Turbulent enhancement ratios used for characterizing local emission sources in a complex urban environment

Abstract

In this study, we introduce the turbulent enhancement ratio (TER) as an experimental approach for characterizing local emission sources in complex urban environments, with a focus on the city of Innsbruck, Austria. The idea behind the approach is to take advantage of highly time-resolved trace gas observations that allow for identifying turbulent air motions, from which a turbulent enhancement ratio can be constructed. Spectral analysis helps in determining the most relevant temporal scales that need to be resolved for the TER at a location. We use a comprehensive measurement setup at the Innsbruck Atmospheric Observatory, utilizing advanced instruments to test the approach. Our dataset, spanning mid-2018 to early 2022, includes periods affected by the COVID-19 pandemic, allowing us to assess the impact of reduced traffic and changes in domestic fuel use on NOx /CO2 emission ratios. We test the method by comparing it with direct eddy covariance flux measurements of these tracers. The results show a statistically significant linear relationship between TER and the flux ratio of NOx over CO2, with regression slopes ranging between 0.96 and 1.1. Weekday TER values are generally higher due to increased traffic, while weekend values are lower, reflecting reduced commuter activity. Seasonal analysis shows that winter TER is influenced significantly by domestic heating, while in summer, traffic is the predominant source of NOx and CO2 emissions within the measurement footprint. The diurnal cycle of TER also highlights the role of valley wind systems in modulating local emissions through changes in the footprint, with up-valley winds bringing higher traffic-related emissions to the site during the day. Our findings demonstrate that by resolving the most relevant turbulent timescales for a location, TER is a robust predictor for emission ratios in urban settings, offering insights into the dynamics of local emissions. The method’s ability to capture turbulent fluctuations provides a more nuanced understanding of source contributions, particularly in environments with complex and mixed emission sources.