Using AI/ML and Bayesian Modeling Techniques to Study Chemical Stressors and Their Impacts

NOAA's Mussel Watch records are examined as a unified framework for coastal contamination. The study combines sediment chemistry, toxicity, benthic condition and tissue measurements, using AMBI and M-AMBI scores at 732 national assessment sites and 33 Hudson–Raritan stations. Random-forest and Bayesian models associate urbanization with benthic condition in the New York region (ρ=0.62), but not nationally (ρ=0.05). A Delft3D estuary model reproduces observed tides and predicts increased deep-channel bed shear under sea-level rise. The work asks whether sediment contaminants mediate urbanization impacts and whether legacy cleanup remains effective as sea level changes. It was conducted through a NOAA NERTO graduate internship with Kimani Kimbrough and Erik Davenport at NCCOS.