Environmental Health

Insight into VOCs source profiles by machine learning: Role of commonalities in synergistic pollution controls.

Under the trend of low-carbon and cost-reduction, achieving efficient control requires identifying the commonalities in volatile organic compounds (VOCs) source profiles and implementing collaborative emissions reduction strategies. This study focuses on the analysis of common pollution characteristics in chemical industrial clusters, examining the emission behaviors of VOCs from nearly 200 emissi...

A high-throughput differential chemical genetic screen uncovers genotype-specific compounds altering plant growth.

The identification of chemical compounds regulating plant growth in a genetic context can greatly enhance our understanding of biological mechanisms. Here, we have developed a high-throughput phenotype-directed chemical screening method in plants to compare two genotypes and identify small molecules inducing genotype-specific phenotypes. We used wild type and , a DNA repair mutant, and screened o...

A method for delineating traffic low emission control zone based on deep learning and multi-objective optimization.

Current methods for defining traffic low emission control zones (TLEZ) often face limitations that hinder their widespread implementation and effectiv...

Deep transfer learning for spatiotemporal mapping of PM nitrate across China: Addressing small data challenges in environmental machine learning.

The proportion of nitrate in PM is increasing in China, leading to rising health risks. However, due to the lack of a publicly accessible nationwide m...

Assessing the impact of traffic restriction interventions on school air quality: a citizen science-based modelling study.

Air pollution poses a significant threat to human health, especially for the vulnerable groups such as children. Given that schools are central to the...

Optimizing Cu adsorption prediction in Undaria pinnatifida using machine learning and isotherm models.

Algae are cost-effective bioadsorbents for heavy metal remediation, yet their potential is underutilized due to limitations in traditional adsorption ...

Assessment of the long RR intervals using convolutional neural networks in single-lead long-term Holter electrocardiogram recordings.

Advancements in medical technology have extended long-term electrocardiogram (ECG) monitoring from the traditional 24 h to 7-14 days, significantly en...

Applications of machine learning in potentially toxic elemental contamination in soils: A review.

Soil contamination by potentially toxic elements (PTEs) poses substantial risks to the environment and human health. Traditional investigational metho...

Microplastics migration mechanisms in high-erosion watersheds under climate warming.

Understanding Microplastics (MPs) migration in small watersheds is crucial for pollution management, but progress has been hindered by limited long-te...

A novel deep learning-based floating garbage detection approach and its effectiveness evaluation in environmentally sustainable development.

Floating garbage removal is an essential environmental strategy to reduce water pollution and achieve environmental sustainability, and it is a pressi...

Joint identification of hydraulic conductivity and groundwater pollution sources using unscented Kalman smoother with multiple data assimilation and deep learning.

Identification of groundwater pollution sources (IGPSs) is a prerequisite for pollution remediation and pollution risk prediction. Data assimilation a...

Artificial Intelligence for the Discovery of Safe and Effective Flame Retardants.

Organophosphorus flame retardants (OPFRs) are important chemical additives that are used in commercial products. However, owing to increasing health c...

U-shaped deep learning networks for algal bloom detection using Sentinel-2 imagery: Exploring model performance and transferability.

Inland water sources, such as lakes, support diverse ecosystems and provide essential services to human societies. However, these valuable resources a...

Predicting and investigating water quality index by robust machine learning methods.

This study addresses the critical challenges of waste management and water quality in urban environments, where accelerated urbanization has exacerbat...

Spatiotemporal evolution and risk thresholds of PM components in China from the human health perspective.

PM is a significant global public health hazard, with its components closely linked to various fatal diseases, thereby significantly increasing mortal...

Machine Learning-Enhanced Prediction for Soil-to-Air VOC Emission and Environmental Impact Pertaining Contaminated Fractured Aquifers.

How to scientifically and efficiently quantify the impact and hazards of volatile organic compounds (VOCs) pollution and volatilization from complex g...

Predicting the amount of toxic metals and metalloids in silt loading using neural networks.

Material deposited on road surfaces, called road dust, are known to contain different toxic elements. According to particle size, there are different ...

Advancing Enzyme-Based Detoxification Prediction with ToxZyme: An Ensemble Machine Learning Approach.

The aaccurate prediction of enzymes with environment detoxification functions is crucial, not only to achieve a better understanding of bioremediation...

Unveiling drug-induced osteotoxicity: A machine learning approach and webserver.

Drug-induced osteotoxicity refers to the harmful effects certain pharmaceuticals have on the skeletal system, posing significant safety risks. These t...

The interpretable machine learning model for depression associated with heavy metals via EMR mining method.

Limited research exists on the association between depression and heavy metal exposure. This study aims to develop an interpretable and efficient mach...