Development of a vehicle emission inventory with high temporal–spatial resolution based on NRT traffic data and its impact on air pollution in Beijing – Part 2: Impact of vehicle emission on urban air quality

Abstract. A companion paper developed a vehicle emission inventory with high temporal–spatial resolution (HTSVE) with a bottom-up methodology based on local emission factors, complemented with the widely used emission factors of COPERT model and near-real-time (NRT) traffic data on a specific road segment for 2013 in urban Beijing (Jing et al., 2016), which is used to investigate the impact of vehicle pollution on air pollution in this study. Based on the sensitivity analysis method of switching on/off pollutant emissions in the Chinese air quality forecasting model CUACE, a modelling study was carried out to evaluate the contributions of vehicle emission to the air pollution in Beijing's main urban areas in the periods of summer (July) and winter (December) 2013. Generally, the CUACE model had good performance of the concentration simulation of pollutants. The model simulation has been improved by using HTSVE. The vehicle emission contribution (VEC) to ambient pollutant concentrations not only changes with seasons but also changes with time. The mean VEC, affected by regional pollutant transports significantly, is 55.4 and 48.5 % for NO₂ and 5.4 and 10.5 % for PM_2.5 in July and December 2013 respectively. Regardless of regional transports, relative vehicle emission contribution (RVEC) to NO₂ is 59.2 and 57.8 % in July and December 2013, while it is 8.7 and 13.9 % for PM_2.5. The RVEC to PM_2.5 is lower than the PM_2.5 contribution rate for vehicle emission in total emission, which may be due to dry deposition of PM_2.5 from vehicle emission in the near-surface layer occuring more easily than from elevated source emission.