Comparing Estimates of Persistence and Long-Range Transport

Cedric Wannaz , Peter Fantke , Joe Lane , Olivier Jolliet .... Pierre Glynn , Gene Whelan , Gary Geller , Nigel Quinn , Michiel Blind , Scott Peckham ...
0 downloads 0 Views 319KB Size
Download PDF
Policy Analysis Comparing Estimates of Persistence and Long-Range Transport Potential among Multimedia Models K A T H R I N F E N N E R , †,‡ M A R T I N S C H E R I N G E R , * ,‡ MATTHEW MACLEOD,§ MICHAEL MATTHIES,| THOMAS MCKONE,§ MAXIMILIAN STROEBE,‡ ANDREAS BEYER,⊥ MARK BONNELL,# ANNE CHRISTINE LE GALL,X JO ¨ RG KLASMEIER,| DONALD MACKAY,O DIK VAN DE MEENT,× DAVID PENNINGTON,+ BERND SCHARENBERG,] NORIYUKI SUZUKI,[ AND FRANK WANIA∇ Swiss Federal Institute for Environmental Science and Technology (EAWAG), CH-8600 Du ¨ bendorf, Switzerland, Swiss Federal Institute of Technology Zu ¨ rich, CH-8093 Zu ¨ rich, Switzerland, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, 90R3058, Berkeley, California 94720, Institute of Environmental Systems Research, University of Osnabru ¨ ck, D-49069 Osnabru ¨ ck, Germany, Institute of Molecular Biotechnology, Theoretical Systems Biology, D-07745 Jena, Germany, New Chemicals Evaluation Division, Environment Canada, Gatineau, Quebec, K1A 0H3 Canada, Modeling and Economic Analysis Unit, Chronic Risk Division, INERIS, F-60550 Verneuil en Halatte, France, Canadian Environmental Modelling Centre, Trent University, Peterborough, Ontario, K9J 7B8 Canada, RIVM Laboratory for Ecological Risk Assessment, NL-3720BA Bilthoven, The Netherlands, European Commission Joint Research Centre, I-21020 Ispra, Italy, Federal Environmental Agency of Germany, Environmental Exposure Assessment, D-14191 Berlin, Germany, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaragi 305-8506, Japan, and Department of Physical and Environmental Sciences, University of Toronto at Scarborough, Toronto, Ontario, M1C 1A4 Canada

Overall persistence (Pov) and long-range transport potential (LRTP) of organic chemicals are environmental hazard metrics calculated with multimedia fate and transport models. Since there are several models of this type, it is important to know whether and how different model designs * Corresponding author phone: +41-44-632 30 62; fax: +41-44632 11 89; e-mail: [email protected]. † Swiss Federal Institute for Environmental Science and Technology (EAWAG). ‡ Swiss Federal Institute of Technology Zu ¨ rich. § Lawrence Berkeley National Laboratory. | University of Osnabru ¨ ck. ⊥ Institute of Molecular Biotechnology. # Environment Canada. X INERIS. O Trent University. × RIVM Laboratory for Ecological Risk Assessment. + European Commission Joint Research Centre. ] Federal Environmental Agency of Germany. [ National Institute for Environmental Studies. ∇ University of Toronto at Scarborough. 1932

9

ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 39, NO. 7, 2005

(model geometry, selection of compartments and processes, process descriptions) affect the results for Pov and LRTP. Using a set of 3175 hypothetical chemicals covering a broad range of partition coefficients and degradation halflives, we systematically analyze the Pov and LRTP results obtained with nine multimedia models. We have developed several methods that make it possible to visualize the model results efficiently and to relate differences in model results to mechanistic differences between models. Rankings of the hypothetical chemicals according to Pov and LRTP are highly correlated among models and are largely determined by the chemical properties. Domains of chemical properties in which model differences lead to different results are identified, and guidance on model selection is provided for model users.

Introduction High persistence and long-range transport potential are recognized as two properties that make a chemical a hazardous environmental contaminant (1, 2). Because there are currently no monitoring strategies that allow these properties to be measured directly, multimedia models are needed to determine numerical indicators of overall persistence (Pov) and long-range transport potential (LRTP) in the environment. However, several alternative models and modeling approaches are being used for that purpose (Tables 1 and 2). This raises the question of how the selection of the model affects the final classification of chemicals according to environmental hazard. In this paper we explore the premise that chemical properties are key factors determining Pov and LRTP classifications and that model differences are of secondary relevance only in making this classification. The available models differ in geometry, parametrization of the environment, and definitions of the metrics for LRTP. The differences arise from different goals and motivations for developing the various models. We therefore expect that the models will produce results with sometimes small, but in some cases also substantial, differences. This could lead to disagreement over prioritization of environmental hazard of individual chemicals, for example, between different countries and organizations using different models for their assessments. For instance, the European Union System for the Evaluation of Substances (EUSES) is applied in risk assessments in the EU member states (15); the Canadian Environmental Modelling Centre (CEMC) models are used in Canada and by the United States Environmental Protection Agency (U.S. EPA) (16, 17); and CalTOX, which was developed for hazardous waste classification in the State of California, is now used for risk assessment by the U.S. EPA and by countries in Asia and Europe (18, 19). Ideally, classification of substance behavior should be consistent among different models, but to encourage effective communication and international collaboration, existing differences between the models must be characterized. This requires examination of the various models for assessing Pov and LRTP and a comprehensive cross-comparison of the models. To address this need, the Organization for Economic Cooperation and Development (OECD) established an expert working group with a mandate to compare models systematically in order to assess their reliability and applicability for LRTP and Pov classification (20). The working group 10.1021/es048917b CCC: $30.25

 2005 American Chemical Society Published on Web 02/19/2005

confronted these challenges using two approaches. The first, which is presented in this paper, compares model results using a comprehensive set of hypothetical chemicals. The second approach evaluates the ability of the models to identify chemicals that exhibit high Pov and LRTP in the real environment and is described in a separate paper (21). Previous model comparison studies for evaluating Pov and LRTP endpoints (4, 22-24) have suggested that absolute values of Pov and LRTP may differ considerably between models, but relative rankings among a set of chemicals are similar for different models. In one study (22), Pov and LRTP of 26 chemicals with different environmental behavior were compared among 12 models. Correlation coefficients between model results were typically between 0.6 and 0.9. Rank correlation coefficients were even higher, between 0.8 and 0.99. However, all previous studies have examined small, albeit diverse, sets of chemicals. It therefore remains unknown whether the high correlation coefficients in substance rankings can be generalized to a broader set of chemicals. In this study, we present a comprehensive comparison of nine multimedia models that can be used to calculate Pov and LRTP. Our objective is to determine how consistently the models characterize substances in terms of Pov and LRTP for defined combinations of chemical property data and to develop methods for identifying differences in the models that lead to different characterizations. We focus on comparing ranks of chemicals in terms of Pov and LRTP, consistent with the goal of identifying high priority chemicals from lists of substances. We characterize the models’ behavior across the full range of plausible partitioning between air, water, and sorbed phases for non-ionizing, organic chemicals and for a set of degradability classes representative of environmental contaminants. A range of plausible “chemical space” is defined with a set of 3175 hypothetical chemical property combinations. Using the hypothetical chemicals allows us to focus on model-to-model differences (such as different environmental parameters, model structures, and assumptions) rather than on parameter uncertainty of real chemicals. To analyze results for this large set of hypothetical chemicals, we have developed novel procedures for efficient and instructive comparison of model results.

Methods for Comparing Models Models Used. Nine multimedia models are investigated (Tables 1 and 2) including evaluative models with uniform environmental properties (CalTOX, CEMC Level II and Level III, ChemRange, ELPOS, and SimpleBox) and coarsely segmented spatially explicit models (BETR North America, Globo-POP, Impact 2002), but not highly spatially resolved models based on global circulation models of the atmosphere and oceans (for example, refs 25 and 26). The latter are excluded to focus on models that are readily available and can be applied by nonexpert users with a modern desktop computer. The different types of models available for assessing environmental fate and transport of contaminants and their range of applicability are discussed in reviews by Scheringer and Wania (27) and OECD (1). The distinguishing features of the nine models are listed in Table 1. Models are characterized with respect to their definitions of Pov and LRTP metrics. In all models, Pov is defined as a chemical’s reactive residence time (i.e., the ratio of the mass present in the model system divided by the mass flux through the model system due to degrading reactions). Eight of the nine models calculate steady-state contaminant levels so that the total removal rate of chemical from the system is equal to the release rate. Globo-POP calculates Pov under non-steady-state conditions where both the contaminant mass in the model system and the mass flux vary in

time. In that case, the mass flux used in the Pov calculation is the sum of all removal fluxes over a defined time period. In contrast, the LRTP is calculated by very different methods in the models. Mathematical definitions of LRTP for each model are included in section 1 of the Supporting Information. A key distinction is between Globo-POP and BETR North America and all other models. The LRTP metrics of the first two models are “target-oriented”, describing the percentage of emitted substance that migrates to surface media in selected target regions as a consequence of transport in air and/or water and subsequent deposition. The LRTP metrics of the other models are “transport-oriented”, describing the potential for transport in the mobile media air and/or water with simultaneous exchange with the surface media. As described below, these different transport metrics cause significant differences between model results for LRTP for chemicals with certain property combinations. The transport-oriented models can be further differentiated as follows: ELPOS treats transport in air or water strictly separated from each other (totally uncoupled model). A characteristic travel distance (CTD) in air or water is derived from the spatial distribution of the chemical after emission into air or water. In CalTOX, SimpleBox, Impact 2002, and ChemRange, transport in both air and water is considered, but the relative importance of transport in water varies and different LRTP metrics are used. In CalTOX, the environmental parameters used represent a continental system that contains so little water that CTD values are similar to those from ELPOS for transport in air only. SimpleBox and Impact 2002 take a similar approach but include higher fractions of water. ChemRange describes simultaneous transport in water and air and exchange between all media in a continuum along the distance traveled. Figure 1 presents a conceptual summary of the LRTP metrics calculated by the nine models, grouped by treatment of simultaneous transport in air and water and by transport-oriented versus target-oriented LRTP metric. Hypothetical Chemicals for Model Comparison. We employ a set of hypothetical chemicals selected to cover the entire space of plausible chemical partitioning properties and half-lives. Using real chemicals for the model comparison would allow for more intuitive interpretation of the results. However, even a set of several hundred chemicals does not densely cover the space of possible chemical property combinations. In addition, using real chemicals makes it difficult to select appropriate chemical properties, which can be highly uncertain due to measurement error and variability between methods, especially near the extremes of property values (28). This would create problems of credibility concerning the properties used that would divert attention from the goal of model comparison. We envisage that this set of hypothetical reference chemicals could become a standard tool that can be used to interpret the results of any model of this type. For example, a similar data set has been applied to analyzing different environmental scenarios of the same model (29). The set of hypothetical chemicals includes all possible combinations of integer values of log Kaw from -11 to 2 and log Kow from -1 to 8, with the restriction that -1 e (log Kow - log Kaw) e 15 (Kaw, air-water partition coefficient; Kow, octanol-water partition coefficient; and assuming Koa ) Kow/ Kaw, octanol-air partition coefficient), see Table 3. The range of possible degradability of organic chemicals is represented in two additional dimensions by defining five half-life categories ranging from 24 h to 10 yr for degradability in water and half-lives in air ranging from 4 h to 1 yr. To limit the number of possible combinations to an acceptable level, half-lives in soil were set to twice the half-lives in water, and half-lives in sediment were set to 10 times those in water. These factors are similar to those used in the U.S. EPA PBT VOL. 39, NO. 7, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

9

1933

1934 9

ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 39, NO. 7, 2005

TABLE 1. Main Features of the Models Included in the Model Comparison model namesa

ChemRange

spatial resolution spatial scale temporal resolution media height/ depth [m]

no global steady state a: 6000 w: 200 s: 0.1

land and water surface fractions transport media

fw ) 0.7 fs ) 0.3 air and water, coupled air, water and soil, any combination Pov, SR

emission media endpointk Φl degradation of aerosolbound fraction minimum of substance data required m

Koa no Kow, Kaw, ks, ka, kw

ELPOS

CalTOX

no regional steady state a: 1000 w: 3 s: 0.2c sed: 0.03 fw ) 0.03 fs ) 0.97 air or water, single air, water and soil, any combinationo Pov, CTDair, CTD water Koa or p0 no

no regional steady stateb a: 700 w: 5 s: three layers sed: 0.05 fw ) 0.02 fs ) 0.98 mainly air

Kow, p0, Kaw or Sw; ks, ka, kw, ksed

SimpleBox

CEMC LIII and LII

Impact 2002 yes continental steady stateb a: 800 w: 17.8 s: 3 layerse

no regional steady state a: 1000 w: 20 s: 0.2

geo-referenced air and water, coupled air, water soil, sediment, any combination Pov, outflow ratio

fw ) 0.1 fs ) 0.9 air or water

air, water, surface soil, root soil, any combination Pov, CTD

nested boxes regional to global steady state a: 1000 w: 3-103 d s: 0.03-1c sed: 0.03 fw ) 0.03j fs ) 0.97j air and water, coupled air, water and soil, any combination Pov, outflow ratio

Koa yes

p0 no

MW; Kow or Ksw; Kaw or Sw, p0; ks; ka; kw; ksed

Kaw, Ksw, results from ready biodegradability tests

BETR North America

Globo-POP latitudinal zones global dynamic a: 4 layersf w: 20-200g s: 0.02-0.2h sed: 0.05 varying

yes continental steady stateb a: 2000i w: 20-80 s: 0.1

air

air, water, soil, sediment, any combination Pov, CTD

air and water, coupled air, water and soil separately Pov, ACP

air, water and soil, any combination Pov, GLTE

Koa yes

p0 yes

Koa no

Koa no

Kow, Kaw, ks, ka, kw, ksed

MW, p0, Sw, Kow, Tm, ka, kw, ks, ksed

MW, Koa, Kaw, Kow, ∆Hoa, ∆Haw, ∆How, ks, kw, ksed, kOH, Ea for all degradation reactions

MW, reference T, 2 out of Kaw, Kow, Koa; ks, ka, kw, ksed, ∆Haw, ∆How, ∆Hoa

geo-referenced

a Acronyms: ELPOS, Environmental Long-range transport and Persistence of Organic Substances model; CEMCLII/III, Canadian Environmental Modelling Centre models for levels II and III; Impact 2002, IMPact Assessment of Chemical Toxics, Version 2002; Globo-POP, GLOBal distribution mOdel for Persistent Organic Pollutants; BETR North America: BErkeley-TRent North America Contaminant Fate Model. b Can be run in dynamic mode, but steady-state version used for this exercise. c Agricultural soil; substance-specific soil depths for natural and industrial soil. d Freshwater, 3 m; seawater, 10 m (regional box), 200 m (continental box), 1000 m (global box). e Surface soil layer, 0.01 m; root zone layer, 0.3 m; unsaturated vadose zone layer, 1.5 m. f Borders between horizontal atmospheric layers are defined based on atmospheric pressure. The heights approximately corresponding to these pressures are bottom of layer 1: 100 kPa, 0 m; bottom of layer 2: 83.5 kPa, 1.3 km; bottom of layer 3: 50 kPa, 5 km; bottom of layer 4: 11 kPa, 15 km; top of layer 4: 1 kPa, 33 km. g Surface ocean, 200 m in all climate zones. Freshwater, 20 m in all climate zones. h Uncultivated soil: 5 cm in all climate zones, except in two polar zones (2 cm). Cultivated soil: 20 cm in all climate zones, except in subpolar (10 cm) and polar zones (2 cm). i Two atmospheric layers with a total height of 2000 m. j Valid for local and regional box. Continental box, fw ) 0.515; global box, fw ) 0.62. k SR, spatial range; CTD, characteristic travel distance; ACP, Arctic contamination potential; GLTE, Great Lakes transfer efficiency. Mathematical definitions of all LRTP metrics are given in the Supporting Information. l Fraction of substance bound to aerosol particles. It can be calculated from the vapor pressure (3) or from Koa (4, 5). m MW, molecular weight; Koa, octanol-air partition coefficient; Kow, octanol-water partition coefficient; Kaw, air-water partition coefficient or Henry’s law constant; p0, vapor pressure; Sw, water solubility; Tm, melting point; ks, ka, kw, ksed, degradation rate constants in soil, air, water, and sediment, respectively; kOH, rate constant for reaction with OH radicals; ∆Hoa, ∆Haw, ∆How, enthalpies of phase transfer between octanol-air, air-water, and octanol-water, respectively; Ea, activation energy. n CEMC LII and LIII treat identical environments, but LII assumes equilibrium between all compartments. LIII is identical to the TaPL3 model (6). o CTD calculation requires emission to air or water only.

12 11 10 9 8

yes not directly downloadable yes http://eetd.lbl. gov/ied/era/

reference

yes www.sust-chem.ethz.ch/ research/product/ chemrange.html 7 availability source

yes www.usf. uos.de/ projects/elpos/

Excel/Visual Basic yes www.epfl.ch/ impact Excel Excel

Excel

log Kaw log Kow log Koaa

t1/2,a

implementation

Excel

13

yes by request from: macleod@ chem.ethz.ch 14

Excel

Excel tables

Excel-readable tables stand-alone software yes www.utsc.utoronto. ca/∼wania Excel-readable tables stand-alone software yes www.trentu. ca/cemc Excel tables Excel tables Excel tables

parameter

Excel tables

Excel tables

TABLE 3. Values of Partition Coefficients and Half-Lives Used To Define the Hypothetical Chemicals

output format

BETR North America Globo-POP CEMCLIII and LII Impact 2002 SimpleBox CalTOX ELPOS ChemRange

TABLE 2. Technical Information on the Models Included in the Model Comparison

FIGURE 1. Comparison of multimedia models with respect to mode of transport (single-medium vs coupled, vertical axis) and LRTP metric (transport- or target-region oriented, horizontal axis). Definitions of LRTP metrics are given in the Supporting Information. CTD, characteristic travel distance; ACP, Arctic contamination potential; GLTE, Great Lakes transfer efficiency.

t1/2,w ) 0.5‚t1/2,s ) 0.1‚t1/2,sed

values

units

-11 to 2 -1 to 8 g-1 e15

(-) (-) (-)

4 24 168 1000 8760 24 168 1000 8760 87 600

(a)b (b) (c) (d) (e) (1) (2) (3) (4) (5)

(h)

(h)

a Under the assumption that log K oa ) log Kow - log Kaw, the boundaries of log Koa determine the possible combinations of log Kaw and log Kow (see also Figures 4 and 5 and Figures SI-1 to SI-19 in the Supporting Information). b Labels used in Figures SI-1 to SI-19 of the Supporting Information.

profiler (17) and those suggested in the EU TGD for extrapolation from water half-lives to soil and sediment halflives (15); in section 4 of the Supporting Information the half-life ratios are compared to half-lives of real substance data. The resulting 25 half-life combinations were combined with each of the 127 possible combinations of partition coefficients to yield a set of 3175 hypothetical chemicals. The emission scenario or “mode-of-entry” influences the model outcomes. Pov was therefore calculated for releases to air, water, and soil separately. Simultaneous input to more than one medium yields Pov values between those obtained with the extreme emission scenarios. Because not all of the LRTP metrics yield interpretable results for emission scenarios other than release to air, LRTP was only determined for release to air. Methods for Model Comparison. To compare model outcomes for the large set of hypothetical chemicals, we required informative, transparent, and manageable comparison methods. Furthermore, the different definitions for LRTP used in various models make it difficult to directly compare absolute values of LRTP. For these reasons, we based the model comparison mainly on the rank orders of the 3175 chemicals with respect to Pov and LRTP. This is supported VOL. 39, NO. 7, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

9

1935

FIGURE 2. Model-to-model rank correlation coefficients for Pov for release into air (A), Pov for release into water (B), Pov for release into soil (C), and for LRTP after release into air (D). Rank correlation coefficients with Globo-POP were determined for a subset of 1474 substances only.

by the recognition that ranking is the most common application of the models. We compare the model results without focusing on the differences among models in the algorithms and assumptions that produce the results unless there is evidence of significant disagreement between two models. In cases where disagreements are observed, it is important to understand the reasons for the discrepancies. For this purpose, we have developed graphical techniques for efficiently identifying the combinations of partition coefficients and half-lives that lead to different classifications by different models. Specifically, the results obtained with the different models are compared in three ways: (i) The rank correlation coefficients (RCCs) between all combinations of two models for the rankings of the 3175 hypothetical chemicals by Pov and LRTP are used as a highly aggregated metric of the level of agreement between each model pairing (22, 30). The different RCCs between models yield correlation matrices for the whole group of models investigated as shown for Pov in Figure 2A-C and for LRTP in Figure 2D. (ii) Another method for comparing the models’ rankings of chemicals is to sort chemicals into a small number of bins whose borders are defined as percentiles of the total of 3175 chemicals and to compare how consistently different models sort the chemicals into the different bins. This method mimics a priority-setting activity in which substances are grouped according to high, intermediate, and low levels of concern. Figure 3 shows an illustration of the binning method for the comparison of ELPOS and ChemRange with the hypothetical chemicals sorted into three bins with borders set at the 40th and 60th percentile of the 3175 chemicals. It is possible to 1936

9

ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 39, NO. 7, 2005

FIGURE 3. Binning results for ChemRange vs ELPOS for LRTP after release to air. The three bins include the ranges of 0-40%, 40-60%, and 60-100% of all hypothetical chemicals. There are a total of 208 opposite classifications for these two models (see also Table 4). identify groups of chemicals for which models disagree by looking at the degradation and partitioning properties of those chemicals that show opposite classifications between two models. (iii) The most detailed method of model comparison is to draw multidimensional plots showing the model results as functions of position in the chemical partitioning and degradation space. In general, any model outcome can be shown in such plots. The plots make it possible to identify specific regions in the chemical space where disagreements between two models occur. We have found it more informative to plot the absolute results for Pov and LRTP rather than

their ranks since the plots present a visual representation of model outcomes. Working with the absolute results also allows a mechanistic interpretation of the observed discrepancies between models.

Results of Model Comparison Comparison by Means of Rank Correlation Coefficients. Rank correlation coefficients between all models for Pov under the three different release scenarios are shown in Figure 2A-C and for LRTP for release to air in Figure 2D. To aid interpretation of these data, correlation coefficients have been color-coded into three categories representing levels of agreement: “high” (green, r > 0.85), “medium” (yellow, 0.85 > r > 0.70), and “low” (red, r < 0.70). The most prominent feature of the correlation coefficients for Pov in Figure 2A-C is the general agreement between models. One exception is the discrepancy between Globo-POP and the other eight models. This is attributable to the inclusion of temperaturedependent degradation rate constants and zonally and temporally variable OH radical concentrations in Globo-POP. The other models use fixed first-order rate constants derived from the half-lives defined for the hypothetical chemicals. The correlation coefficients in Figure 2A (release to air) are greater than 0.9 for all combinations of models except those involving Globo-POP. In several cases, the correlation coefficients exceed 0.95. Correlation coefficients for release to water (Figure 2B) are the lowest among the three release scenarios for Pov. Correlations involving the CEMC LII model are generally low, and there are lower correlations between models with large ocean water compartments (ChemRange, SimpleBox, Impact 2002) relative to models without ocean water (CalTOX, CEMC LIII, ELPOS). CEMC LII is distinctly different from the other models in that it assumes instantaneous equilibrium, i.e., there is no resistance to substance exchange between the media. This assumption leads to mass distributions between environmental compartments that are characteristically different from those in the other models. The CEMC LII model therefore deviates from the other models since Pov is calculated from the mass-weighted average of the degradation rate constants in the different compartments. The correlation coefficients for LRTP (Figure 2D) reflect the range of differently defined LRTP metrics illustrated in Figure 1. Correlations between the transport-oriented models and the target-oriented models in all cases indicate medium or low agreement. For example, the highest correlation value for BETR North America is 0.79, which is lower than all persistence correlations between BETR North America and the transport-oriented models. Among the transport-oriented models, ChemRange (and again CEMC LII) have lower correlations with SimpleBox, CalTOX, Impact 2002, CEMC LIII, and ELPOS than these models have with one another. ELPOS is included in this group because the assumption of no transport in water does not create a strong discrepancy to the other models with some transport in water. The lower correlations with ChemRange are attributed to ChemRange’s simultaneous transport in water and air and its large oceanic water compartment (see more detailed analyses below). The discrepancy of CEMC LII is mainly caused by different Pov and fa values in the calculation of the CTD (CTD ) u‚fa‚Pov, with fraction in air ( fa), and wind speed (u)). In CEMC LII, fa is different because of the instantaneous equilibrium assumption and the fact that aerosols are not considered as an atmospheric subcompartment. Comparison of Binning Results for Different Bin Boundaries. In Table 4, the results of sorting the 3175 hypothetical chemicals into three bins are summarized for three alternative percentile cutoffs between low, intermediate, and high priority chemicals i.e., (20,80), (33,66), (40,60). Table 4 gives the average percentage (taken over all possible combinations of pairs of models) of all chemicals

TABLE 4. Averages of Binning Results for Different Bin Sizes and All Possible One-to-One Model Combinationsa Pov release to bins

air x

≈

LRTP

water ?

x

≈

soil ?

x

≈

air ?

x

≈

?

0/20/80/100 85 15 0 80 19 1 86 14 0 70 29 1 0/33/67/100 85 14 1 77 21 2 83 15 2 61 34 5 0/40/60/100 83 14 3 78 18 4 83 14 3 62 28 10 a Consistency of binning is indicated as percentage of chemicals assigned to the same bin by both models compared (x), percentage of chemicals assigned to adjacent bin (≈), and percentage of chemicals assigned to opposite bins (?).

that, in two different models, are classified in the same bin (x), in adjacent bins (≈), or into opposite bins (?). The last category contains chemicals that are classified as low priority by one model and high priority by another. We cannot conclusively say that one model is a better representation of the actual environmental behavior than another since the models are different in focus and hence put different emphases on the various possible fate processes. Over all combinations of models, an average of less than 1% and up to 4% of all chemicals are given opposite classifications for Pov, whereas opposite classifications for LRTP average between 1% and 10%, depending on the bin boundaries chosen. This implies that the models provide consistent classification for the majority of the hypothetical chemicals if these chemicals are assigned to three bins. For individual pairs of mechanistically very different models, the percentage of oppositely classified chemicals can be higher. The pair ChemRange-BETR North America, for example, has 12% oppositely classified chemicals for LRTP with the bin boundaries set to (40,60). This discrepancy can be attributed to the models’ different geometries, process descriptions, and LRTP metrics. The opposite classifications observed in the binning exercise provide an opportunity to identify influential model differences and for which substance properties these differences matter most. The 193 substances that are classified differently with respect to LRTP in ChemRange and ELPOS (Figure 3, top right, boundaries of the bins set at (40,60)) have a very short half-life in air (4 h), a very long half-life in water (87 600 h), and a low tendency to partition into air (log Kaw e -6 and log Kow < 4). Although they are released to air, they are deposited to and transported in water in ChemRange but have a very low LRTP in the ELPOS model, which only describes transport in air. Such an interpretation of the binning differences can be supported by the more detailed analysis of the chemical space plots, given below. The results of the binning exercise are not strongly influenced by the definition of the bin boundaries, i.e., whether (20,80), (33,66), or (40,60) are used as bin boundaries. This is the case although the criteria become consistently more stringent when the size of the middle bin is reduced from (20,80) to (40,60). It might be feasible to define the bin sizes more realistically with the help of reference chemicals with known Pov/non-Pov and LRTP/non-LRTP behavior by computing how these chemicals rank within the list of hypothetical chemicals. Detailed Comparison of Results for Pov and LRTP from ChemRange, ELPOS, and BETR North America. A compilation of all chemical space plots generated for all models is supplied in the Supporting Information, accompanied by a more detailed discussion of model differences that can be inferred from these plots. Using BETR North America, ChemRange, and ELPOS, we illustrate the utility of the chemical space plots to identify model differences that lead to different outcomes. VOL. 39, NO. 7, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

9

1937

FIGURE 4. Chemical space plots for Pov calculated with ChemRange (A, C) and ELPOS (B, D); release into air. Half-life combinations are t1/2,a ) 168 h and t1/2,w ) 1000 h in panels A and B (corresponding to chemical space plots 3c in the Supporting Information) and t1/2,a ) 168 h and t1/2,w ) 87 600 h in panels C and D (corresponding to chemical space plots 5c in the Supporting Information). The scale is log Pov in days.

For Pov, important model differences are (i) the absence or presence of a sediment and/or groundwater compartment and (ii) differences in compartment dimensions, especially the depth of the water compartment and the land-to-water surface ratio. For LRTP, inclusion or exclusion of transport in water, the size of the water compartment, and inclusion or exclusion of export to the deep ocean with settling particles are important differences between models. We also illustrate the effect of using a target-oriented LRTP metric in BETR North America in comparison to transport-oriented metrics in ChemRange and ELPOS. Other important model differences, such as the influence of the aerosol-sorbed fraction of chemical in the air on Pov and LRTP, are discussed in the Supporting Information. In Figure 4, Pov results are shown for ChemRange and ELPOS for different half-life combinations and as functions of Kaw and Kow. Panels A and B highlight a first difference between the two models that is caused by the presence of 1938

9

ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 39, NO. 7, 2005

a freshwater sediment compartment and by a larger soil compartment in ELPOS. The water compartment in ChemRange represents oceanic surface water and, therefore, covers a large fraction of the model area and is not underlain by a sediment compartment (Table 1). The yellow band observed in ELPOS but not in ChemRange for log Kow > 3 and log Koa > 10 (panel B) is caused by the higher persistence of chemicals strongly partitioning into soil and sediment, which in turn is caused by the assumptions of t1/2,s ) 2‚t1/2,w and t1/2,sed ) 10‚t1/2,w used for all hypothetical chemicals. In other words, the difference between t1/2,s and t1/2,sed, on one hand, and t1/2,w, on the other hand, is discernible in ELPOS but not in ChemRange. The differences in Pov shown in panels C and D are due to different sizes of the bulk water volume in the two models. In ELPOS, the water compartment is considerably smaller than in ChemRange, covering only 3% of the surface with only 3 m depth. There is a band of lower persistence in ELPOS

FIGURE 5. Chemical space plots for LRTP calculated with ChemRange (A), ELPOS (B, D), and BETR North America (C); release into air. Half-life combinations are t1/2,a ) 8760 h and t1/2,w ) 87 600 h in panels A and B (corresponding to chemical space plots 5e in the Supporting Information) and t1/2,a ) 8760 h and t1/2,w ) 8760 h in panels C and D (corresponding to chemical space plots 4e in the Supporting Information). The scale is spatial range in percent of the circumference of the earth (R95) for ChemRange, CTD in km for ELPOS, and GLTE (Great Lakes transfer efficiency) in percent for BETR North America. (panel D) as compared to ChemRange (panel C) for log Kow < 1.3 and log Kaw < -5 because the maximum fraction of chemical in water in ELPOS, even for very water-soluble chemicals, is 0.93 due to the small size of the water compartment. The remaining fraction of 0.07 in air reduces the Pov values of these compounds so that it is lower than in ChemRange, where the corresponding chemicals have fractions in water of 0.999 and higher. In Figure 5, LRTP results for ChemRange (panel A), ELPOS (panels B and D), and BETR North America (panel C) are displayed. Panels A and B illustrate an important difference between ChemRange and ELPOS, namely, inclusion and exclusion of long-range transport in water. Chemicals mainly partitioning into water in ChemRange (log Kow < 6, log Kaw

< -4) have spatial ranges around 0.3-0.4 if they are slowly degraded in water (light blue-green). In ELPOS, however, these chemicals are not significantly transported (CTD values below 10 km; dark blue). If water is not included or plays only a minor role as a means of transport, the chemical space plot is almost insensitive to the half-lives in the surface media, as is visible from panels B and D. Besides for ELPOS, this is the case for CalTOX, Impact 2002, and CEMC LIII, see Supporting Information. In contrast, in global scale models such as ChemRange and SimpleBox, which contain oceanic water compartments that contribute significantly to the transport of water-soluble substances with long half-lives in water, LRTP results clearly depend on surface media halflives. Another feature of models with transport in oceanic VOL. 39, NO. 7, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

9

1939

water and deep sea deposition, such as ChemRange, is the region of reduced spatial ranges in the bottom right corner of panel A (log Kow > 6 and log Kaw e -6). Chemicals in this region sorb strongly to suspended particles and are exported to the deep ocean with settling particles. This reduces, for example, the spatial range of highly chlorinated PCBs (31). Finally, the differences between LRTP values obtained with transport-oriented models and target-oriented models are shown in panels C (GLTE values in BETR North America) and D (CTD in ELPOS). The models produce very different LRTP values for persistent and volatile chemicals (log Kaw > -1, log Kow < 6). Such chemicals are transported over large distances in the atmosphere, which is indicated by their high LRTP in the transport-oriented models, but are not deposited to the surface due to their volatility. The target-oriented metric calculated in BETR North America, however, describes the fraction of emitted substance that is deposited to the surface water in the target region (the Great Lakes Basin). Accordingly, the LRTP decreases from 3% to less than 0.01% when log Kaw increases from -2 to 1. With BETR North America, the highest LRTP is obtained for chemicals with log Kaw values between -1 and -4 and log Kow < 6. These chemicals are volatile enough to be present in the atmosphere in considerable amounts but they are also subject to deposition by wet and dry deposition processes. This region of the partitioning space comprises genuine multimedia-type substances such as semi-volatile organic compounds (SOCs). In that same region, transportoriented LRTP metrics show a transition from high values for volatile chemicals to low values for nonvolatile chemicals. This reflects the fact that a fraction of the substance released partitions to the immobile or only slightly mobile media soil and water, which reduces its transport potential.

Discussion: How to Select the “Best” Model? By applying data analysis techniques that compare models over the full space of plausible chemical partitioning and degradability properties, we are able to show that among nine different models the ranking of chemicals according to Pov and, to a lesser extent, LRTP depends mainly on chemical properties. Significant differences between the models have been shown to be restricted to certain regions of the chemical space. These differences can be identified by detailed analyses of the chemical space plots. A recent study (32) comparing the effect of parameter uncertainty within a given model to the extent of uncertainty among the models (“model uncertainty”) further corroborates these findings. For Pov, the effect of parameter uncertainty, which has been estimated by assuming typical uncertainty ranges for all substance-specific input parameters, is higher than model uncertainty for most of our set of hypothetical chemicals. For LRTP, model uncertainty is higher than parameter uncertainty for approximately 70% of the hypothetical chemicals, however, in 95% of the cases not by more than a factor of 2. Consistent with the findings reported here, the regions in the chemical space where model uncertainty dominates over parameter uncertainty are the same as those where large discrepancies were identified in the previous section. These results illustrate that, especially for LRTP calculations, it is important to select a model that is appropriate to address any specific questions, and for the type of substances that are to be assessed. To guide this selection, we have summarized the results of our comprehensive analysis of the chemical space plots for LRTP of all models in Table 5 (all plots are given in Figures SI-1 to SI-10). Table 5 lists features of each model that lead to differences in LRTP results along with regions in the chemical space plot that show a distinct feature as compared to some or all of the other models. Four regions of the chemical space plots 1940

9

ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 39, NO. 7, 2005

in particular illustrate different results in the various models: (i) For chemicals with low volatility, high water solubility, and high half-life in water, it is important whether and how transport in water is taken into account by the model. (ii) For chemicals that strongly bind to aerosols due to high Koa, it is important whether the aerosol-bound fraction is assumed to be degradable. (iii) For chemicals with high Kow and low Kaw, particle-bound settling to the deep sea reduces their LRTP in global models containing oceanic water compartments. (iv) For very volatile chemicals, the distinction between target- and transport-oriented metrics is important. With the help of Table 5, which details these four aspects for each model, the user can choose, for a selection of chemicals with a certain set of chemical properties, the most appropriate model or suite of models. Because differences in model design influence LRTP results, it is important to check whether the chemicals investigated fall into one of the regions i-iv mentioned above before applying any model. We suggest applying several different models to build a comprehensive understanding of the LRTP under different sets of assumptions. Although this inter-model comparison has yielded useful insights, uncertainties remain that cannot be addressed by this analysis. Key among these is the influence of simplifications common to all models, such as the treatment of rain as a continuous process (33). The continuous-rain assumption may lead to underestimated LRTP of some chemicals for which there is empirical evidence of transport over long distances (34). More generally, there is a possibility of bias because all of the models considered here share some common characteristics: All are based on a mass balance approach, have a compartmental design, and rely on similar empirical submodels to describe partitioning and exchange of chemicals between various environmental compartments and subcompartments. Assessing the reliability of these fundamental assumptions is beyond the scope of this study. An ongoing model comparison study, focusing on differences among model algorithms, provides an opportunity to consider a broader spectrum of models (31). We found that some of the differences among the results are attributable to different original purposes of the models since many were not explicitly designed to calculate Pov and LRTP. These differences include different spatial scales (regional vs global), different regional characteristics (NorthAmerica vs Europe) implying different land-to-water area ratios, and different numbers and types of compartments. There are also differences in how the model developers defined the LRTP metrics. These differences are most prominent between target-region- and transport-focused metrics, but also the choice of spatial range (CTD) or the outflow ratio affects the picture obtained (36). To some degree, target- and transport-oriented metrics address different hazards. While the target-oriented LRTP metrics address the exposure of surface media in remote ecosystems, the transport-oriented metrics more generally address the hazard due to widespread presence of a substance. They also flag chemicals that pose a threat to atmospheric targets such as the ozone layer or that might lead to widespread exposure to atmospheric transformation products. So how does one select the “best” model? Given the results of this study, we suggest that no single best model exists for screening-level identification of chemicals with high Pov and LRTP. While for the calculation of Pov most models except Globo-POP and CEMC LII agree well and can therefore be used interchangeably, we observed more significant differences for LRTP. These are due to structural differences that are strongly linked to the different purposes of the models. Therefore, model selection is not arbitrary but requires careful consideration of the question and the context of the

TABLE 5. Model Features with High Influence on LRTP Results and Regions in the Chemicals Space Plots Showing This Influencea model

defining model features

findings from LRTP chemical space plots

log Kaw < -4, log Kow < 6, and t1/2,w > 8760 h: significant transport in seawater log Kow > 7 and log Koa > 13: LRTP of aerosol-sorbed chemicals constant for all half-lives log Kow > 6 and log Kaw e -6: transport reduced by deep sea export CalTOX continental scale; transport in air and river water; LRTP almost insensitive to half-lives in surface media; degradation of ABF log Kaw < -8 and log Kow < 3: hardly any transport in water log Kow > 5 and log Koa g 11: LRTP of aerosol-sorbed chemicals constant only for t1/2,a g 24 h SimpleBox regional, continental and global scales in nested log Kaw < -6, log Kow < 1, and t1/2,w g 1000 h: structure; transport in air and seawater, significant transport in seawater no degradation of ABF log Kow > 6 and log Koa > 13: LRTP of aerosol-sorbed chemicals constant for all half-lives IMPACT 2002 continental scale; georeferenced surface area ratios LRTP insensitive to half-lives in surface media; for Europe; transport in air and river water; log Kaw e -6 and log Kow e 4: hardly any transport degradation of ABF in water log Kow > 6 and log Koa > 11: LRTP of aerosol-sorbed chemicals constant only for t1/2,a g 1000 h CEMC LIII continental scale; generic environmental conditions; LRTP insensitive to half-lives in surface media; transport in air only; degradation of ABF log Kaw < -6 and log Kow < 4: hardly any transport in water log Kow > 5 and log Koa > 12: LRTP of aerosol-sorbed chemicals constant for t1/2,a g 24 h CEMC LII equilibrium partitioning; continental scale; generic domain with significant LRTP limited by constant environmental conditions with no aerosols log Kow for log Kow < 4 and constant log Koa for in air; transport in air only log Kow > 4; domain with significant LRTP increases with increasing t1/2,w; no transport with particles in the atmosphere ELPOS continental scale; typical European environmental air: LRTP insensitive to half-lives in surface media; conditions; transport in either air or river log Kow g 6 and log Koa > 12: LRTP of aerosol-sorbed water only; no degradation of ABF chemicals constant for all half-lives log Kaw < -7 and log Kow < 4: no transport in water water: LRTP insensitive to half-lives in air; log Kaw < -5 and log Kow < 4: water is important means of transport BETR North continental scale; georeferenced environmental -5 < log Kaw < -1 and log Kow < 6: high LRTP for America conditions for North America; target-oriented chemicals with t1/2,a g 168 h LRTP for atmospheric deposition to the log Kaw > -1: low LRTP for volatile chemicals due Great Lakes; no degradation of ABF to low deposition to target region log Kow > 5, log Koa > 11: LRTP of aerosol-sorbed chemicals constant for all half-lives Globo-POP global scale; transport in air and water; zonal average log Kaw > -5 and log Kaw < -1 or log Kaw > -1 and values for environmental parameters; targetlog Koa > 5: high LRTP for chemicals with oriented LRTP for accumulation in the Arctic; t1/2,w > 1000 h and chemicals with t1/2,w ) no degradation of ABF 1000 h and t1/2,a g 1000 h log Kaw > 0 and log Koa < 6: low LRTP for volatile chemicals due to low deposition to target region

ChemRange

a

global scale; transport in air and seawater; full spatial coupling between media; no degradation of ABF

ABF, aerosol-bound fraction.

assessment. Table 5 is provided to guide the user in the selection of an appropriate model. All nine models, when used in the correct context, provide credible and useful descriptions of the complex interactions between the environment and chemical pollutants.

Acknowledgments We thank Environment Canada, the Federal Environmental Agency of Germany, the Swiss Federal Agency for Environment, Forests and Landscape and the U.S. EPA National Exposure Research for financial support, and Eva Webster and Adam Hodge for running the CEMC models.

Supporting Information Available List of definitions of the LRTP metrics used in the different models (section 1); all chemical space plots of LRTP and Pov results for the nine models, along with an analysis of some

features discernible in the plots for Pov (this analysis complements the analysis of LRTP results in the main text) (sections 2 and 3); and a short discussion of the properties of the hypothetical chemicals (section 4). This material is available free of charge via the Internet at http://pubs.acs.org.

Literature Cited (1) OECD. Guidance Document on the Use of Multimedia Models for Estimating Overall Environmental Persistence and LongRange Transport; Series on Testing and Assessment 45; OECD Environment, Health and Safety Publications: Paris, France, 2004. (2) UNEP. Stockholm Convention on Persistent Organic Pollutants; Geneva, Switzerland, 2001. (3) Junge, C. E. Basic considerations about trace constituents in the atmosphere as related to the fate of global pollutants. In Fate of Pollutants in the Air and Water Environments; Suffet, I. H., Ed.; Wiley: New York, 1977. VOL. 39, NO. 7, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

9

1941

(4) Wania, F.; Dugani, C. B. Assessing the long-range transport potential of polybrominated diphenyl ethers: A comparison of four multimedia models. Environ. Toxicol. Chem. 2003, 22, 1252-1261. (5) Finizio, A.; Mackay, D.; Bidleman, T. F.; Harner, T. Octanol-air partition coefficient as a predictor of partitioning of semi-volatile organic chemicals to aerosols. Atmos. Environ. 1997, 31, 22892296. (6) Beyer, A.; Mackay, D.; Matthies, M.; Wania, F.; Webster, E. Assessing long-range transport potential of persistent organic pollutants. Environ. Sci. Technol. 2000, 34, 699-703. (7) Scheringer, M. Persistence and spatial range as endpoints of an exposure-based assessment of organic chemicals. Environ. Sci. Technol. 1996, 30, 1652-1659. (8) Beyer, A.; Matthies, M. Criteria for Atmospheric Long-Range Transport Potential and Persistence of Pesticides and Industrial Chemicals; Umweltbundesamt ed.; Erich-Schmidt-Verlag: Berlin, Germany, 2002. (9) McKone, T. E.; Enoch, K. G. CalTOX, A Multimedia Total Exposure Model Spreadsheet User’s Guide, Version 4.0; Lawrence Berkeley National Laboratory Report LBNL-47399; 2002; http:// eetd.lbl.gov/ied/era/. (10) Den Hollander, H. A.; Van Eijkeren, J. C. H.; Van de Meent, D. SimpleBox 3.0: Multimedia Mass Balance Model for Evaluating the Fate of Chemical in the Environment; Report 601200003/ 2004; RIVM: Bilthoven, The Netherlands, 2004; www.rivm.nl. (11) Pennington, D. W.; Margni, M.; Ammann, C.; Jolliet, O. Multimedia fate and human intake modeling: spatial versus nonspatial insights for chemical emissions in Western Europe. Environ. Sci. Technol. 2005, 39, 0000-0000. (12) Mackay, D. Multimedia Environmental Models: The Fugacity Approach; Lewis Publishers: Boca Raton, FL, 2001. (13) Wania, F.; Mackay, D. The Global Distribution Model. A NonSteady-State Multi-Compartmental Mass Balance Model of the Fate of Persistent Organic Pollutants in the Global Environment; Technical Report and Computer Program on CD-ROM; 2000. (14) MacLeod, M.; Woodfine, D. G.; Mackay, D.; McKone, T. E.; Bennett, D. H.; Maddalena, R. BETR North America: A regionally segmented multimedia contaminant fate model for North America. Environ. Sci. Pollut. Res. 2001, 8, 156-163. (15) EU. Technical Guidance Document on Risk Assessment in Support of Commission Directive 93/67/EEC on Risk Assessment for New Notified Substances, Commission Regulation (EC) No 1488/94 on Risk Assessment for Existing Substances and Directive 98/8/ EC of the European Parliament and of the Council Concerning the Placing of Biocidal Products on the Market; European Chemicals Bureau: Ispra, Italy, 2003; Parts I, II, and IV. (16) Conducting the Multi-Media Exposure Assessment of New Substances in Canada, 2nd draft report; Prepared under contract for the New Substances Division, Environment Canada; Bonnell Environmental Consulting: Ottawa, Canada, 2001. (17) Office of Pollution Prevention and Toxics. Persistence, Bioaccumulative, and Toxic (PBT) Profiler; U.S. EPA: Washington, DC, 2001; http://www.epa.gov/oppt/pbtprofiler/. (18) Bare, J. C.; Norris, G.; Pennington, D. W.; McKone, T. E. TRACIs The tool for the reduction and assessment of chemical and other environmental impacts. J. Ind. Ecol. 2002, 6 (3/4), 49-78. (19) McKone, T. E. CalTOX, A Multimedia Total-Exposure Model for Hazardous-Wastes Sites Part II: The Dynamic Multimedia Transport and Transformation Model: Prepared for the State of California, Department of Toxic Substances Control, Lawrence Livermore National Laboratory: Livermore, CA 1993; UCRLCR-11146PtII. (20) OECD. Report of the OECD/UNEP Workshop on the Use of Multimedia Models for Estimating Overall Environmental Persistence and Long-Range Transport in the Context of PBTs/ POPs Assessment; Series on Risk Management 36; OECD

1942

9

ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 39, NO. 7, 2005

(21)

(22)

(23)

(24)

(25)

(26)

(27)

(28)

(29)

(30)

(31)

(32)

(33) (34)

(35) (36)

Environment, Health and Safety Publications: Paris, France, 2002. Klasmeier, J.; Matthies, M.; Fenner, K,; Scheringer, M.; MacLeod, M.; Stroebe, M.; Le Gall, A. C.; McKone, T. E.; Pennington, D.; Suzuki, N.; van de Meent, D.; Wania, F. Long-range transport potential and overall persistence in screening and assessment of organic chemicals. Manuscript in preparation. Wania, F.; Mackay, D. A Comparison of Overall Persistence Values and Atmospheric Travel Distances Calculated by Various MultiMedia Fate Models, WECC Report 2/2000; WECC Wania Environmental Chemists Corp.: Toronto, Canada, 2000. Beyer, A.; Scheringer, M.; Schulze, C.; Matthies, M. Comparing representations of the environmental spatial scale of organic chemicals. Environ. Toxicol. Chem. 2001, 20, 922-927. Bennett, D. H.; Scheringer, M.; McKone, T. E.; Hungerbu ¨ hler, K. Predicting long-range transport: A systematic evaluation of two multimedia transport models. Environ. Sci. Technol. 2001, 35, 1181-1189. Malanichev, A.; Mantseva, E.; Shatalov, V.; Strukov, B.; Vulykh, N. Numerical evaluation of the PCBs transport over the Northern Hemisphere. Environ. Pollut. 2004, 128, 279-289. Leip, A.; Lammel, G. Indicators for persistence and long-range transport potential as derived from multicompartment chemistry-transport modelling. Environ. Pollut. 2004, 128, 205-221. Scheringer, M.; Wania, F. Multimedia models of global transport and fate of persistent organic pollutants. In Handbook of Environmental Chemistry; Hutzinger, O., Fiedler, H., Eds.; Springer: Berlin and Heidelberg, 2003. Pontillo, J.; Eganhouse, R. P. The Search for Reliable Sw and Kow Data for Hydrophobic Organic Compounds: DDT and DDE as a Case Study; Water-Resources Investigations Report 01-4201; U.S. Geological Survey: Reston, VA, 2001. Wania, F.; McLachlan, M. S. Estimating the influence of forests on the overall fate of semivolatile organic compounds using a multimedia fate model. Environ. Sci. Technol. 2001, 35, 582590. Cullen, A. C.; Frey, H. C. Probabilistic Techniques in Exposure AssessmentsA Handbook for Dealing with Variability and Uncertainty in Models and Inputs; Plenum Press: New York, 1999. Scheringer, M.; Stroebe, M.; Wania, F.; Wegmann, F.; Hungerbu ¨ hler, K. The effect of export to the deep sea on the long-range transport potential of persistent organic pollutants. Environ. Sci. Pollut. Res. 2004, 11, 41-48. Fenner, K.; MacLeod, M.; Stroebe, M.; Beyer, A.; Scheringer, M. Relative Importance of Model and Parameter Uncertainty in Models Used for Prediction of Persistence and Long-Range Transport Potential of Chemical Pollutants; Proceedings of iEMSs 2004 Conference; Osnabru ¨ ck, Germany, 2004. Hertwich, E. G. Intermittent rainfall in dynamic multimedia fate modeling. Environ. Sci. Technol. 2001, 35, 936-940. Muir, D. C. G.; Teixeira, C.; Wania, F. Empirical and modeling evidence of regional atmospheric transport of current-use pesticides. Environ. Toxicol. Chem. 2004, 23, 2421-2432. EMEP. POPs Multimedia Intercomparison Study; Meteorological Synthesizing Centre East: Moscow, 2004. Stroebe, M. Exploring Multi-Media Fate Models: The Temporal and Spatial Remote States and Investigation of Junge’s Variability-Lifetime Hypothesis. Dissertation, ETH Zu ¨ rich, Zu ¨ rich, Switzerland, 2004.

Received for review July 13, 2004. Revised manuscript received December 23, 2004. Accepted January 4, 2005. ES048917B