Classification of key aerosol types and their frequency distributions based on satellite remote sensing data at an industrially polluted city in the Yangtze River Delta, China

Abstract

ABSTRACTIn the present study, characterization of columnar aerosol optical properties and classifying the major aerosol types was investigated at an urban–industrial city, Nanjing in the Yangtze River Delta (YRD) region over East China using simultaneous data sets retrieved from the MODerate resolution Imaging Spectroradiometer (MODIS) and Ozone Monitoring Instrument (OMI) sensors during 2004–2015. A notable spatiotemporal heterogeneity was observed in the optical properties of aerosols on the seasonal scale over East China. Aerosol optical depth at 550 nm (AOD550) exhibited pronounced seasonal variability over Nanjing in the YRD, with higher values during summer and spring seasons and lower in winter. Ångström exponent (AE470–660) found higher in summer indicating the relative abundance of fine mode aerosols over the coarse mode. We also used the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model for presenting cluster trajectory analysis which revealed that the airmasses from different source regions contributed greatly to aerosol loading during the study period. In addition, we followed two techniques for studying classification of major aerosol types based on the predefined thresholds. Using the AOD–AE method (here called as Technique‐I), five major aerosol types were identified via, continental clean (CC), marine (MA), biomass burning/urban–industrial (BU), desert dust (DD), and mixed (MX). In all the seasons, MX is the dominant aerosol type followed by the BU and DD type aerosols during summer and spring seasons, respectively. Further, the sub‐classification of aerosol types was carried out considering into account of the characteristics of absorbing aerosol index (AAI) (here called as Technique‐II). The two clustering techniques showed reasonable consistency in the obtained results. The various aerosol types (absorbing and non‐absorbing) and their change over a region are highly helpful in fine tuning the models to decrease the uncertainty in the radiative and climatic effects of aerosols.