1 Trace element loads in urbanized watersheds and the potential for treatment of NPS loads Lester McKee Sources Pathways and Loadings Workgroup May 22

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Trace element loads in Trace element loads in urbanized watersheds urbanized watersheds and the potential for and the potential for

treatment oftreatment ofNPS loadsNPS loads

Lester McKeeLester McKee

Sources Pathways and Loadings Sources Pathways and Loadings WorkgroupWorkgroup

May 22May 22ndnd 2007 2007San Francisco Estuary Institute

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HistoryHistory

2000 – 2002: Study design and location 2000 – 2002: Study design and location developed developed and recommended and recommended by SPLWGby SPLWG

WY 2003:WY 2003: CEP ($150k)CEP ($150k)

WY 2004:WY 2004: RMP and CEP ($50k+75k)RMP and CEP ($50k+75k)

WY 2005:WY 2005: RMP, USACE/SCVWD, SCVURPPP RMP, USACE/SCVWD, SCVURPPP ($50k + $100k + $23k)($50k + $100k + $23k)

San Francisco Estuary Institute

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ImpetusImpetus May 15May 15thth 2006 WG meeting. 2006 WG meeting.

Members asked about the trace metals data for Guadalupe Members asked about the trace metals data for Guadalupe and remarked they did not remember it being reported – and remarked they did not remember it being reported – Lester responded that there was three years of data and Lester responded that there was three years of data and that we only reported it once (year 2).that we only reported it once (year 2).

Mike C asked what inter-annual variability might look like Mike C asked what inter-annual variability might look like for contaminants other than Hg. Lester said that he thinks for contaminants other than Hg. Lester said that he thinks the Hg in the Guadalupe River would be more variable the Hg in the Guadalupe River would be more variable than sediment but he cannot say for other metals or the than sediment but he cannot say for other metals or the organics. Mike suggests that Lester do the analysis for the organics. Mike suggests that Lester do the analysis for the others. WG agreed it would be informative.others. WG agreed it would be informative.

Nov 13Nov 13thth WG meeting. WG meeting. ““Try to write future reports so that there is a better Try to write future reports so that there is a better

connection to management alternatives as an endpoint”connection to management alternatives as an endpoint”


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Sampling LocationSampling Location Area: 414 km2

4th largest watershed (8.4%)

178 km2, upstream from reservoirs leaving 236 km2 of which >78% is urban land use

Population: 486,500 (7.2% of Bay Area)

Highest elevation: Loma Prieta (1,155 m)

Precipitation varies from 300-1,100mm and falls during winter (Nov-April: 89%)

Annual Q: 0.42-241 Mm3 or approximately 1-640mm


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Sampling Methods


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USGS flow gage 11169025 at Hwy 101 adjacent to SJ Airport

Sampling based on hypothesis that SSC is a surrogate for predicting TE concentrations

FTS Ltd. DTS 12 turbidity probe with wiper

Water samples collected using D74 or DH48 and analyzed for SSC and GS

LOESS regression used to generate continuous 15-minute SSC

Water samples collected during floods using D95 and analyzed for total Ag, As, Cd, Cr, Cu, Ni, Pb, and Zn

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QA/QCQA/QC

All samples run through the RMP QA/QC programAll samples run through the RMP QA/QC program 2, 3. 2, 3. Silver concentrations in these batches were considered marginally Silver concentrations in these batches were considered marginally

acceptable as the matrix spike and duplicate recoveries were within the acceptable as the matrix spike and duplicate recoveries were within the data quality objectives. data quality objectives.

4. 4. These samples were not rejected because the matrix spike and These samples were not rejected because the matrix spike and duplicate recoveries were within the data quality objectives and more duplicate recoveries were within the data quality objectives and more importantly they were not anomalous with respect to SSCimportantly they were not anomalous with respect to SSC


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Loads EstimationLoads Estimation Linear interpolation when storms are well samplesLinear interpolation when storms are well samples

Stratified regression estimator during other storms Stratified regression estimator during other storms depending on the predominant source of runoff based depending on the predominant source of runoff based on rainfall and runoff gauging information from on rainfall and runoff gauging information from further up the watershed:further up the watershed:

Used “lower watershed urban” regression when water from Used “lower watershed urban” regression when water from therethere

Used “upper watershed non-urban” regression when water Used “upper watershed non-urban” regression when water from therefrom there

Base flow: used upper watershed regression but choice made Base flow: used upper watershed regression but choice made little difference to the annual loads estimation little difference to the annual loads estimation


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Error AnalysisError Analysis Errors accounted for during loads Errors accounted for during loads

estimation were:estimation were: flow (flow (10%)10%) SSC (SSC (2%)2%) SSC-Turbidity regressions SSC-Turbidity regressions

((2%)2%) Interpolation / SSC-trace Interpolation / SSC-trace

element specific regressions:element specific regressions:

Ag: Ag: 23%23% As: As: 31%31% Cd: Cd: 13%13% Cr: Cr: 13%13%

The mean coefficient of The mean coefficient of variation (CV) of duplicate variation (CV) of duplicate field samples was:field samples was:

Ag: Ag: 46%46% As: As: 4.9%4.9% Cd: Cd: 13%13% Cr: Cr: 7.3%7.3%

We did not include an error for We did not include an error for variation in the cross section variation in the cross section because this was accounted for because this was accounted for within the USGS SSC recordswithin the USGS SSC records

The total estimated errors for The total estimated errors for loads calculations were:loads calculations were:

SSC: SSC: 10%10% Ag: Ag: 52%52% As: As: 33%33% Cd: Cd: 21%21% Cr: Cr: 18%18%

Note, that we have found no other Note, that we have found no other papers that have quantified errors papers that have quantified errors in loads calculations, yet clearly, in loads calculations, yet clearly, claims of loads variation between claims of loads variation between years may not be validyears may not be valid


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Cu: Cu: 10%10% Ni: Ni: 13%13% Pb: Pb: 7%7% Zn: Zn: 14%14%

Cu: Cu: 4.3%4.3% Ni: Ni: 6.2%6.2% Pb: Pb: 3.6%3.6% Zn: Zn: 2.3%2.3%

Cu: Cu: 15%15% Ni: Ni: 17%17% Pb: Pb: 13%13% Zn: Zn: 17%17%

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Results – Precipitation and Results – Precipitation and RunoffRunoff

30-year average (July 1st, 1976 - 30-year average (July 1st, 1976 - June 30th, 2005): 388 mm and a June 30th, 2005): 388 mm and a coefficient of variation (CV) of coefficient of variation (CV) of 0.390.39 WY 2003: 100%WY 2003: 100% WY 2004: 90%WY 2004: 90% WY 2005: 164%WY 2005: 164% WY 2006: 150%WY 2006: 150%

Storms were generally of low Storms were generally of low magnitude and duration with the magnitude and duration with the exception of five storms exception of five storms (December 19th 2002; November (December 19th 2002; November 9th 2003; December 21st, 2003; 9th 2003; December 21st, 2003; December 27th, 2004; October December 27th, 2004; October 26th, 2004)26th, 2004)

Runoff similar (MAR = 57.4 MmRunoff similar (MAR = 57.4 Mm33):): WY 2003: 106% WY 2004: 92% WY 2005: 128% WY 2006: 221%


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Results – Concentration Results – Concentration StatisticsStatistics


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Results – Concentration Results – Concentration Scatter PlotsScatter Plots


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Results – ConcentrationsResults – Concentrations


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Correlation AnalysisCorrelation Analysis


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As Cd Cr Cu Ni Pb

Cd 0.49** 1

Cr 0.01 -0.04 1

Cu 0.33* 0.80** 0.08 1

Ni 0.01 -0.07 0.91** 0.02 1

Pb 0.30* 0.79** 0.04 0.83** -0.05 1

Zn 0.39* 0.84** 0.06 0.91** -0.03 0.93**

*p<0.01**p<0.001

Groups:1. Ag2. As3. Pb, Zn, Cd, Cu4. Cr, Ni

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Daily LoadsDaily Loads


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AnnuAnnual al

Wet Wet SeasSeason on

LoadLoadss


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Guadalupe versus the Guadalupe versus the WorldWorld(Peer-reviewed Literature)(Peer-reviewed Literature)


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Ag – Hardly ever reported, Guadalupe similarAg – Hardly ever reported, Guadalupe similar As – Reported previously in 9 urban watersheds, Guadalupe similarAs – Reported previously in 9 urban watersheds, Guadalupe similar Cd – Reported previously in 19 urban watersheds, Guadalupe Cd – Reported previously in 19 urban watersheds, Guadalupe

similar – but time trend in world literaturesimilar – but time trend in world literature Cr – Reported previously in 29 urban watersheds, Guadalupe Cr – Reported previously in 29 urban watersheds, Guadalupe

greater except 2 industrial watershedsgreater except 2 industrial watersheds Cu – Reported previously in 34 urban watersheds, Guadalupe Cu – Reported previously in 34 urban watersheds, Guadalupe

similarsimilar Ni – Reported previously in 20 urban watersheds, Guadalupe Ni – Reported previously in 20 urban watersheds, Guadalupe

greater except 1 industrial watershed and 1 high density greater except 1 industrial watershed and 1 high density residential watershed – but time trend evidentresidential watershed – but time trend evident

Pb – Reported previously in 39 urban watersheds, Guadalupe Pb – Reported previously in 39 urban watersheds, Guadalupe similar – but time trend in world literaturesimilar – but time trend in world literature

Zn – Reported previously in 29 urban watersheds, Guadalupe Zn – Reported previously in 29 urban watersheds, Guadalupe similar similar

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CadmiumCadmiumItem #1

R2 = 0.0269

R2 = 0.0069

0.01

0.1

1

10

100

1000

0.001 0.01 0.1 1 10 100 1000

Area (km2)

Cd

(µg/

L)

R2 = 0.4154

R2 = 0.4762

0.01

0.1

1

10

100

1000

1970 1975 1980 1985 1990 1995 2000 2005 2010

Year

Cd

(µg/

L)


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ChromiumChromiumItem #1

R2 = 0.0629

R2 = 0.33590.1

1

10

100

1000

10000

0.001 0.01 0.1 1 10 100 1000

Area (km2)

Cr

(µg

/L)

R2 = 0.0619

R2 = 0.0022

0.1

1

10

100

1000

10000

1970 1975 1980 1985 1990 1995 2000 2005 2010

Year

Cr

(µg

/L)


Guadalupe

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NickelNickelItem #1

R2 = 0.2802

R2 = 0.0954

0.1

1

10

100

1000

0.01 0.1 1 10 100 1000

Area (km2)

Ni

(µg

/L)

R2 = 0.0006

R2 = 0.3918

0.1

1

10

100

1000

1970 1975 1980 1985 1990 1995 2000 2005 2010

Year

Ni

(µg

/L)


Guadalupe

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LeadLeadItem #1

R2 = 0.459

R2 = 0.1454

0.01

0.1

1

10

100

1000

10000

100000

1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

Year

Pb

(µ

g/L

)

R2 = 0.0298

R2 = 0.1528

0.1

1

10

100

1000

10000

100000

0.001 0.01 0.1 1 10 100 1000

Area (km2)

Pb

(µ

g/L

)


Lee and Bang, 2000 – KoreaChoe et al., 2002Buffleben et al., 2002

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Mahler et al., 2006. Trends in Mahler et al., 2006. Trends in metals in urban and reference lake metals in urban and reference lake

sediments across the United sediments across the United States, 1970-2001 (ET&C 25, States, 1970-2001 (ET&C 25,

1698-1709)1698-1709) Cd decreased by 29% Cd decreased by 29% Cr decreased by 15%Cr decreased by 15% Ni decreased by 22%Ni decreased by 22% Pb decreased by 46%Pb decreased by 46%


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TreatmeTreatment nt

PotentiaPotential?l?


A.A. 11stst storm only storm onlyB.B. 11stst storm and largest storm and largest

floodfloodC.C. All rising stageAll rising stageD.D. Peak shavingPeak shaving

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Documents

1 Trace element loads in urbanized watersheds and the potential for treatment of NPS loads Lester McKee Sources Pathways and Loadings Workgroup May 22