Validation of SOC Estimation Using OC and EC Concentration in PM2.5 Measured at Seoul

Received: December 22, 2021
Revised: February 15, 2022
Accepted: February 20, 2022

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Cite this article:

Yoo, H.Y., Kim, K.A., Kim, Y.P., Jung, C.H., Shin, H.J., Moon, K.J., Park, S.M., Lee, J.Y. (2022). Validation of SOC Estimation using OC and EC Concentration in PM_2.5 Measured at Seoul. Aerosol Air Qual. Res. 22, 210388. https://doi.org/10.4209/aaqr.210388

HIGHLIGHTS

• The (OC/EC)_pri in the EC tracer method was determined from three approaches.
• The approach from linear regression was the most suitable for estimating SOC.
• The (OC/EC)_pri from MRS seems to be influenced by the long-range transport of EC.

Keywords: PM2.5, OC/EC ratio, Primary organic carbon, Secondary organic carbon, EC tracer method

1 INTRODUCTION

Organic carbon (OC) in the ambient particulate matter (PM) can be classified into primary organic carbon (POC) and secondary organic carbon (SOC) based on their production mechanisms. The POC is directly emitted to the atmosphere from sources, such as vehicular exhaust (He et al., 2008) and fossil fuel combustion (Zhang et al., 2008). The SOC is formed from photochemical reactions between gaseous oxidizing agents and volatile organic compounds (VOCs) in the atmosphere (Bowman et al., 1997; Odum et al., 1997). The POC concentration in the atmosphere can be reduced by controlling emissions of POC to the atmosphere. However, if the concentration of SOC far exceeds that of POC, controlling the OC concentrations in the atmosphere cannot be successfully achieved by controlling the emissions of POC alone. Therefore, it is necessary to assess and quantify the secondary formation of OC to effectively reduce OC in fine PM (Larson et al., 1989).

Direct measurement of SOC is difficult due to their complex formation mechanism in the atmosphere. Most studies have indirectly estimated the SOC level based on the measurements of OC and EC. Among these methods, the EC tracer method is the most widely used one. Most EC are diretly emitted from combustion sources similar to POC. This similarity of the emission characteristics of EC with POC allows it to be used as a tracer to assess the characteristics of POC (Chu and Macias, 1981; Wolff et al., 1983; Gray et al., 1986; Turpin and Huntzicker, 1991). The basic concept of EC trace method is based on that OC concentration changes continuously after POC is emitted to the atmosphere due to formation of SOC in the atmosphere; however, EC cannot be formed further in the atmosphere. Based on these characteristics of OC and EC, the (OC/EC)_pri ratio has been developed in the EC tracer method, which is defined as the minimum value of the OC/EC ratio during the measurement period (Strader et al., 1999). The (OC/EC)_pri should vary substantially among different emission sources; however, each source might exhibit rather a unique value of (OC/EC)_pri (Gray, 1986). The problem is that as the atmosphere is affected by various emission sources, the unique (OC/EC)_pri of each emission source cannot be independently analyzed owing to spatiotemporal variations. Thus, the estimation of proper (OC/EC)_pri evidently contains errors during the characterization of emission sources according to time or region. Despite this limitation, the EC tracer method is currently the most frequently applied method for analyzing SOC trend in the atmosphere because the (OC/EC)_pri can be easily calculated using only the measured values of OC and EC. In practice, this method has adequately reflected SOC characteristics (Choi et al., 2012; Jeon et al., 2015; Kim et al., 2018). Several studies have been conducted on predicting adequate (OC/EC)_pri according to time and region (Turpin and Huntzicker, 1995; Castro et al., 1999; Lim and Turpin, 2002; Lin et al., 2009; Wu and Yu, 2016).

In the EC tracer method, the POC is calculated based on Eq. (1). The concentration of OC emitted from primary combustion sources is calculated by multiplying EC concentration by (OC/EC)_pri and OC_non-comb, the OC level from non-combustion emission sources, is added. Subsequently, the SOC is finally estimated by subtracting POC from OC_total (Turpin and Huntzicker, 1995).

where OC_non-comb refers to the OC released from non-combustion emission sources such as plant debris, spore, and pollen though its impact is assessed to be negligible as most of the OC in PM_2.5 is released during combustion (Hildemann et al., 1991).

So far, three approaches have generally been applied to estimate (OC/EC)_pri. First, the minimum value of OC/EC during the measurement period is used to determine (OC/EC)_pri (Castro et al., 1999). This approach has been used when the EC tracer method was first introduced, and it allows a simple estimation of the (OC/EC)_pri. Second, regression analyses have been conducted, whereby the regression equation between OC and EC can be obtained from the lower 5–20% OC/EC data obtained during the measurement period, and the slope of the equation with the highest correlation coefficient can be used to determine the (OC/EC)_pri (Lim and Turpin, 2002; Lin et al., 2009). Third, the OC/EC ratio, with the minimum R squared (MRS) value between EC and SOC, is used to determine (OC/EC)_pri (Wu and Yu, 2016).

In this study, the three above-mentioned SOC estimation approaches were applied to the 1-h data of OC and EC concentrations in PM_2.5 measured at Seoul, an urban area, during October 2012 to September 2013. The SOC values estimated from each method were compared to determine the most adequate (OC/EC)_pri of Seoul area. In addition, to compare the differences of SOC estimates with respect to the time resolution of measurement of the atmospheric OC and EC, the (OC/EC)_pri and SOC were determined from the daily mean value of the 1-h measurement data and 24-h measurement data which is general OC/EC analysis after filter collection.

 
2 METHODS

 
2.1 OC and EC Measurement Data

For the analyses of 1-h interval data for OC and EC concentrations in PM_2.5, the semi-continuous measurement data of OC and EC obtained from the Seoul Air Pollution Intensive Monitoring Stations of the National Institute of Environmental Research (NIER) during the period of October 2012 to September 2013 were used. The 1-h measurement data were obtained by the thermal/optical transmittance and the non-dispersive infrared method based on the NIOSH and U.S. EPA STN protocols. A semi-continuous carbon monitor (SECOC Analyzer, Sunset Laboratory Inc., USA) was used for the 15 min analysis of OC and EC in PM_2.5 which were collected for 45 min in quartz filter at a flow rate of 8 LPM. The differences between the estimates of the upper 25% (Q₃) and the lower 25% (Q₁) are referred as the interquartile range (IQR). Based on the data obtained from 7,324 measurements of OC and EC concentrations during one year, the estimates above Q₃ + 1.5 IQR or below Q₁ – 1.5 IQR were excluded in this study which left 7,080 data of one year to be analyzed. That is, 1,998 data obtained during spring (March‒May), 1,214 during summer (June–August), 1,828 in fall (September‒November), and 2,040 in winter (December–January).

 
2.2 Approaches for the Determination of (OC/EC)_pri

2.2.1 Approach 1: Selection of the minimum value of OC/EC obtained during the measurement period

Determining the (OC/EC)_pri based on the minimum value of the OC/EC ratio is based on the assumption that the OC/EC should be the lowest if POC is the dominant fraction in OC. This is because the atmospheric OC increases with the formation of SOC. Castro et al. (1999) reported that the adequate minimum value of OC/EC to determine (OC/EC)_pri is the value that is consistently repeated and markedly lower than other values. Following previous studies, the OC/EC was obtained in this study for each recorded time and weather by dividing OC by EC, and the minimum OC/EC was selected to determine the (OC/EC)_pri.

 
2.2.2 Approach 2: Regression analysis of OC/EC in lower ranges

As mentioned for the Approach 1, the increase in OC/EC is presumed to be associated with the formation of SOC, and therefore, lower ranges of OC/EC value were selected to determine (OC/EC)_pri. While, the (OC/EC)_pri in Approach 1 was determined based on a single estimate of OC/EC ratio, a specific low range of OC/EC was selected in this Approach 2 and adequate (OC/EC)_pri which reflects all OC and EC data in the low range was calculated from the regression analysis. Herein, the regression line was drawn for the data within the lower 5‒20% of OC/EC and the slope of the linear regression line was selected as the value of (OC/EC)_pri. Thus, this second approach reduces uncertainty of determining the (OC/EC)_pri based on a single value obtained from the Approach 1. In this study, the data of OC/EC in the lower 5%, 10%, 15%, and 20% ranges were tested to draw the regression line. Subsequently, the (OC/EC)_pri values in each range were obtained and compared. In addition, as the influence of the direct emission from primary source becomes dominant for both OC and EC, the correlation between OC and EC becomes stronger and, thus, the obtained (OC/EC)_pri value becomes more reliable. The EC tracer method is most suitable in conditions where the influence of non-combustion emission sources is the lowest and the POC from combustion emission sources is the dominant form of OC. Thus, for data with strong correlations above or equal to 0.9, the OC/EC in the range where the OC_non-comb approximates to 0, was selected for the suitable value as (OC/EC)_pri.

 
2.2.3 Approach 3: Applying the Minimum R Squared (MRS) method

Millet et al. (2005) developed an approach for determining (OC/EC)_pri based on the assumption of the independence between the air pollutants from primary emission and secondary formation. In this approach, the POC and SOC are estimated based on the calculated (OC/EC)_pri from Eq. (1) using a single OC/EC value that falls within the OC/EC range of the measurement data and the correlation (R²) between the calculated SOC and the EC in the measurement data is obtained. This procedure is repeated at continuous intervals (0.01) for the entire OC/EC data within the target range. The OC/EC value corresponding to the minimum R² is the ratio at which the correlation between EC and SOC is the lowest and hence it is selected as the final value of (OC/EC)_pri. Wu and Yu (2016) named the method as the MRS method in the study that validated the applicability of the method for OC and EC values measured for one year in Hong Kong’s atmosphere. Wu and Yu (2016) also provided a computer program, written on Igor Pro (WaveMetrics, Inc. Lake Oswego, OR, USA) for direct calculation based on the of MRS method (https://sites.google.com/site/wuchengust). This program was applied to calculate (OC/EC)_pri of the Approach 3.

 
3 RESULTS AND DISCUSSION

 
3.1 Characteristics of OC and EC in PM_2.5 at Seoul

Table 1 summarizes the OC and EC concentrations measured at Seoul with the mean, minimum, and maximum values of OC/EC measured at 1-h intervals for a period of one year (from October 2012 to September 2013). The seasonal distributions of OC and EC concentrations and OC/EC are presented in Table 2. The mean OC concentration was the highest in winter (4.51 µg m^–3) and the lowest in fall (2.79 µg m^–3). The mean EC concentration was the highest in winter (2.08 µg m^–3) and the lowest in summer (1.62 µg m^–3 µg m^–3) and fall (1.62 µg m^–3). The mean OC/EC ratio was the highest in summer (2.28) and the lowest in fall (1.89).

 
3.2 Determination of (OC/EC)_pri

3.2.1 Approach 1: Selection of the minimum value of OC/EC obtained during the measurement period

The minimum value of OC/EC obtained during the measurement period is shown in Table 1. The minimum OC/EC ((OC/EC)_min) in each season is presented in Table 3. Based on the 1-h interval measurement data, the (OC/EC)_min was found to be 0.37 in spring, 0.46 in summer, 0.35 in fall, and 0.78 in winter, revealing that the ratio is lowest in fall and highest in winter (Table 3). The (OC/EC)_min of 0.35 was observed just once in the 1-h interval data, which reflected the inconsistency in observation. Thus, it was presumed that the estimated (OC/EC)_min did not accurately reflect the actual (OC/EC)_pri, based on which we concluded that the method based on the (OC/EC)_min is not suitable.

 
3.2.2 Approch 2: Regression analysis of OC/EC in lower ranges

Fig. 1 shows the regression analysis results of the lower 5%, 10%, 15%, and 20% values of OC/EC based on the 1-h interval measurement data, respectively. The results satisfied the linearity of regression with R ≥ 0.9 and exhibited a trend of increasing regression slope ((OC/EC)_ratio) with the increase in the data range. However, all of the regression analysis results showed negative y-intercept values which failed to account for the influence of OC_non-comb. Lin et al. (2009) attributed the negative y-intercept to potential errors that arised after necessary adjustments were made to reduce the overestimating factors of OC and EC measurements in the instrument. So, Lin et al. (2009) suggested that, in such cases, approximating the y-intercept value being 0 should be adopted. In this study, the OC and EC correlations were all high with R > 0.9 regardless of the data range. Thus, (OC/EC)_pri value was set for the lower 15% OC/EC data in each season (Table 4) and y-intercept values was approximated to 0. A seasonal variation for (OC/EC)_pri was approximately 2-fold between minimum and maximum of the (OC/EC)_pri. By calculating the POC and SOC using the selected (OC/EC)_pri (Table 5), the POC and SOC of the entire year were estimated to be 2.08 ± 1.24 µg m^–3 and 1.68 ± 1.32 µg m^–3, respectively, and each of them accounted for 58 ± 19% and 42 ± 19% of the total OC, respectively. For the 1-h measurement interval data, the portion of SOC was the highest at 46% (1.91 µg m^–3) in summer, which is reflection of the enhanced photochemical reaction to form SOC in summer (Fig. 2).

Fig. 1. Linear regression analysis of OC and EC using the range of low (OC/EC) ratios at Seoul. 

Fig. 2. Fraction of POC and SOC to OC estimated by Approach 2.

3.2.3 Approch 3: Applying the Minimum R Squared (MRS) method

In this study, the MRS program provided in Wu and Yu (2016) was used to obtain the (OC/EC)_pri across MRS points, as shown in Fig. 3. For 31.64% of all OC/EC ratios estimated based on the 1-h interval measurement data in this study, the (OC/EC)_pri was determined to be 1.77 (Fig. 3). The MRS method was applied to each season (Table 6), and the (OC/EC)_pri was found to plot within the range between 1.28 (fall) and 1.94 (spring).

Fig. 3. (OC/EC) ratio when minimum R² is observed between SOC and EC for 1-h measurement data. The shaded area is the frequency distribution of the OC/EC ratio, and green percentile represents the cumulative frequency rate. N represents the number of the total measurement data set.

The POC and SOC values, calculated using the (OC/EC)_pri values estimated using the MRS method (Table 5), were 3.00 ± 1.74 µg m^–3 and 0.76 ± 0.96 µg m^–3 for the entire year, respectively, and each of them accounted for 82 ± 17% and 18 ± 17% of the total OC, respectively. The POC and SOC estimates in each season demonstrated that the SOC percentage was the highest in summer (27%), which, as in the case of Method 2, was presumed to reflect the atmospheric OC/EC in summer associated with active photochemical reactions (Fig. 4).

Fig. 4. Fraction of POC and SOC to OC concentration estimated by Approach 3.

 
3.3 Comparison of (OC/EC)_pri and Estimated SOC in according with Temporal Resolution of the OC and EC Measurement Data

The value of (OC/EC)_pri might be influenced by temporal resolution of measurement of OC and EC. Thus, to compare the variations of (OC/EC)_pri estimations with respect to the temporal resolution of measurement at Seoul, the 24-h mean OC and EC concentrations from the 1-h interval measurement data and the OC and EC concentrations measured during 24-h (the PM_2.5 samples were collected on the filter for 24 hr and OC and EC were analyzed by lab OCEC analyzer) were used to calculate (OC/EC)_pri and these values were compared. The results from the three individual estimation methods were compared with the (OC/EC)_pri value estimated based on the 1-h interval data (presented in Section 3.2).

Applying the Approach 1 to the 24-h mean OC and EC concentrations determined based on the 1-h measurement data produced (OC/EC)_pri ((OC/EC)_min) = 0.95 which was approximately three times higher than the (OC/EC)_min = 0.35 determined based on the 1-h measurement data. Applying the Approach 1 to the 24-h measurement data of OC and EC concentrations at Seoul generated (OC/EC)_min value of 2.22 which was approximately six times higher than the (OC/EC)_min value of 0.35 determined based on the 1-h measurement data. It suggests that the prediction of SOC using the Approach 1 could result in approximately three- or six-fold deviation in the portion of POC contribution with respect to the time resolution of the measurement with a possibility of the underestimation of SOC concentration. The POC and SOC exhibited 49% and 50% for the 24-h mean data and 48% and 52% for the 24-h measurement data, respectively, whereby these were 18% and 81%, respectively, for the 1-h measurement data.

Applying the Approach 2 (the one using the regression analysis) to the 24-h mean data produced (OC/EC)_pri value of 1.31. Applying the Approach 2 to the 24-h measurement data produced (OC/EC)_pri value of 2.66 which is corresponded to the lower 5% OC/EC data. This value was two times higher than those for the 1-h measurement data case ((OC/EC)_pri value of 1.22). The portion of POC and SOC were 66% and 34% for the 24-h mean data and 54% and 46% for the 24-h measurement data, respectively, and it exhibited ≤ 10% deviation from the 1-h measurement data results, 58% and 42%, respectively. In terms of the 24-h mean data based on the 1-h measurement data, the smallest value was observed during fall (1.12) and the largest in winter (1.83), while in terms of the 24-h measurement data, the smallest value was observed in winter (2.14) and the largest in spring (4.42) showing over the factor of two variation. For the 1-h measurement data, the smallest value was observed in summer (0.76) and the largest in winter (1.57) showing seasonal variation of the (OC/EC)_pri values were different by different time resolution measurement data. It was surmised that the SOC fraction be the highest in summer due to the activity of the photochemical reactions. For the 24-h mean data, the SOC fraction was 37% in summer which was lower than that in spring (63%) but higher than in fall (29%) and winter (21%). For the 24-h measurement data, the SOC fraction was 25% in summer which was lower than in spring (42%) and winter (40%) but higher than in fall (24%). For the 1-h measurement data, the SOC fraction was 46% in summer which was higher than in spring (43%), fall (40%), and winter (28%). Thus, the shortest temporal resolution data, 1-h measurement data would adequately reflecting the seasonal variation of SOC generation.

Applying the Approach 3 to the 24-h mean data, the (OC/EC)_pri value of 1.92 was obtained. When using the 24-h measurement data, the calculated (OC/EC)_pri value was 3.92 which was approximately two times higher than the (OC/EC)_pri value of 1.77 estimated based on the 1-h measurement data. The portion of POC and SOC were 89% and 11% for the 24-h mean data, 77% and 23% for the 24-h measurement data, and 82% and 18% for the 1-h measurement data, respectively. Though the (OC/EC)_pri value for the 24-h measurement data was about three times higher than that for the 1-h measurement data, the POC fraction for the 24-measurement data was lower than that for the 1-h measurement data. It was most likely due to the approximately two times higher mean concentration of OC (7.49 µg m^–3) and the mean OC/EC (5.31) compared to the 1-h measurement data. The (OC/EC)_pri in each season determined using the Approach 3 revealed that the seasonal deviation in (OC/EC)_pri ranged between 1.35 (fall) and 2.16 (spring) for the 24-h mean data and between 2.65 (fall) and 6.45 (winter) for the 24-h measurement data, reflecting large seasonal deviation. However, the deviation was relatively small for the 1-h measurement data, that is, between 1.28 (fall) and 1.94 (spring).

When using the 24-h mean data or 24-h measurement data, the tendency of the increase of (OC/EC)_pri was observed. Thus, the POC fraction becomes higher and the SOC fraction becomes lower compared to the (OC/EC)_pri value obtained from the 1-h measurement data. When POC and SOC are predicted by estimating the (OC/EC)_pri from the 24-h and 1-h measurement data with identical region and period of measurement, despite the difference in specific measurement site the (OC/EC)_pri value for the 1-h measurement data was found to be more than two times larger than that for the 24-h measurement data. This was likely due to the presence of OC in the atmosphere after being released from the emission source with prolonged time of OC and EC concentration measurements which could have led to secondary generation and consequently to the higher (OC/EC)_pri value compared to that of the 1-h measurement data. Thus, Approaches 1 and 3 with the 24-h OC and EC data has overestimated POC concentration. Comparing the POC and SOC fractions in each season revealed that the higher level of SOC in summer was clearly observed from only the 1-hour measurement data determined based on the Approach 2.

 
3.4 Validation of SOC Estimates from (OC/EC)_pri in the Atmosphere of Seoul

Fig. 5 shows the POC and SOC concentrations and their fractions in total OC, calculated based on the (OC/EC)_pri using the three approaches described in Section 3.2.

Fig. 5. (a) Average concentration and (b) fraction of POC and SOC to OC estimated from three Approaches.

While estimating the (OC/EC)_pri, the deviation across the three approaches could be examined and the approach with MRS (Approach 3) was found to exhibit the highest value of (OC/EC)_pri regardless of the temporal resolution of the measurement. Therefore, as shown in Fig. 5, applying the (OC/EC)_pri obtained through the Approach 3 provided the lowest SOC value compared to the values from the Approach 1 or 2. This implied that EC can be correlated with SOC due to the relation of emission between EC and precursors of SOC, and it made high correlation between EC and SOC. Thus, it is possible that the value of OC/EC at the point of the lowest correlation between SOC and EC might not reflect the (OC/EC)_pri of the lowest SOC, and in the actual atmosphere, SOC might be partially reflected in POC, thereby overestimating POC (Wu et al., 2019). In the case of Seoul, with such a significant impact of long-range transport of air pollutants, EC may not be influenced solely from the primary emission sources that existed around Seoul, implicating a possibility of underestimating SOC through the MRS method.

For Approach 1, the uncertainty in SOC estimation was found to be high if the (OC/EC)_pri based on a single low-frequency value was used. It was due to the lack of consistency in the measurements of (OC/EC)_min for the 1-h measurement data. As shown in Fig. 5, POC and SOC concentrations based on the Approach 1 deviated from the Approach 2 or 3 by as low as 1.09 µg m^–3 and as high as 2.66 µg m^–3, whereas for the POC and SOC fractions, the SOC accounted for most of the OC in the Approach 1 in contrast to the Approach 2 or 3. This confirmed that the Approach 1 is not suitable for estimating POC and SOC at Seoul.

For the Approach 2, the resulting (OC/EC)_pri exhibited higher correlations with OC and EC for lower range values. The observed seasonal variations in (OC/EC)_pri corresponded with the previous studies that applied the Approach 2 (Table 7). In addition, the typical high SOC in summer was reflected (Fig. 5). Thus, for the 1-h measurement data, application of the Approach 2 using linear regression analysis of the lower range data of OC/EC was found to estimate the most adequate estimation of (OC/EC)_pri which in turn is essential for estimating SOC in PM_2.5 in the atmosphere of Seoul.

Table 7 summarizes the (OC/EC)_pri obtained using the EC tracer method in the previous studies. The (OC/EC)_pri ranged between 1.0 and 2.8 from the various previous studies, and the (OC/EC)_pri from the Approaches 2 and 3 examined in this study fell within the previous suggested range while the Approach 1 did not. Thus, the (OC/EC)_pri determined from the Approach 2 or 3 confirmed the validity of the EC tracer method which has been widely applied in the previous studies.

4 CONCLUSIONS

Direct measurement of SOC contribution in PM_2.5 is impossible because SOC is formed by the oxidation in the atmosphere. Hence, the EC tracer method was developed and widely used, whereby EC is harnessed as the tracer of POC. Previous studies have hypothesized that (OC/EC)_pri adequately reflects the emission sources of primary combustion which is based on the measured values of OC and EC. So far, various techniques adopting the EC tracer method have been applied in SOC estimation studies (Choi et al., 2012; Jeon et al., 2015; Kim et al., 2018). In this study, three conventional approaches for (OC/EC)_pri determination were applied to 1-h and 24-h measurement data of OC and EC concentrations in PM_2.5 for one year at Seoul, Korea to identify the most suitable approach of SOC estimation at Seoul.

Three approaches were applied to the 1-h measurement data to determine the (OC/EC)_pri and the comparison results regarding the temporal resolution of the measurement showed that the (OC/EC)_pri was high for the 24-h measurement data than for the 1-hour measurement data across all three approaches. This showed that the increase of (OC/EC)_pri was due to the combination of low and high values of OC/EC with prolonged measurement of OC and EC concentrations for 24-h measurement. In addition, it is presumed that (OC/EC)_pri from the 24-h measurement might not reflecte direct emissions of OC and EC from emission sources and seasonal variations in the ambient conditions affected the variation of OC/EC ratio.

Comparison of the three approaches based on the 1-h measurement data revealed that the (OC/EC)_min values were inconsistent when the Approach 1 was used. Thus, it was concluded that the Approach 1 cannot reliably reflect the influence of constant emission sources. The (OC/EC)_pri estimated using the Approach 3 was approximately 1.3 times higher than that estimated using the Approach 2 which was attributed to the inability of the correlation analysis for EC and SOC to adequately account for the independence of EC and SOC due to impact of long-range transport of air pollutants at Seoul, Korera. Lastly, the (OC/EC)_pri estimated using the Approach 2 was more suitable for the basic application of EC tracer approach that provides (OC/EC)_pri for the minimum ratio of OC/EC.

From this study, we concluded that the (OC/EC)_pri estimated using linear regression analysis of OC and EC concentrations is the most suitable for the lower 5–20% OC/EC range using the 1-h measurement of OC and EC.

 
ACKNOWLEDGMENTS

This research was supported by the FRIEND (Fine Particle Research Initiative in East Asia Considering National Differences) Project through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (2020M3G1A1114537) and Technology Development Program to Solve Climate Changes through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (2019M1A2A2103953)