Observation of NO2 air pollution distribution maps in cities with mobile ICAD bicycle measurements

Nitrogen Dioxide (NO₂) is currently the most critical air pollutant in Europe. The main source is traffic, especially diesel engines, and its concentration is highly variable. However, NO₂ levels are only measured in larger cities at few measurement points. Passive samplers can provide a better spatial coverage but contain no temporal information about the NO₂ variability at that location. Electrochemical sensors require a lot of manpower and additional parameters to be measured simultaneously to achieve sufficient accuracy and are thus not practical.

We apply the mobile, low power and high precision ICAD NO₂ / NO_x instrument (Airyx GmbH) to observe the distribution of NO₂ concentration in a city or in industrial facilities. For example, smaller cities are of interest where so far no information about air pollution levels and possible hot spots are available. Measurements are conducted on a bicycle at ~1.6m height and beside the road-line (with a time resolution of 2s and 1ppb accuracy) to be comparable to data from permanent measurement stations. Along a predefined route through the city, covering different street types, repeated measurements at different days and times are performed.

We present results from measurements in multiple cities with focus on the small city of Walldorf in South-West Germany. An NO₂ distribution map was derived from mobile bicycle measurements over a period of 3 months. Locations with increased air pollution levels are clearly identified. Additionally, extrapolated annual average NO₂ level and its distribution were estimated by comparison with an urban air monitoring station in 6km distance. The method for this annual mean extrapolation will be described. For two hot spot locations the derived extrapolated annual mean concentration was validated in a second campaign with intensive stationary measurements using the same instrument in a small trailer. The annual mean concentrations agreed within ~10% and prove the mobile measurement results, not only for these locations, but also in general for this method. Due to the high time resolution of the data additional emission sources can be identified.

This example shows that it is possible to derive reliably annual mean NO₂ air pollution distribution maps with few repeated mobile measurements and thus increase our understanding of real air pollution levels on a broad scale in a city.

Mobile measurements were also performed in industrial facilities like mines. An example of such measurements will be presented.