## 4 Results and Discussions  
### Accuracies of the Generated Urban Maps  
In this section, the degrees of accuracy of the first and second urban maps separately generated from the first and second Landsat images, respectively, are assessed using the 100 checkpoints generated through manual digitization. Figure 7 shows examples of the checkpoints generated through manual digitization for measuring the degrees of accuracy of the first and second urban maps. Table 1 shows the statistical results showing the degrees of accuracy of the first and second urban maps separately generated from the first and second Landsat images, respectively. As can be seen in Table 1, the first and second urban maps separately generated from the first and second Landsat images, respectively, through the SVM technique had high accuracy in identifying the urban areas in the entire Daegu area. There were a few misclassification errors, however, in both urban maps because some urban features (e.g., man-made features) were misclassified as non-urban features (e.g., soil, water, vegetation), or vice versa. These misclassification errors were generally caused by the similar reflectance characteristics of the different materials owing to the shades, etc. [18]. Figure 6: Time series graphs showing the annual PM\({}_{10}\) concentrations in each AQMS in Daegu from 2007 to 2017. AQMS 1(**a**) to AQMS 11(**k**). Figure 7: Examples of the checkpoints generated through manual digitization for measuring the degrees of accuracy of the first and second urban maps. Relationship between the Urban Expansions and the PM\({}_{10}\) Concentrations in Daegu from 2007 to 2017  
Discussed in this section is the relationship between the urban expansion and the annual PM\({}_{10}\) concentration rate in Daegu from 2007 to 2017 determined by calculating the following statistics: those showing the increase of the expanded urban areas in Daegu from 2007 to 2017 and those showing the annual PM\({}_{10}\) concentration changes in each AQMS, each year and each season in Daegu from 2007 to 2017 (see Table 2). As can be seen in Figure 6, Table 2a,b, the urban areas expanded by 55.27 km\({}^{2}\) in the entire Daegu area from 2007 to 2017, but the annual PM\({}_{10}\) concentrations in all the AQMSs in Daegu only slightly increased by 0.45 \(\mu\)g/m\({}^{3}\) in only one station or sharply decreased by 4.12-42.28 \(\mu\)g/m\({}^{3}\) in the other 10 stations, within the same period. Table 2c shows that the annual PM\({}_{10}\) concentrations had decreased in Daegu City from 2007 to 2017 by 17.37 \(\mu\)g/m\({}^{3}\). Table 2b,c show that the annual PM\({}_{10}\) concentrations were measured high in AQMSs 3, 4, and 6, while they were measured low in AQMSs 5, 7, 9, 10 and 11. Table 2c also shows that the highest annual PM\({}_{10}\) concentrations was most frequently measured in AQMS 6 from 2007 to 2017 while the lowest annual PM\({}_{10}\) concentrations most frequently measured in AQMS 11 during the same period. Based on Table 2b,c results, we assume that there are the number of other facilities (e.g., industrial factories) that emit the PM\({}_{10}\) particles near AQMSs 3, 4, and 6, while there are few facilities emitting the PM\({}_{10}\) particles near AQMS 5, 7, 9, 10, and 11. Table 2c,d show that the annual PM\({}_{10}\) concentrations generally measured high in spring and winter compared to summer and autumn. In addition, the annual PM\({}_{10}\) concentrations decreased in all the seasons by 31.11 \(\mu\)g/m\({}^{3}\) for spring, by 8 \(\mu\)g/m\({}^{3}\) for summer, by 8.78 \(\mu\)g/m\({}^{3}\) for autumn, and by 18.77 \(\mu\)g/m\({}^{3}\) for winter. Based on Table 2d, we assume that the climate factors (e.g., air temperature, air pressure, rainfall, humidity, wind speed, and wind direction) can be significant for the annual PM\({}_{10}\) concentrations.