AIMC Topic: Drinking Water
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Occurrence, Sources, and Prioritization of Per- and Polyfluoroalkyl Substances (PFASs) in Drinking Water from Yangtze River Delta, China: Focusing on Emerging PFASs.
Molecules (Basel, Switzerland)
May 25, 2025
As regulations ban legacy PFASs, many emerging PFASs are being developed, leading to their release into the aquatic environment and drinking water. However, research studies on these emerging PFASs in drinking water are limited, and current standards...
A new method for drinking water quality risk assessment based on data-driven.
Environmental geochemistry and health
May 21, 2025
Risk assessment of water quality plays a crucial role in sustainable management of water resource. However, evaluating drinking water quality risk for different types of water within the same framework is a challenging task. The Water Quality Index (...
Predicting determinants of unimproved water supply in Ethiopia using machine learning analysis of EDHS-2019 data.
Scientific reports
Apr 4, 2025
Over 2 billion people worldwide are impacted by the global dilemma of access to clean and safe drinking water. The problem is most acute in low-income nations, where many people still use unimproved water sources such as exposed wells and surface wat...
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...
Machine learning models for water safety enhancement.
Scientific reports
Jan 30, 2025
Humans encounter both natural and artificial radiation sources, including cosmic rays, primordial radionuclides, and radiation generated by human activities. These radionuclides can infiltrate the human body through various pathways, potentially lead...
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...
Geochemical evolution, geostatistical mapping and machine learning predictive modeling of groundwater fluoride: a case study of western Balochistan, Quetta.
Environmental geochemistry and health
Dec 24, 2024
Around 2.6 billion people are at risk of tooth carries and fluorosis worldwide. Quetta is the worst affected district in Balochistan plateau. Endemic abnormal groundwater fluoride ( ) lacks spatiotemporal studies. This research integrates geospatial...
Time series-based machine learning for forecasting multivariate water quality in full-scale drinking water treatment with various reagent dosages.
Water research
Nov 9, 2024
Accurately predicting drinking water quality is critical for intelligent water supply management and for maintaining the stability and efficiency of water treatment processes. This study presents an optimized time series machine learning approach for...
Advanced Low-Cost Technology for Assessing Metal Accumulation in the Body of a Metropolitan Resident Based on a Neural Network Model.
Sensors (Basel, Switzerland)
Nov 7, 2024
This study is devoted to creating a neural network technology for assessing metal accumulation in the body of a metropolis resident with short-term and long-term intake from anthropogenic sources. Direct assessment of metal retention in the human bod...
Machine learning for environmental justice: Dissecting an algorithmic approach to predict drinking water quality in California.
The Science of the total environment
Aug 24, 2024
The potential for machine learning to answer questions of environmental science, monitoring, and regulatory enforcement is evident, but there is cause for concern regarding potential embedded bias: algorithms can codify discrimination and exacerbate ...
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