Functional Location-Scale Model to Forecast Bivariate Pollution Episodes

Manuel Oviedo- de La Fuente, Celestino Ordóñez and Javier Roca-Pardiñas
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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

Mathematics, 2020, vol. 8, issue 6, 1-12

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.

Keywords: pollution episodes; functional data; bivariate analysis; uncertainty region; generalized additive models (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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