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Article

Size-Segregated Particulate Matter Down to PM0.1 and Carbon Content during the Rainy and Dry Seasons in Sumatra Island, Indonesia

by
Muhammad Amin
1,
Rahmi Mulia Putri
1,
Rizki Andre Handika
1,2,
Aulia Ullah
3,
Fadjar Goembira
4,
Worradorn Phairuang
5,6,
Fumikazu Ikemori
7,
Mitsuhiko Hata
6,
Perapong Tekasakul
8,9 and
Masami Furuuchi
6,10,*
1
Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa 920-1192, Japan
2
Department of Environmental Engineering, Faculty of Science and Technology, Universitas Jambi, Jambi City 36364, Indonesia
3
Faculty of Science and Technology, Sultan Syarif Kasim Islamic State University, Pekanbaru 28293, Indonesia
4
Department of Environmental Engineering, Faculty of Engineering, Andalas University, Padang 25163, Indonesia
5
Department of Geography, Faculty of Social Sciences, Chiang Mai University, Chiang Mai 50200, Thailand
6
Faculty of Geoscience and Civil Engineering, Institute of Science and Engineering, Kanazawa University, Kanazawa 920-1192, Japan
7
Nagoya City Institute for Environmental Science, Nagoya 460-8508, Japan
8
Department of Mechanical and Mechatronics Engineering, Faculty of Engineering, Prince of Songkhla University, Songkhla 90110, Thailand
9
Air Pollution and Health Effect Research Center, Prince of Songkla University, Songkhla 90110, Thailand
10
Faculty of Environmental Management, Prince of Songkla University, Songkhla 90110, Thailand
*
Author to whom correspondence should be addressed.
Atmosphere 2021, 12(11), 1441; https://doi.org/10.3390/atmos12111441
Submission received: 29 September 2021 / Revised: 25 October 2021 / Accepted: 28 October 2021 / Published: 31 October 2021
(This article belongs to the Section Air Quality)
Browse Figures
Versions Notes

Abstract

:
Size-segregated particulate matter (PM) including the PM0.1 fraction, particles ≤0.1 µm, was monitored during the rainy and dry seasons at three different cities in Sumatra island, Indonesia in 2018. In order to identify possible emission sources, carbonaceous components in the particles collected by a cascade air sampler that is capable of collecting PM0.1 particles were analyzed by applying a thermal/optical reflectance (IMPROVE-TOR) protocol. The PM0.1 levels in the Jambi and Pekanbaru areas were similar to those in large cities in East Asia, such as Bangkok and Hanoi. During the rainy season, local emissions in the form of vehicle combustion were the main sources of PM. The influence of peatland fires in the dry season was more significant in cities that are located on the east coast of Sumatra island because of the larger number of hotspots and air mass trajectories along the coast. A clear increase in the carbonaceous profiles as OC, TC, and OC/EC ratios in the dry season from the rainy season was observed, particularly in fine fractions such as PM0.5–1. In both seasons, EC vs. OC/EC correlations and soot-EC/TC ratios showed that the PM0.1 fraction in Sumatra island was heavily influenced by vehicle emissions, while the effect of biomass burning was more sensitive with respect to the PM0.5–1 fraction, particularly in Jambi and Pekanbaru sites during the dry season.

Graphical Abstract

1. Introduction

Air pollution is becoming a serious problem in all countries and is believed to cause both short- (illnesses such as pneumonia or bronchitis and irritation to the nose, eyes, or skin) and long-term (heart diseases, lung cancer, or emphysema) adverse effects on human health [1,2,3]. It is also an environment problem that affects visibility and contributes to climate change [4,5], particularly in developing countries such as Indonesia. Indonesia has been facing serious air pollution problems, especially by particulate matter (PM) that is emitted from biomass burning in forests and peatland fires [6,7]. The average concentration of fine particles (PM2.5), for example, was reported to be 7817 µg/m3 during a forest fire period, corresponding to a level that is hundreds of times higher than the daily standard for Indonesia (65 µg/m3) and three hundred times higher than the WHO standard (25 µg/m3) [8]. Indonesia has become a main contributor of emissions derived from forest fires in Equatorial Asia region since the El Niño year in 1997 [9]. Because of the meteorological characteristics over the Southeast Asian (SEA) region, air pollution in Indonesia would not be expected to be a local problem, but in reality, a transboundary smoke-haze problem now exists that affects neighboring countries such as Singapore, Malaysia, Southern Thailand, Brunei, and Philippines [10,11,12,13].
An area that is highly contaminated by PM could have adverse influence on human health. This is particularly true in the case of fine and ultrafine particles that contain higher amounts or fractions of hazardous chemicals than coarse particles and can penetrate deeply inside the lung [14,15]. However, our understanding of the behavior and characteristics of ambient particles with aerodynamic diameters <0.1 µm, or the PM0.1 fraction regarding chemicals including carbonaceous components, remains incomplete [16,17]. This is true for the SEA region, especially in Indonesia. Since the PM0.1 fraction is assumed to contain various harmful components that may exert toxic effects on human health, the National Research Council blueprint for particulate matter (PM) of the US in 1998 singled out the PM0.1 fraction as the main focus of research [18]. A number of studies have now confirmed that PM0.1 particles have adverse effects on human health [14,19,20], including the human brain [21].
As reported to date, nearly 90% of particles emitted from biomass burning are smaller than 1 µm, or PM1, and nearly 30% of these are smaller than 0.1 µm, or PM0.1, even on a mass basis. It was reported in [22,23,24] that carbonaceous components in the PM0.1 fraction are sensitive to carbon-containing particles that are emitted during the burning of agricultural crops. The situation regarding PM in Indonesia may, therefore, be strongly influenced by fine and ultrafine particles that are emitted as the result of the burning of forests and peatlands. However, as of now, little information concerning the characteristics of coarse and fine particles that are emitted along with PM0.1 particles in Indonesia is currently available.
In this study, in order to examine the present status and characteristics of airborne particulate matter in Indonesia, data on size-segregated particulate matter (PM) down to the PM0.1 level was collected in three different cities in Sumatra island, Indonesia in the year 2018. Sumatra island was selected as a typical area, since peatland fires during the dry season are common in that region. A cascade air sampler was used to collect size-segregated particulate matter down to PM0.1 in both rainy and dry seasons at three different locations in Sumatra island, i.e., Padang, Jambi, and Pekanbaru, where the influence of local emission sources as well as the effects of possible peatland fires are discussed. The carbonaceous components of the size fractionated particles were analyzed by a thermal/optical method. In addition, possible local emission sources were identified based on the components in conjunction with the calculation of air mass movement from burned areas, or hotspots, as obtained from satellite imaging data.

2. Methodology

2.1. Sampling Site

Three different cities that are located in three different provinces in Sumatra island, Indonesia were selected. We collected data concerning possible emissions from local sources, e.g., vehicle emission as well as the emission from biomass burning, particularly from peatland or forest fires that frequently occur on Sumatra island. The distance between one site and the other sites varied from 184.86 to 354.28 km, as shown in Figure 1. However, all of the sites had similar characteristics regarding meteorological conditions, such as temperature and humidity. The meteorological conditions are listed in Table 1 to make comparisons among the sites still reasonable. The air mass trajectories near the study sites are shown in Figure S1. Detailed information regarding each site is discussed below.

2.1.1. Padang City

In Padang city, the sampling site was located on the 4th floor of the building that houses the Environmental Engineering Division, Faculty of Engineering, Andalas University (00°54′46.3′′ in the South and 100°27′50.0′′ in the East). Padang city is located on the west coast of North Sumatra island and is the capital city of the Pauh subdistrict (see Figure 1). The site is surrounded by a forest and is located about 13 km from the city center, where the influence of emissions from traffic may not be important. As an industrial emission source that might possibly be affected by the weather, a large cement factory that uses coal as fuel was located about 2.5 km south of the sampling site [25]. The climate in the Padang area is classified as a tropical rain forest climate and is one of Indonesia’s wettest cities with frequent rainfall throughout the year [26].

2.1.2. Muaro Jambi Regency

This sampling site was located at the rooftop of the Environmental Engineering building (3rd floor), Jambi University, Muaro Jambi (01°40.437′ in the South and 103°34.566′ in the East, 48 m above sea level). It is located at the border of Mestong and the Jambi Luar Kota subdistrict, Muaro Jambi on the east cost of Sumatra island (see Figure 1) [27]. The land use of these subdistricts consists of agriculture and residential and industrial areas. Provincial roads that connect the Jambi province and the South Sumatra Province (~1.2 km away from the Jambi site) could also represent possible PM sources in both the rainy and dry seasons, especially in the case of ultrafine and fine particles. The climate in Jambi is also classified as a tropical rain forest climate but is rather dry with a much smaller precipitation in the dry season than that in Padang.

2.1.3. Pekanbaru City

The sampling site was located on the rooftop of the Engineering Faculty building, Riau University, Tampan subdistrict, Pekanbaru City (01°40.437′ in the South and 103°34.566′ in the East, 48 m above sea level) [28] (see Figure 1). The Tampan subdistrict is located at the city center of Pekanbaru with land use dominated by housing, hospitals, roads, offices, restaurants, industries, and hotels. The university is surrounded by roads that involve heavy traffic and is approximately 100 m from a cross-provincial road (Riau and West Sumatra Province). The climate in Pekanbaru is also classified as a tropical rain forest, similar to that in Jambi.

2.2. Sampling Methods

A cascade air sampler, termed here as ANS, was used as the sampler. It was developed by Furuuchi et al. (2010) [29] and can be used to collect PM0.1, PM1, PM2.5, PM10, TSP fractions at an air flow rate of 40 L/min. Quartz fibrous filters (QFF) (2500 QAT-UP, Pall Corp., New York, NY, USA) of Ø 55 mm that had been pre-baked at 350 °C in an oven for 1 h, then conditioned at 21.5 ± 1.5 °C, and 35 ± 5% RH in a PM2.5 weighing chamber (PWS-PM2.5, Tokyo Dylec Corp., Tokyo, Japan) for 48 h before and after sampling were used to collect particles (>10, 2.5–10, 1.0–2.5, 0.5–1.0, and <0.1 µm). This method was adapted from the standard procedure recommended by the [26] Ministry of Environment of Japan (MOEJ) (2019) [30]. The pre-baking temperature was determined by MOEJ so as to avoid the fiber surface from being activated and therefore to absorb more OC after cooling down, as has also been reported by Arp et al., (2007) [31]. An inertial filter (IF) was used for the separation of particles (<0.1 µm) (PM0.1). The IF consisted of webbed stainless steel fibers (average fiber diameter df = 9.8 µm, Nippon Seisen Co. Ltd. (Osaka, Japan), felt type, SUS-316), inserted into a cartridge nozzle of Ø 5.25 mm [29,32]. Before assembling the inertial filter cartridge, the stainless-steel web was cleaned with ethanol to reduce the blank value for carbon. Each prepared filter was wrapped in aluminum foil then stored in a plastic bag during transport to the sampling sites. All of the filters were accompanied by several blank filters to verify possible contaminants during the transport of the filter.

2.3. Sampling Procedure

For a better understanding of seasonal difference regarding size-segregated PM in Sumatra island, sampling was conducted during both the rainy and dry seasons. The rainy season in Sumatra island commonly occurs from November to April, while the dry season generally spans from May to September or October in some parts of Sumatra island [33]. During the dry season, biomass burning by forest and peatland fires as well as crop residue burning is an important factor on air pollution other than meteorological conditions. Based on Table 1, generally, only precipitation and wind direction from five meteorological factors showed significant differences between the two seasons, especially in Jambi and Pekanbaru cities. Regarding the typical area in the west coast of Sumatra island, the difference between the rainy and dry season was unclear, for example, in Padang city.
Due to logistical reasons, we performed the sampling in parallel by using the same ANS in the order of Padang, Jambi, and Pekanbaru city. For the dry season, a simultaneous sampling using three different ANSs at the three sites was conducted. Based on the conditions at each site, the sampler was installed in a suitable type of shelter. The sampling period and duration of sampling at each site are summarized in Table 1 along with information on the three sampling sites above. Meteorological information during the study period was obtained from the Meteorology, Climatology and Geophysical Agency (BMKG) of Indonesia, i.e., Marine Meteorological Station Teluk Bayur, Padang city, Climatological Station of Muaro Jambi, and Meteorological Station of Sultan Syarif Kasim II, Pekanbaru city [33].

2.4. Analysis of Carbonaceous Components

The thermal/optical analysis of carbonaceous components in particles collected on QFF involved the use of a punched filter sample (10 × 15 mm) using a carbon analyzer (Carbon Aerosol Analyzer, Sunset Laboratory, Tigard, OR, USA), following the IMPROVE protocol [34]. Briefly, the OC fractions were determined at four temperature steps in 100% helium: OC1 at 120 °C, OC2 at 250 °C, OC3 at 450 °C, and OC4 at 550 °C. The EC fractions were determined at three temperature steps in a mixture of 2% oxygen and 98% helium: EC1 at 550 °C, EC2 at 700 °C, and EC3 at 800 °C. Prior to each set of carbon analyses, the TC value was calibrated to that of a reference chemical, sucrose (C12H22O11) (196-00015, Sucrose, Wako Pure Chemical Industries, Ltd., Osaka, Japan). OC was defined as OC1 + OC2 + OC3 + OC4 + PyOC, while EC was defined as EC1 + EC2 + EC3 − PyOC. PyOC denotes the pyrolyzed fraction of organic carbon. Char-EC, defined as EC1 − PyOC, and soot-EC, defined as EC2 + EC3, were also evaluated [35]. The repeatability of the analysis of the punched filter samples with deposition spots of ambient particles was preliminarily confirmed to be fairly good with a CV of less than 3.2% for OC and 7.9% for EC, except 17.8% for EC in particles >10 µm. For the quality assurance and quality control of carbon analysis, a reference standard (C12H22O11, 196-00015, Sucrose, Wako Pure Chemical Industries, Ltd., Osaka, Japan) was used. Travel blanks (n = 6) were also evaluated. The detection limit (MDL) based on the analysis of blank filters for OC and EC in this study were, respectively, confirmed as 0.79 and 0.00 µg/cm2. This is smaller enough than minimum values of samples in all sites (1.96 and 0.20 µg/cm2 for OC and EC, respectively). Samples collected on the IF (0.1–0.5 µm) were not analyzed, since the thermal/optical method could not be applied.

2.5. Backward Trajectory and Hotspots

The 72 h backward trajectories of air parcels arriving at the monitoring sites in Padang, Jambi, and Pekanbaru sites at a distance of 500 m from the average ground level (a.g.l.) were calculated using the Hybrid Single-Particle Lagrangian Integrated Trajectory Model version 4 (HYSPLIT4, http://ready.arl.noaa.gov/HYSPLIT.php, accessed on 10 August 2019) [36,37]. The use of 500 m was somewhat of a compromise between model limitations and the data observed at the ground surface [38]. Meteorological data from Global Data Assimilation System (GDAS) resolution 0.5 degree from the National Oceanic and Atmospheric Administration (NOAA) of the Unites Stated of America (USA) were used. Geographic locations of hotspots or active fires in Indonesia with a resolution of 1 km × 1 km that are available from Moderate Resolution Imaging Spectroradiometer (MODIS) (produced by the US NASA, https://firms.modaps.eosdis.nasa.gov/download/) (accessed on 15 August 2019) satellite remote sensing imagery were used to specify possible areas corresponding to biomass burning [39].

3. Result and Discussion

3.1. Status of PM Concentration

The average mass concentrations of PM in all sites during the rainy and dry seasons are shown in Figure 2a–e, respectively, for PM0.1, PM1, PM2.5, PM10, and TSP. The average PM10 and PM2.5 values in the rainy–dry seasons at the Pekanbaru site (56.0–79.3 and 42.8–59.1 µg/m3, respectively) were consistently larger than those in the WHO guidelines for 24 h (50 and 25 µg/m3, respectively), and only the PM10 value (48.31 µg/m3) was less than that in the guidelines at the Jambi site, while all of the guideline values were satisfied at the Padang city site. The PM0.1 level in the rainy season for the cities of Jambi and Pekanbaru (9.2–9.6 and 10.9–15.6 µg/m3, respectively) was somewhat similar to those in other large cities in SEA (e.g., Bangkok (14.80 ± 1.99 µg/m3) [24] and Hanoi (6.06 ± 2.71 µg/m3)) [40] while one at the Padang site (5.36–5.57 µg/m3) was about two times larger than that at Kanazawa (2.7 µg/m3), a local city in Japan [41].
Compared with previous haze episodes in Indonesia, however, the PM levels for the dry season determined in this study were much smaller than previously reported levels. For example, the PM2.5 level during a haze episode in the year 2012 in central Kalimantan was up to 7817 µg/m3 [8]. During the massive peatland fires in the Riau province in June 2013, the PM10 level reached unhealthy and hazardous levels (360–600 µg/m3) [42]. In the most recent serious episode in 2015, the PM10 level in Pekanbaru city reached 600 µg/m3 [43]. This may be explained by a drastic decrease in the frequency of peatland fires that were controlled by the Indonesian government since 2016 [44,45,46]. It might also be related to the fact that it was still categorized as an El Niño year, even though it was weaker than that in 2015. Supplementary Figure S2 shows information on the number of hotspots plotted as an indicator of peatland fires from 2009 to 2018 [47]. There was a periodic fluctuation in the number of hotspots, such that they were increased from May, the beginning of the dry season, reached a peak in July or August, and then decreased drastically after September, the beginning of the rainy season. The difference in PM10 fractions between 2015 and 2018 in Pekanbaru city of about seven times could be reasonably explained by the difference in the peak numbers of hotspots of about six times (2672 in September 2015 and 424 in August 2018). Similar studies reported a positive correlation between the number of hotspots and pollutant levels [42,48,49].

3.2. PM and Carbonaceous Component Affected by Season and Location

Regardless of the season, the PM concentration increased in the order Padang, Jambi, and Pekanbaru sites. This can be attributed to an increase in the levels of pollutants emitted from local sources. Figure 3 and Figure 4, respectively, show the size distribution and mass fractions of each size of PM collected at the study sites. In both seasons, size distributions at the Jambi and Pekanbaru sites were similar, but the PM level was different. There was a peak fraction of around 1 µm in Padang, the level of PM mass concentration was much less than in the other two sites, and the coarse fraction of >1 µm dominated the PM mass. This finding suggests a different contribution or different types of local emission sources in those three cities. To confirm this statistically, non-repeated one-way ANNOVA [50] was performed for the mass concentrations of PM or TSP, PM10, PM2.5, PM1, and PM0.1 evaluated at the study sites, and significant differences (p < 0.05) were found among all sites in both seasons.
Seasonal behaviors of the PM concentration were also different at each sampling site. At the Jambi and Pekanbaru sites, the PM level increased from the rainy to the dry season. However, at Padang city, almost no seasonal differences were observed, either in size distribution or PM level. As shown in Table S1, the p-value obtained through the independent sample t-test [51] between parameters for the rainy and dry seasons at the Padang site was >0.05. This suggests that there was no significant difference between seasons. To the contrary, at the Jambi and Pekanbaru sites, it was <0.05, indicating that seasonal differences at both sites were significant, except for fine particles (PM0.1 and PM1) at the Jambi site.
The influence of haze transport by air mass and meteorological conditions are also important parameters. Figure 5a–f shows air mass trajectories arriving at each sampling site plotted together with hotspots. The air mass approaching the studied sites in the rainy season (Figure 5a,c,e) was from the South China sea, indicating that the influence of peatland fires may not be so serious, although a slight influence in long range transportation from the upper part of SEA cannot be excluded. Hence, differences in the chemical characteristics of PM between sites in the rainy season mainly appear to be from local emissions, except for agricultural open burning, as well as from meteorological conditions. At the Padang site, the air mass came from the ocean, or from the South China sea in the rainy season and from the Indian ocean in the dry season. This may be a reason for the lowest concentrations of PM among the sites, since such an air mass route commonly generates a low particle level [52]. One possible reason for nearly no seasonal difference being observed at the PM level and particle size distribution at the Padang site might be related to climate characteristics. There was only a small difference in the total number of rainy days between the rainy and dry seasons due to climate characteristics [53].
Regarding the effect of season and location on the characteristic of PM, the levels of carbonaceous components listed in Table 2 and displayed in Figure 6 provide important information. Tendencies at the Padang site were different from those at the other two sites. The peak size for soot-EC was in the PM0.1 fraction, while it was in 0.5–1 µm at other sites. This peak value was lower than that in the others. The OC/EC (8.19) at the Padang site was larger than those at other sites (4.05 and 4.55 at Jambi and Pekanbaru sites, respectively). Such differences might be related to the influence of lower traffic emission compared to that at the other two sites.
At Jambi and Pekanbaru, the tendencies were similar between these sites, but the peak level of EC was higher than that at the Padang site observed in the range of 0.5–1 µm. This finding suggests that the surrounding environments of these two sites were more affected by vehicle emissions, since these carbonaceous parameters can be frequently attributed to diesel exhaust [35]. Since both the Jambi and Pekanbaru sites were located in areas of heavy traffic, the characteristics of the surrounding environments may be quite similar. However, more influence of traffic emission could be experienced at the Pekanbaru site, since it was located in the busiest city center. Hence, the level of soot-EC at the Pekanbaru site was consistently higher than that in Jambi for all particle sizes. Regardless of the season, the level of soot-EC was somewhat constant at these two sites for PM0.1 and PM0.5–1. This suggests that the degree of influence of local emissions was stable throughout the year. A possible reason for a decrease in the soot-EC/TC ratio in the dry season is attributed to transboundary particle pollution from surrounding regions or neighboring countries, such as that from peatland field areas.

3.3. Influence of Peatland Fires and Air Mass Transportation during the Dry Season

As shown in Figure 5, the air mass passing through peatland areas arrived at the study sites in the dry season, except for the Padang site. Hence, air mass transportation should have had large influence, particularly at the Jambi and Pekanbaru sites. However, it affected the Pekanbaru site only slightly. The air mass arriving at the Jambi site in the dry season (see Figure 5d) moved through the South Sumatra province, an area that contained many hotspots. These hotspots corresponded to the burning of agricultural crop residues (see Figures S3d and S4d) and peatland fires that emit a considerable amount of PM [54]. Although the air mass movement to the Pekanbaru site was not as simple as that to the Jambi site, an important fraction of the trajectories still moved through hotspot areas (see Figure 5f).
OC, EC, and TC increased in the dry season for PM0.5–1 and PM1–2.5 at the Jambi and Pekanbaru sites, respectively (see Table 2). An increase in the OC/EC ratio was clear in those particle sizes, particularly at the Jambi site. The OC/EC ratio was somewhat similar between these sites for the dry season. This suggests that biomass burning events in Sumatra island are the main reason for the increase in the OC/EC ratio, as previously reported [55]. The OC/EC values found in this study were comparable with those reported by Hayasaka et al. (2014) [56] for Central Kalimantan (3.88–14.75) in the dry season in 2010–2012. However, the present level was much lower than levels reported for the Riau province (36.4 ± 9.08) in the dry season [55]. This may be explained by a larger difference in the amount of PM emitted and the conditions in the vicinity of the sampling site.
To overview the above discussion on carbonaceous parameters, the correlation between OC/EC and EC for PM0.1 and PM0.5–1 was plotted, and the data are shown in Figure 7, following the discussion by Putri et al. (2021) [57]. As reported so far [16,57,58] and already discussed above, characteristics of PM0.1 reflect traffic emission, while those of PM0.5–1 reflect biomass burning. In Figure 7, correlations for reported PM0.1 data from urban areas in Asian countries where vehicle emissions were reported as the main emission are shown [16,24,40,57,58]. As seen from Figure 7, values for PM0.1 from traffic emission-dominated areas fall in a band range with a small fluctuation that is referred to as “urban correlation” [58]. The OC/EC vs. EC correlation for the PM0.1 fraction at the Padang site was nearly in this range in both seasons. This was the same for the fraction of PM0.5–1, indicating that biomass burning contributed weakly at the Padang site, as discussed above. At the Jambi site, the values of PM0.1 were still inside the urban correlation probably because of the strong influence of traffic emission. However, this was not the case for the PM0.1 fraction at the Pekanbaru site and for the PM0.5–1 fraction at the Jambi and Pekanbaru sites. The OC/EC vs. EC correlation during the dry season in both the Jambi and Pekanbaru sites moved slightly from the urban correlation to the upper sites during dry season at both sites. It suggests a greater influence of biomass burning, such as peatland fires. During the dry season, Jambi was affected by peatland fires to a greater extent than the Pekanbaru site, as clearly seen from the air mass trajectory.

4. Conclusions

Seasonal behaviors of size-segregated particulate matter (PM) in Sumatra island, Indonesia were investigated during the rainy and dry seasons based on air sampling at three different locations in the year 2018. In both seasons, the PM2.5 fraction at Jambi and Pekanbaru sites exceeded the WHO guidelines for a 24 h period. The exception was for the PM10 fraction in Jambi city. The PM0.1 levels in both cities were also comparable to those in other large cities in East Asia. The influence of peatland fires in the dry season was significant in cities located on the east coast (Jambi and Pekanbaru) of Sumatra island because of the larger number of hotspots and air mass trajectories along the east coast. In Padang city, regardless of the season, the transboundary effect was not so important throughout the year. The correlation between OC/EC vs. EC for PM0.1 in all sites was shown to be more affected by vehicle emissions. This observation is in line with the highest level of soot-EC in PM0.1 compared to other sizes. Concerning the effect of transboundary particles, 0.5–1 µm particles were more sensitive to biomass burning. PM0.1 was shown to be only slightly influenced by biomass burning. The OC/EC vs. EC correlation and soot-EC/TC ratio implied that during the dry season, Jambi and Pekanbaru cities were affected by biomass burning, especially from peatland areas. Hence, the size range of 0.5–1 µm can be considered to serve as an indicator of biomass burning. More research should be conducted on other chemical components such as trace metals, ions, and PAHs in order to provide comprehensive and reliable understanding of smoke-haze profiles emitted from peatland fires, especially during a catastrophic fire event in Indonesia.

Supplementary Materials

The followings are available online at https://www.mdpi.com/article/10.3390/atmos12111441/s1, Figure S1: Air mass trajectory during rainy and dry season in all sampling site, in Sumatra Island, Figure S2: Numbers of yearly hotspots in Sumatra Island (2009–2018), Indonesia. (Sources: ASMC, 2019), Figure S3: Overlap land cover of Sumatera Island and air mass trajectory during dry and rainy season in all sampling location (a) Padang (rainy), (b) Padang (dry), (c) Jambi (rainy), (d) Jambi (dry), (e) Pekanbaru (rainy) and (f) Pekanbaru (dry), Figure S4: Detail overlap land cover of Sumatera Island and air mass trajectory during dry and rainy season in all sampling location (a) Padang (rainy), (b) Padang (dry), (c) Jambi (rainy), (d) Jambi (dry), (e) Pekanbaru (rainy) and (f) Pekanbaru (dry), Table S1: Summary of the independent sample t-test comparing seasonal different between rainy and dry season in Sumatra Island, Indonesia

Author Contributions

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

Funding

This study was supported by an Indonesia Endowment Fund for Education (LPDP) scholarship, Ministry of Finance, Indonesia and by Sumitomo Foundation (Kankyo Kenkyu Josei 2020–2022) and Japan Society for the Promotion of Science (JSPS) (KAKENHI 21H03618).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The authors acknowledge the contribution of members of the East Asia Nanoparticle Monitoring Network (EA-Nanonet) for field sampling, particularly in Padang and Jambi city, and the authors also wish to thank Milton S. Feather for improving English in this manuscript.

Conflicts of Interest

The authors declare no conflict of interests.

References

  1. Weichenthal, S.; Lavigne, E.; Traub, A.; Umbrio, D.; You, H.; Pollitt, K.; Shin, T.; Kulka, R.; Stieb, D.M.; Korsiak, J.; et al. Association of Sulfur, Transition Metals, and the Oxidative Potential of Outdoor PM2.5 with Acute Cardiovascular Events: A Case-Crossover Study of Canadian Adults. Environ. Health Perspect. 2021, 129, 107005. [Google Scholar] [CrossRef] [PubMed]
  2. Sommar, J.N.; Hvidtfeldt, U.A.; Geels, C.; Frohn, L.M.; Brandt, J.; Christensen, J.H.; Raaschou-Nielsen, O.; Forsberg, B. Long-Term Residential Exposure to Particulate Matter and Its Components, Nitrogen Dioxide and Ozone-A Northern Sweden Cohort Study on Mortality. Int. J. Environ. Res. Public Health 2021, 18, 8476. [Google Scholar] [CrossRef]
  3. Pongpiachan, S. Incremental lifetime cancer risk of PM2.5 bound polycyclic aromatic hydrocarbons (PAHs) before and after the wildland fire episode. Aerosol Air Qual. Res. 2016, 16, 2907–2919. [Google Scholar] [CrossRef] [Green Version]
  4. Doherty, R.M.; Heal, M.R.; O’Connor, F.M. Climate change impacts on human health over Europe through its effect on air quality. Environ. Health 2017, 16, 118. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Manisalidis, I.; Stavropoulou, E.; Stavropoulos, A.; Bezirtzoglou, E. Environmental and Health Impacts of Air Pollution: A Review. Front. Public Health 2020, 8, 14. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  6. Koplitz, S.N.; Mickley, L.J.; Marlier, M.E.; Buonocore, J.J.; Kim, P.S.; Liu, T.; Sulprizio, M.P.; DeFries, R.S.; Jacob, D.J.; Schwartz, J.; et al. Public health impacts of the severe haze in Equatorial Asia in September–October 2015: Demonstration of a new framework for informing fire management strategies to reduce downwind smoke exposure. Environ. Res. Lett. 2016, 11, 94023. [Google Scholar] [CrossRef]
  7. Tham, J.; Sarkar, S.; Jia, S.; Reid, J.; Mishra, S.; Sudiana, I.M.; Swarup, S.; Ong, C.; Yu, L. Impacts of peat-forest smoke on urban PM2.5 in the Maritime Continent during 2012–2015: Carbonaceous profiles and indicators. Environ. Pollut. 2019, 248, 496–505. [Google Scholar] [CrossRef] [PubMed]
  8. Betha, R.; Pradani, M.; Lestari, P.; Joshi, U.M.; Reid, J.S.; Balasubramanian, R. Chemical speciation of trace metals emitted from Indonesian peat fires for health risk assessment. Atmos. Res. 2013, 122, 571–578. [Google Scholar] [CrossRef]
  9. Huijnen, V.; Wooster, M.J.; Kaiser, J.W.; Gaveau, D.L.A.; Flemming, J.; Parrington, M.; Inness, A.; Murdiyarso, D.; Main, B.; van Weele, M. Fire carbon emissions over maritime southeast Asia in 2015 largest since 1997. Sci. Rep. 2016, 6, 26886. [Google Scholar] [CrossRef] [Green Version]
  10. Quah, E.; Varkkey, H.M. The political economy of transboundary pollution: Mitigation of forest fires and haze in Southeast Asia. Asian Community-Its Concepts Prospect. 2013, 23, 323–358. (In Japanese) [Google Scholar]
  11. Vadrevu, K.P.; Lasko, K.; Giglio, L.; Justice, C. Analysis of Southeast Asian pollution episode during June 2013 using satellite remote sensing datasets. Environ. Pollut. 2014, 195, 245–256. [Google Scholar] [CrossRef]
  12. Sheldon, T.; Sankaran, C. The Impact of Indonesian Forest Fires on Singaporean Pollution and Health. Am. Econ. Rev. 2017, 107, 526–529. [Google Scholar] [CrossRef]
  13. ChooChuay, C.; Pongpiachan, S.; Tipmanee, D.; Deelaman, W.; Suttinun, O.; Wang, Q.; Cao, J. Long-range transboundary atmospheric transport of polycyclic aromatic hydrocarbons, carbonaceous compositions, and water-soluble ionic species in southern Thailand. Aerosol Air Qual. Res. 2020, 20, 1591–1606. [Google Scholar] [CrossRef]
  14. Xing, Y.F.; Xu, Y.H.; Shi, M.H.; Lian, Y.X. The impact of PM2.5 on the human respiratory system. J. Thorac. Dis. 2016, 8, E69–E74. [Google Scholar] [CrossRef]
  15. Schraufnagel, D.E. The health effects of ultrafine particles. Exp. Mol. Med. 2020, 52, 311–317. [Google Scholar] [CrossRef]
  16. Kim, K.H.; Sekiguchi, K.; Furuuchi, M.; Sakamoto, K. Seasonal variation of carbonaceous and ionic components in ultrafine and fine particles in an urban area of Japan. Atmos. Environ. 2011, 45, 1581–1590. [Google Scholar] [CrossRef]
  17. Chen, S.C.; Hsu, S.C.; Tsai, C.J.; Chou, C.C.K.; Lin, N.H.; Lee, C.T.; Roam, G.D.; Pui, D.Y.H. Dynamic variations of ultrafine, fine and coarse particles at the Lu-Lin background site in East Asia. Atmos. Environ. 2013, 78, 154–162. [Google Scholar] [CrossRef]
  18. National Research Council (NRC). Research Priorities for Airborne Particulate Matter I, Immediate Priorities and a Long-Range Research Portfolio; National Academy Press: Washington, DC, USA, 1998. [Google Scholar]
  19. Lammers, A.; Janssen, N.; Boere, A.; Berger, M.; Longo, C.; Vijverberg, S.; Neerincx, A.H.; Zee, A.H.; Cassee, F. Effects of short-term exposures to ultrafine particles near an airport in healthy subjects. Environ. Int. 2020, 141, 105779. [Google Scholar] [CrossRef]
  20. He, R.; Gerlofs-Nijland, M.; Boere, J.; Fokkens, P.; Leseman, D.; Janssen, N.; Cassee, F. Comparative toxicity of ultrafine particles around a major airport in human bronchial epithelial (Calu-3) cell model at the air-liquid interface. Toxicol. In Vitro 2020, 68, 104950. [Google Scholar] [CrossRef]
  21. Umezawa, M.; Onoda, A.; Takeda, K. Developmental Toxicity of Nanoparticles on the Brain. Yakugaku Zasshi J. Pharm. Soc. Jpn. 2017, 137, 73–78. [Google Scholar] [CrossRef] [Green Version]
  22. Hata, M.; Chomanee, J.; Thongyen, T.; Bao, L.; Tekasakul, S.; Tekasakul, P.; Otani, Y.; Furuuchi, M. Characteristics of nanoparticles emitted from burning of biomass fuels. J. Environ. Sci. 2014, 26, 10. [Google Scholar] [CrossRef] [PubMed]
  23. Rogula-Kozłowska, W. Size-segregated urban particulate matter: Mass closure, chemical composition, and primary and secondary matter content. Air Qual. Atmos. Health 2016, 9, 533–550. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Phairuang, W.; Suwattiga, P.; Chetiyanukornkul, T.; Hongtieab, S.; Limpaseni, W.; Ikemori, F.; Hata, M.; Furuuchi, M. The influence of the open burning of agricultural biomass and forest fires in Thailand on the carbonaceous components in size-fractionated particles. Environ. Pollut. 2019, 247, 238–247. [Google Scholar] [CrossRef]
  25. Bachtiar, V.S.; Ruslinda, Y.; Wangsa, D.; Kurniawan, E. Mapping of PM 10 Concentrations and Metal Source Identifications in Air Ambient at Surrounding Area of Padang Cement Factory. J. Environ. Sci. Technol. 2016, 9, 390–398. [Google Scholar] [CrossRef] [Green Version]
  26. Badan Pusat Statistik (BPS-Statistics of Padang City), Padang City in Figure; BPS: Padang, Indonesia, 2018.
  27. Badan Pusat Statistik (BPS-Statistics of Muaro Jambi Regency), Muaro Jambi in Figure; BPS: Muaro Jambi, Indonesia, 2018.
  28. Badan Pusat Statistik (BPS-Statistics of Pekanbaru City), Pekanbaru City in Figure; BPS: Pekanbaru, Indonesia, 2018.
  29. Furuuchi, M.; Eryu, K.; Nagura, M.; Hata, M.; Kato, T.; Tajima, N.; Sekiguchi, K.; Ehara, K.; Seto, T.; Otani, Y. Development and performance evaluation of air sampler with inertial filter for nanoparticle sampling. Aerosol. Air. Qual. Res. 2010, 10, 185–192. [Google Scholar] [CrossRef]
  30. MOEJ (Ministry of Environment of Japan). Chapter 4. Carbonaceous Component Analysis Method 483 (Thermal Optical Reflectance Method), 3rd ed.; Fine Particles (PM2.5) Component 484 Measurement Manual; Ministry of Environment: Tokyo, Japan, 2019. Available online: https://www.env.go.jp/air/osen/pm/ca/manual.html (accessed on 18 August 2020). (In Japanese)
  31. Arp, H.P.H.; Schwarzenbach, R.P.; Goss, K.U. Equilibrium sorption of gaseous organic chemicals to fiber filters used for aerosol studies. Atmos. Environ. 2007, 41, 8241–8252. [Google Scholar] [CrossRef]
  32. Otani, Y.; Eryu, K.; Furuuchi, M.; Tajima, N.; Tekasakul, P. Inertial Classification of Nanoparticles with Fibrous Filters. Aerosol. Air. Qual. Res. 2007, 7, 343–352. [Google Scholar] [CrossRef]
  33. Badan Meteorologi, Klimatologi, dan Geofisika. BMKG- Meteorologhy, Climatologhy, and Geophysics Agency of Indonesia. Data Online, Pusat Data Base 2019, Indonesia. Available online: https://www.bmkg.go.id/?lang=EN (accessed on 20 August 2019).
  34. Han, Y.; Cao, J.; Chow, J.C.; Watson, J.G. Evaluation of the thermal/optical reflectance method for discrimination between char- and soot-EC. Chemosphere 2007, 69, 569–574. [Google Scholar] [CrossRef] [PubMed]
  35. Han, Y.M.; Lee, S.C.; Cao, J.J.; Ho, K.F.; An, Z.S. Spatial distribution and seasonal variation of char-EC and soot-EC in the atmosphere over China. Atmos. Environ. 2009, 43, 6066–6073. [Google Scholar] [CrossRef]
  36. Budiwati, T.; Setyawati, W.; Aries, T.D. Chemical Characteristics of Rainwater in Sumatra, Indonesia, during 2001–2010. Int. J. Atmos. Sci. 2016, 10, 1155. [Google Scholar]
  37. Air Resource Laboratory (ALR). The Air Resource Laboratory (HYSPLIP 4). 2019. Available online: https://ready.arl.noaa.gov/HYSPLIT.php (accessed on 10 August 2019).
  38. Murao, N. Air Quality Model-6. Trajectory Analysis. J. Soc. Atmos. Environ. Jpn. 2011, 46, A61–A67. [Google Scholar]
  39. FIRMS. 2019. Available online: https://firms.modaps.eosdis.nasa.gov/download/list.php (accessed on 15 August 2019).
  40. Thuy, N.; Dung, N.; Sekiguchi, K.; Ly, B.; Hien, N.; Yamaguchi, R. Mass Concentrations and Carbonaceous Compositions of PM0.1, PM2.5, and PM10 at Urban Locations of Hanoi, Vietnam. Aerosol. Air. Qual. Res. 2018, 18, 10. [Google Scholar] [CrossRef] [Green Version]
  41. Zhao, T.; Hongtieab, S.; Hata, M.; Matsuki, A.; Yoshikawa, F.; Furuuchi, M. Characteristics Comparison of Ambient Nano-Particles in Asian Cities. Proceeding in JAAST; Osaka Prefectural University: Sakai, Japan, 2016. [Google Scholar]
  42. Kusumaningtyas, S.D.A.; Aldrian, E. Impact of the June 2013 Riau province Sumatra smoke haze event on regional air pollution. Environ. Res. Lett. 2016, 11, 075007. [Google Scholar] [CrossRef]
  43. Crippa, P.; Castruccio, S.; Archer-Nicholls, S.; Lebron, G.B.; Kuwata, M.; Thota, A.; Sumin, S.; Butt, E.; Wiedinmyer, C.; Spracklen, D.V. Population exposure to hazardous air quality due to the 2015 fires in Equatorial Asia. Sci. Rep. 2016, 6, 37074. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Aminingrum. Forest Fire Contest: The Case of Forest Fire Policy Design in Indonesia. Master’s Thesis, Erasmus University, Rotterdam, The Netherlands, 2017. [Google Scholar]
  45. Jikalahari. Catatan Akhir Tahun 2018: Hutan Binasa, Banjir Melanda; Jikalahari: Pekanbaru, Indonesia, 2018. (In Bahasa) [Google Scholar]
  46. Saputra, E. Beyond Fires and Deforestation: Tackling Land Subsidence in Peatland Areas, a Case Study from Riau and Indonesia. Land 2019, 8, 76. [Google Scholar] [CrossRef] [Green Version]
  47. ASMC. 2019. Available online: http://asmc.asean.org/asmc-haze-hotspot-monthly-new#Hotspot (accessed on 15 August 2019).
  48. Anwar, A.; Juneng, L.; Othman, M.R.; Latif, M.T. Correlation between hotspots and air quality in Pekanbaru, Riau, Indonesia in 2006–2007. Sains Malay. 2010, 39, 169–174. [Google Scholar]
  49. Gaveau, D.L.A.; Salim, M.A.; Hergoualc’H, K.; Locatelli, B.; Sloan, S.; Wooster, M.; Marlier, M.E.; Molidena, E.; Yaen, H.; DeFries, R.; et al. Major atmospheric emissions from peat fires in Southeast Asia during non-drought years: Evidence from the 2013 Sumatran fires. Sci. Rep. 2014, 4, 6112. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  50. Pallarés, S.; Gómez, E.T.; Martínez-Poveda, Á.; Jordán, M.M. Distribution Levels of Particulate Matter Fractions (<2.5 μm, 2.5–10 μm and >10 μm) at Seven Primary Schools in a European Ceramic Cluster. Int. J. Environ. Res. Public Health 2021, 18, 4922. [Google Scholar] [CrossRef]
  51. Nway, N.; Aung, W.; Pan-Nu-Yi, E.; Thant, Z.; Yagishita, M.; Ishigaki, Y.; Suzuki, T.; Nakajima, D.; Win-Shwe, T.; Mar, O. Seasonal and regional variation of particulate matter dispersion in Yangon City and Taunggyi City, Myanmar. IOP Con. Ser. Earth Environ. Sci. 2020, 496, 012003. [Google Scholar] [CrossRef]
  52. Bousiotis, D.; Dall´osto, M.; Beddows, D.; Pope, F.; Harrison, R. Analysis of New Particle Formation (NPF) Events at Nearby Rural, Urban Background and Urban Roadside Sites. Atmos. Chem. Phys. 2019, 19, 5679–5694. [Google Scholar] [CrossRef] [Green Version]
  53. Badan Meteorologi, Klimatologi, dan Geofisika (BMKG- Meteorologhy, Climatologhy, and Geophysics Agency of Indonesia). Prakiraan Musim Hujan dan Kemarau di Indonesia 2015/2016. 2015. Indonesia. Available online: https://www.bmkg.go.id/berita/?p=prakiraan-musim-hujan-20152016-di-indonesia&tag=klimatologi&lang=ID (accessed on 20 August 2020).
  54. Permadi, D.A.; Kim Oanh, N.T. Assessment of biomass open burning emissions in Indonesia and potential climate forcing impact. Atmos. Environ. 2013, 78, 250–258. [Google Scholar] [CrossRef]
  55. Fujii, Y.; Iriana, W.; Oda, M.; Puriwigati, A.; Tohno, S.; Lestari, P.; Mizohata, A.; Huboyo, H.S. Characteristics of carbonaceous aerosols emitted from peatland fire in Riau, Sumatra, Indonesia. Atmos. Environ. 2014, 87, 164–169. [Google Scholar] [CrossRef] [Green Version]
  56. Hayasaka, H.; Noguchi, I.; Putra, E.I.; Yulianti, N.; Vadrevu, K. Peat-fire-related air pollution in Central Kalimantan, Indonesia. Environ. Pollut. 2015, 195, 257–266. [Google Scholar] [CrossRef] [PubMed]
  57. Putri, R.M.; Amin, M.; Suciari, T.F.; Al Fattah Faisal, M.; Auliani, R.; Ikemori, F.; Wada, M.; Hata, M.; Tekasakul, P.; Furuuchi, M. Site-specific variation in mass concentrationand chemical components in ambient nanoparticles (PM0.1) in North Sumatra Province-Indonesia. Atmos. Pol. Res. 2021, 12, 101062. [Google Scholar] [CrossRef]
  58. Kim, K.H.; Sekiguchi, K.; Kudo, S.; Sakamoto, K. Characteristics of atmospheric elemental carbon (char and soot) in ultrafine and fine particles in a roadside environment, Japan. Aerosol. Air. Qual. Res. 2011, 11, 1–12. [Google Scholar] [CrossRef] [Green Version]
Atmosphere 12 01441 g001 550
Figure 1. Locations of sampling sites in three different cities on Sumatra island, Indonesia.
Figure 1. Locations of sampling sites in three different cities on Sumatra island, Indonesia.
Atmosphere 12 01441 g001
Atmosphere 12 01441 g002a 550Atmosphere 12 01441 g002b 550
Figure 2. Seasonal particle mass concentrations in Sumatra island, Indonesia: (a) PM0.1, (b) PM1, (c) PM2.5, (d) PM10, and (e) TSP.
Figure 2. Seasonal particle mass concentrations in Sumatra island, Indonesia: (a) PM0.1, (b) PM1, (c) PM2.5, (d) PM10, and (e) TSP.
Atmosphere 12 01441 g002aAtmosphere 12 01441 g002b
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Figure 3. Particle size distribution observed at the study sites in Sumatra island.
Figure 3. Particle size distribution observed at the study sites in Sumatra island.
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Figure 4. Size fraction of particles on a mass basis observed at study sites in Sumatra island.
Figure 4. Size fraction of particles on a mass basis observed at study sites in Sumatra island.
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Figure 5. Seasonal air mass backward trajectory in Sumatra island: (a) Padang (rainy), (b) Padang (dry), (c) Jambi (rainy), (d) Jambi (dry), (e) Pekanbaru (rainy), and (f) Pekanbaru (dry).
Figure 5. Seasonal air mass backward trajectory in Sumatra island: (a) Padang (rainy), (b) Padang (dry), (c) Jambi (rainy), (d) Jambi (dry), (e) Pekanbaru (rainy), and (f) Pekanbaru (dry).
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Figure 6. Carbonaceous components in three different cities in Sumatra island: (a) OC, (b) EC, (c) OC/EC, (d) soot-EC, and (e) soot-EC/TC.
Figure 6. Carbonaceous components in three different cities in Sumatra island: (a) OC, (b) EC, (c) OC/EC, (d) soot-EC, and (e) soot-EC/TC.
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Atmosphere 12 01441 g007a 550Atmosphere 12 01441 g007b 550
Figure 7. Comparison of OC/EC ratio to mass concentration of EC in the PM0.1 and PM0.5–1 in East Asia: (a) Padang, (b) Jambi, and (c) Pekanbaru.
Figure 7. Comparison of OC/EC ratio to mass concentration of EC in the PM0.1 and PM0.5–1 in East Asia: (a) Padang, (b) Jambi, and (c) Pekanbaru.
Atmosphere 12 01441 g007aAtmosphere 12 01441 g007b
Table
Table 1. Sampling period, duration, and meteorological conditions in Sumatra island, Indonesia in 2018. Sources: www.bmkg.go.id (accessed on 20 August 2019).
Table 1. Sampling period, duration, and meteorological conditions in Sumatra island, Indonesia in 2018. Sources: www.bmkg.go.id (accessed on 20 August 2019).
LocationSeasonDateSample (n)Temperature (°C)Humidity (%)Precipitation (mm)Sunlight (Hour)Wind Direction
PadangRainy8–13 March526.53 ± 0.7689.00 ± 4.0411.2 ± 17.955.13 ± 2.13N
Dry17–29 August826.92 ± 0.9275.46 ± 5.717.5 ± 4.946.01 ± 3.84N
JambiRainy14–19 March527.35 ± 0.7583.00 ± 4.104.1 ± 6.225.32 ± 2.73N, NE
Dry17–24 August827.80 ± 0.8578.00 ± 4.440.4 ± 1.066.84 ± 2.04SE
PekanbaruRainy20–25 March527.28 ± 0.8380.33 ± 5.9910.0 ± 17.555.72 ± 2.91N
Dry17–26 August827.86 ± 0.9478.30 ± 4.951.8 ± 4.724.46 ± 2.18S, N
Table
Table 2. Seasonal average concentrations of carbonaceous components in Sumatra island.
Table 2. Seasonal average concentrations of carbonaceous components in Sumatra island.
LocationSeasonSize (µm)OC
(µg/m3)		EC
(µg/m3)		TC
(µg/m3)		Soot-EC
(µg/m3)		OC/EC
(-)		Mass
(µg/m3)		TC/Mass
(-)		Soot-EC/TC
(-)
PadangRainy<0.12.820.343.160.248.195.360.590.08
0.1–0.5N/AN/AN/AN/AN/A2.17N/AN/A
0.5–11.700.221.920.157.686.150.310.08
1–2.53.080.343.420.099.026.730.510.03
2.5–108.340.919.250.149.1313.440.690.02
>100.530.080.620.026.422.130.290.04
Dry<0.11.570.251.820.166.345.570.330.09
0.1–0.5N/AN/AN/AN/AN/A0.77N/AN/A
0.5–11.030.531.560.231.936.560.240.15
1–2.51.580.432.010.123.698.690.230.06
2.5–101.590.442.040.103.6010.360.200.05
>100.190.050.240.014.103.920.060.06
JambiRainy<0.12.400.593.000.404.059.200.330.13
0.1–0.5N/AN/AN/AN/AN/A3.47N/AN/A
0.5–13.281.174.460.472.8112.450.360.11
1–2.52.150.592.740.163.669.090.300.06
2.5–102.860.703.560.134.0714.090.250.04
>100.180.080.260.022.426.050.040.09
Dry<0.12.450.553.000.434.449.610.310.14
0.1–0.5N/AN/AN/AN/AN/A4.56N/AN/A
0.5–14.620.515.130.549.0315.940.320.10
1–2.52.960.733.700.304.0313.180.280.08
2.5–102.550.633.170.164.0718.280.170.05
>100.420.110.530.033.828.770.060.06
PekanbaruRainy<0.13.220.713.930.554.5510.920.360.14
0.1–0.5N/AN/AN/AN/AN/A5.64N/AN/A
0.5–14.470.685.150.576.5515.640.330.11
1–2.53.640.934.570.303.9010.610.430.06
2.5–102.710.723.430.203.7513.170.260.06
>100.280.100.370.042.825.350.070.10
Dry<0.13.300.613.910.505.4115.160.260.13
0.1–0.5N/AN/AN/AN/AN/A4.70N/AN/A
0.5–15.391.296.690.594.1721.480.310.09
1–2.55.271.276.540.514.1617.790.370.08
2.5–105.531.887.410.592.9520.200.370.08
>100.820.170.980.074.9113.360.070.07
Description: N/A: not analyzed.
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Amin, M.; Putri, R.M.; Handika, R.A.; Ullah, A.; Goembira, F.; Phairuang, W.; Ikemori, F.; Hata, M.; Tekasakul, P.; Furuuchi, M. Size-Segregated Particulate Matter Down to PM0.1 and Carbon Content during the Rainy and Dry Seasons in Sumatra Island, Indonesia. Atmosphere 2021, 12, 1441. https://doi.org/10.3390/atmos12111441

AMA Style

Amin M, Putri RM, Handika RA, Ullah A, Goembira F, Phairuang W, Ikemori F, Hata M, Tekasakul P, Furuuchi M. Size-Segregated Particulate Matter Down to PM0.1 and Carbon Content during the Rainy and Dry Seasons in Sumatra Island, Indonesia. Atmosphere. 2021; 12(11):1441. https://doi.org/10.3390/atmos12111441

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Amin, Muhammad, Rahmi Mulia Putri, Rizki Andre Handika, Aulia Ullah, Fadjar Goembira, Worradorn Phairuang, Fumikazu Ikemori, Mitsuhiko Hata, Perapong Tekasakul, and Masami Furuuchi. 2021. "Size-Segregated Particulate Matter Down to PM0.1 and Carbon Content during the Rainy and Dry Seasons in Sumatra Island, Indonesia" Atmosphere 12, no. 11: 1441. https://doi.org/10.3390/atmos12111441

APA Style

Amin, M., Putri, R. M., Handika, R. A., Ullah, A., Goembira, F., Phairuang, W., Ikemori, F., Hata, M., Tekasakul, P., & Furuuchi, M. (2021). Size-Segregated Particulate Matter Down to PM0.1 and Carbon Content during the Rainy and Dry Seasons in Sumatra Island, Indonesia. Atmosphere, 12(11), 1441. https://doi.org/10.3390/atmos12111441

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