AIMC Topic: Particulate Matter
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Is replacing missing values of PM constituents with estimates using machine learning better for source apportionment than exclusion or median replacement?
Environmental pollution (Barking, Essex : 1987)
May 16, 2024
East Asian countries have been conducting source apportionment of fine particulate matter (PM) by applying positive matrix factorization (PMF) to hourly constituent concentrations. However, some of the constituent data from the supersites in South Ko...
Short-term prediction of PM2.5 concentration by hybrid neural network based on sequence decomposition.
PloS one
May 10, 2024
Accurate forecasting of PM2.5 concentrations serves as a critical tool for mitigating air pollution. This study introduces a novel hybrid prediction model, termed MIC-CEEMDAN-CNN-BiGRU, for short-term forecasting of PM2.5 concentrations using a 24-ho...
Explainable geospatial-artificial intelligence models for the estimation of PM concentration variation during commuting rush hours in Taiwan.
Environmental pollution (Barking, Essex : 1987)
Apr 12, 2024
PM concentrations are higher during rush hours at background stations compared to the average concentration across these stations. Few studies have investigated PM concentration and its spatial distribution during rush hours using machine learning mo...
Gelato: a new hybrid deep learning-based Informer model for multivariate air pollution prediction.
Environmental science and pollution research international
Apr 9, 2024
The increase in air pollutants and its adverse effects on human health and the environment has raised significant concerns. This implies the necessity of predicting air pollutant levels. Numerous studies have aimed to provide new models for more accu...
The association between PM components and blood pressure changes in late pregnancy: A combined analysis of traditional and machine learning models.
Environmental research
Apr 3, 2024
BACKGROUND: PM is a harmful mixture of various chemical components that pose a challenge in determining their individual and combined health effects due to multicollinearity issues with traditional linear regression models. This study aimed to develo...
Unmasking the sky: high-resolution PM prediction in Texas using machine learning techniques.
Journal of exposure science & environmental epidemiology
Apr 1, 2024
BACKGROUND: Although PM (fine particulate matter with an aerodynamic diameter less than 2.5 µm) is an air pollutant of great concern in Texas, limited regulatory monitors pose a significant challenge for decision-making and environmental studies.
Large-scale spatiotemporal deep learning predicting urban residential indoor PM concentration.
Environment international
Nov 22, 2023
Indoor PM pollution is one of the leading causes of death and disease worldwide. As monitoring indoor PM concentrations on a large scale is challenging, it is urgent to assess population-level exposure and related health risks to develop an easy-to-u...
Application of artificial intelligence in quantifying lung deposition dose of black carbon in people with exposure to ambient combustion particles.
Journal of exposure science & environmental epidemiology
Oct 17, 2023
BACKGROUND: Understanding lung deposition dose of black carbon is critical to fully reconcile epidemiological evidence of combustion particles induced health effects and inform the development of air quality metrics concerning black carbon. Macrophag...
TFEB/LAMP2 contributes to PM-induced autophagy-lysosome dysfunction and alpha-synuclein dysregulation in astrocytes.
Journal of environmental sciences (China)
Oct 5, 2023
Atmospheric particulate matter (PM) exacerbates the risk factor for Alzheimer's and Parkinson's diseases (PD) by promoting the alpha-synuclein (α-syn) pathology in the brain. However, the molecular mechanisms of astrocytes involvement in α-syn pathol...
Spatio-temporal fusion of meteorological factors for multi-site PM2.5 prediction: A deep learning and time-variant graph approach.
Environmental research
Oct 4, 2023
In the field of environmental science, traditional methods for predicting PM2.5 concentrations primarily focus on singular temporal or spatial dimensions. This approach presents certain limitations when it comes to deeply mining the joint influence o...
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