[Spatial Simulation of Black Carbon Concentrations Based on a Land Use Regression Model and Mobile Monitoring over Shanghai, China].

Black carbon (BC) is an important component of atmospheric pollution and has significant impacts on air quality and human health. Choosing Shanghai city for a case study, this paper explores the statistical characteristics and spatial patterns of BC concentrations using a mobile monitoring method, which differs from traditional fixed-site observations. Land use regression (LUR) modeling was conducted to examine the determinants for on-road BC concentrations, e.g. population, economic development, traffic, etc. These results showed that the average on-road BC concentrations were (9.86±8.68) μg·m, with a significant spatial variation. BC concentrations in suburban areas[(10.47±2.04) μg·m] were 32.03% (2.54 μg·m) higher than those in the city center[(7.93±2.79) μg·m]. Besides, meteorological factors (e.g. wind speed and relative humidity) and traffic variables (e.g. the length of roads, distance to provincial roads, distance to highway) had significant effects on on-road BC concentrations (:0.5-0.7, <0.01). Moreover, the LUR model, including meteorological and traffic variables performed well (adjusted :0.62-0.75, cross validation :0.54-0.69, RMSE:0.15-0.20 μg·m), which demonstrates that on-road BC concentrations in Shanghai are mainly affected by these factors and traffic sources to some extent. Among them, the most accurate LUR model was developed with a 100 m buffer, followed by the LUR model with a 5 km buffer. This study is of great significance for the identification of spatial distribution patterns for on-road BC concentration and exploring their influencing factors in Shanghai, which can provide a scientific basis and theoretical support for simulating and predicting the response mechanisms of BC on human health and the natural environment.