AIMC Topic: Water Pollutants, Chemical
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Sentinel-2 imagery coupled with machine learning to modelling water turbidity in the Doce River Basin, Brazil.
Environmental monitoring and assessment
Mar 22, 2025
Remote sensing and machine learning are techniques that can be used to monitor water quality properties, surpassing the limitations of the conventional techniques. Turbidity is an important water quality property directly influenced by the Fundão dam...
Antioxidant activity of Mentha piperita phenolics on arsenic induced oxidative stress, biochemical alterations, and cyto-genotoxicity in fish, Channa punctatus.
Fish physiology and biochemistry
Mar 20, 2025
The study aims to investigate the synergistic antioxidant effects of the phenolics present in Mentha piperita (MP) against arsenic trioxide-induced oxidative stress, biochemical alteration, and cyto-genotoxicity in the fish, Channa punctatus. The phe...
Artificial intelligence based detection and control strategies for river water pollution: A comprehensive review.
Journal of contaminant hydrology
Mar 17, 2025
Water quality (WQ) is a metric for assessing the overall health and safety of water bodies like a river. Owing to the habitation of anthropogenic habitation around its basin, the rivers can become one of the most contaminated water sources globally. ...
Integrated machine learning based groundwater quality prediction through groundwater quality index for drinking purposes in a semi-arid river basin of south India.
Environmental geochemistry and health
Mar 14, 2025
The main objective of this study is to predict and monitor groundwater quality through the use of modern Machine Learning (ML) techniques. By employing ML techniques, the research effectively evaluates groundwater quality to forecast its future trend...
AI-aided chronic mixture risk assessment along a small European river reveals multiple sites at risk and pharmaceuticals being the main risk drivers.
Environment international
Mar 13, 2025
The vast amount of registered chemicals leads to a high diversity of substances occurring in the environment and the creation of new substances outpaces chemical risk assessment as well as monitoring strategies. Hence, risk assessment strategies need...
Prediction of chlorination degradation rate of emerging contaminants based on machine learning models.
Environmental pollution (Barking, Essex : 1987)
Mar 4, 2025
Assessing the degradation of emerging contaminants in water through chlorination is crucial for regulatory monitoring of these contaminants. In this study, we developed a machine learning model to predict the apparent second-order reaction rate const...
Prediction of surface water pollution using wavelet transform and 1D-CNN.
Water science and technology : a journal of the International Association on Water Pollution Research
Mar 4, 2025
Permanganate index (COD), total nitrogen, and ammonia nitrogen are important indicators that represent the degree of pollution of surface water. This study combined ultraviolet-visible (UV-vis) spectroscopy with a one-dimensional convolutional neural...
Tracking the spatiotemporal evolution of groundwater chemistry in the Quaternary aquifer system of Debrecen area, Hungary: integration of classical and unsupervised learning methods.
Environmental science and pollution research international
Mar 1, 2025
Monitoring changes in groundwater quality over time helps identify time-dependent factors influencing water safety and supports the development of effective management strategies. This study investigates the spatiotemporal evolution of groundwater ch...
Developing a real-time water quality simulation toolbox using machine learning and application programming interface.
Journal of environmental management
Feb 28, 2025
Rivers are vital for sustaining human life as they foster social development, provide drinking water, maintain aquatic ecosystems, and offer recreational spaces. However, most rivers are being increasingly contaminated by pollutants from non-point so...
Comparative immobilization of 30 PFAS mixtures onto biochar, clay, nanoparticle, and polymer derived engineered adsorbents: Machine learning insights into carbon chain length and removal mechanism.
Journal of hazardous materials
Feb 25, 2025
Per- and polyfluoroalkyl substances (PFAS) are a group of fluorinated chemicals that cause potential risk in PFAS-impacted soil and water. The adsorption efficiency of 30 PFAS mixtures using different adsorbents in environmentally relevant concentrat...
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