Environ. Sci. Technol. 2009, 43, 6676–6683
Development and Exploration of an Organic Contaminant Fate Model Using Poly-Parameter Linear Free Energy Relationships TREVOR N. BROWN AND FRANK WANIA* Department of Chemistry and Department of Physical and Environmental Sciences, University of Toronto Scarborough, 1265 Military Trail, Toronto, Ontario, Canada M1C 1A4
Received April 22, 2009. Revised manuscript received July 15, 2009. Accepted July 16, 2009.
Octanol-based partitioning relationships, referred to as singleparameter linear free energy relationships (SP-LFERS), are often criticized for their limited applicability to polar organic substances. Therefore, SP-LFERS describing environmental phase partitioning in CoZMo-POP2, a dynamic multimedia chemical fate model, are replaced with poly-parameter linear free energy relationships (PP-LFERS) which describe temperaturedependent partitioning as the linear sum of various specific and nonspecific molecular interactions. A data set of chemicals with available solute descriptors, which quantify these molecular interactions, is compiled from the literature and, together with a data set of hypothetical chemicals, used to investigate the differences in the predictions of SP-LFER- and PPLFER-based model in relative and absolute terms for three different emission scenarios. Model outputs are manipulated to allow the results to be displayed as a function of log KAW and log KOA. Whereas the primary environmental fate is similar in both models, differences arise mostly in the environmental phases which contain only a small fraction of chemical. Larger differences in model results occur either because a difference in the predicted partitioning between water and organic matter affects the extent of soil-water runoff, or because differences in gas-particle partitioning affect the relative deposition to aqueous and forested surfaces. The two models showed smaller differences for degradable chemicals than for chemicals assumed to be perfectly persistent. Overall, however, the absolute differences between the model results are relatively small in comparison to the precision generally associated with model parametrization. Accordingly, we suggest that the quality of the available chemical input parameters should decide whether a PP-LFER model is preferable over a SP-LFER model. The PP-LFER model is further used to evaluate the effects of various molecular interactions on chemical fate, and the solute descriptor associated with van der Waals dispersive interactions is found to have the most pronounced effect on the environmental distribution of chemicals.
Introduction Several years ago Breivik and Wania proposed that the domain of applicability of multimedia fate models could be expanded * Corresponding author phone: +1-416-287-7225; E-mail:
[email protected]. 6676
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by using poly-parameter linear free energy relationships (PPLFERs) to quantify phase partitioning (1). The argument for implementing models of this type was that single-parameter linear free energy relationships (SP-LFERs), primarily those based on regressions with KOW, make poor predictions of phase partitioning when applied to chemicals outside of the data sets used in their parametrization (2). Subsequent work has shown that this approach is viable for chemicals, such as pharmaceuticals, which may be considered to be outside the range of applicability of SP-LFER based models (3). More recently Go¨tz et al. implemented PP-LFER based partitioning into atmospheric transport models and demonstrated that the model results can be significantly different from SP-LFER based models, especially for polar chemicals (4). The PP-LFER equations applied in these studies were the linear solvation energy relationships developed by Abraham et al. (5), or extensions thereof (6, 7). These equations include terms that describe the interactions of the solute (solute descriptors) and of the phases involved (phase descriptors). Specific intermolecular interactions, such as dipole interactions and hydrogen bonding, and nonspecific intermolecular interactions, such as cavitation energy and dispersive van der Waals interactions, are accounted for separately. Three major factors limited the implementation of a PP-LFER-based multimedia fate model at the time of Breivik and Wania’s publication; PP-LFER equations were missing for several environmentally relevant phases, there was no PP-LFER based method for calculating the temperature dependence of phase partitioning, and the availability of solute descriptors for environmentally relevant chemicals was limited. There has since been progress on all three of these issues, and sufficient data is now available for the full implementation of a PP-LFER-based multimedia fate model. Much work has been done recently to address the lack of PP-LFER equations for some environmentally relevant phases. Goss (8) has proposed a modification of Abraham’s PP-LFERs which uses a single form to describe all partitioning processes, instead of separate forms for partitioning between two condensed phases and between a gas phase and a condensed phase, as in the traditional Abraham equations (5). This modification has a statistical disadvantage in that two of the parameters are highly correlated, but as Goss notes the equations are more convenient for environmental modeling because it allows for the use of thermodynamic cycles to derive equations for partitioning systems that have not been experimentally measured. Goss et al. have subsequently characterized the partitioning of a wide range of chemicals to environmentally relevant phases, including natural organic matter (9, 10) and atmospheric aerosols (11, 12), and provided modified PP-LFERs to describe partitioning to these phases. A solution to the problem of describing the effect of temperature on phase partitioning has also been presented. Mintz et al. derived Abraham PP-LFER equations for the prediction of enthalpies of solvation for water/air and octanol/air partitioning (13), building upon previous work deriving equations for the enthalpy of phase change for systems of two condensed phases (14, 15). Using the enthalpies compiled by Mintz et al. (13), and provided by Niederer et al. (9) and Arp et al. (12) the necessary data are now available to derive modified PP-LFERs for the enthalpies of phase change required for multimedia fate modeling. Constructing a PP-LFER-based multimedia fate model is only a worthwhile endeavor if there are solute descriptors available for environmentally relevant chemicals. Such descriptors have been published for a large number of 10.1021/es901205j CCC: $40.75
2009 American Chemical Society
Published on Web 07/24/2009
TABLE 1. Phase Descriptors for PP-LFER Equations Used in this study
log KAW log KOA log KOW log KHA log KHW log KQA ∆HAW ∆HOA ∆HOW ∆HHA ∆HHW ∆HPA
sjk
ajk
bjk
ljk
vjk
cjk
-2.07 0.66 -1.41 1.01 -1.07 1.38 -0.86 6.04 5.18 -4.59 -5.45 -14.03
-3.67 3.49 -0.18 3.18 -0.49 3.21 33.63 -53.66 -20.03 -31.87 1.76 -20.73
-4.87 1.42 -3.45 1.86 -3.01 0.42 43.79 -9.19 34.60 -17.81 25.98 -0.03
-0.48 0.91 0.43 0.75 0.27 0.63 1.52 -9.66 -8.14 -8.15 -6.63 -3.37
2.55 -0.14 2.41 -0.17 2.38 0.98 16.63 1.57 18.19 -2.82 13.81 -20.22
0.59 -0.25 0.34 -0.24 0.34 -7.24 8.59 -6.67 1.92 -5.90 2.69 -2.89
reference 8 (a) (b) (b) (c) (d) (d) (e) (f) (g) (h)
8 10 10,10 12
Derived by thermodynamic cycle from logKAW and logKOW of ref 8. b Equations from ref 10 for 25 °C, normalized to OC content. c Equation for Berlin Winter aerosols, as recommended for generic terrestrial aerosols, from ref 12. d Derived for this study from the data in the Supporting Information of ref 13. e Derived by thermodynamic cycle from ∆HWA and ∆HOA. f Derived for this study from the data in the Supporting Information of ref 9. g Derived by thermodynamic cycle from ∆HWA and ∆HHA. h Arp et al. suggest using the enthalpy of vaporization as a substitute for ∆HQA, the equation shown is for the enthalpy of vaporization and was derived for this study from the data in the Supporting Information of ref 23. a
chemicals, but due to experimental limitations many of these chemicals are small and have a single functional group (16, 17). This limitation in the data sets used to parametrize the PP-LFERs however does not appear to limit their applicability to environmentally relevant phases (18). Some recent additions to the available solute descriptors are more environmentally relevant; these include a series of commonly used pesticides and pharmaceuticals (19), and polychlorinated biphenyls (PCBs) (20). We outline here the implementation of PP-LFER equations into CoZMo-POP2, a nonequilibrium, nonsteady state, fugacity-based multimedia fate model (21). In working toward the goal of expanding the range of multimedia fate models to more polar chemicals, a nonsteady state model is preferred. Polar chemicals will intuitively be more sensitive to seasonal variations in the water balance, and possibly other environmental parameters, so a nonsteady state model will be required to properly describe their environmental fate. Additionally, a nonsteady state simulation on a relatively short time scale will give the most realistic assessment of the potential differences between SP-LFER- and a PP-LFER-based models, because we expect larger differences in predicted environmental fate to be caused by differences in the description of environmental partitioning as chemicals approach equilibrium.
Materials and Methods The details concerning the selection of PP-LFER equations from the literature, and the derivation of additional equations required are provided here and in the Supporting Information (SI). A far more difficult task than the implementation of a PP-LFER based model though is devising a thorough and systematic comparison of the results of the two models. Where data are available individual chemicals can be run in both the original SP-LFER based and the new PP-LFER based CoZMo-POP2, and the results can be compared directly. This was the strategy of Go¨tz et al. and the differences in the model outputs for the specific chemicals modeled are clearly demonstrated (7). We prefer to evaluate the differences for the entire range of chemicals that the model can be applied to, and to offer specific guidance on what results can be expected for chemicals with diverse environmental fates. Previous studies of this type have made use of chemical space maps which show the variation in model outputs as a function of partitioning coefficients, usually KAW, KOA, and KOW (22). This approach uses hypothetical chemicals with various combinations of the partitioning coefficients to systematically
map the chemical space. Applying this method to a PP-LFERbased model, however, is difficult, because for each combination of partitioning coefficients there are multiple valid combinations of solute descriptors. It is, in theory, also possible that chemicals in close proximity to each other within the chemical space may have widely different model results, but as is discussed below and in the SI this is generally found not to be the case; which allows the chemical space map method of testing and displaying model sensitivity to be modified to evaluate the PP-LFER model. PP-LFER Equations. Equation 1 is the modified PP-LFER equation presented by Goss to describe the partitioning coefficient K of solute i between two phases j and k (8). log Kijk ) sjkSi + ajkAi + bjkBi + ljkLi + vjkVi + cjk
(1)
Lowercase letters denote phase descriptors and uppercase solute descriptors. Each term represents how different types of interactions between the solute and two phases contribute to the overall partitioning. Specific interactions of the solute are described by Si (polarity/dipolarity), Ai (hydrogen bond acidity), and Bi (hydrogen bond basicity) with the corresponding phase descriptors describing the relative affinity of the two phases for those kinds of interactions. Nonspecific interactions are described by Li (log of the hexadecane air partition coefficient) and Vi (McGowan volume) and cjk is a system constant. An analogous equation is used to calculate the enthalpy of phase change. ∆Hijk ) sjkSi + ajkAi + bjkBi + ljkLi + vjkVi + cjk
(2)
Strictly speaking this is a misapplication of the PP-LFER equation, because the solute descriptors are intended to describe free energy changes, not enthalpy changes alone. However previous studies have used this type of equation with good results (13-15): the reason for this success is very likely that in many cases the free energy and the enthalpy of a partitioning process are linearly related, as has been noted by previous studies (2, 15, 23), and recently validated mathematically by MacLeod et al. for the enthalpy of vaporization (24). For the purpose of modeling chemical fate at environmental temperatures and dilute concentrations this assumption of linearity appears to be a fair one. Table 1 summarizes the phase descriptors for eqs 1 and 2 that are used in this study. Phases are symbolized by the subscripted letters A (air), O (octanol), W (water), H (Leonardite humic acid), Q (water-insoluble aerosol fraction), and P (whole aerosol). Phase descriptors provided in Table VOL. 43, NO. 17, 2009 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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1 for the enthalpies of phase change could not be drawn directly from the literature, but have instead been derived for this study using data from the relevant papers. Details concerning the derivation of these phase descriptors are provided in SI Text 1). Implementation of PP-LFERs in CoZMo-POP2. As a fugacity-based model partitioning in CoZMo-POP2 is described by Z-values, which are half of a partition coefficient and describe the capacity of a phase to hold the chemical modeled. The exact equations used to calculate the Z-values in CoZMo-POP2 are provided in ref 21, and the alterations to these equations required to implement PP-LFERs are provided in SI Text 2. Two important SP-LFER equations are replaced with PP-LFER equations; these are the equation for partitioning between organic carbon and water, and the equation for partitioning between atmospheric particles and air. All other Z values are calculated as described in ref 21, the only difference is that all partitioning coefficients are calculated with eq 1 and the phase descriptors from Table 1. Temperature correction of the partitioning coefficients also remains unaltered, but the enthalpies are now calculated with eq 2 using the phase descriptors from Table 1, and then converted to internal energies. One SP-LFER remains in the model; partitioning to the forest canopy, described by a regression with KOA (25). We prefer this equation over the available PP-LFER for partitioning to tomato cuticle (26); the reasons for this are discussed in SI Text 3. Solute Descriptors. Experimentally measured solute descriptors have been compiled from a number of literature sources (16, 17, 19, 27-30). In several of these sources Li values are missing for some (17), or all of the chemicals (19, 28, 30), and the missing values have been filled in from other sources where available (31-33). Values for Li are not available for some chemicals; to fill in these data gaps a regression has been parametrized to predict the Li value from the other solute descriptors, the details of this regression are provided in SI Text 4. Two additional sources of solute descriptors are used which calculated the descriptors from literature values for various partitioning coefficients; these are PCBs (20), and polychlorinated naphthalenes (34). In total, solute descriptors have been obtained for 1460 individual chemicals. Values for logKAW and logKOA have been calculated for all 1460 chemicals using eq 1 and the phase descriptors from Table 1, and are plotted in the logKAW/logKOA chemical space (Figure 1). The domain of reasonable model applicability is outlined in red. This domain is defined primarily by computational costs; outside this range the time step required to ensure stability of the model’s numerical calculation is very short resulting in long computation times. In addition, many of the chemicals outside of this range partition so strongly to a single phase that multimedia fate modeling is a misguided exercise. Chemicals falling outside of the range of reasonable model applicability were removed from the list leaving 932 individual chemicals. Many of the remaining 932 chemicals have very similar solute descriptors, meaning the model results would also be very similar for these chemicals. To save computational costs the list has been further reduced by removing chemicals with very similar solute descriptors. The reduction method is pseudorandom and designed to preserve all of the variability in the combinations of solute descriptors in the full data set. A sample set of 235 chemicals has been selected and used to perform calculations. The list of these chemicals and the details of the reduction procedure are provided in SI Text 5 and Table SI1). A statistical analysis of the sample set of 235 chemicals shows that there are a number of strong intercorrelations in the data, most notably between Li and Vi, and Li and Si (SI Table SI3). This is because solute descriptors are not random 6678
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FIGURE 1. Locations of the 1460 chemicals for which solute descriptors were obtained in the logKOA/logKAW chemical space. Values for log KOA and log KAW are calculated using eq 1 and the phase descriptors from Table 1. The reasonable domain of model applicability is shown in red. but vary systematically within chemical classes. This presents a problem for the evaluation of the model results. Because of the systemic variation in solute descriptors any patterns observed in the model results may be due to the nature of the data set, and not directly related to the properties of the PP-LFER model. To help quantify the extent of this effect a second data set of hypothetical solute descriptors has been created which contains no intercorrelations (SI Table SI4) or systemic variation and the results for this data set are also discussed. Hypothetical chemicals have been created by taking a random number from within the range of each solute descriptor contained in the data set of real chemicals. This second data set contains 246 hypothetical chemicals. The solute descriptor combinations are provided in SI Table SI2. Model Parameters. The parametrization used in this study is for an environment representing the Baltic Sea drainage basin (35). This parametrization includes both the aquatic environment, composed of two water and four sediment compartments, and the terrestrial environment, composed of three soil compartments and two forest canopy compartments. A single air compartment is in contact with all surface compartments. All environmental parameters are the same for both the SP-LFER and PP-LFER models. The model details are provided in SI Text 6 and Table SI5). Aside from partitioning coefficients, the model requires the input of a chemical’s degradation half-lives. Such values cannot be realistically defined for the data set of hypothetical chemicals, and so to ensure comparability between the data sets generic values are used for all calculations. All chemicals are assumed to be perfectly persistent in all compartments. This is not only required because persistence cannot be calculated for the hypothetical chemicals but because it would have a confounding effect on the results; chemicals with very similar solute descriptors might have widely different fates if their susceptibility to degradation is different. Simulated chemical fate can be widely different depending on which model compartment receives the emissions (36). Three different emission scenarios are tested; continuous emissions to air, agricultural soil, and fresh water. Each simulation is run for 10 years and the model outputs for the final year of the simulation are used to evaluate the results. Model Comparison. All partitioning coefficients required for the SP-LFER model are calculated using eq 1 and the phase descriptors from Table 1. The advantage of this method
FIGURE 2. (A) Correlation between the logKOC values obtained by an SP-LFER equation and the PP-LFER for humic acid. (B) Correlation between the logKPA values obtained by an SP-LFER equation and the PP-LFER for whole aerosol. is that there is no need to consider experimental error in the partitioning coefficients when comparing the results, because the only source of error will be from the solute descriptors and this will equally affect the results of both the SP-LFER and PP-LFER models. This means that the only difference between the model inputs is the replacement of octanolbased SP-LFERs with PP-LFERs, and any observed differences will be attributable to this alteration. However, the disadvantage of this approach is that the full variability in the model inputs is not captured for either model because the input data is derived from linear fits of experimental data. This shortcoming should be considered when interpreting the results of the model comparison. The output parameter used to compare the models is the amount in various compartments in moles. Using the amount of chemical as the primary model output is intuitive because it is linearly related to environmental concentrations and intercompartment fluxes. The amount of chemical is summed together for like compartments to obtain five output amounts for air, soil, forest canopy, water, and sediments. Amounts are averaged for the entire final year of the simulation. To display the model outputs as chemical space plots we first calculate the logKOA and logKAW values of each chemical using eq 1 and the phase descriptors from Table 1. As can be observed in Figure 1 the chemicals are unevenly scattered in the chemical space, so the next step is to interpolate the model outputs being plotted in the chemical space to a grid of evenly spaced logKOA and logKAW values. Finally, extrapolation is used to fill in the gaps where interpolation is not possible. A custom interpolation and extrapolation method has been devised to take into consideration the fact that moving in different directions in the chemical space has different chemical meanings. The details of this method are provided in SI Text 7. The validity of this method is primarily demonstrated by the fact that the resultant plots are sensible and relatively smooth, but further discussion of the validity can be also be found in SI Text 8). Linking Solute Descriptors to Chemical Fate. A PP-LFER model allows for the elucidation of the relationship between individual solute descriptors and environmental fate. This is primarily investigated using subsampling statistical analysis. The basic principle is this: a random subsample drawn from a data set of chemicals should have the same mean value and standard deviation for any solute descriptor. For example, the mean value and standard deviation of Bi in the hypothetical data set are 0.574 and 0.367 respectively. Now we take a subsample of this data set, all hypothetical chemicals with Ai above 0.394, and the mean value and standard deviation of this subsample are 0.550 and 0.367 respectively.
The statistics are almost identical, indicating that this was a random subsampling with respect to Bi. If the mean value of the subsample deviates from the mean value of the entire data set then this is a biased subsample and if the standard deviation is significantly reduced compared to the full data set this is a selective subsample. Subsampling statistical analysis of chemicals based on PP-LFER model outputs allows us to detect if environmental phases have biases or selectivity for chemicals with certain solute descriptor values, linking molecular interactions to environmental fate.
Results and Discussion Comparison of KOC Values. Organic carbon/water partitioning coefficients (KOC) of all 932 chemicals within the model’s domain of reasonable applicability, as calculated in the two models are compared in Figure 2A. In the SP-LFER model, KOW is calculated using eq 1, and then KOC is calculated with a regression derived by Seth et al. (37). In the PP-LFER model, the KOC is the KHW taken directly as calculated with eq 1 and the phase descriptors in Table 1. There is surprisingly little scatter in the plot and a very strong correlation between the SP-LFER and PP-LFER values. This is due partly to a strong similarity in the phase descriptors for KOW and KHW (Table 1), but is also due to systematic variations in the solute descriptors of real chemicals which reduce the variability; the same plot for the hypothetical chemicals (SI Figure SI3A) shows far more scatter. From the regression shown in Figure 2A we expect that chemicals with low KOC values will partition more strongly to organic carbon in the PP-LFER model, and chemicals with high KOC values will partition more weakly to organic carbon. The SP-LFER used in CoZMo-POP2 to calculate KOC is KOC ) 0.35 · KOW (37). If the SP-LFER equation is instead changed to KOC ) KOW0.85 then there is a nearly perfect match with the PP-LFER results. This alternate equation is similar to an equation initially presented by Seth et al. (eq 10 of ref 37), which they discarded because some of the data in the regression were suspected to be inaccurate. Comparison of KPA Values. Figure 2B compares values for atmospheric particle/air partitioning coefficients (KPA) calculated using SP-LFERs and PP-LFERs. The SP-LFER for sorption to atmospheric particles in CoZMo-POP2 assumes that the organic matter in the aerosol has the same sorption properties as octanol (38). The PP-LFER values for KPA are calculated using the dual phase model (consisting of waterinsoluble and aqueous fractions) by Arp et al. (11), with an assumed relative humidity of 80% to calculate the aqueous volume fraction of the aerosol. While most chemicals in Figure 2B are close to the 1:1 line, there is significantly more scatter than in the corresponding plot for KOC. On the left side of VOL. 43, NO. 17, 2009 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 3. Chemical space plots of phase distribution of the PP-LFER based model and the sum of absolute differences in the percent phase distribution between the SP-LFER- and PP-LFER-based models for each of the three emission scenarios. Figure 2B (logKPA < 6) the chemicals with the largest deviations have much higher KPA values predicted by the PP-LFER equation. These chemicals are biased toward low Si, Li and Vi, high Ai and Bi values, and also have low calculated KAW values (logKAW < -3.5). We conclude that absorption into the aqueous fraction of the aerosols increases the overall KPA for these chemicals. It should be noted that this is unlikely to affect the model results because sorption of chemicals with logKPA < 6 to atmospheric particles will be negligible. On the right side of Figure 2B (logKPA > 6) the chemicals with the largest deviations have lower KPA values predicted by the PP-LFER equation. These chemicals are biased toward large molecules with hydrogen bond accepting groups (high Bi, Li and Vi values) and also have low KAW values (logKAW < -3.5). Despite partitioning strongly to water these chemicals have a significantly lower predicted KPA using the dual phase PPLFER equation. An inspection of the phase descriptors (Table 1) reveals the reason; KOA increases more rapidly with increasing Bi and Li than KQA, so we conclude that partitioning into the aqueous phase of the aerosols fails to offset the decreased sorption capacity of the water-insoluble fraction of the aerosols for these specific chemicals. A plot similar to that shown in Figure 2B was created using the PP-LFER for partitioning to atmospheric aerosols by Go¨tz et al. (7) (SI Figure SI4A). The KPA from the dualphase absorption mechanism by Arp et al. (12) is comparable to the KPA derived from KOA, lying close to the 1:1 line, as opposed to the KPA from the model of Go¨tz et al. which is consistently higher by an order of magnitude. This is likely because the latter assumes perfect additivity of various absorptive and adsorptive fractions, which probably overestimates the sorptive capacity of atmospheric particles. Arp et al. also have derived a PP-LFER for aerosol collected in southern Sweden (12), which is arguably more appropriate for the current model parametrization. However, the mean absolute deviation in calculated KPA values from the results 6680
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of the equation used here is only 0.34 log units and the fit is very similar, as can be seen in SI Figure SI4B, so we prefer to use the equation recommended by Arp et al. Comparison of Model Results. Figure 3 shows the primary model outputs as six chemical space plots, all with logKOA on the x-axis and logKAW on the y-axis. As there are no chemicals in the data set with a logKOW above approximately 11 (Figure 1), this area is blacked out in Figure 3. Anomalous contour shapes observed in some of the plots are due to irregularly spaced chemical data. The corresponding plots for hypothetical chemicals are provided in SI Figure SI5. Figures 3A-C show the phase distribution of real chemicals in the PPLFER model expressed as a fraction of the total mass of chemical; the colored areas are where more than 50% of a chemical is found in a single phase. No chemical is ever found to be more than 50% in the forest canopy. Figure 3D-F are plots of the absolute difference between the SP-LFER and PP-LFER model results, which is calculated by summing the absolute differences in model results for all five phases and then dividing by 2. This represents the fraction of chemical that shifts from one compartment to another when comparing the model results. In all three emission scenarios partitioning to air and water is very similar, the largest absolute differences in the phase distribution occur at high log KOA values where chemicals primarily partition to the three solid phases; forest canopy, soils, and sediment. Two mechanisms are responsible for most of these differences; deposition to the forest canopy, and soil-water runoff. In the region defined by logKOA > 4.5 and logKOW > 4.5 (marked as region 1) some chemicals show enhanced partitioning to soil at the expense of partitioning to sediments in the PP-LFER model (soil/sediment mass quotient enhanced by a factor of up to 2.2). This is primarily due to decreased sorption to atmospheric particles (lower KPA in PP-LFER model) which in turn results in a faster uptake in the canopy (gaseous deposition to the canopy is faster
FIGURE 4. Vector plots of movement in the logKAW/logKOA chemical space that can be related to the contributions of each phase descriptor. Vector magnitudes are the value of the upper quartile minus the value of the lower quartile of the distribution of solute descriptors multiplied by the corresponding phase descriptor. than particle-bound deposition) and a greater flux of chemical to the soil with falling foliage. In the region defined by logKAW < -1.5 and 1 < logKOW < 4.5 (marked as region 2) chemicals show enhanced partitioning to either soil or sediments, which is due to either enhanced or depressed sorption to particulate organic carbon in runoff water (differences in KOC) depending on the solute descriptors of the chemical (soil/sediment enhanced or depressed by up to a factor of 1.7). The primary effect of these mechanisms is that when emissions are to air or water, most of the differences between the SP-LFER and PP-LFER models occur in the three solid phases (Figures 3D and F). However, when emissions are to soil most of the largest absolute differences between the models occur in the transition zones where chemicals are distributed between air or water and the solid phases (Figure 3E). One of the main effects of the mode of emission is that the amount of chemical in the receiving compartment is elevated. This can be seen in Figures 3A-C: the compartment receiving emissions makes up the largest portion of the phase distribution in each case. Considering this effect, a general rule can be stated as follows: Whereas the primary environmental fate of chemicals is similarly predicted by both models, the largest relative differences in results are found mostly in the environmental phases which contain only a small fraction of emitted chemical (less than 1%). This is because even small absolute differences in the phase distribution will have a significant relative effect (up to a factor of air, 10.9; canopy, 14.5; water, 5.29; soil, 11.8; sediment: 1.73). This is demonstrated by plotting the fraction of chemicals in any single phase and then overlaying this with a plot of the relative differences in model results for that phase; the largest relative differences in model results are always located outside of the area with the highest fraction of chemicals (SI Figures SI6A-C). A good example is the environmental distribution of PCB194 resulting from emissions to water. An uncertainty analysis was performed by calculating the 95% confidence intervals of the SP-LFER parameters and performing additional simulations with these values. However the same could not be done for the PP-LFER model as standard errors were not available for the solute descriptors of PCB-194. In the SPLFER model 98.03% (94.75-99.10%) of PCB-194 is in sediments and 0.008% (0.003-0.025%) is in air, in the PP-LFER model 94.44% is in sediments and 0.033% is in air. The primary environmental fate of PCB-194 in both models is sorption to sediments; the PP-LFER model results are depressed by a negligible factor of 1.04 (1.00-1.05) relative to the SP-LFER model results. However, if we are specifically interested in air concentrations of PCB-194 the PP-LFER model results are elevated by a significant factor of 4.24 (1.33-11.7) relative to the SP-LFER model results. An additional simulation has been performed to test the possible effect of degradation. Real chemicals are assigned uniform degradation half-lives of two days in air, two months in water and six months in soils, sediments and forest canopy. Additionally, the characteristic travel distance (CTD) is
calculated for each chemical as described in ref (39). Chemical space plots corresponding to Figures 3A and D are provided in SI Figure SI7 along with a plot of how CTD varies with location in the chemical space for both the SP-LFER and PP-LFER models (SI Figure SI8). The phase distribution shifts with the incorporation of degradation because a smaller fraction of emissions makes their way from air to the condensed phases. However, the absolute differences in model results follow the same general pattern. Interestingly, the absolute difference in percent distribution of the SPLFER and PP-LFER models is smaller when degradation is included (up to a factor of 23.3). Oxidative degradation in air is assumed to only affect chemical in the gas phase; particle bound chemical is protected. This has the effect of making the relative amount of chemical in the gas phase smaller and therefore chemicals are less susceptible to gas phase deposition to forests, which has been identified above as one of the mechanisms causing differences in model results. Another observation is that the CTD for particle-bound chemicals is enhanced in the PP-LFER model versus the SP-LFER model (up to a factor of 2.61), due to a smaller net loss to the surface media relative to the amount of particle-bound chemicals. A major conclusion resulting from this work is that differences in the output from the two models are relatively small; larger differences in model outputs have been demonstrated by varying the environmental input parameters of a multimedia fate model (22). When using a multimedia fate model as an evaluative or screening tool we recommend that the choice of using a SP-LFER or PP-LFER model should be based on the quality of the available chemical input values. The more accurate mechanistic description of partitioning in a PP-LFER model should allow for better predictive power, but this will only become realized if the quality of the solute descriptors is as good as, or better than, the quality of the octanol-based partition coefficients. Linking Solute Descriptors to Chemical Fate. A simple graphical method of comparing how each solute descriptor affects the location of a chemical in the logKAW/logKOA chemical space is to plot the vectors of the movement. Because the solute descriptors are of different magnitudes plotting the vector of the phase descriptors would be misleading, so the vectors must be normalized to the magnitudes of the phase descriptors. Figure 4 shows the vectors in the logKAW/logKOA chemical space corresponding to the range defined by the upper quartile and lower quartile of the possible values of each solute descriptor. This can be interpreted as the range of movement in the chemical space induced by varying each solute descriptor across the majority of the range of its values. The plot shows that the Li descriptor has the largest effect on location in the chemical space, which in turn controls chemical fate. A more detailed investigation of the link between chemical fate and solute descriptors can be achieved with subsampling statistical analysis. Both real and hypothetical chemicals were divided into four subsamples based on their simulated environmental fate; chemicals which are found primarily in VOL. 43, NO. 17, 2009 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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air in all three emission scenarios, chemicals found primarily in water, chemicals found primarily in one or more of the solid phases (soil, sediment, forest canopy), and multimedia chemicals which have different environmental fates depending on the mode of emission. Mean values and standard deviations for the solute descriptors of the full data sets and for all subsamples are provided in SI Table SI7. A phase is considered to be biased toward a solute descriptor if the mean value of the subsample deviates from the value of the full data set by more than 10%, and the phase is considered to be selective if the standard deviation of the subsample is more than 20% lower than the value for the full data set. Hypothetical chemicals favoring the air compartment are biased and selective toward low values of Si and Li but show no bias or selectivity to the other solute descriptors. Hypothetical chemicals partitioning primarily into water are biased toward high values of Si and Bi and low values of Li and Vi, with selectivity for low Li values. Hypothetical chemicals which are primarily sorbed to the three solid phases are biased toward low Bi values and both biased and selective toward high Li and Vi values. Multimedia hypothetical chemicals have no biases and are selective only to intermediate Li values. The biases and selectivity of each phase are intuitive, for example chemicals found in the air tend to be those that only weakly experience van der Waals dispersive interactions (Li) and dipole interactions (Si). Chemicals sorbed to solid phases tend to be large (Vi) and experience strong van der Waals dispersive interactions (Li). Chemicals which experience strong dipole interactions (Si) and strong specific interactions (Bi) tend to be found in the water. Two features of this analysis are of further interest; first there is no observable bias or selectivity in any of the subsamples for chemicals which are hydrogen bond donors (Ai). Additionally, the multimedia chemicals are very close to a random subsampling of the full data set of hypothetical chemicals. Statistical analysis of the subsamples of the data set of real chemicals reveals far more biases and selectivity than the hypothetical data set; this is undoubtedly because intercorrelations in solute descriptors of real chemicals cause additional biases in the phase distribution. The first notable difference is the Ai solute descriptor; real chemicals in the water phase are biased toward high Ai values, real chemicals in the air and solid phases are biased and selective for low Ai values, and real multiphase chemicals are biased toward low Ai values. To understand this, the data set of real chemicals was divided into two subsamples; chemicals that have Ai ) 0 (n ) 138) and chemicals that have Ai > 0 (n ) 97). The mean and standard deviation for the solute descriptors of these subsamples are provided in SI Table SI7. Chemicals with Ai > 0 are biased and selective for low values of Si and high values of Bi, and biased toward low values of Li and Vi. This means that hydrogen bond donors in the data set are small chemicals and also hydrogen bond acceptors, in short there are too many alcohols. This observation explains most of the differences between the real and hypothetical data sets. The water phase is biased toward chemicals with high Bi, and low Li and Vi, many of the chemicals with these properties also have high Ai values causing the water phase to be biased toward real chemicals with high Ai values. A similar argument explains the differences between the two data sets for chemicals sorbed to the solid phases. The net effect of this is that chemicals with high Ai and Bi end up in the water phase, and chemicals with high Si, Li and Vi end up sorbed to the solid phases. This causes real chemicals in air to be biased and selective for chemicals with low values of all descriptors. A general conclusion is that the PP-LFER model has no selectivity for multimedia chemicals; they are simply those chemicals that are not strongly selected for by any single phase. Real multimedia chemicals are biased and selective 6682
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for large Li and Vi, and are biased toward high Bi and low Ai. A closer inspection of the biases, however, shows that they are smaller in magnitude than the biases of the air, water, or solid phases, so we conclude that these biases are the result of the data set being skewed by the selection process, as discussed in detail in the SI. Multimedia chemicals tend to have intermediate solute descriptor values because chemicals with relatively high or low values of any solute descriptor generally experience a bias toward one or more environmental phases, as demonstrated by the selectivity of the model for multimedia chemicals with intermediate Li values. The fact that the Li descriptor has such a strong influence on chemical fate may provide an explanation for the strong similarity between the SP-LFER and PP-LFER model results: KOA-based SP-LFERs likely capture the majority of the van der Waals interactions represented by the Li descriptor and so on a large scale do a reasonable job of predicting environmental fate.
Acknowledgments We acknowledge funding from the Long-range Research Initiative of the European Chemical Industry Association (CEFIC). The study benefitted greatly from discussions with Kai-Uwe Goss, Michael S. McLachlan and Knut Breivik.
Supporting Information Available Text describing the selection and derivation of PP-LFER equations, alterations and parametrization of the CoZMoPOP2 model, and the methods for refining the data set of real chemicals and interpolating the results to a chemical space plot is provided. Tables are provided which list the chemicals in the data sets, model parameters and statistics. Additional figures provided are chemical space plots for hypothetical chemicals and relative differences in model results. This material is available free of charge via the Internet at http://pubs.acs.org.
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(11) Arp, H. P. H.; Schwarzenbach, R. P.; Goss, K.-U. Ambient gas/ particle partitioning. 1. sorption mechanisms of apolar, polar, and ionizable organic compounds. Environ. Sci. Technol. 2008, 42, 5541–5547. (12) Arp, H. P. H.; Schwarzenbach, R. P.; Goss, K.-U. Ambient gas/ particle partitioning. 2: The influence of particle source and temperature on sorption to dry terrestrial aerosols. Environ. Sci. Technol. 2008, 42, 5951–5957. (13) Mintz, C.; Clark, M.; Acree, W. E.; Abraham, M. H. Enthalpy of solvation correlations for gaseous solutes dissolved in water and in 1-octanol based on the Abraham model. J. Chem. Inf. Model. 2007, 47, 115–121. (14) Abraham, M. H.; Whiting, G. S.; Fuchs, R.; Chambers, E. J. Thermodynamics of solute transfer from water to hexadecane. J. Chem. Soc., Perkin Trans. 2 1990, 291–300. (15) Fuchst, R.; Abraham, M. H.; Kamlet, M. J.; Taft, R. W. Solutesolvent interactions in chemical and biological systems. IV. Correlations of ∆G, ∆H and T∆S of transfer of aliphatic and aromatic solutes from 2,2,4-trimethylpentane to aqueous solution. J. Phys. Org. Chem. 1989, 2, 559–564. (16) Abraham, M. H.; Andonian-Haftvan, J.; Whiting, G. S.; Leo, A.; Taft, R. S. Hydrogen bonding. Part 34. The factors that influence the solubility of gases and vapours in water at 298 K, and a new method for its determination. J. Chem. Soc., Perkin Trans. 2 1994, 1777–1791. (17) Abraham, M. H.; Chadha, H. S.; Whiting, G. S.; Mitchell, R. C. Hydrogen Bonding. 32. An analysis of water-octanol and wateralkane partitioning and the ∆log P parameter of Seiler. J. Pharm. Sci. 1994, 83, 1085–1100. (18) Goss, K.-U.; Arp, H. P. H.; Bronner, G.; Niederer, C. Non-additive effects in the partitioning behaviour of various aliphatic and aromatic molecules. Environ. Toxicol. Chem. 2009, 28, 52–60. (19) Tu ¨ lp, H. C.; Goss, K.-U.; Schwarzenbach, R. P.; Fenner, K. Experimental determination of LSER parameters for a set of 76 diverse pesticides and pharmaceuticals. Environ. Sci. Technol. 2008, 42, 2034–2040. (20) Abraham, M. H.; Al-Hussaini, A. J. M. Solvation parameters for the 209 PCBs: Calculation of physicochemical properties. J. Environ. Monit. 2005, 7, 295–301. (21) Wania, F.; Breivik, K.; Persson, N. J.; McLachlan, M. S. CoZMoPOP 2-A fugacity-based dynamic multi-compartmental mass balance model of the fate of persistent organic pollutants. Environ. Model Software 2006, 21, 868–884. (22) Meyer, T.; Wania, F.; Breivik, K. Illustrating sensitivity and uncertainty in environmental fate models using partitioning maps. Environ. Sci. Technol. 2005, 39, 3186–3196. (23) Goss, K.-U.; Schwarzenbach, R. P. Empirical prediction of heats of vaporization and heats of adsorption of organic compounds. Environ. Sci. Technol. 1999, 33, 3390–3393. (24) MacLeod, M.; Scheringer, M.; Hungerbu ¨ hler, K. Estimating enthalpy of vaporization from vapor pressure using Trouton’s Rule. Environ. Sci. Technol. 2007, 41, 2827–2832. (25) Horstmann, M.; McLachlan, M. S. Atmospheric deposition of semi-volatile compounds to two forest canopies. Atmos. Environ. 1998, 32, 1799–1809.
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