## 4 Results and Discussions
Figure 2: Seasonal variation in (a) R, (b) RMSE, (c) slope, and (d) number of data used in correlations between measured and estimated PM\({}_{2.5}\) (unit: \(\upmu\)g/m\({}^{3}\)) values. Estimated PM\({}_{2.5}\) values here are from the M3-1 model using MODIS-DT (red), MODIS-DB (orange), MODIS-DT for 3 \(\times\) 3 km resolution (yellow green), VIIRS (green), MISR (light blue), and OMI (blue) AOD. MAM means March–April–May (spring), JJA means June–July–August (summer), SON means September–October–November (autumn), and DJF means December–January–February (winter). The unit of RMSE is \(\upmu\)g/m\({}^{3}\). Thus far, we examined the relationship between the performance of PM\({}_{2.5}\) estimation and applied satellite AOD values. Next, similar comparisons were repeated using the various ground-based AODs. As mentioned, we estimated PM\({}_{2.5}\) based on the M3-1 model using AERONET, SKYNET, ultraviolet MFRSR (UV-MFRSR), visible MFRSR (VIS-MFRSR), and Brewer spectrophotometer AOD (Figure 4). Here, one more key point was to investigate the performance of PM\({}_{2.5}\) estimation in terms of the channel (wavelength) of AOD. We mainly compared the basic MLR (M1) and the best MLR (M3-1) model results. M1 results, only considering AOD for PM\({}_{2.5}\) estimation, show a large wavelength dependence: a decrease in the correlation coefficients and slope, and an increase in RMSE for the comparison between observed and estimated PM\({}_{2.5}\) as the wavelength increased. AODs in near-infrared (IR) channels do not result in high-quality PM\({}_{2.5}\) estimation. This feature reveals the importance of channel selection in the simple application of the relationship between AOD and PM\({}_{2.5}\) for the investigation of regional aerosol pollution. Considering the large wavelength dependence of ground-based AOD measurement [47], exact examination of the relationship between AOD and PM\({}_{2.5}\) cannot be guaranteed without the appropriate channel selection. This lesson should be significantly considered for the usage of AOD to connect with the level of PM\({}_{2.5}\) directly [37]. When the M3 model is used, however, the wavelength dependence does not occur (Figure 4d-f). From this result, we find that the wavelength dependence of AOD is not influential on the PM\({}_{2.5}\) estimation if the advanced statistical approach is applied with a number of other effective factors (i.e., PBLH, meteorology, local chemical concentration). The M3 results reveal that both MFRSR AOD values result in a high accuracy of PM\({}_{2.5}\) estimation because MFRSR AODs here are retrieved with the elaborate screening of Rayleigh scattering induced by trace gases such as ozone and NO\({}_{2}\)[23], resulting in the more precise  
Figure 3: Provision of (**a**) R, (**b**) slope, (**c**) RMSE, and (**d**) number of data used for the comparison between measured and estimated PM\({}_{2.5}\) from M3-1 and M3-2 models with the usage of MODIS-DT, MODIS-DB, and MODIS-3 km AODs. Black bars indicate results from AODs of the Terra satellite, and white bars indicate results from AODs of the Aqua satellite. The unit of RMSE is \(\upmu\)g/m\({}^{3}\). capture of local aerosol variation. Compared to the results obtained from using the satellite AODs (Table 4), the usage of ground-based AODs shows similar correlation coefficients but a slightly higher RMSE and lower slope. This feature can probably be explained by the fact that ground-based AODs more reflect the local characteristic of the maximum aerosol pollution because the statistical approach has limitations in the detection of dense aerosol pollution [4]. The interesting result shown in Figure 4--the performance of PM\({}_{2.5}\) estimation using the UV- and VIS-MFRSR AODs-- is described here in more detail. The M1 results show that the quality of estimated PM\({}_{2.5}\) based on MFRSR AODs is lower than that of the others. In contrast, the quality of estimated PM\({}_{2.5}\) based on MFRSR AODs appears to be the best when the M3 model is applied, obtaining the highest correlations and slope values.