AIMC Topic: Environmental Monitoring

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Modeling climate change impacts and predicting future vulnerability in the Mount Kenya forest ecosystem using remote sensing and machine learning.

Environmental monitoring and assessment
The Mount Kenya forest ecosystem (MKFE), a crucial biodiversity hotspot and one of Kenya's key water towers, is increasingly threatened by climate change, putting its ecological integrity and vital ecosystem services at risk. Understanding the intera...

Uncovering rare earth and precious metal in landfill-mined soil-like-fractions: distribution prediction, ecological risk and resource potential.

Environmental pollution (Barking, Essex : 1987)
The introduction of rare earth elements (REEs) and precious metals (PMs) containing wastes in aged landfills leads to a significant pollutant and resource potential. Against this backdrop, the accumulation of REEs and PMs in soil-like-fractions (SLF)...

Modelling and mapping of subsurface nitrate-attenuation index in agricultural landscapes.

Journal of environmental management
Environmental management of nutrient losses from agricultural lands is required to reduce their potential impacts on the quality of groundwater and eutrophication of surface waters in agricultural landscapes. However, accurate accounting and manageme...

A novel method for achieving ecological indicator based on vertical soil bacterial communities coupled with machine learning: A case study of a typical tropical site in China.

Journal of hazardous materials
Global industrialization has resulted in severe contamination of soil with heavy metals (HMs). Nevertheless, it is unclear if it affects the depth-resolved bacterial communities. Herein, we collected soil samples at different depths from a typical HM...

Development of deep learning quantization framework for remote sensing edge device to estimate inland water quality in South Korea.

Water research
Recent achievements in the fields of deep learning and remote sensing have led to their application in monitoring river water quality. One of the most researched methods is the estimation of total suspended solid (TSS) concentrations using multispect...

Unraveling four decades of soil acidification on the Qinghai-Tibetan Plateau: Patterns, drivers, and future projections.

Environmental pollution (Barking, Essex : 1987)
Soil acidification poses escalating threats to ecosystem functions, yet its spatiotemporal dynamics and drivers across vulnerable high-altitude regions remain poorly resolved. Here, we integrate four-decade soil surveys (1980s-2020s) with machine lea...

Machine learning-driven optical microfiltration device for improved nanoplastic sampling and detection in water systems.

Journal of hazardous materials
The rising presence of nanoplastics in water poses toxicity risks and long-term ecological and health impacts. Detecting nanoplastics remains challenging due to their small size, complex chemistry, and environmental interference. Traditional filtrati...

Spatiotemporal evolutions and drivers of ground-level ozone in China (2015-2020): A GTWR-Kriging approach.

Environmental research
Ground-level ozone pollution has emerged as a primary environmental challenge in China. An accurate and high-resolution analysis of ground-level ozone concentrations is crucial for effectively mitigating pollution and achieving sustainability goals. ...

Stricter cadmium and lead standards needed for organic fertilizers in China.

The Science of the total environment
This study aims to evaluate the adequacy of China's national standards for heavy metals in organic fertilizers by predicting their concentrations in grains using machine leaning. A comprehensive dataset was collected from literature, including soil p...

Unveiling PFAS hazard in European surface waters using an interpretable machine-learning model.

Environment international
Per- and polyfluoroalkyl substances (PFAS), commonly known as "forever chemicals", are ubiquitous in surface waters and potentially threaten human health and ecosystems. Despite extensive monitoring efforts, PFAS risk in European surface waters remai...