Measuring Spatial and Temporal Patterns of Urban NO2 Concentrations by combining mobile and stationary DOAS instruments

In many cities around the world the NO₂ concentration levels exceed WHO guideline limits. Urban air quality is typically monitored using a relatively small number or monitoring stations that follow certain guidelines in terms of inlet height and location relative to streets. However, the question remains how a limited number of point measurements can represent the city-wide air quality and capture spatial patterns. Measurement campaigns in Hong Kong and Munich were conducted, using a combination of mobile in-situ and stationary remote sensing differential optical absorption spectroscopy (DOAS) instruments. In order to separate spatial and temporal patterns, we developed an algorithm based on a combination of mobile and stationary data sets that corrects for the diurnal cycle in the mobile measurements.  We constructed pollution maps from the corrected measurements that represent daily average NO₂ exposure. The maps have been used to identify pollution hot spots, determine the spatial dependency of long-term changes, and capture the weekly cycles of on-road NO₂ levels in Hong Kong and Munich. Since our method can also be used to determine the spatial representativeness of the monitoring stations in cities, it is very valuable tool for identifying suitable locations for air quality monitoring stations.