NHESSD Natural Hazards and Earth System Sciences Discussions NHESSD Nat. Hazards Earth Syst. Sci. Discuss. 2195-9269 Göttingen, Germany 10.5194/nhess-2017-341 An inversion of fine particulate matter (PM<sub>2.5</sub>) mass concentrations based on the air quality index (AQI) during dust prone periods in Hotan oasis, Sinkiang Chunyan Ju 1 2 5 Zili Zhang 3 5 Xu Zhou 4 5 Qing He 2 School of Management, Xinjiang Agricultural University, Urumqi, China Institute of Desert Meteorology, China Meteorological Administration, Urumqi, China Zhejiang Environment Monitoring Centre, Hangzhou, China Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China These authors contributed equally to this work. 05 02 2018 2018 1 19 Copyright: © 2018 Ju Chunyan et al. 2018 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ This article is available from https://nhess.copernicus.org/preprints/nhess-2017-341/ The full text article is available as a PDF file from https://nhess.copernicus.org/preprints/nhess-2017-341/nhess-2017-341.pdf

Ambient air pollution has been implicated as a major environmental problem in Urban development process. The objective of this publication is to analyse deeply the correlation coefficient of PM<sub>2.5</sub> and AOD and aerosol optical depth (AOD). Surface PM<sub>2.5</sub> observation data and AOD were investigated from March to June in 2015 and 2016. Hourly PM<sub>2.5</sub> data are sampled from air quality monitoring stations in Hotan oasis. The AOD data are derived from Terra and Aqua at 10&thinsp;km resolution. The satellite passed the area at about 13:30&thinsp;AM and 15:30&thinsp;PM，respectively．By using the matched PM<sub>2.5</sub> and AOD data，the spatial and temporal distribution characteristics are discussed, and the correlation coefficient of PM<sub>2.5</sub> versus AOD are estimated. The results show that PM<sub>2.5</sub> mass concentration and AOD vary greatly in different pollution weather. This phenomenon may be associated with data collection time, and other meteorological factors. Regression analysis based on typical air pollution show subsection fitting effect is relatively good choice, and regression is relatively well in Hazardous and serious pollution weather. Fitting analysis is good for PM<sub>2.5</sub> in different level of air pollution, and sources and pollutants transmission have difference.

 National Natural Science Foundation of China 41641045