October 2011

Antimicrobial Copper Tr

Copper Transport and Fa

Algae-Resistant Shingles and T

Mark Velleux, Paul Paquin

Aaron Redman, and Robert Sa
Antimicrobial Copper Pe

z Copper-containing biocides used 

roofing shingles and wood

z Differs from agricultural pesticide

“[D]etermining human and ecologicaexposure to antimicrobial pesticides different types of measurements and those needed for pesticides largely aand other plants.” (http://www.epa.g

z Exposure scenario designed for t
Exposure Scenario De

z Three factors control scenario de

z Chemical usesz Physical settingz Landscape specific to setting and us

z Antimicrobial copper uses evalua

z Algae-resistant (AR) shinglesz Treated wood

z These products are building mate

z Roofsz Wood decks, fences, and utility pole
Uses Control Setting and L

z Uses evaluated are building mate

z Greatest density of use expected in 

(constructed environments)

z Market share information identifies s

region where product use is greates

z Copper releases depend on rainfa

z Southeast U.S. climate has above av

(Average: 50 inches/year, WY83-92: 6

z An urban landscape in a Southeas

provides expected upper bound f
Physical Setting: Goodwin 

z Well-know watershed in southeas

z Extensively studiedz High runoff/erosion vulnerability (Yaz Detailed database to assist model se

z Loess soils characteristic of many z In a climate with high rainfall (50-6z Database allows model represent

processes to reflect realistic cond

z Further described as part of mode
Landscape: Urbanized 

z Designed to reflect a range of dev

z 1, 4, and 8 house(s) per acrez Includes impervious area for roofs, s

driveways and roadways

z Overall set-up includes other develo

open space, commercial areas with p

z Based on Census Bureau and hou

statistics, antimicrobial market sh

z Reasonable maximum for copper z Further described as part of lands
Setting: Homogeneous vs. He

z Why not PRZM?z Agricultural Use:

z Farm fieldsz Uniform application

z Building Products:

z Urbanized areasz Spatially variable!z Biocide mass released not 

equal to mass installed

z Address variability with 

watershed approach
TERRA: Two-Dimens

Exposure and Rainfall-Runo

(Velleux et al. 2008) 

z z z z z z z 

http://www.engr.colostate.edu/~pierre/ce_old/Projects/TREX%20Web%
Watershed Mode

Set-up and Calibra
Physical Setting is a Real W

Location:

Goodwin CreBasin Size:

21.4 km

2 (5,1

Land Cover:

Grassland, pLand cover wrepresent an Database:

14 stream gaModel Resolu

R. Rojas, 2002 90 m x 90 m (

Panola County, MSYazoo County, MS
Model Set-up

z Set-up based on standard data sourcesz Used 90-m grid scale (2,558 cells)z Watershed topography and basic landcz Soil properties from USDA NRCSz Rainfall from USDA ARSz Calibration to three storm events:

z 17-October-1981, 28-August-1982, and 20-Sez Based on previous publications 

z Simulate solids as 5 particle types:

z clay, silt, sand, fine gravel, coarse gravel
Digital Elevation Model and St

TopographyDigital ElevationModel (DEM)

Stream NetworkDelineation

18 sh
Soils

Spatial Distributionof Soils

Determines grain siinfiltration propertie
Land Cover (Not Urba

Spatial Distributionof Land Cover

Determines interceproughness, land use 
Hydrology and Sediment T

Response to Storms

Compaflow anto measgages a

0.00.51.01.52.02.53.03.54.0

0 50 100 150 200 250 300 350

Time (min)

Intensity (in/h)

1 25

6

7 1012 1434 3541 4252 6164 65

Rain Gage 
Results: October 17, 1981
Results: August 28, 1982
Results: September 20, 1983
Copper Release Ra
Copper Release Rate A

z AR shingles:

z Short-term (3-4 months) and long-term (2-15z Releases were directly proportional to expoz Average release rate: 0.16 mg copper per m

z Treated Wood (decks, fences, utility po

z Short-term and long-term studiesz Releases were proportional to exposed area z Average CCA release rate: 0.26 mg copper z Average non-CCA release rate: 3.2 mg coppz Assumed wood mix: 20% CCA, 80% non-CC

z Expected to represent reasonable maxi
Copper Release Rate: AR 

0.1

1

10

100

1000

10000

1 10 100 1,000 

Cumulative Copper Release (mg m-2)

Cumulative Rainfall (mm

Palo AltoHoustonHomesteadHagerstownMeanU95%L5%

Short-Term (Palo Alto) Study

1 test panel, 3 months

Mean = 0.52 mg m-2 mm-1

Al

Me

11 

Me

6 t

Me

18 t
Copper Sources: Treate
0.1

1

10

AR Shingles CCA Wood No

Copper Release Rate (mg m-2 mm-1)

Summary of Copper Relea

0.16 mg/m2/mm

0.26 mg/m2/mm

Assumed Mix of Wood Preservatives: 20% CCA, 8

3.

Overall: 2.6 mg/m2
Rainfall Data Analy
Rainfall Analysis

z Focused on southeast US:

z Area of expected greatest use of AR shinglez Higher than average rainfall compared to ot

regions of US (Long-term average: 50 inche

z Examined more than 100 years of regio

z Rainfall distributions at four stations (up to z Considered daily, hourly, and 15-minute rec

z Water Years 1983-1992 

Æ 62 inches/yea

z Period with highest 10-year average rainfall z Includes biggest storms on recordz Using 15-minute rainfall data, 772 storm eve
Distributions Rainfall Meas

• • • • • 
WY 1983-1992 Used for Mode

0123456789

10/01/81

10/01/82

10/01/83

09/30/84

10/01/85

10/01/86

10/01/87

09/30/88

10/01/89

Rainfall (inches)

Annual Rainfall for WY 1983-1992: 62 i
Urban Landscape Devel
Urban Landscape Devel

z Simulated urban land cover: looks z Reflects spatial variations in deve

z Housing densities: 1, 4, & 8 house(s)z Houses with AR shingle roofs determ

market share data

z Houses with decks and fences deter

Census Bureau statistics and marke

z Utility pole placement and treatment 

study for Toledo, Ohio urban area

z Represents reasonable maximum 
Copper Sources: Shingles, D

AR Shingle RoofsTreated Wood Decand Fences...

Copper Source Detail
Copper Sources: Utility 

CCA treated Utility Poles
Landscape Includes Imperv

Land Use Total Area (acres) Impervious Area (

Creek Set‐Back 59

0 

Residential Use (total) 4520 921.5 

1 House/Acre (subtotal)4 Houses/Acre (subtotal)

8 Houses/Acre (subtotal)

150715071506

78.4

313.5529.6

Open Space 452 45 

Commercial Use 20 15.8 

Government Use 30

9 

Education 23 13.9 

Elementary/Middle Schools 11 7.5 

High School 12 6.4 Entire Watershed 5104 1005.2 
Density of Antimicrobial Co

All Houses

Development Housing Units Rooftop Area (m2) Deck Area (m2) 

1 house/acre 1,507 350,000 10,800 

4 house/acre 6,028 1,400,100 43,000 

8 house/acre 12,048 2,126,700 53,700 

Houses With Antimicrobial Copper Building Material Cons

1 house/acre 613 140,000 4,500 

4 house/acre 2,411 560,000 18,100 8 house/acre 4,819 850,700 22,600 

Note: Surface area values rounded to simplify display.
Accounting for Urban Landsc

z Many urban landscape features occur at 

smaller than the 90-m model grid scale:

z Individual buildings, sidewalks, roads, individz Impervious area is handled as sub-grid scale f

z Hydraulic conductivity, flow resistance, a

are scaled by impervious cover for each 

(

)

(

) urbanh naturalh impervious impervious

K

K

1

f

0

f , = , −

+

=

(1

−

)

+

(1

−

urban lawn lawn concrete conc

nfnn

f

(

USLE urban USLE natural imperv

K

K

1

f

, = , −
Environmental Quality Be
Environmental Benchmarks 

z Surface water:

z Biotic Ligand Model (BLM)z Water quality for regional sitesz Vary by month: 11.7 to 33.7 

μg/L

z Sediment:

z Equilibrium sediment guidelines 

(ESG) (USEPA, 2000)

z Copper as excess metalz Assumed 5% organic carbonz 320 mg/kg

z Soil:

z Empirical relationships, 10% effects 

level (Rooney et al. 2006)

z Used local soil chemistry dataz 36 mg/kg
Copper Transport Simulati

z TERRA was used to perform simulations 

storm events over a 10 year period

z Daily average EECs calculated for surfac

sediment

z EECs compared to benchmarks
Copper from AR Shingles Over 1

z Over 10 yea

z Mean = 0z St Dev = z Min = 0 μz Max = 7.1 

z Cu releases driven by rainfallz 10% of Cu release goes to 

stream, 1% exported

z EECs at watershed outletz Water benchmark: 11.7–33.7 

μg/L
Copper from AR Shingles After 

z z z z z 
Copper from AR Shingles After 10 

z z z z z 
EECs and Benchmarks: AR 

Media

Concentration

Mean St Dev Min Max 

Water 0.25 0.79

0 7.1 

Sediment 6.7 4.9 1.0 34.5 

Soil 1.7 1.5

0 8.8 

Notes: Benchmarks for surface water are time variable to differences over time. Values shown represent an average from data from six regional sites in the USGS NAWQA dat
Copper from Treated Wood Over 1

z Over 10 yea

z Mean = 0z St Dev = z Min = 0 μz Max = 16

z Cu releases driven by rainfallz 10% of Cu release goes to 

stream, 1% exported

z EECs at watershed outletz Water benchmark: 11.7–33.7 

μg/L
Copper from Treated Wood After 

z z z z z 
Copper from Treated Wood After 

z z z z z 
EECs and Benchmarks: Tre

Media

Concentration

Mean St Dev Min Max 

Water 0.58 1.7

0 16.7 

Sediment 14.1 9.4 2.1 66.8 

Soil 3.8 4.9

0 31.8 

Notes: Benchmarks for surface water are time variable to differences over time. Values shown represent an average from data from six regional sites in the USGS NAWQA dat
Simulated Copper Mass Fate: 

z Over 

z 90% z 10% z 9% z 1% 

z Most 

becauthe di

z Once 

coppesurfacsignif

Algae-Resistant ShinglesTreated Wood (Decks, Fences, Poles)
Benchmark Exceeda

Note: For surface water, three benchmarks were evaluatedsensitive); (2) average; and (3) lower (more sensitive). Excnumber of days in 10 years when dissolved copper concethe benchmark for that day. An exceedance rate of 1 day years) or less is typically considered acceptable (Stephan 

Media AR Shingles Surface Water

UpperAverageLower

ZeroZeroZero

Sediment Zero Soil Zero 
Outcomes

z Results are expected to be conservativ

z Settings include high-end estimates for den

on land surface and in stream

z Low TSS and low Kp maximize dissolved co

z Generally low EECs consistent with low 

z Very low effective application rates compare

uses: 0.15–0.32 lbs/acre/year vs. 1–16 lbs/ac

z Simulated EECs similar to measurements in 

(Boulanger and Nikolaidis, 2003)

z Simulated EECs much lower than measured 

with high application rates (Fernández-Calv
Copper Application Rate C
Antimicrobial Case Study 
Antimicrobial Case Study 

z Examined two separate antimicrobial co

z Algae-resistant (AR) shingles (e.g. Cu

2O)

z Treated wood (e.g. CCA, CA, ACQ, etc.)

z Uses and setting differ from agricultura

z Urban setting with spatially-variable landscaz Rainfall-driven releases from roofs, decks, f

z Simulated copper transport and fate to 

estimated environmental concentration

z Excess copper in surface water, sediment, a
Exposure Simulation Ou

z Generally low EECs consistent with low 

z Very low effective application rates compare

uses: 0.15–0.32 lbs/acre/year vs. 1–16 lbs/ac

z Results consistent with measurements 

z Simulated EECs similar to measurements in 

(Boulanger and Nikolaidis, 2003)

z Simulated EECs much lower than measured 

with high application rates (Fernández-Calv

z Few surface water exceedances occur, 

soil or sediment
Scenario Designed To Be Re

z Physical setting and landscape design 

z Uses (driven by market share data and Censz Material types (building products)z Release mechanism (rainfall-driven, so we w

Æ Urbanized area, southeast U.S., high rainfal

z Goodwin Creek used for physical settin

meets location and climate needs and hKh and other parameters can be defined

z Scenario accounts for impervious area 

z

Kh and other parameters scaled to account 
Why this Scenario De

z Why Goodwin Creek? Why not Seattle 

z Any site could be used, but southeast U.S. i

greatest product use paired with highest rai

z Annual rainfall: Seattle = 36 in/yr, Goodwin Cz Another advantage of Goodwin Creek site is 

exist so we can show that basic model mec

z High runoff/erosion vulnerability (near Yazo

z Over time, more scenarios can be deve

USEPA developed scenarios for agricu

z Scenarios can be designed to include m

over time (more chemical processes, st
Parameterization and Ca

z Values are specified for each model pa

z True for PRZM/EXAMS, true for TERRA, true z Initial parameter values may be selected fro

just one value) presented in publications an

z Unless demonstrated by comparison to mea

parameterization may not agree with field m

z Calibration is a refinement of an initial p

z Parameter values adjusted so model results z Ensures a proper balance exists between trz Most important calibrated parameter used in 

Kh because values scaled to account for im
October 2011

Any Questions?
Extra Slides if Time Perm

Sample Hot Spot Ana
Location of Sites...
Copper EECs: Watershe
Copper EECs: Gag
Copper EECs: Gag
Copper EECs: Gag
Copper EECs: Gag
Copper EECs: Gage 
Copper EEC Probability Dis
Copper Toxic Unit Probability 

Note: Toxic Unit (TU) = Copper Concentratio