Environmental Health

A novel framework for groundwater quality evaluation in industrial zone using unsupervised machine learning methods.

Population density, urbanization and industrialization are the rapidly increasing and it's causes severe damages to freshwater ecosystem. The present study aims to assess the quality of groundwater using integrated methods in the industrial zone of South India. The integrated assessment of groundwater using unsupervised machine learning method associated human health risk assessment are identified...

Ozone concentration prediction using machine learning models integrated with 115 volatile organic compound measurements and dimensionality reduction techniques.

High-quality prediction of ozone concentrations is essential for formulating effective environmental policies. The integration of monitoring data of volatile organic compounds (VOCs), key precursors in ozone formation, through dimensionality reduction techniques can substantially improve predictive accuracy. This study utilized monitoring data from 115 VOC species and evaluated four machine learni...

Machine Learning-Assisted Detection of Phosgene and Acetyl Chloride via a Dual-Probe Fluorescent Platform with Differential Reactivity.

Phosgene and acyl chlorides are highly toxic chemicals that pose serious threats to human health and environmental safety, yet their rapid and reliabl...

Microplastics on the frontline: causes, strategies to combat pollution and protect health with advanced bioremediation-a review.

Microplastic (MP) pollution is a growing global concern with serious implications for ecosystems and human health. The present review provides a compr...

Street-level ambient fine particulate matter and tuberculosis: A machine learning and Bayesian spatiotemporal analysis.

Tuberculosis (TB) remains a leading cause of death from infectious diseases globally, yet evidence linking short-term ambient fine particulate matter ...

A simplified co-culture reveals altered cardiotoxic responses to doxorubicin in hPSC-derived cardiomyocytes in the presence of endothelial cells.

Cardiotoxicity is a significant challenge in cancer therapies, particularly with doxorubicin, a widely used anthracycline. More predictive in vitro mo...

ChronicDPipredictor: an interpretable deep learning framework for chemical chronic and subchronic toxicity assessment.

Chronic and subchronic toxicity are very important endpoints for evaluating the long-term and medium-term toxicity of chemical substances. However, du...

Ensemble learning for air quality index prediction: integrating gradient boosting, XGBoost, and stacking with SHAP-based interpretability.

The increasing challenge of air pollution in cities requires smart methods to make proper predictions and manage the problem. Although machine learnin...

Orbital-level engineering of bonding networks to modulate halogen migration in lead-free double perovskites.

Ionic migration plays a pivotal role in determining the performance and operational stability of a wide range of optoelectronic devices. Halide double...

Facing the Silent Crisis: Suicidal Ideation and Suicide in the Nursing Profession.

BACKGROUND: This manuscript explores the silent yet urgent crisis of suicidal ideation and suicide within the nursing profession. Despite being one of...

Multi-omics characterization of benzo[a]pyrene toxicity networks identifies SERPINE1 and STK3 as prognostic biomarkers and therapeutic targets in head and neck squamous cell carcinoma.

Benzo[a]pyrene (BaP), a ubiquitous environmental carcinogen predominantly found in tobacco smoke and air pollution, plays a pivotal role in head and n...

Smart sewers in practice: a systematic review of instrumentation, applications and pathways to adoption for sustainable urban systems.

Sewer systems are critical to public health and urban economies, yet failures cause overflows, pollution, and costly disruption. Smart sewer systems c...

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 promis...

Utilizing deep learning models for early detection and classification of fruit diseases: towards sustainable agriculture and enhanced food quality.

Productivity and quality of food are crucial for populations around the world. However, food faces challenges due to the threats of fruit diseases, wh...

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 dive...

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...