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

Highlights: • Mobile monitoring of in-traffic black carbon (BC) was conducted in Shanghai, China. • BC was lowest in the urban core and increased towards the outer areas of the city. • Land use regression (LUR) model could explain 68% of the BC spatial variability. • The results implied the effect of traffic policy on the spatial BC in a Mega city. -- 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.