IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i8p3582-d1635753.html
   My bibliography  Save this article

Assessing the Impact of a Low-Emission Zone on Air Quality Using Machine Learning Algorithms in a Business-As-Usual Scenario

Author

Listed:
  • Marta Doval-Miñarro

    (Department of Chemical and Environmental Engineering, Universidad Politécnica de Cartagena, Doctor Fleming s/n, 30202 Cartagena, Spain)

  • María C. Bueso

    (Department of Applied Mathematics and Statistics, Universidad Politécnica de Cartagena, Doctor Fleming s/n, 30202 Cartagena, Spain)

  • Pedro Antonio Guillén-Alcaraz

    (Department of Chemical and Environmental Engineering, Universidad Politécnica de Cartagena, Doctor Fleming s/n, 30202 Cartagena, Spain)

Abstract

The proliferation of low-emission zones (LEZs) across Europe is anticipated to accelerate in the coming years as a measure to enhance air quality in urban areas. Nevertheless, there is a lack of a standardized methodology to evaluate their effectiveness, and some of the proposed strategies may not adequately address air quality issues or overlook meteorological considerations. In this study, we employ three machine learning (ML) algorithms to forecast NO 2 , PM 10 and PM 2.5 concentrations in the air in Madrid in 2022 (post-LEZ) based on data from the period 2015–2018 (pre-LEZ) under a business-as-usual scenario, accounting for seasonal and meteorological factors. According to the models, the reductions in NO 2 concentrations in 2022 varied from 29 to 35% in contrast to a scenario without the LEZ, which is coherent with the observed decrease in 2022 in traffic volume inside the area limited by the LEZ. However, no clear improvement was observed for PM 10 and PM 2.5 concentrations.

Suggested Citation

  • Marta Doval-Miñarro & María C. Bueso & Pedro Antonio Guillén-Alcaraz, 2025. "Assessing the Impact of a Low-Emission Zone on Air Quality Using Machine Learning Algorithms in a Business-As-Usual Scenario," Sustainability, MDPI, vol. 17(8), pages 1-21, April.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:8:p:3582-:d:1635753
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/8/3582/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/8/3582/
    Download Restriction: no
    ---><---

    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:8:p:3582-:d:1635753. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.