IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i12p5274-d1673993.html
   My bibliography  Save this article

Comparison of Selected Ensemble Supervised Learning Algorithms Used for Meteorological Normalisation of Particulate Matter (PM 10 )

Author

Listed:
  • Karolina Gora

    (Faculty of Geo-Data Science, Geodesy and Environmental Engineering, AGH University of Krakow, 30-059 Kraków, Poland)

  • Mateusz Rzeszutek

    (Faculty of Geo-Data Science, Geodesy and Environmental Engineering, AGH University of Krakow, 30-059 Kraków, Poland)

Abstract

Air pollution, particularly PM 10 particulate matter, poses significant health risks related to respiratory and cardiovascular diseases as well as cancer. Accurate identification of PM 10 reduction factors is therefore essential for developing effective sustainable development strategies. According to the current state of knowledge, machine learning methods are most frequently employed for this purpose due to their superior performance compared to classical statistical approaches. This study evaluated the performance of three machine learning algorithms—Decision Tree (CART), Random Forest, and Cubist Rule—in predicting PM 10 concentrations and estimating long-term trends following meteorological normalisation. The research focused on Tarnów, Poland (2010–2022), with comprehensive consideration of meteorological variability. The results demonstrated superior accuracy for the Random Forest and Cubist models (R 2 ~0.88–0.89, RMSE ~14 μg/m 3 ) compared to CART (RMSE 19.96 μg/m 3 ). Air temperature and boundary layer height emerged as the most significant predictive variables across all algorithms. The Cubist algorithm proved particularly effective in detecting the impact of policy interventions, making it valuable for air quality trend analysis. While the study confirmed a statistically significant annual decrease in PM 10 concentrations (0.83–1.03 μg/m 3 ), pollution levels still exceeded both the updated EU air quality standards from 2024 (Directive (EU) 2024/2881), which will come into force in 2030, and the more stringent WHO guidelines from 2021.

Suggested Citation

  • Karolina Gora & Mateusz Rzeszutek, 2025. "Comparison of Selected Ensemble Supervised Learning Algorithms Used for Meteorological Normalisation of Particulate Matter (PM 10 )," Sustainability, MDPI, vol. 17(12), pages 1-16, June.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:12:p:5274-:d:1673993
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/12/5274/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/12/5274/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Matthew A. Cole & Robert J R Elliott & Bowen Liu, 2020. "The Impact of the Wuhan Covid-19 Lockdown on Air Pollution and Health: A Machine Learning and Augmented Synthetic Control Approach," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 76(4), pages 553-580, August.
    2. Wright, Marvin N. & Ziegler, Andreas, 2017. "ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 77(i01).
    3. Sandra Ceballos-Santos & Jaime González-Pardo & David C. Carslaw & Ana Santurtún & Miguel Santibáñez & Ignacio Fernández-Olmo, 2021. "Meteorological Normalisation Using Boosted Regression Trees to Estimate the Impact of COVID-19 Restrictions on Air Quality Levels," IJERPH, MDPI, vol. 18(24), pages 1-18, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Backer, David & Billing, Trey, 2024. "Forecasting the prevalence of child acute malnutrition using environmental and conflict conditions as leading indicators," World Development, Elsevier, vol. 176(C).
    2. Luis A Barboza & Shu-Wei Chou-Chen & Paola Vásquez & Yury E García & Juan G Calvo & Hugo G Hidalgo & Fabio Sanchez, 2023. "Assessing dengue fever risk in Costa Rica by using climate variables and machine learning techniques," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 17(1), pages 1-13, January.
    3. Mariana Oliveira & Luís Torgo & Vítor Santos Costa, 2021. "Evaluation Procedures for Forecasting with Spatiotemporal Data," Mathematics, MDPI, vol. 9(6), pages 1-27, March.
    4. repec:osf:osfxxx:s8ayp_v1 is not listed on IDEAS
    5. repec:wbk:wbrwps:10254 is not listed on IDEAS
    6. Augusto Cerqua & Roberta Di Stefano, 2022. "When did coronavirus arrive in Europe?," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(1), pages 181-195, March.
    7. Pekka Malo & Juha Eskelinen & Xun Zhou & Timo Kuosmanen, 2024. "Computing Synthetic Controls Using Bilevel Optimization," Computational Economics, Springer;Society for Computational Economics, vol. 64(2), pages 1113-1136, August.
    8. Bokelmann, Björn & Lessmann, Stefan, 2024. "Improving uplift model evaluation on randomized controlled trial data," European Journal of Operational Research, Elsevier, vol. 313(2), pages 691-707.
    9. Joel Podgorski & Oliver Kracht & Luis Araguas-Araguas & Stefan Terzer-Wassmuth & Jodie Miller & Ralf Straub & Rolf Kipfer & Michael Berg, 2024. "Groundwater vulnerability to pollution in Africa’s Sahel region," Nature Sustainability, Nature, vol. 7(5), pages 558-567, May.
    10. Heinisch, Katja & Scaramella, Fabio & Schult, Christoph, 2025. "Assumption errors and forecast accuracy: A partial linear instrumental variable and double machine learning approach," IWH Discussion Papers 6/2025, Halle Institute for Economic Research (IWH).
    11. López-Cazar, Ibeth & Papyrakis, Elissaios & Pellegrini, Lorenzo, 2021. "The Extractive Industries Transparency Initiative (EITI) and corruption in Latin America: Evidence from Colombia, Guatemala, Honduras, Peru, and Trinidad and Tobago," Resources Policy, Elsevier, vol. 70(C).
    12. Souknilanh Keola & Kazunobu Hayakawa, 2021. "Do Lockdown Policies Reduce Economic and Social Activities? Evidence from NO2 Emissions," The Developing Economies, Institute of Developing Economies, vol. 59(2), pages 178-205, June.
    13. Chakravorty, Bhaskar & Arulampalam, Wiji & Bhatiya, Apurav Yash & Imbert, Clément & Rathelot, Roland, 2024. "Can information about jobs improve the effectiveness of vocational training? Experimental evidence from India," Journal of Development Economics, Elsevier, vol. 169(C).
    14. Arjan S. Gosal & Janine A. McMahon & Katharine M. Bowgen & Catherine H. Hoppe & Guy Ziv, 2021. "Identifying and Mapping Groups of Protected Area Visitors by Environmental Awareness," Land, MDPI, vol. 10(6), pages 1-14, May.
    15. David Dorn & Florian Schoner & Moritz Seebacher & Lisa Simon & Ludger Woessmann, 2024. "Multidimensional Skills on LinkedIn Profiles: Measuring Human Capital and the Gender Skill Gap," Papers 2409.18638, arXiv.org, revised May 2025.
    16. Albert Stuart Reece & Gary Kenneth Hulse, 2022. "European Epidemiological Patterns of Cannabis- and Substance-Related Congenital Neurological Anomalies: Geospatiotemporal and Causal Inferential Study," IJERPH, MDPI, vol. 20(1), pages 1-35, December.
    17. Foutzopoulos, Giorgos & Pandis, Nikolaos & Tsagris, Michail, 2024. "Predicting full retirement attainment of NBA players," MPRA Paper 121540, University Library of Munich, Germany.
    18. Michael Parzinger & Lucia Hanfstaengl & Ferdinand Sigg & Uli Spindler & Ulrich Wellisch & Markus Wirnsberger, 2020. "Residual Analysis of Predictive Modelling Data for Automated Fault Detection in Building’s Heating, Ventilation and Air Conditioning Systems," Sustainability, MDPI, vol. 12(17), pages 1-18, August.
    19. Kuosmanen, Timo & Zhou, Xun & Eskelinen, Juha & Malo, Pekka, 2021. "Design Flaw of the Synthetic Control Method," MPRA Paper 106328, University Library of Munich, Germany.
    20. Van Belle, Jente & Guns, Tias & Verbeke, Wouter, 2021. "Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains," European Journal of Operational Research, Elsevier, vol. 288(2), pages 466-479.
    21. Tania L. Maxwell & Mark D. Spalding & Daniel A. Friess & Nicholas J. Murray & Kerrylee Rogers & Andre S. Rovai & Lindsey S. Smart & Lukas Weilguny & Maria Fernanda Adame & Janine B. Adams & William E., 2024. "Soil carbon in the world’s tidal marshes," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    22. Albert Stuart Reece & Gary Kenneth Hulse, 2022. "European Epidemiological Patterns of Cannabis- and Substance-Related Body Wall Congenital Anomalies: Geospatiotemporal and Causal Inferential Study," IJERPH, MDPI, vol. 19(15), pages 1-38, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:17:y:2025:i:12:p:5274-:d:1673993. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.