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Article

Atmospheric Ultrafine Particulate Matter (PM0.1)-Bound Carbon Composition in Bangkok, Thailand

by
Worradorn Phairuang
1,2,*,
Surapa Hongtieab
3,
Panwadee Suwattiga
4,
Masami Furuuchi
1,5 and
Mitsuhiko Hata
1
1
Faculty of Geosciences and Civil Engineering, Institute of Science and Engineering, Kanazawa University, Kanazawa 920-1192, Japan
2
Department of Geography, Faculty of Social Sciences, Muang, Chiang Mai University, Chiang Mai 50200, Thailand
3
Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa 920-1192, Japan
4
Department of Agro-Industrial, Food and Environmental Technology, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bangkok 10800, Thailand
5
Faculty of Environmental Management, Prince of Songkla University, Hat Yai 90112, Thailand
*
Author to whom correspondence should be addressed.
Atmosphere 2022, 13(10), 1676; https://doi.org/10.3390/atmos13101676
Submission received: 1 October 2022 / Revised: 11 October 2022 / Accepted: 12 October 2022 / Published: 14 October 2022
(This article belongs to the Special Issue Feature Papers in Air Quality)
Browse Figures
Versions Notes

Abstract

:
Seasonal variations in atmospheric ultrafine particulate matter (PM0.1) were monitored in Bangkok, Thailand, from 2016 to 2017. PM0.1-bound organic carbon (OC) and elemental carbon (EC) were collected by a cascade air sampler that can collect PM0.1 and were analyzed by a Thermal-Optical carbon analyzer following the IMPROVE-TOR protocol. The annual average PM0.1 in Bangkok was 14.5 ± 4.7 µg/m3, which is higher than in large Asian cities such as Shanghai and Hanoi. Biomass burning from neighboring areas was shown to increase the particle concentration. Apparent increases in carbon species such as OC and EC, and the OC/EC ratios in the wet and dry seasons were observed; the Char-EC/Soot-EC ratio revealed that the PM0.1 in the Bangkok atmosphere was influenced mainly by vehicle exhausts, even though the influence of biomass burning was greater during the dry season. The effective carbon ratio (ECR) shows that Bangkok’s carbonaceous aerosol is light-absorbing and -scattering. The higher SOC/OC in the dry season indicates the high level of secondary sources forming smaller particles from the combustion sources in Bangkok, increasing light scattering during these periods, and contributing to climate and air quality. The findings of this work are of great importance to air pollutant control policies in urban areas.

Graphical Abstract

1. Introduction

Air pollutants, particularly airborne particulate matter (PM), have been a source of critical environmental pollution in many countries in recent decades [1,2,3,4]. Natural and anthropologic emission sources can release airborne PM [5,6]. PM components are also classified as materials which are hazardous to human health [7] and induce changes in global climate systems [8]. According to particle size criteria, PM is typically separated into coarse, fine, and ultrafine modes [9]. PM with an aerodynamic diameter of less than 10 µm is categorized as coarse PM (PM10). Those with an aerodynamic diameter of less than 2.5 µm can be classified in the fine fraction (PM2.5). Ultrafine particulate matter (PM0.1) has particle diameters of less than 0.1 µm, or 100 nm [10]. Thailand faces a severe air pollution problem, mainly from ambient PM, primarily released from industrial activities, biomass fires, and land transportation [11]. A PM2.5 average concentration during the dry season was reported to exceed the 24 h standard for Thailand (50 µg/m3), and more significant than the World Health Organization (WHO) standards (10 µg/m3). Moreover, transboundary particulate pollutants have been an increasing concern recently [6,12].
However, in the past decade, there has been an increase in the study of ultrafine particles (UFPs) or PM0.1 [9,13]. These UFPs have recently had greater public health effects [10]. Information on the physio-chemical characteristics of UFPs continues to be lacking worldwide [9,10]. Information on the fraction of UFPs in the Asian atmosphere is still limited, even though the Asian region has been the leading contributor to particulate pollution in the recent decade [14]. Several studies have measured the amount of particulate pollution bound to chemicals in several nations around Asia due to health effects and environmental damage [12,13,14]. Many studies are based on ground-based detecting, scientific modeling, and satellite image techniques [6,15,16,17]. Recent decades exposed the fact that ambient PM plumes during haze episodes exceeded many countries’ national standards, with an extreme value compared to WHO data and other countries’ standards [18]. Thailand and Southeast Asian (SEA) countries displayed UFP mass concentrations comprise around 15–20% of the total suspended particulate matter (TSP) in ambient air [12,13,14,19].
Carbonaceous aerosols (CA) are typically separated into two broad classes based on their Thermal-Optical properties, and the total carbon (TC) is split into operationally defined elemental carbon (EC) and organic carbon (OC) [20]. Total carbon (TC) is mostly composed of particles that have particle mass concentrations of approximately 20–50% [12,13]. The importance of PM in modifying the Earth’s climate has been recognized for a long time [21]. Recently, the significance of the carbon fraction in aerosols to global radiative forcing, air quality, and human health has been stressed in several studies [22,23]. Information on carbon components in airborne PM is also essential to identify the emission sources and regular procedures for ambient particles and carbon material, the two leading causes of atmospheric particulate pollution and global warming [24].
Therefore, the general motivation of this study was to investigate the physicochemical characteristics of atmospheric PM0.1 related to carbon in Bangkok, Thailand, from 2016 to 2017. The main goal of this study was to investigate PM0.1 and its carbon composition, and this information would be valuable for studying possible emission sources of atmospheric PM0.1 in Thailand. The information from this work will provide facts about the chronological variation of atmospheric PM0.1 in typical cities in Thailand, which leads to a life-threatening global warming problem and adverse human health effects in urban areas. Moreover, this study may support future research and policy making on air pollution control and environmental management in the case of developing countries.

2. Methodology

2.1. Sampling Site

The monitoring campaign was conducted at the King Mongkut’s University of Technology, North Bangkok (KMUTNB; 13.83° N, 100.51° E) (Figure 1). With a population of around 8 million in 2015, Bangkok is located in central Thailand, with a daily average temperature ranging from 27.1 to 28.2 °C [25]. All of the main roads are southwest (SW), south (S), and southeast (SE) of the monitoring site, but the north (N) of KMUTNB is exposed to cropland. The sampling site is located about 40 m above ground level. A 24 h sampling was conducted five times/month every six days from May 2016 to April 2017.

2.2. Cascade Sampler for Ambient PM0.1

A cascade air sampler for nanoparticles or ambient nano-sampler (termed here as ANS) was used to collect PM0.1 at a flow rate of 40 L per minute [26]. Quartz fiber filters (QFF) of 55 mm diameter were incubated at 350 °C for 1 h to eliminate contaminants on the filter and then stored for 48 h in a weighing chamber (temperature 21.5 ± 1.5 °C and relative humidity 35 ± 5%) and weighed using a microbalance. The procedure followed one suggested by the Ministry of Environment of Japan. After collecting samples, the filters were kept in the chamber for at least 48 h before being reweighed and held at −20 °C to preserve their chemical constancy until consequent analysis [27]. A total of 60 PM0.1 samples were collected in this study.

2.3. Thermal-Optical Carbon Analysis

A Sunset Thermal-Optical Carbon Analyzer equipped with flame ionization detector (FID) sensors was used to analyze the OC and EC of PM0.1. The thermal/optical reflectance (TOR) protocol was used to measure the OC and EC. The instruments estimated the carbon compositions in PM0.1 samples, i.e., OC and EC, by heating the filter at different temperatures in a stepwise manner. In short, a 1.5 cm2 area of QFF was heated in pure helium using temperatures of 120, 250, 450, and 550 °C set by the instrument’s program. Four OC fractions were separated and quantified as OC1, OC2, OC3, and OC4. Organic materials in the samples were converted entirely into CO2. Following that, oxygen was passed through the instrument to maintain the condition of 2% oxygen and 98% helium. The temperature was gradually increased to 550, 700, and 800 °C. This temperature program isolated and evaluated elemental carbon fractions (EC1, EC2, and EC3). In addition, organic pyrolysis carbon (OPC) was generated during the oxidation phase. OC1 + OC2 + OC3 + OC4 + OPC defined OC while EC1 + EC2 + EC3 − OPC defined EC. The analyzer was calibrated daily with a filter blank and a known concentration of CH4. Moreover, EC can divide into Char-EC (=EC1 − OPC) and Soot-EC (=EC2 + EC3) [24].

2.4. Estimation of Climate Effect

Carbonaceous aerosol affects public health and the global macroclimate since it can modify the global temperature by absorbing and scattering light. As Safai et al. (2014) [28] reported, the effective carbon ratio (ECR) is more amenable to evaluating the carbonaceous aerosol effect of climate change, and ECR can be calculated from the ratio of SOC/(POC + EC). POC and EC can absorb the light commonly emitted from fossil fuel burning, crop residue burning, and domestic cooking. In contrast, SOC can scatter the light emitted from the oxidation of volatile organic compounds.

2.5. Quality Assurance/Quality Control (QA/QC)

The calibration of the carbon analysis was achieved by a TC reference chemical or sucrose (C12H22O11). Blank filters (n = 4) were used for standardization. Ten percent of all samples were analyzed in duplicate, and a full calibration was performed every time a gas cylinder was changed. OC and EC repeatability in duplicates was 90% and 95%, respectively. The minimum detection limits (MDL) for OC and EC based on the estimation of the travel blanks were 0.37 and 0.01 µg/cm2, respectively.

2.6. Hot Spots and Backward Trajectories

Active fires or hot spots representing open fires were derived from a moderate-resolution imaging spectroradiometer (MODIS). The back-trajectories were calculated using the hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model, which was initially advanced in the 1980s by the National Oceanic and Atmospheric Administration (NOAA) and then launched on https://www.ready.noaa.gov/HYSPLIT.php, accessed on 29 September 2022. The model was initialized with two seasons. Three-day backward trajectories were simulated for twelve individual months from sampling hours (0800 UTC) at three different elevations of 100, 500, and 1000 m above ground level.

3. Results and Discussion

3.1. PM0.1 Mass Concentrations

The annual average of PM0.1 in Bangkok, Thailand, from May 2016 to April 2017 was 14.5 ± 4.7 µg/m3 (Figure 2). The concentration of PM0.1 during the wet season from May to October ranged from 7.5 to 15.5 μg/m3 with an average concentration of 12.0 ± 3.2 μg/m3, while during the dry season from November 2016 to April 2017 it ranged from 11.8 to 21.4 μg/m3 with a mean of 17.0 ± 4.6 μg/m3. The monthly concentration of PM0.1 during the wet season was less than during the dry season. The lowest PM0.1 was 5.1 μg/m3, found during 18–19 July 2016, while the highest PM0.1 was 27.0 μg/m3, found during 27–28 February 2017. The mass concentration in Bangkok, Thailand, from 2016 to 2017 was lower than the levels in Chiang Mai, Thailand, from 2014 to 2015 (25.2 ± 4.7 µg/m3) during forest fire episodes [24] and in tunnel measurements by Chen et al. (2010) [29] in Hsinchu, Taiwan (33.2 ± 6.5 µg/m3). Nevertheless, the concentrations of PM0.1 were mainly more significant than those in large cities in Asian countries, e.g., Hat Yai (2018), Thailand (10.2 ± 2.2 µg/m3) [23], Hat Yai (2019), Thailand (10.4 ± 1.2 µg/m3) [12], Shanghai, China (13.4 µg/m3) [30], and Hanoi, Vietnam (6.1 ± 2.7 µg/m3) [4] (Table 1). The PM0.1 in this study was also higher than those in cleaner zones, for instance, in North Sumatra, Indonesia (7.1 µg/m3) [13]; 2.2 ± 0.6 µg/m3 in the traffic area in Hsinchu, Taiwan [29]; 1.4 ± 0.6 µg/m3 in New Taipei, Taiwan [31]; and 4.7 µg/m3 in Kanazawa, Japan [32]. The predictable PM0.1 portion in PM10 in each emission source in the United Kingdom is as follows: waste burning (4%), manufacturing boilers (7%), energy in industry (8%), cropland (9%), off-road transport (9%), other non-road vehicles (14%), and manufacturing processes (15%) [33]. In addition, the information on PM0.1 mass concentration is still limited in Western countries due to a small mass concentration. Most studies of UFPs worldwide use particle number concentration (PNC) [34]. In the United States of America, Venecek et al. (2019) [34] calculated PM0.1 mass concentration in 39 towns and found that PM0.1 levels are relatively low on an annual average (under 1 µg/m3).

3.2. The Analysis of Eight Carbon Fractions of PM0.1

The thermogravimetric EC-OC study produced information for OC1-OC4, OPC, and EC1-EC3 fractions. These fractions were divided based on volatility and contained information regarding possible EC and OC sources in the Bangkok atmosphere. Figure 3 illustrates the percentage contributions of eight carbon fractions (OC1, OC2, OC3, OC4, OPC, EC1, EC2, and EC3) to total carbon in eight stages. Because each fraction differs in its source, it is often used in the source apportionment of CA investigations [23]. Biomass combustion contributes to OC1. OC2 is an indicator for coal combustion and might be derived from secondary organic carbon (SOC) and biomass fires [35]. OC3 and OC4 are found at higher concentrations in the road dust profile. EC1-OPC, also known as Char-EC, is generated by biomass combustion and specifies lower-temperature combustion processes (550 °C). EC2 and EC3, also identified as Soot-EC, result from a high-temperature combustion process and are created through gas-to-particle conversion [20]. The OPC fraction may be due to highly polar organic compounds. There was a noticeable monthly variation in the eight carbon fractions in the PM0.1 particle range. OC2 and OC3 were the most abundant species during the wet season, while OC2, OC3, and EC1 established a place with 60–80% during the dry season. Soot-EC was detected at higher rates than the Char-EC and showed seasonal variation. Char-EC was also characterized by seasonal dependence. Han et al. (2009) [20] claim that Char-EC/Soot-EC ratios are better for identifying sources and comprehending the radiative effect. Both are naturally absorbent; Soot-EC absorbs more lightly than Char-EC [36].

3.3. Carbon Characteristics

The carbonaceous components in ultrafine particles, including OC, EC, Char-EC, and Soot-EC, from May 2016 to April 2017 at KMUTNB, are presented in Figure 4a,b. The OC and EC concentrations in the wet season were slightly higher than in the dry season, with the highest OC in September being 4.2 µg/m3 and EC in May at 1.9 µg/m3. The lowest OC was found in OC for April (1.6 µg/m3) and EC in February and March was found to be 0.5 µg/m3. In Bangkok, the ambient PM0.1 also dominates the total TC concentration (OC and EC), occupying around 16.1–53.1% of the total PM0.1 mass concentration with, on average, around 30.3%.
The OC/EC ratios can identify specific sources of carbonaceous particles. Ratios for diesel exhaust, biomass fires, and coal burning have different levels. The higher ratios come from biomass fires, whereas fossil fuel burning produces lower OC/EC ratios [2]. The OC to EC ratio for biomass fires is higher (~6–8) [2,20], and that from fossil fuel combustion is lower (~0.2–1) [37]. Characteristic carbonaceous sources include diesel consumption (OC/EC~0.06–0.8) [38], biomass fires (OC/EC~3.9–4.2) [39], and distant transportation/aged aerosols (OC/EC~12) [40]. In this study, the OC/EC ratios we accounted for generally agreed with others. The OC/EC ratios for ambient PM0.1, ranging from 1.9 to 6.2 with an average of 3.0, are lower than those cities in Thailand, such as in Chiang Mai, with an ambient PM0.1 during haze episodes of 3.5, and in Bangkok, with values of 3.4. These values are slightly higher than Hat Yai (2.9) [23,24]. Moreover, these results are comparable with Hanoi, Vietnam, which fluctuated from 3.8 to 5.7 [4], but higher than those from Taiwan, which ranged from 0.2 to 1.7 [29]. The OC/EC ratio during the wet season is less than during the dry season. The OC/EC ratio of land transportation ranges from 1 to 2, and the higher value, of about 4, may be from biomass fires [4].
The OC/EC ratios of PM10 from proximate sources in Thailand were as follows: land transportation (1.2–1.3), manufacturing (1.6–2.3), and biomass combustion (3.9–4.2) [39]. These results show that the higher OC/EC of PM0.1 ratios at the KMUTNB site could be attributed to mixed sources dominating the carbonaceous particles in Bangkok, Thailand. OC domination, particularly in PM0.1, may be primarily derived from biomass fires. In contrast, the OC/EC ratios depend on the above three factors to acceptably categorize the emission sources. The three factors involved are primary emission sources, wet deposition, and secondary formation in the atmosphere [16].
Unlike the OC to EC ratio, the Char-EC/Soot-EC ratio is diverse, from different primary sources, and can be used to classify the origin sources [20]. The direct emission source and wet deposition are the two influences affecting the Char-EC/Soot-EC ratio. Higher Char-EC/Soot-EC indicates the dominance of biomass combustion linked to Char-EC contributions to the total EC content [12]. At the same time, ratios less important than 1.0 suggest that soot from diesel exhaust significantly contributes to the total EC concentrations [16]. The ratio of Char-EC/Soot-EC in PM0.1 is almost constant during the wet season, around 0.58 ± 0.45. Char-EC/Soot-EC is an index of fossil fuel burning (<1.0 for diesel soot) [23]. This suggests that fossil energies in local transportation, e.g., diesel exhausts, influence atmospheric PM0.1 above Bangkok. However, the Char-EC/Soot-EC ratio increases during the dry season by around 1.0. The impact of biomass fires is vital for increasing Char-EC, representing biomass burning emissions in this area [24]. Considering the carbon indices, both OC/EC and Char-EC/Soot-EC ratios differed in size distribution [2,23]. Other size-segregated PMs and carbon components are crucial to forthcoming studies in a more detailed investigation into developing carbonaceous aerosol studies.

3.4. Estimation of Secondary Organic Aerosol Concentrations

Because the observed OC concentrations include both POC released directly from emission sources and SOC generated in the ambient air by photochemical reactions, an assumption is required to determine the secondary OC contribution to the measured OC concentrations. A simplified technique was adopted in some circumstances, where OC/EC >2.0 was presumed to imply a substantial SOC contribution [41]. However, a more complicated method known as the EC tracer method is more commonly employed to assess the SOC contribution to the OC concentrations. SOC concentrations can be estimated using an equation of the EC tracer method [42]:
SOC = POC − EC × (OC/EC)min
where POC is the primary organic carbon, SOC refers to secondary organic carbon, and (OC/EC)min is the value of the lowest OC/EC ratio each season. This study calculated the carbon species for the wet and dry seasons. The contribution of POC, SOC, and EC are presented in Figure 5a,b.
The averaged proportion of SOC estimated the contribution of PM0.1 to be 33% and 44% in the wet and dry seasons, respectively (Figure 5). In Thailand, secondary atmospheric PM0.1 has been studied, which is a semi-direct attempt to estimate the SOC in PM0.1 each season. The average SOC in Bangkok, Thailand, matched the corresponding values for Hanoi, Vietnam, and Hat Yai, Thailand [4,23]. Thuy et al. (2018) [4] reported that smaller SOC particles were dominant compared to coarser particles; the SOC in PM0.1 constitutes up to 42.7% of the carbon level in Hanoi, Vietnam. The meteorological conditions during the cold dry season play a vital role in increasing the level of organic species amongst ambient particles. During the dry season, stable atmospheric conditions may strengthen atmospheric oxidation, and the low temperatures could increase the condensation of volatile secondary organic components (VSOC) [43].

3.5. Effective Carbon Ratio (ECR)

The OC/EC ratios play an essential role in the CA in climate models. However, the OC/EC ratio does not provide comprehensive data on the radiative impact of CA or its sources. Since OC contains both primary and secondary OC, each has different source mechanisms and behaviors when interacting with solar radiation. The effective carbon ratio (ECR) can represent the ambient warming effect of combustion CA [28]. A lower ECR ratio during the wet season implies a more absorbing type of CA. During the rainy season, low ECR values (≤0.50), primarily due to the high level of POC and EC, were detected (Figure 6). POC and EC could absorb the light commonly emitted from fossil fuel combustion, agricultural residue burning, and domestic cooking. On the other hand, during hot and cold dry seasons, high values (ECR ≥ 0.60) point in the direction of scattering-type CA. An estimate of the formation of secondary organic carbon (SOC) during smoke aging in Bangkok, Thailand, was attempted using the semi-direct method of the SOC in PM0.1. The averaged proportion of SOC showed that the contribution of PM0.1 is 57.1% in the dry season, while in the wet season, only 46.3% of SOC was found in PM0.1. The higher SOC/OC in the dry season indicates the high level of secondary sources forming smaller particles from the combustion sources in Bangkok and increasing the light scattering during the dry season [4]. A characteristic of SOC is to scatter the light emitted mainly from the oxidation of VOC. Consequently, the concept of ECR is strengthened to estimate the effect of CA on the global climate system.

3.6. Possible Local and Long-Range Transport of PM

The HYSPLIT model was used to study the potential long-term transport of PM-bound carbon at the sampling site [44]. We performed a backward air mass trajectory simulation for two seasons, wet and dry, in this study. The receptor site is at 13.83° N and 100.51° E. PM-bound carbon, with a lifetime of around one week, can travel for long distances in the atmospheric system [45]. Figure 7a,b displays the hot spot distribution in Thailand and neighboring countries. During the wet season, there are few hot spots in this area compared to the dry season. One hot spot shows a dense active fire in upper Southeast Asian countries where several researchers have reported biomass fires, including in the pre- and post-harvesting season of the agricultural crop, as well as forest fires [6,23,24]. In the wet season, most air mass movement originated from a westerly direction (Figure 7c). This was under the influence of the southwest monsoon, in which clean air masses from the Indian Ocean brought a low PM level to Thailand. On the other hand, in this study, high levels of PM0.1 were detected on a few sampling days during the dry season (27–28 February 2017) (Figure 7d). These high concentrations could be contributed to by either domestic sources or from bordering countries through the long-range transport of particulate pollutants. Moreover, all 72 h air mass originated from the northeast and central part of Thailand and some parts of neighboring countries, including Cambodia, Vietnam, and Laos, where open fires have been observed.

4. Conclusions

Seasonal variations of ultrafine particles (PM0.1) in Bangkok, Thailand, were investigated based on cascade air sampling in 2016–2017. The annual average PM0.1 in Bangkok was 14.5 ± 4.7 µg/m3, higher than in large cities in Southeast Asia. Char-EC/Soot-EC values for the PM0.1 particle were a lower amount of 1.0 in both the wet and dry seasons. A higher Char-EC/Soot-EC ratio indicates the dominance of biomass burning associated with Char-EC contributions to total EC content; ratios <1.0 suggest that Soot-EC from fossil fuel combustion largely contributes to the whole and is a significant contributor to total EC. The continuous monitoring of PM0.1 in Thailand will be the subject of a future study relating to the role of atmospheric PM0.1 in Thailand’s atmosphere. It could also be an essential factor in air pollution, which merits a more detailed investigation into the further development of air quality management in Thailand. The authors also recommend that more research be conducted addressing how local sources and transboundary pollution affect not only the PM0.1 mass level but also the chemical components of these fractions; such research will make a more comprehensive and reliable understanding of PM0.1 profiles emitted from an urban area, particularly during a haze episode in Thailand, available. These findings emphasize the importance of focusing emission control strategies on ultrafine particle sizes in Thailand and elsewhere.

Author Contributions

Conceptualization, W.P.; investigation, S.H.; resources, S.H. and P.S.; data curation, W.P.; writing—original draft preparation, W.P.; writing—review and editing, P.S., M.F. and M.H.; visualization, P.S.; supervision, M.H. and M.F.; project administration, W.P. and M.H.; funding acquisition, W.P. All authors have read and agreed to the published version of the manuscript.

Funding

This work was financially supported by the Office of the Permanent Secretary, Ministry of Higher Education, Science, Research and Innovation (Grant No. RGNS 63-253) in Thailand. Moreover, this research work was partially supported by JICA-JST SATREPS, JSPS KAKENHI 21H03618, and Sumitomo Foundation, Japan.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The authors appreciate the contributions to air monitoring by the students of King Mongkut’s University of Technology North Bangkok. Moreover, the authors wish to thank Milton S. Feather for improving this manuscript’s English.

Conflicts of Interest

The authors declare no conflict of interest.

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Atmosphere 13 01676 g001 550
Figure 1. Location of monitoring site in Bangkok, Thailand.
Figure 1. Location of monitoring site in Bangkok, Thailand.
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Figure 2. Monthly average variation in PM0.1 concentrations in Bangkok, Thailand.
Figure 2. Monthly average variation in PM0.1 concentrations in Bangkok, Thailand.
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Figure 3. The percentage of eight carbon fractions in PM0.1.
Figure 3. The percentage of eight carbon fractions in PM0.1.
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Figure 4. (a) OC, EC, and OC/EC ratios; (b) Char-EC, Soot-EC, and Char-EC/Soot-EC ratios.
Figure 4. (a) OC, EC, and OC/EC ratios; (b) Char-EC, Soot-EC, and Char-EC/Soot-EC ratios.
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Figure 5. Carbon distribution of each species for wet (a) and dry (b) seasons in PM0.1. EC, elemental carbon; POC, primary organic carbon; SOC, secondary organic carbon.
Figure 5. Carbon distribution of each species for wet (a) and dry (b) seasons in PM0.1. EC, elemental carbon; POC, primary organic carbon; SOC, secondary organic carbon.
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Figure 6. Effective carbon ratio (ECR) during the wet and dry seasons in Bangkok.
Figure 6. Effective carbon ratio (ECR) during the wet and dry seasons in Bangkok.
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Figure 7. Hot spots and air mass trajectories.
Figure 7. Hot spots and air mass trajectories.
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Table
Table 1. PM0.1 concentrations (µg/m3) at different locations in Asian Countries.
Table 1. PM0.1 concentrations (µg/m3) at different locations in Asian Countries.
LocationSite DescriptionConcentration References
Bangkok, ThailandUrban14.5 ± 4.7This study
Bangkok, ThailandUrban14.8 ± 2.0[24]
Chiang Mai, ThailandSuburban25.2 ± 4.7[24]
Hat Yai, ThailandSuburban10.2 ± 2.2[23]
Hat Yai, ThailandSuburban10.4 ± 1.2[12]
Pathumtani, ThailandSuburban16.9 ± 4.2[23]
North Sumatra, IndonesiaRural7.1[13]
Hanoi, VietnamUrban6.1 ± 2.7[4]
Shanghai, ChinaUrban13.4 [30]
Hsinchu, TaiwanTraffic2.2 ± 0.6[41]
Hsinchu, TaiwanTunnel33.2 ± 6.5[42]
New Taipei, TaiwanUrban1.4 ± 0.6[31]
Kanazawa, JapanSuburban4.7[32]
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Phairuang, W.; Hongtieab, S.; Suwattiga, P.; Furuuchi, M.; Hata, M. Atmospheric Ultrafine Particulate Matter (PM0.1)-Bound Carbon Composition in Bangkok, Thailand. Atmosphere 2022, 13, 1676. https://doi.org/10.3390/atmos13101676

AMA Style

Phairuang W, Hongtieab S, Suwattiga P, Furuuchi M, Hata M. Atmospheric Ultrafine Particulate Matter (PM0.1)-Bound Carbon Composition in Bangkok, Thailand. Atmosphere. 2022; 13(10):1676. https://doi.org/10.3390/atmos13101676

Chicago/Turabian Style

Phairuang, Worradorn, Surapa Hongtieab, Panwadee Suwattiga, Masami Furuuchi, and Mitsuhiko Hata. 2022. "Atmospheric Ultrafine Particulate Matter (PM0.1)-Bound Carbon Composition in Bangkok, Thailand" Atmosphere 13, no. 10: 1676. https://doi.org/10.3390/atmos13101676

APA Style

Phairuang, W., Hongtieab, S., Suwattiga, P., Furuuchi, M., & Hata, M. (2022). Atmospheric Ultrafine Particulate Matter (PM0.1)-Bound Carbon Composition in Bangkok, Thailand. Atmosphere, 13(10), 1676. https://doi.org/10.3390/atmos13101676

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