Environmental Health

Machine Learning Based Water Quality Evolution and Pollution Identification in Reservoir Type Rivers.

Quantifying transport and transformation of pollutants in river systems regulated by reservoirs poses a long-standing scientific challenge. This mainly results from complex interactions between hydrodynamic and biogeochemical factors. In this study, we combined 48 months of high-frequency field monitoring data (January 2020 to December 2023) with Sentinel-2 multispectral imagery to explore the spa...

Air Pollution and Autism Spectrum Disorder: Unveiling Multipollutant Risks and Sociodemographic Influences in California.

BACKGROUND: Autism spectrum disorder (ASD) is a neurodevelopmental condition with increasing prevalence worldwide. Air pollution may be a major contributor to the rise in ASD cases. This study investigated how the risk of ASD associated with prenatal and early postnatal exposure to specific air pollutants is modified by key sociodemographic factors, exploring vulnerable exposure periods.

Widespread distribution of bacteria containing PETases with a functional motif across global oceans.

Accumulating evidence indicates that microorganisms respond to the ubiquitous plastic pollution by evolving plastic-degrading enzymes. However, the fu...

Integrated network toxicology, machine learning and molecular docking reveal the mechanism of benzopyrene-induced periodontitis.

BACKGROUND: Environmental pollutants, particularly from air pollution and tobacco smoke, have emerged as significant risk factors. Benzopyrene (BaP), ...

Urban-rural inequality in soil heavy metal health risks: Insights from Baoding, China.

Soil heavy metal contamination poses serious health risks, but few studies have quantitatively assessed disparities in these risks between urban and r...

Exploring Aerosol Vertical Distributions and Their Influencing Factors: Insight from MAX-DOAS and Machine Learning.

Understanding aerosol vertical distribution is crucial for aerosol pollution mitigation but is hindered by limited observational data. This study empl...

Towards sustainable solutions: Effective waste classification framework via enhanced deep convolutional neural networks.

As industrialization and the development of smart cities progress, effective waste collection, classification, and management have become increasingly...

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.

Agricultural runoff leading to nitrate (NO-N) and orthophosphate (PO-P) contamination poses significant environmental and public health risks. This st...

Role of astroglia and microglia in Alzheimer's disease and multiple therapeutic interventions.

Alzheimer's disease (AD) is characterized by deposition of amyloid-β (Aβ) and neurofibrillary tangles (NFTs) formed by aggregates of hyperphosphorylat...

Environmental Pollution: Statistical Approach on Mobile Tower Radiation.

BACKGROUND: The Electromagnetic Field (EMF) effect is considered an alarming human health issue, dependent on the use of mobile phones. Several nation...

Aerosol Measurements and Decadal Changes: The Role of Climatic Changes and How It Reflects in Respiratory Allergies and Asthma.

The causative agents of respiratory allergies are bioaerosols, such as house dust mite feces, pollen grains, and fungal spores. Climate change and urb...

AquaFlowNet a machine learning based framework for real time wastewater flow management and optimization.

This paper presents AquaFlowNet, a machine learning-based algorithm for real-time wastewater flow management. It addresses critical challenges related...

Machine learning models for forecasting microplastic dynamics in China's coastal waters.

Understanding spatial-temporal microplastic (MP) patterns and regional drivers in China's coastal waters is crucial for pollution interventions. Based...

Identifying factors influencing trace metal concentrations in urban residential soil using an optimal parameter-based geographical detector model.

Australia's national citizen science program VegeSafe has collected and analysed over 26,000 residential garden soil samples for their trace metal con...

Emerging technologies for assessing the occurrence, fate, effects, and remediation of plastics in the environment.

Plastic pollution and contamination originates from raw material handling, polymerization, compounding, and fabrication, contributing to environmental...

Systematic Exploration of Potential Toxicity Targets and Molecular Mechanisms of Emerging Short-Chain PFAS Substitutes: PFBA- and PFBS-Induced Hepatocellular Carcinoma Based on Toxicity Network Analysis, Machine Learning, and Biomimetic Calculations.

Perfluorobutanoic acid (PFBA) and perfluorobutanesulfonic acid (PFBS) are short-chain alternatives to traditional perfluoroalkyl and polyfluoroalkyl s...

Machine learning and explainable artificial intelligence to predict and interpret lead toxicity in pregnant women and unborn baby.

INTRODUCTION: Lead toxicity is a well-recognised environmental health issue, with prenatal exposure posing significant risks to infants. One major pat...

Evaluation of machine learning and deep learning algorithms for fire prediction in Southeast Asia.

Vegetation fires are most common in Southeast Asian (SEA) countries, causing biodiversity loss, habitat destruction, and air pollution. Accurately pre...

Enhancing environmental sustainability and operational efficiency in a case study of limestone quarry in an arid climate.

This study explored innovative approaches to enhance environmental sustainability and operational efficiency in quarry operations in Ethiopia, focusin...

Prediction of pesticide runoff at the global scale and its key influencing factors.

The prediction of pesticide loss in runoff water is a critical step in quantifying pesticide pollution potential and risks. Herein, we compiled a glob...