AIMC Topic: Environmental Monitoring

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Prioritizing geochemical drivers of groundwater quality and health risks in coastal aquifers of Bangladesh using machine learning algorithms.

Environmental geochemistry and health
This study aims to evaluate key parameters of groundwater quality and associated health risks in three coastal aquifers of Cox's Bazar, Bangladesh, with a focus on manganese contamination and geochemical processes. A total of 288 groundwater samples ...

Machine learning framework for forecasting air pollution: Evaluating seasonal and climatic influences in Istanbul, Turkey.

PloS one
Air pollution, driven by seasonal and meteorological variations, poses a significant threat to public health and urban sustainability. Despite numerous forecasting approaches, the influence of seasonal patterns on air pollutant levels remains underex...

Remote sensing-based long-term assessment of bioenergy policy impact on agricultural land cover change: A case study of biogas in the Weser-Ems region in Germany.

The Science of the total environment
Climate change, population growth, and other global challenges are putting pressure on the limited land resources, in particular on agricultural land, to satisfy the demands for food, energy carriers, raw materials for the chemical industry, and ecos...

Particle number emissions on mountainous roads: machine learning insights from on-road testing.

Environmental research
Mountainous roads pose unique challenges for controlling vehicular fine particulate number (PN) emissions, a critical pollutant impacting air quality and public health. This study integrates on-road testing with interpretable machine learning to anal...

Fires enhanced productivity in fire-adapted subtropical pinelands of the Florida Everglades.

The Science of the total environment
Some ecosystems require regular disturbances to maintain their biological and structural diversity. However, shifts in climate and changes in land management practices have altered global fire regimes, making it challenging to determine the most effe...

High-resolution agricultural drought hazard mapping using the potential of geospatial data and machine learning approaches.

Environmental monitoring and assessment
Effective delineation of Agricultural Drought Hazard (ADH) zones is crucial for mitigating crop losses and ensuring water security in semi-arid regions. Conventional agricultural drought assessment methods, reliant on single-index approaches or stati...

Wearable sensors for monitoring workplace chemical exposures in occupational health management.

Analytical methods : advancing methods and applications
This review evaluates the current state of wearable chemical sensors for occupational health applications, emphasizing both their promise and their limitations. We systematically compare electrochemical, optical, photoionization, and chemiresistive p...

Emerging nanosensor technologies for the rapid detection of heavy metal contaminants in agricultural soils.

Analytical methods : advancing methods and applications
The accumulation of heavy metals in agricultural soils presents a growing threat to food safety and human health. Conventional laboratory-based methods for heavy metal detection, while highly sensitive, are impractical for widespread, real-time soil ...

Regional PM2.5 pollution forecasting using a hybrid model based on multi-scales feature fusion and deep learning algorithms.

PloS one
The issue of regional haze pollution has become increasingly prominent. However, early warning models for regional haze pollution are significantly lacking. To accurately predict regional PM2.5 pollution, hourly average concentration data of pollutan...