Environmental Health

Competitive secondary formation and evolving sources drive the stage-dependent evolution of PM2.5-bound PAHs and their derivatives associated health risks.

Polycyclic aromatic hydrocarbons (PAHs) and their oxygenated (OPAHs) and nitrated (NPAHs) derivatives are critical carcinogenic constituents of PM2.5, yet high-resolution observations of their dynamic evolution remain scarce. This study characterized the 4-h spatiotemporal evolution and source-apportioned risks of these compounds across four Guanzhong Plain cities including Xi'an (XA), Baoji (BJ),...

Multimedia ecological risk deciphering and sediment source apportionment of potentially toxic elements in a typical coastal bay: A combined PMF-SOM-GIS approach.

As transitional zones of landsea interactions, bays are facing increasingly severe anthropogenic pollution pressures. To systematically assess these impacts, in this study, a comprehensive multimedia assessment framework encompassing seawater, surface sediments, and typical marine organisms was constructed to investigate the ecological risks, and anthropogenic driving mechanisms of potentially tox...

A Socioeconomic Assessment of Projected Health Impacts From Outdoor Air Pollution Under Current European Climate Policy: A Modelling Study.

BACKGROUND: Outdoor air pollution poses a major threat to human health and is closely interlinked to climate change, as both stem largely from the sam...

Association between long-term PM2.5 exposure and risk of urinary tract infection in children: a nationwide longitudinal cohort study.

BACKGROUND: Urinary tract infection (UTI) is among the most common bacterial infections in children and can lead to both acute and chronic complicatio...

Improving Nitrogen Use Efficiency in Wheat: Integrating Agronomic, Genomics, and Remote Sensing for Sustainable Production.

Improving nitrogen use efficiency (NUE) in wheat is critical for addressing the dual challenges of global food security and environmental sustainabili...

Integrating satellite AOD, trace gases, and meteorological reanalysis to estimate PM₂.₅ and its long-term trends in southern Nepal.

Fine particulate matter (PM₂.₅) poses severe public health and environmental risks in Nepal's Tarai and Dun Valley regions, where ground-based air qua...

Discovery of TYR inhibitors from de novo molecular generation to dual-track lead optimization: "Competition" between AI and chemists.

This study introduces a unified framework combining artificial intelligence (AI)-directed de novo molecular generation with dual-track lead optimizati...

Full-spectrum EEM end-member unmixing with statistical validation: A deep learning framework for organic pollution source apportionment in complex watersheds.

Accurately apportioning organic pollution sources in mixed land-use watersheds remains challenging due to the limited tracer capacity of conventional ...

Seasonal water quality index prediction in Guilin karst water system: an explainable machine learning approach.

Accurate prediction of the water quality index (WQI) in karst water systems remains challenging due to pronounced seasonal hydrological heterogeneity,...

An Intelligent Self-Powered Covalent Organic Framework-Based Triboelectric Nanosensor Platform for On-site Picomolar Hg2+ Ion Monitoring.

Mercury (Hg2+) ions are persistent, highly toxic environmental pollutants that threaten ecosystems and human health through bioaccumulation and long-r...

Toward Precise Management of Groundwater by Combined Heavy Metal(loid)s Contamination at Industrial Sites: A Machine Learning Driven Source-to-Risk Zoning Framework.

Controlling and remediating heavy metal(loid) contamination in groundwater at complex industrial sites remains challenging due to mixed pollution sour...

Design and simulation of a hybrid PLC-AI automotive control module for adaptive lighting and honking.

Excessive honking and improper use of the headlamp beam significantly impact driving safety at night through visual impairment caused by headlamp beam...

Integrating physical modeling with artificial intelligence for predicting fish survival zones in polluted rivers to maintain a sustainable aquaculture industry.

Water quality prediction and management are crucial for ensuring the sustainability of water supplies. Contaminated water can harm humans and aquatic ...

The interplay between actual and perceived air pollution, physical activity, and stress across mobility-based geographic contexts: A double machine learning approach.

The social ecological framework emphasizes the necessity of integrating individual characteristics, behavioral patterns, environmental factors, and he...

Interaction Between Air Pollution and Genetic Predisposition to Blood Pressure and LDL-C on Cardiovascular Events.

AIMS: Particulate matter ≤2.5μm (PM2.5) air pollution is a leading global environmental risk factor. We investigated the impact of PM2.5 on cardiovasc...

Remote sensing for marine oil spill detection, mapping, and monitoring: A systematic review and bibliometric analysis.

Oil pollution is one of the most persistent and harmful anthropogenic pressures on global marine and coastal ecosystems. Accidental discharges, chroni...

Monitoring network design limits detection of sediment-mediated contaminant mobilisation in the River Mersey estuary (UK).

Suspended sediments regulate turbidity and act as major vectors for metals, organic contaminants, and nutrient fluxes in estuarine systems. Effective ...

Integrated toxicological analysis links air pollution-associated acute respiratory distress syndrome signatures to benzo[a]pyrene-induced acute lung injury.

Acute respiratory distress syndrome (ARDS) is associated with high mortality, and increasing evidence suggests that air pollution may contribute to it...

Development and validation of artificial intelligence-assisted volumetric response criteria in pleural mesothelioma (ARTIMES): a retrospective, multicohort, multicentre study.

BACKGROUND: Response evaluation in pleural mesothelioma is challenging because its crescent growth pattern is poorly captured by diameter-based criter...

Explainable machine learning reveals evolutionary signals in influenza haemagglutinin.

Identifying amino acid changes that lead to phenotypic change is critical to viral surveillance. Common metrics, such as site-wise evolutionary rates ...