AIMC Topic: Water Pollutants, Chemical

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Prediction of acute toxicity of organic contaminants to fish: Model development and a novel approach to identify reactive substructures.

Journal of hazardous materials
In this study, count-based Morgan fingerprints (CMF) were employed to represent the fundamental chemical structures of contaminants, and a neural network model (R² = 0.76) was developed to predict acute fish toxicity (AFT) of organic compounds. Model...

Prediction of chlorination degradation rate of emerging contaminants based on machine learning models.

Environmental pollution (Barking, Essex : 1987)
Assessing the degradation of emerging contaminants in water through chlorination is crucial for regulatory monitoring of these contaminants. In this study, we developed a machine learning model to predict the apparent second-order reaction rate const...

Prediction of surface water pollution using wavelet transform and 1D-CNN.

Water science and technology : a journal of the International Association on Water Pollution Research
Permanganate index (COD), total nitrogen, and ammonia nitrogen are important indicators that represent the degree of pollution of surface water. This study combined ultraviolet-visible (UV-vis) spectroscopy with a one-dimensional convolutional neural...

Tracking the spatiotemporal evolution of groundwater chemistry in the Quaternary aquifer system of Debrecen area, Hungary: integration of classical and unsupervised learning methods.

Environmental science and pollution research international
Monitoring changes in groundwater quality over time helps identify time-dependent factors influencing water safety and supports the development of effective management strategies. This study investigates the spatiotemporal evolution of groundwater ch...

Developing a real-time water quality simulation toolbox using machine learning and application programming interface.

Journal of environmental management
Rivers are vital for sustaining human life as they foster social development, provide drinking water, maintain aquatic ecosystems, and offer recreational spaces. However, most rivers are being increasingly contaminated by pollutants from non-point so...

Comparative immobilization of 30 PFAS mixtures onto biochar, clay, nanoparticle, and polymer derived engineered adsorbents: Machine learning insights into carbon chain length and removal mechanism.

Journal of hazardous materials
Per- and polyfluoroalkyl substances (PFAS) are a group of fluorinated chemicals that cause potential risk in PFAS-impacted soil and water. The adsorption efficiency of 30 PFAS mixtures using different adsorbents in environmentally relevant concentrat...

Dissolved organic carbon estimation in lakes: Improving machine learning with data augmentation on fusion of multi-sensor remote sensing observations.

Water research
This paper presents a novel approach for estimating Dissolved Organic Carbon (DOC) concentrations in lakes considering both carbon sources and sink operators. Despite the critical role of DOC, the combined application of machine learning, as a robust...

Driving factors of TOC concentrations in four different types of estuaries (canal, urban, agricultural, and natural estuaries) identified by machine learning technique.

Marine pollution bulletin
Mangroves are among the most significant organic carbon sinks on Earth. However, the drivers of mangrove carbon remain poorly understood due to the lack of data on organic carbon across different types of estuaries. In this study, boosted regression ...

Classification and predictive leaching risk assessment of construction and demolition waste using multivariate statistical and machine learning analyses.

Waste management (New York, N.Y.)
Managing construction and demolition waste (CDW) poses serious concerns regarding landfilling and recycling because of the potential release of hazardous elements after leaching. Ceramic materials such as bricks, tiles, and porcelain account for more...

Using artificial intelligence tools for data quality evaluation in the context of microplastic human health risk assessments.

Environment international
Concerns about the negative impacts of microplastics on human health are increasing in society, while exposure and risk assessments require high-quality, reliable data. Although quality assurance and -control (QA/QC) frameworks exist to evaluate the ...