Exposure Assessment for Potential Risks from Antimicrobial Copper in Urbanized Areas 

Mark Velleux1

, Aaron Redman2, Paul Paquin3, and Robert Santore4

Abstract: Copper is used as an antimicrobial agent in building materials such as algae-resistant 

shingles and treated wood. In urbanized areas, copper-treated building 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 (Two-dimensional Runoff Erosion and Export) 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 and soil characteristics 

for the setting were based on an existent watershed to establish rainfall-runoff parameters. Other 

urban landscape characteristics were based on typical housing densities and upper-end copper 

use in building materials to provide a representative upper-bound scenario with respect to the 

spatial distribution and variety of copper sources at a watershed scale. Copper release rates from 

building materials were defined as functions of surface area and rainfall using measurements 

from manufacturers or other field 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-minute rainfall data. Results were analyzed in 

terms of the frequency with which modeled exposure 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 toxicity endpoints and site-specific soil chemistry as 

well as equilibrium sediment guidelines. Simulated 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 shingles. 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 wood is not likely to pose significant 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 13057 
Introduction 

In the U.S., copper use as an antimicrobial pesticide is regulated under the Federal Insecticide, 

Fungicide, and Rodenticide Act (FIFRA). The U.S. Environmental Protection Agency (USEPA) 

registers new pesticide uses and performs re-registration eligibility determinations for existing 

uses. Re-registration eligibility determination efforts for copper-based pesticides have generally 

focused on pesticides used in agricultural settings, where pesticides are uniformly applied over 

large areas at specific intervals during the growing season (USEPA 2005; USEPA 2006a). 

USEPA uses the Pesticide Root Zone Model (PRZM) 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 such as algae-resistant 

(AR) shingles and treated wood as well as other products. In urbanized areas, copper-containing 

building materials may be used in roofs, decks, fences, and utility poles that are installed in both 

densely-clustered and widely-dispersed 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. Conventional agricultural runoff 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 realistic exposure assessments for urban 

areas where antimicrobial copper is used in building materials because of their spatially-variable, 

time-dependent patterns of release. 

To provide a more realistic assessment, estimated exposure concentrations (EECs) of copper 

released from building materials were determined using the Two-dimensional Runoff, Erosion, 

and Export (TREX)5 spatially-distributed watershed model (Velleux et al. 2008; Velleux et al. 

2006; Velleux, 2005) to drive calculations for a simulated 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 from these specific copper uses could result in 

concentrations that pose significant, 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. Although 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 watershed characteristics representing the occurrence 

of copper sources in an urbanized setting; (3) determining environmental quality benchmarks for 

surface water, soil, and sediment for evaluation 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 potentials 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 independent studies. A short-term (3 months) study 

measured copper accumulation in runoff from AR shingles 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 Houston, Texas (1990-2005) and 

Homestead, Florida, and Hagerstown, Maryland (1992-2006) (Everman and Joedicke, 2006). 

Copper losses over time for all study sites were calculated and were normalized to the installed 

shingle surface area to determine copper release rates as a function of cumulative rainfall depth 

(e.g. mg copper released per m2

 of shingle per mm of rainfall, mg m-2 mm

-1) (Figure 1). Potential 

relationships between copper releases, temperature, and rainfall characteristics (e.g., intensity, 

power, kinetic energy, etc.) were explored but did not provide reliable improvements to release 

rate estimates as a consequence of data limitations. Although experimental design differences 

precluded complete assessment, short-term study release rates were roughly a factor of three 

larger than long-term study rates. It is worth noting that more than 60% of the initial copper 

remained in the shingles at the end of the long-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) treated wood. Trade information from manufacturers indicated that these 
formulations represented the majority of copper-bearing wood preservatives in the lumber 

market at the time of this study. Data from these 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 m2

 of exposed board per mm of rainfall, mg m-2 mm

-1) (Figure 2). 

Rates varied by preservative and wood species (Stefanovic and Cooper, 2006; Chung and 

Ruddick, 2004). Release rates for CCA-treated wood were significantly different 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 pieces 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 Ruddick, 2004; Kennedy, 2004), can be a factor 

of three larger than long-term rates. Long-term release rates measured over 1-2 year periods 

showed a steady, slow decline over time and are expected 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 rates 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 represent a realistic upper bound, the exposure assessment assumed 

that 80% of wood used for outdoor construction was non-CCA wood and the remaining 20% was 

CCA-treated. 

Determination of landscape characteristics for copper sources in an urbanized setting: The 

setting used to simulate antimicrobial copper 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 associated with the spatial distribution and variability of 

copper sources in an urbanized area. Use of a hypothetical setting to simulate EECs is analogous 

to USEPA’s risk assessment approach to evaluate agricultural pesticide uses (USEPA 2005; 

USEPA 2006a). For this exposure assessment, the Goodwin Creek watershed in Panola County, 

Mississippi, was the basis for urbanized setting development (Figure 3). This site was selected 

because: (i) its physical attributes are well-established (Blackmarr, 1995); (ii) it is in a climatic 

zone where rainfall and runoff are relatively high (Hershfield, 1961; Blackmarr, 1995); and (iii) 
manufacturer market share data indicate it is in a region with extensive antimicrobial copper 

building material use. Land surface elevations and soil characteristics in the setting reflect the 

physical attributes of the existent watershed. Land use characteristics for the setting were based 

on typical housing densities 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 assigned into categories for residential, commercial, 

government, educational, and open space uses to mimic development patterns that occur in urban 

areas where antimicrobial copper building product usage would be greatest (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 size and distribution of 

open space areas, and impervious cover associated with roadways and parking areas, were 

determined from U.S. Census Bureau statistics (USCB, 2002), urban design 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 m2

 (2,500 ft2) for houses in the 1 and 4 house per 

acre categories and 176.5 m2

 (1,900 ft2) in the 8 house per acre category based on data reported 

by the National Association of Home Builders (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. Locations of houses with 

AR shingle roofs were randomly distributed within each residential development in the setting 

and included one 40.5 ha (100 acre) development in each density category where all houses had 

AR shingle roofs in order to represent a reasonable upper bound condition for AR shingle use. 

The exposed area wood decks was assumed to be 17.8 m2 (192 ft2) for houses in the 1 and 4 

house per acre categories and 11.1 m2 (120 ft2) 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 construction, 40% of houses were assumed to have a wood 

deck. Similarly, the exposed area of fences was 292.6 m2 (3,150 ft2), 146.3 m2 (1,575 ft2), and 

104.5 m2

 (1,125 ft2) 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 constructed of wood and not all of these 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 manufacturers indicate that 

42% of wood used for fences is copper-treated. Similar statistics for wood used in decks were 

not available. For simplicity, the setting assumes that all decks are constructed from wood and 

that 42% of wood used for decks is copper-treated. Combining these factors, 16.8% of all houses 

have a deck constructed of copper-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 residential development in the setting. 

The exposed area of utility poles was estimated to be 8.76 m2 (94.2 ft2) based on dimensions as 

reported by Jarden and Abraham (2004). In urbanized areas with overhead utilities, the typical 

spacing between poles is 30.5-45.7 m (100-150 ft), corresponding to an average spatial density 

of 5.36 poles per hectare (2.17 poles per acre) (Jarden and Abraham, 2004). The Edison Electric 

Institute reported that utilities in 20-30% of new developments are placed underground (Johnson, 

2006), reducing the likely number of poles in service. For simplicity, the setting assumes that all 

poles are wood and that poles are limited to residential areas with underground utilities in other 

areas. For a total residential 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 and that 31% of all new poles entering service would be CCAtreated. Given this expected shift in copper-based preservative use, the setting assumes that 15% 

of all poles are CCA-treated. With 9,808 utility poles in the watershed, there would be 1,471 

CCA-treated poles. Locations of utility poles and the subset of CCA-treated poles were randomly 

distributed within each residential 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 the 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 represent 
spatial variations of chemical sources is a benefit of this exposure assessment approach. The 

distribution and density of copper sources in this watershed represent a realistic upper bound to 

the expected distribution of copper-treated building materials in an urbanized setting. 

Determination of environmental quality benchmarks: Environmental quality benchmarks for 

water, soil, and sediment were established using bioavailability-based methods. Water quality 

benchmarks were generated using the biotic ligand model (BLM) 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 six 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 instantaneous water quality criteria (IWQC) that were 

then binned by month. Average BLM-based IWQC for 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) framework (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 elongation. This relationship is based on soil cation 

exchange capacity and organic carbon content. Soil properties for Goodwin Creek were obtained 

from the Soil Survey Geographic (SSURGO) database (USDA, 1995) for soils in Panola County, 

MS. Soil properties in the watershed were within the range of the properties Rooney et al. (2006) 

used to establish their relationships. This resulted in a soil benchmark 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 because 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 ensure 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 model set-up were obtained from the U.S. Geological Survey, U.S. 

Department of Agriculture Natural Resource Conservation Service and Agricultural Research 

Service, and other literature. The model was calibrated 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 Rojas, 2002; Rojas et al. 2008): October 17, 1981; 

August 28, 1982; and September, 20, 1983. 

The calibrated watershed model was used to perform copper transport simulations for the urban 

setting. For these simulations, the watershed’s existent land use characteristics were replaced by 

the land use and spatially-variable copper source distributions of the urban setting. Simulated 

copper concentrations represent exogenous (added) copper originating 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 average 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 the 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 dissolved copper from AR shingles had a mean of 

0.25 μg/L, a standard deviation of 0.79 μg/L, and ranged from 0 to 7.06 μg/L. Similarly, dailyaveraged EECs for dissolved copper from treated wood had a mean of 0.58 μg/L, a standard 

deviation of 1.7 μg/L and ranged from 0 to 16.7 μg/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. Similarly, EECs for copper from treated wood 
in 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 averaged 1.7 mg/kg, had a standard deviation of 1.5 

mg/kg, and ranged from 0 to 8.8 mg/kg. Similarly, EECs for copper from treated wood in soil 

averaged 3.8 mg/kg, had a standard deviation of 4.9 mg/kg, and ranged from 0 to 31.8 mg/kg. 

These EECs also represent spatial averages of conditions in the 0-5 cm soil layer at the end of the 

10-year simulation period (Figure 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 algae resistant shingles 

and treated wood is not likely to pose significant adverse environmental risks. With respect to 

surface water benchmarks, USEPA guidelines (Stephan 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 larger than typical copper concentrations because 

releases from copper-treated building materials and their spatial 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 shingles and treated wood are 

relatively low. Over the 10-year simulation period, 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 residential area of 1,829 ha 

(4,520 acres), this corresponds to an average copper application rate of 0.16 kg/ha/year (0.15 

lbs/acre/year) for AR shingles and 0.36 kg/ha/year (0.32 lbs/acre/year) for treated wood. These 

application rates are much lower than for agricultural 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 (USEPA, 2006b). Driven by differences in application 
rates, average surface water EECs (0.25 μg/L for AR shingles and 0.58 μg/L for treated wood) 

are at the low end of EECs for agricultural uses reported by USEPA (2006b) (0.4-80 μg/L). 

Because the urban setting is a hypothetical construct, simulated EECs for each medium were 

compared to measured values for a variety of cases to determine whether model results were 

reasonable. Boulanger and Nikolaidis (2003) measured copper concentrations 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. Although direct comparison to these 

measurements are uncertain because of differences in impervious area, the source and magnitude 

of copper inputs, and other factors, it is nonetheless worth noting that the range of measured 

copper concentrations are similar to the range of surface water 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 occur 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 below 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 background values within 30 cm of posts. Importantly, 

Lebow et al. (2002) found that levels of environmental accumulation detected in a wetland did 

not appear to have measurable biological impacts, 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 than these 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 spatially-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 those 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 vineyards that were regularly 
treated with copper-based fungicides. 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 vineyards 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 copper 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 water at the watershed outlet averaged 40 μg/L and ranged from 

10 to 117 μg/L. Note that measured concentrations 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 concentrations that occur in 

surface water, sediment, and soil. Predominantly as a result of differences in effective application 

rates, copper EECs simulated for the urban setting are lower than those Fernández-Calviño et al. 

(2008) measured. However, measured copper concentrations in the vineyard study are consistent 

with EECs for agricultural copper 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 unlikely 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) demonstrated that exchangeable 

copper initially in soils becomes irreversibly bound and decreases by roughly 20% in 30 days, 

30% in 6-12 months, and by more than 50% after 5-8 years. Arias et al. (2004) examined copper 

in soils from vineyards that had been in use for 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 samples. 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 modeling approach used for this assessment of antimicrobial copper 

offers several benefits compared to standard approaches used to calculate EECs. In particular, 

TREX can represent spatially-variable patterns of use of different product types (e.g. shingles, 

decks, fences, poles) and their temporally-variable 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 characteristic release rate wherever 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 chemical 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 reflect distributions of 

chemical sources across the landscape. 

Conclusions 

The TREX spatially-distributed watershed model was used to simulate EECs for copper in an 

urban setting. The ability of TREX 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. The 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 spatial densities were designed to represent reasonable upper bound values. 

Despite use of upper bound values for copper inputs, 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 Chemistry 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 rate 
and market share information they provided for this study. Watershed model files and related 

information for Goodwin Creek were provided courtesy of Dr. Pierre Julien, Department of Civil 

and Environmental Engineering, Colorado State University. Technical assistance was provided 

by Ruta Rugabandana, Brian George, John Max, Lihong Yang, Alan Ward, and Daniel 

Schwartz, and Kelly Navagh. 

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Figures 
Notes: upper and lower percentiles defined as mean ±1.96 standard deviations of release rates for long-term studies 

(Houston, Hagerstown, and Homestead). 

Figure 1. Copper release from AR shingles with rain over time. 
Figure 2. Copper release from treated wood with rain over time (plotted points indicate median 

values and whiskers indicate the corresponding range). 
Watershed area Stream channel Monitoring station 

a) Eastern portion of United States 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 

8 
Figure 4. Overall spatial distribution of houses showing combinations of different copper 

sources: AR shingles, wood decks, and wood fences. 
October 17, 1981 August 28, 1982 September 20, 1983 

Note: (1) elapsed time represents time since the onset of rainfall; (2) symbols for sand, silt and clay represent model 

results for those particle size classes.

Figure 5. Simulated and measured flow (runoff) and suspended solids (sediment) concentrations 

for October 17, 1981, August 28, 1982, and September 20, 1983 storm events. 
Figure 6. Time series of daily-average estimated environmental concentrations of dissolved 

copper in surface water (μg/L) from algae-resistant shingles (upper panel) and treated wood 

(lower panel) at the watershed outlet over 10 years of simulation 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. 
Tables 
Table 1. Summary of copper 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-1
Table 2. Summary of land use characteristics and total area of algae-resistant shingles, and 

treated wood decks, fences, and utility poles in the simulated urban setting. 

Land Use Total Area (ha) 

Total Area of AlgaeResistant Shingle Roofs 

(m2

) 

Total Area of 

Treated Wood 

Decks and Fences 

(m2

) 

Total Area of 

Treated Wood 

Utility Poles 

(m2) 

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 

 2,150 
Table 3. Summary of copper benchmark concentrations 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 averages 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 area 
Table 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 over the 10-year simulation period; (2) values for sediment 

and soil are spatial averages at the end of the 10-year simulation period; (3) exceedances indicated the number of 

times the benchmark concentration was exceeded; (4) exceedances for water determined at watershed outlet.