Citywide measurements of nitrogen dioxide (NO2) using a combination of remote sensing and in-situ measurement techniques

The high NO2 levels have been observed worldwide in cities and can have negative influence on human health. Since NO2 concentration changes rapidly with time and has a very strong spatio-temporal variability, it is vital to understand its distribution. This work aims to investigate the spatial and temporal distribution of NO2 using multiple Differential Optical Absorption Spectroscopy (DOAS) instruments so as to identify the pollution hotspots and have a long-term ground observation that could be compared with satellite data. In order to look into the spatial and temporal variability of street level concentrations of NO2, a measuring system was established combining open path remote sensing and in-situ measurement techniques. This study was implemented in Munich and Hong Kong, which are both densely populated cities with a high volume of traffic. With this combination technique, a new algorithm was developed which allowed to separate temporal changes and spatial patterns and analyze them independently. Specifically, diurnal cycles, weekly patterns as well as spatially resolved long term changes were examined in order to study the source patten on different time scales. Two measurement campaigns have been conducted, one in June and July 2016 in Munich and one in March 2017 in Hong Kong. The data of the Hong Kong measurement campaign in December 2010 was also analyzed. Mobile measurements were performed during the daytime to cover non-rush hours, morning and evening rush hours. To compare spatial patterns, the accumulation of NO2 measured when stopping at traffic lights was filtered out. In this way, this work focuses on the changes of NO2 spatial distribution instead of comparing traffic flow patterns. For the generation of composite maps, the diurnal cycle has been normalized by scaling the mobile data with coinciding citywide path-averaged measurements. Several data sets were used in this study including the Ozone Monitoring Instrument (OMI) satellite data and the local stationary monitoring data. The Munich long-term LP DOAS data of monthly average and satellite measurements were well correlated when comparing the data of the OMI overpass time. For identifying pollution hotspots in Munich, on-road concentrations of \NO2 were much higher on motorways and the city center than the rest of the area. The NO2 level and the traffic count data offered by the Department of Public Order (KVR) of Munich showed similar weekend reduction during rush hour peaks. The column density of NO2 measured by car-based Multi-AXis (MAX) DOAS and the satellite OMI observed comparable spatial distribution which displayed a higher level in the city center. In Hong Kong, pronounced spatial structures were observed in long-term difference maps, which exhibited a decreasing trend of NO2 in most measured areas except an increasing trend around subway stations. Analysis of the weekend effect showed that NO2 levels for most part of Hong Kong were significantly reduced on Sundays, whereas an opposite trend was revealed around shopping malls. As further implications, the spatial differences have to be considered when discussing city-wide trends and can be used to put local point measurements into perspective. The visualized data sets can provide a better insight into on-road NO2 characteristics in Munich and Hong Kong, which helps to identify heavily polluted areas and represents a useful database for urban planning and the design of pollution control measures.