Regional classification of high PM10 concentrations in the Seoul metropolitan and Chungcheongnam-do areas, Republic of Korea

Since the Seoul metropolitan area is a highly developed megacity, many people are often exposed to high concentrations of particulate matter (PM), with mean aerodynamic diameters equal to or less than 10 μm (PM10), in cold seasons. PM10 concentrations can be influenced by a combination of various factors, including meteorological conditions, anthropogenic emissions, atmospheric chemical reactions, transboundary transport, and geographic characteristics. However, the establishment of an efficient air quality management plan remains challenging due to the limited understanding of the regional PM concentration characteristics. Here, the Seoul metropolitan (Seoul, Gyeonggi-do, and Incheon) and Chungcheongnam-do (Chungcheongnam-do, Daejeon, and Sejong) areas were regionally classified to identify the spatiotemporal air quality in areas where megacities and emission sources are mixed. The four representative regions were determined using the K-means clustering method based on the temporal variations in the observed PM10 concentrations. The first cluster consisted of small cities in the southern and eastern parts of Gyeonggi-do and Chungcheongnam-do, respectively, while the second cluster consisted of Incheon, West Gyeonggi-do, and Seoul. In addition, the third and fourth clusters included West Chungcheongnam-do and East Gyeonggi-do, which are adjacent to the Yellow Sea and downstream area of the westerly wind, respectively. The characteristics of each cluster during the high PM10 concentration events are explainable by wind patterns and the local air pollutant emissions, including nitrogen oxides and sulfur oxides. The obtained regional classification was different from the provincial-level administrative division of South Korea. Therefore, the present study is expected to be a scientific basis for overcoming the limitations of air quality management in administrative districts.


Introduction
The suspended particulate matter (PM), with mean aerodynamic diameters equal to or less than 10 μm (PM 10 ), causes various environmental and health problems, such as limited visibility and respiratory diseases (Dockery et al., 1993;Kim et al., 2020Kim et al., , 2022a;;Pope & Dockery, 2006).It has been known that high concentrations of 24-h mean PM 10 , exceeding 100 μg m −3 , occur over 20 days during the cold season in the Korean Peninsula (Ku et al., 2021;Lee et al., 2011).Indeed, several factors causing high PM concentrations in the Korean Peninsula have been reported.Among them, transboundary transport from the western region of Korean Peninsula by mid-latitude westerly winds and nearby industrial emissions have been widely studied for identifying high PM episodes (Bae et al., 2019;Choi et al., 2019;Jeong et al., 2011;Kim et al., 2018;Lee et al., 2011Lee et al., , 2013Lee et al., , 2019aLee et al., , b, 2021;;Park et al., 2021;Pouyaei et al., 2022).Another major factor causing high PM concentrations is the synoptic pattern of atmospheric stagnation that blocks the outward propagation of aerosols (Cho et al., 2021(Cho et al., , 2022;;Jeon et al., 2019;Jeong & Park, 2017;Jung et al., 2019;Kim et al., 2017a, b, c;Shin et al., 2021;Yun & Yoo, 2019).Moreover, the impacts of secondary aerosol formation have been known to contribute considerably to the air quality deterioration (Jordan et al., 2020;Sun et al., 2013Sun et al., , 2016;;Xu et al., 2022).Given the harmful effects of PM on human health, potentially significant damages are expected in densely populated areas.
The Seoul metropolitan area, including Seoul, Gyeonggi-do, and Incheon, is a megacity in South Korea (Ho et al., 2021).Therefore, various types of air pollutants can be emitted from urban sources, such as residences, vehicles, coal combustion, and usage of organic solvents.Indeed, there are many large-scale coal-fired power plants in Chungcheongnam-do in the southern neighboring region of the Seoul metropolitan area, including Dangjin, Seosan, Boryeong, Taean, and Seocheon, as well as nearby steel mills and petrochemical complexes.Emitted air pollutants from these coal-fired power plants could alter the chemical composition and variability of PM through gas-to-particle conversion in the atmosphere (Cho et al., 2019;Choi et al., 2021;Kim et al., 2017aKim et al., , b, 2022b;;Lee et al., 2015;Vellingiri et al., 2015).Most regional industry-related air quality management policies are implemented by the administrative districts.However, districts are defined by artificial boundaries (or borders) that do not typically reflect topography, airflow, and emission sources of PM.Therefore, coordination among several districts to develop regional air quality management and strategy have not been carried out in consideration of regional variations in PM concentrations.
In this study, the regional air quality in the Seoul metropolitan and Chungcheongnam-do area was classified based on the temporal variations in the PM 10 concentrations.There are many industrial and residential emissions, and dense population (~ 30 million) in study area, so great health and social damages due to exposure are expected.Hence, diagnosis of regional PM 10 variability in target area is required.Numerous factors could be comprehensively considered in the clustering analysis of PM 10 concentrations, including local emissions, airflow transport, topographic effects, and atmospheric chemical reactions.In fact, this regional classification could be essential information not only to understand the mechanism of high PM 10 concentrations but also to examine the factors controlling the regional air quality (Stolz et al., 2020).To investigate the causes of the differences in the PM 10 concentrations by region and to assess the reliability of regional classification, representative cases of high PM 10 concentrations were studied.

Data
In this study, the observed PM 10 concentration data from a total of 87 sites, representing each municipality in the Seoul metropolitan area (Seoul, Gyeonggido, and Incheon) and Chungcheongnam-do region (Chungcheongnam-do, Daejeon, and Sejong), were obtained from AirKorea website (https:// www.airko rea.or.kr/).Measures at each site and monitoring network system are managed by the Ministry of Environment, Korea Republic.The PM 10 concentrations were measured using the β-ray attenuation instrument (e.g., Met One BAM-1020 or Thermo Anderson FH 62 C14 Series; Lee et al., 2006).The basic principle of β-ray attenuation is that suspended particles with a diameter of 10 µm or less in the air were first collected on a filter for at 1-h intervals, then the intensity of the absorbed or dissipated β-ray emitted from the atmospheric β-ray light source was measured by a sensor as it passes through the aerosol-collected filter (Chang & Tsai, 2003).The PM 10 concentrations can be continuously deduced using the difference between the absorbed and dissipated β-ray intensities.Because the high PM 10 concentrations frequently occur in cold seasons (Lee et al., 2011), the observed PM 10 concentration data in 5 months from November to March over the 2018-2021 period, representing a total of three cold seasons, were used in this study for clustering analysis.Although this is an hourly dataset, their normalized daily averages, with an average and standard deviation of 0 and 1, respectively, were used to evaluate the temporal variability rather than the difference in the absolute PM 10 concentrations according to the observatories.
In addition, hourly wind directions and speed, air temperature, and air humidity data were obtained from the Korea Meteorological Administration to assess the regional variations in the PM 10 concentrations.In total, three surface weather stations, namely Boryeong (36.33°N, 126.56°E), Seoul (37.57°N, 126.97°E), and Yangpyeong (37.49°N, 127.49°E), were considered in this study.Observed data during the periods of high PM 10 concentrations (see "Characteristics of the four clusters during the high PM 10 concentration episodes" below) were investigated to reveal the regional PM 10 variability in the study area.

Methodology
In this study, a clustering technique is applied to group together stations that show similar temporal PM 10 concentration variations.The K-means clustering method is one of the representative unsupervised machine learning algorithms (Steinley, 2006).First, a K number of centroids is randomly defined in the dataset to be classified.In this method, each data is classified into the group of the closest centroids based on the Euclidean distance.Second, new centroids are set by calculating the average of the data assigned to each cluster.This process repeatedly determines the centroid of each cluster by calculating the distance between the datasets and classifies the representative groups until achieving slight changes in the cluster centroids.
The distance between datasets in the K-means clustering analysis is usually calculated based on the Euclidean distance, representing the difference between vector elements of each object at the same time.However, the Euclidean distance calculation may not be able to appropriately reflect data characteristics if the dataset includes time dimension (e.g., time series data) even under similar characteristics of objects at different time steps.Therefore, the dynamic time warping (DTW) method, connecting the points with the shortest distances by considering the speed of each time series data, was used in this study (Aach & Church, 2001;Aghabozorgi et al., 2015;Petitjean et al., 2011;Suris et al., 2022).Indeed, this method calculates the distance between two given temporal datasets, with sequential alignment (time = t), and the minimum distances of three neighboring time step combinations, namely (t-1, t), (t, t-1), and (t-1, t-1), of the first and second array.By minimizing the sum of the distances, the DTW can be performed easily to analyze temporal sequences, such as audio and graphic data, resulting in an optimal match by reflecting different time-evolving data.Therefore, the DTW method can be used to classify different datasets within the same cluster in the case of time lags between them according to their variability.
The number of clusters (K) can exhibit a deterministic impact on the cluster analysis results.Although K can be determined subjectively by researchers and according to the purposes of studies, the elbow method was used in this study to find the optimal K number (Kodinariya & Makwana, 2013).This method uses the K-value of the inflection point for determining the distortion scores (i.e., cost function), which is the sum of distances from the centroid of each cluster that rapidly decreases with increasing the number of clusters.
To understand comprehensively the dynamic mechanisms controlling the three representative high-PM 10 concentration cases, backward trajectories were analyzed using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model (Stein et al., 2015).The model results were calculated by considering the Global Data Assimilation System (GDAS) as meteorological input data.These data were, in fact, obtained from the National Oceanic and Atmospheric Administration website (http:// www.arl.noaa.gov/ HYSPL IT_ info.php).The 72-h back trajectories initialized 1000 m above ground level (a.g.l.) at each site were analyzed to trace the air mass movement in this study.

Regional classification of PM 10 according to its concentration variability
Considering the distortion scores as a function of the number of clusters (Syakur et al., 2018), it was found that the elbow point was four (Fig. 1).Therefore, PM 10 variabilities in the Seoul metropolitan and Chungcheongnam-do area were classified into four representative groups.In general, clustering analysis is highly dependent on the dataset, as it finds representative patterns from the original data.However, the regional distributions of the four clusters were similar, even if the analysis was performed separately according to each winter season (not shown).Hence, this regional classification is considered a strong and clear result.
Cluster 1 was observed mainly in the southern and northeastern parts of Gyeonggi-do and Chungcheongnam-do, respectively (red area in Fig. 2).In this area, aerosols are emitted from small industrial facilities, as well as from active movements of ships and vehicles in ports and highways, respectively.Regions of cluster 2 are located in the western parts of Gyeonggi-do, Seoul, and Incheon (blue area in Fig. 2).Since the Seoul metropolitan area is a largely populated megacity, regions within cluster 2 are expected to be affected by emissions of PM from urban activities, such as residential and traffic-related activities.Cluster 3 was mainly observed in the western part of Chungcheongnam-do, bordering the Yellow Sea.Since several large-scale coal-fired power plants are present in this area (e.g., Dangjin, Seosan, Boryeong, Taean, and Seocheon coal-fired power plants), local emissions may influence greatly air quality in this region.It should be noted that small blue and green areas in the southeastern part suggest a kind of noise for clustering analysis.Cluster 4 was observed in the eastern part of Gyeonggi-do.Although there were few local emission sources in cluster 4, pollutants emitted from the western part are expected to impact the air quality in this area.Since westerly and northwesterly winds are climatologically predominant in the Korean Peninsula during cold seasons, all regions could be also influenced by transboundary aerosol transport from other nations (Bae et al., 2019;Kim et al., 2018;Lee et al., 2013;Pouyaei et al., 2022).Overall, representative clusters of the PM 10 variability were classified in these four regions based on various influencing factors, such as local emissions, transport, and secondary generation of PM (Nault et al., 2018;Park et al., 2022).
The variation in the normalized PM 10 concentration within each cluster was examined in this study (Fig. 3).The data from November to March in boreal winter were connected and analyzed to represent three winters in a single time series.Since time series data were normalized for the entire analysis period based on the observation sites, most values were within the range of 0 ± 1.The variations in the PM 10 concentrations in the four clusters were generally similar (Fig. 3e) because the study area (the Seoul metropolitan and Chungcheong area) is smaller than the synoptic scale (~ 1000 km) (Fig. 2).Therefore, it can be expected that the weather and external influences in this area would not be significantly different.However, the PM 10 variability showed distinct patterns in specific periods of the time series.Mid-January 2019, early-March 2019, and late-March 2021 were selected as representative high PM 10 concentration periods, during which the normalized PM 10 concentrations increased, with one or more clusters exceeding a value of 1.However, they exhibited different levels between clusters (yellow shades in Fig. 3e).The characteristics of the regional concentrations during these three periods were further examined in this study.
Among the three representative high PM 10 cases, the regional average PM 10 concentrations were observed on January 14-15, 2019, March 5-6, 2019, and March 29-30, 2021, when the differences in concentrations between clusters were the greatest (Fig. 4).The first case, in January 2019, was a wellknown high PM concentration episode (Oh et al., 2020).The regional PM 10 concentrations of cluster 3 in January 2019 were relatively low, while those of cluster 4 were relatively high (Fig. 3e).This finding was in line with the observed spatial distribution of PM 10 concentrations, showing a PM 10 concentration range of about 100-150 μg m −3 on January 14, 2019, in the western part of the Chungcheongnam-do area, next to the Yellow Sea.However, most eastern areas of Gyeonggi-do exhibited PM 10 concentrations over 150 μg m −3 (Fig. 4a).Although the overall air quality was cleaner on January 15, 2019, than the day before, the same aforementioned regional differences were observed (Fig. 4b).
High concentrations of about 200 μg m −3 were observed on March 5, 2019, over most of the study area due to a well-known long-lasting haze episode (Shim et al., 2022).However, areas of cluster 3 showed a lower concentration range of 100-150 μg m −3 compared to those in other areas (Fig. 4c).This spatial distribution was, indeed, similar to that observed on the next day (March 6, 2019).Although the overall high concentration event was slightly attenuated, relatively low concentrations were also found in the western part of Chungcheongnamdo (Fig. 4d).The average daily PM 10 concentration was up to 800 μg m −3 in the western coastal area of Chungcheongnam-do on March 29-30, 2021, which is inconsistent with the previous cases (Fig. 4e and  f).In contrast, the observed PM 10 concentrations were lower than 400 μg m −3 in most parts of Gyeonggi-do and Seoul, which is approximately half of that observed in Chungcheongnam-do (Fig. 4e).This regional distribution pattern was also observed on March 30, 2021, showing local high PM 10 concentrations in Chungcheongnam-do (Fig. 4f).Although the study region is a small area, there were some regional differences in the PM 10 concentrations differences during the high concentration period.

Characteristics of the four clusters during the high PM 10 concentration episodes
The characteristics of clusters 2, 3, and 4, showing distinct PM 10 concentrations in the three cases, were further investigated in this study.To examine the characteristics of each cluster, the time series of surface wind speeds were examined according to each high PM 10 concentration episode in Seoul (the representative point of cluster 2), Boryeong (cluster 3), and Yangpyeong (cluster 4) (yellow dots in Fig. 2).These sites are manned observatories, reflecting well the PM 10 concentration variability in each cluster.Observatory sites in these three high PM 10 concentration cases were located in areas where environmental conditions were likely to increase PM 10 concentrations due to aerosol accumulation through atmospheric stagnation, with slight hourly wind speeds.Indeed, winds of 1-2 m s −1 were dominant on January 14-15 and March 5-6, 2019 (Fig. 5a and b).On March 29-30, 2021, winds of 2-4 m s −1 prevailed in three sites (Fig. 5c).Overall, a condition of general atmospheric stagnation in winter is constructed.Moreover, the obtained clustering results represented well the general characteristics of the winter air quality.
As a gaseous precursor of aerosol, distributions of SO 2 concentrations by region are investigated for each high PM 10 concentration case (Fig. 6).Considering that the SO 2 concentration on a typical day with low PM 2.5 concentration is about 2-4 (10 −3 ppmv), the SO 2 concentration was also generally high in three representative cases.The SO 2 concentrations in January 2019 were relatively high in the western part of Chungcheongnam-do (cluster 3) and the Seoul metropolitan area compared to other region (Fig. 6a and  b).In addition, extremely high SO 2 concentrations were observed in Dangjin (Fig. 6b), which might be due to the presence of a large-scale coal-fired power plant located in this area.High SO 2 concentrations were also observed in March 2019 in the western part of Chungcheongnam-do, including Dangjin, Incheon, and some areas of Seoul (Fig. 6c and d).Sulfur oxides (SOx) may also be emitted from small factories located in the southern part of Gyeonggido (cluster 1).On the other hand, the measured SO 2 concentrations in March 2021 were relatively high in the western part of Chungcheongnam-do, including Yesan County.In contrast, low SO 2 concentrations were observed in the Seoul metropolitan area (Fig. 6e  and f).Although there was no major emission source in Yesan, it was reported that PM from the coal-fired power plant located near the coastal area often emitted into Yesan, where they are blocked by the mountain ranges, thereby leading to high PM 2.5 concentrations (Gong et al., 2021;Nam et al., 2018).
Similar to SO 2 , the distributions of the NO 2 concentrations in the three representative cases were investigated (Fig. 7).Given the NO 2 concentrations are 1-2 (10 −2 ppmv) during the normal clean day, relatively high NO 2 concentrations were observed in the case of January 2019 in Seoul and neighboring areas, while low NO 2 concentrations were observed in Chungcheongnam-do (Fig. 7a and b).Since there were no major emission sources in the region of cluster 4 and there were significant aerosol precursor emissions in Seoul metropolitan area (Fig. 7a and b), atmospheric chemical reactions might be the main mechanism driving the air quality deterioration in regions of cluster 4.These patterns of high and low concentrations of NO 2 in the Seoul metropolitan area and Chungcheongnam-do, respectively, were similar in March 2019 (Fig. 7c and d).In contrast, although the observed NO 2 concentrations in the metropolitan area in March 2021 were relatively higher than those observed in the other regions, they were generally lower than the other high PM 10 concentration cases (Fig. 7e and f).
The HYSPLIT model was used to analyze the air inflows at three representative stations in Seoul, Boryeong, and Yangpyeong.In the case of January 2019, the air masses at the three sites were transported over a long distance from China's mainland (Fig. 8a and b).In March 2019 case, the longdistance air inflow from Beijing-Tianjin-Hebei to Seoul and Yangpyeong increased the PM 10 concentrations.However, Boryeong was affected by the nearby air mass from the Yellow Sea during the previous two days (Fig. 8c and d).The results of the back trajectories showed a typical yellow dust pathway in March 2021 as airflow from the Gobi Desert in Mongolia (Fig. 8e and f).Overall, it is clear that the regional PM 10 concentrations were modulated by the local influences of atmospheric stagnation and long-distance transportation from China's mainland.

Discussion
To further discuss the variation in the PM 10 concentrations within each cluster, chemical factors that can lead to secondary particle formation were analyzed with respect to the wind directions and speeds.To ensure a comprehensive analysis, major aerosol precursors, such as nitric acid (HNO 3 ) and sulfuric acid (H 2 SO 4 ), should be considered in the investigation.Since ion components are not often measured in observatories, the regional SO 2 and NO 2 concentrations in sulfur and nitrogen oxides, respectively, were considered in this study as alternatives since they are measured at all times like PM 10 .SO 2 and NO 2 can be converted into strong acids, namely, H 2 SO 4 and HNO 3 , by reacting with H 2 O and O 3 in the atmosphere, respectively.Subsequently, these aerosol precursors can be combined with ammonia (NH 3 ) in the atmosphere to form particles, such as ammonium sulfate ((NH 4 ) 2 SO 4 ) and ammonium nitrate (NH 4 NO 3 ) (Bauer et al., 2007;Wang et al., 2005).Sulfur oxides in the study area are mainly emitted from coal-fired power plants, especially from the Dangjin plant, while traffic-related emissions are the major source of nitrogen oxides.In addition, NH 3 might be released from livestock farms in Chungcheongnam-do and agricultural lands in the Gyeonggi-do plains.It is, therefore, necessary to discuss the aerosol concentration variabilities, taking into account the locations and types of the emission sources.
In the three abovementioned cases, the SO 2 and NO 2 concentrations, as well as wind transport, were comprehensively analyzed to reveal the characteristics of the PM 10 concentration variability within each cluster.Since the SO 2 concentrations in the Seoul metropolitan area were similar to those observed in Chungcheongnam-do in January and March 2019, it is assumed that the contribution of NO 2 to the high PM 10 concentration in the Seoul metropolitan area might be high if the regional differences between the direct aerosol emissions and transboundary transport are not substantial.Although there were not many major emission sources in the region of cluster 4, high PM 10 concentrations are often derived from air pollutant inflows from Incheon, Seoul, or North Korea, considering the prevailing northerly and westerly winds.The prediction accuracies of the air quality by subregions may be improved by considering the regional PM 10 concentration characteristics of this study in existing air quality prediction models.
It should be noted that the Korea Meteorological Administration (https:// www.weath er.go.kr/ weath er/ asian dust/ obser vday.jsp? type= 2& stnId= 108& year= 2021&x= 5&y=6) reported yellow dust over the Korean Peninsula on March 29-30, 2021 (Fig. 8e and  f).The SO 2 concentrations in this period were relatively high in the western part of Chungcheongnamdo.However, it was difficult to interpret the variations in the PM 10 concentrations based on SO 2 and NO 2 due to the large-scale transportation of the yellow dust.Although yellow dust from China flowed into the Korean Peninsula and increased the PM 10 concentrations, its regional distribution might differ due to topography, location, and various environmental conditions of each district.
To further investigate the secondary formation of aerosols and their effects on human health, it would be better to analyze the PM concentration data, with mean aerodynamic diameters of ≤ 2.5 μm (PM 2.5 ), which are smaller particles than PM 10 , making it possible to comprehensively interpret the aerosol effects on human health, including respiratory diseases.However, the PM 2.5 concentration dataset is not suitable for multiple station analysis because there are much more missing values than PM 10 concentrations.At each monitoring station, concentrations of PM 2.5 and PM 10 are independently observed at the same time.If observed PM 2.5 concentration is higher than PM 10 (e.g., mainly on clean days), observation of PM 2.5 is considered incorrect and treated as missing, resulting in more missing values of PM 2.5 dataset.Hence, the PM 2.5 concentrations dataset was not appropriate input data for training machine learning algorithms, limiting further discussion of the coarse fractions (i.e., PM 10 -PM 2.5 ) and contributions of PM 2.5 to the PM 10 masses.A comprehensive analysis may be possible if the operational measure of PM 2.5 is stabilized, with more recorded data, in the future.Moreover, considering PM and gas phases of various air pollutants in the classification of the regional characteristics of air quality in future studies may be of great significance for improving air quality and maintaining population health through national policies.More clear statements will be possible in the future by numerical predictions of PM 2.5 concentrations without missing data and various air pollutants relevant to secondary particle formation.
The present study provides further insights into air pollution issues in other megacities worldwide suffering from air pollution problems.Indeed, lowemission zones have been implemented in many cities to reduce traffic volumes in urban areas.In addition, the use of renewable energies, which do not emit PM, such as solar and wind power generation, has been promoted.The air pollution characteristics may vary according to the geographical, climatic, social, and economic situations of each city.Similar to this study, assessing the characteristics of air pollution around megacities may contribute significantly to the development of city-based air quality policies to protect human health from PM and preserve the air environment.This study provides scientific references and baseline approaches for assessing air pollution characteristics in megacities worldwide.

Conclusions
This study aims to assess the characteristics of the Seoul metropolitan area and Chungcheongnam-do region, classified based on the PM 10 concentration variability.Cluster analysis of the average PM 10 concentrations in three winters during 2018/19-2020/21 showed a total of four regional patterns, which differ from the administrative division.The obtained results demonstrated the effects of the locations of PM 10 emission sources, topography, atmospheric chemical reactions, and airflow in the Korean Peninsula on the clustering results.The implemented policies in the study area to reduce air pollutants mostly focus on the management of emission sources at the administrative district scale.The present study suggests that Vol:.( 1234567890) the implementation of region-customized air quality management policies by the defined clusters is more effective in improving air quality compared to those implemented based on administrative districts.
Author contribution Woosuk Choi: conceptualization, formal analysis, methodology, visualization, writing-original draft and editing.Min Young Song: investigation, review, and editing.Jong Bum Kim: investigation, review, and editing.Kwanchul Kim: investigation, review, and editing.Chaeyoon Cho: formal analysis, supervision, investigation, writing-original draft and editing.

Data availability
The data that support the findings of this study are available from the corresponding author, upon reasonable request.

Declarations
Ethics approval All authors have read, understood, and have complied as applicable with the statement on "Ethical responsibilities of Authors" as found in the Instructions for Authors.

Competing interests
The authors declare no competing interests.
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Fig. 1
Fig. 1 Inertia scores of K-means clustering according to the number of clusters

Fig. 2
Fig. 2 Classification of the representative regional clusters shown in different colors according to the PM 10 concentration variations in the capital and surrounding regions of the Republic of Korea.Regions within clusters 1, 2, 3, and 4 are shown

Fig. 4
Fig. 4 Spatial distributions of the average daily PM 10 concentrations (μg m −3 ) in the three high PM 10 concentration episodes

Fig. 7
Fig. 7 Spatial distributions of the mean daily NO 2 concentrations (10 −2 ppmv) in the three high PM 10 concentration episodes This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2022R1C1C1003659) and a grant (2023-MOIS-20024324) of Ministry-Cooperation R&D Program of Disaster-Safety funded by Ministry of Interior and Safety (MOIS, Korea).