气候变化领域集成服务门户(CCPortal): Neighbourhood scale nitrogen dioxide land use regression modelling with regression kriging in an urban transportation corridor

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DOI	10.1016/j.atmosenv.2019.117218
	Neighbourhood scale nitrogen dioxide land use regression modelling with regression kriging in an urban transportation corridor
	Shi T.; Dirienzo N.; Requia W.J.; Hatzopoulou M.; Adams M.D.
发表日期	2020
ISSN	1352-2310
卷号	223
英文摘要	Land use regression models (LUR) associate observed air pollution concentrations with surrounding land use characteristics for air pollution modelling. This technique is common in urban landscapes focused at a city-wide spatial scale. Our study tested the applicability of LUR modelling at a local scale, defined as multiple air monitors within a neighbourhood. The study area was 15.4 km of an urban transportation corridor in Mississauga, Canada. Nitrogen dioxide (NO2) was sampled at 112 sites during the summer in 2018 and observations ranged from 5.8 ppb to 19.65 ppb. A linear regression LUR model explained 69% of the variation in NO2 concentrations at this local scale, with estimated prediction errors less than 1.61 ppb, which were calculated by three cross-validation methods. Traffic volume, major and minor road lengths were key determinants among the predictor variables, and park area and distance to the nearest major intersection were the only variables with negative coefficients in the local-scale model. Extending the linear model approach with regression kriging improved the model's explanatory ability with a coefficient of determination at 0.91; however, smaller improvements were observed during cross-validation. Leave-one-out cross-validation for the linear model LUR model (RMSE = 1.44 ppb and a R2 = 0.64) and the regression kriging LUR model (RMSE = 1.34 ppb and a R2 = 0.69) were similar. Model performance remained stable when 10-fold cross-validation was performed with the regression kriging LUR model (regression kriging, R2 = 0.68 and RMSE = 1.36 ppb). The predicted air pollution levels ranged from 4.5 ppb to 25.6 ppb. This study demonstrates the ability of LUR modelling to perform well for local scale modelling in transportation dominated local urban environments. © 2019 Elsevier Ltd
英文关键词	Air pollution modelling; Land use regression; Nitrogen dioixde; Regression kriging
语种	英语
scopus关键词	Air pollution; Interpolation; Land use; Nitrogen oxides; Regression analysis; 10-fold cross-validation; Air pollution modelling; Cross-validation methods; Land use regression; Land-use regression models; Leave-one-out cross validations; Regression-kriging; Transportation corridors; Urban transportation
来源期刊	Atmospheric Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/129629
作者单位	Department of Geography, University of Toronto Mississauga, 3359 Mississauga Road, Mississauga, Ontario L5L 1C6, Canada; CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, No. 72 Wenhua Road, Shenyang, 110016, China; College of Resources and Environment, University of Chinese Academy of Sciences, No. 19 Yuquan Road, Beijing, 100049, China; Institute of Environmental Science, Carleton University, 1125 Colonel By Drive, Ottawa, Ontario K1S 5B6, Canada; Department of Environmental Health, School of Public Health, Harvard University, 677 Huntington Avenue, Boston, MA 02115, United States; Department of Civil and Mineral Engineering, University of Toronto, 35 St George Street, Toronto, Ontario M5S 1A4, Canada
推荐引用方式 GB/T 7714	Shi T.,Dirienzo N.,Requia W.J.,et al. Neighbourhood scale nitrogen dioxide land use regression modelling with regression kriging in an urban transportation corridor[J],2020,223.
APA	Shi T.,Dirienzo N.,Requia W.J.,Hatzopoulou M.,&Adams M.D..(2020).Neighbourhood scale nitrogen dioxide land use regression modelling with regression kriging in an urban transportation corridor.Atmospheric Environment,223.
MLA	Shi T.,et al."Neighbourhood scale nitrogen dioxide land use regression modelling with regression kriging in an urban transportation corridor".Atmospheric Environment 223(2020).