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Environmental Monitoring

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Assessment of noise pollution-prone areas using an explainable geospatial artificial intelligence approach.

Journal of environmental management
This research aims to use the power of geospatial artificial intelligence (GeoAI), employing the categorical boosting (CatBoost) machine learning model in conjunction with two metaheuristic algorithms, the firefly algorithm (CatBoost-FA) and the frui...

Tracing Microplastic Aging Processes Using Multimodal Deep Learning: A Predictive Model for Enhanced Traceability.

Environmental science & technology
The aging process of microplastics (MPs) affects their surface physicochemical properties, thereby influencing their behaviors in releasing harmful chemicals, adsorption of organic contaminants, sinking, and more. Understanding the aging process is c...

Efficient plastic detection in coastal areas with selected spectral bands.

Marine pollution bulletin
Marine plastic pollution poses significant ecological, economic, and social challenges, necessitating innovative detection, management, and mitigation solutions. Spectral imaging and optical remote sensing have proven valuable tools in detecting and ...

Spatial prediction of groundwater salinity in multiple aquifers of the Mekong Delta region using explainable machine learning models.

Water research
Groundwater salinization is a prevalent issue in coastal regions, yet accurately predicting and understanding its causal factors remains challenging due to the complexity of the groundwater system. Therefore, this study predicted groundwater salinity...

Combining Google traffic map with deep learning model to predict street-level traffic-related air pollutants in a complex urban environment.

Environment international
BACKGROUND: Traffic-related air pollution (TRAP) is a major contributor to urban pollution and varies sharply at the street level, posing a challenge for air quality modeling. Traditional land use regression models combined with data from fixed monit...

Towards reliable data: Validation of a machine learning-based approach for microplastics analysis in marine organisms using Nile red staining.

Marine pollution bulletin
Microplastic (MP) research faces challenges due to costly, time-consuming, and error-prone analysis techniques. Additionally, the variability in data quality across studies limits their comparability. This study addresses the critical need for reliab...

Unveiling the potential of a novel portable air quality platform for assessment of fine and coarse particulate matter: in-field testing, calibration, and machine learning insights.

Environmental monitoring and assessment
Although low-cost air quality sensors facilitate the implementation of denser air quality monitoring networks, enabling a more realistic assessment of individual exposure to airborne pollutants, their sensitivity to multifaceted field conditions is o...

Deep learning insights into spatial patterns of stable isotopes in Iran's precipitation: a novel approach to climatological mapping.

Isotopes in environmental and health studies
Stable isotope techniques are precise methods for studying various aspects of hydrology, such as precipitation characteristics. However, understanding the variations in the stable isotope content in precipitation is challenging in Iran due to numerou...

Hybrid deep learning based prediction for water quality of plain watershed.

Environmental research
Establishing a highly reliable and accurate water quality prediction model is critical for effective water environment management. However, enhancing the performance of these predictive models continues to pose challenges, especially in the plain wat...

Multi-source machine learning and spaceborne remote sensing data accurately predict three-dimensional soil moisture in an in-service uranium disposal cell.

Journal of environmental management
One reason arid and semi-arid environments have been used to store waste is due to low groundwater recharge, presumably limiting the potential for meteoric water to mobilize and transport contaminants into groundwater. The U.S. Department of Energy O...