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Random Forests Assessment of the Role of Atmospheric Circulation in PM 10 in an Urban Area with Complex Topography

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  • Piotr Sekula

    (Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, 30-059 Kraków, Poland
    Institute of Meteorology and Water Management, National Research Institute, IMGW-PIB, 01-673 Warszawa, Poland)

  • Zbigniew Ustrnul

    (Institute of Meteorology and Water Management, National Research Institute, IMGW-PIB, 01-673 Warszawa, Poland
    Institute of Geography and Spatial Management, Jagiellonian University, 30-387 Kraków, Poland)

  • Anita Bokwa

    (Institute of Geography and Spatial Management, Jagiellonian University, 30-387 Kraków, Poland)

  • Bogdan Bochenek

    (Institute of Meteorology and Water Management, National Research Institute, IMGW-PIB, 01-673 Warszawa, Poland)

  • Miroslaw Zimnoch

    (Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, 30-059 Kraków, Poland)

Abstract

This study presents the assessment of the quantitative influence of atmospheric circulation on the pollutant concentration in the area of Kraków, Southern Poland, for the period 2000–2020. The research has been realized with the application of different statistical parameters, synoptic meteorology tools, the Random Forests machine learning method, and multilinear regression analyses. Another aim of the research was to evaluate the types of atmospheric circulation classification methods used in studies on air pollution dispersion and to assess the possibility of their application in air quality management, including short-term PM 10 daily forecasts. During the period analyzed, a significant decreasing trend of pollutants’ concentrations and varying atmospheric circulation conditions was observed. To understand the relation between PM 10 concentration and meteorological conditions and their significance, the Random Forests algorithm was applied. Observations from meteorological stations, air quality measurements and ERA-5 reanalysis were used. The meteorological database was used as an input to models that were trained to predict daily PM 10 concentration and its day-to-day changes. This study made it possible to distinguish the dominant circulation types with the highest probability of occurrence of poor air quality or a significant improvement in air quality conditions. Apart from the parameters whose significant influence on air quality is well established (air temperature and wind speed at the ground and air temperature gradient), the key factor was also the gradient of relative air humidity and wind shear in the lowest troposphere. Partial dependence calculated with the use of the Random Forests model made it possible to better analyze the impact of individual meteorological parameters on the PM 10 daily concentration. The analysis has shown that, for areas with a diversified topography, it is crucial to use the variability of the atmospheric circulation during the day to better forecast air quality.

Suggested Citation

  • Piotr Sekula & Zbigniew Ustrnul & Anita Bokwa & Bogdan Bochenek & Miroslaw Zimnoch, 2022. "Random Forests Assessment of the Role of Atmospheric Circulation in PM 10 in an Urban Area with Complex Topography," Sustainability, MDPI, vol. 14(6), pages 1-43, March.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:6:p:3388-:d:770648
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    1. Cowell, F.A., 2000. "Measurement of inequality," Handbook of Income Distribution, in: A.B. Atkinson & F. Bourguignon (ed.), Handbook of Income Distribution, edition 1, volume 1, chapter 2, pages 87-166, Elsevier.
    2. Abdollah Hadi-Vencheh & Yong Tan & Peter Wanke & Seyed Mohammadreza Loghmanian, 2021. "Air Pollution Assessment in China: A Novel Group Multiple Criteria Decision Making Model under Uncertain Information," Sustainability, MDPI, vol. 13(4), pages 1-13, February.
    3. Daniel E. Horton & Christopher B. Skinner & Deepti Singh & Noah S. Diffenbaugh, 2014. "Occurrence and persistence of future atmospheric stagnation events," Nature Climate Change, Nature, vol. 4(8), pages 698-703, August.
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