Spatio-Temporal Dynamics of Aerosol Optical Thickness derived Using MODIS-MAIAC Algorithm at a High Spatial Resolution Along with the HYSPLIT Trajectory Model

Aerosol is a key component in the climate system. Limited ground monitoring stations impede the acquisition of spatial and temporal aerosol concentration data. However, Remote sensing can provide wider coverage and real-time data, compensating for ground coverage constraints. In the present study, the spatial and temporal variation of Aerosol Optical Thickness (AOT) was analyzed for the Indian cities having significantly different meteorology and geographical conditions like Jaipur and Pune for the years 2020 and 2021 using the Multi-Angle Implementation of the Atmospheric Correction (MAIAC) algorithm. The seasonal mean AOT in winter, pre-monsoon, and post-monsoon are recorded as 0.56, 0.62, and 0.89, respectively, over the entire Jaipur district. However, it was recorded as 0.76, 0.62, and 0.52, respectively, over the entire Pune district. Results of the seasonal analysis indicate that Jaipur and Pune experience high loads of aerosol during post-monsoon and winter, respectively. In this context, the back trajectory, developed through the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, revealed that Jaipur experiences air masses and emissions from the northern region of India during the post-monsoon. However, Pune encounters air masses from the eastern region of India in winter. The mean Angstrom exponent values at Jaipur and Pune aid in understanding the size and type of aerosol. Jaipur and Pune experience biomass burning aerosol and mixed aerosols to a greater extent, respectively. The performance of MAIAC-derived AOT was assessed using Aerosol Robotic Network (AERONET) sun-photometers derived AOT at Jaipur and Pune with coefficient of determination (R²) values of 0.88 and 0.71 and Root Mean Squared Error (RMSE) values of 0.1338 and 0.1869, respectively.