Utilization of the sensor network, remote sensing measurements, and the microscale LES model PALM for the air quality assessment in polluted urban area

The assessment of the air quality in polluted urban areas represents a challenge. Due to strong heterogeneity of the urban canopy types and local emission sources, the air quality conditions are spatially and temporarily strongly inhomogeneous. This requires very local approaches for its assessment. The earlier observation campaigns (see Resler et.al. (2021)) provided a few days of air quality observations for a limited number of locations. To improve information about spatial and temporal distribution of the air pollution, a recent observation campaign done in the framework of the international research project TURBAN utilized a dedicated sensor network placed in heavily polluted street canyons and their surroundings. The monitoring network consisted of twenty air quality sensors, a doppler lidar, and microwave radiometer profiler. The almost one year long observations were complemented by observations from permanent meteorological and air quality monitoring stations operated by the Czech Hydrometeorological Institute (CHMI). These data have been compared with PALM simulations performed for selected episodes of the year. The LES based modeling 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 including air quality processes. The PALM model for presented simulations has been configured in two nested domains with resolution of 10 m and 2 m and the extent of 8×8 km and 1.2×1.6 km respectively. Comparison of the low accuracy and reliability sensor observations with imperfect modeling results represents a significant challenge. The differences in the values of individual sensors as well as their departures from the reference observations reached tens of percent. Sufficiently long co-measurement with the reference monitoring station allowed calibration of the sensor outputs which partially helped to improve the quality of measurements. The model results also include a lot of uncertainties due to e.g. imprecise emission inputs, boundary meteorological conditions, and imperfect description of particular processes inside the model. In spite of all uncertainties, the detailed information in microscale resolution brings an improvement of our knowledge about air quality in urban areas.