Publications

Rupakheti, D; Kang, SC; Bilal, M; Gong, JX; Xia, XG; Cong, ZY (2019). Aerosol optical depth climatology over Central Asian countries based on Aqua-MODIS Collection 6.1 data: Aerosol variations and sources. ATMOSPHERIC ENVIRONMENT, 207, 205-214.

Abstract
The aim of this study is to explore the aerosol optical depth (AOD) - an indicator of air quality, based on satellite data over Central Asia. Due to limited studies and lack of knowledge regarding AOD variations over this region, five countries (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan) and fifteen sites including the capital cities were selected for the analysis. Long-term Aqua-MODIS Collection 6.1 Deep Blue (DB) AOD observations at 10 km spatial resolution were obtained for 2002-2017 and the angstrom ngstrom exponent (AE) was calculated using spectral AOD observations. AOD and AE were explored based on seasonal and inter-annual scale. The relationship between aerosol loading and economic indicator (gross domestic product) was also analyzed. Highest frequencies of low AOD and high AE were observed in all countries. Average AOD (AE) values during the study period were 0.17 +/- 0.09 (1.29 +/- 0.17) for Kazakhstan, 0.11 +/- 0.13 (1.41 +/- 0.13) for Kyrgyzstan, 0.17 +/- 0.14 (1.36 +/- 0.18) for Tajikistan, 0.20 +/- 0.10 (1.10 +/- 0.28) for Turkmenistan, and 0.18 +/- 0.09 (1.18 +/- 0.24) for Uzbekistan. Summer season experienced highest aerosol loading over Kazakhstan, Tajikistan, and Uzbekistan, whereas spring season experienced highest aerosol loading over Kyrgyzstan and Turkmenistan possibly due to the influence of less precipitation and dust from the arid regions. All fifteen cities selected in this study exhibited increasing aerosol load. Aerosol types based on the relationship between AOD and AE were investigated, results suggested the clean continental aerosol as a major aerosol type over the region, whereas the contribution of mixed aerosols was observed during spring and summer seasons. This study suggests for establishment of ground-based aerosol monitoring stations network and inclusions of modeling works to understand the complex nature of aerosol over this vast landmass.

DOI:
10.1016/j.atmosenv.2019.03.020

ISSN:
1352-2310