Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/77897
Title: Using geographical semi-variogram method to quantify the difference between NO2 and PM2.5 spatial distribution characteristics in urban areas
Authors: Song, W
Jia, H
Li, Z 
Tang, D
Keywords: Air pollution
Seasonal difference
Semi-variogram
Spatial autocorrelation
Spatial scale dependence
Spatial variation
Issue Date: 2018
Publisher: Elsevier
Source: Science of the total environment, 2018, v. 631-632, p. 688-694 How to cite?
Journal: Science of the total environment 
Abstract: Urban air pollutant distribution is a concern in environmental and health studies. Particularly, the spatial distribution of NO2 and PM2.5, which represent photochemical smog and haze pollution in urban areas, is of concern. This paper presents a study quantifying the seasonal differences between urban NO2 and PM2.5 distributions in Foshan, China. A geographical semi-variogram analysis was conducted to delineate the spatial variation in daily NO2 and PM2.5 concentrations. The data were collected from 38 sites in the government-operated monitoring network. The results showed that the total spatial variance of NO2 is 38.5% higher than that of PM2.5. The random spatial variance of NO2 was 1.6 times than that of PM2.5. The nugget effect (i.e., random to total spatial variance ratio) values of NO2 and PM2.5 were 29.7 and 20.9%, respectively. This indicates that urban NO2 distribution was affected by both local and regional influencing factors, while urban PM2.5 distribution was dominated by regional influencing factors. NO2 had a larger seasonally averaged spatial autocorrelation distance (48 km) than that of PM2.5 (33 km). The spatial range of NO2 autocorrelation was larger in winter than the other seasons, and PM2.5 has a smaller range of spatial autocorrelation in winter than the other seasons. Overall, the geographical semi-variogram analysis is a very effective method to enrich the understanding of NO2 and PM2.5 distributions. It can provide scientific evidences for the buffering radius selection of spatial predictors for land use regression models. It will also be beneficial for developing the targeted policies and measures to reduce NO2 and PM2.5 pollution levels.
URI: http://hdl.handle.net/10397/77897
ISSN: 0048-9697
EISSN: 1879-1026
DOI: 10.1016/j.scitotenv.2018.03.040
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