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Article

A Study on Elevated Concentrations of Submicrometer Particles in an Urban Atmosphere

by
Hee-Joo Cho
,
Jia Kang
,
Dohyeong Kim
,
Arom Seo
,
Minhan Park
,
Hungsoo Joo
and
Kihong Park
*
National Leading Research Laboratory (Aerosol Technology and Monitoring Laboratory), School of Environmental Science and Engineering, Gwangju Institute of Science and Technology (GIST), 261 Cheomdan-Gwagiro, Buk-gu, Gwangju 61005, Korea
*
Author to whom correspondence should be addressed.
Atmosphere 2018, 9(10), 393; https://doi.org/10.3390/atmos9100393
Submission received: 14 August 2018 / Revised: 28 September 2018 / Accepted: 4 October 2018 / Published: 10 October 2018
(This article belongs to the Special Issue Carbonaceous Aerosols: Sources, Physical and Chemical Characterization, and Toxicity)
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Abstract

:
Mass concentrations of chemical constituents (organics, nitrate, sulfate, ammonium, chloride, and black carbon (BC)) and the number size distribution of submicrometer particles in the ambient atmosphere were continuously measured in urban Gwangju, Korea, during the Megacity Air Pollution Studies (MAPS)-Seoul campaign. Organics (9.1 μg/m3) were the most dominant species, followed by sulfate (4.7 μg/m3), nitrate (3.2 μg/m3), ammonium (2.6 μg/m3), and BC (1.3 μg/m3) in submicrometer particles (particulate matter less than 1 μm (PM1)). The potential source regions of the sulfate were located in the South and East regions of China and South and East regions of Korea, while local sources were responsible for the elevated BC concentration. Diurnal variation showed that concentrations of organics, nitrate, ammonium, chloride, and BC decreased with increasing mixing layer and wind speed (dilution effect), while sulfate and oxidized organics increased possibly due to their strong photochemical production in the afternoon. During the campaign, an elevated mass concentration of PM1 (PM1 event) and number concentration (nanoparticle formation (NPF) event) were observed (one PM1 event and nine NPF events out of 28 days). The PM1 event occurred with Western and Southwestern air masses with increasing sulfate and organics. Long-range transported aerosols and stagnant meteorological conditions favored the elevated mass concentration of submicrometer particles. Most of the NPF events took place between 10:00 and 14:00, and the particle growth rates after the initial nanoparticle formation were 7.2–11.0 nm/h. The times for increased concentration of nanoparticles and their growth were consistent with those for elevated sulfate and oxidized organics in submicrometer particles under strong photochemical activity.

1. Introduction

Fine particulate matter (PM) in the ambient atmosphere is of current interest due to its effects on climate change (radiation balance and cloud formation) [1] and human health [2,3,4]. Due to multiple sources and formation pathways, chemical constituents in fine PM differ significantly among locations and times. Fine PM is directly emitted from various sources (primary aerosols) and/or produced by gas-to-particle conversion processes (secondary aerosols) and rapidly experiences the aging process (condensation, coagulation, oxidation, cloud processing, and so on), leading to different chemical and mixing states from the original particles. Additionally, the existence of ultrafine particles (<100 nm) in the ambient atmosphere have often been observed due to direct emissions from combustion sources and nanoparticle formation (NPF) with subsequent rapid growth [5,6,7]. Due to the rapid dynamic process of fine PM, real-time measurements of their physical and chemical properties are essential to better understand their sources and formation pathways when elevated PM mass and number concentrations are observed.
Long-range transport (LTP) aerosols play an important role in the elevated mass concentration of submicrometer particles in East Asia [8,9,10]. It has often been observed that the chemical constituents in PM less than 1 μm (PM1) or 2.5 μm (PM2.5) were dependent on the types of long-range transported air mass arriving at sites [7,8,11,12]. Sulfate-dominant and organic-dominant LTP events have been found with different air mass types in Korea [8]. A significant number of elevated PM2.5 concentrations in Korea during winter and spring seasons have been explained by long-range transported aerosols [13].
Nanoparticle formation significantly contributes to an enhanced number concentration of submicrometer particles in the ambient atmosphere [12,14]. NPF events have often been observed in many locations worldwide including East Asia [11,12,15]. It has been reported that the low surface area of pre-existing particles and high growth of initial clusters favors NPF in the ambient atmosphere [16].
In this study, mass concentrations of organics, nitrate, sulfate, ammonium, chloride, and black carbon (BC) of submicrometer particles and the number size distribution of particles from 10 nm to 25 μm in an urban atmosphere (Gwangju, Korea) were continuously measured during the period of the Megacity Air Pollution Studies (MAPS)-Seoul 2015 campaign (18 May 2015–14 June 2015). Air mass backward trajectory analysis, residence time analysis (RTA), and potential source contribution function (PSCF) methods were applied to examine the effects of long-range transported aerosols on elevated PM1 and their chemical constituents, as well as to infer possible geographic source regions of the long-range transported aerosols. Number size distribution data were used to examine NPF in relation to the chemical composition of submicrometer particles and meteorological parameters.

2. Experimental Section

The ambient aerosol measurements were conducted at an urban Gwangju site during the period of the MAPS-Seoul 2015 campaign (18 May 2015–14 June 2015). As shown in Figure 1, the sampling site is under the influence of multiple local aerosol sources such as residential/commercial areas (~0.6 km away from the site), highways (~1.5 km away from the site), a small industrial area (~1.5 km away from the site), and an agricultural area (~0.8 km away from the site) [5,17]. The site is also influenced by short-range transported aerosols from a petrochemical industrial complex and steel works located ~90 km Southeast of the site and long-range transported aerosols from China, North Korea, and Mongolia in East Asia.
Table 1 summarizes the measured parameters at the sampling site and nearby national monitoring sites. Chemical constituents (organics, nitrate, sulfate, ammonium, and chloride) of non-refractory submicrometer particles were measured in real time using an aerosol mass spectrometer (AMS) (Aerodyne, Billerica, MA, USA). A detailed description of the AMS is described elsewhere [13,17,18,19]. Briefly, the ambient air was sampled into the AMS through a 100-μm diameter critical orifice with a flow rate of 0.07 liters per minute (lpm). The aerodynamic lens system focused submicrometer particles into the vaporization and ionization region. Particles were vaporized on the V-shaped tip of the vaporizer at the temperature of 600 °C, then the vapors were ionized by electron impaction (70 eV). The produced ions were detected with a quadrupole mass spectrometer. The collection efficiency of ambient particles in the AMS was assumed to be 50% [20]. Calibrations of the electron multiplier gain value, ionization efficiency, and particle size of the AMS were conducted using the previous methods in Jimenez et al. (2003) [21]. Ammonium nitrate particles (300 nm) were used for the ionization efficiency calibration, which was conducted every week. Organic aerosols (OAs) were further classified into hydrocarbon-like OAs (HOAs) and oxygenated OAs (OOAs) by positive matrix factorization (PMF) analysis [13,22]. The HOAs are related to primary OAs produced from combustion sources (the HOAs are often correlated with combustion tracers such as CO, elemental carbon (EC), and NOx), while the OOAs are related to secondary organic aerosols (SOAs) produced by photochemical activity [23]. The number of factors was determined based on dissimilarities in the average mass spectra (m/z 43, 44, 55, and 57), Q/Qexp, and temporal variation of the factors [22,24]. The concentration of BC was measured with an Aethalometer (AE51, Magee Scientific, USA) every 5 min with a flow rate of 0.2 lpm. The Aethalometer measures BC concentration by measuring the attenuation of the transmission of light through the collected samples on the filter [25]. The filter was changed every day [26].
The number size distribution of particles from 10 nm to 25 μm was measured using a NanoScan-scanning mobility particle sizer (SMPS) (3910, TSI, USA), a differential mobility particle sizer (DMPS) consisting of a differential mobility analyzer (DMA) (3081, TSI, USA), a condensation particle counter (CPC) (TSI 3022a, TSI, USA), and an optical particle counter (OPC) (1.108, Grimm, Germany). The scan times for the NanoScan-SMPS, DMPS, and OPC were 1 min, 5 min, and 1 min, respectively. The number size distributions from NanoScan-SMPS (10–133 nm) and DMPS (146–608 nm) were combined together. The combined size distribution was shown in this paper. Size-dependent transport losses of particles through tubes in the NanoScan-SMPS or DMPS were corrected theoretically [27]. The mode diameter was estimated by fitting the number size distribution data into a Gaussian distribution. The number of modes (mono-modal or bi-modal) was selected from the best fit results.
The PM2.5 mass concentration, gaseous components, and meteorological parameters were obtained from the nearest air quality monitoring network stations. Temperature, wind speed, wind direction, precipitation, solar radiation, and relative humidity (RH) were obtained from an automatic weather station (AWS) and the automated surface observation system (ASOS) operated by the Korea Meteorology Administration (KMA).
A 48-h air mass backward trajectory analysis was conducted with 1-h intervals for each trajectory using the NOAA Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT, version 4) with meteorological field data (GDAS1) [28]. A residence time analysis (RTA) method was used to infer the dominant region where air mass passed over, affecting a receptor site. The air mass trajectory consisted of 48 endpoints. The RTA value for each grid cell (0.2° latitude × 0.2° longitude) was calculated using the number of endpoints passing over the grid cell (i.e., the number of endpoints is the residence time of the air mass in each grid cell). Additionally, a PSCF method was applied to find the source regions of the chemical constituents in PM1 [29,30,31] (i.e., elevated concentrations of chemical constituents at a receptor site can be related to the air mass trajectory). The PSCF value for each grid cell was calculated using the ratio of the number of endpoints passing over the grid cell when the concentration of specific chemical constituents was within the upper 35% level to the total number of endpoints passing over the grid. A weight function was applied to place less weight on the cells with a low number of endpoints. Furthermore, aerosol optical thickness (AOT) data were obtained from MODIS sensors aboard the Aqua and Terra satellites [32].

3. Results and Discussion

Figure 2a shows the time series of meteorological data (wind speed, wind direction, RH, precipitation, solar radiation, and temperature) and concentration of gaseous species (CO, O3, SO2, and NO2) during the sampling period. The average temperature and RH were 20.6°C and 56.2%, respectively. The average mixing ratio of O3 was 47.6 ppb, and O3 had a distinct diurnal pattern with correlations with temperature (r = 0.78) and solar radiation (r = 0.56). The average mixing ratios of NO2 and SO2 were 0.018 ppm and 0.003 ppm, respectively. The elevated mixing ratio of SO2 was observed from 7 June 2015 to 8 June 2015 and 10 June 2015 to 11 June 2015.
Temporal variations of PM2.5, PM1, and the number size distribution are shown in Figure 2b. The PM1 was calculated as the summation of organics, nitrate, sulfate, ammonium, chloride, and BC concentrations. In addition to the measured chemical constituents (organics, nitrate, sulfate, ammonium, chloride, and BC), other chemical components from dust and sea salts may exist, although their contribution to PM1 should be small. The average mass concentrations of PM2.5 and PM1 were 30.3 ± 13.5 μg/m3 and 20.8 ± 10.9 μg/m3, respectively, as indicated in Table 2. The PM1 explained approximately 68.5% of PM2.5 with a correlation coefficient of 0.70 between PM1 and PM2.5. The maximum concentrations of PM2.5 and PM1 were 101.0 and 77.1 μg/m3, respectively. The daily PM2.5 level was higher than the WHO standard value (25 μg/m3) on 17 days out of 28 days. One strong PM1 event where daily average PM1 was higher than 30 μg/m3 was found during 11 June 2015 to 13 June 2015. PM1 increased more than 2.1 times when compared to the average concentration during the whole sampling period. More on the characteristics of the PM1 event will be discussed later. The organics were the most dominant species in PM1. The average mass concentrations of organics, sulfate, nitrate, ammonium, and BC were 9.1 μg/m3, 4.7 μg/m3, 3.2 μg/m3, 2.6 μg/m3, and 1.3 μg/m3, and their fractions in PM1 were 43.7%, 22.5%, 15.4%, 12.6%, and 6%, respectively. The fractions were similar to those at other urban sites [33] and at the same sampling site in 2011, 2012, and 2013 [8,13,17].
As shown in Figure 2b, nine NPF events were found in 28 measurement days. The NPF event here was defined when the number of particles less than 50 nm increased significantly with subsequent growth. The growth rate varied in the range of 7.2–11.0 nm/hour. The growth rate was calculated from the change of the mode diameter during particle growth time (the slope in the linear curve between the measured mode diameter and time represents the growth rate) [6,15,34]. The number concentration of particles was not correlated to the PM2.5 mass concentration. When the NPF event occurred, the mass concentration of pre-existing particles (i.e., PM2.5) was low, suggesting that the low surface area of pre-existing particles favored NPF. More discussion on NPF will be provided later.
To determine any relationship among the PM1 mass concentration, chemical constituents in PM1, and air mass, air mass backward trajectories (48 h) were clustered using the K-means algorithm [35]. As shown in Figure 3, five major clusters were found: Western (cluster 1), Northern (cluster 2), Northwestern (cluster 3), Southeastern (cluster 4), and Southwestern (cluster 5) trajectories explaining 15%, 13%, 34%, 23%, and 14% of the total air masses, respectively. With cluster one, the highest PM1 (28.6 μg/m3) was found with dominating species of both organics (41.9%) and sulfate (24.4%). Cluster one passed over an industrial region in the Shandon Peninsula and the Yangtze River Delta (YRD) in China (~664 km west of the site) where elevated SO2 emissions and AOT are often observed [36,37]. The PM2.5 mass concentration also showed a similar pattern to that of PM1, as shown in Figure 3. With cluster four, which had the most stagnant air condition (the air mass speed was as low as 2.8 km/h and the local wind speed was as low as 1.0 m/s), an elevated PM1 concentration was also observed. It is possible that the PM1 produced from local sources was not diluted efficiently at the stagnant condition, leading to the elevated PM1. Additionally, the cluster four air mass passed over a petrochemical industrial complex and steel works located ~90 km Southeast of the site. Our data suggest that long-range transported aerosols and stagnant conditions played an important role in the increased PM1 and PM2.5 concentrations during the sampling period.
The PSCF results for sulfate, organics, and BC in PM1 are shown in Figure 4. Elevated mass concentrations of chemical constituents at a receptor site can be directly related to the air mass trajectory and source regions using the PSCF method. The potential source regions of sulfate were located in the South and East regions of China and South and East regions of Korea, suggesting that long-range transported aerosols played important roles in the elevated concentrations of sulfate at the sampling site. A lower number of source regions with a long distance were found for the elevated organics when compared to the sulfate. The potential source region for the BC was not clearly identified outside of Korea, suggesting that local sources may be responsible for the elevated BC concentration.
The average diurnal variations of the chemical constituents in PM1 are shown in Figure 5a. Organics, nitrate, ammonium, chloride, and BC showed a similar diurnal pattern with increased concentrations in the morning and evening, and low concentrations in the afternoon. This behavior was related to the diurnal variation of mixing height, which led to changes in the wind speed and temperature, as shown in Figure 5b [38]. As the mixing layer increased in the afternoon, the concentrations of organics, nitrate, ammonium, chloride, and BC decreased significantly (i.e., the dilution effect). However, the OOAs decreased a little and sulfate increased, overcoming the mixing layer effect. This occurred as the strong photochemical activity in the afternoon led to the production of significant amounts of sulfate and OOAs.
During the PM1 event (significant increase in PM1 mass concentration) and NPF event (significant increase in the number concentration of submicrometer particles) days, the air mass retention times resulting from the RTA analysis were compared, as shown in Figure 6. During the PM1 event (11 June 2015 and 12 June 2015), the Western and Southwestern air mass influenced the site. Both sulfate and organics increased significantly during the PM1 event. Sulfate and organics were dominant on the first day of the PM1 event (11 June 2015), and organics became more dominant than sulfate on the second day (12 June 2015) with the change in air mass and wind direction as shown in Figure 6a. We believe that the high concentration of PM1 at the receptor site may be caused by long-range transported aerosols. The locations where the air mass passed over included major industrial complexes in China and Korea, possibly contributing to the elevated PM concentration. Satellite data from 12 June 2015 are shown in Figure 6b. The AOT was high in China (east region) and Korea (west region including the sampling site) when the PM1 event occurred. The AOT was determined from the extinction of light in the vertical column. Both ground and satellite data suggested that long-range transported aerosols played an important role in the elevated PM1. The number of particles in the larger size range (650 nm–20 μm) increased significantly during the PM1 event, as shown in Table 2.
Significantly different air masses were found during the NPF events. NPF events occurred when the Western air mass minimally influenced the site as shown in Figure 6a. The Western air mass was related to the elevated PM1 or PM2.5 mass concentration, providing a high surface area of pre-existing particles, which possibly led to suppressing the NPF [39]. The average surface area of particles during PM1 event days was 1007 μm2/cm3. The surface area of particles during the NPF event was small as shown in Table S1 in the Supplementary Materials. During the NPF events, the time for the increased concentration of nanoparticles occurred from 10:00 to 14:00 with subsequent growth in the afternoon with strong solar radiation as shown in Figure 7 and Table S1 in the Supplementary Materials. Furthermore, this is consistent with the times for elevated sulfate and oxidized organics in submicrometer particles, as shown in Figure 7. Our data suggest that a low surface area of pre-existing particles and strong solar radiation favored NPF at the sampling site. Combustion can also contribute to the elevated number concentrations of particles less than 50 nm. However, typical combustion particles did not show significant growth, as we observed here (banana shape in Figure 7). Furthermore, the concentration of BC, one of the major combustion particles, did not increase significantly during NPF events.

4. Conclusions

The chemical composition (organics, nitrate, sulfate, ammonium, chloride, and BC) and number concentration of submicrometer particles were continuously measured to examine elevated mass (PM1) and number concentrations during the MAPS-Seoul campaign. Organics (9.1 μg/m3) were found to be the most dominant species, followed by sulfate (4.7 μg/m3), nitrate (3.2 μg/m3), ammonium (2.6 μg/m3), and BC (1.3 μg/m3). Potential source regions of sulfate were located in the East and Southeast regions of China (West and Southwest of the site) and South and Southeast regions of Korea (south and southeast of the site). Local sources may be more responsible for the elevated BC concentration. One PM1 event and nine NPF events were observed during the campaign. Both sulfate and organics increased significantly in the PM1 event. Long-range transported aerosols and stagnant meteorological conditions played an important role in the elevated mass concentration of submicrometer particles (PM1 event). The locations where the air mass passed over included major industrial complexes in China and Korea, possibly contributing to the elevated PM concentration. NPF started in the late morning and noon, and the newly-formed nanoparticles experienced gradual growth in the afternoon with growth rates of 7.2–11.0 nm/h, suggesting that these particles were produced by photochemical activity. A low surface area of pre-existing particles and strong solar radiation favored the NPF events observed in this study. Diurnal variation of the chemical constituents in PM1 suggested that sulfate and oxidized organics may be involved in nanoparticle formation and growth.

Supplementary Materials

The following are available online at https://www.mdpi.com/2073-4433/9/10/393/s1, Table S1: Comparison of average parameters before, during, and after NPF events.

Author Contributions

K.P. designed the study, H.J.C., A.S., D.K., M.P., and H.J. carried out the field campaign for aerosol measurements. H.J.C. and J.K. analyzed measurement data.

Funding

This work was supported by the National Research Foundation of Korea (Grants NRF-2016R1A2A1A05005532, NRF-2016M1A5A1901779, and NRF-2017M3D8A1092220) funded by the Ministry of Science and ICT, and the National Research Foundation (NRF) of Korea.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. IPCC. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK, 2013. [Google Scholar]
  2. Pope, C.A.; Dockery, D.W. Health Effects of Fine Particulate Air Pollution: Lines That Connect. J. Air Waste Manag. 2006, 56, 709–742. [Google Scholar] [CrossRef]
  3. Cassee, F.R.; Heroux, M.E.; Gerlofs-Nijland, M.E.; Kelly, F.J. Particulate Matter Beyond Mass: Recent Health Evidence on the Role of Fractions, Chemical Constituents and Sources of Emission. Inhal. Toxicol. 2013, 25, 802–812. [Google Scholar] [CrossRef] [PubMed]
  4. Pöschl, U. Atmospheric Aerosols: Composition, Transformation, Climate and Health Effects. Angew. Chem. Int. Ed. 2005, 44, 7520–7540. [Google Scholar] [CrossRef] [PubMed]
  5. Park, K.; Park, J.Y.; Kwak, J.-H.; Cho, G.N.; Kim, J.-S. Seasonal and Diurnal Variations of Ultrafine Particle Concentration in Urban Gwangju, Korea: Observation of Ultrafine Particle Events. Atmos. Environ. 2008, 42, 788–799. [Google Scholar] [CrossRef]
  6. Maskey, S.; Kim, J.S.; Cho, H.J.; Park, K. Ultrafine Particle Events in the Ambient Atmosphere in Korea. Asian J. Atmos. Environ. 2012, 6, 288–303. [Google Scholar] [CrossRef] [Green Version]
  7. Guo, S.; Hu, M.; Zamora, M.L.; Peng, J.; Shang, D.; Zheng, J.; Du, Z.; Wu, Z.; Shao, M.; Zeng, L.; et al. Elucidating Severe Urban Haze Formation in China. Proc. Natl. Acad. Sci. USA 2014, 111, 17373–17378. [Google Scholar] [CrossRef] [PubMed]
  8. Lee, K.; Park, J.; Kang, M.; Kim, D.; Batmunkh, T.; Bae, M.-S.; Park, K. Chemical Characteristics of Aerosols in Coastal and Urban Ambient Atmospheres. Aerosol Air Qual. Res. 2017, 17, 908–919. [Google Scholar] [CrossRef] [Green Version]
  9. Jung, J.; Kim, Y.J. Tracking Sources of Severe Haze Episodes and Their Physicochemical and Hygroscopic Properties under Asian Continental Outflow: Long-Range Transport Pollution, Postharvest Biomass Burning, and Asian Dust. J. Geophys. Res. Atmos. 2011, 116, D02206. [Google Scholar] [CrossRef]
  10. Jung, J.; Lee, K.; Cayetano, M.G.; Batmunkh, T.; Kim, Y.J. Optical and Hygroscopic Properties of Long-Range Transported Haze Plumes Observed at Deokjeok Island Off the West Coast of the Korean Peninsula under the Asian Continental Outflows. J. Geophys. Res. Atmos. 2015, 120, 8861–8877. [Google Scholar] [CrossRef]
  11. Wang, Z.; Wu, Z.; Yue, D.; Shang, D.; Guo, S.; Sun, J.; Ding, A.; Wang, L.; Jiang, J.; Guo, H.; et al. New Particle Formation in China: Current Knowledge and Further Directions. Sci. Total Environ. 2017, 577, 258–266. [Google Scholar] [CrossRef] [PubMed]
  12. Yu, H.; Ren, L.; Kanawade, V.P. New Particle Formation and Growth Mechanisms in Highly Polluted Environments. Curr. Pollut. Rep. 2017, 245–253. [Google Scholar] [CrossRef]
  13. Park, J.; Lee, S.; Kang, M.; Cho, H.J.; Lee, K.; Park, K. Seasonal Characteristics of Submicrometer Organic Aerosols in Urban Gwangju, Korea Using an Aerosol Mass Spectrometer. Atmos. Environ. 2013, 80, 445–454. [Google Scholar] [CrossRef]
  14. Merikanto, J.; Spracklen, D.V.; Mann, G.W.; Pickering, S.J.; Carslaw, K.S. Impact of Nucleation on Global Ccn. Atmos. Chem. Phys. 2009, 9, 8601–8616. [Google Scholar] [CrossRef]
  15. Kulmala, M.; Vehkamäki, H.; Petäjä, T.; Dal Maso, M.; Lauri, A.; Kerminen, V.M.; Birmili, W.; McMurry, P.H. Formation and Growth Rates of Ultrafine Atmospheric Particles: A Review of Observations. J. Aerosol Sci. 2004, 35, 143–176. [Google Scholar] [CrossRef]
  16. McMurry, P.H.; Fink, M.; Sakurai, H.; Stolzenburg, M.R.; Mauldin, R.L.; Smith, J.; Eisele, F.; Moore, K.; Sjostedt, S.; Tanner, D.; et al. A Criterion for New Particle Formation in the Sulfur-Rich Atlanta Atmosphere. J. Geophys. Res. 2005, 110, D22S02. [Google Scholar] [CrossRef]
  17. Park, K.; Park, J.; Lee, S.; Cho, H.J.; Kang, M. Real Time Measurement of Chemical Composition of Submicrometer Aerosols at Urban Gwangju in Korea by Aerosol Mass Spectrometer. Atmos. Enviro. 2012, 62, 281–290. [Google Scholar] [CrossRef]
  18. Jayne, J.T.; Leard, D.C.; Zhang, X.; Davidovits, P.; Smith, K.A.; Kolb, C.E.; Worsnop, D.R. Development of an Aerosol Mass Spectrometer for Size and Composition Analysis of Submicron Particles. Aerosol Sci. Technol. 2000, 33, 49–70. [Google Scholar] [CrossRef] [Green Version]
  19. Allan, J.D.; Jimenez, J.L.; Williams, P.I.; Alfarra, M.R.; Bower, K.N.; Jayne, J.T.; Coe, H.; Worsnop, D.R. Quantitative Sampling Using an Aerodyne Aerosol Mass Spectrometer 1. Techniques of Data Interpretation and Error Analysis. J. Geophys. Res. Atmos. 2003, 108. [Google Scholar] [CrossRef]
  20. Takegawa, N.; Miyazaki, Y.; Kondo, Y.; Komazaki, Y.; Miyakawa, T.; Jimenez, J.L.; Jayne, J.T.; Worsnop, D.R.; Allan, J.D.; Weber, R.J. Characterization of an Aerodyne Aerosol Mass Spectrometer (Ams): Intercomparison with Other Aerosol Instruments. Aerosol Sci. Technol. 2005, 39, 760–770. [Google Scholar] [CrossRef]
  21. Jimenez, J.L.; Jayne, J.T.; Shi, Q.; Kolb, C.E.; Worsnop, D.R.; Yourshaw, I.; Seinfeld, J.H.; Flagan, R.C.; Zhang, X.; Smith, K.A. Ambient Aerosol Sampling Using the Aerodyne Aerosol Mass Spectrometer. J. Geophys. Res. Atmos. 2003, 108. [Google Scholar] [CrossRef]
  22. Ulbrich, I.; Canagaratna, M.; Zhang, Q.; Worsnop, D.; Jimenez, J. Interpretation of Organic Components from Positive Matrix Factorization of Aerosol Mass Spectrometric Data. Atmos. Chem. Phys. 2009, 9, 2891–2918. [Google Scholar] [CrossRef]
  23. Ng, N.; Canagaratna, M.; Jimenez, J.; Zhang, Q.; Ulbrich, I.; Worsnop, D. Real-Time Methods for Estimating Organic Component Mass Concentrations from Aerosol Mass Spectrometer Data. Environ. Sci. Technol. 2010, 45, 910–916. [Google Scholar] [CrossRef] [PubMed]
  24. Zhang, Q.; Jimenez, J.L.; Canagaratna, M.R.; Ulbrich, I.M.; Ng, N.L.; Worsnop, D.R.; Sun, Y. Understanding Atmospheric Organic Aerosols Via Factor Analysis of Aerosol Mass Spectrometry: A Review. Anal. Bioanal. Chem. 2011, 401, 3045–3067. [Google Scholar] [CrossRef] [PubMed]
  25. Hansen, A.; Rosen, H.; Novakov, T. The Aethalometer—an Instrument for the Real-Time Measurement of Optical Absorption by Aerosol Particles. Sci. Total Environ. 1984, 36, 191–196. [Google Scholar] [CrossRef]
  26. Cheng, Y.-H.; Lin, M.-H. Real-Time Performance of the Microaeth® Ae51 and the Effects of Aerosol Loading on Its Measurement Results at a Traffic Site. Aerosol Air Qual. Res. 2013, 13, 1853–1863. [Google Scholar] [CrossRef]
  27. Hinds, W.C. Aerosol Technology: Properties Behavior, and Measurement of airborne Particles, 2nd ed.; John Wiley & Sons: Toronto, ON, Canada, 1999. [Google Scholar]
  28. Air Resources Laboratory. Real-Time Environmental Applications and Display System. Available online: http://ready.arl.noaa.gov (accessed on 30 January 2018).
  29. Ashbaugh, L.L.; Malm, W.C.; Sadeh, W.Z. A Residence Time Probability Analysis of Sulfur Concentrations at Grand Canyon National Park. Atmos. Environ. (1967) 1985, 19, 1263–1270. [Google Scholar] [CrossRef]
  30. Ara Begum, B.; Kim, E.; Jeong, C.-H.; Lee, D.-W.; Hopke, P.K. Evaluation of the Potential Source Contribution Function Using the 2002 Quebec Forest Fire Episode. Atmos. Environ. 2005, 39, 3719–3724. [Google Scholar] [CrossRef]
  31. Kim, I.S.; Wee, D.; Kim, Y.P.; Lee, J.Y. Development and Application of Three-Dimensional Potential Source Contribution Function (3d-Pscf). Environ. Sci. Pollut. Res. Int. 2016, 23, 16946–16954. [Google Scholar] [CrossRef] [PubMed]
  32. NASA Earth Observations. Available online: https://neo.sci.gsfc.nasa.gov (accessed on 30 January 2018).
  33. Jimenez, J.; Canagaratna, M.; Donahue, N.; Prevot, A.; Zhang, Q.; Kroll, J.; DeCarlo, P.; Allan, J.; Coe, H.; Ng, N. Evolution of Organic Aerosols in the Atmosphere. Science 2009, 326, 1525–1529. [Google Scholar] [CrossRef] [PubMed]
  34. Dal Maso, M.; Kulmala, M.; Riipinen, I.; Wagner, R.; Hussein, T.; Aalto, P.P.; Lehtinen, K.E. Formation and Growth of Fresh Atmospheric Aerosols: Eight Years of Aerosol Size Distribution Data from Smear Ii, Hyytiala, Finland. Boreal Environ. Res. 2005, 10, 323. [Google Scholar]
  35. He, Z.; Kim, Y.J.; Ogunjobi, K.O.; Hong, C.S. Characteristics of Pm2.5 Species and Long-Range Transport of Air Masses at Taean Background Station, South Korea. Atmos. Environ. 2003, 37, 219–230. [Google Scholar] [CrossRef]
  36. Huang, R.J.; Zhang, Y.; Bozzetti, C.; Ho, K.F.; Cao, J.J.; Han, Y.; Daellenbach, K.R.; Slowik, J.G.; Platt, S.M.; Canonaco, F.; et al. High Secondary Aerosol Contribution to Particulate Pollution During Haze Events in China. Nature 2014, 514, 218–222. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  37. Xie, X.; Liu, X.; Wang, H.; Wang, Z. Effects of Aerosols on Radiative Forcing and Climate over East Asia with Different So2 Emissions. Atmosphere 2016, 7, 99. [Google Scholar] [CrossRef]
  38. Wallace, J.M.; Hobbs, P.V. Atmospheric Science: An Introductory Survey; Elsevier: San Diego, CA, USA, 2006; Volume 92. [Google Scholar]
  39. Cai, R.; Yang, D.; Fu, Y.; Wang, X.; Li, X.; Ma, Y.; Hao, J.; Zheng, J.; Jiang, J. Aerosol surface area concentration: A governing factor in new particle formation in Beijing. Atmos. Chem. Phys. 2017, 17, 12327–12340. [Google Scholar] [CrossRef]
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Figure 1. Locations of the aerosol measurement site and national monitoring stations.
Figure 1. Locations of the aerosol measurement site and national monitoring stations.
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Figure 2. Time series of (a) meteorological data (wind speed, wind direction, RH, precipitation, solar radiation, and temperature) and concentrations of gaseous species (CO, O3, NO2, and SO2), and (b) PM2.5 and PM1 mass concentrations, concentrations of organics, sulfate, nitrate, ammonium, chloride, BC, and number size distribution.
Figure 2. Time series of (a) meteorological data (wind speed, wind direction, RH, precipitation, solar radiation, and temperature) and concentrations of gaseous species (CO, O3, NO2, and SO2), and (b) PM2.5 and PM1 mass concentrations, concentrations of organics, sulfate, nitrate, ammonium, chloride, BC, and number size distribution.
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Figure 3. Concentrations of PM1 and PM2.5 with clustered air masses based on air mass backward trajectories (48 h).
Figure 3. Concentrations of PM1 and PM2.5 with clustered air masses based on air mass backward trajectories (48 h).
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Figure 4. Potential source contribution function PSCF results for sulfate, organics, and BC.
Figure 4. Potential source contribution function PSCF results for sulfate, organics, and BC.
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Figure 5. Diurnal variations of (a) organics, nitrate, sulfate, ammonium, OOA, HOA, chloride, and BC in PM1 and (b) gases and meteorological data. The solid line indicates the averaged value, and the shaded area represents the standard deviation.
Figure 5. Diurnal variations of (a) organics, nitrate, sulfate, ammonium, OOA, HOA, chloride, and BC in PM1 and (b) gases and meteorological data. The solid line indicates the averaged value, and the shaded area represents the standard deviation.
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Figure 6. (a) Comparison of the residence times of air masses arriving at a receptor site during all sampling periods, the PM1 event, and NPF event, and (b) AOT data obtained from MODIS sensors aboard the Aqua and Terra satellites with air mass backward trajectory (48 h) during the PM1 event (12 June 2015).
Figure 6. (a) Comparison of the residence times of air masses arriving at a receptor site during all sampling periods, the PM1 event, and NPF event, and (b) AOT data obtained from MODIS sensors aboard the Aqua and Terra satellites with air mass backward trajectory (48 h) during the PM1 event (12 June 2015).
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Figure 7. Variation of the number size distribution, N (10–50 nm), surface area, sulfate, OOAs, nitrate, chloride, and BC in PM1 during the NPF event day (28 May 2015).
Figure 7. Variation of the number size distribution, N (10–50 nm), surface area, sulfate, OOAs, nitrate, chloride, and BC in PM1 during the NPF event day (28 May 2015).
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Table
Table 1. Summary of the measured parameters and instruments during the sampling period.
Table 1. Summary of the measured parameters and instruments during the sampling period.
Measured ParametersInstrumentsSampling Period
PM2.5 mass concentration aBAM-1020 (Met One Instruments, USA)18 May 2015–14 June 2015
Chemical constituents in PM1
Sulfate, Nitrate, Ammonium, Chloride, OrganicsAMS (Aerodyne, USA)18 May 2015–14 June 2015
BCAethalometer (AE-51, Magee Scientific, USA)18 May 2015–7 June 2015
Number size distribution
10–420 nmNanoScan SMPS (3910, TSI, USA)18 May 2015–14 June 2015
11–608 nmDMPS (DMA (3081, TSI, USA) and CPC (3022a, TSI, USA)18 May 2015–14 June 2015
0.3–20 μmOPC (1.108, Grimm, Germany)18 May 2015–14 June 2015
Gases a
COCO analyzer (300E, Teledyne API., USA),18 May 2015–14 June 2015
SO2SO2 Analyzer (100E, Teledyne API., USA)18 May 2015–14 June 2015
NO2NOx Analyzer (200E, Teledyne API., USA)18 May 2015–14 June 2015
O3O3 Analyzer (400E, Teledyne API., USA)18 May 2015–14 June 2015
Meteorological parameters
Wind speed bWind speed sensor (JY-WS161C, Jinyang Industrial, Korea)18 May 2015–14 June 2015
Wind direction bWind direction sensor (JY100829, Jinyang Industrial, Korea)18 May 2015–14 June 2015
Precipitation bRain Gauge (JY100097-2, Jinyang Industrial, Korea)18 May 2015–14 June 2015
Temperature bTemperature sensor (JY100829, Jinyang Industrial, Korea)18 May 2015–14 June 2015
Relative humidity cThermo-hygrometer (HTP-20, Wellbian System, Korea)18 May 2015–14 June 2015
Solar radiation cPyrheliometer (CMP-21, Kipp & Zonen, Netherlands)18 May 2015–14 June 2015
a National air quality monitoring network station operated by Korea Environment Corporation. b National AWS observing station operated by the Korean Meteorological Administration. c National ASOS observing station operated by the Korean Meteorological Administration.
Table
Table 2. Average values of the measured parameters during the whole sampling period and PM1 event.
Table 2. Average values of the measured parameters during the whole sampling period and PM1 event.
Measured ParametersUnitAll Sampling PeriodsPM1 Event
T°C20.5 ± 5.4723.7 ± 3.84
RH%54.8 ± 20.358.2 ± 18.3
Wind speedm/s1.2 ± 0.871.4 ± 0.87
Solar radiationW/m2334 ± 336170 ± 235
O3ppb47.6 ± 18.857.8 ± 16.4
NO2ppm0.018 ± 0.0080.015 ± 0.009
COppm0.76 ± 0.110.92 ± 0.08
SO2ppm0.003 ± 0.0010.004 ± 0.001
PM2.5μg/m330.3 ± 13.557.8 ± 20.5
PM1μg/m320.8 ± 10.943.3 ± 12.9
BCμg/m31.3 ± 0.6N/A
Ammoniumμg/m32.6 ± 1.65.8 ± 2.0
Nitrateμg/m33.2 ± 2.66.4 ± 3.9
Sulfateμg/m34.7 ± 2.911.1 ± 2.03
Organicsμg/m39.1 ± 5.519.4 ± 8.83
OOAμg/m33.9 ± 2.29.0 ± 2.5
HOAμg/m34.4 ± 3.28.8 ± 6.2
N (10 nm–608 nm)particles/cm36876 ± 28277624 ± 2465
N (650 nm–20 μm)particles/cm3106 ± 109322 ± 264

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Cho, H.-J.; Kang, J.; Kim, D.; Seo, A.; Park, M.; Joo, H.; Park, K. A Study on Elevated Concentrations of Submicrometer Particles in an Urban Atmosphere. Atmosphere 2018, 9, 393. https://doi.org/10.3390/atmos9100393

AMA Style

Cho H-J, Kang J, Kim D, Seo A, Park M, Joo H, Park K. A Study on Elevated Concentrations of Submicrometer Particles in an Urban Atmosphere. Atmosphere. 2018; 9(10):393. https://doi.org/10.3390/atmos9100393

Chicago/Turabian Style

Cho, Hee-Joo, Jia Kang, Dohyeong Kim, Arom Seo, Minhan Park, Hungsoo Joo, and Kihong Park. 2018. "A Study on Elevated Concentrations of Submicrometer Particles in an Urban Atmosphere" Atmosphere 9, no. 10: 393. https://doi.org/10.3390/atmos9100393

APA Style

Cho, H. -J., Kang, J., Kim, D., Seo, A., Park, M., Joo, H., & Park, K. (2018). A Study on Elevated Concentrations of Submicrometer Particles in an Urban Atmosphere. Atmosphere, 9(10), 393. https://doi.org/10.3390/atmos9100393

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