Headline: Top–down quantification of NOx emissions from traffic in an urban area using a high-resolution regional atmospheric chemistry model

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.

Publikationsjahr
2018
Publikationstyp
Wissenschaftliche Aufsätze
Zitation

Kuik, F., Kerschbaumer, A., Lauer, A., Lupascu, A., von Schneidemesser, E., Butler, T. M. (2018): Top–down quantification of NOx emissions from traffic in an urban area using a high-resolution regional atmospheric chemistry model. - Atmospheric Chemistry and Physics, 18, 11, p. 8203-8225.DOI: http://doi.org/10.5194/acp-18-8203-2018

DOI
10.5194/acp-18-8203-2018 10.5194/acp-18-8203-2018-supplement
Links
http://publications.iass-potsdam.de/pubman/item/escidoc:2885894:6/component/esc… http://publications.iass-potsdam.de/pubman/item/escidoc:2885894:6/component/esc…
Beteiligte Mitarbeiter
Beteiligte Projekte
Modellierung der Luftqualität für Politikberatung