Environmental Health

Multilayer Vertical Interactions of Air Pollution and Meteorological Drivers Revealed by a Unified Data-Driven Model.

Vertical distribution of atmospheric pollutants is critical for refined air quality management, yet continuous vertical observations and traditional numerical models remain limited in resolving vertical structures. Here, long-term vertical observations from a tower building and lidar measurements are integrated to develop a unified machine-learning framework for multipollutant vertical profiling a...

AI-guided multi-omics analysis identifies NPC1-modulated susceptibility to SARS-CoV-2 infection under PM2.5 exposure.

Exposure to airborne fine particulate matter (PM2.5) has been linked to increased risk of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, yet the underlying mechanisms remain unclear. Here, by leveraging a fine-tuned foundation model of single-cell transcriptomics, we uncover shared transcriptional signatures between PM2.5 exposure and SARS-CoV-2 infection. We further v...

Raman spectra for plastics identification (RaSPI) and Raman maps for plastics identification (RaMPI) datasets.

Efforts to expedite accurate identification of environmental plastics pollution have strong focus on machine learning (ML) techniques. We published tw...

Deciphering environmental determinants of soil metals in dust-influenced arid soils through interpretable models across data-availability scenarios.

Accurate prediction of soil potentially toxic metals (PTMs) and identifying their relationships with environmental drivers is essential for environmen...

Precision occupational lead exposure assessment through medical-informed machine learning.

Precise assessment of occupational lead exposure remains a major global public health challenge, as existing assessment methods are limited by overrel...

Diving into AI? Exploring the Potential for AI to Tackle Complex Water Quality Challenges.

Managing risks from water pollution is central to public health, environmental quality, and economic prosperity worldwide. While improvements in water...

Optimal machine learning models and factors for global infectious diseases: An ecological analysis.

BACKGROUND: Infectious diseases are a major global public health challenge. Socioeconomic and environmental changes have complicated the situation, un...

Machine learning approach for heavy metal source identification and spatial distribution in coastal sediments of Tiruchendur, Southern India.

This research focuses on the global issue of heavy metal (HM) contamination in coastal systems and provides the first baseline investigation of the su...

Spatially Optimized Nutrient Management as a Climate-Resilient Strategy to Reduce Nitrogen Runoff from Global Croplands.

Nitrogen fertilizer, as an indispensable input in crop production, has played a crucial role in enhancing crop yields. However, the associated croplan...

SODNet: a scale-oriented detection network for efficient UAV-based sewage outfall detection.

Accurate identification of river sewage outfalls is crucial for effective water pollution control. Unmanned Aerial Vehicles (UAVs), with their high mo...

Exploring seasonal coastal water quality parameter interactions through the self-organizing map neural network in eastern Mediterranean: Lebanon as a case study.

Coastal water pollution poses significant environmental challenges, leading to severe ecological and public health consequences. Thus, new assessment ...

Immune gene MMP9 as a potential key mediator in TCDD exposure-associated atherosclerosis: An integrated study based on network toxicology and experimental validation.

BACKGROUND: 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) is a highly toxic, recalcitrant persistent organic pollutant with ubiquitous environmental dist...

Satellite-based detection of asbestos-cement roofs using WorldView-3 VNIR data: An affordable evaluation tool for sustainable management of hazardous building infrastructure.

Asbestos-cement (AC) roofing remains a critical concern for urban environmental health, particularly in cities where asbestos bans were implemented la...

A Stacking Ensemble Model for KOC Prediction and Environmental Mobility Assessment of Global Neutral Chemicals.

Neutral persistent, mobile, and toxic (PMT) chemicals are an emerging regulatory concern, because they can contaminate drinking water resources. The o...

Agricultural and urban land use intensifies riverine GHG emissions across continents.

Earlier efforts to assess anthropogenic impacts on river greenhouse gas (GHG) emissions mainly relied on local-scale data, overlooking how cross-bound...

Machine Learning for Designing Perovskites and Perovskite-Inspired Solar Materials: Emerging Opportunities and Challenges.

The development of perovskites and perovskite-inspired materials (PIMs) is driven by the need for efficient, non-toxic and stable solar energy convers...

Multi-domain ECG feature extraction enable accurate automated recognition of bipolar disorder, depression and schizophrenia.

OBJECTIVE: To develop and validate a multi-lead electrocardiogram (ECG)-based machine learning system for automated classification of major psychiatri...

A pipeline for meso- and microplastic identification in aquatic systems using machine learning and hyperspectral imaging.

Microplastic pollution presents major environmental and health challenges, requiring accurate identification and quantification to assess its distribu...

A zone-specific water quality modelling framework for medium and large lakes using interpretable machine learning.

The significant spatial heterogeneity of lake characteristics poses a persistent challenge for accurate prediction and effective management of medium ...

Comparison of vacuum ultraviolet-based oxidation as ultrafiltration pretreatment in real water: Performance, risk assessment, and mechanistic insights.

Natural organic matter (NOM), a ubiquitous foulant in aquatic environments, is commonly regarded as a predominant driver of ultrafiltration (UF) membr...