Artificial intelligence can provide significant insights into how complex chemical mixtures in rivers affect aquatic life, paving the way for more effective environmental protection.
A novel methodology developed by academics at the University of Birmingham demonstrates how advanced artificial intelligence (AI) approaches can help identify potentially harmful chemicals in rivers by monitoring their effects on small water fleas (Daphnia).
The team collaborated with scientists from the Research Centre for Eco-Environmental Sciences (RCEES) in China and the Helmholtz Centre for Environmental Research (UFZ) in Germany to analyze water samples from the Chaobai River system near Beijing. This river system is exposed to chemical pollutants from various sources, including agriculture, domestic use, and industry.
Professor John Colbourne, director of the University of Birmingham’s Centre for Environmental Research and Justice and a senior author of the paper, expressed optimism about the technology’s potential. 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 environmental water samples to uncover unknown substances acting together to produce toxicity to animals, including humans.”
The results, published in Environmental Science and Technology, reveal that certain chemical mixtures can interact to impact critical biological processes in aquatic organisms, as indicated by changes in their genes. These chemical combinations create environmental hazards potentially greater than those posed by individual substances.
The research team used water fleas (Daphnia) as test organisms due to their sensitivity to water quality changes and their genetic similarities with other species, making them excellent indicators of environmental hazards.
“Our innovative approach leverages Daphnia as a sentinel species to uncover potential toxic substances in the environment,” explained Dr. Xiaojing Li of the University of Birmingham and the study’s lead author.
“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, led the development of the AI algorithms. He 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, emphasized the innovation in their methodology:
“The study’s key breakthrough 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 for regulatory adoption of the sentinel species Daphnia, alongside new methodological approaches.”
Dr. Timothy Williams, a co-author from the University of Birmingham, noted:
“Typically, aquatic toxicology studies either use high concentrations of individual chemicals to determine detailed biological responses or only measure apical effects, such as mortality and altered reproduction, after exposure to an environmental sample. This study breaks new ground by identifying key classes of chemicals affecting living organisms within genuine environmental mixtures at relatively low concentrations, while simultaneously characterizing the biomolecular changes they elicit.”
