AIMC Topic: Trihalomethanes
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From Model Development to Mitigation: Machine Learning for Predicting and Minimizing Iodinated Trihalomethanes in Water Treatment.
Environmental science & technology
Jun 3, 2025
Disinfection processes in water treatment produce disinfection byproducts (DBPs), such as iodinated trihalomethanes (I-THMs), which pose significant health risks. Mitigating I-THMs remains challenging due to the complex interactions among water quali...
Predicting few disinfection byproducts in the water distribution systems using machine learning models.
Environmental science and pollution research international
Jan 20, 2025
Concerns regarding disinfection byproducts (DBPs) in drinking water persist, with measurements in water treatment plants (WTPs) being relatively easier than those in water distribution systems (WDSs) due to accessibility challenges, especially during...
Investigating the Bromoform Membrane Interactions Using Atomistic Simulations and Machine Learning: Implications for Climate Change Mitigation.
The journal of physical chemistry. B
Dec 6, 2024
Methane emissions from livestock contribute to global warming. Seaweeds used as food additive offer a promising emission mitigation strategy because seaweeds are enriched in bromoform─a methanogenesis inhibitor. Therefore, understanding bromoform sto...
The insightful water quality analysis and predictive model establishment via machine learning in dual-source drinking water distribution system.
Environmental research
Feb 16, 2024
Dual-source drinking water distribution systems (DWDS) over single-source water supply systems are becoming more practical in providing water for megacities. However, the more complex water supply problems are also generated, especially at the hydrau...
Radial basis function artificial neural network able to accurately predict disinfection by-product levels in tap water: Taking haloacetic acids as a case study.
Chemosphere
Jan 22, 2020
Control of risks caused by disinfection by-products (DBPs) requires pre-knowledge of their levels in drinking water. In this study, a radial basis function (RBF) artificial neural network (ANN) was proposed to predict the concentrations of haloacetic...
Prediction of trihalomethane occurrence and cancer risk using interpretable machine learning and virtual data augmentation.
Journal of hazardous materials
Aug 15, 2025
Trihalomethanes (THMs) in drinking water are regulated for carcinogenic health risks. However, frequent water quality monitoring imposes significant resource burdens. This study proposes a framework integrating interpretable machine learning (ML) wit...
Performance analysis of machine learning algorithms for the prediction of disinfection byproducts formation during chlorination: Effect of background water characteristics.
Journal of environmental management
Aug 1, 2025
This study investigated the comparison of the nonlinear machine learning algorithms and linear regression models to predict the formation of trihalomethanes (THM4), haloacetic acids (HAA5 and HAA9), and haloacetonitriles (HAN4 and HAN6) under uniform...
Enhancing drinking water safety: Real-time prediction of trihalomethanes in a water distribution system using machine learning and multisensory technology.
Ecotoxicology and environmental safety
Jun 1, 2025
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 ...
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