1. Selenium in North San Francisco Bay: Conceptual Model and Recommendations for Numerical Model Development Technical Memoranda 4 and 5 Prepared by Tetra Tech Sujoy Roy, Limin Chen, Bill Mills, and Tom Grieb Presentation to Technical Review Committee September 16, 2008

2. Objectives q Explain important selenium-related processes, and lay out broad areas of agreement in the scientific literature. q Summarize spatial and temporal trends in selenium data, with a focus on concentrations in bivalves, waterfowl and fish, so that they can be compared against toxicological and health-based guidelines. q Highlight data gaps and uncertainties of relevance to the TMDL. q Guide the development of a numerical model that is proposed to be used to link selenium sources quantified in TM-2 to biota.

3. Technical Support Documents TM-2: Source Analysis TM-4: Conceptual Model TM-5: Recommendation for Numerical Model TM-6: Model Application and Results

4. Overview of Presentation q Summary of processes in Water column Sediments Phytoplankton/bacteria Fish and birds q Recent data from NSFB q Recommendations for numerical model development

5. Selenium Cycling in North San Francisco Bay

6. Water Column Selenium q Selenium (+VI) (selenate): Present in very oxidizing environments, and does not adsorb strongly to particulates. q Selenium (+IV) (selenite): This form of selenium is common in oxygenated estuarine waters and can be taken up microbes and algae more readily than selenate. q Selenium-II (selenide): Selenides can form through the uptake of oxidized selenium by plankton or microbes, where the selenium is biologically reduced and incorporated into organic compounds. q In addition, particulate selenium can exist in one of these forms or as elemental selenium (Se-0).

7. Relative importance of loadings from different sources Total (kg/yr) Dissolved (kg/yr) Particulate (kg/yr)Uncertainty Sources: Atmospheric deposition 17.8-163.513.7-78.11.4-85.4High Local tributaries354-1511 (354-834 best estimate) -118.2 1 High Municipal and industrial wastewater 250--Low Refineries538--Low Input from Delta1,110-11,752 (mean: 3,962) 814-9,736 (Mean: 3,354) 151-1,509 2 (mean: 698) Moderate Sacramento River at Freeport 670-2,693 (mean: 1,577) for 1991-2007 Moderate San Joaquin River at Vernalis 760-7,270 5 (mean: 2,972) for 1994-2007 838-4,711 (mean: 2,289) for 1991-2007 Moderate Sediment29318.2 4 275Moderate-High South Bay106High Sinks: Outflow4,500 3 3,750 3 750 3 Moderate-High Sediment Dredging82.5 Moderate

8. San Joaquin River at Vernalis

9. Dissolved Selenium Concentrations Although variable, on average, dissolved concentration are lower post 1998 (dashed line compared to solid line)

10. Particulate and Dissolved Se: Low Flow (Source: Cutter Research Group papers) No significant change over 1986-1999 in particulate concentrations

11. Particulate and Dissolved Se: High Flow (Source: Cutter Research Group papers) No significant change over 1986-1999 in particulate concentrations

12. Key Findings-Water and Sediments q Evaluation performed using data from the mid-1980s to 2001, with additional water and sediment data from the RMP q Although variable, field data show minimal change in particulate selenium concentrations patterns (expressed as mg/g) despite changes in dissolved concentrations in the late 1990’s. q The role of particulates in the uptake of selenium implies that the modeling of the freshwater residence time and uptake in the bay is critical to understanding potential risk. q From the sediment coring performed to date, there is limited knowledge of the historical signal of selenium in the bay, especially conditions that were prevalent prior to large scale irrigation in the San Joaquin Valley.

13. Bioaccumulation Definition: Bioaccumulation is a general term for the accumulation of substances in an organism or part of an organism. q For selenium, little direct uptake from the dissolved phase for most species, besides algae and bacteria q Uptake occurs through particulates and higher particulate selenium concentrations should result in greater bioaccumulation

14. In Lab Tests, Algal Uptake Response Non-Linear to Ambient Concentrations With some exceptions, over a wide range of concentrations, algal concentrations are relatively similar. Baines and Fisher, 2001

15. Resident Bivalve Data (USGS)

16. Bivalve Data By Location Sac R SJR

17. Zooplankton Data

18. White Sturgeon Muscle Tissue Data

19. Splittail and Sturgeon Data Compared

20. Concentrations in Diving Ducks

21. Bioconcentration/Bioaccumulation Factors MediaConcentrationBioconcentration/ Bioaccumulation Factors Water Column0.10 μg/l (low flow) 0.12 μg/l (high flow) Seston0.73 μg/g (low flow) 0.49 μg/g (high flow) 7,300 L/kg (low flow) 4,800 L/kg (high flow) Plankton3 μg/g25,000 – 100,000 L/kg Sediment0.25 μg/g2,200 L/kg Bivalve P. amurensis11 μg/g100,000 L/kg 3 – 4 times plankton concentrations White Sturgeon (liver)24.1 μg/g219, 000 L/kg 2 - 3 times P. amurensis concentrations Zooplankton4.5 μg/g (low flow) 1.9 μg/g (high flow) 17, 200 - 40, 900 L/kg 0.6 – 1.5 times plankton concentrations Splittail (liver)11.4 μg/g103,600 L/kg 2 - 6 times zooplankton concentrations

22. Key Findings-Selenium in Biota q In addition to the load changes, there have been recent changes in the ecology of the bay, primarily an increase in phytoplankton productivity to levels seen prior to the P. amurensis invasion. q There is no information now on what the P. amurensis populations are, and whether a decline in its numbers may reduce the levels of bioaccumulation in the future. q Laboratory data show the significant effect of algal species on selenium uptake, although this information is not collected in the field. q Although 1999-2005 particulate, bivalve, and white sturgeon selenium data do not show decreases from prior periods, the very small amount of bird muscle data does indicate a decrease

24. Numerical Model Development q Why needed? An approach to link point and non-point sources to numeric targets adopted in the TMDL q Numeric targets could be in the form of water column and/or tissue concentrations and have not yet been proposed for the NSFB TMDL q Specific scenarios to be modeled will be developed later in the TMDL process

25. Modeling Approaches (1): Presser and Luoma (2006) q Title: Forecasting Selenium Discharges to the San Francisco Bay-Delta Estuary: Ecological Effects of a Proposed San Luis Drain Extension q All loads enter at the head of the estuary, concentration gradient from head of the estuary to Golden Gate a function of salinity q Bioaccumulation in different trophic levels using DYMBAM and regressions from data q Calibrated to field data from the bay q Scenarios tested include baseline and different levels of loads from San Joaquin Valley, different runoff scenarios; and different bioavailability and assimilative efficiency

26. Modeling Approaches (2): Meseck and Cutter (2006) q Title: Evaluating the Biogeochemical Cycle of Selenium in San Francisco Bay through Modeling q Focused on North bay; uses a 1-D model from Rio Vista on Sacramento River to the Golden Gate. Developed using ECoS modeling tool q Representation of selenium biogeochemistry and conversion between different forms, calibrated to Cutter group data collected in the 1980s and 1990s q No representation of bioaccumulation processes

27. Recommendation in TM-5 q Use existing peer-reviewed modeling information to the extent feasible q Employ the Meseck and Cutter (2006) approach for selenium transport, and the Presser and Luoma (2006) approach for bioaccumulation q Calibrate to detailed water chemistry and biological data from ~1999 q Validate for water chemistry to RMP data in 2001 and 2005 q Predictions for other years with a given hydrology