气候变化领域集成服务门户(CCPortal): Land use regression models revealing spatiotemporal co-variation in NO2, NO, and O3 in the Netherlands

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DOI	10.1016/j.atmosenv.2019.117238
	Land use regression models revealing spatiotemporal co-variation in NO2, NO, and O3 in the Netherlands
	Lu M.; Soenario I.; Helbich M.; Schmitz O.; Hoek G.; van der Molen M.; Karssenberg D.
发表日期	2020
ISSN	1352-2310
卷号	223
英文摘要	Land use regression (LUR) modeling has been applied to study the spatiotemporal patterns of air pollution, which when combined with human space-time activity, is important in understanding the health effects of air pollution. However, most of these studies focus either on the temporal or the spatial domain and do not consider the variability in both space and time. A temporally aggregated model does not reflect the temporal variability caused by traffic and atmospheric conditions and leads to inaccurate estimation of personal exposure. Besides, most studies focus on a single air pollutant (e.g., O3, NO2, or NO). These pollutants have a strong interaction due to photochemical processes. For studying relations between spatial and temporal patterns in these pollutants it is preferable to use a uniform data source and modelling approach which makes comparison of pollution surfaces between pollutants more reliable as they are produced with the same methodology. We developed temporal land use regression models of O3, NO2 and NO to study the co-variability of these pollutants and the relations with typical weather conditions over the year. We use hourly concentrations from the measurement network of the Dutch National Institute for Public Health and the Environment and aggregate them by hour, for weekday/weekend and month, and fit a regression model for each hour of the day. 70 candidate predictors that are known to have a strong relationship with combustion-related emissions are evaluated in the LUR modelling process. For all pollutants, the optimal LUR was identified with 4 predictors and the temporal variability was determined by the explained variance of each temporal model. Our temporal models for O3, NO2, and NO strongly reflect the photochemical processes in space and time. O3 shows a high background value throughout the day and only dips in the (close) vicinity of roads. The diminishing rate is affected by traffic intensity. The NO2 LUR is validated against NO2 measurements from the Traffic-Related Air pollution and Children's respiratory HEalth and Allergies (TRACHEA) study, resulting in an R2 of 0.61. © 2019 Elsevier Ltd
语种	英语
scopus关键词	Land use; Nitrogen oxides; Public health; Regression analysis; Atmospheric conditions; Land-use regression models; Measurement networks; Photochemical process; Spatial and temporal patterns; Spatiotemporal patterns; Temporal variability; Traffic-related air pollution; Air pollution
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/129619
作者单位	Department of Physical Geography, Faculty of Geosciences, Utrecht University, Utrecht, Netherlands; Department of Human Geography and Planning, Faculty of Geosciences, Utrecht University, Utrecht, Netherlands; Institute for Risk Assessment Sciences, Utrecht University, Utrecht, Netherlands; Meteorology & Air Quality, Wageningen University, Wageningen, Netherlands
推荐引用方式 GB/T 7714	Lu M.,Soenario I.,Helbich M.,et al. Land use regression models revealing spatiotemporal co-variation in NO2, NO, and O3 in the Netherlands[J],2020,223.
APA	Lu M..,Soenario I..,Helbich M..,Schmitz O..,Hoek G..,...&Karssenberg D..(2020).Land use regression models revealing spatiotemporal co-variation in NO2, NO, and O3 in the Netherlands.Atmospheric Environment,223.
MLA	Lu M.,et al."Land use regression models revealing spatiotemporal co-variation in NO2, NO, and O3 in the Netherlands".Atmospheric Environment 223(2020).