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

Min Liu; Xia Peng; Ziqi Meng; Taoye Zhou; Lingbo Long; Qiannan She

doi:10.1016/j.scitotenv.2018.12.135

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

Sci Total Environ. 2019 Mar 25:658:51-61. doi: 10.1016/j.scitotenv.2018.12.135. Epub 2018 Dec 13.

Authors

Min Liu¹, Xia Peng², Ziqi Meng³, Taoye Zhou⁴, Lingbo Long³, Qiannan She³

Affiliations

¹ Shanghai Key Lab for Urban Ecological Processes and Eco-restoration, School of Ecological and Environmental Sciences, East China Normal University, Shanghai 200241, PR China; Institute of Eco-Chongming (IEC), Shanghai 200062, PR China. Electronic address: mliu@re.ecnu.edu.cn.
² Library of East China Normal University, Shanghai 200241, PR China.
³ Shanghai Key Lab for Urban Ecological Processes and Eco-restoration, School of Ecological and Environmental Sciences, East China Normal University, Shanghai 200241, PR China.
⁴ Pudong New Area Environmental Monitoring Station, Shanghai 200135, PR China.

PMID: 30572214
DOI: 10.1016/j.scitotenv.2018.12.135

Abstract

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 μg/m³. 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 μg/m³) was 57% higher than that in the urban core (7.77 ± 2.24 μg/m³). 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.

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