AIMC Topic: Water Pollutants, Chemical

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Interpretable machine learning framework for predicting pesticide phytotoxicity in wastewater reuse: Integrating molecular, quantum, and experimental descriptors.

Environmental research
Pesticides are essential for crop protection, but their potential toxicity poses significant environmental and health risks. Although numerous toxicological studies have been conducted, accurately predicting pesticide phytotoxicity remains challengin...

Anaerobic microbial degradation of persistent organic pollutants in aquatic sediments: implications of climate change.

Archives of microbiology
Persistent organic pollutants (POPs) are harmful chemicals that resist degradation and remain in the environment for a long time. These pollutants originate from various sources, such as industrial, agricultural, and waste disposal. They contaminate ...

Bacterial cellulose for emerging contaminants: A review of applications for PFAS, nanoplastics, and endocrine disruptors in water treatment.

The Science of the total environment
Emerging contaminants, including per- and polyfluoroalkyl substances (PFAS), nanoplastics, and endocrine-disrupting chemicals (EDCs), pose significant threats to water quality due to their persistence, toxicity, and resistance to conventional treatme...

Explainable machine-learning-based predictions of blood lead levels and school drinking water contamination among children: a case study in Washington DC.

Scientific reports
Water quality degradation poses significant risks to human health, ecosystem, and community. Many cities continue to rely on outdated pipes and water distribution networks that are highly susceptible to leaks, corrosion, and lead contamination. The p...

Multivariate Functional Data Analysis Uncovers Behavioral Fingerprints in Invertebrate Locomotor Response to Micropollutants.

Environmental science & technology
The need for effective biomonitoring in wastewater has become clear due to the impracticality of continuously tracking all chemicals and emerging contaminants in the aquatic exposome. Effect-based biomonitoring provides a cost-effective solution. The...

Deep Fuzzy-NN modeling for the prediction of Zn(II) adsorption in columns using alkaline modified biochar: Integrated experimental and computational insights.

Environmental research
The precise prediction of adsorption process is significant in the optimization of pollutant removal systems. In this research, deep fuzzy neural network (DFNN) model was developed for the prediction of Zn(II) removal efficiency using alkaline activa...

Homogeneous multi-antibiotics residual identification in various actual water via SERS spectra multilayer perceptron algorithm combined with Gaussian kernel density estimation data augmentation.

Analytica chimica acta
BACKGROUND: Antibiotic residues pose varying degrees of potential hazards to the water environment and human health due to their diverse types. Surface-enhanced Raman spectroscopy (SERS) technology can achieve rapid detection of various antibiotic re...

Diffusion for Diffusion: A versatile multiphysics fields refinement framework in pollutants transportation.

Water research
Refining coupled multiphysics fields in pollutant transport from sparse measurements is critical for environmental risk assessment and industrial pollution mitigation. However, existing numerical solvers and machine learning architectures exhibit inh...

Generative AI-Empowered Screening Strategy for Chemical Pollutants: A Case on Per- and Polyfluoroalkyl Substances.

Environmental science & technology
Identifying unknown chemical pollutants is essential for effective risk management. However, current analytical techniques are restricted to structures covered by existing databases, capturing only the tip of the iceberg in the vast pollutant chemica...

Removal of a quaternary ammonium compound by electrocoagulation: Mechanistic analysis and multi-response optimization using response surface methodology and machine learning.

Water research
The widespread use of quaternary ammonium compounds (QACs), intensified by the COVID-19 pandemic, has led to their increasing presence in aquatic environments, thereby demanding effective treatment strategies for shock loads from industrial discharge...