AIMC Topic: Water Pollutants, Chemical

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Prediction of trihalomethane occurrence and cancer risk using interpretable machine learning and virtual data augmentation.

Journal of hazardous materials
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...

Predicting estrogen receptor agonists from plastic additives across various aquatic-related species using machine learning and AlphaFold2.

Journal of hazardous materials
The absence of effective public databases greatly limits high-throughput prediction of hormonal effects mediated by nuclear receptors in aquatic organisms. In this study, we developed novel strategies for multi-species screening of estrogen receptor ...

One-pot synthesized multifunctional Zn-MOF/HOF heterostructure sensor array assisted by machine learning for efficient capture, target discrimination and optosmart sensing of doxycycline analogs.

Journal of hazardous materials
The ideal multifunctional platform that combines the capabilities of effective capture, sensitive detection, and accurate identification of doxycycline analogs (DCs) remains a serious challenge for ensuring the environment and food security. This wor...

Advancing wetland groundwater pollution zoning: A novel integration of Monte Carlo health risk modeling and machine learning.

Journal of hazardous materials
Wetlands serve as crucial water reservoirs, providing essential water resources for the surrounding regions. However, elevated ion concentrations in wetland groundwater may pose health risks to local populations. This study focused on Judian Lake and...

Machine Learning-Assisted Tissue-Residue-Based Risk Assessment for Protecting Threatened and Endangered Fishes in the Yangtze River Basin.

Environmental science & technology
Assessing pollutant risks to threatened and endangered (T&E) species is crucial for their conservation. However, traditional risk assessment methods for bioaccumulative pollutants to T&E fishes is challenging due to uncertainties in exposure-based to...

Assessing risk of groundwater pollution exposure from sea level rise in California.

The Science of the total environment
Sea level rise (SLR) will cause a groundwater table rise in coastal aquifers, and this can trigger exposure to toxic chemicals via direct contact with contaminated water or through vapor intrusion. This study presents deep learning- and explainable a...

TP-Transformer: An Interpretable Model for Predicting the Transformation Pathways of Organic Pollutants in Chemical Oxidation Processes.

Environmental science & technology
Chemical oxidation is pivotal in remediating organic pollutants in aquatic systems; however, it frequently yields transformation products (TPs) with potential toxicological profiles surpassing those of the parent pollutants. Comprehensive identificat...

Performance analysis of machine learning algorithms for the prediction of disinfection byproducts formation during chlorination: Effect of background water characteristics.

Journal of environmental management
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...

Artificial intelligence (AI)-driven morphological assessment of zebrafish larvae for developmental toxicity chemical screening.

Aquatic toxicology (Amsterdam, Netherlands)
Screening chemicals using the zebrafish embryo developmental toxicity assay requires visual assessment of larval morphological changes based on images by experienced screeners. The process is time-consuming and prone to subjectivity. However, deep le...

Unique bioaccumulation and biosynthesis of arsenobetaine in marine fish.

Aquatic toxicology (Amsterdam, Netherlands)
Arsenic (As) contamination represents a significant global concern, particularly prevalent in regions such as China, South Asia, and Southeast Asia. Arsenic permeates the food chain, posing potential hazards to ecosystems and human health. Studies ha...