title: [A Numerical Study of Typical Heavy Air Pollution Episode of PM₂.₅ in Shanghai].
abstract: To analyze the characteristics and formation mechanism of a heavy air pollution episode in Shanghai City from January 23th to January 24th, 2013, the observed data of PM₂.₅ concentration and ground meteorological data and the WRF-Chem model were collected. The analysis revealed that the synoptic necessary mechanism of the heavy air pollution episode could be characterized by the following patterns: Accompanied with weak cold front activities, the city experienced weak winds (i. e. stable atmosphere) at first and then northerly winds (i. e. pollutant transport process ), causing the continuous increase and maintaining of pollutant concentration. The detailed results are shown as follows: Firstly, the stable atmosphere circulation pattern which lasted for 10 hours was not good for air pollution dispersion, as a result, local PM₂.₅ concentrations continued to increase and reached severe pollution level and the high concentrations maintained for 7 hours caused by the stable boundary layer (e. g. static surface winds and low level temperature inversion) during nighttime, and the average PM₂.₅ concentrations during the stable weather process was 172.4 μg · m⁻³. Secondly, the dispersion condition was slightly improved later on with the arrival of a weak cold front, the upstream pollution transportation occurred at the same time, leading to further increase of PM₂.₅ concentration (up to 280 μg · m⁻³), and the average PM₂.₅ concentration during the upstream transportation process was 213.6 μg · m⁻³. Numerical simulation with the WRF-Chem model showed that, average contribution of upstream transportation to local PM₂.₅ concentrations during the episode was 23% . Among them, the contribution during the stable weather and upstream transportation stage was 17.2% and 32.2% . Our results suggested that there were significant differences in the contribution of upstream transportation to the local PM₂.₅ concentration of Shanghai due to variation of weather conditions. Therefore, the government can design effective emission control strategy in advance taking pollution weather forecasting into account.
authors.forename: Lu-yu
authors.initials: LY
authors.lastname: Chang
authors.forename: Jian-ming
authors.initials: JM
authors.lastname: Xu
authors.forename: Guang-qiang
authors.initials: GQ
authors.lastname: Zhou
authors.forename: Jian-bin
authors.initials: JB
authors.lastname: Wu
authors.forename: Ying
authors.initials: Y
authors.lastname: Xie
authors.forename: Zhong-qi
authors.initials: ZQ
authors.lastname: Yu
authors.forename: Chen
authors.initials: C
authors.lastname: Yang
mesh_terms: D000393:Air Pollutants / Q000032:analysis*; D001272:Atmosphere; D002681:China; D002947:Cities; D004784:Environmental Monitoring; D008962:Models, Theoretical; D010316:Particle Size; D052638:Particulate Matter / Q000032:analysis; D014887:Weather; D014919:Wind
chemical_list: D000393:Air Pollutants; D052638:Particulate Matter