Environmental Health

Microplastic contamination and risk profiling in cultured freshwater prawns Litopenaeus vannamei (Boone, 1931) and Macrobrachium rosenbergii (De Man, 1879) from Southwestern India using a machine learning approach.

The presence of microplastic (MP) particles in freshwater species is a growing global concern due to potential impacts on food security and human health. This study investigated MP contamination in two commercially important freshwater species, Macrobrachium rosenbergii (giant freshwater prawn, GP) and Litopenaeus vannamei (white leg shrimp, WS) cultured in inland and semi coastal aquaculture syst...

Management of missing air pollution data within urban environments using machine learning regressions: a case study for Delhi, India.

Machine learning models are increasingly used in air pollution research, yet their application to long-term reconstruction of missing air-quality records remains limited. Pollutants such as PM2.5, PM10, O3, NO2, SO2, and CO pose major environmental challenges in megacities like Delhi, but accurate assessment requires long, complete, and spatially distributed monitoring records that are often unava...

Global surveillance of offshore oil slicks: Linking satellite detection with marine biodiversity patterns and conservation priorities.

By combining satellite technology with ecological risk analysis, this study establishes a scalable global framework for tracking the environmental eff...

Antimicrobial dosing in patients with renal dysfunction.

PURPOSE OF REVIEW: Many antimicrobials used in critically ill patients are predominantly eliminated by the kidneys. Optimizing dosing in this populati...

Magnetoelectrocatalysis at the frontier: advanced quantum materials, mechanistic insights, and application landscapes.

Amid escalating environmental pollution and the urgent surge in sustainable energy demand, electrocatalysis has emerged as a key technology for sustai...

Leakage-controlled machine learning for European Cd, Hg, and Pb emission inventories.

Timely and spatially resolved inventories of cadmium (Cd), mercury (Hg), and lead (Pb) are needed for transboundary pollution assessment and inventory...

Digital Twin-Enabled Toxicology: Current Applications, Opportunities, and Future Directions.

Toxicology has long depended on animal studies and static computational models to evaluate the safety of chemical substances and pharmaceutical compou...

Portable Raman spectroscopy coupled with machine learning for rapid identification and source apportionment of plastic particles in aquaculture wastewater.

Aquaculture expansion has exacerbated microplastics (MPs) contamination in aquaculture water bodies. MPs readily adsorb heavy metals and organic pollu...

Upcycling Vegetable Waste Into Functional Food Ingredients via Synergistic Microbial Engineering and Artificial Intelligence.

The escalating generation of global vegetable waste represents a critical loss of bioactive resources, necessitating a paradigm shift from passive dis...

Predictive modeling of heavy metal pollution and ecological risk for sustainable water quality management in the NY-NJ harbor system.

Evaluating and forecasting surface water quality is essential for protecting aquatic ecosystems and improving water resource management. This study in...

Evaluating regional and seasonal variations of PM2.5 and O3 across Malaysia using XGBoost and SHAP-based interpretability.

Evaluating regional and seasonal air pollution variability is essential for monitoring tropical environments. This study evaluated regional and season...

From traditional retting to precision bioprocessing: microbial ecology, enzymatic selectivity, and systems biology of jute retting.

Jute is one of the world's most important lignocellulosic fibre crops, yet its commercial value remains highly dependent on retting, a biologically me...

Machine learning for monitoring and assessment of potentially toxic elements in soils: a synthesis of spatial validation, explainability, and uncertainty.

Potentially toxic elements (PTEs) in soils pose persistent risks to ecosystems, groundwater, and food systems, creating a need for reliable spatial as...

Daily 1 km satellite-based PM2.5 exposure estimates in Bangladesh, 2020-2025: spatiotemporal patterns and implications for environmental health.

Bangladesh experiences some of the highest ambient PM2.5 concentrations globally and remains one of the most monitoring-sparse high-burden regions. To...

Non-Destructive and High-Fidelity Sensing of Plant Water Content Enabled by Near-Infrared Luminescent Metal Halides.

Real-time, accurate water monitoring is a crucial technical foundation for industrial, environmental, and biological research. However, traditional de...

Machine learning framework for predicting ESDD on high-voltage glass insulators with SHAP-based feature optimization.

This paper introduces the use of machine learning (ML) models to predict the severity of pollution on high-voltage outdoor glass insulators using equi...

Artificial intelligence and machine learning in photocatalytic nanomaterials: applications across diverse environmental pollutants.

Escalating environmental pollution, particularly from persistent organic pollutants, dyes, pharmaceuticals, and other recalcitrant contaminants in was...

Biodiversity and chemical regulation: Status quo, data gaps, and recommendations for future action.

Global biodiversity is declining, driven by multiple, interacting pressures including the 5 most prominent of resource use, habitat loss, invasive spe...

Quantitative decoupling of source-pathway-receptor driving mechanisms for integrated soil risk via interpretable machine learning.

Most existing concentration-based risk assessments of potentially toxic elements (PTEs) in farmland soils tend to overlook source-specific transport p...

Programmable biodegradation: Lipase-driven microplastic degradation via AI and omics.

Microplastic pollution is a pervasive global challenge, with millions of tons of plastic entering terrestrial and aquatic ecosystems each year and per...