Reliability and stability of a statistical model to predict ground-based PM2.5 over 10 years in Karachi, Pakistan, using satellite observations
Darynova Z. Malekipirbazari M. Shabdirov D. Khwaja H.A. Amouei Torkmahalleh M.
April 2023Springer Science and Business Media B.V.
Air Quality, Atmosphere and Health
2023#16Issue 4669 - 679 pp.
Understanding the complex mechanisms of climate change and its environmental consequences requires the collection and subsequent analysis of geospatial data from observations and numerical modeling. Multivariable linear regression and mixed-effects models were used to estimate daily surface fine particulate matter (PM2.5) levels in the megacity of Pakistan. The main parameters for the multivariable linear regression model were the 10-km-resolution satellite aerosol optical depth (AOD) and daily averaged meteorological parameters from ground monitoring (temperature, dew point, relative humidity, wind speed, wind direction, and planetary boundary layer height). Ground-based PM2.5 was measured in two stations in the city, Korangi (industrial/residential) and Tibet Center (commercial/residential). The initial linear regression model was modified using a stepwise selection procedure and adding interaction parameters. Finally, the modified model showed a strong correlation between the PM2.5–satellite AOD and other meteorological parameters (R2 = 0.88–0.92 and p-value = 10−7 depending on the season and station). The mixed-effect technique improved the model performance by increasing the R2 values to 0.99 and 0.93 for the Korangi and Tibet Center sites, respectively. Cross-validation methods were used to confirm the reliability of the model to predict PM2.5 after 10 years.
Korangi , Meteorological parameters , Mixed-effects model , MODIS AOD , Multivariable linear regression model , Tibet Center
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Faculty of Science and Technology, University of Lorraine, Nancy, France
Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, 41296, Sweden
Department of Production Automation and Information Technology, Atyrau Oil and Gas University, Atyrau, 060027, Kazakhstan
Wadsworth Center, New York State Department of Health, Albany, NY, United States
Department of Environmental Health Sciences, School of Public Health, University at Albany, Albany, NY, United States
Division of Environmental and Occupational Health Sciences, School of Public Health, University of Illinois at Chicago, Chicago, 60612, IL, United States
Faculty of Science and Technology
Department of Computer Science and Engineering
Department of Production Automation and Information Technology
Wadsworth Center
Department of Environmental Health Sciences
Division of Environmental and Occupational Health Sciences
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