Frontiers in Sustainable Cities (May 2022)
True Reduction in the Air Pollution Levels in the Community of Madrid During the COVID-19 Lockdown
Abstract
The coronavirus disease (COVID) lockdown was implemented in 2020, which included harsh restrictions on the amount of traffic. As a consequence, a low-emission scenario that could only be simulated before, actually occurred. This constituted a unique and valuable opportunity to study the effect of air quality pollutant concentrations. Although a direct comparison between the observed measured values given by reference air quality stations (AQSs) and values from before the COVID lockdown provides an idea of the pollution reduction, it cannot be separated from the meteorology, and hence, those studies could be misleading. This study used the approach of modeling a normal business day using both air quality and meteorological data from 2017 to 2019 to train machine-learning models to be able to predict what concentration of the three most concerning pollutants (NO2, O3, and PM10) would be given by the meteorological conditions and the time of the year. The XGBoost and LightGBM gradient boosting decision tree-based models were applied to the time series recorded in Madrid and used to predict the expected concentrations in 2020 if no restrictions had been made. The predictions could then be compared to the real observed AQS data to determine the meteorological normalized reductions. The results showed around a 60% reduction in the NO2 at the three types of AQSs (traffic, suburban, and background) during the most restrictive months of the pandemic. The O3 concentration showed a different behavior depending on the type of AQS, pointing to changes in the regime of other pollutants, such as VOCs. The PM10 was the most difficult case to analyze because of its dependence on external transport phenomena, which were difficult to consider in the models. A set of CTM simulations should be done in the future to assess the O3-VOCs-NOx chemistry.
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