DOI: 10.1016/j.atmosenv.2019.117238
论文题名: Land use regression models revealing spatiotemporal co-variation in NO2, NO, and O3 in the Netherlands
作者: Lu M. ; Soenario I. ; Helbich M. ; Schmitz O. ; Hoek G. ; van der Molen M. ; Karssenberg D.
刊名: Atmospheric Environment
ISSN: 1352-2310
出版年: 2020
卷: 223 语种: 英语
英文关键词: 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
中文摘要: 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 Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/161014
Appears in Collections: 气候变化与战略
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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
Recommended Citation:
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]. Atmospheric Environment,2020-01-01,223