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

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 NO2, PM10 and PM2.5 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 NO2 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 PM10 and PM2.5 concentrations