Risk-based Management of
PCBs in San Francisco Bay
All the hard work done by
Frank A.P.C. Gobas and Jon A. Arnot
Presentation attempted by
Adrian M.H. deBruyn
October 30, 2008
Workshop on Climate Change Risk Assessments
SFU

1. Problem Definition
•   Contamination of the San Francisco Bay ecosystem poses a threat to the health of wildlife and humans
•   Focus of this analysis is on PCBs
–   Persistent, Bioaccumulative, Toxic
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2. Management Objectives
•   Aspirational objectives
–   Maintain healthy ecosystems & healthy human populations
•   Operational objectives
–   Prevent contaminant levels in wildlife and sportfish from exceeding levels that cause adverse effects
•   Means objectives
–   Manage historical contamination and ongoing contaminant inputs
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3. Indicators of Risk
•   Some combination of magnitudes and probabilities
–   Fraction of a wildlife population exceeding (or probability that an individual within a population will exceed) a particular threshold exposure level associated with a particular type of effect or
–   Probability of a particular level of adverse effects
(e.g., 0.001% increase in lifetime cancer risk) in humans
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4. Management Options
•   Not explicitly addressed, but
–   Focus of assessment is on linking risk to manageable entities (PCB concentrations and loadings)
–   Assessment includes risk-based derivation of management objectives (targets)
–   Management will likely include remediation of portions of SFB and/or key source areas
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7. Conceptual Model
•   Link manageable entities to risk
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7. Conceptual Model
Biological effect levels in seals, bird eggs
Human health risk levels
3. Indicators of Risk
Concentrations in upper trophic level species
Concentrations in sport fish
Concentrations in prey species
Concentrations in water and sediment
Loadings
4. Management Options
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BSAFs
7. Conceptual Model
Biological effect levels in seals, bird eggs
Human health risk levels
3. Indicators of Risk
Concentrations in upper trophic level species
Concentrations in sport fish
Concentrations in prey species
Concentrations in water and sediment
Loadings
4. Management Options
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Biota-Sediment Accumulation Factor (BSAF)
BSAF = C
B
/ C
S
Sediment
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C sediment
SFB Food-Web
Bioaccumulation
Model
C biota
Input Model Output
C biota
= BSAF x C sediment
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C sediment
SFB Food-Web
Bioaccumulation
Model
C biota
Output Model Input
C sediment
= C biota
/ BSAF
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C sediment
SFB Food-Web
Bioaccumulation
Model
C biota
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5 & 6. Uncertainty Analysis
•   Characterized variability
–   Sediment concentrations ( n > 1000)
–   Measured BSAFs for sampled species
•   Model Bias
–   Empirical catch-all assessment of ability of model to forecast
•   Monte Carlo Simulation
–   PDFs for various bioaccumulation model parameters
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5 & 6. Uncertainty Analysis
BSAF predicted BSAF observed
Model Bias (MB) = 10 n
Σ Log ( i=1
BSAF predicted,i
BSAF observed,i
) / n
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5 & 6. Uncertainty Analysis
1. Monitoring data
2. Monte Carlo Simulations
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Forward Calculation
+ =
SFB Food-Web
Bioaccumulation
Model
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Forward Calculation
+ =
SFB Food-Web
Bioaccumulation
Model
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Threshold Effect Concentration:
Immunotoxicity
(Kannan et al. 2000)
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Backward Calculation
- =
SFB Food-Web
Bioaccumulation
Model
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Backward Calculation
Female Harbor Seal
Male Harbor Seal
Tern Egg
Cormorant Egg
White croaker
Jacksmelt
Shiner surfperch
Back-calculated sediment targets based on thresholds for toxic effects in fish & wildlife
White croaker
Jacksmelt
Shiner surfperch
Current Mean
Sediment
Concentration
Targets based on 10 -5 excess lifetime cancer risk in human consumers of fish
0 10 20 30 40 50 60 70 80
SFEI Sediment PCB Concentration (ug/kg dw)
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8. Sensitivity Analysis
•   Characterized sensitivity of bioaccumulation model
–   Abiotic state variables (e.g., temperature, suspended solids)
–   Biotic state variables (e.g., lipid content, digestive efficiency)
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8. Sensitivity Analysis
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Key Elements
•   Mechanistic exposure model based on state-of-the-science approaches
•   Combined with empirical data to characterize uncertainty/variability (Model
Bias) in exposure
•   Related to a range of toxicological data
•   Links risk to a manageable entity (C sediment
)
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