Environmental Health

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 in predicting pollutant distributions due to their efficiency, low cost, and ability to capture complex nonlinear relationships. To date, however, systematic and quantitative reviews of the full modeling workflow remain limited. In this study, 214 pub...

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 management. Here, we develop an interpretable machine learning framework that integrates an XGBoost model with SHAP (SHapley Additive exPlanations) to diagnose how the key drivers of the ACE inventory's spatiotemporal variability evolved in mainland ...

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...

Multiparametric functional MRI for early detection of renal impairment: clinical applications and future directions.

Early identification of renal impairment is critical for improving patient outcomes. Most current biochemical markers of renal function are not sensit...

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...

Contrasting Impacts of Policy Interventions and Population Mobility on Organic Aerosols and Oxygenated Volatile Organic Compounds in Beijing.

Organic aerosol (OA) is a dominant component of fine particulate matter in megacities, yet disentangling the impacts of emission changes from meteorol...

Advancing One Health: Innovations and Future Directions in Mycotoxin Detection, Management, and Prevention-Narrative Review.

Mycotoxins, toxic secondary metabolites generated by fungi such as Aspergillus, Fusarium, and Penicillium species pose substantial concerns to food sa...

Machine-learning-based cardiovascular mortality prediction using a cumulative PM2.5 exposure metric in high risk groups: a retrospective cohort study.

Classical risk factors for cardiovascular disease (CVD) are well established. Although the association between air pollution (AP) and CVD is also well...

Plastic pollution and its global environmental impacts: a comprehensive review of microbial biodegradation mechanisms, plastic-degrading enzymes, emerging biotechnological strategies and artificial intelligence-driven approaches for sustainable plastic waste management.

Plastic pollution has emerged as a critical global environmental challenge due to the persistence, chemical complexity and resistance of synthetic pol...

Generalization of ML Models Between ECG and VCG Representation.

Integrating heterogeneous data sources is vital for developing and validating robust medical machine learning models. Although the 12-lead format is s...

geoAMBI: A new molluscan biotic index for assessing biological condition of estuaries along the mid-Atlantic coast of the United States.

Biotic indices are used by environmental managers to assess the biological condition of benthic habitats in response to environmental stressors. Succe...

Probabilistic cancer risk assessment from heavy metal exposure in iranian rice and pasta: a novel hybrid framework integrating INAA, ICP-AES, and ensemble machine learning.

This study investigates cancer risk from heavy metal exposure in rice and pasta using experimental data and machine learning approaches, based on 19 e...