Environmental Health

Growing impacts of fire smoke on ozone pollution and associated mortality burden in the United States.

Fire smoke pollution is an emerging health risk factor, yet prior studies have focused mostly on fine particulate matter (PM2.5) and neglected the health impacts from other pollutants, most notably ozone (O3). Here, we quantify changes in ground-level O3 concentrations during smoke episodes across the contiguous US using surface air quality measurements, satellite data, and machine learning models...

Dual framework for rainfall prediction: a multi-seed machine and deep learning evaluation across Pakistan's climatic regimes.

Accurate prediction of rainfall events is vital for agriculture, hydrology, flood preparedness, and climate-adaptive strategies in regions of Pakistan susceptible to monsoons and droughts. Increasing climate variability highlights the need for reliable data-driven forecasting systems capable of precisely representing nonlinear atmospheric dynamics across different forecast intervals. This study in...

Machine learning-driven groundwater quality classification using physicochemical parameters and regulatory thresholds.

Groundwater contamination poses a critical challenge to public health and environmental sustainability, particularly in rapidly urbanizing regions. En...

Innovative approaches to mitigating persistent toxic substances and their impacts on soil health and human well-being.

Persistent toxic substances (PTS), including heavy metals, persistent organic pollutants (POPs), and persistent, mobile, and toxic/very persistent and...

Source characterization of trace elements and impacts of salinization using geochemical modelling with machine learning approach and Monte-Carlo based health risk assessment in coastal aquifer zones.

This work investigated the trace element contamination, spatial distribution and their source of origin along with health risk assessment in the coast...

A physically constrained and interpretable deep learning framework for PM2.5 inversion under sparse monitoring conditions in arid regions.

Monitoring fine particulate matter (PM2.5) in arid regions remains difficult due to sparse ground-based networks and frequent extreme pollution episod...

Global Co-Occurrence Patterns of Ischemic Heart Disease and Metabolic Dysfunction-Associated Steatotic Liver Disease: Spatial Clusters, Risk Factors, and Quality-of-Care Index.

BACKGROUND: Ischemic heart disease and metabolic dysfunction-associated steatotic liver disease represent significant global health challenges, charac...

Machine learning-based disentanglement of meteorological and anthropogenic drivers of ozone pollution in the Chengdu-Chongqing urban agglomeration.

To clarify the relative contributions of meteorological conditions and anthropogenic activities to ozone (O3) pollution in the Chengdu-Chongqing urban...

Unraveling spatial distribution and associated factors of bisphenol analogues in the Yangtze River Delta: A machine learning approach.

Bisphenol analogues (BPs) are widely detected in aquatic environments; however, comprehensive spatial characterization across large river systems rema...

Selective and Sustainable Recovery of High-Value Metals from e-Waste via a Glucose-Mediated Hydrothermal Process.

The rapid fabrication of artificial intelligence-based hardware worldwide is intensifying the electronic waste (e-waste) crisis, threatening ecosystem...

Ultrasensitive, Rapid, and Incubation-Free Surface-Enhanced Raman Scattering Detection of Sulfasalazine with a GF@PDDA-Ag/rGO Functionalized Membrane and Machine Learning.

Developing rapid detection methods with high sensitivity and real-time capabilities is a critical challenge for monitoring antibiotic pollution. This ...

Integrating microbial bioremediation, multi-omics, and emerging technologies for polycyclic aromatic hydrocarbon (PAHs) detoxification.

Environmental organic pollutants, identified as Polycyclic Aromatic Hydrocarbons (PAHs), are widespread and toxic. These hydrocarbons are commonly pro...

Development and simulation of an innovative autonomous knowledge-based smart waste collection system.

Substantial efforts have been made to modernize and decarbonize the waste collection industry. Smart sensor-based waste collection (SWC) systems have ...

Daily Mortality Prediction Under Compound Environmental Stressors: Comparing Epidemiological Time Series Models and Machine Learning Models.

Accurate prediction of daily mortality is vital for effective early warning systems and the strategic allocation of medical resources, particularly gi...

In Silico Prediction of Potential Bile Salt Export Pump Inhibitors Using Pharmacophore Models and Consensus Machine Learning.

The ATP-dependent bile salt export pump (BSEP) is a transporter responsible for moving bile salts from hepatocytes into bile canaliculi. Inhibition of...

Uncovering the Global Burden and Future Trajectories of Nanomagnetite Particle Emissions from Biomass Power Plants.

Biomass power plants (BPPs) are expanding rapidly, yet the most toxicologically potent nanoscale fraction of its particulate emissions remains poorly ...

Deep Learning Model Using Transfer Learning for Detecting Left Ventricular Systolic Dysfunction: Retrospective Algorithm Development and Validation Study.

BACKGROUND: Artificial intelligence-augmented electrocardiogram (AI-ECG) models for detecting left ventricular systolic dysfunction (LVSD) often exhib...

Prediction of the respiratory disease incidence based on environmental factors using machine learning techniques in Penang, Malaysia.

Traditional statistical models often struggle to accurately predict global burden of respiratory diseases due to the complex and interdependent nature...

A Deep Neural Network for Interpreting Wearable Electrocardiogram Data in Atrial Fibrillation: Prospective Observational Diagnostic Accuracy Study.

BACKGROUND: Atrial fibrillation (AF) and atrial flutter (AFL) are common arrhythmias associated with the risk of ischemic stroke, which can be reduced...

The impact of fuel standard on sulfur dioxide emissions: Evidence from machine learning technique.

Green transport is increasingly important in improving urban air quality. Using daily air quality monitoring data at the station level, this paper emp...