Ground-based remote sensing by three ceilometers for mixing layer height detection over Augsburg as well as a Radio- Acoustic Sounding System (RASS) for temperature and wind profile measurements at the campus of Augsburg University are applied together with UAV height profiling with low-weight meteorological sensors and particle counter to monitor the three-dimensional dynamics of the lower atmosphere. Results about meteorological influences upon spatial variation of air pollution exposure are presented on this data basis which is more than one year long. Special focus is on the information about atmospheric layering as well as mixing and transport conditions for emitted particulate matter. Better understanding of these complex processes support knowledge about quality of air, which we breath, and especially high air pollution episodes and hot spot pollution regions.
A pragmatic, data driven approach, which for the first time combines existing in situ and remote sensing data sets with a networked mobile air pollutant measurement strategy in the urban space is an objective of the Smart Air Quality Network (SmartAQnet) project. It aims to implement an intelligent, reproducible, finely-tuned (spatial, temporal), yet cost-effective air quality measuring network, initially in the model region of Augsburg, Germany. Central to this is the development and utilization of partial, already existing (but not yet combined) data on the one hand and the collection and integration of relevant missing data on the other hand. Unmanned aerial vehicles (UAV) with low-weight meteorological sensors and particle counter are used to monitor the three-dimensional dynamics of the lower atmosphere. Ground-based remote sensing by ceilometer for mixing layer height detection as well as a Radio-Acoustic Sounding System (RASS) for temperature and wind profile measurements at the University campus complete the new network architecture and UAV height profiling of atmospheric parameters. <p> </p>The SmartAQnet research initiative focuses on the subject of data access and data-based applications. Such complex monitoring provides the basis of deeper process understanding of air pollution exposure. The network architecture is shown and first results about spatial variation of meteorological influences upon air pollution exposure is presented using ceilometer, UAV and the existing monitoring network data.