globalchange  > 气候变化事实与影响
DOI: 10.1016/j.atmosenv.2015.06.056
Scopus ID: 2-s2.0-84936929937
Land use regression models coupled with meteorology to model spatial and temporal variability of NO2 and PM10 in Changsha, China
Author: Liu W; , Li X; , Chen Z; , Zeng G; , León Tomá; , Liang J; , Huang G; , Gao Z; , Jiao S; , He X; , Lai M
Source Publication: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
Publishing Year: 2015
Volume: 116
pages begin: 272
pages end: 280
Language: 英语
Keyword: Land use regression ; Meteorological factors ; NO2 ; PM10 ; Temporal resolution
Scopus Keyword: Backpropagation ; Balloons ; Land use ; Motor transportation ; Neural networks ; Nitrogen oxides ; Pollution ; Regression analysis ; Wind ; Back-propagation neural networks ; Land use regression ; Land-use regression models ; Meteorological condition ; Meteorological factors ; Spatial and temporal variability ; Spatial and temporal variation ; Temporal resolution ; Air pollution ; nitrogen dioxide ; atmospheric pollution ; concentration (composition) ; epidemiology ; health risk ; human activity ; land use change ; meteorology ; nitrogen dioxide ; numerical model ; particulate matter ; pollution exposure ; resolution ; spatiotemporal analysis ; wind velocity ; air pollutant ; air pollution ; Article ; atmospheric pressure ; China ; cloud ; environmental exposure ; environmental temperature ; humidity ; land use ; land use regression model ; meteorological phenomena ; meteorology ; model ; particulate matter ; priority journal ; residential area ; sanitation ; urban area ; Changsha ; China ; Hunan
Subject of Scopus: Environmental Science: Water Science and Technology ; Earth and Planetary Sciences: Earth-Surface Processes ; Environmental Science: Environmental Chemistry
English Abstract: Land use regression (LUR) models are widely used in epidemiological studies to assess exposure to air pollution. However, most of the existing LUR studies focus on estimating annual or monthly average concentration of air pollutants, with high spatial but low temporal resolution. In this paper, we combined LUR models with meteorological conditions to estimate daily nitrogen dioxide (NO2) and particulate matter (PM10) concentrations in the urban area of Changsha, China. Seventy-four sites for NO2 and thirty-six sites for PM10 were selected to build LUR models. The LUR models explained 51% and 62% of spatial variability for NO2 and PM10. The most important spatial explanatory variables included major roads, residential land and public facilities land, indicating that the spatial distributions of NO2 and PM10 are closely related to traffic conditions and human activities. Meteorological factors were introduced to model the temporal variability of NO2 and PM10 by using meteorological factors regression (MFR) and back propagation neural network (BPNN) modeling techniques. Important temporal explanatory variables included temperature, wind speed, cloud cover and percentage of haze. Pearson's r values between predicted and measured concentrations were much higher in BPNN models than in MFR models. The results demonstrate that the BPNN models showed a better performance than the MFR models in modeling temporal variation of NO2 and PM10. The approach of modeling spatial and temporal variation of air pollutants using LUR models coupled with meteorological conditions has potential usefulness for exposure assessment, especially for medium or short term exposure, in health studies. © 2015 Elsevier Ltd.
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Document Type: 期刊论文
Appears in Collections:气候变化事实与影响

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Affiliation: College of Environmental Science and Engineering, Hunan University, Changsha, China; Key Laboratory of Environmental Biology and Pollution Control (Hunan University), Ministry of Education, Changsha, China; College of Information Science and Technology, Hunan University, Changsha, Hunan, China; School of Public Health, University of California, Berkeley, CA, United States; Faculty of Engineering and Applied Science, University of Regina, Regina, SK, Canada; College of Architecture, Hunan University, Changsha, China

Recommended Citation:
Liu W,, Li X,, Chen Z,et al. Land use regression models coupled with meteorology to model spatial and temporal variability of NO2 and PM10 in Changsha, China[J]. Atmospheric Environment,2015-01-01,116
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