Special Issue on COVID-19 Aerosol Drivers, Impacts and Mitigation (XII)

Racha Dejchanchaiwong1,2, Perapong Tekasakul This email address is being protected from spambots. You need JavaScript enabled to view it.1,3

1 Air Pollution and Health Effect Research Center, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand
2 Department of Chemical Engineering, Faculty of Engineering, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand
3 Department of Mechanical and Mechatronics Engineering, Faculty of Engineering, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand


Received: July 19, 2020
Revised: November 7, 2020
Accepted: December 1, 2020

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.4209/aaqr.200418  

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Cite this article:

Dejchanchaiwong, R., Tekasakul, P. (2021). Effects of Coronavirus Induced City Lockdown on PM2.5 and Gaseous Pollutant Concentrations in Bangkok. Aerosol Air Qual. Res. 21, 200418. https://doi.org/10.4209/aaqr.200418


HIGHLIGHTS

  • Reduction of traffic has no clear influence on PM2.5 concentrations in Bangkok.
  • NO2 and CO decreased during lockdown period while O3 level increased.
  • Transboundary and local transport from biomass burning affect PM2.5 levels.
  • OC/EC ratios show the dominant PM2.5 source from open biomass burning.
 

ABSTRACT 


Partial lockdown measures took effect in Thailand from March 22, 2020 to prevent the spread of the coronavirus. People widely believed that the air quality in Bangkok would improve during the lockdown. This study aims to understand the effects of the traffic on Bangkok PM2.5 and gas concentrations, by comparing the air quality before and during the lockdown. Some pollutant concentrations in pre-lockdown period were higher, but the differences were not significant, except for O3. When results in the full lockdown month, April 2020, were compared to the same period in 2019, the average PM2.5 concentrations at the road sites decreased by 11.1%. On the other hand, it increased by 16.7% at the business area sites. No clear relation of the PM2.5 change to the reduction of traffic and diesel fuel consumption was observed. The reduction of NO2 was clear, caused by the significant drop in traffic and fuel consumption: in turn, it contributed to an increase of O3. The increment of PM2.5 levels during the lockdown was an external effect, even though significant change of local sources occurred. The values of OC/EC ratios in fine particles and the backward trajectory simulation confirmed that the peaks of PM2.5 levels were affected by both transboundary and local aerosol transport from open biomass burning. Hence, it is clear that road traffic, as well as industries or other human activities, were not the most influential factors on high PM2.5 levels in Bangkok in normal conditions. Possible solutions to reduce sources of Bangkok air pollutants include introduction of small-scale machinery for sugarcane harvesting to reduce biomass burning, adoption of higher standards to control diesel engine emission, and mutual agreement and action among ASEAN members in transboundary haze reduction.


Keywords: Air quality, Fine particles, COVID-19, Air pollutants


1 INTRODUCTION


A coronavirus outbreak occurred in late 2019 and caused a COVID-19 pandemic since early 2020. Many countries have used different approaches to handle the pandemic. One of the most used measures was lockdowns of cities, regions or countries in an attempt to control the virus. Details of the measures varied from country to country. It has been interesting to many aerosol scientists or environmental researchers to know how the lockdown and activity constraints, including road traffic, affected air pollution (Jain and Sharma, 2020; Mahato et al., 2020; Nakada and Urban, 2020; Tobías et al., 2020; Xu et al., 2020). Thailand adopted a series of partial lockdown measures since March 22, 2020. In the first phase, from March 22–May 2, 2020, the measure was most strict. All business, including industries, transportation, shopping, tourism and hotels, along with many entertainment and recreation venues, were shutdown and limited access to certain areas was announced. A state of emergency was announced on March 26, 2020, and a curfew, involving prohibition of all activities, outside residences from 10 pm–4 am, was enforced from April 3 until May 16, 2020; curfew hours were reduced to 11 pm–4 am from May 17 to 31 and further reduced to 11 pm–3 am from June 1 to June 14, before it was lifted. All incoming international flights were banned from April 4, 2020 (and remain constricted, even in November, 2020), except for state and military aircraft and emergency and technical landings. It was clear that road traffic in Bangkok city, as well as many cities, was significantly reduced during these periods. Industrial processes and manufacturing activities were halted. The period from March 22–April 31 was then selected as representing the strictest ‘lock down’ period in the present study, where air pollution, due to road traffic would be minimum.

Researchers studied the impact of the COVID-19 pandemic on air quality in many countries, e.g., China, India, Brazil, and Spain. Jain and Sharma (2020) studied the overall effect of lockdown measures on air quality in five Indian megacities - Delhi, Mumbai, Chennai, Kolkata, and Bangalore. Results showed that all pollutants, i.e., PM2.5 PM10, NO2 and CO significantly declined in all the megacities, especially in Delhi, except for O3. The concentration of PM2.5, PM10, NO2 and CO declined by 41%, 52%, 51% and 28%, respectively, during the 2020 lockdown period in comparison to the period in 2020 before the lockdown. This was similar to the decrease in air pollution in Delhi, India reported by Mahato et al. (2020). Xu et al. (2020) reported the impact of COVID-19 control on air pollution levels, including PM2.5, PM10, CO and NO2, in three cities of central China during the lockdown period: it resulted in reduction in concentrations of PM2.5 (3–45%), PM10 (30–48%), NO2 (30–61%) and CO (7–23%), during January to March 2020, compared to January to March 2017–2019. Nakada and Urban (2020) reported reduced concentrations of NO (by 77%), NO2 (by 54%) and CO (by 65%) in São Paulo, Brazil, during the partial lockdown, compared to the same period from 2015 to 2019. In contrast, a 30% increase of O3 concentrations was observed. Tobías et al. (2020) presented the similar reduction in air pollutant trends during the lockdown period in Barcelona, Spain. The significant reduction in NO2, BC and PM10 were 51%, 45% and 231% at traffic air quality monitoring stations. The results from several investigations clearly indicated the similar trends in air pollutant reduction, except for O3.

Bangkok, the capital of Thailand, and a megacity has faced serious air pollution problems, particularly fine particles or PM2.5, for a long time. It has been widely believed that traffic is the most influential factor on Bangkok air quality, especially PM2.5 concentration, though some studies indicated that biomass burning was the major cause of atmospheric aerosols, during the dry period, including haze episodes in January of recent years (Oanh, 2017; Dejchanchaiwong et al., 2020). It is interesting to understand the effect of the traffic on Bangkok PM2.5 concentration by comparing air quality, during the lockdown period, to the same period of the preceding year, a normal situation, as well as nearly a month prior to the city lockdown. In the present study, PM2.5 and gaseous pollutant concentrations data from the Thailand Pollution Control Department (PCD) stations in key areas in Bangkok in four periods are reported. Backward trajectories were used to identify possible external biomass burning sources of PM2.5.

 
2 METHODS



2.1 Real Time Data and Sampling Stations

Ambient PM2.5 (24-hr average), NO2 (1-hr average), O3 (8-hr average) and CO (8-hr average) concentrations were obtained from five selected stations operated by the Thailand Pollution Control Department (PCD). They were located in busy areas of Bangkok, including business districts: Phaya Thai District (13.780965°N, 100.538566°E), Bang Na District (13.667852°N, 100.605512°E), Bang Kruai District (13.810672°N, 100.506430°E), and roadsides: Kanjanapisek Road, Bang Khun Thian District (13.644727°N, 100.408773°E) and Din Deang Road, Din Deang District (13.764134°N, 100.548752°E) – see Fig. 1. All sites used the Tapered Element Oscillating Microbalance (TEOM) or Beta Attenuation Monitoring (BAM) methods for PM2.5 measurements, non-dispersive infrared detection (NDIR) or gas filter correlation method for CO measurements, chemiluminescence method for NO2 measurements and UV absorption method for O3 measurement, approved by the U.S. Environmental Protection Agency (U.S. EPA). The first three stations were ‘business area sites’ in busy areas, where traffic is highly congested from normal to slow speed vehicles.

Fig. 1. Locations of PCD monitoring stations in Bangkok.
Fig
1. Locations of PCD monitoring stations in Bangkok.

In particular, the Phaya Thai District is the center of the business area, where the traffic congestion is one of the worst in Bangkok. The Bang Na District is also a busy area, in the east side of Bangkok, with traffic problems mostly from local roads and the expressway. The Bang Kruai District represents the highly congested area in West Bangkok. Two ‘road site’ stations were beside major roads: the Kanjanapisek Road Station monitored highway traffic south of Bangkok, while the Din Deang Road Station monitored an expressway roadside in the center.

 
2.2 Sampling Periods, Traffic Data and Fuel Consumption

Both the PM2.5 and gas concentrations were measured during March–April 2020, when the COVID-19 pandemic heavily affected Thailand. From March 1–21, 2020, the way of life was normal: this period represented a normal period of 2020, labelled ‘pre-lockdown’ here. During Mar 22–April 30, 2020, labelled the ‘lockdown’ period, most activities were frozen. Many business sectors were shut down. People were urged to stay home and work at home under the ‘Stay home, stop disease for the nation’ campaign. The PM2.5 and gas concentrations, during both periods, were measured, so we could compare levels, pre- and during the lockdown. Continuous average pollutants concentrations at each site during April 1–30, 2019 formed a reference ‘2019 normal’ period, and April 1–30, 2020 was a ‘2020 lockdown’ period – as listed in Table 1. Because vehicles in Bangkok play an important role in air quality, vehicle numbers and diesel and gasoline fuel consumption on air quality in Bangkok were analyzed to find any correlation with air pollutant contributions. The number of passenger vehicles traveling to and from Bangkok were provided by Ministry of Transport Operation Center (Ministry of Transport Operation Center, 2020), while the diesel and gasoline fuel consumption data was provided by Department of Energy Business, Ministry of Energy (Department of Energy Business, 2020).

Table 1. Air pollutants monitoring during 2019 normal and 2020 lockdown periods details.

 
2.3 Chemical Analysis

Carbon components in the atmospheric particles are a good indicator for the sources of aerosol (Phairuang et al., 2019; Dejchanchaiwong et al., 2020). They were analyzed for several particle size ranges. A PM0.1 sampler or nanosampler, capable of segregating particle size to 100 nm, was used to collect samples (Furuuchi et al., 2010). Particles were separated into six size ranges: < 0.1 µm (PM<0.1), 0.1–0.5 µm (PM0.1-0.5), 0.5–1.0 µm (PM0.5-1), 1.0–2.5 µm (PM1-2.5), 2.5–10 µm (PM2.5-10) and > 10 µm (PM>10). A 55-mm quartz fiber filter (Pallflex, 2500QAT-UP) was used for particle collection in each stage. The stage, with an inertial filter (IF), used a stainless steel filter pack (SUS304, fiber diameter = 9.8 µm) to collect PM0.1-0.5. A sample was collected at the King Mongkut's University of Technology North Bangkok (KMUTNB: 13°51'06.4"N, 100°34'22.9"E) site which is 8 km away from Bang Kruai PCD station during March 3-4, 2019. A sample at the same site, during March 16-18, 2020, a few days before lockdown, was collected to study time variation of the sources of carbon. Organic carbon (OC) and elemental carbon (EC) components in PM were investigated to identify major sources. The carbonaceous components in PM on the quartz fiber filters were analyzed using a Carbon Aerosol Analyzer (Sunset Laboratory, Model 5), following the Interagency Monitoring of Protected Visual Environments Thermal/Optical Reflectance (IMPROVE-TOR) protocol (Dejchanchaiwong et al., 2020). Organic carbon fractions were taken in a non-oxidizing helium (He) at temperatures 120, 250, 450 and 550°C for OC1, OC2, OC3 and OC4, respectively, while EC fractions were taken in 2% O2/98% He at temperatures 550, 700, 800°C for EC1, EC2, and EC3, respectively. The samples were punched to 15 mm2, using a rectangular cutter. The number of spots on each punched sample was set at 1, 2, 4 and 8 for samples in the PM>10, PM2.5-10, PM1-2.5 and PM0.5-1 stages, respectively. The analyzer was calibrated with a blank filter and standard sucrose solutions, following the same procedure to confirm the analysis reliability based on the total carbon (TC). The detection limits for EC and OC analysis were below 0.1 µg cm2. However, the IMPROVE-TOR protocol could not be applied to particles collected on an inertial filter, as described by Dejchanchaiwong et al. (2020)

 
2.4 Air Mass Trajectories

48-hour backward trajectories (48-hr BT) of air and aerosol particles from external sources focused on biomass burning outside of Bangkok were simulated using the Hybrid Single-Particle Langrangian Integrated Trajectory Model version 4 (HYSPLIT4) (Air Resources Laboratory, 2020). Wind direction at 1,000 m altitude (AGL) was selected as the height of the mixing layer. The start time for backward trajectories was 00UTC (07:00 am Bangkok time). The sampling site was chosen to represent the receptor in Bangkok (13.84°N, 100.56°E). Hotspots from open biomass burning were obtained from the NASA VIIRS 375 m active fire data (Earthdata, 2020).

 
3 RESULTS AND DISCUSSION


 
3.1 Effect of Lockdown on PM2.5 Mass Concentration in Bangkok

Because PM2.5 is the most important air pollutant affecting Bangkok recently (Oanh et al., 2000; Thongsanit et al., 2003; Chuersuwan et al., 2008; Oanh et al., 2011; Pongpiachan et al., 2014; Oanh, 2017; Phairuang et al., 2019; Dejchanchaiwong et al., 2020), it was investigated first. The 24-hr average PM2.5 mass concentration at five sites in Bangkok were observed in pre-lockdown (March 1–21, 2020) and during the lockdown (March 22–April 30, 2020) periods to compare the air pollution during both periods, when road activities were significantly different. Daily PM2.5 concentrations in both periods are shown in Fig. 2. In pre-lockdown, the daily roadside PM2.5 concentrations ranged from 22.5–47.5 µg m–3, with a mean of 32.0 ± 6.6 µg m–3 and at business sites, it ranged from 12.2–34.1 µg m–3 with a mean 20.9 ± 6.3 µg m–3, i.e., roadside levels were about 34.5% higher than business areas. Most vehicles on the highways were diesel powered and the major PM emitter, compared to gasoline or gasohol passenger vehicles in business areas. During the lockdown, the daily PM2.5 concentrations at the same road sites ranged from 19.4–44.4 µg m–3, mean 28.0 ± 6.4 µg m–3, and ranged from 11.1–33.6 µg m–3, mean 18.6 ± 6.1 µg m–3 in the business areas. PM2.5 concentrations decreased by 12.5% on the roads and 11.0% in business areas, during lockdown compared to immediately before lockdown in 2020. PM2.5 concentrations in pre-lockdown reached 47.5 µg m–3 at road sites on March 16, 2020 while, at business area sites, the peak was 34.1 µg m–3 on March 9, 2020. During lockdown, the highest PM2.concentrations were 44.4 µg m–3 (roads) and 33.6 µg m–3 (business areas) on April 27, 2020. The 24-hr average PM2.concentrations at both sites did not exceed Thai national ambient air quality standards (50 µg m–3 for PM2.5). However, they exceeded the WHO standard (25 µg m–3, 24-hr average) in some periods.

Fig. 2. PM2.5 concentration between pre lockdown (March 1–21, 2020) and during lockdown (March 22–April 30, 2020) periods at road and business area sites in Bangkok.Fig. 2. PM2.5 concentration between pre lockdown (March 1–21, 2020) and during lockdown (March 22–April 30, 2020) periods at road and business area sites in Bangkok.

Apparent effects of road activities on the daily PM2.5 concentrations were compared in the same period of the previous year (2019) and the 2020 lockdown periods to clearly understand the effects. The most strict lockdown in 2020 covered the full month of April. The concentrations at both the road and business area sites are shown in Fig. 3. At road sites, average PM2.5 concentrations during April 2020 were 28.1 ± 7.1 µg m–3, ~11.1% decrease from April 2019 (31.6 ± 6.6 µg m–3). The p-values of 0.1 indicated this was not a significant difference. In contrast, at business sites, a 16.7% increase in daily PM2.5 concentrations was observed in April 2020 (mean 18.9 ± 6.6 µg m–3) vs. April 2019 (mean 16.2 ± 5.2 µg m–3), but the difference was also not significant (p-value = 0.13). Hence, the April 2020 pandemic lockdown had insignificant effect on PM2.5 concentrations. The small observed reductions could be attributed to normal random variations.

Fig. 3. Daily PM2.5 concentration in April: 2019 (blue circles) and 2020 (COVID-19 pandemic period – orange circles) at (a) road and (b) business area sites in Bangkok.Fig. 3. Daily PM2.5 concentration in April: 2019 (blue circles) and 2020 (COVID-19 pandemic period – orange circles) at (a) road and (b) business area sites in Bangkok.

 
3.2 Effect of Lockdown on Gaseous Pollutants

Variations in the levels of NO2, O3, and CO concentrations at road and business area sites in Bangkok between pre- and during 2020 lockdown are shown in Fig. 4. The levels of three air pollutants, i.e., NO2, O3 and CO, were decreased, except for O3 at business area sites. It is important to note that O3 concentrations at many stations increased during the 2020 lockdown period. In pre-lockdown periods, the average NO2, O3, and CO concentrations at the road sites were 19.1 ± 7.1 ppb, 22 ± 11.7 ppb, and 1.2 ± 0.3 ppm, and at the business area sites were 9.8 ± 3.4 ppb, 20.5 ± 7.4 ppb, and 0.3 ± 0.2 ppm, respectively. During the lockdown, average NO2, O3, and CO concentrations at the road site were 17.6 ± 8.4 ppb, 17.6 ± 6.8 ppb, and 1.1 ± 0.2 ppm, while at the business area sites, they were 8.5 ± 2.1 ppb, 25.4 ± 8.9 ppb, and 0.3 ± 0.1 ppm, respectively. 7.9%, 20.0%, and 8.3%, decreases in of NO2, O3 and CO concentrations at road sites during lockdown period were observed, compared to the pre-lockdown period in the year 2020. Similarly, NO2 concentrations at business area sites decreased by 13.3%. In contrast, a 23.9% increase in O3 concentration and no change in CO concentrations were observed in business areas. NO2, and CO in pre-lockdown period were slightly higher, but the difference was not considered significant. Also, average gaseous pollutant concentrations did not exceed Thailand's national ambient air quality standards (NO2 = 170 ppb based on 1-hours average, O3 = 70 ppb and CO = 9 ppm based on 8-hour average).

Fig. 4. Gaseous air pollutants in pre- (black bars) and during the 2020 lockdown period (gray bars) at road sites and business area sites in Bangkok.Fig. 4. Gaseous air pollutants in pre- (black bars) and during the 2020 lockdown period (gray bars) at road sites and business area sites in Bangkok.

Concentrations of three gases between 2019 normal period (April 1–30, 2019) and 2020 lockdown period (April 1–30, 2020) are compared in Fig. 5 and Table 2. In the 2019 normal period, levels at the road sites were NO2 (24.7 ± 9.6 ppb), O(9.2 ± 4.3 ppb) and CO (1.2 ± 0.2 ppm), and at the business area sites were 11.3 ± 6.9 ppb, 17.9 ± 5.5 ppb, and 0.7 ± 0.1 ppm, respectively. In the 2020 lockdown period, the average NO2, O3 and CO concentrations at the road sites were 18.9 ± 10.5 ppb, 15.6 ± 5.0 ppb, and 1.1 ± 0.3 ppm, and, at the business area sites, they were 9.3 ± 7.2 ppb, 26.0 ± 9.8 ppb, and 0.3 ± 0.1 ppm, respectively. NO2 concentrations during 2020 lockdown period were significantly lower than the same period in 2019, 23.5% less near roads and 17.7% less in business areas. CO concentrations reduced by 8.3% (roads) and 57.1% (business areas). Road side CO did not change significantly, but dropped considerably in business areas. In contrast, O3 concentrations significantly increased by 69.6% (road sites) and 45.3% (business area sites). Similar reductions in NO2 and CO and an increase in O3 concentrations was reported in Indian megacities (Jain and Sharma, 2020) and central China (Xu et al., 2020). The higher level of Oduring lockdown could be the result of the more suitable sunlight conditions for photochemical reactions and reductions of NO2 (Jain and Sharma, 2020). O3 production is controlled by either volatile organic compounds (VOCs) or nitrogen oxides (NOx). The O3 formation from these two precursors depends on the hydroxide (OH) radical and NOx. In general, O3 production in business areas is limited by VOCs (U.S. EPA, 2020). During the 2020 lockdown, 42.1% of passenger vehicles (see in section 3.3) and other combustion activities were reduced. This resulted in reduced NOx emissions in a VOC limited condition. As a result, when NOx decreased, more OH radical was available to react with the VOCs, leading to increased Oformation (NRC, 1991; Jain and Sharma, 2020; Tobías et al., 2020; Xu et al., 2020).

Fig. 5. Bangkok gaseous air pollutant concentrations for April: 2019 (normal 2019, blue bars) vs. 2020 (lockdown 2020, orange bars) at road sites and business area sitesFig. 5. Bangkok gaseous air pollutant concentrations for April: 2019 (normal 2019, blue bars) vs. 2020 (lockdown 2020, orange bars) at road sites and business area sites.

Table 2. Change in PM2.5 and gaseous pollutant concentrations at road and business area sites, as well as number of vehicles and fuel consumption in Bangkok during April 2019 normal and April 2020 lockdown periods.

 
3.3 Effects of Vehicle Numbers and Diesel Fuel Consumption on PM2.5 Concentration

Trends of PM2.5 concentration versus numbers of vehicles in Bangkok are shown in Fig. 6. The average number of vehicles in Bangkok ranged from 2.1–2.8M cars (mean 2.4M ± 0.2M cars) for the pre-lockdown periods. It reduced to 0.89–2.1M cars (mean 1.5M ± 0.3M cars) during the lockdown, a significant reduction of 38%. However, the average PM2.5 concentrations during the lockdown only declined by 12.5% (road sites) and 11.0% (business sites). Correlations between PM2.5 concentrations and numbers of vehicles are not clear, with large deviations, as shown in Fig. 7. Linear fits showed a slight increase of PM2.5 concentration with numbers of vehicles, but the correlations were weak - R2 = 0.1145 for roads and R2 = 0.031 for business areas. Most highway vehicles are diesel engines in contrast to gasoline or gasohol engines in passenger cars in the business areas. We concluded that there was no clear correlation between PM2.5 concentrations and the number of vehicles in Bangkok.

Fig. 6. Daily vehicle counts vs. PM2.5 mass during 2020 normal and lockdown periods at (a) road sites and (b) business sites.Fig. 6. Daily vehicle counts vs. PM2.5 mass during 2020 normal and lockdown periods at (a) road sites and (b) business sites.

Fig. 7. Correlations between vehicle counts and PM2.5 concentration in Bangkok during 2020 pandemic at (a) road sites and (b) business sites.Fig. 7. Correlations between vehicle counts and PM2.5 concentration in Bangkok during 2020 pandemic at (a) road sites and (b) business sites.

Variations observed in PM2.5 and gas concentrations at road and business area sites in Bangkok during the 2019 normal and 2020 lockdown periods, along with number of vehicles, diesel and gasoline fuel consumption in Bangkok, are also shown in Table 2. The number of vehicles and gasoline fuel consumption during the 2020 lockdown decreased significantly – vehicle numbers by 42.1% and gasoline by 26.8%. Petroleum sourced diesel consumption reduced by 28.6%. Total diesel (including biodiesel) fuel consumption, on the other hand, remained nearly unchanged. The biodiesels, B10 and B20, contain 10% and 20%, respectively, of methyl ester in petroleum sourced diesel. They were a much larger portion in 2020 than 2019. Many studies indicated that the use of biodiesel helped reduce the PM2.5 concentration. B10 and B20 fuels led to a significant decrease of 5–15% and 10–15% in PM2.5, compared to the diesel (Morris and Jia, 2003; Hutter et al., 2015; Pino-Cortés et al., 2015; Riberio et al., 2016; Dias et al., 2019). Thailand intends to enhance biodiesel use for transport energy, where about 40% increase of biodiesel consumption was observed in 2020 in Bangkok (Department of Energy Business, 2020). However, the increase of B10 and B20 diesel consumption had only a slight influence on the ambient PM2.5 reduction during the lockdown. This implied that the observed PM2.5 was mainly from other sources. Gasoline consumption changed with the number of vehicles, indicating that major portion of vehicles in Bangkok were gasoline engine passenger cars and diesel vehicles were mostly for transportation of goods, that were only slightly affected by the lockdown. The decline of NO2 and CO, during the lockdown, was then attributed to the large decrease in the gasoline vehicle use. In contrast, O3 concentrations increased at business sites.

The decrease in PM2.5 levels during the lockdown may also be attributed to the reduction in NO2 levels, which played an important role in the formation of secondary aerosols. In addition to transportation, industries, construction and many other activities were shut down during the lockdown and this led to a reduction in pollutant levels. There is an indication that, due to the city lockdown, a slightly significant reduction in PM2.5 levels occurred only at road sites in Bangkok. A countertrend of PM2.5 concentrations was observed with a 16.7% increase at business sites during the lockdown period. It is important to highlight that both PM2.5 and O3 increased during the lockdown.

 
3.4 Impact of External Biomass Burning on PM2.5 in Bangkok

Effects of biomass burning on Bangkok PM2.5 concentrations were studied by a 48-hr backward trajectory (BT) simulation during the 2019 normal and 2020 lockdown periods, as shown in Fig. 8. There were open biomass burning areas in central of Thailand as well as Myanmar, Laos and Cambodia. BT simulations on April 1, 2019 showed south wind flowing from the Gulf of Thailand to Bangkok resulting in drops of PM2.5 levels, as shown in Fig. 8(a). Conversely, west wind flowed towards Bangkok from west part of Thailand and some parts of Myanmar, where partial hotspots from open biomass burning occurred with peaks, on April 21, 2019, as shown in Fig. 8(b). On April 26, 2019, wind flowed to Bangkok from every direction, where hotspots from open biomass burning were observed, as shown in Fig. 8(c). During the 2020 lockdown period, backward trajectories on April 14, 2020 showed west-bound air mass movement, passing through some hotspots in Cambodia, to Bangkok, as shown in Fig. 8(d). Moreover, simulations for April 27 (Fig. 8(e)) and April 29, 2020 (Fig. 8(f)) showed northeast wind to Bangkok, passing through open burning in northeast Thailand, Laos, Vietnam and some parts of Cambodia. Although a small number of hotspots were found in Cambodia during this period, it was sufficient for the PM to transport and affect Bangkok air quality.

Fig. 8. Backward trajectory simulation and hotspot overlay for 2019 normal and 2020 lockdown periods: (a) April 1, 2019 low, (b) April 21, 2019 peak, (c) April 26, 2019 peak (d) April 14, 2020 peak, (e) April 27, 2020 peak and (f) April 29, 2020 peak.Fig. 8. Backward trajectory simulation and hotspot overlay for 2019 normal and 2020 lockdown periods: (a) April 1, 2019 low, (b) April 21, 2019 peak, (c) April 26, 2019 peak (d) April 14, 2020 peak, (e) April 27, 2020 peak and (f) April 29, 2020 peak.

Hence, the 48-hrs backward trajectories showed aerosol transported from open biomass burning areas in Thailand, and some parts of Myanmar, Cambodia, Laos and Vietnam affecting the Bangkok air quality. This agrees with previous studies of potential sources of PM in Bangkok (Oanh, 2000; Chuersuwan et al., 2008; Pongpiachan et al., 2014; Oanh et al., 2017; Phairuang et al., 2019; Dejchanchaiwong et al., 2020). Chuersuwan et al. (2008) reported the major sources of PM in Bangkok during years 2002–2003 were vehicle emission and biomass burning. Contribution of biomass burning was approximately 6–41% for PM2.5 and 28–36% for PM10. Oanh (2017) also confirmed that PM2.5 in Bangkok in the dry period had a 35.5% contribution from biomass burning. Dejchanchaiwong et al. (2020) stated that petroleum combustion was the major source of PM during the 2017 non-haze period, whereas the sources were mixed petroleum and biomass combustion during the 2018–2019 haze period, where the contribution of biomass burning was pronounced. Our present study clearly showed that sources of PM2.5 were mixed - between traffic and biomass burning. Effects from traffic were not significant as reduction of traffic and diesel consumption did not lower the PM2.5 concentration in Bangkok. The increment of PM2.5 level during the lockdown could be an external effect as a significant decrease of local sources occurred, from the 2019 normal period to the 2020 lockdown period.

 
3.5 Source Identification

The organic carbon (OC) and elemental carbon (EC) concentrations, along with OC/EC ratios for each particle size during March 3–4, 2019 and March 16-18, 2020 in Bangkok are shown in Fig. 9. Both periods represented normal situations, when traffic activities were comparable. Average concentrations of PM2.5 at Bang Kruai PCD station near the KMUTNB sampling point were 21.8 ± 3.4 and 24.1 ± 7.7 µg m–3 in March 2019 and 2020, respectively. BT simulations over Bangkok, on March 4, 2019 and March 16, 2020, are shown in Fig. 10. Air mass movements to Bangkok on March 4, 2019 were from the Gulf of Thailand, and from the southwest and west, where some hotspots were observed (Fig. 10(a)). Moreover, simulations for March 16, 2020, showed air mass movements, by the east wind from Cambodia and Vietnam, where high concentrations of hotspots from biomass burning were observed. This caused aerosol transport to Bangkok, as shown in Fig. 10(b). Both periods were affected by biomass burning from external sources, especially on March 16, 2020. 


Fig. 9. Size distribution of OC and EC concentrations along with OC/EC ratios during 2019 normal and 2020 lockdown periods.
Fig. 9. Size distribution of OC and EC concentrations along with OC/EC ratios during 2019 normal and 2020 lockdown periods.

Fig. 10. Backward trajectory simulation and hotspot overlay on (a) March 4, 2019 and (b) March 16, 2020.Fig. 10. Backward trajectory simulation and hotspot overlay on (a) March 4, 2019 and (b) March 16, 2020.

OC/EC ratios are commonly used as an index for identifying sources of PM. OC/EC ratios ranging from 0.06 to 0.8 indicate diesel exhaust (Na et al., 2004; Dallman et al., 2014) while values from 3.8 to13.2 indicate biomass burning (Zhang et al., 2007). In the present study, significant carbonaceous components were focused on the nuclei and accumulation mode particles. In PM<0.1, the OC and EC concentrations, during March 16–18, 2020, were 2.6 µg m–3 and 0.7 µg m–3, i.e., slightly increased - by 4.6% and 12.5%, compared to March 3-4, 2019 (OC at 2.5 µg m–3 and EC at 0.6 µg m–3). The OC/EC ratios were 4.0 (March 2019) and 3.7 (March 2020), indicating mixed sources from petroleum and biomass combustions, during both periods. During March 2020, the OC/EC ratios were 5.1 (PM0.5-1) and 9.9 (PM1-2.5). They were significantly higher than March 2019 with 2.9 (PM0.5-1) and 5.1 (PM1-2.5). This was also consistent with previous reports and it confirmed that the dominant size of PM from biomass burning was in PM0.5-1.0 (Hata et al., 2014). This agrees with the PM2.5 concentrations and back trajectory results presented earlier. The ratios for PM0.1-0.5 were not obtained due to limitation in the analysis. Increased ratios in accumulation mode particles clearly suggest the significant influence of biomass burning emission (Phairuang et al., 2019; Dejchanchaiwong et al., 2020).


3.6 Challenges and Solutions

We showed that background air pollution, due to Bangkok local traffic, exists, but effects of biomass burning of agricultural residues in the country, as well as aerosol transport from neighboring countries, cannot be underestimated. The solution to the problem involves long term commitment from many parties.

To reduce the PM2.5 concentration generated by diesel vehicles, the government needs to impose the Euro-5 or Euro-6 standards to control engine emissions. Diesel particulate filters (DPFs) must be introduced. The problem of air pollution from biomass burning is, however, more complicated. Farmers burn crops and agricultural residues, both for harvesting and residue removal. More than 60% of sugarcane, in central, northeast and lower north parts of Thailand, is burned annually to harvest it before sending it for sugar production. Most of sugarcane producers are smallholders and they cannot afford large harvesting machinery, because of the cost and lack of economy of scale. The solution may be an adoption of small-scale machinery. Government may need to support interest-free loans to farmers or farmer groups to purchase such machines. This approach can also be applied to other farmers growing rice and maize, in which the residues are burned for replanting following cropping. The solution to transport of aerosol from neighboring countries requires diplomatic approaches. The ASEAN haze free agreement needs to be put into real action, before the problem expands further.

 
4 CONCLUSIONS


PM2.5 and gaseous pollutants (NO2, O3 and CO) concentrations obtained from different Bangkok PCD monitoring stations in key areas before and during the lockdown resulting from the COVID-19 pandemic during March–April 2020 at a representative selection road and business area sites were investigated. Data from a similar period in 2019 was added to represent a normal situation. Decreases of PM2.5 as a result of the lockdown were not significant, despite significant reduction of traffic and diesel consumption. Backward trajectory simulation and OC/EC ratios showed that open biomass burning impacted PM2.5 levels in Bangkok. The decline of NO2 was, however, pronounced, due to the significant drop in gasoline vehicle use. This then contributed to an increase of O3, as more OH radicals were available to react with VOCs to form O3. CO concentration did not drop at the road sites, compared to business area sites, because highway road traffic was much less affected by the lockdown. Our results highlighted that a significant increase in PM2.5 levels in Bangkok during the 2020 lockdown periods was caused by external effects. The backward trajectory simulations confirmed that both transboundary and local aerosol transport from open biomass burning, in Thailand and some parts of Myanmar, Cambodia, Laos and Vietnam, affected the Bangkok city air quality, when the wind direction favored long distance transport.

 
ACKNOWLEDGMENTS


This research was financially supported by the National Research Council of Thailand under the Haze Free Thailand project grant #ENG590769S. Data was supplied by the Pollution Control Department, Ministry of Transport Operation Center and Department of Energy Business, Ministry of Energy, and the National Research Council of Thailand. Access to the Carbon Aerosol Analyzer at the Faculty of Environment, Kasetsart University and the Nanosampler, provided by the East Asia Nanoparticle Monotoring Network (EA-NanoNet), is gratefully acknowledged. We are also thankful to Asst. Prof. Dr. Panwadee Suwattiga at King Mongkut's University of Technology North Bangkok for assisting in sample collection, and John Morris for language editing.


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