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Aerosol contamination survey during dust storm process in Northwestern China using ground, satellite observations and atmospheric modeling data

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Abstract

The present survey addresses the comprehensive description of geographic locations, transport ways, size, and vertical aerosol distribution during four large dust events which occurred in the Northwest China. Based on the data from 35 ground-based air quality monitoring stations and the satellite data, emission flows for dust events within the period of 2014 to 2017 have been estimated. The data show that maximum peak daily average PM10 and PM2.5 concentrations exceeded 380 and 150 μg/m3, respectively, and the PM2.5/PM10 ratio was ranging within 0.12–0.66. Both satellite data and simulation data of the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) coincide with location and extension of a dust cloud. The Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) found dust at 0 to 10 km altitude which remained at this level during the most part of its trajectory. The vertical aerosol distribution at a wave of 532 nm total attenuated backscatter coefficient range of 0.0025–0.003 km−1 × sr−1. Moderate Resolution Imaging Spectroradiometer (MODIS) (Terra) Collection 6 Level-3 aerosol products data show that aerosol optical depth (AOD) at pollution epicenters exceeds 1. A comprehensive data survey thus demonstrated that the main sources of high aerosol pollutions in the territory were deserted areas of North and Northwest China as well as the most part of the Republic of Mongolia, where one of the largest deserts, Gobi, extends.

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Funding

The work was financially supported by the National Key R&D Program of China (2017YFB0504203 and 2017YFB0504201), the National Natural Science Foundation of China (No. 41371435), and the Talent Innovation Project of Lanzhou (No. 2015-RC-28).

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Correspondence to Haowen Yan.

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Filonchyk, M., Yan, H., Shareef, T.M.E. et al. Aerosol contamination survey during dust storm process in Northwestern China using ground, satellite observations and atmospheric modeling data. Theor Appl Climatol 135, 119–133 (2019). https://doi.org/10.1007/s00704-017-2362-8

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  • DOI: https://doi.org/10.1007/s00704-017-2362-8

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