AIMC Topic: Water Pollutants, Chemical

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Machine learning prediction of DOC-water partitioning coefficients for organic pollutants from diverse DOM origins.

Environmental science. Processes & impacts
This study aims to improve predictions and understanding of dissolved organic carbon-water partitioning coefficients (), a crucial parameter in environmental risk assessment. A dataset encompassing 709 datapoints across 190 unique organic pollutants ...

Ligand Microenvironment-Regulated Nanozymes Enabled Machine Learning-Assisted Sensor Array for Simultaneous Identification of Phenolic Pollutants.

ACS sensors
Phenolic pollutants pose a great threat to human health due to high toxicity, whereas existing methods are difficult to achieve the rapid recognition of multiple phenolic pollutants. In this study, we developed a novel machine learning-assisted senso...

Predicting the Site-Specific Toxicity of Metals to Fishes Using a New Machine Learning-Based Approach.

Environmental science & technology
Fishes of various trophic levels play an important role in the stability and balance of aquatic ecosystems. Metal contaminants can impair the survival and population fitness of fish at elevated concentrations. When universal water quality criteria (W...

Matrix-specific PFAS source allocation machine learning-based models: Identifying differential indicators in soil and water systems.

Environmental research
PER: and polyfluoroalkyl substances (PFAS) pose a significant environmental and human health risk due to their persistence, bioaccumulation, mobility, and toxicity. Current PFAS source-allocation methods often demand data on many compounds while over...

Monitoring the dynamics of irrigated parcels and impacts on phreatic water quality in the Mostaganem Plateau (northwestern Algeria): an integrated analysis using remote sensing and field data for 2010 and 2020.

Environmental monitoring and assessment
Since the early 2000s, Algeria has implemented several agricultural policies to expand its irrigated areas and enhance its national food security. While these efforts have significantly increased irrigated land, they have raised concerns about ground...

Modeling water quality in the brazilian semiarid region using remote sensing: support for water management.

Environmental monitoring and assessment
Water management in semi-arid regions faces challenges due to water scarcity and the need for continuous quality monitoring. This study evaluates the use of remote sensing to analyze a reservoir's water quality status in Brazil's semi-arid region to ...

Prediction of water quality parameters and pollution exceedance analysis in typical rivers of semi-arid regions based on interpretable deep learning models.

Environmental pollution (Barking, Essex : 1987)
Deep learning models that integrate environmental characteristics provide a powerful means for high-precision water quality prediction; however, their black-box nature can limit interpretability and reliability. We proposed an interpretable Attention...

Exploiting the gut microbiota of aquatic animals as indicators of microplastic pollution using interpretable machine learning models.

Journal of hazardous materials
The response of aquatic animal gut microbiota to microplastics has been extensively studied and shows sensitivity, however, the potential of using gut microbiota as indicators for microplastic pollution has not yet been fully explored. To address thi...

Event-Driven Taxonomy (EDT) Screening: Leveraging Effect-Based Spectral Libraries to Accelerate Semiquantitative Nontarget Analysis of AhR Agonists in Sediment in the Era of Big Data.

Environmental science & technology
Sediments contain complex chemical mixtures. While effect-directed analysis (EDA) combined with nontarget screening (NTS) is promising, its large-scale application has been limited by time-consuming workflows. Here, we developed an event-driven taxon...

Harnessing deep learning for fusion-based heavy metal contamination index prediction in groundwater.

Journal of contaminant hydrology
Groundwater contamination by heavy metals presents a major environmental threat with serious implications for public health and resource sustainability. This study proposes a novel deep learning-based data fusion framework to predict heavy metal cont...