Long-Term analysis of the impact of traffic volume and other influencing factors on urban NO2 levels for interpreting the COVID-19 lockdown effects in Munich, Germany

The influence of traffic regulations on urban air quality has been discussed for years, especially during the COVID-19 lockdowns, when significant shifts in urban air quality were observed, particularly in the concentration of nitrogen dioxide (NO₂), a key pollutant linked to vehicular emissions and industrial activities. This study provides a long-term analyzes the variation of NO₂ levels in an urban environment, and an investigation of the interplay of various influencing factors during the lockdown periods in Munich, Germany, including traffic volume, wind speed, radiation, boundary layer height, humidity, and precipitation.

Using a combination of ground-based stationary and mobile NO₂ measurements in Munich coupled with traffic flow records, we apply statistical and machine learning techniques to identify the primary drivers of NO₂ concentration variability. The analysis reveals the extent to which reductions in traffic during the lockdown contributed to NO₂ declines, while highlighting the modulating effects of meteorological conditions such as wind dispersion and atmospheric stability.

Our findings provide insights into the complex dynamics of urban air pollution and its sensitivity to human activity and weather patterns. By comparing pre-lockdown, lockdown, and post-lockdown scenarios, the study underscores the potential for targeted interventions to achieve sustained improvements in air quality and offers valuable guidance in designing evidence-based strategies to mitigate urban air pollution and its health impacts.