The traffic emission-dispersion model for a Central-European city agrees with measured black carbon apportioned to traffic. (July 2018) - Ježek, Irena; Blond, Nadège; Skupinski, Grzegorz; Močnik, Griša
Abstract: The bottom-up traffic emission model EMISENS is used to calculate hourly black carbon (BC) and nitrogen oxides (NOx ) emission rates on an average workday in Maribor (Slovenia), using emission factors that were previously determined with the on-road chasing measurements in Slovenia. From modeled NOx emission rates and in-situ NOx measurements we empirically determined the hourly dispersion of traffic emissions and applied it to model BC concentrations using BC emission rates. We compared the modeled BC concentrations with in-situ BC concentration measurements over three periods in winters 2011–2012 and 2012–2013, a total of 67 workdays. Measured BC concentrations were first apportioned to traffic using the top-down Aethalometer model. We found that the bottom-up modeled BC concentrations overestimated the top-down apportioned values by only 19%, 32% and 6% in each of the three investigated time periods, respectively. We did not find any influence of meteorology on the performance of the model. This is the first time that BC source apportionment results were used to evaluate traffic emissions calculated using the bottom-up modeling approach. We demonstrate that the two independent approaches yield similar results. We use thus validated emission inventory for evaluating different emission reduction scenarios. We show that excluding 10% of vehicles that are highest BC or NOx emitters would reduce the total BC or NOx emissions from traffic in Maribor by 39% and 33% respectively. Highlights: A simple methodology to determine and verify traffic emissions in a city is presented. The first time the Aethalometer model was used to evaluate the emission inventory results. The verified model was used to model emission reduction scenarios. Removing 10% of highest polluting vehicles reduces BC emissions by 39% and NOx by 33%.