Abstract. In this paper we present an investigation of the spatial and temporal variability of street-level concentrations of NO2 in Hong Kong as an example of a densely populated megacity with heavy traffic. For the study we use a combination of open-path remote sensing and in situ measurement techniques that allows us to separate temporal changes and spatial patterns and analyse them separately. Two measurement campaigns have been conducted, one in December 2010 and one in March 2017. Each campaign lasted for a week which allowed us to examine diurnal cycles, weekly patterns as well as spatially resolved long-term changes. We combined a long-path differential optical absorption spectroscopy (DOAS) instrument with a cavity-enhanced DOAS and applied several normalizations to the data sets in order to make the different measurement routes comparable. For the analysis of long-term changes we used the entire unfiltered data set and for the comparison of spatial patterns we filtered out the accumulation of NO2 when stopping at traffic lights for focusing 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 measurement results. An overall descending trend from 2010 to 2017 could be observed, consistent with the observations of the Ozone Monitoring Instrument (OMI) and the Environment Protection Department (EPD) air quality monitoring network data. However, long-term difference maps show pronounced spatial structures with some areas, e.g. around subway stations, revealing an increasing trend. We could also show that the weekend effect, which for the most part of Hong Kong shows reduced NO2 concentrations on Sundays and to a lesser degree on Saturdays, is reversed around shopping malls. Our study shows that spatial differences have to be considered when discussing citywide trends and can be used to put local point measurements into perspective. The resulting data set provides a better insight into on-road NO2 characteristics in Hong Kong, which helps to identify heavily polluted areas and represents a useful database for urban planning and the design of pollution control measures.