Using the Google Earth Engine to estimate a 10 m resolution monthly inventory of soil fugitive dust emissions in Beijing, China.

Emission inventory GEE Particulate matter Soil fugitive dust Wind erosion

Journal

The Science of the total environment
ISSN: 1879-1026
Titre abrégé: Sci Total Environ
Pays: Netherlands
ID NLM: 0330500

Informations de publication

Date de publication:
15 Sep 2020
Historique:
received: 13 03 2020
revised: 30 04 2020
accepted: 30 04 2020
pubmed: 31 5 2020
medline: 31 5 2020
entrez: 31 5 2020
Statut: ppublish

Résumé

Soil fugitive dust (SFD) is an important contributor to ambient particulate matter (PM), but most current SFD emission inventories are updated slowly or have low resolution. In areas where vegetation coverage and climatic conditions undergo significant seasonal changes, the classic wind erosion equation (WEQ) tends to underestimate SFD emissions, increasing the need for higher spatiotemporal data resolution. Continuous acquisition of precise bare soil maps is the key barrier to compiling monthly high-resolution SFD emission inventories. In this study, we proposed taking advantage of the massive Landsat and Sentinel-2 imagery data sets stored in the Google Earth Engine (GEE) cloud platform to enable the rapid production of bare soil maps with spatial resolutions of up to 10 m. The resulting improved spatiotemporal resolution of wind erosion parameters allowed us to estimate SFD emissions in Beijing as being ~5-7 times the level calculated by the WEQ. Spring and winter accounted for >85% of SFD emissions, while April was the dustiest month with SFD emissions of PM

Identifiants

pubmed: 32473441
pii: S0048-9697(20)32691-7
doi: 10.1016/j.scitotenv.2020.139174
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

139174

Informations de copyright

Copyright © 2020 Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Aobo Liu (A)

College of Global Change and Earth System Science, Beijing Normal University, 100875 Beijing, China.

Qizhong Wu (Q)

College of Global Change and Earth System Science, Beijing Normal University, 100875 Beijing, China.

Xiao Cheng (X)

School of Geospatial Engineering and Science, Sun Yat-Sen University, 519082 Zhuhai, China; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), 519082 Zhuhai, China; Joint Center for Global Change Studies, 100875 Beijing, China. Electronic address: chengxiao9@mail.sysu.edu.cn.

Classifications MeSH