Environmental Health

Quantitative analysis of 3-monochloropropane-1,2-diol in fried oil using convolutional neural networks optimizing with a hybrid feature selection based on Fourier transform infrared spectroscopy.

To address challenges in quantifying 3-monochloropropane-1,2-diol (3-MCPD), a toxic compound formed during oil refining with nephrotoxic, reproductive toxic, and carcinogenic properties, a convolutional neural network (CNN) integrated with a hybrid feature selection strategy based on Fourier transform infrared spectroscopy was constructed. Before the construction of CNN, relevant spectral variable...

Decoding the urban-suburban ozone disparities in Shandong, China: A machine learning-driven attribution analysis (2019-2023).

Urban-suburban disparities in ozone (O3) pollution have received growing attention due to their implications for environmental equity and public health. However, the underlying drivers of these disparities remain poorly understood, especially the contrasting mechanisms between coastal and inland cities. Based on observations at 267 air quality monitoring sites in Shandong during 2019-2023, spatiot...

New spectral indices for identifying large plastic accumulations in coastal waters with sentinel-2 imagery.

Plastic pollution in aquatic environments has become a growing global concern, with floating macroplastics posing serious ecological, economic, and he...

Global mapping of pharmaceutical ecological risk in rivers using machine learning: drivers, hotspots, and compounded water stress.

Pharmaceuticals serve as a double-edged sword: while essential for health care, they are also ubiquitous pollutants that threaten ecosystem. The vast ...

Evaluating the role of low-cost sensors in machine learning based European PM2.5 monitoring.

We evaluate the added value of integrating validated Low-Cost Sensor (LCS) data into a Machine Learning (ML) framework for providing surface PM2.5 est...

Integrative metabolomics and machine learning reveal diagnostic biomarkers for gelsenicine intoxication.

ETHNOPHARMACOLOGICAL RELEVANCE: Gelsemium elegans Benth. (G. elegans) is a highly toxic medicinal plant traditionally used to treat pain and inflammat...

Global distribution and evolutionary trends of the PM2.5 health burden predicted with a Geographically Neural Network Weighted Regression model.

PM2.5 pollution, driven by various human activities, has become a significant global threat to both environmental and public health, yet accurately pr...

Synergistic integration of multidimensional differential spectroscopy and deep learning for robust phenol monitoring in complex industrial effluents.

BACKGROUND: Phenol is a key chemical intermediate and a common, toxic pollutant found in industrial wastewater from processes like coking and oil refi...

Plant immune inducers from fundamental research to applications: Past achievements, current challenges, and future perspectives.

Plant pathogens cause substantial global crop losses, posing a serious threat to food security. Conventional chemical pesticides face increasing chall...

Spatial heterogeneity and evolutionary pathways of PM2.5 pollution driven by urbanization in China to 2100.

With the continued acceleration of global urbanization, PM2.5 pollution has become a critical environmental constraint to sustainable urban developmen...

Deep learning model for identifying significant tricuspid regurgitation using standard 12-lead electrocardiogram.

INTRODUCTION: Transthoracic echocardiography (TTE) is the current standard for detecting tricuspid regurgitation (TR); however, it incurs additional c...

Programmable materials: A comprehensive review on shape memory polymers for orthopedic applications.

Shape memory polymers (SMP) are smart, active polymers, which can significantly change their properties by external stimuli such as heat or temperatur...

Optimizing nitrogen-water use efficiency for nitrate reduction: spatiotemporal strategies and potential of climate-smart kiwifruit agriculture across SSP scenarios.

The intensive use of nitrogen (N) fertilizers in agriculture has led to surpluses and widespread nitrate pollution in surface water and groundwater. C...

Predicting the environmental risks of potentially toxic metal(loid)s in mechanochemically treated fly ash using machine learning.

Mechanochemical (MC) treatment is a green and efficient approach that has shown great potential in reducing the environmental risks of potentially tox...

Decoding the transport thresholds of emerging contaminants in watersheds using explainable machine learning.

Understanding watershed emerging contaminants (ECs) transport is vital for pollution control but challenging due to complex land-climate interactions ...

Phthalate ester pollution poses a potential threat to soil organisms: Insights from a global meta and machine-learning analysis.

The widespread accumulation of phthalate esters (PAEs) in agricultural soils has raised global concern, yet their comprehensive ecological impacts on ...

The New A-⊘OHO process Based on DO Gradients and Load-Ratio Allocation Enabling Mainstream PN/A in Toxic Industrial Wastewater.

The partial nitritation-anammox (PN/A) process offers significant benefits in energy conservation and carbon neutrality, however, its application is l...

Analysis of microplastics and nanoplastics emerged from polyethylene bags and polyethylene terephthalate bottles by an artificial intelligence-enabled tool.

This study presents a comprehensive and comparative analysis of the emergence of micro- and nanoplastics (MNPs) from polyethylene (PE) plastic bags an...

Global analysis of mineral aerosol and industrial effects on lake water quality using spectral indices and machine learning.

The complex physical and chemical interactions between desert mineral aerosol, industrial pollutants, and lake water during drought periods pose signi...

GeoAI-based 3D spatial distribution modeling of PAHs in industrial contaminated soils.

Soil pollution threatens human health and food security, particularly in industrial legacy sites. Accurate three-dimensional distribution modeling of ...