Using AI/ML and Bayesian Modeling Techniques to Study Chemical Stressors and Their Impacts
Anais Ortega · NOAA Cooperative Science Center for Earth System Sciences and Remote Sensing Technologies II (CESSRST-II), City College of New York
Fri, Oct 16, 2026 · 15:30 UTC
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 urbanizatio