AIMC Topic: Environmental Monitoring

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Spatial distribution and risk assessment of heavy metal in coastal waters of China.

Marine environmental research
In order to better understand the status of heavy metal pollution in surface seawater of China's coastal waters, this paper compiled research results on seven heavy metals (i.e. Hg, As, Cu, Cd, Cr, Pb, Zn) in surface seawater of 35 coastal areas of C...

Machine learning-based quantification and separation of emissions and meteorological effects on PM in Greater Bangkok.

Scientific reports
This study presents the first-ever application of machine learning (ML)-based meteorological normalization and Shapley additive explanations (SHAP) analysis to quantify, separate, and understand the effect of meteorology on PM over Greater Bangkok (G...

Unlocking the soundscape of coral reefs with artificial intelligence: pretrained networks and unsupervised learning win out.

PLoS computational biology
Passive acoustic monitoring can offer insights into the state of coral reef ecosystems at low-costs and over extended temporal periods. Comparison of whole soundscape properties can rapidly deliver broad insights from acoustic data, in contrast to de...

Metabolomic signatures of pathogen suppression effect of Baltic eelgrass meadows in surrounding seawater.

The Science of the total environment
Organic molecules exuded into water column by marine organisms represent a significant portion of marine dissolved organic matter (DOM) that modulates biochemical interactions. Secreted allelochemicals have been suggested to be involved in regulation...

ExpoPath: A method for identifying and annotating exposure pathways from chemical co-occurrence networks.

The Science of the total environment
Improving risk evaluation for environmental and human health is of paramount concern for the U.S. Environmental Protection Agency (EPA). This includes the identification and assessment of chemical transport from commercial and industrial sources to e...

Inversion of lake transparency using remote sensing and deep hybrid recurrent models.

Ecotoxicology and environmental safety
Utilizing computer technology and remote sensing data, the extraction of water-related features of lakes has become a hot topic in lake ecological research. Addressing challenges like the high optical complexity of lake water bodies, the inadequacy o...

Low-cost video-based air quality estimation system using structured deep learning with selective state space modeling.

Environment international
Air quality is crucial for both public health and environmental sustainability. An efficient and cost-effective model is essential for accurate air quality predictions and proactive pollution control. However, existing research primarily focuses on s...

Bayesian-optimized recursive machine learning for predicting human-induced changes in suspended sediment transport.

Environmental monitoring and assessment
The suspended sediment load (SSL) of a river is a key indicator of water resource management, river morphology, and ecosystem health. This study analyzes historical changes in SSL and evaluates machine learning (ML) models for SSL prediction in the G...

Enhancing tree-based machine learning for chlorophyll-a prediction in coastal seawater through spatiotemporal feature integration.

Marine environmental research
The excessive growth of phytoplankton in water can deplete oxygen, release toxins, harm aquatic life, cause economic losses, and threaten coastal residents. Accurately predicting phytoplankton levels is crucial for safeguarding marine life and coasta...

Estimating soil cadmium concentration using multi-source UAV imagery and machine learning techniques.

Environmental monitoring and assessment
Urbanization and industrialization have led to widespread soil heavy metals contamination, posing significant risks to ecosystems and human health. Conventional methods for mapping heavy metal distribution, which rely on soil sampling followed by che...