Exposure Assessment for Potential Risks from Antimicrobial Copper in Urbanized Areas

Mark Velleux 1 , Aaron Redman 2 , Paul Paquin 3 , and Robert Santore 4

Abstract: Copper is used as an antimicrobial agent inbuilding materials such as algae-resistant shingles and treated wood. In urbanized areas, copper-treated bu ilding materials may be present in roofs, wood decks, wood fences, and utility poles installed in both densely clustered and widely dispersed locations. Releases from these materials may pose risks to aquatic and terrestrial organisms. The TREX (T wo-dimensional R unoff E rosion and Ex port) watershed model was used to estimate antimicrobial copper releases and exposures in surface water, soil, and sediment in a simulated urbanized setting. Land surface elevations an d soil characteristics for the setting were based on an existent watershed to establish rainfall-runoff parameters. Other urban landscape characteristics were based on typical housi ng densities and upper-end copper use in building materials to provide a represen tative upper-bound scenario with respect to the spatial distribution and variety of copper sources at a watershed scale. C opper release rates from building materials were defined as functions of surface area and rainfall using measurements from manufacturers or other fi eld study results. A 2,066 hectare watershed was simulated at a 90-meter resolution using a grid that included 2,558 cells. Simulations were performed for a 10- year period with 772 storms defined using 15-mi nute rainfall data. Results were analyzed in terms of the frequency with which modeled e xposure concentrations exceeded benchmark concentrations for water, sediment, or soil. Surface water benchmarks were determined using the Biotic Ligand Model (BLM) for copper. Benchmarks for copper in soil and sediment were determined using experimentally-derived toxici ty endpoints and site-spe cific soil chemistry as well as equilibrium sediment guidelines. Simu lated copper exposures from algae-resistant shingles were low and did not exceed benchmarks in any media. Simulated copper exposures from treated wood were larger than those for sh ingles. Soil and sediment benchmarks were not exceeded and just two surface water benchmark exceedances (i.e. 2 days in 10 years) were simulated for treated wood. Based on this analysis, copper use as an antimicrobial agent in algae resistant shingles and treated w ood is not likely to pose significan t adverse environmental risks.

1 HDR|HydroQual, 1200 MacArthur Boulevard, Mahwah, NJ 07430 2 HDR| HydroQual, 2 N. Main Street, Suite 9, Providence, UT 84332 3 HDR|HydroQual, 1200 MacArthur Boulevard, Mahwah, NJ 07430 4 HDR|HydroQual, 6700 Kirkville Road, Suite 101, East Syracuse, NY 13057Introduction In the U.S., copper use as an antimicrobial pest icide is regulated under th e Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA). The U. S. Environmental Protection Agency (USEPA) registers new pesticide uses and performs re-regi stration eligibility determinations for existing uses. Re-registration eligibility determination effo rts for copper-based pes ticides have generally focused on pesticides used in agricultural setti ngs, where pesticides are uniformly applied over large areas at specific inte rvals during the growing season (USEPA 2005; USEPA 2006a). USEPA uses the Pesticide Root Zone Model (P RZM) in tandem with the Exposure Analysis Modeling System (EXAMS) to perform exposure assessments and evaluate potential risks for typical agricultural pesticide use. However, copper is also used as an antimicrobial in building materials su ch as algae-resistant (AR) shingles and treated wood as well as othe r products. In urbanized areas, copper-containing building materials may be used in roofs, decks, fe nces, and utility poles that are installed in both densely-clustered and widely-dispe rsed locations. Antimicrobial copper in building materials is slowly released over time from a large number of sources rather than being uniformly applied and rapidly released at discrete intervals. C onventional agricultural r unoff and exposure models such as PRZM/EXAMS assume that pesticides are uniformly applied over broad areas at regular intervals. Such models are not well-suited to perform realisti c exposure assessments for urban areas where antimicrobial copper is used in buildi ng materials because of th eir spatially-variable, time-dependent patterns of release. To provide a more realistic assessment, estima ted exposure concentrations (EECs) of copper released from building material s were determined using the T wo-dimensional R unoff, E rosion, and Ex port (TREX) 5 spatially-distributed wa tershed model (Velleux et al. 2008; Velleux et al. 2006; Velleux, 2005) to drive calculations for a si mulated urban landscape. Copper uses as an antimicrobial agent in AR shingles and treated wood were examined. The objective of this assessment was to determine whether releases fro m these specific copper uses could result in concentrations that pose significan t, adverse environmental risks in water, soil, or sediment.

5 Note that the model name was changed to Two-dimensional Exposure and Rainfall-Runoff Analysis (TERRA) in

June 2011. This change was made to avoid confusion with the Terrestrial Residue Exposure (T-REX) model that the U.S. Environmental Protection Agency (EPA) Office of Pesticide Programs (OPP) uses to estimate pesticide concentrations on avian and mammalian food items. Alth ough the name has been changed, the model is still referred to as “TREX” throughout this report as well as in the model computer code, inputs, and outputs.Methods The exposure assessment was conducted by: (1) determining copper release rates from AR shingles and treated wood; (2) determining wate rshed characteristics representing the occurrence of copper sources in an urbanized setting; (3) de termining environmental quality benchmarks for surface water, soil, and sediment for evaluati on of EECs; and (4) watershed model set-up and simulation of copper EECs for an urban landscape. Calculated EECs were then compared to benchmarks for each media to determine potential s for adverse risks in surface water, soil, and sediment. In the analyses that follow, soil and sediment concentrations reported as mg/kg are assumed to be on a dry weight basis. Determination of copper release rates: Copper releases from AR shingles were determined from measurements collected as part of three independen t studies. A short-term (3 months) study measured copper accumulation in runoff from AR shi ngles during rainfall events for a site near Palo Alto, California (PARWQP, 2000). Long-term (~2 to 15 years) studies measured copper remaining in weathered AR shingles over time for sites near Houst on, Texas (1990-2005) and Homestead, Florida, and Hage rstown, Maryland (1992-2006) (Everman and Joedicke, 2006). Copper losses over time for all study sites were calculated and were norma lized to the installed shingle surface area to determine copper release rates as a functi on of cumulative rainfall depth (e.g. mg copper released per m 2 of shingle per mm of rainfall, mg m -2 mm -1 ) (Figure 1). Potential relationships between co pper releases, temperature, and rain fall characteristics (e.g., intensity, power, kinetic energy, etc.) were explored but di d not provide reliable improvements to release rate estimates as a consequence of data lim itations. Although experiment al design differences precluded complete assessment, short-term study release rates were roughly a factor of three larger than long-term study rate s. It is worth noting that more than 60% of the initial copper remained in the shingles at the end of the l ong-term (e.g. up to 15 years) studies. The mean longterm copper release rate for AR shingles was 0.16 mg m -2 mm -1 (Table 1). Copper releases from intermittently-wetted treated wood were determined from measurements collected as part studies by Kennedy (2004), Lebow et al. (2000, 2003, 2004), Stefanovic and Cooper (2006), and Chung and Ruddick ( 2004). Most experiments used chromated copper arsenate (CCA) treated wood and a smaller number used alkaline copper quaternary (ACQ) or copper azole (CA) trea ted wood. Trade information from ma nufacturers indicated that theseformulations represented the majority of copper-bearing wood preservatives in the lumber market at the time of this study. Data from thes e studies were used to calculate release rates based on copper mass released as a function of exposed surface area and cumulative rainfall (e.g., mg copper per m 2 of exposed board per mm of rainfall, mg m -2 mm -1 ) (Figure 2). Rates varied by preservative and wood speci es (Stefanovic and Cooper, 2006; Chung and Ruddick, 2004). Release rates for CCA-treated w ood were significantly di fferent than non-CCAtreated wood (ACQ and CA) while rates for ACQ and CA were similar. Rate variations with environmental factors such as rainfall intensity and pH (Lebow et al. 2004; Lebow et al. 2003) were less significant than variations by preservative and species. Rates from laboratory studies that used commodity-size wood piec es were comparable to rates measured in field studies. Shortterm release rates, which occur during the first few months of service (approximately the first 160-250 mm of cumulative rainfall) (Chung and R uddick, 2004; Kennedy, 2004), can be a factor of three larger than l ong-term rates. Long-term release rates measured over 1-2 year periods showed a steady, slow decline over time and are e xpected to continue to decline slowly over the service life of the wood. Consequently, long-term release rates measured in field studies are considered to be an upper-bound for release rate s applicable throughout the remaining service life of treated wood (Lebow et al. 2004). When averaged across wood species, mean long-term copper release rates were 0.26 mg m -2 mm -1 for CCA-treated wood and 3.2 mg m -2 mm -1 for nonCCA wood (Table 1). To repr esent a realistic upper bound, th e exposure assessment assumed that 80% of wood used for out door construction was non-CCA w ood and the remaining 20% was CCA-treated. Determination of landscape characteristics fo r copper sources in an urbanized setting: The setting used to simulate antimicrobial coppe r EECs combined a hypothetical urbanized land use scenario with topographic and soil characteristics of an existent watershed. It was designed to provide a realistic upper-bound scenario as sociated with the spatial di stribution and variability of copper sources in an urbanized area. Use of a hypothetical setting to simu late EECs is analogous to USEPA’s risk assessment approach to eval uate agricultural pestic ide uses (USEPA 2005; USEPA 2006a). For this exposure assessment, the Goodwin Creek watershed in Panola County, Mississippi, was the basis for urbanized setting de velopment (Figure 3). This site was selected because: (i) its physical at tributes are well-established (Blackmarr, 1995); (ii) it is in a climatic zone where rainfall and runoff are relatively high (Hershfiel d, 1961; Blackmarr, 1995); and (iii)manufacturer market share data indicate it is in a region with extens ive antimicrobial copper building material use. Land surface elevations and soil characteristics in the setting reflect the physical attributes of the existe nt watershed. Land use characteristics for the setting were based on typical housing densitie s and upper-end copper use in building materials. The watershed area is 2,066 ha ( 5,104 acres) and includes the main stem of the creek and several tributaries. For the setting, land uses were assign ed into categories for residential, commercial, government, educational, and open space uses to mi mic development patterns that occur in urban areas where antimicrobial copper building product usage would be gr eatest (Table 2). Residential areas were the largest land use, 1,829 ha (4,520 acres ), and included development at densities of 1, 4, and 8 houses per acre as identified by USEPA (2006b), yielding a total of 19,583 housing units in the setting. Other setting characteristics, such as population, the si ze and distribution of open space areas, and impervious cover associat ed with roadways and parking areas, were determined from U.S. Census Bureau statisti cs (USCB, 2002), urban de sign guidelines (Beyard and O’Mara, 1999), recreation and park guidelines (NRPA, 1983), and local land use ordinances (Panola County, 2001). Shingled rooftop area was estimated as 232.3 m 2 (2,500 ft 2 ) for houses in the 1 and 4 house per acre categories and 176.5 m 2 (1,900 ft 2 ) in the 8 house per acre cate gory based on data reported by the National Association of Home Builder s (NAHB, 2007) and Census Bureau statistics (USCB, 2004). In each housing density category, 40% of all houses were assumed to have AR shingle roofs based on market share information from manufacturers. Lo cations of houses with AR shingle roofs were randomly distributed with in each residential deve lopment in the setting and included one 40.5 ha (100 acre) development ineach density category where all houses had AR shingle roofs in order to represent a reas onable upper bound condition for AR shingle use. The exposed area wood decks was assumed to be 17.8 m 2 (192 ft 2 ) for houses in the 1 and 4 house per acre categories and 11.1 m 2 (120 ft 2 ) in the 8 house per acre category. The Census Bureau reported that the average percentage of new homes with decks was 37% in 1992, 25% in 2006, and varied by geographic region. Given this range, and considering that homeowners could add a deck to a house after initial constructi on, 40% of houses were assumed to have a wood deck. Similarly, the exposed area of fences was 292.6 m 2 (3,150 ft 2 ), 146.3 m 2 (1,575 ft 2 ), and 104.5 m 2 (1,125 ft 2 ) for houses in the 1, 4, and 8 house per acre categories, respectively.Statistics regarding the percentage of houses with fences were not available. In the absence of statistics, the setting assumes that 33% of all houses have a fence.

Not all decks and fences are c onstructed of wood and not all ofthese structures use coppertreated wood. The U.S. fencing market is approximately 45% wood, 44% metal, 11% other materials (Winandy et al. 2004). Market share studies by fencing manuf acturers indicate that 42% of wood used for fences is copper-treated. Similar statistics for wood used in decks were not available. For simplicity, th e setting assumes that all deck s are constructed from wood and that 42% of wood used for decks is copper-treat ed. Combining these factors, 16.8% of all houses have a deck constructed of c opper-treated wood and 6.3% of all houses have a fence constructed of copper-treated wood. Locations of houses with treated wood decks and fences were randomly distributed within each resident ial development in the setting. The exposed area of utility poles was estimated to be 8.76 m 2 (94.2 ft 2 ) based on dimensions as reported by Jarden and Abraham (2 004). In urbanized areas with ove rhead utilities, the typical spacing between poles is 30.5-45.7 m (100-150 ft), co rresponding to an average spatial density of 5.36 poles per hectare (2.17 poles per acre) (Jarden and Abraha m, 2004). The Edison Electric Institute reported that utilit ies in 20-30% of new developments are placed underground (Johnson, 2006), reducing the likely number of poles in servi ce. For simplicity, the setting assumes that all poles are wood and that poles are limited to resi dential areas with underground utilities in other areas. For a total reside ntial area of 1,829 ha ( 4,520 acres) there would be 9,808 utility poles. The Utility Solid Waste Activities Group (USWAG, 2003) estimated that 9% of all wood utility poles in service were CCA-treated a nd that 31% of all new poles entering service would be CCAtreated. Given this expected shif t in copper-based preservative use, the setting assumes that 15% of all poles are CCA-treated. With 9,808 utility poles in the waters hed, there would be 1,471 CCA-treated poles. Locations of ut ility poles and the subset of CCA-treated poles were randomly distributed within each resident ial development in the setting. Spatial distributions of antimicrobial copper sources (AR shingle roofs, copper-treated wood deck and fences, and CCA-treated utility poles) in the setting are presented in Figure 4. Different combinations of copper sources occur through th e setting. Some housing units have every copper source (i.e. roof, deck, fence, and pole), most have one or more sources (e.g. roof and deck, roof and fence, deck only, etc.) and a very few have no sources. The ability to realistic representspatial variations of chemical sources is a bene fit of this exposure assessment approach. The distribution and density of coppe r sources in this watershed re present a realistic upper bound to the expected distribution of copper-treated building material s in an urbanized setting. Determination of environmental quality benchmarks: Environmental quality benchmarks for water, soil, and sediment were established us ing bioavailability-based methods. Water quality benchmarks were generated us ing the biotic ligand model (B LM) for copper (USEPA 2007; Di Toro et al. 2001; Santore et al. 2001). Water chemistry (e.g., pH, DOC, Ca, etc.) data from the U.S. Geological Survey National Water Quality Program database (USGS, 2008) were used as input to the BLM. The data used were from si x rivers in the region (e.g., Tennessee, Arkansas, Mississippi) and spanned a range of flow conditions and degrees of urbanization (e.g., rural to urban). The BLM was used to calculate instanta neous water quality crite ria (IWQC) that were then binned by month. Average BLM-based IWQC fo r each month were used as benchmarks to simulate water quality compliance. These water quality benchmarks ranged from 5 to 35 μg/L (Table 3). A sediment benchmark was derived using the acid- volatile sulfide (AVS) fr amework (Berry et al 1996; USEPA 2000). As a conservative estimate, all copper in sediments was assumed to be in excess of AVS. Sediments were also assumed to have a 5% organic carbon content. This resulted in a sediment benchmark of 320 mg/kg (Table 3). A soil benchmark was estimated using the method of Rooney et al. (2006), where adverse effects were correlated to soil properties using empirical relationships. The most sensitive endpoint was the 10% effect concentration on barley root el ongation. This relationshi p is based on soil cation exchange capacity and organic carbon content. Soil properties for Goodwin Creek were obtained from the Soil Survey Geographic (SSURGO) data base (USDA, 1995) for soils in Panola County, MS. Soil properties in the watershed were within the range of the propert ies Rooney et al. (2006) used to establish their relations hips. This resulted in a soil be nchmark of 36 mg/kg (Table 3). Watershed model set-up. The TREX watershed model (Velleux et al. 2008; Velleux et al. 2006; Velleux, 2005) was used to perform simulations b ecause of its abilities to simulate chemical transport and represent a diverse landscape with spatially-variable chemical inputs. Model set-up was performed for the Goodwin Creek site to en sure that the model appropriately computed runoff, soil erosion, and sediment transport. This watershed has a rich database of flow and in-stream solids measurements and is also in a region where AR shingle and treated wood use is expected to be high. Data for m odel set-up were obtained from th e U.S. Geological Survey, U.S. Department of Agriculture Natural Resource Co nservation Service and Agricultural Research Service, and other literature. The model was ca librated to match measured runoff (flow) and suspended solids measurements for three storm events (Figure 5) that have been studied by other researchers (Johnson et al. 2000; Julien and Ro jas, 2002; Rojas et al. 2008): October 17, 1981; August 28, 1982; and September, 20, 1983. The calibrated watershed model was used to perf orm copper transport simulations for the urban setting. For these simulations, the watershed’s ex istent land use characteristics were replaced by the land use and spatially-variab le copper source dist ributions of the urban setting. Simulated copper concentrations represent exogenous (added) copper origin ating from AR shingles and treated wood. Rainfall records for a 113-year period at a gage near the watershed were reviewed. The 10-year period with the largest annual averag e rainfall was used for simulations because copper releases in the model are proportional to rainfall. During this period, rainfall averaged 1,575 mm/year (62 inches/year), well above th e long-term average of 1,290 mm/year (50.82 inches/year). From this rainfall record, 772 storms were identified using 15-minute rainfall measurements. Simulations for each storm were performed in sequence, with final conditions for the prior storm providing initial concentrations for the next storm in the sequence. Copper EECs in water, sediment, and soil were calculated and compared to environmental quality benchmarks to tabulate exceedances and assess the potential for adverse risks.

Results For surface water, daily-averaged EECs for dissolv ed copper from AR shingles had a mean of 0.25 μ g/L, a standard deviation of 0.79 μ g/L, and ranged from 0 to 7.06 μ g/L. Similarly, dailyaveraged EECs for dissolved copper from treated wood had a mean of 0.58 μ g/L, a standard deviation of 1.7 μ g/L and ranged from 0 to 16.7 μ g/L. These results and BLM-based benchmarks used to assess compliance are presented in Figures 6 and Table 4. EECs for AR shingles did not exceed surface water benchmarks. EECs for treated wood exceeded benchmark values on two days during the 10-year simulation period. For sediment, EECs for copper from AR shingles averaged 6.7 mg/kg, had a standard deviation of 4.9 mg/kg, and ranged from 1.0 to 34.5 mg/kg. Si milarly, EECs for copper from treated woodin sediment averaged 14.1 mg/kg, had a standard deviation of 9.4 mg/kg, and ranged from 2.1 to 66.8 mg/kg. These EECs represent spatial averages of conditions in the 0-5 cm sediment layer at the end of the 10-year simulation period (Figure 7, panels a and b). EECs for AR shingles and treated wood did not exceed sediment benchmark values. For soil, EECs for copper from AR shingles av eraged 1.7 mg/kg, had a standard deviation of 1.5 mg/kg, and ranged from 0 to 8.8 mg/kg. Similarl y, EECs for copper from treated wood in soil averaged 3.8 mg/kg, had a standard deviati on of 4.9 mg/kg, and ranged from 0 to 31.8 mg/kg. These EECs also represent spatial averages of cond itions in the 0-5 cm soil layer at the end of the 10-year simulation period (Figur e 7, panels c and d). EECs for AR shingles and treated wood did not exceed soil benchmark values.

Discussion Based on these simulation results, copper use as an antimicrobial agent in al gae resistant shingles and treated wood is not likely to pose significant adverse environm ental risks. With respect to surface water benchmarks, USEPA guidelines (Ste phan et al. 1985) indicate that water quality exceedances should occur at a maximum frequency of one day in three years (e.g. 3-4 days in 10 years). For this setting, only two exceedances occurred in 10 years for EECs from treated wood and none occurred for AR shingles. With respect to sediment and soil, no exceedances occurred during the simulation. This urban setting is expected to generate la rger than typical copper concentrations because releases from copper-treated bu ilding materials and their spatia l densities were designed to represent reasonable upper bound values. In this context, the reason why EECs do not exceed benchmarks is that effective application rates for copper from AR shingl es and treated wood are relatively low. Over the 10-year simulation pe riod, 2,980 kg (6,570 lbs) of copper were released from AR shingles and 6,590 kg (14,530 lbs) from treated wood. For a resi dential area of 1,829 ha (4,520 acres), this corresponds to an average copper applicati on rate of 0.16 kg/ha/year (0.15 lbs/acre/year) for AR shingles and 0.36 kg/ha/y ear (0.32 lbs/acre/year) for treated wood. These application rates are much lower than for agricu ltural uses of copper-based pesticides, which typically range from 1.1 kg/ha/year (1 lbs/acre /year) for field crops to 17.9 kg/ha/year (16 lbs/acre/year) for tree fruits and tree nuts (U SEPA, 2006b). Driven by differences in applicationrates, average surface water EECs (0.25 μg/L fo r AR shingles and 0.58 μg/L for treated wood) are at the low end of EECs for agricultural uses reported by USEPA (2 006b) (0.4-80 μg/L). Because the urban setting is a hypothetical cons truct, simulated EECs for each medium were compared to measured values for a variety of cases to determine whether model results were reasonable. Boulanger and Nikolai dis (2003) measured copper con centrations in roof runoff, runoff from impervious areas, and in an urban stream during six storm events and found that dissolved copper concentrations averaged 14 ± 7 μg/L. Altho ugh direct comparison to these measurements are uncertain because of differences in impervious area, the source and magnitude of copper inputs, and other factor s, it is nonetheless worth noti ng that the range of measured copper concentrations are similar to the range of surface wate r EECs calculated for the urban setting using the distributed watershed model (0 to 7.1 μg/L for AR shingles and 0 to 16.7 μg/L for treated wood). Stilwell and Gorny (1997), FPL (2001), Lebow et al. (2002), and Morrell and Huffman (2004) reported that copper accumulation in soils and sediments may occu r very near a source (i.e. 10- 30 cm) but that it has limited mobility otherwise. When expressed as exogenous copper (i.e. total copper minus background), Stilwell and Gorny ( 1997) reported that concentrations were 14-137 mg/kg in the 0-5 cm soil layer immediately belo w decking and decreased from an average of 199 mg/kg to 50 mg/kg within 15 cm of the edge of a deck. Morrell and Huffman (2004) reported that copper concentrations in the vicinity of CCA-treated posts decreased by approximately a factor of 10 within 15 cm and approached back ground values within 30 cm of posts. Importantly, Lebow et al. (2002) found that le vels of environmental accumulati on detected in a wetland did not appear to have measurable biological impact s, supporting conclusions that copper mobility is limited under ambient conditions. Simulated EECs for copper in the soil and sediment of the urban setting are expected to be much lower thanthese measured values because of differences between the spatial scale of measurements (0.1 to 1 m) and the model grid scale (90 m). In this context, the relatively low spa tially-averaged copper EECs in soils (1.7 mg/kg for AR shingles and 3.8 mg/kg for treated wood) and sediments (6.7 mg/kg for AR shingles and 14.1 mg/kg for treated wood) of the urban setting are consistent with t hose field studies. Fernández-Calviño et al. (2008) measured copper levels in 26 soil, 20 sediment, and 71 surface water samples collected from a 173 ha (427 acre) watershed with vineyard s that were regularlytreated with copper-based fungici des. Copper application rates for vineyards were estimated to be 4 kg/ha (3.6 lbs/acre) with 4-6 applications per year, corresponding to effective application rates of 16-24 kg/ha/year (14.3-21.4 lbs/acre/year). All vineyards examined were either in active use or had been used as vineya rds for at least 30 years. Total copper concentrations averaged 31 mg/kg in woodland soils, 30 mg/kg in pasture soils, 139 mg/kg in abandoned vineyards, and 246 mg/kg (ranging from 157 to 434 mg/kg) in active vineyard soils. Total co pper concentrations in sediments averaged 73 mg/kg and ranged from 18 to 209 mg/kg, depending on the proportion of upstream land used as vineyards in the sub-basin that drained to the sampling location. Dissolved copper concentrations in surface wa ter at the watershed outlet averaged 40 μg/L and ranged from 10 to 117 μg/L. Note that measured concentratio ns include copper derived from natural sources as well as exogenous copper inputs from fungicides. Although differences in land use, effective annual copper application rates, watershed area, and other conditions exist, the results of Fernández- Calviño et al. (2008) are illustrative because they demonstrate the temporally and spatially-variable pattern of copper concentr ations that occur in surface water, sediment, and soil. Predominantly as a result of diffe rences in effective application rates, copper EECs simulated for the urban setting are lower than those Fernández-Calviño et al. (2008) measured. However, measured copper concen trations in the vineyard study are consistent with EECs for agricultural coppe r uses simulated by USEPA (2006b). This qualitative similarity suggests that copper EECs for the urban setting are reasonable compared to EECs for agricultural uses as well as independent field measurements. Simulations were performed for a 10-year period. It is possible that EECs could be larger if the simulation period were longer and copper continued to accumulate in soil and sediment. Even if total copper concentrations in sediment and soil were larger, it is unlikel y that benchmarks would be exceeded because exchangeable (bioavailable) copper is expected to decrease significantly over time. Copper aging studies by Ma et al. (2006a, 2006b) dem onstrated that exchangeable copper initially in soil s becomes irreversibly bound and d ecreases by roughly 20% in 30 days, 30% in 6-12 months, and by more than 50% after 5-8 years. Aria s et al. (2004) examined copper in soils from vineyards that had been in use fo r more than 100 years and which were regularly treated with copper fungicides. Total copper concentrations in those soils ranged from 40-301 mg/kg and exceeded 100 mg/kg in 12 of 20 samp les. Despite very high existing total copper concentrations, all samples showed a strong capacity to sorb additional copper and less than 8%of copper added to the samples was subsequently desorbable (Arias et al. 2004). These studies suggest 50% or more of the total copper in soil or sediment would not be in a bioavailable form.

The distributed watershed modeli ng approach used for this asse ssment of antimicrobial copper offers several benefits compared to standard approaches used to calculate EECs. In particular, TREX can represent spa tially-variable patterns of use of different product types (e.g. shingles, decks, fences, poles) and their temporally-variabl e patterns of release. Structures constructed from these products can each have a different surface area. Each can be made from a different product and each product can have its own characteri stic release rate wher ever that a structure with that product occurs on the landscape and whenever releases occur over time. TREX can also provide exposure estimates for the different ch emical formulations used for a product (e.g. CCA, ACQ, and CA treated wood). This spatially-variable modeling approach is useful because it aids development of exposure assessment scenarios that more realistically re flect distributions of chemical sources across the landscape.

Conclusions The TREX spatially-distr ibuted watershed model was used to simulate EECs for copper in an urban setting. The ability of TR EX to represent the distribution of copper sources across the landscape as well as rainfall-driven releases from these sources provides a realistic basis to perform exposure assessments. Th e urban setting used for this study is expected to generate larger than typical EECs because releases from AR shingles and treated wood decks, fences, and utility poles and their sp atial densities were designed to re present reasonable upper bound values. Despite use of upper bound values for copper in puts, EECs were nonetheless relatively low because effective copper application rates are much lower than for typical agricultural uses of copper-based pesticides. Simulation results suggest that copper use as an antimicrobial agent in AR shingles and treated wood is not likely to pose significant adverse environmental risks because copper EECs did not generally exceed benchmark concentrations in surface water, sediment, or soil. Acknowledgements This research was funded by the American Ch emistry Council Copper Task Force Biocides Panel. The authors gratefully acknowledge Dr. Hasmukh Shah of the American Chemistry Council and all members of the Biocides Panel for their support as well as copper release rateand market share information they provided fo r this study. Watershed model files and related information for Goodwin Creek were provided courte sy of Dr. Pierre Julien, Department of Civil and Environmental Engineering, Colorado State Un iversity. Technical as sistance was provided by Ruta Rugabandana, Brian George, John Max, Lihong Yang, Alan Ward, and Daniel Schwartz, and Kelly Navagh.

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a) Eastern portion of United Stat es c) Goodwin Creek watershed

Panola County Watershed location b) Mississippi and Panola County d) Monitored sub-basins (denoted by shaded areas within watershed)
Figure 3. Locations of Panola County, Mississippi, Goodwin Creek watershed, stream channel network, and monitoring stations. 4 8Figure 4. Overall spatial distri bution of houses showing combin ations of different copper sources: AR shingles, wood decks, and wood fences.October 17, 1981August 28, 1982September 20, 1983

Note: (1) elapsed time represents time since the onset of ra infall; (2) symbols for sand, silt and clay represent model results for those particle size classes. Figure 5. Simulated and measured flow (runoff) a nd suspended solids (sediment) concentrations for October 17, 1981, August 28, 1982, and September 20, 1983 storm events.Figure 6. Time series of daily-average estimate d environmental concentrations of dissolved copper in surface water (μg/L) from algae-resi stant shingles (upper pa nel) and treated wood (lower panel) at the watershe d outlet over 10 years of simulati on with water quality criteria: urban setting.a) AR Shingles: copper in sediment b) Treated Wood: copper in sediment

c) AR Shingles: copper in soil d) Treated Wood: copper in soil

Figure 7. Spatial distributions of estimated environmental concentrations of total copper (tCu) from algae-resistant shingles and treated wood (mg/kg) in surface sediment and soil (0-5 cm) after 10 years of simulation.TablesTable 1. Summary of co pper release rates.

Application Material Period Release Rate Units Notes

Roofs AR shingles short-term n/a n/a Average value of three longterm studies. long-term 0.16 mg m -2 mm rain -1

Treated Wood: Terrestrial Uses CCA-treated wood short-term 0.36 mg m -2 mm rain -1

Above ground, intermittentlywetted, short-term rates apply to first 200 mm of rainfall. long-term 0.26 mg m -2 mm rain -1

non-CCAtreated wood short-term 5.7 mg m -2 mm rain -1

long-term 3.2 mg m -2 mm rain -1

Aggregate short-term 4.6 mg m -2 mm rain -1 Same conditions noted above, assumes mix of 20% CCA and 80% non-CCA wood. long-term 2.6 mg m -2 mm rain -1Table 2. Summary of land use characteristics and total area of algae-re sistant shingles, and treated wood decks, fences, and utilit y poles in the simulated urban setting.

Land Use Total Area (ha) Total Area of AlgaeResistant Shingle Roofs (m 2 ) Total Area of Treated Wood Decks and Fences (m 2 ) Total Area of Treated Wood Utility Poles (m 2 ) Open Space 182.9 (452 acres) Commercial 8.1 (20 acres) Government 12.1 (30 acres) Education: 9.3 (23 acres) Residential: 1 house/acre 4 houses/acre 8 houses/acre 1,821 (4,520 acres) 609.9 (1,507 acres) 609.4 (1,507 acres) 609.4 (1,506 acres) 1,551,062 140,051 560,205 850,806 201,163 32,022 68,086 101,055 6,440 2,145 2,145 2,150Table 3. Summary of copper benchmark concentra tions for surface water, sediment, and soil.

Compartment Concentration Units Method of Comparison

Surface Water

Month Value

January 11.83

μg/L Exceedance frequency of daily average dissolved concentrations at watershed outlet (urban setting). Benchmark values are monthly averag es of instantaneous water quality criteria calculated from water quality data measurements for six sites as determined using the Biotic Ligand Model (BLM). February 11.75

March 14.73

April 15.76

May 18.00

June 14.86

July 25.76

August 33.68

September 28.10

October 14.90

November 17.31

December 17.13

Sediment 320 mg/kg Spatial average over top 5 cm of sediment surface area

Soil 36 mg/kg Spatial average over top 5 cm of soil surface areaTable 4. Summary of estimated exposure concentrations and benchmark exceedances for copper in surface water, sediment, and soil in the urbanized setting.

Media Concentration Units Exceedances Mean Standard Deviation Minimum Maximum Benchmark Range

AR Shingles

Water 0.25 0.79 0 7.1 11.7 – 33.7 μg/L 0

Sediment 6.7 4.9 1.0 34.5 320 mg/kg 0

Soil 1.7 1.5 0 8.8 36 mg/kg 0

Treated Wood

Water 0.58 1.7 0 16.7 11.7 – 33.7 μg/L 2

Sediment 14.1 9.4 2.1 66.8 320 mg/kg 0

Soil 3.8 4.9 0 31.8 36 mg/kg 0

Notes: (1) values for surface water are daily averages ov er the 10-year simulation period; (2) values for sediment and soil are spatial averages at the end of the 10-year si mulation period; (3) exceedances indicated the number of times the benchmark concentration wa s exceeded; (4) exceedances for wate r determined at watershed outlet.