Environmental Health

Big data from population surveys and environmental monitoring-based machine learning predictions of indoor PM in 22 cities in China.

Many studies have confirmed that PM exposure can cause a variety of diseases. Because people spend most of their time indoors, exposure to PM in indoor environments is critical to population health. Large-population, long-term, continuous, and accurate indoor PM data are important but scarce because of the difficulties in monitoring the indoor air quality on a large scale. Model simulation provide...

Nordic environmental resilience: balancing air quality and energy efficiency by applying artificial neural network.

Maintaining public health and environmental safety in the Nordic nations calls for a strict plan to define exact benchmarks on air quality and energy efficiency. This study investigates the complicated interaction of decentralized energy production (DEP) with energy efficiency, and air quality index in the Nordic nations from 1990 to 2022 using System GMM and Artificial Neural Network (ANN) approa...

Machine Learning Framework for Conotoxin Class and Molecular Target Prediction.

Conotoxins are small and highly potent neurotoxic peptides derived from the venom of marine cone snails which have captured the interest of the scient...

High spatiotemporal resolution estimation and analysis of global surface CO concentrations using a deep learning model.

Ambient carbon monoxide (CO) is a primary air pollutant that poses significant health risks and contributes to the formation of secondary atmospheric ...

Significant spatiotemporal changes in atmospheric particulate mercury pollution in China: Insights from meta-analysis and machine-learning.

PM bound mercury (PBM) in the atmosphere is a major component of total mercury, which is a pollutant of global concern and a potent neurotoxicant when...

Topic-sentiment analysis of citizen environmental complaints in China: Using a Stacking-BERT model.

Environmental complaints serve as a crucial means for citizens to participate in environmental regulation, providing precise insights for real-time id...

Impacts of micro/nano plastics on the ecotoxicological effects of antibiotics in agricultural soil: A comprehensive study based on meta-analysis and machine learning prediction.

Micro/nano plastics (M/NPs) and antibiotics, as widely coexisting pollutants in environment, pose serious threats to soil ecosystem. The purpose of th...

Deep learning-assisted detection of psychoactive water pollutants using behavioral profiling of zebrafish embryos.

Water pollution poses a significant risk to the environment and human health, necessitating the development of innovative detection methods. In this s...

Air quality index prediction with optimisation enabled deep learning model in IoT application.

The development of industrial and urban places caused air pollution, which has resulted in a variety of effects on individuals and the atmosphere over...

High-throughput prediction of oral acute toxicity in Rat and Mouse of over 100,000 polychlorinated persistent organic pollutants (PC-POPs) by interpretable data fusion-driven machine learning global models.

This study utilized available oral acute toxicity data in Rat and Mouse for polychlorinated persistent organic pollutants (PC-POPs) to construct data ...

Machine learning for air quality index (AQI) forecasting: shallow learning or deep learning?

In this study, several machine learning (ML) models consisting of shallow learning (SL) models (e.g., random forest (RF), K-nearest neighbor (KNN), we...

Innovations in plastic remediation: Catalytic degradation and machine learning for sustainable solutions.

Plastic pollution is an extreme environmental threat, necessitating novel restoration solutions. The present investigation investigates the integratio...

Effects of the co-exposure of microplastic/nanoplastic and heavy metal on plants: Using CiteSpace, meta-analysis, and machine learning.

Micro/nanoplastics (MNPs) and heavy metals (HMs) coexist worldwide. Existing studies have reported different or even contradictory toxic effects of co...

Derivation of marine water quality criteria for copper based on artificial neural network model.

The water chemical effects of copper have been a focus in the study of water quality criteria (WQC). Currently, multiple regression models are commonl...

Multimodal Representation Learning via Graph Isomorphism Network for Toxicity Multitask Learning.

Toxicity is paramount for comprehending compound properties, particularly in the early stages of drug design. Due to the diversity and complexity of t...

An integrated three-stream network model for discriminating fish feeding intensity using multi-feature analysis and deep learning.

Feed costs constitute a significant part of the expenses in the aquaculture industry. However, feeding practices in fish farming often rely on the bre...

Imaging pollen using a Raspberry Pi and LED with deep learning.

The production of low-cost, small footprint imaging sensor would be invaluable for airborne global monitoring of pollen, which could allow for mitigat...

Diagnostic performance of single-lead electrocardiograms for arterial hypertension diagnosis: a machine learning approach.

Awareness and early identification of hypertension is crucial in reducing the burden of cardiovascular disease (CVD). Artificial intelligence-based an...

3DVar sectoral emission inversion based on source apportionment and machine learning.

Air quality models are increasingly important in air pollution forecasting and control. Sectoral emissions significantly impact the accuracy of air qu...

Artificial intelligence application for identifying toxic plant species: A case of poisoning with Datura stramonium.

INTRODUCTION: The management of plant poisonings in the emergency department (ED) presents various challenges. Foremost among these is the identificat...