AIMC Topic: Environmental Monitoring

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A semi-supervised learning-based framework for quantifying litter fluxes in river systems.

Water research
Supervised deep learning methods have been widely employed to detect floating macroplastic litter (>5 mm) in (fresh)water bodies. However, few studies used them to quantify floating litter fluxes in rivers with wide cross-sections, that is important ...

Decoding spatiotemporal dynamics of suspended sediment and vegetation in shallow reservoirs with Sentinel-2 and ANNs: A case study of Lake Tisza, Hungary.

Environmental monitoring and assessment
Shallow reservoirs on large rivers are highly dynamic systems vulnerable to sediment accumulation, eutrophication, and water quality deterioration, posing significant threats to their storage capacity, hydropower generation, and ecological balance. R...

Assessment of climate change impacts on arsenic contamination in groundwater through machine learning, remote sensing, and GIS: a review.

Environmental geochemistry and health
More than 50% of the world's largest countries and cities depend on groundwater for their daily needs. In particular, 80% of the largest cities in the Middle East, South Asia, and Central Asia rely on groundwater for drinking, irrigation, and industr...

Driving mechanisms and high-risk area prediction of arsenic pollution in surface water of the Shaanxi Wei River Basin.

Environmental pollution (Barking, Essex : 1987)
The Weihe River Basin, located within the Yellow River Basin, is an ecologically important region increasingly threatened by arsenic (As) contamination in surface water, which poses risks to both environmental security and public health. This study c...

Advances and challenges in the ecological risk assessment of engineered nanomaterials in aquatic ecosystems: A review.

The Science of the total environment
Generating appropriate ecological risk assessments to support the rapid growth of nanotechnology requires a comprehensive understanding of the potential effects of engineered nanomaterials (ENMs), both toxic and beneficial, and accurate predictions o...

Detection of climate change signals using precipitation and temperature time series by a hybrid deep learning framework.

Environmental monitoring and assessment
Climate change is one of the most extreme challenges of the twenty-first century. Precipitation (pr) and temperature variability are key indicators of climate change detection. Whereas hybrid deep learning (DL) models have been widely applied, their ...

Combining wood traits as a promising timber origin verification and its application in the Brazilian trade chain.

The Science of the total environment
Tracing the geographic origin of wood remains a major challenge in the fight against illegal timber in tropical countries. Methods using anatomical, chemical, isotopic and DNA markers have been successfully tested, but methods are either expensive, u...

Comprehensive monitoring of the spatiotemporal variation of water quality and its associated human health risks in Luvuvhu river catchment, Vhembe biosphere reserve, South Africa.

Scientific reports
This study investigates the spatiotemporal variations in water quality and assesses the associated human health risks in the Luvuvhu River Catchment (LRC), South Africa. Water quality parameters such as pH, total dissolved solids (TDS), turbidity, te...

Discovery of Comprehensive Sets of Chemical Constituents as Markers of PFAS Sources through a Nontarget Screening and Machine Learning Approach.

Environmental science & technology
The objective of this study was to identify chemical constituents as markers of six per- and polyfluoroalkyl substance (PFAS) sources including aqueous film-forming foam-impacted groundwater, landfill leachate, biosolids leachate, municipal wastewate...

Multi-dimensional Attention-Based MOSSM Model for Marine Oil Spill Monitoring in SAR image Remote Sensing.

Environmental monitoring and assessment
Marine oil spills pose severe threats to marine ecosystems, where rapid and accurate oil spill region segmentation is crucial for emergency response to disasters. Synthetic Aperture Radar (SAR), with its all-weather and day-night observation capabili...