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Environmental Monitoring

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Satellite Remote Sensing-Implemented Nontargeted Screening of Emerging Contaminant Fingerprints in a River-to-Ocean Continuum through Interpretable Machine Learning: The Pivotal Intermediary Role of Dissolved Organic Matter.

Environmental science & technology
Emerging contaminants (ECs) can exert irreversible health impacts on humans, even at trace concentrations. Currently, nontargeted screening of ECs has been developed for their assessment, which requires sophisticated instrumentation. Although satelli...

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

Ecotoxicology and environmental safety
Identification of groundwater pollution sources (IGPSs) is a prerequisite for pollution remediation and pollution risk prediction. Data assimilation approaches have been used extensively in IGPSs field in recent years. A data assimilation approach-un...

Traffic-related air pollution backcasting using convolutional neural network and long short-term memory approach.

The Science of the total environment
Air pollution backcasting, especially nitrogen dioxide (NO), is crucial in epidemiological studies, thus enabling the reconstruction of historical exposure levels for assessing long-term health effects. Changes in NO concentrations in urban areas are...

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

Journal of environmental management
Floating garbage removal is an essential environmental strategy to reduce water pollution and achieve environmental sustainability, and it is a pressing issue for global ecological restoration. Under the interference of complex environments, floating...

Statistical analysis and prediction via neural networks of water quality in the Middle Paraíba do Sul (Rio de Janeiro State, Brazil) region in the period (2012-2022).

Environmental science and pollution research international
The aim of this study is to accurately predict the water quality at these points over a decade through the combined use of statistical tools and artificial intelligence. This study brings the innovative use of neural networks implemented with the GRN...

Feasibility study of real-time virtual sensing for water quality parameters in river systems using synthetic data and deep learning models.

Journal of environmental management
With water quality management crucial for environmental sustainability, multiple techniques for real-time monitoring and estimation of water quality parameters have been developed. However, certain data types, such as airborne images, are only access...

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

Journal of environmental management
This study addresses the critical challenges of waste management and water quality in urban environments, where accelerated urbanization has exacerbated environmental degradation and public health risks. Employing advanced machine learning algorithms...

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

Journal of environmental management
Inland water sources, such as lakes, support diverse ecosystems and provide essential services to human societies. However, these valuable resources are under increasing pressure from rapid climate changes and pollution resulting from human activitie...

Water quality parameters-based prediction of dissolved oxygen in estuaries using advanced explainable ensemble machine learning.

Journal of environmental management
The dissolved oxygen (DO) is crucial for the ecological health of estuaries and bays. However, human activities, land-sea interactions, and the unclear impact mechanisms of water quality parameters (WQPs) pose challenges to DO prediction. Water quali...

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

Environmental monitoring and assessment
Material deposited on road surfaces, called road dust, are known to contain different toxic elements. According to particle size, there are different fractions. Particles with an aerodynamic size less than or equal to 75 µm are called silt loading. A...