Surface PM ₁₀ Air Pollution of Karachi, Pakistan: Spatial-Temporal Statistical Modeling Using Aerosol-Optical-Depth Remote Sensing

The levels of particulate matter (PM10) in Karachi, Pakistan, are hazards to public health and environmental degradation. In this study, two statistical techniques, land use regression (LUR) and Pearson Correlation Coefficient (PCC), have been applied to detect PM10 and Moderate Resolution Imaging Spectroradiometer (MODIS) along with four meteorological parameters. The average values of observed PM10-MODIS aerosol optical depth (AOD)-predicted PM10 have been analyzed with PCC correlation. Results, the M4 and M7 models are more reliable, where coefficient of determination (R2 > 0.6) and root mean square error (RMSE) = 2–14. PCC showed a strong positive correlation with significant levels among observed PM10-MODIS AOD-Predicted PM10. The concentrations of PM10 and AOD approximately increased by 182%, and 208%, respectively, and dropped by 28% to 30%, during COVID-19 (2020). In conclusion, utilize remote sensing with hybrid modeling to mitigate and monitor air pollutants in regions lacking ground-based air quality data resources.