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Probabilistic Forecasting of Nitrogen Dioxide Concentrations at an Urban Road Intersection

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  • Joanna A. Kamińska

    (Department of Mathematics, Wroclaw University of Environmental and Life Sciences, Grunwaldzka str., 53, 50-357 Wrocław, Poland)

Abstract

The concentration of nitrogen dioxide in the air along a major route in a large city is affected by very many factors, which are also interdependent. As an alternative to complicated deterministic models based on these complex processes, in this study a probabilistic model for predicting NO 2 concentrations is proposed, using a simple accounting cluster-based method for determining probability distributions for tabulated values of ambient factors. Using the example of hourly values of NO 2 concentration and data on wind speed and traffic flow for the main intersection in Wrocław (Poland), a model is constructed to predict the frequency of occurrence of concentrations in the form of a probability distribution, for given values of the input variables. The model was successfully verified on data for the first six months of 2018. A mean continuous rank probability score (CRPS) of 9.15 μg/m 3 was obtained. In spite of the greater impact of traffic volume on urban NO 2 concentrations, as measured by Pearson’s correlation coefficient, for instance, the model indicates that wind speed is also a very important factor—wind being the principal mechanism causing the evacuation of pollutants. This underlines the importance of sustainable city planning with regard to ensuring suitable conditions for the passage of air.

Suggested Citation

  • Joanna A. Kamińska, 2018. "Probabilistic Forecasting of Nitrogen Dioxide Concentrations at an Urban Road Intersection," Sustainability, MDPI, vol. 10(11), pages 1-16, November.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:11:p:4213-:d:183089
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    References listed on IDEAS

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    1. Kazak, Jan & van Hoof, Joost & Szewranski, Szymon, 2017. "Challenges in the wind turbines location process in Central Europe – The use of spatial decision support systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 425-433.
    2. Catalano, Mario & Galatioto, Fabio & Bell, Margaret & Namdeo, Anil & Bergantino, Angela S., 2016. "Improving the prediction of air pollution peak episodes generated by urban transport networks," Environmental Science & Policy, Elsevier, vol. 60(C), pages 69-83.
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