AI Can Detect Hidden Environmental Risks In River Water
NEW DELHI, (ANI) – Artificial intelligence (AI) is advancing the study of how chemical mixtures in rivers affect aquatic life, offering a new path for environmental protection. Researchers at the University of Birmingham, in collaboration with the Research Centre for Eco-Environmental Sciences in China and the Helmholtz Centre for Environmental Research in Germany, developed a novel AI methodology to detect harmful chemicals in rivers. Their study focused on the Chaobai River system near Beijing, which faces pollution from agricultural, domestic, and industrial sources.
The team used small water fleas (Daphnia) as test organisms, which are highly sensitive to changes in water quality. These tiny crustaceans serve as indicators of environmental hazards due to their genetic similarities to other species. By analyzing water samples and the impact on Daphnia, the researchers discovered that certain chemical combinations in the water could disrupt important biological processes in aquatic organisms.
The findings, published in Environmental Science and Technology, revealed that chemical mixtures could be more toxic to aquatic life than individual chemicals. The AI-driven approach allowed the team to identify harmful chemicals even in low concentrations that would typically go unnoticed.
Professor John Colbourne, director of the Centre for Environmental Research and Justice at the University of Birmingham, expressed hope that this technology could eventually be used to monitor water for toxic substances regularly. Dr. Xiaojing Li, lead author of the study, emphasized that AI helps pinpoint which chemical mixtures are especially harmful. Dr. Jiarui Zhou, co-first author, highlighted how AI algorithms can process vast biological and chemical data to predict environmental risks.
The research challenges traditional ecotoxicology by introducing a data-driven, unbiased method to identify harmful chemical mixtures, potentially influencing future environmental regulations and monitoring practices.
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