## 4 Discussion  
The results of the analysis show that reducing emission from the household sector will significantly improve air quality in Lower Silesia and the Lodzkie region. The concentration of PM\({}_{2.5}\) annually is estimated to drop by 35.6% in Lower Silesia and 31.7% in Lodzkie province and for PM\({}_{10}\) in the corresponding regions by 30.4% and 30.1%. These data are for Scenario 1, which relates to almost no emission (reduction by 94.7%) from the household sector. According to the national statistics on emissions from the National Centre for Emissions Management, the annual share of emissions of PM\({}_{10}\) and PM\({}_{2.5}\) from households is 46.5% as an average for the whole of Poland [33]. Additionally, an international study providing modeling of PM\({}_{2.5}\) concentration with source apportionment shows a household share of 41% as an average for the whole country [34]. Our study results are lower than this, therefore this analysis can be treated as a conservative estimation for Poland. This is most likely caused by still not having a sufficiently comprehensive database of emissions at a local level in the country. Statistics are prepared using a top-down method that does not take into account local issues of air quality. The total number of premature deaths may be somewhat underestimated since it did not include increased air pollution exposure in narrow urban streets. However, the analyzed scenarios did not include changes in transport, therefore it should not affect the relative difference, which was the input for the premature death calculation [35]. An additional limitation is that it only took into account the concentration of particulate matter. The meta-analysis studies on the health impact of air pollution do not focus on the composition of PM\({}_{x}\), which, depending on their composition, have different toxicity. As an extreme example, particulate matter with a content of more than 80% NaCl (salt) has positive health properties [36], which of course is not a case for Poland characterized by high emissions from combustion processes and high contribution of BaP [37]. However, taking into account the composition of particulate matter is not a straightforward issue. For instance, to analyze changes of BaP emissions and concentration, we would need to also include socioeconomic changes, temperature and relative humidity to derive a new relative risk for concentration-response function [38]. Therefore, to tackle this limitation, \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Lower Silesia**} & \multicolumn{3}{c}{**Lodzkie Province**} \\ \hline
**Health Impact** & **Scenario 1** & **Scenario 2** & **Scenario 3** & **Scenario 1** & **Scenario 2** & **Scenario 3** \\ \hline Premature death [cases] & 1122 & 776 & 476 & 1438 & 1 211 & 945 \\
[\% reduction] & 34.3\% & 23.7\% & 14.6\% & 29.9\% & 25.2\% & 19.7\% \\
[per 100k a.] & 38.7 & 26.7 & 16.4 & 57.5 & 48.5 & 37.8 \\ \hline Bronchitis amongst children & 9602 & 7303 & 4628 & 9880 & 8067 & 6932 \\
[new cases] & 27.7\% & 21.1\% & 13.4\% & 26.8\% & 21.9\% & 18.8\% \\
[per 100k a.] & 331 & 252 & 160 & 395 & 323 & 277 \\ \hline Chronic bronchitis amongst adults & 1516 & 1148 & 724 & 1502 & 1221 & 1047 \\
[new cases] & 26.6\% & 20.1\% & 12.7\% & 25.3\% & 20.6\% & 17.7\% \\
[per 100k a.] & 52.3 & 39.6 & 25.0 & 60.1 & 48.9 & 41.9 \\ \hline Work absence [days] & 481161 & 333083 & 204775 & 688981 & 581101 & 454101 \\
[\% reduction] & 34.7\% & 24.0\% & 14.7\% & 30.4\% & 25.6\% & 20.0\% \\
[per 100k a.] & 16 592 & 11 486 & 7 061 & 27 559 & 23 244 & 18 164 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Annual health impact reduction per scenario and region. a new separate study would be need to be conducted. Added value can also be found in presenting health effects other than premature deaths alone. One of the possible approaches is using a DALY indicator, which allows explaining the morbidity.