Top–down quantification of NOx emissions from traffic in an urban area using a high-resolution regional atmospheric chemistry model
Authors: Kuik Friderike , Kerschbaumer Andreas , Lauer Axel , Lupascu Aurelia , von Schneidemesser Erika , Butler Tim M. 
Published: June 2018

Abstract. With NO2 limit values being frequently exceeded in European cities,
complying with the European air quality regulations still poses a problem for
many cities. Traffic is typically a major source of NOx emissions
in urban areas. High-resolution chemistry transport modelling can help to
assess the impact of high urban NOx emissions on air quality
inside and outside of urban areas. However, many modelling studies report an
underestimation of modelled NOx and NO2 compared with
observations. Part of this model bias has been attributed to an
underestimation of NOx emissions, particularly in urban areas.
This is consistent with recent measurement studies quantifying
underestimations of urban NOx emissions by current emission
inventories, identifying the largest discrepancies when the contribution of
traffic NOx emissions is high. This study applies a
high-resolution chemistry transport model in combination with ambient
measurements in order to assess the potential underestimation of traffic
NOx emissions in a frequently used emission inventory. The
emission inventory is based on officially reported values and the
Berlin–Brandenburg area in Germany is used as a case study. The WRF-Chem
model is used at a 3 km × 3 km horizontal resolution, simulating
the whole year of 2014. The emission data are downscaled from an original
resolution of ca. 7 km × 7 km to a resolution of
1 km × 1 km. An in-depth model evaluation including spectral
decomposition of observed and modelled time series and error apportionment
suggests that an underestimation in traffic emissions is likely one of the
main causes of the bias in modelled NO2 concentrations in the urban
background, where NO2 concentrations are underestimated by ca.
8 µg m−3 (−30 %) on average over the whole year.
Furthermore, a diurnal cycle of the bias in modelled NO2 suggests
that a more realistic treatment of the diurnal cycle of traffic emissions
might be needed. Model problems in simulating the correct mixing in the urban
planetary boundary layer probably play an important role in contributing to
the model bias, particularly in summer. Also taking into account this and
other possible sources of model bias, a correction factor for traffic
NOx emissions of ca. 3 is estimated for weekday daytime traffic
emissions in the core urban area, which corresponds to an overall
underestimation of traffic NOx emissions in the core urban area
of ca. 50 %. Sensitivity simulations for the months of January and July
using the calculated correction factor show that the weekday model bias can
be improved from −8.8 µg m−3 (−26 %) to
−5.4 µg m−3 (−16 %) in January on average in the urban
background, and −10.3 µg m−3 (−46 %) to
−7.6 µg m−3 (−34 %) in July. In addition, the negative
bias of weekday NO2 concentrations downwind of the city in the rural
and suburban background can be reduced from −3.4 µg m−3
(−12 %) to −1.2 µg m−3 (−4 %) in January and from
−3.0 µg m−3 (−22 %) to −1.9 µg m−3
(−14 %) in July. The results and their consistency with findings from
other studies suggest that more research is needed in order to more
accurately understand the spatial and temporal variability in real-world
NOx emissions from traffic, and apply this understanding to the
inventories used in high-resolution chemical transport models.