| Autor: |
Darynova, Zhuldyz, Malekipirbazari, Milad, Shabdirov, Daryn, Khwaja, Haider A., Amouei Torkmahalleh, Mehdi |
| Zdroj: |
Air Quality, Atmosphere & Health; Apr2023, Vol. 16 Issue 4, p669-679, 11p |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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