Advances in Agronomy, Volume 99

CONTRIBUTORS

Numbers in Parenthesis indicate the pages on which authors contributors begin

V. C. Baligar (345) USDA-ARS-Sustainable Perennial Crops Lab, Beltsville, Maryland 20705-2350 Guilhem Bourrie´ (227) INRA, UR 1119, Soil and Water Geochemistry, Europoˆle de l’Arbois, B.P. 80, F-13545 Aix-en-Provence (France) J. F. Briat (183) CNRS, Universite´ Montpellier II, SupAgro, INRA, UMR5004 ‘Biochimie et Physiologie Mole´culaire des Plantes’, Place Pierre Viala, F-34060 Montpellier cedex I, France N. K. Fageria (345) National Rice and Bean Research Center of EMBRAPA, Caixa Postal 179, Santo Antoˆnio de Goia´s, GO, CEP. 75375-000, Brazil Rebecca E. Hamon (289) Plant Chemistry Section, Agricultural and Environmental Chemistry Institute, Faculty of Agricultural Sciences, Universita` Cattolica del Sacro Cuore, Via Emilia Parmense 84, I-29100, Piacenza, Italy Alfred E. Hartemink (125) ISRIC - World Soil Information, 6700 AJ Wageningen, The Netherlands P. Hinsinger (183) INRA, SupAgro, UMR1222 ‘Bioge´ochimie du Sol et de la Rhizosphe`re’, Place Pierre Viala, F-34060 Montpellier cedex 1, France Philip M. Jardine (1) Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831 P. Lemanceau (183) INRA, Universite´ de Bourgogne, UMR1229 ‘Microbiologie du Sol et de l’Environnement’, CMSE, BV 86510, F-21034 Dijon cedex, France Enzo Lombi (289) Plant and Soil Science Laboratory, Department of Agricultural Science, Faculty of Life Sciences, University of Copenhagen, Thorvaldsensvej 40, 1871 Frederiksberg C, Denmark

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Contributors

J. M. Meyer (183) CNRS, Universite´ Louis Pasteur, UMR7156 ‘De´partement Environnement, Ge´ne´tique mole´culaire et Microbiologie’, F-67000 Strasbourg, France David R. Parker (101, 289) Soil and Water Sciences Section, Department of Environmental Sciences, University of California, Riverside, California 92521 A. Robin (183) INRA, Universite´ de Bourgogne, UMR1229 ‘Microbiologie du Sol et de l’Environnement’, CMSE, BV 86510, F-21034 Dijon cedex, France Angelia L. Seyfferth (101) Department of Environmental Sciences, University of California, Riverside, California 92521 Fabienne Trolard (227) INRA, UR 1119, Soil and Water Geochemistry, Europoˆle de l’Arbois, B.P. 80, F-13545 Aix-en-Provence (France) G. Vansuyt (183) INRA, Universite´ de Bourgogne, UMR1229 ‘Microbiologie du Sol et de l’Environnement’, CMSE, BV 86510, F-21034 Dijon cedex, France

PREFACE

Volume 99 contains seven comprehensive and timely reviews dealing with plant, soil, and environmental sciences. Chapter 1 is an excellent review on the influence that complex hydrological, geological, and biological processes have on inorganic contaminant fate and transport, with emphasis on field-scale studies. Chapter 2 focuses on the uptake and fate of perchlorate in plants. Chapter 3 is a timely review on the soil and environmental issues related to the use of sugarcane for bioethanol production. Chapter 4 is a comprehensive review on iron dynamics in the rhizosphere including the impact of plants and microorganisms on iron status and iron-mediated interactions in the rhizosphere. Chapter 5 deals with a reevaluation of the Fe cycling in soils in light of recent advances in understanding the geochemistry of green rusts and fougerite. Chapter 6 is a thorough review of recent advances on using isotopic dilution techniques in trace element research including a discussion of methods, benefits, and limitations. Chapter 7 deals with liming of tropical Oxisols and includes factors affecting lime requirements and methods and frequency of lime applications. I thank the authors for their fine contributions. DONALD L. SPARKS University of Delaware

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Influence of Coupled Processes on Contaminant Fate and Transport in Subsurface Environments Philip M. Jardine Contents 1. Introduction and Rationale 2. Chapter Objectives and Outline 3. General Overview on the Impact of Coupled Processes on Subsurface Fate and Transport 3.1. The importance of subsurface media structure 3.2. Influence of subsurface hydrologic processes on biogeochemical reactions 3.3. Influence of the subsurface capillary fringe on couple hydro-bio-geochemical reactions 4. Influence of Coupled Processes on Inorganic Contaminant Fate and Transport 4.1. General overview 4.2. Inorganic metals 4.3. Inorganic radionuclides 4.4. Inorganic ligands 4.5. General inorganics 4.6. Modeling coupled processes involving dissolved aqueous phase inorganic constituents 5. Influence of Coupled Processes on Organic Contaminant Fate and Transport 5.1. General overview 5.2. Chlorinated solvents 5.3. Hydrocarbons 5.4. Pesticides and herbicides 5.5. Modeling coupled processes involving organic constituents 6. Concluding Remarks Acknowledgments References

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Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831 Advances in Agronomy, Volume 99 ISSN 0065-2113, DOI: 10.1016/S0065-2113(08)00401-X

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2008 Elsevier Inc. All rights reserved.

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Abstract The following chapter emphasizes subsurface environmental research investigations over the past 10 to 15 years that couple hydrological, geochemical, and biological processes as related to contaminant fate and transport. An attempt is made to focus on field-scale studies with possible reference to laboratory-scale endeavors. Much of the research discussed reflects investigations of the influence of coupled processes on the fate and transport of inorganic, radionuclide, and organic contaminants in subsurface environments as a result of natural processes or energy and weapons production endeavors that required waste disposal. The chapter provides on overview of the interaction between hydro-biogeochemical processes in structured, heterogeneous subsurface environments and how these interactions control contaminant fate and transport, followed by experimental and numerical subsurface science research and case studies involving specific classes of inorganic and organic contaminants. Lastly, thought provoking insights are highlighted on why the study of subsurface coupled processes is paramount to understanding potential future contaminant fate and transport issues of global concern.

1. Introduction and Rationale Until recently, worldwide waste disposal practices were an afterthought to the desire for economic expansion and national security and defense. In an age full of fear, greed, and the desire for global superiority, waste disposal practices regarding weapons, energy, and food production, and the quest for a higher standard of living, were of little consequence and were deemed an effort that future generations would confront. Unfortunately, cleanup technologies have been slow in development and the resolution of the legacy waste problem persists. An excellent example exists within several government agencies within the United States (U.S.) such as the Department of Energy (DOE) and the Department of Defense (DoD) which face a daunting challenge of remediating huge below ground inventories of legacy radioactive, toxic metal, and mixed organic wastes. The scope of the problem is massive, particularly in the high recharge, humid regions east of the Rocky Mountains, where the off-site migration of contaminants continues to plague soil water, groundwater, and surface water sources. Even in semiarid regimes west of the Rocky Mountains, the threat of contaminant migration through seemingly ‘‘dry’’ porous media persists due to slow water movement along fine sediment layers as a result of tension-driven anisotropic flow. Industrial activities have also contributed to massive legacy waste problems that are associated with accidental and intentional spills and disposal activities. The cleanup of these activities by DOE, DoD, and the U.S. Environmental Protection Agency (EPA) has

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been ongoing for several decades with the pace slowing due to budget cuts and priority shifts in the U.S. government spending portfolio. In this context, it is not surprising that determining the best course of action— large-scale cleanups, focused hotspot remediation, or no action (natural attenuation)—remains exceedingly difficult from a technical standpoint. If a natural system has sufficient capacity for clean-up of contaminants by in situ processes (e.g., adsorption, dilution, precipitation, biodegradation, chemical transformation), perhaps natural attenuation processes should be considered as the first option. The current reality (i.e., 2008) is that contaminated sites are closing rapidly and many remediation strategies have chosen to leave contaminants in-place with little consideration of whether the decision is appropriate. In situ barriers, surface caps, and bioremediation are often the remedial strategies of choice. By choosing to leave contaminants in-place, we must accept the fact that the contaminants will continue to interact with subsurface and surface media. Contaminant interactions with the geosphere are complex and investigating long-term changes and interactive processes is imperative to verifying risks. Since contaminants may be left in-ground, it is critical to understand immobilization and remobilization processes that may operate during long-term stewardship as it is our societal responsibility to ensure a healthy environment for future generations. A deeper understanding of the relevant spatial and temporal scales that govern the fate of transport mechanisms is needed in order to make informed decisions about the applicability of various remediation options including natural attenuation. Understanding the spatial and temporal scales at which coupled hydrobio-geochemical processes operate is essential to designing an efficient and effective monitoring program for long-term stewardship.

2. Chapter Objectives and Outline In the following chapter we emphasize subsurface environmental research investigations that combine hydrological, geochemical, and biological processes as related to contaminant fate and transport. We do not consider coupled subsurface deformation, mechanical, or thermal processes as related to chemical distribution and reactivity. This information can be found in Bai and Elsworth (2000). We attempt to discuss only fieldscale studies with possible reference to laboratory-scale endeavors. A review of environmental investigations involving coupled processes at the laboratory scale can be found in Geesey and Mitchell (2008). Much of the research discussed in this chapter reflects investigation of the influence of coupled processes on the fate and transport of contaminants in subsurface environments as a result of natural processes or energy and weapons production endeavors that require waste disposal. Many of the approaches and research

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findings from these studies have potential application to future investigations on the environmental consequences of contaminant dissemination as a result of shifts in energy and climate policy and man-made changes to the global hydrologic cycle. Section 3 provides an overview of the interaction between hydrological, geochemical, and microbial processes in structured, heterogeneous subsurface environments and how these interactions control contaminant fate and transport. Next, Section 4 highlights recent field relevant research on the influence of these coupled processes on inorganic contaminant fate and transport, and Section 5 provides numerous examples of field-scale research on the impact of coupled processes on organic contaminant fate and transport. Lastly, Section 6 provides concluding remarks of how the study of subsurface coupled processes is paramount to understanding potential future contaminant fate and transport issues of global concern.

3. General Overview on the Impact of Coupled Processes on Subsurface Fate and Transport 3.1. The importance of subsurface media structure Undisturbed subsurface soils and geologic material consist of a complex continuum of pore regions ranging from large macropores and fractures at the millimeter scale to small micropores at the submicrometer scale. Structured media, common to most subsurface environments throughout the world, accentuates this physical condition which often controls the hydrological, geochemical, and microbial processes affecting transport phenomena. More often than not, subsurface media structure controls the rate and extent of geochemical and microbial reactions, all of which ultimately influence contaminant fate and transport processes. Geochemical and biological reactions and activity may, in turn, influence media structure and the hydrodynamics of the system (e.g., biogeochemical pore plugging, earthworm channels). Therefore, the extent and magnitude of subsurface biogeochemical reactions is often controlled by the spatial and temporal variability of the media structure which controls the system hydrodynamics. The physical properties of the media (e.g., structured, layered) coupled with its antecedent water content and the duration and intensity of precipitation events, dictate the avenues of water, solute, and microbe movement as well as their interaction within the subsurface. In humid environments where structured media is commonplace, transient storm events invariably result in the preferential migration of water (Gerke et al., 2007; Hornberger et al., 1991; Jardine et al., 1989, 1990a,b; 1998, 1999a, 2001, 2002; 2006, 2007; Mayes et al., 2003; Shaffer et al., 1979; Shuford et al., 1977; Vogel et al., 2006; Wilson et al., 1989, 1993, 1998).

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Highly conductive voids within the media (e.g., fractures, macropores) carry water around low permeability, high porosity matrix blocks or aggregates resulting in water bypass of the latter (Fig. 1A). Subsurface preferential flow is also a key mechanism controlling water and solute mobility in arid environments (Hendrickx and Yao, 1996; Ho and Webb, 1998; Liu et al., 1998; Mayes et al., 2003, 2005; Pace et al., 2003, 2007; Porro et al., 1993; Ritsema et al., 1993, 1998; Tompson et al., 2006). Lithologic discontinuities and sediment layering promote perched water tables and unstable wetting fronts that drive both lateral and vertical subsurface preferential flow (Fig. 1B). Water that is preferentially flowing through media often remains in intimate contact with the porous matrix, and physical and hydrologic gradients drive the exchange of mass from one pore regime to another. Mass exchange is time dependent and is often controlled by diffusion to and from the matrix. The preferential movement of water and mass through the subsurface therefore significantly impacts geochemical and microbial processes by controlling the extent and rate of various reactions with the solid phase. It imposes kinetic constraints on biogeochemical reactions and limits the surface area of interaction by partially excluding water and mass from the matrix porosity. These concepts are likewise conveyed in the subject area hydropedology which provides a link between the disciplines of pedology (e.g., soil B A

1 cm

10 cm

Structured saprolite Laminated sediments

Figure 1 An example of structured media from (A) humid and (B) semiarid climatic regimes showing a fractured shale-derived saprolite and a layered sediment consisting of laminated coarse- and fine-grained material, respectively. The fractured saprolite in (A) consists of macroporous fast-flowing fractures that surround low permeability, high porosity matrix blocks. The laminated sediments in (B) are irregularly spaced depositional layers of fine- and coarse-grained minerals that have drastically different hydrologic characteristics that often results in tension-driven anisotropic lateral flow along fine layers.

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macro- and micromorphology) and subsurface hydrology and other disciplines involved with land, air, and water interfaces (Kutilek and Nielsen, 2007). The coupling of such processes suggests that anisotropy is a general characteristic of soils and that the formulation of physically meaningful transport parameters requires quantitative knowledge of soil micromorphology. As suggested by Kutilek (1978, 1990), the assumption that soil is an isotropic body is only an approximation of reality. Coupling of hydropedology with geochemistry and microbiology provides new insights into the role of solute and contaminant fate and transport as a function of hydrology and soil structure.

3.2. Influence of subsurface hydrologic processes on biogeochemical reactions Subsurface geochemical and microbial reactions are directly linked to the system hydrodynamics. Soil moisture conditions that promote the onset of preferential flow and thus higher volumetric flux per unit area will minimize geochemical and microbial interfacial reactions due to decreased residence times during transport and potential bypass of the soil matrix (Estrella et al., 1993; Jardine et al., 1988, 1993a; Jarvis, 2007; Jarvis et al., 2007; Kung, 1990a,b; Maraqa et al., 1999). Conversely, soil moisture conditions that do not promote preferential flow will, in general, enhance geochemical retardation and microbial interfacial reactions. In the presence or absence of preferential flow, water content variations affect the extent and rate of geochemical and microbial reactions very differently. The extent of contaminant retardation by the solid phase via geochemical mechanisms (e.g., sorption, redox alteration, and complexation) will be more pronounced when flow is restricted to smaller pore size regimes (e.g., mesopores/micropores). Jardine et al. (1988, 1993a,b) have found that the reactivity of reactive contaminants and chelated radionuclides increased dramatically with a slight decrease in pressure head or water content. The larger surface area and potential reactivity of smaller sized pores versus macropores allow geochemical reactions to proceed to a more significant extent in the subsurface media. Microbial activity and transport in the subsurface are also controlled by physical and chemical interactions with the solid phase as well as the availability of nutrients, sources of carbon, and possible electron acceptors. Hydraulic conductivities can have a severe influence on nutrient transport and delivery within the subsurface and can often be the most limiting aspect of bioremediation. Biotransformation, biosorption, and electron transfer reactions are typical processes that govern the fate and transport of microbes in the subsurface. Unlike solutes that can reside within nearly all of the pore structure of subsurface media, microbes (i.e., bacteria and viruses) are too large to reach a significant fraction of the micropore regime and are restricted to the mesopore and macropore domains. Usually, less than 5–10% of the

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void volume in structured media is accessible to bacteria (Bales et al., 1989; Champ and Schroeter, 1988; Harton, 1996; Harvey et al., 1989, 1993; Jardine et al., 1998; McKay et al., 1993a,b; Smith et al., 1985; Wilson et al., 1993) because most of the soil porosity is contained within the micropore domain. However, microbial activity may actually accelerate solute mass transfer from micropores to larger meso- and macropores. Although bacteria cannot physically access most of the micropore regime, they can form biofilms around the soil aggregates and matrix blocks. These biofilms are permeable to the transfer of water and solutes between the various pore domains. It is possible that active biofilms that surround micropore domains accelerate the mass transfer of contaminants and solutes to the more biologically active pore regions. This may occur since microbial processes maintain a steep concentration gradient between the small and the large pores. The mass transfer process from one pore class to another may still remain quite slow however, and can often be the rate limiting factor governing the success of contaminant bioremediation strategies. Delivery of nutrients to microbes colonizing surfaces of low-permeability media might be diffusion controlled, whereas in high permeability media (coarse grained, fractured, or macroporous) it may primarily be advectively controlled. The rates of microbial growth and activity and propensity to alter or degrade contaminants may be quite different for the two distinct hydrologic regimes (Kieft et al., 1997; Sinclair and Ghiorse, 1989). Thus, faster flowing fracture dominated regimes will most likely be physically more appealing for sustained bioreduction as long as a suitable electron donor can be supplied. In contrast, bioreduction processes in slower flowing matrix regimes will most likely be limited by rate-dependent mass transfer of contaminants from smaller pores into larger pores. Accumulation of biomass on the surfaces of flow paths within geologic media may cause a decrease in the effective pore diameter which restricts flow and solute transport of growth promoting nutrients to organisms (Geesey et al., 1987). Another important consideration regarding bioremediation in structured and unstructured media is that the mechanisms and rates of bacteria retention are proportional to the degree of gas saturation since bacteria are preferentially sorbed to the gas–water interface versus the solid–water interface ( Jewett et al., 1999; Powelson and Mills, 1996, 1998; Schafer et al., 1998; Wan et al., 1994). Bacteria tend to accumulate at the air–water interface and thus the extent of bacterial retardation in the subsurface increases markedly with decreasing water content of the porous media. This mechanism of retention is enhanced by the corresponding loss or decrease of preferential flow and the corresponding increase in available surface area of both the solid surface and the air–water interface. The degree of sorption to the air–water interface is controlled mainly by the hydrophobicity of the cell surface, and the sorption process is essentially irreversible because of capillary forces (Wan et al., 1994).

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Depending on the water content of the subsurface media, unsaturated preferential flow may still significantly contribute to microbial bypass of the soil matrix ( Jewett et al., 1999; Powelson and Gerba, 1994; Powelson and Mills, 1998; Schafer et al., 1998). Wilson (unpublished data, University of Tennessee) found that only 6–15% of the cross-sectional area of an undisturbed block of structured coastal plain sandy sediment exhibited flow during unsaturated bacterial transport, with 88% of this flow occurring through just 4% of the area. Particle size distribution, rather than porosity, was the most significant property controlling microbial transport as areas dominated by fine sand tended to accumulate bacteria. Thus, subtle variations in particle size and arrangement (i.e., media structure) control unsaturated preferential flow paths and the degree of gas saturation which allows for the accumulation of bacteria within the subsurface. Powelson and Gerba (1994) found that virus removal by soil was three times more effective during unsaturated flow relative to saturated conditions; however, the column displacement retardations of virus transport were only 0.8–8% of that predicted by adsorption coefficients determined from batch studies. Chemical adsorption, precipitation, ion exchange, redox, complexation/ chelation, colloid formation, and microbially mediated transformation in subsurface media need to be defined in terms of hydrodynamic parameters which are often time-dependent nonlinear processes. Microbial metabolism can also alter pH, redox potential, and chemistry of the surrounding pore water causing geochemical changes (e.g., mineral precipitation). Cunningham and Fadel (2007) examined the correlation between subsurface groundwater hydraulic conductivity and the degradation rate constant for reactive contaminant transport in heterogeneous aquifers. The authors found that a negative correlation between hydraulic conductivity and the rate of contaminant degradation resulted in fingering of the contaminant plume and the persistence of more contaminant mass relative to a positive correlation. The spatial variability of the degradation rate was thought to be a function of the variability in activity of bacteria responsible for biodegradation which in turn could be the result of geochemical and mineralogical heterogeneities in an aquifer setting. Chapelle (2000) provided an overview of the significance of microbial processes on hydrological and geochemical conditions within groundwater. The author provides examples of electron donor- and electron acceptor-limited subsurface systems and the influence that microbes have on transient geochemical conditions and changes in mineral porosity, and thus groundwater flow and mass exchange.

3.3. Influence of the subsurface capillary fringe on couple hydro-bio-geochemical reactions The capillary fringe is an ill-defined boundary condition separating the water table from the unsaturated zone, without defining it as a significant part of either (Fig. 2). It is the subsurface layer in which groundwater is

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Infiltration

Unsaturated zone

Capillary fringe

Saturated zone (groundwater)

Figure 2 A schematic of the capillary fringe which is a dynamic boundary separating the water table from the unsaturated (vadose) zone. It is a subsurface layer in which groundwater is pulled from the saturated zone into the vadose zone by capillary forces. The blue color represents an aqueous phase and the shade white represents a gaseous phase (from http://oceanworld.tamu.edu/resources/oceanography-book/groundwater. html).

pulled from the saturated zone into the vadose zone by capillary forces. Pores at the base of the capillary fringe are filled with water due to tension saturation. If pore size is small and relatively uniform, it is possible that soils can be completely saturated with water for several feet above the water table. Alternately, the saturated portion will extend only a few inches above the water table when pore size is large. Capillary action supports a vadose zone above the saturated base within which water content decreases with distance above the water table. Subsurface capillary fringe regimes are an extreme example of couple processes undergoing constant dynamic changes due to recharge inputs or lack thereof, and groundwater fluctuations due to changes in surface water stage height. Because of the dynamic condition associated with most system hydrologic cycles, the capillary fringe is a temporally and spatially variable

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regime that is more often than not in a state of nonequilibrium. As such, geochemical and microbial processes are constantly changing within the capillary fringe as the influx of nutrients and oxygenated storm water impact the subsurface. Silliman et al. (2002) and Berkowitz et al. (2004) discuss the importance of the capillary fringe on local flow, chemical migration, and microbiology. They stress the impact of physical heterogeneity and the exchange of water and solutes between the capillary fringe and the region below the water table and how this alters subsurface geochemical and microbial processes. The authors suggest that physical heterogeneity with the subsurface media adjacent to the water table can lead to (1) increased flow and exchange of solutes between the capillary fringe and the underlying saturated zone, (2) preferential transport of solutes moving into the capillary fringe during infiltration events, (3) enhanced horizontal chemical flux above the water table, and (4) increased contact between gas (trapped and free flowing) and liquid phases in the region bounding the water table (Berkowitz et al., 2004; Silliman et al., 2002). Recent column studies by Qafoku et al. (2004) suggested that capillary fringe fluctuations at the DOE’s Hanford Reservation could promote the kinetically limited desorption of U into area groundwater and surface waters. Ronen et al. (2000) investigated the influence of groundwater recharge events from surface precipitation on the capillary fringe of a sandy, phreatic aquifer. During rainy seasons, abrupt changes in media water content and the increases in the height of the water table were observed. Within a 4-m interval, water table heights varied as much as 33% and 50% before and after the rainy season, respectively. Saturated conditions were detected in some regions of the capillary fringe while unsaturated conditions were found in other regions even though they were below the water table. The residence time of recharge water in the unsaturated water table regions (below the water table) was estimated to be several years. This was attributed to the entrapment of air within the pore structure of the media. Thus, multiphase flow and transport processes appear significant in the capillary fringe which will have a dramatic influence on hydrologic, geochemical, and microbial processes in the subsurface.

4. Influence of Coupled Processes on Inorganic Contaminant Fate and Transport 4.1. General overview Metals and radionuclides offer unique challenges for remediation of contaminated subsurface environments since they typically cannot be degraded into innocuous products as can organic contaminants. Because of this, inorganic contaminants are often leached deep into the subsurface where they are unreachable by conventional remedial technologies. Unlike metals

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and radionuclides, some inorganic ligand contaminants such as nitrate and perchlorate can be degraded or transformed into innocuous products via biotic and abiotic pathways. For inorganic metals and radionuclides, one option being explored is the use of microbes to transform soluble inorganic contaminants into sparingly soluble species via elemental redox changes, thereby immobilizing them in situ. Microbes can also alter inorganic contaminants in ways other than transformation, as they can alter pH, redox, and the chemical environment of subsurface systems which in turn can influence metal and radionuclide speciation and reactivity indirectly. Often these metal and radionuclide speciation changes are reversible and this is one reason why long-term stewardship and monitoring of metal and radionuclide contaminated sites is so important. Successful implementation of such a strategy requires an enhanced fundamental understanding of coupled hydrological, geochemical, and microbial processes that control contaminant migration in subsurface environments as a function of space and time. In this section, the influence of coupled processes on subsurface metal, radionuclide, and co-contaminant fate and transport are discussed. The section is divided into specific elemental inorganic contaminant types with a focus on recent field-scale relevant examples. The section ends with a discussion of recent modeling strategies that incorporate coupled processes in the simulation of the fate and transport of dissolved aqueous phase inorganic constituents in subsurface environments.

4.2. Inorganic metals 4.2.1. Arsenic The redox-sensitive toxic metal arsenic (As) is often times significantly impacted by coupled processes in subsurface environments. Sources of As are both natural and anthropogenic and it exists in the metallic state and several ionic forms (Lambert and Lane, 2004; Mansfeldt and Dohrmann, 2004; Polizzotto et al., 2005). Common inorganic species are the negatively charged arsenates (H2AsVO4– and HAsVO42
) and zero-charged arsenite (H3AsIIIO30). Arsenic has been used as a medicinal agent, a pigment, a pesticide, and an agent of criminal intent. It typically accumulates in oxic sediments that contain mineral oxides of Fe and Mn since As forms strong inner sphere bonds with these mineral surfaces (Wang and Mulligan, 2006). Suboxic and anoxic environments favor the reduction of As(V) to As(III) via both geochemical and microbial pathways. Elemental arsenic is not toxic; however, most compounds of this element are extremely poisonous since very few organ systems escape its toxic effects. Arsenic in groundwater has emerged into the largest environmental health disaster of the past several decades with an estimated 100 million people worldwide at risk of exposure to unacceptable arsenic levels in drinking water (Bhattacharya et al., 2007; Ohno et al., 2007; O’Shea et al., 2007). This has become a major public

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health issue in the developing world, primarily Bangladesh and surrounding countries, where many thousands of individuals are suffering from precancerous arsenic-related disease (Fig. 3). Fortunately, several technologies are available for As removal from groundwater, ranging from simple flocculation to sophisticated ion exchange and reverse osmosis (Naidu and Bhattacharya 2006). A low cost, but effective, method for As removal in drinking water is through the use of natural Fe-rich mineral phases (i.e., Oxisols, Bauxsols, and Laterites). Polizzotto et al. (2005) investigated coupled processes responsible for the release and transport of As into aquifers of Bangladesh where nearly 57 million people drink water with As levels exceeding the limits set by the World Health Organization (WHO). The high concentrations of As are indigenous to the area and contaminated sediments wash from the mountains each year and are deposited in flood plains during the rainy season. The near-surface soil As is released to the aqueous phase through cyclic, seasonal redox cycles that impact the biogeochemistry of the subsurface (Saha and

CHINA

NEPAL

BHUTAN Brahmaputra

Ganges BANGLADESH INDIA

Dhaka Meghna Kolkata (Calcutta)

Bay of Bengal

BURMA

Areas where majority of wells contain more than 50 micrograms/liter of arsenic.

Figure 3 Schematic diagram showing the prevalence of groundwater arsenic contaminations above 50 ppb in drinking water for Bangladesh and India. The current USEPA MCL for As is 10 ppb (from http://earthtrends.wri.org/updates/node/176).

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Ali, 2006; Swartz et al., 2004). During the rainy season, subsurface conditions are ideal for microbially mediated iron and metal reduction and As is released from the solid phase. Polizzotto et al. (2005) hypothesized that Fe(III)-respiring bacteria are mobilizing both As(V) and As(III) that is bound to soil ferric oxides by the reductive dissolution of iron-arsenate minerals (Horneman et al., 2004; Islam et al., 2004; Kent and Fox, 2004; Nicholas et al., 2003). However, Kocar et al. (2006) suggests that As retention and release from Fe(III)-oxides is controlled by complex pathways of Fe biotransformation and that reductive dissolution of As-bearing ferrihydrite can promote As sequestration rather than desorption under certain environmental conditions. In the studies of Polizzotto et al. (2005), the reduction of Fe is most likely driven by microbial metabolism of sedimentary organic matter which is present in the soil at concentrations as high as 6% C (Harvey et al., 2005; Nickson et al., 2000). Arsenic released by oxidation of pyrite, due to water draw-down via irrigation and the entry of air, was considered a negligible contributor to As release into the groundwater. Voltammetric measurements in field studies have indicated that more than 95% of the dissolved As is as As(III) (van Geen et al. 2006). The release of As into the aqueous phase coupled with the fact that groundwater recharge is sufficient to continually supply As to the aquifer appears to have created a rather unfortunate situation since As retardation is limited in the aquifer due to insufficient mineralogical and geochemical conditions. Similar investigations of As mobility in Bangladesh soils by Van Geen et al. (2006) found that elevated local recharge in areas where the permeability of surface soils was high, prevented As from accumulating in groundwater. Conversely, dissolved As concentrations were found to be high in regions where local recharge was restricted by surface covers of low permeability. Nickson et al. (2005) found that in central Pakistan, a semiarid environment, canal irrigation has resulted in widespread water-logging of soils and evaporative concentrations of salts has caused As concentrations to significantly increase in groundwater. Efforts to remove As from groundwater prior to use involve filtration and in situ aeration (S.E. Fendorf, Stanford University, personal communication). In situ aeration causes an increase in groundwater dissolved oxygen (DO), which in turn causes Fe(II) to precipitate to amorphous Fe(III)oxides. The newly formed Fe(III) solid phase serves as an excellent sorbent for removing toxic levels of As from solution. Zheng et al. (2005) used hydrological and geochemical data to propose that deeper aquifers, low in As, could be used as a viable source of drinking water as long as withdrawals do not exceed recharge rates comparable to 1 cm/year. Likewise, Yu et al., (2003) suggested that replacing 30% of the existing wells in Bangladesh with deeper wells would reduce As health effects by 70% provided that As concentrations in the deep wells remained low. Efforts to construct deeper tube-wells to 60 m rather than the traditional 30 m is underway since low

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concentrations of Fe(II) and As exist in these deeper groundwater ( Jakariya et al., 2007; von Bromssen et al., 2007). However, infiltration of shallow high-As groundwater into these deeper groundwater sources is of concern due to increase pumping of the latter and shifts in the vertical subsurface hydraulic gradient (Jakariya et al., 2007; Stollenwerk et al., 2007). Hohn et al. (2006) investigated the fate and transport of As(V) in an Fereducing, sandy aquifer at the USGS site in Cape Cod, MA. An oxygenated injectate solution containing As(V) and nonreactive Br was added to the aquifer and numerous geochemical entities were measured downgradient for an extended period of time. Elevated DO in the injectate caused significant Fe(II) oxidation and subsequent adsorption of As(V) onto the freshly precipitated Fe(III)-oxides. Anoxic conditions returned to the aquifer once the injectate was terminated and an increase in As(III) was observed in downgradient monitoring wells. Sediment microbial assays and elevated hydrogen concentrations in groundwater suggested the presence of Asreducing microorganisms that were converting As(V) to As(III). Microbial reduction of As(V) coupled to oxidation of organic C or hydrogen has been shown to be important processes in some systems (Ahmann et al., 1997; Oremland et al., 2000). The investigations of Hohn et al. (2006) showed however, that even in the presence of biological reduction, both As(III) and As(V) transport were delayed relative to Br suggesting geochemical retardation of both species via precipitation and/or sorption. Arsenic is a major contaminant of acid mine drainage that typically results from historical mining activities. Acid mine drainage or acid rock drainage refers to the outflow of acidic water from abandoned metal mines or coal mines. Acid rock drainage occurs naturally within some environments as part of the weathering of sulfide-bearing rocks but is exacerbated by large-scale earth disturbances characteristic of mining and other large construction activities. This highly acidic water is caused by the biological oxidation of sulfidic materials and frequently contains high concentrations of redox-sensitive metals such as As and Fe that interact with the subsurface. The importance of microbial activity in sulfide dissolution and acid generation at mining sites has received significant attention over the years due to (1) the potential for contaminant mobilization and (2) the economic prospects of bioleaching. Biological processes in acid mine drainage are complex and are typically controlled by a variety of coupled physical and chemical processes. Edwards et al. (1999) investigated the impact of seasonal variations and various environmental conditions on microbial populations in acid mine drainage systems. They found that the relative proportions and the absolute numbers of microbial populations were spatially and seasonally correlated with geochemical (e.g., pH and conductivity) and physical conditions (e.g., temperature and rainfall). Studies by Edwards et al. (1999) showed that high concentrations of dissolved solutes occurred in the summer months and correlated with high archaeal populations and lower

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bacterial populations. Eukaryotes were essentially absent during the winter months but increased during the rest of the year in low pH environments (pH  0.5) which correlated with decreasing water temperatures and increasing numbers of prokaryotes. Routh et al. (2007) also investigated the biogeochemical impacts on As dynamics in mining soils from Northern Sweden where soil and groundwater are heavily contaminated with As. The authors found that although oxic conditions prevailed, As-rich surface and groundwater samples contained predominately As(III). Microbially activity was believed to be responsible for the abundant proportions of reduced As (III) since the microorganism A. bolidensis was isolated from the area and it is known that this organism is capable of reducing As(V) to As(III). 4.2.2. Mercury The redox-sensitive toxic element mercury (Hg) is also significantly impacted by coupled processes in subsurface environments. Major uses of Hg in industry are historically for the production of caustic soda and chlorine as well as certain pesticides and antifouling paints. Massive quantities of Hg were also used in the 1950s and early 1960s at the Oak Ridge Tennessee Y12 Plant for the first production-scale separation of lithium isotopes (6Li) during the development of the hydrogen bomb. As of 2005, the world’s largest user of Hg is small-scale gold mining in underdeveloped countries, accounting for nearly 30% of the global Hg demand (Hogue, 2007). The world’s second largest user is China for the production of vinyl chloride (20% of the global demand). Previous and current releases of Hg to the environment have been enormous, with coal-fired electric power plants being the largest current source of human-induced Hg air emissions in the USA (40% of total emissions) (Schnoor, 2004). Atmospheric releases of Hg from coal burning are expected to become worse, since coal is cheap and abundant and has become the fuel of choice in much of the world. Coal burning is powering the economic boom in China and India, and the worldwide demand for coal is projected to rise significantly over the next decade. At the U.S. DOE Y-12 Plant in Oak Ridge, Tennessee, USA, nearly 950,000 kg elemental Hg was disseminated throughout the environment due to historical releases during the 1950s and 1960s, with the environmental implications of these releases still persisting today, some 60 years later (Burger and Campbell, 2004; Burger et al., 2005; Southworth et al., 2000, 2002). The UNEP estimates that small-scale gold mining activities account for the release of 650–1000 metric tons of Hg/year, which is about a third of all Hg releases to the environment from humans. Since Hg has no known metabolic function and it is not easily eliminated by humans or animals, it is considered extremely toxic (Eisler, 1987). Ecological and toxicological effects, however, are highly dependent on speciation (Clarkson, 2002) where Hg attacks the central nervous system, especially sensory, visual, and auditory aspects of coordination. Various forms

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of Hg (e.g., methylmercury—MeHg) can be potent neurotoxins that bioaccumulate as they track through the food chain (Akagi et al., 1995; Kurland et al., 1960; Montuori et al. 2006; Southworth et al., 2000, 2002; Ullrich et al., 2007a,b). Bioaccumulation and toxicity of Hg are strongly connected to its complex biogeochemical cycle within the environment (Fig. 4). Subsurface Hg is often highly reactive with soil and sediment and with a variety of aqueous phase ligands. In terrestrial environments, OH–, Cl–, and S2
ions have the largest influence on ligand formation with Hg where Hg(OH)2, HgCl2, HgOHþ, HgS, and Hg0 are the predominant inorganic Hg forms under oxidized conditions and HgSHþ, HgOHSH, and HgClSH are the predominant forms of Hg under reduced conditions (Barnett et al., 1995, 1997; Gabriel and Williamson, 2004). Hg forms strong inner-sphere complexes with soil and sediments, particularly those with high clay and organic matter (Liu et al., 2006; Miretzky et al., 2005; Wallschlager et al., 1998a,b), with adsorption increasing with increasing pH and decreasing with increased ligand complexation (e.g., Cl–). This is consistent with increasing evidence that Hg is primarily transported from subsurface environments to surface

H2O, O3

Hg2+

Hg+ (vapor)

Oxidation

Volatilization 55~60%

Gold mining by dredging (raft of gold miners)

Rivers in forests pH 4.7~6.0

Mercury discharged in the environment

40~45%

River Hg2+ Bottom sediment Organification

Hg (CH3)+

pH 6.0~7.1 Hg0 (metallic mercury)

Uptake by fish Retention by sediment

pH: an indicator showing acidity or alkalinity; pH7 means neutrality and smaller figures indicates higher acidity.

Figure 4 A schematic example of mercury biogeochemical cycling in terrestrial, aquatic, and atmospheric regimes (from http://www.nimd.go.jp/archives/english/ tenji/d_corner/d04.html).

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waters via particulate forms versus dissolved forms (Barringer et al., 2006; Hultberg et al., 1994; Kolka et al., 2001; Slowey et al., 2005). Both inorganic and organic colloids (Fe-oxides, clays, and DOC) make up this particulate material, all of which have a strong affinity for a variety of Hg species. Anaerobic and aerobic microbial activity via bacteria and fungi can synthesize the potent neurotoxin methyl-mercury (CH3Hgþ) (Choi and Bartha, 1993; Compeau and Bartha, 1985; Gray et al., 2004; Jackson, 1998; Regnell et al., 2001; Slowey and Brown, 2007; Watras et al., 1995; Zhang and Planas, 1994). (CH3)2Hg is sparingly soluble and highly volatile (Cotton and Wilkinson, 1988; Gavis and Ferguson, 1972), whereas CH3Hgþ is quite soluble and poses severe bioaccumulation problems, even at very low concentrations (Bakir et al., 1973; Kurland et al., 1960; Kuwabara et al., 2007; Mason et al., 1995; Southworth et al., 2000, 2002; Wiatrowski and Barkay, 2005). Southworth et al. (2000, 2002) found that the concentration of bioaccumulated CH3Hgþ in fish was more than 10,000-fold greater than its concentration in the surface water where the fish resided. Three major sources of CH3Hgþ to freshwater ecosystems have been identified by Rudd (1995) which consists of precipitation, runoff from wetlands, and in-lake/ stream methylation. The methylation, demethylation, and oxidation of Hg are typically all secondary in magnitude relative to Hg2þ reduction to Hg0 in terrestrial environments (Carpi and Lindberg, 1998). The formation of Hg0 and subsequent volatilization is an important terrestrial reaction that can regulate much of the Hg load to surface waters where bioaccumulation is a major threat. This concept has also guided several remedial strategies that take advantage of microbial reduction of Hg(II) to Hg0 in waste streams and soil (Takeuchi et al., 2001; Wagner-Dobler, 2003). Once formed, the migration of Hg0 is dependent on soil structure and soil ambient air temperature (Carpi and Lindberg, 1998; Lindberg et al., 1979; Schluter, 2000). Various strands of bacteria are known to metabolically mediate the reduction of Hg in subsurface environments (Hansen et al., 1984; Schluter, 2000; Takeuchi et al., 2001). Although Hg volatilization helps to decrease surface water Hg loads, dry deposition of Hg contributes significantly to the atmosphere/surface exchange and biogeochemical cycling of Hg (Gosar et al., 2006; Lindberg et al., 1992). Lechler et al. (1997) investigated Hg migration processes at the Carson River Superfund site in west-central Nevada, USA, where Hg contaminated soils, water, and biota exist due to historical amalgamation milling processes of Ag-Au ores. Their results suggested that Hg was preferentially leached from Hg-Au amalgam particles and subsequently adsorbed onto fine-grained sediments which were deposited downstream. In reducing environments, Hg was converted to relatively insoluble HgS where microbially mediated sulfate reduction most likely provided ample concentrations of the reduced ligand S2
to complex Hg as HgS. Fortunately, HgS is highly surface reactive which helps contribute to its lower bioavailability

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(Barnett and Turner, 2001). Bonzongo et al., (2006), however, found that naturally occurring hydrologic processes within the Carson River caused a buildup of certain anions and oxyanions which interfered with the transformation of Hg within the S cycle. The authors found that low-flow conditions were characterized by high water pH values, high concentrations of oxyanions, and decreased microbial-mediated Hg methylation in the sediments; whereas the reverse was observed during high-flow conditions. The results suggested that changing flow regimes likely affected the rates of MeHg production through a coupling of factors such as a high pH which favors MeHg demethylation, and the occurrence of high concentrations of oxyanions that can interfere with microbial sulfate reduction and MeHg production due to Hg complexation by various anionic ligands. These findings were consistent with the observations of Pettersson et al. (1995) who noted that MeHg transport was highly correlated with humic materials and that the MeHg humic/TOC ratio decreased significantly during high flow conditions suggesting rapid drainage of groundwater storage and a slow microbial production of MeHg during times of watershed depletion. Barringer and Szabo (2006) provided an overview of investigations into Hg in groundwater, soils, and seepage along the New Jersey, USA, southern coastal plain. Investigations by health departments and the USGS in the region, in response to potential human exposure risk, have shown that Hg concentrations in water from more than 600 domestic groundwater wells exceeded the maximum concentration of Hg allowable in drinking water. Through extensive observation and compilation of data, Barringer and Szabo (2006) concluded that soil disturbance caused the downward vertical migration of colloidal organic and inorganic Hg from surface soils to subsoils and that septic system effluent provided dissolved constituents that enhanced Hg mobility through the vadose zone to the saturated zone. Without disturbance, Hg infiltration would be typically limited to the upper 0.5–1.0 m of the soil profile, although deeper migration may occur if fractures or macropores are present (Henke et al., 1993). The coupled hydrological and geochemical processes controlling Hg migration along the New Jersey coastal plain were further complicated by methylation of Hg in the shallow aquifer where redox conditions, organic C, and SO4 were optimal to stimulate the activity of SO4-reducing bacteria. It is the methylated forms of Hg that pose the largest health risk due to enhanced bioavailability relative to other Hg species. Huge quantities of Hg0 were used in the 1950s and early 1960s at the Oak Ridge Tennessee Y-12 Plant to enrich 6Li during the development of the hydrogen bomb. Major releases of Hg to the environment during this period included an estimated 35,000 kg to air, 120,000 kg to floodplain and reservoir sediments, 194,000 kg to onsite soil and rock, and 590,000 kg unaccounted for and presumed lost to the environment (Southworth, ORNL, personal communication, 2007). Present day Hg losses to nearby

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surface waters are about 75 kg/year where the sources are predominately leakage from traps, junction boxes, and building footers in the historical Hguse areas at the Y-12 plant (Southworth et al., 2000, 2002). There is also evidence of significant Hg0 discharges from deep underlying karsts bedrock and near surface clay hardpans that reside under armored fine sediments at the site. This hydrologically active system maintains a strong hydraulic relationship between groundwater and surface water sources which creates significant intermingling of the two water sources and significantly impacts the off-site fate and transport of Hg. At the Oak Ridge site, Hg flux during the rainy season and during storm events appears to be dominated by resuspension of Hg-rich particulates from streambeds and inputs of dissolved Hg in the Y-12 plant storm-drain network. Barnett et al. (1995, 1997) found that the Hg sources from floodplain soils at the site were sparingly soluble mercuric sulfide and metallic Hg, and Liu et al. (2006) noted that much of this Hg was associated with organic matter. Most important, however, is that long-term studies on the Oak Ridge Reservation suggest that significant reductions in waterborne inorganic Hg inputs have not reduced microbially mediated methylmercury (MeHg) concentrations in fish. It appears that a small amount of inorganic Hg goes a long way to produce sufficient methylmercury to allow Hg bioaccumulation to persist. Current research strategies are investigating techniques that decrease in-stream formation of methylmercury without having to further eliminate inorganic Hg inputs. Strategies include (1) blocking key inorganic precursors for microbial production of MeHg via chlorination to eliminate Hg(II) transport, additions of sulfide and other complexants to bind Hg(II), the addition of chemicals to inhibit the photoreduction of Hg(II), (2) reducing net methylation via changes in microbial ecology as a result of simulation and changes in biochemistry, and (3) blocking the uptake or assimilation of MeHg from food by fish or invertebrates via food chain manipulation. Montgomery et al. (2000) present evidence that (2) above can significantly influence MeHg formation in surface waters where flooded reservoir sites were found to have higher levels of autochthonous material (algae/bacteria, i.e., potential sources/methylators of Hg) on fine particular matter relative to freshwater lakes. As well, Driscoll et al. (1995) found that high concentrations of dissolved organic C may complex MeHg, diminishing its bioavailability. Branfireun (2004) investigated the influence of coupled processes on the spatial variability of MeHg in peatlands, with a focus on microtopographical features. Since peatlands show distinctive topographical self-organization (Foster et al., 1983) where pore-water chemistries are known to have considerable vertical and horizontal spatial variability (Hunt et al., 1997), Branfireun (2004) investigated MeHg in porewater beneath several peatland microtopographical landscapes. Concentrations of MeHg were 3.5 times higher in shallow hollows versus deeper hollows which were related to biogeochemical changes associated with water table fluctuations. Branfireun

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suggests that these differences in MeHg concentration at the water table are likely due to subsurface processes that influence both microbial metabolism and inorganic Hg bioavailability in the different landforms. The spatial variability of MeHg in these systems was thought to be a complex synergy of local hydrology and accompanying groundwater–surface water interactions, plant and moss ecology, pore water geochemistry, and microbial consortia. Gray et al. (2006) investigated Hg speciation and microbial transformation of historical mine waste in southwest Texas, USA, and evaluated the propensity for Hg transport into the surrounding ecosystem. The mine waste was found to contain variable amounts of cinnabar, metacinnabar, Hg0, and Hg sorbed onto solid particulates. Stable Hg isotope analysis (see Ridley and Stetson, 2006) revealed that the net methylation rate was high indicating significant microbial Hg methylation at the site which was positively correlated with the geochemical constituents Hg2þ, organic C, and total S. Methylation of Hg was primarily a microbially mediated process that was enhanced in anaerobic, saturated environments and was favored by the highly bioavailable Hg, the presence of sulfate-reducing bacteria (SRB), and ample amounts of nutrients and organic C. Hydrologic factors limited Hg methylation at this site as the arid environment and lack of precipitation inhibited microbial activity downstream from the source. The authors noted that during periods of precipitation, the potential for Hg methylation production increased across the watershed. 4.2.3. Selenium Selenium (Se) is an essential nutrient for the health of humans and animals with recent research even suggesting that Se may reduce liver disease and prevent/cure cancer. Low Se status in humans has been associated with several chronic diseases (Li et al., 2007) such as hypertension (Mihailovic et al., 1998), coronary heart disease (Yoshizawa et al., 2003), cancer (Rayman, 2005), diabetes (Faure, 2003), and many other pathological symptoms. However, excess Se can be toxic to both humans and animals as well. Selenium from the soil is absorbed by plants which may be eaten by livestock over extensive periods resulting in chronic Se toxicity. Chronic Se toxicity in livestock is called ‘‘alkali disease’’ and is characterized by a lack of vitality, roughness of coat, loss of hair, hoof soreness, and so on. Early signs of selenium toxicity in humans include nausea, weakness, and diarrhea. With continued intake of selenium, changes in fingernails and hair loss result, and damage to the nervous system may occur. Soils with high concentrations of Se are widespread in the Rocky Mountain and Great Plains regions of the western USA and in western States with semiarid climates where irrigation is utilized for agricultural production. With regard to irrigation in these regions, excess water is typically applied to fields to flush out salts leached onto the surface soils. This excess water either

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infiltrates into the soil or runs off into nearby basins, ponds, or streams. The irrigation water can mobilize trace elements such as Se through the soil profile, polluting groundwater and surface water sources. The high evaporation rates of semiarid environments can concentrate Se in waters to levels that are toxic to fish and sensitive bird species. The effects of selenium toxicity to fish and birds include impaired reproduction and deformed embryos. Monthly maximum discharge limits have been established for Se in irrigation drainage by the State of CA and the U.S. EPA (Green et al., 2003), and as a result, farmers and drainage districts on the western side of the San Joaquin valley are required to reduce Se concentrations in irrigation drainage discharged to the San Joaquin River. The enormous economic and health impacts posed by Se in drainage waters have prompted investigations of the biologically enhanced volatilization of Se from dewatered seleniferous sediments and what impact coupled hydrological and geochemical processes have on the rates and mechanisms of biovolatilization. Biovolatilization occurs with several metals that undergo methylation when they are taken up by plant or microbial cells. This can potentially make the metal more toxic relative to the elemental form (see Hg discussion above). Studies by de Souza et al. (2001), Frankenberger and Arshad (2001) and Frankenberger and Karlson (1994, 1995) have demonstrated that 30–70% of Se entering wetlands in central California, USA, was volatilized as dimethyl-Se (i.e., (CH3)2Se) as a result of microalgae and bacteria activity. Flury et al. (1997) investigated the potential for long-term depletion of Se from dewatered sediments by taking advantage of the concept that microbial methylation of Se to volatile (CH3)2Se may contribute to a significant loss of Se from seleniferous soils. Field experiments were initiated to investigate the likelihood that microbially mediated volatilization of Se could be used as a bioremediation approach to dissipate Se. Microbial activity within the field plots was stimulated using different organic C and protein amendments and periodic tillage and irrigation. Over a period of 100 months, Flury et al. (1997) observed that 68–88% of the Se in the upper 0–15 cm of the soil profile had dissipated. By monitoring coupled processes, Se depletion was found not to correlate with rainfall events or temperature changes. Since rainfall occurred primarily during the cooler winter months, Se leaching was primarily during this period; whereas, volatilization dominated during the summer months. The highest amount of Se depletion occurred with the amendment of protein casein; however, statistical significance was lacking with regard to nonamendment plots. The results suggested that irrigation and tillage were more important than the addition of organic C or protein amendments and thus soil structure and hydrology were key processes controlling microbial activity and therefore Se methylation and volatilization. Modeling endeavors confirmed that Se depletion from soil was kinetically controlled where the rate limiting mechanisms changed as a function of time.

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In a similar manner as Flury et al. (1997), Frankenberger and Karlson (1995) performed field investigations on biologically enhanced volatilization of Se from dewatered seleniferous sediments in central California, USA, where Se contamination in agricultural drainage waters is of significant concern. Field plots were amended with various organic materials including citrus peels, cattle manure, barley straw, and grape pomace, and several subplots were fertilized with nitrogen and zinc. Over a 22-month period, the greatest emission of gaseous Se was observed during the summer months and the lowest emissions were noted during the cooler winter months. Irrigation and tillage resulted in a 30% loss in soil Se, while plots with manure application lost nearly 60% of their indigenous Se, where cattle manure was the most effective organic amendment. Frankenberger and Karlson (1995) found that the most important parameters responsible for Se volatilization were aeration, an available C source, moisture, and warm temperatures. Thus, microbial-enhanced volatilization of Se proved to be an effective means of detoxifying contaminated sediments when soil physical, hydrological, and geochemical conditions were suitable to support microbial activity. Frankenberger and Arshad (2001) also performed laboratory and field studies to investigate microbial transformation of toxic Se species into nontoxic forms. Investigations considered microbial reduction of toxic oxyanions of Se, such as SeO42
and SeO32
, into insoluble Se0 and methylation of these species into volatile (CH3)2Se. Microorganisms such as Enterobacter cloacae could be stimulated with organic amendments and were found to actively reduce Se oxyanions present in contaminated irrigation water into insoluble Se0. The authors found that the process of Se biomethylation in soil sediments and water was active and highly dependent on specific C amendments such as pectin and proteins, pH, temperature, moisture, and aeration. Further, the process of biomethylation was found to be protein/peptide limited rather than N or C limited. Additional research by Zhang and Frankenberger (2003) suggested the optimum conditions for rapid Se(VI) removal from contaminated irrigation waters were a pH of 6–9, high amounts of sulfate, low amounts of nitrate, and significant amounts of organic C amendments. Siddique et al. (2007) also found that Se-reducing bacteria were present in Se contaminated sediments that were associated with coal tailings and could reduce Se(IV) and Se(VI) to insoluble Se0. The presence of Se0 was confirmed with SEM EDX and numerous Se-reducing bacteria were isolated from the sediments. 4.2.4. Chromium In a similar manner as As, Hg, and Se, the redox-sensitive toxic metal chromium (Cr) can also be significantly impacted by coupled processes in subsurface environments. Subsurface Cr exists as both an anion and a cation depending on its oxidation state. Common inorganic species are negatively charged chromate (HCrVIO2– and CrVIO22
) and the positively charged

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chromium ion, Cr(III). Whereas Cr(III) is an essential element in humans at small doses, Cr(VI) is a powerful oxidant that can quickly reduce to Cr(V) which is a known carcinogen that can lodge in living tissue to form cancerous growths. Cr(VI) has long been used as a corrosion inhibitor and in leather production creating both airborne and aqueous waste. Reduced forms of Fe (e.g., Fe(0) and Fe(II)) and synthetic and natural organics can directly reduce toxic Cr(VI) to the less mobile, less toxic Cr(III) form (Anderson et al., 1994; Deng and Stone, 1996a,b; Fendorf and Li, 1996; Ginder-Vogel et al., 2005; Jardine et al., 1999; Mayes et al., 2000; Powell et al., 1995). Direct microbial reduction of Cr(VI) is also possible if the contaminant concentration does not exceed a toxic effect on the organism (Bank et al., 2007; Cummings et al., 2007; Middleton et al., 2003; Sani et al., 2002). Indirect microbial reduction of Cr(VI) by subsurface dissimilatory bacteria is more common in mixed systems whereby biogenic Fe(II), formed from microbial-induced Fe(III)-oxide reduction, serves to reduce Cr(VI) to Cr(III) (Hansel et al., 2003; Wielinga et al., 2001; Wilkins et al., 2007). Hazen and Tabak (2005) performed field biostimulation investigations at a Cr(VI) contaminated site at the Hanford 100 H area in Richland, WA, USA, where the vadose and saturated zones were contaminated with Cr(VI) due to historical reactor operations. The Hanford 100 area resides adjacent to the pristine Columbia River, and potential migration of Cr(VI) into the river is problematic since Cr(VI) is a noted carcinogen. Conversion of mobile anionic Cr(VI) to sparingly soluble, cationic Cr(III) is highly desirable since Cr(III) precipitates as Cr(OH)3 in pH environments above 5, and the Hanford sediments have pH values near 8. Reoxidation of precipitated Cr(III) to Cr(VI) is unlikely, even in the presence of strong oxidants such as oxygen and even Mn-oxides, as Cr(OH)3 is typically scavenged and stabilized by subsurface Fe(III)-oxides (Fendorf and Zasoski, 1992; Hansel et al., 2003; Stewart et al., 2003a,b). Hazen and coworkers created bioreducing conditions in the Hanford 100 area groundwater by injection of 13C labeled poly-lactate (hydrogen releasing compound—HRC) using a dipole injection/extraction technique. Microbial (direct count and molecular analyses), geophysical (pre- and postinjection seismic and radar), and geochemical (anions, Cr, metals) analyses of groundwater, coupled with stable isotope monitoring (13C), allowed for accurate tracking of microbial processes to confirm that Cr(VI) was successfully removed from groundwater using the HRC as an electron donor and C source. The reduction of Cr(VI) occurred either directly by stimulated bacteria or, more likely, it occurred indirectly via biogenic Fe(II) which is formed by microbial reduction of subsurface Fe(III)-oxides. It is also possible that the formation of hydrogen sulfide contributed to Cr(VI) reduction since the HRC can potentially depress the redox potential of the aquifer toward sulfate-reducing conditions. These results are consistent with laboratory investigations of Tokunaga et al.

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(2001, 2003a,b) and many others that have demonstrated that substrates such as lactate can stimulate Fe-reducing bacteria which produce biogenic Fe(II) which in-turn can effectively reduce Cr(VI) to sparingly soluble Cr(III). The studies of Hazen and Tabak (2005) also used 16S rDNA microarray analysis to perform groundwater microbial community characterization and they found significantly increased microbial diversity as a result of the injected HRC which included nitrate, iron, and sulfate reducers. It is important to note that Cr(VI) reduction occurred even though Fe and sulfate TEA were not depleted from the system. This suggests that multiple electron donors can be simultaneously utilized in the subsurface, most likely due to the presence of microbial biofilms that develop during stimulation.

4.3. Inorganic radionuclides 4.3.1. Uranium Coupled processes have been shown to significantly impact the subsurface fate and transport of the redox-sensitive toxic metal/radionuclide uranium (U). The radioactivity of U found in nature is typically weak, and the chemical toxicity effects are vastly greater than the radiological effects. Uranium poisoning and toxicity are considered rare and typically limited to cases of accidental exposure by uranium miners and workers. These instances have indicated that uranium affects the proximal tubules of the kidney at very high acute doses; however, at lower doses there is generally no diminution in kidney function. Uranium has been, and continues to be used as a nuclear fuel or is converted into plutonium via ‘‘breeder’’ reactors to generate nuclear explosive material. Depleted U (238U) is a huge legacy waste problem for the DOE where massive volumes of solid and liquid waste were generated during the Cold War era. Over the past couple of decades, depleted uranium has also been used by the DoD in most of their ammunitions since the Gulf War. The U containing munitions are preferred since they are pyrophoric and self-sharpening on impact thus resulting in incredible heat and energy focused on a minimal area, for example, armor piercing ammunitions. The munitions are being used domestically (firing ranges) and overseas (war efforts) with hundreds of thousands of tons of the munitions scattered throughout the world. The most common oxidation states of uranium in the environment are U(IV) and U(VI), and their two corresponding oxides are uranium dioxide (UO2) and uranium trioxide (UO3), respectively. The UO22þ ion represents the U (VI) redox state and it is highly soluble, surface reactive, and known to form compounds with ligands such as carbonate, hydroxyl, sulfate, and organics. The U(IV) redox species is sparingly soluble and generally precipitates to form uraninite. The U.S. DOE is faced with considering options for remediating numerous sites contaminated with uranium in highly heterogeneous,

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difficult to characterize subsurface environments (NRC, 1999). The scale of this legacy waste problem is massive and includes 120 DOE sites in the USA alone and many other facilities in Europe and Russia (Lloyd and Renshaw, 2005). In this context, it is not surprising that determining the best course of action—large-scale cleanups, focused hotspot remediation, or no action (natural attenuation)—remains exceedingly difficult from a technical standpoint. Stabilization of U in situ is preferred due to the vast spatial domain of the problem (Lloyd and Renshaw, 2005). Over the past decade, research efforts have sought to use various geochemical- and microbial-based methods to convert mobile U(VI) to sparingly soluble, immobile U(IV). The concept is challenging since subsurface conditions are typically not conducive to U(VI) reduction due to the presence of co-contaminants that act as competing electron acceptors (e.g., O2, NO3–; see Finneran et al., 2002a,b; Istok et al., 2004; Wu et al., 2007) or the lack of sufficient electron donor and C sources to stimulate ample microbial activity. Vrionis et al. (2005) investigated the impact of coupled hydrological, geochemical, and microbial processes on the bioremediation of U at a historical DOE Uranium Mill Tailing Remedial Action (UMTRA) site where uranium ore was processed in the 1950s and 1960s. The UMTRA sites were U processing plants, located in the western and southwestern portions of the USA, that were closed in the 1960s and the tailing piles from mill operations were abandoned in-place. The legacy waste remains at many of the sites and efforts to immobilize U(VI) within the subsurface have involved a variety of methods including biostimulation (Anderson et al., 2003; Senko et al., 2002). Vrionis et al. (2005) utilized a multiple well injection scenario to deliver the electron donor acetate into the subsurface in an effort to stimulate microbially mediated U(VI) reduction. Both horizontal and vertical geochemical gradients were observed at the site, with more reduction of Fe(III)-oxides and sulfate occurring near the injection source and at greater depths. Downgradient from the array of injection wells, acetate utilization created Fe-reducing conditions, and as a result, an increase in abundance of 16S rRNA gene sequences belonging to the dissimilatory Fe(III)- and U(VI)-reducing family Geobacteraceae were noted (Chang et al., 2005; Vrionis et al., 2005). The highest levels of contaminant reduction were correlated with the maximal recovery of Geobacteraceae gene sequences; however, reduction zones were spatially heterogeneous due to the method of acetate injection or heterogeneities in the groundwater hydrologic flow-field. Laboratory studies of Finneran et al. (2002a,b) using UMTRA sediments from the site, also indicated that the addition of acetate to the sediments stimulated anaerobic conditions and the loss of U(VI) from solution. The reduction of U(VI) occurred simultaneously with the formation of Fe(II) and prior to sulfate reduction. 16S rDNA analyses of the simulated microorganisms revealed that U reduction occurred as the microbial communities shifted toward organisms known to

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reduce both Fe(III) and U(VI), such as Geobacteraceae which were greatly enriched (40% of total detectable bacterial community) (Holmes et al., 2002). Davis et al. (2006) also investigated natural in situ processes affecting the transport of U(VI) at another UMTRA site under nonbiostimulated conditions. The researchers noted that the upgradient portion of the contaminated aquifer had very little dissolved Fe(II), few metal-reducing bacteria, and the U(VI) that was present in solution was controlled by the U(VI) adsorption to the solid phase (vs dissolution of a U precipitate). However, in the downgradient portion of the aquifer where redox conditions were more anoxic, Fe(II) concentrations increased, diverse populations of Fe(III)reducing bacteria were observed, and significant reduced U(IV) was detected on the solid phase; all indicative of microbially mediated U(VI) reduction and immobilization to U(IV). Schryver et al. (2006) investigated the relationship between hydrologically impacted groundwater geochemistry and microbial community structure at the Shiprock uranium mill tailing disposal site in New Mexico, USA. The authors applied both nonlinear and generalized linear data analysis methods to relate microbial biomarkers, such as phospholipids fatty acid (PLFA), to groundwater geochemical characteristics where the primary contaminants and solutes of concern were U(VI), SO42
, and NO3–. Neural network models were found to greatly outperform the generalized linear models for describing the data. Modeling results suggested that riverine influences (i.e., nearby river impact on groundwater hydrology and geochemistry) and U(VI) distribution were important in predicting the distribution of the microbially based PLFA classes. Nonlinear principal components were then extracted from the PLFA data using a variant of the feed-forward neural network technique which grouped samples according to similar geochemistry. The PLFA indicators of Gram-negative bacteria and eukaryotes were associated with groundwater with lower concentrations of contaminants. Groundwaters with significantly higher concentrations of contaminants were associated with terminally branched saturated and branched monounsaturates that are indicative of microbial metal reducers, actinomycetes, and Gram-positive bacteria. These findings indicate that microbial community composition at this U contaminated site is strongly coupled to the groundwater geochemistry (i.e., also observed by Palumbo et al., 2004) which is spatially and temporally altered by surface and subsurface hydrologic processes. Groudev et al. (2001a,b) performed laboratory and field investigations to evaluate the propensity for bioremediation of agricultural soils contaminated with radionuclides (U, Ra, and Th) and toxic metals (Cu, Cd, and Pb) in southeastern Bulgaria that have resulted from previous mineral processing and mining activities. Stimulation of heterotrophic and chemolithotrophic aerobic microbes in the near surface resulted in dissolution of contaminants with subsequent hydrologic transport to lower horizons where contaminants

Influence of Coupled Processes on Contaminant Fate

27

became immobilized as sparingly soluble compounds primarily as a result of anaerobic SRB (Gadd, 2004; Geets et al., 2005). The activity of these organisms was enhanced by perturbations in various hydrological and geochemical environmental factors including water, oxygen, and nutrient content. Extensive U bioremediation research has been underway at the former S-3 ponds located within the Y-12 facility in Oak Ridge, Tennessee in the eastern USA, where massive unlined surface impoundments were used to dispose of acidic, highly buffered U, Tc, and NO3 bearing waste during a period from the early 1950s to the early 1980s. The liquid waste was pipelined to the ponds at a rate of 10 million liters/year for 32 years, and during this period infiltration was the primary release mechanism to the surrounding soils and groundwater. In 1984, attempts were made to neutralize and bio-denitrify the S-3 ponds and they were capped in 1988. Subsurface contaminant fate and transport processes are complex at the site since multiregion flow and transport mechanisms are the norm due to fractured weathered saprolite derived from interbedded shale and limestone sequences. The media consist of highly permeability fractures that surround low permeability, high porosity matrix blocks on the centimeter scale, and thus the media is not only conducive to significant preferential flow, it is also a source/sink for contaminants (Fig. 1A). Contaminants, such as U, Tc, NO3, PCE, and toxic metals (e.g., Al, Ni, and Hg) migrate away from the capped waste disposal units following both geologic strike and bedding plane dip, with density effects also being quite significant over great distances from the source. Near source groundwater concentrations of NO3 can be as high as 40,000 ppm and U concentrations can be as high as 60 ppm, with solid phase concentrations near or above 1000 mg U/kg and in some places above 12,000 mg U/kg solid. Elevated nitrate concentrations and significant U have been detected vertically to several hundred feet owing to rapid movement through the saprolite and underlying bedrock. The nitrate plume extends nearly 100 ha down the valley in relation to the source. Since the year 2001, research activities at this site have focused on plot-scale biostimulation studies in an effort to induce in situ bioreduction and immobilization of subsurface U and Tc contamination. The investigations have shown that microorganisms indigenous to the subsurface environments can be stimulated to transform contaminants, such as U and Tc, into chemical species that are less mobile in groundwater. These studies have also tested novel geophysical, hydraulic, and tracer techniques for characterizing and monitoring subsurface coupled processes and groundwater flow. For example, they have tested new inexpensive surface geophysical techniques in which seismic waves and electrical currents are used to create three-dimensional (3D) images of the subsurface geology and of contaminated groundwater plumes. One investigation at the Oak Ridge Y-12 site combined subsurface transport, microbiology, and geochemistry to identify the conditions that

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are conducive to the bioremediation and immobilization of U and Tc (Istok et al., 2004). The investigations focused on the potential to stimulate indigenous microbial communities that could reduce a mixture of mobile U(VI) and Tc(VII) in the presence of elevated initial nitrate (120 mM) cocontamination in the shallow unconfined aquifer. Microbial reduction of these contaminants is desirable since the reduced forms of these radionuclides (e.g., U(IV) and Tc(IV)) are sparingly soluble and significantly less mobile than their oxidized species. The investigations of Istok et al. (2004) utilized small-scale field ‘‘push-pull’’ tests where electron donor such as ethanol, glucose, and acetate were injected radially into the aquifer, and then slowly removed as a function of time. The authors found that when electron donor was added, rapid nitrate utilization via denitrification was observed with nearly simultaneous reduction of Tc(VII). Once Fe-reducing conditions were achieved in the subsurface, U(VI) reduction began to occur (Fig. 5). Changes in viable biomass, community composition, metabolic status, and respiratory state of organisms sampled from down-well microbial samplers during these tests were consistent with enhanced microbial growth, creation of anaerobic conditions, and an increase in the abundance of metal-reducing organisms (e.g., Geobacter and Anaeromyxobacter) (North Fe(II) NO−2 pH

500 250 0

9 8 7

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6 5

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0.6 0.4 0.2 0.0 0

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400 Time (hrs)

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Figure 5 Loss of uranium (U) and technetium (Tc) from groundwater following the injection of ethanol during a field ‘‘push-pull’’ biostimulation experiment at the Oak Ridge Y-12 S-3 ponds site. Note the formation of Fe(II) in solution which results from the reduction of Fe(III)-oxides which acts as a competing terminal electron acceptor (see Istok et al., 2004).

Influence of Coupled Processes on Contaminant Fate

29

et al., 2004; Peacock et al., 2004; Wilkins et al., 2006). These results were further supported by the observations of Petrie et al. (2003) who used phylogenetic analysis of 16S rRNA gene sequences extracted from MPN dilutions to show that the predominant members of Fe(III)-reducing consortia from background sediments were closely related to members of the Geobacteraceae family, while the Fe(III)-reducing bacterium Anaeromyxobacter sp., Paenibacillus sp., and Brevibacillus sp. predominated in the Fe(III)-reducing consortia of the contaminated sediments. Analysis of core samples taken before and after biostimulation using variable-temperature Fe-57 Mossbauer spectroscopy revealed an overall loss of Fe from the system and major changes to the distribution of the Fe-oxide mineral forms relative to prebiostimulated conditions (Stucki et al., 2007). Within biostimulated cores, goethite spectral components were greatly diminished in intensity whereas hematite spectral components were greatly enhanced suggesting preferential loss of goethite from biostimulated samples. This was most likely due to microbially induced reduction of Fe(III) within the goethite minerals to soluble Fe(II) moieties. This is supported by additional data of Stucki et al. (2007) that showed that the Fe(II):Fe(III) ratio in the nonoxide phase (aluminosilicate clay minerals) increased during the biostimulation process. The biogenic Fe(II) that was formed could also have contributed to the reduction of U(VI) and Tc(VII) observed in these systems. In another research effort at the Y-12 site in Oak Ridge, Wu et al. (2006a,b) investigated the rates and mechanisms by which naturally occurring microorganisms transformed solution and solid phase U(VI) to U(IV) in the presence of dynamic flow conditions and complex geochemical reactions. They used a double-dipole, forced gradient injection-extraction strategy where tracers and electron donor (i.e., ethanol) were intermittently injected into the inner loop recirculation zone (inner dipole), and clean water was injected into the outer loop recirculation well (outer dipole) which was designed to protect experimental reactions within the inner loop. Geophysical measurements, including multielectrode resistivity and tomographic seismic refraction, were used to guide monitoring well placement within the inner loop, and to confirm the location of subsurface regimes that were most hydrologically active and most highly contaminated (Chen et al., 2006; Watson et al., 2005). Since the research effort was focused near the source where nitrate concentrations were in the thousands of parts per million, the overall strategy combined an aboveground removal of PCE, NO3, high concentrations of Al, Ca, and Mg (Wu et al., 2006a), and a belowground biostimulated reduction zone for immobilization of solution and solid phase U(VI) (Wu et al., 2006b). The addition of ethanol initially stimulated denitrification of solid-phase matrix ‘‘entrapped’’ NO3 which was subsequently followed by U(VI) reduction as sulfate-reducing conditions were invoked (Fig. 6). Continued additions of electron donor allowed for sustained U reduction over a 13-month investigation, and

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X-ray Absorption Near Edge Structure (XANES) on sediment samples acquired after biostimulation confirmed the formation of significant solidphase U(IV) that was not present prior to biostimulation (Kelly et al., 2008). Wu et al. (2006b) maintained the system (1) at a pH below 6.2, (2) at low bicarbonate levels, and (3) with residual sulfate to suppress methanogenesis and minimize U remobilization. The research was further expanded to consider the application of functional gene arrays (FGAs) to the analysis of the in situ U bioreduction processes (He et al., 2007). The array, known as the GeoChip, is the most comprehensive FGA available for environmental studies and allows for the investigation of microbial community gene functionality and processes in groundwater and soil contaminated with metals, ligands, and organics with excellent resolution. Analysis of groundwater via the FGA before and during subsurface biostimulation showed statistically significant positive hybridization signals with dissimilatory Fe (III)-reducing bacteria (FeRB) such as Geobacter spp. and SRB, such as Desulfovibria spp., which reached their highest levels during the biostimulation period when U(VI) reduction was observed. These results are consistent with classical microbial monitoring methods that have shown these two groups of microorganisms are capable of U(VI) reduction via direct enzymatic or indirect chemical mechanisms (Liger et al., 1999; Lovley et al., After biostimulation 2 pH

pH

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Before biostimulation 2

Figure 6 Groundwater pH, nitrate, and uranium concentration profiles within a field facility at the Y-12 S-3 ponds site before and after biostimulation using ethanol as an electron donor. The pictorial insets show sediment samples acquired before and after biostimulation where sediments on the left are unaltered and samples on the right have been reduced. The black color may be indicative of the U(IV) species uraninite.

Influence of Coupled Processes on Contaminant Fate

31

1993a,b; Petrie et al., 2003; Tebo and Obraztsova, 1998; Truex et al., 1997; Wu et al., 2006b). Microarray analysis indicated that in situ U reduction activity correlated with the abundance of multiheme C-type cytochome genes (R ¼ 0.6), dissimilatory sulfite reductase genes (dsrAB) (R ¼ 0.8) similar to those from Desulfovibrio-like and Geobacter-like species. These results are also consistent with findings from 16S rRNA gene-based library studies which are very labor intensive relative to the FGAs. Fields et al. (2006) investigated changes in microbial community structure along this particular contaminant plume (e.g., NO3, Tc, and U) by monitoring shifts in microbial phylogenetics and functionality and changes in geochemistry. Clonal libraries of multiple genes were analyzed from groundwater that varied in contaminant concentration along the plume and compared this information to over 100 geochemical parameters using principal components analyses. The analyses-suggested sites could be grouped as low, intermediate, and extreme contaminant levels where the low and extreme sites were functionally less diverse than sites with intermediate contaminant concentrations. The ‘‘extreme’’ sites were characterized by not only high contaminant concentrations, but highly buffered acidity; whereas the ‘‘low’’ sites were characterized by low ionic strength conditions and limited nutrients. Both these conditions were thought to contribute to the observation of similar functionality even though they were phylogenetically distinct. Since U transformations can be influenced by both biological and chemical transformation reactions, Christensen et al. (2004) used a stable isotope technique to distinguish sources and pathways. They used ratios of U isotopes to implicate leaking waste storage tanks at the Hanford site in Richland, WA, USA, and their contribution to vadose zone pore water and groundwater. The authors showed that both stable and slowly decaying radioactive isotopes could be used as signatures for source identification and the transformation of metals and radionuclides contaminants in heterogeneous subsurface environments. Brooks et al. (2003) recently showed the pronounced influence of Ca2þ on the bioreduction of U(VI) at circum-neutral pH values. The authors provided evidence for the formation of a Ca–UO2–CO3 complex that is resistant to microbial reduction via metal-reducing bacteria since it is less effective than uncomplexed U(VI) at being a terminal electron acceptor. However, Stewart et al. (2007) has shown that the presence of Fe(III)-oxides strongly influence the complexation reaction between Ca2þ and U(VI) due to the significance of Ca2þ adsorption (resulting in less Ca–UO2–CO3) and the presence of a competing terminal electron acceptor. The authors found that ferrihydrite acts as a competitive electron acceptor and thus, like Ca, decreases U(VI) reduction. However, with increasing Ca2þ concentrations, U(VI) reduction was enhanced in the presence of ferrihydrite (relative to its absence) and U(VI) reduction becomes almost independent of Ca concentration. Several other studies have documented the inhibition of U(VI)

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reduction and/or its reoxidation from reduced U(IV) in the presence of nitrate and NOx (Elias et al., 2003; Finneran et al., 2002b; Istok et al., 2004; Senko et al., 2002; Wu et al., unpublished, ORNL) and dissolved O2 (Wu et al., 2007). Gu et al. (2005) also found that although the presence of humics can accelerate the reduction of U(VI), it can also accelerate the reoxidation process from U(IV) to U(VI) under certain circumstances. 4.3.2. Technetium Technetium is a radioactive chemical element with no stable isotopic forms. Most technetium produced on Earth is a fission by-product of 235U in nuclear reactors and is extracted from nuclear fuel rods. Its short-lived gamma-emitting nuclear isomer 99 mTc (half life, t1/2 ¼ 6 h) is used in nuclear medicine for a variety of diagnostic tests. The 99 mTc isomer decays to 99Tc which has a t1/2 ¼ 212,000 years and is used as a source of beta particles. Although it has a low chemical toxicity, its radioactive toxicity can be potentially harmful. The two most common redox states in the environment are pertechnetate, Tc(VII)O4
, under oxic conditions and Tc(IV) under anoxic conditions. Tc(VII) is a contaminant of concern at a number of U.S. DOE facilities, including sites at Oak Ridge, TN; Paducah, KY; Hanford, WA; and Portsmouth, OH, due to its large migration tendency in groundwater. In oxygenated and suboxygenated environments, Tc(VII) is highly soluble, poorly sorbed by sediment minerals, and is therefore highly mobile in the subsurface (Bondietti and Francis, 1979; Gu and Schulz, 1991; Schulte and Scoppa, 1987; Wildung et al., 1986). In reducing environments, Tc(VII) is readily reduced, either chemically or biologically, to Tc(IV) or Tc(V) species, which have a much lower solubility and thus are retained by sediments and soil humic materials (Bondietti and Francis, 1979; Gu and Schulz, 1991; Lloyd et al., 1998, 2000; Schulte and Scoppa, 1987; Wildung et al., 1986, 2000). A variety of anaerobic microorganisms have been shown to be capable of reducing Tc(VII)O4– in solution to solid-phase Tc(IV) precipitates in the presence of various electron donors (Lloyd et al., 1998, 2000; Wildung et al., 2000). Therefore, bioreduction of Tc(VII) has been proposed as an option to remove or impede the migration of technetium in the subsurface. On the other hand, it is also known that reduced Tc(IV) species can readily form complexes with a number of organic and inorganic ligands such as carbonate, citrate, EDTA, and DOC under reducing conditions and thus render it soluble and mobile in groundwater (Geraedts et al., 2002; Gu and Ruan, 2007; Maes et al., 2004). Istok et al. (2004) investigated the propensity for bioremediation of Tc (VII) at the Oak Ridge Tennessee USA Y-12 site (described above in Section 4.3.1). Investigations focused on the stimulation of indigenous microbial communities to reduce a mixture of mobile U(VI) and Tc(VII) in the presence of elevated initial nitrate (120 mM) co-contamination in the shallow unconfined aquifer. Microbial reduction of Tc(VII) to Tc(IV) is

Influence of Coupled Processes on Contaminant Fate

33

desirable since the reduced form of this radionuclide is sparingly soluble and significantly less mobile than the oxidized species. The investigations utilized small-scale field ‘‘push-pull’’ tests where electron donor was injected radially into the aquifer, followed by slow removal with time. With the addition of electron donor, rapid nitrate utilization via denitrification was observed with a nearly simultaneous loss of Tc(VII) from solution. These results suggest direct microbial enzymatic reduction of Tc(VII), although Mossbauer spectroscopy analyses of postbiostimulated core sample suggested the formation of biogenic Fe(II) (Stucki et al., 2007) which may have contributed to the reduction of Tc(VII). Wildung et al. (2004) observed presumptive evidence of such a process while investigating Tc reduction in Atlantic Coastal Plain sediments from a shallow sandy aquifer that exhibited a Fe(II)/Tc(VII) concentration of <1.1. As well, the authors found that the addition of various electron donors (acetate and lactate) resulted in up to 35% microbial reduction of Fe(III) and a corresponding increase in Fe(II) in media that exhibited the lowest initial Tc(VII) reduction and the highest hydraulic conductivities. This suggested that accelerated microbial reduction of Fe(III) could offer a viable means of reducing and attenuating mobile Tc(VII) as Tc(V). Fredrickson et al. (2004) also showed the potential for biogenic Fe(II)-mediated reduction of Tc(VII) in DOE relevant Hanford and Oak Ridge sediments. The simultaneous reduction of nitrate and Tc(VII) as observed by Istok et al. (2004) conflicts with Abdelouas et al. (2002) who found that denitrifying organisms had no influence on Tc reduction since Tc concentrations did not change during denitrification in soil systems. However, in the presence of SRB, Tc concentrations decreased which was attributed to Tc(VII) reduction and precipitation of TcO2 and TcS2. Abdelouas et al. (2002) also noted that coprecipitation of Tc with newly formed iron sulfide is also expected to contribute to Tc removal under sulfate-reducing conditions. 4.3.3. Strontium Aqueous phase strontium (Sr) often exists as a divalent cation under most environmental conditions. It expresses potential for similar industrial applications to those of calcium and barium, but it is rarely employed because of its higher cost. Principal uses of strontium compounds are in pyrotechnics since it displays a brilliant red color in fireworks and warning flares. It is the radioactive 90Sr isotope that is of environmental concern with 90Sr being produced commercially through nuclear fission for use in medicine and industry. It is also found in the environment from nuclear testing that occurred in the 1950s and 1960s and in nuclear reactor waste. It has a t1/2 of 29 years and its high-energy radiation can be used to generate an electric current that is used in space vehicles, remote weather stations, and navigation buoys. Ingestion of 90Sr through food and water intake is the greatest health concern regarding this radionuclide. Once in the body, 90Sr acts like

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calcium and is readily incorporated into bones and teeth where it can cause cancer of the bone and soft tissues around the bone. Significant legacy waste issues exist within the U.S. DOE concerning 90Sr such as those at the Oak Ridge National Laboratory, Idaho National Laboratory, and the Hanford site in Richland, WA, USA. Typically remedial efforts involving 90Sr are solely hydrological or geochemical in nature. However, one innovative study at INL utilized coupled microbial and hydrogeochemical processes to impede the migration of 90Sr in groundwater. Fujita et al. (2000, 2004) injected dilute molasses and urea into the Snake River Plain Aquifer in Idaho to stimulate ureolytic microorganisms that facilitate the precipitation of calcite for the purpose of co-precipitating the radionuclide 90Sr which was present in the groundwater. The immobilization of 90Sr in groundwater appeared promising since biomass and mineral precipitates rapidly formed during the electron donor and nutrient amendment phase (Fujita et al., 2000, 2004). The results, however, suggested that the permeability of media surrounding the injection regime decreased significantly due to the increase in biomass and mineral precipitation. Stoner et al. (2005) also showed that the bioprecipitation of CaCO3 can alter the flow paths within fractures and that pore plugging depended strongly on the hydrodynamics of the system. The efforts of Fujita et al. (2000, 2004) may have been improved by alteration of the injection well gallery or by using a forced gradient dipole design that would have allowed groundwater movement at a controlled rate and more accurate migration of the electron donor and thus mineral precipitate, to avoid aquifer clogging (e.g., Wu et al., 2006a,b). This is illustrated in laboratory studies by Roden et al. (2000) who found that advective removal of soluble Fe(II) from the surface of Fe(III)oxide during dissimilatory reduction by bacteria, minimized the formation and precipitation of secondary minerals on the Fe(III) surface which impedes electron transfer processes, thus allowing 100 more biomass to accumulate versus diffusion controlled conditions.

4.4. Inorganic ligands 4.4.1. Nitrate Nitrate (NO3–) is a naturally occurring anionic form of nitrogen (N) and is an integral part of the Earths nitrogen cycle (Fig. 7). It is formed from decaying organic residues of plants and animals and is typically used as a fertilizer, an oxidizing agent, and in the production of explosives and certain glassware. Nitrate is more often than not, a plant limiting nutrient and thus is often applied to land surfaces as fertilizers in an effort to enhance agriculture and silviculture productivity. Nitrate is also a byproduct of septic systems and waste disposal lagoons as it is a naturally occurring chemical that is a product of animal or human waste decomposition. Since nitrate is an anion and a weak Lewis base, it typically does not interact with

35

Influence of Coupled Processes on Contaminant Fate

Precipitation

Gaseous losses

Nitrates applied as fertilizer

Surface runoff

Agricultural land

Denitrification

Plant consumption

Organic residue

Nitrates

Nitrification

Organic matter

Leaching

Nitrites

Ammonium

Lake

Figure 7 A schematic representation of the nitrogen cycle where nitrate contributes a significant role in terrestrial, aquatic, and atmospheric N dynamics (from http://www. cst.cmich.edu/centers/mwrc/nitrogen.htm).

subsurface soils and sediments and is rapidly leached through the vadose zone potentially causing contamination of the underlying groundwater. However, nitrate is strongly influenced by microbial, and to a lesser extent, geochemical denitrification and nitrification reactions. It is estimated that 4.5 million people in the USA who rely on well water are exposed to nitrate concentrations exceeding the EPA MCL which is 10 ppm as N or 44 ppm as NO3– (Kite-Powell and Harding, 2006; Fig. 8). Likewise, in 15 European countries, the percentage of population exposed to nitrate level in drinking water above 50 ppm ranges from 0.5% to 50% (ECETOC, 1988; WHO, 1985, 2003) which corresponds to nearly 10 million people. In several developing countries, especially in India, consumption of water that contains nitrate concentrations up to 500 ppm is common. In India, concentrations of nitrate up to 1500 ppm have been found in groundwater within agricultural areas (Jacks and Sharma, 1983) and in the year 2000 it has been reported that more than 33% of water supplies in Punjab and Haryana had nitrate levels above desirable limits

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Increasing risk of ground-water contamination

Philip M. Jardine

Nitrogen input High

Aquifer vulnerability High

High

Low

Low Low

High Low

Figure 8 Schematic showing nitrogen inputs versus aquifer vulnerability for the USA where green and yellow colors indicate areas with a lower risk for groundwater nitrate contamination versus orange and red that indicate areas with a higher risk for groundwater (from http://asuwlink.uwyo.edu/tkc/).

(Malik, 2000). Subramaniyan (2004) also indicated that nitrate concentrations in groundwater from India ranged from 0.1 to 870 ppm with an average concentration of 65 ppm which is well above the EPA MCL for this solute. Thus, leaching of nitrate from agricultural lands and other sources to groundwater is a global phenomenon and one that is on the rise. Nitrate in drinking water is associated with numerous health problems both in animals and humans, the latter being affected by methemoglobinemia, various cancers, vascular dementia, cytogenetic effects in children, and many other adverse health effects (Rao and Puttanna, 2006). In India, recurrent diarrhea affects nearly 20% of the population with the highest incidence of diarrhea being reported in areas that have high concentrations of nitrate in drinking water (Gupta et al., 2001). Gupta et al. found a significant correlation between individuals with recurrent acute diarrhea and high levels of nitrate in drinking water (r2 ¼ 0.84, p ¼ 0.03). As well, accelerated eutrophication of aquatic ecosystems as a result of nitrate and phosphate enrichments has been implicated in the emergence of numerous human and wildlife diseases ( Johnson et al., 2007). Since global agricultural production and nitrate fertilizer application is forecasted to increase over the next century, eutrophication will almost certainly become an increasingly severe problem thus requiring a balance between nutrient-mediated agricultural gains and concurrent increases in disease risk. Denitrification of nitrate in soil can be a significant process in the loss of fertilizer and soil N, but can also serve to remove excess nitrate that is leached below the root zone (Mosier et al., 2002). Denitrification can be

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both microbial and chemical, but microbial processes dominate in most soils through a stepwise reduction to N2. Soil oxygen, which is regulated by the soil water content, available pore space (i.e., texture), availability of labile organic C, and microbial respiration are the main factors controlling microbially mediated denitrification. Several investigations have shown that the addition of a suitable electron donor to nitrate contaminated vadose or groundwater regimes results in enhanced denitification through stimulated microbial activity (Dick et al., 2000; Evans and Trute, 2006; Gierczak et al., 2007; Istok et al., 2004; Smith et al., 2001). Seitzinger et al. (2006) suggested that within a watershed, the amount of land-based N that is denitrified is generally highest in terrestrial soils, with progressively smaller amounts denitrified in groundwater, rivers, lakes, reservoirs, and estuaries. On a per area basis, however, the contribution of denitrification in soils and groundwater is about 1/10 that of lakes, rivers, estuaries, and other water bodies. Barkle et al. (2007) investigated the propensity of denitrification at the soil– groundwater interface at the Lake Taupo catchment in New Zealand where deteriorating water quality has been observed. The authors quantified soil physical, chemical, and mineralogical properties of the vadose soils and used isotopic tracers to quantify the extent of denitrification. The authors found that the denitrification capacities of the vadose zone material was low compared to other studies in the literature and suggested that careful land management was necessary to avoid continued contamination of groundwater due to nitrate leaching below the root zone. The land application of wastes is becoming an increasing popular practice in many countries throughout the world. For example, the application of farm dairy effluent to land, as opposed to direct discharge to surface water, is the preferred regulatory method for disposal in New Zealand (Hawke and Summers, 2006; Muller et al., 2007). The soil chemistry benefits from such a practice are sometimes outweighed by nitrate contamination of groundwater, adverse impacts on soil structure and hydraulic conductivity, and possible microbial contamination issues. Gloaguen et al. (2007) investigated the impact of sewage effluent additions to a tropical Oxisol and noted that Naþ leaching was enhanced in the soils which resulted in improved soil structure due to tactoid formation between clay minerals. They also found that the C and N chemistry of the pore water indicated mineralization of DOC and rapid nitrification from ammoniac and organic nitrogen that was provided by the effluent. The authors cautioned of the possible long-term risk associated with nitrate contamination in shallow groundwater environments via such land application strategies. Hansen and van Berk (2004) investigated the impact of nitrate from long-term (i.e., 40 years) agricultural activities on an aquifer in northern Germany which provides the majority of water for the city of Hanover. The authors found that the extracted raw waters showed little sign of nitrate contamination due to microbial nitrate reduction coupled with iron sulfide

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oxidation in the aquifer. By comparing measured and modeled data using a material flux model (PhreeqC), the authors reconstructed long-term trends in raw water quality. The results suggested that anthropogenic processes such as varying nitrate influx and conversion of wet grasslands into arable land coupled with geochemical processes such as biomass degradation, oxidation of sulfide minerals, and partial nitrate breakthrough due to consumption of iron sulfide minerals, were responsible for observed low nitrate concentrations in the groundwater. In contrast, Kelly (1997) found significant nitrate contamination in a shallow unconfined aquifer in Illinois, USA, due to rapid recharge induced by agricultural irrigation practices. They noted, however, that the nitrate plume was restricted to shallow depths since denitrification was actively removing nitrate from deeper regimes. The authors believed that the denitrification process was coupled to the geochemical oxidation of sulfide minerals. Nitrate that was present in the aquifer was found to be due to rapid water movement through the porous media, most likely along preferential flow paths, which creates short residence times during transport and contributes to limited geochemical or microbial reactions involving nitrate. Tarits et al. (2006) also noted that the rate of groundwater movement within a fractured aquifer underlying aboveground agricultural activity controlled the extent of autotrophic denitrification processes in this pyrite-bearing system. Where preferential groundwater flow was evident, groundwater nitrate concentrations were high due to short residence times within the media. Where preferential groundwater flow was of a lesser significance, groundwater nitrate concentrations were lower and sulfate concentrations were higher suggesting that denitrification processes were prevalent within pyrite-bearing fractures where solute residence times were sufficient to accommodate geochemical and microbial reactions. Hinkle et al. (2007) found that low recharge rates and slow flow velocities in a sandy aquifer in Oregon, USA, were such as to restrict septic tank-derived nitrate to isolated plumes within several meters of the water table. A variety of geochemical and isotopic measurements indicated that denitrification also influenced nitrate gradients in the aquifer with denitrification occurring at an oxic/suboxic subsurface boundary where the distribution of electron donor changed, and most likely the microbial community structure changed as well. The use of isotopic techniques in this study proved a powerful indicator of microbially mediated denitrification since the 13C/12C ratio changed dramatically within the plume which is characteristic of biological processes. Biodegradation processes cause an enriched C isotope signature versus hydrological impacts such as dilution and evaporation that result in a constant isotopic ratio even though the C concentration changes. Nitrate is also a major anion in the DOE waste plumes throughout the USA. It was used in various radioisotope purification and enrichment

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technologies including Pu purification and irradiated fuel processes at Hanford in Richland, WA, USA, and in U metal processing at the Y-12 facility in Oak Ridge, Tennessee, USA. Vast areas of contaminated soils and groundwater exist at these facilities where a 100-m deep, 100-ha long nitrate plume exists at the Y-12 facility in Oak Ridge. At the Hanford site, Singleton et al. (2005) used a nitrogen and oxygen stable isotope technique to track sources of deep vadose zone and groundwater nitrate. The authors identified four potential sources of nitrate in groundwater at the site including two natural sources (i.e., microbially produced nitrate from the soil profile and nitrate buried with the caliche layer which is part of the Ringold formation) and two anthropogenic sources (i.e., isotopically distinct nitrate from industrial sources such as nitric acid and nitrate from high-level radioactive waste from Pu processing). The authors found that the most common sources of high nitrate concentrations in groundwater at Hanford were nitric acid and natural nitrate which was hydrologically flushed from the vadose zone during the disposal of low-level wastewater (i.e., artificial recharge). The studies of Singleton et al. (2005) show the usefulness of stable isotopes as a tool for monitoring transformations of specific elements in the environment by distinguish sources and pathways since both biological and chemical transformations show a preference for specific isotopes (Wiatrowski and Barkay, 2005). 4.4.2. Perchlorate Perchlorate has recently (late 1990s) been identified as a contaminant of concern in groundwater and surface water, particularly in several western states of the USA, owing to an EPA mandated lowering of the MCL to 18 ppb because of new analytical methods that allowed the detection of perchlorate to 4 ppb and lower (Herman and Frankenberger, 1998; Motzer, 2001; Wang et al., 2006). Whether this MCL is justified or not, perchlorate intake is particularly problematic in children as it replaces iodine in the body and causes numerous potential adverse health effects such as thyroid hormone production. There are three recognized sources of perchlorate in the food chain: (1) military—industrial activities; (2) agricultural use of Chilean nitrate fertilizer (origin: Chile, South America); and (3) natural atmospheric sources, with (1) and (2) above dominating global inputs (Dasgupta et al., 2006). Perchlorate is widely used as a propellant in solid rocket fuels which have a limited shelf life and must be periodically replaced. Historically, the rocket fuel washout in the USA was discharged to groundwater and surface water sources, whereas today, the washout is properly contained. As well, Chilean fertilizers were widely used in the USA until the 1960s, and it was not until the year 2000 that Chilean nitrate producers instituted changes that removed perchlorate from their fertilizers. Nevertheless, tens of thousands of metric tons of perchlorate entered the environment annually for many

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decades (Dasgupta et al., 2006). Being a highly soluble, nonreactive anion, perchlorate is extremely mobile in aqueous environments thus contaminating vast subsurface domains. There have been several ex situ remedial techniques proposed for the removal of perchlorate in surface water and groundwater, including bioreactors and anion selective resins that have a strong affinity and large capacity for perchlorate (Gu et al., 2000, 2005; Schaefer et al., 2007; Xu et al., 2003; Zhang et al., 2005). Because of the vast subsurface real estate that perchlorate can potentially occupy, in situ strategies for removing the anion from groundwater are desirable and often take advantage of the ability of certain microorganisms to reduce the anion to innocuous end-products such as Cl and oxygen (Coates and Achenbach, 2004; Evans and Trute, 2006; Hunter, 2002; Nozawa-Inoue et al., 2005; Tan et al., 2004; Waller et al., 2004; Zhang et al., 2005). Several investigators have shown that perchlorate-reducing microorganisms are plentiful in subsurface environments (Hunter, 2002; NozawaInoue et al., 2005) and can actively biodegrade the perchlorate anion in solution. Schaefer et al. (2007) found that biological reduction of perchlorate was more effective than the metal catalysts of Fe and Ni, and Evans and Trute (2006) showed that moisture content and the rate of hydrologic transport of electron donor was critical for the successful bioreduction of perchlorate in contaminated groundwaters. Many investigators have also shown that the presence of nitrate in solution slows the bioreduction of perchlorate, most likely since it is a competitive electron acceptor (NozawaInoue et al., 2005; Tan et al., 2004; Yu et al., 2006). This may explain the persistence of perchlorate in the environment since it typically resides at ppb levels versus ppm levels found for nitrate. Stable isotope ratios of light elements such as H, O, and Cl have also been used to decoupling the influence of abiotic versus biotic losses of perchlorate in contaminated environments. Sturchio et al. (2003) quantified the chlorine isotope fractionation during perchlorate bioreduction and found a large kinetic isotope effect, thus providing a sensitive technique for distinguishing abiotic perchlorate losses due to hydrological dispersion or geochemical reactions versus microbial reduction.

4.5. General inorganics 4.5.1. Landfills The disposal of municipal waste typically occurs as direct land application in designated dump areas that may or may not be capped or covered in the future. Many such landfills can pose a severe threat to human health, especially in Third World nations where dump sites are unrestricted, unmanaged, and publicly accessible. A good example is the Dandora landfill in Kenya, Africa, that receives 2000 tons of ‘‘garbage’’ each day that is

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spread over a 30-acre area. The Dandora site is one of thousand such sites in developing countries around the world. Such dump-sites are often ravaged by local citizens for basic necessities required for survival or for material that is saleable in order to generate income. Children are often sent to scavenge through the debris and are frequently exposed to unacceptable levels of heavy metals and toxins that are present in the dump site soil, water, and air. Restricting individuals from landfills within these poor nations due to environmental issues must unfortunately be weighed against economic and day-to-day survival impacts. Landfill leachates are often strongly reducing and leachate interactions with aquifers may lead to redox alterations of groundwater depending on the redox buffer capacity and the potential reactivities of the reduced and oxidized constituents in the leachate and the aquifer. The local hydrogeologic conditions such as hydraulic conductivity, solid phase cation and anion exchange capacities, buffer capacity of the solid phase, and attenuation processes such as bacterial growth and metal precipitation reactions (e.g., redox changes, porous media clogging) associated with landfill leachate create a multicomponent, multiprocess system of significant complexity. Sequential redox zones of increasing electrochemical potential may develop downgradient of a landfill where regimes of methane production, sulfate reduction, Fe(III) reduction, nitrate reduction, and oxygen reduction may develop if the respective electron acceptors are present in the aquifer (Lyngkilde and Christensen, 1992; Fig. 9). Such redox zones can overlap (Bjerg et al., 1995; Ludvigsen et al., 1998) and this may partly be due to the formation of biofilms that can simultaneously use multiple terminal electrons acceptors that are found in the subsurface. Various laboratory and field studies have suggested that the hydraulic conductivity of subsurface media under a landfill decreases with time primarily due to plugging of soil flow paths as a result of metal precipitation and microbial growth (Brune et al., 1994; Kjeldsen et al., 1998; Rowe et al., 1997). Bacterial degradation of volatile organic acids such as acetate to CO2 accelerate the precipitation of CaCO3, the major component of ‘‘clog slime’’ in leachate-drainage systems due to an increase in system pH (Rittmann et al., 1996). Clogging may also occur as a result of insoluble metal sulfide precipitates that form via SRB. Since sulfate reduction is a major process linked to the degradation of contaminant organics and the transformation of inorganic contaminants, Ulrich et al. (2003) investigated coupled processes influencing sulfate influx and reduction in a landfill leachate contaminated aquifer in Norman, OK, USA. Lithologic, climatic, hydrological, and biogeochemical processes were believed to control the source of sulfate and to moderate the reduction processes in subsurface environments. Ulrich et al. (2003) identified three sources of subsurface sulfate including iron sulfide oxidation via microbial processes, barite dissolution via geochemical processes, and the hydrologic

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Calcite growth Iron carbonate Calcium carbonate Quartz

Quartz

Organics Biofilm

Iron hydroxide

Quartz Landfill

Clay

O2

Methane production O2 Sulfate reduction

Aerobic respiration Iron reduction Denitrification

Figure 9 Schematic of dynamic redox changes that may occur along a contaminant plume originating from an organic-rich landfill. The inset illustrates various coupled hydrological, geochemical, and microbial processes that may result within an Fe-reducing subsurface environment (from http://www.lbl.gov/Science-Articles/ Archive/sabl/2006/Mar/04-underground-view.html).

advective flux of sulfate, with the relative importance of each source dependent on depth within the alluvium profile. 4.5.2. Permeable reactive barriers Subsurface barriers have been used for many years to restrict the movement of contaminant plumes in groundwater. Such barriers are commonly constructed of impermeable grouts, slurries, and sheet pilings to form subsurface walls that impede the discharge of contaminated groundwater to potential receptors thereby eliminating potential adverse environmental impacts. Permeable reactive barriers, on the other hand, serve as preferential conduits for contaminated groundwater with the intent of removing or destroying contamination when the groundwater interacts with the barrier material (Powell and Puls, 1997; Fig. 10). Two basic designs are commonly used in field-scale applications which include (1) the funnel and gate reactive barriers and (2) the continuous trench reactive barriers, each of which is described in detail by Powell and Puls (1997). As might be expected, complete site characterization of coupled hydrological, geochemical, and

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Influence of Coupled Processes on Contaminant Fate

Contaminated groundwater

Ground surface

Surface backfill

PRB Ambient groundwater flow field

Treated groundwater Bedrock

Figure 10 Schematic illustration of a permeable reactive barrier (PRB) that serves to intercept contaminated groundwater and either removes, alters the chemical form, or destroys the contaminant so that downgradient groundwater is less harmful (from http://www.lanl.gov/environment/all/overview.shtml).

microbial processes is important for the successful installation and success of a reactive barrier since design, location, emplacement methodology, and barrier life span are often based on these site characteristics. Gu et al. (2002a) investigated biogeochemical, mineralogical, and hydrological characteristics of a permeable zero-valent iron reactive barrier that was used in the treatment of U and Tc contaminated groundwater at the Y-12 S-3 ponds site in eastern Tennessee, USA. Long-term performance of the barrier was quantified through interrogation of the biogeochemical interactions between Fe0 and the groundwater constituents. Nitrate and sulfate were found to be reduced within the zone of influence of the barrier due to the low redox potential of the barrier regime. An increased anaerobic microbial population was also observed and this was thought to have contributed to the nitrate and sulfate reduction. Microbial populations within the barrier regime were found to be 1 to 3 orders of magnitude greater than background soil/groundwater systems and appeared to increase from upgradient the barrier to downgradient the soil on the other side of the barrier (Gu et al., 2002b). Microbial communities were found to be very diverse and the composition of nitrate and sulfate reducers and methanogens appeared to change with time. Geochemical decreases in Ca and bicarbonate also occurred as a function of time and resulted in the formation of Ca and Fe mineral precipitates aragonite and siderite, respectively, which corresponded to the corrosion of Fe0 and an increased groundwater pH. Numerous other

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mineral precipitates were identified including amorphous Fe-oxides, goethite, ferrous sulfides, and green rust. These minerals were found to clog the Fe barrier via cementation which caused a decrease in the groundwater hydraulic gradient and flow-path connectivity. Microorganisms also may have contributed to barrier fouling via gas production from microbial respiration and denitrification resulting in the formation of CO2, N2, and N2O. Using the data of Gu et al. (2002a), Liang et al. (2003) utilized a chemical equilibrium model to predict permeable reactive barrier (PRB) precipitate formation based on thermodynamic properties of the Fe0 and groundwater constituents. The results suggested that high saturation indices were prevalent with respect to calcite and iron oxides which was consistent with the experimental findings of Gu et al. (2002a). Analyses showed that significant solid phase material can form over a 10-year period leading to severe clogging of the Fe0 media resulting in fouling of the PRB. Powell and Puls (1997) also used Fe0 barriers to impede the migration of chlorinated hydrocarbons and Cr(VI) in groundwater. The interaction of chlorinated hydrocarbons with Fe0 results in reductive dechlorination reactions that ultimately produced the innocuous endproducts ethane and ethane. In a similar manner, the interaction of Cr(VI) with Fe0 results in geochemical reduction of Cr(VI) to sparingly soluble Cr(III) which precipitates as Cr(OH)3 at pH values above 5. If iron is present, a mixed Cr–Fe hydroxide precipitate may also form. Wilkin et al. (2003) investigated the long-term performance of Fe0 PRBs at two contaminated sites, with one western site containing chlorinated hydrocarbons and the one eastern site containing overlapping plumes of chlorinated hydrocarbons and Cr(VI). The permeable barriers at both sites acted as a long-term sink for the elements C, S, Ca, Si, N, and Mg. Consistent patterns of spatially variable mineral precipitation and microbial activity were observed, with mineral precipitation and microbial biomass accumulating most rapidly in the upgradient portion of the Fe0–aquifer interface. Porosity reductions were found to be related to S and carbonate precipitants and ranged from 0.03 to 0.06 cm3 cm–3, which was sufficient to clog pores involved in the advective movement of groundwater. However, Wilkin et al. (2003) found that pore clogging was not significant enough over a 4-year period to influence the overall reactivity of the barrier and its ability to destroy or immobilization contaminants (e.g., reduction of Cr(VI) to sparingly soluble Cr(III)).

4.6. Modeling coupled processes involving dissolved aqueous phase inorganic constituents In a system of multiple inorganic components, the identity of a component becomes chemically distinct from that of a species. Components are a set of linearly independent ‘‘basis’’ chemical entities such that every species can be

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uniquely represented as a combination of these components. A species is then defined as the product of a chemical reaction involving the components as reactants. Thus, numerical models that consider multiple components and multiple processes are computationally intensive, especially when aqueous and solid phase reaction kinetics are considered. In recent years, a limited number of multicomponent reactive inorganic-based transport models have become available that truly couple hydrodynamic transport with multiprocess, time-dependent geochemical and microbial reactions. During the 1990s, Yeh and Tripathi (1991) developed the equilibrium-based, multicomponent model HYDROGEOCHEM that numerically simulates multispecies, multiprocess fate, and transport processes in subsurface environments. Gao et al. (2001) also developed REACTRAN2D which is similar to HYDROGEOCHEM for simulating the transport of chemically reactive components in conjunction with energy in groundwater systems. The code HYDROGEOCHEM was soon upgraded to HYDROGEOCHEM 2.0 which simulates mixed chemical kinetic and equilibrium reactions in an effort to deal with multispecies fate and transport that is controlled by timedependent or thermodynamic-based reactions. Several biogeochemical codes such as BIOKEMOD (Salvage and Yeh, 1998) and BIOGEOCHEM (Fang et al., 2006; Yabusaki et al., 2007) were also formulated during this period to describe equilibrium and kinetic geochemical and biological reactions in the absence of flow. HYDROGEOCHEM 2.0 was further modified to incorporate time-dependent microbial reactions (HYDROBIOGEOCHEM, Yeh et al., 1998; HYDROGEOCHEM 5.0, Yeh et al., 2004) which uses the simulator BIOKEMOD (Salvage and Yeh, 1998; Yeh et al., 2001) and is designed to solve a system of equations describing hydrologic transport and biogeochemical reactions in a reactive multicomponent, fully anisotropic, unsaturated or saturated system. The major transport processes are advection, dispersion/diffusion, and source/sinks. The major chemical processes are aqueous complexation, adsorption, ion-exchange, precipitation/dissolution, redox, and acid–base reactions. The major microbiological processes are biodegradation and microbial respiration. Rate expressions for kinetically controlled geochemical reactions are based on collision theory and microbial growth rates are represented by a modified Monod kinetic expression. A high-performance computing version of HYDROBIOGEOCHEM has also been released (HBGC123D, Gwo et al., 2001) which is a 3D shared-memory parallel computer model that couples hydrogeological and biogeochemical processes. The parallelized code offers the advantage of simulating large-scale, more realistic problems with improved performance and speed. The various versions of the computer code HYDROGEOCHEM and HBGC123D have been used by a variety of authors to describe multiscale experimental contaminant fate and transport processes (Brown et al., 2000; Gwo and Jardine, 2005; Gwo et al., 1999, 2001, 2007; Liu and Chen, 1996; Zhang et al., 2007). Recently, Parker (unpublished data,

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University of Tennessee, 2007) has applied HYDROGEOCHEM 5.0 at the Y-12 site on the Oak Ridge Reservation in a watershed-scale effort aimed at predicting hydraulics and contaminant NO3 and U fate and transport. A higher resolution plot-scale numerical model was also developed to aid experimental design and interpretation of results. Models that include U reduction reactions and immobilization are being used to determine reaction rate coefficients and the scale dependence of those coefficients. In a smallerscale effort, Yabusaki et al. (2007) used a modified version of BIOGEOCHEM (Fang et al., 2006) to simulate the microbially mediated reduction of U(VI) in the unconfined aquifer of the Old Rifle UMTRA site in western Colorado, USA, as described by Anderson et al. (2003) and Vrionis et al. (2005). Yabusaki et al. (2007) found that the consideration of coupled hydrological, geochemical, and microbial processes was imperative to the accurate simulation of the experimental data. Other multicomponent reactive transport models, such as CRUNCH and FERACT, are also available which couple multiple sets of kinetically controlled geochemical and biological processes with advective-dispersive flow (Steefel, 2001; Tebes-Stevens et al., 1998). Still other kinetic-based flow models are available that target specific problems related to timedependent microbially driven redox reactions (Hunter et al., 1998) and complex, time-dependent metal-organo geochemical reactions (Szecsody et al., 1998) in heterogeneous subsurface environments. Steefel et al. (2005) provided a review of reactive transport modeling for the analysis of coupled physical, chemical, and biological processes in Earth systems such as contaminant remediation in the subsurface, the description of solute and nutrient fluxes within ecosystems, and the treatment of deep earth processes such as metamorphism and magma transport. Steefel et al. (2005) stressed that numerical simulation of the complex interplay of material flow, transport, and reactions at multiple spatial and temporal scales requires an integrated approach. One such example relevant to this chapter is that microbial biofilms at the pore scale may depend on some combination of advective and diffusive transport combined with local biogeochemical reactions providing electron donors and acceptors. These processes may result in physical changes to the media through biological growth and mineral precipitation or dissolution, providing a feedback between flow, transport, and reaction. Islam et al. (2001) also provides a review of numerical modeling studies concerned with the simulation of biogeochemical processes in leachate-contaminated soils. Consideration is given to coupled physical processes including advection, diffusion, and hydrodynamic dispersion, and biogeochemical processes including aqueous complexation, precipitation/dissolution, microbial reactions, and redox transformations. Scheibe et al. (2006) integrated field-scale geophysical, hydrological, and geochemical data from a coastal plain field site in eastern Virginia, USA, with laboratory biogeochemical experiments to formulate a 2D numerical

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model of reactive coupled processes transport in heterogeneous subsurface media. The model incorporated time-dependent biological metal reduction with microbial growth and transport, Fe-oxide reactivity and surface-area dynamics, and hard-wired contaminant geochemical reaction scenarios (e.g., U geochemistry). The authors used the model to investigate the impact of physical and biogeochemical heterogeneity on the fate and transport of redox-sensitive contaminants in heterogeneous, subsurface environments. Recent efforts by Scheibe et al. (2007) are also underway to develop a hybrid multiscale subsurface reactive transport modeling framework that integrates other models with diverse representations of physics, chemistry, and biology at scales ranging from subpore to pore and continuum. The modeling framework includes parallel coding and solvers, gridding, data and workflow management, and visualization. Li et al. (2006) considered the impact of coupled processes on mineral precipitation-induced porosity reductions in PRBs and used MODFLOW coupled with RT3D to simulate geochemical reactions as a function of groundwater flow to understand time-dependent mineral fouling of PRBs. MODFLOW is a 3D finite-difference groundwater flow model that simulates advective and dispersive subsurface flow, whereas RT3D is a 3D reactive multispecies mass transport code that models subsurface contaminant reactions, time-dependent microbial metabolism, and microbial transport kinetics (Clement et al., 1998). The model of Li et al. (2006) illustrated that the largest porosity reductions within an operational PBR occur between the entrance and the mid-plane of the PRB as a result of CaCO3 mineral precipitation, and that modest porosity reductions occur from the mid-plane to the exit of the PRB as a result of Fe-oxide mineral precipitation. These simulations were consistent with the experimental results of Gu et al. (2002a) and Wilkins et al. (2006). Li et al. (2006) suggested that three mineral species accounted for essentially all of the predicted porosity reduction, namely CaCO3, FeCO3, and amorphous Fe(III)-oxide. Porosity reduction was found to be sensitive to groundwater carbonate, Ca, DO, the anaerobic Fe0 corrosion rate, and the kinetics of CaCO3 and Fe-oxide precipitation. Porosity reductions were spatially variable owing to groundwater flow path heterogeneities which influenced the spatial distribution of solutes entering the barrier. Modeling results suggested the most important hydraulic variables affecting porosity reduction in PRBs were aquifer heterogeneity and the difference in hydraulic conductivity between the aquifer and the PRB. Mayer et al. (2006) also utilized a process-based reactive transport model to investigate mine drainage treatment by PRBs. Simulations considered microbial-mediated sulfate reduction via organic-C amendments and removal of sulfate and Fe by precipitation of reduced mineral phases such as iron monosulfides and siderite (FeCO3). Long-term changes in barrier performance necessitated the use of temperature-dependent rate coefficients

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and a multimodal Monod-type rate expression accounting for the variable reactivity of multiple organic C fractions during sulfate reduction. The model captured spatial variations, seasonal fluctuations, and time-dependent decreases in PRB reactivity.

5. Influence of Coupled Processes on Organic Contaminant Fate and Transport 5.1. General overview Research on the biogeochemical and microbial processes that underlie the success of natural attenuation, biostimulation, and/or bioaugmentation remedial strategies for contaminant organics in groundwater has received much attenuation in recent years (Scow and Hicks, 2005). Both natural and intentional biodegradation and imbibition of organics can occur in contaminated subsurface environments as long as available electron donor and acceptors, and nutrients are available, and if subsurface geochemical and hydrologic conditions are conducive to microbial growth and activity (Aulenta et al., 2006; Xie et al., 2006). Understanding site hydrology is critical to the evaluation of contaminant plume development since variations in terminal electron accepting processes (TEAPs) and microbial physiological processes occur along groundwater flow paths and with depth within contaminant plumes (Chapelle, 1993; Chapelle et al., 1995; Lovley, 2001; Smith, 1997). Characterization of coupled physicochemical and hydrologic processes is important because the degradative potential of specific compounds within a plume varies depending on the TEAP (Fig. 11). TEAP zones are often delineated by measuring concentrations of electron donors and acceptors in groundwater samples; however, interpretation of this data is difficult because these concentrations are influenced by not only microbial reactions, but by hydrological processes such as advection, dispersion, diffusion, and coupled geochemical reactions. Plumes of contaminated groundwater often exhibit sharp vertical or lateral gradients in contaminant concentration, aquifer geochemistry, and redox conditions (Bennett et al., 2000; Christensen et al., 2000; NRC, 2000), thereby providing opportunities for fundamental investigations of microbial community diversity, ecology, and function within TEAPs. Chemical gradients are also used to infer natural attenuation processes in contaminated aquifers (NRC, 2000) where TEAP shifts are typically accompanied by changes in aquifer geochemistry (Bennett et al., 2000) and chemical shifts due to biodegradation reactions (NRC, 2000). Haack and Bekins (2000) discussed the prevalence of various microbial populations in organic-based contaminant plumes and they focus on nonmanipulated systems and experiments that have been conducted in situ or

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O

CO2

SO42−

S2−

O

CO2

NO−3

O2

N2

Unsaturated zone Groundwater SO42− NO−3 O2 Contaminant source

Methanogenic O

CH4 CO2

Fe(III)-reducing Sulfate reducing

O

CO2

Fe(III)

Fe(II)

Aerobic

r

Nit

ing

uc

ed

r ate

O

CO2

O2−

H2O

Direction of groundwater flow

Figure 11 Schematic illustration of changes in terminal electron acceptor processes (TEAPs) along an organic contaminant plume where highly reducing conditions prevail near the source and oxic conditions exist at the plume fringe (from Lovley, 2001).

under conditions representative of a contaminated field site. They suggested that the effective biodegradation of subsurface organic contaminants requires that the appropriate microbial populations be present and that favorable subsurface geochemical and hydrological conditions prevail. They stress that interdisciplinary approaches to site investigations have significantly improved our understanding of hydrogeochemical and microbiological interactions within these systems. Culture-based and molecular analyses of microbial populations in subsurface contaminant plumes have revealed significant adaptation of microbial populations to plume environmental conditions where spatial and temporal variability of geochemical and hydrological conditions can significantly influence subsurface microbial community structure and thus the rates and mechanisms of biodegradative processes (Krumholz et al., 1996). Such variations in physical and chemical properties of an aquifer and the aqueous concentration of contaminants and other important reactants in turn significantly impact the spatial distribution of microbial communities in aquifers (NRC, 1993). For example, the presence of macroscale depositional layers of different permeability, solution

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channels, and fractures zones, superimposed on microscale media heterogeneities add to the irregularities of subsurface formations and the spatial distribution of microbial habitats (Atlas and Bartha, 1987; Paul and Clark, 1989). As well, microorganisms with specific degradative capacities may only be found in favorable habitats that exist only in a portion of a contaminant plume (Anderson et al., 1998). Thus, understanding spatial and temporal changes in hydrology and chemistry, as related to microbial capabilities over time will better facilitate forecasts of the long-term fate of contaminant plumes. Variability in the physical structure of aquifer materials also influences bacterial transport (Harvey et al., 1993). Lawrence and Hendry (1996) reviewed the role of geologic, hydrologic, and solute variables on bacterial transport through geologic media. They concluded that variations in site hydrogeology may significantly influence the location and community structure of subsurface microbes through differential transport mechanisms influencing bacterial movement. Ulrich et al. (1998) used microscale radiography to show that sulfate-reducing activity was significantly less in clayey sediment layers where water flow was restricted versus more unconsolidated sediment layers where advective flow dominated. However, Ludvigsen et al. (1999) used PFLA analyses to show increased microbial populations and changes in community structure within fine-grained clay and silt layers relative to coarse-grained sand layers in an aquifer contaminated with landfill leachate. Baker and Herson (1990) discussed the use of various manipulative bioremediation strategies for degrading organic hazardous waste in subsurface environments. They suggested that these technologies depend upon the alteration of the physical and chemical conditions in the subsurface environment in order to optimize microbial activity. They reviewed the microbiological, hydrological, and geochemical factors that should be considered in evaluating the appropriateness of bioremediation of hazardous organic waste-contaminated aquifers and subsurface soils. They stress that enhancing biodegradation or biostimulation at contaminated sites requires alterations on the site’s physical and chemical characteristics in order to increase biodegradation by indigenous organisms. Perturbation of environmental factors such as pH, temperature, nutrient availability, and physical structure on microbial abundance, diversity, activity, and function are all important considerations for the successful application of bioremediation at organic contaminated sites. Recently, Scow and Hicks (2005) recommend that three lines of evidence be used to assess the effectiveness of organic contaminant bioremediation: (1) a decrease in contaminant mass and concentration as a function of time, (2) observed changes in hydrological and/or geochemical processes thus providing indirect evidence, and (3) in situ or microcosm studies providing direct evidence for biodegradation (NRC, 2000; Smets

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and Pritchard, 2003). The authors also suggest that stable isotope technologies and various microbial molecular tools have been employed to quantify remedial success and the organisms responsible for contaminant transformation and destruction within organic plumes. In this section, the influence of coupled processes on subsurface contaminant organic fate and transport are discussed. The section is divided into specific organic contaminant types with a focus on recent field-scale relevant examples. The section ends with a discussion of recent modeling strategies that incorporate coupled processes in the simulation of the fate and transport of nonaqueous phase organic constituents in subsurface environments.

5.2. Chlorinated solvents Nonaqueous phase liquids (NAPLs) that have a density greater than water and which tend to sink in subsurface environments are known as dense nonaqueous phase liquids (DNAPLs) with chlorinated solvents, such as trichloroethylene (TCE) and perchloroethylene (PCE), being some of the most common DNAPL constituents. The chlorinated solvents PCE, TCE, and other highly chlorinated aliphatic hydrocarbons (CAHs) such as 1,1,2trichloroethane (TCA) and chloroform (CF) are widely used in various industrial processes, mainly as solvents in dry-cleaning and semiconductor manufacture. They are highly toxic and are known or suspected carcinogens. Chlorinated solvents can be transformed into a variety of products within subsurface environments via biotic and abiotic processes (Aulenta et al., 2006; Lenczewski et al., 2003; Lorah and Voytek, 2004; Vogel et al., 1987). Most CAHs do not support microbial growth under aerobic conditions; however, many are anaerobically transformed to less chlorinated or nonchlorinated compounds through dechlorination reactions. Nonchlorinated products are typically harmless and many of the less chlorinated daughter products of dechlorination are biodegraded under aerobic conditions as well. Two distinct microbial-mediated reductive dechlorination reactions exist: hydrogenolysis and dichloroelimination (Aulenta et al., 2006) where hydrogenolysis (i.e., reductive dechlorination) involves the replacement of chorine with hydrogen with a net input of one proton and two electrons from an external donor; whereas, dichloroelimination results in the replacement of the chlorine substitutes and the formation of a double bond between the two carbon atoms, with an input of two electrons from an external donor. Most known bacterial isolates that utilize CAHs as terminal electron acceptors require H2 as an electron donor, and numerous organic fermentable substrates such as lactate, glucose, ethanol, and so on can be used as a source of H2 for enhancing in situ dechlorination. A drawback of using these substrates is the possible accumulation of fermentation products and the

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DNAPL residual Vadose zone Capillary fringe Water table

DNAPL as separate fluid phase Dissolved DNAPL in ground water Vapors emanating from DNAPL Clay layer

Ground water flow direction

Bedrock After NRC, 1994

Figure 12 Schematic illustration of DNAPL transport through the vadose zone and entry into the groundwater. As DNAPL migrates downward through the vadose zone it leaves residual saturation behind, until it reaches the water table. Pooling occurs on restrictive layers (e.g., clay layers) causing lateral spreading (from http://pangea. stanford.edu/research/hydro/).

generation of large amounts of biomass in the subsurface that can modify groundwater hydrologic flow paths and induce porosity clogging. As mentioned above, PCE and TCE form DNAPL that sinks through permeable soil or aquifer material until a nonpermeable or restrictive layer (e.g., nonuniform soil texture) occurs resulting in the formation of DNAPL pools (Fig. 12). Some of the DNAPL may volatilize to the atmosphere or become trapped in soil pores, while other portions of the DNAPL may be sorbed on soil particles or partitioned into organic matter (Bollag et al., 1980; Schwarzenbach and Giger, 1985). During DNAPL transport, hysteretic capillary forces result in a complex pattern of discontinuous globules of organic liquid in the pore structure of the subsurface. Entrapped DNAPL mass dissolves slowly into flowing soil and groundwater and thereby acts as a long-term source. Because of this, remedial technologies have used alcohol and surfactant flushing to enhance the recovery of the contaminant organic (Christ et al., 2005; Londergan et al., 2001; Ramsburg et al., 2004). As might be expected, effective dechlorination by microorganisms acts to drive the dissolution of DNAPL by reversing the gradient that causes DNAPL formation (Yang and McCarty, 2000). This microbial-enhanced DNAPL dissolution is effective as long as toxic byproducts, such as vinylchloride (VC), do not accumulate in the subsurface. The most effective means of DNAPL removal from the subsurface is to take advantage of coupled processes where aggressive physicochemical processes are used to remove the bulk of the solvent mass and bioremediation is used as a polishing step to the

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contaminant mass flux emanating from the remaining DNAPL. Successful field-scale bioremediation of chlorinated solvents requires careful design, operation, and monitoring of electron donor additions, hydrological and geochemical conditions, and the propensity for contaminant reduction (Morse et al., 1998). Lenczewski et al. (2003) investigated the influence of coupled processes on the fate and transport of chlorinated solvents in a fractured shale groundwater environment on the Oak Ridge Reservation in eastern Tennessee, USA. The field study offered an excellent example of how natural microbial attenuation processes transformed TCE into a variety of endproducts depending on the spatial and temporal changes in hydrology and geochemistry. The field site consisted of a 50-m long transect of multilevel groundwater sampling wells that followed strike parallel geology and extended from numerous waste burial trenches to a seep exiting into a perennial stream. The wells were situated within either a fast flowing fracture regime or a slow flowing matrix regime and the spatial and temporal variability of TCE, 1,2-DCE, VC, ethylene, ethane, methane, and various geochemical redox indicators were quantified as a function of time. The concentration of organics followed the general trend 1,2-DCE>ethylene>VCTCE with TCE concentrations being 10-fold higher in the waste trenches relative to downgradient sampling wells, whereas VC, 1,2-DCE, and ethylene concentrations in the waste trenches were similar or slightly higher than the downgradient groundwater monitoring wells. TCE concentrations disappeared within 10 m from the end of the waste trenches and 1,2-DCE disappeared just prior to the seep, 50 m downgradient the trench source. VC and ethylene were still present at the seep, with ethylene showing peak concentrations at this locale. These results indicated that anaerobic reduction of the chlorinated organics was occurring. In addition, the presence of high concentrations of methane throughout the site was also an indication of anaerobic metabolism (Lenczewski et al., 2003). The accumulation of VC in many locations was consistent with the notion that conversion of VC to ethane is usually the rate limiting step during reductive dechlorination of chlorinated solvents (Ballapragada et al., 1997). Another interesting finding at the site was that the concentration of chlorinated organics was slightly higher in the matrix relative to the fracture regime, whereas the concentration of ethylene was lower in the matrix relative to the fracture regime. These results suggest that the more hydrologically active fracture regime was slightly more effective in the anaerobic reduction of the chlorinated organics. Temporal variability of TCE and its degradation products was slight, with a general increasing concentration trend of chlorinated organics and dissolved gases as the site hydraulic gradient increased (a response to increased storm events during the winter and spring months). This may imply that the intrinsic bioremediation scenario at the site was less effective at higher discharge rates. Measurements

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of redox potential at the site indicated that iron-reduction, sulfate reduction, and potentially methanogenesis were occurring and are conducive to dechlorination of TCE. Bacterial enrichments of groundwater samples revealed the presence of methanotrophs, methanogens, iron-reducing bacteria, and SRB, all of which have previously been implicated in anaerobic biodegradation of TCE. 16S ribosomal DNA (rDNA) sequences from DNA extracted from groundwater were similar to sequences of organisms previously implicated in the anaerobic biodegradation of chlorinated solvents. The combined data strongly suggest that anaerobic dechlorination of TCE to VC and ethylene was occurring (Lenczewski et al., 2003). The presence of methane oxidizers (methanotrophs) also suggested possible zones of oxygenated groundwater, which was confirmed with on-site DO measurements. Groundwater DO was found to increase during the winter and spring months when the site hydraulic gradient increased due to oxygenated recharge from atmospheric precipitation during this period. Thus, a second biological removal mechanism of chlorinated organic compounds may have been occurring at the site, which involved the oxidation of TCE to ethane via methane oxidizing bacteria. Since both ethane and VC-ethylene are all present as possible degradation products of the TCE, it is probable that both mechanisms were operative. Skubal et al. (1999) investigated temporal changes in redox zonation at a mixed hydrocarbon/solvent contaminated aquifer in an effort to quantify the propensity for TCE natural attenuation in situ. Predominant TEAPs as measured by dissolved hydrogen, suggested temporal variations in reoxygenation along the plume transect. It was postulated that the intrusion of oxygen was possibly due to recharge, fluctuations in the water table, and/or microbial activity. Microbial analyses revealed a correlation between bacterial phylogeny, TEAP, and groundwater hydrogen concentrations. An increase in the water table and evidence of methanogenesis corresponded to an order of magnitude increase in archaeal 16S rRNA relative to when it was unsaturated (creation of capillary fringe). Spatial and temporal variations in TEAPs and microbial community structure suggest that the potential for TCE dechlorination varies seasonally within the plume, with reductive reactions (formation of DCE and VC) more likely in the shallow saturated zone or the capillary fringe during wet cycles, and aerobic co-metabolism of TCE and its products more likely in the deep aerobic subsurface or vadose zone where it could be supported by organic co-contaminants or methane from methanogenesis. Nevertheless, natural dechlorination processes at the site were limited despite the abundance of electron donor and C sources. Significant vertical and horizontal variations in TEAP zonation and associated organic contaminant degradation processes have been noted in a variety of settings (Christensen et al., 1994; Norris et al., 1994). At a sewage-effluent plume in Cape Cod, MA, the vertical dispersivity was found to be insignificant relative to the longitudinal dispersivity

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(Garabedian et al., 1991; LeBlanc et al., 1991). This circumstance limited vertical mixing and allowed for a sharp gradient in oxygen and other solutes within the subsurface over long time periods (Smith et al., 1991) thus influencing contaminant fate and transport processes in the different zones. McGuire et al. (1999) also observed significant temporal variations in TEAP zonation within a mixed fuel/chlorinated solvent plume. Simultaneous changes in microbial community structure were noted during the same time period as the TEAP shifts, with methanogen abundance being highest where methanogenesis and sulfate reduction were the predominant TEAP (Haack and Reynolds, 1999; Reynolds and Haack, 1999). Song et al. (2002) used a time-series stable C isotope technique to monitor the degradation of groundwater TCE and its by-products at the Idaho National Laboratory in western USA following subsurface biostimulation using lactate. Large kinetic isotope effects were observed during the dechlorination process which indicated a microbially mediated reaction scenario. The observed changes in the 13C/12C ratio indicated microbialmediated biodegradation of the VOCs, since a constant isotopic ratio would be more indicative of geochemical and hydrological impacts on VOC concentration changes. Morrill et al. (2005) also observed substantial C isotope enrichment in c-DCE at a DoD contaminated site in San Antonio, TX, and the authors were able to calculate the rate of VOC transformation using the stable C isotope technique. Similarly, Chu et al. (2004) suggested that VOC compound-specific isotopic fractionation could assist in determining whether aerobic or anaerobic degradation of VC and c-DCE occur during in situ reductive dechlorination; however, metabolic versus cometabolic reactions could not be distinguished since their isotopic fractionation changes were too similar. Lee et al. (2007) studied stable C isotope fractionation of chloroethenes by dehalorespiring isolates and found that a wide range of isotopic enrichment factors were associated with functionally similar and phylogenetically diverse organisms. Because of this, the authors cautioned that although compound-specific isotope fractionation is a powerful tool for evaluating the progress of in situ bioremediation in the field, care must be exercised when applying enrichment factors for the interpretation of dechlorination results. Numerous investigations have shown that fluctuations in recharge to shallow contaminant plumes can create temporal variability in geochemical conditions that are reflected in microbial population changes. Recharge events that deliver electron acceptors such as O2, NO3, SO4, and Fe(III) to anaerobic, contaminated subsurface environments are likely important considerations for assessing the propensity for organic contaminant natural attenuation (Vroblesky and Chapelle, 1994). McGuire et al. (2005) noted recharge-induced geochemical changes in a sandy aquifer contaminated with waste fuels and chlorinated solvents. Multiple regression analysis indicated that dominant chemical associations and their interpreted

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processes (anaerobic and aerobic microbial processes, mineral precipitation and dissolution, and temperature effects) did not change significantly during spring time recharge events; however, the relative importance of each of the processes within the contaminated plume did in fact change. The authors found that after recharge events, the overall importance of aerobic processes increased and during anaerobic periods the zones with multiple electron accepting processes (TEAPs) likely occurred in the same aquifer unit. It was determined that recharge effects on TEAPs occurred primarily at the interface between infiltrating recharge water and the aquifer (capillary fringe) where rising water table elevations may have enhanced the availability of Fe(III)-oxide coatings as electron acceptors for metal-reducing microorganisms. This hypothesis is supported by Lovley and Anderson (2000) who demonstrated that Fe(III)-reducing bacteria can be effective agents in removing aromatic hydrocarbons from groundwater under anaerobic conditions. The investigations of McGuire et al. (2005) make clear that an understanding of site geology, hydrology, and hydrogeochemistry are required to avoid misinterpreting TEAP zonation and its impact on organic contaminant biodegradation. A good example is Yager et al. (1997) who delineated the hydrogeochemical setting of a fractured dolomite aquifer that was contaminated with chlorinated ethenes. Methane and sulfide analysis in groundwater wells suggested methanogenesis or sulfate reduction was a possible TEAP; however, both degradation pathways were discounted using hydrogen gas analyses and recognizing that the source of methane and sulfide was from deeper noncontaminated portions of the aquifer. Although the transient nature of water levels, flow directions, availability of terminal electron acceptors, and contaminant concentrations within chlorinated solvent plumes has been recognized (Christensen et al., 2000; NRC, 2000), temporal variations in aquifer microbial community structure, and the factors that might govern temporal variations, are not well studied in contaminated groundwater. Haack et al. (2004) investigated spatial and temporal changes in microbial community structure associated with recharge-induced chemical gradients in a contaminated aquifer containing waste fuels and chlorinated solvents (McGuire et al., 2002, 2005). Community amplified ribosomal DNA restriction analysis (ARDRA) using 16S rDNA primers and denaturing gradient gel electrophoresis (DGGE) using 16S rDNA primers indicated that (1) communities in the middle of the aquifer where anoxic/contaminated conditions occurred were similar regardless of recharge, (2) communities at the greatest aquifer depths were similar to those in uncontaminated environments after extended recharge, and (3) communities changed in the upper and lower depths of the aquifer during extended periods of low recharge. General aquifer geochemistry was found to be quite important as was TEAPs with regard to the spatial and temporal variability of microbial communities within the aquifer (Haack et al., 2004). Numerous other investigators have also observed significant

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within-plume microbial heterogeneities at contaminated sites containing chlorinated solvents (Bekins et al., 2001; Christensen et al., 2000; Cozzarelli et al., 2000; Davis et al., 2002; Dojka et al., 1998; Haack and Bekins, 2000; Kinner et al., 2002; Madsen, 2000; Pickup et al., 2001). The propensity for microbial-mediated dechlorination of contaminant organic solvents in the presence of bioavailable groundwater sulfate has resulted in a variety of conflicting findings. Investigations of subsurface hydrology-induced geochemical impacts on the degradation of chlorinated solvents indicated that the presence of sulfate influenced dechlorination via no inhibition (El Mamouni et al., 2002; Hoelen and Reinhard, 2004), partial inhibition (Cabirol et al., 1998; Townsend and Suflita, 1997), or complete inhibition (Nelson et al., 2002; Warner et al., 2002). The lack of dechlorination during sulfate-reducing conditions may result from several factors including (1) competition for electron donor by SRB, (2) dechlorination enzyme inhibition by sulfate, (3) use of the sulfate as a terminal electron acceptor versus the CAHs, and (4) scenarios conducive to larger populations of SRB relative to dechlorinators and thus reaction kinetics toward sulfate reduction versus dechlorination. Similar situations may also apply to the competition for H2 in the presence of nitrate and Fe(III)-reducing conditions (Aulenta et al., 2006). Dybas et al. (1998, 2002) conducted plot- and field-scale bioaugmentation experiments designed to demonstrate the remediation of nitrate and carbon tetrachloride in an aquifer at Schoolcraft, MI. Pseudomonas stutzeri (strain KC) was utilized in the endeavor since it is a denitrifying bacterium that degrades carbon tetrachloride (CT) to CO2 and other inert compounds (Criddle et al., 1990) without producing CF. Subsurface activity of the organisms was maintained by adjusting the pH of the groundwater to more alkaline conditions and using pulsed additions of acetate to the groundwater followed by additions of acetate-free water. Significant losses of both nitrate and CT were observed. Uniform efficiencies of nitrate and CT removal over a 15-m vertical depth profile were observed despite significant variability in groundwater hydraulic conductivity.

5.3. Hydrocarbons 5.3.1. Crude Crude oil, also known as petroleum, is a naturally occurring NAPL within geologic Earth deposits and consists primarily of a complex mixture of alkane hydrocarbons of various lengths ranging from approximately C5H12 to C18H38. The largest quantities of petroleum are used primarily for producing fuel oil and gasoline with nearly 85% of the hydrocarbons present in petroleum being converted into energy-rich fuels, including gasoline, diesel, jet, heating, and other fuel oils and liquefied petroleum gas. Petroleum is also used in the production of many pharmaceuticals

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products, solvents, fertilizers, pesticides, and plastics. With the notion of the world’s oil supplies beginning a downward trend, coupled with current-day stress factors concerning the world’s clean water supply, the presence of oil has significant social and environmental impacts ranging from routine activities such as seismic exploration and drilling to accidents and polluting wastes. Oil spills on land and water bodies are mostly caused by accidents involving tankers, barges, pipelines, refineries, and storage facilities and are caused by human error or carelessness, and sometimes by natural disasters such as hurricanes or earthquakes. Cozzarelli et al. (1999) investigated scale-effects on biogeochemical reactions in a physically and chemically heterogeneous aquifer that was contaminated with gasoline. The aquifer was composed of two hydrologic units with a shallow local aquifer of perched water and an underlying regional sandy aquifer. Vertical heterogeneity was pronounced as reactive groundwater species concentrations varied by an order of magnitude, which significantly impacted the propensity for gasoline biodegradation. Microbially mediated degradation of hydrocarbons was noted to vary over short vertical distances and time, and Cozzarelli et al. (1999) found that anaerobic processes dominated within the low-permeability clay units with nitrate reduction and aerobic hydrocarbon degradation occurring to a greater extent in the more permeable sandy layers where the availability of electron acceptors was plentiful. Because of the limited availability of electron acceptor in the low-permeability layers, hydrocarbon degradation was limited relative to the more permeable sand layers. Degradation processes were still evident in the lower hydrologically conductive clay layer and were linked to the presence of sulfate and iron reduction within this unit. The authors noted that the chemical effects resulting from the microbial degradation of the hydrocarbons led to discrete zones where secondary minerals, such as iron-sulfide may precipitate from solution. Vertical heterogeneity at the site was such that small-scale geochemical changes had to be quantified in order to evaluate changes in biogeochemical processes with depth, and the impact of hydrologic processes was different for the perched water regime versus the regional aquifer due to different hydrodynamics of the two zones. Cozzarelli et al. (1999) found that recharge water entering the perched water was depleted in oxygen and nitrate as it mixed with contaminated groundwater of the shallow, higher permeability zone. Thus, recharge events can be a significant driver of groundwater redox changes, especially in organic contaminated aquifer that are often anaerobic (McGuire et al., 2000; Vroblesky and Chapelle, 1994). Hydrocarbons that concentrate near the capillary fringe serve as abundant electron donors for microbial respiration; however, microbial activity is often limited by the availability of electron acceptors. In reduced environments, recharge events can initiate changes in TEAPs by providing an influx of electron donors such as oxygen, nitrate, and sulfate (Vroblesky and Chapelle,

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1994). Microbial terminal electron processes are often examined for natural and enhanced biodegradation of contaminants (Baedecker et al., 1993; Bjerg et al., 1995; Chapelle et al., 1996; Levine et al., 1997; Lyngkilde and Christensen, 1992; Vroblesky et al., 1997); however, it is often difficult to distinguish between the direct influence of recharge on TEAPs versus dilution of electron donors (organic contaminants and dissolved hydrogen gas) when recharge mixes with indigenous groundwater. As capillary fringe zones are frequently aerobic due to the intrusion of oxygen from recent recharge, many of the aromatic compound degrading genotypes present in groundwater (Hosein et al., 1997; Stapleton and Sayler, 1998; van der Meer et al., 1998) are linked to oxidative processes requiring molecular oxygen and, therefore, are only active in oxygenated areas such as the plume fringe (Davison and Lerner, 2000). At a crude-oil spill site near Bemidji, MN, Essaid et al. (1995) noted variations in hydrologic flow paths due to restricted recharge through nonaqueous oil bodies. Flow path variations resulted in large changes in the overall depth of the anaerobic portion of the plume, where ferric iron reduction and methanogenesis were the dominant TEAPs. Bekins et al. (1999) found that in regimes where methanogenic conditions were prevalent at the site, a shift in the number and types of solid phase culturable organisms was also present. Methanogenic intervals were noted to have an increased number of methanogens and heterotrophic fermenters and fewer iron reducers. Haack et al. (2004) found that a particular type of microbial community within various locations of the Bemidji aquifer was equally influenced by aquifer geochemistry and the dominating terminal electron acceptor that were present. At the same study site in Bemidji, MN, Bekins et al. (2001) investigated controls of coupled processes on the spatial distribution of subsurface microbes and its impact on the propensity of natural attenuation at a crude oil spill site. Microbial populations were analyzed along with aquifer permeability, pore-water chemistry, nonaqueous phase oil content, and extractable sediment Fe-oxides. Vertical profiles through anaerobic portions of the aquifer exhibited regimes that had progressed from iron-reducing conditions to methanogenesis. Methanogenic conditions existed both within the nonaqueous phase contaminated regimes and below the oil where hydrocarbon concentrations were high and aquifer permeability was high. These results suggested that advective transport played an important role in which zones first supported methanogenic activity. It was also found that Fe(II) concentrations and proximity to the water table were also important factors in controlling regimes of methogenesis and hydrocarbon degradation. Sustained methogenesis was found only to occur below the lowest water table elevation during seasonally oscillating conditions of the capillary fringe. Bennett et al. (2000) investigated microbial controls of mineralgroundwater equilibria in a petroleum-contaminated aquifer. The authors investigated the relationships between mineral alteration, groundwater

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chemistry, and microbial colonization. Scale effects were observed for microbial influences on mineral weathering processes at (1) the macroscale through perturbations of groundwater geochemistry and thus mineralwater equilibria, and (2) the microscale where attached organisms influence local-scale mineral-water equilibria releasing trace nutrients from dissolving minerals. In a similar manner, Thorn and Aiken (1998) investigated contaminant hydrocarbon dynamics in an unconfined glacio-fluvial aquifer and found that during oxic conditions, carbonate dissolution was controlled by heterotrophic respiration resulting in the production of excess carbon dioxide. During anoxic conditions, calcite overgrowths occurred on calcite mineral surfaces and were thought to be due to consumption of groundwater acidity by iron-reducing bacteria. At the microscale, microbes tended to preferentially colonize feldspars in anoxic regimes apparently due to the availability of P in apatite inclusions, and feldspar dissolution was found to be accompanied by precipitation of secondary minerals. As groundwater oxygen increased downgradient the oil pools, aerobic microorganisms became dominate resulting in carbonate dissolution and Fe(III)-oxide precipitation and microbial colonization did not appear to be an important mechanism under aerobic conditions. 5.3.2. Btex Benzene, toluene, ethylbenzene, and xylene (BTEX) are a group of volatile organic compounds (VOCs) found in petroleum hydrocarbons and other common environmental contaminants that can have major human health effects and target the central nervous system. BTEX compounds are common groundwater and soil contaminants that occur near petroleum and natural gas production sites, gasoline stations, and other storage areas containing petroleum-related products. They are considered one of the major causes of environmental pollution because of widespread occurrences of leakage from underground petroleum storage tanks and spills at petroleum production wells, refineries, pipelines, and distribution terminals (Fries et al., 1994). It is estimated that more than 35% of the 1.4 million gasoline storage tanks in the USA are leaking into subsurface soil and groundwater (Harwood and Gibson, 1997) resulting in extensive belowground BTEX contaminant plumes (Fig. 13). BTEX compounds can undergo aerobic metabolism which includes biodegradation by a variety of pathways. Whereas benzene is degraded to a substituted catechol, toluene degradation may follow many separate biodegradative pathways. Many microbially mediated pathways also exist for ethylbenzene which can be degraded to 3-ethylcatechol, and xylene compounds can be metabolized to mono-methylated catechols. Anaerobic pathways of BTEX biodegradation are also prevalent in subsurface environments depleted of DO (Heider and Fuchs, 1997) with toluene and ethylbenzene biodegradation generating benzoyl-CoA as an intermediate,

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Drinking well

Gas tank

Volatilization Gas leak Mobile phase

Microbial degradation

Sorption on to soil Dissolved phase Groundwater flow (Q)

Figure 13 Schematic illustration of BTEX contamination resulting from leakage of gasoline from faulty and poorly maintained underground storage tanks. Once released to the environment, BTEX can volatilize (evaporate), dissolve, attach to soil particles, or degrade biologically (from http://www.envirotools.org/factsheets/btex.shtml).

which is the most common central intermediate of anaerobic aromatic metabolism (Heider and Fuchs, 1997). Kao et al. (2006) investigated the influence of coupled hydrologic, geochemical, and microbial processes on the propensity of natural attenuation processes to remediate a petroleum-hydrocarbon spill site. Numerous lines of evidence were used to suggest that natural biodegradation was the major factor observed in contaminant reduction, which included (1) significant depletion of DO, nitrate, and sulfate; (2) production of groundwater Fe(II), S2
, and CO2; (3) decrease in BTEX along the transport path coupled with limited spreading via dispersion; (4) increased alkalinity and microbial populations; and (5) preferential removal of key BTEX components along the transport pathway. As such, successful bioremediation of BTEX contaminants often depends on knowledge of the subsurface mineralogy and aqueous phase geochemistry. Multivariate statistics and artificial neural networks have been used to link geochemistry with microbial community analyses and thus the propensity for biodegradation (Feris et al., 2004; Lee et al., 2001). Maurer and Rittmann (2004) have shown that abiotic geochemical processes such as precipitation and dissolution of calcite and surface interactions with iron sulfide minerals are important in the destruction and attenuation of BTEX. At the Bemidji, MN, crude-oil spill site, Cozzarelli et al. (2001) found that groundwater redox dynamics and changes in Fe reduction had a pronounced influence on the behavior of a subsurface hydrocarbon plume. Pore-scale analysis indicated that the hydrocarbon plume had been growing over a two-decade period due to the depletion of solid phase

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Fe(III)-oxides. However, Lovley et al. (1989) showed bacteria catalyzed oxidation of aromatic hydrocarbons may be coupled to ferric-iron reduction in systems containing ferric oxide minerals. Thus, the depletion of ferric iron that was observed by Cozzarelli et al. (2001) may have also affected the development of other TEAP zones within the contaminated plume as xylene contaminants were shown to migrate along a thin layer within the aquifer that had undergone methanogenic conditions. At another crude oil contaminated site, Baedecker et al. (1993) found that hydrocarbon degradation was initially associated with Fe(III) reduction processes; however, as ferric iron was depleted (Tuccillo et al., 1999), methanogenic zones were formed in areas of high contaminant flux (Bekins et al., 1999). Cozzarelli et al. (2001) also noted that plume-scale observations differed from these smaller-scale observations since the largerscale observation suggested that the extent of the Fe(II) and BTEX plume had changed very little during the second half of the second decade of study. Richnow et al. (2003a,b) quantified in situ microbial degradation of aromatic hydrocarbons in a contaminated anoxic aquifer using geochemical isotopic fractionation. The isotopic fractionation technique confirmed xylene and dimethylbenzene biodegradation as well as the distinction between biodegraded aromatics and untransformed aromatics. Several other studies have also used the stable isotope technique for C and H to track the biodegradation of benzene and other aromatic hydrocarbon contaminants in groundwater (Gray et al., 2002; Griebler et al., 2004; Kuder et al., 2005; Mancini et al., 2003; Morasch et al., 2004; Steinbach et al., 2004). The technique is highly sensitive and informative since the elemental isotopic ratios will change during biodegradation due to preferential enrichment or depletion of one of the isotopes, whereas isotopic ratios remain constant in response to geochemical and hydrological impacts such as adsorption, dilution, and evaporation. 5.3.3. Coal-tar/creosote Coal-tar is a highly viscous liquid that is produced when coal is carbonized or glasified to make coke or coal gas, respectively. Coal-tar products are used in medicines to treat skin diseases such as psoriasis, and are used as wildlife repellents, insecticides, and fungicides. Coal-tar derivatives are also used for roofing, road paving, and coking. Coal-tars are complex mixtures of phenols, polycyclic aromatic hydrocarbons (PAHs), and various heterocyclic compounds. Coal-tar creosote is a thin oily liquid that is typically used as a wood preservative and is classified as a DNAPL since it has a density slightly greater than water. It may consist of as many as 200 different organic compounds and on average is composed of 85% PAHs by mass, 10% phenolic compounds, and 5% heteroaromatic type compounds (Mueller et al., 1989). Williams et al. (2001) investigated the hydro-bio-geochemical characteristics of a subsurface coal-tar distillate plume in a sandstone aquifer described by Bridge (1997). The authors found that redox conditions and

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the production of methane and CO2 suggested natural attenuation of the coal-tar plume; however, the kinetics of degradation were found to be slow. The presence of the electron acceptor sulfate in the plume suggested that methanogenesis was probably limited. Microbial characterization showed a diverse array of microbial communities that had the potential for both aerobic and anaerobic degradation of the coal-tar contaminant. The authors found that microbial activity was greatest at the leading edge of the plume and that degradation in the core of the plume was limited, possibly due to phenol toxicity. It was proposed that once the plume was hydrologically diluted due to groundwater dispersion, natural microbial attenuation of the hydrocarbons could proceed. Pickup et al. (2001) investigated the influence of coupled processes on the biodegradation potential of phenol and other tar acids in a contaminated aquifer in the West Midlands, UK (Thornton et al., 2001). The potential for phenol degradation was found to be influenced by the concentration of the contaminant and the total bacterial cell count that was present in the groundwater. The observed microbial activity complemented results obtained through chemical analysis, and when combined with hydrologic data, provided a realistic profile of plume effects that could be related to the potential for natural attenuation at the site. The authors stressed that microbial data suggesting favorable conditions for natural attenuation without accompanying chemical data may result in an incorrect assertion that microbial attenuation processes are operative (Stapleton and Sayler, 1998; Williams et al., 2001). King and Barker (1999) and King et al. (1999) investigated the influence of hydrological, geochemical, and microbial processes on the fate and transport of coal-tar creosote in the Canadian Forces Base (CFB) Borden site, located near Toronto, Ontario, Canada. The authors tracked a welldefined source of seven representative creosote compounds over a four-year period as they developed into a plume downgradient within the aquifer. It was noted that the different compounds within the common source showed markedly different patterns of plume development and that significant transformations in compound mass occurred during transport which impacted the behavior of the overall contaminant plume. Phenol was found to dissolve quickly from the source, thus migrating downgradient as a discrete slug. The phenol plume was nearly absent after 2 years owing to transformation by microbial degradation. The xylene plume was found to migrate to a maximum distance at around 2 years, after which time the plume receded back toward the source as the rate of xylene mass flux from the source decreased below the rate of xylene microbial degradation. Carbazole exhibited similar behavior as xylene, although the overall kinetic reactions controlling its migration were much slower. King et al. (1999) used several lines of evidence to support that the loss of contaminants were due to microbial degradation reactions. Geochemical redox indicators showed that DO and sulfate decreased in the groundwater within the

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plume while significant increases in dissolved Fe(II), Mn(II), methane, and aromatic acids were observed. Further, measurements of PLFA in the aquifer sediments indicated that microbial biomass and turnover rate were greater within the plume than adjacent to the plume, which is consistent with systems undergoing biodegradative processes. King et al. (1999) also found that the naphthalene plume continued to advance and increase in mass over the 4-year observation period suggesting minimal biodegradation. These results are consistent with Ramaswmi et al. (1994) who investigated the role of physical and chemical mass transfer processes on the biodegradation of PAH compounds that were derived for residual coal-tar that was present within microporous media. The authors found that the release kinetics of naphthalene from coal-tar to the bulk aqueous solution was more rapid than the biotransformation rates for this compound. In a similar manner, Broholm et al. (1999) investigated the transport of biodegradation of 25 organic compounds typical of creosote in a fractured clay till soil from Funen, Denmark. At low contaminant concentrations, significant biodegradation was observed for many of the compounds, with the presence of nitrate and oxygen enhancing the degradation process. Higher concentrations of creosote compounds resulted in little biodegradation and the contaminants were transported through the structured media in a similar manner as nonreactive Br. Thornton et al. (2001) investigated the impact of coupled processes on the distribution and natural attenuation of phenol, cresols, and xylenols in a deep Triassic sandstone aquifer that was contaminated by a historical coaltar distillation plant. Overlapping contaminant plumes existed at the site including phenols, mineral acids, and a highly alkaline condition that resulted from waste NaOH, with their distribution primarily related to historical source releases. The authors found that contaminant degradation was occurring via aerobic respiration nitrate reduction, Mn(IV) and Fe(III) reduction, sulfate reduction, methanogenesis, and fermentation, with accumulation of various products including inorganic carbon, organic metabolites, acetate, methane, dissolved hydrogen, and reduced forms of Fe, Mn, and S. Respiratory processes were found to be rate limiting with regard to the spatial distributions and dynamics of hydrogen and TEAPs, and the aerobic processes were thought to be controlled by the mixing of uncontaminated aquifer groundwater, rich in DO and nitrate, with the contaminant plume. Contaminant transformations by geochemical oxidation reactions were found to be minimal since mineral oxide and sulfate consumption was small relative to their total system mass. Overall biodegradation rates were found to be slow, but were expected to increase with time as contaminant concentrations decreased due to plume hydrologic dispersion and the increased efficiency of intruding DO from outside the plume. The authors suggested that hydrologic transport processes may exert a greater

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Aerial drift Adsorption

Atmospheric deposition Crop removal

Photodegradation

Ru

Volatilization

Chemical degradation

Adsorption desorption

no

ff

Biological degradation

Leaching

Ground water

Figure 14 Schematic illustration of various hydrological, geochemical, and microbial processes that influence the fate and transport of pesticides in terrestrial environments (from http://www1.agric.gov.ab.ca/$department/deptdocs.nsf/all/wat3350).

control on microbial-based natural attenuation of the plume as compared to geochemical factors such as aquifer oxidant availability.

5.4. Pesticides and herbicides Over the past several decades, extensive research has sought to provide an improved understanding and predictive capability of pesticide and herbicide fate and transport in surface water, groundwater, and the vadose zone (Bloomfield et al., 2006; Muller et al., 2007; Sarmah et al., 2004; Fig. 14). Pesticide use as of the year 2000 has increased 50-fold since 1950, and 2.5 million tons of industrial pesticides are now used each year to enhance agriculture production and decrease human disease carrying insects. The major source of nonfarming human exposure to pesticides is through diet and it is believed that long-term chronic exposures can increase the risk of cancer, infertility, and cause disruptions to the endocrine system and possible mutagenic effects. Vinther et al. (2001) investigated the impact of hydrological and geochemical processes on the microbial degradation of pesticides in loamy and sandy soils. Bacterial biomass, enzymatic activity, C utilization patterns, and

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pesticide mineralization were monitored. Bacteria biomass and activity, and C utilization in the macroporous portion of the loamy soil were higher than that of its surrounding matrix with the macroporous portion of the soil having a higher pesticide degradation potential relative to the surrounding matrix portion of the soil. The authors suggested that the higher abundance of nutrients and pesticides in the macropore channels relative to the matrix may have created a more favorable environment for microbial activity and the potential for pesticide degradation. Vinther et al. (1999) also found that macropore-type soils had higher concentrations of nitrate and DOC relative to matrix dominated soils, with the latter having fewer numbers of bacteria. Tariq et al. (2006) evaluated the influence of soil structure, temperature, soil water content, and microbial activity on the persistence of the pesticides carbosulfan, carbofuran, lambda-cyhalothrin, endosulfan, and monocrotophos in a sandy loam soil from Pakistan. Microbially mediated degradation of the pesticides was found to be kinetically controlled with the degradation rate enhanced by an increase in soil temperature and moisture, and the degradation rate slowed by conditions of limited organic carbon. Movement of the pesticides into deeper soil horizons was attributed to preferential hydrologic flow during storm events, where the resident time in the media was short, causing microbial degradation processes to be minimal due to their kinetic nature (Ghodrati and Jury, 1992; Jury et al., 1987; O’Dell et al., 1992). Bolduan and Zehe (2006) investigated the microbial degradation kinetics of the herbicide isoproturon in soil macro- and micropores within a soil catchment in SW Germany. The authors found that herbicide degradation kinetics within earthworm constructed soil macropores was as rapid as nearsurface organic-rich topsoils. This observation may have been the result of organic rich coatings that can develop within the macroporous channels. The authors also noted that herbicide degradation rates for the soil matrix that surrounded the macropores, was an order of magnitude lower than that observed in the macropore domains. This was attributed to the lower microbial activity that was present in the soil matrix (i.e., microporosity). This study stresses the importance of media structure on controlling the degradation rates and preferential transport of herbicides, and other organic contaminants, in macropores versus slow transport through micropores. Pivetz and Steenhuis (1995) investigated the influence of soil structure on the transport and biodegradation of the pesticide 2,4-dichlorophenoxyacetic acid (2,4-D). The authors stressed that preferential flow of pesticides in macropores can lead to a decreased residence time through the soil which can enhance the possibility of groundwater pollution. However, they point out that macropores may present a more favorable environment for biodegradation due to greater oxygen, nutrients, substrate supply, and higher microbial populations, particularly in earthworm burrows, relative to the

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soil matrix where micropores exists. Pivetz and Steenhuis (1995) noted that pesticide degradation rates within macropores was significantly greater than within micropores, presumably due to increased microbial activity and numbers in the larger pore type. Nevertheless, the authors found that both macropore and micropore flow paths resulted in pesticide degradation with the rate of biodegradation increasing with time in each flow path type. Muller et al. (2007) provided an extensive review of the influence of effluent agricultural irrigation on the fate and transport of pesticides in soil. Treated effluents for agricultural land use, otherwise known as reclaimed water, include municipal wastewater, farm effluents (dairy, piggery), and effluents from food and plant processing. These effluents typically have high concentrations of natural and synthetic DOC (e.g., natural humics vs surfactants and solvents) which can influence the geochemical nature and thus transport of pesticides. Muller et al. (2007) stressed the importance of soil properties on the transformation and transport of the organic contaminants and in particular the influence of DOC on pesticide mobility and biodegradation. Co-transport of pesticides via complexation with DOC may result in accelerated transport through soil by decreasing pesticide sorption, or the DOC may enhance pesticide biodegradation, thereby decreasing its mobility, by providing an energy source for microorganisms that are capable of pesticide degradation.

5.5. Modeling coupled processes involving organic constituents Numerous multicomponent reactive transport models involving nonaqueous phase constituents have been developed over the years that couple hydrodynamic transport with multiprocess, time-dependent geochemical and microbial reactions. The multiphase flow and multicomponent reactive transport simulator, PARSim has been linked to a mixed chemical kinetic and equilibrium model (KEMOD) to allow simulation of multiple flowing phases with a full complement of reactions (Arbogast et al., 1996). The model (RPARSim/KEMOD) allows for a more general, nonequilibrium phase transfer for KEMOD-style reactions where the reactants and products are in different phases. The model has been parallelized in order to enhance computational efficiency and the need to simulate larger-scale, more realistic environmental problems. Other kinetic-based models designed to deal with subsurface DNAPL issues tend to emphasize substrate-limited biodegradation. The EPA code, BIOPLUME III is a 2D contaminant transport model that couples DNAPL advective-dispersive transport with sorption, first-order decay, and biodegradation through instantaneous, zero-order, first-order, or Monod kinetics. The model is based on the USGS BIOMOC code where the hydrocarbon source and each active

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electron acceptor (e.g., O2, NO3–, Fe(III), SO42
, and CH4) are simulated as separate plumes. Another numerical simulator named MT3D99, which is an enhancement of MODFLOW, also couples advective-dispersive transport in soil systems with nonequilibrium sorption, time-dependent nonaqueous phase liquid dissolution, and rate-limited microbial processes. It considers BIOPLUME-type reactions, monad reactions, and daughter products, thus enabling the simulation of multispecies transport in a similar manner as the transport code RT3D. Kinetically limited hydrocarbon biodegradation using multiple electron acceptors and time-dependent transport of bacteria, electron acceptors, and hydrocarbons are explicitly considered. MT3D99 also maintains a dual porosity option, where the soil media is divided into an advective dominated mobile domain and a diffusion dominated immobile domain. An empirical first-order parameter accounts for mass transfer between the domains. Recently, Barry et al. (2002) reviewed modeling investigations on the fate of oxidizable organic contaminants in groundwater. A comprehensive modeling framework was specified, including geochemical reactions and interphase mass transfer processes such as sorption/ desorption, nonaqueous phase liquid dissolution, and mineral precipitation/ dissolution, all of which can be equilibrium or kinetically controlled. As well, the framework was specified to simulate microbially mediated transformation/degradation processes and microbial population growth and decay. Microbial degradation reactions allowed for limitations based on the availability of nutrients and electron acceptors (i.e., changing redox states), as well as concomitant secondary reactions. Phanikumar et al. (2005) developed a 3D numerical model to describe microbial transport and biodegradation of CT at the Schoolcraft, MI, site (see Section 5.2 above for experimental details of this investigation). The model simulates transport and reaction of solution and sorbed CT, acetate, electron acceptor nitrate, mobile and immobile microbes, and nonreactive tracers (e.g., Br). Microbial processes included growth, decay, attachment, detachment, and endogenous respiration. The model was found to predict observed acetate and nitrate concentration profiles quite well; however, a lower CT degradation rate, relative to that determined in laboratory studies (Phanikumar et al., 2002), was needed to describe the CT concentrations observed in situ after the inoculation event. Essaid et al. (2003) used the USGS multicomponent solute transport and biodegradation code BIOMOC and inverse modeling code UCODE to simulate BTEX dissolution and biodegradation at a crude oil spill site in Bemidji, MN. Historical experimental data from 1986 to 1997 was used and the inverse modeling strategy successfully described the results when coupled transport and degradation processes were used and a single dissolution rate coefficient was used for all BTEX components. Model parameters consistent with subsurface coupled processes were used including hydraulic

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conductivity, dispersion, dissolution kinetics, and anaerobic microbial degradation rates. The calibrated simulations reproduced the general large-scale evolution of the plume, but did not reproduce small-scale spatial and temporal variabilities in concentration. It was suggested that anaerobic degradation removed 77% of the aqueous phase BTEX versus 17% for aerobic processes. Prommer et al. (2006) investigated field-scale reactive transport modeling strategies to simulate capillary fringe controls on natural attenuation of phenoxy acid herbicides (e.g., mecoprop MCPP) in a landfill plume. Attenuation processes were noted in transition zones between the anaerobic plume core and the overlying aerobic water body. The location of the transition zone was controlled by vertical transverse dispersion processes occurring downgradient the contaminant source term. Simulations involved a 2D vertical cross section to quantify the combined physical, geochemical, and microbial processes influencing the herbicide fate and transport processes. The capillary fringe regime was found to control the aerobic degradation of the phenoxy acids. Yabusaki et al. (2001) also used a multicomponent reactive transport code to assess the in situ destruction of chlorinated hydrocarbons by a Fe0 PRB. Both equilibrium and time-dependent hydrocarbon degradation, iron dissolution, secondary mineral precipitation, and a variety of complexation reactions were considered. Dominant precipitants in the PRB zone were Fe-oxides, siderite, aragonite, brucite, and iron sulfide which are PRB-mediated mineral phases observed in the experimental findings of Gu et al. (2002a) and Wilkins et al. (2006). Model predictions suggested that mineral precipitants could account for a 3% increase in mineral volume per year which could have significant implications for the long-term performance of subsurface barriers of this type. The authors suggested that the inclusion of transport (hydrodynamics) within the simulation was paramount to understanding the interplay between rates of transport and rates of reactions and therefore a more accurate assessment of barrier longevity and performance and the understanding of mechanisms responsible for contaminant destruction and immobilization. Recently, Lichtner and Wolfsberg (2004), Hammond et al. (2005), Lu and Lichtner (2005), and Mills et al. (2005) described a newly developed high-performance simulator, PFLOTRAN, which is a massively parallel 3D multiphase, multicomponent simulator of subsurface flow and reactive transport. Since PFLOTRAN was built on top of PETSc, the Portable, Extensible Toolkit for Scientific Computation (Balay et al., 1997), the code exhibits excellent performance on the world’s largest-scale supercomputers, such as that at Oak Ridge National Laboratory which is a Cray XT3/4 system consisting of 11,706 dual-core Operon processor nodes. The PFLOTRAN code solves a system of mass and energy conservation equations for a number of phases including water, supercritical CO2, black oil, and a

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gaseous phase. It describes coupled thermal, hydrologic, and chemical processes in variably saturated, nonisothermal, porous media in multiple spatial dimensions. The code has been written with parallel scalability in mind and can run on single processors to the largest massively parallel computer architectures. Many enhancements are planned for PFLOTRAN over the next several years, such as methods to upscale soil processes that are typically nonlinear and scale dependent, in an effort to simulate large-scale (watershed and basin scale) contaminant fate and transport scenarios (Mills, ORNL, personal communication).

6. Concluding Remarks This chapter has shown that subsurface contaminant fate and transport processes are invariably impacted by coupled hydrological, geochemical, and microbial processes. Many times the assessment of process contribution to the overall flux of contaminants is difficult since the interactions of the processes are often nonlinear, time dependent, and complex. Published multiscale research endeavors in these areas over the past several decades, such as those described above, have provided excellent scientific knowledge and prediction of contaminant fate and transport issues that can be used for decision-making and assessment of natural attenuation or manipulative remediation strategies. This chapter has focused on the impact of coupled processes on legacy waste issues that have plagued society for many years. It is the author’s belief that the continued investigation of subsurface coupled processes is imperative in order to deal with future environmental issues of global concern. Because of a ‘‘business as usual’’ mentality among the industrial societies, four main topical areas are perceived as standouts with regard to imparting adverse environmental consequences on the world over the next century ( Jack Parker, University of Tennessee, personal communication, 2007). These topical areas include (1) energy availability, (2) climate change, (3) water quality and supply, and (4) land use change. Each one of these topical areas not only exhibits huge potential environmental impacts upon the earth’s terrestrial and aquatic ecosystems, but they potentially will have massive economic and societal implications on the world human population for many years to come. Continued improvements to our conceptual and predictive understanding of these environmental issues will require fundamental knowledge of the coupled processes that dictate behavior responses of associated contaminants in the geosphere. Each of the four perceived environmental issues listed above are briefly discussed below with the intention of providing the reader with some complex environmental issues that are in need of investigation and resolution.

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The notion of the world’s oil supplies beginning a downward trend have prompted the consideration of alternative fuel sources and types (Altun et al., 2006; Hill, 2007; Kok, 2002; Petroll and Tveiten, 2007; Sanderson et al., 2006; Smeets et al., 2007). As easily accessible sources of oil become depleted, alternative crude sources such as those found in deep shale formations will become more attractive. However, the environmental consequences of such action are unpredictable, with current technologies creating vast regions of organic contaminated soil and imparting massive greenhouse effects which soon may be a tremendous financial liability (Kahru et al., 2002; Pollumaa et al., 2001). In addition, in situ shale oil extraction creates large thermal, pressure, and hydraulic gradients that may allow organic and inorganic contaminants to escape from the retort zone into groundwater and surface water sources (Kahru and Pollumaa, 2006). Other alternative energy sources such as biofuels and an increased use of nuclear power plants may also become much more attractive as crude production declines (Boczar et al., 1998; Hahn-Hagerdal et al., 2006; Petroll and Tveiten, 2007). Each of these energy sources imparts its own potential environmental impacts (Converse, 2007; Hill, 2007; Jonsson and Hillring, 2006), where spent-fuel nuclear waste disposal issues continue to plague society in our current environment. Subsurface contaminant fate and transport issues associated with future energy production strategies will most certainly be an issue of global environmental concern. Climate change as a result of anthropogenic emissions is a strongly debated topic and one that most certainly will not be resolved until it is far too late for immediate corrective action (Alcamo et al., 2005; Oppenheimer and Petsonk, 2005). Lal (2007) discusses the daunting environmental consequences of the emerging carbon civilization on the planet Earth. Because of political agendas and ignorance toward technical and scientific realities related to what appear to be certain indicators of climate change, short- and long-term consequences of climate change on the world’s environment are largely unknown. Not only will aboveground processes be influenced by changing climates, but belowground processes as we currently understand them will be altered as well. Belowground temperature increases may accelerate organic C turnover rates and possibly disrupt agricultural and silviculture productivity with unpredictable consequences to the environment. Subsurface solute fate and transport issues associated with future climatic change impacts will again most certainly be an issue of global environmental concern. Future shortages in water supply and quality are foreseen even without the influence of climate change ( Jury and Vaux, 2005; Lal, 2007; Tao et al., 2003). Stress on the world’s water supply will severely impact agriculture and energy productivity as well as human health and quality of life (Vitale et al., 2003) which will in turn create unpredictable environmental consequences of global concern. New strategies will be necessary to optimize

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water use and management of reservoirs and watersheds, and advancements in science and technology will be needed to optimize water recycling and reuse (Arnell and Delaney, 2006; Shelef and Azov, 1996). Groundwater and surface water quality issues will be of increasingly significant concern with regard to human and animal health, agricultural production, and potential disastrous ecological change due to toxins and salinity effects. Important water management problems can only be adequately addressed from a holistic view of the water cycle in a world with hydro-geo-bio- complexity. Understanding and predicting subsurface solute fate and transport processes will be an important component when addressing such water quality issues. Land use change is also envisioned to impart adverse environmental consequences on the world over the next century ( Johnson et al., 2007; Lal, 2007; Liu and Chen, 2006; Tomich et al., 2004). As above- and belowground environments succumb to the aggressive influence of economic productivity, severe environmental impacts to the world’s hydrologic cycle and biodiversity functions are foreseen. The loss of massive sectors of aboveground biomass and diversity, increased soil erosion, and conversion of wetlands into human habitats are examples of changing land characteristics that could have huge impacts on the global hydrologic cycle which will in turn impact global biogeochemical processes in terrestrial and aquatic ecosystems (Liu and Chen, 2006; Zhao et al., 2006). Emerging infections of humans and wildlife are often related to land use change as evolutionary relationships between hosts and pathogens are altered ( Johnson et al., 2007). Ecological changes in aquatic systems typically involve eutrophication which broadly enhances infection and pathology of human and wildlife parasites. Vast municipal landfills, covering large tracks of land, plague many underdeveloped countries and pose a severe threat to human health since dump sites are unrestricted, unmanaged, and publicly accessible. Such dump sites are often ravaged for basic necessities required for survival or for material that is saleable in order to generate income, thereby exposing unsuspecting individuals to unacceptable levels of heavy metals and toxins that are present in the dump site soil, water, and air. It is estimated that 25% of all deaths in poor countries is linked to environmentally related illnesses. The influence of land use change on subsurface solute and contaminant fate and transport processes is therefore an area of concern since disruption of the soil structure, chemical and microbial environment, and increased propensity for soil organic C loss and erosion will create an environment in severe nonequilibrium with a pathway toward stability that is currently unpredictable. Scientific investigation of the environmental consequences of future energy production, climate change, water quality and supply, and land use issues will require an improved experimental and predictive capability of coupled subsurface processes that are spatially and temporally variable across scales ranging from molecular to basin levels. The demand for energy,

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resources, and potable water will require a keen understanding of the relationships between hydrological, geochemical, and biological processes in subsurface environments. As stated by Geesey and Mitchell (2008), continued and expanded research in these areas is necessary for the (1) protection of world’s aquifers and surface waters from contamination, (2) appropriate disposal of hazardous waste, (3) protection of ecosystems from chemical, radioactive, or biological contamination, (4) sustained agricultural productivity, (5) identification and wise use of energy and mineral resources, and (6) mitigation of global climate change.

ACKNOWLEDGMENTS This research was sponsored by the U.S. Department of Energy (DOE), Office of Science, Biological Environmental Research, Environmental Remediation Sciences Division (ERSD). The Environmental Sciences Division (ESD) of the Oak Ridge National Laboratory (ORNL) is managed by UT-Battelle, LLC, for the U.S. Department of Energy under Contract DE-AC05-00OR22725. The author wishes to thank Dr. Donald L. Sparks, editor of this book, for the opportunity to prepare the following chapter, and to thank Beth Bailey of the ESD for reference compilation and editing. The author is also grateful for the financial and moral support provided by the DOE ERSD technical representatives Todd Anderson, Paul Bayer, David Lesmes, and Michael Kuperberg.

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Uptake and Fate of Perchlorate in Higher Plants Angelia L. Seyfferth and David R. Parker Contents 1. Introduction 1.1. Perchlorate in the environment 1.2. Toxicological issues 1.3. Objectives of review 2. Perchlorate Levels in Plants 2.1. Plants growing on highly contaminated sites 2.2. Market surveys 3. Perchlorate Uptake Studies 3.1. Phytoremediation 3.2. Mechanistic studies 4. Conclusions and Future Research References

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Abstract Perchlorate recently emerged as a drinking water contaminant, and its high water solubility and relatively unreactive nature under ambient conditions make it a persistent and mobile contaminant. Perchlorate interferes with iodine uptake by the human thyroid, potentially leading to adverse effects on normal metabolism and cognitive function in sensitive groups. There is an interest in the fate of perchlorate in higher plants because phytoremediation is a promising remediation option, and because there is mounting concern about human exposure to perchlorate from contaminated produce. Perchlorate is taken up by many higher plants and is mainly stored in leaves, although perchlorate is also found in smaller quantities in fruits, stems, seeds, and roots. Transpiration plays a key role in delivery of perchlorate to plant roots, and it appears that perchlorate traverses the root cell membrane via the same ion transporter as for nitrate. Certain plants are able to metabolize high concentrations (mg/liter) of perchlorate within their leaves (phytodegradation) by way of chlorate and chloride intermediates to chloride, although this process Department of Environmental Sciences, University of California, Riverside, California 92521 Advances in Agronomy, Volume 99 ISSN 0065-2113, DOI: 10.1016/S0065-2113(08)00402-1

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is slower than ex situ microbial degradation in the root zone (rhizodegradation). However, it is currently unknown whether higher plants will metabolize smaller quantities (i.e., mg/liter concentrations) of perchlorate in vivo. More research is needed in order to determine the extent of translocation, phytodegradation, and exudation of perchlorate and its metabolites, as well as the ability of modified stems and roots to store perchlorate.

1. Introduction In the last 10 years, perchlorate (ClO
4 ) has emerged as a controversial environmental contaminant. Its detection in ground and surface waters has increased with the advent of new analytical techniques and, more recently, it has been found in a range of concentrations in vegetation (El Aribi et al., 2006; FDA, 2005; Sanchez et al., 2005a,b, 2006), organisms (Smith et al., 2004, 2006), beverages (El Aribi et al., 2006), vitamins (Snyder et al., 2006), breast milk (Kirk et al., 2005, 2007), and baby formula (Pearce et al., 2007). Perchlorate ingestion interferes with iodine uptake by the thyroid gland, and may result in a lower production of key thyroid hormones in humans. The US Environmental Protection Agency has recently adopted a reference dose (RfD) of 0.7 mg per kg of body weight per day (see http://www.epa.gov/ IRIS/subst/1007.htm), but the extent of exposure through consumption of contaminated produce is currently unknown. Perchlorate is taken up by a wide variety of plants, including fresh produce, and the mechanisms involved in its uptake and fate in plants have only recently started to unfold. From both an ecological and a human health standpoint, an understanding of the uptake and fate of perchlorate in higher plants is becoming increasingly important.

1.1. Perchlorate in the environment 1.1.1. Chemical properties Perchlorate is an inorganic ion consisting of one chlorine (VII) atom surrounded by four oxygen atoms (Fig. 1) with a delocalized negative charge (Urbansky, 1998). From a thermodynamic standpoint, perchlorate is a potent oxidant, but its tetrahedral symmetry leads to very sluggish kinetics under typical, ambient conditions (Urbansky, 1998, 2002). The high solubility of perchlorate salts and the unreactive nature of perchlorate under ambient conditions make it both a favored industrial oxidant as well as a persistent environmental contaminant. 1.1.2. Production and use Ammonium perchlorate and/or potassium perchlorate salts are produced and used mainly as oxidizing additives in solid rocket propellant, munitions, and explosives, but perchlorate salts are also used in analytical chemistry,

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O

O Cl O

O

Figure 1

Tetrahedral structure of the perchlorate anion.

airbag manufacture, leather tanning, pyrotechnics, and road flares (Urbansky, 1998). Most of the environmental contamination is due to the manufacture and use of ammonium or potassium perchlorate for the defense industry. Due to the short shelf life of the propellants, unused portions must be disposed of and this is usually done with high-pressure water washes. Since perchlorate has historically not been regulated, large volumes of perchlorate-laden wastes have been legally discharged over the last 50 years (Urbansky, 1998). Because of the chemical properties of perchlorate, environmental disposal is largely responsible for the widespread contamination of ground and surface waters, especially in the western United States where many manufacturers and users of perchlorate salts are located. For instance, the low-level contamination in the Colorado River south of Lake Mead is associated with discharges over the last 50 years from PEPCON and Kerr McGee, which are the two largest manufactures of ammonium perchlorate salts in the United States.

1.1.3. Natural occurrence In addition to its anthropogenic sources, perchlorate also forms naturally under certain environmental conditions. The Atacama Desert of Chile is notoriously associated with natural geologic deposits of perchlorate salts. These soils are nitrate-rich and have been a source of nitrate fertilizer across the United States and elsewhere, but many commercial fertilizers from this Chilean saltpeter contain low levels (<2 mg/kg) of perchlorate (Urbansky et al., 2001). Most of the Chilean saltpeter has been used historically for tobacco and citrus, and US imports have been steadily decreasing over the last 100 years (Dasgupta et al., 2006). Some believe that perchlorate contamination from the use of these

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fertilizers is not of significant concern today, especially when compared with the contamination from the manufacture and use of perchlorate as an oxidant (Foubister, 2006). Some researchers have suggested that perchlorate forms naturally in the atmosphere, and that this mechanism may ultimately be the source of natural perchlorate in places like the Atacama (Dasgupta et al., 2005, 2006). Although some perchlorate may form naturally under certain atmospheric conditions, most of that found in rainfall is in the ng/liter range and is widely considered to be of minimal importance in terms of extensive perchlorate contamination in the United States. Sensitive stable isotopic analyses have revealed that natural perchlorate has a distinguishable isotopic signature from anthropogenic perchlorate, thus the source of contamination can be traced analytically (Bao and Gu, 2004; Duncan et al., 2005; Motzer et al., 2006; Sturchio et al., 2007).

1.2. Toxicological issues 1.2.1. Analytical advancements Perchlorate detection in ground and surface water was facilitated by analytical advancements in ion chromatography (IC) that became available in 1997, leading to detection down to 6 mg/liter (CDHS, 1997). This method was later refined, achieving detection limits of 4 mg/liter (EPA, 1999) and then 1 mg/liter ( Jackson et al., 2000). As a result, perchlorate detection in drinking water became much more widespread, especially in the southwestern United States where many of the manufacturers and users of perchlorate salts are located. In the southwest, perchlorate levels in surface and ground waters have ranged from ca. 8 to 4000 mg/liter (Urbansky, 1998). With new analytical methodology using mass spectrometry (MS) detection, perchlorate has been quantified at levels 0.7 mg/liter in bottled water (Snyder et al., 2005) and from ca. 0.1 to 10 mg/liter in ‘‘pristine’’ groundwater samples from across the United States (Parker et al., 2008). With increasingly lower levels of detection, concern about the health effects of such levels in drinking water and food became more prevalent. Perchlorate was detected at levels from 2 to 8 mg/liter in the lower Colorado River, which is used to irrigate vast acreages of crops in the southwestern United States (Sanchez et al., 2005a). Currently, there is much concern over the health risks associated with consuming contaminated produce. New analytical methodology, including ion chromatography-electrospray ionization-mass spectrometry (IC-ESI-MS) and IC-tandem MS have enabled the detection and accurate quantification of perchlorate in the range of 0.07 to 2.0 mg/kg fresh weight (FW) in fresh produce (El Aribi et al., 2006; Krynitsky et al., 2004; Seyfferth and Parker, 2006).

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1.2.2. Human health effects The primary route of perchlorate exposure is through ingestion of contaminated water or food products. Perchlorate is absorbed by the gastrointestinal tract and elicits its primary response on the human thyroid gland (Greer et al., 2002). Perchlorate may interfere with iodide uptake at the sodium–iodide symporter of the human thyroid, and may thus lead to a lower production of key thyroid hormones if the individual is also hypothyroidic or iodine-deficient. In fact, perchlorate was utilized in the 1950s and 1960s to treat hyperthyroidism, but the practice ceased after several patients developed aplastic anemia (Wolff, 1998). The safe level of daily perchlorate ingestion is a topic of ongoing debate, but it should be emphasized that they key to the issue is iodine nutrition. The United States’ recommended daily intake of iodine is 150 mg for adults and 220 mg for pregnant women. Pregnant and lactating women require more iodine because they are the sole provider of iodine to their developing fetuses or breast-fed infants. Iodine is extremely important for cognitive development in developing neonates. Infants who receive less than adequate iodine are more susceptible to cognitive impairments, and may be at risk for behavioral abnormalities such as attention deficit disorder (Kirk et al., 2007). Fetuses and breast-fed infants are thus potentially more sensitive to the effects of perchlorate, especially if their mothers are iodine-deficient. Several recent studies have indicated that about one-third of US women are iodine-deficient and may be more susceptible than men to the effects of perchlorate (Blount et al., 2006; Pearce et al., 2004). Moreover, many women across the United States contain perchlorate at potentially hazardous levels in their breast milk (Kirk et al., 2005, 2007; Pearce et al., 2007). Kirk et al. (2007) analyzed the breast milk of 10 women volunteers over several days, and found that the US Environmental Protection Agency RfD of 0.7 mg/kg·per day would be exceeded for the first 2 months of life for an average-weight infant consuming an average amount of breast milk with a perchlorate concentration of 4.0 mg/liter, the median value of all samples in the study. While the safe level of perchlorate in drinking water continues to be debated, recent evidence suggests that the focus should instead turn to concentrations in fresh produce. In one study, a woman consuming only reverse osmosis drinking water, which is very effective at perchlorate removal (see, e.g., El Aribi et al., 2006), had the highest concentrations of perchlorate in her breast milk of all women in the study (Kirk et al., 2007). Additionally, the fact that perchlorate concentrations of breast milk were positively correlated with the consumption of fruits and vegetables (Kirk et al., 2007) further demonstrates that fresh produce can be a significant source of perchlorate exposure, potentially more so than drinking water.

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Moreover, cumulative perchlorate exposure from several types of fresh produce and drinking water may lead to perchlorate consumption exceeding the RfD in children (Sanchez et al., 2007). Thus, an understanding of the fate of perchlorate in higher plants, including produce, is becoming increasingly important. 1.2.3. Ecological effects The discovery of perchlorate at levels from 1.7 to 6.4 mg/liter in store-bought dairy milk (Kirk et al., 2003) provides evidence for food chain transfer, but it is unknown whether cows consumed contaminated water or food. Other researchers provided evidence for placental or breast-milk transfer of perchlorate between deer mice that were fed contaminated food or water (Smith et al., 2006). The detection of perchlorate in various vertebrates has indicated that perchlorate may be of concern for entire ecosystems, especially those inhabiting heavily contaminated sites. In rodents, perchlorate has been found from trace levels up to 33,000 mg/kg near the Las Vegas Wash (Smith et al., 2004), and up to 2300 mg/kg near the Longhorn Army Ammunition Plant (Smith et al., 2001). Perchlorate has also been detected in mosquito fish, tree frogs, sunfish, and damselfly larvae (Smith et al., 2001). At high doses, perchlorate may affect reproduction and growth in mosquito fish (Park et al., 2006), but it is not likely to affect insects (e.g., mosquito) at typical concentrations found in the environment (Sorensen et al., 2006).

1.3. Objectives of review The objective of this review is to provide an overview of the fate of perchlorate in higher plants and the factors, as well as the mechanisms, involved in perchlorate uptake by higher plants from both relatively high (mg/liter) and relatively low (mg/liter) concentrations. With the potential for fresh produce to be a significant source of perchlorate exposure in humans, and because phytoremediation has been suggested as a remediation option, a mechanistic understanding of perchlorate’s fate in higher plants is warranted.

2. Perchlorate Levels in Plants 2.1. Plants growing on highly contaminated sites Not long after perchlorate detection in water became widespread, several researchers and the US Environmental Protection Agency became interested in assessing the levels of perchlorate in vegetation surrounding heavily

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contaminated sites. One such site is the Las Vegas Wash near Henderson, Nevada, which is less than 2 miles from the largest producers of ammonium perchlorate and eventually drains into Lake Mead. Urbansky et al. (2000) detected perchlorate in submerged and exposed portions of salt cedar (Tamarix ramosissima Ledeb.) growing in the Wash at concentrations of 300,000 mg/kg of FW and 6000 mg/kg of dry weight (DW), using IC with an early ESI–MS detection method. A few years later, other researchers sampled ‘‘mostly leaves’’ from several ‘‘vegetation types’’ at three sites near the Wash, and found relatively high levels of perchlorate up to 446,000 mg/kg DW, using IC with electrical conductivity detection with a reported 100 mg/kg detection limit (Smith et al., 2004). Terrestrial broad-leaved plants had the highest mean perchlorate concentrations (645,000 mg/kg DW), followed by ‘‘seeding bush’’ (529,000 mg/kg DW) and aquatic broad-leaved plants (421,000 mg/kg DW). Broad-leaved plants had significantly higher perchlorate concentrations than grasses (Smith et al., 2004). In another site assessment, perchlorate was detected in vegetation sampled near the Longhorn Army Ammunition Plant in east central Texas, where solid-propellant rocket motors were once manufactured. Smith et al. (2001) observed that the amount of perchlorate varied with vegetation type and with the portion of plant analyzed; perchlorate was detected at concentrations ranging from 555 to 5,560,000 mg/kg DW, with most of the perchlorate found in leaves and a much smaller amount in seeds, branches, and stems (Smith et al., 2001). However, in contrast to the data for salt cedar (Urbansky et al., 2000), there was more perchlorate found in exposed than submerged bulrush (Scirpus spp.) (Smith et al., 2001). In another assessment, vegetation was sampled over a 1 year period at the Naval Weapons Industrial Reserve Plant in McGregor, Texas. With data from over 40 tree species, the authors observed that the amount of perchlorate found in leaves varied with the plant species, with the highest average perchlorate levels found in smartweed (Polygonum spp.), willow (Salix nigra Marshall), chinaberry (Melia azedarach L.), and watercress (Nasturtium spp.) with averages up to 40,600, 6590, 5040, and 5040 mg/kg DW, respectively (Tan et al., 2004). Of the plants for which both leaves and fruit were analyzed, the perchlorate concentration was always lower in the fruit, if detected. By correlating bulk water perchlorate analyses with vegetation data, the authors observed that higher plant perchlorate concentrations were due to higher perchlorate concentrations in the stream (Tan et al., 2004). These site assessments suggest that perchlorate tends to accumulate to a greater extent in leafy tissue than in other parts of a plant. Furthermore, these studies have shown that the amount of perchlorate taken up by higher plants depends both on the species or plant type as well as the initial concentration of perchlorate in the source water.

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2.2. Market surveys In addition to assessment of contaminated sites, researchers became interested in the perchlorate concentrations of plant items that humans consume, including tobacco products and fresh produce. Ellington et al. (2001) analyzed the perchlorate levels in uncured tobacco (Nicotiana tabacum var. K326) that was grown using Chilean nitrate fertilizer, and also analyzed flue-cured tobacco and various store-bought tobacco products including cigarettes, cigars, and chewing tobacco. The seven brands of cigarettes analyzed ranged in their perchlorate levels from 12,000 to 60,000 mg/kg FW and were higher than the cigar (6300 mg/kg FW). The lamina of the fresh tobacco leaf had a higher concentration of perchlorate (14,600 mg/kg FW) than the midrib (1100 mg/kg FW), while flue-cured tobacco had even higher amounts (24,900 mg/kg FW) (Ellington et al., 2001). While perchlorate in smoked tobacco products may help us to elucidate the fate of perchlorate in higher plants, it is of limited relevance to human health because they are seldom ingested. In the context of human health, it is important to understand whether the presence of low levels of perchlorate in irrigation water, such as those found in the lower Colorado River, leads to perchlorate accumulation in plants to levels of concern. Martinelango et al. (2006) found perchlorate concentrations ranging from 29 to 878 mg/kg DW in seaweed collected off the coast of Maine, where sea water contained an average of only 0.16 mg/liter perchlorate. This suggests that some plants may bioconcentrate perchlorate to levels much higher than those in the medium in which they are growing. Sanchez et al. (2005a) sampled several lettuce cultivars (Lactuca sativa L.) from agricultural fields in the southwestern United States that are irrigated with Colorado River water, which contains perchlorate at low levels (2–8 mg/liter). They found that perchlorate levels in lettuce leaves varied with the cultivar tested, with the highest levels found in butterhead and the lowest in iceberg (a.k.a. crisphead) lettuce (Sanchez et al., 2005a). Analyses of leaf fractions revealed that perchlorate accumulated to a greater extent in the outer leaves and to a lesser extent in the inner leaves in iceberg, but the other lettuce cultivars were not similarly dissected before analysis (Sanchez et al., 2005a). In a follow-up study, different types of leafy produce were obtained from agricultural fields and/or farmers’ markets from across North America, and the highest perchlorate concentrations were found in spinach (Spinacia oleracea L.) (628 mg/kg FW), baby mix (370 mg/kg FW), and arugula (Eurca sativa Miller) (195 mg/kg FW). The five common lettuce cultivars also contained relatively high amounts of perchlorate: iceberg (31 mg/kg FW), romaine (100 mg/kg FW), green leaf (195 mg/kg FW), red leaf (104 mg/kg FW), and butterhead (98 mg/kg FW) (Sanchez et al., 2005b). The ranking of mean perchlorate concentrations in lettuce cultivars was

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butterhead > romaine > green leaf > red leaf > iceberg (Sanchez et al., 2005b). In another study of citrus from California and Arizona, perchlorate was highest in leaves (669–4930 mg/kg DW) of lemon trees (Citrus limon L. Burm f.), less in fruit (64–195 mg/kg DW), and even less in branches (

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These site assessments and market surveys have shown that perchlorate is found in greater concentrations in leaves than in fruits and stems. It may be that transpiration is a key factor in perchlorate accumulation in plants, and because fruits transpire less water than leaves, fruits accumulate less perchlorate. However, it is quite possible that this observation is merely a reflection of a greater extent of in vivo perchlorate metabolism in fruits than in leaves. Mechanistic studies on perchlorate behavior in plants have provided some insight into these possibilities.

3. Perchlorate Uptake Studies Mechanistic studies of perchlorate fate in higher plants have been conducted either to evaluate the feasibility of perchlorate phytoremediation or to determine the fate of perchlorate in fresh produce that could be ingested. In phytoremediation experiments, plants are grown at a relatively high initial perchlorate concentration (mg/liter range) and uptake/accumulation/reduction is studied over time as the concentration in the growth media approaches very low levels. The disappearance of perchlorate from the growth media is due in part to reduction in the rhizosphere (rhizodegradation) and in part to plant uptake. Once taken up by the plant, perchlorate may be reduced within plant tissues (phytodegradation), stored in plant organs (i.e., leaves, stems, roots, branches, and fruits), or exuded through roots or leaves. In studies of the fate of perchlorate in fresh produce, a constant level of perchlorate is supplied that simulates contaminated irrigation water. Once plants reach maturity, edible portions are extracted and analyzed for perchlorate to determine the potential human exposure from ingestion. In both types of experiments, other factors such as transpiration rate and the role of competing ions on perchlorate fate in higher plants have been studied.

3.1. Phytoremediation Most of the experimental work on perchlorate uptake in higher plants has been conducted in the context of phytoremediation. Several of these studies suggest that there are three distinct phases with respect to perchlorate uptake and degradation. Phase 1 is characterized by a rapid depletion of perchlorate from solution as perchlorate is taken up by the plant via transpiration, and Phase 2 is characterized by a plateau in which the perchlorate concentration in solution is steady despite an increase in transpiration. During Phases 1 and 2, the microbial population is stimulated in the rhizosphere, and optimal conditions are eventually attained for the fastest rate of perchlorate removal due to

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rhizodegradation in Phase 3 (Nzengung and McCutcheon, 2003; Nzengung et al., 1999; Susarla et al., 2000). As long as it is not leached or precipitated as a salt, perchlorate in the rhizosphere has two main fates: it is either taken up by a plant or degraded in the rhizosphere (rhizodegradation). Under anaerobic conditions, rhizodegradation dominates and is facilitated by low availability of nitrate (a competing terminal electron acceptor) and high availability of electron donors (Nzengung and McCutcheon, 2003; Nzengung et al., 2004; Shrout et al., 2006). Certain anaerobic bacteria are necessary for this process, and they are capable of reducing perchlorate to chloride via chlorate and chlorite intermediates through a stepwise reaction (Susarla et al., 2000):


ClO
4 ! ClO3 ! ClO2 ! Cl

ð1Þ

In anaerobic microcosms, direct evidence for this stepwise reduction has been demonstrated by the appearance of chlorate and chloride and the disappearance of perchlorate in the rhizosphere; chlorite was not detected and is believed to be a short-lived intermediate (Nzengung et al., 2004). In another experiment, root homogenates from poplar trees (Populus deltoides  nigra DN34) inoculated with perchlorate-reducing bacteria were successful at degrading perchlorate to chloride in anaerobic microcosms, whereas root homogenate alone did not degrade perchlorate (Shrout et al., 2006). This further supports the notion that rhizodegradation is due to bacterial activity. Under aerobic conditions, plant uptake dominates and perchlorate is taken up by roots and eventually stored in tissues where it may be phytodegraded (Nzengung et al., 2004; Susarla et al., 2000). Phytodegradation of perchlorate appears to be an enzymatically driven process by which reduction follows the same stepwise reaction as that in anaerobic bacteria (Nzengung et al., 1999; Susarla et al., 2000). Whereas rhizodegradation of perchlorate takes place on the order of hours, phytodegradation is rather sluggish and takes place on the order of days (Nzengung et al., 2004). After an initial perchlorate dose was depleted from hydroponic solution, perchlorate leaf concentration in black willow (S. nigra Marsh) was high and steadily decreased with no subsequent increase in perchlorate in solution (Nzengung et al., 2004). The authors noted that this observation provides evidence for perchlorate phytodegradation in higher plants, but it may simply reflect a dilution effect as dry matter increased after perchlorate was depleted from the medium. Moreover, no data were provided concerning the presence of intermediate metabolites in the leaves or the appearance of chloride in the hydroponic solution, either of which would have solidified the evidence for phytodegradation. There are very limited data on the presence of chlorate and chlorite intermediates within plant tissues after perchlorate uptake. Susarla et al. (1999)

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claimed to have measured chlorate, chlorite, and chloride intermediates within all eleven plant species that took up perchlorate; however, there is no mention of how these metabolites were analyzed or of their leaf concentrations. In another study, intermediate metabolites were not detected in nutrient solutions or extracts of eucalyptus (Eucalyptus cinerea F. Muell. ex Benth.), eastern cottonwood (P. deltoides Bartr. ex Marsh.), or willow (S. nigra Marsh.), despite the appearance of chloride above background levels in the nutrient solutions after leaf perchlorate concentrations decreased (Nzengung et al., 1999). The lack of detection was likely due in part to crude analytical techniques at that time, the difficulty in analyzing complex plant matrices, and the lack of the use of an isotopic tracer. Tan et al. (2006) noted that there was no evidence for chlorate or chlorite metabolites within smartweed (Polygonum spp.) leaves in a 10-day experiment; however, from previous work, it appears that more time would be needed to see evidence of the rather sluggish phytodegradation (Nzengung et al., 2004). Van Aken and Schnoor (2002) have provided the most unequivocal evidence for perchlorate phytodegradation to date. The authors used radiolabeled perchlorate (36ClO4
) at concentrations of 25 mg/liter in a 4-week uptake experiment using hybrid poplar trees (P. deltoides  nigra). Radiolabeled chlorate, chlorite, and chloride were detected in the poplar leaf extracts, but only radio-labeled chloride was detected in the solution after 30 days (Van Aken and Schnoor, 2002). These data provide convincing evidence that perchlorate is metabolized within poplar plant tissues through chlorate and chlorite intermediates to chloride, which is subsequently exuded from the plant roots to the solution. Because the solutions were aerobic and sterile, there was limited opportunity for rhizodegradation (Van Aken and Schnoor, 2002). It remains unknown whether other plant species are capable of perchlorate phytodegradation or whether phytodegradation differs in various plant organs or as a function of tissue age. In a recent experiment, chlorite and chlorate intermediates were not detected in lettuce extracts of plants grown with 10 or 50 mg/liter perchlorate in nutrient solution, even with low method reporting limits of 19.6 and 1.1 mg/kg FW for chlorite and chlorate, respectively (Seyfferth and Parker, 2008, unpublished results). It is unknown whether phytodegradation takes place in other produce that is grown at mg/liter perchlorate concentrations in irrigation water.

3.2. Mechanistic studies The aforementioned site assessment and market survey data suggest that perchlorate may accumulate to different extents between plant species, and that the amount of perchlorate stored in plant tissues depends on the period of growth in perchlorate solution and on the perchlorate concentration

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(e.g., Table 1). However, these studies do not clearly demonstrate whether different species, when grown under exactly the same conditions and perchlorate concentrations, would result in different tissue perchlorate concentrations. Yu et al. (2004) grew cucumber (Cucumis sativus L.), soybean (Glycine max L. Merr.), and lettuce for 4–8 weeks in small containers of sand with initial perchlorate concentrations of 100 mg/liter, and perchlorate concentrations in leaves varied between the three species with 116,000 mg/ kg DW in lettuce, 35,000 mg/kg DW in cucumber, and 13,000 mg/kg DW in soybean after the first week. Over time, perchlorate levels in leaves appeared to reach a plateau and then decrease; however, this trend can be explained by the fact that perchlorate was only provided initially, and was thus depleted as the experiment proceeded (Yu et al., 2004). If vegetation receives perchlorate through contaminated irrigation water, one would assume that the perchlorate concentration in soil solution is relatively constant throughout the growth period. Thus, a more realistic experimental approach for assessing perchlorate uptake and its potential health risks in produce is to maintain a constant perchlorate concentration as the experiment proceeds, and then to compare the edible tissue concentrations. In addition, plants should be grown to full market size as opposed to just seedlings (Hutchinson, 2003; Yu et al., 2004). Tomatoes (Solanum lycopersicum L.), alfalfa (M. sativa L.), and soybean (G. max L. Merr.) were grown in this way with 50 mg/liter perchlorate in irrigation water provided more or less daily, and they varied with respect to perchlorate accumulation ( Jackson et al., 2005). After 12 weeks of growth in sand, soybean leaves contained an average of 31,000 mg/kg FW whereas tomato leaves contained an average of 11,000 mg/kg FW; alfalfa grown for only 8 weeks contained 8700 mg/kg FW perchlorate in the above-ground biomass ( Jackson et al., 2005). These data show that perchlorate accumulation in higher plants varies between species. The aforementioned studies have also revealed that perchlorate tends to accumulate more in leaves than fruits or seeds: leaves contained higher Table 1 Variation in perchlorate storage in willow leaves grown hydroponically for varying durations given a constant level of different perchlorate concentrations Solution ClO4
(mg/liter)

Days of growth

Leaf ClO4
(mg/kg FW)

112 100 22

120 26 26

2200 850 130

Adapted from Nzengung and McCutcheon (2003).

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amounts of perchlorate than tomato fruit (180 mg/kg FW), soybean pod (7600 mg/kg FW), or seed (600 mg/kg FW) ( Jackson et al., 2005). Thus, produce in which leaves are consumed are likely more hazardous than produce in which fruits are consumed. Roots tend to have very little, if any, detectable perchlorate (Sundberg et al., 2003; Yu et al., 2004). To date, there have been no evaluations of the potential for modified roots and stems that act as storage organs (e.g., tubers) to accumulate perchlorate. These studies demonstrate that perchlorate accumulation varies with plant species, but perchlorate accumulation has also been shown to vary among cultivars of the same species. Five cultivars of winter lettuce were grown under hydroponic conditions with perchlorate concentrations ranging from 1 to 10 mg/liter, and leaf perchlorate concentrations were significantly different after lettuce reached approximate maturity: crisphead > butterhead  romaine > red leaf  green leaf (Seyfferth and Parker, 2007). The perchlorate concentration in crisphead leaves for the 10 mg/liter treatment was approximately 170 mg/kg FW whereas green leaf lettuce contained only approximately 30 mg/kg FW (Seyfferth and Parker, 2007). Because perchlorate is stored to a greater extent in leaves, attention has been given to the role of transpiration in perchlorate accumulation in higher plants. Some authors have suggested that perchlorate may volatilize from cucumber leaves through transpiration (Yu et al., 2004); however, this seems highly unlikely due to the nonvolatility of the perchlorate ion. Seyfferth and Parker (2007) found that perchlorate tends to accumulate in the outer leaves of several cultivars of lettuce, which are older and broader and thus transpire more water than the smaller, younger inner leaves. This effect is less pronounced in lettuce cultivars that have more uniform leaf morphology such as butterhead lettuce, in which the transpirational surface of outer leaves and inner leaves are similar. Plants also tend to take up substantially more perchlorate during the day, when transpiration is occurring, than at night (Fig. 2). In addition, from 1.2- to 2.0-fold differences in perchlorate accumulation were observed in green leaf, butterhead, and crisphead lettuce grown under different climatic conditions that resulted in 2.0- to 2.7-fold differences in transpiration rates (Fig. 3 A,B). Transpiration clearly plays a key role in the mechanism for perchlorate uptake by higher plants. But, transpiration alone cannot be used to predict perchlorate accumulation in higher plants: perchlorate concentrations were always less than those predicted based solely on transpiration rate (Fig. 3C,D). Thus, perchlorate anions are not simply taken up ‘‘passively’’ by higher plants through the transpirational stream, as has been suggested by other researchers (Susarla et al., 2000; Tan et al., 2006). Rather, transpiration provides a mechanism of delivery of perchlorate ions to plant-root surfaces due to convection (Barber, 1995). Perchlorate is most probably then actively taken up by root cell membranes against an electrochemical

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Tissue ClO4− Concentration (mg/kg FW)

60 Perchlorate during day Perchlorate during night 40

20

0 Green leaf

Butterhead

Crisphead

Figure 2 Perchlorate accumulation in three types of hydroponically grown lettuce that were supplied with 5 mg/liter perchlorate only during the day (16 h of light, white bars) or only at night (8 h of dark, black bars). Perchlorate-containing pots were switched with perchlorate-free pots for the appropriate plants twice daily, that is, at the onset of ‘‘day’’ or the onset of ‘‘night’’ in controlled environment chambers. Bars indicate 1 SE of the mean where n ¼ 3. Source: Seyfferth and Parker, 2007, unpublished results; see Seyfferth and Parker (2007) for methodological details.

gradient, which is the accepted model for plant uptake of anions generally (Marschner, 2003). It is widely accepted that anionic plant nutrients are actively taken up by plant roots via ion carriers, and for many anions, it has been illustrated that the mechanism involves cotransport of the anion and protons (Hþ) across the cell membrane (Marschner, 2003). This theory is supported by the observation that pH affects anion uptake by roots of higher plants: an increase in pH results in a decrease in anion uptake in many plants (Cataldo et al., 1978; Hurd, 1958; Olsen, 1953). Such a reduction in perchlorate uptake was recently observed for lettuce, and perchlorate uptake was also reduced by the presence of bicarbonate (a soluble buffer), suggesting that perchlorate uptake by higher plants involves this active cotransport process (Seyfferth et al., 2008; Table 2). The increase in pH or buffering capacity reduces the electrochemical gradient across cell membrane, thereby reducing the efficacy of anion cotransport (Toulon et al., 1989). Marschner (2003) emphasized that plant roots do not have specific transporters for specific ions, but rather transporters that recognize plant nutrients based on the physiochemical properties of the ion, notably ion size and valence. Thus, perchlorate is likely taken up due to its chemical

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1.25 mg/liter

ClO4-

Cloudy, humid, cool Sunny, dry, warm 20

10

0 Green leaf

Butterhead

Iceberg

160

1.25 mg/liter ClO4-

120

80

40

0 Green leaf

Butterhead

Crisphead

D

B Tissue ClO4- concentration (mg/kg FW)

Percentage of predicted tissue concentration

C 30

200

10 mg/liter ClO4-

150

100

50

0 Green leaf

Butterhead

Iceberg

Percentage of predicted tissue concentration

Tissue ClO4- concentration (mg/kg FW)

A

160

10 mg/liter ClO4-

120

80

40

0 Green leaf

Butterhead

Crisphead

Figure 3 Perchlorate accumulation in green leaf, butterhead, and crisphead lettuces (A,B) and the corresponding tissue concentration expressed as a percentage of the predicted value based on transpiration ratio (C,D) grown at 1.25 mg/L or 10 mg/L perchlorate in two growth environments. Error bars represent standard errors of the mean where n ¼ 3 with the exception of the 1.25 and 10 mg/L ‘‘cloudy, humid, cool’’ butterhead treatments and the 1.25 mg/L ‘‘cloudy, humid, cool’’ crisphead treatment where n ¼ 2. Reprinted with permission from Seyfferth and Parker (2007). Copyright 2007 American Chemical Society.

similarity to a plant-essential anionic nutrient, as is the case with sulfate and selenate, and phosphate and arsenate (Marschner, 2003). Recently, Seyfferth et al. (2008) demonstrated that perchlorate uptake was significantly reduced by increasing nitrate solution concentrations in the three types of lettuce tested, whereas an increase of sulfate or chloride had no effect. This suggests that the mechanism of perchlorate uptake across cell membranes of higher plants is via the ion carrier for nitrate (Table 2). These results contrast with Tan et al. (2006) who found no significant relationship between perchlorate and nitrate, sulfate, or chloride in

Table 2 Linear regression analyses of the effects of increasing common soil anions (in mM) and pH on the uptake of perchlorate in three genotypes of lettuce

Tested soil component

Concentration range

NO
3

4–12

SO2
4

1–10

Cl


5–15

HCO
3

0–5

pH

5.5–6.5

Greenleaf

Butterhead

Crisphead

Linear regression equation

Linear regression equation

Linear regression equation

y ¼ 0.86x þ 19.2 y ¼
0.005x þ 14.8 y ¼
0.33x þ 13.8 n/a y ¼
3.60x þ 47.8

r2 value

0.558* 0.028NS 0.320NS n/a 0.530**

y ¼
2.95x þ 56.5 y ¼
0.30x þ 43.6 y ¼
0.45x þ 37.4 y ¼
1.26x þ 46.3 n/a

r2 value

0.857*** 0.014NS 0.072NS 0.524* n/a

y ¼
5.5x þ 121 y ¼ 0.48x þ 98.4 y ¼
1.25x þ 76.0 y ¼
3.70x þ 93.3 y ¼
34.4x þ 343

r2 value

0.897*** 0.00008NS 0.282NS 0.352NS 0.744*

*** p < 0.001; ** p < 0.01; * p < 0.05; NSp > 0.05. Modified from Seyfferth et al. (2008) Linear regression analyses were conducted separately for each genotype using actual values (n ¼ 9). Superscripts on r2 values indicate the level of significance:

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smartweed (Polygonum spp.). An increase of nitrate from 3.6 to 36 mM did result in a decrease in perchlorate uptake from ca. 750 to 350 mg/kg FW, but the high variation within replicates may explain the statistically nonsignificant result (Tan et al., 2006). In other work, Nzengung et al. (1999, 2004) observed that black willow grown with Hoagland solution (nitratebased N source) accumulated more perchlorate in leaves than plants grown with Miracle-GroÒ (ammonium- and urea-based N source). Nzengung et al. (2004) also observed that after spiking 36Cl-labeled perchlorate into either Hoagland or Miracle-Gro solutions in which black willow was growing, 96% of the 36Cl activity remained in Miracle-Gro solution whereas just 76% remained in the Hoagland solution after perchlorate was completely depleted from solution. The authors conclude that plants take up more perchlorate when the N source is nitrate (Hoagland solution) compared with the ammonium (Miracle-Gro); however, there may be other chemical differences between Hoagland solution and Miracle-Gro that may be responsible for their observations (e.g., phosphate or urea concentrations). Moreover, nitrate likely inhibits rhizodegradation of perchlorate to a greater extent than ammonium, thus allowing the more sluggish plant uptake to occur. Thus, their observation is most likely a reflection of a rate differential between rhizodegradation and plant uptake in the presence of different forms of N. Nzengung and McCutcheon (2003) observed the that old-leaf perchlorate concentrations steadily decreased whereas new-leaf concentrations increased, and the authors conclude that perchlorate may move from old leaves to new leaves in some plant species (Fig. 4.). However, it is not clear whether this was due to phloem retranslocation of perchlorate per se or due to phytodegradation of perchlorate in older leaves along with concurrent uptake and translocation of perchlorate in new leaves. In recent work in our laboratory using 37Cl-enriched perchlorate, not more than 1% of the perchlorate stored in outer leaves was transported to new leaves of lettuce that was grown at solution concentrations of 10 or 50 mg/liter (Seyfferth and Parker, 2008, unpublished results). More data is needed in order to determine the mechanism of translocation and the extent (if any) of phloem retranslocation of perchlorate or perchlorate metabolites in various plant species. Data is clearly lacking on the extent of leaf or root exudation of perchlorate in higher plants. As previously mentioned, there is some evidence of exudation of perchlorate metabolites from roots after phytodegradation, but to date, there is no clear evidence of nondegraded perchlorate exudation from roots. Tan et al. (2006) grew smartweed for 30 days in 10,000 mg/liter perchlorate, and allowed one set of plants to desorb in deionized water for 15 days. Upon analyzing various plant organs before and after desorption, the authors found a significant difference between perchlorate root concentrations, but not in stems or leaves, although leaf concentration tended to increase after desorption. The differences in total

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2000

40 1500 30 1000 20 500 10

0 0

20

40

60 80 100 Time (days)

120

140

Leaf concentration of perchlorate (mg/kg FW)

Solution concentration of perchlorate (mg/liter)

50

0 160

Solution perchlorate (mg/liter) Old leaf perchlorate (mg/kg FW) New leaf perchlorate (mg/kg FW)

Figure 4 Temporal perchlorate concentrations in solution, old leaves, and new leaves of black willow (Salix caroliniana Michx.). Adapted from Nzengung and McCutcheon, 2003 and reprinted with permission from John Wiley and Sons, Inc. (2008).

plant perchlorate concentrations before and after desorption were just 4  2% (Tan et al., 2006). More research is needed in order to conclusively determine whether root exudation of perchlorate exists in higher plants. In addition, there have been no studies on perchlorate exudation from leaves in plants that use salt glands for osmoregulation. Martinelango et al. (2006) noted that rinsing removed from 38% to 73% of perchlorate from four seaweed samples, and the authors conclude that surface adsorption may play a role in observed perchlorate concentrations in seaweed. However, given the known nonreactivity of perchlorate, this observation much more likely due to osmotic effects or to flushing out of apoplastic perchlorate. In another study, no significant difference in perchlorate concentration of field-grown wheat samples was found before and after rinsing with deionized water ( Jackson et al., 2005). The fate of perchlorate in dead leaves is not fully understood. In one study, dead leaves collected under willow and sweet gum from the Longhorn Army Ammunition Plant contained no perchlorate despite perchlorate detection in leaves of live trees (Nzengung and McCutcheon, 2003). In another study, leaf litter collected from beneath live trees had significantly

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less perchlorate than those collected from under dead trees (Smith et al., 2004). It is unknown weather perchlorate can be retained by higher plants through resorption of perchlorate upon leaf senescence, or whether the lack of perchlorate in dead leaves simply reflects microbial decomposition or leaching of fallen leaves, or both. In addition, it remains unknown if there is any metabolism of perchlorate in plants after harvesting (i.e., postharvest phytodegradation).

4. Conclusions and Future Research It is clear that a wide variety of plant species as well as plant cultivars accumulate perchlorate mostly in their leaves, followed by fruits, stems, branches, and roots. Overall, the amount of perchlorate found within plants depends on (1) the concentration of perchlorate in the growth media, (2) the duration of plant growth, (3) the plant species, (3) the presence of competing ions in solution, (4) the amount of water transpired, (5) the extent of phytodegradation within the plant, (6) the extent of exudation from the plant, and (7) the portion of the plant analyzed. Evidence suggests that transpiration plays a key role in perchlorate delivery to plant roots, where perchlorate is actively taken up by the nitrate ion transporter through cotransport with protons. The most promising conditions for perchlorate phytoremediation are (1) using plants within which perchlorate is fully phytodegraded to chloride or (2) stimulating rhizodegradation under anaerobic conditions by low nitrate availability and high concentration of electron donors. Of most concern to human health are produce in which mostly leaves are consumed. More research is needed in order to understand the extent of perchlorate metabolism within produce and the extent to which edible modified stems and roots (e.g., tubers) accumulate perchlorate, both pre- and postharvest. Additional research utilizing isotopic labeling to determine the extent of translocation, phytodegradation, and exudation of environmentally relevant levels of perchlorate and its metabolites should also be conducted.

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Cataldo, D. A., Garland, T. R., and Wildung, R. E. (1978). Plant root absorption and metabolic fate of technetium in plants. In ‘‘Technetium in the Environment’’ (G. Desmet and C. Myttenaere, Eds.), Elsevier, London. CDHS (1997). ‘‘Determination of Perchlorate by Ion Chromatography.’’ Department of Health Services, California. Dasgupta, P. K., Martinelango, P. K., Jackson, W. A., Anderson, T. A., Tian, K., Tock, R. W., and Rajagopalan, S. (2005). The origin of naturally occurring perchlorate: The role of atmospheric processes. Environ. Sci. Technol. 39, 1569–1575. Dasgupta, P. K., Dyke, J. V., Kirk, A. B., and Jackson, W. A. (2006). Perchlorate in the United States. Analysis of relative source contributions to the food chain. Environ. Sci. Technol. 40, 6608–6614. Duncan, P. B., Morrison, R. D., and Vavricka, E. (2005). Forensic identification of anthropogenic and naturally occurring sources of perchlorate. Environ. Forensics 6, 205– 215. El Aribi, H., Le Blanc, Y. J. C., Antonsen, S., and Sakuma, T. (2006). Analysis of perchlorate in foods and beverages by ion chromatography coupled with tandem mass spectrometry (IC-ESI-MS/MS). Anal. Chim. Acta 567, 39–47. Ellington, J. J., Wolfe, N. L., Garrison, A. W., Evans, J. J., Avants, J. K., and Teng, Q. C. (2001). Determination of perchlorate in tobacco plants and tobacco products. Environ. Sci. Technol. 35, 3213–3218. EPA (1999). ‘‘Method 314.0 Determination of Perchlorate in Drinking Water Using Ion Chromatography.’’ US Environmental Protection Agency (EPA), National Exposure Research Laboratory, Cincinnati, OH. FDA (2005). Exploratory Survey Data on Perchlorate in Food. Food and Drug Administration http://www.cfsan.fda.gov/~dms/clo4data.html. Foubister, V. (2006). Chilean fertilizer leaves perchlorate legacy. Anal. Chem. 78, 7914– 7915. Greer, M. A., Goodman, G., Pleus, R. C., and Greer, S. E. (2002). Health effects assessment for environmental perchlorate contamination: The dose response for inhibition of thyroidal radioiodine uptake in humans. Environ. Health Perspect. 110, 927–937. Hurd, R. G. (1958). The effect of pH and bicarbonate ions on the uptake of salts by disks of red beet. J. Exp. Bot. 9, 159–174. Hutchinson, S. L. (2003). ‘‘A Study on the Accumulation of Perchlorate in Young Head Lettuce.’’ US Environmental Protection Agency, Athens, GA EPA/600/R-03/003. Jackson, P. E., Gokhale, S., Streib, T., Rohrer, J. S., and Pohl, C. A. (2000). Improved method for the determination of trace perchlorate in ground and drinking waters by ion chromatography. J. Chromatogr. A 888, 151–158. Jackson, W. A., Joseph, P., Laxman, P., Tan, K., Smith, P. N., Yu, L., and Anderson, T. A. (2005). Perchlorate accumulation in forage and edible vegetation. J. Agric. Food Chem. 53, 369–373. Kirk, A. B., Smith, E. E., Tian, K., Anderson, T. A., and Dasgupta, P. K. (2003). Perchlorate in milk. Environ. Sci. Technol. 37, 4979–4981. Kirk, A. B., Martinelango, P. K., Tian, K., Dutta, A., Smith, E. E., and Dasgupta, P. K. (2005). Perchlorate and iodide in dairy and breast milk. Environ. Sci. Technol. 39, 2011–2017. Kirk, A. B., Dyke, J. V., Martin, C. F., and Dasgupta, P. K. (2007). Temporal patterns in perchlorate, thiocyanate, and iodide excretion in human milk. Environ. Health Perspect. 115, 182–186. Krynitsky, A. J., Niemann, R. A., and Nortrup, D. A. (2004). Determination of perchlorate anion in foods by ion chromatography-tandem mass spectrometry. Anal. Chem. 76, 5518–5522. Marschner, H. (2003). ‘‘Mineral Nutrition of Higher Plants’’ 2nd edn. Academic Press, London.

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Martinelango, P. K., Tian, K., and Dasgupta, P. K. (2006). Perchlorate in seawater: Bioconcentration of iodide and perchlorate by various seaweed species. Anal. Chim. Acta 567, 100–107. Motzer, W. E., Mohr, T. K. G., McCraven, S., and Stanin, P. (2006). Stable and other isotope techniques for perchlorate source identification. Environ. Forensics 7, 89–100. Nzengung, V. A., and McCutcheon, S. C. (2003). Phytoremediation of perchlorate. In ‘‘Phytoremediation: Transformation and Control of Contaminants’’ (S. C. McCutcheon and J. L. Schnoor, Eds.), pp. 863–885. John Wiley & Sons Inc., Hoboken. Nzengung, V. A., Penning, H., and O’Niell, W. (2004). Mechanistic changes during phytoremediation of perchlorate under different root-zone conditions. Int. J. Phytoremediation 6, 63–83. Nzengung, V. A., Wang, C. H., and Harvey, G. (1999). Plant-mediated transformation of perchlorate into chloride. Environ. Sci. Technol. 33, 1470–1478. Olsen, C. (1953). The significance of concentration for the rate of ion absorption by higher plants in water culture 4: The influence of hydrogen ion concentration. Physiol. Plantarum 6, 848–858. Park, J. W., Rinchard, J., Liu, F. J., Anderson, T. A., Kendall, R. J., and Theodorakis, C. W. (2006). The thyroid endocrine disruptor perchlorate affects reproduction, growth, and survival of mosquitofish. Ecotox. Environ. Safe 63, 343–352. Parker, D. R., Seyfferth, A. L., and Kiel Reese, B. (2008). Perchlorate in groundwater: A synoptic survey of ‘‘pristine’’ sites in the coterminous United States. Environ. Sci. Technol. 42, 1465–1471. Pearce, E. N., Bazrafshan, H. R., He, X. M., Pino, S., and Braverman, L. E. (2004). Dietary iodine in pregnant women from the Boston, Massachusetts area. Thyroid 14, 327–328. Pearce, E. N., Leung, A. M., Blount, B. C., Bazrafshan, H. R., He, X., Pino, S., ValentinBlasini, L., and Braverman, L. E. (2007). Breast milk iodine and perchlorate concentrations in lactating Boston-area women. J. Clin. Endocr. Metab. 92, 1673–1677. Sanchez, C., Krieger, R., and Blount, B. (2007). Potential perchlorate exposure from horticultural crops irrigated with Colorado River water. Hortscience 42, 884–885. Sanchez, C. A., Krieger, R. I., Khandaker, N., Moore, R. C., Holts, K. C., and Neidel, L. L. (2005a). Accumulation and perchlorate exposure potential of lettuce produced in the Lower Colorado River region. J. Agric. Food Chem. 53, 5479–5486. Sanchez, C. A., Crump, K. S., Krieger, R. I., Khandaker, N. R., and Gibbs, J. P. (2005b). Perchlorate and nitrate in leak vegetables of North America. Environ. Sci. Technol. 39, 9391–9397. Sanchez, C. A., Krieger, R. I., Khandaker, N. R., Valentin-Blasini, L., and Blount, B. C. (2006). Potential perchlorate exposure from Citrus sp irrigated with contaminated water. Anal. Chim. Acta 567, 33–38. Seyfferth, A. L., and Parker, D. R. (2006). Determination of low levels of perchlorate in lettuce and spinach using ion chromatography-electrospray ionization mass spectrometry (IC-ESI-MS). J. Agric. Food Chem. 54, 2012–2017. Seyfferth, A. L., and Parker, D. R. (2007). Effects of genotype and transpiration rate on the uptake and accumulation of perchlorate (ClO4
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C H A P T E R

T H R E E

Sugarcane for Bioethanol: Soil and Environmental Issues Alfred E. Hartemink* Contents 127 128 128 129 133 137 140 143 147 147 149 151 152 153 154 156 158 160 161 161 162 164 166 169 172 172

1. Introduction 2. Changes in Soil Chemical Properties 2.1. Data sources and types 2.2. Monitoring over time 2.3. Samples from different land-use systems 2.4. Soil organic matter dynamics 2.5. Leaching, denitrification, and inorganic fertilizers 2.6. Nutrient balances 3. Changes in Soil Physical Properties 3.1. Compaction and aggregate stability 3.2. Soil erosion 4. Changes in Soil Biological Properties 4.1. Macrofauna 4.2. Microbes 5. Environmental Issues 5.1. Herbicides and pesticides 5.2. Inorganic fertilizers 5.3. Air and water quality 6. Discussion and Conclusions 6.1. Sugarcane for bioethanol 6.2. Effects on the soil 6.3. Effects on air and water 6.4. Sugarcane yields 6.5. The potential for precision farming Acknowledgments References

*

ISRIC - World Soil Information, 6700 AJ Wageningen, The Netherlands

Advances in Agronomy, Volume 99 ISSN 0065-2113, DOI: 10.1016/S0065-2113(08)00403-3

#

2008 Elsevier Inc. All rights reserved.

125

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Alfred E. Hartemink

Abstract Cultivation of sugarcane for bioethanol is increasing and the area under sugarcane is expanding. Much of the sugar for bioethanol comes from large plantations where it is grown with relatively high inputs. Sugarcane puts a high demands on the soil because of the use of heavy machinery and because large amounts of nutrients are removed with the harvest; biocides and inorganic fertilizers introduce risks of groundwater contamination, eutrophication of surface waters, soil pollution, and acidification. This chapter reviews the effect of commercial sugarcane production on soil chemical, physical, and biological properties using data from the main producing areas. Although variation is considerable, soil organic C decreased in most soils under sugarcane and, also, soil acidification is common as a result of the use of N fertilizers. Increased bulk densities, lower water infiltration rates, and lower aggregate stability occur in mechanized systems. There is some evidence for high leaching losses of fertilizer nutrients as well as herbicides and pesticides; eutrophication of surface waters occurs in high-input systems. Soil erosion is a problem on newly planted land in many parts of the world. Trash or green harvesting overcomes many of the problems. It is concluded that sugarcane cultivation can substantially contribute to the supply of renewable energy, but that improved crop husbandry and precision farming principles are needed to sustain and improve the resource base on which production depends.

1. Introduction Bioenergy is energy from biofuels. Biofuel is produced directly or indirectly from biomass such as wood, charcoal, bioethanol, biodiesel, biogas (methane), or biohydrogen (FAO, 2006). It is big business. Demand for biofuels is surging because of the rise in crude oil prices and the global search for renewable energy (Valdes, 2007) and global biofuel production tripled between 2000 and 2007. Currently, the most important biofuel crops are corn, rapeseed, soybean, sugarcane, and oil palm whereas suitable trees for bioenergy production include eucalyptus, poplar, and willow. Biofuel production itself needs fossil energy. Currently, agriculture accounts for about 15% of the global energy demands (fertilizers, transport etc.) but it is estimated that agriculture can produce half to several times the current global energy demand (Smeets et al., 2007). The environmental impact of the shift toward growing crops for energy is still to be assessed. It is a complex matter with economic interests and other factors interacting on several scales. For example, the cultivation of biofuel crops is competing with food crops and may drive up commodity prices (UNEP, 2007)—over the last few years, world food prices have increased because of market demand for corn, wheat, and soybean. There

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is further concern that the expansion of biofuel crops takes place at the expense of rainforest and has negative effects on biodiversity and the environment. Sugarcane as a biofuel crop has much expanded in the last decade, yielding anhydrous ethanol (gasoline additive) and hydrated ethanol by fermentation and distillation of sugarcane juice and molasses (Gunkel et al., 2007; Pessoa et al., 2005). By-products are bagasse and vinasse (stillage or dunder), which is the liquid waste sometimes used for fertigation purposes. Bagasse, a by-product of both sugar and ethanol production, can be burned to generate electricity or be used for the production of biodegradable plastic. It provides most of the fuel for steam and electricity for sugar mills in Australia and Brazil. One hectare of sugarcane land with a yield of 82 t ha
1 produces about 7000 liter of ethanol. Brazil currently produces about 31% of the global production and it is the largest producer, consumer, and exporter of ethanol for fuel (Andrietta et al., 2007). The industry employs more than one million people (Pessoa et al., 2005). The value of the sugar and ethanol industry reached $8 billion in 2006, some 17% of Brazil’s agricultural output (Valdes, 2007). Between 1990 and 2005, global average sugarcane yields increased from 61 to 65 Mg ha
1 (http://faostat.fao.org). In 1990, global production was 1050 million Mg and in 2005, production of sugarcane was 1225 million Mg. Much is grown on large plantations but in some countries sugarcane is grown by smallholders, for example in Thailand where there are more than 100,000 farmers growing sugarcane (Sthiannopkao et al., 2006). In Brazil, less than 20% of the sugarcane is produced on small farms; most is grown in the southeast with over 60% of the production in the Sa˜o Pula district (FAO, 2004). In some countries, sugarcane is the main source of revenue and in Mauritius, sugarcane occupies 90% of the arable land (Ng Kee Kwong et al., 1999). Globally, the area harvested increased by 2.6 million ha in the period 1990–2005; the largest expansion was in India and Brazil. It is expected that the area under sugarcane in Brazil will expand by 3 million ha over the next 5 years whereas the area under sugarcane in China is forecast to rise by 5% or more than 100,000 ha year
1. Brazil has a long tradition of growing sugarcane. In sixteenth century, it was the world’s major supplier of sugar (Courtenay, 1980). In 1975, the area under sugarcane in Brazil was 1.9 million ha (de Resende et al., 2006), now there is about 6.2 million ha under sugarcane in Brazil compared to 21 million ha soybean and 14 million ha corn. Other big sugarcane producers are India (4.2 million ha), China (1.4 million ha), Thailand (1.1 million ha), and Pakistan (0.9 million ha) whereas the sugarcane areas in Australia, Cuba, Indonesia, Mexico, and South Africa cover some 0.5–0.6 million ha in each country. In the United States, there are about 170,000 ha in Louisiana and 167,000 ha in Florida. Against the trend, the area under sugarcane in Hawaii has decreased from

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about 100,000 ha in the 1930s to 6000 ha in 2007, and also the area under sugarcane in Cuba has been more than halved in the last 15 years. Traditionally, sugarcane was harvested manually; the senescent leaves (trash) and stalks were removed by people using big knives. Green harvesting was common in Brazil up to 1940s (de Resende et al., 2006), but the large volume of trash makes manual harvesting difficult (Boddey et al., 2003). As labor shortages developed, it became common practice to burn of the dead leaves prior to the harvest (preharvest burning). In the last two decades, preharvest burning has been replaced by mechanical green- or trash harvesting by cutter-chopper-loader harvesters that leave the trash on the field. Most of the sugarcane in Australia and parts of the West Indies is now arvested like this (Graham et al., 2002a). Up to the 1960s, Australian sugarcane was harvested manually but a decade later, following severe labor shortages, nearly all sugarcane was harvested mechanically (Brennan et al., 1997). Currently, about 30% of the Brazilian sugarcane is greenharvested, the rest is harvested manually with preharvest burning. All sugarcane in the United States is mechanically harvested but over 90% of the fields are burned after the green harvesting, to get rid of the trash blanket. Sugarcane is grown as a ratoon crop: the whole above ground biomass is harvested each year and harvests may continue for a number of years (ratoons). Yields decline with ratooning and, after some years, the land is ploughed and new sugarcane is planted. Much of the world sugarcane is grown with a high degree of mechanization. Also, large amounts of biomass are annually removed with the harvest and herbicides and pesticides are used extensively. Irrigation and large amounts of inorganic fertilizers are often required for high yields. As a consequence, soil properties are likely to change under sugarcane cultivation and the high biocide inputs may affect the environment. Environmental concerns and policies are key factors affecting the future of sugarcane production (Valdes, 2007). There is a also risk that the sugar industry is expanding on marginal lands where the costs or preventing or repairing environmental damage may be high (Arthington et al., 1997). This chapter reviews the main soil and environmental issues under continuous sugarcane cultivation. Most of this work predates the surge of sugarcane production for bioethanol but the results are very relevant for the new situation.

2. Changes in Soil Chemical Properties 2.1. Data sources and types There is fair a body of literature on changes in soil properties under sugarcane cultivation, especially in conference proceedings and books. Increasingly, there have been publications on soil and environmental issues

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in international scientific journals in English. Changes in soil properties under continuous sugarcane have been investigated in two ways. First, soil properties are monitored over time at the same site and this generates Type I data using chronosequential sampling. There are few such data sets because they require long-term research commitment and detailed recordings of soil management and crop husbandry practices. In the second approach, soils under adjacent different land-use systems are sampled at the same time and it is called biosequential sampling (Tan, 1996) generating Type II data (Sanchez et al., 1985). The assumption is that the soils of the cultivated and uncultivated land are the same and that differences in soil properties can be attributed to differences in land use and management (Hartemink, 2003). A considerable number of studies have focused on soil chemical and physical changes, and there are only few studies that included soil biological changes (Table 1). Several studies have been conducted in Brazil, Australia, and South Africa; although sugarcane is important and extensively grown in many other countries, fewer studies have been reported in the literature. Well-researched soil types are Fluvents, Inceptisols, Alfisols, and Oxisols; less data are available from Vertisols, although they are extensively used for sugarcane (Ahmad, 1983).

2.2. Monitoring over time Few studies have monitored soil chemical properties under continuous sugarcane cultivation. In Fiji, Haplic Acrustox were sampled under native vegetation prior to planting sugarcane, and again 6 years later (Masilaca et al., 1985). Exchangeable K decreased, soil P levels were increased in two of the three topsoils, and in one-third of the Oxisols, the topsoil pH had declined from 5.5 to 4.6 (Table 2). Schroeder et al. (1994) measured soil pH over 5 years on sugarcane farms on soils derived from sedimentary rocks in South Africa. These soils had received about 140 kg N ha
1 year
1 and pH declined by 0.4 units. Soil pH in the VMC milling district in the Philippines declined from 5.0 to 4.7 over a 19-year period under sugarcane (Alaban et al., 1990). The decline in pH was accompanied by a decrease in organic C from 14 to 10 g kg
1; also available P and levels of exchangeable cations decreased (Table 3). In Papua New Guinea, Hartemink (1998a,c) compiled soil data at a plantation on Fluvents and Vertisols. Soil chemical data were available from the early 1980s and early 1990s (Table 4). A significant decrease was found in the pH, available P, and CEC of the Fluvents and even in Vertisols, the pH had decreased significantly. A decrease of 0.2–0.4 pH unit was found to a depth of 0.60 m after 10 years of continuous sugarcane (Table 5).

130 Table 1

Studies focusing on changes in soil chemical, physical, and biological properties under sugarcane cultivation Dataa

Soil property investigated Soil order

Alfisols

Country

Australia

Brazil India Swaziland Andosols Fluvents

USA Hawaii Australia

Brazil Fiji USA Hawaii Iran Mexico Papua New Guinea

Chemical

Physical

Biological

p

p

p

p

p

Type I

p

p

p p

p p

p p

p

p

p

p

p

p

p

p

p p

p

p

p

p

p

p p

Type II

p p

p

p

References

Blair, 2000; Bramley et al., 1996; Pankhurst et al., 2005a,b; Skjemstad et al., 1999 Caron et al., 1996; Tominaga et al., 2002 Sundara and Subramanian, 1990 Henry and Ellis, 1995; Nixon and Simmonds, 2004 Zou and Bashkin, 1998 Bramley et al., 1996; Braunack et al., 1993; Pankhurst et al., 2005a,b; Skjemstad et al., 1999 de Resende et al., 2006 Masilaca et al., 1985 Juang and Uehara, 1971; Trouse and Humbert, 1961 Barzegar et al., 2000 de la F et al., 2006 Hartemink, 1998a,c

Inceptisols

Australia

India

Oxisols

Iran South Africa Brazil

Fiji USA Hawaii South Africa

Spodosols Ultisols

Vertisols

Swaziland Australia USA Australia Brazil Indonesia Mexico

p

p

p

p

p

p

p

p

p

p

p

p

p

p

p

p

p

p

p

p p

p

p

p p

p

p

p

p

p

p

p

p

p

p

p p

p

p p

131

p

p

p

p

p p

p

Bramley et al., 1996; Noble et al., 2003; Pankhurst et al., 2005a,b; Skjemstad et al., 1999 Singh et al., 2007; Srivastava, 2003; Suman et al., 2006 Barzegar et al., 2000 Dominy et al., 2002 Caron et al., 1996; Ceddia et al., 1999; Cerri and Andreux, 1990; de Souza et al., 2005; Nunes et al., 2006; Razafimbelo et al., 2006; Silva et al., 2007 Masilaca et al., 1985 Juang and Uehara, 1971; Trouse and Humbert, 1961 Dominy and Haynes, 2002; Dominy et al., 2002; Haynes et al., 2003 Henry and Ellis, 1995 McGarry et al., 1996a,b Muchovej et al., 2000 Pankhurst et al., 2005a,b Ceddia et al., 1999 Sitompul et al., 2000 Carrillo et al., 2003; de la F et al., 2006 Hartemink, 1998b,c (continued)

Table 1

(continued) Dataa

Soil property investigated Soil order

Country

Papua New Guinea South Africa

Not specified

Zimbabwe Australia

India Mexico Philippines South Africa Trinidad a

Chemical

Physical

Biological

p

p

p

p

p

p

p

p

p p

p

p

p

Type I

p

p

p

p p

Type II

p p

p

p

p

References

Graham and Haynes, 2005, 2006; Graham et al., 2002b Rietz and Haynes, 2003 Garside et al., 1997; King et al., 1953; Maclean, 1975; Magarey et al., 1997; Moody and Aitken, 1995, 1997; Wood, 1985 Srivastava, 1984; Yadav and Singh, 1986 Campos et al., 2007 Alaban et al., 1990 Schroeder et al., 1994; Swinford and Boevey, 1984 Georges et al., 1985

Type I are data whereby soil dynamics are followed with time on the same site; Type II are data whereby different land use was sampled simultaneously [see Hartemink (2006)].

133

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Table 2 Changes in soil chemical properties at sugarcane plantations in Fiji CEC and exchangeable cations (mmolc kg
1)

Sampling Site depth (m) pH

C P N (g kg
1) (g kg
1) (mg kg
1) CEC

A

–26.9 þ3.8 –0.6 –14.2 þ1.3 þ0.5 –17.0 þ7.8 –0.2

B

C

0–12 30–40 70–80 0–12 30–40 70–80 0–12 30–40 70–80

–0.7 –0.8 –0.6 –0.3 þ0.1 –0.1 þ0.2 þ0.1 þ0.1

–2.1 –0.2 0 –2.2 þ0.1 –0.2 –0.3 þ0.3 0

þ62.0 þ3.0 –1.0 –2.0 þ1.0 þ2.0 þ64.0 þ6.0 –4.0

–96.0 þ0.3 –18.0 –38.0 þ5.0 þ7.0 –3.0 þ35.0 þ28.0

Ca

Mg

K

–19.9 þ2.4 þ0.2 –26.9 –3.3 –2.9 –29.6 –1.4 0

–1.3 –0.1 –0.2 –10.6 –1.4 –2.2 þ1.8 –0.4 –0.3

–1.8 –0.1 –0.2 –1.1 –0.2 0 þ0.5 –0.9 –0.2

Soils were Oxisols and had been under sugarcane for 6 years. Type I data, modified from Masilaca et al. (1985).

Table 3 Changes in soil chemical properties on sugarcane plantations in the Philippines Exchangeable cations (mmolc kg
1)

Sampling period

pH

Organic C (g kg
1)

Available P (mg kg
1)

Ca

Mg

K

1969–1970 1988–1989

5.0 4.7

13.3 9.9

27.3 17.3

85.7 47.4

11.6 11.1

3.7 3.4

Type I data, modified from Alaban et al. (1990).

2.3. Samples from different land-use systems One of the longest data sets on soil changes under sugarcane cultivation is from the coastal tableland in Alagoas, Brazil (Silva et al., 2007). Soil samples were taken Oxisols in undisturbed forest and compared with soils that had been under sugarcane for 2, 18, and 25 years. Under forest, soil organic C was about 26 g kg
1 in the upper 0.20 m soil layer but had decreased to 19 g C kg
1 after 2 years of sugarcane cultivation. After 18 and 25 years, soil organic C levels were similar to those under forest in both topsoil and subsoil. In South Africa, an experiment established in 1939 on a Vertisol at the Experimental Station at Mount Edgecombe, has trash-burned and unburned treatments and with or without inorganic fertilizers. Fertilized plots received 140 kg N ha
1, 20 kg P ha
1, and 140 kg K ha
1. Soil organic matter was lowest when crop residues (trash) were removed and

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Table 4 Soil chemical properties (0–0.15 m) of Fluvents and Vertisols under sugarcane in the 1980s and 1990s Fluvents (n ¼ 7 pairs)

Vertisols (n ¼ 5 pairs)

Soil chemical properties

1982– 1983

1991– 1994

Difference

1982– 1984

pH H2O (1:2.5 w/v) Available P (mg kg
1) CEC (mmolc kg
1) Exchangeable Ca (mmolc kg
1) Exchangeable Mg (mmolc kg
1) Exchangeable K (mmolc kg
1)

6.3

5.9

p < 0.001

37.2

29.0

p ¼ 0.04

412

354

p < 0.001

229

213

100 11.0

1991– 1994

Difference

6.4

6.0

p < 0.001

35.4

24.6

ns

450

403

ns

ns

269

250

ns

94

ns

109

95

ns

9.5

ns

10.1

ns

13.0

ns ¼ not significant. Type I data, modified from Hartemink (1998c).

highest when residues were retained and inorganic fertilizers were applied. Soil pH decreased from 5.8 under natural grassland to 5.2 under sugarcane with fertilizer applications and also as a result of the trash retention. In soils where there was no trash or inorganic fertilizers, there was no significant decline in pH. Acidification was accompanied by a decrease in the levels of Ca and Mg (Graham and Haynes, 2005; Graham et al., 2002a). Several studies have been conducted in Australia where Type II data are termed samples from ‘‘paired sites’’ or ‘‘paired sampling,’’ sampling ‘‘old and new soils,’’ comparing ‘‘cropped and undeveloped’’ land, or comparing ‘‘virgin and cultivated’’ soils (Hartemink, 2006). King et al. (1953) compared soil chemical properties of uncultivated soils with those that had been under sugarcane for 22 years in the Bundaberg area. The cultivated soils contained on average 22 g C kg
1 whereas the C content of virgin soils was 48 g kg
1. In proportion, total N contents of the soils under sugarcane were also less than half of the N contents in virgin soils. Maclean (1975) found significant differences in topsoil pH between sugarcane and uncultivated land and also topsoil P, Ca, and Mg levels were significantly lower in soils under sugarcane. In the subsoil, available P and exchangeable Mg were significantly lower, but below 0.3 m depth, there was no significant difference between soils under sugarcane and uncultivated soils. Wood (1985) sampled cultivated and adjacent uncultivated land at 19 sites in a range of different soil types. The cultivated sites had been cropped with sugarcane for at least 30 years whereas the uncultivated sites were road reserves, cleared

Table 5 Change in pH H2O with depth based on samples from the same site at different times and from the different land use sampled at the same time Type I data

Type II data

Sampling depth (m)

Sample pairs

1986

1996

Difference

0–0.15 0.15–0.30 0.30–0.45 0.45–0.60

9 9 7 7

6.2 6.2 6.5 6.6

5.8 5.9 6.1 6.4

p < 0.001 p < 0.001 p ¼ 0.02 p ¼ 0.01

a Soils were continuously cultivated with sugarcane for at least 10 years ns ¼ not significant. Modified from Hartemink (1998a).

Sampling depth (m)

Sample pairs

Natural grassland

Continuous sugarcanea

Difference

0–0.15 0.15–0.30 0.30–0.50 0.50–0.70 0.70–0.90

5 5 5 5 5

6.3 6.3 6.6 6.7 6.9

5.8 6.1 6.4 6.6 6.8

p ¼ 0.02 p ¼ 0.02 p ¼ 0.05 ns ns

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Alfred E. Hartemink

Table 6 Changes in soil chemical properties on sugarcane plantations in North Queensland, Australia

Land use

Sugarcane

Sampling depth (m)

0–0.10 0.10–0.20 0.20–0.30 0.30–0.40 Uncultivated 0–0.10 0.10–0.20 0.20–0.30 0.30–0.40

CEC and exchangeable cations (mmolc kg
1)

P C pH (g kg
1) (mg kg
1) CEC Ca

Mg

K

5.0 7.0 4.9 6.5 4.9 5.6 5.0 4.0 5.2 15.0 5.2 8.1 5.1 5.9 5.1 4.9

7.3 5.1 5.6 8.1 14.1 12.3 12.4 15.3

2.0 1.4 1.1 1.0 2.9 1.6 1.3 1.3

35 26 15 9 14 8 7 3

37.0 37.0 39.0 41.3 56.3 47.5 46.8 51.7

15.2 15.5 17.1 18.7 32.8 26.1 23.1 25.0

Average data of various soil types. Sugarcane was cultivated for at least 30 years. Type II data, modified from Wood (1985).

land, or forest. A slightly lower pH was found under sugarcane and differences in soil reaction in the 0.20–0.30 m soil horizon were significant (Table 6). Organic C levels in soils under sugarcane were less than half of the levels in uncultivated soils. Exchangeable cations and the CEC were significantly lower in soils under sugarcane but these soils had significantly higher levels of available P due to high application rates of P fertilizers. Bramley et al. (1996) sampled Dystropepts, Ustropepts, Tropaquepts, Natrustalfs, Haplustalfs, and Fluvents that had been under sugarcane for 20 years or more. Soil fertility decline differed between soil orders and depths. Organic C declined in the Fluvents, but no significant changes were found in the other soils. A significant decline in soil pH was found only in Ustropepts. Skjemstad et al. (1999) investigated the same soils and found little changes in total soil organic C and in the light fraction (<1.6 Mg m
3). Well-established sugarcane sites (20–70 years) had lower soil organic C levels in the subsoils relative to uncultivated soils. No difference was found between Ustropepts, Natrustalfs, and Fluvents, and it appeared that sugarcane production did not lead to an overall decline in total organic C in the soil profile confirming the observations of Bramley et al. (1996). However, Noble et al. (2003) found that soil organic C declined under continuous sugarcane cultivation and levels were 13 g C kg
1 in 1994 and 8 g C kg
1 in 2000. The pH under continuous sugarcane was 6.6 in 1994 and 6.0 in 2000. Caron et al. (1996) sampled a Typic Haplorthox and Typic Paleudalf under primary forest and 20-year-old sugarcane near Sa˜o Paulo, Brazil. Topsoil organic C levels were 34 g kg
1 in the Alfisol under forest and 16 g C kg
1 soil under sugarcane. In Oxisols under forest, there was 45 g C kg
1 compared with 30 g C kg
1 under sugarcane; the difference

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137

between forest and sugarcane extended to 1.2 m in the Oxisol and up to 0.9 m in the Alfisol. The decrease in soil organic C was accompanied by a significant decrease in soil pH in both soil orders but the drop in pH was larger in Alfisols (Caron et al., 1996). In Mexico, Vertisols and Fluvents under different periods of sugarcane were sampled (de la F et al., 2006): a significant decline in N, P, and organic matter levels was found after 30 years of sugarcane cultivation but pH changes were less consistent. Henry and Ellis (1995) investigated changes in Oxisols and Natraqualfs under sugarcane in Swaziland. The Oxisol had been under sugarcane for 18 years and the Alfisols had been under paddy rice for 25 years and were 15 years under sugarcane when sampled. In Oxisols, the difference in organic C between sugarcane and uncultivated soils was only 2 g C kg
1. Exchangeable K in soils under sugarcane was about half the values found in uncultivated soils in both Oxisols and Alfisols. Levels of available P were much higher in the soils under sugarcane. Changes in soil chemical properties were accompanied by a degradation of soil physical and biological properties. Both the Type I and Type II studies showed considerable changes in soil fertility under continuous sugarcane. In most soils, the pH dropped, often accompanied by a decrease in exchangeable cations. Soil acidification has been reported from sugarcane areas in Australia (Moody and Aitken, 1995), Brazil (Silva et al., 2007), Hawaii (Humbert, 1959), Papua New Guinea (Hartemink, 1998a), Puerto Rico (Abrun˜a-Rodriguez and VicenteChandler, 1967), and Florida (Coale, 1993). An important cause of soil acidification is the application of N fertilizers. Because these contain N in the ammonium form, nitrification results in acidification. The soils under sugarcane in Fiji (Table 2) had acidified following the applications of sulfate of ammonia at rates averaging 150 kg N ha
1 year
1. In Papua New Guinea (Tables 3 and 4), most of the N fertilizers in the mid-1990s were applied as sulfate of ammonia; previously urea was applied that is less acidifying but most of the N is lost when urea is applied on the trash blanket. The levels of P increased in many soils, also as a result of fertilizer applications and relatively low removal rates (see also Section 5.2). A decline in organic matter has been reported from several sugarcane areas; the dynamics of soil organic matter are discussed below. No study has been found that looked at changes in soil micronutrients under sugarcane.

2.4. Soil organic matter dynamics Soil organic matter is key for the productive capacity of many tropical soils (Woomer et al., 1994). As shown in the previous sections, soil organic matter has declined in many soils under sugarcane but some studies found little change in soil organic matter levels under continuous sugarcane. Because there are different systems of cultivation (trash harvesting,

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Alfred E. Hartemink

preharvest burning) and sugarcane is grown in different agroecologies that largely affect the soil organic matter status, it is hard to generalize. In Brazil, Cerri and Andreux (1990) measured different C fractions of a Typic Haplorthox under forest and at a sugarcane plantation in Sa˜o Paula State. The natural abundance of the isotope 13C was used to identify organic C sources and to determine the changes in soil organic matter when forest is cleared and sugarcane planted. The approach depends on the difference in the natural 13C abundance between plants having different photosynthetic pathways: mainly C3 (forest) and C4 (sugarcane). The 13C/12C ratio of C3 plants is lower than that of C4 plants. Table 7 presents the C content in soils under forest and sugarcane. Total C levels after 50 years of sugarcane cultivation were 46% of the levels under forest. After 12 years of sugarcane cultivation, more than 80% of the soil organic C still originated from the forest but after 50 years, the forest C formed 55% of the total C contents in the topsoil. The rate of increase in C originating from sugarcane was slower than the decrease in C that had originated from the forest. The data in Table 7 were used in a regression model for soil organic matter dynamics (van Noordwijk et al., 1997). The decline in forest-derived organic matter continued during the 50 years spanned by the investigation; the apparent equilibrium value of total soil organic C is based on a balance between gradual build-up of sugarcane-derived organic matter, and decay of forest-based organic matter. For comparison, soil from pastures showed a larger stable C pool, a more rapid decline of labile forest C but also a much faster accumulation of labile crop C, which returned the total soil organic C levels to that of the forest before deforestation after about 7 years (van Noordwijk et al., 1997). Some of the differences between the pasture and sugarcane patterns can be explained by the lower annual input of C under sugarcane (<1.0 Mg C ha
1) compared with the pasture (7.5 Mg C ha
1) and differences in soil mineralogy and climate (Cerri and Andreux, 1990). Soil texture plays a role; 12 years after conversion from forest to sugarcane, Table 7 Carbon content of soils under forest and after 12 and 50 years of sugarcane cultivation (Mg ha
1, 0–0.20 m depth) Sugarcane

Total C Stable C originating from the forest C originating from the sugarcane

Forest

Soils under 12 years of sugarcane

Soils under 50 years of sugarcane

71.9 71.9

44.6 36.0

38.5 21.0

8.6

17.3

Type II data, modified from Cerri and Andreux (1990).

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139

the majority of the C derived from sugarcane is found in the coarse sand fraction. About 90% of the C in the clay fraction still has the forest signature after 12 years, whereas after 50 years, 70% of the forest-derived C persisted in the clay fraction (Vitorello et al., 1989). These data illustrate the importance of clay–organic matter linkages as a C-protection mechanism (Dominy et al., 2002; van Noordwijk et al., 1997). Another study in Brazil found that soil organic C levels under continuous sugarcane reached the same levels as soils under forest. Soil organic C under forest was about 26 g kg
1 in the soils under forest but had decreased to 19 g C kg
1 in soils that were cultivated with sugarcane for 2 years. After 18 and 25 years of sugarcane cultivation, levels were similar to those under forest in both topsoil and subsoil. The increase in soil organic C under continuous sugarcane was explained by the input of filter cake and vinasse (Silva et al., 2007). Also Graham et al. (2002b) found similar soil organic C levels in natural grassland compared with soils that had been under sugarcane cultivation for 59 years. Soil organic C levels under sugarcane were even higher when the sugarcane was fertilized. Not only is organic matter decline affected by clay content and soil texture, it is also different for different fractions. On a Grossarenic Kandiudult in Sumatra, Indonesia, Sitompul et al. (2000) modeled soil organic matter dynamics under sugarcane using CENTURY. Rates of change differed between particle size fractions. The sum of light, intermediate, and heavy fractions of macro-organic matter (150 mm–2 mm) showed a decline of about 250 to about 100 g C m
2 after 10 years of sugarcane cultivation. In South Africa, Graham and Haynes (2006) investigated soil organic matter and the microbial community under burned and trash-harvested sugarcane on Vertisols. Soil organic C was lower under burned sugarcane but K2SO4-extractable C, light fraction C, microbial biomass C, and basal respiration were much lower; changes occurred to a depth up to 0.30 m. Much organic matter is returned to the soil with trash harvesting but in burned sugarcane systems, the main organic return is through root turnover (rhizodeposition). The authors concluded that the effects of agricultural practice on organic matter status are more obvious and first noted when labile C fractions microbial activity is measured. In these Vertisols, soil organic C levels were similar under natural grassland and sugarcane (Graham et al., 2002b). In Inceptisols and Oxisols in the South African province of KwaZuluNatal, the organic C content was 40–50 g C kg
1 under natural vegetation but it declined exponentially with increasing years under sugarcane (Dominy et al., 2002). After 20–30 years of sugarcane, organic C content had declined to about 33 g kg
1 in the Oxisol and to 17 g kg
1 in the Inceptisol. In the Inceptisol, it reached a new equilibrium level after about 30–40 years. The higher organic matter content in the Oxsiol was attributed to clay protection of organic matter. The natural 13C abundance

140

Alfred E. Hartemink

in Inceptisols was used to calculate the loss of forest-derived, native soil C and the input of sugarcane-derived C. Sugarcane-derived organic C increased over time until it accounted for about 61% of organic C in the surface 10 cm in soils that had been under sugarcane for more than 50 years (Dominy et al., 2002). Alfisols under sugarcane in Australia contained about 11 g C kg
1 whereas under natural grassland, C levels were 34 g kg
1. Levels of soil organic C were much higher under trash-harvesting system than when preharvest burning was practiced, but the organic C levels of the soils under grass were not reached (Blair, 2000). In most studies on soil organic matter dynamics under sugarcane, it was found that the rates of soil organic matter decline differed for different soils (clay protection), soil organic matter fractions, agroecologies (climate), and management (e.g., trash-harvesting, vinasse applications). In most soils, levels decreased in the first years of cultivation and then slowly increased again. The increase is higher with higher levels of organic inputs (trash, vinasse). Rarely, the original soil organic matter levels are reached, typically, the levels settle at 60% of the soil organic matter levels in soils under natural vegetation.

2.5. Leaching, denitrification, and inorganic fertilizers Many studies have investigated the effects of inorganic fertilizer on sugarcane yield, sugar and leaf nutrient content, and the overall response to inorganic fertilizers. The Diagnosis and Recommendation Integrated System, originally developed for rubber, has been adapted to sugarcane in Brazil, United States, and South Africa (El Wali and Gascho, 1984; Reis and Monnerat, 2002; Sumner and Beaufils, 1975). The effects of lime have been well documented. This is important because sugarcane is prone to acidify the soil when ammonia-fertilizers are used. The effects of organic amendments have been studied (e.g., Braunbeck et al., 1999; Ng Kee Kwong and Deville, 1988; Orlando Filho et al., 1991; Sutton et al., 1996) and several studies have followed the fate of applied nutrients. Most have focused on N because sugarcane is a large N consumer (Malavolta, 1994); less attention is given to K as sugarcane is often grown on soils in which the K status may be sufficient for sugarcane (de Geus, 1973). There has been little soil processoriented research on P, possibly because sugarcane has a low P requirement (Malavolta, 1994). 2.5.1. Leaching Comprehensive N work has been conducted at the Sugar Industry Research Institute in Reduit, Mauritius on Ustic Eutropepts (annual rainfall 1550 mm) and Dystropeptic Gibbsiorthox (annual rainfall 3700 mm). In a study, 15N-labeled was given as (NH4)2SO2 or as NaNO3 at the rate of

Sugarcane for Bioethanol: Soil and Environmental Issues

141

100 kg N ha
1 (Ng Kee Kwong and Deville, 1984, 1987). The amount of N leached depended more on the duration and intensity of drying preceding rainfall than on the leachate volume. More N was leached from soils with higher organic matter content. Leaching was greater at the drier site but cumulative N loss over one year was similar for both soils; it appears that frequent, shorter, and less-intense drying and wetting cycles are as effective in mobilizing soil N as less frequent but longer and more-intense drying. The Oxisols were able to retain NO3 by absorption, which reduced N leaching, but K and Ca were more readily leached than N. It was concluded that losses of cations might be more acute than the need for measures to minimize N leaching. Also in Australia, leaching losses were low under sugarcane (Chapman et al., 1994; Wei-Ping et al., 1993). A study on Grossarenic Paleudults in Florida (Unites States) showed that leaching losses varied from 6% to 24% of applied N depending on fertilizer type and irrigation level (El Wali et al., 1980). Leaching of the applied N was mainly as NO3 but when irrigation took place before, the N hydrolyzed from urea was completely nitrified, there was substantial leaching in the NH4 form. Losses were lowest with sulfur-coated urea and increased with irrigation. Amounts of N loss ranged from 17% to 24% of the applied N, and upto 15% of the applied N was not accounted for by the plant, leachate, or soil. A study on Vertic Haplaquepts in Louisiana (United States) showed that N losses by leaching could be substantial (Southwick et al., 1995). Average NO3 leaching ranged from 15% to 60% depending on the leaching period and season. de Oliveira et al. (2002) measured leaching of N and cations under sugarcane using lysimeters in Sa˜o Paulo, Brazil. Inorganic N was applied but loss of N by leaching from the fertilizer (15N) was not detected despite the heavy rainfall and irrigation. There were N losses but these originated from crop residues and the amount of N leached in 11 months was less than 5 kg N ha
1. However, there were high rates of leaching for K, Ca, and Mg. Nitrogen losses under sugarcane may be low but cation losses may be considerable, which is a problem in acid soils. Moreover, not all N that is leached is lost. In very acid subsoils with an anion exchange capacity, NO3 is adsorbed but it may be below the rooting zone of the crop (Dynia, 2000; Rasiah et al., 2003). The maximum rooting depth of sugarcane is about 2 m, although there are considerable genotypic variations (Smith et al., 2005). When the exchange capacity below the rooting zone is saturated, there may be leaching of NO3 to the groundwater. 2.5.2. Gaseous losses Many studies have shown that an appreciable fraction of fertilizer N invariably remains unaccounted (Allison, 1966). It is generally assumed that denitrification and volatilization of NH3 are the major components of this unaccounted N. Weier et al. (1996) studied the potential for biological

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denitrification of fertilizer N in soils under sugarcane. In field studies on Alfisols and Ultisols, denitrification ranged from 1% to 20% of the applied N but differences between soil orders were considerable. In a glasshouse study, denitrification losses ranged from 13% to 39% of the N applied and the majority of the gaseous N loss occurred as N2. It was concluded that denitrification is an important cause of fertilizer N loss from fine-textured soils, with N2O the gaseous N product when soil NO3 concentrations are high (Weier et al., 1996). Fertilizer N uptake and N-use efficiency was investigated in Mauritius using 15N-labeled (NH4)2SO2 at a rate of 100 kg N ha
1. Approximately 10–20 kg ha
1 of the labeled-N was lost from the green tops (i.e., the aerial parts of the sugarcane). These were gaseous N losses from the plant itself. Fertilizer N use efficiency derived from recovered N at harvest grossly underestimates the ability of sugarcane to use fertilizer N. The study showed that the uptake of fertilizer N varied from 20% to 40% whereas it was 13– 18% when the measurements were made at the harvest. Denitrification and volatilization were grossly overestimated because losses of N from the aerial parts constitute a significant proportion of the unaccounted N (Ng Kee Kwong and Deville, 1994). Most studies in Australia have focused on losses of N fertilizer applied on trash-harvested fields. Such losses can be high under relatively dry conditions when urea is applied. Sugarcane, like all plants, contains the enzyme urease that in trash breaks down urea into CO2 and NH3. The trash blanket cannot bind this NH3 (Ralph, 1992) and volatilization of NH3 can be onethird of the applied N. Wei-Ping et al. (1993) found losses of 20–30% of the applied N within 30 days after applications. In heavy rainfall areas, urea was washed from the trash and losses were 17% of the applied N, whereas ammonia losses from ammonia-sulfate were less than 2% of the applied N (Freney et al., 1992). Similar losses were reported from Brazil (Gava et al., 2003). Chapman et al. (1994) investigated the efficiency of fertilizer N uptake using urea, labeled with 15N, which was either broadcast or buried in different trash management systems. The proportion of the applied fertilizer N recovered was 33% when the urea was buried and 18% when the urea was broadcast. It was suspected that denitrification accounted for the majority of the fertilizer-N loss (Chapman et al., 1994). Drilling urea into the soil decreased the N losses although total losses remained relatively high (Prasertsak et al., 2002). In the Australian research, gaseous N losses from the plant itself were not taken into account. Fertilizer N recovery was about the same as was found in Mauritius (20–40%). Low N-recovery values were also found sugarcane grown on Vertisols in Guadeloupe that is attributed to the higher rates of volatilization on these soils (Courtaillac et al., 1998). Leaching and denitrification are difficult to measure because of the inherent variability in the governing factors and the time needed for

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accurate measurements. Modeling contributes to our understanding of these processes and studies have used APSIM-SWIM to estimate N losses under sugarcane in Australia (Stewart et al., 2006). Modeled N-fluxes through 1.5 m soil depth showed that of the 30 kg N ha
1, about 27 kg N ha
1 was taken up by the crop so only 3 kg N ha
1 year
1 was leached. In another model study, it was shown that NO3 leaching was lower under rainfed conditions compared to irrigated conditions. Nitrogen leaching was strongly correlated with rainfall when application rates exceeded 200 kg N ha
1; it was concluded that careful N management is needed under rainfed conditions (Thorburn et al., 2005). Modeling studies may play an increasing role in the quantification of leaching and gaseous losses, both for experimental work and for decision making (Stewart et al., 2006). Low fertilizer-N recovery has been reported from many sugarcane areas. As N rarely accumulates in the soil, leaching and gaseous losses must be considerable with high rates of N applications. Leaching depends on the weather (rainfall), soil physical attributes (wetting, drying), the age of the sugarcane, as well as the type, quantity, timing, and placement of the fertilizer (Prasertsak et al., 2002). The few detailed studies that have been conducted show that leaching losses are generally low despite the low N recovery of inorganic fertilizers. There is also evidence that gaseous losses are generally high from N fertilizers applied on trash-harvested fields and under poorly drained conditions. Related environmental issues are discussed in Section 6.2.

2.6. Nutrient balances Nutrient balances can be used to estimate likely changes in soil chemical properties. In essence, they mimic an accounting procedure that compares inputs (e.g., inorganic fertilizers, manure) to nutrient outputs (e.g., crop removal, leaching) over a given time span. Nutrient balances may give insight into the processes that regulate nutrient cycling and help to formulate system management decisions and direct research (Hartemink, 2005a; Robertson, 1982). Nutrient balance studies have been influential—agronomically and politically—in many tropical regions, but most suffer from methodological problems. The best quantified budget line is often nutrient removal by the crop. It is usually calculated as yield multiplied by nutrient uptake or removal data but nutrient uptake data are variable. For example, Hartemink (1997) showed that, based on 11 literature sources, nutrient removal of Agava sisalana varied from 27 to 33 kg N ha
1, 5 to 7 kg P ha
1, and 59 to 69 kg K ha
1 per Mg of produce. The variation may be attributed to differences in sampling techniques, sampling period, inherent soil conditions, fertilizer applications, and analytical methods. Faerge and Magid (2004)

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Table 8 Partial N-balance (kg ha
1 year
1) for sugarcane cropping systems in Latin America and the Caribbean start Brazil, Ecuador,Peru etc at the same height as Dominician Brazil

Inputs N fixation Inorganic fertilizer Manure Deposition Total Outputs Harvest NH3volatilization Leaching Burning Forage Total Within system Fertilizer recovery (%)

Ecuador Dominican Peru Republic

Trinidad Range of values

15–25 nd 60–100 150

nd 200

nd 200

nd 80

nd 60–200

5 5 100

5–10 nd nd

5–10 5–10 nd

5–10 nd 282

5–10 nd nd

5–10 5–10 nd

50–60 nd

50–60 nd

50–60 nd

150 nd

50–60 nd

50–150 nd

nd nd nd 100

nd nd nd 100

nd nd nd 100

20 45 20 230

nd nd nd 100

nd 30 nd 100–230

50

50

50

70

50

50–70

nd ¼ no data. Modified from Ruschel et al. (1982).

concluded that losses are often overestimated and that modeled losses are rarely compared with direct measurements. Also, there are differences between years depending on the weather and other factors. As a result there is large interannual variation in nutrient balances (Sheldrick et al., 2003) A work group on sugarcane summarized the problems with nutrient balances in sugarcane as follows: A general N balance for this crop is difficult to construct because of widely differing agronomic practices and growing conditions and also a lack of knowledge of certain processes (Ruschel and Vose, 1982). The agronomic variation includes a growing period that ranges from 9 to 22 months, yields that range from 30 to 150 Mg ha
1, preharvest burning or trash harvesting, and different inorganic fertilizer regimes and recycling practices for organic wastes. Partial N-balances for the sugarcane in some Latin American and Caribbean countries are presented in Table 8. A wide range of values was found for the sugarcane systems in the different countries. Inorganic fertilizer applications ranged from 60 to 200 kg N ha
1 year
1. Fertilizer recovery under sugarcane in Latin America is about 50%, which implies that the N balances, shown in Table 8, are negative in most countries. The fertilizer N recovery rate has substantial influence on the

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overall balance. In Mauritius, a fertilizer N recovery of 20–40% was found (Ng Kee Kwong and Deville, 1994), in Australia 20–50% (Vallis et al., 1996; Weier, 1994), and in India 16–45% (Singh et al., 2007). Higher recovery is reported in Brazil (Basanta et al., 2003). In the Peruvian case in Table 8, total N input after correcting for the fertilizer recovery is 182 kg N ha
1 whereas total output is estimated to be 230 kg N ha
1. Losses of N by burning were estimated to be 45 kg N ha
1 and, in another study in Peru, it was shown that burning losses could account for 30% of the N output in a sugarcane system (Valdivia, 1982). Denitrification and losses by erosion were not considered in the balances of Table 8 because the data were not available. In Louisiana (United States), erosion losses under sugarcane were about 17 Mg soil ha
1 and annual nutrient losses by erosion were 18 kg N, 14 kg P, and 104 kg K ha
1 (Bengtson et al., 1998). No data are available on nutrient losses with soil erosion from other sugarcane areas, but considerable amounts of nutrients can also be lost with soil erosion (Hartemink, 2006). A partial N balance for sugarcane on an Entisol in the Sa˜o Paulo region of Brazil showed that N added in vinasse and urea was insufficient to maintain the N levels in the 0.20 m topsoil, but in the 0–0.60 m soil layer, total N levels increased. The input by biological nitrogen fixation (BNF) caused a positive N balance (de Resende et al., 2006). An estimate of the major nutrient inputs and outputs at a sugarcane plantation in Papua New Guinea (Hartemink, 2003) used yield data from 1991 to 1995. The N balance was positive but the P and K balance was negative. The N recovery was not measured but if a 50% recovery assumed the N balance was also negative. In Coimbatore, India, a partial nutrient balance for sugarcane was calculated for sugarcane grown on Alfisols (Sundara and Subramanian, 1990). Data for the plant cane and first ratoon (2 years) are given in Table 9. More N and slightly more P was applied with the inorganic fertilizers than removed with the crop. The difference between K applied and removed was 137 kg ha
1. Despite the positive balance of N Table 9 NPK balance and soil changes in a sugarcane field at Coimbatore, India Balance (kg ha
1 year
1)

Soil changes (0–0.20 m) (kg ha
1)

Applied with inorganic fertilizer

Content after two years

Difference

157 32 341

–25 –3 –180

N 225 P 33 K 100

Removed at harvest

Difference

Level at the beginning

107 29 237

þ118 þ4 –137

182 35 521

Calculated from Sundara and Subramanian (1990).

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and P, the soil levels of N and P declined because not all outputs were measured and much of the applied N may have been lost. If a 50% recovery is assumed, the balance becomes negative and explains the loss of N from the topsoil. There has been no study quantifying all nutrient inputs and outputs in sugarcane cultivation systems, and partial nutrient balances should be interpretated with caution (Hartemink, 2006). Although the removal of nutrients with the crop is fairly well-quantified (de Geus, 1973; Malavolta, 1994; Srivastava, 1992), other outputs and inputs of the nutrient balance have not been studied in sufficient detail, with the exception of BNF. 2.6.1. Biological nitrogen fixation In the late 1950s, it was discovered that N2-fixing bacteria of the genus Beijerinckia were present in the rhizosphere of sugarcane (Do¨bereiner, 1961). Most of the work on BNF in the sugarcane rhizosphere has been conducted in Brazil. Evidence for substantial inputs via N2 fixation by sugarcane has been provided by isotope-dilution measurements; these are consistent with observations in the field (Chalk, 1991). In Brazil, an estimate of BNF holds that about 17% of total plant N is fixed by the sugarcane, which is equivalent to 17 kg ha
1 at yields of 70 Mg ha
1 (Ruschel and Vose, 1982). More recent results of 15N dilution/N balance studies showed that some sugarcane varieties can obtain larger contributions ranging from 60% to 80% of total plant N, equivalent to over 200 kg N ha
1 year
1 (Boddey et al., 1991; de Oliveira et al., 2006; Medeiros et al., 2006). N-fixation is high for most Brazilian cultivars as they have been systematically bred for high yields with low N inputs (Boddey et al., 1995). Depending on the yield, 100–200 kg N ha
1 is removed with the harvest. Annual N application rates on sugarcane in Brazil are on average 50 kg N ha
1 (FAO, 2004), If over 200 kg N ha
1 year
1 is fixed biologically, it can be assumed that N levels in the soils under sugarcane are maintained (Boddey et al., 2003; Lima et al., 1987). However, ecosystems with high rates of N fixation often have high loss rates through leaching or possibly denitrification. The relationship is not fully understood but is related to the plant energy requirements when switching from uptake of atmospheric N to soil mineral N (Pastor and Binkley, 1998). It should be noted that the benefits of high N fixation in sugarcane may only be recorded in soils low in mineral N, when no or little inorganic N fertilizers are applied and when the soil P and Mo status is adequate. Cultivar differences in the potential for BNF are considerable (Urquiaga et al., 1992) and water supply needs to be abundant for high fixation rates (Boddey et al., 2003). These effects have been documented in Brazil, India, and Mexico (de Oliveira et al., 2006; Medeiros et al., 2006).

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3. Changes in Soil Physical Properties At most commercial sugarcane plantations, heavy machinery is used for land preparation, harvesting, and applications of fertilizers, herbicides, and pesticides. The machinery affects soil physical properties like aeration and porosity and the variation in soil physical properties, which is naturally already large within a field (Cassel and Lal, 1992), may be enhanced. In areas where most field work is done manually like in Mexico, soil physical changes are minimal under continuous sugarcane (Carrillo et al., 2003; de la F et al., 2006). This chapter discusses the effects of mechanized sugarcane cultivation on soil bulk density, aggregate stability, water intake (infiltration), and runoff and soil erosion.

3.1. Compaction and aggregate stability Usually, sugarcane is grown in rows on low ridges (intrarows) with tractors and harvesters passing through the interrow. On Spodosols in Australia, McGarry et al. (1996a) found a topsoil bulk density of 1.55 Mg m
3 in the intrarows as compared to 1.85 Mg m
3 in the interrow. An adjoining uncultivated site had a topsoil bulk density of 1.40 Mg m
3. Maclean (1975) and Wood (1985) reported significant increases in bulk density of 0.15–0.18 Mg m
3 in the topsoil compared with uncultivated land; several other reports have confirmed compaction under mechanized harvesting in Australia (Braunack, 2004; Braunack and McGarry, 2006). In South Africa, Dominy and Haynes (2002) sampled Oxisols that had been cultivated for over 30 years with sugarcane and compared these to soils under native grassland. The topsoil bulk density was 1.17 Mg m
3 under grassland but had increased to 1.37 Mg m
3 under sugarcane. Below 0.10 m, there was little difference in the bulk densities of these soils and the increased bulk densities and lower water stable aggregates have negative effects on the growth and yield sugarcane. They also found that bulk density is generally higher in soils with burned sugarcane compared with soils under trashharvested sugarcane (Graham and Haynes, 2006). Also in Brazil where much of the sugarcane is burned before harvesting, it was found that the topsoils of Oxisols after 6 years of cultivation had an increased bulk density (Ceddia et al., 1999; Silva et al., 2007). Absolute and relative increases in soil bulk density are different for different soils. In Papua New Guinea, bulk densities under natural grassland and within the sugarcane rows were similar for all depths of both Fluvents and Vertisols (Table 10). The bulk densities in the interrow were significantly higher and roots were absent. The absolute increase in the topsoil bulk density of the interrow as compared to natural grassland was 0.22 Mg m
3

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Table 10 Difference in bulk density between Fluvents and Vertisols under sugarcane and natural grassland for three depths Sampling depth (m)

0–0.15 0.15–0.30 0.30–0.50

Sugarcane

Natural grassland

Fluvents

Vertisols

Difference

Fluvents

1.19 1.28 1.37

1.09 1.15 1.18

p < 0.05 1.07 p < 0.01 1.17 p < 0.001 1.26

Vertisols

Difference

1.00 1.02 1.12

ns p < 0.05 p < 0.05

ns ¼ not significant. Data from Hartemink (1998c).

(þ21%) in the Fluvents and 0.18 Mg m
3 (þ18%) in the Vertisols. In Fluvents, the bulk density of the interrow increased to 0.50 m soil depth. Soil compaction under sugarcane has been reported worldwide, including India (Rao and Narasimham, 1988; Srivastava, 1984), South Africa (Swinford and Boevey, 1984), Swaziland (Nixon and Simmonds, 2004), Mexico (Campos et al., 2007; Vera et al., 2003), Iran (Barzegar et al., 2000), Brazil (Souza et al., 2004), and Fiji (Masilaca et al., 1985). It is a common problem (Yates, 1978) and it is likely to increase with higher rates of mechanization. Bulk density is higher in ratoons compared with soils that have just been planted. The fraction of water stable aggregates declines with increasing age of the sugarcane (Srivastava, 2003) and declines in soils where the sugarcane is burned before harvesting (Blair, 2000). Soil compaction may occur at once during field operations at moist soil conditions or may be cumulative during the years of cropping. Trouse and Humbert (1961) have shown that the topsoil bulk density of an Oxisol in Hawaii increased from 1.25 Mg m
3 after 10 tractor passes to 1.43 Mg m
3 after 20 passes, and to 1.53 Mg m
3 after 50 tractor passes. Much depends on the ground pressure exerted by the tires of the field equipment and the soil moisture content at the time of field operations. Georges et al. (1985) found that water content was the most important factor affecting soil compaction and that equipment type had only a significant effect at high soil moisture contents. Similar findings were reported by Braunack et al. (1993) who found differences in bulk density between conventional tires and so-called high flotation equipment. In general, bulk densities were 0.1–0.3 Mg m
3 higher under conventional equipment but conditions under which the experiments were conducted were fairly dry (Braunack et al., 1993). Compaction commonly results in an increase in soil strength. In South Africa, Swinford and Boevey (1984) found a penetrometer resistance of 220 N cm
2 in fully compacted topsoils that reduced the root density from about 4 to 2.5 Mg m
3 in uncompacted soils. Uncompacted soils had resistance values of about 140 N cm
2. McGarry et al. (1997) observed soil resistance values in Spodosols in North Queensland of about 2500 kPa

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in the top 10 cm of the interrow whereas the resistance was less than 800 kPa in the intrarow. In Trinidad, Georges et al. (1985) found an increase in penetration resistance from 21 to 26 kg cm
2 after wheel traffic on a clay soil. Water intake is commonly reduced by an increase in topsoil bulk density. On Fluvents in Australia, Braunack et al. (1993) found differences in infiltration rates of 15–60% between the use of conventional and high flotation equipment. In Papua New Guinea, Hartemink (1998c) observed a negative exponential relation between topsoil bulk density and water intake of Vertisols and Fluvents. Bulk densities causing slow water intake (<50 mm h
1) were about 1.20 Mg m
3 in Fluvents and 1.16 Mg m
3 in Vertisols. For both soil types, an increase of about 0.2 Mg m
3 drastically reduced the water intake. Water intake in the interrow was less than 10% of the soils under natural grassland. The slow water intake in the interrows may result in soil erosion, which can be high on Vertisols. Decreasing aggregate stability following loss of soil organic matter (see Section 2.4) may also cause increased soil bulk densities. In Vertisols in South Africa, it was found that aggregate stability was decreased following many years of inorganic fertilizer applications, particularly K. There was an increase in the proportion of monovalent cations (K, Na) and less Ca and Mg, which were leached. It favored dispersion, lowered stable soil aggregates, and increased soil bulk density (Graham et al., 2002a). Under sugarcane, the bulk density of different soils increases at different rates so it is difficult to establish a threshold bulk density value that affects the movement of air and water. Juang and Uehara (1971) mentioned that bulk density, in itself, is not a particularly useful index for predicting crop performance. It is, however, a good indication of what happens to the soil under continuous sugarcane cultivation. Although soil compaction is common, it can be relatively easily reversed. After 3 or 5 years when the sugarcane is plowed out and a new crop is planted, compacted soil layers may be broken up. Tillage usually lowers the bulk density, and sugarcane soils under zero tillage tend to have higher bulk densities than when the soil is tilled. Trash harvesting could lead to lower soil bulk densities because of increased soil organic matter contents (Srivastava, 2003).

3.2. Soil erosion Soil erosion is a common problem under sugarcane. Some soils under sugarcane are heavy textured, for example, Vertisols that are erodible due to their low water infiltration rates after wetting (Ahmad, 1996). When planted, after harvesting, or with excessive furrow irrigation, soils may erode even if the land is nearly flat. In other soils, compaction may be accompanied by surface sealing that reduces infiltration and increases the likelihood for runoff and erosion. Also, the heat of preharvest burning

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makes the topsoil hydrophobic that decreases soil hydraulic conductivity (Robichaud and Hungerford, 2000) and increases the potential for runoff. Putthacharoen et al. (1998) measured runoff and soil erosion under different arable crops on Quartzipsamments in Eastern Thailand. The experimental site was located on a 7% slope with annual rainfall 1300 mm. Runoff and sediment load were measured in ditches. Over a 50-month period, average annual soil erosion losses were 47 Mg ha
1. Erosion was particular severe during the first 3 months after planting but once the crop was established, there was little erosion in the successive 2 years when the canopy protected the soil and contour rows reduced runoff. After 18 months, the sugarcane was trash harvested and erosion was minimal (Putthacharoen et al., 1998). A soil erosion study in the sugarcane areas of Australia, where the industry is largely confined to the high rainfall coastal zones ( Johnson et al., 1997), monitored soil erosion at seven sites with slopes ranging from 5% to 18% (Prove et al., 1995). Soils were Oxisols and annual rainfall was 3300 mm. Soil erosion losses from conventionally cultivated ratoons were in the range of 47–505 Mg ha
1 year
1 with an average soil loss of 148 Mg ha
1 year
1. The variation was largely explained by the variation in the rainfall. Analyses of in situ and eroded soil indicated that sediment from notillage practices may be transported further from the erosion site and carry a more mobile fraction of nutrients (Prove et al., 1995). A time series analysis of remote sensing imagery, daily rainfall, digital soil, and terrain maps combined with the universal soil loss equation and field observations showed that average erosion rates under sugarcane in Australia are 16 Mg ha
1 (Lu et al., 2003). Soil loss is particularly high in newly developed sugarcane lands (Brodie and Mitchell, 2005). In Louisiana (United States), soil erosion losses under sugarcane were on average 17 Mg ha
1 (Bengtson et al., 1998) but rainfall was lower than in the Australian study and ranged from 1300 to 1600 mm year
1. Various studies have been conducted in which soil erosion under sugarcane was not measured but modeled, based on remotely sensed images or models like Universal Soil Loss Equation (USLE) (Sparovek et al., 2000). Erosion under sugarcane in Piracicaba in Southeastern Brazil was estimated to be 31 Mg soil ha
1 (Sparovek and Schnug, 2001b). In South Taiwan, multitemporal remote sensing images and numerical simulation models were used to investigate soil erosion and nonpoint source pollution (Ning et al., 2006). Total N and P measured in the runoff of sugarcane fields were six times larger than in the runoff of soils under forest. However, sugarcane made only a small contribution to total erosion and nutrient input into the river systems. In the upper northwest region of Thailand, sugarcane is an important crop and the area is expanding. Forest conversion to sugarcane accelerated soil erosion and, in some farms, the topsoil was completely eroded within 30 years of sugarcane cultivation (Sthiannopkao et al., 2006).

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3.2.1. Erosion control The available evidence shows that soil erosion under sugarcane can be high, when it is immature, after burning and harvesting, and when the soil is compacted and infiltration reduced. Annual soil loss levels per hectare ranges from 47 Mg (Thailand), 16–505 Mg (Australia), 17 Mg (United States), and 31 Mg (Brazil). On most plantations, erosion control measures are taken: drains, bunds, ridges, strip cropping, and on heavy clays, strip tillage has proven successful to control erosion (de Boer, 1997). In Brazil, bench terracing following the contour is common practice to avoid runoff and soil erosion but the interest in reduced tillage and soil cover based methods to control erosion is increasing (Sparovek and Schnug, 2001a). As much of the sugarcane is cultivated on sloping land, the advantages and lower costs of harvesting mechanically on nonterraced and noncontoured fields do not encourage anti-erosion measures (Sparovek and Schnug, 2001a). Mechanical harvesting can be hindered by hilly relief but also by low labor costs (Gunkel et al., 2007). It may restrict antierosion measures like terraces and contour farming. In Australia, no-tillage practices reduced the rates of erosion to less than 15 Mg ha
1 year
1, and the effect of no-tillage was greater than the effect of a groundcover from trash harvesting (Prove et al., 1995). A recent study in Louisiana focused on the effects of polyacrylamide (PAM) and crop residues to reduce erosion. In the area, agriculture accounts for up to two-thirds of the nonpoint source pollutions and sediments with absorbed pesticides, metals, and nutrients deteriorate aquatic life in the rivers. The addition of PAM to the irrigation water had no effect on sediment load, whereas sugarcane residues significantly reduced soil erosion. Adding PAM as a water solution had no effects on the erosion in the drains, possibly as PAM is degraded by exposure to UV radiation (Kornecki et al., 2006). However, when PAM was applied directly to the primary quarter-drains, soil erosion was significantly reduced (Kornecki et al., 2005).

4. Changes in Soil Biological Properties Changes in the soil physical and chemical properties as a result of continuous sugarcane cultivation affect the biological properties of the soils. Increasing acidity and decreasing soil organic matter as well as increased bulk density and reduced porosity and aeration cause changes in the quantity and diversity of soil life. Likewise, a change in the soil biological properties influences the chemical and physical properties of the soil. Only a few studies of this interrelationship are available (Table 2).

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4.1. Macrofauna The abundance of fire ants was investigated in different soil types in the sugarcane-growing areas of Louisiana, United States (Ali et al., 1986). The ants were found in highest numbers in Vertisols possibly related to the higher soil fertility and moisture content, and lower bulk density. In these clay soils, herbicides are better degraded and sorbed, which favors ants. Increasing ground cover of weeds and trash increased the number of ants, which are predators of the insect pests in sugarcane. In Hydrandepts under long-term sugarcane in Hawaii, no earthworms were present but earthworms were present and increased after the land was reforestated (Zou and Bashkin, 1998). This was attributed to an increase in soil organic C and N and a higher pH. Earthworm abundance and diversity has also been researched in the sugarcane fields on Oxisols of Parana´ state, Brazil (Nunes et al., 2006). Almost 300 earthworm species have been recorded in Brazilian soils but in the sugarcane soils only 6 species were identified. Fewer individuals and species were found in soils under sugarcane compared with pastures, but the lowest number of earthworms were found under forest. Dearth of earthworms under sugarcane was the effect of tillage (plowing, disking). Under sugarcane, native species are lost and exotic species dominate (Nunes et al., 2006). In South African Oxisols, earthworm abundance, biomass, and number of species were investigated under sugarcane and several other land uses (Dlamini and Haynes, 2004; Haynes et al., 2003). Numbers of earthworms, biomass, and the number of species were lowest under sugarcane compared to soils under pasture or forest. Under sugarcane, twice as many worms were found in the plant rows as the interrow is more compacted that lowers earthworm activity as roots were absent and there was low C turnover. Earthworm numbers and biomass were closely correlated with soluble C, microbial biomass activity, and the pH. There were more worms under trash-harvested sugarcane. As was found in Brazil, the earthworms in the soils under sugarcane were mostly exotic species (Dlamini and Haynes, 2004; Haynes et al., 2003). Accidentally introduced worm species dominate in many agricultural soils (Fragoso et al., 1997). The effect of burning on the insect community was investigated in Oratorios, Brazil (Araujo et al., 2005). In this area, fire is used to control pests and diseases but the effects on insect populations are poorly understood. The number of insects was reduced by burning but the insect population soon recovered after the sugarcane was burned. The few available studies suggest that both the population and abundance of the macrofauna are changed under sugarcane cultivation. Tillage, decreased C input, and burning may be the primary causes. The effects of these changes on overall soil functioning as well as on sugarcane production are yet to be quantified. Also the effects of trash harvesting and pesticide and herbicide applications on the soil macrofauna have not been well studied.

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4.2. Microbes Measurements of microbial biomass have been made in cultivated and uncultivated sites in Australia: McGarry et al. (1996a) found large reductions in microbial biomass following cultivation; they suggested that the decrease was a result of the use of pesticides. Holt and Mayer (1998) quantified microbial biomass in new and old sugarcane fields in Australia. Significantly lower microbial biomass was found in soils under long-term sugarcane (Table 11). Microbial biomass rapidly reduces after the introduction of sugarcane. Garside et al. (1997) observed that soil microbial biomass was significantly lower on old sugarcane land than on new land, again concluding that there is a rapid loss of soil microbial biomass under sugarcane, which was also observed in Oxisols in Swaziland (Henry and Ellis, 1995). The cause for such decline is not established but may be related to the use of inorganic fertilizers and biocides, and the reduction in soil organic matter. In South Africa, the effects of inorganic fertilizers on microbial biomass have shown mixed results. In some cases, microbial biomass increased whereas the fertilizer N-induced soil acidification reduced the microbial activity and the activity of exocellular enzymes (Graham and Haynes, 2005). In Australia, Pankhurst et al. (2005a) investigated the effects of soil organisms on sugarcane yield. Root rot fungus and nematodes increase with continuous sugarcane cultivation but long fallows increased biological suppression of soil organisms that may cause yield decline. Root lesion nematodes decrease under fallow but the effects are short-lived (Pankhurst et al., 2005b). Magarey et al. (1997) sampled soils continuously cropped with sugarcane and from land that has never been cultivated (Table 12). Higher levels of some fungal pathogens as well nematodes were found under permanent sugarcane but no clear picture emerged of relationships between fungi, bacteria, and actinomycetes and land use. It was concluded that yield

Table 11

Microbial biomass carbon at six sites in Queensland, Australia Microbial biomass (mg C g
1 soil)

a

Site

New landa

Old landb

Difference

Tully Costanzo Harney Fortini Ingham Kalamia

591  155 590  279 192  20 372  57 732  73 336  134

357  45 519  295 216  11 125  14 313  65 160  70

p < 0.05 ns ns p < 0.001 p < 0.001 p < 0.05

New land is land that not been under sugarcane before or had been cultivated less than 6 months. Old land is land that has been cultivated with sugarcane for prolonged periods. Type II data, modified from Pankhurst 2005b.

b

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Table 12 Soil biological properties under permanent sugarcane, grassland, and rainforest in northeast Australia Sugarcane 6


1

Total fungi (10 g ) Total bacteria (108 g
1) Total actinomycetes (106 g
1) Fungal pathogens Pachymetra chaunorhiza (spores g soil
1) Pythium spp. (% baits colonized) Nematodes Pratylenchus zeae (nematodes kg
1) Helicotylenchus spp. (nematodes kg
1)

Grassland

Rainforest

2.2 3.7 48

3.4 4.1 21.8

36 17

0 33

0 17

273 273

0 0

0 0

4.2 4.1 5.4

Type II data, modified from Magarey et al. (1997).

decline has a major biological component (Magarey et al., 1997), possibly as reduced microbial activity results from decreased soil organic C, which was also found in South Africa (Dominy and Haynes, 2002; Graham and Haynes, 2005) and India (Suman et al., 2006). In several parts of the world, sugarcane is irrigated. This affects the soil moisture regime and thus the microbial activity. A study in Zimbabwe investigated the effects of irrigation-induced salinity on microbes. Soils were sampled under dead and dying sugarcane, poor, satisfactory, and good cane growth, and from adjacent sites under native vegetation. Increasing salinity and sodicity resulted in a progressively smaller, more stressed microbial community that was less metabolically efficient. Agricultureinduced salinity and sodicity influences the chemical and physical characteristics of soils and greatly affects soil microbial and biochemical properties (Rietz and Haynes, 2003). Changes in soil microbial biomass are closely related to changes in the soil organic matter status; the microbial biomass is governed by the same factors. A reduction is commonly perceived to negatively affect the soils productivity through, for example, reduced organic matter mineralization. The soil biological component of sugarcane cultivation has been stressed in various studies and is of importance for improved and sustained production.

5. Environmental Issues Environmental issues resulting from continuous sugarcane cultivation for bioethanol production can be explored at different scales. Energy production is important but also the energy consumption of the production

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system, now more and more sugarcane is being harvested mechanically and increasing rates of inorganic fertilizers are being used. The production of greenhouse gasses is of concern, including the release of methane and NOx by preharvest burning and inorganic fertilizers, and it may be enhanced in trash-harvested systems because of higher soil moisture contents (Macedo, 1998; Tominaga et al., 2002). Locally, contamination of the soil and water resources may occur. Commercial sugarcane is usually grown with herbicides that represent about 50% of all biocides used in many countries (Lanchote et al., 2000). Most studies on the environmental impact of sugarcane have focused on the off-site effects including deterioration of surface water and air quality. Nearly all studies have been conducted in the United States and Australia, although a few have been conducted in Barbados and Brazil, reflecting differences in research priorities and capacities between economic regions (Bouma and Hartemink, 2002; Hartemink, 2002). The Australian sugarcane industry is adjacent to the environmentally sensitive areas Great Barrier Reef and rainforests. The industry is intensifying with fewer and larger farms, using more fertilizers, continuous cropping, and utilizing more marginal soils (Gourley and Ridley, 2005). As reported in Section 3.2, soil erosion rates can be high but the precise rate and impact of sediment delivery to estuarine and marine environment is not well understood ( Johnson et al., 1997). Sugarcane production has significant impact on riverine water quality compared to grazing or forestry (Brodie and Mitchell, 2005). This is mainly because of higher N, P, and suspended solids in streamwater draining from highly fertilized sugarcane lands. Nutrient levels may have increased over the years and Rayment and Bloesch (2006) compiled soil acid P soil tests data of 105 sugarcane sites in Australia; they found that since the 1950s, P levels had increased from about 40 to over 100 mg kg
1. High rates of P applications resulted in high levels of P in the soil, with risks for leaking to the groundwater. Both the sugarcane industry and the broader community realize the potentially adverse ecological effects of discharges to the Great Barrier Reef lagoon. The deterioration of surface and groundwater quality is perceived by the farmers to be a consequence of new farm management strategies (Arakel et al., 1993). Several measures have been taken following concerns about the downstream effects (Bunn et al., 1997). In Australia, the policy has been toward a voluntary rather than regulatory approach and the industry has drawn up a national program to raise awareness among growers and introduced a ‘‘Sustainability in sugar’’ checklist (Gourley and Ridley, 2005). An acute concern in coastal areas is the drainage and oxidation of acid sulfate soils. Almost 10% of the soils under sugarcane in Australia are underlain by acid sulfate soils: 18,000 ha in New South Wales, 20,900 ha in South Queensland, and an unknown area in far North Queensland. Drainage of these soils has lead to acidity

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discharge carrying heavy metals and arsenic into aquatic ecosystems. Such discharge has extremely negative effects on the environment (Dent and Pons, 1995; Kinsela and Melville, 2004). The industry in Australia has responded with management practices to minimize the hazard, notably by avoiding deep drainage, and national legislation now prohibits development of these soils. Such soils are also found in Guyana (Dent, 1986) and any expansion of sugarcane for bioethanol (and oil palm for biodiesel) on coastal plains needs to consider this issue.

5.1. Herbicides and pesticides Herbicides like atrazine, diuron, 2,4-D, and alachlor are extensively used in sugarcane cultivation. Atrazine is probably the most widely used herbicide in sugarcane. These herbicides are water soluble and there is a concern that they may contaminate the soil, vadose zone, and surface and groundwaters. They may leach from sugarcane fields and that may take place along preferential flow paths and cracks in clay soils (McMartin et al., 2003). The herbicides may also wash off the land by runoff and soil erosion. There is some difference in the risks in using these herbicides: atrazine and ametryne are mostly degraded by sunlight and alachlor dissipates faster than atrazine ( Javaroni et al., 1999). Southwick et al. (1992) measured atrazine leaching on Vertic Haplaquepts under sugarcane in Louisiana. Maximum concentrations, found within 11 days after application, ranged from 82 to 403 mg liter
1. The lifetime health advisory limit for drinking water in the United States is 3 mg liter
1; this concentration was reached in 20–30 days after application (Southwick et al., 1992). Southwick et al. (1995) also measured leaching of atrazine and metribuzin: leaching of both herbicides was high directly after application but decreased after some weeks. Total losses ranged from 0.4% to 2.0% for atrazine and from 0.4% to 1.7% for metribuzin. Atrazine concentrations in the drainage water were again above the United States health advisory levels but the lifetime health advisory limit for metribuzin was not reached (Southwick et al., 1995). Water quality data collected over several years in the sugarcane area showed that one in five detections of atrazine is above the maximum contaminant level for drinking water (Southwick et al., 2002). Although many factors are involved, the method of application is a main factor determining the rate of herbicide loss (Bengtson et al., 1998). In Brazil, Lanchote et al. (2000) measured residues of atrazine, simazin, and ametryne in surface and groundwater collected in a sugarcane area near Sa˜o Paulo. Ten water-sampling points were selected in a watershed, of which nine were taken from surface water and one from groundwater. In total, 250 samples were collected but atrazine residues were detected in only 17 samples. The concentrations were below those recommended as

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safe by international agencies of environmental control. Leaching and halflife of the herbicide tebuthiuron were examined under sugarcane in Santa Rita do Passa Quattro, Sa˜o Paulo State. Soils were Typic Quartzipsamments. The herbicide was applied and soil samples were taken at different depths at regular intervals up to 300 days after the application. No herbicide residues were found and there was much more rapid degradation and less mobility than previously assumed; half-life was 20 days and after 180 days, there was no measurable residue in the soil (Cerdeira et al., 2007). In Mauritius, the leaching of herbicides was measured under sugarcane on Vertisols and Andosols. There was a lower risk of herbicide leaching than in temperate regions due to the high temperatures and the highly adsorbent soils. The herbicides were moderately to very immobile, although there was a considerable difference between the herbicides and the two soil types. Overall, the potential for leaching was considered very low, but during the 40 days per year when there are cyclones with high rainfall intensities, there may be considerable leaching losses (Bernard et al., 2005). Most environmental impact studies in sugarcane have focused on herbicides and few on pesticides because they are used less frequently. On an active ingredient basis, about 11 times more herbicides than pesticides are used in the Australian sugarcane industry (Arthington et al., 1997). Environmental regulations caused a shift in the use of biocides in Australia: from the early 1950s until the late 1980s, organochlorine pesticides were widely used but were banned in the 1980s. A survey has investigated residues in sugarcane soils and in the coastal alluvial floodplains. Marine surface sediment samples and three sediment cores had no detectable levels of organochlorine pesticides, but easily detectable concentrations were found in the soils under sugarcane. It is likely that these pesticides move from the sugarcane soils to the near-shore marine environment by runoff and soil erosion (Cavanagh et al., 1999). Until 1985, persistent organochlorine compounds such as aldrin and heptachlor were also commonly used as insecticides on sugarcane in Brazil. Traces of these insecticides were investigated in soils, colluvium, submerged sediments, and organisms (worms, larvae) in a watershed in a sugarcane area. Most insecticides applied in the past were not detected, but organochlorine compounds that remained on the market after 1985 were detectable in significant amounts. It was concluded that a complete ban is probably the only solution for avoiding the dispersion of these products into the environment (Sparovek et al., 2001). Leaching of herbicides and pesticides is of serious concern wherever high input agriculture is practiced. Earlier work in Hawaii has shown that leaching of herbicides under sugarcane is negligible because of high adsorption rates in the soil (Hilton and Yuen, 1966). The available evidence shows no serious leaching losses under sugarcane because many soils have a high clay content (Vertisols), organic matter content (Histosols), or organic

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matter content. More herbicides and pesticides are lost through erosion and runoff but trash harvesting and zero-tillage reduces the risk for such losses. As zero tillage is often combined with higher levels of herbicide use, further studies are needed to investigate how such systems affect herbicide losses.

5.2. Inorganic fertilizers Section 2.5 summarized reports on nutrient losses from fertilized sugarcane systems. High amounts of fertilizer N in the sugarcane and the contamination of surface and groundwater are a concern (Baisre, 2006). Nitrogen concentrations are often not considered to be an important criterion of surface water quality because of denitrification and biological cycling in an open system (Anderson and Rosendahl, 1997). However, there could be increased use of inorganic fertilizers to boost sugarcane production following high ethanol prices and increasing environmental impact regulations for sugarcane growers may follow. 5.2.1. Nitrogen Leaching of N is very likely as in most sugarcane areas application rates are high, rainfall or irrigation are abundant, and N use recovery is low. Studies on N leaching under sugarcane in Brazil have been limited—possibly as N applications are low (40–60 kg N ha
1) and much of the N in sugarcane is derived from BNF (de Oliveira et al., 2006)—see also Section 2.6. In Australia, N is applied at rates of 150–300 kg N ha
1 and excess N has been linked to NO3 contamination of water supplies as well as contributing to greenhouse gas emissions (Dalal et al., 2003; Gourley and Ridley, 2005; Weier, 1998). Surface water quality in forested wetlands of Louisiana is being reduced by nutrient input from adjacent agricultural production areas. A 15N study was undertaken to assess the input of fertilizer N applied to sugarcane fields and to forested wetlands (Lindau et al., 1997). The major soil orders were poorly drained Vertic Haplaquepts and Aeric Fluvaquents. Fertilizer N draining into adjacent forested wetlands was estimated to be only a small fraction of the amount applied and concentrations of NH4 and NO3 were low. About 3–4% of the applied N was removed by runoff. Even after anhydrous NH3 application, no increase was observed in the NH4 and NO3 concentration. This was explained by the high clay contents of the soil and the injection of the anhydrous NH3 at 0.10–0.15 m below the soil surface. In another study in Louisiana, it was found that NO3 and P were present in the surface water but not at high levels and it could also not be directly linked to sugarcane cultivation (Southwick et al., 2002). In order to reduce N losses on sugarcane plantations in Mauritius, research has focused on the use of drip-fertigation (Ng Kee Kwong et al., 1999).

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Applications of fertilizer N could be reduced by 30% from 120 to 80 kg N ha
1 year
1 without a reduction in growth pattern or sugarcane yields. However, investments in drip-fertigation are large and it may not be economically and technically feasible for all sugar producing areas. Alternatively, increased plant densities may reduce the leaching of N in sugarcane systems (Yadav and Prasad, 1997). 5.2.2. Phosphorus In Australia, P is applied at rates of 15–50 kg ha
1. Application rates are often in excess of recommendation to avoid the risk of P-limited yields (Bramley et al., 2003; Thorburn et al., 2005). Application rates do not take into account the differences between different soils in their ability to release or sorb P; industry recommendations do not consider soil properties (Edis et al., 2002). Also, many sugarcane soils have considerable mycorrhizal density that may enhance the P supply (Kelly et al., 2005). As a result, many soils under sugarcane are well supplied with P. This is not necessary an advantage—in the United States, it was found that high soil P levels may increase rust severity ( Johnson et al., 2007a). An evaluation of 105 sugarcane sites in Australia showed that 84% of all soils sampled had excessive P levels, following annual applications of 20 kg P ha
1. It will take a long time to deplete the high soil P levels (Rayment and Bloesch, 2006). Also in Brazil, continual fertilization with P has lead to high levels of organic and inorganic P in the topsoils (Ball-Coelho et al., 1993). Soil erosion, fertilizer P loss, and groundwater flow result in blue-green algae and excessive growth of aquatic macrophytes (Arthington et al., 1997). Algal blooms are also increased by reduced water flow—a problem that occurs in the sugarcane areas of Everglades (United States) (Anderson and Rosendahl, 1997) where regulatory program with best management practices was introduced and has considerably reduced the P level in drainage waters of sugarcane farms (Rice et al., 2002). It needs to be ascertained whether current P levels are acceptable for South Florida wetlands. Such assessment may be hard to make because there is a difference between freshwater and marine water on the response to increased P input from agricultural drainage waters. In Australia, it was found that environmental problems posed by P attached to sediments from sugarcane land is likely to be greater in freshwater than in marine ecosystems (Edis et al., 2002). Several cultural practices could reduce the loss of biocides from sugarcane fields: Southwick et al. (2002) reported variable success in the reduction of runoff losses of biocides as a result of conservation tillage; subsurface drains that increase infiltration seem to be more effective to reduce runoff and reductions up to 25% have been reported; filter strips or water settling areas may also reduce runoff, soil erosion, and sediment loss (Southwick et al., 2002).

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5.2.3. Heavy metals and rare earth elements Little is known about heavy metal accumulation in sugarcane systems although flora and fauna are affected by even low concentrations. Reports from Australia have shown a sevenfold increase in Cd in the topsoils under sugarcane compared with uncultivated sites (Arthington et al., 1997), probably caused by Cd contamination in P fertilizers, which is common (Kirkham, 2006). It is suspected that preharvest burning could dissipate Cd while trash blankets may concentrate Cd at the soil surface, where it could be eroded (Arthington et al., 1997). In the sugarcane areas of southern China, Chua et al. (1998) investigated the accumulation of cerium (Ce), a rare earth element (REE), nonradioactive, and moderately toxic. It was shown that Ce entered the sugarcane plants via the leaves exposed to atmospheric contaminants, via the roots in soils contaminated by Ce and other REEs, or applied with inorganic fertilizers (Rodriguez-Barrueco, 1996). Official limits to residual concentrations are not available but high REE concentrations in the soils under sugarcane could lead to harmful effects for humans consuming sugarcane products (Chua et al., 1998). In the Everglades of Florida, serious mercury contamination of freshwater fish in 1989 was related to preharvest burning of sugarcane (Patrick et al., 1994). Soils are Histosols and the average mercury content of the Histosols was only 0.15 mg kg
1; Hg concentrations in the sugarcane stalks were also low. The study concluded that direct emission of Hg from sugarcane fields during preharvest burning was only a minor source (2%) of atmospheric Hg and left open the question on the origin of the Hg contamination.

5.3. Air and water quality The bioethanol program in Brazil started after the oil crisis in 1973 with the aim to make the country less dependent on imported oil. A big industry developed and gasoline has been replaced in large measure by ethanol from sugarcane. The cleaner air in the cities has been at the expense of increased smoke from preharvest burning in sugarcane areas. Depending on the amounts of crop residues, over 3000 kg ha
1 of C is released as CO2; the smoke is a health problem in many areas and of particular concern in newly developed suburban areas adjacent to plantations (Kornecki et al., 2006). The smoke contains respirable particles that have a size less than 10 mm (Boopathy et al., 2002). Research in Brazil has shown that increases in total suspended particles generated from preharvest burning were associated with asthma hospital admissions (Arbex et al., 2007). Also in Louisiana, smoke from burning sugarcane accounts for much air pollution (Kornecki et al., 2006) and a link was found between asthma admissions hospital visitations and sugarcane burning. As the prevalence of asthma in both adults and

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children is rising in many parts of the world, detailed studies on the effects of weather, pollen counts, and air pollution from sugarcane burning and the pollution from other sources are needed (Boopathy et al., 2002). Water quality is affected by sugarcane cultivation through loss of biocides and inorganic fertilizers (see Sections 2.5 and 5.1) and through sugarcane processing plants. These plants produce waste waters (stillage, vinasse) that are used for fertigation but some are discharged in streams and rivers like in the sugarcane areas in Cuba (Rosabal et al., 2007). In Pernambuco in the northeast of Brazil, the waste water heats the riverwater, contains organic acids, and has a high biological oxygen demand (Gunkel et al., 2007)—all of which deplete aquatic life. A number of treatment options exist including wastewater lagoons, trickling filters, and activated sludge systems. In areas with high risks of water pollution, changes in land use and reforestation may be the only options (Gunkel et al., 2007).

6. Discussion and Conclusions Sugarcane is a major cash crop, increasingly used for bioethanol production. Given the increase in oil prices coupled to the demand for renewable energy sources, it is likely that the area under sugarcane will further expand. Both expansion and further intensification (fewer and bigger farms) affect the soil and wider environment.

6.1. Sugarcane for bioethanol Sugarcane is an ideal crop for renewable energy because of its rapid growth and high energy production per hectare. Fossil energy is needed for growing of the crop and the production of bioethanol, which partly offsets the energy produced. In Brazil, fossil energy costs are minimized by the use of processing products like bagasse for energy. The energy balance (yield over fossil energy) of such systems may range from 9 to 11 (Macedo, 1998), which compares very favorably to many other biofuel crops. In part, this favorable balance is explained by the relatively low N application rates to sugarcane in Brazil because of the high rates of BNF. In many agricultural systems, inorganic fertilizers are a major budget line. Overall, BNF can be considered one of the principal reasons for the success of the bioethanol program in Brazil (Medeiros et al., 2006). Several cultural practices that reduce the energy demand for growing sugarcane. Tillage before planting requires about one-third of the total operational energy. Zero tillage seems to have little effect on crop yield whereas mechanical trash harvesting increases the energy demand as compared to preharvest burning (Srivastava, 2003). However, preharvest

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burning is increasingly criticized because of public health issues related to the smoke and because of the loss of beneficial crop residues. Thus, the use of bioethanol has effectively reduced air pollution in many cities but the urban areas surrounding sugarcane areas are largely affected by the preharvest burning, which takes place about 6 months per year (Arbex et al., 2007). Another aspect that deserves discussion is the expansion of sugarcane in relation to land used for food production. It is projected that in many industrialized regions, the area under agriculture will decrease whereas the area under agriculture in developing regions is increasing (Smeets et al., 2007). Most of the human population increase takes place in the developing regions (Fischer and Heilig, 1997) where the need to increase crop production is largest (Sanchez, 2002; Swaminathan, 2006). It has been calculated that 55% of the present global agricultural land will be needed for food production in the year 2050, if high external input agriculture is practiced (Wolf et al., 2003). The remaining 45% can be used for other purposes including biofuels. There will be no land available for biomass production when low external input agriculture is practiced (Wolf et al., 2003). Little new land is available in developing regions (Young, 1999) so crop production for food and biofuel competes for the same land area. Some expansion is possible through the clearing of forest or savanna, but most of the increased biomass production needs to come from intensification of the present systems. According to Hill et al. (2006), a biofuel should provide a net energy gain, have environmental benefits, be economically competitive, and able to be produced in large quantities. Sugarcane for bioethanol can fulfill these criteria. The net energy gain is several times the input and it is economically grown in many countries without the subsidies that other biofuel crops receive (e.g., corn in the United States or rapeseed in Europe). It is not affecting staple food production in the United States or Australia—where it is grown for sugar. In Brazil and some other tropical countries where sugarcane is mainly grown for bioethanol, a further increase may compete with food production; that assessment is yet to be made.

6.2. Effects on the soil Most studies have shown that soil acidification takes place under sugarcane, principally due to the use of N fertilizers containing or producing NHþ4 . All ammoniacal N fertilizers release protons when NHþ4 is oxidized to NO
3 by nitrifying microorganisms. Also, mineralization of organic matter can contribute to soil acidity by the oxidation of N and S to HNO3 and H2SO4 (Sumner, 1997). Because organic matter declined in most soils under sugarcane, it may have contributed to the increase in soil acidity. Acidity is reversible; liming readily restores productivity but if acidification has also

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taken place in the subsoil, amelioration is much more difficult. There is only a small response of sugarcane to lime on moderately acid soils (Turner et al., 1992) whereas in other studies, a decrease in the sugar content was found after lime applications (Kingston et al., 1996). Sugarcane is fairly tolerant of acidity and high concentrations of exchangeable and soluble Al (Hetherington et al., 1988); avoiding strong soil acidification might be a better option than the use of lime to correct for high acidity inputs. Soil organic C dynamics have received much attention in sugarcane, but there are some conflicting reports. A part of the problem is that total soil organic C determined by the Walkley and Black or the dry combustion method is not very sensitive to short-term changes in land use. Long-term observations are required to pick up statistically significant differences in soil organic C levels. It is also related to the spatial variability in total soil organic C. Notwithstanding these methodological problems, total soil organic C decreased in most topsoils and in most soil types. This may be the effect of tillage that causes increased soil organic matter decomposition compared with soils under natural ecosystems, but, also, because of lower inputs of organic matter in sugarcane systems. Soil texture plays an important role in the rate of change in soil organic C and this change also differs for different size fractions. An equilibrium is reached after many years but it is generally lower than the initial level in the soil under forest. In a number of soils, it was found that levels of soil organic C increased in the subsoil. The decrease in soil organic matter under continuous sugarcane reduces soil biological activity and increases the susceptibility of the soils to physical degradation. Soil compaction is a common problem in mechanized systems, mainly due to the heavy machinery used for field operations at the wrong soil moisture levels. Also, frequent applications of inorganic fertilizers may lower soil aggregate stability of some soils (Graham et al., 2002a) and increase the bulk density and lower the rates of water infiltration (Mills and Fey, 2003). Erosion losses up to 505 Mg soil ha
1 year
1 have been reported under sugarcane. Erosion can be high after the harvest and with replanting, especially on sloping land (Blackburn, 1984). Sugarcane is more prone to soil erosion than other perennial crops because the periodic harvesting removes almost all vegetation from the field (Hartemink, 2005b). On the other hand, sugarcane covers the soil in most parts of the year so reduces the risk for soil erosion. Erosion means loss of productive topsoils but also sedimentation in the lower part of the catchment that may cause environmental problems. In Australia, there seems to be little evidence to support claims that sediment deposition resulting from sugarcane cultivation has had a major impact on the characteristics of the rivers and sugar catchments over the last 50–100 years ( Johnson et al., 1997). However, there is increasing concern about the erosional effects and green harvesting methods have been advocated to reduce soil erosion (Wood, 1991).

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The advantages of green or trash harvesting also include soil water conservation, reduced soil temperature, increased soil fertility and soil organic matter, and improved soil structure. Residue burning leads to a loss of N, reduced soil organic matter with deterioration of physical and microbiological properties, and an increase in greenhouse gases (Basanta et al., 2003). The trash has nematocidal properties (Akhtar, 1993), combats weeds, and there are also more roots in trash-harvested systems that increase nutrient uptake, particularly P (Ball-Coelho et al., 1993). Several reports have indicated that the trash harvesting has the potential to maintain or increase soil organic carbon contents (de Resende et al., 2006; Noble et al., 2003; Razafimbelo et al., 2006) and reduce the susceptibility of the soil to compaction (Barzegar et al., 2000). There are also some disadvantages. Trash may hinder tillage, reduce nutrient availability through immobilization, and cause waterlogging resulting in N losses through denitrification especially on poorly drained soils (Wood, 1991). Some studies have found that sugarcane trash is allelopathic (Viator et al., 2006). In cooler areas (e.g., Louisiana, United States, or North South Wales, Australia), the trash results in increased soil moisture and lower soil temperatures that not only delay the reemergence of a ratoon crop but can also increase sugarcane infection by parasitic soil fungi (Viator et al., 2005). Consequently, sugarcane yield may be reduced by 4.5–13.5 Mg ha
1 ( Johnson et al., 2007b). Trash quantities are large (7–12 Mg DM ha
1) and contain high amounts of C (3–5 Mg C ha
1) and N (28–54 kg N ha
1). In the United States, a trash-harvested field may contain up to 24 Mg DM ha
1. The C/N ratio is typically over 70. In some studies, it was found that the trash is decomposed within a year (Vallis et al., 1996), but it may also take longer (Robertson and Thorburn, 2007a). In the longer term, it may improve soil N levels but that depends on the climate, soil, and management practices (Meier et al., 2006). For example, in South Africa and Australia, it was found that sugarcane yields were higher under trash retention because of better moisture conservation (Thorburn et al., 2005), but soils in trash-harvested systems are more acid (Hartemink, 1998a; Noble et al., 2003). Research has shown that fertilizer N applications should not be reduced in the first 6 years after trash harvesting has started and small reductions (15–40 kg ha
1) may be possible after 15 years of trash harvesting but that is site dependent (Robertson and Thorburn, 2007b). Overall, sugarcane trash is N source of slow availability to the crop (Basanta et al., 2003).

6.3. Effects on air and water Sugarcane is either grown under rainfed conditions with high rainfall or irrigated areas which may enhance leaching of fertilizers. In most sugarcane areas, N applications are high and the recovery of fertilizer N ranges from

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20% to 50%. Research in the United States showed that leaching losses were up to 60% of the applied N; in Australia, leaching, runoff, and denitrification caused loss of 60% of the applied N (Vallis et al., 1996). There is concern about the effects of rising NO3 levels in groundwater resulting from intensive cropping in relation to environmentally sensitive areas. Gaseous losses are also important and there are indications that sugarcane may loose some of its N through the aerial parts of the plant. Nitrogen losses from denitrification and ammoniacal losses from N applied on trash contribute to greenhouse gas emissions. A quantitative link between gaseous and leaching losses of inorganic fertilizers and the wider environment has not been clearly established for sugarcane—this applies to most agricultural crops. Increasing public and political concern and refined measurement techniques might lead to new regulatory measures to minimize nutrient losses to the environment. This also applies to herbicide and pesticide use. Further development of integrated pest management practices that minimize the use of pesticides is needed to reduce the environmental impact of sugarcane cultivation ( Joshi and Viraktamath, 2004; Reay-Jones et al., 2005; Robertson et al., 1995). Sugarcane cultivation affects the balance of CO2 and other greenhouse gas emissions. Methane is emitted with preharvest burning, when stillage is applied as a soil conditioner, and when fertilizer and bagasse is burned. Also NOx is emitted from the soil. Growing sugarcane fixes atmospheric CO2 by photosynthesis but there are emissions from the combustion of fossil fuel for field operations, transport, agrochemical production, irrigation, as well the processing plants. The carbon benefit comes from substituting gasoline by ethanol bagasse for fossil fuels in the processing plants (Macedo, 1998). The net contribution of the sugarcane-bioethanol industry to atmospheric CO2 has not been assessed. Largely unquantified, and ignored in the CO2 footprint discussion, is the net changes in soil organic C. This chapter has shown that soil organic C declines under sugarcane cultivation. The decline is different for different soil types and much depends on the original C level and the period of cultivation. Some soils under sugarcane have released very large amounts of CO2 when cultivated. In the Everglades of Florida (United States), some 167,000 ha of mainly peat (Histosols) is under sugarcane (Muchovej et al., 2000). More than half of the wetlands have been drained (Schrope, 2001) and since 1900, many areas have lost 2.7–4.0 m of surface elevation due to subsidence upon drainage. Clearly, the drainage of such peat lands has emitted very large amounts of CO2 that will not be sequestered by growing sugarcane. In most mineral soils, organic C levels reach equilibrium after many years of sugarcane cultivation, and once soil organic C levels have stabilized, sugarcane cultivation can be seen as a net atmospheric CO2 fixer. The effects of sugarcane cultivation on the atmosphere include smoke from preharvest burning and processing factories. The smoke affects the

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health of people living in the surrounding areas. Trash harvesting is the obvious solution but under many conditions (steep slopes, terraces), and for many small-scale farmers, mechanized harvesting is not feasible and preharvest burning is the only option. An indirect effect of the sugarcane for bioethanol program is that the air quality in Brazilian cities has much improved since use of ethanol for cars (Arbex et al., 2007), as almost onefifth of all cars in Brazil run on ethanol. Sugarcane ethanol is a relatively clean fuel as it contains no sulfur oxides, solid microparticles of carbon, benzene, and lead (Pessoa et al., 2005).

6.4. Sugarcane yields Few of the many reports in the scientific literature on the effects of continuous sugarcane cultivation on the soil and the environment quantify the effects of changes on sugarcane yields. This is, perhaps, not surprising as such relations are hard to establish, or may not be directly measurable—they occur gradually and there is considerable within-field variation of both yield (e.g., Johnson and Richard, 2005a,b) and soil properties (e.g., Tominaga et al., 2002) that may mask the effects of soil changes. Sugarcane yields are indeed highly variable and range from 36 to 134 g ha
1 in Louisiana, from 65 to 150 Mg ha
1 in Australia, and from 70 to 200 Mg ha
1 in Brazil. Many factors other than trends in soil chemical properties may explain yield patterns. One of the factors that have received some attention in the literature is the relation between imbalanced plant nutrition and pests and diseases. Recent work in Louisiana, based on earlier studies in Florida, has shown that sugarcane rust is related to excess P and S levels and soil acidity. A simple way of starting to investigate the relationships between a yield pattern and the trends in soil chemical fertility is to present yield data and soil data from differently producing sugarcane fields. Muchovej et al. (2000) measured soil chemical properties in ‘‘good’’ and ‘‘poor’’ spots in Florida. In the study area, sugarcane often exhibits areas of reduced growth that can comprise up to 25% of a field. Dominant soils were Spodosols. Soil pH, organic C, and exchangeable cations were significantly higher for the areas of good sugarcane growth (Table 13). Nutrients, organic C, and microbial populations were less with increasing depth. Although moisture appeared to be an important factor in the areas of reduced growth, a lower or higher water table was not associated with low-yielding areas in the field. Differences in soil chemical properties may be an important explanation for the differences in sugarcane growth. At a sugarcane plantation in Papua New Guinea, annual sugarcane yields have ranged from 28 to 88 Mg ha
1 over 15 years (Hartemink and Kuniata, 1996). This wide variation was explained by sudden and catastrophic infestation of pests and diseases; to a lesser extent, yields were also affected by weed competition. Changes in soil properties under continuous cultivation

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Table 13 Soil chemical properties from Spodosols under ‘‘good’’ and ‘‘poor’’ sugarcane growth at two sites in Florida, United States Sampling depth (m)

0–0.15

0.15–0.30

Site I

Site II

Soil chemical property

Good

Poor

Good

Poor

pH Organic C (g kg
1) Available P (mg kg
1) Exchangeable Ca (mmolc kg
1) Exchangeable Mg (mmolc kg
1) Exchangeable K (mmolc kg
1) pH Organic C (g kg
1) Available P (mg kg
1) Exchangeable Ca (mmolc kg
1) Exchangeable Mg (mmolc kg
1) Exchangeable K (mmolc kg
1)

6.4 7.3 200 162.7 10.2 3.1 6.7 5.3 55 35.9 3.2 1.5

5.8 4.6 166 90.7 10.2 2.0 6.3 2.9 54 21.9 3.4 1.0

6.8 5.8 38 61.9 1.8 2.0 6.6 3.5 40.0 30.8 1.5 2.3

5.9 4.5 23 23.5 1.6 1.4 6.3 2.7 27.9 11.9 0.5 0.8

Modified from Muchovej et al. (2000).

included decreases in pH, available P and exchangeable K, and soil compaction (Hartemink, 1998c). Leaf nutrient concentrations of N, P, and K also declined (Hartemink, 1998b). It was concluded that the yields were largely influenced by insect pests and diseases but that the management of soil fertility is increasingly important once those problems have been solved. In Australia, there is widespread evidence that sugarcane yield has been declining. Higher yields are usually obtained on soils that have not been cultivated before (Lawes et al., 2000; Wood, 1985). The yield decline is thought to be caused by a combination of enhanced soil-borne pests and diseases (McGarry et al., 1996a), the frequent tillage, and the use of heavy machinery (McGarry et al., 1996b; Pankhurst et al., 2005a). Soil management practices such as excessive cultivation, insufficient fallowing, the burning of crop residues, and the application of large amounts of N fertilizers are believed to be partially responsible for the decline in sugarcane yield in Australia (Wood, 1985). Pankhurst et al. (2005b) investigated the effects of fallow periods and different fallows on sugarcane yield and soil biological properties (Table 14). A fallow period and fumigation resulted in significant higher yields compared with continuously cropped fields, although the effect differed between sites and soil types. The increase in yield was most likely due to reduced populations of soil organisms (e.g., lesion nematodes) that cause yield decline in sugarcane (Pankhurst et al., 2005b). The Australian studies had considerable impact on the way sugarcane was cultivated and several practices evolved to improve soil: less tillage is being practiced and preharvest burning has been replaced by trash

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Table 14 Effect of fallow and fumigation on plant crop sugarcane yield (Mg ha
1) at five sites in Australia

Soil type

Continuous sugarcane Continuous sugarcane fumigated Pasture (grass/legume) Pasture (grass) Pasture (legume)

Tully

Ingham

Burdekin

Mackay

Bundaberg

Alfisols, Ultisols

Alfisols

Inceptisol

Alfisol

Alfisol

44

38

118

60

112

83

83

152

101

143

73

76

153

104 121 116

Modified from Pankhurst et al. (2005b) based on the work of A. Garside.

harvesting. Preharvest burning is only possible in dry weather (Wood, 1991) and it may cause about 30% of the annual N removal in a sugar crop (Valdivia, 1982). There may be considerable K losses as ash being blown off the plots following burning (Graham et al., 2002a). In South Africa, loss of soil organic matter under sugarcane as a result of preharvest burning is assumed to be a major contributor to soil degradation and may have yield effects (Graham and Haynes, 2006). In Brazil, it was found that over a 16-year period, trash harvesting increased sugarcane yield by 25% (de Resende et al., 2006); in India, trash harvesting improved crop yields from 49 to 73 Mg ha
1 (Srivastava, 2003); and in the Philippines, yields are increased by trash harvesting (Mulkins, 2000). Fallow periods after the plowing-out of the sugarcane is common in some areas and may give yield increases: in Alfisols in Swaziland, sugarcane increased from 129 to 140 Mg ha
1 after a fallow period (Nixon and Simmonds, 2004), but it is not known whether the increase is the effect of improvement in soil physical or biological soil attributes, or a decline in pests and diseases, or a combination of factors. The relation between soil changes and sugarcane yield is not always clear. In Mexico, a decline in soil fertility after 30 years of sugarcane cultivation was accompanied by an increase in sugarcane yields. This was explained by improved crop husbandry, although no details are given (de la F et al., 2006). In Bangladesh, sugarcane yields have declined from about 43 Mg ha
1 in the early 1970s to 39 Mg ha
1 in the 1990s. Based on farmers’ interviews, this decline is perceived to be caused by organic matter depletion and a general decline in soil fertility (Hossain, 2001).

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This chapter has shown that soils are much changed under continuous sugarcane cultivation but the effects of these changes on yield are hard to quantify. In many parts of the world, yields have increased whereas in many fields, the soils had adversely changed (i.e., lower pH, loss of soil organic matter, increased bulk density). These yield increases are attributed to better crop husbandry, new cultivars, and higher rates of external inputs, particularly N fertilizer. Sugarcane growers are concerned about soil changes. Management techniques have been adopted to improve soil conditions or reduce the negative effects of continuous cultivation including trash harvesting and zero tillage. Improved soil management strategies should be targeted toward the genetic potential of the sugarcane.

6.5. The potential for precision farming Most environmental impact studies in sugarcane areas have been conducted in the United States and Australia, where the crop is cultivated with high levels of inputs (herbicides, pesticides, inorganic fertilizers etc). The heavy use of agrochemicals is a concern but these inputs also guarantee high yields. Precision agriculture has great potential in sugarcane monocropping systems; it may result in increased yield, savings in fertilizers and biocides, and reduced potential for off-farm environmental damage (Wood et al., 1997). Sugarcane is an ideal crop for precision agriculture as it is capital-intensive, grown on a large scale, and monocropped for several years. Various studies have investigated the possibilities, mainly in Brazil (e.g., Cora et al., 2004; Galvao et al., 2005; Magalhaes and Cerri, 2007; Sparovek and Schnug, 2001a), United States ( Johnson and Richard, 2005b), and Australia (Bramley and Quabba, 2002; Everingham et al., 2007) but also in Mauritius, India, and South Africa. However, the economic and ecological benefits of precision agriculture that have so widely been advocated and used in other cropping systems (Cox, 2002; Pierce and Nowak, 1999; Robert, 2002; Swaminathan, 2006) have not been fully exploited in sugarcane. Given the rapid expansion of the crop in many parts of the world, such technologies are needed for maintaining high yields and sustaining a healthy environment Several growth models have been developed for sugarcane—particularly in Australia (e.g., Cheeroo-Nayamuth et al., 2000; Stewart et al., 2006; Thorburn et al., 2005; Wood et al., 1996): SUCROWS, AUSCANE, CANEGRO, and APSIM-Sugarcane. The two main models are APSIMSugarcane and CANEGRO and are based on the CERES maize model (Thorburn et al., 2005). These models have increased the understanding of sugarcane physiology and served to identify knowledge gaps and research areas. They have also influenced sugarcane farming systems and policy (Lisson et al., 2005). Models can be coupled to studies on soil variation and yield mapping (Johnson and Richard, 2005a; Timm et al., 2003) and be

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combined with newly developed yield-monitoring equipment (Magalhaes and Cerri, 2007). Mechanized precision farming technologies are beyond the financial and technical capacities of smallholders. However, many smallholders have high skills that can match and substitute high-technology principles used in capital-intensive precision farming. Fertilizers may be too expensive or unavailable but biofertilizers that combine mineral rock phosphate, organic amendments, and soluble fertilizer (Stamford et al., 2006) could be an appropriate nutrient management strategy.

ACKNOWLEDGMENTS I am grateful to Dr David Dent for critically commenting on the text. Dr. Russell Yost of the University of Hawaii, and Drs. Edward Richard and Richard Johnson of USDA-ARSSSRC provided information about the sugarcane in the United States; Mr. Bernie Powell kindly provided information on sugarcane and acid sulfate soils in Australia.

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Schrope, M. (2001). Save our swamp. Nature 409, 128–130. Sheldrick, W. F., Syers, J. K., and Lingard, J. (2003). Soil nutrient audits for China to estimate nutrient balances and output/input relationships. Agric. Ecosyst. Environ. 94, 341–354. Silva, A. J. N., Ribeiro, M. R., Carvalho, F. G., Silva, V. N., and Silva, L. E. S. F. (2007). Impact of sugarcane cultivation on soil carbon fractions, consistence limits and aggregate stability of a Yellow Latosol in Northeast Brazil. Soil Tillage Res. 94, 420–424. Singh, K. P., Suman, A., Singh, P. N., and Lal, M. (2007). Yield and soil nutrient balance of a sugarcane plant-ratoon system with conventional and organic nutrient management in sub-tropical India. Nutr. Cycl. Agroecosyst. 79, 209–219. Sitompul, S. M., Hairiah, K., Cadisch, G., and van Noordwijk, M. (2000). Dynamics of density fractions of macro-organic matter after forest conversion to sugarcane and woodlots, accounted for in a modified Century model. Neth. J. Agric. Sci. 48, 61–73. Skjemstad, J. O., Taylor, J. A., Janik, L. J., and Marvanek, S. P. (1999). Soil organic carbon dynamics under long-term sugarcane monoculture. Aust. J. Soil Res. 37, 151–164. Smeets, E. M. W., Faaij, A. P. C., Lewandowski, I. M., and Turkenburg, W. C. (2007). A bottom-up assessment and review of global bio-energy potentials to 2050. Prog. Energy Combust. Sci. 33, 56–106. Smith, D. M., Inman-Bamber, N. G., and Thorburn, P. J. (2005). Growth and function of the sugarcane root system. Field Crops Res. 92, 169–183. Southwick, L. M., Willis, G. H., and Salim, H. M. (1992). Leaching of Atrazine from sugarcane in Southern Louisiana. J. Agric. Food Chem. 40, 1264–1268. Southwick, L. M., Willis, G. H., Johnson, D. C., and Selim, H. M. (1995). Leaching of nitrate, atrazine and metribuzin from sugarcane in southern Louisiana. J. Environ. Qual. 24, 684–690. Southwick, L. M., Grigg, B. C., Kornecki, T. S., and Fouss, J. L. (2002). Potential influence of sugarcane cultivation on estuarine water quality of Louisiana’s gulf coast. J. Agric. Food Chem. 50, 4393–4399. Souza, Z. M., Marques-Junior, J., and Pereira, G. T. (2004). Spatial variability of physical attributes of the soil in different landscape forms under sugarcane. Rev. Bras. Cienc. Solo 28, 937–944. Sparovek, G., and Schnug, E. (2001a). Soil tillage and precision agriculture: A theoretical case study for soil erosion control in Brazilian sugar cane production. Soil Tillage Res. 61, 47–54. Sparovek, G., and Schnug, E. (2001b). Temporal erosion-induced soil degradation and yield loss. Soil Sci. Soc. Am. J. 65, 1479–1486. Sparovek, G., Bacchi, O. O. S., Schnug, E., Ranieri, S. B. L., and De Maria, I. C. (2000). Comparison of three water erosion prediction methods (Cs-137, WEPP, USLE) in south-east Brazilian sugarcane production. Tropenlandwirt 101, 107–118. Sparovek, G., Anisimova, M. A., Kolb, M., Bahadir, M., Wehage, H., and Schnug, E. (2001). Organochlorine compounds in a Brazilian watershed with sugarcane and intense sediment redistribution. J. Environ. Qual. 30, 2006–2010. Srivastava, A. C. (2003). Energy savings through reduced tillage and trash mulching in sugarcane production. Appl. Eng. Agric. 19, 13–18. Srivastava, A. K. (1984). Soil compaction: A problem in sugarcane culture. Indian Sugar 34, 503–504. Srivastava, S. C. (1992). Sugarcane. In ‘‘IFA World Fertilizer use Manual’’ (D. J. Halliday and M. E. Trenkel, Eds.), pp. 257–266. International Fertilizer Industry Association, Paris. Stamford, N. P., Lima, R. A., Santos, C. R. S., and Dias, S. H. L. (2006). Rock biofertilizers with Acidithiobacillus on sugarcane yield and nutrient uptake in a Brazilian soil. Geomicrobiol. J. 23, 261–265.

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C H A P T E R

F O U R

Iron Dynamics in the Rhizosphere: Consequences for Plant Health and Nutrition A. Robin,* G. Vansuyt,* P. Hinsinger,† J. M. Meyer,‡ J. F. Briat,§ and P. Lemanceau* Contents 1. Introduction 2. Iron Status in Soils and Rhizospheres 2.1. Pools of iron in minerals 2.2. Solubility of iron oxides 2.3. Complexes and chelates with organic matter 2.4. Iron bioavailability 3. Impact of Plants and Microorganisms on the Iron Status 3.1. Iron solubilization in the rhizosphere 3.2. Iron homeostasis in plants and microorganisms 4. Iron-Mediated Interactions in the Rhizosphere 4.1. Impact of soil’s chemical properties on plants and rhizospheric microorganisms 4.2. Impact of plant iron nutrition on rhizospheric microorganisms 4.3. Impact of rhizospheric microorganisms on the host plant 5. Conclusions Acknowledgments References

* {

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184 187 187 188 190 192 194 194 202 203 203 204 205 209 211 211

INRA, Universite´ de Bourgogne, UMR1229 ‘Microbiologie du Sol et de l’Environnement’, CMSE, BV 86510, F-21034 Dijon cedex, France INRA, SupAgro, UMR1222 ‘Bioge´ochimie du Sol et de la Rhizosphe`re’, Place Pierre Viala, F-34060 Montpellier cedex 1, France CNRS, Universite´ Louis Pasteur, UMR7156 ‘De´partement Environnement, Ge´ne´tique mole´culaire et Microbiologie’, F-67000 Strasbourg, France CNRS, Universite´ Montpellier II, SupAgro, INRA, UMR5004 ‘Biochimie et Physiologie Mole´culaire des Plantes’, Place Pierre Viala, F-34060 Montpellier cedex I, France

Advances in Agronomy, Volume 99 ISSN 0065-2113, DOI: 10.1016/S0065-2113(08)00404-5

#

2008 Elsevier Inc. All rights reserved.

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Abstract Iron is an essential micronutrient for most organisms due to its role in fundamental metabolic processes. In cultivated soils, soil solution iron is mostly oxidized [Fe(III) species] unless local anoxic conditions develop. The concentration of these Fe(III) species is small in soil solution due to the low solubility of ferric oxides, oxyhydroxides, and hydroxides, which is minimal at neutral and alkaline pH. In the rhizosphere, iron concentration in the soil solution is even lower because of its uptake by aerobic organisms (plants and microorganisms), leading to a high level of competition for Fe(III). In order to face iron competition, these organisms have evolved active uptake strategies based on acidification, chelation, and/or reduction processes. Iron competition plays a major role in microbial and plant–microbe interactions in the rhizosphere. This review summarizes current knowledge on the iron status in soils and rhizospheres, and the acquisition strategies of plants and microbes. This review also shows how the dynamic interactions between soil minerals, plants, and microorganisms impact plant health and nutrition. Analysis of these complex interactions offers an interesting case study of research on rhizosphere ecology integrating different scientific expertises and approaches.

1. Introduction Soils are known to be oligotrophic environments whereas soil microorganisms are mostly heterotrophic so that microbial growth in soil is mainly limited by the scarce sources of readily available organic compounds (Wardle, 1992). Therefore in soils, microorganisms are mostly in stasis (fungistasis/bacteriostasis) (Lockwood, 1977). In contrast, plants are autotrophic organisms responsible for the primary production resulting from the photosynthesis. A significant part of photosynthetates are released from plant roots to the soil through a process called rhizodeposition. These products, the rhizodeposits, are made of exudates, lysates, mucilage, secretions, and dead cell material, as well as gases including respiratory CO2 and ethylene. Depending on plant species, age, and environmental conditions, rhizodeposits can account for up to 40% of net fixed carbon (Lynch and Whipps, 1990). On average, 17% of net fixed carbon appears to be released by the roots (Nguyen, 2003). This significant release of organic compounds by plant roots in soil oligotrophic environments is expected to affect strongly the heterotrophic microorganisms located close to the roots. Indeed, more than a century ago, Hiltner (1904) observed an increased proliferation of heterotrophic bacteria in contact with roots. He proposed the name rhizosphere for designing the volume of soil surrounding roots in which microorganisms are influenced

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by those roots. Since then, further studies have shown that living roots modify the biological, physical, and chemical properties of the surrounding soil determining the rhizosphere effect (Bowen and Rovira, 1999; Curl and Truelove, 1986; Darrah, 1993; Gregory and Hinsinger, 1999; Hinsinger, 1998; Hinsinger et al., 2005; Lemanceau and Heulin, 1998; Lynch and Whipps, 1990; Rovira, 1965). Rhizodeposition affects soil microorganisms leading to (i) an increase in their density (Clark, 1949; Rovira, 1965), biomass (Barber and Lynch, 1977), and activity (Soderberg and Ba˚a˚th, 1998) and (ii) variations in the structure and diversity of their populations (Edel et al., 1997; Lemanceau et al., 1995; Mavingui et al., 1992). In the rhizosphere, trophic interactions and communications lead indeed to (i) the selection of the most adapted microbial groups and populations, (ii) variations in their physiology, and then (iii) shifts in the structure, diversity, and activity of the microbial community compared to that of the bulk soil. Some microbial groups and populations appear to be preferentially adapted and favored in the rhizosphere. Among them, fluorescent pseudomonads were shown to have a higher density and activity in the rhizosphere than in bulk soil and are considered as rhizobacteria (Lemanceau, 1992; Sorensen and Nybroe, 2004). Energetic metabolism of heterotrophic microorganisms is based on electron donors (organic compounds) and electron acceptors (ferric iron, oxygen, and nitrogen oxidized species). Bacteria are usually able to use a wide range of organic compounds as shown for fluorescent pseudomonads (Lemanceau et al., 1995; Stanier et al., 1966), in such a way that different catabolic activities can be induced, upon the organic compounds present, to take advantage of the nutrients available in the rhizosphere (Cheng et al., 1996). In contrast, the range of possible electron acceptors is limited (Latour and Lemanceau, 1997), making the competition for them especially intense. This is the case for ferric iron [Fe(III) species] making up the speciation which include the free metal ion Fe3þ and the whole range of complex species formed with inorganic and organic ligands present in the soil solution (see below). More generally, iron is essential for major metabolic processes in most organisms such as reduction of ribonucleotides and molecular nitrogen, and the energy-yielding electron transfer reactions of respiration and photosynthesis (Guerinot and Ying, 1994). The biological importance of iron is a result of its electronic structure, which is capable of reversible changes in oxidation state over a wide range of redox potentials (pe). Although being the fourth most abundant element of the earth’s crust, at pH values compatible with plant growth and in oxic environments, Fe(III) is mostly precipitated as hydroxides, oxyhydroxides, and oxides, while Fe(II) is predominantly included in the crystal lattice of a range of primary and secondary ferromagnesian silicates (Cornell and Schwertmann, 2003). Because of the poor solubility of iron oxides (used thereafter as a generic name for oxides, oxyhydroxides, and hydroxides), the concentration of

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2

4

pH 6

8

10

Log Fe species activities (M)

0 −5 −10

Fe(OH)4−

−15

Fe(OH)2+

−20

Fe(OH)2+

−25 Fe2(OH)24+

Fe3+ Fe2+

Figure 1 Solubility diagram of an iron oxide (goethite, FeOOH) as a function of pH, calculated on the basis of constants taken from Cornell and Schwertmann (2003) and Lindsay (1979). The black lines indicate the equilibrium activities of the various Fe(III) species that form as a consequence of the hydrolysis of Fe3þ, while the grey line stands for the equilibrium activity of Fe2þ. The grey domain indicates the range of Fe concentrations required for optimal growth of plants (Guerinot and Ying, 1994) while the hatched zone indicates those required for optimal growth of microbes (Loper and Buyer, 1991).

Fe3þ in the soil solution is extremely low (Cornell and Schwertmann, 2003; Lindsay, 1979; Marschner, 1995) (Fig. 1). In the rhizosphere, this concentration is even lower due to the iron uptake by both roots and microbes, and the concentrations of Fe(III) species are generally far below those required for optimal growth of microbes and plants, 105 to 107 M and 104 to 109 M, respectively (Guerinot and Ying, 1994; Loper and Buyer, 1991). Based on the solubility of goethite (Fig. 1), under oxic conditions, the Fe3þ requirements of the least demanding plant species and microorganisms would be met only at very acidic pH, that is, values of 3.5 and 2.8, respectively. If the same calculations were made with the more soluble ferrihydrite [based on the constants for the so-called ‘‘soil iron oxide’’ in Lindsay (1979)], pH values of about 3.9 and 3.4, respectively, would need to be reached, which is still very acidic. The combined low concentration of Fe(III) in soil solution (low supply) together with the requirements of aerobic organisms (plants and microorganisms) (high demand) lead to a considerable level of competition for Fe(III) in the rhizosphere (Guerinot and Ying, 1994; Loper and Buyer, 1991). To acquire this essential element in spite of its low availability, plants and microbes have evolved active strategies of uptake which are based on a range of chemical processes. Basically, these strategies rely on (i) acidification of the soil solution based on the excretion of protons

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or organic acids, (ii) chelation of Fe(III) by plant and microbial ligands showing a high affinity for Fe3þ, and (iii) reduction of Fe3þ to Fe2þ mostly by plant reductases and reducing compounds. Iron dynamics in the rhizosphere are under the control of the combined effects of soil properties, uptake and activities of plants and microorganisms, and interactions between them. In this review, the status of iron in soils and rhizospheres will be examined with a specific focus on oxic environments and on the fraction of iron available for living organisms, that is, the bioavailable iron. The impact of plants and microorganisms on the iron status and dynamics in the rhizosphere, and more specifically on its solubilization and homeostasis, will be analyzed and illustrated. The effects of the resulting iron competition on microbial interactions—competitiveness and antagonism—and on plant–microbe interactions—iron nutrition and induced resistance—will be presented and their consequences for plant health and nutrition summarized.

2. Iron Status in Soils and Rhizospheres Three major pools of iron can be distinguished in soils: (i) iron included in primary and secondary minerals, (ii) soluble iron (and the speciation of iron, i.e., the range of iron species that occur in solutions), and (iii) iron bound to organic matter. Iron availability in soils and rhizospheres is governed by its concentration in the soil solution and more importantly by its partitioning within the solid phases, and the ability of the latter to replenish the soil solution via dissolution/precipitation and dissociation/association of complex species.

2.1. Pools of iron in minerals Iron is the fourth most abundant element in the earth’s crust after oxygen, silicon, and aluminium (Louet, 1986; Ma, 2005). Commonly, iron occurs in two oxidation states in soil minerals, designated as ferrous (II) and ferric (III) iron. These are found in most rocks in a range of primary minerals, and mostly in ferromagnesian silicates such as olivine, augite, hornblende, and biotite (Louet, 1986; Schwertmann and Taylor, 1989; Segalen, 1964). Primary soil minerals, containing principally Fe(II), are generally unstable in soils and thus weather in the presence of water and atmospheric oxygen. The weathering of these minerals can be dramatically accelerated by the activities of living organisms, as shown for microorganisms (e.g., Brantley et al., 1999) and plants (e.g., Hinsinger et al., 2001). During weathering, Fe(II) and Fe(III) ions are released by a range of dissolution and oxidation/reduction mechanisms. However, in the presence of hydroxyl ions (OH), they rapidly hydrolyze and precipitate to form a range of poorly soluble secondary

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minerals such as iron oxides (hematite, magnetite) and oxyhydroxides (goethite, lepidocrocite), as well as iron hydroxides and less organized minerals such as ferrihydrite (Cornell and Schwertmann, 2003; Segalen, 1964). Iron oxides may be associated with clays and sorb ions such as phosphate, leading to a decreased solubility of iron (Borggaard, 2002; Borggaard et al., 1990; Hinsinger, 2001; Segalen, 1964). Depending on soil aeration, the predominant iron oxides contain either Fe(II) or Fe(III), producing the characteristic colors of soils (Cornell and Schwertmann, 2003; Lindsay, 1979; Segalen, 1964). In oxic environments corresponding to the majority of cultivated soils, iron is mostly found as Fe(III) oxides, leading to soil colors varying between brown and yellow–red, whereas in reducing environments, such as waterlogged soils, marsh, and flooded zones, Fe(II) soluble species and oxides are predominant, giving rise to soils colors varying between grey and blue– green. Mixed Fe(II)–Fe(III) hydroxides, commonly called green rusts such as fougerite, largely explain these colors of waterlogged soils or soil horizons (Feder et al., 2005). In the most reducing soil conditions, ferrous iron sulfides (pyrite, FeS2) may ultimately form. Redox processes are reversible and these ferrous iron-bearing minerals may rapidly dissolve in the presence of oxygen, while iron oxides may dissolve in the presence of reducing compounds. Other iron-bearing secondary minerals, ascarbonates (siderites, FeCO3), and more importantly phyllosilicates (clay minerals such as glauconite, which are iron-rich illites, and some smectites such as beidellite) are also present in soils (Louet, 1986; Schwertmann and Taylor, 1989). Besides iron-bearing minerals, iron can be bound to mineral surfaces, especially clay minerals that are prone to adsorb metal cations in an exchangeable pool due to their cation exchange capacity. Because of the small concentrations of iron in soil solution in oxic environments (Fig. 1), iron usually makes up only a minor proportion of exchangeable cations. This proportion, however, may increase under acidic conditions and more so under reducing conditions. Progresses have been made in the knowledge of iron-bearing minerals and their surface reactivity over the years thanks to the fast development of spectroscopic methods (Mo¨ssbauer spectroscopy, synchrotron-based X-ray spectroscopies) and high resolution microscopic techniques (high resolution transmission electron microscopy and atomic force microscopy). The above-referred progresses were mostly achieved for transition metals and their metal oxides, but less for pure iron oxides [as reviewed by, for example, Ford et al. (2001)].

2.2. Solubility of iron oxides In aerated soils, iron-bearing secondary minerals mostly contain Fe(III). Their solubility is controlled by dissolution–precipitation equilibria such as the following for, for example, iron hydroxide: Fe(OH)3 ⇆ Fe3þþ3OH

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Table 1

Information on parameters related to the iron status in soils and rhizospheres

Parameter

Stability constant and concentration

Solubility of iron Fe3þ þ 3OH!Fe(OH)3 Ksp Fe(OH)3 ¼ [Fe3þ] [OH]3 Ksp ¼ 1038 M [Fe3þ] ¼ 107 M (at pH 3.5) [Fe3þ] ¼ 1023 M (at pH 8.5) Stability constants with iron Enterobactin 1052 Ferrioxamine Pyoverdine

1032 1032

Fusarinine Phytosiderophore Organic acids Malate Citrate Oxalate EDDHA EDTA Humic acid Concentrations in rhizospheres Siderophores Phytosiderophores Organic acids

1029 1018

Lindsay (1974, 1979); Lindsay and Schwab (1982)

Pollack and Neilands (1970) Berner et al. (1988) Meyer and Abdallah (1978) Scher and Baker (1982) Sugiura et al. (1981) Jones (1998)

107 1011 108 1033 1025 1013

Lindsay (1979) Lindsay (1979) Takahashi et al. (1997)

107 to 108 M 103 M 5105 M to 9103 M

Powell et al. (1980) Ro¨mheld (1991) Dinkelaker et al. (1989); Jones et al. (1996)

Iron requirement for optimal growth Plants 104 to 109 M Microbes

Reference

105 to 107 M

Guerinot and Ying (1994) Loper and Buyer (1991)

(Table 1). This reaction is fully dependent on soil solution pH as shown in Fig. 1 for the case of a ubiquitous iron oxyhydroxide in soils (goethite), according to: FeOOH þ 3Hþ ⇆ Fe3þ þ 2H2O. For this mineral, which is less soluble than Fe(OH)3 and the so-called ‘‘soil iron oxide’’ in Lindsay

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(1979), the activity of Fe3þ ranges from about 109 M at pH ¼ 3.5 to about 1024 M at pH ¼ 8.5, these concentrations being lower than the plant and microbe requirements. The solubility diagram presented in Fig. 1 shows that Fe3þ is never the dominant Fe(III) species in solution over this range of pH since Fe(OH)2þ appears to be dominant up to pH ¼ 8 and Fe(OH)4 at higher pH values. This diagram also shows that total soluble iron (sum of all iron species in equilibrium with goethite) reaches a minimum at pH ¼ 8, value which is close to that of calcareous soils. Accordingly, the total Fe concentration in the soil solution is dramatically decreased when pH increases up to 8 (Lindsay and Schwab, 1982). The solubility of iron oxides is also very much dependent on redox conditions. When oxidizing conditions prevail, which is the case in most soils, the activity of Fe(II) species is lower than that of Fe(III) species at pH values commonly found in soils, except possibly in the most alkaline conditions (see Fe2þ vs Fe3þ in Fig. 1) (Lindsay, 1979). Thus, at atmospheric pO2 (pe þ pH ¼ 20.61), Fe2þ concentration is rather negligible (close to 10 20 M at pH ¼ 7) and contributes for little to the total soluble iron in soil solution. However, Fe2þ concentration will increase tenfold when the pe drops by 1 unit. Thus, under reducing conditions, when pe þ pH reaches 9, Fe2þ makes up a major contribution to total soil Fe (its concentration getting close to 109 M at pH ¼ 7). However, this does not apply to oxic conditions, and will then not be further discussed here. The solubility of iron oxides is also much affected by their particle size. When the particle size of goethite and hematite decreases, their usually low solubility increases to reach that of the more soluble ferrihydrite (Trolard and Tardy, 1987). This is relevant to soils because goethite (and hematite) particles of nanometric size are ubiquitous in oxic soil environments (Cornell and Schwertmann, 2003). The particle size not only affects iron solubility but also more importantly the kinetics of iron oxide dissolution (Kraemer, 2004). Besides pH, pe, and particle size, ligands, which make complex species with Fe(II) and Fe(III), influence the dissolution/precipitation equilibrium and kinetics (Fig. 2).

2.3. Complexes and chelates with organic matter The solubility of soil iron is also considerably affected by the complexation or chelation of Fe3þ by organic ligands that make up the DOM. According to Van Hees and Lundstro¨m (2000), more than 95% of Fe in soil solution is likely to be chelated. The corresponding organic ligands belong to microbial siderophores (Neilands, 1981), plant root exudates, including phytosiderophores (Takagi et al., 1984), and more complex and diverse macromolecules constitutive of humic substances (Stevenson, 1994). The contribution of microorganisms and plants to the solubilization of iron through siderophores

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Log dissolution rate (mol m-2 s-1)

-7 -8 Reduction Complexation

-9 -10 -11

Protonation 2

3

4

5

6

7

pH

Figure 2 Diagram showing the impact of different mechanisms—protonation, complexation (with oxalate), and reduction (with ascorbate)—on the dissolution of an iron oxide (goethite, FeOOH) as a function of pH. The rate of dissolution as affected by these mechanisms is clearly pH dependent; and appears to decrease as the pH increases (from Stumm and Furrer, 1987, with permission of J. Wiley and sons, NY).

and other ligands will be presented in a separate section. Surprisingly, compared with other metals such as copper, the binding of iron by DOM or humic substances is much less documented (Tipping et al., 2002). These authors successfully modelled the data from Liu and Millero (1999) and confirmed that Fe(III) concentration could be increased by about 100-fold (reaching values above 109 M) upon addition of humic acid (at concentrations below 1 mg dm3 and pH ¼ 8). Concentrations of Fe(III) above 108 M were recorded when adding more than 4 mg dm3 at pH ¼ 8, while micromolar concentrations were attained at acidic pH (below 5). Tipping et al. (2002) even showed that the concentration of iron bound to a specific DOM (fulvic acid), at pH values between 6.5 and 8.5, was more than twice that of all inorganic iron species. They also reported that, whatever the pH value was, the species of iron bound to fulvic acid consisted mostly in Fe(OH)2þ, Fe3þ being hardly not bound except at the most acidic pH values. The high content of oxygen-containing functional groups of humic substances is favorable to the formation of stable complexes with Fe (Chen, 1996; Stevenson, 1994). Binding of iron to humic substances occurs mostly at high soil pH values. These complexes are protected from possible precipitation and subsequent crystal growth processes that would decrease iron solubility (Cesco et al., 2000; Schwertmann, 1991; Varanini and Pinton, 2001). The importance of the complexing capacity of DOM on the availability of micronutrients including iron has long been acknowledged (Hodgson, 1969), and humic substances were shown to provide a pool of iron available for plants (Cesco et al., 2002; Chen and Aviad, 1990; Lobartini and Orioli, 1988; Pinton et al., 1998, 1999; Varanini and Pinton, 1995, 2001). This was illustrated in soilless cultures, whereas contradictory

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information has been reported for soils. Humates were shown to increase the extractability of iron from soils, even in calcareous soils, and to improve iron nutrition (Olmos et al., 1998; Pandeya et al., 1998). However, in other studies, plant iron uptake was not enhanced by such substances (Alva and Obreza, 1998; Kumar and Prasad, 1989). The discrepancy between the observations made in different experimental conditions may be related to differences in the complexing ability of humic substances in different environments. The stability of Fe–humic complexes is indeed influenced by parameters such as pH—with a maximum stability at pH 8—and the Fe/ humic substances ratio (Garcı´a-Mina et al., 2004). Furthermore, modification of iron solubility upon introduction of organic matter may also result from indirect effects such as adsorption of plant and microbial siderophores (Crowley et al., 1991), and many others via the alteration of soil’s physical, chemical, and biological properties (Kraemer et al., 2006). Humic substances not only contribute to increase iron bioavailability through their iron-chelating properties but also have redox-reactive properties (Weber et al., 2006). These properties are related to phenolic groups contributing to Fe(III) reduction (Chen et al., 2003; Deiana et al., 1995; Szilaˆgyi, 1971). The chemical reduction of Fe(III) by humic substances is strongly pH dependent, the highest reduction capacities occurring at pH ¼ 3 (Chen et al., 2003). As pH increases, humic substances are more frequently bound to metal cations and therefore have a decreased reducing ability (Chen et al., 2003).

2.4. Iron bioavailability Bioavailable iron can be defined as the portion of total iron that can be easily assimilated by living organisms, according to the general definition of bioavailability given by Harmsen et al. (2005). Iron bioavailability is expected to be governed by the kinetics of iron dissolution from ironbearing minerals (Kraemer, 2004; Reichard et al., 2005), desorption of iron from exchangeable forms (Crowley et al., 1991), iron speciation in soil solution, and, to a less extent, iron concentration. Chemical analyses of soil iron are commonly based on chemical extraction and quantification, giving access to the exchangeable or extractable iron (Borggaard, 1976; Cornell and Schwertmann, 2003; Haynes, 1983; Lindsay and Norvell, 1978). These methodologies (Baize, 2000) do not provide information on the associations of iron with soil constituents (solid phases) and even less on the bioavailability of iron. These limitations call for alternative methods to characterize these associations in more detail, particularly for examining the stability kinetics of associations between iron and soil constituents (Bermond et al., 2005). Recent techniques, including the use of stable

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iron isotopes, bring new prospects for analyzing the behavior of Fe in soils, in particular the interactions between the various reservoirs of Fe in soils (Emmanuel et al., 2005). None of these chemical methodologies, as common as they are, can replace the final application of living organisms to test their ability to use iron in the soil environment. Iron stress conditions for microorganisms can be assessed in soil indirectly by characterizing their susceptibility to iron starvation. This susceptibility can be evaluated by determining the minimal concentrations of a strong iron chelator (8-hydroxyquinoline) inhibiting the growth in vitro of bacterial isolates from different soil conditions. This strategy unlighted the lower susceptibility to iron starvation of populations from rhizosphere than from bulk soil (Lemanceau et al., 1988b), and from the rhizosphere of a transgenic tobacco line overexpressing ferritin than from the rhizosphere of a control nontransformed plant (Robin et al., 2006b), indicating the lower availability of iron in these environments. Another strategy relies on so-called iron biosensors based on the use of reporter genes which are under the control of the promoters of ironregulated genes, as those encoding siderophore synthesis in fluorescent pseudomonads (Loper and Lindow, 1997). Such constructs were made in Pseudomonas fluorescens Pf-5 (Loper and Lindow, 1994) and in P. putida WCS358 (Duijff et al., 1994a) by fusing a promoter-less ice-nucleation activity gene (inaZ) to an iron-regulated promoter regulating the production of fluorescent siderophores. Expression of ice-nucleation activity from this construct is inversely related to the iron concentration. Use of the Pf-5 construct indicated that the bacterial cells were mildly iron-stressed in the rhizosphere (Loper and Lindow, 1994) and that the ice-nucleation activities were similar in different root zones (Marschner and Crowley, 1997). Icenucleation activity was shown to decline with time, indicating that iron bioavailability increased during plant growth (Loper and Henkels, 1997). As stressed by Harmsen et al. (2005), the bioavailability of a nutrient or a toxic substance varies upon the living organisms according to their different acquisition pathways/capabilities. Variation between organisms may also result from a so-called bioinfluenced zone in which each organism interacts with its environment, thereby altering the availability of the nutrient or toxic substance (Harmsen et al., 2005); for plants, this bioinfluenced zone typically corresponds to the rhizosphere. These variations according to the organisms necessarily stress the limits of biosensors based on the use of a given organism for assessing the bioavailability of soil iron, since it would be relevant only to those which are close enough to the species used as biosensor. For instance, Bontidean et al. (2004) designed a bacterial biosensor for assessing mercury bioavailability. However, the biosensor response appeared to not match with the bioavailability of mercury to a higher plant species (common bean, Phaseolus vulgaris).

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3. Impact of Plants and Microorganisms on the Iron Status Plants and microorganisms have developed active strategies to acquire iron which is essential for their metabolism but has a low availability in soils under oxic environments. The corresponding activities, which include acidification, chelation, and reduction, contribute to increase the dissolution and solubility of iron oxides in the rhizosphere (Figs. 2 and 3). Plants and microorganisms have also evolved strategies for regulating their iron content by homeostasis.

3.1. Iron solubilization in the rhizosphere Acidification results from proton extrusion and secretion of organic acids by plants and microorganisms leading to proton concentrations up to 100-fold greater in the rhizosphere than in bulk soil (Darrah, 1993; Hinsinger et al., 2003; Nye, 1981). Root and microbial respiration also contribute to soil acidification, due to the resulting elevation of pCO2 and dissociation of carbonic acid (Hinsinger et al., 2003; Nye, 1981). Efficacy of acidification by plants and microorganisms depends on the buffering capacity of the soil which is especially high in calcareous soils as a consequence of the consumption of protons by the dissolution of CaCO3 (Hinsinger et al., 2003; Siebner-Freibach et al., 2003).

Protons

Acidification

Organic acids (citric, oxalic, malic acids,...)

Molecules with high iron affinity (phytosiderophores, siderophores)

Chelation

Reducing compounds (phenolic, aliphatic acids,...)

Reductases

Reduction

Iron solubilization

Iron bioavailability

Figure 3 Schematic representations of mechanisms affecting iron availability in the rhizosphere. Plants and microorganisms may increase iron availability by (i) acidification through proton extrusion and organic acid secretion (and possibly respiration), (ii) chelation through secretion of complexing molecules with variable affinity for iron (phytosiderophores, siderophores, phenolics, and carboxylic acids), and (iii) reduction through secretion of compounds characterized by reducing properties or through the expression of a membrane-bound reductase activity.

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3.1.1. Acidification The respiration of living organisms, including plant roots and microorganisms, leads to an increased pCO2 in soils, which is commonly several tens or hundreds times higher than that in the atmosphere (Hinsinger et al., 2003). Accordingly, the concentration of carbonic acid (H2CO3) is expected to be much higher in soils, inducing a significant acidification when dissociating. Given its rather high pK (6.36), H2CO3 dissociates more at neutral and alkaline than at acidic pH. Therefore, the contribution of respiration to soil acidification is especially significant in calcareous soils known for their low iron availability. The pH of these soils, close to 8 at ambient atmospheric pCO2, is known to decrease by 0.67 pH units for every tenfold increase in pCO2 and may then decrease down to 6.7 at a pCO2 of 0.1 mol mol1 corresponding to values of pCO2 reported to occur in the rhizosphere (Gollany et al., 1993; Hinsinger et al., 2003 ). Although seldom accounted for, root and microbial respiration can thus have a significant effect on soil pH and ultimately on the solubility of soil iron oxide. Kraemer et al. (2006) calculated that the total concentration of dissolved iron species in equilibrium with ‘‘soil iron oxide’’ (as defined by Lindsay, 1979) increased from about 1010 M, at ambient atmospheric pCO2 , up to 2 109 M, at a pCO2 of 0.1 mol mol1 , these values of pCO2 being commonly found in the rhizosphere (Gollany et al., 1993). Nongraminaceous plants have developed an active strategy of iron uptake (strategy I) based on (i) the extrusion of protons (Bienfait, 1985; Guerinot and Ying, 1994), (ii) the reduction of Fe3þ to the more soluble Fe2þ species by plasmalemma-bound reductases (Robinson et al., 1999; see paragraph below), and (iii) the absorption, that is, transport, of Fe(II) through the plasmalemma by iron transporters (Curie et al., 2000; Eide et al., 1996; Vert et al., 2001). Proton effluxes from strategy I plants commonly reach 6 mmol Hþ h1 (g root biomass)1 (Ro¨mheld et al., 1984). Proton extrusion results from the activity of Hþ-ATPases that is promoted under iron deficiency (Dell’Orto et al., 2000; Ro¨mheld and Marschner, 1981; Santi et al., 2005) in strategy I plant species. The Hþ-ATPase activity is promoted also by humic substances (Pinton et al., 1997; Varanini et al., 1993) and even more by the root uptake of nutrients (Hinsinger et al., 2003; Marschner, 1995). Indeed, plant root balance cations/anions influx ratio is regulated by plant release of protons when an excess of cations over anions has been taken up (Haynes, 1990; Hinsinger, 1998; Hinsinger et al., 2003; Nye, 1981; Ro¨mheld and Marschner, 1986a). Protons are not extruded homogeneously along the root. Their greater release has been frequently reported in some specific zones such as subapical and basal zones, especially in strategy I species exposed to Fe deficiency ( Jaillard et al., 2002; Plassard et al., 1999; Ro¨mheld and Marschner, 1981). In tobacco (Nicotiana tabaccum) rhizosphere proton efflux appeared to be

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significantly higher [7–9 pmol Hþ (m root length)1 s1] in the basal than in the subapical zone [3–5 pmol Hþ (m root length)1 s1] (Vansuyt et al., 2003). Further information is given in several reviews of root-mediated pH changes in the rhizosphere (Haynes, 1990; Hinsinger, 1998; Hinsinger et al., 2003; Nye, 1981). Microbial metabolism also contributes to pH variations (Latour and Lemanceau, 1997). Observations and in situ measurements indicated reduced pH close to active microbes and biofilms using confocal microscopy (Barker and Banfield, 1998) and micro-electrodes (Yu and Bishop, 2001; Yu et al., 1998). Microbial acidification is especially well documented in bacteria involved in the oxidation of ferrous iron sulfide, such as pyrite, and in that of ammonium leading to the formation of sulfuric and nitric acids, respectively. Ferrous iron sulfides do not commonly occur in oxic environments and only few bacteria show the ability to oxidize these sulfides. Among them, Thiobacillus ferrooxidans, recently renamed Acidithiobacillus ferrooxidans (Lu et al., 2006), is commonly applied for the treatment (bioleaching) of metal sulfide ores (Fernandez et al., 1995). Conversely, acidification resulting from the oxidation of ammonium in nitrate is common in soils, but again relies on the activity of only few bacteria genera, that is, Nitrosomonas and Nitrobacter (Holloway and Dahlgren, 2002; Sitaula et al., 2001). Carboxylates, such as citrate, oxalate, malate, and many others commonly found in root exudates, also contribute to pH decrease in the rhizosphere, when their exudation is coupled with proton efflux (Hoffland, 1992; Jones, 1998; Jones et al., 2003). A metabolic link between citrate excretion and proton extrusion has been proposed (Ohwaki and Sugahara, 1997) and the amount of citrate exuded correlated with the capacity of rhizosphere acidification. The concentration of carboxylates in the rhizosphere ranges from 50 mM ( Jones et al., 1996) to 9000 mM (Dinkelaker et al., 1989). This concentration considerably varies among plant species and depends on environmental constraints, notably phosphorus and iron deficiency (Curl and Truelove, 1986; Hinsinger et al., 2003; Jones, 1998). The exudation of carboxylic acids from maize (Zea mays) roots contributed to less than 0.3% of rhizosphere acidification according to Petersen and Bo¨ttger (1991). Conversely, cluster roots of white lupin (Lupinus albus) were shown to release massive amounts of carboxylates which accounted for a considerable acidification of the rhizosphere (Dinkelaker et al., 1989; Vance et al., 2003). Small organic acids from bacterial metabolism also contribute to pH decrease (Hoberg et al., 2005; Liermann et al., 2000). This is the case for oxalic acid, which is produced in large amounts by ectomycorrhizal fungi (Casarin et al., 2004). Microbial organic acids appear to enhance silicate dissolution rates (Rogers and Bennett, 2004). Carboxylic acids, such as malic and citric acids, could be involved in the dissolution of Fe(III) polymers bound to the surface of microbial cells (Winkelmann, 1979, 2007). These acids as well as oxalic acid

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affect the fate of iron not only via proton-promoted dissolution but also via ligand-promoted dissolution of iron-bearing minerals (see following section). 3.1.2. Chelation and complexation Ferric iron can be chelated by a range of organic ligands showing a high affinity for this metal cation. Among them, siderophores produced by graminaceous plants and by microbes are involved in their active iron-uptake strategies. Phytosiderophores such as mugineic acids produced by graminaceous plants efficiently chelate ferric iron due to their amine and carboxyl groups (Fig. 4A and B) (Kraemer et al., 2006; Takagi, 1976). These root exudates are nonproteinogenic amino acids which are synthesized from methionine via nicotianamine. The strategy of phytosiderophore-mediated iron uptake (strategy II) has been described in graminaceous plants including wheat (Triticum aestivum), barley (Hordeum vulgare), rice (Oryza sativa), and maize (Z. mays), and in a range of pasture grasses (Marschner and Ro¨mheld, 1994). Rice plants have, however, the peculiarity to take up both and Fe3þphytosiderophore and Fe2þ as nongraminaceous strategy I plants (Ishimaru et al., 2006). Phytosiderophores can reach local concentrations of 1 mM in the soil solution, according to model calculations made by Ro¨mheld (1991). Little information is available on in situ measurements of phytosiderophore concentrations except the work by Shi et al. (1988) reporting the presence of only micromolar concentrations of mugineic acid in the rhizosphere of barley (H. vulgare). As stressed by Kraemer et al. (2006), these measurements did not take into account the temporal and spatial patterns of secretion, which indicate that phytosiderophores are secreted at much larger rates near the root apices and between 3 and 6 h after the onset of light (Marschner et al., 1987; Reichman and Parker, 2007; Takagi et al., 1984). At such locations and during these temporal pulses of secretion, phytosiderophore concentration in the rhizosphere may reach that computed by Ro¨mheld (1991). Reichard et al. (2007) recently demonstrated that pulse additions of phytosiderophores led to considerably increased rate of dissolution of goethite, relative to a steady supply of phytosiderophores. Phytosiderophores exhibit a high affinity for iron with a stability constant of the corresponding chelate equal to 1017–1018. This stability constant is high enough for phytosiderophores to compete efficiently for iron bound to the humic fraction of soils (Cesco et al., 2000). Kraemer et al. (2006) further discussed this process in their review and concluded that ligand-exchange reactions between humic substances and organic ligands such as phytosiderophores (and most microbial siderophores) is fast enough to make a portion of this pool of iron available for uptake. More importantly, the chelating capacities of the phytosiderophores allow them to dissolve iron oxides, including poorly soluble minerals such as goethite (Hiradate and Inoue, 1998, Kraemer

A

COO−

COO−

B O

CH O

C

C

N H2C

Avenic acid COO−

COOH

O CH2

N

CH2

O

NH2

NH O OH C

F

E

OH

HN

−

COO

HO

COOH

HOOC

COOH COOH

NH+ 2

NH+

NH

HN OH

OH

OH

HO

H3C O

O

HN

OH OH

O

OH

Enterobactin

D-aThr

H3 C L-Ala O

Rhizoferrin

Hydroxymugineic acid

O

O NH D-OHA sp H O

HN

OH

O N H

G L-Lys

O

HO

COOH O

NH2

H N

HN HO

O

OH

HN HO

O O

O L-Ala

OH

Mugineic acid COO

O

Fusarinine

O

−

O

O

O

CH3

N

H2N

O

O

COOH

NH+ 2

NH+

HN O

CH3

OH

COO−

HO

O

O

2⬘-deoxymugineic acid (DMA) COO−

HO

O

N

CH

C H

O H2N

OH

OH

Fe-DMA

NH+ 2

NH+

HO

O O

O

Fe

OH

COO−

D

CH3 O

OH H2C

C

CH

H2C O

NH+ 2

NH+ 2

HO

COOH

OH N OH

HN

N + NH N H

H2C

H2C

H2C

OH COOH NH

R

N OH OH

CH2

CH2 CH2

N

CH3 O O CH3

O

Schizokinen

L-cOHOrn

Pyoverdine

0

Figure 4 Structures of phytosiderophores, avenic acid, 2 -deoxygemugineic acid (DMA), mugineic acid, 3-hydroxymugineic acid, (A); the phytosiderophore DMA chelated with iron, (B); fusarinine, a trihydroxamate siderophore produced by the fungi Fusarium, (C); enterobactin, a tricatecholate siderophore produced by the bacterium E. coli, (D); rhizoferrin, a polycarboxylate siderophore produced by the fungi Rhizopus spp. and the bacterium Ralstonia pickettii, (E); bacterial monohydroxamate mixed-ligands pyoverdine produced by Pseudomonas sp. strain B10, (F); and schizokinen produced by Bacillus megaterium, (G).

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et al., 2006), by enhancing the rate of ligand-promoted dissolution of these minerals (Reichard et al., 2005). Phytosiderophores show a poor specificity for iron relative to other metals, since most phytosiderophores can efficiently chelate a number of divalent metal cations, especially zinc, nickel, and copper, and form stable chelates (Murakami et al., 1989). As an example, the stability of the chelate of mugineic acid with Cu(II) is even slightly higher than that with Fe(III) and therefore phytosiderophores are expected to be more frequently bound to copper than to iron at pH values above 5 according to the model calculation made by Reichman and Parker (2005). Competition between cations may explain the iron–copper antagonism reported in durum wheat (Triticum turgidum durum) grown in calcareous soils contaminated by copper (Michaud et al., 2007). In this study, iron deficiency symptoms (interveinal chlorosis in leaves) were related to copper toxicity, since they were associated with an elevated copper content of roots and shoots. Such competition of metals for phytosiderophores is less likely to occur in the case of microbial siderophores, which generally show a greater specificity for ferric iron and a much higher stability constant than for other metal cations (Kraemer et al., 2006; Parker et al., 2005). The active uptake strategy of iron by microorganisms relies on the synthesis of siderophores and ferri-siderophore membrane receptors (Neilands, 1981). However, bacterial siderophores differ structurally from phytosiderophores (Fig. 4C–F) and show a higher affinity for ferric iron with stability constants ranging from 1023 to 1052 (Albrecht-Gary and Crumbliss, 1998; Neilands, 1981; Winkelmann, 1991). Microbial siderophores are quite diverse molecules with molecular mass values usually less than 1000 Da (Neilands, 1981), with, however, some of them, the pyoverdines synthesized by the fluorescent Pseudomonas, ranging between 1000 and 1800 Da [1764 for the biggest one reported so far by Meyer et al. (2008)]. More than 500 microbial siderophores have been so far been characterized (Boukhalfa and Crumbliss, 2002), among which are more than 100 different pyoverdines (Budzikiewicz, 2004; Meyer et al., 2008). They are classified according to the functional groups acting as ligands: catecholates, hydroxamates, hydroxypyridonates, hydroxy- or amino-carboxylates (Fig. 4C–F) (Bossier et al., 1988; Winkelmann, 1991, 2002, 2007). Siderophores can efficiently sequester iron adsorbed on different metal oxides and solubilize iron oxides (Hersman et al., 1996; Kraemer, 2004). Further information on siderophore interactions with metal oxides and solid surfaces can be found in several reviews (Cocozza et al., 2002; Hersman et al., 1995, 2000; Holme´n and Casey, 1996; Holme´n et al., 1999; Kraemer, 2004; Liermann et al., 2000; Maurice et al., 2001; Watteau and Berthelin, 1994). Much attention has been dedicated to the major siderophores of fluorescent pseudomonads (pyoverdines) and to their membrane receptors, both synthesized under iron deficiency (Hohnadel and Meyer, 1988; Meyer, 2000; Meyer and Abdallah, 1978). Pyoverdines are chromopeptides

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composed of a quinoleinic chromophore bound together with a peptide and an acyl side chain (Fig. 4E) (Budzikiewicz, 2004). Synthesis of pyoverdines and related protein membrane receptors correspond to a significant metabolic effort for bacterial cells that is expressed only when required via regulation processes. Their synthesis occurs in response to cellular iron deficiency resulting from a low iron availability of the environment (low supply) but is repressed under noniron stress conditions (Meyer et al., 1987). Similarly, pyoverdine synthesis is regulated by the phenomenon of Quorum Sensing through the production of acyl homoserines lactones (AHLs) when the bacterial density is high and corresponds to a significant demand in iron (Stintzi et al., 1998). This synthesis was shown to occur in the rhizosphere by the use of (i) monoclonal antibodies against ferripyoverdine (Buyer et al., 1990) and of (ii) the ice-nucleation reporter gene inaZ (Duijff et al., 1999; Loper and Lindow, 1994). As for pyoverdines, hydroxamate siderophores have been detected in soils with concentrations in soil solution ranging from 107 to 108 M (Powell et al., 1980). In addition to these high-affinity ligands, microorganisms and plants also produce a range of lower-affinity ligands such as phenolics and, more abundantly, carboxylates, oxalate, and citrate, for instance ( Jones, 1998; Jones et al., 1996; Reichard et al., 2005). A large proportion of these carboxylates occur in soil solutions as complex species with a range of metal cations including iron. As an example, 1–40% of citrate in soil solution is present as a Fe–citrate complex ( Jones et al., 1996). The stability constant of the complexes of citric and malic acids with Fe(III) varies from 107 to 1011, respectively (Hue et al., 1986; Jones, 1998; McColl and Pohlman, 1986), which is orders of magnitude less than that of phytosiderophores and even more so for microbial siderophores. Some carboxylates, such as citrate and oxalate, form poorly soluble minerals with Ca and are thus prone to precipitate in calcareous soils, thereby reducing their role in complexing metals such as iron ( Jones, 1998). A more thorough discussion of the interactions of carboxylates with iron oxides and their ligand-promoted dissolution can be found in Kraemer et al. (2006). 3.1.3. Reduction Mechanisms accounting for reduction by plants were recently reviewed (Schmidt, 2003). Fe(III) reduction is an essential and prerequisite component of iron uptake by strategy I plant species since they are equipped only for taking up Fe(II). Fe(III) reduction in strategy I plant species is essentially mediated by reductases, whose activities are stimulated as a response to iron deficiency. This reductase activity in the plasma membrane is promoted by Hþ extrusion into the rhizosphere (Marschner and Ro¨mheld, 1994; Marschner et al., 1986) and is suppressed at high level of HCO3 in calcareous soils (Siebner-Freibach et al., 2003). Reductase activity enables

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strategy I plants to have access to Fe(III) including iron complexed by citrate. The FRO2 gene from Arabidopsis is a member of a reductase gene family encoding the root ferric-chelate reductase activity induced in response to iron deficiency (for review, see Curie and Briat, 2003; Robinson et al., 1997, 1999). Reducing activity by roots has also been attributed to reducing compounds such as phenolic (caffeic acid) or some carboxylic acids (malic acid) (Brown and Ambler, 1973; Ro¨mheld and Marschner, 1983). Their contribution appears, however, to be of limited ecological significance (Bienfait et al., 1983; Ro¨mheld and Marschner, 1983). Microbial reduction of metals such as iron is well documented and has been reviewed by, for example, Lovley (1995). Membrane and extracellular reductases have been described in various pathogenic bacteria (Escherichia coli, Listeria monocytogenes, Pseudomonas aeruginosa, Salmonella thyphimurium, Staphylococcus aureus) (Coulanges et al., 1997; Cowart, 2002; Deneer et al., 1995; Vartivarian and Cowart, 1999). In these bacteria, extracellular iron reduction contributes to iron acquisition from their hosts and from soils during their parasitic and saprophytic life, respectively (Barchini and Cowart, 1996; Schro¨der et al., 2003). Extracellular reductases and siderophores would act together to solubilize and transport iron from the environment (Cowart, 2002). Recently, phenazines, known for their antagonistic activities against phytopathogenic fungi (Thomashow and Weller, 1988), were reported to act as electron shuttles contributing to metal cation reduction (Hernandez et al., 2004; Price-Whelan et al., 2006). Siderophores are also expected to affect the redox status of the rhizosphere through their electronegative characteristic or the modification of the Fe(II)/Fe(III) balance resulting from iron chelation (Emery, 1977; Pidello, 2003). Indeed, inoculation of the pyoverdine-producing strain P. fluorescens C7R12 was shown to increase the pe in soil compared to that of the corresponding nonproducing pyoverdine (pvd-) mutant (Pidello, 2003). In microaerophilic or anaerobic zones (Hojberg et al., 1999), such as waterlogged soils, oxygen can be released by the roots (Amstrong, 1964) allowing iron oxidation as demonstrated by the presence of iron oxide precipitates coating the root surface of many wetland plants, thereby forming the so-called Fe-plaque (Mendelssohn et al., 1995). Microscopic observations have revealed the presence of bacterial cells in this Fe-plaque (St-Cyr et al., 1993) and Fe-oxidizing bacteria have been suggested to contribute to its precipitation (Emerson et al., 1999). In return, Fe(III) oxides of this plaque are used as electron acceptors by dissimilatory iron-reducing bacteria (Luu and Ramsay, 2003; Schro¨der et al., 2003). The high Fe(III) reduction potential of the Fe-plaque promotes a microbial-mediated Fe cycle around the roots of wetland plants (Weiss et al., 2004) and their rhizosphere is the site of an unusually active microbial Fe cycling.

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3.2. Iron homeostasis in plants and microorganisms Although iron is required for the metabolism of aerobic organisms, its excess may be harmful at the cellular level. The same physical properties which make iron acting as an efficient cofactor and catalyst in controlled redox chemistry also make it toxic when not shielded from susceptible biomolecules. Many intracellular reactions use molecular oxygen as electron acceptor, producing superoxide (O2) or hydrogen peroxide (H2O2) that contribute to the generation of the extremely reactive hydroxyl radical (OH). Its formation is catalyzed by iron in the so-called Fenton chemistry (for review, see Briat, 2002):

FeðIIIÞ þ O 2 ! FeðIIÞ þ O2

FeðIIÞ þ H2 O2 ! FeðIIIÞ þ OH þ OH These two equations can be summarized:

O2 þ H2O2 ! O2 þ OH þ OH

The hydroxyl radical OH reacts with almost every molecule found in living cells, causing lipid peroxidation, DNA strand breaks, and degradation of other biomolecules (Briat, 2002; Harrison and Arosio, 1996). It is a major challenge for the cell to keep iron in a continuously bound organic form in order to avoid precipitation due to the neutral cellular pH and to shield iron from oxygen in order to prevent oxidative stress. This iron homeostasis is achieved by complex iron trafficking networks that involve processes based on chelation and reduction mechanisms (Andrews et al., 2003; Briat and Lobre´aux, 1997; Clemens, 2001). Ferritins play a major role in iron homeostasis in both plants and microbes (Andrews et al., 2003; Briat, 1996; Theil, 1987). Ferritins are ubiquitous multimeric protein complexes assembled from 24 homologous or heterologous subunits that are able to store up to 4500 iron atoms in a central cavity that acts as a reservoir (Briat et al., 1999; Harrison and Arosio, 1996), with a supramolecular structure which is conserved in plants and microorganisms (Harrison and Arosio, 1996; Theil, 1987). Iron is sequestered in a safe form, as hydrous ferric oxide-phosphate (Theil, 1987). Ferritin synthesis is controlled by transcriptional and posttranscriptional regulation and its content reflects the cell requirement of iron (Briat et al., 1999). Transgenic plants overexpressing ferritins have been obtained in various plant species such as tobacco (N. tabaccum, Goto et al., 1998; Van Wuytswinkel et al., 1999), rice (O. sativa, Goto et al., 1999; Lucca et al., 2001; Vasconcelos et al., 2003), wheat (T. aestivum, Drakakaki et al., 2000), lettuce (Lactuca sativa, Goto et al., 2000), and Arabidopsis thaliana (Vansuyt et al., 2007). Overexpression of these proteins leads to an enhanced iron uptake resulting from the increase in proton extrusion, ATPase, and root reductase activities (Briat et al., 1999; Van Wuytswinkel et al., 1999).

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4. Iron-Mediated Interactions in the Rhizosphere 4.1. Impact of soil’s chemical properties on plants and rhizospheric microorganisms As indicated above, bioavailability of iron depends on the chemical properties of soils. In soils with high pH and CaCO3 contents, symptoms of chlorosis, indicating an iron deficiency, are commonly reported (for review, see Mengel, 1994). However, the susceptibility of plants to this deficiency differs according to their strategy of iron uptake. Strategy II followed by graminaceous plant species appears to be more efficient to overcome iron deficiency than strategy I, and this is mainly due to the susceptibility of the root ferric reductase activity to neutral and alkaline pH (Ro¨mheld, 1991; Ro¨mheld and Marschner, 1986b). However, the efficacy of strategy II varies among graminaceous plant species. This is illustrated by the differences in susceptibility to chlorosis between calcifuge and calcicole species in calcareous soils (Tyler, 2003), corresponding to variations in their ironuptake efficacy (Zohlen and Tyler, 2000) related to their organic acid exudation (Gries and Runge, 1995; Strom, 1997). It is worth noting that rice plants, although belonging to the graminaceous family, are nevertheless fairly susceptible to iron deficiency. Whether or not it is related to the fact that rice plants do not rely only on a chelation strategy to acquire iron from the soil remains to be determined (Ishimaru et al., 2006). This stands for upland rice only as lowland rice is mostly dealing with the opposite stress of potential Fe toxicity due to the ambient reducing conditions of submerged soils. This may be the reason why acquisition of Fe in rice is rather based on Fe(II) uptake (as for strategy I plant species), as this does make sense in wetland environments where the bulk of soil solution iron is made of Fe(II) species, in contrast with oxic environments (Bughio et al., 2002). In soils with high pH and CaCO3 contents, the low availability of iron contribute to the stasis of microbial species susceptible to iron starvation such as F. oxysporum. Germination of chlamydospores of F. oxysporum is suppressed when Fe(III) activity in solution ranges from 1019 to 1022 M (Simeoni et al., 1987). This reduced saprophytic growth of pathogenic F. oxysporum leads to a decreased number of root infections and therefore to disease suppression. The possible involvement of iron competition in Fusarium wilt suppression was first raised by the observation of natural suppression in soils sharing common properties such as high pH and CaCO3 content (Alabouvette and Lemanceau, 1996). This hypothesis was further demonstrated by assessing the impact of soil supplementation with synthetic ligand (EDDHA) and chelate (FeEDTA) on saprophytic growth of the pathogen and disease severity. FeEDDHA has a significantly higher

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stability constant (K ¼ 1033.9) (Lindsay, 1979) than do Fe-fusarinine (K ¼ 1029), the Fe-chelate formed by Fusarium siderophores (Emery, 1965; Lemanceau et al., 1986). Therefore, EDDHA addition decreases iron availability to F. oxysporum and was shown to reduce germination of chlamydospores and germ tube length in vitro and in soil (Scher and Baker, 1982). In contrast, FeEDTA has a significantly lower stability constant (K ¼ 1025) (Lindsay, 1979) than do Fe-fusarinines and therefore increases iron availability to F. oxysporum. Consequently, while the reduced concentration of iron available for F. oxysporum by EDDHA addition led to a decreased disease severity (Lemanceau, 1989; Lemanceau et al., 1988a; Scher and Baker, 1982), the increased concentration of iron available for F. oxysporum by FeEDTA addition led to an increased disease severity. The low availability of iron in naturally suppressive soils favors microbial populations with a more efficient siderophore-mediated iron uptake such as fluorescent pseudomonads, which express their competitive ability against pathogenic F. oxysporum even more strongly; and natural soil suppressiveness to Fusarium wilts has been ascribed at least partly to these bacterial populations (Lemanceau et al., 1988b; Scher and Baker, 1980).

4.2. Impact of plant iron nutrition on rhizospheric microorganisms The possibility for microbes to take advantage of plant strategies for their iron uptake has been suggested. Thanks to the construct made in P. fluorescens Pf-5 with the reporter gene pvd-inaZ (Loper and Lindow, 1994), phytosiderophores were indeed shown to repress pyoverdine synthesis, suggesting that these phytosiderophores could be a possible source of iron for the bacteria (Marschner and Crowley, 1998). Similar observations were made by Jurkevitch et al. (1993) when measuring incorporation of 55Fe from 55Fe-mugeneic acid by a P. putida strain. However, in most studies the strong competition for iron in the rhizosphere has been shown to lead to the selection of microbial populations adapted to iron starvation (Lemanceau et al., 1988b; Robin et al., 2006a,b, 2007). As an example, populations of fluorescent pseudomonads; from the rhizosphere appear to be less susceptible to iron depletion than those from bulk soil, suggesting that populations adapted to the rhizosphere environment are more efficient for iron acquisition than those from bulk soil (Lemanceau et al., 1988b; Robin et al., 2007). The efficient pyoverdinemediated iron uptake of a P. fluorescens model strain was indeed shown to be involved in its rhizosphere competence by comparing its survival kinetics to that of a pvd- mutant (Mirleau et al., 2000, 2001). More generally, most of the competitive pseudomonad strains in the rhizosphere were shown to share the same type of pyoverdine and to have a low susceptibility to iron depletion, indicating again the importance of the pyoverdine-mediated iron

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uptake in the rhizosphere competence (Delorme et al., unpublished). The impact of the plant iron nutrition on the diversity of pseudomonad populations was recently demonstrated by the use of a transgenic tobacco overexpressing ferritin (Robin et al., 2007). This transgenic line that overaccumulates iron led to a reduction in the iron bioavailability in the rhizosphere (Robin et al., 2006b) and to the selection of pseudomonad populations which are efficient iron competitors (Robin et al., 2007).

4.3. Impact of rhizospheric microorganisms on the host plant 4.3.1. Plant health Interestingly, bacterial populations associated with the transgenic tobacco overexpressing ferritin appeared to be significantly more antagonistic in vitro against a fungal phytopathogen (Pythium aphanidermatum) than those associated with the wild type (Robin et al., 2007). This activity disappeared for the most antagonistic populations when pyoverdine synthesis was repressed by iron supplementation, suggesting that the bacterial antagonism was related to the pyoverdine-mediated iron competition. This type of microbial antagonism has been well documented (Table 2). Studies on the possible involvement of pyoverdines in suppression of soilborne diseases were stimulated by the early report of Kloepper et al. (1980a) indicating that addition of Pseudomonas sp. B10 or its pyoverdine to soils conducive to Fusarium wilts and to take-all rendered them suppressive. They further showed that supplementation of the soils with iron overcame the positive effects of the bacterial inoculation and pyoverdine addition. The demonstration of the involvement of pyoverdines in the antagonism achieved by some fluorescent pseudomonads against plant pathogens (F. oxysporum, Pythium spp.) and by so-called deleterious microorganisms (fluorescent pseudomonads, Pythium spp., Moulin et al., 1996) was achieved by the use of mutants impaired in their ability to synthesize pyoverdines (Bakker and Schippers, 1987; Bakker et al., 1986; Becker and Cook, 1988; Buysens et al., 1996; De Boer et al., 2003; Duijff et al., 1993; Leeman et al., 1996a; Loper, 1988; Raaijmakers et al., 1995). Antagonistic activity against plant pathogens leads to an improvement in plant health (Loper and Buyer, 1991) and that against deleterious microorganisms to an enhanced plant growth (Becker and Cook, 1988; Kloepper et al., 1980b; Schippers et al. 1987). As an example of the strategy based on the use of the pvd- mutant, Lemanceau et al. (1992) showed that P. putida WCS358, but not its pvdmutant, was able to improve the control of Fusarium wilt caused by nonpathogenic F. oxysporum. The pyoverdine-mediated improvement of the control by P. putida WCS358 was related to a reduced saprophytic density and activity of the pathogenic F. oxysporum as assessed by b-glucoronidase reporter gene in gusA-marked derivative of the pathogen (Duijff et al., 1999).

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Table 2

Examples showing evidence for pyoverdine-mediated antagonistic activity and consequences on plant health

Consequence on plant health

Plant

Reference

Macrophomina phaseolina Verticillium dahliae Fusarium oxysporum f. spp.

Peanut Olive Carnation, cucumber, flax, pea, radish

Barley, wheat Bean, cotton, tomato

Buysens et al. (1996); Lemanceau and Samson (1983); Loper (1988)

Bacterial wilt

Gaeumannomyces graminis F. oxysporum, F. solani, Pythium ultimum, Rhizoctonia solani, Sclerotinia sclerotiorum Ralstonia solanacaearum

Gupta et al. (2002) Mercado-Blanco et al. (2004) De Boer et al. (2003); Duijff et al. (1993, 1994a, 1999); Elad and Baker (1985); Kloepper et al. (1980a); Lemanceau et al. (1992,1993); Scher and Baker (1982); Sneh et al. (1984); Van Peer et al. (1990) Kloepper et al. (1980a); Weller et al. (1988)

Eucalyptus

Ran et al. (2005)

Induction of systemic resistance Tobacco necrosis Fusarium wilt Gummy stem rot Grey mould Bacterial speck

Tobacco Necrosis Virus F. oxysporum f. sp. Didymella bryoniae Botrytis cinerea Pseudomonas syringae

Tobacco Radish Watermelon Tomato Arabidopsis

Maurho¨fer et al. (1994) Leeman et al. (1996a,b) YongHoon et al. (2001) Meziane et al. (2005) Meziane et al. (2005)

Disease suppression Charcoal rot Verticillium wilt Fusarium wilt

Take-all Damping-off

A. Robin et al.

Pathogen

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In addition to their antagonistic activity during the saprophytic phase of the life cycle of the pathogens, siderophores and other iron-regulated metabolites were shown to be implicated in the induction of defense reactions during the parasitic phase of their life cycle (Audenaert et al., 2002; Bakker et al., 2003; Ongena et al., 2005; Press et al., 2001; Van Loon et al., 1998). As an example, P. fluorescens CHA0 was shown to induce systemic resistance of tobacco against TNV, whereas its pvd- mutant was less efficient than the wild type. These observations suggest that pyoverdine plays a role in induced systemic resistance (ISR) by CHA0 (Maurho¨fer et al., 1994). Further examples of the contribution of pyoverdines to ISR are given in Table 2. It appears that the contribution of pvd to ISR differs by bacterial strains and also by plant species (Ho¨fte and Bakker, 2007). Mechanisms accounting for pyoverdine ISR remain to be elucidated. 4.3.2. Plants’ iron nutrition Taking into account the strong iron competition in the rhizosphere and the high affinity of pyoverdines for Fe(III), these molecules are expected to interfere with the iron nutrition of plants, as they do with rhizospheric microbes. Recently, we have assessed the impact of ferri-pyoverdine on iron nutrition of a model plant (A. thaliana) belonging to strategy I (Vansuyt et al., 2007). Pyoverdine appeared not to depress but even to improve the iron nutrition of A. thaliana. Indeed, iron chelated to pyoverdine was taken up by plants more efficiently than when chelated to EDTA, leading to an increased plant growth. Our demonstration of the uptake of iron from pyoverdine by a strategy I plant is in agreement with previous studies with other plant species and experimental conditions (Table 3). Other microbial siderophores than pyoverdines, such as ferrioxamine and rhodothorulic acid, were also shown to improve the iron nutrition of various plant species belonging to strategies I and II (Crowley and Gries, 1994; Crowley et al., 1988, 1992; Ho¨rdt et al., 2000; Siebner-Freibach et al., 2003; Yehuda et al., 1996, 2000, 2003). Mechanisms by which plants acquire iron from ferri-pyoverdines remain to be elucidated even though some hypotheses have been proposed. Our recent data suggest that, in contrast with iron chelated to EDTA, iron from pyoverdine would be incorporated by A. thaliana (strategy I) through a different transporter than IRT1. Incorporation of iron by A. thaliana from ferri-pyoverdine was consistent with the presence of pyoverdine in planta as shown by ELISA and by tracing 15N of 15N-pyoverdine (Vansuyt et al., 2007). For strategy II plants, only indirect mechanisms may account for improved plant nutrition by pyoverdines due to their significantly higher affinity for iron compared to phytosiderophores (Meyer and Abdallah, 1978; Sugiura et al., 1981). Duijff et al. (1994c) have proposed that possible degradation of ferri-pyoverdines by microbes could lead to the release of

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Table 3 Examples of positive effects of pyoverdine and ferri-pyoverdine on plant iron nutrition

Plant

Strategy I Peanut

Cotton

Growing conditions

Calcareous soil Nutrient solution Nutrient solution

Cucumber Soil Carnation

Nutrient solution

Pea

Nutrient solution

Vine

Quartz matrix

Strategy II Barley

Nutrient solution

Sorghum

Nutrient solution

Maize

Nutrient solution

Oat

Nutrient solution

Effect on plant

Reference

Enhanced chlorophyll content Presence of 59Fe in roots

Bar-Ness et al. (1991) Jurkevitch et al. (1988) Bar-Ness et al. (1991)

Enhanced chlorophyll content Presence of 59Fe in roots Presence of 59Fe in the roots Enhanced chlorophyll content and ferric reductase activity Reduced plant iron uptake and chlorophyll content Enhanced iron content in shoots only when supplemented with Fe-citrate Enhanced chlorophyll content Presence of 59Fe in the roots Enhanced chlorophyll content Presence of 59Fe by the host plant Reduced plant iron uptake and chlorophyll content Presence of 55Fe or 59Fe in the host plant Presence of 55Fe in the host plant

Walter et al. (1994) Duijff et al. (1994b) Becker et al. (1985) Sharma et al. (2003)

Duijff et al. (1994c)

Bar-Ness et al. (1991)

Becker et al. (1985) Bar-Ness et al. (1992); Walter et al. (1994) Bar-Ness et al. (1992)

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iron that would then be available for phytosiderophores. Possible relations of phytosiderophores with microorganisms in relation with iron depend on their concentration, location, and release kinetics. The diurnal production cycle of phytosiderophores results in pulses (Crowley and Gries, 1994; Reichman and Parker, 2007; Takagi et al., 1984), during which their concentration in the rhizosphere might be higher than that of microbial siderophores, thereby affecting the ligand exchange in favor to phytosiderophores ( Jurkevitch et al., 1993; Yehuda et al., 1996). However, one should be cautious with this hypothesis because of the thermodynamic and kinetic constraints involved in such ligand-exchange processes, which, even if they occurred in the rhizosphere, would be too slow to contribute significantly to plants’ iron nutrition. Besides these temporal aspects, the chelation efficiency of iron by phytosiderophores also relies on their spatial location. Their release occurs at a greater rate in apical root zones (Marschner et al., 1987) corresponding to where the rhizodeposition is maximal (Nguyen, 2003). Despite this observation, interactions between phytosiderophores and microflora are likely to be quite low since the rhizosphere effect on microorganisms, along the root, mostly occur at some distance from root apex (Gamalero et al., 2002; Yang and Crowley, 2000) due to the longer time required for root elongation than for microbial growth upon root exudation. However, in plants producing only low amounts of phytosiderophores (Sorghum bicolor,Z. mays), their degradation by microorganisms could negatively affect plant iron content, whereas such negative effects were not recorded in plants (H. vulgare) producing higher amounts of phytosiderophores (Von Wire´n et al., 1993, 1995). According to the general model of Darrah (1991), secretion of phytosiderophores in massive amounts in restricted spatial and temporal windows would represent an efficient strategy for plants to minimize their microbial degradation in the rhizosphere, compared to the secretion of similar amounts all along the root length and all along the day.

5. Conclusions In cultivated soils, iron is mostly oxidized and precipitated as ferric oxides, oxyhydroxides, and hydroxides. Their rather low solubility results in a low availability of iron for living organisms. However, ferric iron is essential for aerobic organisms such as plants and microorganisms. Plants release a significant part of their photosynthetates as rhizodeposits that promote microbial density and activity. Iron uptake by the roots and the rhizospheric microorganisms reduce the already low bioavailability of Fe(III) in soils even more. The high demand for Fe(III) in the rhizosphere together with its low availability in soils lead to a strong competition for this

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nutrient among living organisms. To face this competition, plants and microorganisms have developed active strategies of iron uptake. Combined expression of these strategies impact the Fe(III) dynamics according to soils’ chemical properties (Fig. 5). Microbial populations selected by the plant in the rhizosphere appear to be adapted to iron stress conditions and therefore to be efficient iron competitors. Indeed their siderophores show a high affinity for iron and thus reduce its availability for less efficient competitors such as fungal phytopathogens, leading to the suppression of their saprophytic growth. This siderophore-mediated microbial antagonism leads to a decreased disease incidence and consequently improves the plant health. Efficient siderophores of microbial populations from the rhizosphere do not compete with the plant harboring them, and even seem to contribute to the plant iron nutrition.

(4) Ferri-siderophores (3)

Siderophores (3)

Phytopathogenic microorganisms

(2)

(3) (3) Antagonistic microorganisms

[FeIII]

(1)

(3)

(2) Proton extrusion reductases phytosiderophores

(1)

Rhizodeposits

Figure 5 Schematic representations of iron-mediated interactions between plants and microbes promoting plant health and iron nutrition. (1) Plants release a variety of organic and inorganic substances (rhizodeposits) which exert a direct influence on soilborne microorganisms including antagonistic and phytopathogens populations. (2) The concentration of Fe(III) in solution is decreased in the rhizosphere due to its uptake by roots and microbes. (3) Thus, active iron acquisition strategies are activated; antagonistic microorganisms produce siderophores showing a higher affinity for iron than those of phytopathogens leading to their suppression (microbial antagonism). (4) Microbial siderophores may also elicitate defense reactions in the host plant and promote plant iron nutrition. Altogether, these different actions promote plant health and iron nutrition.

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Plants have also evolved active iron acquisition strategies based on the acidification and reduction of Fe(III) (strategy I) and on the release of phytosiderophores (strategy II). Altogether, these complex interactions between soils’ chemical properties, plants, and microbes affect the iron dynamics in the rhizosphere, which in turn impact plant health and nutrition (Fig. 5). It is, therefore, a major challenge to better understand this dynamics in order to possibly monitor it for improving plant health and iron nutrition in sustainable agriculture. The corresponding studies require combined expertises in different fields of research and diverse methodologies, in such a way that the iron dynamics is considered as a case study for analyzing rhizosphere ecology.

ACKNOWLEDGMENTS The authors are grateful to K. Klein for correcting the English text. This work was supported by a Ph.D. fellowship to A. Robin by the French Ministry of Research.

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C H A P T E R

F I V E

Geochemistry of Green Rusts and Fougerite: A Reevaluation of Fe cycle in Soils Fabienne Trolard and Guilhem Bourrie´ Contents 1. Introduction 2. Iron as the Main Biogeochemical Marker of Gleyey Soils 2.1. Soil color 2.2. Field tests 2.3. rH measurements 3. Methods for Study of Redox State of Iron in Soil Solutions 3.1. Soil solution sampling 3.2. Characterization of soil solutions 4. Methods for Study of Redox State of Iron in Soil Solids 4.1. Soil sample conditioning 4.2. Characterization of iron in the solid fraction 5. Field Evidence of Fe2þ Mobility in Solution and Seasonal Dynamics 6. Structure of Synthetic Green Rusts 6.1. Structure of GR1s 6.2. Structure of GR2s 7. Structure of Fougerite Mineral 8. Direct Identification of Fougerite Mineral by Decomposition of XRD Spectra and Nature of the Interlayer Anion in Fouge`res–Fougerite 8.1. Material and methods 8.2. Results 8.3. Discussion 9. Thermodynamic Modeling 9.1. General ternary solid solution model for GRs and fougerite 9.2. Constraints on the composition of the solid solution 9.3. Relationship between Gibbs free energies of formation of GRs and the electronegativity of the interlayer anion, revised values

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10. Fe Control in Solution by Mixed Fe(II)–Fe(III) Minerals/Solution Equilibria 11. Redox Interactions Between Iron and Other Elements in Relationship with the Occurrence of GRs 11.1. GRs and nitrogen 11.2. Reaction mechanisms 11.3. GRs and selenium 11.4. GRs and metals 12. Biotic Interaction 13. Geochemical Significance and Place of Fougerite in Iron Oxide Family References

269 271 274 276 278 278 279 280 281

Abstract The oxidation state of Fe in soils plays an important role in determining the mobility of Fe in soil solutions, and the mineralogy of iron oxides. The purpose of this chapter is to review the state of the art regarding Fe cycle in soils, protocols for studying the redox state of iron in soils and soil solutions, the recent data obtained on the natural green rust, fougerite, and the consequences of the reactivity of this mineral on the coupling of iron and other elements’ cycles.

1. Introduction Iron is the fourth element by decreasing order of abundance in the terrestrial crust after O, Si, and Al, and it is mobile in reductive conditions. Its dynamics controls or influences strongly the behavior of most of the other chemical elements such as metals and nutrients (N, P) and of molecules such as xenobiotics. Fe(III) oxides can act as electron acceptors in abiotic processes, facilitating anaerobic respiration, which results in the release of Fe(II) in solution by reductive dissolution. As iron oxides are often associated with clay minerals, this process destabilizes the soil structure and leads to changes in pedogenetic processes. Physical, chemical, and biological processes in soils are, thus, largely dependent on Fe geochemistry. The major Fe oxides and oxyhydroxides have long ago been identified: goethite, lepidocrocite, hematite, maghemite, and ferrihydrite, as Fe(III) oxides, and magnetite, as mixed Fe(II)–Fe(III) oxides (Cornell and Schwertmann, 2003). The recent discovery of fougerite (Trolard et al., 1996, 1997, 2007), the natural mineral of the group of mixed Fe(II)–Fe(III) hydroxides, known as green rusts (GRs), leads to a reevaluation of the Fe cycle in soils, which is the purpose of this review. We will successively examine  

the role of iron as a biogeochemical marker in waterlogged soils; the protocols for studying the redox state of iron in soil solutions and in soil solids;

Geochemistry of Green Rusts and Fougerite

   

229

the structure and chemical composition of synthetic GRs and of fougerite; the thermodynamic model for these complex compounds; Fe control by soil/solution equilibria from field data; the role of fougerite on the redox interactions between iron and other elements, by both abiotic and biotic processes.

2. Iron as the Main Biogeochemical Marker of Gleyey Soils A soil is identified as an hydromorphic or gleyey soil when some of its characteristics are due to an excess of water (Avery, 1973; Baize and Girard, 1995). However, different conditions with regard to hydric variations and biological and biogeochemical processes must be simultaneously fulfilled:     

an excess of water; the restriction of the oxygen sources; the presence of bioavailable substrates; temperature conditions favorable to microflora activity; the presence of elements the oxidation state of which can change and thus record more or less irreversibly the variations in aerobic/anaerobic conditions, named geochemical markers of hydromorphic soils (Trolard et al., 1998).

Among those elements the oxidation state of which can change under earth surface conditions, iron is the most abundant, and the switching between the di- and trivalent redox states of iron initiates a number of significant geochemical reactions. Moreover, the large difference in mobility between Fe2þ, soluble, and Fe3þ, insoluble, results in the segregation of iron in the horizon and/or in soil sequences (Segalen, 1971; Marshall, 1977). These variations are ordered in the landscapes and are considered as responsible for concretioning processes (Marshall, 1977) and for color variations (Taylor, 1981). In all soil classification systems, soil characteristics depending on iron dynamics have been used to differentiate well-drained from poorly drained soils (Soil Survey Staff, 1975, 1976; IUSS Working Group WRB, 1994, 2006; Baize and Girard, 1995; Driessen et al., 2001): soil color, field tests, and rH measurements.

2.1. Soil color Soil color is closely related to the nature of iron oxides, more specifically to their degree of hydration and their amount (Vyssotskii, 1905 [1999]; Taylor, 1981; Cornell and Schwertmann, 2003; Photo 1). Classically,

230

Fabienne Trolard and Guilhem Bourrie´

Photo 1 Soil profile developed in a loamy cover on granite at Quintin, in Brittany, showing all colors from blue (bottom) to yellow, orange and red (top). Photo INRA.

brown, red, or yellow colors have been associated with the occurrence of Fe (III) oxides, while gray, green, blue, or black colors have been associated with the occurrence of Fe(II) in the solids (oxides or sulfides), the white color being interpreted as indicating the absence of any Fe-bearing mineral. Among these colors, moderately reduced waterlogged soils are characterized by the blue-green one (Photo 2) which turns into ochre when the soils are open to the outer atmosphere (Vyssotskii, 1905 [1999]; Ponnamperuma et al., 1967; Photo 3). It has been often ascribed to the occurrence in the milieu of mixed Fe(II)–Fe(III) compounds with a likely structure of GRs. This assumption has been formulated since 1960 and largely discussed in the literature (see the reviews by Taylor, 1981, and Lewis, 1997). GRs were first observed in corrosion products of steel (Stampfl, 1969; Bigham and Tuovinen, 1985; Genin et al., 1994; Al-Agha et al., 1995) then in a waste sludge (Koch and M
rup, 1991), recognized as a

Geochemistry of Green Rusts and Fougerite

231

Photo 2 Gleysol in Fouge`res, showing the characteristic green-blue color. Photo INRA.

Photo 3

Change of soil color in Fouge`res after 30 mn exposure to the air.

mineral in soils (Trolard et al., 1996, 1997), and finally homologated in 2004 by the International Mineralogical Association (Trolard, 2006; Trolard et al., 2007).

2.2. Field tests Field tests that give a semiquantitative appreciation of the redox status of soil have been proposed by Childs (1981) and by Bartlett and James (1995). Childs’ test gives evidence for the presence of Fe(II) in nonsilicated soil Fe, by the appearance of a strong red color on a freshly broken surface of a field-wet soil sample after spraying it with a 0.2% a, a0 -dipyridyl (DIPY) solution in 1 M (pH ¼ 7) ammonium acetate (Childs, 1981). After 1–2 min the intensity of the color is evaluated as follows: positive (red color, Photo 4), weakly positive (pink color or redder patches on clods), and negative or

232

Fabienne Trolard and Guilhem Bourrie´

Photo 4 Red color obtained by Childs’ test on the gleysol in Fouge`res.

barely detectable. Bartlett and James’ test gives evidence for the presence of easily reducible iron oxide fraction: 0.1 M oxalic acid is poured to saturate the soil on a spot plate that is placed in sunlight for 10 min, then five drops of DIPY indicator (10 mM DIPY in pH ¼ 4.8, 1.25 M ammonium acetate) are added. Similarly, distinctions are made: very positive (dark purple color), positive (purple color), weakly positive (pale purple color), and negative or barely detectable. Recent field applications of these two tests have been done in Brittany (Chaplot, 1998; Chaplot et al., 2000) and in Amazonia (Fritsch et al., 2007), and performed on clods as well as on separate features in the case of heterogeneous soil samples, for example, spots, mottles, or nodules. In these two cases, the seasonal dynamics of the redox status of soil was studied. In Brittany, seasonal variations in Fe(II) occurrence (Child’s test) were surveyed over 2 years along a loamy hillslope developed on granite or schist. Results (Fig. 1) showed that in both situations, the depth of the limit between reduced and oxidized horizons varied with the seasons and that reduced horizons were located in the surface organo-mineral and deep saprolite horizons, which were separated by oxidized mineral horizons. In the granite system near the stream channel, an oxidized zone persisted in the saprolite despite the continuous waterlogging of soil, which implies that waterlogging is a necessary but not sufficient condition for hydromorphy. In the Amazonian basin, similar observations were made during the dry and wet seasons (Fritsch et al., 2007). While during the dry season the Childs’ test gave negative results (Fig. 2), during the rainy season it showed a large positive response in different horizons of the sequence. The presence of lepidocrocite in site 1 during the rainy season indicates that fougerite was formed, as fougerite is known as a precursor of lepidocrocite. Strongly positive results of the Bartlett and James’test (Fig. 3)

233

Geochemistry of Green Rusts and Fougerite

Stream channel

Horizon

1m 2m

2m

Topography

1m

Stream channel 10 m

Point of field test

Point of field test

October

January

Topography 10 m Horizon

May

January ? ?

December

April

March

? ? October

? ?

A

B

C

D

Figure 1 Dynamics of iron versus time, as evidenced with Childs’ test, in two sequences of soils on granite (left) and schists (right) in Brittany, France. (A) White: negative or barely visually detectable; (B) dotted gray: weakly positive (pale purple 10R6/3); (C) gray: positive, purple 10R6/8; (D) black: strongly positive, dark purple 10R4/8. Adapted from Chaplot (1998) and Chaplot et al., (2000).

affected a larger thickness of the slightly clay-depleted topsoil in the transition zone (site 3) than in the upslope or low-lying positions. In the clay loam subsoil, the test was commonly strongly positive on separated features such as the bright to dusky red mottles, weakly positive for dark red nodules, and barely detectable or negative for the yellow fringes and white masses. All these observations show that Fe is subject to fast mineralogical transformations in gleysols.

2.3. rH measurements Field tests and rH measurements are scarce, and evidences for Fe mobility and redox state of iron are mainly based upon morphological observations (color), total Fe measurements in soils, and mineralogy by X-ray diffraction (XRD). However, this gives only indirect evidences. No information is gained about the thermodynamic control and kinetics of the processes. The diagnosis for gleyic properties (IUSS Working Group WRB, 2006) is an rH value of 19 or less, where rH( logpH2) is related to the redox potential (Eh) and pH by the relationship: rH ¼ Eh/0.029 þ 2pH, where Eh is expressed in V, and 0.029 stands for the approximate value of (ln 10)RT/ 2F at 298.15 K.

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Fabienne Trolard and Guilhem Bourrie´

6

5

Wet season 4

0m

3

2 1

1m Water table level

2m

6

5

Dry season 4 3

0m

2 1

1m 2m Water table level

A

B

C

0

20 m

Figure 2 Dynamics of iron versus time, assessed with Childs’test, in the toposequence of Humaita, Bresil. (A) Black: positive (purple); (B) Gray: weakly positive (pale purple); (C) White: negative or barely visually detectable. During the wet season (top), test is positive in large parts of the sequence, while during the dry season (bottom), the test is generally negative. Adapted from Fritsch et al., (2007).

This implies measuring Eh and pH in soil solution, as discussed below. When computing rH, one must, however, use the exact temperature of the sample, and rH is more conveniently obtained as a function of pe ( log {e}, where {e} is the activity of the electron), and pH:

FEh ; ðln10ÞRT

ð1Þ

rH ¼ 2ðpe þ pHÞ;

ð2Þ

pe ¼

where F ¼ 96485.309 C mol1 is the Faraday constant, R ¼ 8.314510 J mol1 K1 is the ideal gas constant, T ¼ 273.15 þ t is the absolute temperature (K), and Eh is the redox potential, in Volt, referred to the normal hydrogen electrode. Usually, the potential E is measured with a Pt electrode against either a calomel reference electrode or an Ag/AgCl reference electrode, and Eh must be corrected for the standard potential of the reference electrode.

235

Geochemistry of Green Rusts and Fougerite

Wet season

6

5

0m

4

3

1m

2 1

2m

Water table level

Dry season

6

5

0m

4

1m

3

2

1

2m Water table level

A

B

C

D

0

20 m

Figure 3 Dynamics of iron versus time, assessed with Bartlett and James’ test, in the toposequence of Humaita, Bresil. (A) Black: strongly positive, dark purple; (B) gray: positive, purple; (C) pale gray: weakly positive, pale purple; (D) white: negative or barely visually detectable. Adapted from Fritsch et al., (2007).

For calomel reference electrode (Criaud and Fouillac, 1986)

Eh ¼ E þ 0:2415  0:76  103 ðt  25Þ:

ð3Þ

Although calomel electrode is more reliable, Ag/AgCl reference electrode is preferred due to the risk of pollution by mercury. However, Ag/AgCl electrodes are not highly reproducible (Bates and Macaskill, 1978), and, moreover, their standard potential depends on the nature and the concentration of the internal electrolyte solution. Usually, KCl is used, as transference numbers of Kþ and Cl are similar, which minimizes the error due to the liquid junction potential, but either at saturation or 3, 3.5, or 4 M. The standard potential of the Ag/AgCl electrode is given by

EAg=AgCl ¼ E 0 

@E0 ðT  298:15Þ; @T

ð4Þ

where E 0 is the standard potential of the Ag/AgCl electrode at 298.15 K, 1 bar. Thermodynamic data (Table 1) lead to the practical equation:

Table 1

Thermodynamic data for Ag/AgCl reference electrode

Compound

DfG0/kcal mol1

DfH0/kcal mol1

S0/cal mol1 K1

References

Ag(c) AgCl(c) Cl(aq.) e (1/2 H2(g))

0 26.224 31.350 0

0 30.362 40.023 0

10.206 22.97 13.2 15.6055

a a a a

Equilibrium reaction: AgClðcÞ þ e ⇆ AgðcÞ þ Cl

a b c

DRG0/kcal mol1

DRH0/kcal mol1

DRS0/cal mol1 K1

5.126

9.661

15.1695

E /V

@E 0 @T

0.222 0.22249

0.6578 —

Latimer (1952). From (a), with @E0 =@T ¼ DR S0 =F (Bratsch, 1989), converted with F ¼ 23.060 cal V1 mol1. Bates and Macaskill (1978).

b

=mV K1 a,b c

Geochemistry of Green Rusts and Fougerite

0 3 EAg= AgCl ¼ 0:22249  0:6578  10 ðt  25Þ;

237

ð5Þ

where E0 is from Bates and Macaskill (1978), while its derivative with temperature is obtained from Latimer (1952). The half-cell chemical reaction for the Ag/AgCl electrode can be written as follows:

AgClðc Þ þ e ⇆ Agðc Þ þ Cl and according to Nernst’s law, the theoretical potential is obtained as follows:

EAg=AgCl ¼ E 0 

@ E0 ðln10ÞRT logfCl g; ðT  298:15Þ  F @T

ð6Þ

where the curly braces stand for the activity of Cl in the internal electrolyte filling solution. For KCl 4.5 M, the mean activity coefficientpisffiffiffiffiffiffiffiffiffiffiffi g  ¼ 0.583 at 25  C (Robinson and Stokes, 1970); hence, fCl g ¼ 0:583  4:5 ¼ 3:43, which leads to EAg/AgCl ¼ 191 mV. Practically, because of the difficulty to make reproducible Ag/AgCl electrodes, manufacturers give the corrections to apply. It must be kept in mind that electrodes should be checked periodically, and after replacing the internal filling solution. The correction applied is not always specified in the literature. In our previous studies (Bourrie´ et al., 1999), standard calomel electrode was used and data were corrected according to Eq.(3). Later (Feder et al., 2005), we used Ag/AgCl with KCl in an agar gel manufactured by Ingold and the data were corrected according to

Eh ¼ E þ 0:20671  0:7588  103 ðt  25Þ;

ð7Þ

the electrode being checked against two reference solutions (230 mV and 470 mV  5 mV). The precision obtained is 20 mV, and the sensitivity 0.1 mV.

3. Methods for Study of Redox State of Iron in Soil Solutions The direct study of redox geochemistry of iron is rather difficult in natural environments due to the extreme sensitivity of iron toward aerial oxidation and due to the lack of sensitive detection techniques. The study in the field of Fe dynamics is easier in gleysols where reduction is

238

Fabienne Trolard and Guilhem Bourrie´

generalized in whole horizons, but the phenomena occur in microenvironments too. The originality of our field studies has been to investigate systematically since 1987 both the solution and the solid fractions in soil, to check mineral–solution equilibria (Maıˆtre, 1991a; Trolard et al., 1993; Soulier, 1995; Jaffrezic, 1997; Bourrie´ et al., 1999; Feder, 2001; Feder et al., 2005).

3.1. Soil solution sampling The in situ study of soil solutions requires a specific procedure of sampling and measurements: soil waters are sampled without any contact with oxygen of air to prevent oxidation and in the dark to prevent photoreduction in the presence of dissolved organic matter. Thus, in the field, the devices used were zero-tension lysimeters, made of inert polypropylene flasks, with holes, wrapped in a synthetic tissue to avoid clogging (Fig. 4, from Maıˆtre, 1991a). These devices were preferred to ceramic or PTFE cups that present a large exchange surface area favorable to contamination by mineral compounds added to the PTFE (Maıˆtre, 1991b) or screening. However, the sampling is limited to the free soil waters. Soil solutions are transferred from the collector to a dark syringe and filtered at 0.2 mm under nitrogen atmosphere.

3.2. Characterization of soil solutions Total Fe measured by physical techniques always includes colloidal particles that cannot be quantitatively separated either by ultrafiltration or by dialysis. This leads invariably to an overestimation of aqueous Fe. Indeed, except at

Injection of O2 free air

Water sample

Hole

Bottle wrapped in an insert tissue

Figure 4 Scheme of the device used to sample the soil solution in the field. Adapted from Maıˆtre (1991a).

Geochemistry of Green Rusts and Fougerite

239

extremely low pH, the solubility of Fe(III) is so small that total aqueous Fe consists exclusively of Fe(II). We chose to consider total FeIIaq , pH, and Eh as the master variables. Following Bourrie´ et al. (1999), the activity of Fe2þcan be derived from total Fe(II), providing for the different ion pairs formed by 3þ Fe2þ with Cl, OH, and SO2 4 and the activity of Fe is then derived 2þ from the activity of Fe and pe. The nonconservative parameters were measured in the field: temperature, pH, redox potential, FeIIaq , nitrite, and sulfide. After filtration at 0.45 mm, FeIIaq was analyzed immediately in situ by colorimetry at 660 nm after di-2-pyridyl-ketone-benzoylhydrazone (DPKBH) complexation (Suarez Iha et al., 1994; Bourrie´ et al., 1999) with a portable spectrometer Hach DR/2010. DPKBH is specific of total aqueous FeIIaq in solution, without interference with any cationic complexes and is not influenced by the presence of ‘‘colloidal’’ particles ( Jaffrezic, 1997). Nitrite and sulfide were measured by colorimetry. In the laboratory, after filtration under nitrogen atmosphere, other major cations were analyzed by ICP-AES (Inductively Coupled Plasma Atomic Emission Spectrometry), anions by ionic chromatography (Dionex Isocratic), and alkalinity by acidimetry with Gran-method for detection of the end point. Activity coefficients were computed with the Debye-Hu¨ckel extended equation with provision for ion pair formation using EQUIL(T) (Fritz, 1981) or PHREEQC (Parkhurst and Appelo, 1999) codes, from which activities of free elements were calculated. All equilibrium reactions were written with pe, pH, and the activity of Fe2þ as master variables.

4. Methods for Study of Redox State of Iron in Soil Solids 4.1. Soil sample conditioning The standard protocol in soil science, with sieving sift and air drying, cannot be used. Instead, soil samples must be sampled in a large volume with the surrounding soil solution and maintained in anoxic conditions in an airtight box. The permanence of the original blue or green blue colors during all experiments can be used as a preservation criterion. Analyses of concretions or spots of Fe concentrations, physically separated by microsampling under a binocular in an airtight box, completed the bulk analysis of the samples (Trolard et al., 1993).

4.2. Characterization of iron in the solid fraction Many physical and chemical techniques, such as XRD, electron microscopy, Mo¨ssbauer (Murad, 1988; Murad and Cashion, 2004, and references therein), Raman, or extended X-ray absorption fine structure (EXAFS)

240

Fabienne Trolard and Guilhem Bourrie´

spectroscopies, thermal analysis, and selective extractions (Mehra and Jackson, 1960; Borggaard, 1988; Trolard et al., 1995, and references therein), have been used to study iron minerals in soils and sediments. 4.2.1. Physical techniques In many soils where total Fe content is small, 5%, XRD is not sensitive enough to characterize iron fraction, unless special data treatment is carried (see below). It has been shown that several iron phases, especially GRs and ferrous clay minerals, are very reactive because iron can be oxidized or reduced inside their structures. Their lability makes the study difficult, and special care must be taken, both in the field and in the laboratory, for the conservation of these minerals (Badaut et al., 1985, Trolard et al., 1996). Mo¨ssbauer spectroscopy has emerged as a key tool to study precipitations, transformations, substitutions, reactivities, and so on of iron solid or colloidal fractions (Schwertmann and Cornell, 2003), with a detection limit of 1% (absolute; Murad, 1988), and to define the conditions of syntheses of GRs (Refait and Ge´nin, 1993; Drissi et al., 1994; Hansen and Koch, 1995; Refait et al., 1998 a,b, 2000; Simon et al., 2003). Well-crystallized, Al-free goethite and hematite show sextets, but soil goethite and hematite are frequently small sized and Al substituted, and show only one doublet at room or field temperature (Cornell and Schwertmann, 2003); lepidocrocite is paramagnetic at room or field temperature and its spectrum shows a doublet. Fe(III) oxides, thus, show only ferric doublets, each doublet being characterized by two hyperfine parameters. As synthetic GRs, fougerite contains both Fe(II) and Fe(III), and Mo¨ssbauer spectra show two or three doublets (Trolard et al., 1997), one ferric and one or two ferrous doublets. The presence of a ferrous doublet makes, thus, clear the distinction between fougerite and all other Fe(III) oxides, lepidocrocite, paramagnetic goethite, or hematite, in soils. The distinction between fougerite and Fe(II)–Fe(III) clay minerals is more difficult and needs to take into account the values of both hyperfine parameters at field temperature and spectra obtained at low temperature in the laboratory (Feder et al., 2005). 4.2.2. Selective chemical extractions The presence of fougerite can be confirmed by selective chemical extractions. Chemical extractions have been commonly used in soil science to extract different fractions and then to compare soils from different origins. Soil Survey Staff (1999) and IUSS Working Group WRB (2006) used several chemical reagents to quantify organic or mineral compounds and then to define diagnostic horizons and properties. However, these extractions are not specific for one mineral with a given crystallinity and particle size (Borggaard, 1988) but have been used to operationally define fractions of solids. For example, Fe extracted by Mehra and Jackson’s method using the

241

Geochemistry of Green Rusts and Fougerite

dithionite-citrate-bicarbonate (DCB) reagent has been ascribed to all oxides (s.l.), excluding silicated Fe, while Fe extracted by an acid ammonium oxalate solution has been ascribed to poorly ordered oxides (s.l.) (Tamm, 1922; Schwertmann, 1979). Nguyen Kha and Duchaufour (1969) tried to extract Fe(II) fraction in hydromorphic soils by using oxalate reagent, but this reagent does not dissolve fougerite and GRs because oxalate can be intercalated as the interlayer anion to form a stable oxalate-GR (Refait et al., 1998b). Trolard et al., (1996) showed that citrate-bicarbonate (CB) reagent, that is, DCB without the reductive effect of dithionite, dissolves in a few hours synthetic GRs and fougerite. However, CB-extracted Fe does not consist solely of fougerite, but also includes organo-Fe complexes, for example, as coatings on clay minerals, which can be dissolved by the oxalate reagent. Nanoparticles of Fe(III) oxides can be partly dissolved by oxalate but not by CB. CB extraction is, thus, a better estimator of fougerite fraction and organo-Fe complexes than is oxalate extraction. To separate fougerite from organo-Fe complexes, extractions can be processed kinetically and/or sequentially with mineralogical controls made step by step (Trolard et al., 1996, 2007; Feder et al., 2005): fougerite is more labile than organo-Fe complexes, so that kinetics of CB extraction shows two successive steps. Fougerite is quasi-entirely dissolved after a few hours, while organo-Fe complexes are more resistant. This method can be illustrated by results obtained in gleysol developed on granite in Brittany (France) (Photo 1), where seven microsampled soil features containing iron were distinguished from surface toward the depth (Table 2 and Fig. 5) (Trolard, 2006). In summary, three of them (LV, GRG, and GRB) were associated with coatings located around fine roots and on surface of aggregates, two (BTO, SO) were ochre spots more or less diffuse in a clear gray or white matrix, one (PO) consisted of large rusty spots inside the zone of fluctuation of the water table, and the last one (AR) was the blue green matrix, which turns into ochre when in contact with air. CB selective extraction showed the occurrence of a large Fe labile fraction available without reduction; it is larger in reduced than in oxidized Table 2 Selective extraction of Fe by citrate-bicarbonate (CB) in the different features distinguished in the soil profile in Quintin

Sample

LV

BTO

PO

GRG

GRB

AR (N2)

FEM

FeCBt ¼ 1h =% FeCBtotal

21.8

10.5

11.5

18.0

19.7

39.5

3.6

242

Fabienne Trolard and Guilhem Bourrie´

LV

LV BTO SO

BTO

PO

SO

GRG

PO

GRB

GRG GRB

AR

AR

0% 20% 40% 60% 80% 100%

Scale 0

20 cm

FeCB

FeCBD-CB

Fe (Silicates)

Figure 5 Location of the different microsamples in the soil profile, adapted from Trolard et al., (1998) (left), and selective extraction of Fe by citrate-bicarbonate (CB) (right).

milieu (Fig. 6); 10–40% of this fraction is extracted after only 1 h. This fraction is labile, as even a short contact (a few hours) with air resulted in the immobilization of a large Fe fraction, for example, up to 40% for AR sample (Fig. 6), with respect to the amount extractible by CB under N2 atmosphere for samples showing the characteristic blue green color before CB extraction. This behavior is typical of labile compounds such as GRs and fougerite. Classically, the difference DCB minus oxalate has been used to quantify Fe(III) oxides and explain association, or substitution, of elements (Al and transition metals) in Fe oxides (Cornell et al., 1976; Borggaard, 1988; Singh et al., 1992). However, the oxalate reagent appreciably dissolves gibbsite due to the low pHð’ 3Þ, while CB does not dissolve gibbsite due to the neutral pH. We can, thus, conclude that DCB minus CB extraction is a better estimator of Fe(III) oxides (Trolard et al., 1995), including goethite, lepidocrocite, hematite, and magnetite, but excluding silicated Fe (not dissolved by DCB) (Mitchell et al., 1971), fougerite, and organo-Fe complexes (dissolved by CB). Fougerite can be separated from organo-Fe complexes by using CB extraction kinetically.

5. Field Evidence of Fe2þ Mobility in Solution and Seasonal Dynamics First of all, temperature, pH, and Eh were monitored in the soil solution by using an automatic probe in the field, derived from oceanographic research (Feder et al., 2005; Cary and Trolard, 2008). The results

243

Geochemistry of Green Rusts and Fougerite

12 FEM1 FEM2

10 8 6 4 2 0 0

50

100 150

200 250

300

200

250

300

100 150 200 Time (h)

250

300

12 GRB1 GRB2

Fet/Fetotal (%)

10 8 6 4 2 0 0

50

100

150

12 AR ARN

10 8 6 4 2 0 0

50

Figure 6 Results of selective extractions by citrate-bicarbonate (CB) reagents on the different microsamples, showing the sensitivity of the Fe labile fraction to air contact. In light, samples treated with the standard protocol, that is, extraction in contact with air; in dark, samples treated by the same protocol but under N2 atmosphere (top: petroferric nodule; middle: ferric coating on root channels; and bottom: arena).

obtained at Fouge`res (in Brittany) and in Camargue (in South of France) (Fig. 7) show that the variations in pH and Eh were large and spanned the domains of existence of aqueous Fe(II) and of Fe(III) oxides. From those data, we computed the rH value, diagnosis of the redox status in soils (IUSS Working Group WRB, 2006), according to Eq. (2). In Fouge`res, rH values ranged from 4 to 14.4, well smaller than the threshold value of 19. In Camargue, during the irrigation period, rH ranged from 4 to 5.5, which is even smaller.

10.14

400

6.76

200

3.38

0

0.00

pe

EhNHE/mV

600

-200

-3.38

-400

-6.76

98 98 99 99 99 99 99 99 99 99 00 00 00 /19 1/19 1/19 2/19 4/19 6/19 7/19 9/19 1/19 2/19 2/20 4/20 5/20 0 /1 /1 /0 /0 /0 /0 /0 /0 /1 /1 /0 /0 /0 01 20 09 28 19 08 28 16 05 25 13 03 23

8.5 8.0 7.5 pH

7.0 6.5 6.0 5.5 5.0 9 9 9 8 8 9 9 9 9 9 0 0 0 99 99 99 99 99 99 99 99 99 99 00 00 00 0/1 /11/1 /01/1 /02/1 /04/1 /06/1 /07/1 /09/1 /11/1 /12/1 /02/2 /04/2 /05/2 1 / 01 20 09 28 19 08 28 16 05 25 13 03 23

Irrigation 1/04 -150

21/04

11/05

31/05

20/06

10/07 6

-250

7 Eh

-300 -350 -400

pH

Eh(mV)

-200

8 pH 9

Figure 7 Dynamics of pH and Eh versus time in the soil solution, monitored in situ (top and middle) at 70 cm depth in the forest of Fouge`res (adapted from Feder et al., 2005); (bottom) at 1.6 m in Camargue (adapted from Cary, 2005; Cary and Trolard, 2008).

245

Geochemistry of Green Rusts and Fougerite

“Top” system

“Slope” system

“Colluvio-alluvial” system

Fe(II)batho. (mM/L)

80

1m 10 m Sequence scale

Horizon

20

5 4

1X 2X 3X

80 Fe(II)batho. (mM/L)

1m

6 40

0

1m 10 m Topography

60

J F M A M J J A S Time (month)

60 4X 5X 6X

40 20 0

1

3

2 J F M A M J J Time (month)

A

S

Figure 8 Seasonal dynamics of Fe(II) in soil solution in relationship with their location in the soil and in the landscape on Quintin granite, in Brittany. Adapted from Bourrie´ et al., (1994).

Two main patterns of seasonal dynamics of Fe(II) in solution could be distinguished, in another soil sequence on granite, (Fig. 8), depending on the location of hydromorphic soils in landscapes. The vertical movements of the water table control the first pattern, generally observed in colluvial–alluvial systems, as follows: (1) when the level of the water table was high, soils were waterlogged for a long time, reductive conditions occurred, and significant release of Fe(II) in soil solutions was observed; (2) when the level of the water table decreased, the environment became oxidizing by entry of air, and Fe(II) concentrations in soil solution decreased dramatically to zero (Fig. 8). The recharge of oxygen by rainwater controlled the second, generally observed in reductomorphic soils located at midslopes or over lithological discontinuities, as follows: (1) during the rainy season, as rainwater is saturated with oxygen, enough dissolved oxygen was supplied, though the soils were completely waterlogged, so that the environment stayed oxidizing and no Fe(II) was released in solution; (2) when the rainflow decreased, at the end of spring, the milieu became confined, reductive and Fe(II) concentration increased strongly, until in summer dissolved oxygen was supplied and Fe precipitated in situ.

246

Fabienne Trolard and Guilhem Bourrie´

These two patterns resulted in two different geochemical behaviors from a same initial condition, that is, reductive and waterlogged. In the first case, there was a continuous flow of water when Fe was mobile. This induced the formation of bleached horizons with the exportation of Fe from horizons. In the second case, there was no outflow when Fe was mobile, and Fe was globally conserved in the milieu, though locally it was segregated. Oximorphic properties were, thus, obtained with simultaneous presence of rusty spots, brown matrix, and bleached spots in the same horizon. These results confirmed that Fe is mobile as Fe2þ, but different seasonal dynamics explain different soil color patterns.

6. Structure of Synthetic Green Rusts The GRs are a family of labile Fe(II)–Fe(III) hydroxysalts which can be synthesized in the laboratory (Murad, 1990). They can form by oxidation of Fe(II) solution (Tamaura et al., 1984; Vins et al., 1987; Hansen et al., 1996; Lewis, 1997), by interaction of soluble Fe(II) with Fe(III) oxides (Taylor and Mc Kenzie, 1980), by partial oxidation of Fe(OH)2 (Ge´nin et al., 1994), or by reduction of Fe(III) oxides with bacteria (Tuovinen et al., 1980; Bigham and Tuovinen, 1985; Fredrickson et al., 1998). GRs consist of brucite-like hydroxide layers positively charged due to the presence of Fe(III) cations, which alternate regularly with interlayers made of anions and water molecules. The structure of GRs and parent minerals can accommo2 date a variety of anions, such as OH, Cl,CO2 3 ; and SO4 (Table 3). Depending on the nature of anion, two types of GRs could be distinguished by means of XRD (Bernal et al., 1959; Lewis, 1997) and Mo¨ssbauer spectrometry. Synthetic Fe(II)–Fe(III) GRs show two main doublets at 78 K by classical transmission Mo¨ssbauer spectroscopy (TMS): D1 (isomer shift d ’ 1:27 mm s1 , quadrupole splitting DEQ ’ 2:87 and 2:92 mm s1 ) and D3 ðd ’ 0:47  0:48 mm s1 Þ, DEQ ’ 0:38 and 0:43 mm s1 ). D1 has been ascribed to Fe2þand D3 to Fe3þ. A slightly better fit was sometimes obtained by considering a second ferrous doublet D2 ðd ’ 1:27  1:28 mm s1 ; DEQ ’ 2:55  2:69 mm s1 Þ; this occurs when the structure is GR1.

6.1. Structure of GR1s The GR1s present a stacking sequence similar to that of pyroaurite, . . . AcBiBaCjCbAk . . ., where A, B, C are planes of OH ions; a, b, c, planes of Fe atoms; and i, j, k, the interlayers. The lattice is rhombohedral and the space group R3m (Fig. 9A, Tables 4–6). The interlayered anions are spherical or planar, for example, Cl, Br, 2 2  I , CO2 3 ; C2 O4 ; SO3 . The rhombohedral structure is due to the

Table 3

Structural formula of natural minerals of the fougerite group and nature of the interlayer anion

Mineral

Structural formula

Interlayered anion

Fougerite Meixnerite Woodallite Iowaite Takovite Hydrotalcite Pyroaurite

n ½ðFe2þ ; MgÞ1x Fe3þ x ðOHÞ2 ½x=nA ; mH2 O ; 1=4 x 1=3 [Mg6Al2(OH)16][(OH)2, 4H2O] [Mg6Cr2(OH)16][Cl2, 4H2O [Mg4FeIII(OH)10][Cl2, H2O] [Ni6Al2(OH)16][(OH, CO3), 4H2O] [Mg6Al2(OH)16][CO3, 4H2O] [Mg6Fe2III(OH)16][CO3, 4H2O]

Discussed: OH, Cl, CO32

From Trolard et al. (2007).

OH Cl Cl OH, CO2 3 CO32 CO32

248

Fabienne Trolard and Guilhem Bourrie´

A Stacking C c/3 a B B A

Interlayer Hydroxide layer

c b

Cl-

OH-

H 2O

Fe(III)

A

Stacking C a B

A

Stacking A c B

B c/3

H2O

Interlayer c

Hydroxide layer b

S Interlayer Hydroxide layer

c OH-

C

Fe(II)

B

B

C

b OH-

Fe(III)

H2O

O

OH-

Fe(III)

Fe(II)

Fe(II) Mg(II)

Figure 9 Stacking sequences and positions of water molecules and anions in an interlayer viewed along [001] of: (A) GR1Cl, hydroxychloride green rust (adapted from Ge´nin et al., 2001); (B) fougerite with OH as interlayer anion and (C) GR2, hydroxysulfate green rust (adapted from Simon et al., 2003).

stacking sequence of layers AB-BC-CA. Fe(II) and Fe(III) cations are distributed randomly among the octahedral positions. Water molecules in the interlayer are situated close to the threefold axis that connects the two OH ions of the adjacent hydroxide layers. The general following formula could be proposed for GR1s: þx x n ½FeII1x FeIII x ðOHÞ2 ½x=nA ; m=nH2 O ; where 1=4 x 1=3:

Figure 9A shows an example of the crystal structure of a GR1 such as the hydroxychloride, GR(Cl) (Refait et al., 1998; Ge´nin et al., 2001). More specifically, the XRD study of GR1(Cl) (Refait et al., 1998a) showed that 2þ the structure was close to that of pyroaurite ½MgII6 FeIII 2 ðOHÞ12 2 ½CO3 ; 4H2 O and iowaite, a Mg(II)–Fe(III) pyroaurite-like mineral incorporating Cl.

6.2. Structure of GR2s The GR2s present a stacking sequence similar to the crystal structure of Fe (OH)2, . . . AcBiA . . ., with the same notations as above. The lattice is trigonal and the space group is P3m1 (Fig. 9C) (Simon et al., 2003).

Table 4 X-ray diffraction data (CoKa1 radiation) compared with computed interplanar distances (nm) and intensities of synthetic GR1(Cl) hkl

daobs

dcalc

dobs-dcalc

Iobs/I1

Icalc/I1

Iobs/Icalc

003 006 101 012 009 104 015 107 018 0012 1010 0111 110 113 1013 116 0114 021 202 024 205/0018 1016

0.797 (3) 0.3966 (8) 0.2744 (6) 0.2692 (4) 0.264 (1) 0.253 (2) 0.2392 (3) 0.216 (2) 0.2027 (3) 0.198 (1) 0.1808 (4) 0.1702 (5) 0.1595 (1) 0.1563 (1) 0.1526 (7) 0.1479 (1) 0.1448 (3) 0.1375 (3) 0.1364 (2) 0.1348 (3) 0.1323 (3) 0.1309 (4)

0.7950 0.3975 0.2744 0.2691 0.2650 0.2507 0.2391 0.2146 0.2026 0.1988 0.1805 0.1706 0.1595 0.1564 0.1528 0.1480 0.1450 0.1379 0.1372 0.1346 0.1326 0.1312

0.002 0.0009 0 0.0001 0.001 0.0023 0.0001 0.0014 0.0001 0.0008 0.0003 0.0004 0 0.0001 0.0002 0.0001 0.0002 0.0004 0.0008 0.0002 0.0003 0.0003

100 31.5

100 31.5

1 1

34

30.8

1.104

4.5 21 2 19.2

5.4 21.5 3.0 21.4

0.833 0.977 0.666 0.897

5.5 4.5 9.0 10.4 5 4.3 3

6.3 6.1 8.7 11.3 3.9 5.9 2.6

0.873 0.738 1.034 0.920 1.282 0.729 1.154

5 1.5 6

5.3 0.8 6.2

0.943 1.875 0.968 (continued)

250 Table 4 (continued) hkl

daobs

dcalc

dobs-dcalc

Iobs/I1

027 208/0117 1112 Weighted average

0.1284 (2) 0.1251 (2) 0.1240 (3)

0.1280 0.1252 0.1244

0.0004 0.0001 0.0004 þ0.0001

0.5 5

From Refait et al. (1998a). The structure is R3 m with parameters a ¼ 0.3190(1) nm and c ¼ 2.385(6) nm (V ¼ 0.2102 nm3). The digit in parentheses is the uncertainty (1s) on the last digit.

Icalc/I1

0.8 3.8

Iobs/Icalc

0.625 1.316 1.002

Table 5

Reduced coordinates of atoms in synthetic GR1(Cl) and temperature factor, from Refait et al., (1998a)

Fe O (OH) O (H2O) Cl

x

y

z

U11

U22

U33

U23

U13

U12

0 0 0.1 0.25

0 0 0.1 0.25

0 0.375 0.5 0.5

0.009 0.014 0.15 0.3

0.009 0.014 0.15 0.3

0.025 0.06 0.08 0.08

0 0 0.02 0.02

0 0 0.02 0.02

0.0045 0.007 0.1 0.1

From Refait et al. (1998a).

251

252

Fabienne Trolard and Guilhem Bourrie´

Table 6 Layer-to-layer and interatomic distances in synthetic GR1(Cl)

Layer-to-layer distances/nm Fe–OH: 0.099

OH–OH in hydroxide layer: 0.199 OH–interlayer: 0.298

Interatomic distances/nm Fe–OH: 0.209 OH–OH (6x): 0.319 OH–H2O: 0.300 OH–Cl: 0.309

OH–OH (3x): 0.277 Cl–H2O (min.): 0.320

From Refait et al. (1998a).

2 The interlayered anions are three-dimensional, for example, SO2 4 ; SeO4 . . . Moreover, an ordering of Fe(II) and Fe(III) was observed and the interlayer contained two layers of water molecules and ordered anions inducing a stoichiometry. The D1 doublet has been assigned to Fe(II) in the octahedral sites of the brucite-like sheets, as its hyperfine parameters were close to those of Fe(OH)2 and the D2 doublet to Fe(II) atoms situated close to the anions. Thus, 2þ the following formula could be proposed: ½FeII4 FeIII ½A; 8H2 O 2 , 2 ðOHÞ12 where A is one of the three-dimensional divalent anions cited above.

7. Structure of Fougerite Mineral Fougerite (IMA 2003-057) is a mixed M(II)–M(III) hydroxysalt of the GR group, where M(II) can be Fe or Mg, and M(III) Fe. The general þx x n structural formula is ½ðFeII ; MgÞ1x FeIII x ðOHÞ2 ½x=n A ; mH2 O , where A is the interlayer anion and n is its valency, with 1/4 x 1/3. The structure of the mineral was proposed to be GR1(OH) (Fig. 9B), by analogy to GR1(Cl), with a symmetry group R3m and lattice parameters a ’ 0:32 nm and c ’ 2:25 nm: Mo¨ssbauer, Raman, and X-ray absorption spectroscopies (XAS) at the Fe K edge and XRD gave the structure of the mineral. Four doublets best fitted the Mo¨ssbauer spectra of the mineral obtained at 78 K: D1 and D2 due to Fe2þ (d ’ 1:27 and 1:25 mm s1 , DEQ ’ 2:86 and 2:48 mm s1 , respectively) and D3 and D4 due to Fe3þ (d ’ 0:46 mm s1 , DEQ ’ 0:48 and 0:97 mm s1 ). Microprobe Raman spectra obtained with a laser at 514.53 nm showed the characteristic bands of synthetic GRs at 427 and 518 cm1, respectively. The structure of the mineral was studied by XAS, both by recording the EXAFS signal and the X-ray absorption near edge structure (XANES) signal at the Fe K-edge, along with TMS. The same techniques were applied to synthetic GR1(Cl), GR1(CO3), and to a synthetic pyroaurite with approxi2þ 2 mate formula ½MgII5 FeIII 2 ðOHÞ14 ½CO3 ; nH2 O . Two samples of

Geochemistry of Green Rusts and Fougerite

253

hydroxycarbonates obtained by mixing Fe2þ, Fe3þ, and Mg2þ salts and coprecipitating hydroxides by addition of NaOH in presence of Na2CO3 were also prepared and analyzed by TMS. The x mole ratios were measured by Mo¨ssbauer spectroscopy. Powder X-ray diffractograms were performed on the synthetic samples. The global formulae of the compounds II II III obtained were FeII2 MgII2 FeIII 2 ðOHÞ12 CO3 and Fe4 Mg2 Fe2 ðOHÞ16 CO3 . The main results obtained were the following (Refait et al., 2001): 



 







both Fe2þ and Fe3þ valence states occurred in close neighborhood, as evidenced from the XANES part of the spectrum, which was sensitive to the oxidation state of Fe and to its local symmetry; there were no clusters of Fe2þor Fe3þ; the mineral was confirmed as belonging to the group GR1, because its pseudo-radial distribution functions (PRDF) displayed the same set of peaks (P1–P6) and were in agreement with previous studies on similar compounds (Roussel et al., 2000); the lattice parameter a was obtained at about 0.31–0.32 nm, as predicted earlier, from the second P2 peak of PRDF, that is, the Fe–Fe distance; the positions of further and smaller peaks of the PRDF corresponded to the hexagonal pffiffiffi arraypofffiffiffi cations, second, third . . . neighbors of Fe, in the sequence a 3; 2a; a 7; 3a . . .. Mg substitutes for Fe in the mineral because the radial distribution function obtained for the mineral proved to be intermediate between those of GR1s and synthetic pyroaurite, that is, between Fe2þ–Fe3þ and Mg2þ–Fe3þ hydroxycarbonates; the local ratio Mg/Fe was about 2  1; this was estimated by fitting a linear combination of the contributions of Mg and Fe to the back-Fourier transform of the PRDF curve: in the case of synthetic GR1s, no Mg was present and backscattering atoms were Fe atoms, whereas in synthetic pyroaurite, each Fe atom was surrounded by Mg atoms, so that Mg atoms were the only backscattering atoms; the spectrum of the mineral was intermediate in between these two cases; the nature of the anion present in the mineral could not be determined by XAS, because spectra of various GRs proved to be independent of the interlayer anion.

8. Direct Identification of Fougerite Mineral by Decomposition of XRD Spectra and Nature of the Interlayer Anion in Fouge`res–Fougerite The lattice parameter c of GR1 compounds is mainly determined by the geometry and size of the interlayer anion (see Fig. 9). It is c ’ 2:385 nm in GR1(Cl) (Refait et al., 1998a) and c ’ 2:256 nm in GR1(CO3) (Drissi et al.,

254

Fabienne Trolard and Guilhem Bourrie´

1995; Abdelmoula et al., 1996). In GR1 (CO3), the carbonate ions are parallel to the hydroxide sheets and the width of the interlayer is fixed by the diameter of the oxygen atoms of H2O and CO2 3 . Thus, the parameter c of GR1(OH) should be in the range between that of GR1(Cl) and of GR1(CO3), that is, c 2 [2.256, 2.385] nm. However, it is not accessible by EXAFS spectroscopies that probe only the local environment of the nucleus target and for Fe only see the arrangement of metals in the (a, b) plane. EXAFS spectroscopy of selenate-GR, GR2(SeO4), at the Se K edge only showed that Se is surrounded by O in tetrahedral coordination and did not yield information on the interaction of the anion with the layer. However, a closer examination of the XRD diagrams and decomposition of peaks allowed us to determine the c parameter, which could not be done on raw diagrams.

8.1. Material and methods Samples were taken in the Gleysol in Fouge`res every 20 cm in the beginning of October 1998, from 20 to 100 cm (Table 3). At that time, the first two samples (silty) showed oximorphic conditions, the third (silty) and fourth (saprolite) showed reductimorphic conditions, and the last (saprolite) showed oximorphic conditions. As shown previously (Feder, 2001; Feder et al., 2005), the limit between reducing and oxidized horizons fluctuates seasonally, getting deeper during winter. Clay fractions were separated by sedimentation under inert atmosphere in a glove box, and saturated with Mg, K, and ethyleneglycol or heated to 350  C, 450  C, and 550  C following the classical protocol (Robert and Tessier, 1974; Caille`re et al., 1982). Spectra were acquired with a Siemens D-500 (40 kV, 20 mA) diffractometer, equipped with a graphite monochromator. The line was Co-Ka1, and spectra were acquired from 3 to 45 (2y ), by 0.02 steps, 10 s counting time per step, in scan mode. Labeling of peaks was done with Diffrac AT EVA on the basis of JCPDS ( Joint Committee on Powder Diffraction Standards) database. As the main peak of GRs is very close to the main peak of kaolinite, we used the DECOMPXR software (Lanson and Besson, 1992) in the range 3–15 (2y ), to decompose the peaks.

8.2. Results The raw and decomposed diagrams are shown in Figs. 10–14. ˚ and is masked by kaolinite at The main peak of GR1(Cl) is at 7.97 A ˚ 7.13 A. Quartz, K-feldspar, and plagioclase feldspars are present. Lepido˚. crocite is present in the deepest sample (oxidized arena) at 6.26 A ˚ ˚ that In addition to kaolinite, omnipresent at 7.13 A, the peak at 10.01 A ˚ does not collapse with K can be ascribed to illite, the 14.36 A peak that ˚ peak that does collapses with K (sample 1) to true vermiculite, the 14.1 A ˚ not collapse with K to Al-smectite (samples 3, 4, and 5), the peak at 13.2 A

5

10

20

25

30

2q Co Ka1/degrees

35

40

45

6

10.01 Å

10.53 Å

8.18 Å

6.71 Å

8.39 Å

Intensity

10.77 Å

7.13 Å

10.46 Å

12.33 Å 12.67 Å 12.25 Å

8

8.13 Å 8.39 Å

Saturation potassium

15

12.37 Å

14.36 Å

Saturation magnesium

12.59 Å

Saturation potassium

14.38 Å

Intensity

2.38 Å 2.45 Å

4.44 Å

3.53 Å

Saturation ethylene glycol

12.13 Å

Saturation magnesium

Heating to 350 °C

10.77 Å

2.49 Å

3,24 Å 3.57 Å 6.71 Å

12.25 Å

14.36 Å 14.38 Å 12.24 Å

Saturation ethylene glycol

11.40 Å

3.34 Å 3.19 Å

Heating to 550 °C

Heating to 450 °C

4.70 Å

Heating to 350 °C

0

4.25 Å

7.13 Å

Heating to 450 °C

4.97 Å

9.98 Å

Heating to 550 °C

10

12

2q Co Ka1/degrees

Figure 10 Raw diagram and decomposed diagram of sample 1.

14

16

10.05 Å 10.92 Å

13.69 Å

7.13 Å 11.00 Å

Intensity

7.83 Å 7.65 Å

10.71 Å 10.99 Å

Saturation magnesium

7.94 Å

3.53 Å

Saturation potassium

2.46 Å

Saturation potassium

0

Heating to 350 °C

2.85 Å

Saturation magnesium

12.66 Å

Intensity

2.50 Å

3.25 Å 3.21 Å

2.39 Å

Heating to 350 °C

4.75 Å

12.66 Å

3.58 Å

7.18 Å

4.27 Å

13.69 Å 10.92 Å

5.00 Å

10.09 Å

3,35Å

256 Heating to 550 °C

Heating to 550 °C

5

10

15

20

25

2 q Co Ka1 /degrees

30

35

40

45

6

7

8

9

10

11

12

2 q Co Ka1 /degrees

Figure 11 Raw diagram and decomposed diagram of sample 2.

13

14

15

16

10.03 Å

Intensity 8.91 Å

14.21 Å

Saturation magnesium

5

7.92 Å

3.53 Å

14.02 Å

Saturation potassium

14.02 Å

11.04 Å

Saturation magnésium

0

7.13 Å

8.96 Å

12.68 Å 11.43 Å

14.24 Å

Intensity

Heating to 450 °C

2.38 Å

2.83 Å

10.91 Å

2.49 Å 3.23 Å

4.71 Å

14.20 Å

Saturation ethylene glycol

3.19 Å

7.12 Å

3.57 Å

Heating to 550 °C

Heating to 450 °C

10.05 Å

11.06 Å

9.84 Å

3.34 Å 4.25 Å

4.98 Å

9.99 Å

Heating to 550 °C

Saturation potassium

10

15

20

25

257

2 q Co Ka1 /degrees

Figure 12

30

35

40

45

6

8

10

12

2 q Co Ka1 /degrees

Raw diagram and decomposed diagram of sample 3.

14

16

0

5

7.13 Å

10.01 Å

10.69 Å

Intensity

8.01 Å

14.26 Å

10.58 Å

8.01 Å

Intensity

13.95 Å

2.49 Å

11.30 Å

7.86 Å

14.08 Å

14.06 Å

10

15

20

25

30

2 q Co Ka1/degrees

Figure 13

10.92 Å

35

40

45

6

7.89 Å

Saturation potassium

14.04 Å

3.21 Å

3.53 Å

Saturation magnesium

14.04 Å

Saturation potassium

Heating to 350°C

2.38 Å

Saturation ethylene glycol

2.85 Å

4.72 Å

2.45 Å

Heating to 350 °C

Heating to 450 °C

11.06 Å

3.57 Å

7.13 Å Heating to 450 °C

Saturation magnesium

13.38 Å

Heating to 550 °C

4.25 Å

4.98 Å

10.00 Å

3.33 Å

258 Heating to 550°C

8

10

12

2 q Co Ka1/degrees

Raw diagram and decomposed diagram of sample 4.

14

16

Intensity

7.69 Å

7.83 Å

11.13 Å 10.49 Å

12.59 Å 13.19 Å

7.65 Å

6.26 Å

14.20 Å

10.70 Å

Intensity

7.14 Å

2.50 Å

Heating to 350 °C

2.83 Å

4.26 Å

6.26 Å

4.72 Å

2.38 Å

3.54 Å

13.19 Å

Heating to 450 °C

Saturation magnesium

14,20Å

Saturation ethylene glycol

14.15 Å

Heating to 350 °C

3.19 Å

3.57 Å 3.46 Å

7.90 Å 7.14 Å

12.59 Å

Chauffage à 550 °C

Heating to 450 °C

11.09 Å

12.34 Å

10.03 Å

3.34 Å 3.24 Å

4.98 Å

12.33 Å 10.03 Å

Heating to 550 °C

Saturation potassium

259

0

5

Saturation potassium

10

15

20

25

2 q Co Ka1 /degrees

30

35

40

45

6

7.48 Å

10.64 Å

14.10 Å

14.10 Å

Saturation magnesium

8

10

12

14

2 q Co Ka1 /degrees

Figure 14 Raw diagram and decomposed diagram of sample 5.

16

18

260

Fabienne Trolard and Guilhem Bourrie´

to hydroxy-Al vermiculite (sample 5), and the peak around 11 A˚ either to interstratified illite-smectites or to intergrade minerals. This complex paragenesis is classical in acid brown soils (Alocrisols) on granite in oceanic climate, and the abundance of Al in the interlayers of vermiculites and smectites in acid soils has been described long ago (Gjems, 1963; Tardy and Gac, 1968). In Fouge`res, a micropodzolization in surface superimposes on acid brown pedogenesis, and Al is present at large concentrations in solutions, and largely as ‘‘Al13’’ polymer (Bourrie´, 1981; Aurousseau et al., 1987; Bourrie´ et al., 1989). ˚ . The peak at 8.13–8.39 A ˚ The peak of fougerite should be near 7.97 A in sample 1 is very small and cannot be interpreted. However, samples 2–4 ˚ , respectively. (K-saturated) clearly show peaks at 7.94, 7.92, and 7.89 A This peak disappears on heating and shifts to either smaller or larger values ˚ shifts to 7.65 A ˚ with Mg saturation. In sample 5, the peak present at 7.48 A  with Mg and larger values when heating, until it disappears at 550 C.

8.3. Discussion We propose to ascribe the peaks observed in samples 2–4 to fougerite. We exclude the Mg-saturated samples for the following reason: large concentrations of Mg in the milieu can result in an absorption of Mg by the mineral, with release of Fe, which can explain the variability observed. This is not likely to occur with K, due to the larger size of this cation. The main peak of GR1 is ascribed to d003, which leads to c ¼ 3 d003 ¼ 2.375  0.0075 nm, from the three experimental values above. This value is close to the value of GR1(Cl), that is, c ¼ 2.3856 nm (Table 4), but smaller; it is much larger than the value for GR1(CO3), c ¼ 2.256 nm (Abdelmoula ˚ . Fougerite is, et al., 1996), which would shift the main peak to d003 ¼ 7.52 A thus, closer to GR1(Cl) than to GR1(CO3). Abdelmoula et al., (1996) obtained c ¼ 2.256, 2.255, and 2.274 nm, in the range classically admitted for GR1(CO3), [2.25–2.28] nm. The confipffiffiffi dence interval of the average of our three measurements is given by ts= n. With n ¼ 3, n ¼ 2 degrees of freedom, t ¼ 2.92 from Student distribution, and s ¼ 0.0075 nm, at the probability level a ¼ 0.05, the confidence interval for the average of c is [2.36–2.39], which is completely out of the range given above [2.25–2.28]. The average of our measurements cannot, thus, be taken as an estimate of c of GR1(CO3), and we can rule out definitively CO2 3 as the interlayer anion in Fouge`res—fougerite. For GR1(Cl), the best values are derived from the refinement of the structure (Refait et al., 1998a, Table 4): c ¼ 2.385  0.006 nm. The confidence interval on the average of three measurements of a GR1(Cl) is pffiffiffi given by ts= n, in which we can use t ¼ 1.96, at the probability level a ¼ 0.05, from the normal distribution, as c is derived from a large number of measurements and statistical refinement. The confidence interval on the average is [2.378–2.392]. The average obtained, c ¼ 2.375 nm, is outside

Geochemistry of Green Rusts and Fougerite

261

this confidence interval, though close to it. We cannot rule out definitively Cl, but solutions are always largely undersaturated with respect to chlorofougerite (Feder et al., 2005, and this study), and it is thus highly doubtful that Cl be the interlayer anion. As Cl is larger than OH (0.181 vs 0.135 nm), the decrease observed in c is logical, and in favor of OH as the interlayer anion in Fouge`res—fougerite, as previously assumed on the basis of mineral/solutions equilibria (Ge´nin et al., 1998; Bourrie´ et al., 1999; Feder et al., 2005). The further decrease in c with carbonate in the interlayer cannot be ascribed simply to the radius of carbonate as compared to OH, as oxygen anion in both cases is the larger atom. We could ascribe it to a less compact arrangement with OH due to hydrogen bonding. For sample 5, the data are more complex. This sample is oxidized and showed the presence of lepidocrocite. Lepidocrocite is known as a product of oxidation of GR (Schwertmann and Fechter, 1994; Lin et al., 1996; Srinivasan et al., 1996), in this case fougerite. We can, thus, ascribe the peak in sample 5 to a precursor of lepidocrocite. A good candidate would be the ill-defined ‘‘ferric GR,’’ a Fe(III) compound keeping the layered structure of GRs obtained with partial deprotonation of OH (oxolation) (see below). Abdelmoula et al. (1996) obtained c ¼ 2.202 nm, that is, d003 ¼ 7.34 A˚, for ˚ , intermediate between this compound. The value observed here is 7.48 A fougerite and ‘‘ferric GR.’’ One would, thus, see the beginning of the transformation of fougerite into lepidocrocite through ‘‘ferric-GR’’ (or ‘‘proto-lepidocrocite’’), the oxidation leading to a contraction of the cell, consistent with the smaller size and larger charge of Fe3þ. The criteria for identification of fougerite are reported in Table 7.

9. Thermodynamic Modeling To define the stability domain of fougerite, a general ternary solid solution model was developed and extensively discussed by Bourrie´ et al., (2004) for the case of hydroxy-fougerite and by Feder et al., (2005) for other 2 cases, that is, Cl, SO2 4 , and CO3 fougerite. This model is revised here, ´ due to corrections for the electronegativity of SO2 4 (in error in Bourrie et al., 2004) and the Gibbs free energy of oxalate-GR. The basic equations remain the same.

9.1. General ternary solid solution model for GRs and fougerite The complete structural formula of fougerite considered was þx x II  ½FeIIð1xÞ FeIII x Mgy ðOHÞ2þ2y ½xOH ; mH2 O ; with m < 1  x þ y;

262

Fabienne Trolard and Guilhem Bourrie´

Table 7 Identification criteria proposed for fougerite and characteristics of hydroxyfougerite, from Trolard et al., (2007), extended Method

Criteria

Color of soil or sediment

Bluish-green (Munsell 2.5 Y, 5 Y, 5 G, 5 B) turning to ochreous or reddish brown within a few hours of exposure to air

Selective dissolution

Extractible by citrate-bicarbonate without the necessity of reduction by dithionite

XRD

Main peak depending on the structure and the nature of the interlayer anion: GR1: system trigonal, space group R3m d003¼7.58 A˚, depending on the nature of the interlayer anion: ˚ for carbonate-fougerite, 7.92 A˚ for hydroxy7.5 A ˚ for chloride-fougerite, 8.6–8.7 A˚ for fougerite, 7.97 A sulfate-fougerite with only one plane interlayer (GR1); GR2: system trigonal, space group P3ml d001 ¼ 11.0–11.6 ˚ for sulfate-fougerite with two planes interlayer A (GR2). These values are respectively close to the main peaks ˚ ) and interstratified clay minerals of kaolinite (7.13 A ˚ ), so that the identification may be difficult. (10–14 A At 77–78 K, four doublets for GR1, two ferrous and two ferric. D1: d ’ 1.27 mm s1; DEQ ’ 2.86 mm s1 D2: d ’ 1.25 mm s1; DEQ ’ 2.48 mm s1 D3: d ’ 0.46 mm s1; DEQ ’ 0.48 mm s1

Mo¨ssbauer spectroscopy

D4: d ’ 0.46 mm s1; DEQ ’ 0.97 mm s1 Only two doublets for GR2, one ferrous and one ferric. D1: d ’ 1.27 mm s1; DEQ ’ 2.83 mm s1 D3: d ’ 0.47 mm s1; DEQ ’ 0.45 mm s1 If silicated Fe is present, the distinction may be difficult.

Raman spectroscopy

Bands at 427 and 518 cm1 n ½ðFe2þ ; MgÞ1x Fe3þ x ðOHÞ2 ½x=nA ; mH2 O ;

1=4 x 1=3; Z ¼ 1=3; Structural formula System Unit cell

An ¼ OH for Fouge`res—fougerite. Trigonal, Space Group R3m a ¼ 0.3125(5) nm, c ’ 2:25ð5Þnm, V ¼ 0.1903 nm3

Geochemistry of Green Rusts and Fougerite

263

2 where An is the interlayer anion, for example, Cl, OH, SO2 4 , CO3 ,  and HCO3 . It could be more symmetrically described as a nonideal solid solution n with three end-members Fe(OH)2, ½FeðOHÞþ 2 ½1=nA , and Mg(OH)2, with respective mole fractions X1, X2, and X3, such as

X1 ¼

1x x y X2 ¼ X3 ¼ : 1þy 1þy 1þy

ð8Þ

Under these conditions, and assuming an ideal substitution between FeII and MgII, and a regular solution between FeIII and the bivalent cations, the chemical potentials of the three end-members and of fougerite were obtained as follows:

m1 ¼ m01 þ RT ln X1  A12 X22 ;

ð9Þ

m2 ¼ m02 þ RT ln X2  A12 ð1  X2 Þ2 ;

ð10Þ

m3 ¼ m03 þ RT ln X3  A12 X22 ;

ð11Þ

m ¼ X1 m1 þ X2 m2 þ X3 m3 ;

ð12Þ

which can be combined to give

m ¼ X1 m01 þ X2 m02 þ X3 m03 þ RT ½X1 ln X1 þ X2 ln X2 þ X3 ln X3 þ A12 X2 ð1  X2 Þ:

ð13Þ

The standard chemical potentials of Fe(OH)2 and Mg(OH)2 are known and respectively equal to m01 ¼ 489:8 kJ mol1 and m03 ¼ 832:16 kJ mol1 . The unknown thermodynamic parameters were the standard chemical n potential m02 of the end-member ½FeðOHÞþ 2 ½1=nA and the nonideality

264

Fabienne Trolard and Guilhem Bourrie´

parameter A12. To solve Eq. (13) with two unknown parameters, it was necessary to establish additional equations.

9.2. Constraints on the composition of the solid solution To determine m, structural and geochemical constraints of the mineral were used. Whatever the nature of the interlayer anion, the local organization of the mineral suggested that every Fe(III) must be surrounded by six bivalent cations, either Fe(II) or Mg(II). The upper limit of the x mole ratio was, thus, given by the inequality: [Fe(II) þ Mg(II)]/Fe(III)  2. This inequality is because two neighboring Fe(III) induce an oxolation reaction and the creation of Fe–O–Fe bonds (Jolivet, 1994), which is incompatible with the structure of fougerite. It results that

FeðIIÞ þ MgðIIÞ 1xþy 1þy 2, 2,x

; FeðIIIÞ x 3

ð14Þ

which with Eq. (8) gives

X2 1=3:

ð15Þ

On the other end, the large solubilities of both Fe(OH)2 and Mg(OH)2 defined a lower limit of the mole fraction of Fe(III). In order to build up the hydroxide layer of fougerite, Fe(III) ions must not be too far from each other. pffiffiffi Thus, the maximal distance between two neighbored Fe(III) must be a 3, from which the Fe(III) mole fraction must obey:

X2  1=4:

ð16Þ

In the literature, this value has been usually observed and reported in the laboratory experiments on GR synthesis as the lower value of formation of GRs. Thus, the stability domain of the solid solution could be drawn in a triangular diagram (e.g., for OH-fougerite, from Bourrie´ et al., 2004), and is restricted to the narrow range: 1/4 X2 1/3. This is true even when there is no Mg in the GR. In this range, we assumed that the chemical potential of the mineral was minimum, which gave the relationship:

265

Geochemistry of Green Rusts and Fougerite

@m ¼ m2  m1 ¼ 0: @X2

ð17Þ

At the minimum, that is, for X2,min. ¼ 7/24  1/24, the chemical potentials of the ferric and ferrous end-members are equal, and one has

m ¼ m1 ¼ m2 ;

ð18Þ

which eventually gives

h A12 ¼

m01  m02  RT ln



X2;min: 1X2;min:

ð1  2X2;min: Þ

i ;

ð19Þ

, h A12 ¼ RT

m01 m02 RT

 ln



X2;min: 1X2;min:

ð1  2X2;min: Þ

i :

ð20Þ

We now have two equations, for three unknowns, m0, m02 , and A12.

9.3. Relationship between Gibbs free energies of formation of GRs and the electronegativity of the interlayer anion, revised values 9.3.1. Principle We have shown (Bourrie´ et al., 2004) that experimental Gibbs free energies of formation of synthetic Fe GRs were linearly related to the electronegativities of the anions. The rationale is that the composition of the layer is quasi-constant, and so the Gibbs free energy of formation of the GR depends mainly on the nature of the anion. As the interaction between the layer and the anion is primarily electrostatic, a suitable parameter to seek for a relationship with Gibbs free energy is the electronegativity of the anion. This parameter can be derived from the model of partial charges developed by Jolivet (1994) as follows:

266

Fabienne Trolard and Guilhem Bourrie´

Table 8 Some electronegativities of the elements, taken on the Allred and Rochow’s scale, and electronegativities of anions, computed from the model of partial charges by Jolivet (1994) (Eq. 21) Element

H

C

wi*

2.10 

Anion

Cl

w

0.542

2.50 OH



1.601

O

S

Cl

Fe

Se

3.50

2.48

2.83

1.72

2.5

CO2 3

SO2 4

C2O2 4

SO2 3

SeO2 4

2.001

2.286

2.329

1.996

2.290

P pffiffiffiffi ffi w þ 1:36Z w ¼ i Pi 1 ; pffiffiffi ffi i

ð21Þ

wi

where w i are the electronegativities of the elements (Table 8) taken on Allred and Rochow’s scale and Z the electric charge of the molecule or ion. The w values obtained are reported in Table 8 too. The value used ´ et al., 2004) was erroneous. Moreover, previously for SO2 4 (1.86, Bourrie the Gibbs free energy of formation of oxalate-GR, from Refait et al., (1998b) was in error, and so the preceding relation obtained must be reevaluated. 9.3.2. Sources of thermodynamic data on synthetic GRs The data available are the Gibbs free energies of formation of Fe(OH)2, GR1(Cl), GR1(CO3), GR2(SO4), and GR1(oxalate) (Table 9). The empirical relationship previously obtained was

m0 ¼ 76:887w  491:5206; n

r 2 ¼ 0:9985; N ¼ 4;

ð22Þ

where n is the number of Fe atoms per mole formula, and m is in kJ mol1. The GR1(CO3) value showed a clear discrepancy with respect to the straight line obtained, which was ascribed to the possibility of having 2 HCO 3 mixed with CO3 in the interlayer. The new relationship obtained is

m0 ¼ 56:47w  495:93; n

r 2 ¼ 0:9762; N ¼ 4:

ð23Þ

Table 9

Structural formulas and Gibbs free energies of formation at 298.15 K, 1 bar, of synthetic green rusts (GRs) Type

x*

m0exp.kJ mol1

m0calc.kJ mol1

GR1

1/4a

2145.0  7h

2106

2þ 2 2 ½FeII6 FeIII 2 ðOHÞ16 ½SO3  4H2 O

GR1

1/4b

–

4869

2þ 2 2 ½FeII6 FeIII 2 ðOHÞ16 ½C2 O4  4H2 O

GR1

1/4c

4489  3i

5019

2þ 2 2 ½FeII5:5 FeIII 2 ðOHÞ15 ½SeO4  8H2 O

GR2

1/3.25d

–

4689

2þ 2 2 ½FeII4 FeIII 2 ðOHÞ12 ½SeO4  8H2 O

GR2

1/3d

–

3752

Structural formula

½FeII3 FeIII ðOHÞ8 þ ½Cl  8H2 O 

(continued)

Table 9 (Continued ) Structural formula 2þ 2 2 ½FeII4 FeIII 2 ðOHÞ12 ½SO4  8H2 O

Type

x*

m0exp.kJ mol1

m0calc.kJ mol1

GR2

1/3e

3790  10e

3750

35905

3653

GR1

1/4

f,g

j

2þ 2 2 ½FeII4 FeIII 2 ðOHÞ12 ½CO3  nH2 O

* x¼FeIII/Fetotal mole ratio. a Refait et al., 1998a. b Simon et al., 1998. c Refait et al., 1998b. d Refait et al., 2000. e Refait et al., 1999. f Hansen, 1989. g Drissi et al., 1995. h Bourrie´ et al., 1999. i For the ‘anhydrous form’;from E ¼ 0.135 V( Refait et al., 1998 b) for the equilibrium between GR and Fe(OH)2, with m0(oxalate) ¼ 673.93 kJ mol1 and m0(Fe(OH)2) ¼ 489.80 kJ mol1, from Refait et al., (1999) j For the ‘anhydrous form’;from E0= 0.6475 V, average of the two experimental values ( Drissi et al., 1995) for the equilibrium between GR and Fe(OH)2, with m0 (carbonate) = 527.81 kJ mol1 and m0 (Fe(OH)2) = 489.80 kJ mol1 from Refait et al., (1999). k This paper (see below).

Geochemistry of Green Rusts and Fougerite

269

The data used now are the Gibbs free energies of formation of Fe(OH)2, GR1(Cl), GR2(SO4), and GR1(CO3). By interpolation, with w(OH) ¼ 1.60, m of hydroxy-fougerite is obtained as 586.12 kJ mol1 for 1 Fe (instead of m ¼ 614.5 kJ mol1, in Bourrie´ et al., 2004). Equation (13) can now be completely solved for the nonideality parameter of the ternary solid solution A12 ¼ 1049.2 kJ mol1 (instead of A12 ¼ 1455.8 kJ mol1) and for Fe(OH)3 end-member: m02 ¼ 56:65 kJ mol1 for Fe(OH)3 (instead of m02 ¼ 119 kJ mol1 ). This small negative value, instead of the large positive one, implies that Fe(OH)3 could a priori exist. However, if one considers the reaction FeðOHÞ3 ⇆ FeOOH þ H2 O, the Gibbs free energy of formation is largely negative (DR G0 ’ 661 kJ mol1 ), which confirms that Fe(OH)3 is unstable when it adopts a brucite-type structure, due to the large repulsion of two adjacent FeIII. For pure FeII–FeIII GRs, the maximum degree of oxidation is Fe3(OH)7 (Bourrie´ et al., 2004). For Fe(OH)2-[Fe(OH)2Cl]-Mg(OH)2 solid solution, m02 ¼ 280 kJ mol1 for [Fe(OH)2Cl] and A12¼ 498.2 kJ mol1 for the nonideality parameter (Feder et al., 2005). The fact that m02 is negative means that this end-member could a priori exist. For Fe(OH)2-[Fe(OH)21/2 SO4]-Mg(OH)2 solid solution, m02 ¼ þ217 kJ mol1 for [Fe(OH)21/2 SO4] and A12¼1691.3 kJ mol1 for the nonideality parameter (Feder et al., 2005). For Fe(OH)2-[Fe(OH)2]þ [2/3 HCO3, 1/6 CO3]-Mg(OH)2 solid  solution, we considered a mixture of CO2 3 and HCO3 in the interlayer (Bourrie´ et al., 2004). Thus, the average value of w as equal to 2.33 leads to m02 ¼ þ394 kJ mol1 for [Fe(OH)2]þ [2/3 HCO3, 1/6 CO3] and A12¼2116.1 kJ mol1 for the nonideality parameter (Feder et al., 2005).  However, as the proportion of CO2 3 and HCO3 in the interlayer may vary as a function of pH in the milieu, this introduces an additional uncertainty. Thus, for any FeII-FeIII-MgII A GR, the general solid solution model can be used to compute the chemical potential of the mineral. The range of computed values between x ¼ 1/4 and x ¼ 1/3 is within 10 kJ mol1 of the experimental values, normalized to 1 Fe, that is, less than 2%, except for oxalate (12%), which should be reevaluated.

10. Fe Control in Solution by Mixed Fe(II)–Fe(III) Minerals/Solution Equilibria When dissolved oxygen enters, aqueous Fe precipitates, and the nature of Fe oxide can be discussed on the basis of the composition of soil solutions. Check of Fe minerals/solutions equilibria showed that all soil solutions in the Brittany region, on granites and schists, were

270

Fabienne Trolard and Guilhem Bourrie´



oversaturated with respect to Fe(III) oxides (s.l.), hematite, goethite, and lepidocrocite;  undersaturated with respect to Fe(II) hydroxide;  close to equilibrium with a compound globally written as Fe3(OH)7, Fe2(OH)5, or Fe3(OH)8 (Maıˆtre, 1991a). These results were consistent with previous conclusions proposed in pioneering works by Ponnamperuma et al., (1967) in Philippines under rice crop or by Lindsay (1979) in gleysols in Colorado. However, at this stage, the question remained whether this formula corresponded to a mean compartment of ferrosoferric complexes or to a specific mineral, for which GRs were the best candidate. Thus, additional investigations of the Fe solid fraction of soils were made, which led to the identification of GRs in the gleysol of Fouge`res, the new mineral being since then denominated as fougerite. Interpretation of chemical equilibria led to a chemical formula ´ et al., 1999). But XAS FeII1x FeIII x ðOHÞxþ2 with 0.42 < x < 0.86 (Bourrie results have shown that fougerite contains Mg in addition to Fe, so that the computation must be based upon the ternary solid solution model developed above. By definition, the mole ratio x is related to the mole fractions of endmembers in the solid solution model by

x¼

X2 : X1 þ X2

ð24Þ

From the value of x and with X2 either equal to1/4 or to 1/3, X1 was obtained and X3 ¼ 1(X1þX2), so that the composition of fougerite was obtained from Mo¨ssbauer data and our solid solution model (Feder et al., 2005) and was plotted in a ternary diagram (Fig. 15); values of x larger than 1/3 implied that magnesium was present in the mineral. At the depth where soil solutions were sampled, x was quasi-constant around 0.6. The set of soil solutions sampled at Fouge`res during 16 months was used to check the equilibria. Results showed that the soil solutions were always undersaturated when Cl was the interlayer anion and always oversaturated with OH anion. With carbonate or sulfate anions, solutions were either undersaturated or oversaturated depending on X2 values and seasons; the lowest values of log IAP-log K occurred in summer. The comparison of these results with those (Bourrie´ et al., 1999) obtained at Fouge`res in the same milieu, considering no Mg incorporation in the mineral, showed that the presence of magnesium stabilized the fougerite (Feder et al., 2005).

271

Geochemistry of Green Rusts and Fougerite

Mg (OH)2 0 1

(O Fe tio n fra c le

nM

ctio

Allowed domain

1 2

)2 OH

X

1

C

g(

1 2

2 3

fra

D

le mo

mo

3 4

1 3

X3

H) 2

1 4

2 3

B

3 4 N

1 Fe (OH)2

0

M

A

1 4

1 3

Excluded domain

P

1 3 1 4

0 1 2

2 3

3 4

1

[Fe (OH)2] + [OH−]

−

X2 mole fraction Fe (OH)3

Figure 15 Range of variation in the composition of fougerite in the ternary diagram Fe(OH)2—Mg(OH)2—Fe(OH)3 system, adapted from Bourrie´ et al. (2004). Fougerite is stable in the narrow domain between solid line AD and dashed line MN; it starts forming, for example, at point N and oxidizes from N to P, where it transforms into Fe (III) oxyhydroxide.

11. Redox Interactions Between Iron and Other Elements in Relationship with the Occurrence of GRs The redox chemistry of iron has a strong influence on the fate and distribution of numerous natural and xenobiotic compounds. The nature of the Fe-bearing minerals can modify considerably the oxido-reduction potentials where the transition from ferric to ferrous state occurs. Some oxido-reduction reactions in soils are homogeneous in aqueous solution, but many are heterogeneous and involve both solids and the solution. For this reason, different redox reactions occurring in the soil cannot be simply ordered according to the standard potential E0. Moreover, stoichiometric coefficients of oxidized and reduced species and Hþ are variable from one reaction to another, so that the order of reactions changes with pH. Sposito (1981) proposed to compute the electron potential (pe) at a given pH and for a given value of ½Ared =½Aox , the ratio of the activities of

Table 10 Variations with pH of the ‘‘critical’’ value of pe for the main oxido-reduction reactions in water ‘‘Critical’’ pe at pH ¼ 4

Reactions

1/4 O2(g)þ Hþþ e

⇆1=2H2 Oðl:Þ

16.6

pH ¼ 7

13.6

pH ¼ 9

11.6

þ  1=8 NO 3 þ 5=4H þ e

⇆1=8NHþ 4 þ 3=8H2 Oðl:Þ

9.24

5.4

2.85

þ  NO 3 þ 2H þ 2e

⇆NO 2 þ H2 Oðl:Þ

7.3

4.3

2.3

g FeOOHlepidocrociteþ 3Hþþ e

⇆Fe2þ þ 2H2 Oðl:Þ

11.2

2.7

3.41

a FeOOHgoethiteþ 3Hþþ e

⇆Fe2þ þ 2H2 Oðl:Þ

10.0

1.0

5.0

1/8 SO42 þ Hþ þ e

⇆1/8 S2þ 1/2H2O(l.)

2.2

5.2

7.2

Geochemistry of Green Rusts and Fougerite

273

the oxidized and reduced species. Accordingly, for homogeneous reactions, the ½Ared =½Aox ratio is taken as 106 conventionally. For heterogeneous reactions, where the oxidized species is a solid and dissolves during reduction, [Ared] is taken as 107, with the assumption that the solid phase is pure and its activity [Aox] is equal to 1. The value of pe obtained is designated as ‘‘critical’’ pe. For pe smaller than the ‘‘critical’’ value, the reduction reaction is quasi-complete. From the thermodynamic data (Bratsch, 1989) of the most stable species, it is possible to establish a scale of sequential reduction potentials of O(0)/O (-II), N(V)/N(-III), Fe(III)/Fe(II), and S(VI)/S(-II). The classical order is obtained and steps larger than 100 mV (1.5 pe units) separate the successive redox couples (Table 10). In addition, in this sequence the position of Fe(III)/Fe(II) is based upon the assumption that redox potential is controlled by a ferric oxide/Fe2þ couple at equilibrium. But in soil, different Fe-minerals are observed indeed. In strong reducing conditions (i.e., pe < 2), Fe-sulfides precipitate; in oxidizing conditions (i.e., pe > 10), Fe-oxides such as goethite, lepidocrocite, or hematite predominate. In moderately reducing conditions (i.e., 2 < pe < 9), aqueous Fe is controlled by the equilibrium with mixed Fe(II)–Fe(III) hydroxides (Ponnamperuma, 1972; Lindsay, 1979) belonging to the GRs group (Trolard et al., 1997; Bourrie´ et al., 1999). When GRs are present in a milieu, they can control the redox potential at equilibrium rather than the more stable ferric iron species. The exact nature of the GR depends on the nature and abundance of anions. Ponnamperuma (1972), Olowe and Ge´nin (1991), Refait and Ge´nin (1993), and Drissi et al., (1994) have measured the respective thermodynamic data of the natural hydroxyhydroxide Fe3(OH)8, the synthetic GR compounds of hydroxysulfate, hydroxychloride, and hydroxycarbonate. The ‘‘critical’’ pe data (Table 10) were calculated for redox couples different from the compounds cited above and under the assumptions exposed in the preceding paragraph. The sequences are equally calculated at pH ¼ 6 and pH ¼ 8.5, which are the most frequent edges of pH observed in soils. Figure 16 shows that the reduction of Fe oxides into aqueous Fe2þ is possible throughout a large range of redox potential, as underlined by Stumm and Sulzberger (1992). This fact must be kept in mind to predict the interaction between GRs and some anions in soils, such as nitrate, selenate, or chromate (Ge´nin et al., 2001). In the recent literature, interactions between GRs and several chemical species have been studied in the laboratory or in the field. The principal effect of GRs is the reduction of these species, with the oxidation of GRs into ferric oxides, that is, magnetite, lepidocrocite, or goethite.

274

Fabienne Trolard and Guilhem Bourrie´

Ox Eh (V) (pH7)

Red

3+ FeIII(phen) 3

FeII(phen)23+

1.0

CI − GR(s)

Fe2+(10−7M)

0.8

Fe3+

0.6

SO4 − GR(s) CO3 − GR(s) (≡FeIIIO)2FeIII+(s)

Fe2+

Limit of water stability O2/H2O(I)

Fe2+(10−7M) Fe2+(10−7M) (≡FeO)2FeII(s)

0.4

0.2

FeIIIOH2+ FeIIIsal

FeIIOH+ FeIIsal

FeIIIEDTA− FeIIIporph

FeIIEDTA2− FeIIporph

0 Ferrihydrite(s) −0.2

−0.4

aFe aFeIIIOOH(s) aFe2O3(s) aFeIIIOOH(s) Oxferredoxin

IIIOOH(s)

Fe3O4(s)

Fe2+(10−5M) Fe3O4(s) Fe2+(10−5M) Fe2+(10−5M) FeCO3(s)* Redferredoxin Limit of water stability H2O(I)/H2 Fe2SiO4(s)

Figure 16 Scale of ‘‘critical’’ values of Eh for major Fe redox couples, at pH ¼ 7 and * 3 valid for ½HCO M. Phen, phenanthroline; sal, salicylate; porph, porphyrine; 3 ¼ 10 GR, green rust.

11.1. GRs and nitrogen 2þ While in solution NO 3 cannot be reduced by Fe , Hansen et al., (1996; 2001) showed that in abiotic conditions GR1(Cl) and GR2(SO4) reduced stoichiometrically nitrates into ammonium in several hours. They observed that reduction rates increased with increasing nitrate concentrations up to some threshold concentrations, depending on the initial conditions of the reaction, above which no further increase in reduction rates took place. The kinetics of the reaction depend on the type of the interlayer anion, the layer charge, and the relative content of Fe(II) in the hydroxide layers. In addition, Huang and Zhang (2004) observed that pH modified the rate of reduction by a direct implication of Hþ in the redox reaction following first-order kinetics and Hþ ions affected the nitrate adsorption onto reactive sites of iron grains. Thermodynamic calculations (Trolard and Bourrie´, 1999) showed that the reduction of nitrate proceeds largely before the reduction of ferric oxides, following the classical sequence when GRs were absent. When GRs were present, and irrespective of the nature of

Eh (mV) 760

Eh (mV) O2/H2O

80

Fe(II) ⫻ 10−6 M

70

NO3− ⫻ 10−6

760

M

400

60 256

NO−3/NO−2

50

O2/H2O Fougerite / Fe2+

256

NO3− /NO2−

−88

FeOOH/Fe2+

40 30 −88

FeOOH/Fe2+

20 10 0 S

Summer

O

N

D J Time (month)

F

M Winter

Figure 17 Seasonal dynamics of aqueous Fe(II) and nitrate in soil waters in Brittany, modified from Jaffrezic (1997), showing first the disappearance of nitrate before the release of Fe(II), then the coexistence of Fe(II) and N(V) (see text). The scales of ‘‘critical’’ Eh were calculated for pH ¼ 6.

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the compensating anion, the redox sequence was determined by the pH of the milieu. For example, at pH ¼ 6, the reduction of GRs precedes the reduction of nitrate, whereas at pH ¼ 8.5, the reduction of GRs follows the reduction of nitrate. These results imply that nitrate and GRs can compete as electron acceptors for microflora in anaerobic conditions. At pH ¼ 6, the redox buffering by GRs protects nitrate and thus bypasses denitrification. At pH ¼ 8.5, in presence of GRs, denitrification is possible. However, in this case, as we have shown above, nitrate can be spontaneously reduced by GRs into NHþ 4. In the field, such evidence can be observed on Fe(II) and NO 3 dynamics in soil solution (Jaffrezic, 1997, Fig. 17). At first, at the end of summer, no Fe(II) is present in solution. At the beginning of autumn, nitrate concentration decreases to zero before Fe(II) is released in solution. This is the classical scheme (Fig. 17, left). Then, in winter, fluctuations of Fe(II) are observed, due to partial entry of oxygen from rainwater. This results in the formation of fougerite. When fougerite is reduced, as electron acceptor, it competes with nitrate and coexistence of N(V) and Fe(II) is observed, which is contrary to the classical scheme, but well explained, if the reaction fougerite/Fe(II) is considered in the scale of ‘‘critical’’ Eh (Fig. 17, right), as predicted by Trolard and Bourrie´ (1999). Strictly speaking, this reduction is not denitrification, as ammonium formed must undergo nitrification before any biotic denitrification, and this is not a depolluting process, as ammonium is more toxic than nitrate. But this process can be used to prevent N loss from the soil as nitrate to the groundwater and surface water, or to the atmosphere as N2 and N2O. Ammonium is readily fixed on clay minerals and can be later nitrified to nitrate or released in solution after cation exchange, thus being available to plants.

11.2. Reaction mechanisms The layered structure of the mineral explains the particular reactivity of GRs in abiotic conditions. Figure 18 summarizes, for example, the steps of reduction of nitrate by SO4-GR: the first step is an anion exchange between nitrate and sulfate in the interlayer of the GR, and GR2 transforms into GR1. In these conditions, a minimum of 8 divalent Fe2þ are immediate þ  neighbors of NO 3 . The reduction of NO3 into NH4 can, thus, proceed spontaneously with simultaneous transfer of 8 electrons from Fe2þ to N. In the layer, the Fe(II)–OH–Fe(II) bonds transform into Fe(III)–O–Fe(III) by oxolation, releasing 8e and 8Hþ; 4 additional Hþ equilibrate the two sulfate anions released; 8e and 10Hþ are necessary for each NHþ 4 (Table 10), so that there is a net release of 2 protons. The GR oxidizes into hematite.

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FeII/FeIII layer

H

H

H

H

2−

SO4 , H2O interlayer FeII/FeIII layer

2−

NO3

H

SO4

H

H

2−

H

Fe2+

Fe3+

Fe3+ N+v

N−III +4H+

Figure 18 Reaction scheme for the abiotic reduction of nitrate by sulfate-GR: top: initial state of the interlayer in GR-SO4; middle: first step, exchange of sulfate by nitrate and collapse of the interlayer; bottom: initiation of reduction of nitrate by eight neighboring Fe(II) ions, and final state of ammonium production before ammonium release to solution.

The half-reactions and the net reaction can be written as follows:

2FeII4 FeIII 2 ðOHÞ12 SO4 ! 6Fe2 O3;hematite þ 6H2 O  þ þ 2SO2 4 þ 8e þ 12H   þ NO 3 þ 8e þ 10H ! NH4 þ 3H2 O

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 2FeII4 FeIII 2 ðOHÞ12 SO4 þ NO3 ! 6Fe2 O3;hematite þ þ þ 9H2 O þ 2SO2 4 þ NH4 þ 2H :

11.3. GRs and selenium Other works documented the reduction of selenate Se(VI) by GRs and underlined that it was the only relevant abiotic reaction of reduction pathway in natural environments, soils, and sediments. In contrast, Se(IV) reduction by GRs was less inhibited kinetically and was induced by a variety of abiotic and bacterial processes (McNeal and Balistrieri, 1989). They showed that SO4-GR reduced selenate Se(VI) to Se(0) or Se(-II) (Myneni et al., 1997; Refait et al., 2000). These reactions induced an increase in 80Se/76Se ratio, which shifted by 7.36  0.24 % of dissolved selenate as lighter isotopes were preferentially consumed during reduction by SO4-GR (Johnson and Bullen, 2003). Abiotic selenate reduction by GRs induced much greater isotopic fractionation than does bacteria selenate reduction.

11.4. GRs and metals Many metals or metalloı¨ds, for example, Cu, Hg, Ag, Au, Cr, and As, have multiple valence states within the range of redox conditions encountered in surface or near-surface environments and may interact with Fe. O’Loughlin et al., (2003) reported experiments where aqueous solutions of AgCH3COO, AuCln(OH)4n, CuCl2, or HgCl2 were added to hydroxysulfate GR suspensions. Soluble Ag(I), Au(III), and Cu(II) species are often relatively mobile, thus reduction to comparatively insoluble Ag(0), Au(0), and Cu(0) phases can also be expected to reduce the mobility of silver, gold, and copper. However, unlike Ag(0), Au(0), and Cu(0), Hg(0) is relatively soluble in aqueous solution. Moreover, Hg(0) has a significant vapor pressure and can be lost through volatilization. Results showed that Ag(I), Au(III), Cu(II), and Hg(II) were readily reduced to Ag(0), Au(0), Cu(0), and Hg(0), respectively. The resulting solids from Ag (I)-, Au(III)- and Cu(II)-amended GR suspension observed by transmission microscopy were submicron-sized particles of Ag(0), Au(0), and Cu(0). The reduction of Hg(II) to Hg(0) by GR may actually increase the overall mobility of mercury; indeed the release of Hg(0) vapor to the atmosphere is a significant component of the global cycling of mercury (Stein et al., 1996; Schlu¨ter, 2000). Chromium displays several oxidation states from 0 to þVI but of these, Cr(III) and Cr(VI) are by far most common in nature. Cr(III) is much less soluble, and is considered as nontoxic, than Cr(VI), which is toxic. This

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suggests that in Cr-polluted groundwater and soil, reduction of Cr(VI) to Cr(III) is, therefore, desirable. Loyaux-Lawniczak et al., (1999, 2000) studied the interactions between Cr(VI) and GRs, namely, GR-SO4 and GRCl. They showed that GRs proved to be very reactive; their interaction with potassium chromate solution leads to the rapid and complete reduction of Cr(VI) into Cr(III). The nature of the initial GR proves to have no influence on the process, an outcome mainly due to the structural properties which define these compounds.

12. Biotic Interaction GRs were observed as products of the metabolic activity of bacteria known to be key players in the biogeochemical redox cycling of Fe in aquatic and surface terrestrial environments. Various strains of the dissimilatory iron reducing bacterium (DIRB) Shewanella putrefaciens produced GRs as products of the bioreduction of ferrihydrite or lepidocrocite (Fredrickson et al., 1998; Kukkadapu et al., 2001; Parmar et al., 2001; Glasauer et al., 2002; Ona-Nguema et al., 2002; Zegeye et al., 2005). Dechlorosoma suillum produced GRs by anaerobic biooxidation of Fe(II) (Chaudhuri et al., 2001). DIRB are capable of using solid phase Fe(III) in both oxides, for example, ferrihydrite, goethite, and lepidocrocite, and phyllosilicates, for example, montmorillonite and illite (Stucki et al., 1984a,b) as an electron acceptor for respiration. For example, Shewanella putrefaciens is a facultative anaerobe and substitutes Fe3þ for O2 as the terminal electron acceptor of the electron transport chain to gain energy for growth and metabolism (Nealson and Saffarani, 1994). Kukkadapu et al. (2004) studied the formation of carbonate GR by biotransformation of silica-ferrihydrite by DIRB. They observed two primary routes of GR formation that varied with Si and P concentrations. In the medium with the lowest Si and P concentrations (1 mole % Si and 1 mmole/L P), they observed the formation of bioaltered ferrihydrite with Magnetite Without P

Si - ferrihydrite + bacteria

I mMI−1 P

GR(CO3)

>4 mML−1 P

GR(CO3) + vivianite

Figure 19

With increased (P)

Green rust formation by biotransformation of ferrihydrite.

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superparamagnetic behavior after 1 day of incubation. In absence of P, magnetite is the predominant bacterial transformation product of ferrihydrite (Fredrickson et al., 1998), but P apparently impedes magnetite formation (Couling and Mann, 1985; Fredrickson et al., 1998). The pathways of reaction are summarized in Fig. 19. Si did not inhibit the formation of magnetite. An additional difficulty to determine the pathways of formation of iron minerals by bacterial mediation is that the natural ferrihydrite contains Si (up to 9 mole %) and significant amounts of organic matter (Carlson and Schwertmann, 1981; Fox, 1989; Fortin et al., 1993; Tessier et al., 1996; Perret et al., 2000). Coreacted Si affects the recrystallization of ferrihydrite to crystalline oxides such as goethite or hematite and may influence its reductive transformation by DIRB into GRs.

13. Geochemical Significance and Place of Fougerite in Iron Oxide Family Fougerite is today recognized as a new iron oxide which occurs in waterlogged soils. Fougerite forms as easily as GRs, controls Fe in solution, and has particular properties quite different from the other iron oxides. In addition, Mg enters the mineral. In soils, the following scheme has been proposed: at the end of dry season, the soil profile is completely oxidized and Fe present in oxides such as goethite or lepidocrocite. When reducing conditions appear, such as in autumn in temperate climate or during the wet season in tropical climate, FeIII/Fetotal Goethite

1

Ferrihydrite

Lepidocrocite

Hematite Maghemite

2/3

Magnetite

1/3 Fougerite Fe2+ in solution

1/4

1/2 O/O + OH

3/4

1

Figure 20 Pathways of formation of iron oxides, and role of fougerite and ferrihydrite as reaction intermediates.

Geochemistry of Green Rusts and Fougerite

281

with both a supply of organic matter and water saturation, Fe oxides are partly reduced and Fe2þ is released in solution. When oxygen enters the soil due to fluctuations in the water table or supply by rain, Fe2þ is oxidized and fougerite forms, by co-precipitation of Fe3þ with Fe2þ and Mg2þ. Bacterial activity is strongly suspected as responsible for the formation of this mineral as was observed in laboratory experimentation. Later, with alternate moderately reducing or strongly reducing conditions, the x mole ratio fluctuates, and fougerite is subject to mineralogical transformations, which can be monitored by in situ Mo¨ssbauer spectroscopy (Feder et al., 2005). Oxidation of fougerite at constant Mg mole ratio leads to an increase in the electrical charge of the layer. This can be simply compensated by a deprotonation of water molecules in the interlayer, or OH ions into O2 ions in the hydroxide sheets, or various anions depending on the environmental conditions. Fougerite acts as a transient phase from Fe2þ of the soil solution to Fe oxides and oxyhydroxides and Fig. 20 summarizes from literature and our observations the pathways between Fe2þ and iron oxides implying GRs. Fougerite is a well-crystallized mineral, with a definite structure, stable in its narrow conditions of stability. As it is labile, it transforms easily in other, more stable, iron oxides as soon as conditions become oxidizing. It acts as an intermediate toward oxidation of dissolved Fe2þ, and it also plays a role in the magnesium cycle, and interacts with many other biogeochemical cycles. More generally, fougerite, as other layered double hydroxides (Sparks, 2001), may play an essential role in the formation of clay minerals, both phyllosilicates and iron hydroxides and oxides, and in the control of both major elements (Mg, Fe) and trace metals (Co, Cr, Mn, Ni, and Zn) in the environment.

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C H A P T E R

S I X

Advances in Isotopic Dilution Techniques in Trace Element Research: A Review of Methodologies, Benefits, and Limitations Rebecca E. Hamon,* David R. Parker,† and Enzo Lombi‡ Contents 290 293 306 306 308 309 310 313 314 314 315 317

1. Introduction 2. The Isotopic Dilution Principle 3. Methodologies 3.1. Choice of isotope 3.2. Isotope ‘‘spiking’’ 3.3. Choice of suspension matrix in E-value determinations 3.4. Equilibration time 3.5. L-value determinations 4. Uncertainties and Sources of Errors 4.1. Accuracy and precision 4.2. Spike-derived artifacts 4.3. Error propagation 4.4. Uncertainties and sources of error specific to L-value determination 4.5. Colloidal interferences 4.6. Changes in oxidation state 5. Interpretation of E-Values 6. Interpretation of L-Values 7. Future Applications Acknowledgments References * {

{

318 320 321 325 327 335 336 337

Plant Chemistry Section, Agricultural and Environmental Chemistry Institute, Faculty of Agricultural Sciences, Universita` Cattolica del Sacro Cuore, Via Emilia Parmense 84, I-29100, Piacenza, Italy Soil and Water Sciences Section, Department of Environmental Sciences, University of California, Riverside, California 92521 Plant and Soil Science Laboratory, Department of Agricultural Science, Faculty of Life Sciences, University of Copenhagen, Thorvaldsensvej 40, 1871 Frederiksberg C, Denmark

Advances in Agronomy, Volume 99 ISSN 0065-2113, DOI: 10.1016/S0065-2113(08)00406-9

#

2008 Elsevier Inc. All rights reserved.

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Abstract New insights into factors controlling element bioavailability and mobility in soils have been achieved through the use of isotopic dilution methods. With the advent of robust and relatively simple analytical techniques able to accurately determine stable isotope ratios, the future use of isotopic dilution methods is expected to continue to expand. In both theory and practice, the E- and L-value isotopic dilution methods appear relatively simple to apply. However, this simplicity is deceptive: in reality, there exist a number of pitfalls that can result in collection of flawed data or inappropriate data interpretation. With a focus on trace elements, this chapter reviews studies that have applied isotopic dilution techniques to examine various aspects of soil chemistry and bioavailability, provides guidance on conducting isotopic dilution experiments, and discusses potential future applications for these techniques. The various pitfalls that may be encountered, including precipitation artifacts, colloidal interferences, and labile redox state effects, as well as how to identify and avoid such pitfalls, are also described.

1. Introduction Trace elements, including both metals and metalloids which are the focus of this chapter, are present in soils in a variety of chemical and physical forms and, for some elements, in multiple oxidation states. These diverse forms are the combined result of the geochemical origins of each element in conjunction with its chemical interactions with highly heterogeneous soil systems. Significant research effort over the last 50 years has been devoted to improving our understanding of the various forms, or pools, in which trace elements exist in soils, as this is a key determinant of their bioavailability and mobility. The chemical techniques that have been developed to investigate trace element pools in soils can be divided into five main categories: fractionation, adsorption, desorption, spectroscopic, and isotopic dilution methods. Fractionation methods involve the use of one (batch extraction) or more (sequential extraction) extractants, ranging from water to neutral salt solutions to chelating agents and even Coca ColaÒ (Schnug et al., 1996), to extract trace elements from different chemical pools in soil. The pools that are so extracted are almost always operationally defined (Ahnstrom and Parker, 2001) because the quantity extracted depends on the strength and effectiveness of the extractant used as a solvent for a particular trace element–soil substrate combination; few, if any extractants are highly specific for a particular trace element–soil substrate combination. Extractants also typically alter the chemistry of the system they are extracting; this can sometimes lead to measurable redistribution of elements among the putative

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geochemical pools (Ahnstrom and Parker, 2001; Gleyzes et al., 2002). Fractionation studies can therefore provide broad information regarding the nature of the chemical environment in which the trace element is hosted within the soil, but caution is required to avoid overinterpretation of the results due to the lack of extractant specificity and the possibility of redistribution. A further aim of many fractionation studies has been to identify a ubiquitous extractant, able to act as a bioavailability surrogate across a number of soil types, sources of contamination, environmental endpoints, and for multiple elements. This quest has so far proven largely unsuccessful (Menzies et al., 2007). Adsorption methods assess the strength of binding of trace elements to soils (or soil components) by measuring the concentration remaining in solution following additions of the element of interest to soil suspensions, with the aqueous phase typically being water or a dilute, neutral salt. Plots of sorbed element versus solution concentration at incremental additions of the element can be used to generate adsorption isotherms and, when linear, to estimate the partitioning coefficient, Kd, and thus the soil buffering capacity for a given element (Allen et al., 2001). Correlations between different soil properties and the Kd can provide information on which soil components are important sorbents for trace elements ( Janssen et al., 1997). However, adsorption studies are labor intensive. They also require addition of sufficient additional analyte to register measurable increases in the trace element concentration in solution; hence, the data obtained reflect a perturbation of the original chemical equilibrium of the system. Adsorption studies also implicitly assume that the analyte measured in solution is present in a form that is available to bind to solid-phase sorption sites. However, there is increasing evidence from isotope dilution studies (see Section 4.5) that a significant proportion of analyte in the aqueous phase of soil suspensions may be bound in a nonexchangeable form to soil colloids, which are so small that they can pass through a 0.2 mm filter. Few adsorption studies to date have, prior to measurement, filtered the analyte solution using filters with pore size smaller than 0.2 mm. Inadvertent inclusion of a nonexchangeable form of analyte in Kd determinations results in an underestimate of the Kd, that is an underestimate of the true capacity for the soil to sorb the analyte. Moreover, colloids may also provide binding sites for the introduced analyte, further increasing the apparent solution-phase concentration and thus further contributing to the underestimation of Kd. We believe that the conclusions of at least some of the numerous adsorption studies conducted to date may in fact be compromised by the presence of variable quantities of colloids in the measured solutions. Desorption methods assess the ability of the soil solid phase to retain previously sorbed analytes, and when the results are correlated against different soil properties, can also provide information on the chemical pools that host the trace elements and on the soil factors that influence

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their bioavailability and mobility. Different types of desorption methods include column desorption (Allen et al., 1995), repeated resuspension in fresh electrolyte (Gray et al., 1999), batch desorption with resins (Mason et al., 2008), and diffusion gradients in thin films (DGT) (Zhang et al., 1998). As with adsorption methods, the column and batch desorption methods are labor intensive and may be subject to interferences from colloids. Each of the desorption methods also perturbs the chemical equilibrium of the system. However, the perturbation that occurs during desorption may be similar to the localized depletion that is induced by biotic uptake of trace elements from soils and hence investigation of this (i.e., the rate of resupply of elements from the solid phase) can provide further insights into soil factors affecting bioavailability. Indeed, this is a key premise of the DGT method (Zhang et al., 1998). An in-depth discussion of the DGT method is beyond the scope of this chapter, but it should be noted that this technique, which provides an integrated measure of the soil solution concentration and the rate of resupply, is showing great promise as a tool to predict the bioavailability of trace elements to plants across a wide range of soil types (Nolan et al., 2005). A number of spectroscopic techniques have been applied to the investigation of metal distribution, surface reactions, and solid-phase speciation at the molecular level. Using these methods, the physical and chemical forms and distribution of contaminants in soil and sediments can be investigated in situ. The spectrum of techniques available has increased significantly in the last few decades, and ranges from conventional methods such as X-ray diffraction to more sophisticated techniques such as X-ray microanalyses (EDXA, PIXE), Fourier-transform infrared, nuclear magnetic and paramagnetic resonance, secondary ion mass spectroscopy, and X-ray absorption (XAS) spectroscopy. For instance, X-ray microanalysis allows quantitative investigation of the distribution of an element in samples with a volume as small as a few femtoliters or with a spatial resolution of a few micrometers [for review, see Van Steveninck and Van Steveninck (1991)]. More recently, synchrotron-based techniques such as XAS have greatly improved our ability to gain information regarding oxidation and coordination states, number and type of near neighbors, and bond distances of the elements of interest (Sparks, 2001). XAS techniques have been used to assess solid-phase speciation of a number of soil metals and metalloids such as As, Co, Cr, Ni, Pb, and Zn. These techniques provide powerful tools for the assessment of metal speciation at the microscale. However, translating this detailed assessment into environmentally relevant information in terms of metal toxicity and bioavailability still represents a significant challenge. Also, accessibility to these spectroscopic techniques is still limited so that routine use is problematic. Isotopic dilution techniques have been employed in soil science for more than 50 years and are the focus of the remainder of this chapter. In essence, the isotope dilution technique allows differentiation of trace

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element pools on the basis of the kinetics of exchange of the trace elements with the soil solid phase. While in reality, the kinetics of exchange of elements within the soil represents a continuum, it can be useful and convenient to delineate kinetic boundaries. For example, results obtained during an isotopic dilution study of metal-contaminated soil prompted Hamon et al. (2002a) to propose that soil contaminants be placed into three risk classes based on the kinetics of their association with the soil solid phase, namely, (1) labile, (2) chemically labile, and (3) nonlabile. Labile forms were defined as the solution and readily (within 1–3 days) exchangeable pools of contaminant that pose a current risk and whose bioavailability can be measured, for example, by monitoring concentrations of the contaminant in plants growing in the soil. Nonlabile forms were defined as those that remain in chemically inert pools (i.e., do not readily participate in an exchange equilibrium) despite environmental perturbations, and hence pose little risk either now or in the future. The chemically labile form of contaminant was defined as that which is ‘‘fixed’’ in a nonexchangeable (and nonavailable) pool under current conditions, but which can be released to the available pool if the environmental conditions change, for example, through soil acidification or changes in redox status. The same definitions of labile, chemically labile, and nonlabile forms are used throughout the remainder of this chapter. It should be emphasized from the beginning that the isotopic dilution approach does not aim to directly measure trace element bioavailability but, both separately and in combination with the other methods described above, can provide useful information toward the understanding of various soil processes that influence bioavailability.

2. The Isotopic Dilution Principle The isotopic dilution principle is based on the premise that when a small amount of an isotope of an element of interest is introduced in a soil, it will readily redistribute itself among the solution and exchangeable phases (which are in dynamic equilibrium) in the same way as the other isotopes of the same element. Therefore:

a
sol Msol ¼
aexch Mexch

ð1Þ

where sol and exch represent the activity (a
) of the isotope, or the concentration of the metal (M ), in the solution phase and exchangeably adsorbed on the soil solid phase, respectively. When a
sol (Bq liter1) is converted to the same units as a
exch (Bq kg1) by taking into account the dilution factor for the suspension, D (D ¼ solution volume in liter/soil mass

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in kg), the sum of a
sol and a
exch is equal to the total quantity of tracer isotope introduced (abbreviated hereafter as A
); similarly, the sum of Msol (mg liter1) and Mexch (mg kg1) is equal to the total labile or exchangeable metal pool after multiplying Msol by D to convert to the appropriate units. Sampling and analysis of the solution phase allows determination of isotope distribution between the solution and solid phases. In this case, the labile pool has traditionally been termed an E-value (where E signifies ‘‘exchangeable’’) and it can be calculated by rearranging Eq. (1) and taking into account the dilution factor, D:

E ¼ ðD  Msol þ Mexch Þ ¼

Msol  A
 D
asol

ð2Þ

It is important to note that Eqs. (1) and (2) can only be valid under the following conditions: 1. The small quantity of isotope introduced in the system has not perturbed the equilibrium of the system, and consequently the isotope has no access to any nonlabile metal pool through processes such as (co) precipitation; 2. The introduced isotope behaves exactly as the natural element in the soil; 3. All metal species measured in solution (Msol) are isotopically exchangeable; 4. The introduced isotope has physically mixed with the entire labile metal pool. It should also be noted here that the dilution of the introduced isotope within the preexisting pool of the same element in soil is time-dependent. In theory, at infinite time, the isotope would mix uniformly with the entire soil metal pool (Tiller et al., 1972). Generally, the equilibration time is operationally limited to a few days (usually 1–3 days, see Section 3.4). As pointed out by Hamon et al. (2002b), the determination of an Evalue not only allows the measurement of the total exchangeable pool of an element in soil (E-value or Ea) but also the assessment of the pool present on the soil solid phase in an exchangeable form (Ee ¼ EaDMsol). In many cases, these two E-values are not very different; exceptions occur when the soil buffering capacity is low (Hamon et al., 2002b). Alternatively, plants grown in an isotopically labeled soil can be used to sample the exchangeable pool. In this case, the labile pool is called an L-value (Larsen, 1952). After growing the plants in the labeled soil, the shoot is harvested and the concentration/activity of the isotopes in the plant is measured. The L-value is calculated as follows:

L¼

Mshoot  Mseed  A
a
shoot

ð3Þ

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where shoot indicates the activity (a
) of the isotope, or the concentration of the metal (M), in the plant shoot and Mseed is the contribution of metal from the seed to the metal concentration in the shoot; as before, A
is the total amount of isotope used to label the soil. Since in this case, a living organism is used to sample the soil solution, the L-value can be considered as representative of the potentially bioavailable pool for that organism. In other words, it is the metal pool that the organism can draw from as the most accessible forms (e.g., solution species) are depleted over time, which may include both labile and chemically labile forms of the element. However, it should be noted that the actual bioavailability of this metal pool is controlled by a number of chemical parameters (such as pH, competing ions, ligands in solution) as well as by physiological processes. The procedural frameworks used for the determination of E- and Lvalues are depicted in Fig. 1, and a compendium of the literature that has made use of isotopic dilution techniques to investigate trace elements in soil is given in Table 1.

L-value

Soil spiking

E-value

Shoot harvesting Soil suspension (24/72 h equilibration)

Isotope measurement L-value calculation Soil

Centrifugation filtering < 0.2 mm

Isotope measurement E-value calculation

Non-labile

Labile

Figure 1 Schematic representation of E- and L-value procedures. In the Lvalue procedure, stars denote the added isotope whereas dots represent the native metal.

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Table 1 Compendium of the literature on isotopic dilution techniques (E- and L-values) used to assess the exchangeable pool of heavy metals and metalloids in soil Year

Authors

Cadmium 1986 Fujii and Corey

Isotope

Other elements Soil No.

Equilibration Total mg kg1 Equilibration medium Time

109

Cd

Zn

4

na

Pb

2

0.16, 0.29

0.01 M Ca (NO3)2þ1 M EDTA

16 h–3 days

E (%)

Dalenberg and Van Driel

109

Cd

1990

109

Cd

5

0.04–0.22

–

na

–

109

Cd

33

0.7–2040

0.01 M CaCl2

24 h

3–102

1994

Jensen and Mosbaek Nakhone and Young Riise et al.

109

Cd

2

<1

Modified Tessier

5 min–221 days

~25

1997

Hamon et al.

109

Cd

1

0.2

1998 1998

Hamon et al. Pandeya et al.

109

Cd Cd

1 10

0.03–0.4 na

1999

Gabler et al.

114

Cd

2

0.25, 0.76

1999 2000

Smolders et al. Gerard et al.

109

Cd Cd

10 4

0.3–6.5 0.6–25

115

109

Zn

Cu, Cr, Ni, Pb, Zn Zn

6 months–4 years

0.005 M DTPA, 0.01 M CaCl2, 0.1 M Na-ac. Water, EDTA, NH4NO3 0.01 M CaCl2 Water

L (%)

na

1990

1993

Plant species used for L-values

L. multiflorum, S. oleracea, T. aestivum, D. carota L. sativa

~62–86

B. napus, A. calendula, T. subterraneum, L. sativa, B. vulgaris, L. perenne, T. aestivum, T. turgidum T. turgidum

20–36

na

50–80

48 h

na

2–24 h

~65

7 days IEK

62–90 T. aestivum 55–109 41–66 L. perenne, L. sativa, na (30 days) T. caerulescens

2000

Hutchinson et al.

109

Cd

2000 2000

Stanhope et al. Young et al.

109

Cd Cd

2001

111

2001 2001 2002a

Ahnstrom and Parker Almas and Singh Stacey et al. Hamon et al.

Cd Cd 109 Cd

2003

Ayoub et al.

114

2003 2003 2003 2003

Collins et al. Degryse et al. Gray et al. Lombi et al.

109

2003 2003 2004

Scheifler et al. Tye et al. Degryse et al.

109

2004 2004 2004

Gray et al. Hutchinson et al. Sterckeman et al.

109

2005 2005

Nolan et al. Sappin-Didier et al. Sterckeman et al.

109

2005

109

109 109

Cd

Cd Cd Cd 109 Cd 109

Cd Cd 109 Cd

67–261

48 6–103

4

22–34

0.1 M Ca (NO3)2 48 h 0.1 M Ca (NO3)2 or 48 h CaCl2 0.1 M Sr (NO3)2 2 h–14 days

Zn Zn Zn

2 3 5

0.17, 1.9 0.03–1.06 90

24 h 3 days

6.4–33 1.5–3.2

Zn

2

0.18, 33.8

0.01 M Ca (NO3)2 Water (acidification) Water

1 h–50 days

86 (3 days)

Cu, Zn

2 74 20 8

18.7, 20.3 0.5–118 0.6–3.8 19–42

24 h 16 h–3 days 24 h 3 days

19–73 18–92 33–84 22–63

Zn Zn

1 39 80

20.3 0.03–384 0.6–414

IEK 2 days 16 h–3 days

50 (14 days) Helix aspersa (snail) Na 9–92 (3 days)

20 1 7

0.19–3.0 135 0.13–7

Diluted ligands 0.01 M CaCl2 0.05 M Ca (NO3)2 Waterresin (acidification) Water 0.1 M Ca (NO3)2 0.01 M CaCl2 and 0.1 mM EDTA Water 0.1 M Ca (NO3)2 Water

IEK 48 h IEK

13 2

0.11–86 2.3, 2.4

0.1 M Ca (NO3)2

24 days

43–54 (24 h) 39 59–74 (79 days) na

2

19.5, 19.9

0.01 M CaCl2

Zn

Cd Cd 109 Cd

109

T. caerulescens, T. officinale, H. vulgare

59 Na

109

109

~20–47 40–83

1 66

111

109

L. multiflorum T. aestivum

0.1 M Ca (NO3)2

Cd

Cd Cd

Cu, Zn

Cd

Zn

4.9–49

6–50

59–770

Zn

48 h

T. caerulescens (6 pop), L. heterophullum B. juncea

6

13–49 (14 days)

N. tabacum (2 lines) 7 days

44

35–74

58

86–100

297

L. perenne, L. sativa, 37–76 T. caerulescens, T. pratense, B. napus, A. thaliana

(continued)

Table 1 (continued) 298

Authors

Isotope

Other elements Soil No.

Total mg kg

Equilibration Equilibration medium Time

E (%)

Zn Zn

23 12

3–4 0.01–0.94

0.1 M Ca (NO3)2 0.01 M CaCl2

48 h 3 days

na 50–108

2006

Crout et al. Degryse and Smolders Geebelen et al.

109

7

31

IEK

5–32 (7 days)

2006 2007

Zhang et al. Gabler et al.

109

Water ( acidification) 0.1 M Ca (NO3)2 0.01 M Ca (NO3)2

48 h 24 h

na 4–99

Lopez and Graham

24 h–5 days

na

1972

0.005 M DTPA, 0.10 M Naac.þ0.01 M CaCl2 or 0.01 M LaCl3 0.005 M DTPA, 0.01 M CaCl2, 0.1 M Na-ac. at different pH 0.05 M CaCl2þcarrier 0.005 M DTPA, 0.01 M CaCl2, 0.1 M Na-ac. 0.005 M DTPA, 0.01 M CaCl2, 0.1 M Na-ac. 0.005 M DTPA, 0.01 M CaCl2, 0.1 M Na-ac. at different pH

48 h

na

1–15 days

Year

2006 2006

Cd Cd

109

109

Cd

Zn 17 Cr, Cu, Mo, 115 Ni, Pb, Tl, Zn

3–4 0.04–1.3

65

Cu, Fe, Mn

3

na

Lopez and Graham

65

Co, Cu, Fe, Mn

6

na

1972

Tiller et al.

65

25

4–183

1973

Lopez and Graham

65

Fe, Mn

6

na

1976

Rule and Graham

65

Fe, Mn

4

na

1977

Sinha et al.

65

21

na

Zinc 1970

Cd Cd

1

114

Zn

Zn

Zn Zn

Zn

Zn

Plant species used for L-values

L (%)

T. subterraneum

~3–15

48 h

~4–212 (7 days) na

T. repens

na

48 h

na

T. repens, F. elatior

na

48 h

na

Z. mays, T. aestivum na

1982

Ganai et al.

65

1986 1987

65

1997

Fujii and Corey Sanders and El Kherbawy Hamon et al.

1999

Zn

4

100–1500

Cd

4 26

na 37–224

65

Cd

1

80

Gabler et al.

68

Cd, Cr, Cu, Ni, Pb

2

51

1999

Sinaj et al.

65

11

42–987

1999 2000

Smolders et al. Young et al.

65

Cd Cd

6 25

50–311 na

2001 2001 2002a 2003

Almas and Singh Stacey et al. Hamon et al. Ayoub et al.

65

Cd Cd Cd Cd

2 3 5 2

68, 168 28–214 18540 65, 1231

2003 2003 2003

Collins et al. Degryse et al. Lombi et al.

65 65

Cd Cd, Cu

2 74 8

1400, 3250 53–34100 1756–2920

2003 2004

Tye et al. Degryse et al.

65

Cd Cd

39 109

29–28500 8–35800

Zn Zn

65

Zn

Zn Zn Zn Zn

65

Zn Zn 65 Zn 67 Zn 65

Zn Zn 65 Zn Zn Zn

65

0.005 M DTPA, 0.01 M CaCl2, 0.1 M Na-ac. at different pH 0.01 M Ca (NO3)2 0.01 M CaCl2 þ carrier

Water, EDTA, NH4NO3 Water

48 h

9–96

16 h–3 days na

na na

2–24 h

~5–10

IEK

4–44 (15 days) 23–50 6–48

0.01 M CaCl2 7 days 0.1 M Ca (NO3)2 or 48 h CaCl2

299

0.01 M Ca (NO3)2 Water Water

24 h 3 days 1 h–50 days

0.12–24 1.2–5.9 13–65 (25 days)

Diluted ligands 0.01 M CaCl2 Water, resin ( acidification) 0.1 M Ca (NO3)2 0.01 M CaCl2, 0.1 mM EDTA.

24 h 16 h–3 days 3 days

16–59 5–68 18–53

48 h 16 h–3 days

na 3–72 (3 days)

B. napus, A. calendula, T. subterraneum, L. sativa, B. vulgaris, L. perenne, T. aestivum, T. turgidum

12

T. aestivum

24–74

L. multiflorum T. aestivum

10–28 20–72

T. caerulescens, T. officinale, H. vulgare

11–60

(continued)

Table 1 300 Year

(continued) Authors

Isotope

2004 2004

Sarret et al. ScottFordsmand et al.

65

2004 2005 2005

Sinaj et al. Nolan et al. Sterckeman et al.

65

2006 2006

Crout et al. Degryse and Smolders Zhang et al. Gabler et al.

65

Lopez and Graham

1972

1974

2006 2007

Copper 1970

Other elements Soil No.

Zn Zn

Total mg kg

1

Equilibration Equilibration medium Time

E (%)

3 1

21000 15

0.001 M Ca (NO3)2 48 h 36, 42 days

54–92

Cd, Cu Cd

7 13 2

na 28–21300 1538–3362

Water 0.1 M Ca (NO3)2 0.01 M CaCl2

IEK 48 h 7 days

na na 9–49

Cd Cd

23 12

300–500 4.5–70.7

0.1 M Ca (NO3)2 0.01 M CaCl2

48 h 3 days

na 11–49

Cd 17 Cd, Cr, Cu, 115 Mo, Ni, Pb, Tl

300–500 4.2–163

0.1 M Ca (NO3)2 0.01 M Ca (NO3)2

48 h 24 h

na 0.2–94

64

Fe, Mn, Zn

3

na

Lopez and Graham

64

Co, Fe, Mn, Zn

6

na

McLaren and Crawford

64

24

4.4–64

0.005 M DTPA, 48 h 0.10 M Naac.þ0.01 M CaCl2 or 0.01 M LaCl3 48 h 0.005 M DTPA, 0.01 M CaCl2, 0.1 M Na-ac. at different pH 0.05 M CaCl2 24 h

65

Zn 65 Zn 65 Zn

Zn Zn

65

65

Zn Zn

68

Cu

Cu

Cu

Plant species used for L-values

Eisenia andrei (earthworm)

na

na

2–21

L. sativa L. multiflorum

L (%)

55–60

na

L. perenne., L. sativa, 28–66 T. caerulescens, T. pratense, B. napus, A. thaliana

1999

Gabler et al.

65

2003

Lombi et al.

64

2004

Lombi et al.

64

2004 2005 2006a

Nolan et al. Nolan et al Ma et al.

64,65

2006b 2006c 2006

Ma et al. Ma et al. Oliver et al.

64,65

2007

Gabler et al.

65

Nickel 1998 1999

Echevarria et al. Gabler et al.

63

2002

Denys et al.

2002

Cu

Cd, Cr, Ni, Pb, Zn Cd, Zn

2

13

4

1245

As

7

1058

Cd, Zn

12 13 21

7–432 7–602 12–40000

19 46 6

12–2400 12–2400 84–1058

Water, EDTA, NH4NO3 Water, resin ( acidification) Water, resin ( acidification) Watercarrier 0.1 M Ca (NO3)2 Water, resin, 0.01 M CaCl2 Water Water Water

Cd, Cr, Mo, 89 Ni, Pb, Tl, Zn

0.6–46.1

2 2

33, 35 16

63

7

10–862

Staunton et al.

63

2

20, 45

2003

Pinel et al.

63

2

19, 35

2004

Massoura et al.

63

4

26–863

Cu Cu

Cu Cu 64 Cu 64

Cu Cu Cu

64 65

Cu

Ni Ni

62

Ni Ni Ni

Ni

Cd, Cr, Cu, Pb, Zn

2–24 h

~24

24 h

28–40

24 h 24 h 24 days 24 h

34 (control soil) 3.7–52 na 5–100

24 h 24 h 24 h

30–110 11–100 58–74

0.01 M Ca (NO3)2

24 h

na

Water Water, EDTA, NH4NO3 Water

IEK 2–24 h IEK

Water or 0.01 M CaCl2

24 h

Water

IEK

S. lycopersicum, L. multiflorum

63–81

15–40 ~15

T. pratense

7–36

~8–57 (90 days) na

T. aestivum, T. na pratense A. murale

30–60 (90 days)

T. alexandricum, F. na ovina, F. rubra, R. sativus, B. napus, L. perenne, L. esculentum T. aestivum, T. 30–60 pratense A. murale

301

(continued)

Table 1

(continued)

302 Year

Authors

Isotope

2006

Echevarria et al.

63

Ni

2006

Massoura et al.

63

Ni

2007 2007 2007

Bani et al. Chardot et al. Gabler et al.

63

Manganese 1970 Lopez and Graham

Ni Ni 62 Ni

Total mg kg

Equilibration Equilibration medium Time

E (%)

100

19–12000

Water

IEK

na

16

154–12000

Water

IEK

1 9 115

3440 125–2507 <3–49.9

Water Water 0.01 M Ca (NO3)2

IEK IEK 24 h

~0.1–50 (3 months) 6 (24 h) >50 na

0.005 M DTPA, 0.10 M Naac.þ0.01 M CaCl2 or 0.01 M LaCl3 0.005 M DTPA, 0.01 M CaCl2, 0.1 M Na-ac. at different pH 0.005 M DTPA, 0.01 M CaCl2, 0.1 M Na-ac. 0.005 M DTPA, 0.01 M CaCl2, 0.1 M Na-ac. 0.1 N H3PO4 0.05 M CaCl2 or 0.005 M DTPA 0.05 M CaCl2 or 0.005 M DTPA

24 h–5 days

na

48 h

na

48 h

na

T. repens

na

48 h

Na

T. repens, F. elatior

na

3 days 7–97 up to 5 days ~3–46

S. vulgare

2–17

na

H. vulgare

Other elements Soil No.

63

Cd, Cr, Cu, Mo, Pb, Tl, Zn

1

54

Mn

Cu, Fe, Zn

3

na

1972

Lopez and Graham

54

Mn

Co, Cu, Fe, Zn

6

na

1973

Lopez and Graham

54

Mn

Fe, Zn

6

na

1976

Rule and Graham

54

Mn

Fe, Zn

2

na

1979 1984

Salcedo and Ellis Goldberg and Smith Goldberg and Smith

54

Mn Mn

12 10

96–655 49–1275

Mn

3

na

1985

54

54

na

Plant species used for L-values

T. caerulescens, T. pratense A. murale

L (%)

na

Iron 1970

Lopez and Graham

59

Cu, Mn, Zn

3

na

1972

Lopez and Graham

59

Co, Cu, Mn, Zn

6

na

1973

Lopez and Graham

59

Zn, Mn

6

na

1976

Rule and Graham

59

Mn, Zn

4

na

1985

Dyanand and Sinha

59

22

na

Tye et al.

73

102

4–17200

P

27

13–1080

2004 2004

De Brouwere et al. Hamon et al. Lombi et al.

73

Cu

8 1

Lead 1979

Tjell et al.

210

3

Arsenic 2002 2004

Fe

Fe

Fe

Fe

Fe

As As

73

As As

73

Pb

0.005 M DTPA, 0.10 M Naac.þ0.01 M CaCl2 or 0.01 M LaCl3 0.005 M DTPA, 0.01 M CaCl2, 0.1 M Na-ac. at different pH 0.005 M DTPA, 0.01 M CaCl2, 0.1 M Na-ac. 0.005 M DTPA, 0.01 M CaCl2, 0.1 M Na-ac. 0.005 M DTPA, 0.01 M CaCl2, 0.1 M Na-ac.

24 h–5 days

48 h

na

48 h

na

T. repens

na

48 h

na

T. repens, F. elatior

na

48 h

na

S. vulgare

na

L. multiflorum

na

0.005 M (NH4) 48 h H2PO4 0.005 M Ca (NO3)2 40 h

0.4–60

725–4770 4772

Water Water, resin ( acidification

2–73 3.7 (control soil)

11–17

1:1 HNO3, or 0.02 na M EDTA, or 1 M NH4-ac.

48 h 2 days

1.2–19

na

303

(continued)

304

Table 1

Year

(continued) Authors

Isotope

1989

Mosbaek et al.

210

1990

Dalenberg and Van Driel

210

1999

Gabler et al.

207

2005

Tongtavee et al.

207

2007

Gabler et al.

207

2007

Degryse et al.

208

Cobalt 1969

Tiller et al.

60

Gille and Graham Lopez and Graham

60

McLaren et al.

1971 1972

1986

Selenium 1994 He et al.

Other elements Soil No.

Pb Pb

Pb

1

Total mg kg

2

7–15

Cd

2

4.7, 20.1

Cd, Cr, Cu, Ni, Zn

2

37

5

Equilibration Equilibration medium Time

1:1 HNO3, or 1 M NH4-ac.

>2 years

E (%)

Plant species used for L-values

Na

Na L. multiflorum, S. oleracea, T. aestivum, D. carota

6 months–4 years 2–24 h

~61

21–246

Water, EDTA, NH4NO3 Water

IEK

40–64 (24 h) T. aestivum

<4–80

0.01 M Ca (NO3)2

24 h

na

16–14436

0.01 M CaCl2

3 days

45–>89

25

0.18–97

7–14 days

~0.6–111

1

na

0.05 M CaCl2carrier various

Na

na

6

na

48 h

na

60

20

3–15

0.005 M DTPA, 0.01 M CaCl2, 0.1 M Na-ac. at different pH 0.5 M CaCl2

48 h

1–11

75

3

32–256

0.01 M 24 h CaCl2KH2PO4

37–79

Pb Pb

Pb

Cd, Cr, Cu, 115 Mo, Ni, Tl, Zn 21

Co Co

60

Co

Co Se

Cu, Fe, Mn, Zn

S. vulgare

L (%)

~80– 170 ~100– 2000

~54– 868

na

2003

Goodson et al.

75

Se

5

2–20

0.1 M KCl

2006

Collins et al.

78,76

9

3–1130

Water

24 h

3–78

55

Water, EDTA, NH4NO3 0.01 M Ca (NO3)2

24 h

~0.06

24 h

Na

4 weeks–6 months 24 h

Na

<2–5 Mo

1 M HNO3, 1 MNH4-ac. 0.01 M Ca (NO3)2

na

0.01 M Ca (NO3)2

24 h

Na

Chromium 1999 Gabler et al. 2007

Gabler et al.

Se

53

Cr

53

Cr

Cd, Cu, Ni, 2 Zn, Pb Cd, Cu, Mo, 115 Ni, Pb, Tl, Zn

Molybdenum, Thallium, and Mercury 203 Hg 1988 Mosbaek et al. 2007

Gabler et al.

97

2007

Gabler et al.

203

Mo

Tl

1

Cd, Cr, Cu, 115 Ni, Pb, Tl, Zn Cd, Cr, Cu, 115 Mo, Ni, Pb, Zn

<3–171 Cr

0.2–0.3

4–73

A. bisulcatus, A. 2–37 canadensis, B. juncea, S. airoides, S. pinnata

L. multiflorum, L. sativa, R. sativus

na

Na

E (%) and L (%) refer to the isotopically exchangeable pool (E- and L-values) as percentage of the total soil content. The symbol ‘‘~’’ indicates values that have been calculated from published data. When different amendments were added to the same soil, these treatments were compiled in the table as different soils.

305

306

Rebecca E. Hamon et al.

3. Methodologies 3.1. Choice of isotope Typically, radioactive isotopes of metals and metalloids have been used. Available radioisotopes include 109Cd, 60Co, 64Cu, 59Fe, 203Hg, 54Mn, 63Ni, 210Pb, 75Se, and 65Zn. However, as shown in Table 2, some of these isotopes have very short half-lives (e.g., 64Cu) that limits their use in longer term studies, or have relatively long half-lives (e.g., 63Ni) that can cause a disposal problem once the study is completed. Use of certain radioisotopes may also pose a significant risk due to the nature of the radiation emitted (e.g., 210Pb). Therefore, the use of enriched stable isotopes (Table 3) has gained some favor, driven in part by the availability of sensitive analytical techniques such as inductively coupled plasma mass spectrometry (ICP-MS) (Ahnstrom and Parker, 2001; Gabler et al., 1999; Gray et al., 2003; Nolan et al., 2004; Tongtavee et al., 2005). Even though the use of stable isotopes is analytically more demanding than in the case of radioisotopes, it opens the way to the study of elements for which appropriate radioisotopes are not available (or affordable), and offers the additional Table 2

Some useful radioisotopes for the determination of E- and L-values

Isotope

Half-life

Decay modea

Detectionb

51

Cr Mn 55 Fe 59 Fe 57 Co 63 Ni 64 Cu

28 days 312 days 2.7 years 45 days 272 days 100 years 12.7 h

g 320 keV (100) g 835 keV (100) b 231 keV (100) g 1100 keV (57) g 22 keV (17) b 67 keV (100) g 1346 keV (100)

65

Zn As 75 Se 109 Cd 110m Ag

244 days 80 days 120 days 461 days 250 days

EC!51V EC!54Cr EC!55Mn b!59Co EC!57Fe b!63Cu b, EC!64Zn, 64 Ni EC!65Cu EC!73Ge EC!75As EC!109Ag b!110Cd

113

115 days 47 days

EC!113In b!203Tl

54

73

Sn Hg

203 a

g 115 keV (51) g 53 keV (10) g 265 keV (59) g 88 keV (3.7) g 657 keV (94) or b 83 keV (67) g 392 keV (64) g 279 keV (100)

EC¼electron capture. For g - and b-ray spectrometry, the energy of the strongest emission is given in keV, along with the percent relative intensity in parentheses. Note that the unusually long and short half-lives of 63Ni and 64Cu, respectively, can make their use problematic. b

307

Advances in Isotopic Dilution Techniques in Trace Element Research

Table 3 Some potentially useful stable isotopes for the determination of E- and L-values, typically using ICP-MS for isotope-ratio determinations Element

Number of isotopes

Suggested tracera,b

Suggested referenceb

B Cr Fe Ni Cu Zn Se Mo Ag Cd Sn Hg Pb

2 4 4 5 2 5 6 7 2 8 10 7 4

10

11

53

52

B (20%) Cr (9.5%) 57 Fe (2.2%) 62 Ni (3.6%) 65 Cu (31%) 68 Zn (19%) 77 Se (7.6%) 95 Mo (16%) 109 Ag (48%) 111 Cd (13%) 118 Sn (24%) 201 Hg (13%) 206 Pb (24%)

B Cr 56 Fe 60 Ni 63 Cu 66 Zn 78 Se 97 Mo 107 Ag 114 Cd 120 Sn 202 Hg 207 Pb

a

Natural atomic abundance is shown in parentheses. Many of these isotopes are subject to potential interferences from polyatomic (molecular) species such as 40Ar16OH (amu ¼ 57). Increasingly, these problems are readily overcome through the use of newer ICP-MS instruments with collision/reactor cell technology, most typically in He collision mode. Note that with B and Mo, no radioisotopes are available. b

advantage of an indefinite ‘‘shelf-life’’; the feasibility of in-field isotopic dilution studies is another possible benefit. The measurement of a labile pool (E-value) using stable isotopes requires the accurate knowledge or determination of three isotope ratios: the natural abundance ratio of the isotopes (IRnat), the ratio of the isotopes in the spike solution (IRsp), and the measured isotope ratio in solution after spiking (IRmeas). These isotopic ratios are then used to calculate the labile pool using equations such as the following developed by Nolan et al. (2004) for Cu:

E¼R

AWðMnat Þ IRsp  IRmeas  ðIRnat þ 1Þ AWðM
Þ IRmeas  IRnat

ð4Þ

where, R is the total concentration of the isotope in the spike and AW is the atomic weight of the isotopes in their natural abundance (Mnat) and in the spike (M
). Note that although most isotope ratios were until relatively recently considered invariable in nature (with the exception of a few elements such as Pb or B), high resolution techniques such as multicollector ICP-MS are now showing that many elements exhibit natural massdependent variations in isotope composition (Halliday et al., 1998; Vance and Thirlwall, 2002). Therefore, it is recommended that the natural isotope ratio is measured in each soil tested and the measured value is used in the calculation of E- and L-values.

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3.2. Isotope ‘‘spiking’’ When a radioisotope is used to assess either an E- or an L-value, the soil must be spiked with an amount of isotope that will allow accurate detection of the activity in the solution extracted from the soil or in plants. It is difficult to generalize as to the total activity needed for a given experiment because this will depend on the soil characteristics controlling the partitioning of the radioisotope (such as pH, organic matter), the half-life and counting efficiency of the isotope, and, in the case of L-values, the ability of the organism to accumulate that element. Moreover, the propensity of the element of interest to partition to the soil must be considered, for example, for a given soil, the Kd of Pb is typically larger than that of Cd. Hence, at a given spike rate, less of a Pb spike will be found in solution where it can be counted in comparison to a Cd spike. In the literature, activities varying from much less than 1kBqg1 (e.g., in the case of 63Ni in sandy soil; Echevarria et al., 1998) to over 1MBqg1 (e.g., for the shortlived 64Cu; Lombi et al., 2003) are reported. Generally, the isotope used is virtually ‘‘carrier free’’ radioisotope [i.e., has a high specific activity (MBqmg1 metal)] and the amount of ‘‘cold’’ element added in the spike is negligible and can be ignored. In cases where carrier (‘‘cold’’ element) is deliberately added (e.g., Tiller et al., 1972), or the specific activity is low such that a significant quantity of cold element is added with the radioisotope, this needs to be accounted for during the calculation of E- or L-values. Since the total amount of radioisotope added to the soil is generally extremely small, its addition does not significantly perturb the equilibrium of the system. Consequently, the decrease of radioactivity in solution is due to a homoionic exchange between the added radioisotope and the stable isotope(s) of the same element present as free ions in solution, or as reversibly sorbed ion bound to solution- or solid-phase ligands (Sinaj et al., 1999). In contrast, when a stable isotope is used for the determination of an Eor L-value the amount of metal added to the system must be sufficiently large to cause a quantifiable change in the isotopic ratio of the spiked soil in comparison to the natural isotopic ratio of the system (i.e., IRsp and IRmeas in Eq. (4) must be significantly different). Nolan et al. (2004) used the mean standard deviation of 63Cu/65Cu measurements between replicates and the difference between IRsp and IRmeas to assess the error associated with different amount of 65Cu spiking. The data showed that an addition equivalent to 5% of the E-value produced an acceptably low uncertainty (<5%) in the E-value determination. But when the E-value is unknown, the amount of isotope to add can be estimated from the total elemental concentration in soil, or from a reasonable proxy for the E-value, such as a 1M CaCl2 extraction in the case of Cd (Young et al., 2000). Nolan et al. (2004) suggested an addition of 65Cu equivalent to approximately 1% of total Cu in

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soil, as did Ahnstrom and Parker (2001) using 111Cd. The isotopic natural abundance is also a consideration in determining which isotope to use and the amount of spike to add, with the lower the natural abundance of the spike relative to the natural abundance of the other isotopes, the lower amount of spike that can be added. Hence, in contrast to studies with Cu, Gray et al. (2003) could achieve reliable measurements by adding 111Cd equivalent to ca. 0.1% of the total Cd concentration partly because 111Cd accounts for only 12.8% of the total Cd in natural systems whereas 65Cu represents 30.8% of the isotopic abundance of Cu isotopes. Gabler et al. (2007) added an amount of spike equivalent to one-third of the EDTAextractable metal content and repeated the measurements with an optimized spike addition if the original amount added to the sample was less than 5% of the isotopically exchangeable amount [which is in agreement with the suggestion made by Nolan et al. (2004)]. In case of Pb, Degryse et al. (2007) added 208Pb in a relatively large amount (between 4% and 12% of the total soil Pb concentration) and it is possible that as a consequence, their results may overestimate Pb lability in some soils (see Section 4.2).

3.3. Choice of suspension matrix in E-value determinations Early studies aiming to assess micronutrient lability in soil used a mixture of DTPA, CaCl2, and Na-acetate at different pH (e.g., Lopez and Graham, 1970, 1972; Sinha et al., 1977) as the isotopic equilibration medium. This choice of suspension matrix was probably based on the use of DTPA to assess micronutrient availability. However, it is likely that such a solution, especially when the pH of the soil suspension was modified, could result in dissolution/desorption of nonexchangeable metals from the chemically labile pool, and this should be considered when interpreting the results of these experiments. More recently, either deionized water or a dilute electrolyte solution such as 0.1 M or 0.01 M CaCl2 or Ca(NO3)2 (Smolders et al., 1999; Young et al., 2000) has been used in E-value determination of metals. In case of As, water (Hamon et al., 2004), 5 mM (NH4)H2PO4 (Tye et al., 2002), and 5 mM Ca(NO3)2 (De Brouwere et al., 2004) have all been used. The dilute salt solutions have the advantage of increasing the concentration of the analyte in the extracts due to displacement of the ion of interest from the adsorption sites. In case of CaCl2, used to mimic the Ca concentration in the soil solution, the Cd concentration in solution could also be enhanced by the complexation of Cd by Cl (Young et al., 2000). These aspects are of importance when the concentrations of the analytes in solution are close to their analytical detection limits because increased concentrations usually translate to more accurate measurements. Dilute divalent cation salt solutions also offer the advantage of decreasing the presence of colloids in solution that facilitates filtration of the samples and lessens the potential for colloidal interferences (see Section 4.5). Care is

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needed, however, to ensure that the suspension matrix does not cause precipitation of the analyte of interest that, if it occurs prior to the addition of isotope, will usually result in an underestimation of the labile pool. For example, if the goal is to assess E-values for Ag or As, it would be best to avoid use of salt solutions containing Cl or Ca, respectively, due to the potential risk of precipitation of AgCl or insoluble calcium arsenate salts. There may also be interest in assessing the E-values of cations and anions simultaneously in a soil, and here water may provide the most useful alternative. However, use of water can enhance colloid dispersion and colloids may lead to an overestimation of the E-values (see Section 4.5). To avoid this problem, a resin purification method was originally developed for phosphorus E-values by Hamon and McLaughlin (2002), and subsequently adapted for trace metals by Lombi et al. (2003; Fig. 2). This method also allows quantification of the fraction of colloidal metals that are not isotopically exchangeable and therefore not readily bioavailable. Another advantage of this resin step is that it concentrates the metals (and isotopes) and provides a constant analyte matrix, and therefore allows a more reliable determination of the E-values when metal concentrations and isotope activities in the filtrates are close to detection limits. Generally, if colloidal interferences are resolved using the resin method, differences in E-values measured using either water or CaCl2 are small for elements such as Cd and Zn (unpublished data). However, a recent study found that for Cu, significantly different E-values were obtained using water with the resin purification procedure compared to using CaCl2 (Ma et al., 2006a). In this case, the E-values measured with 0.01M CaCl2 were 30% smaller than those measured in water (þresin). In soils, Cu is more strongly associated with organic matter than either Cd or Zn, and changes in soil organic matter conformation due to an increase in the ionic strength of the soil suspension when CaCl2 was employed may have caused Cu to be sequestered in nonexchangeable form within organic colloids.

3.4. Equilibration time A distinction must be made between the equilibration time for E-values calculated, as discussed above [Eq. (2)], and the isotopically exchangeable kinetic (IEK) method. The latter uses an empirically derived function of time that was originally developed for P (Fardeau and Marini, 1968), and then adapted for other elements (Echevarria et al., 1998; Frossard and Sinaj, 1997), to assess exchangeable pools. When an isotope is added to an equilibrated soil suspension its activity in solution decreases over time. Jose and Krishnamoorthy (1972) suggested that the initial fast process is due to equilibration with soil surface sites with different absorption energies and exchange kinetics. After this initial fast process, the activity in solution slowly decreases over time probably due to

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Soil spiking

Soil suspension (24/72 h equilibration)

Soil

Centrifugation filtering < 0.2 mm

Calculation of labile metals (E-value)

+ Chelex 100 (Ca form)

Calculation of labile metals (Er-value)

Supernatant

Supernatant + colloids

Resin elution 0.5 M HNO3

Supernatant + colloids + resin (24 h equilibration)

Figure 2 Schematic representation of a conventional E-value procedure and of an E-value procedure including a resin purification step to minimize colloidal interferences [after Hamon and McLaughlin (2002) and Lombi et al. (2003)].

diffusion processes (McAuliffe et al., 1948; Tiller et al., 1972). Due to the continued reaction of the isotope with the soil system, an operationally defined equilibration time is chosen when the E-values are calculated using Eq. (2). This equilibration time is generally 1–3 days, and is intended to be long enough for the fast reaction processes to be completed, so that an increase in the equilibration time would have little further influence on the measured E-value. For instance, Young et al. (2000) reported very little change in Cd E-values after 48 h of isotope equilibration, and suggested 2 days as standard for equilibration. Other authors have found that after 2–3 days, the activity in solution stabilizes and remains almost constant for several days or weeks (Goldberg and Smith, 1984; Oliver et al., 2006;

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Tiller et al., 1972; Tongtavee et al., 2005). This indicates that, even though the choice of the equilibration time is operationally defined, in practice most published data suggest that a reasonably distinct labile pool is distinguishable when a 2–3 day equilibration time is used (Young et al., 2000). It should be noted, however, that very few studies have been of sufficient duration to allow any quantification of the more sluggish exchange kinetics that are operative on a months-to-years timescale. Ahnstrom and Parker (2001) showed that E-values for Cd (expressed as a percentage of total soil Cd) increased anywhere from 5% to 20% over 57 weeks of incubation. This slow migration of tracer into marginally labile pools would be almost impossible to detect if only monitored for a few days or weeks. When using the IEK approach, the labile pool is estimated at different equilibration times. In this case, short-term equilibration kinetics (1–100 min) are used to extrapolate E-values at longer equilibration times (Et), generally up to 90 days. These values are calculated with an equation effectively identical to Eq. (2) (Gray et al., 2004):

Et ¼ D  Msol

A
a
t

ð5Þ

where D is the soil:solution ratio, A
is the total amount of radioactivity introduced in the system, and a
t is the activity in solution at time (t). However, in this case instead of being measured directly, the term a
t is estimated from a measurement of the radioactivity remaining in the soil solution after 1 min (a
1 ) using an empirical equation (Fardeau, 1996):

"

1=n #n a
t a
1 a1 a
1 ¼ t þ þ A
A
A
A


ð6Þ

where a
1 is the radioactivity remaining in solution after an infinite exchange time and n is a parameter describing the rate of disappearance of the isotope from the solution for times longer than 1 min of exchange. The ratio a
1 =A
is the maximum possible dilution of the isotope and can be approximated by the ratio of water-soluble metal to the total soil metal concentration (Fardeau, 1996). In practice, this term is approximated by a measurement of the water-soluble metal over the total soil metal at equilibrium (Echevarria et al., 1998; Fardeau, 1996).

a
1 Msol ¼ A
Mtot

ð7Þ

Isotopic lability of Zn as predicted by the IEK method was compared to E-values measured at different equilibration times by Sinaj et al. (1999), and the agreement was very good (r2 ¼ 0.997) for equilibration times of 1–15 days.

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However, it should be noted that Sinaj et al. (1999) used 2M HNO3-extractable Zn rather than total soil Zn as the input parameter for Mtot in Eq. (7). Gray et al. (2004) investigated whether isotopically exchangeable Cd from exchange intervals up to 18 days could be predicted by the IEK method. Their results showed that for 4 of the 6 soils investigated, the IEK method could only predict Cd lability up to 24 h after which the predictions significantly overestimated the measured E-values. One of the fundamental assumptions of IEK (and isotopic dilution in general) is that at infinite time, all soil elements are potentially exchangeable. Gray et al. (2004) speculated that their results (i.e., inability of IEK to predict measured E-values) might indicate that this fundamental assumption is unrealistic and supported this conjecture with the observation that when EDTA-extractable Cd instead of total Cd was used in Eq. (7), IEK could correctly predict E-values for all soils. However, we would argue that their results are in fact a good demonstration that short-term exchange kinetics do not necessarily provide a valid model for exchange processes occurring in the longer term. For example, there is no a priori reason as to why the kinetics of exchange occurring in more recalcitrant pools such as within crystal lattices should be predictable from much more rapid surface-exchange kinetics. It is therefore not surprising that limiting the metal pool under consideration during the IEK calculation (e.g., 2-M HNO3-extractable or EDTA-extractable metal), to one which is more likely to have exchange kinetics similar to the pool sampled in the first few minutes, yields an IEK calculation that compares more favorably with measured E-values. In the first comprehensive investigation of E- and L-values for Pb, Tongtavee et al. (2005) also compared Pb lability using short-term IEK and measured E-values. These authors found that short-term IEK data could not predict the Pb E-values measured (up to 15 days) in the investigated soils. Tongtavee et al. (2005) attributed this to a lack of precision in the values used for time (t). As they pointed out, for short equilibrations, the length of time taken to separate the solution from the soil by centrifugation and filtration could have introduced substantial errors into the first two data points (usually taken at 1 and 10min). As a consequence of both of the issues described above, we would recommend that a great deal of caution be exercised when extrapolating results from very short-term exchange experiments (minutes) to quantify pools of elements that are labile in the medium/longer term (days/months).

3.5. L-value determinations L-value determinations are based on the exposure of a selected organism (traditionally a higher plant) to a soil spiked with an isotope of the element of interest. After exposure, the isotopic ratio between the introduced and native isotope is measured in the organism and the L-value is calculated as

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described in Eq. (2) (for a radioisotope). In addition to the methodological issues described above, which are common to both E- and L-values, some additional methodological aspects have to be considered in the case of L-value determinations. The first issue is related to the need to use a larger amount of soil for L-value determination because the soil has to host the biological organism used in the assessment. This causes L-values to be more labor intensive and require the addition of more isotope than for E-values. When radioisotopes are used, this also means an increased exposure to radioactivity for the experimentalist. From practical point of view, it is also more difficult to homogenize the isotope in a larger amount of dry or moist soil than in the case of an E-value determination where a small amount of soil is used in a batch system involving a soil slurry. However, a homogeneous distribution of the isotope is essential for the correct measurement of L-values (see Section 4.4). An additional issue is the period of exposure that is largely a function of the growth rate and life cycle of the organism of choice. It should be long enough so that quantifiable amounts of both trace and reference isotopes can accumulate in the organisms. This is particularly important in order to minimize errors because of the presence of the element of interest in the seeds (or juveniles) used for the L-value determination (see Section 4.4). In any case, the exposure time is generally much longer than the equilibration time (a few minutes to a few days) used for E-value determination. Therefore, the choice of the isotope for an L-value measurement may occasionally be more problematic than in the case of E-values. An example is provided by Cu wherein the radioisotope 64Cu has a half-life (12.4 h) that is suitable for E-value determinations, but far too short for the assessment of Cu lability using L-values. Therefore, in this case, the choice of the isotope is restricted to the stable isotope 65Cu. L-values using stable isotopes can be calculated using the same equation used for stable isotope E-value determination [Eq. (4)]. However, in this case, IRnat is the natural abundance ratio of the isotopes in plants grown in an unspiked soil (from control pots), and IRmeas is the measured abundance ratio of the metal in plants grown on the labeled soil (Oliver et al., 2006).

4. Uncertainties and Sources of Errors 4.1. Accuracy and precision It is clear from the equations presented above that the determination of an Eor L-value is the end result of a number of independent measurements. Consequently, any error in a single measurement is propagated through the calculation of the labile pool. In the case of E-value determinations made using a radioisotope, three independent measurements are needed: (1) the total

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activity of the tracer isotope added to the system, (2) the activity of the isotope in the solution after equilibration, and (3) the total concentration of the element in the equilibrated solution. Activities of the radioisotopes are assessed using standard methods for g- or b-radiation. These analytical techniques are usually both accurate and precise, providing spectral interferences (e.g., interference of 65Zn on 109Cd) and quenching issues have been accounted for, and if the recorded counts are sufficiently greater than the background levels. Note that appropriate decay correction is also an important consideration, especially for short-lived radioisotopes. The total amount of radioisotope added is usually analyzed in acidified ‘‘blank’’ solutions spiked with the radioisotope, but without the soil. Acidification of the solution is essential to avoid loss of isotope due to sorption to glassware and filters, especially if it is added ‘‘carrier-free’’ whereby the total number of atoms per liter in the spiking solution is very low. In the case of L-values, the total amount of radioisotope added is measured either in a digest of the labeled soil (Smolders et al., 1999) or in the stock solution used to spike the soil (Hutchinson et al., 2000). The accuracy/ precision of measurements of the analyte concentration in solutions depends on the analytical technique used and is beyond the scope of this chapter. It is, however, an important consideration in assessing the overall accuracy of a calculated E- or L-value (see Section 4.3). Generally, the use of dilute salt solutions, or a deionized water extract in conjunction with a resin purification step, both increases the concentration of the analyte in solution (thus improving accuracy) and reduces the problem of colloidal interferences (see Section 4.5).

4.2. Spike-derived artifacts When E- or L-values are determined using stable isotopes, a single instrument (usually an ICP-MS) is used for all measurements. In addition to errors due to isobaric interferences (which vary depending on the instrument utilized and are not discussed here), a significant source of uncertainty can arise from the amount of the isotope added. In fact, the decision regarding the amount of added isotope is a balancing act between two contrasting needs: on the one hand, the statistical errors of the measurements are minimized if the number of added spike atoms equals the number of isotopically exchangeable atoms in the sample (Heumann, 1988) but on the other hand, a large amount of added isotope may cause changes in the speciation of the element investigated. Moreover, if a large amount of isotope is added, some of the isotope may undergo precipitation reactions, thereby not participating in isotopic exchange. This violates one of the conditions for isotope dilution (viz ‘‘the isotope introduced in the system has not perturbed the equilibrium of the system’’) and results in an overestimation of the isotopic exchangeability. Artifacts arising from precipitation of the spike can be tested by examining the effect of an increasing spike

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concentration on measured E-values; if all of the assumptions of isotopic dilution are valid, then the E-value should be independent of the amount of spike added. Figure 3 shows the effect of increasing 206Pb spike (added at 1, 2, 5, 10, 20, 50, and 100% of total soil Pb) on the determination of the Pb E-value in two soils. The results obtained for Soil 1 show an increasing Evalue with increasing amounts of added isotope. This indicates that the E-value is already overestimated at relatively low additions of spike (<2% total soil Pb), which can be attributed to precipitation of the added spike. Note that the E-value may also be overestimated in Soil 1 at the lowest spike addition (1% total soil Pb); however, detection limit issues precluded the addition of a lower amount of isotope to assess this. The solubility of Pb in soils is much lower than the solubility of many other metals, and it would be expected that this would be reflected in a relatively small labile pool of Pb as a proportion of the total soil Pb content. In contrast, Degryse et al. (2007) remarked on the high lability of Pb found in their study in comparison to documented lability for other metals, and other studies have similarly reported relatively large values for the labile Pb pool (Table 1). We hypothesize that precisely because Pb solubility in soils is low, E- and L-value results for this element are more likely to be compromised by precipitation artifacts, resulting in an overestimation of the actual lability, unless great care is taken to prevent this.

1600 Soil 1 Soil 2

E-value (mg kg−1)

1400

1200

1000

800

600 10

100

1000

10,000

Amount of isotope added (mg kg−1)

Figure 3 Lead (Pb) E-values in two soils as a function of increasing amounts of spiked 206 Pb (Hamon and Nolan, unpublished data).

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In contrast, one concern about E- or L-values determined using radioisotopes, discussed at length in the early literature (Tiller et al., 1972), was irreversible binding of the isotope to vacant sites on the soil surface, coined ‘‘isotope fixation’’. This theory was originally proposed to try to explain severe overestimates (i.e., E-value>total soil element) of E-values that were frequently observed in soils where the E-values were expected to be low (e.g., in the case of Zn: soils with high pH and low total Zn content). The essence of this theory is that in some circumstances, isotopic exchange is impeded by the existence of vacant binding sites that instantaneously sequester and ‘‘fix’’ the introduced trace atoms, hence circumventing the exchange process because there are no atoms of native element already present at the sites to exchange with. It was thought that this problem was exacerbated by the small number of tracer atoms added during E-value determination such that a small number of these vacant sites would nonetheless cause a large, positive error in the calculated E-value. Indeed, preequilibrating the soil with carrier (with the aim being to ‘‘saturate’’ any such vacant sites, making them no longer accessible to the added tracer atoms) did yield lower E-values (Tiller et al., 1972). However, it seems thermodynamically implausible that a system that hosts a measurable (if low) concentration of isotopically exchangeable native element in solution would simultaneously host vacant sites that do not bind the soluble native element already present, but which nevertheless can selectively and irreversibly bind radioisotope or other tracer atoms added in a very small amount. One exception would be if the chemistry of the added isotope is sufficiently different from the native element that major isotopic discrimination occurs (i.e., violating the condition that ‘‘the introduced isotope behaves exactly as the natural element’’). This seems unlikely for most metals/metalloids that have a high enough atomic mass that a difference of a few neutrons is of little overall significance, though more investigation may be needed (see Section 4.4). Hamon et al. (2002b) have provided an alternative explanation to account for the results of Tiller et al. (1972) that does not invoke vacant sites. Moreover, other credible candidates that could explain reported overestimates of E-values determined in high pH or uncontaminated soils include the susceptibility of measurements made in such soils to colloidal interferences (see Section 4.5), and the fact that any measurement of analytes near their detection limit is inherently subject to analytical difficulties. Finally, changes in lability can arise if the added spike solution contains components that acidify or otherwise alter the overall sample (Gabler et al., 2007).

4.3. Error propagation Errors in the determination of E- and L-values might arise simply from experimental error, as the values depend on several exacting analyses. Excepting the early work of Tiller et al. (1972), there have been few

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systematic analyses of sources of error and uncertainty in the determination of E- and L-values. As a simple example, consider the determinations of E- and L-values made by Goodson et al. (2003) using 75Se. The quantification of the radioisotope involves two separate g-counter measurements, one for the background and one for the sample. Because the net activity is derived from the difference between the sample counts and the background counts, the overall uncertainty is calculated using standard conventions for the propagation of indeterminate errors (Wang et al., 1975):

Stracer ¼ ðs2total þ s2background Þ2

ð8Þ

where Stracer is the absolute uncertainty in the measured net radioactivity (in cpm) and stotal and sbackground are the standard deviations for the sample and background measurements, respectively (and typically taken to be the square root of the measured count rate). The uncertainty in the measurement of the ‘‘cold’’ Se can be estimated from the standard deviation of several replicate readings of a single sample using HVG-AAS, typically about 0.3 mg liter1 Se in our laboratories (¼Sreference). Then, because E- and L- values are both derived from the quotient of ‘‘cold’’ Se to isotopic tracer, the overall, relative uncertainty is given by

"
%SrðEorLÞ ¼

Stracer
Se

2


þ

Sreference ½Se

2 # 2
100

ð9Þ

where
Se is the measured, background-corrected 75Se activity in cpm and [Se] is the measured solution Se concentration in mg liter1. This uncertainty is depicted in Fig. 4 for representative values of 75Se counting and Se analysis by HVG-AAS as employed by Goodson et al. (2003). The figure suggests that, to achieve overall accuracies to within 5%, data collection should be restricted to cases where (1) total 75Se activity exceeds about 2000 cpm and (2) ‘‘cold’’ Se exceeds about 9mg liter1.

4.4. Uncertainties and sources of error specific to L-value determination In the calculation of L-values [Eq. (3)], the elemental content of the seeds should be subtracted from the total metal content of the plant. Similarly, in the case of labile pools determined using different biological systems such as earthworms or snails, the amount of metal in the juvenile invertebrates should be subtracted from the metal present in the organism at the end of the experiment. However, the seed/juvenile contribution is often negligible if the seed/juvenile size is small, and if the plants/invertebrates are allowed to

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6500 6000

Relative error in E- or L-value (%)

75Se

activity, cpm

5500 5000

32

4500

28

4000

24 20

3500

16 3000

12

2500

8

2000

4

1500

0

1000 1

3

5

7

9

11

13

15

Aqueous [Se], mg L-1

Figure 4 Relative uncertainty in the calculated E- or L-value as a function of the counting rate for 75Se by g-counting and the ‘‘cold’’ Se in solution as determined by HVG-AAS. Background count rate is taken to be constant at 495 cpm. Assumed uncertainty in the HVG-AAS determination of Se is 0.3 mg liter1.

grow to a reasonable size (Tiller and Wassermann, 1972). For instance, Hamon et al. (1997) showed that for the plants in their study, the seed contribution to the total metal content of the shoot could be minimized to <5% if the plants were left to grow for several weeks. Scott-Fordsmand et al. (2004) calculated that the Zn content of juvenile earthworms could lead to a maximum error of 10%. Scheifler et al. (2003) accounted for the initial amount of Cd in snails and the Cd contribution of the food source (lettuce) by subtracting the amount of Cd accumulated in snails grown in an uncontaminated soil from that of snails grown in a contaminated one. Oliver et al. (2006) reported that, under their experimental conditions, seed contribution to Cu L-values was not significant when tomato seeds were used because of their small size and small Cu content. In contrast, when ryegrass was used there was a significant impact on the Lvalues determined, and they had to be corrected for the seed Cu content. This contribution was determined by germinating the seeds in petri dish for 10 days and measuring the Cu content in the seedling shoots. Therefore, seed contribution is not a factor that can be discounted a priori, and care must be taken to avoid overestimation of L-values due to the seed contribution. Another consideration in the determination of L-values is possible discrimination of the isotopes during uptake. One of the key assumptions of any

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L-value determination is that all the isotopes of an element of interest, which are in equilibrium in the labile pool, are taken up and assimilated with equal ease by the organism under study. However, Weiss et al. (2005) have reported isotopic discrimination of Zn in higher plants. In particular, they observed a preferential uptake of the lighter 64Zn isotope over 66Zn when plants were grown in nutrient solutions. The extent of this discrimination is limited (0.13% to 0.26% per atomic mass unit) and as such would not have a significant effect on the determination of the labile pool, but information in this regard is scarce and more investigation is needed. One procedural aspect that is extremely important in the determination of correct L-values is the mixing of the isotope with the soil. As we discussed in Section 1 of this chapter, one of the fundamental assumptions of the isotopic dilution principle is that the introduced isotope has physically mixed with the entire labile metal pool. This condition is obviously easier to achieve in the determination of E-values, which is generally conducted using a batch system and dilute slurry (Fig. 1), than for L-values where the isotope has to be manually or mechanically mixed into the soil in which the plants will be grown (Fig. 1). Because plant roots may only sample an incomplete proportion of the soil (especially in the early stages of growth), any heterogeneity in the distribution of the introduced isotope, as well as the metal present in the soil, will have an effect on the L-values (Hamon et al., 1998). For instance, if the isotope is not homogeneously distributed and the plant roots grow in areas where the introduced isotope is not present, the L-value will be overestimated. The opposite result can occur (L-values underestimated) if the root system grows into areas where the introduced isotope is accumulated. It is therefore imperative that the soil used is homogenized and the added isotope is thoroughly mixed with the soil. The best way to achieve a thorough mixing will depend on the nature of the soil (clay soils are more difficult as they tend to form aggregates when wet) and the isotope used (mechanical mixing, for safety reasons, should be preferred when radioisotopes are used). When a large amount of soil is to be spiked for L-value determinations, adding the isotope diluted in an appropriate amount of water (rather than using a few microliters of concentrated solution) helps to ensure a more thorough mixing.

4.5. Colloidal interferences If nonisotopically exchangeable colloidal metals (Mcol) are present in the filtered solutions (Msol), then the unlabeled metal concentrations, and consequently the E-values, are overestimated as can be deduced from the following equations:

E¼

Msol Msol þ Mcol
 A  D <  A
 D

asol asol

ð10Þ

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As mentioned previously, a resin purification step was developed to remove this interference (Hamon and McLaughlin, 2002; Lombi et al., 2003). Briefly, after equilibration of the isotope with the soil suspension, the liquid phase is separated from the soil by filtration through a 0.45 or 0.2 mm filter. An ion exchange resin is used to separate the element of interest (and the isotope) from any nonlabile colloidal forms present in the filtered extract (Fig. 2). The metals and/or metalloids of interest are then eluted from the resin and their concentration and radioactivity measured. Colloidal interferences generally increase with increased pH and can lead to overestimations of the labile pool that are very significant. For example, overestimations of up to 60% for Cd, Zn, Cu, and As were found by Lombi et al. (2003, 2004). The resin purification method also allows additional information to be obtained during the determination of labile pools, specifically, an assessment of the presence of nonisotopically exchangeable metals/metalloids associated with the colloids. Sinaj et al. (1999, 2004) proposed the use of ion chromatography to assess Msol. This method should avoid colloidal interferences but is less sensitive than other analytical techniques. In addition, if Msol is measured using ion chromatography then a
sol also needs to be measured in the eluant obtained by ion chromatography and not on the original solution. Otherwise, the E-values will be most likely underestimated due to relative isotopic enrichment of the original solution as a result of isotope sorption to surface exchange sites on any colloids that are present in the original solution.

4.6. Changes in oxidation state In the case of redox-labile elements such as As, Co, Cr, Fe, Mn, and Se, there is the potential to incur large errors during E-value determination if the introduced isotope changes redox state during the equilibration period and this is not accounted for (Hamon et al., 2004). In other words, if the isotope changes redox state, and the E-value of the system is determined by the standard procedure of simply measuring the concentration and isotopic activity of the element in solution, hence ignoring the solution-phase speciation of the element and isotope, hidden within the calculated E-value are multiple components (Eq. (11)) and the result of this calculation may be incorrect (Hamon et al., 2004):

PIE ¼

Mox1 þ Mox2 a
ox1 þ a
ox2

!

!  ðA
ox1 þ A
ox2 Þ D

ð11Þ

where PIE is the potentially incorrect E-value; Mox1 and Mox2 are the solution concentrations of the two oxidation states; a
ox1 and a
ox2 are the activities of the isotope with two oxidation states in solution; A
ox1 and A
ox2 equals the total activity, A
, initially added to the system; and D, as before, is

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the dilution factor. In contrast, the correct equation for calculating the total E-value (Etot) for an element having two oxidation states can be written as follows (Hamon et al., 2004):

! Etot ¼

Mox1  A
ox1 a
ox1

!! þ

Mox2  A
ox2 a
ox2

D

ð12Þ

Hamon et al. (2004) observed that the function Etot will be equal to PIEvalue when one of the two oxidation states is not present or, for soils containing appreciable quantities of element in both oxidation states, when the Kd for the two oxidation states are equal. These authors investigated changes in As lability in two soils under different redox conditions. Arsenic exists in two oxidation states, As(V) and As(III), and it can be expected that the Kd for the two states will be very different with As(V) larger than that of As(III) (Smith et al., 1999). Preliminary experiments showed that irrespective of whether it was introduced to the soils as As(V) or As(III), a portion of the isotope converted to the alternate species during the equilibration period. Hence, Hamon et al. (2004) considered that the PIE-value (which to calculate, only requires knowledge of the total amount of isotope added, and the combined solution concentration and activity of cold and radioactive species, respectively) could not be substituted as a measure of Etot in their system. The latter (i.e., Etot) requires much more information in order to calculate, namely, knowledge of the solution concentration and activity of cold and radioactive As(V) and solution concentration and activity of cold and radioactive As(III) as well the Kd of one of the redox species. Hamon et al. (2004) solved this problem by coupling the isotopic dilution technique with a speciation of both the stable and the radioisotope by HPLC-ICP-MS and HPLC-g-counting. In addition, the Kd for the As(III) species was determined after repeated extractions with 0.1 M NH4H2PO4 followed by HPLC-ICP-MS analysis that enabled assessment of the total amount of 73As(III) in the system. However, we have realized that the function Etot will also be equal to PIE-value when the specific activities of the species in solution are the same, that is, when:

a
ox1 a
¼ ox2 Mox1 Mox2

ð13Þ

This observation potentially greatly simplifies the determination of Etot in systems with different redox states because if a determination of the solution concentration and activity of the different species shows that their specific activities are the same, then the extractive step to establish the Kd of one of

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the species is not necessary, as Etot can be calculated using the PIE-value equation. Note further that, in this case, the E-values for the individual redox states can also be determined as follows:

!

Mox1  A
ox1 a
ox1

PIE ¼ Etot ¼

!!

þ

Mox2  A
ox2 a
ox2

D

ð14Þ

and

A
¼ A
ox1 þ A
ox2 Hence,

! Mox1  A
ox1
aox1

þ

!! Mox2

 ðA  Aox1 Þ D ¼ PIE a
ox2

ð15Þ

ð16Þ

Assuming the specific activity of the two redox species has been measured, then the only unknown factor in Eq. (16) is A
ox1 , which can therefore be determined, allowing also the determination of A
ox2 , and thus, the E-values for both the oxidation states, Eox1 , and Eox2 :

Eox1 ¼

Eox2 ¼

! Mox1  A
ox1 D a
ox1

ð17Þ

! Mox2  A
ox2 D a
ox2

ð18Þ

Furthermore, if the specific activities of the species in solution are not the same, this in fact indicates that the system has not yet reached a state of (pseudo) equilibrium. One theoretical corollary of this observation is that it should be possible to achieve identical solution-specific activities for the two species by increasing the equilibration time. We would recommend that as a first step an assessment be made as to whether this is likely to occur for the system under investigation because, as will be seen from the discussion below, this would be the simplest approach toward determining the E-value in systems containing different redox species where the initial measurement gives different specific activities. However, in many cases, it may not be feasible to increase the equilibration time due to practical constraints associated with maintaining a relatively constant redox status in a batch system, which can be very difficult to achieve over longer time periods. Hence, in practical terms, it may never be possible to

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achieve a state of (pseudo) equilibrium for some redox-labile systems. For systems where a (pseudo) equilibrium has not been achieved, the introduced isotope is still being converted to the other redox state. Hence, the size of A
ox1 is either continuing to decrease or continuing to increase depending, respectively, on whether the isotope was added in the same form as A
ox1 or was added as the other redox species; the size of A
ox2 is also changing but in the opposite direction. Whether a reasonable estimate of the E-value is achievable under these circumstances can be tested by comparing E-values obtained following addition of either the A
ox1 or the A
ox2 form of the isotope as follows. Note that here determination of the E-value requires that the total amount of one of the redox forms of the isotope remaining in the system at equilibrium can be measured, for example, by using the extraction method employed in Hamon et al. (2004) for assessing the total 73As(III); the total amount of the other form is determined by difference. When the A
ox1 form of the isotope is used, the value of A
ox1 measured at the end of the equilibration period will be an underestimate compared to the actual average size of A
ox1 that has contributed to exchange with the labile A
ox1 pool because the size of A
ox1 has continually decreased since the beginning of the equilibration period by being converted to the other redox form. In contrast, when the A
ox2 form of the isotope is used, the measured value of A
ox1 will be an overestimate compared to the actual average size of A
ox1 because there was no A
ox1 present at the beginning of the experiment, A
ox1 has only accumulated in the system by being converted from A
ox2 . The degree to which the two Eox1 values calculated from the two measured A
ox1 values also underestimate and overestimate the ‘‘true’’ E-value, and thus, the degree to which they differ from each other, depends on how rapidly the A
ox1 form of the isotope is able to redistribute across the labile pool in response to incremental changes in the amount of A
ox1 in the system during the equilibration period. If, for example, we imagine a system that at all times during the equilibration period responds to changes in A
ox1 with an instantaneous redistribution, then, despite being based on an underestimate and an overestimate of A
ox1 , both of the Eox1 values calculated from measurements taken at time t would exactly match the true (by definition) E-value at time t. In reality, the rate of redistribution is not instantaneous and may change with time (and may differ between redox states so the alternate case relevant to A
ox2 should also be checked), so the two Eox1 (Eox2 ) values will differ, but they provide a boundary within which the ‘‘true’’ E-value is located. Hamon et al. (2004) calculated both the Etot and PIE-values, and found that the PIE-value overestimated Etot by up to more than an order of magnitude in samples that contained high amounts of As(III) relative to As(V). This indicates that for those samples, the system had not yet reached a state of (pseudo) equilibrium. This consequently invalidates both the hypothesis presented by Hamon et al. (2004) that equilibrium of the oxidation states of the isotope would be rapid because only small quantities of the radioisotope were added, and also, therefore, the presumed test of this

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hypothesis [i.e., the same Etot values for each species were obtained irrespective of whether the introduced isotope was in the As(V) or As(III) form]. However, the fact that this hypothesis was invalid fortunately is not a significant problem in terms of the results obtained in that study because the E-values for both redox species were similar irrespective of the form of the isotope that was introduced and hence, as discussed above, the reported Etot values are a good approximation of the actual E-values. A similar issue was tackled by Collins et al. (2006) when investigating the E-values of Se in soils and sediments. Using a double-labeling technique in combination with HPLC-ICP-MS, these authors were able to demonstrate that, in contrast to As, the introduced isotope did not change oxidation state in their soils during their equilibration period (24 h). This facilitated the determination of E-values of selenite and selenate because, in this case, only the solution-phase speciation of the Se isotopes needed to be performed. To our knowledge, these are the only two examples where speciation of redox sensitive species has been taken into account in E-value determination. The approaches used by Hamon et al. (2004) and Collins et al. (2006) were successful in determining the lability of different oxidation species of As and Se, respectively. Any other study conducted on redox-sensitive elements (e.g., Table 1) should be viewed with caution if the speciation of the element of interest has not been considered. This issue is likely to be much more challenging in the case of cationic metals such as Co, Fe, and Mn that are often present in the soil in multiple oxidation states, are strongly sorbed, and/or readily form insoluble oxidation products and/or coprecipitates. As discussed above, if isotope introduced in the soil undergoes a change in oxidation state over the equilibration period (as in the case of As but not Se), but does not attain a state of (pseudo) equilibrium, the isotopic Kd for at least one of the oxidation states needs to be measured to estimate Etot. This was possible, without disturbing the soil equilibrium, for As(III) due to its weak sorption by soil, but may be problematic in the case of strongly sorbed ions such as Co, Fe, and Mn. An early recognition of this problem was suggested by Lopez and Graham (1970) who investigated the lability of Mn, Fe, Zn, and Cu in soil. These authors reported that E-values for Mn and Fe were dependent on the composition and pH of the extracted solutions whereas those for Cu and Zn were not, and suggested that this was due to ‘‘the oxidation–reduction properties of the element concerned.’’

5. Interpretation of E-Values E-values have been compared to elemental concentrations of organisms, or to growth responses, to assess their use for predicting bioavailability (e.g., Ganai et al., 1982; McBeal et al., 2007; Nolan et al., 2005; Pandeya

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et al., 1998). This use may be motivated by the fact that E-values have some commonality with definitions used to describe bioavailability. For instance, Sposito (1989) stated that a chemical element is bioavailable, specifically to terrestrial plants, if it is present as, or can be readily transformed to, the aqueous free ion; if it can move to plant roots on a timescale that is relevant to plant growth and development; and if once absorbed by the root, it affects the life cycle of the plant. However, the E-value is an empirical measure based on soil—solution partitioning of an isotopic tracer within the exchangeable pool, and is not designed to measure bioavailability per se. In other words, while the E-value may quantify the potentially reactive pool of an element over a specified length of time (Tye et al., 2002), there is no reason that this should reflect actual element uptake by, or toxicity to, an organism because these factors are not controlled directly by the size of the labile pool. A simple case to demonstrate this is as follows: one can envision three different soils that have the same sized labile pool (i.e., identical E-values) for a given metal, but in the first soil, most of the labile metal is partitioned to exchange sites on the soil solid phase (high Kd). In the second soil, more of the labile metal is free in the solution phase (low Kd), while in the third soil, the solution-phase metal is exchangeably complexed by various soluble ligands. For example, Collins et al. (2003) clearly showed that decreasing the pH and addition of organic ligands greatly affected the Kd for Cd in an acidic soil, but at the same time had no significant effect on the lability of Cd (E-value) in that soil. Despite the three identical E-values, organisms growing in these three soils will typically exhibit different responses in terms of metal uptake or toxicity. This is because these responses are strongly influenced by the concentration (activity) and speciation of the metal in solution (Campbell, 1995; Parker and Pedler, 1997), which, as can be seen from the foregoing example, bears no direct relation to the E-value per se. Using the IEK method, Fardeau and Jappe (1978) introduced the index r (1)/R [equivalent to a
1 /A in Eq. (6)] to estimate the P-buffering capacity of different soils. Theoretical problems with this approach are discussed elsewhere (Hamon et al., 2002b). More recently, Gray et al. (2004) used the same index to assess the soil-buffering capacity of Cd and argued that the higher the ratio, the less readily the ion is removed from the solution, and hence the more highly buffered the soil is. However, we disagree with this assertion and draw attention to the fact that, in an acidic soil for example, an isotope of a metal could remain in solution simply because the Kd for that metal is small (e.g., as a consequence of a low cation exchange capacity in conjunction with protons). In this case, the system would not be well buffered (the small sorbed pool could not replenish the large pool in solution) but the r(1)/R would nevertheless be large. The aforementioned considerations are important in the proper interpretation of E-values. In particular, we would like to emphasize that there is

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no direct linkage between the labile pool and the amount of metal taken up by an organism. Nonetheless, E-values can provide valuable information and have been employed to: (1) investigate various management options such as in situ remediation using soil amendments (Hamon et al., 2002a; Lombi et al., 2003, 2004); (2) assess the specificity and selectivity of sequential extraction procedures (Ahnstrom and Parker, 1999); (3) investigate the aging process for soil metals (Crout et al., 2006; Ma et al., 2006b,c; Nakhone and Young, 1993; Smolders et al., 1999; Young et al., 2001); (4) predict solubility and free-ion activity of contaminants in soils as a function of soil characteristics (Tye et al., 2002, 2003), and as a result of environmental changes (Hamon et al., 2004). Moreover, in combination with L-value measurements, E-values have been used to assess whether plants (e.g., Denys et al., 2002; Gerard et al., 2000; Goodson et al., 2003; Hammer et al., 2006; Hutchinson et al., 2000), snails (Scheifler et al., 2003), or earthworms (Scott-Fordsmand et al., 2004) can mobilize metals or metalloids from soil (see Section 6). When a comparison of E- and L-values is conducted, it should be remembered that the isotopically exchangeable pool is an operationally defined assay, whose value depends upon equilibration time (Echevarria et al., 1998; Young et al., 2005). This should be taken into account in the measurement of L-values, where contact between the isotope and soil may extend to several weeks or months. In this case, unless a comparable equilibration time is used, E-values measured or estimated by IEK may be lower than the L-value simply due to different equilibration times rather than to any other factor. However, this may not be a serious problem because, as discussed in Section 3.4, E-values seem to be reasonably constant if the equilibration time is long enough to fully embrace the rapid exchange reactions (2–3 days).

6. Interpretation of L-Values Plants and other organisms living in soil absorb ions from the soil solution. (Possible exceptions include earthworms that may additionally sorb ions more ‘‘directly’’ from the solid phase, through the action of gut fluids.) As a consequence of this uptake, exchangeable ions are released from the solid phase into the solution, and this exchangeable pool can be labeled using an isotope. Hence, organisms that access metals or metalloids from only the exchangeable pool will have the same specific activity as the soil solution (assuming free of colloidal interferences) and, for a given soil, E- and L-values should be identical. This is true as long as the contribution from seed/juveniles/food sources can be quantified or is negligible, providing there is no isotopic discrimination during uptake, providing the equilibration times are effectively equivalent, and as long as the isotope is

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physically and homogeneously mixed with the entire exchangeable pool, and the other conditions of isotope dilution are independently met for both the E- and the L-value systems (see Sections 3 and 4). However, plant roots may have the ability to mobilize nutrients in the rhizosphere and access nonexchangeable nutrients [for review, see Hinsinger (2001)]. Therefore, E- and L-values have been used in combination to assess whether organisms can access nonlabile pools of metals and metalloids. It should be noted that, in theory, L-values can only be equal to or larger than corresponding E-values. This statement is based on the assumption that the metal pool in soil that is the most available to plants, which can be assumed to be the free ions in solution, is completely isotopically exchangeable. Therefore, Lvalues smaller than E-values have presumably been obtained as a result of analytical or procedural errors, including the nonhomogeneous distribution of the isotope in soils used for L-value assessment, or colloidal interference in determining E-values. A further reason (S. Young, personal communication) may be due to the fact that the E-value provides a snapshot of the system at a given time, whereas the L-value is an integration of the uptake of isotopes over the whole life of the plant. Thus, in the event of a slow rate of reaction between the isotope and the soil, the L-value may be smaller than an E-value that has been measured (or estimated by IEK) after an equilibration time equal to the plant growth period because when the plant started growing, the L-value was smaller (as was the E-value at that time) than at the final sampling time. In practice, however, because the major reaction processes between the introduced isotope and the soil are typically completed before a sown plant starts to take up significant quantities of the analyte of interest, the probability of the L-value being significantly lower than the E-value due to this reason can be considered very low. A list of studies showing where E- and L-value comparisons have been done can be seen within Table 1 and numerical data from the available literature has been compiled in Fig. 5. The experimental data from seven publications reporting both E- and Lvalues for Cd and eight publications for Zn have been compiled in Fig. 5A and B. The investigations of Ayoub et al. (2003) and Hutchinson et al. (2000) both tried to assess whether the Cd and Zn hyperaccumulator plant Thlaspi caerulescens could access a larger pool of Cd than nonaccumulator plants. The results indicated that all the plant species accessed the same pool. In the former study, the Cd and Zn E-values were generally larger than the L-values. As suggested by the authors, this was probably due to heterogeneity in the distribution of the isotope in the soil used for the L-value experiment; Gerard et al. (2000) explained their results similarly (Fig. 5A and B). The results of Hutchinson et al. (2000) were more variable, and the authors concluded that the E- to L-value ratio was very close to unity. Sterckeman et al. (2005) conducted a similar investigation with Cd and Zn using both an acid and a calcareous soil. Cadmium E- and L-values were

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A 100

L-values (mg kg-1)

Cd

10

Ayoub et al. 2003 Hutchinson et al. 2000 Scheifler et al. 2003 Smolders et al. 1999 Stacey et al. 2001 Sterckeman et al. 2005 Gerard et al. 2000

1

0.1 0.1

1 10 E-values (mg kg-1)

100

B 10,000 Zn

L-values (mg kg-1)

1000

100

10

Ayoub et al. 2003 Sinaj et al. 2004 Sinha et al. 1977 Smolders et al. 1999 Stacey et al. 1999 Sterckeman et al. 2005 Tiller et al. 1972 Rule and Graham 1976

1

0.1 0.1

1

10

100

E-values (mg kg-1)

Figure 5 (Continued )

1000

10,000

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C 1000 Cu, Ni, Pb, Se

L-values (mg kg-1)

100

10

1

Cu: Oliver et al. 2006 Ni: Echevarria et al. 1998 Ni: Massoura et al. 2004 Pb: Tongtavee et al. 2005 Se: Goodson et al. 2003

0.1 0.1

1

10 E-values (mg kg-1)

100

1000

D 10,000 Fe, Mn

L-values (mg kg-1)

1000

100

10

Fe: Dyanand and Sinha 1985 Fe: Rule and Graham 1976 Mn: Golbert and Smith 1985 Mn: Rule and Graham 1976 Mn: Salcedo and Ellis 1979

1 1

10

100

1000

10,000

E-values (mg kg-1)

Figure 5 Relations between experimentally measured L- and E-values for different soils, plants, and metals: (A) Cd (N ¼ 79); (B) Zn (N ¼ 140); (C) Cu (N ¼ 6), Ni (N ¼ 26), Pb (N ¼ 5), and Se (N ¼ 16); (D) Mn (N ¼ 48) and Fe (N ¼ 16).

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similar in the acid soils for all species investigated. In the calcareous soil, L-values for Cd were larger than E-values for all species, with the exception of the hyperaccumulator plant. In contrast, Zn L-values were always larger than E-values in both soils and for all plant species. Among the plant species investigated, ryegrass (Lolium perenne) showed the largest L-values. This result was interpreted according to the view that the Graminaceae may excrete phytometallophores that have a rather general affinity for transition metals (Parker et al., 2005; Welch, 1995), which could mobilize metals in the rhizosphere (i.e., access the chemically labile pool). Smolders et al. (1999) and Sinaj et al. (2004) also compared E- and L-values using graminaceous species. Smolders et al. (1999) found consistent, albeit only marginally, greater L- than E-values for both Cd and Zn. However, Sinaj et al. (2004) did not find any difference between E- and L-values for Zn using ryegrass. Tiller et al. (1972) investigated Zn E- and L-values in 25 different soils using Trifolium spp. No differences were observed in soils with pH < 7 whereas, with calcareous soils, a detailed investigation demonstrated that E-values were overestimated due to methodological problems. The results reported by Stacey et al. (2001) show very large differences between E- and L-values that the authors attributed to the different isotope equilibration times in their determinations. While it is likely that this may be a contributing factor, because the isotopic equilibration time for the E-value determination was only 24 h, the differences appear too large to be solely caused by this experimental parameter. Finally, the results obtained by Sinha et al. (1977) and Rule and Graham (1976) for Zn show a general agreement between E- and L-values but it should be noted that a pH-buffered DTPA solution was used in the E-value determination. Thus, the overall consensus with respect to E- versus L-values in the case of Cd and Zn is that, if these metals are mobilized from nonexchangeable (chemically labile) pools by plants at all, this only occurs to a very limited extent and is species- and soil-specific, with graminaceous plants more likely to mobilize metals than dicotyledonous species (including the Cd and Zn hyperaccumulators). This is reinforced by the observation that the root exudates of the hyperaccumulator T. caerulescens were much less able to mobilize Cd and Zn when compared to the root exudates of Fe- or Zndeficient wheat (Triticum aestivum) (Zhao et al., 2001). Comparisons of E- and L-values for Cu, Ni, Pb, and Se show a generally good agreement between the two techniques (Fig. 5C). An exception is provided with Ni by Echevarria et al. (1998), who presented data showing E-values, which in most cases exceeded L-values by twofold. This may be due to heterogeneous mixing of the isotope in the soil used for the L-value determination (Gerard et al., 2000; Hamon et al., 1998), or to overestimation of the E-values, which were estimated using the IEK technique, for reasons discussed above (see Section 3.4). The Pb data are derived from the study of Tongtavee et al. (2005) who used a stable Pb isotope for

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determination of the E- and L-values. The data show a good agreement between E- and L-values with the exception of the uncontaminated soil where the L-value was clearly erroneous because it was much larger than the total soil Pb content. The authors suggested that this may be due to rapid ‘‘fixation’’ of the introduced Pb isotope. However, this would also be expected to affect E-values determined in the same soil, but that was not the case (Fig. 5C). More likely, either nonhomogeneous mixing of the spike during the L-value determinations or a substantial, unaccounted for contribution of Pb from the seed caused this discrepancy. It is important to emphasize that overestimates of the labile pool arising from precipitation of the added spike, which as previously discussed (Section 4.2) is a particular consideration in studies done with stable Pb isotopes, would not necessarily be revealed by a comparison of E- and L-values, as the precipitation artifact would erroneously inflate both the E- and L-values similarly. Comparison of E- and L-values for Mn and Fe (Fig. 5D) shows large and inconsistent differences. This may be due to several reasons including the fact that E-values were determined using either pH-buffered 5-mM EDTA solutions or a 0.1-M H3PO4 solution (see Table 1). Furthermore, as suggested by Lopez and Graham (1970), both Fe and Mn were added as divalent cations and the isotope added could have been oxidized in the soil and therefore precipitate as oxyhydroxides, as recognized by Dyanand and Sinha (1985), or, as discussed above, could be due to the problems associated with performing isotope dilution experiments on elements that commonly exhibit more than one oxidation state in ambient conditions. Two studies have recently assessed whether soil invertebrates can access nonexchangeable (chemically labile) pools of metals. Scott-Fordsmand et al. (2004) reported that the earthworm Eisenia andrei accessed the same soil Zn pool as lettuce, and that this pool is the isotopically exchangeable Zn. In contrast, Scheifler et al. (2003) suggested that snails could access a part of the nonlabile Cd pool. In this study, the Cd E-value, estimated using IEK, was 10.08 (1.27) mg kg1 whereas the labile Cd measured using the snail was 11.85 (0.40) mg kg1. This difference, even if statistically significant, is rather modest considering the large number of independent measurements required in this experiment (see Section 4.3). This is apparent when the results of Scheifler et al. (2003) are viewed in the context of the other studies investigating Cd mobilization using E- and L-values (Fig. 5A). The variability observed in Fig. 5A–D, and the potential sources of error when comparing E- and L-values, suggests that comparing these two independent measurements is difficult, and that care should be taken when discussing the findings. Perhaps a simpler alternative is to assess whether a specific plant (or invertebrate) can mobilize nonexchangeable (chemically labile) forms of metals in a given soil via a direct comparison of the L-values of different species. This approach was used by Hamon et al. (1997) who investigated the uptake of Cd and Zn by seven plant species and found that,

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with the exception of Cd uptake by canola, all plants seemed to access the same metal pool. A similar approach has been also adopted by several other authors in the last few years (see Table 1), and generally the results yielded small and rarely significant differences between the species investigated. L-values have also been used to assess ageing processes of metals in soils (Hamon et al., 1998) and, in studies conducted in the field, to infer the contribution of atmospheric deposition to the total plant content of metals (e.g., Dalenberg and Vandriel, 1990; Jensen and Mosbaek, 1990; Mosbaek et al., 1988, 1989). Conclusions about deposition rates from some of these studies may need revision as they were not necessarily designed to recognize, or control for one or more of the range of artifacts described above (e.g., contribution of metal from the seed, nonhomogeneous mixing of the isotope with the soil, and so on) that, as well as atmospheric deposition, could also give rise to the inflated L-value, which in these studies was attributed solely to atmospheric deposition. For example, recalculation of data from Dalenberg and Vandriel (1990) shows L-values for Pb for plants grown in a filtered air chamber (where atmospheric deposition should be minimal) that range from 100% to more than 2000% of the total soil Pb. The authors ascribed the lower than expected specific activity in these plants to contaminated air leaking into the chamber, though it is equally if not more likely that one or more of the previously mentioned artifacts was responsible. However, these studies also serve as a reminder that surface contamination can lead to an overestimation of the L-value and hence steps should be taken to prevent this from compromising results. In theory, the isotope dilution E- and L-value methods are simple to use. In practice, however, it is clear from the above discussion that these methods are subject to a number of both theoretical and methodological pitfalls that can easily result in erroneous conclusions being drawn from the data that is generated. Table 4 summarizes the potential methodological sources of error that we have discussed, and aims to provide a troubleshooting guide and check list to help researchers who may be using these methods for the first time. However, it should be noted that these pitfalls are not necessarily intuitive, and many of them have emerged only with the benefit of hindsight and/or through a process of trial-and-error, which is still ongoing. For example, similar issues as occur with redox-sensitive species may exist for elements that are present in significant amounts in solutions as complexes/compounds that can partition to the solid phase, and that are exchangeable with the isotope, but that are only slowly exchangeable such that they do not undergo complete exchange during the equilibration period. In fact, decreased exchangeability of organically complexed Cu arising from condensation of the complexes in Ca electrolyte could contribute to the differences found between E-values determined by Ma et al. (2006a) for Cu using the waterþresin method versus CaCl2 (see section 3.3). However, more investigation is required to assess this issue. Hence,

Table 4 Troubleshooting guide and checklist for E- and L-value determinations Checklist

E > tot

Result status Analytical accuracy and precision poor Precipitation of added isotope Spike solution too acidic/corrosive Reactive suspension matrix for analyte of interest Isotope discrimination occurring Preexisting metal in organism or food unaccounted for Contamination by, e.g., atmospheric deposition Nonhomogeneous mixing of added isotope Colloidal interferences Multiple oxidation states present but not accounted for Spike equilibration time not consistent between E- and L-value method



E < tot

p

L > tot



L < tot

p

E>L



E¼L

E
p

p

 

c co co cu

 

c co co



c co co

c co co cu



c

 

c co



c

c co



co



c



n

c





 

n

co,cu*

n

n

c

n

n

 



co,cu*

co





tot ¼ total concentration of the element of interest in soil. p ¼ Results highly likely to be acceptable. However, possible hidden interferences arising from problems identified in same column should never be completely discounted unless they have been shown to be insignificant.  ¼ Results probably unacceptable.  ¼ Possible sole explanation for unacceptable result. c ¼ Could cause either over- or underestimate of labile pool. co ¼ Will only cause overestimate of labile pool. cu ¼ Will only cause underestimate of labile pool. * ¼ Depends on method used (refer to text). n ¼ Problem possible but unlikely in this circumstance using standard methods.

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while we have endeavored to provide a comprehensive guide in Table 4, it will likely be subject to revisions in the future.

7. Future Applications The future use of isotopic dilution techniques is likely to expand due to the development of robust and relatively simple analytical techniques that are able to accurately determine stable isotope ratios. The use of enriched stable isotopes will not only permit the study of elements for which radioisotopes are not available, too expensive, short lived, or dangerous to use, but may further allow application of isotopic dilution techniques in field studies. In recent years, isotopic dilution techniques have been recognized as an extremely flexible set of tools to investigate a variety of processes related to bioavailability and mobility of micronutrients and contaminants. Moreover, new methodologies and approaches have led to the use of these techniques to investigate new area of research. For instance, new insights on colloidalfacilitated transport of metals have been achieved using the resin technique described previously (Hamon and McLaughlin, 2002; Lombi et al., 2003) as well as by combining the principle of isotopic dilution with ultrafiltration methods (Sivry et al., 2006). The next step forward in this direction could focus on the coupling of isotopic dilution and Field Flow Fractionation (FFF)-ICP-MS. For instance, FFF has been recently employed to separate engineered Zn oxide nanoparticles in soil suspension (Gimbert et al., 2007) and, when combined with isotopic dilution techniques, could provide a powerful tool in the emerging area of environmental risk assessment of nanomaterials. As pointed out by Tye et al. (2003) and Young et al. (2007), isotopic dilution techniques may also provide an ideal starting point for whole soil modeling of metal speciation and partitioning. Solution equilibrium models such as GEOCHEM (Parker et al., 1995), NICA (Gooddy et al., 1995), or WHAM (Tipping, 1994) are being complemented by models based on adsorption on specific solid phases to simulate whole-soil behavior of metals and metalloids. Since these models are based on the assumption that all the elements are free to rapidly equilibrate, situations in which this assumption is invalid can create serious problems in the modeling exercise. This assumption clearly cannot be met in a soil system where a significant part of the metal/metalloid of interest may be entrapped in the crystal lattice of minerals or in precipitates. We have demonstrated that the same problem could be present in soil solution due to the presence of nonexchangeable forms of metals/metalloids associated with colloids (Lombi et al., 2003). As discussed previously, the isotopic dilution principle is based on the dilution

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of the introduced isotope in the exchangeable pool, which is consequently determined. Therefore, this reactive/exchangeable pool represents the ideal starting point for modeling. In our opinion, the use of isotopic dilution technique in combination with speciation techniques (such as HPLC-ICP-MS, secondary ion mass spectroscopy, or synchrotron spectroscopy) has a tremendous potential in terms of investigating soil processes such as metal/metalloids aging or mobilization. For instance, Hamon et al. (2004) combined isotopic dilution with a chromatographic/mass spectroscopic As speciation method to simultaneously determine the labile pools of arsenite and arsenate. Application of this method identified a suite of mechanisms controlling mobility and speciation of As in contaminated soils subjected to changes in microbial activity, pH, and redox conditions. A current limitation of isotopic dilution techniques is a lack of molecular understanding of the chemistry of the labile metal pool associated with the soil solid phase, with only a few studies investigating this aspect (e.g., Bailey et al., 2005; Lamm et al., 1963). Synchrotron-based techniques, which allow characterization of the chemical environment surrounding the solid-phase metal, are ideal to complement, and provide a molecular underpinning for isotopic dilution. This coupling was demonstrated by Sarret et al. (2004) who investigated Zn speciation in soil using isotopic dilution, micro X-ray fluorescence, and extended X-ray absorption fine structure spectroscopy. A similar approach was employed by Bailey et al. (2005) to investigate time-dependent changes in lability of Cd. Similar studies in which different techniques are combined with isotopic dilution methods are likely to become more numerous in the future. Further afield, the analytical progress made in terms of detection of isotopic ratios has opened the door to novel and exciting applications of isotopic dilution techniques to new areas of research. For instance, using stable isotopic dilution, Maddaloni et al. (1998) investigated bioavailability of soil-borne Pb in humans. Sander and Pignatello (2005) developed an isotopic dilution technique using 14C-naphthalene to assess true hysteresis (i.e., irreversible sorption) of this compound to organic matter. We expect that the use of isotopic dilution techniques in the investigation of the behavior of xenobiotics in the environment will increase rapidly in the near future.

ACKNOWLEDGMENTS The authors would like to thank Prof. Scott Young, Nottingham University, for valuable comments on the manuscript.

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Tipping, E. (1994). WHAM - a chemical-equilibrium model and computer code for waters, sediments, and soils incorporating a discrete site electrostatic model of ion-binding by humic substances. Comp. Geosci. 20, 973–1023. Tjell, J. C., Hovmand, M. F., and Mosbaek, H. (1979). Atmospheric lead pollution of grass grown in a background area in Denmark. Nature 280, 425–426. Tongtavee, N., Shiowatana, J., McLaren, R. G., and Gray, C. W. (2005). Assessment of lead availability in contaminated soil using isotope dilution techniques. Sci. Total Environ. 348, 244–256. Tye, A. M., Young, S. D., Crout, N. M. J., Zhang, H., Preston, S., Bailey, E. H., Davison, W., McGrath, S. P., Paton, G. I., and Kilham, K. (2002). Predicting arsenic solubility in contaminated soils using isotopic dilution techniques. Environ. Sci. Technol. 36, 982–988. Tye, A. M., Young, S. D., Crout, N. M. J., Zhang, H., Preston, S., Barbosa-Jefferson, V. L., Davison, W., McGrath, S. P., Paton, G. I., Kilham, K., and Resende, L. (2003). Predicting the activity of Cd2þ and Zn2þ in soil pore water from the radio-labile metal fraction. Geochim. Cosmochim. Acta 67, 375–385. Van Steveninck, R. F. M., and Van Steveninck, M. E. (1991). Microanalysis. In ‘‘Electron Microscopy of Plant Cells’’ ( J. L. Hall and C. Hawes, Eds.), pp. 415–455. Academic Press, London. Vance, D., and Thirlwall, M. (2002). An assessment of mass discrimination in MC-ICPMS using Nd isotopes. Chem. Geol. 185, 227–240. Wang, C. H., Willis, D. L., and Loveland, W. D. (1975). ‘‘Radiotracer Methodology in the Biological, Environmental, and Physical Sciences.’’ Prentice-Hall, Englewood Cliffs, New Jersey. Weiss, D. J., Mason, T. F. D., Zhao, F. J., Kirk, G. J. D., Coles, B. J., and Horstwood, M. S. A. (2005). Isotopic discrimination of zinc in higher plants. New Phytol. 165, 703–710. Welch, R. M. (1995). Micronutrient nutrition of plants. Crit. Rev. Plant Sci. 14, 49–82. Young, S. D., Tye, A., Carstensen, A., Resende, L., and Crout, N. (2000). Methods for determining labile cadmium and zinc in soil. Eur. J. Soil Sci. 51, 129–136. Young, S. D., Tye, A., and Crout, N. M. J. (2001). Rates of metal ion fixation in soils determined by isotopic dilution. In ‘‘Proceedings of the 6th International Conference on the Biogeochemistry of Trace Elements.’’ Guelph, Canada. p. 105. Young, S. D., Zhang, H., Tye, A. M., Maxted, A., Thums, C., and Thornton, I. (2005). Characterizing the availability of metals in contaminated soils. I. The solid phase: Sequential extraction and isotopic dilution. Soil Use Manag. 21, 450–458. Young, S. D., Crout, N. M. J., Hutchinson, J., Tye, A., Tandy, S., and Nakhone, L. (2007). Techniques for measuring attenuation: Isotopic dilution methods. In ‘‘Natural Attenuation of Metal Availability in Soils’’ (R. E. Hamon, E. Lombi, and M. J. McLaughlin, Eds.), pp. 19–42. SETAC Press, Pensacola. Zhang, H., Davison, W., Knight, B., and McGrath, S. P. (1998). In situ measurements of solution concentrations and fluxes of trace metals in soils using DGT. Environ. Sci Technol. 32, 704–710. Zhang, H., Davison, W., Tye, A. M., Crout, N. M. J., and Young, S. D. (2006). Kinetics of zinc and cadmium release in freshly contaminated soils. Environ. Toxicol. Chem. 25, 664–670. Zhao, F. J., Hamon, R. E., and McLaughlin, M. J. (2001). Root exudates of the hyperaccumulator Thlaspi caerulescens do not enhance metal mobilization. New Phytol. 151, 613–620.

C H A P T E R

S E V E N

Ameliorating Soil Acidity of Tropical Oxisols by Liming For Sustainable Crop Production N. K. Fageria* and V. C. Baligar† Contents 1. Introduction 2. Distribution and Characteristics of Oxisols 3. Beneficial Effects of Liming 3.1. Neutralizing soil acidity and improving supply of calcium and magnesium 3.2. Reducing phosphorus immobilization 3.3. Improving activities of beneficial microorganisms 3.4. Reducing solubility and leaching of heavy metals 3.5. Improving soil structure 3.6. Improving nutrient use efficiency 3.7. Controling plant diseases 3.8. Mitigating nitrous oxide emission from soils 4. Disadvantages of Overliming 5. Factors Affecting Lime Requirements 5.1. Quality of liming material 5.2. Soil texture 5.3. Soil fertility 5.4. Crop rotation 5.5. Use of organic manures 5.6. Conservation tillage 5.7. Crop species and genotypes within species 5.8. Interaction of lime with other nutrients 6. Criteria to Determine Liming Material Quantity 6.1. Soil pH 6.2. Base saturation 6.3. Exchangeable aluminum, calcium, and magnesium levels

* {

346 350 352 353 355 356 358 360 360 361 362 363 364 365 366 367 368 369 370 371 372 375 376 377 379

National Rice and Bean Research Center of EMBRAPA, Caixa Postal 179, Santo Antoˆnio de Goia´s, GO, CEP. 75375-000, Brazil USDA-ARS-Sustainable Perennial Crops Lab, Beltsville, Maryland 20705-2350

Advances in Agronomy, Volume 99 ISSN 0065-2113, DOI: 10.1016/S0065-2113(08)00407-0

#

2008 Elsevier Inc. All rights reserved.

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6.4. Aluminum saturation 6.5. Crop responses 7. Methods, Frequency, Depth, and Timing of Lime Application 8. Conclusions Acknowledgment References

381 383 384 386 388 389

Abstract The greatest potential for expanding the world’s agricultural frontier lies in the savanna regions of the tropics, which are dominated by Oxisols. Soil acidity and low native fertility, however, are major constraints for crop production on tropical Oxisols. Soil acidification is an ongoing natural process which can be enhanced by human activities or can be controlled by appropriate soil management practices. Acidity produces complex interactions of plant growth-limiting factors involving physical, chemical, and biological properties of soil. Soil erosion and low water-holding capacity are major physical constraints for growing crops on tropical Oxisols. Calcium, magnesium, and phosphorous deficiencies or unavailabilities and aluminum toxicity are considered major chemical constraints that limit plant growth on Oxisols. Among biological properties, activities of beneficial microorganisms are adversely affected by soil acidity, which has profound effects on the decomposition of organic matter, nutrient mineralization, and immobilization, uptake, and utilization by plants, and consequently on crop yields. Liming is a dominant and effective practice to overcome these constraints and improve crop production on acid soils. Lime is called the foundation of crop production or ‘‘workhorse’’ in acid soils. Lime requirement for crops grown on acid soils is determined by the quality of liming material, status of soil fertility, crop species and cultivar within species, crop management practices, and economic considerations. Soil pH, base saturation, and aluminum saturation are important acidity indices which are used as a basis for determination of liming rates for reducing plant constraints on acid soils. In addition, crop responses to lime rate are vital tools for making liming recommendations for crops grown on acid soils. The objective of this chapter is to provide a comprehensive and updated review of lime requirements for improved annual crop production on Oxisols. Experimental data are provided, especially for Brazilian Oxisols, to make this review as practical as possible for improving crop production.

1. Introduction Soil acidity is one of the most yield-limiting factors for crop production. Land area affected by acidity is estimated at 4 billion ha, representing
30% of the total ice-free land area of the world (Sumner and Noble, 2003). About 16.7% of Africa, 6.1% of Australia and New Zealand, 9.9% of

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Europe, 26.4% of Asia, and 40.9% of America have acid soils (Von Uexkull and Mutert, 1995). They cover a significant part of at least 48 developing countries located mainly in tropical areas, being more frequent in Oxisols and Ultisols in South America and in Oxisols in Africa (Narro et al., 2001). In tropical South America, 85% of the soils are acidic, and
850 million ha of this area is underutilized (Fageria and Baligar, 2001). Acid and low fertility Oxisols and Ultisols cover about 43% of the tropics (Sanchez and Logan, 1992). Most of the central part of Brazil is tropical savanna, known as the cerrado, and covers about 205 million ha or 23% of the country. Most of the soils in this region are Oxisols (46%), Ultisols (15%), and Entisols (15%), with low natural soil fertility, high aluminum saturation, and high P fixation capacity (Fageria and Stone, 1999). Although low fertility is characteristic of acid soils, these vast areas have a large proportion of favorable topography for agriculture, adequate temperatures for plant growth throughout the year, sufficient moisture availability year round in 70% of the region, and for 6–9 months in the remaining 30% of the region (Narro et al., 2001). When the chemical constraints are eliminated by liming and using adequate amounts of fertilizers, the productivity of Oxisols and Ultisols is among the highest in the world (Sanchez and Salinas, 1981). Theoretically, soil acidity is quantified on the basis of hydrogen (Hþ) and aluminum (Al3þ) concentrations of soils. For crop production, however, soil acidity is a complex of numerous factors involving nutrient/element deficiencies and toxicities, low activities of beneficial microorganisms, and reduced plant root growth which limits absorption of nutrients and water (Fageria and Baligar, 2003a). In addition, acid soils have low water-holding capacity and are subject to compaction and water erosion (Fageria and Baligar, 2003a). The situation is further complicated by various interactions among these factors (Foy, 1992). The components of the soil acidity complex have been thoroughly discussed in various publications (Foy, 1984, 1992; Kamprath and Foy, 1985; Tang and Rengel, 2003). Soils become acidic for several reasons. The most common source of hydrogen is the reaction of aluminum ions with water. The equation for this reaction in very acid soils (pH<4.0) is:

Al3þ þ H2 O , AlðOHÞ2þ þ Hþ The species of aluminum ions present vary with pH. Potassium chloride extracted Al and Al saturation has an inverse relationship with pH (Chartres et al., 1990; Kariuki et al., 2007). Increased soil acidity causes solubilization of Al, which is the primary source of toxicity to plants at pH below 5.5 (Bohn et al., 2001; Carson and Dixon, 1979; Ernani et al., 2002; Kariuki et al., 2007; Parker et al., 1989). The forms of aluminum are mostly exchangeable Al3þ under very acidic conditions (pH<4.5) to aluminum-hydroxyl ions at higher pH (4.5–6.5) (Carson and Dixon, 1979). In general, the net positive

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charge of the hydroxyl aluminum species decreases as the pH increases and then becomes negative in the alkaline pH range. The species of aluminum ions generates hydrogen ions through a series of hydrolysis reactions shown below (Lindsay, 1979):

Al3þ þ H2 O , AlðOHÞ2þ þ Hþ þ Al3þ þ 2H2 O , AlðOHÞ þ 2 þ 2H

Al3þ þ 3H2 O , AlðOHÞ 03 þ 3Hþ þ Al3þ þ 4H2 O , AlðOHÞ  4 þ 4H þ Al3þ þ 5H2 O , AlðOHÞ 2 5 þ 5H

The exchangeable Al3þ precipitates as insoluble Al hydroxyl species as pH increases and is reported to decrease 1000-fold for each unit increase in pH (Lindsay, 1979). However, at pH values greater than 6.5, Al becomes increasingly soluble as negatively charged aluminates form (Haynes, 1984). The Al(OH)2þ species is of minor importance and exists over only a narrow pH range. The Al3þ ion is predominant below pH 4.7, Al(OH)þ2 between pH 4.7 and pH 6.5, Al(OH)30 between pH 6.5 and pH 8.0, Al(OH)4 above pH 8.0, and Al (OH)52 species occurs at pH values above those usually found in soils (Bohn et al., 2001). Soils become acidic due to the parent material being acidic and naturally low in the basic cations such as Ca2þ, Mg2þ, Kþ, and Naþ or due to leaching of these elements down the soil profile by excess rains. This situation is common in high rainfall areas, where precipitation exceeds evaporation, and leads to leaching. Soil acidity may also be produced by long-time use of ammonium fertilizers, removal of cations in the harvested portion of crops and leaching process, and release of organic acids in decomposition of crop residues and added organic wastes (Fageria et al., 1990; Sparks, 2003). Use of adequate amounts of nitrogen fertilizer is fundamental for higher yield of crops under all ecosystems. Urea and ammonium sulfate are dominant nitrogen carriers used for crop production around the world. The acidification of soils by using the ammonium form of nitrogen fertilizers can be explained by the following equation:  þ NHþ 4 þ 2O2 , NO3 þ H2 O þ 2H

The oxidation of NH4þ in the above equation is known as nitrification and hetrotrophic and autrotrophic bacterias can carry it out. The most important autrotrophic genera of bacteria are Nitrosomonas and Nitrobacter. Use of legume crops continuously or in rotation can increase soil acidity.

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In Australia and New Zealand, continuous cultivation of legume crops decreased the pH of agricultural soils (Bolan and Hedley, 2003). Legumebased pastures also increased soil acidification (Loss et al., 1993; Williams, 1980). Williams (1980) reported that even the normal growth of clover pasture for 50 years decreased the pH of an Australian soil from 6.0 to 5.0 at a depth of 30 cm. Legumes also increase soil acidification in arable cropping systems (Burle et al., 1997). The reason for generating acidity is associated with higher absorption of basic cations by these crops and the release of Hþ ions by the root of legume crops to maintain ionic balance (Bolan and Hedley, 2003). According to Bolan and Hedley (2003), for different legume species about 0.2–0.7 mol of Hþ were released per mol N2 fixed. These authors also state that the amount of Hþ ions released during N2 fixation is really a function of carbon assimilation and hence depends mainly on the form and amount of amino acids and organic acids synthesized within the plants. Soil acidification is also caused by the release of protons (Hþ) during the transformation and cycling of carbon, nitrogen, and sulfur in the soil– plant–animal system (Bolan and Hedley, 2003; Robson, 1989). Soil acidification is caused by acid precipitation, the result of industrial pollution (Foy, 1984; Ulrich et al., 1980). For a long time, acid soils have been considered less suitable for productive agriculture. However, the generation of modern technology, through intensive research, has brought forth a new reality of increased productivity of grains and other food, fiber and feed crops, pastures, and energy products on these soils. Borlaug and Dowswell (1997) concluded that acid lands are no longer a marginal agriculture frontier, but the most extensive agriculture frontier of the world, providing hope for adequate food supply and a better quality of life for millions of people, especially in the tropics. Within the last few decades, significant advances have been made in management of acid soils of the tropics for improving pasture for cattle raising and increasing productivity of annual and plantation crops (Fageria and Baligar, 2003a; Sumner and Noble, 2003). However, there still remains much to be done to develop technologies that are economically viable, environmentally sound, and socially acceptable. For increase in food supply on acid soils, sustainable cropping systems are essential for agronomic, economic, and environmental reasons. Sustainable crop production is defined as practice that over the long term enhances environmental quality and the resource base on which agriculture depends, provides for basic human food and fiber needs, is economically viable, is social acceptable, and improves the quality of life for farmers and society as a whole (White et al., 1994). Adequately limed soils enhance the sustainability of cropping systems because of higher crop yields, lower cost of production, and reduced environmental pollution. World population reached more than 6 billion in 2007, and is expected to increase to 8 billion by 2025 and over 9 billion by 2050. Over half of the world population currently lives

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in regions dominated by acid soils (Yang et al., 2004). The greatest potential for expanding the world’s agricultural frontier lies in the tropical savanna regions dominated by acid, infertile soils classified mainly as Oxisols and Ultiosls (Sanchez and Salinas, 1981). Furthermore, about 95% of the current population growth has taken place in tropical regions; therefore, a continuing increase in food production is required to meet this demand (Hartemink, 2002). Liming is the most important and most effective practice to ameliorate soil acidity constraints for optimal crop production (Haynes, 1984). The practice of well-planned and execution of liming under these situations is fundamental for increasing crop yield on acid soils. The magnitude of the soil acidity problem of Oxisols in many areas around the world, and the potential that these soils offer in increasing the production of food and fiber, provides a focus for the objectives of this review, that is, to review the nature, causes, and management of Oxisols acidity in order that crop productivity on these acid soils might be improved for the benefit of humankind.

2. Distribution and Characteristics of Oxisols The theory concerning the effects of parent materials on soil formation originated in Russia during the late 19th century. According to this theory, soil formation is a function of climatic conditions, biotic activities, topography, and time span (West et al., 1998). Soil profile development processes are known as pedogenic and involve additions, losses, translocations, and transformations of soil materials (Brady and Weil, 2002). Simonson (1959) outlined a generalized theory of soil genesis in which he proposed to consider soil formation as consisting of two overlapping steps: the accumulation of parent materials and the differentiation of horizons in the solum. The former is largely a geochemical or geogenesis process, whereas the latter is a pedogenesis process (Van Wambeke, 1991; West et al., 1998). Information on the extent and distribution of Oxisols in various parts of the world has increased in recent years. However, soil survey is still incomplete due to lack of infrastructure and low population density in some areas where Oxisols occur (Buol and Eswaran, 2000). Table 1 shows approximate areas of Oxisols at global level and in the tropical regions compared with other soil orders. In the tropics, Oxisols occupy a much larger area than do other soil orders. Acid and low fertility soils meeting such stereotypic concept of ‘‘tropical soils’’ are mainly classified as Oxisols and Ultisols. Because of recent global glaciaton, these soils cover only 7% of the temperate region but 43% of the tropics (Sanchez and Logan, 1992). The largest concentration of Oxisols occurs in the South American savannas, the eastern Amazon, and parts of central Africa (Sanchez and Salinas, 1981). Distribution

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Table 1

Approximate distribution of soil orders globally and in tropical regions

Soil order

Global area (million ha)

Percentage of total global area

Area in the tropics (million ha)

Percentage of total tropical area

Alfisols Andisols Aridisols Entisols Gelisols Histosols Inceptisols Mollisols Oxisols Spodosols Ultisols Vertisols

1790 144 2276 2730 1769 240 1547 1100 840 478 1347 311

12.3 1.0 15.6 18.7 12.1 1.6 10.6 7.5 5.8 3.3 9.3 2.2

559 43 87 574 – 36 532 74 833 20 749 163

15.2 1.2 2.4 15.6 – 0.9 14.4 2.0 23.0 0.5 20.4 4.4

Source: Sanchez and Logan (1992), Buol and Eswaran (2000), and Brady and Weil (2002).

Table 2 Distribution of oxisols and ultisols in South America

Country

Area (106 ha)

Total area of the country (%)

Brazil Colombia Peru Venezuela Bolivia Guiana Surinam Paraguay Equador French Guiana Chile Argentina

572.71 67.45 56.01 51.64 39.54 12.25 11.43 9.55 8.61 8.61 1.37 1.28

68 57 44 58 57 62 62 24 23 94 2 0.4

Source: Cochrane (1978).

of Oxisols and Ultisols in South American countries is shown in Table 2. Extensive areas of Southeast Asia are covered by highly weathered Oxisols and Ultisols (Ismail et al., 1993; Supriyo et al., 1992; Van Wambeke, 1991; Von Uexkull and Mutert, 1995). Sanchez and Salinas (1981) reported that about 15 million ha of Oxisols is found in tropical Asia. Baligar and Ahlrichs

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(1998), Baligar et al. (2004), Van Wambeke (1991), and Von Uexkull and Mutert (1995) have reported extensively on the distribution, nature, and properties of acid soils. Oxisols are also referred to as latosols, laterite, Ferralsols, and red earths. Oxisols are defined as mineral soils having an oxic horizon which is characterized by the virtual absence of weathering primary minerals or 2:1 layer silicate clays, the presence of 1:1 layer silicate clays and highly insoluble minerals such as quartz sand, the presence of hydrated oxides of iron and aluminum, and the absence of water-dispersible clay (Soil Science Society of America, 1997). Oxisols are highly weathered acidic soils, having low basic cation and effective cation exchange capacity (ECEC). The cation exchange capacity (CEC) refers to the value obtained with 1 M NH4OAc (pH 7), and ECEC is the sum of exchangeable cations (Al3þ, Ca2þ, Mg2þ, Kþ, and Naþ) (Sumner and Noble, 2003). These soils have good physical properties (except low water-holding capacity and susceptibility to erosion) and have uniform distribution of clay with increasing depth. On average, these soils have clay contents of more than 300 g kg1. However, the clays are of low activity and have a limited capacity to hold basic cations. These soils are very low in plant-available phosphorus and due to high concentration of iron and aluminum oxides having high P fixation capacity; selected chemical properties of Brazilian Oxisols are presented in Tables 3 and 4.

3. Beneficial Effects of Liming Modern agriculture production requires the implementation of efficient, sustainable, and environmentally sound management practices. In this context, liming is an important practice to achieve optimum yields of all crops grown on acid soils. Liming is the most widely used long-term method of soil

Table 3

Selected chemical properties of Oxisols of the cerrado region of Brazil

Value

pH in H2O

Organic matter (g kg1)

P (mg kg1)

K (mg kg1)

Mg Ca (cmolc (cmolc kg1) kg1)

Minimum Maximum Mean S.D.

4.8 6.3 5.5 0.28

8 31 21.5 5.1

0.6 14.8 4.12 2.93

12 73 31.6 13.7

0.54 3.42 1.77 0.70

Source: Compiled from Fageria and Breseghello (2004). Values are averages of 43 soil samples collected from the State of Mato Grosso.

0.22 2.42 0.84 0.40

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Table 4 Selected soil acidity indices of Oxisols of cerrado region of Brazil

Value

Base saturation (%)

Al saturation ((%)

Ca Saturation (%)

Mg saturation (%)

K saturation (%)

Minimum Maximum Mean SD

9 60 28 10.78

0 26 8 6.33

5 34 18 7.36

2 25 9 4.03

0.25 1.64 0.82 0.31

Source: Compiled from Fageria and Breseghello (2004).

acidity amelioration, and its success is well documented (Conyers et al., 1991; Haynes, 1982; Kaitibie et al., 2002; Scott et al., 2001). Application of lime at an appropriate rate brings several chemical and biological changes in the soil, which are beneficial or helpful in improving crop yields on acid soils. Adequate liming eliminates soil acidity and toxicity of Al, Mn, and H; improves soil structure (aeration); improves availabilities of Ca, P, Mo, and Mg, pH, and N2 fixation; and reduces the availabilities of Mn, Zn, Cu, and Fe and leaching loss of cations. These beneficial effects are discussed in the following section.

3.1. Neutralizing soil acidity and improving supply of calcium and magnesium Liming raises soil pH, base saturation, and Ca and Mg contents, and reduces aluminum concentration in Brazilian Oxisols (Fageria, 2000, 2001a; Fageria and Stone, 2004). The changes in these chemical properties with the use of dolomitic lime [CaMg (CO3)2] can be explained on the basis of following equation (Fageria and Baligar, 2005a): 2þ CaMgðCO3 Þ2 þ 2Hþ , 2HCO  þ Mg2þ 3 þ Ca þ 2HCO 3 þ 2H , 2CO2 þ 2H2 O

CaMgðCO3 Þ2 þ 4Hþ , Ca2þ þ Mg2þ þ 2CO2 þ 2H2 O The above equations show that acidity-neutralizing reactions of lime occur in two steps. In the first step, Ca and Mg react with H on the exchange complex and H is replaced by Ca2þ and Mg2þ on the exchange sites (negatively charged particles of clay or organic matter), forming HCO 3 . In the þ to form CO and H O to increase pH. second step, HCO reacts with H 3 2 2

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Soil moisture and temperature and quantity and quality of liming material mainly determine the reaction rate of lime. To get maximum benefits from liming or for improving crop yields, liming materials should be applied in advance of crop sowing and thoroughly mixed into the soil to enhance its reaction with soil exchange acidity. Selected soil chemical properties influenced by applied lime in a Brazilian Oxisols are presented in Table 5. Highly weathered tropical soils such as Oxisols have very low levels of exchangeable Ca, and crops grown on such soils exhibit Ca deficiency when exchangeable Ca is <1 cmol/kg (Cregan et al., 1989; Kamprath, 1984). Application of limestone (calcium carbonate) and dolomitic lime (Ca and Mg bicarbonate) increases soil-exchangeable Ca and Ca and Mg, respectively. Plant growth improvement in acid soil is not due to addition of basic cations (Ca, Mg), but because of increasing pH reduces toxicity of phytotoxic levels of Al and Mn. Increasing concentration of Ca2þ, Kþ, and Hþ in the soil decreases plant Mg uptake due to competitive inhibition (Clark, 1984; Marschner, 1995).

Table 5 Influence of liming on selected soil chemical properties of Oxisols at 0–10 and 10–20 cm depth Lime rate (Mg ha1) Soil property

0–10 cm depth pH (1:2.5 soil water) Base saturation (%) H þ Al (cmolc kg1) Acidity saturation (%) Ca (cmolc kg1) Mg (cmolc kg1) 10–20 cm depth pH Base saturation (%) H þ Al (cmolc kg1) Acidity saturation (%) Ca (cmolc kg1) Mg (cmolc kg1)

0

12

24

F test

CV (%)

5.4c 28.9c 6.5a 71.1a 1.8c 0.6b

6.7b 72.0b 2.0b 27.8b 3.6b 1.3a

7.1a 84.9a 1.0c 15.1c 4.3a 1.3a

** ** ** ** ** **

2.0 19 14 14 9 12

5.3c 22.9c 6.6a 77.1a 1.4c 0.4b

6.2b 51.2b 3.8b 49.0b 2.7b 1.0a

6.5a 61.7a 2.9c 38.3c 3.3a 1.1a

** ** ** ** ** **

3.0 24 13 12 15 15

** Significant at the 1% probability level, respectively. Means followed by the same letter in the same line under different lime treatments are not statistically significant at the 5% probability level by Tukey’s test. Source: Fageria (2006).

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3.2. Reducing phosphorus immobilization Oxisols are naturally deficient in total and plant-available phosphorus and significant portions of applied P are immobilized due to either precipitation of P as insoluble Fe/Al-phosphates or chemisorption to Fe/Al-oxides and clay minerals (Nurlaeny et al., 1996). Smyth and Cravo (1992) reported that Oxisols are notorious for P immobilization because they have higher iron oxide contents in their surface horizons than do any other kind of soil. The P fixation capacity in Oxisols is directly related to the surface area, and clay contents of the soil material, and inversely related to SiO2/R2O3 ratios (Curi and Camargo, 1988). Bolan et al. (1999) reported that in variable charge soils, a decrease in pH increases the anion exchange capacity, thereby increasing the retention of P. Hence, improving crop yields on these soils requires high rates of P application (Fageria, 1989; Sanchez and Salinas, 1981). Reports regarding the effects of liming on P availability in highly weathered acid soils are in conflict (Friesen et al., 1980a; Haynes, 1984). Liming can increase, decrease, or have no effect on P availability (Anjos and Rowell, 1987; Curtin and Syers, 2001; Fageria, 1984; Haynes, 1982; Mahler and McDole, 1985). However, in a recent study, Fageria and Santos (2008) reported a linear increase in Mehlich 1 extractable P with increasing soil pH in the range of 5.3–6.9 (average of 0–10 and 10–20 cm soil depth) in Brazilian Oxisols (Fig. 1). Mansell et al. (1984) and Edmeades and Perrott (2004) reported that in acid soils of New Zealand, primary benefit of liming occurs through an increase in the availability of P by decreasing P adsorption and stimulating the mineralization of organic P. Fageria (1984) also reported that in Brazilian Oxisols there was a quadratic increase in the Mehlich 1 extractable P in the pH range of 5.0–6.5, and thereafter it was decreased. Increase in availability of P in the pH range of 5.0 to 6.5 was associated with release of P ions from Al and Fe oxides, which were responsible for P fixation (Fageria, 1989). At higher pH (>6.5), the reduction of extractable P was associated with precipitation of P as Ca phosphate (Naidu et al., 1990). These increases in extractable P or liberation of this element in the pH range of 5.0–6.5 and reduction in the higher pH range (>6.5) can be explained by the following equations:

AlPO4 ðP fixedÞ þ 3OH , AlðOHÞ3 þ PO 3 4 ðP releasedÞ CaðH2 PO4 Þ2 ðsoluble PÞ þ 2Ca2þ , Ca3 ðPO4 Þ2 ðinsoluble PÞ þ 4Hþ The liming acid soils result in the release of P for plant uptake; this effect is often referred to as ‘‘P spring effect’’ of lime (Bolan et al., 2003). Bolan et al. (2003) reported that in soils high in exchangeable and soluble Al, liming might increase plant P uptake by decreasing Al, rather than by increasing

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N. K. Fageria and V. C. Baligar

Average of two soil depths 40 30 20

Y = - 1.5594 + 4.9585X R 2 = 0.5417**

Mehlich-1 extractable P (mg kg-1)

10 0

Y = - 5.3937 + 3.9288X R 2 = 0.4032**

10–20 cm 30 20 10 0 60

0–10 cm

50 40 30 Y = - 3.5026 + 5.7293X R 2 = 0.4462**

20 10 0 5.0

5.5

6.0

6.5

7.0

7.5

Soil pH in H2O

Figure 1 Relationship between soil pH and Mehlich 1 extractable soil phosphorus (Fageria and Santos, 2008).

P availability per se. This may be due to improved root growth where Al toxicity is alleviated, allowing a greater volume of soil to be explored (Friesen et al., 1980b).

3.3. Improving activities of beneficial microorganisms Soil microbiological properties can serve as soil quality indicators because soil microorganisms are the second most important (after plants) biological agents in the agricultural ecosystem (Fageria, 2002; Yakovchenko et al., 1996). Soil microorganisms provide the primary driving force for many chemical and biochemical processes and thus affect nutrient cycling, soil fertility, and carbon cycling (He et al., 2003). Plant roots and rhizosphere are colonized by many plant-beneficial microorganisms such as symbiotic and nonsymbiotic dinitrogen (N2) fixing bacteria, plant growth promoting

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rhizobacteria, saprophytic microorganisms, biocontrol agents, and mycorrhizae and free living fungi. Soil acidity restricts the activities of these beneficial microorganisms, except fungi, which grow well over a wide range of soil pH (Brady and Weil, 2002). Enhancing the activities of beneficial microbes such as rhizobia, diazotropic bacteria, and mycorrhizae in the rhizosphere has improved root growth by the fixation of atmospheric nitrogen, suppressing pathogens, and producing phytohormones, enhancing root surface area to facilitate uptake of less mobile nutrients such as P and micronutrients and mobilizing and solubilizing unavailable nutrients (Baligar and Fageria, 1999). Literature provides ample evidence that low soil pH adversely affects activities of rhizobium, including a loss of its ability to fix nitrogen (Angle, 1998). Mulder et al. (1977) showed that low soil pH reduced the activity and their ability to multiply. Holdings and Lowe (1971) further demonstrated that low soil pH increased the number of ineffective rhizobia in soil. Angle (1998) reported that soil pH decline below 5.5 reduced rhizobial populations and rhizobia that survive such a pH lack the capacity to fix atmospheric nitrogen. Ibekwe et al. (1995) showed that plants grown in an unamended control soil with low pH often exhibited low rates of nitrogen fixation. Ibekwe et al. (1995) also reported high rates of nitrogen fixation when high concentrations of heavy metals were present, but soil pH was nearly neutral. Franco and Munns (1982) found that decreasing the pH of nutrient solutions from 5.5 to 5.0 decreased the number of nodules formed by common bean. Bacterias are divided into three groups based on their tolerance to soil acidity. The first group is known as acidophiles (grow well under acidic conditions), the second group is known as neutrophiles (grow well under neutral pH), and the third group is known as alkaliphiles (grow well under alkaline conditions) (Glenn et al., 1997). Soil contains all these groups of bacteria. Most soil bacterias, however, including the nitrogen fixing rhizobium, belong to the neutrophiles group (Glenn et al., 1997). Therefore, acidic pH ranges are detrimental to bacterial activities. Lime ameliorates the harmful effects of soil acidity (Cregan et al., 1989). Studies on bacteria suggest that the success of liming may be due not only to an effect on the soil pH but also to a direct effect on the bacteria themselves (Reeve et al., 1993). Like most neutrophilic bacteria, rhizobia appear to maintain an intracellular pH between 7.2 and 7.5, even when the external environment is acidic (Kashket, 1985). However, differences exist between and among acid-tolerant and acid-sensitive strains (Bhandhari and Nicholas, 1985; Graham et al., 1994). Strain tolerance to lower pH has been reported for rhizobia (Brockwell et al., 1995) and arbuscular mycorrhizal fungi (Habte, 1995; Siqueira and Moreira, 1997). Muchovej et al. (1986) reported that liming of a Brazilian Oxisols improved nodule formation in soybean. Haynes and Swift (1988) reported that liming increased microbial biomass and enzyme activity in acid soils.

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Nurlaeny et al. (1996) found that liming increased shoot dry weight, total root length, and mycorrhizal colonization of roots in soybean and corn grown on tropical acid soils. These authors also reported that mycorrhizal colonization improved P uptake and plant growth. Furthermore, colonization of the roots with arbuscular mycorrhizal fungi can increase plant uptake of P and other nutrients with low mobility, such as Zn and Cu (Marschner and Dell, 1994). Uptake of P by mycorrhizal plants is usually from the same labile soil P pool from which the roots of nonmycorrhizal plants take up P (Morel and Plenchette, 1994). However, the external hyphae can absorb and translocate P to the host from soil outside the root depletion zone of nonmycorrhizal roots (Johansen et al., 1993). Thereby, under conditions where P and other nutrients’ diffusion in soil is slow and root density not very high, more immobile nutrients are spatially available to mycorrhizal plants than to nonmycorrhizal plants (Nurlaeny et al., 1996). Furthermore, mycorrhizal plants may utilize organic soil P due to surface phosphatase activity of hyphae (Tarafdar and Marschner, 1994, 1995), enhanced activity of P-solubilizing bacteria in the mycorrhizosphere (Linderman, 1992; Tarafdar and Marschner, 1995), or reduced immobilization of labile P in organic matter (Joner and Jakobsen, 1994). Additionally, soil pH affects arbuscular mycorrhizal colonization (Mamo and Killham, 1987; Wang et al., 1985), species distribution (Porter et al., 1987), and effectiveness of the mycorrhizal symbiosis with plant species (Hayman and Tavares, 1985).

3.4. Reducing solubility and leaching of heavy metals Heavy metals are those metals having densities >5.0 Mg m3 (Soil Science Society of America, 1997). In soils, these include the elements Cu, Zn, Fe, Mn, Cd, Co, Cr, Hg, Ni, and Pb. Higher concentrations of these heavy metals in soil solution can lead to uptake by crop plants in quantities that are harmful for human and animal health; leaching can also occur and contaminate ground water (Epstein and Bloom, 2005; Hall, 2002). Soil properties such as organic matter content, clay type, redox status, and soil pH are considered the major factors determining the bioavailability of heavy metals in soil (Huang and Chen, 2003; McLean, 1976; Wagner, 1993). Increasing pH by application of lime to acid soils reduces the solubility of most heavy metals (Fageria et al., 2002; Lindsay, 1979; Mortvedt, 2000). In addition, higher soil pH increases the adsorption affinity of iron oxides, organic matter, and other adsorptive surfaces (Sauve et al., 2000). This practice can reduce the leaching of heavy metals to ground water as well as their absorption by plants and consequently improve soil and water quality and subsequently human health. Data in Tables 6 and 7 show the influence of increasing base saturation on soil-extractable Mn, Fe, Zn, and Cu (heavy metals) after harvest of common bean crop grown on Brazilian Oxisols. These data show that with exception of Cu, concentrations of

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Table 6 Influence of base saturation on DTPA extractable soil Mn and Fe after harvest of a common bean crop grown on Brazilian Oxisols Base saturation (%)

Mn (mg kg1)

Base saturation (%)

Fe (mg kg1)

23.4 54.4 67.5

8.1a 6.4b 5.5b

28.5 65.8 75.3

33.1a 21.1b 20.7b

Means in the same column followed by same letter are not significantly different at 5% probability level by Tukey’s test. Source: Fageria and Santos (2005).

Table 7 Influence of base saturation on DTPA extractable soil Zn and Cu after harvest of common bean crop grown on Brazilian Oxisols Base saturation (%)

Zn (mg kg1)

Base saturation (%)

Cu (mg kg1)

26.2 60.1 69.8

5.5a 5.4a 4.8b

23.4 56.7 66.5

3.7a 3.7a 3.5a

Means in the same column followed by same letter are not significantly different at 5% probability level by Tukey’s test. Source: Fageria and Santos (2005).

these microelements in the extractant were significantly reduced with increasing soil base saturation due to liming. Increasing soil pH with lime can significantly affect the adsorption of heavy metals in soils (Adriano, 1986; McBride, 1994). Adsorption of Pb, Cd, Ni, Cu, and Zn is significantly decreased with increasing soil pH (Basta and Tabatabai, 1992; Elliott et al., 1986; Harter, 1983). Ribeiro et al. (2001) reported that among several soil amendments (dolomitic lime, gypsum, vermicompost, sawdust, and solomax), dolomitic lime was most effective in reducing bioavailabilities of these heavy metals: Zn, Cd, Cu, and Pb. Most metals are relatively more mobile under acidic and oxidizing conditions and are strongly retained under alkaline and reducing conditions (Huang and Chen, 2003). Brummer and Herms (1983) reported that Pb, Cd, Hg, Co, Cu, and Zn are more soluble at pH 4.0–5.0 than in a pH range of 5.0–7.0. However, under acidic conditions, As, Se, and Mo are less soluble because of formation of these elements into anionic forms (Huang and Chen, 2003). The risk of food-chain contamination by toxic substances and elements has been a major concern of both producers and consumers (Treder and

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Cieslinski, 2005). The concentrations of heavy metals in plants and their distribution to various parts of the plant are the result of the combined influence of soil properties and biological factors. However, translocation of heavy metals from the soil solids to the soil solution, and thus their availability to plants, depends upon several factors. Soil properties such as organic matter content, clay type, redox potential, and soil pH are considered the major factors that determine the bioavailability of heavy metals in soil (Treder and Cieslinski, 2005). Hence, liming certainly helps in reducing availability of heavy metals to crop plants.

3.5. Improving soil structure The clustering of soil particles (sand, silt, and clay) into aggregates or peds and their arrangement into various patterns resulted in what is termed soil structure. From the agronomic standpoint, soil structure affects plant growth through its influence on infiltration, percolation, and retention of water, soil aeration, and mechanical impedance to root growth. Its general role in soil–water relations can be evaluated in terms of the extent of soil aggregation, aggregate stability, and pore size distribution. These soil characteristics change with tillage practices and cropping systems. The major binding agents responsible for aggregate formation are the silicate clays, oxides of iron and aluminum, and organic matter and its biological decomposition products. The Oxisols and Ultisols, characterized by dominant quantities of iron and aluminum oxides, have a high degree of aggregation and the aggregates are quite stable. The red color of these soils is attributed to iron oxide minerals. Calcium in liming materials helps in the formation of soil aggregates, hence improving soil structure (Chan and Heenan, 1998). The lime-induced improvement in aggregate stability manifested through the effect of liming on dispersion and flocculation of soil particles (Bolan et al., 2003). Liming is often recommended for the successful colonization of earthworm in pasture soils. The lime-induced increase in earthworm activity may influence soil structure and macroporosity through the release of polysaccharide and the burrowing activity of earthworm (Springett and Syers, 1984).

3.6. Improving nutrient use efficiency Improving nutrient use efficiency is becoming increasingly important in modern crop production due to rising costs associated with fertilizer inputs and growing concern about environmental pollution. Nutrient use efficiency is defined in several ways in the literature. The most common definitions are known as agronomic efficiency, physiological efficiency, agrophysiological efficiency, apparent recovery efficiency, and utilization efficiency (Baligar et al., 2001; Fageria and Baligar, 2003b, 2005a; Fageria et al., 1997). Most of these

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definitions deal with nutrient uptake and utilization by plants in dry matter production. In a simple way, efficiency is output of economic produce divided by fertilizer input. This means that a crop species or genotypes of the same species producing higher dry matter yield with low nutrient application rate or accumulation are called efficient plant species or genotypes. According to the Soil Science Society of America (1997), a nutrient-efficient plant is one that absorbs, translocates, or utilizes more of a specific nutrient than another plant under conditions of relatively low nutrient availability in the soil or growth medium. Fischer (1998) reported that rice cultivars released in 1965 produced less than 40 kg of grain per kilogram of N taken up by the plants. However, cultivars released in 1995 produced almost 55 kg of grain per kilogram of N take-up, resulting in a more than 35% increase in N efficiency (Fischer, 1998). Nutrient use efficiency (recovery efficiency) seldom exceeds 50% in most grain production systems (Fageria and Baligar, 2005a; Westerman et al., 2000). Worldwide, N recovery efficiency for cereal production [rice, wheat, sorghum, millet, barley, corn, oat, and rye is
33% (Raun and Johnson, 1999]. The low nutrient use efficiency is associated with the use of low input crop management technology. Such management practices include use of low rate of fertilizers and lime, water deficiency, inadequate control of insects, diseases, and weeds, and planting of inefficient plant species or genotypes of the same species (Fageria, 1992). Low nutrient use efficiency in crop production systems is undesirable, both economically and environmentally. Soil acidity is also responsible for low nutrient use efficiency by crop plants. Fageria et al. (2004) reported that liming of Oxisols improved the use efficiency of P, Zn, Cu, Fe, and Mn by upland rice genotypes. Efficiency of these nutrients was higher under a pH of 6.4 than with pH 4.5. The improvement in efficiency of these nutrients was associated with decreasing soil acidity, improving their availability, and enhanced root system (Fageria et al., 2004).

3.7. Controling plant diseases Mineral nutrition plays an important role in controling plant diseases. Healthy plants provided by adequate essential nutrients in appropriate balance generally have fewer diseases compared with nutrient-deficient plants (Fageria et al., 1997). When evaluating the effects of mineral nutrition on plant diseases, in addition to optimal rate of nutrient application an appropriate nutrient balance is very important. Indiscriminate excess supply of a nutrient will create imbalance with other nutrients and chances of plant infestation with diseases will increase. Furthermore, there is still lack of systematic research data to determine the effects of mineral nutrition on plant diseases, involving soil scientists and plant pathologists. Plant

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pathologists without collaboration of soil scientists have conducted most of the studies in this discipline, and invariably, with very inappropriate fertilizer rates (excess or deficient). In addition, most of the research data related to nutrient–disease interactions are collected under controlled growth conditions and field studies are lacking. However, existing information suggests that in some cases liming decreases diseases and in others the inverse is true. It has been known for nearly 100 years that amending the soil with CaCO3 would provide a significant measure of clubroot (Plasmodiphora brassicae Wor.) control in crucifers (Engelhard, 1989). Conversely, many diseases of potato (Solanum tuberosum L.), such as common scab (Streptomyces scabies), powdery scab (Spongospora subterranea), black scurf (Rhizoctonia solani), and tuber blight (Phytophthera infestans), are favored at higher pH compared to lower pH (Haynes, 1984). Calcium has been implicated in plant resistance to several plant pathogens, including Erwinia phytophthora, R. solani,Sclerotium rolfsii, and Fusarium oxysporum (Kiraly, 1976). Haynes (1984) reported that calcium forms rigid linkages with pectic chains and thus promotes the resistance of plant cell walls to enzymatic degradation by pathogens. Information is lacking on effects of lime on damage by specific insects on plants. Overall, from the available data, it appears that variable levels of macro- and micronutrients in plants have positive to negative or no effects on insect damage to crop plants (Fageria and Scriber, 2002). Lime improves the availability of Ca, Mg, Mo, and P and reduces the availability of Mn, Zn, Cu, and Fe. It would be interesting to evaluate how such changes in availabilities of these nutrients in soil affect insect damage in plant.

3.8. Mitigating nitrous oxide emission from soils Nitrous oxide (N2O) is globally important, due to its role as a greenhouse gas, and once oxidized to NOx it can catalyze stratospheric ozone destruction. Nitrous oxide is a potent greenhouse gas with much greater global warming potential than CO2 (Izaurralde et al., 2004). Nitrous oxide concentration in the atmosphere has increased since preindustrial times and agricultural lands are the main anthropogenic sources (Clough et al., 2004; Perez et al., 2001). The concentration of N2O in the atmosphere, estimated at 2.68102 mL L1 around 1750, has increased by about 17% as a result of human alterations of the global N cycle (IPCC, 2001). Global annual N2O emissions from agricultural soils have been estimated to range between 1.9 and 4.2 Tg N, with about half arising from anthropogenic sources (IPCC, 2001). Since soil pH has a potential effect on N2O production pathways, and the reduction of N2O to N2, it has been suggested that liming may provide an option for the mitigation of N2O emission from agricultural soils (Stevens et al., 1998). Clough et al. (2003, 2004) reported that liming has been promoted as a mitigation option for lowering soil N2O emissions

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when soil moisture content is maintained at field capacity. Nitrous oxide forms in soils primarily during the process of denitrification (Robertson and Tiedje, 1987) and to a lesser extent during nitrification (Tortoso and Hutchinson, 1990).

4. Disadvantages of Overliming Adequate lime rate is essential not only for maximum economic return but also for maintaining appropriate nutrient balance for crop production. Overliming effects are more pronounced on coarse-textured soils than on fine-textured soils. Fine-textured soils have high buffer capacity; hence, changes in chemical properties due to liming are not as pronounced as for coarse-textured soils. Overliming can create deficiencies of micronutrients like Fe, Mn, Zn, Cu, and B. These nutrients are usually adsorbed onto sesquioxide soil surfaces. Table 8 summarizes important changes in micronutrient concentrations as influenced by soil pH and consequent acquisition by plants. Table 9 shows acquisition of Zn, Cu, Fe, and Mn by upland rice grown at various soil pH levels on Brazilian Oxisols. Uptake of these micronutrients has been significantly decreased at higher pH levels. Iron deficiency in upland rice in Brazilian Oxisols is commonly observed with pH higher than 6.5 (Fageria, 2000; Fageria et al., 1994, 2002). Similarly, shoot dry matter, grain yield, and number of panicles in upland rice were significantly decreased with increasing pH in the range of 4.6–6.8 in Brazilian Oxisols (Table 10). The reaction responsible for the reduced solubility of Fe with increasing pH is well understood. It results in the precipitation of Fe (OH)3 as the concentration of OH ions is increased as indicated by the following reaction:

Fe3þ þ 3OH , FeðOHÞ3 ðprecipitationÞ The Fe(OH)3 is chemically equivalent to the hydrated oxide, Fe2O3 3H2O. Acidification shifts the equilibrium, causing a greater release of Fe3þ as a solution ion. In addition to deficiency of some micronutrients, overliming can also create problems of Al toxicity. Aluminum solubility is minimal in the pH range of 5.5–6.5 (Haynes, 1984). At higher pH values, Al becomes increasingly soluble as the negatively charged aluminate form. Farina et al. (1980) reported increased uptake of Al by corn (Zea mays L.) at near neutral pH values and such Al uptake decreased corn yield. This type of detrimental effect of Al can occur at pH higher than 7.0 in water and liming of Oxisols around pH 6.5 in water can produce maximum yields of most food crops (Fageria et al., 1997).

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Table 8

Influence of soil pH on micronutrient concentrations in soil and plant uptake

Micronutrient

Influence on concentration/uptake

Zinc

Zinc solubility is highly soil pH dependent and decreases 100-fold for each increase in pH, and uptake by plant decreases as a consequence. Iron Ferric (Fe3þ) and ferrous (Fe2þ) activities in soil solution decrease 1000-fold and 100-fold, respectively, for each unit increase in soil pH. In most oxidized soils, uptake of Fe by crop plants decreases with increasing soil pH. Manganese The principal ionic Mn species in soil solution is Mn2þ, and concentrations decrease 100-fold for each unit increase in soil pH. In extremely acidic soils, Mn2þ solubility can be sufficiently high to induce toxicity problems in sensitive crop species. Copper Solubility of Cu2þ is very soil pH dependent and decreases 100-fold for each unit increase in pH. Plant uptake also decreases. Boron Sorption of B to Fe and Al oxides is pH dependent and is highest at pH 6.0–9.0. Bioaavailability of B is higher between pH 5.5 and 7.5, decreasing below and above this range mainly due to pH-dependent reactions. Molybdenum Above soil pH 4.2, MoO42 is dominant. Concentration of these species increases with increasing soil pH and plant uptake also increases. Water-soluble Mo increases sixfold as pH increases from 4.7 to 7.5. Replacement of adsorbed Mo by OH is responsible for increases in water-soluble Mo as soil pH increases. Chlorine Chloride is bound tightly by most soils in mildly acidic to neutral pH soils and becomes negligible to pH 7.0. Appreciable amounts can be adsorbed with increasing soil acidity. Source: Adriano (1986), Fageria et al. (1997, 2002), Mortvedt (2000), and Tisdale et al. (1985).

5. Factors Affecting Lime Requirements Lime requirement is defined as the amount of liming material, as calcium carbonate equivalent, required to change a volume of soil to a specific state with respect to pH or soluble Al content (Soil Science Society of America, 1997). However, in economic terms, lime requirement can be defined as the quantity of liming material required to produce maximum economic yield of crops cultivated on acid soils. Quantity of lime required to

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Table 9 Influence of soil pH on acquisition of Zn, Cu, Fe, and Mn by upland rice grown in an Oxisol of Brazil

Soil pH

Zn (mg plant1)

Cu (mg plant1)

Fe (mg plant1)

Mn (mg plant1)

4.6 5.7 6.2 6.4 6.6 6.8 R2

1090 300 242 262 163 142 0.98**

75 105 78 64 61 51 0.89*

4540 1860 1980 1630 1660 1570 0.97**

11,160 5010 4310 3610 2760 2360 0.99**

*,** Significant at 5 and 1% probability levels, respectively. Source: Fageria (2000).

Table 10 Influence of soil pH on shoot dry weight, grain yield, and panicle number of upland rice grown on Oxisols

pH in H2O

Shoot dry weight (g plant1)

Grain yield (g plant1)

Panicle number (plant1)

4.6 5.7 6.2 6.4 6.6 6.8 R2

15.9 14.9 12.2 10.6 7.8 6.0 0.99**

11.0 11.5 11.7 9.5 6.8 5.2 0.91**

5.0 4.6 4.5 3.9 3.4 3.3 0.94**

** Significant at the 1% probability level. Source: Fageria (2000).

produce maximum economic yields of crops grown on acid soils is determined by soil properties, liming material quality, management practices, cropping systems, crop species or genotypes within species, calcium and magnesium interaction with other nutrients, and economic considerations.

5.1. Quality of liming material Quality of liming material is very important in correcting soil acidity. Chemical analysis of the liming material gives its composition. A liming material containing both calcium and magnesium is desirable for correcting soil acidity and improving contents of Ca and Mg of the soil. Two important characteristics that determine lime material quality are its neutralizing

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power or reactivity and fineness. The chemical effectiveness of agricultural limestone is measured by its CaCO3 equivalence. If neutralizing value is lower than CaCO3, a higher quantity of liming material is required and vice a versa. The neutralizing power or value of a liming material is defined as the acid-neutralizing capacity of the material by weight in relation to CaCO3. A dolomitic limestone has a higher neutralizing capacity than a calcitic limestone because of the lower atomic weight of Mg. Pure dolomite has a CaCO3 equivalence of 1.08 (Barber, 1984). Table 11 shows various liming materials and their CaCO3 equivalent values (neutralizing values). The degree of fineness indicates the speed with which lime materials will neutralize soil acidity. Fineness is measured by the proportion of processed agricultural lime which passes through a sieve with an opening of a particular size. A 60-mesh sieve, which is the standard for comparisons of lime finesse and efficiency rating of 100%, is assigned (Caudle, 1991).

5.2. Soil texture The relative proportions of sand, silt, and clay in a given soil determine soil texture, a basic physical property of the soil that remains unchanged by cultural and management practices. The sand group includes all soils whose Table 11 Liming materials, their composition, and neutralizing power Commercial name

Chemical formula

Neutralizing value (%)

Dolomitic lime

CaMg(CO3)2

95–109

Calcitic lime

CaCO3

100

Dolomite lime

MgCO3

100–120

Burned lime

CaO

179

Slaked lime

Ca(OH)2

136

Basic slag

CaSiO3

86

Wood ash

Variable

30–70

Characteristics

Contains 78–120 g kg1 of Mg and 180–210 g kg1 of Ca Contains 284–320 g kg1 of Ca Contains 36–72 g kg1 of Mg Fast reacting and difficult to handle Fast reacting and difficult to handle Byproduct of pig-iron industry, also contains 1–7% P Caustic and water soluble

Source: Fageria (1989), Bolan et al., (2003), Brady and Weil (2002), and Caudle (1991).

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sand content by weight is 85% or more, the silt group has a silt content of 80% or more, and the clay group includes those whose clay content is 40% or more (Soil Science Society of America, 1997). Soil texture determines the buffering capacity of a soil, which refers to the ability of the solid phase soil materials to resist changes in ion concentrations in the solution phase. For liming purposes, the resistance of the soil solution to changes in pH is a main component of soil buffer power. Oxisols contain predominantly iron and aluminum oxide minerals and kaolinite and have characteristically low to moderately low cation exchange capacity, but they also have high buffering capacity. Hence, Oxisols require large amount of liming materials to raise soil pH to a desired level for maximum crop yields. Maximum legume grain yield (common bean, soybean) is achieved at a pH of about 6.5 in Brazilian Oxisols (Fageria, 2001b, 2006; Fageria and Stone, 2004). Data in Fig. 2 show that to raise a pH from 5.3 to 6.5 in Oxisols, a lime rate of about 7 Mg ha1 is needed.

5.3. Soil fertility Soil fertility has a significant influence on the quantity of lime required to correct soil acidity to produce maximum economic crop yields. Quantity of nutrient present in the soil is defined in term of soil fertility, and, generally, soil analysis is used as a criterion to make fertilizer recommendations for

Soil pH in H2O

7.0

6.5

6.0 Y = 5.2809 + 0.2181X - 0.0061X 2 R 2 = 0.9656**

5.5

5.0

0

3

6

9

12

15

18

Lime applied (Mg ha-1)

Figure 2

Relationship between lime rate and soil pH in Brazilian Oxisols.

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Table 12 Influence of liming on base saturation and exchangeable Caþ and Mg2þ at 0–20 cm depth in an Oxisols Lime rate (Mg ha1)

Base saturation (%)

Ca2þ (cmolc kg)

Mg (cmolc kg1)

0 4 8 12 16 20 R2

40 44 51 53 56 66 0.80**

1.9 2.3 3.0 3.1 3.3 3.8 0.72**

1.0 1.1 1.2 1.3 1.3 1.4 0.23*

*,** Significant at the 5 and 1% probability level, respectively. Source: Fageria (2001a,b).

field crops (Fageria and Baligar, 2005b). High fertility soils in terms of exchangeable Ca2þ, Mg2þ, and Kþ require less lime than do those with lower soil fertility. When Ca2þ, Mg2þ, and Kþ contents are higher, a lower lime rate is required, because of higher levels of these basic cations in the soil, meaning relatively higher base saturation and higher pH than with lower levels of these cations (Table 12).

5.4. Crop rotation Crop rotation is defined as a planned sequence of crops growing in a regularly recurring succession on the same area of land, as contrasted to continuous culture of one crop or growing a variable sequence of crops (Soil Science Society of America, 1997). This is a very old system of growing crops and was practiced during the Han dynasty of China more than 3000 years ago (Karlen et al., 1994; MacRae and Meheys, 1985). Romans recognized the benefits of alternating leguminous crops with cereals more than 2000 years ago (Karlen et al., 1994; Robson et al., 2002). Continuous cultivation of a given crop on the same land may reduce productivity. This decline in productivity is associated with a decrease in soil fertility, infestation by weeds, insects, and diseases, loss of soil by erosion, reduction in soil biological diversity, and buildup of allelopathy (Fageria, 2002). Ability of legumes to fix atmospheric nitrogen discovered in the late 19th century was a major reason for crop rotation (cereals with legumes) that became popular in the early 20th century (Karlen et al., 1994). Crop rotation has several benefits. Diversifying crops in rotation can improve crop yields, a response known as the rotation effect (Anderson, 2003). Phosphorus deficiency or unavailability is a major yield-limiting

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factor in Oxisols, and in the Central Great Plains of the United States, crop rotations have been reported to improve P-use efficiency (Bowman and Halvorson, 1997). However, there are some disadvantages in adopting crop rotation. One such disadvantage is soil acidification, a serious form of land degradation associated with crop rotation (Coventry et al., 2003). In crop rotations, the N cycle and C cycle contribute most to the acid input (Coventry and Slattery, 1991; Helyar et al., 1997). Acidification of soil can also result from increasing addition of crop residues due to crop rotation (Poss et al., 1995). It is reported in several studies in southern Australia that in the wheat-based crop rotation, the rates of acidification can be 0.16–3.6 Hþ ha1 year1 for pastures (Loss et al., 1993; Ridley et al., 1990) and 1.0–7.5 Hþ ha1 year1 for cereal–legume rotations (Coventry and Slattery, 1991; Dolling, 1995; Moody and Aitken, 1997). The rate of acidification in intensive cropping systems is also associated with removal of large amounts of calcium and magnesium in the grains. In addition, acidification in the tropical rainforest such as the cerrado region of Brazil is also associated with the replacement of perennial vegetation with shallow-rooted annual pastures and crops. With this change, more water has drained through the soil profile, which leads to removal of bases (Coventry et al., 2003). The rates of acidification are generally more pronounced in higher rainfall areas and soil pH in the range of 4.8–5.5 (CaCl2) is likely to be more susceptible to rapid acidification (Coventry et al., 2003; Haynes, 1983).

5.5. Use of organic manures Organic manures are products from the processing of animal or vegetable substances that contain reasonable amount of plant nutrients to be of value as fertilizers. Brosius et al. (1998) reported that plant- and animal-based organic byproducts may substitute for commercial fertilizers and enhance chemical and biological attributes of soil quality in agricultural production systems. Use of organic manures can also affect lime requirement of a crop. Organic matter increases the soil’s ability to hold and make available essential plant nutrients and to resist the natural tendency of soil to become acidic (Cole et al., 1987). Furthermore, addition of organic manures to acid soils has been shown to increase soil pH, decrease Al saturation, and thereby improve conditions for plant growth (Alter and Mitchell, 1992; Reis and Rodella, 2002; Wong and Swift, 2003). Miyazawa et al. (1993) reported that crop residues of wheat and corn and 20 plant species utilized as green manure increased soil pH and decreased Al content of the soil. Several mechanisms have been proposed for reducing acidity by organic manures. These mechanisms include specific adsorption of organic anions on hydrous Fe and Al surfaces and the corresponding release of hydroxyl ions which increase soil pH (Hue, 1992; Wong and Swift, 2003). Adsorption of Al by organic matter sites and the subsequent isolation of the inorganic phase to

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maintain the equilibrium Al activity in soil solution have been proposed to increase soil pH (Wong and Swift, 2003; Wong et al., 1998). Chelating agents released by decomposing organic matter may detoxify Al ions. Plant roots decay in the soil and form into soil organic matter. Active roots also release organic acids such as citrate, malate, and tartrate. These organic acids react strongly with Al and convert it into less toxic organically bound forms (Yang et al., 2000). The organic Al affinities or stability constants are in the order of citrate > tartrate > malate (Hue et al., 1986). The decrease in Al-activity by addition of organic matter has been reported by Kochain (1995). The functional groups involved in metal complexation by organic matter are COOH and OH (Wong and Swift, 2003). Surface application or surface incorporation of organic matter also decreased phytotoxic subsoil Al3þ activities because dissolved organic matter (DOM) that leached into the subsoil formed nontoxic Al–DOM complexes (Hue, 1992; Hue and Licudine, 1999; Liu and Hue, 1996; Willert and Stehouwer, 2003). The combined application of CaCO3 and organic matter in lime-stabilized biosolids decreased subsoil acidity and increased subsoil Ca saturation, compared with CaCO3 alone (Brown et al., 1997; Tan et al., 1985; Tester, 1990; Willert and Stehouwer, 2003). This effect was attributed to increases in Ca mobility caused by Ca–DOM complexes (Willert and Stehouwer, 2003). Additional benefits of organic matter addition to acid soils are improving nutrient cycling and availability to plants through direct additions as well as through modification in soils’ physical and biological properties. A complementary use of organic manures and chemical fertilizers has proven to be the best soil fertility management strategy in the tropics (Fageria and Baligar, 2005a; Makinde and Agboola, 2002). Enhanced soil organic matter increases soil aggregation and water-holding capacity, provides source of nutrients, and reduces P fixation, toxicities of Al and Mn, and leaching of nutrients (Baligar and Fageria, 1999). Build-up of organic matter through additions of crop and animal residues increases the population and species diversity of microorganisms and their associated enzyme activities and respiration rates (Kirchner et al., 1993; Weil et al., 1993). The use of organic compost may result in a soil that has greater capacity to resist the spread of plant pathogenic organisms. The improvement in overall soil quality may produce more vigorous growing and high yielding crops (Brosius et al., 1998).

5.6. Conservation tillage The mechanical manipulation of the soil profile for crop production is known as tillage. Conservation tillage is minimum manipulation of the soil profile for crop production and 30% or more soil surface is covered with crop residues. In conservation tillage, weeds are controlled by herbicides and this practice helps in conservation of water and nutrients, and reduces soil erosion and labor cost. Conservation tillage reduces oxidation

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of organic matter or conserves soil organic matter content and consequently decreases the adverse effects of soil acidity. Additional research is needed to assess the long-term conservation tillage effects on soil acidity.

5.7. Crop species and genotypes within species Crop species and genotypes within species differ significantly in relation to their tolerance to soil acidity (Baligar and Fageria, 1997; Devine, 1976; Fageria and Baligar, 2003b; Fageria et al., 1989, 2004; Foy, 1984; Garvin and Carver, 2003; Reid, 1976; Sanchez and Salinas, 1981; Yang et al., 2000). Hence, lime requirements also vary from species to species and among cultivars within species. Many of the plant species tolerant to acidity have their center of origin in acid soil regions, suggesting that adaptation to soil constraints is part of the evolution processes (Foy, 1984; Sanchez and Salinas, 1981). A typical example of this evolution is the acid soil tolerance of Brazilian upland rice cultivars. In Brazilian Oxisols, upland rice grows very well without liming, when other essential nutrients are supplied in adequate amount and water is not a limiting factor (Fageria, 2000, 2001a). Experimental results obtained on Brazilian Oxisols with upland rice are good examples of crop acidity tolerance evaluation. Fageria et al. (2004) reported that grain yield and yield components of 20 upland rice genotypes were significantly decreased at low soil acidity (limed to pH 6.4) as compared with high soil acidity (without lime, pH 4.5), demonstrating the tolerance of upland rice genotypes to soil acidity. In Table 13, data are presented showing grain yield and panicle number of six upland rice genotypes at two acidity levels. These authors also reported that grain yield gave significant negative correlations with soil pH, Ca saturation, and base saturation. Further, grain Table 13 Grain yield and panicle number of six upland rice genotypes at two soil acidity levels in Brazilian Oxisols Grain yield (g pot1)

Panicle number (pot1)

Genotype

High acidity (pH 4.5)

Low acidity (pH 6.4)

High acidity (pH 4.5)

Low acidity (pH 6.4)

CRO97505 CNAs8983 Primavera Canastra Bonanc¸a Carisma Average

74.3 55.2 53.0 51.6 48.8 50.8 66.7

52.0 42.9 47.2 38.9 36.5 17.5 47.0

38.0 29.0 25.0 32.0 26.3 43.3 38.7

28.3 25.7 21.7 26.3 20.7 17.7 28.1

Source: Compiled from Fageria et al. (2004).

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yield had significant positive correlations with soil Al and H þ Al, confirming that upland rice genotypes were tolerant to soil acidity. Fageria (1989) reported stimulation of growth of Brazilian rice cultivars at 10 mg Al3þ L1 in nutrient solution compared with a control (no Al) treatment. Okada and Fischer (2001) suggested that the mechanism for the genotype difference of upland rice for tolerance to soil acidity is due to the relationship between regulation of cell elongation and legend-bound Ca at the root apoplast. A substantial number of plant species of economic importance are generally regarded as tolerant to acid soil conditions of the tropics (Sanchez and Salinas, 1981). In addition, there are cultivars within crop species that are tolerant to soil acidity (Fageria et al., 2004; Garvin and Carver, 2003; Yang et al., 2004). Yang et al. (2005) reported significant differences among genotypes of rye, triticale, wheat, and buckwheat to Al toxicity. These crop species or cultivars within these species can be planted on tropical acid soils in combination with reduced rates of lime input. Combination of legume–grass pasture and agroforestry system of management are the other important soil acidity management components useful in tropical ecosystems. For example, Pueraria phaseoloides is used as understory for rubber, Gmelina arborea or Dalbergia nigra, plantations in Brazil, presumably supplying nitrogen to the tree crops (Sanchez and Salinas, 1981) A detailed discussion of combination of legume–grass pasture and agroforestry in tropical America is given by Sanchez and Salinas (1981). These authors reported that when an acid-tolerant legume or legume–grass pasture is grown under young tree crops, the soil is better protected, soil erosion is significantly reduced, and nutrient cycling is enhanced. Some important annual food crops, cover or green manure crops pasture species, and plantation crops tolerant to tropical acid soils are listed in Table 14. Acid soil tolerant crops are useful to establish low input management systems.

5.8. Interaction of lime with other nutrients Recognition of the importance of nutrient balance in crop production is an indirect reflection of the contribution of interactions to yield. The highest yields are obtained where nutrients and other growth factors are in a favorable state of balance. As one moves away from this state of balance, nutrient antagonisms are reflected in reduced yields (Fageria et al., 1997). Nutrient interactions can occur at the root surface or within the plant and can be classified into two major categories. In the first category are interactions which occur between ions because the ions are able to form a chemical bond. Interactions in this case are due to formation of precipitates or complexes either in soil or in the plant. For example, this type of interaction occurs where the liming of acid soils decreases the concentration of almost all micronutrients in soil solution except molybdenum. Such reduction in ion concentration in soil solution decreases the uptake. Increasing soil pH

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Table 14 Some important crop species, pasture species, and plantation crops tolerant to soil acidity in the tropics Annual crop species

Pasture species

Plantation crops

Rice Peanut Cowpea Potato Cassava Pigeon pea Millet Kudzu Mucuna Crotolaria

Brachiaria Andropogon Panicum Digitaria Napiergrass Jaraguagrass Centrosema Stylosanthes

Banana Oil palm Rubber Coconut Cashewnut Coffee Guarana Tea Leucaena Brazilian nut Eucalyptus Papaya

Source: Sanchez and Salinas (1981), Caudle (1991), Fageria et al. (1997), and Brady and Weil (2002).

due to lime will have a more marked effect on Zn than Cu uptake, mainly because Cu is more complexed and protected from precipitation by soluble organic matter (Robson and Pitman, 1983). The second form of interaction is between ions whose chemical properties are sufficiently similar that they compete for site of adsorption (soil components, cell walls), absorption, transport, and function on plant root surfaces or within plant tissues. Such interactions are more common between nutrients of similar size, charge, geometry of coordination, and electronic configuration (Robson and Pitman, 1983). Generally, three types of interactions occur among essential nutrients in plants, and these interactions are known as synergistic, antagonistic, and neutral. If upon addition of two nutrients, an increase in plant growth or yield that is greater than that achieved by adding only one occurs, the interaction is synergistic or positive. Similarly, if adding the two nutrients together produced less plant growth or yield as compared to individual ones, the interaction is negative or antagonistic. When there is no significant change in plant growth or yield with the addition of two nutrients, there is no interaction (Sumner and Farina, 1986). Data in Table 15 show that lime and P fertilization have positive as well as negative interactions depending on crop species. In the case of upland rice and wheat, shoot dry weight yields were higher at zero level of lime compared with 2 and 4 g lime kg1 of soil. Hence, interaction between lime and P in this case was negative. Whereas, shoot dry matter yield of common bean and corn increased with increasing lime as well as P rates, indicating that interaction between lime and P in this case was synergistic. An increasing response to applied P with

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Table 15 Dry matter yield of shoots of upland rice, wheat, common bean, and corn as affected by lime and phosphorus applied to a Brazilian Oxisols

Lime rate (g kg1)

P rate (mg kg1)

Rice (g pot1)

Wheat (g pot1)

Common bean (g pot1)

Corn (g pot1)

0 0 0 2 2 2 4 4 4

0 50 175 0 50 175 0 50 175

0.72 15.08 18.63 0.73 15.23 13.23 0.33 10.20 13.20

0.17 5.83 8.40 0.20 5.20 6.50 0.20 4.90 5.97

1.25 8.30 10.60 1.30 9.00 10.60 1.70 10.50 12.00

1.10 4.93 8.73 1.43 7.13 11.47 1.10 6.60 9.93

Source: Compiled from Fageria et al. (1995).

increasing rates of added lime has been attributed to either an improved rate of supply of P by the soil or an improved ability of the plant to absorb P when Al toxicity has been eliminated (Friesen et al., 1980b). Liming also improves the microbiological activities of acid soils, which, in turn, can increase dinitrogen fixation by legumes and liberate N and other nutrients from incorporated organic materials (Fageria et al., 1995). Nutrient interaction is also evaluated by studying the influence of increasing nutrient concentrations on the uptake of other nutrients and corresponding plant growth. When plant uptake of a given nutrient decreased and corresponding plant growth also decreased, interaction is negative. However, if plant growth and nutrient concentration increased with increasing nutrient supply, the interaction is positive. Liming of acid soils is mainly to supply Ca and Mg to plants and neutralize phytotoxic levels of soil Al3þ. Hence, interaction of Ca2þ, Mg2þ, and Al3þ with other elements is an important aspect for nutrient interaction discussion. Although absolute Ca requirements for plant growth and metabolism are low, it has great significance in providing balance for levels of other plant nutrients, maintaining membrane integrity, and reducing potential for toxicity of other elements (Wilkinson et al., 2000). Potassium, Al3þ, Mg2þ, Mn2þ, and Hþ and heavy metals can reduce Ca uptake by binding at the exterior surface of plasma membrane which increases the Ca requirement (Marschner, 1995). Fertilization with NO3 generally enhances Ca and Mg concentrations in plants driven by the need for cation–anion balance (Wilkinson et al., 2000). Lime and P interactions are mainly associated with Al toxicity, which limits root growth and proliferation, and nutrient uptake. Aluminum absorbed by roots can also precipitate

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375

root-absorbed P and hinder its subsequent translocation to plant tops (Foy, 1983). Mora et al. (2005) reported that the most important Al detoxifying mechanisms in ryegrass were apparently physiological Al-PO4 precipitation inside the root and chemical AlSOþ 4 complex formation in the nutrient solution. The results of solution culture experiments have shown that negative effects of Al on root growth can be reduced by increasing Ca2þ concentration in the growth medium (Nichol et al., 1993; Zysset et al., 1996). Baligar et al. (1992) also reported that Ca ameliorates Al toxicity in wheat plants. Huang and Grunes (1992) reported that adequate supply of Ca and Mg might also ameliorate Al toxicity in wheat plants. Aluminum toxicity continues to be associated with P nutrition of plants (Foy, 1984). Santana and Braga (1977) found that P concentrations in rice tops decreased with increasing Al saturation of the soil. Helyar (1978) concluded that Al toxicity effects were largely associated with Al interference with P metabolism and with Al binding to pectins in root cell wall, stopping root elongation. Fageria and Baligar (1999) studied the interactions between calcium and P, Mg, Zn, Cu, Mn, Fe, and B in common bean and found that uptake of P was quadratically increased, whereas uptake of Mg, Zn, Cu, Mn, Fe, and B significantly decreased as soil Ca increased from 4.9 to 12.5 cmolc kg1 of soil. Calcium  Al3þ interactions are important in acid soils (Foy, 1984). Below pH 5.5, Ca2þ  Al3þ antagonism is probably the most important factor affecting Ca uptake by plants. Lance and Pearson (1969) reported that reduced Ca uptake was the first externally observed symptom of Al damage on cotton seedlings. Franco and Munns (1982) reported that increasing Ca2þ concentrations from 8 to 80 mg L1 decreased Al toxicity in bean plants. Simpson et al. (1977) attributed poor root growth of alfalfa to Al3þ  Ca2þ interaction. Aluminum  Feþ3 interactions are frequently reported in the literature (Foy, 1984). Alam (1981) reported Al-induced Fe deficiency in oat and postulated that Al interfered with the reduction of Fe3þ to Fe2þ within the plant, a process essential for normal Fe metabolism and utilization. Clark et al. (1981) found that Fe-deficiency chlorosis was a common symptom of Al toxicity in sorghum.

6. Criteria to Determine Liming Material Quantity Use of adequate lime rate to correct soil acidity and production of maximum yield of a crop species is an important consideration for economical and ecological reasons. Quantity of liming material required is determined on the basis of soil pH, base saturation, and aluminum saturation adjustment at appropriate levels. Appropriate levels of these acidity indices

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vary with soil type, soil fertility, plant species, and crop genotypes within species (Fageria and Baligar, 2003a). In addition, crop response curves related to lime rate and yield is another criterion that can be used to define lime requirement for any given crop species. Crop response curves to lime levels should be determined for each crop species under different agroecological regions to make liming recommendations effective and economical.

6.1. Soil pH Soil pH or hydrogen ion activity is the most common acidity index used in soil testing program for assessing lime requirements of crops grown on acid soils. Weaver et al. (2004) reported that soil pH buffering capacity, since it varies spatially within crop production fields, may be used to define sampling zones to assess lime requirement, or for modeling changes in soil pH when acid-forming fertilizers or manures are added to a field. The pH is defined as the negative logarithm of the hydrogen ion concentration or activity. It is determined by means of a glass, quinhydrone, or other suitable electrode or indicator at a specified soil to solution ratio in a specified solution, usually distilled water, 0.01 M CaCl2, or 1 M KCl (Soil Science Society of America, 1997). In soil testing laboratories of Brazil, a soil to solution ratio of 1:2.5 is commonly used to determine soil pH (EMBRAPA, 1997). The pH measured in the soil solution represents the active acidity of the soil. Hydrogen and aluminum ions adsorbed by the soil, as well as other soil constituents that generate hydrogen ions, constitute the reserve acidity. The active acidity is neutralized by the addition of lime where more hydrogen ions from the reserve pool go into solution. This results in the resistance of soil to changes in the pH of the soil solution. This is termed ‘‘buffering capacity.’’ The pH of acid soils is lower than 7.0, that of neutral soils is equal to 7.0, and that of alkaline soils is above 7.0. At neutrality, the concentrations of OH ions and Hþ ions are equal, since these ions are derived in equal quantities from the ionization of water. The pH of most agricultural soils is in the range of 4.0–9.0 (Fageria et al., 1990, 1997). Soil acidity is classified into several groups based on soil pH. Slightly acid soils have a pH range of 6.0–7.0, moderately acid soils pH range from 5.5 to 6.0, strongly acid soils pH from 4.5 to 5.0, and extremely acid soils have a pH range below 4.5 (Fageria and Gheyi, 1999). These acidity classifications are arbitrary, and care should be taken when defining adequate pH for crop yields, particularly the extractant used could make a difference. Soil pH in water is higher than that in CaCl2 solution. Soil pH measurements not only indicate the acidity level of a soil but also be used as an initial basis for the prediction of the chemical behavior of soils, particularly in relation to nutrient availability and the presence of toxic elements. Most plant-essential nutrients in soil reach maximal or near-maximal

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availability in the pH range 6.0–7.0, and decrease both above and below this range (McLean, 1973). Optimal pH values for annual crops cultivated on Brazilian Oxisols are given in Table 16. A curve showing the relationship between lime rate and pH change of a Brazilian Oxisol is presented in Fig. 2. Oxisols with application of lime 0–18 Mg ha1 increase pH significantly and quadratically. Figure 3 shows the relationship between pH of an Oxisol and shoots dry weight and grain yield of common bean. Maximum shoot dry weight and grain yields were obtained at a pH of 6.4. Improvement in grain yield was associated with increasing numbers of pods, grains per pod, and weight of 100 seeds when pH was increased from 5.3 to 6.4 (Fig. 4).

6.2. Base saturation Base saturation is another important chemical property of soils used as a criterion for liming recommendations. Base saturation is defined as the proportion of the CEC occupied by exchangeable bases. It is calculated as follows (Fageria et al., 2007):

Table 16

Optimal soil pH for important crop species grown on Brazilian Oxisols

Crop species

Wheat Common bean Upland rice

Type of experiment

Plant part measured

Soil pH in H2O (1:2.5) Reference

Greenhouse Shoot dry 6.0 weight Field Grain yield 6.6 Field

Grain yield 5.6

Common bean Corn Soybean Upland rice

Field

Grain yield 6.2

Field Field Field

Grain yield 6.4 Grain yield 6.8 Grain yield 5.5

Upland rice Upland rice

Greenhouse Grain yield 5.4 Greenhouse Grain yield 5.9

Corn

Field

Grain yield 6.2

Soybean

Field

Grain yield 6.4

Fageria et al. (1997) Fageria and Stone (2004) Fageria and Baligar (2001) Fageria and Baligar (2001) Fageria (2001b) Fageria (2001b) Fageria and Stone (1999) Fageria (2000) Fageria et al. (1990) Gonzales-Erico et al. (1979) Raij and Quaggio (1997)

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Shoot dry weight and grain yield (kg ha−1)

Shoot

Grain a

a

6.4

6.8

3000 b 2000

a

a

b 1000

0

5.3

6.4

6.8

5.3

Soil pH in H2O

Figure 3 Relationship between soil pH and shoot dry weight and grain yield of common bean grown on a Brazilian Oxisol (Fageria and Santos, 2005).

b 200

100

4

a c

b Weight of 100 grains (g)

Number of pods (m−2)

a

Number of grains (pod−1)

a

300

3

2

1

a

a

6.4

6.8

b 30

20

10

0 5.3

6.4

6.8

5.3

6.4

6.8

5.3

Soil pH in H2O

Figure 4 Relationship between soil pH and yield components of common bean grown on Brazilian Oxisols (Fageria and Santos, 2005).

P Base saturationð%Þ ¼

ðCa; Mg; K; NaÞ  100 CEC

where CEC is the sum of Ca, Mg, K, Na, H, and Al expressed in cmolc kg1.

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In Brazil, Naþ is generally not determined because of a very low level of this element in Brazilian Oxisols (Raij, 1991). Hence, Na is not considered in the calculation of CEC or base saturation. For crop production, base saturation levels in soil may be grouped into very low (lower than 25%), low (25–50%), medium (50–75%), and high (>75%) (Fageria and Gheyi, 1999). Very low and low base saturation means a predominance of adsorbed hydrogen and aluminum on the exchange complex. Deficiencies of calcium, magnesium, and potassium are likely to occur in soils with low CEC and very low to low percent base saturation. Quantity of lime required by the base saturation method is calculated by using the following formula (Fageria et al., 1990):

  CECðB2  B1 Þ  df Lime rateðMg ha Þ ¼ TRNP 1

where CEC: cation exchange capacity or total exchangeable cations (Ca2þ, Mg2þ, Kþ, Hþ þ Al3þ) in cmolc kg1, B2: desired optimum base saturation, B1: existing base saturation, TRNP: total relative neutralizing power of liming material, and df: depth factor, 1 for 20 cm depth and 1.5 for 30 cm depth. For Brazilian Oxisols, the desired optimum base saturation for most of the cereals is in the range of 50–60%, and for legumes it is in the range of 60–70% (Fageria et al., 1990). However, there may be exceptions, like upland rice, which is very tolerant to soil acidity and can produce good yield at base saturation lower than 50%. Specific optimal base saturation values for important annual crops grown on Brazilian Oxisols are given in Table 17. Nature of the soil alters the optimum base saturation required by any given crop species. A relationship between lime rate and base saturation in a Brazilian Oxisol is given in Fig. 5. Bean yield was having significant quadratic response in relation to base saturation (Fig. 6). Maximum yield was obtained with base saturation of 73% at 0–10 cm soil depth, with base saturation of 62% at 10–20 cm soil depth and at 67% base saturation when averaged across two soil depths. Hence, at topsoil layer, higher base saturation was required compared with that at lower soil layer (Fageria, 2008).

6.3. Exchangeable aluminum, calcium, and magnesium levels Aluminum has long been recognized as a toxic element for plant growth (Cronan and Grigal, 1995; Foy, 1984). In soil–plant systems, plant-available Al is determined by soil extraction procedure to predict the risk of Al toxicity and the need for liming (Thomas and Hargrove, 1984). In addition, Ca and Mg contents of the Oxisols are important in determining growth of plants. Hence, exchangeable aluminum, calcium, and magnesium contents

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Table 17 Optimal base saturation for important annual crops grown on Brazilian Oxisols Type of experiment

Plant part measured

Common bean Common bean Upland rice

Field

Grain yield 60

Field

Grain yield 69

Field

Grain yield 40

Common bean Corn Soybean Upland rice Upland rice Common bean Corn Wheat Soybean Cotton Sugarcane Soybean

Field

Grain yield 70

Field Field Field Field Field

Grain yield Grain yield Grain yield Grain yield Grain yield

59 63 50 30 71

Field Field Field Field Field Field

Grain yield Grain yield Grain yield Grain yield Cane yield Grain yield

60 60 60 60 50 61

Crop species

Base saturation (%)

Reference

Fageria and Santos (2005) Fageria and Stone (2004) Fageria and Baligar (2001) Lopes et al. (1991) Fageria (2001a) Fageria (2001a) Lopes et al. (1991) Sousa et al. (1996) Fageria and Stone (2004) Raij et al. (1985) Lopes et al. (1991) Raij et al. (1985) Raij et al. (1985) Raij et al. (1985) Gallo et al. (1986)

of the soil are taken into account to determine the rate of lime required for a crop grown on an Oxisol. The equation used for lime rate determination is (Fageria et al., 1990; Raij, 1991):

Lime rateðMg ha1 Þ ¼ ð2  Al3þ Þ þ ½2  ðCa2þ þ Mgþ Þ where values of Al3þ, Ca2þ, and Mg2þ are expressed in cmolc kg1. If values of Ca2þ and Mg2þ cations are more than 2 cmolc kg1, only Al multiplied by factor 2 is considered. This criterion was originally suggested by Kamprath (1970) for tropical soils and is still largely used for liming recommendation for Brazilian acid soils (Paula et al., 1987; Raij, 1991; Raij and Quaggio, 1997). Alvarez and Ribeiro (1999) recommended that the factor used to multiply Al should be varied according to soil texture. These authors suggested that in sandy soil with clay content of 0–15%, the factor 0–1 should be used; for medium-texture soils with clay content of 15–35%, a factor 1–2 should be used; for clayey soil with clay content of 35–60%,

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100

Base saturation (%)

80

60

40 Y = 20.0658 + 8.1171X - 0.2493X 2 R 2 = 0.9635**

20

0

3

6

9

12

18

Lime rate (Mg ha-1)

Figure 5 Relationship between lime rate and base saturation of Brazilian Oxisols.

a factor 2–3 should be used; and for heavy clayey soil with clay content of 60–100%, a factor of 3–4 should be used.

6.4. Aluminum saturation Crops grown in soils with acceptable levels of basic cations do not show Al toxicity symptoms even when the levels of KCl-extractable Al are considered high (Kariuki et al., 2007). Hence, the mere presence of Al in the soil is not an indicator of Al toxicity ( Johnson et al., 1997). A more reliable measure of the potential for Al toxicity is Al saturation (Kariuki et al., 2007). It has been widely reported in the literature that differences in Al tolerance are found among plant species and cultivars within species (Fageria and Baligar, 2003a; Foy, 1992; Kochain, 1995; Okada and Fischer, 2001; Yang et al., 2004). It is evident, therefore, that crop tolerance to Al should be taken into account in estimating the amounts of lime needed to correct Al toxicity. Cochrane et al. (1980) suggested that crop aluminum tolerance should be considered along with levels of exchangeable Ca and Mg, in determination of lime requirement. Aluminum saturation, or the proportion of aluminum among the cations, is calculated by using the following formula:



Al saturationð%Þ ¼

 Al  100 ECEC

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N. K. Fageria and V. C. Baligar

Average of two soil depths 3000

2000

Y = 1248.41 + 56.6337X - 0.4203X 2 R 2 = 0.7736**

1000

0

Grain yield (kg ha-1)

10–20 cm 3000

2000 Y = 1347.17 + 58.6230X - 0.4696X2 R2 = 0.7391**

1000

0 0–10 cm 3000

2000 Y = 1173.82 + 54.2814X - 0.3709X2 R2 = 0.7956**

1000

0

Figure 6

20

40 60 Base saturation (%)

80

Influence of base saturation on grain yield of dry bean (Fageria, 2008).

where ECEC is in cmolc kg1, which is the sum of exchangeable Al3þ, Ca2þ, Mg2þ, and Kþ in cmolc kg1. After determining Al saturation, the following formula is used to calculate lime rate (Cochrane et al., 1980):

 3þ  Al  TASðAl3þ þ Ca2þ þ Mg2þ Þ Lime rateðMg ha Þ ¼ 1:8 100 1

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where 1 M KCL extracts Al, Ca, and Mg and concentrations are expressed in cmolc kg1 and TAS is target Al saturation, which varied from crop species to species. Critical Al saturation values for important plant species are given in Table 18. These values can be used as a reference guide to calculate the lime rate for different crop species. This approach is very useful where lime is difficult to obtain and rather costly and Al-tolerant cultivars are available.

6.5. Crop responses Methods discussed earlier for lime rate determination provide reference guides for determination of lime rates for correcting soil acidity-related constraints for crops. The best criterion, however, for determining lime rate is actual testing of crop responses to applied lime rates. Crop responses to liming are determined by soil, climate, plant species, and cultivar within Table 18 Critical soil aluminum saturation for important field crops at 90–95% of maximum yield

Crop

Type of soil

Critical Al saturation (%)

Cassava Upland rice Cowpea Cowpea Peanut Peanut Soybean Soybean Soybean Soybean Corn Corn Corn Corn Mungbean Mungbean Coffee Sorghum Common bean Common bean Common bean Cotton

Oxisols/Ultisols Oxisols/Ultisols Oxisols/Ultisols Oxisols Oxisols/Ultisols Oxisols Oxisols Oxisols Oxisols/Ultisols Not given Oxisols Oxisols/Ultisols Oxisols/Ultisols Oxisols Oxisols/Ultisols Oxisols/Ultisols Oxisols/Ultisols Oxisols/Ultisols Oxisols/Ultisols Oxisols/Ultisols Oxisols/Ultisols Not given

80 70 55 42 65 54 19 27 15 <20 19 29 25 28 15 5 60 20 10 8–10 23 <10

Source: Compiled from various sources by Fageria et al. (1997).

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species and socioeconomic condition of growers and their interactions. Hence, crop responses to liming should be determined in different agroecological regions. Over the years in different region of Brazil, liming experiments have been conducted on Oxisols with various annual crops. Lime rate to achieve 90% of the maximum grain yield potential is considered an economic lime rate. Raij and Quaggio (1997) reported that in the State of Sao Paulo, depending on grain yield, economic optimum lime rate required for soybean is around 3.6– 5.5 Mg ha1. These authors also reported economic optimum lime rate of 6 Mg ha1 for cotton and 3.8 Mg ha1 for sugarcane. Fageria (2001a) reported that for the State of Goia´s, economic optimum lime rate was 5 Mg ha1 for common bean, 8 Mg ha1 for corn, and 9 Mg ha1 for soybean. Caires et al. (2005) reported that in Brazilian Oxisols (Hapludox), the maximum economic yield of wheat was obtained with application of 4 Mg ha1of lime. Figure 7 shows the relationship between lime rate and grain yield of soybean grown on Brazilian Oxisols in the State of Tocantins. Maximum grain yield was achieved at 12 Mg ha1 of lime. However, 90% of the maximum yield (economical rate) was obtained with the application of 5.3 Mg ha1.

7. Methods, Frequency, Depth, and Timing of Lime Application Methods, frequency, depth, and timing of liming are important practices in improving liming efficiency and crop yields on acid soils. Liming material is applied in large quantity to bring about the desired chemical

Grain yield (kg ha-1)

3500

3000

2000 Y = 2205.7970 + 187.5253X - 7.7712X 2 R2 = 0.8688**

1000

0

3

6

9

12

15

18

Lime rate (Mg ha-1)

Figure 7 Relationship between lime rate and soybean grain yield grown on Brazilian Oxisols.

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changes in acid soils. Hence, the best method is applying it as broadcast as uniformly as possible and mixing thoroughly through the soil profile. Lime broadcasting machines are available for uniform application of liming materials. Liming frequency is mainly determined by intensity of cropping, crop species planted, and levels of Ca2þ, Mg2þ, Al, and pH in a soil after each harvest. The effect of lime is long lasting but not permanent. After several crops, Ca2þ and Mg2þ move downward and beyond the reach of roots. These elements are taken up by crops and, to some extent, are lost through soil erosion. Acid-forming fertilizers and decomposing organic matter lower the soil pH and release more aluminum to the soil solution and cation exchange sites on soil particles. When values of exchangeable Ca2þ, Mg2þ, and pH fall below optimum levels for a given crop species, liming should be repeated. This means that soil samples should be taken periodically to determine changes in soil chemical properties and to decide liming frequency. Effects of lime do last longer than those of most other amendments; however, it is rarely necessary to lime more frequently than every 3 years (Caudle, 1991). Lime study on Oxisols of the cerrado region of Brazil showed that after 4 years, with eight crops grown (two crops each year) in rotation (rice, common bean, corn, and soybean), the soil maintained levels of soil pH and Ca2þ and Mg2þ contents to maintain yields of these crops at an optimal level (Table 19) (Fageria, 2001a). The residual effect of coarse Table 19 Values of soil pH and Ca2þ and Mg2þ contents after harvest of eight crops (upland rice, common bean, corn, and soybean) grown in rotation for 4 years on a Brazilian Oxisol at two soil depths Lime rate (Mg ha1)

0–20 cm soil depth 0 4 8 12 16 20 20–40 cm soil depth 0 4 8 12 16 20 Source: Fageria (2001a).

pH in H2O

Ca2þ (cmolc kg1)

Mg2þ (cmolc kg1)

5.6 6.0 6.2 6.4 6.5 6.8

1.9 2.3 3.0 3.1 3.3 3.8

1.0 1.1 1.2 1.2 1.3 1.4

5.5 5.9 6.1 6.2 6.3 6.7

1.7 1.9 2.3 2.4 2.6 3.3

0.9 1.0 1.1 1.1 1.2 1.3

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lime material is greater than with finer lime material because large lime particles react slowly with soil acidity and tend to remain in the soil longer. Liming material should be mixed thoroughly in the soil as deeply as possible to improve crop-rooting systems in acid soils. However, with currently available machinery, it is generally mixed to a depth of 20– 30 cm. A depth greater than 30 cm required more powers and became more costly in terms of labor and energy. Timing of lime application is important in achieving desirable results. Lime should be applied as far in advance as possible of planting of crop to allow it to react with soil colloids and to bring about significant changes in soil chemical properties. Soil moisture and temperature are determining factors for lime to react with soil colloids. In Brazil, most of the published work shows that liming should be done 3 months in advance of sowing a crop. However, other studies carried at the National Rice and Bean Research Center have shown that in Brazilian Oxisols significant chemical changes can take place 4–6 weeks after applying liming materials if soil has sufficient moisture (Fageria, 1984, 2001a). Hence, to obtain desirable results, it is not necessary to wait for a longer period of time after applying lime.

8. Conclusions The increasing acidification of agricultural soils, as a result of natural processes, industrial pollution, and agricultural practices, is adversely affecting crop production worldwide on significant levels. Highly weathered acid soils cover about two-thirds of tropical America and are of great ecological and economical importance. Soil order Oxisols make up the largest proportion of acid soils of this region. Soils of this order are acidic with low fertility. Most soil acidity problems can be managed successfully, without economic and environmental disruption. Among management factors for acid soils, perhaps most important is supplying sufficient lime initially to correct the majority of growth-limiting factors. Hence, adequate liming and fertilization can reduce the adverse effects of soil acidity and improve crop yields. The central part of Brazil, locally known as the cerrado region, contains predominantly Oxisosls. Modern agricultural practices, including liming and fertilization, could help to achieve economic yields on these acidic and infertile soils. On these soils, the amount of lime needed for maximum yield varies with crop species and cultivar within species. Experimental findings have shown that maximum economic yield for most annual crops on Oxisols can be obtained with application of 4–6 Mg ha1 of lime. Initially, liming acid soils not only improves crop yields but also helps to maintain environmental quality and consequently improves animal and human health. Overliming, however, can significantly reduce the

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Table 20

Common and scientific names of plant species cited in text

Common name

Crop species Corn Wheat Soybean Rice Barley Peanut Cowpea Potato Cassava Pigeon pea Millet Kudzu Graybean Triticale Buckwheat Rye Crotolaria Pasture species (grasses and legumes) Gambagrass or Carimagua Surinamgrass Pangolagrass Jaraguagrass Paragrass Guineagrass Bahiagrass Napiergrass Desmodium Brazilian stylo Pueraria Zornia Plantation crops Banana Cashew Coconut Brazilian nut Eucalyptus Coffee Rubber

Scientific name

Zea mays L. Triticum aestivum L. Glycimne max (L.) Merr. Oryza sativa L. Hordeum vulgare L. Arachis hypogaea L. Vigna unguiculata (L.) Walp. Solanum tuberosum L. Manihot esculenta Crantz Cajanus cajan (L.) Millspaugh Pennisetum glaucum (L.) R. Br. Pueraria phaseoloides (Roxb.) Benth. Mucuna cinerecum L. X Triticosecale Wittmack Fygopyrum esculentum Moench Secale cereale L. Crotolaria breviflora

Andropogon gayanus Kunth Brachiaria decumbens Stapf. Digitaria decumbens Stent Hyparrhenia rufa (Nees) Stapf. Brachiaria mutica (Forsk) Stafp. Panicum maximum Jacq. Paspalum notatum Fluegge Pennisetum purpureum Schumach Desmodium ovalifolium Guillemin & Perrottet Stylosanthes guianiensis (Aubl.) Scv. Pueraria phaseoloides Roxb. Zornia latifolia Musa paradisiaca L. Anacardium occidentale Cocos nucifera Bertholletia excelsa Eucalyptus grandiflora (Gaertn.) Hochr. Coffea arabica L. Hevea brasiliensis (continued)

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Table 20 (continued) Common name

Scientific name

Oil palm Guarana Tea Papaya

Elaeis guineensis Paullinia cupana Cammellia Sinensis (L.) Ktze. Carica papaya

bioavailability of micronutrients (Zn, Cu, Fe, Mn, B), which decreases with increasing pH (Fageria et al., 2002). This can produce plant nutrient deficiencies, particularly that of Fe. Planting acid-tolerant crop species and genotypes within species is another most cost-effective and environmentally sound approach to improve crop production on acid soils. Some high soil acidity-tolerant crop species like millet, rice, cassava, and cowpea could be produced with minimum input of lime. In addition, some pasture grass and legume species have high tolerance to soil acidity and these could be used in developing sustainable cropping system for Oxisols. Some plantation crops also have high tolerance to soil acidity and could be used effectively in acid ecosystem cropping systems, along with annual crops. Low to medium acidity-tolerant crop species such as soybean, sorghum, and peanut are suitable crops for low input farming systems. High acidity-tolerant grasses, such as brachiaria and andropogon and plantation crops such as rubber and oil palm, are suitable crops for extremely acidic soils. In addition, soil acidity can be managed successfully by adopting practices that maximize the biological activities in the soil, such as nutrient mineralization and cycling, biological nitrogen fixation, and mycorrhizal symbiosis. In areas where limestone quarries and transportation systems are well developed, lime is almost always inexpensive and typically provides a very high rate of return on investment. However, in some areas, transportation costs have made lime very costly. In any case, lime should be viewed as an investment in improving the production potentials of acid soils. Because of the long-lasting residual effects of lime applications, the cost of liming may be amortized over several crops. Common and scientific names of crop species cited in the text are listed in Table 20.

ACKNOWLEDGMENT We thank Dr. C. D. Foy for his critical review and valuable suggestions for improving this manuscript.

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Index

A Acidification ammonium nitrate oxidation, 196 CaCO3 dissolution, 194 carboxylates role in, 196–197 pCO2 role in, 195 proton extrusion, 195–196 Acidithiobacillus ferrooxidans, 196 Acid mine drainage, 14 Alkali disease, 20 Aluminium solubilization, 347–348 Anaeromyxobacter sp., 29 Arabidopsis thaliana, 201–202 Arsenic (As) acid mine drainage, 14 in groundwater contamination, 12–13 removal, 13–14 microbial activity, 14–15 sources and uses, 11 Arsenicicoccus bolidensis, 15 Atrazine herbicides, 156 B Bacterial antagonism, plant health, 205–207 Benzene, toluene, ethylbenzene, and xylene (BTEX), 68 contamination and biodegradation, 60–61 and Fe reduction, 61–62 microbial degradation, 62 Bioaccumulation, 16–17, 19 Bioavailable iron, definition, 192 Bioenergy, definition, 126 Bioethanol production, 154–155, 161–162 Biofuel crop, sugarcane cultivation global production, 127–128 trash and green harvesting, 128 Biological nitrogen fixation (BNF), 146 Bioremediation, 3 BTEX contaminants, 61 chlorinated solvents, 52–53, 55 limiting factor, 6–7 organic contaminant, 50 selenium, 21 technetium, 32–33 uranium, 25–28 Biotransformation, 6, 13, 64

Brevibacillus sp., 29 Buffering capacity, soil, 376 C Canadian Forces Base (CFB), 63 Capillary fringe, 8–10 Chemical kinetic and equilibrium model (KEMOD), simulation model, 67 Chlorinated aliphatic hydrocarbons (CAHs), 51, 57 Chlorinated solvents C isotope technique, 55 coupled processes influence, 52–53 microbial-mediated dechlorination, 56–57 subsurface migration of, 51–52 TCE biodegradation, 53–54 TEAP variations, 54–55 Chloroform (CF), 51, 57 Chromium (Cr), 22–24 Coal-tar creosote, 62–64 Coca ColaÒ , 290 Colloidal interferences, 320–321 Community amplified ribosomal DNA restriction analysis (ARDRA), 56 Coupled processes, subsurface environments BIOMOC and UCODE, 68 BTEX, 60–62 coal-tar creosote, 62–64 crude oil, 57–60 CRUNCH and FERACT model, 46 DNAPL transport, 51–53 3D numerical model, 68 herbicide degradation, 66–67 HYDROGEOCHEM model, 45–46 hydrologic processes geochemical reactions, 6 microbial activity, 6–8 landfills, 40–42 metal contaminants arsenic (As), 11–15 chromium (Cr), 22–24 mercury (Hg), 15–20 selenium (Se), 20–22 MODFLOW model, 47 MT3D99 model, 67–68 nitrate in groundwater contamination, 35–37 nitrogen cycle, 34–35 radioactive waste, 38–39

401

402

Index

Coupled processes, subsurface environments (cont.) perchlorate, 39–40 permeable reactive barriers, 42–44 pesticides fate and transport, 65–66 PFLOTRAN model, 69 radionuclide contaminants strontium (Sr), 33–34 technetium (Tc), 32–33 uranium (U), 24–32 RPARSim/KEMOD model, 67 Crop rotation, 368–369 Crude oil, 57, 59, 61, 68 65 Cu spiking, 308–309 D Denaturing gradient gel electrophoresis (DGGE), 56 Dense nonaqueous phase liquids (DNAPLs), 51–52, 62, 67 Department of Defense (DoD), 2, 24, 55 Department of Energy (DOE), 2 Hg dissemination, 15 nitrate waste plumes, 38–39 remediation below ground inventories, 2 strontium, 34 technetium, 32–33 uranium, 24–25 Desulfovibria spp., 30 Dichloroelimination, 51 Diffusion gradients in thin films (DGT), 292 Dissimilatory iron reducing bacterium (DIRB), 278–279 E Enterobacter cloacae, 22 E-value determination, isotopic dilution methods accuracy and precision, 314–315 equilibration time, 310–313 interpretation metal uptake or toxicity, 326 usefulness, 327 isotope fixation, 317 schematic representation, 311 suspension matrix choice, 309–310 F Fenton chemistry, 202 Fe(III)-reducing bacteria (FeRB), 26, 29–30, 56 Ferrihydrite biotransformation, 278–279 solubility, 188, 190 Ferritin, 202 Fertilizer denitrification, 141–142 Fluorescent pseudomonads. See Pyoverdines

Fougerite mineral citrate-bicarbonate (CB) extraction of, 241–242 ferrous doublet in, 240 geochemical and structural constraints of, 264–265 geochemical significance, 280–281 identification, XRD spectra decomposition criteria and characteristics, 262 decomposition peak results, 254–260 interlayer anion in fougeres–fougerite, 260–261 material and methods, 254 Mg-saturated samples in, 260 structural characterization, 247, 252–253 ternary solid solution model, 261–263 G Geobacteraceae, 25–26, 29 Geobacter spp., 30–31 Geochemical transport models inorganic contaminants CRUNCH and FERACT, 46 HYDROGEOCHEM and HBGC123D, 45–46 MODFLOW, 47 organic contaminants BIOPLUME III and KEMOD, 67 3D numerical model, 68 MT3D99 67–68 PFLOTRAN, 69 GeoChip, 30 Gibbs free energy, 265–266 Gleyey soil, iron marker field tests, 231–233 rH measurements Ag/AgCl electrode, 235–236 electron potential, 233–234 Nernst’s law, 237 soil color, 229–231 Goethite DCB and CB for, 242 and DIRB, 279 physical properties, 240 redox interactions, 273 Green rusts (GRs). See also Fougerite mineral; Synthetic green rusts formation, DIRB, 278–279 green rust1 (GR1) crystal structure, 248 interplanar distances and intensities, 249–250 layer-to-layer and interatomic distances, 252 reduced coordinates of atoms, 251 stacking sequences and interlayered anions, 246, 248

403

Index

green rust2 (GR2) crystal structure, 248 interlayered anions, 252 and metals, 228 nitrate reduction mechanism, 276–277 pH, 273–274 seasonal dynamics, 274–276 redox interactions electron potential and pH, 271–272 Fe(II)–Fe(III) hydroxides in, 273 selenate reduction, 277–278 solid solution model chemical potential estimation, 263, 269 fougerite estimation, 270 H Herbicides degradation, 66–67 environmental impact, 157 leaching of, 157–158 macrofauna, 152 and pesticides, 156–158 Humic substances, 191–192 Hydrogen ion activity. See Soil pH Hydrogenolysis, 51 Hydroxychloride green rust, 248 I Icenucleation activity. See Bioavailable iron Inorganic fertilizers effects of microbial biomass, 153 on sugarcane yield, 140 heavy metals and rare earth elements, 160 nitrogen fertilizer, 158–159 phosphorus fertilizer, 159 recovery of, 143 Iron (Fe) acidification, 194–197 bioavailability, 192–193 biological properties, 185–186 chemical properties chelation and complex formation, 190–191 iron oxides solubility, 188–190 oxidation, 188 concentration of, 186 dynamics, 187 ferritin, homeostasis, 202 interactions in rhizosphere bacterial antagonism, 205–207 Fusarium oxysporum role, 203–204 plant nutrition, 207–208 pyoverdin-mediated iron uptake, 204–205 thermodynamic and kinetic constraints, 209

nutrient bioavailability, 193 oxides solubility, 188–190 uptake strategy Fe(III) reduction, 200–201 phytosiderophores role, 197–199 siderophores and pyoverdines, 199–200 Iron, redox geochemistry. See also Green rusts (GRs) chemical extraction citrate-bicarbonate (CB) reagent, 241 dithionite-citrate-bicarbonate (DCB) reagent, 240–241 soil profile, 242 soil solids iron characterization, 239–242 sample conditioning, 239 soil solutions characterization of, 238–239 Fe control, 269–270 mobility and seasonal dynamics, 242–246 nitrate dynamics, 274–276 sampling, 238 Isotopically exchangeable kinetic (IEK) method, 310–313 Isotopic dilution methods accuracy and precision in, 314–315 colloidal interferences, 320–321 and equilibration time, 310–313 error propagation, 317–318 E-value determination interpretation of, 325–327 schematic representation for, 311 suspension matrix choice, 309–310 HVG-AAS determination of Se, 319 isotope choice, 306–307 L-value determination Cd and Zn E-and L-values, 328–331 deposition rates in, 333 methodological sources of error, 333–334 mixing method, 320 seed/juvenile contribution, 318–319 oxidation state changes arsenic redox conditions, 322 equilibration time, 323–324 PIE and E-value (Etot) 321–323 As and Se elements, 325 principle E-and L-value procedures, 294–295 exchangeable pool assessement, 296–305 and soil contaminants, 293 solution equilibrium models, 335 spike-derived artifacts and isotope fixation, 317 206 Pb spike values, 316 spiking for, 308–309 uses of speciation techniques, 336

404

Index L

Lepidocrocite bioreduction, 279 oxidation of green rust, 261 physical properties, 240 Lime requirement, crop production applications of, 384–386 chemical analysis, 365–366 conservation tillage, 370–371 crop rotation, 368–369 crop species and genotypes, 371–372 definition, 364–365 nutrient interactions Ca2þ, Mg2þ, and Al3þ levels in, 374–375 ion-ion, 372–373 types of, 373–374 organic manure benefits, 369–370 quantity determination aluminum saturation, 381–383 base saturation, 377–379 crop responses, 383–384 exchangeable ions, 379–381 soil pH, 376–377 soil fertility, 367–368 soil texture, 366–367 Liming method calcium and magnesium, role of, 353–354 disadvantages of, 363–364 heavy metals leaching and solubility, 358–360 improving soil structure, 360 mineral nutrition, 361–362 mycorrhizal colonization, 358 nitrous oxide mitigation, 362–363 nutrient use efficiency, 360–361 plant-beneficial microorganisms, 356–357 reducing phosphorus immobilization, 355–356 soil acidity amelioration, 350 Lolium perenne. See Ryegrass plant L-value determination, isotope dilution methods consequences of, 313–314 mixing method, 320 seed/juvenile contribution, 318–319 vs. E-values Cd and Zn, 328–331 deposition rates, 333 methodological sources of error, 333–334 M Macrofauna, fire ants, 152 Mercury (Hg) biogeochemical cycle, 16 emissions and toxicological effects, 15–16 in groundwater, 18 microbial activity, 17 migration processes, 17–18 speciation, 20

Metal contaminants arsenic (As), 11–15 chromium (Cr), 22–24 mercury (Hg), 15–20 selenium (Se), 20–22 Methylmercury (MeHg), 16–17, 19 Microbial biomass, 153–154 Miracle-GroÒ 118 Mycorrhizal colonization, 358 N Nernst’s law, 237 Neutralizing power, 366 Nitrate in groundwater contamination agricultural activities, 35–36 denitrification, 36–37 waste application, 37 nitrogen cycle, 34–35 radioactive waste, 38–39 Nitrification, 348 Nitrogen cycle, 34–35 Nitrogen (N) fertilizers application of, 137, 159, 167 gaseous losses efficiency of, 142 fraction of, 141 recovery of, 143 leaching, 140–141, 143, 158–159 Nitrous oxide (N2O) mitigation, 362 Nonaqueous phase liquids (NAPLs), 51 Nutrient balances, soil chemical properties agronomic variation, 144 biological nitrogen fixation (BNF), 145–146 denitrification, 145 O Organochlorine pesticides, 157 Oxisols definition, 352 occurence and distribution, 350–351 P Paenibacillus sp., 29 Perchlorate ions in environment conditions chemical properties, 102 natural occurrence of, 103–104 production of, 102–103 tetrahedral structure of, 103 phytodegradation, 111 in plants highly contaminated site assessment, 106–107 market survey assessment, 108–110 rhizodegradation, 111–112

405

Index

toxicological issues analytical advancements in, 104 ecological effects, 106 human health effects, 105–106 uptake methodologies linear regression analysis, 117 phytoremediation, 110–112 surface adsorption, 119 temporal concentration data, 118–119 transpiration mechanism, 114–115 Perchloroethylene (PCE), 51 Permeable reactive barriers (PRB), 42–44, 47–48, 69 Pesticides erosion control, 151 and herbicides, 156–158 leaching of, 157–158 microbial biomass, 153 microbial degradation of, 64–66 pest management practices, 165 Petroleum, 57, 59–60 Phospholipids fatty acid (PLFA), 26 Phytodegradation, of perchlorate, 111–112 Phytoremediation under aerobic conditions, 111–112 anaerobic microcosms, 111 characterization of, 110 radio-labeled perchlorate detection, 112 Phytosiderophores iron uptake, 199 soil concentration of, 197 structures of, 198 Polycyclic aromatic hydrocarbons (PAHs), 62, 64 Potentially incorrect E-value (PIE), 321–323 Preharvest burning, sugarcane cultivation air pollution, 160–161 bioethanol production, 162 effects of, 165–166 greenhouse gas production, 155 loss of soil organic matter, 168 Pseudomonas fluorescens, 193 Pseudomonas stutzeri, 57 Pseudo-radial distribution functions (PRDF), 253 P spring effect, 355 pvd-inaZ, reporter gene, 204 Pyoverdines composition and synthesis, 199–200 iron uptake, 204–205 R Radio-labeled perchlorate, 112 Radionuclide contaminants strontium (Sr), 33–34 technetium (Tc), 32–33 uranium (U), 24–32 Redox-labile elements, 321 Rhizodegradation, 111–112

Rhizodeposition, 184–185 Rhizosphere iron-mediated interactions impact of plant health, 205–207 plant iron nutrients, 204–205, 207–209 soil’s chemical properties and microorganisms, 203–204 iron solubilization acidification, 195–197 chelation and complexation, 197–200 reduction, 200–201 Ryegrass plant, 331 S Selenate reduction, green rusts, 277–278 Selenium (Se), 20–22 Shewanella putrefaciens, 279 Siderophores. See Phytosiderophores Soil acidity Al solubilization, 347–348 bacteria groups, 357 causes, 349 leaching method, 348 lime requirement, crop production applications of, 384–386 chemical analysis, 365–366 conservation tillage in, 370–371 crop rotation, 368–369 crop species and genotypes, 371–372 definition, 364–365 nutrient interactions, 372–375 organic manure benefits, 369–370 quantity determination, 376–384 liming method amelioration, 350, 352 calcium and magnesium role, 353–354 disadvantages of, 363–364 heavy metals leaching and solubility, 358–360 improving soil structure, 360 mineral nutrition, 361–362 mycorrhizal colonization, 358 nitrous oxide mitigation, 362–363 nutrient use efficiency in, 360–361 plant-beneficial microorganisms, 356–357 reducing phosphorus immobilization, 355–356 occurence and quantification, 347 Soil erosion control, 151 denitrification and nutrient losses, 145 soil compaction, 163 sugarcane cultivation, 149–150 Soil fertility, 367–368 Soil organic matter dynamics alfisols, 140 inceptisols and oxisols, 139–140

406 Soil organic matter dynamics (cont.) regression model, 138 systems of cultivation, 137–138 vertisols, 139 Soil pH, 376–377 Soil properties, sugarcane cultivation biological properties macrofauna, 152 microbial biomass, 153–154 chemical properties biological nitrogen fixation, 146 denitrification and volatilization, 141–142 different land-use system samples, 133–134, 136–137 inorganic fertilizers, 142 leaching, 140–141 monitoring, 129, 133–135 nutrient balances, 142–146 organic matter dynamics, 137–140 Type I and Type II data sources, 129 physical properties compaction and aggregate stability, 147–149, 163 soil erosion and control, 149–151 Soil texture, 366–367 Solid solution model, green rust chemical potential estimation, 263, 269 fougerite estimation, 270 Strontium (Sr), 33–34 Subsurface environments. See also Coupled processes, subsurface environments capillary fringe, 8–10 future environmental issues, 70–72 geochemical and microbial reactions, 6–8 hydrologic processes geochemical reactions, 6 microbial activity, 6–8 structured media, 4–6 terminal electron accepting processes hydrocarbon contamination, 54 recharge effects on, 55–56, 58–59 sewage-effluent plume, 54–55 zones, 48–49 Sugarcane cultivation bioethanol production, 154–155, 161–162 biofuel crop global production, 127–128 effects on air and water greenhouse gas emissions, 165 leaching, 164–165 preharvest burning, 165–166 environmental issues air quality, 160–161 herbicides, 156–157 impact on, 165 inorganic fertilizers, 158–160 pesticides and insecticides, 157–158 soil and water resource contamination, 155

Index

water quality, 161 land-use system samples oxisols, 133 soil organic C contents, 136–137 Type II data, 134, 136 vertisol, 133–134 precision agriculture biofertilizers, 170 economic and ecological benefits of, 169 preharvest burning air pollution, 160–161 bioethanol production, 162 effects of, 165–166 greenhouse gas production, 155 loss of soil organic matter, 168 ratoon crop, 128 soil acidification, 137, 162–163 soil compaction, 147–149, 163 soil degradation, 137, 168 soil organic carbon contents, 165 soil pH, 134–137 sugarcane yields effects of continuous cultivation, 166 and soil changes, 168 soil fertility, 166–167 trash harvesting, 167–168 trash and green harvesting advantages and disadvantages, 164 soil organic matter dynamics, 137–140 Sugarcane monocropping systems, 169 Sulfate-reducing bacteria (SRB), 41, 57 Hg methylation, 20 TCE biodegradation, 53 technetium reduction, 33 uranium remediation, 26–28 Synthetic green rusts anions electronegativities and Gibbs free energies of, 265–266 green rust1 (GR1) crystal structure, 248 interplanar distances and intensities, 249–250 layer-to-layer and interatomic distances, 252 reduced coordinates of atoms, 251 stacking sequences and interlayered anions, 246, 248 green rust2 (GR2) crystal structure, 248 interlayered anions, 252 thermodynamic data, 266–269 T Technetium (Tc), 32–33 Terminal electron accepting processes (TEAPs), subsurface environments hydrocarbon contamination, 54

407

Index

recharge effects on, 55–56, 58–59 sewage-effluent plume, 54 zones, 48–49 Thermodynamic modeling Gibbs free energy vs. electronegativity, 265–266 solid solution composition, 264–265 ternary solid solution model, 261, 263 Thlaspi caerulescens, 328, 331 Trace elements adsorption and desorption methods, 291–292 fractionation methods, 290–291 isotopic dilution methods accuracy and precision, 314–315 colloidal interferences, 320–321 equilibration time, 310–313 isotope choice, 306–307 L-value determination, 313–314 oxidation state changes in, 321–325 principle of, 293–305 and soil contaminants, 293 spike-derived artifacts, 315–317 spiking for, 308–309 spectroscopic techniques, 292 Transpiration mechanism, perchlorate ions, 114–115 Trash and green harvesting, 128 advantages and disadvantages, 164 soil organic matter dynamics alfisols, 140 cultivation systems, 137–138 vertisols, 139 Trash management systems, 142 Trichloroethane (TCA), 51 Trichloroethylene (TCE) dechlorination, 54–55

DNAPL formation and transport, 51–52 groundwater degradation, 55 temporal variability, 53 U Uranium (U), 24 bioreduction, 31 bioremediation, 27–28 biotransformation analysis, 29–31 PLFA indicators, 26 reduction of U(VI), 25–26 US Environmental Protection Agency (EPA), 2, 102, 105–106 BIOPLUME III model, 67 MCL for arsenic, 12 nitrate, 35–36 perchlorate, 39 selenium effects, 21 X X-ray absorption near edge structure (XANES), 30 XRD spectra decomposition, Fougerite criteria and characteristics, 262 decomposition peak results, 254–260 interlayer anion in fougeres–fougerite, 260–261 material and methods, 254 Mg-saturated samples in, 260 Z Zero-tension lysimeters, 238