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Article

Comparison of Particle Number Concentrations Between Small and Large Urban Green Spaces During a PM Pollution Episode in Seoul, South Korea

by
Sumin Choi
1,
Taehee Kim
1,2 and
Chan-Ryul Park
1,*
1
Livable Urban Forests Research Center, National Institute of Forest Science, 57 Hoegi-ro, Dongdaemun-gu, Seoul 02455, Republic of Korea
2
Kangwon University-Industry Cooperation Foundation, 1 Kangwondaehak-gil, Chuncheon-si 24341, Republic of Korea
*
Author to whom correspondence should be addressed.
Land 2026, 15(1), 103; https://doi.org/10.3390/land15010103
Submission received: 18 November 2025 / Revised: 30 December 2025 / Accepted: 31 December 2025 / Published: 5 January 2026
(This article belongs to the Special Issue Healthy and Inclusive Urban Public Spaces)
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Abstract

We analyzed the effect of reducing particulate matter in a forest by comparing concentrations and particle number concentrations (PNCs) between urban and background forest areas with the use of aerodynamic particle sizers. PM was observed at forest and urban sites during the high particulate matter events from 22 to 30 April 2019. Comparing the PM concentrations measured, PM10 and PM2.5 were 61.6 μg/m3 and 36.9 μg/m3, respectively, in the urban site, while PM10 and PM2.5 were 53.9 μg/m3 and 31.8 μg/m3, respectively, in the forest site. Most PNCs at both sites ranged in particle size from less than 0.5 μm (99%). During high-concentration events, the mass concentration of PM10 was not significantly different, but PNCs of the accumulation mode particles (≤0.5 µm) and coarse mode particles (>0.5 µm) were differed between two sites. The re-duction rate of coarse mode particles (>0.5 µm) was lower 20% at large urban green space. A large urban green space showed the high slope value of decrease at the relationship between aerodynamic diameter and PNC at all times. These results indicate that not only mass concentration but PNC could support to understand the PM traits at large urban green space during the PM pollution of episode.

1. Introduction

Recently, high concentrations of particulate matter (PM) have emerged as a social concern. Particulate matter is an air pollutant that originates in urban and industrial areas and often exceeds the regulatory limit, which has detrimental effects on human health, causing severe problems [1]. Particles are composed of various sizes and shapes and can be made from many different chemicals. Direct emissions emitted from construction sites, unpaved roads, fields, and chimney fires, as well as a combination of sulfur dioxide and pollutants emitted from nitrogen oxides, power plants, industries, and cars, form particles in the atmosphere [2]. Particulate matter sizes are classified as ultrafine (<0.1 μm), fine (0.1~2.5 μm), coarse (2.5~10 μm), and super coarse (>10 μm) [2] and are directly linked to potential health risks. Ultrafine and fine particulates can be inhaled into the lungs and are therefore particularly harmful. The effects of coarse airborne particles on health are gaining increasing attention, as there is reason to believe that these effects may have been under-appreciated in the past [3,4,5]. According to a report from the Korea Centers for Disease Control and Prevention, in 2014, symptoms of asthma deterioration increased by 29% with a 10 μg/m3 increase in particulate matter, while visits to emergency rooms and hospitalizations due to worsening asthma also increased by 29% [6].
The management of airborne particulate matter differs according emission sources. Health risks caused by PM pollution sources must be controlled, but methods for reducing particulate matter concentrations via plants also have potential. Forests can reduce the concentration of PM and improve air quality by adsorbing and absorbing particles through filtering functions. The complex surface of leaves, stems, and branches can capture and retain particulate matter, helping to precipitate it to the ground within forests [7,8,9,10]. In Korea, PM-related research has been conducted mainly in cities and industrial complexes. The potential for forests to reduce the amount of fine dust has been well explored in terms of PM mass [11,12] rather than particle number concentration (PNC), so there is a lack of understanding regarding PM reduction mechanisms of trees under field conditions based on practical observations. Also, a couple of data showed the low value of mass concentration of PMx at Beijing [13], Shenyang [14] and Rio de Janeiro [15] between urban parks and cities. However, PNC modeling and vertical distributions [16] have been reported at national and international scales in Asia [17] and the UK [18]. Moreover, the role of trees and forests in regard to PNC distribution patterns in residential areas of megacities has not yet been explored. Meanwhile, in Korea, according to the Clean Air Conservation Act, days on which concentrations of PM10 exceed 50 µm/m3 are designated as PM pollution episodes, and related regulations on traffic and permission activities in industry are managed and regulated under this law. So, the effects of trees on PM has emerged as an important issue during PM pollution episodes. This study tried to comprehend the effects of yellow dust and local pollutants in urban forests; therefore, we selected short periods with simultaneous occurrences of yellow dust and local pollutants. Many studies have investigated the role of tree species in PM reduction capacity at tree stands [19,20,21]. However, this study selected a temporal scale in early spring, April. April is the starting time for leaf emergence for deciduous trees and is also a critical period during which PM pollution episodes frequently occur in Northeastern Asia [22,23]. We selected the temporal period from 22 to 30 April at urban forests of pine trees. The reason for selecting that week was that a situation occurred simultaneously with the influx of yellow dust and the return to normal atmospheric conditions. Therefore, it is necessary to more accurately understand the process by which fine particles develop and settle inside forests, especially when particles enter forests during PM pollution episodes in urban pine forests. This study investigates changes in particle size within forests by comparing PNCs between urban and suburban areas.

2. Methods

2.1. Study Area

This study selected two pine forests: Hongneung (HN; large urban green space) and Cheongnyangni (CN; small urban green space), located in Dongdaemun-gu, Seoul, Korea. The Hongneung site (HN, 37°35′56.53″ N, 127° 2′54.93″ E) is located about 50 m above sea level and is surrounded by an urban forest. The Cheongnyangni site (CN, 37°34′50.37″ N, 127°2′43.26″ E) is located in front of the Cheongnyangni subway station, at a direct distance of 2 km from HN, approximately 10 m above sea level, and is surrounded by roads and residential areas (Figure 1).
The two pine forests demonstrated differences in study area size, tree height, diameter at breast height, and shrub coverage. However, the two pine forests showed no significant differences in tree density or canopy coverage around the PM sampling points (Table 1) [24].

2.2. Particulate Matter Measurement Data

In 2019, aerosol samplers were placed and operated in the pine forests at Hongneung (HN) and Cheongnyangni (CN), respectively. We selected data from high particulate matter episodes from 22 to 30 April 2019. In 2019, a joint investigation report released by three governments indicated that China was responsible for 32% of fine dust pollution in Korea [25]. PM values and particle size distributions in the range of 0.25–32 μm in diameter were observed simultaneously using a multi-channel Grimm aerosol spectrometer (Environmental Dust Monitor #164, GRIMM Aerosol Technic, EDM164). The dust monitor was regularly serviced by the Korea service branch of the manufacturer, GRIMM, and calibrated annually. Calibration of the two monitors was performed on 2 February 2019 at the phytotron of NIFoS. The instrument was able to measure 1 min measurements; however, in this study, 5 min measurements were taken. Ambient air was directly fed into the measuring cell at a rate of 1.2 L per minute using a volume-controlled pump. All aerosol particles passing through the measurement cell were classified by into 31 size distribution channels. In the sampling data, mass concentrations of PM by site and hour were compared, and distribution characteristics of particle concentrations were compared using the particle number concentration (PNC) in 23 sections of 10 μm or less. Furthermore, the contribution of PNC size distribution to changes in PM concentration was analyzed, and particle distributions were confirmed according to regional characteristics. The lognormal distribution is widely utilized for particle number concentration analysis primarily because it provides the best empirical fit to observed aerosol data and offers significant practical advantages for statistical analysis and modeling [26]. For data analysis, PNC data were subdivided into two categories to represent various models. However, there are various opinions about the different sizes of particles [27,28,29,30,31]. In the PNC data, 99% of particles were assigned to the accumulation mode (≤0.5 μm), and only 1% of the particles were assigned to the coarse mode (>0.5 μm). Thus, in this study, the PNC cut-off point between accumulation mode and coarse mode was selected as 0.5 μm [30,32].

2.3. Meteorological Data

We hypothesized that the two sites were located under the same wind effects, and we compared wind velocities and directions between high and low episodes. Also, we utilized the dataset of temperature and relative humidity measured at HN [33] by NIFoS and at CN [34]. Between the two sites, HN showed significantly higher temperature (df = 175, p < 0.0001) and relative humidity (df = 182, p < 0.0001, Figure 2), representing differences in meteorological conditions between the two sites.

2.4. Statistical Analysis

Tree density, diameter at breast height, canopy, shrub coverage percentage, air temperature, and relative humidity were tested by t-tests, while mass concentration and particulate number concentration were analyzed with correlation equations. Statistical analysis was performed using the statistical software R 4.2.0 [35].

3. Results

3.1. Concentration of Particulate Matter in Urban Forest

Time-series plots are displayed in Figure 3. Changes in PMx concentrations were almost identical at the two sites, despite them being 2 km apart. PM concentrations in the forest were lower than the small urban green space, with 9.88% lower PM10 and 14.46% lower PM2.5. The large urban green space demonstrates a low-concentration of PM due to absorption by leaves and branches [10], and particulate matter under the canopy is removed by deposition due to low wind velocity and high humidity. To compare particle properties between the two sites, we compared the PM2.5/PM10 ratio. The large urban green space has a ratio value 0.58 and the small urban green space has a ratio 0.59, both similar to the characteristics of residential areas [36].
Figure 4 illustrates the diurnal variation in PM10 concentration. The highest concentrations were observed between 8:00 and 9:00 at both sites, which is assumed to be related to the increased traffic on nearby roads during rush hour. The large urban green space had the lowest concentration of particulate matter between 18:00 and 20:00, whereas the small urban green space had the lowest concentrations between 11:00 and 14:00. In the small urban green space, PM concentrations are relatively low due to air diffusion; however, in the large urban green space, PM transported under low wind speeds does not spread and remains stagnant, which is assumed to result in higher concentrations during the day.
The mean concentration of aerosol particles from 22 April to 30 April is shown in Table 2. The mean reduction rates of PM10, PM2.5, and PM1.0 during high-concentration event days were 9.2%, 13%, and 13%, respectively. The mean reduction rates of PM10, PM2.5, and PM1.0 during low-concentration event days were 19%, 16%, and 15%, respectively. The results of the PM concentration comparison illustrated that the large urban green space reduced the concentration of PM in the air and revealed that forests have lower PM mass concentrations than urban areas. On high-concentration event days, the smaller the particle size, the greater the small urban green space effect, whereas on low-concentration event days, the larger particle size, the greater the PM reduction effect. However, statistical analysis indicated that PM10 did not differ under high PM episode conditions (Figure 5a, left). It represents the large urban green space showed the high reduction rate during high-concentration episodes but the low reduction rate did during low-concentration episodes.
The average total particle number concentration data and classified particle size data measured from 22 April to 30 April are shown in Table 3. On high-concentration event days, the total average PNC in the large urban green space was 16.9 number/cm3, with maximum and minimum average PNC values of 43.1 number/cm3 and 6.9 number/cm3, respectively. The total average PNC in the small urban green space was 19.1 number/cm3, with maximum and minimum average PNC values of 36.8 number/cm3 and 8.6 number/cm3, respectively. The mean reduction rates of total PNC, accumulation-mode PNC, and coarse-mode PNC were 11.5%, 11.5%, and 20%, respectively.
On low-concentration event days, the total average PNC in the large urban green space was 6.6 number/cm3, with maximum and minimum average PNC values of 20.0 number/cm3 and 0.3 number/cm3, respectively. The total average PNC in the small urban green space was 8.0 number/cm3, with maximum and minimum average PNC values of 19.0 number/cm3 and 0.6 number/cm3, respectively. The mean reduction rates of total PNC was 17.5%, the accumulation-mode PNC reduction rate was 17.9%, and the coarse-mode PNC was the same.
PNC differed statistically between CN and HN at all variables in high- and low-concentration episodes (Figure 5).

3.2. Size Distribution of Aerosol Particle Number Concentration

Figure 6 shows the PM10 mass concentration and aerosol mass size distribution during high-concentration event days. The yellow rectangle spans from 22 to 25 April and is related to the inflow of yellow dust from foreign regions (yellow rectangle in Figure 6a,b), and among them, the coarse-mode particle mass distribution was very high at both sites. The high-coarse particle mass concentrations in the mass concentration graph were related to high concentrations due to the inflow of yellow dust mist [37,38]. On 24 April, a very high-concentration of accumulation-mode particles (red rectangle in Figure 6a,b) was observed, showing a different PNC pattern from the yellow dust period. The mass concentration of particle matter dominated by accumulation-mode particles was high, whereas coarse-mode particles were low. This result is similar to the highest mass concentration reported in the 0.3~0.4 μm range along the road, and the effects of road pollution sources might have influenced the occurrence of high concentrations of particulate matter [39]. In the case of a low-concentration event, PM trends were similar in the small and large urban green spaces.
The aerosol mass size distribution differed between the coarse-mode and accumulation-mode of PNCs at HN (forest) and CN (urban) during the high-concentration PM event day (Figure 6). Among the observed PNC data, 99% of particles were less than 1 μm, and only 1% had particle diameters greater than 1 μm, demonstrating a sharp unimodal distribution dominated by the accumulation mode. To compare aerosol size distributions, PNCs considering particle diameter are compared. The particle number concentration (y) is plotted based on a logarithmic scale against the diameter (μm).
To compare the particle size distribution on high-concentration event days, the particle size lognormal distribution was calculated as y = −75.68ln(x) + 17.56 for the urban site and y = −68.19ln(x) + 15.70 for the forest (Figure 7a). For aerodynamic diameter and particle number concentration, the urban site demonstrated a lower slope value than the forest. Accumulation-mode particle concentrations were higher in the urban area, while coarse-mode particle concentrations were similar. To compare the particle size distribution during a low-concentration event day (Figure 7b), the particle size lognormal distribution was calculated as y = −33.12ln(x) + 73.35 for the urban site and y = −28.85ln(x) + 63.28 for the forest. Furthermore, the urban site demonstrated a lower slope value than the forest. The urban area has a greater slope than forests, which means that there are more small-sized particles in urban areas than in forests, which is related to the capacity of forest leaves, twigs, and branches for the attachment and sedimentation of small-sized particles.

4. Discussion

4.1. Why Are PM Concentrations Low in Forests?

The PM concentration trends between forests and urban areas showed similar patterns, and PM concentrations were significantly lower at the forest site, except for PM10 concentrations during high PM episodes. Large-sized urban forests may have the capacity to reduce PM. In the small urban green space, PM tends to be removed during the daytime due to active diffusion. But the large urban green space is characterized by relatively slow diffusion; therefore, fine dust that enters forests is not eliminated during the day and demonstrates the lowest PM concentrations after 18:00. When comparing PNC values in relation to particle size, coarse-mode PNC values in the large and small urban green spaces were similar during low-concentration periods, but during high-concentration events, such as with yellow dust, coarse-mode PNC is 20% lower than those in the small urban green space. Diurnal differences in pollutant concentrations are pronounced in high-traffic-concentrated areas with high-rise buildings as opposed to areas characterized by a larger fraction of residential apartment complexes in Seoul [40]. Also, in a spatial context, there had been similar results on the spatial heterogeneity of PM distribution were con-ducted at the nearest study areas in 2021 [41] and 2023 [42]. Our study revealed a similar pattern, as explained by the PNC and PM mass data, although the measurements occurred during a short time window at the small and large urban green spaces.
While urban areas do have active processes that affect PM, the large urban green space is more effective at removing PM from the atmosphere during the day [43,44]. Forests in the large urban green space may act as a barrier, trapping and removing particles through processes like dry deposition on leaves and branches, leading to lower PM concentrations compared to the small urban green space, which often have higher and more consistent pollution level [40].

4.2. What Affects the Growth Rate of PM Concentrations Between Urban and Forests?

Forests at the large urban green space have a blocking effect and are expected to grow and remove particles faster due to the meteorological change of wind velocity and low temperatures [44].To analyze this, we compared the PNC distribution of the small and large urban green spaces. Of the total PNC data, accumulation-mode particles accounted for 99% of the total particle distribution, resulting in a sharp unimodal distribution in the accumulation mode (250 nm). Regarding the lognormal distribution of the observed PNCs, the slope for the small urban green space is higher than that for the large urban green space because the PNC distribution includes more small-sized particles in the small urban green space, while coarse-mode particles appear at similar concentrations in both the large and small urban green space. We suppose that the particle concentration is lower in the large and small urban green spaces because particles from the same inflow source are removed faster through absorption and growth processes inside the trees and for-est, due to the wind velocity and low temperatures. Wind velocity and high humidity can affect aerosol movement by increasing particle sizes via water absorption, which can decrease Brownian motion due to increased inertia, while low temperatures can affect aerosol movement indirectly by promoting aqueous phase reactions and affect-ing particle deposition rates [44,45,46]. Therefore, the humid and stagnant conditions at the trees and forests can lead to the formation of secondary aerosols, which may be larger and exhibit different deposition behaviors compared to their dry, primary counterparts.
In forests, high humidity levels can produce vapors that induce cohesive and viscous forces acting between two wet particles or surfaces connected by a liquid bridge. So, these forces cause adhesion between vapors and the micro-structures of hairs, trichomes, and resins at the micro-scale of trees. Ultimately, the PM reduction process involves not only the structures of leaves, twigs, branches, and trunks but also micro-meteorological factors such as air temperature and relative humidity, which could assist interactions between vapors and leaves.
The small urban green space was characterized by a lower shrub coverage percentage than the large urban green space (Table 1). This may reflect a higher occurrence rate of particulate matter resuspension in the urban area [47]. The leaf biomass of shrubs may pr mote attachment and settling of the particulate matter in forests, illustrating the importance of forest size as well as vertical green space.

4.3. Implication for the Management of Urban Forests and Research Limitations

Small-sized urban forests have the potential to not only provide shade but also to reduce air pollutants. However, surrounding roads and buildings may limit the possibility of PM settling down on the ground; resuspension can occur in an urban setting dominated by pollutants. From this viewpoint, this study examined the short-term and localized effects on PM mass and PNC. This study also has limitations in terms of broader applicability at city- or country-level; however, the importance of small-sized urban forests can be carefully observed under conditions of high land price in megacities.
Long-term monitoring may reduce biased interpretations of the role of trees and forests in urban regions. Furthermore, the duration dry periods (low relative humidity) and resistance to urban dry conditions should be fully addressed in the near future.
The effect of green space on reducing PM levels has been debated in relation to specific conditions of street trees, such as isolated patches of green space and vertical green systems [48]; however, mechanistic approaches that apply nano-scale measurement devices will address not only the concentration but also speciation issues important for human health in the near future [49].

5. Conclusions

This study examined particulate matter reduction effects and size distribution characteristics by measuring particulate matter in April 2019 with an EDM 164 at the Hongneung, the large urban green space and Cheongnyangni, the small urban green space. The large and small urban green spaces demonstrated similar trends in particulate matter concentrations over time, as particulate matter is produced by the same type of pollutants. During high-concentration days, the large urban green space displayed mean reduction rates of 9.2%, 13.0%, and 13.0% for PM10, PM2.5, and PM1.0, respectively. During low-concentration days, the large urban green space was characterized by mean reduction rates of 19.0%, 15.9%, and 15.0% for PM10, PM2.5, and PM1.0, respectively. Particles in the accumulation mode (≤0.5 um) accounted for the largest proportion of the overall particle number concentration (99%). In the PNC data, high-concentration event days in the large urban green space had 11.5% less accumulation-mode particles and 20% less coarse-mode particles compared to the small urban green space. When low-concentration event days occurred, coarse-mode particles demonstrated little difference between sites; however, on high-concentration event days, the large urban green space reduced PM levels by 20% by blocking yellow dust particles from inflow.
When comparing the ratio of fine particles affecting PM10 concentration increases, we see that both sites feature residential areas. We believe that daily particulate matter concentrations began to increase at 7 a.m. and reached the highest values at 9 a.m., as increased traffic during rush hour produces large quantities of particulate matter from road sources.
When comparing the distribution of PNCs by particle size, we observe that the PNCs in the accumulation mode, which is characterized by small particles, were lower in the large urban green space, as they were removed via growth and absorption, resulting in lower concentrations than in the city center. When high concentrations of fine dust were introduced, fine dust with larger particle sizes was removed due to the tree’s blocking effect, while small particles were removed by growth and absorption, thereby improving air quality inside at the large urban green space.
Our results show that the forests in the large urban green space absorb, adsorb, block, and reduce particulate matter. The small urban green space might have a role in reducing particulate matter in the urban atmosphere. It is necessary to observe the chemical composition of particulate matter at the site and analyze the chemical composition of particulate matter generated inside urban forests.
We recommend continued monitoring of forests to aid the development of a comprehensive understanding of the aerosol system, including aerosols with BVOC speciation, and the general characteristics of particle generation and growth within forests.

Author Contributions

Conceptualization and methodology, T.K., S.C., and C.-R.P.; data curation, T.K. and S.C.; formal analysis, T.K., S.C., and C.-R.P.; investigation and visualization, T.K., S.C., and C.-R.P.; funding acquisition, project administration, and resources, S.C. and C.-R.P.; supervision, S.C. and C.-R.P.; writing—original draft preparation, T.K., S.C., and C.-R.P.; validation and writing—review and editing, C.-R.P. and S.C. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Institute of Forest Science (NIFoS) of Korea (Project No. FM0500-2022-01-2025).

Data Availability Statement

Data supporting the findings of this study can be provided upon reasonable request to the corresponding author.

Acknowledgments

We would like to thank the anonymous reviewers for their helpful comments.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of the data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. Pope, C.A.; Dockery, D.W. Health Effects of Fine Particulate Air Pollution: Lines that Connect. J. Air Waste Manag. Assoc. 2006, 56, 709–742. [Google Scholar] [CrossRef] [PubMed]
  2. EPA in 2009: Progress Report. 2009. Available online: https://www.epa.gov (accessed on 1 November 2025).
  3. Popek, R.; Gawrońska, H.; Wrochna, M.; Gawroński, S.W.; Sæbø, A. Particulate Matter on Foliage of 13 Woody Species: Deposition on Surfaces and Phytostabilisation in Waxes—A 3-Year Study. Int. J. Phytoremediation 2013, 15, 245–256. [Google Scholar] [CrossRef] [PubMed]
  4. Bates, J.T.; Weber, R.J.; Abrams, J.; Verma, V.; Fang, T.; Klein, M.; Strickland, M.J.; Sarnat, S.E.; Chang, H.H.; Mulholland, J.A.; et al. Reactive Oxygen Species Generation Linked to Sources of Atmospheric Particulate Matter and Cardiorespiratory Effects. Environ. Sci. Technol. 2015, 49, 13605–13612. [Google Scholar] [CrossRef] [PubMed]
  5. Kyung, S.Y.; Kim, Y.S.; Kim, W.J.; Park, M.S.; Song, J.W.; Yum, H.; Yoon, H.K.; Rhee, C.K.; Jeong, S.H. Guideline for the prevention and management of particulate matter/Asian dust particle induced adverse health effect on the patients with pulmonary diseases. J. Korean Med. Assoc. 2015, 58, 1060–1069. [Google Scholar] [CrossRef]
  6. Korea Centers for Disease Control and Prevention (KCDC) Committed to Eradicate Tuberculosis in 2014. Ministry of Health and Welfare. 2014. Available online: https://www.mohw.go.kr (accessed on 1 November 2025).
  7. Beckett, K.; Freer-Smith, P.; Taylor, G. Effective tree species for local air quality management. J. Arboric. 2000, 26, 12–19. [Google Scholar]
  8. Dzierzanowski, K.; Popek, R.; Gawrońska, H.; Saebø, A.; Gawroński, S.W. Deposition of particulate matter of different size fractions on leaf surfaces and in waxes of urban forest species. Int. J. Phytoremediation 2011, 13, 1037–1046. [Google Scholar] [CrossRef]
  9. Sæbø, A.; Popek, R.; Nawrot, B.; Hanslin, H.M.; Gawronska, H.; Gawronski, S.W. Plant species differences in particulate matter accumulation on leaf surfaces. Sci. Total Environ. 2012, 427–428, 347–354. [Google Scholar] [CrossRef]
  10. Nowak, D.J.; Crane, D.E.; Stevens, J.C. Air pollution removal by urban trees and shrubs in the United States. Urban For. Urban Green. 2006, 4, 115–123. [Google Scholar] [CrossRef]
  11. Vellingiri, K.; Kim, K.-H.; Ma, C.-J.; Kang, C.-H.; Lee, J.-H.; Kim, I.-S.; Brown, R.J.C. Ambient particulate matter in a central urban area of Seoul, Korea. Chemosphere 2015, 119, 812–819. [Google Scholar] [CrossRef]
  12. Sou, H.D.; Kim, P.R.; Hwang, B.M.; Oh, J.H. Diurnal and Seasonal Variations of Particulate Matter Concentrations in the Urban Forests of Saetgang Ecological Park in Seoul, Korea. Land 2021, 10, 1213. [Google Scholar] [CrossRef]
  13. Wei, W.; Wang, B.; Niu, X. Forest roles in particle removal during spring dust storms on transport path. Environ. Res. Public Health 2020, 17, 478. [Google Scholar] [CrossRef] [PubMed]
  14. Qu, H.; Lu, X.; Liu, L.; Ye, Y. Effects of traffic and urban parks on PM10 and PM2.5 mass concentrations. Energy Sources Part A: Recovery Util. Environ. Eff. 2019, 45, 5635–5647. [Google Scholar]
  15. Junior, D.P.M.; Bueno, C.; de Silva, C.M. The effect of urban green spaces on reduction of particulate matter concentration. Bull. Environ. Contam. Toxicol. 2022, 108, 1104–1110. [Google Scholar] [CrossRef] [PubMed]
  16. Roostaei, V.; Abbas, A.; Gharibzadeh, F.; Faridi, S.; Naddafi, K.; Yunesian, M.; Hassanvand, M.S. Vertical Distribution of Air Particulate Matter (PM1, PM2.5, and PM10) in Different Regions of Tehran. Aerosol Air Qual. Res. 2024, 24, 240036. [Google Scholar] [CrossRef]
  17. Song, M.; Jeong, R.; Kim, D.; Qiu, Y.; Meng, X.; Wu, Z.; Zuend, A.; Ha, Y.; Kim, C.; Kim, H.; et al. Comparison of Phase States of PM2.5 over Megacities, Seoul and Beijing, and Their Implications on Particle Size Distribution. Environ. Sci. Technol. 2022, 56, 17581–17590. [Google Scholar] [CrossRef]
  18. Power, A.L.; Tennant, R.K.; Stewart, A.G.; Gosden, C.; Worsley, A.T.; Jones, R.; Love, J. The evolution of atmospheric particulate matter in an urban landscape since the Industrial Revolution. Sci. Rep. 2023, 13, 8964. [Google Scholar] [CrossRef]
  19. Chen, L.; Liu, C.; Zhang, L.; Zou, R.; Zhang, Z. Variation in tree species ability to capture and retain airborne fine particulate matter (PM2.5). Sci. Rep. 2017, 7, 3206. [Google Scholar] [CrossRef]
  20. Kim, K.; Jeon, J.; Jung, H.; Kim, T.K.; Hong, J.; Jeon, G.-S.; Kim, H.S. PM2.5 reduction capacities and their relation to morphological and physiological traits in 13 landscaping tree species. Urban For. Urban Green. 2023, 70, 127526. [Google Scholar] [CrossRef]
  21. Hong, J.; Park, C.; Kim, K.; Jeon, J.; Son, J.; Chang, H.; Park, C.-R.; Kim, H.S. Experimental analysis of PM2.5 reduction characteristics between Korean red pine (Pinus densiflora) and sawtooth oak (Quercus acutissima) saplings under different densities and arrangement structures. Environ. Pollut. 2024, 347, 123699. [Google Scholar] [CrossRef]
  22. Zhang, M. Transboundary fine dust pollution in China and Korea: How has international politics impeded environmental negotiations? Asia Pac. Policy Stud. 2024, 11, e384. [Google Scholar] [CrossRef]
  23. Bae, M.; Kang, Y.-H.; Kim, E.; Kim, S.; Kim, S. A multifaceted approach to explain short- and long-term PM2.5 concentration changes in Northeast Asia in the month of January during 2016–2021. Sci. Total Environ. 2023, 880, 163309. [Google Scholar] [CrossRef] [PubMed]
  24. Choi, S.M.; Park, C.R.; Park, W.H.; Yeo, J.H.; Yoo, S.Y.; Oh, S.R.; Joe, J.H. The Greeninfra Survey Report of AiCAN sites 2021. In National Institute of Forest Science Research Report No. 952; National Institute of Forest Science Research: Seoul, Republic of Korea, 2021; Available online: https://www.preprints.org/manuscript/202511.1582 (accessed on 1 November 2025).
  25. Korea’s Ministry of Environment. Summary Report of the 4th Stage (2013–2017) LTP Project, Joint Research Project for Long–Range Transboundary Air Pollutants in Northeast Asia. 2019. Available online: https://me.go.kr/home/file/readDownloadFile.do?fileId=184686&fileSeq=1 (accessed on 13 December 2025).
  26. Zhu, K.; Wang, L. A comprehensive study on the validation and application of multi-lognormal distribution models for atmospheric particles. Atmos. Environ. 2024, 338, 20813. [Google Scholar] [CrossRef]
  27. Rimnácová, D.; Ždímal, V.; Schwarz, J.; Smolík, J.; Rimnác, M. Atmospheric aerosols in suburb of Prague: The dynamics of particle size distributions. Atmos. Res. 2011, 101, 539–552. [Google Scholar] [CrossRef]
  28. Kittelson, D.B.; Watts, W.F.; Johnson, J.P. Nanoparticle emissions on Minnesota highways. Atmos. Environ. 2004, 38, 9–19. [Google Scholar] [CrossRef]
  29. Kittelson, D.B.; Watts, W.F.; Johnson, J.P. On-road and laboratory evaluation of combustion aerosols-Part1: Summary of diesel engine results. J. Aerosol Sci. 2006, 37, 913–930. [Google Scholar] [CrossRef]
  30. Khan, M.F.; Latif, M.T.; Amil, N.; Juneng, L.; Mohamad, N.; Nadzir, M.S.M.; Hoque, H.M.S. Characterization and source apportionment of particle number concentration at a semi-urban tropical environment. Environ. Sci. Pollut. Res. 2015, 22, 13111–13126. [Google Scholar] [CrossRef]
  31. Zhang, Q.; Shen, Z.; Lei, Y.; Wan, Y.; Zeng, Y.; Wang, Q.; Ning, Z.; Cao, J.; Wang, L.; Xu, H. Variations of Particle Size Distribution, Black Carbon, and Brown Carbon during a Severe Winter Pollution Event over Xi’an, China. Aerosol Air Qual. Res. 2018, 18, 1419–1430. [Google Scholar] [CrossRef]
  32. U.S. Environmental Protection Agency. Available online: https://www.epa.gov/sites/default/files/2019-11/documents/overvw1.pdf (accessed on 13 December 2025).
  33. Korea Mountain Meteorology Data. Available online: https://mtweather.nifos.go.kr (accessed on 13 December 2025).
  34. KMA Weather Data Service. Available online: https://data.kma.go.kr (accessed on 13 December 2025).
  35. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2023. [Google Scholar]
  36. Lee, Y.-K.; Lee, K.-J.; Lee, J.-S.; Shin, E.-S. Regional Characteristics of Particle Size Distribution of PM 10. J. Korean Soc. Atmos. Environ. 2012, 28, 666–674. [Google Scholar] [CrossRef]
  37. Kim, J.A.; Jin, H.-A.; Kim, C.-H. Characteristics of Time Variations of PM10 Concentrations in Busan and Interpreting Its Generation Mechanism Using Meteorological Variables. J. Environ. Sci. Int. 2007, 16, 1157–1167. [Google Scholar]
  38. Chen, J.; Yu, X.; Sun, F.; Lun, X.; Fu, Y.; Jia, G.; Zhang, Z.; Liu, X.; Mo, L.; Bi, H. The Concentrations and Reduction of Airborne Particulate Matter (PM10, PM2.5, PM1) at Shelterbelt Site in Beijing. Atmosphere 2015, 6, 650–676. [Google Scholar] [CrossRef]
  39. Cho, B.-Y.; Shin, S.-H.; Jung, C.-S.; Ju, M.-H.; Yoon, M.-H.; Ahn, J.-E.; Bae, G.-S. Characteristics of Particle Size Distribution at the Roadside of Daegu. J. Korean Soc. Atmos. Environ. 2019, 35, 16–26. [Google Scholar] [CrossRef]
  40. Ahn, H.; Lee, J.; Hong, A. Urban form and air pollution: Clustering patterns of urban form factors related to particulate matter in Seoul, Korea. Sustain. Cities Soc. 2022, 81, 103859. [Google Scholar] [CrossRef]
  41. Kim, P.-R.; Park, C.-R. Evaluation of Particulate Matter’s Traits and Reduction Effects in Urban Forest, Seoul. Korean J. Environ. Ecol. 2021, 35, 569–575. [Google Scholar] [CrossRef]
  42. Kim, J.; Eom, K.-W.; Yeo, S.G.; Cho, H.-J.; Woo, S.Y.; Lee, J.K.; Dong, J.I. Analysis of the Distribution of Fine Particulate Matter (PM-2.5) Pollution according to Types of Urban Forest Park. J. Korean Soc. Atmos. Environ. 2023, 39, 283–293. [Google Scholar] [CrossRef]
  43. Park, S.; Kwak, M.J.; Lee, J.; Lim, Y.J.; Kim, H.; Jeong, S.G.; Son, J.-A.; Chang, H.; Je, S.M.; Oh, C.-Y.; et al. Urban Greening: A Sustainable Method for Particulate Matter (PM) Reduction. J. Korean Soc. For. Sci. 2023, 112, 1–10. [Google Scholar] [CrossRef]
  44. Seo, J.; Oh, H.-R.; Park, D.-S.R.; Kim, J.Y.; Chang, D.Y.; Park, C.R.; Sou, H.-D.; Jeong, S. The role of urban forests in mitigation of particulate air pollution: Evidence from ground observations in South Korea. Urban Clim. 2025, 59, 102264. [Google Scholar] [CrossRef]
  45. McRee, M.M.; Moschos, V.; Fiddler, M.N.; Massabò, D.; Surratt, J.D.; Bililign, S. Influence of relative humidity and aging on the optical properties of organic aerosols from burning African biomass fuels. Aerosol Sci. Technol. 2025, 59, 544–566. [Google Scholar] [CrossRef]
  46. He, Y.; Gu, Z.; Lu, W.; Zhang, L.; Okuda, T.; Fujioka, K.; Luo, H.; Yu, C.W. Atmospheric humidity and particle charging state on agglomeration of aerosol particles. Atmos. Environ. 2019, 197, 141–149. [Google Scholar] [CrossRef]
  47. Seo, I.; Park, C.R.; Yoo, G. Particulate matter resuspension from simulated urban green floors using a wind tunnel-mounted closed chamber. PeerJ 2023, 11, e14674. [Google Scholar] [CrossRef]
  48. Liu, J.; Zheng, B.; Li, J. The Positive and Negative Effects of Green Space on PM2.5 Concentrations: A Review. Atmosphere 2025, 16, 1235. [Google Scholar] [CrossRef]
  49. Durand, M.; Lintune, A.; Ezhova, E. Forest canopy interactions with aerosols: Important considerations in approaching future impacts and climate management. New Phytol. 2026, 249, 114–122. [Google Scholar] [CrossRef]
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Figure 1. A map of the study area displaying the particulate matter monitoring sites at Hongneung (HN; large urban green space, green dot) and Cheongnyangni (CN; small urban green space, red dot)—a megacity in Seoul, Korea.
Figure 1. A map of the study area displaying the particulate matter monitoring sites at Hongneung (HN; large urban green space, green dot) and Cheongnyangni (CN; small urban green space, red dot)—a megacity in Seoul, Korea.
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Figure 2. Box plot graphs of quartile distribution of temperature (°C) and relative humidity (%) at the monitoring site at Hongneung (HN) and Cheongnyangni (CN) in a megacity of Seoul, Korea.
Figure 2. Box plot graphs of quartile distribution of temperature (°C) and relative humidity (%) at the monitoring site at Hongneung (HN) and Cheongnyangni (CN) in a megacity of Seoul, Korea.
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Figure 3. Time-series plots of the concentration of PM10 and PM2.5 from 22 to 30 April 2019.
Figure 3. Time-series plots of the concentration of PM10 and PM2.5 from 22 to 30 April 2019.
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Figure 4. Diurnal variation in PM10 concentration in HN (forest) and CN (urban) during a high-concentration event day.
Figure 4. Diurnal variation in PM10 concentration in HN (forest) and CN (urban) during a high-concentration event day.
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Figure 5. Comparison of mass concentration (μg/m3, (left)) of PM10, PM2.5, and PM1.0, and the particle number concentration (#/m3, (right)) at high-concentration episodes (a) and low-concentration episodes (b) between the sites at Hongneung (HN) and Cheongnyangni (CN) in a megacity of Seoul, Korea.
Figure 5. Comparison of mass concentration (μg/m3, (left)) of PM10, PM2.5, and PM1.0, and the particle number concentration (#/m3, (right)) at high-concentration episodes (a) and low-concentration episodes (b) between the sites at Hongneung (HN) and Cheongnyangni (CN) in a megacity of Seoul, Korea.
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Figure 6. The aerosol mass size distribution in the large (HN, (a)) and small (CN, (b)) urban green spaces during high-concentration PM10 event days. The yellow rectangle represents the coarse-mode of the PNC, and the red rectangle represents the accumulation-mode of the PNC.
Figure 6. The aerosol mass size distribution in the large (HN, (a)) and small (CN, (b)) urban green spaces during high-concentration PM10 event days. The yellow rectangle represents the coarse-mode of the PNC, and the red rectangle represents the accumulation-mode of the PNC.
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Figure 7. Size distribution of aerosol particle number concentration in the large (black) and small (gray) urban green spaces during high (a) and low (b) concentration event days.
Figure 7. Size distribution of aerosol particle number concentration in the large (black) and small (gray) urban green spaces during high (a) and low (b) concentration event days.
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Table 1. The status of upper trees at the HN (forest) and CN (urban) sites: tree density (ea/100 m2), tree height (m), diameter at breast height (cm), and canopy and shrub coverage percentage (%).
Table 1. The status of upper trees at the HN (forest) and CN (urban) sites: tree density (ea/100 m2), tree height (m), diameter at breast height (cm), and canopy and shrub coverage percentage (%).
SitesUpper TreesDensityTree
Height		Diameter at Breast HeightCanopy Coverage Percentage (>6 m)Shrub Coverage Percentage (<2 m)Study Area
ea/100 m2mcm%%m2
Hongneung (HN)Pinus densiflora1312.4 ± 3.735.3 ± 11.061~8060100,000
Cheongyangni (CN)Pinus densiflora1510.6 ± 2.623.7 ± 2.861~800700
t-Testnsp < 0.01p < 0.01nsp < 0.01-
Data are presented as mean ± SD. An independent samples t-test was used to assess the differences between the HN and CN sites. ns, not significant.
Table 2. Statistical value of average PM10, PM2.5, and PM1.0 concentrations measured at CN and HN.
Table 2. Statistical value of average PM10, PM2.5, and PM1.0 concentrations measured at CN and HN.
EpisodeVariables
(μg/m3)		MeanMedianMinimumMaximumStd. Dev.Reduction
Rate (%)
CNHNCNHNCNHNCNHNCNHN
High
Conc.		PM10112.8102.394.783.844.741.9268.0260.351.252.49.2
PM2.565.256.766.454.629.027.0110.0119.916.816.413.0
PM1.051.745.052.744.424.121.5100.1111.815.515.113.0
Low
Conc.		PM1031.125.229.824.63.22.076.072.916.214.919.0
PM2.520.116.919.417.22.11.244.545.59.78.815.9
PM1.016.714.216.314.51.50.838.339.98.27.615.0
Table 3. Statistical value of particle number concentration measured at CN and HN sites.
Table 3. Statistical value of particle number concentration measured at CN and HN sites.
EpisodeVariables
(#/cm3)		MeanMedianMinimumMaximumStd. Dev.Reduction
Rate (%)
CNHNCNHNCNHNCNHNCNHN
High
Conc.		PNC (total)19.116.919.416.58.66.936.843.15.96.011.5
PNC(≤0.5 μm)82.472.983.470.836.429.4156.9185.325.025.611.5
PNC(>0.5 μm)1.51.21.31.10.40.44.34.40.80.720.0
Low
Conc.		PNC (total)8.06.67.66.60.60.319.020.04.33.817.5
PNC(≤0.5 μm)31.225.629.725.72.31.272.276.216.715.017.9
PNC(>0.5 μm)0.30.30.30.20.10.01.31.30.20.20.0
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Choi, S.; Kim, T.; Park, C.-R. Comparison of Particle Number Concentrations Between Small and Large Urban Green Spaces During a PM Pollution Episode in Seoul, South Korea. Land 2026, 15, 103. https://doi.org/10.3390/land15010103

AMA Style

Choi S, Kim T, Park C-R. Comparison of Particle Number Concentrations Between Small and Large Urban Green Spaces During a PM Pollution Episode in Seoul, South Korea. Land. 2026; 15(1):103. https://doi.org/10.3390/land15010103

Chicago/Turabian Style

Choi, Sumin, Taehee Kim, and Chan-Ryul Park. 2026. "Comparison of Particle Number Concentrations Between Small and Large Urban Green Spaces During a PM Pollution Episode in Seoul, South Korea" Land 15, no. 1: 103. https://doi.org/10.3390/land15010103

APA Style

Choi, S., Kim, T., & Park, C.-R. (2026). Comparison of Particle Number Concentrations Between Small and Large Urban Green Spaces During a PM Pollution Episode in Seoul, South Korea. Land, 15(1), 103. https://doi.org/10.3390/land15010103

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