AIMC Topic: Water Pollutants, Chemical

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Robust S3Former deep learning model for the direct diagnosis and prediction of natural organic matter (NOM) from three-dimensional excitation-emission-matrix (3D-EEM) data.

Water research
The non-destructive, three-dimensional excitation-emission matrix (3D-EEM) based on fluorescence spectroscopy has been widely used in natural organic matter (NOM) monitoring in aquatic environments. However, the direct recognition of the species and ...

QSAR Model Development for the Environmental Risk Limits and High-Risk List Identification of Phenylurea Herbicides in Aquatic Environments.

Journal of agricultural and food chemistry
Due to the extensive residues of phenylurea herbicides (PUHs) in the environment, it is important for the ecological risk assessment of PUHs to determine their environmental risk limits and identify the high-risk PUHs. This study derived the environm...

Machine learning-driven 3D-QSAR models facilitated rapid on-site broad-spectrum immunoassay of (fluoro)quinolones using evanescent wave fiber-embedded optofluidic biochip.

Biosensors & bioelectronics
(Fluoro)quinolones (FQs) pose significant threats to public health due to their widespread use and persistence in food and water sources. Given the extensive variety of FQs, testing each compound individually is prohibitively expensive and time-consu...

From Model Development to Mitigation: Machine Learning for Predicting and Minimizing Iodinated Trihalomethanes in Water Treatment.

Environmental science & technology
Disinfection processes in water treatment produce disinfection byproducts (DBPs), such as iodinated trihalomethanes (I-THMs), which pose significant health risks. Mitigating I-THMs remains challenging due to the complex interactions among water quali...

Comparative analysis of SWAT and SWAT coupled with XGBoost model using Optuna hyperparameter optimization for nutrient simulation: A case study in the Upper Nan River basin, Thailand.

Journal of environmental management
Agricultural runoff leading to nitrate (NO-N) and orthophosphate (PO-P) contamination poses significant environmental and public health risks. This study integrates the Soil and Water Assessment Tool (SWAT) with eXtreme Gradient Boosting (XGBoost), o...

Occurrence, Sources, and Prioritization of Per- and Polyfluoroalkyl Substances (PFASs) in Drinking Water from Yangtze River Delta, China: Focusing on Emerging PFASs.

Molecules (Basel, Switzerland)
As regulations ban legacy PFASs, many emerging PFASs are being developed, leading to their release into the aquatic environment and drinking water. However, research studies on these emerging PFASs in drinking water are limited, and current standards...

A new method for drinking water quality risk assessment based on data-driven.

Environmental geochemistry and health
Risk assessment of water quality plays a crucial role in sustainable management of water resource. However, evaluating drinking water quality risk for different types of water within the same framework is a challenging task. The Water Quality Index (...

Machine learning-driven optical microfiltration device for improved nanoplastic sampling and detection in water systems.

Journal of hazardous materials
The rising presence of nanoplastics in water poses toxicity risks and long-term ecological and health impacts. Detecting nanoplastics remains challenging due to their small size, complex chemistry, and environmental interference. Traditional filtrati...

Unveiling PFAS hazard in European surface waters using an interpretable machine-learning model.

Environment international
Per- and polyfluoroalkyl substances (PFAS), commonly known as "forever chemicals", are ubiquitous in surface waters and potentially threaten human health and ecosystems. Despite extensive monitoring efforts, PFAS risk in European surface waters remai...

Data assimilation for prediction of ammonium in wastewater treatment plant: From physical to data driven models.

Water research
This study compares various modeling approaches to predict ammonium concentration in wastewater treatment plants (WWTPs), with a focus on integrating data assimilation techniques. It explores white-box, grey-box, and black-box models, evaluating thei...