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Keywords:

  • Copper;
  • Risk;
  • Roof runoff;
  • Modeling;
  • Stormwater

Abstract

  1. Top of page
  2. Abstract
  3. EDITOR'S NOTE:
  4. INTRODUCTION
  5. METHODS AND MATERIALS
  6. RESULTS AND DISCUSSION
  7. CONCLUSIONS
  8. Acknowledgements
  9. References

Copper (Cu) concentrations in waterways of the United States are of widespread concern. Presently, 692 waterway segments around the United States are listed by the U.S. Environmental Protection Agency (USEPA) as having unacceptably high copper concentrations. As part of their water quality management strategy, the USEPA is mandated to understand and manage sources and impacts of nonpoint releases of chemicals of concern. One potential nonpoint source of Cu is the runoff of precipitation falling onto Cu used in external architecture (e.g., roofing). However, few studies of Cu roof runoff have been published. This article is intended to provide estimations of Cu runoff rates and concentrations across the United States. Copper runoff rates and concentrations are predicted at 179 locations with a recently developed model. The average and range (in parentheses) of annual Cu loading rates, based on roof area; Cu export rates, based on amount of precipitation; and Cu concentrations for the United States are 2.12 (1.05–4.85) g Cu/m2/y; 2.72 (0.69–16.48) mg Cu/m2/mm; and 2.72 (0.69–16.48) mg Cu/L as total Cu, respectively. Statistics are presented that describe site-specific data distributions for use in probabilistic exposure and risk assessments. The effects of air quality as well as the potential fate and risks of Cu from roof runoff are discussed.


EDITOR'S NOTE:

  1. Top of page
  2. Abstract
  3. EDITOR'S NOTE:
  4. INTRODUCTION
  5. METHODS AND MATERIALS
  6. RESULTS AND DISCUSSION
  7. CONCLUSIONS
  8. Acknowledgements
  9. References

Additional supporting data are found in an appendix available on the online edition of IEAM volume (1), Number (4).

INTRODUCTION

  1. Top of page
  2. Abstract
  3. EDITOR'S NOTE:
  4. INTRODUCTION
  5. METHODS AND MATERIALS
  6. RESULTS AND DISCUSSION
  7. CONCLUSIONS
  8. Acknowledgements
  9. References

Copper (Cu) is one of the most important metals to man, ranking 3rd in world consumption behind aluminum and steel (Jolly 2000). Man has used Cu and its alloys for approximately 9,000 y (Landner and Lindeström 1999). Copper sheeting has been used for centuries in architectural applications (Sundberg 1998) in both commercial and residential buildings because it is fire-retardant (historically important), durable, and attractive, forming a highly valued patina as it ages. Some applications of copper are in external cladding (e.g., roofs, flashing, gutters, facades, and other decorative ornamentation). Supply of strip, sheet, and plate Cu products, a portion of which is used in exterior architectural applications, averaged 168.3 million kg/y in the United States from 2001 to 2004 (Copper Development Association 2005).

Exterior use exposes copper to weathering forces such as wind and all forms of meteorological precipitation. This leads to dissolution of copper from product surfaces and its introduction into local watersheds. The amount of Cu dissolved and transported from exposed copper surfaces is a function of atmospheric chemistry, precipitation rates, and roof orientation. The potential for the exposure of organisms in local watersheds is a function of the amount of Cu dissolved and transported from the exposed surfaces to the watershed. However, characteristics of a watershed affect exposure and, thus, the risk of copper in runoff. These characteristics include the amount of Cu used in architectural applications in the watershed, as well as the dilution and assimilation capacity of a wide variety of natural and human-made substrates that transform, sequester, and dilute Cu before or soon after runoff enters the watershed.

In most cases, limited input of copper to the environment can be benign or even beneficial. Unlike synthetic environmental contaminants, Cu is an essential micronutrient to humans, aquatic organisms, and other terrestrial organisms (Richter 1978; Fernandes and Henriques 1991; Gabel et al. 1994; Culotta et al. 1995; Harris and Gitlin 1996; Linder and Hazegh-Azam 1996; Olivares and Uauy 1996; Slekar et al. 1996; Dallinger et al. 1997; Uauy et al. 1998). Organisms have evolved in the presence of Cu and developed both a need for and a means to regulate Cu. It is common practice to increase Cu levels in soils through the application of fertilizers to lawns, pastures, and crops and to supplement Cu in foods of both humans and livestock for beneficial reasons. However, Cu is recognized as being toxic to sensitive organisms in water at concentrations in the low μg/L range.

Understanding the risk potential of Cu in roof runoff is complicated, site specific, and beyond the scope of this study. However, quantifying copper in runoff as it leaves rooftops is an essential 1st step in determining potential exposure and risk to organisms in watersheds. Copper runoff data are used along with estimations of the quantity of Cu roofing in a watershed to estimate the percentage of contribution from Cu roofing to Cu loads measured in stormwater discharged to local waterways. This is critical for identifying the actual cause of unacceptable Cu concentrations and designing the best management practices to reduce Cu loadings to local waterways because other sources of Cu (e.g., brake pads, fertilizers, pesticides, industrial site runoff) can also contribute substantial loads of Cu to stormwater runoff.

Spatial and temporal variations in atmospheric conditions affect the rate of dissolution of Cu in roof runoff. Elemental Cu (Cu0), the form found in Cu products, is highly insoluble in water. However, Cu naturally forms moderately soluble corrosion products when exposed to the weather. The corrosion products vary in composition and are directly affected by the chemistry of the atmosphere and precipitation. Chemistry of the atmosphere and precipitation varies geographically and, thus, corrosion products of Cu applications can vary geographically. However, in general, the copper oxide, cuprite (Cu2O), and the copper hydroxysulphates, brochantite [Cu4SO4(OH)6] and posnjakite [Cu4SO4(OH)6-H2O], are common corrosion products of Cu claddings (Graedel 1987; Krätschemer et al. 1997). Near marine environments, chloride may be present in the atmosphere in sufficient amounts to cause a portion of the corrosion product to be the Cu chloride atacamite [Cu2Cl(OH)3] (Graedel 1987). Cuprite layers begin forming within seconds to hours of exposure. Posnjakite and atacamite take weeks to months to begin forming, and brochantite may take years. Cuprite is generally found closest to the Cu metal, with the other corrosion layers closer to the surface (Graedel 1987; Krätschemer et al. 1997). Although a variety of atmospheric chemistries may affect copper dissolution on rooftops, Odnevall Wallinder et al. (2004) have concluded that rain acidity predominately determines the dissolution process, that chloride and sulfate only negligibly increase dissolution, and that nitrate has a small inhibitory effect.

The conversion of insoluble elemental Cu into moderately soluble Cu compounds and subsequent dissolution is slow, accounting for Cu's durability. Although dissolution of Cu is relatively minor from a material-life perspective, this process does create the potential for Cu exposure to organisms. Thus, it is important to understand the amount of soluble Cu in runoff of architectural applications because large quantities of Cu materials are produced and used in architectural applications. Worldwide only a few studies of Cu roof runoff have been published, and only 3 studies have documented runoff rates at 9 locations in the United States. Moreover, the U.S. Environmental Protection Agency (USEPA) total maximum daily load and nonpoint source control programs (USEPA 1991, 1993) make it necessary to determine sources and assess the potential impacts of both point and nonpoint sources of chemicals of concern within potentially impaired watersheds. At the time of the preparation of this article, the USEPA had listed 692 water body segments in the United States as impaired due to Cu. Since 1996, 142 total maximum daily loads assessments limiting Cu discharge to attain acceptable Cu concentrations in listed water bodies had been conducted and approved (USEPA 2005).

Whereas data for point sources are often available, data for nonpoint sources are often lacking. Monitoring of individual buildings is expensive and unlikely to occur on a wide scale. Thus, there is a need for estimates of Cu runoff rates from Cu-covered structures in watersheds across the United States. However, until recently a precise model to predict Cu runoff rates did not exist. Odnevall Wallinder et al. (2004) have recently developed such a model. This model provides a consistent method to make estimates of Cu roof runoff and can be applied locally or across large-scale geographic areas. The objective of this study was to use the model with rainfall monitoring data to predict runoff rates (g Cu/m2 of roof/y), export rates (mg Cu/mm of precipitation/m2 of roof), and concentrations (mg Cu/L) of Cu in roof runoff at locations throughout the United States. Additional estimations are made to quantify the effect of acid rain on copper-loading rates in an effort to evaluate the benefits of air quality management efforts on reducing Cu dissolution.

METHODS AND MATERIALS

  1. Top of page
  2. Abstract
  3. EDITOR'S NOTE:
  4. INTRODUCTION
  5. METHODS AND MATERIALS
  6. RESULTS AND DISCUSSION
  7. CONCLUSIONS
  8. Acknowledgements
  9. References

The model

Experimental data from parallel field and laboratory investigations of pure and naturally patinated (0–145 y old) Cu were used by Odnevall Wallinder et al. (2004) to develop the model. In addition, Odnevall Wallinder et al. (2004) compiled data from 10 publications containing 27 data sets, representing most of the existing laboratory and field studies measuring runoff rates from external Cu structures subjected to various environmental factors. Five of the data sets were not used by Odnevall Wallinder et al. (2004) because the pH of the precipitation was not measured. Based on the data set, a single model was derived to estimate total Cu (i.e., dissolved and particulate Cu) runoff rates. It was determined by Odnevall Wallinder et al. (2004, Equation 8) that annual precipitation rate, precipitation pH, and angle of roof inclination were critical variables in the model

  • equation image((1))

where P(V, θ, pH) is the predicted Cu runoff rate (g Cu/m2/y); V is the annual rate of precipitation (mm/y); pH is the pH of the precipitation; and θ is the angle of the surface (°). The expression cos(θ)/cos45° corrects the prediction of runoff rates for surfaces that deviate from 45°. The addition of the constant (1.04) corrects the model runoff rate to account for the 1st-flush effect, where metal runoff rates are higher during the initial portion of a runoff event, as noted in previous studies by He (2000), as well as Boulanger and Nikolaidis (2001, 2003a, 2003b). Odnevall Wallinder et al. (2004) reported that 70% of the field runoff rate data fall within ±30% of the model prediction. Simply stated, a greater amount of precipitation, lower precipitation pH, and smaller roof angle (i.e., a flatter roof) contribute to higher Cu runoff.

The ranges for each variable of data used to develop the model are as follows:

  • 1.
    Annual precipitation rate: 396 to 3,203 mm/y
  • 2.
    Precipitation pH: 3.9 to 5.8
  • 3.
    Angle of surface: 20° to 70°

Precipitation data

Precipitation rate, pH, and longitude and latitude data for 177 locations throughout the lower 48 states plus Alaska and Puerto Rico from January 1994 to December 2000 were extracted from the U.S. Geological Survey National Atmospheric Deposition Program (NADP) database (USGS 2002). Data for 2 additional locations, Hawaii and the U.S. Virgin Islands, were extracted but differed from the other locations. Data for Hawaii are from 1987 to 1993 (data collection in Hawaii ceased after 1993). Data for the U.S. Virgin Islands are from 1998 to 2000 (data collection began in 1998). Some NADP data fell outside of the range of data used to develop the model. Data for all 179 locations ranged as follows: precipitation pH 4.17 to 6.59 and annual precipitation rate 64.6 to 4,303.5 mm/y. Cu runoff rates and concentrations were estimated for all 179 locations. Locations where data fell outside the range used to develop the model are noted and should be used with caution.

Estimation of Cu runoff rates, export rates, and concentrations

Three derivations of the model developed by Odnevall Wallinder et al. (2004) were used with the data from the NADP to predict Cu runoff rates and concentrations. A derivation of the Equation 8 presented by Odnevall Wallinder et al. (2004) was used to estimate average annual mass of copper in runoff per m2 of Cu surface area,

  • equation image((2))

where PRR(V,pH) is the predicted runoff rate (g Cu/m2/y); V is the annual rate of precipitation (mm/y); and pH is the pH of the precipitation. This version of the model assumes all surfaces are at a 45° angle, a common angle used for roofs and the average angle assumed for the Cu roof runoff rate estimates that follow. To correct the average Cu roof runoff rate at a given location or building within a location that has a slope between 20° and 70°, simply determine the average Cu runoff rate by using Equation 2 and multiply by cos(θ)/cos45°, where θ is the angle of the surface (°). The effect of roof angle deviations from 45° is illustrated in Figure 1.

Another manipulation of the model allows the estimation of annual mass of Cu in runoff per m2 of roof per amount of precipitation. This is useful for site-specific estimations of potential export rates when annual precipitation deviates from the average annual precipitation at a given location and is calculated as

  • equation image((3))

where PPRR(V,pH) is the predicted precipitation runoff rate (mg Cu/m2/mm); V is the annual rate of precipitation (mm/y); and pH is the pH of the precipitation. Multiplying the equation by 1,000 (mg/g) is performed simply to convert grams to milligrams of copper. A similar manipulation of Equation 2 allows for site-specific estimations of average annual Cu concentration of the runoff. Dividing the model by the rate of precipitation and multiplying by 1,000 (mm/m), the units become g/m3 or mg/L and is calculated as

  • equation image((4))

where PRC(V,pH) is the predicted runoff concentration (mg Cu/L); V is the annual rate of precipitation (mm/y); and pH is the pH of the precipitation. Estimations of the mass of Cu per unit of precipitation and concentrations in the runoff are equal. Annual Cu roof runoff rates based upon roof area (i.e., g Cu/m2/y) and Cu roof runoff rates based upon amount of precipitation (i.e., mg Cu/m2/mm), here forward, will be referred to as Cu roof loading and export rates, respectively.

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Figure Figure 1.. Plot of correction factors (correction factor = cosine of roof angle in (°)/ cosine of 45°) used to correct Cu roof runoff rates for roof angles that differ from 45°.

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RESULTS AND DISCUSSION

  1. Top of page
  2. Abstract
  3. EDITOR'S NOTE:
  4. INTRODUCTION
  5. METHODS AND MATERIALS
  6. RESULTS AND DISCUSSION
  7. CONCLUSIONS
  8. Acknowledgements
  9. References

Effects of model modifications on predictability

The utility of using Equation 2 without the correction factor for roof angles that differ from 45° is demonstrated using data compiled by Odnevall Wallinder et al. (2004) (Figure 2). The correlation between measured and predicted Cu roof loading rates remains highly statistically significant (least squares: r2 = 0.78, F = 71.8, p < 0.001; Measured loading rate = 0.98 × Predicted loading rate + 0.09). Predicted Cu loading rates deviate from measured Cu runoff rates by <35% for 77% of the data.

Deviation results are similar to those corrected for roof angle and reported by Odnevall Wallinder et al. (2004). This similarity is not surprising because 17 of the 22 field data sets used by Odnevall Wallinder et al. (2004) were collected from roof surfaces with angles of 45°. Nevertheless, the need to correct for roof angle is supported by empirical data and should be used when available. Moreover, the ability of Equation 2 to predict measured runoff should be evaluated further, as data become available, and should be adjusted as appropriate.

Copper loading rates, export rates, and concentrations for the United States

Yearly data, summary statistics, and cumulative frequency plots for estimates of Cu loading rates, export rates, and concentrations at each of the 179 locations are provided in the Appendix. These data may prove helpful in site-specific exposure and risk assessments. Summary statistics for the United States are provided in Table 1. Mean annual copper loading rate, export rate, and concentration estimates from each location are illustrated in Figures 3 and 4.

Summary statistics were calculated 2 ways. The 1st method uses only the 7-y average loading rate, export rate, or concentration at 177 locations (N = 177). The 2nd method uses yearly average loading rates, export rates, or concentrations at 177 locations (N = 1,239) (i.e., 177 locations × 7 y). Data from Hawaii and the U.S. Virgin Islands were excluded because the time period of precipitation information does not correspond exactly with the other 177 locations.

The mean, standard deviation, and range of predicted average annual loading rates for the United States from 1994 to 2000 (N = 177) were 2.12, 0.74, and 1.12 to 3.80 g Cu/m2/y, respectively. The average, standard deviation, and range of annual predicted loading rates calculated based on the 1,239 location-y of data from 177 locations and 7 consecutive y, 1994–2000 (N = 1,239) were 2.12, 0.77, and 1.05 to 4.85 g Cu/m2/y, respectively.

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Figure Figure 2.. Plot of measured versus predicted Cu runoff loading rates using Equation 2 and data from Odnevall Wallinder et al. (2004). Linear regression of measured and predicted Cu runoff rates is highly statistically significant (least squares, F = 78.1, p < 0.001).

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Table Table 1.. Summary statistics for predicted annual copper roof runoff rates in the United States, based on precipitation data collected at 177 locations for 7 y, 1994–2000ab
ParameterYearly loading rate (g Cu/m2/y)Average location loading rate (g Cu/m2/y)Yearly export rate and concentration (mg Cu/m2/mm or mg Cu/L)Average location export rate and concentration (mg Cu/m2/mm or mg Cu/L)
  1. a Data for Hawaii and U.S. Virgin Islands are not included.

  2. b Cu = total copper (i.e., dissolved plus particulate copper); CL = confidence limit.

No. of values1,2391771,239177
Minimum1.051.120.690.84
25th percentile1.391.382.022.07
Median2.052.162.482.51
75th percentile2.702.693.023.03
Maximum4.853.8016.488.27
Mean2.122.122.722.72
Standard deviation0.770.741.261.13
Standard error0.020.060.040.09
Coefficient of variation0.360.350.460.42
Lower 95% CL2.072.012.652.55
Upper 95% CL2.162.232.792.88
Kurtosis−0.59−1.0819.217.15
Skewness0.530.183.162.20
Normality K-S distance0.120.130.150.14
p Value<0.0010.006<0.0010.002
Pass normality test (0.05)NoNoNoNo
Geometric mean1.981.992.512.54

The mean, standard deviation, and range of runoff concentration and predicted export rates for the United States based on precipitation at 177 locations from 1994 to 2000 (N = 177) were 2.72, 1.13, and 0.84 to 8.27 mg Cu/L or mg Cu/m2/mm, respectively. The average, standard deviation, and range of predicted concentration and export rate based on the 1,239 location-y of data from 177 locations and 7 consecutive y, 1994 to 2000 (N = 1,239) were 2.72, 1.26, and 0.69 to 16.48 mg Cu/L or mg Cu/m2/mm, respectively.

Estimated loading rates >2 g Cu/m2/y occurred predominately in the eastern half of the United States with only a few exceptions (Figure 3). Estimated loading rates >3 g Cu/m2/y occurred primarily in the eastern United States along the various mountain ranges from eastern Tennessee through New Hampshire. Most locations west of the Mississippi River had loading rate estimates of <2 g Cu/m2/y with only a few exceptions. Estimated Cu roof runoff concentrations and export rates were highest at extremely arid locations in the western United States (Figure 4).

Data for pH and annual precipitation rates at some locations were outside of the range of data used to develop the model and are identified in the Appendix. Model estimations for those locations should be used with caution because confidence decreases with the increasing number and magnitude of excursions. Data at 4 locations and a total of 13 yearly averages were higher than the pH range of the model. There were no excursions resulting from low pH data. Data at 3 locations and 7 yearly averages were higher than the precipitation rate range of model verification. Data at 45 locations and 195 yearly averages were lower than the precipitation rate range of model verification.

Potential impacts of improving air quality

Precipitation pH has a profound effect on the dissolution rates of copper and other metals and, thus, the importance of air quality management should not be overlooked as a means of improving water quality in a watershed. Acid precipitation (considered in this discussion as precipitation with pH < 5.6) has the potential to substantially increase metal dissolution and increase metal loading to watersheds. To demonstrate the potential impacts of air quality and to stress the potential benefits of air quality management on water quality, Cu runoff rates and concentrations are estimated based on ambient precipitation quantities at each location and by substituting a constant pH of 5.6 for the ambient pH. Resulting estimates of Cu in roof runoff across the United States are illustrated in Figures 5 and 6. This analysis demonstrates that substantial reductions in Cu mobilization could be expected with improvement of air quality and can have profound implications for management of other metals in stormwater runoff. The average percentage of reduction in Cu from roof runoff across the United States is estimated to be approximately 31% with a maximum of approximately 61% (Figure 7).

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Figure Figure 3.. Estimations of average yearly Cu roof runoff loading rates (g Cu/m2/y) for 7 y (1994–2000) at 177 locations throughout the United States. Estimations for 2 additional locations differ from the others. Estimations for Hawaii are based on a different 7 y (1987–1993) because of the cessation in 1993 of data collection. U.S. Virgin Island estimates are based on 3 y (1998–2000) because of the lack of any other data.

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Considerations when conducting source, exposure, and risk assessments

Determining the relative contribution of different Cu sources to a watershed is very watershed-specific and can be difficult. Contributions from point source discharges such as publicly owned treatment works are relatively easy to estimate because contaminants are routinely monitored and reported. However, determining contributions from nonpoint sources such as runoff from roadways, roofs, and fields can be difficult and highly uncertain. It is beyond the scope of this study to estimate the relative contribution of sheet copper from architectural applications for all watersheds. However, it is possible and perhaps useful to provide a comparison of a variety of Cu sources to a single water body as an example. Estimates of annual nonpoint Cu loadings to San Francisco Bay, California, USA, and their relative uncertainty were made by the Clean Estuary Partnership (2004a) and are summarized in Table 2. An additional 11,020 kg Cu/y is discharged by industrial and publicly owned treatment works (Clean Estuary Partnership 2004b). In the report by the Clean Estuary Partnership (2004a), it is assumed that all Cu leaving the rooftops and gutters enters San Francisco Bay. Using these loading estimates, architectural sheet Cu applications contribute about <5% of the loading to San Francisco Bay and are the 6th largest nonpoint source analyzed.

When conducting exposure and risk assessments using roof runoff data, it is important to understand the pathway that the runoff travels before reaching a waterway. Materials that the stormwater contacts before entering the watershed can alter the concentration or modify the bioavailability of the copper in the stormwater. The scenario producing the highest probability of unacceptable risk is that of Cu roof runoff entering directly into a water body without contacting other surfaces. In site-specific cases where unacceptable risk due to Cu roof runoff is demonstrated, there exist other management options that can be considered such as commercially available coatings or filtration systems.

Designing buildings that allow Cu roof runoff to discharge directly into a waterway should be avoided unless mixing is rapid and dilution is extensive (approximately 3 orders of magnitude). It has been shown that 60 to 90% of the total Cu concentration in roof runoff is in a bioavailable form as it leaves the roof (Karlén 2001). However, Cu roof runoff from commercial buildings more often falls onto hard surfaces or enters some type of conduit (e.g., sewer pipe) before reaching a waterway. Residential roofs often discharge to lawns. In some cases, roof runoff can even be directed to stormwater treatment facilities before being discharged to a waterway.

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Figure Figure 4.. Estimations of average Cu roof runoff export rates (mg Cu/m2/mm) and Cu roof runoff concentrations (mg Cu/L) for 7 y (1994–2000) at 177 locations throughout the United States. Estimations for 2 additional locations differ from the others. Estimations for Hawaii are based on a different 7 y (1987–1993) because of the cessation in 1993 of data collection. U.S. Virgin Island estimates are based on 3 y (1998–2000) because of the lack of any other data.

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Materials such as soils, concrete, and iron can rapidly and extensively transform and sequester bioavailable forms of Cu in runoff before reaching the watershed (Sunda and Guillard 1976; Sunda and Hansen 1979; Cabiniss and Shuman 1988; Cantrell et al. 1995; Sundberg 1998; Shoakes and Moeller 1999; Boulanger and Nikolaidis 2001, 2003a, 2003b; Bertling et al. 2002, 2003). One runoff study of a 9-y-old Cu roof and 16 storm events documents an average 45% reduction in the concentration of Cu after traveling through 46 m of an iron and concrete conduit system (Boulanger and Nikolaidis 2001). Data from Perkins et al. (2005) indicate that Cu is removed from water containing 2.5 mg dissolved Cu/L at a rate of approximately 3.1% per linear meter of new concrete conduit. That study also indicated that Cu did not readily dissolve back at detectable levels (minimum detection level of <7 μg Cu/L) into control water that was subsequently passed through the conduit. A study by Sundberg (1998) of Cu runoff onto concrete gravel placed in the drip line of a gutterless building indicates that approximately 50% of the Cu reacted with the concrete and was removed from the runoff. A study of soil retention of Cu demonstrates 85% and 72% removal of 3.5 mg Cu/L as dissolved CuCl2 and organically complexed Cu, respectively, in 20-cm-long columns of undisturbed soil and 100% removal in columns of disturbed soil (Camobreco et al. 1996).

Boulanger and Nikolaidis (2001, 2003a, 2003b) developed a model framework for the risk assessment of Cu roof runoff in a watershed by incorporating water quality characterization, watershed land use (including copper roofing), hydraulic data, toxicity measurements, chemical speciation modeling, and a probabilistic modeling technique. The speciation mode of the biotic ligand model (Di Toro et al. 2001; Paquin et al. 2002) was used to confirm Cu speciation measurements. However, the toxicity mode of the biotic ligand model could be used in place of toxicity testing to assess the bioavailability of Cu. In the study by Boulanger and Nikolaidis (2001, 2003a, 2003b), runoff from a 1,800-m2 Cu roof was monitored to assess impacts on the receiving stream in a 0.465-km2 developed urban watershed. The runoff flowed directly into a storm-water conduit system and eventually into a small stream. Because of removal within the conduits, dilution, and transformation reactions in transit, Cu concentrations were reduced on average from 3.63 mg Cu/L (total) and 1.73 mg Cu/L (ionic) to 0.046 mg Cu/L (total) and <0.00005 mg Cu/L (ionic) before reaching the stream. Thus, concentrations of Cu were reduced to approximately 1/80th and 1/34,600th of the original concentrations.

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Figure Figure 5.. Estimations of average yearly Cu roof runoff loading rates (g Cu/m2/y) at a constant pH of 5.6 and based on 7 y (1994–2000) of rainfall data at 177 locations throughout the United States. Estimations for 2 additional locations differ from the others. Estimations for Hawaii are based on a different 7 y (1987–1993) because of the cessation in 1993 of data collection. U.S. Virgin Island estimates are based on 3 y (1998–2000) because of the lack of any other data.

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This emphasizes how Cu concentrations and runoff rates can be modified in a simple conduit system and why transformation and sequestering processes must be considered in exposure and risk assessments. Further studies are needed to better quantify the degree that conduits modify Cu concentrations and the duration that they are effective because eventually Cu binding sites may become exhausted. However, the substantial reduction of Cu documented in the study by Boulanger and Nikolaidis (2001) suggests that such systems can have active binding sites for years. Proper quantification of removal rates under controlled conditions will likely aid in making realistic, defensible, and informed watershed management decisions.

Model improvements

Examination of the model and resulting predictions suggest the need for additional roof runoff studies, especially in arid regions. Variation coefficients of predicted annual loading rates at each site range from approximately 4 to 20% between predicted loading rates of 1.5 to 3.7 g Cu/m2/y. However, the range of the coefficient of variation decreases substantially at locations where predicted loading rates are approximately <1.5 g Cu/m2/y (Figure 8). This likely indicates the rate at which the addition of the 1st-flush constant (1.04 g Cu/m2/y, Eqn. 2) begins to dominate the loading rate estimate, and thus, relative variability from year to year begins to decrease.

The lower bound of the model for annual precipitation rate (400 mm/y) is too high to be applied with complete confidence to a substantial amount of arid areas of the United States. In addition, there may be a need to refine the 1st-flush rate constant (1.04 g Cu/m2/y, Eqn. 2). For example, as the annual rate of precipitation approaches 0 mm/y, the runoff rate estimate is explained by the 1st-flush constant. The model ultimately predicts runoff of 1.04 g Cu/m2/y when annual precipitation is 0 mm/y, an obvious impossibility. The highest estimates of runoff concentrations were at locations with extremely low precipitation rate. Thus, overestimation of runoff rates and concentrations can be expected for drier climates, but this is yet to be proven.

As mentioned previously, 17 of the 22 data points used to develop the model were from 45° slopes, thus more data are needed for roofs with angles other than 45°. Lastly, it is hypothesized that accounting for roof length (i.e., distance from roof peak to roof drip edge), which affects the contact time of precipitation with the copper, may further explain variability between measured and predicted roof runoff rates. Studies are underway to test this hypothesis.

CONCLUSIONS

  1. Top of page
  2. Abstract
  3. EDITOR'S NOTE:
  4. INTRODUCTION
  5. METHODS AND MATERIALS
  6. RESULTS AND DISCUSSION
  7. CONCLUSIONS
  8. Acknowledgements
  9. References

Estimates of Cu loading rates, export rates, and concentrations of roof runoff across much of the United States are now available. Loading rates >2 g Cu/m2/y occur predominately east of the Mississippi River with few exceptions. Loading rates >3 g Cu/m2/y occur primarily in the eastern United States along the various mountain ranges from eastern Tennessee through New Hampshire. Most locations west of the Mississippi River have loading rate estimates of <2 g Cu/m2/y with few exceptions. Mean Cu concentrations and export rates range from 0.84 to 8.27 mg Cu/L and mg Cu/m2/mm, respectively, and are highest in the extremely arid western regions of the United States.

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Figure Figure 6.. Estimations of average Cu roof runoff export rates (mg Cu/m2/mm) and Cu roof runoff concentrations (mg Cu/L) at a constant pH of 5.6 and based on 7 y (1994–2000) of rainfall data at 177 locations throughout the United States. Estimations for 2 additional locations differ from the others. Estimations for Hawaii are based on a different 7 y (1987–1993) because of the cessation in 1993 of data collection. U.S. Virgin Island estimates are based on 3 y (1998–2000) because of the lack of any other data.

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Improvement in air quality can lead to decreased Cu mobilization. If air management programs are effective in eliminating acid precipitation (i.e., pH < 5.6) in the United States, it is estimated that copper runoff will be reduced by an average of 31%.

Further refinement of the model is needed. The model should be corrected as data become available, and new estimates for the United States should be made. Correction of the 1st-flush constant to improve estimates for arid regions is a significant need. Incorporation of roof length into the model may improve the predictability of the model and provide the ability to better predict Cu runoff for individual buildings. Nevertheless, the estimates provided here should prove useful for watershed management activities and as a surrogate for Cu runoff studies.

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Figure Figure 7.. Cumulative frequency plot of estimated percent reductions in runoff rates for all locations and dates (N = 179), assuming a constant precipitation pH of 5.6. Percent reduction = (Predicted runoff at measured pH - Predicted runoff at pH 5.6)/(Predicted runoff at measured pH) · 100.

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Figure Figure 8.. Coefficients of variation of predicted annual Cu loading rates as a function of average predicted annual copper loading rates at 177 locations throughout the United States. Data for Hawaii and the U.S. Virgin Islands are omitted.

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Table Table 2.. Nonpoint sources, estimates of annual copper (Cu) loadings and uncertainty descriptors for San Francisco Bay, California, USA (data from CEP 2005)
Nonpoint Cu sourceLoad estimate kg Cu/yUncertainty
Marine antifouling coatings9,072Moderate-high
Vehicle brake pads>4,536High
Copper pesticides<4,536High
Air deposition3,992Low-moderate
Soil erosion3,175Moderate
Copper roofs and gutters2,032Moderate-high
Copper algaecides applied to surface water1,814High
Industrial use1,497Moderate
Domestic water discharge to storm drains1,361Moderate-high
Vehicle fluid leaks272Moderate-high

The data presented here are important in assessing Cu loadings to waterways, as well as exposure and risk of Cu roofing runoff to aquatic organisms in watersheds. These estimates represent total Cu concentrations and loading and export rates of runoff at the point it leaves the roof. Estimated concentrations of Cu in the runoff are high enough to suggest that direct discharge from a Cu roof into a water body should be avoided whenever possible. However, the data should be used with caution. Studies have shown that various surfaces that runoff can contact before entering aquatic environments readily sequester or change the chemical speciation of Cu and thus reduce its bioavailability. Therefore, these data should be used with caution to estimate exposure or risk to aquatic organisms in a watershed. Rather, they should 1st be corrected for the effects of landscapes, conduit materials, and best management practices (e.g., use of filtration systems to remove Cu).

Acknowledgements

  1. Top of page
  2. Abstract
  3. EDITOR'S NOTE:
  4. INTRODUCTION
  5. METHODS AND MATERIALS
  6. RESULTS AND DISCUSSION
  7. CONCLUSIONS
  8. Acknowledgements
  9. References

This work was funded by the Copper Development Association and the International Copper Association, New York, New York, USA.

References

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  2. Abstract
  3. EDITOR'S NOTE:
  4. INTRODUCTION
  5. METHODS AND MATERIALS
  6. RESULTS AND DISCUSSION
  7. CONCLUSIONS
  8. Acknowledgements
  9. References
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