the long term fate of pcbs in san francisco bay jay a. davis san francisco estuary institute
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The Long Term Fate of PCBs in San Francisco Bay Jay A. Davis San Francisco Estuary Institute. Regional Monitoring Program. Regional Monitoring Program for Trace Substances in the San Francisco Estuary Innovative partnership between government, dischargers, and scientists - PowerPoint PPT PresentationTRANSCRIPT
The Long Term Fate of PCBs in San Francisco Bay
Jay A. Davis
San Francisco Estuary Institute
Regional Monitoring Program
Regional Monitoring Program for Trace Substances in the San Francisco Estuary
Innovative partnership between government, dischargers, and scientists
$ 3 million/year of stable funding Began in 1993 World class monitoring www.sfei.org
PCB Concentrations in San Francisco Bay Fish Fillets, 2000
PCBs in mussels
1981-2000
ng/g lipid
~ 50% decline
Questions
Why is the Bay responding so slowly? How long will it take for fish to be safe to eat? What would the response be with reduced inputs? How large are the inputs? What studies are needed to better understand PCB
fate in the Bay?
A PCB Mass Budget
A first step toward a PCB mass budget for the Bay Followed approach described by:
– Gobas et al. 1995 (EST 29: 2038-2046)– Mackay et al. 1994 (JGLR 20: 625-642)
One-box model for the whole Bay A water and sediment model Individual congeners PCB 118 used as “typical” PCB Report benefited from extensive peer review
Input Data
Approximately 30 input parameters– Physical data for the Bay– Flow– Sediment budget– RMP concentration data– Chemical properties
Sensitivity analysis conducted on all parameters
Combined External Loads
Volatilization
Outflow
Dissolved PCB
Sorbed PCB
Dissolved PCB
Sorbed PCB
Burial
Water
Active Sediment Layer
Buried Sediment
Dissolved PCB
Particulate PCB
DegradationDegradation
Degradation Diffusion
Depositionand
Resuspension
PCB Fate in Bay Water and Sediment
Predicted Long Term Trends in PCB Mass with Varying Loads
Year
0 10 20 30 40 50 60 70 80 90 100
PC
B M
ass
in B
ay (
kg)
0
500
1000
1500
2000
2500
3000
50% of present
25% of present
10% of present
80 kg
40 kg
20 kg10 kg 0 kg
Predicted Trends in PCB Mass for Different PCB Congeners
MASS IN BAY
VOLATILIZATION
OUTFLOW
DEG SEDIMENT
0
500
1000
1500
2000
2500
3000
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
YEAR
PC
B 1
18
MA
SS
(K
G)
Predicted Long Term Trends in PCB Mass with Varying Depth of the Active Sediment Layer
Year
0 10 20 30 40 50 60 70 80 90 100
PC
B M
ass
in B
ay
(kg
)
0
1000
2000
3000
4000
50005 CM 10 CM 15 CM 20 CM 25 CM
Conclusions
At this stage, the value of the model is in showing the response to ranges of input values, not in the precision of estimates
The most influential parameters included degradation half-life in sediment, Kow, outflow, average PCB concentration in sediment, and depth of the active sediment layer
Sediment dynamics are very important, including mixing and erosion/burial
Conclusions (continued)
The model suggests that annual loads from 1982 to 2000 were in the 0 to 20 kg range
Annual inputs of 10 to 20 kg could significantly delay declines in PCBs
Different PCB congeners are predicted to have very different response times
For more information or a copy of the report: [email protected]
Next Steps for PCB Fate Modeling in San Francisco Bay
Food web model coming soon (Frank Gobas and John Wilcockson)
Sediment
Amphelisca sp(Amphipod)
Potamocorbula amurensis(Bivalve)
Shiner PerchJack Smelt Small Croaker Large Croaker
Harmothoe sp(Polychaete)
Crangon sp(Shrimp)
Neanthessuccinea
(Polychaete)
Theora lubrica(Bivalve)
Zooplankton
Phytoplankton
Sediment
Amphelisca sp(Amphipod)
Potamocorbula amurensis(Bivalve)
Shiner PerchShiner PerchJack SmeltJack Smelt Small CroakerSmall Croaker Large CroakerLarge Croaker
Harmothoe sp(Polychaete)
Crangon sp(Shrimp)
Neanthessuccinea
(Polychaete)
Theora lubrica(Bivalve)
Zooplankton
Phytoplankton
Modeling PCB Trophic Transfer
Mass Budget for a White Croaker
Dietary Uptake
Growth Dilution
Metabolism
Fecal Egestion
Gill Elimination
Gill Uptake
Next Steps for PCB Fate Modeling in San Francisco Bay
Quantify uncertainty of estimates Go multibox
The Multibox Model
Collaboration with Dave Schoellhamer (USGS, Sacramento)
Builds on existing model calibrated for salinity
RMP really interested in 5 boxes
Next Steps for PCB Modeling in San Francisco Bay
Better characterization of sediment dynamics
Also need better estimates of: Degradation rates Outflow Average concentrations Historic long term trends
Next Steps for Management
Look for manageable PCB loads Watch out for “PCBs” of the future (e.g.,
PBDEs)