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Alleviating Health Risks for Water Safety: A Systematic Review on Artificial Intelligence-Assisted Modelling of Proximity-Dependent Emerging Pollutants in Aquatic Systems
Summary
This systematic review summarizes how artificial intelligence can help track emerging pollutants, including microplastics, in water systems. It highlights that AI-driven models can predict contamination patterns more efficiently than traditional methods, which could help protect drinking water safety and public health.
Emerging pollutants such as pharmaceuticals, industrial chemicals, heavy metals, and microplastics are a growing ecological risk affecting water and soil resources. Another challenge in current wastewater treatments includes tracking and treating these pollutants, which can be costly. As a growing concern, emerging pollutants do not have lower limit levels and can be detrimental to aquatic resources in minuscule amounts. Thus, the assessment of multiple emerging water pollutants in community-based water sources such as surface water and groundwater is a prioritized area of study for water resource management. It provides a basis for the ecological health management of arising diseases such as cancer and dengue caused by unsafe water sources. Accordingly, by utilizing artificial intelligence, wide-range and data-driven insights can be synthesized to assist in water resource management and propose solution pathways without the need for exhaustive experimentation. This systematic review examines the artificial intelligence-assisted modelling of water resource management for emerging water pollutants, notably machine learning and deep learning models, with proximity dependence and correlated synergistic health effects for both humans and aquatic life. This study underscores the increasing accumulation of these emerging pollutants and their toxicological effects on the community and how data-driven modelling can be utilized to assist in addressing research gaps related to water treatment methods for these pollutants.
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