Chemical Composition, Source Appointment and Health Risk of PM2.5 and PM2.5-10 during Forest and Peatland Fires in Riau, Indonesia

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.

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 PM_2.5 and PM_2.5-10 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 PM_2.5 and PM_2.5-10 were identified.
• Biomass burning, secondary aerosol and vehicle exhaust are main particle contributors.
• Both non- and carcinogenic health risk caused by PM_2.5 exceed the acceptable level.

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 PM_2.5 and PM_2.5-10 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).

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 (PM_2.5) and coarse (PM_2.5-10) 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 PM_2.5 and PM_2.5-10 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.

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

PM_2.5 and PM_2.5-10 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 min^–1, respectively, which are based on the height of the sampling location and the correction factors for PM_2.5 and PM_2.5-10. 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 PM_2.5 and 32 PM_2.5-10 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 PM_2.5, PM_2.5-10, 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 PM_2.5 and PM_2.5-10 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 (SO₄^2–), 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 PM_2.5 and PM_2.5-10 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 s^–1) 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 PM_2.5 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 day^–1); EF is the exposure frequency (350 days year^–1 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 day^–1; for carcinogens = 70 years × 365 days year^–1 × 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 PM_2.5 and PM_2.5-10 are shown in Fig. S1. The average concentrations of PM_2.5 and PM_2.5-10 are 63.85 ± 3.22 µg m^–3 and 27.72 ± 2.40 µg m^–3, respectively. The mean concentrations of metal elements in PM_2.5 and PM_2.5-10 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 PM_2.5 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 PM_2.5 and PM₁₀ ranged from 0.69–0.94 (Hopke et al., 1997). PM₁₀ showed a strong positive correlation with PM_2.5 (R² = 0.827), which accounted for approximately 69% of PM₁₀, suggesting that the decrement of PM_2.5 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 PM_2.5 and PM_2.5-10 with standard deviations and ranges. The minimum and maximum mass concentrations for PM_2.5 and PM_2.5-10 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 PM_2.5 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 PM_2.5 (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), PM_2.5 exhibits a strong association with morbidity and mortality related to the effectiveness of PM_2.5 deposition in the human body and the large surface area of PM_2.5, 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 PM_2.5 and PM_2.5-10, 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.

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 PM_2.5 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 PM_2.5 are 47.11% ± 6.32% and 9.74% ± 3.61%, respectively. OM (47%) is the most abundant species in PM_2.5 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 s^–1 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 s^–1.

 
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 PM_2.5 and PM_2.5-10 mass concentrations are shown in Figs. 4 and 5, respectively. The PMF model applied to the concentration data identified six source factors for PM_2.5 and five source factors for PM_2.5-10, 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 PM_2.5 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 PM_2.5-10 mass concentration.

Fig. 6. Source contribution results from PMF for the PM_2.5 and PM_2.5-10 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 PM_2.5 and PM_2.5-10 mass concentrations, respectively. BB is identified as the first source. The PMF analysis shows that BB contributes approximately 28.7% to the PM_2.5 mass concentration and 24% to the PM_2.5-10 mass concentration. This emission source is identified based on high values of marker compounds such as SO₄^2–, K, Cl, Na, and Ca. BC characterizes this factor and occurs in high concentrations, while K, Se, Na, Ca, Cl, NH₄⁺, and SO₄^2– 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 PM_2.5 and 15.3% to the mass concentration of PM_2.5. SA generated through photochemical and other chemical processes is known to be the main constituent of PM_2.5. This result is evident from the high levels of the marker compounds NH₄⁺, NO₃^–, and SO₄^2– emitted directly by anthropogenic or natural sources and/or formed in the atmosphere. Sources rich in NO₃^–, NH₄⁺, and SO₄^2– have been classified as secondary nitrates, and secondary sulfates have been identified as a key source of PM_2.5 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^–, SO₄^2–, NO₃^–, NH₄⁺, Zn, and Fe. Regarding two-stroke engines, the marker compounds included BC, Cl^–, SO₄^2–, 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 PM_2.5 mass concentration and 28.6% to the PM_2.5-10 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^–, SO₄^2–, 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 PM_2.5 and PM_2.5-10 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 PM_2.5 is mainly associated with wood burning, whereas K in PM_2.5-10 is related to road dust and soil (Thorpe and Harrison, 2008). Based on PMF analysis, SD accounts for 11.9% of the PM_2.5 mass and 17.2% of the PM_2.5-10 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 PM_2.5 mass concentration on average.

The above PMF results indicate six major sources of PM_2.5 and five major sources of PM_2.5-10 at the Tuanku Tambusai station without OC data. Regarding PM_2.5, 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 PM_2.5, 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 PM_2.5 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 PM_2.5, 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 SO₄^2–. 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 PM_2.5 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 PM_2.5 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 PM_2.5 is attributable to BB, fossil fuel combustion, IE, VE (Yu et al., 2021), SA, and SD, in contrast to the PM_2.5 source contributions obtained without the OC data.

In the dry season in Pekanbaru, PM_2.5 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 PM_2.5 in the wet season is far below the national standard due to high rainfall.

 
3.3 Conditional Probability Function Study

PM_2.5-10 data were used to analyze the PMF source contribution results. Based on the wind direction, PM_2.5-10 predicts the likelihood that a source contribution will exceed the predetermined threshold (Kim and Hopke, 2004; Lestari and Mauliadi, 2009). In the PM_2.5-10 analysis, hourly wind speed and direction data were required, wind speeds below 1 m s^–1 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.

3.4 Health Risk Assessment

This study calculated the health risks caused by inhalation of PM_2.5. 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 × 10^–6 and 1 × 10^–4 (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 PM_2.5, the HQ values range from 0.0002 to 1.4654 (adults) and 0.0007 to 1.5226 (children). For a single metal in PM_2.5-10, 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 PM_2.5 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 PM_2.5 and PM_2.5-10 for adults and children.

The HQ values for the PM_2.5-10 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 PM_2.5 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 PM_2.5-10 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 PM_2.5 have potential carcinogenic and noncarcinogenic risks for both adults and children. Moreover, for PM_2.5-10, 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 PM_2.5 and five factors for PM_2.5 without considering OC concentration data. PM_2.5 was dominated by BB and SA, while PM_2.5-10 was dominated by VE and BB. In contrast, when considering OC concentration data, seven source factors of PM_2.5 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 PM_2.5 have more potential carcinogenic and noncarcinogenic risks than the metal elements in PM_2.5-10. 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 PM_2.5 and PM_2.5-10 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.