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Detection of a Dust Storm in 2020 by a Multi-Observation Platform over the Northwest China

Authors :
Lili Yang
Zhiyuan Hu
Zhongwei Huang
Lina Wang
Wenyu Han
Yanping Yang
Huijie Tao
Jing Wang
Source :
Remote Sensing, Vol 13, Iss 6, p 1056 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

Dust storms have occurred frequently in northwest China and can dramatically reduce visibility and exacerbate air quality in downwind regions through long-range transport. In order to study the distribution characteristics of dust particles sizes, structures and concentrations in the process of dust storm, especially for the vertical distributions, the multi-observation platform composed of six Lidars and nine aerosol analytical instruments is first used to detect a severe dust storm event, which occurred in Northwest China on 3 May 2020. As a strong weather system process, the dust storm has achieved high intensity and wide range. When the intensity of a dust storm is at its strongest, the ratios of PM2.5 (particulate matter with diameter < 2.5 µm) and PM10 (particulate matter with diameter < 10 µm) (PM2.5/PM10) in cities examined were less than 0.2 and the extinction coefficients became greater than 1 km−1 based on Lidar observations. In addition, the growth rates of PM2.5 were higher than that of PM10. The dust particles mainly concentrated at heights of 2 km, after being transported about 200–300 km, vertical height increased by 1–2 km. Meanwhile, the dust concentration decreased markedly. Furthermore, the depolarization ratio showed that dust in the Tengger Desert was dominated by spherical particles. The linear relationships between 532 nm extinction coefficient and the concentration of PM2.5 and PM10 were found firstly and their R2 were 0.706 to 0.987. Our results could give more information for the physical schemes to simulate dust storms in specific models, which could improve the forecast of dust storms.

Details

Language :
English
ISSN :
20724292
Volume :
13
Issue :
6
Database :
Directory of Open Access Journals
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
edsdoj.0dab3b24a85445afb24802364f1d6486
Document Type :
article
Full Text :
https://doi.org/10.3390/rs13061056