Abstract
In recent years, smog has been one of the main concerns in heavily populated urban areas like Lahore (Pakistan) and Kanpur (India). Atmospheric pollutants like aerosols play an important role in smog. In this paper, aerosol types are identified in smog episodes, based on Aerosol Robotic Network (AERONET) data, during 4-year period i.e., 2015-2018. Aerosols are classified based on fine mode fraction (FMF) and single scattering albedo (SSA). One of the main aerosol types which are abundant in every smog episode is Black Carbon (BC) aerosol while dust is present throughout the year. BC is responsible for radiation imbalance and is considered the main component in climate changes at regional and global levels. Furthermore, time series of aerosol optical depth (AOD) during smog episodes is used to identify the variability of aerosol burden in the atmosphere. Backward trajectories from the HYSPLIT model are used to trace the origin of aerosols during the days of maximum AOD.
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