AIMC Topic: Environmental Monitoring

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Advancing low-cost air quality monitor calibration with machine learning methods.

Environmental pollution (Barking, Essex : 1987)
Low-cost monitors for measuring airborne contaminants have gained popularity due to their affordability, portability, and ease of use. However, they often exhibit significant biases compared to high-cost reference instruments. For optimal accuracy, t...

Meteorological and traffic effects on air pollutants using Bayesian networks and deep learning.

Journal of environmental sciences (China)
Traffic emissions have become the major air pollution source in urban areas. Therefore, understanding the highly non-stational and complex impact of traffic factors on air quality is very important for building air quality prediction models. Using re...

Identifying the key factors of mercury exposure in residents of southwestern Iran using machine learning algorithms.

Environmental geochemistry and health
It is necessary to predict hair mercury (Hg) levels and specify the related effective factors to develop preventive strategies to reduce Hg exposure in different regions. This study is the first effort to investigate the effectiveness of eight machin...

The integrated fuzzy AHP and fuzzy logic techniques for mapping and prioritizing groundwater potential zone based on water quality.

Environmental monitoring and assessment
Groundwater, which is utilized to supply water demand in various sectors such as domestic water consumption, agriculture, and industry, could be achieved by delineating a groundwater potential zone. Although mapping groundwater potential zones has be...

Integrating machine learning and traditional methods for cadmium prediction and bioavailability assessment in Paeoniae Radix Alba: a case study from Bozhou, Anhui Province.

Environmental geochemistry and health
Soil heavy metal contamination, particularly cadmium (Cd), poses a significant risk to ecosystems and human health. This study investigates the distribution and bioavailability of Cd in soil and Paeoniae Radix Alba system from Qiaocheng District, Boz...

Machine learning-assisted source identification and probabilistic ecological-health risk assessment of heavy metal(loid)s in urban park soils.

Scientific reports
The accumulation of heavy metal(loid)s (HMs) in the soils of urban parks in industrial cities has raised global concerns because of their environmental and health impacts. However, traditional deterministic assessments commonly overlook uncertainties...

Brick Kiln Dataset for Pakistan's IGP Region Using AI.

Scientific data
Brick kilns are a major source of air pollution in Pakistan, with many operating without regulation. A key challenge in Pakistan and across the Indo-Gangetic Plain is the limited air quality monitoring and lack of transparent data on pollution source...

Robotic monitoring of European habitats: a labeled dataset for plant detection in Annex I habitats of Italy.

Scientific data
The present data descriptor presents a dataset designed for the detection of plant species in various habitats of the European Union. This dataset is based on images captured using multiple different hardware including quadrupedal robot ANYmal C, ref...

Mapping Regional Meteorological Processes to Ozone Variability in the North China Plain and the Yangtze River Delta, China.

Environmental science & technology
High-concentration ozone threatens human health and ecosystems, modulated by dynamic, multiscale meteorological processes. Existing machine learning studies for ozone prediction rarely incorporate the spatiotemporal evolution of regional meteorologic...

Hidden threats beneath: uncovering the bio-accessible hazards of chromite-asbestos mine waste and their impacts on rice components via multi-machine learning algorithm.

Environmental geochemistry and health
The chromite-asbestos mining leaves behind tonnes of toxic waste, contaminating nearby agricultural fields with potentially toxic elements (PTEs). Over time, wind and water erosion spread these pollutants, severely impacting the ecosystem, food chain...