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Multibox Model of PCB Fate in San Francisco Bay

Multibox Model of PCB Fate in San Francisco Bay. Presented to the CFWG May 29, 2007. Version 1.0 draft report (February 2005) Updated sediment model (March 2005) PCB model updated in response Independent testing by Tetra Tech CFWG discussed findings and recommended next steps

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Multibox Model of PCB Fate in San Francisco Bay

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  1. Multibox Model of PCB Fate in San Francisco Bay Presented to the CFWG May 29, 2007

  2. Version 1.0 draft report (February 2005) Updated sediment model (March 2005) PCB model updated in response Independent testing by Tetra Tech CFWG discussed findings and recommended next steps SFEI addresses findings and comments Uncertainty analysis by Tetra Tech (March 2006) CFWG review (November 2006) SFEI performed uncertainty/sensitivity analyses Progress To Date

  3. Model Overview

  4. Model Overview

  5. Validation : Salinity

  6. Validation : SSC

  7. Preliminary (Uncalibrated) Hindcast Results * Magnitude and spatial distribution of results can be improved.

  8. Considering Tetra Tech’s testing and Workgroup comments, do certain model parameters need revising?  Magnitude of WY 2000 tributary loads.  Temporal trend of historic PCB loads.  Spatial distribution of tributary loads. Targeted Calibration

  9. Historic PCB Loads Breivik et al, 2002

  10. Spatial Distribution of Tributary Loads Model KLI, 2002

  11. Hindcast Results After Targeted Calibration *Still room for improvement. Workgroup recommended spatially explicit Koc.

  12. RMP data insufficient for regional Koc Filter size overestimates dissolved fraction Need objective means of determining Koc Should consider more than just sediment PCBs.  Model Bias Estimator Iteratively run model with small perturbations of regional Koc and search for model bias closest to 1 Objective Calibration of Partitioning (Koc)

  13. Estimate model bias for each Bay segment (j) and each matrix (i). For each segment (j) determine mass-weighted bias for all matrices (i). Determine overall (Bay-wide) model bias. Model Bias Estimator

  14. Hindcast Results After Calibration Error Bars: EMAP & RMP = Standard Deviation of Samples Model = Aggregate Uncertainty

  15. Hindcast Results After Calibration

  16. Hindcast Results After Calibration Net Erosional Net Depositional

  17. 10,000 runs made as part of Tetra Tech testing re-analyzed to determine ‘aggregate uncertainty of model results given uncertainty in input parameters.’ 3 sediment results (SSC, net sedimentation, sediment export) 10 PCB results (wct, sed, mass in water, mass in sed, burial, deposition, degradation, erosion, outflow, volatilization) Uncertainty Analysis

  18. Uncertainty, expressed as std. dev., is function of mean. Use this relation to project uncertainty onto forecast Uncertainty Analysis : Results

  19. Initialized with water and sediment concentrations from EMAP and profiles from end of hindcast Future loads attenuate from current levels with 50yr half-life Relationships of PCBs to SSC @ Mallard Island remain Sedimentation rates/patterns continue Uncertainty determined by hindcast Forecast Setup

  20. Forecast Setup : Sedimentation

  21. Base Forecast : Recovery Due to Natural Attenuation

  22. Base Forecast : Recovery Due to Natural Attenuation Net Erosional Net Depositional

  23. Sensitivity Sensitivity to PCB Profile Base Forecast

  24. Sensitivity Sensitivity to PCB Profile Base Forecast

  25. Sensitivity Sensitivity to PCB Profile Base Forecast

  26. Sensitivity Sensitivity to PCB Profile Base Forecast

  27. Loading Scenarios : Local Tributary Loads

  28. Recovery : Wastewater

  29. Loading Scenarios : Delta Loads

  30. Loading Scenarios : Delta Loads

  31. Loading Scenarios : No External Loads

  32. Scenarios : Attenuation & Degradation

  33. Recovery : Barrel Spill South Bay: 40 kg PCB 9.8e12 g sediment = 4.2 ng/g input

  34. Sediment cores Lower South Bay in particular (depositional) Better info on attenuation/degradation Estimate outflow loads of PCBs (?) Information Gaps

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