Environmental Health

Quantifying the contributions of natural and anthropogenic factors to nitrogen exceedance in shallow groundwater in agricultural areas using a hybrid machine learning approach.

Groundwater nitrogen pollution in intensively farmed regions threatens water safety. Prevailing studies often attribute exceedances solely to anthropogenic sources, overlooking natural background levels. Utilizing data from 706 sites (2011-2020) in China's Sanjiang Plain and a hybrid machine learning model, this study quantifies the predictive influence of natural versus anthropogenic factors. Res...

Reassessing the Heterogeneous Effects of Greenspace on Air Pollution Conditioned on Seasons, Vegetation Types, and Street Structures.

Urban greenspace is generally assumed to mitigate air pollution. However, clear large-scale real-world evidence remains limited, particularly regarding how its effects vary with seasons, vegetation types, and street structures. To bridge this gap, this study integrates 4.254 million historical street view images, 1.894 million records of mobile monitoring air pollution data, and three-dimensional ...

A case-aware feature modulation framework for defect classification in power lines.

Conductors and grounded transmission towers are separated by non-conductive overhead transmission line insulators are known as materials. They frequen...

Incorporating geospatial environmental exposure indicators in individual hypertension risk prediction: a multi-stage machine learning pipeline.

BACKGROUND: Environmental exposures are known contributors to chronic disease but are rarely incorporated into risk prediction models. OBJECTIVE: We d...

Missing data imputation in hourly CO measurements for air quality monitoring: a case study in the city of Salvador, Brazil.

Continuous and uninterrupted air quality monitoring is essential for environmental management and public policy formulation, which requires the absenc...

Probabilistic risk assessment of heavy metals sources by integrating interpretable machine learning with receptor models.

Accurate source apportionment of heavy metals (HMs) in surface water and the development of a source-based risk assessment system is essential for for...

Next generation risk assessment and new approach methodologies for sustainable management of heavy metal contaminated environments: A critical review.

Environmental stability forms the foundation for sustaining healthy ecosystems and protecting human life, but the relentless accumulation of persisten...

Integrative analysis and experimental validation of dioxin-interacting genes reveal diagnostic and prognostic biomarkers in lung adenocarcinoma.

Dioxins are persistent environmental pollutants and key components of the human exposome with established carcinogenic potential. As airborne toxicant...

Machine learning for the spatial prediction of soil and groundwater contamination: An integrative global review.

The spatial distribution of soil and groundwater pollutants is critical for effective remediation. Machine learning methods are increasingly applied i...

Diagnosing the Spatiotemporal Evolution of Anthropogenic CO2 Emission Drivers in China Under Systemic Disruptions Using Interpretable Machine Learning.

Understanding how anthropogenic CO2 emissions (ACE) respond to large-scale systemic disruptions is essential for climate mitigation and environmental ...

Unveiling hidden heavy metal hotspots in mining landscapes using integrated hyperspectral remote sensing.

Regional ecological risk assessments typically rely on interpolated toxic heavy metal (THM) surfaces derived from sparse field samples. However, this ...

Development of a model for design radiation shielding composite aprons using machine learning.

This study presents the development and evaluation of a novel lead-free composite for radiation shielding, designed using an artificial neural network...

Unveiling the role of air pollution in diabetic kidney disease: an integrated study combining network toxicology, machine learning, and Mendelian randomization.

Diabetic kidney disease (DKD) is a major and severe complication associated with diabetes. Air pollution is not only an independent risk factor for me...

Machine learning prediction of dual absorber lead-free perovskite solar cells for boosting PCE.

Curtailing the toxicity level of perovskites is a considerable obstacle resisting the wide-scale commercialization of perovskite solar cells (PSCs). T...

Predicting the future of urban ecological resilience in China's Yellow River Basin: a machine learning approach.

Urban Ecological Resilience (UER) is essential for sustainable development, especially within ecologically sensitive regions such as China's Yellow Ri...

Smart spectral analysis of Najaf Sea water using artificial neural networks.

The Najaf Sea is increasingly affected by seasonal tidal pollution, raising significant concerns for both environmental integrity and public health. I...

Assessing air quality in Tehran: Explainable artificial intelligence for megacities.

Urban air pollution poses significant public health challenges in megacities like Tehran, where complex emission sources and topographical constraints...

Geospatial multi-scale GNN for urban food security in climate-stressed environments.

The increasing global food insecurity driven by climate-induced natural hazards and soil degradation has made the resilience of alternative agricultur...

Microbial systems for azo dye biodegradation: enzymatic mechanisms, microbial consortia, and emerging biotechnological strategies.

Azo dyes are the most widely used class of synthetic colorants in textile and related industries; however, their discharge into natural ecosystems pos...

The role of nitrogen dioxide in the prevalence of adverse cardiovascular and cerebrovascular diseases in China: a national multi-pollutant geospatial analysis.

INTRODUCTION: Cardiovascular and cerebrovascular diseases (CCVDs) pose a severe global health threat, particularly among middle-aged and elderly popul...