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Functional Location-Scale Model to Forecast Bivariate Pollution Episodes

Author

Listed:
  • Manuel Oviedo-de La Fuente

    (Department of Statistics, Mathematical Analysis and Optimization, Universidad de Santiago de Compostela, 15782 Santiago de Compostela, Spain)

  • Celestino Ordóñez

    (Department of Mining Exploitation and Propsecting, Universidad de Oviedo, Escuela Politécnica de Mieres, 33600 Mieres, Spain)

  • Javier Roca-Pardiñas

    (Department of Statistics and Operation Research, Universidad de Vigo, 36310 Vigo, Spain)

Abstract

Predicting anomalous emission of pollutants into the atmosphere well in advance is crucial for industries emitting such elements, since it allows them to take corrective measures aimed to avoid such emissions and their consequences. In this work, we propose a functional location-scale model to predict in advance pollution episodes where two pollutants are involved. Functional generalized additive models (FGAMs) are used to estimate the means and variances of the model, as well as the correlation between both pollutants. The method not only forecasts the concentrations of both pollutants, it also estimates an uncertainty region where the concentrations of both pollutants should be located, given a specific level of uncertainty. The performance of the model was evaluated using real data of SO 2 and NO x emissions from a coal-fired power station, obtaining good results.

Suggested Citation

  • Manuel Oviedo-de La Fuente & Celestino Ordóñez & Javier Roca-Pardiñas, 2020. "Functional Location-Scale Model to Forecast Bivariate Pollution Episodes," Mathematics, MDPI, vol. 8(6), pages 1-12, June.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:6:p:941-:d:368775
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    References listed on IDEAS

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    1. Deqing Wang & Zhangqi Zhong & Kaixu Bai & Lingyun He, 2019. "Spatial and Temporal Variabilities of PM 2.5 Concentrations in China Using Functional Data Analysis," Sustainability, MDPI, vol. 11(6), pages 1-20, March.
    2. Febrero-Bande, Manuel & de la Fuente, Manuel Oviedo, 2012. "Statistical Computing in Functional Data Analysis: The R Package fda.usc," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 51(i04).
    3. Philippe C. Besse & Herve Cardot & David B. Stephenson, 2000. "Autoregressive Forecasting of Some Functional Climatic Variations," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 27(4), pages 673-687, December.
    4. Rui Zhao & Xinxin Gu & Bing Xue & Jianqiang Zhang & Wanxia Ren, 2018. "Short period PM2.5 prediction based on multivariate linear regression model," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-15, July.
    5. Wenqing He & Jerald F. Lawless, 2005. "Bivariate location–scale models for regression analysis, with applications to lifetime data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(1), pages 63-78, February.
    6. Donald Hedeker & Robin J. Mermelstein & Hakan Demirtas, 2008. "An Application of a Mixed-Effects Location Scale Model for Analysis of Ecological Momentary Assessment (EMA) Data," Biometrics, The International Biometric Society, vol. 64(2), pages 627-634, June.
    7. R. A. Rigby & D. M. Stasinopoulos, 2005. "Generalized additive models for location, scale and shape," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(3), pages 507-554, June.
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