Physics-Informed Machine Learning for Climate-Sensitive Agricultural Pesticide Exposure Modeling and Water-Quality Validation
Abstract
Abstract Water-quality monitoring networks are too sparse to track how climate extremes are shifting agricultural pesticide pollution, and screening-level exposure models proposed to fill this gap are rarely tested against independent real-world monitoring data. We built such a model for California and tested it directly. A physics-based fate index – first-order decay from each chemical’s soil half-life – is driven by a LightGBM model that predicts weekly pesticide application by county and chemical from eight years (2016–2023) of public agricultural, weather, and chemical-property data. We validated the resulting exposure index against 3,924 real U.S. Geological Survey stream measurements at 554 California stations, never used in training. For the two most-monitored chemicals, agreement was strong and statistically significant (Malathion, ρ = 0.51, p
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Evidence Hub reads this as a simulation / modelling, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
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