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Abstract:
To confront inflation and reduce private energy consumption, incentives to use public transport are becoming more common; this is the case of the 9€-ticket implemented in Germany during summer 2022 (June-August), which allowed passengers to commute and travel regionally using a reduced-price monthly ticket. However, there is still a lack of knowledge regarding the environmental impacts of the 9€-ticket, e.g., air quality (AQ) improvements derived from changes in private transport trends. In this study, we aim to assess the influence of the 9€-ticket on air quality in Leipzig. Long-term (2018-2022) times series of atmospheric pollutants (PM, NOx, NO2), aerosol concentrations (Black carbon), and meteorological variables have been collected. A random forest machine learning algorithm (RFA) is used for meteorological normalization of air pollution and aerosol data to "discount" the effect of meteorology on AQ. This process allows the robust comparison of the AQ time series from 2022 and normal years (2018-2019). Preliminary use of the RFA successfully normalized NO2 mass concentrations for 2018-2019. Slight reductions of 5% and 3% in the average NO2 concentrations were observed in Summer 2018 and 2019 (compared to spring), respectively. The following steps include the meteorological normalization of 2022 AQ data (in preparation) and further pollutants (PM, NOx, BC). The normalized AQ time series from 2022 and normal years will be compared; the analysis will consider the reductions already observed in 2018-2019 and the school-holidays period effect. Additionally, we will assess traffic volume from permanent counting stations from 2018 to 2022.
