Investigation of the Cold Condensation of Persistent Organic

Investigation of the Cold. Condensation of Persistent Organic. Pollutants with a Global Multimedia. Fate Model. MARTIN SCHERINGER,*. FABIO WEGMANN,...
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Environ. Sci. Technol. 2000, 34, 1842-1850

Investigation of the Cold Condensation of Persistent Organic Pollutants with a Global Multimedia Fate Model MARTIN SCHERINGER,* FABIO WEGMANN, KATHRIN FENNER, AND KONRAD HUNGERBU ¨ HLER Swiss Federal Institute of Technology, Laboratory of Chemical Engineering, ETH-Zentrum, CH-8092 Zu ¨ rich, Switzerland

In this paper we present a new global multimedia fate model that considers the influence of temperature on the environmental transport, degradation, and partitioning of persistent organic pollutants. The model consists of a variable number of latitudinal zones with specific annual temperature courses; each zone contains soil, oceanic surface water, and tropospheric air. The chemicals’ degradation rates and Henry’s law constants (H) are implemented as functions of temperature and the concentrations in the soil, water, and air compartments of each latitudinal zone are calculated as functions of time. The resulting temporal and spatial concentration patterns are characterized by persistence and spatial range. Model calculations are carried out for tetrachloromethane, R-hexachlorocyclohexane (R-HCH), and mirex, and the specific distribution patterns of these three chemicals are discussed. The model results show that the soil and water concentrations of the polar zones are strongly sensitive to changes of the latitudinal gradient of H and of washout ratios, adsorption to aerosol particles, and deposition rates.

Introduction Persistence and potential for long-range transport have been increasingly recognized as relevant criteria for the assessment of chemicals (1-11). Such criteria not only are of particular importance for the characterization and identification of Persistent Organic Pollutants (POPs) (12, 13) but can also be used in a general framework for the assessment of chemicals (14). Several approaches for the calculation of persistence and long-range transport potential with multimedia fate models have been presented (3-10). It has been proposed to calculate the transport potential on regional scales with models of advective transport described by a moving air parcel (8). Two multimedia models which have been used for calculating the distribution of chemicals on a global scale are the models developed by Wania and Mackay (15-17) and by Scheringer (3, 6). The first model provides the latitudinal distribution of chemicals in a global system of 10 climatic zones of different volumes and temperatures. In particular, the model has been used to investigate the accumulation of R-HCH in arctic regions (cold condensation, global distillation (19)). The model presented by Scheringer, on the other hand, consists * Corresponding author Fax: +41-1-632 11 89; E-mail: scheringer@ tech.chem.ethz.ch. 1842

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of 80 identical cells forming a closed loop in which the meridional circulation of a chemical is calculated. From the results of that model, persistence and spatial range have been derived for the 12 presently identified POPs and several other chemicals (6, 13, 14). While the first model covers the influence of temperature and the variability of environmental parameters, its spatial resolution is limited to 10 climatic zones and four atmospheric layers. The second model provides a high spatial resolution but does not cover the variation of degradation rates and phase partitioning coefficients along the climatic zones. For several reasons, there is a need for refined global multimedia models going beyond the existing ones: (i) The present experience with global fate models is limited. A systematic comparison of model algorithms, model parameters, and model results is required for a reliable use of fate models in the assessment of globally relevant chemicals. (ii) Persistence and spatial range have only been estimated with very simple models so far. A refinement of these results with more detailed and flexible models is desirable. (iii) Up to now, there is no conclusive scientific evidence of the cold condensation (20). The influence of different factors (chemical properties, temperature, deposition processes, soil and vegetation, etc.) on the cold condensation can be investigated systematically with global fate models. Here we present a new model (21) which is based on the two above-mentioned global models. The model consists of a variable number of latitudinal zones (typically 10-90) which are characterized by specific annual temperature courses and media volumes. The concentrations of CCl4 (volatile, persistent), R-HCH (semivolatile, moderately persistent), and mirex (semivolatile, persistent) in the environmental media soil, water, and air of all zones are calculated as functions of time, i.e. the model is a dynamic, non-steady-state model (level IV). The exposure patterns of the three different chemicals are characterized by persistence and spatial range. The chemicals’ tendency to accumulate at the poles is analyzed and parameters governing this process are identified.

Model Description Model Geometry. A closed loop of cells as it is used in the circular global model (3) is not suitable for a model with climatic zones because two cells of the same latitude are not in direct contact in the circular model. Fast mixing in a latitudinal zone is not possible in the circular geometry because the chemical has to pass one of the poles in order to reach the other cell of the same latitude. For this reason, we have changed the model geometry from a loop of cells to a sequence of n latitudinal zones (denoted by the index j; see Table 7 for a list of all model parameters) with two terminal zones at the poles, see Figure 1. The main assumption associated with this geometry is that mixing within a latitudinal zone is much faster than transport in the northsouth direction, which is supported by meteorological data (22). However, if single episodes of chemical transport on a time scale of several days are to be modeled, the assumption does not hold so that the model is not suitable for that purpose. Every latitudinal zone contains soil (depth 10 cm), oceanic surface water (depth 200 m), and tropospheric air (height 6 km). These environmental media are denoted by the index i ) s, w, a. Additional media such as freshwater, deep sea water, and sediments are not included for the following reason: A primary aim of our study is to investigate the 10.1021/es991085a CCC: $19.00

 2000 American Chemical Society Published on Web 03/28/2000

FIGURE 1. Geometry of the model. Shown are n ) 8 latitudinal zones; model calculations are carried out with 10-120 zones. ln is the length of the zones in north-south direction; wj is the width of the zones in east-west direction; N and S are the poles. Each zone contains soil (black), water (grey), and air (white). The width of the air compartment is wj; the width of soil and water are 0.3 wj and 0.7 wj, respectively. A list of all model parameters is given in Table 7. influence of the number of zones, n, on the results of the model. This requires considerable computer time for high values of n even with only three media. Since three additional media would increase the computational effort by much more than a factor of 3, we restricted the model to soil, oceanic surface water, and air, which represent the most important sinks and transport mechanisms for POPs on a global scale (18). However, freshwater, deep sea water, and sediments might also be relevant for the global budget of POPs (17), and, therefore, these media will be included into future versions of the model. These versions will have about 30 latitudinal zones because, in the present model, the number of zones has a significant influence as long as n is below 30. Another limitation of the present model is that ice and snow are not included. This is mainly due to a lack of data for the exchange processes between air and these media. Consequently, the condensed phases water and soil are only placeholders for ice and snow in the polar zones of the model. We want to emphasize here that a global multimedia model such as the one presented here should not be understood as a tool for exactly calculating actual concentrations. It mainly has the purpose of identifying key parameters governing the global transport processes. The fractions of water and land are 30% and 70% in each zone. Explorative calculations have shown that more realistic water-to-land ratios do not significantly affect the model results for global parameters such as persistence, spatial range, and the increase of concentrations in the polar zones. Adjacent zones are linked by mutual exchange of air and water which simulates the effect of oceanic and tropospheric eddy diffusion (3, 15). The volumes of the three environmental media of each zone are given by their depth (identical in all zones), their width wj, and their length ln. ln is the ratio L/n with L being the distance between the poles (20,000 km); n is the flexible number of zones. wj is given by the circumference of the parallel at the latitude of the zone j according to wj ) sin Rj‚L with Rj ) (j-0.5)/n‚180, j ) 1, ..., n. Each zone is characterized by a typical annual temperature course. The temperature data are taken from ref 23 where a monthly average of the air temperature is given for every 0.5° × 0.5° area of the earth’s surface. From these highresolution data, average air temperatures for each of the latitudinal zones of the model and each “season” of the year are calculated. The number of seasons per year, nseasons, can be chosen from 1-4, 6, or 12. In Figure 2, the annual temperature courses of zone 15 (of 60 in total) are plotted for nseasons ) 1, 2, 4, and 12. It shows that nseasons ) 4 is sufficient to cover almost the entire temperature range. For this reason and because the computational effort is directly proportional to the number of seasons (see Supporting Information, description of the model algorithm), the model calculations are performed with nseasons ) 4.

FIGURE 2. Annual temperature courses of a northern temperate zone (15 of 60) with 1, 2, 4, and 12 seasons per year. With four seasons, almost the entire temperature range is covered so that nseasons ) 4 is used in all model calculations. Input data from ref 23. In the circular model described in ref 3, all degradation rates, phase transfer parameters, and eddy diffusion coefficients have constant values in all cells. All of these parameters occur in the new model as well, but the three degradation rate constants κi (in s-1) and the Henry’s law constant (denoted by H ) ca/cw, dimensionless) are implemented as functions of temperature as described in the following section. The phase transfer parameters representing volatilization, dry and wet deposition, etc. are then calculated in the same way as described in Tables 2-4 of ref 3. However, as some of these parameters are functions of H, they too are functions of temperature in the new model. The phase transfer parameters are denoted by uik with the sequence i-k describing transfer from phase i to phase k, see below, eq 8. The transport between two adjacent zones j and j + 1 is represented by exchange of water and air. If the two zones have different volumes vj and vj+1, the general mass balance equations are (media index i omitted for sake of simplicity)

vj+1 ‚ dj+1fj ‚ cj+1(t) vj

(1)

vj ‚d ‚ c (t) - dj+1fj ‚ cj+1(t) vj+1 jfj+1 j

(2)

c˘ j(t) ) - djfj+1 ‚ cj(t) + c˘ j+1(t) )

(and similar for j - 1 and j). If a diffusive process is to be simulated, the dynamics described by eqs 1 and 2 must yield equal concentrations cj ) cj+1 in the steady-state (c˘ j ) c˘ j+1 ) 0). Inspection of eqs 1 and 2 shows that this is fulfilled only if the exchange parameters djfj+1 and dj+1fj are related by

djfj+1 ) (vj+1/vj)‚dj+1fj

(3)

In the next step, the absolute values of djfj+1 and dj+1fj are derived from the macroscopic eddy diffusion coefficients Dw (water) and Da (air). For water, Dw ) 1‚108cm2/s (24) is used, and for air, Da is implemented as a function of latitude with Da ) 5‚109 cm2/s at the equator, a maximum of Da ) 4‚1010 cm2/s at 50 degrees latitude and a value of Da ) 2‚1010 cm2/s at the poles; see ref 25 where the latitudinal course of Da is shown. On this basis, di,jfj+1 and di,j+1fj are calculated from the quantity di,j ) Di,j/l2n. With this relation, the eddy diffusion coefficients Di,j are converted to exchange parameters di,j (in s-1) that are required for the discrete formulation of Fick’s second law of diffusion (3). Since (i) the condition of eq 3 has to be fulfilled and (ii) both di,jfj+1 and di,j+1fj should be as close as possible to di,j, their numerical values are set to

di,jfj+1 ) di,j ‚ xvj/vj+1 and di,j+1fj ) di,j ‚ xvj+1/vj (4) VOL. 34, NO. 9, 2000 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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TABLE 1. Input Parameters for the Three Selected Chemicalsa parameter

unit

tetrachloromethane

r-HCH

mirexb

CAS no. κs κw κa EA,s EA,w EA,a A B log Kow p0

s-1 s-1 s-1 kJ/mol kJ/mol kJ/mol K Pa

56-23-5 2.93‚10-8 2.93‚10-8 2.19‚10-9 55.0 55.0 19.0 11.27 3230 2.83 1.5‚104

319-84-6 1.07‚10-7 1.53‚10-7 2.10‚10-6 55.0 78.0 8.31 9.88 2989 3.80 7.4‚10-3

2385-85-5 1.80‚10-9 5.36‚10-8 4.50‚10-10 0 0 0 12.709 4711 6.89 1.3‚10-4

a The degradation rate constants taken from refs 32 and 37 are estimates without specified temperatures and were assigned to the reference temperature of 298 K. Sources for the activation energies EA are given in the text. The A and B values for CCl4 refer to a relation ln H ) A - B/T (27), neglecting the term -ln T from eq 5. For R-HCH, A and B refer to a relation ln KH ) A - B/T with KH being the Henry’s law constant in Pa m3/mol (16). For mirex, the relation is log10 KH ) A - B/T with KH in atm m3/mol (29). H is then obtained as H ) KH/(RA‚T) from KH. b κs from ref 38, κw from ref 39; as mirex is nonreactive in the troposphere, κa describes diffusion to the stratosphere (39). log Kow from ref 39, p0 from ref 40. The degradation of mirex is very slow and not well characterized. Therefore, the assumption EA,i ) 0 is used in the model calculations.

Inclusion of Temperature. The influence of the temperature on the Henry’s law constant H (dimensionless) is described by (26)

ln H(T) ) A - B/T - ln T

(5)

This relation is based on the Clausius-Clapeyron equation for water-gas equilibrium. A and B are taken from refs 27 (CCl4), 28 (R-HCH), and 29 (mirex), see Table 1. When the input temperature from ref 23 is above 270 K, water and air have the same temperature in the model. In this case, the Henry’s law constant, H, is used to describe air-water partitioning. When the input temperature drops below 270 K, the water temperature is kept constant at 270 K, and only air and soil are cooled below 270 K. In this case, the ratio Paw of the fugacity capacities Zair ) 1/(RA‚Tair) and Zwater ) 1/(RA‚Twater‚H(Twater)) is used instead of H (with RA ) 8.3144 J/(mol‚K)):

Paw ) Zair/Zwater ) RA‚Twater‚H(Twater)/(RA‚Tair) ) Twater H(Twater) ‚ (6) Tair Paw serves as an estimate of the nonequilibrium air-water partioning coefficient (30). While H decreases strongly with decreasing temperature, Paw increases slightly with decreasing air temperature (31), see values in Table 2.

In the present version of the model, there is no ice between water and air in the polar zones. For the degradation rate constants κi,j, a general Arrhenius relation

{

κi,j(T) ) κi,j(Tref)‚exp -

EA,i (T -1 - T -1 ref ) RA

}

(7)

is assumed (with Tref ) 298 K). The reference values κi,j(Tref) are taken from ref 32 and for the activation energies EA,i estimates of 10 to 20 kJ/mol for air (18, 33) and 50 to 100 kJ/mol for soil and water (16, 34) are used (see Table 1). EA,i determines how sensitive to temperature changes the degradation rates κi,j are. If the temperature is increased from 273 to 283 K, EA,i ) 10 kJ/mol leads to an exponential factor of 1.17 in eq 7 and EA,i ) 100 kJ/mol to a factor of 4.74. As the temperature is a function of space and time, H and the κi,j are functions of space and time as well. Table 2 shows H(T), Paw(T), and the three degradation rate constants for all three chemicals in the relevant temperature interval [215 K, 301 K]. Model Solution. By combining the degradation and phase partitioning processes with the diffusion dynamics of eqs 1 and 2, one obtaines 3‚n mass balance equations of the type

c˘ i,j(t) ) -(κi,j(t) +

∑u

ik,j(t)

+ di,jfj-1 + di,jfj+1)‚ci,j(t) +

k

∑u

vk vj+1 ‚ck,j(t) + di,j+1fj ‚ci,j+1(t) + vi vj vj-1 di,j-1fj‚ ‚ci,j-1(t) (8) vj

ki,j(t)‚

k

with degradation rates κi,j, phase transfer parameters uik,j, and diffusion parameters di,j. i and k denote the environmental media; j denotes the latitudinal zones. The phase transfer parameters uik,j represent diffusive and advective processes such as volatilization, dry and wet particle deposition, rain washout, etc. The κi,j and uik,j are not continuous functions of time, but for each zone j a set of nseasons values of each κi,j and uik,j are determined by the temperature courses shown in Figure 2. The 3‚n equations form a system of coupled linear differential equations which is solved for the concentrations ci,j(t). The procedure for solving the system is described in the Supporting Information. The computer time required for this procedure is directly proportional to the number of seasons. Solving the rate equations yields for each compartment i in each zone j a concentration function ci,j(t) and an overall exposure ei,j that is a numerical estimate of the total tot exposure ei,j ) ∫∞0 ci,j(t) dt. This means that there are three

TABLE 2. Lowest and Highest Partitioning Coefficients and Degradation Rates of CCl4, r-HCH, and Mirex in the Relevant Temperature Interval [215 K, 301 K] chemical CCl4 R-HCH

mirex

T (K)

H (-)

Paw (-)

Ks (s-1)

Kw (s-1)

Ka (s-1)

215 270 301 215 270 301 215 270 301

2.34‚10-2 5.00‚10-1 1.71 1.10‚10-5 1.46‚10-4 4.08‚10-4 3.55‚10-8 8.23‚10-4 4.63‚10-2

6.28‚10-1 5.00‚10-1 1.71 1.83‚10-4 1.46‚10-4 4.08‚10-4 1.03‚10-3 8.23‚10-4 4.63‚10-2

5.56‚10-12 2.93‚10-9 3.66‚10-8 2.03‚10-11 1.07‚10-8 1.33‚10-7

5.56‚10-12 2.93‚10-9 3.66‚10-8 8.07‚10-13 5.85‚10-9 2.09‚10-7

1.13‚10-10 9.89‚10-10 2.36‚10-9 5.78‚10-7 1.48‚10-6 2.17‚10-6

1.80‚10-9

5.36‚10-8

4.50‚10-10

a The values are derived from the A, B, E , and κ data given in Table 1. Above 270 K, H and P A,i i aw are identical. If Tair is below 270 K, Paw is used instead of the very low values of H. For mirex, constant κi values are assumed for all temperatures (EA,i ) 0).

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environmental media separately and for the averaged exposure distribution with the values

ejj )

vs,j‚es,j + vw,j‚ew,j + va,j‚ea,j j ) 1, ..., n vs,j + vw,j + va,j

(9)

The concentrations ci,j(t) are plotted as spatial distributions at certain points in time, see Figures 4-6. These spatial concentration distributions provide time dependent spatial ranges which characterize the temporal change of the concentration pattern and can be compared with the exposure-based, i.e. time independent, R values.

FIGURE 3. Northern and southern spatial ranges RN and RS of a schematic distribution with release to the zone j0. The unshaded end of each hemispheric branch contains 2.5% of the total weight of the distribution.

Model Calculations

sets of n concentration functions and n overall exposure values for the three environmental media. From these results, the overall persistence τ and the spatial range R of the chemical are derived as described in refs 3 and 6. τ is calculated from the rate of decay of the overall mass contained in the model system. It describes the time span that is required for a decrease of the initially released amount, M0, to approximately 0.37M0. The spatial range is calculated for each medium as the 95% interquantile distance of the spatial exposure distribution, see Figure 3. According to this definition, the spatial range is the distance that contains 95% of the total weight of the exposure distribution (indicated by the gray area in Figure 3). In the case of a homogeneous global distribution, this distance is equal to 95% of the pole-to-pole distance L. In ref 11, the spatial range as it is used here is compared with the related concept of the characteristic travel distance (8). The characteristic travel distance cannot be calculated for spatial distributions with two maxima at the end of the branches as they are obtained in some cases with the global model used here (see the examples shown in Figures 4a and 5b). Because of the different temperature courses in the northern and southern hemispheres, the chemicals have different distributions in the two hemispheres. Thus, northern and southern spatial ranges RS and RN are determined separately. They are calculated such that there are 2.5% of the overall exposure between the points marked by the hemispheric spatial ranges and the poles (Figure 3). In addition, the indicators ccN and ccS quantifying the extent of accumulation at the poles are determined: ccN and ccS represent the ratios of the exposure values of the poles, e1 and en, to the minimal exposure values of both hemispheres: ccN ) e1/eN,min; ccS ) en/eS,min. Finally, the southto-north ratio of the exposure values at the poles, rSN ) en/e1, is calculated. ccS, ccN, and rSN can be determined for the three

Model calculations were performed for tetrachloromethane (CCl4), R-HCH, and mirex. The typical environmental fate of CCl4 is known (27, 35, 36), and the model is checked by comparison of the CCl4 results with the known environmental distribution of CCl4 (partitioning into air, homogeneous atmospheric distribution, atmospheric lifetime of several decades). Subsequently, the more complex behavior of R-HCH and mirex is investigated. The simulation times (nyears) are 50 years for R-HCH and 200 years for CCl4 and mirex. In all calculations, a pulse emission of the amount M0 to the soil of one single zone j0 is assumed, and all concentrations are in arbitrary units of mass per volume. Since pulse releases are the simplest modeling scenarios, we concentrate, in a first approach, on the interpretation of the results obtained for pulse releases. However, the model can also be used with any number of repeated releases at several locations. Such a more realistic scenario will be investigated for R-HCH in a subsequent study. Release to soil is chosen because it shows the effects of volatilization, redeposition, and transport in combination (3, 11). See ref 11 for a comparison of the scenarios “release to soil” and “release to air” with respect to the information that can be gained from the respective modeling results. Input Data. The input parameters for the three chemicals are given in Table 1. For CCl4, κs and κw describe aerobic biodegradation (32); anaerobic biodegradation is not relevant in the model scenarios because sediments and anaerobic soils are not included in the model. κa describes reaction with OH radicals (33). Transfer to the stratosphere is of similar magnitude and is represented by a constant rate of 6.34 ‚ 10-10 s-1 (35) that is added to all κa values of CCl4. For R-HCH, we used assumptions similar to those of Wania et al. (16) (hydrolysis in water, reaction with OH radicals in air, biodegradation in soil). Mirex has a very low reactivity in all compartments (37, Vol. III, p 112), and no temperature dependence was implemented for the degradation of mirex.

FIGURE 4. Concentrations of CCl4 in soil (a) and air (b) after 1 year (4 seasons); emission to the soil at j0 ) 30 with n ) 60. The concentrations are in units of mass per volume. The atmospheric spatial range Ra is 94.5%. The amount in the soil is less than 0.1% of the total amount of CCl4 in the model system. VOL. 34, NO. 9, 2000 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 5. Concentrations of r-HCH in oceanic surface water after 1 year (a) and 5 years (b); release to the soil at j0 ) 30 with n ) 60. (a), bottom, shows the low concentrations after 1 year on an increased scale. The concentrations are in units of mass per volume. The maximum value at j0 ) 30 is 1.8‚10-4 (not shown). The spatial ranges are 10.7% after 1 year and 98.2% after 5 years. The time-independent spatial range derived from the oceanic exposure distribution is 7.7%.

FIGURE 6. Soil concentrations of mirex after 4 years (a) without adsorption to aerosol particles (Φ ) 0) and (b) with a constant value of Φ ) 33% of adsorbed material in all zones. Release to the soil at j0 ) 30, n ) 60. The concentrations are in units of mass per volume; the soil concentration of the zone j0 is about 2.0‚103 in both scenarios (nonmobile fraction). This means that with mirex the influence of the varying airwater partitioning coefficients H and Paw alone are investigated. It has to be emphasized that the degradation rates, activation energies, and partitioning coefficients are subject to considerable uncertainty (at least a factor of 2 for the EA values and one order of magnitude for the κi(Tref)). Nevertheless, the values in Table 1 are considered as estimates which are sufficient to provide a qualitative understanding of the mobility of the three different chemicals. CCl4: Model Check and Calibration of Eddy Diffusion Coefficients. In a first set of calculations, CCl4 is released to the soil at j0 ) n/2, i.e. in the southernmost zone of the northern hemisphere. The number of zones, n, is increased from 10 to 120, and for each of these values, the overall persistence τ, the atmospheric spatial ranges in the northern and southern hemisphere, RN,a and RS,a, the total atmospheric spatial range RS+N,a ) RS,a + RN,a, the cold condensation ratios, ccS and ccN, and the south-to-north ratio rSN are calculated. The results shown in Table 3 indicate that persistence and spatial range approach limiting values with increasing n. RS+N,a indicates a homogeneous global distribution as it is observed in reality for CCl4 (27, 35, 36) (due to the homogeneous atmospheric conditions, the experimentally observed scatter of CCl4 concentrations is not reflected by the model results). 1846

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TABLE 3. Persistence τ (days), Atmospheric Spatial Ranges RN,a, RS,a, and RS+N,a (in % of the Pole-to-Pole Distance L), and Cold Condensation Ratios of the Averaged Exposure Distribution ejj (see eq 9) of CCl4a n

τ (in 103 days)

RS,a

RN,a

RS+N,a

ccN

ccS

rSN

10 30 60 90 120

4.496 4.463 4.460 4.459 4.459

52.43 49.11 48.29 48.02 47.89

42.53 45.85 46.66 46.93 47.07

94.96 94.96 94.96 94.96 94.96

1.021 1.022 1.021 1.021 1.021

1.019 1.018 1.019 1.019 1.019

0.9688 0.9698 0.9831 0.9883 0.9910

a All values are obtained from the spatial exposure distributions (time independent); release to soil at j0 ) n/2.

τ is determined mainly by κa and corresponds to the atmospheric lifetime of CCl4 of several decades (35). The atmospheric spatial range Ra describes the spread of the amount of chemical that volatilizes from the soil at the point of release. However, Ra is nearly independent of the size of this atmospheric fraction. Therefore, we also determine the mobile fraction, fm, of the chemicals. fm describes the fraction of the amount released that is removed from the place of release. fm is determined as follows: The time integral of the amount remaining at the point of release, m j s,j0 ) ∫n0 years ms,j0(t) dt ) vs,j0‚es,j0, divided by the time integral of the overall

TABLE 4. Temporal Changes of the Atmospheric Spatial Ranges RN,a, RS,a, and RS+N,a (in % of the Pole-to-Pole Distance L) of CCl4a time (years)

RN,a

RS,a

RS+N,a

0.25 1.0 1.5 2.0 3.0 10.0

22.98 22.26 21.97 21.82 21.70 21.67

42.32 72.21 72.93 73.17 73.31 73.34

65.30 94.47 94.90 94.99 95.01 95.01

a Release to soil at j ) 15, n ) 60. The values are obtained from the 0 time dependent spatial concentration distributions.

TABLE 5. Persistence τ (days), Atmospheric Spatial Ranges RN,a, RS,a and RS+N,a (in % of the Pole-to-Pole Distance L), and Cold Condensation Ratios of the Averaged Exposure Distribution ejj (see eq 9) of r-HCHa j0 n/2

n τ (in days)

10 30 60 0.3n 10 30 60

97.1 95.3 95.6 366.0 261.2 240.4

RS,a

RN,a

13.00 8.44 8.80 17.48 13.04 11.86

26.20 15.71 11.90 22.13 24.76 25.53

RS+N,a

ccN

39.20 1.00 24.15 1.09 20.70 1.15 39.61 207.0 37.80 407.4 37.39 511.0

ccS

rSN

2.00 2.83 3.02 2.64 3.08 3.29

9.99‚10-2 3.51‚10-1 8.66‚10-1 3.57‚10-5 1.86‚10-5 1.61‚10-5

a Release to soil at j ) n/2 (top) and j ) 0.3 n (bottom). The values 0 0 are obtained from the time independent spatial exposure distributions.

mass, M h ) ∫n0 years ∑i,jmi,j(t) dt ) ∑i,jvi,j‚ei,j, describes the nonmobile fraction. The mobile fraction is then fm ) 1 m j s,j0/M h . For CCl4, fm is 99.96% with 98% of this in air, about 2% in water, and less than 0.1% in soil. Figure 3 shows the spatial concentration distributions of CCl4 in soil and air after 1 year. Note that even CCl4 exhibits a cold condensation in soil (and also in water; not shown) which is driven by the low H values during winter in the polar zones. This is in agreement with the finding of increasing CCl4 concentrations in antarctic ocean water (27). The cc values in soil are ccN ) 7.7 and ccS ) 29.8. However, as the mass fractions of soil and water are only 2% and less than 0.1% (see above, fm values), this cold condensation effect is not relevant for the overall environmental distribution of CCl4. With an identical value of H ) 0.710 (278 K) in all zones, no cold condensation is observed (ccN ) ccS ) 1.00; homogeneous distribution in all media). In order to determine the interhemispheric mixing time in the model, we investigate the temporal development of the CCl4 concentrations. In these calculations, CCl4 is released at j0 ) n/4 ) 15 with n ) 60, i.e. in a northern temperate zone. Table 4 contains the atmospheric spatial ranges that are derived from the spatial concentration distributions at different times (note that these spatial ranges change with time while the spatial ranges given in Table 3 are timeindependent). The results show that the Da values according to ref 25 lead to a homogeneous atmospheric distribution after 1 year, which is the commonly assumed value for the interhemispheric mixing time (22). r-HCH: Cold Condensation in Oceanic Water. In Table 5, the persistences and spatial ranges of R-HCH are shown for increasing n and two different places of release, j0 ) n/2 and j0 ) 0.3 n (release to the soil). In both scenarios, τ is lower than or close to the global mixing time of about 1 year, and, therefore, the place of release has a strong influence on τ. According to the lower temperatures and lower degradation rates at j0 ) 0.3 n, τ is higher by a factor of 3 if R-HCH is released at 0.3 n. In general, τ is governed by the degradation rate in soil. The activation energies EA also have a relevant influence: If the EA values

of R-HCH are increased by a factor of 1.5, the overall persistence τ increases by 10%. RS and RN assume more symmetric and significantly lower values with increasing n if R-HCH is released at j0 ) n/2 (Table 5, top). This is because for n ) 10 the zone of release, n/2, reaches beyond the northern boundary of the Intertropic Convergence Zone (ITCZ) which is an area of low diffusion coefficients and high degradation rates. With increasing n, the zone of release narrows and moves toward the center of the ITCZ so that R-HCH remains longer in this area. This effect of the ITCZ does not occur if R-HCH is released in the temperate zone at j0 ) 0.3 n so that RS and RN decrease to a lesser extent with increasing n in this scenario. The cc values are higher for higher n because the temperatures at the poles are lower if the polar zones are narrower. As a sensitivity analysis has shown, the cc values are strongly influenced by the dry and wet deposition rates. The wet deposition rate is very high at low temperatures because of the high tendency of R-HCH to partition into “subcooled” rainwater (low H). Although this is an artificial feature of the model, the wet deposition rate was not changed to a lower value because high scavenging ratios through snow fall are possible for gaseous as well as particle-bound chemicals (41-43). In these scenarios, the mobile fraction of R-HCH is between 1% and 6%, which is in accordance with the low vapor pressure of R-HCH. The temporal course of the cold condensation process is analyzed for the first scenario, release at the equator. The temporal profiles of the functions ci,j(t) (not shown here) indicate that in all zones the maximum concentrations are reached in the first 2 months (air) or the first 1.5 years (soil, water) after the release. In Figure 4 the oceanic concentration distributions of R-HCH after 1 year (≈4 τ) and 5 years are depicted. It can be seen that the R-HCH concentrations at the poles increase rather quickly within the first year (Figure 4(a), bottom). After this time, degradation is the dominant process in all zones. In the tropical zones, degradation of R-HCH is much faster than in the polar zones, which leads to the concentration profile of Figure 4(b). This result is in qualitative agreement with the experimental data compiled by Wania and Mackay (19) that show an increase of oceanic R-HCH concentrations by a factor of 20-50 from the equator to the Bering Sea. Note that there is no further accumulation of R-HCH at the poles after 1.5 years but only a relative increase of the polar concentrations. This effect can be interpreted as a “cold condensation” but has to be distinguished from a continuous movement of the chemical toward the poles. Due to the high losses of R-HCH in the tropical and temperate zones, the peaked atmospheric concentration distribution of the first year turns into a distribution with two residues in the polar regions (Figure 4(b)). R-HCH is the only chemical of the three substances that shows this change of the shape of the spatial concentration distribution. Mirex: Influence of Aerosol Particles. Mirex is as stable as CCl4 but has considerably lower p0 and H values. Thus, it might be seen as a nonreactive tracer similar to CCl4 but with a strong tendency to adsorb to soil and aerosol particles. In the model, the fraction of air-borne chemical that is adsorbed to or included into aerosol particles is represented by the parameter Φ. Estimates for Φ can be calculated from the properties of the aerosol (aerosol surface per m3 air, chemical composition, size distribution) and from the vapor pressure (44) or the octanol-air partitioning coefficient (45) of the chemical. Here, only the general influence of aerosol particles is explored, and, therefore, Φ is used as an independent parameter that can be set at any value between 0% and 100%. Φ enters the model dynamics of eq 8 at two points, see ref 6: (i) the atmospheric degradation rate and VOL. 34, NO. 9, 2000 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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TABLE 6. Persistence τ (days), Mobile Fraction fm, Atmospheric Spatial Ranges RN,a, RS,a, and RS+N,a (in % of the Pole-to-Pole Distance L), and Cold Condensation Ratios of the Averaged Exposure Distribution ejj of Mirex for Different Fractions of Particle-Adsorbed Material, Φa Φ

τ (in 103 days)

fm

RS,a

RN,a

RS+N,a

ccN

ccS

rSN

0 0.33

6.192 6.221

0.24% 0.13%

42.75 33.35

46.26 39.56

89.01 72.91

17.24 1.043

122.2 3.778

0.756 2.337

a

Release to soil at j0 ) n/2, n ) 60. The values are obtained from the time independent spatial exposure distributions.

TABLE 7. Nomenclature of the Model Parametersa

a

parameter

symbol

value and/or units

Henry parameters concentration in medium i and zone j cold condensation ratios (southern and northern) eddy diffusion coefficients in air and water diffusion parameter in medium i overall exposure of medium i in zone j activation energy in medium i mobile mass fraction Henry’s law constant media index zone index zone of release octanol-water partitioning coefficient pole-to-pole distance zone length in north-south direction mass initially released mass contained in the entire model system at time t time integral of M(t) from 0 to ∞ mass contained in medium i in zone j at time t time integral of mi,j(t) from 0 to ∞ number of zones total simulation time number of seasons per year nonequilibrium air-water partitioning coefficient vapor pressure gas constant spatial ranges (northern, southern, sum of both) south-to-north ratio temperature transfer parameters between media i and k volume of medium i in zone j zone width in east-west direction fugacity capacities degradation rate constant in medium i and zone j particle-adsorbed fraction of air-borne material overall persistence

A and B ci,j(t) ccS, ccN Da, Dw di,j ei,j EA,i fm H i j j0 Kow L ln M0 M(t) M h mi,j(t) m ¯ i,j n nyears nseasons Paw p0 RA RN, RS, RS+N rSN T uik vi,j wj Zair, Zwater κi,j Φ τ

- and K mol/m3 m2/s s-1 s‚mol/m3 J/mol s, w, a 1, ..., n 1, ..., n 20 000 km L/ n mol mol mol‚s mol mol‚s 10-120 years Pa 8.3144 J/(mol‚K) % of L K s-1 m3 km mol/(Pa‚m3) s-1 days

See also refs 3 and 6 for more detailed information.

the deposition and washout of gas-phase material is lowered by the factor (1-Φ) and (ii) the deposition of particle-bound material is increased by the factor Φ. If Φ ) 0 is used, a very high persistence of τ ) 6.19‚103 days and a global atmospheric spatial range of RS+N ) 89.0% is obtained for mirex (Table 6, top). The mobile fraction fm ) 0.24% is very small. Figure 6(a) shows the concentrations in soil after 4 years. The maximum value at j0 ) 30 is 2.0‚103 (in units of mass per volume), which is due to the high nonmobile fraction; the cc values in soil are ccN ) 2.85‚102 and ccS ) 1.97‚103. The maximum concentrations at the poles are reached 4 years (water and air) and 18 years (soil) after the release. If the model is run for mirex with adsorption to aerosol particles, the shape of the spatial concentration distribution changes significantly (Table 6, bottom, and Figure 6(b)): With a constant value of Φ ) 33% in all zones, the cold condensation is almost completely suppressed, and the overall atmospheric spatial range is lowered from 89% to 73%. This means that adsorption to aerosol particles and thereby increased deposition to soil and water reduces the 1848

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mobility of mirex significantly in the model, while the low p0 and H values alone do not prevent the mobile fraction of mirex from being transported to the poles and being accumulated in polar water and soil. The results for mirex underline that there are two different aspects of long-range transport and mobility which should be distinguished: (i) How much material is removed from the place of release? This aspect is represented here by the mobile fraction. (ii) What are the spatial ranges, cc values, etc. of the concentration distributions that represent the environmental distribution of the mobile fraction of the chemical? This aspect is to some extent independent of the absolute quantity of the mobile fraction.

Discussion The main results obtained with the global fate model presented in this study are the following: (1) For CCl4 and R-HCH investigated, transport from the equator to the poles occurs within the first 1 or 2 years after the single pulse release. Mirex, in contrast, exhibits a more delayed transport to the

poles, and especially the concentration in the soil increase during 18 years after the release. (2) The mobile fraction of R-HCH and mirex is below 5% and 1%, respectively. However, even these small mobile fractions can lead to high spatial ranges and increased polar concentrations. Findings (1) and (2) support the hypothesis that the velocity as well as the extent of the transport to the poles is correlated with the vapor pressure of the chemicals. (3) Accumulation in the polar regions can be found for all three chemicals (only in the condensed media water and soil). This effect is mainly driven by the low values of H at the poles (thermodynamic aspect) and by the efficiency of the atmospheric deposition processes such as vapor scavenging by rain and particle deposition (kinetic aspect). The thermodynamic aspect is illustrated with CCl4 which has ccN and ccS of 1.00 if H is the same in all zones. This demonstrates that the gradient but not the absolute value of H is relevant for the accumulation process. The kinetic aspect is demonstrated by the cc values of mirex and R-HCH which are very sensitive to changes of the particle deposition rate and the washout rate. In the case of R-HCH, there are residues in the polar zones after several years because of the lower degradation rates in those zones. In the case of CCl4, the influence of temperature on the degradation rates is not relevant for the accumulation process. However, the cold condensation effect observed with the model might be of minor importance for the actual distribution pattern of a chemical for several reasons: In the case of CCl4, the very large fraction in air dominates the overall picture. In the case of R-HCH, the inverted concentration profile occurs only after some time when most of the chemical has been degraded so that all concentrations are very low and the effect might not be detectable in reality. Third, mirex has a strong gradient of the Henry’s law constant and a very high persistence, which could make it a candidate for transport to and accumulation in polar regions. However, the mobility of mirex is likely to be restricted by efficient wet and dry particle deposition processes. Finally, the actual emission patterns, which are much more complex than the pulse release investigated here, strongly influence the experimentally observed concentration distributions of POPs. For these reasons, the many factors that influence the global distribution of POPs but have not been included in the model (e.g. high spatial and temporal variability of all degradation and deposition processes; influences of vegetation and different soil types; influence of local sources) might compensate for the general trends described above. For an improved understanding of the cold condensation effect, it is therefore necessary that the temperature dependence of partitioning coefficients, deposition and phase transfer velocities (washout by snow fall; transfer to different soils, vegetation, and ice or snow), and degradation rates is further investigated. Knowledge of the variability of these parameters, especially at low temperatures, is crucial for a well-based correlation of modeling results and measurement data. In conclusion, the model makes it possible to gain a qualitative and semiquantitative understanding of the temperature influences on the transport, partitioning, and degradation of environmental chemicals on a global scale. A tendency for the accumulation of chemicals in polar water and soil could be demonstrated and parameters possibly governing this accumulation have been identified. The influence of the number of latitudinal zones has been determined, and it turned out that approximately 30 zones will be sufficient for further applications of this type of global model. On the other hand, there are several aspects requiring further improvement of the model: For the purpose of complete mass balances, freshwater, deep ocean water, and sediments have to be included. The treatment of the soil in

the model is by far not sufficient for describing the complexity of real soils. The sensitivity of the polar concentrations to a broad set of model parameters, especially those describing phase transfer processes, has to be investigated, and a more realistic representation of the most relevant processes is desirable. Finally, it should be kept in mind that the model, despite its increased complexity as compared to the circular model (3), is still an evaluative model which is suitable for the relative comparison of different chemicals within a consistent framework. It is not to be understood as a tool for predicting actual environmental concentrations, in particular if only simplified scenarios of chemical release are used. However, the model provides a useful platform for the systematic combination and evaluation of chemical property data, landscape parameters, and mechanisms of environmental processes. The number of latitudinal zones, the annual temperature course, the location, time, and number of emissions, the simulated period of time, and the diffusion coefficients and other landscape parameters can be changed easily. This flexibility makes the model suitable for the simulation of various scenarios beyond the ones presented in this study.

Acknowledgments We thank U. Fischer, K.-U. Goss, H. Held, H. HoffmannRiem, J. Jaeger, and S. Willitsch for helpful comments. Financial support by the Swiss Federal Agency for Environment, Forest and Landscapes is gratefully acknowledged.

Supporting Information Available Solution of the system of 3‚n linear differential equations of the type of eq 8. This material is available free of charge via the Internet at http://pubs.acs.org.

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Received for review September 21, 1999. Revised manuscript received January 4, 2000. Accepted January 11, 2000. ES991085A