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Estimation of Prediction Error in Regression Air Quality Models

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  • Szymon Hoffman

    (Faculty of Infrastructure and Environment, Czestochowa University of Technology, 69 Dabrowskiego St., 42-200 Czestochowa, Poland)

Abstract

Combustion of energy fuels or organic waste is associated with the emission of harmful gases and aerosols into the atmosphere, which strongly affects air quality. Air quality monitoring devices are unreliable and measurement gaps appear quite often. Missing data modeling techniques can be used to complete the monitoring data. Concentrations of monitored pollutants can be approximated with regression modeling tools, such as artificial neural networks. In this study, a long-term set of data from the air monitoring station in Zabrze (Silesia, South Poland) was analyzed. Concentration prediction was tested for the main air pollutants, i.e., O 3 , NO, NO 2 , SO 2 , PM 10 , CO. Multilayer perceptrons were used to model the concentrations. The predicted concentrations were compared to the observed ones to evaluate the approximation accuracy. Prediction errors were calculated separately for the whole concentration range as well as for the specified concentration subranges. Some different measures of error were estimated. It was stated that the use of a single measure of the approximation accuracy may lead to incorrect interpretation. The application of one neural network to the entire concentration range results in different prediction accuracy in various concentration subranges. Replacing one neural network with several networks adjusted to specific concentration subranges should improve the modeling accuracy.

Suggested Citation

  • Szymon Hoffman, 2021. "Estimation of Prediction Error in Regression Air Quality Models," Energies, MDPI, vol. 14(21), pages 1-13, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:7387-:d:673124
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    References listed on IDEAS

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    1. Hanna, Rema & Oliva, Paulina, 2015. "The effect of pollution on labor supply: Evidence from a natural experiment in Mexico City," Journal of Public Economics, Elsevier, vol. 122(C), pages 68-79.
    2. Joshua Graff Zivin & Matthew Neidell, 2012. "The Impact of Pollution on Worker Productivity," American Economic Review, American Economic Association, vol. 102(7), pages 3652-3673, December.
    3. Tom Chang & Joshua Graff Zivin & Tal Gross & Matthew Neidell, 2016. "Particulate Pollution and the Productivity of Pear Packers," American Economic Journal: Economic Policy, American Economic Association, vol. 8(3), pages 141-169, August.
    4. Aragón, Fernando M. & Miranda, Juan Jose & Oliva, Paulina, 2017. "Particulate matter and labor supply: The role of caregiving and non-linearities," Journal of Environmental Economics and Management, Elsevier, vol. 86(C), pages 295-309.
    5. Mouton, Ans M. & De Baets, Bernard & Goethals, Peter L.M., 2010. "Ecological relevance of performance criteria for species distribution models," Ecological Modelling, Elsevier, vol. 221(16), pages 1995-2002.
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    Cited by:

    1. Szymon Hoffman & Mariusz Filak & Rafał Jasiński, 2022. "Air Quality Modeling with the Use of Regression Neural Networks," IJERPH, MDPI, vol. 19(24), pages 1-33, December.

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