Environmental Health

Deep transfer learning-based bird species classification using mel spectrogram images.

The classification of bird species is of significant importance in the field of ornithology, as it plays an important role in assessing and monitoring environmental dynamics, including habitat modifications, migratory behaviors, levels of pollution, and disease occurrences. Traditional methods of bird classification, such as visual identification, were time-intensive and required a high level of e...

Long-term trend forecast of chlorophyll-a concentration over eutrophic lakes based on time series decomposition and deep learning algorithm.

Long-term trend forecast of chlorophyll-a concentration (Chla) holds significant implications for eutrophication management and pollution control planning on lakes, especially under the background of climate change. However, it is a challenging task due to the mixture of trend, seasonal and residual components in time series and the nonlinear relationships between Chla and the hydro-environmental ...

Identifying heavy metal sources and health risks in soil-vegetable systems of fragmented vegetable fields based on machine learning, positive matrix factorization model and Monte Carlo simulation.

Urban fragmented vegetable fields offer fresh produce but pose a potential risk of heavy metal (HM) exposure. Thus, this study investigated HM sources...

Do machine learning methods improve prediction of ambient air pollutants with high spatial contrast? A systematic review.

BACKGROUND & OBJECTIVE: The use of machine learning for air pollution modelling is rapidly increasing. We conducted a systematic review of studies com...

Optimizing BenMAP health impact assessment with meteorological factor driven machine learning models.

This study aims to address accuracy challenges in assessing air pollution health impacts using Environmental Benefits Mapping and Analysis Program (Be...

Green workspace and urban health: exploring the impacts of industrial robotics in pollution emissions and public health.

INTRODUCTION: This study addresses a critical gap in understanding how technological advancements, specifically industrial robots, influence urban pol...

Machine learning-based analysis of heavy metal contamination in Chinese lake basin sediments: Assessing influencing factors and policy implications.

Sediments are important heavy metal sinks in lakes, crucial for ensuring water environment safety. Existing studies mainly focused on well-studied lak...

Machine Learning Allowed Interpreting Toxicity of a Fe-Doped CuO NM Library Large Data Set─An Environmental In Vivo Case Study.

The wide variation of nanomaterial (NM) characters (size, shape, and properties) and the related impacts on living organisms make it virtually impossi...

Predictive analysis and risk assessment of potentially toxic elements in Beijing gas station soils using machine learning and two-dimensional Monte Carlo simulations.

Gas stations not only serve as sites for oil storage and refueling but also as locations where vehicles frequently brake, significantly enriching the ...

The Lag -Effects of Air Pollutants and Meteorological Factors on COVID-19 Infection Transmission and Severity: Using Machine Learning Techniques.

BACKGROUND: Exposure to air pollution is a major health problem worldwide. This study aimed to investigate the effect of the level of air pollutants a...

Deep mutational scanning and machine learning for the analysis of antimicrobial-peptide features driving membrane selectivity.

Many antimicrobial peptides directly disrupt bacterial membranes yet can also damage mammalian membranes. It is therefore central to their therapeutic...

Pollution loads in the middle-lower Yangtze river by coupling water quality models with machine learning.

Pollution control and environmental protection of the Yangtze River have received major attention in China. However, modeling the river's pollution lo...

Adsorption behavior and mechanism of heavy metals onto microplastics: A meta-analysis assisted by machine learning.

Microplastics (MPs) have the potential to adsorb heavy metals (HMs), resulting in a combined pollution threat in aquatic and terrestrial environments....

Appraisal of microplastic pollution and its related risks for urban indoor environment in Bangladesh using machine learning and diverse risk evolution indices.

The widespread presence of Microplastics (MPs) is increasing in the indoor environment due to increasing annual plastic usage, which is becoming a glo...

An update for various applications of Artificial Intelligence (AI) for detection and identification of marine environmental pollutions: A bibliometric analysis and systematic review.

Marine environmental pollution is one of the growing concerns of humans all over the world. Therefore, managing these marine pollutants has been a cru...

Predicting hospital admissions for upper respiratory tract complaints: An artificial neural network approach integrating air pollution and meteorological factors.

This study uses artificial neural networks (ANNs) to examine the intricate relationship between air pollutants, meteorological factors, and respirator...

Recognition of aggressive driving behavior under abnormal weather based on Convolutional Neural Network and transfer learning.

OBJECTIVES: Aggressive driving behavior can lead to potential traffic collision risks, and abnormal weather conditions can exacerbate this behavior. T...

Interpretable machine learning models for detecting peripheral neuropathy and lower extremity arterial disease in diabetics: an analysis of critical shared and unique risk factors.

BACKGROUND: Diabetic peripheral neuropathy (DPN) and lower extremity arterial disease (LEAD) are significant contributors to diabetic foot ulcers (DFU...

Computational Insights into Reproductive Toxicity: Clustering, Mechanism Analysis, and Predictive Models.

Reproductive toxicity poses significant risks to fertility and progeny health, making its identification in pharmaceutical compounds crucial. In this ...

Exploring the nexus between water quality and land use/land cover change in an urban watershed in Uruguay: a machine learning approach.

The expansion of urban areas contributes to the growth of impervious surfaces, leading to increased pollution and altering the configuration, composit...