IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v16y2019i21p4276-d283315.html
   My bibliography  Save this article

Spatio-Temporal Variation Characteristics of PM 2.5 in the Beijing–Tianjin–Hebei Region, China, from 2013 to 2018

Author

Listed:
  • Lili Wang

    (College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China)

  • Qiulin Xiong

    (Faculty of Geomatics, East China University of Technology, Nanchang 330013, China)

  • Gaofeng Wu

    (College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China)

  • Atul Gautam

    (College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China)

  • Jianfang Jiang

    (College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China)

  • Shuang Liu

    (College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China)

  • Wenji Zhao

    (College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China)

  • Hongliang Guan

    (College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China)

Abstract

Air pollution, including particulate matter (PM 2.5 ) pollution, is extremely harmful to the environment as well as human health. The Beijing–Tianjin–Hebei (BTH) Region has experienced heavy PM 2.5 pollution within China. In this study, a six-year time series (January 2013–December 2018) of PM 2.5 mass concentration data from 102 air quality monitoring stations were studied to understand the spatio-temporal variation characteristics of the BTH region. The average annual PM 2.5 mass concentration in the BTH region decreased from 98.9 μg/m 3 in 2013 to 64.9 μg/m 3 in 2017. Therefore, China has achieved its Air Pollution Prevention and Control Plan goal of reducing the concentration of fine particulate matter in the BTH region by 25% by 2017. The PM 2.5 pollution in BTH plain areas showed a more significant change than mountains areas, with the highest PM 2.5 mass concentration in winter and the lowest in summer. The results of spatial autocorrelation and cluster analyses showed that the PM 2.5 mass concentration in the BTH region from 2013–2018 showed a significant spatial agglomeration, and that spatial distribution characteristics were high in the south and low in the north. Changes in PM 2.5 mass concentration in the BTH region were affected by both socio-economic factors and meteorological factors. Our results can provide a point of reference for making PM 2.5 pollution control decisions.

Suggested Citation

  • Lili Wang & Qiulin Xiong & Gaofeng Wu & Atul Gautam & Jianfang Jiang & Shuang Liu & Wenji Zhao & Hongliang Guan, 2019. "Spatio-Temporal Variation Characteristics of PM 2.5 in the Beijing–Tianjin–Hebei Region, China, from 2013 to 2018," IJERPH, MDPI, vol. 16(21), pages 1-20, November.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:21:p:4276-:d:283315
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/16/21/4276/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/16/21/4276/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Juntao Tan & Pingyu Zhang & Kevin Lo & Jing Li & Shiwei Liu, 2016. "The Urban Transition Performance of Resource-Based Cities in Northeast China," Sustainability, MDPI, vol. 8(10), pages 1-17, October.
    2. Zhang, Na & Jing, Yong-Cai & Liu, Cheng-Yu & Li, Yao & Shen, Jing, 2016. "A cellular automaton model for grasshopper population dynamics in Inner Mongolia steppe habitats," Ecological Modelling, Elsevier, vol. 329(C), pages 5-17.
    3. Jixia Huang & Jinfeng Wang & Yanchen Bo & Chengdong Xu & Maogui Hu & Dacang Huang, 2014. "Identification of Health Risks of Hand, Foot and Mouth Disease in China Using the Geographical Detector Technique," IJERPH, MDPI, vol. 11(3), pages 1-17, March.
    4. Sangalli, Laura M. & Secchi, Piercesare & Vantini, Simone & Vitelli, Valeria, 2010. "k-mean alignment for curve clustering," Computational Statistics & Data Analysis, Elsevier, vol. 54(5), pages 1219-1233, May.
    Full references (including those not matched with items on IDEAS)

    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. Yafei Wu & Ke Hu & Yaofeng Han & Qilin Sheng & Ya Fang, 2020. "Spatial Characteristics of Life Expectancy and Geographical Detection of Its Influencing Factors in China," IJERPH, MDPI, vol. 17(3), pages 1-15, February.
    2. Qiurong Xu & Xinqi Zheng & Chunxiao Zhang, 2018. "Quantitative Analysis of the Determinants Influencing Urban Expansion: A Case Study in Beijing, China," Sustainability, MDPI, vol. 10(5), pages 1-16, May.
    3. Li, Cunfang & Li, Danping & Zhang, Xiaoxu, 2019. "Why can China's coal resource-exhausted enterprises cross the district to transfer?," Resources Policy, Elsevier, vol. 60(C), pages 94-105.
    4. Li, Pai-Ling & Chiou, Jeng-Min, 2011. "Identifying cluster number for subspace projected functional data clustering," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2090-2103, June.
    5. Han Yue & Tao Hu, 2021. "Geographical Detector-Based Spatial Modeling of the COVID-19 Mortality Rate in the Continental United States," IJERPH, MDPI, vol. 18(13), pages 1-16, June.
    6. Dong, Hao & Zheng, Yingrong & Tang, Yuhong, 2024. "Impact of FinTech on the industrial structural transformation: Evidence from China's resource-based cities," Resources Policy, Elsevier, vol. 91(C).
    7. Andrea Martino & Andrea Ghiglietti & Francesca Ieva & Anna Maria Paganoni, 2019. "A k-means procedure based on a Mahalanobis type distance for clustering multivariate functional data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(2), pages 301-322, June.
    8. Hao Wu & Tongtong Shan & Hassan Saif Khan & Lin Dong & Hua Li, 2024. "The Spatiotemporal Differentiation Characteristics and Driving Forces of Carbon Emissions from Major Livestock Farming in the Shaanxi–Gansu–Ningxia Region," Agriculture, MDPI, vol. 14(10), pages 1-16, October.
    9. Sidong Zhao & Weiwei Li & Kaixu Zhao & Ping Zhang, 2021. "Change Characteristics and Multilevel Influencing Factors of Real Estate Inventory—Case Studies from 35 Key Cities in China," Land, MDPI, vol. 10(9), pages 1-29, September.
    10. Mantas Svazas & Valentinas Navickas & Yuriy Bilan & Joanna Nakonieczny & Jana Spankova, 2021. "Biomass Clusterization from a Regional Perspective: The Case of Lithuania," Energies, MDPI, vol. 14(21), pages 1-15, October.
    11. Yunlin He & Yanhua Mo & Jiangming Ma, 2022. "Spatio-Temporal Evolution and Influence Mechanism of Habitat Quality in Guilin City, China," IJERPH, MDPI, vol. 20(1), pages 1-15, December.
    12. Cheng Cui & Baohua Wang & Hongyan Ren & Zhen Wang, 2019. "Spatiotemporal Variations in Gastric Cancer Mortality and Their Relations to Influencing Factors in S County, China," IJERPH, MDPI, vol. 16(5), pages 1-13, March.
    13. Tucker, J. Derek & Wu, Wei & Srivastava, Anuj, 2013. "Generative models for functional data using phase and amplitude separation," Computational Statistics & Data Analysis, Elsevier, vol. 61(C), pages 50-66.
    14. Floriello, Davide & Vitelli, Valeria, 2017. "Sparse clustering of functional data," Journal of Multivariate Analysis, Elsevier, vol. 154(C), pages 1-18.
    15. Wagner, Heiko & Kneip, Alois, 2019. "Nonparametric registration to low-dimensional function spaces," Computational Statistics & Data Analysis, Elsevier, vol. 138(C), pages 49-63.
    16. Marco Grasso & Bianca Maria Colosimo & Fugee Tsung, 2017. "A phase I multi-modelling approach for profile monitoring of signal data," International Journal of Production Research, Taylor & Francis Journals, vol. 55(15), pages 4354-4377, August.
    17. Chengdong Xu, 2017. "Spatio-Temporal Pattern and Risk Factor Analysis of Hand, Foot and Mouth Disease Associated with Under-Five Morbidity in the Beijing–Tianjin–Hebei Region of China," IJERPH, MDPI, vol. 14(4), pages 1-13, April.
    18. Zheng Cao & Tao Liu & Xing Li & Jin Wang & Hualiang Lin & Lingling Chen & Zhifeng Wu & Wenjun Ma, 2017. "Individual and Interactive Effects of Socio-Ecological Factors on Dengue Fever at Fine Spatial Scale: A Geographical Detector-Based Analysis," IJERPH, MDPI, vol. 14(7), pages 1-14, July.
    19. Cai-Rong Lou & Hong-Yu Liu & Yu-Feng Li & Yu-Ling Li, 2016. "Socioeconomic Drivers of PM 2.5 in the Accumulation Phase of Air Pollution Episodes in the Yangtze River Delta of China," IJERPH, MDPI, vol. 13(10), pages 1-19, September.
    20. Dimeglio, Chloé & Gallón, Santiago & Loubes, Jean-Michel & Maza, Elie, 2014. "A robust algorithm for template curve estimation based on manifold embedding," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 373-386.

    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:16:y:2019:i:21:p:4276-:d:283315. 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.