Science activity

This project developed methods to calculate risk of mixtures of pesticides for the Upper San Francisco Estuary (USFE). We used curve fitting to estimate the exposure-response curves for each individual chemical and then the mixture. For the mixtures, the models were normalized for specific ECx values. In that way, the curve fitting was optimized for effects that are comparable to most threshold values. A Bayesian network was built that incorporated five different pesticides and mercury. The input distributions of the contaminants were measured amounts from each of the six risk regions. We also explored three different methods of combining the results of the three pathways – additive, average, and expert judgement. The initial result was the BN model’s Predicted Fish Mortality (%). The Sensitivity analysis (mutual information) identified the most important components of the Bayesian network in determining the toxicity. The top two pathways were the Malathion/Diazinon Mortality pathway and the Mercury Mortality pathway. For the individual nodes Mercury, Bifenthrin and Season were key. Currently, we are completing the risk assessment network by adding Chinook salmon and Delta smelt population pathways to estimate risk to the six Risk Regions. A major accomplishment was the demonstration that curve fitting using additive models for mixtures can be used to estimate fish toxicity in this proof-of-concept model. Bifenthrin, the specific risk region, and season were the inputs that were most important to the calculation. Factors determining macroinvertebrate community structure were identified using multivariate tools. Water quality parameters were the most important in determining clusters of similar macrobenthic communities. Because contaminants were not statistically significant in determining these patterns, further analysis of macroinvertebrate community structure was postponed. At this time, the techniques applied in this program appear applicable to estimating risk due to the variety of chemicals and other stressors to the multiple endpoints under management in the USFE.