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Article

Investigation of Secondary Organic Aerosol Formation during O3 and PM2.5 Episodes in Bangkok, Thailand

by
Pornpan Uttamang
1,
Parkpoom Choomanee
2,*,
Jitlada Phupijit
2,3,
Surat Bualert
2 and
Thunyapat Thongyen
4
1
Environmental Technology Program, Faculty of Science, Maejo University, Chiang Mai 50290, Thailand
2
Department of Environmental Science, Faculty of Environment, Kasetsart University, Bangkok 10900, Thailand
3
Regional Integrated Multi-Hazard Early Warning System, AIT Campus, Pathumthani 12120, Thailand
4
Department of Environmental Technology and Management, Faculty of Environment, Kasetsart University, Bangkok 10900, Thailand
*
Author to whom correspondence should be addressed.
Atmosphere 2023, 14(6), 994; https://doi.org/10.3390/atmos14060994
Submission received: 1 May 2023 / Revised: 31 May 2023 / Accepted: 5 June 2023 / Published: 7 June 2023
(This article belongs to the Special Issue Atmospheric Particulate Matter Hazard Mapping)
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Abstract

:
In Bangkok, the megacity of Thailand, concentrations of fine particulate matter (PM2.5) have often exceeded the National Ambient Air Quality standards. During severe smog events over Bangkok, the air quality has exhibited moderate to unhealthy atmospheric conditions, according to the air quality index of the United States. To investigate the formation of secondary organic aerosols (SOA), a field campaign to estimate secondary organic carbon (SOC) in Bangkok using the EC tracer method was conducted in January 2021, when the concentrations of PM2.5 were high. The monthly period was classified into three pollution groups, including high pollution, high PM, and low pollution events. The study showed that the correlations between PM2.5 and O3 were negative during both the daytime and night-time. The OC/EC ratios varied from 4.32 to 5.43, while the moderate OC/EC values implied that fossil fuel combustion was the major carbonaceous aerosol in Bangkok. The EC tracer-estimated SOC and POC showed that SOC contributed between 32.5 and 46.4% to OC, while the highest SOC contribution occurred during the low pollution event. The heightened formation of SOA during the low pollution event was perhaps owing to the levels of oxides of nitrogen (NOx). Since Bangkok is more likely to have a NOx-rich photochemical reaction regime, an increase in the NOx level tended to decrease the SOA yield ([NOx] was 21.6 ppb, 20.8 ppb, and 17.1 ppb during the high pollution, high PM, and low pollution events, respectively). Together with the high humidity and high light intensity during the low pollution event, the SOA formation was enhanced. Even though the driving factors of SOA formation over Bangkok remain unclear, the results of this study reveal the significance and urgency of local actions to reduce NOx and O3 towards more habitable and sustainable urban environments.

1. Introduction

In the past few years, Bangkok, the megacity of Thailand, has experienced high concentrations of fine particulate matter (PM2.5). According to the 2020 report on the quality of the environmental state, the annual average PM2.5, ranging from 28.6 to 31.5 µg m−3 in Bangkok during 2012–2019, often exceeded the National Ambient Air Quality Standards (NAAQS) (annual PM2.5 standard is 25 µg m−3) [1]. High concentrations of PM2.5 normally occur in the winter (October to February) and in the transitional period between the winter and summer (February to May) [2,3]. In March 2023, the air quality index value of the United States (US AQI) for Bangkok ranged from a moderate level (59) to an unhealthy level (170), with the daily average PM2.5 ranging from 40 to 81 µg m−3 [4]. Furthermore, the comparison between the yearly average concentrations of ultrafine particles (PM0.1) in large cities in Southeast Asia showed that Bangkok had very high concentrations of PM0.1 [5]. Absorbed through the respiratory and circulatory systems, PM2.5 can exert adverse effects on human health, such as aggravated asthma, pulmonary inflammation, cardiopulmonary effects, and the premature death of people with heart or lung disease [6,7]. Biomass burning and fossil fuel combustion from vehicles are considered to be the major emission sources of pollutants, such as particulate matter (PM), ozone (O3), oxide of nitrogen (NOx), and carbon monoxide (CO) in Bangkok [5,8,9,10].
Fine PM (PM2.5) can be formed from various chemical compounds, such as organic carbon (OC), elemental carbon (EC), metals, and inorganic species [10,11]. In Thailand, the major chemical compositions in PM2.5 are OC, anions (the sum of C l − , N O 3 − , S O 4 2 − ), cations (the sum of N a + , N H 4 + , K + , C a 2 + , M g 2 + ), sulfate, and nitrate [12]. OC in aerosols can be classified into primary organic carbon (POC) and secondary organic carbon (SOC). POC originates from, or is directly emitted from, emission sources, whereas SOC is generated from atmospheric chemical mechanisms, such as gas-particle conversion or oxidation processes in the atmosphere [12,13]. To estimate the contribution of SOC to the PM2.5 concentration, the EC tracer method proposed by Turpin and Huntzicker (1991) [14] has been widely used [15,16,17]. Secondary organic aerosols (SOA) can be estimated by multiplying the SOC concentration by the organic mass-to-OC conversion factor [18]. There are several processes in the formation of SOA in the atmosphere. The oxidation of hydrocarbon (HC) by hydroxyl radicals (OH) and O3 is considered a major mechanism for the daytime formation of SOA. Although the formation of OH and O3 are suppressed during night-time, the oxidation of HC by O3 and nitrate radicals (NO3) is the major night-time process that creates SOA [19]. Previous studies have attempted to investigate the correlation between O3 and SOA in many countries, but in Asia, studies have mainly focused on China. In the related literature, a positive correlation between PM2.5 and O3 was reported during a hot season, since O3, a strong oxidiser, enhanced the formation of secondary particulate matter [20,21]. In Beijing–Tianjin–Hebei, China, a 31% O3 reduction decreased SOA by approximately 30% [22]. In the Yangtze River Delta, China, the increased surface level of O3 moderately enhanced the level of SOC [23]. During a cold season in China, a negative correlation between O3 and PM2.5 was found since the high concentration of PM2.5 attenuated sunlight, reduced the photolysis rate, and suppressed O3 formation [20]. In addition, the reactive uptake of hydroperoxy radicals (HO2) and nitrogen oxides (NO2, NO3, and N2O5) can inhibit the reaction between HO2 and NO, thus reducing O3 formation [24].
To analyse the possible causes of the SOA enhancement and the atmospheric conditions and chemical reactions that raised PM2.5 levels over Bangkok, a field campaign (hereinafter called the SOC campaign) was conducted to measure O3, PM2.5, PM10, and meteorological variables in January 2021 via the microclimate and air pollutant monitoring tower located at Kasetsart University, Bangkok. The observations were conducted at 30 m above ground level (agl), with the lowest observation level at the tower. The concentrations of POC, SOC, OC, and EC were estimated using the EC tracer method. The correlations between the chemical species, the ratio of PM2.5 to PM10, and the ratio of OC to EC were analysed. Previous studies have shown that the PM2.5/PM10 ratio could reveal the emission sources and processes of particles in the atmosphere [25,26,27,28,29]. A low PM2.5/PM10 ratio indicates that coarse particles that originate from natural emission sources (e.g., dust or sand dust from long-distance transport) are dominant, whereas a high PM2.5/PM10 ratio suggests that fine particles predominate due to anthropogenic sources and secondary particulate formation [25,26,27,28]. Several studies (e.g., [30,31,32,33]) have reported that the major emission sources could be indicated by the OC/EC ratio. An OC/EC ratio ranging from 1.0 to 4.2 suggests that vehicles are the major source of aerosols. OC/EC ratios ranging from 2.5 to 10.5, 3.8 to 13.2, and 32.9 to 81.6 reveal coal combustion, biomass combustion, and cooking as the main emission sources, respectively [34]. A better understanding of the origin, atmospheric fate, and impact of SOA may enable SOA to be used as an additional metric for monitoring urban air quality in megacities.

2. Materials and Methods

2.1. Sampling Site

The observations in this study were conducted during January 2021 via the microclimate and air pollutant monitoring tower located at Kasetsart University (KU tower), Bangkok, Thailand (latitude: 13.854529N, longitude: 100.570012E) (Figure 1). This study area is considered an urban area, surrounded by commercial buildings, residential buildings, and traffic routes. There is an elevated tollway (Uttaraphimuk elevated tollway), with a high traffic volume of approximately 80,000 vehicles per day [35], located approximately 400 m away on the east of the KU tower. The land-use and land-cover types within a radius of 5 km from the KU tower were classified as buildings and residential communities (94%), roads (4%), water bodies, and others (2%) [9].
Bangkok has a tropical climate where, during the local winter season (October to February), the northeast monsoon wind brings a dry air mass from China and Mongolia, thus resulting in dry and cool weather. February to May is the transitional period from the northeast monsoon to the southwest monsoon. During the local summer season (from February to May), the weather is hot or very hot. During the months from May to October, a southwest monsoon wind with high moisture travels from the Indian Ocean to this region, resulting in the rainy season [2,3]. During the dry seasons, the air pollution levels in Bangkok are normally high due to the northeast monsoon winds that travel past high pollution areas (i.e., high traffic volume, commercial areas, high population density areas, and high biomass burning areas) and carry the pollution to Bangkok [10,36]. This is especially the case during December to February, which can be considered the smog season in Bangkok [37]. On the other hand, during the wet season, the air pollution levels in Bangkok are low due to dilution from the clean marine air masses and rainy washout. Therefore, our study focuses on examining carbonaceous aerosols during January due to the larger concentrations.

2.2. Data Collection

The hourly concentrations of PM2.5, PM10, O3, and NOx and meteorological parameters, including wind speed (WS), wind direction (WD), temperature (T), relative humidity (RH), and light intensity, were measured at 30 m agl. The hourly concentrations of PM2.5 and PM10 were measured using a tapered element oscillating microbalance (TEOM) technique (1405-DF TEOMTM Continuous Dichotomous Ambient Air Monitor; Thermo Fisher Scientific Inc., Waltham, MA, USA). The concentrations of O3 and NOx were detected using an ultraviolet (UV) photometer O3 analyser (49i Ozone analyser; Thermo Fisher Scientific Inc., Waltham, MA, USA) and a NOx analyser (49i Ozone analyser; Thermo Fisher Scientific Inc., Waltham, MA, USA), respectively. For the meteorological parameters, the WS and WD were measured using a wind cup anemometer and a wind vane (LSI LASTEM, DNA827, Milano, Italy). The T and RH were measured using a thermo-hygrometer (LSI LASTEM, DMA867 and DMA875). The light intensity (UV wavelength 300–400 nm) was measured using a spectroradiometer 350–2050 nm (EKO instruments, MS700, Tokyo, Japan).
To estimate the SOC, PM2.5 were continuously collected on a quartz microfiber filter of 37 mm in diameter (TE-QMA-37, Tisch Environmental Inc., OH, USA) using an area dust monitor (ADR1500; Thermo Fisher Scientific Inc., Waltham, MA, USA) with a flow rate of 1.52 L/min. The quartz filters were heated at 550 °C for one hour before placement in the area dust monitor. The samples were collected twice a day, including from 08:00 to 19:00 (daytime) and from 20:00 to 07:00 (night-time). The loaded filters were preserved at approximately 4 °C to prevent the evaporation of chemical species.

2.3. OC and EC Contaminant Analysis and Estimation of POC and SOC

To analyse the OC and EC components in PM2.5, a thermal–optical method (Sunset laboratory carbon analyser with detection limits of 0.2 μg cm–2 for OC and 0.40 μg cm–2 for EC) with the IMPROVE protocol [5,10,38,39] was used. The analyser was calibrated prior to use with a sucrose solution standard (N = 5, R2 = 0.99). The loaded sample quartz filter was cut to a size of 1.5 cm2 and exposed stepwise to temperature. The OC component analysis was conducted in a pure Helium (He) atmosphere, followed by four selected temperature steps at 140 °C, 290 °C, 480 °C, and 580 °C to analyse OC1, OC2, OC3, and OC4, respectively. The analysis of the EC components was conducted in a mixed 2% O2 and 98% He atmosphere, followed by three selected temperature steps at 580 °C for EC1, 740 °C for EC2, and 840 °C for EC3 analysis. The total OC ( O C t o t a l ) and EC ( E C t o t a l ) were calculated using the following equations:
O C t o t a l = O C 1 + O C 2 + O C 3 + O C 4 + P C
E C t o t a l = E C 1 + E C 2 + E C 3 − P C
where P C is the split point between those phases and is automatically set when the measured optical signal returns to the baseline [40] to minimise the uncertainty, due to the formation of pyrolytic carbon (PC) from the OC into the thermally stable form having similarity with EC [41].
We estimated the concentrations of POC and SOC in PM2.5 by performing the EC tracer method, which has been widely used to estimate the partitioning of measured particulate OC into primary and secondary fractions [10,14,42,43], as follows:
P O C = E C × O C E C p r i + b
S O C = O C t o t a l − P O C
where O C E C p r i is the ratio of OC to EC in the primary emissions. In this study, the lowest 10% of O C E C was considered O C E C p r i [17,23,44]. The O C t o t a l measured the particulate OC, and b was the interception from the OC vs. EC scatter plot, which was interpreted as the POC from activities other than the combustion processes, such as road dust resuspension, biogenic emissions, and fuel and solvent evaporation [15,45].
Furthermore, the quantitative criteria to select the OC and EC data in order to determine O C E C p r i were not clear, and O C E C p r i had diurnal and seasonal variations [43,46].

3. Results and Discussion

3.1. Classification of Pollution Events

During the SOC campaign, the hourly maximum, minimum, and average concentrations at 30 m agl were estimated at 141.9 µg m−3, 12.0 µg m−3, and 49.69 ± 25.61µg m−3 for PM2.5; 177.5 µg m−3, 18.5 µg m−3, and 69.80 ± 31.43 µg m−3 for PM10; and 94.2 ppb, 11.6 ppb, and 39.5 ± 19.0 ppb for O3, respectively. The ratio of PM2.5/PM10 in this study ranged from 0.42 to 0.89, and the average was 0.70 ± 0.08 (Table 1).
The study period was classified into three events, including high pollution, high particulate matter (PM), and low pollution events. During the high pollution event, the daily concentrations of PM2.5 were greater than 50 µg m−3 (Thailand’s National Ambient Air Quality Standards (NAAQS) of 50 μg m−3 for daily-average PM2.5), and the hourly concentrations of O3 were greater than 77.4 ppb. Since the hourly O3 concentrations (11.6 to 94.22 ppb) were within the standard (the NAAQS of Thailand for the hourly O3 is 100 ppb), we, therefore, applied the hourly average concentration of O3 + 2SD as the O3 threshold (77.4 ppb). During the high PM event, the daily average concentrations of PM2.5 exceeded the standard, while the hourly concentrations of O3 were at the threshold. When the daily average concentrations of PM2.5 and the hourly concentrations of O3 were at the threshold, the atmosphere was considered to be a low pollution event. The criteria to classify the atmospheric conditions are summarised in Table 2.
During the study period, the 24 h averages of PM2.5 exceeded the standard for 15 days (4, 13 to 24, and 30 to 31 January), with concentrations ranging from 52.4 to 95.2 µg m−3. During those days, the daily average concentrations of PM10 ranged from 69.9 to 119.5 µg m−3. The hourly O3 concentrations during the study period were below the standard, with concentrations ranging from 11.6 to 94.2 ppb. High concentrations of O3 (above 77.4 ppb) were observed from 16 to 20 January and on 23 January. Therefore, from 16 to 20 and on 23 January, the atmospheric condition was classified as a high pollution event (high PM2.5 and O3), with daily average PM2.5 and PM10 and hourly average O3 concentrations of 74.4 ± 16.3 µg m−3, 97.8 ± 17.8 µg m−3, and 59.8 ± 23.7 ppb, respectively. The atmospheric conditions during 4, 13 to 15, 21 to 22, 24, and 29 to 31 January were considered high PM events, with daily average PM2.5 and PM10 concentrations of 70.4 ± 16.3 µg m−3 and 96.9 ± 18.1 µg m−3, respectively, and an hourly average O3 concentration of 36.5 ± 15.3 ppb. The atmospheric condition from 1 to 3, 5 to 12, and 25 to 28 January was classified as a low pollution event, with daily average PM2.5 and PM10 and hourly O3 concentrations of 39.6 ± 12.4 µg m−3, 56.3 ± 14.8 µg m−3, and 33.2 ± 12.4 ppb, respectively. The daily average PM2.5 and PM10 during the high pollution and high PM events were in the range of those observed from megacities in China, such as Beijing (87.0 µg m−3 for PM2.5 and 109.4 µg m−3 for PM10), Shanghai (56.1 µg m−3 for PM2.5 and 79.9 µg m−3 for PM10), and Guangzhou (51.6 µg m−3 for PM2.5 and 72.5 µg m−3 for PM10) [47] and those observed over the Indo-Gangetic Plain of India (PM2.5 ranging from 66 to 98 µg m−3) [48]. The hourly average O3 during the high pollution event was comparable to the hourly average O3 in Beijing (52.5 ppb), Shanghai (56.1 ppb), and Guangzhou (58.3 ppb). The daily average ratios of PM2.5/PM10 were 0.73, 0.71, and 0.67 during the high pollution, high PM, and low pollution events, respectively. The high PM2.5/PM10 ratio suggested that both anthropogenic emissions and the formation of secondary particulate matter enhanced the PM levels over the study area [25,26,27,28]. Figure 2 illustrates a time series of hourly PM2.5, PM10, O3, and PM2.5/PM10 ratios during the high pollution, high PM, and low pollution events during the SOC campaign in January 2021.
The meteorological observations made during the high pollution, high PM, and low pollution events are shown in Table 3 and Figure 3. In January, the winds often originated from the northeast, with the average WS ranging from 1.72 to 2.00 m s−1. The average WS was high (2.00 ± 1.22 m s−1) during the low pollution event and low (1.72 ± 1.06 m s−1) during the high PM event.
Overall, the average T value varied between 25.12 and 26.13 °C. The average RH levels were 56.8 ± 18.10, 50.27 ± 12.59, and 57.35 ± 16.08% during the high pollution, high PM, and low pollution events, respectively. The average light intensity measured from 8:00 to 19:00 during the high pollution event was 74.3 ± 61.8 wm−2 µm−1, during the high PM event was 76.5 ± 62.4 wm−2 µm−1, and during the low pollution event was 76.5 ± 66.0 wm−2 µm−1. The results reveal possible causes of the formation, accumulation, and dispersion of the pollution during the SOC due to meteorological factors. For example, high air pollution levels decreased solar radiation, which could affect photochemical reactions in the atmosphere. However, the meteorological factors alone could not fully account for the high pollutant concentrations during the high pollution event.

3.2. Correlation between PM2.5 and O3

The correlations between PM2.5 and O3 during the three events are shown in Table 4. This study revealed that PM2.5 was inversely correlated with O3. The strongest negative correlation between PM2.5 and O3 occurred during the high pollution event, with a coefficient of correlation (r) value of −0.64, followed by the high PM (r = −0.38) and low pollution (r = −0.18) events. The correlations between PM2.5 and O3 were also analysed separately during the daytime and night-time since the formation process of PM differs between these times.
The daytime correlations were −0.43, −0.40, and −0.17 during the high pollution, high PM, and low pollution events, respectively. The attenuation of sunlight by air pollution appeared to cause the negative correlations between PM2.5 and O3 [20]. The negative correlation was stronger during the high pollution and high PM events than during the low pollution event, with a high concentration of O3, a strong oxidiser, occurring during the high pollution event. The negative correlations between PM2.5 and O3 also occurred at night, with the strongest negative correlation (r = −0.64) being detected during the high pollution event, followed by the low pollution (r = −0.37) and high PM (r = −0.27) events. A negative correlation between O3 and chemical species is often expected during night-time. At night, O3 can react with NO, NO2, and HC to generate NO2, NO3, and ozonides, respectively, even though the formation of O3 is suppressed. Therefore, the O3 concentration always declines during night-time. To clarify the possible chemical pathways of O3 and SOA during night-time, the contributions of OC, EC, POC, and SOC are discussed in Section 3.3.

3.3. Temporal Patterns of OC, EC, POC, and SOC

3.3.1. Identification of Emission Sources

For an in-depth PM2.5 analysis, the temporal changes in OC, EC, POC, and SOC according to the high pollution, high PM, and low pollution events are presented in Table 4. The O C E C p r i values were determined from the whole data (January data set). The average OC concentrations during the daytime (night-time) varied from 34.0 to 92.1 (44.6 to 95.2) µg m−3 during the high pollution event, 43.0 to 89.2 (31.8 to 143.3) µg m−3 during the high PM event, and 26.6 to 63.4 (29.6 to 81.5) µg m−3 during the low pollution event. The EC concentrations in this study during the daytime (night-time) ranged from 6.0 to 19.1 µg m−3 (6.7 to 32.7 µg m−3) during the high pollution event, 5.2 to 17.3 µg m−3 (8.0 to 34.1 µg m−3) during the high PM event, and 3.5 to 19.3 µg m−3 (3.4 to 17.7 µg m−3) during the low pollution event. While the OC concentrations from our study are comparable to the OC concentrations estimated at Patiala, India during the daytime (ranging from 10 to 119 µg m−3) and night-time (ranging from 15 to 190 µg m−3), the EC concentrations from our study were higher than those reported over Patiala (3.1 to 11.2 µg m−3 during the daytime and 3.5 to 12.8 µg m−3 during the night-time) [45]. The average OC and EC concentrations during the night-time were usually higher than those during the daytime. In this study, the percentage differences between OC (EC) during the night-time and daytime during the high pollution, high PM, and low pollution events were approximately 9.8 % (23.4%), 30.7% (46.8%), and 15.7% (19.6%), respectively. The OC/EC ratios were 4.54, 4.32, and 5.43 during the high pollution, high PM, and low pollution events, respectively. The OC/EC ratios in this study were comparable to those in Beijing (4.88), Langfang (4.42), and Tianjin (4.22), China [34] and Delhi (4.24 to 5.66), India [49] but were higher than the mean OC/EC at Varanasi, India (3.9). The moderate OC/EC values implied that fossil fuel combustion (i.e., from traffic) was the major carbonaceous aerosol in Bangkok [34,45].

3.3.2. Estimation of Primary and Secondary Organic Carbon Concentrations

Based on the EC tracer method, the concentrations of POC and SOC during the three events were estimated. During the high pollution event, the average POC and SOC concentrations were 41.72 ± 22.36 µg m−3 and 23.20 ± 9.29 µg m−3 (Table 4), with their contributions to OC comprising 64.3% and 35.7%, respectively (Figure 4). During the high PM event, the average POC and SOC concentrations were 50.92 ± 26.84 µg m−3 and 24.46 ± 11.68 µg m−3, which contributed 67.6% and 32.5% to OC, respectively. During the low pollution event, the average POC and SOC concentrations were 27.21 ± 11.44 µg m−3 and 23.54 ± 7.46 µg m−3, which accounted for 53.6% and 46.4% of the OC, respectively. Most strikingly, during the low pollution event, SOC and POC contributed to the OC mass equally, and the formation of SOA was heightened. The POC and SOC concentrations from our study were higher than those over northern Europe during a highly processed PM pollution event (POC ranged from 2.2 to 3.7 µg m−3 and SOC was 3.3 ± 0.1 µg m−3) [42] and the annual average POC and SOC concentrations over Delhi, India (POC and SOC were 17.0 ± 9.7 and 16.7 ± 9.2 µg m−3, respectively) [50] but were similar to those from Delhi (approximately 50%) [49].
The POC/SOC and OC/EC ratios were separately analysed during the daytime and night-time. SOA formation during the daytime is mainly driven by O3 [23]; therefore, the OC/EC and SOC/POC ratios were expected to be high during the high pollution event. However, the OC/EC and SOC/POC ratios were 4.96 and 0.70, 5.08 and 0.74, and 5.58 and 0.91 during the high pollution, high PM, and low pollution events, respectively. The observed discrepancy may have resulted from the NOx levels. For example, Han and Jang (2023) [50] and Chen et al. (2022) [30] indicated that, under relatively high NO concentrations, the reaction between NO and peroxy radicals (RO2) is the major formation process of SOA. The product of this reaction is alkoxy radicals, a highly volatile chemical species that is unconducive to the formation of SOA. As the NOx concentration increased, the reaction between NO and RO2 was enhanced, resulting in an SOA yield reduction. In this study, the average NOx concentration during the daytime was 21.6 ppb, 20.8 ppb, and 17.1 ppb during the high pollution, high PM, and low pollution events, respectively. Since the O3 formation over Bangkok was more likely to be a volatile organic compound (VOC)-limited (or NOx-rich) regime [51], an increase in the NOx level tended to decrease the SOA yield, resulting in a lower SOA yield during the high pollution event and a higher SOA yield during the low pollution event.
At night, the O3-driven formation of SOA is suppressed due to the formation of OH radicals, and O3 is inhibited through photochemical processes. Therefore, SOA can be formed continuously through the ozonolysis of HC and NO3-driven oxidation [19,52,53,54]. At night, O3 generated during the daytime is titrated by NO, resulting in O3 depletion; however, the reaction is not rapid. Accordingly, O3 can react with NO2 to generate NO3 radicals, and SOA formation occurs through NO3-driven oxidation. The yield of SOA formed from this process rises with increasing NOx [19]. Consequently, the SOC contribution to OC during the high pollution event was expected to be high due to the high concentrations of O3 and NOx. However, the SOC/POC ratios from our study showed the opposite result. The SOC/POC ratio was 0.44, 0.34, and 0.83 during the high pollution, high PM, and low pollution events, respectively (Table 4). The SOC contribution to OC accounted for 31%, 25%, and 45% during the high pollution, high PM, and low pollution events, respectively (Figure 4). This was because O3 can react either with NO to generate NO2 or with NO2 to generate NO3, with the latter leading to SOA formation through NO3-driven oxidation. The NO/NO2 ratio was calculated to evaluate the competition between the reactions of O3 with NO and NO2. The average NO/NO2 ratio was 0.15, 0.20, and 0.16 during the high pollution, high PM, and low pollution events, respectively. During the high PM event, the high NO concentration could be a possible reason to retard the SOA formation through NO3-driven oxidation. The RH level may be the cause of the differences in SOA formation between the high pollution and low pollution events since the high RH level can enhance the partitioning between the gas–particle phases of low-volatile compounds, the surface area of aerosols, and the chemistry of aqueous aerosols, thus increasing the SOA yield [55]. We found that the average RH level was lower during the high pollution event (56.83%) than during the low pollution event (57.35%). However, the reason for the difference in the SOA formation between the high pollution and low pollution events still remains unclear.

4. Conclusions

The SOC campaign was conducted in January 2021 to investigate SOA formation over Bangkok. The chemical species of OC (POC and SOC), EC, O3, PM2.5, and PM10 and meteorological factors were observed at 30 m agl at the KU tower. During the study period, the atmospheric conditions were classified as high pollution, high PM, and low pollution events. Even though this study has some limitations, including a lack of PM2.5 chemical compositions and VOC concentration data during the study period and a short study period, the results from this study reveal the atmospheric fate and chemistry of PM2.5 formation over Bangkok. The correlations between PM2.5 and O3 were negative during the three events. Negative correlations in the daytime implied that the high PM concentrations could attenuate sunlight and suppress the photochemical reactions to form O3. The negative correlations at night-time occurred since O3 could not be formed but was destroyed by several chemical reactions. The moderate OC/EC values implied that fossil fuel combustion (i.e., from traffic) was the major carbonaceous aerosol in Bangkok. The EC tracer-estimated SOC and POC showed that SOC contributed 32.5 to 46.4% to the OC. Most strikingly, during the low pollution event, SOC contributed approximately 50% to the OC, which revealed that SOA formation was higher during the low pollution event than during the high pollution and high PM events. The heightened formation of SOA during the low pollution event was perhaps due to the NOx levels. Since Bangkok is more likely to have a NOx-rich photochemical reaction regime, an increase in the NOx level tends to decrease the SOA yield. Together with the high humidity and high light intensity during the low pollution event, the SOA formation was enhanced. Although the driving factors of SOA formation over Bangkok remain unclear, the results of this study revealed the significance and urgency of local actions to reduce NOx and O3 to achieve more habitable and sustainable urban environments.

Author Contributions

Conceptualization, P.U., P.C., S.B. and T.T.; Methodology, P.U., P.C., S.B. and T.T.; Formal analysis, P.U., P.C., J.P. and T.T.; Investigation, P.U., P.C., J.P. and T.T.; Visualization, P.U., P.C. and J.P.; Writing—Original Draft, P.U., P.C., J.P. and T.T.; Writing—Review and Editing, P.U., P.C., S.B. and T.T.; Supervision, S.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data will be made available upon request.

Acknowledgments

This study was funded by the ‘Atmospheric Science Research Group (ASRG)’ and the Faculty of Environment, Kasetsart University, Bangkok, Thailand.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Ambient Air Quality Standard for PM2.5 Act. 139 § 163. 2022. Available online: https://ratchakitcha2.soc.go.th/pdfdownload/?id=139D163S0000000002100 (accessed on 30 June 2022).
  2. Thai Meteorological Department. The Climate of Thailand. Available online: https://www.tmd.go.th/en/archive/thailand_climate.pdf (accessed on 30 June 2022).
  3. Uttamang, P.; Aneja, V.P.; Hanna, A.H. Assessment of gaseous criteria pollutants in the Bangkok Metropolitan Region, Thailand. Atmos. Chem. Phys. 2018, 18, 12581–12593. [Google Scholar] [CrossRef] [Green Version]
  4. Bangkok Metropolitan Administration. AQI Information. Available online: https://bangkokairquality.com/bma/aqi?lang=en (accessed on 14 March 2023).
  5. 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. [Google Scholar] [CrossRef]
  6. Thangavel, P.; Park, D.; Lee, Y.-C. Recent Insights into Particulate Matter (PM2.5)-Mediated Toxicity in Humans: An Overview. Int. J. Environ. Res. Public Health 2022, 19, 7511. [Google Scholar] [CrossRef]
  7. United State Environmental Protection Agency (US.EPA). Particulate Matter (PM) Pollution Health and Environmental Effects of Particulate Matter (PM). Available online: https://www.epa.gov/pm-pollution/health-and-environmental-effects-particulate-matter-pm (accessed on 14 March 2023).
  8. Wetchayont, P.; Hayasaka, T.; Khatri, P. Air Quality Improvement during COVID-19 Lockdown in Bangkok Metropolitan, Thailand: Effect of the Long-range Transport of Air Pollutants. Aerosol Air Qual. Res. 2021, 21, 200662. [Google Scholar] [CrossRef]
  9. Choomanee, P.; Bualert, S.; Thongyen, T.; Salao, S.; Szymanski, W.W.; Rungratanaubon, T. Vertical variation of carbonaceous aerosol with in the PM2.5 fraction in Bangkok, Thailand. Aerosol Air Qual. Res. 2020, 20, 43–52. [Google Scholar] [CrossRef] [Green Version]
  10. Rattanapotanan, T.; Thongyen, T.; Bualert, S.; Choomanee, P.; Suwattiga, P.; Rungrattanaubon, T.; Utavong, T.; Phupijit, J.; Changplaiy, N. Secondary source of PM2.5 based on the vertical distribution of organic carbon, elemental carbon, and water-soluble ions in Bangkok. Environ. Adv. 2023, 11, 100337. [Google Scholar] [CrossRef]
  11. Zhang, H.-H.; Li, Z.; Liu, Y.; Xinag, P.; Cui, X.-Y.; Ye, H.; Hu, B.-I.; Lou, L.-P. Physical and chemical characteristics of PM2.5 and its toxicity to human bronchial cells BEAS-2B in the winter and summer. J. Zhejiang Univ. Sci. B 2018, 19, 317–326. [Google Scholar] [CrossRef]
  12. Pani, S.K.; Lin, N.-H.; Chantra, S.; Wang, S.-H.; Khamlkaew, C.; Prapamontol, T.; Janjai, S. Radiative response of biomass-burning aerosols over an urban atmosphere in northern peninsular Southeast Asia. Sci. Total Environ. 2018, 633, 892–911. [Google Scholar] [CrossRef]
  13. Yoo, H.Y.; Kim, K.A.; Kim, Y.P.; Jung, C.H.; Shin, H.J.; Moon, K.J.; Park, S.M.; Lee, J.Y. Validation of SOC Estimation using OC and EC Concentration in PM2.5 Measured at Seoul. Aerosol Air Qual. Res. 2022, 22, 210388. [Google Scholar] [CrossRef]
  14. Turpin, B.J.; Huntzicker, J.J. Secondary formation of organic aerosol in the Los Angeles basin: A descriptive analysis of organic and elemental carbon concentrations. Atmos. Environ. 1991, 24A, 207–215. [Google Scholar] [CrossRef]
  15. Day, M.C.; Zhang, M.; Pandis, S.N. Evaluation of the ability of the EC tracer method to estimate secondary organic carbon. Atmos. Environ. 2015, 112, 317–325. [Google Scholar] [CrossRef]
  16. Kaskaoutis, D.G.; Grivas, G.; Theodosi, C.; Tsagkaraki, M.; Paraskevopoulou, D.; Stavroulas, I.; Liakakou, E.; Gkikas, A.; Hatzianastassiou, N.; Wu, C.; et al. Carbonaceous Aerosols in Contrasting Atmospheric Environments in Greek Cities: Evaluation of the EC-tracer Methods for Secondary Organic Carbon Estimation. Atmosphere 2020, 11, 161. [Google Scholar] [CrossRef] [Green Version]
  17. Lim, H.-J.; Turpin, B.J. Origins of primary and secondary organic aerosol in Atlanta: Results of time-resolved measurements during the Atlanta Supersite Experiment. Environ. Sci. Technol. 2002, 36, 4489–4496. [Google Scholar] [CrossRef]
  18. Salma, I.; Varga, P.T.; Vasanits, A.; Machon, A. Secondary organic carbon in different atmospheric environments of a continental region and seasons. Atmos. Res. 2022, 278, 106360. [Google Scholar] [CrossRef]
  19. Han, S.; Jang, M. Modeling daytime and nighttime secondary organic aerosol formation via multiphase reactions of biogenic hydrocarbons. ACP 2023, 23, 1209–1226. [Google Scholar] [CrossRef]
  20. Jia, M.; Zhao, T.; Cheng, X.; Gong, S.; Zhang, X.; Tang, L.; Liu, D.; Wu, X.; Wang, L.; Chen, Y. Inverse relations of PM2.5 and O3 in air compound pollution between cold and hot seasons over an urban area of east China. Atmosphere 2017, 8, 59. [Google Scholar] [CrossRef] [Green Version]
  21. Zhu, J.; Chen, L.; Liao, H.; Dang, R. Correlations between PM2.5 and Ozone over China and Associated Underlying Reasons. Atmosphere 2019, 10, 352. [Google Scholar] [CrossRef] [Green Version]
  22. Feng, T.; Zhao, S.; Bei, N.; Wu, J.; Liu, S.; Li, X.; Liu, L.; Qian, Y.; Yang, Q.; Wang, Y.; et al. Secondary organic aerosol enhanced by increasing atmospheric oxidizing capacity in Beijing–Tianjin–Hebei (BTH), China. Atmos. Chem. Phys. 2019, 19, 7429–7443. [Google Scholar] [CrossRef] [Green Version]
  23. Liu, Y.; Zhao, Q.; Hao, X.; Zhao, J.; Zhang, Y.; Yang, X.; Fu, Q.; Xu, X.; Wang, X.; Huo, J.; et al. Increasing surface ozone and enhanced secondary organic carbon formation at a city junction site: An epitome of the Yangtze River Delta, China (2014–2017). Environ. Pollut. 2020, 265, 114847. [Google Scholar] [CrossRef]
  24. Li, K.; Jacob, D.J.; Liao, H.; Shen, L.; Zhang, Q.; Bates, K.H. Anthropogenic drivers of 2013–2017 trends in summer surface ozone in China. Proc. Natl. Acad. Sci. USA 2019, 116, 422–427. [Google Scholar] [CrossRef] [Green Version]
  25. Fan, H.; Zhao, C.; Yang, Y.; Yang, X. Spatio-Temporal Variations of the PM2.5/PM10 Ratios and Its Application to Air Pollution Type Classification in China. Front. Environ. Sci. 2021, 9, 692440. [Google Scholar] [CrossRef]
  26. Franzin, B.T.; Guizellini, F.C.; de Babos, D.V.; Hojo, O.; Pastre, I.A.; Marchi, M.R.R. Characterization of Atmospheric Aerosol (PM10 and PM2.5) from a Medium Sized City in São Paulo State, Brazil. J. Environ. Sci. 2020, 89, 238–251. [Google Scholar] [CrossRef]
  27. Spandana, B.; Rao, S.S.; Upadhya, A.R.; Kulkarni, P.; Sreekanth, V. PM2.5/PM10 ratio characteristics over urban sites of India. Adv. Space Res. 2021, 67, 3134–3146. [Google Scholar] [CrossRef]
  28. Xu, G.; Jiao, L.; Zhang, B.; Zhao, S.; Yuan, M.; Gu, Y.; Liu, J.; Tang, X. Spatial and Temporal Variability of the PM2.5/PM10 Ratio in Wuhan, Central China. Aerosol Air Qual. Res. 2017, 17, 741–751. [Google Scholar] [CrossRef] [Green Version]
  29. Zhao, D.; Chen, H.; Yu, E.; Luo, T. PM2.5/PM10 Ratios in Eight Economic Regions and Their Relationship with Meteorology in China. Adv. Meteorol. 2019, 2019, 5295726. [Google Scholar] [CrossRef] [Green Version]
  30. Chen, Y.J.; Zhi, G.R.; Feng, Y.L.; Fu, J.M.; Feng, J.L.; Sheng, G.Y.; Simoneit, B.R.T. Measurements of emission factors for primary carbonaceous particles from residential raw-coal combustion in China. Geophys. Res. Lett. 2006, 33, 382. [Google Scholar] [CrossRef]
  31. Chow, J.C.; Watson, J.G.; Chen, L.W.A.; Arnott, W.P.A.; Moosmüller, H.; Fung, K. Equivalence of elemental carbon by thermal/optical reflectance and transmittance with different temperature protocols. Environ. Sci. Technol. 2004, 38, 4414–4422. [Google Scholar] [CrossRef]
  32. He, L.Y.; Hu, M.; Huang, X.F.; Yu, B.D.; Zhang, Y.H.; Liu, D.Q. Measurement of emissions of fine particulate organic matter from Chinese cooking. Atmos. Environ. 2004, 38, 6557–6564. [Google Scholar] [CrossRef]
  33. Zhang, Y.X.; Shao, M.; Zhang, Y.H.; Zeng, L.M.; He, L.Y.; Zhu, B.; Wei, Y.J.; Zhu, X.L. Source profiles of particulate organic matters emitted from cereal straw burnings. J. Environ. Sci. 2007, 19, 167–175. [Google Scholar] [CrossRef]
  34. Qi, M.; Jiang, L.; Liu, Y.; Xiong, Q.; Sun, C.; Li, X.; Zhao, W.; Yang, X. Analysis of the characteristics and sources of carbonaceous aerosols in PM2.5 in the Beijing, Tianjin, and Langfang Region, China. Int. J. Environ. Res. Public Health 2018, 15, 1483. [Google Scholar] [CrossRef] [Green Version]
  35. Department of Land Transport. Nation Transportation Statistics. Available online: https://web.dlt.go.th/statistics/index.php (accessed on 30 November 2022).
  36. Phairuang, W.; Inerb, M.; Furuuchi, M.; Hata, M.; Tekasakul, S.; Tekasakul, P. Size-fractionated carbonaceous aerosols down to PM0.1 in southern Thailand: Local and long-range transport effects. Environ. Pollut. 2020, 260, 114031. [Google Scholar] [CrossRef] [PubMed]
  37. Aman, N.; Manomaiphiboon, K.; Suwattiga, P.; Assareh, N.; Limpaseni, W.; Suwanathada, P.; Soonsin, V.; Wang, Y. Visibility, aerosol optical depth, and low-visibility events in Bangkok during the dry season and associated local weather and synoptic patterns. Environ. Monit. Assess. 2022, 194, 322. [Google Scholar] [CrossRef] [PubMed]
  38. Chow, J.C.; Watson, J.G.; Chen, W.A.; Chang, M.O.; Robinson, N.F.; Trimble, D.; Kohl, S. The IMPROVE_A temperature protocol for thermal/optical carbon analysis: Maintaining consistency with a long-term database. J. Air Waste Manag. Assoc. 2012, 57, 1014–1023. [Google Scholar] [CrossRef] [Green Version]
  39. Han, Y.M.; Cao, J.J.; Chow, J.C.; Watson, J.G.; An, Z.; Jin, Z.S.; Fung, K.; Liu, S. Evaluation of the thermal/optical reflectance method for discrimination between char- and soot-EC. Chemosphere 2007, 69, 569–574. [Google Scholar] [CrossRef]
  40. Karanasiou, A.; Minguilloón, M.C.; Viana, M.; Alastuey, A.; Putaud, J.P.; Maenhaut, W.; Pantelladis, P.; Močnik, G.; Favez, O.; Kuhlbusch, T.A.J. Thermal-optical analysis for the measurement of elemental carbon (EC) and organic carbon (OC) in ambient air a literature review. Atmos. Meas. Technol. 2015, 8, 9649–9712. [Google Scholar]
  41. Bautista VII, A.T.; Pabroa, P.C.B.; Santos, F.L.; Quirit, L.L.; Asis, J.L.B.; Dy, M.A.K.; Martinez, J.P.G. Intercomparison between NIOSH, IMPROVE_A, and EUSAAR_2 protocols: Finding an optimal thermal– optical protocol for Philippines OC/EC samples. Atmos. Pollut. Res. 2015, 6, 334–342. [Google Scholar] [CrossRef]
  42. Srivastava, D.; Daellenbach, K.R.; Zhang, Y.; Bonnaire, N.; Chazeau, B.; Perraudin, E.; Gros, V.; Lucarelli, F.; Villenave, E.; Prévôt, A.S.H.; et al. Comparison of five methodologies to apportion organic aerosol sources during a PM pollution event. Sci. Total Environ. 2021, 757, 143168. [Google Scholar] [CrossRef]
  43. Wu, C.; Wu, D.; Yu, J.Z. Estimation and Uncertainty Analysis of Secondary Organic Carbon Using 1 Year of Hourly Organic and Elemental Carbon Data. Journal of Geophysical Research. Atmospheres 2019, 124, 2774–2795. [Google Scholar]
  44. Coa, J.J.; Zhu, C.S.; Tie, X.X.; Geng, F.H.; Ho, S.S.H.; Wang, G.H.; Han, Y.M.; Ho, K.F. Characteristics and sources of carbonaceous aerosols from Shanghai, China. Atmos. Chem. Phys. 2013, 13, 803–817. [Google Scholar]
  45. Tiwari, S.; Dumka, U.C.; Kaskaoutis, D.G.; Ram, K.; Panicker, A.S.; Srivastava, M.K.; Tiwari, S.; Attri, S.D.; Soni, V.K.; Pandey, A.K. Aerosol chemical characterization and role of carbonaceous aerosol on radiative effect over Varanasi in central Indo-Gangetic Plain. Atmos. Environ. 2016, 125, 437–449. [Google Scholar] [CrossRef]
  46. Wu, C.; Yu, J.Z. Determination of primary combustion source organic carbon-to-elemental carbon (OC /EC) ratio using ambient OC and EC measurements: Secondary OC-EC correlation minimization method. Atmos. Chem. Phys. 2016, 16, 5453–5465. [Google Scholar] [CrossRef] [Green Version]
  47. Zhang, H.; Wang, Y.; Hu, J.; Ying, Q.; Hu, X.-M. Relationships between meteorological parameters and criteria air pollutants in three megacities in China. Environ. Res. 2015, 140, 242–254. [Google Scholar] [CrossRef]
  48. Devi, N.L.; Kumar, A.; Yadav, I.C. PM10 and PM2.5 in Indo-Gangetic Plain (IGP) of India: Chemical characterization, source analysis, and transport pathways. Urban Clim. 2020, 33, 100663. [Google Scholar] [CrossRef]
  49. Dumka, U.C.; Tiwari, S.; Kaskaoutis, D.G.; Hopke, P.K.; Singh, J.; Srivastava, A.K.; Bisht, D.S.; Attri, S.D.; Tyagi, S.; Misra, A.; et al. Assessment of PM2.5 chemical compositions in Delhi: Primary vs. secondary emissions and contribution to light extinction coefficient and visibility degradation. J. Atmos. Chem. 2017, 74, 423–450. [Google Scholar] [CrossRef]
  50. Han, S.; Bian, H.; Feng, Y.; Liu, A.; Li, X.; Zeng, F.; Zhang, X. Analysis of the Relationship between O3, NO and NO2 in Tianjin, China. Aerosol Air Qual. Res. 2011, 11, 128–139. [Google Scholar] [CrossRef] [Green Version]
  51. Uttamang, P.; Campbell, P.C.; Aneja, V.P.; Hanna, A.F. A multi-scale model analysis of ozone formation in the Bangkok Metropolitan Region, Thailand. Atmos. Environ. 2020, 229, 117433. [Google Scholar] [CrossRef]
  52. Gaoa, S.; Lathem, T.; Nyadong, L. Nighttime secondary organic aerosol formation from unburned fuel vapors. Atmos. Environ. 2019, 204, 125–134. [Google Scholar] [CrossRef]
  53. Palm, B.B.; Campuzano-Jost, P.; Day, D.A.; Ortega, A.M.; Fry, J.L.; Brown, S.S.; Zarzana, K.J.; Dube, W.; Wagner, N.L.; Draper, D.C.; et al. Secondary organic aerosol formation from in situ OH, O3, and NO3 oxidation of ambient forest air in an oxidation flow reactor. Atmos. Chem. Phys. 2017, 17, 5331–5354. [Google Scholar] [CrossRef] [Green Version]
  54. Zaveri, R.A.; Shilling, J.E.; Fast, J.D.; Springston, S.R. Efficient nighttime biogenic SOA formation in a polluted residual layer. J. Geophys. Res. Atmos. 2020, 125, e2019JD031583. [Google Scholar] [CrossRef]
  55. Jiang, X.; Tsona, N.T.; Jia, L.; Liu, S.; Zhang, H.; Xu, Y.; Du, L. Secondary organic aerosol formation from photooxidation of furan: Effects of NOx and humidity. Atmos. Chem. Phys. 2019, 19, 13591–13609. [Google Scholar] [CrossRef] [Green Version]
Atmosphere 14 00994 g001 550
Figure 1. Microclimate and air pollutant monitoring tower located at Kasetsart University (KU tower), Bangkok, Thailand, and land-use and land-cover types around the study area.
Figure 1. Microclimate and air pollutant monitoring tower located at Kasetsart University (KU tower), Bangkok, Thailand, and land-use and land-cover types around the study area.
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Atmosphere 14 00994 g002 550
Figure 2. Time series of hourly (a) PM2.5 (µg m−3), (b) PM10 (µg m−3), (c) O3 (ppb) concentrations, and (d) PM2.5/PM10 ratios during 1–31 January 2021. Yellowish and greyish bands refer to the high pollution and high PM events, respectively.
Figure 2. Time series of hourly (a) PM2.5 (µg m−3), (b) PM10 (µg m−3), (c) O3 (ppb) concentrations, and (d) PM2.5/PM10 ratios during 1–31 January 2021. Yellowish and greyish bands refer to the high pollution and high PM events, respectively.
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Figure 3. Wind rose diagrams during the high pollution, high PM, and low pollution events at 30 m agl at the KU tower.
Figure 3. Wind rose diagrams during the high pollution, high PM, and low pollution events at 30 m agl at the KU tower.
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Atmosphere 14 00994 g004 550
Figure 4. POC and SOC contributions to OC in the daytime and night-time during the high pollution, high PM, and low pollution events.
Figure 4. POC and SOC contributions to OC in the daytime and night-time during the high pollution, high PM, and low pollution events.
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Table
Table 1. The hourly maximum, minimum, and average (+ standard deviation [SD]) values of PM2.5, PM10, O3, and PM2.5/PM10 during the SOC campaign.
Table 1. The hourly maximum, minimum, and average (+ standard deviation [SD]) values of PM2.5, PM10, O3, and PM2.5/PM10 during the SOC campaign.
SpeciesMaximumMinimumAverage ± SD
PM2.5 (µg m−3)141.8811.9749.69 ± 25.61
PM10 (µg m−3)177.4618.5369.80 ± 31.43
O3 (ppb)94.2211.639.45 ± 18.96
PM2.5/PM100.890.420.70 ± 0.08
Table
Table 2. The criteria for atmospheric pollution conditions classification during the SOC campaign in January 2022.
Table 2. The criteria for atmospheric pollution conditions classification during the SOC campaign in January 2022.
EpisodePM2.5 (µg m−3)O3 (ppb)Day of the MonthTotal Day
High pollution>50>77.416 to 20 and 236
High PM>50≤77.44, 13 to 15, 21 to 22, 24,
and 29 to 31		10
Low pollution≤50≤77.41 to 3, 5 to 12, and 25 to 2815
Table
Table 3. Meteorological factors during the high pollution, high PM, and low pollution events.
Table 3. Meteorological factors during the high pollution, high PM, and low pollution events.
ParametersEventsMaximumMinimumMean
Wind Speed
(m s−1)		High pollution5.570.161.84 ± 1.21
High PM7.100.101.72 ± 1.06
Low pollution6.830.042.00 ± 1.22
Temperature
(°C)		High pollution31.9918.7825.12 ± 3.21
High PM32.0116.4325.81 ± 3.43
Low pollution33.0916.7426.13 ± 3.47
Relative Humidity (%)High pollution100.00 (34) *32.67 (11)56.83 ± 18.10 (19 ± 6)
High PM100.00 (34)29.32 (10)50.27 ± 12.59 (17 ± 4)
Low pollution100.00 (36)34.42 (12)57.35 ± 16.08 (20 ± 6)
Light Intensity **
(wm−2 µm−1)		High pollution212.30.074.3 ± 61.8
High PM234.70.076.5 ± 62.4
Low pollution235.70.076.5 ± 66.0
Note: * absolute humidity (g m−3) at 1010 hPa. ** UV wavelength (300–400 nm) measured between 8:00 and 19:00.
Table
Table 4. The average total, daytime, and night-time PM2.5, PM10, and O3 concentrations and PM2.5/PM10 ratio and correlations between PM2.5 and O3 (r(PM2.5-O3)), OC, EC, OC/EC ratio, POC, SOC, and POC/SOC ratio during the high pollution, high O3, high PM, and low pollution events.
Table 4. The average total, daytime, and night-time PM2.5, PM10, and O3 concentrations and PM2.5/PM10 ratio and correlations between PM2.5 and O3 (r(PM2.5-O3)), OC, EC, OC/EC ratio, POC, SOC, and POC/SOC ratio during the high pollution, high O3, high PM, and low pollution events.
EventsPM2.5
(µg m−3)		PM10
(µg m−3)		O3
(ppb)		PM2.5/PM10r
(PM2.5-O3)		OC
(µg m−3)		EC
(µg m−3)		OC/ECPOC
(µg m−3)		SOC
(µg m−3)		SOC/
POC
Total
High pollution78.3 ± 48.885.9 ± 30.159.8 ± 23.70.73−0.6464.92 ± 20.7214.31 ± 7.674.5441.72 ± 22.3623.20 ± 9.290.56
High PM65.2 ± 34.588.1 ± 32.336.5 ± 15.30.71−0.3875.38 ± 28.3817.47 ± 9.214.3250.92 ± 26.8424.46 ± 11.680.48
Low pollution31.4 ± 14.050.8 ± 17.233.2 ± 12.40.67−0.1850.75 ± 12.989.34 ± 3.925.4327.21 ± 11.4423.54 ± 7.460.86
Daytime
High pollution72.3 ± 51.281.8 ± 33.161.7 ± 23.00.70−0.4361.57 ± 23.6812.42 ± 5.504.9636.18 ± 16.0225.38 ± 8.180.70
High PM59.9 ± 36.582.1 ± 31.044.9 ± 15.40.69−0.4061.73 ± 17.3412.14 ± 4.195.0835.39 ± 12.2026.34 ± 7.730.74
Low pollution31.8 ± 15.651.4 ± 17.239.0 ± 12.90.65−0.1746.42 ± 9.298.32 ± 3.475.5824.26 ± 10.1022.16 ± 7.150.91
Night-time
High pollution76.2 ± 46.982.9 ± 29.152.3 ± 24.80.74−0.6468.27 ± 18.9016.21 ± 9.524.2147.25 ± 27.7421.03 ± 10.560.44
High PM71.5 ± 31.095.0 ± 32.526.8 ± 7.40.75−0.2789.04 ± 31.4122.80 ± 9.923.9166.45 ± 28.9222.59 ± 14.850.34
Low pollution30.8 ± 11.950.1 ± 17.326.7 ± 7.80.70−0.3755.09 ± 15.1110.35 ± 4.245.3230.17 ± 12.3524.92 ± 7.780.83
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Uttamang, P.; Choomanee, P.; Phupijit, J.; Bualert, S.; Thongyen, T. Investigation of Secondary Organic Aerosol Formation during O3 and PM2.5 Episodes in Bangkok, Thailand. Atmosphere 2023, 14, 994. https://doi.org/10.3390/atmos14060994

AMA Style

Uttamang P, Choomanee P, Phupijit J, Bualert S, Thongyen T. Investigation of Secondary Organic Aerosol Formation during O3 and PM2.5 Episodes in Bangkok, Thailand. Atmosphere. 2023; 14(6):994. https://doi.org/10.3390/atmos14060994

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Uttamang, Pornpan, Parkpoom Choomanee, Jitlada Phupijit, Surat Bualert, and Thunyapat Thongyen. 2023. "Investigation of Secondary Organic Aerosol Formation during O3 and PM2.5 Episodes in Bangkok, Thailand" Atmosphere 14, no. 6: 994. https://doi.org/10.3390/atmos14060994

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