DOI: 10.1016/j.atmosenv.2017.07.042
Scopus记录号: 2-s2.0-85026425348
论文题名: Land use regression models for total particle number concentrations using 2D, 3D and semantic parameters
作者: Ghassoun Y ; , Löwner M ; -O
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2017
卷: 166 起始页码: 362
结束页码: 373
语种: 英语
英文关键词: CityGML
; Frontal Area Index
; Geoinformation system
; Multiple regression
; Process chain
; TNC
; Urban morphology
Scopus关键词: Data flow analysis
; Land use
; Mean square error
; Semantics
; CityGML
; Frontal areas
; Geo-information
; Multiple regressions
; Process chain
; Urban morphology
; Regression analysis
; concentration (composition)
; GIS
; index method
; land use change
; numerical model
; parameter estimation
; particulate matter
; regression analysis
; spatial variation
; urban morphology
; Article
; concentration (parameters)
; Germany
; land use
; priority journal
; regression analysis
; urban area
; Berlin
; Germany
Scopus学科分类: Environmental Science: Water Science and Technology
; Earth and Planetary Sciences: Earth-Surface Processes
; Environmental Science: Environmental Chemistry
英文摘要: Total particle number concentration (TNC) was studied in a 1 × 2 km area in Berlin, the capital of Germany by three Land Use Regression models (LUR). The estimation of TNC was established and compared using one 2D-LUR and two 3D-LUR models. All models predict total number concentrations TNC by using urban morphological (2D resp. 3D) and additional semantical parameters. 2D and semantical parameters were derived from Open Street Map data (OSM) whereas 3D parameters were derived from a CityGML-based 3D city model. While the models are capable to depict the spatial variation of TNC across the study area, the two 3D-LUR showed better results than the 2D-LUR. The 2D-LUR model explained 74% of the variance of TNC for the full data set with root mean square error (RMSE) of 4014 cm−3 while the 3D-LUR explained 79% of the variance with an RMSE of 3477 cm−3. The further introduction of a new spatial parameter, the Frontal Area Index (FAI) that represents the dynamic factor wind direction enhanced the 3D-LUR to explain 82% of the variance with RMSE of 3389 cm−3. Furthermore, the semantical parameters (e.g. streets type) played a significant role in all models. © 2017 Elsevier Ltd Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/82778
Appears in Collections: 气候变化事实与影响
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作者单位: Institute of Geodesy and Photogrammetry, Technische Universität Braunschweig, Pockelsstraße 3, Braunschweig, Germany
Recommended Citation:
Ghassoun Y,, Löwner M,-O. Land use regression models for total particle number concentrations using 2D, 3D and semantic parameters[J]. Atmospheric Environment,2017-01-01,166