Dynamic modeling of chemical fate and transport in multimedia environments at watershed scale—II: Trichloroethylene test case

ARTICLE IN PRESS

Journal of Environmental Management 83 (2007) 56–65 www.elsevier.com/locate/jenvman

Dynamic modeling of chemical fate and transport in multimedia environments at watershed scale—II: Trichloroethylene test case Yuzhou Luoa, Qiong Gaob, Xiusheng Yanga, a

Department of Natural Resources Management and Engineering, University of Connecticut, Storrs, CT 06269, USA b Institute of Resources Science, Beijing Normal University, Beijing 100875, PR China Received 7 April 2005; received in revised form 19 January 2006; accepted 26 January 2006 Available online 5 May 2006

Abstract A multimedia environmental fate model was developed to study the temporal dynamics and spatial distribution of a chemical pollutant at watershed scale. The theoretical considerations and implementation of the model were described in the accompanying paper (Part I). This paper presents the result of a test simulation on the transport of trichloroethylene (TCE) in the Connecticut River Basin. The simulation results were reported as time series of concentrations and inter-media transport fluxes in the compartments of atmosphere, plant, soil, surface water, and sediment. Predicted concentrations from the test simulation were compared with published field data or predictions by validated models. The temporal trends in TCE predictions were evaluated by comparing the simulation results with monthly TCE concentrations in various environmental compartments and monthly fluxes of inter-media transport processes. Results indicated that the simulation results were in reasonable agreement with reported data in the literature. The results also revealed that the mass transport of TCE from the atmosphere compartment to soil and surface water was a major route of TCE dispersion in the environment. r 2006 Elsevier Ltd. All rights reserved. Keywords: Connecticut River Basin; Multimedia environmental model; TCE; Watershed modeling

1. Introduction Hazardous substances released into the environment are transported and dispersed in complex environmental systems that include air, plant, soil, water, and sediment. Effective environmental management practices depend on clear understandings of the source–receptor relations, which demand holistic modeling of the transport and transformation of the materials in the multimedia arena. In response to the high cost for multimedia monitoring and the need for rapid forecasting or retrospective analysis, multimedia models at various levels of complexity have been developed (Mackay and Yeun, 1983; Cohen et al., 1990; Cowan et al., 1995; MacLeod et al., 2001; USEPA, 2001; Toose et al., 2004). The current contention in using multimedia models for environmental management is due to the lack of spatial and temporal resolutions in existing Corresponding author. Tel.: +1 860 486 0135; fax: +1 860 486 5408.

E-mail address: [email protected] (X. Yang). 0301-4797/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.jenvman.2006.01.018

models. The majority of the models in the literature were developed to provide steady-state response to chemical inputs and neglected the spatiotemporal variations in environmental properties in simulating chemical dynamics. With GIS integration, a multimedia environmental fate and transport model was developed for simulating chemical dynamics with spatial resolution at watershed scale. The theoretical considerations and implementation of the model were presented in the accompanying paper (Luo et al., 2006). The primary objective of this paper was to report the testing results for the simulation of the fate and transport of trichloroethylene (TCE) in the Connecticut River Basin using our newly developed multimedia model. In particular, efforts were aimed to estimate the dynamic concentrations and inter-media transport fluxes of TCE in the compartments of atmosphere, plant, soil, surface water, and sediment, and to evaluate the results by comparing the model predictions with available data from the literature and databases. We noted that a full validation of the

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multimedia environmental model was impractical due to the lack of accurate and complete data of emission, concentration, and inter-media fluxes. Therefore, the comparison between simulated and observed time variation and spatial patterns in normalized concentrations, rather than absolute values, was conducted in this study to determine if the spatial and temporal variations in chemical distribution were well predicted by the model even with inaccurate emission data (Sweetman et al., 2002; Lee et al., 2004). Efforts were also made to compare the steady-state concentrations from the model outputs to predictions by other well-reviewed models. 2. Model initialization and simulation design 2.1. Site description The Connecticut River Basin, the largest river ecosystem in the New England region, encompasses approximately 11,000 miles2 (Fig. 1). Its headwater is at the Fourth Connecticut Lake near the Canadian border, and the river runs into the Long Island Sound at Old Saybrook, Connecticut. Contamination investigations of the basin have found specific environmental problems such as toxins in the river, combined sewer overflows, bio-accumulation

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of contaminants, and non-point sources (NEIWPCC, 1998). A recent study also identified the Connecticut River as a major source of pollutants in the Long Island Sound. According to the EPA New England Office of Site Remediation and Restoration, the groundwater and soil in some monitoring sites in the Connecticut River Basin were contaminated with volatile organic compounds (VOCs), primarily TCE. TCE has a high vapor pressure that leads to a rapid dispersion into the atmosphere from various local sources. The physical and chemical properties of TCE are summarized in Table 1. The Connecticut River Basin consists of 14 drainage watersheds with hydrological unit codes (HUC) defined by the USGS (Fig. 1). In this study, each watershed represented a unit environment, connected to each other by air and surface water flows. Table 2 shows the total areas and surface water areas of the watersheds. The major enclosed counties for each watershed are also listed because some of the landscape characteristics and emission rates were provided on a county basis. At watershed scale, GIS technology was used in conjunction with spatial databases to quantify the environmental properties, chemical releases, and background concentrations for the seven modeling compartments, i.e., air, plant canopy, surface soil, root-zone soil, vadose-zone soil, surface water, and sediment.

Fig. 1. Watershed delineations in the Connecticut River Basin (RW ¼ River Watershed).

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2.2. Landscape characterization in the Connecticut River Basin The simulation using our newly developed model required environmental parameters for each compartment as time series at daily time intervals (Table 3). As described in the accompanying paper (Luo et al., 2006), GIS-powered functions were developed to estimate these parameters from digital maps or field measurements. For parameters with temporal variations, missing data in the input time series were first replaced by a default linear interpolation or a user-defined function. Spatial interpolation or aggregation was applied to parameters obtained from field measurements to generate representative values for each watershed. These parameters included meteorological data and aerosol load. The volume fraction and organic carbon Table 1 Physical and chemical properties of trichloroethylene at 20–25 1C (CEPA, 1993; Coulibaly, 2000) Property

Unit

Value

Molecular weight Vapor pressure Solubility Henry’s low constant Octanol–water partition coefficient Organic carbon partition coefficient Diffusivity in pure air Diffusivity in pure water Half-life in bulk air Half-life in plant Half-life in surface soil Half-life in root zone soil Half-life in vadose-zone soil Half-life in surface water Half-life in sediment Half-life in groundwater

g Pa mg l1 Pa m3 mol1 l [water] l1 [octanol] l kg1 m2 day1 m2 day1 day day day day day day day day

131.4 9870 1100 886.675 322.5 85.6 0.681 9.02  105 3.46 2.77 923 930 757 121.5 217 801

content in suspended particles in surface water were set as the averages of field measurements within a watershed. More details for the input data preparation were provided in the accompanying paper (Part I). For parameters that were not available as continuous measurements in the simulation domain, representative values were adopted from the literature. These parameters included mixing layer height, particle densities, rain/snow scavenging ratio, plant dry-mass fraction and fresh-mass density, aquatic biota fraction, surface soil porosity and sediment porosity. The atmospheric compartment was assumed to extend from the ground surface to the top of the troposphere. Based on similar studies at the University of Connecticut (Xu et al., 2000a, b), the horizontal grid size was set to 12 km  12 km for the simulation domain. The atmospheric mixing height was estimated as 700 m for all the watersheds (Hanna et al., 1982). Hourly and daily data were obtained from EPA Clean Air Status and Trends Network (CASTNet) and NOAA National Weather Service for precipitation, temperature, wind speed, wind direction, relative humidity, and solar radiation. The aerosol load in the air compartment was obtained from the monitoring stations of the Interagency Monitoring of Protected Visual Environments Program (IMPROVE, 2002) and EPA AirData system (USEPA, 2004). The annual mean aerosol load was about 25 mg m3 or an air volume fraction of 9.4  1012 throughout the basin. Organic carbon content in the aerosols was obtained from the chemical speciation concentration data from aerosol filters in the CASTNet database. The plant compartment included the above-ground plant biomass only, while the plant root was taken as a thermodynamic sub-compartment within the root-zone soil. Forest was the major vegetation type in this study. Leaf area index (LAI) was obtained from CASTNet measurements and NASA historical estimates (Scurlock

Table 2 Attributes of the fourteen watersheds in the Connecticut River Basin as shown in Fig. 1 HUC

Main stream

Major counties

1080101

Upper Connecticut River

1080102 1080103 1080104 1080105 1080106 1080107 1080201

Passumpsic River Waits River Upper Connecticut- Mascoma River White River Black-Ottauquechee River West River Middle Connecticut River

1080202 1080203

Millers River Deerfield River

1080204 1080205 1080206 1080207 Sum

Chicopee River Lower Connecticut River Westfield River Farmington River

Essex, VT Coos, NH Caledonia, VT Orange, VT Sullivan, NH Orange, VT Windsor, VT Windham, VT Cheshire, NH Hampshire and Franklin, MA Worcester, MA Windham, VT Franklin, MA Worcester, MA Hartford and Middlesex, CT Hampshire and Hampden, MA Hartford, CT

Total area (km2)

Water area (km2)

5147.7

187.7

1310.6 1131.7 3828.9 1849.3 1108.4 1588.6 2608.7

26.3 19.4 105.6 3.8 8.03 6.83 112.1

1000.4 1714.8

105.6 31.9

1866.6 2866.2 1344.2 1566.5 28932.7

217.7 147.6 40.3 80.2 1093.1

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Table 3 Environmental properties in the Connecticut River Basin Environmental properties

Typical values

In the atmosphere compartment Area Mixing layer height Volume fraction of aerosol particles

700 m 9.4  1012

Density of aerosol particles Scavenging ratio for rain Scavenging ratio for snow Meteorological data In the vegetation compartment Area Effective thickness Plant dry-mass fraction Plant fresh-mass density Leaf area index In the soil compartments Area Thickness of surface soil Thickness of root-zone soil Thickness of vadose-zone soil Organic carbon content of soil particles Soil porosity in surface soil Soil porosity in root-zone or vadose-zone soil Air and water content in soil

2650 kg m3 200,000 100,000

22% 830 kg m3

0.5 cm 0.5–1.0 m 3.0–5.5 m 40%

References and databases

Watershed delineation Hanna et al. (1982) IMPROVE data (IMPROVE, 2002) EPA AirData database McKone et al. (1997) Mackay (2001) Daly and Wania (2004) NOAA National Weather Service GIRAS landuse map USDA FIA database (USDA, 2004) Trapp and McFarlane (1994) Trapp and McFarlane (1994) NASA Vegetation Data Sets GIRAS landuse map McKone et al. (1997) Eq. (1) with soil data and HSPF results USGS NWIS groundwater database Calculated from SSURGO database McKone et al. (1997) Calculated from SSURGO database Calculated from water budget

In the surface water compartment Area Hydraulic properties Depth Volume fraction of suspended particles Density of suspended particles Organic carbon content in suspended particles Volume fraction of aquatic biota

7  106 2650 kg m3 5–20% 1  106

GIRAS landuse map National Hydrography Dataset HSPF simulation results USGS NWIS McKone et al. (1997) USGS NWIS ChemCAN (2003) model

In the sediment compartment Area Active depth Organic carbon content of sediment particles Sediment porosity

0.05 m 0.025 80%

National Hydrography Dataset Mackay and Paterson (1991) USGS NWIS Coulibaly et al. (2004b)

et al., 2001). The LAI for each watershed was calculated as the multiplication of the field-based LAI measurements and the fractional canopy cover. The plant growth model described in the accompanying paper (Part I) was used to generate time-dependent LAI values for model simulation. The volume and effective thickness of the vegetation compartment were calculated from the dry biomass of trees and shrubs on forest lands, found in the database of USDA Northeastern Forest Inventory and Analysis. The thickness of surface soil was assumed to be 0.5 cm with 40% porosity, and with the same organic carbon fraction as in the root zone soil. The root-zone soil layer must be thick enough to act as an effective non-escape barrier for contaminant diffusion to the vadose zone. According to McKone and Bennett (2003), the thickness of the root-zone soil (hs, m) was estimated as the steady-state penetration depth derived from a unit value of the

Damkoehler number (Jury, 1990; McKone and Bennett, 2003), M Rs hs ¼ 1, Dts =hs þ uinfil

(1)

where MRs (day1) is the TCE degradation rate constant in root-zone soil, Dts (m2 day1) the bulk diffusivity of TCE in root-zone soil, and uinfil (m day1) the average infiltration velocity. The thickness of the root-zone soil, hs, ranged from 0.5 to 1.0 m for TCE in the basin, varying with TCE bulk diffusivity in root-zone soil in different watersheds. The thickness of the vadose-zone soil was calculated from the annual average of the water table measured in the USGS National Water Information System (NWIS). Soil porosity was estimated as a function of soil clay content, soil bulk density, soil particle density, and soil particle size distribution found in the USDA Soil

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Table 4 Air emission rates of TCE (pound per year) in the Connecticut river basin during 2000–2002 HUC

1080201 1080202 1080204 1080205 1080206 1080207

Watershed

Middle Connecticut River Watershed Millers River Watershed Chicopee River Watershed Lower Connecticut River Watershed Westfield River Watershed Farmington River Watershed

Survey Geographic (SSURGO) database (USDA, 2000). Temporal variations of air and water contents in soil were calculated based on water budget in the hydrological simulation. Organic carbon content was calculated from organic matter content and corrected by the soil particle size distribution (Coulibaly et al., 2004b). Time-dependent air and water contents in the soil were calculated from a water balance scheme based on daily surface and subsurface water flows from hydrological simulation. The environmental properties for the surface water compartment included the surface area, average slope, trapezoidal structure, and other hydraulic properties such as lateral flow recession rate, initial storages, and fraction of remaining evapotranspiration for base flow. These properties were parameterized according to the Geographic Information Retrieval and Analysis (GIRAS) landuse data and the National Hydrography Dataset (NHD) supplied by USGS. Water flow rates were calculated from hydrological simulation, and the effective depths of rivers were estimated from their trapezoidal structure. The load and organic carbon content of suspended sediment was taken from the USGS NWIS. Suspended sediment load in the Connecticut River Basin was about 20 mg l1, or a particle volume fraction of 7  106. The water volume fraction of the aquatic biota (fish) was 1 ppm, a typical value used in the literature (ChemCAN, 2003). The sediment compartment included only the active top layer of sediment. The depth of this layer was assumed to be 0.05 m everywhere in the simulation domain. Below the active layer, there is a deeper inactive layer in which chemicals are essentially isolated from the water column. The porosity of the sediment zone was assumed to be 80% with the remaining 20% being the sediment solids. The organic carbon content of sediment was taken from the USGS NWIS. In the Connecticut River Basin, the average organic carbon concentration in the bed sediment was about 5 g kg1, or an organic carbon content of 0.025 at 80% of porosity. 2.3. TCE inputs and background concentration The TCE inputs by local emissions were extracted from the EPA Toxics Release Inventory (TRI) database. TRI data were collected on a facility or county basis. Geo-

Emission rate (pound per year) in 2000

2001

2002

44.0 11651.4 23302.8 108640.3 8108.0 42947.0

36.0 2900.9 5801.8 82208.2 18.0 34734.5

28.0 1988.6 3977.2 40185.2 14.0 27180

coding techniques were utilized to relocate and aggregate chemical release data into each watershed. According to the TRI database, releases of TCE from local industrial sources were introduced mainly by evaporation into the air. Emissions from other sources, such as surface water discharge, land treatment, and off-site disposal, were either zero or not reported in the Connecticut River Basin. During 2000–2002, non-zero air emissions of TCE were reported only in the watersheds located in the southern part of the basin (Table 4). The emission rates in other watersheds were reported as zero. It was noteworthy that TRI did not address all release sources of disposal, and did not include some mobile point or area sources in the air compartment (USEPA, 2003). Owing to limitations in data collection, only the background concentration of TCE in air was used in this study. The background air TCE concentration was taken from actual in situ measurement by Pankow et al. (2003). The background concentration within the Connecticut River Basin was averaged for each watershed as initial conditions for the simulation runs, while the background concentration in the surroundings of the simulation domain was used to calculate the distance chemical input by advective air flows. The other compartments of vegetation, soils, surface water and sediment were assumed to be clean at the start of simulation. 3. Model evaluation After initialization, simulations driven by actual weather conditions were conducted for a 3-year period from 2000 to 2002 at daily steps. The simulations achieved dynamic equilibrium between compartments in a couple of weeks for the atmosphere compartment and several months for the soil compartments, respectively. Therefore, the model evaluation was based on the model outputs in 2002, including simulated profiles of fugacities and concentrations, and inter-media chemical fluxes among the compartments of atmosphere, plant, root-zone soil, surface water, and sediment. Concentrations for some sub-compartments such as aquatic biota (fish) were also compared with available data. In this study, the model was partially evaluated by comparing the simulation results with available data from

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the literature and monitoring databases. These comparisons focused on (1) compartmental distribution, (2) spatial variation, and (3) temporal trend of TCE concentration in the Connecticut River Basin. The simulated annual mean concentrations of TCE in air, plant, root-zone soil, surface water, sediment, and fish were compared to reported measurements (Pearson and McConnell, 1975) and predictions from other models (Cohen and Ryan, 1985; Mackay and Paterson, 1991; Coulibaly et al., 2004b). To minimize the impact of local air emission and background concentration in the TCE distribution, relative concentrations of TCE were used in the comparison to show the general distribution patterns of TCE in the environment. Relative concentration in any environmental medium was defined as the simulated concentration in that medium divided by the corresponding air concentration. The simulated spatial variation of the TCE concentrations in water were compared with field measurements at the USGS NWIS stations located in the Connecticut River Basin. Monitoring data were only available in six watersheds in the Middle Connecticut River, the Millers River, the Chicopee River, the Lower Connecticut River, the Westfield River, and the Farmington River. Due to lack of accurate TCE release rates to surface water, the simulated water concentration did not reflect the actual chemical levels. Therefore, the simulated and observed spatial variations in normalized concentrations (rather than actual values) were compared to determine if the variabilities in environmental properties and chemical concentrations were well simulated by the model. More detailed comparisons were also performed between predictions from this model and from the EPA Assessment System for Population Exposure Nation-wide (ASPEN) dispersion model (USEPA, 2002). Temporal trends of simulated TCE concentration were examined for the compartments of air, root-zone soil, and surface water. Monthly means of simulated chemical amounts in terms of fugacities were used to compare the temporal responses of these compartments to inputs and inter-media transport processes. Because local emissions were assumed to be invariant over the entire year, the monthly variation in the atmosphere was expected to

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follow the temporal variation in TCE inputs by air flow. The monthly variation of the simulated concentrations in soil and surface water were expected to be consistent with the time-dependent inter-media fluxes that interacted with the atmosphere. 4. Results and discussion 4.1. TCE concentration in environmental compartments Table 5 summarizes the annual mean TCE concentrations in 2002 averaged from daily simulation results in the watersheds in the Upper Connecticut River, Lower Connecticut River, and West River. These selected watersheds represent the headwater, downstream, and independent tributary of the Connecticut River. According to the simulation results, about 99% of the TCE in the basin was residing in the air compartments. This distribution pattern was mainly due to the high volatility of TCE with a vapor pressure of 9870 Pa. This finding was in agreement with the results in Coulibaly et al. (2004a, b). Their studies reported 97% of TCE residing in the air compartment in the Passaic River Watershed, NJ. The comparison between the model results and the reported concentrations in the literature (Pearson and McConnell, 1975; Cohen and Ryan, 1985; Mackay and Paterson, 1991; Coulibaly et al., 2004b) is shown in Table 6. The simulated distribution pattern of TCE was generally in agreement with the reported data in Cohen and Ryan (1985) and Pearson and McConnell (1975), where air emission of TCE was the only local source in their studies. In Mackay and Paterson (1991) and Coulibaly et al. (2004b), TCE discharges to surface water were introduced as 1–8% of the source strength in air. Therefore, elevated concentrations in the aquatic environment of surface water, sediment, and fish were observed in these cases, while the relative concentrations in plants and soil were comparable to the predictions by our model (Table 6). 4.2. Spatial variation of TCE Illustrated in Fig. 2 is the spatial distribution of the simulated annual averaged concentrations of TCE in the

Table 5 Annual means of simulated fugacity and mass of TCE during 2002 in selected watersheds in the Connecticut River Basin Compartment

Air Plant Surface soil Root zone Vadose zone Surface water Sediment

Upper CT River Watershed

West River Watershed

Lower CT River Watershed

Fugacity Pa

Mass kg [TCE]

Fugacity Pa

Mass kg [TCE]

Fugacity Pa

Mass kg [TCE]

9.32E07 8.16E07 9.32E07 9.27E07 1.80E07 9.37E07 2.79E06

188.660 0.027 0.019 0.209 0.718 0.026 0.003

1.35E06 1.16E06 1.35E06 1.17E06 3.57E07 1.28E06 3.81E06

84.573 0.012 0.009 0.083 0.454 0.001 0.000

2.16E06 2.67E06 2.17E06 1.92E06 4.84E07 2.05E06 6.90E06

244.140 0.036 0.025 0.237 1.057 0.045 0.005

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Table 6 Comparisons between the annual means of the simulated relative concentrations of TCE in 2002 with reported data in the literature Study area

Relative concentration in this study Upper CT River Watershed West River Watershed Lower CT River Watershed Relative concentration by San Diego Basin (California)a La Jolla region (California)b Southern Ontario regionc Passaic River Basin (New Jersey)d

Relative concentration in Plant

Root-zone

7.4 7.3 10.5

5.6 5.0 5.1

2.6 2.0 2.3

9.5 7.5 9.8

6.0

3.0 o937.5 24.5 165.6

9.0 15.6 77.5 575.8

8.4

5.0 5.5

Water

Sediment

Fish

51.5 40.8 46.8 60.0 15.6–1562.5 236.4

a

Cohen and Ryan (1985). Pearson and McConnell (1975). c Mackay and Paterson (1991). d Coulibaly et al. (2004b). b

Fig. 2. Spatial distribution of the simulated annual mean concentrations of TCE (mg m3) in the (a) atmosphere, (b) root-zone soil, and (c) surface water in 2002.

compartments of the atmosphere, root-zone soil, and surface water. The spatial variations in the simulated TCE concentrations were similar in these compartments, with elevated concentrations in the southern part of the Connecticut River Basin. The Farmington River Watershed had the highest simulated TCE concentration in all compartments, followed by the lower Connecticut River Watershed. This spatial pattern in TCE distribution was in agreement with the local emission rates (Table 4). According to Pankow et al. (2003), the annual mean air concentration of TCE in New Jersey was about

0.18 mm m3, while that in Maine was only 0.06 mm m3. This implied that local emission was the primary factor in controlling the ambient contaminant level, while distant chemical input via advective airflow was also of importance in the areas without local releases. The intense emissions and poor air quality in the New York–New Jersey industrial area might have significant effects on the spatial pattern of TCE in the Connecticut River Basin. In the surface water compartment, higher simulated TCE concentrations were found in tributaries compared to main streams. The spatial pattern of TCE concentration in

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surface water was compared with observations in the six watersheds where measurements of TCE concentration in surface water were available in USGS NWIS. In order to minimize the uncertainty due to inaccurate estimation of emission rates, the simulated concentrations were normalized by the maximum measured concentrations. The spatial pattern of the model predictions matched the measured data fairly well (Fig. 3). Since only air emission of TCE was quantified in this study, the simulated TCE concentration in air was more reliable compared to the simulation results in other compartments. The simulated air concentrations were compared with simulation results from the EPA ASPEN dispersion model for the respective counties (Table 7). Generally, both simulated results were matched in spatial pattern, but the predictions from this model were slightly lower than those from ASPEN. It was noteworthy that the emission used in ASPEN did not reflect potentially significant emission reductions that have taken effect since

Normalized concentration (ug/l)

0.20

Predicted concentrations Measured concentrations 0.15

0.10

0.05

0.00 Lower CT River

Farmington River

Westfield Chicopee River River Watersheds

Miller River Middle CT River

Fig. 3. Concentration of the simulated and measured TCE concentrations in surface water in the watersheds in the Connecticut River Basin in 2002. The error bars indicate the 95.0% confidence intervals for the observation means. Table 7 Comparison between simulated air concentration of TCE (mg m3) in 2002 and the simulation results from ASPEN in 1996

63

1996 (USEPA, 2002). Therefore, the simulated air concentrations in this study were most comparable to the ASPEN results in the areas with high emission rates, such as the Lower Connecticut River Watershed and the Farmington River Watershed. 4.3. Temporal variation and key fate processes Using the lower Connecticut River Watershed as an example, Fig. 4 illustrates the temporal variations of the simulated TCE concentrations, in terms of fugacity, in the compartments of the atmosphere, root-zone soil, and surface water. Monthly means of TCE concentration were plotted to reduce the fluctuations caused by wet deposition events. As expected, the air concentration closely followed the curve of TCE input by airflow with peak concentrations occurring in spring and summer. The temporal variation of TCE input by atmospheric advection into the watershed was primarily determined by prevailing wind direction. The surface winds in Connecticut are most frequently from the northwest in the cold season and southwest in the warm season (Luo et al., 2002, 2003). Wind from the southwest introduced more TCE in air from the New York–New Jersey industrial area into the basin, and increased air concentration during spring and summer. As shown in Fig. 4, simulated concentrations in air and surface water followed the same temporal variations as the TCE input by airflow. This suggested that the air-water diffusion was a dominant inter-media process in determining TCE levels in water. The inter-media transport fluxes from air to water and from water to air were similar in both quantity and temporal trend as shown in Fig. 5(a), suggesting that an equilibrium was established between the two compartments. The slightly high fluxes from air to water, mainly from wet deposition, revealed that the atmosphere was a continuous source of TCE to surface water. Compared to surface water, the root-zone soil compartment was slower in responding to the temporal variations of air concentrations. This study assumed that there were no TCE inputs to soil compartments by local emissions or

Results from our model Results from ASPEN County

5th percentile 95th percentile

1080101 1080102 1080103 1080104 1080105 1080106 1080107 1080201 1080202 1080203 1080204 1080205 1080206 1080207

0.052 0.073 0.063 0.057 0.080 0.075 0.076 0.081 0.064 0.083 0.082 0.122 0.094 0.135

Essex, VT Caledonia, VT Orange, VT Sullivan, NH Orange, VT Windsor, VT Windham, VT Cheshire, NH Worcester, MA Windham, VT Worcester, MA Middlesex, CT Hampshire, MA Hartford, CT

0.081 0.082 0.082 0.083 0.082 0.082 0.083 0.083 0.114 0.083 0.114 0.098 0.114 0.137

0.082 0.087 0.089 0.091 0.089 0.101 0.102 0.160 0.620 0.102 0.620 0.190 0.669 0.317

3.7E-06

3000 TCE input

3.2E-06

2400

2.7E-06

1800

Root-zone soil Atmosphere

2.2E-06

1200

600

1.7E-06 Surface water

1.2E-06 Jan

Feb

Mar

Apr

May

Jun

Jul

Aug Sep

Oct

TCE input by air flow (mol/day)

Value

Fugacity (Pa)

HUC

0 Nov Dec

Month of 2002

Fig. 4. Temporal variations in the TCE input by air flow, and TCE fugacities in the atmosphere, root-zone soil, and surface water of the Lower Connecticut River Watershed during 2002.

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Inter-media flux (g[TCE]/m2)

25.0 Air to water Water to air

20.0 15.0 10.0 5.0

0.0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

(a)

Month of 2002

Inter-media flux (g[TCE]/m2)

3.2 3.2 3.1 3.1 3.0 3.0 2.9

Air to soil Soil to air

2.9 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec (b)

Month of 2002

Fig. 5. Simulated monthly flux of inter-media transport between (a) air and water, and (b) air and soil in the Lower Connecticut River Watershed during 2002.

distance transport in all watersheds. Therefore, any TCE accumulation in the soil during the simulation period must be attributed to inter-media transport processes. For the root-zone soil compartment, major inter-media processes included air–soil diffusion and atmospheric deposition. Similar seasonal variations of simulated TCE levels in the root-zone soil were found throughout the Connecticut River Basin, with high concentrations during the cold season (Fig. 4). In spring and summer, simulated fugacity in the soil was lower than that in the atmosphere, indicating a net soil absorption of TCE from the air. This finding was also confirmed by the monthly variations seen in the inter-media fluxes between air and soil, as shown in Fig. 5(b). During the warm season net mass transport from air to soil was observed. As the TCE concentration in the air dropped below the equilibrium concentration with the root-zone soil, TCE from soil was transferred back into the air compartment during fall and winter. 5. Conclusions The results of the TCE test case illustrated the dynamic predictive capability of our newly developed model for a complex multimedia environment. This application provided a detailed assessment of TCE concentrations in the seven environmental compartments and of the inter-media mass exchanges in the Connecticut River Basin. Besides the

inter-media mass transfer within each watershed, the advective flows between watersheds and cross-boundary transports from the vicinity of the basin were also included in the output of the model. The integration of GIS into multimedia environmental modeling was demonstrated in this study. GIS-based geospatial analyses provided fineresolution model parameters by making use of very comprehensive landscape and chemical release data available from various public agencies. Comparisons of the test results with reported data in the literature showed that the model can reasonably simulate the cycling of chemicals in the real multimedia environment. The fundamental determinant of the TCE concentration was the chemical inputs introduced by local sources or advective flows. The compartmental distribution and monthly variation in simulated concentrations were also attributed to inter-media transport processes, of which mass transfers of TCE from the air into soil and surface water were the major dispersion routes of TCE in the environment. The model evaluation presented in this paper was a first step towards validating a comprehensive description of the multimedia chemical fate and transport processes. The lack of simultaneous field measurements in various environmental media as a function of time was shown to be the most important data gap in the validation of multimedia modeling approaches. We suggest that further development and validation of a multimedia environmental fate model include a supporting experimental study to collect simultaneous field data of emissions, chemical concentrations, and inter-media fluxes. Acknowledgments This study was conducted with financial support from the University of Connecticut Research Foundation, the Storrs Agricultural Experiment Station, and the Connecticut River Airshed-Watershed Consortium. References CEPA, 1993. CalTOX, A Multimedia Total Exposure Model for Hazardous Waste Sites. Office of Scientific Affairs, Department of Toxic Substance Control, California Environmental Protection Agency, Sacramento, CA. ChemCAN, 2003. Level III fugacity model of regional fate of chemicals version 6.00, September 2003, developed by Mackay, D., Di Guardo, A., Paterson, S., Tam, D.D., Canadian Environmental Modelling Centre. Trent University, Peterborough, Ontario, Canada. Cohen, Y., Ryan, P.A., 1985. Multimedia modeling of environmental transport: trichloroethylene test case. Environmental Science and Technology 19, 412–417. Cohen, Y., Tsai, W., Chetty, S.L., Mayer, G.J., 1990. Dynamic partitioning of organic chemicals in regional environments: a multimedia screening-level modeling approach. Environmental Science and Technology 24 (10), 1549–1558. Coulibaly, L., 2000. Multimedia modeling of organic contaminants in the Passaic River Watershed in New Jersey. Thesis, Department of Environmental Engineering, New Jersey Institute of Technology. Coulibaly, L., Labib, M.E., Meegoda, J.N., 2004a. Multimedia model for analysis of contaminant releases in Passaic River Watershed. Practice

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