PuSH - Publication Server of Helmholtz Zentrum München

Export: TXT Text RIS Endnote (RIS) BIB BibTeX
Liu, X. ; Hadiatullah, H.* ; Zhang, X.* ; Schnelle-Kreis, J. ; Lin, X.* ; Cao, X. ; Zimmermann, R.

Combined land-use and street view image model for estimating black carbon concentrations in urban areas.

Atmos. Environ. 265:118719 (2021)
Postprint DOI
Open Access Green
In this study, we developed a novel land-use street view image random forest (LUSRF) model to estimate the equivalent black carbon (eBC) concentration based on land-use random forest (LURF) and street view imagery (SVI) models and compared their accuracy and precision in the urban city of Augsburg, Germany. The variables of the LUSRF model were constructed by combining LURF and SVI model variables (i.e., land-use, street scene, and meteorological factors). Stratified cross-validation (CV) was used to validate the model performance. Based on R2 and IA (Index of Agreement), LUSRF has superiority (average-R2: 0.73, average-IA: 0.91) compared to the LURF (average-R2: 0.52, average-IA: 0.81) and SVI model (average-R2: 0.68, average-IA: 0.89) in the urban city of Augsburg during the observed period. The main driving factors of the LUSRF model for BC estimation were different in heating and non-heating periods (i.e., elevation, the proportion of moving cars, and relative humidity for the non-heating period; and elevation, the proportion of building, and relative humidity for the heating period), which improves the estimation accuracy of eBC concentration and its sources. The model verification in other areas (i.e., suburban and small towns) further proved that the model has certain generalizability. Overall, the LUSRF model will provide insight for epidemiological studies in urban areas as a personal exposure assessment.
Impact Factor
Scopus SNIP
Scopus
Cited By
Altmetric
4.798
1.556
2
Tags
Annotations
Special Publikation
Hide on homepage

Edit extra information
Edit own tags
Private
Edit own annotation
Private
Hide on publication lists
on hompage
Mark as special
publikation
Publication type Article: Journal article
Document type Scientific Article
Keywords Black Carbon ; Land-use ; Random Forest ; Street View Images; Use Regression-models; Particle Number; Particulate; Pm2.5; Pollutants; Exposure; Bicycle
Language english
Publication Year 2021
HGF-reported in Year 2021
ISSN (print) / ISBN 1352-2310
e-ISSN 1873-2844
Journal Atmospheric Environment
Quellenangaben Volume: 265, Issue: , Pages: , Article Number: 118719 Supplement: ,
Publisher Elsevier
Publishing Place The Boulevard, Langford Lane, Kidlington, Oxford Ox5 1gb, England
Reviewing status Peer reviewed
Institute(s) Cooperation Group Comprehensive Molecular Analytics (CMA)
POF-Topic(s) 30202 - Environmental Health
Research field(s) Environmental Sciences
PSP Element(s) G-504500-001
Grants Peiyang Future Scholar Scholarship
China Scholarship Council (CSC)
High-level Teachers in Beijing Municipal Universities in the Period of 13th Five-year Plan
Germany Federal Ministry of Transport and Digital Infrastructure (BMVI) as part of SmartAQnet
Research Project of the Ministry of Science and Technology of China
National Natural Science Foundation of China
DOI 10.1016/j.atmosenv.2021.118719
WOS ID WOS:000703665600004
Scopus ID 85114907232
Erfassungsdatum 2021-10-11
[➜Report correction]