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
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2016
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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 PM2.5 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 PM2.5. The RVEC to PM2.5 is
lower than the PM2.5 contribution rate for vehicle emission in total
emission, which may be due to dry deposition of PM2.5 from vehicle emission in the near-surface layer occuring more easily than from elevated source emission.
| Reference Key |
he2016atmosphericdevelopment
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|---|---|
| Authors | ;J. He;L. Wu;H. Mao;H. Liu;B. Jing;Y. Yu;P. Ren;C. Feng;X. Liu |
| Journal | Journal of agricultural and food chemistry |
| Year | 2016 |
| DOI |
10.5194/acp-16-3171-2016
|
| URL | |
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