AIMC Topic: Water Pollutants, Chemical

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Machine learning-driven analysis of soil microplastic distribution in the Bang Pakong Watershed, Thailand.

Environmental pollution (Barking, Essex : 1987)
Microplastics (MPs) have emerged as a pervasive environmental pollutant due to their persistence and global distribution. However, MPs relationships with covariables remain largely unexplored. This study investigates factors influencing MPs occurrenc...

Machine Learning Reveals Key Adsorption Mechanisms for Oxyanions Based on Combination of Experimental and Published Literature Data.

Environmental science & technology
The development of new adsorbents for water treatment often involves complex adsorption mechanisms, whose individual contributions are unclear, thereby limiting the understanding of adsorption driving forces, making it difficult to achieve precise de...

Hybrid Physical Mechanism and Artificial Intelligence-Based Model for Evaluating Nonpoint Source Pesticide Pollution at a Megacity Scale.

Environmental science & technology
Large-scale nonpoint source (NPS) pesticide pollution is a growing concern in urban areas; however, modeling of such pollution is constrained by challenges in acquiring urban pipeline data and the scarcity of pollutant monitoring data. This study pre...

Advancements in artificial intelligence-based technologies for PFAS detection, monitoring, and management.

The Science of the total environment
Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants with strong carbon‑fluorine (CF) bonds that contribute to bioaccumulation and long-term environmental and health risks. Traditional PFAS detection and treatment meth...

A Comprehensive Exploration of Groundwater Quality of Ambagarh Chowki Region, Chhattisgarh, India: Water Quality Index, Health Risk, and ANN Predictive Modeling.

Water environment research : a research publication of the Water Environment Federation
Access to safe and clean drinking water remains a critical global challenge, with groundwater as a primary source for billions of people. Further, toxic contaminants increasingly threaten groundwater quality, posing significant health risks. This stu...

Microbial degradation potential of microplastics in urban river sediments: Assessing and predicting the enrichment of PE/PP-degrading bacteria using SourceTracker and machine learning.

Journal of environmental management
Microplastic mitigation strategies that adapt to various actual aquatic environments require the ability to predict their microbial degradation potential. However, the sources and enrichment characteristics of the degrading bacteria in the plastisphe...

Machine learning approach for photocatalysis: An experimentally validated case study of photocatalytic dye degradation.

Journal of environmental management
In this study, machine learning (ML) models coupled with genetic algorithm (GA) and particle swarm optimization (PSO) were applied to predict the relative influence of experimental parameters of photocatalytic dye removal. Specifically, the impact of...

A machine learning multimodal profiling of Per- and Polyfluoroalkyls (PFAS) distribution across animal species organs via clustering and dimensionality reduction techniques.

Food research international (Ottawa, Ont.)
Per- and polyfluoroalkyl substances (PFAS) contamination in aquatic and terrestrial organisms poses significant environmental and health risks. This study quantified 15 PFAS compounds across various tissues (liver, kidney, gill, muscle, skin, lung, b...

Enhancing photocatalytic degradation of hazardous pollutants with green-synthesized catalysts: A machine learning approach.

Journal of environmental management
The effective removal of organic pollutants from wastewater necessitates the development of advanced photocatalytic materials. This study explores the application of machine learning algorithms to predict the degradation efficiency of PRM using green...

Enhancing drinking water safety: Real-time prediction of trihalomethanes in a water distribution system using machine learning and multisensory technology.

Ecotoxicology and environmental safety
Prolonged exposure to high concentrations of trihalomethanes (THMs) may generate human health risks due to their carcinogenic and mutagenic properties. Therefore, monitoring THMs in drinking water distribution systems (DWDS) is essential. This study ...