International Journal of Environmental Monitoring and Analysis

| Peer-Reviewed |

Emission Characteristics of Major Atmospheric Pollutants in Changchun City

Received: 15 July 2020    Accepted:     Published: 28 September 2020
Views:       Downloads:

Share This Article

Abstract

Based on the technical guide for the preparation of air pollutant emission inventory, this paper makes a statistical analysis of the basic data of air pollution related to the Changchun, and according to the relevant literatures, establishes a list of the main air pollutant emissions from the Changchun 2016 air pollution emission sources (including fine particles, sulfur dioxide and nitrogen oxides). Then the uncertainty analysis is made by Monte Carlo method, and the countermeasures of Changchun air pollution control are put forward. The results showed that the total emission of PM2.5 in Changchun in 2016 was 70986.45t, and the emissions from the main sources are fixed combustion source, mobile source, process source, biomass burning source and dust source respectively. According to the analysis, we know that coal-based energy consumption structure not only contributes significantly to the emission of one fine particulate matter, but also emits a large number of two fine particulate precursor materials, such as sulfur dioxide, nitrogen oxides. The total sulfur dioxide emission in Changchun in 2016 is 52183.55t, and the total NOx emission is 13.15 million tons. The uncertainty analysis of the Monte Carlo method shows that the uncertainty range of the emission inventory is small and the estimation results are quite credible.

DOI 10.11648/j.ijema.20200805.14
Published in International Journal of Environmental Monitoring and Analysis (Volume 8, Issue 5, October 2020)
Page(s) 155-160
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Emission Inventory, Fine Particulate Matter, Sulfur Dioxide, Nitrogen Oxides

References
[1] Wang Peng, Ying Qi, Zhang Hongliang, Hu Jianlin, et al. Source apportionment of secondary organic aerosol in China using a regional source-oriented chemical transport model and two emission inventories [J]. Environmental Pollution, 2018, 237: 756-766.
[2] Shuhan Liu, Shenbing Hua, Kun Wang, Peipei Qiu, et al. Spatial-temporal variation characteristics of air pollution in Henan of China: Localized emission inventory, WRF/Chem simulations and potential source contribution analysis [J]. Science of the Total Environment, 2018, 624: 396-406.
[3] Fu X, Wang S X, Zhao B, et al. Emission inventory of primary pollutants and chemical speciation in 2010 for the Yangtze River Delta region, China [J]. Atmospheric Environment, 2013, 70: 39-50.
[4] Ming, L., Jin, L., Li, J., Fu, P., Yang, W., Liu, D., Zhang, G., Wang, Z., Li, X. PM2.5 in the Yangtze River Delta, China: Chemical compositions, seasonal variations, and regional pollution events [J]. Environ Pollut, 2017, 223, 200-212.
[5] Wang, J., Xie, X., Fang, C. Temporal and Spatial Distribution Characteristics of Atmospheric Particulate Matter (PM10 and PM2.5) in Changchun and Analysis of Its Influencing Factors [J]. Atmos, 2019, 10 (11).
[6] Shen, F., Zhang, L., Jiang, L., Tang, M., Gai, X., Chen, M., Ge, X. Temporal variations of six ambient criteria air pollutants from 2015 to 2018, their spatial distributions, health risks and relationships with socioeconomic factors during 2018 in China [J]. Environ Int, 2020, 137, 105556.
[7] Yao, L., Yang, L., Yuan, Q., Yan, C., Dong, C., Meng, C., Sui, X., Yang, F., Lu, Y., Wang, W. Sources apportionment of PM2.5 in a background site in the North China Plain [J]. Sci. Total Environ., 2016, 541.
[8] Alexis Laurent, Michael Z. Hauschild. Impacts of NMVOC emissions on human health in European countries for 2000–2010: Use of sector-specific substance profiles [J]. Atmospheric Environment, 2014, 85: 247-255.
[9] Jiandong Li, Wei-Chyung Wang; Hong Liao, Wenyuan Chang. Past and future direct radiative forcing of nitrate aerosol in East Asia [J]. Theoretical and Applied Climatology, 2015, 121 (3): 44-458.
[10] Jiamin Ou, Junyu Zheng, Rongrong Li, et al. Speciated OVOC and VOC emission inventories and their implications for reactivity-based ozone control strategy in the Pearl River Delta region, China [J]. Science of the Total Environment, 2015, 530: 393-402.
[11] Shuangqi Zhang, Mengsi Deng, Ming Shan, et al. Study on the energy and environmental impacts of substituting molded straw fuels for heating coal in rural areas of northern China based on the amount of straw open burning [J]. Journal of Agro-Environment Science, 2017, 36 (12): 2506-2514.
[12] Chang J, Ren Y, Shi Y, et al. An inventory of biogenic volatile organic compounds for a subtropical urban-rural complex [J]. Atmospheric Environment, 2012, 56: 115-123.
[13] Yanci Liang, Handong Liang, Shuquan Zhu. Mercury emission from coal seam fire at Wuda, Inner Mongolia, China [J]. Atmospheric Environment, 2014, 83: 176-184.
[14] Wenyuan Chang, Hong Liao, Jinyuan Xin, et al. Uncertainties in anthropogenic aerosol concentrations and direct radiative forcing induced by emission inventories in eastern China [J]. Atmospheric Research, 2015, 166 (1): 129-140.
[15] Lili Wang, Jinyuan Xin, Xingru Li, Yuesi Wang. The variability of biomass burning and its influence on regional aerosol properties during the wheat harvest season in North China [J]. Atmospheric Research, 2015, 157: 153-163.
Author Information
  • College of New Energy and Environment, Jilin University, Changchun, China

  • College of Water Conservancy and Hydropower Engineering, Sichuan Agricultural University, Ya’an, China

  • College of New Energy and Environment, Jilin University, Changchun, China

  • College of New Energy and Environment, Jilin University, Changchun, China

Cite This Article
  • APA Style

    Ju Wang, Yilian Zhao, Kexin Xue, Chunsheng Fang. (2020). Emission Characteristics of Major Atmospheric Pollutants in Changchun City. International Journal of Environmental Monitoring and Analysis, 8(5), 155-160. https://doi.org/10.11648/j.ijema.20200805.14

    Copy | Download

    ACS Style

    Ju Wang; Yilian Zhao; Kexin Xue; Chunsheng Fang. Emission Characteristics of Major Atmospheric Pollutants in Changchun City. Int. J. Environ. Monit. Anal. 2020, 8(5), 155-160. doi: 10.11648/j.ijema.20200805.14

    Copy | Download

    AMA Style

    Ju Wang, Yilian Zhao, Kexin Xue, Chunsheng Fang. Emission Characteristics of Major Atmospheric Pollutants in Changchun City. Int J Environ Monit Anal. 2020;8(5):155-160. doi: 10.11648/j.ijema.20200805.14

    Copy | Download

  • @article{10.11648/j.ijema.20200805.14,
      author = {Ju Wang and Yilian Zhao and Kexin Xue and Chunsheng Fang},
      title = {Emission Characteristics of Major Atmospheric Pollutants in Changchun City},
      journal = {International Journal of Environmental Monitoring and Analysis},
      volume = {8},
      number = {5},
      pages = {155-160},
      doi = {10.11648/j.ijema.20200805.14},
      url = {https://doi.org/10.11648/j.ijema.20200805.14},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ijema.20200805.14},
      abstract = {Based on the technical guide for the preparation of air pollutant emission inventory, this paper makes a statistical analysis of the basic data of air pollution related to the Changchun, and according to the relevant literatures, establishes a list of the main air pollutant emissions from the Changchun 2016 air pollution emission sources (including fine particles, sulfur dioxide and nitrogen oxides). Then the uncertainty analysis is made by Monte Carlo method, and the countermeasures of Changchun air pollution control are put forward. The results showed that the total emission of PM2.5 in Changchun in 2016 was 70986.45t, and the emissions from the main sources are fixed combustion source, mobile source, process source, biomass burning source and dust source respectively. According to the analysis, we know that coal-based energy consumption structure not only contributes significantly to the emission of one fine particulate matter, but also emits a large number of two fine particulate precursor materials, such as sulfur dioxide, nitrogen oxides. The total sulfur dioxide emission in Changchun in 2016 is 52183.55t, and the total NOx emission is 13.15 million tons. The uncertainty analysis of the Monte Carlo method shows that the uncertainty range of the emission inventory is small and the estimation results are quite credible.},
     year = {2020}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Emission Characteristics of Major Atmospheric Pollutants in Changchun City
    AU  - Ju Wang
    AU  - Yilian Zhao
    AU  - Kexin Xue
    AU  - Chunsheng Fang
    Y1  - 2020/09/28
    PY  - 2020
    N1  - https://doi.org/10.11648/j.ijema.20200805.14
    DO  - 10.11648/j.ijema.20200805.14
    T2  - International Journal of Environmental Monitoring and Analysis
    JF  - International Journal of Environmental Monitoring and Analysis
    JO  - International Journal of Environmental Monitoring and Analysis
    SP  - 155
    EP  - 160
    PB  - Science Publishing Group
    SN  - 2328-7667
    UR  - https://doi.org/10.11648/j.ijema.20200805.14
    AB  - Based on the technical guide for the preparation of air pollutant emission inventory, this paper makes a statistical analysis of the basic data of air pollution related to the Changchun, and according to the relevant literatures, establishes a list of the main air pollutant emissions from the Changchun 2016 air pollution emission sources (including fine particles, sulfur dioxide and nitrogen oxides). Then the uncertainty analysis is made by Monte Carlo method, and the countermeasures of Changchun air pollution control are put forward. The results showed that the total emission of PM2.5 in Changchun in 2016 was 70986.45t, and the emissions from the main sources are fixed combustion source, mobile source, process source, biomass burning source and dust source respectively. According to the analysis, we know that coal-based energy consumption structure not only contributes significantly to the emission of one fine particulate matter, but also emits a large number of two fine particulate precursor materials, such as sulfur dioxide, nitrogen oxides. The total sulfur dioxide emission in Changchun in 2016 is 52183.55t, and the total NOx emission is 13.15 million tons. The uncertainty analysis of the Monte Carlo method shows that the uncertainty range of the emission inventory is small and the estimation results are quite credible.
    VL  - 8
    IS  - 5
    ER  - 

    Copy | Download

  • Sections