Translator Disclaimer
18 December 2020 Temporal Variations and Potential Source Areas of Fine Particulate Matter in Bangkok, Thailand
Suwimon Kanchanasuta, Sirapong Sooktawee, Aduldech Patpai, Pisit Vatanasomboon
Author Affiliations +

Particulate matter (PM) less than 2.5 micron (PM2.5) issue is 1 of the important targets of concern by the United Nations’ Sustainable Development Goals. Bangkok is a megacity and facing air pollution problems. This study analyzed PM, PM2.5 and PM less than 10 micron (PM10), monitoring data from stations located in Bangkok, and aimed to present their variations in diurnal, weekly, and intra-annual timescales. High PM concentrations are related to calm wind. The diurnal variation of PM2.5/PM10 suggested a greater accumulation of PM2.5 than PMcoarse during the low wind speed. Potential source areas affecting PM rising at each monitoring station were identified using statistical technique, bivariate polar plot, and conditional bivariate probability function. Results showed that Ratchathewi District Monitoring Station identified 3 potential source areas related to emissions from transportation sources creating rising PM concentrations. The first potential source was located in the northwest direction, namely, the Rama VI Road close to the conjunction with Ratchawithi Road. The second potential source area was located around the cross-section between Phaya Thai Road and Rama I Road, while the third was located at the intersection of the Phaya Thai Road to Yothi Street and Rang Nam Road. These potential source areas constitute useful information for managing and reducing PM.


Ambient air pollution has been reported by the World Health Organization (WHO) that caused 4.2 million premature deaths annually in both cities and rural areas.1 Particulate matter (PM) air pollution has been revealed by the WHO to cause approximately 800 000 premature deaths yearly and is ranked as the 13th leading cause of mortality worldwide.2 Particulate matter less than 10 micron (PM10) and PM less than 2.5 micron (PM2.5) are coarse and fine particles, respectively, that are small enough to pass through the thoracic region of the respiratory system. Particulate matter affects respiratory and cardiovascular morbidity including asthma, respiratory symptoms, and increased hospital admissions. Moreover, long-term effects from PM2.5 and PM10 have resulted in mortality from cardiovascular and respiratory diseases including lung cancer. Related studies have reported that PM demonstrated strong effects on the cardiovascular system, and long-term exposure to PM was related to a significantly higher cardiovascular incidence and mortality rate. In addition, short-term acute exposure obviously increases the rate of cardiovascular events within days of a pollution peak.3 Moreover, some studies have reported that exposure to high fine PM levels may also cause various symptoms, including low birth weight among infants, preterm deliveries, and possibly fetal and infant deaths. In addition, PM2.5 exposure may also result in shortness of breath (dyspnea), chest discomfort and pain, and coughing and wheezing.4

Environmental conditions and quality are a part of the United Nations’ Sustainable Development Goals (SDGs) that countries are endeavoring to achieve. The concentration level of PM, especially PM2.5, has been used as an SDG indicator.5 However, PM problems in the atmosphere, that Thailand has been experiencing, such as haze in the northern region, high PM10 levels in ambient air in Saraburi Province (quarrying activity), and air pollution issues in Bangkok,69 constitute obstacles to achieving the SDG goals. Many studies have provided scientific information that haze issues in northern Thailand are related to transboundary pollution, open burning, and potential source area.6,1013 The resuspended dust and mechanical dust are major causes of severe PM10 concentrations in Saraburi Province.7,14 In addition, the problem of air pollution in Bangkok is complicated, especially concerning PM2.5.

Bangkok has been facing air pollution issues for over a decade. The National Ambient Air Quality Standards (NAAQs) of PM2.5 are 25 and 50 µg/m3 for annual average and 24-hour average, respectively.9 Notably, the NAAQ values are higher than the guideline values suggested by the WHO, 10 and 25 µg/m3 for annual average and 24-hour average, respectively.15 The annual average of PM2.5 concentrations over Bangkok in 2011 was 33 µg/m3. The later annual average levels vary year by year and remained over the NAAQs level until 2017 when the annual average equaled the standard value. The number of days that the PM2.5 level exceeded the standard value of 24-hour average in 2011 was 22, but the number exceeding the standard in 2017 was 42 days. Considering emission sources affecting PM2.5 in ambient air is required for effective management. Studies have suggested that traffic congress, construction, and open burning of agricultural residue impacts PM2.5 concentration levels in Bangkok.16,17 From source analysis using the receptor model of chemical mass balance, 2 major sources comprised transportation and biomass burning. The transportation sector contributes 27.4% and 23.0% during wet and dry seasons, respectively. Biomass burning contributes 28.4% and 33.9% during wet and dry seasons, respectively.18 This useful information reveals the significant types of PM2.5 emission sources in Bangkok. Also, long-range transportation would carry PM2.5 from remote areas, eg, Vietnam, Cambodia, Lao People’s Democratic Republic, and other parts of Thailand, to Bangkok.18

A study investigating places not far away, having higher PM2.5 emission, and affecting the receptors (monitoring stations) would be useful. These places are named the potential source areas. Therefore, we aimed to identify the potential source areas of PM2.5 in Bangkok. The result will make PM2.5 reduction actions more effective, and implement interventions in crucial areas rather than throughout Bangkok.


Monitoring data

According to alertness on PM2.5 and PM10 concentrations, the Bangkok Metropolitan Administration (BMA) started to install PM monitoring stations in 2018 for each district over the area. Therefore, we used hourly PM, wind direction (wd), and wind speed (ws) monitoring data in 2018 from the BMA for analyses to determine the capability of data on capturing PM behaviors. Particulate matter less than 2.5 micron and PM10 were measured in units of µg/m3. The units of wd and ws comprised degree from north and m/s, respectively. Forty-six monitoring sites distributed throughout Bangkok are shown in Figure 1. Forty-five sites used the Environmental Beta-Attenuation Mass Monitor (BAM 1020) from Met One Instruments to monitor PM for each district. Another site involved air quality monitoring stations using the Continuous Ambient Particulate Monitor (Model 5014i Beta) from Thermo Scientific to monitor PM2.5 and the Continuous Particulate Monitor (BAM-1020) from Met One Instruments to monitor PM10. In all, 19, 26, and 1 station monitor PM10, PM2.5, and both, respectively. All data were checked for outlier and error before analysis. The completeness of the PM dataset and site details are shown in  Supplemental Table S1.

Figure 1.

Locations of air quality monitoring sites. The graticule lines show meridional and zonal lines with resolution 0.1° (~11 km).


Analysis method

The time series plot is useful to present a variety of pollutant concentrations changing across various timescales. The intra-annual, weekday, and diurnal variations of time series plots were used to present the situations of PM2.5 and PM10. Also, many studies using time variations revealed trends, cycles, and magnitudes of pollutants.8,1921 Only 1 site collected both PM2.5 and PM10 data, used to determine the PM2.5/PM10 ratio. The ratio is useful to inform the classification of source type as Munir et al19 mentioned

It is important to emphasize that resuspended and windblown dusts are mostly in the coarse range (PM10—PM2.5); therefore, a low PM2.5/PM10 ratio. In contrast, PM emitted by combustion processes are mostly in the fine particulate (PM2.5) range and exhibit a higher PM2.5/PM10 ratio.

Two statistical techniques had been used to identify the potential source area of pollutants that affect the level of air quality at the measurement location.2224 The bivariate polar plot (BVP) function and conditional probability function (CPF) of openair package25 wrapped by the R program26 was used to study and determine the potential source areas for each monitoring station. The construction of BVP is based on the concept of pollutant concentration-wind dependence on a polar coordinate that presents pollutant concentrations on the polar coordinate of wind speed and wind direction. Partitioning of concentration, wind speed, and wind direction is used to separate the data and place it in wind speed-wind direction bins. The numbers of wind direction and wind speed intervals that can reveal the behavior of the concentration distribution are 10° and 30° intervals, respectively. The interesting statistical index (such as mean concentrations) and wind component data were used to plot on the surface of polar coordinates, and the detail of BVP calculation and construction was described by Uria-Tellaetxe and D. C. Carslaw.22 Another technique is based on the idea of resident time to diagnose the potential source area contributing to high pollutant concentrations at receptor locations. The potential region can be determined by the spatial probability distribution of air mass placement at a given time interval in the past. The probability (p) of an event where air mass placed in the interested area or grid cell can be calculated using , P = n / N , where (n) represents the number of air mass samples in the cell and (N) is the total number of air mass samples, whereas the probability of an air mass exhibiting high concentrations of pollutant can be determined using Phigh _ conc = m / N, where (m) is the number of air mass samples at high concentration in the cell and (N) represents the total number of air mass samples. To determine the potential source area, the CPF is defined as the ratio of Phigh_conc to P, which is written for polar coordinates as 10.1177_1178622120978203-fi01.gif, where 10.1177_1178622120978203-fi02.gif comprises the number of air mass samples in a given wind direction interval (Δ 10.1177_1178622120978203-fi03.gif), wind speed interval (Δws), and concentration interval (i).22,27 Both techniques constitute another kind of receptor model, and the result presents the level shading in polar coordinates, which cannot be compared with monitoring data similar to an application of the dispersion model.28 Because the graphic result given by the BVP or conditional bivariate probability function (CBPF) does not have a spatial scale incorporated in its information, it could be compared with the geographic image to reveal the consistency of the source areas affecting the level of pollutants at the monitoring stations.22 The technique is used to effectively identify potential source areas in various places including the area of point source influencing the pollution level in rural communities29 and the area of mobile and industrial sources affecting the VOC level at receptors located near industrial estates.30

Results and Discussion

Average concentrations of each monitoring station and each PM level were determined. The spatial graphic of PM2.5 and PM10 average concentrations is shown in Figure 2. The result showed the minimum and maximum values of PM2.5 were 22.3 and 63.2 µg/m3 at Lak Si and Bang Na districts, respectively. The spatial-wise average of PM2.5 throughout Bangkok was 33.6 µg/m3. These were 2.2, 6.3, and 3.4 times the WHO guideline value (10 µg/m3), respectively. However, the minimum value was not greater than the annual NAAQS of Thailand (25 µg/m3). The PM10 minimum and maximum values were 32.2 and 75.1 µg/m3 at Chom Thong and Prawet districts, respectively. The spatial-wise average of PM10 was 53.4 µg/m3. Three were over the annual average of the WHO guideline value for PM10 (20 µg/m3) at 1.6, 3.8, and 2.7 times. However, the NAAQS of Thailand was 50 µg/m3; these results in the minimum value were not over the standard level. Differences in using the threshold as the standard value resulted in differences in exceedance levels. It could be said that the air pollution problem of PM2.5 and PM10 still exceeds the WHO guideline value throughout Bangkok. Because PM2.5 is a part of PM10, PM having size between 2.5 and 10 has been defined as PMcoarse. The coarse fraction is related to emissions from mechanical activity; the fine fraction is more related to the combustion process.19 Mitigation of emission sources producing PM2.5 and PM10 would be effective knowing whether fine or coarse particles comprised the majority. The ratio of PM2.5 to PM10 would be useful to provide this information.

Figure 2.

Mean concentrations of PM2.5 (yellow) and PM10 (blue) at various locations throughout Bangkok. The graticule lines show meridional and zonal lines with resolution 0.1° (~11 km), and the concentration unit is µg/m3.

PM indicates particulate matter.


The ratio of PM2.5/PM10 was 0.63 determined by spatial-wise average of PM2.5 divided by PM10 that implied that overall Bangkok presented more fine PM than coarse fraction. More fine particle fractions are related to higher emissions from fuel combustion processes such as emissions from vehicle tailpipes than emissions from mechanical processes such as construction. The spatial-wise PM2.5/PM10 ratio was in agreement with a related study in Bangkok reporting the ratio was 0.6.31 In other countries, studies have reported the ratio for urban areas.32,33 Xu et al32 reported the annual average of PM2.5/PM10 was 0.62 in urban sites in Wuhan, China, and the ratio was lowest in summer (0.55) and highest during winter (0.75). For the ratios determined in the urban traffic areas in the United Kingdom, the values ranged from 0.58 to 0.79.33 Thus, time variation of PM2.5 and PM10 could be investigated to see the present low and high values of the ratio. Nevertheless, only 1 station monitored both PM2.5 and PM10, namely, the monitoring station at Ratchathewi district. The mean values were 24.4 and 45.5 µg/m3 for PM2.5 and PM10, respectively. The ratio of PM2.5/PM10 is 0.54. This means a little more fine PM was found than the coarse fraction. To reduce the level of PM2.5 and PM10 in Bangkok, fine and coarse emission sources could be controlled related to the timescale in their rising levels.

Temporal variations of PM2.5 and PM10

Variations of intra-annual, weekly, and diurnal cycles of PM at Ratchathewi district are presented in Figure 3. The diurnal cycles of PM2.5 and PM10 (bottom-left panel) revealed lower concentrations in the early morning (before 06:00 hours) than the level during late morning. The highest concentration presented at 09:00 hours, and reduced concentration appeared lowest at 15:00 hours, increasing in the evening (after 18:00 hours). The diurnal cycle pattern did not differ much between days in a week. However, it seemed the patterns on Mondays during the late morning and evening were less sharp than other days. This pattern was quite similar to the other PM diurnal cycles at various districts presented in  Figures S1 and  S2. Further observations in relation to the PM2.5/PM10 ratio should be made (Figure 4). During low concentration before 06:00 hours, the ratio was higher than 0.55 and the maximum was 0.65. This means greater PM2.5 contribution was found in PM10, but the concentration of PM10 during the early morning was lower than the level of PM10 during the late morning. Therefore, higher PM2.5 concentration corresponding to low PM10 concentration would be related to reduce PMcoarse resulting in a high PM2.5/PM10 ratio. The wind speed was also very low close to calm conditions (ws < 0.5 m/s) (Figure 5) resulting in a very stable condition with less pollutant removal by transportation processes. Later times had higher concentrations of both PMs, and the ratio decreased to around 0.54 until noon when wind speed increased slightly. The decreased ratio implied reduced PM2.5 contributed to PM10, and was possibly related to a greater removal of PM2.5 by the higher wind speed due to it having smaller particle size than PM10. On the other hand, decreased PM2.5/PM10 ratio meant that an increase in PMcoarse was caused by the mechanical activities. This was related to the time when people start commuting to work. In the afternoon, wind speed increased to the highest level during the day, resulting in greater removal of PM, and lower concentrations were exhibited. Also, the ratio still decreased caused by PM2.5 being more diluted than PM10, which was influenced by the high wind speed. After 18:00 hours, wind speed decreased with the presence of nocturnal conditions resulting in increasing PM2.5 and PM10 levels. However, less wind speed, resulting in a greater accumulation of PM2.5 than PM10, revealed a higher PM2.5/PM10 ratio.

Figure 3.

Intra-annual, weekly, and diurnal cycles of mean PM2.5 and PM10 concentrations (µg/m3) at Ratchathewi district.

PM indicates particulate matter.


Figure 4.

Intra-annual, weekly, and diurnal cycles of mean PM2.5/PM10 ratio at Ratchathewi district.

PM indicates particulate matter.


Figure 5.

Intra-annual, weekly, and diurnal cycles of mean wind speed (m/s) at Ratchathewi district.


The weekly cycle, presented in the bottom right panel of Figure 3, shows the concentration levels of PM2.5 and PM10 during the weekend were lower than those during the weekday period. This was related to the commuting of most people working from Monday to Friday, whereas Saturday and Sunday are holidays. However, some companies work on Saturday. These resulted in reduced pollutant emissions during the weekend. For weekly cycles of the PM2.5/PM10 ratio and wind speed, we observed small variations between the highest and lowest values within a week. Their differences were 0.02 and 0.05 m/s for the PM2.5/PM10 ratio and wind speed, respectively, as shown in Figures 4 and 5, respectively. Even though, PM concentration was low during the weekend, proportions of PM2.5 and PMcoarse to PM10 varied less during the week. These were caused by decreased activities emitting fine and coarse particles during weekend with similar percentages of PM2.5 and PMcoarse decreasing.

For the intra-annual cycle, climatic factors were used to consider PM2.5 and PM10 variations. The wet season of Thailand is related to the summer monsoon and the moving of the Intertropical Convergence Zone (ITCZ) presenting from June to October.34 The dry season, influenced by the winter monsoon, starts and lasts from November to February.35 The bottom-middle panel of Figure 3 shows lower PM concentrations during the summertime than those of the wintertime. The cause is related to washout and rainout processes during the summer season that remove pollutants from the atmosphere. Another factor is the variation of the planetary boundary layer (PBL). The PBL, representing the height that air mass can mix well in the layer, is lowest during the winter season in the northern hemisphere. In the tropics zone, the average PBL is reduced from 2100 to 1800 km approximately over terrestrial areas.36 A height decrease of 14% means the volume of air would also decrease by 14%. In the case the PM mass in the atmosphere is constant, reduced PBL affects the decreased air mixing volume. The concentration represents the pollutant mass by air volume; therefore, pollutant concentration increases in relation to shortened PBL. Figure 5 shows the wind speed in a calm state from August throughout the winter. This condition also increased the level of PM in ambient air, in addition to the effect of PBL. These meteorological factors strengthened the PM concentration in Bangkok during the winter season.

Thailand’s rainy season is related to the summer monsoon and moving of the ITCZ that presents from June to October,34 whereas the dry season is influenced by the winter monsoon that starts from November to February.35 More precipitation over Thailand is exhibited during the summer monsoon,37 resulting in less photochemical reactions during the rainfall period. Therefore, removal processes are enhanced in reduced PM and gaseous pollutants.38 However, the removal efficiency of PM by rainfall depends on emission rates of pollutants in the ambient air and the amount of rainfall. The obvious reduction of pollutants by rainfall prefers the condition of large amounts of rain of more than 1 mm.38 Wind speed during the rainy season is stronger than the speed in the dry season (Figure 5 bottom-middle). This is a cause of lower PM concentration presented during the rainy season as shown in Figure 3. The intra-annual variation of the PM2.5/PM10 ratio shown in Figure 4 (bottom-middle panel) appears to have a high ratio during the rainy season. The PM2.5/PM10 ratio appears to have a higher ratio during the rainy season than that during the dry season. Based on the assumption that emission sources do not change much in a year and remain quite constant, especially mobile and industrial sources, an increased PM2.5/PM10 ratio implies rereduction of removal processed on PMcoarse. Even though coarse PM’s properties are nonhygroscopic and largely insoluble,39 a greater reduction of coarse particles emitted from mechanical processes and less of resuspended dust result in low PM10 concentrations during the rainy season. Another reason is higher PM2.5 levels during rainy season than dry season. A study reports the contribution percentages of each emission source type. Traffic, biomass burning, and secondary inorganic aerosol are significant sources of PM2.5. The contributions are 27.4%, 28.4%, and 25.1%, and 23.0%, 33.9%, and 16.6% for rainy and dry seasons, respectively.18 Therefore, the differences between rainy and dry seasons (rainy season − dry season) are 4.4%, −5.5%, and 8.5% for traffic, biomass burning, and secondary inorganic aerosol, respectively. The difference values for traffic and secondary inorganic aerosol are positive. These imply a greater impact on the PM2.5 level during the rainy season caused by traffic source and secondary inorganic aerosol than during the dry season. Thus, tailpipe exhaust, secondary aerosol formation, greater reduction of particles emitted from mechanical processes, and less resuspended dust are possible reasons for the high PM2.5/PM10 ratio during rainy season. However, the different values for biomass burning show a negative value during dry season meaning the biomass burning source is an influential factor enhancing PM2.5 during the dry season.

From the time series analysis above, we can say that the diurnal and weekly variation of PM concentration would be related to their varying associated emissions and diurnal weather cycles. Nevertheless, the intra-annual cycle of the PM level depends on intra-annual climatic variability. The effects of biomass burning are greater during the dry season. Transportation and secondary inorganic aerosol exhibit reduced contributions to the PM2.5 level during the dry season. The role of secondary inorganic aerosol is important and would be studied in the future.

Potential source area

This section presents results using statistical techniques to analyze and classify the monitoring data at receptor locations. The BVP gives the spatial mean concentration of PM2.5 associated with various emission source locations identified by wind direction and speed. Many stations showed higher PM2.5 concentration than the NAAQS of Thailand as Watthana, Bang Na, Don Mueang, and Min Buri districts ( Table S1). Overall, potential source area is not far from the monitoring station. Distance between them is no longer than 3 km that implied to the short-range transportation of air mass. Noteworthy is that there are PM2.5 mean concentrations of 4 stations of more than 50 µg/m3, which are 13. Watthana district station 15.Bang Na district station 21.Don Mueang district station, and 26.Min Buri district station. Their observed concentration was mostly influenced by the potential source areas that representing to street. However, the potential source area located at lower right of the Bang Na district station is the area of industrial and logistic area. Airport located at the east of the Don Mueang district station and close to the highway is also its potential source area. For the Min Buri district station, the potential source areas located at north and west of the station are industrial area, and the area at east of the station is residential area. All of them are near the street. Figure 6 shows 2 obvious potential source areas that affected PM concentration at the Ratchathewi District Monitoring Station. The first potential source is located in the northwest direction of the monitoring station, which is an area related to mobile sources. The area identified Rama VI Road close to the conjunction with Ratchawithi Road. Also, over the Rama VI Road is the Si Rat Expressway close to a section leading to the Victory Monument. These road areas have the potential for increasing PM2.5 concentrations, at the mean level, around 80 µg/m3, at the Ratchathewi District Monitoring Station. The second potential source area is located around the cross-section between the Phaya Thai Road and the Rama I Road. The surrounding area of the second location comprises many popular department stores and hotels resulting in high traffic density. Moreover, the Siam station of BTS sky train serves as a transit station to other lines and remains a very cloudy station. However, the yellow area (60 µg/m3) shown in Figure 6 is located in the north not far from the station. To emphasize the areas affecting increasing high concentrations at the monitoring station, the CBPF was used to present the potential source areas related to the presence of high concentrations in a range of 80 to 100 percentile. Figure 7 shows a plot using the results from the CBPF analysis. The high probability (red shading in Figure 7) implies the potential source area result shows high concentrations (80-100 percentile), present in 3 areas. Two are at the same locations indicated by the BVP and are intersections in the northeast and south directions. The third is located at the intersection of the Phaya Thai Road to Yothi Street and Rang Nam Road.

Figure 6.

Bivariate polar plot of PM2.5 concentrations at Ratchathewi district (top) and its map (bottom). The red dot represents the monitoring station. The graticule lines show meridional and zonal lines with resolution 0.01° (~1.1 km).

PM indicates particulate matter.


Figure 7.

The CBPF plot of PM2.5 concentrations in a range of 80 to 100 percentile. The numbers in brackets are concentrations related to 80 and 100 percentile, respectively. The graticule lines show meridional and zonal lines with resolution 0.01° (~1.1 km).

CBPF indicates conditional bivariate probability function; PM, particulate matter.


This area presents a great probability to emit high concentration resulting in enhancing the PM2.5 level at the monitoring station to the 80 to 100 percentile, but overall, it exhibits a lower mean concentration than the other 2 areas. One possible reason is the low concentrations of this area would be smaller than the low concentrations of the other 2 stations. Therefore, these 3 potential source areas are related to emissions from transportation sources, and should be the main focus to control and reduce emissions than other surrounding areas.

Moreover, BVP analysis was performed for the PM10 and PM2.5/PM10 ratio. Figure 8A presents the potential source areas of PM10 that are similar to the results indicated by analyzing PM2.5 data. This implied that the potential source areas of PM2.5 and PM10 are the same. These are potential areas to emit PM2.5 and PM10 affecting the rising concentrations at the Ratchathewi District Monitoring Station. Air pollutant reducing measures should be implemented at the potential areas as a first priority, to obtain the benefits of reduced PM2.5 and PM10 emissions. Another concern is the BVP of the PM2.5/PM10 ratio as shown in Figure 8B. Focusing on this ratio reveals whether PM2.5 or PMcoarse is the major contribution to PM10. The spatial pattern of PM2.5/PM10 does not present any significant potential areas and reveals a likely monotone. The ratio values are in the range of 0.55 to 0.6. This means that the surrounding area of the monitoring station plays a similar role to influence the PM level at the station. The small value of the ratio implied more coarse fractions that originated mostly from mechanical processes, whereas a high ratio was related to emissions from anthropogenic sources.40 A study in China reported the ratios of PM2.5/PM10 of urban, urban fringe, and suburban areas were 0.617, 0.630, and 0.680, respectively.32 The results of this study agreed with those of related studies.32,33,40

Figure 8.

(A) Bivariate polar plot of PM10 concentrations. (B) Bivariate polar plot of the PM2.5/PM10 ratio.

PM indicates particulate matter.


Reducing and controlling PM2.5 at these areas involves possible mitigations. Vehicles are well known to be significant emission sources in urban areas. They can emit PM from tailpipes and nonexhaust processes. The nonexhaust processes generating road wear particles include brake wear, tire wear, and road pavement abrasion, but their contribution to total PM2.5 in urban centers including London, Tokyo, and Los Angeles is less (0.27%).41 Improving fuel quality and reducing the number of vehicles on the road would be useful.

Large numbers of vehicles on the road emit more particles than fewer vehicles.42 However, variation of traffic volume was not strongly related to PM2.5 variation, and was not a significant factor for the variations of increasing PM.43 Also, older vehicles tend to emit more PM2.5 to ambient air.42 Both emissions of gasoline- and diesel-fueled vehicles can contribute to PM2.5. For example, gasoline-fueled vehicles contribute 17.2% and diesel vehicles contribute 8.1% to PM2.5 in Seoul, Korea.44 However, low sulfur content in fuel produces less amount of PM2.5 emissions.42 Therefore, vehicle population, vehicle age, and fuel quality are factors resulting in changed PM2.5 levels.

Another strategy to reducing PM2.5 is using natural methods such as increasing green area and eco-forests performing as a buffer to protect PM2.5 dispersion from the transportation source. The study in Leicester city, UK, presented that PM2.5 deposition on trees could decrease PM2.5 by 2.8%, and deposition on the grass could remove 0.6%. Urban trees have the ability to decrease air pollution in street canyon areas by aerodynamic and deposition processes. The aerodynamic process occurring by rough change due to increasing numbers of trees results in an increase in turbulence production and a decrease in stable conditions suitable for pollutant accumulation.45 Plants can reduce PM from the ambient air and improve air quality. A wide variety of PM sizes including fine and coarse particles suspended in ambient air were deposited on leaf surfaces and trapped by waxes.46 While building surfaces had a much lower ability to trap PM2.5,45 green walls can be used to improve their ability to reduce pollutants.47

To enhance the capability of reducing PM2.5 in the ambient air in cities such as Bangkok, identifying all potential source areas of PM2.5 given by all the air quality monitoring stations over Bangkok is required. This study also provides additional potential source areas of PM2.5 for other monitoring locations in Bangkok as shown in the  Supplemental Material. All potential source areas around Bangkok can provide information to design green corridors to improve Bangkok air quality. The concept of connectivity can be used to increase green areas4749 by connecting built-up green zones at all potential source areas to serve as green corridors.


The 2018 PM monitoring data provided by BMA stations in Bangkok were analyzed to present their time variations and potential source areas. The diurnal cycle of PM2.5 and PM10 at Ratchathewi district station revealed lower concentrations in the early morning (before 06:00 hours), the highest concentration at 09:00 hours, and the lowest at 15:00 hours before increasing after 18:00 hours. The diurnal variation is related to diurnal change in wind speed. High concentrations were related to calm wind, whereas strong wind resulted in reduced concentrations. The diurnal variation of PM2.5/PM10 suggested a greater accumulation of PM2.5 than PMcoarse during low wind speed. Also, the diurnal cycle patterns of PM at other stations did not differ. The PM2.5/PM10 ratio was high during the rainy season implying a greater reduction of course than fine particles. Potential source areas were identified using BVP and CBPF analysis. Ratchathewi District Monitoring Station identified 3 potential source areas: (1) the Rama VI Road, (2) the cross-section between Phaya Thai Road and Rama I Road, and (3) the intersection of the Phaya Thai Road to Yothi Street and Rang Nam Road related to emissions from transportation sources creating rising PM concentrations. These areas should receive a greater focus of controlling and reducing emissions than other surrounding areas of the monitoring stations. Moreover, this study provided potential source areas for other monitoring stations in Bangkok. All potential source areas around Bangkok can be used as information to design green corridors to improve Bangkok air quality and implement other policies to reduce the PM level. However, the annual data provided by the BMA were used limitedly. This study could be improved by using more yearly data and data from other monitoring stations not belonging to the BMA.


The authors acknowledge the Air Quality and Noise Management Division, Department of Environment, Bangkok Metropolitan Administration (BMA) for providing the monitoring data. We also thank the R program and openair package developers for producing useful tools to analyze air quality. The study was partially supported for publication by the Faculty of Public Health, Mahidol University, Bangkok, Thailand.

Author Contributions

SK collected and prepared the input data, discussed the results, and proofed the manuscript. SS analyzed the data, discussed the results, and composed the manuscript. AP discussed the results and contributed to the final manuscript. PW contributed to the final manuscript.

Supplemental Material

Supplemental material for this article is available online.



WHO. Mortality and Burden of Disease from Ambient Air Pollution Global Health Observatory (GHO) Data 2016. Geneva, Switzerland: WHO; 2018. Google Scholar


WHO. Health Effects of Particulate Matter: Policy Implications for Countries in Eastern Europe, Caucasus and Central Asia. Geneva, Switzerland: WHO; 2013. Google Scholar


Anderson JO , Thundiyil JG , Stolbach A. Clearing the air: a review of the effects of particulate matter air pollution on human health. J Med Toxicol. 2012;8:166–175. Google Scholar


Guaita R , Pichiule M , Maté T , Linares C , Díaz J. Short-term impact of particulate matter (PM2.5) on respiratory mortality in Madrid. Int J Environ Health Res. 2011;21:260–274. Google Scholar


UNSD. Sustainable Development Goal (SDG) Indicators Correspondence With the Basic Set of Environment Statistics of the FDES 2013. New York, NY: Environment Statistics Section, United Nations Statistics Division; 2018. Google Scholar


Sooktawee S , Humphries U , Patpai A , Kongsong R , Boonyapitak S , Piemyai N. Visualization and interpretation of PM10 monitoring data related to causes of haze episodes in Northern Thailand. Appl Environ Res. 2015;37:33–48. Google Scholar


Phetrawech T , Thepanondh S. Source contributions of PM-10 concentrations in the Na Phra Lan pollution control zone, Saraburi, Thailand. Sci Technol Asia. 2017;22:60–70. Google Scholar


Aziz TA , Xu R , Fan C , et al. Analysis of spatial and temporal variation of criteria air pollutants in Bangkok Metropolitan Region (BMR) during 2000-2015. Paper presented at: 1st International Electronic Conference on Atmospheric Sciences; July 16-31, 2016:B006. Google Scholar


PCD. Thailand state of pollution report 2017. Publication PCD 06-066. Bangkok, Thailand: Pollution Control Department; 2018. Google Scholar


Sooktawee S , Kongsong R , Boonyapitak S , et al. Identify the plausible potential source areas related to haze episode in the upper Northern Thailand. Paper presented at: 2nd Environment and Natural Resources International Conference; June 14, 2016:18–25; Ayutthaya, Thailand. Google Scholar


Sooktawee S , Patpai A , Boonyapitak S , Kongsong R , Piemyai N , Humphries U . Influence of PM10 from the outside area affecting on the Northern part of Thailand. Paper presented at: 3rd Environment and Natural Resources International Conference; November 22-23, 2018:30–41; Bangsaen, Thailand. Google Scholar


Outapa P , Ivanovitch K. The effect of seasonal variation and meteorological data on PM10 concentrations in Northern Thailand. Int J GEOMATE. 2019;16:46–53. Google Scholar


Sukitpaneenit M , Kim Oanh NT. Satellite monitoring for carbon monoxide and particulate matter during forest fire episodes in Northern Thailand. Environ Monit Assess. 2014;186:2495–2504. Google Scholar


Pimonsree S , Wongwises P , Pan-Aram R , Zhang M. Model analysis of PM10 concentration variations over a mineral products industrial area in Saraburi, Thailand. Water Air Soil Pollut. 2009;201:239–251. Google Scholar


WHO. WHO Air Quality Guidelines for Particulate Matter, Ozone, Nitrogen Dioxide and Sulfur Dioxide: Global Update 2005, Summary of Risk Assessment. Geneva, Switzerland: WHO; 2006. Google Scholar


Thammasaroj P , Jinsart W. Effects of overcrowded traffic and road construction activities in Bangkok on PM2.5, PM10 and heavy metal composition. EnvironmentAsia. 2019;12:28–35. Google Scholar


Tipayarom D , Oanh NTK . Effects from open rice straw burning emission on air quality in the Bangkok Metropolitan Region. Sci Asia. 2007;33:339–345. Google Scholar


Narita D , Oanh NTK , Sato K , et al. Pollution characteristics and policy actions on fine particulate matter in a growing Asian economy: the case of Bangkok Metropolitan Region. Atmosphere. 2019;10:227. Google Scholar


Munir S , Habeebullah TM , Mohammed AMF , Morsy EA , Rehan M , Ali K. Analysing PM2.5 and its association with PM10 and meteorology in the Arid climate of Makkah, Saudi Arabia. Aerosol Air Qual Res. 2017;17:453–464. Google Scholar


Szulecka A , Oleniacz R , Rzeszutek M. Functionality of openair package in air pollution assessment and modeling—a case study of Krakow. Ochr Srodowiska I Zasobow Nat. 2017;28:22–27. Google Scholar


Hayes ET , Chatterton TJ , Barnes JH , Longhurst JWS . Utilising Openair to support multi-stakeholder engagement and the resolution of air quality issues. Clean Air J. 2013;23:23–31. Google Scholar


Uria-Tellaetxe I , Carslaw DC. Conditional bivariate probability function for source identification. Environ Model Softw. 2014;59:1–9. Google Scholar


Grange SK , Lewis AC , Carslaw DC. Source apportionment advances using polar plots of bivariate correlation and regression statistics. Atmos Environ. 2016;145:128–134. Google Scholar


Alas HD , Muller T , Birmili W , et al. Spatial characterization of black carbon mass concentration in the atmosphere of a Southeast Asian megacity: an air quality case study for Metro Manila, Philippines. Aerosol Air Qual Res. 2018;18:2301–2317. Google Scholar


Carslaw DC , Ropkins K. openair—an R package for air quality data analysis. Environ Model Softw. 2012;27–28:52-61. Google Scholar


R Core Team. A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2013. Google Scholar


Ashbaugh LL , Malm WC , Sadeh WZ. A residence time probability analysis of sulfur concentrations at Grand Canyon National Park. Atmos Environ. 1985;19:1263–1270. Google Scholar


Bennett ND , Croke BFW , Guariso G , et al. Characterising performance of environmental models. Environ Model Softw. 2013;40:1–20. Google Scholar


Sooktawee S , Kanchanasuta S , Boonyapitak S , et al. Distinguish potential source areas of PM 2.5 and PM 10 by statistical data analysis. IOP Conf Ser: Earth Environ Sci. 2020;489:012024. Google Scholar


Jindamanee K , Thepanondh S , Aggapongpisit N. Source apportionment analysis of volatile organic compounds using positive matrix factorization coupled with conditional bivariate probability function in the industrial areas. EnvironmentAsia. 2020;13:31–49. Google Scholar


Sahanavin N , Tantrakarnapa K , Prueksasit T. Ambient PM10 AND PM2.5 concentrations at different high traffic-related street configurations in Bangkok, Thailand. Southeast Asian J Trop Med Public Health. 2016;47:528–535. Google Scholar


Xu G , Jiao L , Zhang B , et al. Spatial and temporal variability of the PM2.5/PM10 ratio in Wuhan, Central China. Aerosol Air Qual Res. 2016;17:741–751. Google Scholar


Munir S. Analysing temporal trends in the ratios of PM2.5/PM10 in the UK. Aerosol Air Qual Res. 2017;17:34–48. Google Scholar


Kirtphaiboon S , Wongwises P , Limsakul A , Sooktawee S , Humphries U. Rainfall variability over Thailand related to the El Nino-Southern Oscillation (ENSO). J Sustain Energy Environ. 2014;5:37–42. Google Scholar


Sooktawee S , Humphries U , Limsakul A , Wongwises P. Spatio-temporal variability of winter Monsoon over the Indochina Peninsula. Atmosphere. 2014;5:101–121. Google Scholar


McGrath-Spangler EL , Denning AS. Global seasonal variations of midday planetary boundary layer depth from CALIPSO space-borne LIDAR. J Geophys Res Atmos. 2013;118:1226–1233. Google Scholar


Limsakul A , Limjirakan S , Suttamanuswong B. Asian summer monsoon and its associated rainfall variability in Thailand. EnvironmentAsia. 2010;3:79–89. Google Scholar


Kim S , Hong KH , Jun H , Park YJ , Park M , Sunwoo Y. Effect of precipitation on air pollutant concentration in Seoul, Korea. Asian J Atmos Environ. 2014;8:202–211. Google Scholar


Wilson WE , Suh HH. Fine particles and coarse particles: concentration relationships relevant to epidemiologic studies. J Air Waste Manag Assoc. 1997;47:1238–1249. Google Scholar


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


Panko J , Hitchcock K , Fuller G , Green D. Evaluation of tire wear contribution to PM2.5 in urban environments. Atmosphere. 2019;10:99. Google Scholar


Zavala M , Barrera H , Morante J , Molina LT. Analysis of model-based PM2.5 emission factors for on-road mobile sources in Mexico. Atmosfera. 2013;26:109–124. Google Scholar


Cheng YH , Li YS. Influences of traffic emissions and meteorological conditions on ambient PM10 and PM2.5 levels at a highway toll station. Aerosol Air Qual Res. 2010;10:456–462. Google Scholar


Heo JB , Hopke PK , Yi SM. Source apportionment of PM2.5 in Seoul, Korea. Atmos Chem Phys. 2009;9:4957–4971. Google Scholar


Jeanjean APR , Monks PS , Leigh RJ . Modelling the effectiveness of urban trees and grass on PM2.5 reduction via dispersion and deposition at a city scale. Atmos Environ. 2016;147:1–10. Google Scholar


Dzierzanowski K , Popek R , Gawrońska H , Sæbø A , Gawroński SW. Deposition of particulate matter of different size fractions on leaf surfaces and in waxes of urban forest species. Int J Phytoremediation. 2011;13:1037–1046. Google Scholar


Aruninta A. Green design and planning resolutions for an eco-industrial town: a case study of polluted industrial estate in Rayong province, Thailand. J Environ Prot. 2012;3:1551–1558. Google Scholar


Park S. A preliminary study on connectivity and perceived values of community green spaces. Sustainability. 2017;9:692. Google Scholar


Polenšek M , Pirnat J. Forest patch connectivity: the case of the Kranj–Sora basin, Slovenia. Acta Geogr Slov. 2018;58:84–95. Google Scholar
© The Author(s) 2020 This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (
Suwimon Kanchanasuta, Sirapong Sooktawee, Aduldech Patpai, and Pisit Vatanasomboon "Temporal Variations and Potential Source Areas of Fine Particulate Matter in Bangkok, Thailand," Air, Soil and Water Research 13(1), (18 December 2020).
Received: 19 September 2020; Accepted: 11 November 2020; Published: 18 December 2020

Back to Top