California has numerous aquatic threatened and endangered species and there is evidence that these species may be affected by non-target pesticide runoff. Modeling can play an important role in assessing the impact of pesticides but it requires complete data on pesticide use, monitoring data, and complex issues with land use. Using two small urban land use areas in Folsom and Roseville, California, we were able to overcome these issues using monitoring data from the California Department of Pesticide Regulation with monthly county use data from the California Pesticide Use database, and aerial photography for land use characterization. Using the U.S. Environmental Protection Agency (US EPA) Pesticide Water Calculator model, we successfully reproduced the overall magnitude of pyrethroids detected, frequency of extreme concentrations, and mean and median concentrations. This was a first step in estimating the proportion and location of pesticide contamination from urban areas in the Central Valley of California that may affect threatened and endangered fish. Validation was accomplished with data from adjacent watersheds, while performance was evaluated statistically and graphically. From this work, we identify strategies to better assess potential impact of pesticides, including a joint US EPA/State of California citizen-science initiative involving homeowners with watershed-based tracking of their pesticide use; exploration of the use of homeowner/storeowner anecdotal information; and development of a Smart Phone App to enable interested parties to enter their own information. Professional urban pesticide applicators could be part of the citizen science initiative by identifying their pesticide application amounts with specific dates, instead of monthly. The enhanced information would be used to improve modeling efforts and to educate the public on their watersheds and how their actions affect water quality.

Walking the California county lines with pesticides on the mind:  A tale of two cities

254th National Meeting (2017)

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