Technical Memo: Incorporating Mixture Toxicity into Bayesian Networks to calculate risk to pesticides in the Upper San Francisco Estuary.

This memo presents the methods we have developed 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 mixture the models were normalized for specific ECx values. In that way the curve fitting was optimized for effects that are similar to most threshold values. A Bayesian network was then built that incorporated four different pesticides and a specific mode of action. The input distributions of the pesticides were measured amounts from each of the six risk regions. Sensitivity analysis identified the components of the Bayesian network most important in determining the toxicity. We did demonstrate that curve fitting using additive models for mixtures can be used to estimate fish toxicity in this proof-of-concept model. Bifenthrin and the specific risk region were the two variables that were most important to the risk calculation. These techniques appear applicable to estimating risk due to the variety of chemicals and other stressors in the USFE and to the multiple endpoints under managemen