Environmental Health

Dissolved organic carbon estimation in lakes: Improving machine learning with data augmentation on fusion of multi-sensor remote sensing observations.

This paper presents a novel approach for estimating Dissolved Organic Carbon (DOC) concentrations in lakes considering both carbon sources and sink operators. Despite the critical role of DOC, the combined application of machine learning, as a robust predictor, and remote sensing technology, which reduces costly and time-intensive in-situ sampling, has been underexplored in DOC research. Focusing ...

Uncovering soil heavy metal pollution hotspots and influencing mechanisms through machine learning and spatial analysis.

Soil heavy metal (HM) pollution is a significant and widespread environmental issue in China, highlighting the need to quantify influencing factors and identify priority concern areas for effective prevention and management. Based on published literature data of soil HM concentrations from 2000 to 2022, this study investigated the pollution characteristics and spatial distribution of eight soil HM...

High-resolution spatio-temporal estimation of street-level air pollution using mobile monitoring and machine learning.

High spatio-temporal resolution street-level air pollution (SLAP) estimation is essential for urban air quality management, yet traditional methods fa...

Generalizable deep neural networks for image quality classification of cervical images.

Successful translation of artificial intelligence (AI) models into clinical practice, across clinical domains, is frequently hindered by the lack of i...

Learning from leading indicators to predict long-term dynamics of hourly electricity generation from multiple resources.

Electricity is generated through various resources and then flows between regions via a complex system (grid). Imbalances in electricity generation ca...

A novel graph convolutional neural network model for predicting soil Cd and As pollution: Identification of influencing factors and interpretability.

Soil pollution caused by toxic metals poses serious threats to the ecological environment and human well-being. Accurately predicting toxic metal conc...

Environment sustainability with smart grid sensor.

Environmental sustainability is a pressing global concern, with energy conservation and efficient utilization playing a key role in its achievement. S...

Machine learning insights into calcium phosphate nucleation and aggregation.

In this study, we utilized machine learning interatomic potentials (MLIPs) to investigate the nucleation mechanisms of calcium phosphate, a critical c...

Managerial myopia and its barrier to green innovation in high-pollution enterprises: A machine learning approach.

Green technology innovation has become a vital remedy in response to the world's growing ecological problems and the urgent need for sustainable devel...

A study on the impact of meteorological and emission factors on PM concentrations based on machine learning.

PM pollution, a major environmental and health concern, is influenced by a complex interplay of emission sources and meteorological conditions. Accura...

Exploring the response of bacterial community functions to microplastic features in lake ecosystems through interpretable machine learning.

Microplastics (MPs) are ubiquitous and have various characteristics. However, their impacts on bacterial community functions in lakes remain elusive. ...

A Systematic Study of Popular Software Packages and AI/ML Models for Calibrating In Situ Air Quality Data: An Example with Purple Air Sensors.

Accurate air pollution monitoring is critical to understand and mitigate the impacts of air pollution on human health and ecosystems. Due to the limit...

Quantitative identification of melamine flame retardant in polyolefins with hyperspectral imaging and machine learning.

Non-halogenated flame-retardant Melamine (MEL), an alternative to toxic halogenated flame retardants, has been recognized as a substance of very high ...

Integration of machine learning and meta-analysis reveals the behaviors and mechanisms of antibiotic adsorption on microplastics.

Microplastics (MPs) can adsorb antibiotics (ATs) to cause combined pollution in the environment. Research on this topic has been limited to specific t...

Chemical Space Networks Enhance Toxicity Recognition via Graph Embedding.

Chemical space networks (CSNs) are a new effective strategy for detecting latent chemical patterns irrespective of defined coordinate systems based on...

Combining deep learning and machine learning techniques to track air pollution in relation to vegetation cover utilizing remotely sensed data.

The rapid urban expansion in Dhaka, the capital of Bangladesh, has escalated air pollution levels and led to a significant decrease in green spaces. T...

Advancing Source Apportionment of Atmospheric Particles: Integrating Morphology, Size, and Chemistry Using Electron Microscopy Technology and Machine Learning.

To further reduce atmospheric particulate matter concentrations, there is a need for a more precise identification of their sources. The SEM-EDS techn...

Machine learning-assisted prediction of engineered carbon systems' capacity to treat textile dyeing wastewater via adsorption technology.

Dyes are widely used in industries like printing, cosmetics, paper, leather processing, textiles, and manufacturing to add color to products. However,...

ANN-assisted comprehensive screening of silica gel-alunite composite sorbent system for efficient adsorption of toxic nickel ions: Batch and continuous mode water treatment applications.

Through batch and fixed-bed column operations, nickel ions were extracted from a contaminated aqueous media by adsorption onto silica gel-immobilized ...

Assessing the impact of rainfall, topography, and human disturbances on nutrient levels using integrated machine learning and GAMs models in the Choctawhatchee River Watershed.

Nutrient pollution caused by excessive total nitrogen (TN) and total phosphorus (TP) is a significant environmental challenge globally, threatening wa...