Environmental Health

Introduce multivariate two-dimensional information to establish a data-driven volume estimation model for complex microplastic fibers.

Microplastic (MP) fibers are pervasive in the environment, posing significant ecological risks due to their persistence and potential toxicity. Accurate quantification of fiber volume is crucial for assessing its environmental flux and ecological impacts. Traditional methods, reliant on geometric assumptions, often miscalculate the volume of complex-shaped fibers, particularly those with curvature...

Machine learning and causal inference for disentangling air pollution reduction during the Asian Games in megacity Hangzhou.

Controlling air pollution in megacities remains challenging due to complex pollutant sources, atmospheric chemistry, and weather conditions. During the 19th Asian Games (AG) in Hangzhou, the government enforced strict traffic restrictions in 10 % of the urban area while maintaining industrial operations. The AG coincided with the China's National Day Holiday (CNDH) from September 29th to October 6...

A review of groundwater vulnerability assessment to nitrate pollution in the Mediterranean region.

The concept of groundwater vulnerability , involving the identification of areas susceptible to contamination from surface sources, plays a crucial ro...

Predicting intersystem crossing rate constants of alkoxy-radical pairs with structure-based descriptors and machine learning.

Peroxy radicals (RO) are ubiquitous intermediates in many oxidation processes, especially in the atmospheric gas phase. The recombination reaction of ...

Soil and litter emission sources as important contributors to ozone production from volatile organic compounds in island tropical forests.

While studies have confirmed that volatile organic compounds (VOCs) emitted directly by tropical island forest vegetation significantly influence ozon...

Automatic titration detection method of organic matter content based on machine vision.

This article proposes an automatic titration algorithm for organic matter content detection based on machine vision, which addresses the disadvantages...

Optimizing machine learning methods for groundwater quality prediction: Case study in District Bagh, Azad Kashmir, Pakistan.

Groundwater quality monitoring is crucial for protecting the environment and human health. Machine learning (ML) offers substantial potential for enha...

Air quality monitoring in Mendoza, Argentina: machine learning approaches for PM prediction.

In this study, different statistical methodologies were combined to assess the relationship between PM concentrations and meteorological variables (te...

Predictive modelling of air pollution affecting human tuberculosis risk on Mainland China.

In this study, we investigated the correlation between air pollution indicators and pulmonary tuberculosis (TB) incidence and mortality rates across p...

The effects of PM concentrations on traffic violations and accident severity in Guangdong China.

How does air pollution affect road safety? This study examines the impact of local PM concentrations on traffic violations and accident severity in Gu...

Recent Advances in Analytical Methods for Aconitine Alkaloid in Natural Products: A Systematic Review.

INTRODUCTION: Aconitine, the main toxic and active compound in Aconitum species, is a key component of Traditional Chinese Medicine (TCM), valued for ...

Machine learning based prediction modeling of micro-EDM of Ti-29Nb-13Ta-4.6Zr (TNTZ).

Although Ti-6Al-4V stands out as one of the best in biomedical, automotive, and aerospace applications due to its low density and higher corrosion res...

Fusing satellite imagery and ground-based observations for PM air pollution modeling in Iran using a deep learning approach.

With the rapid advancement of urbanization and industrialization in cities, air pollution has become one of the significant environmental challenges a...

Improving YOLOv11 for marine water quality monitoring and pollution source identification.

Marine pollution has become an increasingly severe environmental issue, with oil spills, marine debris, and turbid water significantly impacting ecosy...

Deep learning framework for hourly air pollutants forecasting using encoding cyclical features across multiple monitoring sites in Beijing.

Environmental managers and citizens alike are concerned with air quality. Early warning systems for air pollution are essential to prevent health issu...

PM concentration 7-day prediction in the Beijing-Tianjin-Hebei region using a novel stacking framework.

High-precision prediction of near-surface PM concentration is a significant theoretical prerequisite for effective monitoring and prevention of air po...

Dielectric characterization of sugar mill wastewater and its impact on soil properties in the khadar and bangar regions.

This study investigates the dielectric properties of sugar mill wastewater and its impact on surrounding soils and crops, prompted by the environmenta...

High-accuracy PM prediction via mutual information filtering and Bayesian-Optimized Spatio-Temporal Convolutional Networks.

Air pollution, particularly fine particulate matter (PM), poses severe threats to human health and ecological sustainability, rendering accurate predi...

Effect of short-term exposure to particulate matter on mortality rate by cardiovascular disease in Colombia: An observational ecological study with causal machine learning.

Cardiovascular disease (CVD) is an important cause of death in older adults with pre-existing CVD. This study aimed to estimate the causal effect of s...

A Novel LIBS-Machine Learning Strategy for Multimetal Detection in Microsized PMMA Particles: Efficient Quantification for Composite Pollution.

Microplastics (MPs) have emerged as a critical environmental pollutant, causing composite pollution through their widespread production, usage, and di...