NATHALIE PATEL
Modeling METAL–PARTICLE INTERACTIONS with an Emphasis on Natural Organic Matter Numerous models exist for predicting metal interactions in the environment, but few have been validated with field observations. PATRICI A MERDY SA NDRINE HUCLIER UNIV ERSIT Y OF TOULON (FR A NCE) LUUK K. KOOPA L WAGENINGEN UNIV ERSIT Y (THE NETHERL A NDS) © 2006 American Chemical Society
M
odeling the binding of metal ions and actinides to natural particles of various origins remains a challenging task. The use of models to understand the speciation and distribution of metals in the environment is essential for predicting metal bioavailability. In this article, we discuss the primary metal–particle interaction models and their limits, with an emphasis on natural organic matter (NOM). These models have varying levels of complexity, but we discuss only those based on parameters that are independent of environmental conditions. We also discuss the application of such models to field predictions. DECEMBER 15, 2006 / Environmental Science & Technology n 7459
Metal diversity and complexity The presence and transport of trace metals (Fe, Al, Cd, Cu, Pb, As, and Hg) and radionuclides (Th, Pu, Cm, and Cs) lead to metal-ion contamination of natural and seminatural ecosystems (sediments, soils, and water) and constitute a potential source of health risk for plants, humans, and other organisms. Whatever their sources, metals may interact with other components present in the natural system, such as dissolved low-molar-mass organic or inorganic ligands, high-molar-mass particulate organic or inorganic materials, and living organisms. These interactions have consequences for metal transport, bioavailability, and risk assessment. Before we can evaluate how contaminants affect the biological populations and what environmental risks they pose, the fate of such contaminants must be known. Insight into the dynamics of metal pollutants in natural systems can be gained by combining experiments and modeling studies, but this approach is feasible only when the metal interactions with the different components can be described adequately. Complexation reactions of metal ions with lowmolar-mass components are well understood (1, 2), and considering the solution complexation reactions in further modeling is common practice. High-molar-mass particles can be present in the aqueous phase, as dispersed or dissolved components, or in the immobile solid phase. These particles include NOM, clays, aluminosilicates, iron and manganese aluminum oxides or oxyhydroxides, carbonates, and silica. They may exist alone or associate together. This makes their interactions with contaminants rather diverse. Therefore, the behaviors of mineral oxides and organic matter must be clearly distinguished in models. In the next section, we discuss the complexity of metal interactions in more detail and present the most relevant models that have been developed. The interaction of metals with living organisms constitutes a very complex and multidisciplinary field of science (3). One example of an integrated approach is the evaluation of the rhizotoxicity of Cd and Cu in soil extract by combining a metal-interaction model for NOM with a metal-binding model for the cell wall of the root (4). Voigt et al. (4) showed that rhizotoxicity was better correlated with root– metal complexes than with the free-metal-ion activity in soil solutions. Such a combination of models is called a biotic-ligand model (BLM; 5); the aquatic receptor site on the organism is considered to be a biotic ligand that competes with NOM for the sorption of metal ions. BLM is an interesting approach to evaluate the biological aspect of the impact of metals on plants (6, 7). Little is known about biotic-ligand–metal interactions, which are beyond the scope of this article.
Metal–particle interactions Models for general-purpose speciation programs should be based on parameters that are intrinsic (i.e., that do not vary with the solution conditions). For such models, the complexity of ion binding 7460 n Environmental Science & Technology / DECEMBER 15, 2006
should be considered explicitly; this inevitably leads to multiple-parameter models. Metal–particle interactions have two main aspects. First, the ions bind to the sites on the mineral surface, the functional groups of the NOM, or both. Second, electrostatic effects occur because the charge of the ions affects the charge of the mineral surface, the NOM particles, or both. The site binding can be described with affinity or stability constants, provided that lateral interactions (except for electrostatic interactions) between the sites can be neglected. The electrostatic interactions are a function of particle charge, which is directly related to ion binding itself—this
Metal interactions have consequences for metal transport, bioavailability, and risk assessment. holds for protons, too. Site competition as well as electrostatic interaction affect proton and metalion binding and the competition between different metal ions. Therefore, when at a given pH, metalion binding is investigated at a series of metal-ion concentrations, both metal-ion and proton loading vary. The ionic strength of the background electrolyte also plays a role: screening of the charges makes particle charging easier because it suppresses the electrostatic particle potential(s). All of these effects are most conveniently taken into account in binding models with two interdependent parts: a site-binding model and an electrostatic model. Commonly used electrostatic models include an electric double layer (EDL) model for impenetrable surfaces and a Donnan model for particles that are penetrable for ions.
Metal–mineral interactions Classic approach. For the mineral oxides and hydroxides, some agreement has been reached about modeling ion binding. The classic approach is the 2-pK-EDL model (see Ref. 8), in which one type of surface group is present that can protonate in two steps, each characterized by a pK H value. Metal-ion binding to sites in the anionic form is characterized by metal-affinity constants. The electrostatic interactions are described by an EDL model that accounts for the surface charge and its countercharge in solution. In most cases, the solution side of the EDL is composed of at least one thin, charge-free layer between the surface plane and the so-called Stern plane as well as a layer in which the interacting ions (mainly background ions) are diffusely bound. Specifically bound metal ions can be placed at either the surface plane or the Stern plane. Modern approach. More realistic is the CD-MUSIC-EDL model (9, 10). In this model, a few types of surface groups can be present, and within the normal pH window, the protonation reaction of each type of group is given by one pK H value. The choice
of the functional groups and the stoichiometry of metal complexes are based on spectroscopic and crystallographic information, and the model yields an independent estimate of the proton-affinity constants. In the EDL model, smeared-out potentials are used, and the ions are allowed to have a spatial distribution of their charge over the surface plane and the Stern plane. See Ref. 11 for a brief review of both models. Amorphous hydroxides and clay minerals. Amorphous iron/aluminum oxyhydroxides and hydroxides can be treated as hydrous ferric oxide (12). In this case, the classical surface complexation model together with an EDL model that has only a surface plane in contact with a diffuse layer (13) is sufficient. Ion binding to clay surfaces is based mainly on ionexchange models.
Metal–NOM interactions The interaction models for metals and NOM, and specifically for the dissolved fraction, are different from the mineral models because of the strong heterogeneity and the complex, diverse structure of humic acids. Generally, a distinction is made between fulvic acids, which can be considered oligomeric polyfunctional acids, and humic acids, which range from structured polyelectrolytes to micellar structures. The diversity of existing models is related to the extent to which the solution conditions (pH, ionic strength, and different metals present) are taken into account. Reviews of the various models can be found in Refs. 14–16. Rigorous models must account for the facts that the number of binding sites differs for different humic acids; binding-site heterogeneity is large; site competition always takes place, because protons are always present and interact primarily with the same sites as the metal ions; the stoichiometry of the binding reactions can differ for different ions; and electrostatic interactions play a role (17). According to the literature reviews, Model VI (15, 18) and the NICA–Donnan model (17, 19) are best equipped to address these aspects. Model VI has appeared in two versions: the original by Tipping (15, 18) and the one we refer to as Model VI-S (20). To explain the differences and similarities of these models, we discuss the main aspects of heterogeneous site binding. Overall binding function. The basis for each model that takes heterogeneity into account is the overall binding equation. To reach a relatively simple overall binding equation, we must make two important assumptions (17). First, the diversity (in size, shape, ion permeability, and functional groups) of humic-acid particles in a given sample can be described by a kind of “average” behavior. This averaging is especially important for modeling the electrostatic interactions. Second, the site heterogeneity is very similar for all the ions that can bind. This assumption simplifies the treatment of ion competition to the heterogeneous sites. Of course, these assumptions may not hold in practice, but most models apply additional simplifications. For the binding of ion i to 1 g of humic-acid particles, the overall binding function may then be
written as a simple weighted summation over all the local contributions: Qi,tot =
� j
QH,j · (ni,j /nH,j)
· �i,j (Ki,j ,..., Kk,j, ci ,..., ck, ni,j ,..., nk,j,�j) where Qi,tot is the total loading (mol/g) of the humic acid with ion i, and QH,j is the maximum loading (mol/g) of sites of type j of the humic acid by protons (reference ions). Each local contribution is the binding of ion i to a group of homogeneous sites of type j expressed as a fraction, i,j (Ki,j, . . . , K k,j, ci , . . . , ck, ni,j, . . . , nk,j, j), of QH,j. The function i,j is called the local binding function. Ki,j is the affinity of ion i for site j, and ci is the concentration of ion i in the aqueous phase. The ratio ni,j /nH,j takes into account the stoichiometry factors, and it expresses that the maximum binding level on sites of type j varies for the different ions. Ideally, nH,j = 1, and ni,j is the fraction of ion i that can bind to one proton site. For mono-, bi-, and tri-dentate binding, ni,j equals (in principle) 1, 1/2, and 1/3, respectively. j is the electrostatic potential of sites of type j. Discrete or continuous distribution. When a wide distribution of site types is considered, a discrete or a continuous function can be used. The disadvantage of a wide discrete distribution is that many parameters are required. For each type of site, the various ion affinities, the stoichiometries with respect to the proton, the proton capacity, and an EDL model are needed. The number of parameters quickly becomes unmanageable. The solution is to fix, a priori, a subset of the parameters so that only the most relevant ones must be optimized. An assumption regarding the heterogeneity must be followed by assumptions about the stoichiometry and the electrostatic interactions. Determining which parameters will be fixed and which are best suited for fitting is a matter of insight and experience. The advantage of a discrete approach is that special reactions can be incorporated relatively easily (at the expense of increasing the number of parameters). An attractive alternative that reduces the number of parameters considerably is to use a continuous distribution function that is the same for all ions (with a common shape and width but a peak position that depends on the ion) and a stoichiometry that is independent of the site type. Because the number of parameters is limited, their optimization is a standard operation. A disadvantage of this treatment is that the separation of stoichiometry and heterogeneity is complex (17). Local binding function. The general equation of the local isotherm function is obtained from a binding polynomial (21) that takes into account the detailed binding reactions and the electrostatic interactions. In the simplest case, with the same stoichiometry for all ions and no electrostatic interactions, the local isotherm can be described by the competitive Langmuir equation; more complex equations are extensions of this one (17). In general, the electrostatic interaction is expressed by a Boltzmann factor that depends on the charge of the metal ion and on j. Together, the local and the overall isotherm equations form the site-binding part of the model. For the calculation DECEMBER 15, 2006 / Environmental Science & Technology n 7461
7462 n Environmental Science & Technology / DECEMBER 15, 2006
average potential is based partly on a mineral-oxidetype EDL model and partly on a Donnan-type model, depending on the humic-acid-to-fulvic-acid ratio. Especially the EDL part is unnecessarily complicated. An example of the quality of fit that can be reached with Model VI is depicted in Figure 1. A set of generic parameters for proton and metal-ion binding to both humic and fulvic acid, based on the analysis of multiple data series, is available for both models. FIGURE 1
(a) Lead and (b) cadmium binding by humic acid (65 mg/L), and the compet itive effects of aluminum The results for lead refer to pH 4.5 and those for cadmium to I = 5 mM. The symbols are experimental data of Pinheiro et al. (44 ); solid lines are optimized fits with Model VI; dotted lines were calculated with the default value of log KMA (Al) = 2.6. Adapted with permission from Ref. 15. (a)
log [Pbbound] (M)
–4
–5
–6
–7 (b)
I = 5 mM, no Al I = 5 mM, [Altot]=31 µM I =0.1 M, [Altot]= 31 µM –7
–6 log [Pb2+] (M)
–5
–4
–4.5 –5.0 log [Cdbound] (M)
of the electrostatic potentials from the amounts of bound ions, an EDL model is required. Patchwise and random heterogeneity. In principle, the local isotherm equation takes into account the fact that stoichiometry and electrostatic interactions can be different for each site type (patchwise heterogeneity approach). In general, this situation is very complex, and the binding behavior is impossible to characterize experimentally at all the different patches. Therefore, in most cases, an overall stoichiometry (ni,j = ñi ) and an overall electrostatic ~ effect (j =), both independent of site type, are assumed—the random- or regular-heterogeneity approach. In this case, the number of parameters is reduced considerably, and good estimates can be obtained of both the electrostatic interactions and the heterogeneity from proton adsorption measurements in the absence of metal ions (17). Electrostatic models. Several models relate the electrostatic potential to the charge of the humicacid particles under the assumption that the site heterogeneity is random or regular (15, 17, 22). Such models include only one electrostatic potential for the entire particle that depends on the total particle charge. For this potential to be calculated from the particle charge, the ionic strength is required, and the location of the particle charge must be assumed. Humic-acid particles have a fairly open structure that allows salt ions to partly penetrate them. If one assumes that each particle carries all its charge at its surface, a diffuse distribution of co- and counterions in solution is sufficient. The charges can also be distributed within the particle volume rather than “fixed” at the particle surface. This is done in the Donnan models, in which both the fixed and mobile countercharge are located in the Donnan volume. Limiting forms. The overall binding equation summarizes the role of the heterogeneity and the local binding function. Neglecting the summation implies neglecting the heterogeneity of the humic particles. Simplifications about competition, stoichiometry, and electrostatic interactions are all related to the local binding function. Models that neglect the ion competition underestimate the important role of the protons. In the charge-neutralization model (23), the metal–proton exchange is considered implicitly by considering metal-ion binding at constant solution conditions as an exchange process with protons at a metal-to-proton ratio equal to the charge ratio of the ions. Heterogeneity and metal– metal competition are not considered. Models VI and VI-S. Models VI (15, 18) and VIS (20) follow the discrete approach with a priori assumptions about a large subset of parameters. With this approach, special reactions and stoichiometries can be incorporated relatively easily. The electrostatic interactions are taken into account by assuming an average electrostatic particle potential. The electrostatic model used in Model VI (15) is a simple Boltzmann factor that depends on the charge number of the particles and the ionic strength. The Boltzmann factor also can vary with the type of humic acid. For Model VI-S (20), the site-binding part is the same as in Model VI, but the calculation of the
–5.5 –6.0 –6.5 –7.0
pH 4.5, no Al pH 4.0, no Al pH 4.5, 31 µM Al pH 4.0, 31 µM Al
–7.5 –7.5 –7.0 –6.5 –6.0 –5.5 –5.0 –4.5 –4.0 –3.5 log [Cd2+] (M)
NICA–Donnan model. The NICA–Donnan approach (17, 19, 24) is based on a summation of two specific continuous distribution functions to avoid having too many specific parameters and to obtain an analytical solution for the overall binding equation. For each distribution function, the width is required; for each ion, an average affinity and an average stoichiometry are required (the NICA part). The electrostatic potential is believed to be spread
FIGURE 2
Example of modeling cadmium–NOM (purified peat humic acid, PPHA) interactions at different pH values and calcium concentrations with the NICA–Donnan model. The symbols are the experimental results; the curves are the NICA–Donnan predictions. (a) Proton binding at a range of salt concentrations; the merging curves on the left are corrected for the electrostatic effect with the Donnan model. (b) Calcium binding at 0.1 mol/L KNO 3 and various pH values. (c) Cadmium binding at 0.1 mol/L KNO 3 and various pH values. (d) Cadmium binding with and without calcium at 0.1 mol/L KNO 3 and 4 pH values. Adapted with permission from Ref. 20. (a)
(b)
0
pH 10 log [Ca2+]bound (mol/kg PPHA)
Charge (mol/kg PPHA)
–1 –2 –3 –4 –5
4
6
(c)
pH
8
–1
–5
–4 –3 log [Ca2+] (mol/L)
–2
–1
0 log [Cd2+]bound (mol/kg PPHA)
log [Cd2+]bound (mol/kg PPHA)
pH 8 pH 6
–0.5
(d)
–1
–2 pH 10
–4 –12
0
–1.5 –6
10
0
–3
0.5
pH 8 –10
pH 6
–1
–2 pH 10 –3
pH 8
pH 4
–8 –6 –4 log [Cd2+] (mol/L)
–2
0
over the entire particle, and a Donnan model is used to calculate this potential. The Donnan volume is made a simple function of the ionic strength. Both the site heterogeneity (NICA) and the Donnan model can easily be adjusted to each type of humic acid. Also, when the NICA–Donnan model is used, generic parameter sets are available for proton and metal-ion binding to either humic or fulvic acids (24, 25). Figure 2 depicts an example of results achieved with this model. Two advantages of the NICA–Donnan model over Model VI are that the equations can also easily be optimized to describe ion binding to compost or cell walls (e.g., in BLMs; 17) and can be used in thermodynamic relations (26).
Metal interactions with organic–mineral complexes Bifunctional polyelectrolyte approach. Minerals and NOM are often present together in natural systems, and the surface properties of the miner-
–4 –12
–10
pH 6 –8
pH 4 –6 –4 log [Cd2+] (mol/L)
with Ca without Ca –2
0
als are modified by the bound humic acids and vice versa. As a result, the sorption of metals to organic–mineral complexes (OMCs) is different from that to humic substances or to oxides alone (27–31). The mechanisms leading to the formation of these complexes and their interactions with metals are not well studied, and efforts to model such systems are still scarce. In the first attempts, humic particles were considered to be high-molar-mass, bifunctional, weak organic polyelectrolytes that adsorb to a variably charged oxide surface. On humic adsorption, proton binding to both the polyelectrolyte and the oxide changes considerably (32, 33), and consequently, metal binding also is altered. MUSIC/NICA–Donnan model. Weng et al. recently demonstrated the effectiveness of the CDMUSIC-EDL and NICA–Donnan models (26, 34). The advantage of this treatment is that all the information captured in the parameters of the two existing models can now be used to predict both the adsorption DECEMBER 15, 2006 / Environmental Science & Technology n 7463
of the humic acid to the oxide and the ion binding to the OMC. Weng et al. show that the charge adaptation of the humic acid is a very important factor in determining its binding to the mineral. This explains why ion binding to the complex is different from the sum of the binding to the individual components. Weng et al. illustrate the method for fulvic-acid adsorption at various pH values and two ionic strengths (34).
Laboratory calibration and field predictions General aspects. The models that have been tested in the laboratory (VI, VI-S, and NICA–Donnan) must be validated for their ability to make predictions in the field. Because the natural environment is much more complex than laboratory experiments, comparing experimental predictions with in situ (field) measurements and unraveling the discrepancies is very important. Various questions may arise. Can the generic parameters calculated from laboratory experiments be used to accurately predict metal–NOM interactions in the field? How much of the organic carbon is fulvic acid, and how much is humic acid? Can freshwater dissolved organic matter (DOM) and peat or soil organic matter (SOM) be described with the same set of model parameters, or must model parameters be optimized for different situations—and if so, to what extent? Can the trace concentrations of free metal be accurately measured when most of the metal is bound as a labile fraction? Freshwater. Several researchers have performed in situ measurements and calculated metal speciation in freshwater. Tessier et al. (35) were among the first to compare metal sorption in two lakes with model predictions for both oxides (2pK-EDL) and humic acids (Model V, a precursor to Model VI). Their results supported the argument that laboratory-derived data sets may be useful for predicting metal adsorption in the field. Good agreement was found between the measured conditional constants and the binding constants derived from Tipping’s Model V (15). Zhang (36) found that Model VI successfully predicted the species distribution of Ni, Zn, and Cu when the competitive binding by Fe(III) was taken into account. Although good model predictions of metal–humic-acid binding were obtained, problems occurred with predicting the speciation of the inorganic compounds. Unsworth et al. (37) compared predictions of Cu, Pb, Cd, and Ni speciation in freshwater with measurement data of free-metal-ion concentrations obtained with various techniques. The results showed that both Model VI and the NICA–Donnan model can predict the bound amounts of trace metals in freshwater reasonably well. However, the concentration of free ions was seriously underestimated when, as for Cu and Pb, it was a small fraction of the concentration of total dissolved ions. This underestimation is of concern, given the importance of these concentrations in models of biological uptake. The discrepancy might be related to inadequacies of the models, of the instrumental techniques used for the measurements, or of both. The comparison can be especially difficult when the labile fraction—as measured, for example, by diffusive gradient in thin 7464 n Environmental Science & Technology / DECEMBER 15, 2006
films—is of the same order of magnitude as the total concentration, whereas the free concentration is several orders of magnitude lower than the total concentration. In surface waters, this might be the case for most metals. Soils. Field applications of the models were also investigated for soil NOM interaction with various metal ions. Benedetti et al. (38) were the first to test the NICA–Donnan model; they predicted Cu and Cd binding in field systems with some success by using model constants extracted from laboratory measurements. Benedetti (39) also has applied various models with generic parameter sets to metal interactions with soil components, such as organic matter, clays, and oxides. Soil solution composition and speciation were predicted successfully by using the characteristics of the major soil constituents. Tipping et al. (40) compared simulated and measured data for soils and streams. They also used lake sediment data to estimate depositional inputs. Simulation and field data gave a reasonable agreement for soil metal pools and streamwater concentrations of metals such as Ni, Cu, Zn, and Cd, but Pb simulation required a different soil binding capacity to fit the data. The researchers incorporated Model VI into a dynamic model called CHUM-AM (41). Its predictions indicated that weakly bound metals are influenced by changes in metal deposition or soil acidification much more quickly than strongly bound metals. The model results also showed that the aluminum in soil can compete with other metals. In an elegant study, Dijkstra et al. (42) characterized the leaching of heavy metals from 8 contaminated soils over a wide pH range (0.4–12). They also compared experimental results with predictions from a multisurface model that incorporated the NICA– Donnan model for DOM and particulate organic matter, the amorphous iron hydroxide (two-layer) model for iron/aluminum hydroxides, and a Donnan ion-exchange model for clay, all with standard parameters. They concluded that the multisurface model could adequately predict the leaching over a wide range of conditions. Good agreement was generally found between the measured metal-ion concentrations and those predicted by the Donnan membrane technique, except for lead and zinc at very basic pH. Two recent studies (20, 43) considered the efficiency of Model VI-S and the NICA–Donnan model to describe SOM–metal interactions. Both studies concluded that the parameter sets derived for DOM were less satisfactory when used for SOM. The work of Gustafsson (20) mainly focuses on the larger effects of salt with SOM than with DOM (i.e., on the electrostatic part of the models). MacDonald et al. (43) provided an overview of recent work and considered the relationship between soil organic carbon and the ratio of fulvic acids to humic acids for 16 soils. Generic constants were found to be inappropriate for modeling metal binding to these soils. To solve this problem, MacDonald et al. better defined the fulvic-to-humic ratio of the soil organic carbon and introduced a new parameter set for the NICA–Donnan model. Good predictions were obtained with respect to copper, cadmium, and zinc
for forest-floor SOM, but lead was underestimated. MacDonald et al. remarked that further parameter optimization should be carried out for more soil types on the basis of a fixed definition of soil organic carbon before a general conclusion can be drawn. In general, lead often seems to lead to discrepancies between model predictions and field data, so this metal requires special attention. Koopal et al. (17) have reviewed various applications of the NICA–Donnan model, including field predictions. In several studies, the NICA–Donnan model with generic model constants provided adequate predictions. The NICA–Donnan model has also been reported to provide descriptions of ion binding to cell-wall material. Given the cell-wall heterogeneity and the fact that metal-binding stoichiometry also plays a role, the NICA–Donnan model is a very good candidate to describe metal-ion binding to biotic ligands. Its analytical form and the relative ease of parameter optimization make it user-friendly.
The models that have been tested in the laboratory must be validated for their ability to make predictions in the field. The success with which interaction models that incorporate the composition of natural materials (humic acids, lignin, clay, and oxides) will be able to predict metal binding in the environment depends on the modeling of not only NOM–metal interactions but also mineral–metal and OMC interactions. Progress has been made, and the prospects for further improvements are good. Nevertheless, more work is required, and NOM still warrants a lot of attention. Models VI and VI-S and the NICA–Donnan model seem well equipped to handle NOM, but field predictions require further study of both the organic matter classification in the field and parameter optimization. OMCs are difficult to investigate experimentally, and the modeling is still in the first stages of development and testing. However, the metal-interaction models and those that can describe interactions of metals with biotic ligands will, without a doubt, be important for risk assessment. The experimental characterization of metal-ion binding to biotic ligands (e.g., cell-wall materials) also requires attention so that models can be formulated that describe this binding process and so that the BLM approach can be improved. Patricia Merdy and Sandrine Huclier are professors in the PROTEE (Processus de Transferts et d’Echanges dans l’Environnement) Laboratory, Department of Chemistry, University of Toulon (France). Luuk K. Koopal is a professor in the Laboratory of Physical Chemistry and Colloid Science, Wageningen University (The Netherlands). Address correspondence about this article to Merdy at
[email protected].
Acknowledgments We thank Edward Tipping, Willem van Riemsdijk, and David Kinniburgh for useful advice and critical comments on the preliminary drafts. We also thank the reviewers for their useful remarks that helped to improve the manuscript.
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National Exposure Research Laboratory Post-Doctoral Program Job Opportunities The National Exposure Research Laboratory (NERL) of the United States Environmental Protection Agency is accepting applications beginning November 20, 2006, through January 31, 2007, for approximately 16 federal, three-year postdoctoral research positions. • Research in areas such as environmental monitoring and characterization; computer modeling of the transport, transformation, and fate of pollutants in multiple media and at multiple scales; human and ecological exposure analysis; remote sensing applications; and landscape ecology. • Positions located in: Research Triangle Park, North Carolina; Cincinnati, Ohio; Las Vegas, Nevada; Athens, Georgia; or Washington, DC metropolitan area. • Full Federal Employment Benefits and a salary range of $51,972 – $84,559 commensurate with qualifications. (Salary range is subject to increase in January 2007.)
Specific job information and application instructions for the NERL post-doctoral program are posted on the NERL Internet site at http://www.epa.gov/nerl. The U.S. EPA is an Equal Opportunity Employer.
GWANGJU INSTITUTE OF SCIENCE AND TECHNOLOGY(GIST) TECHNOLOGY (GIST) DEPARTMENT OF ENVIRONMENTAL SCIENCE AND ENGINEERING FACULTY POSITIONS (Assistant, Associate, and Full Professor) at Department of Environmental Science & Engineering, Gwangju Institute of Science & Technology (GIST) in Korea. GIST seeks candidates whose interest lies in Environmental Biotechnology, Renewable Energy Technology (Photovoltaics, Biofuel), Environmental Nanotechnology and Ecological Engineering. Candidates must possess a Ph.D. degree in a pertinent field. Candidates with postdoctoral experience are preferred. Korean citizenship is NOT required for these positions. At GIST all lectures are conducted in English. GIST started in 1993 as a government-funded science and engineering oriented graduate school and has performed numerous research projects sponsored by the government and private sectors. More information about GIST can be found on our webpage: http://www.gist.ac.kr. We seek dynamic and superb scientists or engineers for these faculty positions. GIST will offer internationally competitive salaries and housing benefits. GIST offers an electronic version of its application forms in Microsoft Word format at http://www. gist.ac.kr/hindex.html. Applicants should submit all application materials by e-mail or fax to the following address: Section of Academic Affairs, Gwangju Institute of Science and Technology, 1 Oryong-dong, Buk-gu, Gwangju 500-712, Republic of Korea.Tel.: 82-62-970-2043, fax: 82-62-970-2059, e-mail:
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