RMIT University
Browse

Aerosol vertical distribution and sources estimation at a site of the Yangtze River Delta region of China

journal contribution
posted on 2024-11-02, 10:02 authored by Wenzhi Fan, Kai Qin, Jian Xu, Limei Yuan, Ding Li, Zi Jin, Kefei ZhangKefei Zhang
The vertical distribution characteristics of aerosols are key uncertain factors for studying the effect on radiative forcing and trans-regional transport of pollutants. This paper used three years (2013-2015) LiDAR measurements at a site in the Yangtze River Delta region of China to investigate the aerosol vertical distribution and transport sources of aerosol-aloft by using the Potential Source Contribution Function (PSCF) and Concentration-Weighted Trajectory (CWT) models. The results indicated that there were 230 haze days accounted for 21% of all the days, including 142 damp haze days and 88 dry haze days during the study period. The aerosols below 2 km accounted for >89% of the total aerosol optical depth (AOD). Compared to other seasons, aerosols in winter were more likely to accumulate below 1 km (>69%). In summer, although atmospheric convention was strong leading to a high planetary boundary layer height (PBLH) and the concentration of PM 2.5 was low, the AOD was largest because of high relative humidity that caused hygroscopic growth of particles. Due to the stable weather condition in winter, the PBLH was low with the largest concentration of PM 2.5 , so the occurrence of haze days was most frequent. The PSCF and CWT results revealed that the high-level aerosols mainly came from local areas; the CWT model showed considerable long-distance transports of dust from northern/northwestern China, as far as Mongolia, Gansu Province and Xinjiang Uygur Autonomous Region, in spring, autumn and winter. Southern sources were more obvious in winter that could contribute more anthropogenic aerosols and biomass burning emissions.

History

Journal

Atmospheric Research

Volume

217

Start page

128

End page

136

Total pages

9

Publisher

Elsevier BV

Place published

Netherlands

Language

English

Copyright

© 2018 Elsevier B.V.

Former Identifier

2006090262

Esploro creation date

2020-06-22

Fedora creation date

2019-03-26

Usage metrics

    Scholarly Works

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC