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Within the TURBAN project, a "Legerova campaign" focusing on air quality and meteorology in the traffic-loaded part of the Prague city (Czech Republic) was carried out from 30 May 2022 to 28 March 2023. The network comprised of 20 combined low-cost sensor (LCS) stations for NO2, O3, PM10 and PM2.5 concentrations, along with a mobile meteorological mast, a single-channel microwave radiometer and Doppler LIDAR for measurement of vertical temperature and wind profiles. Significant individual deviations of LCSs were detected during the 165 day initial field test of all units at the urban background Prague 4-Libuš reference station (coefficient of variation 17–28 %). Implementing the Multivariate Adaptive Regression Splines method for correction reduced the LCS inter-individual variability and improved correlation with reference monitors in all pollutants (R2 0.88–0.97). The LCSs' data drifts and ageing were checked by the double mass curve method for the entire measurement period. During the Legerova campaign, the highest NO2 concentrations were in traffic-loaded street canyons with continuous building blocks and several traffic lights. Aerosol pollution showed very little variation between the monitored streets. The highest PM10 and PM2.5 concentrations were recorded during temperature inversions and an episode involving pollution transported from a large forest fire in northern Czech Republic in July 2022. This report provides valuable data to support the validation of various predictive models dealing with complex urban environment, such as microscale LES model PALM tested in the TURBAN project.
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Bauerová et al. (2024) studied this question.
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