## 4 Discussion
The comparison of metal concentration in PM2.5 by research institutes are illustrated in Figure 1. The mostly detected element in PM2.5 by research institution #1 and #2 was S followed by Al, Si, and K, respectively, whereas Sr and Rb were the least abundant elements detected. Those results are similar to the findings of a study conducted in Istanbul, Turkey that mostly detected Si and Al as PM2.5 metal components in dense traffic area [44]. In institute #3, Al was the most abundant element followed by S and Si. Results of the analysis (ng/mg, ppm) between institute #1 and institute #2 showed 16 out of 25 metals to have high (0.73 or larger) correlation between the two institutes. In particular, correlation coefficient of Fe, S, V, Zn, Mn, Pb, and K was 0.97 (\(p<0.05\)) or higher, implying a very high correlation between the measured values from the two institutes. Compared to the coefficients between institute #1 and institute #2, those between institute #1 and institute #3 were relatively low; we assumed this to be due to the results being affected by low detection rate in the institute #3 analysis. Institute #2 has yielded measurable values for heavy metal analysis of all samples. Using XRF instrumentation with improved sensitivity, PM component analysis studies like this will become more possible in the future using a low-burden PM monitor which can greatly contribute to an accurate health risk assessment. In addition, the coefficient of determination (R\({}^{2}\)) obtained from simple regression analysis for 18 out of 25 heavy metals was 0.76 or more between institute #1 and institute #2. Further, we compared the mean concentration of heavy metal components contained in our PM2.5 samples with those inside residential homes (n = 10) in the vicinity of the Xinqiao mining area, Tongling, China [45]. Although we could not compare the entire metal proportion profile because the latter reported concentration distributions of only 7 selected metals, the concentration level and ratios of most metals were similar. The mean concentration of Zn of this study was 1256 (ng/mg, ppm) while the one from [PERSON] et al. 's study was 979 (ug/g, ppm) [45]. Ratios of mass concentration of Pb, Cr, Cu, Cd, and Ni in this study and [PERSON] et al. 's study were 0.20, 0.28; 0.10, 0.06; 0.09, 0.33; 0.06, 0.01; not detected, 0.05, respectively. In case of Fe, the ratios were 1.83 (this study) and 27.7 in [PERSON] et al. 's study. As characteristics of study areas between the two studies were different, there is some limitation on further interpretation. Nevertheless, the authors thought that our study results are informative in terms of indicating metal composition of PM2.5 collected from an urban residential asthmatic's home. On the contrary, according to previous studies conducted in Nepal's urban area (outside housing) [46] and U.S. urban area (outside housing) [47], this study's indoor Ca, Cr, Fe, and K levels were somewhat similar to the results from outside areas of Bhakrapur and Pennsylvania, with exceptions for S, Pb, and V.  
Use of the same filter samples for consecutive analyses in three global institutes was a unique study design and the major advantage of this study. PM2.5 samples on filters were transported to institute #3 and institute #2 after the first analysis in Korea. They were safely transported, along with blank samples. Since the first sample analysis results by institute #1 and the last sample analysis results by institute #2 were highly correlated, we assumed that there was no serious systemic error during the process of transport. In this study, we used real-world field samples. Institute #1 has had a calibration curve prepared on a 40 mm filter for their daily routine commercial analysis while institute #2 prepared the curve on a 10 mm filter. Field operators used personal monitoring devices with 10 mm filters. The authors recognized that institute #1 used a modified 10 mm nozzle instead of the original 40 mm nozzle on XRF.