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Measurements of atmospheric aerosol vertical distribution above North China Plain using hexacopter

Authors
Zhu, YishuWu, ZhijunPark, YongheeFan, XiaoboBai, DongZong, PengchengQin, BoCai, XuhuiAnn, Kang-Ho
Issue Date
May-2019
Publisher
Elsevier BV
Keywords
Unmanned aerial vehicle; Hexacopter; Air pollution; Particle number size distribution
Citation
Science of the Total Environment, v.665, pp.1095 - 1102
Indexed
SCIE
SCOPUS
Journal Title
Science of the Total Environment
Volume
665
Start Page
1095
End Page
1102
URI
https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/2926
DOI
10.1016/j.scitotenv.2019.02.100
ISSN
0048-9697
Abstract
In this study, a novel setup was developed to measure the vertical profile of particle number size distribution (PNSD) and meteorological parameters by using hexacopter equipped with a portable instrument package including a custom-built scanning mobility particle sizer, an optical particle counter and temperature and relative humidity (RH) sensors. By using this setup, a field experiment was carried out to investigate the vertical profiles of RH, temperature, PNSD ranged from 8 to 245 nm, and particle number concentration with the diameter ranges of 0.3-0.5 mu m and 0.5-1.0 mu m from the ground up to 300 m above ground level in a rural site of the North China Plain. New particle formation (NPF) event in a vertical scale was observed during the daytime cruises. The newly formed particles showed a heterogeneous vertical distribution, indicating the inhomogeneous occurrence of the NPF event vertically. During the daytime, the vertical variations in number concentration of particles larger than 0.3 mu m was not obvious, while, showed a tendency to decrease associated with the increasing altitude in the evening. The newly-developed unmanned aerial vehicle research platform could be applied to study the vertical NPF events and transport processes of air pollutants within the atmospheric boundary layer and urban canopy, and to monitor source emissions with the purpose of environment management. (c) 2019 Elsevier B.V. All rights reserved.
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