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Improving resolution of a spatial air pollution inventory with a statistical inference approach

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  • Joanna Horabik
  • Zbigniew Nahorski

Abstract

This paper presents a novel approach to allocation of spatially correlated data, such as emission inventories, to finer spatial scales, conditional on covariate information observable in a fine grid. Spatial dependence is modelled with the conditional autoregressive structure introduced into a linear model as a random effect. The maximum likelihood approach to inference is employed, and the optimal predictors are developed to assess missing values in a fine grid. An example of ammonia emission inventory is used to illustrate the potential usefulness of the proposed technique. The results indicate that inclusion of a spatial dependence structure can compensate for less adequate covariate information. For the considered ammonia inventory, the fourfold allocation benefited greatly from incorporation of the spatial component, while for the ninefold allocation this advantage was limited, but still evident. In addition, the proposed method allows correction of the prediction bias encountered for the upper range emissions in the linear regression models. Copyright The Author(s) 2014

Suggested Citation

  • Joanna Horabik & Zbigniew Nahorski, 2014. "Improving resolution of a spatial air pollution inventory with a statistical inference approach," Climatic Change, Springer, vol. 124(3), pages 575-589, June.
    • Handle: RePEc:spr:climat:v:124:y:2014:i:3:p:575-589
    DOI: 10.1007/s10584-013-1029-4
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    References listed on IDEAS

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    1. Gotway C.A. & Young L.J., 2002. "Combining Incompatible Spatial Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 632-648, June.
    2. Wolfang Polasek & Carlos Llano & Richard Sellner, 2010. "Bayesian Methods for Completing Data in Spatial Models," Review of Economic Analysis, Digital Initiatives at the University of Waterloo Library, vol. 2(2), pages 194-214, June.
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    1. Khrystyna Boychuk & Rostyslav Bun, 2014. "Regional spatial inventories (cadastres) of GHG emissions in the Energy sector: Accounting for uncertainty," Climatic Change, Springer, vol. 124(3), pages 561-574, June.
    2. Jörg Verstraete, 2019. "Solving the general map overlay problem using a fuzzy inference system designed for spatial disaggregation," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 24(6), pages 1101-1122, August.
    3. Hongjiang Liu & Fengying Yan & Hua Tian, 2020. "A Vector Map of Carbon Emission Based on Point-Line-Area Carbon Emission Classified Allocation Method," Sustainability, MDPI, vol. 12(23), pages 1-21, December.
    4. Nadiia Charkovska & Joanna Horabik-Pyzel & Rostyslav Bun & Olha Danylo & Zbigniew Nahorski & Matthias Jonas & Xu Xiangyang, 2019. "High-resolution spatial distribution and associated uncertainties of greenhouse gas emissions from the agricultural sector," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 24(6), pages 881-905, August.
    5. Rostyslav Bun & Zbigniew Nahorski & Joanna Horabik-Pyzel & Olha Danylo & Linda See & Nadiia Charkovska & Petro Topylko & Mariia Halushchak & Myroslava Lesiv & Mariia Valakh & Vitaliy Kinakh, 2019. "Development of a high-resolution spatial inventory of greenhouse gas emissions for Poland from stationary and mobile sources," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 24(6), pages 853-880, August.

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