AI-Driven Species Sensitivity Distribution (AI-4-SSD) Framework for Predicting Aquatic Ecological Risks of Chemical Pollutants in Global Near-Coastal Environments

Currently, more than 350,000 chemicals and chemical mixtures have been registered for global production and use, and they are inevitably released into global near-coastal environments during their life cycle. However, the multidimensional adverse effects of these chemicals on marine species populations, assemblages, and biodiversity remain unknown. Herein, we proposed an AI-based framework (AI-4-SSD) for whole-chain predictions of chemical exposure, aquatic toxicity, and risk in the global near-coastal environment. As the core of this framework, a multimodal deep learning model was developed to predict population-level aquatic toxicities of diverse chemicals on eight marine species across three phyla, demonstrating excellent predictive power (R2 of 0.85 in the test set). Using the AI-4-SSD framework, we identified six high-risk chemicals threatening marine species assemblages via direct effects on life-history characteristics, including DDT and 6:2/8:2 diPAPs, from approximately 3,000 target chemicals potentially entering the global near-coastal environment. Specifically, in the Black Sea, we found that cumulative risks from coexposure to hundreds of detected chemicals could drive biodiversity loss during 2016–2019, despite individual chemicals posing negligible risks. This work not only provides a user-friendly prediction framework for rapidly identifying high-risk chemicals but also highlights the necessity of mixture risk management for the conservation of marine biodiversity.