Environ. Sci. Technol. 2003, 37, 4934-4943
Expanding the Applicability of Multimedia Fate Models to Polar Organic Chemicals K N U T B R E I V I K † A N D F R A N K W A N I A * ,‡ NILU - Norwegian Institute for Air Research, P.O. Box 100, N-2027 Kjeller, Norway, and Department of Physical and Environmental Sciences, University of Toronto at Scarborough, 1265 Military Trail, Scarborough, Ontario, Canada M1C 1A4
Reliable estimates of environmental phase partitioning are essential for accurate predictions of the environmental fate of organic chemicals. Current fate and transport models use single-parameter linear free energy relationships (SPLFERs) to quantify equilibrium phase partitioning. The applicability of such SP-LFERs is limited because no single parameter is able to describe appropriately all the molecular interactions that contribute to environmental phase distribution processes. Environmental partitioning coefficients predicted by SP-LFERs may thus have errors of up to an order of magnitude. Ranges for several environmental partitioning equilibria are identified, where such errors can result in significantly different fate predictions for individual bulk model compartments. We propose that it is possible to reduce such errors and uncertainties by implementing polyparametric LFER (PP-LFER) approaches in multimedia fate models. A level III fugacity model was modified such that the partitioning properties of chemicals are characterized by five linear solvation energy parameters rather than vapor pressure, water solubility, and octanolwater partition coefficient. A comparison of modified and unmodified models for a set of organic chemicals shows that the approach chosen to simulate environmental phase partitioning can have a large impact on model results, including long-range transport potential, overall persistence, and concentrations in various media. It is argued that PPLFER based environmental fate models are applicable to a much wider range of organic substances, in particular those with polar functional groups. Obstacles to the full implementation of PP-LFER in multimedia fate models are currently the lack of solute descriptors for some chemicals of environmental concern and suitable regression equations for some important environmental phase equilibria, in particular for the partitioning between gas and particle phase in the atmosphere.
Introduction Multimedia environmental fate models, based on the fugacity approach (1, 2) or not (e.g. ref 3), are versatile and widely used tools to understand and predict the fate of chemicals in the environment (4, 5). Expressions that describe environmental phase partitioning are at the core of these models * Corresponding author phone: (416)287-7225; fax: (416)287-7279; e-mail:
[email protected]. † NILU - Norwegian Institute for Air Research. ‡ University of Toronto at Scarborough. 4934
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(e.g. refs 2 and 6). Important phase equilibria are those that describe the distribution between the gas phase, aqueous phases, and various organic phases in soil, sediments, vegetation, and atmospheric particles. Typically, environmentally relevant equilibrium phase partitioning coefficients are estimated using double logarithmic correlations with partition constants involving pure aqueous and organic solvent phases. These correlations are known as singleparameter (or one-parameter) Linear Free Energy Relationships (SP-LFERs) (7) and are widely used for describing the phase distribution of organic chemicals in environmental media. Specifically, the equilibrium partition coefficient between 1-octanol and water (KOW) finds use for estimating the distribution of chemicals between water and organic carbon in soil and sediments (commonly denoted KOC, but referred to as KOC,W here to better distinguish it from the partitioning coefficient between organic carbon and the gasphase KOC,A) (e.g. refs 8 and 9). Likewise, partitioning between air and various organic phases relies on correlations with the octanol-air equilibrium partition coefficient (KOA) (10). Most multimedia fate models rely on such SP-LFERs to describe phase partitioning. Awareness of the limitations of SP-LFERs to accurately predict environmental phase partitioning is increasing (e.g. refs 7, 9, and 11). Goss and Schwarzenbach (7) argued that the predictive power of SP-LFERs is limited because no single parameter is able to describe appropriately all the molecular interactions that determine environmental phase partitioning processes. For example, SP-LFERs based on KOW often fail to accurately predict experimentally derived KOC,W (11), and errors can easily reach 1 order of magnitude. Neither can SP-LFERs explain the observed variability in partitioning between atmospheric particles and air (12) or foliage and air (13). Errors in the predictions of SP-LFERs may in part be due to significant experimental uncertainties of the physicalchemical properties on which they are based, such as KOW, KOA, and water solubility, in particular for highly hydrophobic and relatively involatile compounds (e.g. refs 14 and 15). A more fundamental issue is, however, to what extent octanol can be considered an appropriate surrogate for the organic matter encountered in soils, sediments, suspended particles, plants, and aerosols. It is becoming increasingly obvious that the regression coefficients of SP-LFERs are only valid for a set of closely related compounds that undergo similar molecular interactions with a phase (7). Different compound classes yield and require different SP-LFERs. Much of the error in SP-LFER predictions thus derives from the inappropriate application of unsuitable SP-LFERs to a particular compound. Strictly speaking, multimedia models should thus only be used for chemicals that have a partitioning behavior that is comparable to the partitioning behavior of those chemicals that were used in the derivation of the SP-LFERs that are a part of the model. This would limit the applicability of most existing fate models to fairly nonpolar organic substances. Two issues, in particular, highlight the need to widen the applicability of multimedia environmental fate models. More and more chemicals of environmental concern have partitioning properties quite different from those of the “classical” pollutants for which SP-LFERs exist and were shown to work reasonably well. This includes modern pesticides, pharmaceuticals, and many substances used in industrial applications and consumer products. The other reason is the potential use of models in screening exercises of large numbers of organic substances (16), e.g. to identify those with exceptionally high persistence or long-range transport 10.1021/es034454i CCC: $25.00
2003 American Chemical Society Published on Web 10/02/2003
potential. The likelihood of many of the screened chemicals having more complex partitioning behavior than a typical “classical” pollutant is very high. Several options exist to apply the models to a wider array of organic substances. The simplest is to directly supply the models with empirically derived phase partitioning information, i.e., the models rely on measured partitioning coefficients between water and soil organic carbon, between gas phase and atmospheric particles, and between any other environmentally relevant phases that may be included in the model. This limits the applicability to fairly well characterized substances and prevents the use of models in the screening of a large number of chemicals, for which no such empirical information is available. Another option would be to develop multiple SP-LFERs applicable to clearly defined subsets of substances and allow the model user to specify as input the parameters of the respective regression equations applicable to a particular simulated compound. This, however, still limits applicability to those substances for which suitable SP-LFERs exist. A much more promising approach appears to be the development of models that are based on polyparameter Linear Free Energy Relationships (PP-LFERs). There are a variety of such approaches, but they have in common the use of a number of parameters to account for van der Waals (dispersive and dipole interactions) and Lewis acid-base interactions (hydrogen bonds) between solute and solvent. LFERs that use several molecular descriptors are better suited to describe the whole variance of experimental partitioning data for a large variety of chemical compounds (7). This paper aims to take a first step in the development and application of PP-LFER based environmental fate models and to identify what is required to make them feasible and widely accepted. Specifically, we start out by identifying the partitioning property ranges for which multimedia fate models show the highest sensitivity and thus are likely to yield inaccurate fate predictions if supplied with erroneous partitioning coefficients. Against this background, we present a revised environmental fate modeling approach that omits the use of SP-LFERs in favor of linear solvation energy relationships (LSERs), which are a specific example of the PP-LFER approach. Deviations between the SP-LFER and the PP-LFER approach implemented in one particular multimedia fate model are evaluated and discussed with the aim to show how the modeling strategy with respect to environmental phase partitioning may affect various model outputs for a test set of chemicals. Examples include differences in predicted phase concentrations, overall persistence, and long-range transport potential.
Sensitivity of Predicted Phase Distributions to Partitioning Coefficients The environmental phase distribution of organic chemicals is controlled by various factors related to both environmental and chemical characteristics (e.g. ref 2). It is of considerable interest in the current context to assess for which chemicals and which environmental media correct phase partitioning data are of particular importance, i.e., under what circumstances even small errors in the equilibrium phase partitioning constants may significantly affect model results. To identify zones of high sensitivity to partitioning data, we calculated the range of the partitioning coefficients KOC,W, and KOC,A (partitioning coefficient between the organic carbon in atmospheric particles and the gas phase), in which significant changes in environmental phase distribution occur within the bulk environmental model compartments air, water, soil, and sediment. The partitioning properties of such chemicals are highlighted as transition zones within maps defined by the relevant partitioning coefficients and phase properties (Figure 1). The Supporting Information provides
detail on how the equations underlying these maps were derived and on the generic environmental parameters used in these calculations. Water and Sediment. In the water and sediment compartments of most environmental fate models an organic chemical is either in the dissolved phase or in the organic matter present in particles, and this partitioning equilibrium is expressed through KOC,W. Figure 1A,B displays the fraction present in the dissolved and sorbed state in water and sediment as a function of KOC,W and the concentration of organic carbon, denoted here as the concentration of particulate organic carbon in water (CPOC in g/L) and the mass fraction of organic carbon in sedimentary solids (fSOC), respectively. The diagonal red band indicates the region where less than 90% of the organic chemical is either dissolved in water (blue) or sorbed to the organic carbon (yellow), and where thus a slight error in KOC,W can result in a large shift in predicted phase distribution. For a typical CPOC of 0.1-1 mg/L such sensitivity occurs for a log KOC,W between 5 and 8, i.e., for highly hydrophobic chemicals. This transition range shifts toward lower values of KOC,W if the concentration of CPOC increases and vice versa. For example, Figure 1A indicates that during episodes of elevated CPOC concentrations, such as occur during algal blooms or periods of high runoff, chemicals that are expected to be predominantly in the aqueous phase (i.e. having a log KOC,W between 4 and 5) may become increasingly sorbed to POC. The transition region for the sediment compartment is similar to that for the water compartment but occurs at much lower KOC,W values (Figure 1B, assuming sediment porosity of 0.8) reflecting the much higher concentration of organic matter. Assuming a typical fSOC between 0.05 and 0.1, the sensitive region corresponds to chemicals with a log KOC,W between 1 and 3. Soil. Soils are rarely water saturated, and it is necessary for models to consider partitioning of an organic chemical into the gas phase in addition to the water and soil organic matter phase. Besides KOC,W, the air-water partition coefficient KAW is required to express the propensity of the substance to partition into the air-filled pore space. Figure 1C plots the major soil subcompartment for an organic chemical in a typical soil (porosity 0.5, pore space 60% waterfilled, 4%(w/w) OC) at equilibrium as a function of log KOC,W and log KAW. A chemical with a log KOC,W higher than 4 is likely to be sorbed to soil organic carbon, irrespective of its KAW. A chemical with a log KOC,W between 2 and 4 is still likely to be sorbed to the soil organic carbon, unless the chemical has a log KAW higher than 0 and partitioning into the soil air becomes notable. A chemical with a log KOC,W less than 2 may be predominantly found in soil organic matter, soil water, or soil air, depending on its log KAW. If log KAW of the chemical is higher than 1, soil air may be the dominant phase. If log KAW of the chemical is less than -1, soil air may be of limited significance as compared to water and soil organic matter. Finally, if log KAW of the chemical is between 1 and -1, reliable estimates of both log KOC,W and log KAW are crucial in order to predict soil partitioning accurately. Atmosphere. The equilibrium distribution of organic chemicals in the atmosphere can be expressed using the partitioning coefficient between the organic carbon in atmospheric particles and the gas phase (KOC,A), if it is assumed that sorption to other parts of these particles is negligible. Using an organic carbon content in the particles of 10% (v/v), Figure 1D displays the partitioning between gas and organic particle phase as a function of KOC,A and the concentration of particles CQ in the dry atmosphere. Assuming a typical CQ of 10-100 µg‚m-3, the transition from the gas to the particle phase occurs in the KOC,A range 10.513.5, and it is within this range that atmospheric phase distribution is most sensitive to the value of KOC,A. In a cloud, partitioning into aqueous droplets can again be expressed VOL. 37, NO. 21, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 1. Estimated dominant pools (>90%) of organic chemicals in various environmental compartments of a generic environment. Red color identifies zones of environmental partitioning sensitivity, for which less than 90% of a chemical is estimated to be present in a single phase of an environmental medium. The colored lines (C, E) are the 10% partitioning isolines of the respective environmental phases of interest. using KAW. Figure 1E shows the phase distribution in a cloud as a function of log KOC,A and KAW, assuming a cloudwater content of 10-6 m3‚m-3 and a CQ of 45 µg‚m-3. Chemicals that have a log KOC,A higher than 13 are likely to be sorbed to the organic carbon of atmospheric particles, unless they are very water soluble (log KAW of less than -6), and dissolution in water droplets may become significant. Chemicals that have a log KOC,A lower than 10.5 are either found in the gas phase or dissolved in droplets, with the transition occurring in the log KAW range between -5 and -7. The atmospheric phase distribution of chemicals with a log KOC,A between 10 and 13 is most sensitive to the partitioning properties, and minor uncertainties in either KOC,A or KAW can result in large errors in the predicted equilibrium distribution between gas phase, organic aerosol matter, and water droplets. Reliable estimates of the phase distribution within the subcompartments air, water, soil, and sediment are key prerequisites to accurately describe the overall environmental fate of an organic chemical. Maps such as in Figure 1 may serve to identify the ranges of chemical partitioning combinations for which environmental fate models are likely at elevated risk of making errors. A notable observation of the analysis depicted in Figure 1 is the rather narrow width of the red transition regions, which generally range over a mere 2 orders of magnitude. In other words, if a partitioning property such as KOC,W, KOC,A, and KAW is in error by 1 order of magnitude, predictions of phase distribution can be very misleading. The potential for error is dependent on the position of a compound within these maps, i.e., is highest in or close to the red transition zones. 4936
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In the construction of Figure 1 (especially parts C and E), assumptions with respect to the phase composition of the environmental compartment had to be made. Under a different set of environmental circumstances, the transition zones will shift. It should be emphasized that the maps in Figure 1 do not yet give a complete picture of partitioning sensitivity, as sometimes even uncertainties of very small fractions of chemicals in various subcompartments can strongly impact on model results, i.e., it may matter whether 0.0001 or 0.00001% of a chemical is in a particular subphase. This is the case when the rate of chemical transport is very different in different subcompartments, e.g. a chemical may be transported predominantly in the pore space of sediments and soils, even though the bulk of the chemical is sorbed to solids. In other words, partitioning sensitivity may also occur outside of the red zones indicated in Figure 1.
Linear Solvation Energy Relationships for Environmentally Relevant Phase Partitioning Equilibria PP-LFERs facilitate the description of phase partitioning for a large number of chemicals with diverse properties, using a single equation. So far, such expressions have been applied rarely in environmental chemistry practice, especially when compared to their widespread use in pharmaceutical, analytical, and organic chemistry (7, 17). A large number of PP-LFER expressions have been developed and evaluated to describe phase partitioning for a variety of chemicals and phases (6, 7, 11, 17-24). We have chosen to use the so-called solvation parameter model (e.g. ref 23), or linear solvation energy relationships (LSERs), to illustrate the use of PP-LFERs
TABLE 1. Linear Solvation Energy Relationships for Environmentally Relevant Phase Equilibria (11, 18-21)a n
G
SD
f
using Vx
log KWA log KOW log KOC,W log KPW
[A] [B] [C] [D]
-0.994 + 0.577R2 + 2.549πH2 + 3.813 ∑RH2 + 4.841∑βH2 - 0.869Vx 0.088 + 0.562R2 - 1.054πH2 + 0.034 ∑RH2 - 3.460∑βH2 + 3.814Vx 0.21 + 0.74R2 - 0.31ΣRH2 - 2.27∑β02 + 2.09Vx -0.415 + 0.596R2 - 0.413πH2 - 0.508∑RH2 - 4.096∑βH2 + 3.908Vx
408 613 131 62
0.997 0.997 0.977 0.981
0.151 0.116 0.248 0.236
16 810 23 161 655 566
using log L16
log KWA log KOA log KOC,A log KPA
[E] [F] [G] [H]
-1.271 + 0.822R2 + 2.743πH2 + 3.904∑RH2 + 4.814∑βH2 - 0.213log L16 -0.120-0.203R2 + 0.560πH2 + 3.576∑RH2 + 0.702∑βH2 + 0.939log L16 -0.46 + 0.65R2 + 2.40πH2 + 3.39∑RH2 + 2.57∑βH2 + 0.36log L16 -0.617 + 0.082R2 + 1.282πH2 + 3.120∑RH2 + 0.820∑βH2 + 0.860log L16
392 156 69 62
0.996 0.997 0.991 0.994
0.185 0.125 0.238 0.230
10 229 10 573 667 2361
log SP
LSER expressions
a n ) number of solutes, F ) overall correlation coefficient (cross-validated r 2 in case of K PW and KPA), SD ) standard deviation, f ) Fisher F-statistic). [A] eq 4 and Table 5 in ref 20, [B] eq 7 in ref 21, [C] eq 9 in ref 11, [D] eq 7 in ref 19, [E] eq 3 and Table 5 in ref 20, [F] eq 5 in ref 18, [G] eq 14 in ref 11, [H] eq 4 in ref 19.
in multimedia environmental fate modeling. Solvation parameter models are commonly expressed as
log SP ) c + rR2 + sπΗ 2 + a
∑R
Η 2
+b
Η 2
+b
∑β
Η 2
+ vVx
(1)
or
log SP ) c + rR2 + sπΗ 2 +a
∑R
∑β
Η 2
+ l log L16 (2)
In eqs 1 and 2, SP refers to the phase partition property of interest. In general, eq 1 is commonly applied to processes involving two condensed phases, while eq 2 is typically applied for the distribution between the gas-phase and a condensed phase (19, 20). These equations consist of product terms representing the properties of the chemical (solute descriptors) and the environmental media (system constants)s the latter as identified by lower-case Latin characters. Each product term thus represents the contribution of a specific intermolecular interaction to the overall partition property of interest. The chemically specific solute descriptors in LSERs are the excess molar refraction (R2 in cm3/10), a dipolarity/ polarizability term (πΗ 2 ), the effective or overall hydrogenΗ bond acidity, and hydrogen-bond basicity (∑RΗ 2 and ∑β2 , respectively) as well as an indicator of solute size. LSERs of the type eq 1 use McGowan’s characteristic volume Vx (in cm3 mol-1/100), while those of type eq 2 include the distribution constant for the chemical between the gas phase and n-hexadecane at 298 K (log L16). The coefficients (c, r, s, a, b, v, and l) or system constants in eqs 1 and 2 are defined by their complementary interactions with the solute descriptors (11). The r constant determines the difference in capacity of the two phases considered with respect to their n- or π-electron interaction with the chemical (solute). The s constant is a measure of the difference in capacity of the two phases to participate in dipole-dipole and dipoleinduced dipole interactions with the chemical. The a and b constant express the difference between the two phases with respect to their hydrogen-bond acidity and hydrogen-bond basicity, respectively. Finally, the l and v constants are measures of the relative ease of the chemical to form a cavity in the two phases considered (i.e. the so-called cavity term). Different H-donor and acceptor scales are found in the literature. Specifically, a “solute special overall hydrogenbond basicity”, denoted ∑β02, has been introduced for specific groups of chemicals (e.g. certain sulfoxides, anilines, pyridines, and some heterocyclic compounds) in water/ solvent partitioning systems where the organic phase is quite aqueous (including octanol) (17). The key reason for the development of an additional basicity parameter was that for certain groups of chemicals the solute hydrogen-bond basicity was found to vary with the partitioning system (17).
As stated by Goss and Schwarzenbach (7), “these scales are very similar in their absolute values and variables, but they are not identical and must not be confused, i.e., polyparameter LFERs must be used with just the H-bond parameter scales for which they were established”. We note that changes to the nomenclature identifying solute descriptors and system constants have recently been proposed (25). One advantage of the LSER approach is the availability of solute descriptors for a very large number of organic chemicals (11). Of greater importance, however, is that LSER regressions exist for several environmentally relevant partition coefficients, including those between water and air (20), organic carbon and air or water (11), and between plant material and air or water (19). The latter regressions represent partitioning properties of the cuticle of the fruit of one species only (tomato, Lycopersicon esculentum) and may be expected to be different for leaf cuticles and other plant species. The solvation parameter model has further been applied to characterize the phase equilibria that are commonly used in SP-LFERs, including those between octanol and air (KOA) (18) and octanol and water (KOW) (21). Finally, LSER regressions have been shown to yield accurate descriptions of environmental phase partitioning equilibria (see e.g. ref 11 and statistics, Table 1). LSERs for various environmentally relevant, dimensionless partition coefficients (11, 18-21) are compiled in Table 1. The water-air partition coefficient KWA, KOW, KOC,W and the plant cuticle-water partition coefficient KPW are based on McGowan’s characteristic volume (Vx), whereas the octanolair partition coefficient KOA, the organic carbon-air partition coefficient KOC,A and the plant cuticle-air partition coefficient KPA are based on log L16. ∑βΗ 2 is included in all equations except for log KOC,W which includes ∑β02, as no “variable” basicity compounds were included in seven out of the eight regressions. R2 can be estimated by simple addition of fragments, and Vx can be calculated from atomic constants (see e.g. ref 17 and references therein). If one does not specifically address the groups of chemicals for which ∑β02 is required, only three out of five solute descriptors need to Η Η be determined (πΗ 2 , ∑R2 , β2 ) for use of LSER expressions of type eq 1. In theory then, if three different water/solvent partition coefficients are known, the three unknown solute descriptors may be calculated. However, this will work only if the system constants of the three water/solvent equations are sufficiently different (17). How the various solute properties influence environmentally relevant phase equilibria is illustrated in Figure 2, based on the sign and magnitude of the system constants in Table 1. Considering water-air partitioning (Figure 2A), excess molar refraction (R2), solute dipolarity (πΗ 2 ), solute hydrogen-bond acidity (∑RΗ 2 ), and solute hydrogen-bond basicity (∑βΗ 2 ) all favor water, whereas solute volume or size VOL. 37, NO. 21, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 2. Solute properties influencing environmentally relevant phase partitioning equilibria (modified after ref 17). (log L16 and Vx) favors air. In simple terms, most solute properties (except log L16 and Vx) show a greater propensity for solute-water interaction than for partitioning into air. Figure 2B-D further illustrates how various solute characteristics affect the partitioning between an organic phase (octanol, soil organic carbon, and plant cuticle) and air or water. There are both similarities and differences in the interaction of a solute between water and various organic phases. For example, in all cases excess molar refraction (R2) and solute size (Vx) favor partitioning from water to the organic phase (Figure 2B-D), whereas the overall hydrogen basicity favors water relative to the organic phase. Some system constants are very small or are completely omitted from an LSER equation (πΗ 2 in the LSER for KOC,W), indicating that water and the organic phase have similar propensities to undergo a certain type of interaction (indicated by double arrows in Figure 2). This suggests, for example, that wet soil organic matter is as dipolar/polarizable as water and the propensity for dipolar-type interactions becomes equal in both phases (s system constant is zero) (11). Indeed, the differences in the system constants listed in Table 1 and illustrated in Figure 2 highlight the limitations of using octanol as a surrogate for environmental organic phases (such as plant cuticles and soil organic carbon).
Linear Solvation Energy Relationships for Fugacity Capacities Multimedia fate models based on partition coefficients may directly use the equations in Table 1 to estimate environmental phase partitioning equilibria. Fugacity-based multi4938
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media fate models, however, do not use partition coefficients to express environmental phase partitioning but so-called fugacity capacities or Z-values in units of mol‚m-3‚Pa-1. The relationship between the dimensionless distribution constant of a chemical between two phases, 1 and 2, and the Z-values in these two phases is given by K12 ) Z1/Z2 (2). LSER equations for the Z-values of octanol (ZO) and environmentally relevant phases (water ZW, particulate organic carbon ZPOC, plant cuticles ZP) may thus be deduced from the LSERs equations for the partition coefficients in Table 1. In all regressions that include log L16 as well as the regression for log KWA that is based on Vx, phase 2 refers to air. Since ZAir is usually estimated as 1/RT (where R is the universal gas constant: 8.314 Pa m3 mol-1 K-1 and T is the atmospheric temperature in K), log Z1 can then be calculated as log K1Air - log RT (since Z1 ) K1Air/RT and log Z1 ) log K1Air - log RT). In the other three cases where phase 2 refers to water (eqs [B], [C], and [D] in Table 1), log Z1 can be calculated as log K1Water + log ZWater (since log K1Water ) log Z1 - log ZWater), applying polyparametric expressions for both log K1Water (Table 1) and log ZWater (derived from KWA/RT, as detailed above). The resulting polyparametric Z-value expressions are given in Table 2. Starting out with air, Z-values for the remaining media have been estimated by sequentially proceeding from one medium to the other. In Table 1, the solvation parameter model is applied to the distribution between two phases, and the system constants express differences between these phases (e.g. ref 17). Using the terminology of Mackay (2) a Z-value can be regarded as “half” a partition coefficient. The system constants of the LSERs in Table 2 may therefore be considered measures of the propensity of a chemical to undergo various solute-solvent interactions in one medium. One should thus expect that the two expressions for the log Z of a specific medium in Table 2 are similar. Indeed, the two expressions for log ZW are similar, but for the other Z-values there are some marked differences. Some of these differences may be attributed to the different solute size or volume terms used (Vx and log L16) as well as due to cross-correlation between the different solute descriptors. Abraham and Chadham (17) have previously shown that the maximum cross-correlation is between R2 and π (r2 ) 0.545), and it would appear that some of the most significant differences are found for these two descriptors in the expressions for ZO, ZPOC, and ZP. At present, it is difficult to provide guidance on which polyparametric expressions to choose from Table 2. To investigate quantitatively the differences in Z-values estimated by “classical” SP-LFERs and the PP-LFERs in Table 2, a test set of chemicals representing different compound classes was compiled, for which solute descriptors (both log L16 and Vx) and key physical-chemical properties (KAW and KOW) as well as environmental half-lives are available (see Table 1 in the SI). Solute descriptors for the chemicals were taken from the compilation by Poole and Poole (11) with 16 additional descriptors (∑βΗ 2 and log L ) from Abraham et al. (20, 21). KAW and KOW values and environmental half-lives were in most cases based on the selected values in the compilation by Mackay et al. (26). Figure 3A compares the Z-values for particulate organic carbon in soils, calculated with the well-known and frequently applied KOW-based SP-LFER by Karickhoff (8) (y-axis) and the Vx-based PP-LFER for ZPOC in Table 2 (x-axis). We have also included the 1:1 line and the results of the log L16-based PP-LFER. An inspection of Figure 3A suggests that ZPOC may vary by an order of a magnitude, depending on the approach chosen to estimate environmental phase partitioning. Considering that media concentrations are linearly related to fugacity capacity, the estimated concentrations will vary to the same extent (assuming the same fugacity). Excellent agreement is found between the ZPOC calculated with the
TABLE 2. Linear Solvation Energy Relationships (LSERs) for Fugacity Capacities of Environmentally Relevant Phases Derived from the LSERs for Partition Coefficients in Table 1a
a
based on Vx
log ZW log ZO log ZPOC log ZP
-0.99 + 0.58R2 + 2.55πH2 + 3.81∑RH2 + 4.84∑βH2 - 0.87Vx - log(RT) -0.90 + 1.14R2 + 1.50πH2 + 3.84∑RH2 + 1.38∑βH2 + 2.94Vx - log(RT) -0.78 + 1.32R2 + 2.55πH2 + 3.50∑RH2 + 4.84∑βH2 - 2.27∑β02 + 1.22Vx - log(RT) -1.41 + 1.18R2 + 2.14πH2 + 3.30∑RH2 + 0.74∑βH2 + 3.04Vx - log(RT)
based on log L16
log ZW log ZO log ZPOC log ZP
-1.27 + 0.82R2 + 2.74πH2 + 3.90∑RH2 + 4.81∑βH2 - 0.21log L16 - log(RT) -0.12 - 0.20R2 + 0.56πH2 + 3.58∑RH2 + 0.70∑βH2 + 0.94log L16 - log(RT) -0.46 + 0.65R2 + 2.40πH2 + 3.39∑RH2 + 2.57∑βH2 + 0.36log L16 - log(RT) -0.62 + 0.08R2 + 1.28πH2 + 3.12∑RH2 + 0.82∑βH2 + 0.86log L16 - log(RT)
W ) water, O ) octanol, POC ) particulate organic carbon, P ) plant cuticle.
FIGURE 3. Comparison between various results of a PP-LFERs (x-axis) and a SP-LFERs (y-axis) based level III fugacity model for 40 test chemicals. Shown are log ZPOC in mol‚m-3‚Pa-1 (A), log overall persistence in hours (B), log soil solid concentrations in mol‚m-3 (C), and the log cuticle concentration in mol‚m-3 (D). Also given is the 1:1 line and a comparison between the results of the PP-LFER approach using either equations based on Vx or on log L16 (small circles). two polyparametric equations based on Vx and log L16. One obvious outlier (benzo-a-pyrene), however, had to be excluded and hints at erroneous solute descriptors for that substance.
A Level III Fugacity Model Based on Polyparameter Linear Free Energy Relationships The polyparametric Z-value expressions in Table 2 were implemented into the well-known level III fugacity model, a generic nonequilibrium, steady-state multimedia mass-
balance model (2, 27). Supplied with information on chemical properties, emissions, and environmental parameters, the level III model calculates concentrations in four environmental media (air, water, soil, and sediment), rates of intermedia transport, advective flow, and degradation, and estimates the overall persistence of a chemical. Reference 2 provides a general introduction to fugacity-based environmental fate modeling and a detailed description of the level III model. With respect to phase partitioning, the original level III model requires the input of a chemical’s water VOL. 37, NO. 21, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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solubility, vapor pressure, melting point, and octanol-water partition coefficient. These input parameters are used to estimate KAW (from the ratio of vapor pressure and water solubility), KOC,W (using a SP-LFER with KOW), and the partitioning equilibrium coefficient between gas and atmospheric particle phase (using a SP-LFER with liquid-phase vapor pressure PL). The melting point is used to convert the vapor pressure of solid substances to the liquid state, using a generic entropy of fusion. Z-values for water, soil, and sediment organic carbon and atmospheric particles are then derived from these partition coefficients. A SP-LFER based on KOW is further used to estimate a fugacity capacity for fish lipids in equilibrium with the water phase. For the purposes of this study we made a number of changes to the model: The Z-value for atmospheric particles is calculated using a KOA-based (instead of a PL-based) SPLFER (10). This avoids the need for specifying or estimating a liquid-state vapor pressure. Furthermore, a concentration in plant foliage is estimated by assuming equilibration (i.e. equifugacity) between air and a plant compartment, whose Z-value is estimated using a KOA-based SP-LFER equation for plant cuticles by Welke et al. (28). Most importantly, however, the level III model was modified to alternatively allow the calculation of Z-values using the LSER equations in Table 2. Presently, no PP-LFER for atmospheric particles (or the organic matter of atmospheric particles) exists, that could be incorporated into a PP-LFER based multimedia fate model. To nevertheless estimate all partitioning information from PP-LFERs, we assumed that the organic matter of the atmospheric particles has the same partitioning characteristics as octanol and accordingly used the equations for ZO in Table 2. We are, of course, aware that this is not at all in the spirit of this contribution, because that assumption essentially amounts to the use of a SP-LFER. However, in the absence of a PPLFER for atmospheric particles, this seems the only feasible approach. Please note that because of that assumption, partitioning into atmospheric particles is estimated virtually identically in the SP-LFER based model (SP-LFER for the organic matter in aerosols based on KOA) and the PP-LFER based model (PP-LFER for Z-value of octanol, which is assumed to be a surrogate for the organic matter in aerosol). This implies that any differences in the results of the two models are due to differences in the Z-values for phases other than atmospheric particles. In other words, the comparison below underestimates the potential error that a SP-LFER makes relative to a completely PP-LFER based model, because it does not account for the limitations of SP-LFERs for atmospheric particles. (Incidentally, this also explains the use of a KOA-based approach in the SP-LFER level III model, as it prevents the confounding of the comparison below. Such confusion could have occurred by including a KOAbased approach in the PP-LFER based model and a PL-based approach in the SP-LFERs based model.) In summary, the modified level III fugacity model calculates Z-values either from SP-LFERs or from PP-LFERs. We like to stress the major shift this change implies in terms of the input parameters required for simulating the environmental fate of an organic chemical. The SP-LFER based model requires the input of a chemical’s vapor pressure and water solubility (to estimate KAW) and KOW (KOA is estimated from KAW and KOW), whereas the PP-LFER based model requires the input of a chemical’s LSER solute descriptors.
Potential Errors in Multimedia Fate Model Predictions From Using Erroneous Partitioning Expressions Above we have shown that partitioning properties that are in error by 1 order of magnitude could lead to large errors in the phase distributions estimated for various subcom4940
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partments of multimedia fate models (Figure 1). We further have shown that commonly used SP-LFERs could easily lead to errors in environmentally relevant phase equilibria that reach 1 order of magnitude (Figure 3A). To illustrate the potential implications of such errors, we compared results of the original (SP-LFER based) and the modified (PP-LFER based) level III model. The generic environmental input parameters and default emissions and advective inflow concentrations that were used in the model simulations are given in the Supporting Information. The comparison focused on selected model results, specifically concentrations in various environmental media of interest in exposure analyses, and the compound characteristics overall persistence, and long-range transport potential. Exposure Analysis. Multimedia environmental fate models are often used in the estimation of predicted environmental concentrations (PECs) (e.g. ref 29). Independent of the use of multimedia models, any exposure analysis that relies on PECs in a number of environmental media depends on accurate estimates of environmental phase partitioning. Presumably, PEC values will depend on the way phase partitioning is estimated and parametrized. Although it is not our ambition to assess the full implications of erroneous phase partitioning coefficients in the context of exposure assessment, it is possible to illustrate how relevant environmental concentrations may be affected by the choice of partitioning approach. Water concentrations calculated with the two level III models were very similar (data not shown). In fact, for 36 out of 40 chemicals, the dissolved water concentrations calculated by the PP-LFER based model were within 10% of those calculated by the polyparametric approach. A fair agreement may have been anticipated, considering the lack of overlap between the sensitive transition zone with respect to partitioning in the water compartment (5 < log KOC,W < 8, Figure 1A) and the property combinations represented within our test set of chemicals (not very hydrophobic, log KOW < 5, Table 1, SI). This highlights the practical value of the chemical space maps in Figure 1 in identifying “nonproblematic” zones of environmental phase partitioning. Conversely, it is also possible to use the maps in Figure 1 to deliberately seek out the chemicals that are at elevated risk of being simulated wrongly. As for most of the test chemicals soil could be identified as the dominant environmental compartment, the properties of these chemicals were superimposed onto Figure 1C. In the resultant Figure 4, each chemical is represented by two markers with the partitioning properties KAW and KOC,W calculated using either PP-LFERs or SP-LFERs. Some halogenated and monoaromatic hydrocarbons fall close to the zone of elevated partitioning sensitivity for the soil compartment. Some of the chemicals that are found within the sensitive transition region with respect to KOC,W and KAW are also identified as outliers in a plot comparing the soil concentrations calculated by the SP-LFER and PP-LFER based level III models (Figure 3C). Not surprisingly, the largest differences in soil concentrationssas high as an order of magnitudesare observed for chemicals for which large differences in ZPOC had been noted (Figure 3A). We did not identify the sensitive transition regions with respect to plant-air and plant-water partitioning. Recent studies have, however, identified the physical-chemical property combinations for which inclusion of a canopy compartment affects the simulated overall multimedia fate of organic chemicals (30). Figure 3D compares the concentrations in foliage estimated with the PP-LFER and SP-LFER based level III models. Deviations are large (in excess of 2 orders of magnitude) and systematic, i.e., the PP-LFER based model predicts much higher foliage concentrations for the PAHs and phenols than the SP-LFER based model. These
FIGURE 4. Identification of test chemicals positioned in the sensitive transition zone with respect to soil partitioning. Filled markers represent properties based on PP-LFERs (Vx), whereas open markers represent properties based on SP-LFERs. Some chemicals included in the test set are not displayed to achieve consistency with respect to the scales applied in Figure 1C. compound classes appear to be less volatile, i.e., have higher log KOA-values, than the set of chemicals for which the SPLFER had originally been developed (28). As both LFERs are based on the same data set by Welke et al. (28), the systematic deviation cannot be explained by the fact that the PP-LFER equation refers to tomato cuticles only. Overall Persistence. The persistence of a chemical in the environment is commonly assessed using either multiple individual media-specific half-lives or a single overall halflife or persistence in the environment (31). The latter approach, based on the level III fugacity model, has been advocated and described by Webster et al. (32). In the estimation of an overall persistence, the level III model serves to weigh the relative importance of the individual media half-lives. The weighing factors are the relative mass distributions in the various media, which obviously are related to environmental phase partitioning. Figure 3B compares the overall persistence estimated for selected chemicals using the PP-LFER-based and the SP-LFER based level III model. Whereas the results for some compound groups (alcohols, carboxylic acids, PAHs, halogenated hydrocarbons) are very similar, the SP-LFER-based model generally predicts longer overall persistences for the halogenated benzenes and the monoaromatic hydrocarbons than the PP-LFER based approach. For the halogenated benzenes, the persistence values differ generally by a factor of around 2. The apparent similarity between Figure 3 (parts A and B) provides the explanation for the discrepancy. The SP-LFER-based approach tends to predict a higher ZPOC for these compound classes, i.e., higher sorption to the organic matter of soil and sediment, and these two environmental compartments are generally assigned longer individual half-lives (see Table 1, SI). Finally, we again observe that the two polyparametric approaches (based on either Vx or log L16) yield very consistent estimates. Long-Range Transport Potential. An important criterion for identifying organic chemicals of environmental concern is the potential for long-range transport (LRTP), and multimedia modeling approaches can play an important role in its estimation (e.g. ref 33). The level III model developed here was slightly modified to resemble the LRTP assessment model TaPL3 by Beyer et al. (33). For this calculation, we assumed no advective inflow of chemicals via air and water
and emissions occurred only to the atmosphere. We were then able to calculate a Characteristic Travel Distance (CTD) for our test set of 40 chemicals. The results were virtually the same, independent of the approach selected (results not shown). One reason is that the chemicals in the test set are all rather volatile, i.e., represent chemical property combinations that do not favor retention in surface media, which is one of the key terms affecting LRTP. Another reason is that the LRTP of many organic substances is strongly influenced by the partitioning between gas and particle phase in the atmosphere, which is treated identically in the original and the modified model. The error in estimated LRTP may be substantial for semivolatile substances, for which the SPLFERs for gas-particle partitioning may not be applicable, e.g. because they are more polar than those used in the derivation of the SP-LFER equation. In summary, the comparison of the results by the SPLFER and PP-LFER based level III models revealed that the errors in partitioning coefficients made by SP-LFERs could easily lead to significant errors in those predictions of multimedia fate models, that are used in a regulatory context. There was further consistency between the zones of partitioning sensitivity identified in Figure 1 and the chemicals and model results that did indeed show sensitivity to variability in partitioning data, i.e., deviations in estimated environmental fate occur primarily for chemicals positioned in these sensitive partitioning zones. For such chemicals, subtle differences in environmental partitioning coefficients will yield significantly divergent estimated environmental concentrations. Some differences in model outputs can be explained by unusually large differences in the estimated environmental partitioning coefficients or Z-values. We should stress that the current comparison likely underestimates the potential for errors made by SP-LFER based multimedia models. First, this is because the test set did not include chemicals whichsaccording to Figure 1 (parts A, D, and E)sare sensitive to phase partitioning in the water and air subcompartments. Furthermore, no PP-LFER currently exists that could reveal the potential error made by SP-LFERs for atmospheric particles. However, previous studies comparing the results of multimedia models with different SP-LFERs for atmospheric particles have demonstrated the large impact that the parametrization of that partitioning equilibrium can have (34).
The Promise of, and Obstacles to, PP-LFER Based Environmental Fate Modeling Environmental fate models that estimate environmental phase partitioning using PP-LFERs constitute a significant advance, because they are applicable to a much wider range of organic chemicals and not only to particular classes of compounds. They should be particularly useful for more polar organic chemicals that interact by hydrogen-bonds with various environmental phases. The LFER equations in Table 2 can even be used to calculate Z-values for nonvolatile and nonsoluble substances, because they do not require a value for vapor pressure, water solubility, or KAW. Classical sp-LFER based models, such as the EQC model, require a different set of equations for nonvolatile and nonsoluble chemicals (35). More than the currently used SP-LFERs, polyparametric modeling approaches also provide mechanistic insight and understanding of the various interactions that influence the environmental distribution of organic chemicals. Another advantage is that it is easier to include additional phases in PP-LFER based models. For example, considering the availability of PP-LFERs for the water surface (36) and numerous mineral surfaces (37), it would be possible to account for organic chemical sorption to such surfaces in PP-LFER based multimedia fate models without creating the VOL. 37, NO. 21, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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need for any additional chemical input parameters. SP-LFER based models on the other hand would require the input of surface sorption coefficients, unless they can be estimated from the other physical-chemical properties that the model requires as input. It has been argued that soot carbon is a phase that needs to be included in some multimedia fate models (38). Instead of seeking to establish octanol-based SP-LFER to predict the sorption of organic chemicals to soot (39), which will be valid for only a small subset of organic substances, it would seem more promising to establish PPLFERs that are applicable to a large variety of organic compounds and could be included into PP-LFER based models without any new chemical input parameter. There are still some obstacles to the wide-spread adoption of PP-LFERs in multimedia environmental fate modeling. The most urgent is the lack of empirical data sets that would allow the derivation of polyparameter LFERs for the partitioning between gas phase and atmospheric particles. Existing data sets for gas/particle partitioning are unsuitable because they are highly biased toward nonpolar compounds. Fortunately, work appears to be ongoing to determine gas/ particle partitioning data for a sufficiently large and diverse group of organic chemicals to serve in the development of PP-LFERs (40). Also the other PP-LFER equations used above, in particular the one for plant foliage, should undergo further evaluation, as discrepancies between PP-LFER and SP-LFER approaches could also be due to system constants that are uncertain or only applicable to very specific environmental phases. Another unresolved problem is how to account in PPLFERs for the impact of variable environmental conditions, most notably temperature and relative humidity, on environmental phase partitioning equilibria. It is well established that many of these partition coefficients are highly temperature dependent, and the sorption of polar substances to organic matter in soils and atmospheric particles is likely to be different under different humidity conditions. It may be feasible to derive system constants in PP-LFERs that are functions of relative humidity. So far, however, it is difficult to implement PP-LFERs in models that are describing the fate of chemicals at variable environmental temperatures. Although LSER solute descriptors have been reported to be available for about 4000 compounds (ref 11 and references therein), the limited availability and reliability of such descriptors for many pesticides and other chemicals of environmental concern remains another obstacle. It not only limits the applicability of PP-LFER based models but also prevents the evaluation of PP-LFERs for environmental phase partitioning coefficients of such chemicals. Recently, fragment methods have been introduced to predict LSER solute parameters from molecular structure (41), which should at least provide a temporary solution. However, the empirical estimation of parameters (the solute descriptors) that in turn are used in the empirical estimation of other parameters (the environmental phase partitioning data) is not satisfactory. PP-LFER approaches that are based on theoretically based solute descriptors may be more promising in the long term (42, 43). Interestingly, for some chemicals of more recent environmental concern, namely pharmaceuticals, the availability of solute descriptors is less of an issue. Due to the variety of applications of the solvation parameter model in the field of medicinal chemistry, solute descriptors are available for more than 200 such compounds (17, 44). PPLFER based multimedia models may thus prove to be particularly useful for assessing the environmental fate of pharmaceuticals (45-50). If theoretical solute descriptors are employed in the derivation of the PP-LFERs, no input parameters beyond molecular structure are required to characterize partitioning behavior. In the long term it may even be feasible to express 4942
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other aspects included in environmental fate and transport models in terms of PP-LFERs. Both, selected reaction kinetics (51) and toxic thresholds (52) of organic chemicals have been successfully regressed using PP-LFERs. The chemical risk assessment tools of the future may ultimately require no other chemical input parameter than molecular structure.
Acknowledgments We would like to thank the Long-Range Research Initiative of the European Chemical Industry Association (CEFIC), the Canadian Natural Sciences and Engineering Research Council and the Norwegian Research Council (NFR, Project 140530/ 720) for funding. We are also indebted to Kai-Uwe Goss and Michael McLachlan for valuable comments.
Supporting Information Available Details on how the equations underlying the maps in Figure 1 were derived and on the generic environmental parameters used in these calculations. This material is available free of charge via the Internet at http://pubs.acs.org.
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Received for review May 8, 2003. Revised manuscript received August 15, 2003. Accepted August 26, 2003. ES034454I
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