AIMC Topic: Particulate Matter
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Application of machine learning (individual vs stacking) models on MERRA-2 data to predict surface PM concentrations over India.
Chemosphere
Aug 25, 2023
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)
Jul 29, 2023
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
Jul 22, 2023
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
May 22, 2023
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...
Integrating low-cost sensor monitoring, satellite mapping, and geospatial artificial intelligence for intra-urban air pollution predictions.
Environmental pollution (Barking, Essex : 1987)
May 18, 2023
There is a growing need to apply geospatial artificial intelligence analysis to disparate environmental datasets to find solutions that benefit frontline communities. One such critically needed solution is the prediction of health-relevant ambient gr...
Prediction of atmospheric pollutants in urban environment based on coupled deep learning model and sensitivity analysis.
Chemosphere
May 1, 2023
Accurate and efficient predictions of pollutants in the atmosphere provide a reliable basis for the scientific management of atmospheric pollution. This study develops a model that combines an attention mechanism, convolutional neural network (CNN), ...
Separating Daily 1 km PM Inorganic Chemical Composition in China since 2000 via Deep Learning Integrating Ground, Satellite, and Model Data.
Environmental science & technology
Apr 28, 2023
Fine particulate matter (PM) chemical composition has strong and diverse impacts on the planetary environment, climate, and health. These effects are still not well understood due to limited surface observations and uncertainties in chemical model si...
Cooperative simultaneous inversion of satellite-based real-time PM and ozone levels using an improved deep learning model with attention mechanism.
Environmental pollution (Barking, Essex : 1987)
Mar 24, 2023
Ground-level fine particulate matter (PM) and ozone (O) are air pollutants that can pose severe health risks. Surface PM and O concentrations can be monitored from satellites, but most retrieval methods retrieve PM or O separately and disregard the s...
PM2.5 Concentration Prediction Model: A CNN-RF Ensemble Framework.
International journal of environmental research and public health
Feb 24, 2023
Although many machine learning methods have been widely used to predict PM2.5 concentrations, these single or hybrid methods still have some shortcomings. This study integrated the advantages of convolutional neural network (CNN) feature extraction a...
A hybrid deep learning framework for air quality prediction with spatial autocorrelation during the COVID-19 pandemic.
Scientific reports
Jan 18, 2023
China implemented a strict lockdown policy to prevent the spread of COVID-19 in the worst-affected regions, including Wuhan and Shanghai. This study aims to investigate impact of these lockdowns on air quality index (AQI) using a deep learning framew...
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