What Are the Applications of AI in Environmental Health?

By Rasit Dinc

Artificial intelligence (AI) is rapidly transforming various sectors, and environmental health is no exception. The integration of AI technologies, particularly machine learning (ML), is providing innovative solutions to complex environmental challenges, offering new avenues for research, and enhancing our ability to protect public health. This article explores the diverse applications of AI in environmental health, drawing on recent academic research to highlight the significant strides being made in this critical field.

One of the most significant applications of AI in environmental health is in the treatment of water pollution. Researchers are leveraging ML models to predict the effectiveness of different materials in removing pollutants from water. For instance, a study published in Environment International demonstrated the use of ML to screen for the most effective resins for removing perfluoroalkyl and polyfluoroalkyl substances (PFASs) from water [1]. By analyzing a large dataset of resin properties and operating conditions, the ML model could accurately predict the removal efficiency, thereby optimizing the treatment process. Furthermore, AI is being used to forecast the quality of effluent from wastewater treatment plants (WWTPs) and to predict the global distribution of pollutants in groundwater, enabling early warnings for affected populations [1].

In the realm of air pollution control, AI is proving to be a powerful tool for mitigating the impact of harmful emissions. Researchers are using ML algorithms to predict the performance of materials in capturing greenhouse gases like carbon dioxide (CO2). By combining ML with density functional theory (DFT), scientists can screen for optimal materials for pollutant removal with remarkable accuracy [1]. AI models are also being used to simulate the global distribution of air pollutants, such as methane, providing valuable insights into their sources and transport patterns. This information is crucial for developing effective strategies to reduce air pollution and its associated health risks.

AI is also making significant contributions to soil remediation. ML models are being used to predict the effectiveness of biochar in immobilizing heavy metals in soil, a major environmental concern. A study found that the nitrogen content and application rate of biochar were the most critical factors influencing its immobilization efficiency, with an ML model accurately predicting the outcomes [1]. Additionally, AI is being used to predict the spatio-temporal evolution of soil pollution, such as arsenic contamination, helping to identify high-risk areas and inform remediation efforts.

Perhaps one of the most critical applications of AI is in environmental health risk assessment. AI-powered models are being used to assess the health risks associated with exposure to environmental pollutants. For example, researchers have used ML to evaluate the combined reproductive exposure risks of phthalates (PAEs) and organophosphate esters (OPEs) in atmospheric particulate matter [1]. By analyzing complex datasets, these models can identify potential health risks and inform public health interventions. AI is also being used to identify new environmental obesogens, substances that can contribute to obesity, by analyzing their molecular structures and predicting their biological activity.

In conclusion, AI is revolutionizing the field of environmental health, offering powerful new tools to address some of the most pressing environmental challenges of our time. From treating polluted water and air to remediating contaminated soil and assessing health risks, AI is enabling scientists and health professionals to work more efficiently and effectively. As AI technologies continue to advance, we can expect to see even more innovative applications that will help us to create a healthier and more sustainable future for all.

References

[1] Guo, Z., Huang, L., Yan, J., Zhang, H., Jia, X., Li, M., & Li, H. (2025). Artificial intelligence technology in environmental research and health: Development and prospects. Environment International, 203, 109788. https://doi.org/10.1016/j.envint.2025.109788