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Water Pollutants, Chemical

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Elucidating and forecasting the organochlorine pesticides in suspended particulate matter by a two-stage decomposition based interpretable deep learning approach.

Water research
Accurately predicting the concentration of organochlorine pesticides (OCPs) presents a challenge due to their complex sources and environmental behaviors. In this study, we introduced a novel and advanced model that combined the power of three distin...

Utilizing machine learning to evaluate heavy metal pollution in the world's largest mangrove forest.

The Science of the total environment
The world's largest mangrove forest (Sundarbans) is facing an imminent threat from heavy metal pollution, posing grave ecological and human health risks. Developing an accurate predictive model for heavy metal content in this area has been challengin...

Machine Learning-Assisted Eu(III)-Functionalized HOF-on-HOF Composite-Based Sensor Platform for Precise and Visual Identification of Multiple Pesticides.

Analytical chemistry
Precise and rapid identification of pesticides is crucial to ensure a green environment, food safety, and human health. However, complex sample environments often hinder precise identification, especially for simultaneous differentiation of multiple ...

Exploring the potential of machine learning to understand the occurrence and health risks of haloacetic acids in a drinking water distribution system.

The Science of the total environment
Determining the occurrence of disinfection byproducts (DBPs) in drinking water distribution system (DWDS) remains challenging. Predicting DBPs using readily available water quality parameters can help to understand DBPs associated risks and capture t...

Machine learning based-model to predict catalytic performance on removal of hazardous nitrophenols and azo dyes pollutants from wastewater.

International journal of biological macromolecules
To maintain human health and purity of drinking water, it is crucial to eliminate harmful chemicals such as nitrophenols and azo dyes, considering their natural presence in the surroundings. In this particular research study, the application of machi...

Machine learning-driven QSAR models for predicting the cytotoxicity of five common microplastics.

Toxicology
In the field of microplastics (MPs) toxicity prediction, machine learning (ML) computer simulation techniques are showing great potential. In this study, six ML algorithms were utilized to predict the toxicity of MPs on BEAS-2B cells based on quantit...

Optimized phenol degradation and lipid production by Rhodosporidium toruloides using response surface methodology and genetic algorithm-optimized artificial neural network.

Chemosphere
Oleaginous yeast can produce lipids while degrading phenol in wastewater treatment. In this study, a Plackett-Burman Design (PBD) was adopted to identify key factors of phenol degradation and lipid production using R toruloides 9564. While temperatur...

Plastic particles and fluorescent brightener co-modify Chlorella pyrenoidosa photosynthesis and a machine learning approach predict algae growth.

Journal of hazardous materials
Global release of plastics exerts various impacts on the ecological cycle, particularly on primary photosynthesis, while the impacts of plastic additives are unknown. As a carrier of fluorescent brightener, plastic particles co-modify Chlorella pyren...

Machine learning-based analysis of heavy metal contamination in Chinese lake basin sediments: Assessing influencing factors and policy implications.

Ecotoxicology and environmental safety
Sediments are important heavy metal sinks in lakes, crucial for ensuring water environment safety. Existing studies mainly focused on well-studied lakes, leaving gaps in understanding pollution patterns in specific basins and influencing factors.We c...

Pollution loads in the middle-lower Yangtze river by coupling water quality models with machine learning.

Water research
Pollution control and environmental protection of the Yangtze River have received major attention in China. However, modeling the river's pollution load remains challenging due to limited monitoring and unclear spatiotemporal distribution of pollutio...