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Particulate Matter

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Unmasking the sky: high-resolution PM prediction in Texas using machine learning techniques.

Journal of exposure science & environmental epidemiology
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.

Interpreting hourly mass concentrations of PM chemical components with an optimal deep-learning model.

Journal of environmental sciences (China)
PM constitutes a complex and diverse mixture that significantly impacts the environment, human health, and climate change. However, existing observation and numerical simulation techniques have limitations, such as a lack of data, high acquisition co...

Large-scale spatiotemporal deep learning predicting urban residential indoor PM concentration.

Environment international
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
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)
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
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...

Application of machine learning (individual vs stacking) models on MERRA-2 data to predict surface PM concentrations over India.

Chemosphere
The spatial coverage of PM monitoring is non-uniform across India due to the limited number of ground monitoring stations. Alternatively, Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), is an atmospheric reanalys...

Suspended particulate matter promotes epithelial-to-mesenchymal transition in alveolar epithelial cells via TGF-β1-mediated ROS/IL-8/SMAD3 axis.

Journal of environmental sciences (China)
Epidemiological evidence presents that dust storms are related to respiratory diseases, such as pulmonary fibrosis (PF). However, the precise underlying mechanisms of SPM-elicited adverse effects still need to be investigated. Epithelial-mesenchymal ...

Predicting spatial variations in annual average outdoor ultrafine particle concentrations in Montreal and Toronto, Canada: Integrating land use regression and deep learning models.

Environment international
BACKGROUND: Concentrations of outdoor ultrafine particles (UFP; <0.1 µm) and black carbon (BC) can vary greatly within cities and long-term exposures to these pollutants have been associated with a variety of adverse health outcomes.

PM2.5 concentration prediction using weighted CEEMDAN and improved LSTM neural network.

Environmental science and pollution research international
As the core of pollution prevention and management, accurate PM2.5 concentration prediction is crucial for human survival. However, due to the nonstationarity and nonlinearity of PM2.5 concentration data, the accurate prediction for PM2.5 concentrati...