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Water Pollutants, Chemical

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Explainable machine learning models for predicting the acute toxicity of pesticides to sheepshead minnow (Cyprinodon variegatus).

The Science of the total environment
A quantitative structure-activity relationship (QSAR) study was conducted on 313 pesticides to predict their acute toxicity to Sheepshead minnow (Cyprinodon variegatus) by using DRAGON descriptors. Essentials accounting for a reliable model were all ...

Identifying and quantifying multiple pollution sources in estuaries using fluorescence spectra and gradient-based deep learning.

Marine pollution bulletin
This study developed an intelligent method for identifying and quantifying water pollution sources in estuarine areas. It characterized the excitation-emission matrix (EEM) fluorescence spectra from seven end-members, including seawater, rainwater, a...

Unravelling integrated groundwater management in pollution-prone agricultural cities: A synergistic approach combining probabilistic risk, source apportionment and artificial intelligence.

Journal of hazardous materials
Groundwater is vital for agricultural cities, but intensive farming and fertilizer use have increased contamination risks, particularly for non-carcinogenic health hazards. This study reveals the sources of contaminants in groundwater, their health i...

Efficient deep learning surrogate method for predicting the transport of particle patches in coastal environments.

Marine pollution bulletin
Several coastal regions require operational forecast systems for predicting the transport of pollutants released during marine accidents. In response to this need, surrogate models offer cost-effective solutions. Here, we propose a surrogate modeling...

Applications of artificial intelligence for chemical analysis and monitoring of pharmaceutical and personal care products in water and wastewater: A review.

Chemosphere
Specifying and interpreting the occurrence of emerging pollutants is essential for assessing treatment processes and plants, conducting wastewater-based epidemiology, and advancing environmental toxicology research. In recent years, artificial intell...

Machine learning integration with response surface methodology to enhance the removal efficacy of arsenate (V) through sulfur-functionalized mxene coated QPPO/PVA AEM.

Journal of environmental management
Arsenic, a poisonous and carcinogenic heavy metal in drinking water, presents severe health risks to humans, including skin lesions, neurological damage, and circulatory disorders. Despite extensive research efforts have been carried out on removing ...

Assessment of groundwater quality variation characteristics and influencing factors in an intensified agricultural area: An integrated hydrochemical and machine learning approach.

Journal of environmental management
The decline in groundwater quality in intensive agricultural areas in recent years, driven by environmental change and intensified human activity, poses a significant threat to agricultural production and public health, requiring attention and effect...

Nanomaterial Texture-Based Machine Learning of Ciprofloxacin Adsorption on Nanoporous Carbon.

International journal of molecular sciences
Drug substances in water bodies and groundwater have become a significant threat to the surrounding environment. This study focuses on the ability of the nanoporous carbon materials to remove ciprofloxacin from aqueous solutions under specific experi...

Deep learning-assisted detection of psychoactive water pollutants using behavioral profiling of zebrafish embryos.

Journal of hazardous materials
Water pollution poses a significant risk to the environment and human health, necessitating the development of innovative detection methods. In this study, a series of representative psychoactive compounds were selected as model pollutants, and a new...

Biosorption of cobalt and chromium from wastewater using manganese dioxide and iron oxide nanoparticles loaded on cellulose-based biochar: Modeling and optimization with machine learning (artificial neural network).

International journal of biological macromolecules
In this study, two nanomaterials with excellent adsorption capacities were developed to remove heavy metals efficiently from wastewater. Manganese dioxide MnO nanoparticles and iron oxide FeO nanoparticles were successfully synthesized using cassava ...