Oil Spill Science and Technology

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Oil Spill Science and Technology

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Oil Spill Science and Technology Prevention, Response, and Cleanup

Edited by

Mervin Fingas

Amsterdam l Boston l Heidelberg l London l New York l Oxford Paris l San Diego l San Francisco l Singapore l Sydney l Tokyo Gulf Professional Publishing is an imprint of Elsevier

Gulf Professional Publishing is an imprint of Elsevier 30 Corporate Drive, Suite 400, Burlington, MA 01803, USA The Boulevard, Langford Lane, Kidlington, Oxford, OX5 1GB, UK Copyright Ó 2011 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. Library of Congress Cataloging in Publication Data Oil spill science and technology : prevention, response, and clean up / edited by Mervin Fingas. – 1st ed. p. cm. Summary: “The National Academy of Sciences estimate that 1.7 to 8.8 million tons of oil are released into world’s water every year, of which more than 70% is directly related to human activities. The effects of these spills are all too apparent: dead wildlife, oil covered marshlands and contaminated water chief among them. This reference will provide scientists, engineers and practitioners with the latest methods use for identify and eliminating spills before they occur and develop the best available techniques, equipment and materials for dealing with oil spills in every environment. Topics covered include: spill dynamics and behaviour, spill treating agents, and cleanup techniques such as: in situ burning, mechanical containment or recovery, chemical and biological methods and physical methods are used to clean up shorelines. Also included are the fate and effects of oil spills and means to assess damage”– Provided by publisher. ISBN 978-1-85617-943-0 1. Oil spills–Prevention. 2. Oil spills–Cleanup. 3. Oil spils–Managements. I. Fingas, Mervin F. TD427.P4O38785 2010 628.1’6833–dc22 2010033465 British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library. ISBN: 978-1-85617-943-0 For information on all Gulf Professional Publishing publications visit our Web site at www.elsevierdirect.com

11 12 13 10 9 8 7 6 5 4 3 2 1 Printed and bound in the USA

Contents

Preface About the Contributors

xxv xxvii

Part I Introduction and the Oil Spill Problem 1.

2.

Introduction

3

Merv Fingas 1.1. Introduction 1.2. A Word on the Frequency of Spills

3 4

Spill Occurrences: A World Overview

7

Dagmar Schmidt-Etkin 2.1. Introduction 2.2. Executive Summary 2.3. Overview of Spill Occurrences 2.3.1. Natural Oil Seepage 2.3.2. Historical Concern Over Oil Pollution 2.3.3. Sources of Oil Spills and Patterns of Spillage 2.3.4. Spillage from Oil Exploration and Production Activities 2.3.5. Spills During Oil Transport 2.3.6. Spillage from Oil Refining 2.3.7. Spillage Related to Oil Consumption and Usage 2.3.8. Oil Inputs from Potentially Polluting Sunken Shipwrecks 2.3.9. Summary of Oil Spillage References

7 8 8 8 11 12 17 23 28 32 39 41 46

Part II Types of Oils and Their Properties 3.

Introduction to Oil Chemistry and Properties

51

Merv Fingas 3.1. Introduction

51

v

vi

Contents

3.2. The Composition of Oil 3.3. Properties of Oil References

51 54 59

Part III Oil Analysis and Remote Sensing 4.

5.

Measurement of Oil Physical Properties

63

Bruce Hollebone 4.1. Introduction 4.2. Bulk Properties of Crude Oil and Fuel Products 4.2.1. Density and API Gravity 4.2.2. Dynamic Viscosity 4.2.3. Surface and Interfacial Tensions 4.2.4. Flash Point 4.2.5. Pour Point 4.2.6. Sulphur Content 4.2.7. Water Content 4.2.8. Evaluation of the Stability of Emulsions Formed from Brine and Oils and Oil Products 4.2.9. Evaluation of the Relative Dispersability of Oil and Oil Products 4.2.10. Adhesion to Stainless Steel 4.3. Hydrocarbon Groups 4.4. Quality Assurance and Control 4.5. Effects of Evaporative Weathering on Oil Bulk Properties 4.5.1. Weathering 4.5.2. Preparing Evaporated (Weathered) Samples of Oils 4.5.3. Quantifying Equation(s) for Predicting Evaporation References Appendix 4.1

71 72 73 77 78 78 79 81 83 85

Introduction to Oil Chemical Analysis

87

Merv Fingas 5.1. Introduction 5.2. Sampling and Laboratory Analysis 5.2.1. Incorrect and Obsolete Methods 5.3. Chromatography 5.3.1. Introduction to Gas Chromatography 5.3.2. Methodology 5.4. Identification and Forensic Analysis 5.4.1. Biomarkers 5.4.2. Sesquiterpanes and Diamondoids 5.5. Field Analysis References

63 63 66 67 67 69 70 70 70 71

87 87 88 89 89 93 96 99 105 107 107

Contents

6.

Oil Spill Remote Sensing: A Review Merv Fingas and Carl E. Brown 6.1. Introduction 6.2. Visible Indications of Oil 6.3. Optical Sensors 6.3.1. Visible 6.3.2. Infrared 6.3.3. Ultraviolet 6.4. Laser Fluorosensors 6.5. Microwave Sensors 6.5.1. Radiometers 6.5.2. Radar 6.5.3. Microwave Scatterometers 6.5.4. Surface Wave Radars 6.5.5. Interferometric Radar 6.6. Slick Thickness Determination 6.6.1. Visual Thickness Indications 6.6.2. Slick Thickness Relationships in Remote Sensors 6.6.3. Specific Thickness Sensors 6.7. Acoustic Systems 6.8. Integrated Airborne Sensor Systems 6.9. Satellite Remote Sensing 6.10. Oil Under Ice Detection 6.11. Underwater Detection and Tracking 6.12. Small Remote-Controlled Aircraft 6.13. Real-Time Displays and Printers 6.14. Routine Surveillance 6.15. Future Trends 6.16. Recommendations Acknowledgments References

7.

vii

111 111 112 114 114 120 123 123 124 124 125 134 135 135 135 135 136 138 139 139 140 144 145 149 150 150 153 154 158 158

Laser Fluorosensors

171

Carl E. Brown 7.1. Principles of Operation 7.1.1. Active versus Passive Sensors 7.1.2. Sensor Features 7.1.3. Pros/Cons 7.2. Oil Classification 7.2.1. Real-Time Analysis 7.2.2. Sensor Outputs 7.3. Existing Operational Units 7.3.1. Airborne 7.3.2. Ship-Borne 7.4. Aircraft Requirements 7.4.1. Power

171 171 171 174 175 175 176 179 179 179 180 180

viii

Contents

7.4.2. Weight 7.4.3. Operational Altitude 7.5. Cost Estimates 7.6. Conclusions References

181 181 182 182 182

Part IV Behaviour of Oil in the Environment and Spill Modeling 8.

9.

Introduction to Spill Modeling

187

Merv Fingas 8.1. Introduction 8.2. An Overview of Weathering 8.2.1. Evaporation 8.2.2. Emulsification 8.2.3. Natural Dispersion 8.2.4. Dissolution 8.2.5. Photo-Oxidation 8.2.6. Sedimentation, Adhesion to Surfaces, and Oil-Fines Interaction 8.2.7. Biodegradation 8.2.8. Sinking and Overwashing 8.2.9. Formation of Tarballs 8.3. Movement of Oil and Oil Spill Modeling 8.3.1. Spreading 8.3.2. Movement of Oil Slicks 8.3.3. Spill Modeling References

192 193 194 195 196 196 197 198 199

Evaporation Modeling

201

Merv Fingas 9.1. Introduction 9.2. Review of Theoretical Concepts 9.3. Development of New Diffusion-Regulated Models 9.3.1. Wind Experiments 9.3.2. Evaporation Rate and Area 9.3.3. Study of Mass and Evaporation Rate 9.3.4. Study of the Evaporation of Pure Hydrocarbonsdwith and Without Wind 9.3.5. Other Factors 9.3.6. Temperature Variation and Generic Equations Using Distillation Data 9.3.7. A Simplified Means of Estimation 9.4. Complexities to the Diffusion-Regulated Model 9.4.1. Thickness of the Oil

187 187 188 190 191 192 192

201 205 212 212 215 215 216 217 217 227 229 229

Contents

9.4.2. The Bottle Effect 9.4.3. Skinning 9.4.4. Rises from the 0-Wind Values 9.5. Use of Evaporation Equations in Spill Models 9.6. Comparison of Model Approaches 9.7. Summary References

10.

11.

ix 229 230 233 233 235 240 241

Models for Water-in-Oil Emulsion Formation

243

Merv Fingas 10.1. Introduction 10.2. Early Modeling of Emulsification 10.3. First Two Model Developments 10.4. New Model Development 10.5. Development of an Emulsion Kinetics Estimator 10.6. Discussion 10.7. Conclusions References

243 249 251 253 260 260 269 270

Oil Spill Trajectory Forecasting Uncertainty and Emergency Response

275

Debra 11.1. 11.2. 11.3.

Simecek-Beatty Introduction: The Importance of Forecast Uncertainty The Basics of Oil Spill Modeling Trajectory Model Uncertainties 11.3.1. Release Details 11.3.2. Wind 11.3.3. Current 11.3.4. Turbulent Diffusion 11.3.5. Oil Weathering 11.3.6. Ensemble Forecasting 11.3.7. Communicating Trajectory Forecast Uncertainty 11.4. Trajectory Forecast Verification 11.4.1. Diagnostic Verification 11.5. Summary and Conclusions Acknowledgments References

275 276 280 281 282 284 287 288 289 291 292 294 295 297 297

Part V Physical Spill Countermeasures on Water 12.

Physical Spill Countermeasures

303

Merv Fingas 12.1. Containment on Water

303

x

13.

Contents

12.1.1. Types of Booms and Their Construction 12.1.2. Uses of Booms 12.1.3. Boom Failures 12.1.4. Ancillary Equipment 12.1.5. Sorbent Booms and Barriers 12.1.6. Special-Purpose Booms 12.2. Skimmers 12.2.1. Oleophilic Surface Skimmers 12.2.2. Weir Skimmers 12.2.3. Suction or Vacuum Skimmers 12.2.4. Elevating Skimmers 12.2.5. Submersion Skimmers 12.2.6. Skimmer Performance 12.2.7. Special-Purpose Ships 12.3. Sorbents 12.4. Manual Recovery 12.5. Temporary Storage 12.6. Pumps 12.6.1. Performance of Pumps 12.7. Separation 12.8. Disposal Acknowledgments References

303 306 309 313 314 314 315 316 320 321 322 323 323 325 325 329 330 332 334 334 335 337 337

Weather Effects on Oil Spill Countermeasures

339

Merv Fingas 13.1. Introduction 13.1.1. Spreading Compared to Weathering 13.1.2. Important Components of Weather 13.1.3. Oil Properties Regardless of Weathering 13.2. Review of Literature on Spill Countermeasures and Weather 13.2.1. A Priori Decision Guides 13.2.2. General Countermeasures 13.2.3. Booms 13.2.4. Skimmers 13.2.5. Dispersants 13.2.6. In-Situ Burning 13.2.7. Others 13.2.8. Ice Conditions 13.3. Development of Models for Effectiveness of Countermeasures 13.3.1. Overall 13.3.2. Booms 13.3.3. Skimmers 13.3.4. Dispersants 13.3.5. In-Situ Burning

339 340 340 343 343 343 345 345 353 372 378 381 381 383 383 383 383 398 403

Contents

13.3.6. Others 13.4. Overview of Weather Limitations 13.5. Summary and Conclusions Acknowledgments References

xi 404 405 407 416 416

Part VI Treating Agents 14.

15.

Spill-Treating Agents

429

Merv Fingas 14.1. Introduction 14.2. Dispersants 14.3. Surface-Washing Agents 14.4. Emulsion Breakers and Inhibitors 14.5. Recovery Enhancers 14.6. Solidifiers 14.7. Sinking Agents 14.8. Biodegradation Agents

429 429 430 430 431 431 431 432

Oil Spill Dispersants: A Technical Summary

435

Merv Fingas 15.1. Introduction 15.1.1. What Are Dispersants? 15.2. The Basic Physics and Chemistry of Dispersants 15.2.1. Formulations 15.2.2. Nature of Surfactant Interaction with Oil 15.3. The Basic Nature of Dispersions or Oil-in-Water Emulsions 15.3.1. Forces of Destabilization 15.3.2. The Science of Stabilization 15.3.3. Oil Spill Dispersions 15.3.4. Significance of Emulsion Stability 15.4. Effectiveness 15.4.1. Introduction to Effectiveness 15.4.2. Field Trials 15.4.3. Laboratory Tests 15.4.4. Tank Tests 15.4.5. Analytical Means 15.5. Monitoring 15.5.1. Introduction to Monitoring 15.5.2. Review of SMART Protocol 15.5.3. The SERVS Protocol 15.5.4. Review of Other Protocols 15.5.5. Review of Goodman Analysis of SMART 15.5.6. Considerations for Monitoring in the Field

435 437 437 437 438 440 441 443 447 449 451 452 454 464 467 480 481 481 482 483 486 487 488

xii

Contents

15.5.7. 15.5.8. 15.5.9. 15.5.10. 15.5.11. 15.5.12. 15.5.13. 15.5.14. 15.5.15. 15.6.

15.7.

15.8. 15.9.

15.10.

Visual Surveillance Remote Sensing Tracking of Oil on Surface Tracking of Oil Underwater Mass Balance Use of Undispersed Slick(s) as a Control Background Levels of Hydrocarbons Using and Computing Values Recommended Procedures for Monitoring Dispersant Applications Studies Energy Composition of Oil Amount of Dispersant Temperature Salinity Particle or Droplet Size

Physical 15.6.1. 15.6.2. 15.6.3. 15.6.4. 15.6.5. 15.6.6. Toxicity 15.7.1. Toxicity of Dispersants 15.7.2. Photoenhanced Toxicity 15.7.3. Testing Protocols Biodegradation Other Information 15.9.1. Component Separation 15.9.2. Dispersant Use 15.9.3. Application of Dispersants 15.9.4. Assessment of the Use of Dispersants 15.9.5. Spills-of-Opportunity Research 15.9.6. Interaction with Sediment Particles 15.9.7. Modeling Oil and Dispersed Oil Behavior and Fate 15.9.8. Separation of Dispersants from Water 15.9.9. Dispersant Breakthrough Oil Slicks 15.9.10. Overall Effects of Weather on Dispersion 15.9.11. Joint Effect of Temperature and Salinity on Effectiveness 15.9.12. Dispersibility of Biodiesels 15.9.13. Application Systems 15.9.14. Accelerated Weathering Summary and Conclusions 15.10.1. Effectiveness Testing Overall 15.10.2. Laboratory Effectiveness Tests 15.10.3. Tank Testing 15.10.4. Analytical Methods for Effectiveness 15.10.5. Toxicity of Dispersed Oil and Dispersants 15.10.6. Biodegradation of Oil Treated by Dispersants

492 493 494 494 494 495 495 496 496 500 500 506 512 512 513 519 519 532 533 534 535 539 539 539 551 553 555 555 556 557 557 557 558 559 560 560 562 563 563 564 564 564 565

xiii

Contents

15.10.7. 15.10.8. 15.10.9. 15.10.10. 15.10.11. 15.10.12. 15.10.13. 15.10.14. Acknowledgments References

16.

Spill-of-Opportunity Research 565 Monitoring Dispersant Applications 565 Dispersant Use in Recent Times 566 Interaction with Sediment Particles 566 Stability of Dispersions and Resurfacing with Time 566 Fate of Dispersed Oil 566 Application Technology and Issues 566 Correlation of Oil Properties with Effectiveness 566 566 567

A Practical Guide to Chemical Dispersion for Oil Spills Merv Fingas 16.1. Introduction and Decision Making 16.1.1. An OverviewdHow, When, and Where Dispersants Are Used 16.1.2. Net Environmental Benefit Analysis 16.1.3. Scenarios For Which Dispersants Might Be Used 16.1.4. Planning Process and Checklists 16.2. How Dispersants Are Used 16.2.1. Dispersion Spray Equipment 16.2.2. Spray Aircraft 16.2.3. Spray Nomograms and Calculations 16.2.4. Monitoring, Sampling, and Analytical Equipment 16.2.5. Equipment Availability 16.2.6. Equipment Checklist 16.2.7. Conducting the Operation 16.3. Safety and Postdispersion Actions 16.3.1. Worker Health and Safety Precautions 16.3.2. Follow-Up Monitoring Additional Information Appendix A. Specific Spill Scenarios and Dispersion Strategies Appendix B. Nomograms to Calculate Spreading and Viscosity with Time

17.

583 583 584 587 589 589 591 592 593 594 596 596 597 597 598 598 599 601 603 605

Procedures for the Testing and Approval of Oil Spill Treatment Products in the United Kingdomd What They Are and Considerations for Development

611

Mark Kirby 17.1. Background and Introduction 17.1.1. Preassessment Requirements 17.2. Toxicity Testing Procedures

611 612 613

xiv

18.

Contents

17.2.1. Reference Oil 17.2.2. Test water 17.2.3. The Sea Test 17.3. Test Description 17.3.1. The Rocky Shore Test 17.3.2. Rationale 17.3.3. Test Species 17.3.4. Test Description 17.3.5. Test Validity and Pass/Fail Assessment 17.4. Testing with Heavy Fuel Oils 17.5. The 2007 UK Scheme Review 17.5.1. Review and Improvement 17.5.2. Specific Issues 17.6. Conclusions References

613 613 615 615 616 617 618 618 619 619 620 620 620 626 627

Formulation Changes in Oil Spill Dispersants: Are They Toxicologically Significant?

629

Mark F. Kirby, Paula Neall, Jennifer Rooke, and Heather Yardley 18.1. Introduction 18.2. Materials and Methods 18.2.1. General Approach 18.2.2. Dispersants and Constituents 18.2.3. Toxicity Tests 18.2.4. Testing Schedule 18.3. Results 18.3.1. Inherent Toxicity of Constituent Chemicals and Dispersants 18.3.2. Toxicity of Reformulated Dispersants in the Sea Test 18.3.3. Toxicity of Reformulated Dispersants in the Rocky Shore Test 18.3.4. Inherent Toxicity of Reformulated Dispersants 18.4. Discussion 18.4.1. Do Formulation Changes Matter? 18.4.2. Sea Test 18.4.3. Rocky Shore Test 18.4.4. Are Specific Constituents of Concern? 18.4.5. Significance of Inherent Toxicity Changes of Formulations? Acknowledgments References

19.

Environment Canada’s Methods for Assessing Oil Spill Treating Agents Carl E. Brown, Ben Fieldhouse, Trevor C. Lumley, Patrick Lambert and Bruce P. Hollebone

629 630 630 631 631 633 633 633 634 635 635 638 638 639 639 640 641 641 642

643

Contents

19.1. 19.2.

Introduction Toxicity and Effectiveness of Treating Agents for Oil Spills 19.2.1. Dispersants 19.2.2. Shoreline-Washing Agents 19.2.3. Deemulsifiers and Emulsion Inhibitors 19.2.4. Herding Agents 19.2.5. Recovery Agents 19.2.6. Solidifiers and Gelling Agents 19.2.7. Biodegradation Agents 19.2.8. Sinking Agents 19.3. Approval for Use of Treating Agents in Canadian Waters 19.4. Challenges to Current Toxicity Test Protocols 19.4.1. Endocrine Disrupting Capacity 19.4.2. Genotoxicity 19.4.3. Sublethal Effects 19.5. Conclusions References

20.

The United States Environmental Protection Agency: National Oil and Hazardous Substances Pollution Contingency Plan, Subpart J Product Schedule (40 Code of Federal Regulations 300.900) William 20.1. 20.2. 20.3. 20.4. 20.5. 20.6. 20.7. 20.8.

21.

xv 643 645 645 653 657 658 658 658 659 661 662 662 664 664 665 666 667

673

J. Nichols Introduction Why Is There a Product Schedule? Authorities for a Product Schedule Information Requested from Manufacturers Agency Activities Practical Utility of the Data Authorities for Use Federal Agencies’ Role within the Regional Response Team 20.9. Does Listing Mean the Environmental Protection Agency Approves and Endorses a Product? 20.10. Conclusions 20.10.1. Proper Uses and Lessons Learned References

681 681 682 682

Surface-Washing Agents or Beach Cleaners

683

Merv Fingas and Ben Fieldhouse 21.1. Introduction to Surface-Washing Agents

683

673 674 675 675 679 679 680 680

xvi

22.

Contents

21.1.1. Motivations for Using Surface-Washing Agents 21.1.2. Surface Washing Agent Issues 21.1.3. Surface-Washing Agent Chemistry 21.2. Review of Major Surface-Washing Agent Issues 21.2.1. Effectiveness 21.2.2. Toxicity 21.3. Other Issues 21.3.1. Application 21.3.2. Dispersion with Higher Applied Energy 21.3.3. Assessment of the Use of Surface-Washing Agents References Appendix 21.1. Environment Canada’s Test Method Summary Method EPA Draft Protocol Summary Fieldhouse High-Energy Protocol

685 685 686 686 686 697 697 697 700 700 704 707 707 707 709 709 709

Review of Solidifiers

713

Merv Fingas and Ben Fieldhouse 22.1. Introduction to Solidifiers 22.1.1. Motivations for Using Solidifiers 22.1.2. Solidifier Issues 22.1.3. Solidifier Chemistry 22.2. Review of Major Solidifier Issues 22.2.1. Effectiveness 22.2.2. Toxicity 22.2.3. Biodegradation 22.3. Other Issues 22.3.1. Spill Size 22.3.2. Solidifier Use in Recent Times 22.3.3. Solidifiers or Sorbents 22.3.4. Potential for Sinking 22.3.5. Modeling Solidifier and Solidified Oil Behavior and Fate 22.3.6. Solidified Oil Stability 22.3.7. Fate of Unreacted Solidifier 22.3.8. Recovery of Solidified Oil 22.3.9. Solidification Time 22.3.10. Application Systems 22.3.11. Reduction of Flash Point 22.3.12. Assessment of the Use of Solidifiers 22.3.13. Disposal Methods or Recycling 22.4. Summary Acknowledgments References

713 713 714 714 717 717 728 728 728 728 729 729 729 729 729 729 729 730 730 730 730 730 730 731 731

Contents

Appendix 22.1. Testing Procedures from Environment Canada Solidifier Test Procedures Used in Early Years Oil Solidifier Effectiveness Test Used 1998 to Present Brief Description of the Test Equipment and Supplies Procedure Calculation

xvii 732 732 732 733 733 733 733

Part VII In-Situ Burning 23.

An Overview of In-Situ Burning 737 Merv Fingas 23.1. Introduction 737 23.2. An Overview of In-Situ Burning 737 23.2.1. The Science of Burning 737 23.2.2. Summary of In-Situ Burning Research and Trials 743 23.2.3. How Burns at Sea Are Conducted 750 23.2.4. Advantages and Disadvantages 755 23.2.5. Comparison of Burning to Other Response Measures 756 23.3. Assessment of Feasibility of Burning 758 23.3.1. Burn Evaluation Process 758 23.3.2. Areas Where Burning May Be Prohibited 758 23.3.3. Regulatory Approvals 763 23.3.4. Environmental and Health Concerns 765 23.3.5. Oil Properties and Conditions 793 23.3.6. Weather and Ambient Conditions 799 23.3.7. Burning in Special Locations 801 23.3.8. Burning on Land 806 23.3.9. Burning In or On Ice 809 23.4. EquipmentdSelection, Deployment, and Operation 811 23.4.1. Burning Without Containment 811 23.4.2. Oil Containment and Diversion Methods 814 23.4.3. Ignition Devices 834 23.4.4. Treating Agents 849 23.4.5. Support Vessels/Aircraft for At-Sea Burns 851 23.4.6. Monitoring, Sampling, and Analytical Equipment 852 23.4.7. Final Recovery of Residue 856 23.4.8. Equipment Checklist 858 23.5. Possible Spill Situations 858 23.6. Post-Burn Actions 870 23.6.1. Follow-Up Monitoring 870 23.6.2. Estimation of Burn Efficiency 873 23.6.3. Burn Rate 877 23.7. Health and Safety Precautions during Burning 878

xviii

Contents

23.7.1. Worker Health and Safety Precautions 23.7.2. Public Health and Safety Precautions 23.7.3. Establishing Safety Zones 23.7.4. Monitoring Burn Emissions Acknowledgments References

878 887 888 888 894 894

Part VIII Shoreline Countermeasures 24.

25.

Shoreline Countermeasures

907

Edward H. Owens 24.1. Introduction 24.1.1. Control At or Near the Source 24.1.2. Control on Water 24.1.3. Shoreline Protection Strategy 24.1.4. Shoreline Treatment 24.2. Shoreline Treatment Decision Process 24.3. Treatment Options 24.3.1. Natural Recovery 24.3.2. Physical Removal 24.3.3. In-Situ Treatment 24.4. Treatment by Shore Type 24.5. Waste Generation References

907 908 908 909 909 910 912 912 913 915 916 919 920

Automated Assessment and Data Management

923

Alain Lamarche 25.1. Introduction 25.2. Automated Processing and Data Management: Goals and Definition 25.2.1. Understanding the Use of Shoreline Assessment Data During a Response 25.2.2. The Nature of Shoreline Assessment Data 25.2.3. Practical Use of Shoreline Observations 25.3. Shoreline Observations Data Processing 25.3.1. Data Processing Organization 25.3.2. Responsibilities of the Shoreline Assessment Data Management Team 25.3.3. Data Management Tasks and Processes 25.3.4. Why and When to Establish a Shoreline Assessment Data Management Team 25.4. Assessment Automation Methods and Tools 25.4.1. Basic Tools 25.4.2. Combining Tools Within a Data Management Support System

923 924 924 924 927 929 929 931 935 939 939 940 944

xix

Contents

25.4.3. Information Distribution Shoreline Assessment Data Management Issues 25.5.1. Equipment Failure 25.5.2. Software Corruption 25.5.3. Overwhelming Amounts of Data 25.5.4. Conditions Unique to the Response References 25.5.

947 948 948 949 949 949 955

Part IX Submerged Oil 26.

Submerged Oil Jacqueline Michel 26.1. Introduction 26.2. Submerged Oil Characteristics 26.3. Review of Recent Submerged Oil Spills 26.3.1. M/V Athos I 26.3.2. T/B DBL-152 26.3.3. Lake Wabamun Spill 26.4. Submerged Oil Spill Response Methods and Recommendations for Future Work 26.4.1. Methods for Detection of Oil Suspended in the Water Column 26.4.2. Methods for Detection of Oil on the Bottom 26.4.3. Containment of Suspended Oil/Protection of Water Intakes 26.4.4. Containment of Submerged Oil on the Bottom 26.4.5. Recovery of Submerged Oil on the Bottom References

959 959 961 965 965 967 972 975 975 976 978 979 979 981

Part X Effects of Oil in the Environment 27.

Effects of Oil in the Environment Gary 27.1. 27.2. 27.3. 27.4. 27.5. 27.6. 27.7. 27.8. 27.9. 27.10. 27.11.

Shigenaka Introduction Some Definitions Size Matters: Seeps vs. Spills An “Equation” to Convey Toxic Impact Route of Exposure: The Anthrax Example Route of Exposure: Oil Oil Chemistry, Physical Behavior, and Oil Effects Freshwater/Saltwater Differences Tropical Environments Arctic Environments Ecological Effects of Oil Spills

985 985 987 989 991 999 1000 1003 1008 1010 1013 1014

xx

Contents

27.12. The Future of Oil Effects Science 27.13. Summary and Conclusions Acknowledgments Disclaimer References

1017 1019 1019 1019 1020

Part XI Contingency Planning and Command 28.

29.

Introduction to Oil Spill Contingency Planning and Response Initiation

1027

Merv Fingas 28.1. An Overview of Response to Oil Spills 28.2. Activation of Contingency Plans 28.3. Training 28.4. Structure of Response Organizations 28.5. Oil Spill Cooperatives 28.6. Private and Government Response Organizations

1027 1028 1029 1030 1030 1031

The Role of the International Tanker Owners Pollution Federation Limited

1033

Karen Purnell

30.

Safety Issues at Spills

1037

Quek Qiuhui 30.1. Introduction 30.2. Organization Structure 30.3. Health and Safety Risk Analysis/Risk Assessment 30.4. Air Monitoring 30.5. Site Safety and Health Plan 30.6. Different Types of Hazards on Site 30.7. Recommended Safety Procedures 30.7.1. Site Evaluation Process 30.7.2. Site Control Measures 30.7.3. Personal Protective Equipment 30.7.4. Excessive Noise 30.7.5. Heat Stress 30.7.6. Cold Stress 30.7.7. Monitoring Program 30.8. Emergency Procedures During a Response 30.8.1. Fire and Explosion 30.8.2. Hazardous Atmosphere/Hazardous Chemicals 30.8.3. Medical Emergencies 30.9. Other Issues

1037 1037 1038 1038 1043 1048 1049 1049 1050 1052 1052 1052 1054 1054 1054 1054 1058 1058 1059

Contents

30.9.1. Personnel Training 30.9.2. Volunteers 30.10. Conclusion Acknowledgments References

xxi 1059 1059 1062 1062 1062

Part XII Postassessment and Restoration 31.

32.

Natural Resource Damage Assessment

1067

Gary S. Mauseth and Heather Parker 31.1. Introduction 31.2. Regulatory Regimes 31.3. Objectives 31.4. Making the Public Whole 31.4.1. Injury Assessment 31.4.2. Interpretation of Restoration or Reinstatement 31.5. Alternative Sites 31.6. Use of Models 31.7. The NRDA Process in the United States 31.7.1. DOI CERCLA NRDA Regulations 31.7.2. NOAA NRDA Regulations Acronyms References

1067 1067 1069 1070 1071 1072 1075 1076 1077 1078 1079 1081 1082

Seafood Safety and Oil Spills

1083

Greg Challenger and Gary Mauseth 32.1. Introduction 32.2. Seafood Exposure to Oil 32.3. Spill Response and Seafood Safety Management 32.4. Seafood Safety Assessment: Reopening a Closed Fishery 32.5. Chemical Analytical Evaluation 32.6. Seafood Sensory Evaluation 32.7. Trends in Lifting Fishery Bans 32.8. Long-Term Implications of Oil Spills on Seafood References

1083 1085 1087 1090 1090 1092 1096 1098 1099

Part XIII Specific Case Studies 33.

The Torrey Canyon Oil Spill, 1967

1103

Robin J. Law 33.1. Case Study References

1103 1105

xxii

34.

35.

36.

Contents

The Ekofisk Bravo Blowout, 1977

1107

Robin J. Law 34.1. Case Study References

1107 1108

The Sea Empress Oil Spill, 1996

1109

Robin J. Law 35.1. Introduction 35.2. Mechanical Recovery at Sea 35.3. Dispersant Spraying at Sea 35.4. Shoreline Cleanup 35.5. Dispersant Use on Beaches 35.6. Impacts on Seabirds 35.7. Mortalities of Fish and Shellfish 35.8. Effects on Fish and Shellfish Stocks and Plankton 35.9. Contamination of Fish and Shellfish 35.9.1. Finfish 35.9.2. Crustacea 35.9.3. Whelks 35.9.4. Bivalve Mollusks 35.10. Removal of Fishery Restrictions 35.11. Conclusion References

1109 1110 1111 1112 1113 1113 1113 1114 1114 1114 1115 1115 1115 1115 1116 1116

The Braer Oil Spill, 1993

1119

Robin 36.1. 36.2. 36.3. 36.4.

37.

J. Law and Colin F. Moffat Introduction At-Sea and Shoreline Response Fate of the Braer Oil Impacts of the Braer Oil 36.4.1. On Land 36.4.2. On Seabirds 36.4.3. On Otters and Seals 36.4.4. On Commercial Fish and Shellfish 36.4.5. On Farmed Salmon 36.4.6. On Benthic Communities 36.4.7. On the Human Population 36.5. Conclusion References

1119 1119 1121 1121 1121 1121 1121 1123 1124 1125 1125 1125 1126

1991 Gulf War Oil Spill

1127

Jacqueline Michel 37.1. Review of the Spill References

1127 1131

Contents

38.

xxiii

Tanker SOLAR 1 Oil Spill, Guimaras, Philippines: Impacts and Response Challenges

1133

Ruth Yender and Katharina Stanzel 38.1. Incident Summary 38.2. Impact Summary 38.3. Shoreline Cleanup 38.4. Mangrove Cleanup and Recovery 38.5. Fisheries Impacts and Health Concerns 38.6. Summary Disclaimer References

1133 1134 1139 1143 1144 1145 1146 1146

Conversions Index

1147 1149

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Preface

Oil spill studies continue to evolve. While there are few books on the topic, there are regular conferences and symposiums which provide updates. This is the first book on the topic of oil spills for some time. As such, this book focuses on providing material that is more practical and somewhat introductory. While every attempt was made to include the essential material, there may be some gaps. The importance of many sub-topics changes with time and current spill situations. All material in this book, including introductions have been peer reviewed by at least two persons. The following peer reviewers are acknowledged (in alphabetical order): Carl Brown, Phil Campagna, Francois Charbonneau, Dagmar Schmidt Etkin, Ken Doe, Eric Gundlach, Kurt Hansen, Mike Kirby, Debra French McCay, Hugh Parker, Roger Percy, Karen Purnell, Doug Reimer, Gary Sergy, Debra Simecek-Beatty, Heidi Stout, Jordan Stout, Zhendi Wang, and Chun Yang. A special thanks goes out to the following reviewers who reviewed several papers (again in alphabetical order): Fred Beech, Leigh de Haven, Ben Fieldhouse, Anita George-Ares, Ron Goodman, Peter Lane, Robin Law, Bill Lehr, Jacqui Michel, and William Nichols. A special thanks goes out to the authors, many of whom put in their own time to complete their chapters. Their names appear throughout the text. Following this forward, I have a brief biography of each of them. I would also like to thank the many people who provided support and encouragement throughout this project, especially Meibing. I also thank Environment Canada and my former colleagues for their help and support. Environment Canada is acknowledged for permission to use materials and photos from my former employment.

xxv

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About the Contributors

Carl Brown Dr. Carl E. Brown is the Manager of the Emergencies Science and Technology Section, in the Water Science and Technology Directorate of Environment Canada. Dr. Brown has a doctorate degree in Physical Chemistry from McMaster University and a BSc in Laboratory Science from Ryerson Polytechnical University. Prior to joining Environment Canada, Dr. Brown was a research scientist on a Natural Sciences and Engineering Research Council (NSERC) Industrial Fellowship with Intera Information Technologies (now Intermap). Dr. Brown has post-doctoral experience as a research associate with the Organic Reaction Dynamics and the Laser Chemistry Groups at the Steacie Institute for Molecular Sciences, at the National Research Council of Canada and held a Canadian Government Laboratory Visiting Fellowship in Chemistry, with the Laser Chemistry Group, Division of Chemistry, National Research Council of Canada in Ottawa. His specialities include airborne oil spill sensor development, and the application of laser technologies to environmental problems. He has authored over 180 scientific papers and publications. Dr. Brown is the Chemical Science Cluster Leader for the CRTI (Chemical, Biological, Radiological, Nuclear, and Explosives Research and Technology Initiative) Program lead by Defence Research and Development Canada (DRDC) and Public Safety Canada. Dr. Brown was one of 24 scientists who recently completed the inaugural Scientists as Leaders program. Greg Challenger Greg Challenger is a Principal Marine Scientist for Polaris Applied Sciences, Incorporated in Seattle, Washington, U.S.A. Mr. Challenger is a marine ecologist by training and is involved in scientific support for oil spill and ship grounding response, natural resource injury assessment and development of habitat restoration programs. He has been a lead investigator for nearly 50 large vessel groundings, oil spills, and wreck removal operations in the Western Atlantic, Caribbean Sea, and Indo-Pacific Oceans. Dagmar Etkin Dagmar Schmidt Etkin has 35 years of experience in environmental analysis e 14 years investigating issues in population biology and ecological systems, and 21 years specializing in the analysis of oil spills. For the past 10 years, she has been president of Environmental Research Consulting (ERC), focusing on providing regulatory agencies and industry with sound scientific data and perspectives for responsible environmental decision-making. Dr. Etkin has a BA from University of Rochester, and M.A. and PhD from Harvard University. She is a member of the American Salvage

xxvii

xxviii

About the Contributors

Association, Maritime Law Association, and the UN Joint Group of Experts on the Scientific Aspects of Marine Protection. Merv Fingas Merv Fingas is a scientist focussing on oil and chemical spills. He was a spill researcher in Environment Canada for over 30 years and is currently working privately in Western Canada. Mr. Fingas has a PhD in Environmental Physics from McGill University, three masters degrees; Chemistry, Business, and Mathematics, all from University of Ottawa. His specialities include: spill dynamics and behaviour, spill treating agent studies, remote sensing and detection, and in-situ burning. He has over 750 papers and publications in the field. Dr. Fingas has been editor of the Journal of Hazardous Materials for 6 years. He has served on two committees on the U.S. National Academy of Sciences on oil spills including the recent ‘Oil in the Sea’. He is chairman of several ASTM and inter-governmental committees on spill matters. Bruce Hollebone Bruce Hollebone is a chemist with 14 years experience in the field of chemical and oil spill research and development. He has a PhD in Chemistry from the University of British Colombia. His research interests include: the fate and behaviour of oil and petroleum products in the environment, including simulation of spill behaviours in the laboratory; the development of new methods for physical and chemical analyses relevant to spills studies; environmental forensics for oil spill suspect-source identification; and environmental emergencies response. He currently works at the Oil Research Laboratory of Environment Canada. Mark Kirby Mark is an internationally recognised senior Ecotoxicologist with over 20 years experience working on studies pertaining to aquatic pollution. He has worked extensively on the toxicological impacts of oil and chemical spills and the assessment of appropriate methods of mitigation and has been involved in impact assessments in the UK from the Sea Empress to the MSC Napoli. He is a key advisor to the UK government and industry on the effects of oil and chemical spills in the marine environment and of any subsequent treatment actions (e.g. dispersants, sorbents etc.). Mark oversees the toxicological testing and approval of oil spill treatment products for use in UK waters and is the coordinator of a national initiative in the UK, PREMIAM (www.premiam.org), to implement improved post spill monitoring and impact assessment practices. He is first author of over 15 scientific papers and numerous reports in the field and continues to be actively involved in associated environmental research. Alain Lamarche Mr Lamarche is a recognized expert in spill response management systems. He has been involved in the analysis and management of environmental data since 1979. Mr. Lamarche has been responsible for the development and implementation of many computerized environmental decision support systems databases. He is also the original designer of the ShoreCleanÒ and ShoreAssess software, dedicated to the provision of Shoreline Cleanup Assessment Technique (SCAT) data management support, and

About the Contributors

xxix

Personal Digital Assistant (PDA) based geo-referenced field data acquisition tools. Mr. Lamarche has acted as a SCAT data manager and Geographical Information System (GIS) specialist during a number of oil spills, including: the Kure, New Carissa, Swanson Creek, Lake Wabamun, Westridge Line and Cosco Busan incidents. As principal of EPDS, Mr Lamarche is also responsible for all aspects of environmental software development projects including design, management, and implementation. Robin Law Robin Law is a chemist who joined Cefas (The UK Centre for Environment, Fisheries, and Aquaculture Science) in 1975. During the last 35 years he has been involved in the response and impact assessment activities following a number of major oil and chemical incidents, including the blowout on the Ekofisk Bravo platform, and from the oil tankers Amoco Cadiz, Eleni V, and Sea Empress, and the chemical tankers Ievoli Sun and Ece. Most recently, he designed and operated an environmental monitoring programme targeting oil and chemicals following the grounding of the container ship MSC Napoli on the south coast of the UK in 2007. Currently, he leads an emergency response team that advises UK government following oil and chemical spills at sea. Gary Mauseth Mr. Gary Mauseth has over 35 years of experience in the management and technical aspects of a wide variety of projects in the marine and freshwater environments. He has provided scientific support to vessel interests in over 90 spills, groundings, and natural resource damage assessment cases in the United States and its territories, as well as Canada, Mexico, the Caribbean and Mediterranean Seas, Micronesia, South America, and Europe. He has conducted research on the fate and effects of spilled oil, as well as the environmental effectiveness of response techniques, and has authored numerous publications and presentations on oil spill response, NRDA, and ecological restoration. Mr. Mauseth is a principal and President of Polaris Applied Sciences in Kirkland, Washington, USA. He has a Bachelor of Science in Biology from Whitman College in Walla Walla, Washington and a Master of Marine Biology from University of the Pacific, Pacific Marine Station, Dillon Beach, California. Jacqui Michel Dr. Jacqueline Michel is the President of Research Planning, Inc., and an internationally recognized expert in oil and hazardous materials spill planning and response. Her primary areas of expertise are in oil fates and effects, non-floating oils, shoreline cleanup, alternative response technologies, and natural resource damage assessment. Much of her expertise is derived from her role, since 1978, as part of the Scientific Support Team to the U.S. Coast Guard provided by the National Oceanic and Atmospheric Administration (NOAA). Under this role, she is on 24-hour call and provides technical support for 50-100 spill events per year. She leads shoreline assessment teams and assists in selecting cleanup methods to minimize the environmental impacts of the spill. She has evaluated and used a wide range of alternative response technologies, including surface washing agents, solidifiers, bioremediation

xxx

About the Contributors

agents, in-situ burning (mostly on wetlands and inland habitats), and methods to track and recover non-floating oils. William Nichols William (Nick) Nichols was born in Baltimore, Maryland and now lives in Ellicott City, Maryland. He has a Bachelor’s in Economics \Geography from Salisbury State University, Salisbury, Maryland and a Masters of Environmental Science from Johns Hopkins University, Baltimore. He has been an environmental scientist in the U.S. Environmental Protection Agency Office of Emergency Management (OEM) from 1997 and was U.S. National Contingency Plan Product Schedule Manager from 1998 to 2006. He is the national expert on chemical and biological oil spill countermeasures. He is also the OEM Tribal Coordinator from 2004 to the present. Ed Owens Dr. Owens is recognized internationally as an expert on oil spill shoreline cleanup and has worked on spill-related projects in the Arctic, North-South America, Africa, Russia, the Caspian, Australia, throughout South America, and in the Middle East. He has over 40 years experience providing technical and scientific support on oil response operations worldwide including: T/V Arrow (Canada), Hasbah 6 blowout (Arabian Gulf), T/V Exxon Valdez (USA), Arabian Gulf/Desert Storm (Bahrain, Qatar), Komineft pipeline (Russia), M/V Iron Baron (Australia), T/V Estrella Pampeana (Argentina), Desaguadero River (Bolivia), and M/V Cosco Busan spill (USA). Dr. Owens has conducted oil spill related missions as a United Nations Expert Consultant for the International Maritime Organization and as a consultant for the World Bank and the European Bank of Reconstruction and Development, and was a member of the U.S. National Academy of Science Oil Spill R&D Committee. Karen Purnell Dr Karen Purnell has been Managing Director of ITOPF since May 2009. She is a graduate of the Royal Society of Chemistry, with a PhD in Chemical Physics. Before joining ITOPF as a technical adviser in 1994, she worked on toxic waste management and environmental remediation in the nuclear industry and as a research chemist at several universities. Whilst at ITOPF, she has attended several major oil spills, including the sea empress (UK, 1996), prestige (Spain, 2002), and tasman spirit (Pakistan, 2003). Prominent amongst Karen’s achievements is the expansion of ITOPF’s capability to respond to spills of HNS (Hazardous & Noxious Substances). She has also worked closely with key U.S. agencies and the International Group of P&I Clubs on environmental issues. Dr Purnell has established a constructive dialogue with shipowners and is highly respected in the maritime community. Qiuhui Quek Qiuhui Quek holds a Bachelor’s degree in Environmental Engineering and has attended a number of responses in Australia, India, Indonesia, Korea, Libya, and Singapore. She has also delivered several workshops for Oil Spill Response members in various countries in the region. Qiuhui recently completed a secondment in Southampton as part of the Duty Manager rotation program. She also presented papers in international conferences such

About the Contributors

xxxi

as IOSC, Interspill, and SPE. Qiuhui has since left Oil Spill Response and is working in the HSE Management Unit of A)STAR Research Institutes. Gary Shigenaka Gary Shigenaka is the lead marine biologist with the Emergency Response Division (ERD) of the NOAA, based in Seattle. Gary received both his bachelor of science and masters degrees from the University of Washington in Seattle. As a graduate student, he served as a Knauss Sea Grant Policy Fellow in Washington D.C., and was awarded the Donald L. McKernan prize for outstanding marine affairs thesis. He has provided biological and shoreline assessment support during spills of oil and hazardous chemicals across the country and internationally over the last two decades. Gary was part of the early scientific mobilization for the Exxon Valdez oil spill in 1989, and continues to monitor the long-term effects in Prince William Sound. He also oversees other research initiatives for NOAA/ERD designed to improve oil spill impact understanding, and also to develop and improve biological tools for response and assessment. He has published numerous articles on the science and applied aspects of his spill-related research. Debra Simecek-Beatty Debra Simecek-Beatty has been a physical scientist for the NOAA’s Emergency Response Division for 25 years. She has a Masters degree in Marine Affairs from the University of Washington. During an emergency response, she is responsible for providing estimates of the movement and behavior of the spill. This includes collecting visual observations, remote sensing information, wind and current data, and computer modeling output to form an analysis. In addition, she is responsible for interfacing with local experts (i.e., meteorologist, academia, researchers) in formulating the trajectory analysis. Ruth Yender Ruth Yender is a marine ecologist with NOAA’s Office of Response and Restoration, based in Seattle, Washington. As NOAA’s Scientific Support Coordinator for the U.S. Pacific Northwest and Pacific Islands regions, Ruth provides remote and on-scene support to the U.S. Coast Guard during responses to oil and hazardous materials spills. Since joining NOAA in 1992, she has responded to more than 100 oil and chemical spills in the U.S. and internationally. Ruth also participates in spill response planning, conducts training for responders, and writes response technical guides.

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Part I

Introduction and the Oil Spill Problem

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Chapter 1

Introduction Merv Fingas

Chapter Outline 1.1. Introduction 1.2. A Word on the Frequency of Spills

3 4

1.1. INTRODUCTION Major oil spills attract the attention of both the public and the media. In past years, this attention created a global awareness of the risks of oil spills and the damage they do to the environment. In recent years, there have been fewer major spill incidents, as noted by Dagmar Etkin in Chapter 2. The public usually becomes aware of major spills, but generally does not recognize that spills are a daily fact of life. Oil is a necessity in our industrial society and a major element of our lifestyle. Most of the energy used in much of the developed world is for transportation that runs on oil and petroleum products. As current energy usage trends show, this situation is not likely to change much in the future. Industry uses oil and petroleum derivatives to manufacture such vital products as plastics, fertilizers, and chemical feedstocks, all of which will continue to be required in the future. In fact, production and consumption of oil and petroleum products are increasing worldwide, and the risk of oil pollution is increasing accordingly. The movement of petroleum from the oil fields to the consumer involves as many as 10 to 15 transfers between many different modes of transportation, including tankers, pipelines, railcars, and tank trucks. Oil is stored at transfer points and at terminals and refineries along the route. Accidents can occur during any of these transportation steps or storage times. Fortunately, in the past few years the actual number of spills has decreased. Obviously, an important part of protecting the environment is ensuring that there are as few spills as possible. Both government and industry are working to reduce the risk of oil spills by introducing strict new legislation and stringent operating codes. Industry has invoked new operating and maintenance Oil Spill Science and Technology. DOI: 10.1016/B978-1-85617-943-0.10001-2 Copyright Ó 2011 Elsevier Inc. All rights reserved.

3

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Introduction and the Oil Spill Problem

procedures to reduce accidents that lead to spills. Intensive training programs have been developed to reduce the potential for human error. Despite these measures, spill experts estimate that 30 to 50% of oil spills are either directly or indirectly caused by human error, with 20 to 40% of these incidents caused by equipment failure or malfunction. There are also many deterrents to oil spills, including government fines, loss of reputation, and high cleanup costs. In Canada, it costs an average of $20 to clean up each liter (about 1/4 gallon) of oil spilled. In the United States, these costs average about $100 per liter spilled, whereas the average cost of cleanup worldwide ranges from $20 to $200 per liter, depending on the type of oil and where it is spilled. Cleaning up oil on shorelines is usually the most expensive cleanup process.

1.2. A WORD ON THE FREQUENCY OF SPILLS Smaller oil spills are a frequent occurrence in the world, particularly because of the heavy use of oil and petroleum products in our daily lives. Canada uses about 260,000 tons of these products every day; the United States uses about 10 times this amount, and, worldwide, about 10 million tons are used per day. Most domestic oil production in Canada comes from approximately 350,000 oil wells in Alberta and Saskatchewan. There are 22 oil refineries in Canada, 5 of which are classified as large. Canada imports about 100,000 tons of crude oil or other products per day but exports about 600,000 tons per day, mostly to the United States. In the United States, more than half of the approximately 3 million tons of oil and petroleum products used daily is imported, primarily from Canada, Africa, Saudi Arabia, and other Arabic countries. About 40% of the daily demand in the United States is for automotive gasoline, and about 15% is for diesel fuel used in transportation. About 40% of the energy used in the United States comes from petroleum, 35% from natural gas, and 24% from coal. Spill statistics are collected by a number of agencies around the world. In Canada, provincial offices collect data, and Environment Canada maintains a database of spills. In the United States, the Coast Guard handles a database of spills into navigable waters, while state agencies keep statistics on spills on land which are sometimes gathered into national statistics. The Minerals Management Service (MMS) in the United States maintains records of spills from offshore exploration and production activities. It can sometimes be misleading to compare oil spill statistics, however, because different countries use different methods to collect the data. In general, statistics on oil spills are not easily obtainable, and any data set should be viewed with caution. Determining or estimating the spill volume or amount is the most difficult aspect of data collection. For example, in the case of a vessel accident, the exact volume in a given compartment may be known before the accident, but the remaining oil may have been transferred to other ships

Chapter | 1

Introduction

5

immediately after the accident. Some spill accident data banks do not include the amounts burned, if and when that occurs, whereas others include all the oil lost by whatever means. Sometimes the exact character or physical properties of the oil lost are not known, thereby leading to different estimates of the amount lost. Spill data are often collected for purposes other than future improvement of the spill response. Reporting procedures vary in different jurisdictions and organizations, such as government or private companies. Minimum spill amounts that must be reported according to federal regulations in Canada and the United States vary from 400 to 8,000 liters (100 to 2000 gals), depending on the product spilled. Spill statistics compiled in the past are less reliable than those based on more recent data because few agencies or individuals collected spill statistics before about 1975. Today, although techniques for collecting statistics are continually being improved, the resources allocated for this purpose have been reduced. The number of spills reported also depends on the minimum size or volume of the spill. In both Canada and the United States, most oil spills reported total more than 4000 L (about 1000 gals). In Canada, about 12 such oil spills take place every day, of which only about one of these spills is into navigable waters. These 12 spills amount to about 40 tons of oil or petroleum product. In the United States, there are about 25 spills per day into navigable waters and an estimated 75 spills on land. Despite the apparently large number of spills, only a small percentage of oil used in the world is actually spilled. There are proportionately more spills into navigable waters in the United States than in Canada because more oil is imported by sea and more fuel is transported by barge. In fact, the largest volume of oil spilled in U.S. waters comes from barges, while the largest number of spills is from vessels other than tankers, bulk carriers, or freighters. In Canada, most spills take place on land, and this accounts for a high volume of oil spilled. Pipeline spills account for the highest volume of oil spilled. In terms of the actual number of spills, most oil spills happen at petroleum production facilities, wells, production collection facilities, and battery sites. On water, the greatest volume of oil spilled comes from marine or refinery terminals, although the largest number of spills is from the same source as in the United Statesdvessels other than tankers, bulk carriers, or freighters. The public has the wide misconception that oil spills from tankers are the primary source of oil pollution in the marine environment. Although some of the large spills are indeed from tankers, these spills still make up less than about 5% of all oil pollution on the seas. The sheer volume of oil spilled from tankers and the high profile given these incidents in the media have contributed to this misconception. In fact, as stated earlier, half of the oil spilled in the seas is the runoff of oil and fuel from land-based sources rather than from accidental spills. In conclusion, it is important to study spill incidents from the past to learn how the oil has affected the environment, what cleanup techniques work, and what improvements can be made, as well as to identify the gaps in technology.

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Chapter 2

Spill Occurrences: A World Overview Dagmar Schmidt-Etkin

Chapter Outline 2.1. Introduction 2.2. Executive Summary

7 8

2.3. Overview of Spill Occurrences

8

2.1. INTRODUCTION Asked to picture an oil spill, most people envision a large tank ship (tanker) grounded on a large rock or reef after having gone off-course in a storm or due to navigational errors. Depending on one’s frame of reference and nationality, this might be the Exxon Valdez incident, the Hebei Spirit spill, or perhaps the Prestige spill. Oil-coated beaches, dead birds, angry fishermen, and massive cleanup efforts complete the picture. Although these types of “catastrophic” spill incidents do indeed occur occasionally and receive considerable media coverage, they are, fortunately, relatively rare events. Much more commonly, oil spills are much smaller in scope. On any given day, hundreds, if not thousands, of spills are likely to occur worldwide in many different types of environments, on land, at sea, and in inland freshwater systems. The spills are coming from the various parts of the oil industrydfrom oil exploration and production activities, from transport of that oil by tank ships, pipelines, and railroad tankcars to the refineries, and from the refineries where the oil is refined to create the many types of fuels that are then transported by pipeline, rail, truck, or tank vessel to the consumers of that oil. Consumption-related spillage comes from manufacturing facilities, nontank vessels that carry oil only as fuel and for machinery, tanker trucks bringing oils to service stations and heating oil tanks, and many miscellaneous sources. The spills occur because of structural failures, operational errors, weather-related Oil Spill Science and Technology. DOI: 10.1016/B978-1-85617-943-0.10002-4 Copyright Ó D.S. Etkin 2011.

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Introduction and the Oil Spill Problem

events, earthquakes, human errors and negligence, and even vandalism or terrorism. The spills involve many different types of oil ranging from various types of crude oil to a large array of refined products, from heavy persistent fuels to lighter, less persistent, but very toxic lighter fuels. Because each spill occurs in a different location under different circumstances of oil type and volume, proximity to sensitive resources, season, weather effects, and currents, each spill is a relatively unique event in terms of impacts, damages, and response challenges.

2.2. EXECUTIVE SUMMARY Worldwide oil spillage rates have decreased dramatically since the 1960s and 1970s, from about 635,000 tons annually to about 300,000 tons per year from all sources, not counting the anomalous intentional spillage associated with the 1991 Gulf War, which amounted to over 82 million tons on land and at sea. The largest sources of oil spills in the last two decades have been related to oil transportation by tank ships (tankers) or through pipelines. Oil inputs from spills and other chronic discharge sources, such as urban runoff, refinery effluents, and vessel operational discharges, currently total about 1.2 million tons worldwide annually. While most spills are relatively small and cause localized impacts, occasionally very large spills occur that cause significant environmental and socioeconomic damages. Despite significant progress in reducing spillage through a variety of technological and regulatory prevention measures along with better industry practices, the risk for significant oil spills remains. A more detailed analysis of oil spillage in the United States, for which there are more accurate data than many other parts of the world, reveals that during the decade of 1998e2007, inland pipelines spilled an average of nearly 11,000 tons annually, with the next largest source being refineries, which spilled 1,700 tons. Inland tanker truck spills amounted to 1,300 tons annually. Tank ships only spilled an average of 500 tons annually during this decade. Nevertheless, the risk for large spills from tank ships, facilities, and offshore oil exploration and production, all of which contain large volumes of oil, remains a concern for contingency planners and spill responders.

2.3. OVERVIEW OF SPILL OCCURRENCES 2.3.1. Natural Oil Seepage Oil slicks on water and oiled shorelines are not new phenomena. A considerable amount of crude oil is discharged each year from “natural seeps”dnatural springs from which liquid and gaseous hydrocarbons (hydrogen-carbon compounds) leak out of the ground. Oil seeps are fed by natural underground

Chapter | 2

Spill Occurrences: A World Overview

9

accumulations of oil and natural gas. Oil from submarine (and inland subterranean) oil reservoirs comes to the surface each year, as it has for millions of years due to geological processes. Natural discharges of petroleum from submarine seeps have been recorded throughout history going back to the writings of Herodotus1 and Marco Polo.2 Archaeological studies have shown that products of oil seeps were used by Native American groups living in California, including the Yokuts, Chumash, Achomawi, and Maidu tribes, well before the arrival of European settlers.3 In recent times, the locations of natural seeps have been used for exploration purposes to determine feasible locations for oil extraction. Regional assessments of natural seepage have been conducted in some locations, particularly nearshore in California,4-7 the Indian Ocean,8-10 and the Gulf of Mexico.11 The most comprehensive worldwide assessment of natural seepage is still the study conducted by Wilson et al.12 Even the two more recent international assessments of oil inputs into the sea13 relied heavily on the estimates of natural oil seepage conducted by Wilson et al.,12 having found no more recent comprehensive studies. While industry studies have been conducted for the purpose of determining potential locations for oil exploration and production using various forms of increasingly sophisticated technology, no results have been openly published in the scientific or technical literature. Natural seeps are of such great magnitude that, according to the prominent geologists Kvenvolden and Cooper,14 “natural oil seeps may be the single most important source of oil that enters the ocean, exceeding each of the various sources of crude oil that enters the ocean through its exploitation by humankind.” Assessments of natural oil seepage involve few actual measurements, though certain seep locations along the Southern California coast of the Pacific Ocean have been studied to some extent. Natural seep studies have also included identification of hydrothermically sourced hydrocarbons (especially polycyclic aromatic hydrocarbons) in sediments. The most well-known studies have relied on estimation methodologies based on field data, observations, and various basic assumptions. Wilson et al. estimated that total worldwide natural seepage ranged from 0.2 to 6.0 106 tonsy annually, with the best estimate being 0.6 106 tons, based largely on observations of seepage rates off California and western Canada.12 Estimates of the areas of ocean with natural seeps are shown in Table 2.1, and estimates of seepage rates by ocean are shown in Table 2.2. y

Oil measurements are in metric tons (tons). Within the industry, oil is often measured in barrels (equivalent of 42 U.S. gallons or 159 liters), roughly equal to one-seventh of a ton, depending on specific gravity. Conversion between tons (weight) and barrels (volume) is per the formula: tons ¼ 0.173 barrels specific gravity.

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Introduction and the Oil Spill Problem

TABLE 2.1 Seepage-prone Areas of the World’s Oceans (based on Wilson et al.12) Number of 1,000 Square Kilometers

Ocean

High-potential Seepage

Moderate-potential Seepage

Low-potential Seepage

Pacific

1,943

9,285

4,244

Atlantic

1,303

10,363

11,248

Indian

496

7,928

3,010

Arctic

0

5,636

2,456

Southern

0

486

458

3,741

33,697

21,416

Total

Wilson et al. based their estimates on five basic assumptions: More seeps exist in offshore basins than have been observed; factors that determine seepage rates in a particular area are related to general geological structural type and stage of sedimentary basin evolution; seepage is dependent on the area of exposed rock rather than on rock volume; most marine seeps are clustered at continental margins; and seepage rates are lognormally distributed.12

TABLE 2.2 Summary of World Seepage Rates (based on Wilson et al.12) Estimated Oil Seepage (106 tons per year) Ocean

Case I, P16z

Case II, P1.0x

Case III, P0.3**

Pacific

2.83 106

2.69 105

0.689 105

Atlantic

2.06 106

1.96 105

5.04 104

Indian

9.30 105

8.85 104

2.28 104

Arctic

2.14 105

2.30 103

5.20 103

Southern

1.88 104

1.74 103

4.51 102

Total

6.05 3 106

0.558 3 105

0.148 3 106

z

Probability percentile 16 with a worldwide estimate of 6 106 tons annually, likely a high estimate. Probability percentile 1.0 with a worldwide estimate of 0.6 106 tons annually. **Probability percentile 0.3 with a worldwide estimate of 0.2 106 tons annually, likely a minimal estimate. x

Chapter | 2

Spill Occurrences: A World Overview

11

Kvenvolden and Harbaugh15 concluded that the minimal worldwide estimate (0.2 106 tons annually) from the Wilson et al.12 study is most likely correct and that an error margin of an order of magnitude above and below this value should be applied (i.e., 0.02 106 to 2.0 106 tons annually). Their theory was based on a reduced value for the assumed and known oil resources that would be available for seepage. There is some evidence that seepage rates are decreasing in some locations, such as those near Coal Point, off Santa Barbara, California.16 In a 2003 National Research Council (NRC) study, a worldwide estimate of natural seepage into the marine environment of between 0.02 106 to 2.0 106 tons annually was made, with a “best estimate” of 600,000 tons.17 These estimates were made based on the Kvenvolden and Harbaugh15 reassessment of the estimates made by Wilson et al.,12 as well as an acceptance of the original estimates of Wilson et al.,12 resulting from a “new appreciation” for the magnitude of natural seepage, particularly in the Gulf of Mexico. Relying largely on the Wilson et al.12 and Kvenvolden and Harbaugh15 studies, the 2007 Joint Group of Experts on Scientific Aspects of Marine Protection (known as GESAMP) study on oil inputs into the marine environment included an estimate of the range of natural seepage as 0.22.0 106 tons per year, with a best estimate of 600,000 tons per year.13 Natural seeps often release oil sporadically in relatively small amounts, but occasionally release larger amounts that can have the same environmental impacts as crude oil spills from tankers or other sources. But while natural seeps have had impacts on the marine and terrestrial environment since prehistoric times, it was not until the occurrence of several larger anthropogenic oil spills in the late 1960s, which coincided with a greater public awareness of general environmental issues, that concern over oil pollution came to the forefront.

2.3.2. Historical Concern Over Oil Pollution When the tanker Torrey Canyon spilled 130,000 tons of crude oil off the western coast of the UK in March 1967, killing 15,000 seabirds and oiling nearly 300 kilometers of English and French coastline, there was a large public outcry. The environmental damage from this spill was multiplied by the use of highly toxic first-generation dispersant chemicals in the response. The Torrey Canyon spill was not the first oil tanker spill by any means. A large number of oil tankers were torpedoed and sunk during World War II. According to Campbell et al., during the first six months of 1942 alone, a total of 484,200 tons of oil were released from torpedoed tankers within 90 kilometers of the eastern U.S. coast.18 This came to about one tanker spill of about 20,000 tons per week over six months. Cleanup efforts consisted of burning incidental to the torpedoing and minimal cosmetic actions on swimming beaches. While the occurrence of these incidents during wartime may explain the

12

PART | I

Introduction and the Oil Spill Problem

relatively low concern about environmental damage from the spilled oil, there was, arguably, a general lesser awareness of environmental protection in these times as well. The Torrey Canyon spill in 1967 was notable in that when it occurred, it is the largest spill to date. The tanker’s capacity had recently been increased to hold 130,000 tons of oil cargo. Subsequently, there were at least five significantly larger worst-case discharge (complete cargo loss) tanker spills, as well as several other large spills associated with oil wells and pipelines. Following on the 1967 Torrey Canyon incident, the 1969 Union Alpha Well 21 blowout off Santa Barbara, California, which released 14,300 tons of crude oil, is often credited with being the impetus for the environmental movement in the United States, as well as for the establishment of the federal Environmental Protection Agency (EPA).19 In the 1970s, other significant oil spills around the world brought greater attention to the problem on an international scaledthe tanker Metula (Chile in 1974), the tanker Urquiola (Spain in 1977), the tanker Amoco Cadiz (France in 1978), the largest tanker spill of all time, Atlantic Empress (Trinidad and Tobago/Barbados in 1979), and the largest nonewar-related spill in historydthe Ixtoc I well blowout (Gulf of Mexico in 1979).20 The largest oil spills in history are listed in Table 2.3. The 1989 tanker Exxon Valdez spill in Alaska is perhaps the most notorious spill incident, though it is by no means the largest. The spillage of over 37,000 tons of Alaskan crude oil into what was considered to be a “pristine” location, Prince William Sound, precipitated the most expensive and the lengthiest spill response and damage settlements in history. Its repercussions were felt worldwide, resulting in the passage of significant spill prevention and liability legislation in the United Statesdthe Oil Pollution Act of 1990 (OPA 90)das well as international conventions on spill prevention that included such measures as the requirement for double-hulls on tankers by 2015 and increased financial liability. The significant financial consequences for tanker owners and operators as a result of the Exxon Valdez spill and the spiller liability inherent in subsequent regulations brought the consequences for spills to an unprecedented level. The financial risk associated with large spills may have had as much impact on spill prevention as any actual preventive measures, such as double-hulls on tankers.

2.3.3. Sources of Oil Spills and Patterns of Spillage Spills occur around the worlddanywhere that oil is produced, transported, stored, or consumed. The vast majority of spills are relatively small. As shown in Figure 2.1, 72% of spills are 0.003 to 0.03 ton or less. The total of amount of these small spills comes to 0.4% of the total spillage. The largest spills (over 30 tons) make up 0.1% of incidents but involve nearly 60% of the total amount spilled. Naturally, the relatively rare large spill incidents get the most public attention owing to their greater impact and visibility, though spill size itself is

Chapter | 2

13

Spill Occurrences: A World Overview

TABLE 2.3 Largest Oil Spills in History Worldwide Environmental Research Consulting (ERC data)** Date

Source Name*

Location

Tons

10-Mar-1991

700 oil wells

y

Kuwait

71,428,571

20-Jan-1991

Min al Ahmadi Terminalyz

Kuwait

857,143

3-Aug-2000

oil wells

Russia

700,000

3-Jun-1979

Ixtoc I well

Mexico

476,190

Iraq

377,537

Uzbekistan

299,320

Trinidad/Tobago

286,354

Russia

285,714

y

1-Feb-1991

Bahra oil fields

2-Mar-1990

oil well x

19-Jul-1979

T/V Atlantic Empress

25-Oct-1994

Kharyaga-Usinsk Pipeline y

4-Feb-1983

No. 3 Well (Nowruz)

Iran

272,109

6-Aug-1983

T/V Castillo de Bellver

South Africa

267,007

16-Mar-1978

T/V Amoco Cadiz

France

233,565

10-Nov-1988

T/V Odyssey

Canada

146,599

11-Apr-1991

T/V Haven

Italy

144,000

1-Aug-1980

D-103 concession well

Libya

142,857

6-Jan-2001

pipeline

Nigeria

142,857

Kuwait

139,690

19-Jan-1991

yz

T/V Al Qadasiyah y

19-Jan-1991

T/V Hileen

Kuwait

139,690

18-Mar-1967

T/V Torrey Canyon

United Kingdom

129,857

19-Dec-1972

T/V Sea Star

Oman

128,891

23-Feb-1980

T/V Irenes Serenade

Greece

124,490

yz

19-Jan-1991

T/V Al-Mulanabbi

Kuwait

117,239

7-Dec-1971

T/V Texaco Denmark

Belgium

107,143

19-Jan-1991

T/V Tariq Ibn Ziyadyz

Kuwait

106,325

20-Aug-1981

storage tanks

Kuwait

106,003

yz

26-Jan-1991

Min al Bakar Terminal

Kuwait

100,000

15-Nov-1979

T/V Independentza

Turkey

98,255

11-Feb-1969

T/V Julius Schindler

Portugal

96,429

(Continued )

14

PART | I

Introduction and the Oil Spill Problem

TABLE 2.3 Largest Oil Spills in History Worldwide Environmental Research Consulting (ERC data)**dcont’d Date

Source Name*

Location

12-May-1976

T/V Urquiola

Spain

95,714

25-May-1978

No. 126 Well/pipeline

Iran

95,238

28-Mar-1995

pipeline

Nigeria

90,000

5-Jan-1993

T/V Braer

United Kingdom

85,034

Kuwait

83,897

yz

Tons

1-Mar-1991

pipeline

29-Jan-1975

T/V Jakob Maersk

Portugal

82,503

6-Jul-1979

storage tank (Tank #6)

Nigeria

81,429

19-Nov-2002

T/V Prestige

Spain

77,000

3-Dec-1992

T/V Aegean Sea

Spain

74,490

6-Dec-1985

T/V Nova

Iran

72,626

15-Feb-1996

T/V Sea Empress

United Kingdom

72,361

19-Dec-1989

T/V Khark 5

Morocco

70,068

27-Feb-1971

T/V Wafra

South Africa

68,571

11-Dec-1978

fuel storage depot

Zimbabwe

68,027

26-Apr-1992

T/V Katina P.

South Africa

66,700

12-Jun-1978

Sendai Oil Refinery

Japan

60,204

6-Dec-1960

T/V Sinclair Petrolore

Brazil

60,000

7-Jan-1983

T/V Assimi

Oman

53,741

9-Nov-1974

T/V Yuyo Maru No. 10

Japan

53,571

28-May-1991

T/V ABT Summer

Angola

51,020

22-May-1965

T/V Heimvard

Japan

50,000

31-Dec-1978

T/V Andros Patria

Spain

49,660

30-Jan-1991

T/V Ain Zalah yz

Kuwait

49,543

13-Jun-1968

T/V World Glory

South Africa

48,214

13-Jan-1975

T/V British Ambassador

Japan

48,214

9-Dec-1983

T/V Pericles GC

Qatar

47,619

9-Aug-1974

T/V Metula

Chile

47,143

Chapter | 2

15

Spill Occurrences: A World Overview

TABLE 2.3 Largest Oil Spills in History Worldwide Environmental Research Consulting (ERC data)**dcont’d Date

Source Name*

Location

Tons

1-Jun-1970

T/V Ennerdale

Seychelles

46,939

7-Dec-1978

T/V Tadotsu

Indonesia

44,878

29-Feb-1968

T/V Mandoil

United States

42,857

10-Jun-1973

T/V Napier

Chile

38,571

13-Mar-1994

T/V Nassia

Turkey

38,500

26-Aug-1979

T/V Patianna

United Arab Emirates

38,000

11-Jun-1972

T/V Trader

Greece

37,500

24-Mar-1989

T/V Exxon Valdez

United States

37,415

29-Dec-1980

T/V Juan Antonio Lavalleja

Algeria

37,279

21-Oct-1994

T/V Thanassis A.

Hong Kong

37,075

22-Apr-1988

T/V Athenian Venture

Canada

36,061

7-Feb-1977

T/V Borag

Taiwan

35,357

Mar-1986

Pemex Abkatun 91

Mexico

35,286

6-Feb-1976

T/V St. Peter

Colombia

35,100

*“T/V” ¼ “tank vessel” and refers to tank ships or tankers. **Ended in January 2010. y War-related intentional spillage. z Several intentional spills occurred nearly simultaneously during the 1991 Gulf War. They are often aggregated into one large “spill.” In this list, the individual spill sources are separated. x T/V Atlantic Empress spilled 145,250 tons of oil off Trinidad and Tobago on 19 July 1979, then another 141,000 tons while under tow off Barbados.

not a direct measure of damage. Location and oil type are extremely important in determining the degree of environmental and socioeconomic damage. Oil spills and discharges* can occur at any point in the “life cycle” of petroleumdduring oil exploration and production; transport by vessel, pipeline, railroad, or tanker truck; refining; storage, consumption or usage as fuel or as raw material for manufacturing; or waste disposal. The regional and national patterns of spillage depend on the oil-related activities in those *

A “spill” is a discrete event in which oil is accidentally or, occasionally, intentionally released. A “discharge” is a legal permitted release of oil (usually in a highly diluted state in water) as part of normal operations.

16

PART | I

Introduction and the Oil Spill Problem

% Total Spills 80%

71.9%

70%

# spills

60%

amount

50% 39.5%

40% 30%

22.8%

22.2%

20.3%

20% 6.2%

10% 0%

11.7%

0.4%

0.003

1.6%

0.03

4.3%

0.3

1.6%

3

0.3%

3,000

0.1%

30

0.0%

300

Spill Size (tonnes) FIGURE 2.1 Size classes of U.S. marine oil spills, 1990e1999 (ERC data).

locations, the amount of oil handled, and the degree to which oil prevention measures have been implemented and enforced. Overall, oil spillage has decreased significantly in the United States and internationally due to the implementation and enforcement of prevention measures as well as more responsible operations on the part of the shipping and oil industries.13,17,21,22 In the 1970s, an estimated 6.3 million tons of oil spilled into marine waters from all sources, excluding war-related incidents.22 By the 1980s, an estimated 3.8 million tons of oil spilled worldwide, a 40% reduction since the decade 1988e1997. Spillage reduced another 20% by the 1990s. These reductions in spillage are all the more remarkable considering the increases in production, shipping, and handling of oil during this time period (Table 2.4). In a series of studies that estimated total oil inputs into the marine environment from spills, as well as from operational discharges* from shipping and other sources, especially urban runoff,y a definitive trend of input reduction is apparent (Table 2.5). It is important to note that some of the variations between the studies are due to differences in methodology rather than to actual differences in inputs. *

A legal permitted release of oil (usually in a highly diluted state in water) as part of normal operations. y “Urban runoff” is the accumulation of drops of oil that leak from automobiles, trucks, and other vehicles, as well as small chronic spillages that occur from other land-based sources. The oil washes off into storm sewers, culverts, and other waterways into streams and rivers that enter marine waters. Because the exact source of this spillage cannot be pinpointed, it is termed nonpoint source pollution.

Chapter | 2

17

Spill Occurrences: A World Overview

TABLE 2.4 Annual Worldwide Marine Oil Spillage (ERC Data) Estimated Average Annual Tons Spilledy Source Type

1970s

1980s

1990s

428,646

190,180

126,743

2,735

23,811

10,248

Pipelines

59,087

36,744

85,664

Facilities

66,067

58,047

35,655

Offshore Exploration/ Production

69,111

68,099

38,351

9,241

1,775

3,905

634,887

378,656

300,546

Tank Vessels Nontank Vessels

Unknown/Other Total y

Excluding war-related spills.

The tracking of oil spills is generally conducted by those authorities involved in initiating emergency spill response operations, such as Coast Guard agencies or state and local governments. The accuracy of reporting, particularly of smaller spills, varies considerably from one jurisdiction to another. There have been increases in the reporting of increasingly smaller spills, though not necessarily in the actual incidence of such spills, which reflects broader public awareness of spills and greater concern about and responsibility for these incidents by spillers. As larger spills become increasingly rarer, it is important that contingency planners and spill responders maintain preparedness for these large spills owing to the potential damages associated with them.22,23 A detailed recent overview of oil spills in the United States is presented here based on Environmental Research Consulting (ERC) data, along with analytical results from some past international studies on oil spills.24-28

2.3.4. Spillage from Oil Exploration and Production Activities During the years 1998e2007, an estimated 182 tons of crude oil spilled annually from offshore exploration and production platforms into U.S. waters. An additional 373 tons spilled annually from pipelines associated with offshore oil production, for a total of 555 tons per year. This represents a nearly 66% reduction in spillage since 1988e1997, and an 87% reduction in spillage since the 1970s (1969e1977). Oil spillage from offshore platforms in U.S. Outer Continental Shelf (OCS) and state waters is shown in Figure 2.2 for 1969e2007.

TABLE 2.5 Estimated Worldwide Oil Inputs Based on Various Studies 18

Oil Input Estimates (Tons) Source Natural Seeps Municipal/Industrial Urban Runoff Coastal Refinery Other Coastal

Tanker Accidents Other Shipping

Atmospheric

zzz

Offshore Expl/Prod TOTAL z

1981**

1981yy

1990zz

1997xx

2000***

600,000

600,000

300,000

200,000

258,500

600,000

600,000

2,700,000

2,250,000

1,480,000

1,230,000

1,175,000

114,900

156,900

2,500,000

2,100,000

1,430,000

1,080,000

e

n/a

140,000

200,000

e

e

100,000

e

112,500

4,900

e

150,000

50,000

50,000

e

2,400

12,000

2,130,000

1,100,000

1,440,000

1,420,000

564,000

389,000

413,100

300,000

300,000

390,000

400,000

e

157,900

100,000

yyy

7,100

750,000

200,000

340,000

320,000

e

586,500

1,080,000

600,000

710,000

700,000

e

225,800

306,000

600,000

600,000

300,000

300,000

305,000

68,000

24,700

80,000

60,000

50,000

50,000

47,000

19,750

38,000

10,940,000

7,960,000

6,490,000

5,850,000

2,349,500

2,246,750xxx

1,802,700

[24] [25] **[26] yy [27] zz [28] xx [13] ***[17] yyy Includes 53,000 tons from small-craft activity. zzz Atmospheric deposition of petroleum hydrocarbons from volatile organic compounds (VOCs) that evaporate during the handling of oil and incomplete fuel combustion that are then deposited into the sea. xxx Does not include urban runoff. x

Introduction and the Oil Spill Problem

Operational

1979x

PART | I

Transportation

1973z

Chapter | 2

19

Spill Occurrences: A World Overview

Tonnes 18,000 16,000 14,000 12,000 10,000 8,000 6,000 4,000 2,000 0 1969

1974

1979

1984

1989

1994

1999

2004

FIGURE 2.2 Annual U.S. offshore oil platform spillage, 1969e2007 (ERC data).

Average platform spillage by decade is shown in Table 2.6. There has been a 30% reduction in annual spillage since 1988e1997 and a 95% reduction since the 1970s. Annual oil spillage from pipelines connected to offshore platforms is shown in Figure 2.3, and by decade in Table 2.7. There has been a 68% reduction in offshore pipeline spillage since 1988e1997. Of the total spillage, 96% is in the Gulf of Mexico. Offshore oil exploration and production spillage was combined to include offshore platforms and pipelines, as well as offshore supply vessels servicing the platforms, as shown in Table 2.8. There has been a 61% reduction in total spillage since 1988e1997 and an 87% reduction since the 1970s.

TABLE 2.6 Average Annual Spillage from U.S. Offshore Oil Platforms (ERC data) Years

Average Annual Spills One Ton or More

Average Annual Tons Spilled

1969e1977

45

3,694

1978e1987

29

192

1988e1997

14

259

1998e2007

20

182

1969e2007

27

1,015

20

PART | I

Introduction and the Oil Spill Problem

Tonnes 5,000 4,500 4,000 3,500 3,000 2,500 2,000 1,500 1,000 500 0 1969

1974

1979

1984

1989

1994

1999

2004

FIGURE 2.3 Annual oil spillage from U.S. offshore pipelines, 1969e2007 (ERC data).

TABLE 2.7 Average Annual Spillage from U.S. Offshore Oil Pipelines (ERC data) Years

Average Annual Spills One Ton or More

Average Annual Tons Spilled

1969e1977

15

640

1978e1987

10

495

1988e1997

14

1,161

1998e2007

13

373

1969e2007

13

668

Oil spillage per production (i.e., barrels spilled per barrels produced) has decreased over time, as shown in Table 2.9. In other words, despite increases in production, spillage rates have decreased. For every ton of oil produced in the United States, less than 0.000005 tons have spilled from offshore exploration and production activities in the last decade. This is a 71% reduction since the 1988e1997 decade and an 87% reduction since the 1969e1977 decade. While the majority of oil production spills have been recorded in offshore waters, there are reported spills of inland-based oil production wells to inland areas, as shown in Table 2.10. During the oil extraction process at offshore oil platforms, water in the oil reservoir is also pumped to the surface. Industry practice is to treat this

Chapter | 2

21

Spill Occurrences: A World Overview

TABLE 2.8 Average Annual Spillage (tons) from U.S. Offshore Oil Activities (ERC data) Years

Platforms

Pipelines

Offshore Vessels

Total

1969e1977

3,694

640

14

4,348

1978e1987

192

495

39

726

1988e1997

259

1,161

7

1,427

1998e2007

182

373

1

556

1969e2007

1,015

668

15

1,698

TABLE 2.9 U.S. Offshore Oil Exploration/Production Spillage per Production (ERC data) Years

Average Annual Tons Spilled per Tons Produced

1969e1977

0.0000089

1978e1987

0.0000015

1988e1997

0.0000040

1998e2007

0.0000012

1969e2007

0.0000038

TABLE 2.10 Average Annual U.S. Inland Oil Exploration/Production Spillage (ERC data) Years

Average Annual Tons Spilled

1980e1987

521

1988e1997

742

1998e2004

863

1980e2004

705

“produced water” to separate free crude oil, and then to inject the water back into the reservoir, or to discharge the water overboard from the platform. Increasingly, the reinjection process is becoming the preferred technique. The highly diluted oil content in produced water (with a maximum allowable oil

22

PART | I

Introduction and the Oil Spill Problem

TABLE 2.11 Estimated Oil Inputs in Produced Water from U.S. Offshore Oil Exploration/Production

U.S. Region

Produced Water (tons/yr)

Oil/Grease Content (ppm)

Oil/Grease Discharge (tons/yr)

Low

High

“Best”*

Low

High

“Best”

Gulf of Mexico OCS

67,571,429

15

29

20

1,300

2,500

1,700

Louisiana State

26,571,429

15

29

20

450

860

600

e

e

6.6

0

0

5

Texas State

614,286

California Offshore

5,157,143

15

29

18

85

170

85

Alaska State

6,528,571

15

29

15

110

210

110

e

e

20

2,000

3,740

2,500

Total US

106,442,858

*Best estimate as determined by panel of experts in the 2003 NRC study.17

content of 29 ppm) from offshore oil exploration and production processes is generally dispersed very quickly in the open waters where offshore oil platforms are located. The impacts from these inputs in offshore waters have been studied extensively, and, as concluded by the 2003 NRC study, “there is little evidence of significant effects from petroleum around offshore platforms in deep water.”17 The oil inputs from produced water are calculated as shown in Table 2.11don average, 2,500 tons per year, based on the methodology used by the 2003 NRC study based on measurements and assumptions of maximum allowable oil content in produced water (“high”) or lower oil content as reported by offshore operators.17 It is important to note that these inputs are permitted operational discharges that are distinct from accidental spillage previously reviewed. Worldwide estimates on oil spillage and discharges from offshore oil exploration and production activities are shown in Table 2.12. The greatest concern associated with oil pollution from offshore oil and gas exploration is the unlikely event of a catastrophic well “blowout”*. The largest well blowout incidents worldwide are shown in Table 2.13. Fortunately, most blowouts release relatively little oil.29 *

Loss of well control or a blowout is defined as: the uncontrolled flow of formation or other fluids, including flow to an exposed formation (an underground blowout) or at the surface (a surface blowout), flow through a diverter, or uncontrolled flow resulting from a failure of surface equipment or procedures.

Chapter | 2

23

Spill Occurrences: A World Overview

TABLE 2.12 Worldwide Spillage and Discharge from Offshore Oil Exploration and Production Annual Oil Input Estimate (tons)

Study

Estimate Year

Spillage

Operational

Atmospheric

Total

NRC, 1975 [24]

1973

e

e

e

80,000

Kornberg, 1981 [25]

1979

e

e

e

60,000

Baker, 1983 [26]

1981

e

e

e

50,000

NRC, 1985 [27]

1981

e

e

e

50,000

GESAMP, 1993 [28]

1990

e

e

e

47,000

GESAMP, 2007 [13]

1997

3,400

16,350

e

19,750

NRC, 2003 [17]

2000

860

19,000

1,300

21,160

2.3.5. Spills During Oil Transport After extraction from offshore or terrestrial wells, oil is transported by a variety of means to refineries and ultimately to industrial or individual consumersdby tank vessel (tank ships or tankers; tank barges), pipeline, railroad, and tanker truck, each potentially a source of spillage.

2.3.5.1. Spillage from Tank Vessels Tank ships can carry the greatest amount of oildas much as 300,000 tonsdand thus can be the sources of the largest transport-related spills. Tank ships (tankers) carrying crude oil or refined petroleum as cargo spilled an average of 514 tons of oil annually in U.S. waters over the last decade, a 90% reduction since the decade 1988e1997. A breakdown of annual spillage from oil tankers is shown in Figure 2.4. Average annual spillage by decade is shown in Table 2.14. Tank barges carrying oil as cargo spilled an average of 771 tons of oil annually over the last decade, a nearly 67% reduction from the spillage in the decade 1988e1997. Annual spillage volumes are shown in Figure 2.5. A breakdown of average annual spillage from oil tank barges is shown in Table 2.15. Oil transport by tank vessels (tankers and barges) has decreased over the last decades in the United States. Oil spillage from tank vessels in the United States in relation to oil transported by this mode decreased by 71% since the decade 1988e1997 and 81% since the 1980s (Table 2.16). Worldwide estimates of tanker and tank barge spillage made in international studies are shown in Table 2.17.

24

PART | I

Introduction and the Oil Spill Problem

TABLE 2.13 Largest Offshore Exploration and Production Well Blowouts Worldwide (ERC data) Well

Location

Date

Tons

Ixtoc I

Bahia del Campeche, Mexico

June 1979

471,430

Pemex Abkatun 91

Bahia del Campeche, Mexico

October 1986

35,286

Phillips Ekofisk Bravo

North Sea, Norway

April 1977

28,912

Nigerian National Funiwa 5

Forcados, Nigeria

January 1980

28,571

Aramco Hasbah 6

Gulf, off Saudi Arabia

October 1980

15,000

Iran Marine International

Gulf, off Laban Island, Iran

December 1971

14,286

Union Alpha Well 21

Santa Barbara, California, USA

January 1969

14,286

Chevron Main Pass 41-C

Gulf of Mexico, Venice, Louisiana, USA

March 1970

9,286

Pemex Yum II/Zapoteca

Bahia del Campeche, Mexico

October 1987

8,378

Shell South Timabalier B-26

Gulf of Mexico, Bay Marchand, Louisiana, USA

December 1970

7,585

2.3.5.2. Spills from Pipelines In inland areas, underground and above-ground pipelines transport large quantities of crude oil and refined fuels, particularly diesel, gasoline, heavy fuel oil, and trans-mix.* Spillage from pipelines in coastal and inland areas is shown in Table 2.18 and Figure 2.6. During the last decade, coastal and inland pipelines spilled an average of 11,000 tons of oil annually. This represents a 35% reduction in spillage since 1988e1997 and 70% since the 1970s. In these analyses, coastal and inland pipelines were considered to encompass all parts of the pipeline system, including gathering pipes, transmission pipes, breakout tanks, pump stations, and tank farms directly associated with and operated by pipeline companies. Offshore pipelines were considered separately under *

Usually a combination of No. 2 fuel oil (diesel) and No. 6 heavy fuel oil.

Chapter | 2

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Spill Occurrences: A World Overview

Tonnes 90,000 80,000 70,000 60,000 50,000 40,000 30,000 20,000 10,000 0

1962

1967

1972

1977

1982

1987

1992

1997

2002

2007

FIGURE 2.4 Spills into U.S. waters from tank ships, 1962e2007 (ERC data).

TABLE 2.14 Average Annual Oil Spillage from Tank Ships in U.S. Waters (ERC data) Years

Average Number of Spills One Ton or More

Average Annual Tons Spilled

1962e1967

e

7,162

1968e1977

301

27,513

1978e1987

153

8,607

1988e1997

55

6,028

1998e2007

19

514

offshore exploration and production. It should be noted that a significant portion of oil (about 85%) that spills from inland pipelines goes to containment areas around breakout tanks or to solid ground rather than directly into surface waters. With concerns about the aging pipeline infrastructure and vulnerability of pipelines for spillage, there have been a number of regulatory changes for pipelinesdthe Oil Pollution Act of 1990 (OPA 90), the 2002 Pipeline Safety Act (PSA), and the 2006 Pipeline Integrity, Protection, Enforcement, and Safety (PIPES) Act, which have improved pipeline safety and reduced spillage. Pipeline spillage amounts by oil type and per unit of oil transport are shown in Table 2.19. Spillage per unit transport has decreased 37% since the decade 1988e1997, and 57% since the 1980s.

26

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Introduction and the Oil Spill Problem

Tonnes 16,000 14,000 12,000 10,000 8,000 6,000 4,000 2,000 0

1968

1973

1978

1983

1988

1993

1998

2003

FIGURE 2.5 Spills into U.S. waters from tank barges, 1968e2007 (ERC data).

TABLE 2.15 Average Annual Oil Spillage from Tank Barges in U.S. Waters (ERC data) Years

Average Number of Spills One Ton or More

Average Annual Tons Spilled

1968e1977

368

4,547

1978e1987

290

7,570

1988e1997

123

3,269

1998e2007

54

776

1968e2007

186

4,040

2.3.5.3. Spills from Railroads Railroads spilled 200 tons of oil annually as cargo in tankcars and as fuel. This is a 34% reduction since the decade 1988e1997. Average annual railroad spillage and spillage by ton-miles transported are shown in Table 2.20. (A tonmile is a measure of the transport of oil one ton the distance of one mile.) The spillage rate has decreased in the last three decades. Spills from railroads often go to ballast and do not always directly impact waterways. 2.3.5.4. Spillage from Tanker Trucks Tanker trucks carrying oil (usually fuels) as cargo spilled an average of 1,300 tons of oil annually in the last decade, a 76% increase since the decade 1988e1997.

Chapter | 2

27

Spill Occurrences: A World Overview

TABLE 2.16 Oil Spillage by Tank Vessels in Relation to Oil Transported in U.S. Waters (ERC data) Average Annual Spillage (tons) Tankers

Tank Barges

Combined

Average Annual Spillage per Billion Ton-Miles* Oil Transport

1978e1987

8,607

7,570

16,177

27.40

1988e1997

6,028

3,269

9,297

18.22

1998e2007

514

776

1,290

5.28

Time Period

*Ton-miles combine volume and distance of transport.

TABLE 2.17 Estimates of Worldwide Annual Tank Vessel Spillage Estimate of Average Annual Tank Vessel (Tank Ship and Tank Barge) Spillage (tons) Study

1970s

1980s

1990s

NRC, 1975

300,000

e

e

Kornberg, 1981

300,000

e

e

Baker, 1983

e

390,000

e

NRC, 1985

e

400,000

e

GESAMP, 1993

e

564,000*

e

GESAMP, 2007

e

e

157,900

NRC, 2003

e

e

100,000

Etkin, 2001

372,878

98,866

184,460y

ERC Data

431,381

213,991

136,991z

*Includes operational discharges from vessels. y Includes 1991 Gulf War-related tanker spillage. z Excludes 1991 Gulf War-related tanker spillage.

This may be attributed to better reporting of these incidents to local authorities that usually handle these incident responses. Spills from tanker trucks often go to pavements and do not directly impact waterways. Average annual spillage is in Table 2.21. There are no reliable international data on this source type.

28

PART | I

Introduction and the Oil Spill Problem

TABLE 2.18 Oil Spillage from U.S. Inland and Coastal Pipeline Systems (ERC data) Years

Average Annual Number of Spills One Ton or More

Average Annual Tons

1968e1977

276

37,049

1978e1987

172

25,885

1988e1997

140

16,900

1998e2007

195

10,965

1968e2007

196

22,828

Tonnes 70,000 60,000 50,000 40,000 30,000 20,000 10,000 0

1968

1974

1979

1984

1989

1994

1999

2004

FIGURE 2.6 Spills into U.S. waters from pipelines, 1968e2007 (ERC data).

2.3.6. Spillage from Oil Refining Each year, on average, over 8.6 billion tons of imported and domesticallyproduced crude oil are refined into hundreds of petroleum-based products and fuels at the 162 refineries in the United States. Spillage from oil refineries averaged 1,700 barrels annually over the last decade, about a 19% reduction since the decade 1988e1997 and a 27% reduction in spillage per barrel of oil throughput at refineries. Average annual spillage is shown in Table 2.22. The lower spillage for the 1980e1987 time period is likely a data artifact since spill sources in reports were not always accurately identified (e.g.,

Chapter | 2

29

Spill Occurrences: A World Overview

TABLE 2.19 Average Annual U.S. Oil Pipeline Spillage by Oil Type and Transport (ERC data) Tons Spilled per Billion Ton-Miles* Transport

Spillage (tons) Years

Crude

Refined

Total

Crude

Refined

Total

1980e1987

11,314

6,157

17,471

35.24

25.06

30.71

1988e1997

16,384

9,292

25,676

48.99

40.42

45.50

1998e2007

10,855

6,558

17,413

32.38

25.95

29.71

1980e2007

7,716

3,249

10,965

27.11

11.89

19.88

*A ton-mile is one ton of oil being transported one mile.

TABLE 2.20 Average Annual Estimated U.S. Oil Spillage from Railroads (ERC data) Years

Tons Spilled per Year

Tons Spilled per Billion Ton-Miles Transport

1980e1987

332

26.7

1988e1997

309

18.6

1998e2007

204

10.3

1980e2007

278

16.2

TABLE 2.21 Average Annual Estimated U.S. Oil Spillage from Tanker Trucks (ERC data) Years

Tons per Year

Tons Spilled per Billion Ton-Miles Transport

1980e1987

698

25.5

1988e1997

745

26.2

1998e2007

1,312

41.2

1980e2007

934

31.6

30

PART | I

Introduction and the Oil Spill Problem

TABLE 2.22 Average Annual Oil Spillage from U.S. Refineries (ERC data)

Years

Tons Spilled

Annual Refining Capacity (tons)

Refinery Utilization

Annual Throughput (tons)

Spillage per Refinery Throughput

1980e1987

502

9.17 109

78.95%

0.66 109

0.00000076

1988e1997

2,145

9.41 109

89.71%

0.72 109

0.00000296

91.62%

0.80 10

9

0.00000216

87.32%

0.73 10

9

0.00000208

1998e2007 1980e2007

1,734

10.33 10

1,529

9.66 10

9

9

a refinery may merely have been identified as a “facility”). There were also changes in reporting requirements in 1986 that authorized the Toxics Release Inventory (TRI) to track facility releases of a variety of chemicals and toxic substances. While crude oil and refined petroleum products themselves are not encompassed by the TRI-reporting requirements, some of their additives and chemical components are listed. Overall, this created a greater awareness of the need to report discharges from refineries. During refining, wastewater containing minute concentrations of oil is legally discharged in effluents, as permitted under the National Pollutant Discharge Elimination System (NPDES). The NPDES-permitted refinery effluents contain no more than five parts of oil per million parts of wastewater. The effluents are generally discharged in rivers and coastal areas where the already dilute oil concentrations are quickly diluted even further. The environmental impacts of refinery effluents have been studied fairly extensively. Environmental impacts from the oil in the effluents are extremely low and localized. Refineries are, however, generally located in industrial areas that have other permitted discharges, making it difficult to separate the effects of oil in effluents from those of background concentrations of other contaminants from other point and nonpoint sources. A comprehensive review of the ecological impacts of refinery effluents concluded that any minor impacts are limited to the areas close to the outfalls, but that it is difficult to distinguish these impacts from other pollution sources.30 The total amount of aqueous effluent discharged from oil refineries has decreased by 20% over the last 40 years due to increases in the use of air cooling and recirculation of cooling water. In addition, the toxicity of effluent discharges has decreased significantly owing to the implementation of various wastewater treatment systems.30,31 The estimated maximum discharge of oil in refinery effluents over the last decade is 7,700 tons per year. This estimate is the equivalent of less than 0.00001 ton of oil for each ton of oil processed, and is based on the following assumptions: wastewater production as a function of refinery capacity average for the last decade is 2.37

Chapter | 2

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Spill Occurrences: A World Overview

TABLE 2.23 Estimated Annual Oil Discharged in U.S. Oil Refinery Effluents (ERC data)

Years

Throughput (billion tons)

Wastewater Tons per Ton Throughput

Wastewater (billion bbl)

Oil in Effluent (tons)

1985e2007

0.66

2.15

1.17

5,837

1985e1987

0.72

1.69

1.49

7,465

1988e1997

0.80

2.05

1.91

9,538

1998e2007

0.73

2.38

1.55

7,740

barrels of wastewater (refinery effluent) produced per barrel of refining capacity; and effluents contain 5 ppm of oil (based on NPDES guidelines). There are a number of estimates of the amount of wastewater produced per unit of refining capacity. The average of the two best-documented sources was taken.31,32 Average annual refinery effluent discharges in the United States are shown in Table 2.23. The average annual oil in legally-permitted refinery effluent discharges is based on an assumption of maximum effluent oil concentration of 5 ppm. This value is the maximum allowed. Actual oil concentrations in effluents are likely to be lower. Estimates of international coastal refinery spillage and effluent oil content were made as shown in Table 2.24. Estimates in the 2007 GESAMP study were made using the same methodology as for U.S. refinery effluents, with the exception that the oil content in the effluent was assumed to vary between 5 ppm and 25 ppm, depending on national laws and practices.13

TABLE 2.24 Estimates of Worldwide Oil Refinery Spillage and Effluent Discharges Estimated Average Annual Inputs to Marine Waters (tons)

Study

Year(s)

Refinery Spillage

Oil in Refinery Effluent

Total Refinery Inputs

NRC, 1975

1970s

200,000

e

200,000

Baker, 1983

1980s

100,000

e

100,000

GESAMP, 2007

1990s

e

179,547

179,547

32

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Introduction and the Oil Spill Problem

2.3.7. Spillage Related to Oil Consumption and Usage Refined petroleum products are used in a wide variety of applications, including fuels for transportation, heating, manufacturing, and electricity production. Spillage of oil from sources that “consume” or use oil is generally outside of the realm of the petroleum industry itself, but is presented here for perspective on total oil inputs.

2.3.7.1. Spillage from Nontank Vessels “Nontank vessels” (e.g., cargo ships) carrying oil as bunker fuel and for operations spill an average of 230 tons of oil annually, a 43% reduction since the decade 1988e1997. Average annual spillage from these vessels is shown in Table 2.25. At the same time, the shipment of dry cargo (i.e., nonpetroleum shipments) by vessels has increased by 43% over the last 30 years in the United States (U.S. Army Corps of Engineers). The rate of spillage from these cargo ships in relation to the tonnage of cargo moved in U.S. waters during that time period is shown in Table 2.26. Spillage per cargo shipment has declined by 50% since the decade 1988e1997. Worldwide nontank vessel spillage was estimated in several international studies (Table 2.27). Spillage from smaller vessels (e.g., passenger, fishing, recreational, and unclassified vessels) averaged under 600 tons annually over the last decade in the United States, a nearly 34% reduction since the decade 1988e1997. Average annual spillage from these vessels is shown in Table 2.28. There are no reliable worldwide estimates of spillage from smaller vessels. Operational discharges of lubricant oils from the vessels within ports, during transit within ports, and while moored at docks contribute a significant amount of oil to U.S. watersdabout 2,800 tons annually.33 Leakages occur from stern tubes and other submerged machinery, as well as from on-deck

TABLE 2.25 Average Annual Oil Spillage from Nontank Vessels in U.S. Waters (ERC data) Years

Annual Number of Spills (1 ton or more)

Annual Tons Spilled

1973e1977

100

149

1978e1987

85

969

1988e1997

83

402

1998e2007

37

229

1973e2007

73

479

Chapter | 2

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Spill Occurrences: A World Overview

TABLE 2.26 Cargo Vessel Oil Spillage per Dry Cargo Shipments in U.S. Waters (ERC data) Years

Dry Cargo Shipment (million short tons)

Annual Tons Spilled

Tons Spilled per Million Short Tons Shipped

1978e1987

1,057

969

0.90

1988e1997

1,256

402

0.32

1998e2007

1,382

229

0.16

1978e2007

1,232

534

0.46

TABLE 2.27 Estimates of Worldwide Nontank Vessel Spillage Estimate of Average Annual Nontank Vessel Spillage (tons) Study

1970s

1980s

1990s

NRC, 1975

750,000*

e

e

Kornberg, 1981

200,000*

e

e

Baker, 1983

e

340,000*

e

NRC, 1985

e

320,000*

e

GESAMP, 2007

e

NRC, 2003

e

533,000* 7,100

Etkin, 2001

1,000

4,024

5,454

ERC Data

2,735

23,811

10,248

*Includes operational discharges from nontank vessels.

machinery. Based on a study by Etkin, these inputs are estimated to be as shown in Table 2.29, as calculated for the past five years.33 Previous time periods were adjusted based on the overall amount of shipping in U.S. waters. Operational inputs of oil and gasoline from two-stroke engines in the United States were estimated by the 2003 NRC study17 to average 7,000 tons annually and by the 2007 GESAMP study13 to average 53,000 tons annually worldwide (Table 2.30). It should be noted that this estimate has been questioned by a number of researchers (personal communications) with regard to the assumption that all of the gasoline enters the water rather than combusts or evaporates. The use of two-stroke engines of the type mentioned in this study has significantly decreased in the last few years.

34

PART | I

Introduction and the Oil Spill Problem

TABLE 2.28 Estimated Oil Spillage from Smaller Vessels in U.S. Waters (ERC data) Years

Annual Tons Spilled

1973e1977

2,123

1978e1987

939

1988e1997

900

1998e2007

595

1973e2007

999

TABLE 2.29 Vessel Operational Lubricant Leakage in U.S. and Worldwide Ports33 Lubricant Discharges (average annual tons) Stern Tube

Other Operational

Total

Years

U.S.

Worldwide

U.S.

Worldwide

U.S.

Worldwide

1980e1987

1,064

19,558

1,036

22,082

2,101

41,488

1988e1997

1,246

22,903

1,213

25,859

2,460

48,584

1998e2007

1,400

25,734

1,363

29,055

2,764

54,589

1980e2007

1,237

22,738

1,204

25,672

2,442

48,233

2.3.7.2. Spillage from Facilities Coastal facilities (other than refineries) spill an estimated 600 tons of oil in the United States annually, a 72% reduction from the decade 1988e1997. Average annual facility spillage in the United States and worldwide is presented in Table 2.31. It is important to note that spillage volumes from coastal facilities often include oil that spills into secondary containment. A secondary containment system provides an essential line of defense in preventing oil from spreading and reaching waterways in the event of the failure of an oil container (e.g., a storage tank) or the primary containment. The system provides temporary containment of the spilled oil until a response can be mounted. In the last decade, gas stations and truck stops spilled an average of 100 tons of oil annually, a nearly 48% decrease since the decade 1988e1997. This

Chapter | 2

Spill Occurrences: A World Overview

35

TABLE 2.30 Estimates of Oil Inputs from Two-Stroke Recreational Vessels in the United States and Worldwide Estimated Average Annual Tons Input

Region U.S. Atlantic*

3,100

U.S. Gulf of Mexico*

1,540

U.S. Pacific and Alaska*

2,306

U.S. Total*

6,946

Worldwide*

53,000

*Estimates based on analyses in the 2003 NRC study.17

TABLE 2.31 Estimated Oil Spillage from Coastal Marine Facilities in U.S. and Worldwide Waters Years

U.S. Annual Tons Spilledy

Worldwide Annual Tons Spilled

1973e1977

8,889

150,000z

1978e1987

6,112

50,000x

1988e1997

2,151

1998e2007

604

1973e2007

3,803

e 2,400*e12,000** e 13

*From 2007 GESAMP study based on methods in the 2003 NRC study.17 The 2007 GESAMP study13 had a second estimate of 3.9 106 tons annually. y ERC data z [24] x [25, 26] **[23]

includes all spillages that occur at gas station facilities and truck stops, including spills that occur during the transfer of fuels from tanker trucks. Average annual spillage from these sources is shown in Table 2.32. Spills at gas stations and truck stops often go to pavements and other substrates, reducing the direct impacts to waterways. There are no reliable estimates of worldwide spillage rates. Inland facilities regulated under the United States’ Environmental Protection Agency’s Spill Prevention, Control, and Countermeasures (SPCC) program other than refineries and production wells, covered separately in these analyses,

36

PART | I

Introduction and the Oil Spill Problem

TABLE 2.32 Estimated U.S. Oil Spillage from Gas Stations and Truck Stops (ERC data)* Year

Annual Spillage (tons)

1980e1987

171

1988e1997

223

1998e2007

116

1980e2007

170

*Based on reported data reported to the relevant state and local authorities in the 50 U.S. states, as well as data reported to federal authorities. These data do not include leaking underground storage tanks that leak over long periods of time. These data are tracked separately and are not considered emergency spill incidents. Since gas stations are regulated by the EPA, facilities spillage at these facilities of at least 50 gallons (0.17 tons) that occur during these facilities are included. Smaller spills (less than 50 gallons) are not included.

TABLE 2.33 Estimated U.S. Oil Spillage from Inland EPA-Regulated Facilities (ERC data) Years

Annual Spillage (tons)

1980e1987

4,963

1988e1997

35,002

1998e2007

8,525

1980e2007

16,963

spill an average of 8,500 tons of oil annually, a 76% reduction since the decade 1988e1997 (Table 2.33). Spills at inland facilities often go to pavements and other substrates, including secondary containments, reducing direct impacts to waterways. There are no reliable estimates of worldwide spillage rates. Oil spillage from home-heating oil tanks, which are not regulated by the EPA unless the tanks are in sizes larger than 34 tons, amounts to 70 tons of oil annually, a slight decrease from the decade 1988e1997 (Table 2.34). Note that this does not include slow leakages from underground storage tanks. There are no reliable estimates of worldwide spillage rates from residential tanks. Motor vehicles that carry oil as fuel rather than cargo spill about 285 tons of oil annually in the United States, double that for the decade 1988e1997 (Table 2.35). The spillage is associated with greater motor vehicle traffic, as well as

Chapter | 2

Spill Occurrences: A World Overview

37

TABLE 2.34 Estimated Oil Spillage from U.S. Residential Heating Oil Tanks (ERC data) Years

Annual Tons Spilled

1980e1987

26

1988e1997

74

1998e2007

71

1980e2007

59

TABLE 2.35 Estimated U.S. Oil Spillage from Motor Vehicles (excluding tanker trucks) (ERC data) Year

Annual Tons Spilled

Average 1980e1987

39

Average 1988e1997

170

Average 1998e2007

295

Average 1980e2007

168

better reporting by local authorities that are often the emergency responders. Motor vehicle spills* often go to pavements and do not directly impact waterways. Since the data only include spills of less than 1 ton, most passenger vehicles are excluded. There are no reliable estimates of worldwide spillage from motor vehicles.

2.3.7.3. Spillage from Aircraft and Other Sources Aircraft spill an estimated 50 tons of jet fuel annually to inland areas. These spills generally occur at airports during fueling, or occasionally from an accident. Aircraft spill an additional 530 tons annually to U.S. marine waters, based on a 2003 NRC study.17 These spills occur from two sources: through the deliberate discharge or jettisoning of jet fuel due to emergency conditions aboard an aircraft, or through the release of partially burned fuel in inefficient engines or operating modes.17,34 This type of spillage also occurs over inland areas, but there are no *

Note that tanker trucks carrying oil as cargo are considered separately.

38

PART | I

Introduction and the Oil Spill Problem

TABLE 2.36 Estimated U.S. Oil Spillage (bbl) from Other Inland Sources (ERC data) Years

Inland Aircraft (annual tons)

Inland Unknown (annual tons)

1980e1987

2

138

1988e1997

23

314

1998e2007

49

74

1980e2007

26

190

current estimates of these inputs. Total aircraft input in the United States is estimated to be about 580 tons of oil annually. The 2003 NRC study estimated worldwide marine inputs from jettisoned aircraft fuel to be about 7,500 tons annually.17 Average annual spillage from aircraft and miscellaneous unknown (unidentified) inland sources in the United States is shown in Table 2.36.

2.3.7.4. Oil Inputs from Urban Runoff About 50,000 tons of oil enters U.S. marine waters each year through urban runoff, based on a 2003 NRC study.17 Urban runoff is the accumulation of drops of oil that leak from automobiles, trucks, and other vehicles, as well as small chronic spillages that occur from other land-based sources. Oil washes off into storm sewers, culverts, and other waterways into streams and rivers that enter marine waters. Because the exact spillage source cannot be pinpointed, it is termed “nonpoint source” pollution. The U.S. inputs are broken down by region in Table 2.37. Studies that included worldwide estimates of oil in urban runoff are shown in Table 2.38.

TABLE 2.37 Estimates of U.S. Oil Inputs from Urban Runoff Region

Estimated Average Annual Tons of Oil Input*

Atlantic

31,500

Gulf of Mexico

12,600

Pacific

5,829

Alaska

80

Total

50,009

*Estimates based on analyses in the NRC 2003 study.17

Chapter | 2

39

Spill Occurrences: A World Overview

TABLE 2.38 Estimates of Worldwide Marine Oil Inputs from Urban Runoff Estimate of Average Annual Oil Inputs from Urban Runoff (tons) Study

1970s

1980s

1990s

NRC, 1975

2,500,000

e

e

Kornberg, 1981

2,100,000

e

e

Baker, 1983

e

1,430,000

e

NRC, 1985

e

1,080,000

e

NRC, 2003

e

e

140,000

2.3.8. Oil Inputs from Potentially Polluting Sunken Shipwrecks Potential future and documented current oil leakage and discharges from sunken ships in marine waters is an issue of concern worldwide. A study conducted in 1977 drew attention to the oil discharges from a large number of oil tankers sunk during military operations in World War II along the U.S. western, eastern, and southern (Gulf of Mexico) coasts.18 While the tankers had been sunk over 30 years earlier, oil was still periodically leaking from the vessels, which were acting as “seeps.” Many of the tankers were still relatively intact, though their structural integrity was uncertain. The issue of oil pollution from sunken World War II tankers and military vessels was further brought to public attention after several incidents of oil leaking from several vessels (notably the S.S. Jacob Luckenbach off the Pacific coast of the United States, the USS Mississinewa in Micronesia, and the German warship Blu¨cher off Oslo, Norway) in the late 1990s to 2004. These sunken vessels were identified as the sources of “mystery spills” and discharges that impacted shorelines and other resources.35 The South Pacific Environment Programme (SPREP) has conducted surveys of wrecks in the South Pacific region particularly impacted by World War II military vessel sinkings.36,37 In 2005, the American Petroleum Institute and the sponsors of the International Oil Spill Conference* commissioned a study, Potentially Polluting Wrecks in Marine Waters, which involved developing a databasey of recorded *

International Maritime Organization, U.S. Coast Guard, U.S. Environmental Protection Agency, International Petroleum Industry Environmental Conservation Association, Minerals Management Service, and National Oceanic and Atmospheric Administration.

y

The proprietary database was developed by ERC.

40

PART | I

Introduction and the Oil Spill Problem

FIGURE 2.7 Approximate location of potentially polluting shipwrecks* (ERC data).

vessel sinkings for tankships of at least 150 gross registered tons (GRTs) carrying oil and nontank vessels of at least 400 GRTs that carried oil as fuel/ bunkers (and for operations); an analysis of the distribution of and likely amount of oil contained in these vessels; and an examination of the environmental, regulatory, political, technical, and financial issues associated with these sources of petroleum.38 The data analysis revealed that there were at least 8,569 recorded vessel sinkings worldwide, of which 1,583 were tankships and 6,986 were nontank vessels. An estimated 2.5 to 20.4 million tons of oil is thought to be present in these shipwrecks. The shipwrecks are distributed throughout the world, as shown in Figure 2.7. The data in the 2005 Michel et al. study were analyzed regionally, as summarized in Table 2.39.38 This oil will not necessarily discharge, but there is the potential that it will, with the actual probability of discharge depending on vessel integrity and condition, age, depth at which the wrecks rest, temperature of the waters, and type of oil. Heavier fuels at greater depths may be nearly solid, and many of the vessels may be largely intact. On the other hand, the greatest potential for spillage is with the older vessels, particularly those from World War II, which were often built according to lower standards than more modern vessels. The potential for impacts depends largely on the location of the wrecks. Those in nearshore waters tend to present the greatest potential for impacting *

Dots indicate approximate locations based on Marsden square (10-degree latitude/longitude). Because many of the vessels are “war-graves” and there are also safety concerns, authorities aim to prevent plundering or diving exploration. The exact locations of many vessels are uncertain or are classified or confidential.

Chapter | 2

Spill Occurrences: A World Overview

41

TABLE 2.39 Worldwide Potential Pollution from Sunken Tankers and Nontank Vessels (ERC data) Estimated Oil Content of Shipwrecks (tons) Region

Minimum

Maximum

North Atlantic Ocean

951,000

7.5 million

South Atlantic Ocean

165,000

0.5 million

North Pacific Ocean

221,000

1.7 million

South Pacific Ocean

521,000

4.2 million

Indian Ocean

264,000

2.2 million

shorelines. The impacts of discharges from these vessels in the open ocean are likely to be less severe than those closer to shore because of the natural dispersion that would break the oil into smaller concentrations. Much of the oil involved is likely to be heavier and would most likely form tar balls rather than larger slicks unless released in a large mass. The experts who conducted the 2005 Michel et al. study concluded that most of the vessels were likely to release oil in small quantities over a longer period of time or had already started to do so, acting almost as a “natural seep.” Nevertheless, there is the potential for a vessel to suddenly release a much larger quantity of oil if a radical change takes place in the vessel’s structural integrity.38 The political, regulatory, and financial issues associated with these shipwrecks are extremely complex due to jurisdictional concerns. Removing the oil and other hazardous materials, as well as munitions, from these vessels involves complex, dangerous, and expensive salvage operations. It is unclear who would finance or regulate these operations, especially for the large number of World War II vessels involved. Because of the complex issues presented by these wrecks and the overwhelming number of potentially polluting wrecks, an approach involving scientifically based risk assessments and cost-benefit analyses has been promoted by several organizations, government agencies, and researchers to prioritize those wrecks that poset the highest environmental risk for oil and hazardous material removal operations.38-44

2.3.9. Summary of Oil Spillage Estimates of average annual U.S. oil spillage by decade from all source categories are summarized in Table 2.40. Over the last decade, the largest source category of spillage is inland pipelines, followed by EPA-regulated facilities. The oil spillage reported here does not reflect the amounts of oil

42

PART | I

Introduction and the Oil Spill Problem

TABLE 2.40 Estimated Total Average Annual U.S. Oil Spillage (tons) 1969e 1977

1978e 1987

1988e 1997

1998e 2007

Production

4,491

1,243

2,169

1,420

5.07%

Offshore Platform Spills

3,694

192

259

182

0.65%

640

495

1,161

373

1.33%

Offshore Supply Vessels

14

35

7

1

0.00%

Inland Production Wells

143

521

742

863

3.08%

Refining

429

502

2,145

1,734

6.19%

Refinery Spills

429

502

2,145

1,734

6.19%

Transport

69,809

43,092

27,250

13,770

49.16%

Inland Pipelines

37,049

25,885

16,900

10,965

39.15%

Tanker Trucks

429

698

745

1,312

4.68%

Railroads

286

332

309

204

0.73%

Tank Ships

27,499

8,607

6,028

514

1.83%

Tank Barges

4,547

7,570

3,269

776

2.77%

16,932

13,887

39,789

11,088

39.58%

714

969

402

229

0.82%

2,123

939

900

595

2.12%

171

171

223

116

0.41%

21

26

74

71

0.25%

4,286

4,963

35,002

8,525

30.43%

529

531

552

578

2.06%

Coastal Facilities

8,889

6,112

2,151

604

2.16%

Inland Unknown

129

138

314

74

0.26%

71

39

170

295

1.05%

91,660

58,723

71,354

28,011

100.00%

Source Type

Offshore Pipelines

Storage and Consumption Nontank Vessels Other Vessels Gas Stations and Truck Stops Residential Inland EPA-Reg Facilities* Aircrafty

Motor Vehicles Total

% Total 1998e2007

*Excludes refineries, gas stations, and production wells. y Includes aircraft in inland areas plus estimates of marine inputs (based on NRC, 2003).

Chapter | 2

43

Spill Occurrences: A World Overview

that were contained or recovered. It also does not reflect the differences between oil that is spilled directly into marine or freshwater systems and oil that is spilled onto other surfaces, including containment areas around storage tanks in tank farms. The properties of the oil spilled (crude vs. refined, heavy vs. light) and the locations in which the oil spills (marine waters, inland waters, dry surfaces, wetlands, industrial zones) will largely determine the impacts of these spills and should be considered in addition to the actual amounts of oil spilled. Total U.S. oil inputs to marine and inland waters, including spills, runoff, and all operational discharges are shown in Table 2.41. TABLE 2.41 Estimated Total Average Annual U.S. Oil Inputs (tons) Source Type

1969e 1977

1978e 1987

1988e 1997

1998e 2007

% Total 1998e2007

Production

5,876

2,431

3,438

2,930

2.15%

Offshore Platform Spills

3,694

192

259

182

0.13%

Offshore Pipeline Spills

640

495

1,161

373

0.27%

Offshore Supply Vessel Spills

14

39

7

1

0.00%

Inland Production Well Spills

521

742

863

705

0.52%

1,007

963

1,148

1,669

1.23%

35,963

52,758

68,910

55,915

41.09%

429

502

2,145

1,734

1.27%

Refinery Effluents

35,534

52,256

66,765

54,181

39.82%

Transport

69,882

43,084

27,163

13,864

10.19%

Inland Pipelines

37,049

25,885

16,900

10,965

8.06%

Tanker Trucks

429

698

745

1,312

0.96%

Railroads

332

309

204

278

0.20%

Tank Ships

27,513

8,607

6,028

514

0.38%

Tank Barges

4,547

7,570

3,269

776

0.57%

12

15

17

19

0.01%

67,841

65,497

91,595

63,357

46.56%

Produced Water Refining Refinery Spills

Tank Vessel Operational Discharge Storage and Consumption

(Continued )

44

PART | I

Introduction and the Oil Spill Problem

TABLE 2.41 Estimated Total Average Annual U.S. Oil Inputs (tons)dcont’d Source Type Nontank Vessels

1969e 1977

1978e 1987

1988e 1997

1998e 2007

% Total 1998e2007

149

969

402

229

0.17%

Other Vessels

2,123

939

900

595

0.44%

Vessel Operational Discharge

2,000

2,086

2,443

2,745

2.02%

171

223

116

170

0.12%

21

26

74

71

0.05%

4,286

4,963

35,002

8,525

6.27%

2

2

23

49

0.04%

Coastal Facilities (Nonrefining)

8,889

6,112

2,151

604

0.44%

Inland Unknown

129

138

314

74

0.05%

71

39

170

295

0.22%

50,000

50,000

50,000

50,000

36.75%

179,562

163,770

191,106

136,066

100.00%

Gas Stations and Truck Stops Residential Inland EPA-Regulated Facilities** Aircrafty

Motor Vehicles Urban Runoff Total

**Excludes refineries, gas stations, and production wells. y Includes aircraft in inland areas, plus estimates of marine inputs based on the 2003 NRC study [17].

Although annual spill amounts vary from year to year, often due to one or two particularly large incidents, there has been a general downward trend in U.S. spills in the past decade, and an even greater downward trend since 1989, the year of the Exxon Valdez spill (Figure 2.8). Worldwide trends are shown in Figure 2.9. The large spill in 1979 from the Ixtoc I well blowout dominates the spillage. War-related intentional spillage, such as that in the 1991 Gulf War, has not been included. Despite general downward trends in spills in the United States and worldwide, it is important that spill response preparedness be maintained due to the continuing risk of spills, including worst-case discharge scenarios. Most spills will continue to be “routine” in that they are relatively small and easily responded to with local resources. At the same time, occasional large spills, along with increasing public expectations for effective spill response and increased spiller liability, have necessitated complex contingency planning for

Chapter | 2

45

Spill Occurrences: A World Overview

Tonnes 140,000 120,000 100,000 80,000 60,000 40,000 20,000 0

1968

1973

1978

1983

1988

1993

1998

2003

FIGURE 2.8 Annual oil spillage into U.S. waters with reduction trends (ERC data).

Tonnes 2,000,000 1,800,000 1,600,000 1,400,000 1,200,000 1,000,000 800,000 600,000 400,000 200,000 0

1970

1975

1980

1985

1990

1995

2000

FIGURE 2.9 Worldwide oil spillage with reduction trends (ERC data).

increasingly rare high-impact events.22 For example, the United States with its experience in the 1989 tanker Exxon Valdez, which involved the spillage of over 37,000 tons of oil, has not experienced a worst-case discharge scenario, defined as the complete release of the contents of a fully loaded oil tanker or large storage facility. Had the Exxon Valdez released its entire contents, about five times as much oil would have spilled into Prince William Sound. The complexity of the spill response and the impact of the spill is difficult to envision, but must be planned for. The magnitude of the spill from the MC-252

46

PART | I

Introduction and the Oil Spill Problem

well (otherwise referred to as Deepwater Horizon spill) that occurred during April through July 2010 has not yet been verified, though it has been been confirmed to be the largest spill in US history.

REFERENCES 1. Lees GM, et al. The Eastern Hemisphere. In: Pratt WE, Good D, editors. World Geography of Petroleum, 159. Princeton University Press; 1950. 2. Levorson AI. Geology of Petroleum, 14. San Francisco, CA: Freeman Press; 1954. 3. Hodgson SF. Onshore Oil and Gas Seeps in California, California Division of Oil and Gas. Department of Conservation; 1987. 4. Allen A, Schlueter RS, Mikolaj PG. Natural Oil Seepage at Coal Oil Point, Santa Barbara, California. Science 1970;974. 5. Hornafius JS, Quigley D, Luyendyk BP. The World’s Most Spectacular Marine Hydrocarbon Seeps (Coal Point, Santa Barbara Channel, California): Quantification of Emissions. J. Geophys. Res 1999;703. 6. Kvenvolden KA, Simoneit BRT. Hydrothermically Derived Petroleum: Examples from Guaymas Basin, Gulf of California, and Escanaba Trough, Northeast Pacific Ocean. Amer. Assoc. Petrol. Geolog. Bull. 1990;223. 7. Leifer I, Luyendyk B, Broderick K. Tracking Seep Oil from Seabed to Sea Surface and beyond at Coal Oil Point, California, Proceedings of the American Association of Petroleum Geologists (AAPG). Salt Lake City: Utah; 2003. 8. Chernova TG, Rao PS, Pikovskii Y, Alekseeva TA, Nagender NB, Ramalingeswara RB, et al. The Composition and the Source of Hydrocarbons in Sediments Taken from the Tectonically Active Andaman Backarc Basin, Indian Ocean. Mar. Chem. 2001;1. 9. Gupta RS, Qasim SZ, Fondekar SP, Topgi RS. Dissolved Petroleum Hydrocarbons in Some Regions of the Northern Indian Ocean. Mar. Pollut. Bull. 1980;65. 10. Venkatesan MI, Ruth E, Rao PS, Nath BN, Rao BR. Hydrothermal Petroleum in the Sediments of the Andaman Backarc Basin, Indian Ocean. Appl. Geochem. 2003;845. 11. MacDonald IR. Natural Oil Spills. Scientific American 1998;57 (Nov). 12. Wilson RD, Monaghan PH, Osanik A, Price LC, Rogers MA. Natural Marine Oil Seepage. Science 1974;857. 13. GESAMP (IMO/FAO/UNESCO-IOC/UNIDO/WMO/IAEA/UN/UNEP Joint Group of Experts on the Scientific Aspects of Marine Environmental Protection). Estimates of Oil Entering the Marine Environment from Sea-Based Activities. GESAMP Reports and Studies 2007;75. 14. Kvenvolden KA, Cooper CK. Natural Seepage of Crude Oil into the Marine Environment. Geo-Marine Letters 2003;140. 15. Kvenvolden KA, Harbaugh JW. Reassessment of the Rates at Which Oil from Natural Sources Enters the Marine Environment. Mar. Environ. Res. 1983;223. 16. Quigley DC, Hornafius JS, Luyendyk BP, Francis RD, Clark J, Washburn L. Decrease in Natural Marine Hydrocarbon Seepage near Coal Oil Point, California, Associated with Offshore Oil Production. Geology 1999;1:047. 17. National Research Council Committee on Oil in the Sea. Oil in the Sea III: Inputs, Fates, and Effects, National Research Council Ocean Studies Board and Marine Board Divisions of Earth and Life Studies and Transportation Research Board. Washington, DC: National Academy Press; 2003.

Chapter | 2

Spill Occurrences: A World Overview

47

18. Campbell B, Kern E, Horn D. Impact of Oil Spillage from World War II Tanker Sinkings, Report No. MITSG 77e4 Index No. 77-304-Nnt. Cambridge: Massachusetts Institute of Technology Sea Grant Program; 1977. 19. Easton R. Black Tide: The Santa Barbara Oil Spill and Its Consequences. New York: Delacorte Press; 1999. 20. Hayes MO. Black Tides. Austin: University of Texas Press; 1999. 21. Etkin DS. Analysis of Oil Spill Trends US and Worldwide. IOSC 2001;291. 22. Etkin DS. Analysis of Past Marine Oil Spill Rates and Trends for Future Contingency Planning. AMOP 2002;227. 23. Etkin DS. Analysis of US Oil Spill Trends to Develop Scenarios for Contingency Planning. IOSC 2003;47. 24. National Research Council. Petroleum in the Marine Environment. Washington, DC: National Academy of Sciences; 1975. 25. Kornberg H. Royal Commission on Environmental Pollution: 8th Report. London: Her Majesty’s Stationery Office; 1981. 26. Baker JM. Impact of Oil Pollution on Living Resources, Comm. Ecology Paper No. 4. Gland, Switzerland: International Union for Conservation of Nature and Natural Resources; 1983. 27. National Research Council. Oil in the Sea: Inputs, Fates, and Effects. Washington, DC: National Academy Press; 1985. 28. GESAMP (IMO/FAO/UNESCO/WMO/WHO/IAEA/UN/UNEP Joint Group of Experts on the Scientific Aspects of Marine Pollution). Impact of Oil and Related Chemicals and Wastes on the Marine Environment. GESAMP Reports and Studies 1993;Vol. 50. 29. Etkin DS. Analysis of US Oil Spillage. American Petroleum Institute Publication 356, Environmental Research Consulting; 2009. 30. Wake H. Oil Refineries: A Review of Their Ecological Impacts on the Aquatic Environment. Estuarine, Coastal and Shelf Science 2005;131. 31. CONCAWE, Trends in Oil Discharged in Aqueous Effluents from Oil Refineries in Europe: 2000 Survey, Report No. 4/04, CONCAWE (The Oil Companies’ European Association for Environmental, Health, and Safety in Refining and Distribution), Brussels, 2004. 32. American Petroleum Institute (API). Water Reuse Studies. Washington, DC: API Publication No. 949. American Petroleum Institute; 1977. 33. Etkin DS. Worldwide Analysis of In-Port Vessel Operational Lubricant Discharges and Leakages. AMOP; 2010. 34. Canadian Environmental Assessment Agency (CEAA), Military Flying Activities in Labrador and Quebec, Ottawa, 1995. 35. Symons L, Hodges MK. Undersea Pollution Threats and Trajectory Modeling. Mar. Techn, Soc. J. 2004;78. 36. Nawadra S, Gilbert TD. Risk of Marine Spills in the Pacific Island Region and Its Evolving Response Arrangements. Sydney, Australia: Proceedings of the International Spill Conference, SpilCon 2002; 2002. 37. South Pacific Regional Environment Programme (SPREP). Regional Strategy to Address Marine Pollution from World War II Shipwrecks. Majuro, Marshall Islands: Thirteenth SPREP Meetings of Officials (Item 7.2.2.1); July 2002, 21e25. 38. Michel J, Etkin DS, Gilbert T, Urban R, Waldron J, Blocksidge CT. Potentially Polluting Wrecks in Marine Waters. IOSC; 2005. 39. Etkin DS, van Rooij JAC, French-McCay D. Risk Assessment Modeling Approach for the Prioritization of Oil Removal Operations from Sunken Wrecks. Interspill; 2009.

48

PART | I

Introduction and the Oil Spill Problem

40. Etkin DS. Cost-Benefit Analyses for Wreck Oil Removal Projects, Proceedings of the Wrecks of the World: Hidden Risks of the Deep Conference. Linthicum, MD: Maritime Institute of Technology (MITAGS); 2009. 41. Etkin DS. Magnitude of Worldwide Potentially-Polluting Wreck Problem, Proceedings of the Wrecks of the World: Hidden Risks of the Deep Conference. Linthicum, MD: Maritime Institute of Technology (MITAGS); 2009. 42. Hassello¨v I-M, Morrison G, Rose´n L, Dahllo¨f I, Lindgren F, Knutsson J. Development of a Protocol for Risk Assessment of Potentially Polluting Shipwrecks in Scandinavian Waters, Proceedings of the Wrecks of the World: Hidden Risks of the Deep Conference. Linthicum, MD: Maritime Institute of Technology (MITAGS); 2009. 43. Cabioc’h F. The Wreck Concern in France and European Waters: Prioritization, Proceedings of the Wrecks of the World: Hidden Risks of the Deep Conference. Linthicum, MD: Maritime Institute of Technology (MITAGS); 2009. 44. Westerholm D. Repercussions of a Reactive Strategy and Need for a Proactive Strategy, Proceedings of the Wrecks of the World: Hidden Risks of the Deep Conference. Linthicum, MD: Maritime Institute of Technology (MITAGS); 2009.

Part II

Types of Oils and Their Properties

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Chapter 3

Introduction to Oil Chemistry and Properties Merv Fingas

Chapter Outline 3.1. Introduction 3.2. The Composition of Oil

51 51

3.3. Properties of Oil

54

3.1. INTRODUCTION Oil is a general term that describes a wide variety of natural substances of plant, animal, or mineral origin, as well as a range of synthetic compounds.1 This section covers mineral oil or petroleum oil. The many different types of crude oil are made up of hundreds of major constituents and thousands of minor ones. As their composition varies, each type of oil or petroleum product has certain unique characteristics or properties. These properties influence how the oil behaves when it is spilled and determines the fate and effects of the oil in the environment. These properties also influence the efficiency of cleanup operations. This section deals specifically with crude oils and petroleum products derived from crude oils and describes the chemical composition and physical properties.

3.2. THE COMPOSITION OF OIL Crude oils are mixtures of hydrocarbon compounds ranging from smaller, volatile compounds to very large, nonvolatile compounds.2 This mixture of compounds varies according to the geological formation of the area in which the oil is found and strongly influences the properties of the oil. For example, crude oils that consist primarily of large compounds are viscous and dense. Petroleum products such as gasoline or diesel fuel are mixtures of fewer compounds, and thus their properties are more specific and less variable. Crude oil contains many compounds of different sizes and different classes. In fact, Oil Spill Science and Technology. DOI: 10.1016/B978-1-85617-943-0.10003-6 Copyright Ó 2011 Elsevier Inc. All rights reserved.

51

52

PART | II

Types of Oils and Their Properties

there are so many that as time goes by more and more compounds are identified in oil.3 Figure 3.1 shows the number of compounds that are identified and quantified in oils by year as well as the prediction for the future. Some analysts have preliminarily identified up to 17,500 compounds in an oil. Hydrocarbon compounds are composed of hydrogen and carbon, which are therefore the main elements in oils. Oils also contain varying amounts of sulphur, nitrogen, oxygen, and sometimes mineral salts, as well as trace metals such as nickel, vanadium, and chromium. In general, the hydrocarbons found in oils are characterized by their structure. A common and older method of classification is by SARA dsaturates, aromatics, resins, and asphaltenes. Figure 3.2 illustrates the SARA classification along with classes of compounds typically found in this overall classification. The saturate group of components in oils consists primarily of alkanes, which are compounds of hydrogen and carbon with the maximum number of hydrogen atoms around each carbon. Thus, the term saturate is used because the carbons are “saturated” with hydrogen. The saturate group includes straight-chain alkanes and branched-chain alkanes and also includes cycloalkanes, which are compounds made up of the same carbon and hydrogen constituents, but with the carbon atoms bonded to each other in rings or circles. Straight-chain saturate compounds from C18 and up are often referred to as waxes. The aromatic compounds include at least one benzene ring of six carbons. Three carbon-to-carbon double-bonds float around the ring and provide

Compounds Identified

4000

3000

Prediction

2000

1000

0 1970

1980

1990

2000

Year

2010

2020

FIGURE 3.1 The number of compounds identified and quantified in crude oils by year, including prediction in the future.

Chapter | 3

53

Introduction to Oil Chemistry and Properties

Groupings

Example Classes, Names, and Compounds

Saturates

alkanes

Chemical class Alternate name

Description

paraffins

Example compound

dodecane C12H26

cycloalkanes waxes

Aromatics

naphthanates

decalin n-alkanes C18-C80

Benzenes BTEX

benzene Benzene, Toluene, Ethylbenzene, Xylenes

PAHs

Naphthenoaromatics

Resins Asphaltenes

anthracene

combinations of aromatics and cycloalkanes

tetralin

class of mostly anomalous polar compounds carbazole sometimes containing oxygen, nitrogen, sulphur, or metals

N

class of large anomalous compounds structures not known sometimes containing oxygen, nitrogen, metals, or sulphur

FIGURE 3.2 An overview of the classification of compounds with specific examples.

stability. Because of this stability, benzene rings are very persistent and can have toxic effects on the environment. The most common smaller aromatic compounds found in oil are often referred to as BTEX, or Benzene, Toluene, Ethyl-benzene, and Xylenes. Polyaromatic hydrocarbons or PAHs are compounds consisting of at least two benzene rings. PAHs make up between 0 and 60% of the composition of oil. The olefins, or unsaturated compounds, are another group of compounds that contain less hydrogen atoms than the maximum possible. Olefins have at least one double carbon-to-carbon bond that displaces two hydrogen atoms. Significant amounts of olefins are found only in refined products. Polar compounds are those that have a significant molecular charge as a result of bonding with compounds such as sulphur, nitrogen, or oxygen. The “polarity” or charge that the molecule carries results in behavior that may be different from that of other compounds. In the petroleum industry, the smallest polar compounds are called resins, which are largely responsible for oil adhesion. The larger polar compounds are called asphaltenes because they often make up the largest percentage of the asphalt commonly used for road construction. Asphaltenes often have very large molecules and, if in abundance in an oil, they have a significant effect on oil behavior.4 Bitumen, which comes from heavy oil deposits or tar sands, consists largely of asphaltenes that must be broken down to smaller compounds before refining. Crude oil is processed in refineries to yield petroleum products that are used for heating, transport, and chemical synthesis. Table 3.1 lists some of the

54

PART | II

Types of Oils and Their Properties

TABLE 3.1 General Characterizations of Product Distillation Ranges Product

Distillation Temperature Range ( C)

Approximate Carbon Number Range

Gasoline

30e200

5e12

Naphtha

100e200

8e12

Jet Fuel & Kerosene

150e250

11e13

Diesel fuel

160e400

13e17

Gas-Oil

220e350

Heavy fuel oils

315e540

20e45

Atmospheric residue

>450

30þ

Vacuum residue

>600

60þ

products produced by distillation, a primary refinery process. Table 3.2 gives the general composition of some typical fuels and oils.5 The following are the oils or fuels that be used to illustrate the fate, behavior, and cleanup of oil spills: l l l l l

l

gasolinedas used in automobiles diesel fueldas used in trucks, trains, and buses a light crude oil a heavy crude oil an intermediate fuel oil (IFO)da mixture of a heavy residual oil and diesel fuel used primarily as a propulsion fuel for ships (the intermediate refers to the fact that the fuel is between a diesel and a heavy residual fuel) bunker fueldsuch as Bunker C, which is a heavy residual fuel remaining after the production of gasoline and diesel fuel in refineries and often used in heating plants

3.3. PROPERTIES OF OIL The properties of oil discussed here are viscosity, density, specific gravity, solubility, flash point, pour point, distillation fractions, interfacial tension, and vapor pressure. These properties for the oils noted as examples above are listed in Table 3.3.5 Viscosity is the resistance to flow in a liquid. The lower the viscosity, the more readily the liquid flows. For example, water has a low viscosity and flows readily, whereas honey, with a high viscosity, flows slowly. The viscosity of the

(% e except for metals) Group

Compound Class

alkanes cyclo-alkanes

Diesel

Light Crude

50 to 60

65 to 95

55 to 90

25 to 80

25 to 35

20 to 30

45 to 55

35 to 45

0 to 20

0 to 10

2 to 10

5 to 15

5

waxes Olefins Aromatics BTEX

IFO

Bunker C

30 to 50 0 to 1

5 to 10

0 to 10

25 to 40

5 to 25

10 to 35

15 to 40

40 to 60

30 to 50

15 to 25

0.5 to 2.0

0.1 to 2.5

0.01 to 2.0

0.05 to 1.0

0.00 to 1.0

0 to 5

10 to 35

15 to 40

30 to 50

30 to 50

0 to 2

1 to 15

5 to 40

15 to 25

10 to 30

0 to 2

0 to 10

2 to 25

10 to 15

10 to 20

0 to 10

0 to 20

5 to 10

5 to 20

30 to 250

100 to 500

100 to 1000

100 to 2000

0 to 2

0 to 5

0.5 to 2.0

2 to 4

PAHs Polar Compounds resins asphaltenes Metals (in parts per million) Sulphur

Heavy Crude

0.02

0.1 to 0.5

Introduction to Oil Chemistry and Properties

Saturates

Gasoline

Chapter | 3

TABLE 3.2 Typical Composition of Some Oils and Petroleum Products

55

56

TABLE 3.3 Typical Oil Properties Property Viscosity

Units

mPa.s at 15 C

Gasoline

Diesel

0.5

2

Light Crude 5 to 50

Heavy Crude 50 to 50,000

0.72

0.84

0.78 to 0.88

0.88 to 1.00

Flash Point

C

35

45

30 to 30

30 to 60

Solubility in Water

ppm

200

40

10 to 50

Pour Point

NR

35 to 10 40 to 30

65

35

27

100 C 200 C

C

API Gravity Interfacial Tension

mN/m at 15 C

1000 to 15,000

10,000 to 50,000

0.94 to 0.99

0.96 to 1.04

80 to 100

>100

5 to 30

10 to 30

1 to 5

40 to 30

10 to 10

5 to 20

30 to 50

10 to 30

10 to 20

5 to 15

27

10 to 30

15 to 30

25 to 30

25 to 35

70

1

2 to 15

1 to 10

e

e

100

2 to 5

2 to 5

Distillation Fractions % distilled at

30

15 to 40

2 to 25

85

30 to 60

15 to 45

15 to 25

5 to 15

100

45 to 85

25 to 75

30 to 40

15 to 25

15 to 55

25 to 75

60 to 70

75 to 85

300 C 400 C residual NR ¼ not relevant

Types of Oils and Their Properties

g/mL at 15 C

Bunker C

PART | II

Density

Intemediate Fuel Oil

Chapter | 3

Introduction to Oil Chemistry and Properties

57

oil is largely determined by the amount of lighter and heavier fractions that it contains. The greater the percentage of light components, such as small saturates, and the lesser the amount of asphaltenes, the lower the viscosity. As with other physical properties, viscosity is affected by temperature, with a lower temperature giving a higher viscosity. For most oils, the viscosity varies as the logarithm of the temperature, which is a very significant variation. Oils that flow readily at high temperatures can become a slow-moving, viscous mass at low temperatures. In terms of oil spill cleanup, viscosity can affect the oil’s behavior. Viscous oils do not spread rapidly, do not penetrate soil as readily, and are difficult to pump and skim. Density is the mass (weight) of a given volume of oil and is typically expressed in grams per cubic centimeter (g/cm3). It is the property used by the petroleum industry to define light or heavy crude oils. Density is also important as it indicates whether a particular oil will float or sink in water. As the density of fresh water is 1.0 g/cm3 at 15 C and the density of most oils ranges from 0.7 to 0.99 g/cm3, most oils will float on water. As the density of seawater is 1.03 g/cm3, even heavier oils will usually float on it. The density of oil increases with time, as the light fractions evaporate. Occasionally, when the density of an oil becomes greater than the density of freshwater or seawater, the oil will sink. Sinking is rare, however, and happens only with a few oils, usually residual fuels such as Bunker C. Significant amounts of oil have sunk in only about 25 incidents out of thousands. However, as heavier and heavier oils are being used more frequently, this may become more common in the future. Another measure of density is specific gravity, which is an oil’s relative density compared to that of water. If the oil-specific gravity is greater than 1, it sinks; if it is less than 1, it floats. Another gravity scale is that of the American Petroleum Institute (API). The API gravity is based on the density of pure water that has an arbitrarily assigned API gravity value of 10 (10 degrees). Oils with progressively lower specific gravities have higher API gravities. The following is the formula for calculating API gravity: API gravity ¼ [141.5 O (oil density at 15.5 C)] 131.5. Oils with high densities have low API gravities and vice versa. Solubility in water is the measure of how much of an oil will dissolve in the water column on a molecular basis. Solubility is important in that the soluble fractions of the oil are sometimes toxic to aquatic life, especially at higher concentrations. As the amount of oil lost to solubility is always small, this is not as great a loss mechanism as evaporation. In fact, the solubility of oil in water is so low (generally less than 100 parts per million) that it would be the equivalent of approximately one grain of sugar dissolving in a cup of water. Yet, even this small amount is important to the environment as even small amounts may be toxic to certain biota. The flash point of an oil is the temperature at which the liquid gives off sufficient vapors to ignite upon exposure to an open flame. A liquid is considered to be flammable if its flash point is less than 60 C. There is a broad range of flash points for oils and petroleum products, many of which are

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Types of Oils and Their Properties

considered flammable, especially when fresh. Gasoline, which is flammable under all ambient conditions, poses a serious hazard when spilled. Many fresh crude oils have an abundance of volatile components and may be flammable for as long as one day until the more volatile components have evaporated. On the other hand, Bunker C and heavy crude oils generally are not flammable even when spilled. The pour point of an oil is the temperature at which it takes longer than a specified time to pour from a standard measuring vessel. As oils are made up of hundreds of compounds, some of which may still be liquid at the pour point, the pour point is not the temperature at which the oil will no longer pour. The pour point represents a consistent temperature at which an oil will pour very slowly and therefore has limited use as an indicator of the state of the oil. In fact, pour point has been overused in the past to predict how oils will behave in the environment. For example, waxy oils can have very low pour points, but may continue to spread slowly at that temperature and can evaporate to a significant degree. It is important to note that pour point is not the solidification temperature. As produced crude oils become heavier, pour point becomes less relevant. Distillation fractions of an oil represent the fraction (generally measured by volume) of an oil that is boiled off at a given temperature. This data is obtained on most crude oils so that oil companies can adjust parameters in their refineries to handle the oil. This data also provides environmentalists with useful insights into the chemical composition of oils. For example, while 70% of gasoline will boil off at 100 C, only about 5% of a crude oil will boil off at that temperature and an even smaller amount of a typical Bunker C. The distillation fractions correlate strongly to the composition as well as to other physical properties of the oil. Equations to predict evaporation can use distillation fraction data as input. The oil/water interfacial tension, sometimes called surface tension, is the force of attraction or repulsion between the surface molecules of oil and water. Together with viscosity, surface tension is an indication of how rapidly and to what extent an oil will spread on water. The lower the interfacial tension with water, the greater the extent of spreading. In actual practice, the interfacial tension must be considered along with the viscosity because it has been found that interfacial tension alone does not account for spreading behavior. The vapor pressure of an oil is a measure of how the oil partitions between the liquid and gas phases, or how much vapor is in the space above a given amount of liquid oil at a fixed temperature. Because oils are a mixture of many compounds, the vapor pressure changes as the oil weathers. Vapor pressure is difficult to measure and is not frequently used to assess oil spills. Again as oil is a mixture of hundreds of compounds, vapor pressure is not entirely relevant. Although there is a high correlation between the various properties of an oil, these correlations should be used cautiously as oils vary so much in composition. For example, the density of many oils can be predicted based on their

Chapter | 3

Introduction to Oil Chemistry and Properties

59

viscosity. For other oils, however, this could result in errors. For example, waxy oils have much higher viscosities than would be implied from their densities. There are several mathematical equations for predicting one property of an oil from another property, but these must be used carefully as there are many exceptions.

REFERENCES 1. Neumann H-J, Paczynska-Lahme B, Severin D. Composition and Properties of Petroleum. New York: Halsted Press; 1981. 2. Speight JG. The Chemistry and Technology of Petroleum. 4th ed. Boca Raton, FL: CRC Press; 2007. 3. Marshall AG, Hendrickson CL. High-Resolution Mass Spectrometers, chapter in Annual Review of Analytical Chemistry, Volume 1, 2008, Young ES and Zare RN, editors., Annual Reviews, Palo Alto, CA, p. 579e99, 2008. 4. Groenzin H, Mullins OC. Asphaltene Molecular Size and Weight by Time-Resolved Fluorescence Depolarization, Chapter 2 in Asphaltenes, Heavy Oils and Petroleomics. In: Mullins OC, Sheu EY, Hammami A, Marshall AG, editors. New York: Springer Publications; 2007. p. 17. 5. Fingas MF. The Basics of Oil Spill Cleanup. 2nd ed. Boca Raton, FL: CRC Press; 2000.

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Part III

Oil Analysis and Remote Sensing

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Chapter 4

Measurement of Oil Physical Properties Bruce Hollebone

Chapter Outline 4.1. Introduction 4.2. Bulk Properties of Crude Oil and Fuel Products 4.3. Hydrocarbon Groups 4.4. Quality Assurance and Control

63 63 73 77

4.5. Effects of Evaporative Weathering on Oil Bulk Properties Appendix 4.1

78

85

4.1. INTRODUCTION During any uncontrolled release of oil, the properties of the spilled oil, including the bulk physical property changes due to weathering, must be immediately available, so that models can be used to predict the environmental impacts of the spill and guide the selection of various remediation alternatives. Unfortunately, the properties routinely measured by oil producers and refiners are not the ones that spill responders need to know most urgently. Questions important to responders include the following: l l

l l l l

the physical properties of the oil and how these change over time how the compositional and bulk property changes affect an oil’s behavior and fate whether emulsions will form whether the oil is likely to submerge the hazard to on-site personnel during cleanup the oil toxicity to marine or aquatic organisms

4.2. BULK PROPERTIES OF CRUDE OIL AND FUEL PRODUCTS The physical properties of the almost limitless variety of crude oils are generally correlated with aspects of chemical composition. Some of these key Oil Spill Science and Technology. DOI: 10.1016/B978-1-85617-943-0.10004-8 Copyright Ó 2011 Elsevier Inc. All rights reserved.

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properties for determining the fate and behavior of oil and petroleum products in the environment are viscosity, density, specific gravity (density relative to water), flash point, pour point, distillation, and interfacial tension. These properties for the oils are listed in Table 4.1. Viscosity is the resistance to flow in a liquid. The lower the viscosity, the more readily the liquid flows. The viscosity of an oil is a function of its composition; therefore, crude oil has a wide range of viscosities. For example, the viscosity of Federated oil from Alberta is 5 mPa$s, while a Sockeye oil from California is 45 mPa$s at 15 C . In general, the greater the fraction of saturates and aromatics and the lower the amount of asphaltenes and resins, the lower the viscosity. As oil weathers, the evaporation of the lighter components leads to increased viscosity. As with other physical properties, viscosity is affected by temperature, lower temperatures giving higher viscosities. For most oils, the viscosity varies approximately exponentially with temperature. Oils that flow readily at high temperature can become a slow-moving, viscous mass at low temperature. In terms of oil spill cleanup, viscous oils do not spread rapidly, do not penetrate soils readily, and affect the ability of pumps and skimmers to handle the oil. The dynamic viscosity of an oil can be measured by a viscometer using a variety of standard cup-and-spindle sensors at controlled temperatures. Density is the mass of a unit volume of oil, usually expressed as grams per millilitre (g/mL) or, equivalently, as kilograms per cubic metre (kg/m3). It is used by the petroleum industry to grade light or heavy crude oils. Density is also important because it indicates whether a particular oil will float or sink in water. As the density of water is 1.0 g/mL at 15 C and the density of most oils ranges from 0.7 to 0.99 g/mL, oils typically float on water. As the density of seawater is 1.03 g/mL, even heavier oils will usually float on it. Only a few bitumens have densities greater than water at higher temperatures. However, as water has a minimum density at 4 C and oils will continue to contract as temperature decreases, heavier oils, including heavy crudes and residual fuel oils, may sink in freezing waters. Furthermore, as density increases as the light ends of the oil evaporate off, a heavily weathered oil, long after a spill event, may sink or be prone to overwashing, where the fresh oil, immediately after the spill, may have floated readily. A related measure is specific gravity, an oil’s density relative to that of water. As the densities of both water and oil vary differently with temperature, this quantity can be highly variable. The American Petroleum Institute (API) uses the specific gravity of petroleum at 50 F (15.56 C) as a quality indicator for oil. Pure water has an API gravity of 10. Oils with progressively lower specific gravities have higher API gravities. Heavy, inexpensive oils have less than 25 API; medium oils are 25 to 35API; and light commercially valuable oils are 35 to 45API. API gravities generally vary inversely with viscosity and asphaltene content. Interfacial tensions are the net stresses at the boundaries between different substances. They are expressed as the increased energy per unit area (relative to the bulk materials), or equivalently as force per unit length. The ‘Standard

Chapter | 4

Intermediate Fuel Oil

Bunker C

Crude Oil Emulsion

1,000 to 15,000

10,000 to 50,000

20,000 to 100,000

0.88 to 1.00

0.94 to 0.99

0.96 to 1.04

30 to 50

10 to 30

10 to 20

5 to 15

10 to 15

10 to 30

15 to 30

25 to 30

25 to 35

N/A

80 to 100

>100

>80

5 to 20

>50

Property

Units

Gasoline

Diesel

Light Crude

Viscosity

m.Pa$s

0.5

2

5 to 50

Density

g/mL

0.72

0.84

0.78 to 0.88

50 to 65

35 to 40

27

27

API Gravity Interfacial Tension

mN/m

Heavy Crude 50 to 50,000

Flash Point

C

35

55 to 65

30 to 30

30 to 60

Pour Point

C

N/A

60

55 to 0

30 to 30

10 to 10

0.95 to 1.0

Measurement of Oil Physical Properties

TABLE 4.1 Typical Oil and Fuel Properties at 15 C

65

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International (SI)’ units for interfacial tension are milliNewtons per meter (mN/ m). Surface tension is thought to be related to the final size of a slick. The lower the interfacial tension of oil with water, the greater the extent of spreading and thinner terminal thickness of oil. In actual practice, the interfacial tension alone does not apparently account for spreading behavior; environmental effects and other effects seem to be dominant. The flash point of an oil is the temperature at which the vapor over the liquid can be ignited. A liquid is considered to be flammable if its flash point is less than 60 C. Flash point is an important consideration for the safety of spill cleanup operations. Gasoline and other light fuels can ignite under most ambient conditions and therefore are a serious hazard when spilled. Many freshly spilled crude oils also have low flash points until the lighter components have evaporated or dispersed. On the other hand, Bunker C and heavy crude oils generally are not flammable when spilled. The pour point of an oil is the temperature at which no flow of the oil is visible over a period of 5 seconds from a standard measuring vessel. The pour point of crude oils ranges from 60 C to 30 C. Lighter oils with low viscosities generally have lower pour points. As oils are made up of hundreds of compounds, some of which may still be liquid at the pour point, the pour point is not the temperature at which an oil will no longer pour. The pour point represents a consistent temperature at which an oil will pour very slowly and therefore has limited use as an indicator of the state of the oil. For example, waxy oils can have a very low pour point, but may continue to spread slowly at that temperature and can evaporate to a significant degree.

4.2.1. Density and API Gravity The density of an oil sample, in g/mL, is best measured using a digital density meter following American Society for Testing and Materials (ASTM) method D 5002.1 The instrument is calibrated using air and distilled, deionized water. Acoustically measured densities must be corrected for sample viscosity, as specified by the instrument manufacturer. API gravity (API 82) is calculated using the specific gravity of an oil at 60 F (15.56 C).2 The oil density at 15.56 C can be estimated by exponential extrapolation from the higher (THi) and lower (TLo) data points, if necessary. This is converted to specific gravity by division by the density of water at 15.5 C, using the following equation: s:g:15:56 ¼ rTHi exp

h

.

THi TLo i. THi 15:56 þ In rTHi rðH2 OÞ15:56 In rTHi In rTLo

(1)

where s.g.15.56 is the specific gravity of the oil or product at 15.56 C (60 F), rTLo and rTHi are the measured oil densities at TLo and THi, respectively, and

Chapter | 4

Measurement of Oil Physical Properties

67

r(H2O)15.56 is the density of water at 15.56 C. The API gravity is then determined using the formula (API 82): API ¼ 141:5= s:g:15:56 131:5 (2)

4.2.2. Dynamic Viscosity The dynamic viscosity of an oil sample, in mPa$s or cP, is measured using an enclosed spinning cup viscometer using standard NV and SV1 cup-and-spindle sensors.3 Check standards of pure ethylene glycol and glycerine can be conveniently used to validate the NV and SV1 methods, respectively. From a qualitative observation of the oil, either the NV or the SV1 sensor is chosen to measure the sample. The NV sensor is used for oils with viscosities below 100 mPa$s, and the SV1 sensor, for oils above 70 mPa$s to 10,000 mPa$s. For oils with higher viscosity, measurements must be made on cone and plate or parallel plate instruments (see below). For both cases using the rotary viscometer, the measurement cup is filled with a sample to the edge or the rotating surface. The sensor is mounted onto the instrument, and the sample volume is adjusted to the proper level. The sample is allowed to equilibrate until the sample temperature probe stabilizes at the measurement temperature and remains stable for 5 minutes. Samples and sensors are kept chilled at the appropriate temperature prior to use. For the NV sensor, the rotational shear rate is set at 1,000/s, the SV1 sensor at 50/s. If the oil is observed to be non-Newtonian, single samples are run at shear rates of 1/s, 10/s, and 100/s. In all cases, the sensors are ramped up to speed over a period of 5 minutes. The viscosity is measured for a subsequent 5 minutes, sampled once per second. The viscosity reported is that at time zero of the second, constant-shear rate interval. This may be obtained by the mean of the constant-shear rate interval data or by linear fit to the time-viscosity series if friction-heating has occurred during the measurement. For Newtonian samples, triplicate measurements are averaged and the mean is reported as the absolute or dynamic viscosity. For non-Newtonian samples, viscosities are reported for each of the three shear rates. Viscosities above 50,000 mPa$s are measured on a parallel plate rheometer with an air bearing. Measurement for most oils can be performed with a 35 mm plate/plate geometry at a gap of 2 mm between plates. A stress sweep in forced oscillation mode at 1 Hz performed over an appropriate range will determine the stress independent regions. A creep test can then be performed at a stress value selected in the stable “sol” range of flow response for the material. This provides the zero shear viscosity value.

4.2.3. Surface and Interfacial Tensions Surface and interfacial tensions, in mN/m, are normally determined by one of two methods. The de No€ uy ring is a common technique, used by many laboratories,

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Oil Analysis and Remote Sensing

and has been codified as ASTM method D 971.4 It depends on accurate measurement of the maximum force that a platinum ring can exert on the surface of a liquid before detachment. A second emerging technique that shows much promise for improved speed and accuracy is the pendant/rising drop method, which depends on shape calculations of a droplet of oil in air or water.5,6 The values that are important for spill responders include the oil/air, oil/ water, and the oil/seawater interfacial tensions. The oil/air interfacial tension is often called surface tension. As interfacial tensions are temperature dependent, it is often convenient to determine these quantities for several temperatures. Two measurements at freezing, 0 C, and at ambient temperature, 25 C, allow for a wide range of interpolated values. Measurement at 50 F/15 C also allows determination of common marine temperatures.

€ Ring Determination of Interfacial Tensions De Nouy A measurement apparatus specific to the de No€ uy ring test is required. Manual machines are common, but automated systems are now available that make measurements much quicker and repeatable. All measurement equipment, rings, measurement vessels, transfer, and storage containers must be scrupulously clean before measurement. Surface and interfacial tension measurements are very sensitive to contamination by organic chemicals or salts. For sample/air surface tensions, the instrument is zeroed with the measurement ring in the air. A small amount of sample, approximately 15 mL, is poured into a vessel of sufficient diameter that the wall effects on the meniscus do not affect the area through which the ring will pass. The ring is dipped into the sample to a depth of no more than 5 mm and is then pulled up such that it is just visible on the surface of the liquid. The system is allowed to rest for 30 seconds. The measurement is initiated, terminating when the upward pulling force on the ring just balances the downward force exerted by the liquid. The apparent surface tension, sAPP, is recorded. For sample/water and sample/brine interfacial tensions, the ring is zeroed in the sample at a depth of not more than 5 mm. The ring is removed and cleaned. A volume of water or brine is dispensed into the measurement vessel. The ring is dipped 5 mm into the aqueous phase. A small volume of sample is carefully poured down the side of the vessel wall, with great care taken so as to disturb the aqueous/oil interface as little as possible. The overlying layer should be at least 5 mm thick. The ring is then raised to the bottom on the interface, and the system is allowed to rest for exactly 30 seconds. The measurement is started, and the apparent interfacial tension is recorded, sAPP, when the force balance is reached. The apparent surface tension is corrected for mass of the upper phase lifted by the ring during measurement using the Zuidema and Waters6 correction: sﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ 1:452sAPP 1:679 þ 0:04534 (3) s ¼ sAPP 0:7250 þ C2 ðD dÞ R=r

Chapter | 4

Measurement of Oil Physical Properties

69

where s is the interfacial tension, sAPP is the instrument scale reading, C is the ring diameter, D is the density of the lower phase, d is the density of the upper phase, R is the radius of the du No€ uy ring, and r is the radius of the ring wire. As these measurements depend on temperature, samples, aqueous phases and glassware should be kept at the measurement temperature for a minimum of 30 minutes before a determination is made.

Pendant/Rising Drop Determination of Interfacial Tensions In this test, the interfacial tension is determined by calculation with comparison to the shape of a drop hanging from the end of a needle. A camera is used to photograph a picture of a drop hanging from a needle. The digital picture is analyzed by software; then a parameterized curve shape is developed, from which the surface tension is calculated.6 In the case of a liquideliquid interfacial tension, the surrounding fluid must be clear, so that a good image may be generated. For oil in water, this requires that the oil be suspended in water. However, as most oils are less dense than water, the rising oil bubble, rather than the pendant drop, must be measured. In this case, the image is inverted in software and, instead of the force of gravity, the buoyant force, determined as the fraction of gravity based on the specific gravity of the oil is used: b ¼ gðrwater roil Þ=rwater

(4)

where b is the buoyant force, g is the acceleration due to gravity, rwater is the density of water at the measurement temperature, and roil is the oil density.

4.2.4. Flash Point The flash point of an oil product can be determined by several methods, depending on the oil product and the quantity available. Lower viscosity products, including light fuel oils and most fresh crudes, are measured by the Tag closed-cup method. This follows ASTM method D 1310.7 Though accurate, the Tag method uses a comparatively large volume of oil, 50 to 70 mL. Smaller volumes, 1e2 mL, can be measured by ASTM D6450.8 The practical working range of these two methods is e10 C to approximately 100 C. With subambient cooling, using dry ice baths and/or liquid nitrogen baths, much lower flash point temperatures can be measured, but this is often not necessary for emergency response considerations. Heavier products, including intermediate and heavy fuel oils, can be measured by a Pensky-Martins analyzer, following ASTM D 93.9 As with the Tag method, this method uses 50e70 mL of crude oil. Smaller volumes can be used with the newer method ASTM D7094, which uses only 2 mL of oil.10 The working range for these heavier type tests is approximately 50 C to 225 C.

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PART | III

Oil Analysis and Remote Sensing

The standard test material for assuring quality control for a lowertemperature flash point apparatus historically has been para-xylene; however, heavier normal alkane standards, n-decane, n-undecane, n-tetradecane, and n-hexadecane have also been found to be suitable and offer a wider range of test temperatures.11

4.2.5. Pour Point The pour point of an oil sample, in degrees Celsius, can only be determined by following ASTM method D 97.12 Sample aliquots are poured into ASTMapproved jars, stopped and fixed with ASTM-certified thermometers. The temperature regime described in the standard is critical; particularly in waxy oils, with high normal alkane contents, a crust of waxy crystals can form on the surface of the oil as it cools. The ASTM D 97 heating and cooling process for oil is designed to ensure that the formation of these microstructures does not interfere with reproducible measurement of the pour point.

4.2.6. Sulphur Content The mass fraction of atomic sulphur in oil is conveniently determined using X-ray fluorescence closely following ASTM method D 4294.13 In brief, the method is as follows: approximately 3 g of oil is weighed out into standard 31 mm XRF cells. The sealed cells are then measured in an XRF spectrometer. The spectrometer response is calibrated using a series of certified reference material standards. Spectra should be corrected for interference by chlorine by subtraction, based on a calibration curve established by the certified reference materials. Matrix effects, X-ray absorption by the base oil, can be corrected by subtraction of a spectrum of an oil free of sulphur, such as a mineral or lubricating oil.

4.2.7. Water Content The mass fraction of water in oil or an emulsion, expressed as a percentage, is best determined by Karl Fischer titration, using ASTM method D 4377.14 The Karl Fischer reaction is an amine-catalyzed reduction of water in a methanolic solution: CH3 OH þ SO2 þ RN/½RNHþ þ ½SO3 CH3 2RN þ H2 O þ I2 þ ½RNHþ ½SO3 CH3 /½RNHþ ½SO4 CH3 þ 2½RNHþ I

(5)

The amine, RN, or mixture of amines is proprietary to each manufacturer. An aliquot of approximately 1 g of oil is accurately weighed, then introduced to the reaction vessel of the autotitrator. A solution of 1:1:2 (by volume) mixture of methanol:chloroform:toluene is used as a working fluid.

Chapter | 4

Measurement of Oil Physical Properties

71

4.2.8. Evaluation of the Stability of Emulsions Formed from Brine and Oils and Oil Products Water-in-oil emulsions are formed in 2.2-liter fluorinated vessels on an endover-end rotary mixer at a rotational speed of 50 RPM.15,16 1. 600 mL of salt water (3.3% w/v NaCl) is placed in each mixing vessel. 2. 30 mL of oil is added to each vessel for a 1:20 oil:water ratio. 3. The vessels are sealed and placed in the rotary mixer such that the cap of each mixing vessel follows, rather than leads, the direction of rotation. The rotary mixer is kept in a temperature-controlled cold room at 15 C. 4. The vessels and their contents are allowed to stand for approximately 4 hours before rotation begins, then mixed continuously for 12 hours. 5. At the conclusion of the mixing time, the emulsions are collected from the vessels for measurement of water content, viscosity, and the complex modulus. The emulsions are stored at 15 C for one week, then observed for changes in physical appearance. Water content for the emulsions should be determined. The Karl-Fischer titration method works well for all types of emulsion and watereoil mixtures. The complex modulus of the emulsion is measured on a rheometer using a 35 mm plate-plate geometry. A stress sweep is performed in the range 100 to 10,000 mPa in the oscillation mode at a frequency of 1 Hz. The complex modulus value in the linear viscoelastic region is reported.

4.2.9. Evaluation of the Relative Dispersability of Oil and Oil Products This method determines the relative ranking of effectiveness for the dispersibility of an oil sample by to a dispersant test mixture. It is used either to determine the effectiveness of a dispersant product for a standard crude oil or to test the dispersability of a crude oil against a standard dispersant. This method follows ASTM F 2059 closely.17 A premix of 1:25.0 dispersant:oil is made up by adding oil to 100 mg of dispersant (approximately 2.50 mL of oil in total). Six ASTM-standard swirling conical flasks modified with side spouts, containing 120 mL of 33& brine, are placed into an incubator-shaker. An aliquot of 100 mL of premix is added to the surface of the liquid in each flask, care being taken not to disturb the bulk brine. The flasks are mechanically shaken at 20.0 C with a rotation speed of 150 rpm for exactly 20 minutes. The solutions are allowed to settle for 10 minutes. Using the side spout, 30 mL of the oil-in-water phase is transferred to a 250 mL separatory funnel, first clearing the spout by draining 3 mL of liquid. The 30 mL aliquot is extracted with 35 mL of 70:30 (v:v) dichloromethane:pentane, collected into a 25 mL graduated cylinder.

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PART | III

Oil Analysis and Remote Sensing

A Gas Chromatograph-Flame Ionization Detector (GC/FID) is used to determine the oil concentration in the solvent. A 900 mL aliquot of the 15 mL solvent extract is combined with 100 mL of internal standard (200 ppm of 5-aandrostane in hexane) in a crimp-top injection vial and shaken well. The total petroleum hydrocarbon content of the sample is quantified by the internal standard method using the total resolved peak area and the average hydrocarbon response factor over the entire analytical range: RPH ¼ ATOTAL =AI:S: =RRF 20 15 120=30=0:9

(6)

where RPH is the resolved petroleum hydrocarbon (mg/mL), ATOTAL is the total resolved peak area, AI.S. is the internal standard peak area, and RRF is the relative response factor for a series of alkane standards covering the analytical range. The method is calibrated using a series of six oil-in-solvent mixtures prepared from the premix for each oil. The volume of premix dispersant/oil solution for each standard is selected to represent a percentage efficiency of the dispersed oil. The volume of the premix is then carefully applied to the surface of the brine in a shaker flask and shaken exactly as one of the samples, as described previously. Upon removal from the shaker however, the entire contents of the flask is transferred to the separatory funnel. This is extracted with 3 20 mL of 70:30 (v:v) dichloromethane:pentane and made up to 60 mL. Chromatographic quantitation is then performed using the formula: RPH ¼ ATOTAL =AI:S: =RRF 20 60 120=120=0:9

(7)

The RPH values as a function of % effectiveness for the calibration standards are plotted. The sample RPH values are then used to determine the percentage effectiveness of the dispersant. Note that these effectiveness percentages are not expected to correlate to real-world dispersabilities. It is important to remember that these values are relative rankings only.

4.2.10. Adhesion to Stainless Steel Adhesion to stainless steel is useful to responders in order to judge the “stickiness” of oil to certain drum skimmer configurations. Environment Canada has developed a quantitative test for this purpose.18,19 An analytical balance is prepared by hanging an ASTM method D 6 standard penetrometer needle from the balance hook and allowing the apparatus to stabilize and tare. Approximately 80 mL of oil sample is poured into a 100 mL beaker. The beaker is elevated until the oil reaches the top of the stainless steel needle. Care is taken not to coat the brass segment of the needle. The needle rests for 30 seconds immersed in the oil. The beaker is lowered until the needle is clear of the oil. The system is left undisturbed, closed inside a draft shield. After 30 minutes, the weight of the oil adhering to the needle is recorded. The

Chapter | 4

Measurement of Oil Physical Properties

73

mass of the oil divided by the surface area of the needle is the adhesion of the oil in g/cm2. Typically, four measurements are taken for each oil sample and the mean reported as the final value.

4.3. HYDROCARBON GROUPS The fate and behavior of crude oils and petroleum products are strongly determined by their chemistries. The main constituents of oils can be grouped into four categories: saturated hydrocarbons (including waxes), aromatics, resins, and asphaltenes. Saturates: A group of hydrocarbons composed of only carbon and hydrogen with no double bonds or aromaticity. They are said to be “saturated” with hydrogen. They may by straight-chain (normal), branched, or cyclic. Typically, however, the group of “saturates” refers to the aliphatics generally including alkanes, as well as a small amount of alkenes. The lighter saturates, those less than ~C18, make up the components of an oil most prone to weathering. The larger saturates, generally those heavier than C18, are termed waxes. Aromatics: These are cyclic organic compounds that are stabilized by a delocalized p-electron system. They include such compounds as BTEX (benzene, toluene, ethylbenzene, and the three xylene isomers), polycyclic aromatic hydrocarbons (PAHs, such as naphthalene), and some heterocyclic aromatics such as the dibenzothiophenes. Benzene and its alkylated derivatives can constitute several percent in crude oils. PAHs and their alkylated derivatives can also make up as much as a percent in crude oils. Resins: This is the name given to a large group of polar compounds in oil. They include heterosubstituted aromatics (typically oxygen- or nitrogencontaining PAHs), acids, ketones, alcohols, and monoaromatic steroids. Because of their polarity, these compounds are more soluble in polar solvents than the nonpolar compounds, such as waxes and aromatics, of similar molecular weight. Asphaltenes: A complex mixture of very large organic compounds that precipitate from oils and bitumen by natural processes. For the purposes of this method, asphaltenes are defined as the fraction that precipitates in n-pentane. The separation of petroleum and its products into these four characteristic groups is known as fractionation. The quantification of the groups is often referred to as SARA analysis, an acronym of the characteristic groups: saturates, aromatics, resins, and asphaltenes. Historically, many techniques have been used to perform this separation, including distillation, solvent precipitation (ASTM D6560)20, treatment with strong acids (ASTM D2006)21, adsorption (ASTM D2007 and D4124)22,23, and thin-layer chromatography.24 For reviews of the methods, see Speight and Becker.24-26 While excellent methods for the determination of the SARA groups have been developed using thin-layer chromatograph (TLC), there has been continuing interest in alternate

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test methods based on solvent separation and adsorption techniques.22-24 Gravimetric methods are typically based on the solubilities of the groups in n-pentane, hexane/benzene, and methanol.3 Such methods can rely on gravimetric determinations of all components, including the saturate and aromatic groups. However, the drawback of such methods is that they contain significant volatile components. This is particularly true of crude oils and lighter fuels. More sophisticated methods rely on a combination method involving determination of the saturate and aromatic fractions by gas chromatography, an adaptation of total petroleum hydrocarbon methods, while gravimetrically determining the nonvolatile resin and asphaltene components.27,28

Resin and Asphaltene Gravimetric Determination A 100 mL quantity of n-pentane is added to a preweighed sample of approximately 5 g of oil. The flask is shaken well and allowed to stand for 30 minutes.27 The sample is filtered through a 0.45 mm membrane using a minimum of rinsings of n-pentane. The precipitate is allowed to dry, then weighed. The weight of the precipitate as a fraction of the initial oil sample weight is reported as the percentage asphaltenes. The filtrate from the precipitation, the “maltene” fraction, is recovered and made up to 100 mL with n-pentane. A 15 g, a 1 cm diameter column of activated silica gel is prepared. The top of the column is protected by a 1 cm layer of sodium sulphate. A 5 mL aliquot of the maltene fraction is loaded onto the column. A 60 mL volume of 1:1 (v:v) benzene:hexane is eluted through the column and discarded. A 60 mL volume of methanol, followed by a 60 mL volume of dichloromethane, are eluted through the column and combined. The methanol/dichloromethane fractions are reduced by rotary evaporation and blown down to dryness under nitrogen. The mass fraction of this dried eluent, compensating for the volume fraction used, is reported as the percentage of resins in the sample. Resin and Asphaltene Thin-Layer Chromatography Determination While no standard method for this technique exists, it has the advantages over the gravimetric methods of being much faster, requiring much less oil or product and being more reproducible. It has the disadvantage of requiring a sophisticated instrument, a TLC with a flame ionization detector (FID). A TLC that quantifies analytes developed on silica gel-coated glass rods, such as the Iatroscan Mark 6, is necessary for this method. Briefly, an aliquot of sample dissolved in dichloromethane at a concentration of 1 mg/mL is spotted at a point, the origin, near one end of a rod, the foot of the rod. The rods are then developed by immersion of the feet into a series of solvents to separate the four hydrocarbon groups. The origin points must remain above the liquid surface, but the feet of the rods must be immersed sufficiently to cause solvent to travel up the rods by capillary action.

Chapter | 4

Measurement of Oil Physical Properties

75

The first solvent used is n-hexane to develop the saturates. Toluene develops the aromatics. Finally, a 95% dichloromethane, 5% methanol mixture is used to develop the resins. The asphaltenes remain at the spotting origin. The hydrocarbon groups that are not quantified by this method, the saturates and aromatics, are removed by pyrolysis. A known standard is then applied to the chromarod and then quantified using an FID and an internal standard. A sample of 1 octadecanol at 1 mg/mL concentration is a convenient internal standard. This is spotted on the rod just prior to measurement, on the part of the rod pyrolyzed to remove the saturate and aromatic fractions. The development of the chemicals on the rods critically depends on the conditions. The rods must be developed in tanks to control the vapors in atmosphere. Also, temperature and humidity must remain as consistent as possible in order to achieve reproducible results. When drying after each development, the rods must rest in a controlled humidity chamber. Resin and asphaltene contents are determined as follows: %Resin ¼ CIS VIS AR =AIS

(8)

%Asphaltene ¼ CIS VIS AA =AIS

(9)

where: CIS: Internal standard concentration VIS: Internal standard volume AIS: Internal standard area from TLC integration AR: Resin area from TLC integration AA: Asphaltene area from TLC integration Note that while saturate and aromatic fractions are separated by the development process and could, in principle, be measured by TLC-FID, the drying process between development stages requires significant evaporation. This level of evaporation is significant enough to remove most of the volatile components, which includes a large fraction of both saturates and aromatics (but not the resins or asphaltenes). For this reason, this TLC-FID method is not suitable for saturate or aromatic determination.

Saturate and Aromatic Chromatographic Determination This method is adapted and simplified from a previously published method for crude oil and petroleum product determination.28 An 80 mg/mL solution of oil is prepared in hexane. A 3.0 g column of activated silica-gel is prepared, topped with 0.5 cm anhydrous sodium sulphate. The column is conditioned with 20 mL of hexane. An amount of 200 mL of the oil solution, approximately 16 mg of oil, is quantitatively transferred onto the column using an additional 3 mL of hexane to complete the transfer. The eluent is also discarded. Just prior to exposure of

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Oil Analysis and Remote Sensing

the sodium sulphate to the air, 12 mL of hexane is added to the column. The eluent is labeled fraction “F1.” F1 is considered to contain all the saturates, including the waxy components in the oil. The column is then eluted with 15 mL of 1:1 (volume:volume) benzene/ hexane or dichloromethane/hexane. The eluent is collected and labeled fraction “F2.” F2 is considered to contain the aromatic compounds in the oil, including the BTEX compounds, other alkylated benzene species, PAHs, and the alkylated PAH homologues. Half of fractions F1 and F2 are combined. This composite fraction is labeled “F3.” This fraction is used for analysis of total petroleum hydrocarbons (TPH). All the three fractions are concentrated under dry nitrogen. The fractions are then spiked with the internal standard, 100 mL of 200 ppm 5-a-androstane, and made up with hexane to 1 mL. The analysis for total petroleum hydrocarbons and saturates is performed by high-resolution capillary GC/FID using the following conditions: Column: Carrier Gas: Injection volume: Injector temperature: Detector temperature: Oven program:

30 m 0.32 mm ID HP DB5-HT fused silica column (0.10 mm film thickness); Helium, 3.0 mL/min, constant flow; 1.0 mL; 290 C; 325 C; 40 C for 2 minutes, followed by 25 C/minute to a final temperature of 340 C, then held for 15 minutes. The total run time is 29 minutes.

To calculate the concentration of hydrocarbons in each fraction, the area response attributed to the petroleum hydrocarbons must be determined. This area includes all of the resolved peaks and unresolved “hump.” This total area must be adjusted to remove the area response of the internal standards and GC column bleed. Column bleed is the reproducible baseline shift that occurs during the oven cycle of the GC. To determine this area, a hexane blank injection is analyzed before and after every 10 samples to determine the baseline response. The integration baseline is then set at a stable reproducible point just before the solvent peak. This baseline area for the blank run is subtracted from the actual sample run. The total areas of the chromatograms of F1, F2, and F3 are obtained by integration of all peaks, corrected by removal of the baseline. The area response attributable to the internal standard is calculated. The F3 fraction is used to calculate the TPH values for the oil.28 The F1 and F2 fractions are used to calculate the total saturate (TSH) and total aromatic (TAH) contents. Note that TPH should be within 10% of TSH þ TAH.

Chapter | 4

Measurement of Oil Physical Properties

77

As not all the oil is passed through the GC column, a simple sum of TSH, TAH, resin, and asphaltene contents will not equal 100%. This missing portion of the oil, which does not precipitate or get analyzed by the GC method, is approximated by proportionally dividing it into the saturate and aromatic portions. Thus the saturate content of the oil is commuted using: % Saturates ¼ TSH=ðTSH þ TAHÞð1 % Asphaltenes % ResinsÞ (10) Likewise, the aromatic content is computed using: % Aromatic ¼ TAH=ðTSH þ TAHÞð1 % Asphaltenes % ResinsÞ (11) Note that the asphaltene and resin contents may be determined by either gravimetric or TLC-FID method described earlier. For crude oils or products with high water content, it is necessary to dry the sample prior to the gravimetric determination of the hydrocarbon group contents. If a Karl-Fischer water content determination can be made, then the composition of the original product can be reported and adjusted for the observed water content. If not, the values should be reported as for dried product only.

4.4. QUALITY ASSURANCE AND CONTROL Most of the physical property methods described here rely on a single instrument and involve a simple measurement with little sample manipulation.28 For these methods, the instruments are calibrated as directed by the manufacturer or the appropriate ASTM method with chemical and/or gravimetric standards as appropriate. In addition, instrumental and operator performance should be monitored by periodic measurement of check standards. A control chart should be kept for each procedure, for the check or performance standard measurements. The check standard measurements are monitored closely. Failure of the check standard measurement to fall within the smaller of either a historical 95% confidence limit or the appropriate ASTM required repeatability should result in an investigation of the procedure. This typically includes required instrument maintenance, cleaning, recalibration, and measurement of the check standard until the desired precision and accuracy is reached. The chromatographic methods described here, including the dispersability tests and the hydrocarbon group analysis, involves significant sample preparation, followed by a measurement by gas chromatography. Such techniques require a higher level of effort to maintain quality assurance. Check or surrogate samples of either pure materials or certified reference standards should be processed in the same manner as the samples. Calibration should be accomplished with a second, separate set of certified reference materials. Internal standards should also be certified reference materials from reputable suppliers. Surrogate recovery, calibration stability, and internal standard response control charts should all be checked regularly to ensure procedure

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and measurement accuracy. Chromatograms should be checked to ensure that chromatographic quality, including good peak shape, baseline drift, column bleed, sample carryover, and chromatographic resolution are within acceptable limits.

4.5. EFFECTS OF EVAPORATIVE WEATHERING ON OIL BULK PROPERTIES Long experience has shown that the physical characteristics and chemical fingerprint of a crude oil can change greatly over the course of a spill incident. These changes have a profound effect on the fate, behavior, and effects of an oil in the environment. The oil may transmute to other states, evaporating, dissolving in water, or condensing to a semisolid residue, each new state having unique behaviors and eventual fates. In order to aid in the estimation and prediction of spill behavior, it is useful to know not only the characteristics of the fresh crude oil, but also those of oils at different stages of “weathering” in the environment. Previous work has shown that immediately after a spill, the dominant process of oil weathering is evaporation. The following discussion focuses on the effects of evaporative weathering on changes of oil physical properties and chemical compositions.

4.5.1. Weathering When oil is spilled, on either water or land, a number of transformation processes operate on the oil. In general, there are two types of transformation processes: the first is weathering, and the second is a group of processes (including spreading, movement of oil slicks, and sinking and ove-washing) related to the movement of oil in the environment. Weathering and movement processes overlap, with weathering strongly influencing how oil moves in the environment and vice versa. These processes depend very much on the type of oil spilled and the weather conditions during and after the spill. Thoroughly understanding the behavior of spilled oil in the environment is extremely important for development of oil spill models. Today’s sophisticated spill models combine the latest information on oil fate and behavior with computer technology to predict where the oil will go, what state it will be in, and when it gets there. “Weathering” is the term referring to a combination of a wide variety physical, chemical, and biological processes of a spilled oil in the environment. The weathering processes include evaporation, emulsification, natural dispersion, dissolution, microbial degradation, photo-oxidation, and other processes such as sedimentation, and oil-suspended particle interactions. Weathering has a very significant effect on most bulk oil properties. Unlike the chemical compositions, however, where environmental parameters only affect the rate and type of weathering, bulk properties of the oil are also highly variable depending on the physical conditions. The most important of these is

Chapter | 4

Measurement of Oil Physical Properties

79

temperature, but other factors such as pressure and the materials with which the oil is in contact also play a role. As an oil loses mass and changes in composition, several general trends in physical property changes can be observed: l

l

l

Density increases approximately linearly with increasing weathering. Density decreases approximately linearly with temperature. Viscosity increases with increasing weathering, but a simple functional relationship is not easy to develop. Viscosity increases approximately exponentially with decreasing temperature. Surface and interfacial tensions tend to increase slightly with increasing weathering.

4.5.2. Preparing Evaporated (Weathered) Samples of Oils A common technique for simulating weathering in the laboratory is evaporation. While this is only one of the possible processes in the natural environment, it is probably the dominant one for most spills, particularly in the first few hours or days following a spill. A laboratory oil-weathering technique by rotary evaporation allows for convenient preparation of artificially weathered oils with varying degrees of weight loss. A typical oil-weathering system consists of a rotary evaporator. The bath temperature of the evaporator should be variable from 20 C to 100 C 0.5 C. The rotation speed should be continuously variable from 10 to 135 rpm. The following evaporation procedure is used to evaporate oils: (1) The water bath is brought to a temperature of 80 C. (2) The empty rotary flask is weighed, and no more than one-third the volume of the rotary flask in oil is added and the flask reweighed. (3) The flask is mounted on the apparatus and the flask partially immersed in the water bath and spun at high speed, at least 120 rpm. A constant flow of air through the apparatus should be maintained by a vacuum pump. (4) At set intervals, the sample flask is removed and weighed. It is convenient to prepare two to three weathered samples for each type of oil measured. With a moderate flow rate through the instrument, a duration of 48 hours evaporation will come close, within 5 to 10%, to simulating the eventual final state of an oil in the environment. Intermediate fractions of approximately one- and two-thirds of the 48-hour loss by weight will simulate approximately the condition of the oil after a few hours to days and a few days to weeks of natural evaporation. The exact time taken to prepare these intermediate fractions is determined by estimation from the measured fractional mass-loss as a function of time for the 48-hour sample. The fraction mass-loss is calculated as: % weathering ¼ ðmi mf Þ=ðmi me Þ x 100%

(12)

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PART | III

Oil Analysis and Remote Sensing

where % weathering is the percentage evaporative mass-loss over the 48-hour period, mi is the initial mass of the flask and oil, mf is the final mass of the flask and oil, and me is the mass of the empty flask. A graph of % weathering as a function of time is plotted using the interval weighing data. The times for one-third (t1/3) and two-thirds (t2/3) of the 48-hour mass loss are interpolated from a time-weathering graph. Typical times for t1/3 range from 30 minutes to 2 hours, for t2/3, 8 to 12 hours. This technique allows for precise control of the evaporative weight loss for a target oil and can be directly correlated to bulk property and compositional changes of the weathered oil. By tracking weight loss as a function of time, an equation for predicting evaporation can be found. Also, from this same graph, it is possible to determine a point at which the evaporation rate is sufficiently slow that the oil may be considered to have achieved the maximum evaporative loss likely to be observed under the conditions of a marine spill. 0.94

0.94

Cook Inlet 2003 vs.T

Density (g/mL)

0.92

Cook Inlet 2003 0.92

0.90

25.0%

0.88

11.4%

0.86 Fresh 0.84 0.82

vs.W(%)

34.4%

Density (g/mL)

(a)

0.90 0.88 0.86

5 °C 15 °C

0.84 30 °C 0.82

0

5

10

15

20

25

30

0.80

35

0

10

Temperature (°C)

(b) 1.02

40

Platform Elly vs.W(%) 1.00

0.98

13.3% 7.9% 4.6%

0.96

Density (g/mL)

1.00

Density (g/mL)

30

1.02

Platform Elly vs.T

0.98 5 °C 0.96

Fresh 0.94

20

Weathering (%)

0

5

10

15

20

25

Temperature (°C)

30

35

15 °C 30 °C

0.94

0

2

4

6

8

10

12

14

Weathering (%)

FIGURE 4.1 Density versus temperature and weathering for a light (Cook Inlet) (a) and heavy (Platform Elly) (b) crude oil.

Chapter | 4

81

Measurement of Oil Physical Properties

4.5.3. Quantifying Equation(s) for Predicting Evaporation The evaporation kinetics are determined for each oil by measuring the weight loss over time from a shallow dish.30,31 Approximately 20 g of oil is weighed into a 139 mm petri dish. The oil weight is recorded by an electronic balance accurate to 0.01 g at set intervals and collected on a computer logging system. Measurements are conducted in a climate-controlled chamber at 15 C. Temperatures are monitored by a digital thermometer. The evaporation period can last from a few days for light oils to weeks for heavier products. The time versus weight-loss data series are fitted to a set of simple equations. The best curve-fit is chosen as the equation for predicting evaporation.

Effects of Evaporative Weathering on Crude Oil Density Densities of oils typically increase approximately 5 to 10% as oil weathers. Cook Inlet, a light oil, changes from 0.84 g/mL to 0.91 g/mL at 30 C (see 10000

10000

Cook Inlet 2003 vs.W(%)

1000

Viscosity (mPas)

Viscosity (mPas)

Cook Inlet 2003 vs.T

100 34.4% 25.0% 10

1000

100

10

11.4% Fresh

1

0

5

10

15

20

25

30

5 °C 15 °C 30 °C

1

35

0

10

1e+7

1e+5 13.3% 1e+4

7.9% 4.6%

1e+3

Fresh

0

5

10

15

20

25

Temperature (°C)

30

35

1e+6

Viscosity (mPas)

Viscosity (mPas)

30

40

1e+7

Platform Elly vs.T

1e+6

1e+2

20

Weathering (%)

Temperature (°C)

Platform Elly vs.W(%)

1e+5

1e+4

1e+3

1e+2

5 °C 15 °C 30 °C

0

2

4

6

8

10

12

14

Weathering (%)

FIGURE 4.2 Viscosity versus temperature and weathering for light (Cook Inlet) and heavy (Platform Elly) crude oils.

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Oil Analysis and Remote Sensing

Figure 4.1a), while Platform Elly, a very heavy crude oil, has a fresh density of 0.9531 g/mL and increases to 0.9843 g/mL in its most weathered state at 30 C (Figure 4.1b). From Figure 4.1, it can be seen that, to a first approximation,

30

34.4%

29

25.0%

28 27 26

Fresh

25 0

5

10

15

20

25

30

34 o/w

vs.T

32 30 28 26

34.4% 25.0%

24 22

11.4% Fresh 0

5

10

15

20

25

30

35

34

vs.T

o/b

32 30 28 26 24

34.4% 25.0% 11.4%

22

Fresh

20

0

5

10

15

20

25

Temperature (°C)

30

35

31

Cook Inlet 2003 vs.W(%) o/a

5 °C 15 °C

30

30 °C

29 28 27 26 25 24

35

Interfacial Tension (oil/water) (mN/m)

Interfacial Tension (oil/water) (mN/m)

11.4%

Surface Tension (oil/air) (mN/m)

31

24

Interfacial Tension (oil/3.3%brine) (mN/m)

32

Cook Inlet 2003 vs. T o/a

Interfacial Tension (oil/3.3%brine) (mN/m)

Surface Tension (oil/air) (mN/m)

32

30 °C 15 °C 5 °C 0

10

20

30

40

34

vs.W(%)

o/a

32 30 28 26

5 °C 15 °C

24 30 °C 22

0

10

20

30

40

34

vs.W(%)

32

o/b

30 28 26 24

5 °C 15 °C

22 30 °C 20

0

10

20

30

40

Weathering (%)

FIGURE 4.3 Surface and interfacial tensions as a function of temperature and weathering for Cook Inlet (2003).

Chapter | 4

Measurement of Oil Physical Properties

83

density increases linearly with increasing mass-loss and decreasing temperature. Better extrapolations can be made from log-log extrapolations of both quantities. Note that the uncertainties in density are very small: 0.0002 g/mLdapproximately 1 part in 5,000.

Effects of Evaporative Weathering on Crude Oil Viscosity In contrast to most other physical properties, the viscosity of an oil can change by orders of magnitude with weathering and changes in temperature. For example, the viscosity of Cook Inlet (2003) changes from 5.8 mPa s to 67.0 mPa s at 30 C (see Figure 4.2), while fresh Platform Elly has a viscosity of 1070 mPa s, and reaches 52280 mPa s in the most weathered fraction (Figure 4.3). As can be seen from the logarithm of viscosity is roughly inversely linear with temperature, but the effects of weathering on viscosity are more complex. Uncertainties in viscosity are 5%. Effects of Evaporative Weathering on Crude Oil Surface and Interfacial Tensions Surface and interfacial tensions have no simple quantitative relationships in general to either the degree of weathering or the temperature. Surface tensions however, do not vary greatly from oil to oil; values from 25 mN/m to 32 mN/m are typical for almost all types of oil. Interfacial tensions for oil/water and oil/ 3.3% brine are often marginally lower than the corresponding oil/air surface tension. Oil/brine interfacial tensions are usually somewhat higher than the corresponding oil/(pure) water values. Typical values for both range from 18 mN/m to 32 mN/m. Surface and interfacial tensions tend to decrease with temperature and increase with weathering. Care should be taken not to overinterpret the significance of surface and interfacial tension values; however, the errors on these measurements are relatively large, 15%, and the relative variations of the values are fairly small.

REFERENCES 1. ASTM D 5002. Standard Test Method for Density and Relative Density of Crude Oils by Digital Density Analyzer. Conshohocken, PA: American Society for Testing and Materials (ASTM); 2009. 2. API 82. American Petroleum Institute (API), Petroleum Measurement TablesdVolume XI/XII. West Conshohocken, PA: American Society for Testing and Materials; 1982. 3. Jokuty P, Fingas M, Whiticar S. Oil Analytical Techniques for Environmental Purposes. AMOP 1994;245. 4. ASTM D 971. Standard Test Method for Interfacial Tension of Oil Against Water by the Ring Method. West Conshohocken, PA: American Society for Testing and Materials; 2009. 5. Jokuty P, Fingas M, Whiticar S, Fieldhouse B. A Study of Viscosity and Interfacial Tension of Oils and Emulsions, Manuscript Report EE-153, Ottawa, ON: Environment Canada, 1995.

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6. Song B, Springer J. Determination of Interfacial Tension from the Profile of a Pendant Drop Using Computer-aided Image Processing. Colloid Interface Sci. 1996;64. 7. ASTM D1310. Standard Test Method for Flash Point and Fire Point of Liquids by Tag Open-Cup Apparatus. West Conshohocken, PA: American Society for Testing and Materials; 2007. 8. ASTM D 6450. , Standard Test Method for Flash Point by Continuously Tester. West Conshohocken, PA: American Society for Testing and Materials; 2009. 9. ASTM D 93. American Society for Testing and Materials (ASTM), Standard Test Method for Flash Point by Pensky-Martens Closed Cup Tester. West Conshohocken, PA: American Society for Testing and Materials; 2009. 10. ASTM D 7094. Standard Test Method for Flash Point by Modified Continuously Closed Tester. West Conshohocken, PA: American Society for Testing and Materials; 2009. 11. Montemayor RG, Rogerson JE, Colbert JC, Schiller SB. Reference Verification Fluids for Flash Point Determination. J. Test. Eval. 1999;27. 12. ASTM D 97. Standard Test Method for Pour Point of Petroleum Oils. West Conshohocken, PA: American Society for Testing and Materials; 2009. 13. ASTM D 4294. Standard Test Method for Sulfur in Petroleum Products by Energy-Dispersive X-ray Fluorescence Spectroscopy. West Conshohocken, PA: American Society for Testing and Materials; 2009. 14. ASTM D 4377. Standard Test Method for Water in Crude Oils by Potentiometric Karl Fischer Titration. West Conshohocken, PA: American Society for Testing and Materials; 2009. 15. Fingas M, Fieldhouse B, Mullin J. Studies of Water-in-oil Emulsions: Stability and Oil Properties. AMOP 1998;1. 16. Fingas M, Fieldhouse B. Studies on Crude Oil and Petroleum Product Emulsions: Water Resolution and Rheology. Colloids Surf. A. 2009;67. 17. ASTM F 2059. Standard Test Method for Laboratory Oil Spill Dispersant Effectiveness Using the Swirling Flask. West Conshohocken, PA: American Society for Testing and Materials; 2007. 18. Jokuty P, Whiticar S, McRoberts K, Mullin J. Oil Adhesion TestingdRecent Results. AMOP 1996;9. 19. ASTM D 5. Standard Test Method for Penetration of Bituminous Materials. West Conshohocken, PA: American Society for Testing and Materials; 2009. 20. ASTM D 6560. Standard Test Method for Determination of Asphaltenes (Heptane Insolubles) in Crude Petroleum and Petroleum Products. West Conshohocken, PA: American Society for Testing and Materials; 2006. 21. ASTM D 2006. Method of Test for Characteristic Groups in Rubber Extender and Processing Oils by the Precipitation Method (Withdrawn 1975). West Conshohocken, PA: American Society for Testing and Materials; 1965. 22. ASTM D 2007. American Society for Testing and Materials (ASTM), Standard Test Method for Characteristic Groups in Rubber Extender and Processing Oils and Other PetroleumDerived Oils by Clay-Gel Absorption Chromatographic Method. West Conshohocken, PA: American Society for Testing and Materials; 2007. 23. ASTM D 4124. Standard Test Methods for Separation of Asphalt into Four Fractions. West Conshohocken, PA: American Society for Testing and Materials; 2006. 24. Barman BN. Hydrocarbon-Type Analysis of Base Oils and Other Heavy Distillates by ThinLayer Chromatography with Flame-Ionization Detection and by the Clay-Gel Method. J. Chromat. Sci. 1996;219. 25. Speight JG. The Chemistry and Technology of Petroleum. New York: Marcel Dekker; 2007. 26. Becker JR. Chapter 13, Asphaltene Test Methods, Crude Oil Waxes, Emulsions and Asphaltenes. Tulsa, OK: Penn Well Publishing Co; 1991.

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27. Hollebone B, Wang Z, Landriault M, Smith P. A New Method for the Determination of the Hydrocarbon Groups in Oils: Saturates, Aromatics, Resins, and Asphaltenes (SARA). AMOP 2003;31. 28. Wang ZD, Fingas M, Li K. Fractionation of ASMB Oil, Identification and Quantitation of Aliphatic Aromatic and Biomarker Compounds by GC/FID and GC/MSD (Parts I and II). J. Chromat. Sci. 1994;361. 29. Environment Canada, Oil Properties Database, http://www.etc-cte.ec.gc.ca/databases/ OilProperties/oil_prop_e.html, accessed May 2010. 30. Fingas M. The Evaporation of Oil Spills. AMOP 1995;43. 31. Fingas M. Modeling Evaporation Using Models That Are Not Boundary-Layer Regulated. J. Haz. Mat. 2004;27.

APPENDIX 4.1 Table A4.1 gives the environmentally relevant properties of selected crude oils. TABLE A4.1 Environmentally-Relevant Properties of Selected Crude Oils29 Alaska North Slope Prudhoe Bay, Alaska, USA

Saudi Arabia

Mississippi Brent Canyon Blend Federated Block 807 Gulf of North Mexico, Sea, United Alberta, Louisiana, USA Kingdom Canada

Arabian Light

West Texas Intermediate

Texas USA

0 C

0.8777

0.8776

0.8472

0.8413

0.9310

0.8594

15 C

0.8663

0.8641

0.8351

0.8293

0.9461

0.8474

30.89

31.30

37.8

38.9

17.5

34.38

0 C

23.2

32.6

16

10

88.1

19.2

11.5

13

6

4

4.8

8.6

mN/m 0 C

27.3

27.2

28.0

27.3

28.8

27.4

26.4

26

25.5

25.8

28.2

26.0

mN/m 0 C

26.7

23.5

25.7

18.7

24.4

19.3

23.6

23.8

22.7

15.9

24.1

15.8

Oil-sea water Interfacial tension

mN/m 0 C

22.5

21.3

24.9

17.6

26.0

18.8

20.2

21.6

22.5

16.2

26.6

15.6

Flashpoint

C

<8

<10

<30

15

<0

<10

Pour Point

C

32

21

42

57

22

Density

g/mL

API Gravity Viscosity

mPas

15 C Surface Tension Oil-water Interfacial tension

15 C

15 C

15 C

(Continued )

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Oil Analysis and Remote Sensing

TABLE A4.1 Environmentally-Relevant Properties of Selected Crude Oils29dcont’d Alaska North Slope Prudhoe Bay, Alaska, USA Emulsion Formation

Arabian Light

Saudi Arabia

Visual Unstable Meso Stability Complex 92.7 Modulus (Pa) 91.1 Emulsion Water Content

Mississippi Brent Canyon Blend Federated Block 807 Gulf of North Mexico, Sea, United Alberta, Louisiana, USA Kingdom Canada NM

NM

Meso

West Texas Intermediate

Texas USA Unstable

26 79.7

Water

% (w/w)

<0.1

<0.1

0.4

NM

11.9

<0.1

Sulphur

% (w/w)

1.11

1.93

0.39

0 29

0.83

0.86

Saturate

% (w/w)

75.0

75.5

72

74

73.3

78.5

Aromatic

% (w/w)

15.0

15.2

23

21

15.45

14.8

Resin

% (w/w)

6.1

5.7

4

3

6.22

6.0

Asphaltene

% (w/w)

4.0

3.6

1

1

5.03

0.7

Wax

% (w/w)

2.6

2.7

7

0.6

2.8

Adhesion

g/m2

20

17

12

2

NM

12

47

19

45

61

49.6

28

Chemical % (Corexit Dispersability 9500)

Chapter 5

Introduction to Oil Chemical Analysis Merv Fingas

Chapter Outline 5.1. Introduction 87 5.2. Sampling and Laboratory 87 Analysis 5.3. Chromatography 89

5.4. Identification and Forensic 96 Analysis 5.5. Field Analysis 107

5.1. INTRODUCTION An important part of the field of oil spill control is the analysis of oil in various media. Oil analytical techniques are a necessary part of the scientific, environmental, and engineering aspects of oil spills.1-4 Analytical techniques are used extensively in environmental assessments of fate and effects. Laboratory analysis can provide information to help identify an oil if its source is unknown or what its sources might be. With a sample of the source oil, the degree of weathering and the amount of evaporation or biodegradation can be determined for the spilled oil. Through laboratory analysis, the more toxic compounds in the oil can be measured, and the relative composition of the oil at various stages of the spill can be determined. This is valuable information to have as the spill progresses. In nonspill situations, analytical techniques are used extensively to measure the oil content of soil and water for environmental quality purposes. Many jurisdictions have standards on the petroleum content of waters and soils for various uses. In addition, many laws exist for the maximum oil content in soils. Soils must often be removed for treatment before lands can be transferred from one owner to another.

5.2. SAMPLING AND LABORATORY ANALYSIS Taking a sample of oil and then transporting it to a laboratory for subsequent analysis is common practice. While there are many procedures for taking oil Oil Spill Science and Technology. DOI: 10.1016/B978-1-85617-943-0.10005-X Copyright Ó 2011 Elsevier Inc. All rights reserved.

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samples, it is always important to ensure that the oil is not tainted from contact with other materials and that the sample bottles are precleaned with solvents, such as hexane, that are suitable for the oil.5 The simplest and most common form of analysis is to measure how much oil is in a water, soil, or sediment sample.6 Such analysis results in a value known as total petroleum hydrocarbons (TPH). The TPH measurement can be obtained in many ways, including extracting the soil, or evaporating a solvent such as hexane and measuring the weight of the residue that is presumed to be oil. There now exist certified laboratories that use certified petroleum hydrocarbon measurement techniques.6 These should be used for all studies. One of the most serious difficulties in older studies occurred when inexperienced staff tried to conduct chemical procedures. Analytical methods are complex and cannot be conducted correctly without chemists familiar with the exact procedures. Furthermore, field instrumentation requires calibration using standard procedures and field samples during the actual test. These samples must be taken and handled by standard procedures. Certified standards must be used throughout to ensure good Quality Assurance/Quality Control (QA/QC) procedures. In this era, it is simply unacceptable not to use certified methods, laboratories, and chemists.

5.2.1. Incorrect and Obsolete Methods Several attempts to perform oil analysis have been made using methods that are not scientifically valid. One of these is the use of colorimetry. This method has never been scientifically valid for oil measurement as oil does not have what is known as a color center, that is, a molecular absorption center for a specific band of light.7-9 As oil is a mixture of hundreds of compounds, there is obviously not a single light-absorbing centre. This method results in oil measurements that are typically 100% incorrect. Another series of methods involved extracting oil from soil or water using fluorinated or chlorinated hydrocarbons. Since these extractants were ozonedepleting, they were removed from the market over 20 years ago. The extracted hydrocarbons were then “measured” using infrared light, as hydrocarbons in such solvents do absorb at specific wavelengths. The method was the standard oil in soil technique in several countries and yielded repeatable results. The ozone-depleting substances were replaced with hexane. Hexane is not a good extractant of oil from soil, and thus these methods are not as popular as the older methods were. The use of hexane also led to renaming the results of these methods from Total Petroleum Hydrocarbons (TPH) to hexaneextractables.10 There are several “meters” sold on the market that offer oil readings; however, none of these are reliable or accurate.8

Chapter | 5

Introduction to Oil Chemical Analysis

89

Fluorometry is a technique sometimes used for measuring or estimating concentrations of oil in water. A fluorometer uses UV or near UV to activate aromatic species in the oil.11 The UV activation energy is more sensitive to the naphthalenes and phenanthrenes, whereas the near UV is more sensitive to large species such as fluorenes. The composition of the oil changes with respect to aromatic content as it weathers and is dispersed in water, with the concentration of aromatics increasing. Thus, the apparent fluorescent quantity increases in this process. It must be noted that fluorometers cannot truly be calibrated for the oil as there are many variables, as explained above. The errors encountered all increase the apparent value of the oil concentration in the water column. Incorrect calibration procedures can distort concentration values up to 10 times their actual value, or even more. Correct analytical methods involve performing accurate gas chromatograph (GC) measurements both in the laboratory and in the field.

5.3. CHROMATOGRAPHY The primary method for oil analysis, as well as for many chemicals in the environment, is gas chromatography; this method will be described in the subsequent section. One should note that other chromatography methods and other analytical methods are sometimes used for oils. These include liquid chromatography, sometimes used for PAH analysis and inductively coupled plasma (ICP) instruments for measuring metals in oils. These and many other techniques are not described in this section.

5.3.1. Introduction to Gas Chromatography The standard method for oil analysis is to use a GC.2,3,12,13,14 A small sample of the oil extract (typically measured in microliters, mL), often in hexane, and a carrier gas, usually helium or hydrogen, are passed through a capillary column. The sample is injected into a heated chamber from where its vapors pass into the silica column. The silica column is coated with absorbing materials, and, because the various components of the oil have varying rates of adhesion, the oil separates because these components are absorbed at different rates onto the column walls. The gases then pass through a sensitive detector. The injector, column, and detector are often maintained at constant temperatures to ensure repeatability. The system is calibrated by passing known amounts of standard materials through the unit. The amount of many individual components in the oil is thereby measured. The components that pass through the detector can also be totaled, and a TPH value determined. It is important to note that only the vapors pass into the column initially. Heavier contaminants can foul the injector and first part of the column. It is therefore important that the sample be subjected to a cleanup procedure before it is injected into the column. Cleanup procedures can be complex and involve several steps.

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While a GC measurement is highly accurate, this measurement does not include resins, asphaltenes, and some other components of the oil with higher molecular weight that do not vaporize and pass through the column. These heavier components can be determined separately using open column chromatography or precipitation techniques. The detectors used in chromatography are important. An important detector for petroleum hydrocarbons is the flame ionization detector (FID).2,3 The principle behind this instrument is simple as most compounds show variable ion conductivity burned in a hydrogen flame. This detector is simple and has the advantage of yielding relatively similar signals for different hydrocarbons, thus making calibration and quantification simple. Another detector commonly used for oils is the mass spectrometric detector. The analytes from the GC column are introduced into a vacuum chamber and ionized, and then these ions are separated according to mass and passed to a detector. The detected signals are then analyzed by a computer and output to the user as peaks of given mass or even possible compound identification. The mass spectrometer provides information about the structure of the substance so that each peak in the chromatogram can be more positively identified. The methods are abbreviated as GC-FID, if a gas chromatograph and flame ionization detector are used or GC-MS, if a gas chromatograph and a mass spectrometer detector are used. A typical GC-FID chromatogram of a light crude oil with some of the more prominent components of the oil identified is shown in Figure 5.1.2,3 This chromatogram shows some of the many features of oil that are identified by this analytical technique. The bulk of crude oils, especially light ones, have a large proportion of n-alkanes, as can be seen by the large peaks that constitute a large portion of this chromatogram. This is a light crude oil that can be evidenced by the fact that the highest peak is C15. A more weathered crude might have its highest peak at C18 or more. The top of the chromatogram is typically shaped as a curve, peaking to C15, as it is here. Under the peaks is a hump, often called the unresolved complex mixture, or UCM. This is an aggregate of largely unresolved peaks of alkane origin. At C16, for example, there are already thousands of isomers that cannot be resolved by typical GC methods. Between the n-alkane peaks are smaller peaks, most of which are aromatic compounds. Two standard biomarkers (here, isoprenoids and branched alkanes) are usually evident in such a chromatogram, Pristane (near nC17) and Phytane (near nC18). These have been used to assess state of weathering; however, other compounds are now typically used. Figure 5.2 shows the GC-FID chromatograms of 10 oils. Figure 5.2A to C shows the chromatograms of three lighter crude oils: Arabian medium crude oil, Hedrun crude oil, and Gullfaks,2,3 Figure 5.2D shows a chromatogram of Orimulsion, a bitumen. In this chromatogram one notes that almost all n-alkanes are not present, and the chromatogram largely consists of the

91

C28

C26

C24

C22

C20

Introduction to Oil Chemical Analysis

C21

Chapter | 5

Chromatographic retention time FIGURE 5.1 GC-FID of a light crude oil. This chromatogram illustrates many features of the chromatograms of crude oils. The bulk of crude oils, especially light ones, have a large proportion of n-alkanes, as can be seen by the large peaks that constitute a large portion of this chromatogram.

unresolved complex mixtures, or UCM. Figure 5.2E shows the chromatogram of IFO-30, Intermediate fuel oil, which is a mixture of a diesel fraction and Bunker C. Figure 5.2F shows the chromatogram of Bunker C, and Figures 5.2G-J show jet fuel, diesel fuel, lubrication oil, and number 6 fuel oil, respectively. The mass spectrometer provides information about the structure of the substance so that each peak in the chromatogram can be positively identified. An important technique is that of SIM (selective ion monitoring), where one can monitor the ion most typically associated with the target compound. This technique enables the detection and quantification of many compounds in oil that otherwise would not be separately resolved.2,3 Figure 5.3 shows three chromatograms first by GC-FID and then by GC-MS using SIM. Figures 5.3A and B are chromatograms of a light Alberta crude oil; Figures 5.3C and D are chromatograms of California heavy crude oil, and Figures 5.3E and 5.3F are chromatograms of Orimulsion Bitumen. The two chromatograms (e.g., FID/ SIM) are quite different. The SIM chromatograms are no longer recognized as the types of oils as shown by FID. However, one can see that the peaks are more clearly defined and that the SIM can provide different and important information. The disadvantage of the SIM is that each peak must be quantified separately using an internal standard. Most fuels and oils show a typical distribution pattern in GC-FID. The idealized patterns can be seen in Figure 5.4, a figure that shows the alkane distribution. All graphs are n-alkanes except for the lube oils where the alkanes are highly branched. These bar graphs were created from

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Oil Analysis and Remote Sensing

FIGURE 5.2 GC-FIDs of several oils. Figure 5.2A shows the chromatogram of Arabian medium crude oil. Figure 5.2B is a chromatogram of Hedrun crude oil, a light oil. Figure 5.2C is a chromatogram of Gullfaks, another light oil. Figure 5.2D shows a chromatogram of Orimulsion, a bitumen. In this chromatogram one notes that almost all n-alkanes are not present, and the chromatogram largely consists of the unresolved complex mixtures or UCM. Figure 5.2E shows the chromatogram of IFO-39, Intermediate fuel oil, which is a mixture of a diesel fraction and Bunker C. One can see the peaks of diesel fuel, peaking at about C14 and that of Bunker C, peaking at about C28. In Figure 5.2F the chromatogram of Bunker C is shown. Figures 5.2G, 2H, 2I, and 2J show jet fuel B, diesel fuel, lubrication oil, and number 6 fuel oil, respectively.

Chapter | 5

Introduction to Oil Chemical Analysis

93

FIGURE 5.2 Continued

quantitative analysis and approximate the alkane chromatograms of the same oils.

5.3.2. Methodology Modern chromatographic methods require that the injected sample contents be of certain types and that they do not foul the injector or column. Thus, several cleanup methods have developed over the years.1,6 The basic methods involve extracting the oil using dichloromethane (DCM), sometimes in combination with other solvents such as hexane. This procedure will leave the DCM insoluble material, such as soil and wood, and remove the DCM soluble material, which is largely petroleum oil. Surrogate chemicals are often added at this stage; these substances are compounds, typically deuterated hydrocarbons, that are not present in oil and will serve to identify peaks in subsequent analyses. The DCM extract is often filtered and treated to

94

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Oil Analysis and Remote Sensing

FIGURE 5.3 The GC-FID and GC-MS with SIM at ion 85m/e. The left-hand columns are the GC-FID chromatograms, and the right-hand side are the GC-MS and SIM chromatograms. Figures A and B are chromatograms of a light Alberta crude oil; Figures C and D are chromatograms of California heavy crude oil, and Figures E and F are chromatograms of Orimulsion Bitumen. One notes that the two chromatograms (e.g., FID/SIM) are quite different. The SIM chromatograms no longer have the recognizability of the types of oils as shown by FID. However, one can see that the peaks are more clearly defined and that the SIM can provide separate information. The disadvantage of the SIM is that each peak must be quantified separately using a standard.

Chapter | 5

95

Introduction to Oil Chemical Analysis

Lube Oil

Gasoline

4 6

8 10 12 14 Carbon Number

15

20

25

30

35

40

Carbon Number

Jet Fuel

Typical Crude

6 8 10 12 14 16 18 Carbon Number 0

5

15 10 Carbon Number

20

25

30

35

40

35

40

Diesel Fuel Bunker C 6 8 10 12 14 16 18 20 22 24 Carbon Number

10

15

20

25 30 Carbon Number

FIGURE 5.4 The bar graph distribution of alkanes for typical oils and fuels. These graphs were generated from the quantitative analysis of several oils. It should be noted that the alkanes are typically n-alkanes for all oils except for lube oils, where they are highly branched alkanes.

remove water before injection into the GC. At each point in this cleanup, the sample is quantified to allow measurement of those groups of materials removed. These measurements then form the basis for various forms of TPH measurement. One such method as developed by Dr. Zhendi Wang of Environment Canada is shown in Figure 5.5.2,3 In the method illustrated in Figure 5.5, the sample is separated into aliphatic, aromatic, and polar fractions using an open silica column. Several tests of this have been carried out to ensure that separation is complete. Having these fractions separated ensures that subsequent chromatographic analysis is not affected by interferences between the three fractions. The peaks that are typically quantified for analysis and possibly for identification are listed in Table 5.1.2,3 As described later, many of the these peaks are useful when combined in ratios. Often these ratios are unique and can be used for positive identification of an oil. There are many published methods and standards for oil analysis; several of these are listed in Table 5.2.

96

PART | III

Oil Analysis and Remote Sensing

Weigh Sample add surrogates

serially extract sample with dichloromethane/hexane

filter and concentrate extract

gravimetrically determine TPH

silica column fractionation fraction 1 aliphatics

fraction 2

fraction 3

aromatics

mixed

50% DCM/Hexane hexane gravimetric saturates aromatics determinations 5-- -Androstane d14-Terphenyl internal standards Hopane

GC/MS SIM

GC/MS (SIM)

GC/FID PAHs n-alkane hopanes n-alkane quantification distribution & steranes

half F1&F2 TPH 5- -Androstane

fraction 4 polars Methanol polars polars

GC/FID

Benzenes PAH alkylated homologues

TPH

FIGURE 5.5 Illustration of the Dr. Wang analysis method developed for Environment Canada. After cleanup procedures, the sample is separated into aliphatic, aromatic, and polar fractions. This enables very clear chromatographic analysis without interference between these fractions. This method yields many analytical parameters.

5.4. IDENTIFICATION AND FORENSIC ANALYSIS The foregoing information can then be used to predict how long the oil has been in the environment and what percentage of it has evaporated or biodegraded.15-23 This is possible because some of the components in oils, particularly crude oils, are very resistant to biodegradation, whereas others are resistant to evaporation. This difference in the distribution of components then allows the degree of weathering of the oil to be measured. The same technique can be used to “fingerprint” an oil and positively identify its source. Certain compounds are consistently distributed in oil, regardless of weathering, and these are used to identify the specific type of oil. The effect of weathering is particularly important as it may negate the use of standard GC-FID to positively identify an oil.23, 28-31 Figure 5.6 shows the effect of weathering on the GC-FID chromatogram of a light crude oil. As the oil weathers, more and more of the lower n-alkanes are lost to evaporation, the most important component of weathering. Figure 5.6A shows the

Chapter | 5

97

Introduction to Oil Chemical Analysis

TABLE 5.1 Target Analytes/Compounds for Oil Spill Studies Analyte

Analyte

Target Ion

Aliphatic Hydrocarbons

BTEX and C3 Benzenes

n-C8

Benzene

78

n-C9

Toluene

91

n-C10

Ethylbenzene

105

n-C11

Xylenes

105

n-C12

C2 - benzenes

105

n-C13

C3 - benzenes

105

n-C14

PAHs

n-C15

Naphthalene

128

n-C16

C1 - naphthalene

142

n-C17

C2 - naphthalene

156

Pristane

C3 - naphthalene

170

n-C18

C4 - naphthalene

184

Phytane

Phenanthrene

178

n-C19

C1 - phenanthrene

192

n-C20

C2 - phenanthrene

206

n-C21

C3 - phenanthrene

220

n-C22

C4 - phenanthrene

234

n-C23

Fluorene

166

n-C24

C1 - fluorene

180

n-C25

C2 - fluorene

194

n-C26

C3 - fluorene

208

n-C27

Chrysene

228

n-C28

C1 - chrysene

242

n-C29

C2 - chrysene

256

n-C30

C3 - chrysene

270

n-C31

Biphenyl

154

(Continued )

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TABLE 5.1 Target Analytes/Compounds for Oil Spill Studiesdcont’d Analyte

Analyte

Target Ion

n-C32

Benzo[e]pyrene

252

n-C33

Benzo[a]pyrene

252

n-C34

Perylene

252

n-C35

Dibenzothiophene

184

n-C36

C1 - Dibenzothiophene

198

n-C37

C2 - Dibenzothiophene

212

n-C38

C3 - Dibenzothiophene

226

n-C39 n-C40 EPA Priority PAH pollutants Naphthalene

128

Phenanthrene

178

Fluorene

166

Chrysene

228

Acenaphthylene

152

Acenaphthene

153

Anthracene

178

Fluoranthene

202

Pyrene

202

Benz[a]anthracene

228

Benz[b]fluoranthene

252

Benzo[k]fluoranthene

252

Benzo(g,h,i)perylene

276

Biomarkers Triterpanes Tricyclic terpanes

191

Chapter | 5

99

Introduction to Oil Chemical Analysis

TABLE 5.1 Target Analytes/Compounds for Oil Spill Studiesdcont’d Analyte

Analyte

Target Ion

Tetracyclic terpanes

191

Pentacyclic terpanes

191

C23H42

191

C24H44

191

C27H46 (Ts) & (Tm)

191

C29H50&C30H52 ab-hopane

191

C30-35H52-62 22S/22R

191

Steranes C27 20 R/S-cholestanes

217,218

C28 20 R/S-ergostanes

217,218

C29 20 R/S-stigmastanes

217,218

unweathered oils with the Cn Benzenes and naphthalenes very obvious. After about a 30% loss of oil through evaporation, the Cn Benzenes are lost, and many of the lower alkanes are shown in Figure 5.6B. After 44.5% of the mass is evaporated, as shown in Figure 5.6C, even the naphthalenes are not clearly present. It would be very difficult to simply compare weathered and unweathered oils simply by comparing the chromatograms. Although there are some ways to partially compensate for this, modern technology uses other components of the oil to forensically identify oils.

5.4.1. Biomarkers Biological markers or biomarkers are an important hydrocarbon group in petroleum analysis.32-34 Biomarkers are complex molecules derived from formerly living organisms. Biomarkers found in crude oils, rocks, and sediments show little change in structures from their parent organic molecules, or so-called biogenic precursors (for example, hopanoids, and steroids), in living organisms. Biomarker concentrations are relatively low in oil, often in the range of several hundred ppm. Biomarkers are useful because they retain all or most of the original carbon skeleton of the original natural product; this structural similarity reveals more information about oil origins than other compounds. Petroleum geochemists have historically used biomarker

100

TABLE 5.2 List of Standards Applicable to Oil Measurement Standards Organization

Method Number

ASTM

Analyte

Description

Reference

D5739-06

Standard Practice for Oil Spill Source Identification by Gas Chromatography and Positive Ion Electron Impact Low Resolution Mass Spectrometry

GC-EI

GC pattern

Fingerprinting for oil identification

24

ASTM

D3328-06

Standard Test Methods for Comparison of Waterborne Petroleum Oils by Gas Chromatography

GC-EI

GC pattern

Fingerprinting for oil identification

24

ASTM

D3415-98

Standard Practice for Identification of Waterborne Oils

GC-FID

oil ID

Identification of oils on water

24

ASTM

D3326-07

Standard Practice for Preparation of Samples for Identification of Waterborne Oils

GC-FID

oil ID

Identification of oils on water

24

ASTM

D5739-00

Standard Practice for Oil Spill Source Identification by Gas Chromatography and Positive Ion Electron Impact Low Resolution Mass Spectrometry

GC-EI

GC pattern

Fingerprinting for oil identification

24

Oil Analysis and Remote Sensing

Technique

PART | III

Title

TABLE 5.2 List of Standards Applicable to Oil Measurementdcont’d Method Number

ASTM

Technique

Analyte

Description

Reference

F2059-06

Standard Test Method for Laboratory Oil Spill Dispersant Effectiveness Using the Swirling Flask

GC-FID

TPH

TPH for water, chemical dispersion quantification

24

ASTM

D5412-93

Standard Test Method for Quantification of Complex Polycyclic Aromatic Hydrocarbon Mixtures or Petroleum Oils in Water

GC-EI

PAHs

Quantification of PAHS

24

ASTM

D6352-04

Standard Test Method for Boiling Range Distribution of Petroleum Distillates in Boiling Range from 174 to 700 C by Gas Chromatography

SIM-DIS

Boiling Distribution

Method to carry out a simulated distillation on oil e extended range

24

ASTM

D2887-08

Standard Test Method for Boiling Range Distribution of Petroleum Fractions by Gas Chromatography

SIM-DIS

Boiling Distribution

Method to carry out a simulated distillation on oil

24

ASTM

D5307-97

Standard Test Method for Determination of Boiling Range Distribution of Crude Petroleum by Gas Chromatography

SIM-DIS

Boiling Distribution

Method to carry out a simulated distillation on crude oil

24

ASTM

D7169-05

Standard Test Method for Boiling Point Distribution of Samples with Residues Such as Crude Oils and Atmospheric

SIM-DIS

Boiling Distribution

Method to carry out a simulated distillation

24

101

(Continued )

Introduction to Oil Chemical Analysis

Title

Chapter | 5

Standards Organization

102

TABLE 5.2 List of Standards Applicable to Oil Measurementdcont’d Standards Organization

Method Number

Title

Technique

Analyte

Reference

Method to preserve oil samples

24

and Vacuum Residues by High Temperature Gas Chromatography D3325-90

Standard Practice for Preservation of Waterborne Oil Samples

Sample Preservation

API

PHC

Determination of Petroleum Hydrocarbons

GC-FID

PHC

Petroleum Hydrocarbons

25

API

GRO

Determination of Petroleum Hydrocarbons

GC-FID

GRO

Gasoline range organic compounds

25

API

DRO

Determination of Petroleum Hydrocarbons

GC-FID

DRO

Diesel range organic compounds

25

EPA

846

Gas Chromatographic (GC) Methods

GC-FID/EI

organics

General GC method

26

Oil Analysis and Remote Sensing

ASTM

PART | III

Description

TABLE 5.2 List of Standards Applicable to Oil Measurementdcont’d Method Number

EPA

Technique

Analyte

Description

Reference

610

Determination of priority PAHS in municipal and industrial wastes

GC-EI

PAHs

PAHs in wastes

26

EPA

1664

N-hexane Extractable Material (HEM; Oil and Grease) and Silica Gel Treated N-Hexane Extractable Material (SGT-HEM: Non-polar Material) by Extraction and Gravimetry

gravimetry

TPH

N-hexane extractables in various substrates

26

CCME

1397

Canada-Wide Standard for Petroleum Hydrcarbons (PHC) in Soil

GC-FID/EI

TPH

Suite of analysis techniques for hydrocarbons in soil

27

CCME

1399

Canada-Wide Standard for Petroleum Hydrcarbons (PHC) in Soil: Scientific Rationale

GC-FID/EI

TPH

Background to suite of analysis techniques for hydrocarbons in soil

27

Reference Method for CanadaWide Standard for Petroleum Hydrcarbons (PHC) in Soil- Tier 1 Method

GC-FID/EI

TPH

Reference method to above for hydrocarbons in soil

27

CCME

Introduction to Oil Chemical Analysis

Title

Chapter | 5

Standards Organization

103

104

PART | III

Oil Analysis and Remote Sensing

FIGURE 5.6 Illustration of the effect of oil weathering on the chromatograms. As the oil weathers, more and more of the lower n-alkanes are lost to evaporation, the most important component of weathering. Figure A shows the unweathered oils with the Cn Benzenes and naphthalenes marked. After about 30% loss of oil through evaporation, the Cn Benzenes are lost and also many of the lower alkanes as shown in Figure B. After 44.5% of the mass is evaporated as shown in Figure C, even the naphthalenes are not clearly present. It would be very difficult to compare weathered and unweathered oils simply by comparing the chromatograms.

fingerprinting in characterizing oils in terms of (1) the type(s) of precursor organic matter in the source rock (such as bacteria, algae, or higher plants); (2) correlation of oils with their source rocks; (3) determination of depositional

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environmental conditions (such as marine, terrestrial, deltaic, or hypersaline environments); (4) assessment of thermal maturity and thermal history of oil and the degree of oil biodegradation; and (5) providing information on the age of the source rock for petroleum. For example, oleanane (C30H52) is a biomarker characteristic of angiosperms (flowering plants) found only in Tertiary and Cretaceous (<130 million years) oils.35 The conversion of precursor biochemical compounds from living organisms into biomarkers creates a vast suite of compounds in crude oils that have distinct structures. Due to the wide variety of geological conditions under which oil has formed, every crude oil exhibits a unique biomarker fingerprint. Biomarkers can be detected in very low quantities (ppm and below) in the presence of many other types of petroleum hydrocarbon by using GC-MS. Relative to other hydrocarbon groups such as alkanes and many aromatic compounds, biomarkers are highly stable and degradation-resistant. Therefore, the usefulness of analyzing biomarkers is that they generate information useful in determining the source of spilled oil, monitoring the degradation process, and weathering the state of oils. They have proven useful in identifying petroleum-derived contaminants in the marine environment.22,36-38 In the past decades, the use of biomarker fingerprinting techniques to study spilled oils has rapidly increased, and biomarker parameters have been playing a prominent role in almost all oil-spill forensic investigations. Table 5.1 lists some typical biomarkers used in forensic analysis. The concentration of the biomarkers is important, while the ratio of various biomarkers in an oil is extremely useful in making the comparisons. The ratios of the biomarker contents in most oils are constant throughout processes such as weathering and biodegradation, and thus serve as an identity tag for an oil.

5.4.2. Sesquiterpanes and Diamondoids The commonly used biomarkers that occur within crudes and heavier refined products include pentacyclic triterpanes (e.g., hopanes), regular and rearranged steranes, and mono- and tri-aromatic steranes.2,39,40 However, the high boiling point pentacyclic triterpanes and steranes are generally absent or in very low abundances in lighter petroleum products such as jet fuels and midrange diesels. For lighter petroleum products, refining processes have removed most high-molecular-weight biomarkers from the crude oil feed stocks, while the smaller compounds of bicyclic sesquiterpanes are greatly concentrated in these petroleum products. Figure 5.7 shows some of the typical biomarkers used for oil identification. Sesquiterpanes are ubiquitous components of crude oils and ancient sediments. Bicyclic sesquiterpanes are also widely found in intermediate petroleum distillates and finished petroleum products. The abundance of the three major compounds in the m/z 123 chromatograms has been employed to differentiate the organic matter input from various sedimentary environments. Early studies

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hopane - general

Steranes

general sterane structure

Sesquiterpanes

8 (H) - drimane

Diamondoid

Adamantane

Oil Analysis and Remote Sensing

FIGURE 5.7 Illustration of some typical biomarkers used for oil identification.

focused mainly on geological application of sesquiterpane compounds. The naturally occurring bicyclic sesquiterpanes are stable in biodegradation, and therefore in recent years, they found potential applications in oil-source correlation and differentiation. Recently, environmental scientists have also considered fingerprinting the diamondoid hydrocarbons as a promising forensic technique for oil spill studies.41-47 These naturally occurring compounds are thermodynamically stable, and therefore, they may have potential applications both in oil-source correlation and differentiation for those cases where the traditional biomarker terpanes and steranes are absent due to removal in the refining processes. There is increased awareness of possible application of diamondoid compounds for source identification. Diamondoids have the general molecular formula C4nþ6H4nþ12 and are a class of saturated hydrocarbons that consist of threedimensional fused cyclohexane rings, which results in a diamond-like structure. The simplest compound of these polycyclic diamondoids is adamantane (C10H16), followed by its homologues diamantane (C14H20), tri-, tetra-, penta-, and hexamantane. Adamantine and diamantane and their various substituted equivalents are widely found in crude oils, intermediate petroleum distillates, and other petroleum products. Diamondoid compounds (adamantanes and diamantanes) in petroleum are believed to be the result of carbonium ion rearrangements of suitable cyclic precursors on clay substances in the source rock. The higher homologues of diamondoids are considered to be formed from

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the smaller diamondoid compounds under extreme temperature and pressure conditions.

5.5. FIELD ANALYSIS Analysis performed in the field is faster and more economical than analysis done in a laboratory.8 As analytical techniques are constantly improving and lighter and more portable equipment is being developed, more analytical work can be carried out directly in the field. Test kits have also been developed that can measure total petroleum hydrocarbons directly in the field. Some new methods use enzymes that are selectively affected by oil components. While these test kits are less accurate than laboratory methods, they are a rapid screening tool that minimize laboratory analysis and may provide adequate data for making response decisions. It is important to stress, however, that these field kits may have limitations and that results should be verified by laboratory analysis.

REFERENCES 1. Environment Canada, Analytical Method for the Determination of Individual and Total Petroleum Hydrocarbons, PAHs and Biomarkers in Oil and Oil-Spill-Related Environmental Samples (ETC Standard Method 5.3/1.4/M), Environment Canada, 2003. 2. Wang Z, Fingas M. Oil and Petroleum Product Fingerprinting Analysis by Gas Chromatographic Techniques, Chapter 27. In: Nollet Leo ML, editor. Chromatographic Analysis of the Environment, 1027. Boca Raton, FL: Taylor and Francis; 2005. 3. Wang ZD, Fingas M, Page D. Oil Spill Identification: Review. J Chromatogr 1999;369. 4. Wang ZD, Fingas M. Development of Oil Hydrocarbon Fingerprinting and Identification Techniques. Mar Pollut Bull 2003;423. 5. Wang ZD, Hollebone B, Fingas M, Fieldhouse B, Sigouin L, Landriault M, et al. Development of a Composition Database for Selected Multicomponent Oils. Vancouver: Environment Canada, 2002. 6. Wang Z, Fingas M, Sigouin L, Yang C, Hollebone B. Comparison Study for the CCME Reference Method for Determination of PHC in Soil by Using Internal and External Standard Methods and by Using Silica Gel Column Cleanup and in-situ Silica Gel Cleanup Methods. AMOP 2003;193. 7. Encyclopedia of Chemistry. New York: Wiley-Interscience; 2005. p. 241. 8. Lambert P, Fingas MF, Goldthorp M. An Evaluation of Field Total Petroleum Hydrocarbon (TPH) Systems. J Haz Mat 2001;65. 9. Fingas MF, Kyle DA, Lambert P, Wang Z, Mullin JV. Analytical Procedures for Measuring Oil Spill Dispersant Effectiveness in the Laboratory. AMOP 1995;339. 10. Index to EPA Test Methods, April 2003 revised edition, http://epa.gov/, accessed 2010. 11. Lambert P, Goldthorp M, Fieldhouse B, Wang Z, Fingas M, Pearson L, et al. A Review of Oil-in-Water Monitoring Techniques. IOSC 2001;1375. 12. Wang ZD, Fingas M, Li K. Fractionation of ASMB Oil, Identification and Quantitation of Aliphatic Aromatic and Biomarker Compounds by GC/FID and GC/MSD (Part I). J Chromatogr Sci 1994;361.

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13. Blomberg J, Schoenmakers PJ, Brinkman UA. Gas Chromatographic Methods for Oil Analysis. J Chromatogr 2002;137. 14. Beens J, Brinkman UA. The Role of Gas Chromatography in Compositional Analyses in the Petroleum Industry. TrACdTrend Anal 2000;260. 15. Wang Z, Fingas M, Sigouin L. Using Multiple Criteria for Fingerprinting Unknown Oil Samples Having Similar Chemical Composition. Environ Forens 2002;251. 16. Wang Z, Fingas M. Oil Spill Fingerprinting. J Int Soc Environ Forens 2002;3/4. 17. Wang Z, Fingas M, Yang C, Christensen JH. Crude Oil and Refined Product Fingerprinting: Principles, Chapter 16. In: Morrison Robert D, Murphy Brian L, editors. Environmental Forensics; Contaminant Specific Guide, 339. Amsterdam: Academic Press/ Elsevier; 2006. 18. Wang Z, Yang C, Hollebone BP, Fingas M, Lambert P, Landriault M. Characterization and Identification of Spill Samples from Recent Spill Incidents. AMOP 2006;113. 20. Daling PS, Faksness LG, Hansen AB, Stout SA. Improved and Standardized Methodology for Oil Spill Fingerprinting. Environ Forens 2002;263. 21. Stout SA, Uhler AD, Naymik TG, McCarthy KJ. Environmental Forensics: Unravelling Site Liability. Environ Sci Techn 1998;260A. 22. Stout SA, Uhler AD, McCarthy KJ, Emsbo-Mattingly S. Chapter 6: Chemical Fingerprinting of Hydrocarbons. In: Murphy BL, Morrison RD, editors. Introduction to Environmental Forensics, 137. London: Academic Press; 2002. 23. Wang ZD, Yang C, Fingas M, Hollebone B, Peng XZ, Hansen AB, et al. Characterization, Weathering, and Application to Source Identification of Spilled Oils. Environ Sci Technol 2005;8700. 24. ASTM standards, http://www.astm.org. 25. API test methods, http://www.api.org/Publications. 26. Index to EPA Test Methods, April 2003 revised edition, http://www.epa.gov. 27. CCME test methods, http://www.ccme.ca/publications. 28. Wang ZD, Fingas M. Study of the Effects of Weathering on the Chemical Composition of a Light Crude Oil Using GC/MS and GC/FID. J Microcolumn Sep 1995;617. 29. Wang ZD, Fingas M, Sergy G. Chemical Characterization of Crude Oil Residues from an Arctic Beach by GC/MS and GC/FID. Environ Sci Techn 1995;2622. 30. Wang ZD, Fingas M, Blenkinsopp S, Sergy G, Landriault M, Sigouin L, et al. Comparison of Oil Composition Changes Due to Biodegradation and Physical Weathering in Different Oils. J Chromatogr 1998;89. 31. Douglas GS, Bence AE, Prince RC, McMillen SJ, Butler EL. Environmental Stability of Selected Petroleum Hydrocarbon Source and Weathering Ratios. Environ Sci Technol 1996;2332. 32. Wang Z, Fingas M, Yang C, Hollebone B, Peng X. Biomarker Fingerprinting: Applications and Limitations for Source Identification and Correlation of Oils and Petroleum Products. AMOP 2004;103. 33. Wang Z, Yang C, Fingas M, Hollebone B, Yim U, Oh J. Petroleum Biomarker Fingerprinting for Oil Spill Characterization and Source Identification, Chapter 3. In: Wang Z, Stout S, editors. Oil Spill Environmental Forensics: Fingerprinting and Source Identification, 73. Amsterdam: Academic Press; 2007. 34. Prince RC, Elmendorf DL, Lute JR, Hsu CS, Haith CE, Senius JD, et al. 17_(H), 21_(H)Hopane as a Conserved Internal Marker for Estimating the Biodegradation of Crude Oil. Environ Sci Techn 1994;142. 35. Peters KE, Moldowan JW. The Biomarker Guide: Interpreting Molecular Fossils in Petroleum and Ancient Sediments. Old Tappen, NJ Englewood Cliffs, NJ: Prentice Hall; 1993.

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36. Kvenvolden KA, Hostettler FD, Rosenbauer RW, Lorenson TD, Castle WT, Sugarman S. Hydrocarbons in Recent Sediment of the Monterey Bay, National Marine Sanctuary. Marine Geology 2002;181:101e13. 37. Kaplan IR, Galperin Y, Lu S, Lee RP. Forensic Environmental Geochemistry Differentiation of Fuel-types, Their Sources, and Release Time. Org Geochem 1997;289. 38. Volkman JK, Holdsworth DG, Neill GP, Bavor Jr HJ. Identification of Natural, Anthropogenic and Petroleum Hydrocarbons in Aquatic Environments. Sci Total Environ 1992;203. 39. Wang Z, Yang C, Fingas M, Hollebone B, Peng X, Hansen AB, et al. Characterization, Weathering and Application of Sesquiterpanes to Source Identification of Spilled Lighter Petroleum Products. Environ Sci Techn 2005;8700. 40. Stout SA, Uhler AD, McCarthy KJ. Middle Distillate Fuel Fingerprinting Using Drimanebased Bicyclic Sesquiterpanes, Environ. Forensics 2005;6:241e51. 41. Yang C, Wang ZD, Hollebone B, Peng X, Fingas M, Landriault M. GC/MS Quantitation Analysis of Diamondoid Compounds in Crude Oils and Petroleum Products. Environ Forensics 2006;7:292. 42. Yang C, Wang ZD, Hollebone BP, Fingas M, Peng X, Landriault M. GC/MS Quantitation of Diamondoid Compounds in Crude Oils and Petroleum Products. AMOP 2006;91. 43. Wang Z, Yang C, Hollebone B, Fingas M. Forensic Fingerprinting of Diamondoids for Correlation and Differentiation of Spilled Oil and Petroleum Products. Environ Sci Techn 2006;5636. 44. Yang C, Wang ZD, Hollebone BP, Peng X, Fingas MF, Landriault MA. GC/MS Quantitation of Diamondoid Compounds in Crude oils and Petroleum Products. Forensics 2006;370. 45. Dahl JE, Liu SG, Carlson RMK. Isolation and Structure of Higher Diamondoids, Nanometersized Diamond Molecules. Science 2003;96. 46. Grice K, Alexander R, Kagi R. Diamondoid Hydrocarbons as Indicators of Biodegradation in Australian Crude Oils. Org Geochem 2000;67. 47. Stout SA, Douglas GS. Diamondoid Hydrocarbons-Application in the Chemical Fingerprinting of Natural Gas Condensate and Gasoline. Environ Foren 2004;225.

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Chapter 6

Oil Spill Remote Sensing: A Review Merv Fingas and Carl E. Brown

Chapter Outline 6.1. Introduction 6.2. Visible Indications of Oil 6.3. Optical Sensors 6.4. Laser Fluorosensors 6.5. Microwave Sensors 6.6. Slick Thickness Determination 6.7. Acoustic Systems 6.8. Integrated Airborne Sensor Systems

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6.9. Satellite Remote Sensing 6.10. Oil under Ice Detection 6.11. Underwater Detection and Tracking 6.12. Small Remote-controlled Aircraft 6.13. Real-time Displays and Printers 6.14. Routine Surveillance 6.15. Future Trends 6.16. Recommendations

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6.1. INTRODUCTION Large spills of oil and related petroleum products in the marine environment can have serious biological and economic impacts. Public and media scrutiny is usually intense following a spill, with demands that the location and extent of the oil spill be determined. Remote sensing is playing an increasingly important role in oil spill response efforts. Through the use of modern remote-sensing instrumentation, oil can be monitored on the open ocean around the clock. With knowledge of slick locations and movement, response personnel can more effectively plan countermeasures in an effort to lessen the effects of the pollution. In recent years, there has been a strong interest in detection of illegal discharges, especially in view of the large seabird mortality associated with such discharges.1 Even though sensor design and electronics are becoming increasingly sophisticated and much less expensive, the operational use of remote-sensing Oil Spill Science and Technology. DOI: 10.1016/B978-1-85617-943-0.10006-1 Copyright Ó 2011 Elsevier Inc. All rights reserved.

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equipment lags behind the technology. In remote sensing, a sensor, other than the eye or conventional photography, is used to detect the target of interest at a distance. The most common forms of oil spill surveillance and mapping are still sometimes carried out with simple still or video photography. Remote sensing from an aircraft is still the most common form of oil spill tracking. Attempts to use satellite remote sensing for oil spills continue, although success is not necessarily as claimed and is generally limited to identifying features at sites where known oil spills have occurred or for mapping discharges or known spills. It is important to divide the uses of remote sensing into the end use or objective, as the utility of the sensor or sensor system is best defined that way. Remote-sensing systems for oil spills used for routine surveillance certainly differ from those used to detect oil on shorelines or land. A single tool does not serve for all functions. For a given nation and several functions, many types of systems may, in fact, be needed. Furthermore, it is necessary to consider the end use of the data. The end use of the data, be it location of the spill, enforcement, or support to cleanup, may also dictate the resolution or character of the data needed. Several general reviews of oil spill remote sensing have been prepared.2-7 These reviews show that although progress has been made in oil spill remote sensing, this progress has been slow. Furthermore, these reviews show that specialized sensors offer advantages to oil spill remote sensing. Off-the-shelf sensors have very limited application to oil spills.

6.2. VISIBLE INDICATIONS OF OIL Under many circumstances oil on the surface is not visible to the eye.8 Other than the obvious situations of nighttime and fog, in many situations oil cannot be seen. A very common situation is that of thin oil, such as from ship discharges, or

FIGURE 6.1 An example of problems in detecting slicks visually. There is no oil in this image. The differences in water color are caused by mineral fines at the top of the pictures and the meeting of darker water from the open ocean.

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FIGURE 6.2 Another example of confusion in the visible region. This anomaly is caused by the front between a river and seawater. Again there is no oil in this image.

FIGURE 6.3 An image of Herring “milk” on the water surface. This is often mistaken for oil in various sensors, and again there is no oil in this image.

the presence of materials, such as sea weed, ice, and debris, that mask oil presence. Often there are conditions on the sea that may appear like oil, when indeed there is no oil. These include wind shadows from land forms, surface wind patterns on the sea, surface dampening by submerged objects or weed beds, natural oils or biogenic material, and oceanic fronts. In the case of large spills, the area may be too great to be mapped visually. Several of these cases are illustrated in Figures 6.1 to 6.12. All of these factors dictate that remotesensing systems be used to assist in the task of mapping and identifying oil. In many cases, aerial observation and remote sensing are necessary to direct cleanup crews to slicks. Figure 6.13 shows a case where no aerial direction

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FIGURE 6.4 This image again shows no oil and shows open seawater at a front with mineralladen bay water.

FIGURE 6.5 An image of the Exxon Valdez tanker at Naked Island. The apparent oil is actually reflections from clean water and some wind ruffles on the sea. There is no oil in this image.

was given and a skimmer crew is missing the slick by about half a kilometer. Figure 6.14 shows a skimmer crew that was directed to the thicker slick in the area.

6.3. OPTICAL SENSORS 6.3.1. Visible The use of human vision alone is not considered remote sensing; however, it still represents the most common technique for oil spill surveillance. In the

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FIGURE 6.6 An image looking into a bay. The foreground material is oil; however what appears somewhat like oil further into the bay are actually surface wind calms.

FIGURE 6.7 An image of sheen from a major spill. One can see sheen to a distance of about 30 km and about 10 km wide. Large areas like this are hard to map without the aid of remote sensing.

past, major campaigns using only human vision were mounted with varying degrees of success.9 Optical techniques, using the same range of the visible spectrum detection, are the most common means of remote sensing. Cameras, both still and video, are common because of their low price and commercial availability. In recent years, visual or camera observation has been enhanced by the use of GPS (Global Positioning Systems).10 Systems are now available to directly map remote-sensing data onto base maps. In the visible region of the electromagnetic spectrum (approximately 400 to 700 nm), oil has a higher surface reflectance than water, but shows limited nonspecific absorption tendencies. Oil generally manifests throughout the

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FIGURE 6.8 An image of water from an airplane during foggy conditions. There is no oil in this image.

FIGURE 6.9 A visible image of a slick that had just been illegally discharged from a ship. The multiple colors are due to the light path interference and indicates a thickness of about 1 mm.

entire visible spectrum. Sheen shows up silvery and reflects light over a wide spectral region down to the blue. As there is no strong information in the 500 to 600 nm region, this region is often filtered out to improve contrast.11 Overall, however, oil has no specific characteristics that distinguish it from the background.12 Taylor studied oil spectra in the laboratory and the field and observed flat spectra with no usable features distinguishing it from the background.13 Therefore, techniques that separate specific spectral regions do not increase detection capability. Some researchers noted that while the oil spectra is flat, the presence of oil may slightly alter water spectra.14 It has been suggested that

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FIGURE 6.10 A visible image of a cleanup operation. Notice the various false indications of oil further away from the scene. Photography by Environment Canada.

FIGURE 6.11 An infrared image of a slick as taken in 1981. Note the annotation providing essential times and positions.

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FIGURE 6.12 A visible image of the same slick and at the same time as the one shown in Figure 6.11. This illustrates the higher capability that infrared imaging has under these specific conditions.

FIGURE 6.13 A visible image of a cleanup crew missing a slick by at least a half kilometer. The actual slick is noted on the image. Aerial direction of cleanup crews is not only desirable but necessary in many cases. Photography by Environment Canada.

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FIGURE 6.14 A visible image of a cleanup crew aiming toward the thickest slicks in the area as directed by an aerial surveillance team.

the water peaks are raised slightly at 570 to 590, 780 to 710, and 810 to 710 nm. At the same time there are depressions or troughs at 650 to 680 nm and 740 to 760 nm. It has been found that high contrast in visible imagery can be achieved by setting the camera at the Brewster angle (53 degrees from vertical) and using a horizontally aligned polarizing filter that passes only that light reflected from the water surface.15 This is the component that contains the information on surface oil.11 It has been reported that this technique increases contrast by up to 100%. Filters with band-pass below 450 nm can be used to improve contrast. View angle is important, and some researchers have noted that the thickness changes the optimal view angle.16 On land, hyperspectral data (use of multiple bands, typically 10 to 100) has been used to delineate the extent of an oil well blowout.17 The technique used was spectral reflectance in the various channels, as well as the usual black coloration. Video cameras are often used in conjunction with filters to improve the contrast in a manner similar to that noted for still cameras. This technique has had limited success for oil spill remote sensing because of poor contrast and lack of positive discrimination. Despite this, video systems have been proposed as remote-sensing systems.18 With new light-enhancement technology (low lux), video cameras can be operated even in darkness. Tests of a generation III night vision camera shows that this technology is capable of providing imagery in very dark night conditions.19,20 Scanners were used in the past as sensors in the visible region of the spectrum. A rotating mirror or prism sweeps the field-of-view (FOV) and directs the light toward a detector. Before the advent of CCD (charge-coupled device) detectors, this sensor provided much more sensitivity and selectivity than a video camera. Another advantage of scanners was that signals were

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digitized and processed before display. Recently, newer technology has evolved, and similar digitization can now be achieved without scanning by using a CCD imager and continually recording all elements, each of which is directed to a different FOVon the ground. This type of sensor, known as a pushbroom scanner, has many advantages over the older scanning types. It can overcome several types of aberrations and errors, the units are more reliable than mechanical ones, and all data are collected simultaneously for a given line perpendicular to the direction of the aircraft’s flight. Several types of scanners were developed. In Canada, the MEIS (Multidetector Electro-optical Imaging Scanner) and the CASI (Compact Airborne Spectrographic Imager) have been developed, and in the Netherlands, the Caesar system was developed.11, 21, 22 Digital photography has enabled the combination of photographs and the processing of images. Locke et al. used digital photography from vertical images to form a mosaic for an area impacted by an oil spill.23 It was then possible to form a singular image and to classify oil types by color within the image. The area impacted by the spill was also carried out. Video cameras are often used in conjunction with filters to improve the contrast in a manner similar to that noted for still cameras. This technique has had limited success for oil spill remote sensing because of poor contrast and lack of positive discrimination. The detection or measurement of oil in water has never been successfully accomplished using remote visible technology. There may be potential for light scattering technology. Stelmaszewski and coworkers measured the light scattering of crude oil in water emulsions and noted that scattering increases with wavelength in the UV range and decreases slightly with the wavelength of visible light.24 The use of visible techniques in oil spill remote sensing is largely restricted to documentation of the spill because there is no mechanism for positive oil detection. Furthermore, there are many interferences or false alarms. Sun glint and wind sheens can be mistaken for oil sheens. Biogenic material such as surface seaweeds or sunken kelp beds can be mistaken for oil. Oil on shorelines is difficult to identify positively because seaweeds look similar to oil and oil cannot be detected on darker shorelines. In summary, the usefulness of the visible spectrum for oil detection is limited. It is, however, an economical way to document spills and provide baseline data on shorelines or relative positions.

6.3.2. Infrared Oil, which is optically thick, absorbs solar radiation and reemits a portion of this radiation as thermal energy, primarily in the 8 to 14 mm region. In infrared (IR) images, thick oil appears hot, intermediate thicknesses of oil appear cool, and thin oil or sheens are not detected. The thicknesses at which these transitions occur are poorly understood, but evidence indicates that the transition between the hot and cold layer lies between 50 and 150 mm and the minimum

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detectable layer is between 10 and 70 mm.25-28 The reason for the appearance of the "cool" slick is not fully understood. A plausible theory is that a moderately thin layer of oil on the water surface causes destructive interference of the thermal radiation waves emitted by the water, thereby reducing the amount of thermal radiation emitted.8 This may be analogous to the appearance of the rainbow sheen, which is explained in Section 6.2. The cool slick would correspond to the thicknesses as observed above because the minimum destructive thickness would be about two times the wavelength, which is between 8 and 10 mm. This would yield a destructive onset of about 16 to 20 mm to about 4 wavelengths or about 32 to 40 mm. The destructive area is usually only seen with test slicks, which is explained by the fact that the more rapidly spreading oil is more transparent than the remaining oil. The onset of the hot thermal layer would in theory then be at thicknesses greater than this or at about 50 mm. IR devices cannot detect emulsions (water-in-oil emulsions) under most circumstances.29 This is probably a result of the high thermal conductivity of emulsions as they typically contain 70% water and thus do not show a temperature difference. IR cameras are now very common, and commercial units are available from several manufacturers. In the past, scanners with IR detectors were largely used. A disadvantage of the older type of IR detector, however, is that they required cooling to avoid thermal noise, which would overwhelm any useful signal. Liquid nitrogen, which provides about 4 hours of service, had traditionally been used to cool the detector. Some, smaller sensors use closed-cycle or Sterling coolers, which operate on the cooling effect created by expanding gas. While a gas cylinder or compressor must be transported with this type of cooler, refills or servicing may not be required for days at a time.30 In recent times, uncooled detectors are commonplace and have entirely replaced the older, cooled detectors. Most IR sensing of oil spills takes place in the thermal IR at wavelengths of 8 to 14 mm. A slightly different sensor, which is designed as a fixed-mounted unit, uses the differential reflectance of oil and water at 2.5 and 3.1 mm.31 Tests of a mid-band IR system (3.4 to 5.4 mm) over the Tenyo Maru oil spill showed no detection in this range, but ship scars were visible.32-34 Specific studies in the thermal IR (8 to 14 mm) show that there is no spectral structure in this region.35 Tests of a number of IR systems show that spatial resolution is extremely important when the oil is distributed in windrows and patches, emulsions are not always visible in the IR, and cameras operating in the 3 to 5 mm range are only marginally useful.36 Nighttime tests of IR sensors show that there is detection of oil (oil appears cold on a warmer ocean), however, the contrast is not as good as during daytime.36-38 The relative thickness information in the thermal IR can be used to direct skimmers and other countermeasure equipment to thicker portions of the slick. Figures 6.11, 6.12, 6.15, and 6.16 illustrate the utility of IR oil imaging. Oil

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FIGURE 6.15 An image of an oil slick formed from a composite of infrared and ultraviolet images. The red represents the thermal infrared and the thickest oil. The darker spots are intermediate thicknesses. The light blue area represents thin oil or sheen and is taken from the ultraviolet image.

FIGURE 6.16 A composite image of the infrared and ultraviolet images of a slick similar to that in Figure 6.11. The outlined areas are from the infrared sensor and represent the thicker oil. Areas of the infrared and ultraviolet sensors are also annotated.

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detection in the IR is not positive, however, as several false targets can interfere, including seaweed, shoreline, and oceanic fronts.39 IR is reasonably inexpensive, however, and is currently the prime tool used by the spill remote-sensor operator.

6.3.3. Ultraviolet Ultraviolet (UV) sensors can be used to map sheens of oil as oil slicks display high reflectivity of UV radiation even at thin layers (<0.1 mm). Overlaid UV and IR images are often used to produce a relative thickness map of oil spills. This has been illustrated in Figures 6.15 and 6.16. UV cameras, though inexpensive, are not often used in this process, however, as it is difficult to overlay camera images.30 Data from IR scanners and derived from push-broom scanners can be easily superimposed to produce these IR/ UV overlay maps. UV data are also subject to many interferences or false images such as wind slicks, sun glints, and biogenic material. Since these interferences are often different from those for IR sensing, combining IR and UV can provide a more positive indication of oil than using either technique alone.

6.4. LASER FLUOROSENSORS Laser fluorosensors are sensors that take advantage of the fact that certain compounds in petroleum oils absorb UV light and become electronically excited. This excitation is rapidly removed through the process of fluorescence emission, primarily in the visible region of the spectrum. Since very few other compounds show this tendency, fluorescence is a strong indication of the presence of oil. Natural fluorescing substances, such as chlorophyll, fluoresce at sufficiently different wavelengths than oil to avoid confusion. As different types of oil yield slightly different fluorescent intensities and spectral signatures, it is possible to differentiate between classes of oil under ideal conditions.40-50 Readers are referred to a separate subsection in this book for a review of laser fluorosensors. This section on remote sensing will just give a brief introduction. Most laser fluorosensors used for oil spill detection employ a laser operating in the UV region of 300 to 355 nm.40,50-52 With this wavelength of activation, there exists a broad range of fluorescent response for organic matter, centered at 420 nm. This is referred to as Gelbstoff or yellow matter, which can be easily annulled. Chlorophyll yields a sharp peak at 685 nm. The fluorescent response of crude oil ranges from 400 to 650 nm with peak centers in the 480 nm region. The use of laser fluorosensors for chlorophyll and other applications has been well documented.53 One laser fluorosensor operating at 488 nm from an Argon ion laser was successful in detecting oil from a ship platform.54

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Another phenomenon, known as Raman scattering, involves energy transfer between the incident light and the water molecules. When the incident UV light interacts with the water molecules, Raman scattering occurs. This involves an energy transfer between the incident light and the water molecules. The water molecules absorb some of the energy as rotational-vibrational energy and emit light at wavelengths, which are the sum or difference between the incident radiation and the vibration-rotational energy of the molecule. The Raman signal for water occurs at 344 nm when the incident wavelength is 308 nm (XeCl laser). The water Raman signal is useful for maintaining wavelength calibration of the fluorosensor in operation, but it has also been used in a limited way to estimate oil thickness because the strong absorption by oil on the surface will suppress the water Raman signal in proportion to thickness.55,56 The point at which the Raman signal is entirely suppressed depends on the type of oil, since each oil has a different absorption coefficient. The Raman signal suppression has led to estimates of sensor detection limits of about 0.05 to 0.1 mm.57 The principle of fluorescence can also be used on a smaller scale. A handheld UV light has been developed to detect oil spills at night at short range.58 Another related instrument is the Fraunhofer Line Discriminator, which is essentially a passive fluorosensor using solar irradiance instead of laser light.11 This instrument was not very successful because of the limited discrimination and the low signal-to-noise ratio. Laser fluorosensors have significant potential as they may be the only means to discriminate between oiled and unoiled seaweed and to detect oil on different types of beaches. Tests on shorelines show that this technique has been very successful.59 Algorithms for the detection of oil on shorelines have been developed.60 Work has been conducted on detecting oil in the water column, such as occurs with the product, Orimulsion.61-65 The fluorosensor is also the only reliable means of detecting oil in certain ice and snow situations. Operational use shows that the laser fluorosensor is a powerful tool for oil spill remote sensing.19,43

6.5. MICROWAVE SENSORS 6.5.1. Radiometers Microwave radiometers detect the presence of an oil film on water by measuring an interference pattern excited by the radiation from free space. The apparent emissivity factor of water is 0.4 compared to 0.8 for oil.11,66 This passive device can detect this difference in emissivity and could therefore be used to detect oil. In addition, as the signal changes with thickness, in theory, the device could be used to measure thickness. This detection method has not been very successful in the field, however, as several environmental and oilspecific parameters must be known. In addition, the signal return is dependent

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on oil thickness but in a cyclical fashion. A given signal strength can imply any one of two or three signal film thicknesses within a given slick. Microwave energy emission is greatest when the effective thickness of the oil equals an odd multiple of one quarter of the wavelength of the observed energy. Biogenic materials also interfere, and the signal-to-noise ratio is low. In addition, it is difficult to achieve high spatial resolution (might need resolution in meters rather than the typical tens of meters for a radiometer).67 The Swedish Space Agency has carried out work with different systems, including a dual-band, 22.4- and 31-GHz device, and a single band 37-GHz device.68 Skou, Sorensen, and Poulson describe a two-channel device operating at 37.5 and 10.7 GHz.69 Mussetto and coworkers at TRW described the tests of 44-94-GHz and 94-154-GHz, two-channel devices over oil slicks.70 They showed that correlation with slick thickness is poor and suggest that factors other than thickness also change surface brightness. They suggest that a singlechannel device might be useful as an all-weather, relative-thickness instrument. Tests of single-channel devices over oil slicks have also been described in the literature, specifically a 36-GHz and a 90-GHz device.71,72 A new method of microwave radiometry has recently been developed in which the polarization contrasts at two orthogonal polarizations are measured in an attempt to measure oil slick thickness.73 A series of frequency-scanning radiometers have been built and appear to have overcome the difficulties with the cyclical behavior.74,75 In summary, passive microwave radiometers may have potential as allweather oil sensors. Their potential as a reliable device for measuring slick thickness, however, is uncertain at this time.

6.5.2. Radar Capillary waves on the ocean reflect radar energy, producing a “bright” image known as sea clutter. Since oil on the sea surface dampens capillary waves, the presence of an oil slick can be detected as a “dark” sea or one with an absence of this sea clutter.76 Unfortunately, the oil slick is not the only phenomenon detected in this way. There are many interferences or false targets, including freshwater slicks, wind slicks (calms), wave shadows behind land or structures, seaweed beds that calm the water just above them, glacial flour, biogenic oils, and whale and fish sperm.77-81 As a result, radar can be ineffective in locations such as Prince William Sound, Alaska where dozens of islands, freshwater inflows, ice, and other features produce hundreds of such false targets. Despite these limitations, radar is an important tool for oil spill remote sensing because it is the only sensor that can be used for searches of large areas and it is one of the few sensors that can “see” at night and through clouds or fog. Figures 6.17 to 6.23 illustrate the many slick look-alikes that appear in radar displays.

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FIGURE 6.17 Airborne radar image of a small test slick attended by two boats. Note that the boats cast a radar shadow on both their sides. A ship is passing to the top right of the image, and the ship’s wake also casts a radar shadow.

FIGURE 6.18 A satellite Radarsat-I image of a large area of sea during the raising of the Irving Whale barge. Note that the area to the left that appears darker is caused by wind shadows and low winds. Only the small areas noted are actually slicks. One might have to know beforehand where the slicks were before interpreting this image.

The two basic types of imaging radar that can be used to detect oil spills and for environmental remote sensing in general are Synthetic Aperture Radar (SAR) and Side-Looking Airborne Radar (SLAR). SLAR is an older but less expensive technology that uses a long antenna to achieve spatial resolution. SAR uses the forward motion of the aircraft to synthesize a very long antenna, thereby achieving very good spatial resolution, which is independent of range, with the disadvantage of requiring sophisticated electronic processing. Though inherently more expensive, the SAR has greater range and resolution than the

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FIGURE 6.19 A close-up of the area shown in Figure 6.18 from radar satellite. These dark areas are actually oil, as confirmed by ground observation. The white spots in the center are ships.

FIGURE 6.20 An image of the source of the oil shown in Figure 6.19. The ships shown here appear as white spots in the radar image in Figure 6.19. Photography by Environment Canada.

SLAR. In fact, comparative tests show that SAR is vastly superior.82-84 Search radar systems, such as those frequently used by the military, cannot be used for oil spills because they usually remove the clutter signal, which is the primary signal of interest for oil spill detection. Furthermore, the signal processing of this type of radar is optimized to pinpoint small, hard objects, such as periscopes. This signal processing is very detrimental to oil spill detection.

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FIGURE 6.21 A radar satellite image of a coastline. There is no oil in this image. The track through the image is the wake of a vessel. It should be noted that all ship wakes leave a shadow like this, making it very hard to use radar to detect ship discharges. The dark areas near the coastline are low-wind areas, probably caused by the coast wind shadows.

FIGURE 6.22 A view of an area near ships and platforms. A possible slick is pointed out; however, as it is very near a major low-wind area, it is difficult to say whether or not this is really a slick.

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FIGURE 6.23 A view of the track of a vessel. Despite the interpretation that there was a slick behind the vessel, the black line may be simply a ship wake. Note also the other dark areas from low winds and coast wind shadows.

SLAR has predominated airborne oil spill remote sensing, primarily because of the lower price.85,86 There is some recognition among the operators that SLAR is very subject to false hits, but solutions are not offered. Experimental work on oil spills has shown that X-band radar yields better data than L- or C-band radar.87,88 It has also been shown that vertical antenna

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polarizations for both transmission and reception (VV) yield better results than other configurations.82,89-91 The ability of radar to detect oil is also limited by sea state. Sea states that are too low will not produce enough sea clutter in the surrounding sea to contrast to the oil, and very high seas will scatter radar sufficiently to block detection inside the troughs. Indications are that minimum wind speeds of 1.5 m/s (~3 knots) are required to allow detectability, and a maximum wind speed of 6 m/s (~12 knots) will again remove the effect.92-94 The most accepted limits are 1.5 m/s (~3 knots) to 10 m/s (~20 knots). This limits the environmental window of application of radar for detecting oil slicks. Gade et al. studied the difference between extensive systems from a space-borne mission and a helicopter-borne system.95 They found that at high winds, it was not possible to discriminate biogenic slicks from oil. At low-wind speeds, it was found that images in the L-band showed discrimination. Under these conditions, the biogenic material showed greater damping behavior in the L-band. Okamoto et al. studied the use of ERS-1 using an artificial oil (oleyl alcohol) and found that an image was detected at a wind speed of 11m/s, but not at 13.7 m/s.96 SAR can be polarimetric imaging that is horizontal-horizontal (HH), vertical-vertical (VV), and cross combinations of these. Several researchers have shown that VV is best for oil spill detection and discrimination.97-100 Migliaccio et al. showed that the co-polarized phase differencedfor example, the difference between the HH and VV phases can be used to discriminate oil FIGURE 6.24 An HH polarized view of the sea surface.

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131 FIGURE 6.25 A VV polarized view of the same area of sea surface. Note that this polarization yields a slightly clearer image of sea-surface details than shown in Figure 6.24.

slicks from biogenic slicks.97 A larger standard deviation for the slick, compared to the sea, typically indicates that it is oil. Figures 6.24 and 6.25 show the difference between a VV and HH polarization. Radar has also been used to measure currents and predict oil spill movements by observing frontal movements.101 Work has shown that frontal currents and other features can be detected by SAR.102 Shipborne radar has similar limitations and the additional handicap of low altitude, which restricts its range to between 8 and 30 km, depending on the height of the antenna. Ship radars can be adjusted to reduce the effect of sea clutter deenhancement. Shipborne radar successfully detected a surface slick in the Baltic Sea from 8 km away and during a trial off the coast of Canada at a maximum range of 17 km.103 During the Prestige spill, a Netherlands vessel successfully used this technique to guide a recovery vessel into slicks. The technique is, however, very limited by sea state, and in all cases where it was used, the presence and location of the slick were already known or suspected. Recently, researchers have carried out work on improving the imaging of slicks from shipborne radars.104 Today there are some commercial products that enhance the images from shipborne radar to enable some oil imaging. Gangeskar has proposed an automatic system that can be mounted on oil drilling platforms.105 This system would use standard X-band ship navigation units and would provide an alert if an oil spill was present. The system includes

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an extensive postprocessing system to provide both a user-friendly GUI and an automatic detection and alert system. The system has not been fully tested to date. In summary, radar optimized for oil spills is useful in oil spill remote sensing, particularly for searches of large areas and for nighttime or foul weather work. The technique is highly prone to false targets, however, and is limited to a narrow range of wind speeds. Because of the all-weather and daynight capability, radar is now the most common means of remote sensing.

6.5.2.1. Radar Processing Because radar detection of oil spills is so highly susceptible to false images, much work has taken place on means to differentiate oil slicks and false targets, often called look-alikes. These look-alikes include: low-wind areas, areas sheltered by land, rain cells, organic films, grease ice, wind fronts, up-welling zones, oceanic fronts, algae blooms, current shear zones, and so on.106 The discussion in this subsection is relevant to both satellite and airborne SAR systems. Several “automatic” systems have been designed for slick detection.107 Limited testing with actual satellite output has shown that many false signals are present in most locations.108,109 Extensive effort on data processing appears to improve the chances of oil detection.110 In recent years, automatic systems have given way to systems involving smart algorithms that are manipulated by operators.111-113 The most common way to eliminate wind-origin look-alikes is to map the wind fields in the same coordinates as the radar data.110,114 The most common slick look-alikes are low-wind areas. One group of researchers used radar wind data calibrated to wind data from an ocean buoy to map oil seeps in the southern Gulf of Mexico.114 Most researchers used some form of neural networks or fuzzy logic to assist in the discrimination of look-alikes and the intended targets.115-118 Others used various forms of models such as range dependence models.119 Topouzelis and coworkers developed several series of mathematical networks for differentiating slicks from look-alikes.120-123 The basis of these networks is the idea that generally oil slicks are imaged through a complex series of processes and conditions. Thus imaging is not a simple statistical manipulation. The same group developed a fuzzy classification to differentiate look-alikes from oil spills. The methodology involved four procedures. The first is the segmentation of the image into large image segments with different statistical values. In the second procedure, a detailed scale segmentation is carried out, and statistical values of each segment are compared to the threshold of the large segment from which it came. Third, the dark portions are classified according to the properties of the surrounding areas. Finally, the dark areas are classified using knowlege bases. The group also examined the use of

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forward-feed neural networks to discriminate slicks from look-alikes.120,124 Several topologies of forward-feed neural networks were examined, and none were better than others. The networks yielded classification accuracies as high as 91.3 to 93.6% for the given example. A recent work by Topouzelis used the inputs of shape texture, asymmetry, mean difference to neighbours, and power to the mean images in a neural network.106 The workers used forward-feed neural networks. It was found that the classification accuracy was 99.4% for the MLP network in the test case. Later, Topouzelis and co workers used a similar method to test a data set of 69 oil spills and 90 look-alikes.122 They found a combination of 11 features out of a possible 25 features. The 11 features found to be best for discrimination are perimeter, shape factor object mean value, ratio of the power to mean ratios, local area contrast ratio, mean border gradient, maximum border gradient, standard deviation border gradient, maximum border gradient, mean difference to neighbors, and spectral texture. Use of these factors resulted in classification accuracies of 85.3% for oil spills and 84.4% for look-alikes. A similar approach is to use a classification scheme that incorporates some of the same input parameters. Karantzalos and Argialas proposed a classification scheme involving processes and then a classification scheme. The first processing step involves filtering and levels.125 The second step is segmentation of the images to include all suspected slicks. The final step is to classify the potential slicks according to area, perimeter, shape complexity eccentricity, orientation, segment mean border gradient, inside segment standard deviation, and outside segment standard deviation. Several researchers have used Geographic Information System (GIS) databases to assist in the interpretation of SAR imagery.125-131 The technique divides the area of interest into segments and notes data such as currents, proximity to land, wind, and sea lanes. These parameters are then correlated to the SAR images. For example, oil spills are much more likely under the correct wind conditions, in sea lanes, and far from land. Tahvonen used data sets including wind speed and direction, sea-surface temperature, heavy rain, and location of algae blooms to assist in the discrimination.126 Muellenhoff proposed a data set consisting of wind information, sea-surface temperature, chlorophyll-a concentration, geostrophic currents, wave information, contextual background information, and existing oil spill databases.129 The assigned influences were wind speedd30%; wind directiond12%; sea-surface temperatured14%, chlorophyll-a concentrationd10%; oil portsd10%; and main traffic linesd20%. Wave and current direction only accounted for 2% each. Migliaccio and group studied the processing of SAR images from an aircraft-based sensor.132-134 It was noted that the main obstacle to analysis was speckle in the images. Speckle is caused by stray reflectances, such as from rough seas. Speckle is also caused by random constructive and destructive interference. Since speckle is temporary, multi-look imaging is one way to decrease speckle by a large amount. Further processing can then be achieved by

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combining multi-look data with wind data, best obtained from satellite scatterometers. The technique proposed for multi-look data is to divide the SAR imagery into subbands and then generate lower-resolution imagery. Then the images are averaged. This results in reduction of speckle. To process singlelook data with high speckle content, filters are used. First speckle is removed, and then an ROA (ratio of average) filter is used. In both techniques, edge detection is used to find the actual limits of the slicks or look-alikes. Marghany and co workers used a fractal method to analyze SAR data.135 The images are broken into fractals, and these fractals have dimensions that are different for oil spills and look-alikes. A further study under different wind speeds showed that there were differences only in the wide beam mode for lowwind zones and current shear features between real oil slicks.136 Danisi et al. utilized a similar approach.137 Another method employed by researchers to separate oil slicks from lookalikes is to use textural analysis.138,139 Direct statistical methods are also employed. Tello et al. noted that an algorithm characterizing the border between oil spill candidates and the surrounding sea allows for good classification.139 Lounis et al. used a measure of similarity between the local probability density function of clean water and of the dark area to be examined.140 Comparing the two values is said to result in discrimination between oil and look-alikes. Pelizzari employed a similar technique using graph cuts to estimate a smoothness factor.141 Ferraro et al. describe the development of an operational system for the Mediterranean Sea and show a procedure for identifying oil spills as (1) isolation and contouring of all dark signatures, (2) extraction of shape and backscattering contrast signatures, (3) test of these values against standard values, and (4) calculation of the probabilities of each patch.142-144 Another series of techniques involves the use of two streams of information. Several researchers used both SAR and visible information from the MODIS (Moderate Resolution Imaging Spectroradiometer) satellites to discriminate between look-alikes and oil slicks.145 The visible imagery is subject to false images, but not the same ones as satellites, and thus discrimination can be achieved to a degree. Similarly, Sipelgas used visible imagery from the MODIS satellite to assist in discrimination of false images from oil slicks in the Gulf of Finland.146 Adamo et al. used three streams for informationdSAR data, MODIS, and MERIS datadto discriminate look-alikes from actual spills.145

6.5.3. Microwave Scatterometers A microwave scatterometer is a device that measures the scattering of radar energy by a target. One radar scatterometer was flown over several oil slicks and used a low-power transmitter operating in the Ku band (13.3 GHz).11 The scatterometer detected the oil, but discrimination was poor. The “Heliscat,” a device with five frequencies, has been used to investigate capillary wave

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damping.92 The advantage of a microwave scatterometer is that it has an aerial coverage similar to optical sensors and it can look at several incident angles. The main disadvantages include the lack of discrimination for oil and the lack of imaging capability.

6.5.4. Surface Wave Radars It is possible to send radio waves along the sea using high frequency. The conductivity of the sea acts as a form of wave guide. These radars can be used to detect ships as far out as 500 km.147 Since these are surface wave phenomena, only targets above the surface are detected; thus slicks may not be detected by this technique.148 Modeling of the technique does not show whether there is potential for this method.149

6.5.5. Interferometric Radar Radars can be used to measure height, currents, and other surface elevation phenomena using interferometric techniques. Some radar systems on aircraft are fitted for this application, such as the government of Canada Convair 580. This can also be carried out in space using two satellites traveling in tandem. One research group employed the tandem satellite pairs of ERS-2 and ENVISAT to carry out such work, but there are no reports on the use on oil spills.150

6.6. SLICK THICKNESS DETERMINATION There has long been a need to measure oil slick thickness; this need has been expressed both within the oil spill response community and among academics in the field. There are presently no reliable methods, either in the laboratory or in the field, for accurately measuring oil-on-water slick thickness. The ability to do so would significantly increase understanding of the dynamics of oil spreading and behavior. Knowledge of slick thickness would make it possible to determine the effectiveness of certain oil spill countermeasures, including dispersant application and in-situ burning. Indeed, the effectiveness of individual dispersants could be determined quantitatively if the oil remaining on the water surface following dispersant application could be accurately measured.151,152

6.6.1. Visual Thickness Indications A very important tool for working with oil spills has been the relationship between appearance and thickness. Careful study of the literature on this relationship and comparison of this to field experience shows that there is limited potential to scale thicknesses to visual appearance.8 The only physical-based appearances that occurs are thicknesses of about 0.7 to 2.5 mm

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TABLE 6.1 Relationship of Thickness to Appearance Visibility Thresholds (mm)

Typical thickness

Darkening Minimum Silvery Rainbow Colors

Dull Colors

Dark

0.09

2.7

8.5

0.1

0.6*

0.9

*Note this is the only physical-based appearance factor

FIGURE 6.26 A rainbow sheen above a sunken vessel. The appearance of a rainbow sheen is the only strong visible indicator of slick thickness, and thickness may be between about 0.7 and 2.5 mm. Photography by Environment Canada.

at which the rainbow colors appear as a result of multiple constructive and destructive interferences by light. Table 6.1 presents a summation of the best knowledge on this phenomenon. Figures 6.26 and 6.27 show typical rainbow sheens for which we can estimate that the thickness is about 1 mm. This is the only color appearance that has a physical slick thickness associated with it.

6.6.2. Slick Thickness Relationships in Remote Sensors A number of investigators tried to correlate slick thickness with appearance in various remote-sensing instruments. Hollinger and Mennella conducted a series of eight controlled oil spills off Virginia to investigate the use of microwave radiometry to delineate oil spills.153 They used 19.4 and 69.8 GHz radiometers on the spills. Measurements using sorbents were used to calibrate the

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FIGURE 6.27 A rainbow sheen above another sunken vessel. The appearance of a rainbow sheen is the only strong visible indicator of slick thickness, and thickness may be between about 0.7 and 2.5 mm. Photography by Environment Canada.

radiometer. It was noted that the sheens typically had a thickness of 2 to 4 mm. It was found that 90% of the oil was in 10% of the slick area and that the microwave threshold was about 0.1 mm (100 mm). A series of experiments was carried out in 1979 to evaluate IR and SLAR for oil spill detection.154 The imagery was correlated against visual and sorbent measurements, which were used to derive a thickness estimate. It was concluded that the IR threshold was between 25 and 50 mm and for SLAR 100 nm. Furthermore, manipulation of data showed that a mass balance could be achieved if the thickness at which the IR showed oil to be colder at the sea occurred at 100 mm and for the heated portion of the oil at 1,000 mm. The United Kingdom conducted Isowake Experiments in 1982.155,156 On the basis of estimations and calculations, it was concluded that the lowest detectable slick thickness for IR was between 10 and 50 mm, whereas hot spots in the IR image could be as much as 1,000 mm. MacDonald et al. used photography from the space shuttle to define up to 124 slicks in an area of the Gulf of Mexico, offshore Louisiana.157 Similarly, a thematic image from Landsat showed at least 66 slicks in one large area. Some of the thickness relationships were based on unpublished experimental data from Duckworth. Brown et al. conducted experiments to measure the visibility of oil slicks. The observers and a visible UV camera were mounted in a crane basket 30 m over the slick.12,158,159 It was found that the detection ability decreased by over 50% for most oils and for the cameras when the angle was changed from 90 to 55 degrees from the horizontal (equivalent incidence angle of 0 to 35 degrees). Detectability degraded to 70% and sometimes to nil as the viewing angle was

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decreased past 55 through 35 degrees. Brown et al. conducted several experiments to ascertain the relationship between thickness of slicks and the density (or intensity) of the IR image.39 The thicknesses varied between 1 and 10 mm, and thicknesses were measured using an acoustic system. No relationship between slick thickness and IR brightness was found.

6.6.3. Specific Thickness Sensors The suppression of the water Raman peak in laser fluorosensor data has not been fully exploited or tested. This technique may work for thin slicks, but not necessarily for thick ones, at least not with a single excitation frequency. Attempts have been made to calibrate the thickness appearance of IR imagery, but also without success. It is suspected that the temperatures of the slick as seen in the IR are highly dependent on oil type, sun angle, and weather conditions. If so, it may not be possible to use IR as a calibrated tool for measuring thickness. Because accurate ground-truth methods do not exist, it is very difficult to calibrate existing equipment.160,161 The use of sorbent techniques to measure surface thickness yields highly variable results.151 As noted in the section on microwave radiometers, the signal strength measured by these instruments can imply one of several thicknesses. This methodology does not appear to have potential other than for measuring relative oil thickness. A variety of electrical, optical, and acoustic techniques for measuring oil thickness have been investigated.161,162 Two promising techniques were pursued in a series of laboratory measurements. In the first technique, known as thermal mapping, a laser is used to heat a region of oil, and the resultant temperature profiles created over a small region near this heating are examined using an IR camera.163 The temperature profiles created are dependent on the oil thickness. A more promising technique involves laser acoustics.164,165 The Laser Ultrasonic Remote Sensing of Oil Thickness (LURSOT) sensor consists of three lasers, one of which is coupled to an interferometer to accurately measure oil thickness.160,165-168 The sensing process is initiated with a thermal pulse created in the oil layer by the absorption of a powerful CO2 laser pulse. Rapid thermal expansion of the oil occurs near the surface where the laser beam was absorbed, which causes a steplike rise of the sample surface as well as an acoustic pulse of high frequency and large bandwidth (~15 MHz for oil). The acoustic pulse travels down through the oil until it reaches the oilewater interface where it is partially transmitted and partially reflected back toward the oileair interface, where it slightly displaces the oil’s surface. The time required for the acoustic pulse to travel through the oil and back to the surface again is a function of the thickness and the acoustic velocity of the oil. The displacement of the surface is measured by a second laser probe beam aimed at the surface. Motion of the surface induces a phase or frequency shift (Doppler shift) in the reflected probe beam. This phase or frequency modulation of the probe beam can then be demodulated with an interferometer.169 The thickness

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FIGURE 6.28 The signal from a 3-laser-thickness sensor. The time corresponds to a thickness of about 6 mm. This was measured by a prototype sensor mounted in an aircraft and flying over bins with various thicknesses of oil on water.

can be determined from the time of propagation of the acoustic wave between the upper and lower surfaces of the oil slick. This is a very reliable means of studying oil thickness and has great potential. Laboratory tests have confirmed the viability of the method, and a test unit has been flown to confirm its operability.160 Figure 6.28 shows the first airborne measurement of slick thickness. Several attempts have been made to measure thickness by using visible spectral imaging. As there are no visual indications other than the rainbow sheen area around 0.8 mm, these efforts are wasted.8,170

6.7. ACOUSTIC SYSTEMS Pogorzelski has shown that acoustic means can be used to measure oil viscosities on the surface.171 A directional acoustic system employing highfrequency forward specular scattering was used in the laboratory and at sea. Signals scattered are related to the rheological film properties. It is not known at this time if the system is scalable or exactly what the limitations are.

6.8. INTEGRATED AIRBORNE SENSOR SYSTEMS Increasingly, a number of different types of airborne oil spill remote sensors are being consolidated into sensor systems. The reason for this integration is to take advantage of the different information provided by each of the specific sensors and combine the information to provide a more complete and comprehensive

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information product. Although each of the individual sensors has specific inherent weaknesses such as false detections, these false detections are often different for each sensor type; hence a consolidation of information can help resolve and remove some of the uncertainties that exist from a single data source. Furthermore, additional information such as the relative thickness of the oil slick can be deduced from the overlaying of imagery from several sensor types. Although the absolute thickness of an oil slick remains the subject of continued research and scientific opinion, the ability to locate the thicker portions of the slick is essential in terms of operational spill cleanup and response. In addition to the integration of a number of remote sensors into a sensor system, information from other sources such as marine vessel traffic surveillance systems (i.e., automatic identification system, AIS) can be integrated and can play an essential role in identifying the source of the marine pollution. Two commercially available airborne marine oil spill remote-sensing systems are the MEDUSA and the MSS 6000.172-174 MEDUSA incorporates a number of sensor technologies such as laser fluorosensors, IR/UV line scanners, forward-looking IR sensors, microwave radiometers, SLAR systems, and camera systems, as well processing software into a flexible realtime data acquisition and processing system. The data from the various sensors are geo-referenced and fused with information from Airborne Information Systems (AIS) and marine surveillance radars into a GISdbased display output format. The processing software is known as the Oil Spill Scene Analysis System (OSSAS) and allows for the extraction of features such as the area of oil coverage, including areas of intermediate and thicker portions of the slick. The MSS 6000 Maritime Surveillance System is comprised of a flexible suite of sensors such as SLAR systems, IR/UV line scanners, forward-looking IR sensors, microwave radiometers, and camera systems, along with data processing and mission management software in order to perform the oil spill remote-sensing surveillance task. The MSS 6000 also focuses on sensor integration and includes AIS and marine search radar inputs. All sensor data, imagery, slick targets, vessels, and the like are annotated using navigation data from a single source to form an integrated part of a GIS). Both the MEDUSA and MSS 6000 can distribute their data in neardreal time via direct downlink or satellite communications to vessels or shore-based communications centers. A large number of maritime nations are now employing integrated airborne sensor systems.174,175

6.9. SATELLITE REMOTE SENSING The use of optical satellite remote sensing for oil spills has been attempted several times. The slick from the IXTOC I well blowout in Mexico was detected using GOES (Geostationary Operational Environmental Satellite) and by the AVHRR (Advanced Very High Resolution Radiometer) on the

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LANDSAT satellite.11 A blowout in the Persian Gulf was subsequently detected. The massive Exxon Valdez slick was detected on SPOT (Satellite Pour l’Observation de la Terre) satellite data.176 Oiled ice in Gabarus Bay resulting from the Kurdistan spill was detected using LANDSAT data.177,178 Several workers were able to detect the Arabian Gulf War Spill in 1991.179-182 The Haven spill near Italy was also monitored by satellite.183 A spill in the Barents Sea was tracked using an IR band on NOAA 10.184 It is significant to note that, in all these cases, the position of the oil was known and data had to be processed to actually see the oil, which usually took several weeks. Newer findings show that the ability to detect oil may be a complex function of conditions, oil types, and view angles.185-187 Figure 6.29 shows a visible satellite image of an area in Russia in which there was a massive pipeline spill. As noted in the caption for this image, the oil is not visible; however, a round lake in the image was mistaken for oil. Figure 6.30 shows an oiled ice area off Canada in which sediment and oil appear alike. There are several problems associated with relying on satellites operating in optical ranges, for oil spill remote sensing. The first is the timing and frequency of overpasses and the absolute need for clear skies to perform optical work.188 The chances of the overpass and the clear skies occurring at the same time give

FIGURE 6.29 A SPOT satellite visible image of an area in Russia where a large oil spill occurred. The oil spill is shown by the arrows. The black round objects are lakes. This illustrates that satellite visible imagery is difficult to interpret.

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FIGURE 6.30 A satellite visible image of an area off the east coast of Canada where an oil spill had occurred. Some of the black stripes in the white ice are oil mixed with sediment; others are sediment. The black on the right is land and the black on the left is sea. This image also illustrates the lack of discrimination of targets using visible satellite imagery.

a very low probability of seeing a spill on a satellite image. This point is well illustrated in the case of the Exxon Valdez spill.189 Although the spill covered vast amounts of ocean for over a month, there was only one clear day that coincided with a satellite overpass, and that was on April 7, 1989. Another disadvantage of satellite remote sensing is the difficulty in developing algorithms to highlight the oil slicks and the long time required to do so. For the Exxon Valdez spill, it took over two months before the first group managed to “see” the oil slick in the satellite imagery, although its location was precisely known. Recently, several workers have attempted to use MODIS visible data to detect oil spills.190,191 To be successful, these techniques generally rely on ancillary data such as suspected position or other satellite data. There is some information on slicks available from angular information. For example, Chust and Sagarminaga used the Multi-angle Imaging SpectroRadiometer (MISR) sensor aboard a satellite to detect oil spills on Lake Maracaibo, Venezuela.192 This sensor uses nine push-broom cameras at fixed angles from nadir to 70.5o to examine particular surfaces. A comparison of this angular sensor shows that better contrast was obtained than a simple nadir camera on another satellite. Data analysis showed that oil spills appear in

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greater contrast in those view angles affected by sun glitter because of the presence of oil. Recently, IR data from satellite has been used to map the land oil pollution in Kuwait.193 It was found that the old hydrocarbon-contaminated areas showed as much as 10oC difference from the surrounding land. Ground-truthing was used extensively in compiling the data. Casciello et al. also made an attempt to use IR imagery from the thermal IR region of the AVHRR satellite to locate known oil spills.194 Radar satellites, including ERS-1 and -2, Radarsat-1 and -2, and ENVISAT, are useful in detecting large offshore spills and in spotting anomalies.195-198 Radarsat has been used for detecting oil seeps and smaller spills resulting from an oil barge.199,200 The relative location of these smaller slicks was known before the detection. A novel application of Radarsat has been the study of oil lakes in the deserts of Kuwait.201,202 A number of nations now use radar satellites routinely to provide imagery for larger spills and to give indications of ship discharges. ERS-1 and 2 have been used for mapping oil spills in the Caspian Sea.202 Fortuny et al. describe the use of ERS-2 and ENVISAT to provide imagery during the Prestige incident off Spain.203 Torres Palenzuela and co workers used two ASAR (Advanced SAR) images from the Envisat satellite to study the Prestige spill off Spain.204 Using several techniques that were readily-available, such as filtering and comparison to GIS data of the areas, several slicks were identified. These slicks were confirmed by recorded sightings from helicopters and ships. Several countries have instituted satellite monitoring systems for oil pollution.205,206 Many of these countries use processing methods as described above. Extensive programs are in place in the Baltic Sea, North Sea, and English Channel.143 There are now beginning programs in the Black, Caspian, and Azov seas.206,207 Canada has had a program in place for several years.205 The Mediterranean Sea has had such a program for a long time.141,142 A constellation of monitoring satellites is proposed for the Mediterranean sea. In recent years, a number of new satellite-borne SAR sensors have been launched; see Table 6.2. While one of these sensors, Radarsat-2, operates in the traditional Cdband, TerraSARdX and Cosmos Skymed operate in the Xdband, while the PALSAR sensor on ALOS operates in the Ldband. As noted above, Xdband is the preferred band for oil spill remote sensing in terms of Bragg scattering. All four of these new SAR satellites have polarimetric imaging modes (some are experimental vs. operational modes) and much higher spatial resolution (down to 3 m), which may have application for oil spill remote sensing. Radarsat-2, like its predecessor, is an operational commercial satellite that can be tasked to respond to emergency situations such as major oil spills. The time required to task Radarsat-2 in emergency mode is now 4 hours, which is a large improvement from the 12 hours required to task its predecessor. As noted above, VV polarization provides a superior clutter-to-noise ratio (CNR) over HH polarization for oil spill detection. Radarsat-2 is fully

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TABLE 6.2 Current and Future Satellite-Borne SAR Sensors Satellite

Launch Date

Owner/Operator

Band

ERS-2

1995

European Space Agency

C

RADARSAT-1

1995

Canadian Space Agency

C

RADARSAT-2

2007

Canadian Space Agency

C

ENVISAT (ASAR)

2002

European Space Agency

C

ALOS (PALSAR)

2006

Japan Aerospace Exploration Agency

L

TerraSAR-X

2007

German Aerospace Centre

X

Tandem -X

tbd

German Aerospace Centre

X

Cosmos Skymed-1/2

2007

Italian Space Agency

X

TecSAR

2008

Israel Aerospace Industries

X

Sentinel-1

2012

European Space Agency

C

RADARSAT-Constellation (3-satellites)

2014

Canadian Space Agency

C

polarimetric, and there is interest in investigating whether a dual polarization ScanSAR mode utilizing VV/VH polarizations will work for oil and ship detection, respectively, as part of the Integrated Satellite Tracking of Pollution (ISTOP) program.208 The increased number of SAR satellites, as well as the plans to operate constellations of small satellites such as Cosmos (Constellation of Small Satellites for Mediterranean basin Observation), will provide increased temporal coverage with revisit times down to a few hours in some circumstances. The opportunity for increased frequency of image collection should prove useful to the oil spill response community. Figures 6.31 to 6.36 show the use of radar satellites and the look-alikes to oil that sometimes appear in the images.

6.10. OIL UNDER ICE DETECTION The difficulties in detecting oil in or under ice are numerous. Ice is never a homogeneous material but rather incorporates air, sediment, salt, and water, many of which may present false oil-in-ice signals to the detection mechanisms. In addition, snow on top of the ice or even incorporated into the ice adds complications. During freeze-up and thaw in the spring, there may not be distinct layers of water and ice. There are many different types of ice and

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FIGURE 6.31 A satellite radar image of a known spill off the Galapagos Islands. Note that most of the image consists of slick look-alikes.

different ice crystalline orientations. A separate subsection in this book provides a review of oil in and under ice.

6.11. UNDERWATER DETECTION AND TRACKING Many different techniques have been tried for underwater oil detection. First, the division should be made between oil in the water column or floating on a pycnocline, and oil on the bottom. Quite different physics and conditions can apply to these different situations. Several parties have tried to use standard sonars to detect submerged oil on the bottom. Oil on the bottom can appear as a softer surface than ordinary bottom sediment.209 The problem arises in that vegetation on the bottom also appears similar, and thus many false positives arise. In the water column, sonar can be useful as it can locate intermediate oil on pycnoclines; however, there is

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FIGURE 6.32 A satellite radar image of the large Sea Empress spill off the United Kingdom. The dark areas near the shore are calm areas. Note that the slick and these calm areas blend so that there is no delineation between them near the shoreline.

FIGURE 6.33 A Radarsat-1 image of the large Sea Empress spill off the United Kingdom. Similar features as in Figure 6.32 are noted.

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FIGURE 6.34 A third satellite radar image of the large Sea Empress spill off the United Kingdom. This image was taken a few days after the images in Figures 6.32 and 6.33. It appears that the oil slick has separated and some has moved to the bottom of the photograph and part toward the shore. This was never confirmed, and the “slick” at the bottom of the photograph may have indeed been oceanographic features.

no unique signature, and there are often weeds and other debris on pynoclines. Wendelboe et al. report on tests using a 200 and 400 kHz (dual-frequency) multibeam system.210 The contributing signal is the lower acoustic reflectivity of the oil than typical bottom geological formation or the better reflection than weed beds. Wendelboe et al. used the backscatter signals from several tests to develop algorithms for oil detection. This was tested in a tank with a 90% success rate and a 23% false detection rate. Oil on the bottom has successfully been mapped by underwater cameras, often mounted on sleds.210-213 The problems with this technique are the bottom visibilityd which is often insufficient to discriminatedand the difficulty in towing the camera vehicle as slow as 1 knot, the necessary speed. Pfeifer et al. were successful in employing mosaics of photographs to determine the aerial extent of oil on the seafloor.212-213 A low-technology approach had been historically employed. Heavy oil, oil such as would sink, often adheres to oil snares or pom-poms, which are polypropylene strips mounted much as a cheerleader’s pom-pom. These can be mounted on a beam and towed over the bottom and then raised periodically to see if oil has adhered.210 Alternatively, they can be mounted on an anchor with

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FIGURE 6.35 A radar image of the Nakhodka oil spill off the west coast of Japan (Radarsat-1). Only the black features on the lower half of the photograph and near the shoreline have been confirmed as oil. The remainder of the black areas are oceanographic features.

a marker buoy. These are then raised periodically to check whether the subsurface oil has contacted them. Camilli et al. have successfully applied mass spectrometry to the detection of sunken heavy oil (Fuel Oil #6).214 Using the small and enclosed mass spectrometer, TETHYS, the low-molecular-weight hydrocarbons coming from sunken oil masses are monitored. The mass spectrometer is mounted in a submersible that is driven over the seafloor. The exact position of the submersible is monitored closely using an acoustic positioning system on the surface. Signals then can be correlated closely to the position on the seafloor. Three ion peaks of m/z 43, 41, 27 are monitored to establish hydrocarbon

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FIGURE 6.36 A satellite radar image of an oil slick in the North Sea. This slick was confirmed by aerial observation.

presence. Tests show that the ion peaks provide sensitivity as low as 0.4 ppb. This is fully sufficient to monitor sunken oil. Tests were conducted in a test tank and later over actual spills in the Gulf of Mexico. The technique was able to find concentrations of sunken oil and place the locations within 1 meter. The tests in the Gulf of Mexico were conducted at depths of 200 meters and confirmed by using cameras on the submersible.

6.12. SMALL REMOTE-CONTROLLED AIRCRAFT Several parties have suggested using remote-controlled aircraft to provide more economical solutions for response personnel.215,216 In fact, remote-controlled aircraft have been used by a number of parties for monitoring a variety of pollutants since the 1970s.217 Belgium employs an Unmanned Aerial Vehicle (UAV) of the B-Hunter class to routinely monitor its portion of the North Sea.216 This is a large UAV that has visible and IR camera systems aboard. The unit has a 10-hour endurance over the targets. A variety of commercial platforms are now available that can carry small sensors such as visible and IR cameras. Furthermore, automatic navigation technology has now made these units, especially helicopters, very much easier to fly than in previous years.

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6.13. REAL-TIME DISPLAYS AND PRINTERS A very important aspect of remote sensing is the production of data so that operations people can quickly and directly use it. Real-time displays are important, so that remote sensor operators can adjust instruments directly in flight and provide information quickly on the location or state of the spill. A major concern of the client is that data be rapidly available.218 An additional concern is that the data from various sensors be available in a combined or fused form.85 Furthermore, there is a need to correct this data for aircraft motion and to annotate the data with time and position. At this time, existing hardware and software must be adapted as commercial off-the-shelf equipment for directly outputting and printing sensor data is not yet available. The displays and operators of a remote sensing aircraft are shown in Figure 6.37.

6.14. ROUTINE SURVEILLANCE One application of oil spill remote-sensing equipment is to detect and map slicks resulting from illegal discharges of oil from ships and offshore platforms. Historically, this task has always been performed using visual techniques, but in the past decade it has increasingly been turned over to aircraft with some

FIGURE 6.37 The interior of a remote sensing aircraft showing operators and displays. Photography by Environment Canada.

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instrumentation. Typical instrumentation includes a SLAR, IR/UV scanner, and cameras. This sensor package is economical compared to more ideal packages and greatly improves capability beyond just visual observation. Limitations include limited ability to ‘look into’ ship wakes, limited night operations, and inability to positively identify oil slicks. Recent additions such as improved SLAR systems, better display systems, and nighttime cameras have added to the capability but do not overcome these limitations. Figure 6.38 shows pilots overflying the stern of a ship to ascertain whether it is discharging oil. Many efforts have been made to perform surveillance of illegal discharges. Most existing operative remote systems are dedicated to this function. These are estimated to be around 35, most of these being around Europe.219 There are intensive programs in some areas, for example, in the North Sea. Carpenter reports on the 18-year program of surveillance in the North Sea.220 Some interesting statistics are noted. In 2004, 418 unidentified slicks were found, 65 slicks from oil rigs, and 57 slicks from ships. In 2004, 3,314 hours were flown in daylight and 594 in darkness. In the same year 91 slicks were found in the darkness and 449 in daylight. Ferraro et al. describe a routine surveillance program using satellite and aircraft data for the Mediterranean Sea.142 Future work in the Mediterranean Sea proposes a cluster of radar satellites to constantly monitor oil pollution.221 A word about aircraft is suitable here. A variety of aircraft are deployed as remote-sensing aircraft. Typically, different types are deployed for routine surveillance and for remote-sensing research. The latter requires flexibility in mounting sensors and in access to the outside of the aircraft. Figures 6.39 to 6.42 show some remote-sensing aircraft and highlight the modifications necessary.

FIGURE 6.38 A ship viewed from the cockpit of a remote sensing airplane. The ship will be overflown to ascertain whether it is discharging oil. Photography by Environment Canada.

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FIGURE 6.39 A view of a remote-sensing aircraft. The extensive airframe modifications are not visible in this photograph. The aircraft has extensive modifications in the interior to provide racks for equipment.

FIGURE 6.40 The bottom of the aircraft shown in Figure 6.39. This particular aircraft has 4 onemeter ports and one half-meter port. The direct opening in the one port is for a laser beam exit. Photography by Environment Canada.

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FIGURE 6.41 A view of another remote-sensing aircraft. This aircraft carries experimental radars, and one of the radomes is visible under the aircraft. Such modification can cost millions of dollars and take two years to complete.

6.15. FUTURE TRENDS Advances in sensor technology will continue to drive the use of remote sensors as operational oil spill response tools in the future. Cameras and thermal IR cameras that offer high sensitivity are cheap and plentiful. This improvement not only reduces the size and complexity of the sensor, but also the cost. In the next decade, advances in solid-state laser technology, in particular diodepumped solid-state lasers, will greatly reduce the size and energy consumption of laser-based remote sensors. This will promote the use of these sensors in smaller, more economical aircraft within the budget of many more regulatory agencies and maritime countries. Rapidly improving computer capabilities will allow for true real-time processing. At the present time and for the foreseeable future, there is no single “Magic Bullet” sensor that will provide all the information required to detect, classify, and quantify oil in the marine and coastal environment. An example of the improvement in recent years is that of the night-vision camera. It is now possible to use this sensor to visualize oil at night. An illustration of this appears in Figure 6.43. It will require the combined advances in sensor technologies and computer capabilities to gather, integrate, and merge several sources of data into a realtime format, usable by response crews in the field. If this type of information can be made available to response crews in a short enough timeframe following

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FIGURE 6.42 A view of the other side of the aircraft shown in Figure 6.41. This shows a lateral radome on the side of the aircraft as well as the radome on the underside. This aircraft also has extensive modifications in the interior to provide racks for equipment and power to sensors. Photography by Environment Canada.

a spill incident, then it can be used to lessen the potentially disastrous effects of a major oil spill on the marine ecosystem. As technology in remote-controlled systems evolve, it is possible to employ such technology in oil spill remote sensing. First efforts in the deployment of remote-controlled sensing aircraft have posted success and will, no doubt, be expanded in the future.222

6.16. RECOMMENDATIONS Recommendations are based on the above considerations and include economy as a major factor. Table 6.3 shows the considerations related to the development state, cost, and use of the sensor, and Table 6.4 shows the applicability of the sensor to various functions. The laser fluorosensor offers the only potential for discriminating between oiled and unoiled weeds or shoreline, and for positively identifying oil pollution on ice, among ice, and in a variety of other situations. This instrument, however, is large and expensive. A cheap sensor recommended for oil spill work is an IR camera. This is the cheapest undiscriminating device. This is the only piece of equipment that can be purchased off-the-shelf.

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FIGURE 6.43 A night-vision display. The annotation shows the various features of the image. Photography by Environment Canada.

All other sensors require special order and, often, development. Radar, though low in priority for purchase, offers the only potential for large area searches and foul weather remote sensing. Most other sensors are experimental or do not offer good potential for oil detection or mapping. Any sensor package should include a real-time printer and display, and a downlink. In order to respond effectively to major marine oil spills, a combination of airborne and satellite-borne sensor systems is recommended. Improvements in the resolution of satellite-based systems, particularly SAR systems combined with the increased number of such systems and the ability to steer them to image the area of the oil spill, will lead to their increased use in a tactical role. Being capable of imaging vast areas of the open ocean will ensure that satelliteborne sensors will also continue to be used in a strategic manner. There are a number of commercially available airborne sensor systems that provide near real-time information on oil slick location and indications of thicker areas of the pollution in an easily interpretable graphical manner. These airborne sensor systems are currently being employed by a large number of maritime nations in conjunction with satellite-based sensor systems. Historically, satellite sensors suffered from problems of low resolution and the low frequency of scene observation. These inadequacies are now being addressed by higher resolution systems with multiple imaging modes and the

156

TABLE 6.3 Attributes for Airborne Sensor Selection Specific to Oil

Immunity to False Targets

Typical Coverage (km)

Acquisition Cost Range k$

Aircraft Physical Requirements

Still Camera

High

High

Poor

Poor

0.25 to 2

1 to 5

no

Video

High

High

Poor

Poor

0.25 to 5

1 to 10

no

Night Time Vision Camera

Medium

Medium

Poor

Poor

0.25 to 2

5 to 20

no

IR Camera (8e14 mm)

High

Medium

Medium

Medium

0.25 to 2

20 to 50

no

UV Camera

Medium

Medium

Poor

Poor

0.25 to 2

4 to 20

no

MultiSpectral Scanner

Medium

Medium

Poor

Poor

0.25 to 2

100 to 200

Radar

High

High

Medium

Poor

5 to 50

1200 to 8000

yes-Dedicated

Microwave Radiometer

Medium

Medium

Medium

Medium

1 to 5

400 to 1000

yes-Dedicated

Laser Fluorosensor

Medium

Limited

Good

Good

0.01 to 0.1

300 to 1000

yes-Dedicated

some

Oil Analysis and Remote Sensing

Amount of Experience in Use

PART | III

Sensor

State of Development

Night & Fog Operation

Detection of Oil with Debris

Oiled Shoreline Survey

Spill Mapping

Ship Discharge Surveillance

Enforcement and Prosecution

Still Camera

2

n/a

1

2

2

2

2

Video

2

n/a

1

2

2

2

2

Night Time Vision Camera

3

4

1

n/a

2

2

2

IR Camera (8e14 mm)

4

2

1

n/a

3

3

3

UV Camera

2

n/a

n/a

n/a

3

2

1

UV/IR Scanner

4

2

1

n/a

4

3

3

Multi-Spectral Scanner

1

n/a

n/a

1

2

1

1

Radar

n/a

4

n/a

n/a

4

3

2

Microwave Radiometer

1

3

n/a

n/a

2

2

1

Laser Fluorosensor

4

3

5

5

1

5

5

Oil Spill Remote Sensing: A Review

Sensor

Support for Cleanup

Chapter | 6

TABLE 6.4 Sensor Suitability for Various Missions

Key: n/a ¼ not applicable; numerical values represent a scale from 1 ¼ poorly suited to 5 ¼ ideally suited

157

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ability to steer the sensor to look in the direction of the target of interest. There are an increasing number of satellite-borne SAR and optical sensors, some of which currently or soon will operate in constellations to provide increased coverage of the Earth’s surface. These enhanced capabilities will allow for the possible use of these sensors in a tactical mode of operation. In spite of these increased capabilities, there remains an essential role for airborne oil spill remote-sensing platforms. The ability to collect and deliver real-time oil slick location information will ensure the continued use of airborne systems in spite of their high operational costs. If this type of real-time oil spill remote-sensing information can be made available to response crews in a short enough timeframe following a spill incident, the information can be used to mitigate the potentially disastrous effects of a major oil spill on the marine ecosystem.

ACKNOWLEDGMENTS The authors acknowledge the many parties who contributed to this chapter, including the Canada Centre for Remote Sensing, and the Canadian Space Agency. In particular, they acknowledge Environment Canada for the many photographs in this chapter.

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29. Bolus RL. Airborne Testing of a Suite of Remote Sensors for Oil Spill Detecting on Water, Proceedings of the Second Thematic International Airborne Remote Sensing Conference and Exhibition, ERIM, 1996;III 743. 30. Goodman RH. Simple Remote Sensing System for the Detection of Oil on Water, Environmental Studies Research Fund Report Number 98, 1988. 31. Seakem Oceanography, Remote Sensing Chronic Oil Discharges, Environment Canada Report EE-108, 1988. 32. Kennicutt MC, MacDonald IR, Rogne T, Giammona C, Englehardt R. The Tenyo Maru Oil Spill: A Multi-Spectral and Sea Truth Experiment. AMOP 1992;349. 33. Rogne T, Macdonald I, Smith A, Kennicutt MC, Giammona C. Multi-Spectral Remote Sensing and Truth Data from the Tenyo Maru Oil Spill, Proceedings of the First Thematic Conference on Remote Sensing for Marine and Coastal Environments, ERIM 37 [also published in Photogramm Eng Rem Sens, 1993, 1992;391. 34. Rogne TJ, Smith AM. Tenyo Maru Oil Spill Remote Sensing Data Analysis. Washington, D.C: Marine Spill Response Corporation; 1992. MSRC Technical Report Series 92-003. 35. Salisbury JW, D’Aria DM, Sabins FF. Thermal Infrared Remote Sensing of Crude Oil Slicks. Remote Sens Environ 1993;225. 36. Hover GL. Testing of Infrared Sensors for U.S. Coast Guard Oil Spill Response Applications, Proceedings of the Second Thematic Conference on Remote Sensing for Marine and Coastal Environments: Needs, Solutions and Applications, ERIM 1994;I-47. 37. Grierson IT. Use of Airborne Thermal Imagery to Detect and Monitor Inshore Oil Spill Residues During Darkness Hours. Environ Manage 1998;905. 38. Shih W-C, Andrews AB. Infrared Contrast of Crude-Oil-Covered Water Surfaces. Opt Lett 2008;3019. 39. Brown HM, Baschuk JJ, Goodman RH. The Limits of Visibility of Spilled Oil Sheens. AMOP 1998;805. 40. Brown CE, Fingas MF. Review of the Development of Laser Fluorosensors for Oil Spill Application. Mar Pollut Bull 2003;477. 41. Brown CE, Fruhwirth M, Wang Z, Lambert P, Fingas M. Airborne Oil Spill Sensor Test Program, Proceedings of the Second Thematic Conference on Remote Sensing for Marine and Coastal Environments: Needs, Solutions and Applications, ERIM, 1994;I-19. 42. Brown CE, Fingas MF, An J. Laser Fluorosensors: A Survey of Applications and Developments of a Versatile Sensor. AMOP 2001;485. 43. Brown CE, Nelson R, Fingas MF, Mullin JV. Airborne Laser Fluorosensing: Overflights During Lift Operations of a Sunken Oil Barge, Proceedings of the Fourth Thematic Conference on Remote Sensing for Marine and Coastal Environments, ERIM 1997;I 23. 44. Brown CE, Marois R, Fingas MF, Choquet M, Monchalin J-P, Mullin J, et al. Airborne Oil Spill Sensor Testing: Progress and Recent Developments. IOSC 2001;917. 45. Brown CE, Fingas MF. Review of the Development of Laser Fluorosensors for Oil Spill Application. Mar Pollut Bull 2003;477. 46. Hengstermann T, Reuter R. Lidar Fluorosensing of Mineral Oil Spills on the Sea Surface. Appl Optics 1990;3218. 47. Balick L, DiBenedetto JA, Lutz SS. Fluorescence Emission Spectral Measurements for the Detection of Oil on Shore, Proceedings of the Fourth Thematic Conference on Remote Sensing for Marine and Coastal Environments, ERIM 1997;I 13. 48. Sarma AK, Ryder AG. Comparison of the Fluoresence Behaviour of a Biocrude Oil and Crude Petroleum Oils. Energy & Fuels 2006;783.

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212. Pfeifer CE, Brzozowski R, Markian, Redman R. Quantifying Percent Cover of Submerged Oil Using Underwater Video Imagery. IOSC 2008;269. 213. Pfeifer CE, Brzozowski R, Markian, Redman R. Long-Term Monitoring of Submerged Oil in the Gulf of Mexico Following the T/B DBL 152 Incident. IOSC 2008;275. 214. Camilli R, Bingham B, Reddy CM, Nelson RK, Duryea AN. Method for Rapid Localization of Seafloor Petroleum Contamination Using Concurrent Mass Spectrometry and Acoustic Positioning. Mar Pollut Bull 2009;1505. 215. Lehr WJ. The Potential Use of Small UAS in Spill Response. IOSC 2008;431. 216. Donnay E. Use of Unmanned Aerial Vehicle (UAV) for the Detection and Surveillance of Marine Oil Spills in the Belgian Part of the North Sea. AMOP 2009;771. 217. Li K, Fingas MF, Pare´ JRP, Boileau P, Beaudry P, Dainty E. The Use of Remote-Controlled Helicopters for Air Sampling in an Emergency Response Situation, in. AMOP 1994;139. 218. Goodman RH. Overview and Future Trends in Oil Spill Remote Sensing. Spill Sci Techn 1994;11. 219. Huisman J. Use of Surveillance Technology to Support Response Decision Making and Impact Assessment. Interspill 2006. 220. Carpenter A. The Bonn Agreement Aerial Surveillance Programme: Trends in North Sea Oil Pollution: 1986e2004. Marine Pollution Bulletin 2007;149. 221. De Dominicis M, Pinardi N, Coppini G, Tonani M, Guarnieri A, et al. Interspill 2009. 222. Allen J, Walsh B. Enhanced Oil Spill Surveillance, Detection and Monitoring through the Applied Technology of Unmanned Air Systems. IOSC 2008;113.

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Chapter 7

Laser Fluorosensors Carl E. Brown

Chapter Outline 7.1. Principles of Operation 171 7.2. Oil Classification 175 7.3. Existing Operational 179 Units

7.4. Aircraft Requirements 7.5. Cost Estimates 7.6. Conclusions

180 182 182

7.1. PRINCIPLES OF OPERATION 7.1.1. Active versus Passive Sensors Passive sensors are those that measure naturally available energy such as that produced by the sun.1 Passive sensors can only be used when the naturally occurring energy is available, that is, during periods of daylight when the sun is illuminating the earth. Passive sensors cannot therefore be used during periods of darkness, which is at night. Some naturally emitted energy such as thermal infrared energy can be detected during the day or night, providing there is enough energy to be detected. Active sensors, on the other hand, provide their own energy or source of excitation for illumination. The sensor illuminates or excites the target to be investigated. The energy or radiation reflected from that target is then detected and measured by the active sensor. The main advantage of active sensors is the ability to obtain measurements anytime day or night. Furthermore, active sensors can be used at wavelengths that are not provided by the sun, such as the microwave region. It should be noted that active remote sensors require the generation of a large amount of energy in order to adequately illuminate the targets. Examples of active remote sensors include synthetic aperture radars (SARs) and laser fluorosensors.

7.1.2. Sensor Features Laser fluorosensors are the only sensors that detect a primary characteristic of oil, namely, the unique oil fluorescence spectral signature. Other generic sensors rely Oil Spill Science and Technology. DOI: 10.1016/B978-1-85617-943-0.10007-3 Copyright Ó 2011 Elsevier Inc. All rights reserved.

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on secondary characteristics of oil such as the reflection of light of various wavelengths, scattering of microwaves, and emission of infrared energy. Laser fluorosensors are active sensors that take advantage of the fact that certain compounds in petroleum oils absorb ultraviolet light and become electronically excited. This excitation is rapidly removed through the process of fluorescence emission, primarily in the visible region of the spectrum. Since very few other compounds show this tendency, fluorescence is a strong indication of the presence of oil. Natural fluorescing substances, such as chlorophyll, fluoresce at sufficiently different wavelengths than oil to avoid confusion. As different types of oil yield slightly different fluorescent intensities and spectral signatures, it is possible to differentiate between classes of oil under ideal conditions.2-10

7.1.2.1. Excitation Source and Wavelength Selection Most laser fluorosensors used for oil spill detection employ a laser excitation source operating in the ultraviolet region of 300 to 355 nm.4 The fluorescence response of crude oil when excited with an ultraviolet laser ranges from 400 to 650 nm, with peak centers in the 480 nm region. A typical laser fluorosensor system with excimer laser and scanner is shown in Figure 7.1. These excitation wavelengths are a compromise in that they excite all three classes of oil with reasonable efficiency (shorter wavelength lasers would excite lighter oils efficiently but would be rather poor at exciting crude and heavy refined oils). There FIGURE 7.1 Scanning Laser Environmental Airborne Fluorosensor (SLEAF). (Environment Canada)

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are several reasonably priced, commercially available ultraviolet lasers in the 300e355 nm region, including the XeCl excimer laser (308 nm), the nitrogen laser (337 nm), the XeF excimer laser at 351 nm, and the frequency-tripled Nd:YAG at 355 nm. With excitation in this wavelength region, there exists a broad organic matter fluorescent return, centered at 420 nm. This is referred to as Gelbstoff or yellow matter, which must be accounted for. While Gelbstoff disappears if the oil thickness is greater than 10e20 mm (i.e., where the oil is optically thick), it can be an interfering signal when attempting to detect thin films of light oils on water. Chlorophyll yields a sharp peak at 685 nm. Another phenomenon, known as Raman scattering, involves energy transfer between the incident light and the water molecules. When the incident ultraviolet light interacts with the water molecules, Raman scattering occurs. The water molecules absorb some of the energy as rotational vibrational energy and emit light at wavelengths that are the sum or difference between the incident radiation and the vibration rotational energy of the molecule. The Raman signal for water occurs at 344 nm when the incident wavelength is 308 nm (XeCl laser). The water Raman signal is useful for maintaining wavelength calibration of the fluorosensor in operation, but has also been used in a limited way to estimate oil thickness because the strong absorption by oil on the surface will suppress the water Raman signal in proportion to thickness,11 where transmittance ¼ EXP ( thickness absorption coefficient). The point at which the Raman signal is entirely suppressed depends on the type of oil, since each oil has a different absorption coefficient. The Raman signal suppression has led to estimates of sensor detection limits of about 0.05 to 0.1 mm.12 Details of the use of Raman scattering to measure oil slick thickness can be found in the early work of Hoge and Swift13 and the recent studies by Patsayeva et al.14

7.1.2.2. Detection System The detection systems in most laser fluorosensor systems usually involve the collection of laser-induced fluorescence by a telescope and the focusing of the fluorescence onto the entrance slit of a grating spectrometer and then onto either photomultiplier tubes or intensified diode-arrays. The fluorescence spectrum is then recorded at a number of selected wavelengths or over a wide spectral range covering the ultraviolet through the visible. 7.1.2.3. Range-Gating The majority of modern laser fluorosensors are equipped with range-gated detection systems. Range-gating is simply the turning on of the detection system at precisely the time at which the laser-induced fluorescence is expected to return to the laser fluorosensor. This is accomplished by turning the detection system on and off at a precise time based on the known altitude. To accomplish this, the timing of the laser pulse is monitored prior to exiting the aircraft and the elastic backscatter from the surface is then monitored to determine the

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precise aircraft altitude, which is then used to control the range-gating electronics. This allows the detector to observe only the fluorescence induced by the excitation laser and neglect the majority of the background solar radiation.

7.1.2.4. Field of ViewdFixed versus Scanning Systems As noted earlier, active sensors need to deliver sufficient energy to the target, the surface of the earth containing oil contamination, to excite sufficient fluorescence to allow for the detection and classification of the oil. With most airborne laser fluorosensor systems, this means illuminating a field of view (FOV) of about 1 3 mrad, giving a footprint on the surface of about 0.1 m 0.3 m at 100 m altitude. This does not allow for a large amount of the surface to be interrogated by each laser pulse. With higher-powered ultraviolet lasers, one can fly at higher altitudes and enlarge the footprint of the sensor. The repetition rate of the laser and the ground speed of the aircraft are also major factors in the sampling of the surface where the oil contamination is being examined. At ground speeds of 100e140 knots (nautical miles) at a laser repetition rate of 100 Hz, a fluorescence spectrum is collected approximately every 60 cm along the flight path (at 100 m altitude). Some laser fluorosensors only “look” directly below the aircraft and collect fluorescence spectra in a straight line; this is referred to as a “fixed” FOV system. As spilled oil often piles up in narrow bands at the high-tide line, detection of this oil with a fixed FOV system is not optimal. This means that the oil might not be detected because the sensor is striking the surface of the earth on either side of the high-tide line. To compensate for this tendency of the oil to accumulate in a narrow band, it is preferable to change the laser FOV by employing a scanner. The scanner can either be moved in a conical (circular) fashion or back and forth across the surface to increase the likelihood of striking the oil contamination. There are conical scanning laser fluorosensor systems that have been developed in Germany15 and Canada.16 An example of a conical scanner is shown in Figure 7.2.

7.1.3. Pros/Cons Laser fluorosensors are capable of detecting oil and related petroleum products in complex marine, coastal, and terrestrial environments. These sensors are extremely sensitive and can discriminate between oiled and unoiled naturally occurring substances such as kelp and seaweed. It is under these circumstances that the laser fluorosensor can aid in the direction of oil spill countermeasures by discriminating between contaminated and clean areas in the marine and terrestrial environment. Laser fluorosensors are the only sensors that detect a primary characteristic of oil, namely, the unique oil fluorescence spectral signature. Other generic sensors rely on secondary characteristics of oil such as the reflection of light of various wavelengths, scattering of microwaves, and emission of infrared energy.

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FIGURE 7.2 Scanner unit (narrow/wide swath) SLEAF system. (Environment Canada)

At the current time, laser fluorosensors are very large, heavy, and powerhungry systems. These characteristics necessitate the use of large multiengined aircraft to house the systems. These conditions will remain until much smaller diode-pumped solid-state lasers are developed in the ultraviolet region of the electomagnetic spectrum. This development has lagged behind that of solid-state lasers in the visible region and might be another decade in coming to fruition.

7.2. OIL CLASSIFICATION 7.2.1. Real-Time Analysis One of the benefits of modern laser fluorosensors is the ability to detect and classify oil contamination in real time. This availability of real-time oil contamination is essential for rapid oil spill response and environmental damage mitigation. A recent analysis of oil spill detection algorithms for laser fluorosensors has been undertaken by Jha et al.17 In earlier fluorosensors like the LEAF system, Pearson correlation coefficients were calculated to determine the presence of oil contamination and to broadly classify the petroleum products.18 Standard reference fluorescence spectra for light refined, crude, and heavy refined classes of oil, along with a standard water reference spectrum, were stored in the LEAF data analysis computer. Correlation coefficients were calculated for the live spectrum versus the three broad classes of petroleum products and water at the rate of 100 Hz. When the value of the correlation coefficient versus a class of petroleum product was above a certain level and greater than the correlation with the water spectrum, the live spectrum was identified as being of that class of petroleum.

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With modern computer technology, it is now possible to analyze a large amount of laser fluorosensor data in real time. For example, with the Scanning Laser Environmental Airborne Fluorosensor (SLEAF), it is possible to analyze fluorescence data at a rate of nearly 400 Hz and display oil detections along with the flight path of the aircraft on a geo-referenced map output.8,19 With the SLEAF system, fluorescence spectra are analyzed in real time to determine the presence or lack of oil in the sensor field-of-view. Principal component analysis20 is used to classify the oil class as light, medium, or heavy and to estimate the extent of oil coverage in the field of view as clean, light, moderate, or heavy.

7.2.2. Sensor Outputs As noted earlier, a high volume of fluorescence spectral data can be analyzed in real time. In most laser fluorosensor systems, the fluorescence data is georeferenced (i.e., the location of the oil contamination is well known) and the data can be presented in either spectral or map display outputs. While displays of spectral data are important for the sensor operator to verify the proper operation of the sensor, they are of little use to the spill responder. What is essential for the spill responder is knowledge of the location of the oil contamination so that spill response equipment can be rapidly deployed to the spill scene and cleanup operations undertaken. The positive identification of oil contamination afforded through the use of laser fluorosensors is one of their main advantages.

7.2.2.1. Spectral Data The display of spectral data is essential for the effective operation of modern laser fluorosensors. Laser fluorosensor display monitors often include a representation of sensor parameters such as laser pulse energy, operating altitude, laser backscatter energy, reference spectra, and live or real-time fluorescence spectra. The observation of each of these parameters is extremely useful to the sensor operator. In particular, real-time spectra are useful for providing an indication of the interaction of the laser beam with the surface. By observing the live spectrum, the operator has an indication of the water clarity through observation of the water Raman scattering spectrum. A trained sensor operator can easily recognize the presence of oil contamination by the characteristic spectrum of light refined, crude, or heavy refined petroleum products. Figure 7.3 shows typical laser-induced fluorescence spectra of a crude oil. The lack of a proper spectral signature can indicate a problem with the fluorosensor system such as low laser power, low laser backscatter signal, incorrect rangegate timing, or laser misalignment. It is impossible to display all of the spectral data collected with a high sampling rate laser fluorosensor system; a subsample of the spectral data is all that is needed for an experienced operator to monitor system operations.

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FIGURE 7.3 Laser-induced fluorescence spectradlight crude oildSLEAF system.

7.2.2.2. Map Display An efficient map display of oil contamination location(s) is essential for the rapid mitigation of the environmental effects of spilled oil. Displays of oil contamination superimposed over aircraft flight lines are useful for spill responders who participate in oil spill remote sensing overflights. Figure 7.4 shows the operator’s

FIGURE 7.4 Operator’s monitor for, sensor parameters, and map display of SLEAF system. (Environment Canada)

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FIGURE 7.5 Oil contamination indicated with colored bars perpendicular to aircraft flight path on SLEAF system map display. (Environment Canada)

display from Environment Canada’s SLEAF system. Figure 7.5 shows the operator’s display with areas of oil contamination illustrated as colored bars perpendicular to the flight path. Similar information is presented in Figure 7.6, which overlays the scanner pattern on the flight path along with the oil

FIGURE 7.6 Scanner pattern superimposed on aircraft flight path; colors are an indication of oil classification, SLEAF system. (Environment Canada)

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contamination information (different colors for clean, light, medium, or heavy oil classifications). Spill response organizations and personnel are not interested in sensor parameters or spectral data. What is needed are geo-referenced maps showing oil locations. These maps, or at least the geo-referenced oil contamination locations, should be in a format that can be transmitted electronically and be compatible with commonly used geographical information systems (GIS). These maps will help in the rapid and efficient deployment of spill response resources and equipment to the location(s) where oil contamination is the heaviest and the cleanup of contamination is most needed.

7.3. EXISTING OPERATIONAL UNITS 7.3.1. Airborne There are a number of operational airborne laser fluorosensor units operating around the globe. Some of these are research and development units that have progressed to become operational spill response sensors such as the SLEAF system.21 Other laser fluorosensors have been combined with other sensors in commercially available sensor packages, while others are standalone systems or remain as research instruments. There are a number of recent reviews of airborne laser fluorosensor systems in the literature, including those by Samberg,10 as well as Brown and Fingas.18, 22 There are a number of commercially available airborne laser fluorosensors in the marketplace and a few examples are provided here. The first is the Fluorescent LiDAR Spectrometer (FLS-AU) developed by Laser Diagnostics Instruments International Incorporated. The FLS series of LiDARs is designed for pollution monitoring of terrestrial, river, lake, and ocean targets, oil and gas pipeline leak detection, and oil exploration.23 The second example is the laser fluorosensor system developed by Optimare Sensorsysteme AG as part of the MEDUSA system.24 MEDUSA is a flexible real-time data acquisition and processing system that combines a number of sensor technologies for the detection, mapping, quantification, and classification of marine pollution. MEDUSA incorporates a number of unique sensor systems, for example, laser fluorosensors, infrared/ ultraviolet (IR/UV) line scanners, microwave radiometers, radar systems, camera systems, as well as the corresponding processing software. The final example is the Swedish Space Corporation’s MSS 6000 Maritime Surveillance System that can be tailored to integrate with a number of sensors, including a selection of side-looking airborne radar (SLAR), IR/UV cameras, microwave radiometers, forward-looking infrared (FLIR), and laser fluorosensors.25

7.3.2. Ship-Borne A small number of ship-borne laser fluorosensors have been developed. Most ship-borne laser fluorosensors are research and development technologies, although there have been recent commercial developments. Two examples of

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ship-borne laser fluorosensors are the FLIDAR (Fluorescence Lidar) developed by the research group at the Istituto di Ricerca sulle Onde Elettromagnetiche “Nello Carrarra” IROE-CNR26 and the compact lidar system developed by the Japanese Ship Research Institute.27 The FLIDAR system incorporates an XeCl excimer laser, a 12 spectral channel detection system, and a conical scanner to direct the ultraviolet laser beam onto the surface of the ocean alongside the marine vessel onto which the system is mounted. The compact lidar system at the Japanese Ship Research Institute is a frequency-tripled Nd:YAG laser coupled to an intensified CCD camera and uses a series of optical band-pass filters. One commercially available ship-borne laser fluorosensor system is the FLS-S (Fluorescence LiDAR SystemeShip-borne) developed by Laser Diagnostics Instruments International Incorporated. The FLS-S is designed to detect, measure, and map natural Dissolved Organic Matter (DOM), oil pollution, photosynthetic algae, and other contaminants in water.23

7.4. AIRCRAFT REQUIREMENTS The combination of large-size, heavyweight, and demanding power requirements for the ultraviolet lasers detailed below necessitate the use of midsized fixed-wing propeller or turboprop aircraft for laser fluorosensor system installation. Typical aircraft housing laser fluorosensors have included the Dornier 228-212 (see Figure 7.7), Douglas DC-3, CASA C-295, P-3B, and Beech B-99.18

7.4.1. Power The high-powered excimer lasers often employed in airborne laser fluorosensors for oil spill detection require a significant amount of power. This power is typically supplied in the form of 3-phase 208 VAC at 400 Hz for the excimer laser. Additional power is required for systems such as the laser

FIGURE 7.7 Dornier 228-212 twin turboprop aircraft.

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scanner head in the form of 28 VDC and for sensor-controller electronics at 220 VAC, 60 Hz. These power requirements necessitate the use of heavy-duty aircraft power generators and a number of power invertors to supply the energy in the proper voltage, phase, and frequency.

7.4.2. Weight Most laser fluorosensors for oil spill detection employ an ultraviolet excitation laser. In order to deliver enough laser pulse energy and a sufficient repetition rate for use in a fixed-wing aircraft, an excimer laser is usually required. Laser fluorosensor systems that employ excimer lasers such as the XeCl laser operating at 308 nm, capable of producing 150 mJ/pulse at a repetition rate of 400 Hz (such as that used in the SLEAF system shown in Figure 7.8),21 are large and heavy, weighing over 450 kg. Smaller systems are possible with the use of frequency-tripled Nd:YAG lasers operating at 355 nm; however, these lasers are unable to produce the high laser pulse energies and repetition rates to fly at higher altitudes with sufficient areal coverage. These systems are more suitable for helicopter-based platforms that can fly low and slowly and hover over a location for an extended period of time.

7.4.3. Operational Altitude Operational altitudes for laser fluorosensors are entirely dependent on the output energy of the excitation laser and field of view of the system optics. Typical operating altitudes for lasers operating in ultraviolet would be approximately 100 m for a system with 10 mJ/pulse of laser power up to

FIGURE 7.8 Lambda Physik Excimer LaserdSLEAF System. (Environment Canada)

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approximately 500 m for a laser output power of 150 mJ/pulse. The operational altitude will also determine the swath width achieved with a conical scanner; for details refer to Brown and Fingas.18

7.5. COST ESTIMATES The cost of a laser fluorosensor system is significant, partially due to the low production volume of these unique sensors. Cost estimates have been reported by Tebeau et al. for three laser fluorosensor systems ranging from $150,000 to $500,000 $US.28

7.6. CONCLUSIONS Laser fluorosensors capable of detecting oil and related petroleum products in complex marine, coastal, and terrestrial environments have been developed at a number of locations around the world. These sensors are extremely sensitive and can discriminate between oiled and unoiled naturally occurring substances such as kelp and seaweed. It is under these circumstances that the laser fluorosensor can aid in the direction of oil spill countermeasures by discriminating between contaminated and clean areas in the marine and terrestrial environment. Laser fluorosensors are the only sensors that detect a primary characteristic of oil, namely, the unique oil fluorescence spectral signature. Other generic sensors rely on secondary characteristics of oil such as the reflection of light of various wavelengths, scattering of microwaves, and emission of infrared energy. Advances in the fields of lasers, solid-state electronics, and computer operating hardware/software continue to fuel the development of advanced laser fluorosensors. While many of the current systems are large and require dedicated aircraft, the unique data sets available from these laser fluorosensors will ensure their continued development for years to come. Wide acceptance of laser fluorosensors as viable spill response tools will not be achieved until the size of the systems is reduced to a point where they can be flown routinely on small twin-engine aircraft. This reduction in size will come about when highpower, high-repetition rate, diode-pumped solid-state lasers are available.

REFERENCES 1. NRCan, http://www.ccrs.nrcan.gc.ca/resource/tutor/fundam/chapter1/06_e.php, 2010. 2. Brown CE, Fingas MF, Gamble RL, Myslicki GE. The Remote Detection of Submerged Oil, In: Proc. Third R&D Forum on High-Density Oil Spill Response. International Maritime Organization; 2002;46. 3. Brown CE, Marois R, Myslicki G, Fingas MF. Initial Studies on the Remote Detection of Submerged Orimulsion with a Range-Gated Laser Fluorosensor. AMOP 2002;773. 4. Brown CE, Marois R, Myslicki G, Fingas MF, MacKay R. Remote Detection of Submerged Orimulsion with a Range-Gated Laser Fluorosensor. IOSC 2003;779.

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5. Brown CE, Marois R, Gamble RL, Fingas MF. Further Studies on the Remote Detection of Submerged Orimulsion with a Range-Gated Laser Fluorosensor. AMOP 2003;279. 6. Brown CE, Fingas MF, Marois R. Oil Spill Remote Sensing: Laser Fluorosensor Demonstration Flights off the East Coast of Canada. AMOP 2004;317. 7. Brown CE, Fingas M, Marois R, Fieldhouse B, Gamble RL. Remote Sensing of Water-in-Oil Emulsions: Initial Laser Fluorosensor Studies. AMOP 2004;295. 8. Brown CE, Fingas MF, Marois R. Oil Spill Remote Sensing Flights in the Coastal Waters Around Newfoundland. In: Proc. Eighth Intern. Conf. Remote Sensing for Marine and Coastal Environments. Ann Arbor, MI: Altarum; 2005. 9. Hengstermann T, Reuter R. Lidar Fluorosensing of Mineral Oil Spills on the Sea Surface. Appl Opt 1990;3218. 10. Samberg A. The State-of-the-Art of Airborne Laser Systems for Oil Mapping. Can J Rem Sens 2007;143. 11. Piskozub J, Drozdowska V, Varlamov V. A Lidar System for Remote Measurement of Oil Film Thickness on Sea Surface. In: Proc. Fourth Inter. Conf. Remote Sensing for Marine and Coastal Environments. Ann Arbor, MI: Environmental Research Institute of Michigan; 1997; 1:386. 12. Goodman R, Brown CE. Oil Detection Limits for a Number of Remote Sensing Systems. In: Proc. Eighth Inter. Conf. Remote Sensing for Marine and Coastal Environments. Ann Arbor, MI: Altarum Conferences; 2005. 13. Hoge FE, Swift RN. Oil Film Thickness Measurement Using Airborne Laser-Induced Water Raman Backscatter. Appl Opt 1980;3269. 14. Patsayeva S, Yuzhakov V, Varlamov V, Barbini R, Fantoni F, Frassanito C, et al. Laser Spectroscopy of Mineral Oils on Water Surface. EARSeL eProceedings; 2000;. 1:106. 15. Zielinski O, Andrews R, Go¨bel J, Hanslik M, Hunsa¨nger T, Reuter R. Operational Airborne Hydrographic Laser Fluorosensing. In: Proc. Fourth EARSel Workshop, Lidar Remote Sensing of Land and Sea. Dresden; 2001. 16. Brown CE, Marois R, Fingas MF. Preliminary Testing of the Scanning Laser Environmental Airborne Fluorosensor. AMOP 2000;519. 17. Jha NM, Gao Y, Levy J. An Analysis of Oil Spill Detection Algorithms Using Laser Fluorosensor Data. AMOP 2008;741. 18. Brown CE, Fingas MF. Review of the Development of Laser Fluorosensors for Oil Spill Application. Mar Pollut Bull 2003;477. 19. Brown CE, Fingas MF, Marois R. Oil Spill Remote Sensing Flights Around Vancouver Island. AMOP 2006;921. 20. James RTB, Dick R. Design of Algorithms for the Real-Time Airborne Detection of Littoral Oil Spills by Laser-Induced Fluorescence. AMOP 1996;1599. 21. Brown CE, Marois R. Laser Fluorosensor Demonstration Flights over Newfoundland Coastal Waters. AMOP 2007;437. 22. Brown CE, Fingas MF. The Latest Developments in Remote Sensing Technology for Oil Spill Detection. INTERSPILL; 2009. 23. LDI3, http://www.ldi3.com/index.php?main¼16, 2010. 24. Optimare, http://www.optimare.de/cms/en/divisions/fek.html, 2010. 25. Swedish Space Corporation, http://www.ssc.se/filearchive/7/7729/MSS%206000%20pamphlet %20.pdf, 2010. 26. Niccolai F, Bazzani M, Cecchi G, Innamorati M, Massi L, Nuccio C, Santoleri R. A Study for the Remote Monitoring of Organic Matter in the Ocean. In: Proc. EUROPTO Conf. Remote

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Sensing of the Ocean and the Sea Ice V, Remote Sensing for Earth Science, Ocean, and Sea Ice Applications. SPIE 1999;3868:567. 27. Yamagashi S, Hitomi K, Yamanouchi H, Yamaguchi Y. Determination of a Lidar Signal from Images of Backscattered Natural Light on Water Surface. IOSC 2001;929. 28. Tebeau PA, Hansen KA, Fant JW, Terrien MM. Assessing the Long-term Implementation Costs versus Benefits Associated with Laser Fluorosensor Spill Response Technology. AMOP 2007;451.

Part IV

Behaviour of Oil in the Environment and Spill Modeling

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Chapter 8

Introduction to Spill Modeling Merv Fingas

Chapter Outline 8.1. Introduction 8.2. An Overview of Weathering

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8.1. INTRODUCTION When oil is spilled, whether on water or land, a number of transformation processes occur; many of these processes are referred to as the behavior of the oil. The first process is weathering, a series of processes whereby the physical and chemical properties of the oil change after the spill, of which the most important are evaporation and emulsification.1-4 A second group of processes are related to the movement of oil in the environment. Spill modeling combines the knowledge of both these sets of processes and provides the user with information on future locations of the oil as well as information on the state of the oil.5-8 Weathering and movement processes can overlap, with weathering strongly influencing how oil is moved in the environment and vice versa. All processes depend very much on the type of oil spilled and weather conditions during and after the spill.

8.2. AN OVERVIEW OF WEATHERING The specific behavior processes that occur after an oil spill determine how the oil should be cleaned up and its effect on the environment. For example, if an oil evaporates rapidly, cleanup is less intense, but the hydrocarbons in the oil enter the atmosphere. An oil slick could be carried by surface currents or winds to the vicinity of a bird colony or to a shore where seals or sea lions are breeding and severely affect the wildlife and their habitat. On the other hand, a slick could be carried out to sea where it disperses naturally and has less direct effect on the environment. Oil Spill Science and Technology. DOI: 10.1016/B978-1-85617-943-0.10008-5 Copyright Ó 2011 Elsevier Inc. All rights reserved.

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The fate and effects of a particular spill are determined by the behavior processes, which in turn are almost entirely determined by the type of oil and the environmental conditions at the time of the spill. Spill responders need to know the ultimate fate of the oil in order to take measures to minimize the overall impact of the spill. Oil spilled on water undergoes a series of changes in physical and chemical properties that in combination are termed weathering. Weathering processes occur at very different rates but begin immediately after oil is spilled into the environment. Weathering rates are not consistent throughout the duration of an oil spill and are usually highest immediately after the spill. Both weathering processes and the rates at which they occur depend more on the type of oil than on environmental conditions. Most weathering processes are highly temperature-dependent, however, and will often slow to insignificant rates as temperatures approach zero degrees. The processes included in weathering are evaporation, emulsification, natural dispersion, dissolution, photo-oxidation, sedimentation, adhesion to materials, interaction with mineral fines, biodegradation, and the formation of tarballs. These processes are listed in order of importance in terms of their effect on the percentage of total mass balance, that is, the greatest loss from the slick in terms of percentage, and what is known about the process.

8.2.1. Evaporation Evaporation is usually the most important weathering process.9 It has the greatest effect on the amount of oil remaining on water or land after a spill. Over a period of several days, a light fuel such as gasoline evaporates completely at typical ambient temperatures, whereas only a small percentage of a heavier Bunker C oil evaporates. The rate at which an oil evaporates depends primarily on the oil’s composition. Figure 8.1 shows the differential evaporation of several typical oils. The more volatile components an oil or fuel contains, the greater the extent and rate of its evaporation. Many components of heavier oils will not evaporate at all, even over long periods of time and at high temperatures. A separate section on evaporation appears in this section of the book. Oil and petroleum products evaporate in a slightly different manner than water, and the process is much less dependent on wind speed and surface area. Oil evaporation can be considerably slowed down, however, by the formation of a “crust” or “skin” on top of the oil. This happens primarily on land or in calm areas where the oil layer does not get mixed. The skin or crust is formed when the smaller compounds in the oil are removed, leaving the larger compounds, such as waxes and resins, at the surface. This crust then seals off the remainder of the oil and slows evaporation. Stranded oil from old spills has been reexamined over many years, and it has been found that when this crust has formed, there is no significant evaporation in the oil underneath. When this

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40

Time in Hours FIGURE 8.1 Oil evaporation curves for several typical oilsddata from experiments.

crust has not formed, a similar oil could be weathered to the hardness of wood over the same amount of years. The rate of evaporation is very rapid immediately after a spill and then slows considerably. About 80% of evaporation that does take place occurs in the first two days after a spill. The evaporation of most oils follows a logarithmic curve with time. Some oils such as diesel fuel, however, evaporate as the square root of time, at least for the first few days. This means that the evaporation rate slows very rapidly in both cases after a few days. This can be seen in Figure 8.1.

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The properties of an oil can change significantly with the extent of evaporation. If about 40% (by weight) of an oil evaporates, its viscosity could increase by as much as a thousandfold. Its density could rise by as much as 10% and its flash point by as much as 400%. The extent of evaporation can be the most important factor in determining properties of an oil at a given time after the spill and in changing the behavior of the oil.

8.2.2. Emulsification Emulsification is the process by which one liquid is dispersed into another one in the form of small droplets.10 Water droplets can remain in an oil layer in a stable form, and the resulting material is completely different. These waterin-oil emulsions are sometimes called mousse or chocolate mousse, as they resemble this dessert. In fact, both the actual version of chocolate mousse and butter are common examples of water-in-oil emulsions. The mechanism of emulsion formation is not yet fully understood, but it probably starts with sea energy forcing the entry of small water droplets, about 10 to 25 mm (or 0.010 to 0.025 mm) in size, into the oil. If the oil is only slightly viscous, these small droplets will not leave the oil quickly. On the other hand, if the oil is too viscous, droplets will not enter the oil to any significant extent. Once in the oil, the droplets slowly gravitate to the bottom of the oil layer. Asphaltenes and resins in the oil will interact with the water droplets to stabilize them. Depending on the quantity of asphaltenes and resins, as well as other conditions, an emulsion may be formed. The conditions required for emulsions of any stability to form may only be reached after a period of evaporation. Evaporation increases the viscosity to the critical value and increases the resin and asphaltene percentage in the oil. Further discussion on emulsion formation is found in a separate subsection following this introduction.10 Water can be present in oil in four ways. First, some oils contain about 1% water as soluble water. This water does not significantly change the physical or chemical properties of the oil. The second way is called entrainment, whereby water droplets are simply held in the oil by its viscosity to form an unstable emulsion. These are formed when water droplets are incorporated into oil by the sea’s wave action and there are not enough asphaltenes and resins in the oil. Unstable emulsions break down into water and oil within minutes or a few hours, at most, once the sea energy diminishes. The properties and appearance of the unstable emulsion are almost the same as those of the starting oil, although the water droplets may be large enough to be seen with the naked eye. Semi- or meso-stable emulsions represent the third way water can be present in oil. These emulsions are formed when the small droplets of water are stabilized to a certain extent by a combination of the viscosity of the oil and the interfacial action of asphaltenes and resins. These emulsions generally break down into oil and water or sometimes into water, oil, and stable emulsion within a few days. Meso-stable emulsions are viscous liquids that are reddish-brown in color.

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The fourth way that water exists in oil is in the form of stable emulsions. These form in a way similar to meso-stable emulsions except that the oil contains a sufficient amount of resins and asphaltenes to stabilize the water droplets. The viscosity of stable emulsions is 500 to 800 times higher than that of the starting oil, and the emulsion will remain stable for weeks and even months after formation. Stable emulsions are reddish-brown in color and appear to be nearly solid. Because of their high viscosity and near solidity, these emulsions do not spread and tend to remain in lumps or mats on the sea or shore. The formation of emulsions is an important event in an oil spill. First, and most importantly, it substantially increases the actual volume of the spill. Emulsions that contain about 70% water triple the volume of the oil spill. Even more significantly, the viscosity of the oil increases by as much as 1,000 times, depending on the type of emulsion formed. For example, an oil that has the viscosity of motor oil can triple in volume and become almost solid through the process of emulsification. These increases in volume and viscosity make cleanup operations more difficult. Emulsified oil is difficult or impossible to disperse, to recover with skimmers, or to burn. Emulsions can be broken down with special chemicals in order to recover the oil with skimmers or to burn it. It is thought that emulsions break down into oil and water by further weathering, oxidation, and freezethaw action. Meso–or semi-stable emulsions are relatively easy to break down, whereas stable emulsions may take months or years to break down naturally, if they ever do break down. Emulsion formation also changes the fate of the oil. It has been noted that when oil forms stable or meso-stable emulsions, evaporation slows considerably. Biodegradation also appears to slow down. The dissolution of soluble components from oil may also cease once emulsification has occurred.

8.2.3. Natural Dispersion Natural dispersion occurs when fine droplets of oil are transferred into the water column by wave action or turbulence. Small oil droplets (less than 20 mm or 0.020 mm) are relatively stable in water and will remain so for long periods of time. Large droplets tend to rise, and larger droplets (more than 50 mm) will not stay in the water column for more than a few seconds. Depending on oil conditions and the amount of sea energy available, natural dispersion can be insignificant or it can remove the bulk of the oil. In 1993, the oil from a stricken ship, the Braer, dispersed almost entirely as a result of high seas off Scotland at the time of the spill and the dispersible nature of the oil cargo.11 Natural dispersion is dependent on both the oil properties and the amount of sea energy.12 Heavy oils such as Bunker C or a heavy crude will not disperse naturally to any significant extent, whereas diesel fuel and even light crudes can disperse significantly if the saturate content is high and the asphaltene and resin

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contents are low. In addition, significant wave action is needed to disperse oil. In 40 years of monitoring spills on the oceans, those spills where oil has dispersed naturally have all occurred in very energetic seas. The long-term fate of dispersed oil is not known, although it may degrade to some extent as it consists primarily of saturate components. Some of the dispersed oil may also rise and form another surface slick, or it may become associated with sediment and be precipitated to the bottom.

8.2.4. Dissolution Through the process of dissolution, some of the soluble components of the oil are lost to the water under the slick.13,14 These include some of the lowermolecular-weight aromatics. As only a small amount, usually much less than a fraction of a percent of the oil, actually enters the water column, dissolution does not measurably change the mass balance of the oil. The significance of dissolution is that the soluble aromatic compounds are particularly toxic to fish and other aquatic life. If a spill of oil containing a large amount of soluble aromatic components occurs in shallow water and creates a high localized concentration of compounds, then significant numbers of aquatic organisms can be killed. Gasoline, diesel fuel, and light crude oils are the most likely to cause aquatic toxicity. A highly weathered oil is unlikely to dissolve into the water. On open water, the concentrations of hydrocarbons in the water column are unlikely to kill aquatic organisms. Dissolution occurs immediately after the spill occurs, and the rate of dissolution decreases rapidly after the spill as soluble substances are quickly depleted. Some of the soluble compounds also evaporate rapidly.

8.2.5. Photo-oxidation Photo-oxidation can change the composition of an oil.15-18 It occurs when the sun’s action on an oil slick causes oxygen and carbons to combine and form new products that may be resins. The resins may be somewhat soluble and dissolve into the water, or they may cause water-in-oil emulsions to form. It is not well understood how photo-oxidation specifically affects oils, although certain oils are susceptible to the process, whereas others are not. For most oils, photo-oxidation is not an important process in terms of changing their immediate fate or mass balance after a spill.

8.2.6. Sedimentation, Adhesion to Surfaces, and Oil-Fines Interaction Sedimentation is the process by which oil is deposited on the bottom of the sea or other water body. While the process itself is not well understood, certain

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facts about it are. Most sedimentation noted in the past has occurred when oil droplets reached a higher density than water after interacting with mineral matter in the water column. This interaction sometimes occurs on the shoreline or very close to the shore. Once oil is on the bottom, it is usually covered by other sediment and degrades very slowly. In a few well-studied spills, a significant amount (about 10%) of the oil was sedimented on the seafloor. Such amounts can be very harmful to biota that inevitably come in contact with the oil on the sea bottom. Because of the difficulty of studying sedimentation, data are limited. Oil is very adhesive, especially when it is moderately weathered, and binds to shoreline materials or other mineral material with which it comes in contact. A significant amount of oil can be left in the environment after a spill in the form of residual amounts adhering to shorelines and man-made structures such as piers and artificial shorelines. As this oil usually contains a high percentage of aromatics and asphaltenes with high molecular weight, it does not degrade significantly and can remain in the environment for decades. Oil slicks and oil on shorelines sometimes interact with mineral fines suspended in the water column, and the oil is thereby transferred to the water column.19 Particles of mineral with oil attached may be heavier than water and sink to the bottom as sediment or the oil may detach and refloat. Oil-fines interaction does not generally play a significant role in the fate of most oil spills in their early stages, but can have an impact on the rejuvenation of an oiled shoreline over the long term.

8.2.7. Biodegradation A large number of microorganisms are capable of degrading petroleum hydrocarbons. Many species of bacteria, fungi, and yeasts metabolize petroleum hydrocarbons as a food energy source.20,21 Bacteria and other degrading organisms are most abundant on land in areas where there have been petroleum seeps, although these microorganisms are found everywhere in the environment. As each species can utilize only a few related compounds at most, however, broad-spectrum degradation does not occur. Hydrocarbons metabolized by microorganisms are generally converted to an oxidized compound, which may be further degraded, may be soluble, or may accumulate in the remaining oil. The aquatic toxicity of the biodegradation products is sometimes greater than that of the parent compounds. The rate of biodegradation depends primarily on the nature of the hydrocarbons and then on the temperature. Generally, rates of degradation tend to increase as the temperature rises. Some groupings of bacteria, however, function better at lower temperatures, and others function better at higher temperatures. Indigenous bacteria and other microorganisms are often the best adapted and most effective at degrading oil as they are acclimatized to the temperatures and other conditions of the area. Adding “superbugs” to the oil does not necessarily improve the degradation rate.

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The rate of biodegradation is greatest on saturates, particularly those containing approximately 12 to 20 carbons. Aromatics and asphaltenes, which have a high molecular weight, biodegrade very slowly, if at all. This explains the durability of roof shingles containing tar and roads made of asphalt, as both tar and asphalt consist primarily of aromatics and asphaltenes. On the other hand, diesel fuel is a highly degradable product, for it is largely composed of degradable saturates and lower aromatics. Light crudes are also degradable to a degree. While gasoline contains degradable components, it also contains some compounds that are toxic to some microorganisms. These compounds generally evaporate more rapidly, but in almost all cases, most of the gasoline will evaporate before it can degrade. Heavy crudes contain little material that is readily degradable, and Bunker C contains almost none. The rate of biodegradation is also highly dependent on the availability of oxygen. On land, oils such as diesel can degrade rapidly at the surface, but very slowly if at all only a few centimeters below the surface, depending on oxygen availability. In water, oxygen levels can be so low that degradation is limited. It is estimated that it would take all the dissolved oxygen in approximately 400,000 L of seawater to completely degrade 1 L of oil. The rate of degradation also depends on the availability of nutrients such as nitrogen and phosphorus, which are most likely to be available on shorelines or on land. Finally, the rate of biodegradation also depends on the availability of the oil to the bacteria or microorganism. Oil degrades significantly at the oil-water interface at sea and, on land, mostly at the interface between soil and the oil. Biodegradation can be a very slow process for some oils. It may take weeks for 50% of a diesel fuel to biodegrade under optimal conditions and years for 10% of a crude oil to biodegrade under similar conditions. For this reason, biodegradation is not considered an important weathering process in the short term.

8.2.8. Sinking and Overwashing If oil is more dense than the surface water, it may sometimes actually sink. Some rare types of heavy crudes and Bunker C can reach these densities and sink. When this occurs, the oil may sink to a denser layer of water rather than to the bottom. Less dense layers of water may override these denser layers of water. This occurs, for example, when seas are not high and warmer fresh water from land overrides dense seawater. The fresh water has a density of about 1.00 g/mL and the sea water a density of about 1.03 g/mL. An oil with a density greater than 1.00 but less than 1.03 would sink through the layer of fresh water and ride on the layer of salt water. The layer of fresh water usually varies in depth from about 1 to 10 m. If the sea energy increases, the oil may actually reappear on the surface, as the density of the water increases from 1.00 to about 1.03.

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It is important to note that sinking of any form, whether to the bottom or to the top of a layer of dense sea water, is rare. When oil does sink, it complicates cleanup operations as the oil can be recovered only with underwater suction devices or special dredges. Overwashing is another phenomenon that occurs quite frequently and can hamper cleanup efforts. At moderate sea states, a dense slick can be overwashed with water. When this occurs, the oil can disappear from view, especially if the spill is being observed from an oblique angle, as would occur if someone is observing a slick from a ship. Overwashing causes confusion about the fate of an oil spill as it can give the impression that the oil has sunk and then resurfaced.

8.2.9. Formation of Tarballs Tarballs are agglomerations of thick oil less than about 10 cm in diameter. Larger accumulations of the same material ranging from about 10 cm to 1 m in diameter are called tar mats. Tar mats are pancake-shaped rather than round. Their formation is still not completely understood, but it is known that they are formed from the residuals of heavy crudes and Bunker C. After these oils weather at sea and slicks are broken up, the residuals remain in tarballs or tar mats. The re-formation of droplets into tarballs and tar mats has also been observed, with the binding force being simply adhesion. The formation of tarballs is the ultimate fate of many oils.19 These tarballs are then deposited on shorelines around the world. The oil may come from spills, but it is also residual oil from natural oil seeps or from deliberate operational releases such as from ships. Tarballs are regularly recovered by machine or by hand from recreational beaches. Figure 8.2 shows such a tarball on a beach.

FIGURE 8.2 Tarball on a beach. This is the fate of spills on the sea, if not cleaned up.

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8.3. MOVEMENT OF OIL AND OIL SPILL MODELING 8.3.1. Spreading

Area (square kilometres)

Oil spreads to a lesser extent and more slowly on land than on water. Oil spilled on or under ice spreads relatively rapidly but does not spread to as thin a slick as on water.21 On any surface other than water, such as ice or land, a large amount of oil is retained in depressions, cracks, and other surface irregularities. After an oil spill on water, the oil tends to spread into a slick over the water surface. This is especially true of the lighter products such as gasoline, diesel fuel, and light crude oils, which form very thin slicks. Heavier crudes and Bunker C initially spread to slicks several millimeters thick. Heavy oils may also form tarballs and tar mats and thus may not go through progressive stages of thinning. The area of spreading for different types of oil is illustrated in Figure 8.3. Oil spreads horizontally over the water surface even in the complete absence of wind and water currents. This spreading is caused by the force of gravity and the interfacial tension between oil and water. The viscosity of the oil opposes these forces. As time passes, the effect of gravity on the oil diminishes, but the force of the interfacial tension continues to spread the oil. The transition between these forces takes place in the first few hours after the spill occurs. As a general rule, an oil slick on water spreads relatively quickly immediately after a spill. The outer edges of a typical slick are usually thinner than the inside of the slick at this stage so that the slick may resemble a “fried egg.” After a day or so of spreading, this effect diminishes.

10

Crude oil Diesel

1

Bunker C

0.1 Gasoline

0.01 1

10

100

Time (minutes)

1000 1 day

10000 7 days

FIGURE 8.3 The spreading of several oils on the sea, less the amount evaporated. This is taken over a time period of one week. Gasoline initially spreads rapidly, and then its area shrinks because of rapid evaporation.

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Winds and currents also spread the oil out and speed up the process. Oil slicks will elongate in the direction of the wind and currents and, as spreading progresses, take on many shapes depending on the driving forces. Oil sheens often precede heavier or thicker oil concentrations. If the winds are high (more than 20 km/h), the sheen may separate from thicker slicks and move downwind. A slick often breaks into “windrows” on the sea under the influence of either waves or zones of convergence or divergence. Oil tends to concentrate between the crests of waves simply because of the force of gravity. There are often vertical circulation cells in the top 20 m of the sea. When two circulation cells meet, a zone of convergence is formed. When two currents diverge, it forms a zone of divergence. Oil moving along these zones is alternately concentrated and spread out by the circulation currents to form ribbons or windrows of oil rather than continuous slicks. In some locations close to shore, zones of convergence and divergence often occur in similar locations so that oil spills may appear to have similar trajectories and spreading behavior in these areas.

8.3.2. Movement of Oil Slicks In addition to their natural tendency to spread, oil slicks on water are moved along the water surface, primarily by surface currents and winds. If the oil slick is close to land and the wind speed is less than 10 km/h, the slick generally moves at a rate that is 100% of the surface current and approximately 3% of the wind speed. In this case, wind does not generally play an important role. If the wind is more than about 20 km/h, however, and the slick is on the open sea, wind predominates in determining the slick’s movement. Both the wind and surface current must be considered for most situations. The movement resulting from both wind and current inputs is illustrated in Figure 8.4. When attempting to determine the movement of an oil slick, two factors affect accuracy. The more significant factor is the inability to obtain accurate wind and current speeds at the time of a spill. The other, very minor factor is a phenomenon commonly known as the Coriolis effect, whereby the Earth’s rotation deflects a moving object slightly to the right in the northern hemisphere and to the left in the southern hemisphere.

3% of wind component

Resulting movement

Resulting movement

Current component

3% of wind component Current component

FIGURE 8.4 The effect of different wind and current directions on the resulting movement of an oil slick.

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8.3.3. Spill Modeling Spill response personnel need to know the direction in which an oil spill is moving in order to protect sensitive resources and coastline. To assist them with this, computerized mathematical models have been developed to predict the trajectory or pathway and fate of oil. Outputs of one such spill model are shown in Figure 8.5. Today’s sophisticated spill models combine the latest information on oil fate and behavior with computer technology to predict where the oil will go and what state it will be in when it gets there. Their major limitation to accurately predicting an oil slick’s movement is the lack of accurate estimates of water current and wind speeds along the predicted path. This is likely to remain a major limitation in the future. In addition to predicting the trajectory, these models can estimate the amount of evaporation, the possibility of emulsification, the amount of dissolution and the trajectory of the dissolved component, the amount and trajectory of the portion that is naturally dispersed, and the amount of oil deposited and remaining on shorelines. Accurate spill modeling is now a very important part of both contingency planning and actual spill response.

Spil source Current vectors

Net movement of slick

Path traveled by slick

Present location of oil parcels Scene 1 - About 10 hours after spill

Red shows shoreline hit

Scene 3 - About one day after spill

Scene 2 - About 10 hours after spill - with current vectors shown

Oil moves back into main channel from shoreline

Scene 4 - About three days after spill

FIGURE 8.5 Illustration of the outputs of a spill trajectory model.

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Spill models operate in a variety of modes. The most typical is the trajectory mode, which predicts the trajectory and weathering of the oil. The stochastic mode uses available data to predict a variety of scenarios for the oil spill, which includes the direction, fate, and property changes in the oil slick. In another mode, often called the receptor mode, a site on the shore or water is chosen and the trajectory from the source of the oil is calculated. Increasingly, statistically generated estimates are added to oil spill models to compensate for the lack of accurate knowledge of winds and currents.

REFERENCES 1. Boehm PD, Page DS, Brown JS, Neff JM, Bragg JR, Atlas RM. Distribution and Weathering of Crude Oil Residues on Shorelines 18 Years after the Exxon Valdez Spill. Env Sci Techn 2008;9210. 2. Wardlaw GD, Arey JS, Reddy CM, Nelson RK, Ventura GT, Valentine DL. Disentangling Oil Weathering at a Marine Seep Using GCxGC: Broad Metabolic Specificity Accompanies Subsurface Petroleum Biodegradation. Env Sci Techn 2008;7166. 3. Dı´ez S, Jover E, Bayona JM, Albaige´s J. Prestige Oil Spill. III. Fate of a Heavy Oil in the Marine Environment. Env Sci Techn 2007;3075. 4. Short JW, Irvine GV, Mann DH, Maselko JM, Pella JJ, et al. Slightly Weathered Exxon Valdez Oil Persists in Gulf of Alaska Beach Sediments after 16 Years. Env Sci Techn 2007;1245. 5. French-McCay DP. Modeling Impacts of Oil and Chemical Releases. Sea Techn 2006;21. 6. French-McCay DP. Modeling as a Scientific Tool in NRDA for Oil and Chemical Spills. IOSC; 2008. 7. Etkin DS, Michel J, McCay DF, Boufadel M, Li H. Development of a Practical Methodology for Integrating Shoreline Oil-Holding Capacity into Spill Modeling. AMOP 2008;564. 8. French-McCay D, Rowe J, Etkin DS. Transport and Impacts of Oil Spills in San Francisco BaydImplications for Response. AMOP 2008;159. 9. Fingas M. Evaporation Modeling, Chapter 9 in the present work; 2010. 10. Fingas M. Models for Water-in-Oil Emulsion Formation, Chapter 10 in the Present Work; 2010. 11. Lunel T. The Braer spill: Oil fate Governed by Dispersion. IOSC 2005;790. 12. Farwell C, Reddy CM, Peacock E, Nelson RK, Washburn L, Valentine DL. Weathering and the Fallout Plume of Heavy Oil from Strong Petroleum Seeps Near Coal Oil Point, CA. Env Sci Techn 2009;3542. 13. Danchuk S, Willson CS. Numerical Modeling of Oil Spills in the Inland Waterways of the Lower Mississippi River Delta. IOSC 2008;887. 14. Faksness L-G, Brandvik PJ. Distribution of Water Soluble Components from Arctic Marine Oil SpillsdA Combined Laboratory and Field Study. Cold Regions Sci Techn 2008;97. 15. Taghvaei Ganjali S, Nahri Niknafs B, Khosravi M. Photooxidation of Crude Petroleum Maltenic Fraction in Natural Simulated Conditions and Structural Elucidation of Photoproducts. Iranian J Env Health Sci Eng 2007;37. 16. Fernandez-Varela R, Gomez-Carracedo MP, Fresco-Rivera P, Andrade JM, Muniategui S, Prada D. Monitoring Photooxidation of the Prestige’s Oil Spill by Attenuated Total Reflectance Infrared Spectroscopy. Talanta 2006;409. 17. Plata DL, Sharpless CM, Reddy CM. Photochemical Degradation of Polycyclic Aromatic Hydrocarbons in Oil Films. Environ Sci Techn 2008;2432.

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18. Plata D, Reddy CM. Photochemical Degradation of Select Polycyclic Aromatic Hydrocarbons: First-Order Disappearance Rates and Primary Degradation Mechanisms in OilContaminated Coastal Zones. ACS Nat Meet Book of Abstr 2005;230. 19. Khelifa A, Gamble L. Prediction of Tar Ball Formation. AMOP 2006;79. 20. Lepo JE, Cripe CR, Kavanaugh JL, Zhang S, Norton GP. The Effect of Amount of Crude Oil on Extent of Its Biodegradation in Open waterdand Sandy BeachdLaboratory Simulations. Envir Techn 2003;1291. 21. Spaulding ML. A State-of-the-Art Review of Oil Spill Trajectory and Fate Modeling. Oil Chem Poll 1988;39.

Chapter 9

Evaporation Modeling Merv Fingas

Chapter Outline 9.1. Introduction 9.2. Review of Theoretical Concepts 9.3. Development of New Diffusion-Regulated Models 9.4. Complexities to the Diffusion-Regulated Model

201 205 212 229

9.5. Use of Evaporation Equations in Spill Models 9.6. Comparison of Model Approaches 9.7. Summary

233

235 240

9.1. INTRODUCTION Evaporation is a very important process for most oil spills. In a few days, typical crude oils can lose up to 40% of their volume.1 Most oil spill behavior models include evaporation as a process and in the output of the model. Despite the importance of the process, relatively little work has been conducted on the basic physics and chemistry of oil spill evaporation.2 The difficulty with studying oil evaporation is that oil is a mixture of hundreds of compounds and this mixture varies from source to source and even over time. Much of the work described in the literature focuses on “calibrating” equations developed for water evaporation.2 Furthermore, very little empirical data on oil evaporation was published until a decade ago. An important concept in understanding evaporation is to understand the mechanisms that regulate evaporation. If there were no regulation, evaporation would proceed nearly instantly. Figure 9.1 shows a schematic of the airboundary-layer regulated mechanism. The liquid would evaporate at a very high rate if it was not for the regulation caused by the slow transfer of vapor through the air boundary layer. The most common example of this type of regulation is applicable to water, and this concept enters into most people’s common view of evaporation. Evaporation of water can be increased by spreading it out or by increasing the wind speed. Oil Spill Science and Technology. DOI: 10.1016/B978-1-85617-943-0.10009-7 Copyright Ó 2011 Elsevier Inc. All rights reserved.

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Evaporation limited by diffusion rate into the air therefore air-boundary-layer regulated Air Air boundary layer

Liquid

FIGURE 9.1 Illustration of the air-boundary-layer regulation mechanism. The diffusion into the air layer is the limiting factor and serves to regulate the evaporation rate. This rate is affected by turbulence in the air, which will increase the transfer of the molecules across the boundary layer. This regulatory mechanism is true for pure liquids that have a high evaporation rate. Water is an example of such a liquid and is the most common concept held.

Many liquids are not air-boundary-layer regulated primarily because they evaporate too slowly to have the vapors saturate the air boundary layer above them. Many mixtures are often regulated by the diffusion of molecules inside the liquid to the surface of the liquid. This situation is illustrated in Figure 9.2. Such a mechanism is true for many slowly evaporating mixtures of compounds, such as oils and fuels. Some of the outcomes of this mechanism may seem counterintuitive to some people in that increasing the area may not increase the evaporation rate except to a small degree if the initial pool is very thick, such as over about 10 to 20 mm. Also, and perhaps more importantly, increasing wind speed does not increase evaporation. Evaporation limited by diffusion rate through liquid and through surface layer-therefore diffusion regulated Air Liquid surface

Liquid

FIGURE 9.2 Illustration of the diffusion-controlled regulation mechanism. The diffusion through the evaporating liquid is the limiting factor and thus the regulation mechanism. This mechanism is generally true for oils, fuels, and many other mixtures of liquids that both evaporate more slowly than water and are mixtures.

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Scientific and quantitative work on water evaporation is decades old.3,4 The basis for the oil evaporation work in the literature is water evaporation. There are several fundamental differences between the evaporation of a pure liquid such as water and that of a multicomponent system such as crude oil. Most obviously, the evaporation rate for a single-component liquid such as water is a constant with respect to time.3,4 Evaporative loss, by total weight or volume, is not linear with time for crude oils and other multicomponent fuel mixtures.5 Evaporation of a liquid can be considered as the movement of molecules from the surface into the vapor phase above it. The layer of air above the evaporation surface is known as the boundary layer.6 The characteristics of this air layer, or boundary layer, can influence evaporation. In the case of water, the boundary layer regulates the evaporation rate. Air can hold a variable amount of water, depending on temperature, as expressed by the relative humidity. Under conditions where the boundary layer is not moving (no wind) or has low turbulence, the air immediately above the water quickly becomes saturated and evaporation slows or ceases. In practice, the actual evaporation of water proceeds at a small fraction of the possible evaporation rate because of the saturation of the boundary layer. The air-boundary-layer physics are then said to regulate the evaporation of water. This regulation manifests itself in the sensitivity of evaporation to wind or turbulence. When turbulence is weak or absent, evaporation can slow down by orders-of-magnitude. The molecular diffusion of water molecules is at least 103 times slower than turbulent diffusion.6 Evaporation can be viewed as consisting of two components, fundamental evaporation and regulation mechanisms. Fundamental evaporation is that process consisting of the evaporation of the liquid directly into the vapor phase without regulation other than by the thermodynamics of the liquid itself. Regulation mechanisms are those processes that serve to regulate the final evaporation rate into the environment. For water, the main regulation factor is the air-boundary-layer regulation discussed above. Air-boundary-layer regulation is manifested by the limited rate of diffusion, both molecular and turbulent diffusion, and by saturation dynamics. Molecular diffusion is based on exchange of molecules over the mean-free path in the gas. The rate of molecular diffusion for water is about 105 slower than the maximum rate of evaporation possible, purely from thermodynamic considerations.6 The rate for turbulent diffusion, the combination of molecular diffusion and movement with turbulent air, is on the order of 102 slower than that for maximum evaporation. In fact, in the case of water, maximum evaporation is not known and has only been estimated by experiments in artificial environments or by calculation.3 If the evaporation of oil was like that of water and was air-boundary-layer regulated, one could write the mass-transfer rate in semi-empirical form (also in generic and unitless form) as: E K C Tu S

(1)

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where E is the evaporation rate in mass per unit area, K is the mass-transfer rate of the evaporating liquid, presumed constant for a given set of physical conditions, sometimes denoted as kg (gas phase mass-transfer coefficient, which may incorporate some of the other parameters noted here), C is the concentration (mass) of the evaporating fluid as a mass per volume, Tu is a factor characterizing the relative intensity of turbulence, and S is a factor that relates to the saturation of the boundary layer above the evaporating liquid. The saturation parameter, S, represents the effects of local advection on saturation dynamics. If the air is already saturated with the compound in question, the evaporation rate approaches zero. This also relates to the scale length of an evaporating pool. If one views a large pool over which a wind is blowing, there is a high probability that the air is saturated downwind and the evaporation rate per unit area is lower than for a smaller pool. It should be noted that there many equivalent ways of expressing this fundamental evaporation equation. Much of the pioneering work for evaporation studies was performed by Sutton who proposed an equation based largely on empirical work:7 E ¼ K Cs U

7=9

d 1=9 Scr

(2)

where Cs is the concentration of the evaporating fluid (mass/volume), U is the wind speed, d is the area of the pool, Sc is the Schmidt number, and r is the empirical exponent assigned values from 0 to 2/3. Other parameters are defined as above. The terms in this equation are analogous to the very generic equation, (1), proposed above. The turbulence is expressed by a combination of the wind speed, U, and the Schmidt number, Sc. The Schmidt number is the ratio of kinematic viscosity of air (v) to the molecular diffusivity (D) of the diffusing gas in air, that is, a dimensionless expression of the molecular diffusivity of the evaporating substance in air. The coefficient of the wind power typifies the turbulence level. The value of 0.78 (7/9) as chosen by Sutton, represents a turbulent wind, whereas a coefficient of 0.5 would represent a wind flow that was more laminar. The scale length is represented by d and has been given an empirical exponent of 1/9. For water, this represents a weak dependence on size. The exponent of the Schmidt number, r, represents the effect of the diffusivity of the particular chemical, and historically was assigned values between 0 and 2/3.7 This expression for water evaporation was subsequently used by those working on oil spills to predict and describe oil and petroleum evaporation. Much of the literature follows the work of Mackay.8,9 Mackay and co workers adapted the equations for hydrocarbons using the evaporation rate of cumene. Data on the evaporation of water and cumene have been used to correlate the gas phase mass-transfer coefficient as a function of wind speed and pool size by the equation: Km ¼ 0:0292 U 0:78 X 0:11 Sc0:67

(3)

Chapter | 9

Evaporation Modeling

205

where Km is the mass-transfer coefficient in units of mass per unit time and X is the pool diameter or the scale size of evaporating area. Stiver and Mackay subsequently developed this further by adding a second equation:9 N ¼ km AP=ðRTÞ

(4)

where N is the evaporative molar flux (mol/s), km is the mass-transfer coefficient at the prevailing wind (m/s), A is the area (m2), P is the vapor pressure of the bulk liquid (Pascals), R is the gas constant [8.314 Joules/(mol-K)], and T is the temperature (K ). Thus, boundary-layer regulation was assumed to be the primary regulation mechanism for oil and petroleum evaporation. This assumption was never well tested by experimentation, as revealed by a literature search.2 The implications of these assumptions are that evaporation rate for a given oil is increased by: l l l

increasing turbulence increasing wind speed increasing the surface area of a given mass of oil

These factors can then be verified experimentally to test whether oil is boundary-layer regulated.

9.2. REVIEW OF THEORETICAL CONCEPTS Blokker was the first to develop oil evaporation equations for oil evaporation at sea.10 His starting basis was theoretical. Oil was presumed to be a onecomponent liquid. The ASTM (American Society for Testing and Materials) distillation data and the average boiling points of successive fractions were used as the starting point to predict an overall vapor pressure. The average vapor pressure of these fractions was then calculated from the ClausiusClapeyron equation to yield: log

ps qM 1 1 ¼ p 4:57 T Ts

(5)

where p is the vapor pressure at the absolute temperature, T, ps is the vapor pressure at the boiling point, Ts (for ps, 760 mm Hg was used), q is the heat of evaporation in cal/g, and M is the molecular weight. The term qM/(4.57 Ts) was taken to be nearly constant for hydrocarbons (¼5.0 þ/ 0.2), and thus the expression was simplified to: log ps =p ¼ 5:0½ðTs TÞ=T

(6)

From the data obtained, the weathering curve was calculated, assuming that Raoult’s law is valid for this situation giving qM as a function of the percentage

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evaporated. Pasquill’s equation10 was applied stepwise, and the total evaporation time was obtained by summation: t ¼

DhDb X 1 Kev U a PM

(7)

where t is the total evaporation time in hours, Dh is the decrease in layer thickness in m, D is the diameter of the oil spill, b is a meteorological constant (assigned a value of 0.11), Kev is a constant for atmospheric stability (taken to be 1.2 108), a is a meteorological constant (assigned a value of 0.78), P is the vapor pressure at the absolute temperature, T, and M is the molecular weight of the component or oil mass. Thus the model is partially air-boundary-layer regulated. Blokker constructed a small wind tunnel and tested this equation against the evaporation of gasoline and a medium crude oil. The observed gasoline evaporation rate was much higher than was predicted, and the crude oil rate was much lower than predicted. The times of evaporation, however, were somewhat close, and the equation was accepted for further use. The above equations were then incorporated into spreading equations to yield equations to predict the simultaneous spreading and evaporation of oil and petroleum products. Mackay and Matsugu approached the problem by using the classical water evaporation and experimental work.8 The water evaporation equation was corrected to hydrocarbons using the evaporation rate of cumene. It was noted that the difference in constants was related to the enthalpy differences between water and cumene. Data on the evaporation of water and cumene were used to correlate the gas phase mass-transfer coefficient as a function of wind speed and pool size by the equation: Km ¼ 0:0292 U 0:78 X 0:11 Sc0:67

(8)

where Km is the mass-transfer coefficient in units of mass per unit time and X is the pool diameter or the scale size of evaporating area. Note that the exponent of the wind speed, U, is 0.78, which is equal to the classical water evaporation-derived coefficient. Mackay and Matsugu noted that for hydrocarbon mixtures, the evaporation process is more complex, being dependent on the liquid diffusion resistance being present. Experimental data on gasoline evaporation were compared with computed rates. The computed rates showed some agreement and suggested the presence of a liquid-phase masstransfer resistance. This work was subsequently extended by the same group to show that the evaporative loss of a mass of oil spilled can be estimated using a mass-transfer coefficient, as shown above.11 This approach was investigated with some laboratory data and tested against some known mass-transfer conditions on the sea. The conclusion was that this mass-transfer approach could result in predictions of evaporation at sea.

Chapter | 9

Evaporation Modeling

207

Butler developed a model to examine evaporation of specific hydrocarbon components.12 The weathering rate was taken as proportional to the equilibrium vapor pressure, P, of the compound and to the fraction remaining: dx=dt ¼ kPðx=xo Þ

(9)

where x is the amount of a particular component of a crude oil at time, t, xo is the amount of that same component present at the beginning of weathering (t ¼ 0), k is an empirical rate coefficient, and P is the vapor pressure of the oil component. Since petroleum is a complicated mixture of compounds, P is not equal to the vapor pressure of the pure compound, but neither would there be large variations in the activity coefficient as the weathering process occurs. For this reason, the activity coefficients were subsumed in the empirical rate coefficient k. P and k were taken as independent of the amount, x, for a fairly wide range of oils. The equation was then directly integrated to give the fraction of the original compound remaining after weathering as (similar to Henry’s law): x=xo ¼ expðktP=xo Þ

(10)

The vapor pressure of individual components was fit using a regression line to yield a predictor equation for vapor pressure: P ¼ expð10:94 1:06 NÞ

(11)

where P is the vapor pressure in Torr and N is the carbon number of the compound in question. This combined with Equation (10) yielded the following expression: x=xo ¼ exp½ðkt=xo Þexpð10:94 1:06 NÞ

(12)

where x/xo is the fraction of the component left after weathering, k is an empirical constant, xo is the original quantity of the component, and N is the carbon number of the component in question. Equation (12) predicts that the fraction weathered is a function of the carbon number and decreases at a rate that is faster than predicted from simple exponential decay. If the initial distribution of compounds is essentially uniform (xo independent of N ), then the above equation predicts that the carbon number where a constant fraction (e.g., half) of the initial amount has been lost (x ¼ 0.5 xo) is a logarithmic function of the time of weathering: N1=2 ¼ 10:66 þ 2:17 logðkt=xo Þ

(13)

where N1/2 is half the volume fraction of the oil. The equation was tested using data from some patches of oil on shoreline, whose age was known. The equation was able to predict the age of the samples relatively well. It was suggested that the equation was applicable to open water spills; however, this was never subsequently applied in models. It should be noted that this approach is somewhat similar to the model that will be described

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Behaviour of Oil in the Environment and Spill Modeling

later in this paper and also that it is not air-boundary-layer based but is a diffusion-like model based partially on empirical values. Yang and Wang developed an equation using the Mackay and Matsugu molecular diffusion process.13 The vapor phase mass-transfer process was expressed by: Die ¼ km ð pi piN Þ=½RTS

(14)

where Die is the vapor phase mass-transfer rate, km is a coefficient that lumps all the unknown factors that affect the value of Die, pi is the hydrocarbon vapor pressure of fraction, I, at the interface, piN is the hydrocarbon vapor pressure of fraction, I, at infinite altitude of the atmosphere, R is the universal gas constant, and Ts is the absolute temperature of the oil slick. The following functional relationship was proposed: km ¼ aAg eqU

(15)

where A is the slick area, U is the over-water wind speed, and a, q, and g are empirical coefficients. This functional relationship was based on the results of past investigations, including, for instance, those of MacKay and Matsugu who suggested the value of g to be in the range from 0.025 to 0.055.8 Further experiments were performed by Yang and Wang to determine the values of a and q.13 The results were found to be twofold. Experiments showed that a film formed on evaporating oils and that this film severely retarded evaporation. Before the surface film has developed (rt/ro < 1.0078): Kmb ¼ 69A0:0055 e0:42U

(16)

where Kmb is the coefficient that groups all factors affecting evaporation before the surface film has formed and A is the area. After the surface film has developed (rt/ro > 1.0078): Kma ¼ 1=5 kmb

(17)

where ro is initial oil density, rt is weathered oil density at time t, and Kma is the coefficient that groups all factors affecting evaporation after the surface film has formed. The evaporation rate was found to be reduced fivefold after the formation of the surface film. Brighton proposed that the standard formulation used by many workers required refining.14 E ¼ K Cs U 7=9 d1=9 Scr

(18)

His starting point for water evaporation was similar to that proposed by Sutton7 where E is the mean evaporation rate per unit area, K is an empirically determined constant, Cs is the concentration of the evaporation fluid (mass/volume), d is the area of the square or circular pool, and r is an empirical exponent assigned values from 0 to 2/3.

Chapter | 9

209

Evaporation Modeling

Brighton suggested that this equation does not conform to the basic dimensionless form involving the parameters U and Zo (wind speed and roughness length, respectively), which define the boundary-layer conditions. The key factor in Brighton’s analysis was to use a linear eddy-diffusivity profile. This feature implied that concentration profiles become logarithmic near the surface, which is suspected to be more realistic compared to the more finite values previously used. Using a power profile to provide an estimation of the turbulence, Brighton was able to substitute the following identities into the classical relationship: U ¼

u n k

(19)

where u* is the friction velocity, z1 is the reference height above the surface, z0 is the roughness length, and n is the power law dimensionless term. It should be noted that these are applicable to neutrally stable atmospheres. z1 (20) n ¼ In z0 The evaporation equation now becomes: z dX d ku zdX U ¼ z1 dx dz s dz

(21)

where z is the height above the surface, X is the concentration of the evaporating compounds, x is the dimension of the evaporating pool, k is given by K/u)z, and is the von Karman constant, and s is the turbulent Schmidt number (taken as 0.85). Brighton subsequently compared his model with several runs of experimental evaporation experiments in the field and in the laboratory. This included laboratory oil evaporation data.15 The model only correlated well with laboratory water evaporation data, and the reason given was that other data sets were “noisy.” The most frequently used work in spill modeling is that of Stiver and Mackay.9 It is based on some of the earlier work by Mackay and Matsugu, but significant additions were made.8 Additional information is given in a thesis by Stiver.16 The formulation was initiated with assumptions about the evaporation of a liquid. If a liquid is spilled, the rate of evaporation is given by: N ¼ KAP=ðRTÞ

(22)

where N is the evaporative molar flux (mol/s), K is the mass-transfer coefficient under the prevailing wind (ms1), A is the area (m2), and P is the vapor pressure of the bulk liquid. This equation was arranged to give: dFv =dt ¼ KAPv=ðVo RTÞ

(23)

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PART | IV

Behaviour of Oil in the Environment and Spill Modeling

where Fv is the volume fraction evaporated, v is the liquid’s molar volume (m3/mol), and Vo is the initial volume of spilled liquid (m3). By rearranging, we obtain:

or:

dFv ¼ ½Pv=ðRTÞðKAdt=Vo Þ

(24)

dFv ¼ Hdq

(25)

where H is Henry’s law constant and q is the evaporative exposure. The right-hand side of the second to last equation has been separated into two dimensionless groups. The group, KAdt/Vo, represents the time rate of what has been termed the “evaporative exposure” and was denoted as dq. The evaporative exposure is a function of time, the spill area and volume (or thickness), and the mass-transfer coefficient (which is dependent on the wind speed). The evaporative exposure can be viewed as the ratio of exposed vapor volume to the initial liquid volume. The group Pv/(RT) or H is a dimensionless Henry’s law constant or ratio of the equilibrium concentration of the substance in the vapor phase [P/(RT)] to that in the liquid (l/v). H is a function of temperature. The product qH is thus the ratio of the amount that has evaporated (oil concentration in vapor times vapor volume) to the amount originally present. For a pure liquid, H is independent of Fv and Equation 2.26 was integrated directly to give: Fv ¼ Hq

(26)

If K, A, and temperature are constant, the evaporation rate is constant and evaporation is complete (Fv is unity) when q achieves a value of 1/H. If the liquid is a mixture, H depends on Fv and the basic equation can only be integrated if H is expressed as a function of Fv; that is, the principal variable of vapor pressure is expressed as a function of composition. The evaporation rate slows as evaporation proceeds in such cases. Equation (26) was replaced with a new equation developed using data from evaporation experiments: Fv ¼ ðT=K1 Þ Inð1 þ K1 q=TÞ expðK2 K3 =TÞ

(27)

where Fv is the volume fraction evaporated and K1,2,3 are empirical constants. A value for K1 was obtained from the slope of the Fv versus log q curve from pan or bubble evaporation experiments. For q greater than 104, K1 was found to be approximately 2.3T divided by the slope. The expression exp(K2 K3/T) was then calculated, and K2 and K3 were determined individually from evaporation curves at two different temperatures. Variations of all the above equations have been used extensively by many other experimenters and for model application. Brown and Nicholson studied the weathering of a heavy oil, bitumen.17 They compared experimental data using a large-scale weathering tank with two

Chapter | 9

Evaporation Modeling

211

spill model outputs. In the FOOS model, the evaporative exposure concept is used in which the fraction of oil evaporated is given by a variant of the Mackay equation: F ¼ ½InðPÞ þ InðCEÞ þ 1=P=C

(28)

where F is the fraction evaporated, C is an empirical constant, and E is a measure of the evaporative exposure, defined as; E ¼ ðKm AvtÞ=ðRTVo Þ

(29)

Km ¼ 0:0048 U 0:78 Z 0:11 Sc0:67

(30)

where;

and where Km is the mass-transfer coefficient, A is the slick area, v is the oil molar volume, Vo is the initial slick volume, Z is the pool size scale factor, and Sc is the Schmidt number (taken as 2.7). Brown and Nicholson compared the measured evaporation for a 5 ms1 wind at an ambient temperature of 20 C, and evaluation was done with the equation above.17 A spill volume of 100 m3 was assumed. A value of about 105 m3/mol was used for the average molar volume. The model generally described the observed evaporation quite well, particularly during the first few hours. Later, however, the model consistently overpredicted the evaporation rate. A simple method of correcting the equation was implemented by assuming that the vapor phase Schmidt number decreases slightly as the skin on the oil thickens. In response, the evaporative exposure was modified to: Km ¼ ð0:0025 0:000021 tÞ U 0:78

(31)

The predicted evaporation then compared favorably with the measured values. Bobra conducted laboratory studies on the evaporation of crude oils.18 The evaporation curves for several crude oils and petroleum products were measured under several different environmental conditions. These data were compared to the equation developed by Stiver and Mackay.9 The equation used was: Fv ¼ In½1 þ BðTG =TÞ q expðA B To =TÞfT=BTG g

(32)

where FV is the fraction evaporated, TG is the gradient of the modified distillation curve, A and B are dimensionless constants, To is the initial boiling point of the oil, and q is the evaporative exposure as previously defined. The comparison by Bobra et al. showed that the Stiver and Mackay equation predicts the evaporation of most oils relatively well until time exceeds about 8 hours; after that it overpredicted the evaporation. The “overshoot” can be as much as 10% evaporative loss at the 24-hour mark. This is especially true for very light oils. The Stiver and Mackay equation was also found to underpredict or overpredict the evaporation of oils in the initial phases. In addition, it was found that the basic

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assumptions in the bubble experiment as proposed by early researchers, namely, that the air bubbles are saturated with vapor, was not correct.9 Bobra also noted that most oil evaporation follows a logarithmic curve with time and that a simple approach to this was much more accurate than using Equation (32).

9.3. DEVELOPMENT OF NEW DIFFUSION-REGULATED MODELS A review of the theoretical work above reveals that air-boundary-layer concepts are very limited and cannot accurately explain long-term evaporation. Fingas conducted a series of experiments over several years to examine the concepts further.19 The results of the boundary-regulation experiments are presented in the order of the experimental series.

9.3.1. Wind Experiments A major factor in determining whether or not oil evaporation is air-boundarylayer regulated is if the evaporation rate increases with wind as would be predicted by Equations (2) and (8) above. Experiments on the evaporation of oil with and without wind were conducted with ASMB (Alberta Sweet Mixed Blend), gasoline, and water. Water formed a baseline data set since much is known about its evaporation behavior.3 Regressions on the data were performed, and the equation parameters calculated. Curve coefficients are the constants from the best fit equation [Evap ¼ a ln(t)], t ¼ time in minutes, for logarithmic equations or Evap ¼ a Ot, for the square root equations. Oils with few components evaporating at one time have a tendency to fit square root curves.5 While data were calculated separately for percentage of weight lost and absolute weight, the latter is usually used because it is more convenient. The plots of wind speed versus the evaporation rate (as a percentage of weight lost) for each oil type are shown in Figures 9.3 to Figure 9.5. These figures show that the evaporation rates for oils and even the light product gasoline are not increased by a significant amount with increasing wind speed. In most cases, there is a small rise from the 0-wind level to the 1-m/s level, but after that, the rate remains relatively constant. The evaporation rate after the 0-wind value is nearly identical for all oils. This is due to the stirring effect on the oil, which increases the diffusion rate to the surface. The oil evaporation data can be compared to the evaporation of water, as illustrated in Figure 9.5. These data show the classical relationship of the water evaporation rate correlated with the wind speed (evaporation varies as U0.78, where U is wind speed). This, by itself, indicates that the oils studied here are not boundary-layer-regulated, but a small effect is seen in moving from 0-wind to 1 m/s, and not thereafter. This, as noted above, is due to the stirring effect of the wind thus increasing the diffusion rate.

Chapter | 9

213

Evaporation Modeling 50

Percent Evaporated

40

30

20 wind = 0 m/s wind = 1 m/s wind = 1.6 m/s wind = 2.1 m/s wind = 2.6 m/s

10

0

0

500

1000

1500

2000

2500

Time - Minutes FIGURE 9.3 Evaporation of ASMB with varying wind velocities. This figure shows that there is little variation with wind velocity except in going from the 0-wind-level up to the others. This is due to the stirring effect of wind and not air-boundary-layer regulation.

FIGURE 9.4 Evaporation of gasoline with varying wind velocities. This figure also shows that there is little variation with wind velocity except in going from the 0-wind-level up to the others. This again is due to the stirring effect of wind and not air-boundary layer regulation.

100

Percent Evaporated

80

60

40 wind = 0m/s wind =1 m/s wind = 1.6m/s wind = 2.1m/s wind = 2.6m/s

20

0

0

20

40

60

80 100 120 140 160 180

Time - Minutes

214

PART | IV

Behaviour of Oil in the Environment and Spill Modeling

FIGURE 9.5 Evaporation of water with varying wind velocities. This figure shows dramatic differences in the evaporation rate of water with wind velocity. This is typical of air-boundary-layer regulation. Compare Figure 9.5 with oil evaporation in Figures 9.3 and 9.4, which do not show this trend of variance with wind velocity.

100

Percent Evaporated

80

60

40

20

wind = 0 m/s wind = 1 m/s wind = 1.6 m/s wind = 2.1 m/s wind = 2.6 m/s regression line

0 0

20

40

60

80

100 120 140 160 180

Time - Minutes

Figure 9.6 shows the rates of evaporation compared to the wind speed for all the liquids used in this study. This figure shows the evaporation rates of all test liquids versus wind speed. The lines shown are those calculated by linear regression. This clearly shows that water evaporation rate increased, as expected, with increasing wind velocity. The oils, ASMB, and gasoline, do not show any significant increase with increasing wind speed. In any case, they do not show the typical U0.78 relationship that water shows. All the above data show that oil is not boundary-layer-regulated. FIGURE 9.6 Correlation of evaporation rates and wind velocity. The lines are drawn through the data points from experimental values. This clearly shows no correlation of oil evaporation rates with wind velocity and the strong and expected high correlation of water with wind velocity. The water evaporation line is moved to fit on the vertical scale, but otherwise is unaltered.

Evaporation Rate (%/min. or %/ln min.)

25 Gasoline

20

15

10

Water

ASMB

5 FCC Heavy Cycle

0 0

1

Wind Velocity - m/s

2

Chapter | 9

215

Evaporation Modeling

9.3.2. Evaporation Rate and Area Air-boundary-layer regulated liquids evaporate much faster if one increases the area. A small spill of water on the kitchen cupboard can be evaporated quickly by spreading it out on the cupboard. A test of this tendency will also confirm the proposition that oil is diffusion regulated. ASMB was also used to conduct a series of experiments with varying evaporation area. The mass of the oil was kept constant so that the thickness of the oil would also vary. However, the greater the area, the less the thickness, and both factors would increase oil evaporation if it were boundary-layer regulated. The experiments show no correlation between area and evaporation rate. One can conclude that evaporation rate is not highly correlated with area, and thus the evaporation of oil is not air-boundary-layer regulated.

9.3.3. Study of Mass and Evaporation Rate Air-boundary-layer liquids show no correlation between the mass of oil evaporated and the evaporation rate; however, diffusion-regulated liquids do. ASMB oil was again used to conduct a series of experiments with volume as the major variant. Alternatively, thickness and area were held constant to ensure that the strict relationship between these two variables did not affect the final regression results. Figure 9.7 illustrates the relationship between evaporation rate and volume of evaporation material (also equivalent to mass of evaporating material). This figure illustrates a strong correlation between oil mass (or volume) and evaporation rate. This again proves that there is no boundary-layer regulation.

FIGURE 9.7 Correlation of oil mass with evaporation rate. The plots are the equation factors from the evaporation equation, which is approximately equivalent to evaporation rate. This shows a direct relationship between evaporation rate of oil and mass of oil. It indicates that oil is diffusion regulated.

Evaporation Rate - g/ln min.

5

4

3

2

1

0 0

20

40

60

80

Weight - Grams

100

120

140

216

PART | IV

Behaviour of Oil in the Environment and Spill Modeling

9.3.4. Study of the Evaporation of Pure Hydrocarbonsdwith and Without Wind A study of the evaporation rate of pure hydrocarbons was conducted to test the classic boundary-layer evaporation theory as applied to the hydrocarbon constituents of oils. The evaporation rate data are illustrated in Figure 9.8. This figure shows that the evaporation rates of the pure hydrocarbons have a variable response to wind. Heptane (hydrocarbon number 7) shows a large difference between evaporation rate in wind and no wind conditions, indicating boundarylayer regulation. Decane (carbon number 10) shows a lesser effect, and dodecane (carbon number 12) shows a negligible difference between the two experimental conditions. This experiment shows the extent of boundary regulation and the reason for the small or negligible degree of boundary regulation shown by crude oils and petroleum products. Crude oil contains very little material with carbon numbers less than dodecane, often less than 3% of its composition. Even the more volatile petroleum products, gasoline and diesel fuel, only have limited amounts of compounds more volatile than decane, and thus are also not strongly boundary-layer regulated, if at all. 0.6

Evaporation Rate - g/min.

0.5

0.4

Wind

0.3

0.2

0.1

No Wind

0.0 6

8

10

12

14

16

Hydrocarbon Number FIGURE 9.8 Evaporation rate of pure hydrocarbons. This shows that hydrocarbons up to about C12 (dodecane) show some air-boundary-regulated behavior, whereas those above show no airboundary-regulated behavior. As most compounds in oil are higher than dodecane, the bulk oil would not show air-boundary-regulated behavior.

Chapter | 9

217

Evaporation Modeling

9.3.5. Other Factors Another evaluation of evaporation regulation is that of saturation concentration, the maximum concentration soluble in air. The saturation concentrations of water and several oil components are listed in Table 9.1.20 This table shows that saturation concentration of water is less than that of many common oil components. The saturation concentration of water is, in fact, about two orders of magnitude less than the saturation concentration of volatile oil components such as pentane. This further explains why even light oil components have little boundary-layer limitation. Further, the saturation concentration of water is so regulating that with a high relative humidity, there is little that can be added to the air.

9.3.6. Temperature Variation and Generic Equations Using Distillation Data Comparing the evaporation data with temperature, one finds that the evaporation equations for oils show some differences. The resulting finding that unique

TABLE 9.1 Saturation Concentration of Water and Hydrocarbons Substance

Saturation Concentration* in g/m3 at 25 C

water

20

n-pentane

1689

hexane

564

cyclohexane

357

benzene

319

n-heptane

196

methylcyclohexane

192

toluene

110

ethybenzene

40

p-xylene

38

m-xylene

35

o-xylene

29

*Values taken from Ullmann’s Encyclopedia20

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PART | IV

Behaviour of Oil in the Environment and Spill Modeling

equations may be needed for each oil is a significant disadvantage to practical end use, and a way to accurately predict evaporation using other readilyavailable data is necessary. Findings show that distillation data can be used to predict evaporation. Distillation data are very common and are often the only data used to characterize oils. This is because the data are crucial in operating refineries. Crudes are sometimes priced on the basis of their distillation data. New procedures to measure distillation data are very simple, fast, and repeatable. Further, it was noted that oils and fuels evaporated as two distinct typesd those that evaporated as a logarithm of time and those that evaporated as a square root of time.5 Most oils typically evaporated as a logarithm (natural) with time. Diesel fuel and similar oils, such as jet fuel, kerosene, and the like, evaporate as a square root of time. The reasons for this are simply that diesel fuel and the like have a narrower range of compounds that evaporate at similar yield rates, which are a summation of a square root.5 The empirically measured parameters at 15 C were correlated with both the slopes and the intercepts of the temperature equations. Full details of this correlation are given in the literature.22,23 For the variation with temperature, the resulting equation is: Percentage evaporated ¼ ½B þ 0:045ðT 15Þ InðtÞ

(33)

where B is the equation parameter at 15 C, T is temperature in degrees Celsius, and t is the time in minutes. Distillation data were directly correlated to the evaporation rates determined by experimentation. The distillation data used were the type that are stated as the temperature at which a fixed amount of material is lost. The optimal point, or point at which the regression coefficient is maximum, was found to be 180 C by using peak functions. The percent mass distilled at 180 degrees was used to calculate the relationship between the distillation values and the equation parameters. The equations used were derived from correlations of the data. The data from those oils that were better fitted with square root equationsddiesel, Bunker C light, and Fluid Catalytic Cracker (FCC) Heavy Cycledwere calculated separately. The equations derived from the regressions are as follows: For oils that follow a logarithmic equation: Percentage evaporated ¼ 0:165ð%DÞ InðtÞ

(34)

For oils that follow a square root equation: pﬃ Percentage evaporated ¼ 0:0254ð%DÞ t where %D is the percentage (by weight) distilled at 180 C.

(35)

Chapter | 9

219

Evaporation Modeling

These equations can be combined with the equations generated in previous work as shown in Equation (33) above to account for the temperature variations: For oils that follow a logarithmic equation: Percentage evaporated ¼ ½:165ð%DÞ þ :045ðT 15Þ lnðtÞ

(36)

For oils that follow a square root equation:

pﬃ Percentage evaporated ¼ ½:0254ð%DÞ þ :01ðT 15Þ t 180 C.

(37)

where %D is the percentage (by weight) distilled at In addition, a large number of experiments were performed on oils to directly measure their evaporation curves. The empirical equations that result are given in Table 9.2.

TABLE 9.2 Equations for Predicting Evaporation Oil Type

Equation

Adgo

%Ev ¼ (.11 þ .013T)/t

Adgo e long term

%Ev ¼ (.68 þ .045T)ln(t)

Alaminos Canyon Block 25

%Ev ¼ (2.01 þ .045T)ln(t)

Alaska North Slope (2002)

%Ev ¼ (2.86 þ .045T)ln(t)

Alberta Sweet Mixed Blend

%Ev ¼ (3.24 þ .054T)ln(t)

Amauligak

%Ev ¼ (1.63 þ .045T)ln(t)

Amauligak e f24

%Ev ¼ (1.91 þ .045T)ln(t)

Anadarko H1A-376

%Ev ¼ (2.66 þ .013T)/t

Arabian Medium

%Ev ¼ (1.89 þ .045T)ln(t)

Arabian Heavy

%Ev ¼ (1.31 þ .045T)ln(t)

Arabian Heavy

%Ev ¼ (2.71 þ .045T)ln(t)

Arabian Light

%Ev ¼ (2.52 þ .037T)ln(t)

Arabian Light

%Ev ¼ (3.41 þ .045T)ln(t)

Arabian Light (2001)

%Ev ¼ (2.4 þ .045T)ln(t)

ASMB e Standard #5

%Ev ¼ (3.35 þ .045T)ln(t)

ASMB (offshore)

%Ev ¼ (2.2 þ .045T)ln(t)

Av Gas 80

%Ev ¼ (15.4 þ .045T)ln(t)

(Continued )

220

PART | IV

Behaviour of Oil in the Environment and Spill Modeling

TABLE 9.2 Equations for Predicting Evaporationdcont’d Oil Type

Equation

Avalon

%Ev ¼ (1.41 þ .045T)ln(t)

Avalon J-34

%Ev ¼ (1.58 þ .045T)ln(t)

Aviation Gasoline 100 LL

ln(%Ev) ¼ (0.5 þ .045T)ln(t)

Azeri e long term

%Ev ¼ (1.3 þ .045T)ln(t)

Azeri e short term

%Ev ¼ (0.09 þ .013T)/t

Barrow Island

%Ev ¼ (4.67 þ .045T)ln(t)

BCF-24

%Ev ¼ (1.08 þ .045T)ln(t)

Belridge Crude

%Ev ¼ (.01 þ .013T)/t

Bent Horn A-02

%Ev ¼ (3.19 þ .045T)ln(t)

Beta

%Ev ¼ (0.08 þ .013T)/t

Beta e long term

%Ev ¼ (0.29 þ .045T)ln(t)

Boscan

%Ev ¼ (0.15 þ .013T)/t

Brent

%Ev ¼ (3.39 þ .048T)ln(t)

Bunker C e Light (IFO~250)

%Ev ¼ (.0035 þ .0026T)/t

Bunker C e long term

%Ev ¼ (.21 þ .045T)ln(t)

Bunker C (2002)

%Ev ¼ (0.16 þ .013T)/t

Bunker C (short term)

%Ev ¼ (.35 þ .013T)/t

Bunker C Anchorage

%Ev ¼ (0.13 þ .013T)/t

Bunker C Anchorage (long term)

%Ev ¼ (0.31 þ .045T)ln(t)

California API 11

%Ev ¼ (0.13 þ .013T)/t

California API 15

%Ev ¼ (0.14 þ .013T)/t

Cano Limon

%Ev ¼ (1.71 þ .045T)ln(t)

Canola Oil

Litte

Carpenteria

%Ev ¼ (1.68 þ .045T)ln(t)

Cat Cracking Feed

%Ev ¼ (0.18 þ .013T)/t

Chavyo

%Ev ¼ (3.52 þ .045T)ln(t)

Cold Lake Bitumen

%Ev ¼ (0.16 þ .013T)/t

Combined Oil/Gas

%Ev ¼ (0.08 þ .013T)/t

Chapter | 9

221

Evaporation Modeling

TABLE 9.2 Equations for Predicting Evaporationdcont’d Oil Type

Equation

Compressor Lube Oil -New

%Ev ¼ (0.68 þ .045T)ln(t)

Cook Inlet e Granite Point

%Ev ¼ (4.54 þ .045T)ln(t)

Cook Inlet e Swanson River

%Ev ¼ (3.58 þ .045T)ln(t)

Cook Inlet New Batch

%Ev ¼ (3.1 þ .045T)ln(t)

Cook Inlet Trading Bay

%Ev ¼ (3.15 þ .045T)ln(t)

Corrosion Inhibitor Solvent

%Ev ¼ (0.02 þ .013T)/t

Crude Castor oil

Litte

Cusiana

%Ev ¼ (3.39 þ .045T)ln(t)

Delta West Block 97

%Ev ¼ (6.57 þ .045T)ln(t)

Diesel e Anchorage e long term

%Ev ¼ (4.54 þ .045T)ln(t)

Diesel e Anchorage e short term

%Ev ¼ (.51 þ .013T)/t

Diesel e long term

%Ev ¼ (5.8 þ .045T)ln(t)

Diesel Mobile1997

%Ev ¼ (0.03 þ .013T)/t

Diesel (2002)

%Ev ¼ (0.02 þ .013T)/t

Diesel (regular stock)

%Ev ¼ (.31 þ .018T)/t

Diesel fuel e Southern e long term

%Ev ¼ (2.18 þ .045T)ln(t)

Diesel fuel e Southern e short term

%Ev ¼ (0.02 þ .013T)/t

Diesel Fuel 2002

%Ev ¼ (5.91 þ .045T)ln(t)

Diesel Fuel 2002 short term

%Ev ¼ (0.39 þ .013T)/t

Diesel Mobile 1997 long term

%Ev ¼ (0.02 þ .013T)/t

Dos Cuadros

%Ev ¼ (1.88 þ .045T)ln(t)

Ekofisk

%Ev ¼ (4.92 þ .045T)ln(t)

Empire Crude

%Ev ¼ (2.21 þ .045T)ln(t)

Endicott

%Ev ¼ (0.9þ .045T)ln(t)

Esso Spartan EP-680 Industrial Oil

%Ev ¼ (0.66 þ .045T)ln(t)

Eugene Is. 224-condensate

%Ev ¼ (9.53 þ .045T)ln(t)

Eugene Island Block 32

%Ev ¼ (0.77þ .045T)ln(t)

(Continued )

222

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Behaviour of Oil in the Environment and Spill Modeling

TABLE 9.2 Equations for Predicting Evaporationdcont’d Oil Type

Equation

Eugene Island Block 43

%Ev ¼ (1.57 þ .045T)ln(t)

Evendell

%Ev ¼ (3.38 þ .045T)ln(t)

FCC Heavy cycle

%Ev ¼ (.17 þ .013T)/t

FCC Light

%Ev ¼ (0.17 þ .013T)/t

FCC Medium cycle

%Ev ¼ (.16 þ .013T)/t

FCC-VGO

%Ev ¼ (2.5 þ .013T)/t

Federated

%Ev ¼ (3.47 þ .045T)ln(t)

Federated (new-1999)

%Ev ¼ (3.45 þ .045T)ln(t)

Fuel Oil #5

%Ev ¼ (0.14 þ .013T)/t

Garden Banks 387

%Ev ¼ (1.84 þ .045T)ln(t)

Garden Banks 426

%Ev ¼ (3.44 þ .045T)ln(t)

Gasoline

%Ev ¼ (13.2 þ .21T)ln(t)

Genesis

%Ev ¼ (2.12 þ .045T)ln(t)

Green Canyon Block 109

%Ev ¼ (1.58 þ .045T)ln(t)

Green Canyon Block 184

%Ev ¼ (3.55 þ .045T)ln(t)

Green Canyon Block 200

%Ev ¼ (3.11 þ .045T)ln(t)

Green Canyon Block 65

%Ev ¼ (1.56þ .045T)ln(t)

Greenplus Hydraulic Oil

%Ev ¼ (0.68 þ .045T)ln(t)

Greenplus Hydraulic Oil

%Ev ¼ (0.68 þ .045T)ln(t)

Gulfaks

%Ev ¼ (2.29 þ .034T)ln(t)

Heavy Reformate

%Ev ¼ (0.17 þ .013T)/t

Hebron MD-4

%Ev ¼ (1.01 þ .045T)ln(t)

Heidrun

%Ev ¼ (1.95 þ .045T)ln(t)

Hibernia

%Ev ¼ (2.18 þ .045T)ln(t)

High Viscosity Fuel Oil

%Ev ¼ (0.12 þ .013T)/t

Hondo

%Ev ¼ (1.49 þ .045T)ln(t)

Hout

%Ev ¼ (2.29 þ .045T)ln(t)

IFO-180

%Ev ¼ (0.12 þ .013T)/t

Chapter | 9

223

Evaporation Modeling

TABLE 9.2 Equations for Predicting Evaporationdcont’d Oil Type

Equation

IFO-30 (Svalbard)

%Ev ¼ (0.04 þ .045T)ln(t)

IFO-300 (old Bunker C)

%Ev ¼ (0.15 þ .013T)/t

Iranian Heavy

%Ev ¼ (2.27 þ .045T)ln(t)

Issungnak

%Ev ¼ (1.56 þ .045T)ln(t)

Isthmus

%Ev ¼ (2.48 þ .045T)ln(t)

Jet 40 Fuel

%Ev ¼ (8.96 þ .045T)ln(t)

Jet A1

%Ev ¼ (.59 þ .013T)/t

Jet Fuel (Anch)

%Ev ¼ (7.19 þ .045T)ln(t)

Jet Fuel (Anch) short term

%Ev ¼ (1.06 þ .013T)/t

Komineft

%Ev ¼ (2.73 þ .045T)ln(t)

Lago

%Ev ¼ (1.13 þ .045T)ln(t)

Lago Treco

%Ev ¼ (1.12 þ .045T)ln(t)

Lucula

%Ev ¼ (2.17 þ .045T)ln(t)

Main Pass Block 306

%Ev ¼ (2.86 þ .045T)ln(t)

Main Pass Block 37

%Ev ¼ (3.04 þ .045T)ln(t)

Malongo

%Ev ¼ (1.67 þ .045T)ln(t)

Marinus Turbine Oil

%Ev ¼ (0.68 þ .045T)ln(t)

Marinus Valve Oil

%Ev ¼ (0.68 þ .045T)ln(t)

Mars TLP

%Ev ¼ (2.18 þ .045T)ln(t)

Maui

%Ev ¼ (0.14 þ .013T)/t

Maya

%Ev ¼ (1.38 þ .045T)ln(t)

Mayan crude

%Ev ¼ (1.45 þ .045T)ln(t)

Mississipi Canyon Block 807

%Ev ¼ (2.28 þ .045T)ln(t)

Mississippi Canyon Bk. 72

%Ev ¼ (2.15 þ .045T)ln(t)

Mississippi Canyon Block 194

%Ev ¼ (2.62 þ .045T)ln(t)

Mississippi Canyon Block 807

%Ev ¼ (2.05 þ .045T)ln(t)

Morpeth

%Ev ¼ (1.58 þ .013T)/t

(Continued )

224

PART | IV

Behaviour of Oil in the Environment and Spill Modeling

TABLE 9.2 Equations for Predicting Evaporationdcont’d Oil Type

Equation

Nektoralik

%Ev ¼ (0.62 þ .045T)ln(t)

Neptune Spar (Viosca Knoll 826)

%Ev ¼ (3.75 þ .045T)ln(t)

Nerlerk

%Ev ¼ (2.01 þ .045T)ln(t)

Ninian

%Ev ¼ (2.65 þ .045T)ln(t)

Norman Wells

%Ev ¼ (3.11 þ .045T)ln(t)

North Slope - Middle Pipeline

%Ev ¼ (2.64 þ .045T)ln(t)

North Slope - Northern Pipeline

%Ev ¼ (2.64 þ .045T)ln(t)

North Slope - Southern Pipeline

%Ev ¼ (2.47 þ .045T)ln(t)

Nugini

%Ev ¼ (1.64 þ .045T)ln(t)

Odoptu

%Ev ¼ (4.27 þ .045T)ln(t)

Olive Oil

Litte

Oriente

%Ev ¼ (1.32 þ .045T)ln(t)

Oriente

%Ev ¼ (1.57 þ .045T)ln(t)

Orimulsion 400 e dewatered

%Ev ¼ (3.6)ln(t) (at 15oC)

Orimulsion plus water

%Ev ¼ (3 þ .045T)ln(t)

Oseberg

%Ev ¼ (2.68 þ .045T)ln(t)

Panuke

%Ev ¼ (7.12 þ .045T)ln(t)

Petronius VK981A

%Ev ¼ (2.27 þ .013T)/t

Pitas Point

%Ev ¼ (7.04 þ .045T)ln(t)

Platform Gail (Sockeye)

%Ev ¼ (1.68 þ .045T)ln(t)

Platform Holly

%Ev ¼ (1.09 þ .045T)ln(t)

Platform Irene e long term

%Ev ¼ (0.74 þ .045T)ln(t)

Platform Irene-short term

%Ev ¼ (0.05 þ .013T)/t

Point Arguello-comingled

%Ev ¼ (1.43 þ .045T)ln(t)

Point Arguello Heavy

%Ev ¼ (0.94 þ .045T)ln(t)

Point Arguello Light

%Ev ¼ (2.44 þ .045T)ln(t)

Point Arguello Light e b

%Ev ¼ (2.3 þ .045T)ln(t)

Chapter | 9

225

Evaporation Modeling

TABLE 9.2 Equations for Predicting Evaporationdcont’d Oil Type

Equation

Polypropylene Tetramer

%Ev ¼ (0.25)(t) (at 15oC)

Port Hueneme

%Ev ¼ (0.3 þ .045T)ln(t)

Prudhoe Bay e old stock

%Ev ¼ (1.69 þ .045T)ln(t)

Prudhoe Bay (new stock)

%Ev ¼ (2.37 þ .045T)ln(t)

Prudhoe stock b

%Ev ¼ (1.4 þ .045T)ln(t)

Rangely

%Ev ¼ (1.89 þ .045T)ln(t)

Sahara Blend

%Ev ¼ (0.001 þ .013T)/t

Sahara Blend (long term)

%Ev ¼ (1.09 þ .045T)ln(t)

Sakalin

%Ev ¼ (4.16 þ .045T)ln(t)

Santa Clara

%Ev ¼ (1.63 þ .045T)ln(t)

Scotia Light

%Ev ¼ (6.87 þ .045T)ln(t)

Scotia Light

%Ev ¼ (6.92 þ .045T)ln(t)

Ship Shoal Block 239

%Ev ¼ (2.71 þ .045T)ln(t)

Ship Shoal Block 269

%Ev ¼ (3.37 þ .045T)ln(t)

Sockeye

%Ev ¼ (2.14 þ .045T)ln(t)

Sockeye (2001)

%Ev ¼ (1.52 þ .045T)ln(t)

Sockeye Comingled

%Ev ¼ (1.38 þ .045T)ln(t)

Sockeye Sour

%Ev ¼ (1.32 þ .045T)ln(t)

Sockeye Sweet

%Ev ¼ (2.39 þ .045T)ln(t)

South Louisiana

%Ev ¼ (2.39 þ .045T)ln(t)

South Louisiana (2001)

%Ev ¼ (2.74 þ .045T)ln(t)

South Pass Block 60

%Ev ¼ (2.91 þ .045T)ln(t)

South Pass Block 67

%Ev ¼ (2.17 þ .045T)ln(t)

South Pass Block 93

%Ev ¼ (1.5 þ .045T)ln(t)

South Timbalier Block 130

%Ev ¼ (2.77 þ .045T)ln(t)

Soybeam oil

Litte

Statfjord

%Ev ¼ (2.67 þ .06T)ln(t)

(Continued )

226

PART | IV

Behaviour of Oil in the Environment and Spill Modeling

TABLE 9.2 Equations for Predicting Evaporationdcont’d Oil Type

Equation

Sumatran Heavy

%Ev ¼ (0.11 þ .013T)/t

Sumatran Light

%Ev ¼ (0.96 þ .045T)ln(t)

Taching

%Ev ¼ (0.11 þ .013T)/t

Takula

%Ev ¼ (1.95 þ .045T)ln(t)

Tapis

%Ev ¼ (3.04 þ .045T)ln(t)

Tchatamba Crude

%Ev ¼ (3.8 þ .045T)ln(t)

Terra Nova

%Ev ¼ (1.36 þ .045T)ln(t)

Terresso 150

%Ev ¼ (0.68 þ .045T)ln(t)

Terresso 220

%Ev ¼ (0.66 þ .045T)ln(t)

Terresso 46 Industrial oil

%Ev ¼ (0.67 þ .045T)ln(t)

Thevenard Island

%Ev ¼ (5.74 þ .045T)ln(t)

Troll

%Ev ¼ (2.26 þ .045T)ln(t)

Turbine Oil STO 90

%Ev ¼ (0.68 þ .045T)ln(t)

Turbine Oil STO 120

%Ev ¼ (0.68 þ .045T)ln(t)

Udang

%Ev ¼ (0.14 þ .013T)/t

Udang (long term)

%Ev ¼ (0.06 þ .045T)ln(t)

Vasconia

%Ev ¼ (0.84 þ .045T)ln(t)

Viosca Knoll Block 826

%Ev ¼ (2.04 þ .045T)ln(t)

Viosca Knoll Block 990

%Ev ¼ (3.16 þ .045T)ln(t)

Voltesso 35

%Ev ¼ (0.18 þ .013T)/t

Waxy Light and Heavy

%Ev ¼ (1.52 þ .045T)ln(t)

West Delta Block 143

%Ev ¼ (2.18 þ .045T)ln(t)

West Delta Block 30 w/water

%Ev ¼ (0.04 þ .013T)/t

West Texas Intermediate

%Ev ¼ (2.77 þ .045T)ln(t)

West Texas Intermediate

%Ev ¼ (3.08 þ .045T)ln(t)

West Texas Sour

%Ev ¼ (2.57 þ .045T)ln(t)

White Rose

%Ev ¼ (1.44 þ .045T)ln(t)

Zaire

%Ev ¼ (1.36 þ .045T)ln(t)

Chapter | 9

227

Evaporation Modeling

70

Percent Evaporated

60

Diesel at 20 degrees

50

Diesel at 5 degrees

40

North Slope at 20 degrees

30 20

North Slope at 5 degrees

10 0

0

20

40

60

80

100

120

Time in Hours FIGURE 9.9 Comparison of evaporation curves for diesel and Alaska North Slope oils at two different temperatures.

Since the equations described above require only time and temperature (or at the very worst, the percentage of oil distilled at 180 C), it is relatively simple to apply these forms of equations. They can also be applied in models as increments where t, the time, becomes the total time and the previous evaporation is subtracted. For example, if one was modeling the evaporation of ASMB oil in the time step from 12 to 15 hours. The equation is (from Table 9.2): Percentage evaporation ¼ ð3:24 þ :054TÞ InðtÞ

(38)

Substituting for the temperature of 15 C and with a time of 12 hours or 720 minutes, we get a percentage of 26.65. With 18 hours, we get a percentage of 27.72 with a difference of 1.07%, the amount evaporated in the interval between 15 and 18 hours. The variation of evaporation is illustrated in Figure 9.9, which shows the evaporation of two oils, diesel fuel and North Slope Crude, at two temperatures.

9.3.7. A Simplified Means of Estimation This section will present another simple means to calculate the evaporation rate or parameter B in Equation (33).24 This method is not as accurate as the empirical equations such as are presented in Table 9.2. However, the exact evaporation data may not be available. The evaporation parameters (before temperature correction) are the simplest way to proceed. From the work described above, these parameters are given as parameter B above and e0.675 for oils that follow a logarithmic curve and e0.195 for oils that follow a square

228

PART | IV

Behaviour of Oil in the Environment and Spill Modeling

root curve.1 Data on a number of oil evaporation rates, gathered experimentally and basic properties of oil were collected, primarily from a previous paper, and also from literature sources.24-26 While it is known that density and viscosity are not necessarily good indicators of other oil properties, these may be the only data available at the site of a spill, at least until some time passes.21,26 The data show that the general trends of correlation of evaporation parameters with density and viscosity are shown, with significant variance that is only captured by specific oil experiments. The evaporation equation parameters were measured in specific experiments and often are the result of several repeat experiments. The data were correlated with the evaporation parameter in order to derive a simplified model using oil density and viscosity or Saturates, Aromatics, Resins and Asphaltenes (SARA) data.24 The curve-fitting exercise with the logarithmic evaporation data and density and viscosity data resulted in a best-fit simplest equation: Evap ¼ 15:4 14:5 density þ 2:58=viscosity

(39)

where Evap is the evaporation equation parameter and as in Equation (33) is Be0.675 to compensate for the temperature parameter as explained above. Similarly, the data for those oils that evaporate as a square root with time were correlated with density and viscosity. The equation for predicting those oils that evaporate as a square root of time is: Evap ¼ 2:3 1:47 density 0:073 InðviscosityÞ

(40)

where Evap is the evaporation equation parameter or B in Equation (33), but for the square root equivalent. Other data that may be available are SARA data. Statistical analysis shows that only the saturate content correlates, and this does not so well (r2 < 0.6). The addition of the other SARA data only marginally assists in the correlation. However, an equation for the prediction of the evaporation parameter using SARA data is: Evap ¼ 0:048 S þ 0:006 A 0:025 R 0:0003As þ 0:27

(41)

where Evap is the evaporation equation parameter or Be0.675 as in (33) above: S is the saturate content in percent, A is the aromatic content in percent, R is the resin content in percent, and As is the asphaltene content in percent. It is important to understand that such simplified predictions are much less accurate than empirical data such as those shown in Table 9.2.

Chapter | 9

229

Evaporation Modeling

9.4. COMPLEXITIES TO THE DIFFUSION-REGULATED MODEL 9.4.1. Thickness of the Oil Studies show that under diffusion regulation very thick slicks (much more than 5 mm) evaporate slower than other slicks due to the increased path length that volatile components must diffuse in a thicker slick. This can certainly be confused with air-boundary-layer regulation. Figure 9.10 shows the evaporation rate of various thicknesses of oil by the volume to thickness ratio. As can be seen, there is very little difference in this standard presentation. However, one can exaggerate the thickness by a volume factor and this is presented in Figure 9.11. This figure shows that there is a small difference beginning at about 6 mm to about 10 mm. The difference amounts to about 10% slower at 10 mm compared to the rate at about 2 mm. There are few data at very great thicknesses, so this phenomenon requires further investigation. In any case, actual slicks at sea would never reach this thickness. Figure 9.12 shows the concept of slower evaporation with increased path length, that is, increased oil thickness. As noted, there are insufficient data to fully evaluate this at this time, but there is not much motivation as slicks at sea do not reach this thickness.

9.4.2. The Bottle Effect Another confusing phenomenon encountered in trying to understand evaporation is the bottle effect. This effect is illustrated in Figure 9.13. If all the 6.0

Evaporation Rate (%)

5.5 5.0 4.5 4.0 3.5 3.0 2.5

5

10

15

20

Volume to Thickness Ratio FIGURE 9.10 The relationship of volume over thickness (area) to evaporation rate (given as the equation parameter) for one light crude oil. This shows that there is little relationship. Volume largely dictates the evaporation rate; however, for very thick slicks of thickness more than about 10 mm, the evaporation rate slows due to the increased diffusion distance through the liquid.

230 (Volume/Thickness)/Evaporation Rate

PART | IV

Behaviour of Oil in the Environment and Spill Modeling

4.5 4.0 3.5 3.0 2.5 2.0 0

2

4

6

8

10

Thickness mm FIGURE 9.11 A plot of the exaggerated evaporation rate (equation factor, raised by the volume factor) versus oil thickness for one light crude oil. The exaggeration is about an order of magnitude. This shows that evaporation slows somewhat after 6 mm and is slowed more at 10 mm. The effect is only about 10% at 10 mm. This is due to the increased diffusion length through the slick. Typical oil spills are much thinner than about 2 mm, and thus this effect would not be important.

evaporating oil mass is not exposed, such as in a bottle, more oil vapors than can readily diffuse through the air layer at the bottle mouth may yield a partial air-boundary-layer regulation effect. This air-boundary-layer regulatory effect may end when the evaporation of the oil mass lowers past the rate at which the vapors can readily diffuse through the opening. Such effects could occur in reality in situations such as oil under ice, partially exposed to air or when a thick skin forms over parts of the oil, blocking evaporation. During a recent experiment in the Arctic, it was noted that the evaporation rate varied with the amount of exposure to the air.27 This phenomenon was probably caused by a combination of the bottle effect and partially as a result of the increased thickness in the more confined ice situations.

9.4.3. Skinning Several workers have noted that some crude oil and petroleum products form “skins” on their surfaces.28,29 These are largely due to the accumulation of compounds such as resins on the surface, some possibly created by photo oxidation. These can retard the evaporation of the compounds to a great extent. Figure 9.14 shows the results of some evaporation experiments carried out on two oils, Terra Nova crude and Statfjord crude. Simultaneous experiments were carried out on the oils, one of which was stirred, the other not.1 As can be seen in Figure 9.14, the stirred oils evaporated to a greater extent than the unstirred oils. The relevance of this effect at sea may not be as great as wind and waves

Chapter | 9

231

Evaporation Modeling Evaporation limited by diffusion rate through liquid and through surface layer - therefore diffusion regulated Air

Liquid surface

Liquid

Air Liquid surface

In very thick layers, evaporation limited by diffusion rate through liquid but after about 20 to 40 mm, diffusion slows significantly

Liquid

FIGURE 9.12 An illustration of the effect of very great thicknesses of oil. The evaporation rate is slower because of the longer diffusion difference. The difference becomes measurable after about 6 to 10 mm of oil thickness. This is much greater than typical slicks at sea.

may accomplish; the stirring and skin formation may thus be prevented or retarded. It should be noted that during these experiments, one could see the skin formation on the unstirred oil, and this skin developed more toward the end of the 200-hour experiments. Grose used the Mackay and Matsugu equations with some modification and also to account for the skinning factor:30 =ðRKÞ Pi Sk Mi L ¼ C U 0:78 D0:11 (42) o

232

PART | IV

Behaviour of Oil in the Environment and Spill Modeling

More vapor than can easily diffuse through opening - therefore partially air-boundary layer regulated Opening

Air Air

Liquid surface Evaporation limited by diffusion rate through liquid and through surface layer Liquid

FIGURE 9.13 An illustration of the bottle effect. If all the evaporating oil mass is not exposed, more oil vapors than can readily diffuse through the air layer at the bottle mouth may yield a partial air-boundary-layer regulation effect. This regulatory effect may end when the evaporation of the oil mass lowers past the rate at which the vapors can readily diffuse through the opening.

stirred

% Evaporated

40

Statfjord crude

not stirred

stirred

30 not stirred Terra Nova crude

20 Terra Nova Terra Nova - stirred Statfjord Statfjord - stirred

10

0 0

50

100

150

200

Time (Hours) FIGURE 9.14 Results of an experiment to show the effect of “skinning” on oil evaporation. The upper curve in each case is the evaporation of the oil shown with stirring, thus preventing or retarding skin formation. The lower curve is the evaporation without stirring. The effect of skinning for these two oils amounts to several percent differential in evaporation over 200 hours. At sea, wind and waves may mix the oil sufficiently to minimize the skinning effect.

Chapter | 9

Evaporation Modeling

233

where L is the mass of oil evaporated with time (kg/s), C is the environmental transfer constant, U is the wind speed at the surface (m/hr), Do is the diameter of the oiled area (m), K is the oil temperature in Kelvin, Pi is the vapor pressure of the particular component, Sk is the skin factor, and Mi is the molecular-weight equivalent of the particular oil fraction. The skin factor, Sk, ranges from 0.1 to 8 and accounts for the effect of skinning (the formation of a semipermeable surface layer). Yang and Wang suggested a value of Sk ¼ 0.2 after the density of their test oils had increased by 0.78%.13 A value of 1.0 was used in testing the model. In addition, the massloss rate depends on the vapor pressure, Pi, and the molecular weight, MWi, of each fraction. C is a dimensionless environmental transfer constant whose magnitude depends on the units used. The value used for C (0.00024) includes the constant 0.015 after Mackay and Matsugu.8

9.4.4. Rises from the 0-Wind Values Experimentation shows that studies of oil evaporation at absolutely no turbulence or air flow show a slight decrease in evaporation rate from those experiments carried out with slight air movement such as are found in an ordinary room.19 This is due to the slight stirring in the oil mass which increases the diffusion rate somewhat. Tests of this phenomena show that further increases in evaporation rate do not occur with increased air movement or turbulence, thus confirming that this is a phenomenon only at 0-wind or turbulence conditions. These are seen only during capped vessel experiments, and the “jump” in the evaporation rate is seen when the restriction is removed.

9.5. USE OF EVAPORATION EQUATIONS IN SPILL MODELS Evaporation equations are the prime physical change equations used in spill models. This is because evaporation is usually the most significant change that occurs in an oil’s composition. Many models after 1984 have used the Stiver and Mackay approach.9 The equations developed by Mackay and co workers can be implemented in a variety of ways. Often the difference in models is the manner in which the models are applied. Mackay and co workers developed an extensive oil spill model incorporating a number of process equations including evaporation.31 The earlier work of Leinonen and Mackay was used with the modification proposed by Yang and Wang.13,32 The process includes dividing the oil into a number of different fractions and analyzing each fraction for evaporation loss. The mass-transfer function used is the familiar one proposed by Mackay and Matsugu.8 These equations can be solved to obtain V and ci as functions of time. Solutions were developed by assuming a five-component crude oil that spreads on the water surface according to the correlations for the area.

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Payne and co workers developed an oil spill model using the pseudocomponent approach.33-35 Given the boiling point (1 atm) and API gravity of each cut (or pseudocomponent), the vapor pressure of the cut as a function of temperature was calculated. First, the molecular weight and critical temperature of the cut were calculated according to the following correlation: y ¼ C1 þ C2 X1 þ C3 X2 þ C4 X1 X2 þ C5 X12 þ C6 X22

(43)

where y is the vapor pressure of the cut, X 1 is the boiling point (oF) at one atmosphere, X2 is the API gravity, and C16 are constants empirically determined. Similarly, the critical temperature was calculated from the same equation form using the calculated and empirical constant values. Next, the equivalent paraffin carbon number, Nc, was calculated according to: Nc ¼ ðM 2Þ=14

(44)

where M is the molecular weight assigned to the particular cut. The critical volume, Vc, was then calculated according to: Vc ¼ ð1:88 þ 2:44Nc Þ=0:044

(45)

and the critical pressure, Pc, was calculated from: Pc ¼

20:8 Tc þ P0c ðVc 8Þ

(46)

where Tc is the critical temperature and Pc0 correction factor for critical pressure. The factor Pc0 was set to 10 to correct the critical pressure correlation from a strictly paraffinic mixture to a naphtha-aromatic-paraffin mixture. Next the parameter, b, was calculated according to:

where:

b ¼ b0 0:02

(47)

b0 ¼ C1 þ C2 Nc þ C3 Nc2 þ C4 Nc3

(48)

and the values of the constants C 1 to C 4 were calculated and tables of the values are available.19 A final parameter designated as A is then calculated according to: A ¼

Tr b ½log10 ðPrb Þ þ expð20ðTrb bÞ2 Þ Tr b 1

(49)

where A is an intermediary parameter, Trb is the reduced temperature at the normal boiling point, Prb is the reduced pressure at the normal boiling point, and b is an intermediary parameter determined in (48) above.

Chapter | 9

Evaporation Modeling

235

The vapor pressure equation that can be used down to 10 mm Hg can be expressed in terms of A and b as: log10 Pr ¼

Að1 Tr Þ exp½20ðTr bÞ2 Tr

(50)

where Pr is the reduced pressure and Tr is the reduced temperature. A, b, Tc and Pc were determined from the normal boiling point and API gravity of the cut. The temperature at which the vapor pressure is 10 mm Hg was obtained by the root-finding algorithm of Newton-Raphson.34 Below 10 mm Hg, the vapor pressure between two temperatures, Tr1 and Tr2, was calculated according to the Clausius-Clapeyron equation as follows: Z Tr 0:38 2 ð1 T Þ P2 lo r In ¼ dTr (51) P1 RTc Tr1 Tr2 where P1 is the vapor pressure at temperature 1, P2 is the vapor pressure at temperature 2, l0 is the heat of vaporization at 0 K, and Tc is the critical temperature. This was based on the fact that the ratio of the heat of vaporization, l, to (1 e T )0.38 is a constant at any temperature. The latent heat of vaporization was calculated from the slope of the natural log of the vapor pressure equation with respect to the temperature where the vapor pressure is 10 mm Hg. Thus, in the above equation, P2 is the 10 mm Hg vapor pressure at the temperature, Tr, previously determined. Variations of the Payne model were used in older oil spill models. Several models had used the pseudocomponent approach but with the Mackay evaporation method.36 Currently, there are a number of oil spill models using the empirical equations of Fingas.25

9.6. COMPARISON OF MODEL APPROACHES The comparison of air-boundary-layer models with the empirical equations lead to some interesting conclusions on their applicability. Figure 9.15 shows a comparison of the prediction of evaporation of diesel fuel using an air-layerboundary model and an empirical curve. The 0-wind diesel evaporation calculated using an air-layer-boundary model comes closest to the empirical curve. However, prediction is of the wrong curvature. The prediction of diesel evaporation using the wind levels shown results in prediction errors as great as 100% over about 200 hours. Figure 9.16 shows a comparison of the evaporation of Bunker C using two air-layer-boundary models and an empirical curve. The 0-wind evaporation air-boundary-layer prediction comes closest to the empirical curve. As most comparisons show, the evaporation rate up to about 10 hours is similar to the empirical curve. The prediction of Bunker C evaporation

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Percent Evaporated

80

Behaviour of Oil in the Environment and Spill Modeling

Traditional with varying winds

60 Traditional no wind

40 Trad. no wind Trad. 10 m/s Trad. 20 m/s Empirical curve Trad. B. no wind Trad B. 10 m/s Trad. B. 20 m/s

Empirical curve

20

0 0

20

40

60

80

100

120

140

160

Time in Days FIGURE 9.15 A comparison of the evaporation of diesel fuel using an air-layer-boundary model and an empirical curve. The 0-wind diesel evaporation calculated using an air-layer-boundary model comes closest to the empirical curve, but it is of the wrong curvature. The prediction of diesel evaporation using the wind levels shows errors as great as 100% over about 200 hours.

using the wind levels shown results in prediction errors as great as 400% over about 200 hours. These high values of Bunker C evaporation as predicted by air-boundary-layer models with wind conditions are completely impossible, as shown by extensive experimentation and field measurements.19 Figure 9.17 shows a comparison of the evaporation of Prudhoe Bay crude using two airlayer-boundary models with an empirical curve. The 0-wind evaporation prediction using air-boundary-layer methods comes closest to the empirical curve. The evaporation rate calculated by most means up to about 10 hours is similar to the empirical curve. The prediction of Prudhoe Bay evaporation using the wind levels shown results in prediction errors as great as 100% over about 200 hours. These high values of Prudhoe Bay evaporation as predicted by air-boundary-layer models with wind conditions are not realistic. The importance of evaporation modeling can be shown in Figures 9.18 and 9.19. The fate of oil spills is often dictated by evaporation. Accurate modeling of evaporation then becomes of key importance to the useful prediction of oil fate. Thus there are three major errors resulting from the use of air-boundarylayer models. First and most important is that they cannot accurately deal with long-term evaporation; second, the wind factor results in unrealistic values; and finally, they have not been adjusted for the different curvature for diesel-like evaporation. Some modelers have adjusted their models using air-boundary-layer models to avoid very high values at long evaporation times

Chapter | 9

Trad. 0 m/s Trad. 10 m/s Trad. 20 m/s Trad. 40 m/s Trad. 80 m/s Empirical Trad. B 0 m/s Trad. B 10 m/s Trad. B 20 m/s Trad. B 35 m/s

20

Percent Evaporated

237

Evaporation Modeling

15

10 Empirical curve

5

Empirical curve

0 0

20

40

60 80 Time (Hours)

100

120

140

FIGURE 9.16 A comparison of the evaporation of Bunker C using two air-layer-boundary models and an empirical curve. The 0-wind evaporation prediction comes closest to the empirical curve. The prediction of Bunker C evaporation using the wind levels shown results in prediction errors as great as 400% over about 200 hours. These high values of Bunker C evaporation as predicted by air-boundary-layer models with wind conditions are completely impossible. As most comparisons show, the evaporation rate calculated by most means up to about 10 hours is similar to the empirical curve.

by setting a maximum evaporation value. This does avoid very unrealistic high values after a point in time, but does so artificially. Most models of any type will require that one sets a maximum rate to avoid overprediction or values over 100%, for example. This can be best illustrated using a long-term example. A spill in northern Alberta of Pembina oil was sampled 30 years after its spill. Analysis shows that this was weathered to the extent of 58%.37,38 Figure 9.20 shows the comparison of the actual value, the empirical projection, and the air-boundary-layer predicted value. This shows that the air-boundary-predicted value overshoots the estimate by over 60%, despite using only two low wind values of 2 and 7 m/s. Use of higher wind values increases the evaporation to well over 100%.

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Percent Evaporated

40

30

Empirical curve

20

Trad. 0 m/s Trad. 10 m/s Trad. 20 m/s Empirical Curve Trad. B. 0 m/s Trad. B. 10 m/s Trad. B. 20 m/s

Empirical curve

10

0 0

50

100

150

200

250

300

Time (Hours) FIGURE 9.17 A comparison of the evaporation of Prudhoe Bay crude using two air-layerboundary models and an empirical curve. The 0-wind evaporation prediction using air-boundarylayer methods comes closest to the empirical curve. The evaporation rate up to about 10 hours is similar to the empirical curve. The prediction of Prudhoe Bay evaporation using the wind levels shown results in prediction errors as great as 100% over about 200 hours. These high values of Prudhoe Bay evaporation as predicted by air-boundary-layer models with wind conditions are not realistic.

FIGURE 9.18 Highly-evaporated oils can form tar balls that typically strand on beaches. This shows a typical-size tar ball stranded along with other debris.

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Evaporation Modeling

FIGURE 9.19 This shows highly evaporated Bunker C oil, which has just arrived on a beach. In this case, evaporation is a very important factor dictating how an oil spill can be cleaned up.

100 Air-boundary-layer model 7 m/s wind

Percent Evaporated

80

Air-boundary-layer model 2 m/s wind

60 Empirical curve

Analysis value at 30 years

40

Empircal curve Actual analytical Air-Bound., 2 m.s wind Air-Bound. 7 m.s wind

20

0

1 year

0.0

5.0e+4

6 years

1.0e+5

15 years

1.5e+5

2.0e+5

30 years

2.5e+5

Time - Hours FIGURE 9.20 A comparison of the evaporation of Pembina crude using an air-layer-boundary model, an actual analysis after 30 years, and an empirical curve. The evaporation rate up to about 100 hours is similar to the empirical curve. The prediction of long-term evaporation using even small wind levels shown results in prediction errors as great as 60% over about 10 years. These high values of evaporation as predicted by air-boundary-layer models with wind conditions are not realistic.

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9.7. SUMMARY A review of the physics of oil evaporation shows that oil evaporation is not strictly air-boundary-layer regulated. The results of the following experimental series have shown the lack of boundary-layer regulation. 1. A study of the evaporation rate of several oils with increasing wind speed shows that the evaporation rate does not change past the 0-level wind. Water, known to be boundary-layer regulated, does show the predicted increase with wind speed, U (Ux, where x varies from 0.5 to 0.78, depending on the turbulence level). 2. Increasing area does not change the oil evaporation rate. This is directly contrary to the prediction resulting from boundary-layer regulation. 3. The volume or mass of oil evaporating correlates with the evaporation rate. This is a strong indicator of the lack of boundary-layer regulation because with water, volume (rather than area) and rate do not correlate. 4. Evaporation of pure hydrocarbons with and without wind (turbulence) shows that compounds larger than nonane and decane are not boundarylayer-regulated. Most oil and hydrocarbon products consist of compounds larger than these two and thus would not be expected to be boundarylayer-regulated. The fact that oil evaporation is not strictly boundary-layer-regulated implies that a simplistic evaporation equation will suffice to describe the process. The following factors do not require consideration: wind velocity, turbulence level, area, thickness, and scale size. The factors important to evaporation include time and temperature. A comparison of the various models used for oil spill evaporation shows that air-boundary-layer models result in erroneous predictions. There are three issues: air-boundary-layer models cannot accurately deal with long-term evaporation; second, the wind factor results in unrealistic values; and finally, they have not been adjusted for the different curvature for diesel-like evaporation. Modelers have made some effort to adjust air-boundary-layer models to be more realistic for longer-term evaporation, but these may be artificial and result in other errors such as underestimation for long-term prediction. A diffusion-regulated model has been presented along with many empirically developed equations for many oils. The equations are found to be of the form: Percentage evaporated ¼ ½B þ 0:045ðT 15Þ InðtÞ

(52)

where B is the equation parameter at 15 C, T is temperature in degrees Celsius, and t is the time in minutes. It is also noted that with diesel fuel and similar oils, the curve is different and follows a generic curve, such as: pﬃ Percentage evaporated ¼ ½B þ 0:01ðT 15Þ t (53)

Chapter | 9

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Evaporation Modeling

The most accurate predictions are carried out using the empirical equations as noted in Table 9.2. If these are not available, the parameters can be estimated using distillation data, such as: For oils that follow a logarithmic equation: Percentage evaporated ¼ ½:165ð%DÞ þ :045ðT 15ÞlnðtÞ

(54)

For oils that follow a square root equation:

pﬃ Percentage evaporated ¼ ½:0254ð%DÞ þ :01ðT 15Þ t

(55)

180 C,

where D is the percentage distilled at T is the temperature in Celcius, and t is the time in minutes. Equations are also given that allow estimation of evaporation from density, viscosity, or SARA data; however, these are much less accurate than the direct empirically-derived equations.

REFERENCES 1. Fingas MF. The Evaporation of Oil Spills: Development and Implementation of New Prediction Methodology. IOSC 1999;281. 2. Fingas MF. A Literature Review of the Physics and Predictive Modelling of Oil Spill Evaporation. J Haz Mat 1995;157. 3. Jones FE. Evaporation of Water. Chelsea, Michigan: Lewis Publishers; 1992. 4. Brutsaert W. Evaporation into the Atmosphere. Dordrecht, Holland: Reidel Publishing Company; 1982. 5. Fingas MF. Studies on the Evaporation of Crude Oil and Petroleum Products: I. The Relationship between Evaporation Rate and Time. J Haz Mat 1997;227. 6. Monteith JL, Unsworth MH. Principles of Environmental Physics. London: Hodder and Stoughton; 1990. 7. Sutton OG. Wind Structure and Evaporation in a Turbulent Atmosphere. P Royal Society of London 1934;701. 8. Mackay D, Matsugu RS. Evaporation Rates of Liquid Hydrocarbon Spills on Land and Water. Can J Chem Eng 1973;434. 9. Stiver W, Mackay D. Evaporation Rate of Spills of Hydrocarbons and Petroleum Mixtures. Env Sci Tech 1984;834. 10. Blokker PC. Spreading and Evaporation of Petroleum Products on Water. Proceedings of the Fourth International Harbour Conference, 1964;911. 11. Goodwin SR, Mackay D, Shiu WY. Characterization of the Evaporation Rates of Complex Hydrocarbon Mixtures under Environmental Conditions. Can J Chem Eng 1976;290. 12. Butler JN. Transfer of Petroleum Residues from Sea to Air: Evaporative Weathering. In: Windom HL, Duce RA, editors. Marine Pollutant Transfer, 201. Toronto: Lexington Books; 1976. 13. Yang WC, Wang H. Modelling of Oil Evaporation in Aqueous Environments. Water Res 1977;879. 14. Brighton PWM. Further Verification of a Theory for Mass and Heat Transfer from Evaporating Pools. J Haz Mat 1990;215. 15. Brighton PWM. Evaporation from a Plane Liquid Surface into a Turbulent Boundary Layer. J Fluid Mech 1995;323. 16. Stiver W. Weathering Properties of Crude Oils wWhen Spilled on Water, Master of Applied Science Thesis, Department of Chemical Engineering and Applied Chemistry. Toronto: University of Toronto; 1984:4,110,132.

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17. Brown HM, Nicholson P. The Physical-Chemical Properties of Bitumen. AMOP 1991;107. 18. Bobra M. A Study of the Evaporation of Petroleum Oils, Manuscript Report Number EE-135. Ottawa, ON: Environment Canada, 1992. 19. Fingas MF. Studies on the Evaporation of Crude Oil and Petroleum Products: II. Boundary Layer Regulation. J Haz Mat 1998;41. 20. Ullmann Encyclopedia. Hamburg: Ullmann Publishing; 1989-2005. 21. Jokuty PS, Whiticar S, Wang Z, Fingas MF, Fieldhouse B, et al. Properties of Crude Oils and Oil Products. Environment Canada Manuscript Report EE-165, Ottawa, ON 1999. 22. Fingas MF. The Evaporation of Oil Spills: Prediction of Equations Using Distillation Data. Spill Sci Tech Bull 1996;191. 23. Fingas MF. The Evaporation of Oil Spills: Prediction of Equations Using Distillation Data. AMOP 1997;1. 24. Fingas MF. Estimation of Oil Spill Behaviour Parameters from Readily-Available Oil Properties. AMOP 2007;1. 25. Fingas MF. Modeling Evaporation Using Models That Are Not Boundary-Layer Regulated. J Haz Mat 2004;27. 26. Oil Catalogue, http://www.etc-cte.ec.gc.ca/databases/spills/oil_prop_e.html, accesssed April, 15 2010. 27. Brandvik PJ, Faksness L-G. Weathering Processes in Arctic Oil Spills: Meso-Scale Experiments with Different Ice Conditions. Cold Reg Sci Technol 2009;160. 28. Bobra M, Tennyson EJ. Photooxidation of Petroleum. AMOP 1989;129. 29. Garrett RM, Pickering IJ, Haith CE, Prince RC. Photooxidation of Crude Oils. Environ Sci Technol 1998;3719. 30. Grose PL. A Preliminary Model to Predict the Thickness Distribution of Spilled Oil, Proceedings of a Workshop on The Physical Behaviour of Oil in The Marine Environment, Princeton University, 1979. 31. Mackay D, Buist I, Mascarenhas R, Paterson S. Oil Spill Processes and Models, EE-8. Ottawa, ON: Environment Canada, 1980. 32. Leinonen PJ, Mackay D. A Mathematical Model of Evaporation and Dissolution from Oil Spills on Ice, Land, Water, and Under Ice, Proceedings Tenth Canadian Symposium 1975: Water Pollution Research Canada, 1975;132. 33. Payne JR, McNabb Jr GD, Hachmeister LE, Kirstein BE, Clayton Jr JR, et al. Development of a Predictive Model for the Weathering of Oil in the Presence of Sea Ice, Outer Continental Shelf Environmental Assessment Program, vol. 59, NOAA, 1988. 34. Payne JR, Kirstein BE, McNabb Jr GD, Lambach JL, Redding R, et al. Multivariate Analysis of Petroleum Weathering in the Marine EnvironmentdSubarctic, U.S. Department of Commerce, NOAA, OCSEAP, vol. 21, 1984. 35. Payne JR, McNabb Jr GD. Weathering of Petroleum in the Marine Environment, Marine Technology Society Journal, Washington, DC, 1984;24:18. 36. Buist I, Belore R, Guarino A, Hackenberg D, Dickins D, Wang Z. Empirical Weathering Properties of Oil in Ice and Snow. AMOP 2009;56. 37. Wang Z, Fingas M, Yang C, Hollebone B, Peng X. Biomarker Fingerprinting: Applications and Limitations for Source Identification and Correlation of Oils and Petroleum Products. AMOP 2004;103. 38. McIntyre CP, Harvey PM, Ferguson S, Wressnig AM, Snape I, George SC. Determining the Extent of Weathering of Spilled Fuel in Contaminated Soil Using the Diastereomers of Pristane and Phytane. Org Geochem 2007;2131.

Chapter 10

Models for Water-in-Oil Emulsion Formation Merv Fingas

Chapter Outline 10.1. Introduction 10.2. Early Modeling of Emulsification 10.3. First Two Model Developments 10.4. New Model Development

243 249 251

10.5. Development of an Emulsion Kinetics Estimator 10.6. Discussion 10.7. Conclusions

260

260 269

253

10.1. INTRODUCTION Water-in-oil emulsions sometimes form after oil or petroleum products are spilled. These emulsions, often called chocolate mousse or mousse by oil spill workers, make the cleanup of oil spills very difficult.1 When water-in-oil emulsions form, the physical properties and characteristics of oil spills change dramatically. For example, stable emulsions contain from 60 to 90% water, thus expanding the spilled material from 2 to 5 times the original volume. Most significantly, the viscosity of the oil typically changes from a few hundred mPa.s to about 100,000 mPa.s, an increase of 500 or more. A liquid product is changed into a heavy, semisolid material. These emulsions are difficult to recover with conventional spill recovery equipment. Recently, Fingas and Fieldhouse published a paper on emulsion formation as it relates to spilled oil.2 They found that four clearly defined water-in-oil types are formed by oil and petroleum products when mixed with water. This was shown by water resolution over time, by a number of rheological measurements, and by the water-in-oil product’s visual appearance, both on the day of formation and one week later. Some emulsions were observed for over a year, with the same results. The types are named stable water-in-oil Oil Spill Science and Technology. DOI: 10.1016/B978-1-85617-943-0.10010-3 Copyright Ó 2011 Elsevier Inc. All rights reserved.

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emulsions, mesostable water-in-oil emulsions, entrained, and unstable waterin-oil emulsions. The differences among the four types are quite large and are based on at least two water-content measurements and five rheological measurements. More than 300 oils or petroleum products were studied. Fingas and Fieldhouse carried out tests of several indices of stability, a single value that could provide good discrimination between water-in-oil types even on day one.2 It was found that all of these indices could be used to some extent to differentiate the four water-in-oil types. One index of stability was the ratio of the complex modulus of the product on the first day divided by the starting oil viscosity. This index was named stability A. Stability B was the elastic modulus on the first day divided by the starting oil viscosity. Stability C was a combination of these two indices, the log of the cross product of stability A and B. Another index, the viscosity increase, the ratio of the oil viscosity after mixing divided by the starting oil viscosity, was also evaluated. Another test, first day water times stability A, was a simple composite of the first day water content times stability A. The last two parameters evaluated were the tan delta on the day of formation and the change in tan delta over one week. The index called stability C proved to be the index that best differentiated all four water-in-oil types. Scientists reported that asphaltenes were a major factor in water-in-oil emulsions more than 40 years ago.3 The specific roles of asphaltenes in emulsions have not been defined until recently. The basics of water-in-oil emulsification are now understood to a much better degree.2,4-6 The basic principle is that water-inoil emulsions are stabilized by the formation of high-strength viscoelastic asphaltene films around water droplets in oil. Resins also form emulsions, but they do not form stable emulsions, and they actually aid in asphaltene emulsion stability by acting as asphaltene solvents and by providing temporary stability during the time of the slow asphaltene migration. In all, a wide spectrum of researchers have found out that oil composition is a key factor to water-in-oil emulsion formation and that several key factors include the asphaltene, resin, and saturate contents as well as the density and viscosity of the starting oil. McLean and Kilpatrick studied asphaltene aggregation in model emulsions made from heptane and toluene.7 The asphaltenes were extracted using heptane and the resins using open-column silica columns. Although some emulsions could be generated using resins, they were much less stable than those generated by asphaltenes. It was also found that the concentration of asphaltenes and the availability of solvating resins were important. McLean and Kilpatrick put forward the thesis that asphaltenes were most effective in stabilizing emulsions when they are near the point of incipient precipitation.8 It was noted that there are specific resineasphaltene interactions because differing combinations yielded different results in the model emulsions. The most effective emulsion resin and asphaltene stabilizers were the most polar and the most condensed. McLean and Kilpatrick concluded that the most significant factor in emulsion formation is the solubility state of asphaltenes.

Chapter | 10 Models for Water-in-Oil Emulsion Formation

245

Subsequently, several workers reviewed emulsions and concluded that the asphaltene content is the single most important factor in the formation of emulsions.9-11 Even in the absence of any other possibly-synergistic compounds such as resins, asphaltenes were found to be capable of forming rigid, cross-linked, elastic films that are the primary agents in stabilizing waterin-crude oil emulsions. The exact conformations by which asphaltenes organize at oilewater interfaces and the corresponding intermolecular interactions have not been elucidated. McLean and colleagues suggest that the intermolecular interactions must be either p-bonds between fused aromatic sheets; H-bonds mediated by carboxyl, pyrrolic, and sulfoxide functional groups; or electron donoreacceptor interactions mediated by porphyrin rings, heavy metals, or heteroatomic functional groups.9 Workers studying only crude oil emulsions concluded that water-in-oil emulsions are exclusively stabilized by asphaltenes.12 Even though the emulsions contain inorganic solids, waxes, and other organic solids, the main stabilization comes from asphaltenes. Other workers have noted that solid particles, such as clays, when present, can stabilize or contribute to the stabilization of emulsions.13 This is true of emulsions formed by clay-containing bitumens. These clay-stabilized emulsions may have differences from the crude oil and petroleum product emulsions noted in this subchapter. Asphaltenes are defined by their precipitation from oil in pentane, hexane, or heptane. The specific structure of asphaltenes is unknown; however, the molecular weight averages about 750, and there is a planar aromatic structure surrounded by alkane groups, some with heteroatoms, S, N, and O.14 Studies of the time dependency of film strength by viscosity measurements showed that the complex modulus increased about twofold between 2 and 4 hours. This indicates an increased film strength, probably due to asphaltene aggregation and cross-linking. The mechanism by which emulsions form begins when asphaltenes migrate to the oilewater interface, a process that is regulated by the diffusion of the soluble asphaltenes. At the interface, the asphaltenes selfassemble to produce an elastic, rigid, stable film.5,15,16 The absorption of asphaltenes at the watereoil interface proceeds for a long time and may still proceed after a year.17 This implies that the absorption at the interface lowers the net energy of the system and thus is favored thermodynamically. The bulk concentration of asphaltenes is important and drives the amount that is absorbed at the interface. Asphaltenes have their greatest tendency to absorb and make the strongest interfacial film at their limit of solubility. Several workers noted that there were differences in the stability of emulsions, depending on the fractions of asphaltenes taken and also by the amount of asphaltene aggregation present.18,19 Asphaltene aggregation may be an important topic in emulsion stability. Asphaltenes may self-associate in a manner that is oligimerization rather than simple stacking. Several researchers studied the role of resins in water-in-oil formation.2,12 They noted that the main role appears to be solvation of the asphaltenes in the

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oil solution. They conducted experiments showing that the addition of resins at ratios of 2:1 (resins:asphaltenes) could increase the stability of the water-in-oil emulsions by as much as twice. Others have noted that resins and asphaltenes are somehow tied together in emulsion stability. Some researchers noted that with increasing asphaltene:resin ratio, the emulsions in wellheads were more stable.20 The interfacial properties of asphaltenes in several Norwegian offshore crude oils were studied.21 Asphaltenes were shown to be the agent responsible for stabilizing the crude oils tested; however, the resins were also noted as being important. Some effect from naphthenic acids was also observed.22 Sjo¨blom and coworkers noted that many of the stability differences in emulsions can be explained by the interactions between asphaltenes and resins.23 They stated that asphaltenes are believed to be suspended as colloids in the oil with stabilization by resins. Each particle is believed to consist of one or more sheets of asphaltene monomers and absorbed resins to stabilize the suspension. Under certain conditions, the resins can desorb from the asphaltenes, leading to increased asphaltene aggregation and precipitation of the larger asphaltene aggregates. Several workers then noted that there is significant interplay between asphaltenes and resins and that resins solvate the asphaltenes.24,25 The availability of methodologies to study emulsions is very important. In the past 15 years, dielectric methods and rheological methods and many other methods have been exploited to study formation mechanisms and stability of emulsions.5,15 Standard chemical techniques, including nuclear magnetic resonance (NMR), chemical analysis techniques, near-infrared spectroscopy (NIR), microscopy, interfacial pressure, and interfacial tension, are also being applied to emulsions. These techniques have largely confirmed findings noted in the dielectric and rheological mechanisms. The use of high-pressure NIR has been used by one group to study asphaltene aggregation of live crude oils.23,26,27 Further NIR information on the amount of asphaltenes and resins was tied to the emulsion stability.27 Many of the measurements can now be intercorrelated. Most researchers studied the stability of emulsions by measuring the amount of water resolved with time.10,12,28-32 This certainly is the absolute means. Some researchers also subjected the emulsions to centrifugation to assess stability. Dielectric spectroscopy has been used to study emulsions. The electrical permittivity of the emulsion can be used to characterize an emulsion and assign a stability.5,22,25,33,34 The Sjo¨blom group has measured the dielectric spectra using the time domain spectroscopy (TDS) technique. A sample is placed at the end of a coaxial line to measure total reflection. Reflected pulses are observed in time windows of 20 ns and Fourier transformed in the frequency range from 50 MHz to 2 GHz. Many studies on the rheology of emulsions have been performed.35-37 Emulsions stabilized by surfactant films (such as resins and asphaltenes)

Chapter | 10 Models for Water-in-Oil Emulsion Formation

247

behave like hard-sphere dispersions and display viscoelastic behavior. Relaxation time can be determined for the system, which increases with the volume fraction of the discontinuous phase. It has been noted that the emulsion stability is highly dependent on the rheological properties of the water/oil interface and that a high elasticity will increase the level of stability.6 Several workers in the field have used the interface forces on single droplets to study emulsions. One group used an oscillating pendant drop apparatus and correlated this to asphaltene solubility as measured by NIR.38 Yarranton et al. report on the use of interfacial rheology to study emulsion coalesence.39 Asphaltenes from Athabasca Bitumen were mixed with a solvent and injected into an optical cuvette and the droplet was monitored by camera. Interfacial tension was measured from static measurements on the droplet. Elasticity was measured by manipulating the droplet so that sinusoidal oscillations occurred and the elasticity was measured using Fourier analysis. The findings were that the interfacial modulus was a function of asphaltene content and reached a maximum at an asphaltene concentration of about 1 kg/m3. The modulus increased as the interface aged. The data were found to be consistent with the gradual formation of a cross-linked asphaltene network on the interface. Moran et al. studied emulsions using a micropipette technique in which individual emulsion drops are elongated into a cantilever for force measurements.40 The stress-strain measurements are converted to interfacial rheological behavior. Khristov et al. studied emulsions as a film with toluene dilution and the thin liquid film-pressure balance technique.41 Many researchers studied emulsification by using model oils or modified crude oils. Other researchers studied emulsions as thin layers or by droplets.8,42 Hemmingston et al. studied the water-in-oil emulsions formed from 27 crude oils of different origins.43 They found an increase in stability as viscosity increases. Viscosity also correlated with the SARA (Saturates, Aromatics Resins, and Asphaltenes) data taken on the crude oil. Sjo¨blom et al. studied 16 crudes from the North Sea and 5 from North Africa.34 Stability was measured using the results from an electric field cell. A voltage was applied to the cell until coalescence was observed using a microscope. The value of the voltage at the critical point was taken as a measure of stability. The findings of Fingas and Fieldhouse, studying over 400 crudes and petroleum products, point to four groups of emulsions formed by crude oils and petroleum products: stable, mesostable, entrained, and unstable.2 Stable emulsions are reddish-brown solid-like materials with an average water content of about 80% on the day of formation and about the same one week later. Stable emulsions remain stable for at least 4 weeks under laboratory conditions. All of the stable emulsions studied remained so for at least one year. The viscosity increase over the day of formation averages a multiple of 400 and one week later averages 850. The average properties of the starting oil required to form a stable emulsion are: density 0.9 g/mL; viscosity 300 mPa.s; resin content 9%; asphaltene content 5%; and asphaltene-to-resin ratio 0.6.

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Mesostable water-in-oil emulsions are reddish-brown viscous liquids with an average water content of 64% on the first day of formation and less than 30% one week later. Mesostable emulsions generally break down fairly completely within one week. The viscosity increase over the day of formation averages a factor of 7 and one week later averages 5. The average properties of the starting oil required to form a mesostable emulsion are: density 0.9 g/mL; viscosity 1300 mPa.s; resin content 16%; asphaltene content 8%; and asphaltene-to-resin ratio 0.5. The greatest difference between the starting oils for stable and mesostable emulsions are the ratio of viscosity increase (stable 400, first day and 850 in one week; mesostable 7, first day and 5 in one week) and resin content (stable e9%; mesostable e16%). Entrained water-in-oil types are black viscous liquids with an average water content of 45% on the first day of formation and less than 28% one week later.2 The viscosity increase over the day of formation averages a multiple of two and one week later still averages two. The average properties of the starting oil required to form entrained water are: density 0.97 g/mL; viscosity 60,000 mPa.s; resin content 18%; asphaltene content 12%; and asphaltene-to-resin ratio 0.75. The greatest differences between the starting oils for entrained water-in-oil compared to stable and mesostable emulsions are the viscosity of the starting oil (entrained starting oil averages 60,000 mPa.s compared to 200 mPa.s for stable emulsions and 1300 mPa.s for mesostable emulsions) and the ratio of viscosity increase (entrained ¼ 2, first day and 2 in one week; stable 400, first day and 850 in one week; mesostable 7, first day and 5 in one week). Entrained water-in-oil types appear to be applicable to viscous oils and petroleum products, but not extremely viscous products. Unstable water-in-oil emulsions are characterized by the fact that the oil does not hold significant amounts of water. There is a much broader range of properties of the starting oil than for the other three water-in-oil states. For example, viscosities are very low or very high. Included in this group are light fuels such as diesel fuel and very heavy, viscous oil products. The data suggest that the water-in-oil types are stabilized by both asphaltenes and resins, but that, for greater stability, resin content should exceed the asphaltene content slightly.2 Excess resin content (A/R > about 0.6) apparently destabilizes the emulsion. This does not consider the question of different types of asphaltenes or resins. A high asphaltene content (typically > 10%) increases the viscosity of the oil such that a stable emulsion will not form. Viscous oils will only uptake water as entrained water and will slowly lose much of this water over a period of about one week. Viscous oils (typically >1000 mPa.s) will not form stable or mesostable emulsions. Oils or low viscosity or without significant amounts of asphaltenes and resins will not form any water-in-oil type and will retain less than about 6% water. Oils of very high viscosity (typically > 10,000 mPa.s) will also not form any of these water-in-oil types and thus are classified as unstable. This is probably due to the inability of water droplets to penetrate the oil mass.

Chapter | 10 Models for Water-in-Oil Emulsion Formation

249

Several new models for the prediction of water-in-oil emulsions were recently developed.44-48 These models were the first available to calculate the formation of emulsions using a continuous function and employing the physical and chemical properties of oil. Since this initial model was developed, the emulsification properties of more oils were measured, and the properties of some of the oils in the existing set oils have been remeasured. Some of these data were found to be deficient and were eliminated. This enables the models to be recalculated with sound data on over 316 oils out of over 400 studied. The basis of these models is the result of the knowledge demonstrated abovednamely, that models are stabilized by asphaltenes, with the participation of resins. Other components of oil, especially aromatics, may actually interfere with this process. Findings of this group and other groups show that the entire SARA (Saturates, Aromatic, Resins, and Asphaltene) component affects the formation of emulsions as the prime stabilizers; asphaltenes, and secondarily resins, are available for emulsion formation only when the concentration of the saturates and aromatics are at a certain level and when the density and viscosity are correct.2 The very early emulsion formation modeling equations at that time did not use specific knowledge of emulsion formation processes.44 In the past, the rate of emulsion formation was assumed to be first-order with time. This was then approximated with a logarithmic (or exponential) curve. Although not consistent with the knowledge of how emulsions formed, this assumption has been used extensively in oil spill models. The processes outlined in the introduction above were not discovered until many years after the simple assumptions were proposed. Furthermore, the presence of different water-in-oil states dictates that one simple equation is not adequate to predict emulsion formation. The stability of an emulsion, as represented by stability C, was correlated with starting oil properties to start model development. The different waterin-oil types could be differentiated by correlating individually with starting oil viscosity, density, saturate content, asphaltene content, resin content, or asphaltene/resin ratio. Combinations of these oil properties yielded even greater differentiation between water-in-oil types. Examples of good separation of water-in-oil types were achieved by plotting asphaltene content times asphaltene/resin ratio, along with starting oil viscosity and stability. Other correlations with similar input also yielded differentiation between water-in-oil types.

10.2. EARLY MODELING OF EMULSIFICATION The emulsification processes described above were not apparent until about 15 years ago and have since been translated into modeling equations. Furthermore, the presence of different water-in-oil states dictates that one simple equation is not adequate to predict formation. Information on the kinetics of formation at sea and other modeling data was less abundant in the past. It is now known that emulsion formation is a result of surfactant-like behavior of the polar

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asphaltene and resin compounds. While these are similar compounds that both behave like surfactants when they are not in solution, asphaltenes form much more stable emulsions. Emulsions begin to form when the required chemical conditions are met and when there is sufficient sea energy. Furthermore, as pointed out above, three different water-in-oil types are producedddepending on the oil type and its state of weathering. In the past, the rate of emulsion formation was assumed to be first-order with time. This can be approximated with a logarithmic (or exponential) curve. The physical assumption is that all oils uptake water on a first-order basis. Although not consistent with the knowledge of how emulsions formed, this assumption has been used extensively in oil spill models. As will be shown later, this does not yield correct results. Most models that incorporate the phenomenon use the estimation technique of Mackay and co workers or a variation of this technique.49-51 Mackay suggested the following equation to model water uptake: DW ¼ Ka ðU þ 1Þ2 ð1 Kb WÞDt;

(1)

where DW is the water uptake rate, W is the fractional water content, Ka is an empirical constant, U is the wind speed, Kb is a constant with the value of approximately 1.33, and t is time. Because Equation (1) predicts that most oils will form emulsions rapidly given a high wind speed, most users have adjusted the equation by changing constants or the form slightly. Mackay and Zagorski proposed two relationships to predict the formation of emulsions on the sea. They proposed that the stability could be predicted as follows:51 S ¼ Xa ga exp½Kao ð1 xa xw Þ2 þ Kaw x2w exp½0:04ðT293Þ ;

(2)

where S is the stability index in relative units, high numbers indicate stability, xa is the fraction of asphaltenes, ga is the activity of asphaltenes, Kao is a constant which here is 3.3, xw is the fraction of waxes, Kaw is a constant which is 200 at 293 K, and T is the temperature in kelvin. Water uptake was given as: DWT ¼ DWL þ DWS ¼ DT½kf k1 W1 ;

(3)

where DWT is the total change in water content, DWL is the change in water content for large droplets, DWs is the change in water content for small droplets, DT is time, kf is the rate constant for formation, typically 1 h1, kl is the rate constant for large droplet formation and is about 3 h1, and Wl is the fraction of large droplets, which is typically 3 to 4. Kirstein and Redding used a variation of the Mackay equation to predict emulsification:52 ð1 k2 WÞ exp

2:5W ¼ exp ðk5 k3 tÞ; 1 k1 W

(4)

Chapter | 10 Models for Water-in-Oil Emulsion Formation

251

where k2 is a coalescing constant that is the inverse of the maximum weight fraction water in the mixture, W is the weight fraction water in the mixture, k1 is the Mooney constant that is 0.62 to 0.65, k5 is the increase in mousse formation by weathering, k3 is the lumped water incorporation rate constant and is a function of wind speed in knots, and t is the time in days. The change in viscosity due to emulsion formation was given by: m ¼ m0 exp

2:5W ; 1 k1 W

(5)

where m is the resulting viscosity, m0 is the starting oil viscosity, and the remainder are identical to the above. Reed used the Mackay equations in a series of models.53 The constants were adjusted to correspond to field observations: dFwc Fwc ¼ 2 105 ðW þ 1Þ2 ð1 Þ; dt C3

(6)

where dFwc/dt is the rate of water incorporation, W is the wind speed in m/s, Fwc is the fraction of water in oil, and C3 is the rate constant equal to 0.7 for crude oils and heavy fuel oils. The viscosity of the emulsion was predicted using the following variant of the Mooney equation: m 2:5Fwc ¼ exp ; 1 0:65Fwc m0

(7)

where m is the viscosity of the mixture, m0 is the oil starting viscosity, and Fwc is the fraction of water in oil. The effect of evaporation on viscosity was modeled as: m ¼ m0 expðC4 Fevap Þ;

(8)

where m is the viscosity of the mixture, m0 is the viscosity of the starting oil, C4 is a constant that is 1 for light fuels and 10 for heavy fuels, and Fevap is the fraction evaporated from the slick. All of the above work has a basis in the Mackay equations, which were developed before extensive work on emulsion physics took place. It is now known that both the formation and the characteristics of emulsions could be predicted with orders of magnitude more accurately using empirical data as described below.

10.3. FIRST TWO MODEL DEVELOPMENTS The approaches to model development were implemented and are detailed in the literature.44,45 The approaches used empirical data. One approach was to curve fit the physical and content data to the “stability” index, which was at that time the complex modulus of the water-in-oil product divided by the starting oil

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viscosity. Then this stability factor was used in turn to predict a class (stable, mesostable, entrained, or unstable). Another approach was to predict the class directly from the data which approach was later used in an “improved” model. It is important to note that the class was assigned an arbitrary numerical value, which was later adjusted to optimize the fit. This contrasts very much with the new approach described in this chapter, which uses a newly developed stability index. The empirical data were used to develop mathematical correlation.46 The value for each parameter was correlated in a series of models using DataFit (Oakdale Engineering), which calculates linear models. A two-step process is necessary as DataFit is not able to calculate the specific mathematical function with more than two variables, due to the large number of possibilities. Thus, the functions (e.g., linear, square, log) were calculated using a two-way regression (TableCurve), and these functions were in turn used in developing a predictor model for emulsification. The model that predicts class directly will be summarized here. The steps to produce the model are summarized in an earlier paper.45,46 First, the parameters available were correlated one at a time. Regression coefficients were optimized by adjusting the class criteria from a starting value of 1 to 4 and recalculating these class values several times until they remained relatively stationary. The resulting criteria were: 0.608 is unstable; 0.686 is entrained; 0.657 is mesostable; and 0.674 is stable. The input parameters were density, viscosity, saturates, resins, asphaltenes, a/rdthe asphalteneeresin ratio, and the aromatic content. Several of these parameters can have a zero value, which causes calculation problems. If this is the case, the zero is adjusted either to delete these values or to adjust it to the typical high value for the parameter. A second transformation is performed to adjust the data to a singular increasing or decreasing function. Most parameters have an optimal value with respect to class; that is, the values have a peak function with respect to stability or class. After this correction is made to the values, the regression coefficient increases. Arithmetic was used to convert values in front of the peak to values behind the peak, thus yielding a singular declining or increasing function. The optimal value of this manipulation is found by trial and error, beginning with the estimated peak from the first correlation. The arithmetic to perform this manipulation is as follows: if the initial value is less than the peak value, then the adjusted value is the peak value less the initial value; and if the initial value is more than the peak value, the adjusted value is the initial value less the peak value. The values were adjusted by trial and error until a maximum regression coefficient for the whole model was achieved. The values of the second round of calculations were then correlated using the multiparameter regression package, DataFit. The best model was one that included only four input parameters: density, viscosity, resins, and asphaltenes. It was found that the regression coefficients of class with aromatic content, asphaltene/resin ratio, and waxes were too low to include in the model.

Chapter | 10 Models for Water-in-Oil Emulsion Formation

253

Saturates were eliminated in this new round as it was found that this parameter did not contribute to the overall model. There are some problems with the fundamental process of categorizing water-in-oil states at the onset. Some crude oils are enhanced by the addition of emulsion preventors (also called asphaltene suspenders) directly at the wellhead. This is because they are very emulsion prone. Thus, some emulsion-prone oils may not form emulsions during the laboratory or field tests because of the addition of these emulsion-preventing materials. Although attempts are made to receive oils that do not contain these emulsion-preventing materials, it is impossible to know this fact in every case. Several models were achieved. The best value obtained is 0.996 for the revised model; this was a significant improvement over the old value of 0.51. The fit of the class of water-in-oil type was over 70% correct. Oils that form the entrained state generally have densities over 0.96 and viscosities exceeding 10,000 mPa.s. This can be used to discriminate the entrained states from the other states, which may overlap.

10.4. NEW MODEL DEVELOPMENT The older modeling method as noted in Section 10.3, used an assigned number for each water-in-oil type, and then this was regressed with the various oil starting properties. Further, the functional relationship (square, log, exponential, etc.) of the starting oil properties was determined beforehand and then correlated with a multiparameter linear method to yield a predictor model. Since a stability index has been established, this can be used as the target of the correlation.2 This new modeling has a different approach than the old one. Furthermore, this new approach used a multiregression program directly, using various multifunctional transformations of the input oil property data. This will allow the software of the regression model to assign portions of the functions necessary to achieve the highest correlation factor. Other differences are also that the transformations are automated using a peak function fit, yielding slightly different transformation values than the old values; use of a newer data set; and incorporation of several functional input parameters, dependent on the statistics of the model, into the final equation. A transformation is needed to adjust the data to a singular increasing or decreasing function. Regression methods will not respond correctly to a function that varies both directly and inversely with the target parameter. Most parameters have an optimal value with respect to class; that is, the values have a peak function with respect to stability or class. This is illustrated in Figure 10.1 and Figure 10.2. The resin content without any adjustment is plotted against the stability in Figure 10.1. The stability as shown in these figures is Stability C, as described above. As can be seen in this figure, the values of stability peak at about 5% resins. After this correction is made to the values, the regression coefficient increases. The modified distribution is shown

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30

20

Index

10

0

–10

–20

–30 0

10

20

30

Resins FIGURE 10.1 Graph of resin content (uncorrected) versus stability parameter C. The curve fit is the calculated peak. This is typical of the scatter associated with multiple parameter fits, where each parameter only accounts for a small amount of the variance.

in Figure 10.2. The arithmetic converts values in front of the peak to values behind the peak, thus yielding a singular declining function. The optimal value of this manipulation is found by using a peak function as shown by the line in Figure 10.1. This peak function fit is available from TableCurve, regression software. In the past this was achieved by trial and error, beginning with the estimated peak from the first correlation. The accuracy of the new method is obviously much greater. It is important to note that the scatter observed in Figures 10.1 and 10.2 are typical of scatter observed for a single parameter when a large number of parameters are operative. If one plotted the total number of parameters simultaneously (e.g., in multidimensions), then a singular function would appear with much less scatter. The arithmetic to perform the transformation is as follows: if the initial value is less than the peak value, then the adjusted value is the peak value less the initial value; and if the initial value is more than the peak value, then the adjusted value is the initial value less the peak value. The values found for the transformations are listed in Table 10.1. It should be noted that the exponential of density was used along with the natural log of the viscosity. Previous modeling work had shown that these mathematical changes are necessary to achieve higher correlations.

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Chapter | 10 Models for Water-in-Oil Emulsion Formation

30

20

Index

10

0

–10

–20

–30 0

5

10

15

20

25

Resins FIGURE 10.2 Graph of corrected resin content versus stability parameter C. The curve fit is the best fit. This amount of scatter is typical of multiple parameter fits, where each parameter accounts for only a small amount of the variance.

Having the transformed values, the new model proceeds directly to fit a multiple linear equation to the data. The choice of functions was achieved by correlating the stability function directly with the data and taking the best of the functions (e.g., square, log, etc.) further into the regression process. The functionalities of square, logarithmic or exponential curves are

TABLE 10.1 Values Used to Correct Oil Property Input Parameters Parameter

Form

Correction Value

Density

Exponential

2.5

Viscosity

Natural Logarithm

5.8

Saturates

Standard %

45

Resins

Standard %

10

Asphaltenes

Standard %

4

Asphaltene/Resin Ratio

Standard

0.6

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achieved by correlating the straight value of the input properties plus their expanded values, taken here as the cube of the starting parameter as well as the square of the exponential of the starting value; and their companded values, the natural log (ln), and the logarithm (base 10) of the parameter divided by the square of the value. Thus each parameter is correlated with the stability C index in five sets of mathematical statements. This is similar to the standard Gaussian expansion regression technique.54 In this method the regression is expanded to functionalities above and below linear until the entire entity is optimized. For example, a linear function would be included, then a square, and then a square root and so on until tests of the complete regression show that there are no more gains in increased expansions. Using this technique, six input parametersdexponential of density, ln of viscosity, saturate content, resin content, asphaltene content, and the A/R ratiod were found to be optimal. Thus with four transformations and the original values of these input parameters, there are six times five, or 30, input combinations. Using Datafit, a multiple regression software, a maximum of 20 of these could be taken at a time to test the goodness-of-fit. Values that yield Prob(t) factors of greater than 0.9 were dropped until all remaining factors could be calculated at once. The Prob(t) is the probability that input can be dropped without affecting the regression or goodness-of-fit. Over 20 regressions were carried out until the resulting model was optimal. The r2, the regression coefficient, was 0.75, which is quite high considering the many potential sources of error, and so on. The statistics on the new model are shown in Table 10.2, along with the parameters to create the model. Table 10.2 shows that the 14 remaining parameters all contribute to the accuracy of the final result and that none of them can be cut without affecting the outcome of the model. The procedures for using the new model are given below. The first step is to transform the input data so that it forms a continuous declining or increasing function. Density: take the exponential of the density. If the exponential of density is less than 2.5, then the density parameter is 2.5 less the density and if it is greater than 2.5, it becomes the density less 2.5. The value used in the equation is this transformed value. (9) Viscosity: take the natural logarithm (ln) of the viscosity. If the natural log of the viscosity is less than 5.8, then the viscosity parameter is 5.8 less the viscosity natural log, and if it is greater than 5.8, it becomes the natural log of viscosity less 8.7. The value used in the equation is this transformed value. (10) Saturate content: if the saturate content is less than 45, then the saturate content parameter is 45 less the saturate content, and if it is greater than 45, it becomes the saturate content less 45. The value used in the equation is this transformed value. (11)

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Chapter | 10 Models for Water-in-Oil Emulsion Formation

TABLE 10.2 Regression Results Regression Variable Results

Variable

Value

Standard Error t-ratio

Prob(t)

Input Variable

a

0.259

0.015

16.81

0.0

Saturates

b

1.605

0.098

16.43

0.0

Resins

c

17.198

2.215

7.76

0.0

A/R

d

0.050

0.003

19.17

0.0

ln viscosity

e

0.002

0.000

8.63

0.0

Resins

f

0.001

0.000

4.54

0.00001

Asphaltenes

g

8.505

3.348

2.54

0.01

A/R

h

1.159

0.143

8.11

0.0

ln viscosity

i

0.700

0.170

4.11

0.00004

Resins

j

2.971

0.386

7.70

0.0

A/R

k

0.00000006 0.000

10.90

0.0

ln viscosity A/R

l

1.956

0.663

2.95

0.00

m

0.000004

0.000

10.24

0.0

Exp density

n

0.000146

0.000

2.70

0.01

A/R

1.600

7.69

0.0

o

12.305

Math Applied

Cubed

ln

Exp squared

log/ square x

Contant

Resin content: if the value of the resins is zero, then set this value to 20. If the resin content is less than 10, then the resin content parameter is 10 less the resin content, and if it is greater than 10, it becomes the resin content less 10. The value used in the equation is this transformed value. (12) Asphaltene content: if the value of the asphaltene content is zero, then set the value to 20. If the asphaltene content is less than 4, then the asphaltene content parameter is 4 less the asphaltene content, and if it is greater than 4, it becomes the asphaltene content less 4. The value used in the equation is this transformed value. (13) A/R or asphaltene/resin ratio: this is taken as the direct value of the asphaltene content in percent (untransformed) divided by the resin content in percent (again untransformed). If A/R is less than 0.6, then the A/R parameter is

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0.6 less the A/R, and if it is greater than 0.6, it becomes the A/R less0.6. The value used in the equation is this transformed value. (14) The class of the resulting emulsion is then calculated as follows: Stability C ¼ 12:3 þ 0:259 St 1:601 Rt 17:2 A=Rt 0:50 Vt3 þ 0:002 Rt3 þ 0:001 At3 þ 8:51 A=Rt3 1:12 ln Vt þ 0:700 lnRt þ 2:97 lnA=R þ 6:0108 ExplnVt2 1:96 ExpA=Rt2 4:0106 logExpDt=expDt2 1:5104 logA=Rt=A=Rt2

(15)

where Class is the Stability C of the resulting water-in-oil type, St is the transformed saturate content as calculated in Equation (11), abbreviated A here, Rt is the transformed resin content as calculated in Equation (12), abbreviated B, A/Rt is the transformed asphaltene/resin ratio as calculated in Equation (14), abbreviated C, Vt3 is the cube of the transformed ln viscosity as calculated in Equation (10), abbreviated D, Rt3 is the cube of the transformed resin content as calculated in Equation (12), abbreviated E, At3 is the cube of the transformed asphaltene content as calculated in Equation (13), abbreviated F, A/Rt3 is the cube of the transformed A/R ratio as calculated in Equation (14), abbreviated G, lnVt is the natural logarithm (ln) of the transformed viscosity as calculated in Equation (10), abbreviated H, lnRt is the natural logarithm (ln) of the transformed resin content as calculated in Equation (12), abbreviated I, lnA/R is the natural logarithm (ln) of transformed asphaltene/resin ratio as calculated in (14), abbreviated J, ExplnVt2 is the exponential of the transformed viscositydsquareddas calculated in Equation (10), abbreviated K, ExpA/Rt2 is the is the exponential of the A/R ratio e squareddas calculated in Equation (14), abbreviated L, logExpDt/expDt2 is the is the logarithm (base 10) of exponential of the densityddivided by the square of the transformed densityd the transformed density as calculated in Equation (9), abbreviated M, logA/Rt/A/Rt2 is the logarithm (base 10) of exponential of the A/R ratioddivided by the square of the A/R ratiodthe transformed A/R ratio as calculated in Equation (14), abbreviated N.

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Chapter | 10 Models for Water-in-Oil Emulsion Formation

A simplified version of the equation is then: Stability C ¼ 12:3 þ 0:259A 1:601B 17:2C 0:50D þ 0:002E þ 0:001E þ 8:51G 1:12H þ 0:700l þ 2:97J þ 6:0108 K 1:96L 4:0106 M 1:5104 N (16) where the parameters A to N are defined as above. The Stability C of the resulting product is calculated using the rheological measurements of the water-in-oil product formed.2 The basic uncorrected Stability C or cross product is: Xpr ¼ Complex Modulus Starting Oil ViscosityðStability AÞ Elastic Modulus Starting Oil ViscosityðStability BÞ

(17)

The corrected stability C is: Stability C ¼ In ðXpr XprÞ=1000

(18)

where the Xpr is the value from Equation (17). Or, equivalently, the Stability C can be calculated from: Stability C ¼ InðStab A Stab BÞ2 =1011

(20)

The values of Stability C that are assigned to each class are given in Table 10.3. The viscosity of the resulting product can be taken as the average of the types at a given time as shown in Table 10.4.

TABLE 10.3 Conditions for Type Calculations Calculated C Stability Minimum

Maximum

2.2

Other Conditions

State

Error (%)

15

Stable

0

12

0.7

Mesostable

9

18.3

9.1

density >0.96 viscosity > 6000

Entrained

7

7.1

39.1

density <0.85 or >1.0 viscosity<100 or >800000 Asphaltenes or Resins <1%

Unstable

10

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TABLE 10.4 Viscosity Increases From Starting Oil Viscosity Increase On First Day

Week

Year

Entrained

1.9

1.9

2.1

Mesostable

7.2

11

32

Stable

405

1054

991

Unstable

0.99

1.0

1.0

10.5. DEVELOPMENT OF AN EMULSION KINETICS ESTIMATOR The kinetics of emulsion formation have been studied, and data are available to compute the time to formation.45,46 This kinetics study has shown that the times to formation for stable emulsions is particularly rapid and that of entrainment is also rapiddboth in a matter of minutes. This study yielded data in terms of relative formation time and energy (rpm) of the mixing apparatus. A study in a large test-tank has also yielded data on the formation time of the various water-in-oil states. The data of the relative formation times and the wave height are available. The average data over 25 runs were used to calculate the formation time, which was taken as that time at which 75% of the maximum stability measured occurs. The conditions under which these tests took place and the measurements taken are described in the literature.55,56 The wave height for each experiment was measured and used to indicate relative sea energy, taken for a fully developed sea. The laboratory data was converted from relative rotational energy to wave height by equating formation times and then using this multiplier to calculate the equivalent wave height. Formulae were fitted to each of the three categories, and the common formula among all three relevant classes was found to be 1/x1.5 (24), as shown in Table 10.5. The regression coefficients for this formula are also given. Application of the equations in Table 10.5 will then provide a user with a time to formation of a particular water-in-oil state, given the wave height.

10.6. DISCUSSION The first point to be discussed here will be the error or goodness-of-fit. Table 10.2 shows the specific regression statistics by fit parameter. The first column is the value that is used in the predictor equations. The data in the Standard Error column are the estimates of the standard deviations of the fitted regression parameters. The third column is the t-ratio, which is the ratio of the estimated parameter value to the estimated parameter standard deviation. The larger the ratio is, the more significant the parameter is in the regression model. This is

261

Chapter | 10 Models for Water-in-Oil Emulsion Formation

TABLE 10.5 Wave Height Prediction equation y ¼ a þ b/x1.5(24)

Resulting Equation Predictor

a

b

R2

Stable

27.1

7520

0.51

Mesostable

47

49100

0.95

Entrained

30.8

18300

0.94

x is wave height in cm y ¼ time to formation in minutes

a test statistic to determine if the actual parameter value is zero. If the value were zero, then it should not be included in the equation. As can be seen in Table 10.2, the t-ratios are very high; thus, all remaining input parameters are valid. This exercise had begun with 30 input parameters, and only 14 remained that are highly significant. The fourth column is the Prob(t), which is the probability that a parameter can be dropped without losing significance to the model. If Prob(t) ¼ 0.95, there is a 95% chance that the actual parameter value is zero. In cases like the latter, the parameter in question can usually be removed from the model without affecting the regression accuracy. As can be seen from Table 10.2, all the 14 values are highly significant and should not be dropped. Table 10.3 shows the error percentage by water-in-oil type. There is little error for more stable types, but more error for the unstable types. This was noted in past modeling as well.45,46 It is suspected that the reasons for this are as follows: 1. Unstable types generally consist of three widely separate classes of oils or fuels, very light oils such as the fuels that have little or no resins or asphaltenes; those very heavy oils that are so viscous that they will not uptake water; and those oils that have the incorrect ratio or amounts of resins or asphaltenes. It is difficult to mathematically incorporate all three of these variances into one grouping. 2. Some of the oils may be able to form different water-in-oil types, but emulsion inhibitors or asphaltene suspenders have been added to the products. These types of oils make prediction very difficult as they mislead the regression process. 3. There are many different asphaltenes, some of which make much more stable emulsions than other. Recent work has shown that there are hundreds of asphaltene subcomponents varying very much in composition and molecular size.14 Thus the percent of asphaltenes (or resins) certainly does not tell the whole story about the emulsion-stabilizers in the emulsions. It is important to recognize the properties and behaviors of the four waterin-oil types; they are very different from each other. Table 10.6 shows the

262

TABLE 10.6 Average Properties of the Four Water-in-Oil Types Rheological Properties on Day of Formation Viscosity Increase From Starting

Complex Modulus (mPa)

Elasticity Modulus (mPa)

tan delta (V/E)

Complex Viscosity (mPa.s)

Appearance

Entrained

Viscous Black

44.5

27.5*

1.9

829750

514010

1.73

132305

Mesostable

Viscous Reddish

64.3

29.6*

7.2

132780.5

107294.5

1.7

21219.9

Stable

Solid Reddish

80.7

77.4

405

749991.4

710445.9

0.7

119683.2

Unstable

Oil-Like

6.1

6.85*

0.0

10965637.5

3367562.5

2.4

1828062.5

Rheological Properties One Week After Formation

Appearance

Viscosity Increase From Starting

Complex Modulus Change

Elasticity Modulus Change

Tan Delta Change

Complex Viscosity Change

Entrained

Viscous Black

1.9

0.8

0.6

1.7

1.3

Mesostable

Broken

5.4

0.6

0.5

2.2

0.5

Stable

Solid Reddish

859

1.1

1.1

1.1

1.2

Unstable

Oil-Like

0.0

3.8

2.9

1.7

3.6

(Increase is shown as number larger than 1.00) *These water content values are high as most were not measured as they were obviously low.

Behaviour of Oil in the Environment and Spill Modeling

Averages

PART | IV

Week Water Content (%w/w)

Day Water Content (%w/w)

263

Percent Water Loss (Day–1) per Starting Value

Chapter | 10 Models for Water-in-Oil Emulsion Formation 3.0 Entrained Mesostable Stable Unstable

2.5 2.0 Unstable

1.5 1.0 0.5 Stable

0.0

Entrained

Mesostable

e–1

e0 1

e1

e2

7

e3

e4

e5

e6 1 Year

Time (Days) FIGURE 10.3 Graph of water loss as a rate over time for the four water-in-oil types.

different average properties of the four water-in-oil types. It should be noted that on the day of formation, only mesostable and stable may be somewhat similar, this similarity disappears after one week when the mesostable emulsions break down. The change in water content with time is portrayed in Figure 10.3. As can be seen, the unstable type loses the most water in the first few days. The water content itself is shown in Figure 10.4. As the figure shows, the water content of the four types is very different at the start and especially after one week. Figures 10.3 and 10.4 show that the rate of water loss and the amount of water are indeed different aspects of these water-in-oil types. The other significant point in these figures is that the change in water content of the entrained types is the slowest of all. Another comparison of the four water-in-oil types is shown in Figures 10.5 and 10.6 and relates to the viscosities of types. The rate of change in viscosity is quite different for the four types as seen in Figure 10.4. The rise of viscosity for the mesostable types is more than expected and is due to weathering of the oil, which proceeds more rapidly without water content. The apparent viscosity of the four water-in-oil types is shown in Figure 10.5. As the viscosity axis is logarithmic, the differences are very significant between the four types. In summary, the four water-in-oil types differ by as much as orders-of-magnitude in viscosity and water content at one day, one week, and one year. A question that remains is, how good is this model compared to other models? Table 10.7 shows a comparison for several oil types. These oil types were chosen as common types that are in commerce or as they are well known for their properties. The first six columns give the oil identification and its

264

PART | IV

Behaviour of Oil in the Environment and Spill Modeling

80

Stable

70

Mesostable

Water Content %

60

Entrained Mesostable Stable Unstable

50 40

Entrained

30

A

20

Unstable

10 0

e0

e1

e2

e3

e4

e5

e6

ln Time (Days) FIGURE 10.4

Graph of absolute water content of the four water-in-oil types.

Log Rate of Change in Viscosity

3.5 3.0

Stable

2.5

Entrained Mesostable Stable Unstable

2.0 1.5 1.0 Mesostable

0.5

Entrained

0.0 –0.5

Unstable

e-1

FIGURE 10.5

e0 1

e1

e2

e3 7 Time (Days)

e4

e5

1 Year

e6

Graph of the change in viscosity for the four water-in-oil types.

actual properties of water content and viscosity on the first day of formation. The first model compared shows the traditional exponential water uptake as predicted using the equations presented in Section 10.2. The shaded and boxed values are those predictions in error. A criterion was set for at least 25% in

Chapter | 10 Models for Water-in-Oil Emulsion Formation

T

265

D

FIGURE 10.6 Graph of the apparent viscosity of the four water-in-oil types with time.

water content or incorrect typedif predicted. The number of cases predicted correctly are given at the bottom, and the average orders-of-magnitude are different from the actual value. Table 10.7 shows that the old exponential water uptake model predicted the correct water content only 7% of the time in this set of oils and the order of magnitude of the magnitude of viscosity was over two order-of-magnitudes from the actual viscosity on the day of formation. This comparison would indicate that exponential water uptake models are not useful and are, in fact, misleading. The second model compared in Table 10.7 is that of ADIOS, a widely available model from National Oceanic and Atmospheric Administration (NOAA). The water content is predicted within about 25% in about 49% of cases, and the viscosity averages about 1.6 orders-of-magnitude in error from the actual value. The third model compared is the older model by the present author. It predicted the water content correctly in 72% of cases as well the correct water-in-oil type. The last model compared is the one presented for the first time in this chapter. This model was excellent and predicted the water content and type 100% correct for all types and the viscosity error average 0.2 orders-of-magnitude. The prediction capability of the models is illustrated in Figures 10.7 and 10.8. Figure 10.7 shows the percent correctness in assigning state (by water content in the case of the older models). Figure 10.8 shows the ordersof-magnitude in errors for the water content and the viscosity for the models. As can be seen by these figures, the old exponential model predicts so few correctly that it is probably misleading.

266

TABLE 10.7 Comparison of Predictions of Various Models

%

Actual

Oil

Actual

Water

Starting

Day

Viscosity

Water

Viscosity

Water

Viscosity

Water

Viscosity

(mPa.s)

Content

(mPa.s)

Content

(mPa.s)

Content

Content Viscosity

Adios2

Old Exp. Uptake1

First Two State Models3 Type

New Model In This Paper Viscosity

Water

(mPa.s)

Content

Type

Stability

(%w/w)

(mPa.s)

(mPa.s)

0.0

Entrained

57.9

12610

42000

2853140

90

25000

0

50400

44

Entrained

24000

42.5

Entrained

Belridge Heavy

2.7

Entrained

59.6

17105

47000

3870180

90

25000

0

68400

44

Entrained

32500

42.5

Entrained

Bunker C (1987)

0.0

Entrained

26.4

45030

110000

10188510

90

8000

0

180100

44

Entrained

85600

42.5

Entrained

Bunker C (Anchorage)

0.0

Entrained

34.7

8706

28000

1969820

90

25000

0

34800

44

Entrained

16500

42.5

Entrained

High Viscosity Fuel Oil

0.0

Entrained

47.6

13460

73626

3045470

90

40000

0

53800

44

Entrained

25600

42.5

Entrained

IFO - 180

7.8

Entrained

58.4

27280

149617

6172380

90

2000

0

109100

44

Entrained

51800

42.5

Entrained

IFO - 300

0.0

Entrained

52.3

14470

96610.5

3273990

90

200000

0

57900

44

Entrained

27500

42.5

Entrained

Carpenteria

14.9

Meso

54.3

3426

29000

775170

90

400000

70

13700

44

Entrained

24700

64.6

Meso

Dos Cuadras

20.3

Meso

68.6

741

9800

167660

90

20000

47

33300

67

Meso

5300

64.6

Meso

North Slope (Middle Pipeline)

30.5

Meso

61.9

900

2600

203630

90

80000

0

40500

67

Meso

6500

64.6

Meso

North Slope (Northern Pipeline) 31.1

Meso

69.8

748

1400

169240

90

80000

0

33700

67

Meso

5400

64.6

Meso

North Slope (Southern Pipeline) 29.6

Meso

53.5

961

1900

217440

90

80000

0

43200

67

Meso

6900

64.6

Meso

Arabian Light

12.0

Stable

88.9

33

46000

7470

90

1200000

87

18915000

74

Stable

13400

74.7

Stable

Arabian Light

24.2

Stable

84.7

94

48000

21270

90

1200000

87

25658000

74

Stable

38100

74.7

Stable

Cook Inlet - Swanson River

39.7

Stable

81.5

152

29000

34390

90

250000

90

67545000

74

Stable

61600

74.7

Stable

Point Arguello Comingled

0.0

Stable

82.3

533

180000

120600

90

2000000

82

13059000

74

Stable

215900

74.7

Stable

Point Arguello Light

0.0

Stable

93.1

22

67000

4980

90

4000000

93

20190000

74

Stable

8900

74.7

Stable

Point Arguello Light

10.2

Stable

88.8

76

280000

17200

90

4000000

93

40920000

74

Stable

30800

74.7

Stable

Point Arguello Light

19.0

Stable

85.5

183

270000

41410

90

4000000

93

21705000

74

Stable

74100

74.7

Stable

Point Arguello Light

28.3

Stable

79.8

671

140000

151820

90

4000000

93

5139000

74

Stable

271800

74.7

Stable

Prudhoe Bay (1995)

9.3

Stable

85.1

55

46117.95

12440

90

4000000

93

1112000

74

Stable

22300

74.7

Stable

Sockeye

0.0

Stable

86.5

45

685395

10180

90

450000

87

100

6

Unstable

18200

74.7

Stable

Sockeye

12.5

Stable

80.7

163

201945.375

36880

90

450000

87

1122000

74

Stable

66000

74.7

Stable

Sockeye

22.1

Stable

79.1

628

249183.75

142090

90

450000

87

1442000

74

Stable

254300

74.7

Stable

Sockeye Sweet

26.9

Stable

75.5

321

47956.5

72630

90

450000

87

50000

74

Stable

130000

74.7

Stable

(%w/w)

(%w/w)

(%w/w)

(%w/w)

Takula

0.0

Stable

84.8

110

44678.66763

24890

90

1000000

79

141000

74

Stable

44600

74.7

Stable

Takula

11.0

Stable

81.3

844

83238.125

190960

90

1000000

79

228000

74

Stable

341800

74.7

Stable

Behaviour of Oil in the Environment and Spill Modeling

Evap. Belridge Heavy

PART | IV

Oil

Actual Day

0.0

Unstable

3.7

1

7

0

10

1

0

45

67

value problem

1

6.1

Barrow Island

0.0

Unstable

3.7

2

2

0

10

4000

90

100

67

value problem

2

6.1

Unstable

Brent

0.0

Unstable

3.7

6

6

1360

90

160000

90

300

67

value problem

6

6.1

Unstable

Bunker C (Anchorage)

8.4

Unstable

6.0

280000

63352920

90

25000

0

420000

6

Unstable

277200

6.1

Unstable

Diesel (Anchorage)

0.0

Unstable

3.7

2

2

0

10

6.5

0

100

67

value problem

2

6.1

Unstable

Eugene Island Block 32

0.0

Unstable

3.7

10

10

2260

90

160000

90

450

not done

10

6.1

Unstable

Garden Banks 387

4600

29

6.1

Unstable

Green Canyon 200

2.8E+05

Unstable

0.0

Unstable

3.7

29

29

6560

90

37

1305

6

Unstable

19.1

Unstable

3.7

39

39

8820

90

4000

69

1800

67

mesostable

39

6.1

Unstable

Gulfaks

0.0

Unstable

3.7

13

13

2940

90

90000

90

585

not done

13

6.1

Unstable

Jet A1

0.0

Unstable

3.7

2

2

0

10

45

0

100

67

value problem

2

6.1

Unstable

Malongo

0.0

Unstable

3.7

63

63

14250

90

400000

43

2800

67

mesostable

100

6.1

Unstable

Ship Shoal Block 269

26.0

Unstable

3.7

18

18

4070

90

220000

90

810

not done

18

6.1

Unstable

South Louisiana

0.0

Unstable

3.7

8

8

1810

90

160000

90

400

67

mesostable

8

6.1

Unstable

Taching

0.0

Unstable

3.5

5138000

1162526170

90

100000

81

7707000

6

Unstable

5086600

6.1

Unstable

Viosca Knoll 826

0.0

Unstable

1.7

16

3620

90

120000

90

700

67

mesostable

16

6.1

Unstable

West Texas (2000)

0.0

Unstable

3.7

9

1950

90

12000

90

400

67

mesostable

9

6.1

Unstable

Zaire

23.0

Unstable

5.3

533100

120619440

90

120000

38

799700

6

Unstable

527800

6.1

Unstable

1.64

49 1.17

1.26

72 0.40

0.28

100 0.12

Comparison Statistics

5.1E+06 16 9 5.3E+05

% of cases predicted correctly Average orders-of-magnitude from actual value 2.28

Shaded and boxed values highlight incorrect values 1 Calculated by Old Exponential Model as described in this paper 2 Calculated using ADIOS (NOAA) 3 Calculated Using Earlier State Model (Fingas and Fieldhouse, 2005)

7 0.52

Chapter | 10 Models for Water-in-Oil Emulsion Formation

Aviation Gasoline 100LL

267

268

PART | IV

Behaviour of Oil in the Environment and Spill Modeling

Percent Correct in Assigning State

120 100 80 60 40 20 ADIOS

0

0

1 Old Exponential

Early State

2

3

New

4

5

Model

FIGURE 10.7 Graph of the percent correct predictions of the four models compared.

Average Orders of Magnitude Error

2.5

2.0 Viscosity

1.5 Water Content

1.0

0.5

0.0

0

1 Old Exponential

2

3

4

ADIOS

Early State

New

5

FIGURE 10.8 Graph of the amount of error in predictions of the four models compared.

Chapter | 10 Models for Water-in-Oil Emulsion Formation

269

FIGURE 10.9 An emulsion beginning to form at sea. This emulsion is still fluid and thus is probably a mesostable emulsion.

FIGURE 10.10 An emulsion from a spill in the Arabian Gulf. Note that the thin sheen portion of the oil did not form an emulsion. A minimum thickness, probably the size of a few drops or about 0.5 mm, is necessary for an emulsion to form.

Figures 10.9 and 10.10 show typical emulsions at sea. Emulsion formation is important to actions to deal with spills. Therefore, it is important to correctly predict their formation.

10.7. CONCLUSIONS The knowledge that water-in-oil types exist and that a new scheme to classify their stability, herein called Stability C, enables the development of new and much more accurate emulsion formation models. In this chapter, the

270

PART | IV

Behaviour of Oil in the Environment and Spill Modeling

development of a new modeling scheme was described. The density, viscosity, asphaltene, and resin contents are used to fulfill a regression equation to Stability C, which in turn predicts either an unstable or an entrained waterin-oil state or a mesostable or stable emulsion. A prediction scheme is also given to estimate the water content and viscosity of the resulting water-in-oil state and the time to formation given a sea wave-height. The new model can provide accurate prediction of class about 90% of the time. The major inaccuracy lies with the unstable types because of the fact that there are three distinct types of oils or fuels in this class, each very different, and because of the possible presence of emulsion breakers or asphaltene suspenders in the oils. The new model uses only resin, saturate, asphaltene, viscosity, and density data as input. These data are readily available for most oils. The predictions were compared to the actual data from some common oils and against an old model from the present author. Also compared were an old exponential water uptake model dating from the 1980s and the ADIOS model. It was found that the old exponential water uptake model was incorrect most of the time and would not be considered useful.

REFERENCES 1. NAS. Oil in the Sea III, Inputs, Fates, and Effects. Washington, DC: National Research Council, National Academies Press; 2002. 2. Fingas M, Fieldhouse B. Studies on Crude Oil and Petroleum Product Emulsions: Water Resolution and Rheology. Colloids Surf A 2009;67. 3. Berridge SA, Dean RA, Fallows RG, Fish A. The Properties of Persistent Oils at Sea. J Instit of Petr 1968;300. 4. McLean JD, Spiecker PM, Sullivan AP, Kilpatrick PK. The Role of Petroleum Asphaltenes in the Stabilization of Water-in-Oil Emulsions. In: Mullins OC, Sheu EY, editors. Structure and Dynamics of Asphaltenes, 377. New Jersey: Plenum Press; 1998. 5. Sjo¨blom J, Aske N, Auflem IH, Brandal O, Havre TE, Saether O, et al. Our Current Understanding of Water-in-Crude Oil Emulsions: Recent Characterization Techniques and High Pressure Performance. Adv Colloid and Interfac 2003;399. 6. Sjo¨blom J, Hemmingsen PV, Kallevik H. The Role of Asphaltenes in Stabilizing Waterin-Crude Oil Emulsions. In: Mullins OC, Sheu EY, Hammami A, Marshall AG, editors. Asphaltenes, Heavy Oils and Petroleomics, 549. Amsterdam: Springer Publications; 2007. 7. McLean JD, Kilpatrick PK. Effects of Asphaltene Solvency on Stability of Water-in-Oil Emulsions. Colloid Interface Sci 1997;242. 8. McLean JD, Kilpatrick PK. Effects of Asphaltene Aggregation in Model Heptane-Toluene Mixtures on Stability of Water-in-Oil Emulsions. Colloid Interface Sci 1997;23. 9. McLean JD, Spiecker PM, Sullivan AP, Kilpatrick PK. The Role of Petroleum Asphaltenes in the Stabilization of Water-in-Oil Emulsion. In: Mullins, Sheu, editors. Structure and Dynamics of Asphaltenes, 377. New Jersey: Plenum Press; 1998. 10. Gu G, Xu Z, Nandakumar K, Masliyah JH. Influence of Water-Soluble and Water-Insoluble Natural Surface Active Components on the Stability of Water-in-Toluene-Diluted Bitumen Emulsion. Fuel 2002;1859.

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11. Yarranton HW, Hussein H, Masliyah JH. Water-in-Hydrocarbon Emulsions Stabilized by Asphaltenes at Low Concentrations. Colloid Interface Sci 2000;52. 12. Kilpatrick PE, Spiecker PM. Asphaltene Emulsions. In: Sjoblom J, editor. Encyclopedic Handbook of Emulsion Technology, 707. Amsterdam: Marcel Dekker; 2001. 13. Sztukowski DM, Yarranton HW. Characterization and Interfacial Behaviour of Oil Sands Solids Implicated in Emulsion Stability. J Disp Sci Technol 2004;299. 14. Groenzin H, Mullins OC. Asphaltene Molecular Size and Weight by Time-Resolved Fluorescence Depolarization. In: Mullins OC, Sheu EY, Hammami A, Marshall AG, editors. Asphaltenes, Heavy Oils and Petroleomics, 17. Amsterdam: Springer Publications; 2007. 15. Zhang LY, Lopetinsky R, Xu Z, Masliyah JH. Asphaltene Monolayers at a Toluene/Water Interface. Energy Fuels 2005;1330. 16. Lobato MD, Pedrosa JM, Hortal AR, Martinez-Haya B, Lebron-Aguilar R, Lago S. Characterization and Langmuir Film Properties of Asphaltenes Extracted from Arabian Light Crude Oil. Colloids Surf A 2007;72. 17. Fingas MF, Fieldhouse B. A Review of Knowledge on Water-in-oil Emulsions. AMOP 2006;1. 18. Spiecker PM, Gawrys KL, Kilpatrick PK. Aggregation and Solubility Behavior of Asphaltenes and Their Subfractions. Colloid Interface Sci 2003;178. 19. Yang X, Hamza H, Czarnecki J. Investigation of Subfractions of Athabasca Asphaltenes and Their Role in Emulsion Stability. Energy Fuels 2004;770. 20. Ali MF, Alqam MH. The Role of Asphaltenes, Resins and Other Solids in the Stabilization of Water in Oil Emulsions and Its Effects on Oil Production in Saudi Oil Fields. Fuel 2000;1309. 21. Nordli KG, Sjo¨blom J, Stenius P. Water-in-Crude Oil Emulsions from the Norwegian Continental Shelf: 4. Monolayer Properties of the Interfacially Active Crude Oil Fraction. Colloids Surf A 1991;83. 22. Havre TE, Sjo¨blom J. Emulsion Stabilization by Means of Combined Surfactant Multilayer (D-phase) and Asphaltene Particles. Colloids Surf A 2003;131. 23. Sjo¨blom J, Hemmingsen PV, Kallevik H. The Role of Asphaltenes in Stabilizing Waterin-Crude Oil Emulsions. In: Mullins OC, Sheu EY, Hammami A, Marshall AG, editors. Asphaltenes, Heavy Oils and Petroleomics, 549. Amsterdam: Springer Publications; 2007. 24. Spiecker PM, Gawrys KL, Trail CB, Kilpatrick PK. Effects of Petroleum Resins on Asphaltene Aggregation and Water-in-Oil Emulsion Formation. Colloids Surf A 2003;9. 25. Sjo¨blom J, Skodvin T, Jakobsen T, Dukhin SS. Dielectric Spectroscopy and Emulsions: A Theoretical and Experimental Approach. J Disp Sci Technol 1994;401. 26. Kallevik H, Kvalheim OM, Sjo¨blom J. Qualitative Determination of Asphaltenes and Resins in Solution by Means of Near-Infrared Spectroscopy: Correlations to Emulsion Stability. Colloid Interface Sci 2000;494. 27. Kallevik H, Hansen SB, Saether Ø, Kvalheim OM, Sjo¨blom J. Crude Oil Model Emulsion Characterized by Means of Near Infrared Spectroscopy and Multivariate Techniques. J Disp Sci Technol 2000;245. 28. Song M-G, Jho S-H, Kim J-Y, Kim J-D. Rapid Evaluation of Water-in-Oil (W/O) Emulsion Stability by Turbidity Ratio Measurements. Colloid Interface Sci 2000;213. 29. Dicharry C, Arla D, Sinquin A, Graciaa A, Bouriat P. Stability of Water/Crude Oil Emulsions Based in Interfacial Dilational Rheology. Colloid Interface Sci 2006;785. 30. Xia L, Lu S, Cao G. Stability and Demulsification of Emulsions Stabilized by Asphaltenes or Resins. Colloid Interface Sci 2004;504. 31. Sztukowski DM, Yarranton HW. Oilfield Solids and Water-in-Oil Emulsion Stability. Colloid Interface Sci 2005;821.

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32. Yarranton HW, Urrutia P, Sztukowski DM. Effect of Interfacial Rheology on Model Emulsion Coalescence: II Emulsion Coalesence. Colloid Interface Sci 2007;253. 33. Midttun Ø, Kallevik H, Sjo¨blom J, Kvalheim OM. Multivariate Screening Analysis of Waterin-Oil Emulsions in High External Electric Fields as Studied by Means of Dielectric Time Domain Spectroscopy. Colloid Interface Sci 2000;262. 34. Aske N, Kallevik H, Sjo¨blom J. Water-in-Crude Oil Emulsion Stability Studied by Critical Electric Field Measurement: Correlation to Physico-Chemical Parameters and Near-Infrared Spectroscopy. J Petr Sci Eng 2002;1. 35. Tadros ThF. Fundamental Principles of Emulsion Rheology and Their Applications. Colloids Surf A 1994;39. 36. Johnsen EE, Rønningsen HP. Viscosity of "Live" Water-in-Crude-Oil Emulsions: Experimental Work and Validation of Correlations. J Petr Sci Eng 2003;23. 37. Spiecker PM, Kilpatrick PK. Interfacial Rheology of Petroleum Asphaltenes at the Oil-Water Interface. Langmuir 2004;4022. 38. Aske N, Orr HR, Sjo¨blom J. Interfacial Properties of Water-Crude Oil Systems Using the Oscillating Pendant Drop. Correlations to Asphaltene Solubility by Near Infrared Spectroscopy. J Disp Sci Technol 2004;263. 39. Yarranton HW, Sztukowski DM, Urrutia P. Effect of Interfacial Rheology on Model Emulsion Coalescence: I Interfacial Rheology. Colloid Interface Sci 2007;246. 40. Moran K, Yeung A, Masliyah J. The Viscoplastic Properties of Crude Oil-Water Interfaces. Chem Eng Sci 2006;6016. 41. Khristov K, Taylor SD, Czarnecki J, Masliyah J. Thin Liquid Film Technique: Application to Water-Oil-Water Bitumen Emulsion Films. Colloids Surf A 2000;183. 42. Yang X, Czarnecki J. The Effect of Naphtha to Bitumen Ratio on Properties of Water in Diluted Bitumen Emulsions. Colloids Surf A 2002;213. 43. Hemmingston PV, Silset A, Hannisdal A, Sjo¨blom J. Emulsions of Heavy Crude Oils. I: Influence of Viscosity, Temperature and Dilution. J Disp Sci Technol 2005;615. 44. Fingas MF, Fieldhouse B. Formation of Water-in-Oil Emulsions and Application to Oil Spill Modelling. J Haz Mat 2004;37. 45. Fingas MF, Fieldhouse B. Modelling of Water-in-Oil Emulsions. AMOP 2004;335. 46. Fingas MF, Fieldhouse B. An Update to the Modelling of Water-in-Oil Emulsions. AMOP 2005;923. 47. Fingas MF, Fieldhouse B. A Review of Knowledge on Water-in-Oil Emulsions. AMOP 2006;1. 48. Fingas MF. A New Generation of Models for Water-in-Oil Emulsion Formation. AMOP 2009;577. 49. Mackay D, Buist IA, Mascarenhas R, Paterson S. Oil Spill Processes and Models. Environment Canada Manuscript Report EE-8; 1980. 50. Mackay D. A Mathematical Model of Oil Spill Behaviour. Environment Canada Manuscript Report EE-7; 1980. 51. Mackay D, Zagorski W. Studies of Water-in-Oil Emulsions. Environment Canada Manuscript Report EE-34; 1982. 52. Kirstein BE, Redding RT. Ocean-Ice Oil-Weathering Computer Program User’s Manual. Outer Continental Shelf Environmental Assessment Program, vol. 59. Seattle, WA: NOAA; 1988. 53. Reed M. The Physical Fates Component of the Natural Resource Damage Assessment Model System. Oil Chem Poll 1989;99. 54. Araki Y, Konishi S, Kawano S, Matsui H. Functional Regression Modeling via Regularized Gaussian Basis Expansions. Ann I Stat Math 2008;1.

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55. Fingas MF, Fieldhouse B, Noonan J, Lambert P, Lane J, Mullin J. Studies of Water-in-Oil Emulsions: Testing of Emulsion Formation in OHMSETT, Year II. AMOP 2002;29. 56. Fingas MF, Fieldhouse B. Water-in-Oil Emulsions and Application to Oil Spill Modelling, Proceedings of the Fifth International Marine Environmental Modelling Seminar. Sintef 2001;339.

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Chapter 11

Oil Spill Trajectory Forecasting Uncertainty and Emergency Response Debra Simecek-Beatty

Chapter Outline 11.1. Introduction: The 275 Importance of Forecast Uncertainty 11.2. The Basics of Oil Spill 276 Modeling

11.3. Trajectory Model Uncertainties 11.4. Trajectory Forecast Verification 11.5. Summary and Conclusions

280 292 295

11.1. INTRODUCTION: THE IMPORTANCE OF FORECAST UNCERTAINTY Winds and currents play an important role in oil spill transport; and, occasionally, oil moves in a direction that results in unexpected outcomes. One of the most dramatic examples of the latter phenomenon occurred during the 1984 explosion and subsequent breakup of the T/V Puerto Rican. The accident resulted in more than 5,678,000 liters of oil spilling into the Gulf of the Farallones in California. Initially, the oil slick moved southerly as forecasted, thereby avoiding the large seabird and mammal colonies at the Farallone Islands. Oil protection and recovery equipment were deployed to the south, leaving the Farallone Islands and the northern California shoreline unprotected and exposed. On day 5 of the spill, the slick made a sudden and remarkable reversal, and overnight, the oil moved northward approximately 50 km from its location on the previous day. The trajectory forecast completely missed the reversal. Oiled birds and shoreline oiling were reported on the Farallone Islands.1 By day 10, the spill made landfall along the northern California coast Oil Spill Science and Technology. DOI: 10.1016/B978-1-85617-943-0.10011-5 Copyright Ó 2011 Elsevier Inc. All rights reserved.

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at Point Reyes.2 Oil observations and trajectory forecasts were a critical factor in forming daily operational oil recovery and protection decisions. In this instance, the consequences of an inaccurate trajectory forecast were devastating. An in-depth analysis of the meteorological and oceanographic data collected during the T/V Puerto Rican incident suggested that a reversal in the outer continental shelf current transported the oil rapidly to the north. This “dramatic” reversal was likely related to the onset of the Davidson Current or other larger-scale phenomena, which was not predictable with the available oceanographic measurement data.3 Given these sparse real-time environmental data, today’s models would still have difficulty accurately forecasting the current reversal, particularly in the short period required during an emergency response. The difference, however, is that current-day modelers now include uncertainty as part of the trajectory forecast. Today, emergency responders are briefed with both the estimate of the oil movement and alternative possibilities that could present a significant threat to valuable resources. Most decision makers understand that forecasting is imperfect. The physical processes acting on the oil spill are chaotic and complex, and trajectory forecast uncertainty is inevitable. As shown in the T/V Puerto Rican incident and countless other oil spills, there are good practical reasons for disseminating trajectory uncertainty and ensuring that the response community understands the consequences of uncertainty. Figure 11.1 shows a rough representation of the actual and predicted oil movement for the T/V Puerto Rican incident on the fifth day of the spill. The circle is a hypothetical boundary and introduced here for demonstration. The circle represents the possible errors in the model input data and plausible variations in the transport processes. This includes a possible scenario of surface current reversal. In this instance, the area is especially complex and difficult to model so that the level of forecast uncertainty is high. The large bounded area provides a visual cue to the response community about the limitation of the spill model(s). If a high-value resource is within the uncertainty but not within the “best estimate,” responders should seriously consider protecting the resource from oil impact. This example demonstrates that communicating uncertainty information can avoid misrepresenting the capability of oil spill modeling, better convey “what we do know” and “what we don’t know,” and help responders make more informed decisions and avoid problems.4 This is “a minimum regret” approach to protecting high-value resources.

11.2. THE BASICS OF OIL SPILL MODELING Responders, particularly those interested in the operational aspects of a spill, are often in need of a quick, “back-of-the-envelope” estimate of the spill’s trajectory. They have a general idea about oil behavior and understand that wind and current are important factors in a trajectory forecast. The technique

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FIGURE 11.1 Actual and predicted oil movement for the T/V Puerto Rican spill on day 5. Bounding circle represents uncertainty.6

depicted below is a learning tool. It can be very difficult to get a feel for oil spill modeling due to the complicated interactions of the various processes. The main characteristic of a “back-of-the-envelope” trajectory is the use of simplified assumptions for computational simplicity. In this type of estimate, there is no oil weathering, oil spreading, or mixing, and the current is assumed steady and persistent over time. Before using this type of approach, be mindful of these assumptions and recognize that this “best estimate” of the slick movement can have significant errors when extrapolating too far out in time. The calculation is explained in Figure 11.2 and involves plotting the wind drift and surface vectors on a nautical chart. The sum of the two vectors, the

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resultant vector, is the distance traveled by the spill (Figure 11.3). Oil drifts with the surface current at 100% of the current speed, but only at a fraction of the wind speed. Perhaps one of the best known rules of thumb in oil spill modeling is the “3% rule.”6,7 This rule has some theoretical basis and has

1. Plot the last known location of the spill on a nautical chart. Note the time of the observation. 2. Determine the direction and velocity of the surface current. Using oceanographic convention, the surface current is reported as the direction ‘to’. 3. Calculate the length of the surface current vector by multiplying the velocity by hours of drift. The hours of drift will be the total duration of the trajectory forecast period. For example, if the surface current velocity is 5 cm/s and the forecast period (hours of drift) is 3 hours, then the length of the surface current vector 0.6 km. 4. Draw a line on the chart extending from the last known location of the spill in the direction of the surface current. Use the compass rose on the chart to orient the line. The length of the line is the length of the surface current vector. In the example, the length of the line would be 0.3 nautical miles. To properly scale the line, use either the scale on the chart or use the latitude as a scale (1degree of latitude equals approximately 111 km). 5. Using the following table, collect the wind data. Time

Wind Period

No. of Hours

Wind Direction

Wind Velocity

*Leeway (0.03)

Vector Contribution km km km km

The time field is time of the observation (or forecast); wind period is start and end time for wind speed and direction; number of hours is duration in hours; wind direction is direction the wind is coming from; wind velocity is wind speed in miles per hour. For these calculations, 3% of the wind speed (0.03) is the leeway or wind drift factor for an oil spill. Multiply wind velocity by 0.03 and enter the value in *leeway field. The vector contribution is the length of the wind vector. It is calculated by multiplying *leeway by number of hours (similar to step 3). 6. Returning to the nautical chart, draw a line extending from the end of the surface current vector (from step 4) in the direction and distance of the first entry in the vector contribution field. At the end of this vector, draw a line in the direction and distance of the second entry in the vector contribution field. Continue this process until all wind vectors are plotted on the chart. 7. The predicted location of the slick is at the end of the last vector plotted. The time for the predicted location is the sum of the number of hours added to the time of the last reported location of the slick. Remember, the surface current is assumed constant for this time period

FIGURE 11.2 A simple prediction of the oil slick movement using vector addition of the components due to wind and current. Modified from USCG.16

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FIGURE 11.3 The sum of the surface current and wind drift vectors are the resultant oil movement.

been verified in the field and laboratory experiments.8,9 The 3% rule has been successfully used as wind drift factor or leeway for most fresh oil spills. Uncertainty can be calculated by considering other possible factors. For instance, suppose the spilled oil is a viscous residual fuel oil. The 3% rule represents average conditions, but the actual factor ranges from 1 to 6%.10 Viscous oils are often subject to overwash by waves. While submerged, viscous oils will only drift at the speed of the water current and, hence, will have a net lower drift speed than that given by the 3% rule. On the other hand, oil caught in the convergences in windrows will move faster than the average 3%.11 To use uncertainty in the rough estimate, do the calculation with 1% and then 6% of the wind speed. For a 6-hour forecast at a constant 7.7 m/s wind speed, the oil will travel between 1.6 km at 1% and 10 km at 6%. The resulting forecast will be a best guess of a 5 km (3%) displacement with an uncertainty spanning 1.6 km, 1%, to 10 km, 6%. Similar calculations could be employed for uncertainty in the location and direction and speed of the current and wind. Rather quickly, rough calculations using simple vector addition become unwieldy. At this point, serious consideration should be given to applying a more sophisticated approach to the problem. But what oil spill model(s) should be used? Without a grasp of the underlying principles and assumptions, the mere use of a model does not necessarily lead to a good or better answer. Depending on the spill incident, more than one model may be used because a particular model may perform better in certain situations. Performance varies because models assume different things, represent the physics in different ways, have different resolutions, are initialized differently, and often solve the equations in different ways. Therefore, one model’s simulation of a particular aspect of the spill fate and behavior may be rigorous, but it is likely to be weaker in other aspects. A key point to remember is that a model’s uncertainty will vary over time as environmental conditions change, and also spatially due to resolution and boundary limitations. Discussions of the strengths and weaknesses of oil spill models can be found in the literature.12-15 In general, oil spill models use a combination of Eulerian and Lagrangian methods to simulate oil behavior. The velocity field for winds and currents are derived using Eulerian techniques and are represented as individual velocity

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FIGURE 11.4 Examples of current velocity field (A) and particles (B).

vectors at fixed points in the model domain (Figure 11.4A). Oil patches are represented as individual particles that may be referred to as Lagrangian elements (Le’s), spillets, or splots.17,18 The paths of the particles are tracked as they move along the map (Figure 11.4B). Algorithms may vary but most models will need to account for winds, currents, turbulence, and spill details as input data to initialize and move the particles. In most instances, these processes are parameterized from other models or submodels, and they all come with their own uncertainty.

11.3. TRAJECTORY MODEL UNCERTAINTIES Oil spill models are very sensitive to errors in the initial input data, such as the details of the release and the wind and current forecasts. Furthermore, the mathematical calculations used to simulate oil movement are likely based on empirical approximations and assumptions and are subject to time step and grid limitations. Trajectory model uncertainty refers to changes in the forecast as a result of these errors. Unfortunately, quantitative assessment of the errors in trajectory modeling is difficult and limited. In addition, oil spills are notorious for occurring in areas where the environmental data are temporally and spatially incomplete. This leads to a forecast process that often relies on the forecaster’s subjective judgment and approximated input. The ranking of uncertainty as low, medium, and high for trajectory forecasts and the model inputs presented here are subjective. But the forecaster’s subjective judgment can be an invaluable resource, and, at least as anecdotal data suggest, it may be better than a model alone at estimating errors. The fact that the initial estimates are inaccurate and the model itself has inadequacies leads to forecast errors that grow over time. For this reason shortrange forecasts usually have less error than long-range forecasts (Table 11.1). For larger spill events, the model input data should contain fewer errors due to better field observations, such as remote sensing and visual overflights of the spill. The result is that the multiple forecasts produced daily should actually

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TABLE 11.1 Uncertainty for Trajectory Forecasts Oil Spill Trajectory Forecast

Uncertainty

24-hrs

Low e Medium

24 to 48-hrs

Medium

48 to 72-hrs

Medium e High

72þ hrs

High

improve over time. On the first day of a big spill, the uncertainty for the initial forecast will likely range from low to high. On the second day, with more onscene observations, the uncertainty typically ranges from low to medium. By the third day, the uncertainty should be lower. A sophisticated model with extensive data input requirements does not necessarily produce a better forecast. There are an optimal number of input parameters that will determine the total model uncertainty. The model output is only as good as the largest error input. This is the reason that the performances of complex models are often no better, and sometimes worse, than the predictions of the simpler models. The back-of-the-envelope calculation in Section 2 used only a one-time surface current measurement with a constant speed and direction lasting for a few hours. This approach has serious limitations in regard to time and spatially varying currents. The advantage is that the results can be quickly passed on to the decision maker. In contrast, an oil spill model that uses forecast currents from a hydrodynamic model with extensive input data requirements (e.g., real-time salinity and temperature data at various depths) may not yield a successful result or be as useful because, for most emergency spill incidents, the input data to initiate a three-dimensional hydrodynamic model is not available in a timely manner. In fact, the three-dimensional model may have to rely on historical data rather than input conditions specific to the spill event. Complex models work well only when the extensive data requirements are satisfied, which rarely can be fulfilled at an oil spill response.

11.3.1. Release Details In 1987, the barge Hana encountered rough seas while transporting Bunker C fuel oil to the Maui power plant in Hawaii. On the southwest side of Molokai Island, the barge reported spilling approximately 11,360 liters of oil. At the time of the incident, the wind forecast was northeast at 13 to 15 m/s for the next 24 hours. Using this information, the trajectory forecast did not indicate any beaching of the oil and indicated the slick would move to the southwest and out to sea. The next day, “a lot of oil” came ashore on Oahu. How could the

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TABLE 11.2 Uncertainties for Oil Spill Release Details Release Details

Uncertainty

Location

Low e Medium

Time

Low e Medium

Day

Low

Night

Low e Medium

Oil Properties

Medium e High

Potential Spill Volume

Low e Medium

Actual Spill Volume

High

Leak Rate

High

trajectory be so wrong? First, the trajectory forecaster was given incorrect information about the release. In fact, the location of the actual release site was off by 18.5 km. Second, the spill volume was later determined to be over 227,000 liters of oil and not 11,360 liters as initially reported. The larger spill volume affected the trajectory as more oil was spread out over a larger area. Third, the overnight winds were actually from the east and not the northeast as initially forecasted. Unfortunately, there is no reliable way to quantify the errors related to the details of a release. Table 11.2 provides uncertainty for oil spill releases based on decades of experience. If the spill occurs during daylight and there is an experienced overflight observer who can provide coordinates for the spill with a description of the slick, confirmation about the likely spill volume, and a source, then the uncertainty is relatively low. Conversely, release details for a spill occurring at night during a storm or in fog without confirmation from an experienced observer will likely carry a high uncertainty.

11.3.2. Wind Discussions with the local meteorologist can provide valuable insight about the availability of atmospheric models for a specific area and the model limitations. Ideally, time-dependent and spatially varying wind field from an atmospheric model is imported directly into the oil spill model. However, careful consideration is needed before bringing in the wind forecast. Localized phenomena, which are at a smaller scale than the resolution of the atmospheric model, may have a great influence on the oil spill trajectory. Oil spills spread out quickly, but, even for the larger spills, the slick dimensions are frequently smaller than

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283

the resolution of many atmospheric models. This means, for instance, that the wind at the source of the spill could be different from the wind at the leading edge of the slick. A coarse-resolution atmospheric model may have only one wind vector to represent the entire spill area, much like the back-of-theenvelope calculation in Section 2. Table 11.3 provides examples of typical atmospheric model resolutions. Nested grid systems use a low-resolution, global weather model to provide boundary conditions for high-resolution, regional models. A review of a specific atmospheric model will likely reveal qualitative errors. The other challenge is the time resolution of models. The oil trajectory model may have time steps of 15 minutes, but the wind model may be resolving winds at every hour. For most spills in estuaries, the regional models are suited for oil spill trajectory modeling. But even with regional models, local effects, such as the landesea breeze, may not be sufficiently resolved. This can wreak havoc with a trajectory forecast. Shoreline oiling is enhanced with an onshore wind and a falling tide (Figure 11.5A); accurately forecasting the onshore wind is important to getting the trajectory forecast correct. As the tide ebbs, the intertidal areas are exposed, and, if the wind is blowing onshore, the oil adheres and smears down the beach face (Figure 11.5B). An example of the landesea breeze phenomenon and the difficulty forecasting the timing of shoreline oiling occurred during the 1990 T/V American Trader incident. The vessel ran over its anchor, punctured the hull, and spilled over 1.5 million liters of North Slope crude oil. The spill occurred about 1.5 km off Huntington Beach, California. The net oil slick drift was small due to light winds and a weak surface current. The trajectory forecast repeatedly missed the timing of the shoreline oiling due to the interaction of the landesea breeze and tide. For a few days, the tides and winds were synchronized such that the falling tide coincided with an offshore wind due to the sea breeze. The oil floated up the beach face with the rising tide, but the oil did not adhere as an offshore wind (land breeze) pushed the oil out to sea. This pattern continued for several days

TABLE 11.3 Grid Resolutions of Atmospheric Models (Modified from Kalnay19) Atmospheric Models

Grid Resolution

Climate

Several hundred kilometers

Global weather

50e100 km

Regional meso-scale

10e50 km

Storm scale

1e10 km

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FIGURE 11.5 Falling tide and onshore wind (A), and shoreline oiling due to falling tide and onshore wind (B).

until the tides and land breeze were no longer synchronized, and then the oil stranded on the beach. When local details are important, a higher spatial resolution model should be used and the uncertainty should be carefully conveyed. If a suitable atmospheric model is unavailable, the marine forecaster can provide details about the wind forecast and its likely error bounds. This requires a good verbal briefing by the meteorologist. The meteorologist can provide information about wind shift timing, the strength of the pressure gradient, location of high/low fronts, and local effects. The result can be a wind data file containing the meteorologist’s best estimate and error estimate, which can then be fed directly into the model. As an example, the wind forecast may indicate wind from the south at 7.7 m/s for 12 hours, becoming southwest at 5 m/s. This data is used to compute the best estimate of the wind and is entered into the spill model. If the meteorologist indicates that the forecast wind shift could be off by 3 hours, the wind direction off by 20 degrees, and the speeds by 2.5 m/s, the original wind file is modified or an additional file is created with this data. This represents uncertainty in the wind forecast. The accuracy of the forecast depends, among other things, on special weather features, length of the forecast period, and ability of the forecasters to localize their prediction to the spill site (Table 11.4). Optimum wind forecast periods are usually between 6 and 24 hours. For a wind forecast beyond five days, serious consideration should be given to using climatological winds and generating a probability guidance product as a trajectory forecast.

11.3.3. Current In some regions, oil spill modelers have the capability to import time and spatially varying surface current forecasts from ocean circulation models. These models are updated every few hours in a manner similar to atmospheric models. Figure 11.6 shows the expected movement of a hypothetical spill from a continuous release of oil. In this scenario, there are no winds, or turbulent

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285

TABLE 11.4 Uncertainty for the Surface Wind Forecast Surface Wind Forecast

Uncertainty

24-hr

Low e Medium

48-hr

Medium

72-hr

Medium e High

96þhr

High

mixing processes. There are only surface currents from five different sources: the Global Navy Coastal Ocean Model,20the Global Navy Layered Ocean Model,21 the Global Hybrid Coordinate Ocean Model,22 California High Frequency Radar,23 and the Global Sea Surface Height (SSH 2010) model.24 The NCOM, NLOM, and HyCOM models have similar physics but were initialized with different data, have different grid resolutions, and different numerical methods. The HFR and SSH model forecast currents from observations. It is interesting to note that the HyCOM and NLOM circulation models move the spill in opposite directions, whereas in the short term, a consensus

FIGURE 11.6 Particle tracking of a hypothetical spill using multiple current models.

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begins to take shape with the HFR, SSH, and NCOM forecasts as the oil is moved offshore. The five-model runs display the uncertainty in the trajectory forecast using just the surface currents from different sources. Further exploration by the forecaster is needed to seek out an explanation of why the model runs differ. Another word of caution: because a model yields results that compare favorably with observations one day or one week, doesn’t mean it will do well another day or week. For example, the model may perform better if the surface wind speed is within a specific range. In addition, a model that does well in a certain region may not do well in another region. In coastal areas without a regional circulation model, simulating the current may become a challenge. Three-dimensional hydrodynamic models will require extensive oceanographic data for input. In a spill response situation, acquiring relevant real-time data is highly unlikely. To work around this problem, modelers may use a combination of real-time observations (e.g., overflights), astronomical tidal predictions, and historical data for the ocean currents, along with a simplified approach to generating currents. All of this takes time to collect and enter into a model. In an emergency response, decision makers need a forecast quickly. Typically, simplified two-dimensional and one-dimensional models can be more easily calibrated to fit the actual movement of the oil from day to day. It is not unusual that these simple approaches that calibrate currents to daily observations provide better results than large sophisticated models that are difficult to adjust and calibrate. Large, complicated models are often calibrated with historical records that are often short and are collected under environmental conditions very different from those of the spill. Table 11.5 provides a subjective assessment of the uncertainty in the surface circulation of various water bodies. Closer inspection of a specific hydrodynamic model will likely reveal quantitative error assessment. Many rivers are gauged and controlled by locks and dam systems, so that the uncertainty in the predicted flow is generally low. If the river forecaster provides uncertainty in the flow, this information can be included in the analysis. For spills that occur in tidal-driven estuaries or an ungauged river system, the uncertainty in direction is relatively low (Table 11.5), but the strength of the current may not be accurately known; hence, the overall uncertainty is low to medium. A few coastal areas in the United States have the Physical Oceanographic Real-Time System network that combines real-time monitoring of the water level and meteorological conditions with numerical circulation models for water-level forecasting. The inner continental shelf extends from the shoreline to where the depth increases to about 120 m. In this area, most of the oil releases result in shoreline impacts, and the uncertainty, unfortunately, is medium to high (Table 11.5). Currents in this zone are dominated by long-shore winds, freshwater runoff, and tides. In the 2002 oil recovery operation of the sunken vessel SS Jacob Luckenbach, all of these forces were apparent over the course of the oil removal. The vessel sank in 1953, approximately 30 km southwest of the

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TABLE 11.5 Uncertainties in Surface Current Surface Current

Uncertainty

River Gauged

Low

Un-gauged

Low e Medium

Lake

Low e Medium

Shallow water lagoon

Low e Medium

Tidally dominated estuary

Medium

Inner continental shelf

Medium e High

Deep ocean (off continental shelf)

High

Under ice cover

High

Golden Gate Bridge, San Francisco, California. During the course of the operations, when the winds were particularly light, smaller slicks moved to the south and, a few hours later, moved to the north with a weak tide. Without a dominant mechanism forcing the circulation, it became difficult to predict the overall transport of the oil. In contrast to the T/V Puerto Rican incident, the inability to predict strong, large-scale forces responsible for the abrupt changes in the current direction and speed resulted in an erroneous forecast on the scale of a few kilometers over the time span of a few hours. The deep ocean, off the continental shelf, is dominated by drifting oceanic eddies. These density-driven currents have a slow net drift and typically do not affect the currents on the inner shelf. Therefore, their uncertainty is of less importance for most oil spills unless they occur where the shelf is short or nonexistent (e.g., Hawaii). Figure 11.7 shows a snapshot of the SSH-derived currents with large oceanic eddies with current velocity ranging from 5 to 13 cm/s.

11.3.4. Turbulent Diffusion To the spill modeler, processes smaller than the resolution of the model and timescale motions are most often represented as turbulent mixing and present a challenging problem in oil transport. Virtually all oil spill models use simplified formulas to simulate the horizontal and vertical “mixing” of oil. This term could also be considered the “ignorance coefficient” because it represents the effects of mechanisms that are poorly understood and represented.26 A common approach is to represent turbulence using a constant diffusion

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FIGURE 11.7 SSH derived currents. Modified from CoastWatch.24

coefficient,26 but there are other options.27,28 The effects of turbulence will mask small errors in the surface circulation and winds and smooth out the effects of subgrid-scale processes (e.g., Langmuir circulation and convergence zones). The consequence of this turbulent diffusion approach is a loss of resolution that increases over time.

11.3.5. Oil Weathering The rate and degree with which an oil weathers affects its wind-drift factor (or leeway) and hence, its trajectory. As oil weathers, its chemical properties change. Density will increase as the light fractions evaporate, and both viscosity and density will increase if the oil emulsifies. These property changes will affect wind drift and the oil’s ability to disperse. Uncertainty in weathering predictions is generally lower for spills of light refined products, which rapidly dissipate and do not form stable emulsions (e.g., gasoline and diesel). A few crude oils have also been studied, both in the lab and in field trials for weathering behavior. This extra information makes prediction about their behavior more reliable. For other types of oils, such as intermediate fuel oils, where the available data only vaguely characterizes their weathering characteristics, uncertainty is high for the transport, fate, and effects of the oil, and the uncertainty grows over time. For the best estimate trajectory, the modeler may select the oil in the model that best represents the product spilled. To define uncertainty bounds, the oil can be modeled as a conservative quantity, which is neither evaporated nor dispersed into the water column. Field observations can be used to help calibrate weathering of the oil, which in turn will help improve trajectory estimates. The slick drift factor or leeway changes over time because, initially, the spill appears as a large cohesive film but eventually tears apart into smaller patches or tarballs. Table 11.6 shows different wind drifts for various oils and the

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TABLE 11.6 Wind Drift Uncertainty and Distance Traveled for Various Oils Oil Type

No. of Hours

Wind Velocity (m/s)

Wind Drift or Leeway (%)

Vector Contribution (km)

Gasoline

24

7.7

3 to 4

22e30

Diesel

24

7.7

3 to 4

22e30

Fresh IFO

24

7.7

3

22

Fresh crude oil

24

7.7

3 to 4

22e30

Weathered IFO

24

7.7

2 to 3

13e22

Emulsified oil

24

7.7

1 to 2

5.5e13

Scattered tarballs

24

7.7

0.5 to 2

3.7e13

distance likely to travel with a 7.7 m/s wind for 24 hours. The drift factors are estimates based on the modeler’s experience matching visual observations of the slick with the trajectory forecast. For oils with ranges, like the scattered tarballs, this represents the uncertainty in the wind drift and can be modeled by randomly selecting a slick drift between 0.5 and 2% of the wind speed for each patch of oil at each model time step. Since the wind speed is not likely constant, this modeling technique can also simulate wind gusts. Weathering of oil will also determine the type and severity of impacts expected from the oil spill and, consequently, the amount of response personnel and equipment. For example, if the expected impact from a spill were scattered coin-sized tarballs every 10 m along the shoreline, the cleanup response effort would be very different than that for a spill resulting in a 2-m-wide band of emulsified oil. Therefore, it is important to not only forecast where and when oil will go but what type of impact to expect. Any uncertainty related to the fate of the oil should be conveyed with the trajectory forecast.

11.3.6. Ensemble Forecasting The 1976 Argo Merchant grounding off Nantucket Island, Massachusetts, was one of the most studied oil spills in history with over 200 scientists participating in the response effort. Five independent research teams provided operational forecasting of the oil distribution.29,30 The on-scene commander was presented with five forecasts; each displaying different trajectories. This was the beginning of ensemble forecasting in spill response. Ensemble forecasting involves generating a collection of forecasts based on varying initial conditions, model parameters, and physics. The forecasts can be a compilation of outputs from different models31 or from the same model using different boundary conditions

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and data choices32,33. Ensemble forecasting has developed into the primary means of presenting trajectory forecast uncertainties. The results of ensemble forecasting must be communicated so that the decision maker can interpret and understand the information. Figure 11.8A shows an example a visual graphic of the trajectory forecast that uses the best available input data. Here, particles simulating oil movement are converted so that darker contours indicate a higher concentration of particles.4 The forecast provides only one prediction of the future, with no information about uncertainty. Decision makers are likely to move much of the available oil recovery and protection resources to the area where the contour contacts the shoreline. This is often the type of forecast requested by emergency responders to support operational decisions, even though it is not the complete picture needed for optimum response. Figure 11.8B shows a visual representation of ensemble forecasting. The confidence limit represents the output from a series of trajectory forecasts. In addition to output from multiple models, the forecaster may have used his or her subjective judgment and considered other plausible, what-if, scenarios. The scenarios may have included what if the weather forecast of a frontal passage is off by 12 hours; the release time is off by 1 hour, and the surface current speed off by 20 cm/ s? How would this affect the oil movement? The confidence limit is a visual cue to the decision maker that represents the boundary of the output from multiple models and/or output from multiple runs from one model. The product conveys the likely locations of oil and provides responders with not only a best estimate trajectory, but also other possibilities that could result in a significant threat.

FIGURE 11.8 tainty (B).

Examples of a trajectory forecast without uncertainty (A) and with uncer-

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11.3.7. Communicating Trajectory Forecast Uncertainty Communicating the uncertainty of the trajectory forecast is critical to users. It allows them to make decisions based on the reliability of the forecast and the consequences from inaccuracies in the forecast. The general public is familiar with probabilities associated with forecasts thanks in large part to National Oceanic and Atmospheric Administration’s (NOAA’s) National Weather Service producing forecasts for hurricanes, tornadoes, and precipitation in terms of probabilities. However, for spill movement, it is not possible to compute the uncertainty probabilities for where and when the oil will come ashore. The number of spills with adequate field observations is not sufficient for statistical analysis. Well-documented marine oil spills with robust data sets are the exception, and experimental spills in the ocean are quite few in number. Ocean-surface current drifter studies cannot provide probabilities of the oil movement for any given day, under any given condition. Given the environmental variability and model shortcomings, there is not enough data to generate probabilities for oil spill trajectory forecast. As a result, the oil spill trajectory forecast uncertainty must be conveyed in a way other than with probabilities. Galt proposed a digital standard that presents uncertainty of the trajectory forecast that alleviates the “language of probabilities” problem.4 The trajectory model is first used with the best available input data. It is then run a second time to set the uncertainty or confidence bounds. In the uncertainty model run, each of the particles can be thought of as a centroid of an independent spill and is assigned its own wind and current data. The resultant spread of the particles represents an ensemble of spills. The distribution is not related to oil concentration but represents an ensemble of different spills. However, to make this work, the expert forecaster needs to specify uncertainty bounds for the currents, winds, and other various inputs parameters. A standardized method does not exist, and the approach relies on the forecaster’s subjective judgment. Figure 11.9 shows an example of the NOAA standard for visually representing uncertainty. The graphic is designed to express the amount of complexity and uncertainty in a particular forecast without presenting probabilities. Postprocessing software, independent of the oil spill model, was used to generate the graphic. The product includes a base map, contoured particles, and an outer confidence limit. The bottom of Figure 11.8 contains a scale with eight patterns of oil distribution. By looking at the scale bar, emergency responders can quickly determine how the light, medium, and heavy contours relate to the oil distribution observed on-scene and, from this, develop response options. Regardless of the way uncertainty is expressed to the decision maker, it needs to be done. To do this successfully, the forecaster realistically expresses uncertainty for every input parameter as well as the numerical uncertainty inherent with the model. This is a daunting task, particularly for estimating uncertainty with oil type, oil volume, spill location, spill time, and oil slick observational data.

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FIGURE 11.9 Sample trajectory forecast product.

11.4. TRAJECTORY FORECAST VERIFICATION How accurate are oil spill models? The question is an obvious one but difficult to answer. The back-of-the-envelope calculation presented in Section 2 is a good place to start. For demonstration purposes, the hypothetical calculation

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or forecast indicates that the oil spill remains offshore. Field observation data is needed for verification, and for this demonstration, the location of every piece of oil is known. Suppose the field data indicates the bulk of the oil remained offshore and only a small amount of oil came ashore. Was the model or, in this case, the calculation accurate? The skills of a trained forecaster in this process are important. The forecaster can make multiple calculations or multiple models runs and include uncertainty. If the small amount of oil onshore was within the uncertainty of the calculation, then the forecast was accurate. Model errors can occur for various reasons and are not consistent over time or space; therefore, it is important to have a skilled forecaster verify the model output. Oil spill models cannot precisely predict the movement of every patch of oil. Some models may perform better than others under different conditions, but, inevitably, oil spill models will be wrong. Quantifying the model’s error is not easy due to the constraints found at most oil spill incidents (e.g., observations of surface oil that are both temporally and spatially incomplete). This contrasts with forecasting in other fields. For example, NOAA’s National Hurricane Center has precise metrics to measure hurricane forecasts versus observations. At this time, precise metrics to measure oil spill trajectory forecasts do not exist. Ideally, a formal methodology would be developed for the comparison of the trajectory forecast with observed field data. Such a comparison would provide a means for assessing the model’s performance relative to other spill models. A challenge for the oil spill trajectory forecaster is determining whether a model, despite its uncertainties, can be used to make a useful forecast. The challenge for decision makers is to determine how to use the forecast and its inherent uncertainties to make an informed decision. This section provides a brief description within which a forecaster and decision maker can determine a model’s performance for accurately predicting the oil movement. Field observation data are the basis of model verification. However, collecting data from field experiments and during an emergency response is not simple. It is extremely difficult and often illegal or impossible to stage experimental oil spills in the open ocean. If permission is granted, the experiments are small-scale and conducted over a short time period: usually hours, not the days needed for characterizing a specific set of conditions. This makes it difficult to test models against field data due to the varying environmental conditions and a mismatch between model scales and experiment scales. Attempts to use data from emergency response are always problematic because the on-scene observations of the oil distribution contain significant errors. It is not always known how much of the oil was spotted by the observer or what part of the slick was seen. Overflights of the spill may not be conducted due to poor weather or aircraft availability resulting in large time gaps between observations. Observational errors can also result from observers reporting “false positives” such as kelp beds, silt plumes, algae, and jellyfish, to name but a few. Remote-sensing techniques are imperfect as well because of limitations of the

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sensor, availability of assets (aircraft and satellite), and weather conditions. Due to these constraints, other approaches need to be developed to evaluate the performance of oil spill models. A common approach for evaluating an oil spill model’s performance is a hindcast. In a hindcasting, the release details of wind and current data at the time of the incident are entered into a model to see how well the output matches the reported location of oil. If the hindcast accurately shows the oil movement as known to have occurred, the model is considered successful. The comparison between the hindcast and observations mostly consist of visual inspection rather than statistical evaluation due to the problems with collecting oil observation data.34-37 Hence, there is a need for an experienced forecaster who understands the uncertainty associated with oil fate and observations. Oil observations can be used to make model adjustments so that the hindcast matches the observed distribution of the oil. This is model calibration, and an example can be found in Turrell.38 Parameters within the model are calibrated to match the movement of the spill. Again, the process is subject to error due to problems in collecting field observations and requires a knowledgeable forecaster who knows which model parameters to modify for a best fit. Other approaches are to compare model estimates and measurements to field data on a spill-by-spill basis and then calibrate a model with that comparison.17,39 However, caution is needed in this approach to avoid using a calibrated model for different geographic locations and environmental conditions. Every spill is a unique event, and every location has its own environmental challenges. The remaining technique for evaluating oil spill model performance that appears in the literature is validation. Oil spill trajectory models can never be conclusively validated because they never completely simulate reality.40 In general, validated models are those that have shown correspondence to experimental data. A more accepted approach is a model evaluation process in which the results of the model are determined to be sufficient and, that despite the uncertainties, can be used in decision making.41 In all cases, the model’s documentation should provide clear understanding of why and how the model can be used.

11.4.1. Diagnostic Verification Forecast verification is an integral part of the forecast process in an emergency response as the spill situation and environmental conditions can change very rapidly. As an example, the vessel(s) involved in the accident may be unstable. A submerged pipeline may have a small continuous leak with a potential for a much larger release of oil. The weather is constantly changing, and the currents are changing with tides and coastal events. Therefore, the model results need to be continuously compared to observed data by a skilled forecaster during a spill response. The forecaster needs to compare the predictions with field reports and decide if the model parameters are sufficiently correct or

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require modifications to match the field data. Calibrating the model(s) with the previous day’s overflight observations does not ensure that the forecast will match the next day’s overflight, but it will give the forecaster an idea of which model parameters to monitor. During the T/V Puerto Rican incident, discussed in Section 1, daily adjustments were made to the model, but the forecasters never anticipated a reversal in the surface current, not even with a predicted wind shift. The more serious the consequence of forecast error, the more important monitoring and collection of field data. Essentially, the forecaster is calibrating the model to the spill during the response. Figure 11.10A shows an example of a map of the oil distribution for an oil spill. The map was used to verify the spill model. The model is run from the start of the spill and stopped at the time of the overflight observation. Since no model can simulate reality perfectly, visual inspection of the overflight map and the model run likely indicates differences in the oil location. Adjustments are likely made to model parameters so that the model matches the overflight map. The calibrated model can then be used to generate a forecast (Figure 11.10B). In a spill, forecast verification is an integral part of the forecast process.

11.5. SUMMARY AND CONCLUSIONS In this chapter, the fundamentals of uncertainty related to oil spill fate and transport forecasting were presented. The T/V Puerto Rican incident was used as an example of the importance of uncertainty in the trajectory forecast. This event showed that an estimate of the uncertainty in the forecast provides more information than a single best estimate that uses the initial model input data. Decision makers, who only consider the single best estimate and largely ignore the forecast uncertainty, tend to make less than optimal decisions. If an incident similar to the T/V Puerto Rican were to occur today, close monitoring of the spill by field observations (e.g., overflights, surface current buoys, and remote sensing) and communicating the trajectory forecast uncertainty will help responders make more informed decisions and avoid problems. Presenting both the best estimate and the uncertainty in the trajectory forecast provides the decision maker the opportunity to support a minimumregret decision-making strategy.42 At nearly every spill, there is always a limited amount of resources available for shoreline protection and cleanup. With both the best estimate and uncertainty, decision makers can weigh the wisdom of directing the cleanup toward the most likely spot for oil as opposed to defending less likely but more environmentally important locations.43,44 Overflight operations can conduct more intelligent surveillance, using uncertainty or confidence forecast boundaries to determine their flight paths and prevent any oil from sneaking past the response efforts. The public can be provided with a more realistic representation of what is known about the slick location, avoiding false expectations concerning trajectory accuracy.

296 PART | IV Behaviour of Oil in the Environment and Spill Modeling

FIGURE 11.10 Map of oil distribution (A) and oil spill trajectory forecast (B).

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297

The conclusions from this chapter are simple but important. Oil spill fate and transport forecasting contains errors and, under certain circumstances very large errors. As a result, it is important to convey uncertainty bounds with the forecast. Good field data and a skilled forecaster are needed to adequately calculate, portray, and communicate the uncertainty in the predictions.

ACKNOWLEDGMENTS The findings and conclusions in this chapter are those of the author and do not necessarily represent the views of the National Oceanic and Atmospheric Administration (NOAA). This chapter arose from a series of training conducted by NOAA for the U.S. Coast Guard. We hope this chapter performs the function of introducing relevant principles of modeling uncertainty to the next generation of spill responders. The author would like to acknowledge Glen Watabayashi, Dr. Alan Mearns, and Mark Dix for their assistance in preparing this chapter. Thanks also to Jeffery Lankford for kindly providing Figure 11.1.

REFERENCES 1. PRBO (Point Reyes Bird Observatory). The Impacts of the T/V Puerto Rican Oil Spill on Marine Bird and Mammal Populations in the Gulf of Farallones. 2. FMSA (Farallones Marine Sanctuary Association), Coastal Ecosystem Curriculum: Oil Spills. http://www.farallones.org/documents/education/oilspills.pdf, 2002. 3. Breaker LC, Bratkovich A. CoastaldOcean Processes and Their Influences on the Oil Spilled of San Francisco by the M/V Puerto Rican. Mar Environ Res 1993;1003. 4. Galt JA. Uncertainty Analysis Related to Oil Spill Modeling. Spill Sci Techn Bull 1998;231:4. 5. Lehr WJ. Personal Communication to Debra Simecek-Beatty; February 8, 2010. 6. Smith CL. Determination of the Leeway of Oil Slicks. In: Wolfe DA, Anderson JW, Button DK, Malins DC, Roubal T, Varanasi U, editors. Fate and Effects of Petroleum Hydrocarbons in Marine Ecosystems and Organisms, 351. New York: Pergamon Press; 1976. 7. Huang J. A Review of the State-of-the-art of the Oil Spill Fate/Behavior Models. IOSC 1983;313. 8. Wu J. Sea-Surface Drift Currents Induced by Wind and Waves. J Phys Oceanogr 1983;1441. 9. Fallah MH, Stark RM. Random Drift of an Idealized Oil Patch. Ocean Eng 1976;89. 10. Lehr WJ, Simecek-Beatty D. The Relation of Langmuir Circulation Processes to the Standard Oil Spill Spreading, Dispersion and Transport Algorithms. Spill Sci Tech Bull 2000;247. 11. Leibovich S. Surface and Near-surface Motion of Oil in the Sea, Contract 14-35-0000130612, Minerals Management Service, U.S. Department of Interior; 1997. 12. Yapa PD, Shen HT. Modeling River Oil-SpillsdA Review. J Hydr Res 1994;765. 13. ASCE Task Committee on Oil Spills. State-of-the-Art Review of Modeling Transport and Fate of Oil Spills. J Hydraulic Eng 1996;594. 14. Cekirge HM, Palmer SL. Mathematical Modeling of Oil Spilled into Marine Waters. In: Brebbia CA, editor. Oil Spill Modeling and Process. Southhampton, UK: WIT Press; 2001. 15. French-McCay DP. Oil Spill Impact Modeling: Development and Validation. Environ Toxicol Chem 2004;2441. 16. USCG, United States Coast Guard National Search and Rescue Manual, Vol. II: Planning Handbook; 1991.

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17. French-McCay D. Development and Application of Damage Assessment Modeling: Example Assessment for the North Cape Oil Spill. Mar Poll Bull 2003;341. 18. Beegle-Krause CJ. Advantages of Separating the Circulation Model and Trajectory Model: GNOME Trajectory Model Used with Outside Circulation Models. AMOP 2003;825. 19. Kalnay E. Atmospheric Modeling, Data Assimilation, and Predictability. Cambridge, UK: Cambridge Univ. Press 2003. 20. NCOM (Navy Coastal Model), Naval Research Laboratory. http://www7320.nrlssc.navy.mil/ global_ncom, 2010. 21. NLOM (Global Navy Layered Model), Naval Research Laboratory. http://www7320.nrlssc. navy.mil/global_nlom, 2010. 22. HyCom (Global Hybrid Coordinate Ocean Model), National Ocean Partnership Program. http://www.hycom.org/, 2010. 23. HFR (California High Frequency Radar), Southern California Coastal Ocean Observing System. http://sccoos.org/data/hfrnet, 2010. 24. CoastWatch. http://coastwatch.noaa.gov/, 2010. 25. NRC (National Research Council). Principles for Evaluating Chemicals in the Environment. National Academy of Sciences; 1975. 26. Okubu A. Diffusion and Ecological Problems: Mathematical Models. Dordrecht, Holland: Springer-Verlag; 1980. 27. Elliot AJ, Dale AC, Proctor R. Modeling the Movement of Pollutants in the UK Shelf Seas. Mar Poll Bull 1992;614. 28. Thibodeaux L. Chemodynamics: Environmental Movement of Chemicals in Air, Water, and Soil. New York, NY: John Wiley & Sons; 1979. 29. Grouse PL, Mattson JS. The Argo Merchant Oil Spill, National Oceanic and Atmospheric Administration. Boulder, CO: Environmental Research Laboratory; 1977. 30. Pollack AM, Stolzenbach KD, Investigations in Response to the Argo Merchant Oil Spill, Sea Grant Program, Report No. MITSG 78-8. Cambridge, MA: Crisis Science; 1978. 31. Daniel P, Dandin P, Josse P, Skandrani C, Benshila R, Tiercelin C, et al. Towards Better Forecasting of Oil Slick Movement at Sea Based on Information from the Erika. In: Proc. Third R&D Forum on High-Density Oil Spill Response. Brest, France: Int’l. Maritime Org; 2002. 32. Sebastiao P, Guedes Soares C. Uncertainty in Predictions of Oil Spill Trajectories in a Coastal Zone. J Mar Sys 2006;257. 33. Sebastiao P, Guedes Soares C. Uncertainty in Predictions of Oil Spill Trajectories in Open Sea. Ocean Eng 2007;576. 34. Venkatesh S. Model Simulations of the Drift and Spread of the Exxon Valdez Oil Spill. Atmosphere-Ocean 1990;90. 35. Venkatesh S, Crawford WR. Spread of Oil from the Tenyo Maru, off the Southwest Coast of Vancouver Island. Natural Hazards 1993;75. 36. Proctor R, Elliot AJ, Flather RA. Forecast and Hindcast Simulations of the Braer Oil Spill. Mar Pollut Bull 1994;219. 37. WDOE (Washington Department of Energy), Puget Sound Trajectory Analysis Planner (TAP) Technical Documentation. Spill Prevention, Preparedness, and Response Program, Publication#03-08-007; 2003. 38. Turrell WR. Modeling the Braer Oil SpilldA Retrospective view. Mar Pollut Bull 1994;4. 39. Vethamony PK, Sudheesh MT, Babu S, Jayakumar R, Manimurali AK, Saran LH, et al. Trajectory of an Oil Spill of Goa, Eastern Arabian Sea: Field Observations and Simulations. Environ Pollut 2007;438.

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40. Anderson MG, Bates PD. Model Validation Perspectives in Hydrological Science. New York, NY: John Wiley & Sons; 2001. 41. Pascual P, Stiber N, Sunderland E. Draft Guidance on the Development, Evaluation, and Application of Regulatory Environmental Models. U.S. Environmental Protection Agency, http://www.epa.gov/crem/knowbase; 2003. 42. Galt JA. The Integration of Trajectory Models and Analysis into Spill Response Information Systems. Spill Sci Tech 1997;23. 43. Wirtz KW, Liu X. Integrating Economy and Uncertainty in an Oil-Spill. DSS: The Prestige Accident in Spain. Estuar Coast Shelf Sci 2006;525. 44. Wirtz KW, Baumberger N, Adam S, Liu X. Oil Spill Impact Minimization Under Uncertainty: Evaluating Contingency Simulations of the Prestige Accident. Ecolog.l Econ 2007;417:61.

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Part V

Physical Spill Countermeasures on Water

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Chapter 12

Physical Spill Countermeasures Merv Fingas

Chapter Outline 12.1. 12.2. 12.3. 12.4.

Containment on Water 303 Skimmers 315 Sorbents 325 Manual Recovery 329

12.5. 12.6. 12.7. 12.8.

Temporary Storage Pumps Separation Disposal

330 332 334 335

12.1. CONTAINMENT ON WATER Containment of an oil spill is the process of confining the oil, either to prevent it from spreading to a particular area, to divert it to another area where it can be recovered or treated, or to concentrate the oil so that it can be recovered, burned, or otherwise treated. Containment booms are the most frequently used piece of equipment for containing an oil spill on water. Booms are generally the first equipment mobilized at a spill and are often used throughout the operation. This portion of text covers the types of booms, their construction, operating principles and uses, as well as how they sometimes fail. It also covers ancillary equipment used with booms, sorbent booms, and special-purpose and improvised booms. The topic of fire-resistant booms for use when burning oil on water is covered in the subsection on in-situ burning.

12.1.1. Types of Booms and Their Construction A boom is a floating mechanical barrier designed to stop or divert the movement of oil on water. Booms resemble a vertical curtain with portions extending above and below the water line. Most commercial booms consist of four basic components: a means of flotation, a freeboard member (or section) to prevent oil from flowing over the top of the boom, a skirt to prevent oil from being swept underneath the boom, and one or more tension members to support the entire boom. Booms are constructed in sections, usually 15 or 30 m long, with connectors installed on each end so that sections of the boom can Oil Spill Science and Technology. DOI: 10.1016/B978-1-85617-943-0.10012-7 Copyright Ó 2011 Elsevier Inc. All rights reserved.

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be attached to each other, towed, or anchored. A section of a typical boom is shown in Figure 12.1. Some typical commercial booms are illustrated in Figure 12.2. The floats determine the buoyancy of the boom and the level above the water surface. Floats are located along the center line, outboard, on one side, or on outriggers. Booms either have solid floats or the boom itself is inflatable. Solid floats are usually made of a plastic foam such as expanded polyurethane or polyethylene and are segmented or flexible so that the boom can ride the surface of the waves. Inflatable booms are either self-inflating or are inflated using a powered air source. They require little storage space but are generally less rugged than booms with fixed floats. The freeboard member is the portion of the boom above the water, which prevents oil from washing over the top of the boom. The term freeboard is also used to refer to the height from the water line to the top of the boom. The skirt is the portion of the boom below the floats, which helps to contain the oil. It is usually made of the same types of fabric as the freeboard member and the covering of the floats. Typical materials include polyvinyl chloride (PVC), polyester, nylon, or aramid, sometimes coated with a spray-on protector or another covering such as PVC, polyester, polyurethane, nitrile, and polyether urethane to resist degradation from oil.1 Most booms are also fitted with one or more tension members that run along the bottom of the boom and reinforce it against the horizontal load imposed by waves and currents. Tension members are usually made of steel cables or chains but sometimes consist of nylon or polyester ropes. The boom fabric itself is not strong enough to withstand the powerful forces to which booms are subjected, except in protected waters. For example, the force on a 100-m long section of

Means of Flotation

rd

boa

Free

Weighted Ballast

Skirt Tension Member

FIGURE 12.1 Basic boom construction.

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305

FIGURE 12.2 Typical commercial containment booms.

boom could be as much as 10,000 kg, depending on sea conditions and the construction of the boom.2 Booms are sometimes constructed with ballast or weights designed to maintain the boom in an upright position. Lead weights have been used for this, but steel chain in the bottom of the boom often serves as both ballast and a tension member. A few booms also use a chamber filled with water as ballast. Many booms nowadays are constructed without ballast, however, and their position in the water is maintained by balancing the forces on the top and bottom of the boom. Another construction feature common in larger booms is the addition of stiffeners or rigid strips, often consisting of plastic or steel bars, which are designed to keep the boom in an upright position. The three basic types of booms are fence, curtain booms, which are most common, and external tension member booms, which are relatively rare. Booms are also classified according to where they are used, that is, offshore, inshore, harbor, and river booms, based on their size and ruggedness of construction. The fence boom is constructed with a freeboard member above the float. Though relatively inexpensive, these booms are not recommended for use in high winds or strong water currents. Curtain booms are constructed with a skirt below the floats and no freeboard member above the float. Curtain

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booms are most suitable for use in strong water currents. External tension member booms, which are constructed with a tension member outside the main structure, are used in strong currents and in water containing ice or debris. The characteristics of booms that are important in determining their operating ability are the buoyancy-to-weight ratio or reserve buoyancy, the heave response, and the roll response. The buoyancy-to-weight ratio or reserve buoyancy is determined by the amount of flotation and the weight of the boom. This means that the float must provide enough buoyancy to balance the weight of the boom with the force exerted by currents and waves, thereby maintaining the boom’s stability. The greater a boom’s reserve buoyancy, the greater its ability to rise and fall with the waves and remain on the surface of the water. The heave response is the boom’s ability to conform to sharp waves. It is indicated by the reserve buoyancy and the flexibility of the boom. A boom with good heave response will move with the waves on the surface of the water and not be alternately submerged and thrust out of the water by the wave action. The roll response refers to the boom’s ability to remain upright in the water and not roll over.3

12.1.2. Uses of Booms Booms are used to enclose oil and prevent it from spreading, to protect harbors, bays, and biologically sensitive areas, to divert oil to areas where it can be recovered or treated, and to concentrate oil and maintain an sufficient thickness so that skimmers can be used or other cleanup techniques, such as in-situ burning, can be applied. Booms are used primarily to contain oil, although they are also used to deflect oil. When used for containment, booms are often arranged in a U-, V-, or J-configuration. The U-configuration is the most common and is achieved by towing the boom behind two vessels, anchoring the boom, or combining these two techniques. The U-shape is created by the current pushing against the center of the boom. The critical requirement is that the current in the apex of the U does not exceed 0.5 m/s or 1 knot, which is referred to as the critical velocity. This is the speed of the current flowing perpendicular to the boom, above which oil will be lost from the boom. In open water, the U-configuration can also be achieved by allowing the entire boom system to move downcurrent so that the velocity of the current, as opposed to that of the boom, does not exceed the critical velocity. If this velocity is exceeded, first small amounts of oil and then as the relative velocity increases, massive amounts will be lost. This leads to several types of boom failure that are described in the next section. Figure 12.3 shows failure of a U-configuration. If used in areas where the currents are likely to exceed 0.5 m/s or 1 knot, such as in rivers and estuaries, booms are often used in the deflection mode. The boom is then deployed at various angles to the current, shown in Table 12.1, so

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FIGURE 12.3 A U-configuration that is failing. The current here is too high, and thus entrainment failure is occurring.

that the critical velocity is not exceeded. The oil can then be deflected to areas where it can be collected or to less sensitive areas as shown in Figure 12.4. If strong currents prevent the best positioning of the boom in relation to the current, several booms can be deployed in a cascading pattern to progressively move oil toward one side of the watercourse. This technique is effective in wide rivers or where strong currents may cause a single boom to fail. The deflection is intended to be in a straight line, but usually cusps form in the boom as a result of the current. When booms are used for deflection, the forces of the current on the boom are usually so powerful that stronger booms are required and they must be anchored along their entire length.

TABLE 12.1 Deflection Angles and Critical Current Velocities Angle (degrees)

Critical Velocity of Perpendicular Current*

90

0.5

75

0.5

60

0.6

45

0.7

35

0.9

15

1.9

*The current that would be encountered if the boom was perpendicular to the current

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Boom Secured by Anchor or Boat

Anchor Current

Recovery Operation Oil Diverted to Shore Area

FIGURE 12.4 Diagram showing how a deflection boom is used.

The U-configuration is also used to keep oil from spreading into bays or other sensitive areas, as well as to collect oil so that cleanup measures can be applied. The J-configuration is a variation of the U-configuration and is usually used to contain oil as well as to deflect it to the containment area. The V-configuration usually consists of two booms with a counterforce such as a skimmer at the apex of the two booms. Figure 12.5 shows a V-configuration used to direct oil to a skimmer. Encirclement is another way that booms can be used for containment. Stricken ships in shallow waters are often encircled or surrounded by booms to prevent further movement of oil away from the ship. Oil losses usually still occur because the boom’s capacity is exceeded or strong currents may sweep the oil under the boom. In many cases, however, this is all that can be done to prevent further spillage and spreading of the oil. Encirclement is often used as a preventative measure at tanker loading and unloading facilities. Because these facilities are usually situated in calm waters, oil from minor spills can often be contained using this technique. Booms are also used in fixed systems attached to docks, piers, harbor walls, or other permanent structures with sliding-type connectors that allow the boom to move up and down with the waves and tide. Their purpose is to protect certain areas from an oil spill. They are also used to enclose an area where oil is frequently loaded or unloaded or to provide backup containment for operations such as oil/water separators on shore. As these booms are often in place for 10 years before replacement, special long-lasting booms are usually used. Booms are also used in a “sweep” configuration to either deflect oil or contain it for pickup by skimmers. The sweep is held away from the vessel by

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FIGURE 12.5 A boom V-configuration used to direct oil to a skimmer.

a fixed arm, and the boom is allowed to form a U shape, as shown in Figure 12.6. A skimmer is usually placed in the U or is sometimes fixed in the vessel’s hull, and the oil is deflected to this position. Special vessels are required that can maneuver while moving slowly so that the boom does not fail. The various configurations in which booms can be deployed are shown in Figure 12.6.

12.1.3. Boom Failures A boom’s performance and its ability to contain oil are affected by water currents, waves, and winds.3-6 Either alone or in combination, these forces often lead to boom failure and loss of oil. Eight common ways in which booms fail are discussed here. Some of these are illustrated in Figure 12.7. Entrainment FailuredThis type of failure is caused by the speed of the water current and is more likely to happen with a lighter oil. When oil is being contained by a boom in moving water, if the current is fast enough, the boom acts like a dam and the surface water being held back is diverted downward and accelerates in an attempt to keep up with the water flowing directly under the boom. The resulting turbulence causes droplets to break away from the oil that has built up in front of the boom, referred to as the oil headwave, pass under the boom, and resurface behind it. The water speed at which the headwave becomes unstable and the oil droplets begin to break away is referred to as the critical velocity. It is the speed of the current flowing perpendicular to the boom, above which oil losses occur. For most booms riding perpendicular to the

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Skimmer

U-Configuration

V-Configuration

Leaking tanker

Encirclement

J-Configuration

Bay

Sweep

Exclusion

Anchored booms

Current

Current Recovery area

Diversion

Cascade

FIGURE 12.6 An illustration of various configurations used in boom deployment.

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Water current Water current

Entrainment

Drainage Failure

Water current

Splashover

Water current

Submergence

Critical Accumulation

Water current

Planing

FIGURE 12.7 An illustration of several modes of boom failure.

current, this critical velocity is about 0.5 m/s (about 1 knot). Figure 12.3 shows a boom that is losing oil by entrainment failure. At current speeds greater than the critical velocity, this type of boom failure can be overcome by placing the boom at an angle to the current or in the deflection mode. Since currents in most rivers and many estuaries exceed the critical velocity of 0.5 m/s (1 knot), this is the only way the oil can be contained. The approximate critical velocities for booms riding at other angles to the current are listed in Table 12.1. Drainage FailuredSimilar to entrainment, this type of failure is related to the speed of the water current, except that it affects the oil directly at the boom. After critical velocity is reached, large amounts of the oil contained directly at the boom can be swept under the boom by the current. Both entrainment and drainage failure are more likely to occur with lighter oils. One or both of these

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FIGURE 12.8 Drainage failure occurring on a boom.

two types of failure can occur, depending on the currents and the design of the boom. Figure 12.8 shows this type of boom failure. Critical AccumulationdThis type of failure usually occurs when heavier oils, which are not likely to become entrained in water, are being contained. Heavier oils tend to accumulate close to the leading edge of the boom and are swept underneath the boom when a certain critical accumulation point occurs. This accumulation is often reached at current velocities approaching the critical velocities listed in Table 12.1, but can also be reached at lower current velocities. SplashoverdThis failure occurs in rough or high seas when the waves are higher than the boom’s freeboard and oil splashes over the boom’s float or freeboard member. It can also occur as a result of extensive oil accumulation in the boom compared to the freeboard. Submergence FailuredThis type of failure occurs when water goes over the boom. Often the boom is not buoyant enough to follow the wave motion, and some of the boom sinks below the water line and oil passes over it. Submergence failure is usually the result of poor heave response, which is measured by both the reserve buoyancy and the flexibility of the boom. Failure due to submergence is not that common as other forms of failure, such as entrainment, usually occur first. PlaningdPlaning occurs when the boom moves from its designed vertical position to almost a horizontal position on the water. Oil passes over or under a planing boom. Planing occurs if the tension members are poorly designed and do not hold the boom in a vertical position or if the boom is towed in currents far exceeding the critical velocity. Figure 12.9 shows this type of failure. Structural Failure dThis failure occurs when any of the boom’s components fail and the boom lets oil escape. Sometimes structural failure is so

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FIGURE 12.9 Boom failure due to planing.

serious that the boom is carried away by the current. This does not happen often in normal currents and conditions. Floating debris, such as logs and ice, can contribute to structural failure. Shallow Water BlockagedThis type of failure occurs when rapid currents form under a boom when it is used in shallow waters. With the boom acting like a dam, the flow of water under it increases and oil is lost in several of the ways already described. Shallow water is probably the only situation in which a smaller boom might work better than a larger one. It should be noted, however, that booms are not often used in shallow water.

12.1.4. Ancillary Equipment A wide variety of ancillary equipment is used with booms. Hand-holds are often installed on smaller booms that can be lifted by hand and lifting points are installed on larger booms for lifting by crane. Without such provision for lifting, booms must often be lifted using ropes or cables placed around the boom; such lifting can cause damage. All booms have some form of end connector for joining them to other booms or to other pieces of hardware for towing or anchoring. While there are some standard connectors, they also vary among different manufacturers of booms, which can complicate the hookup. Towing bridles and towing paravanes are pieces of equipment that are designed to be attached to the boom so that it can be towed without being submerged or stressed. Booms are usually towed to the site of a spill in a straight line and must withstand stresses associated with this mode of transport. Anchors, anchor attachments, and lines are also available for use with booms.

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Booms are often stored on reels or in special containers designed for fast and efficient deployment. This is particularly important with heavier fireresistant booms as a 50-m section of such a boom could weigh hundreds of kilograms.

12.1.5. Sorbent Booms and Barriers Sorbent booms are specialized containment and recovery devices made of porous sorbent material such as woven or fabric polypropylene, which absorbs the oil while it is being contained. Sorbent booms are used when the oil slick is relatively thin, that is, for the final “polishing” of an oil spill, to remove small traces of oil or sheen, or as a backup to other booms. Sorbent booms are often placed off a shoreline that is relatively unoiled or freshly cleaned to remove traces of oil that may recontaminate the shoreline. They are not absorbent enough to be used as a primary countermeasure technique for any significant amount of oil. Sorbent booms require considerable additional support to prevent breakage under the force of strong water currents. They also require some form of flotation so that they won’t sink once they are saturated with oil and water. Oil sorbent booms must also be removed from the water carefully to ensure that oil is not forced from them and the area recontaminated. Figure 12.10 shows the use of a sorbent boom to contain a small spill.

12.1.6. Special-Purpose Booms A variety of special-purpose booms is available. A tidal seal boom floats up and down, but forms a seal against the bottom during low tide. These are often used

FIGURE 12.10 Use of a sorbent boom to contain a small spill.

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315 FIGURE 12.11 Illustration of the bubble barrier concept.

Oil

Entrained Water Air Inlet

Perforated Pipe

to protect beaches or other stretches of shoreline from oiling. An ice boom is used to contain or divert oil in ice-infested waters. It is not used to contain or divert ice. An ice boom usually has slots at the water line so that oil and water can pass through but ice cannot. A bubble barrier consists of an underwater air delivery system, which creates a curtain of rising bubbles that deflect the oil. Bubble barriers are occasionally used at fixed facilities such as harbors and loading platforms where the water is generally calm. The concept of bubble barriers is illustrated in Figure 12.11. High-pressure air or water streams can also be used to contain and deflect oil. Because of their high-power requirements, they are usually used only to deflect oil in front of skimmers or fixed separator systems. Chemical barriers use chemicals that solidify the oil and prevent its spread. Large amounts of chemicals are required, however, and the potential for containment is low. Net booms made from fine nets are used to collect viscous oils, tarballs, and oiled debris without having the large hydrodynamic forces of a solid boom. Oil trawls are similar to net booms, but are made in the shape of a U so that oil is contained in the net pocket.

12.2. SKIMMERS Skimmers are mechanical devices designed to remove oil from the water surface. They vary greatly in size, application, and capacity, as well as in recovery efficiency.7,8 Skimmers are sometimes classified according to the area where they are used, for example, inshore, offshore, in shallow water, or in rivers, or sometimes by the viscosity of the oil they are intended to recover, that is, heavy or light oil. Often skimmers are classified by the operating principle for example, disk, brush or weir skimmers. Skimmers are available in a variety of forms, including independent units built into a vessel or containment device and units that operate in either a stationary or mobile (advancing) mode. Some skimmers have storage space

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for the recovered oil, and some of these also have other equipment such as separators to treat the recovered oil. The effectiveness of a skimmer is rated according to the amount of oil that it recovers, as well as the amount of water picked up with the oil. Removing water from the recovered oil can be as difficult as the initial recovery. Effectiveness depends on a variety of factors including the type of oil spilled, the properties of the oil such as viscosity, the thickness of the slick, sea conditions, wind speed, ambient temperature, and the presence of ice or debris. Most skimmers function best when the oil slick is relatively thick, and most will not function efficiently on thin slicks. The oil must therefore be collected in booms before skimmers can be used effectively. The skimmer is placed in front of the boom or wherever the oil is most concentrated in order to recover as much oil as possible. Skimmers are often placed downwind from the boom, so that the wind will push the oil toward them. Small skimmers are usually attached to light mooring lines so that they can be moved around within the slick. Weather conditions at a spill site have a major effect on the efficiency of skimmers. Most skimmers work best in calm waters. Depending on the type of skimmer, most will not work effectively in waves greater than 1 m or in currents exceeding 1 knot. Most skimmers do not operate effectively in waters with ice or debris such as branches, seaweed, and floating waste. Some skimmers have screens around the intake to prevent debris or ice from entering, conveyors or similar devices to remove or deflect debris, and cutters to deal with sea weed. Very viscous oils, tarballs, or oiled debris can clog the intake or entrance of skimmers and make it impossible to pump oil from the skimmer’s recovery system. Skimmers are also classified according to their basic operating principles: oleophilic surface skimmers; weir skimmers; suction skimmers or vacuum devices; elevating skimmers; submersion skimmers; and vortex or centrifugal skimmers. Each type of skimmer has distinct advantages and disadvantages, which are discussed in this section. Other miscellaneous devices used to recover oil are discussed after the skimmers are covered.

12.2.1. Oleophilic Surface Skimmers Oleophilic surface skimmers, sometimes called sorbent surface skimmers, use a surface to which oil can adhere to remove the oil from the water surface. This oleophilic surface can be in the form of a disc, drum, belt, brush, or rope, which is moved through the oil on the top of the water. A wiper blade or pressure roller removes the oil and deposits it into an onboard container or the oil is directly pumped to storage facilities on a barge or on shore. The oleophilic surface itself can be steel, aluminum, fabric, or plastics such as polypropylene and polyvinyl chloride. Oleophilic skimmers pick up very little water compared to the amount of oil recovered, which means they have a high oil-to-water recovery ratio. They therefore operate efficiently on relatively thin oil slicks. They are not as

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susceptible to ice and debris as the other types of skimmers. These skimmers are available in a range of sizes and work best with light crude oils, although their suitability for different types of oil varies with the design of the skimmer and the type of oleophilic surface used. The operating principles of oleophilic skimmers are illustrated in Figure 12.12. The disc skimmer is a common type of oleophilic surface device. The discs are usually made of either polyvinyl chloride or steel. Disc skimmers work best with light crude oil and are well suited to working in waves and among weeds

FIGURE 12.12 Illustration of the principles of oleophilic surface skimmers.

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FIGURE 12.13 A disc skimmer under test.

or debris. These skimmers are usually small and can be deployed by one or two people. Disadvantages are that the recovery rate is slow and they work poorly with light fuels or heavy oils. Figure 12.13 shows a disc skimmer undergoing testing. The drum skimmer is another type of oleophilic surface skimmer. The drums are made of either a proprietary polymer or steel. The drum skimmer works relatively well with fuels and light crude, but is ineffective with heavy oils. Drum skimmers are often smaller in size like the disc skimmer. Figure 12.14 shows a dual drum skimmer. Belt skimmers are constructed of a variety of oleophilic materials ranging from fabric to conveyor belting. Most belt skimmers function by lifting oil up

FIGURE 12.14 A dual drum skimmer.

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from the water surface to a recovery well. As the motion of the belt through the water drives oil away from the skimmer, oil must be forced to the belt manually or with a water spray. Belt skimmers have been designed to overcome this problem, including one that pumps the oily water through a porous belt and the inverted belt skimmer that carries the oil under the water. The oil is subsequently removed from the belt by scrapers and rollers after the belt returns to a selected position at the bottom of the skimmer. Belt skimmers of all types work best with heavier crudes, and some are specially constructed to recover tarballs and very heavy oils. Belt skimmers are large and are usually built into a specialized cleanup vessel. Figure 12.15 shows a belt skimmer. Brush skimmers use tufts of plastic attached to drums or chains to recover the oil from the water surface. The oil is usually removed from the brushes by wedge-shaped scrapers. Brush skimmers are particularly useful for recovering heavier oils, but are ineffective for fuels and light crudes. Some skimmers include a drum for recovering light fuels and a brush for use with heavier oils. These skimmers can also be used with limited amounts of debris or ice. Brush skimmers are available in a variety of sizes, from small portable units to large units installed on specialized vessels or barges. Figure 12.16 shows a brush skimmer. Rope skimmers remove oil from the water surface with an oleophilic rope of polymer, usually polypropylene. Some skimmers have one or two long ropes that are held in the slick by a floating, anchored pulley. Others use a series of small ropes that hang down to the water surface from a suspended skimmer body. The rope skimmer works best with medium viscosity oils and is particularly useful for recovering oil from debris- and ice-laden waters. Rope skimmers vary in size from small portable units to large units installed on specialized vessels or barges. Figure 12.17 shows a rope skimmer.

FIGURE 12.15 An oleophilic belt skimmer.

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FIGURE 12.16 A brush skimmer under test.

FIGURE 12.17 A rope skimmer in use in a heavily oiled situation.

12.2.2. Weir Skimmers Weir skimmers are a major group of skimmers that use gravity to drain the oil from the surface of the water into a submerged holding tank. The configurations of weir skimmers are illustrated in Figure 12.18. In their simplest form, these devices consist of a weir or dam, a holding tank, and a connection to an external or internal pump to remove the oil. Many different models and sizes of weir skimmers are available. A weir skimmer at work is shown in Figure 12.19. A problem with some older weir skimmers is their tendency to rock back and forth in choppy water, alternately sucking in air above the slick and water

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Pump to Storage

Oil Slick

Weir-Type Skimmer

Side View

to External Pump

FIGURE 12.18 Illustration of the weir skimmer concept.

below. This increases the amount of water and reduces the amount of oil recovered. Some models include features for self-levelling and adjustable skimming depths so that the edge of the weir is precisely at the oilewater interface, minimizing the amount of water collected. Weir skimmers do not work well in ice and debris or in rough waters, and they are not effective for very heavy oils or tarballs. Weir skimmers are economical, however, and they can have large capacities. They are best used in calm, protected waters. Weir skimmers have also been built into booms and have been moderately successful in providing high recovery rates of lighter crudes.

12.2.3. Suction or Vacuum Skimmers Suction or vacuum skimmers use a vacuum or slight differential in pressure to remove oil from the water surface. Often the “skimmer” is only a small floating

FIGURE 12.19 A weir skimmer at work.

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FIGURE 12.20 Illustration of suction skimmer concepts. Oil Slick Side View

Suction Skimmer Oil Slick

to External Pump Intake Tubes

Suction Skimmer

Top View

head connected to an external source of vacuum, such as a vacuum truck. The head of the skimmer is simply an enlargement of the end of a suction hose and a float. The principle of operation of a suction skimmer is shown in Figure 12.20. Suction skimmers are similar to weir skimmers in that they sit on the water surface, generally use an external vacuum pump system such as a vacuum truck, and are adjusted to float at the oilewater interface. They also tend to be susceptible to the same problems as weir skimmers. They are prone to clogging with debris, which can stop the oil flow and damage the pump. They also experience the problem of rocking in choppy waters, which causes massive water intake, followed by air intake. Their use is restricted to light to medium oils. Figure 12.21 shows the use of a vacuum truck and suction hose to clean a beach. Despite their disadvantages, suction skimmers are the most economical of all skimmers. Their compactness and shallow draft make them particularly useful in shallow water and in confined spaces. They operate best in calm water with thick slicks and no debris. Very large vacuum pumps, called air conveyors, and suction dredges have been used to recover oil, sometimes directly without a head. Both of these adaptations, however, have the same limitations as smaller suction skimmers.

12.2.4. Elevating Skimmers Elevating skimmers or devices use conveyors to lift oil from the water surface into a recovery area as illustrated in Figure 12.22. A paddle belt or wheel or

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FIGURE 12.21 Use of a vacuum truck and suction hose to clean up oil on a beach.

a conveyor belt with ridges is adjusted to the top of the water layer, and oil is moved up the recovery device on a plate or another moving belt. The operation is similar to removing liquid from a floor with a squeegee. The oil is usually removed from the conveyor by gravity. When operating these skimmers, it is difficult to maintain the conveyor at the water line. In addition, they cannot operate in rough waters or in waters with large pieces of debris, and they cannot deal with light or very heavy oils. Elevating skimmers work best with medium to somewhat heavy oils in calm waters. They are generally large and are sometimes built into specialized vessels. Figure 12.23 shows an elevating skimmer at work.

12.2.5. Submersion Skimmers Submersion skimmers use a belt or an inclined plane to force the water beneath the surface. The belt or plane forces the oil downward toward a collection well where it is removed from the belt by a scraper or by gravity. The oil then flows upward into the collection well and is removed by a pump. Submersion skimmers move faster than other skimmers and can therefore cover a large area, making them suitable for use at larger spills. They are most effective with light oils with a low viscosity and when the slick is relatively thin. Disadvantages include a poor tolerance to debris compared to other skimmers, and they cannot be used in shallow waters. Submersion skimmers are larger than other types of skimmers and are usually mounted on a powered vessel.

12.2.6. Skimmer Performance A skimmer’s performance is affected by a number of factors including the thickness of the oil being recovered, the extent of weathering and emulsification

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Moving Paddles or Scrappers

Oil Slick Fixed Inclined Plane

Collection Well

Movement of Vessel

Squeegee or Roller Collection Well Oil Slick Rotating Belt

Movement of Vessel

FIGURE 12.22 Illustration of elevating skimmer concepts.

of the oil, the presence of debris, and weather conditions at the time of recovery operations. A skimmer’s overall performance is usually determined by a combination of its recovery rate and the percentage of oil recovered. The maximum amount of oil that a skimmer could recover is called the Nameplate Recovery Rate and is typically provided by the manufacturer of a skimmer.9-11 A similar definition is the Effective Daily Recovery Capacity, which is the amount that a skimmer could recover in daylight hours under ideal conditions. The recovery rate is the volume of oil recovered under specific conditions. It is measured as volume per unit of time, for example, m3/h, and is usually given as a range. If a skimmer takes in a lot of water, it is detrimental to the overall efficiency of an oil spill recovery operation. The results of performance testing on various types of skimmers are given in Table 12.2.1,7,9-11

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FIGURE 12.23 An elevating skimmer at work.

In addition to these characteristics, other important measures of a skimmer’s performance include the amount of emulsification caused by the skimmer, its ability to deal with debris, ease of deployment, ruggedness, applicability to specific situations, and reliability.

12.2.7. Special-Purpose Ships Special-purpose ships have been built specifically to deal with oil spills. Some ships have been built with a hull that splits to form a V-shaped containment boom with skimmers built into the hull, although this requires very expensive design features so that the ship can withstand severe weather conditions. Other ships have been built with holes in the hull to hold skimmers, with sweeps mounted on the side to direct oil to the skimmer area. Many small vessels have been custom-built to hold skimmers.

12.3. SORBENTS Sorbents are materials that recover oil through either absorption or adsorption. They play an important role in oil spill cleanup and are used in the following ways: to clean up the final traces of oil spills on water or land; as a backup to other containment means, such as sorbent booms; as a primary recovery means for very small spills; and as a passive means of cleanup. An example of such passive cleanup is when sorbent booms are anchored off lightly oiled shorelines to absorb any remaining oil released from the shore and prevent further reoiling of the shoreline. Sorbents can be natural or synthetic materials. Natural sorbents are divided into organic materials, such as peat moss or wood products, and inorganic

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TABLE 12.2 Performance of Typical Skimmers Recovery Rate (m3/hr) for given oil type*

Skimmer Type

Diesel

Light Crude

Heavy Crude

Oleophilic Skimmers small disc 0.4 to 1

0.2 to 2

large disc

10 to 20

10 to 50

0.5 to 20

0.5 to 2

brush

0.2 to 0.8

large drum small drum large belt

0.5 to 5 1 to 5

inverted belt

large weir

80 to 95

1 to 20

3 to 20

3 to 10

0.5 to 5

2 to 10 2 to 20

5 to 25

Elevating Skimmers paddle conveyer

1 to 10

1 to 20

Submersion Skimmers large 0.5 to 1

1 to 80

1 to 20

0.3 to 1

0.3 to 2

large trawl unit

2 to 40

large vacuum unit

3 to 20

Vortex/Centrifugal Skimmers centrifugal unit 0.2 to 0.8

0.2 to 10

75 to 95 85 to 95

5 to 30

1 to 10

80 to 95

0.5 to 5

5 to 10

Suction Skimmers small

0.5 to 2

80 to 95

30 to 100

advancing weir

80 to 95

10 to 30

2 to 20 0.2 to 10

Percent Oil** 80 to 95

10 to 30

rope Weir Skimmers small weir

Bunker C

20 to 80 3 to 5

50 to 90 30 to 70

1 to 5

10 to 40 70 to 95 3 to 10 20 to 90

3 to 10

10 to 80 2 to 20

*Recovery rate depends very much on the thickness of the oil, type of oil, sea state, and many other factors **This is the percentage of oil in the recovered product. The higher the value, the less the amount of water and thus the better the skimmers’ performance

materials, such as vermiculite or clay. Sorbents are available in a loose form, which includes granules, powder, chunks, and cubes, often contained in bags, nets, or socks. Sorbents are also available formed into pads, rolls, blankets, and pillows. Formed sorbents are also made into sorbent booms and sweeps. One

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type of plastic sorbent is formed into flat strips or “pom-poms.” which are particularly useful for recovering very heavy oils. The use of synthetic sorbents in oil spill recovery has increased in the last few years. These sorbents are often used to wipe other oil spill recovery equipment, such as skimmers and booms, after a spill cleanup operation. Sheets or rolls of sorbent are often used for this purpose. Synthetic sorbents can often be reused by squeezing the oil out of them, although extracting small amounts of oil from sorbents is sometimes more expensive than using new sorbent. Furthermore, oil-soaked sorbent is difficult to handle and can result in minor releases of oil between the regeneration area and the area where the sorbent is used. The capacity of a sorbent depends on the amount of surface area to which the oil can adhere as well as the type of surface. A fine porous sorbent with many small capillaries has a large amount of surface area and is best for recovering light crude oils or fuels. Sorbents with a coarse surface would be used for cleaning up a heavy crude oil or Bunker. Pom-poms intended for recovering heavy Bunker or residual oil consist of ribbons of plastic with no capillary structure. General-purpose sorbents are available that have both fine and coarse structure, but these are not as efficient as products designed for specific oils. Some sorbents are treated with oleophilic (oil-attracting) and hydrophobic (water-repelling) agents to improve the ability of the material to preferentially absorb oil rather than water. As natural sorbents often recover large amounts of water along with the oil, they can be treated to prevent water uptake. This type of treatment usually increases the ability of certain sorbents to remain afloat. The performance of sorbents is measured in terms of total oil recovery and water pickup, similar to skimmers. “Oil recovery” is the weight of a particular oil recovered compared to the original weight of the sorbent. For example, highly efficient synthetic sorbent may recover up to 30 times its own weight in oil, and an inorganic sorbent may recover only twice its weight in oil. The amount of water picked up is also important, with an ideal sorbent not recovering any water. Some results of performance testing of typical sorbents with various types of oils are given in Table 12.3.11-14 A number of precautions must be considered when using sorbents. First, the excessive use of sorbents at a spill scene, especially in a granular or particulate form, can compound cleanup problems and make it impossible to use most mechanical skimmers. Sorbents may cause plugging in discharge lines or even in the pumps themselves. Second, sorbents that sink should not be used as they could be harmful to the environment. Sinking is a problem with many sorbents, such as untreated peat moss, most inorganic sorbents, and many wood products. Many countries do not allow the use of sorbents that sink in applications on water, as the oil will usually be released from the sorbent over time and both the oil and the sorbent are very harmful to benthic life.

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TABLE 12.3 Performance of Some Sorbents Typical Oil Recovery with Oil Type (weight:weight)*

Sorbent Type

Diesel

Light Crude

Heavy Crude

Bunker C

Percent Oil**

Synthetic Sorbents polyester pads

7

9

12

20

90þ

polyethylene pads

25

30

35

40

90þ

polyolefin pom-poms

2

2

3

8

90þ

polypropylene pads

6

8

10

13

90þ

polypropylene pom-poms

3

6

6

15

90þ

polyurethane pads

20

30

40

45

90þ

Natural Organic Sorbents bark or wood fibre

1

3

3

5

70

bird feathers

1

3

3

2

80þ

peat moss

2

3

4

5

80þ

treated peat moss

5

6

8

10

80þ

straw

2

2

3

4

70

vegetable fibre

9

4

4

10

80þ

Natural Inorganic Sorbents clay (kitty litter) 3

3

3

2

70

treated pearylite

8

8

8

9

70

treated vermiculite

3

3

4

8

70

Vermulite

2

2

3

5

70

*Recovery depends very much on the thickness of the oil, type of oil, surface type and many other factors **This is the percentage of oil in the recovered product. The higher the value, the less the amount of water and thus the better the sorbent’s performance

Finally, recovery and disposal of the oiled sorbent material must be considered. As oiled sorbent is most often burned or buried, the sorbent must retain the oil long enough so that it is not lost during recovery operations or in transport to disposal sites. The excessive use of sorbents is illustrated in Figure 12.24. A sorbent used for heavy oil is illustrated in Figure 12.25.

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FIGURE 12.24 The excessive use of particulate sorbent on a spill. Sorbents are best used in a polishing role.

FIGURE 12.25 A sorbent for heavy oil spill polishing. This type of sorbent is sometimes called a pom-pom as it resembles those used by cheer leaders.

12.4. MANUAL RECOVERY Small oil spills or those in remote areas are sometimes recovered by hand. Heavier oils are easier to remove this way than lighter oils. Spills on water close to shorelines are sometimes cleaned up with shovels, rakes, or by cutting the oiled vegetation. Hand bailers, which resemble a small bucket on the end of a handle, are sometimes used to recover oil from the water surface. Manual recovery is tedious and may involve dangers such as physical injury from falls

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FIGURE 12.26 Manual cleanup near a shoreline.

on the shore. Much shoreline cleanup is usually done manually. Figure 12.26 shows manual cleanup near a shoreline.

12.5. TEMPORARY STORAGE When oil is recovered, sufficient storage space must be available for the recovered product. The recovered oil often contains large amounts of water and debris that increase the amount of storage space required. Figure 12.27 shows a massive collection of drums that are used as temporary storage after a successful manual cleanup. Several types of specially built tanks are available to store recovered oil. Flexible portable tanks, often constructed of plastic sheeting and a frame, are the most common type of storage used for spills recovered on land and from rivers and lakes. These are available in a range of sizes from approximately 1 to 100 m3 and require little storage space before assembly. Most of these types of tanks do not have a roof, however, so rain or snow can enter the tank and vapors can escape. Figure 12.28 shows a flexible portable tank being used for oil recovery. Rigid tanks, which are usually constructed of metal, are also available but are less common than flexible tanks. Pillow tanks, constructed of polymers and heavy fabrics, are usually used to store oil recovered on land. These are placed on a solid platform so that rocks cannot puncture the tank when full. Pillow tanks are also sometimes used on the decks of barges and ships to hold oil recovered at sea. Oil recovered on land is often stored in stationary tanks built for other purposes, and in dump trucks and modular containers, lined with plastic. Recovered oil can also be temporarily stored in pits or berms lined with polymer sheets, although this open type of storage is not suitable for volatile oils. Towable, flexible tanks, usually bullet-shaped, are also used to contain oil recovered at sea. Their capacity varies, but they can hold up to several tons.

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FIGURE 12.27 An illustration of a successful cleanup with little bulk temporary storage available.

These tanks are also constructed of polymers, with fabric materials sometimes used as a base. Since most oils are less dense than water, these tanks will float throughout the recovery process. When full, these tanks can be difficult to maneuver, however, and they can be difficult to empty, especially if the oil is viscous and contains debris. Oil recovered at sea is often temporarily stored in barges. Many cleanup organizations have barges that are used solely for storing recovered oil and lease barges for use at larger spills. Recovered oil is also stored in the holds of ships, usually using older vessels. This is more economical than using designated tanks on land, especially when the recovered oil has to be stored for long periods of time until a final disposal method is found. Drums, small tanks,

FIGURE 12.28 A portable tank used for spilled oil recovery.

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livestock watering troughs, and even bags have also been used to contain oil from smaller spills, both on land and at sea.

12.6. PUMPS Pumps play an important role in oil spill recovery. They are an integral part of most skimmers and are also used to transfer oil from skimmers to storage tanks. Pumps used for recovered oil differ from water pumps in that they must be capable of pumping very viscous oils and dealing with water, air, and debris. The three basic types of pumps used for pumping oil recovered from spills are centrifugal pumps, vacuum systems, and positive displacement pumps. The operating principles of some pumps are shown in Figure 12.29.

Centrifugal Pumps Centrifugal pumps have a spinning vane that moves the liquid out of a chamber by centrifugal force. These pumps, which are regularly used for pumping water and wastewater, are not designed for use with oil and are generally not capable of dealing with material more viscous than light crudes. Centrifugal pumps

Inlet

Outlet

Inlet

Wiper

Outlet

Centrifugal

Screw or Auger

Inlet

Inlet valve Piston

Air Outlet valve

Piston

Outlet

Diaphragm

FIGURE 12.29 The operating principle of some common oil recovery pumps.

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cannot typically deal with debris. They are economical and universally available, however, and are often used in oil spill cleanup operations.

Vacuum Systems Vacuum systems consist of vacuum pumps and tanks mounted on a skid or truck. The vacuum pump creates a vacuum in the tank and the oil moves directly through a hose or pipe to the tank from the skimmer or the source of the oil. The oil does not go through the pump, but moves directly from its source into the tank. Vacuum systems can handle debris, viscous oils, and the intake of air or water. The vacuum tank requires emptying, however, which is usually done by opening the entire end of the tank and letting the material move out by gravity. Positive Displacement Pumps Positive displacement pumps are often built directly into skimmers to recover more viscous oils. These pumps have a variety of operating principles, all of which have some common schemes. Oil enters a chamber in the pump where it is pushed by a moving blade, shoe, or piston to the exit of the pump. The oil and other material with it must move through the chamber because there is no alternative passagedthus the name positive displacement. The screw or auger pump is a common type of positive displacement pump. The oil drops into part of the screw and is carried to an output. Wiper blades remove oil from the auger flights to prevent it from remaining on the auger shaft. The screw pump can deal with very viscous oils and is often built into skimmers. A gear or lobe pump uses gears or lobes mounted on a shaft to accomplish the positive displacement of oil through a chamber. Neither of these pumps can handle debris or highly viscous oil. The diaphragm pump uses a flexible plate or diaphragm to move oil from a chamber. This type of pump usually requires a valve, which limits its use to material that can pass through the valve, making it unsuitable for oil containing debris. A vane pump, which uses a movable metal or polymer plate to move oil in a chamber, functions in a manner similar to a centrifugal pump, but with positive displacement. The peristaltic pump uses a hose that is progressively squeezed by rollers moving along the top of its surface. As the oil never comes into contact with any material other than the hose, this pump is suitable for use with a variety of hazardous materials. Both the vane pump and peristaltic pump can handle medium viscosity oils and small debris. A piston-like plunger in the sliding shoe pump moves oil along between the input and output ports. This pump does not require valves, although certain models do include them. The piston pump is similar to the sliding shoe pump except that oil is simply pushed out of the cylinder from the input valve to the output valve. Both sliding shoe and piston pumps can handle viscous oils, but generally cannot handle debris.

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Finally, the progressive cavity pump uses a rotating member inside a molded cylinder that together form a cavity that moves from input to output as the centre is rotated. This pump can handle very viscous oils and debris, but is heavy and more expensive than other types of pumps.

12.6.1. Performance of Pumps The performance of pumps is usually measured in terms of the volume displaced per unit of time at a given viscosity, suction head, and pressure head.15-17 The suction head is the maximum height that a pump can draw the target liquid, and the pressure head is the maximum height that a pump can push the target liquid. These heads are not important in most pump applications in oil spill recovery. It is important, however, that pumps used for pumping oil are selfpriming rather than requiring that a flow of liquid be established before the pump is functional. Other important factors to consider are the pump’s ability to deal with emulsions and debris and the degree of emulsion formation that takes place in the pump itself.

12.7. SEPARATION As all skimmers recover some water with the oil, a device to separate oil and water is usually required. The oil must be separated from the recovery mixture for disposal, recycling, or direct reuse by a refinery. Sometimes settling tanks or gravity separators are incorporated into skimmers, but separators are more often installed on recovery ships or barges. Portable storage tanks are often used as separators, with outlets installed on the bottom of the tanks so that water that has settled to the bottom of the tank can be drained off, leaving the oil in the tank. Vacuum trucks are also used in this way to separate oil and water. Screens or other devices for removing debris are sometimes incorporated into separators. A gravity separator is the most common type of separator. In its simplest form, it consists of a large holding tank in which the oil and water mixture is held long enough for the oil to separate by gravity alone. This is referred to as the residence time and varies from minutes to hours. When inflow volumes are large, it can be difficult to find large enough separators to provide the long residence times required. Oil refineries have large separators that may cover several hectares and are used for treating refinery waste and are sometimes also used to treat oil recovered from spills. Separators are often made with baffles or other interior devices that increase the residence time and thus the degree of separation. The parallel plate separator is a special model of gravity separator. Many parallel plates are placed perpendicular to the flow, creating areas of low water turbulence where drops of oil can re-coalesce from the water and rise to the surface. Centrifugal separators have spinning members that drive the heavier water from the lighter oil, which collects at the center of the vessel. These separators are very efficient but have less capacity than gravity separators and cannot

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handle large debris. They are best suited to constant amounts of oil and water. Sometimes centrifugal separators are used in tandem with gravity separators to provide an optimal system. As emulsions are not broken down in separators, emulsion-breaking chemicals are often added to the recovered mixture before it enters the separator. Heating the emulsions to 80 or 90 C usually results in separation, and the water can then be removed, although this process uses a lot of energy. Separator performance is measured by the water-removal efficiency and the throughput volume. Important factors affecting performance include the ability to handle small debris (larger debris is usually removed) and a wide variety of oil and water ratios, with oil content and flow rate sometimes changing suddenly.

12.8. DISPOSAL Disposing of the recovered oil and oiled debris can be the most difficult aspects of an oil spill cleanup operation.18-20 Any form of disposal is subject to a complex system of local, provincial or state, and federal legislation. Unfortunately, most recovered oil consists of a wide range of contents and material states and cannot be classified as simply liquid or solid waste. The recovered oil may contain water that is difficult to separate from the oil and many types of debris, including vegetation, sand, gravel, logs, branches, garbage, and pieces of containment booms. This debris may be too difficult to remove, and thus the entire bulk material may have to be disposed of. Spilled material is sometimes directly reused either by reprocessing in a refinery or as a heating fuel. Some power plants and even small heating plants such as those in greenhouses can use a broad spectrum of hydrocarbon fuels. Often the equipment at refineries cannot handle oils with debris, excessive amounts of water, or other contaminants and the cost of pretreating the oils can far exceed the value that might be obtained from using them. Heavier oils are sometimes sufficiently free of debris to be used as a road cover when mixed with regular asphalt. Recovered material from cleaning up beaches can be used in this way. If the material is of the correct consistency, usually sand, the entire mixture might be mixed with road asphalt. Incineration is a frequent means of disposal for recovered oil, as large quantities of oil and debris can be disposed of in a relatively short time. Disadvantages are the high cost, which may include the cost of transporting the material to the facility. In addition, approval must be obtained from government regulatory authorities. Local emission guidelines for incinerators may preclude simply placing the material into an incinerator. Spill disposal is sometimes exempt from regulations, or special permits are available. Several incinerators have been developed for disposing of either liquid or solid materials, but these all require special permits or authority to operate. In remote locations, it may be necessary to burn oiled debris directly on the recovery sites without an incinerator because it is too bulky to transport to the nearest incinerator.

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Contaminated beach material is difficult to incinerate because of the sand and gravel content. Special burners have been constructed to process these materials, and the beach material can then be returned to the original site. There are also machines to wash oily sand or gravel. The oil recovered from this process must be separated from the wash water and then disposed of separately. Similarly, heat treatment devices remove oil from sand or gravel without burning. The resulting vapors are trapped, condensed, and the oil is disposed of. This method is not common as it requires large amounts of heat energy. Figure 12.30 shows bags of mixed debris from shoreline cleanup. This material was put into a special landfill. Incineration should be differentiated from in-situ burning, which involves burning the material directly on site without the use of a special device. This is usually only applied to lightly oiled driftwood, and special permission must be obtained from the appropriate authorities. Oiled debris, beach material, and sorbents are sometimes disposed of at landfill sites. Legislation requires that this material not contain free oil that could migrate from the site and contaminate groundwater. Some governments have standard leachability test procedures that determine whether the material will release oil. Several stabilization processes have been developed to ensure that free oil does not contaminate soil or groundwater. One process uses quick lime (calcium oxide) to form a cement-like material, which can be used on roads as a dust-inhibitor. Land-farming is the application of oil and refinery waste to land where it degrades. This practice is now banned in most jurisdictions since many oil components do not break down, and contaminants, such as metals and polyaromatic hydrocarbons, are carried away from the site, often in groundwater. Lightly contaminated water from skimmers, less than 15 parts-per-million (ppm) by weight of oil, can usually be returned to the water body from where it

FIGURE 12.30 Bags from shoreline cleanup. These are typically disposed of in special landfills.

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came. More contaminated water may require further treatment in separators or at a municipal wastewater treatment plant.

ACKNOWLEDGMENTS Environment Canada is acknowledged for the use of photographs and other materials in the preparation of this document.

REFERENCES 1. Fingas MF. The Basics of Oil Spill Cleanup. 2nd ed. Boca Raton, FL: CRC Press; 2000. 2. Potter S, McCourt J, Smith R. Estimation of Towing Forces on Oil Spill Containment Booms. AMOP 1999;825. 3. Castro A, Iglesias G, Carballo R, Fraguela JA. Floating Boom Performance Under Waves and Currents. J Haz Mat 2010;226. 4. Amini A, Bollaert E, Boillat J-L, Schleiss AJ. Dynamics of Low-Viscosity Oils Retained by Rigid and Flexible Barriers. Ocean Eng 2008;1479. 5. Muttin F. Oil Spill Boom Modelling by the Finite-Element Method. WIT Trans Ecol Environ 2008;67. 6. Muttin F. Structural Analysis of Oil-Spill Containment Booms in Coastal and Estuary Waters. Appl Ocean Res 2008;107. 7. Schulze R. Oil Spill Response Performance Review of Skimmers, ASTM Manual Series. ASTM 1998. 8. Schwartz SH. Performance Tests of Four Selected Oil Spill Skimmers. AMOP 1979;493. 9. Fingas MF. Weather Effects on Oil Spill Countermeasures. This Work; 2010:339. 10. Meyer P, Schmidt W, Delgado J-E, DeVitis D, Potter S, Haugstad E, et al. Application of the American Society of Testing and Materials (ASTM) New Skimmer Test Protocol. AMOP 2009;323. 11. Potter S. The Use of Consensus-Based Standards to Improve Oil Spill Equipment Testing and Selection Protocols. IOSC 2008;427. 12. Cooper D, Gausemel I. Oil Spill Sorbents: Testing Protocol and Certification Listing Program. IOSC; 2005:393. 13. Cooper D, Dumouchel A, Brown CE. Multi-Track Sorbent Boom and Sweep Testing. AMOP 2005;393. 14. Carmody O, Frost R, Xi Y, Kokot S. Surface Characterisation of Selected Sorbent Materials for Common Hydrocarbon Fuels. Surf Sci 2007;2066. 15. Potter S, Bronson M, Thompson E, Majors L. Testing of Various Options for Improving the Pumping of Viscous Oils and Emulsions. AMOP 2007;387. 16. Drieu MD, Nourse PC, MacKay R, Cooper DA, Hvidbak F. Latest Update of Tests and Improvements to US Coast Guard Viscous Oil Pumping System. Mar Pollut Bull 2003;470. 17. Cooper DW, MacKay R. Assisted Pumping of Extremely Viscous Oils. AMOP 2001;373. 18. Richardson C. Waste Management in Major Marine Oil Spills: The Importance of Its Strategy. International Conference on Health, Safety, and Environment in Oil and Gas Exploration and Production 2004;1065. 19. Jones MT, Najafi FT. The Disposal of Used Oil, Spilled Crude Oils, and Their Associated Sorbent Materials. Annual ConferencedCanadian Society for Civil Engineering 2002;2723. 20. Davies G. The Management of Oily Waste. Interspill 2000;151.

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Chapter 13

Weather Effects on Oil Spill Countermeasures Merv Fingas

Chapter Outline 13.1. Introduction 339 13.2. Review of Literature on 343 Spill Countermeasures and Weather 13.3. Development of Models 383 for Effectiveness of Countermeasures

13.4. Overview of Weather Limitations 13.5. Summary and Conclusions

405 407

13.1. INTRODUCTION Weather has been recognized as one of the most important factors in predicting oil spill fate and behavior.1,2 Weather has not, however, been well recognized in designing oil spill countermeasures.1,3 Bad weather is usually recognized as a condition that hinders or stops effective oil spill countermeasures.4 The traditional concept of “windows of opportunity” began with the topic of dispersants in about the mid-1990s. This concept largely related to the window of opportunity for dispersal as time progressed and the oil became more weathered and less dispersible. As time progresses, the window of opportunity to disperse closes.5 Thus, the prime variable for generating the window was weathering with time. Subsequently, some of the same concepts, but only including the same parameters, were extended to physical recovery and containment.6-10 The windows of opportunity for Alaska North Slope oil, based on weathering only, were stated by Champ et al. to be 0 to 36 hours for burning, 0 to 36 hours for dispersing, 0 to 18 hours for oilewater separator, and 26 to 120 hours for reduced dispersant effectiveness.9 It is important to note that outside of dispersion, the windows of opportunity concept, in actual fact, has little relevance. Before the publication of the window of opportunity concept, it was not included in logistic planning, nor was weather considered an essential part in Oil Spill Science and Technology. DOI: 10.1016/B978-1-85617-943-0.10013-9 Copyright Ó 2011 Elsevier Inc. All rights reserved.

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planning oil spill countermeasures.11 Historically, most reference to windows of opportunity were to the time factor. The biggest concern was the evaporation of the oil, which leads to large increases in viscosity and therefore increased difficulty in recovery and other countermeasures. Another concern was the emulsification of the oil. The secondary time window is that of spreading. A typical crude oil can spread over dozens of square kilometers in the first day after the spill.12,13 Furthermore, once spreading progresses, the thickness approaches or is less than 1 mm, a thickness that cannot be dealt with by any form of countermeasure. Oil that had been at sea for a period of time was sometimes thought to be lost, and countermeasures were hopeless.14 Only recently has it been recognized that many of these problems do not occur with heavy oils.15 Currently, weather is sometimes incorporated into contingency plans. Some countries have even incorporated weather and its effects into shoreline mapping.16 The presence of ice, though not strictly a weather condition, is weather related and can severely affect the recovery of standard equipment.17 The ability to perform multiple tasks at sea is typically recognized, although the ability to use chemical dispersants and mechanical recovery are not compatible with one another and in-situ burning is incompatible with both of these.18 The costs of spills have not been evaluated in terms of weather conditions.19-21 There is insufficient information in the literature with which to judge the effect of weather conditions on the costs and general progress of oil spill cleanup. Harper et al. reviewed costs and did not include the effects of weather.22 They also noted that the costs of offshore cleanup are less than shoreline cleanup by a factor of 2.5 to 4. In-situ burning is estimated to be about 5 times cheaper than offshore recovery and 10 times cheaper than shoreline cleanup. Recently, Robertson and Kumar prepared a “response gap” analysis for Prince William Sound and the Hitchinbrook Entrance.23 They placed cut-off values for wind, wave height, temperature, and visibility and then used actual weather buoy data to evaluate the operability of physical countermeasures. The criteria are shown in Table 13.1 and the resulting cut-off values in Table 13.2. One point that should be noted is that Arctic conditions, particularly in winter, are much more extreme than others and should bear special consideration.24-26 Many documents on Arctic oil spills exist, and thus Arctic countermeasures per se will not be covered here.

13.1.1. Spreading Compared to Weathering Spreading, which is a function of time and oil properties, will result in thin slicks, which may not be recoverable, burnable, or dispersible.27 Oceanic processes including Langmuir circulation and the presence of ocean fronts may result in the collection of material, thus reversing the effects of spreading to a certain degree.28-31 Initial spreading equations such as those by Fay do not consider weather factors such as transport by wind and waves and, consequently, underpredict the spreading when wind is a factor.32

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TABLE 13.1 Robertson and Kumar’s Response Physical Countermeasures Limits Environmental Factor

Green Response Not Impaired

Yellow Response Impaired

Red Response Not Possible/Effective

Wind (speed in Knots) (m/s)

0 to <21 (0 to <10 m/s)

21 to <30 (10 to <15 m/s)

> 30 (>15 m/s)

Sea State (waves in metres)

<1 if wave steepness considered

1 to <2 also considered wave steepness

>2 also considered wave steepness

Temperature ( C)

> 3 C

8 C to 3 C and wind speed >12 knots (6 m/s)

<8 C and wind speed >5 knots (3 m/s)

Visibility (km)

>0.8 (day light)

0.4 to 0.08

<0.4

not present

Red any factor is red if two or more are yellow

Combination of Factors Green if all factors are green if only one factor yellow

13.1.2. Important Components of Weather A review of weather factors that are relevant to the effectiveness of countermeasures at sea shows that wind speed and the resulting wave heights are the most important factor. Current or speed of water movement is not usually the result of changing weather, but will be covered briefly in this chapter. Temperature affects countermeasures such as dispersion but is not as significant in terms of other countermeasures. Temperature can be significant if it results in icing. The relevant temperature changes at sea are only from 5 to 20 C. This spread in water temperature does not cause viscosity changes that are significant as it relates to skimmers and especially not to booms. On land, however, much wider temperature differences exist. Wind is the component that generates waves and by itself can cause significant changes in oil behaviour.33 Wind can also change the rate of surface drift of oil. Youssef and Spaulding found that the drift factor (normally taken as 3.5%) varies with wind. In shallow water, the drift factor increases with increasing wind speed.34 The deflection angle, however, was found to be insensitive to variations in depth, but increases slightly as wind speed increases. For waves, the most important component to consider is the type of wave.35,36 A regular wave does not impose as great a constraint on performance

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TABLE 13.2 Robertson and Kumar’s Response Limits Applied to Prince William Sound Environmental Factor

Green Response Not Impaired

Yellow Response Impaired

Red Response Not Possible/Effective

Wind (speed in Knots) (m/s)

0 to <21 (0 to <10 m/s)

21 to <30 (10 to <15 m/s)

>30 (>15 m/s)

Sea State (waves in metres)

<1 if wave steepness considered

1 to <2 also considered wavesteepness

>2 also considered wave steepness

Temperature ( C)

>3 C

8 C to 3 C and wind speed > 12 knots (6 m/s)

<-8 C and wind speed > 5 knots (3 m/s)

Visibility (km)

>0.8 (day light)

0.4 to 0.08

<0.4

Green if all factors are green if only one factor yellow

Red any factor is red if two or more are yellow

Combination of Factors

Results Location Central Prince William Sound

Percent of Time

Percent of Time

Entire year

87.4

12.6

Summer (April to September)

95.8

4.2

Winter (October to March)

76.9

23.1

Location Hitchinbrook Entrance

Percent of Time

Percent of Time

Entire year

61.5

38.5

Summer (April to September)

83.8

16.2

Winter (October to March)

33.9

66.1

as does an irregular wave. A typical wave situation that can cause countermeasures difficulty is shown in Figure 13.1. At sea, many different types of wave energies have been recognized. For example, breaking waves display more energy than nonbreaking waves.37,38 Goodman noted that wave energy was a most important factor, but one that was not understood.39 Cheng et al. designed waves for a test tank and noted that wave shape was an important factor.40 Payne et al. studied the weathering of the Exxon Valdez oil in the field and in test tanks.41 They note that weather, especially the waves, is an important factor in

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FIGURE 13.1 A ship in distress and leaking oil; however, the waves are such that it is difficult to apply spill countermeasures.

the fate and behavior of the oil. Simecek-Beatty et al. developed a model of chemical dispersion and use the fact that the mixing layer is 1.5 times the wave height.42 This shows that wave height is a strong predictor of dispersion amount. Some observers state that weather is a very important factor as it relates to spill countermeasures effectiveness.43,44 The resolution of the data is also very important. Elliot and Jones review the prediction of oil spill behavior and fate during the Sea Empress incident and note that the use of coarse grid, nonoperational data resulted in prediction errors.45 They maintain that accuracy could be improved by using high-quality and high-resolution weather data.

13.1.3. Oil Properties Regardless of Weathering Oil properties play a large role in the behavior and fate of oil at sea, including how the oil relates to changes in the weather. Buist et al. studied waxy crudes from eastern Canada and found that, from a countermeasures perspective, they behaved differently than other oils, regardless of small differences in temperature.46 Fingas et al. noted that the dynamics of orimulsion were changed by variations in temperature.47 The lower the temperature, the more orimulsion surfaces.

13.2. REVIEW OF LITERATURE ON SPILL COUNTERMEASURES AND WEATHER 13.2.1. A Priori Decision Guides A number of a priori guides have been issued; these guides are based on logic and not on specific tests. A classic guide described by Al Allen in 198848 was

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Physical Spill Countermeasures on Water

meant to serve as an intermediate in calculating volume rates for each countermeasure analyzed. The logic that Allen uses is summarized here. For burning, it was assumed that a sufficient thickness (2 to 3 mm) is needed for oil to burn and that ignition cannot be carried out at winds greater than about 10 m/s (20 knots) but that, once ignited, burns can be sustained much past this value. Allen notes that mechanical cleanup techniques typically work best on thick oil layers in calm seas and that this drops off quickly at winds of 7 to 10 m/s (Beaufort 3 to 4). The thickness relationships are also crucial, as assumed earlier. High-seas skimming equipment may have an extended envelope, but this is not defined. Allen notes that, as short-period wind-waves build to 0.6 to 0.9 m, booms will suffer significant losses due to entrainment and splashover. The lower bound of dispersant use was estimated to be a wind of about 5 m/s (Beaufort 2) on the basis of the mixing required. The upper limit of dispersant application was estimated to be about 12 to 14 m/s wind (Beaufort 5 to 6) on the basis of the benefits compared to natural dispersion, which should be great at this sea level. Allen combines these weather options with a volume recovery versus spill thickness to provide an assessment basis for offshore countermeasures.48 Exxon Mobil lists the weather limitations (upper limit) for dispersant application as: work boats (tugboat type)dwind speed 3 to 11 m/s (7 to 21 knots), significant wave height 0 to 3 m; single-engine airplanesdwind speed 8 to 11 m/s (17 to 21 knots), significant wave height 2 to 3 m; medium-sized helicoptersdwind speed 9 to 11 m/s (17 to 27 knots, significant wave height 2 to 8 m); and large multiple-engine airplanesdwind speed 15 to 18 m/s (30 to 35 knots), significant wave height 9 to 12 m.49 DeCola presented a survey of world guidelines and decision trees for dispersant use.50 Of the about 30 guides or decision trees presented, only two decision trees note restrictions on weather conditions. The US Environmental Protection Agency (EPA) Region 6 guideline indicates an upper limit of 13 m/s (25 knots) on dispersant use, and the Washington/Oregon guidelines specify a lower limit of a sea state of 1 and an upper sea state of 4 (1 to 10 m/s winds). The latter guideline also specifies an upper limit of 0.5 m/s current for mechanical countermeasures. Koops and Huisman give a priori limits of Beaufort 6 for skimmers and other mechanical recovery, a limit between 2 and 8 Beaufort (2 to 20 m/s wind) for dispersion, and a lower threshold of greater than 3 Beaufort for natural dispersion.51 Koops gives the limit of skimmers as 1.5 m wave heights and notes that swell has no effect on the capability to mechanically recover.52 The Mechanical Equipment Calculator, part of National Oceanic and Atmospheric Administration (NOAA’s) Spill Tools on the Internet, does not include the effects of weather on containment and recovery, although effectiveness is calculated in the program.53 The program includes primarily the inputs of slick thickness and efficiency. Etkin and coworkers describe modeling of countermeasures to include the effect of currents.54 Dempsey suggests a limit of a wind speed of 12 m/s (25 knots) and a sea state with a wave height not exceeding 3 m for offshore work.55

Chapter | 13 Weather Effects on Oil Spill Countermeasures

345

Reed described the OSCAR spill model and noted that the mechanical recovery efficiencies in the model were set as 80% with a 5 m/s wind and 60% with a 10 m/s wind.56-58 It is important to note that most of the above estimates are not based on quantitative results. Quantitative results are presented in the remainder of this subsection.

13.2.2. General Countermeasures Most authors presume low weather limits for at-sea countermeasures. The presumption is typically that countermeasures cannot be conducted if the sea is not dead calm.59,60 Steen et al. suggest a limit of 1 m waves, while at the same symposium, Koops and Huisman suggest a limit of five times that amount.51,60 Det Norske Veritas, in cooperation with the Norwegian Pollution Authority, has developed standards for the certification of oil spill recovery technologies.61 The certification focuses on test methods specifically geared to smaller test tanks as exist in Norway and not on developing minimum specifications for such equipment. Weather conditions have not been included.

13.2.3. Booms Schulze and coworkers reviewed the performance of booms and summarized 20 years of testing.62,63 They note that it is important to relate first loss velocity as well as test oil viscosity, freeboard, boom draft, and the boom buoyancy-toweight ratio. Much of the boom testing was conducted in OHMSETT. The results of boom testing in OHMSETT have varied over the years. Devitis and Hannon note that the results of testing have gone up over the years; that is, the first loss tow velocity has increased.64 Specifically, Devitis and Hannon report that there was a jump between the 1982 and 1992 results. During that period, no testing was conducted in OHMSETT, and when it resumed the first loss failures jumped from about 0.5 to 0.6 m/s. However, Devitis and Hannon state first that the range of results is about the same and that the differences may stem from the fact that the earlier results were conservative and second that boom designs have improved somewhat. The results of testing from 1975 to 1982 and the procedures for offshore testing for oil spill containment booms were reviewed by Nash and Hillger.65 The first loss speed ranged from 0.1 to 0.57 m/s for a calm situation and from 0 to 0.56 m/s for regular waves and 0 to 0.38 m/s for chop. Testing results from OHMSETT are summarized in Table 13.3. The classic failure of oil spill containment booms at currents of about 0.4 m/s (0.8 knots) is well established by hydrodyamic models and tests. Much of this theory was established by Wicks.67 Milgram and van Houten summarized the classic theories and demonstrated these in a test tank.68 Delvigne described boom failure by critical accumulation of viscous oils.69 Delvigne also noted

TABLE 13.3 Tests of Boom Performance with Changing Weather Conditions Current/Tow Speed

Boom

Reference

Year of Test

First Loss Speed m/s

Typical booms Fence - catenary

62

1977

0.2

Fence - catenary

62

1977

0.23

Fence - diversionary

62

1977

0.6

Fence - diversionary

62

1977

0.7

Fence - diversionary

62

1977

0.5

Fence - catenary

62

1991

Fence - catenary

62

Fence - catenary

Critical Speed m/s

Wave Height Summary Speed/ Wave/s

Oil Viscosity mPa. S

Number of Tests

Wave Height m

Wave Conditions

300

8

calm or 0.3

long regular waves

300

0.6

short regular waves

300

calm

0.7

300

0.3

long regular waves

1.3

300

0.6

short regular waves

0.45

64

calm

1991

0.5

64

calm

62

1991

0.6

64

calm

Curtain - catenary

62

1977

0.45

333

Curtain - catenary

62

1977

0.25

Curtain - diversionary

62

1977

0.6

Curtain - diversionary

62

1977

0.4

Curtain - catenary

62

1977

0.45

Curtain - catenary

62

1977

0.35

Curtain - diversionary

62

1977

0.4

Curtain - diversionary

62

1977

0.45

0.17

1.3

1.3

0.7

0.3

Oil Type

16

calm or 0.3

long regular waves

333

0.6

short regular waves

1462

calm or 0.3

long regular waves

1462

0.6

short regular waves

230

calm

230

0.3

336

calm

336

0.3

long regular waves

long regular waves

Curtain - diversionary

62

1977

0.38

Curtain - catenary

62

1977

Curtain - diversionary

62

Curtain - catenary

0.5

336

0.6

0.4

649

calm, or short or long reg waves

1977

0.4

333

calm, or short or long reg waves

62

1977

0.45

97

calm, or short or long reg waves

Curtain - diversionary

62

1977

0.7

235

calm, or short or long reg waves

Curtain EF- catenary

62

1977

0.35

194

calm, or short or long reg waves

Curtain EF- diversionary

62

1977

0.45

134

calm, or short or long reg waves

Self-inf - catenary

62

1977

0.25

300/177

calm

Self-inf - catenary

62

1977

0.3

300/177

0.3

long regular waves

Self-inf - catenary

62

1977

0.4

300/177

0.6

short regular waves

Self-inf - catenary

62

1977

0.2

300/10

calm or 0.3

long regular waves

Self-inf - catenary

62

1977

0.35

300/10

0.6

short regular waves

Self-inf - diversionary

62

1977

0.75

300/238

calm

Self-inf - diversionary

62

1977

0.5

0.85

300/238

0.3

long regular waves

Self-inf - diversionary

62

1977

.6-.8

0.15

300/238

0.6

short regular waves

Self-inf - diversionary

62

1977

0.2

1

300/238

0.3

harbour chop

0.35

0.5

short regular waves

(Continued )

TABLE 13.3 Tests of Boom Performance with Changing Weather Conditionsdcont’d Current/Tow Speed

Boom

Reference

Year of Test

First Loss Speed m/s

Self-inf - catenary

62

1980

.4/.55

Self-inf - catenary

62

1980

0.45

Self-inf - catenary

62

1980

.4/0.57

Self-inf - catenary

62

1991

Press-inf - diversionary

62

Press-inf - diversionary

Critical Speed m/s

Wave Height Summary Speed/ Wave/s

Wave Height m

Wave Conditions

1026/3000

0.2

long regular waves

1026/3000

0.2

long regular waves

1026/3000

0.4

long regular waves

0.5

10/64

calm

1993

.5/.7

100/370

calm

62

1993

0.7/0.9

100/9300

calm

Press-inf - diversionary

62

1993

0.7/0.65

0.55

Press-inf - diversionary

62

1993

0.8/0.9

0.25

Press-inf - diversionary

62

1993

0.7/0.8

0.25

Press-inf - diversionary

62

1993

0.62/0.8

Press-inf - diversionary

62

1993

0.5/0.8

Press-inf - diversionary

62

1993

0.6/0.8

Press-inf - diversionary

62

1993

0.6/0.7

Press-inf - diversionary

62

1993

Press-inf - diversionary

62

Press-inf - diversionary

62

0.13

Oil Type

Oil Viscosity mPa. S

Number of Tests

0.3

long regular waves

100/9900

0.6

short regular waves

100/9900

0.3

harbour chop

100/9900

calm

900/7500

0.3

900/10400

calm

900/3600

0.6

0.72/0.88

100/850

calm

1993

0.63/0.7

300/870

calm

1993

0.65/0.8

300/870

0.3

0

0.2

0.15

harbour chop

short regular waves

long regular waves

Press-inf - diversionary

62

1993

0.63/0.85

Press-inf - diversionary

62

1993

0.6/0.7

Press-inf - diversionary

62

1993

0.5/0.62

Press-inf - catenary

62

1991

Press-inf - catenary

62

Press-inf - catenary

0.35

300/630

0.3

harbour chop

300/1050

calm or 0.3

long regular waves

300/1050

0.3

harbour chop

0.6

10/64

calm

1991

0.45

10/64

calm

62

1991

0.75

10/64

calm

Fence

62

1997

0.5

0.63

600/3000

calm

Fence

62

1997

0.35

0.45

0.63

600/3000

0.24

regular waves

Fence

62

1997

0.55

0.65

0.17

600/3000

0.3

long regular waves

Fence

62

1997

0.48

0.55

0.07

600/3000

0.2

harbour chop

Fence

62

1997

0.48

0.66

500/2900

calm

Fence

62

1997

0.37

0.53

0.46

500/2900

0.24

regular waves

Fence

62

1997

0.48

0.6

0

500/2900

0.3

long regular waves

Fence

62

1997

0.5

0.53

0.07

500/2900

0.2

harbour chop

Fence

62

1999

0.45

0.6

400/200

calm

Fence

62

1999

0.38

0.45

0.29

400/200

0.24

regular waves

Fence

62

1999

0.45

0.6

0

400/200

0.3

long regular waves

Fence

62

1999

0.45

0.6

0

400/200

0.2

harbour chop

0.35

Fire-resistant booms

(Continued )

TABLE 13.3 Tests of Boom Performance with Changing Weather Conditionsdcont’d Current/Tow Speed

Boom

Reference

Year of Test

First Loss Speed m/s

Critical Speed m/s

Intern foam

62

1999

0.48

0.63

Intern foam

62

1999

0.42

0.55

Intern foam

62

1999

0.55

Intern foam

62

1999

Press inflat

62

Press inflat

Wave Height Summary Speed/ Wave/s

Oil Type

Oil Viscosity mPa. S

Number of Tests

Wave Height m

Wave Conditions

360/1940

calm

0.25

360/1940

0.24

regular waves

0.74

0.23

360/1940

0.3

long regular waves

0.53

0.65

0.19

360/1940

0.2

harbour chop

1997

0.45

0.61

500/1730

calm

62

1997

0.4

0.21

500/1730

0.24

regular waves

Press inflat

62

1997

0.54

0.3

500/1730

0.3

long regular waves

Press inflat

62

1997

0.5

0.17

500/1730

0.2

harbour chop

Exterior tension

62

1999

0.45

0.63

360/2064

calm

Exterior tension

62

1999

0.3

0.48

360/2064

0.24

regular waves

Exterior tension

62

1999

0.5

0.65

360/2064

0.3

long regular waves

Exterior tension

62

1999

0.35

0.55

360/2064

0.2

harbour chop

Ceramic

66

1982

1.1

heavy

1300

calm

Ceramic

66

1982

1.1

0

heavy

1300

0.2

Ceramic

66

1982

0.9

0.5

heavy

1300

0.4

Ceramic

66

1982

0.7

2

heavy

1300

0.2

harbour chop

Chapter | 13 Weather Effects on Oil Spill Countermeasures

351

that the classical droplet breakaway failure varied as a square of the current velocity. Tedeschi reviews booms and notes that the typical failure is at winds of 8 to 9 m/s (15 to 18 knots).70 Fitzmaurice reviews containment and failure modes and suggests that multiple boom configurations might be used to avoid critical accumulation.71 Several workers utilized models to study failure modes.72-82 Although most did not include waves as a failure mode, Castro included waves and boom shape in physical model study.83 Chen also reviews containment and proposed to raise containment limits by using porous nets in front of the booms to slow the currents.84 Johnston et al. identified a boom failure mechanism just before critical accumulation occurs with heavy oils. This mode is characterized by a surging under the boom.85 Zhu and Strunin propose a new containment model utilizing the Froude number submergence depth and the amount of oil trapped by the barrier.86 The effect of waves has been studied by Van Dyck and Bruno.87 They conducted a series of tow tank tests showing that short-wavelength waves are the most difficult sea conditions for a floating boom to follow, even at optimum tow speeds or relative currents. Wave height was found not to be the limiting parameter, provided the wavelength/height ratio is 12:1 or greater. Short length/ height ratios lead to breaking waves where the height of the breaker must not exceed the available freeboard of the floating boom or there will be significant oil loss. Optimum catenary tow speeds are verified to be near 0.5 knot full scale for all wave sizes tested. Lee and Kang review the performance of booms in currents and waves.88 They suggest that the effect of waves is to increase the horizontal current velocity. They present a graphical algorithm to predict the failure resulting from symmetrical waves. Kordyban developed a model to examine the effect of waves on boom failure.89 His approach is similar to Lee and Kang, and a sinusoidal wave increases the effective velocity. Marks et al. analyzed the forces on a boom and calculated that the total force was directly related to the wave amplitude as well as 12 other factors.90 Milgram reviewed the specific mechanical design features of booms.91 He derived the following relationship to predict fabric tearing force as: fr ¼ ðCr rgðd þ bÞ2 1H 1=3 Þ=10

(1)

where the factors of interest to this paper are: fr ¼ tearing force, and H ¼ the significant wave height Milgram also notes that fabric tearing is the most significant type of physical damage to the boom and that it relates to the one-third power of the significant wave height. Several workers have noted that the wave period is a serious factor in determining boom capabilities in waves.92 Oebius noted that a height-to-period ratio of 0.04 is optimal for boom containment, but that shorter periods such as 0.056 would result in loss of containment.92

352

PART | V

Physical Spill Countermeasures on Water

Potter et al. studied the towing forces on booms.93 The first formulation that they review has the wave height as a direct and linear factor. The second formulation has the wave height as a constant that varies with boom and wave type. A typical constant for wave height is about 2 for a calm condition, 3.5 for regular waves, and 4 for harbor chop. Allen-Jones reviewed the towing forces and suggested that the direct use of significant wave height might be appropriate.94 Schulze and Potter subsequently conducted a series of tests in OHMSETT to measure the tow forces.95 This resulted in a new estimator of tow force, which includes the tension caused by the force of water being directly proportional to the significant wave height. Several attempts have been made to increase the containment capability of booms in fast waters using energy dissipative devices.96-99 Wong and Witmer used an elliptical shape to increase containment capability.100 Fang and Wong used a ramp and a series of vertical barriers to create a calm recovery area.77 Sloan et al. and Nordvik et al. tested several booms offshore in the New York/New Jersey area.101-103 Findings included the result that many of the booms tested were not suitable for the conditions encountered in offshore waters and that the wave height was only one of the factors that caused an excess of tow forces for the boom designs. It was also noted that a strong relationship existed between tow speed submergence initiation and reserve buoyancy. Also, it was found that the calculation methods for tow force underestimated the tow force. It was suggested that this might be a result of not adding the second tow vessel’s acceleration. Nash and Molsberry discuss offshore testing and report that the size of the system is a factor.104 They observed that larger booms performed better in an offshore environment. Suzuki et al. tested booms in ice and found that the ice presence actually increased the critical tow speed.105 The critical tow speed with ice present generally increased from 0.4 to 0.5 m/s, depending on a variety of conditions.

13.2.3.1. Typical Booms Yazaki described the Japanese government’s requirements for two types of booms as being able to contain oil in winds of 10 or 20 m/s (type C, small harbor, or type D, larger, boom); wave heights of 1 and 1.5 m, and 0.25 and 0.5 m/s current.106 Brown et al. noted that booms would fail at tow velocities of 0.25 m/s if the oil was heavier.107 13.2.3.2. Special and High-Current Booms Getman reported on the tests of three fast current oil recovery devices: the Shell ZRV skimmer, the Seaward streaming fibre skimmer, and the French Cyclonet 050.108 Getman stated that the Shell ZRV performed well in a high

Chapter | 13 Weather Effects on Oil Spill Countermeasures

353

current and in a wave train and that the other two devices performed poorly in a wave train. Bitting reported on the testing of the NOFI Vee-Sweep system, observing that this device retains oil at higher speeds than most other devices.109 Hansen measured the performance of a number of devices including sorbent booms in fast water.110-111 The JBF 6001 and Current Buster worked up to 3 and 3.5 m/s currents, respectively. Hansen described a series of tests to measure the performance of fast water booms and deflectors.112 Brekne et al. described a boom designed to work in up to 2.5 m significant wave height and 20 m/s winds (50 knots).113 This new specialized boom, known as the Uniboom, uses a net for the lower portion of the skirt and is self-inflating. No subsequent data was actually provided on measured performance. Williams and Cooke studied and tested bubble barriers for containing and burning oil.114 They found that the depth of slick contained varied as the square of the current velocity. For example, in 8 to 10 m/s wind, the floating bubble barrier held a 3.2 cm slick several meters away. Eryuzlu and Hauser describe the use of floating deflectors to move oil out of currents to calm areas.115 A prototype deflector was tested in currents from 0.8 to 2.1 m/s and successfully deflected oil simulant to a calm area. Folsom and Johnson describe a streamlined boom that was claimed to contain or deflect oil up to 3 m/s.116 Nash and Johnson report on the use of plunging water jets to converge oil slicks. They found that by using this technology they could converge slicks at current speeds of up to 3 m/s.117 Brown et al. report on the study and testing of several high-speed containment concepts, including vertical cylinders, vertical slats with and without a back wall, vertical walls with fins at an angle, a hydrofoil, and horizontal structures including inclined screens and a variety of filters.107,118 Several of these innovations improved the containment ability, but no single system was recommended. Swift et al. reported on tests of an inclined submergence bow plane device that retained oil at currents up to 1.03 m/s.119 Wong et al. describe new booms for high-current application.120,121 Cooper et al. describe a modified boom to contain heavy oil.122

13.2.4. Skimmers Extensive testing of skimmers has been conducted in the OHMSETT facility. Tests from the 1970s are summarized by Smith and Lichte and by Farlow and Griffiths.123,124 Additional data are given by Lichte et al.125,126 These tests are summarized again in Schulze.127 The book by Schulze is used as the prime source of data here, as summarized in Table 13.4. New testing is described in several papers but quantitative results are not given.128-130

TABLE 13.4 Tests of Skimmer Performance with Changing Weather Conditions Current/Tow Speed Summary

Skimmer

Wave Height Summary

Year Slope

ORR

Slope Slope Slope

ORR

of

ORR

Slope

TE

Slope TE

RE

Oil

Viscosity Thick.

# of Speed Height Wave

ORR TE

RE

m2s/h

%s/m

%s/m %s/m m2/h

%/m

%/m

Type

mPa. S

mm

Tests m/s

m

m3/h %

%

200

120

3

0.25

calm

58.2

56

Reference Test

RE

ORR

Slope Slope %/m

Oil

Slick

Wave Conditions

Harbour/Small Skimmers Skimming barrier

127

1977

Skimming barrier

127

1977

200

120

5

0.25

0.3

47.4

34.5

Skimming barrier

127

1977 115.4

198.3

134.6

36

61.9

71.7

200

120

2

0.38

calm

harbour chop

73.2

73.5

Skimming barrier

127

1977 57.6

99

12.8

200

120

6

0.5

calm

72.6

59.2

Skimming barrier

127

1977

18

30.9

41.3

200

120

2

0.38

0.3

63.6

43.6

Skimming barrier

127

1977

45

77.3

23.7

200

120

4

0.5

0.3

regular

71.7

48.9

Skimming barrier

127

1977

0

0

50.3

200

120

4

0.5

0.3

regular

58.2

40.9

Skimming barrier

127

1977

15.2

26.1

49.4

200

120

1

0.25

0.5

regular

50.6

31.3

Skimming barrier

127

1977

12.6

27.8

34.6

200

120

4

0.5

0.5

regular

66.3

41.9

Skimming barrier

127

1977

18.8

17.2

38.8

200

120

4

0.5

0.5

regular

63.2

39.8

Skimming barrier

127

1977

25.3

43.5

46.7

200

120

2

0.25

0.6

harbour chop

43

28

Skimming barrier

127

1977

85

8

60.2

200

120

3

0.38

0.6

harbour chop

61

37.4

Skimming barrier

127

1977

30

10.3

40.7

200

120

6

0.5

0.6

harbour chop

54.6

34.8

Skimming barrier

127

1977

30.7

11.5

41.5

200

120

6

0.5

0.6

harbour chop

54.2

34.3

Sirene skimming barrier

127

1979

545

3.1

1

0.38

calm

18

100

23

Sirene skimming barrier

127

1979 87.5

486.1

175

158.3

545

3.3

1

0.5

calm

28.5

79

42

Sirene skimming barrier

127

1979 41.6

231.1

288

104

545

3.2

1

0.63

calm

28.4

28

49

Sirene skimming barrier

127

1979 5.9

33

240.5

8.1

Sirene skimming barrier

127

1979

1

5.6

Sirene skimming barrier

127

1979

35.8

Sirene skimming barrier

127

1979

545

3

1

0.75

calm

15.8

11

26

18.2

545

3.2

1

0.38

0.6

harbour chop

18.6

99

31

199.1 85

48.5

545

3.3

1

0.63

0.6

harbour chop

39.5

49

71

25.7

142.6 115

26.8

545

2.6

1

0.75

0.6

harbour chop

33.4

31

58

1.7

Sirene skimming barrier

127

1979

23.3

129.6

306.7

86.3

545

2.7

2

1

0.3

harbour chop

11

8

16

Sirene skimming barrier

127

1979

3.2

17.8

2

29.8

545

3.2

1

0.38

0.5

regular

16.4

99

27

Sirene skimming barrier

127

1979

35.4

196.7 94

26.2

545

2.7

1

0.63

0.5

regular

35.7

53

55

Sirene skimming barrier

127

1979

178

3.1

1

0.38

calm

16.6

99

22

Sirene skimming barrier

127

1979 155

933.7

258.3

183.3

178

3.3

1

0.5

calm

35.2

68

44

Sirene skimming barrier

127

1979 16

96.4

244

80

178

3.1

1

0.63

calm

12.6

38

42

Sirene skimming barrier

127

1979 7

42.3

237.8

5.4

178

2.6

1

0.75

calm

14

11

24

Sirene skimming barrier

127

1979

1.8

10.8

2

178

3.2

1

0.38

0.5

regular

15.7

99

21

Sirene skimming barrier

127

1979

46.4

279.5 96

74

178

3.1

1

0.5

0.5

regular

39.8

51

59

Sirene skimming barrier

127

1979

21.6

130.1 158

44

178

3.5

1

0.63

0.5

regular

27.4

20

44

Sirene skimming barrier

127

1979

1.6

9.6

170

14

178

2.5

1

0.75

0.5

regular

15.8

14

29

Sirene skimming barrier

127

1979

1.3

7.7

0

4.3

178

3.1

1

0.38

0.7

harbour chop

15.7

99

25

Sirene skimming barrier

127

1979

4

24.1

82.9

17.1

178

2

1

0.5

0.7

harbour chop

19.4

41

34

Sirene skimming barrier

127

1979

11.4

68.8

61.4

64.3

178

3.3

1

0.63

0.7

harbour chop

24.6

56

67

Sirene skimming barrier

127

1979

0

0

122.9

10

178

2.9

1

0.75

0.7

harbour chop

16.6

13

29

0

(Continued )

TABLE 13.4 Tests of Skimmer Performance with Changing Weather Conditionsdcont’d Current/Tow Speed Summary

Skimmer

Wave Height Summary

Year Slope

ORR

Slope Slope Slope

ORR

of

ORR

Slope

TE

Slope TE

RE

Oil

Viscosity Thick.

# of Speed Height Wave

ORR TE

RE

m2s/h

%s/m

%s/m %s/m m2/h

%/m

%/m

Type

mPa. S

mm

Tests m/s

m

m3/h %

%

Reference Test

RE

ORR

Slope Slope %/m

Oil

Slick

Wave Conditions

Lori brush skimmer

127

1979

med. oil 600

ns

1

0.75

calm

0.31

60

Lori brush skimmer

127

1979 0.6

182.8

86.7

med. oil 600

ns

1

1.05

calm

0.48

86

Lori brush skimmer

127

1979 0.8

258.1

40

med. oil 600

ns

1

1.3

calm

0.75

82

Lori brush skimmer

127

1979 0.9

279.6

24

med. oil 600

ns

1

1.5

calm

0.96

78

82.9

5.7

Lori brush skimmer

127

1979 0.3

med. oil 600

ns

1

1.8

calm

0.58

66

Lori brush skimmer

127

1979

0.3

80.6

131.3

med. oil 600

ns

1

0.75

0.16

regular

0.35

81

Lori brush skimmer

127

1979

2.6

842.3

83.3

med. oil 600

ns

1

1

0.18

regular

0.78

75

Lori brush skimmer

127

1979

2.5

821.1

72.7

med. oil 600

ns

1

1.3

0.22

regular

0.87

76

Lori brush skimmer

127

1979

2

645.2

56

med. oil 600

ns

1

1.5

0.25

regular

0.81

74

Lori brush skimmer

127

1979

1.1

349.5

33.3

med. oil 600

ns

1

1.8

0.24

regular

0.57

68

Scoop weir skimmer

127

1978

heav. oil 1000

ns

2

0.25

calm

Scoop weir skimmer

127

1978 6.9

75.3

0

heav. oil 1000

ns

1

0.38

calm

10.1

100

87

Scoop weir skimmer

127

1978 13.6

147.8

220

heav. oil 1000

ns

3

0.5

calm

5.8

45

100

Scoop weir skimmer

127

1978 13.4

145.9

176.3

heav. oil 1000

ns

4

0.63

calm

4.1

33

Scoop weir skimmer

127

1978

heav. oil 1000

ns

1

0.38

0.3

10.6

91

4.7

50.7

30

9.2

regular

100

57

Scoop weir skimmer

127

1978

7.5

81.5

71.7

heav. oil 1000

ns

1

0.25

0.6

harbour chop

4.7

57

Scoop weir skimmer

127

1978

6.3

68.8

53.3

heav. oil 1000

ns

3

0.38

0.6

harbour chop

5.4

68

100

Scoop weir skimmer

127

1978

4.2

45.3

51.7

heav. oil 1000

ns

3

0.5

0.6

harbour chop

6.7

69

Scoop weir skimmer

127

1978

7.5

81.5

118.3

heav. oil 1000

ns

2

0.63

0.6

harbour chop

4.7

29

Disc skim. - flat -CCG tests

127

1993

lt. crude 5 to 50

10

0

calm

Disc skim. - flat -CCG tests

127

1993

85

lt. crude 5 to 50

10

0

0.4

regular

65

Disc skim. - flat -CCG tests

127

1993

63.8

lt. crude 5 to 50

10

0

0.8

harbour chop

48

Disc skim. - flat -CCG tests

127

1993

lt. crude 5 to 50

25

0

calm

Disc skim. - flat -CCG tests

127

1993

32.5

lt. crude 5 to 50

25

0

0.4

regular

83

Disc skim. - flat -CCG tests

127

1993

38.8

lt. crude 5 to 50

25

0

0.8

harbour chop

65

Disc skim. -T-disk -CCG 127 tests

1993

lt. crude 5 to 50

10

0

calm

Disc skim. -T-disk -CCG 127 tests

1993

47.5

lt. crude 5 to 50

10

0

0.4

regular

46

Disc skim. -T-disk -CCG 127 tests

1993

51.3

lt. crude 5 to 50

10

0

0.8

harbour chop

24

Disc skim. -T-disk -CCG 127 tests

1993

lt. crude 5 to 50

25

0

calm

Disc skim. -T-disk -CCG 127 tests

1993

lt. crude 5 to 50

25

0

0.4

37.5

95

99

96

99

100

regular

85

(Continued )

TABLE 13.4 Tests of Skimmer Performance with Changing Weather Conditionsdcont’d Current/Tow Speed Summary

Skimmer

Wave Height Summary

Year Slope

ORR

Slope Slope Slope

ORR

of

ORR

Slope

TE

Slope TE

RE

Oil

Viscosity Thick.

# of Speed Height Wave

ORR TE

RE

m2s/h

%s/m

%s/m %s/m m2/h

%/m

%/m

Type

mPa. S

Tests m/s

m3/h %

%

67.5

lt. crude 5 to 50

25

heav. oil 1900

Reference Test

RE

Disc skim. -T-disk -CCG 127 tests

1993

Paddle skimmer

127

1977

Paddle skimmer

127

1977

Rope mop stationary

127

1977

Rope mop stationary

127

1977

Rope mop stationary

127

1977

Rope mop stationary

127

1977

Rope mop stationary

127

1977

Rope mop stationary

127

1977

Rope mop towed single

127

1978

Rope mop towed single

127

1978 4.2

90.9

1.8

Rope mop towed single

127

1978 0.5

7.2

105

Rope mop towed single

127

1978

Rope mop towed single

127

1978 3.1

53.3

Rope mop towed single

127

1978 5.5

73.3

ORR

23

1.5

244.7

57.7

Slope Slope %/m

105

330

35

1.7

53.8

15

7

225.8

10

Oil

Slick mm

Wave m

Conditions

0

0.8

harbour chop

26

0

calm

heav. oil 1900

26

0

0.2

lt. crude 6

20

0

calm

regular

harbour chop

46

9.4

91

84

4.8

70

18

2.6

89

3.5

68

3.1

79

4.1

70

10

98

7.3

73

lt. crude 6

20

0

0.6

lt. crude 14

20

0

calm

lt. crude 14

20

0

0.6

lt. crude 79

20

0

calm

lt. crude 79

20

0

0.6

med. oil 793

5

0.75

calm

4.6

55

med. oil 793

5

1.3

calm

6.9

56

harbour chop

harbour chop

med. oil 793

5

1.5

calm

6.8

35

8

173.9

40

med. oil 793

5

0.75

0.15

regular

5.8

61

14.5

4

58

20

med. oil 793

5

1.3

0.15

regular

7.5

53

10

2.7

39.2

133.3

med. oil 793

5

1.5

0.15

regular

6.4

55

Rope mop towed single

127

1978

Rope mop towed single

127

1978 5.1

59.9 61.4

6.5

141.3

3.3

med. oil 793

5

0.75

0.6

harbour chop

8.5

53

7.3

2

29

11.7

med. oil 793

5

1.3

0.6

harbour chop

5.7

49

15

3

44.1

18.3

med. oil 793

5

1.5

0.6

harbour chop

lt. crude 65

4 ave

1.25

calm

7

36

23

Rope mop towed single

127

1978 3.5

Oil mop ZRV

127

1976

Oil mop ZRV

127

1976 0

0

84

0

lt. crude 65

4 ave

1.5

calm

7

15

23

Oil mop ZRV

127

1976 0.6

8.6

18

2

lt. crude 65

4 ave

1.75

calm

7.3

27

24

Oil mop ZRV

127

1976

lt. crude 65

4 ave

1.5

0.6

4.8

21

10

Oil mop ZRV

127

1977

heav. oil 3000

3

3

0.5

calm

4.3

71

52

Oil mop ZRV

127

1977 7.2

167.4

44

50

heav. oil 3000

3

6

1

calm

7.9

49

77

Oil mop ZRV

127

1977 8.5

197.7

7

23

heav. oil 3000

3

2

1.5

calm

12.8

64

75

Oil mop ZRV

127

1977

heav. oil 3000

3

4

0.5

0.6

10

80

35

Oil mop ZRV

127

1977

lt. crude 3

3

9

1

calm

9.4

65

66

Oil mop ZRV

127

1977 8.1

86.2

2

4

lt. crude 3

3

2

2

calm

17.5

67

62

Oil mop ZRV

127

1977 3

31.9

14.7

12

Oil mop ZRV

127

1977

Oil mop ZRV

127

1977 3.5

Oil mop ZRV

127

1977

3.7

9.5

37.2

16

13

52.4

10

220.9 15

21.7

28.3

harbour chop

harbour chop

5

46

lt. crude 3

3

3

2.5

calm

13.9

43

48

0

0

11.7 20

lt. crude 3

3

4

1

0.6

harbour chop

9.4

72

54

7.7

43.8

18.3

35

lt. crude 3

3

5

2

0.6

harbour chop

12.9

56

41

5

28.6

2.5

21.3

lt. crude 3

3

2

2

0.8

regular

13.5

65

45

Marco belt skimmer

127

1976

heav. oil 837

8 to 11

6

0.5

calm

11.5

85

57

Marco belt skimmer

127

1976 24.2

210.4

10

18

heav. oil 837

8 to 11

5

1

calm

23.6

90

66

Marco belt skimmer

127

1976 9.1

79.1

23

19

heav. oil 837

8 to 11

4

1.5

calm

harbour chop

20.6

62

76

Marco belt skimmer

127

1976

heav. oil 837

8 to 11

1

0.5

0.6

harbour chop

10.7

76

74

1.3

11.6

15

28.3

(Continued )

TABLE 13.4 Tests of Skimmer Performance with Changing Weather Conditionsdcont’d Current/Tow Speed Summary

Wave Height Summary

Year Slope

ORR

Slope Slope Slope

ORR

of

ORR

Slope

TE

Slope TE

RE

Oil

Viscosity Thick.

# of Speed Height Wave

ORR TE

RE

m2s/h

%s/m

%s/m %s/m m2/h

%/m

%/m

%/m

Type

mPa. S

mm

Tests m/s

m

Conditions

m3/h %

%

RE

ORR

Slope Slope

Oil

Slick

Wave

Skimmer

Reference Test

Marco belt skimmer

127

1976 2.4

22.4

62

46

19.5

82.6

75

25

heav. oil 837

8 to 11

1

1

0.6

harbour chop

11.9

45

51

Marco belt skimmer

127

1976 1.8

16.8

48

37

13.5

65.5

56.7

65

heav. oil 837

8 to 11

1

1.5

0.6

harbour chop

12.5

28

37

Marco belt skimmer

127

1976

20.8

18.3

heav. oil 837

8 to 11

1

0.5

1.2

harbour chop

.

60

35

Marco belt skimmer

127

1976

41.7

26.7

heav. oil 837

8 to 11

1

1

1.2

harbour chop

9.9

40

34

heav. oil 784

3

1

0.25

calm

3

74

87

heav. oil 784

3

22

0.5

calm

6.1

71

79

heav. oil 784

6

1

0.5

calm

9.9

71

84

40

2

12

32

Marco belt skimmer

127

1977

Marco belt skimmer

127

1977 12.4

Marco belt skimmer

127

1977

Marco belt skimmer

127

1977 8.8

293.3

12

2

heav. oil 784

3

5

0.75

calm

7.4

68

88

Marco belt skimmer

127

1977 25.2

254.5

8

16

heav. oil 784

6

1

0.75

calm

16.2

69

88

Marco belt skimmer

127

1977 6.9

231.1

29.3

8

heav. oil 784

3

31

1

calm

8.2

52

81

Marco belt skimmer

127

1977 17.4

175.8

20

4

heav. oil 784

6

1

1

calm

18.6

61

86

Marco belt skimmer

127

1977 2.6

85.3

40

12

heav. oil 784

3

8

1.5

calm

6.2

24

72

Marco belt skimmer

127

1977

0.8

27.8

43.3

61.7

heav. oil 784

3

5

0.5

0.6

harbour chop

3.5

48

50

Marco belt skimmer

127

1977

1.2

38.9

71.7

63.3

heav. oil 784

3

1

0.75

0.6

harbour chop

3.7

31

49

Marco belt skimmer

127

1977

9.5

96

95

65

heav. oil 784

6

2

1

0.6

harbour chop

4.2

14

45

413.3

Marco belt skimmer

127

1977

0.5

16.7

35.8

34.2

heav. oil 784

3

2

0.5

1.2

harbour chop

2.4

31

46

Marco belt skimmer

127

1977

0.3

8.3

38.3

33.3

heav. oil 784

3

1

0.75

1.2

harbour chop

3.3

28

47

Fixed submersion plane

127

1978

lt. oil

19

3

2

0.75

calm

14.8

51

Fixed submersion plane

127

1978 40

270.3

36

lt. oil

19

3

2

1

calm

24.8

78

Fixed submersion plane

127

1978 31.7

214.4

20.8

lt. oil

19

3

3

1.5

calm

38.6

77

Fixed submersion plane

127

1978 13.1

88.6

1.1

lt. oil

19

3

5

2

calm

31.2

49

Fixed submersion plane

127

1978 5.8

39.4

10.7

Fixed submersion plane

127

1978

Fixed submersion plane

127

1978 1.6

18.2

28

lt. oil

19

3

2

2.5

calm

25

27

20

135.1

56.7

lt. oil

19

3

2

0.75

0.3

harbour chop

8.8

34

52

209.7

170

lt. oil

19

3

2

1

0.3

harbour chop

9.2

27

Fixed submersion plane

127

1978 9.6

109.1

2.7

75.3

195.2

150

lt. oil

19

3

5

1.5

0.3

harbour chop

16

32

Fixed submersion plane

127

1978 14.3

162.7

6.4

15

48.1

23.3

lt. oil

19

3

4

2

0.3

harbour chop

26.7

42

Fixed submersion plane

127

1978

11.2

75.5

18.3

lt. oil

19

3

1

0.75

0.6

harbour chop

8.1

40

Fixed submersion plane

127

1978

17.6

118.9

52

lt. oil

19

3

1

0.75

0.5

regular

6

25

Fixed submersion plane

127

1978 8.4

140

4

33.4

134.7

108

lt. oil

19

3

1

1

0.5

regular

8.1

24

Fixed submersion plane

127

1978 1.2

20

20

67

173.6

134

lt. oil

19

3

1

1.5

0.5

regular

5.1

10

Fixed submersion plane

127

1978

heav. oil 1230

10

1

1

calm

87.8

90

Fixed submersion plane

127

1978 217.2

heav. oil 1230

10

2

1.25

calm

33.5

87

247.4

12

Fixed submersion plane

127

1978 107.8

122.8

36

heav. oil 1230

10

2

1.5

calm

33.9

72

Fixed submersion plane

127

1978 51.2

58.3

29

heav. oil 1230

10

4

2

calm

36.6

61

Fixed submersion plane

127

1978

heav. oil 1230

3

1

0.5

calm

3.2

8

Fixed submersion plane

127

1978 18.8

heav. oil 1230

3

3

1.38

calm

19.7

77

585.9

78.4

(Continued )

TABLE 13.4 Tests of Skimmer Performance with Changing Weather Conditionsdcont’d Current/Tow Speed Summary

Wave Height Summary

Year Slope

ORR

Slope Slope Slope

ORR

of

ORR

Slope

TE

Slope TE

RE

Oil

Viscosity Thick.

# of Speed Height Wave

ORR TE

RE

m2s/h

%s/m

%s/m %s/m m2/h

%/m

%/m

Type

mPa. S

Tests m/s

m3/h %

%

Skimmer

Reference Test

Fixed submersion plane

127

1978 46.6

Fixed submersion plane

127

1978

Fixed submersion plane

127

1978 28.4

355

60

Fixed submersion plane

127

1978 16.3

203.3

20

RE

ORR

Slope Slope %/m

1456.3 148

Fixed submersion plane

127

1978

Fixed submersion plane

127

1978 37.2

516.7

104

Fixed submersion plane

127

1978 3.5

48.1

24

38

16.7

143.4

62.9

116.7

45

Oil

Slick mm

Wave m

Conditions

heav. oil 1230

3

3

1

calm

26.5

82

heav. oil 1230

3

1

0.75

0.3

harbour chop

8

32

heav. oil 1230

3

2

1

0.3

harbour chop

15.1

47

heav. oil 1230

3

1

1.5

0.3

harbour chop

20.2

47

heav. oil 1230

3

1

0.75

0.6

harbour chop

7.2

29

heav. oil 1230

3

1

1

0.6

harbour chop

16.5

55

heav. oil 1230

3

1

1.5

0.6

harbour chop

4.6

11

Fixed submersion plane

127

1978

heav. oil 1230

3

2

0.75

0.5

regular

4.3

20

Fixed submersion plane

127

1978 4.7

109.8

6.4

heav. oil 1230

3

1

2

0.5

regular

10.2

28

Fixed submersion plane

127

1978 0.9

21.7

16

heav. oil 1230

3

1

1.5

0.5

regular

3.6

8

DIP 2001

127

1973

Alberta crude

8

.7 ave

1

1.3

calm

2.7

88

30

DIP 2001

127

1973

Alberta crude

8

.7 ave

1

1.6

0.6

3.5

49

20

DIP 2001

127

1975

Arab crude

24

1

1

0.5

calm

1.1

94

96

DIP 2001

127

1975 0.4

Arab crude

24

0.5

1

1

calm

0.9

77

94

1.3

36.4

34

49.4

65

regular

DIP 2001

127

1975

DIP 2001

127

1975 1.8

Stationary skim. - Manta 127 Ray

1975

Stationary skim. - Manta 127 Ray

1975

Stationary skim. - skim pak

127

1980

Stationary skim. - skim pak

127

1980

Stationary skim. - skim pak

127

1980

Stationary skim. - skim pak

127

1980

Stationary skim. - skim pak

127

1980

Stationary skim. - skim pak

127

1980

Slurp skimmer

127

1975

Slurp skimmer

127

1975

Slurp skimmer

127

1975

Slurp skimmer

127

1975

Slurp skimmer

127

1975

200

6

0

0

10

Arab crude

24

1

1

0.5

0.4

natural

0.9

81

95

2.3

250

7.5

Arab crude

24

1

1

1

0.4

natural

1.8

78

96

DOP

79

20

6

calm

DOP

79

20

1

0.6

medium 200

7

3

calm

medium 200

7

1

0.26

medium 200

23

2

calm

medium 200

29

3

0.26

medium 200

16

3

calm

medium 200

16

1

0.19

crude

24

1

1

calm

crude

24

1

1

0.2

crude

24

5

1

calm

crude

24

8.2

1.9

2.7

4.2

1

0.1

40.6

76.9

67.3

50.1

558.8

10.9

8.3

3.8

15.4

15.8

30

0

Emulsion 3500

5

1

0.2

5

1

calm

harbour chop

regular

regular

regular

natural

natural

20.1

27

15.2

22

2.5

8

2

7

4

31

3.3

27

8.4

29

7.6

28

0.17

5

0.36

11

0.46

15

0.47

15

0.49

25

(Continued )

TABLE 13.4 Tests of Skimmer Performance with Changing Weather Conditionsdcont’d Current/Tow Speed Summary

Wave Height Summary

Year Slope

ORR

Slope Slope Slope

ORR

of

ORR

Slope

TE

Slope TE

RE

Oil

Viscosity Thick.

# of Speed Height Wave

ORR TE

RE

m2s/h

%s/m

%s/m %s/m m2/h

%/m

%/m

Type

mPa. S

mm

Tests m/s

m3/h %

%

1

Skimmer

Reference Test

Slurp skimmer

RE

ORR

0.4

71.4

Slope Slope %/m

55

Oil

Slick

Wave m

Conditions

0.2

natural

127

1975

Emulsion 3500

5

0.42

14

Harbour Mate weir skim. 127

1993

Diesel

4.5

10

calm

0.2

5

Harbour Mate weir skim. 127

1993

Diesel

4.5

25

calm

1.3

28

Harbour Mate weir skim. 127

1993

3.5

1750

5

Diesel

4.5

10

0.4

regular

1.6

3

Harbour Mate weir skim. 127

1993

0.3

19.2

10

Diesel

4.5

25

0.4

regular

1.2

24

Harbour Mate weir skim. 127

1993

0

0

2.5

Diesel

4.5

10

0.8

harbour chop

0.2

3

Harbour Mate weir skim. 127

1993

0.6

48.1

22.5

Diesel

4.5

25

0.8

harbour chop

0.8

10

Harbour Mate weir skim. 127

1993

crude

50 to 300 10

calm

0.08

1

Harbour Mate weir skim. 127

1993

crude

50 to 300 25

calm

1.3

21

Harbour Mate weir skim. 127

1993

0

31.3

0

crude

50 to 300 10

0.4

regular

0.09

1

Harbour Mate weir skim. 127

1993

2

153.8

32.5

crude

50 to 300 25

0.4

regular

0.5

8

Harbour Mate weir skim. 127

1993

0

31.3

1.3

crude

50 to 300 10

0.8

harbour chop

0.1

2

Harbour Mate weir skim. 127

1993

0.3

19.2

2.5

crude

50 to 300 25

0.8

harbour chop

1.1

19

Destroil weir skimmer

127

1979

heavy

810

5

5

calm

16.2

69

Destroil weir skimmer

127

1979

10

61.7

21.3

heavy

810

5

2

0.47

harbour chop

11.5

59

Destroil weir skimmer

127

1979

24.2

149.4

78.9

heavy

810

5

2

0.19

regular

11.6

54

18.1

111.6

Destroil weir skimmer

127

1979

Destroil weir skimmer

127

1979

Destroil weir skimmer

127

1979

GT-185

127

1988

GT-185

127

1988

GT-185

127

1988

GT-185

127

1988

1.7

5.6

GT-185

127

1988

37.5

125

Walosep

127

1988

Walosep

127

1988

Veegarm towed weir

127

1980

Veegarm towed weir

127

1980 28

254.5

0

Veegarm towed weir

127

1980 7

63.6

Veegarm towed weir

127

1980 22.7

206.1

Veegarm towed weir

127

1980

Veegarm towed weir

127

1980 56

509.1

0

Veegarm towed weir

127

1980 10

90.9

Veegarm towed weir

127

1980 26.7

Veegarm towed weir

127

1980 17

Veegarm towed weir

127

1980 4.8

34.2

15

186

71.4

92.3

123.1

heavy

810

5

1

0.26

light

9

5

2

calm

light

9

5

1

0.26

Bunker C 11700

calm

Bunker C 11700

0.4

Terra Nova

100-600

calm

26.7

Terra Nova

100-600

0.3

125

Terra Nova

100-600

0.4

65

Bunker C >100k 70

184.2

0

regular

regular

0.4

93

18.4

77

9.5

45

21

76

15

50

30

100

regular

29.5

92

regular

15

50

38

2

regular

calm

Bunker C >100k

20.9

regular

10

2

1

0.25

calm

11

100

8

48

1

0.5

calm

18

100

20

8

10

1

1.25

calm

18

92

18

0

5.3

1

1

calm

28

100

12

2

0.25

calm

11

100

18

16

2

0.5

calm

25

100

22

18

10

2

1.25

calm

21

82

28

242.4

30.7

13.3

2

1

calm

31

77

28

154.5

50

6

2

1.25

calm

28

50

24

43.6

56

3.2

2

1.5

calm

17

30

22

(Continued )

TABLE 13.4 Tests of Skimmer Performance with Changing Weather Conditionsdcont’d Current/Tow Speed Summary

Wave Height Summary

Year Slope

ORR

Slope Slope Slope

ORR

of

ORR

Slope

TE

Slope TE

RE

Oil

Viscosity Thick.

# of Speed Height Wave

ORR TE

RE

m2s/h

%s/m

%s/m %s/m m2/h

%/m

%/m

Type

mPa. S

Tests m/s

m3/h %

%

RE

ORR

Slope Slope %/m

Oil

Slick mm

Wave m

Conditions

Skimmer

Reference Test

Veegarm towed weir

127

1980

5

0.25

calm

18

100

35

Veegarm towed weir

127

1980 80

444.4

0

20

5

0.5

calm

38

100

40

Veegarm towed weir

127

1980 5

27.8

22

5

5

1.25

calm

23

78

40

Veegarm towed weir

127

1980 24

133.3

50.7

5.3

5

1

calm

36

62

39

Veegarm towed weir

127

1980

light

9

2

0.25

calm

5.4

100

5

Veegarm towed weir

127

1980 4.8

88.9

0

0

light

9

2

0.5

calm

4.2

100

5

Veegarm towed weir

127

1980 1.4

25.9

70

1

light

9

2

1.25

calm

4

30

4

Veegarm towed weir

127

1980 0.3

4.9

90.7

1.3

light

9

2

1

calm

5.6

32

4

Veegarm towed weir

127

1980

heavy

1300

2

0.25

calm

14

100

9

Veegarm towed weir

127

1980 8

57.1

0

4

heavy

1300

2

0.5

calm

16

100

8

Veegarm towed weir

127

1980 6

42.9

18

2

heavy

1300

2

1.25

calm

8

82

7

Veegarm towed weir

127

1980

Veegarm towed weir

127

1980 8

80

8

8

Veegarm towed weir

127

1980 4

40

40

2

Veegarm towed weir

127

1980

Veegarm towed weir

127

1980 12

80

0

16

0.5

4.8

31.6

6.8

0.25

1.9

regular

10

40

5

6.8

27.4

30.5

7.9

0.5

1.9

regular

12

42

7

0.75

1.9

regular

8

20

6

20

181.8 0

25

0.25

0.2

regular

15

100

13

35

140

25

0.5

0.2

regular

18

100

17

0

Veegarm towed weir

127

1980 12

80

20

14

Veegarm towed weir

127

1980 6.7

44.4

34.7

5.3

55

177.4

15

55

0.75

0.2

regular

21

90

20

1

0.2

regular

20

74

17

Veegarm towed weir

127

1980

2.1

39

210.5

0

light

9

2

0.25

0.19

harbour chop

5

60

5

Veegarm towed weir

127

1980 2

40

152

0

1.6

37.6

410.5

0

light

9

2

0.5

0.19

harbour chop

4.5

22

5

Veegarm towed weir

127

1980 2

40

96

2

0

0

94.7

0

light

9

2

0.75

0.19

harbour chop

4

12

4

Veegarm towed weir

127

1980 0

0

64

1.3

3.2

56.4

105.3

0

light

9

2

1

0.19

harbour chop

5

12

4

Veegarm towed weir

127

1980

4.2

30.1

26.3

0

heavy

1300

2

0.25

0.19

harbour chop

13.2

95

9

Veegarm towed weir

127

1980 7.6

57.6

140

4

4.7

29.6

210.5

0

heavy

1300

2

0.5

0.19

harbour chop

15.1

60

8

Veegarm towed weir

127

1980 0.6

4.5

138

4

28.9

361.8 294.7

0

heavy

1300

2

0.75

0.19

harbour chop

13.5

26

7

Veegarm towed weir

127

1980

12.5

89.3

12.5

2.5

heavy

1300

2

0.25

0.4

harbour chop

9

95

10

Veegarm towed weir

127

1980 14

155.6

200

16

26.3

164.1

137.5

5

heavy

1300

2

0.5

0.4

harbour chop

5.5

45

6

Veegarm towed weir

127

1980 8

88.9

154

12

7.5

93.8

160

7.5

heavy

1300

2

0.75

0.4

harbour chop

5

18

4

Veegarm towed weir

127

1980

2.2

41.2

60.3

0

light

9

2

0.25

0.63

harbour chop

4

62

5

3.7

86.9

101.6

1.6

light

9

2

harbour chop

6.5

36

Veegarm towed weir

127

1980 10

RST Advancing weir

127

1992

RST Advancing weir

127

1992

Transrec

128

19951998

Transrec

128

19951998

Transrec

128

19951998

250

104

4

50

211

0.5

0.63

medium 380

6

0.38

calm

medium 380

7

0.38

0.27

various

2

5

1

80

1.3

various

2

5

1.5

78

5

various

2

5

2

70

0

11.1

regular

4

23.7

97

10.2

94

Offshore skimmers

(Continued )

TABLE 13.4 Tests of Skimmer Performance with Changing Weather Conditionsdcont’d Current/Tow Speed Summary

Wave Height Summary

Year Slope

ORR

Slope Slope Slope

ORR

of

ORR

Slope

TE

Slope TE

RE

Oil

Viscosity Thick.

# of Speed Height Wave

ORR TE

RE

m2s/h

%s/m

%s/m %s/m m2/h

%/m

%/m

Type

mPa. S

Tests m/s

m

m3/h %

%

2

2.5

65

Skimmer

Reference Test

Transrec

128

19951998

RE

ORR

Slope Slope %/m 6

Oil

various

Special skimmers

Slick mm

Wave

5

Conditions

2 to 5

USCG HSS

112

1999

27

Sundex

3 to 4

calm

98 to 18

USCG HSS

112

1997

10

Sundex

2 to 4

calm

69 to 79

NOFI

112

1999

8

Sundex

2 to 5

calm

98 to 90

UNH FS

112

2000

17

Sundex

3 to 4

calm

98 to 46

USCG ZRV

112

1977

11

Sundex

2 to 4

calm

72 to 61

LPI fixed plane

112

1978

6

Sundex

3 to 5

calm

82 to 61

Fasflo

112

1999

14

Sundex

2 to 5

calm

72 to 45

USCG HSS

112

1997

18

Hydrocal

2 to 5

calm

83 to 29

USCG HSS

112

1999

22

Hydrocal

2 to 3

calm

72 to 6

USCG HSS

112

2000

11

Hydrocal

2 to 3.5 calm

72 to 61

NOFI

112

1999

0

Hydrocal

2 to 5

calm

91 to 91

USCG ZRV

112

1977

6

Hydrocal

2 to 3

calm

66 to 49

High Speed Circus

112

1999

Stream Stripper

112

2000

Sorbent booms

L/m

40

Hydrocal

12

Hydrocal

2 to 4

calm

90 to 50

calm

80 to 66

1

7

Boom 1

111

2000

1

calm

8

Boom 1

111

2000

1.5

calm

14

Boom 1

111

2000 3

1

calm

12

Boom 1

111

2000

1

calm

14

Boom 2

111

2000

1

calm

10

Boom 2

111

2000 3

1

calm

13

Boom 2

111

2000 0

1.7

calm

10

Boom 2

111

2000 7

1.7

calm

11

Boom 2

111

2000 7

2.5

calm

10

Boom 2

111

2000 9

2.5

calm

6

Boom 2

111

2000

1

calm

13

Boom 3

111

2000

1

calm

6

Boom 3

111

2000 7

1.7

calm

7

Boom 3

111

2000 8

2.5

calm

6

Boom 3

111

2000 11

2.5

calm

6

Boom 3

111

2000 11

370

PART | V

Physical Spill Countermeasures on Water

Watkins notes that a measure of the effectiveness of a skimmer is the ability to recover the most oil in the least time.131 Suzuki et al. tested model skimmers at scale models and described the flow mathematically.132 Similar work was carried out by Brekne.113 Guenette and Buist reported on extensive testing of the Lori brush skimmer, as did Cooper and coworkers on a brush skimmer.133,134 Similarly, Broje and Keller analyzed a disk skimmer.135-137 Similar work was also carried out on a cold climate skimmer.138 While it is acknowledged that skimmers may have similar limits as booms (about 0.5 m/s (1 knot)), these limitations can be overcome by several means, most typically by moving the skimmer at a lesser relative velocity to the oil on the sea. Several workers have proposed skimmer designs to overcome these limitations. Several earlier works used air herding or sheltering of the recovery area.139-142 Clauss and Ku¨hnlein proposed and have demonstrated the use of massive wave-shielding hulls and energy-absorbing designs to allow at-sea recovery.143 Ueda et al. tested a model skimmer that avoids the effects of waves by diverting oil into a calm area.144 Hara et al. proposed to use a moon pool inside a vessel to increase the wave limit from 1 to 2 or 3 m wave height.145 Akahoshi et al. suggest that the current limit of sea recovery is a wave height of 2.5 m and propose using a compensating float-pump system to deal with motion of the recovery platform.146 Hvidbak and Gunter note that heavy oil imposes additional restrictions on recovery at sea.147 Provant described the capabilities of the recovery equipment for the weather conditions of Prince William Sound.148 Provant provides tables of expected wave heights with winds in the sound. The degradation of performance of skimmers is given as follows: sea height up to 0.9 m, 80%; 1 to 2 m, 70%, 2.1 to 2.7 m, 50%; and over 2.8 m, 10%. Nordvik described modifications of the Transrec oil recovery system to improve its recovery efficiency and to enable high seas recovery.128 The unit recovered 15 to 85% of the oil presented to it during individual tests in 1.5 m waves and was thought to be capable of operating in waves up to 4 m. Nordvik suggested limits for windows of opportunity for skimmers based on increase in viscosity.6 Nordvik suggested a limit of 3 to 10 hours for a disc skimmer and 10 hours for a brush skimmer for the heavy oil BCF 17. For BCF 24, the window was estimated to be 2 to 3 days for a disk skimmer and after 3 days for a brush skimmer. These limits were based on the viscosity number generated from an oil viscosity model. A test of skimmers and booms in cold weather conditions showed no differences from warmer weather tests of the same skimmers.149 Shum and Borst tested a rope-mop skimmer in a test tank with ice and found that the recovery rates were not affected by up to 50% ice concentration.150 After 50% ice concentration, the mouth of the skimmer became jammed with ice. These tests show that the most significant factor in temperature is the formation of ice. Gates and Corradino described a test of a weir skimmer in a test tank.151 The recovery did not deteriorate significantly when waves were increased from 0.18 to 0.47 meters.

Chapter | 13 Weather Effects on Oil Spill Countermeasures

371

13.2.4.1. Harbor/Small Skimmers The standard specifications for a harbor skimmer are that they should be able to achieve the specified performance with a 0.3 m wave.152 Velocity of the skimming surface is important, and studies have shown that significant performance improvements in a skimmer can be achieved if the rotation speed is slowed.153 Yazaki noted that the government of Japan specified that the small skimmer must be able to operate at winds of 10 m/s and up to 0.5 m wave height and larger skimmers at winds of up to 15 m/s and wave heights of up to 1.2 m.106 13.2.4.2. Offshore/Larger Skimmers Nordvik noted successful recoveries on the open seas at up to 10 to 13 m/s winds and in waves up to 2.5 m/s.6 Peigne reports on the test of the Sirene and ESCA offshore skimming systems.154 Both systems were able to recover oil during offshore tests in winds up to 10 m/s and with accompanying waves of 1 to 2 m. Leigh described efforts to build and test two skimming devices to withstand sea state 5 (equivalent to winds of 10 to 14 m/s and wave heights of about 2 m).155 The two devices were a disc-drum skimmer and a weir basin that sheltered the basin face from oncoming waves. Wilson reported on the testing of a high-seas weir boom and noted that high recoveries were obtained in seas with up to 3 m waves and winds of up to 15 m/s. Up to 7,500 tons of oil or emulsion was collected in high seas.156 13.2.4.3. Special/High-Current Skimmers Schwartz describes some of the first tests on higher-speed skimmers, noting the performance decrease with both waves and tow speed.157 Hansen described a series of tests to measure the performance of fast water skimmers.112 The “Ocean Buster” skimmer, a unit using a weir and calm area behind the weir, has been successfully tested at tow speeds up to 2 m/s (4 knots).113 The same skimmer was tested in seas up to 3 m significant wave height at a tow speed of 0.8 m/s (1.5 knots). Coe describes a similar effort in which the United States Coast Guard (USCG) high-speed skimmer capability was increased up to 3 m/s (6 knots) by using a deflector.158 Many of these data have not been verified by other workers. 13.2.4.4. Skimming Ships Several Dutch efforts have taken place over the past 20 years to use dredger vessels with their systems for the recovery of oil.159 Recent efforts have focused on modifications to the input system to allow the dredge vessels to use their own pumps, thereby utilizing this enormous capacity. The heart of the system is the floating unit known as a dredge skimmer, which replaces the regular draghead. This skimmer follows the wave movements, and the suction pipeline is kept in position about 2 m under the water level by the suction tube gantries. A sweeping arm is connected to the skimmer through a double hinge, allowing the arm freedom of movement horizontally and vertically. Koops et al. describe the use of

372

PART | V

Physical Spill Countermeasures on Water

the earlier versions of these skimming vessels to recover the heavy oil during the Katina oil spill.160 The modified dredges recovered oil up to waves of 2 m, at which point the sweeping operation was stopped to avoid damage. Recent versions of the vessel were used during the Prestige spill and recovered up to 13,000 L of pure heavy oil during a three-month operation period.161

13.2.5. Dispersants Over 20 years ago, neither weathering of the oil nor weather itself was considered in planning for dispersant application.162 More recently, dispersant applications have been assessed using only weathering and not weather as a criterion for results.163-167 Nordvik proposed a weathering-based window of 26 hours for ANS crude and 2 hours for Bonnie Light.6 A period of reduced dispersibility was estimated to be 26 to 120 hours for ANS crude and 2 to 4 hours for Bonnie Light. After 4 hours, Bonnie Light was deemed not dispersible. Several workers noted that the weathering of the oil itself was a major factor in dispersant effectiveness.168-172 If one calculates both the time as it relates to spreading and the viscosity change of most oils as they weather, dispersability for most oils is severely diminished after 12 to 36 hours.173-175 Most heavy oils are barely, if at all, dispersible only in the first 12 hours.173-175 In past years, temperature was thought to be unimportant, but as time progressed, most researchers found more significant effects. Martinelli and Lynch reviewed the factors affecting chemical dispersion and found that the most two important factors are the oil composition and sea energy. Martinelli and Lynch concluded that temperature was semi-important, and several other factors were not investigated.176 Farmwald and Nelson tested the effect of temperature on dispersibility on Prudhoe Bay crude and found a variable temperature effect.177 Daling and Brandvik et al. measured dispersant effectiveness in the laboratory and found that, as temperature was lowered, effectiveness fell by as much as 20%.178-180 The researchers also noted that photolysis of the oil resulted in less effectiveness. Lunel and Lewis proposed that the bottom threshold for dispersibility be established as 15% for moderate sea conditions and 30% for calm sea conditions.181 Lunel et al. suggested that winds of 0 to 5 m/s be classified as low energy and that winds of 6 to 10 m/s be classified as high energy.182 The natural dispersion on a medium fuel oil was 0.8% at low energy and 3% at high energy. The dispersion for the medium fuel oil for Slickgone was 8% at low energy and 17% at high energy. Scholz et al. suggest a minimum threshold of energy of a sea state of 1 to 5 (corresponding to winds of 2.5 to 12 m/s and waves of 0.1 to 2 m).163 At higher sea states, many crude oils will disperse naturally. Most of the oil spilled during the Braer incident, which was a Gulfaks crude oil, dispersed naturally.182-184 Lunel noted that the winds were of force 8 to 10 (16 to 26 m/s, yielding waves of about 6 to 12 m and gusts of wind up to 35 m/s) during that incident. It is important to state that the oil, Gulfaks, was dispersible and that

Chapter | 13 Weather Effects on Oil Spill Countermeasures

373

the same conditions applied to a Bunker C, for example, would not result in significant dispersion. Lunel also stated that natural dispersion is slower and moves only to about 1 m depth. This had been stated earlier by Cormack and Nichols.185 Fuentes et al. studied natural dispersion in a turbulent laboratory apparatus and found that natural dispersion increased with increased oil weathering.186 The oil was a lighter Arabian crude. The winds during the Sea Empress incident were up to 20 m/s, and thus much of the dispersion that occurred during that incident may have been natural.187 Natural dispersion has been studied and described by several other workers.58,188 Mackay et al. described the natural dispersion of oil as:189 Fraction dispersed ¼ 0:11ðW þ 1Þ2 ð1 þ 50m1=2 dst Þ 1

(2)

where W is the wind speed in m/s, m is the viscosity in mPa.s, d is the slick thickness in cm, and St is the oil-water interfacial tension in dyne cm1. Most recent models use the equations of Delvigne and Sweeney:190 Q ¼ CD0:57 S F d0:7 Dd

(3)

where Q is the entrainment rate of oil droplets, C is an empirical constant dependent on oil type, D is the dissipated breaking wave energy, S is the fraction of the sea surface covered by the oil, F is the fraction of the sea hit by breaking waves, d is the oil particle diameter, and Dd is the oil particle diameter interval. The wave energy, D, is given by: D ¼ 0:0034rgH 2

(4)

2

where D is the energy in J/m , r is the density of seawater, g is the gravitational constant, and H is the rms value of wave height. Thus in both the Mackay and the Delvigne formulation, the amount of oil that enters the water varies as the square of the wave height. Delvigne and Hulsen subsequently developed a small laboratory method to measure the dispersibility of an oil.191 Koops also presents a formulation for natural dispersion:52 Vdisp ¼ Voð1 e7:6105 Ht=Vo0:62 Þ where Vdisp is the volume of oil dispersed naturally, Vo is the initial oil volume, H is the significant wave height, and t is the time.

(5)

374

PART | V

Physical Spill Countermeasures on Water

In the Koops formulation, the oil dispersion varies linearly with the wave height. Mackay later developed a relationship for breaking waves only, and the parameter that determined the rate of dispersion was only the amount of dispersant applied.192 The effectiveness of Corexit 9500 and Corexit 9527 was tested on Alaska North Slope crude oil at various salinities and temperatures representative of conditions found in southern Alaskan waters.193 The oil was weathered to different degrees. Tests were conducted in a swirling flask at temperatures of 3, 10, and 22 C with salinities of 22 and 32%/. Analysis was by GC. The authors concluded that, at the common temperatures found in the estuaries and marine waters of Alaska, the dispersants were largely ineffective. They also found an interactive effect between temperature and salinity. A high effectiveness for “emulsion,” an uncharacterized mixture of oil and water, was attributed to “osmotic shock” because of the difference in the salinity of preparation (33% / oo) and the test salinity. Later, Fingas et al. studied this same mixture and concluded that this was a matter of ionic strength that varies as both salinity and temperature.194 Deposition of dispersant droplets depends on the droplet sizes discharged from the nozzles. Lindblom and Cashion presented a relationship that describes the droplet sizes:195 VMD ¼ kðma sb rc V d De Þ

(6)

where VMD is the droplet volume mean diameter, m is the dispersant viscosity, s is the surface tension, r is the density of the dispersant, V is the exit velocity relative to the surrounding airstream, D is the nozzle diameter, and k, a, b, c, d, e are empirical constants. It should be noted that d varies from e0.6 to e1.3 depending on dispersant; thus the effect of increasing wind velocity is to decrease the droplet diameter discharged. Smedley studied the deposition of dispersants from DC-4 and Canadair spray aircraft. Smedley noted that droplets less than 100 mm and sometimes up to 500 mm may be entrained in aircraft vortices and may not be deposited near the intended target. Tests also showed that crosswinds cause poor deposition.196 Fay reported on a series of tests on the MASS spray system at Crosbytown, Texas,197 and found that 20 to 80% of the dispersant was lost and did not hit the targeted area. The results of these tests are shown in Table 13.5. Giammona et al. conducted a major campaign to measure the deposition of dispersant from several aircraft platforms near Alpine, Texas.198 These data have been used here to provide prediction of the relation of wind to deposition efficiency and thus weather cut-offs.

Altitude ft

Wind Speed (kn)

Cross Wind

Deposition % **

Droplet Information Average (sm)

(mm) Maximum

Approximate VMD*

Dispersant

Spray System

Gallons Sprayed

Litres Sprayed

Ground Speed (kn)

9527

ADDS

45

171

140

50

13

9

68

104

969

300

9527

ADDS

45

171

140

100

9

8

15

162

1108

450

9527

ADDS

45

171

140

100

13

9

59

78

630

300

9527

ADDS

45

171

140

150

17

12

48

116

758

300

9527

ADDS

45

171

140

120

17

13

73

120

671

250

9527

ADDS

45

171

140

50

18

15

66

68

671

200

9527

ADDS

45

171

140

100

14

12

75

68

888

300

9527

ADDS

45

171

140

150

15

15

56

93

687

400

9527

ADDS

56

212

140

50

8.5

2

46

35

544

400

9527

ADDS

45

171

140

100

9.3

1

78

93

630

300

9527

ADDS

60

227

140

150

6

0

38

76

778

450

9527

ADDS

30

114

140

120

10.2

7

52

137

646

350

9527

ADDS

38

144

140

50

13

1

50

70

758

400

9527

ADDS

41

155

140

120

9.5

9

75

96

785

250

375

(Continued )

Chapter | 13 Weather Effects on Oil Spill Countermeasures

TABLE 13.5 Dispersant Spray Trials at Alpine, Texas

376

TABLE 13.5 Dispersant Spray Trials at Alpine, Texasdcont’d

Cross Wind 5

Deposition % **

Droplet Information Average (sm)

(mm) Maximum

Approximate VMD*

33

101

689

400

42

87

639

150

Spray System

Gallons Sprayed

Litres Sprayed

9527

ADDS

56

212

140

150

12

9527

MASS

116

440

200

100

2

9527

MASS

59

224

200

100

2

38

61

1018

200

9527

MASS

41

155

200

100

31

76

838

250

9527

MASS

40

152

200

100

29

85

903

150

9527

MASS

40

152

200

100

19

81

656

150

9527

MASS

52

197

200

100

6

4

86

94

809

200

9527

MASS

41

155

200

100

7

4

40

105

510

200

9527

MASS

36

136

200

100

9.5

1

49

55

879

250

9527

MASS

60

227

200

100

7

1

44

52

506

150

9527

MASS

93

352

200

100

4.5

1

54

56

796

150

9527

MASS

65

246

200

100

7

2

23

94

810

200

1

Physical Spill Countermeasures on Water

Dispersant

PART | V

Altitude ft

Wind Speed (kn)

Ground Speed (kn)

MASS

113

428

200

100

12

5

20

91

832

100

9527

DC-4

32

121

136

50

17

15

31

60

317

150

9527

DC-4

0

0

136

50

9554

MASS

52

197

200

100

3.5

3

56

67

842

150

9554

MASS

48

182

200

100

3.5

0

44

76

918

150

9527

MASS

71

269

200

100

2.5

1

47

30

348

100

9527

DC-3

37

140

115

50

46

50

594

150

9527

DC-3

33

125

115

50

4.5

1

53

83

690

300

9527

DC-4

38

144

115

50

6.5

0

20

54

316

200

9527

DC-4

43

163

136

50

5.5

1

22

57

591

150

9527

DC-4

71

269

136

50

5.5

2

34

57

591

150

9527

DC-4

81

307

136

50

9

2

13

49

405

150

9527

Air Tr.

0

0

119

15

2.5

8

40

31

711

100

9527

Air Tr.

0

0

119

15

2.5

1

35

38

488

200

1

74

9527

Air Tr.

0

0

119

30

2.5

2

9527

Air Tr.

0

0

119

30

7

4

31

377

150

42

405

150

Chapter | 13 Weather Effects on Oil Spill Countermeasures

9527

*where volume hits 50% in middle of distribution. **calculated in this work.

377

378

PART | V

Physical Spill Countermeasures on Water

Lewis calibrated a helicopter bucket system, finding that the standard conditions of 30 m/s (60 knots) forward speed and altitude of 14 m resulted in about 70% deposition.199 A forward speed of 8 m/s (15 knots) and 6 m resulted in better or optimal deposition, although the ground effect of the helicopter rotors was extreme. A deposition of about 3 to 6 mL/m2 was obtained at the standard conditions, with a loss of about 20% of the material due to wind drift under the prevailing weather conditions of 2 to 5 m/s winds. At this wind speed the deposition was about 3.4 mL/m2 (about 3.5 US gal/acre) with a swath width of about 21 m. Reducing the air speed to 15 m/s (30 knots) and an altitude of 7 m increased the dosage rate to 14 mL/m2 (about 14 US gal/acre) with a deposition rate of at least 70%. Brandvik et al. tested a new helicopter bucket but did not gauge the effect of wind.200 Payne et al. studied the use of dispersant on the Pac Baroness and noted that winds of 7 to10 m/s caused problems in deposition.201 Furthermore, it was impossible to tell if the dispersant hit the slick or was blown off by the crosswind. Payne et al. also studied the test application of dispersant on the Mega Borg spill and noted that there was significant wind drift of the dispersants.202 Lunel noted in several sea trials that winds of 5 to 10 m/s were not a problem for aerial application, although targeting is a problem and that this is best done with the aid of remote sensing.203-213 Guyomarch et al. describe a field experiment in which the wind ranged from 3 to 20 m/s. This wind was not noted as a factor.214 Several workers noted that dispersion using spray booms on vessels was relatively unaffected by winds up to 15 m/s.215

13.2.5.1. Other Agents Pope et al. tested surface collecting agents and evaluated them over a range of temperatures.216 The efficiencies of the agents only decreased slightly with air temperatures down to less than 0 C. The effect of wind of 2 to 3 m/s was to assist in herding.

13.2.6. In-Situ Burning Several workers have reviewed the use of burning in various weather conditions.217 Buist et al. studied the burn rates and conditions of some Alaskan oils with and without ice.218 This data has been used for prediction purposes in Table 13.6. Buist discusses the windows of opportunity for in-situ burning.219 He proposes that the maximum wind speed for successful ignition is 10 to 12 m/s, although no basis for this is given. Later in the paper, Buist also proposes a series of weathering percentages and water content for which oils would or would not burn or where efficiency is severely hampered. Further limitations discussed include operational limits and the fact that VFR flying rules for the helicopter would require greater than 4 km visibility and a minimum 300 m ceiling. Buist et al. studied in-situ burning in a test tank on the North Slope of

TABLE 13.6 Tests of Burning Performance with Changing Weather Conditions

Burn

Reference

Year of Test

Rate Change mm/min

Burn Rate mm/min

Oil Type

Oil Weathering %

Test Condition

0.9

ANS

0

open water

0.8

ANS

0

frazil

1.5

Northstar

0

open water

0.8

Northstar

0

frazil

1

Northstar

33.8

open water

0.6

Northstar

33.8

frazil

1

Northstar

43.8

open water

0.4

Northstar

43.8

frazil

1.6

Pt. McIntyre

0

open water

Change with Frazil/Slush Ice Alaska mid-scale

Buist 2003

2002

Alaska mid-scale

Buist 2003

2002

Alaska mid-scale

Buist 2003

2002

Alaska mid-scale

Buist 2003

2002

Alaska mid-scale

Buist 2003

2002

Alaska mid-scale

Buist 2003

2002

Alaska mid-scale

Buist 2003

2002

Alaska mid-scale

Buist 2003

2002

Alaska mid-scale

Buist 2003

2002

0.1

0.7

0.4

0.6

average

0.45

Change with Brash Ice Buist 2003

2002

0.6

0.3

ANS

0

Brash

Alaska mid-scale

Buist 2003

2002

1.1

0.4

Northstar

0

Brash

Alaska mid-scale

Buist 2003

2002

0.8

0.2

Northstar

33.8

Brash

Alaska mid-scale

Buist 2003

2002

0.7

0.3

Northstar

43.8

Brash

Alaska mid-scale

Buist 2003

2002

1.3

0.3

Pt. McIntyre

0

Brash

average

0.8

379

Alaska mid-scale

Chapter | 13 Weather Effects on Oil Spill Countermeasures

Rate Summary

380

PART | V

Physical Spill Countermeasures on Water

Alaska and found that the wave steepness reduced both the burn rate and effectiveness.220 Bech et al. found that increasing wave action reduced burn efficiency.221 Thornborough described a series of in-situ burns conducted off the United Kingdom in 1996.222 The trial was specifically designed to examine the limits of ignition and combustion under sea conditions. Burn 1 was ignited using a small hand-held unit with a sea state of 4/5 (waves of 1 to 2 m), wind of 10 to 13 m/s (20 to 25 knots), and a current speed of 0.9 m/s (1.8 knots). The boom and the ignition were successful and were not significantly hampered by the prevalent weather conditions. The second burn was ignited using a Helitorch at sea state of 4 (waves about 1.5 m), wind of 10 m/s (20 knots), current of 0.9 m/s (1.8 knots), and again at 120 degrees from the current direction and that of the towing vessel. The second burn involved oil containing 25% water. The burning fuel from the Helitorch reached the sea surface from a height of 60 to 70 ft (20 to 25 m) and a helicopter speed of 10 to 13 m/s (20 to 25 knots). Above this height and speed, the gelled gasoline did not stay ignited. Nordvik et al. reviewed windows of opportunity for burning, noting that the lower flammability limit and weathering of the oil are important.6,223 This paper also proposes an upper wind limit of 10 to 12 m/s (20 knots) for the ignition of the oil. The source of this limit, other than a related report, is not given. A weathering diagram generated from the IKU model is given as the means for estimating the weathering time at which the flash point is reasonable, given a certain wind velocity. Earlier, Nordvik had proposed a window based on formation of emulsion for ANS crude of 36 hours and of 1 hour for Bonnie Light crude.6

13.2.6.1. Ignition Farmwald and Nelson tested the effect of temperature on flammability and ignition of Prudhoe Bay crude and found temperature had little effect.177 Guenette and Wighus reported that ignition was difficult when winds exceeded 10 m/s.224 Bech et al. found that ignition was difficult with emulsified oils.221 D’Atri and King conducted a burn in cold conditions and found that gelled gasoline did not work; however, a propane weed-burner worked well.225 Guenette and Thornborough reported on the use of two types of igniters used off the coast of the United Kingdom.226 Burn 1 was ignited using a small handheld unit with a sea state of 4/5 (waves of 1 to 2 m), wind of 10 to 13 m/s (20 to 25 knots), and a current speed of 0.9 m/s (1.8 knots). The boom and the ignition were successful and not significantly hampered by the prevalent weather conditions. The second burn was ignited using a Helitorch at sea state of 4 (waves about 1.5 m), wind of 10 m/s (20 knots), a current of 0.9 m/s (1.8 knots), and again at 120 degrees from the current direction and that of the towing vessel. The hand-held unit consisted of a jar of gelled gasoline and a flare. The unit was floated out to the oil after lighting the flare; the flame from the flare melted the wall of the plastic and then ignited the gelled gasoline. The second

Chapter | 13 Weather Effects on Oil Spill Countermeasures

381

device used a modified Helitorch in which the fluid included an emulsion breaker as well as the usual gelled fuel. Moffat and Hankins tested a flare-type igniter specially built for oil spills.227 The flares showed a temperature of 1370 C at the center, and this was maintained for a 3-minute burn. The igniter was used to ignite diesel fuel in ambient temperatures of 3 C with winds of 8 to 10 m/s.

13.2.6.2. Fire-Resistant Boom Meikle reported on the test of a ceramic fire-resistant boom.66 Measurements on the loss rate versus tow speed were taken, as well as the rate of burning at different tow speeds. These are reported in Table 13.3. Buist et al. also reported on the testing of a fire-resistant boom.228 The boom was successfully tested in a towing mode up to wave heights of 4 m and contained oil in currents up to 0.4 m/s. Several workers reported on the tests on fire-resistant booms as containment booms.229,230 Many of the booms showed performance near that of conventional booms. The first loss and critical failure velocities were reported. McCourt et al. tested a series of fire-resistant booms for ruggedness while exposed to a propane fire and found that most booms could tolerate the propane fires.231

13.2.7. Others Pumps and transfer devices are not really limited by the weather, but are affected significantly by viscosity, which increases as the oil weathers or at low temperatures. Lower temperatures would, of course, increase viscosity somewhat and make pumping more difficult. Cooper and Mackay, and Hvidbak and Gunter review viscous pumping technologies.232,233 Nordvik proposed a window of opportunity limit for using sorbents based on weathering and increased viscosity of oil.5 Nordvik suggested that a viscosity of 15,000 mPa.s would constitute a reasonable upper limit. The window of opportunity for heavy oil such as BCF-17 and BCF-24 became 4 and 10 days, respectively. The effectiveness was felt to be reduced to 50% after 36 hours. Nordvik also proposed a window of opportunity limit for centrifugal separators based on the closing density gap between oil and seawater.5 This limit was calculated to be 18 hours for ANS and 24 hours for Bonnie Light. Provant studied the effects of wave height on separation by decanting.148 Provant rated the decrease in performance with wave height as follows: wave height up to 0.9 m, 0.8 decanting factor (eg. 80% effective); waves of 1 to 2 m, 0.8; waves of 2.1 to 2.7 m, 0.7; and over 2.8 m, 0.6. McCourt and Shier studied the sediment interaction on the Yukon River and found in laboratory experiments that there were temperature and energy relationships.234 The mean oil loading was 0.006 goil/gsolids at an arc (shaking angle) of 4 degrees, 0.1 at 7 degrees, and 0.11 at 10 degrees. This is explained by increasing contact and coalescence. The loading went from the 0.11 noted at 15 C up to 0.26 goil/gsolids at 2 C.

382

PART | V

Physical Spill Countermeasures on Water

13.2.8. Ice Conditions While it was not one of the purposes of this study to examine the cleanup efficiencies with ice inasmuch as this is more than a factor of weather, somedata were found that make it possible to calculate the degradation of certain

FIGURE 13.2 Recovery of oil under ice using a rope mop skimmer. This type of Arctic cleanup is not covered in this subsection. FIGURE 13.3 Extreme snow cover can hamper oil spill countermeasures. Although this type of situation is relevant to land spill situations, little study has been carried out.

Chapter | 13 Weather Effects on Oil Spill Countermeasures

383

recovery techniques with increasing ice concentrations.235 Abdlenour et al. summarized tests of a water-spray boom/weir combination in increasing ice conditions.236 Shum and Borst tested a rope mop in ice-infested conditions and found a sharp drop-off in effectiveness.150 Figure 13.2 shows oil-in-ice recovery, a topic not covered here, and Figure 13.3 covers heavy snow conditions, a situation that may be relevant here but is not often studied. Several workers have noted that ice, not temperature, is an issue.235,237 Tsang and Vanderkooy described the development of an ice boom that shows the capability of deflecting ice floes on rivers and allowing oil to pass through.238 The boom was successfully tested in heavy ice concentrations and with currents up to 0.2 m/s.

13.3. DEVELOPMENT OF MODELS FOR EFFECTIVENESS OF COUNTERMEASURES 13.3.1. Overall The basic procedure to develop a model for countermeasure effectiveness was to use literature data on testing of the particular countermeasure and then to correlate this data with the weather factor to form a model. The main advantage of this method is that it yields a relatively realistic outlook on the actual relationship between performance and the weather factor under consideration. Its main disadvantage is that often there are no data on the performance of oil spill countermeasures at high winds, waves, or extremes in temperature. Thus, to a certain extent, the existing quantitative data must be extrapolated past the typical measurement points.

13.3.2. Booms The quantitative data obtained for boom performance with weather is summarized in Table 13.3. The variance of critical velocity and wave height can be seen in the table. Figures 13.4 to 13.6 illustrate boom failures. An analysis of the variation of critical loss rate and wave height shows some correlation. This allows one to use the values presented in Table 13.3 directly to predict the decrease in first loss and critical velocities. Figure 13.2 shows the decrease in first loss and critical tow velocities with increasing wave height using the averages of all booms and then the average of fire-resistant booms. The decrease in performance of booms with increasing wave height as shown in Figure 13.7 is expected and has been relatively well known for several years. Table 13.7 shows the correlation between various performance parameters. As can be seen, there is a poor correlation between the oil viscosity, wave height, and first loss speed.

13.3.3. Skimmers The quantitative data obtained for skimmer performance with weather is summarized in Table 13.4. These data come largely from Schulze and represent

384

PART | V

Physical Spill Countermeasures on Water

FIGURE 13.4 The rollover failure of an oil spill containment boom.

FIGURE 13.5 Typical failure of a boom due to droplet breakaway under the contained slick. This typically occurs at about 0.35 m/s (0.7 knots).

data collected over 25 years of skimmer testing.127 Table 13.4 shows the three most important values of skimmer performance, ORR, TE, and RE. The oil recovery rate (ORR) is the quantitative rate in volume per unit time, usually m3/hour, and is corrected for water recovery. The throughput efficiency (TE) is applicable only to advancing skimmers. It is the percentage of oil presented to a skimmer versus that recovered, in percent. The recovery efficiency (RE) is the percent of oil recovered out of the total oil and water recovered. Table 13.8 shows the calculations of the rate of change of the ORR, TE, and RE with increasing current and wave height. These are calculated directly from the data shown in Table 13.4. The rate of change is taken when other parameters of the test, including viscosity and oil type, tow rate, and wave height and type, are held constant.

385

Chapter | 13 Weather Effects on Oil Spill Countermeasures

FIGURE 13.6 Closeup of boom failure; this one is also due to droplet breakaway under the contained slick but is accelerated by the waves present.

Critical or First Loss Speed (m/s)

1.0

Average boom - critical speed Average boom - first loss Fire boom - critical speed Fire boom - first loss

0.8

0.6

0.4

0.2

0.0 0.0

0.5

1.0

1.5

2.0

2.5

Wave Height m FIGURE 13.7 Effect of waves on boom performance.

The following points must be made about skimmer test data. 1. The most important point is that most skimmers show unique data and response to current or tow speed. It is difficult to generalize about skimmers without making the point that there are many exceptions. In this report,

386

PART | V

Physical Spill Countermeasures on Water

TABLE 13.7 Correlation of Performance and Test Parameters for Booms First Loss Speed

Critical Speed/Wave

Oil Viscosity

Wave Height

First Loss Speed

1.00

0.01

0.39

L0.40

Critical/wave

0.01

1.00

0.55

0.05

Oil Viscosity

0.39

0.55

1.00

0.13

Wave height

L0.40

0.05

0.13

1.00

–those items noted in bold show possible, but not significant correlation.

2.

3. 4.

5.

6.

7.

some generalizations are made, but the author is fully aware of the difficulty of doing so. The test data presented in Tables 13.4 and 13.8 are summarized from a variety of literature sources. The accuracy of these sources is unknown in every case, although Schulze is known to be a highly accurate summary.127 Several of the literature sources behind this reference were checked, and no errors were noted. It should also be recognized that the accuracy and precision of some of the data behind the collection are questionable. Furthermore, many data points were collected in the 1970s. The variability of the data may no doubt be due to the variability in test conditions and also in the capability to measure the necessary parameters. Some of the data summarized here are actually estimates. It will be noted very readily that the change rate of skimmer effectiveness, particularly ORR, will usually increase with increasing tow speed. Schulze had noted this.127 The reason, as Schulze also noted, is no doubt the increased oil encounter rate when tow speed increases. This complicates calculations of the “decrease” in ORR with increasing tow speed. The effect noted in point 4 above also occurs with some increasing wave activity. The same reason is probably pertinent here. This also complicates calculations for decline in recovery with wave height; however, as the wave increases, the performance generally falls and one can use these values separately. Some skimmers may show unusually high or low performance in these data. This is largely due to the test conditions. For example, if a skimmer is not tested at a high tow speed or a high wave, its performance may appear to be very good compared to a similar skimmer that is tested under more rigorous conditions. There is no easy way to deal with these variances. The effect of ice on performance is shown in Table 13.9. The average ORR declines somewhat with increasing ice percentage. Both TE and RE appear, on average, to be unaffected by ice concentration.

Current/Tow Speed Summary

Wave Height Summary

Skimmer

% ORR/ TE/ RE/ %ORR/ curr % curr % curr % wave s/m s/m s/m %/m

Overall Average

L112

Skimming barrier Sirene skimming barrier

62

L26

L54

148.7

73.7

0.7

246.5 240.6

89.9

62.2

39.1

547.7

Lori brush skimmer 200.9 Scoop weir skimmer

72.8

132.1

45.3

RE/ wave %/m

%ORR/ ORR TE RE wave m3/h % % %/m

TE/ wave %/m

RE/ wave %/m

%ORR/ wave %/m

TE/ wave %/m

RE/ wave %/m

59

17

12

64 50 L80.1

53.1

16.1

15.3

90.8

22.2

45.4

60

43 21.2

39.7

26.9

13.8

23

53 38 74.3

20

46.5

115.5

75.3

1

77.3

81.1

92.5

65

7 53

Paddle skimmer 23.2

Rope mop towed single

9.6

Oil mop ZRV

75.6

26

21.7

43.5

6.6

19.2

Harbour Chop

TE/ wave %/m

Disc skimmers e flat and T

Rope mop stationary

Regular Waves

105

3.2

106.5

75 547.7 66 88 50.7

81 51

78.3

5

80

8.5

7

51 64.2

25.3

10

52 45 28.6

6.2

75.3 30

71 7

54.8

50.6

2.5

56.7

68.9

32.9

51.1

56.2

7.5

21.3

41.6

4.6

Chapter | 13 Weather Effects on Oil Spill Countermeasures

TABLE 13.8 Summary Skimmer Performance with Changing Weather Conditions

26.3

387 (Continued )

388

TABLE 13.8 Summary Skimmer Performance with Changing Weather Conditionsdcont’d Current/Tow Speed Summary

Skimmer

Wave Height Summary

% ORR/ TE/ RE/ %ORR/ curr % curr % curr % wave s/m s/m s/m %/m 7.1

TE/ wave %/m

RE/ wave %/m

%ORR/ ORR TE RE wave m3/h % % %/m

24.7

49.3

36.4

10

54 63

Fixed submersion plane

187.6 14.8

129.7

87.4

19

44

DIP 2001

81.8

99.8

20.8

2

78 72 149.7

20

142.4

TE/ wave %/m

RE/ wave %/m

98

10.8

8

22 64.8

11.7

Slurp skimmer

166.1

8.3

0.4

14 166.1

12.5

Harbour Mate weir skimmer

196.5

9.2

1

10 402.1

Weir skimmers, Destroil, GT-185

84

43.5

19

59 87.1

7.7

14

68 14 33.6

25.7

73

4.1

Transrec Various high speed skimmers

4.7

13.2

111.9 4

13

75

TE/ wave %/m

RE/ wave %/m

24.7

49.3

36.4

124.3

82.9

40.6

8.3

15.8

12

6.6

54.4

61.7

21.3

23.9

5.2

152

2.3

Physical Spill Countermeasures on Water

58.7

53.4

%ORR/ wave %/m

20.8

Stationary skimmer - skim pak

Veegam towed weir 79.5

Harbour Chop

PART | V

Marco belt skimmer 178.2 25.8

Regular Waves

Positive Values Only Regular Waves

Average

Harbour Chop

Regular Waves

Harbour Chop

%ORR/ wave %/m

TE/ wave %/m

RE/ wave %/m

%ORR/ wave %/m

TE/ wave %/m

RE/ wave %/m

%ORR/ wave %/m

TE/ wave %/m

RE/ wave %/m

%ORR/ wave %/m

TE/ wave %/m

RE/ wave %/m

Overall Average

90.8

54.9

50

67.4

96.7

32.5

5.4

54

33.1

41.4

93.8

27.4

Skimming barrier

26.1

39.7

38.6

54.8

2.5

39.7

32.8

0

54.8

Sirene skimming barrier

12.7

15.9

68.7

52.3

30.8

2.1

11.1

115.5

23.1

77.3

81.1

106.5

115.5

106.5

273.9

Lori brush skimmer Scoop weir skimmer

30

Disc skimmers e flat and T

77.3

25.4

81.1

50.6

30

56.7

e

e

e

e

Rope mop stationary

244.7

105

330

61.9

32.9

122.4

Rope mop towed single

39.2

20

29

7.5

12.5

Oil mop ZRV

28.6

21.3

48.1

26.3

28.6

e

18.3

0 50.6

Paddle skimmer

2.5

37.7

56.7

e 52.5

2.5

165

3.5

0

32.9

15.6

13.6

0

7.5

21.3

3.3

6.9

26.3

389

(Continued )

Chapter | 13 Weather Effects on Oil Spill Countermeasures

Calculated and Summarized Values

390

TABLE 13.8 Summary Skimmer Performance with Changing Weather Conditionsdcont’d Calculated and Summarized Values Positive Values Only Regular Waves %ORR/ wave %/m

TE/ wave %/m

RE/ wave %/m

Fixed submersion plane

142.4

%ORR/ wave %/m

TE/ wave %/m

RE/ wave %/m

54.5

49.3

43.6

124.3

82.9

36.3

Stationary skimmer e skim pak

64.8

11.7

Slurp skimmer

71.4

55

Harbour Mate weir skimmer

86.5

15.8

33.7

Weir skimmers, Destroil, GT-185

120.3

83.7

61.7

Veegam towed weir

87.4

23.9

67.9

Transrec

25.7 4.1

40.6

8.3

152

Regular Waves %ORR/ wave %/m

TE/ wave %/m

142.4

98

74.9

28.6

RE/ wave %/m

Harbour Chop %ORR/ wave %/m

TE/ wave %/m

RE/ wave %/m

39.6

49.3

40

124.3

82.9

64.8

11.7

40.6

8.3

47.4

33.8

0

0

9.2

157.8

15.8

22.9

7.9

21.3

103.7

69.1

61.7

21.3

4.7

60.5

23.9

36.6

25.7 4.1

152

3.5

Physical Spill Countermeasures on Water

DIP 2001

98

Harbour Chop

PART | V

Marco belt skimmer

Average

Performance in Ice Slope of ORR m3/h %

ORR Slope %/%

Slope of TE %/%

Conditions

Slope of RE %/%

Ice Conc. %

Oil Type

Oil Viscosity mPa. S

Slick Thick mm

Number of Tests

ORR m3/h

TE %

0

medium

460

6 ave

4

1.2

6.8

RE %

Skimmer

Reference

Year of Test

Skimming bow

236

1984

Skimming bow

236

1984

0.01

0.5

0.1

30

medium

460

10 ave

3

1.03

9

Skimming bow

236

1984

0.01

0.8

0

50

light/ medium

22/460

6 ave

5

0.7

8

Skimming bow

236

1984

0.01

0.9

0.1

70

light/ medium

22/460

8 ave

9

0.46

11

Rope mop

127

1984

0

light

17

4

1

2.9

53

50

Rope mop

127

1984

0.02

0.7

0.6

0.1

25

light

17

3

2

2.4

68

47

Rope mop

127

1984

0.05

1.8

0.2

0.6

25

light

17

8

2

4.2

49

66

Rope mop

127

1984

0.03

1.1

0.1

0.3

50

light

17

3

2

1.3

58

67

Rope mop

127

1984

0.04

1.3

0.7

0.7

75

light

17

3

1

0

0

0

Rope mop

127

1984

0

light

17

3

2

2.7

59

29

Rope mop

127

1984

0

light

17

8

1

4.9

41

50

391

(Continued )

Chapter | 13 Weather Effects on Oil Spill Countermeasures

TABLE 13.9 Tests of Skimmer Performance with Changing Ice Conditions

392

TABLE 13.9 Tests of Skimmer Performance with Changing Ice Conditionsdcont’d Performance in Ice

Conditions

ORR Slope %/%

Slope of TE %/%

Slope of RE %/%

Ice Conc. %

Oil Type

Oil Viscosity mPa. S

Slick Thick mm

Number of Tests

ORR m3/h

TE %

RE %

Reference

Rope mop

127

1984

0.03

1.2

1.7

0.2

12.5

light

17

3

1

3.1

80

32

Rope mop

127

1984

0.1

2.1

1.2

0.2

12.5

light

17

8

1

6.2

56

52

Rope mop

127

1984

0.01

0.4

0.5

0.1

25

light

17

3

4

2.4

72

31

Rope mop

127

1984

0.06

1.2

1.2

0.4

25

light

17

8

3

6.4

70

59

Rope mop

127

1984

0.02

0.6

0.3

0.1

37.5

light

17

3

1

2.1

71

26

Rope mop

127

1984

0.03

1.2

0.2

0.3

50

light

17

3

1

1.1

49

15

Rope mop

127

1984

0.04

0.7

0.2

0.2

50

light

17

8

1

3.1

51

41

average

0

0.14

L0.35

L0.04

Physical Spill Countermeasures on Water

Skimmer

PART | V

Slope of ORR m3/h %

Year of Test

Chapter | 13 Weather Effects on Oil Spill Countermeasures

393

8. Many other factors influence oil recovery for a skimmer, including oil viscosity and thickness of oil presented to a skimmer. These factors were kept constant throughout this exercise by taking the same conditions with only the interest, tow speed, or wave height as a variable. The cross-correlation matrix for all skimmers in this study is given in Table 13.10, for the Marco belt skimmer in Table 13.11, and for the Veegarm system, an advancing weir, in Table 13.12. These tables show that combining data such as is done in Table 13.10 results in little, if any, correlation between different factors. The reason for this is that different factors influence different skimmer systems differently. For example, an increasing viscosity positively affects the ORR of the Marco skimmer (as can be seen in Table 13.11) and negatively affects a disk skimmer. These correlations show that many of the common-sense relationships do hold true if individual skimmers or skimming principles are examined. Another question that arises is the relationship between the decrease in performance and the parameter that is being examined. As with booms described above, it was found that the general relationship with performance and waves is one of a square root type. A typical analysis is shown in Figure 13.8. Models then used to predict the performance of skimmers were based on a square root function and the decreases in performance from empirical data as summarized in Table 13.8. Figure 13.9 shows the ORR change for groups of skimmers and the average of all the skimmer data. This shows that the recovery is significantly decreased by harbor chop more than by regular waves. Figure 13.10 shows the ORR for a variety of specific skimmers. It can be seen from this figure that ORR changes significantly between specific skimmers. Similarly, the changes in TE and RE are shown in Figures 13.11 and 13.12, respectively. The differences between individual units will again be noted. The change in the performance indicators with increasing current or tow speed were also examined. As was noted earlier and can be seen in Tables 13.4 and 13.8, often the ORR increases with increasing tow speed. This is because the oil encounter rate is increased, at least up to the point that the skimmer can handle the increased velocity. Often skimmers show an increase up to a point and then a decrease. This was handled in averaging the decrease in performance with current by looking at the positive values (decrease in performance only). The change in ORR with increasing current or tow speed cannot be shown because generally it is an increase. The TE does generally decrease with increasing tow speed as seen in Figure 13.13. One sees a significant difference in the throughput efficiency of the different types of skimmers with the changing speed or current. Recent efforts have shown that suction skimmers can remove oil even in high seas.161,239 A good example of this is the use of suction skimmers to recover over 10,000 tons of oil during the Prestige incident. The process,

394

TABLE 13.10 Cross-Correlation Matrix for Factors Influencing Performance for All Skimmers in this Study TE/ curr

RE/ curr

ORR/ wave

Parameter %ORR/ TE/ wave wave

RE/ wave

Oil Visc

Slick mm

Tow m/s

ORR/curr

1.00

0.61

0.03

0.71

0.21

0.11

0.31

0.35

0.19

0.26

0.28

%ORR/curr

0.61

1.00

0.32

0.58

0.11

0.06

0.31

0.29

0.08

0.01

TE/curr

0.03

0.32

1.00

0.47

0.41

0.26

0.59

0.53

0.11

RE/curr

0.71

0.58

0.47

1.00

0.04

0.06

0.03

0.37

ORR/wave

0.21

0.11

0.41

0.04

1.00

0.39

0.01

%ORR/wave

0.11

0.06

0.26

0.06

0.39

1.00

TE/wave

0.31

0.31

0.59

0.03

0.01

RE/wave

0.35

0.29

0.53

0.37

Oil Visc

0.19

0.08

0.11

Slick mm

0.26

0.01

Tow m/s

0.28

Wave m

0.06

ORR

Wave m

ORR

TE

RE

0.06

0.31

0.13

0.11

0.15

0.14

0.28

0.35

0.27

0.08

0.37

0.09

0.14

0.40

0.15

0.06

0.26

0.34

0.19

0.44

0.18

0.21

0.35

0.02

0.04

0.19

0.11

0.17

0.11

0.15

0.03

0.38

0.06

0.04

0.03

0.09

0.07

0.12

0.13

0.03

1.00

0.03

0.03

0.01

0.13

0.18

0.00

0.58

0.32

0.35

0.38

0.03

1.00

0.17

0.26

0.32

0.05

0.12

0.05

0.18

0.06

0.02

0.06

0.03

0.17

1.00

0.18

0.02

0.06

0.04

0.18

0.11

0.08

0.26

0.04

0.04

0.01

0.26

0.18

1.00

0.27

0.12

0.68

0.11

0.06

0.15

0.37

0.34

0.19

0.03

0.13

0.32

0.02

0.27

1.00

0.16

0.05

0.16

0.08

0.14

0.09

0.19

0.11

0.09

0.18

0.05

0.06

0.12

0.16

1.00

0.06

0.26

0.18

0.31

0.28

0.14

0.44

0.17

0.07

0.00

0.12

0.04

0.68

0.05

0.06

1.00

0.25

0.06

TE

0.13

0.35

0.40

0.18

0.11

0.12

0.58

0.05

0.18

0.11

0.16

0.26

0.25

1.00

0.09

RE

0.11

0.27

0.15

0.21

0.15

0.13

0.32

0.18

0.11

0.06

0.08

0.18

0.06

0.09

1.00

Values in bold are significant cross-correlation factors.

Physical Spill Countermeasures on Water

%ORR/ curr

PART | V

ORR/ curr

ORR/ curr

%ORR/ curr

TE/ curr

RE/ curr

% ORR/ ORR/ wave wave

TE/ wave

ORR/curr

1.00

0.56

0.78

0.63

1.00 1.00

%ORR/curr

0.56

1.00

0.82

0.22

TE/curr

0.78

0.82

1.00

RE/curr

0.63

0.22

ORR/wave

1.00

%ORR/wave

Oil Visc

Slick mm

Tow m/s

Wave m

ORR

1.00 1.00

0.50

0.13

0.55

0.55

0.43 L0.74 0.69

1.00 1.00

1.00 1.00

0.75

0.73

0.85

0.65

0.37

0.65

1.00

1.00

1.00

0.19

0.67

0.53

0.53

0.65

0.12 0.81

L0.80

0.65

1.00

1.00

1.00

0.93

0.29

0.27

0.08

0.02 0.35

0.48 0.44

0.53

1.00

1.00

1.00

1.00

0.87

0.54

0.09

0.65

0.75

0.76

0.40

0.72

0.16

0.28

1.00

1.00

1.00

1.00

0.87

1.00

0.53

0.04

0.46

0.69

0.66

0.25

0.52

0.28

0.25

TE/wave

1.00

1.00

1.00

0.93

0.54

0.53

1.00

0.68

0.31 0.13 0.56

0.52

0.07 0.72

0.20

RE/wave

1.00

1.00

0.19

0.29

0.09

0.04

0.68

1.00

0.54 0.47 0.44

0.24

0.39 L0.80 0.55

Oil Visc

0.50

0.75

0.67

0.27

0.65

0.46

0.31 0.54

1.00

0.90

0.32

0.37

0.50

0.06

0.43

Slick mm

0.13

0.73

0.53

0.08

0.75

0.69

0.13 0.47

0.90

1.00

0.33

0.25

0.69

0.11

0.31

Tow m/s

0.55

0.85

0.53

0.02 0.76

0.66

0.56

0.32

0.33

1.00

0.16

0.50

0.48

0.13

Wave m

0.55

0.65

0.65

0.35

0.52 0.24

0.37

0.25

0.16 1.00

0.39 0.50

L0.89

ORR

0.43

0.37

0.12

0.48 0.72

0.07 0.39

0.50

0.69

0.50

0.39

1.00

0.38

0.33

TE

L0.74 L0.75

L0.81 0.44 0.16 0.28

0.72 L0.80 0.06

0.11

0.48 0.50

0.38

1.00

0.70

RE

0.69

L0.80 0.53 0.28 0.25

0.20 0.55

0.43 0.31 0.13 0.89

0.33

0.70

1.00

0.72

0.52

0.44

TE

RE

L0.75 L0.72

395

Values in bold are significant cross-correlation factors.

0.40 0.25

RE/ wave

Chapter | 13 Weather Effects on Oil Spill Countermeasures

TABLE 13.11 Cross-Correlation Matrix for Factors Influencing Performance for the Marco Belt Skimmer

396

TABLE 13.12 Cross-Correlation Matrix for Factors Influencing Performance for the Veegarm System ORR/ curr

%ORR/ curr

TE/ curr

RE/ curr

ORR/ wave

%ORR/ wave

TE/ wave

RE/ wave

Oil Visc

Slick mm

Tow m/s

Wave m ORR TE

ORR/curr

1.00

0.94

0.48

0.64

0.12

0.09

0.38

0.33

0.15

0.38

0.20

0.23

L0.78 0.53 0.66

%ORR/curr

0.94

1.00

0.48

0.64

0.06

0.23

0.31

0.13

0.20

0.19

0.17

0.18

L0.71 0.50 0.56

TE/curr

0.48

0.48

1.00

0.65

0.38

0.23

0.70

0.51

0.21

0.10

0.15

0.04

0.50 0.71 0.45

RE/curr

0.64

0.64

0.65

1.00

0.32

0.11

0.42

0.47

0.61

0.02

0.08

0.14

0.53 0.57 0.53

ORR/wave

0.12

0.06

0.38 0.32 1.00

0.87

0.38 0.62

0.17

0.28

0.05 0.32

%ORR/wave

0.09

0.23

0.23 0.11 0.87

1.00

0.37 0.27

0.02

0.08

0.04

TE/wave

0.38

0.31

0.70

0.37

1.00

0.11

0.18

0.31 0.37 0.56 0.47

RE/wave

0.33

0.13

0.51 0.47 0.62

0.27

0.42 1.00

0.27

0.33

0.07 0.69

0.40

0.80

Oil Visc

0.15

0.20

0.21

0.02

0.11 0.27

1.00

0.11

0.01 0.74

0.34

0.78

Slick mm

0.38

0.19

0.10 0.02

0.01

0.17 0.45

0.11

0.71

Tow m/s

0.20

0.17

0.15 0.08

0.28

0.08

0.18

0.11 0.01

1.00

0.26 0.29

0.31 0.28

Wave m

0.23

0.18

0.04

0.05

0.04

0.31 0.07

0.01 0.17

0.26

1.00

ORR

L0.78

L0.71

0.50 0.53 0.32

0.04

0.37 0.69

0.74

0.45

0.29

0.29 1.00

0.44

0.85

TE

0.53

0.50

L0.71 0.57 0.30

0.20

0.56 0.40

0.34

0.11

0.31

0.40 0.44

1.00

0.42

RE

0.66

0.56

0.45 0.53 0.54

0.21

0.47 0.80

0.78

0.71

0.28

0.35 0.85

0.42

1.00

0.14

Values in bold are significant cross-correlation factors.

0.17

0.42

1.00 0.33

0.54

0.04 0.20

0.21

0.29 0.40 0.35

Physical Spill Countermeasures on Water

0.61

0.38

0.30

PART | V

0.42

RE

397

Chapter | 13 Weather Effects on Oil Spill Countermeasures

FIGURE 13.8 Relationship between ORR (oil recovery rate) and wave height (m).

Oil Recovery Rate (from maximum)

100 All skimmers - reg. waves All skimmers - harbour chop Average skimmer - reg. waves Average skimmer - harbour chop

80

60

40

20

0 0

1

2

3

4

Wave Height m FIGURE 13.9 Effect of waves on skimmer performance.

however, involves recovering large amounts of surface material and then separating this water and oil on board the vessel. In conclusion, use of the average decrease in performance (ORR, TE, or RE) with increasing current appears to yield a reasonable estimate of performance with increased wave energy.

398

PART | V

Physical Spill Countermeasures on Water

Oil Recovery Rate (from maximum)

100

ZRV 80

Sirene 60

Skimming barrier

40

Skimming Barrier Sirene Scoop weir Oil Mop ZRV Marco belt Fixed Sub. plane Skim pack Harbor Mate Weir Skimmers, GT. Veegam moving weir

Weir skimmers 20

0 0

1

2

3

4

5

Wave Height m FIGURE 13.10 Effect of waves on specific skimmers. 100

ZRV

Through put Efficiency

80

Marco belt skimmer 60

40

Average of all Skimming barrier Scoop weir Oil mop ZRV Marco belt Fixed sub. plane Veegarm

20

0 0.0

0.5

1.0

1.5

2.0

2.5

Wave Height m FIGURE 13.11 Effect of waves on throughput efficiency.

13.3.4. Dispersants The variance of dispersant effectiveness with wind speed is a complex function.240 It is known from the literature that effectiveness goes down with

399

Chapter | 13 Weather Effects on Oil Spill Countermeasures 100

Veegarm

Recovery Efficiency %

80

Weir skimmers

Skimming barrier, ZRV and towed rope mop

60

40

20

average 0 0.0

0.5

1.0

1.5

2.0

2.5

Wave Height m Average skimmer Skimming barrier Sirene Disc skimmers Rope mop stationary Rope mop towed

Rope mop ZRV Marco Skim Pak Harbor Mate Weir skimmers Veegarm

FIGURE 13.12 Effect of waves on recovery efficiency. 100

Relative Effectiveness %

High speed skimmer 80 Ave skimmer ZRV Offshore arm High speed skimmer Boom critical Boom first loss

60

40

20

Average skimmer

Boom critical and first loss speed

ZRV Offshore arm

0 0.5

1.0

1.5

2.0

2.5

3.0

Current or Tow Speed m/s FIGURE 13.13 Effect of tow or current speed on recovery efficiency.

400

PART | V

Physical Spill Countermeasures on Water

decreasing dispersant but goes up with increasing energy. The deposition is known to vary with wind speed.198 The empirical data in Table 13.5 from various sources, but mainly Giammona et al., was correlated to observe whether simple relationships could predict the relationship between wind and deposition.198 Included in the correlation were aircraft speed, head wind, cross wind, measured droplet diameters, and altitude with the deposition percentage. Surprisingly, it was found that altitude had little relation to the deposition percentage, but the other factors, such as wind, did. Dropping all parameters except wind speed and the particle average volume diameter, it was found that a relatively simple relationship could be established for deposition with wind speed. This relationship is shown in Figure 13.14. This figure shows the threeway correlation between volume mean diameter (VMD), wind, and the percent deposition. VMD here is the actual value less the optimal diameter of 300 mm. The correlations had shown that the best deposition was obtained with this VMD and that lesser VMD would result in poorer deposition. The correlation achieved was 0.54, which is rather good considering the many variables in deposition, including those mentioned earlier. From this a simple correlation for deposition with wind relationship was derived: Depositionð%Þ ¼ ð80 3X Wind SpeedÞ

(7)

This was used in subsequent calculations for relative dispersant effectiveness.

FIGURE 13.14 Correlation of wind, droplet size, and deposition. The VMD is the particle size smaller than 300 mm, and the three-dimensional view also shows the fit surface. The circles above and below the fit surface show data scatter.

Chapter | 13 Weather Effects on Oil Spill Countermeasures

401

The final model to be achieved should conceptually consist of the deposition times the decrease in dispersion caused by the decrease in deposition, then times the increase in dispersion caused by the increasing wave energy. The next step is to separate natural and chemical dispersion so that the effect of dispersants alone can be estimated. The first step is to calculate the fall-off in deposition with wind speed, which was done with the formula developed from the analysis shown in Figure 13.15. Then the drop in chemical dispersion effectiveness with decreasing dispersant amount was calculated using the square root function as determined from empirical data as illustrated in Figure 13.15.240 Next the natural dispersion was calculated using the Delvigne and Mackay natural dispersion approximations in which the natural dispersion varies as the square of the wave height. It is presumed that a light oil would disperse naturally almost completely by the wave height of about 25 m or a wind of about 25 m/s as was observed during the Braer incident. See Section 13.5 for discussion of this issue. Figure 13.16 shows the standard wind-wave conversion.241 Using this conversion, the total dispersant effectiveness model was created as shown in Figure 13.17. It is important to stress that what is shown here is relative effectiveness, that is, the rationalized effectiveness with the maximum set to 100%. The value typically measured in laboratory tests or other such similar effectiveness tests is absolute effectiveness. Thus if one wanted to know the effectiveness percent at a particular wind or wave condition, then the relative effectiveness is multiplied by the absolute effectiveness. For example, if a dispersant has a value of 40% in the laboratory, then at a wind speed of about 12 m/s (25 knots) the relative effectiveness is 60% and the effectiveness then becomes the two factors multiplied or 0.4 X 0.6 or 24%.

FIGURE 13.15 Correlation of dose (dispersant to oil ratio) and dispersant effectiveness.

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Physical Spill Countermeasures on Water

FIGURE 13.16 Correlation of wind speed and wave height.

Relative Percent Effectiveness

100

Deposition Natural Dispersion Chemical Dispersion Total Dispersion

80

Total Dispersion

60

Chemical Dispersion

40

Natural Dispersion

Deposition

20

Chemical Dispersion

0 0

5

10

15

20

25

30

Wind Speed m/s FIGURE 13.17 Model of dispersion of various types with wind speed.

The dispersion model will be described in greater detail in this section. The natural dispersion follows Delvigne and Mackay approximations, specifically: Natural dispersionfðwave height þ 1:2Þ2 =5

(8)

403

Chapter | 13 Weather Effects on Oil Spill Countermeasures

5 degrees C 15 degrees C 25 degrees C Moles 9527 3 deg Moles 9527 10 deg Moles 9527 22 deg Moles 9500 3 deg Moles 9500 10 deg Moles 9500 22 deg

70 60

Effectiveness%

50

Fingas 2006 5 deg 10 deg

40

Moles 9527 - 22 deg

30 Moles tests

20 10

Fingas 2006 25 deg C

0

0

10

20

30

Salinity o/oo FIGURE 13.18 The effectiveness of test dispersions with temperature and salinity.

Chemical dispersion is modeled using a four-step process. The first step is to calculate the loss in effectiveness with deposition. The deposition was calculated as above using empirical data as shown in Figure 13.14. This value is rationalized then to be a relative number, that is, starting from a maximum of 100%. The next step is to bring in the wave height as well as the wind with a Delvigne-like relationship. Relative dispersionfðloss with windÞ2 wave height

(9)

The relative dispersion was then averaged over immediate increments to ensure that it went in smoother increments with increasing wind and wave height. Chemical dispersion is also known to vary with temperature.193,194 The graph of the temperature versus effectiveness is shown in Figure 13.18. On average, the effectiveness decreases as the square root of the temperature between 3 and 22 C.

13.3.5. In-Situ Burning The relation between burning and weather conditions involves several components. If containment is involved, the limitations are those of the

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containment boom, already discussed under booms above. The relationships are shown in Figure 13.7. Whether or not containment is required, there are two factors important to burning: the ability to ignite and the ability to maintain burning in a wind. The second factor, the ability to maintain burning in wind, is probably not of concern. The limitation on burning may be the ability to ignite in a wind. The section above on burning noted that no specific tests were conducted on ignition limits. However, there are some pieces of information that can be used to make an estimate on igniting using current techniques. It should be noted that techniques that involve some form of sheltering may increase the limits of ignition in wind. Thornborough noted a success in ignition at 13 m/s (25 knots) winds.222 If this is assigned a probability of 0.8, and 10 m/s is assigned a probability of 1, since this has been performed several times, one can extrapolate a probability of ignition using a linear form as shown in Figure 13.19. The burning of oil in ice reduces the efficiency somewhat.218 This data can be used to show the decrease in effectiveness. Table 13.8 shows that, on average, Frazil or slush ice slows burning by 0.45 mm/min and brash ice by 0.8 mm/min.

13.3.6. Others Provant rated the decrease in performance of decanting (gravity separation) with wave height as: wave height up to 0.9 m, 0.8 decanting factor (e.g., 80% effective); waves of 1 to 2 m, 0.8; waves of 2.1 to 2.7 m, 0.7; and over 2.8 m, 0.6.148 These data result in a simple nomogram as shown in Figure 13.20.

Probability of Ignition

100

80

60

40

20

0

10

12

14

16

18

20

22

24

Wind Speed m/s FIGURE 13.19 The probability of ignition with wind speed.

26

405

Chapter | 13 Weather Effects on Oil Spill Countermeasures

Success in Decanting %

100

80

60

40

1

2

3

4

5

Wave Height m FIGURE 13.20 The ability to decant with wind speed.

13.4. OVERVIEW OF WEATHER LIMITATIONS The changes in the effectiveness of countermeasures with waves are shown in Figure 13.21. This figure shows the plots of typical skimmers, booms, and the decanting estimation. Thus there is a wide discrepancy between the capability of various skimmers to deal with wave height but the sea performance of booms is less variable and has limitations at the lower end of the wave scale. Figure 13.22 summarizes the wind speed limitations of several countermeasures. The waves have been equated to wind for this diagram.242,243 This shows how booms are limited by wind speed (and the resulting waves) to a low wind speed of about 3 m/s. Harbor skimmers are limited to similar wind/wave regimes. High-speed skimmers can deal with great wind/wave systems. Chemical dispersion peaks at winds of about 12 m/s as this is the point at which decreasing deposition is matched by increasing sea energy. The probability of ignition persists throughout the diagram to yield a lower probability at winds of about 25 m/s. Figure 13.23 shows the current or tow speed limitations. The average skimmer and boom is limited to below about 1 m/s, while high-speed and zero relative velocity skimmers (ZRVs) have potentially much greater potentials. Figure 13.24 shows an overall view of the effect of wind on several countermeasures. Figure 13.24 The relative performance of countermeasures with wind speed. The first (yellow) bar shows the point at which the effectiveness is at 50%, and the final (green) bar shows the where the performance is decreased to 25%. Temperature does not limit most physical recovery or burning. It does, however, limit chemical dispersion.

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All skimmers reg. waves All skimmers harbour chop Average Skimmer reg. waves Average Skimmer habour chop All booms Critical velocity All booms first loss Decanting

Relative Performance %

100

80

Booms critical velocity

60

Average skimmer regular waves

40

20

All skimmers in harbour chop

0 0

1

2

3

4

5

Wave Height m FIGURE 13.21 Relative performance of countermeasures with wave height.

Dispersant deposition Chemical dispersion Boom Critical failure Boom first loss Ave. Skimmer - reg. waves Ave. Skimmer - harbour chop High speed skimmer Probability of ignition

100

Probability of ignition Relative Effectiveness %

80

High speed skimmer 60

Chemical dispersion

Skimmers 40

Booms Dispersant deposition

20

0 0

5

10

15

20

25

30

Wind Speed m/s FIGURE 13.22 Relative performance of countermeasures with wind speed.

407

Chapter | 13 Weather Effects on Oil Spill Countermeasures 100

Relative Effectiveness%

High speed skimmers 80 Ave skimmer ZRV Offshore arm High speed skimmer Boom critical Boom first loss

60

40

ZRV and offshore arm

20

Booms critical Average skimmer or first loss

0 0.5

1.0

1.5

2.0

2.5

3.0

Current or Tow Speed m/s FIGURE 13.23 Relative performance of countermeasures with current or tow speed.

45

Wind Speed m/s

40 35

25% value 50% value

30 25 20 15 10

Ignition (probability of ignition)

Chemical dispersion

Fast current skimmers

Offshore skimmers

Typical booms

0

Harbour skimmers

5

FIGURE 13.24 The relative performance of countermeasures with wind speed. The first (yellow) bar shows the point at which the effectiveness is at 50%, and the final (green) bar shows the where the performance is decreased to 25%.

13.5. SUMMARY AND CONCLUSIONS Spill countermeasures are affected by weather conditions, and, in some cases, they cannot continue under adverse weather conditions. Although the literature did not show any quantitative guides for the performance of countermeasures under varying weather conditions, many a priori estimates are available. These

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a priori guides may not be useful because they lack quantitative backing. Data could be extracted from a number of papers to enable assessment of changes in the performance of countermeasures related to weather conditions. Many estimates or traditional limits are found in the literature, but these vary considerably and may not be useful. Table 13.13 summarizes the many limits found in the literature, and the numerical findings of this report. Table 13.13 shows the high scatter of the a priori limits around the 25 and 50% performance limits developed in this report. The total countermeasures performance is shown in Figure 13.25. Note that this is relative effectiveness compared to a nominal 100% effectiveness at no wind or in the case of chemical dispersion at high energy or wind. The most important factors influencing the performance of countermeasures are the wind and wave height. These two factors are related and, given a typical sea, can be interconverted. These factors must sometimes be considered separately, however, so that specific weather effects can be examined. Other weather conditions affecting countermeasures include currents and temperature. Currents are important in that they become the critical factor for certain countermeasures such as booms. Temperature primarily affects dispersants and has been shown to have only minimal effect on other countermeasures. Booms are the countermeasures most susceptible to weather conditions. Booms will fail at a current of 0.5 m/s (1 knot) regardless of design or other conditions. This is due to inherent hydrodynamic limitations. There is waveassociated degradation of this value, and this is dependent on design. Nomograms are given for some typical booms. Skimmers show degradation of recovery potential with increasing wave height and also with relative current. Skimmer performance is very individual, and a number of skimmer performance curves have been developed for this study. Some skimmers can only function effectively in absolutely calm waters, while others have recovered oil in sea states of up to 5 or 6 (wave heights of up to 3 m with corresponding winds of up to 15 m/s or 30 knots). Sufficient data exist to predict performance with waves and currents for over 30 specific skimmers and 10 generic types. Advancing skimmers generally show an increase in oil recovery with increasing current or tow speed because this presents more oil to them or increases the encounter rate. The weather affects dispersant application and effectiveness in three ways: the amount of dispersant that contacts the target is highly wind-dependent; the amount of oil dispersed on the surface is very dependent on ocean turbulence and energy; and the amount of oil remaining in the water column is dependent on the same energy. Nomograms for effectiveness have been created. At highsea energies, many lighter oils can disperse naturally. The weather affects in-situ burning in two ways: the ability to ignite oil in a given wind and the ability to sustain ignition in a given wind. While there are few data on these, estimates of ignition success were made based on prior experience.

TABLE 13.13 Summary of Weather Limits or Functions Noted in This Report Variable Limits

Countermeasure Reference

Type

Booms

first loss

This work

critical velocity

Wind

m/s

25%

50% 25%

3

4

Wave

1.2

m

Current

50% 25%

2

0.5

A Priori Limits m/s

Wind

50% m/s

Other

Wave

Current

Wind

Wave

Current

m

m/s

m/s

m

m/s

Notes

0.5 to .06

measured

0.4

typical

0.7

Yazaki 1983

government specification

10

1

0.25

spec for type C boom

Yazaki 1983

government specification

20

1.5

0.5

spec for type D boom

Williams and Cooke 1985

bubble barrier critical velocity

8 to 10

Brekne et al. 2003

critical velocity

20

Tedeschi 1999

critical velocity

8 to 9

Nash and Hillger

first loss

0.1 to 0.6

measured calm

Nash and Hillger

first loss

0 to 0.6

measured regulaqr waves

Nash and Hillger

first loss

0 to 0.38

measured harbour chop

Van Dyck and Bruno

catenary tow optimal

0.5

Marks et al. 1971

total force on boom

Function

oil held several m away

2.5

design for new boom

varies directly as wave height

(Continued )

TABLE 13.13 Summary of Weather Limits or Functions Noted in This Reportdcont’d Variable Limits

Countermeasure Reference

Type

Wind

m/s

25%

50% 25%

Wave

m

Current

50% 25%

A Priori Limits m/s

Wind

50% m/s

Other

Wave

Current

Wind

Wave

Current

m

m/s

m/s

m

m/s

Notes

Function

Milgram 1977

fabric tearing strength

one-third power of wave height

Potter et al. 1999

towing force

varies directly as wave height

Schulz and Potter 2002

towing force

varies directly as wave height

Suzuki et al. 1985

critical velocity

Brown et al. 1993

critical velocity

0.25

if oil was heavy oil

Hansen 2001

critical velocity

3

test for JBF 6001

Hansen 2001

critical velocity

3.5

test for Current Buster

Eryuzlu and Hausser 1977

floating deflector

0.8 to 2.1

successful deflection

Folsom and Johnson 1981

critical velocity

3

claim for boom

Nash and Johnson

critical velocity

3

test for plunging jet

Swift et al. 2000b

critical velocity

1.03

containment for submerged bow plane

increased in ice from 0.4 to 0.5

Skimmers

Meikle 1983

fire resistant boom test

1

Buist et al. 1983

fire resistant boom test

This work

typical harbour

7

10

1.5

2

0.6

1

typical offshore

7

10

2.5

5

4

7

fast current

2

4

10

15

5

8

4

0.4

in towing mode primarily

Reed 1995

mechanical recovery efficiencies

5

80% efficiency

Peigne 1985

successful recovery

10

Reed 1995

mechanical recovery efficiencies

10

Wilson 1981

tests

15

3

weir boom tests

Nordvik 1995

successful recovery

10 to 13

2.5

successful recovery

Leigh 1973

design objectives

10 to 14

1.2

drum skimmer and weir basin objectives

Koops 1985

successful recovery

2

skimming vessel recovery

Brekne et al. 2003

tests

3

Gates and Gordiano 1985

effect of waves

0.18 to 0.47

1 to 2

Sirene and ESCA tests 60% efficiency

0.8

Ocean Buster tests no decrease for wave increase

(Continued )

TABLE 13.13 Summary of Weather Limits or Functions Noted in This Reportdcont’d Variable Limits

Countermeasure Reference Hara et al. 2002

Skimmers

Type

Wind

m/s

25%

50% 25%

Wave

m

Current

50% 25%

A Priori Limits m/s

Wind

50% m/s

Other

Wave

Current

Wind

Wave

Current

m

m/s

m/s

m

m/s

increase in capability

from 1 to 2 or 3

Notes increase in capability by moon pool

Allen 1988

mechanical cleanup

6

0.8

Dempsey 2002

offshore work generally

12

3

Koops and Huisman 2002

skimmers generally

13

Decola 2003

Wash./Or. Guide mechanical cleanup

Koops 1988

skimmers generally

1.5

Steen 2002

general limit

1

Koops and Huisman 2002

general limit

5

Akahoshi et al. 2002

sea recovery limit

2.5

Provant 1992

degradation in performance

0.9

80% effective

Provant 1992

degradation in performance

1 to 2

70% effective

Provant 1992

degradation in performance

2.1 to 2.7

50% effective

0.5

Function

Chemical dispersion

Provant 1992

degradation in performance

over 2.8

10% effective

Nordvik 1995

Transrec performance

1.5

15 to 85% TE

Nordvik 1995

Transrec limitations

4

expected limits of operation

Tech. Serv. Br. 1984

temperature limitations

temperature no difference

Shum and Borst 1985

ice limitations

50% ice caused no difference

Hansen 20021

tests

3

Ocean Buster tests

Coe 1999

tests

3

USCG high speed skimmer

This work

typical rising/falling

10 to 18

22

Decola 2003

Wash./Or. Guide

Koops and Huisman 2002

natural dispersion

1 to 10

Allen 1988

general dispersion

12

Decola 2003

EPA region size guide

13

Exxon Mobil 2000

application - large aircraft

15 to 18

Koops and Huisman 2002

chemical dispersion

2 to 20

3

minimum

5

minimum

3

9 to 12

(Continued )

TABLE 13.13 Summary of Weather Limits or Functions Noted in This Reportdcont’d Variable Limits

Countermeasure Reference

Type

Wind

m/s

25%

50% 25%

Wave

m

Current

50% 25%

A Priori Limits m/s

Wind

50% m/s

Other

Wave

Current

Wind

Wave

Current

m

m/s

m/s

m

m/s

Notes

Exxon Mobil 2000

application e workboat

3 to 11 0 to 3

Exxon Mobil 2000

application e single-engine aircraft

8 to 11 2 to 3

Exxon Mobil 2000

application e helicopters

9 to 11 2 to 8

Daling 1988

dispersion and temperature

20% fall off with lower temperature

Lunel and Lewis 1999

lower sea threshold

15% for low sea, 30% for calm

Lunel et al. 1995a

classification of energy

0 to 5 m/s ¼ low; 6 to 10 m/s high energy

Scholz et al. 1999

lower energy threshold

winds 2.5 to 12 m/s waves 0.1 to 2 m

Lunel 1995a

Braer sea energy

16 to 26 m/s waves 6 to 12 m; winds gusts up to 35 m/s

Mackay et al. 1980

natural dispersion

Function

directly with square of wind

Chemical dispersion

Burning

Delvigne and Sweeney 1988

natural dispersion

directly with square of wind

Koops 1988

natural dispersion

directly with wave height

Moles et al. 2001

temperature effects

Lindblom 1983

aerial application droplet size

Lewis 1995

helicopter bucket deposition

speed of 7 m/s caused 30% deposition

Lewis 1995

helicopter bucket deposition

wind of 2 to 5 m/s caused 20% loss

Payne et al. 19991b

deposition with wind

wind of 7 to 10 m/ s caused loss of deposition

This work

probability of ignition

Thornborough 1997

actual ignition

10

1.5

0.9

Helitorch

Thornborough 1997

actual ignition

10 to 13

1 to 2

0.9

hand held igniter

Moffat and Hankins 1997

ignitor test

8 to 10

Allen 1988

effectiveness decreased with lowering temperature directly with wind velocity

18

24

11

Nordvik 2002

ignition limit

10 to 12

Buist et al. 2003b

ignition limit

1o to 12

1.1

successful test

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Ignition of oil Chemical dispersion Boom Critical failure Boom first loss Ave. Skimmer - reg. waves Ave. Skimmer - harbour chop High speed skimmer Probability of ignition

Relative Effectiveness %

100

80

60 boom boom critical loss first loss ave skimmer

40

chemical dispersion

Ignition of oil

regular waves high speed skimmer

20 ave. skimmer harbour chop 0 0

5

10

15

20

25

30

Wind Speed m/s FIGURE 13.25 The overall effect of wind on oil spill countermeasures. Note that this is relative effectiveness, compared to a nominal 100 % effectiveness at no wind or in the case of chemical dispersion at high energy or wind.

ACKNOWLEDGMENTS The Prince William Sound Regional Citizens Advisory Council is acknowledged for sponsoring an earlier study on weather aspects of countermeasures. Some of the data here is used from that study.

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Chapter | 13 Weather Effects on Oil Spill Countermeasures

417

8. Nordvik AB, MA, Bitting KR. In: Ornitz BE, Champ MA, editors. Oil Spills First Principles: Prevention and Best Response. Review of the Processes for Estimating Time Windows for InSitu Burning of Spilled Oil at Sea, 573. Amsterdam: Elsevier Publishing; 2002. 9. Champ MA, Nordvik AB, Simmons JK. Utilization of Technology Windows of Opportunity in Marine Oil Spill Contingency Planning, Response and Training. IOSC 1997;993. 10. Champ MA. In: Ornitz BE, Champ MA, editors. Oil Spills First Principles: Prevention and Best Response. The Technology Windows-of-Opportunity Oil Spill Response Strategy, 289. Amsterdam: Elsevier Publishing; 2002. 11. Hann RW. Unit Operations, Unit Processes, and Level of Resource Requirements for the Cleanup of the Oil Spill from the Supertanker Amoco Cadiz. IOSC 1979;147. 12. Jeffery PG. Large-Scale Experiments on the Spreading of Oil at Sea and Its Disappearance by Natural Factors. IOSC 1971;469. 13. Thalich P, Xizobo C. Accurate Simulation of Oil Slicks. IOSC 2001;1133. 14. Beynon LR. Codes of Practice for Dealing with Oil Spills at Sea and on Shore: A European View. IOSC 1969;617. 15. Dicks B, Ansell DV, Guenette CC, Moller TH, Santner RS, White IC. A Review of the Problems Posed by Spills of Heavy Fuel Oils, Proceedings of the Third Research and Development Forum on High-Density Oil Spill Response, IMO; 2002. 16. Kokkonen T, Ihaksi T, Jolma A, Kuikka S, Dynamic Mapping of Nature Values to Support Prioritization of Coastal Oil Combating. Environ Model Soft, 2020;248. 17. Lamp HJ. Lake Champlain: A Case History on the Cleanup of #6 Fuel Through Five Feet of Solid Ice at Near-Zero Temperatures. IOSC 1971;579. 18. Harris C. The Sea Empress Incident: Overview and Response at Sea. IOSC 1999;177. 19. Etkin DS. Estimating Cleanup Costs for Oil Spills. IOSC; 1999. 20. Etkin DS. Comparative Methodologies for Estimating on-Water Response Costs for Marine Oil Spills. IOSC 2001;1281. 21. Etkin DS, Tebeau P. Assessing Progress and Benefits of Oil Spill Response Technology Development since Exxon Valdez. IOSC; 2003. 22. Harper J, Godon A, Allen AA. Costs Associated with the Cleanup of Marine Oil Spills. IOSC; 1995. 23. Robertson TL, Kumar SA. Estimating the Response Gap for Two Operating Areas in Prince William Sound, Alaska. IOSC 2008;615. 24. Oskins CJ, Bradley D. “Extreme” Cold Weather Oil Spill Response Techniques and StrategiesdIce and Snow Environments. IOSC 2003;1128. 25. Hazel III WE, Raunchily MR. Health and Safety Issues During Cold Weather Oil Spill Responses. IOSC 2003;109. 26. Kowalski T. Oil Spill Cleanup in Severe Weather and Open Ocean Conditions. IOSC; 2001. 27. Lasted M, Martinson EJ, Currents Computed with a Barotropic Ocean Model: Application to Simulation of Oil Slick Movements in Actual Weather Conditions, Mechanics of Oil Slicks, vol. 133. Paris: Ancient Editions; 1981. 28. Thorpe SA. Langmuir Circulation and the Dispersion of Oil Spills in Shallow Seas. Spill Sci Techn Bull 2000;213. 29. McWilliams JC, Sullivan PP. Vertical Mixing by Langmuir Circulations. Spill Sci Techn Bull 2000;225. 30. Lehr WJ, Simecek-Beatty D. The Relation of Langmuir Circulation Processes to the Standard Oil Spill Spreading, Dispersion, and Transport Algorithms. Spill Sci Techn Bull 2000;247. 31. Simecek-Beatty D, Lehr WJ. NOAA-MMS Joint Langmuir Circulation and Oil Spill Trajectory Models Workshop. AMOP 2000;601.

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136. Broje V, Keller AA. Interfacial Interactions Between Hydrocarbon Liquids and Solid Surfaces Used in Mechanical Oil Spill Recovery. J Coll Interface Sci 2007;286. 137. Broje V, Keller AA. Improved Mechanical Oil Spill Recovery Using an Optimized Geometry for the Skimmer Surface. Env Sci Techn 2006;7914. 138. Keller AA, Clark K. Oil Recovery with Novel Skimmer Surfaces under Cold Climate Conditions. IOSC 2008;667. 139. Ayers RR, Fraser JP, Kazmierczak LJ. Developing and Open-Seas Skimmer. IOSC 1975;401. 140. Freestone FJ, Anderson RA, Trentacoste NP. United States Environmental Protection Agency Research in High-Speed Devices for the Recovery of Thin-Film Oil Spills. IOSC 1975;409. 141. Neal RW, Bianchi RA, Johanson EE. The Design and Demonstration of a RemotelyControlled High-Seas Oil Recovery System. IOSC 1975;395. 142. Widawsky A. Development of Harbor Oil Spill Removal-Recovery Systems: Phase I. IOSC 1975;379. 143. Claus GF, Ku¨hnlein WL. Efficiency of Selected Oil Skimming Systems in Irregular Seas. IOSC 1991;115. 144. Ueda K, Yamanouchi H, Hikida K. Recovery of Spilled Oil in Waves. AMOP 2001;275. 145. Hara S, Yamakawa K, Hoshino K, Yukawa K, Hikida K, Kagemoto H, et al. Improvement of Performance on Multi-purpose Work Vessel, Proceedings of the Third Research and Development Forum on High-Density Oil Spill Response, IMO; 2002. 146. Akahoshi S, Ono S, Iwasaki T, Kobayashi E. Study of Large Oil Recovery System for Use Under Rough Sea Weather Conditions, Proceedings of the Third Research and Development Forum on High-Density Oil Spill Response, IMO; 2002. 147. Hvidbak F, Gunter PA. Preparation for Recovery of Heavy Oil Spills, with Emphasis on Mechanical Feeder Skimmers, Proceedings of the Third Research and Development Forum on High-Density Oil Spill Response, IMO; 2002. 148. Provant SG. Assessment of the Transrec-350 Mechanical Recovery Capacity of the Oil Spill Response Equipment of the Oil Spill Response Equipment in Prince William Sound. AMOP 1992;219. 149. Technical Services Branch. A Winter Evaluation of Oil Skimmers and Booms. Environment Canada Report EPS 4-EP-84-1; 1984. 150. Shum JS, Borst M. OHMSETT Tests of a Rope-Mop Skimmer in Ice-Infested Waters. IOSC 1985;31. 151. Gates DC, Corradino KM. OHMSETT Tests of the Toscon Weir Skimmer and Gravity Differential Separator. IOSC 1985;35. 152. Smith JBH, McLellan C, Pintler LR. Development of An Oil Skimming System to Meet Navy Specifications. IOSC 1989;91. 153. Christodoulou MS, Turner JT. Experimental Study and Improvement of the Rotating Disc Skimmer. IOSC 1987;101. 154. Peigne G. Ecumoire II: Evaluation of Three Oil Recovery Devices Offshore. IOSC 1985;13. 155. Leigh JT. Oil Recovery on the High Seas. IOSC 1973;351. 156. Wilson HB. Development and Testing of a Weir Boom for Oil Recovery at Sea. IOSC 1981;643. 157. Schwartz SH. Performance Tests of Four Selected Oil Spill Skimmers. AMOP 1979;493. 158. Coe T. Control of Oil Spills in Fast Water CurrentsdA Technology Assessment. IOSC; 1999. 159. Ouwerkerk MR, Verbeek PRH, Schut T. A New Tool for Large-Scale Oil Combat. IOSC; 1995. 160. Koops W, Sanders FJ, Gubbens JM. The Katina Oil Spill 1982, Combatting Operation at Sea. IOSC 1985;299.

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161. Huisman S. Private Communication 2008. 162. Lindstedt-Siva J. Advance Planning for Dispersant Use. IOSC 1987;329. 163. Scholz DK, Kucklick JH, Pond R, Walker AH, Bostrom A, Fischbeck P. A Decision-Maker’s Guide to Dispersants: A Review of the Theory and Operational Requirements. American Petroleum Institute, Publication Number 4692; 1999. 164. Lewis A, Aurand D. Putting Dispersants to Work: Overcoming Obstacles. Technical Report IOSC-004, API; 1997. 165. Aurand D. The Application of Ecological Risk Assessment Principles to Dispersant Use Planning. Spill Sci Techn Bull. 1995;241. 166. Trudel K, Ross S, Belore R, Buffington S, Rainey G. Technical Assessment of the Use of Dispersants on Spills From Drilling and Production Facilities in the Gulf of Mexico Outer Continental Shelf. AMOP 2001;531. 167. Trudel K, Ross S, Belore R, Buffington S, Rainey G, Ogawa C, et al. Technical Assessment of Using Dispersants on Marine Oil Spills in the U.S. Gulf of Mexico and California. IOSC 2003;1. 168. Lewis A, Daling PS, Kristiansen TS, Singsaas I, Fiocco RJ, Nordvik AB. Chemical Dispersion of Oil and Water-in-Oil Emulsions: A Comparison of Bench Scale Test Methods and Dispersant Treatment in Meso-Scale Flume. AMOP 1994;979. 169. Lewis A, Daling PS, Strom-Kristiansen T, Brandvik PJ. The Behaviour of Sture Blend Crude Oil Spilled at Sea and Treated with Dispersant. AMOP 1995;453. 170. Lewis A, Daling PS, Nordvik AB. The Effect of Oil Weathering on the Laboratory Determined Effectiveness of Oil Spill Dispersants, Marine Spill Response Corporation Workshop. MSRC; 1995. 171. Lewis A, Crosbie A, Davies L, Lunel T. Large Scale Field Experiments Into Oil Weathering at Sea and Aerial Application of Dispersants. AMOP 1998;319. 172. Lewis A, Crosbie A, Davies L, Lunel T. The AEA ‘97 North Sea Fields Trials on Oil Weathering and Aerial Application of Dispersants. In: Dispersant Application in Alaska: A Technical Update, 78. Cordova: Prince William Sound Oil Spill Recovery Institute (OSRI); 1998. 173. Fingas MF. A Practical Guide to Chemical Dispersion for Oil Spills, Chapter 16 in this work; 2010. 174. Fingas MF. Models of Oil Spill Dispersion Stability. AMOP; 2010. in press. 175. Fingas MF. Oil Spill Dispersants: A Technical Summary, Oil Spill Science and Technology 2010:435. 176. Martinelli FN, Lynch BWJ. Factors Affecting the Efficiency of Dispersants. LR 363 (OP), Warren Spring Laboratory, Stevenage; 1980. 177. Farmwald JW, Nelson WG. Dispersion Characteristics and Flammability of Oil under Low Ambient Temperatures Conditions. AMOP 1982;217. 178. Daling PS. A Study of the Chemical Dispersability of Fresh and Weathered Crude Oils. AMOP 1988;481. 179. Daling PS, Brandvik PJ, Singsaas I. Weathering of Oil and Use of Dispersants: Methods for Assessing Oils’ Properties at Sea and the Feasibility of Oil Spill Dispersants. NOSCA Seminar on Oil Pollution Control; 1995. 180. Brandvik PJ, Daling PS, Aareskjold K. Chemical Dispersability Testing of Fresh and Weathered OilsdAn Extended Study with Eight Oil Types. Report No. 02.0786.00/12/90, IKU SINTEF Group, Norway; 1991. 181. Lunel T, Lewis A. Optimisation of Oil Spill Dispersant Use. IOSC 1999;187. 182. Lunel T. Dispersant Effectiveness at Sea. IOSC 1995;147. 183. Thomas D, Lunel T. The Braer Incident: Dispersion in Action. AMOP 1993;843.

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184. Lunel T. The Braer Spill: Oil Fate Governed by Dispersion. IOSC; 1995. 185. Cormack D, Nichols JA. The Concentrations of Oil in Sea Water Resulting from Natural and Chemically Induced Dispersion of Oil Slicks. IOSC 1977;381. 186. Fuentes HR, Jaffe R, Tsihrintzis VA, Boye D. In-Water Natural Dispersion. Water Res 1995;638. 187. Lunel T, Davies L. Dispersant Effectiveness in the Field on Fresh Oils and Emulsions. AMOP 1996;1355. 188. Sebastiao P, Soares CG. Modeling the Fate of Oil Spills at Sea. Spill Sci Techn Bull 1995;121. 189. Mackay D, Buist I, Mascarenhas R, Petersen S, Oil Spill Processes and Models, Manuscript Report EE-8, Environment Canada; 1980. 190. Delvigne GAL, Sweeney CE. Natural Dispersion of Oil. Oil Chem Poll 1988;281. 191. Delvigne GAL, Hulsen LJM. Simplified Laboratory Measurement of Oil Dispersion CoefficientdApplication in Computations of Natural Oil Dispersion. AMOP 1994;173. 192. Mackay D. Effectiveness of Chemical Dispersants Under Breaking Wave Conditions, The Use of Chemicals in Oil Spill Response, STP 1252. ASTM 1985;310. 193. Moles A, Holland L, Short J. The Effectiveness of Corexit 9527 and 9500 in Dispersing Fresh, Weathered and Emulsion of Alaska North Slope Crude Oil Under Subarctic Conditions. Anchorage: Prince William Sound Regional Citizens’ Advisory Council; 2001. 194. Fingas MF, Fieldhouse B, Wang Z. The Effectiveness of Oil Spill Dispersants on Alaskan North Slope Crude Oils Under Various Temperature and Salinity Regimes. AMOP 2006;821. 195. Lindblom GP, Cashion BS. Operational Considerations for Optimum Deposition Efficiency in Aerial Application of Dispersants. IOSC 1983;53. 196. Smedley JB. Assessment of Aerial Application of Oil Spill Dispersants. IOSC; 1981. 197. Fay RR. Measuring the Aerial Application of Oil Dispersant from Very Large Aircraft at Moderate Altitude. AMOP 1993;1057. 198. Giammona C, Binkley K, Fay R, Denoux G, Champ M, Geyer R, et al. Aerial Dispersant Application: Field Testing Research Program, Alpine, Texas, MSRC Technical Report Series 94-019; 1994. 199. Lewis A. Calibration and Use of a Helicopter Bucket to Apply Oil Spill Dispersants. AMOP 1995;505. 200. Brandvik PJ, Lewis A, Daling PS, Strøm-Kristiansen T. On-Land and Offshore Testing of a New Helicopter Bucket for Dispersant ApplicationdResponse 3000D. AMOP 1997;499. 201. Payne JR, Clayton JR, Phillips CR, Robinson JU, Kennedy D, Talbot J, et al. Dispersant Trials Using the Pac Baroness, A Spill of Opportunity. IOSC 1991;427. 202. Payne JR, Reilly TJ, Martrano RJ, Lindblom GP, Kennicutt MC, Brooks JM. Spill-ofOpportunity Testing of Dispersant Effectiveness at the Mega Borg Oil Spill. IOSC 1993;791. 203. Lunel T. Dispersion of a Large Experimental Slick by Aerial Application of Dispersant. AMOP 1994;951. 204. Lunel T. Field Trials to Determine Quantitative Estimates of Dispersant Efficiency at Sea. AMOP 1994;1011. 205. Lunel T. Research Requirements to Determine Dispersant Effectiveness at Sea, Second International Oil Spill Research and Development Forum. IMO 1995;271. 206. Lunel T. Understanding the Mechanism of Dispersion Through Oil Droplet Size Measurements at Sea, The Use of Chemicals in Oil Spill Response, STP 1252. ASTM 1995;240. 207. Lunel T, Baldwin G, Merlin F. Comparison of Meso-Scale and Laboratory Dispersant Tests with Dispersant Effectiveness Measured at Sea. AMOP 1995;629.

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208. Lunel T, Davies L, Brandvik PJ. Field Trials to Determine Dispersant Effectiveness at Sea. AMOP 1995;603. 209. Lunel T, Wood P. A Laboratory Dispersant Effectiveness Test Which Reflects Dispersant Efficiency in the Field. AMOP 1996;461. 210. Lunel T, Russin J, Bailey N, Halliwell C, Davies Louise. A Successful at Sea Response to the Sea Empress Spill. AMOP 1996;1499. 211. Lunel T, Rusin J, Bailey N, Halliwell C, Davies L. The Net Environmental Benefit of a Successful Dispersant Operation at the Sea Empress Incident. IOSC 1997;185. 212. Lunel. T. Sea Empress Spill: Dispersant Operations, Effectiveness, and Effectiveness Monitoring, Dispersant Application in Alaska: A Technical Update. Cordova: Prince William Sound Oil Spill Recovery Institute (OSRI); 1998; 59. 213. Lunel T. Dispersant Pre-Approvals: Best Practice. IOSC 2001;441. 214. Guyomarch J, Mamaca E, Champs M, Merlin F-X. Oil Weathering and Dispersibility Studies: Laboratory, Flume, Mesocosms, and Field Experiments, In: Proceedings of the Third Research and Development Forum on High-Density Oil Spill Response, IMO 2002;166. 215. Lichtenthaler RG, Daling PS. Dispersion of Chemically Treated Crude Oil in Norwegian Offshore Waters. IOSC 1983;7. 216. Pope P, Allen A, Nelson WG. Assessment of Three Surface Collecting Agents During Temperature and Arctic Conditions. IOSC; 1985. 217. Fingas MF, An Overview of In-Situ Burning, Chapter 23 in this work; 2010. 218. Buist I, Majors L, Linderman K, Dickins D, Mullin J, Owens C. Tests to Determine the Limits of In-Situ Burning of Thin Oil Slicks in Brash and Frazil Ice. AMOP 2003;629. 219. Buist IA. Window-of-Opportunity for In-Situ Burning. Spill Sci Techn Bull 2003;341. 220. Buist I, McCourt J, Mullin J, Glover N, Hutton C, McHale J. Mid-Scale Tests of In-Situ Burning in a New Wave Tank at Prudhoe Bay, AK. AMOP 1998;399. 221. Bech C, Sveum P, Buist I. The Effect of Wind, Ice, and Waves on the In-Situ Burning of Emulsions and Aged Oils. AMOP 1993;735. 222. Thornborough J. United Kingdom In-Situ Burn Trials, Lowenstoft. IOSC 1996;131:1997. 223. Nordvik AB, Champ MA, Bitting KR. Estimating Time Windows for Burning Oil at Sea: Processes and Factors. Spill Sci Techn Bull. 2003;347. 224. Guenette CC, Wighus R. In-Situ Burning of Crude Oil and Emulsions in Broken Ice. AMOP 1996;895. 225. D’Atri B, King T. Flicking Your Bic at 25 Below. AMOP 1993;669. 226. Guenette CC, Thornborough J. An Assessment of Two Off-Shore Igniter Concepts. AMOP 1997;795. 227. Moffatt C, Hankins P. Results of Experiments with Flare Type Igniters on Diesel Fuel and Crude Oil Emulsions. AMOP 1997;1197. 228. Buist IA, Pistruzak WM, Potter SG, Vanderkooy N, McAllister IR. The Development and Testing of a Fireproof Boom. IOSC 1983;43. 229. Bitting KR, Coyne PM. Oil Containment Tests of Fire Booms. AMOP 1997;735. 230. Hansen KA. Second Phase Evaluation of Fire Resistant Booms: Containment Performance. AMOP 2000;429. 231. McCourt J, Buist I, Pratte B, Jamieson W, Mullin J. Testing Fire Resistant Booms in Waves and Flames. AMOP 1997;823. 232. Cooper D, Mackay R. Assisted Viscous Oil Pumping. AMOP 2002;143. 233. Hvidbak F, Gunter PA, The Development and Test of Techniques for Emergency Transfer of Extreme Viscosity Oil (Refloated Bitumen), In: Proceedings of the Third Research and Development Forum on High-Density Oil Spill Response, IMO; 2002.

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234. McCourt J, Shier L. Interaction between Oil and Suspended Particulate Material in the Yukon River. AMOP 1998;79. 235. Grenon S. How to Prioritize Numerous Environmental Issues? Case Study of the HavreSaint-Pierre Spill. AMOP 2000;943. 236. Abdelnour R, Johnstone T, Howard D. Laboratory Testing of an Oil Skimmer Boom in Broken Ice Fields. AMOP 1985;131. 237. Arita M, Yamaguchi M, Narita S, Koyama K, Ueda K, Izumiyama K. Countermeasures for Oil Spills in Cold WaterdIn the Case of Japan. AMOP 1998;265. 238. Tsang G, Vanderkooy N. Development of a Novel Ice Oil Boom for Flowing Waters. IOSC 1979;377. 239. Ono S, Yamaga H, Kato Y, Shigeiri Y, et al. Research and Development of Oil Skimmer for Stormy Weather. Proceedings of the International Offshore and Polar Engineering Conference, 2001;374. 240. Fingas MF. In: Laurier L, Schramm, editors. Surfactants: Fundamentals and Applications to the Petroleum Industry. Use of Surfactants for Environmental Applications, Chapter 12, 461. Cambridge University Press; 2000. 241. Hunt LM, Groves DG. A Glossary of Ocean Science and Undersea Technology Terms. New York: Compass Publications; 1976. 242. Barker CH, Wesley D. Modeling Potential Skimmer and Booming Strategy Effectiveness. AMOP 2004;397. 243. Chrastansky A, Gallies U. Model-Base Long-Term Reconstruction of Weather-Driver Variations in Chronic Oil Pollution Along the German North Sea Coast. Mar Pollut Bull 2009;967.

Part VI

Treating Agents

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Chapter 14

Spill-Treating Agents Merv Fingas

Chapter Outline 14.1. Introduction 14.2. Dispersants 14.3. Surface-Washing Agents 14.4. Emulsion Breakers and Inhibitors

429 429 430

14.5. 14.6. 14.7. 14.8.

Recovery Enhancers Solidifiers Sinking Agents Biodegradation Agents

431 431 431 432

430

14.1. INTRODUCTION Treating the oil with specially prepared chemicals is another option for dealing with oil spills. An assortment of chemical spill-treating agents are available to assist in cleaning up or removing oil. It should be noted, however, that approval must be obtained from the appropriate authorities before these chemical agents can be used. In addition, these agents are not always effective, and the treated oil or the treating chemical may be toxic to aquatic and other wildlife.

14.2. DISPERSANTS Dispersant is a common term used to label chemical spill-treating agents that promote the formation of small droplets of oil that “disperse” throughout the top layer of the water column. Dispersants contain surfactants, chemicals like those in soaps and detergents, that have molecules with both a water-soluble and oil-soluble component. Depending on the nature of these components, surfactants cause oil to behave in different ways in water. Surfactants or surfactant mixtures used in dispersants have approximately the same solubility in oil and water, which stabilizes oil droplets in water so that the oil will temporarily disperse into the water column. Two major issues associated with the use of dispersantsdtheir effectiveness and the toxicity of the resulting oil dispersion in the water columndhave generated controversy in the last 40 years. There is an extensive section on dispersants following this introduction. Oil Spill Science and Technology. DOI: 10.1016/B978-1-85617-943-0.10014-0 Copyright Ó 2011 Elsevier Inc. All rights reserved.

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14.3. SURFACE-WASHING AGENTS Surface-washing agents, or beach cleaners, are different from dispersants. Surface-washing agents are effective in some situations, but they have not been widely accepted partially because of this confusion with dispersants. While toxicity has been a problem with some dispersants in the past, testing has shown that the better surface-washing agents have very little aquatic toxicity and their use could prevent damage to shoreline species. While both products contain surfactants, those in dispersants are equally soluble in both water and oil, whereas in surface-washing agents, the surfactants are more soluble in water than in oil. Surface-washing agents operate by a different mechanism than dispersants. This mechanism is known as detergency and is similar to the use of detergents for washing clothes. In fact, dispersants and surface-washing agents may be quite different. Testing has shown that a product that is a good surface-washing agent is often a poor dispersant and vice versa. Dispersants and surface-washing agents are used for quite different purposes. Rather than causing the oil to disperse, surface-washing agents are intended to be applied to shorelines or structures to release the oil from the surface. During low tide, the oil is sprayed with the surface-washing agent, which is then left to soak for as long as possible. It is washed off with a lowpressure water stream in an area that has been isolated using booms and skimmers. Laboratory- and field-scale tests have shown that these agents substantially reduce the adhesion of the oil so that as much as 90 to 95% of the oil can be released from rocks or other surfaces. There is an extensive section on oil spill surface washing agents in this chapter.

14.4. EMULSION BREAKERS AND INHIBITORS Emulsion breakers and inhibitors are agents used to prevent the formation of water-in-oil emulsions or to cause such emulsions to revert to oil and water. Several formulations can perform both functions. Water-in-oil emulsions can seriously complicate a cleanup operation by increasing the amount of material to be recovered, disposed of, and stored by up to three times. Water-in-oil emulsions are so viscous that skimmers and pumps often cannot handle them. There are different types of emulsion breakers and inhibitors, some of which are best used when little water is present, referred to as a closed system, and others that are best used on the open water, referred to as an open system. For example, some contain surfactants that are very soluble in water and are best used in closed systems so that they are not lost to the water column. Others contain polymers that have a low water solubility and thus are best used on open water. The aquatic toxicity of the products also varies widely. The effectiveness of emulsion breakers and inhibitors is measured as the minimum dose required to break a stable emulsion or prevent one from

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forming. As with dispersants, the use of emulsion breakers or inhibitors is subject to rules and regulations in most countries. Only a few agents have passed both the effectiveness and toxicity criteria, and permission is required to use them. Similar legislation exists in many countries, especially for the use of these products on open waters. Emulsion breakers are not often used on open water or in cleanup operations in general.

14.5. RECOVERY ENHANCERS Recovery enhancers, or viscoelastic agents, are formulations intended to improve the recovery efficiency of oil spill skimmers or suction devices by increasing the adhesiveness of oil. These agents can increase the recovery rate of sorbent surface skimmers for products such as diesel fuel by up to ten times. These products are not useful, however, with normally adhesive products such as heavy crude oils and Bunker C. One recovery enhancer consists of a nontoxic polymer in the form of microsprings, or coiled molecular forms, which increase the adhesion of one portion of the oil to the other.

14.6. SOLIDIFIERS Soldifiers are intended to change liquid oil to a solid compound that can be collected from the water surface with nets or mechanical means. They are sometimes referred to as gelling agents or collecting agents. Collecting agents are actually a different category of agent that are the opposite of dispersants and are not yet fully developed. Solidifiers consist of cross-linking chemicals that couple two molecules or more, or polymerization catalysts that cause molecules to link to each other. Solidifiers usually consist of powders that rapidly react with and fuse the oil. Depending on the agent, about 10 to 40% by weight of the agent is required to solidify the oil, under ideal mixing conditions. An extensive section on solidifiers follows this introduction. Solidifiers have not been used in the past for a number of reasons. Most importantly, if oil is solidified at sea, it makes recovery more difficult as skimming equipment, pumps, tanks, and separators are built to deal with liquid or very viscous liquid. Second, such a large amount of agent is required to solidify oil that it would be impossible to treat even a moderate spill. Third, the faster solidifiers react with the oil, the less likely the oil is to become solidified because the oil initially solidified forms a barrier that prevents the agent from penetrating the remaining oil. Trials at sea have shown that solidifiers often do not solidify the oil mass even when large amounts of treating agents are used.

14.7. SINKING AGENTS Sinking agents are any material, usually minerals, that absorbs oil in water and then sinks to the bottom. The use of sinking agents is banned in almost all countries, however, due to serious environmental concerns. These agents can

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jeopardize bottom-dwelling aquatic life, and the oil is eventually released to reenter the water column in the original spill area.

14.8. BIODEGRADATION AGENTS Biodegradation agents are used to accelerate the biodegradation of oil in the environment. They are used primarily on shorelines or land. They are not effective when used at sea because of the high degree of dilution and the rapid movement of oil. Many studies have been conducted on biodegradation and the use of these agents. Hundreds of species of naturally occurring bacteria and fungi have been found that degrade certain components of oil, particularly the saturate component, which contains molecules with 12 to 20 carbon atoms. Some species will also degrade the lower-molecular-weight aromatic compounds. Hydrocarbondegrading organisms are abundant in areas where there is oil, such as near seeps on land or in water. Studies have shown that many of these native microorganisms, which are already thriving in the local climatic and soil conditions, are better at degrading oil than introduced species that are not yet acclimatized to local conditions. Different types of oil have different potential for biodegradation, based primarily on their saturate content, which is the most degradable component. For example, diesel fuel, which is almost 95% saturates, will degrade readily under the right conditions. However, some types of Bunker C that contain few saturates will not degrade to any extent under any circumstances. This explains why asphalt, the asphaltene, and heavy aromatic fraction of oils that does not degrade is often used in building roads and in roofing shingles. Biodegradation agents include bioenhancement agents that contain fertilizers or other materials to enhance the activity of hydrocarbon-degrading organisms, bioaugmentation agents that contain microbes to degrade oil, and combinations of these two. Studies have shown that adding bioenhancement agents to oil spilled on land can enhance the removal rate of the saturate and some of the aromatic fraction of the oil, so that as much as 40% of the oil is degraded in time periods from one month to a year. It is most important to recognize that one cannot ever degrade all the oil, especially if it is a heavier type. Much of the oil will remain undegraded and relatively unaltered, even years later. Furthermore, it may be difficult to distinguish between oil removed by biodegradation and other weathering processes. It has been found that the agents are most effective when added at an oilto-nitrogen-to-phosphorus ratio of 100:10:1. Fertilizers that maintain the soil at a more neutral level are best for degrading oil. Fertilizers that make the soil acidic usually slow biodegradation. Fertilizers that are more oil-soluble and less water-soluble are most effective as they are not as likely to be washed away.

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Bioaugmentation agents are not used as extensively as bioenhancement agents at oil-contaminated sites. This is because bioaugmentation agents add new microorganisms, which is not usually as effective as stimulating existing bacteria. There are strict government regulations about introducing new, nonindigenous, and possibly pathogenic species to an area. All types of biodegradation agents are subject to government regulations and approval before use. It should be noted that, while biodegradation does remove the saturates and some aromatic fractions of the oil, it can take weeks or even years to remove the degradable fraction, even under ideal conditions. Furthermore, the undegradable components of the oil, which constitute the bulk of heavier crudes, remain at the spill site, usually as a tarry mat often called asphalt pavement. It has been found that biodegradation is useful for treating oil on grasslands or other land not used to grow crops where the undegraded asphaltenes, resins, aromatics and other undegradable components are not likely to pose a problem.

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Chapter 15

Oil Spill Dispersants: A Technical Summary Merv Fingas

Chapter Outline 15.1. Introduction 15.2. The Basic Physics and Chemistry of Dispersants 15.3. The Basic Nature of Dispersions or Oil-inWater Emulsions 15.4. Effectiveness

435 437

440

15.5. Monitoring 15.6. Physical Studies 15.7. Toxicity 15.8. Biodegradation 15.9. Other Information 15.10. Summary and Conclusions

481 500 519 535 539 562

451

15.1. INTRODUCTION The use of dispersants still generates debate four decades after the Torrey Canyon incident. Some of the same issues predominate.1 The motivations for using dispersants are the same: reduce the possibility of shoreline impact, lessen the impact on birds and mammals, and promote the biodegradation of oil. The issues surrounding dispersants also remain the same: effectiveness, toxicity, the effect of dispersants on biodegradation, and long-term considerations. Recently, the National Academy of Sciences released its study of the use of chemical dispersants in the United States.1 This report is particularly instructive and provides some useful assessments of the situation. Their assessments and recommendations will be summarized in the applicable sections of this chapter. The prime motivation for using dispersants has been stated to be reduction of the impact of oil on shorelines. To accomplish this reduction, the dispersant application must be highly successful and effectiveness high. As some oil would still come ashore following treatment, there is much discussion on what effectiveness is required to significantly reduce the shoreline impact.2 A major issue that remains is the actual effectiveness during spills so that these values Oil Spill Science and Technology. DOI: 10.1016/B978-1-85617-943-0.10015-2 Copyright Ó 2011 Elsevier Inc. All rights reserved.

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can be used in estimates and models in the future. A significant physical fact must also be considereddthat is, the lifetime of the dispersion. Because not all dispersions are stable and will degrade to surface slick and some residual dispersion, the utility of dispersants in any case should consider this fact. The second motivation for using dispersants is to reduce the impact on birds and mammals on the water surface. As the National Academy of Sciences (NAS) committee on dispersants states, little or no research on this matter has been carried out anytime since the 1980s. In their report (p. 274) they note the following: Of additional concern is the effect of dispersed oil and dispersants on the waterproof properties of feathers and their role as thermal insulators. One of the recommendations of the NRC (1989) report was that studies be undertaken to “assess the ability of fur and feathers to maintain the water-repellency critical for thermal insulation under dispersed oil exposure conditions comparable to those expected in the field.” This recommendation is reaffirmed because of the importance of this assumption in evaluating the environmental trade-offs associated with the use of oil dispersants in nearshore and estuarine systems because it has not been adequately addressed.1

The third motivation for using dispersants is to “promote the biodegradation of oil in the water column.” The effect of dispersants on biodegradation is still a matter of dispute. A number of papers state that dispersants do not promote biodegradation, whereas others indicate that dispersants suppress biodegradation. The most recent papers, however, confirm that promotion or suppression is a matter of the surfactant in the dispersant itself and the factors of environmental conditions. More details of recent findings will appear in the subsequent discussion. What is very clear at this time is that the surfactants in some of the current dispersant formulations can either suppress or have no effect on biodegradation. Further, there are issues about the biodegradability of the surfactants themselves, and this fact can confound many tests of dispersed oil biodegradation. Several questions remain unanswered, however. An important issue that never comes up is that it is known that oil-degrading bacteria largely live on the water surface, where they would feed on natural hydrocarbons in the absence of spills. Would not putting oil in the water column then remove it from these bacteria? However, in the case of oil seeps or oil-contaminated sediments, there are microbial colonies associated at depth. Another serious question is that of timescale. Biodegradation takes place over weeks, months, and years. Dispersion half-lives are 12 to 24 hours. This author prepared a review of dispersants in 2002 and covered the period to 1997.2 Another review covered the period from 2002 to mid-2008.3 The latter review was combined with the earlier review to provide coverage from 1997 to 2008.4 The comprehensive review contained over 450 references. Literature not covered in this present summary is covered in the reviews.5 Another bibliographic search was published during this time as well but did not contain a review.6 Previous reviews covered the various oil spill dispersant topics.7 All these are reviews of the literature, and most cover similar topics as this review.

Chapter | 15 Oil Spill Dispersants: A Technical Summary

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15.1.1. What Are Dispersants? Many surfactant mixtures for treating oil spills have been promoted in the past four decades to overcome the extensive problems and costs of physical recovery. Of particular interest in this section are dispersants; these are formulations containing surfactants as active ingredients. Surfactants have varying solubilities in water and varying actions toward oil and water.8 The parameter used to characterize surfactants is the hydrophiliclipophilic balance (HLB). HLB is determined using theoretical equations that relate the length of the water-soluble portion of the surfactant to the oil-soluble portion of the surfactant. A surfactant with an HLB between 1 and 8 promotes the formation of water-in-oil emulsions and one with an HLB between 12 and 20 promotes the formation of oil-in-water emulsions. A surfactant with an HLB between 8 and 12 may promote either type of emulsion, but generally promotes oil-in-water emulsions. Dispersants have an HLB in this range. Dispersants are formulated to “disperse” oil slicks into the sea or another water body. Surface-washing agents, or beach cleaners as they are sometimes called, are surfactant formulations designed to remove oil from solid surfaces such as beaches. Emulsion breakers and inhibitors are intended to break waterin-oil emulsions or to prevent their formation. Although many of these treating agents have been promoted, few are still being produced. More than 100 dispersants have been tested for toxicity and effectiveness by Environment Canada, but only 2 remain on the department’s list of accepted products.9 The compendium of oil spill treating agents prepared by the American Petroleum Institute in 1972 lists 69 dispersants and 43 surfacewashing agents, most of which are also listed as dispersants.10 Only two of these are commercially available today, each being produced in a different formulation. More than 300 surface-washing agents have been sold in the North American market, but only about 36 of these are still commercially available. There were 26 surface-washing agents on the U.S. National Contingency Plan List in 2010. It is estimated that approximately 600 dispersants have been sold worldwide, of which only about 200 were ever tested in the lab or field, even in a limited way. The abundance of products makes it difficult for potential buyers and environmentalists to discriminate between effective products and those that are ineffective or could actually cause more damage than if the oil were left without intervention.

15.2. THE BASIC PHYSICS AND CHEMISTRY OF DISPERSANTS 15.2.1. Formulations Dispersants are oil spill treating agents formulated to disperse oil into water in the form of fine droplets. Typically, the HLB of dispersants ranges from 9 to 11. Ionic surfactants can be rated using an expanded scale and have HLBs ranging from 25 to 40. Ionic surfactants are strong water-in-oil emulsifiers, very soluble in water, and relatively insoluble in oil, which generally work from the water

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onto any oil present. Such products disappear rapidly in the water column and are not effective on oil. Because they are readily available at a reasonable price, however, many ionic surfactants are proposed for use as dispersants. These agents are better classified as surface-washing agents. Some dispersants contain ionic surfactants in small proportions, yielding an average HLB more toward 15 than 10. Studies on the specific effect of this mixing on effectiveness or mode of action have not been done. A typical dispersant formulation consists of a pair of nonionic surfactants in proportions to yield an average HLB of 10 and some proportion of ionic surfactants. Studies have been done on this mixture, one of which used statistical procedures in an attempt to determine the best mixture of the three ingredients.11 An improvement in performance was claimed by adjusting the three ingredients. Several patents are held on dispersants.12-14 The typical ingredients, from patents, are listed in Table 15.1. Some dispersants listed for use in Canada, the United States, and Europe are listed in Table 15.2.

15.2.2. Nature of Surfactant Interaction with Oil Surfactants interact with oil and oil droplets to yield a temporary lower-energy statedgiven many conditions and circumstances. 15-18 The disperse state is often called an emulsion, and in the oil spill trade it is known as a dispersiondto distinguish these oil-in-water emulsions from water-in-oil emulsions (called emulsions and sometimes mousse). Some surfactants will align along slick and droplet interfaces and thus promote the temporary stabilization of droplets in water. This droplet stabilization is enhanced by the presence of surfactants at the interface TABLE 15.1 Contents of Dispersants (patent information) Type

Surfactants and Solvents

Hydrocarbon-based -1

Sorbitan monooleate Ethoxylated monooleate Na dioctyl sulfosuccinate Solvent - hydrocarbon and butyl cellosolve

Hydrocarbon-based-2

Sorbitan monooleate Ethoxylated sorbitan monooleate Ethoxylated sorbitan trioleate Na tridecyl sulfosuccinate Solvent - hydrocarbon and butanols

Hydrocarbon-based-3

Mixtures of polyethylene glycol monoleate Solvent- hydrocarbon

Aqueous-based-1

Tall oil esters (35%), ethyl dioxitol (47%) Sorbitan monolaurate (7%), water (10%) Calcium Sulfonate (1%)

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Chapter | 15 Oil Spill Dispersants: A Technical Summary

TABLE 15.2 Listed Dispersants in Various Countries (lists may not be complete due to changes with time) Product

Manufacturer/Origin

United Canada States

Corexit 9500 (now EC9500A)

Exxon, Houston

U

U

Corexit 9527 (now EC9527A)

Exxon, Houston

U

U

Enersperse xx

BP, Britain (old stocks)

U

Biodispers

USA, Newport, NH

U

Dispersit SPC 1000

Polychem, Chestnut Ridge, NY

U

Finasol OSR 62

Total Fluides, France

U

JD (109, 2000)

Globemark, Houston, TX

U

Mare Clean (20, 200, 505)

Taiho, Japan

U

NEOS AB-300

Neos, Japan

U

Nokomis (3-AA, 3-F4)

Mar-Len, Hayward, CA

U

Saf-Ron Gold

Sus. Env. Tech., Mesa, AZ

U

Sea Brat #4

Alabaster, Pasadena, TX

U

ZI-400

Studio City, CA

U

Agma DR 379, OSD 569

Agma, UK

U

Caflon OSD

Univar, UK

U

Dasic Slickgone LS

Dasic, UK

U

Emulsol LW

Arrow Chemicals, UK

U

Gard Slicksol

Larragard, UK

U

NU CRU

Ara Chem, San Diego, CA

U

OD 4000, OSR 4000

Innospec, UK

U

OSD/LT

Ashland Chem., Boonton, NJ

U

Britain France U

U

Radiagreen OSD, OSD-2B Oleon NV, Belgium

U

Seacare Ecosperse, 52, OSD

U

Unitor Chemicals, Norway

U

U

U

U

(Continued )

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TABLE 15.2 Listed Dispersants in Various Countries (lists may not be complete due to changes with time)dcont’d United Canada States

Product

Manufacturer/Origin

Britain France

Super-dispersant 25

Oil Slick Dispersants, UK

U

Veclean Oil Dispersant

Westchem B.V., NL

U

W-2096

Baker Petrolite, UK

U

BIOREICO R93

France

U

Disper 12 M

France

U

Disperep 12

France

U

Dispolene 36s

France

U

Emulgal C-100

France

U

Inipol IP 80, IP 90, IPC

France

U

Neutralex C

France

U

Oceania 1000

France

U

OD 4000 (PE 998)

France

U

15.3. THE BASIC NATURE OF DISPERSIONS OR OIL-IN-WATER EMULSIONS It is well known that most emulsions are not stable and will break down into their constituent parts. This effect is due to a large number of forces, as will be described in this section, but also to the fact that the stabilizers or surfactants act using weak forces. It is also known that chemically dispersed oil destabilizes after the initial dispersion. There is an extensive body of literature on surfactants and interfacial chemistry, which includes an abundance of experimental data on the topic as well as many theoretical approaches to it. This report will summarize both the data and the theory. The phenomenon of resurfacing oil is the result of two separate processes: destabilization of an oil-in-water emulsion and desorption of surfactant from the oil-water interface. Almost every paper on the topic of the stability of emulsions notes that emulsions are not stable.15-20 There are also many books on the topic.21-24 It is noted that emulsions are not thermodynamically stable, but may be kinetically stable depending on the timescale considered. In the case of kinetic stability, emulsions are not stable in terms of years, and the scale of time considered typically relates to consumer products and may be months. In terms of oil spill dispersions, half-life may be only a matter of hours. The destabilization of

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oil-in-water emulsions such as chemical oil dispersions is a consequence of the fact that emulsions are not thermodynamically stable. Natural forces move the emulsions to a stable state, which consists of separated oil and water. What is important is the rate at which this occurs. An emulsion that stays sufficiently stable until long past its practical use consideration may be said to be kinetically stable. There are several forces and processes that result in the destabilization and resurfacing of oil-in-water emulsions such as chemically dispersed oils. These include gravitational forces, surfactant interchange with water and subsequent loss of surfactant to the water column, creaming, coalescence, flocculation, Ostwald ripening, and sedimentation.

15.3.1. Forces of Destabilization 15.3.1.1. Droplet Separation The most important force in resurfacing oil droplets from an oil-in-water emulsion is gravitational separation.25 Droplets in an emulsion tend to move upward when their density is lower than that of water. This is true for almost all crude oil and petroleum dispersions as they usually have droplets with a density lower than that of the surrounding water. Dense or heavy oils are poorly, if at all, dispersible. The rate at which oil droplets will rise due to gravitational forces is dependent on the difference in density of the oil droplet and the water, the size of the droplets (Stokes’s Law, as will be described in Section 15.3.2), and the rheological properties of the continuous phase. The rise rate is also influenced by the hydrodynamical and colloidal interactions between droplets, the physical state of the droplets, the rheological properties of the dispersed phased, the electrical charge on the droplets, and the nature of the interfacial film. Creaming is a process that is simply described by the appearance of the starting dispersed phase at the surface.25 Creaming is the process that might be described in the oil spill world as resurfacing. Robins describes creaming at length, noting that it is a very important phenomenon in the food-processing business.19 As much as 40% of the cost of developing a new food emulsion involves the long-term testing related to creaming. Examples of this include yogurt, whipped cream, jam, and many other types of food. Sedimentation is the reverse of creaming and occurs when the dispersed phase is denser than water. Coalescence is the joining of two or more droplets to form a larger droplet. Coalescence is an important destabilization process in oil spills. Changes in droplet size resulting in coalescence have been monitored as an emulsion destabilizes. Ostwald ripening may be an understated mechanism in the destabilization of oil-in-water emulsions.25 Basically, Ostwald ripening is the growth of larger emulsion droplets by absorption of soluble components from the water column. The effect is to remove soluble material from the water column and smaller droplets, resulting in an increased growth of the larger droplets. The

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phenomenon occurs because the soluble components of the dispersed phase are more soluble in the larger droplets than in the water and the smaller droplets. Although the Ostwald ripening phenomenon has not been investigated with oilin-water emulsions to the same extent as other phenomena, it is believed to be quite important. Studies of undecane, hexadecane, benzene, and octane-inwater emulsions have shown that Ostwald ripening is an important factor in destabilization.25 Flocculation is another process that occurs when two particles come together to form an agglomerate of particles, but the particles do not coalesce.

15.3.1.2. Surfactant Separation It is well known that there is an exchange of surfactants between the target droplet and the surrounding water.18 This promotes destabilization of the emulsion. When the water is in a large ratio to the droplet concentration, surfactant is largely lost and destabilization is relatively rapid. In laboratory tests, a small ratio of oil-to-water then becomes important in simulating the conditions at sea. Surfactants will distribute between the bulk phase (water) and the interface to achieve equilibrium between the two phases. This equilibrium depends on the watereoil solubility characteristics of the surfactant. In a closed system, this equilibrium is achieved rapidly with little loss of surfactant. In an open system, however, equilibrium is never achieved, the surfactant leaches into the water, and over a period of hours, little surfactant is left in the oil droplets. The Marangoni effect is an important phenomenon in terms of surfactant stability and dynamics.26 This effect is due to the tendency of surfactant concentrations to quickly distribute over an interface. If there is a deficit in surfactant concentration on one side of a droplet, the surfactant quickly moves to restore the equilibrium concentration over the droplet. The restoration of equilibrium is known as Marangoni stabilization. Marangoni instability arises as a result of this surfactant flow because the flow continues and results in areas of greater and lesser surfactant concentration over the droplet interface. Some researchers have noted that Marangoni instability was periodic and was about on the order of 1000 seconds for one particular system.27 It was noted that convective instability periodically switched between a slow and a more rapid transport regime. During a convective stage, fast absorption of surfactant occurred with rapid inflow of surfactant to the interface. During a diffusive stage, desorption occurred and gradients built up until the system became unstable again. Several studies have shown, both experimentally and theoretically, that small surfactants will displace larger surfactants or polymers at the interface. Although studies have shown that mixed surfactant systems yield more stable emulsions as a rule, the size difference between surfactants is critical to this. A mixed surfactant system with large and small surfactants will essentially be more stable than one stabilized by the small surfactants alone, as these small surfactants will displace the larger surfactants at the interface. This condition is

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Chapter | 15 Oil Spill Dispersants: A Technical Summary

Marangoni circulation Surface tension Sub-layer viscosity

Radius

Surfactant concentration Surfactant diffusion Absorption Desorption Surfactant type Surfactant chain length Steric stabilization Surfactant precipitation

Ostwald ripening

Dissolution

Doublet formation

Van der Waals force Brownian movement

Bulk diffusivity Micelle formation

Oscillatory structure Interactions

Surface deformation force

Film strength Film thinning Film (Gibbs) elasticity Film thickness Film viscosity

Electrostatic force Steric forces

Capillary force Velocity of approach

Legend Force Flux or movement Influence

FIGURE 15.1

Capillary wave Thermal fluctuation Pulse

Flocculation Depletion flocculation

Hydrodynamic forces

The forces and influences on two droplets approaching each other.

predicated on the fact that the concentration of surfactants at the interface is great and there actually is interference between surfactants. Several additional forces have been described and are summarized in the literature.25 These are depicted graphically in Figure 15.1.

15.3.2. The Science of Stabilization The basis for much of the physics and chemistry surrounding emulsion stability is that emulsions are not thermodynamically stable.15 One view of this is that two immiscible liquids are combined or one immiscible liquid is dispersed into another immiscible liquid. Since the interfacial tension between these two

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liquids will always be greater than zero despite the amount or type of surfactants, there is a force or energy leading toward destabilization. Furthermore, the interfacial energy is vastly increased by increasing the area between the two liquids through the process of increasing the number of droplets. This results in an energy imbalance that will tend to force the two media to separate. Kinetic stability is another consideration when describing an emulsion. An emulsion is said to be kinetically stable when significant separation, usually considered to be half or 50% of the dispersed phase, occurs outside of the usable time. Therefore, if the time of use is one day, an emulsion with a half-life of more than one day may be considered to be usable. In food emulsions, this stability would be well past the stated shelf life. It should be noted, however, that food emulsions are poor examples for crude oil-in-water emulsions because their stability can be controlled in closed systems by adding enough surfactants and gelling the water media, thereby negating coalescence and suppressing surfactant loss. The function of any emulsifying agent or surfactant is to stabilize (somewhat) an otherwise unstable system. The emulsifying agent does so by absorption at the liquideliquid interface as an oriented interfacial film. This oriented film performs two functions: (1) it reduces the interfacial tension between the two liquids and consequently the thermodynamic instability of the system resulting from the increase in the interfacial area between the two phases, and (2) it decreases the rate of coalescence of the dispersed liquid droplets by forming mechanical, steric, and/or electrical barriers around them. The steric and electrical barriers inhibit the close approach of one droplet to another. The mechanical barrier increases the resistance of the dispersed particles to mechanical shock and inhibits them from coalescing when they do collide. When emulsions form, the emulsifying agents or surfactants reduce the amount of work required for formation. Stability can be defined as the resistance of the droplets to coalescence.15 Creaming, or standard gravity separation, was not considered to be destabilization in classical terms because it occurs with or without emulsifier stabilization. The classic destabilization processes were considered to be coalescence, flocculation, and phase inversion. The rate of coalescence was stated to be the only quantitative measure of emulsion stability. These factors have now changed to encompass broader areas, as shown in the present review. It has been found that the rate at which the droplets of a macroemulsion coalesce from larger droplets depends on a number of factors: the physical nature of the interfacial film, the existence of an electrical or steric barrier on the droplets, the viscosity of the continuous phase, the size distribution of the droplets, the phase volume ratio, and the temperature. These factors are dealt with in greater detail in this section. The physical nature of the interfacial film is important. The droplets of dispersed liquid in an emulsion are in constant motion and frequently collide. If the interfacial film surrounding the two colliding droplets in an emulsion ruptures, the droplets will coalesce to form a larger droplet, and eventually the

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emulsion will separate and break. The strength of the film then becomes an important factor in emulsion stability. Highly purified surfactants tend to form weak interfacial films, whereas mixtures of different types of surfactants tend to form stronger films, although there is evidence that smaller surfactants displace larger ones at the interface. Mixtures of a water-soluble and an oil-soluble surfactant are often used in oil spill dispersants, for example, a mixture of Span and Tween surfactants. As these surfactants may be of different sizes, there may be a problem with destabilization. The presence of an electrical charge on the dispersed droplets can create an electrical barrier, preventing two particles from closely approaching each other. While ionic surfactants are sometimes used in oil spill dispersants for this purpose, these surfactants will rapidly partition to the water phase in dilute systems such as at sea. Nonionic surfactants, such as those that typically constitute the bulk of oil spill dispersants, have a lesser charge and pose a weak electrical barrier to coalescence. The viscosity of the continuous phase is an important factor in dispersion stability. An increase in the viscosity of the continuous phase reduces the diffusion of the droplets and thus the frequency of collisions. This is given by the classic equation:25 D ¼

kT 6pha

(1)

where D is the diffusion rate, k is the Boltzmann constant, T is the absolute temperature, h is the viscosity of the liquid continuous phase, and a is the radius of the droplets. It is obvious from this equation that the diffusion occurs inversely to the viscosity of the continuous phase. This is very important for oil spill dispersion as the viscosity of the continuous phase is that of water and is, in fact, very low. Therefore, there is a high diffusion rate, high collision rate, and potential for coalescence. This contrasts with many food emulsions in which the continuous phase is deliberately rendered viscous to reduce coalescence and thus increase stability. Equation (1) predicts that oil spill dispersions will not be as stable as many other emulsions. Another factor influencing the rate of coalescence of the droplets is the size distribution of the droplets. The smaller the range of sizes, the more stable the emulsion. The larger particles have less surface area for the volume and thus are more thermodynamically stable and tend to grow at the expense of the smaller droplets. As this process continues, the emulsion destabilizes. An emulsion with a fairly uniform size distribution is more stable than one with a wide distribution of particle sizes. This factor is also significant when discussing oil spill dispersions. Oil spill dispersions have wide distribution of droplet sizes because of the nature of crude oil and the wide distribution of compounds

446

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in it.1 Oil spill dispersions are therefore less stable by nature than many other types of emulsions. Temperature is an important factor in emulsion stability, for a change in temperature causes changes in interfacial tension between the two phases. Temperature can also cause differential changes in other factors such as the relative solubility of the surfactant in the two phases and in the diffusion in the system. Emulsifying agents are usually most effective when near the point of minimum solubility in the solvent in which they are dissolved because at this point they are most surface-active. Since the solubility of the emulsifying agent usually changes with temperature, the stability of the emulsion also changes with temperature. The classic equation to describe this is by Smoluchowski:15 dn ¼ 4pDrn2 dt

(2)

where dn dt is the rate of diffusion-controlled coalescence, D is the diffusion, r is the collision radius (distance between centres when coalescence begins), and n is the number of particles per cm3. Combining this equation with diffusion equations yields an expression for the rate of coalescence of particles and thus for the stability of the emulsion: dV 4 Vkt E=kT ¼ AeE=kt ¼ e dt 3 h

(3)

where dV dt is the rate of the coalescence of droplets or the stability of the emulsion, V is the volume of the dispersed phase, for example, volume per unit volume, k is the Boltzmann constant, T is the absolute temperature, E is the energy barrier to coalescence, h is the viscosity of the liquid continuous phase, and A is the collision factor as defined by the left portion of the equation. This is the most important equation in describing the stability of oil-inwater emulsions, for it shows that volume and the viscosity of the continuous phase (e.g., water) are critical parameters in describing stability or increased coalescence. In other words, oil spill dispersions will always have low stability. Further, this shows that the effect of temperature is exponentiald that is, collisions are increased with temperaturedor emulsions are actually more stable at lower temperaturedthat is, from the collision point of view only. The forces between particles or droplets are an important physical consideration in describing stability.15 The stronger the force between particles, if opposite sign, the greater the stability of an emulsion. These forces might be considered to be of four types: soft or electrostatic forces, hard sphere, van der Waals, and steric forces.

Chapter | 15 Oil Spill Dispersants: A Technical Summary

447

The soft or electrostatic forces and van der Waals interparticle forces are described in the well-established theory of the stability of dispersions by Derajaguin and Landau (in 1941) and Verwey and Overbeek (in 1948); thus this theory is now called the DLVO theory.15 The theory presumes a balance between the repulsive and attractive potential energies of interaction between the dispersed particles or droplets. Repulsive interactions are due to either the similarly charged electrical double layers surrounding the particles or to solvent-particle interactions. Attractive interactions are believed to be mainly due to the van der Waals forces between particles. For dispersion to occur, the repulsive forces must be larger than the attractive forces. Stokes’s rising rate e The classic Stokes’s equation is: s ¼

2Drga2 9h

(4)

where s is the rise rate, Dr is the density difference between the disperse and droplet phases, g is the gravitational constant, a is the droplet radius, F(F0) is a volume dependent correction factor and is 1 for dilute solutions, and Dh is the difference between the viscosity of the disperse and droplet phases. This equation is very important in terms of understanding the resurfacing of oil spill dispersions. It shows that for the smallest droplets at 1 m below the slick, the rise rate would be about a year (or forever) and for the largest droplets immediately below the slick, rise rate is a few seconds. Several researchers have shown that surfactants do not affect the base rise rate, but others question whether the Stokes’s rate is far too slow.25,27 Many researchers have shown that the rise rate predicted by the Stokes’s equation is far too slow compared to experimental measurements. These might be explained by the destabilization processes described in this report, namely, coalescence, flocculation, and Ostwald ripening. All of these processes serve to increase particle diameter and thus significantly increase the rise rate. A doubling of a droplet radius results in a quadrupling of the rise rate for that particular droplet.

15.3.3. Oil Spill Dispersions There are some measurements of the half-lives of oil and hydrocarbon emulsions in the literature.25 Some of these papers presented data from which the half-life of the particular emulsion could be calculated.25 This is shown in Table 15.3. The half-life data for crude oil emulsions are all very similar with an average half-life of about 12 hours. Resurfacing has been noted during several large tank tests as well.28 Sterling et al. studied the coalescence of Arabian crude oil emulsions with the dispersant Corexit 9500.29 For the range of pH from 4 to 10 and salinity, 10%,

448

TABLE 15.3 Oil Spill Dispersion Half-Lives from the Literature Summary

Literature Data

Oil Type

Dispersant/ Surfactant

Average Half-Life (hours)

Nominal

Arabian Crude

Corexit 9500

4.5

Arabian Crude

Corexit 9500

Arabian Crude

Lower Range

Upper Range

Units

Other Factors

4.5

Hours

Shear rate 5 s1

4

4

Hours

Shear rate 10 s1

Corexit 9500

2.5

2.5

Hours

Shear rate 20 s1

Alaska North Slope

Corexit 9500

15

10

20

Hours

Alaska North Slope

Corexit 9527

15

10

20

Hours

Alberta Sweet Mixed Blend

Corexit 9500

18

10

25

Hours

Alberta Sweet Mixed Blend

Corexit 9527

18

10

25

Hours

Milling Lubricant

Surfact mix

24

Kerosene

Various

3

0.5

5

Hours

Gesium Crude

Various

40

0.5

96

Hours

Crude Oil

Surfact & polyacrylamide

1.5

1

2

Hours

Crude Oil

Natural surfactants

0.2

0.01

0.5

Hours

Hydrocarbons

Average

12

Decane and Decanol

SDS

0.4

0.25

0.5

Hours

Toluene

Ionic

10

5

15

Hours

Tetradecane

SDS

3

0.1

6

Hours

Tetradecane

Tween 20

24

24

Treating Agents

Hours

Closed system

PART | VI

24

Hours

Chapter | 15 Oil Spill Dispersants: A Technical Summary

449

30%, and 40%, the z potentials range from e3 to e10 mV. This potential would not be sufficient to produce significant resistance to coalescence. Coalescence kinetics of the premixed crude oil and dispersant were determined with a range of shear rates and salinity. It was found that increasing shear rate increases coalescence as predicted by the extensive body of literature on the topic. Sterling and coworkers found that the dispersed oil fraction decreased with increasing coalescence and especially with time. The half-life extrapolated from the data implies that the half-life of the Arabian crude oil emulsion with Corexit 9500 dispersant was about 5 hours for a shear rate of 5 s1, about 4.5 hours for 10 s1, about 4 hours for 15 s1, and about 3.5 hours for a shear rate of 20 s1. It is important to emphasize that, although increased turbulence enhances coalescence, increased turbulence at the sea surface may also result in redispersion. The two processes do not appear to balance off because in experiments, it was noted that maintaining energy only decreased the resurfacing rate by about 10% over time periods of 8 to 96 hours.25

15.3.4. Significance of Emulsion Stability Crude oil-in-water dispersions are similar to many other types of emulsions in that they are stable under some conditions for a period of hours. During this time, destabilization processes are underway that result in oil resurfacing. Because of movement of the slick, from which the dispersion occurred and because the water column may have differential movement from the slick, resurfacing oil will likely appear in areas outside the residual slick. As resurfacing is a slow process and goes on for many hours, most of the oil will not be visible on the surface unless processes such as Langmuir cells were to reconcentrate the oil into slicks or unless there was no relative movement between the surface slick and the water column. There is a vast body of information and experimentation and a broad consensus on the stability of such emulsions. The stability and resurfacing of crude oil emulsions are influenced by the following forces. 1. Natural stabilization/destabilization forces The most important force is gravity. As most oils are less dense than water, their emulsion droplets are also less dense than water and will rise. The reappearance of oil on the surface is known as creaming. There are many destabilizing forces to emulsions such as chemically dispersed oil, including coalescence, flocculation, Ostwald ripening, and phase inversion. It is known that coalescence of droplets is the most important destabilization process for emulsions similar to dispersed crude oil emulsions. 2. Standard tendency of emulsions to instability There are many repulsive forces and attractive forces between droplets. The net result of these forces is to destabilize the droplet after some period of time.

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PART | VI

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3. Instability of interfacial film with surfactants The interfacial films stabilized by surfactants are subject to a number of destabilization processes, including Marangoni circulation, hydrodynamic destabilization, oscillatory forces, pulsing, thermal instabilities, surfactant desorption, and others. These forces weaken the interfacial film and consequently destabilize the emulsion. 4. Loss of surfactant In addition to the mechanisms of interfacial stability reduction noted in point 3, there is a net loss of surfactant in dilute emulsions such as oil spill dispersions. This net loss is caused by the tendency of the surfactants to equilibrate between the water bulk phase and the oil droplet interface. As crude oil emulsions are continually being diluted, surfactant movement from the interface to achieve equilibrium constitutes a loss of surfactant to the system. This loss of surfactant accelerates the destabilization of the emulsion. 5. The heterogeneous mixture of compounds in oil Oil consists of dozens of major constituents, most of which are very different in size and properties. This results in the formation of very different droplet sizes. In addition, the effect of surfactant is quite different on the various fractions of the oil. 6. Wide distribution of droplet sizes Because crude oil dispersions have a wide distribution of droplet sizes with much of the volume in the micron-sized area, the emulsions have a lower stability. It has been demonstrated that emulsions of micron-sized droplets are less stable. It has also been shown that the presence of even a few larger droplets will destabilize an emulsion, as this triggers destabilization processes such as Ostwald ripening. 7. Low viscosity of water Because the viscosity of water is low, destabilization processes are more prevalent in water than in other bulk fluids. The low viscosity of water increases coalescence and the diffusion of surfactants away from oil droplets in oil-in-water emulsions. 8. Increasing dilution of the emulsion For dilute emulsions, surfactant desorption becomes surfactant loss. Further surfactant absorption would not occur. As crude oil dispersions or emulsions are dilute and become increasingly dilute with time, they destabilize through surfactant loss and through many of the other processes noted in this report. Crude oil dispersions would be considered less stable than most other emulsions typically studied. 9. Effect of sea energy or turbulence Increased sea energy or turbulent energy increases the amount of coalescence that occurs, resulting in greater resurfacing. However, increased turbulence also causes redispersion, thus offsetting the effect of the recoalescence somewhat.

451

Chapter | 15 Oil Spill Dispersants: A Technical Summary

60 Typical redispersion 2%/hour redispersion

Effectiveness (%)

50

40

30 Typical re-dispersion

20

10

2%/hour additional redispersion

0 0

200

400

600

Time (minutes) FIGURE 15.2 The results of a typical series of runs of dispersion model with a 60% effectiveness as a start and a 50% redispersion effectiveness. This figure shows the typical decline in oil in the water column as time proceeds. The two curves shows the difference in assumptions, the first with typical redispersion and the second with an additional 2%/hour decline in effectiveness.

Thus in summary, dispersions are at best a transient phenomenon. The effectiveness value is a changing value at a given point in time. To illustrate this, a model was developed, based on the stability equations above, to look at the implications of these physics for the effectiveness and half-lives of emulsions.30 Figure 15.2 illustrates the typical effectiveness change over a period of time. Figure 15.3 shows the change in half-life with increase depth of mixing. as indicated by the wave height.

15.4. EFFECTIVENESS Effectiveness remains a major issue with oil spill dispersants. It is important to recognize that many factors influence dispersant effectiveness, including oil composition, sea energy, state of oil weathering, type of dispersant used and amount applied, temperature, and salinity of the water. The most important of these factors is the composition of the oil, followed closely by sea energy and the amount of dispersant applied.1 It is equally important to recognize that the only thing that really counts is effectiveness on real spills at sea. More emphasis should be put on monitoring this real effectiveness so that there is real information for assessment and modeling. Effectiveness issues are confounded by the simple fact that many tests, regardless of scale, show highly different results depending on how they are constructed and operated. Detailed scientific examination of most of these tests

452

PART | VI

Treating Agents

160

Half Life (min.)

140

Change in half-life typical redispersion

120

100

80 Change in half-life reduced redispersion (2%/hr.)

60

40 0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

Wave Height (m) FIGURE 15.3 Plot of the half-lives of the dispersions with the two models of redispersion. It can be seen that half-lives are not affected greatly by which model of redispersion is chosen, because redispersion is much more an influence after about 120 minutes and model consideration here is about 48 hours.

shows major deficiencies in procedure or analytical methods. Further, testers should recognize the fact that effectiveness changes with time, and this factor should be built into testing procedures. More emphasis is needed on looking at the real results from real spills. Another major issue is that of the toxicity of dispersants and dispersed oil, an issue that will be discussed later. Another issue to keep in mind is that of long-term effects. The long-term effects of chemically dispersed oil have not been well studied and therefore remain largely a topic for speculation. On a community level, there have been very few studies such as the TROPICS study; however, no molecular-level studies were undertaken on any of these studies.1,31 These issues will be discussed later and form a “cluster” of major concerns on oil spill dispersants.

15.4.1. Introduction to Effectiveness Dispersant effectiveness is typically defined as the amount of oil that the dispersant puts into the water column compared to the amount of oil that remains on the surface. Many factors influence dispersant effectiveness, including oil composition, sea energy, state of oil weathering, type of dispersant used and the amount applied, temperature, and salinity of the water. The most important of these is the composition of the oil, followed closely by sea energy and the amount of dispersant applied.

Chapter | 15 Oil Spill Dispersants: A Technical Summary

453

One of the major confusions that persist is the relationship of effectiveness to viscosity. There is a certain belief that a “viscosity cut-off” of effectiveness for dispersants exists.4 In fact, certain components of oil, such as resins, asphaltenes, and larger aromatics or waxes, are barely dispersible, if at all. Oils that are made up primarily of these components will disperse poorly when dispersants are applied. On the other hand, oils that contain mostly saturates, such as diesel fuel, will readily disperse both naturally and when dispersants are added. The additional amount of diesel dispersed when dispersants are used compared to the amount that would disperse naturally depends primarily on the amount of sea energy present. In general, less sea energy implies that a higher dose of dispersant is needed to yield the same degree of dispersion as when the sea energy is high. This should not be attributed to viscosity alone, but primarily to oil composition. Oils that typically contain a larger amount of resins, asphaltenes, and other heavier components are typically more viscous and less dispersible. Viscosity, however, does not track composition very well and thus is only an indicator of dispersibility. A “viscosity cut-off” does not exist. While it is easier to measure the effectiveness of dispersants in the laboratory than in the field, laboratory tests may not be representative of actual conditions. Important factors that influence effectiveness, such as sea energy and salinity, may not be accurately reflected in laboratory tests. Results obtained from laboratory testing should therefore be viewed as representative only and not necessarily reflecting what would take place in actual conditions. When testing dispersant effectiveness in the field, it is very difficult to measure the concentration of oil in the water column over large areas and at frequent enough time periods. It is also difficult to determine how much oil is left on the water surface as there are no methods available for measuring the thickness of an oil slick and the oil at the subsurface often moves differently than an oil slick on the surface. Any field measurement at this time is best viewed as an estimate. The NAS committee on dispersants reviewed effectiveness testing.1 It noted that as the physical scale of the effectiveness increases, the cost and realism increase, but the degree to which factors that affect dispersion can be controlled and the ability to quantitatively measure effectiveness decrease. The committee also states that when modeling or prediction is carried out, viscosity is an insufficient predictor of dispersion efficiency. The chemical composition of oil is important, and several factors of composition have been shown to correlate well with dispersant effectiveness. Two other factors relating to dispersant effectiveness are the dispersant-to-oil ratio and the oil-to-water ratio, but the most important factor may be the energy applied, energy dissipation rate, or mixing energy. In reviewing testing, the NAS committee notes that several important principles of experimental design are often ignored, including systematic errors that affect the outcome in one direction and random errors. Common systematic errors in dispersant effectiveness measurement included ignoring the evaporation of volatile compounds, poor analytical methods, and

454

PART | VI

Treating Agents

incomplete recovery of floating oil. These three errors, as an example given in the NAS report, introduce a positive bias in the estimates of dispersant effectiveness.

15.4.2. Field Trials Previous workers and reviews have put forward discussion on field trials.1,32 Field tests can provide opportunities to test and train on full-scale application equipment as well as to develop and test full-scale monitoring equipment and to verify oil fate and transport models. Field tests, however, are subject to high costs, and legal issues may impede the carrying out of these tests. A major limitation on field trials is the limited data set that can be obtained from one given trial. The experimental design of field trials is an issue, and a primary objective should be to obtain an unbiased estimate of the variation that exists between two experimental slicks. Another major limitation of field trials is the inability to measure remaining oil slick thickness. NAS or most scientists do not believe sorbent testing to be an accurate method. Measurement of oil in the water column is also fraught with difficulties; use of fluorometers, it has been observed, only gives a relative measurement. The output of fluorometers also changes with time, aromatic composition, and so on. Visual observation has been used, but a suggestion to improve visual results is to use ‘blind’ observers who are not aware of the particular treatment applied. Visual observation is subject to many variables including position of the sun, cloud cover, and viewing angle. The NAS committee notes that results from field trials are generally lower than those obtained in the laboratory, suggesting that the energy regimes in the laboratory are higher than those encountered in field trials.1 Mass balances should be attempted on field trials. In conclusion, the complexities and costs of carrying out meaningful field trials suggest that more effort be placed on improving bench-scale and mesocosm research projects. As a recommendation, the NAS committee stated that future field-scale work should be based on systematic and coordinated bench-scale and wave-tank testing. Many field trials have been conducted in the past to assess the effectiveness of dispersants. Several papers have assessed the techniques used to measure effectiveness in these tests.33 There is no general consensus that effectiveness and other parameters can actually be measured in the field using some of the current methodologies. The effectiveness determined during field trials varies significantly. Recent results, which may be more reliable, claim that dispersants removed about 10 to 40% of the oil to the subsurface.1,33 The purpose of these tests were: 1. To quantify the effectiveness of dispersants on a given oil in a given application situation 2. To demonstrate the effectiveness of dispersants and/or application techniques

Chapter | 15 Oil Spill Dispersants: A Technical Summary

455

3. To measure concentrations of oil in the subsurface as a result of dispersant use 4. To determine dispersibility conditions and relationships between factors 5. To quantify application factors, such as effect of application rate and droplet size Table 15.4 shows the tests conducted in the past.35-62 Several papers have assessed the techniques used to measure effectiveness in these tests.33,63 The effectiveness determined during these trials varies significantly. The validity of the older results is even more questionable because of both the analytical methodology, which is now known to be incorrect, and data treatment methods.1,33,34,63,64 Most tests relied heavily on developing a mass balance of oil in the water column and that left on the surface.33 In early tests, samples from under the oil plume were analyzed in a laboratory using colorimetric methods. Colorimetric methods are not valid for this type of analysis and are no longer used. Fluorometry has recently been used, but this method is also unreliable because it measures only a small and varying portion of the oil (middle aromatics) and does not discriminate between dissolved components and oil that actually dispersed. Furthermore, it is impossible to calibrate fluorometers for whole oil dispersions in the laboratory without using accurate techniques such as extraction and gas chromatographic analysis. It is known that the aromatic ratio of the oil changes as a result of the dispersion process.33 In early tests, it was not recognized that the plume of dispersed oil forms near the thicker oil in the tail of the slick and that this plume often moves off in a separate trajectory from the slick.63 Many researchers tried to measure the hydrocarbon concentrations beneath the slick and then integrated this over the whole slick area. As the area of the plume is always far less than this area, the amount of hydrocarbons in the water column was greatly exaggerated. Since the colorimetric techniques used at the time always yield some value of hydrocarbons, the effectiveness values were significantly increased. When effectiveness values from past tests were recalculated using only the area where the plume was known to be, those values decreased by factors as much as 2 to 5.33,63 Although no applications of dispersants on freshwater spill have been found, one field test was carried out in fresh water.65 Effectiveness was not measured. The ASTM standards on the use of dispersants in fresh water such as lakes and rivers suggest that they not be used in fresh water primarily because most lakes and rivers are used as sources of drinking water.66-68 In summary, testing in the field is difficult because effectiveness values depend on establishing a mass balance between oil in the water column and on the surface. Because this mass balance is difficult to achieve, results are questionable. All tests relied heavily on developing a mass balance between oil in the water column and that left on the surface. In early tests, samples from under the oil plume were analyzed in a laboratory using colorimetric methods,

TABLE 15.4 Dispersant Field Tests Location/ Identifier

Reference Year

Number Oil Type

Amount, m3 Dispersant

Application Method

Rate, D: 0

Sea Effectiveness State Claimed % unless noted otherwise

North Sea Great Britain

North Sea- Norway

North Sea Great Britain

35

36

37

1997

1995

1995

136

Forties Crude - Weathered 50

Corexit 9500

airplane

1:19

3-4

good

135

Forties Crude - Weathered 50

Slickgone NS

airplane

1:19

3-4

good

134

Alaska North Slope Weathered

30

Corexit 9500

airplane

1:30

good

133

Troll - Weathered

15

Corexit 9500

helicopter

1:20

1-2

good

132

Troll - Weathered

15

Corexit 9500

ship

1:20

1-2

good

131

Troll - Weathered

15

Corexit 9500

control

control

1-2

130

Medium Fuel Oil

continuous 50L/min

OSR 5

boat

1:20

>2

33%

129

Medium Fuel Oil

continuous 50L/min

Corexit 9527

boat

1:20

>2

32

128

Medium Fuel Oil

continuous 50L/min

Slickgone NS

boat

1:20

>2

23

127

Medium Fuel Oil

continuous 50L/min

BP 1100X

boat

1:20

>2

10

126

Medium Fuel Oil

continuous 50L/min

LA 1834

boat

1:20

>2

9

125

Medium Fuel Oil

continuous 50L/min

Control

boat

1:20

>2

4

North Sea -Norway

North Sea - Great Britain

North Sea - Great Britain

124

Forties Crude

continuous 50L/min

Slickgone NS

boat

1:20

>2

16

123

Forties Crude

continuous 50L/min

Control

boat

1:20

>2

6

122

Medium Fuel Oil

continuous 50L/min

Slickgone NS

boat

1:20

<2

10.5

121

Medium Fuel Oil

continuous 50L/min

Control

boat

1:20

<2

1.5

120

MFO Emulsion

continuous 50L/min

Corexit 9527

boat

1:20

<2

4

119

MFO Emulsion

continuous 50L/min

Slickgone NS

boat

1:20

<2

6

118

MFO Emulsion

continuous 50L/min

Control

boat

1:20

<2

0.2

117

Sture Blend Crude

20

Corexit 9500

helicopter

1:12

4-5

good

116

Sture Blend Crude

20

control then-Corexit 9500

helicopter - next day

control then 1:20

4-5

good

115

MF/GO (Medium Fuel Oil/ Gas Oil)

20

Dasic Slickgone NS

airplane

1:10

2-3

good

114

MF/GO

20

Control

113

MF/GO (Medium Fuel Oil/ Gas Oil)

continuous 50L/min

OSR 5

boat

!:20

5-6

29.5

41

112

MF/GO

continuous 50L/min

Slickgone NS

boat

!:20

5-6

17

42

111

MF/GO

continuous 50L/min

BP 1100X

boat

!:20

5-6

10

38

39

40

1994

1993

1993

2-3

(Continued )

TABLE 15.4 Dispersant Field Testsdcont’d Location/ Identifier

North Sea - Great Britain

Beaufort Sea - Canada

North Sea Haltenbanken

Reference Year

43

44

45

1992

1986

1985

Norway

Brest, Protecmar VI

46

1985

Number Oil Type

Amount, m3 Dispersant

Application Method

Rate, D: 0

Sea Effectiveness State Claimed

110

MF/GO

continuous 50L/min

Control

boat

Control

5-6

1.6

109

Forties Crude

12.3

LA 1834 then Dasic LTSW

airplane

1:100, 1:28

4-5

good

airplane

1:100

108

Forties Crude

12.3

LA 1834

107

Forties Crude

12.3

Control

4-5

106

Topped Federated Crude

2.5

control

.

.

105

Topped Federated Crude

2.5

Corexit CRX-8

helicopter

1:1

2-3

poor

104

Topped Federated Crude

2.5

BP MA700

helicopter

1:1

2-3

poor

103

Topped Federated Crude

2.5

BP MA700

helicopter

1:10

2-3

102

Topped Federated Crude

2.5

control

.

.

101

Topped Statfjord Crude

12.5

Alcopol

premixed

250 ppm

101

Topped Federated Crude

2.5

control

.

.

100

Topped Statfjord Crude

12.5

control

.

.

1-2

99

Topped Statfjord Crude

12.5

Finasol

premixed, 3m below surface

1:50

1-2

.

98

Topped Statfjord Crude

12.5

control

.

.

1-2

.

97

Fuel Oil

part of below Dispolene 355

ship-aerosol

1:9

1

.

4-5

1-2

France

Norway

Halifax, Canada

Holland

96

Fuel Oil

part of below Dispolene 355

ship-spray

1:9

1

.

95

Fuel Oil

28

Dispolene 355

helicopter

1:9

1

.

94

Fuel Oil

5

control

control

.

1

93

Statfjord

10

Corexit 9527

airplane

1:50

-

-

92

Statfjord

12

Corexit 9527

premixed

1:33

2

.

91

Statfiord

10

Corexit 9527

airplane

l:80

2

.

90

Statfjord

10

control

control

.

2

.

89

Statfjord

10

Corexit 9527

airplane, Islander

1:75

1

.

88

Statfiord

10

control

control

.

1

87

ASMB

2.5

control

control

.

2-3

7

50

86

ASMB

2.5

BP MA700

helicopter

1:10

2-3

10-41

51

85

ASMB

2.5

control

control

.

1

1

84

ASMB

2.5

Corexit 9550

helicopter

1:10

1

1.3

83

ASMB

2.5

control

control

.

1

1

82

ASMB

2.5

Corexit 9527

helicopter

1:20

1

2.5

81

Statfjord

2

Finasol OSR-5

airplane

1:10-30

1-2

2

80

Statfjord

2

Finasol OSR-5

airplane

1:10-30

1-2

2

79

Light Fuel

2

control

control

.

2-3

2

78

Statfjord

2

Finasol OSR-5

premixed

1:20

2-3

100

77

Light Fuel

2

Finasol OSR-5

airplane

1:10-30

1

2

47

48

49

52

1984

1983

1983

(Continued )

TABLE 15.4 Dispersant Field Testsdcont’d Location/ Identifier

Reference Year

Mediterranean Protecmar V

47

France

53

North Sea, Britain

49

1982

1982

54

Norway

55

1982

Number Oil Type

Amount, m3 Dispersant

Application Method

Rate, D: 0

Sea Effectiveness State Claimed

76

Statfjord

2

Finasol OSR-5

airplane

1:10-30

1

2

75

Statfjord

2

control

control

.

1

2

74

Light Fuel

2

control

control

.

1-2

2

73

Statfjord

2

control

control

.

1-2

2

72

Light Fuel

5

control

control

.

2

71

Light Fuel

2

premixed

premixed

1:20

1-2

40-50

70

Light Fuel

4

Dispolene 325

helicopter

1:2.9

1-2

.

69

Light Fuel

3.5

Dispolene 325

ship

1:2.6

1-2

.

68

Light Fuel

5

Dispolene 325

airplane, CL215

1:2.8

2

.

67

Light Fuel

5

Dispolene 325

ship

l:2.8

2

.

66

Light Fuel

5

Dispolene 325

airplane, CL215

1:2.4

3

.

65

Light Fuel

3

10% Dispolene

ship

1:2

3

.

64

Arabian

20

Corexit 9527

airplane, Islander

1:4

1

63

Arabian

20

Corexit 9527

airplane, Islander

1:2

1

62

Arabian

20

control

control

.

1

61

Statfjord

0.2

10% Corexit 9527

ship

1:13

2-3

.

2

Newfoundland

56

Mediterranean, Protecmar III

57

France

47

Mediterranean, Protecmar II

47

France

57

1981

1981

1980

60

Statfjord

0.2

10% Corexit 9527

ship

1:18

2-3

22

59

Statfjord

0.2

10% Corexit 9527

ship

1:17

2-3

1.9

58

Statfjord

0.2

control

control

.

2-3

2.6

57

Statfjord

0.2

10% Corexit 9527

ship

1:10

2-3

1.7

56

Statfjord

0.2

10% Corexit 9527

ship

1:10

2-3

6

55

Statfjord

0.2

control

control

.

2-3

0.6

54

ASMB

2.5

Corexit 9527

airplane, DC-6

1:10

1

.

53

ASMB

2.5

control

control

.

1

52

Light Fuel

6.5

control

control

.

1-2

.

51

Light Fuel

6.5

Shell

airplane, CL215

1:3

2-3

.

50

Light Fuel

6.5

Dispolene 325

airplane, CL215

1:3

1-2

50

46-49

9527

45

5 Corexit

CL215

44

Finasol OSR-

airplane

43

Mediterranean, Protecmar I France

47

1979

BPIIOOWD

various and

42

Light Fuel

1-5.5

BPIIOOX

ship, helicopter,

.

1-3

.

28-41

Light Fuel

3 each

BPIIOOX

ship, helicopter,

.

1-3

.

32

9527

(Continued )

TABLE 15.4 Dispersant Field Testsdcont’d Location/ Identifier

Reference Year

Number Oil Type

Amount, Dispersant m3

31

5 Corexit

CL215

30

Finasol OSR-

airplane

29 Long Beach, USA

Victoria, BC, Canada

Southern California, USA

58

59

60

61

1979

1978

1978

Application Method

Rate, D: 0

Sea Effectiveness State Claimed

BPIIOOWD

various and

27

Prudhoe Bay

1.6

2% Corexit 9527

ship

1:11

2-3

62

26

Prudhoe Bay

1.6

2% Corexit 9527

ship

1:11

2-3

11

25

Prudhoe Bay

3.2

conc.

airplane, DC-4

1:27

2-3

60

24

Prudhoe Bay

1.6

control

control

.

2-3

1

23

Prudhoe Bay

1.6

conc.

airplane, DC-4

1:25

2-3

45

22

Prudhoe Bay

3.2

conc.

airplane, DC-4

1:20

2-3

78

21

Prudhoe Bay

1.6

2% Corexit 9527

ship

1:67

2-3

5

20

Prudhoe Bay

1.6

2% Corexit 9527

ship

1:67

2-3

8

19

Prudhoe Bay

1.6

control

control

.

2-3

0.5

18

North Slope

0.2

10%, 9527

ship, WSL

1:1

I

.

17

North Slope

0.4

10%, 9527

ship, WSL

1:1

1

.

16

North Slope

0.2

10%, 9527

ship, WSL

1:1

2

.

15

North Slope

0.6

Several, demonstration

Several, demonstration .

1-2

.

Wallops Island, USA

60

1978

61

North Sea, Britain

49 62

1976

14

North Slope

0.8

BPI IOOWD

ship, WSL

>1:5

1-2

.

13

North Slope

0.8

Corexit 9527

ship

> 1:5

0-1

.

13

North Slope

0.8

Corexit 9527

ship

>1:5

1-2

.

12

North Slope

3.2

Corexit 9527

airplane, Cessna

> 1:5

1-2

.

10

North Slope

0.8

BPI IOOWD

ship, WSL

> 1:5

0-1

.

9

North Slope

1.7

Recovery þ

helicopter

> 1:5

0-1

.

8

North Slope

3.2

Corexit 9527

airplane, Cessna

> 1:5

0-1

.

7

North Slope

1.7

Control later Corexit 9527

control then helicopter > 1:5

0-1

.

6

La Rosa

1.7

Corexit 9527

helicopter

1

50

1:11

5

Murban

1.7

Corexit 9527

helicopter

1:11

I

100

4

La Rosa

1.7

Corexit 9527

helicopter

1:5

1

.

3

Murban

1.7

Corexit 9527

helicopter

1:5

1

.

2

Kuwait

10% BP 1100

ship, WSL

1:20

2-3

100

1

Ekofisk

10% BP 1100

ship, WSL

.

I

.

0.5

464

PART | VI

Treating Agents

which are notoriously inaccurate. Fluorometry has recently been used, but this method is also unreliable because it measures only a small and varying portion of the oil (middle aromatics) and does not discriminate between dissolved components and oil that actually dispersed. There is further discussion on analytical techniques in a later section of this report. The points raised in Section 15.4: Tank Tests are valid for field tests as well. In summary, testing in the field is fraught with measurement difficulties; however most of the past tests showed poor effectiveness, and the overall average of those that assigned values was 16%.

15.4.3. Laboratory Tests Many different types of procedures and apparatus for testing dispersants are described in the literature. Fifty different tests or procedures are described in one paper.69 Only a handful of these are now used, however, including the Labofina, Warren Springs, or rotating flask test; the swirling flask test; and the baffled flask test. Most of these procedures are used only on occasion for special studies. Some are used for regulatory purposes to screen dispersants for effectiveness prior to national approval. Some common tests are listed in Table 15.5. Several investigators have reported results of apparatus comparison tests conducted in early years.70-76 In the several papers reviewed, all authors concluded that the results of the different tests do not correlate well, but some conclude that some of the rankings are preserved in different tests. Generally, the more different types of oil tested, the less the results correlate. It has been shown that laboratory tests can be designed to give a comparable value of oil dispersion if the parameters of turbulent energy, oil-to-water ratio, and settling time are set at similar valuesdbut most importantly if correct analytical procedures are applied.73 In the literature, different protocols are sometimes described for the same apparatus. The testing protocol used can sometimes change the data more than the actual physical test. Fingas measured, calculated, or estimated energy and work in several laboratory vessels and compared to estimates of energy/work at sea.77-79 Some measurements completed by particle image velocimetry (PIV) and anemometry were compared to these calculated values. The initial measurements and estimates indicate that the energy in several laboratory vessels is similar and that it may be equivalent to those encountered at sea under moderate wind and wave conditions. Two techniques have been initiated to measure energy. The measurement technique chosen to do this is PIV. In this method, seed particlesdwhich could be oil dropletsdare put into the fluid and the fluid is illuminated with a laser. The movement of a particle in a given cell is measured as a function of time. This can occur as fast as 30 to 200 Hz, depending on the apparatus. Turbulent energy can be calculated at each point in the image frame. The other method used is hot wire anemometry. Although this method can yield data similar to PIV, it requires the intrusion of a probe into the area. The

TABLE 15.5 Apparatus for Laboratory Testing of Dispersant Effectiveness Alternate Names (s)

Energy Source

Water Volume (L)

Prime Use

Where Used

Swirling Flask

ASTM

vessel movement

0.12

regulatory general

ASTM, Canada USA, others

vessel movement

0.12

general

USA

Baffled Flask LABOFINA

Warren Springs Rolling Flask

vessel rotation

0.25

regulatory general

Britain

Mackay

MNS Mackay-Nadeau-Steelman

air stream

6

regulatory general

Norway

High-Energy

moving vessel

5

experimental

Canada

EXDET

wrist-action shaker

0.25

experimental

Exxon

IFP

French Standard

oscillating hoop

16

regulatory general

France

SET

Simulated Environmental Test Tank

circulating pump

119

regulatory

not used at present

Cascading Weir

Flume

fall over weir

constant flow 0.5 L/s

experimental

not used at present

fall down tube

1 e flowing

experimental

not used at present

water flow

constant flow (~0.05 L/s)

experimental

not used at present

Flowing Column

Concentric Tube

Bobra

Oscillating Hoop

35

experimental

rarely used

South African BP Sunbury

moving plates

30

regulatory general

not used at present

Spinning Drop

Interfacial

water movement

<0.05

experimental

not used at present

propeller

1.5

experimental

not used at present

Blender

465

oscillating hoop

Wave-Plate Tank

Chapter | 15 Oil Spill Dispersants: A Technical Summary

Test Name

466

PART | VI

Treating Agents

methods are compared in several laboratory vessels under several energy conditions. Kaku and Boufadel have conducted similar measurements in some laboratory apparatuses.80,81 The laboratory apparatuses compared are the swirling flask and the baffled flask. Some of these laboratory data were compared to the field data by Lunel and coworkers, and the results are shown in Table 15.6.1,75,76,82 While the data correlate somewhat to the field data, with a wide spread in effectiveness numbers and the few data points, this correlation should not be overstated. Another interesting point is that the effectiveness values obtained in the field are lower than the data obtained in the laboratory, indicating that the energy levels may be much higher in laboratory tests than those in the field conditions described here. Furthermore, all of the laboratory tests yield effectiveness values much higher than field values. This is contrary to what was thought in previous years. The results of a number of dispersant effectiveness tests taken from published laboratory results were compared in one study.8 The correlation among tests varies from high to low. This may be due to errors associated with the measurement, such as errors in measurement of volumes and variances in energy of the apparatus. It was also noted that the ranking of effectiveness is generally consistent; that is, those oils and dispersants that show the highest or lowest effectiveness do so in all tests. A lot of work has been done recently on the new Environmental Protection Agency (EPA) test entitled the baffled flask. 83-86 This apparatus has been TABLE 15.6 Intercomparison of Laboratory and Field Effectiveness Results Effectiveness Results in Percent Field Test SF SF [16e17] GC CA

IFP

WSL WSL Lab 1 Lab 2 Exdet

Medium Fuel Oil Corexit 9527

26

54

50

91

42

42

67

Medium Fuel Oil Slickgone NS

17

49

46

94

29

23

50

Medium Fuel Oil LA 1834/Sur

4

2

2

50

16

11

38

Forties Crude

Slickgone NS

16

47

65

95

28

25

60

Forties Crude

LA 1834/Sur

5

2

2

61

15

12

53

0.54 0.87

0.94

0.41

0.35 0.19 0.56

0.62

0.27

Oil Type

Dispersant

2

Correlation with field test (R )

0.89 0.7

Ratio Lab test/field test

0.4

Legend: SF ¼ Swirling Flask, GC ¼ analysis by Gas Chromatography, CA ¼ Colorimetric Analysis, IFP ¼ French Institute for Petroleum test, WSL ¼ Warren Springs Laboratory Test, EXDET e an Exxon test.

Chapter | 15 Oil Spill Dispersants: A Technical Summary

467

studied extensively including energy studies, variation with temperature, salinity, and operational parameters. This test is a high-energy test and uses an obsolescent colorimetric analytical method. More description of this will be given in an analytical section below. Reviews of testing have noted several conclusions on laboratory testing.1,8 Bench-scale testing is widely used to evaluate the performance of dispersants and the physical and chemical mechanisms of oil dispersion. A major disadvantage is, of course, that it is difficult to scale the results of these tests to predict performance in the field. Several factors that are difficult to extrapolate include energy regimes, dilution due to horizontal and vertical advection, and turbulent diffusion. Bench-scale tests are very useful for determining the effectiveness of various dispersanteoil combinations, salinity, temperature effects, effects of oil composition, and effects of oil weathering. It has also been noted that the conditions for some tests are not realistic and result in unrealistically high effectiveness values. Many operators of these tests are not using valid analytical techniques.

15.4.4. Tank Tests There has been high interest in tank tests recently. The U.S. National Academy of Sciences focused much attention on tank testing in its recent report.1 It notes that the physical characteristics of wave tanks imply that the encounter probability of the dispersant with the oil slick will be higher than can be achieved during a real spill response. Thus, wave-tank tests provide upper limits on operational effectiveness. There is concern that wave-tank tests may also not count for the skinning of oil that often occurs with weathering. Skinning occurs when heavier components of the oil, typically resins, float to the top and coat the rest of the oil so that penetration from either side is slowed. Another concern is that the dispersant application system should simulate the droplet-size distributions and impact velocities in real application systems. The wave energies used in tanks should be scalable to actual sea states. It is also noted that coalescence and resurfacing of dispersed oil droplets occur and that wave-tank experiments should include investigation of these phenomena. In summary, the advantage of wave tanks is to investigate operational effectiveness components and observe diffusion of droplets more like those at sea. The dispersant droplet size generation in tanks may be an important factor. The committee feels that the measurement of effectiveness should also include the measurement of dispersed oil droplet size. Further, factors such as mass balance and analytical methods require careful consideration. 1,87 The EPA and the Canadian Department of Fisheries constructed a new test tank at Bedford Nova Scotia. Extensive calibration, wave, and energy measurement were carried out at this facility.88-92 In recent times, more extensive testing has been carried out, and quantitative relationships have developed between energy dissipation rate and dispersion.90 Recent testing

468

PART | VI

Treating Agents

showed effectiveness amounts ranging from 53 to 95%, depending on conditions and dispersants. In a related test, effectiveness was found to be 21 to 36% under regular waves and 42 to 62% under breaking waves.91 This is very similar to the findings at other well-controlled facilities such as at the Texas A&M facility and the Imperial Oil facility. The energy factor under breaking waves in the Bedford Tank created much smaller droplet sizes as would be expected. In a related study, a model was created for droplet sizes produced under breaking waves based on tank test results.92 OHMSETT, a government-run test tank in New Jersey, has continued work on dispersant tank testing.93-100 The facility is still working on the recommendations for analytical methods. Tank testing began with the Imperial test tank facility in Calgary, Alberta. 101 Workers at this facility were pioneers in measuring mass balance, energy, and other essential parameters to tank dispersant testing. The range of effectiveness achieved was generally from 20 to 60% in a few hours and 10 to 30% over one day. Texas A&M followed up with a new test facility near Corpus Christi.102 This facility advanced the art of measuring mass balance, energy, and other tank testing parameters. The effectiveness they measured was similar to that of the Imperial tank crew. The author and coworkers prepared extensive studies on tank testing.103-106 The following presents 17 critical factors that need to be considered and included in any test for measuring the effectiveness of dispersants in a tank or in the field in order for that test to be valid. 1. Mass balance The measurement of effectiveness should include the determination of mass balances.87 It is noted that in tanks where this is attempted, accounted-for mass balances typically vary from 50 to 75%. It is recommended that mass balance should be calculated in all wave-tank studies of dispersant effectiveness. Mass balance remains a large issue. Several testing groups were able to establish mass balances in their tanks to enable more accurate assessments of dispersant effectiveness. Bonner et al. developed a materials balance approach in conducting petroleum experiments at the Shoreline Emergency Research Facility (SERF).107-109 The SERF facility is located on land outside Corpus Christi, Texas, and is run by Texas A&M University. The first attempt at a materials balance was during a 1998 study on the fate/ effects of dispersant use on crude oil. Both water column and beach sediment samples were collected. For the materials balance, the defined environmental compartments for oil accumulation were sediments, water column, and the water surface, while the discharge from the tanks was presumed to be the primary sink. The factors that required development included a need to quantify oil adhesion to the tank surfaces. This was resolved by adhering strips of the polymer tank lining to the tank sides so

469

Chapter | 15 Oil Spill Dispersants: A Technical Summary

that the strips could be later removed and extracted for oil. A water-surface oil slick quantification protocol was developed, using solid-phase extraction disks. Initially the group was able to account for only 10 to 33% of the oil originally placed in the tank. After considerable effort, the mass balance was improved to about 50 to 75%. This illustrates the problems of attaining a mass balance. Mass balance is very difficult to achieve in large test tanks, especially in full-scale field tests. Brown et al. reported on tank tests of dispersant effectiveness.101,110 Effectiveness was measured in two ways: by accumulating the concentrations of oil in the water column by fluorometric measurements and by removing and weighing oil on the surface. The results of these two measurements, the amount of oil unaccounted for, and the difference between the two measurements, are shown in Table 15.7. These data show that between 0 and 68% of the oil in the tank can be unaccounted for. Furthermore, in two cases (2 and 3 in the table), the amount of oil was overcalculated. This shows the difficulty in attaining a mass balance, even in a confined test tank. Brown et al. observed that the problem was accentuated by the heterogeneities in oil concentration in the tank.101 Some of the unaccounted oil may have been in regions where the concentrations of oil were higher than average. It should also be noted that surface removal exaggerated the amount of oil dispersed from a factor of 1 to 8, with an average of 4 times. Mass balance is very important in test situations because it relates directly to the reliability of the data. If the mass balance is not accounted for, the TABLE 15.7 Results of Effectiveness Tests in The Imperial Tank Results of Effectiveness by Different Methods

Dispersant Oil Combination

Water Column e 3 Hour

Surface Removal

Percentage Unaccounted For

Percent Difference Between Methods

1

9

53

38

44

2

24

77

1

53

3

33

77

10

44

4

9

76

15

67

5

11

39

50

28

6

14

43

43

29

7

16

16

68

0

470

PART | VI

Treating Agents

numbers are meaningless. The above examples show that mass balance losses, even in the more controlled tank tests, can vary from a few percent and higher. If the measurement made does not account for the discrepancies in mass balance, then very high errors result. A typical example of this is using only the oil remaining on the surface as an indicator of dispersant effectiveness. In a very highly controlled test series, this number can be from 0 to 67% greater than the oil actually dispersed (factors of 1 to 8 times the amount recovered). The experiences of Bonner et al. show that there are major losses of oil in three areas that historically have not been considered in performing a mass balance.107-109 These are adhesion to walls, adhesion to sediments, and formation of invisible slicks. These three losses can account for over 50% of the oil loss in certain cases. Methods for the measurement of each of these oil losses were developed and applied. The adhesion to the walls was measured by placing strips of wall material into the test tank and later removing and quantifying the oil on these strips. It was noted that the age and conditions of these strips were important as the more weathered tank would hold more oil than the newer, unweathered strips. Bonner and coworkers noted that the sediment (even that from the apparently clean tank bottom) must be collected and oil content measured.107109 Oil in thin slicks was measured using a solid-phase extraction disk held by vacuum to retain both the disk and oil adhered to the disk. A question that must be dealt with is, as in the title of the Brown et al. paper, “where has all the oil gone?” 110 In summary, the mass balance problems revolve around analytical problems; loss of oil through thin, invisible sheens; calculation difficulties; inability to recover surface oil after dispersant applied; and loss to tank walls and also in the presence of large heterogeneities in oil concentrations in the water column. 2. Proper controls A proper control experiment is needed in order to accurately assess a dispersant tank test or a field trial. The control slick must be treated equally to the test slick in every respect except for the application of dispersant. The measurement of any factor of the dispersant slick should then be compared to the same measured factor of the control. 3. Analytical methods Few analytical methods can be used in field situations even in a test tank. Very early in the tank testing program, fluorometers, particularly Turner fluorometers, were used. In early years before Global Positioning Systems (GPS), it was difficult to assess the position at which samples were taken if the sampling probes were not fixed to the tank. Now accurate GPS data coupled directly to fluorometer data can provide reasonable positional data for the fluorometric readings. Some of the earlier trials used grab samples that were subsequently taken for analysis by Ultraviolet (UV) or Infrared (IR) absorption.63,103 These

Chapter | 15 Oil Spill Dispersants: A Technical Summary

471

methods are notoriously inaccurate and scientifically incorrect and have long since been replaced by gas chromatography methods. A further problem is that of sample preservation. Samples must be chilled immediately and treated to prevent bacteria growth and hydrocarbon loss. Standard procedures are available, but in early trials these were not applied. The use of fluorometry for oil measurement has been examined in detail.94-97 These studies show that fluorometry is a sensitive, but not necessarily accurate, means of oil determination. A fluorometer uses UV or near UV to activate aromatic species in the oil. The UV activation energy is more sensitive to the naphthalenes and phenanthrenes, whereas the near UV is more sensitive to large species such as fluorenes. The composition of the oil changes with respect to aromatic content as it weathers and is dispersed, with the concentration of aromatics increasing. Thus, the apparent fluorescent quantity increases in this process. Studies then showed that because the amount and distribution of PAHs, the target compound for fluorometers, change with time during the course of a chemical dispersion event, a fluorometer can never be truly “calibrated” for a particular oil and dispersant combination.111-114 The composition of the oil changes with respect to aromatic content as it weathers and is dispersed, with the concentration of aromatics increasing. A fluorometer reading will always remain a relative value and even with careful calibration can only give indications that are as much an order of magnitude from the true value. Efforts continue on fluorescent measures, but there needs to be more recognition that this method will always be relative and highly prone to error.115 Even with good calibration and taking samples directly from the output of a fluorometer, there are significant differences because the target compounds of the PAHs have different distributions and concentrations with time, conditions, and weathering of the oil. Figure 15.4 shows the lack of relation between the total petroleum hydrocarbon (TPH) measured by Gas Chromatography-Flame Ionization Detection (GC-FID) and the reading of the fluorometer. Samples for analysis were taken directly from the output of the fluorometer, and readings were recorded for comparison. Figure 15.4 shows that the correlation between readings is poor. The calibration of fluorometric readings is critical, but one should bear in mind that an exact reading is impossible, as pointed out earlier.113-116 The most important factor is how the oil is introduced to the fluorometer and the subsequent readings made. The physical factors that influence how much of the oil the fluorometer sees are the solubility and dispersibility of the particular oil and the subsequent evaporation or volatilization of the oil. A typical procedure is to add oil and dispersant to a container (e.g., a bucket) and then pump this through a flow-through fluorometer. Most often, that amount of oil added is taken as the amount of oil read by the fluorometer. The problem with this method is that most of the

472

PART | VI

Treating Agents

100

140

35 30

TPH (ppm)

25 20 15 10 5 0

0

20

40

60

80

120

160

Fluorometer Reading (relative units) FIGURE 15.4 Comparison of fluorometer readings compared to actual readings. These results were taken from an experiment in a test tank.

oil is not dispersed into the water column and that some large amounts of soluble species are present, which would not be the case in the sea. Tests of these types of methods show that the fluorometer calibration curve is generally between 5 and 10 times greater than is the actual case. Thus, a reading of 15 ppm in the field is actually a reading of somewhere between 1.5 and 3 ppm. As this was generally the case in a few testtank trials, the actual ppm readings provided are far too high and cannot simply be converted into actual values. A somewhat better method of calibrating a fluorometer is to use weathered oil (to about the percentage expected in the field) and introduce this to a closed container. After about 15 minutes of pumping, take a sample and analyze it by a standard gas chromatography (GC) method.114 Then the addition continues, one increment at a time, with sampling and analysis at each increment. After the numbers are collected, this will form a relatively good calibration curve. But because of the differences in chemical composition, this calibration curve could also give results as high as twice that of actual concentration. The most reliable method of calibrating a fluorometer is to perform the above calibration procedure, but repeat it throughout the actual experiment. Almost simultaneous samples are relatively easy to collect from the fluorometer as the flow from the output of the fluorometer can be captured and preserved for later analysis. This is generally done when the fluorometer reading is

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relatively stable to ensure correspondence between the sample and the fluorometric value. The actual values and the previously prepared calibration curve can be compared to examine the differences in composition. It should be noted that this method was followed in some of the past test-tank work. This is the case for the data illustrated in Figure 15.4. One must remember that, in fact, the aromatic concentrations in the dispersed plume are a composite of dispersed and dissolved aromatics and this total concentration is changing with time. The effects of running probes into the water column have not been fully examined. Several devices have been created in the past to examine the subsurface water column; however the standby usually ends up being weighted hose. Tests show that there is significant retention on Tygon tubing and that pumping for up to one hour may be required to clear this line to the point of background measurements. Teflon tubing appears to show a smaller effect, although less testing has been conducted on this. There may be a serious effect on measurements, depending on how the tubes or sampling devices are deployed. Some test-tank work used fixed probes with Teflon tubing. Another complication to sampling is the retention of surface oil on the sampling tubes, weights, and pumps that are lowered into the water. As the equipment goes through the surface slick, which is present over greater areas than the in-water plume, some of the surface oil will be retained on the sampling equipment and will be read as oil concentration at that depth. Although some experimenters have dragged the submerged sampling train to the next sample point to avoid this problem, this action may also drag oil on the outside of the sampling gear. In summary, fluorometry is a technique sometimes used for measuring relative concentrations of oil in the water column at test tanks. It must be noted that fluorometers cannot truly be calibrated for the oil as there are many variables, as explained earlier. The errors encountered all increase the apparent value of the oil concentration in the water column. Incorrect calibration procedures can distort concentration values up to 10 times their actual value, or even more. Correct analytical methods involve performing accurate GC measurements both in the laboratory and in the field during the actual experiment. Furthermore, water sampling gear must be deployed in such a way as to avoid disturbing the underwater plume or carrying oil from one level or area to another. Fixed probes and sampling after a period of time may assist in minimizing this disturbance problem. 4. Time lag and length of time plume is followed There are certain time characteristics of the dispersion process that must be understood. First, the time to visible action after the dispersant application varies from 15 to 90 minutes. Fast action is herding and not dispersion. The visible action is generally taken as the appearance of a yellow-to-coffeecolored plume in the water. The second item of timing to note is that the

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dispersant may continue to act for up to an hour after application. Third, the movement and dispersion of the plume are generally slow, although the plume is generally visible for about 3 hours and is never visible after about 8 hours. Finally, the oil in the plume will resurface slowly over the next several days. It is important to take measurements as long as possible. The Beaufort Sea experiment is a good example. Three slicks were laid and two left as controls.44 Two days later, three slicks were found at sea, and each had the same orientation and geometry as one on the first day of the experiment. The largest slick, in area, was the dispersed slick, although the oil content was not known. The interpretation of the results would have been quite different if the slick had not been followed for 2 days. Brown et al. noted that they had to measure their test tank after 24 hours to yield a reasonable result. Measurements before about 6 hours were found to be of little value.110 In summary, as the oil concentration in the water is constantly changing, any value should be expressed as a function of time and place. Further, since the dynamics of the dispersion process change rapidly in the first few hours, values should only be used after about 6 hours. 5. Mathematics of calculation and integration Several examples of the effects of integrating and averaging incorrectly are given in a past paper.63 This effect is exacerbated if nonzero oil concentration values are measured in areas outside of the plume. These errors are illustrated in Figure 15.5. Methods of integration and handling background values can easily change the values by as much as threefold, even for the identical case.33 Another concern regarding the mathematics is that related to the use of fixed water column concentration values to determine effectiveness. Although this method has not been used in recent years, it was thought to be a reliable means of estimating effectiveness. The assumption that is made is that the slick is evenly distributed in 1, 2, or 3 meters. Then once the concentration is measured, an effectiveness is assigned. Table 15.8 illustrates the variances in using this type of scheme. This shows that one concentration could yield a wide range of effectiveness values depending on what assumption one makes. Because one cannot make oil thickness measurements and because the depth of mixing is not simply a fixed depth, this type of procedure is not a valid method for determining effectiveness. 6. Lower and upper limits of analytical methods The lower and upper limits of the analytical methods applied are another important factor, especially in field situations. If the measurement is less than the lower limit, use of these values can result in serious errors, as shown above. The lower analytical limit should be taken as twice the standard deviation, or about 0.3 ppm for an older fluorometer or about 0.1 ppm for a newer unit. Use of double the standard deviation is standard

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Case 1 Actual situation 2

2 2

4

Area dimensions

Concentrations

4 5 10 5 10 0

Integration First part = 2 x 4 x 5 = 40 Second part = 2 x 4 x 5 = 80 Third part = 2 x 4 x 5 = 40 Remaining part = 10 x 10 -(4 x 6) x 0 = 0 Sum = 40 + 80 + 40 = 160

Case 2 Illustration of taking non-zero values 2

2 2

4

4 5 10 5 10 0.5

Integration First part = 2 x 4 x 5 = 40 Second part = 2 x 4 x 5 = 80 Third part = 2 x 4 x 5 = 40 Remaining part = 10 x 10 -(4 x 6) x 0.5 = 38 Sum = 40 + 80 + 40 + 38 = 198

Case 3 Illustration of averaging values 10 Integration Total =10 x 10 x 4.1 = 410 4.1

10

FIGURE 15.5 Illustration of the effect of integration and averaging techniques on the final values e A 2-d example.

laboratory practice, and, in fact, newer practices advocate three times the standard deviation. Values below this should be taken as no-detect levels and not zero, but for calculation purposes, zero is the only choice. The upper limit is equally important since the amount of oil in the water column could exceed the upper limit of some analytical procedures. If this were to occur in practice, the effectiveness would be underestimated. Fluorometers are nonlinear in concentrations approaching or exceeding about 100 ppm oil-in-water; therefore very high concentrations might be missed, although such high concentrations have never been measured in the field or lab. 7. Thickness measurements Several researchers have tried to estimate the amount of oil remaining on the surface by estimating thickness. One of the most common means to do so was by touching the surface with a sorbent. The amount of oil in the sorbent was determined by a number of means such as colorimetric or

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TABLE 15.8 Illustration of the Use of Concentration Values and Mixing Assumptions Assumptions 1 mm slick

3 mm slick

10 mm slick

mixed in mixed in mixed in mixed in mixed in mixed in 1m 3m 1m 3m 1m 3m Concentration Found in Water ppm

Value of Effectiveness %

Value of Effectiveness %

Value of Effectiveness %

1

0

0

0

0

1

0

5

1

0

2

1

5

1

10

1

0

3

1

10

1

15

2

1

5

2

15

2

20

2

1

6

2

20

2

25

3

1

8

3

25

3

30

3

1

9

3

30

3

40

4

1

12

4

40

4

50

5

2

15

5

50

5

75

8

3

23

8

75

8

IR analysis. This was then presumed to relate directly to the oil thickness. Careful laboratory tests of these techniques have shown that they do not yield a good quantitative thickness result.109,117 The removal of oil from the surface is not necessarily total for several reasons. The edges of the sorbent may trap more oil, it may not be possible to calibrate the sorbents in the laboratory, and there may be poor extraction from the sorbent. Sorbents cannot be “calibrated” in the laboratory because it is very difficult to get a uniform thickness of oil in a vessel in the lab. Oil often does not spread uniformly and can form “blobs” interconnected by sheen. Oil will be herded to one side even by the minimal air circulation in the laboratory. Also, most oils will form a concave or convex lens with more oil on either the edge or the middle. The use of sampling tubes and similar devices is fraught with similar difficulties. In summary, the thickness of oil on the surface of the test tank cannot be measured. Therefore, thickness cannot be measured as one

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way to determine dispersant effectiveness. Recent work by Bonner et al. has resulted in thin slick estimation methods as a means of examining mass balance.107,109 Goodman and Brown have successfully developed acoustics in the Imperial test tank to measure the thickness of the oil, given thicknesses of 4 or more mm. 8. Behavior of oil with surfactant content Oil behavior that is strongly affected by surfactant content other than dispersion includes lesser containment capability and lower adhesion. These also affect the ability to measure oil remaining on the surface. If the oil were to be contained, dispersant applied, and the remaining oil measured, errors as large as an order of magnitude would occur because the oil would pass under the boom. The value that is important is the critical velocity of containment. The critical velocity of containment is the velocity at which oil is lost under the boom through several failure mechanisms. The critical velocity of containment can be given by Lee and Kang:118 Ucr ¼ f2½gTo=w ðr ro Þ1=2 ðr þ ro Þ=ðrro Þg1=2

(5)

where: Ucr is the critical velocity, To/w is the interfacial tension between oil and water, r is the water density, and ro is the oil density. A very low ratio of dispersant or surfactant to oil (about 1:100) will lower the interfacial tension to about half its previous value.8 Thus, according to the equation, this would lower the critical velocity to about 0.7 of the previous value. If an experiment were set up that measured the oil left behind a containment boom where the oil was being held close to critical velocity, even a small amount of dispersant would release the oil. If the oil left were measured as the effectiveness of the dispersant, this effectiveness value would be highly exaggerated and would represent containment failure and not dispersion. The other factor changed by adding dispersant to oil is the adhesion of the oil. While quantitative studies have not been performed on this, practical tests have shown that it is difficult, if not impossible, to remove the remaining oil after dispersant application using a sorbent surface skimmer.110 Such a skimmer relies on the adhesion of the oil to remove it from the water surface. Again, because of the effect of the dispersant, the oil remaining on the surface is likely to be underestimated, leading to an increase in the apparent effectiveness of the dispersant. While this effect is not felt to be as large as that of containment failure, it is significant nevertheless. The combination of errors resulting from using contained oil slicks and lack of mass balance is at least a factor of 4, as noted in Brown and coworkers, and is possibly as large as no dispersion at all, even though the surface appears to be clear.110

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9. Surfactant stripping It is relatively well known that an exchange of surfactants takes place between the target droplet and the surrounding water.18 This results in destabilization of the emulsion. In situations where the water is a large ratio to the droplet concentration, surfactant is largely lost and destabilization is relatively rapid. In laboratory tests, the ratio of the oil to water then becomes important in simulating the conditions at sea. In the swirling flask test, the oil-to-water ratio is 1:1200, which may be somewhat representative of a more open situation. Some other laboratory tests use only 1:50. The relationship of the energy, the dilution, and other factors in the laboratory test to open water conditions is not well understood at this time. Chemically dispersed oil has been known to destabilize due to the loss of surfactants to the water column. Once droplets lose a critical amount of surfactant, they are less likely to remain in the water column. 10. Recovering surface oil Some experimenters have tried to recover surface oil in an attempt to directly determine effectiveness by presuming that the entire remainder is dispersed. This is incorrect because the loss from the surface includes the amount evaporated, the amount in very thin slicks, the amount that is physically unrecoverable, oil adhered to booms or other surface objects, errors in the amounts of all the oil compartments, and oil that is simply unaccounted for. Controlled tests in a test tank have shown that the difference between oil accounted for in the water column and the amount on the surface can vary from 0 to 80%.101,110 This again represents the typical error of trying to perform a surface-only measurement. Once oil is treated with dispersant, it becomes less adhesive and therefore much more difficult to recover from the surface using typical skimmers and sorbents. This fact can contribute to the error. Some experimenters have recovered surface oil.107-109,119 While a typical experimental procedure, it should be noted, for the reasons described earlier, that this surface oil number is fraught with error and great care must be taken to ensure good recovery as well as subsequent interpretation of the results. 11. Background levels of hydrocarbons The background level of hydrocarbons is important for several reasons, some of which have been discussed. A good background value is needed first to subtract concentration values and second to know when to terminate integration of the spill. It is suggested that the same techniques, along with the grab samples for calibration, be applied in the area before dispersant application and also after, if practical, to determine the range of background values in the area. These values can then be judged for use in correcting the values and for ending integration. Another problem associated with the background levels is that hydrocarbons will adhere to sample tubes and equipment. This will result in higher

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than background values at the end of a run through the plume. There is no easy solution to the problem. One solution is to examine the values and look at where the signal drops off significantly, probably at the end of the plume, and use this value as a “corrected” background. Some experimentation at the scene of the measurements can be used to define the carry-through of hydrocarbons in the system. It should be noted that, if the carry-through is not corrected for, gross errors could occur in the amount of oil calculated. 12. Fluorescence of the dispersant While the dispersant mixtures per se should not fluoresce, most of them show a significant signal when placed in a Turner Fluorometer.113 The reason for this fluorescence is the reflection of UV and other light into the detection path and the actual fluorescence of small amounts of fluorescent material in the dispersant or picked up through the system. Most experimenters have ignored the fluorescence of the dispersant in the past because it was presumed that there was no contribution. Furthermore, in an actual application or experiment, the pickup of even a small amount of oil by the dispersant will result in a significant signal. While this is difficult to correct for, one way is to correct all the readings to accurate GC analytical results. 13. Herding Herding is the phenomenon that occurs when the oil is pushed aside by the dispersant.120 Herding takes place because the spreading pressure of the dispersant can be more than that of the oil slick, especially when the oil slick is thin. The dispersant must directly contact the water surface in order to cause herding. This readily occurs with thin oil slicks because aerially applied droplets are generally 300 to 1200 mm in size, while the oil slick could easily be as thin as 100 mm (appearing as a thick slick).120 There are several problems associated with herding, the major one being that often little dispersion occurs if the oil is herded. The larger droplets will land on the surface first and cause herding if the conditions are correct, and then much of the dispersant that follows in smaller droplets can land directly on the water. A problem in test tanks is that herding could be interpreted as dispersant effectiveness. 14. Heterogeneity of the slick and plume As slicks are rarely homogeneous in thickness, the dispersant applied may be insufficient in areas or may break through and cause herding in other areas.120,121 Furthermore, slick heterogeneities will result in heterogeneities in the dispersant plume, which will again result in difficulties integrating the plume. Using peak values will result in overestimating the dispersant effectiveness and vice versa. This difficulty can be mitigated by integrating very small areas of the subsurface plume. In tanks, this can be overcome somewhat by continuing circulation for 24 hours and then measuring.110

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15. True analytical standards There exist certified laboratories that use certified petroleum hydrocarbon measurement techniques. These techniques should be used for tank studies. One of the most serious difficulties in older field and tank trials occurred when inexperienced staff tried to conduct chemical procedures. Analytical methods are complex and cannot be conducted correctly without chemists familiar with the exact procedures. Furthermore, field instrumentation such as fluorometers require calibration using standard procedures and field samples during the actual test-tank trial. These samples must be taken and handled by standard procedures. Certified standards must be used throughout to ensure good Quality Assurance/Quality Control (QA/QC) procedures. In this era, it is simply unacceptable not to use certified methods, laboratories, and chemists. 16. Weathering of the oil Dispersant effectiveness decreases with weathering of the oil. The weathering trend is characteristic of that oil, but every oil shows this decrease.122 The oil used for any dispersant test should be weathered to an extent that it would represent a realistic situation, for example, equivalent to about 1 day. The weathering of the oil will also assist in maintaining a more correct mass balance. 17. Other issues It is important during any experiment to alter only one variable at a time. Otherwise, the outcome may be a result of the combination of the inputs, leading to confusion as to what the effect was of a given variable. Another distinct issue is that of scaling wave energy.107 Dispersant effectiveness is largely affected by energy inputs, and therefore scaling and control of energy in a test tank is an important factor. In the laboratory, energy input to the dispersant/oil mixture can be very tightly controlled, but in a test tank it could be highly variable and subject to influences such as winds. Bonner et al. provide mathematical tests of energy scaling and means to estimate wave reflection, which is another source of variability.107

15.4.5. Analytical Means Analytical means in any test system continues to be a major concern. It should be made very clear that only careful GC/FID or MS techniques produce a true answer.111-115 Few analytical methods can be used outdoors or in field situations. Very early in the field testing program, fluorometers, particularly Turner fluorometers, were used. Some of the earlier trials used grab samples that were subsequently taken for analysis by UV or IR absorption. These methods are notoriously inaccurate and have long since been replaced by gas chromatography methods. A further problem is that of sample preservation. Samples must be chilled immediately

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and treated to prevent bacterial growth and hydrocarbon loss. Standard procedures are available, but in early trials these were not applied. Another analytical issue in the field of effectiveness measurement is the use of colorimetric measures. The basic science of the issue is as follows: To be a valid colorimetric measurement, the analyte must have a chromophore or color-absorbing center and the system must obey the Beer-Lambert law (linear absorption over broad range of concentrations).115 Oil does neither of these two things. Oil is a mixture of dozens to hundreds of compounds, none with a chromophore, or visible light-absorbing center. Furthermore, what occurs in an oil-in-solvent system is simply light blockage. In analytical chemistry, colorimetry is never used, even when valid, because of the many problems, interferences, and inaccuracies. Only gas chromatography and detection by mass spectrometry or flame ionization are considered valid techniques.

15.5. MONITORING 15.5.1. Introduction to Monitoring An important aspect of the whole dispersant issue is the actual effectiveness in the field. This aspect has not been measured in the past except at a very few spills. Much of the discussion on dispersant effectiveness could have been saved had dispersant effectiveness been monitored more closely during actual applications. While it is easier to measure the effectiveness of dispersants in the laboratory than in the field, there are few standard testing procedures and tests may not represent actual conditions. For example, important factors that influence effectiveness, such as sea energy and salinity, may not be accurately reflected in laboratory tests. However, dispersant effectiveness at sea is very difficult to measure and such measurements are subject to many types of errors. When testing dispersant effectiveness in the field, it is very difficult to measure the concentration of oil in the water column over large areas and at frequent enough time intervals. It is also difficult to determine how much oil is left on the water surface as there are no methods available for measuring the thickness of an oil slick and the oil at the subsurface often moves differently from the oil on the surface. The quantitative method is not used in modern monitoring practices. Instead, a relative measure of dispersant effectiveness is made. Several papers have assessed the techniques used to measure effectiveness in field tests.123,124 There is no general consensus that effectiveness and other parameters can actually be measured in the field using some of the current methodologies. Many historical tests relied heavily on developing a mass balance of oil in the water column and that left on the surface.123,124 Fluorometry has recently been used, but this method is also quantitatively unreliable,

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for it measures only a small and varying portion of the oil (aromatics with two to five rings) and does not discriminate between dissolved components and oil that actually dispersed. Furthermore, it is difficult to calibrate fluorometers for whole oil dispersions in the laboratory without using accurate techniques such as extraction and gas chromatographic analysis. It is known that the aromatic ratio of the oil changes as a result of the dispersion process. In early tests, it was not recognized that the plume of dispersed oil forms near the heavy oil in the tail of the slick and that this plume often moves off in a separate trajectory from the slick.124 Many researchers “measured” the hydrocarbon concentrations beneath the slick and then integrated this over the whole slick area. As the area of the plume is always far less than this area, the amount of hydrocarbons in the water column was greatly exaggerated. In recent years, many of these factors have been recognized, and monitoring is being instituted to simply determine whether or not a dispersant application has any measurable effectiveness. Most of the current monitoring protocols do not try to quantify the effectiveness of dispersants. Furthermore, the purpose of the monitoring is to derive this limited indication of effectiveness because it is also now recognized that there is a potential to have little or no dispersion in actual situations. In summary, dispersant effectiveness testing in the field is necessary to indicate whether or not the dispersant was effective to some degree. Quantitative measures are difficult because effectiveness values depend on establishing a mass balance between oil in the water column and on the surface. Furthermore, it is difficult to quantify the oil at sea. Because this mass balance and oil concentration values are difficult to achieve, specific quantitative results are questionable. Recent protocols have focused on developing relative measures to simply ascertain whether there is some relative effectiveness as compared to little or no effectiveness.

15.5.2. Review of SMART Protocol The most common monitoring protocol in the United States is SMART (Special Monitoring of Applied Response Technologies).125-127 SMART is a nonregulatory protocol promulgated by the United States Coast Guard, the National Oceanic and Atmospheric Administration (NOAA), the U.S. EPA, and Minerals Management Service (MMS). The purpose of the protocol is to provide information for decision making. It is supposed that if the dispersant application were found to be ineffective, further dispersion would not be carried out. The SMART protocol proposes three tiers of monitoring: Tier I is visual monitoring only. Tier II includes fluorometer monitoring of the underwater dispersed oil plume. Tier III includes fluorometer monitoring at several depths and the possibility of performing other in-situ water analysis.

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Sampling the water from the fluorometer and subsequent laboratory analysis is suggested, although no protocols or standards are given for the laboratory analysis. Detailed procedures are given for on-site work. For the visual surveillance recommended for all tiers, the SMART protocol suggests that a visual aid observer such as the NOAA Dispersant Application Observer Job Aid be used to provide the user with imagery comparison.128,129 The protocol also suggests that thermal infrared imaging would provide “a higher degree of sensitivity” l in determining dispersant effectiveness. Tier II is the use of a continuous-flow fluorometer and the output from this on a relative basis. The suggestion is that a reading of about 5 times the background level indicates that the dispersant is working. The data is to be collected at three locations: in a clean area to provide background; under the oiled slick before dispersant was applied; and then after the oil has been treated with dispersants. Data are to be collected both electronically and some manually in a provided form. Positions are to be recorded with a GPS. Water samples are taken from the fluorometer output, preserved on ice, and analyzed later. No procedures are given for the subsequent analysis, although sampling procedures are given in detail. Tier III monitoring has two alternatives: multiple depths with one fluorometer or a transect at 1 and 5 m (or other depths as negotiated) with two fluorometers. Data are treated as before. When one fluorometer is used, the instrument is to be positioned where a high reading is obtained and then readings are taken down as far as 10 m. The data are to be used only as an indication of the difference between the oil-only and dispersed levels. A level of 5 times is suggested as indicating dispersant effectiveness. The protocol suggests that an s-shaped passage be made though the slick to perform measurements as shown in Figure 15.6, as drawn from the SMART protocol. The protocol also suggests strong links between the command post and the field sampling and observation platforms as shown in Figure 15.7. The protocol recommends that the subsurface dispersed oil plume be tracked using a Davis Drifter. Figures 15.8 and 15.9 show the suggested deployment of Davis Drifters. SMART also advocates a box coordinate method in which coordinates of the target are provided from the monitoring aircraft. The SMART protocol also recommends that in Tier III monitoring, the concentration be measured from 1 to 10 m. This is illustrated in Figure 15.10. It should be noted that Tier III has never been used and there only has been limited use of Tier II.

15.5.3. The SERVS Protocol The Ship Escort Response Vessel System (SERVS) of the Alyeska Pipeline Service Company (APSC) has published its own extensive monitoring system.130 The protocol is detailed and includes visual monitoring and

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FIGURE 15.6 Sample path as recommended in the Smart document.

FIGURE 15.7 Illustration of the communication between field and command post.

FIGURE 15.8 Suggested initial deployment of Davis Drifter.

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FIGURE 15.9 Deployment of a second Davis Drifter.

FIGURE 15.10 Concept of SMART monitoring with a fluorometer.

on-water monitoring by taking samples as well as by collecting fluorometric data. As with SMART, the procedures are not intended to quantify the amount of dispersant effectiveness or to yield a mass balance. The SERVS protocol is intended to answer the question of whether the application dispersed oil and to provide data for future use, including for scientific purposes. After the application of dispersant, a vessel held in the vicinity moves to place drogues or markers at the upcurrent and at the downcurrent areas of the dispersed area. Drogues are initially to be rigged to follow currents at the 2-m depth. The sampling vessel moves to a standby position upwind and upcurrent of the dispersant target and outside the oil. Background water samples are taken at this location, as are fluorometer data. This location is also marked with a drogue. The locations are marked using the drogues as reference points. The sampling vessel then moves to sample the area under the first dispersant application area. Samples are also taken under the untreated slick. Guidance is

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provided from the spotter aircraft as well as by reference to the drogues. Detailed instructions are given for each move and operations at each sample point. The fluorometer is specified as a Turner model 10 with a short-wave light kit. A tube is used to sample at depths of between 0.5 and 5 m. The 2-m depth is recommended for the initial transit. The fluorometer is used to give relative results. The fluorometer is to be calibrated in continuous flow mode before and after each survey. A bench calibration procedure is also prescribed, and this includes detailed procedures using Alaska North Slope (ANS) crude oil. Very detailed operation and decontamination procedures are given for the fluorometer. The water grab samples are collected from the outflow port of the fluorometer. Vertical samples are also taken using a Valskon sampler at stations from 10, 5, and 1 m. Two types of samples are prepared: total petroleum hydrocarbon samples and volatile aromatic samples. The former are preserved at 4 C without acid and the latter with acid. Large numbers of samples are taken, and up to 30 coolers must be provided to hold these samples. The Volatile Organic Compound (VOC) samples are measured by purge and trap and injection into a GC/MS. The PHC analysis is conducted by EPA method 602 and 610. This includes TPH and PAH results. Documentation procedures are given on sampling and fluorometer datataking. Neither the airborne visual monitoring nor analytical procedures are described in detail.

15.5.4. Review of Other Protocols Most jurisdictions in the United States recommend the use of SMART protocols. 131-135 The SMART protocol has been used in two dispersant applications in the Gulf of Mexico.134,135 A survey of dispersant regulations in most countries shows that they do not include monitoring protocols.136 The author could find only the SMART protocol in the United States, the SERVS protocol, and a protocol in the New Zealand dispersant policy. The New Zealand protocol includes provisions for monitoring.137,138 The protocol is short and consists mostly of a monitoring report form. Two types of monitoring are proposed: visual and fluorometer. Neither guidelines nor values are given for the fluorometer. The visual guidelines are that a coffee-colored plume indicates effectiveness and a white plume indicates excessive dispersants. Much of the recent monitoring techniques evolves from earlier British field trials.139,140 Lunel advocates the dual type of monitoring such as is described in SMART; however, the specifications and procedures are somewhat different. Lunel recommends visual monitoring of the dispersed plume (coffee-colored) and notes that a white plume indicates excess dispersant use. Such monitoring methods are hinted at in Australian documentation but are not required.141

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15.5.5. Review of Goodman Analysis of SMART A critique of the SMART protocol was published in the 2003 AMOP Proceedings.142 The first point raised is the question of why dispersants and burning, of all the possible countermeasures possible, are the only ones to require monitoring. The second question is that of the possible delay caused by selecting, mobilizing, and deploying the team. Goodman notes that, even though the document states that such activities should not hold up the dispersant operation, it inevitably will. The third question raised is that of the purpose of effectiveness versus toxicity. Goodman supposes that the main issue is toxicity and notes that SMART does not address this question. It is also noted that the SMART protocol would not give a realistic measure of dispersant effectiveness. Goodman explains that SMART does not cover three areas mentioned in the “Dispersant Application Observer Job Aid”: (1) time delay in dispersion after application; (2) dispersed oil plume variations; and (3) the occurrence of overand underdoses of dispersant.128 Goodman also points out that neither the job aid nor SMART describes how the suggested still and video photography should be done. According to Goodman, the prescription of a water-sampling protocol complicates the issue. The extra time, logistical support, and arrangements may not result in any improvement in determining dispersant effectiveness. Goodman states that the problems of interpreting oil concentrations is very difficult in view of the dynamic nature of the slicks and dispersed oil plume, and because of the heterogeneities observed, and the short time of a traverse. The results are that these noisy data may not lead to a correct interpretation of effectiveness. Goodman also notes that fluorometric measures on the open ocean are quite complex and subject to many problems and variations and thus may be unreliable. According to Goodman, the illustrations in the SMART document portray unrealistic scenarios of the whole slick being treated and the whole slick turning into a dispersed oil plume that is the same size as the oil slick. He also observes that the dispersed plume and the undispersed surface slick may not separate under some circumstances and thus may be missed in the sampling process. Goodman states that the SMART protocol makes no provision for compensating for the Eckman spiral, which is the effect of the Coriolis force on the water flow from the direction of the wind at the surface 90 to the right in the northern hemisphere. (Simply stated, this is the effect in the northern hemisphere that wind influences curve to the right due to the Earth’s rotation.) Goodman notes that Tier II sampling at various depths does not solve any problems or limitations of sampling at a single depth. As Goodman points out, SMART should have addressed the following three major problems: 1. The net environmental benefit of dispersant use i. Impact on surface organisms ii. Impact on subsurface organisms

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iii. Relative environmental importance of surface and subsurface organisms and their population dynamics 2. The perceived and actual public acceptance of dispersant use i. Fishery concerns in terms of catch, tainting, and fishing gear contamination ii. The concern of the public and environmentalists about adding another chemical to the ecosystem iii. The cheap factor 3. The effectiveness of dispersant use compared to other response techniques i. The amount of oil treated in a given time period ii. The reduction of surface oil and oiling of shorelines iii. The response time iv. The area of the slick covered v. The effectiveness of dispersing the oil Goodman notes that SMART covers only item 3(v). He suggests that Tier I, visual monitoring, is adequate for this purpose.

15.5.6. Considerations for Monitoring in the Field 15.5.6.1. Behavior of the Slick or Plume The dispersed oil plume can move in a different direction than the surface slick.124 Furthermore, the plume’s geometry generally has no relation to the surface slick. Almost any combination of movement and geometry is possible, depending on the differential between surface and subsurface currents and wind speeds. A major problem is therefore created in locating sample boats and later in trying to quantify the oil in the plume, whose extent is unknown. The best solution to this problem is to perform good aerial surveillance, preferably from a helicopter, or have a series of aerial photographs taken with good time stamps. The samplers will require direction from a helicopter platform to ensure that measurements are conducted on representative portions of the underwater plume. Generally, the plume can be seen better from the air. The underwater plume can initially be seen from the surface and the air as a yellow-colored mass, but later it is difficult to see from the surface or air. If the plume moves with the surface slick, neither surface nor airborne observers can see it, and it is difficult to develop an accurate measurement and calculation plan to determine the amount of oil in the plume. 15.5.6.2. Safety A prime consideration should be the safety of the operation. The crew in a small sample boat is at risk when an aircraft application is going on and a rapid change in weather is possible. As suggested in SMART or similar protocols, sampling is feasible only in small boats. These small boats should therefore be launched from large, safer vessels that remain in the vicinity while sampling is

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being done. A compromise to this might be to perform sampling from larger, more seaworthy vessels.

15.5.6.3. Purpose and Objectives The prime purpose and objective of monitoring processes such as SMART should be to determine whether or not a particular dispersant is relatively effective. Some users appear to misinterpret the protocol as actually yielding an effectiveness value. As this report will show, there are many nuances to a dispersant application, and thus it is impossible to simply say whether or not a particular application was effective. It might be more appropriate to rate the application as somewhat effective, slightly effective, or apparently ineffective. These are probably the three most accurate things that could be said about a particular application. Goodman has raised the question of whether there is any need for monitoring the dispersant application.142 This report will show that there are many false indications, both from a visual and an analytical point of view. These false indications point to a strong need to properly monitor a dispersant application to see whether there was effectiveness. Other spill countermeasures do not have false indications such as this and thus do not need the same type of monitoring. 15.5.6.4. Misleading Indications There are many visual and other indications that may be misleading in determining the effectiveness or lack of effectiveness in some particular applications. These indications are discussed in this section. 15.5.6.5. Visual Indications That Show More Effectiveness Than Actually Occurred HerdingdHerding is the phenomenon whereby the oil is pushed aside by the dispersant.120 This effect occurs because the spreading pressure of the dispersant can be more than that of the oil slick, especially in thin oil slicks. In order to cause herding, the dispersant must directly contact the water surface. This readily occurs with thin oil slicks because aerially applied droplets are generally 300 to 1200 mm in size, while the oil slick could easily be as thin as 100 mm (appearing as a thick slick).120 Herding presents several problems, the major one being that often little dispersion occurs if the oil is herded. If the conditions for herding are present, the larger droplets will land on the surface first and cause herding, and then much of the dispersant that follows in smaller droplets will land directly on the water. The appearance of open water leads some to believe that dispersion and not herding has occurred. Furthermore, the remaining dispersant appears as a white plume in the open water and can also lead to misimpressions.

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Dispersant-only plumedDispersant can run off more viscous oil or can land directly on the water as noted in the description of herding. Once in the water, dispersant forms a whitish plume until it mixes to a greater extent with the water. Such plumes could be mistaken for dispersed oil as opposed to dispersant only. Herding into smaller unseen stripsdHerding does not necessarily occur in a broad swath. Oil is often herded into small strips that are not visible from the air. These will re-spread after a period of time. Spreading dOne of the side effects of dispersants is to reduce the oil’s interfacial tension and thus increase its tendency to spread. This has been observed at several field trials.143 The result is that the surface slick may be spread to thicknesses that are not visible or at least are not visible under the conditions that apply. This can lead to the assumption that the oil is dispersed into the water column, while it has actually been spread out over a much larger area. Lacing dAnother phenomenon that has been observed is the formation of “lace.” This is a sheen of oil with “holes” in it. The holes are caused by smaller drops of dispersant leading to herding. The lace is usually visible only from the surface and not from the air. Thus what appears to be sheen disappears after dispersant application, but actually portions of it have been herded by dispersant droplets.

15.5.6.6. Visual Indications That Show Less Effectiveness Than Actually Occurred A number of visual indications would lead one to conclude that little or no dispersion is occurring, when in fact there is some or even significant dispersion. These indications are as follows. Plume under remaining slickdThe dispersed oil plume may move under the remaining slick. As the surface is never 100% clear of oil under the dispersant application path, the dispersant operation appears to have had no effect. This is unlikely to last for a long time as the plume could emerge from under the remaining slick within about one hour depending on the size of the slick and the plume. Plume not developed at time of observationdThe dispersed oil plume can take 15 to 60 minutes to develop to a maximum. Observation may take place before the plume is fully developed, leading to a conclusion that there is no plume. This and many other points raised here emphasize the need for good and continual surveillance during the first few hours after a dispersant application and for at least one hour after a trial dispersion. Poor visibility conditionsdThe dispersed plume is not highly visible and can be obscured by haze and some fog. It is unlikely, however, that a test application would be conducted under such conditions.

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15.5.6.7. Fluorescent Indications That Show More Effectiveness Than Actually Occurred There are also indications using fluorometers that can be misleading in terms of the effectiveness of a particular dispersant. These indications are discussed next. Resurfacing after measurementdResurfacing of dispersed oil occurs in every case.25,30 The phenomenon was described earlier in this chapter. The point here is that, if the fluorometry measurement is taken before much resurfacing takes place, the calculated effectiveness is lower. Repeatedly measuring one part of the plumedThe surface sampling crew does not have a good fixed frame of reference to guide them, and it is very easy to repeatedly sample the same small dispersed oil plume. This is true even with high-resolution GPS data. This re-measurement of the same area will lead to a large overestimation of the amount of dispersed oil in the area and can be prevented by good aerial directions and can be documented by plotting the course of the small boat using good GPS data and the plumes after the operation is completed. Dispersant-only plumedWhen aerially applied dispersant lands on heavier or emulsified oils, the dispersant generally runs off without much dispersant penetrating the oil and without any measurable effect on the oil. As mentioned earlier, the dispersant begins to mix with the water and forms a milky mixture that may be mistaken for dispersant effectiveness. This should be noted by direct surface observation to ensure that runoff is not mistaken for dispersant effectiveness. Although the dispersant mixtures per se should not fluoresce, most of them show a significant signal when placed in a Turner Fluorometer.113,114 The reason for this fluorescence is the reflection of UV and other light into the detection path and the actual fluorescence of small amounts of fluorescent material in the dispersant or picked up through the system. Most experimenters have ignored the fluorescence of the dispersant in the past because it was presumed that there was no contribution. In an actual application or experiment, the pickup of even a small amount of oil by the dispersant will result in a significant signal. Although this is difficult to correct for, one way is to correct all the readings to quality GC analytical results. Dissolved aromaticsdAfter an oil spill occurs, a significant plume of aromatics forms. These aromatics are the prime target of a fluorometer and will give significant readings. A dissolved aromatic plume will be as significant as a dispersed oil plume under certain circumstances. There is no way to distinguish this in the field and even in the lab without special analytical procedures. Other fluorescent material in areadFluorometers do not discriminate between sources of fluorescence. Fluorometers operating at long wavelengths will readily pick up organic material and those at short wavelengths, less so.113,114 During a survey of Vancouver harbor several years ago, this author encountered a large submerged plume of fluorescent material using a Turner

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fluorometer. Sample analysis and contact with a local refinery showed that this was aromatic material that had been inadvertently released by the refinery. Dispersant and aromatics onlydA probable occurrence is that dispersants and aromatics from the oil are in an area and these fluoresce and could be mistaken for dispersed oil.

15.5.6.8. Fluorescent Indications That Show Less Effectiveness Than Actually Occurred Fluorometer misses plumedIt is very easy to miss the dispersed oil plume with a surface-sampling rig. As noted before, there are few points of reference for the surface sampling team and their field of view is very narrow. This again points to the need for good aerial support at this type of operation. Misdirected by aerial observerdThe surface sampling team could be misdirected by the aerial observers through a series of errors. This is more likely with a fixed-wing aircraft as overpasses might occur at about 15-minute intervals and the aerial observers can easily lose track of where the surface crew was in the past sequence. Measurement before plume developsdCertain time characteristics of the dispersion process must be understood. First, the time to visible action after the dispersant application varies from 15 to 40 minutes. Fast action is herding and not dispersion. The visible action is generally taken as the appearance of a yellow-to-coffee-colored plume in the water. The second item of timing to note is that the action of the dispersant may continue for up to an hour after application. Third, the movement and dispersion of the plume are often slow, although the plume is generally visible for about 3 hours and is never visible for more than about 8 hours. Finally, the oil in the plume can resurface slowly over several days. Since the resurfaced oil is usually thinner than the visibility limits, this will not be noticed unless there is little differential movement between the slick and the dispersed plume. It is important to track and follow the undispersed oil, control slick, and dispersed plumes for as long as possible. The Beaufort Sea experiment is a good example. Three slicks were laid and two were left as controls.143 Two days later, three slicks were found at sea, and each had the same orientation and general geometry as on the first day of the experiment. The largest slick was the dispersed slick, although the oil content was not known. The interpretation of the results would have been quite different if the slick had not been followed for days.

15.5.7. Visual Surveillance Visual surveillance has been a standard tool for examining the effectiveness of dispersants. The primary indicators are the visual appearance of a yellow-tocoffee-colored plume in the water from dispersed oil. Indicators of poor effectiveness are the appearance of herding or dispersant-only plumes in the water (whitish).

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A very important tool for working with oil spills has been the relationship between the appearance and thickness of the oil. This relationship is occasionally used to estimate the amount of oil before or after dispersion. Present thickness charts actually date from 1930. Before this date, it was already recognized that slicks on water had consistent or nearly consistent appearances. A series of experiments conducted at that time resulted in charts that are still used today. Only a few experiments have been done in recent years. These appearance factors are very important because they provide the only means of estimating the amount of oil in thin sheens on the sea. There are no means for estimating the amount of thick slicks on the sea. The literature is in general agreement that the lower limit of oil visibility ranges from 0.03 to 1.6 mm with a typical average of 0.1 mm.144 Below this average of 0.1 mm, oil is simply not visible to the human eye, and light is transmitted through these thin slicks. Often oil spill observers presume that, if they do not see a slick or sheen, no oil is present. After spreading, which is enhanced by dispersant application, a significant portion of the oil can reside in the “invisible” sheen. This is another reason that surface measurements may be unreliable. These very thin sheens cannot be recovered, seen, or measured at this time.

15.5.8. Remote Sensing Although remote sensing can be useful to assess dispersant trials, some of the data can be misinterpreted. Careful use must be made of data, and recognition must be given to the physical basis of these data. Very importantly, it should be recognized that no capability exists to measure oil thickness using current airborne sensors. There was once a popular myth that infrared sensors could be used to “measure” oil thickness, but theory and tests have shown otherwise.117 Furthermore, attempts were made to use sorbent tests to “calibrate” infrared imagery, and this too was shown to be incorrect.117 The only thickness information available to the oil spill worker is the fact that the rainbow appearance has a thickness of between 0.15 and 0.8 mm, as described above.144 This occurs because of multipath interferences in visible light and is well understood on a physical basis. After the slick becomes thicker, the black/brown appearance has no thickness associated with it. Remote sensing is thought to be a necessary tool for measuring the extent of the surface slick and of the dispersed plume.145,146 While color photography with good time marks is essential, nadir-looking, spatially corrected color imagery is much better. Infrared (IR) photography can give a picture of the relative thickness, but can be misled by the presence of a dispersed oil plume. Infrared photography was to be the prime measure of effectiveness in the Beaufort Sea trials in 1986.143 A computer device had been built to directly yield areas of thick slicks (i.e., IR “hot” area). As the trial progressed, the area of the dispersed slick grew

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rapidly, far beyond that of the two control slicks. While the naive conclusion would be that dispersants were very ineffective and actually increased the amount of oil on the water surface, the actual fact was that the dispersant increased the area of oil on the surface and the dispersed plume was also hotter than the surrounding water due to the absorption of IR radiation. Several reviews of remote-sensing technology and of sensors can provide useful imagery.147,148

15.5.9. Tracking of Oil on Surface Because the long-term effectiveness of the dispersant should be understood as well as the short-term effectiveness (in terms of hours), sea trials should include an appropriate plan to track and sample the slick, the plume, and resurfaced oil. Technologies exist to monitor the surface slick.149-151 Tracking is essential to ensure that the geometries and positions of both the plume and surface slick are well established for the airborne and surface crews. Tracking resurfaced oil might be difficult because it is not highly visible. The use of GPS can now accurately locate the position of the entire surface track. GPS information can be noted at each sampling station, along with the exact GPS time. Software now exists to readily plot these points onto maps.

15.5.10. Tracking of Oil Underwater While technologies to track the underwater plume are not as well tested as those to track oil on the surface, success has been recorded using drogued buoys such as the Davis Drifter.126,145,146 Both the resurfaced oil and the plume should be tracked using remote sensing or drogue and sampling techniques. Tracking is essential to ensure that the geometries and positions of both the plume and the surface slick are well established for the airborne and surface crews. Sampling of the subsurface plume should be guided by airborne crews as well as by observing the plume and the drogued buoys in the plume. Misplaced sampling can result in underestimation of the dispersed amount or in large overestimations if the extent of the plume is overestimated. The dispersed oil plume spreads out over time and becomes increasingly more difficult to track. Eventually it becomes invisible to surface and aerial observation, at which time it can be tracked using fluorometer probes and drogued buoys. The buoys may require repositioning based on fluorometry information as noted in the SMART protocol.

15.5.11. Mass Balance The SMART protocol and other monitoring protocols do not purport to establish a percentage of effectiveness, nor should they. To achieve a percentage effectiveness, the experiment would have to establish a mass balance. Mass

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balance is very difficult to achieve in open field tests. In the 1993 North Sea dispersant trials, the dispersed oil in the water column measured shortly after the dispersant treatment accounted for only 1.8 to 3.5% of the initial volume of the oil released.39,40 Similarly, only 0.1 to 0.2% could be accounted for under the control slick, so the difference between the two was emphasized, for example, 16 to 27 times the amount of oil. Even in enclosed test tanks, it is very difficult to establish a mass balance. Several examples of this are given above.

15.5.12. Use of Undispersed Slick(s) as a Control In order to properly assess a dispersant field test, a proper control slick is needed. The control must be treated equally to the treated slick in every respect except for the application of dispersant. The SMART protocol suggests that the slick before treatment be used as the control. The importance of using a control slick can be illustrated by two field dispersant trials, treatment of emulsified oil from the Exxon Valdez and the Beaufort Sea Trial. Both were attended by the author of this chapter. In the Exxon Valdez test of dispersant application to an emulsified oil slick, two slicks were chosen in the Gulf of Alaska, south of Seward. One was left as a control, and the other was treated with large amounts of dispersant. Sampling was conducted from a ship and aircraft, some equipped with remote-sensing gear, from which the slicks were observed for about 6 hours. The dispersant failed to break the emulsion and did not disperse the oil. Coincidentally, the control slick broke up somewhat after about 5 hours. This was probably due to its great exposure to waves as it was up-sea of the treated slick. Without a control, the experimental results could be interpreted differently. Use of the same slick as a control as the target slick requires further analysis. The control would in this case act only to compare fluorometric readings on an initial basis. Some smaller slick should be left as a control for comparison over a longer term if the dispersant application is continued.

15.5.13. Background Levels of Hydrocarbons The background level of hydrocarbons is important for several reasons, some of which are mentioned above. A good background value is needed, first to subtract concentration values and second, to know when to terminate integration of the spill. The background of hydrocarbons in the sea varies widely.121,152-154 This is especially true in estuarine and riverine outputs into the sea. These areas are often the same areas that were used for dispersant experiments in the past and possibly also the areas where dispersant may be applied. It is suggested that the same techniques, along with the grab samples for calibration, be applied in the area before dispersant application as well as after, if practical, to determine the range of background values in the area. These values can then be judged for use in correcting the values and for ending integration.

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Another problem associated with background levels is that hydrocarbons will adhere to sample tubes and equipment. This will result in higher than background values at the end of a run through the plume. There is no easy solution to the problem. One of the solutions is to examine the values and look at where the signal drops off significantly, probably the end of the plume, and use this value as a “corrected” background. Some experimentation at the scene of the measurements can be used to define the carry-though of hydrocarbons in the system. It should be noted that if the carry-through is not corrected for, gross errors could occur in the amount of oil calculated.

15.5.14. Using and Computing Values SMART and its other counterparts are not intended to result in percentage effectiveness. Nevertheless, users will inevitably try to use these values to “calculate” effectiveness. This should not be done as it will lead to errors, as described above. Another issue is the use of a sampling protocol to determine effectiveness on a trial application. Though a useful concept, this author suggests that it is not practical. The cost of mounting a dispersant operation, be it a trial or not, is too great to stop it after it has begun. In a typical situation, it would not be acceptable to leave the slick with no countermeasures during decision time or to delay countermeasures for further decisions. It may be much more advantageous to obtain a sample of the oil and perform one of the quick bottle tests on the oil as it now is in the field. This would form a better decision point in view of the costs and acceptability of approach.

15.5.15. Recommended Procedures for Monitoring Dispersant Applications 15.5.15.1. Overall In view of what has already been covered in this chapter, it should be apparent that there are many nuances to monitoring the effectiveness of a dispersant application. Furthermore, use of a trial aerial application as a decision point for continuance is questionable in most circumstances. It is suggested that a field test be conducted on a small sample of the oil instead. The aerial application is too expensive to stop if it is not effective. The trial application should only proceed if there is reasonable certainty that the oil is dispersible. It should be noted that the procedures described in this section will not lead to a quantitative value of effectiveness. 15.5.15.2. Field Pretest It is recommended that a small pretest of effectiveness be carried out rather than relying on monitoring as described above. Several tests have been developed and

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are summarized in Table 15.9. The prime purpose of these tests is to screen the effectiveness before application proceeded. No test or agency recommending them, however, suggested any level of effectiveness (even relative) that should be achieved before full application proceeded. The advantage of a pretest is that, before application proceeds, a screen is carried out. This screening takes little logistics and does not interfere with other operations or organization for an actual dispersant application. If the screen test shows that there is potential for dispersant effectiveness, then planning for the next stage can proceed. The last row of Table 15.9 shows a recommended test.124 The concept behind this is that a very simple test would suffice. The procedure for this test is that a sample of the actual spilled oil and a sample of the water in the area are obtained. As soon as practical after the samples are obtained, about 1 L of the water sample is placed into a bottle with a narrower neck (to exaggerate the oil measurement) and filled to the start of the neck. A line is placed at the top of the water level to indicate where the oil would start. This can be done with an etching tool or a special marker. About 1 mL (about 5 drops) of the dispersant to be used is added to 10 mL of oil, is mixed briefly, and is then poured into the test vessel. A mark is placed at the top of the oil. The test vessel is vigorously shaken for 1 minute and left standing for 10 minutes, and a mark is placed at the top of the new oil level. The criterion suggested is that about half of the oil should be dispersed before proceeding with full-scale dispersant application. For information purposes, the oil and dispersant laboratory effectiveness result could be obtained and compared to this value.

15.5.15.3. Visual Surveillance It is suggested that visual surveillance is a prime method for determining whether or not the initial spray had any effect. The many factors noted in this report must be considered, and a good field guide is also needed. At least one experienced person should be employed for the visual surveillance to be effective. It is recommended that buoys be used to track the plume and the remaining slick. Davis Drifters can be used to track the plume, and spill tracker buoys can be used to track the remaining slick. Further visual surveillance on the slicks is necessary for at least one day. The visual surveillance requires documentation by photography. Good quality digital still pictures are the best. The color quality must be good in order to be able to distinguish between white (dispersant only) and yellow-to-brown (dispersed oil) plumes. All images require time-coding. 15.5.15.4. Measurements From the Surface The measurements from the surface, such as by using fluorometry, may provide little additional information over pretesting and visual surveillance. The basic question that any monitoring protocol should ask is: “Is there significant dispersant effectiveness or not?” Surface monitoring may result in confusing

498

TABLE 15.9 Field Tests for Screening Effectiveness Oil to Water Ratio

Dispersant Added (mL)

Dispersant to Oil

battery tester 2 cm d 21 cm long

50

1

1:50

0.2 premixed

1:20

156

1-litre flask

1000

10

1:100

1

1:10

US EPA test

157

test tube

5 cm in 1 cm tube (~6 mL)

10 drops (~2 mL)

~1:3

1 drop (~0.2 mL)

~1:10

Fina Spill Test Kit

158

graduated cylinder

100

2

1:50

0.1 0.2

1:10 1:20

Pelletier Screen Test

159

25 mL vial

20

0.1

1:200

0.05

2

Suggested

124

wine bottle or similar

~1000

~10 (50 drops)

~1:100

~1 (5 drops)

~1:10

Vessel

American Petroleum Institute

155

Environment Canada

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Oil Added (mL)

Reference

PART | VI

Water Capacity (mL)

Test Sponsor

Shake Time (min.)

Settling Time (min)

American Petroleum Institute

end over end 0.5/second

2

Environment Canada

rotated to 140 30 times

US EPA test

Measurement Description

Effectiveness Criteria

5

extraction by toluene colorimetric comparison

none

~15

3

visual measurement of oil height

none

vertical 2/second

1

10

height-O-ring movement light obscuration

none

Fina Spill Test Kit

shaking

0.17

0.5

colour comparison to scale provided

none

Pelletier Screen Test

vortex with magnetic stirrer 2000 rpm

1

1

visual estimation

none

Suggested

stopper flask and shake

1

10

height of lines on bottles top, bottom, after settling of oil

>~40% of oil gone (about half)

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information that is not directly relevant to this question. Therefore, from a strictly monitoring point of view, surface monitoring is not recommended as a prime method. For scientific and documentation purposes, however, it is suggested that water column sampling would be extremely useful. Good quality data from surface monitoring could be very useful for future purposes. For this purpose, the protocols as proposed for SERVS are recommended.130 These field procedures and the accompanying lab procedures require updating.

15.6. PHYSICAL STUDIES 15.6.1. Energy Traditionally, the effectiveness of a dispersant was viewed as simply a result of interfacial phenomena, that is, the lowering of the surface tension of the oil by the use of surfactants.160 It is now apparent that many factors influence the effectiveness of dispersants, the most important of which are the basic physics of dispersion, sea energy, the composition of the oil, the type of dispersant and the amount applied, temperature, and salinity of the water.8,30,162 Given a certain type of oil and salinity, the important considerations are the sea energy and the amount of dispersant. In the past, some experiments focused on determining the relationship between energy and dispersant effectiveness. An energy-dispersant amount diagram for Alberta Sweet Mixed Blend, a common oil in North America, is shown in Figure 15.11. The diagram is based on older experimental data.161 Energy is indicated by the rotational rate

% Dispersion 100

100

80

80

60

60

40

40

20 0 400 350 300 250 200 150 100 Relative Energy

20 0 1/12.5 1/25

50

0

0

1/50 1/100 1/200 Dispersant to Oil Ratio 1/400

FIGURE 15.11 Typical relationship between energy, amount of dispersant, and the effectiveness of dispersion.

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of the shaker unit, which shows that there is a predictable relationship between the three factors of effectiveness, energy, and dispersant quantity. While the figure shows that energy is a very important factor, the same dispersant effectiveness can be achieved at several different energy-dispersant combinations. This older work did not quantify energy. New work shows more specifically what the energy is and at what scales it operates in the test vessel or the sea.

15.6.1.1. Theoretical Basis An important aspect of oil spill processes is the energy applied to the oil on the water surface. As it turns out, sea energy is a very important part of the amount of dispersion. The energy and work applied are known to affect the kinetics of water-in-oil emulsion.77 Furthermore, it has been found that energy is critical in understanding the effectiveness of chemical dispersion of oil.162 The kinetic energy and turbulence in small laboratory apparatuses have recently been studied because turbulent energy is felt to be the most important form of energy related to emulsion formation and dispersion. Turbulence is the fluctuation of velocity.163 If a velocity assemblage is viewed, the description of the overall velocity is given as: U ¼ U þ u0

(6)

where U is the overall velocity component, U is the average or constant velocity, and u0 is the fluctuating component or turbulence The intensity of turbulence is given by: I ¼ ðu2 þ v2 þ w2 Þ1=2

(7)

where I is the turbulence intensity and, u, v, and w are the average turbulence in the x, y, and z directions. The turbulent kinetic energy can be given by: k ¼ 1=2m ðu2 þ v2 þ w2 Þ

(8)

where k is the kinetic energy, m is the mass, typically one unit in the Standard International Units (SIU) system, u, v, and w are the average turbulent velocity components in the x, y, and z directions, and Average turbulence is the average standard deviation in velocity. Turbulence in natural systems decays as the force that initiated is no longer applied. Kolmogorov developed the classic decay law:164 u2 wt10=7 where u is the turbulence in a particular vector, and t is time.

(9)

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The work imparted by wind can be estimated as:165 W ¼ rair gV 2 =2p

(10)

where W is the work rair is the density of the air, g is gravity, and V) is wind stress, the actual impact of wind on a surface. The energy dissipation rate per unit surface area can be estimated from:165 3 ¼ 15 V 3

(11)

where 3 is the dissipation rate and, V is the wind speed. Mellor describes the relationship between wave energy and amplitude as:166 E ¼ g a2 =2

(12)

where E is the wave energy per unit area, g is the gravitational constant, and a is the wave amplitude. A classic method of presenting energy and turbulence energy and decay is in the form of a power density spectrum or density function.167 In this type of presentation, the energy or dissipation of energy is presented versus a logarithm of wave number. The classic decrease in energy with wave number is 5/3. This is said to be a “natural” decay or energy distribution as it is found in many natural systems. The turbulent and kinetic energies of several natural systems have been measured. Horne et al. measured the turbulent energy and dissipation rates of five sites on Georges Bank using a shear probe array.168 The vertically integrated dissipation or energy production was measured as ranging from 0.007 to 0.91 W/m2. The average measured dissipation rate ranged from 2.3 to 46 X 107 W/kg seawater. Lough and Mountain also measured turbulence on Georges Bank.169 Dissipation rates were 1011 to 107 W kg1 but at the surface was 1 to 2 orders of magnitude higher. Sterling et al. report that energy dissipation in an estuary ranges from 101 to 100 W/m3 and for open water, 100 to 101W/m3.170 Barbarosa and Metais describe ocean energies, noting that storms can initiate strong downward velocities of up to 10 cm s1 and surface energy losses as great as 1,000 W/m2.171 Maar et al. measured the turbulence in cruises off Denmark and Greece and found that there was a large range in turbulent diffusion (0.2 to 250 cm2 s1).172 Stocker and Imberger measured the horizontal transport and dispersion on a lake, Kinneret, in Israel, finding that the mean horizontal diffusion dispersion coefficient was 17.1 m2 s1 while the vertical dispersion was negligible.173

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15.6.1.2. Velocity, Energy, and Work Measurement Thermal Anemometry Thermal anemometers can be used to measure fluid velocity by sensing the changes in heat transfer from a small, electrically heated element immersed in the fluid.174 In one form of the instrument, the constant temperature anemometer, the cooling effect produced by fluid flowing over the element is balanced by the electrical current to the element. The change in current is measured as voltage change and forms the anemometer output. The anemometer output is typically coupled to a computer where that data can be collected and analyzed. An important feature of thermal anemometers is the ability to measure very rapid changes in velocity. Frequency changes up to 30,000 Hz or fluctuations as short as 30 microseconds can be measured. This high frequency is accomplished by coupling very fine sensing elements such as a wire of 4 to 6 micrometers in diameter or a platinum thin-film deposited on a quartz substrate. This size also makes the probe less obtrusive than older design probes. The probe will still interfere with fluid flow. Several side effects have been classified and procedures developed to deal with these side effects.175 Particle Image Velocimetry In PIV, the flow as marked by micron-sized seeder particles is illuminated by a light sheet.176 Two images of each particle are recorded in a short time interval. Processing the two images yields a local velocity vector by tracking individual particles. As the time interval is small compared to the flow timescales, PIV can deliver instantaneous velocity maps in a plane. Illumination is typically accomplished using a laser. Common lasers are Nd:YAG and argon ion lasers. The laser can easily produce a very thin light sheet, thus avoiding problems of multiple targets. A light sheet is created using a cylindrical lens or series of lenses. Most modern systems use Charge-coupled Device (CCD) cameras gated to the pulse repetition frequency of the laser. The advantage of using PIV for the applications in oil spill is that surface seeding can be employed. This then results in data relevant only to the surface of the water, such as would be true for an oil slick. Furthermore, this type of data can result in a depiction of surface flow, an important first step in studying the energy of a particular apparatus. Laser Doppler Anemometry The laser doppler anemometer uses laser transmitters and receivers to interrogate a small volume of water or air.177 The transmitter produces periodic short laser pulses. Ambient scatterers such as bubbles or seeding material scatter a portion of the laser light. The receiver detects these scattered laser pulses. The frequency of the returned laser pulses is doppler shifted by the

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speed of the scattering particles. The return signals can then be demodulated to calculate speeds within the defined sample volume. Acoustic Doppler Velocimetry The acoustic doppler velocimeter uses focuses on acoustic transmitters and receivers to interrogate a small volume of water.178 The transmitter produces periodic short acoustic pulses. Ambient scatterers such as bubbles or seeding material scatter a portion of the acoustic energy. The receiver detects these scattered acoustic pulses in the defined sample volume. The frequency of the returned acoustic pulses is doppler shifted by the velocity of the scattering particles. The return acoustic signals can then be used to calculate velocities within the defined sample volume. Calculation by Energy Imparted Brocart et al. developed a method for online water-in-oil emulsification. The route to energy calculation is typical:179 The velocity can be split into two parts: the constant, slow changing component and turbulence: U ¼ Ua þ u0

(13)

where U is the total velocity, Ua is the average or slow-moving velocity component, and u0 is the rapidly changing component or turbulence. The power density (z) is the primary energy term that can be used to calculate droplet size and other mixing parameters. Brocart et al. calculated the power density of a propeller mixing system as:179 z ¼

kinetic energy rV 3 ¼ 4c application time

(14)

where 3 is the power density for a rotor-stator mixer, r is the fluid density, V is the tip velocity of the rotor, and c is the number of teeth on the rotor. Bocart et al. (2002) noted that the power density for this rotor-stator system ranged between 108 and 1010 W/m3, which is higher than other calculation methods. Fingas and coworkers calculated the energy and work of a rotating emulsification apparatus where the motion is end over end. The total kinetic energy in each bottle was given by:180 KE ¼ 1=2MV2 where KE is the total energy in ergs,

(15)

Chapter | 15 Oil Spill Dispersants: A Technical Summary

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M is the mass being agitated in grams, here approximately 620 g of water and oil, and V is the velocity in cm/s, which is 2prdwhich is rpm/60 7.5 cm. Kinetic energy by this formula is then 196 x rpm2 ergs. Ergs were used in this study because they are a much more convenient unit than the SIU Joules at these low energy levels. This simple formulation was used to assign an energy level to each rotational velocity. Again, it is important to note that the energy estimated here was the total energy input to the system, and not turbulent energy, which may be the prime factor in emulsion formation. Work can be defined by looking at the force applied to the system by gravity. Since F ¼ ma (16) where F ¼ force applied to the system in Newtons, m ¼ mass, which here is 0.62 kg, a ¼ the acceleration due to gravity, which is 9.8 m/s2, and thus F ¼ 6.08 Newtons. Work ¼ F D

(17)

where F ¼ the force in Newtons ¼ 6.08, D ¼ distance through which the force moves, which here is the average height through which the water falls, which is 15/2 cm or 0.075 m. Thus, work is 6.08 0.075 J per revolution of the apparatus, or 0.456 J per revolution of the apparatus. Saiz and coworkers calculated the energy dissipation rate in a grid-stirred laboratory vessel as:181 3 ¼ 8:5 104 freq3:003 (18) where 3 is the dissipation rate in cm2 s0.3 and freq is the frequency of the grid stroke. Camp and Stein developed a relationship to calculate energy imparted to a system with paddles as:182 W ¼ C A V 2 =2

(19)

where W is the work, C is the drag coefficient, A is the area of the paddle normal to the movement, and V is the velocity of the paddles with respect to the liquid. The levels of turbulence, velocity, and other factors for the swirling flask, septum flask, a variant of the swirling flask without the spout, and a 300-L tank with a central stirring propeller can be calculated. Testing shows that the velocity, energy, and other similar factors increase as the rpm is increased; however, there is some noise. This noise is caused by the change in flow patterns with changing velocity and with random motions in the vessels as well as small-scale turbulence. Calculation shows that the energy dissipation varies in a nonlinear manner with rotational speed in the swirling flask, but closer to

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linear for the 300 L tank. Placement of the probe affects the results. Comparison of placing the probe at the funnel location compared to opposite the funnel location in the swirling flask does change the values. The energy rises faster with increasing rpm at the location directly by the funnel or spout location as there is an increased flow at this location. Fingas carried out dual or cross-wire probe measurements in three vessels.77-79 This probe simultaneously measures the velocity and turbulence components in the U direction, in this case the horizontal component, and in the V direction, in this case in the vertical or vessel depth component. The U and V data were mathematically processed to provide an equation so that a smoothed value was used at the rotational speeds at which the vessels are normally operated. The flow patterns change as the vessel rotation speed changes, and the probes may no longer be directly facing the flow origin as they were at low speeds. In the large 300-L tank, a downward flow developed further with increased rpm, and thus the flow pattern changed significantly. The U component (horizontal flow) is very high compared to the V component in the swirling flask compared to the V componentdin fact, about 40 times higher as an average in some vessels. This indicates that vertical mixing is much lower in the swirling flask than in the large tank. Thus, simply stated, the turbulence energy in the horizontal direction is about 5 times the energy in the vertical direction. Several laboratory vessels were examined on the basis of the energy and work calculations. These data are shown in Table 15.10 along with the experimental values noted above. The work and energy associated with agitation are calculated using the calculation equations noted. It is interesting to note that once the work and energy are adjusted for the volume of water present in the vessel, the energy level and work input are similar and within about an order of magnitude of one another. The energy and work input are also similar to that noted for wind and wave equivalents. All the same, caution should be noted in making this comparison, as all the calculated values are based on simple assumptions. Table 15.10 shows that all of the laboratory apparatuses studied have energy levels similar to those of low-wind and sea states. Furthermore, Table 15.10 shows that there are variances in energy levels, but that the agreement between experimental methods and calculated energy levels is generally within an order of magnitude. A comparison of the calculated energies between the vessels and waves shows that overall energies in some of the apparatuses are similar to those encountered at sea but that some vessels have very high energies, much greater than at sea.

15.6.2. Composition of Oil In the distant past, viscosity was thought to be the only quality of an oil that influenced the effectiveness of a dispersant.183 It soon became apparent, however, that the chemical constituents of oil had a major influence on the effectiveness of dispersants. Later, studies correlating effectiveness and oil

TABLE 15.10 Work, Energy, and Turbulence Levels of Various Laboratory Apparatuses Compared to Calculated Sea Levels Adjusted for Volume /L

Experimental Dual Probe

Near Surface Energy by Single Thermal Probe

Energy Application

Typical rpm Setting rpm

Calculation Calculated Equation Water Work from Volume Input this mL Joules/min Paper

Calculated Calculated Work Energy Input Level Joules/ Joules Lmin

Calculated Energy Level Joules/L

Energy Dissipation (U and V sum)

PIV Data Turbulence Total Turbulence Turbulence % Joules % %

Apparatus

Use

Swirling Flask

Dispersant screening

moving table

150

120

0.35

10, 12

3.50E-05

2.9

2.92E-04

6.50E-04

360

1.40E- 105 04

Septum Flask

Dispersant screening

moving table

150

120

0.17

10,12

3.50E-05

1.4

2.92E-04

5.67E-04

781

1.00E- 70 04

Standard Beaker

Various

moving table

150

400

1.2

10,12

1.20E-04

3

3.00E-04

Emulsion Unit

Emulsion formation

end-over-end rotation

50

600

22.8

10, 12

0.05

38

8.33E-02

Labofina Unit

Dispersant screening

end-over-end rotation

50

250

4.56

10, 12

0.01

18.2

4.00E-02

High Energy Unit

Dispersant physics

moving table

100e250 150 average

5000

294

10, 12

8.22E-03

58.8

1.64E-03

Tank

Large-scale testing

agitator

120 240 480

300000 33 300000 66 300000 132

14 14 14

0.28 1.1 4.5

0.11 0.22 0.44

9.33E-04 3.67E-03 1.50E-02

90

8.22E-03 1.23E-02 1.77E-02

376 467 322

90

80

5.90E- 40 02 5 2.00E- 38 01 6.00E01

(Continued )

TABLE 15.10 Work, Energy, and Turbulence Levels of Various Laboratory Apparatuses Compared to Calculated Sea Levelsdcont’d Adjusted for Volume /L

Apparatus

Use

Tank

Large-scale testing

Energy Application

Typical rpm Setting rpm

Calculation Calculated Equation Water Work from Volume Input this mL Joules/min Paper

Calculated Calculated Work Energy Input Level Joules/ Joules Lmin

Calculated Energy Level Joules/L

oscillating hoop

60 strokes

300000 18

0.9

3.00E-03

13

0.06

The following are approximations based on estimation equations given in reference 77-79 Wind Equivalent

comparison

over cubic metre of water

5 m/s 10 m/s 20 m/s 30 m/s 40 m/s

1000 1000 1000 1000 1000

1.2 4.9 20 44 78

5 5 5 5 5

0 0 0 0 0.01

1.2 4.9 20 44 78

1.00E-04 2.00E-04 4.00E-04 1.60E-03 6.40E-03

Wave Height Equivalent

comparison

per metres wave height

0.5 1 2 3 4

1000 1000 1000 1000 1000

1.2 4.9 20 44 78

7 7 7 7 7

0 0 0 0 0.01

1.2 4.9 20 44 78

1.00E-04 2.00E-04 4.00E-04 1.60E-03 6.40E-03

Experimental Dual Probe

Energy Dissipation (U and V sum)

Near Surface Energy by Single Thermal Probe

PIV Data Turbulence Total Turbulence Turbulence % Joules % %

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Chapter | 15 Oil Spill Dispersants: A Technical Summary

1.0

Regression Coefficient

0.8

disperses

0.6 0.4 0.2 0.0 -0.2

hinders dispersion

-0.4 -0.6 -0.8

12

14

16

18

20

22

24

26

Carbon Number FIGURE 15.12 Diagram of the effectiveness values for specific hydrocarbons.

composition revealed that the most important factor was the amount of saturates in the oil.184 It was also found that the effectiveness of dispersants decreases with increasing amounts of resins and asphaltenes in the oil. Furthermore, it was found that effectiveness could be predicted using a simple model of saturates, less the other components of the oil, including resins, asphaltenes, and aromatics. In the past decade more precise laboratory effectiveness data and compositional data have enabled correlation with higher precision. Thirteen models for the prediction of chemical dispersibility have been developed.185,186 The models range widely in terms of input parameters and statistical quality. These models can be used to predict the chemical dispersibility of oils, given the required input parameters. Table 15.11 shows the models, statistics on the fits, and parameters used to predict effectiveness. The development of these models reveals essentials of chemical dispersion.185 The results show that small n-alkanes are prone to dispersion and that this ends at about C20. Hydrocarbons as large as C26 resist dispersion, as is illustrated in Figure 15.12 in which the regression coefficients (R2) or the correlation with effectiveness are plotted against the n-alkane carbon number. It can be seen that there is a steady progression downward beginning at C12 and crossing 0 at about the C20 carbon number. The aromatic component may show a similar tendency, but sufficient data were not available to provide details. The naphthalene component showed a high regression coefficient (R2 ¼ 0.76), and the total PAHs were relatively high (R2 ¼ 0.67). This indicates that the larger PAHs are relatively indispersible and that the smaller ones (naphthalenes) are highly dispersible.

510

TABLE 15.11 Models to Predict Effectiveness Using Oil Co