A new scientific perspective suggests that large language models, the artificial intelligence systems behind tools such as modern chatbots, could revolutionize how scientists evaluate environmental risks in aquatic ecosystems.
Aquatic environments are essential for ecological stability and human health, yet assessing pollution risks in rivers, lakes, and coastal waters remains challenging. Critical information about pollutant exposure, toxicity, and ecological effects often exists in scattered forms across scientific papers, monitoring reports, and policy documents. Bringing this fragmented knowledge together has long been a bottleneck in environmental risk assessment.
The new study reviews how large language models, or LLMs, may help solve this problem by extracting and integrating information from vast amounts of unstructured environmental data. The authors argue that these systems can identify pollutants, connect them to their toxic effects, and even generate insights that support decision making in environmental management.
"Environmental data are often dispersed across heterogeneous sources and difficult to synthesize," said the authors. "Large language models offer a promising pathway to mine, integrate, and reason over this information in ways that were previously impossible."
Traditional natural language processing tools typically require manual feature engineering and specialized model design, making them difficult to adapt across disciplines or datasets. In contrast, LLMs rely on transformer architectures that can interpret long text passages, recognize complex terminology, and capture relationships between concepts. These abilities enable them to perform tasks such as named entity recognition, relation extraction, and semantic reasoning within a single framework.
The review highlights that LLMs have already demonstrated strong performance in related scientific fields, including chemistry, materials science, and biomedical research. For environmental science, these tools could help identify pollutants in monitoring data, link them to toxicity thresholds, and reconstruct ecological risk pathways. Such capabilities could support more dynamic and data driven risk management strategies.
However, the authors stress that the application of LLMs in aquatic risk assessment remains in its early stages. Major challenges include the lack of high quality environmental corpora, the risk of generating incorrect associations, and the large computational resources required to train advanced models. Reliable data curation and expert validation will therefore remain essential components of future AI assisted risk assessment workflows.
Despite these limitations, the researchers believe that hybrid approaches combining domain specific datasets, retrieval augmented generation techniques, and expert feedback could significantly improve model reliability. Over time, AI driven data integration platforms may help scientists and policymakers better identify priority pollutants, understand ecological threats, and design more effective environmental protection strategies.
"By systematically integrating diverse environmental data, large language models could become powerful tools for scientific risk assessment and intelligent environmental management," the authors noted.
If these technologies mature as expected, they may enable a new generation of evidence based policies that protect aquatic ecosystems while supporting sustainable development worldwide.
===
Journal reference: Li Q, Cheng F, You J. 2026. Large language models in aquatic risk assessment: research status and future perspectives. Environmental and Biogeochemical Processes 2: e007 doi: 10.48130/ebp-0026-0002
https://www.maxapress.com/article/doi/10.48130/ebp-0026-0002
===
About the Journal:
Environmental and Biogeochemical Processes (e-ISSN 3070-1708) is a multidisciplinary platform for communicating advances in fundamental and applied research on the interactions and processes involving the cycling of elements and compounds between the biological, geological, and chemical components of the environment.
