Articles | Volume 16, issue 5
Atmos. Chem. Phys., 16, 3171–3184, 2016
https://doi.org/10.5194/acp-16-3171-2016

Special issue: Haze-fog forecasts and near real time (NRT) data application...

Atmos. Chem. Phys., 16, 3171–3184, 2016
https://doi.org/10.5194/acp-16-3171-2016
Research article
10 Mar 2016
Research article | 10 Mar 2016

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

Jianjun He et al.

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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 1: Development and evaluation of vehicle emission inventory
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Atmos. Chem. Phys., 16, 3161–3170, https://doi.org/10.5194/acp-16-3161-2016,https://doi.org/10.5194/acp-16-3161-2016, 2016
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Subject: Aerosols | Research Activity: Atmospheric Modelling | Altitude Range: Troposphere | Science Focus: Physics (physical properties and processes)
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Cited articles

An, X., Zhu, T., Wang, Z., Li, C., and Wang, Y.: A modeling analysis of a heavy air pollution episode occurred in Beijing, Atmos. Chem. Phys., 7, 3103–3114, https://doi.org/10.5194/acp-7-3103-2007, 2007.
An, X. Q., Zhai, S. X., Jin, M., Gong, S. L., and Wang, Y.: Tracking influential haze source areas in North China using an adjoint model, GRAPES–CUACE, Geosci. Model Dev. Discuss., 8, 7313–7345, https://doi.org/10.5194/gmdd-8-7313-2015, 2015.
Bowden, J. H., Nolte, C. G., and Otte, T. L.: Simulating the impact of the large-scale circulation on the 2-m temperature and precipitation climatology, Clim. Dynam., 40, 1903–1920, https://doi.org/10.1007/s00382-012-1440-y, 2013.
Burr, M. and Zhang, Y.: Source apportionment of fine particulate matter over the Eastern U.S. Part I: source sensitivity simulations using CMAQ with the brute force method, Atmos. Pollut. Res., 2, 299–316, 2011.
Cao, G. L., Zhang, X. Y., Gong, S. L., An, X. Q., and Wang, Y. Q.: Emission inventories of primary particles and pollutant gases for China, Chinese Sci. Bull., 56, 781–788, https://doi.org/10.1007/s11434-011-4373-7, 2011.
Short summary
Based on a vehicle emission inventory with high temporal–spatial resolution established by bottom-up methodology, this paper evaluates the performance of the CUACE model and investigates the vehicle emission contribution (VEC) to ambient NO2 and PM2.5 with an emission source sensitivity method. With good performance of pollutant concentrations, numerical simulation revealed that the mean VEC is 55.4 and 48.5 % for NO2 and 5.4 and 10.5 % for PM2.5 in July and December 2013 respectively.
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