IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v13y2016i12p1202-d85305.html
   My bibliography  Save this article

High Contributions of Secondary Inorganic Aerosols to PM 2.5 under Polluted Levels at a Regional Station in Northern China

Author

Listed:
  • Yang Li

    (Meteorological Observation Center, China Meteorological Administration, Beijing 100081, China)

  • Jun Tao

    (South China Institute of Environmental Sciences, Ministry of Environmental Protection, Guangzhou 510655, China)

  • Leiming Zhang

    (Air Quality Research Division, Science and Technology Branch, Environment and Climate Change Canada, Toronto, ON M3H 5T4, Canada)

  • Xiaofang Jia

    (Meteorological Observation Center, China Meteorological Administration, Beijing 100081, China)

  • Yunfei Wu

    (Key Laboratory of Regional Climate-Environment for Temperate East Asia (RCE-TEA), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China)

Abstract

Daily PM 2.5 samples were collected at Shangdianzi (SDZ) regional site in Beijing–Tianjin–Hebei (BTH) region in 2015. Samples were subject to chemical analysis for organic carbon (OC), elemental carbon (EC), and major water-soluble inorganic ions. The annual average PM 2.5 mass concentration was 53 ± 36 μg·m −3 with the highest seasonal average concentration in spring and the lowest in summer. Water-soluble inorganic ions and carbonaceous aerosols accounted for 34% ± 15% and 33% ± 9%, respectively, of PM 2.5 mass on annual average. The excellent, good, lightly polluted, moderately polluted, and heavily polluted days based on the Air Quality Index (AQI) of PM 2.5 accounted for 40%, 42%, 11%, 4%, and 3%, respectively, of the year. The sum of the average concentration of sulfate, nitrate, and ammonium (SNA) increased from 4.2 ± 2.9 μg·m −3 during excellent days to 85.9 ± 22.4 μg·m −3 during heavily polluted days, and their contributions to PM 2.5 increased from 15% ± 8% to 49% ± 10% accordingly. In contrast, the average concentration of carbonaceous aerosols increased from 9.2 ± 2.8 μg·m −3 to 51.2 ± 14.1 μg·m −3 , and their contributions to PM 2.5 decreased from 34% ± 6% to 29% ± 7%. Potential source contribution function (PSCF) analysis revealed that the major sources for high PM 2.5 and its dominant chemical components were within the area mainly covering Shandong, Henan, and Hebei provinces. Regional pollutant transport from Shanxi province and Inner Mongolia autonomous region located in the west direction of SDZ was also important during the heating season.

Suggested Citation

  • Yang Li & Jun Tao & Leiming Zhang & Xiaofang Jia & Yunfei Wu, 2016. "High Contributions of Secondary Inorganic Aerosols to PM 2.5 under Polluted Levels at a Regional Station in Northern China," IJERPH, MDPI, vol. 13(12), pages 1-15, December.
  • Handle: RePEc:gam:jijerp:v:13:y:2016:i:12:p:1202-:d:85305
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/13/12/1202/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/13/12/1202/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ling Yao & Ning Lu & Xiafang Yue & Jia Du & Cundong Yang, 2015. "Comparison of Hourly PM 2.5 Observations Between Urban and Suburban Areas in Beijing, China," IJERPH, MDPI, vol. 12(10), pages 1-13, September.
    2. Tianhao Zhang & Gang Liu & Zhongmin Zhu & Wei Gong & Yuxi Ji & Yusi Huang, 2016. "Real-Time Estimation of Satellite-Derived PM 2.5 Based on a Semi-Physical Geographically Weighted Regression Model," IJERPH, MDPI, vol. 13(10), pages 1-13, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ruiling Sun & Yi Zhou & Jie Wu & Zaiwu Gong, 2019. "Influencing Factors of PM 2.5 Pollution: Disaster Points of Meteorological Factors," IJERPH, MDPI, vol. 16(20), pages 1-31, October.
    2. Hujia Zhao & Huizheng Che & Yanjun Ma & Yangfeng Wang & Hongbin Yang & Yuche Liu & Yaqiang Wang & Hong Wang & Xiaoye Zhang, 2017. "The Relationship of PM Variation with Visibility and Mixing-Layer Height under Hazy/Foggy Conditions in the Multi-Cities of Northeast China," IJERPH, MDPI, vol. 14(5), pages 1-18, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wei Xue & Qingming Zhan & Qi Zhang & Zhonghua Wu, 2019. "Spatiotemporal Variations of Particulate and Gaseous Pollutants and Their Relations to Meteorological Parameters: The Case of Xiangyang, China," IJERPH, MDPI, vol. 17(1), pages 1-23, December.
    2. Ling Yao & Changchun Huang & Wenlong Jing & Xiafang Yue & Yuyue Xu, 2018. "Quantitative Assessment of Relationship between Population Exposure to PM 2.5 and Socio-Economic Factors at Multiple Spatial Scales over Mainland China," IJERPH, MDPI, vol. 15(9), pages 1-13, September.
    3. Tianhao Zhang & Wei Gong & Wei Wang & Yuxi Ji & Zhongmin Zhu & Yusi Huang, 2016. "Ground Level PM 2.5 Estimates over China Using Satellite-Based Geographically Weighted Regression (GWR) Models Are Improved by Including NO 2 and Enhanced Vegetation Index (EVI)," IJERPH, MDPI, vol. 13(12), pages 1-12, December.
    4. Hujia Zhao & Huizheng Che & Yanjun Ma & Yangfeng Wang & Hongbin Yang & Yuche Liu & Yaqiang Wang & Hong Wang & Xiaoye Zhang, 2017. "The Relationship of PM Variation with Visibility and Mixing-Layer Height under Hazy/Foggy Conditions in the Multi-Cities of Northeast China," IJERPH, MDPI, vol. 14(5), pages 1-18, April.
    5. Jamal Jokar Arsanjani, 2017. "Remote Sensing, Crowd Sensing, and Geospatial Technologies for Public Health: An Editorial," IJERPH, MDPI, vol. 14(4), pages 1-3, April.
    6. Akmaral Agibayeva & Rustem Khalikhan & Mert Guney & Ferhat Karaca & Aisulu Torezhan & Egemen Avcu, 2022. "An Air Quality Modeling and Disability-Adjusted Life Years (DALY) Risk Assessment Case Study: Comparing Statistical and Machine Learning Approaches for PM 2.5 Forecasting," Sustainability, MDPI, vol. 14(24), pages 1-18, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:13:y:2016:i:12:p:1202-:d:85305. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.