## 3 Results and Discussion
### Corresponding Health Outcomes  
Based on the results of the concentration compared to the control period, the relative risks (RRs) and attributable factors (AFs) for each pollutant from January 2020 were calculated and are shown in Tables S2 and S3. In Seoul, the RR was 1.05-1.20 for PM\({}_{2.5}\), 1.02-1.08 for PM\({}_{10}\), and 0.99-1.06 for NO\({}_{2}\). In Daegu, the RR was 1.08-1.19 for PM\({}_{2.5}\), 1.03-1.08 for PM\({}_{10}\), and 1.01-1.05 for NO\({}_{2}\). In week 8, when the largest reduction in air pollutants was observed, the RR was the highest, and the AF values derived from the RRs were in the order of PM\({}_{2.5}\) (0.12) \(>\) PM\({}_{10}\) (0.05) \(>\) NO\({}_{2}\) (0.03) in both cities. Noticeably, compared to the AFs observed in Sao Paulo, Brazil [11], a megacity similar to Seoul, the PM\({}_{2.5}\) and PM\({}_{10}\) had greater AFs in Seoul, whereas NO\({}_{2}\) had greater AFs in Sao Paulo. This difference seems to have occurred because the main indicator of air quality is NO\({}_{2}\) in Sao Paulo whereas, in Korea, PM\({}_{2.5}\) is the contributory pollutant.  
Figure 8: Comparison of daily AQI distribution of PM\({}_{2.5}\) and PM\({}_{10}\). Each AQI color indicates a different level of health concern. (**(a)**: Seoul and **(b)**: Daegu)  
Based on the AF values, we assessed the preventable deaths and monetary values in each city (Table 2). The analysis showed many more preventable deaths due to reduced air pollution in Seoul, which has a large population. The decrease in PM\({}_{10}\) and NO\({}_{2}\) saved the lives of at least 55 people, and especially PM\({}_{2.5}\) prevented about 250 premature deaths. The effects in Daegu were not as appreciable as those in Seoul, but due to a significant drop in PM\({}_{2.5}\), the number of avoidable deaths was estimated to reach about 78. The number of deaths from COVID-19 differed greatly from two in Seoul to 154 in Daegu during the investigation period.  
Health benefits based on the VSL approach were USD 884 million and USD 278 million in Seoul and Daegu, respectively (Figure 9). Interestingly, Seoul had two COVID-19 deaths, with economic losses of USD 7.1 million, while Daegu had 154 deaths and USD 543.6 million in health costs, showing sharply contrasting results. In Seoul, despite its dense population, social distancing was effective, whereas Daegu saw the rapid spread of secondary, tertiary, and other infections from cluster infection cases at the early stages of COVID-19. The results suggest that the initial spread rate of infectious diseases can have a significant effect on subsequent health outcomes.  
The number of prevented deaths related to reductions in transboundary PM\({}_{2.5}\) before SD was 78 and 26 in Seoul and Daegu, respectively. After 8 weeks, since the effects of reductions in transboundary PM\({}_{2.5}\) were synergistic with the SD effect, it could not be evaluated separately, but the COVID-19 control actions of the neighboring countries probably continued to affect Korea. Thus, the premature deaths avoided before the 8 th week of SD suggests that changes in air pollution in the neighboring countries could affect not only air quality but also health outcomes in other countries.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{6}{c}{**Estimated Avoided Deaths**} \\ \hline
**Location** & \multicolumn{3}{c}{**Seoul**} & \multicolumn{3}{c}{**Daegu**} \\ \hline
**Weeks** & **PM\({}_{2.5}\)** & **PM\({}_{10}\)** & **NO\({}_{2}\)** & **PM\({}_{2.5}\)** & **PM\({}_{10}\)** & **NO\({}_{2}\)** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 2: Assessment of weekly avoided deaths due to reductions in PM\({}_{2.5}\), PM\({}_{10}\), and NO\({}_{2}\).