AI-Driven Approach Uncovers Hidden Hazards of Chemical Mixtures in Rivers

Chemical mixtures in rivers pose unknown threats to aquatic life - Earth.com

A new dawn in environmental protection is emerging - one where artificial intelligence (AI) plays an integral role in understanding and mitigating the effects of chemicals on aquatic life. It's a fascinating blend of cutting-edge technology and environmental science that could revolutionize our approach to safeguarding water health. The breakthrough comes from an unexpected source - tiny water fleas known as Daphnia. Researchers have found that these minute crustaceans, which are highly sensitive to changes in water quality, can serve as excellent markers of potential environmental hazards. This innovative study was a collaborative effort involving scientists from the University of Birmingham, the Research Centre for Eco-Environmental Sciences (RCEES) in China, and the Hemholtz Centre for Environmental Research (UFZ) in Germany. The team analyzed water samples from the Chaobai River system near Beijing, a body of water that is exposed to pollutants from several sources, including agriculture, domestic waste, and industry. Study senior author Professor John Colbourne, director of the University of Birmingham's Centre for Environmental Research and Justice, elaborated on the research. "There is a vast array of chemicals in the environment. Water safety cannot be assessed one substance at a time," said Professor Colbourne. "Now we have the means to monitor the totality of chemicals in sampled water from the environment to uncover what unknown substances act together to produce toxicity to animals, including humans." Published in the journal Environmental Science and Technology, the study reveals that these tiny water fleas can indicate the presence (and potential harm) of various mixtures of chemicals in the aquatic environment. Some chemicals, especially in combination, may affect significant biological processes in aquatic organisms. What's particularly concerning is that these chemical combinations can create environmental hazards that are potentially greater than when chemicals are present individually. "Our innovative approach leverages Daphnia as the sentinel species to uncover potential toxic substances in the environment," explained lead author Dr. Xiaojing Li of the University of Birmingham. "By using AI methods, we can identify which subsets of chemicals might be particularly harmful to aquatic life, even at low concentrations that wouldn't normally raise concerns." The team's use of advanced artificial intelligence is a game changer. Dr. Jiarui Zhou of the University of Birmingham, who is also co-first author of the paper, led the development of the AI algorithms. "Our approach demonstrates how advanced computational methods can help solve pressing environmental challenges," said Dr. Zhou. "By analyzing vast amounts of biological and chemical data simultaneously, we can better understand and predict environmental risks." This breakthrough gives us a new tool to identify previously unknown chemical combinations that pose risks to aquatic life, enabling more comprehensive environmental monitoring. The research will also support better-informed regulations for chemical discharge into waterways, ultimately leading to improved environmental protection. Funding was provided by the Royal Society International Collaboration Award, the European Union's Horizon 2020 research and innovation program, and the Natural Environmental Research Council Innovation People program. With continued support, AI and the diligent water flea will keep playing their part in protecting our precious water bodies. Artificial intelligence plays a central role in this research. By leveraging advanced computational methods, the research team has significantly advanced environmental science. AI enables the analysis of vast datasets, including biological responses of Daphnia and chemical profiles of polluted water samples, thus uncovering intricate patterns and interactions that traditional methods often miss. One of the most notable outcomes is the ability to identify harmful chemical combinations that might be overlooked with conventional testing. Machine learning algorithms can predict which mixtures pose the greatest risks to aquatic life, even at concentrations previously considered safe. This predictive capability not only improves the efficiency of monitoring but also provides actionable insights for environmental regulators. Looking ahead, the potential applications of AI extend beyond water fleas and rivers. Similar approaches can be applied to other ecosystems, paving the way for a comprehensive toolkit for environmental monitoring and conservation. The full study was published in the journal Environmental Science & Technology. -- - Like what you read? Subscribe to our newsletter for engaging articles, exclusive content, and the latest updates.

AI-driven approach reveals hidden hazards of chemical mixtures in rivers

Artificial intelligence can provide critical insights into how complex mixtures of chemicals in rivers affect aquatic life -- paving the way for better environmental protection. A new approach, developed by researchers at the University of Birmingham, demonstrates how advanced artificial intelligence (AI) methods can help identify potentially harmful chemical substances in rivers by monitoring their effects on tiny water fleas (Daphnia). The team worked with scientists at the Research Center for Eco-Environmental Sciences (RCEES), in China, and the Hemholtz Center for Environmental Research (UFZ), in Germany, to analyze water samples from the Chaobai River system near Beijing. This river system is receiving chemical pollutants from a number of different sources, including agricultural, domestic and industrial. Professor John Colbourne is the director of the University of Birmingham's Center for Environmental Research and Justice and one of the senior authors on the paper. He expressed optimism that, by building upon these early findings, such technology can one day be deployed to routinely monitor water for toxic substances that would otherwise be undetected. He said, "There is a vast array of chemicals in the environment. Water safety cannot be assessed one substance at a time. Now we have the means to monitor the totality of chemicals in sampled water from the environment to uncover what unknown substances act together to produce toxicity to animals, including humans." The results, published in Environmental Science and Technology, reveal that certain mixtures of chemicals can work together to affect important biological processes in aquatic organisms, which are measured by their genes. The combinations of these chemicals create environmental hazards that are potentially greater than when chemicals are present individually. The research team used water fleas (Daphnia) as test organisms in the study because these tiny crustaceans are highly sensitive to water quality changes and share many genes with other species, making them excellent indicators of potential environmental hazards. "Our innovative approach leverages Daphnia as the sentinel species to uncover potential toxic substances in the environment," explains Dr. Xiaojing Li, of the University of Birmingham (UoB) and the lead author of this study. "By using AI methods, we can identify which subsets of chemicals might be particularly harmful to aquatic life, even at low concentrations that wouldn't normally raise concerns." Dr. Jiarui Zhou, also at the University of Birmingham and co-first author of the paper, who led the development of the AI algorithms, said, "Our approach demonstrates how advanced computational methods can help solve pressing environmental challenges. By analyzing vast amounts of biological and chemical data simultaneously, we can better understand and predict environmental risks." Professor Luisa Orsini, another senior author of the study, added, "The study's key innovation lies in our data-driven, unbiased approach to uncovering how environmentally relevant concentrations of chemical mixtures can cause harm. This challenges conventional ecotoxicology and paves the way to regulatory adoption of the sentinel species Daphnia, alongside new approach methodologies." Dr. Timothy Williams of the University of Birmingham and co-author of the paper also noted, "Typically, aquatic toxicology studies either use a high concentration of an individual chemical to determine detailed biological responses or only determine apical effects like mortality and altered reproduction after exposure to an environmental sample. "However, this study breaks new ground by allowing us to identify key classes of chemicals that affect living organisms within a genuine environmental mixture at relatively low concentration while simultaneously characterizing the biomolecular changes elicited." The findings could help improve environmental protection by identifying previously unknown chemical combinations that pose risks to aquatic life, enabling more comprehensive environmental monitoring, and supporting better-informed regulations for chemical discharge into waterways.

AI-driven approach reveals hidden hazards of chemical mixtures in rivers

Artificial intelligence can provide critical insights into how complex mixtures of chemicals in rivers affect aquatic life -- paving the way for better environmental protection. A new approach, developed by researchers at the University of Birmingham, demonstrates how advanced artificial intelligence (AI) methods can help identify potentially harmful chemical substances in rivers by monitoring their effects on tiny water fleas (Daphnia). The team worked with scientists at the Research Centre for Eco-Environmental Sciences (RCEES), in China, and the Hemholtz Centre for Environmental Research (UFZ), in Germany, to analyse water samples from the Chaobai River system near Beijing. This river system is receiving chemical pollutants from a number of different sources, including agricultural, domestic and industrial. Professor John Colbourne is the director of the University of Birmingham's Centre for Environmental Research and Justice and one of the senior authors on the paper. He expressed optimism that, by building upon these early findings, such technology can one day be deployed to routinely monitor water for toxic substances that would otherwise be undetected. He said: "There is a vast array of chemicals in the environment. Water safety cannot be assessed one substance at a time. Now we have the means to monitor the totality of chemicals in sampled water from the environment to uncover what unknown substances act together to produce toxicity to animals, including humans." The results, published in Environmental Science and Technology, reveal that certain mixtures of chemicals can work together to affect important biological processes in aquatic organisms, which are measured by their genes. The combinations of these chemicals create environmental hazards that are potentially greater than when chemicals are present individually. The research team used water fleas (Daphnia) as test organisms in the study because these tiny crustaceans are highly sensitive to water quality changes and share many genes with other species, making them excellent indicators of potential environmental hazards. "Our innovative approach leverages Daphnia as the sentinel species to uncover potential toxic substances in the environment," explains Dr Xiaojing Li, of the University of Birmingham (UoB) and the lead author of this study. "By using AI methods, we can identify which subsets of chemicals might be particularly harmful to aquatic life, even at low concentrations that wouldn't normally raise concerns." Dr Jiarui Zhou, also at the University of Birmingham and co-first author of the paper, who led the development of the AI algorithms, said: "Our approach demonstrates how advanced computational methods can help solve pressing environmental challenges. By analysing vast amounts of biological and chemical data simultaneously, we can better understand and predict environmental risks." Professor Luisa Orsini, another senior author of the study, added: "The study's key innovation lies in our data-driven, unbiased approach to uncovering how environmentally relevant concentrations of chemical mixtures can cause harm. This challenges conventional ecotoxicology and paves the way to regulatory adoption of the sentinel species Daphnia, alongside new approach methodologies." Dr Timothy Williams of the University of Birmingham and co-author of the paper also noted that: "Typically, aquatic toxicology studies either use a high concentration of an individual chemical to determine detailed biological responses or only determine apical effects like mortality and altered reproduction after exposure to an environmental sample. However, this study breaks new ground by allowing us to identify key classes of chemicals that affect living organisms within a genuine environmental mixture at relatively low concentration while simultaneously characterising the biomolecular changes elicited." The findings could help improve environmental protection by: This research was funded by the Royal Society International Collaboration Award, the European Union's Horizon 2020 research and innovation programme, and the Natural Environmental Research Council Innovation People programme.