Urban air quality monitoring for environmentally sensitive traffic management

In the pilot region of Leipzig, the Helmholtz Centre for Environmental Research (UFZ) is implementing a sensor network comprising 25 mid-cost measuring devices for the continuous monitoring of air quality parameters PM₂.₅, PM₁₀, O₃, and NOₓ. The initiative is conducted within the project of AIAMO (Artificial Intelligence and Mobility), which aims to expand the existing digital infrastructure for an environmental digital twin which supports data-driven decision-making in sustainable urban development. 
The sensor network focuses on a defined study area (6 km x 6 km) within the city. In close cooperation with various municipal authorities – including the Environment Agency and the Transport and Civil Engineering Agency – traffic-related scenarios are being developed to facilitate the effects of upcoming urban air quality limit changes at an early stage. 

The selection of locations was based on existing or potential air quality hot spots where exceedances of limit values are either observed or expected. Both areas with high traffic volumes and urban background areas, such as parks, were considered. The locations were selected iteratively with all relevant authorities from the city of Leipzig. To this end, various inner-city scenarios have been defined. 
Within these scenarios, continuous air quality measurements are integrated with traffic data and model simulations to produce a consistent, spatially and temporally resolved representation of local emission sources, dispersion dynamics, and possible mitigation strategies. Based on long-term measurement series, targeted measures are identified, tested within virtual environments, and iteratively evaluated in cooperation with municipal stakeholders of Leipzig. The overarching goal is to ensure compliance with air quality standards and to promote environmentally sensitive traffic management strategies.
The project illustrates how regionally anchored urban sensor networks, data-driven analyses, and scenario-based modelling approaches can contribute to the development of sustainable and urban air quality services in contemporary city contexts.