## 3 Results and Discussion
### Application of the Modified Methodology  
Table 3 shows the number of days exceeding the 50 \(\mathrm{\SIUnitSymbolMicro g}\)/\(\mathrm{m}^{3}\) daily PM\({}_{10}\) limit at the two investigated stations DB and GS as well as the number of days exceeding this limit after the subtraction of the NDL\({}_{\mathrm{MB}}\). Throughout the six-year period 242 days exceed the daily limit value at the urban-traffic station DB, which comes up to five out of six years exceeding the 35 day/year limit. At the urban-background station GS 196 days exceed the daily limit value, whereas only in 2017 the 35 day/year limit was exceeded due to a longer lasting pollution episode in January and February. Subtracting the NDL\({}_{\mathrm{MB}}\) without any further evaluation of the presence of DD, 40 days of the 242 days at DB fall below the 50 \(\mathrm{\SIUnitSymbolMicro g}\)/\(\mathrm{m}^{3}\) limit, reducing the years which exceed the 35 day/year limit to three years (2013, 2017 and 2018) in contrast to the five years given before. For GS 35 days of 196 days fall below the 50 \(\mathrm{\SIUnitSymbolMicro g}\)/\(\mathrm{m}^{3}\) limit, not influencing the exceedance of the 35 day/year limit. After an identification of DD days based on model result, back trajectories and optical aerosol measurements as described earlier, results reveal that of the 40 and 35 days at DB and GS, only 20 (DB) and 15 (GS) days show an influence of DD (compare Table 3, columns DD days). Obviously only for those days a subtraction of the NDL\({}_{\mathrm{MB}}\) is allowed and the solely determination of NDL\({}_{\mathrm{MB}}\) is not sufficient. The selection of DD days could be included before subtracting the NDL\({}_{\mathrm{MB}}\), corresponding to the EC methodology. This would identify 51 out of 242 days at DB and 37 out of the 196 days at GS with a possible influence of DD. Subtractions of NDL\({}_{\mathrm{MB}}\) for these special days would again give the number of reductions listed in Table 3 (5 th column for DB and 9 th column for GS). As already discussed, the identification of DD days using models together with back trajectories is a very subjective process. [PERSON] et al [7] developed an automated and user-independent process for the identification of DD days, still using model calculations. As such, a user-independent process was not yet established for Austria. Therefore, we evaluate a method for the identification of DD days based on the time series of the PM\({}_{10}\) measurements at the background site only. Considering only days with a possible contribution of DD similar features of NDL\({}_{\mathrm{MB}}\) and BGL\({}_{\mathrm{MB}}\) become visible, which define additional criteria to avoid undu \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Before Setting Negative NDLs to Zero** & **After Setting Negative NDLs to Zero** \\ \hline Minimum & \(-\)16.82 & 0 \\
25 th percentile & \(-\)3.79 & 0 \\ Median & \(-\)0.91 & 0 \\
75 th percentile & 2.79 & 2.79 \\ Maximum & 50.60 & 50.60 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: NDL in \(\mathrm{\SIUnitSymbolMicro g}\)/\(\mathrm{m}^{3}\) of the Masenberg station, most suitable to act as regional background station, before and after negative values were set to zero.