With attention increasing regarding the level of air pollution in different metropolitan and industrial areas worldwide, interest in expanding the monitoring networks by low-cost air quality sensors is also increasing. Although the role of these small and affordable sensors is rather supplementary, determination of the measurement uncertainty is one of the main questions of their applicability because there is no certificate for quality assurance of these non-reference technologies. This paper presents the results of almost one-year field testing measurements, when the data from different low-cost sensors (for SO2, NO2, O3, and CO: Cairclip, Envea, FR; for PM1, PM2.5, and PM10: PMS7003, Plantower, CHN, and OPC-N2, Alphasense, UK) were compared with co-located reference monitors used within the Czech national ambient air quality monitoring network. The results showed that in addition to the given reduced measurement accuracy of the sensors, the data quality depends on the early detection of defective units and changes caused by the effect of meteorological conditions (effect of air temperature and humidity on gas sensors and effect of air humidity with condensation conditions on particle counters), or by the interference of different pollutants (especially in gas sensors). Comparative measurement is necessary prior to each sensor’s field applications.

The LES based modelling system PALM has been widely extended during recent years. A lot of the development focused on the processes needed for simulation of complex urban environments. Besides individual validations of the newly developed processes, an evaluation of the complete modelling system in a real urban environment is necessary to ensure the reliability of modelling results. Such comprehensive validation requires the construction of additional monitoring networks within the areas of interest to obtain sufficient data support. Our earlier campaigns, e.g. Resler et.al. (2017, 2020), focused mainly on validation of the energy exchange related processes in urban area. As part of the currently running international TURBAN project, a measurement campaign was implemented in the center of Prague, Czech Republic. It focuses mainly on evaluation of the street level meteorological and air quality dynamical processes by utilisation of a specially built sensor network placed in heavily polluted street canyons (traffic-related pollution). Twenty air quality sensors were complemented by a doppler lidar and microwave radiometer profile observations, and by observations from permanent meteorological and air quality monitoring stations operated by the Czech Hydrometeorological Institute (CHMI). These data were compared with PALM simulations performed for selected episodes of the year. The PALM model was configured in two nested domains with resolution of 10 m / 2 m and the extent of 8×8 km / 1.2×1.6 km. A significant challenge to the measurement campaign was the low accuracy and reliability of air quality sensor observations. The differences in the values of individual sensors as well as their deviations from the reference observations reached tens of percent. Using sufficiently long co-measurement with the reference monitoring station and advanced statistical methods, sufficiently accurate and reliable data suitable for model evaluation were obtained. To ensure long-term quality control of the observations, two selected sensors were co-located with a continuous reference CHMI station within the simulation domain. Similarly, setting up a profile measurement (especially with Doppler LIDAR) to obtain the maximum possible information in a given space and processing a large amount of data was a challenge. The presentation shows the details of the observations and their processing as well as the preliminary results of the model evaluation.