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Environmental Monitoring

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

Environmental pollution (Barking, Essex : 1987)
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 litera...

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

Journal of environmental management
High spatio-temporal resolution street-level air pollution (SLAP) estimation is essential for urban air quality management, yet traditional methods face significant challenges in capturing the detailed spatial and temporal variability of pollution. M...

The reanalysis of a new strategy for groundwater level prediction using combined simulation of machine learning and Muskingum methods under ecological water replenishment.

Environmental research
Due to its multi-functionality, ecological water replenishment (EWR) has been an important measure for restoring aquifers. However, suitable prediction methods need to be selected for the unique fluctuation exhibited by groundwater level (GWL) in the...

Seafloor debris detection using underwater images and deep learning-driven image restoration: A case study from Koh Tao, Thailand.

Marine pollution bulletin
Traditional detection and monitoring of seafloor debris present considerable challenges due to the high costs associated with underwater imaging devices and the complex environmental conditions in marine ecosystems. In response to these challenges, t...

Constructing the 3D Spatial Distribution of the HCHO/NO Ratio via Satellite Observation and Machine Learning Model.

Environmental science & technology
The satellite-based tropospheric column ratio of HCHO to NO (FNR) is widely used to diagnose ozone formation sensitivity; however, its representation of surface conditions remains controversial. In this study, an approach to construct the 3D spatial ...

Advancing exposure science through artificial intelligence: Neural ordinary differential equations for predicting blood concentrations of volatile organic compounds.

Ecotoxicology and environmental safety
Volatile organic compounds (VOCs) are a significant concern for human health and environmental safety, requiring accurate models to predict their concentrations in body fluids for effective risk assessments. This study evaluates the application of ne...

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

Ecotoxicology and environmental safety
Soil pollution caused by toxic metals poses serious threats to the ecological environment and human well-being. Accurately predicting toxic metal concentrations is critical for safeguarding soil environmental security. However, the distribution of so...

Long-term solar radiation forecasting in India using EMD, EEMD, and advanced machine learning algorithms.

Environmental monitoring and assessment
Solar radiation plays a critical role in the carbon sequestration processes of terrestrial ecosystems, making it a key factor in environmental sustainability among various renewable energy sources. This study integrates two advanced signal processing...

Change analysis of surface water clarity in the Persian Gulf and the Oman Sea by remote sensing data and an interpretable deep learning model.

Environmental science and pollution research international
The health of an ecosystem and the quality of water can be determined by the clarity of the water. The Persian Gulf and Oman Sea have a unique ecosystem, and monitoring their water clarity is necessary for sustainable development. Here, various crite...

Using artificial intelligence tools for data quality evaluation in the context of microplastic human health risk assessments.

Environment international
Concerns about the negative impacts of microplastics on human health are increasing in society, while exposure and risk assessments require high-quality, reliable data. Although quality assurance and -control (QA/QC) frameworks exist to evaluate the ...