Use of new measurement device to build a high-resolution network in Augsburg city – Smart Air Quality Network-Project

In traditional approach, the air quality in urban environment is monitored by relatively few measuring stations using stationary measuring technology. However, the spatial distribution of air pollutants in cities is very inhomogeneous and depends on various factors. The Smart Air Quality Network project (SAQN) is aimed to set up a dense network of sensor devices for particle mass and number concentration in the city of Augsburg (Germany) as well as conducting of intensive mobile measurements including walking and bike measurements. The main target is to generate real-time data that can be used for several actions and measures in order to reduce pollution levels as well as to inform people about the current levels of air pollutants. Furthermore, the data will be also used for the development and validation of dispersion and land use regression models. For the purpose of this project, a novel measuring device is being developed by GRIMM Aerosol company, so-called “scientific scouts” (autonomous, mobile smart dust measurement devices that are auto-calibrated to a high-quality reference instrument within an intelligent monitoring network). In Phase 1 we already installed 15 scientific scouts at different sampling sites within the Augsburg city. The sampling sites are located in different environments: close to traffic and traffic hotspots, near combustion sources, in the city center and in urban background. In Phase II of this project 35 updated scientific scouts will be installed until summer 2019. The scientific scouts will be able to measure particle mass and number concentrations. The preliminary results suggest good performance of the scientific scouts and their applicability for the purpose of SAQN project. Ongoing calibration will help to improve the performance and efficiency of the low-cost devices. On the other hand, the planned extension of the network in the following months will provide necessary data for the modelling approach