AIMC Topic: Particulate Matter
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Low-cost video-based air quality estimation system using structured deep learning with selective state space modeling.
Environment international
Apr 26, 2025
Air quality is crucial for both public health and environmental sustainability. An efficient and cost-effective model is essential for accurate air quality predictions and proactive pollution control. However, existing research primarily focuses on s...
Effect of pregnancy and infancy exposure to outdoor particulate matter (PM, PM, PM) and SO on childhood pneumonia in preschool children in Taiyuan City, China.
Environmental pollution (Barking, Essex : 1987)
Apr 24, 2025
There is currently a paucity of research on the effects of early life exposure to particulate matter (PM) of various size fractions on pneumonia in preschool-aged children. We explored the connections between antenatal and postnatal exposure to atmos...
Source apportionment of PM particles in the urban atmosphere using PMF and LPO-XGBoost.
Environmental research
Apr 22, 2025
Atmospheric particulate matter (PM), as a leading part of air pollution, affects health in many ways. Thus, identifying and quantifying the contribution of atmospheric particulate matter sources of PM is vital for developing effective air quality man...
Temporally boosting neural network for improving dynamic prediction of PM concentration with changing and unbalanced distribution.
Journal of environmental management
Apr 22, 2025
Increasing medical research evidence suggests that even low PM concentrations may trigger significant health issues. Hence, an accurate prediction of PM holds immense significance in securing public health safety. However, current data-drive predicti...
A method for delineating traffic low emission control zone based on deep learning and multi-objective optimization.
Environmental monitoring and assessment
Apr 8, 2025
Current methods for defining traffic low emission control zones (TLEZ) often face limitations that hinder their widespread implementation and effectiveness. This study addresses these challenges by employing a comprehensive approach to analyze PM con...
Spatiotemporal evolution and risk thresholds of PM components in China from the human health perspective.
Environmental pollution (Barking, Essex : 1987)
Apr 2, 2025
PM is a significant global public health hazard, with its components closely linked to various fatal diseases, thereby significantly increasing mortality rates. This study analysed the spatiotemporal evolution of PM-related mortality and death rates ...
Forecasting the concentration of the components of the particulate matter in Poland using neural networks.
Environmental science and pollution research international
Mar 21, 2025
Air pollution is a significant global challenge with profound impacts on human health and the environment. Elevated concentrations of various air pollutants contribute to numerous premature deaths each year. In Europe, and particularly in Poland, air...
PM concentration prediction using machine learning algorithms: an approach to virtual monitoring stations.
Scientific reports
Mar 8, 2025
One of the most important pollutants is PM, which is particularly important to monitor pollutant levels to keep the pollutant concentration under control. In this research, an attempt has been made to predict the concentrations of PM using four Machi...
PM concentration prediction using a whale optimization algorithm based hybrid deep learning model in Beijing, China.
Environmental pollution (Barking, Essex : 1987)
Mar 1, 2025
PM is a significant global atmospheric pollutant impacting visibility, climate, and public health. Accurate prediction of PM concentrations is critical for assessing air pollution risks and providing early warnings for effective management. This stud...
Prediction of school PM by an attention-based deep learning approach informed with data from nearby air quality monitoring stations.
Chemosphere
Feb 24, 2025
Predicting indoor air pollutants concentrations in schools is essential for ensuring a healthy learning environment. Traditional measurements methods pose challenges in cost, maintenance, and time. This study proposes a new approach using a deep lear...
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