4.7 Article

Spatial characteristics and determinants of in-traffic black carbon in Shanghai, China: Combination of mobile monitoring and land use regression model

期刊

SCIENCE OF THE TOTAL ENVIRONMENT
卷 658, 期 -, 页码 51-61

出版社

ELSEVIER
DOI: 10.1016/j.scitotenv.2018.12.135

关键词

Black carbon (BC); Mobile measurement; Urban environment; Land use regression model (LUR); Shanghai

资金

  1. National Key Research and Development Program [2017YFC0500204, 2016YFC0500204]
  2. Natural Science Foundation of Shanghai [17ZR1408700]
  3. Shanghai Technology innovation action plan of Yangtze River Delta joint research projects [17295810603]
  4. National Natural Science Foundation of China [41471076]
  5. Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Fuzhou University [2017LSDMIS07]

向作者/读者索取更多资源

Black carbon (BC) has emerged as a major contributor to global climate change. Cities play an important role in global BC emission.The present study investigated the spatial pattern of in-traffic BC at a high spatial resolution in Shanghai, the commercial and financial center in Mainland China. The determinants including road network, social economic status and point-source pollutants, which may influence the BC spatial variability were also discussed. From October to December 2016, mobile monitoring was conducted to assess the BC concentrations on three sampling routes in Shanghai with a total length of 116 km. The results showed that the mean in traffic BC among three sampling routes was 10.77 +/- 3.50 mu g/m(3). BC concentrations showed a significant spatial heterogeneity. The highest BC concentrations were near industrial sources and that those high concentrations were associated with either direct emissions from the industries, freight traffic, or both. With the widely distributed polluting enterprises and high emitting vehicles, the average BC in the low urbanization areas (12.80 +/- 4.54 mu g/m(3)) was 57% higher than that in the urban core (7.77 +/- 224 mu g/m(3)). Furthermore, a land use regression (LUR) model based on mobile monitoring was developed to examine the determinants and its spatial variability of BC measurements which corresponded to 17 predictor variables, e.g. road network, land use, meteorological condition etc., in 7 buffer distances (100 m to 10 km). The variables of meteorological, socio-economical and the distance to BC point-sources were selected as the independent variables. It was found that the established LUR model could explain a proportion (68%) of the variability of BC. LUR modeling from mobile measurements was possible, but more work related to the effect of traffic regulation on BC could be helpful for informing best model practice. (C) 2018 Elsevier B.V. All rights reserved.

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