IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i16p2878-d886040.html
   My bibliography  Save this article

Bayesian Aerosol Retrieval-Based PM 2.5 Estimation through Hierarchical Gaussian Process Models

Author

Listed:
  • Junbo Zhang

    (School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China)

  • Daoji Li

    (Department of Information Systems and Decision Sciences, California State University, Fullerton, CA 92831, USA)

  • Yingzhi Xia

    (School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China)

  • Qifeng Liao

    (School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China)

Abstract

Satellite-based aerosol optical depth (AOD) data are widely used to estimate land surface PM 2.5 concentrations in areas not covered by ground PM 2.5 monitoring stations. However, AOD data obtained from satellites are typically at coarse spatial resolutions, limiting their applications on small or medium scales. In this paper, we propose a new two-step approach to estimate 1-km-resolution PM 2.5 concentrations in Shanghai using high spatial resolution AOD retrievals from MODIS. In the first step, AOD data are refined to a 1 × 1 km 2 resolution via a Bayesian AOD retrieval method. In the second step, a hierarchical Gaussian process model is used to estimate PM 2.5 concentrations. We evaluate our approach by model fitting and out-of-sample cross-validation. Our results show that the proposed approach enjoys accurate predictive performance in estimating PM 2.5 concentrations.

Suggested Citation

  • Junbo Zhang & Daoji Li & Yingzhi Xia & Qifeng Liao, 2022. "Bayesian Aerosol Retrieval-Based PM 2.5 Estimation through Hierarchical Gaussian Process Models," Mathematics, MDPI, vol. 10(16), pages 1-13, August.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:16:p:2878-:d:886040
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/16/2878/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/16/2878/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Finley, Andrew O. & Banerjee, Sudipto & Carlin, Bradley P., 2007. "spBayes: An R Package for Univariate and Multivariate Hierarchical Point-referenced Spatial Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 19(i04).
    2. Yueqing Wang & Xin Jiang & Bin Yu & Ming Jiang, 2013. "A Hierarchical Bayesian Approach for Aerosol Retrieval Using MISR Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(502), pages 483-493, June.
    3. Finley, Andrew O. & Banerjee, Sudipto & Gelfand, Alan E., 2015. "spBayes for Large Univariate and Multivariate Point-Referenced Spatio-Temporal Data Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i13).
    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. Gianfranco Piras & Mauricio Sarrias, 2023. "Heterogeneous spatial models in R: spatial regimes models," Journal of Spatial Econometrics, Springer, vol. 4(1), pages 1-32, December.
    2. Yanlan Liu & William J. Riley & Trevor F. Keenan & Zelalem A. Mekonnen & Jennifer A. Holm & Qing Zhu & Margaret S. Torn, 2022. "Dispersal and fire limit Arctic shrub expansion," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    3. Jenny Brynjarsdottir & Jonathan Hobbs & Amy Braverman & Lukas Mandrake, 2018. "Optimal Estimation Versus MCMC for $$\mathrm{{CO}}_{2}$$ CO 2 Retrievals," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(2), pages 297-316, June.
    4. Isabelle Grenier & Bruno Sansó & Jessica L. Matthews, 2024. "Multivariate nearest‐neighbors Gaussian processes with random covariance matrices," Environmetrics, John Wiley & Sons, Ltd., vol. 35(3), May.
    5. Tilman M. Davies & Sudipto Banerjee & Adam P. Martin & Rose E. Turnbull, 2022. "A nearest‐neighbour Gaussian process spatial factor model for censored, multi‐depth geochemical data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(4), pages 1014-1043, August.
    6. Sameh Abdulah & Yuxiao Li & Jian Cao & Hatem Ltaief & David E. Keyes & Marc G. Genton & Ying Sun, 2023. "Large‐scale environmental data science with ExaGeoStatR," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.
    7. Xavier Barber & David Conesa & Antonio López-Quílez & Javier Morales, 2019. "Multivariate Bioclimatic Indices Modelling: A Coregionalised Approach," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(2), pages 225-244, June.
    8. Fangpo Wang & Anirban Bhattacharya & Alan E. Gelfand, 2018. "Process modeling for slope and aspect with application to elevation data maps," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(4), pages 749-772, December.
    9. Maria Terres & Alan Gelfand, 2015. "Using spatial gradient analysis to clarify species distributions with application to South African protea," Journal of Geographical Systems, Springer, vol. 17(3), pages 227-247, July.
    10. Jiafang Song & Joshua L. Warren, 2022. "A Directionally Varying Change Points Model for Quantifying the Impact of a Point Source," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(1), pages 46-62, March.
    11. Jingjing Yang & Dennis D. Cox & Jong Soo Lee & Peng Ren & Taeryon Choi, 2017. "Efficient Bayesian hierarchical functional data analysis with basis function approximations using Gaussian–Wishart processes," Biometrics, The International Biometric Society, vol. 73(4), pages 1082-1091, December.
    12. Lu Zhang & Sudipto Banerjee, 2022. "Spatial factor modeling: A Bayesian matrix‐normal approach for misaligned data," Biometrics, The International Biometric Society, vol. 78(2), pages 560-573, June.
    13. K. Shuvo Bakar, 2020. "Interpolation of daily rainfall data using censored Bayesian spatially varying model," Computational Statistics, Springer, vol. 35(1), pages 135-152, March.
    14. Pebesma, Edzer & Bivand, Roger & Ribeiro, Paulo Justiniano, 2015. "Software for Spatial Statistics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i01).
    15. Sebastain Awondo & Genti Kostandini, 2022. "Leveraging optimal portfolio of Drought-Tolerant Maize Varieties for weather index insurance and food security," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 47(1), pages 45-65, March.
    16. Yi Liu & Gavin Shaddick & James V. Zidek, 2017. "Incorporating High-Dimensional Exposure Modelling into Studies of Air Pollution and Health," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(2), pages 559-581, December.
    17. Xiaotian Zheng & Athanasios Kottas & Bruno Sansó, 2023. "Bayesian geostatistical modeling for discrete‐valued processes," Environmetrics, John Wiley & Sons, Ltd., vol. 34(7), November.
    18. Katerina Spanoudaki & Panayiotis Dimitriadis & Emmanouil A. Varouchakis & Gerald A. Corzo Perez, 2022. "Estimation of Hydropower Potential Using Bayesian and Stochastic Approaches for Streamflow Simulation and Accounting for the Intermediate Storage Retention," Energies, MDPI, vol. 15(4), pages 1-20, February.
    19. Waley W. J. Liang & Herbert K. H. Lee, 2019. "Bayesian nonstationary Gaussian process models via treed process convolutions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(3), pages 797-818, September.
    20. Maitreyee Bose & James S. Hodges & Sudipto Banerjee, 2018. "Toward a diagnostic toolkit for linear models with Gaussian‐process distributed random effects," Biometrics, The International Biometric Society, vol. 74(3), pages 863-873, September.

    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:jmathe:v:10:y:2022:i:16:p:2878-:d:886040. 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.