气候变化领域集成服务门户(CCPortal): Effects of spatial sampling density and spatial extent on linear land use regression modelling of NO2 estimates in an automobile-oriented city

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DOI	10.1016/j.atmosenv.2020.117735
	Effects of spatial sampling density and spatial extent on linear land use regression modelling of NO2 estimates in an automobile-oriented city
	Maddix M.; Adams M.D.
发表日期	2020
ISSN	1352-2310
卷号	238
英文摘要	Residents of densely populated cities face elevated exposure risk to ambient air pollution. Epidemiological studies often estimate exposure risk with land use regression (LUR) models that predict the spatial distribution of air pollutants using surrounding land use characteristics. We examined the effects of estimating city-wide air pollution concentrations using three spatial extents of sampling (31 km2, 94 km2, and 292 km2). Passive sampling of NO2 was completed with 33 samples allocated to each of the three spatial extents. Observed concentrations varied from 6.8 ppb to 19.9 ppb. Linear land use regression models were developed using a manual stepwise approach to ensure consistency between models. The LUR model developed with the largest sampling extent (292 km2 demonstrated the highest performance (Adj. R2 = 0.78; LOOCV R2 = 0.76); however, the resulting air pollution surface showed the least variability. Model performance was not related to spatial extent, with the smallest extent (31 km2) performing better (Adj. R2 = 0.67; LOOCV R2 = 0.56) than the medium extent (Adj. R2 = 0.63; LOOCV R2 = 0.54). Variability in the air pollution surface was related to spatial extent with the highest variability generated by the small extent model. A cross-model validation was completed to examine how well models performed for predicting the other spatial extents. All models demonstrated strong performance with RMSE≤2.5 ppb for all cases, with a mean RMSE of 2.0 ppb. No relationship was apparent between spatial extent and the ability to predict another spatial extent. Our findings indicate linear LUR modelling is robust to variations in spatial extent and density of sampling. © 2020 Elsevier Ltd
关键词	Air pollution Land use regression Nitrogen dioxide Spatial density
语种	英语
scopus关键词	Air pollution; Forecasting; Nitrogen oxides; Regression analysis; Risk perception; Ambient air pollution; Epidemiological studies; Land use regression; Land-use regression models; Model performance; Pollution concentration; Spatial sampling; Stepwise approach; Land use; nitrogen dioxide; ambient air; atmospheric pollution; concentration (composition); land use change; pollutant source; regression analysis; spatial analysis; spatial distribution; air and air related phenomena; air pollution; air quality; ambient air; Article; car; environmental exposure; environmental monitoring; humidity; land use; leave one out cross validation; linear regression analysis; model; Ontario; prediction; priority journal; spatial analysis; surface property; urban area; winter
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/249017
作者单位	Department of Geography, University of Toronto Mississauga, 3359 Mississauga Rd., Mississauga, ON L5L 1C6, Canada
推荐引用方式 GB/T 7714	Maddix M.,Adams M.D.. Effects of spatial sampling density and spatial extent on linear land use regression modelling of NO2 estimates in an automobile-oriented city[J],2020,238.
APA	Maddix M.,&Adams M.D..(2020).Effects of spatial sampling density and spatial extent on linear land use regression modelling of NO2 estimates in an automobile-oriented city.ATMOSPHERIC ENVIRONMENT,238.
MLA	Maddix M.,et al."Effects of spatial sampling density and spatial extent on linear land use regression modelling of NO2 estimates in an automobile-oriented city".ATMOSPHERIC ENVIRONMENT 238(2020).