Environmental Health

Stress Detection Using Heart Rate Variability and Respiratory Signals Derived From a Single-Lead ECG.

Stress detection is a widely studied field due to its significant implications for mental and physical health. While multimodal approaches show promising results, they present challenges related to hardware constraints and computational requirements that limits real time implementation in wearable devices. We propose a hybrid methodology combining feature extraction with ma chine learning (ML) for...

Recent advances in pretreatment technologies for the analysis of per- and polyfluoroalkyl substances.

Per- and polyfluoroalkyl substances (PFASs) are persistent pollutants with shifting pollution characteristics toward short-chain and structurally diverse variants, posing great challenges for precise analysis. Pretreatment, as a rate-limiting step, lacks systematic guidance for technology selection. This review makes a unique contribution by constructing a "target substance characteristics-sample ...

A machine learning-based QSAR model for predicting toxicity of commercial pesticides to Eisenia fetida.

Commercial pesticides are indispensable for modern agriculture but may exert unintended toxicity toward non-target soil fauna, notably earthworms. Her...

Self-supervised learning for aflatoxin B1 detection using masked spectra.

Aflatoxins are highly toxic secondary metabolites widely found in peanut and maize kernels. This study proposes a strategy based on self-supervised le...

The reliability gap: Why high predictive accuracy doesn't guarantee stable feature importance.

Marine Pollution Bulletin increasingly applies machine learning and explainable AI to pollutant and shellfish poisoning risk, exemplified by PCA-based...

Particulate matter (PM2.5 and PM10) prediction using fourier series decomposition in combination with LSTM and SVM.

Sustainable development globally is highly impacted by increased air pollution which is attributed to increasing population, commercial and industrial...

Recent progress and technological advancements for detection of micro/nano-plastics in the environment.

Micro and nanoplastics (MNPs) are an omnipresent and analytically challenging contaminant class that has been emerged in aquatic, terrestrial, atmosph...

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 adsorpti...

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 ...

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 ...