PuSH - Publikationsserver des Helmholtz Zentrums 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
Anmerkungen
Besondere Publikation
Auf Hompepage verbergern

Zusatzinfos bearbeiten
Eigene Tags bearbeiten
Privat
Eigene Anmerkung bearbeiten
Privat
Auf Publikationslisten für
Homepage nicht anzeigen
Als besondere Publikation
markieren
Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Black Carbon ; Land-use ; Random Forest ; Street View Images; Use Regression-models; Particle Number; Particulate; Pm2.5; Pollutants; Exposure; Bicycle
Sprache englisch
Veröffentlichungsjahr 2021
HGF-Berichtsjahr 2021
ISSN (print) / ISBN 1352-2310
e-ISSN 1873-2844
Zeitschrift Atmospheric Environment
Quellenangaben Band: 265, Heft: , Seiten: , Artikelnummer: 118719 Supplement: ,
Verlag Elsevier
Verlagsort The Boulevard, Langford Lane, Kidlington, Oxford Ox5 1gb, England
Begutachtungsstatus Peer reviewed
Institut(e) Cooperation Group Comprehensive Molecular Analytics (CMA)
POF Topic(s) 30202 - Environmental Health
Forschungsfeld(er) Environmental Sciences
PSP-Element(e) G-504500-001
Förderungen 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
[➜Korrektur melden]