Special Issue on Air Pollution and its Impact in South and Southeast Asia (V)

Sepridawati Siregar1,2, Nora Idiawati3, Puji Lestari4, Abiyu Kerebo Berekute5,6, Wen-Chi Pan5,6, Kuo-Pin Yu   5,6 

1 Institute of Public Health, National Yang Ming Chiao Tung University, Taipei, Taiwan
2 Faculty of Mineral Technology, AKPRIND Institute of Science & Technology, Yogyakarta, Indonesia
3 Faculty of Math and Science, Tanjungpura University, Pontianak, Indonesia
4 Faculty of Civil and Environmental Engineering, Bandung Institute of Technology (ITB), Bandung, Indonesia
5 International Ph.D. Program in Environmental Science and Technology, National Yang Ming Chiao Tung University, Taipei, Taiwan
6 Institute of Environmental and Occupational Health Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan


Received: January 9, 2022
Revised: May 11, 2022
Accepted: July 4, 2022

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


Download Citation: ||https://doi.org/10.4209/aaqr.220015  


Cite this article:

Siregar, S., Idiawati, N., Lestari, P., Berekute, A.K., Pan, W.C., Yu, K.P. (2022). Chemical Composition, Source Appointment and Health Risk of PM2.5 and PM2.5-10 during Forest and Peatland Fires in Riau, Indonesia. Aerosol Air Qual. Res. 22, 220015. https://doi.org/10.4209/aaqr.220015


HIGHLIGHTS

  • Chemical characterization of particles during forest and peatland fires in Indonesia.
  • Six and five major sources for PM2.5 and PM2.5-10 were identified.
  • Biomass burning, secondary aerosol and vehicle exhaust are main particle contributors.
  • Both non- and carcinogenic health risk caused by PM2.5 exceed the acceptable level.
 

ABSTRACT


This study investigated the contributions of particulate matter (PM) from various emission sources during the dry season, which resulted from frequent fires occurring in degraded forests and peatlands in Indonesia. Samples of fine (PM2.5) and coarse (PM2.5-10) particles collected during the dry season in Riau, Indonesia were analyzed to determine the mass concentrations of metallic trace elements, ionic compound, black carbon (BC), and organic carbon (OC). The average concentrations of PM2.5 and PM2.5-10 at Riau, Indonesia were 63.85 ± 3.22 µg m–3 and 27.72 ± 2.40 µg m–3, respectively. The positive matrix factorization (PMF) model was adopted to identify possible PM sources and their contributions to the ambient PM level. The PMF results identified six major PM2.5 sources, including biomass burning (BB) (28.7%), secondary aerosols (SA) (26.9%), vehicle exhaust (VE) (12.8%), industrial emissions (IE) (12.3%), soil dust (SD) (11.9%), and sea salt (SS) (7.5%). Moreover, there were five primary PM2.5-10 sources, including VE (28.6%) and BB (24%), followed by IE (19.9%), SD (17.2%), and SA (15.3%). A conditional probability function (CPF) analysis revealed that the southeast sector dominated among source direction-dependent contributions. The noncarcinogenic health risks for both adults and children resulting from exposure to PM2.5 were mainly contributed by Co, Ni, and Mn, and carcinogenic risks were caused by the toxic metals Cr and Co. Both noncarcinogenic and carcinogenic health risks resulting from cumulative multielement exposure for both adults and children exceeded acceptable levels. Clearly, more attention should be devoted to reducing the noncarcinogenic and carcinogenic health risks caused by particulate-bound toxic elements through inhalation exposure.


Keywords: Metal elements, Ionic compound, Black and organic carbon, Carcinogenic, Noncarcinogenic


1 INTRODUCTION


Riau is a province in Indonesia; its capital, Pekanbaru, is located on Sumatra's east-central coast along the Strait of Malacca (Colombijn, 2002). Riau Province exhibits a tropical climate with two seasons: a dry season from March–August and a wet season from September–March (Abdul Kadir et al., 2019). During the dry season, numerous fires occur in degraded forests and peatland every year (Fig. 1), which constitute the primary source of particulate matter (PM) in Pekanbaru (Anwar et al., 2010); other PM sources include motor vehicle, industrial, household, and waste management sources (See et al., 2007). Sources are apportioned and their average contributions (%) to ambient PM2.5 and PM2.5-10 levels in Indonesia and other countries are summarized in Table 1.

Fig. 1. Number of Fire Hotspot Warnings in Indonesia from April 1, to July 10, 2014 (Source: NASA Active Fire Point Data, Fire Information for Resource Management).
Fig. 1. Number of Fire Hotspot Warnings in Indonesia from April 1, to July 10, 2014 (Source: NASA Active Fire Point Data, Fire Information for Resource Management).

Table 1. Source apportionment and their average contributions (%) to ambient PM2.5 and PM2.5-10 levels in Indonesia and other countries.

Previous studies and discussions have highlighted the relationship between severe air pollution and premature mortality and morbidity (Lelieveld et al., 2015). Comprehensive epidemiological studies have indicated that exposure to high concentrations of fine (PM2.5) and coarse (PM2.5-10) particles, nitrogen dioxide, and elemental carbon is associated with a range of adverse health effects, particularly cardiovascular and respiratory diseases (Reid et al., 2016).

Industrial farmers have widely applied slash-and-burn techniques to clear land for cultivation (Jayachandran, 2009). This activity produces considerable smoke pollution due to widespread burning of peatlands and forests, which threatens Indonesia and neighboring countries (Hyer et al., 2013). This smoke pollution, referred to as smoke haze, contains notable amounts of greenhouse gases and hazardous PM (Hayasaka et al., 2014). Particulate pollutants affect the environment for weeks to months, resulting in regional pollution (Radojevic, 2003), and are becoming a serious problem in Pekanbaru, Riau, Indonesia.

The source identification process constitutes an important step in air quality management (Liu et al., 2017). Receptor modeling offers a method to measure pollutant concentrations at sampling locations, and positive matrix factorization (PMF) has been successfully implemented worldwide (Jaiprakash et al., 2017). The advantage of PMF is the model’s ability to process incomplete data, such as missing data, data below the detection limit, and negative-value data (Chueinta et al., 2000). Therefore, if some samples were discarded due to flow rate errors or other accidents, the remaining samples could be analyzed for use in the modeling procedure.

In this study, fine and coarse particles were collected from a sampling location in Pekanbaru during the dry season. We used PMF to determine the sources of particulate pollutants and their contribution to particulate air pollution in Pekanbaru. The aims of this research were (1) to identify the sources of fine and coarse particles and compare the dominant pollutant sources, types of pollutant sources, and contributions of each pollutant source to pollution PM2.5 and PM2.5-10 in Pekanbaru city and (2) to estimate the health risk to residents (both children and adults) via inhalation exposure to several toxic metal elements (V, Cr, Co, Ni, As, Cd, Pb, Al, Mn, Zn, and Cu).

 
2 MATERIALS AND METHODS


 
2.1 Sampling Site Description

This study was conducted on Tuanku Tambusai Road in the Payung Sekaki area, Pekanbaru city, and the GPS coordinates of the study area are 0°32'7"N, 101°26'9"E (Fig. 2). The selected site is located at the center of the city along the border with the Siak Regency in the northern part of Pekanbaru; the Siak Regency and Pelalawan Regency border the eastern part of the city, and the Pelalawan Regency and Kampar Regency border the southern part (Irfan and Yulyanti, 2020). Pekanbaru is also located along the Sumatra Island transportation route, which occupies a strategic location and is expected to become increasingly crucial due to developments in the Sumatra region, Malaysia, and Singapore (Franck, 2013). Tuanku Tambusai road is currently the main street of the city and region (primary arterial) and functions as a link between Pekanbaru and other cities and as a secondary arterial road connecting the western and eastern areas of Pekanbaru city (Prawira et al., 2017).


Fig. 2. Sampling site location in Pekanbaru city.Fig. 2. Sampling site location in Pekanbaru city.

The study site represents a mixed urban area surrounded by commercial, industrial, and traffic areas with a road capacity of approximately 1,978 passenger car units per hour (Prawira et al., 2017). The traffic flow is heavy during morning, afternoon, and evening rush hours, resulting in traffic jams. Rubber and palm oil industrial facilities are located approximately 5 and 13 km west of the sampling location, respectively. Frequent fires occur in degraded forests and peatland throughout the dry season, exposing commuters and neighborhood residents to high PM concentrations (Betha et al., 2013).

 
2.2 Sampling Method

PM2.5 and PM2.5-10 samples were collected with a dichotomous sampler (Sierra Instrument model 244, Andersen Inc., Georgia, USA) at flow rates of 15.97 and 17.18 L min1, respectively, which are based on the height of the sampling location and the correction factors for PM2.5 and PM2.5-10. The filters employed in the study were Teflon filters with a diameter of 37 mm (Lestari and Mauliadi, 2009). Before sampling, the filters were conditioned for 24 hours in a temperature- and relative humidity (RH)-controlled room (at 22 ± 1°C and 40 ± 5% RH) (Hwang and Hopke, 2007), after which they were weighed. Meteorological conditions were measured every 2 hours at the sampling location and included temperature, wind speed, air pressure, and wind direction. After sampling was completes, the filter papers were stored in Petri dishes and placed in a storage box to avoid sample contamination (Cutroneo et al., 2020).

The sampler was placed on a platform at a height of one meter above the ground at 0°32'7"N, 101°26'9"E. The sampling location was approximately 50 meters from the ambient air quality monitoring stations operated by Balai Lingkungan Hidup (BLH) Pekanbaru. Sampling occurred during the dry season from 21 April to 5 July 2014 (Fig. 1 demonstrates the number of fire points for this period). A total of 64 samples were collected in this study, consisting of 32 PM2.5 and 32 PM2.5-10 samples with a sampling duration of 24 hours.

 
2.3 Sample Analysis

The PM concentration was measured in the laboratory via the gravimetric method, and the filter weight was determined before and after sampling and corrected by a blank. For each sample, the mass of each filter was determined with a semi-microbalance (Precisa 320XR Series). Moreover, black carbon (BC) analyses were performed using an M43D EEL smoke stain reflectometer (Evans Electroselenium Ltd., Halstead, Essex); the basic principles of BC analysis using this reflectometer are adsorption and light reflection (Cohen et al., 2000), where the mass concentration of particulates in a filter can be related to the level of particulate density by comparing the reflectance of the sample filter with that of a standard filter. The standard filters used are white and black. Theoretically, a white filter will give 100% reflectance, while a black filter will give 0% reflectance (Lestari and Mauliadi, 2009). This EEL smoke stain reflectometer consists of a measuring head and mask, a readout unit, and a standard.

The organic carbon (OC) concentration was analyzed via the Interagency Monitoring of Protected Visual Environments (IMPROVE) thermal optical reflectance (TOR) method. Quality assurance/quality control for OC analyses with IMPROVE is available in Chow et al. (2011). The concentrations of PM2.5, PM2.5-10, PM10, BC, and OC are provided in the supplementary materials (Fig. S2).

Analyses of metallic elements were performed with a fast sequential Flame AAS-AA280FS (atomic absorption spectrometer, Agilent, USA) instrument. For quality control, calibration curves with five different concentrations of each standard solution were applied. The concentrations of standard solutions and absorbance values showed good linear correlations for all calibration curves. All R2 were found to be ≥ 0.997. The metals analyzed in this study included Na, Mg, Al, Si, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Cd, Sb, Pb, Li, and Mo. Using these same conditions, blank solutions were also measured before the sample analyses. The element concentrations of PM2.5 and PM2.5-10 are shown in Figs. S3 and S4.

OC, metallic element and ionic compounds were analyzed in the Meteorology and Geophysics Chemical Laboratory in Jakarta. Ionic compounds were analyzed via ion chromatography (IC), which included chloride (Cl), nitrate (NO3), sulfate (SO42), sodium (Na+), ammonium (NH4+), potassium (K+), magnesium (Mg2+), and calcium (Ca2+). Both field and laboratory blank samples were prepared and analyzed for each sample and analysis. All data were corrected using filter blanks. The ion concentrations of PM2.5 and PM2.5-10 are shown in Figs. S5 and S6. The uncertainty of BC, OC, elements, and ions and the method detection limit (MDL) of elements and ions are provided in Table S1. All monitoring equipment was operated by professional operators according to standard operating procedures. After passing the standard test, the monitoring equipment performed monitoring under the best conditions. All monitoring equipment underwent a complete inspection and calibration prior to the experiments.

The adopted factor analysis method was PMF, which utilizes error estimates to provide optimum data scaling (Paatero and Tapper, 1993). The technique uses the least-squares approach and is based on solving the factor analysis problem via the data point weighting method. The advantage of PMF is the model's ability to process incomplete data, such as missing data, data below the detection limit, and negative-value data (Chueinta et al., 2000). Two out of 64 samples were discarded due to flow rate errors; the remaining samples were analyzed for use in the modeling procedure.

In this study, missing data, including some data for Li, V and As, were replaced by the median of all measured concentrations, and the accompanying error was set to four times the median value. Concentration data below the MDL were substituted by half of the MDL value, and any missing values were replaced by the arithmetic mean (Liu et al., 2017). Regarding the concentration data above the MDL, the uncertainty was calculated as the sum of 1/3 of the MDL value, the analytical uncertainty, and the C2* concentration (where C2 is the optimal percentage identified via PMF trial and error runs to determine the appropriate uncertainty weightings and explainable factor profiles). If the concentration was lower than or equal to the MDL, the uncertainty was set to 5/6 of the MDL value (Polissar et al., 1998). In this study, the United States Environmental Protection Agency (U.S. EPA) PMF model version 5.0 was used, which relies on the bootstrap technique to determine the variability in the PMF solution, where a new data set consistent with the original data is generated. Each data set was decomposed into profile and contribution matrices, and the resulting profile and contribution matrices were compared with the base run. The FPEAK value of the PMF model was adjusted to 0 after several trials.

The conditional probability function (CPF) was also applied to analyze the PMF results. The CPF predicts the probability that a given source contribution originating from a given wind direction exceeds a predetermined threshold criterion (Kim and Hopke, 2004). The same daily contribution was assigned to each hour of a given day to match the hourly wind data. The CPF is defined by the following equation:

 

where m∆θ is the number of occurrences of wind in sector ∆θ that exceed the threshold criterion and n is the total number of wind data points in the same sector. In this study, 24 sectors were considered (∆θ = 15°). Calm-wind (< 1 m s1) periods were excluded from this analysis due to isotropic wind vane behavior under calm winds.

In this study, meteorological data management was carried out by generating wind rose diagrams of the wind speed and direction data collected during sampling and then comparing them with the data obtained at the nearest monitoring station. The wind rose charts were generated with WRPLOT View software version 7.0.0 (Lakes Environmental Software).

 
2.4 Health Risk Assessment

In the present study, metals can be divided into carcinogens and noncarcinogens based on the International Agency for Research on Cancer (IARC) and U.S. EPA Integrated Risk Information System (IRIS). We analyzed the noncarcinogenic health risks due to 11 toxic metal elements (V, Cr, Co, Ni, As, Cd, Pb, Al, Mn, Zn, and Cu) and the carcinogenic health risks due to seven toxic metal elements (V, Cr, Co, Ni, As, Cd, and Pb).

To determine the risk to human health, the probability of noncarcinogenic risk was evaluated by the hazard quotient (HQ), and the carcinogenic risk (CR) was examined (Xiao et al., 2021). Fine particles can travel deep into the lower lung area and are primarily deposited in bronchioles and alveoli, so they are difficult to eliminate because the alveolar area does not have protective mucus layers; in the inhalation route, fine particles play an essential role in endangering human health (Reid et al., 2016). We calculated the HQ and CR posed by toxic elements in PM2.5 via inhalation using Eqs. (2–4) (U.S. EPA, 1989, 2009).

 

where EC is the exposure concentration (µg m–3); Ci is the average concentration of individual metals in PM of size i (µg m–3); ET is the exposure time (24 h day1); EF is the exposure frequency (350 days year1 for residents); ED is the exposure duration (6 years for children and 24 years for adults); ATn is the average time (for noncarcinogens = ED × 365 days × 24 h day1; for carcinogens = 70 years × 365 days year1 × 24 h); RfCi is the inhalation reference concentration (µg m–3); and UR is the unit risk ((µg m–3)1) (U.S. EPA, 2013). The UR and RfCi values of some potentially toxic metals from the U.S. EPA database are provided in the supplementary data (Table S2).

 
3 RESULTS AND DISCUSSION


 
3.1 Elemental Concentrations

The elemental concentrations of PM2.5 and PM2.5-10 are shown in Fig. S1. The average concentrations of PM2.5 and PM2.5-10 are 63.85 ± 3.22 µg m–3 and 27.72 ± 2.40 µg m–3, respectively. The mean concentrations of metal elements in PM2.5 and PM2.5-10 are 7.97 ± 1.87 µg m–3 and 3.47 ± 1.03 µg m–3, respectively, and those of the ionic compounds are 19.64 ± 4.02 µg m–3 and 6.80 ± 3.43 µg m–3, respectively. The correlation coefficient between PM2.5 and PM10 is R2 = 0.827, which is comparable with that determined in the research of Hopke et al. (1997) in Jakarta, where the correlation coefficients between PM2.5 and PM10 ranged from 0.69–0.94 (Hopke et al., 1997). PM10 showed a strong positive correlation with PM2.5 (R2 = 0.827), which accounted for approximately 69% of PM10, suggesting that the decrement of PM2.5 is crucial for reducing PM pollution and hence improving the air quality in Pekanbaru, Riau.

Table 2 presents the average mass and BC, OC, elemental, and ion concentrations for PM2.5 and PM2.5-10 with standard deviations and ranges. The minimum and maximum mass concentrations for PM2.5 and PM2.5-10 are 56.42 and 67.84 µg m–3, respectively. The mass concentration measurements for both fractions at the Tuanku Tambusai site still meet the national ambient air quality standards based on Indonesian Government Regulation no. 41 in 1999 on air pollution control, with limits of 65 µg m–3 for PM2.5 and 150 µg m–3 for PM10 (Jayachandran, 2009). However, concerning the ambient air quality standards of the US EPA, which include a baseline of 35 µg m–3 for PM2.5 (U.S. EPA, 2012), the air quality in Pekanbaru city exceeds the threshold for very severe health impacts (Fig. S6). According to See et al. (2007), PM2.5 exhibits a strong association with morbidity and mortality related to the effectiveness of PM2.5 deposition in the human body and the large surface area of PM2.5, enabling high absorption of toxic compounds per unit mass (See et al., 2007). The results of this study indicate contributions of 69.73% and 30.25% to PM2.5 and PM2.5-10, respectively. This finding demonstrates that in Pekanbaru city in 2014, particulate emissions were dominated by fine particles during the dry season. Field conditions influenced the differences in emission concentrations between samples. The concentration of compounds measured at a particular place and time is a function of the source emission intensity, meteorological state, potential for atmospheric dispersion (wind, wind speed and direction, humidity, solar radiation, pressure, and temperature), and distance of the measurement point to the source.

Table 2. Means and standard deviations for PM2.5 and PM2.5-10.

Fig. 3. Wind rose during April 21–July 5, 2014 at the Tuanku Tambusai site.Fig. 3. Wind rose during April 21–July 5, 2014 at the Tuanku Tambusai site.

In Pekanbaru, the highly influential natural pollutant sources include soil and road dust. Moreover, the identified anthropogenic pollutants originate from burning activities in degraded forests and peatlands, as well as from transportation and industry. During sampling, fires occurred in degraded forests and peatlands in several areas of Riau, and the closest distance to Pekanbaru was approximately 10 km and the farthest distance was approximately 200 km, which greatly affected the particulate concentrations in Pekanbaru. According to Reid et al. (2005), 50–60% of forest fire-related particulates are dominated by organic compounds, followed by inorganic compounds (Reid et al., 2005). The mean concentrations of organic matter (OM = OC × 1.6) and BC in PM2.5 are 30.08 ± 5.54 µg m–3 and 6.2150 ± 0.82 µg m–3, respectively, and the percentages of OM and BC in PM2.5 are 47.11% ± 6.32% and 9.74% ± 3.61%, respectively. OM (47%) is the most abundant species in PM2.5 at the monitoring station.

The east and west monsoon seasons influence Pekanbaru city. Average wind conditions occur from June-September during the east monsoon season and from November-February during the west monsoon season. The Pekanbaru area exhibited an average temperature of 29.3°C, average air pressure of 1,007.8 mb, and RH of 75.4% during sampling.

Meteorological data in the form of wind speed and direction were added to the wind rose model. The resulting average wind speed was 0.7 m s1 with a calm-wind percentage of 55.05%. As shown in Fig. 3, during sampling, winds predominantly originated from the northeast and southeast directions with an average wind speed of 2 m s1.

 
3.2 Source Apportionment

The PMF model requires input data on measured species concentrations and uncertainty values. The output produced by the PMF model consists of source profiles requiring interpretation, the contribution of each identified source, and a graph of the ratio of the measured mass and the mass calculated with the PMF model for model validation. The mass fraction distributions of the various species were evaluated to identify the sources, which included biomass burning (BB), secondary aerosols (SA), vehicle exhaust (VE), industrial emissions (IE), soil dust (SD), and sea salt (SS). The identified source profiles for the PM2.5 and PM2.5-10 mass concentrations are shown in Figs. 4 and 5, respectively. The PMF model applied to the concentration data identified six source factors for PM2.5 and five source factors for PM2.5-10, which are shown in Fig. 6.

Fig. 4. PMF source profiles of biomass burning (BB), secondary aerosols (SA), vehicle exhaust (VE), industrial emissions (IE), soil dust (SD), and sea salt (SS) at the Tuanku Tambusai site for the PM2.5 mass concentration.Fig. 4. PMF source profiles of biomass burning (BB), secondary aerosols (SA), vehicle exhaust (VE), industrial emissions (IE), soil dust (SD), and sea salt (SS) at the Tuanku Tambusai site for the PM2.5 mass concentration.

 Fig. 5. PMF source profiles of biomass burning (BB), secondary aerosols (SA), vehicle exhaust, industrial emissions (IE), and soil dust (SD) at the Tuanku Tambusai site for the PM2.5-10 mass concentration.Fig. 5. PMF source profiles of biomass burning (BB), secondary aerosols (SA), vehicle exhaust, industrial emissions (IE), and soil dust (SD) at the Tuanku Tambusai site for the PM2.5-10 mass concentration.

Fig. 6. Source contribution results from PMF for the PM2.5 and PM2.5-10 mass concentrations without OC data (biomass burning (BB), secondary aerosols (SA), vehicle exhaust (VE), industrial emissions (IE), soil dust (SD), and sea salt (SS)).Fig. 6. Source contribution results from PMF for the PM2.5 and PM2.5-10 mass concentrations without OC data (biomass burning (BB), secondary aerosols (SA), vehicle exhaust (VE), industrial emissions (IE), soil dust (SD), and sea salt (SS)).

The PMF analysis results indicate that BB (28.7%) and VE (28.6%) are the major sources of PM2.5 and PM2.5-10 mass concentrations, respectively. BB is identified as the first source. The PMF analysis shows that BB contributes approximately 28.7% to the PM2.5 mass concentration and 24% to the PM2.5-10 mass concentration. This emission source is identified based on high values of marker compounds such as SO42, K, Cl, Na, and Ca. BC characterizes this factor and occurs in high concentrations, while K, Se, Na, Ca, Cl, NH4+, and SO42 act as tracers. Smoke is the largest source of particles in Pekanbaru because the BB activities in Pekanbaru mostly include forest, peatland, and plantation burning activities that occur every year during the dry season. This finding is also verified by the dominant wind movement direction from the southwest and northwest. During sampling, burning activities occurred in the Pelalawan, Duri, and Bengkalis areas. The presence of Na and Cl in this factor suggests the mixing of sea spray particles during transport.

The second source of emissions is identified as SA. PMF analysis indicates that SA contributes approximately 26.9% to the mass concentration of PM2.5 and 15.3% to the mass concentration of PM2.5. SA generated through photochemical and other chemical processes is known to be the main constituent of PM2.5. This result is evident from the high levels of the marker compounds NH4+, NO3, and SO42 emitted directly by anthropogenic or natural sources and/or formed in the atmosphere. Sources rich in NO3, NH4+, and SO42 have been classified as secondary nitrates, and secondary sulfates have been identified as a key source of PM2.5 in various source apportionment studies (Kim and Hopke, 2004).

VE is identified as the third source. Kim Oanh et al. (2006) studied source profiles related to diesel engine vehicles and motorcycles. The marker compounds obtained for diesel engine vehicles were BC, Cl, SO42, NO3, NH4+, Zn, and Fe. Regarding two-stroke engines, the marker compounds included BC, Cl, SO42, NO3, NH4+, Fe, Pb, Ca, Mg, Al, K, Na, and Si. In two-stroke engines, lubricant and fuel are commingled and burned together in piston chambers, thereby emitting Zn (Kim Oanh et al., 2006). In four-stroke engines, lubricant is separately introduced to the various cylinders, and Zn is emitted during combustion (Amirante et al., 2017). Begum et al. (2005) revealed that Zn emissions are generated from the mixing and combustion of fuel and lubricant in the piston chambers of two-stroke engines (Begum et al., 2005). In this respect, vehicular emissions are generally associated with high Cu, Zn, and Sb concentrations. Cu and Sb have been adopted as chemical fingerprints for brake wear, while Zn has been widely considered for tire wear (Furusjö et al., 2007). The PMF analysis results show that vehicular emissions contribute approximately 12.8% to the PM2.5 mass concentration and 28.6% to the PM2.5-10 mass concentration at the Tuanku Tambusai station. During sampling, the sources of emissions originating from the transport sector included the highway and Mayang market terminal.

The fourth source identified is IE. Metal elements, such as Al, Si, Mg, K, V, Cr, Mn, Zn, Fe, Ni, Pb, and As, are observed at high concentrations and attributed to industrial sources. Riau Province contains many large-scale industries, such as pulp and paper, palm oil, oil and gas, rubber, and other industries. Wind may transport particles emitted by incinerators and boilers to other regions through the long-distance transport mechanism under mesoscale meteorological conditions. The high percentage of Pb is related to industrial sources and positive contributions of K, Sb, Cl, SO42, and NH4+ (Lim et al., 2010). The observed patterns are caused by metal manufacturing plants and household industries surrounding the sampling site. The model predicts high Al and Cr values due to the presence of small-scale electroplating facilities (Lestari and Mauliadi, 2009), which are widely distributed on the west side of Pekanbaru city. PMF analysis shows that IE contributes approximately 12.3% and 19.9% on average to the PM2.5 and PM2.5-10 mass concentrations, respectively.

The fifth source indicates an abundance of crustal elements with high BC, Al, Si, Ti, Fe, K, Na, and Ca concentrations. These elements identify the source of emissions as SD. This source is characterized by marker compounds such as those in airborne soil and road dust and usually contributes to coarse aerosols (Lough et al., 2005). Fe and Zn are closely associated with SD, indicating that this source type may contain a mixture of crustal elements and road dust; moreover, K in PM2.5 is mainly associated with wood burning, whereas K in PM2.5-10 is related to road dust and soil (Thorpe and Harrison, 2008). Based on PMF analysis, SD accounts for 11.9% of the PM2.5 mass and 17.2% of the PM2.5-10 mass.

The sixth source is SS, with the highest mass fractions of Na and Cl, and these elements have been adopted as tracers of this source. The Na+ and Cl contributing to this factor likely originate from the mixing of marine particles during air transport (Lestari and Mauliadi, 2009). More specifically, this factor is categorized as an aged SS source owing to the presence of NO3. Depletion of chloride occurs due to the reaction between sodium chloride (NaCl) and acids, such as sulfuric and nitric acids (H2SO4 and HNO3), which form sodium nitrate (NaNO3) and sodium sulfate (Na2SO4), respectively (Song et al., 2001). PMF analysis reveals that SS contributes approximately 7.5% to the PM2.5 mass concentration on average.

The above PMF results indicate six major sources of PM2.5 and five major sources of PM2.5-10 at the Tuanku Tambusai station without OC data. Regarding PM2.5, the six sources identified via PMF analysis are BB (28.7%), SA (26.9%), VE (12.8%), IE (12.3%), SD (11.9%), and SS (7.5%). OC concentration data are only available for PM2.5, and PMF analysis identified the following seven source profile categories: VE (14.8%), BB (19.5%), SD (9.2%), SS (10.2%), fossil fuel combustion (15.3%), IE (13.4%), and SA (17.7%). The source contributions and source profiles of the PM2.5 mass concentration considering the OC concentration data are shown in Figs. 7 and 8, respectively. These figures show that BB is the largest source contributing to PM2.5, which is related to the degraded forest and peatland fires occurring during the dry season. This source corresponded to an abundance of BC and OC with high concentrations of K, Cl, Na, Ca, NH4+, and SO42. These compounds are typically emitted during the combustion of wood, peat, and other forms of biomass. High values of BC, OC, and K are indicators of BB (Lestari and Mauliadi, 2009).

Fig. 7. Source contribution results from PMF for the PM2.5 mass concentration with OC mass concentration data (biomass burning (BB), secondary aerosols (SA), vehicle exhaust (VE), industrial emissions (IE), soil dust (SD), and sea salt (SS) and fossil fuel combustion (FFC)).Fig. 7. Source contribution results from PMF for the PM2.5 mass concentration with OC mass concentration data (biomass burning (BB), secondary aerosols (SA), vehicle exhaust (VE), industrial emissions (IE), soil dust (SD), and sea salt (SS) and fossil fuel combustion (FFC)).

 Fig. 8. PMF source profiles of vehicle exhaust (VE), biomass burning (BB), soil dust (SD), sea salt (SS), fossil fuel combustion (FFC), industrial emissions (IE), and (secondary aerosols (SA) at the Tuanku Tambusai site for the PM2.5 mass concentration with OC mass concentration data.Fig. 8. PMF source profiles of vehicle exhaust (VE), biomass burning (BB), soil dust (SD), sea salt (SS), fossil fuel combustion (FFC), industrial emissions (IE), and (secondary aerosols (SA) at the Tuanku Tambusai site for the PM2.5 mass concentration with OC mass concentration data.

High concentrations of OC, BC, and metal elements, such as Cd, Cr, Zn, Ni, Pb, Cu, and As, are marker species generally attributed to fossil fuel combustion, indicating oil-fired power plants, crude oil, and coal combustion. Crude oil is a heterogeneous liquid containing hundreds to thousands of chemical compounds. Crude oil is rich in aliphatic and aromatic hydrocarbons and contains trace elements, such as V, Ni, Fe, Al, Cu, and heavy metals, such as Pb and Cd. This source depends on the dominant wind direction being from the northeast. These emissions likely originated from coal combustion, drilling, and oil and gas burning activities in the southern and northeastern parts of Pekanbaru city. The results considering OC data indicate that PM2.5 is attributable to BB, fossil fuel combustion, IE, VE (Yu et al., 2021), SA, and SD, in contrast to the PM2.5 source contributions obtained without the OC data.

In the dry season in Pekanbaru, PM2.5 generation is dominated by BB, while in Bandung and other areas of Indonesia, it is dominated by SA. Our findings are similar to those for India, which were dominated by BB (21.2%). We did not do an analysis for the wet season because, based on the results reported by Lestari and Mauliadi (2009), the concentration of PM2.5 in the wet season is far below the national standard due to high rainfall.

 
3.3 Conditional Probability Function Study

PM2.5-10 data were used to analyze the PMF source contribution results. Based on the wind direction, PM2.5-10 predicts the likelihood that a source contribution will exceed the predetermined threshold (Kim and Hopke, 2004; Lestari and Mauliadi, 2009). In the PM2.5-10 analysis, hourly wind speed and direction data were required, wind speeds below 1 m s1 were omitted, and a source contribution threshold of 25% was set.

Fig. 9 shows the CPF results above the source contribution threshold of 25% based on peak winds in the various sectors obtained from the daily time series plots. Because intense winds occurred in the southeastern sector during the sampling period, most of the source factor directional contributions were also dominated by the southeastern sector, as expected, and BB, IE, and SA were directionally attributed to the southeastern sector. The CPF plot for the VE factor shows that this source is influenced by the south and northwest directions due to highways and heavy traffic. In contrast, regarding the fossil fuel combustion factor, the CPF plot shows that the northern sector strongly influences this source due to drilling and oil and gas burning activities. Regarding the SS factor, the CPF plot shows that this source is influenced by the northeast and south sectors, confirming that sea breeze is the major contributing factor to sea spray. Moreover, the CPF plot shows that the SD source is influenced by the northeast and southwest sectors.

 Fig. 9. CPF plot for the source contributions identified at the Tuanku Tambusai site.Fig. 9. CPF plot for the source contributions identified at the Tuanku Tambusai site.


3.4 Health Risk Assessment

This study calculated the health risks caused by inhalation of PM2.5. According to U.S. EPA risk management guidelines, CR is the likelihood that an individual will develop any cancer after being exposed to a cancer risk during their lifetime, and the acceptable risk range for CR is between 1 × 106 and 1 × 104 (Sun et al., 2014). The hazard index (HI) is the sum of the HQs applied to examine the overall potential for noncarcinogenic impacts caused by chemicals (Xiao et al., 2021). If HI or HQ is higher than one, there are noncarcinogenic risks, and the probability increases as the value of HI or HQ increases (U.S. EPA, 1989).

This study determined the cumulative risk values for carcinogenic and noncarcinogenic elements; the results are reported in Fig. 10 and described in the supplementary material (Table S3). For a single metal in PM2.5, the HQ values range from 0.0002 to 1.4654 (adults) and 0.0007 to 1.5226 (children). For a single metal in PM2.5-10, the HQ values range from 0.0005 to 0.7339 for adults and 0.0002 to 0.4638 for children. Co, Ni, and Mn in PM2.5 for both adults and children were higher than the safe level (= 1), indicating potential noncarcinogenic risks to residents from inhalation exposure to Co, Ni, and Mn. In contrast, the results for V, Cr, As, Cd, Pb, Al, Zn, and Cu were lower than the safe level (= 1), indicating no noncarcinogenic risks from V, Cr, As, Cd, Pb, Al, Zn, and Cu via inhalation exposure for both adults and children. The sums of HQ values for all elements (HIs) for adults (6.26) and children (6.00) were much higher than the safe level, indicating cumulative noncarcinogenic risks to both adults and children via inhalation.

Fig. 10. Noncarcinogenic and carcinogenic risks of toxic elements within PM2.5 and PM2.5-10 for adults and children.Fig. 10. Noncarcinogenic and carcinogenic risks of toxic elements within PM2.5 and PM2.5-10 for adults and children.

The HQ values for the PM2.5-10 elements V, Cr, Co, Ni, As, Cd, Pb, Al, Mn, Zn, and Cu for both adults and children were < 1, indicating no noncarcinogenic risks to local residents. Ni had the highest HQ for both adults and children. Only Ni considered the propagation of the uncertainties for every single metal; the HQ was > 1. Hence, to improve safety, increased control of Ni could be established to help prevent potential risks to the health of residents. The HI values for adults (1.95) and children (1.69) were > 1, indicating cumulative noncarcinogenic risks to the community in Pekanbaru city.

The carcinogenic risks from toxic elements in PM2.5 via inhalation exposure for both adults and children mainly resulted from Cr (adults = 2.14 × 10–3; children= 1.94 × 10–3) and Co (adults = 3.33 × 10–4; children = 3.02 × 10-4), and the values were higher than the acceptable risk range (between 1 × 10–6 and 1 × 10-4). The integrated risks of these elements were also higher than the acceptable levels (adults = 2.70 × 10–3; children = 2.45 × 10–3), indicating a potential carcinogenic risk posed by these toxic elements to local residents. In addition, for the toxic elements in PM2.5-10 samples, the carcinogenic risks for both children and adults to a single element were below the acceptable level, indicating that the carcinogenic risk posed by these toxic elements to both adults and children was acceptable. In contrast, the integrated risks of these elements are above the acceptable level, indicating cumulative carcinogenic risks to local adults and children. HQ, CR, and cumulative PM2.5 have potential carcinogenic and noncarcinogenic risks for both adults and children. Moreover, for PM2.5-10, only the integrated risks of HI and CR have potential carcinogenic and noncarcinogenic risks for both adults and children.

 
4 CONCLUSIONS


PMF analysis identified six factors for PM2.5 and five factors for PM2.5 without considering OC concentration data. PM2.5 was dominated by BB and SA, while PM2.5-10 was dominated by VE and BB. In contrast, when considering OC concentration data, seven source factors of PM2.5 were primarily attributed to BB and SA. Based on the analysis of the CPF plot, the southeastern sector dominated BB, IE, and SA. The health risk assessment shows that during fires occurring in degraded forests and peatlands in Pekanbaru city, adults and children have the same potential carcinogenic and noncarcinogenic risks. The metal elements in PM2.5 have more potential carcinogenic and noncarcinogenic risks than the metal elements in PM2.5-10. Short-term exposure to particles has an impact on the human respiratory system (Xing et al., 2016). The combination of chemical fractionation and health risk assessment provides more information to stakeholders and policymakers to better understand the influence of regional and local PM2.5 and PM2.5-10 sources on urban areas and formulate effective emission control strategies.

 
ACKNOWLEDGMENTS


We thank the Pekanbaru Provincial Government for kindly providing the sampling location and information on air quality.


REFERENCES


  1. Abdul Kadir, E., Rahim, S.K.A., Rosa, S.L. (2019). Multi-sensor system for land and forest fire detection application in Peatland Area. IJEEI 7, 789–799. https://doi.org/10.52549/ijeei.v7i4.​1604

  2. Alolayan, M.A., Brown, K.W., Evans, J.S., Bouhamra, W.S., Koutrakis, P. (2013). Source apportionment of fine particles in Kuwait City. Sci. Total Environ. 448, 14–25. https://doi.org/​10.1016/j.scitotenv.2012.11.090

  3. Amirante, R., Distaso, E., Napolitano, M., Tamburrano, P., Iorio, S.D., Sementa, P., Vaglieco, B.M., Reitz, R.D. (2017). Effects of lubricant oil on particulate emissions from port-fuel and direct-injection spark-ignition engines. Int. J. Engine Res. 18, 606–620. https://doi.org/10.1177/​1468087417706602

  4. Anwar, A., Juneng, L., Othman, M., Latif, M.T. (2010). Correlation between hotspots and air quality in Pekanbaru, Riau, Indonesia in 2006-2007. Sains Malaysiana 39, 169–174.

  5. Begum, B.A., Biswas, S.K., Kim, E., Hopke, P.K., Khaliquzzaman, M. (2005). Investigation of sources of atmospheric aerosol at a hot spot area in Dhaka, Bangladesh. J. Air Waste Manage. Assoc. 55, 227–240. https://doi.org/10.1080/10473289.2005.10464606

  6. Betha, R., Pradani, M., Lestari, P., Joshi, U., Reid, J., Balasubramanian, R. (2013). Chemical speciation of trace metals emitted from Indonesian peat fires for health risk assessment. Atmos. Res. 122, 571–578. https://doi.org/10.1016/j.atmosres.2012.05.024

  7. Chow, J., Watson, J., Robles, J., Wang, X., Chen, L.W.A., Trimble, D., Kohl, S., Tropp, R., Fung, K. (2011). Quality assurance and quality control for thermal/optical analysis of aerosol samples for organic and elemental carbon. Anal. Bioanal.Chem. 401, 3141–3152. 

  8. Chueinta, W., Hopke, P.K., Paatero, P. (2000). Investigation of sources of atmospheric aerosol at urban and suburban residential areas in Thailand by positive matrix factorization. Atmos. Environ. 34, 3319–3329. https://doi.org/10.1016/S1352-2310(99)00433-1

  9. Cohen, L., Manion, L., Morrison, K. (2000). Research Methods in Education, 5th ed. Routledge, London. https://doi.org/10.4324/9780203224342

  10. Colombijn, F. (2002). A wild west frontier on Sumatra’s east coast: The Pekanbaru-Dumai Road. Bijdragen tot de Taal-, Land- en Volkenkunde 158, 743–768. 

  11. Cutroneo, L., Reboa, A., Besio, G., Borgogno, F., Canesi, L., Canuto, S., Dara, M., Enrile, F., Forioso, I., Greco, G., Lenoble, V., Malatesta, A., Mounier, S., Petrillo, M., Rovetta, R., Stocchino, A., Tesan, J., Vagge, G., Capello, M. (2020). Microplastics in seawater: Sampling strategies, laboratory methodologies, and identification techniques applied to port environment. Environ. Sci. Pollut. Res. 27, 8938–8952. https://doi.org/10.1007/s11356-020-07783-8

  12. Esmaeilirad, S., Lai, A., Abbaszade, G., Schnelle-Kreis, J., Zimmermann, R., Uzu, G., Daellenbach, K., Canonaco, F., Hassankhany, H., Arhami, M., Baltensperger, U., Prévôt, A.S.H., Schauer, J.J., Jaffrezo, J.L., Hosseini, V., El Haddad, I. (2020). Source apportionment of fine particulate matter in a Middle Eastern Metropolis, Tehran-Iran, using PMF with organic and inorganic markers. Sci. Total Environ. 705, 135330. https://doi.org//10.1016/j.scitotenv.2019.135330

  13. Franck, M. (2013). Twin cities and urban pairs, a new level in urban hierar chies structuring transnatinal corridors? A case study of the Pekanbaru Dumai urban pair, pp. 271–298.

  14. Furusjö, E., Sternbeck, J., Cousins, A.P. (2007). PM10 source characterization at urban and highway roadside locations. Sci. Total Environ. 387, 206–219. https://doi.org/10.1016/j.scitotenv.2007.​07.021

  15. Hayasaka, H., Noguchi, I., Putra, E.I., Yulianti, N., Vadrevu, K. (2014). Peat-fire-related air pollution in Central Kalimantan, Indonesia. Environ. Pollut. 195, 257–266. https://doi.org/10.1016/j.​envpol.2014.06.031

  16. Ho, W.Y., Tseng, K.H., Liou, M.L., Chan, C.C., Wang, C. (2018). Application of positive matrix factorization in the identification of the sources of PM2.5 in Taipei City. Int. J. Environ. Res. Public Health 15, 1305. https://doi.org/10.3390/ijerph15071305

  17. Hopke, P.K., Xie, Y., Raunemaa, T., Biegalski, S., Landsberger, S., Maenhaut, W., Artaxo, P., Cohen, D. (1997). Characterization of the Gent stacked filter unit PM10 sampler. Aerosol Sci. Technol. 27, 726–735. https://doi.org/10.1080/02786829708965507

  18. Hwang, I., Hopke, P.K. (2007). Estimation of source apportionment and potential source locations of PM2.5 at a west coastal IMPROVE site. Atmos. Environ. 41, 506–518. https://doi.org/10.1016/​j.atmosenv.2006.08.043

  19. Hyer, E.J., Reid, J.S., Prins, E.M., Hoffman, J.P., Schmidt, C.C., Miettinen, J.I., Giglio, L. (2013). Patterns of fire activity over Indonesia and Malaysia from polar and geostationary satellite observations. Atmos. Res. 122, 504–519. https://doi.org/10.1016/j.atmosres.2012.06.011

  20. Irfan, M., Yulyanti, S. (2020). The effect of regional spillovers on economic growth in Pekanbaru city, Riau Province, Indonesia. IJEDR 1, 186–199. https://doi.org/10.37385/ijedr.v1i3.167

  21. Jaiprakash, Singhai, A., Habib, G., Raman, R.S., Gupta, T. (2017). Chemical characterization of PM1.0 aerosol in Delhi and source apportionment using positive matrix factorization. Environ. Sci. Pollut. Res. 24, 445–462. https://doi.org/10.1007/s11356-016-7708-8

  22. Jayachandran, S. (2009). Air quality and early-life mortality evidence from Indonesia’s wildfires. J. Hum. Resour. 44, 916–954. https://doi.org/10.3368/jhr.44.4.916

  23. Karnae, S., John, K. (2019). Source apportionment of PM2.5 measured in South Texas near U.S.A. – Mexico border. Atmos. Pollut. Res. 10, 1663–1676. https://doi.org/10.1016/j.apr.2019.​06.007

  24. Kim Oanh, N.T., Upadhyay, N., Zhuang, Y.H., Hao, Z.P., Murthy, D.V.S., Lestari, P., Villarin, J.T., Chengchua, K., Co, H.X., Dung, N.T., Lindgren, E.S. (2006). Particulate air pollution in six Asian cities: Spatial and temporal distributions, and associated sources. Atmos. Environ. 40, 3367–3380. https://doi.org/10.1016/j.atmosenv.2006.01.050

  25. Kim, E., Hopke, P.K. (2004). Improving source identification of fine particles in a rural northeastern US area utilizing temperature‐resolved carbon fractions. J. Geophys. Res. 109, D09204. https://doi.org/10.1029/2003JD004199

  26. Lelieveld, J., Evans, J.S., Fnais, M., Giannadaki, D., Pozzer, A. (2015). The contribution of outdoor air pollution sources to premature mortality on a global scale. Nature 525, 367–371. https://doi.org/10.1038/nature15371

  27. Lestari, P., Mauliadi, Y.D. (2009). Source apportionment of particulate matter at urban mixed site in Indonesia using PMF. Atmos. Environ. 43, 1760–1770. https://doi.org/10.1016/j.atmosenv.​2008.12.044

  28. Lim, J.M., Lee, J.H., Moon, J.H., Chung, Y.S., Kim, K.H. (2010). Source apportionment of PM10 at a small industrial area using Positive Matrix Factorization. Atmos. Res. 95, 88–100. https://doi.org/10.1016/j.atmosres.2009.08.009

  29. Liu, B., Wu, J., Zhang, J., Wang, L., Yang, J., Liang, D., Dai, Q., Bi, X., Feng, Y., Zhang, Y. (2017). Characterization and source apportionment of PM2.5 based on error estimation from EPA PMF 5.0 model at a medium city in China. Environ. Pollut. 222, 10–22. https://doi.org/10.1016/j.​envpol.2017.01.005

  30. Lough, G.C., Schauer, J.J., Park, J.S., Shafer, M.M., Deminter, J.T., Weinstein, J.P. (2005). Emissions of metals associated with motor vehicle roadways. Environ. Sci. Technol. 39, 826–836. https://doi.org/10.1021/es048715f

  31. Masiol, M., Hopke, P., Felton, D., Frank, B., Rattigan, O., Wurth, M., LaDuke, G.H. (2016). Source apportionment of PM2.5 chemically speciated mass and particle number concentrations in New York City. Atmos. Environ. 148, 215–229. https://doi.org/10.1016/j.atmosenv.2016.10.044

  32. Masri, S., Kang, C.M., Koutrakis, P. (2015). Composition and sources of fine and coarse particles collected during 2002–2010 in Boston, MA. J. Air Waste Manage. Assoc. 65, 287–297. https://doi.org/10.1080/10962247.2014.982307

  33. Paatero, P., Tapper, U. (1993). Analysis of different modes of factor analysis as least squares fit problems. Chemom. Intell. Lab. Syst. 18, 183–194. https://doi.org/10.1016/0169-7439(93)800​55-M

  34. Park, J.M., Lee, T.J., Kim, D.S. (2022). Improving PMF source reconciliation with cluster analysis for PM2.5 hourly data from Seoul, Korea. Atmos. Pollut. Res. 13, 101398. https://doi.org/​10.1016/j.apr.2022.101398

  35. Polissar, A.V., Hopke, P.K., Paatero, P., Malm, W.C., Sisler, J.F. (1998). Atmospheric aerosol over Alaska: 2. Elemental composition and sources. J. Geophys. Res. 103, 19045–19057. https://doi.org/10.1029/98JD01212

  36. Prawira, L., Zaini, A.K., Astuti, P. (2017). Traffic Jam in Tuanku Tambusai Road, Pekanbaru City, in: Taufik, T., Prabasari, I., Rineksane, I.A., Yaya, R., Widowati, R., Putra Rosyidi, S.A., Riyadi, S., Harsanto, P. (Eds.), ICoSI 2014, Springer, Singapore, pp. 285–297. https://doi.org/10.1007/​978-981-287-661-4_29

  37. Radojevic, M. (2003). Chemistry of forest fires and regional haze with emphasis on southeast Asia. Pure Appl. Geophys. 160, 157–187. https://doi.org/10.1007/s00024-003-8771-x

  38. Reid, C.E., Jerrett, M., Tager, I.B., Petersen, M.L., Mann, J.K., Balmes, J.R. (2016). Differential respiratory health effects from the 2008 northern California wildfires: A spatiotemporal approach. Environ. Res. 150, 227–235. https://doi.org/10.1016/j.envres.2016.06.012

  39. Reid, J.S., Koppmann, R., Eck, T.F., Eleuterio, D.P. (2005). A review of biomass burning emissions part II: Intensive physical properties of biomass burning particles. Atmos. Chem. Phys. 5, 799–825. https://doi.org/10.5194/acp-5-799-2005

  40. Saggu, G.S., Mittal, S.K. (2020). Source apportionment of PM10 by positive matrix factorization model at a source region of biomass burning. J. Environ. Manage. 266, 110545. https://doi.org/​10.1016/j.jenvman.2020.110545

  41. See, S.W., Balasubramanian, R., Rianawati, E., Karthikeyan, S., Streets, D.G. (2007). Characterization and source apportionment of particulate matter ≤ 2.5 μm in Sumatra, Indonesia, during a recent peat fire episode. Environ. Sci. Technol. 41, 3488–3494. https://doi.org/10.1021/​es061943k

  42. Song, X.H., Polissar, A.V., Hopke, P.K. (2001). Sources of fine particle composition in the northeastern US. Atmos. Environ. 35, 5277–5286. https://doi.org/10.1016/S1352-2310(01)0​0338-7

  43. Sun, Y., Hu, X., Wu, J., Lian, H., Chen, Y. (2014). Fractionation and health risks of atmospheric particle-bound As and heavy metals in summer and winter. Sci. Total Environ. 493, 487–494. https://doi.org/10.1016/j.scitotenv.2014.06.017

  44. Thorpe, A., Harrison, R.M. (2008). Sources and properties of non-exhaust particulate matter from road traffic: A review. Sci. Total Environ. 400, 270–282. https://doi.org/10.1016/j.scitotenv.​2008.06.007

  45. U.S. Environmental Protection Agency (U.S. EPA) (1989). Risk Assessment Guidance for Superfund, Volume I, Human Health Evaluation Manual (Part A). U.S. Environmental Protection Agency. 

  46. U.S. Environmental Protection Agency (U.S. EPA) (2009). Risk Assessment Guidance for. Superfund (RAGS), Volume 1: Human Health Evaluation Manual (Part F Supplemental. Guidance for Inhalation Risk Assessment). U.S. Environmental Protection Agency, pp. 7–82. 

  47. U.S. Environmental Protection Agency (U.S. EPA) (2012). The National Ambient Air Quality Standards for Particle Pollution. Revised air quality standards for particle pollution and updates to the air quality index (AQI). 

  48. U.S. Environmental Protection Agency (U.S. EPA) (2013). User's guide/technical background document for US EPA Region 9's RSL (regional screening levels) tables. U.S. Environmental Protection Agency. 

  49. Xiao, K., Qin, A., Wang, W., Lu, S., Wang, Q. (2021). Study on the characteristics of size-segregated particulate water-soluble inorganic ions and potentially toxic metals during wintertime in a high population residential area in Beijing, China. Processes 9, 552. https://doi.org/10.3390/​pr9030552

  50. Xing, Y.F., Xu, Y.H., Shi, M.H., Lian, Y.X. (2016). The impact of PM2.5 on the human respiratory system. J. Thorac. Dis. 8, E69–E74. https://doi.org/10.3978/j.issn.2072-1439.2016.01.19

  51. Yu, S., Chang, C.T., Ma, C.M. (2021). Simulation and measurement of air quality in the traffic congestion area. Sustainable Environ. Res. 31, 26. https://doi.org/10.1186/s42834-021-00099-3

Share this article with your colleagues 

 

Subscribe to our Newsletter 

Aerosol and Air Quality Research has published over 2,000 peer-reviewed articles. Enter your email address to receive latest updates and research articles to your inbox every second week.