Next Article in Journal
Climatology of the Nonmigrating Tides Based on Long-Term SABER/TIMED Measurements and Their Impact on the Longitudinal Structures Observed in the Ionosphere
Previous Article in Journal
The Quantitative Reconstruction of Paleoclimate in the Huangling Region of the Chinese Loess Plateau during the Middle and Late Holocene
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Atmosphere
Volume 15
Issue 4
10.3390/atmos15040477
atmosphere-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Modification of Hybrid Receptor Model for Atmospheric Fine Particles (PM2.5) in 2020 Daejeon, Korea, Using an ACERWT Model

by
Sang-woo Han
1,
Hung-soo Joo
2,
Kyoung-chan Kim
2,
Jin-sik Cho
1,
Kwang-joo Moon
3 and
Jin-seok Han
2,*
1
E2M3 Inc., Anyang 14059, Republic of Korea
2
Department of Environmental Engineering, Anyang University, Anyang 14028, Republic of Korea
3
National Institute of Environmental Research, Incheon 22689, Republic of Korea
*
Author to whom correspondence should be addressed.
Atmosphere 2024, 15(4), 477; https://doi.org/10.3390/atmos15040477
Submission received: 30 January 2024 / Revised: 27 March 2024 / Accepted: 9 April 2024 / Published: 12 April 2024
(This article belongs to the Section Air Quality)
Browse Figures
Review Reports Versions Notes

Abstract

:
Hybrid receptor models overestimate the contribution of background areas (no specific emission sources), like the Yellow Sea in Korea. This study aimed to improve model performances using Advanced Concentration Emission and Retention Time Weighted Trajectory (ACERWT). ACERWT was combined with a positive matrix factorization (PMF), back trajectory, and Regional Emission Inventory in Asia (REAS). The PMF receptor model used one year of data from Korea’s Central Air Environment Research Center. In the PMF receptor model, eight sources (dust/soil, secondary nitrate, biomass burning, vehicles, secondary sulfate, industry, coal combustion and sea salt) influenced PM2.5 pollution at the receptor site (Daejeon, Korea). Secondary sulfate was the most dominant source, followed by secondary nitrate and vehicle sources. ACERWT results showed high contributions from China, Japan, and Korean regions, while the contribution from the Yellow Sea was significantly lower. Several regions, such as the eastern and south-eastern areas of China, the southern area of Taiwan, the western area of Tokyo, and the central area of Korea, showed high contributions due to large-scale emission facilities and industrial complexes. In this study, the ACERWT model significantly improved its performance regarding regional contributions to PM2.5 pollution at the receptor site.

1. Introduction

The region of East Asia emits a large amount of air pollutants owing to its high population density, industrial activities, and energy consumption [1]. Thus, various challenges in this region have been tried to identify high-concentration events and significant emission sources of fine particles (PM2.5) [2]. A dispersion model based on pollution sources was used earlier in the year. Receptor models have been developed to overcome some limitations of the earlier dispersion models and have been recognized as a valuable tool in air pollution modeling [3]. Since chemical element balance (CEB) was presented by Miller et al. (1972), receptor models have been continuously developed and upgraded up to now [4].
Paatero (1997) studied using various methods to find out the effect of fine particles at the receptor site and tried to calculate the relationship between the generation sources and concentrations of fine particles. Eventually, the positive matrix factorization (PMF) model was developed to estimate the source profile and mass contribution of fine particles [5]. Many studies using the PMF receptor model have been conducted to discover primary sources and regions (or areas) contributing to receptor sites. Recently, hybrid receptor models have been actively applied in East Asia to track source locations to receptor sites [2,6,7,8,9,10,11,12]. Most studies using the hybrid receptor model in Korea focused on the source apportionment of PM2.5 concentration in the capital and background areas [2,7,11,12]. Recently, various trials were carried out to identify information about the source location. Linkage analysis with a back trajectory model has been frequently used for tracing source locations [6,7,8,9,10,11,12].
Owing to the fact that the PMF receptor model has limitations in identifying the source location, hybrid receptor models, which are a combined PMF receptor model and back trajectory models of air masses, have been frequently studied, e.g., Potential Source Concentration Function (PSCF), Concentration Weight Trajectory (CWT) and Residence Time Weight Concentration (RTWC) [13,14,15]. However, these models tend to show the contribution of PM2.5, especially in regions with no emission source (i.e., ocean areas), to be unexpectedly high [16,17]. Several hybrid receptor models developed to overcome this limitation still lack information on source locations [18,19,20,21,22,23] and need improvement.
In our previous study using the CWT model (modified CWT model, MCWT), we tried to improve the limitation of the regional contribution of the hybrid receptor model caused by overlapping several air masses. Although the contribution of the Yellow Sea to the receptor site in the MCWT model was lowered, the result of the Yellow Sea contribution was still high and should be complemented [24]. Therefore, as an improvement of the hybrid receptor model, which identifies primary sources and regional contributions to the receptor site, in this study, the Advanced Concentration, Emission, and Retention Time Weighted Trajectory (ACERWT) model was newly introduced and used to identify source locations and those contributions more clearly to the receptor site.
Moreover, it can be expected that the approach introducing the ACERWT model in this study has a significant benefit in figuring out the PM2.5 contributions of surrounding regions on a large scale.

2. Experimenter Method

2.1. Sampling Location and Monitoring Site

The monitoring site was one of the supersites (Central Air Environment Research Center), which was located in the central region of Korea in the Daejeon Metropolitan region (36°19′21.4″ N (latitude), 127°24′49.7″ E (longitude)). The location of the monitoring site is described in Figure 1.
Daejeon metropolitan region is one of the critical regions for interpreting Korea’s national air pollution. This region is located in the center of Korea. Thus, it is surrounded by the capital region (north), large industrial complexes (west), and agricultural areas (south), and these surrounding regions influence the air pollution in this area.

2.2. Sampling and Data Analysis

This study used one year of full measurement data from the Central Air Environment Research Center. Measurement parameters of PM2.5 are mass concentration, eight ions (SO42−, NO3−, Cl−, Na+, NH4+, K+, Mg2+, Ca2+), organic carbon (OC), elemental carbon (EC), and 17 heavy metals (Si, S, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, As, Se, Br, Ba, and Pb). PM2.5 mass concentration was measured in real-time by a BAM-1020 (Met One Ins., Grants Pass, OR, USA), and ions were measured by an AIM-9000D (AIM, URG Co., Chapel Hill, NC, USA) [25,26] and those lower detection limits were 0.0008~0.008 µg/m3. Carbonaceous compounds (OC and EC) were measured by a Sunset OC/EC analyzer using the thermal/optical transmittance (TOT) method (OCEC Aerosol Analyzer, Sunset Laboratory Inc., Oregon, USA). Those detection limits were 0.2 µg/m3 for OC and 0.0007 µg/m3 for EC, respectively. Table 1 shows the gas and temperature conditions of the carbon analyzer [25]. Heavy metals were analyzed by an Online XRF (Xact® 625i, SailBri Cooper, Inc., Tigard, OR, USA).
The mass concentration of 17 elements was detected by the non-destructive analysis method. Further details on sampling and analysis methods are provided in the references [25,27]. Uncertainties of measurement data were examined by the procedure of the PMF receptor model [27] and Equation (1), i.e., the elimination of missing values, chemical species with low reliability, signal-to-noise ration < 0.2, measurement detection limits (MDL), and so on. Table 2 shows the measurement detection limits of PM2.5 components.
( e r r o r   f r a c t i o n × c o n c e n t r a t i o n ) 2 + ( 0.5 × M D L s ) 2

2.3. Positive Matrix Factorization (PMF)

Concentration data of PM2.5 components were used in the PMF receptor model to trace primary emission sources at the receptor site. The PMF receptor model was operated using the methods reported in the 2020 Annual Intensive Air Quality Monitoring Station report by the National Institute of Environmental Research (NIER) [27]. Input and uncertainty data were pre-treated using concentration data and the standard errors of PM2.5 components. Finally, the total input data was 5257, used in the PMF receptor model.

2.4. Emission Inventories

Regional Emission Inventory in Asia (REAS) version 3.2, provided by the National Institute for Environmental Science (NIES) in Japan, was used as East Asia’s air pollution emission data. This emission inventory was updated by Kurokawa et al. in 2020, and it included the real emission data set of East-North Asia from 1950 to 2015 [28]. This emission inventory was composed of 10 parameters (SO2, NOx, CO, NMVOC, PM10, PM2.5, BC, OC, NH3, and CO2) and seven sources (power plant, industry, transportation, domestic fuel combustion, industrial process, agriculture, etc. (suspended emission, solvent use, and human being). Monthly emission data were obtained with a 0.25 × 0.25 latitude and longitude resolution.

2.5. Advanced Concentration, Emission, and Retention Time-Weighted Trajectory (ACERWT)

The CWT model is frequently applied in the related analysis of the hybrid receptor model in Korea. The CWT model expresses the regional contributions using the weighted trajectories of input air masses based on air pollutant concentration at the receptor site. The equation of CWT is shown in Equation (2).
C W T i , j = ∑ l = 1 L C l τ i , j , l ∑ l = 1 L τ i , j , l
C W T i , j means the model results at the grids i   l a t i t u d e , j   ( l o n g i t u d e ) , C l is the concentration (μg/m3) when trajectory l reaches the receptor site, τ i , j , l is the retention time of trajectory l in the grid ( i , j ) . The CWT model has a limitation of overestimation in some grids owing to the overlapping of several trajectories [24]. Thus, a weighted method in concentrations and emissions was tried to overcome this limitation.
ACERWT model is the combination method using each grid’s emission data of East-North Asia and the CWT method. As the first step in ACERWT, the emissions at the grid ( i , j ) which l trajectory passes are estimated as like Equation (3) [24].
C k , l = S k , l ∑ k = 1 N l S k , l = C l S k , l S l
C k , l means the concentration considered the emission data and its weighting factor at the receptor site for l trajectory. k is the area where l trajectory passes, S k , l is the emissions at the k area in the l trajectory. N l is the total number of k in the trajectory l , S l is the total emission on the trajectory l . As the second step, Equation (4) shows the calculation of the ACERWT model result.
A C E R W T i , j = ∑ l = 1 M ∑ k = 1 N l C k , l τ i , j , k , l ∑ l = 1 M ∑ k = 1 N l τ i , j , k , l  
A C E R W T i , j is the relative contribution of PM2.5 concentration to the receptor site at the grid ( i , j ) . τ i , j , k , l is used as the retention time at the k area contained in the grid ( i , j ) on the l trajectory.
Figure 2 shows the flow chart of the ACERWT model. ACERWT is the combination model to identify the regional contribution to the receptor site, PMF receptor model, HYSLIT (back trajectory), and emission inventory. The size of the grid was 1° and the region of Korea was located in the center of Northeast Asia.

3. Results and Discussions

3.1. Emission Inventory

To figure out the regional source contribution of major sources in the grids of the ACERWT model, Figure 3 shows the summated emissions of PM2.5, SOx and NOx for vehicles, industries, coal combustions, and the total source in East Asia. The contribution of vehicle sources is higher than that of other sources in Korea. At the same time, the contributions of industrial and coal combustion_domestic (residential coal combustion) sources are higher in China. Mainly, coal combustion sources for power plants are concentrated in the Sandung Peninsula, near Korea.

3.2. Chemical Composition of PM2.5

Table 3 shows the concentrations of chemical species of PM2.5. PM2.5 concentrations were 22.2 ± 15.3 µg/m3 in the measurement periods, and the maximum concentration was reached by 104 µg/m3. Fractions of ions, carbonaceous compounds, and heavy metals are 51%, 22%, and 12%, respectively.
Figure 4 and Table 4 show the fractions of chemical species depending on PM2.5 concentration level and frequencies depending on PM2.5 concentration level by season. NO3− and NH4+ increased with increasing PM2.5 concentration, and high concentrations of PM2.5 were frequently observed during the winter. This means that the formation of secondary aerosols such as (NH4)2SO4 and NH4NO3 actively progressed with increasing PM2.5 concentration, especially the formation of NH4NO3, which was more active in winter. Guo et al. 2010 reported that the reaction of NH4NO3 ↔ HNO3 + NH3 could be well progressed by the heterogeneous reaction with temperature conditions [29]. In addition, ammonia concentration at 2021 Daejeon was high in the winter season, and the formation of NH4NO3 progressed well owing to heterogeneous reactions in our recent study [30]. Sulfate (SO42−−) and OC are well-known pollutants of long-range transportation and vehicle emissions, respectively. The contribution to high PM2.5 concentration by these two species was not higher than NO3−. The nitrate effect mainly caused the high PM2.5 concentrations.

3.3. Source Apportionment Using PMF Receptor Model

The correlation analysis between predicted and observed data using input data in the PMF receptor model evaluated correlation coefficients, slopes, and intercepts as all affordable values. Correlation coefficients for major components of PM2.5, such as SO42−, NO3−, NH4+, OC, EC, K, and so on, indicated 0.8 or bigger values. Figure 5 shows the correlation between the predicted PMF receptor model and measurement data of PM2.5. The correlation coefficient was 0.94. This coefficient was reasonably affordable and similar to previous studies [7].
Figure 6 and Figure 7, and Table 5, show the results of the PMF receptor model. Eight factors were determined: dust/soil, secondary nitrate, biomass burning, vehicles, secondary sulfate, industry, coal combustion, and sea salt sources. Seasonal contribution (unit: μg/m3) for every eight sources is shown in Figure 8, i.e., spring: March to May 2020, summer: June to August 2020, autumn: September to November 2020, and winter: January to February 2020 and December 2020, respectively.
The first factor showed a high contribution of Ca2+, Mg2+, Ti, and Fe, and it was classified as a Dust/Soil source. The average concentration of dust was 1.73 µg/m3 (6.2%) (Table 5), and the seasonal contribution in summer was the highest (Figure 8). Similar to this study, Ca2+, Mg2+, Si, Ti, Fe, and Mn were used as markers for soil and dust sources in previous studies [24,31]. The second factor showed a high contribution by NO3− and NH4+, and it was classified as a secondary nitrate source. The average concentration of this source was 7.2 µg/m3 (25.7%) (Table 5), and the seasonal contribution in the winter season was the highest (Figure 8). NO3− and NH4+ were used as markers for secondary nitrate sources [24,30,31], as similar to this study. Secondary nitrates are composed of NO3− and NH4+, which are oxidized NO2 combined with NH4+ and exist as the form of NH4NO3 in the atmosphere [9,32,33]. NH4NO3 is actively formed in the winter season by a heterogeneous reaction. Similarly, ammonia concentration was high in the winter season at 2021 Daejeon, and the formation of NH4NO3 was high in our previous study [30]. The third factor showed a high contribution of Cl− and K, and it was classified as a biomass-burning source. Cl−, K and OC were well-known as markers for biomass-burning sources [30,31]. The average concentration of this source was 1.8 µg/m3 (6.5%) (Table 5), and the seasonal contribution in the autumn and winter seasons was high (Figure 8). Biomass burning sources are frequently observed from open burnings in agricultural areas during the late fall to the winter season in Korea. The fourth factor showed a high contribution by OC, EC, and Cu, and it was classified as a vehicle source. The average concentration of this source was 4.38 µg/m3 (15.6%) (Table 5). The seasonal contribution was not significantly affected by season changes (Figure 8) and is the typical urban site pattern. Generally, OC and EC are well known as the emission species from vehicle exhaust. Previous reports show OC is high in gasoline exhaust, while EC is high in diesel exhaust [34,35,36,37]. Unfortunately, gasoline and diesel vehicles were not distinguished in this study. The fifth factor showed a high contribution of SO42− and NH4+ and was classified as a secondary sulfate source. The average concentration of this source was 9.82 µg/m3 (34.9%) (Table 5), and the contribution of PM2.5 in the Daejeon region was the highest in 8 factors. The seasonal contribution in summer was slightly higher than other seasons (Figure 8). Secondary sulfate is composed of SO42− and NH4+, which is the oxidized SO2 combined with NH4+, and it exists in the form of (NH4)2SO4 in the atmosphere. Dockery et al. 2007 reported that high humidity and temperature influenced the conversion from SO2 to SO42−. Thus, the formation of (NH4)2SO4 was more active in the summer and daytime than in the winter and nighttime seasons [7,38]. The sixth factor showed a high contribution by heavy metals such as Mn, Fe, Ni, Cu, and Zn, classified as industry sources. The average concentration of this source was 0.57 µg/m3 (5.6%) (Table 5). The seasonal contribution was not significantly affected by season changes (Figure 8) [39]. The seventh factor showed a high contribution by As, and it was classified as a coal combustion source. The average concentration of this source was 0.38 µg/m3 (1.4%) (Table 5), and the seasonal contribution in the winter season was higher compared to other seasons (Figure 8). The 8th factor showed a high contribution of Na+ and Cl−, which was classified as a sea salt source. Sea salt, composed of Na+ and Cl−, is produced from the bubbles bursting in the ocean and coast. The contribution of Cl− was considerably low, which means that the loss of Cl− might be caused by the reaction of HNO3 (produced from the reaction of NH4NO3 ↔ HNO3 + NH3) and NaCl [40].

3.4. Results of the Regional Contributions by ACERWT

The model results between CWT and ACERWT are compared in Figure 9. Unlike the CWT result, the ACERWT model showed no contribution from the Yellow Sea region (Figure 9, left), while the CWT result showed a high contribution from the overlapping of several trajectories (Figure 9, right) [24]. This result suggests that the ACERWT model can be an alternative to overcome the limitations of the CWT model. In addition, the CWT model showed a high contribution from the near area of the receptor site and Yellow Sea regions, and the contributions of Japan and South China were low. In contrast, the ACERWT model showed a high contribution from East China and North-East China. From the point of view of the distribution of industries and population, the result of the ACERWT model is relatively reasonable.
In the results of the ACERWT model, the regional contribution of North-Eastern China and Eastern China was the highest, followed by Southern Japan and South Korea. This result was based on the differences between back-tracking periods and emission inventories. The regional contributions by Yellow Dust were from Mongolia, a desert area in China. Filonchyk et al. (2022) mentioned that Yellow dust was produced from sandstorms in desert areas in China [41]. The contributions of eastern and south-eastern areas were high in the China region. According to Li et al.’s (2016) report, large-scale emission facilities and industrial complexes were located with a high density in Jiangsu province, Shanghai, and Henan province in Eastern China [42]. Beijing and Shanghai, i.e., China’s largest metropolitan cities, have many anthropogenic emission sources in this Eastern or South-Eastern China. In the Southern Taiwan region, which contains a large harbor and the urban city of Kaohsiung, Tainan also influenced PM2.5 pollution at the receptor site. In Japan, the regional contribution of the western area of Tokyo, Southern Japan, and Western Japan contributed to PM2.5 pollution at the receptor site. Tokyo is the largest city in Japan, and it has the Isogo coal power plant and the Hekinan coal power plant located in the Nagoya region (Southern Japan). Goto and Kumamoto cities, with energy power plants and anthropogenic emission sources due to the high population density in those urban cities, are located in Western Japan [43].
In the domestic region, the regional contribution of Gaeseong city in North Korea is high because of the high population density and industrial complex in Gaeseong city. Moreover, the regional contribution of the Capital region (Seoul, Gyeonggi, and Incheon), Chungcheong region (Dangjin, Cheongju, and Sejong) and Gyeongsang region (Busan and Ulsan) was high; the population density of the capital region is considerably high; Gyeonggi has many industrial complexes with high density, and Incheon has a large scale of harbors and several industrial complexes. Large-scale emission sources such as the iron and steel industry and coal power plants are distributed in Dangjin; Cheongju has industrial complexes and large populations; and Sejong has a high-density population as an administrative capital. Busan and Ulsan metropolitan cities are located in the Gyeongsang region, and both cities have large-scale harbors and high populations.

4. Conclusions

In this study, we improved the hybrid receptor model using a model of Advanced Concentration, Emission, and Retention Time Weighted Trajectory (ACERWT). Using the CWT model (modified CWT model, MCWT) in our previous study, we tried to improve the limitation of the regional contribution of the hybrid receptor model caused by overlapping several air masses. The MCWT model could improve the performance of the regional contribution model. However, it suggested further studies. Thus, we tried to use the ACERWT model combined with a Positive Matrix Factorization (PMF), the HYSPLIT (back trajectory) model and the Regional Emission Inventory. In the PMF receptor model, secondary sulfate source showed the highest contribution (35%), followed by secondary nitrate sources (26%), vehicle sources (16%), biomass burning, dust/soil and industry sources (6%, respectively), sea salt sources (5%), and coal sources (1%), respectively. The result in ACERWT showed that the limitation of the Yellow Sea impact was significantly improved in the previous studies. The contribution of the Yellow Sea to the receptor site was significantly decreased, while the contribution of China, Japan, and Korea was increased. Regions such as the eastern and south-eastern areas of China, the southern area of Taiwan, the western area of Tokyo, and the central area of Korea, which significantly showed high contributions, have large-scale emission facilities and industrial complexes. Regions of eastern and south-eastern areas of China showed a high contribution. Beijing and Shanghai, i.e., the largest metropolitan cities in China, have many anthropogenic emission sources in this eastern or south-eastern region of China. Southern Taiwan, which contains a large harbor and the urban cities of Kaohsiung and Tainan, has also highly influenced PM2.5 pollution at the receptor site. The regional contribution of the Western area of Tokyo, Southern Japan, and Western Japan regions influenced the regional contribution of Gaeseong city in North Korea, which was also high because of high population density and industrial complexes. This study’s modified ACERWT model could estimate the regional contribution of PM2.5 pollution at the receptor site. The regional contribution results were reasonable, and the ACERWT model is significantly upgraded compared to the previously applied models. However, it still has a limitation for the quantitative contribution. In future studies, the improvement of the pretreatment for model application and the quantification of the regional contribution should be studied.

Author Contributions

Conceptualization, J.-s.H.; Methodology, S.-w.H. and J.-s.H.; Software, J.-s.C.; Formal analysis, S.-w.H.; Investigation, K.-j.M.; Data curation, K.-c.K.; Writing—original draft, S.-w.H.; Writing—review & editing, H.-s.J. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable

Informed Consent Statement

Not applicable

Data Availability Statement

The data presented in this study are openly available at [https://doi.org/10.3390/atmos13111902], reference number [24].

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 (2020M3G1A1114999) and Experts Training Graduate Program for Particulate Matter Management from the Ministry of Environment, Korea.

Conflicts of Interest

The authors declare no conflict of interest. Sang-woo Han, Jin-sik Cho are employees of E2M3 Inc. But this paper was not funded by E2M3. The company had no roles in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript, or in the decision to publish the articles. The paper reflects the views of the scientists and not the company.

References

  1. Jung, E.M.; Kim, K.N.; Park, H.; Shin, H.H.; Kim, H.S.; Cho, S.J.; Kim, S.T.; Ha, E.H. Association between prenatal exposure to PM2.5 and the increased risk of specified infant mortality in South Korea. Environ. Int. 2020, 144, 105997. [Google Scholar] [CrossRef]
  2. Han, J.S.; Moon, K.J.; Kim, Y.J. Identification of potential sources and source regions of fine ambient particles measured at Gosan background site in Korea using advanced hybrid receptor model combined with positive matrix factorization. J. Geophys. Res. 2006, 111, D22217. [Google Scholar] [CrossRef]
  3. Hwang, I.J.; Kim, D.S. Research trends of receptor models in Korea and Foreign Countries and Improvement Directions for Air Quality Management. J. Korean Soc. Atmos. Environ. 2013, 29, 459–476. [Google Scholar] [CrossRef]
  4. Miller, M.S.; Friedlander, S.K.; Hidy, G.M. A chemical element balance for the Pasadena aerosol. J. Colloid Interface Sci. 1972, 39, 165–176. [Google Scholar] [CrossRef]
  5. Paatero, P. Least squares formulation of robust non-negative factor analysis. Chemom. Intell. Lab. Syst. 1997, 37, 23–35. [Google Scholar] [CrossRef]
  6. Reff, A.; Eberly, S.I.; Bhave, P.V. Receptor modeling of ambient particulate matter data using positive matrix factorization: Review of existing methods. J. Air Waste Manag. Assoc. 2007, 57, 146–154. [Google Scholar] [CrossRef]
  7. Hwang, I.; Hopke, P.K. Estimation of source apportionment and potential source locations of PM2.5 at a west coastal IMPROVE site. Atmos. Environ. 2007, 41, 506–518. [Google Scholar] [CrossRef]
  8. Thurston, G.D.; Ito, K.; Lall, R. A source apportionment of U.S. fine particulate matter air pollution. Atmos. Environ. 2011, 45, 3924–3936. [Google Scholar] [CrossRef]
  9. Belis, C.A.; Karagulian, F.; Larsen, B.R.; Hopke, P.K. Critical review and meta-analysis of ambient particulate matter source apportionment using receptor models in Europe. Atmos. Environ. 2013, 69, 94–108. [Google Scholar] [CrossRef]
  10. Lu, Z.J.; Liu, Q.Y.; Xiong, Y.; Huang, F.; Zhou, J.B.; Schauer, J.J. A hybrid source apportionment strategy using positive matrix factorization (PMF) and molecular marker chemical mass balance (MM-CMB) models. Environ. Pollut. 2018, 238, 39–51. [Google Scholar] [CrossRef]
  11. Park, M.B.; Lee, T.J.; Lee, E.S.; Kim, D.S. Enhancing source identification of hourly PM2.5 data in Seoul based on a dataset segmentation scheme by positive matrix factorization (PMF). Atmos. Pollut. Res. 2019, 10, 1042–1059. [Google Scholar] [CrossRef]
  12. Park, J.E.; Kim, H.W.; Kim, Y.K.; Heo, J.B.; Kim, S.W.; Jeon, K.H.; Yi, S.M.; Hopke, P.K. Source apportionment of PM2.5 in Seoul, South Korea and Beijing, China using dispersion normalized PMF. Sci. Total Environ. 2022, 833, 155056. [Google Scholar] [CrossRef]
  13. Hopke, P.K.; Gao, N.; Cheng, M.D. Combining chemical and meteorological data to infer source areas of airborne pollutants. Chemom. Intell. Lab. Syst. 1993, 19, 187–199. [Google Scholar] [CrossRef]
  14. Seibert, P.; Kromp-Kolb, H.; Baltensperger, U.; Jost, D.T.; Schwikowski, M.; Kasper, A.; Puxbaum, H. Trajectory analysis of aerosol measurements at high Alpine sites. In Transport and Transformation of Pollutants in the Troposphere; Borrell, P.M., Ed.; Elsevier: New York, NY, USA, 1994; Volumes 689–693. [Google Scholar]
  15. Stohl, A. Trajectory statistics—A new method to establish sourcereceptor relationships of air pollutants and its application to the transport of particulate sulfate in Europe. Atmos. Environ. 1996, 30, 579–587. [Google Scholar] [CrossRef]
  16. Han, Y.J.; Holsen, T.M.; Hopke, P.K.; Cheong, J.P.; Kim, H.; Yi, S.M. Identification of source locations for atmospheric dry deposition of heavy metals during yellow-sand events in Seoul, Korea in 1998 using hybrid receptor models. Atmos. Environ. 2004, 38, 5353–5361. [Google Scholar] [CrossRef]
  17. Lupu, A.; Maenhaut, W. Application and comparison of two statistical trajectory techniques for identification of source regions of atmospheric aerosol species. Atmos. Environ. 2002, 36, 5607–5618. [Google Scholar] [CrossRef]
  18. Fan, W.; Qin, K.; Xu, J.; Yuan, L.; Li, D.; Jin, Z.; Zhang, K. Aerosol vertical distribution and sources estimation at a site of the Yangtze River Delta region of China. Atmos. Res. 2019, 217, 128–136. [Google Scholar] [CrossRef]
  19. Dimitriou, K.; Mihalopoulos, N.; Leeson, S.R.; Twigg, M.M. Sources of PM2.5-bound water soluble ions at EMEP’s Auchencorth Moss (UK) Supersite revealed by 3D-Concentration Weighted Trajectory (CWT) model. Chemosphere 2021, 274, 129979. [Google Scholar] [CrossRef]
  20. Wang, Y.Q.; Zhang, X.Y.; Arimoto, R. The contribution from distant dust sources to the atmospheric particulate matter loadings at XiAn, China during spring. Sci. Total Environ. 2006, 368, 875–883. [Google Scholar] [CrossRef]
  21. Jeong, U.K.; Kim, J.H.; Lee, H.L.; Jung, J.S.; Kim, Y.J.; Song, C.H.; Koo, J.H. Estimation of the contributions of long range transported aerosol in East Asia to carbonaceous aerosol and PM concentrations in Seoul, Korea using highly time resolved measurements: A PSCF model approach. J. Environ. Monit. 2011, 13, 1905–1918. [Google Scholar] [CrossRef]
  22. Zachary, M.; Yin, L.; Zacharia, M. Application of PSCF and CWT to Identify Potential Sources of Aerosol Optical Depth in ICIPE Mbita. Open Access Libr. J. 2018, 5, e4487. [Google Scholar] [CrossRef]
  23. Do, W.G.; Jung, W.S. Estimation of PM10 source locations in Busan using PSCF model. J. Environ. Sci. Int. 2015, 24, 793–806. [Google Scholar] [CrossRef]
  24. Han, S.; Joo, H.; Song, H.; Lee, S.; Han, J. Source apportionment of PM2.5 in Daejeon metropolitan region during January and May to June, 2021 in Korea using a hybrid receptor model. Atmosphere 2022, 13, 1902. [Google Scholar] [CrossRef]
  25. National Institute of Environmental Research (NIER). Annual Report of Intensive Air Quality Monitoring Station; NIER: Incheon, Republic of Korea, 2020; NIER-GP2020–2208. [Google Scholar]
  26. Nuhoglu, Y.; Yazici, M.; Nuhoglu, C.; Kam, E.; Adar, E.; Kuzu, L.; Osmanlioglu, A.E. XRF Analysis of Airborne Heavy Metals and Distribution of Environment in Sivas City Turkey through Dust Samples. In Proceedings of the EurAsia Waste Management Symposium, Istanbul, Turkey, 26–28 October 2020; pp. 26–28. [Google Scholar]
  27. National Institute of Environmental Research (NIER). Establishment of Guidelines for the PMF Modeling and Applications; NIER: Incheon, Republic of Korea, 2020; NIER-SP2020–2273. [Google Scholar]
  28. Kurokawa, J.; Ohara, T. Long-Term Historical Trends in Air Pollutant Emissions in Asia: Regional Emission Inventory in Asia (REAS) version 3. Atmos. Chem. Phys. 2020, 20, 12761–12793. [Google Scholar] [CrossRef]
  29. Guo, S.; Hu, M.; Wang, Z.B.; Slanina, J.; Zhao, Y.L. Size-resolved aerosol water-soluble ionic compositions in the summer of Beijing: Implication of regional secondary formation. Atmos. Chem. Phys. 2010, 10, 947–959. [Google Scholar] [CrossRef]
  30. National Research Foundation of Korea (NRF). A study on the physicochemical properties and formation mechanism considering the characteristics of domestic and foreign influences of fine particles in the central area. In Fine Particle Reseach Initiative in East Asian Considering National Differences (FRIEND) Project, 2020MG1A1114999; National Research Foundation of Korea (NRF): Daejeon, Republic of Korea, 2022. [Google Scholar]
  31. Hwang, I.J.; Yi, S.M.; Park, J.S. Estimation of Source Apportionment for Filter-based PM2.5 Data using the EPA-PMF Model at Air Pollution Monitoring Supersites. J. Korean Soc. Atmos. Environ. 2020, 36, 620–632. [Google Scholar] [CrossRef]
  32. Taghvaee, S.; Sowlat, M.H.; Mousavi, A.; Hassanvand, M.S.; Yunesian, M.; Naddafi, K.; Sioutas, C. Source apportionment of ambient PM2.5 in two locations in central Tehran using the Positive Matrix Factorization (PMF) model. Sci. Total Environ. 2018, 628–629, 672–686. [Google Scholar] [CrossRef]
  33. Chun, M.Y.; Lee, Y.J.; Kim, H.K. Concentration of NH4NO3 in TSP in Seoul Ambient Air. J. Korean Soc. Atmos. Environ. 1994, 10, 130–136. [Google Scholar]
  34. Bayramoğlu Karşı, M.B.; Berberler, E.; Berberler, T.; Aslan, Ö.; Yenisoy-Karakaş, S.; Karakaş, D. Correction and source apportionment of vehicle emission factors obtained from Bolu Mountain Highway Tunnel, Turkey. Atmos. Pollut. Res. 2020, 11, 2133–2141. [Google Scholar] [CrossRef]
  35. Hao, Y.; Gao, C.; Deng, S.; Yuan, M.; Song, W.; Lu, Z.; Qiu, Z. Chemical characterisation of PM2.5 emitted from motor vehicles powered by diesel, gasoline, natural gas, and methanol fuel. Sci. Total Environ. 2019, 674, 128–139. [Google Scholar] [CrossRef]
  36. Coufalík, P.; Matoušek, T.; Křůmal, K.; Vojtíšek-Lom, M.; Beránek, V.; Mikuška, P. Content of metals in emissions from gasoline, diesel, and alternative mixed biofuels. Environ. Sci. Pollut. Res. Int. 2019, 26, 29012–29019. [Google Scholar] [CrossRef]
  37. Cheung, K.L.; Ntziachristos, L.; Tzamkiozis, T.; Schauer, J.J.; Samaras, Z.; Moore, K.F.; Sioutas, C. Emissions of particulate trace elements, metals and organic species from gasoline, diesel, and biodiesel passenger vehicles and their relation to oxidative potential. Aerosol Sci. Technol. 2010, 44, 500–513. [Google Scholar] [CrossRef]
  38. Dockery, D.W.; Stone, P.H. Cardiovascular risks from fine particulate air pollution. N. Engl. J. Med. 2007, 356, 511–513. [Google Scholar] [CrossRef]
  39. Son, S.E.; Park, S.S.; Bae, M.A.; Kim, S.T. A study on characteristics of high pollution observed around large scale stationary sources in Chungcheongnam-do province. J. Korean Soc. Atmos. Environ. 2020, 36, 669–687. [Google Scholar] [CrossRef]
  40. Gonçalves, S.J.; Weis, J.; China, S.; Evangelista, H.; Harder, T.H.; Müller, S.; Sampaio, M.; Laskin, A.; Gilles, M.K.; Godoi, R.H.M. Photochemical reactions on aerosols at West Antarctica: A molecular case-study of nitrate formation among sea salt aerosols. Sci. Total Environ. 2021, 758, 143586. [Google Scholar] [CrossRef]
  41. Filonchyk, M.; Peterson, M. Development, progression, and impact on urban air quality of the dust storm in Asia in March 15–18, 2021. Urban Clim. 2022, 41, 101080. [Google Scholar] [CrossRef]
  42. Li, Y.; Lin, T.; Hu, L.; Feng, J.; Guo, Z. Time trends of polybrominated diphenyl ethers in east China seas: Response to the booming of PBDE pollution industry in China. Environ. Int. 2016, 92–93, 507–514. [Google Scholar] [CrossRef]
  43. Garcia Novo, P.; Kyozuka, Y. Tidal stream energy as a potential continuous power producer: A case study for west Japan. Energy Convers. Manag. 2021, 245, 114533. [Google Scholar] [CrossRef]
Atmosphere 15 00477 g001
Figure 1. Location of the sampling and monitoring site (star symbol) [24].
Figure 1. Location of the sampling and monitoring site (star symbol) [24].
Atmosphere 15 00477 g001
Atmosphere 15 00477 g002
Figure 2. Flow chart of the ACERWT model applied to this study.
Figure 2. Flow chart of the ACERWT model applied to this study.
Atmosphere 15 00477 g002
Atmosphere 15 00477 g003aAtmosphere 15 00477 g003b
Figure 3. Summated emission of PM2.5, SOx and NOx in East Asia region (2020).
Figure 3. Summated emission of PM2.5, SOx and NOx in East Asia region (2020).
Atmosphere 15 00477 g003aAtmosphere 15 00477 g003b
Atmosphere 15 00477 g004
Figure 4. Fractions of chemical species depending on PM2.5 concentration level.
Figure 4. Fractions of chemical species depending on PM2.5 concentration level.
Atmosphere 15 00477 g004
Atmosphere 15 00477 g005
Figure 5. Scatter plot for observed and predicted PM2.5 concentrations.
Figure 5. Scatter plot for observed and predicted PM2.5 concentrations.
Atmosphere 15 00477 g005
Atmosphere 15 00477 g006
Figure 6. Factor profiles at the receptor site (Daejeon) by the PMF model.
Figure 6. Factor profiles at the receptor site (Daejeon) by the PMF model.
Atmosphere 15 00477 g006
Atmosphere 15 00477 g007
Figure 7. Factor contributions at the receptor site (Daejeon) by time series analysis.
Figure 7. Factor contributions at the receptor site (Daejeon) by time series analysis.
Atmosphere 15 00477 g007
Atmosphere 15 00477 g008
Figure 8. Seasonal contribution for each of the eight sources.
Figure 8. Seasonal contribution for each of the eight sources.
Atmosphere 15 00477 g008
Atmosphere 15 00477 g009
Figure 9. Comparison of ACERWT (left) and CWT (right) model results.
Figure 9. Comparison of ACERWT (left) and CWT (right) model results.
Atmosphere 15 00477 g009
Table 1. Gas/temperature conditions of the TOT analyzer.
Table 1. Gas/temperature conditions of the TOT analyzer.
Program ActivityCarrier GasRamp Time
(Second)		Program
Temperature
Oven PurgeHelium101
1stRampHelium70310
2edRampHelium60480
3rdRampHelium60615
4thRampHelium90840
-Helium300
1stRampO2/Helium35550
2ndRampO2/Helium105850
Internal
Std. Calibration		CH4/Helium1200
Cool downHelium10
Table 2. MDL of PM2.5 components.
Table 2. MDL of PM2.5 components.
ComponentsMDL ComponentsMDLComponentsMDL
IonsSO42−0.00595ElementsSi0.03690Cu0.00022
NO3−0.01018S0.00515Zn0.00019
Cl−0.00966K0.00309As0.00016
Na+0.00328Ca0.00069Se0.00021
NH4+0.00218Ti0.00036Br0.00025
K+0.04444V0.00034Ba0.00088
Mg2+0.00106Cr0.00025Pb0.00030
Ca2+0.00286Mn0.00032
CarbonsOC0.29731Fe0.00042
EC0.00084Ni0.00024
Table 3. Average concentrations of chemical species (PMF input data) during the measurement period (µg/m3).
Table 3. Average concentrations of chemical species (PMF input data) during the measurement period (µg/m3).
AVG.MAX.MIN.STD.Sample No.
PM2.522.1104115.48460
SO42−3.7515.90.062.476244
NO3−5.5246.50.016.426258
Cl−0.315.070.010.356257
Anion9.5658.20.117.928693
Anion/PM2.50.380.810.060.208693
Na+0.152.620.010.176255
NH4+2.9117.90.012.556258
K+0.151.140.010.126050
Mg2+0.020.950.010.046218
Ca2+0.112.270.010.156223
Cation3.3018.20.052.678693
Cation/PM2.50.0020.070.00010.0068785
Ion/PM2.50.510.990.040.278693
OC3.4216.10.272.057367
EC0.954.690.020.587367
Carbon 4.3819.20.462.858744
Carbon/PM2.50.220.970.040.128744
Metal2.4110.40.061.668780
Metal/PM2.50.120.860.010.068780
Table 4. Frequency (days) depending on PM2.5 concentration level by seasons.
Table 4. Frequency (days) depending on PM2.5 concentration level by seasons.
PeriodSeasonPM2.5 ≤ 1515 < PM2.5 ≤ 3030 < PM2.5 ≤ 4545 < PM2.5 ≤ 6060 < PM2.5
1~2, 12.2020Winter560748480230165
3~5.2020Spring69010273558223
6~8.2020Summer1180633247171
9~11.2020Autumn9866792297652
Table 5. Source contribution during the whole measurement period.
Table 5. Source contribution during the whole measurement period.
SourceContribution
Secondary Sulfate35%
Secondary Nitrate26%
Vehicle16%
Biomass burning6%
Industry6%
Dust/soil6%
Sea salt4%
Coal combustion1%
Pie chart
Atmosphere 15 00477 i001
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Han, S.-w.; Joo, H.-s.; Kim, K.-c.; Cho, J.-s.; Moon, K.-j.; Han, J.-s. Modification of Hybrid Receptor Model for Atmospheric Fine Particles (PM2.5) in 2020 Daejeon, Korea, Using an ACERWT Model. Atmosphere 2024, 15, 477. https://doi.org/10.3390/atmos15040477

AMA Style

Han S-w, Joo H-s, Kim K-c, Cho J-s, Moon K-j, Han J-s. Modification of Hybrid Receptor Model for Atmospheric Fine Particles (PM2.5) in 2020 Daejeon, Korea, Using an ACERWT Model. Atmosphere. 2024; 15(4):477. https://doi.org/10.3390/atmos15040477

Chicago/Turabian Style

Han, Sang-woo, Hung-soo Joo, Kyoung-chan Kim, Jin-sik Cho, Kwang-joo Moon, and Jin-seok Han. 2024. "Modification of Hybrid Receptor Model for Atmospheric Fine Particles (PM2.5) in 2020 Daejeon, Korea, Using an ACERWT Model" Atmosphere 15, no. 4: 477. https://doi.org/10.3390/atmos15040477

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop