PE PM2.5) filter samples were collected using portative devices in unique source-dominated environments for different target groups of humans, including a domestic fire site (DF) for women and a waste burning site (WB) for students in Abidjan, Côte d'Ivoire, and a motorcycle traffic site (MT) for drivers in Cotonou, Benin (Fig. 1). Abidjan (5∘20′ N, 4∘1′ W) is the economic capital of Côte d'Ivoire, with 6.5 million inhabitants in 2016. It is characterized by the highest level of industrialization and urbanization in the sWA area. Cotonou (6∘21′ N, 2∘26′ W) is the largest city and economic center of Benin, with about 1.5 million inhabitants in 2016. Both the cities experience a tropical wet and dry mixed climate, with relatively constant ambient temperatures (in a range of 24–30 ∘C) and an average of relative humidity (RH) >80 % throughout a year.

The DF site in Abidjan is located in the market of Yopougon-Lubafrique (5∘19.7′ N, 4∘6.4′ W), which is a large courtyard with about 25 fireplaces (Fig. 2). The major fuel used is essentially hevea wood, which is a kind of local rubber tree. Several female adult workers are employed for grilling meat and/or roasting peanuts from 06:00 to 15:00 UTC (working hours) in the working day. In this study, we selected two healthy and nonsmoking female workers (an average age of 32.5 years old) to measure personal exposure to PM2.5 from domestic fires and related sources such as grilling (Fig. 2). The WB site in Abidjan is near the public landfill of Akouédo (5∘21.2′ N, 3∘56.3′ W), which has received all the waste collected from Abidjan for the past 50 years (Fig. 2). We selected two healthy and nonsmoking primary school students (an average age of 11 years old) who live and study next to the WB site (within 100 m straight-line distance) to determine the personal exposure features to PM2.5 from waste burning (spontaneous combustion at high ambient temperatures and irregular combustion by the landfill workers) emissions at landfill and other daily sources. Lastly, the MT site in Cotonou is located in the Dantokpa area (6∘22.1′ N, 2∘25.9′ E), one of the biggest markets in western Africa (Fig. 2). It is largely dominated by a mass of emissions from motorcycle traffic (two-wheeled vehicle powered by petrol, also named zemidjan in the local language) and a small quantity of other motor vehicles. We chose two healthy and nonsmoking male motorcycle drivers (an average age of 50 years old) to survey PM2.5 personal exposure from motorcycle emission and related sources (such as road dust).

Two women (woman A and B) involved in this study at DF were both in charge of cooking at home, using charcoal and butane gas as fuel (Fig. S1a, b, c in the Supplement), and daily household cleaning. One student participator (student A, boy, 8 years old) at WB was not involved in cooking activities at home (energy source for cooking is charcoal and liquefied petroleum gas, LPG) (Fig. S1a, c), but another student (student B, girl, 14 years old) is usually responsible for household cooking using solid fuels (wood) (Fig. S1d). Two motorcycle drivers (driver A and B) at MT work for a local motorcycle operation company whose working time is usually from 06:30 to 10:30, 12:00 to 17:00 and 18:30 to 21:00 UTC. They were on the road almost all the working hours and returned home for meals. They did not participate in any cooking at home (energy source for cooking is charcoal) (Fig. S1a).

Twelve-hour time-integrated (daytime: 07:30 to 19:30 UTC; nighttime: 19:30 to 07:30 the next day UTC) PE PM2.5 samples were collected in two major southwestern African cities (Fig. 1) during the dry season (from 6 to 11 January) and wet season (from 5 to 10 July) in 2016. PE PM2.5 sampling was conducted for three consecutive days synchronously using the PEM (Personal Environmental Monitor) sampling devices with a SKC pump (SKC Inc., Fullerton, CA, USA) at a flow rate of 10 lpm (liters per minute). The PEM PM2.5 sampling heads were worn in the breathing zone (within 20 cm of the nose and mouth area) of participants in this study. PM2.5 was collected on 37 mm pre-baked quartz filters (800 ∘C, 3 h, QM/A^®, Whatman Inc., UK). A total of 72 PE samples, including 24 samples (12 pairs of diurnal samples) for women at DF, 24 (12 pairs) for students at WB and 24 (12 pairs) for drivers at MT, were collected in this study. Moreover, 12 PE PM2.5 field blanks (one field blank for each participant in one season collected on the second day of the three consecutive sampling days) were obtained as well.

In order to verify the comparability of PE samples and data caused by nonidentical sampling devices, 10 pairs of PM2.5 samples were synchronously collected by two sets of actual PEMs with SKC pumps. The comparison results show a significant correlation between the PM2.5 mass concentrations obtained from two sampling devices (y=0.986x + 0.189, R2=0.974, P<0.0001). Identical membrane type (quartz fiber) and analytical treatments were applied in this study. After sampling, the filter samples were placed in Petri dishes, sealed with parafilm and stored in a freezer at -20 ∘C to prevent loss of mass through volatilization prior to analysis. Blank values from blank filter samples were used to account for any artifacts caused by gas absorption and subtract the background PM2.5 and chemical composition concentrations in this area.

The meteorological observations during the dry (December 2015 to March 2016) and wet (April to July 2016) seasons at the sampling sites were shown in Table 1. The meteorological data were retrieved from the NOAA Global Surface Summary of the Day I (GSOD) at the airports of each cities, namely Félix Houphouët-Boigny Airport (Abidjan) and Cardinal Bernadin Gantin International Airport (Benin).

PE PM2.5 filter samples were analyzed gravimetrically for mass concentrations with a high-precision electronic microbalance (Sartorius MC21S, Germany) at Laboratoire d'Aérologie (Toulouse, France) before and after sampling in the weighing room after equilibration at 20–23 ∘C and RH of 35 %–45 % for at least 24 h. The absolute errors between replicate weights were less than 0.015 mg for blank filters and 0.020 mg for sampled filters.

Total carbon (TC) was determined on 0.5 cm2 punch-out of the filters using a carbon analyzer (Ströhlein Coulomat 702C, Germany) at the Observatoire Midi-Pyrenees (OMP; Toulouse, France). The quartz filter samples were subjected to a thermal pretreatment step (kept at 60 ∘C for 20 min) in order to remove the volatile organic compounds (VOCs) and eliminate water vapor. Subsequently, the filters were combusted at 1200 ∘C under O2 and detected as CO2 in the carbon analyzer. EC was obtained using a two-step thermal method: step 1 consisted of a pre-combustion at 340 ∘C under O2 for 2 h in order to remove OC. Step 2 consisted of the oxidation of the remaining EC at 1200 ∘C under O2. The difference (TC - EC) yielded the OC concentration (Benchrif et al., 2018; Cachier et al., 2005).

To extract the water-soluble inorganic ions from the quartz filters, a quarter of the filter was placed in separate 15 mL vials containing 10 mL distilled–deionized water (18.2 MΩ cm resistivity). The vials were placed in an ultrasonic water bath and shaken with a mechanical shaker for 45 min (15 min, three times) to extract the ions. The extracts were filtered through 0.45 µm pore size microporous membranes. After that, three anions (Cl-, NO3- and SO42-) and five cations (Na+, NH4+, K+, Mg2+ and Ca2+) in aqueous extracts of the filters were determined using an ion chromatograph (IC) analyzer (Dionex-600, Dionex, Sunnyvale, CA, USA), which is equipped with an AS11-HC anion column and a CS12 cation column for separation. Details of the IC measurement method are described in Bahino et al. (2018) and Cachier et al. (2005).

One element of Fe (representing earth's crust emission) and 10 heavy metals (i.e., V, Cr, Mn, Co, Ni, Cu, Zn, Sb, Ba and Pb) in PE PM2.5 samples were determined using Energy Dispersive X-Ray Fluorescence (ED-XRF) spectrometry (the PANalytical Epsilon 5 ED-XRF analyzer, PANalytical B.V., the Netherlands) with a quarter of the filter. The relative errors for all measured elements were <6 % between the NIST Standard Reference Material (SRM) 2783 and our ED-XRF results, which is well within the required range of error, demonstrating the accuracy of method. Replicate analysis of one quartz-fiber filter sample (five times) yielded an analytical precision between 5.2 % and 13.9 %. Details of the ED-XRF measurements are shown in Brouwer (2003) and Xu et al. (2012).

Aliquot punches (0.1–1.0 cm2) from the quartz filters were used to quantify organic compounds, including PAHs, phthalate esters (PAEs) and hopanes (details of target organic species and their abbreviations are shown in Table 5) using an in-injection port thermal desorption gas chromatography–mass spectrometry (TD-GC/MS) method. The approach has the advantages of a shorter sample preparation time (<1 min), minimizing contamination from solvent impurities, and a higher sensitivity, compared with the traditional solvent extraction GC/MS method. The detailed analytical procedures have been reported in previous publications (Ho and Yu, 2004; Ho et al., 2008, 2011; Xu et al., 2013, 2016a). The results of the blank analyses showed only traces of contamination (<5.0 %) of PE PM2.5 sample concentrations.

A number of heavy metals and toxic organic species are associated with negative PE health effects (Škrbic et al., 2016; Val et al., 2013; J. Z. Wang et al., 2017; Xu et al., 2018a). In this study, four heavy metals (Mn, Ni, Zn and Pb) and all measured PAH and PAE species in PE PM2.5 were selected to determine the PE inhalation health risks (Xu et al., 2018a). The heavy metal non-carcinogenic risks and toxic organics carcinogenic risks of PM2.5 via inhalation were calculated according to the U.S. EPA health risk assessment model (U.S. EPA, 2004, 2011). The average daily exposure dose (D) via inhalation was estimated to assess the risk using Eq. (1) as follows: 1D=(C×R×EF×ED×cf)/(BW×AT). The definitions and recommended values of parameters are shown in Table 2.

A hazard quotient (HQ) for the non-cancer risk of heavy metals in PE PM2.5 samples can be obtained from Eq. (2): 2HQ=D/RfD. The threshold value of RfD indicates whether there is an adverse health effect during a certain period. The hazard index (HI) can be obtained by summing up the individual HQs to estimate the total non-cancer risks. If HI < 1, then a non-carcinogenic effect is impossible; if HI ≥ 1, an adverse health effect is likely to appear (Hu et al., 2012).

The incremental lifetime cancer risk (ILCR) of PAHs and PAEs in PE PM2.5 samples can be calculated by multiplying the cancer slope factor (CSF) of PAHs and PAEs by D using Eq. (3): 3ILCR=D×CSF. For cancer risk, the value of 1×10-6 is internationally accepted as the precautionary or threshold value above which the risk is unacceptable (Jedrychowski et al., 2015).

It is worth noting that, among the 19 PAHs, benzo[a]pyrene (BaP) has been used as an indicator of PAH carcinogenicity (Wang et al., 2006). The carcinogenic health risk of PAH species can be assessed by [BaP]eq instead (Yassaa et al., 2001) using Eq. (4): 4Σ[BaP]eq=Σ(Ci×TEFi). Additionally, the carcinogenic risk for PAEs was assessed by bis(2-ethylhexyl) phthalate (DEHP), which is identified as possibly carcinogenic to humans by the International Agency for Research on Cancer (IARC, 1982; Li et al., 2016). The definitions and recommended values of the parameters in Eqs. (2)–(4) are also shown in Tables 2 and 3.

A questionnaire (Supplement A–C) and time-activity diary (Supplement D) were collected from each participant during the sampling period, respectively, to fully grasp the basic information, potential personal exposure sources and activities of participants. In the questionnaire, each participant was asked for personal information, family status, dermatological and asthma issues, medical history, current health status and so on. In addition, the questions for women include (1) living habits and environment (past and current living conditions, general living habits, cooking habits and domestic fuel type/usage); (2) work environment and travel habits (workplace, work nature, working hours and daily travel mode/time); and (3) whether they are affected by the burning of domestic solid fuels and roasting meat. The questions for students include (1) living habits and environment (past and current living conditions, general living habits, participation in household duties, family cooking habits and domestic fuel type/usage, distance from home to WB site); (2) school environment and travel habits (school location and related environment and daily travel mode/time); and (3) whether they are affected by the burning of waste and household air pollution sources. The questions for drivers include (1) living habits and environment (past and current living environments, general living habits, participation in household duties, family cooking habits and domestic fuel type/usage); (2) working environment and travel habits (motorcycle power type, driving conditions, working hours and daily travel mode/time); and (3) whether they are affected by the motorcycle emission and household air pollution sources.

The time-activity diaries requested the participants to record information on a half an hour basis (sleeping time excluded) to assess in detail what time is spent in each microenvironment on different activities.

The calculation of the mass balance of the PE PM2.5 is an effective method to figure out the principal components in PM2.5 and distinguish the pollution sources (Gokhale et al., 2008). PE PM2.5 mass in this study can be classified into six parts: organic matter (OM), EC, water-soluble inorganic ions, geological material (GM), heavy metals and an unresolved fraction (Fig. 5). The first five main resolved fractions can explain 78.3 % to 90.6 % of total PE PM2.5 mass concentrations in this study. The unresolved fraction may include water and other undetected substances. For OM, since there are no full organic composition profiles for the PE PM2.5, a conversion factor of 1.4 (1.4 corrects the organic carbon mass for other constituents associated with the organic carbon molecule) is generally used (Turpin and Lim, 2001) to quantify OM using Eq. (5): 5OM=1.4×OC. OM accounted for 34.1±6.3 %, 23.3±2.8 % and 24.9±6.9 % of the PE PM2.5 mass for women at DF, students at WB and drivers at MT, respectively. The results show that there are distinct sources of PE OC for women at DF. According to the information gathered from the questionnaires, the combustion sources, such as roasting meat/peanuts and burning wood, are the major contributors to PE OC for women in this study.

In addition, Fe has been widely used to estimate the upper limit of GM (Taylor and McLennan, 1985). Fe constitutes ∼4.0 % of dust of the earth's crust (Cao et al., 2005b; Hao et al., 2007; Kabata-Pendias and Mukherjee, 2007; Sun et al., 2014; Wu et al., 2012; Xu et al., 2016b). The amount of GM is calculated using Eq. (6): 6GM=(1/4.0%)×Fe. It is found that GM contributed 35.8±2.1 %, 46.0±3.7 % and 42.4±4.7 % of PE PM2.5 mass concentrations for women at DF, students at WB and drivers at MT, respectively. Fugitive dusts, including road dust resuspension from the disturbance of motor vehicles and human activities, construction dust from uncovered construction sites and the dusts generated from burning, could be the dominant sources of PE PM2.5 in this study. OM and GM showed similar proportions (34.1 % and 35.8 %, respectively) of PE PM2.5 mass for women at DF. The fractions of GM in PE samples for students and drivers were approximately 10 % and 7 % higher than that for women. Therefore, the fugitive dust was the most important source of PE PM2.5 in this less developed area, shown by nearly 50 % contribution for students and drivers, attributable to human physical activities and a large amount of covered land. It is surprising to note that the secondary formed ions (i.e., SO42-, NO3- and NH4+) and the total quantified water-soluble inorganic ions were shown to have exceedingly low proportions of PE PM2.5 for all groups. This reconfirms the limited contribution to PE PM2.5 from secondary ionic formation.

In Fig. 5, evident diurnal distinctions can be observed in the two major chemical compositions of OM and GM. GM exhibited a lower proportion at nighttime (35.3 %) than daytime (47.5 %), suggesting its close relationship with human activities. Higher GM was found for all groups in the dry season because of harmattan haze which introduced mineral dusts and the lack of precipitation which increased road dust resuspension. Moreover, OM showed equal or lower proportions in the daytime (25.0 %) than nighttime (30.0 %), relative to the meteorological parameters (i.e., factors that affected the formation of secondary organic carbonaceous aerosol) and diurnal changes of combustion sources around subjects. An exception is that OM proportion of women's PE PM2.5 in the daytime (50.8 %) was much higher than nighttime (38.2 %) in the wet season, due to influences of damp wood burning during working hours. Burning biomass fuel with high moisture often results in low combustion efficiency, a long smoldering period and high air pollutant emissions (Grandesso et al., 2011; Shen et al., 2012, 2013). The emission factor of OC usually increases with the fuel moisture content (Chen et al., 2010; Keita et al., 2018). Therefore, burning damp wood led to higher OC emission than dry wood, in line with the observation for women's PE results in this study.

Benzo[b]fluoranthene (BbF) was the most abundant PAH for women at DF, followed by benzo[a]pyrene (BaP) and indeno[1,2,3-cd]pyrene (IcdP). The average concentration of BbF (a marker of low temperature combustion, such as wood burning) was 11.6±19.2 ng m-3, accounting for approximately 15.0 % of the ΣPAHs for women (Wang et al., 2006) (Table 5). The most abundant PAH species for students at WB and drivers at MT were IcdP (6.4±4.5 ng m-3) and benzo[ghi]perylene (BghiP) (6.4±0.5 ng m-3), respectively, indicating the contributions from waste incineration and/or high temperature combustion of fuel (e.g., gasoline vehicle emission) (Baek et al., 1991; Wang et al., 2006). The average ΣPAHs of women at DF (125.4±54.8 ng m-3) and drivers at MT (44.6±10.8 ng m-3) in the wet season were 326 % and 52 % higher than those in the dry season (29.4±5.6 and 29.4±4.4 ng m-3, respectively), while ΣPAHs in the wet season (36.8±15.7 ng m-3) were 42 % lower than that dry season (62.9±45.0 ng m-3) for students at WB. The dramatic increase in the PE of women to PAHs is mainly due to an increase in moisture content in the wood used for grilling meat in the wet season, promoting more PAH emission from wood combustion processes (Shen et al., 2013). The restraint of waste combustion in the wet season is the main factor for the lower PE PM2.5-bound PAHs at landfill, in accordance with seasonal patterns of PE PM2.5 mass. Fanou et al. (2006) measured the PE PAH concentrations in Cotonou and found that the PAH level associated with particles ranged from 76.21 to 103.23 ng m-3 for 35 motorbike taxi drivers in March 2001. Our values for drivers at the MT site were 50 %–64 % lower than their values, suggesting that the exposure to PAHs for the motorbike drivers in this region has been improved.

In Fig. 6a, PE PAHs showed fluctuating diurnal variations for the three kinds of subjects. For the women at DF, the daytime concentrations in wet and dry seasons were both higher than those at nighttime due to the intensive roasting meat and burning wood during working hours. For the students at WB, PE PAHs at night were higher in the dry season but lower in the wet season. Both the PAH profiles showed high combustion markers of BbF and benzo[e]pyrene (BeP) and high gasoline vehicle emission markers of dibenzo[a,h]anthracene (DahA) and BghiP (Baek et al., 1991; Wang et al., 2006). For the drivers at MT, the average D / N ratios in dry and wet seasons were 0.8 and 0.3, respectively. The higher PE ΣPAH concentrations at night and lower D / N ratios for drivers may be explained by the potential combustion sources of PAHs close to the participants (e.g., sources nearby the drivers' homes) in Cotonou, Benin, especially in the wet season. This can be deduced by the combustion marker of BaP, which was the highest PAH species at night in the wet season, even though the drivers were exposed to traffic emissions during the night working hours (18:30 to 21:00 UTC). Further studies are thus required to confirm findings and figure out reasons for them.

In the study of Titcombe and Simcik (2011), the authors found that the 5 h average total PAH personal exposure concentration was 5040 ng m-3 (±909 ng m-3, n=3) for open wood fires in households in the Njombe district of Tanzania, which was much higher (∼65 times) than women's exposure to PAHs at the DF site in the current research. The highest 12 h exposure to PAHs for women at the DF site in this study was 469.7 ng m-3 (daytime in the wet season, 6 July), approximately one-tenth of the PAH concentrations from open wood fires in Tanzania mentioned above. The large PE PAH concentration difference between these two studies may be influenced by many factors such as wood type, combustion state, stove structure and sampling time.

Diagnostic ratios of PAHs have been widely used in source identification (Tobiszewski and Namiesnik, 2012; Yunker et al., 2002). In our study, the average values of BeP / (BeP + BaP) and IcdP / (IcdP + BghiP) were 0.47 and 0.52 for women at DF, 0.51 and 0.52 for students at WB and 0.64 and 0.34 for drivers at MT, respectively (Fig. 7), indicating the unique impacts on the PE PM2.5 from different atmospheric pollution sources. The average BeP / (BeP + BaP) ratios ranged from 0.47 to 0.64, comparable with those reported in the Chinese megacities of Guangzhou (0.41–0.72) and Xi'an (0.59–0.73) (Li et al., 2005; Xu et al., 2018c), but lower than the value measured in Shanghai (>0.70) (Feng et al., 2006). This implies the low oxidability of the PAHs in the less-developed cities in sWA. PAHs in drivers' PE samples were more prone to aging (i.e., the average ratio was 1.3–1.4 times those for women and students) because of their re-suspension onto road dusts (i.e., longer residence lifetime) and longer outdoor activity time (i.e., exposure to more sunlight). Fine and ultra-fine particle-bound PAHs are emitted in high-temperature combustion from motor vehicular engine, which are more easily photochemically oxidized in the atmosphere (Baek et al., 1991; Lima et al., 2005). The differences of BeP / (BeP + BaP) ratios between dry and wet seasons were not obvious, without a general pattern. However, the ratio exhibited a significant day–night variation, with an average of 0.59 and 0.49 in the daytime and nighttime, respectively. This shows that more favorable meteorological conditions (i.e., higher light intensity) and more physical activities (i.e., extended time for particulate resuspension) in the daytime are more conducive to the aging of PM2.5 and its bounded PAHs. Moreover, IcdP / (BghiP + IcdP) ratios of <0.2, 0.2–0.5 and >0.5 were used to identify petrogenic, petroleum combustion and a mix of grass, wood and coal combustion, respectively (Yunker et al., 2002). The relatively low ratio for drivers at MT (0.34) demonstrates that the PAHs were mainly produced from motor vehicles, while grass, wood and coal combustion was more dominant for women at DF (0.52) and students at WB (0.52) (Fig. 7). The IcdP / (IcdP + BghiP) ratio did not show significant seasonal variation.

Phthalate esters are widely used as plasticizers in materials and can be released into the air through matrix evaporation and combustion (Gu et al., 2010; J. Z. Wang et al., 2017). The PE levels of PAEs could be mainly attributed to the usage of household products, painting material, plastic waste incineration and municipal sewage release (L. B. Zhang et al., 2014). The total concentrations of six phthalate esters (the first six species of PAEs in Table 5) and one plasticizer (bis(2-ethylhexyl) adipate, DEHA) (abbreviated as ΣPAEs for the total seven species) were 882.0±193.3, 1380.4±335.2 and 698.1±192.4 ng m-3, respectively, for women at DF, students at WB and drivers at MT (Table 5). Bis(2-ethylhexyl) phthalate (DEHP) was the most dominant PAE species, followed by di-n-butyl phthalate (DBP) for all the three groups of participants. DEHP is mainly used as a plasticizer for the manufacture of polyvinyl chloride (PVC); and together with DBP, they are the most widely used PAEs globally (Meng et al., 2014). The average DEHP and DBP concentrations were 543.6 and 304.6 ng m-3, accounting for approximately 55.1 % and 30.9 % of the ΣPAEs, respectively (Fig. 6b). The elevated ΣPAEs for students can be ascribed to the combustion of plastic products at landfill nearby. Our results are similar to previous studies conducted in Xi'an and Tianjin, China (Kong et al., 2013; J. Z. Wang et al., 2017). The ΣPAEs ranged from 376.6 to 1074 ng m-3 outdoors and from 469.2 to 1537 ng m-3 in classrooms (J. Z. Wang et al., 2017), where DEHP and DBP were also the most abundant PAEs, with a sum of composition of 68 % and 73 % of the ΣPAEs outdoors and indoors, respectively.

The average concentrations of the ΣPAEs for women at DF, students at WB and drivers at MT were comparable in the dry season. However, the average concentrations were 927.2±154.9, 1929.8±340.4 and 594.6±16.6 ng m-3 in the wet season, 1.1, 2.3 and 0.7 times the ΣPAEs in the dry season (Fig. 6b). A significant increase in PE ΣPAEs for students at WB can be attributed to the enhanced PAE emission in the daytime with high RH (3173.6±1028.3 ng m-3), consistent with the findings on PE PM2.5. Dry and wet seasons had similar PAE profiles with different diurnal variations (Fig. 6b). The average D / N ratios of the ΣPAEs in the dry season demonstrate constant concentrations, with an average of 1.0, 1.0 and 1.3, respectively, for women, students and drivers, while much larger variations of 1.1, 4.6 and 0.7 were found for the wet season. Noticeably different diurnal D / N ratios for students at WB are interrelated with human activities (especially the emissions from plastic materials) and the decreased waste combustion influenced by meteorological conditions (i.e., more precipitation at night in the wet season), which was mentioned in Sect. 3.1.1.

Hopanes are markers for fossil fuel (e.g., petroleum) combustion (Simoneit, 1999; Wang et al., 2009). The average PE to the sum of eight quantified hopanes (Σhopanes) for the drivers was 50.9±7.9 ng m-3, 2.0 and 2.3 times higher than the women (17.1±6.4 ng m-3) and students (15.6±6.1 ng m-3), respectively (Table 5). The results indicate an extremely high personal respiratory exposure contribution from the motor vehicle emissions (e.g., gasoline combustion) for the drivers. It is important to note that number of automobiles is rapidly increasing in sWA cities, further exacerbating the air pollution and consequential health issues. The Σhopanes showed unobvious seasonal variations for three types of PE participants. The Σhopanes in the dry season were 0.9, 1.8 and 0.7 times those in the wet season. Even though the Σhopanes varied among the three sites, their profiles on individual species were similar. 17α(H)-21β(H),30-norhopane (αβ-NH) and 17α(H)-21β(H)-hopane (αβ-HH) were the two most abundant hopanes in all PE PM2.5 samples, with average concentrations of 6.0 and 6.5 ng m-3 and compositions of 21.4 % and 23.3 % of the Σhopanes, respectively (Table 5 and Fig. 6c).

Compared with D / N ratios of ΣPAHs and ΣPAEs, Σhopanes exhibited a more stable diurnal trend, higher in the daytime due to heavier traffic emissions. For women at DF, the D / N ratio was both 2.0 in dry and wet seasons, with Σhopanes of 24.0±11.1 and 12.2±5.0 ng m-3 for daytime and nighttime in the dry season and 21.4±17.5 and 10.9±3.6 ng m-3 in the wet season. The D / N ratio of Σhopane for drivers at MT had the highest value of 11.5, with 78.0±19.1 and 44.9±16.4 ng m-3 for daytime and nighttime in the dry season and 74.2±16.3 and 6.5±1.7 ng m-3 in the wet season. It is notable that the daytime concentrations for drivers were comparable between the two seasons, while the nighttime hopanes in the wet season were mostly washed away by rainfall, resulting in a very large decline in its concentrations.

Even though these organic groups are not major fractions in PE PM2.5, their fingerprints can more accurately illustrate the contributions of air pollution sources to PM2.5. PAHs, PAEs and hopanes are source markers for combustion activities, plastics emissions and fossil fuel emissions (e.g., from gasoline vehicles), respectively, well matching the potential air pollution sources that impact the PE PM2.5 for participants in this study. Our results not only indicate that the PM2.5 respiratory exposure can be strongly contributed from environmental pollution sources and individual activities but also prove the study to be a reliable application of organic tracers for the characterization of personal exposure.