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

Marta Doval-Miñarro (), María C. Bueso and Pedro Antonio Guillén-Alcaraz
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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

Sustainability, 2025, vol. 17, issue 8, 1-21

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.

Keywords: air quality; low-emission zone; machine learning; business-as-usual; nitrogen dioxide; particulate matter (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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