A physics-informed machine learning model predicted pesticide exposure in California; against 3,924 stream measurements, the two most-monitored chemicals correlated significantly while the pooled fourteen-chemical correlation was weak
The authors built a physics-informed screening model for California pesticide exposure: a first-order decay fate index based on soil half-life drives a LightGBM model that predicts weekly county-level pesticide application from 2016-2023 public agricultural, weather, and chemical-property data, and they validated the resulting exposure index against 3,924 U.S. Geological Survey stream measurements at 554 California stations never used in training; agreement was strong and significant for the two most-monitored chemicals (Malathion rho = 0.51, p < 10^-6; Metolachlor rho = 0.28, p < 10^-8), the pooled correlation across all fourteen monitored chemicals was weak, the application model generalized well across space (R2 = 0.64) and time (R2 = 0.