Water Research Volume 45 Issue 9

WATER RESEARCH A Journal of the International Water Association

Editor-in-Chief Mark van Loosdrecht Delft University of Technology Department of Biochemical Engineering Julianalaan 67 2628 BC Delft The Netherlands Tel: +31 15 27 81618 E-mail:[email protected]

Editors J. Block Université H. Poincaré, Nancy I France David Dixon University of Melbourne Australia Hiroaki Furumai The University of Tokyo Japan Xiaodi Hao Beijing University of Civil Engineering and Architecture China Gregory Korshin University of Washington USA Anna Ledin Formas Sweden Eberhard Morgenroth Swiss Federal Institute of Aquatic Science and Technology (EAWAG) Switzerland W. Rauch University Innsbruck Austria Maria Reis Universidade Nova de Lisboa/FCT Portugal Hang-Shik Shin Korea Advanced Institute of Science and Technology Korea Thomas Ternes Bundesanstalt für Gewässerkunde Germany Stefan Wuertz Univ. of California, Davis USA

Associate Editors Andrew Baker University of New South Wales Australia

Damien Batstone The University of Queensland Australia G-H. Chen The Hong Kong University of Science & Technology Hong Kong China Tom Curtis Univ. of Newcastle upon Tyne UK Ana Deletic Monash University USA Francis de los Reyes III North Carolina State University USA Rob Eldridge The University of Melbourne Australia Rosina Girones University of Barcelona Spain Stephen Gray Victoria University Australia Kate Grudpan Chiang Mai University Thailand E.E. Herricks University of Illinois - Urbana USA Peter Hillis United Utilities Plc UK H-Y. Hu Tsinghua University China P.M. Huck University of Waterloo Canada Bruce Jefferson Cranfield University UK Ulf Jeppsson Lund University Sweden Sergey Kalyuzhnyi Moscow State University Russian Federation Jaehong Kim Georgia Institute of Technology USA Jes La Cour Jansen Lund Institute of Technology Sweden G. Langergraber BOKU/Univ. of Natural Res. and Applied Life Scs. Austria S-L. Lo National Taiwan University Taiwan Dionisis Mantzavinos Technical University of Crete Greece

Y. Matsui Hokkaido University Japan A. Maul Université Paul Verlaine-Metz France Max Maurer EAWAG Switzerland How Yong Ng National University of Singapore Singapore Satoshi Okabe Hokkaido University Japan S.L. Ong National University of Singapore Singapore Jong M. Park Pohang University of Science & Technology Korea Susan Richardson U.S. Environmental Protection Agency USA Miguel Salgot University of Barcelona Spain David Sedlak University of California, Berkeley USA Jean-Philippe Steyer LBE-INRA France M. Takahashi Hokkaido University Japan Kai Udert EAWAG Switzerland V.P. Venugopalan BARC Facilities India E. von Sperling Federal University of Minas Gerais Brazil Hanqing Yu University of Science & Technology of China China J. Zilles University of Illinois Urbana USA A.I. Zouboulis Aristotle University Greece

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Review

Natural and enhanced anaerobic degradation of 1,1,1-trichloroethane and its degradation products in the subsurface e A critical review Charlotte Scheutz a,1, Neal D. Durant b,*, Maria H. Hansen a,2, Poul L. Bjerg a,3 a

Department of Environmental Engineering, Technical University of Denmark, Bygningstorvet e Building 115, DK-2800 Kgs. Lyngby, Denmark b Geosyntec Consultants, 10220 Old Columbia Rd., Suite A, Columbia, MD 21046, USA

article info

abstract

Article history:

1,1,1-Trichloroethane (TCA) in groundwater is susceptible to a variety of natural degra-

Received 7 November 2010

dation mechanisms. Evidence of intrinsic decay of TCA in aquifers is commonly observed;

Received in revised form

however, TCA remains a persistent pollutant at many sites and some of the daughter

3 February 2011

products that accumulate from intrinsic decay of TCA have been determined to be more

Accepted 23 February 2011

toxic than the parent compound. Research advances from the past decade indicate that in

Available online 2 March 2011

situ enhanced reductive dechlorination (ERD) offers promise as a cost-effective solution toward the cleanup of groundwater contaminated with TCA and its transformation

Keywords:

daughter products. Laboratory studies have demonstrated that pure or mixed cultures

Dehalobacter

containing certain Dehalobacter (Dhb) bacteria can catalyze respiratory dechlorination of

Bioremediation

TCA and 1,1-dichloroethane (1,1-DCA) to monochloroethane (CA) in groundwater systems.

Bioaugmentation

16S rRNA Dhb gene probes have been used as biomarkers in groundwater samples to both

Chloroethane

assess ERD potential and quantify growth of Dhb in ERD applications at TCA sites. Labo-

Biomarker

ratory findings suggest that iron-bearing minerals and methanogenic bacteria that cooccur in reduced aquifers may synergistically affect dechlorination of TCA. Despite these advances, a number of significant challenges remain, including an inability of any known cultures to completely dechlorinate TCA to ethane. CA is commonly observed as a terminal product of the biological reductive dechlorination of TCA and 1,1-DCA. Also important is the lack of rigorous field studies demonstrating the utility of bioaugmentation with Dhb cultures for remediation of TCA in the field. In this paper we review the state-of-thescience of TCA degradation in aquifers, examining results from both laboratory experiments and twenty-two field case studies, focusing on the capabilities and limits of ERD technology, and identifying aspects of the technology that warrant further development. ª 2011 Elsevier Ltd. All rights reserved.

* Corresponding author. Tel.: þ1 410 381 4333; fax: þ1 410 381 4499. E-mail addresses: [email protected] (C. Scheutz), [email protected] (N.D. Durant), [email protected] (M.H. Hansen), [email protected]. dk (P.L. Bjerg). 1 Tel.: þ45 4525 1607; fax: þ45 4593 2850. 2 Now at: Sortemosevej 2, 3450 Allerød, Denmark. Tel.: þ45 4810 4200; fax: þ45 4810 4300. 3 Tel.: þ45 4525 1615; fax: þ45 4593 2850. 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.02.027

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Contents 1. 2.

3.

4.

5.

1.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Primary transformation mechanisms and processes for TCA and its daughter products in aquifers . . . . . . . . . . . . . . 2.1. Abiotic degradation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.1. Hydrolysis and dehydrohalogenation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.2. Metal-catalyzed reduction by naturally occurring reductants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2. Biological anaerobic reductive dechlorination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1. Chloroethanes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.2. Chloroethenes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3. Inhibitory substrate interactions in systems containing mixed TCA and CAH co-contaminants . . . . . . . . . . . . . 2.4. Biological oxidation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.1. Direct aerobic oxidation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.2. Anaerobic oxidation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Novel tools for assessing TCA biodegradation and ERD performance in the field . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Molecular biological tools (MBTs) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Compound-specific isotope analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Field experience with natural and enhanced anaerobic dechlorination of TCA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. Natural attenuation trends at TCA sites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.1. General trends in groundwater chemistry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.2. Use of molecular monitoring to screen biodegradation capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.3. Use of microcosm studies to screen intrinsic biodegradation capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. General experience with ERD of TCA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.1. Treatment zone characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.2. Microcosm studies as an ERD design tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.3. ERD layout and injection design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.4. Electron donors and pH buffers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.5. Bioaugmentation and molecular monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.6. Overall treatment performance in the field . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions and research needs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1. Field demonstration/validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2. Occurrence, roles, and bioaugmentation of Dehalobacter in TCA ERD systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3. Significance and fate of CA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Introduction

1,1,1-Trichloroethane (TCA) is a common groundwater contaminant that has been detected at numerous industrial facilities and waste disposal sites, including 23% of the active sites listed on the U.S. Environmental Protection Agency (USEPA) National Priorities List (search of USEPA database November 2010). Some physicochemical properties and characteristics of TCA are summarized in Table 1. TCA is denser than water (specific gravity of 1.34) and when it escapes to the subsurface as a pure phase or mixed with other chlorinated solvents it typically forms a dense nonaqueous phase liquid (DNAPL) that can migrate through an aquifer vertically by force of gravity. The dissolution of TCA DNAPL in the subsurface is a slow process, as the aqueous solubility of TCA is relatively low (1135 mg/L). The USEPA maximum contaminant level (MCL) for TCA (200 mg/L) is more than three orders of magnitude less than its solubility. Consequently, TCA DNAPL at many sites may act as a slowly dissolving in situ source of TCA that can impact

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groundwater supplies for decades. TCA is slightly hydrophobic (Log KOW ¼ 2.48) and can sorb to organic matter in the subsurface, but in general can be highly mobile when dissolved in groundwater. TCA is also volatile (KH ¼ 1.32102 atm-m3/mol) and vapors from residual TCA DNAPL above the water table may impact soil gas as well as groundwater. TCA is susceptible to a variety of natural abiotic and biotic transformations; however, the rate of natural transformation is often insufficient to prevent its migration in groundwater, particularly at sites where TCA is present as a DNAPL. Common TCA daughter products including 1,1-dichloroethane (1,1-DCA), 1,1-dichloroethene (1,1-DCE), vinyl chloride (VC), and monochloroethane (CA) all have aqueous solubilities that far exceed that of TCA and corresponding drinking water criteria that are typically lower than that for TCA (Table 1). VC is a known human carcinogen, and USEPA has classified 1,1-DCA as a possible human carcinogen and 1,1-DCE as possessing potentially carcinogenic characteristics (Table 1). All of these daughter products are susceptible to a variety of degradation processes,

Table 1 e Properties, characteristics, and drinking water criteria for 1,1,1-TCA, PCE, TCE, and their respective transformation daughter products. Property/ characteristic a

Regulatory jurisdiction

Chloroethanes 1,1,1-TCA

1,1-DCA

Chloroethenes CA

PCE

Reference

TCE

cDCE

1,1-DCE

VC

e e e e U.S.

1.339 1135 2.48 0.0132 393

1.176 4962 1.79 0.0043 326

0.898 5740 1.43 0.0093 630

1.623 151 2.88 0.0131 494

1.464 1198 2.42 0.0071 634

1.284 3500 1.86 0.0029 225

1.218 2250 2.02 0.0206 182

0.911 2792 0.6 0.0217 518

Noted below Noted below Noted below Gossett, 1987 USEPA, 2010a

Example drinking water and/or groundwater cleanup criteriaf

U.S. Denmarke California Louisiana Maryland New York Oregon Pennsylvania Wisconsin

200 1 200 200 200 5 N/A N/A 200

N/A SCAHs < 1 5 81 90 5 N/A N/A 850

N/A SCAHs < 1 N/A 10 3.6 5 N/A N/A 400

5 1 5 5 5 5 0.8 0.69 5

5 1 5 5 5 5 2.7 2.5 5

70 1 6 70 70 5 0.033 N/A 70

7 1 6 7 7 5 0.033 33 7

2 0.2 0.5 2 2 2 2 0.025 0.2

USEPA, 2010b Danish EPA, 2010 CDPH, 2008 LDEQ, 2003 MDE, 2008 NYSDEC, 2010 ODEQ, 2010 PADEP, 2010 WDNR, 2009

Cancer class/groupg

USEPA & IARC

D

C

Group 3

Group 2A

B2

D

S

H

USEPA, 2010c; IARC, 2010



a 20 C, values from Montgomery (1991) except cDCE and VC from USEPA (1992). b mg/L, 25  C, values from Schwarzenbach et al. (1993), except 1,1-DCE is from USEPA (1986) and CA is for 20  C and is from USEPA (1992). c KOW ¼ octanolewater partition coefficient at 25  C, (mol L1 octanol)/(mol L1 water), values from Schwarzenbach et al. (1993), except cDCE is from USEPA (1994), 1,1-DCE is from Verschueren (1996), and CA is from USEPA (1992). d KH ¼ Henry’s Law constant, 20  C, atm m3 mol1. e CAHs ¼ chlorinated aliphatic hydrocarbons. f mg/L. g B2 ¼ Probable human carcinogen, sufficient evidence in animals and inadequate or no evidence in humans (USEPA Cancer Group); C ¼ possible human carcinogen (USEPA Cancer Group); D ¼ Not classifiable as to human carcinogenicity (USEPA Cancer Group); Group 2A ¼ Probable human carcinogen (IARC Cancer Classification); Group 3 ¼ Not classifiable as to human carcinogenicity (IARC Cancer Classification); H ¼ carcinogenic to humans (USEPA Cancer Classification); S ¼ Suggestive evidence of carcinogenic potential (USEPA Cancer Classification).

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 7 0 1 e2 7 2 3

Specific gravity Solubilityb Log KOWc KHd Superfund sites detected

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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 7 0 1 e2 7 2 3

but degradation rates vary widely and in general these products can be equally or more persistent in groundwater than TCA. Biodegradation of TCA may also be subject to rate limitations by substrate interactions. Laboratory data indicate that at sites where TCA has been co-disposed with other common chlorinated solvents such as tetrachloroethene (PCE) and trichloroethene (TCE), the rate of chloroethane and chloroethene dechlorination can be limited by inhibitory substrate interactions depending on the system microbiology (Duhamel et al., 2002; Grostern and Edwards, 2006; Grostern et al., 2009). Biological enhanced reductive dechlorination (ERD) is an in situ remediation technology that offers promise for cleanup of TCA sites. ERD is a proven and cost-effective technology for in situ treatment of chloroethenes in groundwater under certain aquifer conditions (McCarty, 1997a; USEPA, 2000; Major et al., 2002; AFCEE, 2004; Loeffler and Edwards, 2006; Scheutz et al., 2008). ERD involves subsurface injection of fermentable organic substrates and, in some cases, dehalorespiring bacteria and other amendments (e.g., pH buffer) to promote sequential dechlorination of chlorinated constituents to innocuous end products. Fermentation of these organic substrates yields dissolved hydrogen, which serves as the electron donor that provides reducing equivalents that catalyze reductive transformations. A variety of electron donors have been used in ERD applications, including low-solubility (“slow-release”) donors such as emulsified soybean oil (ESO), polylactate esters, and chitin (shredded crustacean parts), and soluble donors such as alcohols, volatile fatty acids, whey, and sugars. ERD systems may use passive or active designs; the former typically involve low-solubility electron donors that are batch injected and dissolve slowly, while the latter typically involve soluble electron donors that are injected via forced gradient (pumping). Much of the ERD technology development to date has focused on elucidating the biochemistry and microbiology of chloroethene dechlorination, as PCE and TCE in groundwater are considered to be a higher priority due to their higher incidence of detection and higher toxicity relative to TCA (see Table 1). Understanding of the capabilities of ERD for treating TCA has improved through advances in ERD research for chloroethenes, but the state-of-the-science with respect to TCA is less mature and faces a number of challenges. While pure and mixed cultures that respire TCA have been developed (Sun et al., 2002; Grostern and Edwards, 2006), rigorous field-scale demonstrations of bioaugmentation for treatment of TCA have not been reported and the general need for bioaugmentation in ERD applications at TCA sites remains uncertain. The occurrence and distribution of chloroethane-respiring Dhb in subsurface is not yet known. It is encouraging that 16S rRNA biomarkers for the detection of certain TCA dehalorespiring Dhb strains have been developed (Grostern and Edwards, 2006); however, the utility and performance of these biomarkers in the field has not been reported in the peer-reviewed literature. Perhaps of greater significance is the fact that, for the TCA dehalorespiring cultures reported to date, CA has been observed to be a terminal product of TCA dechlorination (de Best et al., 1999; Adamson and Parkin, 2000; Sun et al., 2002; Grostern and Edwards, 2006). The USEPA has not issued a drinking water criterion for CA; however, it is a regulated drinking water contaminant in certain U.S. States and European countries (see Table 1) and may pose a human health concern.

The fate of TCA in aquifers has been addressed in prior review papers (e.g., Vogel and McCarty, 1987a; Vogel, 1994; McCarty, 1997b); however, significant research advances have occurred since those papers were written, and an updated review is warranted. Herein we review the state-of-the-science regarding the degradation of TCA and its transformation daughter products in natural and ERD aquifer systems. Recent advances in the understanding of TCA dechlorination microbiology are emphasized, including characteristics of TCA dehalorespiring cultures, substrate interactions in mixed TCA and chloroethene systems, biomarkers for assessing ERD performance at TCA sites, and compound-specific isotopic tools for quantifying biotransformation. Findings from twenty-two ERD TCA case studies are reviewed to summarize observations regarding the fate of TCA and its transformation products in natural and engineered bioremediation systems. This paper provides a state-of-the-science review of the fate processes governing TCA in groundwater, and the capabilities and limits of current ERD technology for in situ treatment of TCA.

2. Primary transformation mechanisms and processes for TCA and its daughter products in aquifers This section presents a review of TCA transformation processes, focusing on natural and engineered anoxic decay mechanisms. Direct aerobic oxidative transformations are also reviewed, recognizing that natural aerobic biooxidation can be an important degradation mechanism where dissolved oxygen is present. While TCA and its reductive dechlorination daughter products are also susceptible to transformation via aerobic cometabolism in systems where dissolved oxygen and primary substrates (e.g., methane) are present, such as engineered cometabolic bioremediation systems, aerobic cometabolism is not reviewed in this paper. Individual pathways for the primary abiotic and biotic anoxic transformation mechanisms in aquifers are illustrated in Fig. 1. The broad range of degradation mechanisms for TCA is further integrated into Fig. 2, which illustrates the collective suite of pathways and transformation products that are possible for TCA in aquifers.

2.1.

Abiotic degradation

2.1.1.

Hydrolysis and dehydrohalogenation

TCA in water degrades abiotically by hydrolysis and dehydrohalogenation (elimination), reactions that occur under both oxic and anoxic conditions. During hydrolysis of TCA, a chlorine substituent is replaced with a hydroxyl group to form a chlorinated alcohol that is subsequently hydrolyzed to acetate (Fig. 1). Data indicate that TCA only undergoes neutral hydrolysis (Jeffers et al., 1989) and, as such, the rate of reaction appears to be independent of pH in typical groundwater systems. When TCA undergoes elimination, a chlorine substituent is removed from one carbon atom and one hydrogen atom is removed from the adjacent carbon atom, forming 1,1-DCE (Fig. 1). TCA degrades concurrently by hydrolysis and elimination, but the rate of hydrolysis exceeds the rate of elimination approximately by a factor of five (Vogel and McCarty, 1987b; Haag and Mill, 1988; Cline and Delfino,

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 7 0 1 e2 7 2 3

2705

Fig. 1 e Primary abiotic and biotic transformation pathways for 1,1,1-TCA in groundwater.

1989). Consequently, degradation of TCA by hydrolysis and elimination yields approximately 80% acetate and 20% 1,1-DCE. Degradation of TCA in water by hydrolysis and elimination generally follows pseudo-first order kinetics with reported half-lives that range from 1.1 to 3.8 years at 20e25 and increase to 9.3 years at 10  C (see Table 2). In general, the average half-life of TCA at 10  C is about 16 times longer than it is at 25  C (Washington, 1995). The detection of 1,1-DCE in groundwater is often considered to be a hallmark indicator of a past TCA release, as well as

a signature daughter product of TCA attenuation (McCarty, 1997b). Transformation of TCA to 1,1-DCE does not achieve an improvement in water quality because 1,1-DCE is more toxic than TCA and typically has a lower cleanup level (see Table 1). As discussed in the following section, 1,1-DCE under certain natural conditions degrades via biological reductive dechlorination to VC and ethene. Abiotic hydrolysis may also affect the fate of CA, which can be produced in the biological dechlorination of TCA (see following section). Laughton and Robertson (1959) reported that CA in water at 80  C undergoes abiotic hydrolysis to ethanol, which subsequently may be hydrolyzed to acetate. In experiments in which a degradation mechanism was not determined, Vogel and McCarty (1987a) inferred a CA hydrolysis half-life of approximately 680 days at 20  C. In general, the significance of CA hydrolysis in groundwater systems remains uncertain, as there is a scarcity of laboratory and field data that quantify this process. 1,1-DCA, which also occurs as a product of TCA reductive dechlorination, can undergo abiotic hydrolysis, but not within a timeframe relevant for groundwater systems (t½ ¼ 61.3 years at 25  C; Jeffers et al., 1989).

2.1.2. Metal-catalyzed reduction by naturally occurring reductants

Fig. 2 e Overview of typical abiotic and biotic degradation pathways for TCA in aquifers. Pathways shown are for both aerobic and anaerobic conditions unless noted otherwise. R.D. denotes reductive dechlorination under anaerobic conditions. Dashed lines represent potential pathways that have been reported in peer-reviewed literature but not confirmed by repeated, reproducible studies.

Iron sulfides, green rusts, and magnetite can occur naturally in anoxic aquifers and/or aerobiceanaerobic transition zones (Berner, 1964; Rickard, 1974; Trolard et al., 1997; Genin et al., 1998; Ferry et al., 2004). These natural ferro-containing minerals are known to serve as strong reductants for the abiotic reductive dechlorination of certain chlorinated aliphatic hydrocarbons (CAHs) (Kriegman-King and Reinhard, 1992, 1994; Butler and Hayes, 2000; Lee and Batchelor, 2002a,b). Mackinawite (FeS) has been observed to catalyze the reductive transformation of TCA partially to 1,1-DCA, 2-butyne, and other unidentified products, but 1,1-DCA appears to resist transformation by FeS (Butler and Hayes, 1999, 2000; Gander

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Table 2 e Rate constants, half-lives, and degradation products for abiotic degradation of TCA and CA by hydrolysis and elimination in aqueous systems without sediment. Pseudo 1st order rate constant (yr1)a

Half-life (yr1)

Temperature ( C)

Observed degradation products

TCA

1.4 0.04 0.10 1.2 0.50 0.10 0.11 0.18e0.22 0.07 0.41 0.56 0.65

0.5 17 6.9 0.6b 1.4b 7b >2.8 3.2e3.8 9.3b 1.7 1.2 1.1

25 20 20 25 20 10 20 20 10 20 25 25

Not determined 1,1-DCE only 1,1-DCE only HAc HAc HAc 1,1-DCE þ Unidentified Products HAc and 1,1-DCE HAc and 1,1-DCE HAc and 1,1-DCE HAc and 1,1-DCE Not determined

Dilling et al., 1975 Vogel and McCarty, 1987b Gerkens and Franklin, 1989 Mabey et al., 1983 Mabey et al., 1983 Mabey et al., 1983 Vogel and McCarty, 1987b Klecka et al., 1990,c Gerkens and Franklin, 1989 Gerkens and Franklin, 1989 Haag and Mill, 1988 Jeffers et al., 1989

CA

5.8 0.37

0.12b 1.9

25 20

EtOH Not determined

Laughton and Robertson, 1959 Vogel and McCarty, 1987a

Chemical

Reference

a For those references where only half-life was reported, rate constant shown here was calculated from half-life; first-order reaction was assumed. b Extrapolated from results of tests performed at higher temperature. c Experimental system consisted of sterile soil and groundwater slurry.

et al., 2002). FeS-catalyzed transformation of TCA in field-scale studies has not been reported in the peer-reviewed literature. However, Ferry et al. (2004) concluded that 1,1-DCE in groundwater at a field site was degraded via magnetite-catalyzed reduction. Data suggest that biogeochemical interactions may act synergistically to enhance metal-catalyzed reduction of TCA. Gander et al. (2002) hypothesized that FeS and dechlorinating bacteria in combination synergistically dechlorinate TCA, with bacterial exudates working to enhance FeS reactivity (Gander et al., 2002). Cervini-Silva et al. (2003) found that Fe(II) produced via microbial reduction of Fe(III) in smectite clay catalyzes elimination of TCA to 1,1-DCE, while chemically reduced Fe(II) in the smectite did not. The authors concluded that microbial reduction of Fe(III) affected the surface properties of smectite (e.g., cation exchange capacity and surface area) in a way that favored transformation of TCA. Although knowledge of natural metal-catalyzed reduction mechanisms for TCA and 1,1-DCE is increasing, data available to quantify these processes in the field are scant. Moreover, quantification of natural metal-catalyzed decay of TCA and its daughter products in aquifer systems can be a very difficult task because abiotic/biotic decay mechanisms can occur simultaneously, the rate can depend on microbially produced iron species, the daughter products may be rapidly degraded, and/or the analytical methods for detection of the daughter products are not commonly performed on groundwater samples. Perhaps more importantly, the mechanisms and daughter products for TCA transformations catalyzed by natural metal reductants in aquifers are not well understood.

2.2.

Biological anaerobic reductive dechlorination

2.2.1.

Chloroethanes

Sequential anaerobic reductive dechlorination of TCA to 1,1-DCA and CA (Fig. 1) has been observed in laboratory experiments with marine sediments (Wood et al., 1981, 1985), methanogenic biofilm reactors (Bouwer and McCarty, 1982,

1983; Vogel and McCarty, 1987a; de Best et al., 1997), pure cultures in batch reactors (Egli et al., 1987; Ga¨lli and McCarty, 1989; Holliger et al., 1990; Long et al., 1993; Sun et al., 2002), mixed cultures in batch reactors (de Best et al., 1999; Adamson and Parkin, 2000; Gander et al., 2002; Grostern and Edwards, 2006), and aquifer microcosms (Parsons and Lage, 1985; Klecka et al., 1990; Kromann and Christensen, 1998). TCA dechlorination has been observed under sulfate-reducing (Klecka et al., 1990; de Best et al., 1997, 1999) and methanogenic conditions (Klecka et al., 1990; Kromann and Christensen, 1998; de Best et al., 1999; Ru¨gge et al., 1999). In some of these studies, 1,1-DCA was the primary product of TCA dechlorination (e.g., Egli et al., 1987; Ga¨lli and McCarty, 1989; Klecka et al., 1990; Long et al., 1993), while in other studies CA was the observed terminal dechlorination product (de Best et al., 1999; Chen et al., 1999; Adamson and Parkin, 2000; Sun et al., 2002; Grostern and Edwards, 2006). Overall, the results of these studies show that: (1) biological reductive dechlorination of TCA to CA is possible in anaerobic systems; (2) dechlorination of 1,1-DCA occurs more slowly than dechlorination of TCA, and; (3) 1,1-DCA or CA may form as terminal products of the dechlorination reaction, depending on the microbiology and/or redox chemistry of a given system. Some have reported reductive dechlorination of TCA to CO2, acetate, and other unidentified products, but in these cases the end-products only accounted for a minor percentage of the TCA transformed (Vogel and McCarty, 1987a; Ga¨lli and McCarty, 1989). Holliger et al. (1990) reported dechlorination of CA to ethane by a Methanosarcina barkeri pure culture, but many more studies have observed CA to resist biological dechlorination. In systems containing mixed chloroethenes and chloroethanes, ethane generation may be caused by reduction of ethene (de Bruin et al., 1992). The emergence of ethane as TCA daughter product is illustrated in Fig. 2. The common persistence of CA at field sites where TCA dechlorination is evident indicates that CA is often the apparent terminal product of TCA dechlorination in the environment (e.g., Hoekstra et al., 2005; Borden, 2007; Duchesneau et al., 2007).

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 7 0 1 e2 7 2 3

Anaerobic reductive dechlorination of TCA and 1,1-DCA has been reported to occur by both fortuitous cometabolism and growth-linked dehalorespiration. Prior to 2002, biological reductive dechlorination of TCA was largely thought to be a cometabolic process (Bouwer and McCarty, 1983; Klecka et al., 1990; McCarty and Semprini, 1994), although the thermodynamic feasibility of respiratory dechlorination of TCA had been recognized (El Fantroussi et al., 1998; de Best et al., 1999; Adamson and Parkin, 2000; Gander et al., 2002; De Wildeman and Verstraete, 2003). Cometabolic dechlorination of TCA to 1,1-DCA has been reported for the pure cultures Clostridium sp. (strain TCAIIB) (Ga¨lli and McCarty, 1989), Desulfobacterium autotrophicum and Methanobacterium thermoautotrophicum (Egli et al., 1987). Sun et al. (2002) isolated strain TCA1, the first pure culture shown to gain energy and grow during dechlorination of TCA to 1,1-DCA and 1,1-DCA to CA. This culture dechlorinated 45 mM (60 mg/L) of TCA to CA over 5 weeks. Isolated from aquifer microcosm enrichments, Strain TCA1 uses formate or H2 as electron donors, TCA or 1,1-DCA as electron acceptors, with acetate serving as a putative carbon source. Strain TCA1 dechlorinates TCA more rapidly than 1,1-DCA, resulting in transient accumulation of 1,1-DCA prior to generation of CA as an apparent terminal end product. Analysis of 16S ribosomal DNA (rDNA) in strain TCA1 indicates that it is closely related to the PCE-respiring bacterium, Dhb restrictus, suggesting that TCA1 is a member of the Dhb genus. It is noteworthy that while strain TCA1 dechlorinates TCA, Dhb restrictus has been shown to be incapable of dechlorinating TCA (Holliger et al., 1998). The first mixed culture shown to dechlorinate TCA via dehalorespiration, the Dhb-TCA culture, was reported by Grostern and Edwards (2006). The culture was enriched from aquifer sediments using a mixture of methanol, ethanol, acetate and lactate as electron donors. Similar to strain TCA1, the Dhb-TCA culture contains a Dhb strain that grows during dechlorination of TCA and 1,1-DCA, and yields CA as a terminal product of dechlorination. Respiratory growth of Dhb in the culture was confirmed using qPCR enumeration of Dhb 16S rRNA gene copies. For initial concentrations up to 1.5 mM (200 mg/L), the culture was shown to dechlorinate TCA to CA; at initial TCA concentrations ranging from 1.5 to 2.2 mM (300 mg/L), the culture dechlorinated TCA to 1,1-DCA; and at TCA concentrations above 2.2 mM, dechlorination was completely inhibited. Results indicate that dechlorination of TCA and 1,1DCA by Dhb-TCA culture is mediated by two different reductive dehalogenase (RDase) enzymes, potentially within the same Dhbtype organism (Grostern et al., 2009). The development of dehalorespiratory cultures for TCA dechlorination (i.e., strain TCA1 and Dhb-TCA) may represent a significant advancement in the capacity of ERD to treat sites contaminated with TCA, as dehalorespiration is recognized to achieve significantly faster dechlorination rates than cometabolism (Gossett and Zinder, 1997; Sun et al., 2002). In general, dechlorination rates by dehalorespiring bacteria are thought to be 2e5 orders of magnitude higher than cometabolic transformation rates (De Wildeman and Verstraete, 2003). The environmental occurrence of Dhb capable of TCA and 1,1-DCA dehalorespiration is not yet known. Reported half-lives and pseudo-first order degradation rate coefficients for anaerobic reductive dechlorination of TCA and

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1,1-DCA are summarized in Table 3. Data in Table 3 encompass a range of initial TCA concentrations (0.025e60 mg/L), temperatures (10e35  C), microbial cultures, biomass concentrations, and experimental scales (batch, column, and field). TCA biodegradation half-lives listed in Table 3 range from 0.003 to 0.82 years, with the shortest half-lives reported for either high temperature (35  C) column reactors with low TCA concentrations (0.1 mg/L) or batch systems shown to contain bacteria that respire TCA and 1,1-DCA and high TCA concentrations (25e60 mg/L). It should be recognized that most of the data presented in Table 3 were from experimental systems that included sediment and, as such, it is possible that naturally occurring metal reductants may have contributed to degradation in some cases. Comparison of the degradation half-lives in Tables 2 and 3 shows that the rate of TCA degradation by biological reductive dechlorination is significantly faster than the rate of degradation by hydrolysis and elimination. For example, biodegradation half-lives for TCA-respiring cultures at 20e25  C (Sun et al., 2002; Grostern and Edwards, 2006) were more than 100 times faster than combined hydrolysis and elimination degradation half-lives at the same temperature (Haag and Mill, 1988; Gerkens and Franklin, 1989; Jeffers et al., 1989). Klecka et al. (1990) measured biotic and abiotic TCA degradation half-lives in the same experiment and found that the rate of reductive dechlorination of TCA was 34e150 times faster than abiotic degradation of TCA when requisite bacteria and labile organic carbon were present in excess supply. These data suggest that biological dechlorination of TCA to 1,1-DCA will dominate over abiotic decay of TCA to 1,1-DCE and acetate at sites where natural or exogenous electron donors are present.

2.2.2.

Chloroethenes

As illustrated in Figs. 1 and 2, 1,1-DCE formed from the abiotic degradation of TCA can be biotically dechlorinated to ethene or ethane under certain conditions (de Bruin et al., 1992; Tandoi et al., 1994; Maymo´-Gatell et al., 1999). Evidence of intrinsic dechlorination of 1,1-DCE and VC under anaerobic conditions at field sites has been presented widely (e.g., Semprini et al., 1995; Yager et al., 1997; Witt et al., 2002; Lenczewski et al., 2003). Presently, only bacteria belonging to the Dehalococcoides (Dhc) genus are known to reductively dechlorinate 1,1-DCE and VC to ethene. The pure culture Dhc ethenogenes strain 195 has been shown to dechlorinate 1,1DCE rapidly via dehalorespiration using H2 as an electron donor, but can only degrade VC via a slower, cometabolic process (Maymo´-Gatell et al., 1999, 2001). Dhc strains containing genes encoded for the reductive dehalogenase enzymes vcrA and bvcA have been shown to dechlorinate 1,1DCE and VC as a respiratory, growth-linked process (He et al., 2003; Mu¨ller et al., 2004). Dhc capable of respiratory dechlorination of VC have been detected at a growing number of sites (e.g., Mu¨ller et al., 2004; Lee et al., 2008; Scheutz et al., 2008, 2010), and reported success with bioaugmentation of Dhc at chloroethene-impacted sites is increasing (e.g., Ellis et al., 2000; Major et al., 2002; Lendvay et al., 2003). These developments offer promise for biodegradation of 1,1-DCE and VC that may accumulate as a result of the abiotic degradation of TCA.

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Table 3 e Some reported half-lives for anaerobic biodegradation of TCA and 1,1-DCA. Experimental conditions

Initial TCA Conc. (mg/L)

Cometabolism or dehalorespiration

Pseudo 1st order rate constant (yr1)a

Temp. ( C)

Half-life (yr1)

Observed degradation products

Reference

0.025

Acetate provided as primary substrate

>126

<0.005

22.5

TCA / ?

Bouwer and McCarty, 1983

Methanogenic fixed-film column

0.1

Not determined; primary substrates provided

253 <42

0.003 >0.02

35

TCA / 1,1-DCA 1,1-DCA / CA

Vogel and McCarty, 1987a

Batch reactors with anaerobic sediment

0.18

Not reported

16

0.04

TCA / 1,1-DCA

Wood et al., 1981, 1985

Methanogenic aquifer microcosms

0.1e0.5

Cometabolism inferred; Primary substrate not added

1.4e5.4

0.13e0.49b

20

TCA / 1,1-DCA

Klecka et al., 1990

Sulfidogenic aquifer microcosms

0.1e0.5

0.11e0.47b

20

TCA / 1,1-DCA

Batch reactors with mixed anaerobic bacterial culture

0.1e0.5

Acetate provided as primary substrate

1.3e11

0.063e0.53

35

Products not analyzed

Doong and Wu, 1996

Batch reactors with mixed anaerobic bacterial culture

0.1

0.8e2.8

0.25e0.82

35

Products not analyzed

Doong and Wu, 1997

Field injection in landfill plume (iron-reducing to methanogenic conditions)

0.1

Humic acid, acetate, and glucose added as primary substrates Not determined

1.6e2.0

0.35e0.43

10

1,1-DCA, but not analyzed for other products

Ru¨gge et al., 1999

Batch reactors with lactate and mixed PCE-dehalorespiring culture

2.66

Not determined

>253

<0.003

20  C

TCA / 1,1-DCA, partially to CA

Adamson and Parkin, 2000

Strain TCA1 e Pure culture enrichment from aquifer material; batch microcosms

60

Dehalorespiration

18 36

0.04c 0.02c

25  C

TCA / 1,1-DCA 1,1-DCA / CA

Sun et al., 2002

Dhb-TCA Culture; Mixed bacterial enrichment from aquifer material; batch microcosms

25d

Dehalorespiration

72 84

0.01c 0.01c

Room Temp.

TCA / 1,1-DCA 1,1-DCA / CA

Grostern and Edwards, 2006

1.5e6.1

a First-order decay rates assumed for references where only half-lives reported. b Half-lives shortest at 0.1 mg/L initial TCA; longer at 0.5 mg/L. c Half-lives estimated from C/Co ¼ 0.5 in graphs shown in reference. d TCA and 1,1-DCA tested separately, each at initial concentration of 25 mg/L. Up to 200 mg/L TCA was completely degraded to CA. At initial TCA concentrations ranging between 200 and 300 mg/L, dechlorination to 1,1-DCA occurred, but not past 1,1-DCA. Half-lives shown are for 25 mg/L.

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Methanogenic columns

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2.3. Inhibitory substrate interactions in systems containing mixed TCA and CAH co-contaminants As TCA, PCE, and TCE historically have been used as degreasers by some of the same industries, TCA commonly occurs as a co-contaminant with other chlorinated solvents in groundwater. For example, TCA and TCE are listed as co-contaminants at approximately 20% of the sites listed on the USEPA National Priorities List (Grostern and Edwards, 2006), and chloroethenes were found as co-contaminants at 18 out of 22 TCA sites reviewed in this paper (see Section 4). The co-occurrence of TCA and chloroethenes is important because this mixture of CAHs has been shown under certain conditions to cause inhibitory substrate interactions that can reduce the overall rate of CAH biodegradation. While TCA is not known to inhibit dechlorination of PCE or TCE, TCA reversibly inhibits dechlorination of cDCE and VC by certain strains of Dhc, absent Dhb (Duhamel et al., 2002; Grostern and Edwards, 2006). In batch reactors containing a mixed culture (KB-1) that included two strains of Dhc that respire cDCE and VC, Duhamel et al. (2002) observed that TCA at a concentration of 700 mg/L (5.2 mM) completely inhibited dechlorination of cDCE and VC. Grostern and Edwards (2006) found that TCA inhibition of VC dechlorination in Dhc can be reversed (overcome) by coaugmentation with a mixed culture containing Dhb strains that respire TCA and 1,1-DCA (i.e., the Dhb-TCA culture). Grostern and Edwards (2006) also showed that TCE, cDCE and VC inhibited dechlorination of TCA and 1,1-DCA by the Dhb-TCA culture in laboratory reactors. These results were confirmed by Grostern et al. (2009) who determined that the inhibitory interactions between TCA, 1,1-DCA and chloroethenes in a mixed substrate system fit an uncompetitive inhibition model in which the inhibiting compound binds to the enzymeesubstrate complex (e.g., TCA would inhibit VC dechlorination by binding to the complex formed between VC and the RDase enzyme that catalyzes growth-linked dechlorination of VC). It is also possible that the inhibitory effect of TCA on dechlorination of cDCE and VC is related, in part, to inhibition of bacteria that support the Dhc bacteria that mediate dechlorination. TCA is known to inhibit methanogenesis (Benson and Hunter, 1976; de Best et al., 1997; Adamson and Parkin, 2000; Grostern and Edwards, 2006) and acetogenesis (Vargas and Ahlert, 1987), two processes that are believed to support Dhc in mixed cultures. Methanogens in dehalorespiring systems are thought to serve a syntrophic role and provide growth-factors that support Dhc (Seshadri et al., 2005; Duhamel and Edwards, 2006, 2007). Acetogens in these systems may serve multiple purposes, including degrading carbohydrates and producing acetate that can be used to as an electron donor by certain dechlorinating populations (McCarty, 1997a; He et al., 2002). As such, inhibition of methanogens and acetogens by TCA may slow production of co-factors and/or electron donors that support dechlorination. In general, the importance of methanogenesis and acetogenesis in ERD systems is not well understood and, consequently, we can only speculate as to the importance that TCA inhibition of methanogens and acetogens might have on dechlorination of chloroethenes. Many laboratory and field studies have concluded that complete dechlorination of chloroethenes is favored under methanogenic conditions (Semprini

2709

et al., 1995; Chapelle, 1996; Yang and McCarty, 1998; AFCEE, 2004). However, some of the most rapid and complete dechlorination of PCE and TCE has been observed in laboratory systems absent methanogenesis or acetogenesis (DiStefano et al., 1991; Maymo´-Gatell et al., 1995; Duhamel et al., 2004). Moreover, methanogens and acetogens are known to compete with Dhc for dissolved hydrogen and therefore can reduce the efficiency of dechlorination (Yang and McCarty, 1998).

2.4.

Biological oxidation

2.4.1.

Direct aerobic oxidation

TCA and 1,1-DCA are not known to degrade via direct aerobic oxidation; however, CA has been reported to serve as a growth substrate for certain aerobic bacteria (Keuning et al., 1985; Scholtz et al., 1987). Field data regarding aerobic biodegradation of CA are scant, but the reported feasibility of aerobic biooxidation suggests that CA formed in anaerobic zones at TCA sites is susceptible to biodegradation in areas where dissolved oxygen is present such as the water table or plume fringe. Similarly, VC that may form from anaerobic dechlorination of 1,1-DCE can be biooxidized aerobically (Davis and Carpenter, 1990) and used as a growth substrate by certain aerobic bacteria (Verce et al., 2000). As such, natural aerobic biooxidation may limit the migration of CA and VC in groundwater at certain TCA sites, and engineered aerobic biobarriers may be a viable approach for treating these TCA daughter products. Sequenced anaerobic/aerobic bioremediation has been proposed as a strategy to achieve complete treatment of TCA to innocuous end products (Long et al., 1993; Vogel, 1994; de Best et al., 1999); however, the feasibility of direct aerobic oxidation of 1,1-DCE remains uncertain. While cDCE can serve as a respiratory substrate for certain aerobic cultures (Coleman et al., 2002), no aerobic cultures reported to date have been shown to be capable of using 1,1-DCE as a growth substrate.

2.4.2.

Anaerobic oxidation

There is little evidence to support the hypothesis that TCA or its chlorinated transformation daughter products can be degraded by anaerobic oxidation. No bacteria capable of anaerobically oxidizing TCA, 1,1-DCA, 1,1-DCE, or VC have been identified or isolated. Vogel and McCarty (1987a) reported that minor fractions of 14C-labelled TCA, 1,1-DCA, CA, and 1,1-DCE in methanogenic laboratory reactors were mineralized to 14CO2; however, the data showed that the majority of transformations in the reactors were reductive, and it was not evident whether any of the chlorinated constituents were directly oxidized. The possibility that mono- and dichlorinated CAHs might degrade via anaerobic oxidation was later suggested in reports by Bradley and others that cDCE and VC could be anaerobically oxidized under methanogenic, Fe(III)reducing, or Mn(IV)-reducing conditions (Bradley and Chapelle, 1997, 1998; Bradley et al., 1998). Those researchers subsequently recognized that prior reports of anaerobic oxidation of cDCE and VC may have been subject to experimental artifacts, including entrainment of very low levels of oxygen in test reactors intended for anaerobic experimentation (Bradley et al., 2008). Gossett (2010) demonstrated that direct aerobic oxidation of VC can be sustained at dissolved

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oxygen concentrations below 20 mg/L, and concluded that some prior reports of VC disappearance under assumed anaerobic conditions may have been the result of aerobic oxidation at very low oxygen levels. The common persistence of CA in laboratory and field ERD systems also suggests that CA is not susceptible to anaerobic oxidation, or that if such oxidation is possible, it is a very slow process.

3. Novel tools for assessing TCA biodegradation and ERD performance in the field 3.1.

Molecular biological tools (MBTs)

Sun et al. (2002) used Dhb 16S rRNA gene probes and qPCR to demonstrate that dechlorination of TCA and 1,1-DCA in strain TCA1 is linked to growth of Dhb. Grostern and Edwards (2006) similarly showed that Dhb strains in the Dhb-TCA culture grow during dechlorination of TCA and 1,1-DCA. The findings of those two studies suggest that Dhb that respire TCA and 1,1-DCA play a key role in controlling the rate of TCA and 1,1DCA biodegradation in natural and engineered systems. On the basis of this hypothesis, Dhb 16S rRNA biomarkers have emerged as a potential tool for screening the anaerobic biodegradation capacity in aquifers contaminated with TCA and 1,1-DCA and monitoring the growth of indigenous and/or exogenous Dhb in engineered ERD systems (e.g., see Box Inset A). Commercial molecular tools available for analysis of chloroethane-respiring Dhb are not as sensitive as they are for Dhc, as with the latter it is possible to purchase commercial analyses that measure RDase genes found in specific Dhc strains. The recent isolation and sequencing of two Dhb-type RDase gene homologues in the Dhb-TCA culture (Grostern et al., 2009) suggests the possibility that more sensitive biomarkers for monitoring Dhb in environmental samples may become commercially available in the future.

3.2.

Compound-specific isotope analysis

In many cases, contaminant concentration data alone are insufficient to determine mechanisms and processes controlling contaminant fate. Compound-specific isotope analysis (CSIA) is a powerful tool for quantifying biotic and abiotic transformation of organic chemicals in groundwater (Hunkeler et al., 2008). During transformation of organic chemicals the ratio of stable isotopes changes because the molecules with light isotopes (e.g., 12 C) typically react faster and are more susceptible to degradation than those with heavy isotopes (e.g., 13C). In the case of carbon isotopes in organic molecules, the reactant becomes enriched in 13C over time, and the isotopic ratio of 13C/12C (d13C values) shifts as the transformation reaction proceeds. Carbon isotopes are the most common type of CSIA used for quantifying organic contaminant transformations in aquifers, although investigation of 2H/1H and 37Cl/35Cl ratios is also increasing. CSIA, used in conjunction with contaminant concentration data, offers a variety of useful applications in aquifer systems including distinguishing transformation from dilution, distinguishing abiotic from biotic degradation (VanStone et al., 2008), determining transformation pathways among complex mixtures of contaminants (Hunkeler et al., 2005), and

quantifying transformation in source areas where dissolution flux from DNAPL may otherwise mask transformation (Morrill et al., 2009). There are few published field studies involving CSIA and chloroethanes, and fewer still involving TCA. Recently, however, Sherwood Lollar et al. (2010) reported that dechlorination of TCA and 1,1-DCA by the Dhb-TCA culture in batch reactors resulted in enrichment factors (e) of 1.8& and 10.5& for TCA and 1,1-DCA, respectively. Additionally, it was shown that TCA enrichment factors differed for biotic and abiotic transformations, demonstrating the ability of CSIA to distinguish transformation mechanisms. Buscheck et al. (2006) used CSIA for a mixed PCE/TCE/TCA plume to determine whether VC in the plume originated from degradation of PCE/TCE or TCA (See Box Inset B). In general, there is a need for additional research to quantify relevant enrichment factors and demonstrate the utility of CSIA for quantifying transformations at TCA sites.

4. Field experience with natural and enhanced anaerobic dechlorination of TCA Relative to use of ERD at chloroethene-contaminated sites, field experience with ERD at TCA-contaminated sites is not nearly as extensive; however, the number of TCA sites where ERD has been used is growing. We identified and reviewed case studies of 22 field sites (see Table 4) reported over the last decade where ERD has been pilot-tested (12 cases) or implemented at full-scale (10 cases). All but two of the cases were reported in gray literature, and none of the cases achieved a level of comprehensive technology validation equivalent to that which has been established for use of ERD with PCE/TCE (e.g., Ellis et al., 2000; Major et al., 2002; Scheutz et al., 2008). Some of the cases are subject to a variety of limitations, including incomplete laboratory analysis (e.g., no analyses of ethene, ethane), spatially limited monitoring networks, selective presentation of data, short experimental timeframe, or use of treatment wells for monitoring performance. These limitations made it difficult to identify TCA/1,1-DCA/CA loss mechanisms in some cases. Collectively, however, these studies present valuable information regarding the state-of-the-practice in use of ERD for TCA remediation. Trends and general observations regarding site characteristics, chemistry, and ERD implementation and performance at TCA sites are discussed below.

4.1.

Natural attenuation trends at TCA sites

4.1.1.

General trends in groundwater chemistry

Of the 22 sites reviewed herein, 18 sites (82%) were contaminated with both chloroethenes and TCA, while only 4 sites were contaminated with TCA alone. Other chlorinated co-contaminants were observed at a few of the sites, including dichloromethane, 1,2-DCA, 1,1,2-trichloroethane, and perchlorate. It was evident that the co-occurrence of TCA and PCE/ TCE and their respective transformation daughter products in some cases confounded attempts to identify sources of VC, ethene, and ethane. Fourteen of the sites exhibited capacity for natural anaerobic biodegradation of TCA as evidenced by at least trace detections of 1,1-DCA and/or CA prior to ERD implementation; however, CA was not detected or reported as a baseline condition in

Table 4 e Summary of field case studies involving ERD at TCA sites. Gray literature unless noted otherwise. Max concentrations of primary contaminants

Geology of treatment zone

Amendments & injection method

Confidential industrial site/ USA. Lee et al., 2003

Pilot, source area, 3 years

TVOCs (539 mg/L); TCA (178 mg/L), CA (5.35 mg/L), cDCE (0.257 mg/L), 1,1-DCA (38.2 mg/L)

Sequenced sand, silt, gravel, clay

Passive; batch injection of ESO

Illinois site. Markley and Sieczkowski, 2003

Pilot, source area, 2.5 years

Clay and silt, silty sand

Passive; direct push injection of polylactate ester (HRC)

Caldwell Trucking, NJ. Finn et al., 2003

Pilot, source area, 20 months

Sum of TCA/1,1-DCA/1, 2-DCA/CA (29,780 mg/L); Sum of PCE/TCE/cDCE/VC (36,680 mg/L) TCE (700 mg/L), PCE (27 mg/L), TCA (CNR), CF (1.2 mg/L), 1,1-DCA (0.6 mg/L)

Glacial deposits and fractured rock

Daily batch addition of methanol þ lactate þ acetate solution. One-time batch injection of KB-1.

St. Louis, MO. Hippensteel et al., 2003

Full, plume, 10 months

TCE (1800 mg/L), 1,1-DCE (176 mg/L), TCA (37 mg/L)

Silty clay and clay

Passive; direct push injection of polylactate ester (HRC)

Trenton, Ontario. Lyew et al., 2004

Pilot, source area, 2 months

Silt, glacial till, fractured rock

Active recirculation; Molasses

Eindhoven, NL. Hoekstra et al., 2005

Pilot, plume, 2 years

TCA (150 mg/L), 1,1-DCA (CNR), 1,1-DCE (CNR), CA (CNR), PCE (CNR), TCE (CNR), VC (CNR) cDCE (10e70 mg/L), VC (2e5 mg/L), 1,1-DCA (1e1.5 mg/L)

Sequenced sand, peat, silt, clay

Active recirculation in a biobarrier; sodium and ethyl lactate, molasses

Wisconsin Site. Sellwood and Koch, 2005

Full, source and plume, 2 years

Silty clay overlying outwash sand

Passive; Batch injection of lactate (2e4 g/L), yeast extract, sodium sulfide, and carbonate. 4 injections over 18 months

Edison, NJ. Chu et al., 2005

Pilot, source area, 13 months

1,1-DCA (47 mg/L), TCA (<0.3 mg/L), VC (0.53 mg/L), cDCE (<0.4 mg/L), TCE (<0.2 mg/L), CA (0.031 mg/L), petroleum hydrocarbon TCA (10 mg/L), 1,1-DCE (3.4 mg/L), 1,1-DCA (0.39 mg/L), CA (0.026 mg/L)

Fractured shale

Passive; batch injection of Newman Zone, lactate, nano scale ZVI

Kontich, Belgium. Lookman et al., 2005 (peer reviewed)

Pilot, source area and plume, 15 months

TCA (15 mg/L), 1,1-DCA (2.2 mg/L), 1,1-DCE (1.5 mg/L). Trace ethene and ethane.

Fine to medium silty sand

Passive; Batch injection of polylactate ester (HRC)

Reported results TCA dechlorinated to 11-DCA and CA; average reduction in TCA of 96% over 3 years. TVOCs decreased by 82%. TCE concentrations increased in two wells; but in general dechlorination to ethene was observed in a majority of locations. In one source area well: chloroethanes degraded by 99% (with transient accumulation of CA); chloroethenes degraded by 80%. PCE & TCE 0 average decrease of 93e94%, cDCE and VC produced. Average reduction in TCA of 51%, significant production of 1,1-DCA, 1,1-DCE, cDCE, VC, ethene. TCE reduced by 95% in some locations, but not others. TCA concentrations reduced by 50%. 1,1-DCE concentrations were not reduced. cDCE and VC accumulated. Insufficient data to assess performance.

70e90% dechlorination of chloroethenes to ethene (cDCE reduced from 15 mg/L to 10 mg/L). 1,1-DCA degraded to CA (from 1 mg/L to 100 mg/L). 98.5% reduction in DCA to CA. CA accumulated, and it appears that CA was terminal end product. VC concentrations reduced by 90%.

93% Reduction in TCA; 79% reduction in 1,1-DCE; 1,1-DCA increased 1.5X; VC and CA increased by >10X. Ethane accumulated due to ZVI. TCA dechlorinated to 1,1-DCA. 90% Decrease in TCA; 60% decrease in DCA. TCA/DCA ratio decreased. (continued on next page)

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Scale, source area or plume, duration

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Site, location, reference

Scale, source area or plume, duration

Max concentrations of primary contaminants

Geology of treatment zone

Amendments & injection method

Confidential Industrial Site/ USA e Part 1 of 2. Fiacco et al., 2005

Pilot, source area and plume, 17 months

TCA (2.66 mg/L)

Glaciolacustrine sand silt

Confidential Industrial Site/ USA e Part 2 of 2. Fam et al., 2008

Full, source area, 1.5 years

TCA (300e600 mg/L); TCE (2e30 mg/L)

Medium grained sand

Eden Prairie, Minnesota. Postiglione et al., 2006

Pilot, source area and plume, 6 mos

TCA (58,000 mg/L), 1,1-DCA (4219 mg/L), 1,1-DCE (11,000 mg/L), PCE (4463 mg/L), TCE (4731 mg/L), cDCE (556 mg/L)

Layered clay and sand

Passive; batch injection dilute solution of sodium lactate (200e400 mg/L).

Tampa Florida Site. Newman and Pelle, 2006

Full, source area and plume, 4 years

TCA (16 mg/L), TCE (64 mg/L), cDCE (4.1 mg/L), 1,1-DCE (4.1 mg/L), 1,1-DCA (CNR), VC (0.67 mg/L), ETH (NM), CA (CNR)

Interbedded fine sand, medium sand, clayey sand

Passive; batch DPT injection of ESO/lactate solution (Newman Zone).

Orlando Florida Site. Brown et al., 2009; Turner et al., 2007

Full, source area and plume, 15 months

Sequence of sand, clayey sand, and sandy clay

Active recirculation using horizontal extraction wells; potassium lactate

Kansas Site. Duchesneau et al., 2007; Dennis et al., 2007

Full, source and plume, 2 years

Sum chlorinated solvents ranges from 1000 mg/L to 2300 mg/L. TCA (ca 100 mg/L); 1,1-DCE (ca 100 mg/L), DCM (CNR), PCE (CNR), TCE (CNR), cDCE (CNR), VC (CNR), 1,1-DCA (CNR) TCA (40 mg/L), TCE (200 mg/L), VC (20 mg/L). Ethene not detected.

Alternating silt, organic material, and limestone

Passive; batch injection of lactate/ESO into injection well grid. Batch injection of ACT-3, Followed by KB-1 þ ACT-3.

Maryland Site. Borden, 2007 (peer reviewed)

Pilot, plume, 2.5 years

Perchlorate (3100e20,000 mg/L), TCA (5700e17,000 mg/L)

Silty sand and gravel

Passive biobarrier consisting of ESO (EOS) injected into array of injection wells.

Passive; Batch injection of sodium lactate solution (3e6 g/L). 5 injections over 17 months. Yeast and NH4 (2)PO4 also added. Sodium lactate (10%) and whey (90%). Recirculation, 15 GPM. NaHCO3 and NaOH for pH adjust.

Reported results Effective dechlorination of TCA to DCA and CA over 12e17 months for the wells shown.

TCA concentration reductions in range of 17e69%. 1,1-DCA increases from 0 to 30 mg/L; CA increases from 0 to 44 mg/L. 1,1-DCA and CA persist. TCA and TCE reduced to below drinking water standards in most wells. VC, ethene, ethene not reported. Relative contributions of dilution and dechlorination not determined. Plume: Chloroethanes and chloroethenes reduced to near non-detect levels. Source area: TCA reduced from 16 mg/L to 8.2 mg/L; TCE from 29 mg/L to 9 mg/L; 1,1-DCE from 4.1 mg/L to 1.4 mg/L. cDCE, VC, and ethene increased significantly. CA not reported. 90% decrease in TVOCs; significant ethene and ethane generation

TCA, VC, cDCE concentrations decreased by >70% each; 1,1-DCA and CA increased. Significant growth of Dhc and Dhb evident. CA and ethene not detected until after bioaugmentation. Rapid dechlorination of perchlorate in barrier; but dechlorination of TCA was incomplete, resulting in accumulation of 1,1-DCA and CA. Barrier thickness insufficient to achieve residence time necessary for 1,1-DCA dechlorination to CA.

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Site, location, reference

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Table 4 (continued)

Pilot, plume, 7 months

TCE (1800 mg/L), TCA (1100 mg/L)

Sequenced silt/clay, sand/gravel, saprolite

Passive; batch injection into grid; 0.5% ESO; BCI Culture containing Dhc and Dhb.

California Site. Wymore et al., 2009

Pilot, source and plume, 15 months

Sand

Active recirculation; Whey (2.6% average concentration)

Confidential site. Shoup et al., 2010

Full, plume, barrier, 5 months

1,1-DCE (8e10 mg/L), TCE (CNR), TCA (CNR), DCM (CNR), MEK (CNR) TCE (3.5 mg/L), TCA (1.0 mg/L), 1,1-DCE (0.5 mg/L)

Coarse grained alluvium

Ormond Beach Florida. Buser et al., 2010

Full, source

TCA (7300 mg/L), PCE (870 mg/L)

Not reported

Colorado Site. Mysona et al., 2010

Full, source and plume, 4 years

TCA (CNR), 1,1-DCA (CNR), 1,1-DCE (CNR), CA (CNR)

Not reported

Passive biobarrier; batch injection ESO, lactate with pH buffer, and KB-1 Plus Passive; shredded crab parts (ChitoRem) backfilled into excavation Passive; batch injection ESO and lactate

Rhode Island Site. Cote et al., 2010 Confidential Site. Brady et al., 2010

Full, plume, 3.5 years Pilot, plume, 10 months

TCA (CNR), 1,1-DCA (CNR), CA (CNR) 1,1,2-TCA (83 mg/L), VC (8 mg/L), 1,2-DCA (4 mg/L), 1,1-DCE (7 mg/L), TCE (5 mg/L), PCE (2.7 mg/L), 1,1-DCA (0.6 mg/L)

Not reported Sand

Passive; batch injection of lactate solution Passive; sodium lactate solution, KB-1 Plus

98% decrease in TCE and 77% decrease in TCA in a limited monitoring network. Trace detections (near detection limit) of cDCE, VC, CA, ethene, ethane. As such, loss mechanism unclear. Average CVOC concentration reduced 87% in downgradient wells, 58% in crossgradient wells. Ethene generated. TCE reduced by 95%; TCA reduced by 94%. Daughter products and ethene generation occurring. 90% reduction for most constituents

TCA, 1,1-DCA, and 1,1-DCE reduced to below detection or action levels. CA remained elevated above cleanup limits. TCA concentrations decreased. 1,1-DCA and CA increased, and then decreased. 1,1,2-TCA degraded by >95%; VC increased 6X; 1,1-DCE degraded by >90%; TCE degraded by 80%; 1.1-DCA generally stable.

Notes: BCI e Bioremediation Consulting Inc. CF e chloroform. CNR e concentration not reported. CVOCs e chlorinated volatile organic compounds. ESO e emulsified soybean oil. GPM e gallons per minute. Dhb e Dehalobacter. Dhc e Dehalococcoides. MEK e methyl ethyl ketone (2-butanone). NM e not measured. TVOCs e total volatile organic compounds. ZVI e zero valent iron.

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Confidential Industrial Site/ USA. Morris, 2008

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a majority of cases. For most of the sites that were impacted with both chloroethenes and TCA, cDCE and 1,1-DCA comprised the largest molar fraction of CAHs. Observations of 1,1-DCE were reported at ten of the sites. In a few cases (e.g., Hoekstra et al., 2005; Sellwood and Koch, 2005), TCA was detected in trace concentrations only, and the presence of 1,1-DCE and 1,1-DCA provided the only indication that TCA had once been present at the site. Given the tendency of TCA to decompose biotically and abiotically, it is not unusual for TCA degradation products to persist longer than TCA itself (see example in Box Inset C). Baseline organic carbon concentrations were not measured in most of the case studies; however, 1,1-DCA and 1,1-DCE molar ratios prior to ERD implementation suggested that biological processes appeared to dominate in a majority of cases, as indicated by higher molar fractions of 1,1-DCA. Carbon sources available to create reducing conditions at those sites included co-disposed petroleum hydrocarbons and dichloromethane. 1,1-DCE mole fractions exceeded those for 1,1-DCA at sites where labile carbon sources were not detected or reported in significant quantities. At sites where TCA comprised the largest molar fraction of chloroethanes prior to treatment, 1,1-DCE was also present in significant molar fractions. In some cases, 1,1-DCE comprised the largest molar fraction of the TCA group. Ethene and ethane were not analyzed in the baseline sampling programs for a majority of the cases. Interestingly, ethene and ethane were detected at most of the sites where samples were analyzed for these constituents, and most of the sites where they were detected were impacted with a mixture of TCA and chloroethenes. In those cases it was difficult to determine whether ethane was derived from CA or VC. In one case, an absence of ethene was attributed to potential inhibition of VC dechlorination by TCA (Duchesneau et al., 2007; Dennis et al., 2007). The omission of ethene and ethane as analytes in most of the cases, as well as CA in some cases, represented a significant data gap. Given the potential for multiple precursors for ethene (e.g., TCA, 1,1-DCE, TCE, etc) and ethane (ethene, and possibly CA), monitoring programs for natural attenuation and ERD at TCA sites should include ethene, ethane, and CA as standard monitoring parameters.

4.1.2. Use of molecular monitoring to screen biodegradation capacity While use of MBTs for monitoring dehalorespiring bacteria was reported in ten cases, seven of these cases only involved use of Dhc MBTs, and only two involved used of a Dhb MBT (16S rRNA) to assess natural attenuation and pre-ERD conditions. In one of the cases the detection of 9  103 cells/mL Dhb in groundwater as measured by 16S rRNA and qPCR led the investigators to conclude that bioaugmentation with a Dhb culture was not necessary for ERD (Postiglione et al., 2006). In another case, 16S rRNA Dhb measurements in groundwater samples collected after implementation of biostimulation indicated that Dhb were indigenous to the site, and were present at moderate concentrations (106e107 gene copies per liter) (Duchesneau et al., 2007). These two case studies illustrate that indigenous Dhb were present and detectable at both sites where 16S rRNA Dhb MBTs were used; however, these cases did not yield information sufficient to draw conclusions about occurrence of Dhb nor Dhb population thresholds necessary to sustain significant dechlorination of TCA and 1,1-DCA. Clearly, there is a need for more

research to determine the utility of 16S rRNA Dhb biomarkers as a tool for predicting performance of natural and engineered dechlorination at TCA sites.

4.1.3. Use of microcosm studies to screen intrinsic biodegradation capacity In eight of the cases, laboratory microcosm assays were used to assess the capabilities of the indigenous bacteria toward anaerobic dechlorination of TCA and TCE in the presence and absence of exogenous electron donors. In one case the microcosm assay successfully proved the presence of indigenous bacteria capable of dechlorinating TCA to CA in the presence of molasses as an electron donor (Lyew et al., 2004); in a second case microcosm analysis indicated that the native bacteria could not dechlorinate 1,1-DCA in the presence of either molasses or lactate (Lookman et al., 2005); and in a third case in which petroleum hydrocarbons were the only known electron donor in the microcosms, the results for TCA degradation were inconclusive (Sellwood and Koch, 2005). Collectively, the cases indicate that microcosm assays remain an effective tool for screening biodegradation capacity and bioremediation treatment options. As Dhb RDase MBTs are still under development, and Dhb 16S rRNA gene probes are not capable of distinguishing between Dhb that respire chloroethanes and those that do not, microcosm assays may still offer the most sensitive method currently available for predicting TCA biodegradation capacity.

4.2.

General experience with ERD of TCA

4.2.1.

Treatment zone characteristics

Consistent with proper deployment of ERD in general, ERD design in some of these cases was largely a function of the permeability and contaminant characteristics of the treatment zone. Nineteen of the cases were applications in unconsolidated (overburden) deposits, while three cases were applications in fractured bedrock. A majority of cases involved ERD implementation in source areas and/or site-wide applications. Total CAH concentrations exceeding 100 mg/L (i.e., approaching 10% of individual compound solubility, see Table 1) were reported in several cases, suggesting the presence of DNAPL. TCA concentrations in two cases exceeded the TCA inhibition threshold (w200 mg/L) reported for certain Dhb cultures (Grostern and Edwards, 2006).

4.2.2.

Microcosm studies as an ERD design tool

Laboratory microcosm or column studies were conducted for eight of the cases to evaluate the performance of various electron donors, bioaugmentation cultures, and/or pH buffers for treating TCA under site-specific conditions. These studies typically measured rates, extent, and duration of treatment in comparison to sterile controls, yielding a prediction of ERD performance under field conditions. In one case, ERD was compared against direct aerobic oxidation and aerobic cometabolism as alternative treatment options; and in two cases treatment was assessed at high and low TCA concentrations (Lyew et al., 2004; Brady et al., 2010). Various electron donors evaluated in these tests included both soluble and low-solubility donors. Low-solubility donors tested included ESO and polylactate ester, and soluble donors included lactate, whey, and molasses. Microcosm studies that

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compared low-solubility and soluble donors generated data potentially useful for both passive and active ERD designs. However, a majority of the microcosm studies did not compare electron donor performance, and instead served to provide confirmation of the performance of a single donor treatment. Whey was found to be an effective donor in both of the studies where it was tested (Fam et al., 2008; Wymore et al., 2009), lactate was found to out-perform ESO in one case study (Shoup et al., 2010), molasses was found to be effective in another study (Lyew et al., 2004). It is not surprising that each of these donors should achieve success with ERD at the bench scale as it is generally accepted that most fermentable organic substrates are capable of promoting ERD (AFCEE, 2004). The choice of electron donor at a given site, therefore, is largely a function of site-specific treatment performance objectives, geology, presence of DNAPL, and perceived cost-effectiveness. Bioaugmentation cultures were tested in five of the microcosm studies. Two of the studies tested KB-1 Plus (SiREM Laboratory) (Brady et al., 2010; Shoup et al., 2010) and a third study tested a Bioremediation Consulting Inc. (BCI) culture (Morris, 2008). Both of these cultures are reported to contain Dhc and Dhb that respire chloroethenes and chloroethanes, respectively, and KB-1 Plus product literature indicates that Dhb in KB-1 Plus was derived from the same enrichment as the University of Toronto Dhb-TCA culture. Two other cultures (SDC-9 and KB-1, both of which are known to contain Dhc that respire chloroethenes) were tested at sites where chloroethenes comprised a major fraction of the contamination (Finn et al., 2003; Wymore et al., 2009). All five of these studies found that the fastest and most extensive treatment was achieved when bioaugmentation was used, an outcome that led to selection of bioaugmentation as part of the ERD field design in these cases. The microcosm assay was also a useful tool for detecting inhibition due to low pH in at least one case (Fam et al., 2008). In that case it was determined that the low pH at the site (5e5.5) was inhibitory to dechlorination. It was speculated that low pH was due to the presence of TCA DNAPL, and generation of HCl and acetic acid from TCA degradation. The microcosm test found that pH neutralization improved ERD performance, and consequently NaOH and NaHCO3 were incorporated as additives for the field scale system.

4.2.3.

ERD layout and injection design

Sixteen of the cases used passive designs and six used active designs (see Table 4), all of which are common ERD designs for chloroethene sites (e.g., AFCEE, 2004). Passive designs were implemented in source areas as grid injections into direct push borings or permanent injection wells (Newman and Pelle, 2006; Duchesneau et al., 2007), in plumes as passive biobarriers (Borden, 2007; Shoup et al., 2010), and in the base of excavations (e.g., Buser et al., 2010). Box Inset D describes one of the biobarrier cases. Passive designs were implemented in both low- and high-permeability deposits. The active ERD designs typically involved recirculatory flushing of soluble donors in DNAPL source areas (e.g., Fam et al., 2008; Brown et al., 2009). Active recirculation was also used in one case to create a biobarrier for plume treatment (Hoekstra et al., 2005). Few trends emerged from the electron donor data, and it appeared in some cases that electron donor selection was based either on simplicity of delivery and/or perceived cost

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minimization benefits. Active designs were favored in DNAPL source areas and more permeable formations where groundwater flushing was physically possible and offered a benefit in terms of enhanced dissolution of DNAPL (e.g., Fam et al., 2008; Wymore et al., 2009). Passive ERD designs were preferred in low-permeability formations where yields were insufficient to support hydraulic manipulation. In general, both active and passive ERD designs proved effective for delivering ERD amendments to treatment zones. Given the range in test durations, variability in bioremediation additives, and limitations in the types of data reported, however, it was not possible to discern trends in performance across different types of bioremediation designs reported. It was noted in one of the cases that biofouling of injection wells is a potential disadvantage of active ERD systems, and recommended that pulsed injection be used to avoid biofouling (Wymore et al., 2009).

4.2.4.

Electron donors and pH buffers

ESO and lactate mixed with ESO were the most common electron donors (8 cases), followed by sodium and potassium lactate (6 cases), polylactate ester (3 cases), whey and lactate (1 case), whey (1 case), chitin (1 case), molasses (1 case), and mixed methanol/lactate/acetate (1 case). Vitamin B12 and other nutrients were included in one of the ESO formulations (Shoup et al., 2010). A mixture of ESO and nanoscale ZVI were also used in one case with the goal of catalyzing both biotic and abiotic transformation of chloroethanes (Chu et al., 2005). The passive ERD designs used ESO, soluble lactate, polylactate ester, or chitin, while the active designs used soluble lactate, whey, molasses, or methanol/lactate/acetate. In general, most of passive cases did not test the reactive longevity of donors that were injected, and batch injections for passive systems were repeated when soluble donors were employed. Given the variability in the types of performance monitoring data presented, and the different durations of the case study projects, it was not possible to discern trends in the performance of the various electron donors. However, the collective results demonstrated that all of the electron donors tested were capable of successfully enhancing dechlorination of TCA to CA in those areas of the treatment zone where donor was effectively delivered and brought into contact with contaminant mass. These results may suggest that injection design and reagent delivery is a more important design parameter than electron donor selection. Some designs involved addition of pH buffers (e.g., NaHCO3 and NaOH) or nutrients (e.g., (NH4)2PO4 and yeast extract) to optimize ERD conditions. In one case it was reported that alkaline solids, injected with ESO, were an effective tool for buffering groundwater pH a site where pH was low (Shoup et al., 2010). In another case use of NaOH and NaHCO3 in a recirculation system raised pH from below to 5.5 to greater than 6 across the site. The need to maintain neutral pH in ERD systems is a requirement of effective and complete ERD treatment (Robinson et al., 2009); however, this requirement is often overlooked in ERD designs. The two case studies reported here illustrate that pH buffering may be needed for ERD at some TCA sites, particularly in DNAPL source areas where HCl production takes place.

4.2.5.

Bioaugmentation and molecular monitoring

Bioaugmentation was used in five of the cases, in each case as a one-time seeding of bacterial cultures at a mixed chloroethane/

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chloroethene site. Four of the cases used mixed cultures containing Dhc and Dhb (two of these used KB-1 Plus, and a fourth used a culture from BCI). A fifth case (Finn et al., 2003) completed prior to the development of commercial Dhb cultures used a mixed Dhc culture (KB-1). The bioaugmentation cultures were used with a variety of electron donors, including sodium lactate, methanol/ethanol/acetate, and ESO, both in passive and active ERD designs. In one case, a combination of ESO and KB-1 Plus was used to treat a mixed TCA/chloroethene plume and (see Box Inset A). In that case, application of Dhb in KB-1 Plus was intended to treat TCA and thereby reduce any inhibitory effect TCA might have imposed on dechlorination of cDCE and VC. A few of the cases utilized Dhc and Dhb MBTs for assessing ERD performance, with and without bioaugmentation; however, only one of these cases involved Dhb MBTs (Duchesneau et al., 2007). In that case, 16S rRNA gene probes were used to correlate in situ growth of Dhb with dechlorination of TCA and 1,1-DCA to CA (see Box Inset A). Collectively, the cases show that while MBTs are not employed routinely in ERD performance monitoring programs, their use appears to be increasing, particularly for monitoring Dhc. Recognition of the need for monitoring Dhb also appears to be increasing, but until functional genes that synthesize RDases in Dhb are identified and sequenced, the MBTs for Dhb will not be as sensitive as those that are available for Dhc. The majority of ERD field applications reported to date at TCA sites have not included bioaugmentation; however, laboratory results from Sun et al. (2002) and Grostern and Edwards (2006) suggest that bioaugmentation with TCA- and 1,1-DCArespiring Dhb can improve ERD performance in TCA systems. ERD performance in the cases reviewed herein did not reveal a clear performance difference between bioaugmented and non-bioaugmented sites. This outcome may be more a reflection of the wide range of conditions and test designs between cases evaluated than it is a reflection of the capabilities of bioaugmentation. Regardless, none of the cases qualified as a rigorous field demonstration of bioaugmentation for ERD of TCA (e.g., performance in bioaugmented plots was not compared to non-bioaugmented plots).

2007; Mysona et al., 2010) regardless of the bioremediation additives used, suggesting that ERD alone is not an effective technology for treatment of CA. A few cases hypothesized that CA was dechlorinated to ethane, but in those cases ethene was also present, and the source of the ethane was not determined. It is noteworthy that none of the ERD applications resulted in site closure and/or site-wide attainment of cleanup criteria, and the experimental timeframes typically were insufficient to allow an assessment of concentration rebound following treatment. In general, treatment performance among the cases did not appear to achieve a clear correlation with specific electron donors, injection design, or bioaugmentation. Successful dechlorination of TCA to 1,1-DCA and CA was observed for multiple electron donors, and results indicated that bacteria capable of dechlorinating TCA to CA occur naturally at many sites. Some investigators hypothesized that bioaugmentation with mixed Dhc/Dhb cultures may accelerate the rate of ERD treatment at mixed TCA/ chloroethene sites (Duchesneau et al., 2007; Morris, 2008; Shoup et al., 2010), but it was not possible to validate this hypothesis from the experimental designs that were used.

5.

ERD is a simple and promising technology for cost-effective remediation of TCA and its transformation daughter products (except CA) in groundwater. Results of gray literature case studies indicate that fermentable organic substrates injected into the subsurface can promote dechlorination of TCA to 1,1-DCA, and ultimately to CA. Use of ERD for TCA source area and plume remediation continues to increase, and tools for facilitating successful deployment of the technology are under development, including microbial cultures that respire TCA and 1,1-DCA, and MBTs to monitor the performance of specific chloroethane degrading bacteria. Despite these advancements, the technology faces several challenges that warrant further research and development. Some examples are summarized below.

5.1. 4.2.6.

Conclusions and research needs

Field demonstration/validation

Overall treatment performance in the field

ERD achieved success in stimulating dechlorination of TCA to 1,1-DCA in almost every case, and dechlorination to CA in a majority of cases. In general, reported TCA and TCE concentration reductions were on the order of 70e90%, and the extent of treatment and longevity of transformation daughter products depended, in part, on the timeframe for treatment as well as the initial concentration of total CAHs in the treatment zone. More rapid and complete treatment was typically observed in plumes and longer treatment timeframes were required for highstrength DNAPL source areas (e.g., Finn et al., 2003; Newman and Pelle, 2006; Fam et al., 2008). The longevity and time trends for 1,1-DCA, 1,1-DCE, cDCE, and VC varied considerably between the cases. Production of 1,1-DCA typically dominated production of 1,1-DCE in cases where ERD was successful at treating TCA, and 1,1-DCE production exceeded 1,1-DCA production in cases where ERD performance with TCA was sub-optimal. In some cases 1,1-DCA concentrations at the end of the performance monitoring period exceeded initial conditions (e.g., Chu et al., 2005; Borden, 2007). In most cases, CA was observed to accumulate and persist (e.g., Borden, 2007; Duchesneau et al.,

There is a need for rigorous field studies to quantify and validate the performance of ERD for TCA remediation. Field demonstrations at TCA sites are needed to more precisely quantify the rate and extent of TCA treatment by ERD, and to identify design requirements for optimal ERD conditions, both for active and passive systems. Such demonstrations should aim to identify chloroethane treatment mechanisms in ERD systems, including biotic and abiotic processes, and utilize experimental designs that promote mass balance evaluation of system chemistry and microbiology upgradient, within, and downgradient of target treatment zones.

5.2. Occurrence, roles, and bioaugmentation of Dehalobacter in TCA ERD systems A growing body of laboratory research indicates that the most rapid and complete conversion of TCA to CA is attained in ERD systems that include Dhb that respire TCA and 1,1-DCA. There is a need for additional research to determine factors controlling the natural occurrence and distribution of chloroethane

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respirers in aquifers, and the effectiveness of bioaugmentation with exogenous Dhb cultures. Recent laboratory studies indicate that co-bioaugmentation with certain Dhb and Dhc strains accelerates combined treatment of chloroethenes and chloroethanes, but field demonstration/validation of this ERD design approach has not been reported in the peer-reviewed literature. To further elucidate the role of Dhb in natural engineered ERD systems, more sensitive MBTs are needed, including sequences and primers for Dhb genes that synthesize RDases that mediate dechlorination of TCA and 1,1-DCA.

5.3.

Significance and fate of CA

A majority of laboratory and field tests of ERD for treatment of TCA observe CA as terminal end product. That is, CA typically is not observed to dechlorinate to ethane, and instead is found to persist within and downgradient of ERD treatment zones. CA is reported to undergo hydrolysis to ethanol, as well as aerobic biooxidation by heterotrophic bacteria, but there are few published laboratory studies and no published field studies that quantify these processes. There is a need for research to quantify the fate of CA in aquifers, possibly with CSIA to determine if ethane detections can be linked to CA or

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other sources. In addition, if aerobic biooxidation of CA is shown to occur in situ, sequenced ERD/aerobic biooxidation should be investigated as a remedy for TCA sites.

Acknowledgments This work was funded by the former Copenhagen County (now the Capital Region of Denmark), and REMTEC, Innovative REMediation and assessment TEChnologies for contaminated soil and groundwater, Danish Council for Strategic Research, contract 2104-07-0009. We thank Bob Borden (North Carolina State University), Tim Buscheck (Chevron), and Phil Dennis (SiREM Laboratory) for contributions to case studies presented herein.

Appendix Box Inset A – Sequenced Bioaugmentation of Dehalococcoides and Dehalobacter for Mixed TCE/TCA Remediation

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Box Inset B – Application of CSIA to Elucidate Natural Attenuation Pathways for Mixed Chloroethenes and Chloroethanes

Box Inset C – Natural Biodegradation Trends Downgradient of a Mixed TCE/TCA DNAPL Source Area

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Box Inset D – Biobarrier for Containment of Mixed Perchlorate, TCA, and TCE Plume

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Available at www.sciencedirect.com

journal homepage: www.elsevier.com/locate/watres

Modelling Escherichia coli concentrations in the tidal Scheldt river and estuary Anouk de Brauwere a,b,c,*, Benjamin de Brye b,c, Pierre Servais d, Julien Passerat d, Eric Deleersnijder b,e a

Vrije Universiteit Brussel, Analytical and Environmental Chemistry, Pleinlaan 2, B-1050 Brussels, Belgium Universite´ catholique de Louvain, Institute of Mechanics, Materials and Civil Engineering (IMMC), 4 Avenue G. Lemaıˆtre, B-1348 Louvain-la-Neuve, Belgium c Universite´ catholique de Louvain, Georges Lemaıˆtre Centre for Earth and Climate Research (TECLIM), 2 Chemin du Cyclotron, B-1348 Louvain-la-Neuve, Belgium d Universite´ Libre de Bruxelles, Ecologies des Syste`mes Aquatiques, Campus de la Plaine, CP 221, B-1050 Brussels, Belgium e Universite´ catholique de Louvain, Earth and Life Institute (ELI), Georges Lemaıˆtre Centre for Earth and Climate Research (TECLIM), 2 Chemin du Cyclotron, B-1348 Louvain-la-Neuve, Belgium b

article info

abstract

Article history:

Recent observations in the tidal Scheldt River and Estuary revealed a poor microbiological

Received 27 July 2010

water quality and substantial variability of this quality which can hardly be assigned to

Received in revised form

a single factor. To assess the importance of tides, river discharge, point sources, upstream

31 January 2011

concentrations, mortality and settling a new model (SLIM-EC) was built. This model was

Accepted 3 February 2011

first validated by comparison with the available field measurements of Escherichia coli

Available online 13 February 2011

(E. coli, a common fecal bacterial indicator) concentrations. The model simulations agreed well with the observations, and in particular were able to reproduce the observed long-

Keywords:

term median concentrations and variability. Next, the model was used to perform sensi-

Escherichia coli

tivity runs in which one process/forcing was removed at a time. These simulations

Fecal indicators

revealed that the tide, upstream concentrations and the mortality process are the primary

Microbiological water quality

factors controlling the long-term median E. coli concentrations and the observed variability.

Waste water

The tide is crucial to explain the increased concentrations upstream of important inputs,

Modelling

as well as a generally increased variability. Remarkably, the wastewater treatment plants

Scheldt

discharging in the study domain do not seem to have a significant impact. This is due to

Estuary

a dilution effect, and to the fact that the concentrations coming from upstream (where

Tidal rivers

large cities are located) are high. Overall, the settling process as it is presently described in the model does not significantly affect the simulated E. coli concentrations. ª 2011 Elsevier Ltd. All rights reserved.

1.

Introduction

With its population density of more than 500 inhabitants per km2, its active industrial development and its intensive

agriculture and animal farming, the Scheldt watershed (20,000 km2 from the North of France to the BelgianeDutch border, see Fig. 1) represents an extreme case of surface water and groundwater pollution (EEA, 2004). Improvement of water

* Corresponding author. Vrije Universiteit Brussel, Analytical and Environmental Chemistry, Pleinlaan 2, B-1050 Brussels, Belgium. Tel.: þ32 2 629 32 64; fax: þ32 2 629 32 74. E-mail address: [email protected] (B. de Brye). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.02.003

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Fig. 1 e Model domain and grid, showing the area of interest (Scheldt River and Estuary) covering only a small fraction, but containing a significant number of grid cells. (a) Complete mesh; (b) zoom on estuary and tidal rivers, also showing the connection between the 1D and 2D models, the different tributaries modelled as well as a few important locations. Important cities are encircled, sampling locations are indicated by coloured circles (blue: our monitorings, green: VMM stations, red: estuarine stations sampled during cruises). The same colours are used throughout the figures. Km indications refer to the longitudinal axis along the Scheldt used for visualising the simulations. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

quality is however expected for 2015, owing to the ongoing implementation of the EU water framework directive (EU, 2000). Identification of pollutant sources, description of their fate along the Scheldt land-sea continuum and prediction of the evolution of water quality in response to future implemented environmental policies and climate change e these are the objectives of the Interuniversity Attraction Pole (IAP) TIMOTHY (www.

climate.be/timothy). This must be achieved through the integration of different existing and new mathematical models for describing the water flows and biogeochemical and microbial transformations for all aquatic compartments of the Scheldt land-sea continuum. The current study is to be situated in this broad framework, and more particularly focuses on the understanding of the microbiological water quality in the part of the

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Scheldt influenced by the tide. Recent field measurements (Ouattara et al., 2011) have demonstrated a rather poor microbial water quality in the Scheldt watershed concentrations above the minimal water quality standards of the new EU Directive for bathing water (EU, 2006). In addition, a large variability in the measured concentrations was observed. Understanding these observations is the primary motivation for the current study. The monitoring of microbiological water quality is based on the concept of fecal bacterial indicators, whose abundance is related to the risk of pathogens being present (Havelaar et al., 2001). Today, Escherichia coli (E. coli) is the more commonly used fecal bacterial indicator, as there was evidence from epidemiological studies (Kay et al., 2004) that its abundance is a good indicator to predict the sanitary risks associated with waters (Edberg et al., 2000). E. coli concentrations measured in river waters often exhibit a variability which is so high that the concentrations are classically visualised on a log-scale. This variability is especially important in systems under tidal influence, as the part of the Scheldt studied here. Table 1 summarises the factors generally thought to affect E. coli concentrations and variability in natural waters. However, it is often not clear which factors are the main drivers explaining the mean concentrations and the concentration variability. Hydrological factors include the tide, river discharge and lateral runoff, which all influence the local transport, and hence the local residence time, of the bacteria. These factors vary at different scales (and interact with each other); but it is clear that short term variations at the scale of the hour cannot be neglected. Inputs of E. coli bacteria into the domain are also major factors controlling the E. coli concentrations in the system. Indeed, it is generally assumed that fecal bacteria cannot grow in natural water, and hence must be brought into the system

Table 1 e Summary of factors affecting E. coli concentration in natural surface waters and the way these factors are represented in the model used in this study (SLIM-EC) for the Scheldt simulations. Factor affecting E. coli concentration Hydrological factors Tide Upstream discharges Lateral runoff E. coli inputs Upstream concentrations (boundaries) Concentration entering by tributaries WWTP point sources Diffuse sources E. coli processes Mortality

Sedimentation

Representation in SLIM-EC

15 min resolution Daily resolution Parameterised (only in river part), at daily resolution Constant concentration Main tributaries explicitly in model Constant discharge No First order kinetic process, with time-dependent coefficient (seasonal variation linked to temperature) First order process, coefficient vsed/H (with constant vsed)

through external sources. Regarding the tidal Scheldt River and Estuary, bacteria can enter through the upstream boundaries and tributaries. Obviously these inputs are highly variable. In addition, E. coli are brought into the domain by point sources of domestic waste water. Domestic wastewater is released into the aquatic system after treatment in waste water treatment plants (WWTPs); the type of treatment greatly affects the concentration of fecal bacteria in the released effluents (George et al., 2002; Servais et al., 2007b). Wastewater discharges are expected to vary greatly on short time scales, especially during rain events. Finally, fecal pollution can also be brought to surface waters through diffuse sources (surface runoff and soil leaching). In a recent study, (Ouattara et al., (2011) compared the respective contribution of point and non-point sources of fecal contamination at the scale of the whole Scheldt watershed. They concluded that point sources were largely predominant when compared to non-point sources (around 30 times more for E. coli at the scale of the Scheldt watershed). Predominance of point sources was also demonstrated for the Seine watershed which is just south of the Scheldt one and is also highly urbanised (Garcia-Armisen and Servais, 2007; Servais et al., 2007b). However, these results are based on catchment-scale calculations and diffuse sources can still have a significant local impact on the E. coli concentrations, especially in small rivers in rural areas. After their release in rivers, fecal bacteria abundance decreases more or less rapidly. The disappearance of fecal bacteria in aquatic environments results from the combined actions of various biological (grazing by protozoa, virusinduced cell lysis and autolysis) and physico-chemical conditions (stress due to osmotic shock when released in seawater, nutrient depletion, exposition to sunlight and temperature decrease) and also to possible settling to the sediments (Barcina et al., 1997; Rozen and Belkin, 2001). Unfortunately, it is difficult to identify the respective contribution of each of these factors to the decay rate at a given moment but it can be expected that their rate of disappearance varies on timescales from hours to years. In models, the decay of fecal bacteria is usually described by a first order kinetics (Servais et al., 2007b). From the above overview it is clear that many factors act on the local E. coli concentrations, and most of them vary on short time scales. The goal of this study is to bring some insight into the (relative) importance of these different factors in causing the observed E. coli concentrations in the tidal Scheldt River and Estuary. The focus will be on understanding both the long-term median concentrations (varying in space) and the local variability in concentration. For this purpose, the SLIMEC model is set up which includes as many of these factors as possible (Table 1). This is the first E. coli model developed for the Scheldt tidal River, tributaries and Estuary, and the current paper presents the first realistic simulation results. As a number of factors can be included only approximately (due to a lack of information), it is not expected that concentrations can be predicted for a specific point and time. Furthermore, although the model is capable of simulating the intra-tidal E. coli concentrations, the necessary high-resolution observations and boundary conditions are not available to evaluate the model performance at this scale. Rather, the

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 7 2 4 e2 7 3 8

objective is to reconstruct the right median E. coli concentrations (taken over time periods of the order of one day to a year) and concentration variability, both in time and in space. The ability of the model to achieve this goal is assessed by comparison with the available data.

2.

Model domain and mesh

The computational domain (see Fig. 1) is identical to that used by de Brye et al. (2010): although the focus is on the Scheldt Estuary (indicated by the rectangle in Fig. 1a and shown in the zoom of Fig. 1b), the domain is extended both upstream and downstream. Upstream the domain reaches as far as the tidal influence is significant, covering a riverine network of the Scheldt and its tributaries. So, although the Scheldt is the main focus of this study, all main (tidally influenced) tributaries are also modelled explicitly. This riverine part of the model is 1D (averaged over the cross section), while the estuary and the downstream extension covering the whole North-Western European continental shelf are modelled by 2D, depth-averaged equations. Fig. 1 also shows the unstructured mesh used, constructed by Gmsh (Geuzaine and Remacle, 2009; Lambrechts et al., 2008), which is made up of approximately 21,000 triangles (in the 2D part) and 400 line segments (in the 1D part). In the current study a mesh was used with triangle sizes covering several orders of magnitude (the ratio of the size of the largest triangle to the smallest exceeds 1000, the smallest with a characteristic length of w60 m are in the Scheldt Estuary). For a more detailed discussion of the computational domain and construction of the mesh, please refer to de Brye et al. (2010).

2.2.

- by wind fields at 10 m above the sea level. These fields are 4 times daily NCEP Reanalysis data provided by the NOAA/ OAR/ESRL PSD; - at the upstream river boundaries, the mouths of the Seine, Thames, Rhine/Meuse, the Bath Canal, Ghent-Terneuzen Canal and the Antwerp Harbour locks: by discharges interpolated from daily measurements.

Model description

The model used in this study is a version of the Second generation Louvain-la-Neuve Ice-ocean Model (SLIM: www. climate.be/slim). As its name indicates, this model originally focuses on the physical processes in the aquatic environment, and does so by solving the governing equations using the finite element method on unstructured meshes (“second generation”). Unstructured grids offer the possibility of a more accurate representation of coastlines and grid sizes varying in space (and time) e without having to increase the total number of discrete unknowns. A validated SLIM version for the hydrodynamics in the Scheldt (de Brye et al., 2010) is combined with a simple reactive tracer module for the simulation of E. coli concentrations, forming SLIM-EC. Table 1 summarises the main processes and inputs and at which temporal resolution they are represented by the model.

2.1.

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Hydrodynamics

A detailed presentation and validation of the hydrodynamical model SLIM can be found in de Brye et al. (2010). We only repeat here the aspects determining its temporal resolution. The model has a time step of 15 min. It is forced - at the shelf break: by elevation and velocity harmonics of the global ocean tidal model TPXO7.1;

2.3.

E. coli module

SLIM-EC combines the hydrodynamic SLIM with a module describing the dynamics of E. coli in the aquatic system. In this module the bacteria are modelled as a single type of reactive tracer, i.e. once they enter the model domain (through external sources), they are transported by the hydrodynamics and their concentration is affected by E. coli-specific processes. In the 2D part of the model domain, the depth-averaged concentration C of E. coli is described by the following advection-diffusion-reaction equation: v ðHCÞ þ V  ðHuCÞ ¼ V  ðKHVCÞ þ HR vt

(1)

where t is the time, V the horizontal del operator, H the water depth, u the depth-averaged velocity vector, K the diffusivity coefficient and R the reaction term (which will be described in more detail below). As the mesh size varies greatly over the computational domain, it is essential to that the horizontal diffusivity varies with the mesh size. In this study the diffusivity coefficient K depends on the mesh size D according to a relation inspired by Okubo (1971): K ¼ a D1.15, with a ¼ 0.03 m 0.85s1. In the 1D part of the model the following advection-diffusion-reaction equation is solved for the section-averaged concentration C of E. coli:   v v v v ðSCÞ þ ðSuCÞ ¼ KS þ SR vt vx vx vx

(2)

where S is the section of the river and u the section-averaged velocity. The variable x represents the along-river distance. The processes affecting E. coli concentration in the water column that are considered in the SLIM-EC model are mortality and sedimentation. The approach used to model these processes is similar to that of Servais et al. (2007a, b) to model the dynamics of fecal coliforms in the rivers of the Seine drainage network. Both mortality and settling are modelled by first order (type) reaction terms: R ¼ kmort C 

vsed C H

(3)

The sedimentation velocity vsed is assumed to be constant and equal to 5.56  106 ms1. This value is based on experiments conducted to study the fecal bacteria settling rate in rivers from the Scheldt and Seine watersheds (Garcia-Armisen and Servais, 2008). Note that this representation of the disappearance rate by sedimentation is a parameterisation for depth-averaged models, implying that the water column is well-mixed. In practice, this assumption may not be entirely valid, but it has been shown that the error made remains relatively small (de Brauwere and Deleersnijder, 2010).

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The mortality rate varies with temperature following a sigmoid relation (Servais et al., 2007a, 2007b):   2 exp  ðT25Þ 400
(4) kmort ðTÞ ¼ k20 25 exp 400 with T representing temperature in  C and k20¼1.25  105 s1. We do not have high-frequency high-resolution temperature measurements in the Scheldt. But using the temperature measurements made at the monthly intervals during 2007e2008 at several locations, we fitted a sine through these points in order to get the average seasonal temperature in the whole domain as a function of time (Fig. 2). Using this relation, we can now approximate the temperature at any time during the simulations. Substituting this in equation (4), we effectively get a mortality rate varying seasonally. The value of the mortality rate was similar to the one used by Servais et al. (2007a, b) for modelling the dynamics of fecal bacteria in the Seine watershed. We verified in batch experiments (data not shown) that the mortality rates in the large rivers of the Scheldt watershed were not significantly different from those estimated for the large rivers of the Seine watershed. In this model, to the “base mortality” no additional mortality term was added related to solar effects, as is done in some other studies (Liu et al., 2006; Thupaki et al., 2010). The main reason for this is that in the modelled domain water is quite turbid (from 20 mg/l of suspended matter to more than 1 g/l in the maximum turbidity zone of the estuary), resulting in a low light penetration and thus a limited impact of solar irradiation on fecal bacteria.

2.4.

Input of E. coli into the system

2.4.1.

Input by WWTPs

As Ouattara et al., (2011) showed that E. coli enter the Scheldt mostly through point sources (cf. Introduction), WWTP outlets are the only sources included in the model (see also Table 1). WWTP data are compiled from information provided by the Vlaamse Milieumaatschappij (Flemish Environmental Agency, VMM), Rijkswaterstaat Zeeland and Waterschap Zeeuwse Eilanden for the whole (tidal) basin. Data processing steps involved the localisation of the WWTP outlet, the actual discharge point in the model domain, and the distance between these two points. The number of E. coli discharged by a WWTP per second was approximated to be proportional to

the average volume treated in the WWTP (which depends on the number of inhabitants-equivalents connected to the WWTP) multiplied by an E. coli concentration depending on the treatment type applied in the WWTP (George et al., 2002; Servais et al., 2007b). The E. coli concentrations considered in the treated effluents was 2.8  105 E. coli (100 ml)1 when a the primary treatment followed by an activated sludge process was applied, 1.7  105 E. coli (100 ml)1 when the N removal treatment (nitrification þ denitrification) was added to an activated sludge process and 1.1  105 E. coli (100 ml)1 when the treatment included activated sludge followed by N and P removal; these values result from measurements performed in treated effluents of various WWTPs located in the Scheldt watershed. After this procedure, the E. coli discharges in the model domain by the WWTPs ranged from 8  106s1 to 8  108s1.

2.4.2.

Open boundary concentrations

The concentration of E. coli entering the domain through the open boundaries must also be assigned (see Fig. 1 for location and Table 2 for values). The concentration at the shelf break was assumed to be zero, as well as the concentrations entering the estuary laterally (the Bath and Terneuzen Canals, and water coming from the Antwerp harbour locks). The assumption for the shelf break seems undisputable, due to its large distance from land. The concentrations in the canals were not measured but estuarine observations indicate that their effect is very limited (see below). The effect of the harbour was neglected based on specific measurements made inside and outside the locks, which were quasi-identical (unpublished data). Furthermore the harbour authorities estimated the average residence time in the harbour to be of the order of several months, suggesting that bacteria entering the harbour are probably long dead before they could reach the locks. The only boundaries through which a significant amount of bacteria enters the domain are the upstream river boundaries. These boundary concentrations are based on field measurements taken at the boundary locations (unpublished data). If only one measurement is available, this value was considered, otherwise the median value of all measurements available at that point was used. The data did not allow to impose boundary concentrations varying in time e although we did investigate whether the measured concentrations correlated with discharge, but no significant relation was revealed (Ouattara et al., 2011).

24 22

temperature (°C)

20 18 16 14 12 10 8 6 4

27.03.2007

28.05.2007

25.07.2007

24.09.2007 06.11.2007

04.02.2008 17.03.2008

14.05.2008

date

Fig. 2 e Fitted sine (black line) through temperature measurements made at several locations in the Scheldt and its tributaries (dots).

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 7 2 4 e2 7 3 8

Table 2 e E. coli concentrations imposed at the model boundaries in SLIM-EC. Boundary concentrations in E. coli (100 ml)1 Durme Scheldt upper branch Scheldt lower branch Kleine Nete Grote Nete Dender Dijle Zenne Shelf break, rivers discharging in North Sea and canals discharging in estuary

3.

2600 10000 15000 1900 1500 700 3400 400000 0

Validation measurements

The E. coli concentrations calculated by the model were compared to field measurements made in the study domain in order to validate the model. The modelling period was chosen such that it covers the measurements made in the scope of the IAP TIMOTHY project, i.e. February 2007eJune 2008. Two types of sampling campaigns were conducted during this period:  From 26 March 2007 to 13 June 2008, monthly samples were taken at several monitoring stations in the Scheldt watershed. This gives monthly timeseries at several locations, but also enables to assess the long-term variability.  In February 2007 and 2008, two one day cruises along the saline estuary were conducted. This resulted in two longitudinal estuarine profiles. The results of the latter monitoring survey are fully described in Ouattara et al., (2011). E. coli concentrations were estimated by a plate count method using Chromocult Coliform agar medium. By performing replicates, the coefficient of variation (CV) of the plate counts on specific media used in this study was estimated to be 25%. This value of CV is usual for this type of bacterial enumeration (Prats et al., 2008). In addition, a second set of data was used: measurements of fecal coliforms made by the VMM at one station in the Scheldt River (Zele) and three locations in the estuary very close to each other (around Doel). The fecal coliform concentrations were converted into E. coli concentrations by multiplying the fecal coliform data by 0.77; this value is the average ratio between E. coli and fecal coliforms numbers measured in river water samples (Garcia-Armisen et al., 2007). The VMM measurements span different periods, ranging from 2000 to 2008, and hence do not exactly correspond to the modelled period. Therefore, these measurements should be regarded with some caution.

4.

Results and discussion

4.1.

Reference simulation

The simulations are compared to the available observations in three different ways, enabling model validation from different perspectives:

2729

(1) Simulated median and range (over the period of the our monthly monitoring) of E. coli concentrations along the Scheldt axis (Fig. 3) and along the Rupel-Nete-Grote Nete axis (Fig. 4) are compared to the median and range of measured values. This enables an assessment of the simulated median and variability, and its variation in space. (2) Simulated and measured timeseries at a given point in space (two locations in the Scheldt River, Fig. 6). This comparison more clearly visualises the simulated and measured long-term variability in time. (3) Simulated and measured concentrations on two specific days, at a number of specific estuarine stations (sampled during two cruises, Fig. 5). This comparison focuses on the estuarine part; it visualises the short term model variability, but only point-wise comparisons with the observations are possible. Fig. 3a shows that the model is able to reproduce the measured median concentrations and concentration range in the tidal Scheldt River (1D model). The median values correspond very well to the observed medians (Table 3). The difference certainly falls within the measurement precision of approximately 25% (cf. section 3). On the other hand, it appears that the model finds a larger range of concentrations than those measured (when expressed as interquartile range, cf. Table 3). This is probably due to the fact that the model covers a much wider range of hydrological regimes than the monthly measurements. Indeed, the modelled range is primarily a reflection of extreme events occurring during the simulation period. It is not surprising that these brief extreme conditions are not captured by a monthly point sample. Furthermore, it was attempted to carry out the monitoring samplings approximately at low water, but due to logistic constraints this is not exactly the case for all stations. This could be an additional factor lowering the observed range. According to Fig. 3 the WWTPs have little effect on the concentrations, while the tributaries and the water from upstream have a more significant influence. This is especially true for the water coming from the Rupel, as this river also carries water coming from the Zenne crossing the city of Brussels (cf. Fig. 1). Fig. 4 shows the simulation results for the Rupel, including the measurements made during the monthly monitoring, clearly illustrating the huge concentrations entering through the Dyle/Zenne. Ouattara et al., (2011) reported on the Zenne water quality in more detail, noting that the section downstream of Brussels is heavily contaminated with E. coli abundances comparable to those usually measured in treated waste waters. The effect of the tide is also clearly visible in Figs. 3 and 4, as high concentrations are also transported upstream of the input point (e.g. when the Dyle/Zenne join the Rupel in Fig. 4, or when the Rupel joins the Scheldt in Fig. 5). Indeed, the tides periodically push water up the rivers, thus counteracting the “normal”, downstream directed, river flow. Without tides, the high concentrations would primarily be transported downstream. This important feature could only be captured by a model resolving tides, and suggests that the tidal process may indeed be an important factor explaining the observed concentrations and/or variability. In particular, it seems that the concentrations measured at Temse (Fig. 3a) are highly

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4

← Temse ← Bornem

← Sint−Amands

← Dendermonde

← Zele

← Berlare

← Lede & Wichelen

← Wetteren

a ← Aartselaar

E. coli (100 ml)−1

3.5

← Overschelde

4

← Destelbergen

x 10 4.5

3 2.5 2 1.5 1 Uitbergen Temse

0.5 Zele

2000

20

30

Dender ↑

40

50

Durme ↑

60

Rupel ↑

70

1500

SLIM−EC interquartile band SLIM−EC min − max values SLIM−EC median value measurements

← Walcheren

distance from Ghent (km) − 1D model ← Willem Annapolder

2500

10

← Bath & Waarde

E. coli (100 ml)−1

3000

southern ↑ Scheldt branch ← Burcht ← Antwerpen−Zuid

0 0

b

1000 500 0

Doel

80

90

100

110

120

130

140

150

distance from Ghent (km) − 2D model Fig. 3 e E. coli concentration profile along the Scheldt, from Ghent (km 0, cf. Fig. 1) to the mouth. (a) Results from 1D model. (b) Results from the 2D model. Red vertical lines indicate the location of the WWTPs, blue vertical lines the location of tributaries joining the Scheldt. Only the simulation results covering the our monthly monitoring period are considered. The simulations are summarised as their median value at every position (black line), the interquartile range (grey band) and the min-max range (dotted lines). The available measurements are shown as dots: cyan dots referring to our monthly monitoring, and green dots referring to VMM measurements (only the bigger dots represent samples taken during the simulation period), squares indicate the median value of the measurements at each location. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

influenced by the Rupel although Temse is situated upstream of the Rupel connection. The importance of the tide will be further discussed in section 4.2. In the estuary, the major feature is a steep decrease in simulated concentrations (Fig. 3b). This decrease is coincident with the maximum turbidity zone (MTZ) in the Scheldt, which is reported approximately between km 60 and 100, or between salinity values 2 and 10 (Baeyens et al., 1998; Chen et al., 2005; Muylaert and Sabbe, 1999). Measured timeseries in the estuary are scarce. The only timeseries in the estuary available to us are those performed by VMM. As discussed in section 3, these measurements are to be interpreted with care, but it appears that the model underestimates the concentrations in this part of the estuary, or at least cannot reproduce some of the higher values measured. The model performance in the estuary is further assessed in Fig. 5, comparing measurements made during two estuarine cruises with model outputs from the same days. These results suggest that the model predicts the correct concentrations in the beginning and at the mouth of the estuary, but simulates too fast a decrease between these two extremes. Again, the concentration decrease occurs in the MTZ. Therefore, the poor model performance in this part of the Scheldt is probably related to the fact that the E. coli dynamics are modelled as independent of

suspended matter. For instance, explicitly modelling resuspension and longer survival times for E. coli bacteria attached to sediment particles (Craig et al., 2004; Davies and Bavor, 2000; Davies et al., 1995) may indeed increase the modelled concentrations in the MTZ. A second possible explanation for the model underestimation is missing sources. WWTPs are included in the model, but not the possible pollution effect of canals, or of diffuse sources (most of the estuary lies in a rural area). In Fig. 6 the model results are visualised as timeseries at two monitoring stations in the Scheldt. These figures visualise more explicitly the temporal variability in the observations and simulations. It can be seen that the model is not able to reproduce the observations exactly, i.e. the model is not accurate for predictions of the exact concentration at a given time and location. However, the median value and range are satisfactorily modelled, especially when comparing with the generally reported performances of microbial quality models described in the literature, where one is generally satisfied with model simulations falling within half a log unit of the observations (Collins and Rutherford, 2004; Garcia-Armisen et al., 2006; Sanders et al., 2005). The modelled variability has a different nature at the two locations: in Temse (Fig. 6a) a large portion of the variability is due to the tide (compare raw outputs with

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upstream than Temse. It is also in agreement with Ouattara et al., (2011), who identified a positive correlation between E. coli concentrations and discharge at Uitbergen, while there was no significant correlation at Temse. The tidal influence in Temse was already suggested when inspecting Fig. 3a, and is related to the Rupel joining the Scheldt downstream of Temse. The high E. coli concentrations carried by the Rupel are pushed upstream (to Temse) at a tidal frequency, explaining the important tidal fingerprint in the timeseries at this location. Conversely, at Uitbergen, there is no important source in the vicinity which could cause a similar tidal influence. In this section, the model results of the reference simulation were assessed and generally a good agreement is found for the median concentration and its variability. This validation is not trivial as the model parameters (for mortality and settling) and inputs (WWTPs and boundary concentrations) were not tuned, but directly taken from field measurements or external studies. The (potential) influence of tide, river discharge, WWTP inputs and upstream concentrations have been briefly discussed. The importance of these factors will be further investigated in the next section.

5

← Boom

x 10

2.5

E. coli (100 ml)

−1

2

1.5

1

Boom

0.5 Duffel

0 0

5

10

15

20

↑ Kleine Nete

25

30

35

↑ Dijle/Zenne

40

distance from upstream Grote Nete boundary (km) Fig. 4 e E. coli concentration profile along the Rupel-NeteGrote Nete axis (cf. Fig. 1). Km 0 refers to the upstream boundary of the model in the Grote Nete. For legend refer to Fig. 3.

4.2. Impact of different processes on E. coli concentrations

tidally averaged concentrations), while in Uitbergen (Fig. 6b) most of the variability seems to occur at longer timescales and is probably more related to the hydrological regime. This agrees with what we could expect as Uitbergen is located more

One of the objectives of this study is to better understand the importance of the different factors affecting the long-term median E. coli concentration and its variability in the Scheldt River and Estuary. Starting from the reference simulation

8000

a

S20

b

1400

7000 1200

6000

S18

S22

E. coli (100 ml)−1

E. coli (100 ml)

−1

1000

5000

4000 S22

3000

800

600

400

2000 S20

200

1000 S18

0 0

5 S15 S12

10 S09

15

salinity

S07

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30 150

S03

0 0

5 S22 S18 S20 S15 S12

10 S09

15

salinity

S07

20 S04

25

30

S01 150

S01

Fig. 5 e Estuarine profiles of E. coli concentrations on two specific days of longitudinal cruises in the estuary: (a) 14 February 2007, (b) 12 February 2008. Black dots represent the simulated E. coli concentrations at the same location as the cruise stations during the whole cruise day. The larger black circle shows the simulated value approximately at the time of sampling. The crosses represent the measurements. Station names are also added to facilitate localisation of the stations (see Fig. 1, station 150 is a sea station outside the mouth of the Scheldt).

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Fig. 6 e E. coli concentration timeseries at two locations in the Scheldt River (see Figs. 1 and 3 for location): (a) Temse, (b) Uitbergen. Simulation period covers our monthly monitoring. Black line shows the model output, grey line the tidal moving average of these outputs. Dots represent field measurements made during this monitoring.

presented in the previous section, we removed, one by one, the major processes (cf. Table 1). Table 3 summarises the results of these different simulations.

4.2.1.

Tide and upstream discharge

To assess the role of the tides, a simulation was run with the tides removed from the hydrodynamics, while all other forcings and processes are kept identical.

Table 3 e Comparison of observed and simulated median and interquartile concentrations all expressed as E. coli (100 ml)L1. The comparison is done at two monitoring locations, where samples were taken at approximately monthly intervals from 26 March 2007 to 13 June 2008. The simulations cover the same period, but all model outputs (at 15 min intervals) are used to compute the statistics. Temse

Uitbergen

Median Interquartile Median Interquartile range range Observations Simulations Reference No tide No upstream conc. No WWTPs kmort ¼ 0 vsed ¼ 0

1400

1200

3500

3700

1500 80 110

3000 280 51

3600 5500 300

4700 4700 120

1400 16000 1900

3000 20000 3800

3300 10000 4700

4900 2900 5100

First inspecting what happens at the two monitoring stations Temse and Uitbergen (Table 3), it is seen that the change is largest at Temse. Indeed, both median concentration and variability (interquartile range) are significantly reduced. Surprisingly, the median concentration at Uitbergen increases, while the variability remains equal. This confirms the hypothesis formulated when discussing Fig. 6 that Temse is much more influenced by the tide, because it is the tide that allows water mass to flow from downstream to upstream and thus brings the high Rupel concentrations upstream. When the tide is switched off, the Rupel concentration cannot reach as far upstream anymore (Fig. 7). Fig. 8a shows the simulated timeseries at Temse, showing the reduced concentrations and variability. The remaining variability is related to the upstream discharge (average daily discharges are prescribed). Fig. 8c shows the daily water discharge at Melle (see Fig. 1 for location) and there is indeed a clear similarity with the concentration timeseries at Temse. High concentrations at Temse generally coincide with high discharge periods. The concentrations at Uitbergen are overall less influenced by the tide. Therefore, it is no surprise that the simulated concentration timeseries at Uitbergen (without tide, Fig. 8b) also exhibits a clear similarity with the discharge timeseries, although the concentrations seem to be less “sensitive” to high discharges than was the case at Temse. This suggests that the two counteracting effects of high discharge e reduced transit time (increasing E. coli concentrations downstream) and increased dilution (decreasing concentrations) e are balanced differently at these two locations. But the overall result at both locations is an increase of the E. coli concentrations with discharge.

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4

← Temse ← Bornem

← Sint−Amands

← Dendermonde

← Zele

← Berlare

← Lede & Wichelen

← Wetteren

← Overschelde

4

a

← Aartselaar

E. coli (100 ml)−1

5

← Destelbergen

x 10

3

2

1 Uitbergen

Temse

Zele

2000

20

30

Dender ↑

40

50

Durme ↑

60

Rupel ↑

70

1500

SLIM−EC interquartile band SLIM−EC min − max values SLIM−EC median value measurements

← Walcheren

distance from Ghent (km) − 1D model ← Willem Annapolder

2500

10

← Bath & Waarde

E. coli (100 ml)−1

3000

southern ↑ Scheldt branch ← Burcht ← Antwerpen−Zuid

0 0

b

1000 500 0

Doel

80

90

100

110

120

130

140

150

distance from Ghent (km) − 2D model

Fig. 7 e E. coli concentration profile along the Scheldt (cf. Fig. 3 for legend). Model results refer to simulation without tide.

Further inspecting the simulations at Uitbergen without tides, it is remarkable that the median simulated concentration is increased, while the interquartile range remains unchanged. When comparing Fig. 8b with Fig. 6b, it appears that switching off the tide induces two main changes: (i) the short term variability due to the tidal effect vanishes, as expected. Because this variability has a smaller amplitude than the long-term variations, this barely influences the overall interquartile range. (ii) the minimal concentrations are higher (although the maximal concentrations remain quasi-identical). Indeed, in the simulation with tides, the concentrations drop to lower, almost-zero values. As for Uitbergen no major sources lie downstream, during rising tide, waters with lower E. coli concentrations are brought upstream to Uitbergen, effectively reducing the concentration at Uitbergen. It is remarkable that the concentrations remain at these low levels for significantly longer periods than a tidal cycle. Therefore, these low values cannot (only) result from the periodic tidal current upstream. Rather, it seems that the tidal oscillation has a mixing effect acting on longer timescales, especially during periods of low discharge, when there is less counteraction from the river flow. These results clearly demonstrate that the concentrations at both monitoring locations are influenced by the tides, but in a different manner. In order to get a more detailed picture of the spatially varying effect of the tides on median concentration and variability, we visualised the differences between Fig. 3 (with tides) and Fig. 7 (without tides) in Fig. 9. This figure

reveals a complex role of the tides: they can locally either increase or decrease the median concentration and, surprisingly, the same holds for the variability. Indeed, in the central part (between km 22 and 50) the tides effectively reduce the observed variability in E. coli concentrations. Further downstream (from km 50 to the Rupel) the tides hugely increase both the variability and the median concentrations, until almost 100% of their value is due to the tides. This is the upstream Rupel influence zone, as discussed for the sampling station Temse. Upstream of km 50 the median concentrations are lowered by the tides (cf. discussion for Uitbergen), and this reduction is higher than 50% for a significant section of the river. In order to better understand why the tides reduce median and interquartile range in the central part of the river, we performed an additional model test. A narrow patch of tracer was initialised at Uitbergen on 1/2/2007 at 0:00 and followed during 10 days e once transported by the “full” hydrodynamics (tides þ river flow), and once with only the river flow. For simplicity, all other sources and decay reactions were removed (passive tracer). Fig. 10 shows the results of these two simulations. It is seen that, in addition to moving the patch up and down the river, the tides increase the width of the patch and accordingly reduce the maximal concentration. This suggests that the tides indeed have an increased “mixing” effect, smoothing the patch more efficiently than without tidal action, which is compatible with the observed lower median concentrations and variability in this section of the river. In the estuary, the picture does not change so much by removing the tidal effect (Fig. 7b). Without tides, the high Rupel concentrations propagate less far downstream. Only

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Fig. 8 e E. coli concentration timeseries at (a) Temse and (b) Uitbergen (cf. Fig. 1 for location). Model results refer to simulation without tide. (c) Measured daily discharge at Melle.

the residual current drives the concentrations downstream, resulting in slightly higher concentrations close to the Rupel and a faster decrease to quasi-zero values. In conclusion, the tide appears to have a significant influence on the E. coli concentrations (median and range) e but the effect is different depending on the location. Overall, the tide has the effect to enlarge the influence radius of a source (or tributary) by pushing water upstream and further downstream than if there were no tides. In zones lying (not too far) upstream of important sources, the tides therefore cause an increase of the average concentrations, otherwise the average concentrations tend to decrease. In this particular case of the Scheldt, this means that the extent of the region influenced by the high Rupel concentrations is significantly enlarged by the presence of tides, mostly upstream but also downstream. Conversely, the most upstream section of the Scheldt is mainly influenced by what comes from further upstream, and only to a lesser extent by the tide. In this part, the tides rather have the

effect to decrease the concentrations by bringing downstream water which contains lower concentrations of E. coli. Finally, by removing the tidal forcing it was also clearly seen that both at Temse and Uitbergen the modelled E. coli concentrations correlate positively with upstream discharge, although their response is different. Clearly, the impact of the tides on the E. coli concentrations is crucial but very complex, implying that “tidal corrections” in models which would not explicitly simulate the tides are unlikely to be reliable.

4.2.2.

Upstream concentrations and WWTPs

Table 3 clearly shows that from the two inputs considered in this study (upstream concentrations and WWTPs), the main “source” of E. coli in the Scheldt is what comes through the upstream boundaries. This is probably due to the fact that (i) a huge amount of bacteria enter the model domain through the Zenne boundary, caused by the large volumes of

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4000 medians

IQRs

E.coli (100 ml)

−1

2000

0

a Southern ↑ 10 Scheldt branch

Temse

−4000 0

Uitbergen

−2000

30 Dender ↑ km

20

40

50

Durme ↑

60

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60

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70

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70

100 medians

IQRs

%

50

0

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20

Temse

−100 0

Uitbergen

−50

30 Dender ↑ km

40

Fig. 9 e Difference between simulation with tides and simulation without tides. (a) Absolute difference and (b) relative difference between medians (full line) and interquartile ranges (IQRs, dashed line). Positive values mean that the simulation with tides is associated with higher median or IQR. The location of tributaries joining the Scheldt and the two monitoring stations Temse and Uitbergen are also indicated.

waste water discharged in the Brussels area (upstream of the model boundary) in the relatively small river Zenne. These massive concentrations propagate through the Rupel into the Scheldt, where they overwhelm the effect of local WWTPs. (ii) the largest WWTPs in the Scheldt (the part under tidal influence) have a limited effect. Most of them are located in the Antwerp area, where they either discharge in canals or in the Antwerp harbour, avoiding a direct effect on the Scheldt. The few large WWTPs that discharge directly in the Scheldt (e.g. Antwerpen-Zuid and Aartselaar), do so in the downstream part of the river (downstream of the Rupel connection) where water discharges are much higher and therefore their impact is immediately reduced by dilution. Although Table 3 only focuses on Temse and Uitbergen, the concentrations are reduced in the whole domain when the boundary concentrations are set to zero (not shown). The effect of the WWTPs is then more visible but remains only very local, suggesting an efficient mixing/dilution.

4.2.3.

Disappearance processes

Finally, we tested the impact of taking out either of the two considered disappearance processes: mortality and settling. Table 3 shows that sedimentation has a negligible effect, but mortality certainly not. In other words, it is the mortality process which is primarily responsible for the decrease in concentrations following the input by a WWTP or tributary (Fig. 3). The negligible importance of the settling process on the overall disappearance rate is probably due to the fact that the rivers considered in this study are relatively deep,

implying that bacteria need to cross a significant water depth before they actually disappear by settling. The (local) relative importance of mortality versus sedimentation can be expressed as q ¼ kmortH/vsed, with H the water height. In the freshwater (1D) part of the Scheldt and during the study period (26 March 2007e15 June 2008) this ratio ranges between 2 and 35, with a median value of 9. In other words, disappearance by mortality is always faster than by sedimentation. For the Seine watershed, it was already found that the relative importance of settling versus mortality in the total disappearance rate decreases with increasing hydrological order of the stream (Servais et al., 2009). For small streams, settling was the dominant cause of E. coli disappearance, while its importance became negligible in the largest rivers of the watershed. Nevertheless, we must keep in mind that the settling process was modelled by means of a very simple first order parameterisation, while a more accurate representation would include an explicit model of suspended matter (including resuspension). It was already discussed that such a representation is expected to improve the model performance in the estuarine MTZ. However, it is not obvious whether it will significantly influence the results in the riverine part. Based on the E. coli concentrations measured in the bottom sediments and the concentrations of suspended matter, Ouattara et al., (2011) estimated the potential contribution of sediment resuspension to the E. coli concentration in the water column. Sediment resuspension contributed significantly to the water contamination only at two sites in the Scheldt watershed. These results suggest that resuspension can have important but localised impacts in the rivers. Modelling these effect will be a challenge for the future.

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a

0.015

concentration

tracer concentration

0.02

0.01

0.005 0

0.005 0

0.01

0

2

0

4

0.1

6

0.2 0.3 days

0.4

8

10

km from Ghent (1D model)

days

b

60

40

20

0

with tide without tide 0

2

4

6

8

10

6

8

10

days 10

c

km

8 6 4 2 0

0

2

4 days

Fig. 10 e Results of simulation in which a patch of passive tracer was released at Uitbergen. No sources or decay processes were considered. In a first simulation, the tracer was transported by the “full” hydrodynamics (tides D river flow, thick lines) and in a second simulation only the river flow was considered (thin lines). Results shown: (a) evolution of maximal concentration (arbitrary units), with inset showing zoom on first hours; (b) position of maximum; (c) measure for the width of the patch.

5.

Summary and conclusions

The current study aimed at providing some insight into the (observed) E. coli concentration in the tidal Scheldt River and Estuary. At a few locations along the tidal Scheldt long-term monitorings (>1 year) have been performed, and the resulting (monthly) measurements exhibited a remarkable variability, which could not readily be explained. Although measurements are available only at a monthly interval, we hypothesised that the short term physical processes (tide and upstream discharge) could be major drivers. To verify this hypothesis, the SLIM-EC model was built, in order to simulate the spatio-temporal distribution of E. coli concentrations, including these high-resolution physical forcings, in addition to specific E. coli sources (WWTPs, boundary concentrations) and processes (mortality and settling). The E. coli dynamics are

kept relatively simple, motivated by analogous studies (e.g. disregard of diffuse sources) and by lack of data (constant WWTP discharge, boundary concentrations, single pool of bacteria). Nevertheless, the model simulations were capable of reproducing the long-term median and range of E. coli concentrations in the Scheldt. The main deficiency of the model is its inability to accurately simulate the decrease in concentration in the MTZ e which is most probably due to the lack of sediment-related dynamics for E. coli. This is not the first E. coli model resolving the tide, but previous studies did not investigate the long-term effect of this forcing. Kashefipour et al. (2002) focus on single days, Garcia-Armisen et al. (2006) only study the concentration profile after a 28 days simulation with constant upstream discharge. On the other hand, we must admit that the current model is not fit for “point predictions” at a precise time and location. Still, the model has proven accurate in predicting

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 7 2 4 e2 7 3 8

long-term median and range, making it a potentially interesting tool for long-term risk assessment studies. Indeed, for risk studies, understanding of the median behaviour is not sufficient; it is crucial to have some insight into the variability and the processes driving it. Comparing the reference simulation to reduced model setups, a deeper understanding of the controlling processes was possible: (1) The tide, the concentrations coming from upstream and the mortality process are the main factors causing the observed E. coli concentrations and variability. (2) The tide is crucial to find correct median and range of concentrations. However, its effect is complex: it can either increase or decrease the local (median) concentrations (depending on the location of the closest sources) and increase or decrease the local variability. (3) The impact of the WWTPs inside the model domain are minor, suggesting that investment in these WWTPs may not be the most efficient management action to improve the water quality in terms of fecal contamination. At the opposite, improving wastewater treatment in some WWTPs located upstream of the studied domain (especially in the Brussels area) would be important from a water quality point of view. These results point towards a few directions for future developments: (1) Model improvements: a. A better model representation of the estuarine decrease in E. coli concentrations may be achieved by complexifying the E. coli module by including a direct link with sediment dynamics. b. Include further variability in the forcings, especially the boundary concentrations. Including varying WWTP discharges does not seem relevant, due to the small impact of these sources. However, a more accurate representation of what enters from upstream could be achieved by extending the model to the more upstream (non-tidal) river sections, especially the Zenne section crossing Brussels, as this appears to be a major source of contamination. (2) Additional data. Indeed, the above-mentioned model improvement are only possible if additional measurements are made/become available. But also for the validation of the model additional data are necessary. Visually it is clear that data (timeseries) are lacking in the estuary, but also in the riverine part additional monitoring stations would be useful. The model may be a useful guide to determine the optimal position and/or timing of future samples (e.g. de Brauwere et al., 2009).

Acknowledgements The authors wish to thank the Vlaamse Milieumaatschappij for providing data on fecal coliforms in the Scheldt. Anouk de Brauwere performed this study while she was a postdoctoral

2737

researcher with the Research Foundation Flanders (FWO), and with the Belgian National Fund for Scientific Research (FRS-FNRS). Eric Deleersnijder is a Research associate with the Belgian National Fund for Scientific Research (FRS-FNRS). The research was conducted within the framework of the Interuniversity Attraction Pole TIMOTHY (IAP VI.13), funded by the Belgian Science Policy (BELSPO). SLIM is developed under the auspices of the programme ARC 04/09-316 and ARC 10/15-028 (Communaute´ franc¸aise de Belgique).

references

Baeyens, W., van Eck, B., Lambert, C., Wollast, R., Goeyens, L., 1998. General description of the Scheldt estuary. Hydrobiologia 366, 1e14. Barcina, I., Lebaron, P., VivesRego, J., 1997. Survival of allochthonous bacteria in aquatic systems: A biological approach. Fems Microbiology Ecology 23, 1e9. Chen, M.S., Wartel, S., Van Eck, B., Van Maldegem, D., 2005. Suspended matter in the Scheldt estuary. Hydrobiologia 540, 79e104. Collins, R., Rutherford, K., 2004. Modelling bacterial water quality in streams draining pastoral land. Water Research 38, 700e712. Craig, D.L., Fallowfield, H.J., Cromar, N.J., 2004. Use of macrocosms to determine persistence of Escherichia coil in recreational coastal water and sediment and validation with in situ measurements. Journal of Applied Microbiology 96, 922e930. Davies, C.M., Bavor, H.J., 2000. The fate of stormwaterassociated bacteria in constructed wetland and water pollution control pond systems. Journal of Applied Microbiology 89, 349e360. Davies, C.M., Long, J.A.H., Donald, M., Ashbolt, N.J., 1995. Survival of fecal microorganisms in marine and fresh-water sediments. Applied and Environmental Microbiology 61, 1888e1896. de Brauwere, A., De Ridder, F., Gourgue, O., Lambrechts, J., Comblen, R., Pintelon, R., Passerat, J., Servais, P., Elskens, M., Baeyens, W., Ka¨rna¨, T., de Brye, B., Deleersnijder, E., 2009. Design of a sampling strategy to optimally calibrate a reactive transport model: Exploring the potential for Escherichia coli in the Scheldt Estuary. Environmental Modelling & Software 24, 969e981. de Brauwere, A., Deleersnijder, E., 2010. Assessing the parameterisation of the settling flux in a depth-integrated model of the fate of decaying and sinking particles, with application to fecal bacteria in the Scheldt Estuary. Environmental Fluid Mechanics 10, 157e175. de Brye, B., de Brauwere, A., Gourgue, O., Ka¨rna¨, T., Lambrechts, J., Comblen, R., Deleersnijder, E., 2010. A finite-element, multiscale model of the Scheldt tributaries, River, Estuary and ROFI. Coastal Engineering 57, 850e863. Edberg, S.C., Rice, E.W., Karlin, R.J., Allen, M.J., 2000. Escherichia coli: the best biological drinking water indicator for public health protection. Journal of Applied Microbiology 88, 106Se116S. EEA, 2004. Impacts of Europe’s Changing Climate. An Indicatorbased Assessment. Report n 2/2004. Office for Official Publications of the EC, Luxembourg. EU, 2000. Directive 2000/60/EC of the European Parliament and the Council of 23 October 2000-Establishing a framework for Community action in the field of water policy. 72p. EU, 2006. Directive 2006/7/EC of the European Parliament and of the COuncil of 15 February 2006 concerning the management

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of bathing water quality. Official Journal of the European Union 64, 37e51. Garcia-Armisen, T., Prats, J., Servais, P., 2007. Comparison of culturable fecal coliforms and Escherichia coli enumeration in freshwaters. Canadian Journal of Microbiology 53, 798e801. Garcia-Armisen, T., Servais, P., 2007. Respective contributions of point and non-point sources of E. coli and enterococci in a large urbanized watershed (the Seine river, France). Journal of Environmental Management 82, 512e518. Garcia-Armisen, T., Servais, P., 2008. Partitioning and fate of particle-associated E. coli in river waters. Water Environment Research 81, 21e28. Garcia-Armisen, T., Thouvenin, B., Servais, P., 2006. Modelling faecal coliforms dynamics in the Seine estuary, France. Water Science and Technology 54, 177e184. George, I., Crop, P., Servais, P., 2002. Fecal coliform removal in wastewater treatment plants studied by plate counts and enzymatic methods. Water Research 36, 2607e2617. Geuzaine, C., Remacle, J.F., 2009. Gmsh: A 3-D finite element mesh generator with built-in pre- and post-processing facilities. International Journal for Numerical Methods in Engineering 79, 1309e1331. Havelaar, A., Blummenthal, U.J., Strauss, M., Kay, D., Bartram, J., 2001. Guidelines the current position. In: Fewtrell, L., Bartram, J. (Eds.), Water Quality: Guidelines, Standards and Health. In World Health Organization Water Series. IWA Publishing, London. Kashefipour, S.M., Lin, B., Harris, E., Falconer, R.A., 2002. Hydroenvironmental modelling for bathing water compliance of an estuarine basin. Water Research 36, 1854e1868. Kay, D., Bartram, J., Pru¨ss, A., Ashbolt, N., Wyer, M.D., Fleisher, J.M., Fewtrell, L., Rogers, A., Rees, G., 2004. Derivation of numerical values for the World Health Organization guidelines for recreational waters. Water Research 38, 1236e1304. Lambrechts, J., Comblen, R., Legat, V., Geuzaine, C., Remacle, J.F., 2008. Multiscale mesh generation on the sphere. Ocean Dynamics 58, 461e473. Liu, L., Phanikumar, M.S., Molloy, S.L., Whitman, R.L., Shively, D. A., Nevers, M.B., Schwab, D.J., Rose, J.B., 2006. Modeling the transport and inactivation of E. coli and enterococci in the

near-shore region of lake michigan. Environmental Science & Technology 40, 5022e5028. Muylaert, K., Sabbe, K., 1999. Spring phytoplankton assemblages in and around the maximum turbidity zone of the estuaries of the Elbe (Germany), the Schelde (Belgium/The Netherlands) and the Gironde (France). Journal of Marine Systems 22, 133e149. Okubo, A., 1971. Oceanic diffusion diagrams. Deep-sea Reserach 18, 789e802. Ouattara, N.K., Passerat, J., Servais, P. 2011. Faecal contamination of water and sediment in the rivers of the Scheldt drainage network. Environmental Monitoring and Assessment. doi:10. 1007/s10661-011-1918-9. Prats, J., Garcia-Armisen, T., Larrea, J., Servais, P., 2008. Comparison of culture-based methods to enumerate Escherichia coli in tropical and temperate freshwaters. Letters in Applied Microbiology 46, 243e246. Rozen, Y., Belkin, S., 2001. Survival of enteric bacteria in seawater. Fems Microbiology Reviews 25, 513e529. Sanders, B.F., Arega, F., Sutula, M., 2005. Modeling the dryweather tidal cycling of fecal indicator bacteria in surface waters of an intertidal wetland. Water Research 39, 3394e3408. ServaisP, Billen, G., Garcia-Armisen, T., George, I., Goncalves, A., Thibert, S., 2009. La contamination microbienne du bassin de la Seine. Programme Interdisciplinaire de Recherche sur l’Environnement de la Seine. PIREN-Seine, ISBN 978-2-918251-07-1. Servais, P., Billen, G., Goncalves, A., Garcia-Armisen, T., 2007a. Modelling microbiological water quality in the Seine river drainage network: past, present and future situations. Hydrology and Earth System Sciences 11, 1581e1592. Servais, P., Garcia-Armisen, T., George, I., Billen, G., 2007b. Fecal bacteria in the rivers of the Seine drainage network (France): sources, fate and modelling. Science of the Total Environment 375, 152e167. Thupaki, P., Phanikumar, M.S., Beletsky, D., Schwab, D.J., Nevers, M.B., Whitman, R.L., 2010. Budget analysis of Escherichia coli at a southern lake michigan beach. Environmental Science & Technology 44, 1010e1016.

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 7 3 9 e2 7 5 0

Available at www.sciencedirect.com

journal homepage: www.elsevier.com/locate/watres

Removal of human enteric viruses by a full-scale membrane bioreactor during municipal wastewater processing Fredrick J. Simmons a, David H.-W. Kuo b, Irene Xagoraraki a,* a b

Department of Civil and Environmental Engineering, Michigan State University, USA Department of Environmental Science and Engineering, Tunghai University, Taichung, Taiwan

article info

abstract

Article history:

In the US, human enteric viruses are the main etiologic agents of childhood gastroenteritis,

Received 19 October 2010

resulting in several hospitalizations and deaths each year. These viruses have been linked to

Received in revised form

several waterborne diseases, such as acute gastroenteritis, conjunctivitis and respiratory

24 January 2011

illness. The removal of human enterovirus (EV) and norovirus genogroup II (NoV GGII) was

Accepted 2 February 2011

studied in a full-scale membrane bioreactor (MBR) wastewater treatment plant (WWTP) and

Available online 29 March 2011

compared with the removal of human adenovirus (HAdV). In total, 32 samples were quantified using real-time reverse transcription-PCR (RT-PCR) from four separate locations

Keywords:

throughout the treatment process; influent, primary settling effluent, membrane influent

Full-scale MBR system

(which includes the MLSS) and membrane effluent. EV was detected in all 32 samples (100%)

Human adenovirus

with an average concentration of 1.1
107 and 7.8
101 viruses/L for the membrane influent

Human enterovirus

and membrane effluent, respectively. NoV GGII was detected in 20 of 32 samples (63%) with

Human norovirus

an average membrane influent and membrane effluent concentration of 2.8
105 and

Enteric virus removal

1.2
101 viruses/L, respectively. HAdV was detected in all 32 samples with an average

Enteric virus sorption

membrane influent concentration of 5.2
108 and 2.7
103 viruses/L in the membrane effluent. Our findings indicate that this particular full-scale MBR treatment was able to reduce the viral loads by approximately 5.1 and 3.9 log units for EV and NoV GGII as compared to 5.5 log units for HAdV. This full-scale MBR system outperformed the removal observed in previous pilot and bench scale studies by 1 to 2 log units. To the best of our knowledge, this is the first study focusing on the removal of EV in a full-scale MBR WWTP using real-time RTPCR, and on the solideliquid distribution of EV and NoV GII in secondary biological treatment. ª 2011 Elsevier Ltd. All rights reserved.

1.

Introduction

Membrane filtration is the physical process that separates particles and colloidal material present in the raw feed water from the permeate effluent. Membrane bioreactors (MBR) are a modification of the activated sludge process in which separation of solids is achieved without the requirement of a secondary clarifier as compared to conventional activated

sludge systems. In the past decade, MBRs have been increasingly used in the wastewater treatment industry as an advanced treatment technology to improve treated water quality especially when water reuse is required (Ahn et al., 2001). As of 2003, there were over 1000 MBR’s in operation around the world, 66% of these are used throughout Japan, while the rest were found in Europe and the US (Cicek, 2003). As the population worldwide continues to rise, there is a greater demand and increased

* Corresponding author. Department of Civil and Environmental Engineering, A124 Engineering Research Complex, Michigan State University, East Lansing, MI 48824, USA. Tel.: þ1 (517) 353 8539; fax: þ1 (517) 355 0250. E-mail address: [email protected] (I. Xagoraraki). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.02.001

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pressure from the public to be able to treat wastewater efficiently and effectively while trying to minimize the risk of exposure to biological contaminants. Human enteric viruses are one of the main pathogens on the United States Environmental Protection Agency Contaminant Candidate List (USEPA CCL) of emerging contaminants. Human Adenovirus (HAdV), Human Enterovirus (EV), Norovirus genogroup 1, 2 and 4, (NoV GGI) and (NoV GGII) are some of the enteric viruses of concern because of their low infectious dose. Human enteric viruses have been linked to several waterborne diseases, such as acute gastroenteritis, conjunctivitis and respiratory illness (Kuo et al., 2010; Kitajima et al., 2009; da Silva et al., 2007; Haramoto et al., 2007; Kageyama et al., 2003). The main pathways of exposure are often direct fecal-oral route or dermal contact through secondary exposure (Godfree and Farrell, 2005). In the US alone, enteric viruses are the main origin of gastroenteritis detected in children whereby averaging roughly 100 deaths per year (Gerba et al., 2002). The degree of viral infection can often vary depending on the species, serotypes, concentration, age of individual, high-risk category individuals and exposure rates to these viruses (Gerba et al., 2002; Rose et al., 1996). Wastewater effluent discharge is often the major source of enteric viruses detected in natural waterways (Kuo et al., 2010; Kitajima et al., 2009; da Silva et al., 2007; Haramoto et al., 2007; Kageyama et al., 2003). Presently, the USEPA does not require wastewater treatment plants (WWTPs) to monitor the concentration of these viruses in the final effluent. In the past, several studies have used norovirus, enterovirus, poliovirus and viral indicators (i.e., coliphage or bacteriophage) to determine overall removal capabilities of membranes in both bench and pilot scale MBR systems (Ueda and Horan, 2000; Hu et al., 2003; Ottoson et al., 2006; Zheng and Liu, 2006; Zhang and Farahbakhsh, 2007). In addition, two past studies have determined the performance of full-scale MBR’s for adenovirus and norovirus (Kuo et al., 2010; da Silva et al., 2007). The focus of this study is to (i) quantify Human Enterovirus (EV) and Norovirus genogroup I and II (NoV GGI) and (NoV GGII) and determine their removal by a full-scale MBR WWTP, (ii) compare with HAdV removal in the same plant (Kuo et al., 2010) and (iii) describe the solideliquid distribution of enteric viruses (HAdV, EV and NoV) in the membrane influent.

2.

Methods and materials

2.1.

Sample collection

Eight sampling events took place at the Traverse City Wastewater Treatment Plant (TCWWTP) monthly (except April, which had two events and no samples taken in June) between January and August 2008. The WWTP is described in Kuo et al. (2010). Briefly, the TCWWTP is designed to treat maximum monthly wastewater loads of 9200 kg/day biological oxygen demand (BOD) (20,200 lb/ day), 16,550 kg/day total suspended solids (TSS), and 1000 kg/day ammonia at a flow of 32,000 m3/day (8.5 mgd), with peak flows up to 64,000 m3/day (17 mgd). The hydraulic retention times for both the MBR and the entire WWTP are 11e12 and 14.5e15 h, respectively. The MBR system is combined with a biological

nutrient removal technology. The membranes for the MBR system are Zenon’s ZeeWeed 500c cassettes (Zenon Environmental Inc., Oakville, Ontario, Canada) made by hydrophilic and non-ionic proprietary polymer. They are immersed, hollowfiber ultrafiltration membranes with a nominal pore size (pore size at which a challenge organism of a particular size will be retained with 60e98% efficiency) of 0.04 micron (or micrometer) and absolute pore size (pore size at which a challenge organism of a particular size will be retained with 100% efficiency under strictly defined test conditions) of 0.1 micron. There were four sampling locations; influent, primary settling effluent, membrane bioreactor influent (which includes the mixed liquor suspended solids (MLSS)) and membrane effluent. The MLSS is a mixture of solids resulting from combining the influent wastewater with the returned activated sludge (RAS) to maintain the desired food-to-mass ratio in secondary biological treatment. In total there were 32 viral samples collected using 1MDS cartridge filters as explained in the USEPA Manual of Methods for Virology (USEPA, 2001). An average of 20 L of influent, 30 L of primary settling effluent, 50 L of membrane influent an average 400 L of membrane effluent were sampled through the 1MDS electropositive cartridge filter (Table 1). Each sample was pumped through the apparatus at a rate of about 11e12 L/min (3 gal/min) except the membrane influent. The pH in the MBR ranged between 6.8 and 7.2. Approximately 190 L of membrane influent was collected in a large tank and allowed to settle for 30 min due to the high amount of MLSS (w2000 mg/L). After that time, the supernatant was passed through the 1MDS filter and 15 mL of sludge was collected for analysis. All samples collected were stored on ice and shipped the same day using overnight delivery to the Michigan State University Water Quality Engineering Laboratory in East Lansing, MI. Upon delivery, samples were placed in a 4  C cooler for 12e24 h before processing.

2.2.

Virus elution process for filters

All samples collected were eluted 12e24 h after initial sampling according to the Concentration and Processing of Waterborne Viruses by Positive Charge 1MDS Cartridge Filters and Organic Flocculation (USEPA, 2001). Briefly, the filters were eluted using

Table 1 e List of the eight different sampling events and the volumes sampled at each point during this study. The Mem Inf consists of two volumes (Membrane Influent as Supernatant/Membrane Influent as Activated Sludge (total volume)). Sampling date

January February March April #1 April #2 May July August

Volume Sampled (L) Influent

Pri Eff

Mem In

Mem Eff

16 17 24 14 16 13 17 14

30 8 42 45 51 21 14 17

42/91 29/85 47/67 37/77 47/67 86/27 39/74 51/63

379 338 438 193 696 574 170 632

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a 1.5% w/v beef extract (0.05 M glycine, pH 9.0e9.5) solution. The filters were submerged for a total of 2 min (2 separate 1 min elutions) in filter housings with 1 L of beef extract added to the pressure vessel. The filter housing was disinfected between filters using 0.17% bleach solution for a 1 min contact time and then dechlorinated using 2% sodium thiosulfate for another 1 min contact time. After the beef extract was passed through each filter, the 1 L of beef extract and eluted particles had the pH adjusted to 3.5  0.1 using 1 M HCl and slowly flocculated for 30 min. Afterward, the 1 L solution was further concentrated by placing 500 mL into a centrifuge bottle and placed into a refrigerated (w4  C) centrifuge for 15 min at 2500
g. The supernatant was then slowly poured off and the process was repeated until all the beef extract solution was centrifuged. After all the samples were centrifuged the accumulated pellets were resuspended using 30 mL of 0.15 M sodium phosphate (pH 9.0e9.5), mixed until the pellet was mostly dissolved and the pH was adjusted to 9.0e9.5 using 1 M HCl. Next, the solution was placed into a 40 mL centrifuge tube and placed in the refrigerated centrifuge for 10 min at 7000
g. The supernatant was poured off into a 50 mL centrifuge bottle, the pH was adjusted to 7.0e7.5 for stabilization of the virus particles and the pellet was discarded. The supernatant was loaded into a 60 mL syringe and passed through a 0.22 mm sterilized filter for removal of bacteria, fungi and other contaminating agents. All samples were completely mixed and placed into 2 mL cryogenic tubes and stored at 80  C until further analysis.

2.3.

Nucleic acid extraction

All viral samples (except the activated sludge) were extracted using the MagNa Pure Compact System automatic machine (Roche Applied Sciences, Indianapolis, IN). The extraction kits used were the MagNA Pure Compact Nucleic Acid Isolation KitLarge Volume. The program used required 1000 mL of sample to be extracted and concentrated for a final volume of 100 mL. Immediately following the completion of the extraction, all samples were placed in 80  C freezer to preserve the integrity of the RNA molecule. However, due to the high concentration of suspended solids in the activated sludge samples the viral nucleic acids were hand extracted using the QIAamp Viral RNA Mini Kit (Qiagen, Valencia, CA) which incorporates the spin column protocol listed in the manufacturer’s handbook.

Following extraction the quantity of viral nucleic acid extracts from all samples were checked using the NanoDrop Spectrophotometer (NanoDrop ND-1000, Wilmington, DE).

2.4.

Real-time PCR standard curves and detection limits

The standard curves for the molecular detection of EV, NoV GGI and NoV GGII were created using stock cultures of Coxsackie virus B5 (ATCC VR-1036AS/MK), and NoV GGI and NoV GGII stool samples were supplied by the Ingham County Health Department following a confirmed outbreak. The cloning primers used in this study are summarized in Table 2. All standard curve assays were run in the LightCycler 1.5 Instrument (Roche Applied Sciences, Indianapolis, IN). Briefly, the PCR amplicons from EV and NoV GGII from pure culture and stool sample extracts were cloned into a plasmid vector (i.e., pCR4-TOPO) which follows the one-shot chemical transformation described in the manufacturer instructions (TOPO TA Cloning Kit for Sequencing, Invitrogen, Carlsbad, CA). The plasmids carrying the cloned EV, NoV GGI and NoV GGII were purified using Wizard Plus SV Minipreps DNA Purification System (Promega, Madison, WI) and sent for sequencing at the Research Technology Support Facility at Michigan State University. All target gene sequences were compared with those published in the National Center for Biotechnology Information (NCBI) database by using the program of Basic Local Alignment Search Tool (BLAST). The concentrations were determined by using the NanoDrop spectrophotometer and then the samples were serial diluted 10-fold and used for creating the standard curves for all target viruses. All standard curve reactions were run in triplicate. The detection limit for EV and NoV GGII was viruses/reaction (viruses/rxn), and the detection limit for HAdV and NoV GGI was 100 viruses/rxn.

2.5.

Molecular detection for EV, NoV GGI and NoV GGII

The extracted samples were reverse transcribed before the real-time PCR was performed for the three parameters (EV, NoV GGI and NoV GGII). Each reverse transcription reaction mix included 2.5 mL of 10 mM reverse primer (for each target), 1 mL of reverse transcriptase (Promega Corporation, Madison, WI), 4 mL of 5X transcriptor reaction buffer (Roche), 0.5 mm of protector Rnase inhibitor (Roche Applied Sciences,

Table 2 e Cloning primers used for the creation of the standard curves. Degenerate base code: B [ C, G or T; D [ A, G or T; H [ A, C, or T; M [ A or C; N [ A, C, G or T; R [ A or G; Y [ C or T. Sequence (50 to 30 )

Reference

EV

Forward Reverse

CCCAGTAGCACTATGAAAGTTGCGAG GGCTAAGTGGTATAAATCCAACAAAGAGGT

Dierssen et al., 2007

NoV GGI

Forward 1 Forward 2 Forward 3 Reverse

ATHGAAOGYCAAATYTTCTGGAC ATHGAAAGACAAATCTACTGGAC ATHGARAGRCARCTNTGGTGGAC CCAACCCARCCATTRTACA

da Silva et al., 2007

NoV GGII

Forward 1 Forward 2 Forward 3 Reverse

GGHCCMBMDTTYTACAGCAA GGHCCMBMDTTYTACAAGAA GGHCCMBMDTTYTACARNAA CCRCCNGCATRHCCRTTRTACAT

Kageyama et al., 2003

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Indianapolis, IN), and 2 mL of 10 mM deoxynucleotide, all RT reactions were run in a Bio-Rad thermal cycler (iCycler, Bio Rad, Hercules, CA) and the conditions are as follow: 55 C for  30 min and then 85 C for 5 min to inactivate the enzyme and a final hold cycle at 4  C for indefinite (the RT conditions are the same for all three target viruses). The RT samples were then placed into 20  C until further analysis. The primers and probes used for the standard curve and molecular detection are summarized in Table 3. EV was run using both the one-step and two-step approach for detection of the target gene. The conditions for the separate assays, EV and NoV GGII are as previously described (Dierssen et al., 2007; Kageyama et al., 2003). Briefly, each assay condition is as follow: EV one-step, reverse transcription 50  C for 30 min, denaturation at 95  C for 15 min, 45 cycles of amplification 60  C for 1 min and 95  C for 10 s, for two-step, denaturation at 95  C for 10 min, 50 cycles of amplification 95  C for 10 s, 58  C for 30 s and 72  C for 30 s, followed by 1 cooling cycle at 40  C for 30 s. NoV GGII, denaturation at 95  C for 10 min followed by 45 cycles of amplification at 95  C for 15 s with annealing and extension at 56  C for 1 min. The realtime PCR reaction master mix for two-step included 10 mL of 2X LightCycler 480 TaqMan Master Mix (Roche Applied Sciences, Indianapolis, IN), appropriate volume of primers and probes to obtained the concentration described in Table 3, 5 mL of cDNA sample, and appropriate volume of PCR-grade water to make up a final 20 mL reaction mix. However, for EV one-step, the real-time PCR reaction master mix included 10 mL of QuantiTect Probe RTPCR Master Mix, 0.2 mL QuantiTect Probe RT-Mix. All samples were run in triplicate and included a negative control reaction (PCR-grade H2O without template) and a positive control reaction for all targets.

2.6.

2.7.

Inhibition control

To determine if inhibition occurred during viral analysis, the methods previously explained (Viau and Peccia, 2009; Rajal et al., 2007) were used. Bovine Enterovirus (BEV) was chosen as the virus to spike all samples to determine if inhibition was present, BEV was quantified following the methods previously published (Jime´nez-Clavero et al., 2005). Prior to the inhibition check, all samples were initially analyzed for BEV using realtime PCR. Next, all extracted samples and molecular grade H2O were spiked with a final concentration of 105 viruses/rxn of BEV. Following the analysis, the Cp values of the extracted water and wastewater samples were compared to see if inhibition was present in the samples. If the Cp values of both the spiked water and wastewater samples were within an acceptable level (5%), then inhibition did not affect our analysis.

2.8.

Calculations for virus concentration

All samples were quantified using the following equation:

Viruses ¼ L

Viruses 1 rxn 1

100 mL

30; 000 mL rxn 5 mL 1000 mL Initial Sample VolumeðLÞ

(1)

Where the 5 mL is the amount of sample per reaction tube, the 1000 and 100 mL are the amounts of sample extracted and the volume of the extract, respectively. The 30,000 mL is the amount of concentrated eluent after the final filtration through a sterilized 0.22 mm PVDF (polyvinylidene fluoride) syringe filter (Millipore, Billerica, MA) from the elution process stated above.

Human adenovirus data 2.9.

Overall HAdV data published in Kuo et al. (2010) are also reported in this paper for comparison purposes. The HAdV data were obtained from the analysis of the same samples presented in the current study.

Membrane influent concentration

During each sampling event, approximately 190 L of activated sludge was collected and allowed to settle. The supernatant was passed through the 1MDS filter and 15 mL of settled

Table 3 e Primers, probes and real-time PCR conditions used for the standard curves and molecular detection assays. Virus type HAdV40/41

EV

NoV GGI

NoV GGII

Gene region Hexon

50 e Untranscribed Region

Junction ORF1-ORF2

Junction ORF1-ORF2

Primers/Probes

Sequence (50 to 30 )

Reaction condition (temp ( C), time)

Reference

Forward Reverse-1 Reverse-2 Probe

ACCCACGATGTAACCACAGAC ACTTTGTAAGAGTAGGCGGTTTC CACTTTGTAAGAATAAGCGGTGTC CGACKGGCACGAAKCGCAGCGT

95, 10 s e denaturation

Xagoraraki et al., 2007 e Modified from Jiang et al., 2005

Forward

95, 15 s e denaturation

Reverse Probe

ACATGGTGTGAAGAGTCTATT GAGCT CCAAAGTAGTCGGTTCCGC TCCGGCCCCTGAATGCGGCTAAT

Forward Reverse Probe

CGCTGGATGCGNTTCCAT CCTTAGACGCCATCATCATTTAC TGGACAGGAGAYCGCRATCT

95, 15 s e denaturation

Forward

CARGARBCNATGTTYAGRTG GATGAG TCGACGCCATCTTCATTCACA TGGGAGGGCGATCGCAATCT

Reverse Probe

60, 30 s e annealing 72, 12 s e extension

Dierssen et al., 2007

60, 60 s e annealing da Silva et al., 2007

60, 60 s e annealing 95, 15 s e denaturation

56, 60 s e annealing

Kageyama et al., 2003

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sludge was also collected. According to the mass balance for each membrane influent sample, concentrations were calculated by adding up the virus concentration in the supernatant and the settled activated sludge volumetrically. The equation is listed as follows,

There was an increase in the membrane influent samples ranging from 7.9 to 9.1 (average 8.9) log units/L and the membrane effluent ranged from 2.7 to 4.5 (average 3.4) log units/ L. It was concluded that no significant seasonal variation ( pvalue > 0.05) was observed during the 7-month sampling period.



  ½Supernatant
V Supernatant þ ½Sludge
V Sludge
membrance influent ¼ V Supernatant þ V Sludge

In the equation above, [Supernatant]/V Supernatant and [Sludge]/V Sludge are the virus concentration and volume of the supernatant and settled sludge, respectively.

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3.1.2.

(2)

EV

3.

Results

EV was detected in all 32 samples. As shown in Fig. 2, an average log influent concentration of 6.5 viruses/L (0.7 standard deviation (std)) with a range of 5.8 to 6.7 was observed for all 8 sampling events. The highest and lowest log concentrations were detected in the August and April #2 samples at 6.1 and 4.1 viruses/L, respectively. However the average concentration for the winter months was approximately 5.7 log units/L. According to the data (Fig. 2), the detection in the influent ranged from 4.1 to 6.1 log units/L with an average 5.8 (0.7 std.). The average log concentration for the primary effluent samples was approximately 5.0 log units/L (0.8 std). The highest concentrations were observed in the February and August samples, both sampling events concentrations were approximately 5.4 log units/L. However, the April #2 sample had the lowest log concentration approximately 3.2 log units/L. Primary settling only accounted for approximately 0.6 log unit reduction before reaching the secondary biological process. The membrane influent concentration was significantly higher ( p-value < 0.05) as compared to the primary effluent concentration. The elevated concentration occurred because the membrane influent sample was collected at the activated sludge tank after the point of addition of returned sludge (Kuo et al., 2010). The average EV log membrane influent concentration was 7.1 log units/L (0.6 std) indicating that the return activated sludge has increased the concentration to the membrane by 2 log units/L. The membrane effluent log concentration averaged 1.9 log units/L (0.5 std) ranging between 0.9 and 2.4 log units/L for both April #2 and May samples, respectively. Overall, the concentration of EV throughout the WWTP remained relatively stable for the eight-month sampling period. No significant ( p-value > 0.05) seasonal variation was observed during the current study for EV quantification.

3.1.

Quantification of enteric viruses

3.1.3.

2.10.

Log removal

Following the quantification of viruses, the overall log removal achieved by the MBR and the entire WWTP’s was calculated using equations (3) and (4) for HAdV, EV and NoV.   Membrane Influent Concentration Log Removal¼log10 Membrance Effluent Concentration (3)   Influent Concentration Log Removal ¼ log10 Effluent Concentration

(4)

For membrane and post-secondary treatment samples that were below the detection limit, the log removal values were calculated by using the detection limit of the individual assays. This indicates that certain removal values may be greater than reported. However, this will allow for proper calculations when this value is needed for comparison. It was assumed that using the detection limit would give the lowest removal value when it may be higher.

2.11.

Statistical analysis

Log removal values for each WWTP was analyzed using t-test in Microsoft Excel using an alpha value (a-value of 0.05), showing a 95% confidence interval.

3.1.1.

HAdV

HAdV quantification was previously reported by Kuo et al. (2010). The HAdV data used in the current study is to compare with the occurrences and removal of both EV and NoV. The results showed 32/32 samples were positive for HAdV. Fig. 1 shows the overall log concentration of HAdV that was detected over the course of the entire study. The influent concentration ranged from 5.8 to 6.7 (average 6.5) log units/L, and the primary effluent ranged from 5.7 to 7.2 (average 6.2) log units/L.

NoV

All 32 samples were analyzed for both NoV GGI and NoV GGII using the methods described above. NoV GGI was not detected in any of the samples (0/32) but NoV GGII was detected in 20/32 samples. Fig. 3 shows the overall distribution of NoV GGII that was detected for all sampling events during the current study. NoV GGII was detected in 8/8, 6/8, 4/8 and 0/8 samples in the influent, primary effluent, membrane influent and effluent, respectively. The average log concentration of the influent samples was 7.7 log units/L (1.0 std) and the highest log concentration was detected in the January sample (8.6 log

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Fig. 1 e Overall HAdV distribution within the Influent (n [ 8), Pri Eff e Primary Effluent (n [ 8), Mem In e Membrane Influent (n [ 8) and Mem Eff e Membrane Effluent (n [ 8) for all eight sampling events.

units/L). The lowest concentration (5.1 log units/L) was detected in the July sample was approximately 3 log units/L below the highest sample (January) and 2 log units/L below the average. However, this concentration level was also reported in the February, March, April #2 and May samples. The average concentration in the primary settling effluent, membrane influent and membrane effluent was approximately 7.7 (2.0 std), 5.5 log units/L (0.6 std) and below detection limit, respectively.

3.2.

Inhibition control

BEV was not initially detected in the 32 wastewater (0/32) samples. All 32 samples collected were then spiked with 105 viruses/rxn of BEV following extraction including a PCR-grade H2O. The Cp values for both WWTP samples (average Cp value 26.13, std 0.09) and H2O (average Cp value 26.23, std 0.05) were within 2% of each other. This indicates that any inhibition that may be present in the extracted samples was not able to suppress

Fig. 2 e Overall EV distribution within the Influent (n [ 8), Pri Eff e Primary Effluent (n [ 8), Mem In e Membrane Influent (n [ 8) and Mem Eff e Membrane Effluent (n [ 8) for all eight sampling events.

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Fig. 3 e Overall NoV GGII distribution within the Influent (n [ 8), Pri Eff e Primary Effluent (n [ 8), Mem In e Membrane Influent (n [ 8) and Mem Eff e Membrane Effluent (n [ 8) for all eight sampling events.

the detection of the viruses in this study. This was concluded based on the average Cp values for all samples spiked with BEV and a p-value > 0.05.

3.3.

Enteric virus removal by MBR

The log removal values for HAdV (Kuo et al., 2010), EV and NoV GGII were calculated from the MBR alone (membrane influent and effluent sample points) and the entire WWTP (influent and

membrane effluent sample points). Fig. 4 shows the log removal values between the membrane influent and effluent from the eight different samples for HAdV, EV and NoV GGII. As shown in the figure, HAdV removal ranged from 4.1 to 6.3 (average 5.5 and 0.8 std) as compared to 4.1 to 6.8 (average 5.1 and 0.9 std) and 3.5 to 4.8 (average of 3.9 and 0.5 std) log units for EV and NoV GGII, respectively for removal by the MBR. In addition to the membrane removal efficiency, we also determined the removal achieved by the entire WWTP. As shown in Fig. 5, HAdV removal

Fig. 4 e Virus removal by the MBR (membrane influent and effluent). HAdV (n [ 8), EV (n [ 8) and NoV GGII (n [ 8). Detection limit was used for 4/8 membrane effluent samples for NoV GGII.

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Fig. 5 e Virus removal between the influent and MBR effluent. HAdV (n [ 8), EV (n [ 8) and NoV GGII (n [ 8). Detection limit was used for 4/8 membrane effluent samples for NoV GGII.

ranged from 2.2 to 3.6 (average 3.0 and 0.5 std) and the entire WWTP ranged from 1.9 to 4.6 log units (average 3.6 log units and 0.9 std) and log units for the MBR. According to our results, NoV GGII was able to achieve a higher overall removal (4.7 log units) as compared to HAdV (3.1 log units) and EV (3.6 log units) for the entire treatment process. There was no significant difference ( p-value > 0.05) observed between HAdV and EV removal by the MBR for all eight sampling events.

3.4. Distribution of viruses between settled particles and supernatant The viral nucleic acids from 16 different samples were analyzed for overall virus concentration detected in the membrane influent (settled sludge and filtered supernatant) by real-time PCR and calculated using Equation (2) for HAdV, EV and NoV GII. The results were compiled over the duration of eight months to observe if any fluctuations occurred in the concentration of HAdV, EV and NoV GII as compared to the concentrations for each individual sampling event. As shown in Fig. 6, the overall HAdV membrane influent concentration (Kuo et al., 2010) ranged from 7.6 to 9.1 log units/L (average 8.5). The concentration in the settled sludge had an average of 9.0 (range of 8.5 to 9.2 log units) and 4.9 log units/L (range of 4.9 to 5.9 log units) for the filtered supernatant Fig. 6 also shows the distribution for the 24 individual samples analyzed for EV. The membrane influent ranged from 5.9 to 7.6 log units/L (average 6.8 log units/L), while the concentration in the settled sludge and filtered supernatant ranged from 6.2 to 7.9 (average 7.0 log units/L) and 2.3 to 5.6 log units/L (average 4.1 log units/L), respectively. The average log influent concentration of NoV GII ranged between 5.3 and 7.0 log units/L (average 6.2 log units/L), while the concentration in the settled sludge and filtered supernatant ranged from 5.2 to 5.6 (average 5.4 log units/ L) and 1.7 to 3.4 log units/L (average 2.5 log units/L), respectively.

Interestingly, NoV GGII was detected in all eight influent samples, but was only detected in four of the eight membrane influent samples. This observation could suggest additional removal due to sorption and settling prior to secondary biological treatment. Furthermore, as shown in Fig. 7, HAdV influent log concentration averaged 6.3 viruses/L and entering the MBR at an average 8.5 viruses/L. EV log concentration averaged 5.2 and 6.8 viruses/L for influent and MBR influent, respectively. Interestingly, NoV GGII averaged a log concentration of 6.2 viruses/L as influent but decreased to 5.1 viruses/L in the MBR influent.

4.

Discussion

4.1.

Enteric virus occurrence

The results presented in the current study provide conclusive evidence of the removal efficiencies of EV and NoV with the use of membrane technology. Our study is one of few (Kuo et al., 2010; da Silva et al., 2007) that have looked at the removal of enteric viruses in full-scale MBR WWTP systems. To the best of our knowledge, this is the first study to determine the concentration and removal of EV in full-scale MBR WWTP. In this study we evaluated the removal of EV and NoV GGII over a period of eight months (JanuaryeAugust 2008) from a WWTP located in Traverse City, Michigan. All samples were analyzed below detection limit for NoV GGI throughout the study. Our findings are consistent (within 10%) with past full-scale conventional and MBR WWTPs studies (Kitajima et al., 2009; Laverick et al., 2004; Nordgren et al., 2009; da Silva et al., 2007) showing an average 72% NoV detection in both influent and effluent samples as compared to 63% of our samples. It was observed in the current study that NoV GGII was present in the influent for all eight sampling events over a period of eight months at an average concentration of 7.7 log units/L

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Fig. 6 e Overall distribution of HAdV (1), EV (2) and NoV GGII (3) for the entire eight month sampling period. Inf-Membrane Influent (n [ 8), Settled Sludge (n [ 8) and Filtered Supernatant (n [ 8) for each HAdV and EV. Detection limit used for 4/8 of the NoV GGII samples.

(5.1
107 viruses/L) from the winter through summer months. Interestingly, NoV GGI was not detected in any of the thirty-two samples over the course of eight months. It is possible that NoV GGI was present in our samples at a concentration below our detection limit (100 viruses/rxn). However, in our study NoV GGII was detected in 8/8 influent (average 4 to 6 log units/L), 6/8 primary effluent (2 to 8 log units/L), 4/8 membrane influent (5 to 6 log units/L) and 0/8 effluent samples. The results here indicate the highest and lowest concentration of NoV GGII was in January at 8.6 log units/L (4.0
108 viruses/L) and 5.9 log units/L in July

(1.2
105 viruses/L), respectively. However, the samples for February, March, April (#1 and #2) and August averaged 5.9 log units/L (7.8
105 viruses/L), suggesting no significant ( p-value > 0.05) seasonal variation was found in the concentration of NoV GGII in the influent samples.

4.2.

Enteric virus removal by MBR

The removal values determined throughout this study clearly indicate that the MBR system is capable of achieving an

Fig. 7 e Concentration of HAdV (1), EV (2) and NoV GGII (3) between the influent (inf) (n [ 8) and membrane influent (Mem inf) (n [ 8) samples. Membrane influent samples are comprised of both the settled sludge and supernatant.

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average HAdV removal of 5.5 log unit (0.8 std) as compared to 5.1 (0.9 std) and 3.9 (0.4 std) for EV and NoV GGII, respectively for removal by the MBR. Interestingly, in the current study NoV GGII was only found in 4/8 membrane influent samples as compared to 8/8 in the influent samples. We observed almost complete removal (>5.0 log units) of NoV GGII during primary settling in half of the samples or an average 1.0 log unit for the positive membrane influent samples. In comparison, these results are not consistent with those reported by Katayama et al. (2008) where NoV GGI and NoV GGII were routinely detected (92% and 89% of samples, respectively) in the final effluent. The NoV removal results observed in the current study are consistent with those reported by da Silva et al. (2007), where a full-scale MBR WWTP in France was able to achieve up to 5.5 and 5.2 logs reduction of NoV GGI and NoV GGII, respectively. In that study, NoV GGI was detected in 73% of the influents and only 18% of the effluent samples and NoV GGII in 100% and 0% of the influent and effluent samples, respectively. However, the assay used had a detection limit of 5.0
103 and 2.0
102 viruses/L for NoV GGI and NoV GGII, respectively. As a result of a higher detection limit NoV GGI could still be present in the effluent at a concentration of 500 viruses/L and NoV GGII at 200 viruses/L. During the current study we used the NoV GGI assay published by da Silva et al. (2007). However, we were able to optimize the assay to a sensitivity of 100 viruses/rxn; depending on the sample volume is approximately 100e200 viruses/L for the volume ranges we sampled. The most interesting result observed during the current study was the overall removal of NoV GGII in the January sample. It was observed that the combined unit treatment processes achieved a 7.4 log reduction, approximately 2 log units higher removal than the average removal observed in the current study and reported by da Silva et al. (2007). The differences observed between these studies are attributed to the high concentration of NoV GGII that was detected in our influent sample and the lack of detection in the final membrane effluent. Our results indicate that a full-scale MBR system is able to attain at least a 4.1 log (7.4 log unit was the maximum removal observed) reduction for NoV GGII through the membrane alone and 5.0 log reduction for the entire treatment process. Several past studies (Zheng and Liu, 2006, 2007; Lv et al., 2006; Ahn et al., 2001; Hu et al., 2003; Oota et al., 2005; Poyatos et al., 2007; Shang et al., 2005; Tam et al., 2007; Ueda and Horan, 2000; Zhang and Farahbakhsh, 2007; Ottoson et al., 2006) have also determined the removal of both viruses and bacteriophage through bench and pilot scale experiments. Ottoson et al. (2006), observed removal values for EV and NoV of 0.5e1.8 and 1.0e1.1 log units, respectively in a pilot scale study. However, in the current study we determined the average removal values for EV and NoV GGII of 5.1 and 3.9 log units, respectively. We have shown in a full-scale system, EV and NoV GGII removal was approximately 3.9 and 3.0 log unit higher as compared to lab and bench scale systems, respectively. It is plausible that the difference between our study and Ottoson et al. (2006), is due to the fact that we sampled an average 69 L (maximum, 91 L) for the membrane influent and 428 L (maximum, 696 L) for membrane effluent samples (Table 1), as compared to only 1 L grab samples (for both influent and effluent samples) in their study. The reduced sample volumes

used in their study could have significantly under estimated the actual concentrations. Sampling higher volumes of water, especially in waters where the viral concentrations are expected to be lower has been suggested (USEPA, 2001; Sobsey and Glass, 1980; Polaczyk et al., 2007).

4.3. Removal efficiency by MBR treatment for different viruses It is not fully understood why HAdV was removed more efficiently as compared to both EV and NoV during the membrane process. It is possible that HAdV (90e100 nm) could be removed at an increased log unit since this virus is approximately 2e3 times the size of both NoV (27e38 nm) and EV (27e30 nm). Furthermore, the concentration of HAdV in the influent was higher on average as compared to both EV and NoV. It was assumed that due to the high influent concentration, a higher removal might be achieved within our detection limits. Conversely, we observed a much higher removal (5.0 log units) of NoV as compared to HAdV and EV (3.9 and 3.6 log units, respectively) for the entire treatment process. It is plausible that the adsorptive behavior depends on each individual virus, and differs even between each serotype (Gerba, 1984). The outer protective layer of each virus is composed of various protein polypeptides containing amino acids and upon ionization the viral capsid takes on an electrical charge. As previously stated (Gerba, 1984), depending on the virus being studied, the acquired surface charge and hydrophobicity play a significant role on the interaction between particles. This could possibly explain why the removal of HAdV, EV and NoV are not consistent. More research is needed to determine the reasons why different log removal values were observed using MBR treatment.

4.4.

Association of viruses with solid particles

Sorption to organic matter and particles during secondary biological treatment reduces virus concentration before reaching the disinfection process. Virus sorption and removal within biological treatment are often dependent on several factors, including isoelectric point, hydrophobicity, temperature, pH, suspended solids, hydraulic retention time and type and strain of EV (Gerba, 1984). To date, human enteric virus sorption in full-scale wastewater treatment has not been thoroughly researched. During the current study, it was observed that EV and HAdV (Kuo et al., 2010) were detected in every influent (08/08 for each virus) and almost every membrane effluent sample (08/08 and 05/08, respectively). However, NoV GGII was detected in all eight influent samples but was not detected in the membrane effluent. Interestingly, both HAdV and EV log concentration increased by approximately 2.2 and 1.6 viruses/L, respectively but NoV GGII decreased by 1.1 viruses/L between the influent and secondary biological treatment (Fig. 7). It is possible that increased removal NoV in primary sludge would reduce the concentration entering secondary biological treatment and could account for the decreased concentration of NoV in the RAS. This could possibly be explained by the surface properties and physicochemical conditions prior to secondary treatment, possibly resulting in a stronger attraction to primary sludge particles.

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Our results concluded that NoV GGII was below detection limit in the membrane effluent suggesting almost complete removal. In contrast, as previously reported (da Silva et al., 2007), NoV GI and NoV GII were both detected in the effluent on two different samples. It was concluded that an MBR is not an absolute barrier for restricting virus passage. However, in the current study it was shown that NoV GGII was unable to pass through the membrane within our detection limit. In addition, HAdV and EV were routinely detected in both membrane influent and effluent samples (16/16 and 16/16, respectively). Our results indicate that HAdV, EV and NoV GGII are associated more with the settled sludge as compared to the filtered supernatant during secondary biological treatment for each individual sampling event. HAdV, EV and NoV GII were detected in an average 99.8%, 97.1% and 96.0% of the membrane influent as settled sludge (Fig. 6). Suggesting that less than 1% of HAdV, 3% of EV and approximately 4% of NoV GII concentration contributes to the viral load in the membrane influent as supernatant for viruses that did not settle out. Interestingly, during the January sample the EV concentration in the settled sludge reduced to approximately 77% of the viral load into the membrane. This may indicate that during the winter months, EV attachment to flocs could be reduced as compared to warmer months but further study is required to confirm and explain the observation. However, it is also plausible that various concentrations of different types and strains of EV could have been present in the wastewater matrix during the winter sampling months (mainly January and February). This type of occurrence was previously observed (Gerba, 1984) where EV adsorption to natural solids appeared to be dependent on both type and specific strain. However, further analysis would have to be conducted on our samples to compare the different species of HAdV, EV and NoV GGII present throughout the current study. This study only focused on the removal of viruses and viral particles by means of physical separation and the capability of the MBR to restrict nanoparticles from passing into the permeate. No infectivity analysis (culture method) was conducted during this study. Without an infectivity analysis the potential for underestimation of infectious virus removal is possible. Future research is needed to help understand the ability of the MBR system to restrict infectious viruses from exiting the secondary biological treatment and possibly being introduced into a receiving waterway.

5.

Conclusion

 In this study, average removal values for HAdV, EV and NoV GGII were 5.5 (4.1 to 6.3, range), 5.1 (4.1 to 6.8, range) and 3.9 (3.5 to 4.8, range) log units, respectively by the MBR process.  HAdV, EV and NoV GGII were removed at approximately 3.0 (2.2 to 3.6, range), 3.6 (1.9 to 4.6, range) and 4.7 (4.6 to 5.1, range) log units throughout the entire treatment process.  After the membrane influent samples were allowed to settle, 99.8%, 97.1% and 96.0% of HAdV, EV and NoV GII concentration was associated with the settled solids showing a high affinity of the viruses for the suspended solids.  NoV GII was only detected in 4/8 membrane influent samples suggesting a potential stronger affinity toward

2749

primary sludge particles prior to secondary biological treatment as compared to HAdV and EV.

references

Ahn, K.H., Song, K.G., Yeom, I.T., Park, K.Y., 2001. Performance comparison of direct membrane separation and membrane bioreactor for domestic wastewater treatment and water reuse. Water Science and Technology 1, 315e323. Cicek, N., 2003. A review of membrane bioreactors and their potential application in the treatment of agricultural wastewater. Canadian Biosystems Engineering 45, 637e649. da Silva, K., Le Saux, J.C., Parnaudeau, S., Pommepuy, M., Elimelech, M., Le Guyader, F.S., 2007. Evaluation of removal of noroviruses during wastewater treatment, using real-time reverse transcription-PCR: different behaviors of genogroups I and II. Applied and Environmental Microbiology 73, 7891e7897. Dierssen, U., Rehren, F., Henke-Gendo, C., Harste, G., Heim, A., 2007. Rapid routine detection of enterovirus RNA in cerebrospinal fluid by a one-step real-time RT-PCR assay. Journal of Clinical Virology 42, 58e64. Gerba, C.P., 1984. In: Laskin, A.I. (Ed.), Applied and theoretical aspects of virus adsorption to surfaces. Advances in Applied Microbiology, vol. 30. Academic Press, Inc, Orlando, FL, USA, pp. 133e168. Gerba, C.P., Pepper, I.L., Whitehead III, L.F., 2002. A risk assessment of emerging pathogens of concern in the land application of biosolids. Water Science and Technology 46, 225e230. Godfree, A., Farrell, J., 2005. Processes for managing pathogens. Journal of Applied Environmental Quality 34, 105e113. Haramoto, E., Katayama, H., Oguma, K., Ohgaki, S., 2007. Quantitative analysis of human enteric adenoviruses in aquatic environments. Journal of Applied Microbiology 103, 2153e2159. Hu, J.Y., Ong, S.L., Song, L.F., Feng, Y.Y., Tan, W.T., Lee, L.Y., Ng, W.J., 2003. Removal of MS2 bacteriophage using membrane technologies. Water Science and Technology 47 (12), 163e168. Jiang, S., Dezfulian, H., Chu, W., 2005. Real-time quantitative PCR for enteric adenovirus serotype 40 in environmental waters. Canadian Journal of Microbiology 51, 393e398. Jime´nez-Clavero, M.A., Escribano-Romero, E., Mansilla, C., Go´mez, N., Co´rdoba, L., Roblas, N., Ponz, P., Ley, V., Sa´iz, J.C., 2005. Survey of bovine enterovirus in biological and environmental samples by a highly sensitive real-time reverse transcription-PCR. Applied and Environmental Microbiology 71, 3536e3543. Kageyama, T., Kojima, S., Shinohara, M., Uchida, K., Fukushi, F., Hoshino, F.B., Takeda, N., Katayama, K., 2003. Broadly reactive and highly sensitive assay for Norwalk-like viruses based on real-time quantitative reverse transcription-PCR. Journal of Clinical Microbiology 41, 1548e1557. Katayama, H., Haramoto, E., Oguma, K., Yamashita, A., Tajima, H., Nakajima, H., Ohgaki, S., 2008. One-year monthly quantitative survey of noroviruses, enteroviruses, and adenoviruses in wastewater collected from six plants in Japan. Water Research 42, 1441e1448. Kitajima, M., Haramoto, E., Phanuwan, C., Katayama, H., Ohgaki, S., 2009. Detection of genogroup IV norovirus in wastewater and river water in Japan. Letters in Applied Microbiology 49, 655e658. Kuo, D., Simmons, F.J., Blair, S., Hart, E., Rose, J.B., Xagoraraki, I., 2010. Assessment of human adenovirus removal in a full-scale membrane bioreactor treating municipal wastewater. Water Research 44, 1520e1530.

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Laverick, M.A., Wyn-Jones, A.P., Carter, M.J., 2004. Quantitative rtpcr for the enumeration of noroviruses (Norwalk-like viruses) in water and sewage. Letters in Applied Microbiology 39, 127e136. Lv, W., Zheng, X., Yang, M., Zhang, Y., Liu, Y., Liu, J., 2006. Virus removal performance and mechanism of a submerged membrane bioreactor. Process Biochemistry 41, 299e304. Nordgren, J., Matussek, A., Mattson, A., Svensson, L., Lindgren, P. E., 2009. Prevalence of norovirus and factors influencing virus concentrations during one year in a full-scale wastewater treatment Plant. Water Research 43, 1117e1125. Oota, S., Murakami, T., Takemura, K., Noto, K., 2005. Evaluation of MBR effluent characteristics for reuse purposes. Water Science and Technology 51 (6e7), 441e446. Ottoson, J., Hansen, A., Westrell, T., Johansen, K., Norder, H., Stenstrom, T.A., 2006. Removal of noro- and enteroviruses, Giardia cysts, Cryptosporidium oocysts, and fecal indicators at four secondary treatment plants in Sweden. Water Environment Research 78, 828e834. Polaczyk, A., Jacqueline, L., Roberts, M., Hill, V.R., 2007. Evaluation of 1MDS electropositive microfilters for simultaneous recovery of multiple microbe classes from tap water. Journal of Microbiological Methods 68, 260e266. Poyatos, J.M., Molina-Munoz, M., Moreno, B., Gonzalez-Lopez, J., Hontoria, E., 2007. Effect of the mixed liquor suspended solid on permeate in a membrane bioreactor system applied for the treatment of a sewage mixed with wastewater of the milk from the dairy industry. Journal of Environmental Science and Health, Part A 42, 1005e1012. Rajal, V.B., McSwain, B.S., Thompson, D.E., Leutenegger, C.M., Kildare, B.J., Wuertz, S., 2007. Validation of hollow fiber ultrafiltration and real-time PCR using bacteriophage PP7 as surrogate for the quantification of viruses from water samples. Water Research 41, 1411e1422. Rose, J., Dickson, L., Farrah, S., Carnahan, R., 1996. Removal of pathogenic and indicator microorganisms by a full-

scale water reclamation facility. Water Research 30, 2785e2797. Shang, C., Wong, H.M., Chen, G., 2005. Bacteriophage MS-2 removal by submerged membrane bioreactor. Water Research 39, 4211e4219. Sobsey, M.D., Glass, J.S., 1980. Poliovirus concentration from tap water with electropositive absorbent filters. Applied and Environmental Microbiology 40, 201e210. Tam, L.S., Tang, T.W., Lau, G.N., Sharma, K.R., Chen, G.H., 2007. A pilot study for the wastewater reclamation and reuse with MBR/RO and MF/RO systems. Desalination 202, 106e113. Ueda, T., Horan, N.J., 2000. Fate of indigenous bacteriophage in a membrane bioreactor. Waste Research 34, 2151e2159. USEPA, 2001. Manual of Methods for Virology, Chapter 14. EPA 600/4e84/013. Office of Water, U.S. Environmental Protection Agency, Washington, DC. Viau, E., Peccia, J., 2009. Survey of wastewater indicators and human pathogen genomes in biosolids produced by class A and class B stabilization treatments. Applied and Environmental Microbiology 75, 164e174. Xagoraraki, I., Kuo, D.H.-W., Wong, K., Wong, M., Rose, J.B., 2007. Occurrence of human adenoviruses at two recreational beaches of the Great Lakes. Applied and Environmental Microbiology 73, 7874e7881. Zhang, K., Farahbakhsh, K., 2007. Removal of native coliphages and coliform bacteria from municipal wastewater by various wastewater treatment processes: implications to water reuse. Water Research 41, 2816e2824. Zheng, X., Liu, J.X., 2006. Mechanism investigation of virus removal in a membrane bioreactor. Water Science Technology: Water Supply 6 (6), 51e59. Zheng, X., Liu, J., 2007. Virus rejection with two model human enteric viruses in membrane bioreactor system. Science in China Series B e Chemistry 50, 397e404.

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Available at www.sciencedirect.com

journal homepage: www.elsevier.com/locate/watres

Biofiltration of wastewater treatment plant effluent: Effective removal of pharmaceuticals and personal care products and reduction of toxicity J. Reungoat a,*, B.I. Escher b, M. Macova b, J. Keller a a b

The University of Queensland, Advanced Water Management Centre (AWMC), QLD 4072, Australia The University of Queensland, National Research Centre for Environmental Toxicology (Entox), QLD 4108, Australia

article info

abstract

Article history:

This study investigates biofiltration for the removal of dissolved organic carbon (DOC),

Received 6 December 2010

pharmaceuticals and personal care products (PPCPs), and for the reduction of non-specific

Received in revised form

toxicity expressed as baseline toxicity equivalent concentration (baseline-TEQ). Two

11 February 2011

filtering media, sand and granular activated carbon, were tested. The influence of pre-

Accepted 12 February 2011

ozonation and empty-bed contact time (EBCT, from 30 to 120 min) was determined. The

Available online 19 February 2011

experiments were performed at a pilot-scale with real WWTP effluent. A previous study showed that biological activity had developed on the filtering media and dissolved organic

Keywords:

removal had reached a steady state before sampling commenced. The results show that

Organic micropollutants

biological activated carbon (BAC) has a good potential for the removal of DOC (35e60%),

Biological activated carbon filtration

PPCPs (>90%) and baseline-TEQ (28e68%) even without pre-ozonation. On the contrary, the

Sand filtration

sand shows limited improvement of effluent quality. Varying the EBCT does not influence

Baseline toxicity

the performance of the BAC filters; however, dissolved oxygen concentration could be

equivalent concentrations

a limiting factor. The performances of the BAC filters were stable for over two years sug-

Wastewater reclamation

gesting that the main mechanism of organic matter and PPCPs removal is biodegradation. It is concluded that BAC filtration without pre-ozonation could be implemented as a low cost advanced treatment option to improve WWTP effluent chemical quality. ª 2011 Elsevier Ltd. All rights reserved.

1.

Introduction

In the last decade, numerous studies have demonstrated the presence of pharmaceuticals and personal care products (PPCPs) in domestic wastewater worldwide. These PPCPs are removed to different degrees by the biological processes commonly used in wastewater treatment plants (WWTP). While some PPCPs (e.g. ibuprofen, paracetamol) are effectively removed, others (e.g. carbamazepine, diclofenac) are barely affected (Onesios et al., 2009). As a result, PPCPs are released into surface waters via WWTPs effluents. This situation is of

concern, particularly regarding pharmaceuticals as these compounds have been designed to be bioactive and the effects of low-level but long term exposure on aquatic life are still largely unknown. Even though there is no sound evidence of impact on human health, the precautionary principle should be applied when treated wastewater is discharged to water bodies that are used as drinking water sources or considered for indirect potable reuse. Therefore, additional steps have to be considered for the advanced treatment of WWTP effluents to reduce the discharge load of PPCPs into sensitive receiving waters.

* Corresponding author. Tel.: þ61 734466251; fax: þ61 733654726. E-mail address: [email protected] (J. Reungoat). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.02.013

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Several technologies have proven to be effective in removing PPCPs from waters of various qualities: activated carbon adsorption (Nowotny et al., 2007; Snyder et al., 2007; Ternes et al., 2002; Westerhoff et al., 2005; Yu et al., 2008), ozonation and advanced oxidation processes (Esplugas et al., 2007; Hollender et al., 2009; Huber et al., 2003, 2005; Kim et al., 2008; Nakada et al., 2007; Reungoat et al., 2010; Ternes et al., 2003; Zwiener and Frimmel, 2000) and membrane filtration (Kimura et al., 2004; Snyder et al., 2007; Yoon et al., 2007). Activated carbon adsorption and ozonation are considered to be economically feasible for advanced treatment of WWTPs effluents (Joss et al., 2008). Their combination has proven to be very effective in removing organic micropollutants and decrease nonspecific and specific toxicity in a treated wastewater (Reungoat et al., 2010). However, ozonation is known to lead to the formation of by-products largely not identified to date, which raises concerns regarding their potential impact on the environment and human health (Benner and Ternes, 2009; Dodd et al., 2010; Radjenovic et al., 2009; Stalter et al., 2010). While activated carbon does not generate by-products, it has to be renewed regularly and disposed of or regenerated, generally off site. Biofiltration systems are typically robust, simple to construct and have low energy requirements (Pipe-Martin et al., 2010). They therefore potentially represent an interesting alternative technology for the removal of organic micropollutants. The most common technologies are sand filtration, biological activated carbon (BAC) filtration, riverbank filtration and managed aquifer recharge. Whereas the removal of PPCPs from drinking water sources and treated wastewater has been investigated in riverbank filtration and managed aquifer recharge systems (Baumgarten et al., 2010; Petrovic et al., 2009; Rauch-Williams et al., 2010), no study has been published specifically on the treatment of WWTP effluents with engineered BAC filters to our knowledge. A BAC filter consists of a fixed bed of granular activated carbon (GAC) supporting the growth of bacteria attached on the GAC surface. This technology has been used for many years for drinking water treatment, usually after ozonation, and has proven to be able to significantly remove natural organic matter, ozonation by-products, disinfection by-products precursors as well as odour and taste compounds (e.g. geosmin and 2-methylisoborneol) (Simpson, 2008). A Swiss study estimated the cost of several options to upgrade WWTPs for PPCPs removal, sand filtration and ozonation were in the same range, 5.9 to 32.2 and 4.8 to 36.7 CHF EP1 a1 respectively (depending on the plant size) whereas activated carbon adsorption cost was higher, between 21.5 and 95 CHF EP1 a1 (Moser, 2008). BAC filtration costs can be expected to lie in the same range as sand filtration and therefore it potentially represents an interesting alternative technology for the removal of organic micropollutants. In the present study, we investigated the removal of selected PPCPs and the decrease of non-specific toxicity quantified with the Microtox assay, which is based on the bioluminescence inhibition of the marine bacterium Vibrio fischeri, in pilot-scale biofilters treating the effluent from a municipal WWTP. Bioanalytical tools complement chemical analysis to evaluate water quality (Macova et al., 2010;

Reungoat et al., 2010). Out of a test battery of six bioassays, the Microtox assays was selected here to obtain a measure of the sum of all organic micropollutants in a water sample, as it reacts rather non-specifically to all chemicals, and to estimate which fraction of the total effective organic micropollutants is constituted by the quantified PPCPs. The objective of the study was to evaluate and compare the performance of sand and granular activated carbon as biofiltering media during long term operation and assess the influence of pre-ozonation and empty-bed contact time (EBCT) on the treatment efficiency. The results obtained were also compared with a full-scale biological activated carbon filter operating on the same feed water.

2.

Materials and methods

2.1.

Pilot-scale biofilters

Three pilot-scale biofilters (Fig. 1) were set up in December 2006 at the South Caboolture Water Reclamation Plant (Reungoat et al., 2010; van Leeuwen et al., 2003). The reclamation plant receives water from a 40,000 equivalent people WWTP using a sequencing batch reactor process which achieves partial nutrient removal. The columns are 3 m high and 22.5 cm internal diameter; they consist of 80  1 cm filtering bed supported by a 20 cm layer of gravel at the bottom, the top of the columns are filled with water. One column uses sand as filtering medium and the other two are filled with “Acticarb BAC GA1000N” granular activated carbon (Activated Carbon Technologies Pty Ltd, Australia). Details on the filtering media can be found in the supplementary information (Table SI 1). The filters were fed with water from the main stream of the reclamation plant. The sand filter was originally fed with non-ozonated water (referred to as SAND 1) and later with ozonated water (referred to as SAND 2). The activated carbon filters BAC 1 and BAC 2 were continuously fed with non-ozonated and ozonated water respectively. Non-ozonated water refers here and in the rest of the manuscript to the effluent before the main ozonation stage but after the denitrification, the pre-ozonation and the dissolved air flotation and filtration. A prior study showed that the ozone dose added in the pre-ozonation is very low relatively to the DOC concentration at this stage (0.1 mgO3 mg1DOC) and does not lead to any significant removal of DOC or PPCPs (Reungoat et al., 2010). Compressed air was bubbled in the water above the filtering bed to ensure a high level of dissolved oxygen to support biological activity; this was later switched to 90% oxygen. The empty bed contact time (EBCT) was controlled by adjusting the effluent flow rate at the bottom of the columns. The top layer of each filtering bed (sand and BAC filters) was stirred weekly to avoid clogging of the columns and water was withdrawn from above the filter at the same time. This operation removed some of the biomass from the top of the filter; however no backwash of the entire filter was performed. A previous study showed that biological activity had developed on the filtering media and dissolved organic removal had reached a steady state by June 2007 (Pipe-Martin et al., 2010).

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 7 5 1 e2 7 6 2

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Fig. 1 e South Caboolture Water Reclamation Plant (a) and pilot-scale biofilters (b) WWTP [ wastewater treatment plant, HRT [ hydraulic residence time, SRT [ sludge residence time.

2.2.

Full-scale activated carbon filter

The activated carbon filter of the reclamation plant has an empty-bed contact time of 18 min. The filtering media is of the same type of GAC as the one used in the pilot-scale columns with a slightly higher particle diameter (Table SI 1). The GAC was replaced in March 2008, 4 months prior to the first sampling campaign.

2.3.

Sample collection

Samples were collected during 4 campaigns from the influent and effluent streams of the filters. For the first campaign (July and August 2008), four sets of 24 h composite samples were collected. The pilot-scale filters were then operating with an empty-bed contact time (EBCT) of 120  10 min. During the second campaign (December 2008), four sets of 24 h composite samples were collected from both BAC filters operating with various EBCTs (30  1, 60  2, 90  4 and 120  6 min) to investigate the influence of this parameter. A waiting period of one week was observed from the moment the EBCT was modified prior to the sample collection. Finally, a third and fourth sampling campaigns were carried out in October 2009 and July 2010 to confirm the results obtained in previous campaigns on a longer term and to investigate the

performance of sand filtration after ozonation. The feed to the sand filter was changed from non-ozonated to ozonated water 6 months prior to the third campaign. Two sets of 24 h composite samples and 3 grab samples were collected in the third and fourth campaign respectively. Pilot-scale filters were operating with an EBCT of 60 min. Three month prior to the fourth campaign, the air supply at the top of BAC 1 and BAC 2 was switched to 90% oxygen used in the reclamation plant’s ozone generators to ensure higher dissolved oxygen levels in the influent water. As the flow rates through the plant and the biofilters were steady during each of the various sampling campaigns, representative 24 h composite samples were obtained using a continuous flow pump (7 mL min1). Samples were collected into glass bottles pre-washed with MilliQ water and HPLC grade methanol and rinsed with the water sampled moments before sampling commenced. The samples were protected from light and refrigerated during collection. In the fourth campaign, the grab samples were collected directly in the amber glass bottles (see below). The water temperatures were 22  2  C, 27  2  C, 26  1  C, and 22  1  C during the first, second, third and fourth sampling campaigns respectively and pH was 7.0  0.5 for all sampling events. For micropollutant analysis, 1e2 L of sample were transferred into methanol washed amber glass bottles and

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preserved with sodium thiosulfate (80 mg L1) when sent to Queensland Health Forensic and Scientific Services (QHFSS). For the bioassays, 2 L of sample were transferred into methanol washed amber glass bottles and hydrochloric acid (36%) was added to a final concentration of 5 mM for preservation. For dissolved organic carbon (DOC) measurements, 100 mL were collected in plastic (HDPE) bottles. All bottles were rinsed with sample before filling. All samples were transported on ice and stored frozen or at 4  C prior to analysis.

2.4.

Analytical methods

2.4.1.

Dissolved oxygen

During the second sampling campaign, dissolved oxygen (DO) concentration was measured with a YSI 6562 Dissolved Oxygen Probe connected to a YSI MDS 650 multi-parameter display system. A YSI 6560 conductivity and temperature probe connected to the same multi-parameter display system allowed to simultaneously correct the DO concentration value and display it directly as a concentration. During the third sampling campaign, DO was measured using a CyberScan PCD 650 multiparameter instrument (Eutech Instruments) equipped with temperature, pH, DO and conductivity probes. The simultaneous measurement of DO, temperature and conductivity allowed correcting the DO concentration value and displaying it directly as a concentration.

2.4.2.

Dissolved organic carbon

Prior to analysis, samples were filtered through a 0.45 mm PTFE membrane. The dissolved organic carbon (DOC) was measured as non-purgeable organic carbon (NPOC) with an Analytik Jena multi N/C 3100 instrument. For each sample, 2e3 replicates were measured, giving a relative standard deviation of less than 3%.

2.4.3.

Micropollutants

For the first and second campaigns, 57 PPCPs were quantified by QHFSS according to the method described in Reungoat et al. (2010). For the third and the fourth campaigns, 29 PPCPs were quantified at the AWMC using a different method described in the supplementary information (SI 2). Both methods consisted of solid phase extraction (SPE), elution, concentration, and analysis by liquid chromatography coupled with tandem mass spectrometry (LC/MSeMS).A list of compounds with some of their properties is available in the supplementary information (Table SI 4).

2.4.4.

required to produce the same effect as the mixture of the various different compounds in the sample (Escher et al., 2008). baseline  TEQ bio ¼

EC50 ðvirtual baseline toxicantÞ EC50 ðsampleÞ

(1)

The EC50(sample) was experimentally determined and is given in dimensionless units of relative enrichment factor and the EC50(virtual baseline toxicant) was derived for a virtual compound with a logKow of 3 and a molecular weight of 300 g mol1 from a Quantitative Structure Activity Relationship (QSAR) for baseline toxicity (Eq. (2)), which was parameterized with a set of known baseline toxicants in a previous study (Escher et al., 2008).
 logð1=EC50 ðin units of mol=LÞÞ ¼ 1:69 þ 0:84log Dlipw ðpH7Þ (2) If the concentrations of all chemical constituents i of a mixture and their relative potency RPi are known, one can also calculate the baseline-TEQchem. A comparison between baseline-TEQbio and baseline-TEQchem allows us to evaluate how much the quantified PPCPs contribute to the observed mixture toxicity.

baseline  TEQ chem ¼

n X

baseline  TEQ i ¼

i¼1

n X

RPi ,Ci

(3)

i¼1

The relative potency RPi is a measure of how toxic a given chemical i would be in comparison to the reference compound, the virtual baseline toxicant (Eq. (3)). A RPi > 1 indicates a higher toxicity than the reference compound, a RPi < 1 a lower toxicity. RPi ¼

EC50 ðvirtual baseline toxicantÞ EC50 ðiÞ

(4)

As the EC50(i) were not available for most of the analysed PPCP, we estimated them with the QSAR given in (Eq. (2) (Escher et al., 2008)). The liposome water distribution ratios logDlipw(pH 7), which are the input parameters of this QSAR and which were experimentally determined for the calibration chemicals but are not experimentally available for all PPCPs

Bioassays

Six bioassays, discussed in details in Macova et al. (2010), were applied to the samples collected in the study. For the purpose of comparison with PPCPs, we only use the bioluminescence inhibition test with Vibrio fischeri, which is widely recognised in the field of ecotoxicology as the standard assay for acute cytotoxicity and reflects the mixture baseline toxicity of a broad spectrum of compounds. Water samples were cleaned and enriched by solid phase extraction as described previously. Results were expressed as baseline toxicity equivalent concentrations (baseline-TEQbio) (Eq. (1)). The TEQ represents the concentration of a virtual baseline toxicant that would be

Fig. 2 e Mean removals of baseline-TEQbio, baselineTEQchem and DOC observed after SAND 1, BAC 1, OZONATION, BAC 2 and the full-scale activated carbon filter (AC) relatively to the feed for the first sampling campaign. EBCT [ 120 min for SAND 1, BAC 1, BAC 2 and EBCT [ 18 min for AC. Error bars represent the standard deviation of the mean of the four independent samples collected and therefore reflect the temporal variability.

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 7 5 1 e2 7 6 2

investigated here, were calculated from the octanol-water partition coefficients (logKow) of the neutral species and acidity constants pKa according to a procedure described in (Escher et al., 2011). As many of the analysed chemicals were acids and bases, this procedure is necessary and it is not sufficient to use Kow as a measure of bioaccumulation potential (Escher and Schwarzenbach, 2002). A full list of thus calculated RPis is available in the supplementary information (Table SI 4). More details of this approach are described in (Vermeirssen et al., 2010).

3.

Results and discussion

3.1.

Removal of dissolved organic carbon

In the first campaign, the DOC in the feed water (non-ozonated) was 11.2  0.4 mg L1. With an EBCT of 120 min, SAND 1 reduced the DOC by 22  3% (Fig. 2). This is in agreement with what has been previously observed by other researchers in sand columns simulating riverbank filtration or managed aquifer recharge: Maeng et al. (2008) observed up to 20% DOC removal for an EBCT of 4 days; Rauch and Drewes (2004) obtained a removal of 25% after 18 hours of residence time. The investigators observed a faster removal at the top of the columns (corresponding to shorter EBCTs) which is consistent with the result of the present study. The effect of the full scale sand filter preceding the pilot scale filter is assumed to be negligible since the EBCT there is only 15 minutes and backwashes are performed typically every hour preventing the establishment of a biologically active layer. It is therefore suggested that the fraction of DOC removed here corresponds to the more easily biodegradable fraction of the effluent organic matter (EfOM). In BAC 1, the DOC influent level was reduced by 63  1%, which is much higher than what was observed in SAND 1 suggesting that the biodegradable fraction of EfOM is not totally removed in SAND 1. It is also possible that the higher removal observed is due to adsorption of EfOM onto activated carbon. However, a previous study on these filters showed that DOC removal had reached a steady state one year prior to the collection of the samples for the present study (PipeMartin et al., 2010). This indicates that complete breakthrough of EfOM has been reached and it is therefore suggested that biodegradation is responsible for the removal observed even though adsorption might still play a role in the mechanism. Indeed, the surface of the activated carbon is not totally covered by the biofilm and the free areas might still take part in adsorption/desorption processes leading to an increased flux of pollutants to the biofilm (Herzberg et al., 2003). Ozonation reduced the DOC concentration by less than 10% to 10.3  0.6 mg L1 showing that oxidation did not lead to extensive mineralisation of the EfOM but rather a transformation of the organics present. The BAC 2 reduced the DOC by 60  2% reaching the same effluent level as BAC 1. The similar DOC removal observed in BAC 2 compared to BAC 1 is surprising because ozonation is known to increase biodegradability of the organic matter. This indicates that there might be another factor limiting the degradation of EfOM (e.g. EBCT, DO).

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The activated carbon filter of the full-scale plant reduced the DOC concentration by 36% only but with a much shorter EBCT of 18 min. The activated carbon in this filter was renewed 4 months before the sampling campaign took place but that is considered to be a sufficient time for the biological activity to establish (Pipe-Martin et al., 2010; Simpson, 2008). Initial DOC removal efficiencies in GAC filters have been reported to be in the order of 40e90% and then gradually decrease as the DOC breaks through the filter and the biomass establishes. When complete breakthrough of DOC has been reached and the biomass is fully established, the DOC removal stabilises and is only due to biodegradation (Simpson, 2008). Here, it is difficult to say in which phase the filter is given the lack of data before the sample was collected. However, the lower DOC removal observed here compared to the other filters could indicate that the initial phase has already ended and the EfOM is removed mainly by biodegradation. In that case, the lower removal efficiency compared to BAC 1 and BAC 2 is most likely due to the shorter EBCT. However, with an EBCT almost 7 times shorter than in the BACs, the DOC removal was reduced by a factor of 1.5 only consistently with the fact that the most easily (fastest) biodegradable fraction of EfOM would be removed first.

3.2. Removal of pharmaceuticals and personal care products 3.2.1.

Chemical analysis

During the first sampling campaign, 37 PPCPs out of the 57 targeted had a median concentration above their Limit of Quantification (LOQ) in the feed water with gabapentin reaching 3.05 mg L1 (Table SI 5). The concentration of each compound remained in the same range during the whole sampling campaign as indicated by the maximum and minimum values measured. Most of the compounds quantified were reported to be poorly to moderately removed in WWTPs except caffeine, gabapentin and paracetamol (Table SI 4). Caffeine and paracetamol are typically present in very high amounts in raw wastewaters (tens of mg L1 in the present case, data not shown) and therefore can still be quantified in the treated wastewater despite the high removal rates observed. Gabapentin has been reported to be well degraded in WWTPs (>99%) but this is based on one study only (Yu et al., 2006). A preliminary investigation of the WWTP producing the effluent used in this study showed limited removal of around 30% (data not shown). Among these 37 compounds 21 had a median concentration at least 10 times higher than their LOQ; removal percentages are reported for these compounds only in order to be able to express removals in the range of 0 to > 90% and to avoid over-interpretation of variations for the other compounds that could be due to limitations in the chemical analysis method. These compounds still cover a wide range of classes and physicochemical properties. After filtration through SAND 1, 32 compounds still had a median concentration above their LOQ. The 5 compounds removed (atorvastatine, fluoxetine, sertraline, sulfadiazine, triclosan) had initial concentrations that were close to their LOQ before filtration. Among the 21 selected compounds, 12 were not or poorly removed (20%), 8 experienced intermediate removal (23e54%) and only one compound,

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Fig. 3 e Mean removals of selected pharmaceuticals observed after SAND 1, BAC 1, O3 (ozonation), BAC 2 and the full-scale activated carbon filter (AC) relatively to concentrations in the feed (i.e. before the main ozonation stage) for the first sampling campaign. EBCT [ 120 min for SAND 1, BAC 1, BAC 2 and EBCT [ 18 min for AC. Error bars represent the standard deviation of the mean of the four independent samples collected and therefore reflect the temporal variability. No error bar means that the removal indicated is the minimum observed (i.e. compound was below LOQ after treatment).

paracetamol, was well removed (85%) (Fig. 3). Even though no direct conclusion can be made, the fate of PPCPs in WWTPs can be used as a qualitative indication of their biodegradability and/or their propensity to adsorb on the biomass. Indeed, the behaviour of most of the compounds is in accordance with their fate in WWTPs (Table SI 4). It has to be highlighted that the contact time in the sand filter is much shorter than typical hydraulic residence time in WWTPs and the biomass density can be assumed to be far lower than in an activated sludge process. Nevertheless, erythromycin, trimethoprim and roxythromycin experienced intermediate removals of 30, 38 and 54% respectively even though they are poorly removed in WWTPs. Go¨bel et al. (2007) also observed significant removal of these compounds in a sand filter with prior aeration (similarly to this study). This observation indicates clearly a difference in the biodegradation rates of these compounds between the activated sludge and biofiltration processes. Sulfamethoxazole’s concentration consistently increased between 15 and 83%. This fact has already been observed by several researchers in WWTPs and is likely due to the de-conjugation of a sulfamethoxazole metabolite, N4acetyl-sulfamethoxazole (Bendz et al., 2005; Clara et al., 2005; Go¨bel et al., 2007,2005). Filtration through BAC 1 removed 35 PPCPs to levels below their LOQ. Only gabapentin and caffeine were quantified in the effluent with median concentrations of 0.20 and 0.03 mg L1 respectively. Among the 21 selected compounds, 11 were

removed by 90e95% and 10 by more than 95% (Fig. 3). Some of these compounds have been repeatedly reported to be poorly removed in WWTPs: carbamazepine, diclofenac, erythromycin, metoprolol, roxithromycin, sulfamethoxazole, trimethoprim. Another compound known to be poorly removed in WWTPs, iopromide, is here removed by more than 80%. The high removal efficiencies observed for these compounds could be due to adsorption onto the activated carbon surface. Adsorption onto activated carbon is difficult to predict as the mechanism involves several types of interactions. Westerhoff et al. (2005) showed that removal efficiencies of PPCPs by powder activated carbon tend to increase with increasing logKow but some protonated bases and deprotonated acids did not follow this general trend. The GAC contained in BAC 1 has been exposed to typical concentrations of these compounds for several months before sampling. Biomass exposed to low concentration of trace organic chemicals can adapt over time and become able to significantly degrade even compounds considered as persistent (Rauch-Williams et al., 2010). However, GAC filters can also retain an adsorption capacity for compounds present at micrograms per litre levels even after the breakthrough has been observed for DOC (Wang et al., 2007). Therefore, it cannot be concluded at this point if the organic micropollutants are merely adsorbed or also biodegraded. After ozonation, only 16 compounds were quantified with a median concentration above their LOQ. Ozone is known to be able to oxidise many PPCPs but some compounds such as

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 7 5 1 e2 7 6 2

Table 1 e Mean DOC, baseline-TEQbio, baseline-TEQchem and percentage of baseline-TEQbio explained by baselineTEQchem (± standard deviation) for the first sampling campaign. baselinebaseline- TEQchem/ DOC (mg L1) TEQbio (mg L1) TEQchem TEQbio (%) (mg L1) FEED 11.2  0.4 SAND 1 8.8  0.6 BAC 1 4.2  0.1 OZONATION 10.3  0.6 BAC 2 4.1  0.1 AC 6.6  0.7

1373 1170 380 905 335 505

     

686 316 128 172 125 219

3.03  0.77 0.23  0.07 1.96  0.52 0.17  0.03 0 0 0.18  0.07 0.02  0.01 0 0 0 0

gabapentin and iopromide can be refractory (Reungoat et al., 2010). As mentioned in the introduction, ozonation has been extensively studied and was not the focus of this work; it will therefore not be discussed further here. Filtration through BAC 2 removed the remaining compounds below their LOQ except for gabapentin and caffeine, which had remaining median concentrations of 0.10 and 0.01 mg L1 respectively. In the ozonated water, 5 compounds had a concentration at least ten times above their LOQ: gabapentin (1.30 mg L1), oxazepam (0.20 mg L1), temazepam (0.12 mg L1), tramadol (0.14 mg L1) and venlafaxine (0.17 mg L1). These 5 compounds were further removed by more than 90% in BAC 2 alone, in accordance with their fate in BAC 1. Among the 21 selected compounds, 9 were removed by 90e95% and 12 by more than 95% by the combination of ozonation and BAC 2. Following the same reasoning as for BAC 1, both adsorption and biodegradation could be responsible for the removal observed. In the full-scale plant, after filtration through the activated carbon, 2 compounds were quantified with a median concentration above LOQ: gabapentin (0.70 mg L1) and roxythromycin (0.01 mg L1). The 5 compounds with initial concentrations of at least ten times their LOQ were removed by more than 90% except gabapentin, which was removed by only 52%. Among the 21 selected compounds, 9 were removed by 90e95% and 11 by more than 95% by the combination of ozonation and the full-scale filter.

3.2.2.

Reduction of non-specific toxicity

While the chemical analytical concentrations of many PPCPs fell below detection limit after treatment, the baseline-TEQbio of all samples (excluding the blanks) were above detection limit and significantly different from the blank in all samples (Table 1). Thus it was possible to calculate robust removal efficiencies without evoking any assumptions with respect to the detection limit. The SAND 1 filter showed limited decrease of baselineTEQbio (9  30%) whereas BAC 1 was able to reduce it by 68  17% which is higher than what was achieved by ozonation alone (31  12%). After ozonation, BAC 2 and AC further decreased the baseline-TEQbio by 63  13% and 41  31% confirming that EBCT is an important operational parameter. Overall, the combination of ozonation and filtration through BAC 2 and AC achieved 75  9% and 60  20% removal of baseline-TEQbio respectively. With similar EBCT (120 min) in the filters, prior ozonation allowed an overall higher removal of baseline-TEQbio compared to filtration alone. However, when a shorter EBCT

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(18 min) was used after ozonation, the removal of baselineTEQbio was lower compared to filtration without pre-ozonation but with long EBCT (120 min).

3.2.3. tools

Comparison of chemical analysis and bioanalytical

The baseline-TEQbio were compared with the baseline-TEQchem derived for a mixture of all quantified PPCPs in order to evaluate how large is the tip of the iceberg of the identified chemicals in comparison to the overall burden of biologically active pollutants. As is discussed in Section 3.2.1, with every treatment step more PPCPs fell below their LOQ in the chemical analysis. This does not mean that they disappeared altogether, they might still contribute to mixture toxicity when present at concentrations below the LOQ as even concentrations below any theoretical expected effect may contribute to mixture toxicity. This so-called “something from nothing” effect was first shown by Silva et al. for estrogenic chemicals (Silva et al., 2002) but later confirmed for many other endpoints. If the baseline-TEQchem was calculated by Eq. (2)e(4), and the PPCPs that fell below their LOQ were not considered, then the percentage of baseline-TEQbio that could be explained by the baseline-TEQchem fell from 0.23% to less than 0.0001% during the treatment process, incrementally decreasing with every treatment step (Table 1). A fraction of 0.1 of 1% of toxicity in this assay explained by the detected chemicals is a typical result for wastewater effluents and surface waters as has been recently demonstrated by Vermeirssen et al. (2010). The unknown fraction accounts for PCPPs not on the analytical target list, but also for other compounds such as pesticides, industrial chemicals and natural compounds, which in addition to exhibiting a defined specific effect also add to the underlying baseline toxicity. Furthermore, transformation products of micropollutants may also contribute to the observed mixture toxicity. The variability of the fraction of toxicity explained by chemical analysis and its decrease with advanced water treatment indicates that chemical analysis is not necessarily a robust parameter for assessing overall removal efficiencies for a given process but that the toxicity sum parameter of baseline-TEQbio might be more appropriate to estimate the reduction of the mixture of micropollutants and other small molecules. This phenomenon is also illustrated by Fig. 2, where the mean removals of baseline-TEQbio and baseline-TEQchem are depicted for all filtration steps. Using baseline-TEQchem it looks as if the removal efficiency is always >90% but this is somewhat misleading as many PPCPs fall below their LOQ and consequently do not contribute to the calculation of baseline-TEQchem using Eq. (4). The baseline-TEQbio gives a more subtle picture of the different processes, which is also consistent with the removal of DOC (Fig. 2) and individual PPCPs, as has been discussed above.

3.3.

Influence of empty-bed contact time

In the second sampling campaign, the DOC levels in the feed water were slightly lower than during the first campaign except for 90 min (Table SI 6). However, among the 57 target compounds, 32 were quantified with a median concentration above their quantification limit (Table SI 5). These 32 compounds

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Fig. 4 e Influence of EBCT on numbers of PPCPs detected concentrations >10ng L-1 and on DOC, Gabapentin and baselineTEQbio removal in BAC 1 (a) and BAC 2 (b).

were also detected in the first sampling campaign and their concentrations were in the same range. Fluoxetine, salicylic acid, sertraline and triclosan were not detected in this second campaign; their respective concentrations were close to their LOQ in the first campaign. Diclofenac could not be quantified in the second campaign due to the presence of interferences in the analytical instrument. Baseline-TEQbio levels were also similar to the first campaign and remained in the same range during the sampling campaign except for the influent of BAC 1 with EBCT ¼ 30 min (Table SI 7). For 120 min EBCT, the removal of DOC was lower in both BACs compared to the first campaign (Fig. 4). This is probably due to the fact that the feed water contained less easily biodegradable EfOM as the DOC level was lower. Indeed, BAC 1 and BAC 2 reached similar removal levels of DOC as in the first campaign for an EBCT of 90 min for which the level in the feed water was comparable to the first campaign (Fig. 4). Contrary to what was observed in the first campaign, the removal of DOC in BAC 2 (after ozonation) was higher than in BAC 1. This is also consistent with the hypothesis that there was less easily biodegradable EfOM in the feed water. A majority of the targeted PPCPs’ concentrations were reduced to levels below their LOQ in the effluents of BAC 1 and BAC 2 consistently with the observation made for the first sampling campaign. Two compounds were quantified in the effluent of BAC 1, DEET and gabapentin, with respective concentrations of 0.02 and 0.24 mg L1. Gabapentin was also detected in the effluents of BAC 1 and BAC 2 in the first campaign with median concentrations of 0.20 and 0.10 mg L1 respectively. The concentration of DEET was below 0.01 mg L1 in the effluent of BAC 1 in the first campaign but its influent concentration was also lower: 0.05e0.07 mg L1 compared to 0.12 mg L1 here. The removal of gabapentin observed here, 95% and 85% for BAC 1 and BAC 2 respectively, is consistent with the median removal observed in the first campaign (i.e. 93% in both filters). These facts support the hypothesis that the removal of PPCPs is mainly due to biodegradation (or adsorption followed by biodegradation) rather than to a net adsorption because adsorption typically decreases with operating time as adsorption sites are gradually saturated. The reduction of baseline-TEQbio in BAC 1 (62%) was similar to the first campaign (68%) despite the fact

that the DOC removal was lower. On the contrary, in BAC 2, the reduction of the baseline-TEQbio was lower (32%) compared to the first campaign (63%) consistently with the lower DOC removal observed. Decreasing the EBCT from 120 to 30 min did not affect the performance of BAC 1 regarding the removal of DOC, it remained in every case between 34 and 47% (Fig. 4). However, there was a consistent decrease of baseline-TEQbio removal from 62 to 32% when the EBCT was shortened from 120 to 30 min (Fig. 4). This shows that compounds contributing to baseline toxicity need a longer contact time to be degraded by the biomass but their concentration is probably too low to have an influence on DOC removal. Indeed, an increasing number of compounds, from 2 to 5, were quantified at a concentration above 10 ng L1 in BAC 1 effluent when the EBCT was decreased from 120 to 30 min respectively. The concentrations of these compounds were always close to their LOQ after filtration except for gabapentin. Gabapentin removal decreased from 95% to 52% when EBCT decreased from 120 to 30 min (Fig. 2). This effect cannot be attributed to an increase in the gabapentin concentration in the feed water as it actually decreased from 4.60 to 1.86 mg L1 in the meantime (Table SI 8). Similar observations can be made from the results obtained with BAC 2: the decrease in EBCT did not seem to affect the removal of DOC (the higher removal of 60% observed for 90 min EBCT is due to a higher inlet DOC) but more PPCPs were detected at a concentration above 10 ng L1 in the effluent for shorter contact times and the removal of gabapentin decreased (Fig. 4). However, the removal of baseline-TEQbio in BAC 2 was steady when the EBCT was reduced showing that the ozonation by-products contributing to baseline-TEQbio are more easily biodegraded than the original compounds. Overall, the performances of both BAC filters remained very similar to what was observed in the first campaign even for contact time as short as 30 min. The EBCT does not seem to strongly affect the removal of DOC in the range studied but the quantification of PPCPs and the baseline-TEQbio suggests that the removal of these compounds is affected, particularly in BAC 1. An increasing removal of phenol with increasing EBCT has also been observed by Seredynska-Sobecka et al. (2006) in a BAC treating ozonated river water.

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Table 2 e DOC, baseline-TEQbio and baseline-TEQchem in the third and fourth sampling campaigns (EBCT [ 60 min). Removals are calculated based on the feed. For the third campaign, results are from one day only (24 h composite samples) as for the second day some samples were contaminated which affected DOC and baseline-TEQbio. Baseline-TEQbio is given with standard deviation from duplicates when available. Diclofenac was not included in baseline-TEQchem due to interference in the chemical analysis. For the fourth campaign, results are the mean of 3 grab samples ± standard deviation. 3rd campaign

DOC 1

mg L Feed BAC 1 Ozonation SAND 2 BAC 2 AC 4th campaign Feed BAC 1 Ozonation SAND 2 BAC 2 AC

7.4 4.6 6.4 4.7 3.2 4.8

7.0 4.1 6.4 5.0 3.3 4.8

 1.0  0.3  0.7  0.3  0.3  0.3

Baseline-TEQbio Removal

1

mg L

Removal

n/a 36% 13% 37% 54% 36%

1000 150  71 700 350  71 400 300

n/a 85% 30% 65% 60% 70%

n/a 41  5% 7  7% 27  7% 53  2% 30  5%

2290  444 909  168 1493  139 1175  221 601  259 685  165

n/a 60  4% 33  18% 49  3% 74  10% 70  4%

The dissolved oxygen (DO) concentrations were measured in the influent and effluent of both filters at the beginning and end of each 24 h sampling periods and oxygen consumption across the filters were calculated (Table SI 9). The DO concentrations decreased by several mg L1 through the filters confirming that they are biologically active. The

Baseline-TEQchem mg L

1

1.92 0.02 0.11 0.08 0.03 0.02

2.27 0.01 0.36 0.21 0.02 0.02

 0.46  0.01  0.10  0.06  0.01  0.01

TEQchem/TEQbio

Removal n/a 99% 94% 96% 99% 99%

0.192% 0.013% 0.015% 0.024% 0.007% 0.019%

n/a 99  1% 84  2% 91  1% 99  1% 99  1%

0.099  0.006% 0.002  0.001% 0.025  0.009% 0.018  0.002% 0.003  0.001% 0.003  0.001%

oxygen consumption per litre across the filters varied little from one EBCT to another in accordance with the observation that DOC removal also did not vary. The oxygen consumption in BAC 2 was higher than in BAC 1, which is consistent with a higher amount of DOC removed. Moreover, the DO concentrations in the effluent of both filters were low, below

Fig. 5 e Removal of the PPCPs in the third (a) and fourth (b) sampling campaign compared to concentration in feed water. Bars represent the mean (n [ 2 or 3) and error bars show standard deviation. No bar means no removal could be calculated (due to low concentration or interference).

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0.8 and 1.5 mg L1 for BAC 1 and BAC 2 respectively. This indicates that the DOC removal might be limited by the DO concentration and not by the reaction rate, explaining the fact that decreasing the EBCT did not affect the effectiveness of DOC removal in the BAC filters.

3.4.

Long term performance

A third and a fourth sampling campaigns were carried out with the EBCT set at 60 min to verify the performance of the filters on a long term. Three month prior to the fourth campaign, the air supply was changed to 90% oxygen to increase DO concentration in the influent and ensure a fully aerobic filter. The DOC in the feed water was lower compared to the first and second campaigns; however, the PPCPs concentrations (Table SI 5) and the baseline-TEQbio levels (Table 2) were still in the same range as for the two other campaigns. Overall, results were in agreement with observations made during the first and second campaign (Table 2 and Fig. 5). This strongly supports the hypothesis that the removal of organic matter and PPCPs observed in the BAC filters is due to biodegradation (or adsorption followed by biodegradation) rather than adsorption alone as adsorption efficiency would typically decrease over time. The SAND 2 filter, placed after ozonation, showed limited removal of the PPCPs remaining after ozonation which is consistent with the findings of Hollender et al. (2009). Accordingly, filtration through SAND 2 did not improve the baselineTEQbio level either, in agreement with the findings of Escher et al. (2009). After the switch to 90% oxygen supply, dissolved oxygen was measured in the effluent of both BAC 1 and BAC 2 (Table SI 11), showing the filters were fully aerobic. This was accompanied by an increase in DO consumption across the filters compared to the third campaign which suggests DO was a limiting factor in the other campaigns. However, no significant increase in DOC removal and baseline toxicity could be observed (Table 2). The removal of micropollutant was also similar in both campaigns (Fig. 5). Further experiments are necessary to clearly identify the impact of DO concentration.

3.5.

Influence of filtering media

Under similar operating conditions, BAC filtration is more effective than sand filtration to remove the EfOM and a wide range of PPCPs from a WWTP effluent as well as to decrease baseline toxicity equivalent concentration. This can be observed with or without pre-ozonation. Whereas a sand filter can rely only on biodegradation to remove the EfOM and PPCPs, a BAC filter also have adsorption properties. However, after an initial period during which the removal of organic compound is due to adsorption, the adsorption efficiency decreases while biomass develops in the filter and eventually, the removal observed is due mainly to biodegradation (or adsorption followed by biodegradation) (Simpson, 2008). The higher effectiveness of BAC 1 and BAC 2 compared to SAND 1 and SAND 2, respectively, could therefore be due to more biomass attached on the surface of activated carbon or the combined effects of adsorption and biodegradation. Activated carbon typically has a surface area of several hundred square metres per gram due to its high porosity but most of this surface is not accessible to bacteria as it is located in micropores with a diameter smaller than 2 nm. However, the

external surface of the activated carbon grains is much rougher and uneven than the surface of sand grains as can be seen on Fig. SI 1 and therefore potentially provides more sites for the bacteria to attach (Wang et al., 2007). Some authors have also hypothesised that the biodegradation continuously regenerates adsorption sites by degrading adsorbed molecules (Simpson, 2008). Adsorption of organic compounds onto the activated carbon surface could therefore increase their residence time within the filter and allow degradation by the bacteria, particularly for the compounds known to be poorly biodegradable. This mechanism might be relevant when spikes of pollutants occur as an increase of the concentration in the liquid phase will lead to an increase of the adsorbed quantity and compounds will later desorb when the liquid concentration decreases again. The present filters have been continuously exposed to relatively steady, low concentrations of PPCPs and it is therefore unlikely that this mechanism occurred here. Another explanation of the higher removal observed in BAC filters compared to sand filters could be an increased flux of pollutants to the biofilm as mentioned in section 3.1.

3.6.

Influence of pre-ozonation

Classically, in drinking water treatment, sand and BAC filters are placed after an ozonation process to degrade the by-products formed by oxidation of the natural organic matter. These transformation products have been shown to be more biodegradable than their parent compounds. The influence of preozonation on sand filtration is difficult to assess as SAND 1 and SAND 2 were investigated at different times with different influent water quality. Nevertheless, the combination of ozonation and sand filtration appears to be more effective for the removal of DOC compared to sand filtration alone as it could be expected. The overall removal of PPCPs observed after ozonation and filtration through SAND 2 is higher than the removal observed in SAND 1. However, this is mainly due to the effect of ozonation and SAND 2 itself showed poor removal of PPCPs similarly to SAND 1. In the first campaign there was no significant difference between the DOC concentrations in the effluents of both BAC filters whereas in the second and third campaign DOC levels where always lower after BAC 2 compared to BAC 1. The combination of ozonation and BAC 2 removed 15e20% more DOC compared to BAC 1 of which only 5e13% was directly due to ozonation alone showing that pre-ozonation primarily increased the biodegradability of EfOM. Similarly, the baselineTEQbio was generally lower after BAC 2 compared to BAC 1, by up to 54%. Removals of PPCPs by both BAC 1 and the combination of ozonation and BAC 2 were similar and no clear difference could be observed except for gabapentin, which was consistently detected at lower concentrations after BAC 2 compared to BAC 1 as well as perindopril in the third and fourth campaigns.

4.

Conclusions

The results of this study show that direct filtration (i.e. without pre-ozonation) of WWTP effluent through biological activated carbon can significantly:

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 7 5 1 e2 7 6 2

 reduce the DOC concentration by 35e60% which potentially limits the formation of disinfection by-products if water is chlorinated before discharge or reuse as well as bacterial regrowth in the distribution system in the latter case;  reduce the concentrations of a wide range of PPCPs by more than 90%, most of them down to levels below 10 ng L1 which lowers the potential risk of environmental and/or human health impact;  reduce the baseline toxicity equivalent concentration, which is a measure of all chemicals present including PPCPs and small natural organic molecules, by 28e85% but less than the individual chemicals quantified by chemical analysis because transformation products formed during the biodegradation process and natural compounds may also contribute to the mixture toxicity measured with the bioassay. On the contrary, under similar conditions, sand filtration showed limited improvement of water quality. Moreover, the long term study of the BAC filters showed steady performance which suggests that EfOM and PPCPs are biodegraded, the filters could therefore potentially be used for many years without replacing the media. BAC filtration could be implemented as an advanced treatment in WWTP to reduce the impact of the effluent discharged into the environment and/or to produce a water of a higher quality for reuse. In the meantime, further investigations are necessary to fully understand the mechanisms involved in EfOM and PPCPs removal, particularly the role of adsorption, and to clearly identify the key parameters that have to be taken into account for the design of full-scale filters (e.g. initial DO concentration, EBCT).

Acknowledgements This work was co-funded by the Urban Water Security Research Alliance under the Enhanced Treatment Project and the CRC Water Quality and Treatment Project No. 2.0.2.4.1.1 e Dissolved Organic Carbon Removal by Biological Treatment. The National Research Centre for Environmental Toxicology (Entox) is a joint venture of the University of Queensland and Queensland Health Forensic and Scientific Services. The authors acknowledge the following institutions and individuals who contributed to this study: Moreton Bay Water for giving access to the South Caboolture Water Reclamation Plant; Ray McSweeny and Paul McDonnell (Moreton Bay Water) for their help during sampling; Chris Pipe-Martin for providing information on the South Caboolture Water Reclamation Plant; Geoff Eaglesham, Steve Carter and Mary Hodge (Queensland Health Forensic and Scientific Services) for the analysis of micropollutants and discussions on the analytical method; Beatrice Keller and Jelena Radjenovic for their help in establishing the analytical method at the AWMC; Christiane Espendiller and Franc¸ois-Xavier Argaud for the experimental work they carried out during their stay at the AWMC.

Appendix. Supplementary material Supplementary data related to this article can be found online at doi:10.1016/j.watres.2011.02.013.

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references

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Gunten, U., 2005. Oxidation of pharmaceuticals during ozonation of municipal wastewater effluents: a pilot study. Environmental Science & Technology 39 (11), 4290e4299. Joss, A., Siegrist, H., Ternes, T.A., 2008. Are we about to upgrade wastewater treatment for removing organic micropollutants? Environmental Science & Technology 57 (2), 251e255. Kim, I.H., Tanaka, H., Iwasaki, T., Takubo, T., Morioka, T., Kato, Y., 2008. Classification of the degradability of 30 pharmaceuticals in water with ozone, UV and H2O2. Water Science & Technology 57 (2), 195e200. Kimura, K., Toshima, S., Amy, G., Watanabe, Y., 2004. Rejection of neutral endocrine disrupting compounds (EDCs) and pharmaceutical active compounds (PhACs) by RO membranes. Journal of Membrane Science 245 (1e2), 71e78. Macova, M., Escher, B.I., Reungoat, J., Carswell, S., Lee, C.K., Keller, J., Mueller, J.F., 2010. Monitoring the biological activity of micropollutants during advanced wastewater treatment with ozonation and activated carbon filtration. Water Research 44 (2), 477e492. Maeng, S.K., Sharma, S.K., Magic-Knezev, A., Amy, G., 2008. Fate of effluent organic matter (EfOM) and natural organic matter (NOM) through riverbank filtration. Water Science and Technology 57 (12), 1999e2007. Moser, R. 2008. Massnahmen in ARA zur weitergehenden Elimination von Mikroverunreinigungen e Kostenstudie. Hunziker Betatech AG (Ed.), Winterthur (Switzerland). Nakada, N., Shinohara, H., Murata, A., Kiri, K., Managaki, S., Sato, N., Takada, H., 2007. Removal of selected pharmaceuticals and personal care products (PPCPs) and endocrine-disrupting chemicals (EDCs) during sand filtration and ozonation at a municipal sewage treatment plant. Water Research 41 (19), 4373e4382. Nowotny, N., Epp, B., vonSonntag, C., Fahlenkamp, H., 2007. Quantification and modeling of the elimination behavior of ecologically problematic wastewater micropollutants by adsorption on powdered and granulated activated carbon. Environmental Science & Technology 41 (6), 2050e2055. Onesios, K.M., Yu, J.T., Bouwer, E.J., 2009. Biodegradation and removal of pharmaceuticals and personal care products in treatment systems: a review. Biodegradation 20 (4), 441e466. Petrovic, M., De Alda, M.J.L., Diaz-Cruz, S., Postigo, C., Radjenovic, J., Gros, M., Barcelo, D., 2009. Fate and removal of pharmaceuticals and illicit drugs in conventional and membrane bioreactor wastewater treatment plants and by riverbank filtration. Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences 367 (1904), 3979e4003. Pipe-Martin, C., Reungoat, J., Keller, J. (Eds.), 2010. Dissolved Organic Carbon Removal by Biological Treatment. Water Quality Research Australia, Adelaı¨de (Australia). Radjenovic, J., Godehardt, M., Petrovic, M., Hein, A., Farre, M., Jekel, M., Barcelo, D., 2009. Evidencing generation of persistent ozonation products of antibiotics roxithromycin and trimethoprim. Environmental Science & Technology 43 (17), 6808e6815. Rauch-Williams, T., Hoppe-Jones, C., Drewes, J.E., 2010. The role of organic matter in the removal of emerging trace organic chemicals during managed aquifer recharge. Water Research 44 (2), 449e460. Rauch, T., Drewes, L., 2004. Assessing the removal potential of soil-aquifer treatment systems for bulk organic matter. Water Science and Technology 50 (2), 245e253. Reungoat, J., Macova, M., Escher, B.I., Carswell, S., Mueller, J.F., Keller, J., 2010. Removal of micropollutants and reduction of biological activity in a full scale reclamation plant using

ozonation and activated carbon filtration. Water Research 44 (2), 625e637. Seredynska-Sobecka, B., Tomaszewska, M., Janus, M., Morawski, A.W., 2006. Biological activation of carbon filters. Water Research 40 (2), 355e363. Silva, E., Rajapakse, N., Kortenkamp, A., 2002. Something from nothing - Eight weak estrogenic chemicals combined at concentrations below NOECs produce significant mixture effects. Environmental Science & Technology 36 (8), 1751e1756. Simpson, D.R., 2008. Biofilm processes in biologically active carbon water purification. Water Research 42 (12), 2839e2848. Snyder, S.A., Adham, S., Redding, A.M., Cannon, F.S., DeCarolis, J., Oppenheimer, J., Wert, E.C., Yoon, Y., 2007. Role of membranes and activated carbon in the removal of endocrine disruptors and pharmaceuticals. Desalination 202 (1e3), 156e181. Stalter, D., Magdeburg, A., Oehlmann, J., 2010. Comparative toxicity assessment of ozone and activated carbon treated sewage effluents using an in vivo test battery. Water Research 44 (8), 2610e2620. Ternes, T.A., Meisenheimer, M., McDowell, D., Sacher, F., Brauch, H.J., Gulde, B.H., Preuss, G., Wilme, U., Seibert, N.Z., 2002. Removal of pharmaceuticals during drinking water treatment. Environmental Science & Technology 36 (17), 3855e3863. Ternes, T.A., Stuber, J., Herrmann, N., McDowell, D., Ried, A., Kampmann, M., Teiser, B., 2003. Ozonation: a tool for removal of pharmaceuticals, contrast media and musk fragrances from wastewater? Water Research 37 (8), 1976e1982. van Leeuwen, J., Pipe-Martin, C., Lehmann, R.M., 2003. Water reclamation at South Caboolture, Queensland, Australia. Ozone: Science & Engineering 25 (2), 107e120. Vermeirssen, E.L.M., Hollender, J., Bramaz, N., von der Voet, J., Escher, B.I., 2010. Linking toxicity in algal and bacterial assays with chemical analysis in passive samplers exposed to treated sewage effluent. Environmental Toxicology & Chemistry 29 (11), 2575e2582. Wang, H.X., Ho, L., Lewis, D.M., Brookes, J.D., Newcombe, G., 2007. Discriminating and assessing adsorption and biodegradation removal mechanisms during granular activated carbon filtration of microcystin toxins. Water Research 41 (18), 4262e4270. Westerhoff, P., Yoon, Y., Snyder, S., Wert, E., 2005. Fate of endocrine-disruptor, pharmaceutical, and personal care product chemicals during simulated drinking water treatment processes. Environmental Science & Technology 39 (17), 6649e6663. Yoon, Y., Westerhoff, P., Snyder, S.A., Wert, E.C., Yoon, J., 2007. Removal of endocrine disrupting compounds and pharmaceuticals by nanofiltration and ultrafiltration membranes. Desalination 202 (1e3), 16e23. Yu, J.T., Bouwer, E.J., Coelhan, M., 2006. Occurrence and biodegradability studies of selected pharmaceuticals and personal care products in sewage effluent. Agricultural Water Management 86 (1e2), 72e80. Yu, Z.R., Peldszus, S., Huck, P.M., 2008. Adsorption characteristics of selected pharmaceuticals and an endocrine disrupting compounddnaproxen, carbamazepine and nonylphenol on activated carbon. Water Research 42 (12), 2873e2882. Zwiener, C., Frimmel, F.H., 2000. Oxidative treatment of pharmaceuticals in water. Water Research 34 (6), 1881e1885.

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Available at www.sciencedirect.com

journal homepage: www.elsevier.com/locate/watres

Effect of pH on the concentrations of lead and trace contaminants in drinking water: A combined batch, pipe loop and sentinel home study Eun Jung Kim a, Jose E. Herrera a,*, Dan Huggins b, John Braam b, Scott Koshowski b a b

Department of Civil and Environmental Engineering, University of Western Ontario, London, Ontario N6A 5B9, Canada City of London, London, Ontario, Canada

article info

abstract

Article history:

High lead levels in drinking water are still a concern for households serviced by lead pipes

Received 2 August 2010

in many parts of North America and Europe. This contribution focuses on the effect of pH

Received in revised form

on lead concentrations in drinking water delivered through lead pipes. Though this has

13 January 2011

been addressed in the past, we have conducted a combined batch, pipe loop and sentinel

Accepted 20 February 2011

study aiming at filling some of the gaps present in the literature. Exhumed lead pipes and

Available online 11 March 2011

water quality data from the City of London’s water distribution system were used in this study. As expected, the lead solubility of corrosion scale generally decreased as pH

Keywords:

increased; whereas dissolution of other accumulated metals present in the corrosion scale

Lead

followed a variety of trends. Moreover, dissolved arsenic and aluminum concentrations

Drinking water

showed a strong correlation, indicating that the aluminosilicate phase present in the scale

Dissolution

accumulates arsenic. A significant fraction of the total lead concentration in water was

Pipe loop

traced to particulate lead. Our results indicate that particulate lead is the primary

Sentinel home

contributor to total lead concentration in flowing systems, whereas particulate lead contribution to total lead concentrations for stagnated systems becomes significant only at high water pH values. ª 2011 Elsevier Ltd. All rights reserved.

1.

Introduction

Lead is a toxic heavy metal mainly introduced to drinking water through corrosion of lead bearing plumbing materials. Although lead plumbing is no longer used in new construction, high lead levels in drinking water are still a major concern for older homes serviced by lead pipes. The main source of lead in drinking water has been traced to the destabilization of the corrosion scale formed on the inner walls of lead pipes over time. The most common corrosion products present in this scale include cerussite (PbCO3), hydrocerussite (Pb3(CO3)2(OH)2), plumbonacrite (Pb10(CO3)6(OH)6O), litharge (PbO), and plattnerite (PbO2) (AWWARF, 1990). It has been recognized that this

corrosion scale decreases the amount of lead directly leached into water by forming passivating layers on the inner walls of the pipe. The formation of these layers also benefits water quality, since in some cases they can trap and accumulate toxic elements; arsenic being one of particular concern (Lytle et al., 2004; Schock et al., 2008). The formation and stability of these corrosion products strongly depend on the characteristics of the water running through the pipes, such as pH, alkalinity, temperature, concentrations of chlorine residual, dissolved oxygen, chloride, sulfate, phosphate, and organic matter (AWWARF, 1990). Consequently, changes in water chemistry can cause chemical transformations that eventually lead to the dissolution of these corrosion products into the aqueous phase.

* Corresponding author. Tel.: þ1 519 661 2111x81262; fax: þ1 519 661 3498. E-mail address: [email protected] (J.E. Herrera). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.02.023

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The effect of water quality on the stability of the scale can be quite significant. Sudden increases of lead levels in drinking water, exceeding the EPA action limit of 15 mg/L, were found in Washington DC in 2003 due to the switch of disinfectants from chlorine to chloramines (AWWARF, 2008; Switzer et al., 2006). This change of disinfectant lowered the redox potential of the aqueous phase, causing the destabilization and dissolution of PbO2 present in the lead scale. Dissolution of the scale results not only in higher concentrations of lead in drinking water, but can also lead to the release of toxic elements accumulated in the corrosion scale (Copeland et al., 2007). Among different corrosion control strategies deployed by utilities, adjustment of pH and alkalinity as well as addition of corrosion inhibitors such as phosphate and silicate have been used (Schock, 1989). Among these strategies, pH control has been commonly used by utilities to control lead release because pH strongly affects the solubility of lead corrosion scale (AWWARF, 1990; Schock, 1989). In 2007, lead concentrations in about 25% of sampled tap water from older houses in London, ON, Canada exceeded the Canadian water quality standard of 10 mg/L (Health Canada, 2008; Huggins, 2008). These unusually high lead levels are believed to be caused by the low pH levels that resulted from the addition of acidified alum during water treatment process, applied since the early 1990’s. The pH of London’s distribution water was normally maintained 8.0 or higher before this modification. Acidified alum was introduced at the treatment plants to optimize coagulant use and to minimize the amount of dissolved aluminum present in the City’s water. This change resulted in a gradual decrease in the pH of the water sent to the distribution system, from 8.0 to around 7.0, with the consequent destabilization of the corrosion scale present in lead service lines throughout the distribution system (Huggins, 2008). The present study reports the effect of pH on the stability of corrosion scale products present in lead service pipes. Although the solubility of pure lead compounds at different pH values has been predicted using equilibrium models, it remains a challenge to link these predictions to lead levels observed in the field (Schock, 1980, 1989). This might be caused by different dissolution rates, complex chemical composition of actual corrosion scales and/or different local conditions in the field. To address these issues, and aiming to reduce the gap observed between model systems and field results, we have undertaken three different approaches to evaluate the effect of pH on scale stability. These include batch and pipe loop experiments together with a sentinel study of the City’s distribution system. Batch dissolution experiments were conducted using corrosion products harvested from lead pipes exhumed from London’s drinking water distribution system. Pipe loop tests were performed using a 20-foot lead pipe also reclaimed from London’s distribution system. This pipe was servicing a specific home for over 100 years. Finally, we monitored lead levels in drinking water at eight lead serviced homes in London from December 2007 to February 2009. During part of this time-frame, water pH was gradually increased at the treatment plant. These results are part of a wider study carried out by the City of London in order to develop an effective lead control program. Furthermore, we have evaluated the effect of pH on the release of toxic elements accumulated in the corrosion scale.

2.

Materials and methods

2.1.

Batch dissolution experiments

Batch dissolution experiments were conducted to investigate the solubility of lead pipe corrosion scales at different pH values. Corrosion scale samples were harvested from a lead pipe sample exhumed during the Lead Service Replacement Program of the City of London, ON, Canada in 2009. The pipe was originally installed in 1927 and had been used for domestic drinking water distribution until April 2009 when it was removed for replacement. The main phase on the pipe body was metallic lead as the EDX analysis revealed. The pipe was cut longitudinally and the corrosion scale sample was carefully harvested using stainless steel spatulas. Dissolution kinetic experiments of the corrosion scale were performed at pH 6, 7, 8, 9, and 10. Dissolution tests using pure hydrocerrusite were also carried out under the same conditions. Experiments were initiated by adding 0.2 g of the solid to a 200 mL solution in a capped 250 mL polypropylene bottle (1 g/L initial solid concentration). These solutions contained 0.01 M NaNO3 and 20 mg C/L dissolved inorganic carbon (DIC) along with a 0.01 M pH buffer solution (MES for pH 6; MOPS for pH 7; EPPS for pH 8; CHES for pH 9; CAPS for pH 10). The suspensions were continuously mixed on a shaker at 170 rpm and a 10-mL aliquot was sampled from the suspension at each reaction time for up to 30 days (0.5, 1, 3, 6, and 12 h and 1, 2, 3, 5, 7, 10, 15, 20, and 30 days). The samples were immediately filtered using 0.2-mm membrane filters, and the filtrates were acidified to 2% nitric acid and stored at 2  C until analysis. The concentrations of lead and other metals in the filtrate were analyzed using an ICP-OES (Varian, Inc., VistaPro Axial). The solid corrosion scale was characterized before and after 30 day reaction time by X-ray diffraction (XRD) and Fourier transform infrared (FTIR) spectroscopy. The XRD data were collected on a Rigaku-Miniflex powder diffractometer ˚ ) radiation obtained at 30 kV and using Cu K-a (l ¼ 1.54059A 15 mA. Scans were taken over the range of 10e90 2q with 0.05 step sizes. The absorbance spectra of the corrosion scales in the middle IR (400e4000 cm1) were obtained using a Bruker Vector 22 FTIR spectrometer equipped with an ATR cell.

2.2.

Pipe loop test

A pipe loop test was conducted using reclaimed 0.5 inch diameter lead pipe installed in 1907 and actively used for drinking water distribution service in London, ON, Canada. The pipe was cautiously excavated and removed from the ground to minimize mechanical disturbance of internal corrosion scale during transport and pipe loop system assembly. A lead pipe approximately 20 feet in length was connected to a pump and pressure tank supplying pH adjusted water. Treated water obtained from London’s drinking water distribution system was used for this test; its water quality parameters are summarized at Table S1 (Supplemental Information). Initially the pH of the source water was 7.1. This water was pumped through the pipe for 7 weeks in order to remove any interior pipe scale that may have been disturbed during excavation/ transportation. After this 7 week period, the pH of the source water was gradually increased by 0.3 pH units each week for 8

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 7 6 3 e2 7 7 4

weeks to a final pH value of 9.6 using a 50% sodium hydroxide (NaOH) solution. During both the “conditioning” phase and the “pH adjustment” phase, water was pumped through the pipe at different flow rates selected to simulate typical domestic consumption rates (Table 1). Approximately 800 L of water were pumped through the pipe section daily to simulate typical domestic consumption in London, ON, Canada. Two different kinds of water samples were taken from the pipe section at the end of each week; stagnated and flow samples. Stagnated samples were taken following a 12e24-h period of no flow, with water standing inside the pipe. This was followed by flowthrough experiments, where samples were taken while flowing water through the pipe section at 0.25, 3.0, and 32.75 L/min, respectively. Samples were collected in 125 mL sample bottles, and half of each sample was filtered using 0.45-mm membrane filters. Both unfiltered and filtered samples were acidified to 2% HNO3 and the concentration of lead was analyzed using an ICPMS (Varian, Inc., Ultramass).

2.3.

Monitoring of lead serviced houses

Eight lead serviced houses in London were selected and have been monitored since pH was increased at the water treatment plant. The data presented in this contribution covers the time period from December 2007 to March 2010. The pH of the treated water pumped into the City’s distribution system was gradually increased from 7.1 to approximately 8.1 from January to June 2008. This gradual increase was achieved by adding NaOH as the last stage of the water treatment process. Since June 2008, pH values have been maintained at around 8.1. The sampling protocol includes tap flushing for 5 min at about 5 L/min flow rate, and then stagnation for 30 min. After the 30 min stagnation period a 1 L sample was collected at approximately the same flow rate (5 L/min). The pH, chlorine residual, temperature and turbidity were measured on-site during sampling. Each sample was filtered using 0.2-mm membrane filters, and both total and soluble lead and other metal concentrations were measured using an ICP-MS (Varian, Inc., Ultramass).

3.

Results and discussion

3.1.

Batch dissolution tests

The effect of pH was studied with respect to the solubility of lead corrosion products obtained from a lead pipe used for

Table 1 e Flow rate and flow duration used in the pipe loop experiment each week. Flow rate # 1 2 3 4 5 6 7

Average flow rate (L/min)

Flow duration (minutes)

0.25 0.75 1.5 3.0 6.0 11.5 32.75

100 35 40 70 35 17 5

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drinking water distribution. Hydrocerussite (Pb3(CO3)2(OH)2) was the major lead corrosion product identified, while cerussite (PbCO3) and minium (Pb3O4) phases were present as minor components (Fig. 3). In addition to lead corrosion products, an aluminosilicate phase was also observed in the corrosion scale (Supplemental Information, Fig. S1). Elemental chemical analysis indicated that Al, Fe, Ca, and Mn were the main metallic elements accumulated in the corrosion scales besides lead (Table 2). Among them, Al was the most abundant in all samples. Presence of toxic contaminants such as As, V, Cu, and Cr was also observed with concentrations of 157, 654, 642, and 84.7 mg/kg of scale, respectively. Accumulations of toxic elements such as As, Cr, Ni, and V have been previously reported in lead pipe corrosion scales from drinking water distribution systems (Gerke et al., 2009; Kim and Herrera, 2010; Schock et al., 2008). These accumulated contaminants in the corrosion scales could potentially be released to drinking water with soluble and particulate lead when physical or chemical disturbance of corrosion scale occurs. Batch dissolution experiments were conducted at pH 6, 7, 8, 9, and 10 for 30 days using both a well characterized corrosion scale and hydrocerussite, which was identified as the major lead component of the corrosion scale used in this study. The dissolution of corrosion products is strongly dependent on the solution pH value. The solubility of both hydrocerussite and corrosion scale was highest at pH 6 and decreased as pH increased to pH 8 (Fig. 1). Dissolved lead concentration slightly increased as pH increased above 8. Dissolved lead concentrations observed at pH 6 were about two orders of magnitude higher than those obtained at higher pH values. Our result suggests that the dissolution of corrosion products is relatively fast and strongly affected by the solution pH. Dissolved lead concentrations from both hydrocerussite and corrosion scale at all pH values increased rapidly within the first 24 h and then remained relatively constant for 30 days. Maximum dissolved lead concentrations were observed within 30 min on pure hydrocerussite samples in all cases except for pH 6. On the other hand, dissolution of lead from corrosion scale reached about 90, 60, 30, and 40% of the observed maximum dissolved concentrations within 30 min at pH 7, 8, 9, and 10, respectively. In general, the dissolution of the corrosion scale took more time as pH increased from 7 to 9. Many water utilities have increased pH levels to control lead corrosion. Our results indicate that increasing pH can be effective in lowering lead concentrations due to both the relatively low solubility of lead corrosion products at higher pH values and slower dissolution at higher pH values. The accumulated metals in the corrosion scale were also partially released to the aqueous phase during these dissolution experiments. Fifteen metal ions commonly present in water were monitored, and dissolved concentrations of 11 elements including lead were detected during the corrosion scale dissolution experiments (Fig. 2). The release of the accumulated metals in the corrosion scales was also strongly dependent on the solution pH values (Fig. 2). Dissolved concentrations of Ba, Ca, and Mg were highest at pH 6 and decreased as pH increased. Dissolution of Cd, Mn and Zn were only observed at pH 6. Meanwhile, dissolutions of Al, Cr, and As were highest at pH 10. Al was the second most abundant element in the scale after Pb, and was present as an

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a 50000

Pb concentration (µg/L)

40000 30000 pH 6 pH 7 pH 8 pH 9 pH 10

20000 10000 600 500 400 300 200 100 0 0

5

10

15

20

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30

Time (day)

b 30000

Pb concentration (µg/L)

20000

pH 6 pH 7 pH 8 pH 9 pH 10

10000 600 500 400 300 200 100 0 0

5

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15

20

25

30

Time (day) Fig. 1 e Dissolved lead concentrations observed during (a) hydrocerussite and (b) corrosion scale dissolution experiments at various pH values (initial solid concentration: 1 g/L).

aluminosilicate phase (Table 2 and Fig. S1). Poorly crystalline aluminosilicate minerals are reported to play an important role in regulating metal concentrations in the environment, due to their high specific surface areas and surface reactivity (Harsh et al., 2002). The dissolved concentrations of Al, Cr, and As suggest that Cr and As are accumulated in aluminum phase and are released when dissolution of this phase occurs. In a previous study we have reported that there is a strong correlation between arsenic and aluminum concentrations in the corrosion scale as well as a correlation between arsenic and iron concentrations (Kim and Herrera, 2010), which indicated that aluminum and iron solid phases present might play important roles in arsenic accumulation in the corrosion scale. Indeed, arsenic adsorption onto amorphous aluminosilicate (allophane) has been reported to occur by ligand exchange reactions between arsenate, surface coordinated water molecules and hydroxyl and silicate ions at near neutral pH (Arai et al., 2005).

Fig. 3 shows the X-ray diffraction patterns observed for corrosion scale and hydrocerussite samples before (Fig. 3a top) and after dissolution for 30 days at different pH values. The XRD obtained for the hydrocerussite sample after dissolution (Fig. 3b) shows that only cerussite is present after dissolution at pH 6, indicating that hydrocerussite was transformed to cerussite at this pH value. On the other hand, only hydrocerussite was observed after dissolution at pH 10. The XRD pattern of the corrosion scale (Fig. 3a) before dissolution indicates the presence of two main crystalline lead phases: hydrocerussite as a major crystalline phase component and cerussite as a minor one. When the corrosion scale is dissolved at pH 6, the intensity of the characteristic peaks of cerussite increased compared to those of hydrocerussite, indicating that either hydrocerussite is being transformed into cerussite or that hydrocerrusite is dissolving to the aqueous phase. To evaluate these possibilities, we performed an XRD experiment after dissolving pure hydrocerussite at the

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a

6000

3.0

5000 4000 3000 2000

As

2.5

Concentration (µ g/L)

Concentration (µg/L)

b

Al

7000

2.0 1.5 1.0 0.5

1000 0

0.0 0

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0

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c

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2

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Time (day)

e

20

Ca

3500

Concentration (µ g/L)

Concentration (µg/L)

d

Ba

12

15

Time (day)

1.4

15

Time (day)

f

Cd

10

Cr

8

Concentration (µ g/L)

Concentration (µ g/L)

1.2 1.0 0.8 0.6 0.4

6

4

2 0.2 0.0

0 0

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0

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Time (day)

g

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Time (day)

h

35

Cu

Mg

350

Concentration (µg/L)

Concentration (µ g/L)

30 25 20 15 10 5

300 250 200 150 100 50

0

0 0

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Time (day)

i

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Time (day)

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Concentration (µ g/L)

Concentration ( µg/L)

20

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5

0

0 0

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Time (day)

20

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Fig. 2 e Observed dissolved metal concentration during corrosion scale dissolution experiments carried at different pH values. (a) Al, (b) As, (c) Ba, (d) Ca, (e) Cd, (f) Cr, (g) Cu, (h) Mg, (i) Mn, (j) Zn (C pH 6; B pH 7; ; pH 8; 7 pH 9; - pH 10).

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Fig. 3 e X-ray diffraction patterns of reacted (a) corrosion scale and (b) hydrocerussite at different pH values for 30 days.

same pH (Fig. 3b). The results indicate that hydrocerrusite to cerussite transformation is taking place. When the dissolution experiments are carried out at higher pH values, the relative intensity of cerussite peaks decrease compared to hydrocerussite. Finally, only characteristic hydrocerussite peaks are observed at pH 10, resembling those

observed in the corrosion scale before reaction. Both corrosion scale and hydrocerussite dissolution experiments show that hydrocerussite is transformed to cerussite, resulting in a relative increase of the cerussite phase at low pH. These observations are consistent with previous reports indicating that hydrocerussite is transformed to cerussite at pH values

Table 2 e Concentrations of metals accumulated in the solid corrosion scale of lead pipe (Unit: mg/g of scale). Al 39.6

As

Ba

Ca

Cd

Cr

Cu

Fe

Mg

Mn

Ni

Pb

Se

V

Zn

0.157

0.0257

5.11

0.0192

0.0847

0.642

21.3

0.823

4.2

0.021

190

0.0024

0.654

0.0835

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below 8 (Dando and Glasson, 1989). In addition, the results indicate that lead dissolution from the corrosion scale is governed by hydrocerussite.

3.2.

Pipe loop study

Pipe loop testing was conducted by gradually increasing the water pH. However, during the first 7 weeks water at pH 7.1 (no pH adjustment) was pumped through a lead pipe in order to remove any interior pipe scale that may have been disturbed during pipe exhumation/transportation. The total lead concentrations during this conditioning period remained relatively stable at all flow rates (Fig. S2). After this conditioning period, the water pH was gradually increased (at 0.3 pH units per week) for the next 8 weeks. The pH adjusted water was pumped through the pipe, and both stagnated and flow samples were obtained (see experimental section). Fig. 4 displays the total lead concentrations observed for the stagnated and flow samples over these different pH ranges. When the pH of the water was increased from 7.1 to 7.8, a significant decrease in total lead concentration was observed in the stagnated samples, which can be linked to a decrease insoluble lead concentrations at higher pH as observed in section 3.1. Further increase in pH did not significantly affect the total lead concentrations observed. For the case of the flow samples, total lead concentrations gradually decreased as the pH of the water increased. The lead concentrations observed in the stagnation samples were about one order of magnitude higher than those observed for the flow samples for all experiments (Fig. 4 and Fig. S2). The quality of water running through the pipes strongly affects the stability of the corrosion products present on the inner walls of the pipe. The variation of water quality can induce chemical transformations and dissolution of corrosion products in the form of aqueous ionic species of lead, and/or release of particulate and colloidal lead-containing solids into the aqueous phase. The high lead concentrations in the stagnated samples indicate increased dissolution of corrosion products during stagnation. Relatively long contact times between the water and the pipes during the

stagnation experiments resulted in increased lead dissolution. Indeed fast dissolution of lead from corrosion products was observed within the first 24 h in the batch dissolution tests (section 3.1). In general, flow samples with higher flow rates showed lower lead concentrations than those observed for lower flow rates, this is attributed to the shorter residence times of the water within the pipe. Lead in water has been reported to exist in both soluble and particulate forms, which strongly depend on water quality parameters such as pH and alkalinity (Bisogni et al., 2000; Hulsmann, 1990; McNeill and Edwards, 2004). Soluble lead concentrations are mainly affected by the chemical solubility of lead corrosion products such as lead carbonates and lead oxides present on the inner walls of the pipe (AWWARF, 1990). On the other hand, particulate lead in water is generated when the corrosion scale layer is disturbed physically or chemically (Hulsmann, 1990; McNeill and Edwards, 2004). Fig. 5 shows the contributions of particulate lead to the total amount of lead observed for both stagnated (0 L/min) and flow (3 L/min) samples over the pH range studied. The particulate lead fractions for the stagnated samples were about 10% of the total lead concentrations observed at pH 7.1, increasing to over 60% as pH values increased. The contribution of particulate lead to total lead concentration was even larger for the case of flow samples; though a marked trend toward pH dependence was not observed. Two different types of particulate lead have been reported to occur: colloidal lead (0.08mm < particle size < 12 mm) and flaking lead (particle size > 12 mm) (Hulsmann, 1990). Colloidal lead is reported to be associated with colloidal particles such iron oxides, whereas flaking lead is caused by mechanical disturbance of the corrosion layer present inside lead pipes (Hulsmann, 1990; McNeill and Edwards, 2004). In our pipe loop study, we did not differentiate between colloidal and flaking lead separately since we used a single size filter (0.45 mm). However, it is plausible to assume that the high lead particulate contribution observed for the flow samples over all the pH ranges studied is likely related to an increase in the flaking lead caused by mechanical disturbance of the corrosion layer. On the other hand, the increase of the fraction of particulate lead at high pH

Pb concentration (µg/L)

400

300

200

100

Ratio of Particulate Pb to Total Pb (%)

100

0 L/min 0.25 L/min 3 L/min 32.75 L/min

80

60

40

20 0 L/min 3 L/min 0 6.5

0 7.0

7.5

8.0

8.5

9.0

9.5

pH

Fig. 4 e Total lead concentration observed during pipe loop test at different pH values and water flow rates.

7.0

7.5

8.0

8.5

9.0

9.5

10.0

pH

Fig. 5 e Contributions of soluble and particulate lead concentrations to total lead concentrations at various pH and flow rate of (a) 0 L/min (stagnation) and (b) 3 L/min.

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values (clearly observed for the case of stagnated samples in the pipe loop study could be linked to the formation of colloidal iron oxides (see section 3.3) at high pH values. In previous studies, a significant fraction of the total lead present in sampled water has been identified as particulate species; leading to an emphasis on the importance of particulate lead control in drinking water systems (Bisogni et al., 2000; Hulsmann, 1990; McNeill and Edwards, 2004; Triantafyllidou et al., 2007). Our pipe loop tests showed that the contribution of particulate lead to total lead concentration is indeed quite significant for flow samples. However, particulate contribution for the stagnated samples becomes significant only at water pH values above 8.5. It should be emphasized that as pH increased, a decrease in total lead concentrations was observed in the stagnated samples. Therefore, we can attribute the observed decrease in total lead concentration at higher pH values to a decrease in chemically soluble lead. On the other hand, the effect of pH on total lead concentration in flow samples seemed to be less prominent than that observed for stagnated samples. This is attributed to the fact that total lead concentrations observed in flow samples are mostly governed by particulate lead, rather than chemically soluble lead.

3.3.

Monitoring of lead serviced houses

Lead concentrations in drinking water sampled from eight selected lead serviced houses in London’s drinking water distribution system were monitored while the City increased the pH of the water pumped into the distribution system (December 2007 to March 2010). It should be mentioned that total lead concentration was either below or near detection limits (0.02 mg/L) at the City’s distribution plant. Therefore, the observed lead concentrations in this section must be traced to the corrosion scales present in the inner surface of pipes delivering water to each household. Fig. 6 shows the variations observed in averaged pH values obtained during this time period. Specifically, water pH was gradually increased from 7.1 to around 8.1 from January 2008 to June 2008 and pH was slightly increased further to pH 8.3 and maintained at this value until March 2010. The pH was adjusted by adding NaOH

8.6 8.4 8.2 8.0

pH

7.8 7.6 7.4 7.2 7.0 6.8 Nov 2007

May 2008

Nov 2008

May 2009

Nov 2009

May 2010

Date

Fig. 6 e Change of averaged pH values of 8 lead serviced houses with time over the pH adjustment period from December 2007 to March 2010 in London, ON, Canada.

to the treated water before it entered the distribution system. However, pH values dropped to around pH 7.6 on February 2009. The pH drop on February 2009 was due to the temporary malfunction of a pump that feeds NaOH to the treated water. Table S2 summarizes the averaged water quality parameters obtained on-site during sampling. Fig. 7 shows the change in lead concentrations observed at these 8 lead serviced houses from December 2007 to March 2010. During the first six months (December 2007 to June 2008) of this monitoring period water pH was slowly adjusted from 7.1 to 8.1 In December 2007, six of these houses (L3-L8) showed lead levels above the action level (10 mg/L). Over 70% of the total lead concentration in most of the water samples was identified as particulate lead. A clear trend in dissolved lead levels over the time period in which pH adjustment took place was not observed in Fig. 7. However a careful inspection of the data indicates that during the first 6 months (December 2007 to June 2008) lead concentrations steadily decreased in all monitored households. In fact, as the pH of the water increased from 7.1 to 7.6 in February 2008, a significant decrease in the total lead concentration to approximately half of the values recorded before pH adjustment was observed. This is consistent with the results obtained in both the batch and pipe loop experiments where an increase in pH from 7 to 9 yielded lower levels of total dissolved lead. Further pH increase to values close to pH 8.1 (June 2008) did not result in further decrease on total lead concentrations. This result is also consistent with those obtained on the pipe loop test, which do not show a change in leached lead when water pH values are varied between 7.8 and 8.7. After June 2008 while pH was maintained constant, variations of lead concentrations were still observed. These variations do not follow a clear trend; they seem random, suggesting that other factors, such as temperature, play important roles in controlling lead release. The following observation supports this hypothesis: when the water pH was maintained between pH 7.6 and pH 8.3 in the distribution system, an increase of both soluble and particulate lead concentrations were observed in September 2008 and September 2009 for most lead serviced houses. Indeed, effects of seasonal changes in water temperature on lead concentration has been previously observed (Karalekas et al., 1983). Increased water temperature might increase both soluble and particulate lead concentrations. Moreover, the higher water consumption rates normally associated with the summer months could increase particulate lead concentrations. A sudden increase in total lead concentration was observed in February 2009 for house LS5 and in March 2010 for house LS8. The data clearly indicates that the observed increases in lead concentration for houses LS5 and LS8 were mainly caused by an increase in particulate lead (Fig. 7). Table 3 shows the observed particulate lead, particulate iron and particulate aluminum concentrations recorded during the monitoring period. In general, lead and aluminum were the main metals present in the particulate, while particulate iron concentrations were low or not observed. The average particulate iron concentration of L5 house was 50 mg/L through the monitoring period, with an unusually high concentration (170 mg/L) recorded in February 2009. For the case of household LS8, particulate iron concentrations of 60 mg/L in March 2010 were

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10

8

LS2

LS1

7

Pb cocentration ( g/L)

Pb cocentration ( g/L)

8 6 5 4 3

6

4

2

2 1 0 Nov 2007

May 2008

Nov 2008

May 2009

Nov 2009

0 Nov 2007

May 2010

May 2008

Nov 2008

May 2009

10

LS4 20

Pb cocentration ( g/L)

Pb cocentration ( g/L)

8

6

4

2

15

10

5

May 2008

Nov 2008

May 2009

Nov 2009

0 Nov 2007

May 2010

May 2008

Nov 2008

Date

May 2009

May 2010

30

LS6

LS5 25

Pb cocentration ( g/L)

40

Pb cocentration ( g/L)

Nov 2009

Date

50

30

20

10

0 Nov 2007

May 2010

25

LS3

0 Nov 2007

Nov 2009

Date

Date

20

15

10

5

May 2008

Nov 2008

May 2009

Nov 2009

0 Nov 2007

May 2010

May 2008

Nov 2008

Date

May 2009

Nov 2009

May 2010

Date

30

80

LS8

LS7 Pb cocentration ( g/L)

Pb cocentration ( g/L)

25

20

15

10

60

40

20 5

0 Nov 2007

May 2008

Nov 2008

May 2009

Date

Nov 2009

May 2010

0 Nov 2007

May 2008

Nov 2008

May 2009

Nov 2009

May 2010

Date

Fig. 7 e Change in lead concentrations at 8 lead serviced houses with time over the pH adjustment period from December 2007 to February 2009 in London, ON, Canada (C total lead concentration; B soluble lead concentration).

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Table 3 e Particulate lead, iron, and aluminum concentrations observed for the eight lead serviced houses from December 2007 to March 2010 (units: mg/L). Sampling

12/17/07 01/07/08 01/14/08 01/21/08 02/04/08 02/11/08 05/05/08 06/09/08 09/16/08 02/17/09 09/14/09 03/17/10

LS1

LS2

LS3

LS4

LS5

LS6

LS7

LS8

Pb

Fe

Al

Pb

Fe

Al

Pb

Fe

Al

Pb

Fe

Al

Pb

Fe

Al

Pb

Fe

Al

Pb

Fe

Al

Pb

Fe

Al

5.73 5.44 5.07 4.87 2.83 3.6 0.96 3.03 3.65 2.45 4.52 1.75

23 21 27 26 12 15 ND ND ND ND ND ND

7.8 7.6 4.9 5.9 3.7 4.3 8.2 44.4 63.7 3.1 49.4 11.4

5.19 5.92 6.08 4.53 4.07 0 1.73 3.61 3.08 3.37 0.44 4.96

ND 13 15 11 10 ND ND ND ND ND ND ND

6.6 8.4 5.5 5 5.4 0.8 6.2 33.5 48.2 4.2 ND 11.9

6.21 8.78 8.14 6.38 4.61 1.87 1.49 2.9 3.7 3.85 4.75 1.65

109 167 146 134 88 58 32 42 37 73 20 23

7.3 7.9 6.2 5.7 4.8 3.7 6.8 56.5 69.8 4.8 46.3 9.8

14.2 17.1 16.5 12.2 13.4 8.48 7.51 6.51 11.1 7.84 3.1 1

19 66 31 14 14 ND ND 35 ND ND ND ND

2.2 18.3 ND 4.6 5.9 7.2 13.6 54.5 67.4 6.7 4 18.2

18.8 15.8 12.5 11.3 12.2 7.98 3.34 14.0 9.61 40.7 e e

73 59 48 53 47 43 18 77 19 168 e e

6.8 5.9 4 ND 6.5 6.7 5.3 82.5 58.8 9.3 e e

16.9 18.2 16.4 18.1 11.7 9.46 5.98 e 13.6 7.64 7.5 7.52

20 36 25 18 16 13 ND e ND ND ND ND

6.6 7.2 5.5 9 6.5 8.5 12.2 e 66.7 3.9 10.4 17.2

19.9 14.2 16.3 13.6 12.0 8.56 4.62 10.3 17.0 8.61 e 8.89

33 29 10 21 18 14 1 ND ND ND e ND

9.1 9 1.8 5.2 5.7 5 9.3 66.9 66.1 6.3 e 14.4

e e 19.3 12.9 10.8 11.5 4.81 11.4 26.7 7.7 9.0 66.3

e e 45 26 15 14 ND 11 15 ND ND 61

e e 9.1 5.3 3.9 3.6 6.3 43.7 77.5 5.3 15.6 26.7

ND: Not Detected.

observed, even though the average for this household was 14 mg/L at other times. It has been previously reported that a sudden increase in particulate lead concentration by disturbance of the corrosion layer could yield very high lead concentrations in water (Hulsmann, 1990). The unusual increase in leached particulate lead could be explained by the presence of iron oxide particles. Indeed, unusually high particulate iron concentrations were observed at the same time for which high particulate lead concentrations were detected for houses LS5 and LS8. A careful inspection of the data indicates that in these cases, both particulate lead and iron concentrations increased simultaneously. This is in contrast to the particulate aluminum concentration which did not show a clear trend, nor a sudden increase during the same time period. Thus, it is plausible to link this sudden increase in the amount of particulate lead to an increase in iron particulate and not to a mechanical disturbance which would have resulted in an increase in all elements (Pb, Fe and Al) in particulate form. This is consistent with previous reports which indicate that the presence of iron oxide particles is linked to an increase in particulate lead; lead sorption onto colloidal iron being the proposed mechanism (Deshommes et al., 2010; Hulsmann, 1990). Indeed, iron oxide/hydroxide minerals have been reported to have a strong affinity for lead (Mohapatra et al., 2009; O’Reilly and Hochella, 2003). A Pearson correlation analysis was conducted to examine the relationships between observed lead concentrations (total, soluble and particulate), water quality parameters and other metal concentrations over the entire monitoring period (December 2007 to March 2010) (Table 4). As expected, due to the reasons outlined above, significant correlations between lead concentrations and pH were not observed when the data is analyzed over the entire time span of pH monitoring. However, strong positive correlations were observed between leached lead and hardness indicating that lead concentration in water increased as water hardness increased. Although it has been proposed that hard water is less corrosive than soft water, the effect of hardness on lead solubilityis still controversial (AWWARF, 1990; Schock, 1998).

Particulate iron showed a strong positive correlation with particulate lead concentration, which is consistent with previous results (Deshommes et al., 2010; Hulsmann, 1990) and with the sudden particulate lead increase observed for houses LS5 and LS8 in our study. A strong correlation was observed between soluble lead concentration and temperature, which confirms that seasonal variations affect lead concentration. In addition, soluble lead showed a strong negative correlation with free chlorine concentration, indicating that the presence of free chlorine decreases lead levels. Chlorine is a strong oxidizing agent and insoluble lead (IV) oxides have been reported to formin chlorinated water (Lytle and Schock, 2005). Therefore high chlorine levels provide favorable conditions for lead (IV) oxide formation, decreasing the amount of soluble lead. In a previous study, we identified an amorphous phase of lead(IV) oxide in the outermost layer of lead corrosion scales in lead pipes obtained from London’s drinking water system, and this observation supports this hypothesis (Kim and Herrera, 2010). A strong correlation between the concentrations of aluminum and arsenic was also observed. Both aluminum and arsenic showed positive correlation with pH and temperature, and negative correlation with the concentration of free chlorine. Water pH did not show a strong correlation with lead concentration in the water over the time period of the correlation. However, as mentioned above, the increase of pH from 7.1 to 7.6 (December 2007 to February 2008) in the treated water pumped into the City’s distribution system resulted in significant decrease in the total lead concentration. However, after the pH adjustment when the water pH was maintained constant (June 2008 to March 2010), lead concentrations in the household were affected by other various water parameters such as temperature, free chlorine residual, hardness and the presence of other metal concentrations such as calcium and manganese.

4.

Conclusions

In order to investigate the effect of pH on lead concentrations in drinking water, we have conducted batch and pipe loop

Table 4 e Pearson correlation matrix of lead concentration and other water parameters observed in 8 lead serviced houses. pH 1 0.48b 0.31b 0.00 0.69b 0.09 0.16 0.15 0.34b 0.74b 0.53b 0.10 0.06 0.01 0.43b 0.09 0.13 0.08 0.25a 0.32b 0.47b

1 0.66b 0.04 0.23a 0.03 0.26b 0.12 0.18 0.80b 0.58b 0.07 0.14 0.04 0.24a 0.21a 0.32b 0.05 0.26b 0.52b 0.20a

1 0.08 0.20a 0.19 0.30b 0.11 0.03 0.49b 0.34b 0.02 0.25b 0.01 0.26b 0.06 0.07 0.00 0.07 0.31b 0.05

Hardness

Alkalinity

Total Pb

Sol Pb

Part Pb

Part Fe

Al

As

Ba

Ca

Cd

Cu

Cr

Mn

Mg

Ni

Si

Zn

1 0.18 0.28b 0.35b 0.20a 0.02 0.15 0.14 0.09 0.73b 0.02 0.04 0.02 0.01 0.41b 0.05 0.11 0.02

1 0.04 0.13 0.09 0.34b 0.40b 0.27b 0.11 0.30b 0.01 0.10 0.10 0.21a 0.16 0.21a 0.16 0.44b

1 0.49b 0.96b 0.16 0.02 0.03 0.01 0.38b 0.14 0.10 0.08 0.57b 0.09 0.05 0.02 0.03

1 0.22a 0.29b 0.12 0.15 0.01 0.45b 0.06 0.06 0.07 0.06 0.18 0.06 0.16 0.18

1 0.28b 0.06 0.02 0.02 0.27b 0.14 0.13 0.06 0.66b 0.03 0.03 0.04 0.09

1 0.25a 0.17 0.03 0.04 0.01 0.05 0.02 0.45b 0.01 0.10 0.17 0.78b

1 0.70b 0.07 0.05 0.00 0.36b 0.09 0.16 0.15 0.19 0.56b 0.36b

1 0.08 0.06 0.01 0.21a 0.19 0.09 0.35b 0.26b 0.49b 0.25a

1 0.09 0.28b 0.08 0.01 0.07 0.06 0.01 0.05 0.02

1 0.17 0.11 0.08 0.07 0.54b 0.01 0.07 0.01

1 0.09 0.06 0.01 0.10 0.01 0.17 0.13

1 0.01 0.11 0.04 0.02 0.26b 0.10

1 0.08 0.02 0.92b 0.00 0.03

1 0.02 0.09 0.14 0.26b

1 0.01 0.15 0.01

1 0.01 0.18

1 0.12

1

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 7 6 3 e2 7 7 4

pH Temp Free Cl Hardness Alkalinity Total Pb Sol Pb Part Pb Part Fe Al As Ba Ca Cd Cu Cr Mn Mg Ni Si Zn

Temp Free Cl

a Correlation is significant at the 0.05 level (2-tailed). (N ¼ 102). b Correlation is significant at the 0.01 level (2-tailed).

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experiments and coupled these results to monitored lead levels in drinking water obtained at lead serviced households in London, Canada. The lead solubility of corrosion scale is strongly dependent on pH, increasing at low pH values and decreasing as pH increased. Accumulated metals in the corrosion scale were partially released to the aqueous phase during dissolution of corrosion scale; their maximum concentrations in water strongly depending on pH values. A significant fraction of total lead concentrations in water was linked to particulate lead concentrations. Total lead concentrations in flow samples are mostly governed by particulate lead, while the particulate lead contribution to the total lead concentration for the stagnated samples becomes significant only at higher water pH. These results indicate that a lead control strategy needs to take into account not only the chemistry of dissolution of lead minerals, but also patterns in the distribution system such as time of pipe installation and, more importantly, the rate of water consumption of individual households. For instance, if a lead corrosion strategy is developed addressing only water quality (such as a pH increase or the use of corrosion inhibitors) lead could potentially still leach into water, if water is allowed to sit for long residence times in a lead service line. In addition, lead concentrations in water are affected by other various water parameters such as temperature, free chlorine residual, and presence of other metal concentrations; particulate iron being of particular importance. An effective lead control program must incorporate a comprehensive understanding of the effects of all these parameters on both soluble and particulate lead concentrations.

Acknowledgments This study has been supported by the Walkerton Clean Water Center and the City of London.

Appendix. Supplementary data Supplementary data associated with this article can be found in online version at doi:10.1016/j.watres.2011.02.023.

references

Arai, Y., Sparks, D.L., Davis, J.A., 2005. Arsenate adsorption mechanisms at the allophane-water interface. Environmental Science and Technology 39, 2537e2544. AWWARF (AWWA Research Foundation), 1990. Lead Control Strategies. Colorado, Denver. AWWARF (AWWA Research Foundation), 2008. Contribution of Service Line and Plumbing Fixtures to Lead and Copper Rule Compliance Issues. Colorado, Denver. Bisogni, J.J., Nassar, I.S., Menegaux, A.M., 2000. Effect of calcium on lead in soft-water distribution systems. Journal of Environmental Engineering 126, 475e478. Copeland, R.C., Lytle, D.A., Dionysiou, D.D., 2007. Desorption of arsenic from drinking water distribution system solids. Environmental Monitoring and Assessment 127, 523e535.

Dando, K.J., Glasson, D.R., 1989. Vacuum microbalance studies of lead deposits from natural waters. Termochimica Acta 152, 87e96. Deshommes, E., Laroche, L., Nour, S., Cartier, C., Pre´vost, M., 2010. Source and occurrence of particulate lead in tap water. Water Research 44 (12), 3734e3744. Gerke, T.L., Scheckel, K.G., Schock, M.R., 2009. Identification and distribution of vanadinite in lead pipe corrosion by-products. Environmental Science and Technology 43, 4412e4418. Harsh, J., Chorover, J., Nizeyimana, E., 2002. Ch. 9 Allophane and Imogolite. Soil Science of AmericaSociety , Inc, Madison, WI. Health Canada, May 2008. Guideline for Canadian Drinking Water Quality Environment, Federal-Provincial-Territorial Committee on Drinking Water and Federal-ProvincialTerritorial Committee on Health and the Environment (Ottawa, Ontario). Huggins, D., 2008. Remediation of Lead Levels in Drinking Water: The City of London’s Experience. Ontario Water Works Association, London, ON, Canada. Hulsmann, A.D., 1990. Particulate lead in water supplies. Journal of the Institution of Water and Environment Management 4 (1), 19e25. Karalekas, P.C., Ryan, C.R., Taylor, F.B., 1983. Control of lead, copper, and iron pipe corrosion in Boston. Journal of the American Water Works Association 75, 92e95. Kim, E.J., Herrera, J.E., 2010. Characteristics of lead corrosion scales formed during drinking water distribution and their potential influence on the release of lead and other contaminants. Environmental Science and Technology 44, 6054e6061. Lytle, D.A., Schock, M.R., 2005. Formation of Pb(IV) oxides in chlorinated water. Journal of the American Water Works Association 97 (11), 102e114. Lytle, D.A., Sorg, T.J., Frietch, C., 2004. Accumulation of arsenic in drinking water distribution sytems. Environmental Science and Technology 38, 5365e5372. McNeill, L.S., Edwards, M., 2004. Importance of Pb and Cu particulate species for corrosion control. Journal of Environmental Engineering 130, 136e144. Mohapatra, M., Rout, K., Mohapatra, B.K., Anand, S., 2009. Sorption behavior of Pb(II) and Cd(II) on iron ore slime and characterization of metal ion loaded sorbent. Journal of Hazardous Materials 166 (2e3), 1506e1513. O’Reilly, S.E., Hochella, M.F., 2003. Lead sorption efficiencies of natural and synthetic Mn and Fe-oxides. Geochimica et Cosmochimica Acta 67 (23), 4471e4487. Schock, M.R., 1980. Response of lead solubility to dissolved carbonate in drinking water. Journal of the American Water Works Association 72 (12), 695e704. Schock, M.R., 1989. Understanding corrosion control strategies for lead. Journal of the American Water Works Association 81, 88e100. Schock, M.R., 1998. Reason for Corrosion Control Other than the Lead and Copper Rule, pp. 113e150, Marlborough, MA. Schock, M.R., Hyland, R.N., Welch, M.M., 2008. Occurrence of contaminant accumulation in lead pipe scales from domestic drinking-water distribution system. Environmental Science and Technology 42, 4285e4291. Switzer, J.A., Rajasekharan, V.V., Boonsalee, S., Kulp, E.A., Bohannan, E.W., 2006. Evidence that monochloramine disinfectant could lead to elevated Pb levels in drinking water. Environmental Science and Technology 40, 3384e3387. Triantafyllidou, S., Parks, J., Edwards, M., 2007. Lead particles in potable water. Journal of the American Water Works Association 99, 107e117.

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Inactivation and reactivation of antibiotic-resistant bacteria by chlorination in secondary effluents of a municipal wastewater treatment plant Jing-Jing Huang a, Hong-Ying Hu a,b,*, Fang Tang a, Yi Li c, Sun-Qin Lu c, Yun Lu a a

State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, PR China b Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, PR China c College of Environmental Science and Engineering, Hohai University, Nanjing 210098, PR China

article info

abstract

Article history:

Reports state that chlorination of drinking water and wastewater affects the proportions of

Received 30 December 2010

antibiotic-resistant bacteria by potentially assisting in microbial selection. Studies on the

Received in revised form

effect of chlorination on like species of antibiotic-resistant bacteria, however, have shown

15 February 2011

to be conflicting; furthermore, few studies have inspected the regrowth or reactivation of

Accepted 21 February 2011

antibiotic-resistant bacteria after chlorination in wastewater. To understand the risks of

Available online 2 March 2011

chlorination resulting from potentially selecting for antibiotic-resistant bacteria, inactivation and reactivation rates of both total heterotrophic bacteria and antibiotic-resistant

Keywords:

bacteria (including penicillin-, ampicillin-, tetracycline-, chloramphenicol-, and rifampicin-

Antibiotic-resistant bacteria

resistant bacteria) were examined after chlorinating secondary effluent samples from

Chlorination

a municipal wastewater treatment plant in this study.

Reactivation Regrowth Reclaimed water

Our experimental results indicated similar inactivation rates of both total heterotrophic bacteria and antibiotic-resistant bacteria. Microbial community composition, however, was affected by chlorination: treating samples with 10 mg Cl2/L for 10 min resulted in chloramphenicol-resistant bacteria accounting for nearly 100% of the microbial population in contrast to 78% before chlorination. This trend shows that chlorination contributes to selection of some antibiotic-resistant strains. Reactivation of antibiotic-resistant bacteria occurred at 2.0 mg Cl2/L for 10 min; specifically, chloramphenicol-, ampicillin-, and penicillin-resistant bacteria were the three prevalent groups present, and the reactivation of chloramphenicol-resistant bacteria exceeded 50%. Regrowth and reactivation of antibioticresistant bacteria in secondary effluents after chlorination with a long retention time could threaten public health security during wastewater reuse. ª 2011 Elsevier Ltd. All rights reserved.

1.

Introduction

Reclaiming and reusing wastewater are important parts of the total water cycle in cities, therefore these practices are often employed to reduce the required amounts of water for use in

municipal, landscape, recreation areas, etc. (Yang and Abbaspour, 2007). Reclaiming and reusing wastewater before thorough treatment to reduce the concentrations of waterborne pathogens such as helminthes, protozoa, fungi, bacteria, and viruses poses a health risk (Toze, 2006; WHO,

* Corresponding author. Tel.: þ86 10 6279 4005; fax: þ86 10 6279 7265. E-mail address: [email protected] (H.-Y. Hu). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.02.026

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2003; Hunter, 2002; Amahmid and Bouhoum, 2005; Campos, 2008). Hence, disinfection is necessary to control microbial health risks in reclaimed water. Among the many kinds of wastewater disinfection, chlorination has gained wide acceptance commercially, because of its simple application and moderate cost. Despite the potential problems associated with harmful disinfection by-products generated by this treatment (Wang et al., 2007; Wu et al., 2009), chlorine and chlorine-based compounds are still the most widely used in wastewater disinfection today (Rusin and Gerba, 2001). Antibiotic-resistant bacteria are emerging as important waterborne contaminants (Pruden et al., 2006; Sapkota et al., 2007; Li et al., 2009). Acquisition and further spread of antibiotic resistance determinants among pathogens is becoming one of the most relevant problems for treatment of infectious diseases (WHO, 2007; Kumarasamy et al., 2010). In addition, antibiotic resistance in organisms which are not considered primary pathogens is also important because of their ability to potentially transmit resistance to other organisms by means of transmissible resistance factors (Se´veno et al., 2002; Bennett, 2008; Martı´nez, 2008). Because sewage from communities and hospitals is treated, municipal wastewater treatment plants (WWTPs) could be important reservoirs for various antibiotic-resistant bacteria and genes (Zhang et al., 2009; Pruden et al., 2006; Reinthaler et al., 2003). Hence, controlling antibiotic-resistant bacteria in the effluents of WWTPs should be a concern in order to help reduce health risks from microbial pathogens during reclaimed water reuse. Using chemical disinfection to inactivate pathogens also plays an important role in controlling antibiotic-resistant bacteria in WWTPs. Studies on the effect of chlorination on antibiotic-resistant bacteria can be traced back to 1970s, where chlorination was shown to influence the proportion of multiple-antibiotic-resistant bacteria in drinking water and wastewater (Grabow and van Zyl, 1976; Armstrong et al., 1982; Murray et al., 1984). Research examining the effect of treating the same types of antibiotic-resistant bacteria with chlorine, however, is conflicting. For example, the percentage of ampicillin-resistant bacteria in sewage after chlorination at different doses decreased according to Grabow and van Zyl (1976), but increased in similar research done by Murray et al. (1984). Templeton et al. (2009) discussed inactivation of ampicillin-resistant Escherichia coli compared to the antibioticsusceptible strain by chlorination; results indicated that the inactivation of ampicillin-resistant E. coli was greater than the antibiotic-sensitive one. Conversely, this same study also found that trimethoprim-resistant E. coli was more resistant to chlorine than the antibiotic-sensitive one. There is still limited evidence showing that there is any difference between inactivation of antibiotic-resistant bacteria and antibioticsensitive bacteria in sewage to indicate selection of antibioticresistant bacteria by chlorination. After chlorination of wastewater, microbial health risks still exist. More and more research indicates that bacteria in drinking water are able to reproduce in distribution system pipes even after chlorination (Power et al., 1997; Zhang and DiGiano, 2002). There are few studies, however, that investigated the reactivation of antibiotic-resistant bacteria after chlorination in secondary effluents of municipal WWTPs. Murray et al. (1984) inspected the variation in proportions of 11

kinds of antibiotic-resistant bacteria between chlorinated influent samples and chlorinated influents neutralized by sodium thiosulfate after standing for 24 h. They found that the proportion of 7 kinds of antibiotic-resistant bacteria increased after the standing period. Nevertheless, an increase in the proportion of antibiotic-resistant bacteria in the previous studies could be due to an increase of antibiotic-resistant bacteria or a decrease of antibiotic-sensitive bacteria in the microbial community of the effluent. Therefore, it is still not clear whether regrowth or reactivation of antibiotic-resistant bacteria is more common than antibiotic-sensitive bacterial regrowth in drinking water or wastewater. The main objective of this study was to inspect the effect of chlorination on antibiotic-resistant bacteria in the secondary effluents of a WWTP to assist in estimating microbial health risks from antibiotic-resistant bacteria. In order to judge viability and recovery of antibiotic-resistant bacteria in the wastewater, inactivation, regrowth and reactivation between total heterotrophic bacteria and antibiotic-resistant bacteria (including penicillin-, ampicillin-, tetracycline-, chloramphenicol-, and rifampicin-resistant bacteria) were compared under a series of doses and three operation modes of chlorination. Finally, the changes in proportions of antibiotic-resistant bacteria in the microbial communities of the secondary effluents were analyzed to indicate survival rates of antibioticresistant bacteria in reclaimed water. The specific antibioticresistant bacteria chosen represent four kinds of mechanisms of action utilized by antibiotics which inhibit bacteria, and also mechanisms of antibiotic resistance. Penicillin and ampicillin inhibit the formation of peptidoglycan cross-linkages in the bacterial cell wall, while the mechanism of resistance is producing beta-lactamases to destroy penicillin or ampicillin and altering the affinity of penicillin-binding proteins in the membrane (Waxman and Strominger, 1983; Georgopapadakou, 1993). Tetracycline binds to the 30S ribosomal subunit through an interaction with 16S rRNA; the mechanism of resistance is decreased uptake and increased efflux of tetracycline (Schnappinger and Hillen, 1996). Chloramphenicol inhibits bacterial protein synthesis; while the mechanism of resistance is acetylation of the drug and inhibition of transport proteins (Balbi, 2004). The mode of action of rifampicin is inhibition of mRNA synthesis and the mechanism of resistance is caused by altering the target site of RNA polymerase (Wehrli, 1983).

2.

Materials and methods

2.1.

Water samples

Wastewater samples were collected from the secondary sedimentation tank of a municipal WWTP in Beijing, China. The treatment process of this plant is shown in Fig. 1. All samples were aseptically collected in sterile containers and transported to the lab on ice for immediate processing. The concentration

Fig. 1 e Treatment process of the municipal wastewater treatment plant studied in this study.

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of chemical oxygen demand (COD), total organic carbon (TOC) and ammonia nitrogen of the secondary effluents were 105e112 mg/L, 9.0e11.6 mg/L and 17.1e37.7 mg/L, respectively. The pH of the secondary effluents was 7.8e8.1. The absorbance at 254 nm (UV254) of the secondary effluents was 0.18e0.23. Total heterotrophic bacteria of the secondary effluents was 1.5  105e2.3  105 CFU/mL.

2.2.

Laboratory chlorination of secondary effluents

The chlorination of secondary effluent sample was carried out in 90-mm Petri dishes with a magnetic stirbar to gently mix samples at room temperature (25  2  C). The samples were exposed to sodium hypochlorite to establish different doses of chlorine (following DPD method), (0, 0.5, 1.0, 2.0, 5.0 and 10.0 mg Cl2/L) for a contact time of 10 min. The CT value (the product of initial sodium hypochlorite concentration and contact time) was used to represent the dosage of chlorination. The CT value was fixed at 50 mg Cl2 min/L for three operation modes of chlorination and compared between 2.0 mg Cl2/L with a contact time of 25 min, 5.0 mg Cl2/L for 10 min and 25.0 mg Cl2/L for 2 min. Chlorination was terminated by addition of a sodium thiosulfate solution (1.5%).

2.3.

shows the log ratio of inactivated specific bacteria to specific bacteria before chlorination: ! j N0 Inactivation of specific bacteria j ¼ log j Ni Here, specific bacteria j, included total heterotrophic bacteria, penicillin-resistant bacteria, ampicillin-resistant bacteria, tetracycline-resistant bacteria chloramphenicol-resistant bacteria, and rifampicin-resistant bacteria, j N0 : plate count of the specific bacteria j, before chlorination (CFU/mL), j Ni : immediate survival of the specific bacteria j, after chlorination at a chlorine dosage of i (CFU/mL). To evaluate the change in the microbial community of the secondary effluent after chlorination, the percentage of antibiotic-resistant bacteria was quantified as follows. j

Percentage of antibiotic-resistant bacteria ð%Þ ¼ j

Here, Ni : immediate survival of the specific bacteria j after chlorination (CFU/mL), NTi : immediate survival of total heterotrophic bacteria after chlorination (CFU/mL), i ¼ 0, when the dosage of chlorination was 0.

Regrowth and reactivation experiments 2.6.

Regrowth and reactivation experiments were carried out in the dark to simulate conditions of reclaimed water stored in a tank. The unchlorinated secondary effluents and chlorinated secondary effluents after neutralization by sodium thiosulfate were allowed to stand in Petri dishes (90 mm) at room temperature (25  2  C) for 22 h.

2.4.

Ni  100% NTi

Quantitative evaluation of reactivation

To evaluate reactivation after chlorination, the degree of reactivation was quantified according to the degree of photoreactivation (Guo et al., 2009a) as follows: the formula shows the percentage of repaired bacteria among bacteria inactivated by chlorination. The degree of reactivation could be considered as the degree of decay if the value is negative.

Microbial analysis Percentage reactivation or decay ð%Þ ¼

Total heterotrophic bacteria (Heterotrophic plate counts bacteria, HPC) (ISO6222, 1999) were enumerated by diluting 1 mL of sample into 10 mL nutrient agar (peptone: 10 g/L, beef extract: 3 g/L, NaCl: 5 g/L, agar: 15 g/L, pH ¼ 7.2). To obtain colony counts between 30 w 300 per plate, all water samples were diluted by serial ten-fold dilutions in phosphate-buffered saline (PBS, pH ¼ 7.4). The plates were then incubated at 37  C for 24 h. Antibiotic-resistant bacteria were also enumerated by diluting 1 mL of sample into 10 mL nutrient agar containing an antibiotic at a defined concentration (penicillin: 16 mg/L; ampicillin: 32 mg/L; tetracycline: 16 mg/L; chloramphenicol: 32 mg/L; rifampicin: 4 mg/L). Likewise, water samples were diluted using phosphate-buffered saline (PBS, pH ¼ 7.4) to obtain 30e300 colonies of antibiotic-resistant bacteria per plate. Again, the plates were incubated at 37  C for 24 h. Concentrations of antibiotics were defined as the maximum value of all minimum inhibitory concentrations (MICs) for pathogens listed in CLSI (Clinical and Laboratory Standards Institute) documentation (CLSI, 2006).

2.5. Quantitative evaluation of inactivation and percentage of antibiotic-resistant bacteria To evaluate chlorination effects on secondary effluents, the degree of inactivation was quantified as follows. The equation

j

j

j N0

 Ni

Nr  Ni

j

 100%

j

Here, Nr : plate count of the reactivated specific bacteria j after 22 h incubation (CFU/mL), j Ni : immediate survival of the specific bacteria j after chlorination (CFU/mL), j N0 : plate count of the specific bacteria j before chlorination (CFU/mL).

3.

Results and discussion

3.1. Chlorination of antibiotic-resistant bacteria in the secondary effluents Inactivation rates of antibiotic-resistant bacteria by chlorination reflect chlorine tolerance compared with that of total heterotrophic bacteria. The survival curves and inactivation curves of total heterotrophic bacteria and antibiotic-resistant bacteria were shown in Fig. 2 to determine bacterial response to chlorination. The apparent inactivation of penicillin- and ampicillin-resistant bacteria in the secondary effluent was significantly higher than others. Thus, it can be inferred that penicillin- and ampicillin-resistant bacteria were more susceptible to chlorine compounds. A similar phenomenon was reported by Templeton et al. (2009), whose results showed

2778

10

6

10

5

10

4

10

3

10

2

10

1

10

0

10

b

HPC TET

PEN CHL

AMP RIF

-1

0 2 4 6 8 10 12 Concentration of sodium hypochlorite ( mg Cl2/L)

6 5

log(N0/Ni)

4 3 2 1 0

HPC TET

PEN CHL

AMP RIF

0 2 4 6 8 10 12 Concentration of sodium hypochlorite (mg Cl2/L)

Fig. 2 e The survival curves (a) and inactivation curves (b) of total heterotrophic bacteria and antibiotic-resistant bacteria by chlorination in the secondary effluent. The contact time of chlorination was 10 min. HPC (-), PEN (C), AMP (:), TET (,), CHL (B), and RIF (6) represent total heterotrophic bacteria, penicillin-, ampicillin-, tetracycline-, chloramphenicol-, and rifampicin-resistant bacteria, respectively.

that an ampicillin-resistant E. coli had less tolerance to chlorine than an antibioitic-sensitive one. This may occur due to the fact that chlorine compounds affect and destroy the bacterial outer membrane and cell wall, then b-lactams (including penicillin and ampicillin) act in combination with altered outer membrane permeability in gram-negative bacteria and probably with cell wall in gram-positive bacteria (Georgopapadakou, 1993). Affecting penicillin-binding proteins in bacterial membrane by chlorination could lead to a loss of bacterial ability to bind penicillin or ampicillin, which targets cell wall-synthesizing enzymes and inactive bacteria (Georgopapadakou, 1993). Another possibility could be that the majority of penicillin- and ampicillin-resistant bacteria in water were gram-negative species (Sanders and Sanders, 1992; Li et al., 2009). Furthermore, Mir et al. (1997) suggested that gram-negative bacteria were generally more sensitive to chlorine than gram-positive bacteria in freshwater because of

different bacterial membrane and cell wall structural responses to chlorine exposure. The majority of inactivation of penicillin- and ampicillin-gram-negative bacteria leads to higher inactivation of penicillin- and ampicillin-resistant bacteria than that of total heterotrophic bacteria. Inactivation rates of tetracycline-, chloramphenicol-, and rifampicin-resistant bacteria, however, were at similar levels to those of total heterotrophic bacteria in the secondary effluent. These combined results show that the response of antibiotic-resistant bacteria is no more tolerant than total heterotrophic bacteria to chlorination. This also suggests that, in this study, there is no significant resistance to chlorine compounds among penicillin-, ampicillin-, chloramphenicol-, tetracycline- and rifampicin-resistant bacteria; which is further supported by other researchers (Rusin and Gerba, 2001). The inactivation of total heterotrophic bacteria and antibiotic-resistant bacteria in the secondary effluent by different operation modes of chlorination was demonstrated (Fig. 3). In general, at a constant CT value of 50 mg Cl2 min/L, the inactivation of total heterotrophic bacteria and antibiotic-resistant bacteria at an operation mode of 25 mg Cl2/L with 2 min of exposure time was significantly more effective than that of 2 mg Cl2/L for 25 min. The inactivation of tetracycline- and chloramphenicol-resistant bacteria was lower than total heterotrophic bacteria when exposed to 2.0 mg Cl2/L, while there was no significant difference between tetracyline- and chloramphenicol-resistant bacteria and total heterotrophic bacteria under a dosage of 5.0 or 25 mg Cl2/L. The results indicated that tetracycline- and chloramphenicol-resistant bacteria were more tolerant when exposed to a lower concentration of chlorine with a longer contact time. It may be inferred that storing reclaimed water with residual chlorine may be conducive to the prevalence of tetracycline- and chloramphenicol-resistant bacteria.

3.2. Effect of chlorination on the ratio of antibioticresistant bacteria in the secondary effluents Since the ratios of antibiotic-resistant bacteria in a microbial community could indicate the potential for developing 6 control 25 mg Cl2/L x 2min

5

5 mg Cl2/L x 10min 2 mg Cl2/L x 25min

4 log(N0/Ni)

CF U/m L

a

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 7 7 5 e2 7 8 1

3 2 1 0 HPC

PEN

AMP

TET

CHL

RIF

Fig. 3 e Inactivation of total heterotrophic bacteria and antibiotic-resistant bacteria by chlorination in different operation modes (CT [ 50 mg Cl2 min/L). The abbreviations are the same as those in Fig. 2.

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antibiotic-resistant bacteria in chlorinated effluent pipes, the ratios of antibiotic-resistant bacteria in the secondary effluent after chlorination were discussed (Fig. 4). The proportion of antibiotic-resistant bacteria after chlorination changed as dosage of chlorination increased. The proportion of penicillinand ampicillin-resistant bacteria decreased as the concentration of sodium hypochlorite increased, while the percentage of tetracycline- and rifampicin-resistant bacteria varied slightly. Remarkably, when the concentration of sodium hypochlorite was 10 mg Cl2/L, the percentage of chloramphenicol-resistant bacteria reached almost 100%, which was significantly (P < 0.05, by student-test) higher than that before chlorination. It can be directly inferred that the proportions of antibioticresistant bacteria in the microbial community of the secondary effluent will be changed by chlorination. Furthermore, chlorination may lead to chloramphenicol-resistant bacteria becoming a dominant species in the secondary effluent. The experimental results showed that the effect of chlorination on antibiotic-resistant bacteria differed depending on the dosage of chlorine, which can partly explain the conflicting results of previous research (Armstrong et al., 1982; Murray et al., 1984; Shrivastava et al., 2004). For example, the percentage of tetracycline-resistant bacteria after chlorination decreased in the report of Armstrong et al. (1982), but increased as reported by Murray et al. (1984). In addition, data from Staley et al. (1988, cited by Rusin and Gerba, 2001) showed that the proportion change of tetracycline-resistant bacteria in the chlorinated effluent was quite different under different chlorination conditions. Hence, although chlorine resistance is typically not a trait of antibiotic-resistant bacteria; due to the proportional change in microbial communities exposed to chlorine, chlorination should be considered as a selective pressure for antibiotic-resistant bacteria.

3.3. Reactivation of antibiotic-resistant bacteria after chlorination

10

6

10

5

10

4

10

3

10

unchlorinated effluents unchlorinated effluents after 22h

2

HPC

PEN

AMP

TET

CHL

RIF

Fig. 5 e Regrowth of total heterotrophic bacteria and antibiotic-resistant bacteria in the secondary effluents before chlorination. The abbreviations are the same as those in Fig. 2.

reach consumers (Guo et al., 2009b). To simulate these conditions, the concentrations of total heterotrophic bacteria and antibiotic-resistant bacteria in unchlorinated and chlorinated effluents were investigated after a retention time of 22 h. In general, the reproduction of bacteria after chlorination includes regrowth of living bacteria, reactivation of inactivated bacteria and regrowth of reactivated ones. The results showed that there was no significant regrowth of either total heterotrophic bacteria or antibiotic-resistant bacteria in the unchlorinated secondary effluents after retention in the dark at room temperature (Fig. 5). Power et al. (1997) also observed this trend where bacterial levels were stable in the untreated water for 48 h. Therefore, it can likely be considered that there was no significant regrowth of bacteria in the effluents. Furthermore, the reproduction of bacteria after chlorination could be the sum of reactivation of inactivated bacteria and the regrowth of reactivated ones. The reactivation of bacteria mentioned in the following paragraphs includes reactivation of inactivated bacteria and regrowth of reactivated ones.

100

P ercentage repair and deca y (%)

Percentage of antibiotic-resistant bacteria (%)

The rate of consuming reclaimed water is usually irregular; it may take several days or even a week for reclaimed water to

CF U/m L

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 7 7 5 e2 7 8 1

PEN AMP TET CHL RIF

80

60

40

20

0 0

2

4

6

8

10

12

Concentration of sodium hypochlorite ( mg Cl2/L)

Fig. 4 e Ratio of antibiotic-resistant bacteria in the secondary effluent after chlorination. The contact time of chlorination was 10 min. The abbreviations are the same as those in Fig. 2.

1000

100

HPC PEN AMP TET CHL RIF

800 600

80 60 40 20 0 -20

0.5

1.0

2.0

5.0

10.0

-40 -60

400

-80 -100

200 0 -200

0.5

1.0

2.0

5.0

10.0

Concentration of sodium hypochlorite (mg Cl2 /L)

Fig. 6 e Reactivation of total heterotrophic bacteria and antibiotic-resistant bacteria in the secondary effluent after chlorination. The dark time was 22 h. The abbreviations are the same as those in Fig. 2.

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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 7 7 5 e2 7 8 1

1.0

Per cent age repa ir (% )

0.8 0.6

HPC PEN AMP TET CHL RIF

0.4 0.2 0.0

25mg Cl2/L x 2min 5mg Cl2/L x 10min 2.5mg Cl2/L x 20min

Fig. 7 e Reactivation of total heterotrophic bacteria and antibiotic-resistant bacteria in the secondary effluent after three disinfection operation modes (CT [ 50 mg Cl2 min/L). The dark time was 22 h. The abbreviations are the same as those in Fig. 2.

Percentages of reactivation and decay of total heterotrophic bacteria and antibiotic-resistant bacteria are shown in Fig. 6. Reactivation and decay of total heterotrophic bacteria and antibiotic-resistant bacteria occurred when the dosage of chlorine was lower than 2.0 mg Cl2/L for 10 min. The extent of reactivation and decay of total heterotrophic bacteria and antibiotic-resistant bacteria decreased gradually as the dosage of chlorination increased. No reactivation or decay of total heterotrophic bacteria and antibiotic-resistant bacteria was observed when the dosage reached 5.0 mg Cl2/L for 10 min. Reactivation of chloramphenicol-, ampicillin-, and penicillinresistant bacteria was most likely to occur after exposure to a low dosage of chlorination. Total heterotrophic bacteria, tetracycline- and rifampicin-resistant bacteria decayed under a concentration of 0.5 and 1.0 mg Cl2/L, but reactivation occurred under 2.0 mg Cl2/L. According to the reactivation and decay of total heterotrophic bacteria and antibiotic-resistant bacteria, the proportion of antibiotic-resistant bacteria in the secondary effluents had a significant increase after standing for 22 h while the concentrations of chlorine were 0.5 and 1.0 mg Cl2/L. Lastly, reactivation of total heterotrophic bacteria and antibiotic-resistant bacteria in the secondary effluents in different operation modes of chlorination was inspected (Fig. 7). Due to the fact that higher concentration values with shorter exposure times resulted in a lower probability of reactivation, at a constant chlorination CT value, a higher concentration of chlorine with a shorter contact time was more efficient to control antibiotic-resistant bacteria in secondary effluents than a lower concentration of chlorine with a longer contact time.

4.

Conclusions

The inactivation rates of antibiotic-resistant bacteria studied in this paper were not lower than that of total heterotrophic

bacteria, nevertheless, the risk of antibiotic-resistant bacteria prevalence still exists. Firstly, the proportion of several antibiotic-resistant bacteria increased after chlorination, especially for chloramphenicol-resistant bacteria when exposed to 10 mg Cl2/L for 10 min; here antibiotic-resistant bacteria became the dominant species in the microbial community of chlorinated effluents. Secondly, the reactivation of some antibiotic-resistant bacteria occurred under a low chlorination dose. Chloramphenicol-, ampicillin-, and penicillin-resistant bacteria reactivated at higher rates when the concentration of sodium hypochlorite was lower than 2.0 mg Cl2/L. Finally, operation modes of chlorination influenced both the inactivation and reactivation of antibiotic-resistant bacteria. With a constant CT value, a higher concentration of chlorine with a shorter contact time is advantageous to help control the reactivation of inactivated antibiotic-resistant bacteria. Selection of antibiotic-resistant bacteria by chlorination in secondary effluents may depend on many factors, including but not limited to: type of antibiotic resistance, chlorination dose concentration and mode of operation and recovery time after chlorination.

Acknowledgements This study was funded by Chinese National Science Fund for Distinguished Young Scholars (No. 50825801) and Chinese National Science Fund (Key Program) (No. 51078209). The authors thank Professor Marylynn V. Yates and Dane C. Reano in Department of Environmental Sciences in University of California, Riverside for polishment of manuscript.

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Photooxidation of the antidepressant drug Fluoxetine (Prozac
) in aqueous media by hybrid catalytic/ozonation processes Fabiola Me´ndez-Arriaga a,b, Tomohiko Otsu a, Toshiyuki Oyama a, Jaime Gimenez b, Santiago Esplugas b, Hisao Hidaka a,*, Nick Serpone c,** a

Frontier Research Center for the Global Environmental Science, Meisei University, 2-1-1 Hodokubo, Hino, Tokyo 191-8506, Japan Chemical Engineering Department, University of Barcelona, Marti i Franque`s 1, 08028 Barcelona, Spain c Gruppo Fotochimico, Dipartimento di Chimica, Universita di Pavia, Via Taramelli 10, Pavia 27100, Italy b

article info

abstract

Article history:

This article examines the oxidative disposal of Prozac
(also known as Fluoxetine, FXT)

Received 18 October 2010

through several oxidative processes with and without UV irradiation: for example, TiO2

Received in revised form

alone, O3 alone, and the hybrid methods comprised of O3 þ H2O2 (PEROXONE process),

21 February 2011

TiO2 þ O3 and TiO2 þ O3 þ H2O2 at the laboratory scale. Results show a strong pH dependence

Accepted 23 February 2011

of the adsorption of FXT on TiO2 and the crucial role of adsorption in the whole degradation

Available online 8 March 2011

process. Photolysis of FXT is remarkable only under alkaline pH. The heterogeneous photoassisted process removes 0.11 mM FXT (initial concentration) within ca. 60 min with

Keywords:

a concomitant 50% mineralization at pH 11 (TiO2 loading, 0.050 g L1). The presence of H2O2

Drug pollution

enhances the mineralization further to >70%. UV/ozonation leads to the elimination of FXT

Fluoxetine

to a greater extent than does UV/TiO2: i.e., 100% elimination of FXT is achieved by UV/O3 in

Prozac

the first 10 min of reaction and almost 97% mineralization is attained under UV irradiation in

Selective serotonin reuptake inhib-

the presence of H2O2. The hybrid configuration UV þ TiO2 þ O3 þ H2O2 enhances removal of

itor (SSRI)

dissolved organic carbon (DOC) in ca. 30 min leaving, however, an important inorganic

Advanced oxidation processes

carbon (IC) content. In all cases, the presence of H2O2 improves the elimination of DOC, but

AOP

not without a detrimental effect on the biodegradability of FXT owing to the low organic

TiO2/O3

carbon content in the final treated effluent, together with significant levels of inorganic byproducts remaining. The photoassisted TiO2/O3 hybrid method may prove to be an efficient combination to enhance wastewater treatment of recalcitrant drug pollutants in aquatic environments. Crown Copyright ª 2011 Published by Elsevier Ltd. All rights reserved.

1.

Introduction

Fluoxetine hydrochloride {FXT$HCl; i.e., N-methyl-(3-phenyl3-(4-trifluoromethyl-phenoxy)-propyl)-amine} is a selective

serotonin reuptake inhibitor (SSRI) first launched as a drug in Belgium and subsequently approved and introduced in the United States in the mid-1980s to treat depressive disorder symptoms; a 1990 report indicated that some 19 million

* Corresponding author. Tel.: þ81 42 591 6635; fax: þ81 42 599 7785. ** Corresponding author. E-mail addresses: [email protected] (F. Me´ndez-Arriaga), [email protected] (H. Hidaka), [email protected], nickser@ alcor.concordia.ca (N. Serpone). 0043-1354/$ e see front matter Crown Copyright ª 2011 Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.02.030

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 7 8 2 e2 7 9 4

American adults suffered from depressive disorders (Robins and Regier, 1990). The pharmacological drug Fluoxetine has been widely accepted by the medical community to treat major depression disorders, but not without consequences. In this regard, a team of researchers from the University of Ottawa (Canada) has in fact shown that prescription of this antidepressant drug has ultimately led to some unexpected environmental consequences through discharge of the drug (e.g. the fraction of FXT non-metabolized by the liver) in various ecosystems (UOttawa, 2011).

Fluoxetine and its principal metabolite Norfluoxetine are found in surface waters owing to their incomplete degradation after therapeutic use and to partial elimination after the various treatments in wastewater treatment plants. Marketed as Prozac
and recently as Sarafem
by Eli Lilly, FXT is metabolized via N-demethylation to either S- or R-norfluoxetine and via O-dealkylation to 4-trifluoro-methylphenol (Altamura et al., 1994; Liu et al., 2002). Concentrations of FXT in tertiary wastewater effluents lay in the range of 30e82 ng L1 (Wert et al., 2009) and in surface waters as high as 12 ng L1 in the USA (Kolpin et al., 2002) and as high as 99 ng L1 in Canada (Metcalfe et al., 2003). An important fraction of FXT ([70%) has been found in bio-solids as a result of sorption onto humic acids and organic matter. Persistence of FXT in the environment has been demonstrated experimentally in batch reactors incorporating activated sludge (Kwon and Armbrust, 2006). FXT could be portioned on organic matter without being biodegraded for over 28 days. Results of ready-biodegradability investigations also showed that fluoxetine was not expected to biodegrade rapidly in wastewater treatment plants. Laboratory studies by Kwon and Armbrust (Kwon and Armbrust, 2006) also showed that fluoxetine was relatively recalcitrant to hydrolysis and photolysis over a 30-day period, and recalcitrant to microbial degradation. However, FXT could be removed rapidly from

2783

surface waters by adsorption to sediment, where it persists for relatively long times. Not surprisingly then that the presence of FXT in surface waters has had harmful consequences on aquatic species, such as, for example spawning in some crustaceans and bivalves (Brooks et al., 2003a; Brooks et al., 2003b,, 2005; Fong and Molnar, 2008; Nakamura et al., 2008). Except for few reports, the number of studies devoted to the elimination of FXT by advanced oxidation processes (AOPs) has been rather limited. In this regard, Benotti and coworkers (Benotti et al., 2009) reported the degradation of several mixed contaminants that included FXT using a TiO2 membrane reactor, with an FXT removal constant of 1.3  0.1 m3 kW h1. Ozonation was used by Wert et al. (Wert et al., 2009) against tertiary-treated effluents that included FXT. Degradation of effluent contaminants depended on the O3/TOC ratio (TOC, total organic carbon). As, Lam and coworkers (Lam et al., 2005) eliminated FXT, albeit slowly, by direct photolysis in sunlightirradiated surface waters; half-life of the process was 55.2  3.6 h; i.e. k ¼ 1.26  0.10  102 h1. Advanced oxidation processes (AOPs) are oxidative processes that underscore the treatment of contaminants in water, soils and air, based on the presence and reactivity of hydroxyl radicals (OH) generated in atmospheric or under subcritical conditions of temperature and pressure with or without catalyst and/or reactive energy (electrochemical, UVeVis or ultrasounds) (Me´ndez-Arriaga, 2009). The most typical AOPs used in environmental applications are those that involve the photoassisted TiO2 process, the Fenton and photoFenton reactions, sonolysis, and ozonation in alkaline media, among others, together with various hybrid process combinations such as electrophotocatalysis and sonophoto-Fenton processes. In the photoassisted TiO2 process, UV irradiation of this metal oxide generates OH radicals by valence band hole oxidation of surface bound OH groups and/or water. Ozone (O3) is also an attractive oxidant (Eo ¼ 2.07 V) with the ability to attack organic matter directly and/or otherwise lead to the indirect formation of OH radicals under alkaline conditions, thereby promoting the unselective attack of compounds present in aqueous media. Among the various oxidants available, the OH radical is the strongest oxidizing species (Eo ¼ 2.80 V) used in water and wastewater treatments that can lead to greatly accelerated rates of contaminant oxidation. The latter can be achieved by generation of OH radicals through the combination of ozone, hydrogen peroxide (H2O2; Eo ¼ 1.78 V), titanium dioxide, and within the present context also UV radiation (Zhou and Smith, 2001; Bolton and Carter, 2001; Yao and Mills, 2001). Among these, peroxone (O3/H2O2), UV/O3, UV/H2O2, UV/peroxone and heterogeneous photoassisted processes involving in most cases UV/TiO2 have been the most attractive combinations to detoxify waters and wastewaters. Whenever O3 and O2 radical anions are photogenerated in the above combinations, they can initiate a series of radical chain reactions (Buxton et al., 1988). In addition, to the extent that the oxidant hydrogen peroxide (pKa ¼ 11.6) forms in the various methods used, reactions are a priori expected to proceed faster in alkaline media. Accordingly, with the various hybrid procedures examined herein we can envisage certain reactions in aerated aqueous media (Gimeno et al., 2007; Xu and Goddard, 2002) to generate the oxidizing agent(s) that best represents the entity (OH radical) that causes the

2784

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photodegradation and ultimate mineralization of pollutants. Direct addition of O3 to the aromatic rings of FXT is not precluded a priori in initiating the degradation of a substrate such as FXT. AOP hybrid processes that can dispose of FXT in aqueous media have not been reported previously. Accordingly, the principal goal of our investigation was to evaluate the synergies between various advanced oxidation processes such as UV/ TiO2, ozonation and UV/ozonation, peroxone (O3/H2O2) and UV/ peroxone, together with the hybrid UV/TiO2/O3/H2O2 process to eliminate FXT from simulated polluted waters at different pHs.

2.

Experimental

2.1.

Reagents and materials

Fluoxetine hydrochloride (FXT$HCl) was used as received from LKT Laboratories Inc. Table 1 summarizes the major physicochemical properties of FXT (Lam et al., 2005; Fluoxetine-1, 2010;Fluoxetine-2, 2010; Fluoxetine-3, 2011; Fluoxetine-4, 2010). The TiO2 was Degussa P-25 and was employed without previous treatment; H2O2 (Wako) was 30% pure and unless noted otherwise its concentration was 0.12 mM in the simulated waters. The pHs of the aqueous media were adjusted either with a NaOH solution (Wako, 1.0 M) or with concentrated HCl solutions. Deionized water was used throughout. Solutions of FXT were prepared fresh daily in either acidic or alkaline media as needed.

2.2.

Experimental devices and analytical procedures

FXT solutions (0.11 mM), with or without a catalyst, were placed in a 100-mL batch hermetic flask reactor, were constantly stirred magnetically, and then were irradiated with a 75-W high pressure Hg lamp (Toshiba SHL-100UVQ2) emitting a maximal emission centered at 360 nm; irradiance was 2e4 mW cm2 (Topcon UVR-2 radiometer). Experiments were also carried out on FXT solutions in the presence of suspended titania (TiO2) particles at various initial loadings and previously homogenized by ultrasounds for ca. 20 min. An EcoDesign EDOG ozone generator converted pure oxygen gas into ozone for ozonation reactions; ozone was bubbled directly into the solution or the dispersion.

Samples were withdrawn periodically after treatment, filtered with PTFE 0.20 mm Hydrophilic DISMIC-13 HP filters, and then analyzed for unreacted FXT, for dissolved organic carbon (DOC), for inorganic carbon (IC), for ionic species, and for determinations of biochemical oxygen demand (BOD5). The presence and quantification of unreacted FXT were carried out either by UV spectroscopy or by HPLC techniques (Lake, 2010) (wavelength, 227 nm) using an isocratic flow mode at pH 3; the mobile phase consisted of 50/50, v/v acetonitrile (Wako, 99.8%) and potassium monophosphate (10 mM; Wako); the C18 column was Jasco Crestapak C18S, and the temperature was ambient (22.5  2  C); the flow rate was 1 mL min1; injection volume was 40 mL. Dissolved organic carbon (and inorganic carbon content) was determined on a Shimadzu TOC-5000A instrument. Chromatographic analyses for ionic species were carried out on a Jasco Ion Chromatograph equipped with a Shodex IC column. BOD5 analyses were carried out on a BOD Sensor instrument (VELP Scientifica srl, Italy) using a procedure supplied by the manufacturer. Identification of intermediates produced in the photocatalytic degradation of FXT was done using the time-of-flight mass spectral technique (TOF-MS) with a JEOL TOF CS JMST100CS mass spectrometer (applied voltage for ESI þ was 2000 V; detection voltage was 2500 V; temperature of vaporization of the degraded sample in methanolic aqueous media was 100  C; temperature of orifice 1 was 80  C; the ring lens voltage was 15 V, while that of orifice 1 was 40 V and that of orifice 2 was 7 V). Subsequent to a tenfold dilution (methanol) of the degraded samples, the samples were injected into the probe at 10 mL min1. The MS data were recorded in both the positive-ion mode (M  Hþ) and in the negative-ion mode (M).

3.

Results and discussion

3.1. Exploratory experiments: thermolysis, adsorption on titania surface, and photolysis Control experiments carried out under dark conditions showed no changes in the concentration of FXT in the temperature range of 20e55  C in 15  C steps after 60 min of exposure to the various temperatures. To evaluate the extent of adsorption of FXT on the TiO2 particle surface several suspensions of TiO2 of various concentrations were added to

Table 1 e Some physicochemical properties of Fluoxetine [Lam et al., 2005; Fluoxetine-1, 2010; Fluoxetine-2, 2010; Fluoxetine-3, 2011; Fluoxetine-4, 2010]. Chemical name

Fluoxetine Methyl-[3-phenyl-3(4-trifluoromethyl-phenoxy)-propyl]-amine

Molecular formula Molecular Weight: Water solubility as FXT$HCl as FXT Log KOW pKb Henry’s Law Constant  OH Rate Constant (atmosphere)  OH Rate Constant (deionized water)

C17H18F3NO 309.3 g mol1 (as FXT) 14 mg mL1 Sparingly soluble to insoluble in water 1.22 3.95 8.9  108 atm m3/mol; (25  C) 2.2  1011 M1 s1; (25  C) 8.4  0.5  109 M1 s1; 9.6  0.8  109 M1 s1

References

(Fluoxetine-3, 2011) (Fluoxetine-2, 2010) (Fluoxetine-4, 2010) (Fluoxetine-2, 2010) (Fluoxetine-1, 2010) (Fluoxetine-1, 2010) (Lam et al., 2005)

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 7 8 2 e2 7 9 4

a 10 mL flask containing a 0.11 mM aqueous solution of FXT at pH 5 and 11. Increase of the surface area of the metal oxide was achieved by sonicating the contents with ultrasounds for a 20-min period. Samples were then constantly stirred in the dark under isothermal conditions for 24 h, after which samples were withdrawn and filtered with PTFE 0.20 mm (DISMIC-13HP) filters. The FXT concentrations were determined by UV spectroscopy at the wavelength of 227 nm. In weak acidic media (pH 5) adsorption of FXT on the titania surface was less than 7% of the initial substrate for a wide range of TiO2 loadings (0.00010 g L1 < TiO2 < 1.14 g L1). By contrast, the extent of adsorption of FXT in alkaline media (pH 11) was 4-fold greater under otherwise identical conditions. Changes of pH near the pKa of FXT (pKa ¼ 10.05, Table 1) can lead to strong chemical effects. Ionic species such as the protonated FXT-Hþ species in acidic media (pH 5) easily dissolve in water contrary to the non-ionic sparingly soluble parent substrate FXT. At pHs below the pzc for TiO2 (pH ca. 6.5) the cationic FXT-Hþ will therefore be repelled from the positively charged TiO2 surface. Under dark conditions, no depletion of FXT occurred in the presence of H2O2 in both acidic and alkaline media. Photolysis of FXT was evaluated at several pHs with and without the presence of another oxidant, such as H2O2. Results showed that no photolytic degradation of FXT occurred in acidic media (pH 3) and in near-neutral media (pH ca. 6) under UV irradiation (maximal emission, 360 nm; irradiance, 3 mW cm2) as evidenced by absorption spectral changes of FXT in aqueous media at 227 nm (Kwon and Armbrust, 2006), in line with the study of Risley and Bopp (Risley and Bopp, 1990) who noted no remarkable changes occurred when exposing FXT to UV radiation for ca. 8 weeks. In alkaline media, however, the concentration of FXT decreased by ca. 15% after 60 min of UV illumination. In the presence of H2O2 (0.12 mM) depletion of FXT was more than 2-fold greater, indicating that the increase is not solely due to direct H2O2 photolysis, but could also involve reaction of the peroxide with byproducts without necessarily causing further mineralization. About 10% of DOC removal occurred both in the presence and absence of H2O2 in alkaline media. Formation of a small quantity of F ions was seen only in the presence of H2O2.

3.2.

Photoassisted TiO2-mediated disposal of FXT

The time profiles of the degradation and mineralization of FXT at different TiO2 loadings are reported in Fig. 1 in alkaline media (pH 11) and in one instance in acid media (pH 5); specifically (i) depletion of FXT in solution under various conditions including one instance where H2O2 was added (ii) evolution of F ions, and (iii) loss of dissolved organic carbon (DOC). The relevant dynamics of degradation of FXT are reported in Table 2. An increase in TiO2 loading from 0.010 to 0.050 to 0.10 g L1 in the dispersions decreased the time for complete depletion of FXT from 15 to 9 to 8 min, respectively (Fig. 1a). The corresponding first-order rates were 0.38 min1, 0.55 min1 and 0.77 min1. At the lowest TiO2 loading (0.010 g L1), addition of H2O2 to the alkaline dispersion inhibited FXT depletion with the rate being threefold slower (0.12 min1). However, for the same TiO2 loading of 0.10 g L1 a decrease of pH from pH 11 to pH 5 led to a substantive 10-fold decrease in the degradation dynamics,

2785

from 0.77 min1 to 0.075 min1. Adsorption of FXT on the TiO2 particle surface in alkaline media was greater than in acidic media. This less favorable condition for degradation of FXT (and also for mineralization) in acid media was due to the presence of a major barrier to mass transfer (Coulombic repulsive forces between protonated FXT and TiO2 particle surface) necessary to bring FXT close to the metal oxide surface. Fig. 1b illustrates the formation of F ions (defluorination process) during the degradation of FXT with the corresponding rates summarized in Table 2. In line with the degradation process, rates of defluorination increased with increase in TiO2 loading in alkaline media (pH 11): 0.056 min1, 0.091 min1 and 0.14 min1 for 0.010, 0.050 and 0.10 g L1 of TiO2, respectively. As with degradation, defluorination was also inhibited in acidic media for equal TiO2 loadings: k ¼ 0.019 min1 at pH 5 versus k ¼ 0.14 min1 at pH 11 (Table 2) under otherwise identical conditions. Addition of H2O2 to the 0.010 g L1 aqueous TiO2 dispersions impacted negatively on the defluorination process: k w 0.0098 min1 with H2O2 versus k ¼ 0.056 min1 in the absence of the peroxide. The extent of fluoride ions formed represents about 30% of the expected stoichiometric amount (0.33 mM). It is unlikely that F ions are adsorbed on the TiO2 particle surface in alkaline media because of strong Coulomb repulsions (Vohra et al., 2003). Rather, the smaller than expected quantity of free F detected may be due, in part, to non-degraded fluorinated intermediates remaining in the dispersion after the 60 min of irradiation, and/or to the strong likelihood that only one F detached from the eCF3 group to yield a defluorinated quinonoid-type species (Lam et al., 2005). Further examination of the data of Table 2 indicates that the degradation dynamics were in all cases faster than the corresponding defluorination dynamics, from which we infer that the major oxidizing agent, the OH radical, added preferentially to the unsubstituted phenyl ring of FXT and subsequently to the phenoxy aromatic ring. The amine function on the FXT structure was oxidatively converted to nitrite (NO2) and then to nitrate (NO3) ions with no evidence of NH3 under our conditions, in contrast to reports by Garrido and coworkers (Garrido et al., 2009) who inferred demethylation of FXT to form an amine (RNH2) through electrochemical oxidation. The maximal quantity of NO3 ions formed ranged between 0.02 mM and 0.04 mM, whereas the quantity of NO2 produced was significantly less than 0.01 mM. In acidic media (pH 5) and the presence of H2O2 in alkaline media (pH 11) had a negligible effect on the formation of nitrate ions; however, no NO2 ions were detected under these conditions. To the extent that the pH of the aqueous TiO2 dispersions in acid media was adjusted by addition of HCl acid and that in alkaline media the FXT solution was made up with the FXThydrochloride, we examined whether the presence of Cl ions might affect the degradation and mineralization of FXT (Bedner and MacCrehan, 2006; Vione et al., 2005a). Ion chromatographic analyses for chloride ions indicated that no changes in its concentration occurred and thus had no consequence on the oxidative processes occurring at the TiO2 surface under our conditions. Moreover, no chlorinated byproducts were detected and thus we infer that none likely formed.

2786

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 7 8 2 e2 7 9 4

b

30 0.10 g/L TiO2 pH11

25

0.10 g/L TiO2 pH5 0.050 g/L TiO2 pH11 0.010 g/L TiO2 pH11 0.010 g/L TiO2 pH11 H2 O2

20 15 10

1

0.08

3

4

0.06 2

0.04

5

0.02

5 0 0

0.10

F- ions (mM)

FXT (mg L-1)

a

10

20

30

40

50

0.00 0

60

10

DOC (mg L-1)

30

40

50

60

70

Irradiation time (min)

Irradiation time (min)

c

20

20 16

2

12 3 1 4 5

8 4 0 0

10

20

30

40

50

60

70

Irradiation time (min) Fig. 1 e (a) Depletion of FXT at various TiO2 loadings and at pH 5 and 11 together with addition of H2O2 in one instance (b) evolution of FL ions at different TiO2 loadings and (c) time profile of the loss of dissolved organic carbon (DOC) during the photoassisted degradation and mineralization of FXT. Other conditions: TiO2 loadings: (1) 0.10 g LL1, pH 11; (2) 0.10 g LL1, pH 5; (3) 0.050 g LL1, pH 11; (4) 0.010 g LL1, pH 11; (5) 0.010 g LL1, pH 11 plus added H2O2 (0.12 mM). Unless noted otherwise, all experiments were carried out for an initial FXT concentration of 0.11 mM (34 mg LL1; 100 mL) under O2 saturated conditions except for (1); irradiation was provided by the Hg lamp (see text).

The time profiles of the extent of mineralization of FXT (loss of DOC) at various TiO2 loadings and other conditions illustrated in Fig. 1c shows that ca. 60e80% of FXT was mineralized after 60 min of UV irradiation in alkaline dispersions. By contrast, at pH 5 the extent of mineralization, under otherwise identical conditions, was only ca. 20% complete. Evidence of a byproduct with m/z ¼ 416 (and others) from time-of-flight mass spectral experiments in the positive-ion mode suggests formation of some higher molecular weight byproducts subsequent to O-dealkylation and parallel hydroxylation of the aromatic rings; a likely candidate is the product formed from the reaction of FXT with a (CF2C(OH)e CH2CH2) fragment.

3.3.

(k [ w0.7 min1). Similarly, FXT degraded faster by UV/ ozonation than by the UV/peroxone process ([0.6 min1 versus 0.56 min1). Evidently, under these conditions the presence of 0.12 mM H2O2 appears to have had an inhibitory effect on the degradation dynamics of FXT. More than 25% depletion of DOC was successfully attained by the ozonation process in the dark in acidic media (Fig. 2b). Addition of H2O2 (0.12 mM) under the latter conditions increased somewhat the extent of loss of DOC and nearly doubled the rate of depletion of DOC (k ¼ 0.0068 min1 versus k ¼ 0.011 min1). Ozonation alone in the dark in alkaline media led to ca. 75% of DOC being depleted, whereas UV/ ozonation and UV/peroxone led to nearly a quantitative (w97%) depletion of the DOC of the antidepressant FXT

Ozonation and peroxone (O3 þ H2O2) processes

Fig. 2 displays (a) the degradation of FXT in the various solutions (b) the loss of dissolved organic carbon (DOC) (c) the defluorination of FXT, and formation of (d) NO2 and (e) NO3 ions, as well as the (f) evolution of inorganic carbon (IC) during the ozonation and peroxone degradation of FXT (0.11 mM; 34 mg L1). Clearly, degradation of FXT from the solutions to some intermediate products occurred fairly rapidly, in three cases in less than 10 min and in the other two cases in less than 2 min (Fig. 2a). Under dark conditions, degradation of FXT at pH 3 by ozonation was faster than the peroxone process (k ¼ 0.65 min1 versus k ¼ 0.39 min1; see Table 3), whereas ozonation at pH 11 was considerably faster than at pH 3

Table 2 e Rates of degradation and defluorination during the oxidative transformation of FXT in aqueous TiO2 dispersions at various TiO2 loadings, different pH and in the presence of H2O2. TiO2 loading (g L1)

pH

kdeg(min1)

kF(min1)

0.10 0.10 0.050 0.010 0.010 (þH2O2)

5 11 11 11 11

0.075  0.002 0.77  0.09 0.55  0.04 0.38  0.05 0.12  0.04

0.019  0.005 0.14  0.01 0.091  0.006 0.056  0.004 w0.0098

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b

30 O3 pH 3 (dark)

24

DOC (mg L-1)

FXT (mg L-1)

a

O3 pH 3 (dark)+H2O2 O3 pH 11 (dark)

18

O3 pH 11 + UV O3 pH 11+UV +H2O2

12 6 0

20 16 1

12

2

8

0

2

4

6

8

0 0

10

10

20

Time (min)

d

0.10 0.08 4 5

0.06 0.04

0.00 0

3

2

0.02

30

20

30

40

50

60

O3 pH 3 dark+H2O2 O3 pH 11 dark O3 pH 11 + UV O3 pH 11 + UV+H2O2

0.0000 0

70

10

20

Inorganic Carbon (mg L-1)

NO3- (mM)

4

0.03 5

1 2

0.01 0.00 0

3

10

20

30

40

30

40

50

60

70

Time (min)

f

0.02

70

O3 pH 3 dark

0.0050 0.0025

0.05 0.04

60

0.0075

Time (min)

e

50

0.0100

1

10

40

Time (min)

NO2- (mM)

F- (mM)

c

3 4 5

4

50

60

70

16 12 8

O3 pH 11 + UV O3 pH 11 UV+H2O2 O3 pH 3 dark O3 pH 3 dark+H2O2

4 0 0

10

Time (min)

20

30

40

50

60

70

Time (min)

Fig. 2 e (a) Depletion of FXT with time (b) loss of dissolved organic carbon (DOC), and formation of (c) FL ions (d) NO2L ions and (e) NO3L ions, together with (f) the evolution of inorganic carbon (IC) in the ozone-assisted degradation of FXT: (1) O3 dark pH 3; (2) O3 dark pH 3 D H2O2; (3) O3 dark pH 11; (4) UV/O3, pH 11; (5) UV/O3, pH 11 D H2O2. All experiments were carried out for initial FXT concentration of 0.11 ± 0.01 mM (34 mg LL1; 100 mL); 0.12 mM H2O2; ca. 25 mg LL1 of O3 and, where indicated UV illumination provided by the Hg lamp.

(Fig. 2b) within 60 min, even though the UV/peroxone process was twofold faster than the UV/O3 process (k ¼ 0.097 min1 vs k ¼ 0.042 min1). In this case, the presence of H2O2 assisted in achieving near complete elimination of DOC. Although the conditions were not optimized, nonetheless under the present

conditions removal efficiencies of DOC by the ozonation processes were greater than by the photoassisted TiO2 processes (compare Figs. 1c and 2b). Significant mineralization of FXT occurred in the first 10 min of UV/ozonation and UV/peroxonation in alkaline media, whereas in the dark at pH

Table 3 e Dynamics of the depletion of dissolved organic carbon of FXT under various conditions by ozonation, peroxone (O3/H2O2) processes, and hybrid process configurations that included TiO2 and UV irradiation. Ozone concentration was 25 mg LL1 throughout. Process configuration

pH

kdeg(min1)

kDOC(min1)

H2O2(mM)

TiO2 loading (g L1)

O3 (dark) O3/H2O2 (dark) O3 (dark) UV/O3 UV/O3/H2O2 UV/TiO2/O3 UV/TiO2/O3/H2O2 UV/TiO2/O3/H2O2

3 3 11 11 11 11 11 11

0.65  0.04 0.39  0.02 ([0.7) ([0.6) 0.56  0.10 0.76  0.11 0.52  0.12 0.80  0.15

0.0068 0.011 0.018 0.042 0.097 0.074  0.007 0.070  0.008 0.11  0.01

0 0.12 0 0 0.12 0 0.12 0.12

e e e e e 0.010 0.010 0.10

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3 the ozonation and peroxone processes tended to be rather inefficient (see Fig. 2f). Formation of F ions (Fig. 2c) and NO3 ions (Fig. 2e) with small quantities of NO2 ions (Fig. 2d) was also a main characteristic of the degradation of FXT by ozonation and peroxone processes. Under all experimental conditions, formation of free fluoride ions increased at first followed by a decrease of free F ions in the dispersion, inferring formation of some fluorinated intermediate species at longer times. For example, partial defluorination of the trifluoromethyl group in FXT to yield a difluoroquinonoid-type species (Lam et al., 2005), which could be followed in some cases by re-fluorination to produce the more stable trifluoromethylphenol intermediate. The initial rate of formation of free F ions by ozonation in acidic media (pH 3) was slower than in alkaline media (pH 11) under dark conditions: initial rates, 0.012 mM min1 vs w0.06 mM min1. The presence of H2O2 in the peroxone process benefited the extent of free F ions produced under acidic conditions, in contrast to alkaline media in which a smaller quantity of free F ions was present after 60 min.

FXT (mg L-1)

20 15

5 0 -2

3 0

2

4

Irradiation time (min)

d

0.12 0.09

1

0.06

3 2

0.03 0.00 0

20

TiO2 0.010g/L+O3+UV

16

TiO2 0.010g/L+O3+UV+H2O2 TiO2 0.10g/L+O3+UV+H2O2

12 8 4 0 0

6

NO2- (mM)

F- (mM)

c

1

2

1. TiO2 0.010g/L+O3+UV 2. TiO2 0.010g/L+O3+UV +H2O2 3. TiO2 0.10g/L+O3+UV +H2O2

10

20

50

60

70

0.012 1

0.009 0.006

10

20

30

40

50

60

0.000 0

70

f 1

0.032

2

0.024 0.016

3

0.008 10

20

30

40

50

2

3

10

20

30

40

50

60

70

Irradiation time (min)

Inorganic carbon (mg L-1)

NO3- (mM)

40

0.003

0.040

0.000 0

30

Irradiation time (min)

Irradiation time (min)

e

(1)

Fig. 2f shows the evolution of inorganic carbon (IC) in alkaline and acidic media, which was negligible in acidic media in the dark, contrary to evolution of IC in alkaline media under UV illumination by both ozonation and peroxone processes. The latter processes probably give rise to formation of some inorganic acids. Even though chloride ions were present under our conditions, no changes in their concentration were seen indicating their non-involvement in all the processes examined (Bedner and MacCrehan, 2006; Vione et al., 2005a).

b

25

10



H2 O2 þ OH/HO2 þ H2 O k ¼ 3:3  107 M1 s1

DOC (mg L-1)

a

Formation of NO3 ions during the UV/ozonation process (Fig. 2e) was significant. However, H2O2 inhibited the process under illuminated and alkaline conditions because of the competition between FXT and H2O2 for OH radicals (reaction 1 (Buxton et al., 1988; Neta et al., 2010). NO3 ion formation was slower in acidic media, whereas formation of NO2 ions occurred only in alkaline media (Fig. 2d), with H2O2 also having a detrimental effect on the process.

60

Irradiation time (min)

70

16

1 2

12

3

8 4 0 0

10

20 30 40 50 60 Irradiation time (min)

70

Fig. 3 e (a) Degradation of FXT in alkaline dispersions (b) depletion of dissolved organic carbon (DOC), and formation of (c) FL ions (d) NO2L ions and (e) NO3L ions, and (f) evolution of inorganic carbon (IC) during the degradation of FXT by three different hybrid procedures. Conditions: (1) 0.010 g/L TiO2 D O3 D UV; (2) 0.010 g/L TiO2 D O3 D UV D H2O2; (3) 0.10 g/L TiO2 D O3 D UV D H2O2. All experiments were carried for an initial concentration of FXT of 0.11 ± 0.01 mM (100 mL); H2O2, (0.12 mM); ca. 25 mg/L of O3; pH 11; UV illumination provided by the Hg lamp (see text).

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3.4. Photoassisted depletion of FXT by UV/TiO2/O3 and UV/TiO2/O3/H2O2 processes The degradation of FXT in alkaline aqueous TiO2 dispersions together with loss of dissolved organic carbon (DOC), defluorination of FXT, formation of NO2 and NO3 ions, as well as the evolution of inorganic carbon during the photoassisted degradation of FXT under UV irradiation by the TiO2/ ozone and TiO2/peroxone processes are illustrated in Fig. 3. Degradation of FXT was again relatively fast occurring in less than 4e5 min in alkaline media (pH 11) for equal TiO2 loadings of 0.010 g L1 (Fig. 3a): k ¼ 0.76 min1 for UV/TiO2/O3 and k ¼ 0.52 min1 for UV/TiO2/O3/H2O2 (Table 3); in the latter case, increasing the TiO2 loading to 0.10 g L1 enhanced the process dynamics (k ¼ 0.80 min1). Depletion of DOC (Fig. 3b) by the UV/TiO2/O3 process (0.010 g L1 TiO2) in alkaline media took place through identical first-order dynamics as with the UV/TiO2/O3/H2O2 process (k ¼ 0.074  0.007 min1 vs k ¼ 0.070  0.008 min1); hydrogen peroxide affected neither the dynamics nor the complete depletion of DOC attained within 60 min of UV irradiation. Accordingly, the UV/TiO2/O3 process presents an advantage (no need for H2O2) in future hybrid process configurations. A tenfold increase in TiO2 loading to 0.10 g L1 enhanced the DOC depletion dynamics (k ¼ 0.11  0.01 min1). The presence of H2O2 in the defluorination of FXT (Fig. 3c) caused a smaller quantity of free F ions being formed, whereas an increase in TiO2 loading from 0.010 g L1 to 0.10 g L1 had only a negligible effect. By contrast, NO3 ion formation was faster at the higher TiO2 loading under otherwise identical conditions (compare curves 2 and 3 in Fig. 3e). The absence of H2O2 led to a greater amount of NO3 ions being produced (compare curve 1 with curve 2). Nonetheless, after 60 min into the reactions by all three processes, the final concentrations of NO3 in the effluents were identical. Comparison of the data in Fig. 3d and e shows that formation of nitrite ions preceded formation of nitrate ions. Here also, the absence of H2O2 was beneficial with respect to the quantity of NO2 ions formed. The evolution of IC appeared was not to depend greatly on the conditions of the processes (Fig. 3f). In summary, the hybrid process configurations that involve UV/TiO2/O3 and UV/TiO2/O3/H2O2 have a greater influence on the degradation of the antidepressant FXT than do the

3.5.

b -1

150

-1

120 90 1 hr 2 hrs 4 hrs 8 hrs

60 30 0 0

25

50

75

100 125 150

Incubation time (hr)

Biodegradability of the antidepressant FXT

Biodegradability tests were carried out by respirometric measurements on a VELP Scientifica srl (Italy) instrument. The daily consumption of biochemical oxygen demand (BOD) was determined for deionized water (control), for FXT samples, and for final FXT effluents after treatment with a hybrid procedure under different experimental conditions. In all cases, an initial normal sludge activity was observed. Normal BOD consumption of 8e10 mg O2 L1 day1 was observed for triplicate samples of deionized water. The biodegradability of FXT solutions depended on the initial concentrations of FXT. For 0.11 mM of untreated FXT, no biodegradation was observed within a 5-day period. However, exploratory studies showed a BOD of ca. 90 mg O2 L1 day1 at half the FXT concentration for the same incubation period. Four samples of FXT effluents (400 mL, 0.11 mM) previously treated for periods of 1, 2, 4 and 8 h by a hybrid procedure consisting of 0.010 g L1 of TiO2 and 25 mg L1 of O3 were collected and their biodegradability measured as quantity of BOD consumed at various incubation times (Fig. 4a). BOD increased with incubation time, indicating significant increases in biodegradability of the FXT solutions that ultimately necessitated 100e125 mg L1 day1 of O2. Differences in the biodegradability between the 1-, 2- and 4-h treated samples were negligible, all three samples requiring a BOD of ca. 90 mg O2 L1 day1 after ca. 5.5 days of incubation. By contrast, the biodegradability of the 8-h treated sample improved remarkably requiring a BOD5 of ca. 120 mg L1 day1 of O2 after ca. 5 days of incubation. By contrast, biodegradation of FXT samples under similar experimental conditions but in the presence of added H2O2 showed that the H2O2 had a detrimental effect on the BOD (Fig. 4b). Previous removal of H2O2 with sodium sulfite was

BOD (mg O2 L day )

-1

-1

BOD (mg O2 L day )

a

configurations that exclude TiO2 (Fig. 2a vs Fig. 3a) In any case, under the optimal conditions of UV/ozonation and UV/peroxone processes (25 ppm O3 and 0.12 mM of H2O2) occurring at pH 11 it is possible to reach reasonable degradation levels even in the absence of TiO2. However, the high final IC content seen in Fig. 3f, compared with the UV/O3 process (Fig. 2f), suggests a further improvement in the quality of the final effluent treated by the hybrid UV/TiO2/O3 configuration.

30 25 20 15

1 hr 2 hrs 4 hrs 8.hrs

10 5 0 0

25 50 75 100 Incubation time (hr)

125

Fig. 4 e Biochemical oxygen demand (BOD5) results for 1, 2, 4 and 8 h of treatment by the UV/TiO2/O3 hybrid process. Conditions: (a) 0.11 mM of FXT (400 mL); 0.010 g/L of TiO2; 25 mg/L of O3; (b) same as (a) but with 0.12 mM H2O2 added. Other conditions see text.

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Table 4 e BOD consumed during the oxidative transformation of FXT (0.11 mM) in aqueous TiO2 dispersions at various TiO2 loadings in the presence of O3 (25 mg LL1) and H2O2 (0.12 mM) after a 4-h treatment period. FXT sample tested (No.)

TiO2 loading (g L1)

BOD after 1 day incubation (mg O2 L1 day1)

Max BOD observed (mg O2 L1 day1)

0a 0.10b 0.10 0.050 0.010

0 26.3 13.1 19.7 7.1

9.8 92 15.3 19.7 10.9

Untreated 1 2 3 4

a Tested in the absence of O3 and H2O2. b Tested in the absence of H2O2.

3.6. Some mechanistic considerations and identification of intermediates

fully corroborated for each experiment after the 1-, 2-, 4- and 8-h treatments. During the early incubation period (ca. 12 h) BOD increased at first and then decreased, an observation that cannot be attributed to losses through seals. Rather, such a BOD decrease was likely due to inactivation of sludge processes with time; the decrease extended over 100 h for the 8-h treated sample, whereas negligible differences were again observed for the 1-, 2- and 4-h treated cases. Inactivation of the biochemical process probably originated (a) from the significant quantity of DOC removed at the long treatment times in the presence of H2O2 (b) from the small organic carbon content of byproducts remaining in solution, thereby limiting the substrate in the biological incubation (c) from inorganic F- and N-based compounds of high oxidation state remaining in solution, and (d) generation of some volatile organic compounds (e.g. VOCs different from CO2) which makes the difference in pressure between the VOCs produced and oxygen consumed positive. Table 4 summarizes the 1-day incubation and maximum BOD achieved in several hybrid process configurations applied for a 4-h period. Clearly, in the absence of H2O2 the biodegradability of the solution increased in comparison with an untreated FXT solution. Moreover, the use of low loadings of TiO2 also reflects an increase in the biodegradability of FXT.

H3C

H N

.OH

O CF3

indirect pathway

H3C

H N

O CF3 OH

direct pathway A

O

Previously reported laboratory photolysis studies (Xe lamp; irradiance, 765 W m2) by Lam and coworkers (Lam et al., 2005) showed that FXT photodegrades to O-dealkylated product (species I and II in Scheme 1) and potentially to carboxylic acid photoproducts (species III). Indirect photolysis in simulated natural waters via oxidation with hydroxyl radicals was faster than direct photolysis; bimolecular rate constant was ca. 9.0  109 M1 s1 (Lam et al., 2005). Defluorination of the trifluoromethyl group in FXT and analogous trifluorinated methyl systems (e.g. fluometuron and flutalanil) appears to occur through a common direct photolytic pathway. Indirect photodegradation reactions could also lead to formation of hydroxylated and O-dealkylated byproducts. In this regard, Lam et al (Lam et al., 2005) also proposed two different degradation pathways for direct solar photolysis of FXT, which with the indirect pathway yielded no less than four principal byproducts (species IeIV). Related is the study by Garrido and coworkers (Garrido et al., 2009) who reported that electrochemical oxidation of FXT is pH dependent owing to the presence of a secondary amine group and a substituted

direct pathway B hv + H2O

HO

IV

NH CH3

H 3C

H N

O

+

COOH

F 2C

I

II

III

Scheme 1 e Products formed from the direct photodegradation of fluoxetine: O-dealkylated products such as the difluoroquinonoid species (I) and 3-methylamino-1-phenylpropan-1-ol (II) produced by the direct pathway A, and the hypothesized carboxylic acid photoproduct 4-(3-methylamino-1-phenylpropoxy)benzoic acid (III) produced by the direct pathway B. The hydroxylated FXT product (IV) was obtained from an indirect pathway. Adapted from Lam and coworkers [Lam et al., 2005].

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b

180 310

160 140

t = 0 hr

120 100 80 60 40

60

Intensity (x103 counts)

Intensity (x103 counts)

a

44

20 0

100

150

200

250

300

350

400

450

m/z 44

50

t = 5 hrs

40 30 310

20 104 129 166

10 0

326

100

150

200

250

300

350

400

450

166

20 44

326

10 50

100

150

200

Intensity (x103 counts)

300

350

400

450

500

t = 10 hrs 30 44

129

89

10 32

0

500

104

20

60

50

151

214 310

100 150 200 250

358 374 390

300 350

400 450

500

m/z

m/z

e

250

40

342

50

310

30

m/z

d

60

t = 2 hrs 40

0

500

Intensity (x103 counts)

Intensity (x103 counts)

c

50

50

40

30 t = 24 hrs

20

358 374

10 32

129

47 60

0

50

100

150

214

200

280 283

250

300

342

350

416 432 390

400

450

500

m/z Fig. 5 e Time-of-flight mass spectra at various times during the photoassisted degradation of FXT by the UV/TiO2 advanced oxidation process in alkaline (pH 11) aqueous dispersions. To access the various intermediates formed during the degradation of FXT by the TOF mass spectral technique, experimental conditions other than pH were different from those used for Fig. 1a so as to retard the degradation of FXT.

aromatic ring. Oxidation at both these functions produced unstable cation radicals that led to formation of FXT dimers. Hydroxylation of the phenyl rings and depletion of both was the main pathway to aliphatic acids and inorganic compounds (Garrido et al., 2009). Fig. 5 displays the time-of-flight mass spectra obtained in the positive-ion mode during the photoassisted UV/TiO2 degradation of FXT in aqueous alkaline dispersions (pH 11). At time 0 (Fig. 5a), the spectrum shows the molecular mass peak of FXT at m/z ¼ 310 along with a peak attributable to carbon dioxide (m/z ¼ 44) present during the mass spectral measurements. After 2 h into the photodegradation, the mass spectral results (Fig. 5b) reveal a considerably decreased molecular mass peak of FXT together with mass peaks at

m/z ¼ 166 and m/z ¼ 326. The former is assigned to species II of Scheme 1 formed subsequent to O-dealkylation, whereas the latter is attributed to the hydroxylated FXT species V upon addition of an OH radical to the unsubstituted aromatic ring of FXT coworkers (Lam et al., 2005), or for the expected (see above) trifluoromethyl-phenol from the O-dealkylation step, unless both such species underwent very rapid degradation within this time. After 5 h, additional intermediates were detected at m/z below 200, namely species VI at m/z ¼ 104 together with a spectral peak at m/z ¼ 129 that we attribute to species VII {CF3eCH(OH)eCHO} e see Fig. 5c; in addition the mass spectral peak at m/z ¼ 342 is ascribed to the dihydroxylated FXT species VIII. Continued UV irradiation of the aqueous TiO2 dispersion to 10 h revealed (Fig. 5d) mass peaks

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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 7 8 2 e2 7 9 4

CH3

NH

O

CF3 FXT (m/z = 310) UV/TiO2 NH CH3

2 hrs

OH

NH CH3

CF3

HO

II (m/z=166)

5 hrs

O

V (m/z=326)

NH CH3

CH3-NH-CH2CH2CH(OH)CH3 CF3-CH(OH)-CHO

O

VII (m/z=129)

VI (m/z=104)

CF3 HO

OH VIII (m/z=342)

10 hrs

CH3NH2

NH CH3

NH CH3

O

IX (m/z=32) OH

HO

OH

O

CH3

24 hrs

NH CH3

CF3

HO

OH

XI (m/z=374)

OH

HO

OH HO

OH

HO

CF3

X (m/z=358)

NH

O

CF3

XII (m/z=390)

OH

NH CH3

O

O

OH

CF3

CF3 CF2=C(OH)-CH2CH2

CF2=C(OH)-CH2CH2 XIII (m/z=416)

XIV (m/z=432)

Scheme 2 e Intermediates identified by TOF-MS techniques during the TiO2 photoassisted degradation of FXT in aqueous media at pH 11.

at m/z ¼ 32 for species IX {CH3NH2}, m/z ¼ 358 for the trihydroxylated FXT species X, the tetrahydroxylated FXT species XI with m/z ¼ 374, as well as the pentahydroxylated FXT species XII with m/z ¼ 390. After 24 h of UV irradiation of the dispersion produced additional intermediates (Fig. 5e) at m/ z ¼ 416 and m/z ¼ 432 that we tentatively ascribe to species XIII for the former and to the hydroxylated species XIV. Other mass spectral peaks were also observed but the nature of those intermediates remains elusive.

3.7.

Fate of the amine function

Examination of Figs. 2d,e and 3d,e shows a fairly rapid oxidation of the amine function in FXT to nitrite and nitrate ions with the former disappearing in less than 30 min and the nitrate more slowly requiring times longer than 60 min. No

doubt the precursor to nitrate is the nitrite ion, at least in part. Nonetheless, loss of nitrite could partially also be due to other venues, one of which is through direct UV photolysis (Fischer and Warneck, 1996; Mack and Bolton, 1999; Vione et al., 2005b). In this regard, the absorption spectra of NO2 and NO3 are dominated by intense p / p* bands at 205 nm (3 ¼ 5500 M1 cm1) and 200 nm (3 ¼ 9900 M1 cm1), respectively, and by weak n / p* bands at 360 nm (3 ¼ 22.5 M1 cm1) and 310 nm (3 ¼ 7.4 M1 cm1) (Vione et al., 2005b). Accordingly, they can absorb UVB and UVA radiation at l > 295 nm. Nitrite/nitrate photolysis in aqueous media can be a primary source of OH radicals when used in advanced oxidation technologies designed to dispose of organic contaminants. Under our present conditions, however, it is unlikely, given the quantity of nitrite ion formed (<0.01 mM), that UV photolysis will have any consequence. Rather, to the extent

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 7 8 2 e2 7 9 4

that a significant number of OH radicals are formed, any loss of nitrite ions is likely the result of reaction with such radicals. The reaction of OH with NO2 is essentially diffusion controlled (reaction 2; k ¼ 1.0  1010 M1 s1 (Mack and Bolton, 1999; Graetzel et al., 1969). Once formed, the NO2 radical can    NO 2 þ OH/NO2 þ OH

(2)

NO2  þ NO2 /N2 O4

(3)

 N2 O4 þ OH =H2 O/NO 2 þ NO3

(4) 8

1

1

dimerize to form N2O4 (reaction 3; k ¼ 4.5  10 M s ) which then hydrolyzes yielding NO2 and NO3 ions (reaction 4; k ¼ 6.9  103 s1 (Graetzel et al., 1969). Thus reactions 2e4 convert nitrite to nitrate ions. However, the presence of FXT and several intermediates plus OH radicals make dimerization of NO2 radicals unlikely. Alternatively, nitrite radicals can react further with OH radicals (reaction 5; k ¼ 5  109 M1 s1 pH 9.5 (Loegager and Sehested, 1993) to yield the peroxynitrous acid (pKa ¼ 6.5) which subsequently isomerizes to NO3 ions (reaction 6; k z 1.4 s1 (Mack and Bolton, 1999) in alkaline media and/or can undergo nitration of the aromatic rings in FXT and in some intermediates (Vione et al., 2005b). The fairly rapid loss of NO2 can also occur through its reaction with O3 (reaction 7; k ¼ 5  105 M1 s1) to yield NO3 which also appears plausible (Warneck and Wurzinger, 1998) from the results of Figs. 2d,e and 3d,e. 

OH þ NO2 /ONOOH

(5)

OH

ONOOH / NO 3

(6)

 O3 þ NO 2 /O2 þ NO3

(7)

 NO 3 þ hv/NO3

(8)

 NO 3 /NO2



 H2 O







þ O ! NO2 þ OH þ OH

(9)

The disappearance of nitrate ions (Figs. 2e and 3e), however, is somewhat enigmatic as NO3, unlike HNO3 in acid media, is not a known scavenger of oxidizing radicals in alkaline media (Neta et al., 2010). The only other pathway for its demise is thus likely UV photolysis through reactions 8 and 9 or otherwise through other complex yet elusive reactions (see e.g. Scheme 2 in the review by Mack and Bolton (Mack and Bolton, 1999).

4.

Concluding remarks

The present study has shown that application of hybrid oxidative process configurations for the elimination of FXT had some interesting consequences: (1) there exists a strong pH dependence in the degradation of FXT under all the experimental configurations used; (2) where TiO2 was present and based on past experience, adsorption of FXT on the metal oxide particles no doubt played a non-insignificant role in the whole degradation process; (3) the heterogeneous photoassisted process degraded 0.11 mM of FXT within 60 min of UV irradiation with a 50% mineralization at a TiO2 loading of

2793

0.050 g L1 in alkaline aqueous media of pH 11, and addition of H2O2 to the latter further enhanced the mineralization to more than 70%; (4) ozonation alone was able to degrade FXT within 10 min, and in combination with H2O2 and UV irradiation ca. 97% of FXT was mineralized within ca. 60 min; (5) the hybrid UV/TiO2/O3/H2O2 process configuration improved significantly the removal of DOC occurring in ca. 30 min; however, an important inorganic carbon content remained in the final treated effluent, with such inorganic byproducts likely responsible for the detrimental effect on the biodegradability of FXT. Results of the present study suggest that the UV/TiO2/ O3 hybrid procedure can enhance the disposal of nondegradable and recalcitrant pharmaceutical products ever present in aquatic ecosystems.

Acknowledgments We are grateful to the University of Barcelona for financial support to F.M-A to carry out the studies at Meisei University, Tokyo. We also thank the Japanese Ministry of Education, Culture, Sports, Science and Technology for financial support through a Grant-in-Aid (2010e2012) for Scientific Research (C) 22550141 (to H.H.). In addition we wish to thank Prof. T. Machinami and Dr. T. Fujimoto for the assistance with the TOF-MS measurements. One of us (N.S.) wishes to acknowledge Prof. Albini of the University of Pavia for the kind hospitality throughout various winter semesters spent in his laboratory since 2002.

references

Altamura, A.C., Moro, A.R., Percudani, M., 1994. Clinical pharmacokinetics of fluoxetine. Clin. Pharmacokinet. 26, 201e214. Bedner, M., MacCrehan, W.A., 2006. Reactions of the aminecontaining drugs fluoxetine and metoprolol during chlorination and dechlorination processes used in wastewater treatment. Chemosphere 65, 2130e2137. Benotti, M., Stanford, B., Wert, E., Snyder, S., 2009. Evaluation of a photocatalytic reactor membrane pilot system for the removal of pharmaceuticals and endocrine disrupting compounds from water. Water Res. 43, 1513e1522. Bolton, J.R., Carter, S.R., 2001. Homogeneous photodegradation of pollutants in contaminated water: an introduction. In: Helz, G. R., Zepp, R.G., Crosby, D.G. (Eds.), Aquatic and Surface Photochemistry. Lewis Publishers, Boca Raton, FL, pp. 467e490. Brooks, B., Foran, C., Richards, S., Weston, J., Turner, P., Stanley, J., 2003a. Aquatic ecotoxicity of Fluoxetine. Toxicol. Lett. 142, 169e183. Brooks, B., Turner, P., Stanley, J., Weston, J., Glidewell, E., Foran, C., Slattery, M., La Point, T., Huggett, D., 2003b. Waterbone and sediment toxicity of fluoxetine to select organism. Chemosphere 52, 135e142. Brooks, B.W., Chambliss, C.K., Stanley, J.K., Ramirez, A., Banks, K. E., Johnson, R.D., Lewis, R.J., 2005. Determination of select antidepressants in fish from an effluent-dominated stream. Environ. Toxicol. Chem. 24, 464e469. Buxton, G.V., Greenstock, C.L., Helman, W.P., Ross, A.B., 1988. Critical review of rate constants for reactions of hydrated

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electrons, hydrogen atoms and hydroxyl radicals ( OH/ O) in aqueous solutions. J. Phys. Chem. Ref. Data 17, 513e886. Fischer, M., Warneck, P., 1996. Photodecomposition of nitrite and undissociated nitrous acid in aqueous solution. J. Phys. Chem. 100, 18749e18756. Fluoxetine-1, SRC PhysProp Database., 2010, see: http://www. syrres.com/what-we-do/databaseforms.aspx?id¼386, (accessed February 2010) Fluoxetine-2, 2010, see http://www.lookchem.com/FLUOXETINE/ (accessed March 2010). Fluoxetine-3, 2011; see: http://pharma-help.com/fluoxetine/ fluoxetine-ingredients-and-composition (accessed January 2011). Fluoxetine-4, 2010; see: http://cfpub.epa.gov/ncer_abstracts/ index.cfm/fuseaction/display.abstractDetail/abstract/1755/ report/F Fong, P.P., Molnar, N., 2008. Norfluoxetine induces spawning and parturition in estuarine and freshwater bivalves. Bull. Environ. Contam. Toxicol. 81, 535e538. Garrido, E.M., Garrido, J., Calheiros, R., Marques, M.P.M., Borges, F., 2009. Fluoxetine and norfluoxetine revisited: new insights into the electrochemical and spectroscopic properties. J. Phys. Chem. A 113, 9934e9944. Gimeno, O., Rivas, F.J., Beltran, F.J., Garbajo, M., 2007. Photocatalytic ozonation of Winery wastewaters. J. Agric. Food Chem. 55, 9944e9950. Graetzel, M., Henglein, A., Lilie, J., Beck, G., 1969. Pulsradiolytische untersuchung einiger elementar-prozesse der oxidation und reduktion des nitritions. Ber. Bunsen-Ges. Physik. Chem. 73, 646e653. Kolpin, D.W., Furlong, E.T., Meyer, M.T., Thurman, E.M., Zaugg, S. D., Barber, L.B., Buxton, H.T., 2002. Pharmaceuticals, hormones, and other organic wastewater contaminants in U. S. streams, 1999e2000: a national reconnaissance. Environ. Sci. Technol. 36, 1202e1211. Kwon, J.-W., Armbrust, K.L., 2006. Laboratory persistence and fate of fluoxetine in aquatic environments. Environ. Toxicol. Chem. 25, 2561e2568. Lake, R., 2010. RP-HPLC analysis of selective serotonin reuptake inhibitors using polar stationary phases: using polar stationary phases, Restek Corp. 110 Benner Circle, Bellefonte, PA 16823 See. http://www.restek.com/aoi_pharm_A002.asp (accessed February 2010). Lam, M., Young, C., Mabury, S., 2005. Aqueous photochemical reaction kinetics and transformations of fluoxetine. Environ. Sci. Technol. 39, 513e522. Liu, Z.-Q., Zhu, B., Tan, Y.-F., Tan, Z.-R., Wang, L.-S., Huang, S.-L., Shu, Y., Zhou, H.-H., 2002. O-Dealkylation of fluoxetine in relation toCYP2C19 gene dose and involvement of CYP3A4 in human liver microsomes. J. Pharmacol. Exp. Ther. 299, 106e111. Loegager, T., Sehested, K., 1993. Formation and decay of peroxynitrous acid: a pulse radiolysis study. J. Phys. Chem. 97, 6664e6669. 



Mack, J., Bolton, J.R., 1999. Photochemistry of nitrite and nitrate in aqueous solution e a review. J. Photochem. Photobiol. A: Chem. 128, 1e13 (references therein). Me´ndez-Arriaga, F., 2009., Advanced Oxidation Processes (Photocatalysis, Photo-Fenton and Sonolysis) for degradation of pharmaceutical compounds in water, Ph.D. thesis. March 2009. University of Barcelona, Barcelona, Spain. Metcalfe, C.D., Miao, X.-S., Koenig, B., Struger, J., 2003. Distribution of acidic and neutral drugs in surface waters near sewage treatment plants in the lower Great Lakes, Canada. Environ. Toxicol. Chem. 22, 2881e2889. Nakamura, Y., Yamamoto, H., Sekizawa, J., Kondo, T., Hirai, N., Tatarazako, N., 2008. The effects of pH on fluoxetine in Japanese medaka (Oryzias latipes): acute toxicity in fish larvae and bioaccumulation in juvenile fish. Chemosphere 70, 865e873. Neta, P., Huie, R.E., Ross, A.B., 2010. Rate constants for Reactions of Inorganic Radicals in Aqueous Solution. see. Radiation Laboratory, University of Notre Dame, Notre Dame, IN. http:// www.rcdc.nd.edu/compilations/Ino/Ino.pdf (accessed March 2010). Risley, D.S., Bopp, R.J., 1990. In: Florey, K. (Ed.), Analytical Profiles of Drug Substances, vol. 19. Academic Press, New York,, pp. 193e219. Robins, L.N., Regier, D.A. (Eds.), 1990. Psychiatric Disorders in America e The Epidemiologic Catchment Area Study. The Free Press, New York. Uottawa, 2011, see: http://www.media.uottawa.ca/mediaroom/ news-details_2104.html (accessed January 2011) Vione, D., Maurino, V., Minero, C., Calza, P., Pelizzetti, E., 2005a. Phenol chlorination and photochlorination in the presence of chloride ions in homogeneous aqueous solution. Environ. Sci. Technol. 39, 5066e5075. Vione, D., Maurino, V., Monero, C., Pelizzetti, E., 2005b. Nitration and photonitration of naphthalene in aqueous systems. Environ. Sci. Technol. 39, 1101e1110. Vohra, M.S., Kim, S., Choi, W., 2003. Effects of surface fluoridation of TiO2 on the photocatalytic degradation of tetramethylammonium. J. Photochem. Photobiol. A Chem. 160, 55e60. Warneck, P., Wurzinger, C., 1998. Product quantum yields for the 305-nm photodecomposition of NO3 in aqueous solution. J. Phys. Chem. 92, 6278e6283. Wert, E., Rosario-Ortiz, F., Snyder, S., 2009. Effect of ozone exposure on the oxidation of trace organic contaminants in wastewater. Water Res. 43, 1005e1014. Xu, X., Goddard III, W.A., 2002. Peroxone chemistry: formation of H2O3 and ring-(HO2)(HO3) from O3/H2O2. Proc. Natl. Acad. Sci. U.S.A, 9915308e9915312. Yao, C.C.D., Mills, T., 2001. Reaction Pathways in Advanced Oxidation Processes. In: Helze, G.R., Zepp, R.G., Crosby, D.G. (Eds.), Aquatic and Surface Photochemistry. Lewis Publishers, Boca Raton, FL, pp. 499e515. Zhou, H., Smith, D.W., 2001. Advanced technologies in water and wastewater treatment. Can. J. Civil Eng. 28 (Suppl. 1), 49e66.

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Electro-dewatering of wastewater sludge: Influence of the operating conditions and their interactions effects Akrama Mahmoud a,*, Je´re´my Olivier a, Jean Vaxelaire a, Andrew F.A. Hoadley b a b

Laboratoire de Thermique Energe´tique et Proce´de´s (EAD 1932), ENSGTI, rue Jules Ferry, BP 7511, 64075 Pau, France Department of Chemical Engineering, Building 35, Clayton Campus, Monash University, Victoria 3800, Australia

article info

abstract

Article history:

Electric field-assisted dewatering, also called electro-dewatering (EDW), is a technology in

Received 20 September 2010

which a conventional dewatering mechanism such a pressure dewatering is combined

Received in revised form

with electrokinetic effects to realize an improved liquid/solids separation, to increase the

8 February 2011

final dry solids content and to accelerate the dewatering process with low energy

Accepted 23 February 2011

consumption compared to thermal drying. The application of these additional fields can be

Available online 3 March 2011

applied to either or both dewatering stages (filtration and/or compression), or as a pre-or post-treatment of the dewatering process. In this study, the performance of the EDW on

Keywords:

wastewater sludge was investigated. Experiments were carried out on a laboratory filtra-

Dewatering

tion/compression cell, provided with electrodes, in order to apply an electrical field. The

Electro-dewatering

chosen operating conditions pressure (200e1200 kPa) and voltage (10e50 V) are sufficient to

Wastewater sludge

remove a significant proportion of the water that cannot be removed using mechanical

Response surface methodology

dewatering technologies alone. A response surface methodology (RSM) was used to evaluate the effects of the processing parameters of EDW on (i) the final dry solids content, which is a fundamental dewatering parameter and an excellent indicator of the extent of EDW and (ii) the energy consumption calculated for each additional mass of water removed. A two-factor central composite design was used to establish the optimum conditions for the EDW of wastewater sludge. Experiments showed that the use of an electric field combined with mechanical compression requires less than 10 and 25% of the theoretical thermal drying energy for the low and moderate voltages cases, respectively. Crown Copyright ª 2011 Published by Elsevier Ltd. All rights reserved.

1.

Introduction

Over the last decades the increase in municipal and industrial wastewater purification activities have been confronted with a dramatically increasing flow of sewage sludge. A common characteristic of different type of sludge is the very high water content, the colloidal and compressible nature of the sludge. Activated sludge is an important class of these waste products and has to be treated and disposed of. After gravitational thickening, sewage sludge still contains only as little as about 1e5% (wt%) on a wet basis of dry solids content, the remaining

fraction being water (95e99%) (Saveyn et al., 2005). This excessive water content of sludge increases the volume and the cost for truckling to ultimate disposal site. Moreover, sewage sludge requires more supplemental bulking agent during composting and cannot be incinerated as its energy content is low. Therefore, it is both economically and technologically feasible to decrease the water content. This facilitates the possible use of sludge as a fuel and reduces transport and disposal costs. It is well documented that sludge dewatering is one of the most challenging technical tasks in the field of wastewater

* Corresponding author. Tel.: þ33 0540175193; fax: þ33 0559407801. E-mail address: [email protected] (A. Mahmoud). 0043-1354/$ e see front matter Crown Copyright ª 2011 Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.02.029

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engineering (Tchobanoglous et al., 2003; Glendinning et al., 2007). In fact, when compared with thermal (evaporative processes) for water reduction, mechanical dewatering is often selected due to its low energy requirement (Vaxelaire et al., 1999). This operation not only reduces the total waste volume but also increases the caloric value of the product (Vaxelaire and Olivier, 2006; Tuan and Sillanpa¨a¨, 2010a). Dewatering is mainly performed by mechanical techniques based on gravitational settling centrifugation, or filtration/ compression, e.g. by belt or filter presses. However, the colloidal and compressible nature of the sludge will hamper its dewatering without pretreatment. The presence of organic components, mainly bacterial cells and EPS (Extracellular Polymeric Substances), in the sludge makes it very difficult to dewater even at high pressure. Chemical conditioning prior to mechanical dewatering is usually used to overcome partially these problems. The selection of the appropriated chemical and dosage is quite difficult because it depends on both the sludge composition and the dewatering device and remains widely empirical (Vaxelaire and Olivier, 2006; Saveyn et al., 2008). However, one of the steps generally involved in thickening and dewatering is the flocculation of sludge with inorganic (ferric or aluminium chloride) or organic flocculant (synthetic polyelectrolyte), called conditioning. In this way, an inorganic or organic flocculant may be added to induce the formation of flocculated particle networks, resulting in an improved structure with reduced water retention. Despite conditioning and many technical improvements during recent years, wastewater sludge remains hard to dewater and, for many applications, it cannot achieve a sufficiently low water content. A plateau value of 35% (wt%) dry solids content seems to be the highest efficiency which can be commonly reached by the three most employed mechanical dewatering techniques: centrifugation, dewatering by belt filter press or dewatering by filter press (La Heij et al., 1996). The difficulty has been attributed mainly to the fact that particles are very fine, colloidal in nature and gel like structure due to polymeric flocculation. Therefore an improvement in the traditional pressurized dewatering equipment is desirable. As a consequence, current research tends to propose potential alternatives to enhance the dewatering ability of conventional processes (Tarleton, 1992; Mahmoud et al., 2010). Different options have been investigated to enhance the wastewater sludge dewatering, one of the most successful used is the pressure dewatering assisted by an electrical field (D.C. or A.C.). In fact most mechanical dewatering processes (MDW) involve two stages; the first is the filter cake formation stage and the second is the compression stage where further water is squeezed from the cake by the application of a mechanical force. The application of additional fields can be applied to either or both dewatering stages, or as a pre-or post-treatment of the dewatering process. According to Friehmelt et al., 1995; Miller et al., 1998; Barton et al., 1999; Lee et al., 2002 and Saveyn et al., 2005 who noticed that application of the electric field at the start of the whole dewatering run, in the filtration stage, did not show any beneficial effect to the dewatering result, the additional effect of a vertically electric field applied is investigated during compression stage in this paper.

The operating conditions of the electric field and pressure used in the electrically assisted mechanical dewatering are sufficient to remove a significant proportion of the water that cannot be removed using mechanical dewatering technologies alone. Thus electro-dewatering (EDW) has the potential to be viable for a range of slurries, which either could not be sufficiently dewatered or would otherwise require extreme conditions using conventional dewatering devices. Many experimental factors can influence the reduction of water content and, consequently, the process yield. The critical processing factors are voltage (current or electrical field), pressure, time, floc size distribution, electrochemical properties, conditioning parameters, polyelectrolyte characteristics etc. A study of the influence of certain of these factors should indicate the optimum experimental conditions. Treating each factor separately would be very time consuming; furthermore, if several factors play a role, their interactions would not be discernible even if they were dominant. Hence, the use of the experimental factorial design and of the RSM, already successfully applied in other fields, is well suited to the study of the main and interaction effects of the factors on the dewatering yield. The present paper investigates the effects of the processing parameters (pressure, voltage) of the electrically assisted mechanical dewatering, using the RSM, for the reduction of water content using moderate process conditions. Other parameters, such as floc size distribution, pH, time, electrochemical properties, conditioning parameters, polyelectrolyte characteristics etc as mentioned above, can affect the dewatering behavior, but these are not discussed in this paper. The feasibility of the EDW-process on activated sludge sampled from the Lescar Municipal Waste Water Treatment Plant (Pau, France) is tested. Experimental runs are modelled for the estimation of the final dry solids content and the energy consumption calculated per the additional mass of water removed, allowing the significance of the various phenomena to be discussed. Finally, in order to design efficient processes, the RSM is used to achieve the optimum dewatering performance.

2.

Materials and methods

2.1.

Experimental set-up

For the investigation of activated sludge dewatering by electro-dewatering, a lab scale set-up was made with a pressure controlled piston moving in a filtration/compression cell of 70 mm inner diameter. The filtration/compression cell, represented in Fig. 1(a), consists of a compressive piston made of Teflon, a cylindrical vessel and a filter medium. The filtration chamber has a diameter of 70 mm and a maximum height of 145 mm. In spite of its low mechanical resistance, Teflon was selected as constitutive material of the vessel walls not only to minimize the friction with the piston but also to ensure the electrical insulation. Consequently, a stainless steel external jacket was added to ensure the mechanical resistance of the unit. The cell is fitted with a planar medium of SEFAR TETEX MONO SK025, provided by CHOQUENET S.A.S (Chauny, France), deposited on

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 7 9 5 e2 8 1 0

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Fig. 1 e (a) Laboratory-scale of EDW device. (1): Filtration/compression cell; (2): Electronic scale; (3): Computer and data acquisition; (4): Movement sensor; (5): DC power supply; (6): Digital multimeter; (7): Filtrate collector; (8): Teflon piston; (9): Electrodes. (b) Schematic representation of the different stages of the EDW-process. (adapted from Vijh, 1999a,b; EMICO Water Technologies CINETIK; Mahmoud et al., 2008, 2010).

a Teflon grid. To investigate the Ohmic heating impact, two thermocouples are introduced to measure the temperatures. The first is inserted into the bottom part of the compressive piston. The second is put in the outlet channel of the filtrate to minimize cooling effects. The accuracy of these sensors with their acquisition line was estimated at 0.5  C. The dimensionally stable (not consumed coulometrically) electrodes manufactured by Industrie De Nora (Italy) and supplied by ECS (Electro Chemical Services, Saint-Genis-Pouilly, France) of 65 mm of diameter were tested. The upper perforated disk electrode (dimensionally stable anode DSA) made of titanium coated with mixed metal oxide (MMO) was attached to the piston. The lower perforated disk electrode (cathode) made of Titan was covered by the SEFAR TETEX MONO SK025 filter medium. A DC power supply EV202 CONSORT (maximum 300 V and 2 A), operating under constant voltage, was connected to the anode and cathode electrodes for electro-dewatering. A movement sensor gives the thickness of the cake as a function of time. Two digital multimeter (ISO-TECH IDM 73) were used to control the voltage and to monitor current fluctuations in the electro-dewatering cell. The pressure was controlled by pressurized air. Finally, to control the filtrate quality, the filtrate pH and the electrical conductivity were measured by a pH/Ion meter (pHM240, MeterLab) and a conductivity meter (CDM210, MeterLab), respectively while the turbidity and total

suspended solids were measured by a spectrophotometer (DR/2010, Hach, USA). To carry out an electro-dewatering experiment, the product is poured into the cylindrical vessel. The filtrate is collected in a beaker deposited on a scale and data are transferred to an on-line computer. The chosen operating conditions pressure (200e1200 kPa) and voltage (10e50 V) are sufficient to remove a significant proportion of the water that cannot be removing using mechanical dewatering technologies. These conditions also guarantee that water will remain in the liquid phase. Control of the operation and logging of the different measurement data was performed by a personal computer with Labview software. Filtrate mass, current, voltage, anode and cathode temperature were recorded during the experiments on an online computer. The initial and final dry solids contents (Sin and Sf respectively) of the product are measured according to the AFNOR standard procedure N X31-505 (AFNOR, 1994). They were determined by drying at 105  C during 24 h and weighing.

2.2.

Experimental procedures

2.2.1.

Suspension preparation (stage 1)

2.2.1.1. Materials. The activated sludges were sampled from the Lescar Waste Water Treatment Plant (Pau, France). After

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sampling, sludge was stored at 4  C for maximum 4 days. This procedure reduces the effect of biochemical composition change and enables an acceptable reliability of the experiments. During the first day, five samples of 20 g of activated sludge were selected immediately and their initial dry solids were measured according to AFNOR standard procedure by drying at 105  C during 24 h and weighing. The averaged initial dry solids content of this activated sludge measured on the sample of 20 g was about 0.5% (wt%). The electro-dewatering tests were carried out within the three following days (two experiments per day and 6 experiments per series of three days). Each series of three days of experiments was repeated at least three times because of the variability of the initial sludge samples.

2.2.1.2. Conditioning. Before testing, a 2000 g sludge sample was left for 30 min of acclimatization in the open air in order to reach temperature around 20 1  C before conditioning (Fig. 1(b)). Polyelectrolyte chemicals were provided from SNF FLOERGER (Andre´zieux, France), and were delivered as liquid dispersion formulation. All products EM 640 L, EM 640 MBL, EM 640 TRM, EM 640 TBD (from linear chain polymer to cross linked backbone) have a charge density or cationicity 60% with different molecular weight (Mw) polyelectrolyte. Aqueous polyelectrolyte solutions were prepared at a 5 g/l active polyelectrolyte concentration (0.5%), according to instructions given by the manufacturer (SNF FLOERGER). Polyelectrolyte solutions were prepared at least 20 min prior to application, in order to allow the polyelectrolyte chains to completely unfold for optimized contact efficiency. All products were shown to yield clear flocculation already at low doses (3.5, 5, and 6.25 g of active polyelectrolyte/kg dry solids (g/kgDS), respectively), which was thought to be due to the bridging effect. The conditioner dose was fixed from preliminary experiments carried out on the different polymers. A laboratory batch drainage apparatus was used for these preliminary tests (Olivier et al., 2004; Vaxelaire and Olivier, 2006). The batch drainage test consisted of a cloth filter attached between two Plexiglass pipes: one functioned as a filter holder and the other as a retainer ring. Sludge samples were placed on the whole surface of the cloth filter and the filtrate was collected in a beaker. In this study, only the linear polymer EM 640 L, with a medium molecular weight, at a fixed dose (3.5 g of active polyelectrolyte/kgDS) has been envisaged. Despite of the important influence of polyelectrolyte characteristics on dewatering performance (Eriksson and Alm, 1993; Parker et al., 1997; Schuster et al., 1997; Dentel et al., 2000; Dentel, 2001; Saveyn et al., 2005; Thapa et al., 2009), the investigation of polyelectrolyte characteristics on the EDW mechanisms and performance of used materials is not discussed in this paper and requires further and continuing investigations in future works. For activated sludge conditioning, a conventional Jar Test device was used, operating at 270 rpm during 1 min for intense mixing of the polyelectrolyte into the sludge, followed by a 1 min slowly stirring period at 20 rpm to promote floc growth. 2000 g of activated sludge was mixed with the precalculated dose (15 g) of a polyelectrolyte solution. Then, in order to separate the free water in the conditioned sludge, the

conditioned sludge obtained was filtered using the laboratory batch drainage apparatus (Olivier et al., 2004; Vaxelaire and Olivier, 2006). The formed sludge filter cake and the filtrate acquired weights were measured. These values were used to calculate the new dry solids content. Results showed that the new final dry solids content of the conditioned sludge can reach more than 5.5% (wt%). The quantity of conditioned sludge obtained was diluted to a suitable dry solids content (2e3.15%) by the supernatant (filtrate) obtained. About 286 g of conditioned sludge was poured at room temperature into the electro-dewatering cell, corresponding to an initial bed height of 80 mm.

2.2.2.

Dewatering stages (stage 2 and 3)

The dewatering procedure, as shown in Fig. 1(b), consists of two successive stages: -filtration/compression with a pressure applied (stage 2): 2 h were necessary to reach the equilibrium phase; -and electrically compression at the selected operating voltage with a pressure applied (stage 3): the end of this dewatering stage (2 h were also necessary) was automatically detected when no more than two drops of filtrate were collected in 10 min. For the sake of completeness, it has to be mentioned that it is not expedient to impose the voltage at the beginning of the dewatering run (stage 2) because the pressure-driven filtrate flow rate is much higher than the electroosmotic flow rate at these early stages of dewatering. In addition, the electrical resistance is low due to the high liquid content of the sludge and hence the power consumption would be very high (Friehmelt et al., 1995; Miller et al., 1998; Barton et al., 1999; Lee et al., 2002; Saveyn et al., 2005b). Hence, it is advisable to delay the application of the electric field to the filter cake compression phase. Experiments were monitored by a personal computer with Labview. Parameters sampled by the computer are: the filtrate mass at the cathode, the voltage and the current intensity, the temperatures of the anode and cathode filter cake recorded at set time intervals of 3 s. At the end of the experiment, the dewatered cake was released and weighted. Its dry solids mass was determined by drying at 105  C during 24 h and weighing (AFNOR, 1994). The final dry solids content after each experiment was calculated by a mass balance and defined as the weight of dry sample divided by the weight of the wet sample times 100. The mass balance assumes that all feed suspension is filtered to form a cake, that is, no feed remains in the filter as suspension. As mentioned earlier, the initial dry solids content (Sin) of the product should be ascertained before a test and this can be converted into the corresponding water content of liquid/ solids mixtures (X ) using: X¼

1  Sin Sin

(1)

The initial mass of liquid (min L ) present in the conditioned sludge can be defined by: min total Sin min L ¼ X 100

(2)

where min total is the total mass of conditioned sludge poured into the electro-dewatering cell.

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These values can used to calculate the percentage of water removal in each dewatering stage as following: Percentage of water removal during the MDW stage ¼ mMDW filtrate min L

 100

(3)

Percentage of water removal during the EDW stage ¼ mEDW filtrate min L

 100

(4)

EDW where mMDW filtrate and mfiltrate are the filtrate mass removed during the MDW stage and EDW stage, respectively.

3.

Experimental design methodology

3.1.

Statistical design of experiments

undertaken, in comparison to a classical approach for the same number of estimated parameters (Box and Draper, 1987); (iv) to estimate interaction and even quadratic effects, i.e. the effects of a variable on the response, conditional on the level of another variable, and (v) to find improved or optimal process settings. Response surface method (RSM) is used to establish an empirical model that map the response surface using data from a design experiment and to identify the direction and parameter ranges for the response optimization. In addition to the definition of the aim of the study, the following stages are crucial in the implementation of an experimental design (Gouby and Creighton, 2009): the choice of the answer, the research of the factors which could be significant, the choice of their levels and finally the existence of interactions between these factors (Mahmoud et al., 2008, 2011).

3.2. Statistical design of experiments for activated sludge electro-dewatering

A physical phenomenon can always be represented in the following mathematical form: Y ¼ f ðX1 ; X2 ; .; Xn Þ

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(5)

where Y is the output variable (or the response) of interest, Xi are the input variables (or the factors) on which the experimenter can act and f is a mathematical function which, as well as possible, explains the variations of Y with Xi. The usual way to investigate this phenomenon consists in fixing the level of all the variables, except one, and measuring the response according to several values of the not fixed variable. At the end of this experimentation on the first variable, the curve Y ¼ f(X1) can be plotted. This methodology must be repeated for all the other variables, which results to a high number of experiments to perform. The main purpose of statistically designing a series of experiments is to collect the maximum amount of relevant information with a minimum expenditure of time and resources. It is also important to remember that the design of experiments should be as simple as possible and consistent with the requirements of the problem. The selection of the responses is one of the most important problems of a preliminary study on a research subject, since a good definition of research objective leads to a good response choice. Depending on the subject and research objective, optimization parameters or responses may be quite different. For a research subject parameter to be a response, it has to fulfill certain conditions. A response should be: quantitative, singular, statistically effective, universal, physically realistic, simple and easily measurable. The main advantages of experimental designs are: (i) the ability to discriminate one a prior important factor, i.e. a factor which induces a significant change in the response for different levels of that factor. For this comparative design, randomized block designs are advised; (ii) to screen out the few important effects of the individual variables, called the main effects, from the many less important ones. In this case, full or fractional factorial designs should be selected; (iii) the potential for reducing the number of experiments to be

The objectives of the present study were (i) to evaluate the effects of the processing parameters in a large parameter space, (ii) to determine if the separation enhancement, if any, results only from electrical effects or from coupled electricalmechanical effects, (iii) to maximize the final dry solids content (Sf) which is an excellent indicator of the extent of the EDW-process of the proceed activated sludge and (iv) to minimize the energy consumption calculated per the additional mass of water removed. As a consequence, RSM seems to be the most adapted design. A central composite design (CCD) is one of the most useful approaches in determining optimum conditions of many processes (Cochran and Cox, 1957; Khuri and Cornell, 1987). Therefore, in this study, CCD was used to point out the relationship existing between the response function, final dry solids content (Sf) and the energy consumption calculated per the additional mass of water removed, process variables, voltage (X1) and pressure (X2). The range of the independent variables for the EDW-process conditions were the voltage, X1 (10e50 V), the pressure X2 (200e1200 kPa). These moderate conditions were very suited to guarantee that water will remain in the liquid phase (Temperature < Temperature of boiling at applied pressure) and to induce sufficient additional water removal without major heat losses. According to the number of factors, i.e. the two process variables, a central composite design (CCD) was selected (Cochran and Cox, 1957; Khuri and Cornell, 1987). The CCD consists of a 22 factorial design, which is the simplest and most useful design, plus two replicates of the central run and a group of (star points) (four axial points) that permits estimation of finer features of the response surface such as curvature. More precisely, a circumscribed central composite design (CCCD) in 10 runs was retained. Indeed, CCCD designs explore the largest process space and gives good accuracy of the estimates over the entire design space. The star points are at some distance a from the centre based on the properties desired for the design and the number of factors in the design. The (star points) establish new extremes for the low and high settings for all factors. The number of levels of a CCCD is five per factor. The central point,

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the two levels associated to the 22 factorial design and the two levels of the star points. The location of the star points on the axes of the factors depends on the selected criterion of optimality. In general, the points are set so that the errors on the regression coefficients of the empirical model be the smallest possible or the best distributed possible. Rotatability is one of these optimality criterions: the answers calculated with the model resulting from the experimental design have an identical error for all the points located at the same distance from the centre of the experimental field. To maintain rotatability, the typical value of a for a 22 factorial design is [22]1/4 ¼ 1.414. This value, which allows simultaneous rotatability and orthogonality, serves as a guide for choosing the a-value, which, in any case, should also be analyzed for its convenience and feasibility. If this criterion is too restricting, near orthogonality or rotatability seems to be a reasonable criterion (Gouby and Creighton, 2009). In our study, the distance of the star points was slightly reduced from the rotatable value of 1.414 to a value of 1. The experimental conditions selected by this design are listed in Table 1. The dependent variables (the final dry solids content and the energy consumption) were expressed as a function of the independent variables known as the response function. The variance for each factor assessed was partitioned into linear, quadratic and interactive components and were represented using the second order polynomial function in order to predict each response in all experimental regions (Box and Draper, 1987): Y ¼ a0 þ a1 X1 þ a2 X2 þ a12 X1 X2 þ a11 X21 þ a22 X22

(6)

Where Y is the predicted response (the final dry solids content/or the energy consumption calculated per the additional mass of water removed), a0 is the interception coefficient, a1 and a2 are the linear terms, a12 is the interaction term, a11 and a22 are the quadratic terms, X1 and X2 represent the coded levels of the process variables (processing voltage and pressure). Knowing this approximation of the real dependence between Y and the X’s, we can find directions on

Table 1 e Experimental design matrix and observed and predicted responses with the correlation coefficients. No. Exp.

V P (V) (kPa)

Final dry solids content (Sf) (%)

(X1) (X2)

Energy consumption (kWh/kg additionally water removed)

Observed Predicted Observed Predicted 1 10 2 30 3 50 4 10 5 10 6 30 7 50 8 50 9 30 10 30 R2 R2 (adjusted)

1200 1200 1200 700 200 200 700 200 700 700

19.00 43.37 64.00 15.76 14.00 40.00 67.00 55.00 37.60 39.10 0.903 0.879

18.09 42.45 65.83 16.37 14.29 36.66 62.12 58.04 39.73 39.73

0.041 0.177 0.325 0.037 0.034 0.175 0.488 0.312 0.171 0.201 0.897 0.835

0.023 0.154 0.365 0.068 0.0198 0.147 0.406 0.354 0.196 0.196

the Y ¼ f(V, P) surface that lead to the general vicinity of the response surface optima. The relation between the coded and natural variables voltage and for pressure, are given as: X1 ¼

V  Vc DV

(7)

X2 ¼

P  Pc DP

(8)

Where X1 and X2 are the coded values, V and P are the corresponding natural values. Vc and PC are the natural values in the centre of the domain: Vc ¼ 30 V and PC ¼ 700 kPa DV and DP are the increments of V and P corresponding to one unit of X1 and X2 respectively. The significance of all terms in the polynomial functions were assessed statistically using the F-ratio at probability ( p) of 0.05. The p-value tests the statistical significance of the estimated correlations. p-value below 0.05 indicates statistically significant non zero correlations at the 95% confidence level. The fitness of the model was evaluated by the correlation coefficient R2, the fraction of the variation explained by the model, and an analysis of variance F-ratio. The F-ratio is applied to confirm whether the variance explained by the regression model is significantly larger than the variance of the residual and to evaluate the model lack-of-fit (model error). The lack-of-fit (model error) tests are used to determine whether the selected models are adequate to describe the observed data, or whether another model should be used (Hastie et al., 2001). The parameters of the model are estimated from experimental responses by the least squares regression using STATGRAPHICS Centurion XVI.I software. Following the model fitted for each response, we graphically represent isoresponse surfaces which are three-dimensional of the relationship between the responses and the two factors. This response surfaces methodology allows experimental responses behaviour to be described as precisely as possible as a function of factor variation and optimal conditions of the factors to be determined for each experimental response. The model can either be calculated on standardized values or on the experimental values. Both calculations yield useful information: the standardized values allow a mutual comparison of the importance of the different factors with respect to a certain response, whereas the experimental values deliver a predictive model.

4.

Results and discussion

4.1. Evaluation of process repeatability and estimate of the pure error Usually, centre points are used to evaluate the process repeatability and estimate of the pure error. But, taking into account the strong heterogeneity between two activated sludge samples, a preliminary investigation was undertaken to assess the repeatability of the process, prior to conducting the RSM experiments. The electro-dewatering experiments were repeated at least 3 times over two days with the same process operating conditions to evaluate the reproducibility of the results.

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For example, the measurement dispersion resulting from four replicated tests for only one series of experiments can be estimated from the dewatering kinetics obtained for the processing conditions (400 kPa, 50 V) plotted in Fig. 2. In spite of the heterogeneity of the activated sludge, the kinetics are appreciably the same (except for the second test (II) during the third stage from 8800 s onwards), the standard deviation on the filtrate mass being particularly weak during the two stages of the dewatering. The standard deviation on the final dry solids content of the dewatered cake is 3%. The measurement dispersion resulting from different activated sludge samplings from the other series is quantitatively much higher but the trends are similar. The main difference in the dewatering kinetics appears during the third dewatering stage, as it will be seen in next sections. Fig. 2(a,b) shows the typical dewatering kinetics as recorded during a mechanical dewatering run (MDW) and an electro-dewatering run (EDW). In the course of the MDW, it can be discerned that the initial filtrate removal occurs very fast: after a few minutes, more than 70% of the final filtrate volume is obtained. 7200 s of MDW run were necessary to reach the equilibrium state. Furthermore, it was decided to combine a voltage with MDW runs from 7200 s onwards in all cases. The experimental results indicated that the application of a voltage in the dewatering third stage indeed appeared to be an effective method to enhance the dewatering kinetics in

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practice and to significantly increase the dry solids content of the sludge compared to conventional MDW process, as shown in Fig. 2(a,b). The results show that an additionally 65 g of water is removed during the EDW phase and the final dry solids content of the sludge achieved more than 55% (wt%). The principal mechanism almost certainly is electro-osmosis, which is removing water that cannot be accessed by pressure dewatering alone. Electro-osmosis creates a force generated by the applied electric field on a thin layer of charged fluid in the pore liquid that forms adjacent to charged sludge particles. This layer, known as the Debye sheath or double layer, results from the repulsion of co-ions and the attraction of counterions by the pore wall. This force causes the liquid in the double layer to move, which in turn, sets the bulk liquid in motion by viscous interactions (Probstein, 1994; Mahmoud et al., 2010). However, the dry solids content was increased from 13 to 56% (wt%), corresponding to a 53% increasing of the dry solids content originally present in the conditioned activated sludge (2.8% wt%). The percentage of water removal in each dewatering stage, as well as the amount of water remaining after processing is shown in Fig. 2(c). In the course of the MDW with a processing pressure of 400 kPa, the percentage of water removal was more than 70%. The simultaneous combination of an applied voltage (50 V) with an applied pressure (400 kPa) removed 24% of additional water, which cannot be accessed by the pressure forces alone. Fig. 2(d) shows the time variation

Fig. 2 e Reproducibility of the experiments with the conditioned activated sludge coming from the same sampling after processing conditions at 400 kPa and 50 V in terms of (a) Filtrate mass removed; (b) Dry solids content; (c) Column chart illustrating the percentage of water removal in each EDW-process stage and the water remaining after processing conditions; (d) Current and electrical resistance.

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Fig. 3 e Left: (a) Dry solids content versus time for dewatering testing of conditioned activated sludge at an applied pressure of 200 kPa with different voltages. (b) Dry solids content versus time for dewatering testing of conditioned activated sludge at an applied pressure of 600 kPa with different voltages; (c) Dry solids content versus time for dewatering testing of conditioned activated sludge at an applied pressure of 1200 kPa with different voltages; (d) Dry solids content versus time for dewatering testing of conditioned activated sludge at three different pressures with different voltages. Right: (e, f, g) Column chart illustrating the percentage of water removal in EDW stage and the water remaining after these processing conditions. (h) Percentage of water removal in EDW stage and the water remaining in press cake as a function of three different pressures with 20 V.

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of the current and electrical resistance during the replicate EDW runs. The current reached a maximum value shortly after EDW began and then steadily decreased. The current peak is the result of two opposing effects. As water is removed, the bed height decreases and this decreases the bed resistance. At the same time, as water is removed, the percent of solids increases, thus increasing the bed resistance. Also, since cracks appeared in the upper part of the bed, electrical contact between the bed and the upper electrode (anode) is progressively disrupted as the bed dried. On the other hand, increasing of the electrical resistance causes a drop in the electrical field, and thus in the driving force, inside the cake. Near the end of the tests, the electrical resistance of the cake becomes too high and the electrical current reduces and eventually ceases and electro-dewatering stops and no more water is removed. Fig. 2(d) also shows, a significantly deviation during the second test (II) compared to (I, III, V) tests in terms of current and electrical resistance, resulting in the main difference in the dewatering kinetics from 8800 s onwards. Finally, replicate data for energy consumption from four runs shows good reproducibility. The standard deviation on the energy consumption is approximately 1%. In fact, the average value of the energy consumption calculated per the additional mass of water removed is 0.34 kWh/kg(additionally water removed). The corresponding average value of the final dry solids content is 56%. For comparison, thermal drying requires around 0.617e1.20 kWh/kg of water removed (Gazbar et al., 1994; Mujumdar, 2007; Mahmoud et al., 2010). These results emphasize the potential of the EDW-process for activated sludge dewatering.

4.2. Effect of processing pressure and voltage on dewatering 4.2.1. The dry solids content and the significance of the two stages of dewatering For the sake of clarity only three processing pressure with different processing voltage were presented in this section. Fig. 3 depicts a typical MDW experiment as well as an EDW experiment with different voltages applied for the same applied pressure. It shows (i) the dry final solids content; (ii) a column chart illustrating the percentage of water removal over the EDW stage (stage 3) and the water remaining after processing conditions. Fig. 3(a,b,c,d), clearly shows that the application of the voltage has a dramatic influence on the dewatering kinetics and the total water removed (in terms of filtrate mass). Whereas in the stage before application of the electric field, dewatering kinetics was comparable in all experiments, the magnitude of the electric field dramatically influences the dewatering kinetics during the EDW phase. The results of the dry solids contents of the filter cakes that were achieved during dewatering stages were shown in Fig. 3 (a,b,c,d). It is clear from a perusal of all these curve plots that high voltages resulted in the best dewatered cakes. On the other hand, an increase in the applied pressure from 200 kPa to 1200 kPa also led to an increase in the dry solids content. From the viewpoint of the effectiveness of water removal, the percentage of water removal is a far more meaningful parameter. Therefore, an analysis of the extent of water removal

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associated with each stage of the process was investigated for the different processing conditions. The comparative dewatering performance of the third stage (EDW) and the water remaining as a function of processing voltage for each pressure are shown in Fig. 3(e,f,g,h). The results indicated that more than 70% of the total water was removed by MDW alone. When a voltage was combined with MDW, the percentage of additionally water removed was increased considerably by 10 to more than 24%. These experimental observations are in line with findings by Saveyn et al., 2006a,b; Miller et al., 1997, 1998. Moreover, it can be inferred, as shown in Fig. 3(h), that at low levels of X1 (voltage) an increased processing pressure has a relatively pronounced effect on the water removal. Not only is more liquid removed by the higher pressure, but it seems that also the electrical contact is improved and thus the energy efficiency. These observations agree with the results by other researchers (Kondoh and Hiraoka, 1990; Gazbar et al., 1994; Miller et al., 1998; Gingerich et al., 1999; Lee et al., 2002). The results also revealed that the dewatering rate increased with an increase in voltage. To treat this point, we analyzed the electro-dewatering efficiency in terms of the dewatering time and power consumption at the target (desired) cake dryness. For example, there is a large reduction in time required to reach 42% solids content. The time is reduced from 4100 s to 800 s with increasing processing voltage from 30 to 50 V at 1200 kPa, as shown in Fig. 3(d). The corresponding power consumption was ranging from 0.169 to 0.231 kWh/kg(additionally water removed), respectively. For comparison, the power consumption at this target cake dryness was 0.273 and 0.241 kWh/kg(additionally water removed) for the processing conditions (400 kPa, 50 V) and (600 kPa, 50 V), respectively.

4.2.2.

Filtrate analysis

The electrochemical reactions at the electrodes guarantee the continuity of the electrical transportation. When a voltage is applied across the electrodes in an aqueous solution, electrolysis of water occurs in order to maintain charge equilibrium. The electrolysis of water produces oxygen gas and protons, Hþ, at the anode, while hydrogen gas and hydroxyl anion, OH, are formed at the cathode. As a result, the pH near the cathode increases while the pH near the anode decreases. The pH, electrical conductivity, total suspended solids (TSS), and turbidity of the filtrate near the cathode were measured during the experimental run at the end of each stage. The pH is plotted as a function of the voltage for different series of applied pressures in Fig. 4(a). For reasons of clarity, second stage results were removed from the curve plots. The filtrate pH remained at 7 to 7.2 for both the first and the second stage run (MDW). At the beginning of the EDW stage, the results revealed increasing alkalinity with increasing applied voltage (up to a pH of 12 for moderate voltages). Under the most severe processing conditions (40 V and 50 V), the pH decreased. This decrease can be explained by the local deposition of impurities e.g. metal hydroxide at the cathode and, possibly also on the filter cloth surface, as can be seen from Fig. 4(e). This phenomenon requires more thorough investigation. However, increase the pH value can increase the magnitude of zeta potential and whence increases the rate of the EDW (Tuan et al., 2008). On the other hand, the pH developed

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Fig. 4 e Influence of applied processing parameters on the physico-chemical properties of filtrate during electro-dewatering of waste activated sludge: (a) Filtrate pH, (b) Filtrate conductivity, (c) Filtrate turbidity and (d) Filtrate total suspended solids.

along the bed results in the generation of an open circuit potential within the system, and hence reduces the dewatering efficiency (Rabie et al., 1994; Mahmoud et al., 2010). In addition, the gas evolution at both electrodes creates voids within the bed which increases the electrical resistance of the system. The filtrate conductivity, in the order of 0.5 mS/cm initially did not significantly change during MDW. However, during EDW processing the creation of ions at both the anode and cathode increased the filtrate electrical conductivity. For

example, the filtrate electrical conductivity increased from about 1 mS/cm at 10 V to up to 2.5 mS/cm at 50 V at an applied pressure of 400 kPa (Fig. 4(b)). Moreover, the excessive electrical conductivity increment may dramatically increase the Ohmic heating losses and hence a clear negative impact on the energy efficiency. Total suspended solids (TSS) and turbidity were also monitored during EDW processing to control the filtrate quality. Fig. 4(c,d) shows the total suspended solids as well as the turbidity during the experiments with different voltages at

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a given applied pressure. Each value was measured several times and the average values are reported here. As can be seen from this figure, TSS ranges from 16 to about 320 mg/l while turbidity ranges from 10 to 510 FAU. Not surprisingly, the highest values were obtained with the most severe conditions. The increase in both the turbidity and the TSS with voltage can be explained by the electromigration of heavy metals, microbial cells, ions and organic matter presented initially in the sludge (Hwang and Min, 2003; Tuan and Sillanpa¨a¨, 2010a,b) and by the electrophoresis phenomena of the fine-particles. In fact, heavy metals occur in sludges in various abiotic forms, such as, soluble, adsorbed, exchangeable, precipitated, organically complexed, and residual phases. Heavy metals may also exist in biotic forms, such as, extracellular and intracellular species. The electrochemical reactions at the electrodes cause heavy metals in sludge to be desorbed and or/dissociated, and as a result of the electromigration of ions at either cathode or anode under the influence of the applied voltage. More research is required to determine the concentrations of heavy metals and their biotic and abiotic speciations in both sludge and filtrate before and after the EDW-process. During the experiment it was also observed that using EDW under severe voltage caused colouration of the filtrate, giving it a dark green-brown appearance instead of grey colour, as shown in Fig. 4(f). The dark green colour intensity profile may be quantified by investigating the green intensity level. Colours can be defined by a RGB (RedeGreeneBlue) code with values for each colour component reaching from 0 to 255 for so called 24 bit colours (Saveyn et al., 2005a). Moreover, EDW processing might have resulted in an accumulation of soluble organic matter and even odour in the filtrate. This needs to be further investigated and considered for filtrate treatment. Finally, looking at all different parameters measured, it appeared that the effect of the pressure was always of minor importance compared to the effect of the applied voltage.

4.2.3.

Current variation and electrical energy consumption

Fig. 5 shows the transient behaviour of current and electrical resistance for different voltages at a fixed processing pressure. If all other factors are the same, the current will increase when the voltage is increased. As mentioned earlier in section (4.1.), the electrical current increases in the early period, and after reaching a maximum it decreases with time. In this early period the electric resistance of the sludge bed decreases as the anode to cathode distance decreases with dewatering. Thereafter it increases under the influence of the unsaturated sludge layer formed in the dewatered sludge. On the other hand, the gas (oxygen and hydrogen) evolution at both electrodes leads to the appearance of void spaces within the bed and increases the electrical resistance of the system. This may produce an electrically insulating layer, if the electro-dewatering operation takes more time. Finally, the electrical resistance of the cake formed is too high and the electrical current reduces and eventually ceases and electro-dewatering stops and no more water is removed. Moreover, the non-uniformity of the current distributions caused by the heterogeneous local changes in bed

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Fig. 5 e Current and electrical resistance variation during EDW run for different voltages.

conductivity is one of the multiple reasons which explain the variation in the dewatering kinetics for the same sample with the same process operating conditions. A comparison of operating cost of electro-dewatering and thermal drying processes favours electro-dewatering, if the additionally power consumption cost is significantly lower than thermal energy cost of the drying process. The minimal drying energy requirement refers to the latent heat of vaporisation of water of about 0.617 kWh/kg (Perry and Green, 1997). In industrial devices it can reach as high as 1.2 kWh/kg (Gazbar et al., 1994; Mujumdar, 2007; Mahmoud et al., 2010). Fig. 6, gives an overview of the different specific electric energy consumptions based on water removal, calculated as the difference between the amount of water removed by pressure only dewatering and the amount of water removed by the electro-dewatering process. As shown in Fig. 3(a,b,c,d), it was found that high voltage resulted in the best dewatered cakes. It was also found that the electro-dewatering technique requires much less energy than thermal drying techniques. For example, this technique is much more energy efficient requiring less than 10 and 25% of the theoretical thermal drying energy for the low and moderate voltages cases, respectively. These findings seem to indicate that the application of a voltage seems to be an interesting technique to enhance wastewater sludge dewatering.

4.2.4.

Ohmic heating

Voltages used in electro-dewatering applications usually exceed the decomposition voltage of water (Larue and

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Fig. 6 e Energy consumption of conditioned activated sludge as a function of applied voltage for three different pressures.

Vorobiev, 2004). This implies that the electrochemical cell exhibits a considerable Ohmic loss, expressed by the IRcell term (where I is the electric current (A) and Rcell is the cell resistance (Ohm)). This Ohmic loss causes heating in the dewatering device and thus a decreasing liquid viscosity. The viscosity decrease results in enhanced dewatering kinetics (Weber, 2002; Weber and Stahl, 2002). This heating effect is obviously more pronounced, if the electro-dewatering operation takes more time. To investigate the Ohmic heating impact, two thermocouples are introduced to measure the temperatures.

The first is inserted into the bottom part of the compressive piston. The second is put in the outlet channel of the filtrate to minimize cooling effects. At the cathode, the temperature profiles show an increase where voltage is applied, followed by a decrease, as shown in Fig. 7(a). This decrease can be explained by the fact that toward the end of the dewatering process, the filtrate flow rate has almost dropped to zero, so that the slow discharging filtrate drops are cooled before they reach the temperature sensor. Moreover, it is believed that the generated gas at the cathode creating bubbles and even enlarging surface for heat loss may cause an additional cooling. As shown in Fig. 7(a), a maximum temperature of about 40  C is reached for the highest processing voltage. On the other hand, Fig. 7(b) also reveals that the temperature at the anode increment at different voltages increased with an increase in time, followed by a decrease. The rising temperature rate increased considerably with an increase in voltage, and thus a dramatically decreasing liquid viscosity. To investigate the variation of the dynamic viscosity (m), the average temperature of the cake was calculated (Fig. 7(c)). Then the change in the viscosity normalized with its value at 20  C, as can be seen from Fig. 7(d), was computed according to the relation: mTroom  mTaverage:cake mTroom

 100

(9)

Fig. 7(d) shows the average temperature dependent change in the viscosity of water at different processing voltages. The

Fig. 7 e Influence of applied electrode voltage on instantaneous filtrate temperature/cathode (a), (b) anode, and (c) press cake during electro-dewatering of waste activated sludge. (d) Viscosity variation as a function of the press cake temperature.

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Fig. 8 e Standardized Pareto Chart showing the effects of the independent variables X1 (voltage) and X2 (pressure) and their combined effects on both the final dry solids content (a) and the energy consumption.

Ohmic heating effect is obviously more pronounced for incremental increases in applied processing voltage. This increment resulted in a considerable change in the viscosity and therefore an essential acceleration of the dewatering rate, facilitating the removal of some of the remaining water. Therefore it can be concluded that Ohmic heating has a beneficial effect in enhanced dewatering kinetics.

4.3.

Analysis of the CCD design

4.3.1.

Standardized Pareto chart

The model calculation based on the standardized values allows a comparison of the relative influence of the factors on

2807

the response, by comparing the square roots of the sum of squares of all the coefficients related to a factor. To determine which factors have a significant impact on the response variable, the Pareto Chart was used. The standardized Pareto Chart contains a bar for each effect, classed from the most significant to the least significant. The length of each bar is proportional to the standardized effect. The length of each bar indicates the effect of these factors and the level of their effects on responses. A vertical line (reference line) is drawn at the location of the 0.05 critical value. Any bars that extend to the right of that line indicate effects that are statistically significant at the 5% significance level. Fig. 8 shows the standardized Pareto Chart and depicts the main effect of the independent variables on the final dry solids content and the energy consumption calculated per additional mass of water removed. It can be inferred, as shown in Fig. 8(a), that the factor X1 (voltage), X2 (pressure), and (X1X2) extending behind the reference line, have a significant effect on the dry solids content. As shown in Fig. 8(a), the voltage has a bigger impact on the response than the pressure. The main effect of voltage is 45.75 while the main effect of pressure is only 5.79 in the case of Sf. Note that the voltage (X1) is the only significant main effect at the 95% confidence level on the energy consumption calculated per the additional mass of water removed, as can be seen from Fig. 8(b). The main effect of voltage is 0.34 while the main effect of pressure is only 0.007.

4.3.2.

Adequacy test of the model

According to the statistical method, a second order polynomial function was assumed to approximate the final dry solids content. The adequacy test of the models, estimated by the correlation coefficients, R2, given in Table 1, revealed that they are quite adequate with a value always greater than 80%. It indicates that the regression model can predict the results from EDW accurately over a range of 80% of the variation in the pressure and voltage. Fig. 9(a,b) shows the predicted values plotted versus the measured values from the experiment for both the final dry solids content and the energy consumption calculated per the additional mass of water removed. It reveals that all data points lie close to the 45 dash line, implying that the model provides an adequate approximation to the experimental data. Moreover, comparing the reproducibility to the average

Fig. 9 e (a) Model predicted final cake dry solids content values versus experimental values. (b) Model predicted energy consumption values calculated per the additional mass of water removed versus experimental values.

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standard error on the predicted values, it appears that the model yields relatively reliable results, which makes it very well suited for predictive purposes.

4.3.3.

Optimization step

In order to optimize the EDW variables for the dry solids content response Y, the RSM was used. The response surfaces for both the final dry solids content (Sf) and the energy consumption generated by STATGRAPHICS Centurion XVI.I software are displayed in Fig. 10. Using the analysis options dialog box (energy consumption STATGRAPHICS Centurion XVI.I software) of the RSM, the range of each factor allowing the optimum dry solids content to be determined. After generating the polynomial equations relating the dependent and independent variables, the process was optimized for the response Y. Optimization was performed to obtain the levels of X1eX2 which maximize Y. The “optimum” point (voltage (V), pressure (kPa)) for dry solids content was 50 V, 1200 kPa, corresponding dry solids content was 66% (wt%). As discussed previously energy consumption is very important as well. Therefore, the best combination of process variables for the energy consumption response function was determined in order to limit the energetic cost of the process and simultaneously obtain a satisfactory final dry solids content. The optimum processing conditions were a voltage and pressure of 40 V, 728 kPa, which gave a dry solids content and energy consumption of 51.2% and 0.30 kWh/kg(additionally water removed), respectively.

4.3.4.

Energy consumption trend

As shown in Fig. 11, a clear nonlinear relationship was found between the energy consumption and the final dry solids

Fig. 11 e Energy consumption during EDW as a function of the final dry solids obtained for the processing conditions (200e1200 kPa, 10e50 V) compared to thermal drying techniques. The EDW tests (5 experiments per series) for the chosen operating conditions pressure (200e1200 kPa) and voltage (10e50 V) were repeated 3 or 4 times (I, II, III, V) except for the series of (1200 kP, 10e50 V).

content obtained after the EDW run. This relationship is a complex sum of several interdependent parameters which determine the behaviour of the sludge during EDW run and thus the overall shape of the curve. However it is possible to discern some trends from Fig. 11. When regarding the theoretical thermal drying energy requirement, which is about 0.617 kWh per kg evaporated water (Perry and Green, 1997), it is clear that the electrodewatering technique is much more energy efficient, using down to less than 10% of this amount in case of low voltages. The actual working conditions in a practical application will largely depend on the costs for electricity. When increasing the voltage, the additional electricity costs will be too high at a certain stage to justify the additional amount of water which can be removed by it. This break point will largely depend on local electricity market situations. It is however important, when making an economic analysis, to note that electro-dewatering is a one-step process, that does not need expensive transport or handling to enhance the dry solids content of the sludge to be treated. Finally, Fig. 11 shows that the energy used to reach a dryness of 32e60% is significantly lower (10e25% lower) than that required for the thermal drying techniques. Additionally, and in most cases, the intermediate/higher dry solids contents directly relate to lower transportation and disposal costs because of the smaller volumes. Thus, this research showed that power consumption/cost can be altered, based upon the dry solids content that is desired.

5.

Fig. 10 e Estimated response surface for the final dry solids content of the conditioned activated sludge (a) and the energy consumption as a simultaneous function of processing voltage and pressure (b).

Conclusions

The EDW was used to effectively dewater activated wastewater sludge, under a variety of processing conditions ranging from 10 to 50 V and from 200 to 1200 kPa. Importantly, it has been illustrated that the EDW can be used to remove a significant proportion of the inherent water from conditioned

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activated wastewater sludge. It is found that, as a generalization, the EDW can remove approximately from 10 to more than 24% of additional water, which cannot be accessed by the conventional dewatering process (MDW) alone. The analysis from the RMS emphasized that both increasing voltage and pressure increase the dry solids content. On the other hand, it can be inferred that the voltage is the only significant main effect at the 95% confidence level and that there are no significant interaction effects in case of the energy consumption. Moreover, the RMS appeared very well suited to predict the optima set of operating variables in order to achieve the desired treatment level for the dry solids content taking into account the energy aspect. Finally, fundamental studies and further experimental are required to gather data (i) to determine the concentrations of heavy metals and their biotic and abiotic speciations in both sludge and filtrate before and after the EDW-process and (ii) to quantify the local deposition of metal at the cathode and possibly also on the filter media which can be altered the electrical resistance of the system and, consequently, the dewatering rate and the power consumption.

Acknowledgment The authors would like to acknowledge Jean Marc LEGROSADRIAN for kind technical assistance in the construction and development of the experimental set-up. The authors also would like to thank Charlene MARMIGNON for kind, thorough assistance in the experiments. Finally, the authors gratefully acknowledge the financial and support received for this research from the Agence Nationale de la Recherche (ANR-08ECOT-018-004).

references

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Saveyn, H., Meersseman, S., Thas, O., Van Der Meeren, P., 2005. Influence of polyelectolyte characteristics on pressure-driven activated sludge dewatering. Colloids and Surfaces A: Physicochemical and Engineering Aspects 262 (163), 40e51. Saveyn, H., Meeren, P.V., Hofmann, R., Stahl, W., 2005a. Modelling two-sided electrofiltration of quartz suspensions: importance of electrochemical reactions. Chemical Engineering Science 60 (23), 6768e6779. Saveyn, H., Pauwels, G., Timmerman, R., Meeren, P.V., 2005b. Effect of polyelectrolyte conditioning on the enhanced dewatering of activated sludge by application of an electric field during the expression phase. Water Research 39 (13), 3012e3020. Saveyn, H., Meeren, P.V., Pauwels, G., Timmerman, R., 2006a. Bench-and pilot-scale sludge electrodewatering in a diaphragm filter press. Water Science and Technology 54 (9), 53e60. Saveyn, H., Curvers, D., Pel, L., De Bondt, P., Meeren, P.V., 2006b. In situ determination of solidosity profiles during activated sludge electrodewatering. Water Science and Technology 40 (11), 2135e2142. Saveyn, H., Curvers, D., Thas, O., Meeren, P.V., 2008. Optimisation of sewage sludge conditioning and pressure dewatering by statistical modelling. Water Research 42 (4e5), 1061e1074. Schuster, C., Kotz, J., Kulicke, W.M., Parker, S., Bohm, N., Jaeger, W., 1997. Dewatering behaviour of sludge in the presence of polyelectrolytes. Acta Hydrochimica et Hydrobiologica 25 (1), 27e33. Tarleton, E.S., 1992. The role of field-assisted techniques in solid/ liquid separation. Filtration & Separation 29 (3), 246e252. Tchobanoglous, G., Burton, F.L., Stensel, H.D., 2003. Wastewater Engineering: Treatment and Reuse, fourth ed. Metcalf and Eddy Inc./McGraw-Hill, New York, U.S.A.

Thapa, K.B., Qi, Y., Hoadley, A.F.A., 2009. Using FBRM to investigate the sewage sludge flocculation efficiency of cationic polyelectrolytes. Water Science and Technology 59 (3), 583e593. Tuan, P.A., Sillanpa¨a¨, M., 2010a. Migration of ions and organic matter during electro-dewatering of anaerobic sludge. Journal of Hazardous Materials 173 (1e3), 54e61. Tuan, P.A., Sillanpa¨a¨, M., 2010b. Fractionation of macro and trace metals due to off-time interrupted electrodewatering. Drying Technology 28 (6), 762e772. Tuan, P.A., Jurate, V., Mika, S., 2008. Electro-dewatering of sludge under pressure and non-pressure conditions. Environmental Technology 29 (10), 1075e1084. Vaxelaire, J., Olivier, J., 2006. Conditioning for municipal sludge dewatering. From filtration compression cell tests to belt press. Drying Technology 24 (10), 1225e1233. Vaxelaire, J., Bongiovanni, J.M., Puiggali, J.R., 1999. Mechanical dewatering and thermal drying of residual sludge. Environmental Technology 20 (1), 29e36. Vijh, A.K., 1999a. The Significance of current observed during combined field and pressure electroosmotic dewatering of clays. Drying Technology 17 (3), 555e563. Vijh, A.K., 1999b. Electroosmotic dewatering (EOD) of clays and suspensions: components of voltage in an electroosmotic cell. Drying Technology 17 (3), 565e574. Weber, K., 2002. Untersuchungen zum Einfluss eines elektrischen Feldes auf die kuchenbildende Pressfiltration. VDI Verlag GmbH, p. 208. Weber, K., Stahl, W., 2002. Improvement of filtration kinetics by pressure electrofiltration. Separation and Purification Technology 26 (1), 69e80.

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 8 1 1 e2 8 2 1

Available at www.sciencedirect.com

journal homepage: www.elsevier.com/locate/watres

Floc-based sequential partial nitritation and anammox at full scale with contrasting N2O emissions Joachim Desloover a, Hayde´e De Clippeleir a, Pascal Boeckx b, Gijs Du Laing c, Joop Colsen d, Willy Verstraete a,*, Siegfried E. Vlaeminck a a

Laboratory of Microbial Ecology and Technology (LabMET), Ghent University, Coupure Links 653, 9000 Gent, Belgium Laboratory of Applied Physical Chemistry (ISOFYS), Ghent University, Coupure Links 653, 9000 Gent, Belgium c Laboratory of Analytical Chemistry and Applied Ecochemistry (EcoChem), Ghent University, Coupure Links 653, 9000 Gent, Belgium d Environment and Energy Colsen International b.v., Kreekzoom 5, 4561 GX Hulst, The Netherlands b

article info

abstract

Article history:

New Activated Sludge (NAS
) is a hybrid, floc-based nitrogen removal process without

Received 20 July 2010

carbon addition, based on the control of sludge retention times (SRT) and dissolved oxygen

Received in revised form

(DO) levels. The aim of this study was to examine the performance of a retrofitted four-

21 February 2011

stage NAS
plant, including on-line measurements of greenhouse gas emissions (N2O and

Accepted 23 February 2011

CH4). The plant treated anaerobically digested industrial wastewater, containing 264 mg

Available online 2 March 2011

N L
1, 1154 mg chemical oxygen demand (COD) L
1 and an inorganic carbon alkalinity of 34 meq L
1. The batch-fed partial nitritation step received an overall nitrogen loading rate

Keywords:

of 0.18e0.22 kg N m
3 d
1, thereby oxidized nitrogen to nitrite (45e47%) and some nitrate

Ammonia oxidizing bacteria

(13e15%), but also to N2O (5.1e6.6%). This was achieved at a SRT of 1.7 d and DO around

Fluorescent in-situ

1.0 mg O2 L
1. Subsequently, anammox, denitrification and nitrification compartments

hybridization (FISH)

were followed by a final settler, at an overall SRT of 46 d. None of the latter three reactors

Greenhouse gas (GHG)

emitted N2O. In the anammox step, 0.26 kg N m
3 d
1 was removed, with an estimated

Methane

contribution of 71% by the genus Kuenenia, which constituted 3.1% of the biomass. Overall,

Nitrous oxide

a nitrogen removal efficiency of 95% was obtained, yielding a dischargeable effluent. Ret-

Sustainable

rofitting floc-based nitrification/denitrification with carbon addition to NAS
allowed to save 40% of the operational wastewater treatment costs. Yet, a decrease of the N2O emissions by about 50% is necessary in order to obtain a CO2 neutral footprint. The impact of emitted CH4 was 20 times lower. ª 2011 Elsevier Ltd. All rights reserved.

1.

Introduction

Biological nitrogen removal is economically preferred above physicochemical nitrogen recovery for wastewaters containing less than 5 g N L
1 (Mulder, 2003). If the ratio of biodegradable chemical oxygen demand (bCOD) to nitrogen is relatively low (typically  3), nitrogen removal with partial nitritation/anammox is possible, decreasing 30e40% of the

overall nitrogen removal costs compared to nitrification/ denitrification (Fux and Siegrist, 2004). Partial nitritation oxidizes about half of the ammonium with oxygen to nitrite, and subsequent anammox oxidizes the residual ammonium with the formed nitrite to nitrogen gas and some nitrate. Depending on the wastewater characteristics and reactor operation, additional nitrogen conversions can take place, including aerobic nitrite oxidation to nitrate (nitratation) and

* Corresponding author. Tel.: þ32 9 264 59 76; fax: þ32 9 264 62 48. E-mail address: [email protected] (W. Verstraete). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.02.028

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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 8 1 1 e2 8 2 1

reduction of nitrate or nitrite with organic carbon to nitrogen gas (heterotrophic denitrification). The latter requires at least 4.1 g bCOD g
1 NO3
eN and 2.7 g bCOD g
1 NO2
eN using wastewater organics (Mateju et al., 1992). Partial nitritation and anammox can be executed in one reactor stage (Jeanningros et al., 2010; Joss et al., 2009; Vlaeminck et al., 2010; Wett, 2006), or in two sequential stages (van der Star et al., 2007; van Dongen et al., 2001). Although separate conversion stages entail higher investment costs related to the construction of the different reactors, such configuration allows to attune and optimize the conversion stages individually. Furthermore, in case of higher bCOD levels in the influent, the anammox bacteria will experience less bCOD in case of separate stages, leading to a lower chance that denitrifiers overgrow the anammox bacteria (Lackner et al., 2008). For an anammox stage, biomass retention is crucial given the high doubling time of the anammox bacteria (7e14 d; Strous et al., 1998). All application reports so far relied on the growth of anammox bacteria in biofilms or granules to obtain a sufficiently high sludge retention time (SRT); Ferna´ndez et al., 2008; Lo´pez et al., 2008; van der Star et al., 2007). Given the fact that some floccular applications exist for one-stage partial nitritation and anammox in sequential batch reactors (Joss et al., 2009; Wett, 2006), the feasibility of a separate floccular anammox step could be expected, yet remained to be demonstrated with realistic operational parameters. Next to energy- and cost-efficiency, sustainability is evolving as a benchmark in wastewater treatment industry. An important sustainability aspect is the CO2 footprint of a wastewater treatment plant (WWTP). Since 1 kg CH4 and 1 kg N2O have the global warming potential of 25 and 298 kg CO2 on a 100-yr time horizon, respectively (Solomon et al., 2007), follow-up of these emissions is particularly warranted. The formation of methane through methanogenesis is well understood and the sustainability aspect of direct CH4 emissions has been taken into account for quite some time (e.g. Keller and Hartley, 2003). In contrast, N2O emissions concern a more complex matter, as the interplay of many parameters determines N2O production from nitritation, denitrification and chemical reactions (Ahn et al., 2010; Kampschreur et al., 2009b). Furthermore, given the highly dynamic nature of N2O emissions, accurate quantifications can only be obtained from grab samples taken at high frequency (e.g. 5e15 min) or from continuous on-line measurements (e.g. 0.5e5 min). The aim of this study was to examine the performance of a novel, floc-based partial nitritation and anammox process, including quantification of the emissions of the greenhouse gases CH4 and N2O with a continuous on-line measurement set-up. The characterized full-scale nitrogen removal process discharges effluent to surface water and is preceded by

anaerobic digestion and struvite precipitation (Anphos
), jointly representing the WWTP of a potato-processing factory. Previously, the nitrogen removal plant was operated as a floccular nitrification/denitrification system comprising a first nitrification stage followed by two subsequent denitrification stages and a final nitrification stage. As the COD/N ratio of the wastewater entering the denitrification stage was around 2, additional carbon was added by deviating 10% of the anaerobic digestor influent, hence lowering the biogas production. However, by choosing appropriate DO setpoints and SRT, the system was retrofitted to a hybrid nitrogen removal process, consisting of partial nitritation, anammox, denitrification and nitrification (Fig. 1). This novel process was designated New Activated Sludge (NAS
), removing nitrogen without external carbon addition nor pH or temperature control.

2.

Material and methods

2.1.

Plant operation and sampling strategy

The operation of the industrial WWTP (Bergen op Zoom, the Netherlands) follows the production cycle of the potato company, i.e. in cycles of 2 weeks consisting of 12 days of factory effluent treatment and 2 idle days. In the idle days, the nitrogen plant receives no influent, but DO setpoints and recirculation flow rates are maintained. In weeks 10e17 (2010), reactor operation parameters and wastewater characteristics of each nitrogen removal compartment were monitored on a daily basis. In week 16 (2010), on day 1 of the operation cycle, exploratory measurements of the gaseous emissions of the partial nitritation compartment were performed over a 3-h period. In week 17 (2010), at days 8e11 of the operation cycle, the snapshot characterization of each of the four reactor compartments comprised on-line gas sampling of the reactor off-gas, and liquid grab sampling of all streams entering and leaving a reactor compartment. The latter was executed every 30 min over a 3-h period, i.e. the cycle duration of the partial nitritation reactor. Since the exploratory test yielded relatively high N2O emissions from the partial nitritation, this basin was monitored for three snapshot periods (batches 1, 2 and 3), whereas the other basins were only measured for one snapshot period. Also, automated on-line gas sampling was continued overnight for each reactor compartment. Table 1 presents an overview of the snapshot sampling schedule per day and night. Given the inherent variability of factory wastewater, the snapshot characterizations of each stream deviated slightly in composition and flow rate between subsequent snapshot periods.

Fig. 1 e Schematic overview of the examined four-stage hybrid nitrogen removal process. The processes and flows were operated in a continuous mode, except for the feeding of the partial nitritation step and the wasting of excess sludge.

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 8 1 1 e2 8 2 1

2.2.

On-line gas sampling

The on-line gas sampling set-up of the snapshot sampling period (week 17) is presented in Fig. 2. The used Lindvall hood (Lindvall et al., 1974) is an example of the wind tunnel approach to quantify gaseous emissions, whereas Ahn et al. (2010) previously quantified N2O emissions with the dynamic flux chamber approach. Both techniques provide sensitive fluxes, yet the former allows to control the sweep velocity of the mimicked wind more precisely (Gostelow et al., 2003). A floating, aluminium Lindvall gas hood was used to capture the gaseous emissions from 0.864 m2 of covered reactor surface. A fan with air intake opposite to the wind direction was used to blow ambient air through the internal snake pattern of the hood (Fig. 2 inset), mimicking relatively low wind conditions at the water surface under the hood, i.e. 0.4e0.5 m s
1 over a section of 20 800 mm2. The gas velocities and temperature were measured in triplicate every 30 min with a Testo hot-bulb probe (Ternat, Belgium) at the outlet of the hood (7090 mm2), and were 1.8  0.3 m s
1 at 30  2  C (partial nitritation), 1.2  0.4 m s
1 at 30  1  C (anammox), 1.4  0.2 m s
1 at 28  1  C (denitrification), and 1.8  0.2 m s
1 at 25  1  C (nitrification). Hence, the aeration in the partial nitritation and nitrification compartments contributed only 22e33% of the measured gas velocity, and superficial gas velocities in these reactors were 0.0033e0.0049 m3 m
2 s
1. A Teflon tube at the hood outlet was connected to a photo-acoustic infrared Bru¨el & Kjær Multi-Gas Monitor 1302 (Nærum, Denmark), measuring and storing the N2O and CH4 level every 3 min. Calibration was done with 250 ppmv N2O and 50 ppmv CH4. Measured off-gas concentrations were corrected by subtraction of background levels, which were recorded for about 30 min before and after the 3-h intensive sampling period. Average background concentrations of N2O and CH4 were 1.63  1.26 and

2813

6.73  1.01 ppmv, 1.13  0.38 and 9.37  2.51 ppmv, 0.54  0.27 and 7.03  1.24 ppmv, 0.58  0.22 and 5.76  0.11 ppmv during the intensive sampling periods of the partial nitritation, anammox, denitrification and nitrification reactor, respectively. Calculation of the gas emissions (kg d
1) was based on the concentration corrected for background levels, converted to molar concentrations with the ideal gas law at atmospheric pressure and at the measured gas temperature. The measured gas velocity and cross-sectional area of the gas hood outlet, with a diameter of 95 mm (Fig. 2), yielded the off-gas flow rate. The overall emissions were obtained by extrapolating the flux from the covered surface (0.864 m2) to the overall surface area of the relevant reactor compartment.

2.3.

Grab liquid sampling

During the long-term monitoring (weeks 10e17), the pH, DO, inorganic nitrogen species, COD and sludge characteristics were monitored for every reactor compartment, and the total inorganic carbon (TIC) and phosphorus were measured in the influent and effluent. During the snapshot characterization (week 17), DO, pH and water temperature were measured in triplicate close to the gas hood every 30 min (Fig. 2). For the inorganic nitrogen species and dissolved N2O, grab samples were taken every 30 min from every stream entering or leaving the reactor, except for the influent samples of the batch-fed nitritation where only one sample per batch was taken. The latter samples were additionally examined for Kjeldahl nitrogen. A Consort C532 m with probe was used for pH measurements (Turnhout, Belgium), and DO concentration and water temperature were measured with a Hach-Lange LDO meter (Du¨sseldorf, Germany). Hach-Lange cuvette tests (Du¨sseldorf, Germany) were used for ammonium (LCK302, 303 or

Fig. 2 e Set-up for the on-line measurement of gaseous N2O and CH4 emissions, with position of the liquid grab sampling, and detail of the Lindvall gas hood (inset). Q: flow rate; T: temperature; DO: dissolved oxygen; N2O (l) and N2O (g): dissolved and gaseous N2O, respectively.

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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 8 1 1 e2 8 2 1

304), nitrite (LCK341 or 342) and nitrate analysis (LCK339). Free ammonia and free nitrous acid levels were calculated based on the reactor ammonium and nitrite concentration, pH and water temperature (Anthonisen et al., 1976). Kjeldahl nitrogen was analyzed according to standard methods (Greenberg et al., 1992). For dissolved N2O measurement, a 1-mL filtered (0.45 mm) sample was brought into a 7 mL vacutainer (
900 h Pa) and measured afterwards by pressure adjustment with He and immediate injection at 21  C in a Shimadzu GC-14B gas chromatograph equipped with an electron capture detector (Columbia, Maryland). In a control experiment, dissolved N2O concentrations with and without prior filtration were on average 6.06  0.16 and 6.13  0.05 mg N L
1, respectively, showing no significant influence of the filtration step (t-test p-value > 0.05). A Shimadzu TOC-VCPN analyzer and ASI-V autosampler (Columbia, Maryland) were used for TIC determination, and the IC alkalinity was calculated from the TIC concentrations by taking into account pH and temperature (Crittenden et al., 2005). Total and volatile suspended solids (TSS and VSS) content, sludge volume index (SVI), COD and total phosphorus were determined according to standard methods (Greenberg et al., 1992).

2.4.

2.5.

Fluorescent in-situ hybridization

Fluorescent in-situ hybridization (FISH) was performed to identify the anammox bacteria, and to quantify nitritation bacteria and anammox bacteria in each reactor compartment. One mixed liquor sample of each compartment was fixed in a 4% paraformaldehyde solution and FISH was performed according to Amann et al. (1990). An equimolar probe mixture of Nso1225 and Nso190 for the b-proteobacterial nitritation bacteria genera Nitrosomonas and Nitrosospira, and probes Kst157, Amx1240, Sca1309 and Amx820 for the anammox bacterium “Candidatus Kuenenia stuttgartiensis”, “Candidatus Brocadia anammoxidans”, “Candidatus Scalindua” and “Candidatus Brocadia and Kuenenia”, respectively. Probe sequences and formamide concentrations were applied according to probeBase (Loy et al., 2003), unless for the equimolar mixture of Nso1225 and Nso190, 35% formamide was applied (Pynaert et al., 2003). The target group was quantified by dividing the signal of the specific probe to the signal of the DNA stain 40 ,6-diamidino-2-phenylindole (DAPI). Images from ten random fields of view were acquired on a Carl Zeiss Axioskop 2 Plus epifluorescence microscope (Jena, Germany), which were subsequently analyzed with ImageJ freeware.

Anammox batch tests

One grab sample was harvested from the anammox and from the denitrification compartment to determine the specific anammox activity rates. Prior to the batch activity tests, the biomass was washed with a phosphate buffer (100 mg P L
1, pH 8) to remove residual dissolved reactor compounds. Anoxic ammonium conversion tests were previously described in detail (Vlaeminck et al., 2007), and were performed in triplicate.

3.

Results

3.1.

Partial nitritation reactor

Reactor operation parameters and wastewater characteristics during the long-term monitoring period are presented in Tables 1 and 2, respectively. The incoming streams of the

Table 1 e Overview of the reactor parameters over the long-term monitoring period and during the snapshot characterizations (average ± standard deviation). For the latter, the sampling strategy was clarified with day and night numbers indicating the relative position in the 14-d production cycle. Numbers between brackets refer to the numbers in the schematic process overview (Fig. 1). Flow rates (Q) are the sum of the different streams entering a compartment. HRT: hydraulic retention time; DO: dissolved oxygen; TSS and VSS: total and volatile suspended solids concentration, respectively; SRT: sludge retention time; SVI: sludge volume index; am: morning; pm: afternoon; T: temperature. Reactor stage

Partial nitritation (1)

Volume (m3)

2370

Long-term (weeks 10e17)

Q (m3 d
1) HRT (d) pH (
) DO (mg O2 L
1) VSS (g VSS L
1) SRT (d) SVI (mL g
1 TSS)

Snapshot (week 17)

Night monitoring

Anammox (2) Denitrification (3) Nitrification (4) 1650

1851  298 1.3  0.2 7.5  0.1 0.75  0.05 0.25  0.03 1.7  0.5 100  23

5931  298 0.28  0.01 7.9  0.1 0.0  0.0

Batch 1

Night 9 Batch 2

Batch 3

Day monitoring

Day 9, am

Day 9, pm

Day 10, am

pH (
) DO (mg O2 L
1) T ( C)

7.6  0.1 1.0  0.1 36  0

7.6  0.1 0.9  0.2 36  0

7.8  0.1 0.4  0.1 36  0

1600 10 731  298 0.15  0.00 7.7  0.1 0.0  0.0 3.25  0.23 46  41a 167  34

2300 10 731  298 0.21  0.00 7.6  0.1 5.7  0.7

Night 10

Night 11

Night 8

Day 10, pm

Day 11, am

Day 8, pm

8.0  0.1 0.0  0.0 32  0

8.0  0.1 0.0  0.0 31  0

7.6  0.1 3.0  0.2 30  0

a Average and standard deviations were calculated on weekly wasted sludge amounts, and since no sludge was wasted in weeks 12 and 13, this yielded a high standard deviation.

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Table 2 e Overview of long-term wastewater characteristics (weeks 10e17) of the influent and different reactor compartments (average ± standard deviation). Numbers between brackets refer to the numbers in the schematic process overview (Fig. 1). Inorganic carbon (IC) alkalinity was calculated from total inorganic carbon (TIC), pH and temperature values. COD: chemical oxygen demand, respectively; Kj-N: Kjeldahl nitrogen; Ptot: total phosphorus;
Influent (0)

Partial nitritation (1)

Anammox (2)

Denitrification (3)

Nitrification (4)

9.0  0.1 1154  110 264  39 193  39
7.5  0.1 442  69

7.9  0.1 123  49

7.7  0.1 95  34

48  13 115  25 39  8

4.7  5.2 12  6 7.4  2.7

2.4  0.8 1.6  1.2 6.5  4.1

7.6  0.1 48  7
partial nitritation reactor included (i) batches of effluent from the struvite precipitation reactor, containing mainly ammonium, and (ii) a continuous recirculation stream from the anammox reactor (Fig. 1). The partial nitritation reactor received a new influent batch every 3 h during a feeding phase of 0.5 h, exchanging around 10% of the reactor volume per cycle (Fig. 3A). Although the partial nitritation influent was fed in batches, the reactor’s effluent was pumped continuously to the following anammox step. During regular factory operation, the nitrogen streams of the partial nitritation reactor were closely monitored for three influent batches. For batches 1 and 2, the reactor was operated at the normal DO setpoint (0.9e1.0 mg O2 L
1), while from the start of batch 3, the DO was set at 0.4 mg O2 L
1, to test the effect of a lower aeration rate on the emission of N2O. The batch feeding process had a clear influence on the reactor pH and DO. The influent was characterized by a relatively high pH (9.0  0.1) and oxygen demand due to the ammonium (223e243 mg N L
1; Table 3) and COD (1154  110 mg COD L
1; Table 2) content, resulting in pH peaks and DO valleys upon the addition of fresh influent (Fig. 3B, C). The partial nitritation reactor was not heated and was at a constant temperature of 36  0  C. On average, the snapshot reactor total nitrogen loading rates were 0.18e0.22 kg N m
3 d
1, also taking into account the organic nitrogen loads of 36, 72 and 25 kg N d
1 for the batches 1, 2 and 3, respectively (Table 3). The incoming nitrogen was mainly oxidized to nitrite (45e47%) and nitrate (13e15%). Effluent nitrite to ammonium ratios were 1.37e1.53, which is in the vicinity of the required ratio of 1.32 for the subsequent anammox step (Strous et al., 1998). In congruence herewith, b-proteobacterial nitritation bacteria represented 30  10% of the biomass, as determined by FISH analyses (Fig. S.4). Concentrations of ammonium, nitrite and nitrate were relatively stable along the batch cycle. After the feeding phase, ammonium decreased slightly whereas nitrite accumulated near the end of the batch (Fig. 3E). However, the free ammonia (NH3) and free nitrous acid (HNO2) changes were more pronounced, because of the pH decrease due to the proton production associated with nitritation. At high pH values, i.e. at the beginning of a new batch, free ammonia reached up to 2.7e3.8 mg N L
1, whereas free nitrous acid obtained a maximum of 5.6e9.3 mg N L
1 at the end of the batch cycle (Fig. 3H).

At the unsteady state conditions of the onset of a twoweekly operation cycle, 9.0  1.0% of the nitrogen load, i.e. the sum of Kjeldahl nitrogen, NO2
and NO3
, was emitted as N2O (Fig. S.2). The gaseous N2O emissions during normal operation were lower, amounting to 5.1e6.6% of the overall nitrogen load (Table 3). In comparison, the dissolved N2O stream in the effluent of the partial nitritation reactor, was 25e40 times lower, representing 0.16e0.21% of the nitrogen load. In batch 3, the DO setpoint was lowered from 0.9 to 0.4 mg O2 L
1 (Fig. 3C). Despite the lower aeration rate of batch 3 (Fig. 3F), the emitted N2O flow was not lower in batch 3, which might have been caused by the higher dissolved N2O concentrations (Fig. 3D). Dissolved N2O concentrations showed increasing trends in batches 1 and 3, and reached the highest levels in batch 3 with the lower DO setpoint and slightly higher nitrite levels (Fig. 3B, D). In agreement with the latter, gaseous N2O concentrations showed an increasing trend in batch 3, but were not higher compared to batches 1 and 2 (Fig. 3D). The latter could be due to the higher dilution of the aeration flow with the wind flow in batch 3, as the gaseous concentrations were determined on this flow mixture. The overnight trends of gaseous N2O levels were slightly decreasing per cycle, similar to the decrease in aeration rate (Fig. S.1). Overall, no clear or uniform N2O formation or emission trend could be derived. It should be noted that it is in practice difficult to obtain fully closed mass balances from snapshot measurements, given the impact of a number of previous batches on the actual reactor concentrations. Nevertheless, besides the significant loss as gaseous N2O, the balances indicate that no considerable quantities of nitrogen gas were removed from the liquid phase (Table 3). Next to N2O, some CH4 was emitted during the partial nitritation step. The average emission from the three batches corresponded to 0.9  0.0 g CH4 m
3 d
1 (2.1  0.0 kg CH4 d
1), and the CH4 peaks at the beginning of a new batch (Fig. 3G and Fig. S.1) indicated that these emissions were mainly due to stripping of residual dissolved CH4 from the anaerobic digestion step.

3.2.

Anammox reactor

The anammox step was fed with partial nitritation effluent and with return sludge (Fig. 1). A hydraulic minor fraction of the

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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 8 1 1 e2 8 2 1

Fig. 3 e Profiles of reactor parameters and concentrations during three monitoring batches on the partial nitritation reactor. Dashed lines indicate beginning or end of a sampling period. An additional liquid grab sample was taken 10 min prior to the start of batch 1 and 3. Panel D: Concentrations of dissolved N2O (circles) and gaseous N2O (solid line); panel E: Concentrations of nitrite (full circles), ammonium (empty circles) and nitrate (triangles); panel H: concentrations of free ammonia (empty circles) and free nitrous acid (full circles).

anammox effluent was recycled to the partial nitritation stage, whereas the majority was delivered to the denitrification stage. Over weeks 10e17 (2010), the combined anammox, denitrification and nitrification stage was operated at an SRT of 46 d and a floccular sludge was obtained with a fair settleability (Table 1). During the snapshot sampling period, the anammox stage was loaded with 0.33 kg N m
3 d
1 of ammonium, nitrite and nitrate and removed 77% of this nitrogen load, at a nitrite to

ammonium nitrogen consumption ratio of 1.45/1 (Table 4). Assuming that only anammox bacteria were responsible for the anoxic ammonium removal (0.090 kg N m
3 d
1), this would result in a concurrent nitrite removal rate of 0.118 kg N m
3 d
1 and a nitrate production rate of 0.023 kg N m
3 d
1, as calculated from the anammox stoichiometry (Strous et al., 1998). Hence, anammox bacteria actually removed 0.19 kg N m
3 d
1. The biomass from the anammox stage exerted a specific ammonium oxidation rate of 25  2 mg NH4þeN g
1

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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 8 1 1 e2 8 2 1

Table 3 e Water and nitrogen flows from the snapshot characterization (week 17) of the partial nitritation reactor (averages ± standard deviations). The number or label of each flow refers to the number in the schematic process overview (Fig. 1). The gaseous and dissolved N2O streams, respectively labeled N2O (g) and N2O (l), were also expressed as percentages of the incoming nitrogen load, which further consisted of 36, 72 and 25 kg organic N dL1 for the batches 1, 2 and 3, respectively. Q: flow rate;
Partial nitritation (1) Batch 1

Flow Q (m3 d
1) NH4þ (kg N d
1) NO2
(kg N d
1) NO3
(kg N d
1) N2O (l) (kg N d
1) N2O (g) (kg N d
1)

0 1740 388 1.5 1.0

þRec1 36 0.3  0.0 0.3  0.0 0.1  0.0
Batch 2 /1 1776 133  7 202  7 65  7 0.7  0.1 (0.17%) 28  1 (6.6  0.2%)

0 1756 420 0.8 0.1

þRec1 36 0.3  0.0 0.3  0.0 0.8  0.0
VSS d
1, as determined in a batch activity test, and consisted for 3.1  2.0% out of the anammox genus Kuenenia (Fig. S.4). The anammox genera Brocadia and Scalindua could not be detected with FISH. Besides the anammox activity, concurrent nitrite and nitrate denitrification was supported by the results. Firstly, expected nitrite removal by the anammox bacteria was 0.012 kg N m
3 d
1 lower than the actual nitrite consumption, indicating nitrite denitrification. Secondly, nitrate was consumed at 0.041 kg N m
3 d
1 (Table 4) and also the expected nitrate production from anammox (0.024 kg N m
3 d
1) could not be detected, indicating in-situ consumption by nitrate denitrification. Overall, the denitrification rate estimate amounts to 0.076 kg (NO2
þ NO3
)eN m
3 d
1, or 29% of the overall nitrogen removal. The anammox reactor pH (8.0), DO level (0.0 mg O2 L
1) and temperature (32  C) were constant and not controlled (Table 1). In comparison with the preceding partial nitritation, the pH was higher, probably due to concurrent denitrification, and the temperature was lower, given the absence of heating and the mixing with the colder recirculation stream. No gaseous N2O emissions could be detected during the 3-h sampling period or the overnight gas measurement period and moreover, the incoming dissolved N2O from the partial nitritation reactor was consumed in the anammox stage (Table 4). However, some CH4 was detected in the off-gas, corresponding to an average emission of 0.8  0.1 g CH4 m
3 d
1 (1.3  0.2 kg

Batch 3 /1 1792 136  7 209  15 65  2 0.8  0.1 (0.16%) 27  1 (5.5  0.2%)

0 2087 507
þRec1 36 0.2  0.0 0.4  0.0 0.1  0.0
/1 2123 174  13 238  6 71  6 1.1  0.2 (0.21%) 27  0 (5.1  0.0%)

CH4 d
1). The emitted CH4 levels were constantly around 11  3 ppmv, and since no concentration trends were observed in 3-h cycles (Fig. S.3), CH4 was likely formed in-situ, in contrast to stripping of dissolved CH4 entering the partial nitritation reactor. Given the presence of low nitrite and nitrate levels (3e8 mg N L
1; Table 4), methanogenesis might have occurred in anaerobic microniches inside the flocs.

3.3.

Denitrification and nitrification reactors

The final denitrification and nitrification steps provided effluent polishing. The denitrification received influent from the anammox reactor and a recirculation stream from the nitrification reactor (Fig. 1). During weeks 10e17 (2010), the nitrification effluent contained on average 9.1  3.8 mg NO3
eN L
1, and no other nitrogen species (Table 2). Hence, over this period the four-stage nitrogen removal plant yielded a dischargeable effluent (<10 mg N L
1), and an overall nitrogen removal efficiency 95  2%. In the denitrification reactor, pH (8.0) and anoxic conditions were identical to the preceding anammox reactor (Table 1). In the following nitrification stage, a DO setpoint of 5.7 mg O2 L
1 was applied, yielding a significantly lower pH (7.6), probably due to proton production associated with nitritation and the stripping of some CO2 formed in the previous stages. During the snapshot sampling, the denitrification stage obtained a combined nitrite and nitrate removal rate of

Table 4 e Water and nitrogen flows from the snapshot characterization (week 17) of the anammox, denitrification and nitrification reactors (averages ± standard deviations). The number or label of each flow refers to the number presented in the schematic overview of the nitrogen removal process (Fig. 1). Gaseous and dissolved N2O streams are labeled N2O (g) and N2O (l), respectively. Q: flow rate;
Anammox (2) 1 2366 203  15 266  9 79  8 0.6  0.1

þRec2 4080
/Rec1 36 0.3  0.0 0.3  0.0 0.1  0.0
Denitrification (3) þ2 6410 55  1 52  3 21  4 0.0  0.1
2 5601
þRec3 4800
/3 10 401
Nitrification (4) 3 10 997 74  3 1.0  0.0 27  1 0.3  0.2

/Rec3 4800
þ4 6197
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0.058 kg N m
3 d
1, i.e. 70% of the loading rate. The specific anammox activity of the denitrification stage biomass was 29  6 mg NH4þeN g
1 VSS d
1, as determined in a batch activity test, and consisted for 2.6  2.0% out of the anammox genus Kuenenia. As the same sludge is cycled over the anammox and denitrification compartments, the detection of anammox activity and bacteria in the denitrification compartment was expected. Following denitrification, the nitrification stage oxidized all residual ammonium and nitrite to nitrate, without removing any nitrogen. Besides the once-only measured presence of some dissolved N2O in the influent of the nitrification stage, no N2O flows were observed during the monitoring periods of both denitrification and nitrification compartments (Table 4). Also, these stages did not emit any CH4.

4.

Discussion

4.1.

NAS
operation and technology

The NAS
process is one of the first examples applying anammox in a separate floccular stage under realistic operational conditions. As such, a dischargeable effluent (<10 mg N L
1) was obtained through a hybrid nitrogen treatment train without external carbon addition, treating an industrial digestate with medium nitrogen concentrations (264 mg N L
1), a relatively low COD/N ratio (4.4) and a relatively high alkalinity/N (0.13 meq mg
1 N). The effluent nitrite to ammonium nitrogen ratios from partial nitritation were above 1.32 and hence suitable for ammonium removal through anammox. For the partial nitritation stage, the inorganic carbon content of the wastewater played an important buffering role. Given the proton production during nitritation, a buffering capacity of 0.13 meq mg
1 N oxidized is required (Barnes and Bliss, 1983). Equilibrating this with nitratation (Barnes and Bliss, 1983), anammox (Strous et al., 1998) and/or denitrification stoichiometries (Mateju et al., 1992) shows that a similar alkalinity is required for partial nitritation/anammox and nitrification/denitrification, i.e. 0.073 and 0.065 meq mg
1 N removed, respectively. The industrial wastewater in this study contained around 0.13 meq mg
1 N (Table 2), which is hence more than sufficient for any biological removal process. Besides nitrite, some nitrate was produced in the first reactor, i.e. 13e15% of the oxidized ammonium nitrogen. The minimal nitrate production obtained in the partial nitritation reactor were likely the combined result of the chosen DO setpoint (0.9e1.0 mg O2 L
1) and SRT (1.7 d), as well as the prevailing free ammonia concentrations (2.7e3.8 mg N L
1). Firstly, although the applied DO levels were relatively low, often levels of around 0.3 mg O2 L
1 are, in practice, required for longer term suppression of nitratation bacteria (Joss et al., 2009; Vlaeminck et al., 2009; Wett, 2006). Secondly, at higher temperatures the lower doubling time of nitratation bacteria compared to nitritation bacteria can be exploited by choosing an intermediate SRT which does not largely exceed 1 d at 35  C (Hellinga et al., 1998). Finally, in addition to the growth rate differences, also the pH strongly influences the required SRT, through its effect on the NH4þ/NH3 and NO2
/HNO2 equilibria.

Anthonisen et al. (1976) reported inhibition of nitratation bacteria by 0.08e0.82 mg NH3eN L
1 and 0.06e0.83 mg HNO2eN L
1, although some studies reported only inhibition of nitratation bacteria at higher ammonia levels (6.0 mg N L
1) and lower free nitrous acid levels (0.02 mg N L
1) (Vadivelu et al., 2007). It should be noted that nitratation suppression through high free ammonia levels and a controlled SRT is an advantage of a separated partial nitritation reactor, although the concomitant high nitrite levels likely play a role in N2O formation (Kampschreur et al., 2009b). In comparison with highly loaded anammox systems (10 kg N m
3 d
1; van der Star et al., 2007), the anammox step in the current study had an intermediate loading rate (0.33 kg N m
3 d
1), requiring a larger reactor volume. Since retrofitting of the studied plant used the existing reactor compartments and external settler, space requirement or construction costs were no issue. In case of a limited area availability to construct a new plant, the NAS
technology can be executed in a more compact way by applying higher nitrogen loading rates and by operating the final process stage as a membrane bioreactor. In the latter configuration, a 2200 m3 NAS
plant was built in Bergen (the Netherlands), which treated in 2009 a high-strength digestate (3350 mg N L
1) at an overall nitrogen loading rate of 0.5 kg N m
3 d
1 and a nitrogen removal efficiency of 99.5%. The anammox stage removed 77% of its loading rate, with an estimated contribution of 71% by anammox and 29% by denitrification. As a major part of the influent COD was removed in the preceding nitritation step, the wastewater entered the anammox reactor at a COD/N ratio of 2.2 (Table 2), which was sufficiently low to prevent heterotrophic denitrifiers from overgrowing anammox bacteria at an SRT of 46 d. The cooccurrence of anammox and denitrification was even advantageous for the nitrogen removal efficiency, allowing for net nitrate consumption, whereas another full-scale anammox reactor produced on average 0.25 kg NO3
eN kg
1 NH4þeN oxidized (van der Star et al., 2007). Although anammox was the dominant nitrogen removing process (71%) in the anammox reactor, anammox bacteria only represented 3.1% of the bacterial community. Furthermore, the enduring anammox activity was remarkable taking into account the periodical exposure of the biomass to oxygen. Indeed, the settler hydraulic retention time (HRT) was 2.3 h, and the HRT over the anammox, denitrification and nitrification stages were 6.7, 3.6 and 5.1 h, respectively (Table 1), exposing the sludge at least 29% of its cycle time to high DO levels (5.7 mg O2 L
1; Table 1) in the nitrification compartment, which apparently caused no irreversible inhibition in contrast to previous observations (Egli et al., 2001; Strous et al., 1998). In line with the results of Jeanningros et al. (2010), no inoculum enriched in anammox bacteria was required for the plant retrofitting, in contrast to the start-up of most other plants comprising anammox (e.g. Joss et al., 2009; van der Star et al., 2007; Wett, 2006). On the long term, retrofitting the plant from nitrification/ denitrification with carbon addition to NAS
had economical advantages. Since no more carbon was deviated from the anaerobic digestor, biogas production increased with 10% (450 m3 d
1), recovering an additional 302 m3 CH4 d
1 or 52 EUR d
1. Furthermore, electricity consumption for aeration decreased with 33% (859 kWhel d
1), saving 69 EUR d
1, sludge production decreased with 50% (2.7 ton dewatered sludge d
1),

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 8 1 1 e2 8 2 1

saving 110 EUR d
1. Overall, these savings amount to 230 EUR d
1 or 40% of the operational costs for the wastewater treatment plant, not taking into account the value of the sold struvite.

4.2.

Greenhouse gas emissions

During partial nitritation, 5.1e6.6% of the total nitrogen load was emitted as N2O during normal reactor operation, and even 9.0% after the two-weekly non-feeding period. An average N2O emission load of 27 kg N d
1 (Table 3), corresponds to an equivalent of 12 644 kg CO2 d
1. In contrast, no N2O emissions were detected from the anammox, denitrification and nitrification stages. In comparison, reported N2O emissions from full-scale WWTPs range from 0.01 to 3.3% of the nitrogen load, as determined with an intensive gas sampling methodology (Ahn et al., 2010; Joss et al., 2009; Kampschreur et al., 2009a, 2009b; Weissenbacher et al., 2010). The critical parameters for the production of N2O during nitritation include high nitrite and ammonium values, DO setpoint around 1.0 mg O2 L
1, a DO increase after anoxia, as well as rapidly changing operational conditions (Ahn et al., 2010; Kampschreur et al., 2009b; Yu et al., 2010). Moreover, Yoshinari (1990) reported that chemical production of N2O can take place if nitrite concentrations exceed 14 mg N L
1, and known mechanisms for chemical N2O formation require the presence of the nitritation intermediate hydroxylamine (NH2OH) (Kampschreur et al., 2009b). For the final nitrification stage, none of the aforementioned triggers for N2O emission were fulfilled, in agreement with the absence of N2O emissions from this stage. In contrast, the conditions in a partial nitritation reactor are inherently more likely to induce some N2O production, though the measured emissions in the current study were substantially higher than the 1.7% from another full-scale separated partial nitritation step (Kampschreur et al., 2008). Firstly, nitrite and ammonium in the current study were simultaneously high (resp. 114e117 and 75e82 mg N L
1; Fig. 3E), although still considerably lower than the 600e700 mg N L
1 levels reported in the study of Kampschreur et al. (2008). Secondly, DO levels in our study (1 mg O2 L
1) were below the setpoint of 2.5 mg O2 L
1 in the reactor of Kampschreur et al. (2008), and might have been closer to the ‘optimum’ for high N2O emissions (Tallec et al., 2006). During batch 3, the significantly lower DO level of 0.4  0.1 mg O2 L
1 resulted in a build-up of dissolved N2O towards the end of the batch, which can have been caused by an increased N2O production, but also by a decreased N2O stripping, given the lower aeration rate at low DO levels. Finally, the reactor studied by Kampschreur et al. (2008) was fed continuously, in contrast to the relatively more variable process conditions of our study, inherent to a batch feeding operation mode, which might also have contributed to the high nitritation emissions in the current study. The effect of variable process conditions was most apparent from the higher emissions from the introduction of fresh influent after two days of non-feeding. Although it awaits quantification of the N2O emissions from more partial nitritation reactors, it may be that higher N2O emissions are partly inherent to configurations with a separate nitritation step, mainly due to the higher nitrite concentrations. The volumetric mass transfer coefficient (kLa) for N2O in the partial nitritation reactor could be estimated from two different approaches as described by Foley et al. (2010). Using

2819

the superficial gas flow velocities and the reactor depth in Equation S.1 yielded a kLa of 21e29 d
1. A similar kLa of 28 d
1 was obtained from Equation S.2 using the emitted N2O load and the measured dissolved N2O concentrations. These kLa values are consistent with values obtained from full-scale plants (Foley et al., 2010). The production of N2O during denitrification is generally enhanced by rapidly changing process conditions, high DO and nitrite levels and low COD/N ratios (Ahn et al., 2010; Kampschreur et al., 2009b). The absence of residual DO in the anammox and denitrification stages (Table 1) and the relatively low nitrite levels in the anammox and denitrification compartments (8 and 3 mg N L
1, respectively; Table 4) as well as the high COD/N ratio of 5.1 in the denitrification stage (Table 2) were in agreement with the absence of N2O emissions. Apparently, the low COD/N ratio of 2.2 in the anammox compartment (Table 2) was not a trigger for anoxic N2O emissions. The only other study on a separate full-scale anammox step reported N2O emissions of 0.6% of the nitrogen load, attributed to the activity of washed-out nitritation bacteria (Kampschreur et al., 2008). Interestingly, this was not the case in our study. Possibly the intermediate anammox loading rate, as well the absence of a stripping gas led to the insitu consumption of any dissolved N2O, either derived from the influent (0.6e1.1 kg N d
1; Table 3) or locally formed by nitritation bacteria or denitrifiers. Methane emissions were observed from the partial nitritation and anammox stages. Emissions from the first stage (2.1 kg CH4 d
1) likely derived from the stripping of residual dissolved CH4 from the anaerobic digester. At 67% CH4 atmosphere and 35  C, the CH4 solubility is around 12 mg CH4 L
1 (Perry et al., 1997), so 22.4 kg CH4 d
1 was expected to leave the digester dissolved in the effluent, of which around 10% was stripped in the partial nitritation step, and presumably 90% in the preceding struvite precipitation step. Giving the stripping of residual dissolved CH4 in the partial nitritation reactor and the relatively constant CH4 concentrations in the off-gas of the anammox reactor, emissions from the latter reactor (1.3 kg CH4 d
1) likely derived from in-situ production through methanogenesis. Hence, the overall WWTP CH4 emissions were estimated at 23.8 kg CH4 d
1, equivalent to 594 kg CO2 d
1. The importance of the N2O emissions follows from the calculation of the simplified operational CO2 footprint of the WWTP, comprising energy consumption and recovery, and the emission of CH4 and N2O. It should be noted that CO2 emissions from the degradation of organic carbon present in the wastewater are not taken into account, since this carbon is of biogenic origin (Metz et al., 2007). Over weeks 10e17, the anaerobic digester produced 4163 m3 biogas d
1, on average. With a CH4 content of 67% and an energy content of 10 kWh m
3 CH4, this is equivalent to 11 160 kWhel d
1 and 12 550 kWhth d
1, given electrical and thermal conversion efficiencies of 40 and 45%, respectively, in the combined heat and power generation unit. Note that the factory effluent is already at 40  C and that the recovered heat from biogas is fully used to produce steam. Given the average omitted fossilfuel sources of 0.45 kg CO2 kWhel
1 and 0.28 kg CO2 kWhth
1 in the EU15 (Fruergaard et al., 2009), this sums up to a sequestration of 8535 kg CO2 d
1. On the other side, WWTP energy consumption (4475 kWhel d
1, equivalent to 2014 kg CO2 d
1),

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estimated CH4 emissions (594 kg CO2 d
1) and measured N2O emissions (12 644 kg CO2 d
1) give rise to an emitted equivalent of 15 251 kg CO2 d
1. From the latter it is clear that a decrease of about 50% of the N2O emissions from partial nitritation would render the carbon footprint neutral, whereas the impact of direct CH4 emissions was a factor 20 lower. However, reducing the N2O emissions in the given process configuration is not straightforward, giving the difficulty to elucidate the most critical parameter influencing N2O production. However, the results indicated the production cycle variability as an important trigger for N2O emission. Inserting a volumetric buffer tank prior to the partial nitritation reactor would make it possible to operate the partial nitritation step with a lower variability by a continuous influent flow. This would eliminate the possible effect of idle days without fresh influent provision with continued recirculation, and the batch feeding operation mode.

5.

Conclusion

The main findings of the NAS
plant characterization can be listed as follows: Advantages:  Industrial wastewater with a relatively low COD/N ratio (4.5) was treated without carbon addition at high removal percentages (95%) yielding dischargeable effluent qualities (<10 mg N L
1).  A floccular anammox stage was achieved at a COD/N ratio of 2.2, which was sufficiently low to prevent heterotrophic denitrifiers from overgrowing anammox bacteria at an SRT of 46 d, and which allowed for concurrent denitrification and hence higher nitrogen removal efficiencies.  Retrofitting from nitrification/denitrification to NAS
operation allowed to save 40% of the operational costs, due to 10% higher biogas production, 33% lower aeration and 50% lower sludge production. Inoculation with anammox bacteria was not required. Challenges:  Nitrous oxide emissions from partial nitritation constituted 5.1e6.6% of the nitrogen load. These emissions should be decreased by about 50% to render the operational CO2 footprint of the industrial wastewater treatment plant neutral. High N2O emissions may be partly inherent to a separate nitritation step.  Intermediate nitrogen loading rates and an external settler represented a considerable areal footprint for the nitrogen removal plant. New plants with higher loading rates and a membrane bioreactor can be made more compact.

Acknowledgments J.D. and H.D.C. are recipients of a PhD grant from the Institute for the Promotion of Innovation by Science and Technology in Flanders (IWT-Vlaanderen, numbers SB-091144

and SB-81068) and S.E.V. was supported as a postdoctoral fellow from the Research Foundation Flanders (FWOVlaanderen). The NAS
technology was designed and built by Colsen b.v. (www.colsen.nl). The authors gratefully thank Senternovem for the financial support through the ‘Innowator’ programme (DWZ0644224, project number IWA06012); Davey Smet and Marc van Waes for the technical insights from Colsen b.v.; Huib Nagelkerke and Leon Nelen for the assistance at the LambWeston Meijer plant in Bergen op Zoom, the Netherlands; Tim Lacoere and Katja Van Nieuland for the technical expertise; and Nico Boon, Suzanne Read and Anthony G. Hay for the inspiring scientific discussions.

Appendix. Supplementary data Supplementary data related to this article can be found online at doi:10.1016/j.watres.2011.02.028.

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Greenberg, A.E., Clesceri, L.S., Eaton, A.D., 1992. Standard Methods for the Examination of Water and Wastewater. American Public Health Association, Washington DC. Hellinga, C., Schellen, A., Mulder, J.W., van Loosdrecht, M.C.M., Heijnen, J.J., 1998. The SHARON process: an innovative method for nitrogen removal from ammonium-rich waste water. Water Science and Technology 37 (9), 135e142. Jeanningros, Y., Vlaeminck, S.E., Kaldate, A., Verstraete, W., Graveleau, L., 2010. Fast start-up of a pilot-scale deammonification sequencing batch reactor from an activated sludge inoculum. Water Science and Technology 61 (6), 1393e1400. Joss, A., Salzgeber, D., Eugster, J., Konig, R., Rottermann, K., Burger, S., Fabijan, P., Leumann, S., Mohn, J., Siegrist, H., 2009. Full-scale nitrogen removal from digester liquid with partial nitritation and anammox in one SBR. Environmental Science & Technology 43 (14), 5301e5306. Kampschreur, M.J., Poldermans, R., Kleerebezem, R., van der Star, W.R.L., Haarhuis, R., Abma, W.R., Jetten, M.S.M., van Loosdrecht, M.C.M., 2009a. Emission of nitrous oxide and nitric oxide from a full-scale single-stage nitritation-anammox reactor. Water Science and Technology 60 (12), 3211e3217. Kampschreur, M.J., Temmink, H., Kleerebezem, R., Jetten, M.S.M., van Loosdrecht, M.C.M., 2009b. Nitrous oxide emission during wastewater treatment. Water Research 43 (17), 4093e4103. Kampschreur, M.J., van der Star, W.R.L., Wielders, H.A., Mulder, J.W., Jetten, M.S.M., van Loosdrecht, M.C.M., 2008. Dynamics of nitric oxide and nitrous oxide emission during fullscale reject water treatment. Water Research 42 (3), 812e826. Keller, J., Hartley, K., 2003. Greenhouse gas production in wastewater treatment: process selection is the major factor. Water Science and Technology 47 (12), 43e48. Lackner, S., Terada, A., Smets, B.F., 2008. Heterotrophic activity compromises autotrophic nitrogen removal in membraneaerated biofilms: results of a modeling study. Water Research 42 (4e5), 1102e1112. Lindvall, T., Noren, O., Thyseliu, L., 1974. Odor reduction for liquid manure systems. Transactions of the Asae 17 (3), 508e512. Lo´pez, H., Puig, S., Ganigue´, R., Ruscalleda, M., Balaguer, M.D., Colprim, J., 2008. Start-up and enrichment of a granular anammox SBR to treat high nitrogen load wastewaters. Journal of Chemical Technology and Biotechnology 83 (3), 233e241. Loy, A., Horn, M., Wagner, M., 2003. probeBase: an online resource for rRNA-targeted oligonucleotide probes. Nucleic Acids Research 31 (1), 514e516. Mateju, V., Cizinska, S., Krejci, J., Janoch, T., 1992. Biological water denitrification e a review. Enzyme and Microbial Technology 14 (3), 170e183. Metz, B., Davidson, O.R., Bosch, P.R., Dave, R., Meyer, L.A., 2007. Climate Change 2007: Mitigation of Climate Change. Cambridge University Press, Cambridge. Mulder, A., 2003. The quest for sustainable nitrogen removal technologies. Water Science and Technology 48 (1), 67e75. Perry, R.H., Green, D.W., Maloney, J.O., 1997. Perry’s Chemical Engineers’ Handbook. McGraw-Hill, New York. Pynaert, K., Smets, B.F., Wyffels, S., Beheydt, D., Siciliano, S.D., Verstraete, W., 2003. Characterization of an autotrophic nitrogen-removing biofilm from a highly loaded lab-scale

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rotating biological contactor. Applied and Environmental Microbiology 69 (6), 3626e3635. Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K.B., Tignor, M., Miller, H.L., 2007. Climate Change 2007: The Physical Science Basis. Cambridge University Press, Cambridge. Strous, M., Heijnen, J.J., Kuenen, J.G., Jetten, M.S.M., 1998. The sequencing batch reactor as a powerful tool for the study of slowly growing anaerobic ammonium-oxidizing microorganisms. Applied Microbiology and Biotechnology 50 (5), 589e596. Tallec, G., Garnier, J., Billen, G., Gousailles, M., 2006. Nitrous oxide emissions from secondary activated sludge in nitrifying conditions of urban wastewater treatment plants: effect of oxygenation level. Water Research 40 (15), 2972e2980. Vadivelu, V.M., Keller, J., Yuan, Z., 2007. Free ammonia and free nitrous acid inhibition on the anabolic and catabolic processes of Nitrosomonas and Nitrobacter. Water Science and Technology 56 (7), 89e97. van der Star, W.R.L., Abma, W.R., Blommers, D., Mulder, J.W., Tokutomi, T., Strous, M., Picioreanu, C., Van Loosdrecht, M.C. M., 2007. Startup of reactors for anoxic ammonium oxidation: experiences from the first full-scale anammox reactor in Rotterdam. Water Research 41 (18), 4149e4163. van Dongen, U., Jetten, M.S.M., van Loosdrecht, M.C.M., 2001. The SHARON
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Evaluation of continuous mesophilic, thermophilic and temperature phased anaerobic digestion of microwaved activated sludge Nuno Miguel Gabriel Coelho*, Ronald L. Droste, Kevin J. Kennedy University of Ottawa, Department of Civil Engineering, Ottawa, Ontario K1N 6N5, Canada

article info

abstract

Article history:

The effects of microwave (MW) pretreatment, staging and digestion temperature on

Received 21 September 2010

anaerobic digestion were investigated in a setup of ten reactors. A mesophilic reactor was

Received in revised form

used as a control. Its performance was compared to single-stage mesophilic and thermo-

5 December 2010

philic reactors treating pretreated and non-pretreated sludge, temperature-phased (TPAD)

Accepted 25 February 2011

thermophilic-mesophilic reactors treating pretreated and non-pretreated sludge and

Available online 12 March 2011

thermophilicethermophilic reactors also treating pretreated and non-pretreated sludge. Four different sludge retention times (SRTs) (20, 15, 10 and 5 d) were tested for all reactors.

Keywords:

Two-stage thermoethermo reactors treating pretreated sludge produced more biogas than

Sludge pretreatment

all other reactors and removed more volatile solids. Maximum volatile solids (VS) removal

Microwaves

was 53.1% at an SRT of 15 d and maximum biogas increase relative to control was 106% at

Anaerobic digestion

the shortest SRT tested. Both the maximum VS removal and biogas relative increase were

Thermophilic

measured for a system with thermophilic acidogenic reactor and thermophilic methano-

Mesophilic

genic reactor. All the two-stage systems treating microwaved sludge produced sludge free

Two-stage digestion

of pathogen indicator bacteria, at all tested conditions even at a total system SRT of only 5 d. MW pretreatment and staging reactors allowed the application of very short SRT (5 d) with no significant decrease in performance in terms of VS removal in comparison with the control reactor. MW pretreatment caused the solubilization of organic material in sludge but also allowed more extensive hydrolysis of organic material in downstream reactors. The association of MW pretreatment and thermophilic operation improves dewaterability of digested sludge. ª 2011 Elsevier Ltd. All rights reserved.

1.

Introduction

Anaerobic digestion is commonly used in wastewater sludge treatment. However, low biodegradability of sludges, particularly waste activated sludge (WAS) is an issue. Hydrolysis is a rate limiting step when degrading this type of complex organic material, and most of the biodegradable material is

either enclosed inside the microbial cell wall (Park et al., 2004) or enmeshed in a extracellular polymeric matrix (Neyens and Baeyens, 2003), which further contributes to limit the biodegradability of these sludges to 35e45% reduction in volatile solids (VS) (Bolzonella et al., 2005; Bhattacharya et al., 1996). Microwaving is a novel method to thermally pretreat sludges that increases digestion efficiency and decreases

* Corresponding author. University of Ottawa, Department of Civil Engineering, 161 Louis Pasteur, Room A106, Ottawa, Ontario K1N 6N5, Canada. Tel.: þ1 613 562 5766. E-mail address: [email protected] (N.M.G. Coelho). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.02.032

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 8 2 2 e2 8 3 4

pathogen content. It is an energy efficient method, since it eliminates heat losses that occur in energy transmission in conventional heating. MWs can also provide rapid increases in the inner temperature of bulk liquids, decreasing pretreatment time (Metaxas and Meredith, 1983). Hong (2002) applied MW radiation to different types of sludge in order to check the effect on biodegradability. The effect in solubilizing chemical oxygen demand (COD) was effective in activated sludge since the fraction of soluble COD (sCOD) to total COD (tCOD) increased from 8.5 to 18%. The pretreatment consisted of heating the sludge to a temperature of 70  C. The increase in this ratio for primary sludge was only 1%. For higher pretreatment temperature (100  C) the digestion of sludge showed an increase in the amount of methane produced of 23% for primary sludge (PS) and 15% for activated sludge (Hong, 2002). Eskicioglu et al. (2007a,b) investigated the effects of MW intensity, temperature and sludge concentration on the solubilization of WAS (taken from an activated sludge unit operating at 5 d SRT). It was reported that the MW intensity was not a significant factor influencing digestion but temperature of pretreatment and sludge concentration did show an influence on both WAS solubilization and biogas production. Sludge irradiated to 96  C had a greater production of biogas than sludge irradiated to 75  C and this sludge in turn produced more biogas than sludge irradiated to 50  C. Sludge pretreated to 96  C showed an increase of 20% in biogas production compared to the control in the assays at 3% total solids (TS). For the assays at 1.4% TS the increase in biogas production was 15%. The authors also performed a study based on the ultrafiltration membrane fractionation of the soluble fraction of the pretreated sludge that confirmed that digesters treating high molecular weight materials resulted in smaller biodegradation rate constants. Toreci et al. (2009) tested MW pretreatment at temperatures above the boiling point (175  C) and reported increase of 31% in biogas production in mesophilic anaerobic digestion compared to controls without pretreatment. The authors noted also the occurrence of inhibition in the early stages of digestion. In previous experiments the same authors reported higher percentages of solubilization of tCOD at MW pretreatment temperatures of 175  C than those obtained at pretreatment below boiling point (Toreci et al., 2008). The dual-stage thermophilic/mesophilic process or temperature-phased anaerobic digestion (TPAD) has gained some interest due to the fact that it tries to combine the advantages of thermophilic systems in terms of pathogen control and VS reduction, makes use of process optimization due to staging, and it is still economical to operate because the bulk of the digestion takes place in the mesophilic stage (Han et al., 1997; Sung and Santha, 2003). Some of the reasons proposed to explain better performance of dual-stage TPAD include the setting of optimal conditions for two different bacterial populations (mesophilic methanogens and thermophilic hydrolytic/acidogenic) in terms of pH, temperature and residence time. It is known that the methanogens and hydrolytic/acidogenic bacteria have different optimal pH, and the thermophilic acidogens growth rate is higher than mesophilic methanogens (Kiyohara et al., 2000). Also, some compounds that are inhibitory to methanogenesis such as phenol or unsaturated fatty acids, are less inhibitory after

2823

being acidified (Kobayashi et al, 1989). Finally, a lower pH in the first reactor may cause a different distribution of the VFA produced by the acidogenic bacteria, one that includes a smaller proportion of more difficult to degrade VFAs, such as propionate (Breure and van Andel, 1984; Azbar and Speece, 2001). Very few studies have been published that report the use of pretreatment methods prior to TPAD or two-stage digestion. Toreci et al. (2009) tested high temperature MW pretreatment (175  C) combined with two-stage mesophilic digestion for three different SRTs (20, 10 and 5 d) with somewhat inconclusive results. Although MW pretreatment alone improved biogas production and VS removal for all SRT in comparison with non-pretreated sludge, and dual-stage digestion alone showed greater biogas production and higher VS removal, MW pretreatment associated with dual-stage digestion did not show any advantages regarding VS removal and biogas production. Variations on the composition of sludge, the type of sludge being tested, viz., the SRT and MW pretreatment process, particularly MW intensity and pretreatment duration, may have interacted and caused the observed results. The combination of two different techniques or two different pretreatment methods is not original. However, microwaving has not yet been used in combination with other methanization enhancement techniques (either other pretreatment options, or digestor set-ups other than mesophilic single or two-stage). So, there is an interest to evaluate what a novel pretreatment technology that is energy efficient and has proved to increase digestion efficiency can provide in terms of methane production or solids reduction when combined with another pretreatment technique or variations in digestion setup from the conventional mesophilic digestor. Given the aforementioned results by previous authors and in order to tackle insufficient or nonexistent experience and results with MW pretreatment and TPAD, a set of tests was devised to evaluate the influence of these parameters in global digestion performance.

2.

Materials and methods

A total of 10 semi-continuous reactors were setup to study the effect of MW pretreatment, staging, digestion temperature and SRT in process performance. The experimental setup is depicted in Fig. 1. The reactors used were 1000 mL Schott borosilicate glass bottles, with a useful volume of 800 mL. The reactors were sealed with black butyl rubber stoppers (VWR, Montreal, QC) with two holes: one to sample, waste and feed the reactors and the other to collect and measure the biogas. Biogas was collected in 2 L Tedlar bags. The tedlar bags (Chromatographic Specialties Inc., ON) were equipped with on/off valves and a septum fitting that was used for gas composition sampling. The volume of biogas produced daily was measured using a manometer. The mesophilic reactors receiving pretreated sludge were inoculated with acclimatized sludge. This sludge was taken from the anaerobic reactors of the Ottawa, ON municipal wastewater treatment plant [Robert O. Pickard Environmental Center (ROPEC)] that digest primary and secondary sludges at

2824

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 8 2 2 e2 8 3 4

Fig. 1 e Experimental setup of reactors.

a feed ratio of 48:52. This seed sludge was acclimatized using a completely mixed reactor operating at 20 d SRT fed with microwaved sludge for more than a year. The remaining mesophilic reactors were inoculated directly using sludge from ROPEC mesophilic digesters. Thermophilic reactors testing pretreated sludge were inoculated using thermophilic sludge collected in Annacis Island Wastewater Treatment Plant (Vancouver, BC) that was acclimatized for more than one year using a 20 d SRT mixed reactor heated at 55  C fed everyday with microwaved sludge. The remaining thermophilic reactors were directly inoculated with non-acclimatized Annacis Island WTP thermophilic sludge. The use of thermophilic sludge to inoculate thermophilic reactors is based on the fact that thermophilic sludge provides a faster start-up to thermophilic reactors, along with a more stable operation since it avoids a rapid temperature change from mesophilic to thermophilic that may bring about a population shift if the groups are not compatible, specially a decrease in thermophilic methanogens, crucial to digestion stability (MataAlvarez, 2002; Nozhevnikova et al., 1999). Feed sludge was comprised of thickened WAS collected at ROPEC. ROPEC has a conventional aerobic activated sludge process with an SRT of 5 days and a primary settling step prior to the activated sludge aerobic tank. Ferric chloride is added for phosphorous removal and biosolids are stabilized by anaerobic digestion. Feed sludge was divided into two types, a non-pretreated sludge (NPT), and a microwave pretreated sludge (PT). MW pretreatment was performed by heating 500 mL sludge samples in a closed plastic container in a conventional domestic MW oven (Panasonic NNS53W þ inverter, 0.045 m3 capacity, 1250 W, 2450 MHz frequency and 12.24 cm wavelength), working at 100% MW intensity up to the boiling point (around 96  C). The closed container was used to minimize evaporation of water and volatile compounds. Sludge and container were weighed before and after pretreatment and distilled water was added in case weight was lost during MW pretreatment. A thermal

profile of sludge samples was determined and a temperature ramp of 14.4  C/min was calculated when heating sludge samples of approximately 500 g at full power. The heating time required to reach boiling point from a room temperature of approximately 20  C was 6 min. This pretreatment time was used for all microwaved samples. Four different SRTs were tested in all the setups 20, 15, 10 and 5 d, with the total SRT of two-stage systems being equal to the single-stage SRT. For two-stage systems, the SRT applied in the acidogenic thermophilic stage (reactors A1 and A2) was 2 d, to avoid excessive methanization in that stage. The subsequent SRT used in the methanogenic stages were 18 d (for a total SRT of 20 d), 13 d (total SRT 15 d), 8 d (total SRT 10 d), and 3 d (total SRT 5 d). The mesophilic reactors were kept in a constant temperature controlled shaker at 35  1  C and 90 rpm (PhycroTherm, New Brunswick Scientific Co. Inc., NB), while the thermophilic ones were kept at 55  1  C and 90 rpm in a similar controlled temperature shaker. Reactors were fed semi-continuously once a day, with non-pretreated sludge and microwaved sludge being fed to the mesophilic the single-stage, thermophilic single-stage and acidogenic reactors and with the effluent of the acidogenic thermophilic reactors being fed to the second reactor of the two-stage systems. All reactors were started with an SRT of 20 d, (18 d for the two-stage ones plus 2 d for the acidogenic reactors), and were operated with the same SRT until two conditions were met: a) fluctuation of less than 10% in the biogas production was observed and b) the fluctuation of less than 10% over an average value was observed over a period of at least three SRTs. To characterize reactors performance, several parameters were measured during operation. tCOD, sCOD, TS, VS and pH were measured twice a week; ammonia, VFA, alkalinity, dewaterability and biogas composition were measured once a week. Bacterial count tests were also performed every two weeks. TS and VS were determined based on Standard Methods procedure 2540G (APHA, 1995). Ammonia measurements were carried out using an ORION Model 95-12 ammonia gas sensing electrode connected to a Fisher Accumet pH meter model 750. The analysis was conducted according to Standard Methods 4500D procedure (APHA, 1995) and reported as ammonia-N. Colorimetric COD measurements were done based on Standard Methods with a Coleman PerkineElmer spectrophotometer Model 295 at 600 nm light absorbance. Before sCOD determination, sludge samples were centrifuged and filtered through membrane disc filters with 0.45 mm pore size. Total VFA were measured by injecting supernatants to an HP 5840A GC with glass packed column and a flame ionization detector. Biogas composition was determined using an HP GC model 5710 equipped with a thermal conductivity detector. Dewaterability was determined using a capillary suction timer (Fann Instrument Company, Model 440, TX) without polymer addition, according to procedure 2710G (APHA, 1995). For bacterial enumeration, namely Escherichia coli and total coliforms, a semi-automated test for presence and quantification based on MPN after incubation for 24 h was used (IDEXX Colilert Quanti-tray 2000). The method provides 95% confidence limits comparable to the membrane filtration method and can count up to 2000 CFU/mL without dilution.

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3.

Results and discussion

Previous studies have shown that MW pretreatment is more effective for sludge with high solids concentration (Eskicioglu et al. (2007a,b)), so no dilution was made to sludge collected at ROPEC before feeding the reactors. The operation of all the reactors started with the highest SRT (20 d), in order to minimize instability due to high organic load. The time required to obtain a stable 3 hydraulic retention time period (meaning a period where no more than 10% variation was observed in biogas production), was not longer than two to three weeks, except when a 5 d SRT was applied which resulted in some reactors (M1, M2 and M4) not reaching stable operating conditions. The average properties of feed sludge used during the different periods are shown in Table 1. The values are averages calculated in each of the periods, along with the confidence interval assuming a normal distribution of the values around the mean and a confidence level of 95%. The use of pretreatment markedly increased the amount of soluble organic matter, as measured by soluble COD, with solubilization as a fraction of tCOD that is in soluble form increasing from around 0.06 to 0.2, indicating a potentially easier or faster digestion of organic matter present in the sludge. There is also a noticeable increase in ammonia on microwaved sludge, most likely due to release and breakdown of proteinaceous material due to MW and temperature effects during pretreatment. Sludge fed to the reactors during the test periods had slightly different characteristics depending on the period and pretreatment applied. Solids concentration is higher for the SRT 20 and 15 d periods, due most likely to seasonal variations in sludge properties in ROPEC. Solids concentration is also generally a bit higher after MW pretreatment even though deionized water was added to compensate for evaporation. The same trend is visible for COD in (Fig. 2), with tCOD for microwaved samples generally being higher than in nonmicrowaved samples. sCOD concentration is significantly higher in all microwaved sludges. The decrease in tCOD during period of SRT 10 d is a reflection of the seasonal variations of ROPEC waste sludge characteristics. Even though the average values for VS and COD are different for MW and non MW sludge for each period, statistically, the difference is not significant, as can be seen by the error bars in the total COD values in Fig. 2.

Fig. 2 e COD distribution in feed sludge during the tested periods.

Hydrolysis of substrates that contain large percentages of particulate matter, such as wastewater sludge, was identified as a limiting step in anaerobic digestion of these types of substrates (Eastman and Ferguson, 1981; Miron et al., 2000). All the subsequent processes in anaerobic digestion occur at faster rates; thus an increase in hydrolysis results in more solubilized substrate ready to be acidified and transformed into methane and a more efficient and fast digestion. Particulate COD hydrolysis is generally considered a first-order process and can be calculated using a COD mass balance (Puchajda and Oleszkiewicz, 2006; Schmit and Ellis, 2001) according to the following equations:

Specific hydrolysis rate ¼

    mass pCOD mass pCOD  d d influent effluent mass of volatile solids within reactor (1)

pCOD ¼ particulateCOD ¼ totalCOD  solubleCOD

(2)

MW pretreatment increased sCOD but also caused the particulate COD that did not solubilize to be more easily hydrolyzed in the following stage. As observed from the hydrolysis rates in Table 2, rates were higher in reactors fed

Table 1 e Properties of sludge fed at the different SRTs tested. Properties

TS (%) VS (%) VS/TS tCOD (g/L) sCOD (g/L) sCOD/tCOD Alkalinity NH3-N TVFA

SRT 20

SRT 15

SRT 10

SRT 5

NMW

MW

NMW

MW

NMW

MW

NMW

MW

5.14  0.09 3.61  0.08 0.70  0.02 60.26  0.36 3.82  0.32 0.06  0.01 1578  123 751  185 228  128

5.80  0.06 3.94  0.09 0.68  0.01 69.55  0.45 13.94  0.44 0.20  0.01 1755  201 853  102 560  132

5.41  0.03 3.96  0.04 0.73  0.01 63.39  0.41 3.91  0.34 0.06  0.01 2135  114 887  114 441  130

5.70  0.04 3.91  0.04 0.69  0.01 69.54  0.40 13.82  0.40 0.20  0.01 1699  99 1023  195 712  211

4.32  0.04 3.23  0.02 0.76  0.06 52.90  2.90 3.94  0.40 0.07  0.01 1651  102 702  102 197  172

5.73  0.06 3.94  0.04 0.69  0.01 68.24  0.32 13.00  0.65 0.19  0.01 1418  112 1203  112 702  155

4.81  0.06 3.14  0.03 0.68  0.12 61.17  3.35 3.67  0.47 0.06  0.01 2004  201 874  124 225  99

5.89  0.06 3.92  0.04 0.67  0.01 68.91  0.73 13.80  0.81 0.20  0.01 1989  135 1320  119 802  131

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Table 2 e Rates of hydrolysis for all reactors in the SRTs tested. A1

A2

M1

Specific Hydrolysis rate mgCOD/mgVS.d 20 0.595 0.405 0.049 15 0.540 0.497 0.054 10 0.403 0.301 0.063 5 0.546 0.460 0.087 Hydrolysis rate mgCOD/L.d 20 18685.6 12161.9 1082.9 15 15945.1 13866.6 1242.5 10 10969.4 7496.7 1473.4 5 14598.5 11446.3 2244.4

T1

M2

T2

M3

T3

M4

T4

0.074 0.077 0.095 0.148

0.048 0.050 0.045 0.048

0.074 0.067 0.071 0.129

0.021 0.038 0.027 0.160

0.011 0.027 0.006 0.102

0.042 0.007 0.008 0.026

0.021 0.005 0.024 0.003

1548.5 1591.9 2197.8 3496.2

1094.7 1183.5 995.1 1135.0

1555.2 1572.6 1516.7 2971.9

440.7 785.6 532.7 3161.6

212.3 488.2 118.9 1951.0

428.4 37.9 89.6 1487.5

424.4 112.5 481.6 71.5

with microwaved sludge as is the case of A1 in comparison with the other acidification reactor fed with non-microwaved sludge, A2. The same observation occurs when comparing reactor M1 with M2; both the hydrolysis and specific hydrolysis rates are higher in the reactor fed pretreated sludge in comparison with reactor M2, in the same operating conditions except the pretreatment applied to sludge. For T1 and T2 this is true for all but the highest SRT (20 d) and it can be argued that thermophilic sludge has higher intrinsic reaction rates due to higher temperature compared with mesophilic reactors, so a difference in performance between pretreated and non-pretreated sludge is only observable when the organic load is not too low. The MW pretreatment, besides solubilizing organic material, may have caused a partial hydrolysis that despite not creating soluble material, modified the solid substrate to such extent as to make its solubilization easier in the following stage. The occurrence of partial hydrolysis has already been observed in the context of two-stage digestion (Watts et al., 2006), with the authors attributing it to a combination of both a heating process as well as chemical and biological activity. Rates of hydrolysis in reactors M3 and T3 are either significantly lower than those for single-stage reactors or negative, showing that most, if not all, of the hydrolyzable substrate was solubilized either in the microwaving process or the acidifying reactors. The negative numbers are a consequence of production of cellular material that is washed out in the effluent. This washout occurs in all reactors to some extent, but for reactors M3 and T3, all the hydrolyzable substrate is hydrolyzed prior to entering T3 and M3, so when calculating the balance, there is no hydrolysis occurring inside the reactor to compensate for the loss of bacterial cell mass as happens is all the other reactors. So for T3 and M3, the particulate fraction of COD is greater in the exit than in the entrance of the reactor due to biomass production inside the reactors using soluble COD. In the case of M4 and T4 some hydrolysis still occurs given that they show positive values (though smaller) for all the periods tested (except in the case of 20 d SRT). This shows that reactor A2 does not solubilise organic material to the same extent as A1 and some of it is still solubilized in the methanogenic reactor. Table 3 displays a summary of steady state characteristics for all reactors after being fed pretreated and non-pretreated sludge. Values are the means calculated for each period after steady state was achieved, along with the 95% confidence interval values.

3.1.

Biogas production

The results obtained show that MW pretreatment has a positive effect on digestion, both in a single- or two-stage process. Single-stage reactors fed with microwaved sludge, both meso and thermophilic (M1 and T1) produced more biogas than reactors fed with non-microwaved sludge (M2 and T2) for all the SRT tested, with the exception of SRT 5 d where the difference between biogas production for M1 (0.62  0.04 L/d) and M2 (0.60  0.06 L/d) is not statistically significant (t-test, a ¼ 0.05, P ¼ 0.580 for m1 ¼ m2). The maximum biogas production for single-stage reactors for each SRT occurs always in the thermophilic reactor T1. Reactor M1 shows the second best biogas production rates with rates statistically superior to T2 for SRT 20 (t-test a ¼ 0.05, P ¼ 6.72E-29 for m1 ¼ m2), 15 (t-test, a ¼ 0.05, P ¼ 8.68E-5 for m1 ¼ m2) and 10 d (t-test, a ¼ 0.05, P ¼ 4.77E-14 for m1 ¼ m2). At SRT 5 d, the thermophilic reactor fed with non MW sludge T2 produces a higher amount of biogas than M1 (fed with MW sludge); however, the average for M1 was calculated without reaching steady state, since a stable state was not achieved during the period. Two-stage reactors also show that MW pretreatment has a positive effect in the digestion of sludge. Reactors digesting sludge from A1 (that acidifies sludge after MW pretreatment) had more biogas production than reactors fed with sludge from A2, that acidifies sludge not pretreated with MWs. Among reactors fed by A1, more biogas was produced in the thermophilic reactor T3 than the mesophilic reactor M3. The maximum biogas production for two-stage reactors was observed for T3 at the shortest SRT tested, 5 d with a value of 1.24  0.01 L/d, (value calculated adding T3 biogas production plus A1 corrected for sludge volume fed). Reactor T3 produced more biogas in every SRT tested than any other single- or two-stage reactor tested. Also noticeable is biogas production from two-stage mesophilic reactor M3 which was always higher than two-stage M4 as somewhat expected, but also higher than T4. In both cases, the difference is statistically significant (t-test, a ¼ 0.05, P < 0.05 for all the pairs for m1 ¼ m2). Reactor M2 (mesophilic without MW pretreatment) can be used as a control reactor to evaluate the relative improvements obtained since the majority of anaerobic reactors in use today are mesophilic digesters digesting sludge with no pretreatment (De Baere, 2000). Biogas production improvements for two-stage reactors, (M3, M4 T3 and T4) were calculated including the contribution of the respective

Table 3 e Steady state characterization of reactors at tested SRTs. SRT ¼ 20 d Parameters

A1

A2

M1

26.27  20.3  0.38  18  43.5  89 

0.57 3.7 0.01 0.61 4.2 4.7

24.06 16.7 0.29 15 43.1 90

     

0.53 4.7 0.01 0.61 3.6 5.4

1.97  44.1  0.40  254  53.8  576 

0.04 3.9 0.01 8.18 1.9 30.9

OLR (kgVS/m3.d) VS rem % Biogas prod. L/d L/kgVSadded CH4 % Biogas Yield L/kgVSrem

26.09  24.5  0.65  31  44.3  127 

0.25 4.4 0.01 0.56 2.9 3.0

26.37 29.5 0.63 30 39.8 101

     

0.27 4.7 0.01 0.57 2.7 2.9

2.61  40.7  0.50  239  52.5  585 

0.02 4.4 0.01 5.12 1.8 13.8

OLR (kgVS/m3.d) VS rem % Biogas prod. L/d L/kgVSadded CH4 % Biogas Yield L/kgVSrem

26.25  30.8  0.83  40  42.0  129 

0.28 4.3 0.02 1.05 3.3 6.53

21.52 22.9 0.68 40 40.7 173

     

1.30 4.4 0.01 2.48 3.4 14.98

3.94  40.9  0.60  192  53.9  468 

0.04 5.0 0.01 3.74 3.9 24.64

OLR (kgVS/m3.d) VS rem % Biogas prod. L/d L/kgVSadded CH4 % Biogas Yield L/kgVSrem

26.13  0.29 31.8  2.3 0.83  0.01 40.0  0.66 41.1  2.0 125  9.15

20.95  1.72 20.8  1.7 0.69  0.01 41.0  3.42 40.0  1.9 199  23.30

7.84  0.09 34.4  3.0 0.62  0.04 100  6.55 50.9  3.9 289  20.71

1.75  0.05 47  5.9 0.40  0.01 287  10.90 52.6  4.1 612  31.5 SRT ¼ 15 d 2.64  0.03 2.27  0.03 39.9  5.4 46.7  4.8 0.39  0.01 0.56  0.01 183  2.08 310  6.89 54.4  3.2 56.6  2.9 459  13.8 665  18.4 SRT ¼ 10 d 3.23  0.20 3.41  0.14 31.5  5.0 50.0  5.6 0.55  0.01 0.68  0.01 212  13.68 249  10.86 53.7  3.9 56.7  4.8 672  59.96 499  22.54 SRT ¼ 5 d 6.29  0.52 8.91  0.63 24.9  2.2 49.6  4.6 0.60  0.06 0.90  0.01 120  15.57 126  9.02 45.9  3.9 55.9  2.2 480  74.92 255  18.51 1.80 37.4 0.28 192 52.8 514

     

M3

0.04 5.8 0.01 5.69 4.0 29.3

M4

T1

1.67  43.5  0.37  277  54.5  636 

0.06 4.1 0.01 12.45 2.9 33.0

1.97 47.0 0.51 321 64.1 684

     

2.14  45.7  0.48  280  52.5  612 

0.04 6.7 0.01 7.84 3.0 19.2

2.61 47.5 0.65 309 64.0 651

3.11  36.1  0.65  261  51.6  724 

0.02 4.6 0.01 4.35 5.1 52.55

3.94 41.2 0.83 265 59.9 643

8.30  0.07 23.6  2.2 0.76  0.04 114  6.08 53.0  2.1 482  51.05

T2

T4

0.04 5.9 0.01 8.83 2.9 33.2

1.75 50.2 0.53 383 63.2 720

     

0.05 6.5 0.01 13.11 2.7 43.9

1.67 45.1 0.34 256 62.7 568

 0.06  5.4  0.01  11.89  2.1  40.1

 0.02  7.3  0.01  5.31  1.9  20.6

2.64  0.03 40.6  6.6 0.48  0.01 228  5.41 62.0  1.6 563  16.0

2.27 53.1 0.68 372 63.1 718

     

0.03 6.9 0.01 6.91 1.9 18.7

2.14 45.4 0.54 315 61.3 695

 0.04  3.5  0.01  8.29  1.7  19.8

 0.04  4.7  0.01  4.18  3.5  15.25

3.23  0.20 33.8  4.1 0.55  0.01 214  13.81 59.0  2.4 633  56.06

3.41 51.8 0.79 291 61.7 561

     

0.14 6.6 0.01 12.50 3.9 25.08

3.11 37.9 0.66 263 62.8 694

 0.02  4.3  0.01  4.33  2.5  43.64

7.84  0.09 39.6  2.9 1.00  0.01 159  2.42 59.0  1.5 401  11.47

6.29  0.52 26.8  2.2 0.70  0.01 139  11.66 61.0  1.4 521  61.29

8.91 51.4 0.98 137 62.8 266

     

0.63 4.7 0.01 9.79 1.7 19.36

8.30 29.6 0.85 129 63.5 435

 0.07  2.4  0.01  1.87  2.1  36.41

0.04 6.6 0.01 9.06 1.9 42.7

1.80  42.0  0.35  244  57.9  580 

T3

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 8 2 2 e2 8 3 4

OLR (kgVS/m3.d) VS rem % Biogas prod. L/d L/kgVSadded.d CH4 % Biogas Yield L/kgVSrem

M2

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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 8 2 2 e2 8 3 4

acidifying reactor (A1 for M3 and T3; A2 for M4 and T4). Improvements are visible for all reactors except for T2 at 10 d SRT where the difference was not significant (t-test, a ¼ 0.05, P ¼ 0.315 for m1 ¼ m2), with the higher improvements being recorded in reactor T3. It shows higher improvements at all SRTs when compared with the other two-stage reactors (M3, M4 and T4) and with all the single-stage reactors. The highest improvement occurs at SRT 5 d where T3 shows an increase of 106% compared to biogas production in M2. When considering only single-stage reactors, thermophilic reactor T1 showed higher improvements for all SRT in comparison with the other single-stage reactors (M1 and T2). Thermophilic operation alone made T2 perform better in terms of biogas production compared to the control; however, performance was not as good as mesophilic reactor M1 digesting microwaved sludge for all SRTs except at an SRT of 5 d (Fig. 3). Previous studies observed that MW pretreatment efficiency (degree of improvement over a control) increased with smaller SRT, or higher loads applied (Toreci et al., 2009, Eskicioglu et al. (2007a,b)), and that differences between reactors digesting pretreated and non-pretreated sludges were not significant at high SRT (20 d). In contrast, in the results obtained in this study, improvements were measured at all SRTs. It seems logical that pretreated sludges, in which the material available to digestion is comprised of extracellular polymeric substances (EPS) plus all the material that is released after cell wall breakdown due to pretreatment has a higher biodegradable potential than sludges where bacterial cell walls are intact, reducing the pool of easily biodegradable material to EPS. In the case of singlestage reactors, particularly for M1, the degree of improvement seems to decrease with higher SRT applied, while an opposite trend is visible in two-stage digesters. One should not rule out the fact that biogas production measurements at least for M1 at low SRT could have underestimated the real value of biogas production, since biogas yield for M1 at SRT 5 was 289  20.71 L/ d, which is a relatively low value compared to biogas yields of 576  30.9, 585  13.8 and 468  24.64 L/d for SRT 20, 15 and 10 d, respectively. Gas leaks were detected and repaired for measuring gas production. Other authors also reported the same problems with leaks especially when applying high loads

(Eskicioglu et al. (2007a,b)). For the 4 two-stage digesters tested, biogas production improvement seems to increase with lower SRT, since for all reactors biogas production improvement is higher at SRT 5 d than at 20 d, with this trend particularly visible in reactors M3 and T4. The results for SRT 10 d were somehow dissonant of this trend most likely because they were affected by the composition of the original sludge collected in the wastewater plant. The average tCOD and sCOD of untreated sludge was noticeably lower than corresponding values measured in the three other periods, which might have lowered substantially the improvement measured at this SRT. When comparing the effects of staging and microwaving it is interesting to note that for mesophilic conditions, staging alone increases more the biogas production than microwaving alone (M4 produces more biogas than M1) however, for thermophilic conditions, the opposite happens, since microwaving alone has a greater positive effect on biogas production than just staging (T2 produces more biogas than T4 in three of the four SRT tested).

3.2.

VS removal

Microwaved sludge provides for greater VS reduction than non-microwaved sludge, given that single-stage reactors fed with microwaved sludge exhibit higher removal percentages than reactors fed with non-microwaved sludge, as is the case of single-stage reactor M1 compared with M2 and T1 compared with T2. For both cases (M1-M2 and T1-T2) the values are statistically different except for the longest SRT (20 d) (P < 0.05 for pairs at SRT 15, 10 and 5d, P > 0.05 for SRT ¼ 20d). It is likely that for such a long retention time, bacteria are capable of biodegrading all biodegradable solids, so the difference between pretreated and non pretreating performance is not as pronounced. For single-stage reactors, thermophilic conditions resulted in higher removal than corresponding mesophilic operated reactors. T1 performs better than M1 for all SRTs except SRT 10 d where removal percentage is statistically not different (t-test, a ¼ 0.05, P ¼ 0.863 for m1 ¼ m2), and T2 performs better than M2 for SRT 20 and 10 d, while at SRT 15 d (t-test,a ¼ 0.05, P ¼ 0.101 for

Fig. 3 e Improvement percentages on biogas production relative to control reactor (M2).

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 8 2 2 e2 8 3 4

m1 ¼ m2) and 5 d (t-test,a ¼ 0.05, P ¼ 0.126 for m1 ¼ m2), although the average value is higher, the difference is not statistically significant. Again, the change in the characteristics of feed sludge for the period tested at SRT 10 d may explain the lack of statistical relevancy of the difference calculated for SRT 10 d. and non attainment of stable conditions at SRT 5 d for M2 and consequent high variance could explain the lack of statistical significance in the difference between the means. Two-stage reactors generally achieve higher VS removals than the correspondent single-stage reactors (M3 in comparison with M1, T3 with T1, M4 with M2 and T4 with T2). The removal efficiency of two-stage reactors was calculated based on the VS concentration before the acidifying reactor and VS concentration after the methenogenic reactor, treating then the two-stage reactors as a single system. For SRT 5 d, despite average removal being higher for M2 compared to M4, the difference is not significant (t-test,a ¼ 0.05, P ¼ 0.489 for m1 ¼ m2). The highest VS removal for all reactors was obtained at SRT 15 d for T3 (53.1  6.9%), a value that is relatively high considering that the feed sludge was comprised of activated sludge only. Sludge used in this test was young (SRT 5 d) which means it contained a higher proportion of biodegradable organic matter compared with older sludge, particularly sludge produced in processes where nutrient removal is performed. The most striking fact from the values for VS removal calculated for two-stage reactors is that solids removal percentage did not significantly decrease when SRT was decreased for T3 and M3, in contrast with M4 and T4 that had their removal percentages decrease from 44 to 24% and 45 to 30%,respectively, when SRT decreased from 20 to 5 d. Pretreatment causes a large part of influent feed to be easily digestible so the decrease of time available to bacteria to metabolize them apparently is not limiting in these reactors. Two-stage reactors M3 and T3 show consequently more solids removal than M4 and T4, for all but the higher SRT (20 d), where the difference between M3 and M4 is not significant (t-test,a ¼ 0.05, P ¼ 0.359 for m1 ¼ m2), as well as T3 and T4 (ttest,a ¼ 0.05, P ¼ 0.773 for m1 ¼ m2). Digestion temperature also

2829

had an effect in solids removal, since reactor T3 removes more solids at all SRTs than similarly fed M3. In the case of twostage reactors fed with non-pretreated sludge, the effect of digestion temperature is only visible at SRT 5 d since it is the only condition where the difference is statistically significant (t-test,a ¼ 0.05, P ¼ 0.004 for m1 ¼ m2).; Improvements in VS removal relative to control reactor, as shown in Fig. 4, show that pretreatment is more effective for short SRT. All the reactors fed with pretreated sludge had increased relative improvements for SRT 10 and 5 d, compared with the initial SRT of 20 d. The increase is more pronounced in two-stage reactors fed pretreated sludge, because these reactors (M3 and T3) retained high solids removal efficiencies while the control reactor showed a drop in performance. Staging increases solids removal capacity; therefore high removal efficiencies are maintained in a high level even at SRTs where single-stage reactors show signs of overloading (Han et al., 1997) state that staging alone reduces the volume necessary for a removal efficiency of 60%; therefore it is not suprising that T3 and M3 (and to a lesser extent M4 and T4) showed such high removal efficiencies. Microwaving alone, though, seems to have a more beneficial effect in terms of solids removal than just staged digestion. M1 shows higher improvements percentages for SRT 20, 10 and 5 d than M4 and the same happens with T1 for all SRTs in comparison with T4. This seems reasonable since microwaving has a high and direct impact on VS because it solubilizes particulate matter to a greater extent allowing it to be easily transformed into methane and carbon dioxide. It can be hypothesized that having a two-stage system provided better conditions to accommodate higher loadings in comparison with the single-stage systems, particularly the control, and microwaving increased the fraction of those higher loadings that were readily usable by bacteria. Microwaving feed sludge allowed two-stage reactors to use all the optimized capacity staging provides with increased proportion of methanogenic bacteria in the second reactor allowing it to handle higher substrate loading without losing performance.

Fig. 4 e Improvement percentages on VS removal relative to control reactor (M2).

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3.3.

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 8 2 2 e2 8 3 4

VFA, sCOD and pH

Effluent characteristics for thermophilic reactors show a markedly higher concentration of VFA, both for single and two-stage reactors which was already reported as occurring for thermophilic digestion in steady state in previous studies (Moen et al., 1997). One of the reasons for this might be that thermophilic methanogens have higher half-velocity constants compared to mesophilic methanogens (Gavala et al., 2003; Moen et al., 1997). Also, thermophilic methanogens are generally thought to be more susceptible to inhibition and toxicity effects which can explain in part the accumulation of these methane precursors. The same happens with sCOD, reflecting partially what happens with VFA. However, VFA alone does not account for all the sCOD difference between thermophilic and mesophilic reactors. One likely reason might be that part of the hydrolyzates produced in thermophilic second stage reactors are not easily biodegradable, and subsequently are included in the effluent. Another reason might be that thermophilic sludge seems to produce much more EPS than mesophilic sludge, and part of that EPS will be accounted when measuring the soluble fraction of tCOD. For the acidification reactors, it was already shown that hydrolysis rates in the reactors fed microwaved sludge were higher, resulting in a significantly higher concentration of sCOD in the effluent of A1 at all periods. Total VFA is higher in A2 which can be a consequence of lower biogas production observed in that reactor that can cause a higher buildup of VFA. Interestingly, pH in these two reactors was never below 6, most likely due to the buffering capacity provided by the significant biogas production with a reasonable methane content. Thermophilic pH values are generally, slightly higher than those measured for mesophilic reactors. This difference in pH can be attributed in part to the higher temperature in thermophilic reactors. Gas solubility in liquid is described using the Henry’s Law that can be expressed as follows: p (3) kH;pc ¼ kH,pc ¼ cHenry’s constant (L atm/mol);

c ¼ amount concentration of gas in solution (in mol/L) p ¼ partial pressure of gas above the solution (in atm) and temperature has an effect on the Henry’s constants for carbon dioxide, according to the expression:    1 1 c 
T Tq (4) kH;pc ðTÞ ¼ kH;pc Tq e CðCO2 Þ ¼ 2400 K Henry’s constant is 29.41 L atm/mol at 298 K, so, using eq (4), kH,pc (35  C) ¼ 38.34 L atm/mol and kH,pc (55  C) ¼ 61.64 L atm/ mol. The ratio of concentration of CO for these two temperatures is: p kH;pc ð55 CÞ cð55 CÞ 61:64 cð35 CÞ cð35 CÞ  ¼ p /38:34 ¼ cð55 CÞ/cð55 CÞ ¼ 1:61 kH;pc ð35 CÞ cð35 CÞ Since CO2 is an acidic gas, lower concentrations in the liquid phase at 55  C results in a higher pH, when alkalinity values are similar which can explain the difference.

3.4.

Pathogen removal

Total coliforms and E.coli were measured and the results were used to assess the adequacy of the processes to produce Class A sludge biosolids according to the requirements laid down in 40 CFR Part 503 regulations (EPA, 1994), meaning that total fecal coliforms cannot be above a value of 1000 CFU/g TS. Total coliforms are a broader class of coliforms that include (but are not limited to) fecal coliforms and because of that, are always more numerous or, in limited cases, equal to the fecal coliforms present in the sample. Results are summarized in Fig. 5. Microwaving coupled with two-stage digestion produced sludge with pathogen indicator content below the detection limit for all SRTs tested (3.7 CFU/g TS), and consequently with quality to be classified as Class A. Microwaving alone is capable of an average 2.1 log reduction (approximately 99%) in total coliform population (Hong et al., 2006) determined that a temperature of 86  C would be sufficient to eliminate all coliforms from WAS; however, a smaller penetration depth of

Fig. 5 e Total coliforms in each reactor effluent for the tested periods.

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 8 2 2 e2 8 3 4

MWs in activated sludge (1.11 cm in WAS; 2.16 cm in tap water) combined with a lack of homogenization in the heating of sludge samples (the vessel used to heat sludge in the MW oven had no mixing mechanism) may have created spots where temperature did not rise above the necessary temperature to achieve inactivation; therefore, removal of coliforms was not complete. Conventional mesophilic digestion was also capable of some indicator removal but final density of coliforms is still very high (>1  107). Mesophilic single-stage digestion with MW pretreatment was also not sufficient for complete removal of coliforms below the limit of 1000 CFU/g TS; however, due to MW pretreatment, the coliform values in effluent sludge are significantly lower than mesophilic single-stage reactor (M2), used as a control. Both thermophilic single-stage reactors removed coliforms below detection levels for SRT 20 and 15 d, with T1 having a higher reduction, even marginally approaching the required limit. Reactor T4 also shows the same behavior suggesting that a minimum retention time of more than 10 d in thermophilic conditions is necessary to eliminate all pathogenic bacteria present. Staging alone was able to produce sludge with coliforms below the maximum level provided that a total SRT for the system was above 10 days. The thermophilic temperature combined with high VFA concentration in the acidogenic reactor provided enough reduction in coliform density to obtain compliant sludge even when using mesophilic second stage reactor for SRTs of 20 and 15 d. Minimum retention times in thermophilic conditions for coliform inactivation were also reported in other studies with variable results. Riau and De La Rubia (2010) reported minimum retention time of 4 d in a TPAD of a system total of 19 d, and Han et al. (1997) obtained Class A sludge with 4 days thermophilic reactor SRT plus 10 days in a mesophilic second stage reactor, while Cheunbarn and Pagilla (2000) reported also sludge compliant with the limit using thermophilic SRT of just 1 d plus 15 d SRT in a second stage mesophilic reactor. In this study, MW pretreatment allowed a TPAD system with a total SRT of just 5 d, with 2 d thermophilic SRT to consistently produce Class A sludge.

3.5.

2831

Dewaterability

Sludge flocs contain a high amount of free or bounded water that is attached to the sludge floc structure EPS by electrostatic interactions and hydrogen bonds. These flocs can then retain large amounts of water, negatively affecting sludge dewaterability. Thermophilic digestion is thought to produce up to 10 times more the amount of EPS than is observed in mesophilic sludge (Zhou et al, 2003) and, consequently, with higher amounts of water retained in the EPS mesh in the floc, thermophilic sludge is thought to be more difficult to dewater (Bivins and Novak, 2001). MWs, on the other hand, have a direct effect on the bounded water, since they destabilize the floc structure, breaking hydrogen bonds between hydroxyl groups of EPS polymers and water molecules and electrostatic interactions between water molecules and induced dipoles of other functional groups in the EPS structure. This can lead to the release of bounded water, increasing the dewaterability of sludge. Dewaterability was tested using capillary suction time (CST) testing and results (Fig. 6) show that thermophilic reactors without MW pretreatment (T2 and T4) show worse dewaterability properties (higher CST values) than T1 and T3. Reactors T3 and T4 had improved dewaterability compared with the control reactor (M2), particularly for lower SRTs. Staging also seems to have an effect since values for T3, are generally smaller than values for T1. The lowest values however, were measured for mesophilic reactors digesting pretreated sludge (M1 and M3). Staging seems to have a more significant effect in thermophilic reactors than in mesophilic ones, since CSTs tend to be lower in two-stage T3 and T4 in comparison with T1 and T2, respectively. T3 and T1 are both fed microwaved sludge and differ only in the staging setup, and the same happens with T4 and T2, with the difference that are both fed non-pretreated sludge. The results show that thermophilic reactors have higher CST values than the corresponding mesophilic reactors (ex: T1 in comparison with M1), however, some thermophilic reactors (T1, T3 and T4) are able to

Fig. 6 e Specific capillary suction time for all tested periods.

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improve dewatering characteristics in comparison with the control reactor.

4.

Microwaving coupled with staged digestion and thermophilic acidogenisis is capable of eliminating pathogens indicators completely even for total retention times of 5 d. Although thermophilic operation alone decreased dewaterability of sludge, the association of MW pretreatment and thermophilic operation produced sludge that dewaters better than control sludge. Even though the association of two techniques to maximize digestion performance is not a novel technique it was proved that combining MW pretreatment and TPAD has the effect of decreasing volume requirements for digestors, while at the same time, maintaining or in some cases even increasing digestion performance in terms of solid removal and biogas production.

Conclusions

MW pretreatment increases solubilization of organic matter and increases hydrolysis of matter not solubilized in the pretreatment step, thus causing a partial hydrolyzation, that despite not creating soluble COD, facilitated solubilization of solid substrate in anaerobic reactors, consequently increasing production of sCOD in acidogenic reactors. MW has a positive effect in biogas production and VS removal, since reactors fed with microwaved sludge produced more biogas and removed more solids. The improvement seems to be not only in terms of speed of reaction but also in extent, since even at the highest SRT, more biogas and solids are removed in comparison with the control. Digestion temperature was an important factor in digestion, since thermophilic reactors produced more biogas and removed more solids than mesophilic reactors in similar conditions. Staging allows the maintenance of a longer interval of observed high biogas and solids removals (similar to those observed at the highest SRT) of microwaved sludge even at organic loadings where single-stage reactors are not capable of reaching stable operation.

Acknowledgments N. M. Coelho received a PhD scholarship (SFRH/BD/18870/ 2004) from the FCT (Fundac¸a˜o para a Cieˆncia e Tecnologia), Portugal. We would like also to acknowledge the help provided by staff at Robert O. Pickard Wastewater Treatment Plant (Ottawa, ON).

Appendix

SRT ¼ 20 d Parameters

A1

A2

M1

M2

M3

M4

T1

T2

T3

T4

OLR (kg VS/ 26.270.57 24.060.53 1.970.04 1.800.04 1.750.05 1.670.06 1.970.04 1.800.04 1.750.05 1.670.06 m3.d) OLR (kg COD/ 40.170.47 46.370.30 3.860.02 3.350.02 2.600.23 2.620.24 3.860.02 3.350.02 2.600.23 2.620.24 m3.d) VS rem % 20.33.7 16.74.7 44.13.9 37.45.8 475.9 43.54.1 47.06.6 42.05.9 50.26.5 45.15.4 TS rem% 13.43.3 5.63.2 29.84.6 26.85.0 34.05.7 23.14.6 35.55.0 28.96.9 32.96.0 26.96.7 55.34.7 61.92.8 56.22.7 tCOD rem% 32.32.9 21.72.0 50.41.8 47.02.1 60.52.7 59.31.2 55.62.5 Biogas 0.380.01 0.290.01 0.400.01 0.510.01 0.400.01 0.370.01 0.510.01 0.350.01 0.530.01 0.340.01 prod. L/d L/kg VS 180.61 150.61 2548.18 1925.69 28710.90 27712.45 3219.06 2448.83 38313.11 25611.89 added L/kg tCOD 201.9 60.6 14413.9 1009.5 23122.3 20820.1 18217.3 12712.4 30229.4 19418.9 added CH4 % 43.54.2 43.13.6 53.81.9 52.84.0 52.64.1 54.52.9 64.11.9 57.92.9 63.22.7 62.72.1 Lbiogas/kg 894.7 905.4 57630.9 51429.3 61231.5 63633.0 68442.7 58033.2 72043.9 56840.1 VS rem LCH4/kg 38.74.3 38.84.0 309.919.9 271.425.7 321.930.1 346.625.8 438.430.3 335.825.5 455.033.9 356.127.8 VS rem TVFA (mg/L) 4118457 4876554 228138 10039 10838 30918 2013430 1864469 1799472 2410634 sCOD (mg/L) 28543662 15043338 57534 13624 43834 96845 3362181 1610454 3986363 2763428 1634144 805214 854110 1237155 1124154 1200124 984114 1541225 1347117 NH3-N (mg/L) 1745102 pH 6.310.09 6.400.08 7.210.08 7.320.08 7.360.12 7.110.14 7.620.09 7.640.08 7.530.09 7.730.09

SRT ¼ 15 d Parameters

A1

A2

M1

OLR (kg VS/m3.d) 26.090.25 26.370.27 2.610.02 OLR (kg COD/m3.d) 42.260.27 46.360.26 5.350.03 VS rem % 24.54.4 29.54.7 40.74.4 TS rem% 11.15.2 21.75.4 31.35.3 tCOD rem% 21.02.2 25.41.7 36.41.2

M2

M3

M4

T1

T2

T3

T4

2.640.03 3.220.03 39.95.4 27.55.2 33.92.6

2.270.03 4.230.43 46.74.8 34.55.3 48.32.5

2.140.04 3.640.24 45.76.7 31.45.3 48.53.7

2.610.02 5.350.03 47.57.3 36.35.7 41.91.5

2.640.03 3.220.03 40.66.6 27.45.4 37.83.5

2.270.03 4.230.43 53.16.9 40.46.3 50.43.7

2.140.04 3.640.24 45.43.5 31.24.3 47.53.7

(continued on next page)

2833

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 8 2 2 e2 8 3 4

(continued ) SRT ¼ 15 d Parameters

A1

A2

Biogas prod. L/d 0.650.01 0.630.01 L/kgVS added 310.56 300.57 L/kg tCOD added 201.34 181.23 44.32.9 39.82.7 CH4 % Lbiogas/kg VS rem 1273.0 1012.9 56.33.9 40.23.0 LCH4/kg VS rem TVFA (mg/L) 4431451 5788756 sCOD (mg/L) 311166379 15555536 141554 1354110 NH3-N (mg/L) pH 6.210.10 6.310.10

M1

M2

M3

M4

T1

T2

T3

T4

0.500.01 2395.12 1439.96 52.51.8 58513.8 307.112.8 247159 241.422.9 1024125 7.360.08

0.390.01 1832.08 1117.93 54.43.2 45913.8 249.716.5 211137 150.056.1 1044147 7.320.08

0.560.01 3106.89 22915.81 56.62.9 66518.4 376.421.9 11035 337.443.6 1430152 7.200.10

0.480.01 2807.84 22916.00 52.53.0 61219.2 321.320.9 398134 321.94.19 1124123 7.250.12

0.650.01 3095.31 18612.73 64.01.9 65120.6 416.618.1 1361754 3458245 1333321 7.700.13

0.480.01 2285.41 1399.71 62.01.6 56316.0 349.113.4 20051063 3551668 1035114 7.770.10

0.680.01 3726.91 27018.44 63.11.9 71818.7 453.118.0 1186862 3360404 1445141 7.630.12

0.540.01 3158.29 25517.65 61.31.7 69519.8 426.016.9 2129599 3248336 1256132 7.720.14

SRT ¼ 10 d Parameters

A1

A2

M1

M2

M3

M4

OLR (kgVS/ 26.250.28 21.521.30 3.940.04 3.230.20 3.410.14 3.110.02 m3.d) OLR (kg COD/ 35.271.94 45.490.22 8.530.04 6.610.36 7.320.99 6.100.93 m3.d) VS rem % 30.84.3 22.94.4 40.95.0 31.55.0 50.05.6 36.14.6 TS rem% 12.51.2 14.52.2 35.15.4 20.22.8 40.55.4 20.83.0 tCOD rem% 14.21.9 7.71.3 22.62.4 26.02.4 43.03.9 33.52.2 Biogas prod. 0.830.02 0.680.01 0.600.01 0.550.01 0.680.01 0.650.01 L/d L/kgVSadded 401.05 402.48 1923.74 21213.68 24910.86 2614.35 L/kg tCOD 314.80 203.07 11117.08 10015.40 9614.76 19429.83 added 42.03.3 40.73.4 53.93.9 53.73.9 56.74.8 51.65.1 CH4 % Lbiogas/kg 1296.53 17314.98 46824.64 67259.96 49922.54 72452.55 VS rem LCH4/kg 54.25.1 70.48.5 252.322.6 360.941.5 282.927.1 373.645.8 VS rem TVFA (mg/L) 3459931 42131057 329117 55054 44060 43753 sCOD (mg/L) 252684227 14862544 248.416.5 120.748.2 244.039.7 289.640.5 1554222 1420247 1544161 1998111 1234215 NH3-N (mg/L) 1832156 pH 6.410.11 6.110.13 7.240.10 7.210.18 7.120.12 7.420.10

T1

T2

T3

T4

3.940.04

3.230.20

3.410.14

3.110.02

8.530.04

6.610.36

7.320.99

6.100.93

41.24.7 36.13.2 37.72.7 0.830.01

33.84.1 16.51.4 27.42.6 0.550.01

51.86.6 39.93.6 44.52.4 0.790.01

37.94.3 18.82.6 36.02.2 0.660.01

2654.18 15323.48

21413.81 10115.56

29112.50 19730.24

2634.33 19630.13

59.93.5 64315.25

59.02.4 63356.06

61.73.9 56125.08

62.82.5 69443.64

385.224.3

373.536.4

346.126.8

435.832.4

2099907 4030304 1557226 7.650.12

1837136 4603687 1452286 7.750.11

1903166 3388434 1444236 7.540.13

25921193 4698537 1963269 7.710.14

T1

T2

T3

T4

7.840.09

6.290.52

8.910.63

8.300.07

22.970.24

20.391.12

17.561.95

16.571.98

39.62.9 33.03.0 30.92.0 1.000.01

26.82.2 18.42.3 22.91.3 0.700.01

51.44.7 40.83.7 43.12.1 0.980.01

29.62.4 22.82.8 35.32.0 0.850.01

1592.42

13911.66

1379.79

1291.87

728.63

14717.56

13516.14

61.01.4 52161.29

62.81.7 26619.36

63.52.1 43536.41

SRT ¼ 5 d Parameters

A1

A2

M1a

M2a

M3

M4a

OLR (kg VS/ 26.130.29 20.951.72 7.840.09 6.290.52 8.910.63 8.300.07 m3.d) OLR (kg COD/ 40.782.23 45.940.48 22.970.24 20.391.12 17.561.95 16.571.98 m3.d) VS rem % 31.82.3 20.81.7 34.43.0 24.92.2 49.64.6 23.62.2 TS rem% 13.62.3 14.31.8 28.62.8 20.22.5 42.13.4 18.12.3 tCOD rem% 23.62.6 18.72.5 21.72.0 14.91.6 39.03.7 30.53.1 Biogas prod. 0.830.01 0.690.01 0.620.04 0.600.06 0.900.01 0.760.04 L/d L/kg VS 40.00.66 41.03.42 1006.55 12015.57 1269.02 1146.08 added L/kg tCOD 273.23 203.40 577.72 619.48 13816.49 12316.00 added CH4 % 41.12.0 40.01.9 50.93.9 45.93.9 55.92.2 53.02.1 Lbiogas/kg 1259.15 19923.30 28920.71 48074.92 25518.51 48251.05 VS rem LCH4/kg 51.44.5 79.610.1 147.115.4 220.339.2 142.511.8 255.528.9 VS rem TVFA (mg/L) 3753900 41431046 415215 64049 559265 507182 sCOD (mg/L) 26754908 15105932 30546 20047 31252 48252 1023120 1478165 1144121 1564321 NH3-N (mg/L) 1920167 1699177 pH 6.320.21 6.240.15 7.010.32 6.990.29 7.320.13 7.540.13 a Steady state was not observed during test period.

9010.75 59.01.5 40111.47 236.69.1

317.838.1

167.013.0

276.224.9

1349474 4874300 1778113 7.610.10

1431838 4540661 1132323 7.550.12

1766886 3567486 1560235 7.610.11

2781980 4636534 1657265 7.720.09

2834

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 8 2 2 e2 8 3 4

references

APHA, 1995. Standard Methods for the Examination of Water and Wastewater, 19th. ed. American Public Health Association, Washington, DC, USA. Azbar, Nuri, Speece, Richard E, 2001. Two-phase, two-stage, and single -stage anaerobic process comparison. Journal of Environmental Engineering 127 (3), 240e248. Bhattacharya, Sanjoy, Madura, Richard, Walling, David, Farrell, Joseph, 1996. Volatile solids reduction in two-phase and conventional anaerobic sludge digestion. Water Research 30 (5), 1041e1048. Bivins, Jason L., Novak, John T., 2001. Changes in dewatering properties between the thermophilic and mesophilic stages in temperature-phased anaerobic digestion systems. Water Environment Research 73 (4), 444e449. Bolzonella, D., Pavan, P., Battistoni, P., Cecchi, F., 2005. Mesophilic anaerobic digestion of waste activated sludge: influence of the solid retention time in the wastewater treatment. Process Biochemistry 40 (3e4), 1453e1460. Breure, A.M., van Andel, J.G., 1984. Hydrolysis and acidogenic fermentation of a protein, gelatin, in an anaerobic continuous culture. Applied Microbiology and Biotechnology 20, 40e45. Cheunbarn, Tapana, Pagilla, Krishna R, 2000. Anaerobic thermophilic/mesophilic dual-stage sludge treatment. Journal of Environmental Engineering 126 (9), 796e801. De Baere, L., 2000. Anaerobic digestion of solid waste: state-ofthe-art. Water Science and Technology 41 (3), 283e290. EPA., 1994. Control of Pathogens and Vector Attraction in Sewage Sludge (Including Domestic Septage) under 40 CFR Part 503. 625/Re92/013. Environmental Protection Agency, Washington, D.C.: United States. Eastman, J.A., Ferguson, J.F., 1981. Solubilization of particulate organic carbon during the acid phase of anaerobic digestion. Journal of the Water Pollution Control Federation 53 (3), 352e366. Eskicioglu, Cigdem, Kennedy, Kevin J, Droste, Ronald L, 2007a. Enhancement of batch waste activated sludge digestion by microwave pretreatment. Water Environment Research 79 (11), 2304e2317. Eskicioglu, Cigdem, Droste, Ronald L, Kennedy, Kevin J, 2007b. Performance of anaerobic waste activated sludge digesters after microwave pretreatment. Water Environment Research 79 (11), 2265e2273. Gavala, H.N., Yenal, U., V Skiadas, I., Westermann, P., K Ahring, B., 2003. Mesophilic and thermophilic anaerobic digestion of primary and secondary sludge. Effect of pretreatment at elevated temperature. Water Research 37 (19), 4561e4572. Han, Y., Sung, S., Dague, R., 1997. Temperature-phased anaerobic digestion of wastewater sludges. Water Science and Technology 36 (6e7), 367e374. Hong, Seung M, Park, Jae K, Teeradej, N., Lee, Y.O., Cho, Y.K., Park, C.H., 2006. Pretreatment of sludge with microwaves for pathogen destruction and improved anaerobic digestion performance. Water Environment Research 78 (1), 76e83. Hong, Seung-Mo., 2002. Enhancement of pathogen destruction and anaerobic digestibility using microwaves. PhD Thesis, University of Wisconsin, Madison, USA.

Kiyohara, Y., Miyahara, T., Noike, T., 2000. A comparative study of thermophilic and mesophilic sludge digestion. Water and Environment Journal CIWEM 14, 150e154. Kobayashi, T., Hashinaga, T., Mikami, E., Suzuki, T., 1989. Methanogenic degradation of phenol and benzoate in acclimated sludges. Water Science and Technology 21 (4e5), 55e65. Mata-Alvarez, J., 2002. Biomethanization of the Organic Fraction Municipal Solid Wastes. IWA Publishing, UK. Metaxas, A.C., Meredith, R.J., 1983. Industrial Microwave Heating, IEE Power Engineering Series 4. P. Peter Peregrinus, London, UK: IEE. Miron, Y., Zeeman, G., van Lier, J., Lettinga, G., 2000. The role of sludge retention time in the hydrolysis and acidification of lipids, carbohydrates and proteins during digestion of primary sludge in CSTR systems. Water Research 34 (5), 1705e1713. Moen, G., Stensel, H.D., Lepisto¨, R., Ferguson, J.F., 1997. Effect of solids retention time on the performance of thermophilic and mesophilic digestion of combined municipal wastewater sludges. Water Environment Research 75 (6), 539e548. Neyens, E., Baeyens, J., 2003. A review of thermal sludge pretreatment processes to improve dewaterability. Journal of Hazardous Materials 98 (1e3), 51e67. Nozhevnikova, A.N., Kostsyurbenko, O.R., Parshina, S.N., 1999. Anaerobic manure treatment under extreme temperature conditions. Water Science and Technology 40 (1), 215e221. Park, B., Ahn, J., Kim, J., Hwang, S., 2004. Use of microwave pretreatment for enhanced anaerobiosis of secondary sludge. Water Science and Technology 50 (9), 17e24. Puchajda, Bartek, Oleszkiewicz, Jan, 2006. Thermophilic anaerobic acid digestion of biosolids: hydrolysis, acidification, and optimization of retention time of acid digestion. Journal of Environmental Engineering and Science 5 (3), 187e195. Riau, Vı´ctor, Angeles De La Rubia, M., 2010. Temperature-phased anaerobic digestion (TPAD) to obtain class A biosolids: a semicontinuous study. Bioresource Technology 101 (8), 2706e2712. Schmit, K.H., Ellis, T.G., 2001. Comparison of temperature-phased and two-phase anaerobic co-digestion of primary sludge and municipal solid waste. Water Environment Research 73 (3), 314e321. Sung, S., Santha, H., 2003. Performance of temperature-phased anaerobic digestion (TPAD) system treating dairy cattle wastes. Water Research 37 (7), 1628e1636. Toreci, I., Kennedy, K.J., Droste, R.L., 2008. Effect of high temperature microwave irradiation on municipal thickened waste activated sludge solubilization. In: 11th Conference on Process Integration, Modeling and Optimization for Energy Saving and Pollution Reduction (PRES) (Prague, Czech Republic). Toreci, Isil, Kennedy, Kevin J, Droste, Ronald L, 2009. Evaluation of continuous mesophilic anaerobic sludge digestion after high temperature microwave pretreatment. Water Research 43 (5), 1273e1284. Watts, S., Hamilton, G., Keller, J., 2006. Two-stage thermophilic mesophilic anaerobic digestion of waste activated sludge from a biological nutrient removal plant. Water Science and Technology 53 (8), 149e157. Zhou, Jianpeng, Mavinic, Donald S, Kelly, Harlan G, Ramey, William D, 2003. Effects of temperatures and extracellular proteins on dewaterability of thermophilically digested biosolids. Journal of Environmental Engineering and Science 1, 409e415.

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 8 3 5 e2 8 4 4

Available at www.sciencedirect.com

journal homepage: www.elsevier.com/locate/watres

Analysis of the build-up of semi and non volatile organic compounds on urban roads Parvez Mahbub a,*, Godwin A. Ayoko b, Ashantha Goonetilleke a, Prasanna Egodawatta a a b

School of Urban Development, Queensland University of Technology, GPO Box 2434, Brisbane 4001, Queensland, Australia Chemistry Discipline, Queensland University of Technology, GPO Box 2434, Brisbane 4001, Queensland, Australia

article info

abstract

Article history:

Vehicular traffic in urban areas may adversely affect urban water quality through the

Received 22 November 2010

build-up of traffic generated semi and non volatile organic compounds (SVOCs and NVOCs)

Received in revised form

on road surfaces. The characterisation of the build-up processes is the key to developing

20 February 2011

mitigation measures for the removal of such pollutants from urban stormwater. An in-

Accepted 26 February 2011

depth analysis of the build-up of SVOCs and NVOCs was undertaken in the Gold Coast

Available online 11 March 2011

region in Australia. Principal Component Analysis (PCA) and Multicriteria Decision tools such as PROMETHEE and GAIA were employed to understand the SVOC and NVOC build-up

Keywords:

under combined traffic scenarios of low, moderate, and high traffic in different land uses. It

Semi volatile organic compounds

was found that congestion in the commercial areas and use of lubricants and motor oils in

Non volatile organic compounds

the industrial areas were the main sources of SVOCs and NVOCs on urban roads, respec-

Traffic pollutants

tively. The contribution from residential areas to the build-up of such pollutants was

Pollutant build-up

hardly noticeable. It was also revealed through this investigation that the target SVOCs and

Multicriteria decision tools

NVOCs were mainly attached to particulate fractions of 75e300 mm whilst the redistribution of coarse fractions due to vehicle activity mainly occurred in the >300 mm size range. Lastly, under combined traffic scenario, moderate traffic with average daily traffic ranging from 2300 to 5900 and average congestion of 0.47 were found to dominate SVOC and NVOC build-up on roads. ª 2011 Elsevier Ltd. All rights reserved.

1.

Introduction

Urban traffic activities are one of the predominant sources of stormwater pollutants that accumulate on urban roads and are eventually transported to receiving water bodies. In the context of traffic generated pollutants on urban roads, semi volatile organic compounds (SVOCs) are mainly associated with diesel, fuel oil 1e6 and kerosene, whilst the non volatile organic compounds (NVOCs) are mainly associated with motor oils and lubricants (Draper et al., 1996). In a broader sense, these pollutants are part of a larger family of

hydrocarbons which are assessed as total petroleum hydrocarbon (Morrison and Boyd, 1992). According to the criteria stipulated by the American Petroleum Institute (API), products such as diesel fuels, fuel oils 1e6 and heavier engine oils and lubricants are classified as diesel range organics (DROs) (API, 1994). These are the most widely used and distributed traffic related products. Homologous series of n-alkanes from decane to tetracontane are amongst the most common constituents of these products (Draper et al., 1996). In this context, particulate n-alkane concentrations on roads can also result from tyre abrasion and brake lining dust

* Corresponding author. Tel.: þ61 7 3138 9945; fax: þ61 7 3138 1170. E-mail addresses: [email protected] (P. Mahbub), [email protected] (G.A. Ayoko), [email protected] (A. Goonetilleke), [email protected] (P. Egodawatta). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.02.033

2836

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 8 3 5 e2 8 4 4

(Rogge et al., 1993). Brown et al. (1985) reported significant concentrations of vehicle generated SVOCs and NVOCs in urban runoff which may alter the quality of the receiving water, thus harming the endemic biological community. Whilst, both petrol and diesel engine vehicles emit gaseous and particulate hydrocarbons as a result of incomplete combustion (Neeft et al., 1996), Andreou and Rapsomanikis (2009) noted that past studies mainly characterised only one organic group (e.g., polycyclic aromatic hydrocarbons). As the characteristics of urban traffic in terms of traffic volume and congestion is rapidly changing with increased urbanisation throughout the world, an in-depth understanding of the impacts of traffic generated semi and non volatile organic compounds on the urban water environment is needed in order to develop appropriate mitigation measures. The characterisation of the build-up of semi and non volatile organic compounds on urban roads due to changing traffic characteristics under rapid urbanisation is the key to the formulation of appropriate mitigation measures. In this context, the current state of knowledge on the build-up processes of semi and non volatile organic compounds on urban roads is limited. Brandenberger et al. (2005) in their investigation of the emissions of diesel fuels and lubricating oils under different driving conditions found that poor combustion, reduced conversion efficiency of the oxidation catalyst, and increased mean load of the vehicle driving cycle were the primary reasons for increased particulate emissions of lubricating oils and diesel fuels. However, while their results represented the effects of different driving cycles of the motor vehicles on the ambient concentrations of particulate pollutants, it is important to note that not all of the vehicular emissions are necessarily deposited on impervious surfaces. Ning et al. (2005) reported that the initial pollutant concentration at the exhaust pipe, exit velocity, exit angle, and crosswind intensity affect the pollutant dispersion pattern significantly even at the idle condition. Traffic parameters such as, average daily traffic (ADT) and congestion on the road (volume to capacity ratio, V/C ) along with pavement characteristics such as surface texture depth (STD) are reported to significantly influence pollutant build-up on urban roads (Mahbub et al., 2010a; Brown et al., 2004; Pitt et al., 1995). The dynamic variability of the traffic characteristics mentioned above poses a significant threat to urban water bodies through the accumulation of semi and non volatile organic compounds in the urban environment. In this study, the build-up processes of semi and non volatile organic pollutants have been characterised with respect to physicochemical (e.g., particle size distribution), traffic and land use parameters, and pavement characteristics. The outcome of this study is expected to provide guidance for mitigating the impacts of semi and non volatile organic pollutants transported by urban stormwater runoff to receiving waters.

2.

Materials and methods

2.1.

Site selection

The site selection criteria were formulated using a suburb based approach. Two suburbs namely, Helensvale and Coomera in the Gold Coast region in Southeast Queensland, Australia were

selected. The two selected suburbs also represent the transport infrastructure developed within the Gold Coast City region in the past decade. Eleven road sites (Table 1) located in three different land uses, namely, residential, commercial and industrial were selected for build-up sample collection. The selection of different land uses ensured a cross-section of traffic activities on road surfaces within the Gold Coast region.

2.2.

Key study parameters

In the study, the key traffic parameter used was the Daily Traffic (ADT) instead of Average Annual Daily Traffic (AADT), as the former is predicted by a sophisticated transport model called ZENITH (GCCC, 2006) which is currently being used by the Gold Coast City Council. Gardiner and Armstrong (2007) have found that traffic levels measured as AADT are a poor proxy for stormwater runoff quality. Kayhanian et al. (2003) also reported that AADT itself does not have any direct correlation with pollutant build-up on road surfaces. The Volume to Capacity ratio (V/C ) of a roadway describes the traffic characteristics on the stretch of road during the peak hour (Ogden and Taylor, 1999). This parameter was found to vary quite significantly for the different sites that were selected for the study. Studies have shown that vehicle congestion due to increased traffic volumes in the urban areas had a direct influence on pollutant emission levels on roads (Smit et al., 2008). As such, Average Daily Traffic (ADT) and Volume to Capacity Ratio (V/C ) were incorporated as the two principal traffic parameters that would influence the build-up of semi and non volatile organic compounds on urban roads. The US Federal Highway Administration recommend specific pavement surface texture depths so that current and predicted traffic needs could be accommodated in a safe, durable, and cost effective manner (FHWA, 2005). The texture depth can influence pollutant build-up and wash-off from pavement surfaces (Pitt et al., 1995; Legret and Colandini, 1999). The road texture also affects the interactions between the vehicle tyres and the driving surface (Kreider et al., 2010). Hence, the surface texture depth of the pavement surfaces at the selected road sites was also incorporated into the study. Table 1 lists the selected sites with the identifiers adopted and the corresponding traffic and pavement characteristics.

2.3.

Build-up sample collection

The pollutant build-up process was characterised as having four main functional forms such as, linear, power, exponential, and MichaeliseMenten (Huber, 1986). Amongst these, the non-linear asymptotic form proposed by Sartor et al. (1974) has been most often cited and also used in several stormwater quality models such as, DR3-QUAL, FHWA, SWMM (Huber, 1986). In this context, Egodawatta (2007) noted that pollutant build-up on road surfaces asymptote to an almost constant value after a seven-day antecedent dry period. Hence, in this study, seven dry days were allowed at each site prior to any sample collection. Samples were collected over a two-month period in April and May 2009. The weather was dry and the temperature during the sampling ranged between 22  C and 25  C. Three different time periods including 8e9 am in the morning, 12e1 pm at noon as well as 3e4 pm in the

2837

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 8 3 5 e2 8 4 4

Table 1 e Selected road sites with traffic and pavement parameters (partially adapted from Mahbub et al., 2010a). Site Name Identifier Abraham Road CA Reserve Road RR Peanba Park Road RP Billinghurst Cres RB Beattie Road IBT Shipper Drive IS Hope Island Road CH Lindfield Road CL Town Centre Drive CT Dalley Park Drive RD Discovery Drive RDS

Land Use

Commercial Residential Residential

Geo-Coordinates Average Daily Volume to Surface Age of the Top Coat Traffic (ADT), Capacity Texture Depth Road Material % of vehicles/day Ratio (V/C) (STD), mm Section, (yrs) Aggregate Binder 27.865 S 153.307 E 27.870 S 153.301 E 27.851 S 153.281 E

13,028

1.11

0.6467

DG14a

3 5.1

6339

0.45

0.7505

DG14a

3 5.1

581

0.15

0.6844

DG10b

4 5.3

Residential Industrial

27.856 S 153.298 E 27.868 S 153.324 E

5936

0.74

0.7015

DG10b

10 5.3

2670

0.24

0.7074

DG14a

2 5.1

27.861 S 155.332 E Commercial 27.882 S 153.328 E Commercial 27.922 S 153.334 E Industrial

7530

0.55

0.6788

DG14a

6 5.1

7534

0.57

0.7254

DG14a

3 5.1

2312

0.33

0.9417

DG10b

10 5.3

Commercial Residential Residential

27.929 S 153.337 E 27.887 S 153.346 E 27.899 S 153.327 E

24,506

0.62

0.6416

DG14a

4 5.1

3534

0.42

0.8342

DG10b

10 5.3

9116

0.25

0.6957

DG14a

2 5.1

a Dense Grade Bitumen Asphalt with 5.1% aggregate binder. b Dense Grade Bitumen Asphalt with 5.3% aggregate binder.

afternoon were chosen as sample collection time from the eleven sites to incorporate both rush hour and normal traffic. A pilot study, reported in Mahbub et al. (2010b), was undertaken and 90% sample collection efficiency was achieved through a domestic vacuum cleaner with a water filtration system. This collection efficiency of the vacuum cleaner was for sand dust that passed 100% through 420 mm sieve and retained 100% on 0.7 mm Whatman GF F glass fibre filter. The test was performed on the middle of the lanes of actual road surface subject to daily traffic. Three build-up plots of 2  1.5 m2 area were initially cleaned with deionised water and allowed to dry up for 1 h. It was assumed that the build-up of pollutants during 1 h was uniform for the three plots. Two of the plots were applied with 100 gm sand dust and the third plot was kept without applying any sand dust. The ‘wet and dry vacuum’ system (Mahbub et al., 2010b), which incorporates vacuuming of the build-up plot in dry and subsequently in wet condition was then applied at different combinations of pressure and time. The wet condition was created by a sprayer. The difference in the collected sand dust from the first two plots compared with the third plot at various combinations indicated that optimum pressure of 2 bar for 3 min was required to achieve to 90% collection efficiency. The total build-up sample was collected in 8 L deionised water.

2.4.

Sample preparation

The collected samples were transported to the laboratory and 500 mL sub-samples were prepared using a churn splitter. The total particulate analytes were fractioned into four size ranges, namely, >300 mm, 150e300 mm, 75e150 mm, 1e75 mm

using wet sieving. The filtrate passing through a 1 mm Whatman GF B glass fibre filter was considered as the potential total dissolved fraction. In each case, 500 mL homogeneous sub-samples were prepared by mixing with deionised water, stored in 500 mL amber glass bottles with PTFE seals, preserved with 5 mL of 50% HCl at 4  C in the laboratory and analysed within 40 days of collection.

2.5.

Sample testing

The target SVOCs for the study were octane (OCT), decane (DEC), dodecane (DOD), tetradecane (TED), hexadecane (HXD), octadecane (OCD), Eicosane (EIC), docosane (DOC), tetracosane (TTC), hexacosane (HXC), and octacosane (OCC) having boiling points ranging from 125  C to 432  C. The target NVOCs were triacontane (TCT), dotriacontane (DTT), tetratriacontane (TRT), hexatriacontane (HXT), octatriacontane (OTT), and tetracontane (TTT) with boiling points ranging from 449  C to 525  C (Kudchadker and Zwolinski, 1966). The test methods adopted for the determination of SVOCs were USEPA 3510C, 8015, 8021, and 8260 (EPA, 2008). Draper et al. (1996) proposed modifications to the EPA methods to include the determination of motor oil with a carbon number up to C38 which was used as a guide to establish the Gas Chromatographic (GC) temperature programme in this study for the determination of both SVOC and NVOC simultaneously. Calibration standards, internal standards, surrogate spikes and blanks were used in order to maintain quality control and quality assurance of the testing. Nine different calibration standards (17 component FTRPH calibration standards from Accustandard) were prepared at 0.1, 0.5, 0.7, 1, 1.4, 7, 10, 28,

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50 mg/L concentrations for each target analyte. The DRO internal standard (SigmaeAldrich) consisting of acenaphthene-d10, chrysene-d12, naphthalene-d8, perylene-d12, phenanthrene-d10, 1, 4-dichlorobenzene-d4 was added to each sample and standards at 5 mg/L concentration. Field blanks were used for each field sample collection episode and all results were blank corrected. Three quality control standards (TPH Mix-1-DRO certified reference materials from SigmaeAldrich) at 1, 10 and 50 mg/L concentrations were prepared independently of the calibration standards and were included in each batch for comparison with the calibration standards. The sample batch was reanalysed if deviation of >10% from the certified value was observed for at least half of the target analytes in the quality control standards. One sample from each batch was spiked with another quality control standard at a concentration of 35 mg/L. Surrogate standards (Accustandard) consisting of 10 mg/L of n-triacontane-d62 were added to seven randomly chosen samples. Seven field blanks were used to establish the limits of detection (LOD) for each analyte. Values less than LODs were replaced by half of the LOD values and values above the highest concentration limit of the calibration standard were discarded as outliers. Seven replicate sub-samples were prepared from randomly chosen samples from each of the eleven sites. The intra-site relative standard deviation was found within the range of 8e19% for each replicate. The intersite relative standard deviation was found within the range of 15e21% for each analyte. This was within the range of the relative standard deviation suggested by Horwitz (1982) for ppm level concentrations. Table 2 shows the recoveries of the surrogates and the spikes. The test results for each of the five

size fractions are provided as Supplementary Data available online with this study. USEPA method 3510C (EPA, 2008) was used to extract SVOCs and NVOCs using the separatory funnel liquideliquid extraction technique with 250 mL hexane as the exchange solvent. The samples were cleaned using standard column cleanup protocol with 5 cm silica gel and 5 cm pyrex glass wool topped with 5 cm anhydrous Na2SO4 (EPA, 2008). Further concentration was carried out using the Kuderna-Danish apparatus followed by the nitrogen blowdown technique (EPA, 2008). The extractions and concentrations were carried out until a final extracted volume of 1 mL was achieved for Gas Chromatographic (GC) analyses. A specially built HP5MS Agilent capillary column of 30 m length, 0.32 mm internal diameter and 0.25 mm film thickness was used in the GC analyses. The column was temperature programmed to separate the analytes, which were then detected by a mass spectrometer interfaced to the GC. A splitless sample injection of 2 mL at an inlet temperature of 280  C, inlet pressure of 35.58 kN/m2 (5.16 psi) and a flowrate of 2.4 mL/min was used. The initial oven temperature was set at 40  C, held at that temperature for 12 min, followed by an increase of 10  C per min. until the oven temperature reached 300  C and finally the temperature was held at 300  C for 20 min. Hence, the total GC runtime was 58 min per sample. The identification of target analytes was performed by comparing their mass spectra with the electron impact spectra of authentic standards. Other physico-chemical variables such as particle size distribution (PSD) of the sub-samples were determined using a Malvern Mastersizer S particle size analyser capable of

Table 2 e Percent recoveries of spikes applied at 35 mg/L and surrogate applied at 10 mg/L along with limits of detection for the target compounds. Analytes

Limits of detection (LOD), mg/L

% recovery of spikes

% recovery of surrogate in randomly chosen samples

Batch Batch Batch Sample Sample Sample Sample Sample Sample Sample 1 2 3 1 2 3 4 5 6 7 Surrogate n-triacontane-d62 Spiked Octane SVOCs Decane Dodecane Tetradecane Hexadecane Octadecane Eicosane Docosane Tetracosane Hexacosane Octacosane Spiked Triacontane NVOCs Dotriacontane Tetratriacontane Hexatriacontane Octatriacontane Tetracontane

e 0.54 0.32 0.44 0.38 0.85 0.71 0.34 0.54 0.05 1.07 1.16 1.02 1.32 1.23 1.06 0.60 0.85

e 128 80 131 71 75 72 90 124 110 79 78 84 69 85 98 e 80

e 110 82 124 76 88 79 86 129 91 84 83 70 79 78 84 82 81

e 101 83 97 88 84 91 104 e 92 86 97 71 75 74 65 71 86

76

79

104

88

81

96

113

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analysing particle size between 0.05 and 900 mm diameter (Malvern, 1994). Total suspended solids (TSS) and total organic carbon (TOC) were analysed by methods 2540D and 5310B (APHA, 2005). The PSD of the sub-samples were compared with each other and used as a guide for homogeneity maintained in the sub-sampling process. The surface texture depths (STD) of the pavement surface at the selected road sites were determined according to method T250 (Main Roads, 2009). Additionally, the pH and electrical conductivity (EC) of each sample were measured using standard pH and EC probes in the laboratory according to methods 4500-Hþ B and 2510B respectively (APHA, 2005).

prioritise objects (Keller et al., 1991). The PROMETHEE method calculates the positive and negative outranking flows, fþ and f, respectively based on the preference functions in order to rank the objects. The fþ value indicates how each object outranks all the others, whilst the f value indicates how each object is outranked by all the others. This procedure is known as PROMETHEE I ranking. However, in some instances, two objects cannot be compared as they perform equally on different criteria. In these cases, the net outranking flow, f which is the algebraic difference between fþ and f, is calculated in order to facilitate the comparison. This procedure is known as PROMETHEE II ranking.

2.6.

2.6.3.

Data analyses

The data matrices consisted of 11 objects and 25 variables for each of the five particle size fractions noted above. The 11 road sites were considered as the 11 objects with identifiers listed in Table 1 with the prefixes C, I, or R for commercial, industrial, and residential land uses, respectively. Variables such as, ADT, V/C, STD, pH, EC, PSD, TSS, and TOC were considered as attributes of the objects responsible for the build-up of the target SVOCs and NVOCs, and hence considered as independent variables. After initial investigation of the probability distribution of the objects and variables in the data matrices, standardisation of the variables was performed as a pretreatment measure so that each variable could be treated with equal importance in the data analysis. Chemometric multivariate data analyses techniques such as, principal component analysis (PCA), preference ranking organisation method for enrichment evaluation (PROMETHEE) and geometric analysis for interactive aid (GAIA) were employed. Component extraction processes such as PCA and multicriteria decision-making processes such as PROMETHEE and GAIA have been used recently to characterise the incorporation of pollutants in stormwater runoff from urban roads (Jartun et al., 2008; Ayoko et al., 2007). A brief description of these techniques is discussed below.

2.6.1.

PCA

The principal component analysis (PCA) is a data pattern recognition technique that extracts information from a data matrix by the projection of objects and variables to the principal components (PCs). The PCs are considered as the latent variables which are linear combinations of the original variables of the dataset. The PCA technique transforms the original variables to a new orthogonal set of PCs in such a way that they contain the data variance in a decreasing order, i.e., the first PC contains most of the data variance and the second PC contains the second largest variance and so on. Consequently, the data can be presented diagrammatically by plotting the loading of each variable in the form of a vector and the score of each object in the form of a data point. This type of plot is referred to as a ‘Biplot’. More insight into the PCA technique can be found in Massart et al. (1997). In this study, SIRIUS2008 software (Sirius, 2008) was used to perform the PCA procedures.

2.6.2.

PROMETHEE

PROMETHEE is an object ranking technique based on data criteria that uses some user defined preference functions to

GAIA

GAIA is essentially a PCA biplot which facilitates a sensitivity analysis for multicriteria decision methods such as PROMETHEE (Keller et al., 1991). GAIA provides a graphical view of the objects and variables for net outranking flow f in the form of a PCA biplot by decomposing the values from PROMETHEE II into unicriterion flows for each variable. The advantages of GAIA over a PCA biplot is that it produces a decision axis that takes into account the weights associated with the variables. These weights can be interactively adjusted for maximum achievable ‘f’ net ranking values obtained by PROMETHEE II. This helps the decision-maker with an enriched understanding of the problem in terms of the detection of clusters of objects, conflicts in variables, inability to compare objects and so on. More details on the PROMETHEE and GAIA methods are discussed in Keller et al. (1991) and Ayoko et al. (2004). The DecisionLab 2000 software (Decision, 2000) was used to perform PROMETHEE and GAIA analysis.

3.

Results and discussion

3.1.

Trends in the original data

The bulk volume of the original data (presented as Supplementary Tables 1e5) makes it hard to discern any meaningful trends. Simple bi-variate correlations between the target variables at each of the five size fractions in Supplementary Tables 6e10 showed that the correlation of PSD, pH, EC, solids and organic carbon with the target SVOCs and NVOCs were very low, within a range of 0.2 in the dissolved fraction of <1 mm. With the exception of EC, the correlations of PSD, pH, TSS and TOC with the target compounds started to increase from 1 mm to 300 mm size fractions. This suggested that the target compounds were mainly associated in non-ionic form with the particulate fraction 1e300 mm. More intrusive data analyses techniques such as, PCA, PROMETHEE and GAIA were employed to further investigate the trends noted in the original data matrices.

3.2.

Exploratory PCA

Initially PCA was performed on the pre-treated data matrices starting with the total particulate fractions from 1 mm to >300 mm as well as the potential dissolved fraction of <1 mm taken together as shown in Fig. 1. All the physico-chemical,

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traffic, pavement, and land use variables were included along with the target semi volatile and non volatile compounds. The traffic parameters V/C and STD were found to be more strongly correlated with the target SVOCs and NVOCs than ADT in Fig. 1. This suggested that congestion on the road as well as the road texture conditions affected the build-up of SVOCs and NVOCs directly whilst ADT may have influenced the redistribution of particles on the road surface. Whilst the bulk of the free-flowing traffic was in the commercial and most of the residential areas, low traffic volumes were noted in the industrial areas. This explains the strong association of ADT with commercial and residential sites on PC1 in Fig. 1. The age and the grade of the top coat on the road as described in Table 1 was also found to be important as the STD in Fig. 1 positively correlates with most of the target variables. In Fig. 1, only four objects (two residential and two industrial) were found to be associated with the target pollutants. This suggested that there is little or varying influence exerted by the land use parameters on the build-up of SVOCs and NVOCs. However, without detailed studies on the individual particle size fractions, these findings could not be validated. Fig. 2 shows biplots of the build-up of five individual size fractions from >300 mm to <1 mm. In Fig. 2(a), the higher molecular weight NVOCs (422e562 g mol1) with boiling points ranging from 449  C to 525  C are strongly associated with the industrial sites whilst the comparatively lighter molecular weight SVOCs (114e394 g mol1) with boiling points ranging from 125  C to 432  C are mainly associated with the commercial sites for the >300 mm particulate fraction on PC1. There are some associations of residential sites (RDS, RP, RD, and RR) with octane (OCT) and tetradecane (TED) in Fig. 2(a). However, the association of residential sites with the build-up of either SVOCs or NOVCs

4 RD 3

DOD TSS DTT

2

TOC

CH

PC 2 (12.4%)

1

0

RP

TED

-1

OCC TRT TCT OCD STD TTC TTT DOC HXD pH EIC OTT HXC

ADT

-2

RB

EC

RR CT CL CA RDS

IBT HXT

-3 V/C

IS

-4 DEC

OCT

-5 -5

0

5

10

PC 1 (70.8%)

Fig. 1 e PCA biplot of total particulate fractions from <1 mm to >300 mm taken together.

was found to be generally negligible on both PCs for the >300 mm fraction. In Fig. 2(b)e(d), similar findings suggested that the semi volatile components of petrol and diesel fuels are predominantly associated with the commercial areas whilst the non volatile heavier compounds were mainly associated with the industrial areas. The commercial areas in this study were close to carparks, shopping centres as well as service stations and the industrial areas mainly comprised of marine and light metal industries. According to Table 1, the average congestion (0.66  0.33) in the commercial areas was much higher than the average congestions (0.40  0.22) in both the industrial and residential areas. The average volume of traffic in the commercial areas is almost twice the volumes for the residential and industrial areas. This suggested that slow moving traffic in the commercial areas were contributing significantly towards the build-up of SVOCs through exhaust and non-exhaust emissions, whereas, the strong correlations between NVOCs and the industrial sites observed in particulate fractions from >300 mm to 1 mm in Fig. 2 suggested that these NVOCs in the industrial areas may not necessarily originate from traffic alone. Usage of different types of motor oils and lubricants by machinery in the industrial areas may also contribute to the build-up of NVOCs in these areas. In either case, the contribution of residential areas to the build-up of such pollutants on urban roads is hardly noticeable. It is important to note that traffic generated SVOCs are prominently associated with particulate matter from 1 mm to >300 mm in the commercial areas in Fig. 2(a)e(d). In Fig. 2(e), for the potential dissolved fraction of <1 mm, the three different land uses are not directly associated with the build-up of SVOCs and NVOCs as no clear separation of land use with target variables was identified in either of the PCs. There are some associations of residential objects (e.g., RB, RD, and RP) with the build-up of a few SVOCs and NVOCs in Fig. 2 (e). However, as the average volume of daily traffic in the residential study areas was quite similar (around 5100 vehicles per day) to the industrial areas, it is understandable that the traffic in the residential areas did not directly influence the build-up of SVOCs and NVOCs. Patra et al. (2008) noted that coarser particles resuspend and redistribute faster than the finer particles due to vehicle induced turbulence and the reservoir of finer particles get replenished by grinding of the coarser particles under the vehicle wheels. The role of organic matter as a binding agent between solids and other pollutants has been discussed by Charlesworth and Lees (1999). They reported that organic matter acts as a predominant binder for particle sizes ranging from 63 mm to 2 mm during build-up. Hence, the ‘land use independent’ loadings of SVOCs and NVOCs in the potential dissolved fraction of <1 mm in Fig. 2(e) suggest that traffic may have caused the resuspension and redistribution of coarser particles generated elsewhere and replenished the fine particles of <1 mm size which has adsorbed the target organics which is independent of the land use. However, the extent of adsorption of target pollutants by the finer fraction of <1 mm may be very limited as the loading vectors of the target pollutants in the dissolved fraction of <1 mm are quite similar in magnitude to the particulate fractions in Fig. 2(a)e(d). This suggests that the variances of target pollutant concentrations in the dissolved

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Fig. 2 e Individual PCA biplots for (a) >300 mm; (b) 150e300 mm; (c) 75e150 mm; (d) 1e75 mm; and (e) <1 mm size fractions.

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fraction are quite similar to the particulate fractions. This is attributed to the fact that organic compounds have very limited solubility in most of the solvents which may cause the target pollutants to remain free without adsorbing to the fine particles and hence the potential dissolved fraction manifested similar variances as the particulate fractions. The PCA analysis provides a fundamental characterisation of the build-up of traffic related SVOCs and NVOCs for different land uses. In order to characterise such build-up in terms of particle size fractions as well as the predominant urban traffic scenarios that influence build-up, PROMETHEE ranking and GAIA analysis were employed.

3.3.

PROMETHEE

The preference ranking organisation method for enrichment evaluation (PROMETHEE) was applied to the same data matrices that were used for the PCA. In the context of ranking the study sites as urban traffic objects with variable traffic parameters, Mahbub et al. (2010a) proposed high, moderate, and low urban traffic scenarios based on a moderately soft fuzzy clustering technique that allows traffic attributes of different scenarios to intersect with each other. The high traffic scenario comprised of traffic volumes ranging from 9000 to 24,000 ADT with relatively high congestion; moderate traffic scenario comprised of ADT values ranging from 2300 to 5900 with moderate congestion whilst low traffic scenario was associated with low traffic volume ranging from 500 to 3500 ADT with low congestion. This study adopted the same classification of urban traffic scenarios to interpret the PROMETHEE ranking. According to this urban traffic classification system, high traffic scenario comprised of objects IS, CT, CA, and RDS; moderate traffic scenario comprised of CH, CL, IBT, RB, and RR whilst low scenario comprised of RD and RP. Fig. 3 shows the ‘f’ net outranking flows of the 11 traffic objects. The three different land use types and the five different size fractions were incorporated in the ranking. The Gaussian preferential function (Brans et al., 1986) with the threshold value set equal to the standard deviation of each criterion was used in the PROMETHEE model. This function was chosen according to the suggestion of Brans et al. (1986) who showed that the Gaussian function provided the least discontinuities and guaranteed the most stable results out of the six different preference functions in PROMETHEE. In Fig. 3, all the objects with positive outranking flows are from commercial and residential sites. These along with the negatively ranked industrial sites suggest that traffic related

build-up of SVOCs and NVOCs mainly occur in commercial and residential sites. However, the objects with negative outranking flows in Fig. 3 comprised of all three land uses (e.g., CA, RDS, IS, and CT). According to the above noted classification of traffic scenarios, most of the negatively ranked objects fall into the high traffic scenario. To the contrary, the top three objects (CL, CH, and RR) are from the moderate traffic cluster. Therefore, it is evident from the PROMETHEE ranking that the moderate traffic scenario with ADT values ranging from 2300 to 5900 with average congestion of 0.47 would dominate the SVOCs and NVOCs build-up. The low traffic scenario (objects: RD and RP with very low positive outranking flow values) may have some impacts on such build-up through the resuspension and redistribution of coarse particles as both of them fall into residential land use. However, the high traffic scenario (objects: CA, CT, IS, and RDS) did not affect the build-up. Whilst the high traffic scenario had the highest average traffic volume and congestion, the role of texture depths may also play an important role in the SVOCs and NVOCs build-up. The average texture depths of the high traffic objects was 0.67 mm which was comparatively lower than the moderate and low traffic objects (0.77 mm). This difference could have led to weaker correlations between high traffic objects and the different particle size fractions investigated in the study. In order to facilitate the sensitivity of the findings derived through the PROMTHEE ranking, the geometric application for interactive aid (GAIA) was performed on the same data matrices used for PCA and PROMETHEE.

3.4.

GAIA

The GAIA method provided a PCA biplot with a decision axis (pi) for all traffic scenarios and size fractions. The quality of the decision axis was tested for its stability by interactively changing the weights of the different variables in the data matrix for the maximum achievable ‘f’ net ranking values and the optimised GAIA biplot is shown in Fig. 4. The GAIA biplot in Fig. 4 isolates most of the moderate traffic objects from the high traffic objects. Additionally, the decision axis (pi) is strongly correlated with the higher particulate fractions of 75e300 mm fractions as well as the moderate traffic objects on both axes. This suggests that the target organic compounds are predominantly present in the 75e300 mm particulate fractions. The low traffic objects RD and RP as well as moderate objects IBT are also strongly correlated with the particulate fraction >300 mm, suggesting that the redistribution of particulate matter occurred in this

Fig. 3 e Combined PROMETHEE II net outranking flows of traffic objects showing commercial sites as predominant sources of SVOCs and NVOCs build-up.

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SVOCs and NVOCs build-up. Particulate fraction > 300 mm primarily influences the redistribution of coarser particle due to vehicular activities. The potential dissolved fraction <1 mm is not associated with the build-up of SVOCs and NVOCs in any of the land uses investigated in the study. Therefore, mitigation measures for removal of SVOCs and NVOCs from build-up should target the 75e300 mm particulate fractions.

Acknowledgements This research study was undertaken as a part of an Australian Research Council funded Linkage project (LP0882637). The first author gratefully acknowledges the postgraduate scholarship awarded by Queensland University of Technology to conduct his doctoral research. The help and support from Gold Coast City Council, Queensland Department of Transport and Main Roads as well as Queensland Police is also gratefully acknowledged.

Fig. 4 e GAIA biplot for the build-up of SVOCs and NVOCs incorporating all size fractions as well as all traffic scenarios.

fraction. The potentially dissolved fraction <1 mm is not correlated with any of the traffic objects even though the magnitude of its loading vector is significant. This suggested that the presence of the fine fraction <1 mm did not contribute to the build-up of SVOCs or NVOCs and only the resuspension and the replenishment of the finer materials as described earlier are active in this fraction.

4.

Conclusions

The build-up of traffic generated semi and non volatile organic compounds under combined traffic scenarios of low, moderate, and high has been characterised in this study. The key findings can be summarised as follows:  The build-up of lighter semi volatile compounds is mainly associated with the commercial areas whilst non volatile lubricants and motor oil compounds are associated with the industrial areas. The residential areas do not significantly contribute to the build-up of such pollutants on urban roads. Congestion in the commercial areas appears to be the main source of build-up of SVOCs whilst industrial usage of lubricants and heavier oils may also contribute to the buildup of NVOCs in industrial areas.  Moderate traffic scenario with ADT ranging from 2300 to 5900 and average congestion of 0.47 would predominate SVOCs and NVOCs build-up on urban roads under combined traffic scenarios. As a practical outcome of this finding, a moderate traffic scenario in any type of land use can be targeted as a significant source of such pollutants.  Amongst the different size fractions, the particulate fraction 75e300 mm is the most predominant in associating with the

Appendix. Supplementary data Supplementary data associated with this article can be found in the online version at doi:10.1016/j.watres.2011.02.033.

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Legret, M., Colandini, V., 1999. Effects of a porous pavement with reservoir structure on runoff water: water quality and fate of heavy metals. Water Science Technology 39 (2), 111e117. Mahbub, P., Ayoko, G.A., Goonetilleke, A., Egodawatta, P., Kokot, S., 2010a. Impacts of traffic and rainfall characteristics on heavy metals build-up and wash-off from urban roads. Environmental Science and Technology 44 (23), 8904e8910. Mahbub, P., Ayoko, G., Egodawatta, P., Yigitcanlar, T., Goonetilleke, A., 2010b. Traffic and climate change impacts on water quality: measuring build-up and wash-off of heavy metals and petroleum hydrocarbons. In: Yigitcanlar, T. (Ed.), Rethinking Sustainable Development: Urban Management, Engineering and Design. Engineering Science Reference, New York, pp. 147e167. Main Roads, 2009. Austraods Test Manual, Method AGPT/T250. http://www.austroads.com.au/pavement/testmethods.html (accessed 05.01.09.). Malvern, 1994. Mastersizer S Long Bed Version 2.19ª. Malvern Instruments Ltd. 1992e1994. Massart, D.L., Vandeginste, B.G.M., Buydens, L.M.C., De Jong, S., Lewi, P.J., Smeyers-Verbeke, J., 1997. Handbook of Chemometrics and Qualimetrics Part A. Elsevier. 771e804. Morrison, R.T., Boyd, R.N., 1992. Organic Chemistry, sixth ed. Prentice-Hall Inc., pp. 94e97. Neeft, J.P.A., Makkee, M., Moulijn, J.A., 1996. Diesel particulate emission control. Fuel Processing Technology 47 (1), 1e69. Ning, Z., Cheung, C.S., Lu, Y., Liu, M.A., Hung, W.T., 2005. Experimental and numerical study of the dispersion of motor vehicle pollutants under idle condition. Atmospheric Environment 39 (40), 7880e7893. Ogden, K.W., Taylor, S.Y., 1999. Traffic Engineering and Management. Institute of Transport Studies. Monash University, Australia, pp. 592e594. Patra, A., Colvile, R., Arnold, S., Bowen, E., Shallcross, D., Martin, D., Price, C., Tate, J., Apsimon, H., Robbins, A., 2008. On street observations of particulate matter movement and dispersion due to traffic on an urban road. Atmospheric Environment 42 (17), 3911e3926. Pitt, R., Field, R., Lalor, M., Brown, M., 1995. Urban stormwater toxic pollutants: assessment, sources, and treatability. Water Environment Research 67 (3), 260e275. Rogge, W.F., Hildemann, L.M., Mazurek, M.A., Cass, G.R., Simoneit, B.R.T., 1993. Sources of fine organic aerosol. 3. Road dust, tire debris, and organometallic brake lining dust: roads as sources and sinks. Environmental Science and Technology 27 (9), 1892e1904. Sartor, J.D., Boyd, G.B., Agardy, F.J., 1974. Water pollution aspects of street surface contaminants. Journal Water Pollution Control Federation 46 (3), 458e467. Sirius, 2008. SIRIUS Version 7.1. ª Copyright 1987e2008. Pattern Recognition Systems AS. http://www.prs.no Help Topics. Smit, R., Brown, A.L., Chan, Y.C., 2008. Do air pollution emissions and fuel consumption models for roadways include the effects of congestion in the roadway traffic flow? Environmental Modelling & Software 23 (10e11), 1262e1270.

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journal homepage: www.elsevier.com/locate/watres

A sustainable, carbon neutral methane oxidation by a partnership of methane oxidizing communities and microalgae David van der Ha, Bert Bundervoet, Willy Verstraete, Nico Boon* Laboratory of Microbial Ecology and Technology (LabMET), Ghent University, Coupure Links 653, B-9000 Gent, Belgium

article info

abstract

Article history:

Effluents of anaerobic wastewater treatment plants are saturated with methane, an

Received 2 July 2010

effective greenhouse gas. We propose a novel approach to treat such effluents using

Received in revised form

a coculture of methane oxidizing communities and microalgae, further indicated as

25 February 2011

methalgae, which would allow microbial methane oxidation with minimal CO2 emissions.

Accepted 1 March 2011

Coculturing a methane oxidizing community with microalgae in sequence batch reactors

Available online 10 March 2011

under continuous lightning yielded a factor of about 1.6 more biomass relative to the control without microalgae. Moreover, 55% less external oxygen supply was needed to

Keywords:

maintain the methane oxidation, as oxygen was produced in situ by the microalgae. An

Methanotroph

overall methane oxidation rate of 171
27 mg CH4 L1 liquid phase d1 was accomplished

Autotroph

in a semi-batch setup, while the excess CO2 production was lower than 1 mg CO2 L1 d1.

Phytoplankton

Both nitrate and ammonium were feasible nitrogen sources for the methalgae. These

Biofloc

results show that a coculture of microalgae and methane oxidizing communities can be used to oxidize dissolved methane under O2-limiting conditions, which could lead to a novel treatment for dissolved methane in anaerobic effluents. ª 2011 Elsevier Ltd. All rights reserved.

1.

Introduction

There is an increasing interest for anaerobic treatment technology, such as upflow anaerobic sludge blanket digestion, which allows recovery of energy and nutrients (van Lier et al., 2001; Verstraete et al., 2009). However, during such processes, part of the generated biogas dissolves in the liquid effluent, resulting in a considerable release of methane into the atmosphere and consequently a loss of energy. Methane has a global warming potential of 25 and accounts for 15% of the anthropogenic greenhouse gas discharge, so additional emissions should be avoided (Forster et al., 2007). Although under typical conditions of an anaerobic digestion process

(35  C, 60% CH4, 40% CO2 (v/v)), only 11 mg CH4 L1 is dissolved in the effluent, CH4 losses can be up to 25% of the produced methane, especially when treating low strength wastewater (Cakir and Stenstrom, 2005; Hartley and Lant, 2006). When the loading rate is lower than 0.7 g biological oxygen demand L1, anaerobic treatment can emit even more CO2-equivalents than treatment with traditional activated sludge processes (Cakir and Stenstrom, 2005). The reason for these high emissions is twofold: firstly, there is a continuous outflow of relatively large volumes of CH4-saturated effluent, and secondly oversaturation up to a factor of 4 has been observed in digester effluents (Hartley and Lant, 2006). Only few studies have described reactors to treat dissolved methane in such

* Corresponding author. Tel.: þ32 9 264 59 76; fax: þ32 9 264 62 48. E-mail address: [email protected] (N. Boon). URL: http://www.labmet.ugent.be 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.03.005

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effluents (Hatamoto et al., 2010; Matsuura et al., 2010). The introduction of an economically feasible methane oxidizing unit in the typical polishing step of anaerobic digester effluents, could lower the greenhouse gas emissions of anaerobic treatment installations (Verstraete et al., 2009). Moreover, if dissolved CH4 and CO2 could be transformed into products with added value, treatment of low-strength wastewater would effectively be seen as a valuable service. Methane is particularly unreactive: it requires 435 kJ mol1 to break the chemical bond between the carbon and hydrogen atom (Dalton, 2005). As a result it is not easily removed with physico-chemical techniques. Aerobic methane-oxidizing bacteria (MOB), a group of Gram negative bacteria, are able to break this strong CeH bound by means of the enzyme methane monooxygenase, which exists in a soluble and particulate form (Dalton, 2005; Hanson and Hanson, 1996). One group of MOB, the high concentration methanotrophs (HCM), are abundant at concentrations above 1000 ppmv CH4 and use O2 as electron acceptor (Bender and Conrad, 1992; Hanson and Hanson, 1996; van der Ha et al., 2010). A drawback of applying MOB in anaerobic effluents is that oxygen needs to be added externally under such conditions that the methane is not stripped from the liquid. To avoid external aeration and stripping, oxygen can be provided through the addition of microalgae in the system. Both prokaryotic cyanobacteria, eukaryotic microalgae and diatoms produce O2 in situ during the photosynthetic reactions, when sufficient light energy is provided for CO2 fixation (Mata et al., 2010; Melis, 2009; Thajuddin and Subramanian, 2005). The synergistic relationship whereby microalgae provide oxygen for bacterial processes has been successfully applied for tertiary treatment of different industrial wastewaters, with the focus on removal of organic carbon, nitrogen and phosphorus (Molinuevo-Salces et al., 2010; Munoz and Guieysse, 2006). Algal-bacterial processes have also been used for treatment of hazardous contaminants. For example, different algal-bacterial cocultures were able to degrade phenanthrene, acetonitrile and salicylate by pollutant-specific bacteria without an external oxygen supply (Borde et al., 2003; Guieysse et al., 2002; Munoz et al., 2005). As far as the authors know, no research has been performed on cocultures of microalgae and methane oxidizing communities for methane mitigation. In nature however, synergistic relationships are ubiquitous and have been observed between MOB and photosynthetically active mosses (Kip et al., 2010; Raghoebarsing et al., 2005). While the MOB provide the carbon source for the prokaryotes, oxygen is provided in return by the photosynthetically active species. This type of relationship has also been observed previously between MOB and microalgae in fresh water lakes (Kankaala et al., 2006; Trimmer et al., 2010). In this study, a coculture of MOB and microalgae, further indicated as methalgae, was evaluated in the scope of a greenhouse gas free methane oxidation. The aim of this study was to achieve a sustainable methane oxidation with a lower need for externally supplied O2, while observing the influence of the nitrogen source. The outcomes of this study provided a first successful attempt towards the development of a treatment unit for dissolved methane in O2-depleted effluents.

2.

Materials and methods

2.1.

Sampling and inoculation

Nine identical gastight bottles, each with a total volume of 1150 mL served as reactor. Six of the latter were filled with 200 mL dNMS-medium (per liter: 1 g KNO3; 0.2 g MgSO4.7H2O; 3.59 g Na2HPO4.2H2O; 1.36 g KH2PO4; 45 mg CaCl2.6H2O; 4.5 mg Na2-EDTA; 3.5 mg FeCl3.6H2O) according to Whittenbury et al. (1970). The three other gastight bottles were filled with 200 mL dAMS-medium, which has the same composition as dNMSmedium, except that 140 mg NO3eN was replaced with 140 mg NH4þeN, in the form of NH4Cl. Additionally 1 mL trace solution L1 was added to the dNMS or dAMS medium, according to van der Ha et al. (2010). The pH of both media was 6.9. An active methane oxidizing community, previously enriched at the laboratory, was used as inoculum (van der Ha et al., 2010). Equal volumes (20% v/v liquid phase) of this mixed inoculum were added to each reactor. The gas phase (950 mL) contained 20% (v/v) methane in air.

2.2.

Experimental setup

Three different treatments were setup over a period of 30 days: reactors with methane oxidizing communities, referred as “MOC-reactors” and two sets of reactors containing a coculture of methane oxidizing bacteria and algae (MAC), with nitrate (NMAC) or ammonium (A-MAC) as respective nitrogen sources. Firstly, three reactors with a methane oxidizing community (MOC), not inoculated with microalgae, were shielded from the light and had nitrate as N-source. Secondly, six reactors with a methalgae community were inoculated with a mixed microalgae culture (5% v/v liquid phase), originating from an open pond type photobioreactor, which was used to grow a mixed algal culture in mineral medium under continuous bubbling of CO2 (De Schamphelaire and Verstraete, 2009). Three of these reactors contained 140 mg NO3eN as N-source (N-MAC), while three contained 140 mg NH4þeN as N-source (A-MAC). The initial VSS concentration was 61
1 mg L1 for the reactors with a MOC and 76
mg L1 for the N-MAC and AMAC reactors. All reactor where placed on a shaker at 120 rpm. All nine reactors were exposed to four TL-lamps (36 W, Master TL-D 90 Deluxe, Philips, Eindhoven, The Netherlands), which posses a light spectrum comparable with the solar radiation spectrum. The light intensity (photosynthetic active range; PAR) at the level of the liquid/gas interphase (inside the gastight bottles) was 80 mmol photons-PAR m2 s1. The illuminated area and illuminated area/volume ratio were 195 cm2 and 0.97 cm2 cm3 respectively. The temperature of the water phase in the reactors was measured after each cycle and was 22
2  C. After each cycle of 72 h, the liquid phases of the triplicates were merged together and four fifths of the well-mixed liquid phase was replenished with freshly made medium. Hence, the hydraulic retention time and sludge retention time were both 90 h. To maintain the microbial diversity, 2% (v/v) tertiary effluent of a WWTP (Ossemeersen, Gent, Belgium), wherein microalgae were visibly present, was added in between all cycles. The reactors were open for at least 30 min, which

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 8 4 5 e2 8 5 4

2847

allowed the gas phase to equilibrate with the outside air. A volume of 200 mL of methane (99.95% pure, Air Liquide, Lie`ge, Belgium) was then added to the closed reactors. The gas phase of a control reactor after 15 min of equilibration with the water phase contained respectively 193 mL of O2, 235 mL of CH4, 793 mL of N2 and 0.07 mL of CO2 per L of gas phase. After 10 cycles, the reactors with MOC and N-MAC were discontinued.

carbon (TIC) and total organic carbon (TOC) content was determined with a LCK 381 TC/TOC cuvette test (difference method). This method has a detection range between 60 and 735 mg TOC L1 and was assessed using a Xion 500 model LPG385 photospectrometer (Hach Lange GMBH, Du¨sseldorf, Germany). The architecture of the methalgae flocs was examined with a Zeiss Axioskop 2 Plus epifluorescence microscope (Carl Zeiss, Jena, Germany).

2.3. Effect of the nitrogen source, ethyn addition and autoclavation

2.5.

Additional tests were performed, all with an active A-MAC. In all cases, three A-MAC reactors were setup in parallel, as mentioned above, and applied as controls.

2.3.1.

Nitrogen source

To test the influence of the N-source, three reactors with an AMAC were given 140 mg NO3eN L1 instead of NH4þeN L1 as N-source.

2.3.2.

Ethyn addition

The influence of the methane oxidation inhibitor ethyn (C2H2) was analyzed by adding 0.2% (v/v) C2H2 to three reactors with an A-MAC. In another three reactors, the composition of the gas phase was altered to 13% (v/v) CH4, 8% (v/v) CO2 and 0.2% (v/v) C2H2 in air, to investigate the influence of C2H2 on the algal CO2-fixating activity.

2.3.3.

Autoclavation

When the tests were completed, the reactors were autoclaved (20 min, 121  C, 1 bar) to estimate the influence of losses and chemical adsorption processes. The gastight bottles were cooled down to room temperature and placed on a shaker, with new methane added.

2.4.

Chemical analyses

At the end of each cycle, samples were taken for further analysis. Filtered samples (0.45 mm filter, Millipore, Brussels, Belgium) were analyzed for Cl, NO3, NO2, SO42, PO43 and CHOO by means of a 761 Compact Ion Chromatograph, equipped with a conductivity detector (Metrohm, Zofingen, Switzerland). The ammonium concentration was determined by steam distillation, according to Greenberg et al. (1992). Kjeldahl nitrogen was analyzed by standard methods (Greenberg et al., 1992) and pH was determined with a SP10B pH electrode, connected to a Consort C532 multimeter analyzer (Turnhout, Belgium). A specific color reaction for aldehydes (Schiff’s reagent, Merck, Belgium; detection limit: 2 mg L1) was used to monitor formaldehyde formation, while quantification of the soluble methane monooxygenase activity, by means of a naphthalene oxidation assay, was performed according to van der Ha et al. (2010): a crystal of naphthalene is oxidized by the soluble methane monooxygenase to naphtanol, which reacts with a tetrazotized-odianisidine solution. The reaction product was measured with a spectrophotometer at a wavelength of 525 nm. Volatile suspended solids (VSS) were assessed according to Greenberg et al. (1992). The dissolved total carbon (TC), total inorganic

Gas composition analyses

For each cycle, a gas sample (1 mL) was taken immediately after the addition of methane, after 24 h, 48 h and at the end of each cycle (72 h), with a gastight syringe (Hamilton, Sigma Aldrich, Bornem, Belgium). The gas phase composition was analyzed with a Compact GC (Global Analyser Solutions, Breda, The Netherlands), equipped with a Porabond precolumn and a Molsieve SA column. Concentrations of CH4, O2, CO2, N2O and N2 were determined by means of a thermal conductivity detector with a detection limit of 1 ppmv for each gas component. Significant changes in the N2 concentration of the gas phase were never observed.

2.6. Determination of the relative chlorophyll peak height Standard methods to quantify chlorophyll content failed due to partial extraction of the chlorophyll. Therefore, a semiquantitative analysis was developed that allowed to measure differences in chlorophyll content within the methalgae flocs: well-mixed samples were sonicated (Labsonic M, B. Braun Biotechnology Enterprise GmbH, Melsungen, Germany) for 5 min and transferred to a cuvette, followed by measurement of the chlorophyll peak. The latter was detected at an absorbance wavelength of 685 nm whereas the cultures without microalgae showed a linear pattern between 600 and 800 nm. Thus, the relative chlorophyll peak height was calculated as absorbance685nm  (absorbance635nm þ absorbance735nm)/2. To validate this parameter, different mixtures of a MOC with the algal inoculum were tested, which resulted in a linear relationship between the algal concentration and the measured ratio, with a coefficient of determination (R2) value of 0.98 (n ¼ 28). At the end of the 10th cycle, the relative peak height was also measured before sonication. This allowed the estimation of the partition of the algal biomass over the liquid phase and the flocs, respectively.

2.7.

Statistical analysis

Throughout this study, the standard deviation is given for the triplicates, with average values being calculated over the last five cycles (cycle 6ecycle 10), unless otherwise mentioned. Likewise, concentrations and rates are expressed per liter liquid phase, unless otherwise mentioned. Statistical analysis was performed with SPSS for Windows, version 15 (SPSS Inc., Chicago, Illinois, USA). Homogeneity of variances and normality of the data were determined with a Levene’s test and a Kolmogorov Smirnov test, respectively. When a normal distribution and homogeneity of variances were observed, significant differences between mean values were analysed by

2848

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 8 4 5 e2 8 5 4

a One-way ANOVA test, with a significance level of 0.05 (LSD). If no normal distribution was observed, the differences in the means were statistically analysed by KruskaleWallis, followed by a ManneWhitney post hoc test including a Bonferroni correction.

3.

Results

3.1.

Methane oxidation rates

Day 1 Day 2 Day 3 Overall

The overall methane oxidation rates of the methane oxidizing communities with NO3 as N-source (MOC), the methalgae communities with NO3 as N-source (N-MAC), and the methalgae communities with NH4þ as N-source (A-MAC) were measured over ten cycles. During the first two cycles, a significantly lower methane oxidation rate was found for the A-MAC in comparison with the MOC and N-MAC (Fig. 1). Thereafter, no consistent differences ( p > 0,05) in methane oxidation rates were found. During each cycle, the daily observed methane oxidation rate differed for the MOC and MAC, respectively. During the first 24 h, the MOC had a significantly higher methane oxidation rates than both N-MAC and A-MAC (Table 1). However, during the second day of the cycle, the methane oxidation rates were very similar. During the last day of the cycle, the methane oxidation rate decreased in the MOC to 54
16 mg CH4 L1 d1, while an almost identical methane oxidation rate was found for the N-MAC (169
27 mg CH4 L1 d1) and A-MAC (161
35 mg CH4 L1 d1) (Table 1).

3.2.

Carbon balances

On average, 386
60 (N-MAC) and 407
51 (A-MAC) mg CH4eC L1 cycle1 were metabolized. A comparable value of 394
27 mg CH4eC L1 cycle1 was found for the MOC (Table 2). After 72 h, an average CO2 production of 138
10 mg

220

180

4

L

-1

-1

d )

200

160 140 120 100 MOC N-MAC A-MAC

80

40 2

4

6

8

Cycle

Fig. 1 e The overall methane oxidation rate (mg CH4 LL1 dL1; average over 72 h) for the methane oxidizing communities (MOC; circle), methalgae communities with NO3LeN (N-MAC; triangle) and methalgae communities with NH4DeN (A-MAC; square), showing a stable trend in time for all communities.

N-MAC

A-MAC

266
57 210
46 54
16 177
12

135
39 210
44 169
27 171
27

173
53 208
37 161
35 181
23

CO2eC L1 cycle1 was detected in the MOC-reactors, due to dissimilatory methane oxidation and microbial respiration. This concentration corresponded with a CO2eCproduced: CH4eCconsumed ratio of 0.35
0.03. In the reactors with MAC however, the overall CO2 production was in all MAC-reactors lower than 2 mg CO2eC L1 cycle1 from the second cycle on (Table 2). The concentration of formaldehyde, a metabolite of the methane oxidation reactions, was always lower than the detection limit (2 mg L1), while the concentration of formic acid never exceeded a concentration of 2 mg HCOOH L1. When applying a ratio of 1.49 mg COD (chemical oxygen demand) mg1 VSS as observed in comparable reactors (van der Ha et al., 2010), an average growth yield of 0.29
0.05 g CDWeCOD g1 CH4eCOD was observed in the MOC. Analysis of the total organic carbon (TOC) and inorganic carbon (TIC) content of the liquid phase resulted in a TOCaccumulated:CH4 e Cconsumed ratio of 0.85
0.13 and 0.91
0.13 for the N-MAC and A-MAC respectively, where a ratio of 0.40
0.03 was observed for the MOC (Table 2). Based on the TOC measurements, the N-MAC assimilated a factor 2.3 more CH4 oxidizedeC into biomass, compared to the MOC (Fig. 2). The TIC content was with 10
4 (N-MAC) and 5
1 (AMAC) mg C L1 significantly lower than the TIC content of the

60

0

MOC

Table 2 e Overview of the CH4 oxidation (mg CH4eC LL1 cycleL1), the CO2 production (mg CO2eC LL1 cycleL1), the ratio of total organic carbon over CH4eC (mg TOC mgL1 CH4-Cconsumed), the accumulation of volatile suspended solids (mg VSS LL1 cycleL1), the nitrogen consumption (mg N LL1 cycleL1), the ratio of total organic nitrogen over total organic carbon (mg TON mgL1 TOC), the average pH change and the molar O2 consumed:CH4 oxidized ratio over a cycle for the reactors with a methane oxidizing community (MOC), a methalgae community with nitrate as N-source (N-MAC) and a methalgae community with ammonium as N-source (A-MAC) respectively.

240

MOR (mg CH

Table 1 e The average daily and overall methane oxidation rate (MOR; mg CH4 LL1 dL1) over the whole cycle (72 h) for the methane oxidizing community (MOC), the methalgae community with nitrate as N-source (N-MAC) and the methalgae community with ammonium as N-source (A-MAC) from cycle 6 to 10.

10

CH4 oxidation CO2 production TOC ratio:CH4eCa VSS accumulation N-consumption TON:TOC ratioa pH change O2:CH4 ratio

MOC

N-MAC

A-MAC

394
27 138
10 0.40
0.03 411
80 38
6 0.18
0.01 0.2 1.14
0.09

386
60 <2 0.85
0.13 641
83 76
15 0.22
0.03 þ0.6 0.50
0.11

407
51 <2 0.91
0.13 688
106 61
9 0.19
0.02 0.3 0.69
0.12

a Average value for the 10th cycle.

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 8 4 5 e2 8 5 4

3.4.

-1

Carbon species (mg C L liquid phase)

500

400

300

200 Not defined CO2-C TIC TOC

100

0 MOC

N-MAC

A-MAC

Fig. 2 e Overview of the average carbon balances for the methane oxidizing community (MOC), methalgae community with nitrate as N-source (N-MAC) and the methalgae community with ammonium as N-source (A-MAC) during the 10th cycle. The height of the columns shows the total amount of oxidized CH4eC (mg CH4eC LL1 liquid phase) during one cycle The total organic carbon (TOC), total inorganic carbon (TIC) and CO2 production in the gas phase (CO2eC) are expressed as mg C LL1 liquid phase. The difference between the measured CH4eC removal and the total production of TIC, TOC and CO2eC is shown as ‘not defined’.

2849

Macro- and microscopic community organisation

In the algal inoculum, the microalgae Chlorella sp., Scenedesmus sp., Closterium sp., Euglena sp. and Cosmarium sp. and the cyanobacterium Phormidium sp. were classified, based on their cell morphology. Over time, there was a clear selection for Scenedesmus sp., which became dominant in all reactors. The addition of microalgae had an influence on the macroscopic, as well as the microscopic organisation of the community. While most bacteria of the MOC were in suspension or formed smaller flocs, larger flocs (up to 2 mm) were formed by the MAC (Fig. 3A). On a microscopic scale, randomly organized microalgae and bacteria were observed in the methalgae flocs (Fig. 3B). Until the sixth cycle, the relative chlorophyll content increased for the MAC, which indicated an accumulation of algal biomass (Fig. 4). The total amount of chlorophyll was significantly higher in the N-MAC-reactors compared to the A-MAC-reactors until the 8th cycle (Fig. 4). Based on the relative chlorophyll peak height before and after sonication, 51
3 (N-MAC) and 47
2% (A-MAC) of the algal biomass was present in the flocs. To test the settleability of the cultures, the VSS content of both the decanted liquid phase (90% of total volume) and the sludge phase were measured after 5 min of sedimentation. Respectively 65
6% (N-MAC) and 61
5% (A-MAC) of the VSS were present in the sludge phase (10% of total volume), where this was only 23
4% for the MOC.

3.5. The influence of heat treatment, ethyn addition and incubation in the dark MOC (35
8 mg C L1) (Fig. 2). Although it was impossible to quantify the separate VSS accumulation of the microalgae and bacteria respectively, analysis of the total VSS content confirmed the higher biomass accumulation when microalgae were present. The observed VSS accumulation of 641
83 (N-MAC) and 688
106 (A-MAC) mg VSS L1 cycle1 was a factor 1.6 and 1.7 higher than in the MOC-reactors, respectively (Table 2).

3.3.

Apparent oxygen consumption

The apparent oxygen consumption was always significantly lower for the MAC than for the MOC. The average O2 consumed:CH4 consumed ratio (mol/mol) was 0.50
0.11 (NMAC) and 0.69
0.12 (A-MAC) respectively, i.e. 55 and 43% lower than in the MOC-reactors (1.14
0.08). The O2 consumed:CH4 oxidized ratio was significantly higher for the A-MAC compared to the N-MAC. This difference was also observed when the N-source of the N-MAC was changed to ammonium, as the O2 consumed:CH4 consumed ratio rose from 0.48
0.04 to 0.72
0.18. There was also a change in the ratio when the N-source of an A-MAC was changed to nitrate. The O2 consumed:CH4 oxidized ratio dropped to 0.59
0.08 with nitrate as N-source, while with ammonium as N-source it was still 0.75
0.05 mol. In contrast, no change in the ratio was found when the N-source of the MOC was altered to NH4þ (data not shown). Also, no ammonium oxidizing activity was observed when A-MAC were grown in the absence of methane (data not shown).

The microbial nature of the methane oxidation activity was confirmed by the fact that the methane oxidizing rate declined to 0.2
2.1 mg CH4 L1 d1 after heat treatment (20 min, 121  C, 1 bar). Addition of 0.2% (v/v) C2H2, a known inhibitor of MOB, also led to a complete inhibition of the methane oxidation activity. To verify the methane oxidation capacity of the microalgae, the A-MAC reactors were incubated with a gas phase consisting out of 13% (v/v) CH4, 8% (v/v) CO2 and 0.2% (v/v) C2H2 in air. After 72 h, all CO2 was incorporated into biomass by the microalgae, while no significant methane oxidizing rate could be observed. The presence of the soluble methane monooxygenase was detected in the three communities. A naphthalene oxidation rate of 155
33 n mol mg1 protein h1 was found for the MOC (Koh et al., 1993). Although naphthalene oxidation was observed visually for the MAC, no quantification of the soluble methane monooxygenase was possible, since the microalgae interfered with the color measurement. The A-MAC was also incubated under dark conditions. The overall methane oxidizing rate of 131
1 mg CH4 L1 d1 under dark conditions was 29% lower than under lighted conditions (182
10 mg CH4 L1 d1). This difference was the largest (44%) during the first 24 h of the cycle.

3.6.

The influence of the N-source

The nitrogen source influenced the methane oxidizing rate during the first two cycles (Fig. 1). During those cycles, the overall methane oxidizing rate of the A-MAC was 101
34 mg

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Fig. 3 e Images of the reactors with a methane oxidizing community (AeB), a methalgae community with nitrate as N-source (CeD) and a methalgae community with ammonium as N-source (EeF), after the first (upper row) and fifth (lower row) cycle. Microscopic images of the bioflocs from a methalgae community with nitrate as N-source (cycle 5) are shown with a 1003 enlargement (G) and 10003 enlargement (H), respectively.

CH4 L1 d1, significantly lower than the methane oxidizing rate of 148
11 mg CH4 L1 d1 observed for the N-MAC. From the third cycle on, no consistent differences in the overall methane oxidizing rate or in the VSS accumulation could be observed between the MOC, N-MAC and A-MAC (Table 2). Although the absolute nitrogen consumption of the N-MAC and A-MAC was a factor 2 and 1.6 higher than for the MOC (Table 3), respectively, the TONaccumulated:TOCaccumulated ratios were comparable (Table 2). An evaluation of the nitrogen balances was performed for the three communities (Table 3). The nitrite production was in all cases relatively low, with maximum values of respectively

Relative chlorophyll peak height

0,7 0,6 MOC N-MAC A-MAC

0,5 0,4 0,3

0.5
0.1 (MOC), 1.0
0.2 (N-MAC) and 0.4
0.4 (A-MAC) mg NO2eN L1 cycle1. Note that in the MOC-reactors, N2O production was observed when O2 concentrations decreased below 10% (v/v) (Table 3). The nitrogen source also influenced the pH evolution. The average pH increased with 0.6 units per cycle in the N-MAC reactors. The A-MAC on their turn lowered

Table 3 e Overview of the consumed and produced nitrogen species for the methane oxidizing community (MOC), the methalgae community with nitrate as N-source (N-MAC) and the methalgae community with ammonium as N-source (A-MAC), during the 10th cycle. The N-consumption is expressed as mg NO3eN LL1 liquid phase cycleL1 for the MOC and N-MAC, and as NH4DeN for the A-MAC. The TON-, NO3L-, NH4D- and NO2Lconcentrations are expressed as mg N LL1 liquid phase cycleL1. The N2O is expressed as mg N2OeN in the gas phase LL1 liquid phase. The difference between the observed nitrogen consumption and production, respectively, is shown as ‘not defined’. MOC

N-MAC

A-MAC

N-consumption NO3eN NH4þeN

41.7
3.7 n.a.

73.6
1.9 n.a.

n.a. 63.0
2.3

N-production NO3eN NH4þeN NO2eN N2OeN TON Not defined

n.a. 1.2
0.1 0.5
0.1 5.2
0.8 25.4
1.9 9.4
0.8

n.a. 1.8
0.9 1.0
0.2 u.d.l. 67.6
1.7 3.2
0.4

0.1
0.2 n.a. 0.4
0.4 u.d.l. 64.6
8.3 2.1
0.1

0,2 0,1 0,0 -0,1 0

2

4

6

8

10

Cycle

Fig. 4 e The relative chlorophyll peak height for the three types of communities. No chlorophyll was found back in the methane oxidizing community (MOC). For both the methalgae community with nitrate as N-source (N-MAC) and ammonium as N-source (A-MAC), an upward trend in algal growth was observed.

u.d.l.: under detection limit. n.a.: not applicable.

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 8 4 5 e2 8 5 4

the pH with 0.3 units per cycle. The average pH of the MOCreactors, with nitrate as N-source, decreased on average with 0.2 during each cycle (Table 2).

4.

Discussion

4.1. Methane oxidation rates in the presence of microalgae

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between 0.17 and 0.40 was observed for mixed methane oxidizing cultures (Borjesson et al., 1998; Whittenbury et al., 1970). In the MAC reactors, almost no CO2 was measured in the gas phase at the end of each cycle. This shows that there was enough light energy present for the CO2 fixation of the algae, so light intensity was not a limiting factor for the photosynthetic processes. No (bi)carbonate was added to the medium, which means that algal growth was completely dependent on the microbially produced CO2. This indicated that the CH4eC was dissimilated to CO2eC by the MOB, and was to a large extent transformed to TOC, as the microalgae were able to readily use the microbial produced CO2 for their metabolism. This algal CO2 fixation led to a 130
20% higher TOC accumulation, compared to the MOC. Based on the average methane oxidation rate of 210
44 mg CH4 L1 d1 in the N-MAC reactors (Table 1) and the ratio of 0.85
0.13 mg total organic carbonproduced for every mg of CH4eCconsumed (Table 2), an average carbon biofixation of about 135 mg CH4eC L1 d1 can be calculated. This value is similar to the carbon removal rate of 127
26 mg TOCeC L1 d1 that was observed with a similar reactor type for an algal-bacterial coculture treating four times diluted swine slurry under anaerobic conditions (Gonzalez et al., 2008).

To accomplish a technically feasible and environmentally sustainable methane oxidation, a high methane oxidation rate is needed. The presence of microalgae did not lead to a significant decrease in the methane oxidation rate, which shows that no significant inhibitory effect from the microalgae on the methane oxidizing bacteria was present. Only during the first 2 cycles, a less performing A-MAC was observed, which may have been due to the inoculant being precultured with NO3 as Nsource. The overall methane oxidizing rates over one cycle of 177
12 (MOC) and 171
27 (N-MAC) mg CH4 L1 d1 were a factor of 1.5 higher than previously achieved with the same type of reactor (van der Ha et al., 2010). The higher sludge retention time (90 h versus 140 h) and the higher ratio of gas phase over liquid phase (5 versus 2) probably led to a longer period without CH4 and O2 limitations. These methane oxidizing rates are in the range of continuous methane oxidizing reactors reported in the literature, where methane oxidizing rates were achieved in a range of 24e696 mg CH4 L1 d1 (Melse and Van der Werf, 2005; Nikiema et al., 2005). The significantly lower methane oxidizing rate of the MAC compared to the MOC during the start-up phase of a cycle could be due to the observed floc formation. In the MAC, more bacteria and microalgae were observed living closely together in methalgae-flocs (Fig. 3). Therefore, competition for available O2, CH4 and nutrients must take place inside the flocs (Hamdi, 1995; Modin et al., 2008). Additionally, a longer lag time may have been due to CH4 and O2 diffusion through the methalgae-flocs (Hamdi, 1995; Xavier et al., 2005). During a cycle, newly formed flocs were observed with a smaller diameter, thus encountering less diffusion limitations, which may explain the higher methane oxidizing rate during the second day. It was also observed that the methane oxidizing rate of the MOC decreased significantly during the third day of each cycle, concomitant with the limiting concentration of available O2. In contrast, the methane oxidizing rates of the N-MAC and A-MAC was 4 times higher, as the microalgae produced enough O2 to maintain the methane oxidation. With the experimental setup used in this work, the gas composition varied during each cycle, which explains the variation in the methane oxidation rate. However, the methane oxidation rate during the second day was the most representative value, as both the effect of the start-up adaptation and the limiting CH4/O2-concentrations were negligible.

In the MOC, a molar O2 consumed:CH4 consumed ratio of 1.14
0.09 was observed, which is comparable to the ratio of 1.2e1.4, observed with a comparable previous study (Modin et al., 2010). The algal O2 production, due to photosynthetic processes, lowered the overall O2 need in the N-MAC with 55%. The presence of oxygen producing algae allowed the MOB in the MAC-reactors to sustain methane oxidation during the third day of each cycle, when the bacteria were deprived from oxygen originally present in the gas phase. When additional (bi)carbonate or CO2 would be supplied, microalgae could provide enough O2 for the bacterial community to make external O2 supply obsolete. This could lower the cost for a methane oxidizing biofilter drastically if an efficient transfer of light energy could be achieved. This CO2-supplementation is easily achieved, as CO2 is, due to the biogas production, already present in the effluent of the anaerobic digestion process. The O2 consumption was dependent of the applied N-source: with NH4þ as N-source, the O2 consumption of the methalgae was a factor 1.4 higher than with NO3 as N-source. The fact that this difference was not observed with the MOC, indicates that the difference in apparent O2 consumption is caused by the algal metabolism. The underlying reason is unknown, although it seems to be related to nitrogen assimilation processes.

4.2. CO2 fixation supported an increased biomass production

4.4. The coculture of bacteria and microalgae has the tendency to form flocs

The dissimilatory methane oxidation processes, in combination with the heterotrophic respiration, led to a CO2eCproduced:CH4eCconsumed ratio of 0.35 for the MOC. This has also been mentioned in previous studies, where a ratio

The coculture of bacteria and microalgae tends to form flocs, where about half of the algal and bacterial biomass accumulates. This floc formation may be initiated through the attractive forces of surface hydrophobicity (van Loosdrecht

4.3. Methalgae lowered the need for an external oxygen supply

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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 8 4 5 e2 8 5 4

et al., 1987) or production of algal or/and bacterial EPS (Fuentes et al., 1999; Grossart et al., 2006; Wilshusen et al., 2004). Moreover, it could be a mutually beneficial syntrophic relationship, as they are interdependent for their nutrient sources. This floc formation did not seem to lead to a differentiation in the floc configuration. However, the tendency of the methalgae to form flocs, made it easier to separate the biomass from the liquid phase.

4.5.

The bacterial methane oxidation activity

The methane oxidation activity was clearly of microbial origin as a heat treatment stopped the methane oxidation activity almost completely. The methane oxidation activity of the MOB was also completely stopped by the addition of C2H2, a known inhibitor of MOB (Chan and Parkin, 2000; Prior and Dalton, 1985). This indicates that MOB were responsible for the methane oxidation activity. Their presence was confirmed by the detection of the soluble methane monooxygenase, an enzyme that is only present in MOB living under high concentrations of CH4 (>1% CH4 (v/v)) (Dalton, 2005). Moreover, microalgae did not show any methane oxidizing capacity. After addition of C2H2 and CO2 in the gas phase of the methalgae culture, no methane oxidation activity was observed, although all the readily available CO2 was removed, due to algal carbon fixation. This shows that C2H2 is a good selective inhibitor for MOB versus microalgae. The lack of methane oxidizing activity of the microalgae was also observed under dark incubation conditions: a distinctive methane oxidation activity was observed, although no light energy was present, necessary for the algal photosynthetic processes. However, the measured methane oxidation rate was significantly lower than the methane oxidation rate of the control reactors, incubated in the light. This implies that the O2 production of the microalgae inside the methalgae-flocs positively influenced the methane oxidation activity.

4.6.

The influence of the nitrogen source

The nitrogen need of both microalgae and MOB is higher than 0.15 mg N mg1 incorporated C (Klausmeier et al., 2004; Scheutz et al., 2009). Microalgae, as well as MOB, can use NO3 and NH4þ as N-source (Bodelier and Laanbroek, 2004; Li et al., 2010; Lourenco et al., 1998). However, it has been observed that ammonium can have an inhibitory effect on MOB (Begonja and Hrsak, 2001; Nyerges and Stein, 2009). Microalgae on the other hand are sensitive for both ammonium as ammonia: 28 mg NH3eN L1 inhibited the growth of Scenedesmus obliquus, at a pH of 8 (Azov and Goldman, 1982; Kallqvist and Svenson, 2003). In the current study, no inhibition was observed related to the N-source, as no significant differences in methane oxidation rate or VSS accumulation were found between N-MAC and A-MAC. Due to a higher VSS accumulation, the nitrogen consumption of the N-MAC was a factor 2 higher than the N-consumption of the MOC. The TONaccumulated:TOCaccumulated ratio was however not significantly different for the three communities. The relatively low nitrite concentrations did not induce inhibitory effects (King and Schnell, 1994; Nyerges and Stein, 2009). The N2O production of the MOC was probably caused by aerobic methane

oxidation coupled to denitrification (AME-D), a known process when O2 levels are low (Modin et al., 2010). It was however unexpected that this process could occur when 4 mg O2 L1 was still present in the liquid phase. The nitrogen source also influenced the pH changes in the MAC-reactors. The pH drop in the MOC reactors was caused by the acidifying effect of the produced CO2 and (bi)carbonates that dissolved in the water phase. In the MAC-reactors, the acidifying effect of the bacterially produced CO2 was neutralized by the algal fixation. This however cannot explain the pH rise, as no other carbon source was present in considerable amounts. The rise of the pH in the N-MAC probably was due to the alkalizing effect of assimilatory nitrate reductase (Eisele and Ullrich, 1975; Fuggi et al., 1981). In the A-MAC however, the pH decreased as the assimilation of ammonium into microbial biomass produces protons (Fuggi et al., 1981; Li et al., 2008).

4.7.

Future perspectives

The current study showed that under optimized conditions, a coculture of microalgae and methane oxidizing communities can be applied for methane oxidation without a release of CO2. Further research will focus on the treatment of industrial anaerobic wastewaters in a continuous mode. The influence of higher ammonium, salt, bicarbonate and suspended solids will have an influence on the overall performance of the coculture (Gonzalez et al., 2008; Munoz and Guieysse, 2006). However, several research groups already succeeded to apply algal-bacterial cocultures to degrade chemical components from industrial waste streams (Molinuevo-Salces et al., 2010; Munoz and Guieysse, 2006). Next to the physico-chemical parameters, the design of a closed photobioreactor is of importance (Kunjapur and Eldridge, 2010). The applied reactor should work efficient with low light intensities, as this is an important operational cost. Reactor types with tubular columns as described by Munoz and Guieysse (2006) and Mata et al. (2010) seems good candidates as they allow treatment of anaerobic waters with acceptable hydraulic retention times and a good biomass retention.

5.

Conclusions

 Coculturing methane oxidizing communities and microalgae allowed a greenhouse gas neutral oxidation of CH4 to microbial biomass. Microalgae converted almost all CO2, produced by methane oxidizing bacteria, to biomass.  In this configuration, the presence of photosynthetic active microalgae lowered the need of externally supplied O2 for the methane oxidizing community with 55%. The in situ O2 production of the microalgae could make a complete methane oxidation in O2-depleted effluents feasible.  The observed cooperation between microalgae and methane oxidizing communities opens perspectives for a sustainable treatment of methane saturated anaerobically treated effluents. The low light intensity needed, the observed floc formation and the interchangeability of the N-source support this conceptual approach, that needs confirmation by tests providing complete oxygen supply by

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 8 4 5 e2 8 5 4

the algae. Implementation of a continuous system under anoxic conditions with a high methane oxidation rate per unit reactor volume, an efficient transfer of light energy and a low spatial footprint are prerequisites for application in practice.

Acknowledgements This research was funded by a PhD grant for David van der Ha from the Institute for the Promotion of Innovation through Science and Technology in Flanders (IWT-Vlaanderen, SB83259) and a research grant from the Geconcerteerde Onderzoeksactie (GOA) of Ghent University (BOF09/GOA/005). The authors gratefully thank Suzanne Read and Simon De Corte for proof reading the manuscript. We thank Tim Lacoere for the graphic design. Marie Linthout and Sofie Van Den Hende are greatly acknowledged for their assistance.

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Flocculation of harmful algal blooms by modified attapulgite and its safety evaluation Yi Tang, Hong Zhang, Xianan Liu, Dongqing Cai, Huiyun Feng, Chunguang Miao, Xiangqin Wang, Zhengyan Wu*, Zengliang Yu* Key Laboratory of Ion Beam Bioengineering, Hefei Institutes of Physical Science, Chinese Academy of Science, Hefei, Anhui 230031, PR China

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abstract

Article history:

Natural attapulgite (N-AT) and modified attapulgite (M-AT) were used in this study to

Received 26 October 2010

evaluate their flocculation efficiencies and mechanisms in freshwater containing harmful

Received in revised form

algal blooms through conventional jar test procedure. The experimental results showed

1 March 2011

that the efficiency of flocculation can be significantly improved by M-AT under appropriate

Accepted 1 March 2011

conditions. It was found that the attapulgite modified by hydrochloric acid was similar to

Available online 10 March 2011

polyaluminum ferric silicate chloride (PAFSiC). The high efficiency for M-AT to flocculate Microcystis aeruginosa in freshwater was due to the mechanism of bridging and netting

Keywords:

effect. Caenorhabditis elegans was used to detect the toxicity of N-AT and M-AT. The results

Harmful algal blooms

showed that there was no significant toxicity on this organism. Attapulgite is a natural

Attapulgite

material, which can be readily available, abundant, and relatively inexpensive. Using

Flocculation

modified attapulgite to remove the harmful algal blooms could have the advantages of high

Mechanism

effectiveness, low cost, and low impact on the environment. ª 2011 Published by Elsevier Ltd.

Toxicity

1.

Introduction

Harmful algal blooms (HABs) pose a serious threat to aquatic life, human health, local tourism, and coastal aesthetics (Beaulieu et al., 2005). These effects are caused by rapid growth of algae, some of which produce toxins. More than half of the fresh waters in China suffer from HABs, and the cyanobacterial Microcystis aeruginosa is one of the dominant species (Pan et al., 2006). Both direct and indirect methods have been developed to control HABs. Direct countermeasures remove HABs through physical (clays, flocculants, synthetic polymers, ultraviolet radiation etc.), chemical (hydrogen peroxide, hydroxide radicals, ozone, copper sulfate, disinfectants etc.) or biological control approaches (algicidal bacteria, algicidal viruses and plankton grazers) (Kang et al., 2007; Sengco, 2009). The most commonly used method to control HABs has been to spray with a chemical such as copper sulfate. Even though

chemical treatment is considered fairly inexpensive and has a rapid mode of action, this method leads to undesired effects on co-occurring organisms. Biological control of HABs has been suggested. But the potential effects of the addition of cultured grazers or parasites to the ecosystem are largely unknown at the moment. The use of clay to control HABs has successfully been practiced in Asia since the mid-1990s. Clay flocculation is based on the physical and chemical properties of clay particles in aqueous suspensions. Clay particles form aggregates, or flocs, with other clay or non-clay particles due to their different surface charges. These flocs with algal cells will sink to the bottom due to the added weight from clay particles. Algal cells may also lyse or be impaired by the contact with the clay particles. Compared with chemical and biological methods that are of relative high cost and great environmental impact, clay flocculation is considered the most promising and practical control/mitigation technique

* Corresponding authors. P. O. Box 1138, Hefei, Anhui 230031, PR China. Tel.: þ86 551 5592189; fax: þ86 551 5591310. E-mail addresses: [email protected] (Z. Wu), [email protected] (Z. Yu). 0043-1354/$ e see front matter ª 2011 Published by Elsevier Ltd. doi:10.1016/j.watres.2011.03.003

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(Sengco and Anderson, 2004; Pan et al., 2006; Hagstro¨m et al., 2010). Attapulgite/palygorskite is a type of natural clay which has a wide range of industrial applications especially in petroleum and pharmaceutical industries (Baltar, 2009). Currently attapulgite has been widely concerned as a novel porous material (Murray, 2000). It is often used as an adsorbent in environment pollution control and restoration due to its unique layered structure and pores (Koutsopoulou et al., 2010). Cai et al. (2009) has developed a novel fertilizer in which some modified attapulgite has been added. It was found that this fertilizer could prevent the loss of fertilizer nutrients through local surface runoff and volatilization, so the fertilizer has a promising application prospect in the prevention of non-point pollution. But attapulgite, to our knowledge, has rarely been reported as a flocculant. To control the algal bloom caused by eutrophication and other environmental problems, and to expand the application of attapulgite, a novel clay flocculation for M. aeruginosa removal has been studied. The flocculation effect was focused on and the underlying mechanisms were investigated. In addition, a rapid and biologically relevant test, using the nematode Caenorhabditis elegans as animal model, was performed to evaluate the potential toxicity of clay/cell flocs in the benthos, which is one of the concerns regarding HABs removal from fresh water (Pierce et al., 2004). The data collected from a number of studies suggest that the toxicological effects observed in the C. elegans closely reflected the effects observed in mammalian models for most compounds tested (Zhang et al., 2003; Bargmann, 1998; Cole et al., 2004). Typically toxicity tests using C. elegans involves exposing the nematodes to test compounds in liquid culture or agar culture inoculated with feeder bacteria. Various functional biomarkers of toxicity including longevity, fecundity, survivability, behavior, growth and development are determined after a set time of exposure (Sprando et al., 2009). However, the reports concerning the toxicological effects of clay flocculation using C. elegans are very few. In this article, the survival of C. elegans exposed to water treated with modified attapulgite was used as the standard of the toxicological effects.

2.

Materials and methods

2.1.

M. aeruginosa culture

The microcystin-producing strain M. aeruginosa FACHB 905 used in our experiment was obtained from the Institute of Hydrobiology, Chinese Academy of Sciences. Cells were cultured in BG11 medium at 25  1  C (Rippka et al., 1979), under illumination of approximately 90 mmol photons/(m2 s) with a photoperiod cycle of 12 h light, 12 h dark.

2.2.

Flocculant preparation and characterization

2.2.1.

Flocculant preparation

The attapulgite used in this study came from the Anhui Mingguang Minerals Co., Ltd., China with a particle size  0.075 mm (200-mesh) and the purity above 90% as reported by the supplier. 2.5 g attapulgite and 10 mL of either

5 M or 10 M hydrochloric acid (HCl) were mixed in a ceramic pot. The mixture was stirred uniformly, standing for 24 h, dried at 75  C, and grounded to 200 mesh size. The modified clays were designated as 5AT and 10AT for 5 M and 10 M HCl-treated respectively, and the original non-treated clay as N-AT.

2.2.2.

Morphology analysis

Scanning electron microscopy (SEM) (JEOL-2100F, Japan) was used to observe the morphology of N-AT, 5AT, a simple mixture of 5AT and algae cells, and the flocs after the flocculation experiment. N-AT and 5AT samples were stirred in distilled water, and small amounts were placed on glass slides and dried in air. The mixture of algae and 5AT was prepared by placing a drop of algae solution on a glass slide before a small amount of 5AT was added on top, and the mixture was dried in air. Instead of fixation as traditionally used to prepare for SEM, small amounts of flocs from flocculation of clays and algae cells were simply placed on glass slides and air dried to avoid damaging the flocs structure. Algal cells in such preparations normally remained contact. Dried samples were mounted on copper stubs and sputter coated with goldepalladium. The specimens were observed at 5 kV.

2.2.3.

FTIR spectroscopy

The FTIR (Fourier transform-infrared) spectra of N-AT, 5AT and 10AT were obtained in an FTIR spectrometer (Alpha-T, Bruker). Bulk materials were dried and finely grounded. The samples were prepared for analysis by mixing as follows: mixing 350 mg of KBr with about 1 mg of the material and then compressing the mixture to pellets for FTIR analyses (Huang et al., 2007).

2.2.4. BET surface area measurement and BJH pore size distribution Surface area and relative measurements of N-AT, 5AT, W-5AT (5AT after ultrasonic cleaning) and 10AT were performed using ASAP 2020 Accelerated Surface Area and Porosimetry (America). Adsorption and desorption experiments using N2 were carried out at 77 K. Prior to each measurement the samples were degassed at 353 K for 24 h. The surface area (SBET) and the adsorption average pore size were determined using multipoint BET (BrunauereEmmetteTeller) method (Brunauer et al., 1938). The total pore volume (Vtotal) was estimated to be the liquid volume of nitrogen at P/P0 ¼ 0.99. Pore size distribution branch of the isotherm was determined by using the BJH (BarretteJoynereHalenda) method. Micropore volume (Vmicro) was calculated by integrating the area with pore diameters less than 2 nm under the micropore size distribution curve (Shi et al., 2009).

2.3.

Flocculation experiments

M. aeruginosa cells at late exponential growth stage were harvested by centrifugation (12000 rpm). The initial cell concentration for all flocculation experiments was set to 2.85  106 cells/mL (Pan et al., 2006). Flocculation experiments replicated 3 times were performed for both non HCl-treated as control and 5 M HCl-treated attapulgite. Flocculation was carried out in 1 L plastic beakers at ambient temperature by mixing 0.3 g attapulgite with 800 mL algae cell suspension

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 8 5 5 e2 8 6 2

(clay load was 0.37 g/L) (Sengco et al., 2001). The mixture was stirred at 300 rpm for 5 min, followed by 50 rpm for another 3 min (Liu et al., 2010). The mixture was then incubated at room temperature without stirring and cell suspensions were sampled at 5, 10, 20, 30, 40, 50, 60, 90, 120, 150, 180 min for chlorophyll-a (Chl-a). The content of iron, aluminum and silica was measured at 10, 90, 180 min. Chl-a was measured at 665 and 649 nm in the absorption spectra (UV-2550 spectrophotometer, Japan) after extraction with 95% ethanol (Zhang et al., 2010). After the pellets had been removed from water samples by centrifugation, the concentrations of iron, silica and aluminum in supernatant were determined by using 1, 10-phenanthroline (Yegorov et al., 1993), blue molybdosilicate (Pakalns, 1971) and pyrocatechol violet (Simpson et al., 1998) spectrophotometry respectively.

2.4.

Toxicity test

2.4.1.

Nematode culture

C. elegans wild type strain N2, and Escherichia coli strain OP50 were obtained from the Key Laboratory of Ion Beam Bioengineering, Chinese Academy of Sciences. C. elegans was routinely cultured at 20  C on NGM (nematode growth medium) agar plates with E. coli OP50 as a food source (Brenner, 1974). Eggs were collected by treating gravid adults with hypochlorite. Collected eggs were washed with sterile distilled water and allowed to hatch on NGM plates. Hatchings were maintained on plates without OP50 for 1 day to obtain synchronized L1 (the first larval stage) animals for the study.

2.4.2.

Toxicity test

Survival of L1 C. elegans was tested with four groups of water samples: sterile distilled water as control, algae water without flocculation, two algae water samples after flocculation by N-AT and 5AT respectively. To prevent contaminations from other bacteria, all water samples were sterilized by filtering through a 0.22 mm membrane. Nematodes (synchronized 1 day old L1’s) (n ¼ 1500e2000 worms/group) were transferred into the water samples in petri dish and maintained at 20  C for either a short period (4 h) or long periods (12 h, 24 h and 48 h). After the designated time, the worms were transferred to NGM agar plates with E. coli OP50 and maintained at 20  C (n ¼ 150e250 worms/time point). After 24 h, live and dead animals on the plates were counted, and survivability was expressed as the percent of live animals.

3.

Results and discussion

3.1.

Characterization of flocculation

3.1.1.

SEM characterization

SEM micrographs of N-AT, 5AT and flocs are shown in Fig. 1. As can be seen in Fig. 1a, b, compared with N-AT, 5AT shows that bundled attapulgite fibers with a lower density and higher roughness became shorter. It is worth noting that in the upper right of Fig. 1b, there was acid-soluble state covering fibers which were not fully dissolved. Fig. 1c shows the amplification of this region. It was found that the acid-soluble state was composed of many nanoparticles which launched along the

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rough surface forming a membrane. Fig. 1d shows the homogenate with more cells and less 5AT, which can reflect the morphological structure of both cells and 5AT (the arrows indicate algae cells). In this image, acid-soluble state of 5AT attached to the M. aeruginosa cells surface, forming a layer of nano-film. Such attachment can probably explain how the cells were captured and fixed. The fibers which were not fully dissolved by acid and remained the original state distributed in an emission-like way along all directions of cells. Fig. 1e shows the flocs which were sampled after the flocculation experiments. Because there were more 5AT and fewer cells compared with Fig. 1d, the flocs were denser. There formed some network structure at the edge of the cells. The amplification of the network structure is shown in Fig. 1f. The netting was probably formed due to clay bridging by the fibers, some other solutes of 5AT (some cations) and perhaps some extracellular secretion of M. aeruginosa cells (Takaara et al., 2007). When two algae cells approach each other, the clay may form a bridge between them, holding them together. This mechanism of agglomerate formation is known as “bridging flocculation” (Runkana et al., 2006).

3.1.2.

FTIR analysis

Grauer et al. (1986) have studied the IR spectrum of attapulgite in details. The authors report that there are three major bands: (1) 3700e3200 cm1 representing eOH stretching vibration of adsorbed water, (2) 1600 cm1 indicative of eOH deformation vibration of adsorbed water, and (3) 1300e400 cm1 corresponding to the bonding region of Al and Si. In this study, similar distribution of bands was obtained. The FTIR spectra of the N-AT, 5AT and 10AT are given in Fig. 2. The assignments of the bands were made according to previous data (McKeown et al., 2002; Wang et al., 2009). After modification, although the 5AT (Fig. 2b) and 10AT (Fig. 2c) maintained the characteristic peaks of attapulgite: 3475e3610, 1640e1660, 1190, 1030, 980, 510, and 470 cm1 (Lynwood and Schwint, 1967), the two peaks assigned to the OH stretching vibration of water in coordination with Al3þor Mg2þ and Fe3þ ions (3614 cm1 and 3545 cm1) were reduced. This indicates that after the natural attapulgite has been modified by hydrochloric acid, the Mg, Fe, Al ions might have dissolved into the mixture. The bands near 1034 and 985 cm1 are assigned to the SieOeSi stretching vibration and 477 cm1 corresponded to SieOeSi deformation vibration. For higher concentration of hydrochloric acid there was more free amorphous silica.

3.1.3.

Specific surface area and pore size distribution

Attapulgite has many internal channels, a lot of grooves on the surface and high porosity with a specific surface area in the range of 150e200 m2/g (Helmy et al., 2007). Many studies on attapulgite focus on the development of its adsorption properties which are mainly affected by specific surface area and pore size. Previous studies have demonstrated that appropriate acid treatments on attapulgite can increase the surface area (Balci, 1999; Belzunee et al., 1998). Ultrasonic washing to remove the acid-soluble substance and impurity was normally performed after acid treatment to obtain a surface area as large as possible. But our preliminary experiments showed that unwashed 5AT and 10AT can

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Fig. 1 e SEM images of clays and the flocs: (a) Natural attapulgite (N-AT) (500003); (b) attapulgite modified by 5 M hydrochloric acid (5AT) (1000003); (c) Acid-soluble state of 5AT (1500003), amplification of the squared region in (b); (d) Homogenate formed by simple mixing of 5AT and algae cells (400003), arrows pointing two algae cells; (e) The flocs after the flocculation experiment by algae cells and 5AT (the arrows indicate algae cells) (200003); (f) The netting structure of the flocs after the flocculation experiment (1000003).

promote flocculation, probably due to the acid-soluble substance and impurity, therefore the unwashed modified attapulgite was used as the flocculant (5AT and 10AT). Also the specific surface area and pore size distribution of clays 5AT and W-5AT (5AT after ultrasonic cleaning) were compared. The textural characteristics are shown in Table 1. Compared with N-AT, the surface area of 5AT and 10AT greatly decreased probably because some of their channels and pores have been filled with soluble substance. The microporous volume decreased to 0.0019 cm3/g and 0.0005 cm3/g from 0.0061 cm3/g after treating with 5 M and 10 M HCl, respectively. This supported that with the increasing concentration of HCl, the microporous volume became smaller because soluble substances increased. The specific surface area of W-5AT was much larger than that of 5AT because the former has been ultrasonic cleaned, therefore, the filled pores were released. Fig. 3 shows the distribution of pores in samples. The difference between N-AT and 5AT was not obvious. On the other hand, there were considerable changes in the curve of

W-5AT compared to N-AT and 5AT. N-AT showed two peaks of pore size distribution. The pores about 50 nm are the irregular pseudopores between particles (corresponding to external surface area), while the 3e4 nm pores are mesoporous in internal channels (corresponding to internal surface area) (Chen et al., 2003). Compared to N-AT, pore size distribution of 5AT has little change except that the peak of 3 nm mesopores disappeared, probably because they were filled by dissolved substance after HCl modification. After ultrasonic cleaning, W-5AT retained mesoporous but medium pores of about 35 nm appeared, indicating washing might have released the filled channels and transformed the large pseudopores into medium pores.

3.2.

Flocculation studies

As described in Section 2.3, the Chl-a, the concentration of iron, aluminum and silica at different contact times were measured. The effect of contact time on the flocculation of M. aeruginosa by natural and acid-treated attapulgite is illustrated in Fig. 4.

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Fig. 2 e Fourier transform infrared (FTIR) spectra of the clays: (a) natural attapulgite (N-AT); (b) attapulgite modified by 5 M hydrochloric acid (5AT); (c) attapulgite modified by 10 M hydrochloric acid (10AT).

It can be seen that 5AT removed algae cells much faster than N-AT. The former reached the maximum removal within 60 min while the latter in more than 180 min. For 5AT, within the first 10 min, the removal efficiency reached nearly 90%; in the subsequent 170 min, the increase of removal was not obvious. In contrast, the removal by N-AT has been maintained between 5% and 20% within 180 min (Fig. 4). 5AT also had a much higher flocculation capacity for M. aeruginosa as the removal efficiency of Chl-a by 5AT reached 95%, in great contrast to 20% by N-AT. Such remarkable difference was probably due to the different structures and components of the two clays. Among these, three components (silica, iron and aluminum) were measured. Table 2 shows the effective components existing in algae suspension before flocculation (a), in N-AT and 5AT (b), and consumed by flocculation (c) at different contact times. The residual content (d) in algae suspension after flocculation was also determined but was not shown. Component (c) was not determined directly. However, according to the equation: (c) ¼ (a) þ (b)  (d), (c) can be determined. Whether silica, iron or aluminum in flocculants or consumed by flocculation, 5AT had a higher concentration than that of N-AT at 10, 90, 180 min. This supported that silica, iron and aluminum should be the effective components in flocculation. In fact, the three components can be used to make polyaluminum ferric silicate chloride (PAFSiC) which was developed by some international companies in the 1990 s (Cheng et al., 2006). Although there were some silica, iron and aluminum in N-AT, they have not been activated by hydrochloric acid just like that in 5AT. The hydrochloric acid, as an oxidant and Cl provider, is used for the

preparation of polysilicate, the oxidation of ferrous and aluminum (Xu et al., 2009; Gao et al., 2002), therefore, silicon, iron and aluminum in the N-AT and 5AT were in different forms. Because the chemical components dissolved after hydrochloric acid modification are essential to flocculation, they should be retained when the attapulgite is used as flocculant, rather than as adsorbent, for which large surface area is desired and obtained by cleaning the acid-soluble states. Just because of activation by hydrochloric acid and the retention of components, there was such a great disparity in flocculating effect between N-AT and 5AT.

3.3.

The mechanism of flocculation

A comparison on the flocculation performance between N-AT and 5AT supports that flocculating effect of these two

Table 1 e The data of specific surface area and pore size for natural attapulgite (N-AT), non-washed modified attapulgite (5AT, 10AT) and washed modified attapulgite (W-5AT). Samples

SBET (m2/g)

Vtotal (cm3/g)

Vmicro (cm3/g)

Average pore size(nm)

N-AT 5AT 10AT W-5AT

136.98 89.59 95.94 191.23

0.456 0.310 0.303 0.391

0.0061 0.0019 0.0005 0.0150

13.32 13.85 14.63 13.02

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Fig. 3 e The BJH Adsorption dV/dlog(D) Pore Volume of clays: (N-AT) natural attapulgite; (5AT) attapulgite modified by 5 M hydrochloric acid; (W-5AT) 5AT after ultrasonic cleaning.

flocculants is closely linked to the preparation procedure, especially the hydrochloric acid activation. Compared to the natural attapulgite, the modified material has the feature of loose structure and rough surface, as shown in Fig. 1a and b. These structures change after modification increased the dispersion of modified material, and improved the reaction efficiency, therefore the removal efficiency increased due to the sufficient contact of clays and algae cells. Studies on chemical composition and structure information determined by FTIR show that after modification, the Mg, Fe, Al ions were released and more free amorphous silica formed which was also supported by data in Table 2. Just because of ion release and formation of amorphous silica structure, the flocculation ability of the modified samples was significantly enhanced. As shown in Fig. 1, there were some netting and bridging structures in the flocculation which supported that from the aspect of chemical components, the modified attapulgite by hydrochloric acid was similar to the PAFSiC. Some studies reported that the incorporation of polymerized silica can result in the enhancement of flocs size and growth (Zouboulis and Tzoupanos, 2009; Xu et al., 2009) which may explain why

Fig. 4 e Effect of contact time on removal efficiency of Chl-a by natural attapulgite (N-AT) and attapulgite modified by 5 M hydrochloric acid (5AT) (n [ 3, mean ± SD).

5AT can flocculate M. aeruginosa cells with high efficiency. Studies on mechanism supported that silica, iron and aluminum which trap charges and work as polymers can interact with charged particles by bridging flocculation, charge neutralization or other interactions (Stechemesser et al., 2005). According to the SEM analysis and flocculation studies presented in this work, there was a net attraction due to clay bridging, which was the dominant mechanism that regulated clay-cell flocculation.

3.4.

Toxicity studies

Fig. 5 compares the survival of synchronized C. elegans after being maintained for 4, 12, 24, 48 h in the four types of water samples. The results show that, within 48 h, the survival efficiency decreased gradually with time, but actual number of deaths was still trivial. After 48 h, the survival efficiency of the treated water by N-AT and 5AT were 96.2% and 97.3% respectively. No matter how long the contact time was the difference on survival efficiency between natural water and treated water by N-AT or 5AT was not significant. The result shows that there was no clear toxicity to C. elegans of the algae

Table 2 e The concentration of silica, iron and aluminum existing in the clays and consumed by flocculation at 10, 90, 180 min. Effective components

SiO2(mg/L) Fe(mg/L) Al(mg/L)

Clays

N-AT 5AT N-AT 5AT N-AT 5AT

Initial concentration of effective components in algae water (a) 4.911 0.022 39.320

Concentration of effective components in clays (b)

Concentration of effective components consumed by flocculation (c)

10 min

90 min

180 min

10 min

90 min

180 min

0.259 0.698 0.436 4.205 9.761 739.337

0.402 0.764 0.138 3.901 86.127 730.739

0.583 1.005 0.064 3.224 137.091 734.659

0.139 2.696 0.445 3.546 30.803 306.701

0.064 2.740 0.130 3.292 92.193 230.714

0.185 2.823 0.046 2.599 141.121 246.417

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4.

Fig. 5 e Survival of Caenorhabditis elegans in the four types of water (Distilled water; Algae water; Treated water (N-AT): algae water treated by natural attapulgite; Treated water (5AT): algae water treated by modified attapulgite using 5 M hydrochloric acid) after 4, 12, 24, 48 h of contact time (n [ 3, mean ± SD).

water which has been treated by 5AT, so this clay could be used in the harmful algal blooms removal.

3.5.

Strength and weaknesses

The approach that the attapulgite was modified by hydrochloric acid to remove the M. aeruginosa cells has several advantages. Firstly, compared with modification of clays with polyacrylamide (PAM), Fe3þ and chitosan (Zou et al., 2006), the modification with hydrochloric acid is straightforward, practically feasible and cost-effective. Secondly, after modification with hydrochloric acid, 0.37 g/L of attapulgite could remove 90% M. aeruginosa cells in less than 10 min, as efficient as previously reported approaches (Zou et al., 2006; Pan et al., 2006). Last, no significant adverse impacts from modified attapulgite were observed on C. elegans, thus this approach could be biologically safe. Although stirring operation could be one of the main disadvantage of this approach in practical application, a pilot test system for controlling HABs in some regions of Chaohu Lake, the fifth largest lake in China, has been constructed, the debugging and optimization work is carried out in an orderly way. Spreading clay cannot only remove HABs effectively, but also implies some impact on the benthic flora and fauna. Burkholder (1992) studied the effect of sediment loading on phytoplankton community composition. It was noted that the cyanobacterium formed colonies soon after sediment was added. Furthermore, dinoflagellates switched to heterotrophy in various degrees, and some dinoflagellates formed temporary cysts (Burkholder, 1992). Even though further improvements are needed, clay is expected to continue to be one of the most used HABs control methods, especially in Asia (Kang et al., 2007).

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Conclusions

Removal efficiency and mechanisms of flocculation on M. aeruginosa cells by natural and modified attapulgite were studied. The modified clays had much higher abilities to flocculate M. aeruginosa cells than that of natural attapulgite. The results show that under 5AT treatment, the Chl-a removal efficiency was above 90% in 10 min while N-AT had an efficiency below 15%. The outstanding flocculating ability of 5AT was attributed to the unique fibrous structure of the clay and the chemical components such as silica, iron and aluminum released after modification. The studies found that the dominant mechanism was the chemical bridging and netting which conferred flocculating quickly and effectively. Therefore the attapulgite may be used as not only adsorbent, but also flocculant. The modification attapulgite toxicity studies showed that there was no significant toxicity on C. elegans. This method could have the advantages of high removal efficiency, reasonable processing cost and low impact on the environment. In conclusion, modified attapulgite and the technique developed have the potential to be used in large scale to treat the harmful algal blooms.

Acknowledgments This research was supported by theNational Natural Science Foundation of China (No. 10975154) and the grant of the President Foundation of Hefei Institutes of Physical Science, Chinese Academy of Sciences (075AH24581). On the experimental design and analysis, Anhui Lake Environment Technology Ld. Corporation has provided us with great support. We also want to thank Shengquan Fu and Yanwei Ding of the Laboratories & Research Center, University of Science and Technology of China for their help with the Electron Microscopy and Specific Surface Area and Pore Size Distribution respectively. The authors would also like to thank the anonymous reviewers for their comments, which are significantly helpful to the improvement of this manuscript.

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Burkholder, J.M., 1992. Phytoplankton and episodic suspended sediment loading: phosphate partitioning and mechanisms for survival. Limnology and Oceanography 37, 974e988. Cai, D.Q., Wu, Z.Y., Jiang, J., Ding, K.J., Tong, L.P., Chu, P.K., Yu, Z.L., 2009. A unique technology to transform inorganic nanorods into nano-networks. Nanotechnology 20, 255302. Chen, T.H., Xu, X.C., Yue, S., 2003. Nanometer Mineralogy and Geochemistry of Palygorskite Clays in the Border of Jiangsu and Anhui Provinces. Science Press, Beijing. Cheng, W.P., Chi, F.H., Yu, R.F., 2006. Evaluation the ability of polyaluminum silicate chloride coagulants for turbidity removal efficiency. Separation Science and Technology 41, 297e308. Cole, R.D., Anderson, G.I., Williams, P.L., 2004. The nematode Caenorhabditis elegans as a model of organophosphate-induced mammalian neurotoxicity. Toxicology and Applied Pharmacology 194, 248e256. Gao, B.Y., Hahn, H.H., Hoffmann, E., 2002. Evaluation of aluminumesilicate polymer composite as a coagulant for water treatment. Water Research 36, 3573e3581. Grauer, Z., Peled, H., Avnir, D., Yariv, S., Heller-Kallai, L., 1986. The effect of induced aromaticity on sorption of organic molecules by montmorillonite: comparison of dibenzosuberone with dibenzotropone. Journal of Colloid and Interface Science 111, 261e268. Hagstro¨m, J.A., Sengco, M.R., Villareal, T.A., 2010. Potential methods for managing Prymnesium parvum blooms and toxicity, with emphasis on clay and barley straw: a review. Journal of the American Water Resources Association 46, 187e198. Helmy, A.K., de Bussetti, S.G., Ferreiro, E.A., 2007. The surface energy of palygorskite. Powder Technology 171, 126e131. Huang, J.H., Wang, X.G., Jin, Q.Z., Liu, Y.F., Wang, Y., 2007. Removal of phenol from aqueous solutions by adsorption onto OTMAC-modified attapulgite. Journal of Environmental Management 84, 229e236. Kang, X., Zhou, M., Fukuyo, Y., Matsuda, O., Lee, S.-G., Kim, H.-G., Shulkin, V., Orlova, T., Yu, R., 2007. Booklet of countermeasures against Harmful Algal Blooms (HABs) in the NOWPAP Region. Northwest Pacific Region Environmental Cooperation Center (NPEC), Toyama City, Japan. http://cearac. nowpap.org/. Koutsopoulou, E., Papoulis, D., Tsolis-Katagas, P., Kornaros, M., 2010. Clay minerals used in sanitary landfills for the retention of organic and inorganic pollutants. Applied Clay Science 49, 372e382. Liu, G.F., Fan, C.X., Zhong, J.C., Zhang, L., Ding, S.M., Yan, S.H., Han, S.Q., 2010. Using hexadecyl trimethyl ammonium bromide (CTAB) modified clays to clean the Microcystis aeruginosa blooms in Lake Taihu, China. Harmful Algae 9, 413e418. Lynwood, H.W., Schwint, I.A., 1967. Attapulgite, its properties and application. Industrial and Engineering Chemistry 59, 58e69. McKeown, D.A., Post, J.E., Etz, E.S., 2002. Vibrational analysis of palygorskite and sepiolite. Clays and Clay Minerals 50, 667e680. Murray, H.H., 2000. Traditional and new applications for kaolin, smectite, and palygorskite: a general overview. Applied Clay Science 17, 207e221. Pakalns, P., 1971. Spectrophotometric determination of silicon by a standard addition procedure. Analytica Chimica Acta 54, 281e294. Pan, G., Zhang, M.M., Chen, H., Zou, H., Yan, H., 2006. Removal of cyanobacterial blooms in Taihu Lake using local soils. I. Equilibrium and kinetic screening on the flocculation of Microcystis aeruginosa using commercially available clays and minerals. Environmental Pollution 141, 195e200. Pierce, R.H., Henry, M.S., Higham, C.J., Blum, P., Sengco, M.R., Anderson, D.M., 2004. Removal of harmful algal cells (Karenia brevis) and toxins from seawater culture by clay flocculation. Harmful Algae 3, 141e148.

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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 8 6 3 e2 8 7 4

Available at www.sciencedirect.com

journal homepage: www.elsevier.com/locate/watres

A model framework to assess the effect of dairy farms and wild fowl on microbial water quality during base-flow conditions Richard W. Muirhead a,*, Alexander H. Elliott b, Ross M. Monaghan a a b

Climate, Land and Environment, AgResearch Limited, Invermay Research Centre, Private Bag 50034, Mosgiel 9053, New Zealand National Institute of Water and Atmospheric Research, P.O. Box 11-115, Hamilton 3216, New Zealand

article info

abstract

Article history:

There is concern regarding microbial water quality in many pastoral catchments in New

Received 26 August 2010

Zealand which are home to numerous livestock and wild animals. Information on

Received in revised form

microbial impacts on water quality from these animals is scarce. A framework is needed to

1 February 2011

summarise our current knowledge and identify gaps at the scale of an individual farm. We

Accepted 1 March 2011

applied a Monte Carlo modelling approach to a hypothetical dairy farm based on the

Available online 15 March 2011

extensive data sets available for the Toenepi Catchment, Waikato, New Zealand. The model focused on quantifiable direct inputs to the stream from ducks, cows and farm dairy

Keywords:

effluent (FDE) during base-flow conditions. Most of the inputs of Escherichia coli from dairy

Dairy farming

farms occur sporadically and, therefore, have little effect on the expected median stream

Cattle

concentrations. These sporadic inputs do however, have a strong influence on extrema

Modelling

such as 95th percentile values. Current farm mitigations of fencing streams and using

Ducks

improved management practices for applying FDE to land, such as low application rate

Non-point pollution

deferred FDE irrigation systems, would appreciably reduce faecal microbial inputs to the

Base-flow

stream. However, the concentrations of E. coli in rural streams may not reduce as much as expected as wild fowl living in streams would have a larger effect on water quality than a farm in which environmental mitigations are widely implemented. ª 2011 Elsevier Ltd. All rights reserved.

1.

Introduction

Diffuse pollution from agriculture is recognised as an important source of faecal microbial contamination of surface waters (Kay et al., 2007; Monaghan et al., 2008; Till et al., 2008). There have been a number of reviews published presenting mitigation options to reduce faecal indicator organism (FIO) losses from livestock farming (Collins et al., 2007; Oliver et al., 2007). However, an important gap in all of these reviews is the limited quantification of the effectiveness of these mitigations to reduce the number of FIOs discharged by farms, or of the

ultimate effect on stream FIO concentrations (McDowell et al., 2008). Numerous models have been written to predict stream FIO concentrations in agricultural catchments, with most of these existing models based on the hydrological processes that deliver overland flow contaminated with faecal microbes to a stream network (Jamieson et al., 2004). The microbial sources in these models are dependent on the overland flow generated during rainfall events which is of more relevance to storm-flow conditions. However, there are other sources of faecal microbes, such as direct animal inputs and runoff of farm dairy effluent (FDE), which can discharge to the stream

* Corresponding author. Tel.: þ64 3 489 9261; fax þ64 3 489 3739. E-mail addresses: [email protected] (R.W. Muirhead), [email protected] (A.H. Elliott), [email protected] (R.M. Monaghan). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.03.001

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network independent of rainfall (Muirhead et al., 2008). These rainfall-independent sources are the focus of this work as they are the source of microbes discharged to streams during base-flow conditions. Furthermore, there are significant gaps in our understanding of FIO transfer from land to water which means these catchment scale models are of limited value in predicting the effectiveness of on-farm mitigations (Collins and Rutherford, 2004). A farm-scale approach may be more effective at identifying the value of specific mitigation options (Monaghan et al., 2008). The New Zealand dairy industry has recognised the importance of water quality and developed a strategy for sustainable environmental management (Anon, 2006). Within this strategy is a research goal to design a dairy farm that is capable of delivering contact recreational water quality in all water leaving a farm. A logical first step towards addressing this question is the development of a conceptual model to synthesise our existing knowledge and to identify gaps required to answer this question. It is important to note that this water quality target is set at the scale of an individual farm and will therefore, need a farm-scale model to answer this question. Water quality guidelines in New Zealand have set the contact recreational water quality standard at a 95th percentile of <260 Escherichia coli 100 mL1 (MfE, 2003). Concentrations of E. coli measured in the runoff from grazed pastures are significantly higher than the water quality standards making it extremely unlikely that a dairy farm could achieve the water quality standards during storm-flow conditions (Kay et al., 2007; McDowell et al., 2008). However, storm-flow conditions are generally unattractive for contact recreation activities, such as fishing and swimming, due to the high flows and dirty water conditions. As contact recreation occurs predominantly during base-flow conditions, it is probably more relevant to determine, in the first instance, if a dairy farm can achieve these water quality standards during these flow conditions. A complicating factor in predicting water quality in agricultural landscapes is the impact of water fowl which have long been implicated as a source of faecal microorganisms in waterways (Geldreich, 1966). Discussions with farmers about water quality issues usually include questions about the potential impact of ducks and other wild animals living in the stream channels as other potential sources of contamination. New Zealand is home to 4.1 million cattle and approximately 4.5 million mallard ducks. Palmer (1983) was able to demonstrate that faecal coliform concentrations in a river were higher when birds were roosting on bridges over the river, than when the birds were removed. A study in the UK concluded that both cattle and ducks were important sources of Campylobacter jejuni in the stream (Obiri-Danso and Jones, 1999). A numeric framework to quantify the impact of dairy farms and water fowl on microbial water quality would be useful for farmers and for water quality managers. Microbiological data is variable with concentrations of E. coli in cow faeces ranging over six orders of magnitude (Muirhead et al., 2006). The process of choosing single point estimates of such variables for modelling is fraught (Muirhead et al., 2008). An obvious alternative is to use a modelling approach that can take into account natural variability in the input data, such as the Monte Carlo approach frequently used

in microbial risk assessment (Hass et al., 1999). Processedbased models are useful for increasing our understanding of how faecal microbes are delivered to stream networks by specific pathways under certain hydrological conditions (Jamieson et al., 2004). Farmers and water managers are less interested in the processes, but more interested in the long term effectiveness of mitigations to reduce the load of faecal microbes to the stream over a range of hydrological conditions. The stochastic modelling approach presented here was chosen as it can incorporate the natural variability of the input data and generate long term averages or distributions that better describe the potential effectiveness of different mitigation options. The work presented here had three main objectives. The first was to build a farm-scale model, using a Monte Carlo simulation approach, to determine if this could enhance our understanding of the relative importance of different farm sources of FIOs. The second objective was to use this framework to assess whether a dairy farm could achieve contact recreational water quality in water leaving the farm under base-flow conditions, a modification to the research target set by the dairy environment strategy (Anon, 2006). The third objective was to predict the potential impact that ducks may have on microbial water quality.

2.

Model development

2.1.

Overview of the model framework

Few farms are the headwater for streams. Most farms will have a reach of stream flowing through the farm and will receive water already contaminated from upstream activities. We therefore structured the farm-scale model as a single farm (Section 2.2.1) with a single stream flowing through the farm (Fig. 1). Model calculations are based on steady state conditions (more relevant to base-flow conditions) with inputs and outputs averaged over a day; variations from day to day are considered through the Monte Carlo process. The model inputs upstream of the farm are flow rate and stream E. coli concentration. As the stream flows through the farm it

Fig. 1 e Structure of the daily base-flow water quality model. Model farm assumed to be 80 ha, 18% of which is occupied by soils directly connected to the stream due to either close proximity or via artificial drainage networks.

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receives inputs from different farm sources (Section 2.2), and from ducks living in the stream (Section 2.3). The upstream, farm and duck inputs are attenuated according to an in-stream E. coli attenuation rate (Section 2.4.3). The output of the model is the E. coli concentration in the water leaving the farm downstream. The model was constructed using @RISK 5.5 (Pallisade Corporation, Ithaca, New York, USA) in Microsoft Excel2007 (Microsoft Corporation), with 5000 iterations per run using the Latin Hypercube sampling method. An iteration represents the conditions occurring at a random time, and uses parameter values drawn randomly from the distributions of parameter values (except where correlations between distributions are noted in Section 2.2.2). The individual results from each of the 5000 iterations were entered into SigmaPlot10 (Systat Software, Inc., California), and the distribution of the outputs displayed as box plots.

FDE management systems: (1) a typical 2-pond treatment system discharging directly to the stream, (2) an advanced pond treatment system (APS; Craggs et al., 2004) discharging directly to the stream, (3) irrigation of the untreated effluent directly to land from a sump via a high application rate travelling irrigator (Houlbrooke et al., 2004a), and (4) current best management practice (BMP) where the FDE is irrigated via a low application rate system from a holding pond only when soil moisture levels are sufficiently low for safe irrigation to land (Houlbrooke et al., 2004b; Houlbrooke et al., 2006). The four options for the management of FDE were modelled independently, and a switch was used to turn the options on or off to test the effect of the different effluent management systems on downstream concentrations. The source of E. coli from dairy farms is the faeces excreted by the animals. The daily number of E. coli excreted per cow (Ec, E. coli cow1 day1), is calculated from:

2.2.

Farm-scale E. coli loss module

Ec ¼ Np Mp

2.2.1.

Description of the modelled farm

where Np is the number of E. coli in a cowpat and Mp is the daily number of cowpats produced (cow1 day1). Np is determined from:

The modelled farm is based on the extensive data available for the Toenepi catchment in Waikato, New Zealand (Wilcock et al., 2006). The farm is 80 ha in area with a mix of free draining allophonic soils on the ridges and poor draining soils containing artificial drainage predominantly along the stream network. There is 1 km of stream flowing through the farm for a steam density of 0.0125 km ha1. The farm operates a year round pasture grazing system milking 230 cows on a seasonal basis at a stocking rate of 2.9 cows ha1.

2.2.2.

Description of the E. coli sources

The two dominant sources of FIO inputs to streams during base-flow conditions are assumed to be direct inputs from dairy cattle (and other animals including wild fowl) to streams, and discharges of farm dairy effluent (FDE) generated from the hard standing areas where the cows wait for milking (Muirhead et al., 2008). The direct inputs can come from two different processes whereby cattle have contact with water: (a) via the cows having access to an (unfenced) stream from pasture, or (b) via the cows being herded through the stream rather than across bridges (Davies-Colley et al., 2004; McDowell et al., 2008). The management of FDE is variable, with potential systems ranging from direct discharges of the FDE to a drain or stream, through automated land irrigation systems. For this analysis we have modelled four different

(1)

Np ¼ Cf W

(2) 1

where Cf is the concentration of E. coli in the faeces (E. coli g wet weight) and W is the weight of a cowpat (g wet weight). Mp, Cf, and W were sampled from probability distributions described in Table 1. The notation for log-normal distributions used in Table 1 and elsewhere in this paper is as follows. If a variable X follows a log-normal distribution, then log10X follows a normal distribution with a mean value of log10-mean and a standard deviation of log10-SD. W and Np were negatively correlated to prevent the unlikely occurrence of a cow producing 16 large cowpats per day, which is unfeasible (Table 1).

2.2.3.

E. coli losses due to direct deposition to the stream

The calculation of daily loads of faeces deposited to the stream from cows that have access to the stream is complicated by the fact that the stream may not flow through every paddock on the farm, and therefore cows may or may not have access to the stream on a specific day. The daily load of E. coli deposited in the stream (Ld, E. coli ha1 day1) is calculated from: Ld ¼ ad aa Ec SR

(3)

Table 1 e Input variables and the distributions used for the Monte Carlo simulations to predicting daily outputs of E. coli from an individual cow. The notation for log-normal distributions is discussed in Section 2.2.2. Input variable Cf W

Mp

Concentration of E. coli in cow faeces (g1 wet weight) Wet weight of individual cowpata (g)

Number of cowpats excreteda (cow1 day1)

Distribution chosen

Distribution parameters

Source

Log-normal

log10-mean ¼ 4.4, log10-SD ¼ 1.3 Most likely ¼ 2000, maximum ¼ 2700, min ¼ 1500 n ¼ 16, p ¼ 0.75

Muirhead et al. (2006)

Triangular

Binomial

a Input factors were negatively correlated, see Section 2.2.2.

Haynes and Williams (1993) Haynes and Williams (1993)

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where ad is the proportion of the herd’s faeces deposited directly into the stream if there is stock access to the stream and aa is the proportion of days per year that cows that have access to a stream, with probability distributions described in Table 2. For this exercise we assumed that the stream flows through 25% of the paddocks, so the herd has access on 25% of the days, which determines the probability distribution of aa. The stocking rate (SR, cows ha1) is 2.9 cows ha1 (Section 2.2.1). The management option to mitigate these direct inputs is to fence off the stream, and this is modelled as being 100% effective. A switch to turn this load on or off was used in the model to indicate if the stream is unfenced or fenced, respectively. The effect of partially fencing the farm can be explored by changing aa.

2.2.4. E. coli losses due to cows walking through an unbridged stream crossing

(4)

where ac is the proportion of cows that defecate directly into the stream during a crossing and ap is the proportion of days per year that the stream crossing is required, with frequency distributions described in Table 2. It is assumed that when a cow defecates into a stream, one cowpat is deposited. The probability distribution for ap can be estimated from the proportion of paddocks on the farm that are separated from the milking shed by a stream crossing. The factor of four allows for four stream crossings per day for twice a day milking. The management option to mitigate these inputs is to put a bridge over the stream. This was modelled as being 100% effective under base-flow conditions, using a switch in the model to turn this load off if a bridge was present.

2.2.5.

LP ¼ 10bCP VSR

FDE losses from the 2-pond system

The 2-pond system is a traditional pond treatment method that has been used in New Zealand for many years to treat FDE

(5)

where CP is the concentration of E. coli in the 2-pond effluent (100 mL1); V is the daily volume of effluent discharge per cow (L cow1 day1); and b (which takes a value of 1or 0) indicates whether or not effluent is produced, which relates to the fraction of days per year that cows are milked. The probability distributions used for CP, V and b are presented in Table 3. The factor of 10 converts the CP concentration units from 100 mL1 to L1.

2.2.6.

A dairy farm that milks cows twice a day requires the cows to walk between the paddock and the milking shed four times a day. This frequency of walking would mean most stream crossings on a farm would be bridged to avoid the animals and farm machinery from having to ford the stream. Un-bridged crossings, if they exist, are generally only used infrequently to access a few paddocks (Wilcock et al., 2006). In this model we calculated the daily inputs from the cows crossing the stream (Lc, E. coli ha1 day1) from: Lc ¼ 4ac ap Np SR

before it is discharged to a stream or drain (Hickey et al., 1989). The daily load of E. coli discharged to the stream from the 2-pond effluent treatment system (LP, E. coli ha1 day1) was calculated from:

FDE losses from the advanced pond system (APS)

The APS is an upgrade for pond treatment of FDE that replaces the oxidation pond of a traditional 2-pond system with high rate algae and maturation ponds (Craggs et al., 2004). The load discharged from the APS effluent system (LA, E. coli ha1 day1) is modelled using: LA ¼ 10bCA VSR

(6)

where CA is the concentration of E. coli in the APS effluent discharge (100 mL1) with a distribution described in Table 3. The factor of 10 converts the CA concentrations from 100 mL1 to L1.

2.2.7. FDE losses from the high application rate effluent irrigation system (high) The proportion of irrigated FDE that is discharged to the stream was estimated from a series of soil water balance models (Muirhead et al., 2010) based on six years of daily rainfall and evaporation potential (PET) data obtained from a weather station in the Toenepi catchment. Briefly, the rainfall and PET data was used to calculate a daily soil water deficit. If the depth of FDE irrigated on a particular day exceeded the soil water deficit, then a proportion of FDE drained through the topsoil. Thus, the FDE drainage calculated is a direct loss from the irrigation water only and not from rainfall-derived drainage during storm events. The proportion of the effluent that drained through the topsoil was calculated for each day over the 6-year period and the results summarized as a cumulative probability function (Fig. 2). The distribution of the modelled

Table 2 e Input variables and the distributions used for the Monte Carlo simulations predicting the daily load of E. coli deposited directly into the stream. Input variable ad aa ac ap

Proportion of the herd’s faeces deposited directly into the stream if they have access to the stream Proportion of days per year that cows have access to the stream Proportion of cows in the herd that defecate directly into the stream at each crossing Proportion of days per year that cows require an un-bridged stream crossing to be milked

Distribution chosen

Distribution parameters

Source

Triangular

Min ¼ 0.017, most likely ¼ 0.061, maximum ¼ 0.105 1(0.25), 0(0.75)

McDowell et al. (2008) Wilcock et al. (1999) Davies-Colley et al. (2004) Wilcock et al. (1999)

Discretea Normal Discretea

Mean ¼ 0.1, Std. dev ¼ 0.02, truncate at 0 1(0.1), 0(0.9)

a For discrete distributions, the values in parentheses show the proportion of time that the variable is equal to the value to the left of the parentheses.

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Table 3 e Input variables and the distributions used for the Monte Carlo simulations predicting the daily loads of E. coli generated from the FDE management systems. Input variable

Distribution chosen a

b

Variable indicating the number of days per year that FDE is produced (0 for no effluent, or 1)

Discrete

V

Volume of FDE produced (L cow1 day1)

Triangular

CP

Concentration of E. coli in 2-pond effluent discharge (100 mL1) Concentration of E. coli in APS effluent discharge (100 mL1)

Log-normal

CA

Log-normal

Distribution parameters 1(0.74), 0(0.36)

Min ¼ 50, most likely ¼ 80, max ¼ 120 log10-mean ¼ 4.8, log10-SD ¼ 0.7 log10-mean ¼ 2.9, log10-SD ¼ 0.6

Source Based on 270 days of milking per year (Wilcock et al., 1999). Hickey et al. (1989) and unpublished data Donnison et al. (2008) Craggs et al. (2004)

a For discrete distributions, the values in parentheses show the proportion of time that the variable is equal to the value to the left of the parentheses.

losses for the high application rate system indicated no drainage on 71% of the days, with a maximum of 100% losses when the soils were saturated (Fig. 2). The load of E. coli discharged to the stream from the high application rate irrigation system was modelled using Eq. (7): LH ¼ aH at bci Ec SR

(7)

where LH is the load discharged to the stream channel from the high application rate effluent system (E. coli ha1 day1); aH is the proportion of applied FDE passing through the topsoil which is determined from a probability distribution (Fig. 2); at is the average proportion of time per day the cows spent standing on the concrete areas that generate the FDE and was set to 0.1; and ci is the connectivity between the effluent irrigation area and the stream. The value of ci was set to 0.18, based on unpublished research in the Toenepi catchment showing that 18% of the farmed land could be defined as a “critical source area,” i.e. containing either a poorly drained soil that was artificially drained, or a well-drained soil type within 10 m of the stream.

2.2.8. FDE losses from a low application rate irrigation system with pond storage (low) The low application rate irrigation system also had 60 days pond storage available (Muirhead et al., 2010). There is little

data available on E. coli concentrations in effluent storage ponds, so we used data for 2-pond system discharges which are typically designed to have a 60-day retention time (Hickey et al., 1989). Soil water balance modelling of this FDE management scenario indicated no drainage occurred on 88% of the days (Fig. 2). The load of E. coli discharged to the stream from this ‘low application rate’ effluent system (LL, E. coli ha1 day1) was calculated by: LL ¼ 10aL as bci Cp VSR

(8)

where aL is the proportion of FDE lost from the topsoil with a probability distribution determined from the separate model as per the high application rate system (Fig. 2); ci is the connectivity between the effluent irrigation area and the stream and was set to 0.18 as per the high application rate system; as is the soil attenuation factor for E. coli when effluent is applied using low application rate systems and was set to 0.05 (Houlbrooke et al., 2006). The factor of 10 converts the Cp units from 100 mL1 to L1.

2.3.

E. coli inputs to the stream from water fowl

There is very limited data on the amounts of faecal material deposited in streams by water fowl. For this analysis we estimated the daily loadings from ducks which are the most common water fowl found in or near NZ streams. The daily loading of E. coli deposited into a stream by ducks (LB, E. coli ha1 day1) was calculated using: LB ¼ aB CB UZD

(9)

where aB is the proportion of faeces deposited directly in the stream, CB is the concentration of E. coli in duck faeces (E. coli g1 wet weight), U is the weight of faeces produced by a duck (g day1 wet weight), and Z is the density of ducks living in the stream (ducks km1), with probability distributions as described in Table 4. D is the stream density which was set at 0.0125 km ha1 for the model farm (Section 2.2.1).

Fig. 2 e Cumulative probability curves used to represent the proportion of effluent lost from the topsoil by the high and low application rate effluent irrigation systems (aH and aL).

2.4.

Stream module

2.4.1.

Purpose of the stream module

The purpose of the stream module is to convert the daily farm and duck loadings into a concentration of E. coli in the stream water leaving the farm. The key factors in the stream module

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Table 4 e Input variables and the distributions used for the Monte Carlo simulations predicting the daily load of E. coli deposited to the stream by ducks. Input variable CB U aB Z

E. coli concentration in duck faeces (g1 wet weight) Wet weight of duck faeces produced (g duck1 day1) Proportion of duck faeces deposited in water Number of ducks living in stream (km1)

Distribution chosen

Distribution parameters

Source

Log-normal

log10-mean ¼ 5.5, log10-SD ¼ 1.5 Min ¼ 100, most likely ¼ 336, max ¼ 400 Min ¼ 0.10, most likely ¼ 0.35, max ¼ 0.60 l¼4

Moriority et al., personal communication Geldreich (1966) and Hann et al. (2008) Expert opiniona

Triangular Triangular Poisson

Expert opiniona

a There is no published data on the density of ducks living in small streams and the percentage of time ducks spend in the water, so modelled distributions were based on the expert opinion of a wildlife field officer from Fish and Game NZ.

are the flow rate, inflow E. coli concentration, length of stream through the farm, and the attenuation rate of the E. coli as it flows downstream (Fig. 1, Table 5).

where A is the area of the farm (ha) which is equal to 80 ha for the modelled farm (Section 2.2.1).

2.4.3. 2.4.2.

Load entering the farm upstream

The load of E. coli in the stream water entering the farm from upstream (Lu, E. coli day1) was calculated by: Lu ¼ 864; 000Cu Q

(10)

Q is the flow rate in the stream (L s1) and Cu is the E. coli concentration in the upstream water, with distributions as shown in Table 5. The factor of 864,000 allows for 86,400 s per day and also converts the Cu units from 100 mL1 to L1. Q was represented by a log-normal distribution with a log10mean of 1.75 and a log10-SD of 0.5 (Table 5). This stream flow rate distribution translates to 5th, 50th and 95th percentile Q values of 8.5, 56 and 370 L s1, respectively, which is similar to expected base-flow rates in the Toenepi catchment (Wilcock et al., 1999). The distribution used for Cu was log-normal with a log10-mean of 1.75 and log10-SD of 0.4 (Table 5). This E. coli concentration distribution translates to 5th, 50th and 95th percentile concentration values of 12, 56 and 256 E. coli 100 mL1, respectively. For the E. coli concentration in the upstream water, the log10-SD of 0.4 was chosen as it appears to be a typical number for E. coli concentration distributions in freshwaters (Table 5; G. McBride, personal communication). The log10-mean concentration was then set to 1.75, as this distribution would meet the criteria for category B recreational waters in New Zealand (95th percentile < 261 E. coli 100 mL1; MfE, 2003). The total E. coli load entering the stream from the farm and the ducks (Li, E. coli day1) was calculated by: Li ¼ A½LB þ Ld þ Lc þ ðLP or LA or LH or LL Þ

(11)

In-stream attenuation

There are two main processes that will attenuate E. coli in streams: sunlight inactivation and sediment bed uptake. The attenuation due to these processes was modelled in a separate exercise, giving a distribution for the attenuation ratio, R (a dimensionless ratio), which is the proportion lost over 1 km of stream. The distribution of the attenuation ratio was represented by a log-normal distribution with a log10-mean of 0.633 and log10-standard deviation of 0.339 (Table 5), with an upper value of 1. This attenuation distribution translates to a median attenuation of 23% over 1 km and a maximum of 100%. This attenuation distribution was based on published sunlight inactivation rates (Sinton et al., 2007) and stream sedimentation effects (Wilkinson et al., 2006) and was modelled using the stream flow rates given in Table 5. The instream attenuation ratio over 1 km was converted to a first order decay constant (K, km1) using: K ¼ loge ð1  RÞ

2.4.4.

(12)

Downstream load and concentration

The upstream load was attenuated over the full length of the stream (F, km) while the farm and duck load were attenuated over half the length (Fig. 1). The load leaving the farm at the downstream end of the stream (Ldown, E. coli day1) was therefore: KF Ldown ¼ Lu eKF þ Li e 2 (13) F was 1 km (Section 2.2.1). The downstream concentration of E. coli (Cd, E. coli 100 mL1) was calculated from Ldown divided by the daily volume of water discharged from the farm:

Table 5 e Input variables and the distributions used for the Monte Carlo simulations of stream E. coli concentrations. Input variable Cu Q R

E. coli concentration in water upstream (100 mL1) Flow rate in the stream (L s1) E. coli in-stream attenuation ratio over 1 km (dimensionless)

Distribution chosen

Distribution parameters

Source

Log-normal

log10-mean ¼ 1.75, log10-SD ¼ 0.4 log10-mean ¼ 1.75, log10-SD ¼ 0.5 log10-mean ¼ 0.633, log10-SD ¼ 0.339, truncated at 0

MfE (2003) and G. McBride, personal communication Wilcock et al. (1999) Unpublished modelling analysis

Log-normal Log-normal

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Cd ¼

Ldown 864; 000Q

(14)

where the factor 864,000 accounts for conversion of time and concentration units.

3.

Results and discussion

3.1. Comparison with measurements and sensitivity testing of the stream module The framework for the model is based on a hypothetical farm in the Toenepi catchment, with a 2-pond effluent system where stock have some access to the stream and ducks are living in the stream (Wilcock et al., 2006). The catchment (17.2 km2) is predominantly for dairy land use, and parameters for the model were selected to be representative of catchment-average conditions. A comparison of the modelled outputs from this hypothetical farm with actual stream E. coli concentrations measured in the Toenepi catchment (n ¼ 102, from Wilcock et al., 2006) is shown in Fig. 3. The range of concentrations predicted by the model appears realistic, and the medians for the modelled and measured distributions are very similar (Fig. 3). The modelled concentrations have a wider distribution than the measured values, which can be attributable to averaging of spatial variability at the larger scale. A sensitivity analysis of the stream module was conducted by replacing the distributions of the stream inputs with fixed values (while maintaining all other parameters with standard distributions) to determine their impact on predicted concentrations. The flow rate distribution of the stream was replaced with fixed values ranging from 10 to 1000 L s1 (Fig. 4A). The concentrations predicted in the stream decreased as flow rates increased, because the daily load of E. coli was diluted in a larger volume of water. The distribution of E. coli concentrations in the water entering the farm (upstream) was replaced with fixed

Fig. 3 e Comparison of measured E. coli concentrations in the Toenepi stream (Wilcock et al., 2006) with the output concentrations predicted by the model for a typical farm in the Toenepi catchment. Horizontal line is the median, boxes are the 25th to 75th percentile, whiskers 10th and 90th percentile and points are the 5th and 95th percentile values.

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values ranging from 50 to 1000 MPN 100 mL1 (Fig. 4B). As the concentration of E. coli in the inflowing water increased, so did the median and lower percentiles, but the higher percentiles remained relatively constant (Fig. 4B). This indicates that the farm inputs drive the high concentrations leaving the farm, but that the inflow concentration limits the water quality that can be achieved leaving the farm. The distribution of in-stream attenuation ratios was replaced with fixed values ranging from 0.01 to 0.5 km1 using two different distances of 1 and 10 km (Fig. 4C and D). Over a distance of 1 km, the in-stream attenuation rate had very little effect on the predicted concentrations leaving the farm, indicating that attenuation rate has little effect at a farm-scale (Fig. 4C). This finding supports our simplification of the treatment of attenuation in the model, i.e. using a distribution of attenuation rates and attenuating the farm inputs from a point in the centre of the farm. However, over a distance of 10 km the in-stream attenuation rate had a much greater effect, particularly as the attenuation rate increased (Fig. 4D). This implies that the in-stream attenuation rate will be an important parameter for catchment scale modelling. The sensitivity of the stream to inputs from the farm was tested by replacing the farm inputs with a series of fixed values ranging from 104 to 109 E. coli ha1 day1 (Fig. 5). This shows that farm inputs of less than 106 E. coli ha1 day1 did not have an impact on the water quality leaving the farm. Previous attempts to determine a point estimate of an acceptable daily input to the stream calculated a value of 107 E. coli ha1 day1 (Muirhead et al., 2008), which is slightly more than that indicated by the Monte Carlo approach used here.

3.2.

Daily farm and duck loadings to the stream

The distributions of the expected daily loads from the different sources are shown in Fig. 6. As expected, the distributions for daily E. coli losses from the farm are all highly skewed as the outputs are highly sporadic. For loads generated due to direct access to the stream, the 90th and 95th percentiles are similar in range to the 2-pond effluent system discharge. This is consistent with calculations of point estimate loadings from these pathways that indicate that, when they occur, the loads per day are similar (Muirhead et al., 2008). The distribution for the loadings from the cows crossing the stream is more skewed than the direct access distribution, because these occur less frequently (only 10% of the time) and the 95th percentile for crossings is less than for direct access. This is also consistent with previous calculations of point estimates (Muirhead et al., 2008). The advanced pond system (APS) produces the same volume of effluent as the 2-pond system, but the E. coli concentrations are about two orders of magnitude less, significantly reducing predicted daily loadings (Fig. 6). The 95th percentile for the high application rate effluent irrigation system is almost as high as the 95th percentile for the 2-pond effluent system (Fig. 6). Houlbrooke et al. (2004a) demonstrated that applying effluent at high rates, under a worst case scenario where soils are saturated, can result in 100% loss of the applied effluent through the drainage network. Therefore, we would expect the loads under a worst case scenario to be greater than a 2-pond system discharge,

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A

B

C

D

Fig. 4 e Sensitivity analysis of the stream module components on the concentration of E. coli in water leaving the farm. (A) Effect of stream flow rate; (B) Effect of upstream E. coli concentration (square symbols represent upstream concentrations); (C) Effect of in-stream attenuation rate over a distance of 1 km; and (D) Effect of in-stream attenuation rate over a distance of 10 km. Horizontal line is the median, boxes are the 25th to 75th percentile, whiskers 10th and 90th percentile and points are the 5th and 95th percentile values.

but only for a few days per year. However, for drier parts of the year there would be no losses of effluent to the stream from the high application rate system, and this is shown by the skewedness of the distribution with a median value of insignificant losses (Fig. 6). In contrast, the modelled low application rate effluent irrigation system significantly reduced the 75th, 90th and 95th percentiles relative to the high rate system (Fig. 6). This was due to three reasons. The modelled low application rate system included a storage component which allowed for some die-off of the E. coli. Secondly, the storage allowed for a deferred irrigation strategy so that irrigation did not take place during saturated soil conditions until the storage pond was full (Houlbrooke et al., 2004b). Thirdly, the low application rate effluent irrigation systems have been shown to attenuate the E. coli concentrations measured in farm drainage (Monaghan et al., 2010). The amounts and distribution of loads from the low application rate effluent system are therefore considerably less than for the high application rate system (Fig. 6).

The modelled daily loadings from ducks show a large range, reflecting the uncertainty of the input variables estimated for this part of the model (Table 4, Fig. 6). However, the 90th and 95th percentiles for the duck loadings are similar to the loadings estimated for many of the farm losses. This indicates that a large wild bird population in a stream has the potential to have a similar effect on water quality as other factors on a poorly managed dairy farm. The sensitivity analysis indicated that farm inputs of <106 E. coli ha1 day1 would not have an impact on the E. coli concentrations in the water leaving the farm (Fig. 5). Only the 2-Pond discharge had a median value greater than 106 E. coli ha1 day1 indicating that this would be the largest input to the stream (Fig. 6). Both the APS and the ducks had median inputs of approximately 106 E. coli ha1 day1. All of the inputs, except the low-rate effluent irrigation system, had 95th percentiles >106 E. coli ha1 day1 indicating that they would have an impact on the stream for some days each year.

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Fig. 5 e Sensitivity of the downstream E. coli concentrations to a range of daily farm inputs. Horizontal line is the median, boxes are the 25th to 75th percentile, whiskers 10th and 90th percentile and points are the 5th and 95th percentile values.

Sensitivity analysis of the individual input parameters was conducted using the regression coefficient method in @RISK. Sensitivity analysis was conducted for each individual source of E. coli to the stream. For each source in turn, a simulation run was conducted where only one source contributed to the downstream concentration and the sensitivity analysis was then conducted for that run. For each simulation run, the three most sensitive input parameters for the daily load and the downstream concentration were recorded. The effect of adding and removing various combinations of sources is explored in Section 3.3 on mitigation scenarios. The sensitivity of the daily input loads and concentrations at the stream outlet to parameters associated with each source of microbes are shown in Table 6. For the source arising from direct animal access to the stream, the load delivered to the

stream was most sensitive to the concentration of E. coli in the faeces (Cf) and the proportion of days per year that the animals have access to the stream (aa). The stream concentration was also sensitive to the stream flow rate (Q). The impact of animals having to walk through the stream to access grazing was most sensitive to the proportion of days per year the animals had to cross the stream (ap) and the concentration of E. coli in the faeces (Cf). The outputs from the ducks living in the stream were most sensitive to the concentration of E. coli in the duck faeces (Cf) and the proportion of duck faeces deposited directly in the stream (aB) and the flow rate of the stream (Q). Sensitivity analysis of the sources associated with the four different effluent management systems indicates some patterns. The daily loads were all sensitive to the concentration of E. coli in the source (Cp, CA and Cf) and the number of days per year that effluent was generated (B) (Table 6). The daily loads from the two different pond systems (2-Pond and APS) were sensitive to the volume of effluent generated (V). Whereas the two irrigation systems (High and Low) were sensitive to the proportion of effluent that drained through the topsoil (aH and aL). Thus, all 4 effluent management systems were sensitive to the volume of effluent that discharged to the stream. The impact of the effluent management systems on the stream showed that the two management systems that generated the highest 95th percentile loads, the 2-pond and high systems (Fig. 6), were sensitive to the flow rate in the stream (Q). In contrast, the two effluent management systems that generated the lowest 95th percentile loads, the APS and low systems (Fig. 6), were sensitive to the E. coli concentration in the stream entering the farm (Cu) and the in-steam attenuation rate (R). This last observation is expected, as when the inputs to the stream are very small (Fig. 5), the downstream output is effectively the upstream input attenuated through the farm. This sensitivity analysis indicates that all outputs are sensitive to the concentration of E. coli in the source material and implies the need for good input data sets for the

Table 6 e Sensitivity analysis of each individual source of E. coli to the stream. For each source, the three most sensitive inputs for the daily load of E. coli discharge to the stream and the effect of this load on the downstream concentration are listed. The R2 value for each input parameter is given in brackets. Source

Fig. 6 e Distributions of the daily load of E. coli discharged to the stream from the farm managements or from ducks living in the stream. Horizontal line is the median, boxes are the 25th to 75th percentile, whiskers are the 10th and 90th percentile and points are the 5th and 95th percentile values.

Output

Direct access Daily load: Ld (Eq. (3)) Stream conc: Cd Crossings Daily load: Lc (Eq. (4)) Stream conc: Cd 2-Pond Daily load: Lp (Eq. (5)) Stream conc: Cd APS Daily load: LA (Eq. (6)) Stream conc: Cd High Daily load: LH (Eq. (7)) Stream conc: Cd Low Daily load: LL (Eq. (8)) Stream conc: Cd Ducks Daily load: LB (Eq. (9)) Stream conc: Cd

1 Cf (0.13) Cf (0.14) ap (0.12) ap (0.11) Cp (0.41) Cp (0.26) CA (0.53) Cu (0.64) Cf (0.11) Cf (0.10) aL (0.31) Cu (0.77) CB (0.11) CB (0.10)

a Input parameter not statistically significant.

2 aa (0.09) aa (0.11) Cf (0.09) Cf (0.08) B (0.15) Q (0.18) B (0.23) CA (0.16) aH (0.09) aH (0.09) Cp (0.14) R (0.16) aB (0.03) aB (0.03)

3 a

ns Q (0.09) ns ns V (0.03) B (0.09) V (0.06) R (0.16) B (0.03) Q (0.04) B (0.06) aL (0.02) ns Q (0.03)

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distribution of E. coli in faecal material. This will be very important if this modelling framework is used for pathogen risk analysis. It would be useful if studies of pathogen excretion rates could be published as distributions, rather than means or prevalence.

3.3.

Mitigation scenarios

The modelling framework presented here was able to be used to test the effect of various farm-scale mitigation options on the microbial quality of the water leaving an individual farm. The model was re-run several times to predict the effect of the four different effluent management systems under three different scenarios (Fig. 7). The 2-pond effluent system delivered the highest consistent load to the stream, (Fig. 6), and therefore produced the worst water quality leaving the farm (Fig. 7). This result is expected, as effluent pond discharges have been identified as a major source of stream contamination (Wilcock et al., 1999, 2006). The three alternative effluent management systems all improved the water quality relative to the 2-pond system, and surprisingly produced similar median concentrations (Fig. 7A). This observation is likely due to the fact that all 3 systems generated median inputs of 106 E. coli ha1 day1 or less (Fig. 6). The APS and low-rate effluent systems produced a similar range of in-stream concentrations, whereas the high-rate effluent system consistently produced higher upper percentile concentrations (Fig. 7) caused by the higher losses predicted by this system (Fig. 6). The effect of direct animal access to the stream can be seen by comparing Fig. 7A and B. Fencing off the entire stream and bridging the crossings had very little effect on median concentrations, but reduced the upper percentiles. This is because the direct inputs are sporadic, and occurred <50% of the time. This effect was particularly noticeable with APS and low application rate effluent systems, which also reduced the upper percentiles (Fig. 7B). Likewise, the ducks had very little impact on the median concentration, but did increase the 90th and 95th percentiles (Fig. 7C). The lack of effect of the ducks on the stream median in this analysis is likely due to the median input from the ducks of approximately 106 E. coli ha1 day1 which is the sensitivity limit for this scenario (Figs. 5 and 6). Obviously, higher population densities of ducks in the stream (higher than the mean of 4 km1 modelled here) will lead to increased stream median concentrations. The mitigation analyses showed that there are only two scenarios (no direct inputs or crossings, and either the APS or low effluent treatment system) where the water leaving the farm could achieve the standards for category B water quality (Fig. 7B). This applies only to base-flow conditions and assumes that there are no wild fowl living in the stream. Under storm-flow conditions both the inflow water to the farm and losses from the farm will be higher, creating significant problems in achieving water quality guidelines in the stream and receiving waters (Kay et al., 2007). The assumption that there are no wild fowl in the stream is not plausible as they are often a desired component of the landscape for hunting recreational users. The purpose for including wild fowl in this analysis is to illustrate that, even if a farm implements currently available BMPs, the water

quality leaving the farm may still exceed the desired guideline values due to wild fowl inputs.

A

B

C

Fig. 7 e Effect of the 4 different effluent management systems on modelled E. coli concentrations in water leaving the farm for 3 different scenarios. In Panel A, the cows had access to the stream 25% of the time and forded the stream 10% of the time, with no inputs from ducks. In Panel B, the stream was fully fenced and the animals used a bridge to cross the stream, with no inputs from ducks. In Panel C, the stream was fully fenced and the animals used a bridge to cross the stream, but ducks were living in the stream. Horizontal line is the median, boxes are the 25th to 75th percentile, whiskers 10th and 90th percentile and points are the 5th and 95th percentile values. The dashed line represents the 95th percentile water quality target for contact recreation in NZ (MfE, 2003) which the water entering the model farm achieved.

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3.4.

General discussion

The work presented here had three main objectives. The first objective was to use a Monte Carlo simulation framework to represent the variability inherent in microbiological data (Muirhead et al., 2006). This Monte Carlo simulation framework has identified some key principles not considered in previous models. Most microbial pollution models are based on modified sediment models driven by hydrological processes (Jamieson et al., 2004). However, many of the sources of faecal microbes to streams during base-flow conditions are independent of rainfall driven processes (Muirhead et al., 2008), and are often not represented in sediment-based models (Jamieson et al., 2004). The other important finding from this analysis is how different mitigations can affect the distribution of E. coli concentrations in the stream. Collins and Rutherford (2004) developed a model based on the expected mean concentration, and observed little sensitivity to direct deposition of E. coli to the stream network. They concluded that this lack of observed effect on water quality was caused by the sporadic access of cows to the stream network. The analysis presented here supports the findings of Collins and Rutherford (2004) by clearly illustrating that sporadic stock access to stream networks will have little effect on the predicted median concentration, but will increase the upper percentiles at the farm-scale. At a catchment scale the multiple farm inputs will tend to average out, but the variability and sporadic nature of the farm inputs are likely to be a key cause of the variability in FIO concentrations measured in agricultural catchments (Wilcock et al., 2006). The second objective was to consider whether our model dairy farm could achieve contact recreational water quality in all water leaving the farm during base-flow conditions. The analysis presented here indicates that this is achievable in the Toenepi catchment, provided that the water entering the farm upstream is of good quality, the farm is fully fenced to prevent any animal access to the stream and that the farm dairy effluent is managed according to current good environmental practice of low application rate FDE system in conjunction with a deferred irrigation strategy (Houlbrooke et al., 2004b, 2006). These general principles are probably relevant to many catchments due to that fact that, (a) fencing to keep animals out of the stream will be effective in most situations, and (b) the low application rate FDE management system is designed to only apply effluent when there is a suitable soil water deficit (Houlbrooke et al., 2004b) resulting in minimal environmental losses (Fig. 6). The third objective was the inclusion of the ducks in the framework to provide a comparison with inputs from farming activities. This analysis indicates that as FIO losses from farms decrease, wild animal populations are likely to have a greater proportional effect on water quality in streams (Obiri-Danso and Jones, 1999). However, the impact of wild fowl at a catchment scale may be less than indicated by this farmscale model. As the losses of E. coli from the farms decrease, this will effectively reduce the concentration of E. coli in the water entering the farm, resulting in greater capacity to absorb the load from the wild fowl and still achieve water quality guidelines. Furthermore, the sensitivity analysis

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indicated that in-stream attenuation will be an important factor in catchment scale models which will absorb a larger load of E. coli than indicated in this farm-scale model. A much better understanding of in-stream attenuation of microbes is required to advance catchment scale modelling.

4.

Conclusions

A framework has been developed for predicting the effects of some common dairy farm management practices and wild fowl on microbial quality of water leaving a farm under baseflow conditions. Modelling simulations using the framework have demonstrated three key findings. First, many sources of E. coli discharged from farms are sporadic and therefore have little effect on expected downstream median concentrations, but instead increase 95th percentiles. Secondly, currently available BMPs should be sufficient to achieve contact recreational water quality standards in the water leaving the farm during base-flow conditions provided upstream water quality is good and there are no water fowl living in the stream. Thirdly, wild fowl populations in streams also have the potential to impact microbial water quality, with effects generally comparable to those predicted for some common farm management practices.

Acknowledgments This work was funded by the Foundation for Research, Science and Technology contract C10X0320. Thanks to Frank Kelliher (AgResearch), Graham McBride (NIWA) and Rob Davies-Colley (NIWA) for their review of earlier drafts of this manuscript.

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New Zealand Land Treatment Collective, 16e18 April, Queenstown, New Zealand, pp. 222e227. Geldreich, E.E., 1966. Sanitary significance of fecal coliforms in the environment. U.S. Department of the Interior, Federal Water Pollution Control Administration, Water Pollution Control Research, No. WP-20-3, pp. 102e103. Hann, S., Bauer, S., Klaassen, M., 2008. Quantification of allochthonous nutrient input into freshwater bodies by herbivorous water birds. Freshwater Biology 53, 181e193. Hass, C.N., Rose, J.B., Gerba, C.P., 1999. Quantitative Microbial Risk Assessment. John Wiley and Sons, Inc, New York, USA. Haynes, R.J., Williams, P.H., 1993. Nutrient cycling and soil fertility in the grazed pasture ecosystem. Advances in Agronomy 49, 119e199. Hickey, C.W., Quinn, J.M., Davies-Colley, R.J., 1989. Effluent characteristics of dairy shed oxidation ponds and their potential impacts on rivers. New Zealand Journal of Marine and Freshwater Research 23, 569e584. Houlbrooke, D.J., Horne, D.J., Hedley, M.J., Hanly, J.A., 2004a. The performance of travelling effluent irrigators: assessment, modification, and implications for nutrient loss in drainage water. New Zealand Journal of Agricultural Research 47, 587e596. Houlbrooke, D.J., Horne, D.J., Hedley, M.J., Scotter, D.R., Snow, V. O., 2004b. Minimising surface water pollution resulting from farm-dairy effluent application to mole-pipe drained soils. 1. An evaluation of the deferred irrigation system for sustainable land treatment in the Manawatu. New Zealand Journal of Agricultural Research 47, 405e415. Houlbrooke, D., Monaghan, R., Smith, C., Nicolson, C., 2006. Reducing contaminant losses following application of farm dairy effluent to land using a K-line irrigation system. In: Currie, L.D., Hanley, J.A. (Eds.), Implementing Sustainable Nutrient Management Strategies in Agriculture. Occasional Report No. 19. Fertilizer and Lime Research Centre, Massey University, Palmerston North, New Zealand, pp. 290e300. Jamieson, R., Gordon, R., Joy, D., Lee, H., 2004. Assessing microbial pollution of rural surface waters: a review of current watershed scale modelling approaches. Agricultural Water Management 70, 1e17. Kay, D., Aitken, M., Crowther, J., Dickson, I., Edwards, A.C., Francis, C., Hopkins, M., Jeffrey, W., Kay, C., McDonald, A.T., McDonald, D., Stapleton, C.M., Watkins, J., Wilkinson, J., Wyer, M.D., 2007. Reducing fluxes of faecal indicator compliance parameters to bathing waters from diffuse agricultural sources: the Brighouse Bay study, Scotland. Environmental Pollution 147, 138e149. McDowell, R.W., Houlbrooke, D.J., Muirhead, R.W., Muller, K., Shepherd, M., Cuttle, S.P., 2008. Grazed Pastures and Surface Water Quality. Nova Science Publishers Inc, New York, 238 p. MfE, 2003. Microbiological water quality guidelines for marine and freshwater recreational areas. Ministry for the Environment, P.O. Box 10-362, Wellington, New Zealand. Available from: http://www.mfe.govt.nz/publications/water/ microbiological-quality-jun03. Monaghan, R.M., de Klein, C.A.M., Muirhead, R.W., 2008. Prioritisation of farm scale remediation efforts for reducing losses of nutrients and faecal indicator organisms to

waterways: a case study of New Zealand dairy farming. Journal of Environmental Management 87 (4), 609e622. Monaghan, R.M., Houlbrooke, D.J., Smith, L.C., 2010. The use of low-rate sprinkler application systems for applying farm dairy effluent to land to reduce contaminant transfers. New Zealand Journal of Agricultural Research 53 (4), 389e402. Muirhead, R.W., Collins, R.P., Bremer, P.J., 2006. Numbers and transported state of Escherichia coli in runoff direct from fresh cowpats under simulated rainfall. Letters in Applied Microbiology 42, 83e87. Muirhead, R., Houlbrooke, D., Monaghan, R., 2010. Risk assessment of farm dairy effluent irrigation systems: faecal indicator organisms. In: Wang, H., Heaphy, M. (Eds.), Managing Wastes in Rural and Agricultural Landscapes. New Zealand Land Treatment Collective, Proceedings for the 2010 Annual Conference, Dunedin, 17e19 March, pp. 61e66. Muirhead, R.W., Monaghan, R.M., Donnison, A.M., Ross, C., 2008. Effectiveness of current best management practices to achieve faecal microbial water quality standards. In: Currie, L.D., Yates, L.J. (Eds.), Carbon and Nutrient Management in Agriculture. Occasional Report No. 21. Fertilizer and Lime Research Centre, Massey University, Palmerston North, New Zealand, pp. 382e397. Obiri-Danso, K., Jones, K., 1999. Distribution and seasonality of microbiological indicators and thermophilic campylobacters in two freshwater bathing sites on the River Lune in northwest England. Journal of Applied Microbiology 87, 822e832. Oliver, D.M., Heathwaite, A.L., Hodgson, C.J., Chadwick, D.R., 2007. Mitigation and current management attempts to limit pathogen survival and movement within farmed grassland. Advances in Agronomy 93, 95e152. Palmer, M.D., 1983. Fecal coliform loadings from birds on bridges. Canadian Journal of Civil Engineering 10, 241e247. Sinton, L., Hall, C., Braithwaite, R., 2007. Sunlight inactivation of Campylobacter jejuni and Salmonella enterica, compared with Escherichia coli, in seawater and river water. Journal of Water and Health 5 (3), 357e365. Till, D., McBride, G., Ball, A., Taylor, K., Pyle, E., 2008. Large-scale microbiological study: rational, results and risks. Journal of Water and Health 6 (4), 443e460. Wilcock, R.J., Monaghan, R.M., Quinn, J.M., Campbell, A.M., Thorrold, B.S., Duncan, M.J., McGowan, A.W., Betteridge, K., 2006. Land-use impacts and water quality targets in the intensive dairying catchment of the Toenepi Stream, New Zealand. New Zealand Journal of Marine and Freshwater Research 40, 123e140. Wilcock, R.J., Nagels, J.W., Rodda, H.J.E., O’Connor, M.B., Thorrold, B.S., Barnett, J.W., 1999. Water quality of a lowland stream in a New Zealand dairy farming catchment. New Zealand Journal of Marine and Freshwater Research 33, 683e696. Wilkinson, J., Kay, D., Wyer, M., Jenkins, A., 2006. Processes driving the episodic flux of faecal indicator organisms in streams impacting on recreational and shellfish harvesting waters. Water Research 40, 153e161.

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 8 7 5 e2 8 8 6

Available at www.sciencedirect.com

journal homepage: www.elsevier.com/locate/watres

Long-term performance of bicarbonate-form anion exchange: Removal of dissolved organic matter and bromide from the St. Johns River, FL, USA Krystal M. Walker, Treavor H. Boyer* Department of Environmental Engineering Sciences, University of Florida, P.O. Box 116450, Gainesville, FL 32611-6450, USA

article info

abstract

Article history:

The goal of this research was to evaluate the long-term performance of magnetic ion

Received 24 December 2010

exchange (MIEX) treatment using bicarbonate as the mobile counter ion (i.e., MIEX-HCO3)

Received in revised form

and sodium bicarbonate for regeneration. This work is important because there are many

25 February 2011

unknowns concerning the affinity and regeneration efficiency of bicarbonate-form anion

Accepted 2 March 2011

exchange, whereas chloride-form anion exchange (i.e., MIEX-Cl resin) is well-studied. Raw

Available online 10 March 2011

water samples were collected approximately two times per month for one year from a single location on the St. Johns River (SJR), FL, USA. The SJR is characterized by high

Keywords:

concentrations of dissolved organic carbon (DOC; 12e26 mg C/L) and bromide

Alternative water supply

(550e1100 mg/L), and is being considered as an alternative drinking water supply. Jar tests

Magnetic ion exchange

were conducted using MIEX-HCO3 resin, and MIEX-Cl resin was used as a baseline for

Regeneration

comparison. The same batch of MIEX-HCO3 and MIEX-Cl resin was used for the entire

Scanning electron microscopy

study, which was accomplished by regenerating the resins after each jar test in concen-

Sulfate

trated solutions of sodium bicarbonate and sodium chloride, respectively, and resulted in 21 regeneration cycles. Maximum removal efficiency was achieved with fresh MIEX-HCO3 resin and virgin MIEX-Cl resin. Both forms of fresh/virgin MIEX resin also had the same affinity sequence with sulfate z UV-absorbing substance > DOC > bromide. The removal efficiency of both forms of MIEX resin decreased as the number of regeneration cycles increased, with MIEX-HCO3 resin showing 7e18% lower removals than MIEX-Cl resin after 21 regeneration cycles. The affinity sequence of regenerated MIEX-HCO3 and MIEX-Cl resins differed from fresh resin with UV-absorbing substances > DOC > sulfate > bromide. Scanning electron microscopy and simulated MIEX-HCO3 treatment under rapidly changing water quality were also used to improve the understanding of bicarbonate-form anion exchange. The major contribution of this research is a systematic study of the extended use of bicarbonate-form anion exchange resin in the context of affinity, regeneration efficiency, and changing water quality. ª 2011 Elsevier Ltd. All rights reserved.

1.

Introduction

The St. Johns River (SJR) is located in FL, USA and has high concentrations of dissolved organic matter (DOM) and bromide.

The water quality in the SJR is important because the river is being considered as an alternative drinking water source, and is estimated to supply up to 155 million gallons per day (587 ML/d) (St. Johns River Water Management District (SJRWMD), 2005).

* Corresponding author. Tel.: þ1 352 846 3351; fax: þ1 352 392 3076. E-mail address: [email protected] (T.H. Boyer). 0043-1354/$ e see front matter ª 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2011.03.004

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Alternative drinking water sources characterized by high concentrations of DOM are also being considered in other parts of the world where supplies of pristine quality drinking water are diminishing (Wang et al., 2010). It is well known that DOM affects water treatment processes and finished water quality. For example, DOM adds color, taste, and odor to raw drinking water; forms disinfection byproducts (DBPs) during water treatment (Hua and Reckhow, 2007); exerts a demand on oxidation and coagulation chemicals (Johnson and Singer, 2004; Matilainen et al., 2010); fouls membranes (Li and Elimelech, 2004); and contributes to biological growth in distribution systems (Escobar et al., 2001). Also during water treatment, bromide is oxidized to hypobromous acid by free chlorine and other oxidants, and reacts with DOM to form brominated DBPs (Cowman and Singer, 1996; von Gunten, 2003), which are reported to be more toxic than corresponding chlorinated DBPs (Plewa et al., 2002). Thus, advanced processes, such as ion exchange, nanofiltration, and reverse osmosis, will be required to effectively treat water from the SJR and other alternative water supplies to produce high-quality drinking water. Magnetic ion exchange (MIEX) resin, using chloride as the mobile counter ion, has been shown in previous research to be an effective process for removing DOM and to a limited extent bromide (Kitis et al., 2007; Mergen et al., 2008; Hsu and Singer, 2010), and has been shown to be as effective as or more effective than activated carbon adsorption and enhanced coagulation (Humbert et al., 2008; Jarvis et al., 2008). A majority of the previous MIEX studies have used bench-scale experiments and virgin resin to evaluate MIEX treatment of raw drinking water (Singer and Bilyk, 2002; Fearing et al., 2004; Boyer and Singer, 2005; Humbert et al., 2005). A few studies have evaluated MIEX treatment as a function of varying water quality, regeneration efficiency, and downstream unit processes using pilot plant studies (Boyer and Singer, 2006; Singer et al., 2007; Drikas et al., 2009; Dixon et al., 2010), and used multiple regeneration cycles in bench-scale experiments (Mergen et al., 2008; Apell and Boyer, 2010). However, a major limitation of previous MIEX research is that the long-term performance of MIEX treatment due to changing water quality has not been systematically studied under controlled laboratory conditions. A growing concern about MIEX treatment and ion exchange in general is the production waste regenerant brine that is difficult to treat or dispose of (McAdam and Judd, 2008; Neale and Schafer, 2009). One approach to minimize the problems associated with waste regenerant brine is to use a different, more benign mobile counter ion such as bicarbonate. Previous work by Holl and Kiehling (1981), Matosic et al. (2000), and Jelinek et al. (2004) demonstrated the use of bicarbonate as a mobile counter ion in anion exchange reactions. Recent work by Dahlke et al. (2007) and Rokicki and Boyer (2011) demonstrated the use of sodium bicarbonate solution for regeneration of MIEX resin, and showed that MIEX resin with bicarbonate as the mobile counter ion (MIEX-HCO3) performed similar to MIEX resin with chloride as the mobile counter ion (MIEX-Cl). However, no work has been done to evaluate the long-term performance of MIEX-HCO3 resin due to changing water quality, to compare its long-term performance with MIEX-Cl resin, and to evaluate its regeneration efficiency after multiple uses.

The goal of this work was to systematically evaluate the extended use of MIEX-HCO3 treatment for an alternative water supply with high concentrations of DOM and bromide. The specific objectives of this work were: (1) to evaluate the longterm performance of MIEX-HCO3 and MIEX-Cl resins for removal of dissolved organic carbon (DOC) and UV-absorbing substances; (2) to evaluate the long-term performance of MIEX-HCO3 and MIEX-Cl resins for removal of bromide and sulfate; (3) to examine the changes in the physical structure of MIEX resin after multiple regeneration cycles; and (4) to test MIEX-HCO3 resin under conditions that simulate full-scale treatment and rapidly changing water quality. The long-term performance of MIEX-HCO3 and MIEX-Cl resins was studied by conducting 22 jar tests over a 1 year period. The water for the jar tests was collected approximately two times per month from a single location on the SJR. The same MIEX-HCO3 and MIEX-Cl resins were used for the entire study by regenerating the resins after each jar test. The physical structure of regenerated MIEX-HCO3, regenerated MIEX-Cl, and virgin MIEX-Cl resins was analyzed using scanning electron microscopy. A modified jar test procedure and synthetic model waters were used to simulate full-scale MIEX-HCO3 treatment for a water supply with rapidly changing water quality.

2.

Materials and methods

2.1.

Sampling location

All samples were collected from the SJR at Astor, FL, which is located 204 km upstream from the mouth of the river (United States Geological Survey (USGS), 2010). Sources of recharge for the river are groundwater, stormwater runoff, and rainfall. The sampling location was chosen because it is near proposed locations for surface water withdrawal (SJRWMD, 2005) and has a USGS gage station (02236125) that monitors gage height, discharge, water temperature, dissolved oxygen, turbidity, and specific conductance (USGS, 2010). The location of the gage station is latitude 29 100 0000 and longitude 81 310 2000 . The drainage area for the gage station is 8352 km2 (SJRWMD, 2010). There are no wastewater treatment plants within an 8 km radius of the sample location, and the land use for the drainage area is wetland (32%), agriculture (24%), forest (16%), urban (13%), and other (15%) (SJRWMD, 2010). The water quality for the SJR at Astor is characterized by high concentrations of salts (235 mg/L as Cl), hardness (198 mg/L as CaCO3), and DOM (17 mg/L as total organic carbon); concentrations are median values based on 13 years of data (SJRWMD, 2010). Samples were collected by either the authors or staff from the SJRWMD approximately two times per month for a one year period from October 2009 through October 2010. Samples collected by the authors were taken from either the east or west side of the river, from a dock w4 m from the river bank, using a plastic container and transferred to plastic carboys. Samples collected by the SJRWMD were collected from the side of a boat, at approximately the center of the river, using a plastic tube w2 m long and blended in a plastic container before being dispensed into plastic carboys. Preliminary samples were collected from both sides of the river, and the difference in the DOC concentration was only 2%. Samples

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were delivered to the Department of Environmental Engineering Sciences, University of Florida within 24 h of collection and stored at 4  C. Samples were used in experiments and analyzed within 2 weeks of collection.

2.3.

Jar tests

system (Boyer and Singer, 2006). To begin, virgin MIEX-Cl resin was converted to MIEX-HCO3 resin as described in Section 2.4. One jar was used for the entire experiment, which contained 10 mL of resin and 1 L of synthetic model water. The synthetic model waters were prepared by adding sodium chloride, sodium bromide, and potassium sulfate to DI water. The composition of the synthetic model water was based on chloride, bromide, and sulfate levels in the SJR (see Table 1). The resin was mixed for 20 min at 100 rpm and allowed to settle for 5 min. After each jar test, the treated water was decanted and analyzed for chloride, bromide, and sulfate. In addition, 1 mL of exhausted MIEX-HCO3 resin was removed and 1 mL of fresh MIEX-HCO3 resin was added. Finally, 1 L of synthetic model water was added to the jar and the procedure described above was repeated. A total of 20 jar tests were conducted. Thus, this modified jar test procedure was equivalent to a 10% regeneration ratio in a full-scale MIEX process. In addition, the composition of the synthetic model water was altered to simulate periods of low and high sulfate concentration.

2.3.1.

SJR

2.4.

2.2.

Materials

MIEX resin from Orica Watercare was used in this work. Two mobile counter ions: bicarbonate and chloride were evaluated based on previous research by Rokicki and Boyer (2011). Fresh MIEX-HCO3 resin was produced by regenerating virgin MIEXCl resin in a concentrated sodium bicarbonate solution, and both exhausted MIEX-Cl and MIEX-HCO3 resins were regenerated using concentrated salt solutions as described in Section 2.4. Both fresh and virgin resins were previously unused in any jar tests, with the additional qualification that virgin resin was used as received from the manufacturer without pre-treatment.

Jar tests were conducted using both MIEX-Cl and MIEX-HCO3 resins for every water sample collected from the SJR. All jar tests were conducted at ambient laboratory temperature and open to the atmosphere. Jar tests were performed using a Phipps & Bird PB-700 jar tester with 2 L jars. One liter of sample water was transferred to each jar. Four 10 mL batches of MIEX resin (i.e., MIEX-Cl resin in duplicate and MIEX-HCO3 resin in duplicate) were measured as the volume of wet settled resin in graduated cylinders. The graduated cylinders were rinsed with regeneration solution to ensure that all resin was transferred to the jars. The four batches of MIEX resin were measured only once because the initial batches of MIEX resin were used for the entire study. The jar tests were conducted at 100 rpm for 20 min and the resin was allowed to settle for 5 min before treated water was decanted from the jar. Following the jar test, MIEX resin was rinsed two times with deionized (DI) water and stored until regeneration (see Section 2.4). All jar tests were performed using duplicate doses of MIEX resin and the data points shown are the mean value of duplicate samples. The reproducibility of the MIEX resin jar tests was quantified by calculating the relative difference (RD) between duplicate resin doses. The DOC concentration was used to calculate the RD as jDOCa  DOCbj/((DOCa þ DOCb)/2), where DOCa and DOCb are the DOC measurements after treatment with duplicate resin doses. The RDs for MIEX-Cl and MIEX-HCO3 were 0e14% and 0e11%, respectively, with one MIEX-HCO3 jar test having an RD of 27%. The following measurements were made for all jar tests: pH, DOC, UV absorbance at 254 nm (UV254), chloride, bromide, and sulfate.

2.3.2. Simulated full-scale treatment and rapidly changing water quality A series of jar tests were conducted to evaluate MIEX-HCO3 resin under conditions of changing water quality. The jar test procedure was also designed to simulate full-scale MIEX treatment in which a majority of the resin is recycled and a fraction of resin is removed for regeneration while an equivalent fraction of freshly regenerated resin is added to the

Resin regeneration

MIEX resin was regenerated following previous work by the authors (Apell and Boyer, 2010; Rokicki and Boyer, 2011). All resins were regenerated with 1 L of salt solution (NaCl or NaHCO3), which contained the desired mobile counter ion. The concentration of the mobile counter ion in the regeneration solution was based on the equivalent capacity of MIEX-Cl resin, which was previously determined to be 0.52 meq/mL (Boyer and Singer, 2008). Previous experiments also showed that a regeneration solution that contained the mobile counter ion at an amount 10 the equivalent capacity of the resin dose was sufficient to regenerate the resin (Rokicki and Boyer, 2011). Accordingly, the sodium chloride regeneration solution contained 1820 mg/L Cl (52 mM Cl) and the sodium bicarbonate regeneration solution contained 3172 mg/L HCO3 (52 mM HCO3) based on a 10 mL/L resin dose. The initial conversion of MIEX-Cl resin to MIEX-HCO3 resin and subsequent regenerations of MIEX-HCO3 resin were conducted using the same strength regeneration solution (i.e., 52 mM HCO3). This approach was taken so that the removal efficiencies of regenerated MIEX-HCO3 resin could be compared with fresh MIEX-HCO3 resin. In addition, the intention was not necessarily complete regeneration rather comparing chloride and bicarbonate at the same equivalent concentrations. The justification for using lower concentrations of bicarbonate and chloride was to differentiate the regeneration efficiency of the two mobile counter ions.

Table 1 e Synthetic model waters used in jar tests to simulate full-scale MIEX-HCO3 treatment and changing water quality. Jar test number 1e10 11e15 16e20

Chloride (mg/L)

Bromide (mg/L)

Sulfate (mg/L)

200 200 200

1000 1000 1000

0 50 0

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The MIEX resin was mixed in the regeneration solution using the jar test apparatus described previously for 20 min at 100 rpm, allowed to settle for 5 min, and supernatant liquid was decanted. To ensure that there was no regeneration solution remaining in the jar, the resin was washed with DI water by adding 1 L of DI water, mixed for 10 min at 100 rpm, and settled for 5 min before decanting the supernatant liquid. The washing procedure was repeated a second time before the resin was stored in DI water until the next jar test. The SJR jar test, resin regeneration, and washing of the resin was repeated for every water sample collected from October 2009 through October 2010, which resulted in one jar test each for fresh MIEX-HCO3 resin and virgin MIEX-Cl resin and 21 subsequent regeneration cycles. Table 2 lists the dates that water samples were collected from the SJR and the corresponding regeneration number.

2.5.

Analytical methods

All standards were prepared from ACS grade chemicals and DI water. Dry materials were weighed on a Mettler AE 160 analytical balance. All samples were vacuum-filtered with a 0.45 mm nylon membrane (Millipore) to remove any MIEX resin particles and to ensure only dissolved species were analyzed. Filtered samples were analyzed for DOC, UV254, chloride, bromide, and sulfate. DOC was measured on a Shimadzu TOCeVCPH total organic carbon analyzer using combustion at 680  C, equipped with an ASI-V autosampler. The standard solution for DOC was prepared from potassium hydrogen phthalate. Chloride, sulfate, and bromide were measured on a Dionex ICS-3000 ion chromatograph equipped with IonPac AG22 guard column, AS22 analytical column, and

Table 2 e Number of regenerations and corresponding dates that raw water samples were collected from the SJR for jar tests. Regeneration 0 corresponds to virgin MIEX-Cl resin and fresh MIEX-HCO3 resin. Number of regenerations 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

Sampling date 10/21/09 11/04/09 11/20/09 12/16/09 01/07/10 02/17/10 02/24/10 03/17/10 04/04/10 04/27/10 05/10/10 06/01/10 06/14/10 06/28/10 07/08/10 07/21/10 08/06/10 08/22/10 09/10/10 09/23/10 10/11/10 10/17/10

ARS-4 mm suppressor running 4.8 mM Na2CO3/1.0 mM NaHCO3 eluent with 100 mL sample loop and a flow rate of 1.5 mL/min. The temperature of the column and detector compartments was set to 35  C and the suppressor current was set at 40 mA. Standard solutions for chloride, sulfate, and bromide were prepared from sodium chloride, potassium sulfate, and sodium bromide, respectively. All DOC, chloride, sulfate, and bromide measurements were made in duplicate and averaged. The precision of the DOC and anion measurements was monitored by calculating the percent difference between duplicate samples. The accuracy of each run on the TOCeVCPH and ICS3000 was monitored using calibration check standards to ensure that the measured concentration fell within 10% of the known value. UV254 was measured on a Hitachi U-2900 spectrophotometer using a 1 cm quartz cuvette. Regenerated MEX-HCO3, regenerated MIEX-Cl, and virgin MIEX-Cl resins were characterized using a field emission scanning electron microscope (SEM; JEOL JSM-6335F). The regenerated MIEX-HCO3 and MIEX-Cl resins were sampled after 14 regeneration cycles. Each MIEX resin was prepared for microscopy by lyophilizing w0.1 mL of wet resin to prevent any distortion of the resin during drying. The resin samples were then mounted to the pedestal with carbon tape and analyzed. Resin surface morphologies were characterized without coating using an accelerating voltage of 5.0 kV and an w15 mm working distance in order to minimize charging, enhance surface features, and observe the resin at magnifications ranging from 40 to 5000.

3.

Results and discussion

3.1.

Removal of DOC and UV254-absorbing substances

Data for DOM and inorganic anions in the SJR was limited to those results relevant to MIEX treatment efficiency because the geochemistry of the SJR is the subject of a separate publication. The DOM concentration of the SJR and DOM removal by both forms of MIEX resin was evaluated in terms of DOC and UV254 absorbance, as shown in Fig. 1. The specific UV254 absorbance (SUVA254 ¼ UV254/DOC) of the SJR varied from 3.5 to 4.5 L/mg C m during the study (results not shown), which suggests that the DOM was terrestrially derived and its composition was relatively constant over the study timeframe (Weishaar et al., 2003). The DOC concentration of the SJR varied by greater than a factor of two during the study, with a general trend of decreasing concentration over the study period. The mean DOC concentrations for MIEX-HCO3 and MIEX-Cl treated waters were 5.1  0.7 and 4.4  0.6 mg C/L, respectively. The DOC concentration in the MIEX-treated waters was essentially constant despite the changes in the DOC concentration of the SJR. The data indicate that w5 mg C/L of DOC was nonremovable by anion exchange. The DOC remaining in the MIEX-treated waters was likely characterized by low carboxyl acidity and low aromatic carbon content, which are DOM properties that are not favorable for anion exchange uptake (Boyer et al., 2008b). Competition by anions, such as sulfate, for exchange sites on MIEX resin was also likely contributing to a fraction of DOC being non-removable by anion exchange (Fu and Symons, 1990; Boyer and Singer, 2006).

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a

30

Raw (18, 3.9) MIEX-HCO3 (5.1, 0.7) MIEX-Cl (4.4, 0.6)

20 15 10

70 60 50 40 30 20 10 0

0 0

2

4

6 8 10 12 14 16 18 Number of regenerations

20

0

22

2

4

6

8

10 12 14 16 18 20 22

Number of regenerations

Raw (0.74, 0.20) MIEX-HCO3 (0.12, 0.03) MIEX-Cl (0.10, 0.02)

1.2

d

0.8 0.6 0.4

MIEX-HCO3 (82, 5) MIEX-Cl (86, 3)

100 90

UV254 removal (%)

1 UV254 (1/cm)

MIEX-HCO3 (69, 8) MIEX-Cl (74, 6)

80

5

c

100 90

DOC removal (%)

DOC (mg C/L)

25

b

80 70 60 50 40 30 20

0.2

10 0

0 0

2

4

6 8 10 12 14 16 18 Number of regenerations

20

22

0

2

4

6 8 10 12 14 16 Number of regenerations

18

20

22

Fig. 1 e Removal of DOM by MIEX-HCO3 and MIEX-Cl treatment of SJR water over multiple regeneration cycles: (a) DOC concentration, (b) percent DOC removal, (c) UV254 absorbance, and (d) percent UV254 removal. The data in parenthesis (mean, one standard deviation) have the same units as the y-axis and were calculated for the 1 year study period.

Maximum and similar removal of DOC by MIEX-HCO3 resin (87%) and MIEX-Cl resin (88%) was achieved with fresh/virgin resin (i.e., regeneration 0). Removal of DOC by both forms of MIEX resin steadily decreased as the number of regeneration cycles increased. For example, DOC removal by MIEX-HCO3 resin decreased from 87% (regeneration 0) to 75% (regeneration 10) to 54% (regeneration 21). MIEX-Cl resin followed a very similar trend as MIEX-HCO3 resin up to 10 regeneration cycles, at which point MIEX-HCO3 resin showed less DOC removal than MIEX-Cl resin for the remainder of the study. The decreased removal of DOC by MIEX-HCO3 resin was most apparent after 21 regeneration cycles, with MIEX-HCO3 resin removing 10% less DOC than MIEX-Cl resin. The UV254 data followed nearly an identical trend as the DOC data. UV254 removal was maximum and similar for both forms of MIEX resin at regeneration 0. UV254 removal decreased for both forms of MIEX resin as the number of regeneration cycles increased. MIEX-HCO3 resin showed decreased removal of UV254-absorbing substances relative to MIEX-Cl resin after approximately 10 regeneration cycles. MEX-HCO3 resin removed 7% less UV254-absorbing substances than MIEX-Cl resin after 21 regeneration cycles, which was the greatest difference in UV254 removal between the two forms of

MIEX resin. Finally, both fresh/virgin and regenerated MIEXHCO3 and MIEX-Cl resins showed greater removal of UV254absorbing substances than DOC. The affinity of MIEX resin for the UV254-absorbing fraction of DOM is in agreement with previous literature for both MIEX-HCO3 resin (Rokicki and Boyer, 2011) and MIEX-Cl resin (Fearing et al., 2004; Boyer and Singer, 2005; Ates et al., 2007; Kitis et al., 2007). In summary, there was a greater decrease in removal of DOC and UV254 in going from fresh/virgin MIEX resin to resin that was regenerated 21 times, than between MIEX-HCO3 and MIEX-Cl resins at any point during the study. For example, DOC removal by MIEX-HCO3 and MIEX-Cl resin decreased by 33% and 24%, respectively, from regeneration 0 to 21, whereas there was only a 10% difference in DOC removal between MIEX-HCO3 and MIEX-Cl resins after 21 regeneration cycles. Previous work evaluating MIEX-HCO3 and MIEX-Cl resins, using a similar regeneration procedure as this study, did not observe a substantial difference between the removal efficiencies of the two forms of resin (Rokicki and Boyer, 2011). However, the previous work only evaluated 3 regeneration cycles, whereas the current work evaluated 21 regeneration cycles over an extended period of time. Overall, the results in Fig. 1 illustrate that MIEX-HCO3 resin has a similar affinity for

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a

w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 2 8 7 5 e2 8 8 6

Bromide removal (%)

1200 Bromide (µg/L)

b 100

Raw (780, 170) MIEX-HCO3 (520, 150) MIEX-Cl (540, 250)

1400

1000 800 600 400

60 40 20 0

-40

0 0

2

4

6 8 10 12 14 16 18 20 22 Number of regenerations

0

Raw (54, 19) MIEX-HCO3 (26, 16) MIEX-Cl (18, 12)

110 100

2

4

6 8 10 12 14 16 18 20 22 Number of regenerations

d 100

MIEX-HCO3 (58, 20) MIEX-Cl (72, 15)

90

90

80 Sulfate removal (%)

80 Sulfate (mg/L)

80

-20

200

c

MIEX-HCO3 (34, 12) MIEX-Cl (34, 18)

70 60 50 40 30 20

70 60 50 40 30 20 10

10 0

0 0

2

4

6

8

0

10 12 14 16 18 20 22

2

4

6

Number of regenerations

8

10 12 14

16 18 20

22

Number of regenerations

Fig. 2 e Removal of inorganic anions by MIEX-HCO3 and MIEX-Cl treatment of SJR water over multiple regeneration cycles: (a) bromide concentration, (b) percent bromide removal, (c) sulfate concentration, and (d) percent sulfate removal. The data in parenthesis (mean, one standard deviation) have the same units as the y-axis and were calculated for the 1 year study period. The statistics for bromide removal by MIEX-Cl resin were calculated using 0% removal for all data indicating bromide release.

Anion release or uptake (meq/L)

1.5

release uptake

1

0.5

0 0

2

4

6

8

10

12

14

16

18

20

22

Number of regenerations Fig. 3 e Anion release and uptake for MIEX-Cl treatment of SJR water over multiple regeneration cycles. Anion release [ ClLtreated L ClLraw (meq/L). Anion uptake [ (DOCraw L DOCtreated) D (SO42Lraw L SO42Ltreated) D (BrLraw L BrLtreated) (meq/L).

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DOC and UV254-absorbing substances as MIEX-Cl resin, and regeneration with sodium bicarbonate is able to achieve a similar level of regeneration efficiency as sodium chloride when the amounts of bicarbonate and chloride are 10 the equivalent capacity of the resin dose. Finally, it is likely that increasing the concentration of sodium bicarbonate and sodium chloride in the regeneration solutions would further minimize any difference in the performance of MIEX-HCO3 and MIEX-Cl resins (Dahlke et al., 2007; Rokicki and Boyer, 2011).

3.2.

Removal of bromide and sulfate

Uptake of bromide and sulfate by MIEX resin was evaluated because bromide is a precursor to DBPs, whereas sulfate is a competitive species for anion exchange reactions. The concentrations of bromide and sulfate in the SJR, and the removal efficiencies by MIEX resin varied greatly during the study (see Fig. 2). The bromide concentration was >500 mg/ L for the entire study and showed a general trend of increasing concentration with time. The very high concentration of bromide in the SJR is expected to result in substantial formation of brominated DBPs upon treatment. The sulfate concentration varied by a factor of four and also showed a general trend of increasing concentration with time. Competition between sulfate and DOM during anion exchange reactions was expected based on the sulfate levels in the SJR. There is no published data on the affinity of MIEX-HCO3 resin, and bicarbonate-form anion exchange in general, for bromide. Bromide was the only parameter for which fresh MIEX-HCO3 resin showed greater removal than virgin MIEX-Cl resin (i.e., 61% vs. 53%). Bromide removal by both forms of MIEX resin decreased as the number of regeneration cycles increased, however, MIEX-Cl resin showed more variability in the extent of bromide removal and on three occasions increased the bromide concentration of the treated water. Possible explanations for the release of bromide from MIEX-Cl resin include incomplete regeneration of the resin and subsequent bromide release due to the presence of more favorable anions, such as sulfate or DOM, or changes in ionic strength. However, periods of high sulfate in the SJR do not appear to track bromide release (see Fig. 2), and the specific conductance in the SJR (monitored by USGS gage station 02236125) was also relatively constant during periods of bromide release (results not shown). The results for bromide removal by MIEX resin and changing water quality in the SJR illustrate the challenges of conducting research using natural water. As a result, the effect of changing water quality on the performance of MIEX treatment under controlled laboratory conditions is discussed in Section 3.4. Finally, the bromide data for both fresh/virgin and regenerated MIEX-HCO3 and MIEX-Cl resins are in agreement with the range of bromide removal by MIEX-Cl treatment published in the literature (Boyer and Singer, 2006; Hsu and Singer, 2010). The sulfate data showed the clearest trend of maximum and similar removal for fresh MIEX-HCO3 resin and virgin MIEX-Cl resin, and decreasing removal for both forms of resin with increasing number of regeneration cycles. Furthermore, the decrease in sulfate removal with increasing number of regeneration cycles was much greater than the difference

Fig. 4 e Field emission SEM images of MIEX resin at 403 magnification: (a) virgin MIEX-Cl resin, (b) regenerated MIEX-Cl resin analyzed after 14 regeneration cycles, and (c) regenerated MIEX-HCO3 resin analyzed after 14 regeneration cycles. between MIEX-HCO3 and MIEX-Cl resins at any point during the study. Sulfate removal by MIEX-HCO3 and MIEX-Cl resin decreased by 66% and 49%, respectively, from regeneration 0 to 21, whereas there was only an 18% difference in sulfate

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removal between MIEX-HCO3 and MIEX-Cl resins after 21 regeneration cycles. Although MIEX-HCO3 resin removed less sulfate than MIEX-Cl resin, it did not achieve greater removal of DOM or bromide. Thus, considering the DOM and inorganic anion data suggests that sodium bicarbonate is less effective than sodium chloride for regenerating MIEX resin at the concentrations of bicarbonate and chloride tested. However, the concentrations of sodium bicarbonate and sodium chloride used in this study (w3 g NaCl/L) are much lower than fullscale MIEX processes, which typically use saturated sodium chloride solution (w359 g NaCl/L). As stated earlier, the justification for using lower concentrations of sodium bicarbonate and sodium chloride was to differentiate the regeneration efficiency of the two mobile counter ions. The DOC and inorganic anion data was used to investigate the stoichiometry of MIEX-Cl treatment. Fig. 3 shows anion release and uptake by MIEX-Cl resin after treating SJR water for each regeneration cycle. Similar results for MIEX-HCO3 resin could not be calculated because inorganic carbon was not measured. Anion release was equal to (Cltreated  Clraw) in units of meq/L, and anion uptake was equal to (DOCraw  DOCtreated) þ (SO42raw  SO42treated) þ (Brraw  Brtreated) in units of meq/L. DOC in mg C/L was converted to meq/L by assuming the carboxyl acidity of DOM in the SJR is

the same as Suwannee River fulvic acid (11.8 meq/g C; Boyer et al., 2008b). This is a reasonable approximation because both sources of DOM have a similar SUVA254 (i.e., SJR ¼ 3.5e4.5 L/mg C m; Suwannee River fulvic acid ¼ 4.0 L/mg C m (Boyer et al., 2008b)). Fig. 3 shows that anion uptake closely tracks anion release for a majority of the regeneration cycles, which is the expected result for an ideal ion exchange process. Removal of DOC and sulfate accounted for 10e36% and 64e90%, respectively, of the anion uptake. Previous work using synthetic model waters and DOM isolates have demonstrated that DOC uptake by MIEX resin results in stoichiometric release of the mobile counter ion (Boyer et al., 2008b; Rokicki and Boyer, 2011). Data points that show anion release greater than anion uptake indicate that anions in addition to DOM, sulfate, and bromide are being removed. The results in Fig. 3 are important because they demonstrate that MIEX treatment of natural water is a stoichiometric process. Finally, the affinity of fresh MIEX-HCO3 resin and virgin MIEX-Cl resin (i.e., regeneration 0) was sulfate z UV254 > DOC > bromide, whereas the affinity of regenerated MIEXHCO3 and MIEX-Cl resins (i.e., regeneration 21) was UV254 > DOC > sulfate > bromide. These results demonstrate that it is critical to conduct regeneration experiments to understand the affinity of anion exchange reactions.

Fig. 5 e Field emission SEM images of MIEX resin at 2503 magnification: (a) virgin MIEX-Cl resin, (b) regenerated MIEX-Cl resin analyzed after 14 regeneration cycles, (c) regenerated MIEX-HCO3 resin analyzed after 14 regeneration cycles, and (d) regenerated MIEX-HCO3 resin analyzed after 14 regeneration cycles (5003 magnification).

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3.3.

MIEX resin characteristics

MIEX resin is a macroporous polyacrylic resin with strongbase quaternary ammonium functional groups for anion exchange, and magnetic iron oxide incorporated into the polymer matrix to aid in settling. There is very little published information on the properties of MIEX resin. For example, Neale et al. (2010) shows an SEM image of MIEX resin, however, the history of the resin, its preparation for SEM, and the SEM analysis conditions are not clearly described. Specific to this study, the regenerated MIEX-HCO3 and MIEX-Cl resins began to behave differently in the SJR jar tests after approximately 10 regeneration cycles. In particular, under identical mixing conditions the regenerated MIEX-HCO3 resin began to agglomerate and clump to the bottom of the jar, whereas the MIEX-Cl resin was well dispersed. As a result, SEM images of regenerated MIEX-HCO3 and MIEX-Cl resins (sampled after 14 regeneration cycles) were compared with virgin MIEX-Cl resin to provide insights into the physical characteristics of MIEX resin. Fig. 4 shows all three types of MIEX resin at a 40 magnification and illustrates that MIEX resin is not a single particle size, but rather a distribution of particle sizes. Similar particle size data for virgin MIEX-Cl resin is presented in (Boyer et al.,

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2008a). Qualitatively, based on the spherical shape of the resin and distribution of particle sizes, there does not appear to be any mechanical attrition of the regenerated MIEX-HCO3 or MIEX-Cl resins after 14 regeneration cycles relative to virgin MIEX-Cl resin. Fig. 5 shows all three types of MIEX resin at a 250 magnification. The surface of the regenerated MIEXHCO3 resin appears different than the virgin and regenerated MIEX-Cl resins, with both a greater number and more variability in surface features. Fig. 5d shows MIEX-HCO3 resin at a 500 magnification and further illustrates the surface features on the resin. For example, precipitation of CaCO3 on MIEX-HCO3 resin is likely due to the reaction of resin-phase bicarbonate and the high concentration of calcium in the SJR. The virgin and regenerated MIEX-Cl resins appear very similar with a much smoother surface. SEM alone is not able to determine whether the surface features are inorganic, organic, or biological. As a result, future work is needed to quantitatively study changes on the surface of MIEX resin under full-scale operating conditions. Combined techniques such as SEM, focused ion beam, and wavelength-dispersive spectroscopy could be used to quantify the pore structure and elemental composition of MIEX resin. Overall, the results in Figs. 4 and 5 contribute new knowledge to the current understanding of the properties of MIEX resin.

Fig. 6 e Concentration of (a) chloride, (b) bromide, and (c) sulfate from modified jar test procedure to simulate full-scale MIEXHCO3 treatment during changing water quality (resin [ 10 mL/L, contact time [ 20 min). After each jar test, 1 mL of exhausted MIEX-HCO3 resin was removed and replaced with 1 mL of fresh MIEX-HCO3 resin. The diagonal striped bar shows the concentration in the untreated, synthetic mode water (see Table 1). The black bars show the jar tests in which only chloride and bromide are present. The white bars show the jar tests in which chloride, bromide, and sulfate are present.

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3.4. Simulated full-scale treatment and rapidly changing water quality MIEX-Cl resin released bromide at different points during the SJR jar tests (see Section 3.2), which was likely a result of changing water quality. In addition, Ishii and Boyer (2011) observed that MIEX-Cl resin released sulfate when a raw water supply was switched from a well with a high sulfate concentration to a well with a low sulfate concentration. As a result, a series of jar tests were conducted to evaluate MIEXHCO3 resin under controlled laboratory conditions that simulated rapidly changing water quality. In addition, the jar test procedure was designed to simulate the full-scale MIEX process with a regeneration ratio of 10% (see Section 2.3.2). The results of the jar tests are shown in Fig. 6 in which jar test 0 shows the anion concentrations in the untreated synthetic model waters and jar tests 1e20 show the anion concentrations in the treated water (see Table 1). Maximum removal of bromide (73%) and chloride (45%) was achieved in jar test 1 because the test conditions corresponded to the maximum dose of fresh MIEX-HCO3 resin and no sulfate. For jar tests 2e10, the removal of chloride and bromide decreased until a steady-state removal of approximately 10% and 20%, respectively, was reached. A similar transition from nonsteady-state to steady-state removal is illustrated in Boyer et al. (2010) using mathematical modeling simulations of the full-scale MIEX process. For jar tests 11e15, the synthetic model water was adjusted to include bromide, chloride, and sulfate. The addition of sulfate essentially stopped the exchange of chloride and bromide with MIEX-HCO3 resin due to competitive ion exchange with sulfate. In several jar tests, sulfate exchange resulted in the release of chloride and bromide. Sulfate removal decreased from 96% to 49% as the MIEX-HCO3resin became loaded with sulfate. For jar tests 16e20, the synthetic model water was returned to chloride and bromide only. Chloride removal returned to approximately the same steady-state removal seen in jar tests 2e10, whereas bromide removal did not follow a clear trend. The sulfate measured in the treated water (for jar tests 16e20) was the result of sulfate released from the resin, most likely from exchange with chloride. Thus, this controlled laboratory study illustrates that MIEX resin can retain anions and then release the anions into treated water when a more preferred anion is present. This result is especially relevant to the MIEX process because a large fraction of exhausted resin is maintained in the system, and can serve as a source for anion release to treated water due to competitive ion exchange during conditions of rapidly changing water quality.

test. Concentrated solutions of sodium bicarbonate and sodium chloride were used to regenerate the MIEX-HCO3 and MIEX-Cl resin, respectively, where the amounts of bicarbonate and chloride were 10 the equivalent capacity of the resin dose. The major conclusions of this research are:  Fresh MIEX-HCO3 resin and virgin MIEX-Cl resin performed identically in terms of DOC, UV254, and sulfate, whereas fresh MIEX-HCO3 resin showed greater removal of bromide than virgin MIEX-Cl resin. Fresh MIEX-HCO3 resin and virgin MIEX-Cl resin had the same affinity sequence for DOM and inorganic anions with the order of removal: sulfate z UV254 > DOC > bromide.  The removal efficiency of MIEX treatment decreased as the number of regeneration cycles increased for both MIEXHCO3 and MIEX-Cl resins. MIEX-HCO3 resin showed 7e18% lower removals than MIEX-Cl resin after 21 regeneration cycles. Nevertheless, the results indicated that sodium bicarbonate resulted in a similar level of regeneration efficiency as sodium chloride. The affinity sequence of both regenerated MIEX-HCO3 and MIEX-Cl resins was the same with the order of removal: UV254 > DOC > sulfate > bromide. In addition, the regenerated MIEX resins had a different affinity sequence than fresh/virgin resin.  The mean concentration of DOC and bromide in the SJR over the study period was 18 mg C/L and 780 mg/L, respectively. The mean removals of DOC and bromide achieved by 10 mL/ L of MIEX-HCO3 resin were 69% and 34%, respectively.  Uptake of DOC, sulfate, and bromide (in meq/L) by MIEX-Cl treatment of SJR water was approximately equal to chloride release (in meq/L), which demonstrated that MIEX treatment of natural water is a stoichiometric process.  SEM analysis of regenerated MIEX-HCO3, regenerated MIEXCl, and virgin MIEX-Cl resins suggested that the surface of MIEX-HCO3 resin had a greater level of fouling than the MIEXCl resins. SEM analysis was not able to determine whether the fouling layer was organic, inorganic, or biological.  Controlled laboratory experiments showed that MIEX-HCO3 resin released bromide and chloride when the sulfate concentration was increased from 0 to 50 mg/L. The steadystate removal of bromide was w20%, which was lower than the SJR jar tests because of the experimental procedure. In a full-scale MIEX process, a majority of the resin is exhausted and loaded with organic and inorganic anions. When there is a rapid change in water quality, previously exchanged anions can be released from the resin into the treated water.

Acknowledgements 4.

Conclusions

The motivation for this research was the gap in knowledge concerning the long-term performance of MIEX-HCO3 resin. The research approach was to conduct jar tests with MIEX-HCO3 and MIEX-Cl resins using water from the SJR, FL, USA. Water samples were collected from the SJR approximately two times per month for one year. The same MIEX-HCO3 and MIEX-Cl resins were used for the entire study by regenerating the resins after each jar

This work was funded in part by a grant from the University of Florida (UF) Water Institute, Program Initiation Fund: Scenario-Based Analysis of Surface Water Withdrawals from the St. Johns River Basin. The views expressed in this paper do not necessarily reflect the views of the UF Water Institute. The authors would like to thank Orica Watercare for providing MIEX resin, and staff from the SJRWMD for their assistance with collecting water samples.

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