Full text

Turn on search term navigation

Copyright © 2014 Yi-Hsiu Jen et al. Yi-Hsiu Jen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This study used the backward trajectory calculation to obtain the transportation routes of Asian dusts and further combined the chemical composition with the enrichment factor (EF) and the grey relational analysis (GR) to identify the potential sources of eighteen Asian dust storm (ADS) events. The results showed that the chemical compositions of atmospheric particles sampled at the Pescadores Islands were very similar to source soils fugitively emitted from Inner Mongolia, which could assist in identifying the source regions of Asian dusts. This study further compared the source allocation of Asian dusts obtained from EF, GR, and backward trajectory, which showed that the source regions of Asian dusts obtained from these three methods were quite similar. The similarity of backward trajectory and GR reached as high as 83.3%. Moreover, the similarity of backward trajectory calculation and EF or GR was up to 77.8% while that of the GR and EF was up to 83.3%. Overall, these three methods can successfully allocate the source regions of Asian dusts by 66.7%. Moreover, these innovative chemical-assisted methods can be successfully applied to identify the source regions of Asian dusts for 18 ADS events.

Details

Title
Source Allocation of Long-Range Asian Dusts Transportation across the Taiwan Strait by Innovative Chemical-Assisted Identification Methods
Author
Yi-Hsiu Jen; Yi-Chi, Liu; Iau-Ren Ie; Chung-Shin, Yuan; Chung-Hsuang Hung
Publication year
2014
Publication date
2014
Publisher
John Wiley & Sons, Inc.
ISSN
16879309
e-ISSN
16879317
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
1560860522
Copyright
Copyright © 2014 Yi-Hsiu Jen et al. Yi-Hsiu Jen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.