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An artificial intelligence-based model for predicting reproductive toxicity of bisphenol analogues mixtures to the rotifer Brachionus calyciflorus.

The Science of the total environment
The joint toxicity effects of mixtures, particularly reproductive toxicity, one of the main causes of aquatic ecosystem degradation, are often overlooked as it is impractical to test all mixtures. This study developed and evaluated the following mode...

Application of machine learning in the study of cobalt-based oxide catalysts for antibiotic degradation: An innovative reverse synthesis strategy.

Journal of hazardous materials
This study addresses antibiotic pollution in global water bodies by integrating machine learning and optimization algorithms to develop a novel reverse synthesis strategy for inorganic catalysts. We meticulously analyzed data from 96 studies, ensurin...

Identification of agricultural surface source pollution in plain river network areas based on 3D-EEMs and convolutional neural networks.

Water science and technology : a journal of the International Association on Water Pollution Research
Agricultural non-point sources, as major sources of organic pollution, continue to flow into the river network area of the Jiangnan Plain, posing a serious threat to the quality of water bodies, the ecological environment, and human health. Therefore...

Predictive modeling of copper (II) adsorption from aqueous solutions by sawdust: a comparative analysis of adaptive neuro-fuzzy interference system (ANFIS) and artificial neural network (ANN) approaches.

Journal of environmental science and health. Part A, Toxic/hazardous substances & environmental engineering
Heavy metal ions are considered to be the most prevalent and toxic water contaminants. The objective of thois work was to investigate the effectiveness of employing the adsorption technique in a laboratory-size reactor to remove copper (II) ions from...

Machine learning-assisted chromium speciation using a single-well ratiometric fluorescent nanoprobe.

Chemosphere
Chromium is widely recognized as a significant pollutant discharged into the environment by various industrial activities. The toxicity of this element is dependent on its oxidation state, making speciation analysis crucial for monitoring the quality...

Applications of artificial intelligence (AI) in drinking water treatment processes: Possibilities.

Chemosphere
In water treatment processes (WTPs), artificial intelligence (AI) based techniques, particularly machine learning (ML) models have been increasingly applied in decision-making activities, process control and optimization, and cost management. At leas...

Integrated AI-driven optimization of Fenton process for the treatment of antibiotic sulfamethoxazole: Insights into mechanistic approach.

Chemosphere
Antibiotics, as a class of environmental pollutants, pose a significant challenge due to their persistent nature and resistance to easy degradation. This study delves into modeling and optimizing conventional Fenton degradation of antibiotic sulfamet...

Prediction of Cr(VI) and As(V) adsorption on goethite using hybrid surface complexation-machine learning model.

Water research
This study aimed to develop surface complexation modeling-machine learning (SCM-ML) hybrid model for chromate and arsenate adsorption on goethite. The feasibility of two SCM-ML hybrid modeling approaches was investigated. Firstly, we attempted to uti...

Amino-functionalized novel biosorbent for effective removal of fluoride from water: process optimization using artificial neural network and mechanistic insights.

Environmental science and pollution research international
Aqueous fluoride ( ) pollution is a global threat to potable water security. The present research envisions the development of novel adsorbents from indigenous Limonia acidissima L. (fruit pericarp) for effective aqueous defluoridation. The adsorben...