IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v15y2018i1p114-d126411.html
   My bibliography  Save this article

Spatiotemporal Patterns of Ground Monitored PM 2.5 Concentrations in China in Recent Years

Author

Listed:
  • Junming Li

    (School of Statistics, Shanxi University of Finance & Economics, 696 Wucheng Road, Taiyuan 030006, China
    LREIS, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Datun Road 11A, Beijing 10010, China)

  • Xiulan Han

    (School of Statistics, Shanxi University of Finance & Economics, 696 Wucheng Road, Taiyuan 030006, China)

  • Xiao Li

    (School of Statistics, Shanxi University of Finance & Economics, 696 Wucheng Road, Taiyuan 030006, China)

  • Jianping Yang

    (School of Statistics, Shanxi University of Finance & Economics, 696 Wucheng Road, Taiyuan 030006, China)

  • Xuejiao Li

    (School of Statistics, Shanxi University of Finance & Economics, 696 Wucheng Road, Taiyuan 030006, China)

Abstract

This paper firstly explores the space-time evolution of city-level PM 2.5 concentrations showed a very significant seasonal cycle type fluctuation during the period between 13 May 2014 and 30 May 2017. The period from October to April following each year was a heavy pollution period, whereas the phase from April to October of the current year was part of a light pollution period. The average monthly PM 2.5 concentrations in mainland China based on ground monitoring, employing a descriptive statistics method and a Bayesian spatiotemporal hierarchy model. Daily and weekly average PM 2.5 concentrations in 338 cities in mainland China presented no significant spatial difference during the severe pollution period but a large spatial difference during light pollution periods. The severe PM 2.5 pollution areas were mainly distributed in the Beijing-Tianjin-Hebei urban agglomeration in the North China Plain during the beginning of each autumn-winter season (September), spreading to the Northeast Plains after October, then later continuing to spread to other cities in mainland China, eventually covering most cities. PM 2.5 pollution in China appeared to be a cyclic characteristic of first spreading and then centralizing in the space in two spring-summer seasons, and showed an obvious process of first diffusing then transferring to shrinkage alternation during the spring-summer season of 2015, but showed no obvious diffusion during the spring-summer season of 2016, maintaining a stable spatial structure after the shrinkage in June, as well as being more concentrated. The heavily polluted areas are continuously and steadily concentrated in East China, Central China and Xinjiang Province.

Suggested Citation

  • Junming Li & Xiulan Han & Xiao Li & Jianping Yang & Xuejiao Li, 2018. "Spatiotemporal Patterns of Ground Monitored PM 2.5 Concentrations in China in Recent Years," IJERPH, MDPI, vol. 15(1), pages 1-15, January.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:1:p:114-:d:126411
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/15/1/114/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/15/1/114/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Andrew Gelman, 2004. "Prior distributions for variance parameters in hierarchical models," EERI Research Paper Series EERI_RP_2004_06, Economics and Econometrics Research Institute (EERI), Brussels.
    2. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
    3. O. Stramer & R. L. Tweedie, 1999. "Langevin-Type Models II: Self-Targeting Candidates for MCMC Algorithms," Methodology and Computing in Applied Probability, Springer, vol. 1(3), pages 307-328, October.
    4. Andrew Gelman, 2004. "Prior distributions for variance parameters in hierarchical models," Econometrics 0404001, University Library of Munich, Germany.
    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. Xiaobing Yu & Chenliang Li & Hong Chen & Zhonghui Ji, 2020. "Evaluate Air Pollution by Promethee Ranking in Yangtze River Delta of China," IJERPH, MDPI, vol. 17(2), pages 1-18, January.
    2. 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.

    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. Ciprian Crainiceanu & David Ruppert & Raymond Carroll, 2004. "Spatially Adaptive Bayesian P-Splines with Heteroscedastic Errors," Johns Hopkins University Dept. of Biostatistics Working Paper Series 1061, Berkeley Electronic Press.
    2. repec:jss:jstsof:14:i05 is not listed on IDEAS
    3. Cabrini, Silvina M. & Irwin, Scott H. & Good, Darrel L., 2005. "Efficient Portfolios of Market Advisory Services: An Application of Shrinkage Estimators," 2005 Annual meeting, July 24-27, Providence, RI 19469, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    4. Crainiceanu, Ciprian M. & Ruppert, David & Wand, Matthew P., 2005. "Bayesian Analysis for Penalized Spline Regression Using WinBUGS," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 14(i14).
    5. repec:jss:jstsof:14:i14 is not listed on IDEAS
    6. Katie Wilson & Jon Wakefield, 2022. "A probabilistic model for analyzing summary birth history data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 47(11), pages 291-344.
    7. Eibich, Peter & Ziebarth, Nicolas, 2014. "Examining the Structure of Spatial Health Effects in Germany Using Hierarchical Bayes Models," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 49, pages 305-320.
    8. Shreosi Sanyal & Thierry Rochereau & Cara Nichole Maesano & Laure Com-Ruelle & Isabella Annesi-Maesano, 2018. "Long-Term Effect of Outdoor Air Pollution on Mortality and Morbidity: A 12-Year Follow-Up Study for Metropolitan France," IJERPH, MDPI, vol. 15(11), pages 1-8, November.
    9. Mayer Alvo & Jingrui Mu, 2023. "COVID-19 Data Analysis Using Bayesian Models and Nonparametric Geostatistical Models," Mathematics, MDPI, vol. 11(6), pages 1-13, March.
    10. Gil, Guilherme Dôco Roberti & Costa, Marcelo Azevedo & Lopes, Ana Lúcia Miranda & Mayrink, Vinícius Diniz, 2017. "Spatial statistical methods applied to the 2015 Brazilian energy distribution benchmarking model: Accounting for unobserved determinants of inefficiencies," Energy Economics, Elsevier, vol. 64(C), pages 373-383.
    11. Vanessa Santos-Sánchez & Juan Antonio Córdoba-Doña & Javier García-Pérez & Antonio Escolar-Pujolar & Lucia Pozzi & Rebeca Ramis, 2020. "Cancer Mortality and Deprivation in the Proximity of Polluting Industrial Facilities in an Industrial Region of Spain," IJERPH, MDPI, vol. 17(6), pages 1-15, March.
    12. Berti, Patrizia & Dreassi, Emanuela & Rigo, Pietro, 2014. "Compatibility results for conditional distributions," Journal of Multivariate Analysis, Elsevier, vol. 125(C), pages 190-203.
    13. Louise Choo & Stephen G. Walker, 2008. "A new approach to investigating spatial variations of disease," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(2), pages 395-405, April.
    14. Young‐Geun Choi & Lawrence P. Hanrahan & Derek Norton & Ying‐Qi Zhao, 2022. "Simultaneous spatial smoothing and outlier detection using penalized regression, with application to childhood obesity surveillance from electronic health records," Biometrics, The International Biometric Society, vol. 78(1), pages 324-336, March.
    15. Zhengyi Zhou & David S. Matteson & Dawn B. Woodard & Shane G. Henderson & Athanasios C. Micheas, 2015. "A Spatio-Temporal Point Process Model for Ambulance Demand," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 6-15, March.
    16. Eric C. Tassone & Marie Lynn Miranda & Alan E. Gelfand, 2010. "Disaggregated spatial modelling for areal unit categorical data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(1), pages 175-190, January.
    17. Sanjay Chaudhuri & Debashis Mondal & Teng Yin, 2017. "Hamiltonian Monte Carlo sampling in Bayesian empirical likelihood computation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(1), pages 293-320, January.
    18. Dolores Catelan & Annibale Biggeri & Corrado Lagazio, 2009. "On the clustering term in ecological analysis: how do different prior specifications affect results?," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 18(1), pages 49-61, March.
    19. Massimo Bilancia & Giacomo Demarinis, 2014. "Bayesian scanning of spatial disease rates with integrated nested Laplace approximation (INLA)," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(1), pages 71-94, March.
    20. Douglas R. M. Azevedo & Marcos O. Prates & Dipankar Bandyopadhyay, 2021. "MSPOCK: Alleviating Spatial Confounding in Multivariate Disease Mapping Models," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(3), pages 464-491, September.
    21. Dalalyan, Arnak S. & Karagulyan, Avetik, 2019. "User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient," Stochastic Processes and their Applications, Elsevier, vol. 129(12), pages 5278-5311.
    22. Edgar Santos‐Fernandez & Erin E. Peterson & Julie Vercelloni & Em Rushworth & Kerrie Mengersen, 2021. "Correcting misclassification errors in crowdsourced ecological data: A Bayesian perspective," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(1), pages 147-173, January.

    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:15:y:2018:i:1:p:114-:d:126411. 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.