Environmental Health

Development of a high-resolution PM2.5 model in Taiwan using integrated low-cost air quality sensors and satellite observations.

BACKGROUND: Air pollution modeling generally suffers from the limited and uneven distribution of fixed air quality monitoring (AQM) stations. Low-cost sensors have emerged to fill these monitoring gaps; however, few studies have integrated low-cost sensor data with satellite-based measurements. OBJECTIVE: We aimed to develop a daily 250-m resolution fine particulate matter (PM2.5) estimation model...

Recent advances in radioisotope geochronology for quaternary environmental and paleoclimate reconstruction.

Reliable chronological frameworks are fundamental for reconstructing Quaternary environmental change, paleoclimate evolution, landscape development, and human history. Recent advances in radioisotope geochronology have substantially improved the accuracy, precision, and temporal resolution of dating across diverse geological and environmental archives. This review synthesizes developments in major...

Source-specific Control Leverage for Reducing Oxidative Potential of PM2.5 Revealed by Explainable Machine Learning.

Oxidative potential (OP) of fine particulate matter (PM2.5) has been increasingly recognized as a health-relevant metric beyond mass concentration. Ho...

Integrating Audiovisual Data for Short-Term Particulate Matter Concentration Prediction on Urban Sidewalks.

Urban air pollution severely impacts pedestrians on sidewalks, yet traditional fixed-site monitoring lacks the spatial coverage needed for fine-scale ...

Artificial Intelligence-Guided Cosolvent Design for High-Performance Perovskite/Silicon Tandem Solar Cells.

Realizing high-performance perovskite/silicon tandem solar cells requires precise control of wide-bandgap perovskite crystallization. Solvent engineer...

Attribution of PM2.5-Induced Transcriptomic Perturbation to Toxic Components.

Ambient fine particulate matter (PM2.5) is a chemically complex mixture whose health impacts are not fully captured by particle mass. Here, we develop...

Interface Defects and Recombination in HTL-Free and Bilayer Cs2SnI6 Perovskite Solar Cells: Numerical Modeling and Machine Learning Analysis.

Perovskite solar cells (PSCs) are a promising technology for sustainable energy generation. Among lead-free perovskites, Cs2SnI6 offers high structura...

Predictive modeling of fluid status in hemodialysis: model development and internal validation using the MONitoring dialysis outcomes (MONDO) global database.

BACKGROUND: Optimized fluid management is crucial in dialysis care because extracellular volume overload drives adverse cardiovascular outcomes. At th...

Integration of network toxicology, machine learning and single-cell sequencing identifies candidate molecular links between air pollutants and hepatocellular carcinoma.

BACKGROUND: Epidemiological studies link long-term air pollution to an increased risk of hepatocellular carcinoma (HCC), but the underlying toxicologi...

Regenerable "Two-in-One" molecularly imprinted nanozymes colorimetric sensor array combined with deep learning enables selective removing and visual discriminate of tetracycline antibiotics.

Tetracycline (TC) and its analogues are widely employed broad-spectrum antibiotics. Their abuse and environmental residues have raised severe ecologic...

Collaborative discrimination of Cadmium and Lead contamination types and levels in Bok Choy based on multi-temporal hyperspectral data and RCBA-HMTL.

The type and degree of heavy metal contamination in green vegetables are key factors for evaluating food safety. Current evaluation methods based on H...

Research on quality homogenization of preparations from multi-part botanical materials based on pseudotargeted metabolomics and machine learning: a case study of Cocculus orbiculatus (L.) DC.

The use of multiple medicinal parts from a single herbal source for disease treatment is a common practice in traditional Chinese medicine. However, v...

Conditional superiorities and unaddressed bottlenecks: a critical review of artificial intelligence for waterborne microbial detection.

Although conventional microbial detection approaches for water samples are widely applied, they still suffer from prolonged assay durations (24-72 h),...

A deep learning model for predicting daily PM2.5 concentration in response to emission reduction.

Air pollution remains a leading global health threat, with fine particulate matters (PM2.5) causing millions of premature deaths annually. Chemical tr...

Protective Effects of p-Coumaric Acid Against Lead-Induced Hepatic Oxidative Stress, Inflammation, and Apoptosis: Integrated Molecular and Machine Learning-Based Analysis.

Lead (Pb) exposure is a major environmental health concern that induces hepatic injury through oxidative stress, inflammation, and apoptosis. This stu...

Exploring the non-linear impacts of environmental and anthropogenic factors on land surface temperature in southwestern Bangladesh using explainable AI.

Land surface temperature (LST) in southwestern Bangladesh has changed rapidly in recent years due to both environmental conditions and human activitie...

Screening of core targets for Di(2-ethylhexyl) Phthalate-related gastric cancer based on machine learning, molecular docking, and SHAP analysis.

PURPOSE: Given the existing uncertainties regarding the link between Di(2-ethylhexyl) phthalate (DEHP) exposure and gastric cancer (GC) progression, t...

Long-Term Exposure to PM2.5 Constituents and Coronary Heart Disease Risk in China.

BACKGROUND: In China, the associations between long-term fine particulate matter with aerodynamic diameter ≤2.5 μm (PM2.5) exposure and coronary heart...

Acute Aluminum Phosphide Poisoning: Evolving Perspectives on Toxic Mechanisms and Evidence-Based Clinical Management.

Aluminum phosphide (AlP) is a chemical compound that is used as a pesticide for suicidal purposes and can cause death, and it poses a challenge to hea...

Automated analysis of wearable ECG: machine learning methods, preprocessing pipelines, and benchmark datasets.

Wearable and mobile electrocardiography (ECG) has rapidly expanded access to rhythm monitoring outside of clinical settings, but the single-or few-lea...