Environmental Health

Predicting heavy metal distribution coefficient in global soil via machine learning: the effect of mineral heterogeneity.

Heavy metal adsorption by soil particles significantly influences its pollution risk in global soil. However, the global distribution of soil adsorption ability is still lacking due to the difficulty of obtaining large-scale adsorption data. Here, we proposed a novel method to evaluate the adsorption ability in a global scale through predicting the distribution coefficient (Kd) of HMs in soil via ...

Remote sensing analysis of forest fire impacts on ecosystem productivity, greenhouse gas emissions, and fire risk in Pakistan.

This study investigates the spatial variability of forest fire intensity, burn indices, ecosystem productivity, and Greenhouse Gas (GHG) emissions in Pakistan from 2001 to 2023. Using satellite-derived burn indices such as SAVI, LST, NMDI, LSWI, NBR, and MSAVI2, the study examines the relationship between forest fires and net primary productivity (NPP) across diverse ecological regions. The analys...

Toward an AI Foundation Model Integrating Climate Change, Air Pollution, Socioeconomics, and Human Health.

Source apportionment, drinking water quality prediction and health risk appraisal of groundwater nitrate using hydrochemistry, machine learning and Monte Carlo simulation - A case study from the Suruliyar River basin, South India.

The main objective of this study is to evaluate the nitrate contamination in the groundwater of the Suruliyar River basin using modern technologies su...

Evaluating human health risk assessment tools for contaminated soil: a comparative review.

At the European Union level, approximately 60-70% of soil is in unhealthy or degraded conditions. One of the soil threats is the legacy of industrial ...

Recent Trends in the Development and Clinical Translation of Polymer-based Targeted Therapeutic Nanoparticle.

Polymeric nanoparticles are a state-of-the-art innovation in nanomedicine, offering site-specific drug delivery, an improved pharmacokinetic profile, ...

Screening toxic transformation products of emerging pollutants in advanced oxidation processes with 3D deep learning and in vitro assays.

The rapid and precise toxicity assessment of chemical pollutants and their byproducts formed during water treatment and in aquatic environments remain...

Machine learning-enabled meta-analysis reveals the effect of microplastics on nitrogen removal performance in constructed wetlands and its potential mechanisms.

Constructed wetlands (CWs) are an effective wastewater treatment system and an important sink for microplastics (MPs). However, MPs impacts on nitroge...

Using deep learning to assess the toxicological effects of sublethal exposure of a novel green pesticide in a stored-product beetle.

BACKGROUND: Managing stored-grain pests requires new strategies to limit economic and health risks. This study analyses the sublethal effects of the n...

FHGNet: A Feature-Centric Hierarchical Network with Graph Attention Layer for Supraventricular Tachycardia Classification.

Automated electrocardiogram (ECG) classification plays a critical role in arrhythmia diagnosis. However, current deep learning-based methodologies fre...

Estimating chemical oxygen demand in municipal landfill leachate using multilayer perceptron artificial neural networks based on seasonal monitoring data.

Landfill leachate is a hazardous by-product of municipal solid waste (MSW) and chemical oxygen demand (COD) is a key indicator for evaluating its poll...

Air quality prediction model based on deep learning hybrid framework.

As modernization and industrialization continue to accelerate, air pollution has become an increasingly pressing problem. Air quality prediction is co...

Contrasting land-use sources of microplastic and endocrine-disrupting compound pollution in a major coastal river network.

Environmental monitoring in coastal megacities prioritizes land-based microplastics (MPs) and endocrine-disrupting compounds (EDCs) as key threats to ...

Complex networks for modeling texture and spectral features of hyperspectral images for environmental analysis.

A complex network method is introduced for high-resolution hyperspectral image analysis and classification. The method is applied to detecting environ...

ML-Assisted Olfactory Epidemiology Survey Urbanizing China: A Population-Based Study in Yancheng.

OBJECTIVES: Urbanization-related air pollution may be associated with olfactory dysfunction (OD) in China, yet population studies are lacking. METHODS...

MMRCL: An interpretable multi-modal deep learning framework for predicting hERG blockers.

The human ether-a-go-go-related gene (hERG) encodes a voltage-gated potassium channel essential for cardiac action potential repolarization. Drug-indu...

From conventional monitoring to intelligent prediction: data-driven analysis of inorganic elements in atmospheric wet deposition at an urban site in Lanzhou.

Atmospheric wet deposition represents a key pathway linking atmospheric pollution to terrestrial ecosystems, with its chemical composition and deposit...

Artificial intelligence driven protein design and sustainable nanomedicine for advanced theranostics.

The integration of artificial intelligence, protein engineering, and sustainable nanomedicine is driving a paradigm shift in theranostics by enabling ...

Circulating tumor DNA dynamics predict pathological response and guide therapy personalization in the neoadjuvant setting.

Neoadjuvant therapy is a cornerstone of modern oncology, yet its efficacy is traditionally assessed only after treatment completion, creating a "black...

Childhood dyslexia risk elevated by heavy metal mixtures from e-waste: A machine learning-driven mixture modeling study.

Environmental heavy metal mixtures from informal e-waste recycling are potential neurotoxicants, but their link to developmental dyslexia remains uncl...