Potential sources and health risk assessment of polycyclic aromatic hydrocarbons in street dusts of Karaj urban area, northern Iran

Abstract

In the present study chemical fingerprinting approach (isomeric ratios), a receptor-oriented model (principal component analysis with multiple linear regression, PCA/MLR) and a probabilistic health risk framework were employed to characterization, source appointment and carcinogenic risk assessment of polycyclic aromatic hydrocarbons (PAHs) in street dusts of Karaj urban area (northern Iran). Thirty street dusts samples were collected from the different functional areas in the city of Karaj and analyzed for PAHs by gas chromatography/mass spectrometry (GS/MS). The results obtained showed that ∑16PAHs concentrations varied widely from 16.2 to 1236.2 with a mean of 624 μg/kg and decreased in the following order of functional areas; traffic> residential > green/park areas. PAHs profile in the majority of dust samples were dominated by 5–6 rings PAHs, accounting for 25%–95% of the total PAHs. Qualitative source apportionment using the molecular isomeric ratios indicated mixed sources of PAHs in street dusts while PCA/MLR receptor model quantitatively identified three major sources with following relative contributions to the total dust PAH burden; 51% for pyrogenic-traffic sources, 32% for traffic-stationary sources and, 16% for petrogenic sources. The results of health risk assessment based on probabilistic model indicated that at the 95% percentiles, total cancer risks for children and adults are 8.43 × 10⁻⁴ and 3.34 × 10⁻⁵, respectively which both are higher than the acceptable baseline (10⁻⁶) indicating potential carcinogenic risk for local residents. It was also revealed that dust ingestion pathway is the most important contributor to the total carcinogenic risks of PAHs for both children and adults although the cancer risk level for adults through dermal and inhalation was 10 times greater than that for children. Based on the sensitivity analysis using the Monte Carlo simulation, benzo[a]pyrene equivalent concentration, exposure duration, dermal exposure area and ingestion rate were found to be the most sensitive exposure parameters which could introduce uncertainties into the cancer risk estimated.

Introduction

With the rapid growth of population and urbanization, environmental quality in urban areas has severely been deteriorated. Anthropogenic activities associated with urban development generate significant amounts of organic and inorganic chemical pollutants which eventually make their way into the surrounding environment (water bodies, soils, and atmosphere). The number and diversity of these pollutants in the urbanized systems have been noticeably increased in recent years.

Out of those environmental media in an urban system, air-born particulates such as street dusts are considered as a major reservoir and sink for various pollutants released by anthropic sources. The street dust particles with a large specific surface area provide a reactive site for adsorption of pollutants on their surfaces. These particles behave similar to atmospheric aerosols and may be re-suspended onto or re-deposited from the atmosphere [1]. As street dust consists of large proportion of fine particles, organic or inorganic contaminants in street dust can easily enter the human body particularly via inhalation, ingestion or direct absorption by skin [2, 3]. Exposure to street dust of respirable size itself presents a health risk and can lead to a range of lung diseases. This situation will be worsened if the dust particles contain significantly high concentrations of toxic compounds.

Among numerous environmental pollutants, polycyclic aromatic hydrocarbons (PAHs) are especially detrimental and have attracted considerable attention from governmental or urban regulatory bodies. These complex compounds are a class of organic chemicals, which contain at least 2 benzene rings in their molecular structure [4]. PAHs are emitted into the urban environment mainly through anthropogenic activities such as vehicular emissions, domestic heating systems, biomass/coal or wood burning and oil spills [5]. The main concern of PAHs is related to their high toxicity, mutagenic properties and carcinogenicity [6], especially for some isomers including benzo[b]fluoranthene (BbF), benzo[a]pyrene (BaP), and benz[a]anthracene (BaA) [7, 8]. Most of the PAH congeners with high molecular weight have a high tendency to accumulate in dust particles and for this reason street dust particles can act as the major carrier for PAHs in the urban environment.

In order to be able to control the source and reduce the environmental risks posed by PAHs, it is necessary to have quantitative insight into the potential sources of these contaminants in urban street dusts [9]. Generally, there are three categories of approaches to source apportionment of PAHs in the environmental media: chemical fingerprint, receptor modelling and CSIA (Compound Specific Isotope Analysis) [10]. In the chemical fingerprint approach, the relative isomeric ratios of PAHs with similar physicochemical properties and the same molar weights are used [10]. These diagnostic ratios can provide a qualitative fingerprint tracer to pyrogenic or petrogenic sources of PAH [11]. In contrast, the receptor-oriented approaches are capable to make quantitative assessment of the relative contribution of each potential source to overall PAH pollution [12]. In these models, the contribution of each source is usually determined by best-fitting of linear combination of equations for polluting sources or by the multivariate statistical techniques. The positive matrix factorization model (PMF), principal component analysis with multiple linear regression (PCA/MLR), Unmix model and chemical mass balance (CMB) are commonly used receptor models which their usefulness has been proven in studies of source apportionment of PAHs in the different environmental matrices. To data, PCA/MLR model has been the most widely used receptor model for PAH source identification studies, particularly in urban areas [12–15].

In addition to source apportionment studies, it is also very essential to assess the potential human health risks resulted from exposure to particulate PAHs in an urbanized area. According to the USEPA, human health risk assessment is a framework in which the nature and probability of adverse human health effects resulting from exposure to chemicals in a contaminated environmental media are evaluated. Human health risk is often assessed by means of two different methods [16]: Deterministic and probabilistic risk assessment models. Deterministic model is in fact a point estimation technique in which a single estimate of the probable risk is determined [17, 18]. This approach almost certainly overestimates the risks involved and tends to result in conservative, but hopefully not unrealistic, point estimates [19]. In contrast, the probabilistic approach attempts to characterize uncertainty and variability in the input (exposure) parameters [18] and consequently, a range of risk values is generated from probability distribution functions (PDFs) which is more sensible than the single point estimate of risk. Therefore, there is need to incorporate the uncertainty in the risks estimation process in order to provide more meaningful and realistic information to risk managers and the public.

As a developing country, Iran has witnessed a rapid growth both in urbanization and population during the last two decades. According to national censuses [20], nearly 74% of the Iran population lives in urban areas. Karaj city (near Tehran, capital of Iran) have experienced the high rate of urbanization growth (3.14%) in recent years which have made it one of the most densely populated cities of Iran [21]. This trend has also brought about increasingly environmental challenges including air pollution which can adversely affect the health of local inhabitants. However, data concerning the concentrations, potential sources and cancer risk of presence of PAHs in the street dust of Karaj urban area are not available. Since the street dusts can serve as an excellent carrier for PAHs urban in urban environment, this study focuses the fine fraction of the street dust (with particle size of<63) for PAH determination. The major objectives of the present study are therefore to (1) determine the concentrations, contamination level and distribution of PAH compounds in street dusts collected from Karaj urban area; (2) identify the potential sources of PAHs in street dusts by means of PCA/MRL receptor model and fingerprinting techniques; and finally (3) assess the human health (cancer) risks posed by PAHs in street dusts using a probabilistic-based model.

Materials and methods

Description of the study area

As the Iran’s fourth largest city, Karaj with a population of about 1.9 million people is situated in the 40 km west of the Tehran metropolitan area. The city is characterized by a semi-arid climate with annual rainfall of 246 mm and annual average temperature of 14 °C. Because of its economic importance and also proximity to country’s capital (i.e. Tehran), Karaj’s urban population has been growing considerably in recent years. According to Statistical center of Iran [20], Karaj is one of the most densely populated areas in Iran with approximately 1188 inhabitants per km². This city also serves as a hub connecting the major cities in the north and central Iran. Consequently, a significant number of vehicles travel through this area each day (approximately 70,000 vehicles/day), leading to the emergence of many environmental issues including air pollution on a local-scale.

Experimental procedures

Dust sampling and pre-treatment

A total of 30 representative street dust samples were collected from different functional areas within the entire city of Karaj (Fig. 1). The sampling campaign was set up during the winter of 2017 (from 15th December to 15th November). A detailed description of the sampling sites was given in Table 1. More than 250 g of street dust was gathered from both sides of the street and from pavement edges within a circle of 5 m radius around the each sampling point [22]. The samples were taken using polyethylene brush and dusts were slowly swept into a dustpan. Brushes and dustpans were cleaned by ultra-purified water between sampling campaigns. All samples were then sealed in aluminum foils for transport to the laboratory. In the laboratory, the dust samples were passed through a 63 μm nylon sieve and kept at −20 C° until analysis.

Fig. 1.

Map of sampling sites of street dust in Karaj (the number corresponds with that in Table 1)

Table 1.

Description of sampling sites of street dust in Karaj urban area

Sampling site no	UTM		Location	Characteristics of functional area
	Northing	Easting
S-1	3,964,465	501,758	Chalus road exit	Traffic area
S-2	3,964,389	499,866	Mofateh street	Traffic area
S-3	3,963,442	500,997	Chalus street	Traffic area
S-4	3,963,131	500,660	Karaj square	Traffic area
S-5	3,964,106	499,129	Taleghani crossroad	Traffic area
S-6	3,967,443	498,546	Islamic azad university	Traffic area
S-7	3,965,958	501,437	Azimieh quarter	Residential area
S-8	3,961,556	500,037	Haft-e-Tir blvd	Traffic area
S-9	3,965,348	494,959	Gloshar	Residential area
S-10	3,963,779	493,405	Karaj-Qazvin freeway	Traffic area
S-11	3,968,556	495,231	Zafar quarter	Residential area
S-12	3,966,151	492,863	Mellat park	Green/park area
S-13	3,962,149	497,430	Bus terminal	Traffic area
S-14	3,966,068	497,403	Beheshti park	Green/park area
S-15	3,964,001	497,582	Tohid square	Traffic area
S-16	3,960,449	500,333	Metro-Bus station	Traffic area
S-17	3,964,668	498,063	Jomhori blvd	Traffic area
S-18	3,965,336	498,093	Fateh garden	Green/park area
S-19	3,964,563	495,638	Dehghan vila	Residential area
S-20	3,968,104	496,612	Narges park	Green/park area
S-21	3,967,784	492,809	Kharazmi blvd	Traffic area
S-22	3,954,957	499,664	Fardis thermal power plant	Traffic-Industrial area
S-23	3,961,868	491,694	Mehrshar	Residential area
S-24	3,965,181	495,959	Dehghan Vila	Residential area
S-25	3,965,388	499,152	Family park	Green/park area
S-26	3,966,685	495,407	Ebnesina park	Green/park area
S-27	3,957,381	500,212	Malard road	Traffic area
S-28	3,963,529	498,417	Chahrsad dastgah	Residential area
S-29	3,969,361	497,199	Gloestan quarter	Residential area
S-30	3,963,324	494,659	Meharshar quarter	Residential area

Extraction and analysis of PAHs

The Soxhlet method was used to extract the PAHs from the dust samples. Briefly, 5.0 g of each freeze-dried dust sample was first spiked with 100 μl of deuterated surrogate standards (anthracene-d₁₀, naphthalene-d₈, chrysene-d₁₂ and perylene-d₁). The spiked samples were then extracted with 160 ml dichloromethane (DCM) by Soxhlet for 16 h (USEPA Method 3540C) [23]. Under a rotary evaporator, the resulting extracts were concentrated and transferred into a standard column filled with 10 g silica gel (60–100 mesh). The silica gel column was eluted with 25 ml of n-hexane and 40 ml of dichloromethane in order to isolate aliphatic fraction from aromatics (PAH) in the samples. The aliphatic fraction was discarded, while PAH fraction was re-concentrated using ultrapure nitrogen to exact volume of 1 ml under a vacuum rotary evaporator. Helium (as the carrier gas) was flowed into the column at a constant rate of 1 ml/min. Concentrations of 16 target PAHs (prioritized by the USEPA) in the concentrated extracts were finally determined by Gas Chromatography/Mass Spectrometric system (GC/MS) (model Agilent 6890 N equipped with a mass selective and a capillary column with the dimension of 30 m × 0.25 mm × 0.25 mm). In order to control the quality of laboratory procedures, sample duplicates along with the analytical spiked blanks and matrix spikes were included with all the samples. The spiked blanks were applied to check the recovery efficiencies. Recoveries of individual PAHs were found to be in the range of 70% to 120% with an average value of 103%. Additionally, the limit of detection (LOD) and limit of quantification (LOQ) were determined as the minimum concentration of the analyte in the spike samples that gives rise to a peak with a signal-to-noise ratio(S/N) equal or higher than 3 and 10, respectively. The LOD and LOQ values for all measured PAHs ranged from 0.1 to 0.8 μg/kg and from 0.3 to 2.7 μg/kg, respectively.

Source apportionment and statistical treatments

In order to determinate potential sources of PAHs in dust samples, a chemical fingerprinting technique i.e. Diagnostic Molecular Ratios (MDRs) and a receptor-oriented model .i.e., Principal Component Analysis (PCA) in conjunction with step-wise Multiple Linear Regression (MLR) were applied on the dataset.

A diagnostic molecular ratio usually involves pairs of PAHs with the same molecular mass and similar physicochemical characteristics undergoing similar fate processes in the environment [24]. The diagnostic ratios used in this study to distinguish between the pyrogenic and petrogenic sources of PAHs in street dusts are listed in Table 2. More than one diagnostic ratio is recommended to be used to confirm the results, because the use of a single ratio might lead to confounding results in source appointment.

Table 2.

Typical diagnostic ratios used for PAH source appointment in this study

PAH diagnostic ratios	Value	Possible source	References
BaP/BghiP	< 0.6	Traffic	[25]
	> 0.6	Non-traffic
ΣHMW/ΣLMW	> 1	Pyrogenic	[26]
	< 1	Petrogenic
BaA/(BaA + Chry)	< 0.2	Petrogenic	[27]
	> 0.35	Combustion
IcdP/(IcdP+BghiP)	< 0.2	Petrogenic	[28]
	0.2–0.5	Petroleum combustion
	> 0.5	Biomass and coal combustion
Flu/(Flu+Pyr)	< 0.4	Petrogenic	[29]
	0.4–0.5	Fossil fuel combustion
	> 0.5	Biomass and coal combustion
Ant/(Ant+Phe)	< 0.1	Petrogenic	[30]
	> 0.1	Pyrogenic

PCA/MLR, as a receptor oriented model, was also applied on the PAH dataset to determine the contribution percentage of each possible source of PAHs in street dusts. PCA itself is a pattern recognition process that reduces the set of original variables (i.e. measured PAH contents in samples) and extracts a small number of latent factors (principal components). In conjunction with MLR, PCA can quantitatively estimate the contribution of each possible source of PAHs in street dust samples. This model has been successfully employed by many researchers for source identification of PAHs in air-borne particulate matrices [31]. According to Hopke [32], PCA can be defined by the following equation:

$$x_{\mathit{ip}}=\sum _{p=1}^j{x}_{\mathit{ij}}{f}_{\mathit{jp}}+e_{\mathit{ip}}$$ 1

where x_ip is the ith PAH concentration for the pth sample; x_ij is the ith PAH concentration from the jth source; f_jp is the jth source to the pth sample and e_ip is the error or uncertainty involved in the analysis [33]. To execute the PCA/MLR model, the original dataset of PAH concentrations were first standardized by scaling the values to mean and standard deviation and then arranged in a matrix. In this matrix, rows exhibit the sampling sites and columns represent the individual PAH concentrations. PCA with varimax rotation and Kaiser normalization were applied to determine the source groupings of PAHs. Only factors with eigenvalues over 1 were considered as the principle components. MLR (in stepwise mode) was then performed on the extracted factors (score matrix). The contribution percentage of each possible source (i) of PAHs were determined based on the obtained regression coefficients, according to the following equation [34, 35]:

$$i=\raisebox{1ex}{$B_i$}\!\left/ \!\raisebox{-1ex}{$\sum B_i$}\right.\times 100$$ 2

where B_i is the regression coefficient of each source (factor) and ΣB_i represents the sum of all regression coefficients. All these statistical treatments were carried out using SPSS 23.0 and XLSTAT version 2016.02.

Probabilistic model of human health risk assessment

In the present study, we used a probabilistic model developed by the US Environment Protection Agency [36] to estimate the cancer risk of people exposed to the PAHs in the street dust.

Exposure is often defined in terms of chronic daily intake (CDI, mg/kg/day) and calculated individually for exposure pathway using following equations [36]:

$$\mathit{CD}{I}_{\mathit{\text{ingest}}}=\frac{C_{\mathit{\text{dust}}}\times \mathit{\text{IngR}}\times \mathit{EF}\times \mathit{ED}}{\mathit{BW}\times \mathit{AT}}\times \mathit{CF}$$ 3

$$\mathit{CD}{I}_{\mathit{\text{dermal}}}=\frac{C_{\mathit{\text{dust}}}\times \mathit{SA}\times \mathit{AF}\times \mathit{ABS}\times \mathit{EF}\times \mathit{ED}}{\mathit{BW}\times \mathit{AT}}\times \mathit{CF}$$ 4

$$\mathit{CD}{I}_{\mathit{\text{inhale}}}=\frac{C_{\mathit{\text{dust}}}\times \mathit{\text{InhR}}\times \mathit{EF}\times \mathit{ED}}{\mathit{PEF}\times \mathit{BW}\times \mathit{AT}}$$ 5

where CDI_ingest, CDI_dermal and CDI_inhale are the chronic daily intake associated with ingestion, and dermal contact and inhalation of dust particles, respectively. In these equations, C is the total BaP-equivalent concentration (BaP_eq) for 7 carcinogenic PAHs (BaA, Chry, BbF, BkF, BaP, DahA and IcdP) which is calculated as Total BaP_aq = ∑C_i × TEF_i where C_i is the concentration of individual carcinogenic PAHs in the dust sample and TEF_i is the toxicity equivalency factor relative to BaP as listed in Table 3. The parameters used in the health risk assessment along with their probability distributions for each receptor group (child and adult) are presented in Table 4. Probability distributions for these parameters (considered as random variables) were used to estimate chronic daily intake and cancer risk via three major exposure routes. For the total and BaPeq concentrations, the probability distributions were also evaluated by the goodness of fit chi-square (χ²) test.

Table 3.

Carcinogenic evidence and toxicity equivalency factor (TEF) for PAHs relative to BaP [37]

Name	Abbreviation	TEF	IARC (1984)a
Naphthalene	Nap	0.001	2B
Acenaphthylene	Acy	0.001
Acenaphthene	Ace	0.001
Fluorene	Fl	0.001	3
Phenanthrene	Phe	0.001	3
Anthracene	Ant	0.01	3
Fluoranthene	Flu	0.001	3
Pyrene	Pyr	0.001	3
Benzo[a]anthracene	BaA	0.1	2A
Chrysene	Chry	0.01	3
Benzo[b]fluoranthene	BbF	0.1	2B
Benzo[k]-fluoranthene	BkF	0.1	2B
Benzo[a]pyrene	BaP	1	2A
Dibenzo[a,h]anthracene	DahA	1	2A
Indeno[1,2,3-c,d]pyrene	IcdP	0.1
Benzo[g,h,i]perylene	BghiP	0.01	3

2A: probably carcinogenic to human 2B: possibly carcinogenic for human cancer, 3: not classifiable as carcinogenic to human and animal 

^a International Agency for Research on Cancer

Table 4.

Parameters used in health risk assessment and their probability distribution

Parameters	Units	Probability distribution	Receptor groups		Reference
			Children	Adults
Ingestion rate (IngR)	mg/day	LN	12.24, 1.90	26.95,1.88	[38]
Exposure frequency (EF)	day/year	LN	252, 1.01	252, 1.01	[39]
Exposure duration (ED)	year	U	0, 11	0, 52	[40]
Body weight (BW)	kg	LN	42.66, 17.24	77.45, 13.60	[41, 42]
Average life span (AT)	days		25,550	25,550	[43]
Dermal exposure area (SA)	cm2	LN	2196, 1.08	3067, 1.06	[44]
Dermal adherence factor (AF)	mg/cm2	LN	0.04, 3.41	0.02, 2.67	[45]
Dermal adsorption fraction (ABS)	–	LN	0.13, 1.26	0.13, 1.26	[45]
Inhalation rate (InhR)	m3/day	LN	14.10, 1.72	32.73, 1.14	[45]
CSFingestion	mg/kg/day	LN	7.3, 1.56	7.3, 1.56	[39]
CSFinhalation	mg/kg/day	LN	3.14, 1.80	3.14, 1.80	[39]
CSFdermal	mg/kg/day		37.47	37.47	[43]
Particle emission factor (PEF)	m3/kg		1.36 × 109	1.36 × 109	[43]
Conversion factor	kg/mg		1 × 10−6	1 × 10−6	[43]

LN denotes lognormal distribution (mean, standard deviation)

U denotes uniform distribution (minimum, maximum)

The following equation was applied to calculate the carcinogenic risk (CR) of PAHs in street dust samples [46]:

$${\mathrm{CR}}_{\mathrm{i}}=\sum {\mathrm{CDI}}_{\mathrm{i}}\times {\mathrm{CSF}}_{\mathrm{i}}$$ 6

where i = different exposure routes and CSF is carcinogenic slope factor. The total cancer risk is finally calculated as the summation of risks associated with each exposure route:

$${\mathrm{CR}}_{\text{total}}={\mathrm{CR}}_{\text{ingestion}+}{\mathrm{CR}}_{\text{inhalation}+}{\mathrm{CR}}_{\text{dermal contact}}$$ 7

Cancer risk estimates corresponding to the 95th percentile of the risk distribution are considered as unacceptable if their values exceed the safe level of 10⁻⁶ [47].

Uncertainty and sensitivity analysis

Uncertainty in risk assessment arises from the variability in individual human characteristics. In probabilistic risk method, such uncertainties are incorporated into risk calculation. It does so by the repeated random sampling of the input parameter distributions to produce an output (risk or exposure) distribution. In this study, the Monte Carlo simulation was applied to quantity and minimize the uncertainties associated with the above calculations using the software Oracle Crystal Ball 7.2 (version 7.2, Denver, USA). The simulation calculates the cancer risk by selecting a random value of each variable parameter according to their distribution function. The individual simulations runs were performed at 50000 iterations. Also, to find the most important parameters (variables) that effect the cancer risk, a sensitivity test was conducted using Crystal ball Tornado analysis.

Results and discussion

Concentration of total PAHs in street dusts

Summery statistics for the analyzed 16 PAHs in street dust samples are shown in Table 5. Most of PAH congeners (except acenaphthene) are present in detectable levels in all studied samples. This indicates that PAHs studied tend to accumulate in street dust particles, which might have implications (consequences) for public health and urban environmental quality in the investigated area. The total PAHs concentrations (Σ₁₆PAHs) ranged from 16.2 to 1236.2 with a mean of 624.5 μg/kg. Among the studied 16 PAHs, Chry, BkF, Flu, Pyr, BbF, BaP and BghiP compounds, accounting for 12.3%, 11%, 10.7%, 10%, 9.9%, 8.9% and 7.1% of ΣPAHs, respectively, are more abundant than the other PAHs. These PAHs are known to be indicators of combustion (pyrogenic) sources. For instance, the combustion of diesel fuel in vehicle engines may be associated with emission of BbF, BghiP, Flu, Pyr and Chry [48]. Benzo(a)pyrene (BaP) is the most well-studied carcinogenic PAH and, for this reason, it was often selected as the reference compound [49]. The results showed that the total BaP_eq (sum of 16 BaP_eq) of the measured PAHs in all street dust samples was in the range of 7.5–584.2 μg/kg, with a mean of 278.10 μgBaP_eqkg.

Table 5.

Concentration of 16 target PAHs (μg/kg) in dust samples

^aStandard deviation. ^b Not detected. ^c Sum of carcinogenic PAHs including BaA, Chry, BbF, BkF, BaP, DahA and IcdP. ^d Low molecular weight 2–3 ring PAHs. ^e High molecular weight 4–6 ring PAHs. ^f Sum of combustion -derived PAHs (BaA, BaP, BbkF, BghiP, Chry, Flu, IcdP and Pyr)

To put our data into perspective, the mean level of ∑PAHs found in this study was compared with data reported by other studies on PAHs levels in urban areas. Compared with other Iranian cities, the mean level of ∑PAHs in this study was similar to that from Tehran (330 μg/ kg) [50] and lower than that from Isfahan (1074.5 μg/ kg) [51] and Busheher (1116.02 μg/kg) [52]. In comparison with some major cities abroad, the average of total PAH concentrations recorded in this study is much lower than that from New Delhi (1100 μg/kg) [53], Birmingham (2020 μg/kg) [54], Shanghai (140,000 μg/kg) [55] and Sydney (2910 μg/kg) [56].

Spatial distribution and contamination level of PAHs in street dusts

Spatially, the highest concentrations of ∑₁₆PAHs are observed at sites 1, 3, 4 followed by sites 16, 13 and 22 (Fig. 2). Among these, sites 1 and 3 are near the Karaj-Chalus roadway. This road has usually a high daily traffic density, particularly on weekends and holidays. Sites 16 and 13 are located near bus stations (terminals) with a heavy traffic density of diesel buses and truck, which may lead to the higher concentrations of PAHs in street dust from these sites compared to other sites. The other sampling sites with a relatively high traffic flow (sites S-2, S-5, S-6, S-8, S-10, S-12, S-14, S-15 and S-17) also showed elevated concentrations of total PAHs (Fig. 2). Therefore, the re-deposition of PAHs-bearing particulates emitted from the vehicular exhaust could be responsible for elevated levels of PAHs in these sites. Site S-22 is situated near a thermal power plant. The combustion of fossil fuels (natural gas) in this plant may also be a potential source of PAHs into the surrounding environment. The industrial emissions in concert with the traffic -related emissions have substantially elevated the total PAH concentration in the street dusts at site S-22. In contrast, the lower concentrations of PAHs occurred in sites S-7, S-9, S-12, S-18, S-20, S-23, S-25, and S-29 which are located in residential or green/park areas, respectively. Lower levels at these sites could be attributed to low traffic density/congestion as compared to the other sites. So, the observed spatial pattern of the PAHs may be explained in terms of traffic density in streets and proximity to the sources.

Fig. 2.

Contamination levels of PAHs in street dusts collected from different sampling sites

Maliszewska-Kordybach [57] classification scheme was applied to assess the contamination levels of PAHs in each sampling site. According to the classification (Fig. 2), sites S-29, S-23, S-20, and S-25 are classified as “not contaminated” (<200 μg/kg), sites S-7, S-9, S-12, S-and S-18 as “slightly contaminated” (200 to 600 μg/kg), sites S-2, S-5, S-6, S-8, S-10, S-12, S-14, S-15, S-17, S-21 and S-24 as “contaminated” (600 to 1000 μg/ kg) and sites S-1, S-3, S-4, S-13, S-16, S-22 and S-27 are categorized as “heavily contaminated” (>1000 μg/kg).

Composition profile of PAHs in street dusts

The composition of PAHs in terms of ring numbers is depicted in Fig. 3. The results of compositional analysis indicated that in dust samples the level of low- molecular weight (LMW) PAHs varied from 20.1 μg/kg to 148.9 μg/kg (mean 63.8 μg/kg) accounting for 22%–70% of total PAHs. By contrast, high -molecular weight (HMW) PAHs varied from 22.3 μg/kg to 1099.4 μg/kg (mean: 813.9 μg/kg) accounting for 29%–95% of Σ₁₆PAHs. As compared to LMW-PAHs, much higher concentrations of HMW-PAHs were found in street dust samples particularly from the busy traffic areas (sites S-1, S-3, S-4, S-16 and S-22). At these sites, 6-ring PAHs and 5-ring PAHs contributed 27–77% and 34–75% of the total PAHs, respectively (Fig. 3).

Fig. 3.

Percentage composition of two, three and four, five and six- ring PAHs in street dust samples

The high percentage of HMW- PAHs implies the major sources of PAHs in the study area are related to pyrolytic processes, such as the incomplete combustion of fuels in vehicle engines. Due to their low vapor pressure, high molecular weight (HMW) PAHs are resistant to air–surface exchange and consequently have a high tendency to accumulate in solid phase (dust particles). Close to the roadways or streets, these particles rapidly deposited or re-suspended through aerodynamic process such as sedimentation and turbulent diffusion [58].

It should be note that at residential /green areas (e.g. sites S-2, S-7, S-9, S-11, S-12, S-14, S-19, S-20, S-21 and S-23) where the contamination level and traffic density are relatively low, the composition profile of PAHs was dominated by 3-ring PAHs and 4-ring PAHs (52–66%) (Fig. 3), indicating that sources other than traffic emissions most likely contribute to street dust PAH concentration in these sites.

PAH source apportionment

Molecular diagnostic (isomeric) ratios

Ratios of PAHs with same molecular weights have been widely used as chemical fingerprints for source apportionment in different environmental matrices. The isomeric ratios listed in Table 4 were employed in the study to identify the pyrogenic and petrogenic sources of PAHs in Karaj street dusts. The statistical summary (maximum, minimum and mean) of these ratios calculated for the dust samples in the three functional areas are also tabulated in Table 6. The ratios of Flu/ (Flu+Pyr) and IcdP/ (IcdP+BghiP) in the studied samples ranged from 0.11 to 0.52 and from 0.01 to 0.54 respectively, suggesting a mixed source profile of petrogenic and pyrogenic (combustion) related emissions for PAHs in the dust samples. On the other hand, the ratios of ∑HMW/∑LMW, BaA/(BaA + Chry) and Ant/(Ant+Phe) corroborated a strong contribution of pyrogenic (combustion) sources for majority of dust samples in the all functional areas. In addition, the average values of BaP/BghiP ratio (which is an indicator of traffic emissions) for samples taken from the busy traffic areas are found to be less than 0.6, revealing that vehicular traffic emissions are the main source of PAHs in these sites. The incomplete combustion of petroleum fuel (diesel or gasoline) in the vehicle engines, especially during the slow-down of the vehicle in the traffic jams, produces a large amount of PAHs [59]. However, at residential and green areas the BaP/BghiP ratio was more than 0.6 which shows that these compounds are not solely derived from traffic related sources. Based on the above compositional and individual ratio analysis it can be therefore postulated that mixed sources of both pyrogenic (e.g., fuel incomplete combustion and vehicle engine emissions) and petrogenic inputs (e.g., unburned oil or petroleum products) contributed to the total concentrations of PAHs in street dusts of the entire study area but the pyrogenic (combustion) sources appeared to be the major contributor to PAH composition profile in the dusts.

Table 6.

Statistical summary of molecular fingerprint ratios for street dusts sampled from the different functional area

Functional areas	BaP/BghiP			Ant/(Ant+Phe)			Flu/(Flu+Pyr)			IcdP/(IcdP+BghiP)			ΣHMW/ΣLMW			BaA/(BaA + Chry)
	Max	Min	Mean	Max	Min	Mean	Max	Min	Mean	Max	Min	Mean	Max	Min	Mean	Max	Min	Mean
Busy Traffic	0.55	0.12	0.35	1.22	0.34	0.52	0.52	0.40	0.47	0.47	0.35	0.41	5.37	0.75	4.52	20.31	0.95	2.11
Residential	1.25	0.45	0.93	0.45	0.98	0.32	0.42	0.02	0.24	0.42	0.54	0.33	3.33	0.21	3.21	0.33	0.42	1.24
Green/park	2.32	0.70	0.75	0.01	0.52	0.12	0.05	0.15	0.11	0.32	0.01	0.15	1.02	0.11	0.42	1.11	0.07	0.75

PCA-MLR receptor model

Although the molecular diagnostic ratios (MDRs) can give some indication about the different source of PAHs in airborne particles, they are capable to provide only qualitative information on potential sources of PAHs and for this reason these ratios are not sensitive enough to precisely determine the multiple sources of PAHs in the dust samples.

To accurately identify the possible sources of PAH and quantitatively apportion the contribution percentage of each possible PAH source, PCA/MLR receptor model was used in this study. PCA is performed on the original data matrix of PAH concentrations. Each extracted component can be considered as a source type or signature for PAHs. After Varimax rotation, three principal components with eigen values >1 were extracted (Table 7). These three components (PCs) explained more than 50% of the total variance of the original PAH dataset.

Table 7.

Rotated matrix of two extracted component

	PC-1	PC-2	PC-3
Nap	0.012	0.321	0.801
Acy	0.129	0.572	0.731
Ace	0.065	0.060	0.107
Fl	0.022	0.237	0.669
Phe	−0.121	0.566	0.211
Ant	−0.321	0.03	−0.133
Flu	0.149	0.168	0.650
Pyr	0.120	0.671	0.181
BaA	0.871	0.731	0.442
Chry	0.205	0.754	0.178
BbF	0.701	0.472	−0.158
BkF	0.665	0.132	0.181
BaP	0.871	0.622	−0.126
DahA	0.632	0.113	0.081
IcdP	0.721	0.389	0.033
BghiP	0.932	0.314	−0.379
Eigen Values	6.581	1.694	1.278
% of Total variance	51.314	27.705	11.981
Cumulative %	51.314	79.019	91.000

Extraction method: Principal component analysis

Rotation method: Varimax with Kaiser normalization

Bold emphasis signify the moderate and good correlations (>0.5)

The PC-1 contributing 51% of the total variance is heavily loaded on BaA, BaP, DahA, BghiP, BbF, BkF and IcdP. These 5–6 ring PAHs are known to be derived from high temperature combustion of fossil fuels in vehicular engines. Among these, BaA and BaF are indicators of gasoline-powered vehicles while BaP, DahA, BghiP and BkF are often emitted from combustion in diesel powered engines [60, 61]. Therefore, the first component can be attributed to pyrogenic sources related to traffic-emissions.

PC-2, accounting for 27% of the total variability, is predominantly composed of Acy, Phe, Pyr, BaA, Chry, BaF. These compounds are also related to combustion of fossil fuels such as gasoline or natural gas. Simcik et al. [62] considered BaA, Chry, Pyr and Phe as tracers for natural gas combustion. In most of Iran’s cities, natural gas is being used widely for cooking and heating in households or as the main energy source for the thermal power plants. In addition, a great number of cars in Iran have been equipped with natural gas powered engines which may act as a potential mobile source for PAHs in street dust of the urban areas. Thus, the second factor (PC-2) is inferred to have originated from pyrogenic sources related to traffic and stationary (households) emissions.

PC-3 with 12% of the total variance is dominated by Flu, Acy and Nap. These compounds belong to LMW PAHs and are predominately derived from petrogenic sources such as indiscriminate spillage of lubricating oil, crankcase oil or any other petroleum products from vehicles onto street surfaces [63, 64]. For instance, Nap is a major constitute of the crude oils and Flu is an alkyl-substituted PAH originated from oil products [65, 66]. Consequently, a petrogenic or petroleum -related source can be assigned to the third component.

We then estimate the percentage contribution of each potential source by means of multiple liner regression model (MLR). In this model, the standardized normal deviation of ΣPAH contents (z score of sum PAHs) and component (factor) scores were entered as dependent and independent variables, respectively. The MLR equation was calculated to be:

$$Z_{\mathit{\text{ΣPAH}}}=0.825F{S}_1+0.505F{S}_2+0.257F{S}_3\left(R^2=0.902\right)$$

where 0.825, 0.505 and 0.257 are the regression coefficient (B_i) of the three factor scores (FS) calculated by the MLR model with a stipulated minimum 95% confidence interval. The percentage contribution of each component (or source) as determined by Bi /∑B_i × 100 were found to be 51.98% for FS1 (pyrogenic source related to traffic emissions), 31.82% for FS2 (pyrogenic source related to traffic and stationary emissions) and 16.20% for FS3 (petrogenic sources) (Fig. 4). Therefore, the pyrogenic-related sources collectively accounted for almost 83% of the PAH concentration profile, demonstrating that pyrogenic sources have a dominant contribution to PAH levels in in street dust of Karaj urban area. These findings are in broad agreement with other previous studies conducted on street dust- bound PAHs e.g., in Asansol city-India [46], Dresden-Germany [67], Guangzhou-China [68] and Newcastle upon Tyne-England [69].

Fig. 4.

Dot plot (10 × 10) indicating the contribution percentage of each potential source of PAHs in street dust

Carcinogenic risk of PAHs in street dusts

It has been shown that chronic exposure to PAHs significantly increases the risk of developing cancer in human body [70]. According to the International Agency for Research on Cancer [71], seven PAH compounds i.e. BaA, BaP, BbF, BkF, Chr, DahA, and IcdP are considered as potentially carcinogenic to humans. As indicated in Table 5, the total concentration of these seven carcinogenic PAHs (Σ_7cPAHs) in dust samples ranged from 15.40 to 1075 μg/kg, with a mean concentration of 365 μg/kg and standard deviation of 647.31, making up 72.2% of the total PAHs. The results of good of fit Chi-square test (p < 0.05) also indicated that the measured total concentration of these 7 carcinogenic PAHs (relative to BaP concentration) followed a lognormal distribution (Fig. 5).

Fig. 5.

Lognormal fitting curve for seven carcinogenic PAHs (relative to BaP concentration) in dust samples

According to eqs. (3) through (7) and considering the different health characteristics of the two receptor group (adults and children), cancer risks resulted from exposure to PAHs were calculated. The risk estimation was made at the 95% confidence interval using the Monte Carlo simulation. The Monte Carlo simulation assigns probability density functions (PDFs) to each parameter, PDFs repeatedly randomly selects values from each parameter’s PDF, then enters these values into the risk [17]. The predicated probability density plots of the total carcinogenic risk for children and adults were depicted in Fig. 6a-b, respectively. The overall cancer risk varied from 1.90 × 10⁻¹⁰ to 8.58 × 10⁻³ with a mean of 2.69 × 10⁻⁴ for children. The 95th percentile of cancer risk was found to be 8.43 × 10⁻⁴ which is higher than the lower end of the acceptable range for carcinogenic risk (1 × 10⁻⁶, one case cancer per million people), indicating a potential risk of cancer may be developed for children in the study area.

Fig. 6.

Predicated (forecast) probability density plot of the total cancer risk for children (a) and for adults (b)

For adults (Fig.6b), the total cancer risk of PAHs lies between 7.44 × 10⁻¹² and 2.65 × 10⁻⁴ with a mean of 1.04 × 10⁻⁵. The 95th percentile of the cancer risk for adults was 3.34 × 10⁻⁵ which is again greater than the acceptable level of 10⁻⁶, suggesting high potential cancer risk for adults. As it is clear from the results, at the 95th percentiles the total cancer risk for children was significantly (2.5 times) higher than that for adults. Generally, physiological or behavioral characteristics of children (e.g., increased gastrointestinal absorption or hand-to mouth activity) make them more susceptible to environmental contaminants as compared to adults [72, 73].

The contribution of each exposure pathway to the total cancer risk was also quantitatively determined for the two age groups at the 95th percentiles. For children, the carcinogenic risk via dust ingestion, inhalation and dermal contract pathways is 1.83 × 10⁻⁵, 7.38 × 10⁻⁸ and 2.0 × 10⁻⁹, respectively. The contribution percentage of exposure pathways based on their cancer risk levels was in the order of ingestion (94%) > dermal contact (4%) > inhalation (2%) (Fig. 7a). As it can be seen, exposure from inhalation pathway is substantially negligible for children compared with the other two exposure routs.

Fig. 7.

Contribution of each exposure pathway to the total health risk of PAHs at 95th percentiles for children (a) and adults (b)

For adults, the 95th percentile of the cancer risk through dust ingestion, inhalation and dermal contact is 1.79 × 10⁻⁵, 1.44 × 10⁻¹² and 4.50 × 10⁻³, respectively. The contribution percentage of each exposure pathways for adults decreases in the following order: ingestion (46%) > dermal contact (36%) > inhalation (18%) (Fig. 7b).

The cancer risk level via the ingestion pathway for children was 2 times higher than that of adults. In addition, the carcinogenic risk incurred from inhalation and dermal contact pathways for adults was almost 10 times higher than that of children. The high level of cancer risk via ingestion for children can be attributed to high rate of dust ingestion (InR) as well as low body weight (BW) of children as compared to adults. Conversely, the SA (dermal exposure area) and ED (exposure duration) of adults are higher than those of children. Similar findings with this study have also been reported in many other studies on PAHs in urban street dusts [e.g., 8, 72–74].

Sensitivity of risk to input parameters and uncertainty analysis

In order to determine which input parameters (or their probability distributions) have the greatest effect on the final risk distribution, a sensitivity analysis was conducted using the Monte Carlo simulation. During the simulation, the normalized rank correlation coefficients were calculated to assess the sensitivity of each parameter relative to another one. Figure 8a-b demonstrate the results of sensitivity analysis in the form of Tornado plots for the two receptor groups.

Fig. 8.

The sensitivity analysis results on the exposure parameters to the cancer risk model for children (a) and adults (b)

For both children and adults, BaP_aq concentration, ED (exposure duration), IngR (dust ingestion) and SA (dermal exposure area) with sensitivity values of 0.45–0.54, 0.24–0.35, 0.19–0.31, and 0.10–0.21 respectively, were the most sensitive variables which lead to a variance in cancer risk assessment. The exposure parameters associated with inhalation pathway (InhR and CSF_inhalation) were almost zero for the two age groups. The sensitive value for BW was negative, confirming that the cancer risk level is inversely correlated with BW of children and adults. Therefore, to enhance the accuracy of risk estimates, risk assessors should focus on a better characterizing the probability distribution of the BaP_aq concentration, ED, IR and SA parameters.

It should bear in mind, however, that health risk assessment is inherently associated with uncertainty arising from the lack of knowledge about the factors influencing on it. Therefore, there still exist other uncertainties in the cancer risk assessment process that can propagate into the final risk estimate. For instance, uncertainty may be involved in the cancer risk assessment due to the limited data on the dose-response relationship for carcinogenicity of mixture PAHs [40, 44]. Also, TEF values used for calculating the BaP equivalent concentration were obtained from animal experiments and uncertainties may be associated with extrapolating animal study results to humans. Furthermore, the exposure parameters used in this study based on the data from the USEPA and other relevant resources which can cause a bias or uncertainty in health risk estimation. Other uncertainties linked to effects of joint exposures or possible synergetic/antagonistic effects among PAH compounds were not also taken into account in the risk assessment model. In spite of these limitations, health risk assessment based on probabilistic approach reported in this study can be very informative since it considers the reliability of results and actual PAH profiles encountered in environmental setting.

Summery and conclusions

Urban street dust serves as a sink for various pollutants including PAHs released by a variety of sources in the urban areas. As a preliminary assessment, this study aims to provide initial insights on the contamination level, possible sources, and potential human health risks of PAHs in street dusts of a densely populated city in Iran. The results showed that the sum of sixteen PAHs (∑₁₆PAHs) in street dusts varied widely from 16.2 to 1236.2 μg/kg depending on traffic density in streets and proximity to the sources. The composition profile of PAHs was dominated by higher molecular weight PAHs with the highest level in the busy traffic area and the lowest in residential/green zone. Based on the results of qualitative MDR and quantitative PCA-MLR source appointment techniques, three significant sources of PAHs were tentatively apportioned: 52% of PAH burden was attributed to pyrogenic-mobile sources (combustion of either gasoline or diesel fuels in vehicle engines), 32% was ascribed to pyrogenic- stationary sources (natural gas combustion in households and thermal plant) and 16% was related to petrogenic sources (oil products). The pyrogenic sources collectively contribute 83% of anthropogenic PAHs to Karaj urban street dusts. Thus, PAHs in street dust of Karaj were mostly derived from pyrogenic (combustion–related) sources with some contribution from petrogenic sources. Considering the strong influence of traffic activities on the contamination status of street dusts in Karaj urban area, management strategies should be developed to control or minimize the traffic emissions in this densely populated city.

Carcinogenic risk assessment based on the probabilistic approach and Monte Carlo simulation showed that the cancer risk at the 95% percentiles was 8.43 × 10⁻⁴ for children and 3.34 × 10⁻⁵ for adults, suggesting that both children and adults likely to develop cancer via exposure to dust-borne PAHs. Among different exposure pathways, dust ingestion contribute most to the total carcinogenic risks for both receptor groups. On the basis of sensitivity analysis, it was also revealed that the observed cancer risk is highly sensitive to changes in BaP concentration, ED, SA and IngR parameters which must be better characterized to reach the accurate risk estimate results.

The findings of this study may serve as a framework for further assessment using finer size of atmospheric particulates (such as PM_2.5 or PM₁₀) and including other contaminants (such as toxic metals) to achieve a comprehensive insight into environment quality and human health status in Karaj urban area.

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.