GRAPHICAL ABSTRACT

INTRODUCTION

The growing reliance on private vehicles over public transportation has led to excessive pollution levels, posing significant public health challenges [1]. The widespread use of fossil fuels greatly contributes to air pollution, causing nearly one million premature deaths worldwide each year [2-4]. Air pollution, particularly from transportation, is recognized as one of the most pressing public health problems of the 21st century [5,6]. Particulate matter (PM), especially PM with an aerodynamic diameter of less than 2.5 µm (PM_2.5), is a major concern. Although PM with an aerodynamic diameter of less than 10.0 µm particles are larger [7,8], PM_2.5 particles, being finer, are more dangerous because they can penetrate deeply into the lungs and accumulate, causing severe health problems such as bronchitis, asthma, and upper respiratory tract infections [9,10]. Projections indicate that air pollution could cause 6.6 million premature deaths by 2050 [11].

The United States Environmental Protection Agency (EPA) estimates that transportation emissions contribute to 61% of carbon monoxide (CO), 53% of nitrogen dioxide, 17% of volatile organic compounds, and 13-15% of PM_2.5 [12]. Vehicle emissions, particularly in high-traffic areas, release harmful pollutants like CO and particulates into the atmosphere, thereby increasing health risks [13]. Among those most vulnerable to particulate pollution are traffic volunteers who work near roads, as well as children, the elderly, and individuals with preexisting heart or lung conditions [14]. Epidemiological studies have established strong links between PM_2.5 exposure and increased mortality, as well as heightened rates of cardiovascular and respiratory diseases [15,16]. For example, exposure to PM_2.5 from transportation emissions in New York City is responsible for approximately 320 deaths annually [17].

The development pattern in Indonesia is dominated by industry and is predominantly concentrated in major cities and metropolitan areas. One contributing factor is the rapid growth of transportation [18]. Vehicle growth in Indonesia reaches 12% per year, with motorcycles comprising the largest share. This expansion leads to increased traffic congestion, accidents, and air pollution, contributing to 70% of urban pollutants [19].

The development dynamics of Makassar City follow a similar trend. Air pollution levels are frequently categorized as unhealthy, with an air pollution index ranging from 87.83% to 90.00%, posing a threat to public health [20]. During peak hours, vehicle volumes on urban roads—especially on the Urip Sumoharjo-Perintis road—reach 903.108 pcu/hr/day, comprising 872.480 private vehicles, 13.681 public transportation vehicles, 41.042 freight vehicles, and 719.069 two-wheeled vehicles [18]. PM concentration data in Makassar ranged from 32.9 μg/m³. Although this value is lower than those recorded in major cities such as Bandung (61.0 μg/m³) and Serpong (51.8 μg/m³), Makassar remains at risk of air pollution as one of the rapidly growing cities in eastern Indonesia [21,22]. A study conducted in major Indonesian cities exposed to traffic pollutants revealed that PM_2.5 concentrations exceeded World Health Organization (WHO) standards, with risk analysis indicating health quotient (HQ) values >1, demonstrating that increased PM_2.5 levels directly correlate with health risks for both adults and children [13,23]. Another study on the risk of PM_2.5 exposure in traffic volunteers on Semarang City’s main street showed similar results, with HQ values >1 [24].

The increase in motor vehicles has been identified as a major contributor to this pollution, raising concerns about its impact on public health. Experimental research has demonstrated that individuals engaged in activities near roads—and thus exposed to traffic air pollution—experience higher rates of respiratory disturbances compared to those active in parks near roadways [25]. This study aimed to assess PM_2.5 levels in Makassar’s ambient air and evaluate the potential health risks associated with exposure. Although numerous risk analysis studies have been conducted, few have focused on informal workers such as traffic volunteers. Traffic volunteers are at heightened risk due to their prolonged exposure duration, higher exposure intensity, and challenging working conditions near highways. This study emphasizes an environmental health risk analysis in an under-researched group and incorporates spatial analysis to examine pollutant distribution patterns. The primary objective is to evaluate the human health risks associated with PM_2.5 exposure and to analyze its spatial distribution, particularly from anthropogenic sources such as vehicle emissions.

METHODS

Research Design

This analytic observational study employed a cross-sectional design using a health risk assessment approach. A cross-sectional design collects data from a population at a single point in time to measure the prevalence of a phenomenon and analyze relationships present at that moment. Health risk analysis was used to estimate human health risks, both carcinogenic and non-carcinogenic. PM_2.5 concentrations were measured from May 2024 to July 2024 at 36 locations along major roads in Makassar City (Figure 1). The spatial distribution of these pollutant concentrations was analyzed to determine their dispersion, and kriging analysis was used to estimate values at unmeasured locations based on data from nearby measured points.

Study Area

PM_2.5 samples were collected along the main roads of Makassar. Although all samples were taken from major roads, differences in characteristics were evident, with high-traffic areas including Alauddin, Perintis Kemerdekaan, Hertasning, Urip Sumohardjo, and AP Pettarani Roads, and lower-traffic areas including Boulevard and Veteran Roads (Figure 1). PM_2.5 measurements were conducted in accordance with the Indonesian National Standard No. 1971-19.62005. This research focused on the main roads of Makassar City, where traffic volunteers are most active, in order to represent all roads in Makassar, as shown in Figure 1.

Population and Sample

The research sample comprised all traffic volunteers present during the measurement period, using an accidental sampling technique. No additional inclusion criteria were applied, meaning that all respondents encountered during the sampling period were eligible. However, those who withdrew during the interview process were excluded. In total, 175 respondents participated in the study. The accidental sampling technique was chosen because it provided easy access to the specific target group—traffic volunteers who routinely carry out their activities—thereby minimizing potential bias.

Research Instrument

To ensure data validity, PM_2.5 measurements were conducted in collaboration with the Public Health Laboratory Center of Makassar, an institution accredited by the Ministry of Health and the National Accreditation Committee of Indonesia. A high-volume air sampler employing the gravimetric method was used. Measurement points were determined using a grab sampling technique, with samples collected and analyzed in the laboratory. Measurements were taken in the morning (07:00-11:00), afternoon (11:00-17:00), and evening (17:00-22:00), with each session lasting one hour at each point to obtain daily average pollutant values. The measurement points were recorded using a Garmin eTrex 10 GPS, as shown in Figure 1.

Health Risk Assessment and Evaluation

The United States EPA has employed human health risk analysis to calculate the risk associated with PM_2.5 exposure. Although PM_2.5 levels were below Indonesia’s regulatory limit of 65 µg/m³, they exceeded the WHO guideline of 15 µg/m³. Since inhalation is the primary route of exposure for PM_2.5, this pathway was assessed using the HQ formulation to determine the magnitude of individual health risks. The non-carcinogenic risk ratio for PM_2.5 was determined for all respondents. The estimated daily intake of PM_2.5 through inhalation—the primary exposure route—is shown in equations 1 and 2 [26,27].

(1)

ADD Iinh:C x I inh R x LE x EF x EDW b x t Avg

Where:

ADD I_inh: exposure dose µg/m³,

I_inh: average daily dose for inhalation=0.83 m³/hr

C: exposure concentration of PM_2.5,

LE: length of exposure (day/y),

EF: frequency of exposure (day/y),

ED: duration of exposure (y),

Wb: body weight,

t Avg: mean time in days; this value for a non-carcinogen equals 30×365 days, and for carcinogens, it equals 70 years (lifetime) ×365 days.

Risk Characteristics

The risk level (HQ) was calculated by dividing the non-carcinogenic intake of each risk agent by the RfC.

(2)

RfC:C x R x LE x EF x EDW b x t Avg

(3)

HQ=ADDInhRfC

RfC: The dose/concentration of daily exposure to a non-carcinogenic risk agent that is estimated not to cause adverse effects, even with lifetime exposure.

The RfC was determined based on dose-response analysis using the safe exposure limit for PM_2.5 established by the National Ambient Air Quality Standard of the United States EPA in 2006, which is 35 µg/m³, as the default value for anthropometric exposure subjects. The PM_2.5 quality standard according to Government Regulation of the Republic of Indonesia Number 41 of 1999 was not used because the default anthropometric value is not yet known. Based on calculations using the default value, RfC was determined to be 0.001 mg/kg/day, equivalent to 10 µg/kg/day.

Ethics Statement

This research received approval from the Health Research Ethics Committee (KEPK) of the Polytechnic of the Ministry of Health Makassar (No. 0455/M/KEPK-PTKMS/IV/2024).

RESULTS

PM_2.5 levels were measured on major roads in Makassar City. These seven locations were selected because of their high traffic volumes. The results indicated an increase in pollutant concentrations in the research area due to its role as a frequently used transportation route. The highest concentration was recorded on Hertasning Street at 65 µg/m³, while the lowest was recorded on AP Pettarani Street at 2 µg/m³ (Figure 2). Mean-while, the highest average concentration was measured on Hertasning Street at 45.16 µg/m³, and the lowest on Veteran Street at 7.80 µg/m³. Pollutant concentrations exceeded the WHO threshold of 15 µg/m³ (Table 1). The air quality on these roads is primarily affected by vehicular emissions, particularly from private vehicles and large trucks, especially on Hertasning and Sultan Alauddin Roads (Figure 2).

Table 1 shows the average PM_2.5 measurement results. The variation in concentration is attributed to the higher vehicle density on Hertasning Street—the main road of Makassar City and Gowa Regency—particularly during morning and evening peak hours due to increased worker and student activity. In contrast, traffic on Veteran Road is lighter because it is not located near educational institutions or shopping centers. Table 1 also displays the averages of other variables used in the calculation of HQ values, such as body weight, length of exposure, frequency of exposure, duration of exposure, and average annual exposure.

HQ values are shown in Table 2. All respondents were at risk of non-carcinogenic PM_2.5 exposure. The risk categories, based on the HQ calculation, were higher for respondents on Sultan Alauddin and Hertasning Streets compared to those on other main roads.

DISCUSSION

Figure 2 shows the distribution map of PM_2.5 concentrations in Makassar. Concentrations ranging from 0-15 µg/m³ are represented in light green, 15-25 µg/m³ in dark green, 25-35 µg/m³ in a deeper green, 35-45 µg/m³ in yellow, 45-60 µg/m³ in orange, and values exceeding 60 µg/m³ in red. The kriging analysis indicated that light green areas, representing lower PM_2.5 concentrations, cover most of the northern and western regions of Makassar. Red areas depict the highest PM_2.5 concentrations, concentrated around Alauddin Road and Hertasning Road. The distribution map, which predicts PM_2.5 values in areas without measurement points using available data, shows that the low concentration zone marked in green around Jalan Veteran and Urip Sumohardjo exhibits the lowest PM_2.5 levels. This can be attributed to the presence of substantial green open spaces that absorb pollutants. The moderate concentration zone, marked in yellow around Rappocini, reflects vehicle activity and moderate urbanization. In contrast, high concentration zones, marked in orange and red on Jalan Sultan Alauddin and Urip Sumohardjo, are caused by vehicle pollution and traffic density. These findings align with research indicating that the presence of green open spaces influences pollutant concentrations [28].

Areas with higher PM_2.5 concentrations are typically associated with heavy transportation or industrial activities. Roads such as Alauddin, Perintis Kemerdekaan, and Hertasning exhibit the highest levels, reflecting significant traffic flow. Perintis Kemerdekaan Road, which connects Makassar City to Maros Regency, experiences heavy congestion due to the proximity of educational institutions and shopping centers. AP Pettarani, a central hub in Makassar, along with Boulevard and Veteran Roads, supports dense business and commercial activities. Meanwhile, Sultan Alauddin and Hertasning Roads, which connect Makassar to Gowa Regency, are busy with commuters from Gowa. Veteran Street benefits from more green spaces. This observation aligns with studies demonstrating that planting vegetation along road corridors effectively reduces air pollution [29,30].

The study found that the average PM_2.5 concentrations in certain areas exceeded the WHO air quality recommendations for both daily and long-term exposure. For short-term (24-hour average) and long-term (annual average) exposure, the WHO guideline is 15 µg/m³. However, while the PM_2.5 levels observed in the study exceeded WHO standards, they remained below Indonesian air quality standards. The elevated pollutant levels may be attributed to the distribution of PM_2.5 from various vehicles, particularly at sites with heavy traffic [30,31].

In Southeast Asia, the primary sources of PM_2.5 include automobile exhaust, industrial pollution, and secondary aerosols [32]. Vehicle activity, industrial by-products, and the re-suspension of crustal soils are the main anthropogenic drivers releasing particulate pollutants into the environment [33,34]. Consequently, PM_2.5 exposure increases health risks and may elevate pollutant concentrations above environmental quality standards. The research findings indicate that PM_2.5 concentrations exceeded these standards [35].

Regarding PM_2.5 exposure, the highest HQ was observed on Hertasning Road and the lowest on Veteran Road; however, all locations exhibited HQ values greater than 1. Specifically, HQ values exceeded 1 for respondents on Sultan Alauddin, Urip Sumohardjo, and Perintis Kemerdekaan Roads, while they were below 1 for respondents on Veteran, Boulevard, and AP Pettarani Roads [36]. PM_2.5 particles can penetrate the respiratory system, and excessive exposure may result in respiratory distress, reduced lung capacity, cardiovascular disease, and even death. Several studies have established a link between total suspended particulates and adverse health effects [37]. Exposure to toxic chemicals associated with PM_2.5 can lead to both short-term and long-term health issues. Routine activities may increase vulnerability to diseases such as childhood cancer, asthma, birth defects, neurodevelopmental disorders, and obesity. Areas with high levels of metal and gaseous air pollutants tend to produce more PM_2.5. Additionally, factors such as anthropogenic activities, weather conditions, and soil resuspension from roads can contribute to PM_2.5 pollution [38].

Observations at the measurement sites suggest that local topography influences PM_2.5 distribution. PM_2.5 particles are carried by wind in all directions, and turbulence is particularly pronounced on roads located in educational and commercial centers, resulting in higher concentrations in the southern region compared to other areas. This finding is consistent with evidence that high PM_2.5 concentrations are linked to anthropogenic activities, especially from the cement industry [5,38]. The increased concentrations observed during the dry season indicate that climate, weather, and wind direction significantly influence pollutant levels compared to the rainy season. Similar research has shown that PM_2.5 concentrations are higher in the dry season due to wind direction, wind speed, and hotter weather [36,39,40]. Air pollution control for PM_2.5 can be achieved by increasing roadside vegetation, improving traffic management systems, enforcing stricter vehicle emission standards, and providing public information about the Air Quality Index to help people plan their outdoor activities.

This study indicates that PM_2.5 concentrations from motor vehicle emissions at several major congestion points in Makassar City have exceeded WHO guidelines, although they remain below Indonesia’s regulatory limits. The HQ analysis suggests non-carcinogenic health risks, with HQ values exceeding 1 in certain locations—particularly in high-congestion areas. Spatial analysis using the kriging method revealed a direct correlation between areas with higher pollutant concentrations and congestion points, indicating that the spatial distribution of PM_2.5 is significantly influenced by traffic intensity. Therefore, effective air quality management strategies—such as the use of masks, the enhancement of green transportation, and the expansion of green open spaces—are essential to reduce air pollution and minimize health risks, especially for individuals with regular exposure.

Notes

Conflict of Interest

The authors have no conflicts of interest associated with the material presented in this paper.

Funding

This research was funded by a grant from the Makassar Ministry of Health Polytechnic with the number: DP.04.03/F.XX.12.1/1627/2024.

Acknowledgements

The authors thank the Poltekkes Kemenkes Makassar for providing national research, development and innovation funding sources.

Author Contributions

Conceptualization: Suryadi I, Rachmawati S. Data curation: Suryadi I, Rachmawati S, Juherah J, Fitriani N, Kahfi M, Basri S. Formal analysis: Suryadi I, Rachmawati S, Basri S. Funding acquisition: Suryadi I, Juherah J, Rachmawati S. Methodology: Suryadi I, Rachmawati S. Project administration: Suryadi I, Rachmawati S. Visualization: Suryadi I, Rachmawati S, Kahfi M. Writing – original draft: Suryadi I, Rachmawati S. Writing – review & editing: Suryadi I, Juherah J, Rachmawati S, Fitriani N, Kahfi M, Basri S.

Figure. 1.

Particulate matter with an aerodynamic diameter of less than 2.5 μm (PM_2.5) sampling points in Makassar City main road.

Figure. 2.

Spatial distribution of particulate matter with an aerodynamic diameter of less than 2.5 μm (PM_2.5) levels around Makassar’s main street with krigging analysis.

REFERENCES

• 1. Gao C, Gao C, Song K, Xing Y, Chen W. Vehicle emissions inventory in high spatial–temporal resolution and emission reduction strategy in Harbin-Changchun Megalopolis. Process Saf Environ Prot 2020;138: 236-245. https://doi.org/10.1016/j.psep.2020.03.027Article
• 2. Rao S, Pachauri S, Dentener F, Kinney P, Klimont Z, Riahi K, et al. Better air for better health: forging synergies in policies for energy access, climate change and air pollution. Glob Environ Change 2013;23(5):1122-1130. https://doi.org/10.1016/j.gloenvcha.2013.05.003Article
• 3. Zhang S, Worrell E, Crijns-Graus W, Krol M, de BruineM, Geng G, et al. Modeling energy efficiency to improve air quality and health effects of China’s cement industry. Appl Energy 2016;184: 574-593. https://doi.org/10.1016/j.apenergy.2016.10.030Article
• 4. Anenberg SC, Schwartz J, Shindell D, Amann M, Faluvegi G, Klimont Z, et al. Global air quality and health co-benefits of mitigating near-term climate change through methane and black carbon emission controls. Environ Health Perspect 2012;120(6):831-839. https://doi.org/10.1289/ehp.1104301ArticlePubMedPMC
• 5. Lelieveld J, Evans JS, Fnais M, Giannadaki D, Pozzer A. The contribution of outdoor air pollution sources to premature mortality on a global scale. Nature 2015;525(7569):367-371. https://doi.org/10.1038/nature15371ArticlePubMed
• 6. World Health Organization. Climate and health country frofiles - 2015: a global review; 2015 [cited 2025 Jul 21]. Available from: https://www.who.int/publications/i/item/climate-and-health-country-profiles-2015
• 7. Faraji Ghasemi F, Dobaradaran S, Saeedi R, Nabipour I, Nazmara S, Ranjbar Vakil Abadi D, et al. Levels and ecological and health risk assessment of PM2.5-bound heavy metals in the northern part of the Persian Gulf. Environ Sci Pollut Res Int 2020;27(5):5305-5313. https://doi.org/10.1007/s11356-019-07272-7ArticlePubMed
• 8. Khaefi M, Geravandi S, Hassani G, Yari AR, Soltani F, Dobaradaran S, et al. Association of particulate matter impact on prevalence of chronic obstructive pulmonary disease in Ahvaz, southwest Iran during 2009-2013. Aerosol Air Qual Res 2017;17(1):230-237. https://doi.org/10.4209/aaqr.2015.11.0628Article
• 9. Khaniabadi YO, Sicard P, Taiwo AM, De Marco A, Esmaeili S, Rashidi R. Modeling of particulate matter dispersion from a cement plant: upwind-downwind case study. J Environ Chem Eng 2018;6(2):3104-3110. https://doi.org/10.1016/j.jece.2018.04.022Article
• 10. Johnston HJ, Mueller W, Steinle S, Vardoulakis S, Tantrakarnapa K, Loh M, et al. How harmful is particulate matter emitted from biomass burning? A Thailand perspective. Curr Pollut Rep 2019;5: 353-377. https://doi.org/10.1007/s40726-019-00125-4Article
• 11. Zhang D, Aunan K, Seip HM, Larssen S, Liu J, Zhang D. The assessment of health damage caused by air pollution and its implication for policy making in Taiyuan, Shanxi, China. Energy Policy 2010;38(1):491-502. https://doi.org/10.1016/j.enpol.2009.09.039Article
• 12. Bureau of Transportation Statistics. Transportation statistics annual report 2022 [cited 2025 Jan 5]. Available from: https://trid.trb.org/View/2091985
• 13. Ernyasih E, Mallongi A, Daud A, Palutturi S, Stang S, Thaha R, et al. Health risk assessment through probabilistic sensitivity analysis of carbon monoxide and fine particulate transportation exposure. Glob J Environ Sci Manag 2023;9(4):933-950. https://doi.org/10.22035/gjesm.2023.04.18Article
• 14. Huang J, Yang T, Gulliver J, Hansell AL, Mamouei M, Cai YS, et al. Road traffic noise and incidence of primary hypertension: a prospective analysis in UK Biobank. JACC Adv 2023;2(2):100262. https://doi.org/10.1016/j.jacadv.2023.100262ArticlePubMedPMC
• 15. Bowe B, Xie Y, Li T, Yan Y, Xian H, Al-Aly Z. Associations of ambient coarse particulate matter, nitrogen dioxide, and carbon monoxide with the risk of kidney disease: a cohort study. Lancet Planet Health 2017;1(7):e267-e276. https://doi.org/10.1016/S2542-5196(17)30117-1ArticlePubMed
• 16. Rivai A, Rasman R, Sahani W, Inayah I, Ahmad H, Suryadi I. Risk assessment of ambient air pollution PM2.5 exposure to communities in the cement industrial area, Pangkep Regency, Indonesia. Malays J Med Health Sci 2024;20(2):210-217. https://doi.org/10.47836/mjmhs.20.2.28Article
• 17. Kheirbek I, Haney J, Douglas S, Ito K, Matte T. The contribution of motor vehicle emissions to ambient fine particulate matter public health impacts in New York City: a health burden assessment. Environ Health 2016;15(1):89. https://doi.org/10.1186/s12940-016-0172-6ArticlePubMedPMC
• 18. Surya B, Syafri S, Sahban H, Sakti HH. Natural resource conservation based on community economic empowerment: perspectives on watershed management and slum settlements in Makassar City, South Sulawesi, Indonesia. Land 2020;9(4):104. https://doi.org/10.3390/land9040104Article
• 19. Piątkowski MJ. Expectations and challenges in the labour market in the context of industrial revolution 4.0. The agglomeration method-based analysis for Poland and other EU member states. Sustainability 2020;12(13):5437. https://doi.org/10.3390/su12135437Article
• 20. Martins F, Felgueiras C, Smitkova M, Caetano N. Analysis of fossil fuel energy consumption and environmental impacts in European countries. Energies 2019;12(6):964. https://doi.org/10.3390/en12060964Article
• 21. Zhang K, Batterman S. Air pollution and health risks due to vehicle traffic. Sci Total Environ 2013;450-451: 307-316. https://doi.org/10.1016/j.scitotenv.2013.01.074ArticlePubMedPMC
• 22. Kusumaningtyas SD, Khoir AN, Fibriantika E, Heriyanto E. Effect of meteorological parameter to variability of particulate matter (PM) concentration in urban Jakarta city, Indonesia. IOP Conf Ser Earth Environ Sci 2021;724(1):012050. https://doi.org/10.1088/1755-1315/724/1/012050Article
• 23. Mallongi A, Daud A, Palutturi S, Thaha R, Ibrahim E, Al Moudhun W. Model prediction of potential disease effects from PM2.5 emission among school children in coming 30 years in South Tangerang. Pharmacogn J 2023;15(3):400-404. https://doi.org/10.5530/pj.2023.15.91Article
• 24. Pertiwi KD, Lestari IP, Afandi A. Environmental health risk analysis of PM10 and PM2.5 dust exposure in traffic volunteers on Diponegoro street, Ungaran. Pro Health 2024;6(2):85-91. (Indonesian)
• 25. Zhu X, Zhang Q, Du X, Jiang Y, Niu Y, Wei Y, et al. Respiratory effects of traffic-related air pollution: a randomized, crossover analysis of lung function, airway metabolome, and biomarkers of airway injury. Environ Health Perspect 2023;131(5):57002. https://doi.org/10.1289/EHP11139ArticlePubMedPMC
• 26. United States Environmental Protection Agency. Cement manufacturing enforcement initiative; 2024 [cited 2025 Jan 5]. Available from: https://www.epa.gov/enforcement/cement-manufacturing-enforcement-initiative
• 27. United States Environmental Protection Agency. Exposure factors handbook 2011 edition (final report) [cited 2025 Jan 5]. Available from: https://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=236252
• 28. Brantley HL, Hagler GS, Herndon SC, Massoli P, Bergin MH, Russell AG. Characterization of spatial air pollution patterns near a large railyard area in Atlanta, Georgia. Int J Environ Res Public Health 2019;16(4):535. https://doi.org/10.3390/ijerph16040535ArticlePubMedPMC
• 29. Deshmukh P, Isakov V, Venkatram A, Yang B, Zhang KM, Logan R, et al. The effects of roadside vegetation characteristics on local, near-road air quality. Air Qual Atmos Health 2019;12: 259-270. https://doi.org/10.1007/s11869-018-0651-8ArticlePubMedPMC
• 30. Sakunkoo P, Thonglua T, Sangkham S, Jirapornkul C, Limmongkon Y, Daduang S, et al. Human health risk assessment of PM2.5-bound heavy metal of anthropogenic sources in the Khon Kaen Province of Northeast Thailand. Heliyon 2022;8(6):e09572. https://doi.org/10.1016/j.heliyon.2022.e09572ArticlePubMedPMC
• 31. Chuersuwan N, Nimrat S, Lekphet S, Kerdkumrai T. Levels and major sources of PM2.5 and PM10 in Bangkok Metropolitan Region. Environ Int 2008;34(5):671-677. https://doi.org/10.1016/j.envint.2007.12.018ArticlePubMed
• 32. Singh N, Murari V, Kumar M, Barman SC, Banerjee T. Fine particulates over South Asia: review and meta-analysis of PM2.5 source apportionment through receptor model. Environ Pollut 2017;223: 121-136. https://doi.org/10.1016/j.envpol.2016.12.071ArticlePubMed
• 33. Hazarika N, Srivastava A. Estimation of risk factor of elements and PAHs in size-differentiated particles in the National Capital Region of India. Air Qual Atmos Health 2017;10: 469-482. https://doi.org/10.1007/s11869-016-0438-8Article
• 34. Bodor K, Szép R, Bodor Z. The human health risk assessment of particulate air pollution (PM2.5 and PM10) in Romania. Toxicol Rep 2022;9: 556-562. https://doi.org/10.1016/j.toxrep.2022.03.022ArticlePubMedPMC
• 35. Surya B, Hamsina H, Ridwan R, Baharuddin B, Menne F, Fitriyah AT, et al. The complexity of space utilization and environmental pollution control in the main corridor of Makassar City, South Sulawesi, Indonesia. Sustainability 2020;12(21):9244. https://doi.org/10.3390/su12219244Article
• 36. Fadel M, Ledoux F, Afif C, Courcot D. Human health risk assessment for PAHs, phthalates, elements, PCDD/Fs, and DL-PCBs in PM2.5 and for NMVOCs in two East-Mediterranean urban sites under industrial influence. Atmos Pollut Res 2022;13(1):101261. https://doi.org/10.1016/j.apr.2021.101261Article
• 37. Manisalidis I, Stavropoulou E, Stavropoulos A, Bezirtzoglou E. Environmental and health impacts of air pollution: a review. Front Public Health 2020;8: 14. https://doi.org/10.3389/fpubh.2020.00014ArticlePubMedPMC
• 38. Mallongi A, Stang S, Astuti RD, Rauf AU, Natsir MF. Risk assessment of fine particulate matter exposure attributed to the presence of the cement industry. Glob J Environ Sci Manag 2023;9(1):43-58. https://doi.org/10.22034/gjesm.2023.01.04Article
• 39. Yang J, Ji Z, Kang S, Zhang Q, Chen X, Lee SY. Spatiotemporal variations of air pollutants in western China and their relationship to meteorological factors and emission sources. Environ Pollut 2019;254(Pt A):112952. https://doi.org/10.1016/j.envpol.2019.07.120ArticlePubMed
• 40. Liu J, Mauzerall DL, Chen Q, Zhang Q, Song Y, Peng W, et al. Air pollutant emissions from Chinese households: a major and underappreciated ambient pollution source. Proc Natl Acad Sci U S A 2016;113(28):7756-7761. https://doi.org/10.1073/pnas.1604537113ArticlePubMedPMC

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