Environmental Health

Linking molecular heterogeneity of artificial humic acid to divergent cadmium coordination behaviors.

Humic substances strongly influence the environmental behavior of toxic metals, but the molecular basis by which compositionally similar humic systems promote either metal immobilization or activation remains unclear. To resolve this, cadmium (Cd) was selected as a model divalent metal with well-constrained coordination chemistry, while artificial humic acid (A-HA) was used as a structurally tunab...

Data fusion in air pollution exposure assessment: Methods, applications, and future directions.

Over the past two decades, major advances in observational techniques and modeling capabilities have enabled the development of air pollution data fusion methods that integrate information from regulatory monitoring networks, low-cost sensors, satellite remote sensing, and chemical transport models (CTMs). These approaches combine measurements from regulatory monitoring networks with complementary...

Mitigating Algorithmic Bias in AI-Powered Toxicology: Frameworks for Explainable and Equitable Predictions in Human Health and Environmental Safety.

The rapid integration of artificial intelligence (AI) and machine learning into predictive toxicology has transformed chemical hazard identification, ...

Multi-dimensional analysis revealing the mechanism of T-2 toxin driving liver fibrosis: integration of network toxicology, machine learning, experimental verification, and molecular docking.

BACKGROUND: T-2 toxin is a highly toxic mycotoxin commonly present in food and the environment, with accumulating evidence supporting its hepatotoxic ...

AI-driven insights into the impact of tourism on local cultures: a machine learning approach.

Tourism is increasingly recognized as a powerful driver of economic growth, spatial restructuring, and cultural change, yet empirical evidence on how ...

Optimizing single-lead ECG axis for AI-based detection of myocardial diseases.

Wearable devices enable electrocardiograms (ECGs) outside traditional healthcare settings. While these devices are usually equipped with single-lead E...

Identifying heavy metal sources in extreme-arid oasis soils of Southern Xinjiang: a combined analytical approach.

The extreme arid environment of the southern Xinjiang oasis makes it ecologically fragile and highly sensitive to soil changes, including heavy metal ...

Seasonality and aerosol microenvironment-associated processing of organic aerosol in a humid inland city.

Organic aerosol (OA) constitutes a major fraction of atmospheric fine particulate matter, yet quantifying its sources and transformation pathways rema...

Machine learning and dose response effect models: an integrated approach to analyze the association between environmental variables and Cuneo Emergency Department admissions for Acute Otitis Media (2007-2015).

Acute otitis media (AOM) is a leading cause of pediatric Emergency Department visits, particularly among children under five years of age. Although it...

Dual-channel synergistic conduction in layered potassium titanates for ppb-level H2S sensing and real-time leak localization.

H2S is a highly toxic and flammable gas, and mitigating its excessive emissions to protect the environment and human health remains a global challenge...

Air pollution-related health impacts from domestic waste burning and associated interventions: the merits of a traditional versus machine learning scoping review methodology.

Regularly updating repositories of air pollution impacts on health is essential for evidence-based policies, interventions, and progress monitoring. H...

Machine Learning-Based Soft Voting Ensemble Model for the Prediction of Oral Drug-Likeness of Chemical Structures.

Drug discovery is a costly process with low success rates. Early prioritization of compounds based on drug-likeness estimation can help reduce attriti...

Demographic and emission drivers of household air pollution in biomass using communities.

This study investigates household air pollution from biomass combustion by integrating a household survey, experimental stove testing, and data-driven...

Parent compounds and soil properties as primary drivers of neonicotinoid transformation products in surface soils from an intensive agricultural Basin in Central China.

Some transformation products of neonicotinoid insecticides (TPNNIs) exhibit ecological and potential human health risks comparable to or exceeding tho...

Assessment of ambient air pollution and health risks using satellite remote sensing in the garment industrial belt of Savar and Gazipur, Bangladesh.

Particulate matter (PM₂.₅ and PM₁₀) pollution in rapidly industrializing regions poses significant environmental and public health risks. This study p...

A portable source-informed machine learning framework for predicting heavy metals in legacy smelting sites with strong spatial heterogeneity.

Legacy smelting sites often exhibit strong point-source control, highly right-skewed concentration distributions, and pronounced vertical stratificati...

Multi-year monitoring and modeling of benthic environmental improvement by oyster shell capping on coastal sediments.

This study evaluated the multi-year efficacy of oyster shell (OS) capping for reducing benthic phosphorus release and improving coastal ecosystems by ...

Photodegradation behavior, toxicity assessment, and machine learning model of liquid crystal monomers on soil minerals and soils.

Liquid crystal monomers (LCMs) are proposed as persistent, bioaccumulative, and toxic emerging contaminants of global concern; however, their transfor...

Efficacy and safety evaluation of artificial intelligence-identified antimicrobial peptides targeting avian pathogenic Escherichia coli in broiler chickens.

BACKGROUND: The overuse of antibiotics in both veterinary and human medicine has resulted in the emergence of antibiotic-resistant bacteria, prompting...

The association between artificial intelligence anxiety and occupational anxiety among emergency department healthcare workers.

INTRODUCTION: While the integration of artificial intelligence (AI) into healthcare services has the potential to improve the quality of patient care ...