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...
Iodine Water Pollutants, Chemical Water Purification Halogenation Trihalomethanes Disinfectants Disinfection Machine Learning
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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...
Water Supply Trihalomethanes Drinking Water Water Pollutants, Chemical Disinfectants Disinfection Water Purification Ontario Machine Learning Neural Networks, Computer
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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...
Machine Learning Hydrocarbons, Brominated Lipid Bilayers Molecular Dynamics Simulation Hydrophobic and Hydrophilic Interactions Trihalomethanes Climate Change
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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...
Water Supply Trihalomethanes Water Quality Drinking Water Chlorine Models, Theoretical Water Pollutants, Chemical Machine Learning
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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...
Drinking Water Environmental Monitoring Disinfectants Water Purification Disinfection Neural Networks, Computer Trihalomethanes Water Pollutants, Chemical China
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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 ...
Environmental Monitoring Water Pollutants, Chemical Machine Learning Humans Linear Models Water Supply Water Quality Drinking Water Trihalomethanes
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