AIMC Topic: Air Pollution
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Input strategy analysis for an air quality data modelling procedure at a local scale based on neural network.
Environmental monitoring and assessment
Apr 30, 2015
In recent years, a significant part of the studies on air pollutants has been devoted to improve statistical techniques for forecasting the values of their concentrations in the atmosphere. Reliable predictions of pollutant trends are essential not o...
Assessment of ultrafine particles and noise measurements using fuzzy logic and data mining techniques.
The Science of the total environment
Jan 20, 2015
This study focuses on correlations between total number concentrations, road traffic emissions and noise levels in an urban area in the southwest of Spain during the winter and summer of 2009. The high temporal correlation between sound pressure leve...
Quantifying regional transport contributions to PM-bound trace elements in a southeast coastal island of China: Insights from a machine learning approach.
Environmental pollution (Barking, Essex : 1987)
Jul 15, 2025
Identifying and quantifying pollution sources and their associated health risks are essential for formulating effective pollution control policies. This study analyzed PM-bound trace elements based on one year of sampling data collected from a low-PM...
Low-cost sensors for atmospheric NO measurement: A review.
Environmental pollution (Barking, Essex : 1987)
Jul 15, 2025
Nitrogen dioxide (NO) is a major air pollutant in urban areas, prompting the development of numerous analytical methods for its monitoring. Among these, the chemiluminescence method stands out as the most commonly used and is widely regarded as a ref...
Prenatal exposure to criteria air pollution and traffic-related air toxics and risk of autism spectrum disorder: A population-based cohort study of California births (1990-2018).
Environment international
Jul 1, 2025
BACKGROUND: Autism spectrum disorder (ASD) prevalence has risen steadily in California (CA) over several decades, with environmental factors like air pollution (AP) increasingly implicated. This study investigates associations between prenatal exposu...
Using machine learning to unravel chemical and meteorological effects on ground-level ozone: Insights for ozone-climate control strategies.
Environment international
Jul 1, 2025
In the context of climate change, various countries/regions across East Asia have witnessed severe ground-level ozone (O) pollution, which poses potential health risks to the public. The complex relationships between O and its drivers, including the ...
Non-traditional socio-environmental and geospatial determinants of Alzheimer's disease-related dementia mortality.
The Science of the total environment
Jul 1, 2025
IMPORTANCE: Recent data point to the impact of non-traditional environmental and social factors on Alzheimer's Disease-Related Dementias (ADRD) mortality. Our study aimed to determine the extent to which antecedent air pollution, social vulnerability...
Revealing the impacts of the built environment factors on pedestrian-weighted air pollutant concentration using automated and interpretable machine learning.
Journal of environmental management
Jul 1, 2025
Urban air pollution poses significant health risks, especially to pedestrians due to their proximity to pollutants and lack of physical protection. Understanding the influence of built environment factors is essential to mitigate this pollution and s...
Machine learning-based prediction of ambient CO and CH concentrations with high temporal resolution in Seoul metropolitan area.
Environmental pollution (Barking, Essex : 1987)
Jul 1, 2025
Machine learning has the potential to support the growing need for high-resolution greenhouse gas monitoring in urban and industrial environments, where deploying extensive sensor networks is often limited by cost and operational challenges. This stu...
Deep learning-based forecasting of daily maximum ozone levels and assessment of socioeconomic and health impacts in South Korea.
The Science of the total environment
Jun 25, 2025
Accurate forecasting of ground-level ozone (O) is essential for assessing its public health and socioeconomic impacts. This study evaluates the performance of three deep learning models-Deep Convolutional Neural Networks (Deep-CNN), Long Short-Term M...
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