Seasonal effect and source apportionment of ...

Atmospheric Environment 106 (2015) 178e190

Contents lists available at ScienceDirect

Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv

Seasonal effect and source apportionment of polycyclic aromatic hydrocarbons in PM2.5 Md Firoz Khan a, *, Mohd Talib Latif a, b, Chee Hou Lim b, Norhaniza Amil b, c, Shoffian Amin Jaafar b, Doreena Dominick a, b, Mohd Shahrul Mohd Nadzir a, b, Mazrura Sahani d, Norhayati Mohd Tahir e a

Centre for Tropical Climate Change System (IKLIM), Institute for Climate Change, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia School of Environmental and Natural Resource Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia c School of Industrial Technology (Environmental Division), Universiti Sains Malaysia, 11800 Penang, Malaysia d Environmental Health and Industrial Safety Program, School of Diagnostic Science and Applied Health, Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Jalan Raja Muda Abdul Aziz, 50300 Kuala Lumpur, Malaysia e Environmental Research Group, School of Marine Science and Environment, Universiti Malaysia Terengganu, 21030 Kuala Terengganu, Terengganu, Malaysia b

h i g h l i g h t s

g r a p h i c a l a b s t r a c t

Sixteen USEPA priority PAHs determined in PM2.5 at a tropical semiurban site. High molecular weight PAHs are significantly higher in PM2.5. The combustion of gasoline, diesel and heavy oil are dominant sources of PAHs. No potential carcinogenic risk of the airborne BaPeq was found at current site. Monsoon effect influences the PAHs distributions as well as health risk.

a r t i c l e i n f o

a b s t r a c t

Article history: Received 6 August 2014 Received in revised form 28 January 2015 Accepted 30 January 2015 Available online 2 February 2015

This study aims to investigate distribution and sources of 16 polycyclic aromatic hydrocarbons (PAHs) bound to fine particulate matter (PM2.5) captured in a semi-urban area in Malaysia during different seasons, and to assess their health risks. PM2.5 samples were collected using a high volume air sampler on quartz filter paper at a flow rate of 1 m3 min1 for 24 h. PAHs on the filter paper were extracted with dichloromethane (DCM) using an ultrasonic centrifuge solid-phase extraction method and measured by gas chromatographyemass spectroscopy. The results showed that the range of PAHs concentrations in the study period was between 0.21 and 12.08 ng m3. The concentrations of PAHs were higher during the south-west monsoon (0.21e12.08 ng m3) compared to the north-east monsoon (0.68e3.80 ng m3). The high molecular weight (HMW) PAHs (5 ring) are significantly prominent (>70%) compared to the low molecular weight (LMW) PAHs (4 ring) in PM2.5. The Spearman correlation indicates that the LMW and HMW PAHs correlate strongly among themselves. The diagnostic ratios (DRs) of I[c]P/I[c]P þ BgP and B[a]P/B[g]P suggest that the HMW PAHs originated from fuel combustion sources. The source apportionment analysis of PAHs was resolved using DRs-positive matrix factorization (PMF)-multiple linear regression (MLR). The main sources identified were (a) gasoline combustion (65%), (b) diesel and heavy

Keywords: Monsoon effect PAH diagnostic ratio Positive matrix factorization Health risk

* Corresponding author. E-mail addresses: mdfi[email protected], mdfi[email protected] (M.F. Khan). http://dx.doi.org/10.1016/j.atmosenv.2015.01.077 1352-2310/© 2015 Elsevier Ltd. All rights reserved.

M.F. Khan et al. / Atmospheric Environment 106 (2015) 178e190

179

oil combustion (19%) and (c) natural gas and coal burning (15%). The health risk evaluation, by means of the lifetime lung cancer risk (LLCR), showed no potential carcinogenic risk from the airborne BaPeq (which represents total PAHs at the present study area in Malaysia). The seasonal LLCR showed that the carcinogenic risk of total PAHs were two fold higher during south-westerly monsoon compared to northeasterly monsoon. © 2015 Elsevier Ltd. All rights reserved.

1. Introduction Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous, semivolatile, persistent organic pollutants (POPs) and also environmental carcinogens. PAHs are released into ambient air through the incomplete combustion of organic materials, with major anthropogenic sources including fossil fuel burning, motor vehicle emissions, waste incineration, oil refining, the coke and steel industry n et al., 2014; Harrison et al., 1996; and coal combustion (Calle Hedberg et al., 2005; Jang et al., 2013). In urban, industrial-urban and semi-urban areas, emissions from motor vehicles are recognized as the main source of PAHs in ambient air (Fang et al., 2006; Jiang et al., 2009; Sarkar and Khillare, 2013; Sharma et al., 2007). In general, sources of PAHs can be broadly classified into two main groups: pyrogenic and petrogenic, where the pyrogenic are known as the dominant sources. The pyrogenic sources include oil derivatives, coal combustion, natural gas, and traffic-related pollution, whereas petrogenic sources consist of direct contamination such as the spillage of oil products (Liu et al., 2009). Particle-bound PAHs are considered to be very hazardous to human health. The inhalation of PAHs has been associated with an increase in cancer risk (Armstrong et al., 2004). A study on occupation-related PAH exposure has reported increased lung and bladder cancer risks (Bosetti et al., 2007). Due to their toxicokinetic effects, seven PAHs congeners have been classified as carcinogenic by the United States Environmental Protection Agency (US EPA) (US EPA, 2010). These are benzo(a)anthracene, benzo(a)pyrene, benzo(b)fluoranthene, benzo(k)fluoranthene, chrysene, dibenzo(a,h) anthracene and indeno(1,2,3-cd)pyrene. At levels above 1.0 ng m3, benzo(a)pyrene has been predicted to cause a greater genomic frequency of translocation, micronuclei and DNA fragmentation. PAHs are also of great concern due to their non-carcinogenic effects, i.e. intrauterine growth restriction, bronchitis, asthma and asthmalike symptoms and fatal ischaemic heart disease (Choi et al., 2010). Based on the long list of health effects on humans, 16 PAHs have been selected by the United States (US) Environmental Protection Agency (EPA) and China EPA to be monitored by their regulatory bodies, while the European Union has selected 15 þ 1 (the additional was PAH highlighted by the joint Food and Agriculture Organization/World Health Organization (FAO/WHO) expert committee on food additives (JECFA)) priority PAHs; eight of them are also listed in the US EPA 16 PAHs (EU, 2005). As an industrial developing country, the emission of PAHs to the ambient air cannot be avoided in Malaysia. An initial study by Abas and Simoneit (1996) has suggested that organic matter in urban areas was derived from biogenic sources and the anthropogenic utilization of fossil fuel products, with greater PAH concentrations found during haze episodes. Omar et al. (2002) first presented vehicular emissions as the dominant source of PAHs in PM10 in Kuala Lumpur atmospheric particles, with benzo(g,h,i)perylene and coronene reported as the most abundant PAHs. A detailed analysis of the PAHs in total suspended particulate matter (TSP), during haze and non-haze episodes at two urban areas, revealed that biomass burning, vehicular emissions, urban activities and natural sources

are contributors to PAH concentrations back in 1997 (Abas et al., 2004). A further study by Omar et al. (2006) revealed that the ambient and street level distribution of PAHs were similar and were attributed to vehicular emissions. In addition, they also reported a different pattern of selected PAHs during haze episodes and that smoke haze particles had a potential health risk four times higher than that of street level particles. A study at a very busy highway toll station has shown that PAHs with car exhaust characteristics were present in plant leaves, which further indicates the influence of traffic on the concentrations of PAHs (Azhari et al., 2011). A recent study conducted by Jamhari et al. (2014) reintroduced Benzo(g,h,i)perylene as an abundant PAH in PM10 and further suggested traffic emissions are the main source of PAHs in Malaysian city areas. Overall, PAHs in urban and sub-urban areas in Malaysia have been associated with traffic and haze. Multivariate receptor models are very useful tools in the studies of source apportionment of pollutants, especially in environmental studies. This established method is also used in the studies of sources of PAHs. The most commonly and widely used receptor models are: a) chemical mass balance models (CMB) (Watson et al., 1990), b) positive matrix factorization (PMF) (Paatero and Tapper, 1994), c) UNMIX (Henry, 1987), d) principal component analysis coupled with absolute principal component score (PCA/APCS) (Thurston and Spengler, 1985). Among these multivariate receptor modelling techniques, PMF is the most preferred and trusted one. The first and foremost advantage of this procedure is that the prior source information, or priori knowledge, of pollutants is not necessary. This model uses a weighted least-squares fit, includes errors as an input and can impose non-negativity constraints, weighing each data point individually (Paatero, 1997; Paatero and Tapper, 1994). Missing values, noisy data, outliers, and values below detection limit can be treated and made use of in the PMF procedure (Baumann et al., 2008; Khan et al., 2012; Polissar et al., 1998a, 1998b). Application of PMF in the apportionment of particle-bound PAHs can provide robust and accurate results compared to the PCA-multiple linear regression (MLR). Studies of the source apportionment and health impact of PAHs are very important in Malaysia due to various sources of combustion, particularly from motor vehicles, industrial activities and biomass burning. Identification of the sources of PAHs in PM2.5 by a robust and accurate receptor model has yet to be performed in Malaysia. With the advancement of receptor modelling as well as analytical analysis, together with health risk assessment, accurate and detailed analysis of the distribution of PAHs is now possible. Therefore, the main objectives of this study are to investigate the seasonal distribution of 16 PAHs in PM2.5 ambient aerosol and to determine the source of 16 PAHs by means of a diagnostic ratio (DR)-PMF-MLR model. This study also aims to assess the health effects of PAHs using a health risk assessment approach which determines the lifetime lung cancer risk (LLCR) based on airborne BaPeq that represents the 16 primary PAHs in PM2.5.

180


the backward trajectories. Overall, the air mass was transported from the south-westerly direction in the month of June, 2013 to September, 2013 and the north-easterly wind was predominant in the month of December, 2013 to March, 2014.

2. Material and methods 2.1. Description of sampling site This sampling campaign was stationed on the rooftop of the Biology Building (5 storeys) at the Faculty of Science and Technology, Universiti Kebangsaan Malaysia (UKM), about 65 m above sea level. Located in a semi-urban area, UKM Bangi is about 20 km to the south of central Kuala Lumpur (Fig. 1). The nearest main roads are about 300 m from the site. Predominantly light duty vehicles travel on these roads. The two nearest highways (PLUS Highway and SILK Highways) are about 1e2 km away from the monitoring site, used by both heavy duty and light vehicles. In addition, the site itself is surrounded by dense forest. Sampling period covers two major monsoons of Malaysia i.e. the south-west monsoon (SW; June 21, 2013 to September 5, 2013) and the north-east monsoon (NE; January 29, 2014 to February 18, 2014). The total number of samples taken was 34 for the entire sampling period. The yearly ambient temperature and relative humidity in this area were 29 C (25 Ce36 C) and 80% (33%e100%), respectively. During the SW monsoon, the temperature and relative humidity were 29 C (25 Ce36 C) and 77% (33%e100%), respectively. However, in the NE monsoon, the prevailing temperature and relative humidity were 28 C (21 Ce38 C) and 74% (20%e100%), respectively (http:// www.wunderground.com). The backward trajectories calculated at the sampling location are shown in Fig. 2. The Hybrid SingleParticle Lagrangian Integrated Trajectory Model (HYSPLIT 4.9) and fire hotspot data from the Moderate Resolution Imaging Spectroradiometer (MODIS) were employed for biomass fire hotspots and

2.2. PM2.5 sampling The PM2.5 samples were collected on quartz microfibre filters (203 mm 254 mm, Whatman™, UK) using a PM2.5 Tisch High Volume Sampler at a flow rate of 1.13 m3 h1. As part of the preparation, the filters were prebaked at 500 C for 3 h to remove any deposited organic compounds. Prior to weighing, the blank filters were conditioned in a desiccator for 24 h to ensure the equilibrium of mass concentration. Likewise, after sampling, the exposed filter papers were left in a desiccator for 24 h prior to weighing. The filter papers were weighed using a five-digit high resolution electronic balance (A&D, GR-202, Japan) with a 0.01 mg detection limit. The filter samples were then refrigerated at 4 C until the extraction of PAHs was carried out. 2.3. Extraction of PAHs using solid phase extraction-silica column The filter samples were cut into pieces which were placed directly into 50 mL centrifuge tubes. Dichloromethane (DCM) (R & M Chemicals, UK) was used as extraction solvent. 20 mL of DCM were added into the centrifuge tube with filter samples. In the extraction procedure, ultrasonic vibration, centrifuge and mechanical shaking were applied as described by Sun et al. (1998). The samples were then sonicated in an ultrasonic bath (Elmasonic

Fig. 1. Sampling location of PM2.5 at Bangi area, Malaysia.


Fig. 2. The cluster of backtrajectories and biomass burning fire hotspot during (a) south-westerly (SW) and (b) north-easterly (NE) monsoon in 2013e2014.

181

182


S70H, Elma, Germany) for 20 min in total. The extraction solutions were then centrifuged at 2500 rpm (Kubota 5100, Japan) for 10 min before shaken using a vortexmixer for 10 min. The sonication and centrifuged steps were repeated for three times before the extract was filtered using glass microfibre filters (Whatman™, UK). The solution volume was then reduced to about 200 mL under a gentle stream of nitrogen gas (N2) before 800 mL of n-hexane was added to reconstitute the residue. The volume suppression process was repeated for two more times. Next, silica SPE cartridges (Lichrolut® RP-18, Merck, Germany) were used for the clean-up process and pre-concentration of the PAH samples and RP-18 was subjected to conditioning by 10 mL of n-hexane (Friendemann Schmidt, Germany) before the extraction solutions were loaded and passed through the cartridge under gentle vacuum. The RP-18 cartridges were then eluted by DCM:n-hexane (1:9) at a flow rate of 1 mL min1. The n-hexane was used as an eluent at this stage. The eluates were collected into 20 mL centrifuge tubes. The volume of eluates were further reduced under a gentle stream of N2 gas to 500 mL and reconstituted with n-hexane up to 1.5 mL into vial to be used in the GCeMS analysis.

are used with confidence as these molecules are quite stable (Alam et al., 2013). However, like every other analysis tool or method, there is a shortcoming (Yunker et al., 2002; Jamhari et al., 2014). The DR of low molecular weight (LMW) compounds (4-ring), which are less stable and more susceptible to atmospheric processes, requires well-defined samples to establish the threshold values (Alam et al., 2013). For example, ANT, B[a]A, B[a]P and dibenzo[a]pyrene are the most reactive compounds towards ozone oxidation, and due to rapid photodegradation, the atmospheric lifetime of these compounds is relatively short (Perraudin et al., 2007). Since well-defined samples are necessary to establish the threshold values (Alam et al., 2013), LMW compounds are therefore an issue in source apportionment analysis. To avoid these shortcomings, numerical i.e. the PMF has been used as an alternative and/or complementary tool to identify the sources of PAHs. As mentioned earlier, PMF imposes non-negativity constraints and actually weighs each data point individually. The factors in PMF are not necessarily orthogonal to each other which resembles the observation of real-source signatures that are also not orthogonal to each other therefore overcoming the limitations of DR (Aydin et al., 2014; Galarneau, 2008).

2.4. Analysis of PAHs using GCeMS The samples were analysed using gas chromatographyemass spectroscopy (GCeMS) (Agilent, 5975C, USA). A capillary column (HP-5MS) of internal diameter (id) 0.25 mm, length 30 m and thickness 0.2 5 mm was used with the GCeMS. The selected ion monitoring (SIM) mode was used to collect the data which gives more sensitivity than the full scan mode. External calibration was used for the quantification of each of 16 PAHs with the standard mixtures of PAHs (SS EPA 610 PAH Mix, Supelco, USA). The list of the US EPA 16 PAHs is: naphthalene (NAP), acenaphthene (ACP), acenaphthylene (ACY), anthracene (ANT), fluorene (FLR), phenanthrene (PHE), fluoranthene (FLT), pyrene (PYR), benzo(a) anthracene (B[a]A), chrycene (CHY), benzo(b)fluoranthene (B[b]F), benzo(k)fluoranthene (B[k]F), benzo(a)pyrene (B[a]P), indeno [1,2,3-cd]pyrene (I[c]P), dibenzo[h]anthracene (D[h]A), and benzo [g,h,i]perylene (B[g]P). In addition, at the beginning of the extraction, we added 500 ppb each of Chrysene-D12 (Supelco, USA) and Perylene-D12 (Supelco, USA) as surrogate standards to the randomly selected samples. The average recoveries (%) were 92 and 84 for Chrysene-D12 and Perylene-D12, respectively. The respective ranges of recovery were 76e106% and 76e90% for Chrysene-D12 and Perylene-D12. The overall average recovery (%) of surrogate standards of Chrysene-D12 and Perylene-D12 was used to make correction of the concentration of each of 16 PAHs. The overall recovery of each PAH ranges from 59% for NAP to 175% for B[a]P, determined from the standard mixtures of 16 PAHs (SS EPA 610 PAH Mix, Supelco, USA) by external calibration. The limit of detection (LOD) of each PAH was calculated as three times the standard deviation of the eight replicates. The estimated LOD (ng m3) of each PAH were: NAP e 0.08, ACP e 0.08, ACY e 0.07, ANT e 0.10, FLR e 0.12, PHE e 0.08, FLT e 0.10, PYR e 0.04, B[a]A e 0.13, CHY e 0.10, B [b]F e 0.10, B[k]F e 0.08, B[a]P e 0.08, I[c]P e 0.13, D[h]A e 0.11, and B[g]P e 0.05. 2.5. Source apportionment techniques 2.5.1. Diagnostic ratio (DR) The use of DRs is a common conventional method for determining the potential sources of PAH congeners. The biggest advantage of this method is that it is less complicated and easier to be interpreted than other methods. DRs of two PAH congeners or one PAH and the total PAHs are used as an indicator of particular source. The DR of higher molecular weight (HMW) (5-ring) PAHs

2.5.2. Positive matrix factorization (PMF) and multiple linear regression (MLR) PAH source apportionment analyses were conducted using the US EPA PMF 5.0 model of the United States Environmental Protection Agency (US EPA) as suggested by Norris et al. (2014). PMF is a factor-based receptor model that decomposes a matrix of sample data into two matrices, i.e. chemical compositions and the contribution of each factor to each sample. Mathematically, PMF can be defined as (Eq. (1))

X IJ ¼

P X

g ik f kj þ eij

(1)

K¼1

where, X IJ is the concentration of jth species on ith day, p is the number of factors, gik is the contribution of kth factor on the ith day, fkj is the factor profile of jth species on kth factor and eij is the residual matrix for the jth species measured in the ith sample. The PMF solution minimizes the object function Q with the adjusted value of g, f and p. Q is defined by (Eq. (2))

Q ðEÞ ¼

" #2 " #2 P m X n m X n X X X ij pk¼1 g ik f kj eij ¼ S ij S ij i¼1 j¼1

(2)

i¼1 j¼1

where Sij is an estimate of the uncertainty in the jth species in the jth sample. PMF 5.0 operates in a robust mode by default, which downweighs the outliers affecting the fitting of the contributions and profiles. To run PMF, two data files are needed as input for each sample: 1) concentration and 2) uncertainty. The concentration of each PAH was pretreated and validated based on the noisy or outliers, missing and/or values below method detection limit (MDL). The variables with outliers are excluded if there were any. The missing values were replaced with half of the mean value and the species with concentrations below MDL were replaced with the half of the MDL (Baumann et al., 2008; Polissar et al., 1998a, 1998b). The second data file is the uncertainty value of each variable by each sample. As we could not have any measurement or methodological data to calculate errors, the following empirical equation (Eq. (3)) was used to estimate the error of the species concentration:


sij ¼ 0:01 X ij þ X j

(3)

where sij is the estimated measurement error for jth species in the ith sample, X ij is the observed PAHs concentration and X j is the mean value of each PAH. The factor 0.01 was determined through trial and error procedures. Ogulei et al. (2006a; 2006b) uses this method in estimation of uncertainty. Thus, the measurement of uncertainty (S ij ) can be computed with the following (Eq. (4)):

S ij ¼ sij þ C 3 X ij

(4)

where sij is the estimation of measurement error (Eq. (4)) and C3 is a constant. This empirical procedure was used to estimate the uncertainty of variables if there were measurements or methodological data to estimate errors (Ogulei et al., 2006a,b). We also applied similar a step for the values below detection limit. Harrison et al. (2011) and Hedberg et al. (2005) applied this procedure of uncertainty estimation to their work. Our observations of each PMF run using the variability of C factor (Eq. (4) in the methodology of PMF 5.0) value are summarized in Supplementary Table 1. In the procedure of PMF 5.0, we selected the value of 0.2 for C as the end calculation was optimized with lower error (%) and the Q true/Q exp was 1.17. An additional 5% uncertainty was added to account for methodological errors in the preparation of filter papers, gravimetric mass measurements and preparing the calibration curves. The model output of source contribution is provided as normalized or dimensionless (average of each factor contribution is one). Therefore, the mass concentrations of the identified sources were scaled by using the following MLR analysis (Eq. (5))

Mi ¼ S0 þ

p X

S k g ik

(5)

k¼1

where, Mi is the concentration of total concentration of PAHs in ith sample, Sk is the scaling constant, and gik the source contribution (average ¼ 1) found in the result of PMF modelling. Several other researchers have successfully applied this MLR approach to express n et al., 2014; Hedberg et al., 2005; Khan the output of PMF (Calle et al., 2012).

LLCR ¼

X

183

BaPeq URBaP

(7)

BaPeq is estimated by Eq. (6). Recommended by the WHO, the inhalation cancer unit risk (URBaP) for PAHs is 8.7 105 which signifies that there are 8.7 cases per 100,000 people with chronic inhalation exposure to 1 ng m3 BaP over a 70 year lifetime (WHO, 2000). 3. Results and discussions 3.1. Level of PAHs associated with PM2.5 Table 1 shows a summary of the concentrations of PAHs in PM2.5. The individual and total PAHs data is shown as overall mean values and values for the SW and NE monsoon seasons. The average and ranges of total PAHs were 2.79 (0.21e12.08), 3.85 (0.21e12.08) and 1.85 (0.68e3.80) ng m3 for the samples collected during the total sampling campaign, the SW monsoon and NE monsoon respectively. The concentrations of PAHs during the SW monsoon are almost double the NE monsoon results which indicates more carbonaceous aerosol in the surrounding area during that time period. The high concentrations of particles during the SW monsoon, due to biomass burning and haze episodes in Southeast Asia, is expected to contribute to the amount of PAHs in ambient air. According to Anwar et al. (2010), the high amount of particles in ambient air during these episodes has the ability to trap local emissions, particularly from motor vehicles and industrial activities. The individual PAH concentrations in PM2.5 are shown in Table 1. The average concentrations of PAHs have the decreasing order of B [b]F > B[g]P > I[c]P > B[a]P > B[k]F > D[h] A > ACP > FLT > NAP > CHY > PYR > FLR > PHE > ANT > B[a]A > ACY. Overall, the 5- and 6-ring PAHs, i.e. B[k]F, B[a]P, B[b]F, I[c]P, D[h]A, and B[g]P, are dominant. The same results are reported for both the SW and NE monsoons (Table 1). However, the concentrations of 6ring PAHs in the overall dataset (40%) are higher than in the 5-ring PAH (38%). Furthermore, the 6-ring PAHs were more dominant in the SW monsoon than the NE monsoon (Fig. 3b and c). Lower concentrations are represented by the 2, 3- and 4-ring PAHs (NAP,

Table 1 Summary results of PAHs in PM2.5 samples 2013e2014.

2.6. Health risk assessment The most appropriate indicator to assess the carcinogenic potential of PAHs in air is B[a]P, as recommended by the World Health Organization (WHO) and it has been often used in relevant studies as a reference compound (WHO, 1987). B[a]P is the most widely studied PAH, and much of the information on the toxicity and occurrence of PAHs is based on this particular compound. However, the use of B[a]P alone might underestimate the carcinogenic potential of airborne PAH mixtures, since co-occurring substances are also carcinogenic (WHO, 1987). Therefore, the Benzo[a]pyrene equivalent concentration (BaPeq) was estimated using (Eq. (6))

X

BaPeq ¼

n¼1 X

Ci TEFi

(6)

i

Where Ci is the concentration of the ith target PAH, TEFi is the toxic equivalency factor of the ith target compound. The toxic equivalency factor (TEF) of the PAHs was taken from a study by Nisbet and LaGoy (1992). The carcinogenic risk of each PAH as a lifetime lung cancer risk (LLCR) was calculated using: (Eq. (7))

PAHs (ng m3)

PAHs Overall mean (range of concentration)

a

SW (range of concentration)

b NE (range of concentration)

NAP ACY ACP FLR ANT PHE FLT PYR B[a]A CYR B[k]F B[a]P B[b]F I[c]P D[h]A B[g]P Total PAHs

0.09 (n.d.e0.25) 0.03 (n.d.e0.16) 0.12 (0.02e0.65) 0.06 (n.d.e0.35) 0.04 (n.d.e0.16) 0.04 (n.d.e0.19) 0.09 (n.d.e0.37) 0.07 (n.d.e0.28) 0.04 (n.d.e0.16) 0.09 (n.d.e0.43) 0.25 (n.d.e0.97) 0.30 (0.04e1.08) 0.57(0.01e2.67) 0.36 (0.04e1.72) 0.16 (0.01e0.64) 0.54 (0.06e3.16) 2.79 (0.21e12.08)

0.10 0.04 0.18 0.09 0.06 0.06 0.16 0.12 0.06 0.13 0.33 0.37 0.75 0.51 0.22 0.81 3.85

0.08 0.03 0.07 0.04 0.02 0.02 0.04 0.04 0.03 0.05 0.18 0.23 0.42 0.22 0.11 0.31 1.85

n.d.: not detected. a SW: south-westerly. b NE: north-easterly.

(0.01e0.22) (n.d.e0.16) (0.02e0.65) (n.d.e0.35) (n.d.e0.16) (n.d.e0.19) (n.d.e0.37) (n.d.e0.28) (n.d.e0.16) (n.d.e0.43) (n.d.e0.97) (0.04e1.08) (0.01e2.67) (0.04e1.72) (0.01e0.64) (0.06e3.16) (0.21e12.08)

(n.d.e0.25) (n.d.e0.12) (0.03e0.21) (n.d.e0.10) (n.d.e0.05) (n.d.e0.08) (0.01e0.09) (0.01e0.07) (n.d.e0.06) (0.01e0.09) (0.05e0.40) (0.09e0.47) (0.13e0.89) (0.06e0.53) (0.04e0.27) (0.07e0.80) (0.68e3.80)

184


Fig. 3. Contributions of PAHs by number of rings (%) to total PAHs at (a) overall period, (b) south-westerly (SW) and (c) north-easterly (NE) monsoon.

ACY, ACP, FLR, ANT, PHE, FLT, PYR, B[a]A and CHY) which were relatively lower in the NE monsoon compared to the SW monsoon. The seasonal variability of the paired samples t-test shows a significant difference (t ¼ 2.23, p < 0.05) between the SW and NE monsoons. The Klang Valley in Peninsular Malaysia was heavily affected by transboundary haze pollution from Sumatra of Indonesia during the SW monsoon (Koe et al., 2001). A study by Juneng et al. (2011) found that local meteorological conditions governed the largest day-to-day variations of PM10 concentrations during the dry SW monsoon. However, during the wet NE monsoon, the transboundary pollution is thought to have an effect on the concentration of aerosol particles from the Chinese region. Thus, a strong seasonal dependency of the total PAHs was also

observed in this study. Direct comparison of PAHs in PM2.5 found in this study with those found in the surrounding area in Malaysia is not possible as to date all reported work have focused on PAHs in PM10. For example, studies by Omar et al. (2002, 2006) reported that the concentrations of PAHs in PM10 in the capital city of Kuala Lumpur were 6.28 ± 4.35 ng m3 and 3.10 ± 2.92 ng m3, respectively. More recently, Jamhari et al. (2014) conducted a study to determine sources of PAHs in PM10 at similar location to the present study and found that total concentration of PAHs ranged from 1.64 to 3.45 ng m3. All these earlier studies found that vehicular emission sources are the main contributor of total PAHs in the particulate matter.

Table 2 Comparison of ambient PAHs concentrations (ng m3) with other studies. City

Location type

PM fractions

Concentrations (ng m3)

References

Bangi, Malaysia Bangi, Malaysia Kuala Lumpur, Malaysia Kuala Lumpur, Malaysia Qingdao, China Huaniao Island, China Mount Taishan, China Guangzhou, China Hung Hom, Hong Kong Saitama, Japan Seoul, South Korea Barcelona, Spain Montseny, Spain Atlanta, USA North Carolina, USA Houston, USA Rio de Janeiro, Brazil Sao Paulo, Brazil

Semi-urban Semi-urban Urban Urban Urban roadside Island Mountain Urban Roadside Roadside Urban Urban Rural Urban Urban Urban Urban Urban

PM2.5 PM10 PM10 PM10 PM2.5 PM2.5 PM2.5 PM2.5 PM2.5 PM2.5 PM2.5 PM1 PM1 PM2.5 PM2.5 PM2.5 PM2.5 PM2.5

2.79 2.54 6.28 ± 4.35 3.10 ± 2.92 89 5.24 ± 5.81 6.88 15.46 33.96 3.37 ± 2.90 26.3 ± 29.4 4.31 0.85 3.16 1.91 0.78 3.80 ± 2.88 10.8

This study Jamhari et al., 2014 Omar et al., 2002 Omar et al., 2006 Guo et al., 2009 Wang et al., 2014 Li et al., 2010 Gao et al., 2012 Guo et al., 2003 Naser et al., 2008 Park et al., 2002 van Drooge et al., 2012 van Drooge et al., 2012 Li et al., 2009 Pleil et al., 2004 Fraser et al., 2002 Oliveira et al., 2014 Bourotte et al., 2005


Table 2 presents a summary of results from selected studies worldwide on PAHs in PM2.5. In general, the total PAHs concentrations at the current study location are comparatively lower than the most of the locations reviewed in Table 2. Concentrations of total PAHs in PM2.5 varied with locations due to various factors such as variations in population density, vehicle volume, extent of urbanization and industrialization in the area, meteorological conditions and geographical locations. For example mega urban cities like those in China, Hong Kong (Guo et al., 2003), Seoul, South Korea (Park et al., 2002) and Rio de Janeiro and Sao Paulo, Brazil (Oliveira et al., 2014; Bourotte et al., 2005) exhibited much higher PAHs concentration than the present study which has a much lower population density. However, two locations in USA (Pleil et al., 2004; Fraser et al., 2002) and a location in Spain (van Drooge et al., 2012) that showed lower concentration of PAHs compared to all other sites. 3.2. Spearman correlations and types of traffic dependency among the PAH compounds To understand the correlation among the PAHs, we conducted a Spearman correlation analysis as shown in Supplementary Table 2. We focused only on the correlation coefficients (r) of 0.80 where significant values were highlighted for p < 0.05 and p < 0.01. The results for the datasets as a whole show that the lighter or smaller PAH compounds correlate strongly among themselves and the

185

heavier or bigger compounds also correlate strongly among themselves. There is an obvious separation of low and high molecular weight molecules within the individual PAHs. The correlation between these two groups was found to be poor with positive r values. However, the correlation results differ between the samples in the SW and NE monsoons. The NE monsoon samples in particular show no significant correlation among the LMW PAHs. The pair of each PAH consisting of HMW PAHs shows significantly strong correlations at each of three situations (the whole dataset, the SW monsoon dataset and the NE monsoon dataset) providing the evidence that the PAHs of this group might share a similar emission source. Light vehicles, heavy vehicles and total number of vehicles data was applied to the model to study the dependency of motor vehicle type on the release of PM2.5 PAH compounds. The traffic data was made available by the Malaysian Public Works Department (PWD) under the Malaysian Ministry of Works (MOW). According to PWD, light vehicles include motor cars and motor cycles which are less than 3000 kg while heavy vehicles weighed more than that and include lorries, trucks, vans, and buses. The latest 2 years available traffic data (2010 and 2011) recorded by PWD for 16 h daily (6:00e22:00) were used to represent the traffic data for the study period. This is base on the patterns of motor vehicles surrounding the sampling area are almost consistent for the past 5 years. For a clearer understanding, the 16 PAHs have been classified into five groups based on their chemical characteristics (i.e.: 2-ring,

Fig. 4. Sensitivity of PAHs to the traffic frequency by (a) Light vehicle, (b) Heavy duty vehicle and (c) Total vehicle.

186


3-ring, 4-ring, 5-ring and 6-ring). Sensitivity analysis, using the one-factor-at-a-time (OAT) technique and standardized regression determination coefficient (R2) values, was used to examine the level of importance of each of the five groups of PAH compounds towards the different type of vehicles. The results of the dependency of motor vehicle type on the concentrations of PAHs are shown in Fig. 4 (a, b and c). Each type of vehicle gives a unique set of results for the five PAH groups. Overall, the groups least affected by light, heavy and total numbers of vehicles are 4-ring and 3-ring with percentage ranges between 1% and 4%. The 5-ring are 18%, 25% and 18% for LV, HV and total vehicles, respectively. In contrast, light vehicles influence the 4-ring group the most with a percentage of 32%. Heavy vehicles influence the 3-ring group the most (30%) while the total number of vehicles influences the concentrations of the 6-ring group the most (45%). A strong dependency was found for HMW PAHs on the total number of vehicles. 3.3. Source apportionment of PAHs 3.3.1. Diagnostic ratios (DRs) One way to identify sources of PAHs is to employ a qualitative identification. Table 3 shows the DRs of the selected PAHs related to their respective sources while Fig. 5 shows bi-variate plots of PAHs ratios illustrating the different source types. The DR of ANT/ ANT þ PHE is about 0.46, indicating the dominance of a pyrogenic source. An indication of a fuel combustion source was revealed based on the DR of FLT/FLT þ PYR. The DR of BaA/BaA þ CHY suggests that the combustion of coal is predominant to the pyrogenic sources of these PAHs. The characteristics of DRs for larger molecules of PAHs (5-ring) e.g. I[c]P, B[g]P and B[a]P were also reported

Table 3 Diagnostic ratios (DRs) of PAHs associated with PM2.5. DRs

Indicator sources

0.1: Pyrogenica,b,c 0.4: Pyrogenicb,d 0.4e0.5: fuel oilb,d >0.5: Grass, wood, coalb,d 0.6e0.7: Diesele,f 0.4: Gasolinee,f BaA/ 0.35: Pyrogenicg,b,h 0.2e0.35: Coalg,b,h >0.5: Wood burningg,b,h IcP/ 0.2: Pyrogenicb,c,i,j 0.2e0.5: Petroleum/gasolineb,c,i,j >0.5: Grass, wood and coalb,c,i,j 0.82: Oil combustionb,c,i,j 0.35e0.70: Dieselb,c,i,j BaP/ 0.6: Trafficb,c BaA/ 0.66e0.92: Woodk 0.54e0.66: Industryk CHY

ANT/ ANT þ PHE FLT/ FLT þ PYR

a b c d e f g h i j k

Pies et al. (2008). Yunker et al. (2002). Br€ andli et al. (2008). De La Torre-Roche et al. (2009). Sicre et al. (1987). Rogge et al. (1993). Manoli et al. (2004). Akyüz and Çabuk (2010). Manoli et al. (2004). Khalili et al. (1995). Dickhut et al. (2000).

This Factor 1 Factor 2 Factor 3 study 0.46

0.54

0.46

0.44

0.53

0.55

0.69

e

0.33

0.31

0.50

0.32

0.41

0.42

0.39

0.47

0.76

0.49

0.39

4.69

0.50

0.46

0.99

0.46

in Table 3 as well as in the bi-variate plots (Fig. 5a). The DRs values of I[c]P/I[c]P þ BgP and B[a]P/B[g]P are about 0.41 and 0.76 respectively, which confirms that these representative HMW PAHs are from fuel combustion sources. The average ratio value of BaA/ CHY is 0.50 which highlights the strong influence of industrial emissions. Thus, the main source of PAHs was from pyrogenic origin where fuel combustion, coal burning and industrial emission identified based on their respective threshold values published in the literature (Akyüz and Çabuk, 2010; Br€ andli et al., 2008; De La Torre-Roche et al., 2009; Dickhut et al., 2000; Khalili et al., 1995; Manoli et al., 2004; Pies et al., 2008; Rogge et al., 1993; Sicre et al., 1987; Yunker et al., 2002). 3.3.2. PMF model Introducing a 34 17 matrix (sample number 17 PAHs (including total PAHs)) data set to US EPA PMF 5.0, three factors were obtained as presented in Fig. 6a. Before further discussion, we would like to highlight the steps undertaken during the model run that led to the presented results. The concentrations of each variable were scanned using the inbuilt time series function to determine whether the expected temporal patterns are present in the data, and if there are any unusual events. Based on the temporal pattern, the extreme events were noted for possible exclusion from the dataset. Then, the base model was run using the PAH data with a specific seed. In this study, 20 runs and a seed of 25 were selected. The numbers of factors were chosen depending on the understanding of the sources. The lowest or optimized goodness-of-fit by Q value was selected as Q (robust) (calculated excluding the outliers). On the other hand, Q (true) was calculated including all points. The lowest values of Q (robust) and Q (true) was 498.7 for both, for 34 samples and 172 computational steps. The Q (theoretical) can also be calculated using the formula as nm-p (n þ m), where n is the number of species as 17 (including total PAHs), m is the number of samples as 34, and p is the number of factors as 3. Thus, the value of Q (theoretical) was estimated as 425. Bootstrapping was also performed using a selected base run number, seed number, number of bootstraps, minimum correlation, and block size which were 8, 9, 100, 0.6 and 4, respectively. The optimized values of Q (robust) and Q (true) were generated as 498.7 and 498.7, respectively and 172 computational steps were converged. The stability and uncertainty of the solution was compared to the output of the base run as shown in Supplementary Fig. S1 by bootstrapping procedure. Fpeak rotations were made based on 1 to þ1. The output results without rotation were chosen to describe the data as the results were interpretable and showed clear physical meaning (Norris et al., 2014). Factor 1: The individual PAH tracers heavily contributed to Factor 1 were B[k]F, B[a]P, B[b]Fth, I[c]P, D[h]A and B[g]P (Fig. 6a). This factor reflects the influence of the molecules with 5-ring. The 5-ring and larger PAHs were released into atmosphere entirely from a vehicle source (Venkataraman and Friedlander, 1994). Some studies described I[c]P and B[g]P as related to a gasoline source (Miguel et al., 1998). B[k]F, B[b]F, B[a]P, I[c]P and D[h]A were indicative of gasoline exhaust according to Gupta et al. (2011). Okuda et al. (2010) also described a gasoline source due to the dominance of HMW PAHs. Furthermore, the molecular DR value of B[a]P/B[g]P in this study confirms that these larger molecule PAHs €ndli et al., 2008; Yunker et al., originate from a traffic source (Bra 2002). The HMW PAHs are generated significantly from pyrogenic sources. As evidenced by the DR value of I[c]P/I[c]P þ B[g]P for overall data (0.41) and Factor 1 (0.42), the combustion of petroleum €ndli oil or gasoline produces the HMW PAHs in most cases (Bra et al., 2008; Khalili et al., 1995; Manoli et al., 2004; Yunker et al., 2002). Thus, the predominance of B[k]F, B[a]P, B[b]Fth, I[c]P, D[h] A and B[g]P in Factor 1 is an indication that the source is gasoline or


187

Fig. 5. Bivariate plots for the ratios of (a) BaA/BaA þ CHY vs. IcP/IcP þ BgP and (b) BaA/CHY vs. FLT/FLT þ PYR.

Fig. 6. (a) Source profiles of PAHs by PMF 3.0, and (b) comparison of the estimated PAHs modelled by PMF and the observed PAHs in this study.

fuel oil combustion. Factor 2: In this factor, the dominant individual PAHs were ANT, PHE, FLT, PYR, B[g]P, and I[c]P. The mass fraction contribution of PYR along with PHE and FLT in this factor is higher compared to other variables (Fig. 6a). Several studies have shown that PYR is an indicator tracer for emissions from diesel combustion (Guo et al., 2003; Ho et al., 2002; Khalili et al., 1995; Miguel et al., 1998; Sarkar and Khillare, 2013). The DR ratios of FLT/FLT þ PYR for overall data and Factor 2 show the value of 0.53 and 69, respectively, which imply that these compounds are the atmospheric signatures of diesel combustion (Sicre et al., 1987; Rogge et al., 1993). However, the combination of FLR, FLT and PYR with a few HMW PAHs, e.g. B[b]F and I[c]P, are typically indicative of oil combustion (Harrison et al., 1996). Thus, Factor 2 is associated with a diesel and heavy oil combustion source. Factor 3: NAP, ACY, ACP, FLR, and ANT are 2- and 3-ring PAHs and are abundant in Factor 3 (Fig. 6a). Yunker et al. (2002) suggested that the generic source of LMW PAHs (2- and 3-ring) is of

petrogenic origin. In contrast to LMW PAHs, the HMW PAHs (4ring) are emitted into ambient air from a pyro-synthesis or pyrolysis source. However, the DR values of ANT/ANT þ PHE for overall data and Factor 3 by factors of 0.46 and 0.44, respectively, show that these compounds are associated with a pyrogenic source compared to the studies performed by Pies et al. (2008), Yunker et al. (2002), and Br€ andli et al. (2008). However, a study by Alam et al. (2013) suggested that ANT/ANT þ PHE should not be used as an indicator to differentiate source as the reactivity and volatility of LMW PAHs are faster than HMW PAHs. Lighter PAHs were dominant in a natural gas source. For example, the LMW PAHs, e.g. ANT and FLR, evaporate from the combustion of natural gas (Hanedar et al., 2013). FLR and ANT are tracers representing a coal combustion source (Liu et al., 2009). Thus, Factor 3 is related to natural gas and coal burning sources. The source contribution by each factor was scaled using the PMF-MLR procedure. The PMF outputs of three source contributions were regressed against total PAHs (p < 0.01, R2 ¼ 0.99, n ¼ 34).

188


Fig. 7. Contribution of various sources (%) of PAHs at (a) overall period, (b) south-westerly (SW) and (c) north-easterly (NE) monsoon.

The correlation of the predicted and the measured PAHs shows a strong and significant correlation (R2 ¼ 0.97, p < 0.01, conf. int. ¼ 0.95) (Fig. 5b). Fig. 7 shows the contribution of each source in the overall data, the SW monsoon data and the NE monsoon data. For overall data, gasoline, diesel and heavy oil combustion, natural gas and coal burning sources contributed 65%, 19%, and 15% respectively, leaving 1% unaccounted or determined for within total PAHs (Fig. 7). Total PAHs originating from the gasoline source was slightly higher in the north-east than the south-west monsoon data

sets, followed by the diesel and heavy oil combustion sources. The local circulation of air pollutants from the Kuala Lumpur city centre to the southern part of the sub-urban areas during north-east as mentioned by Latif et al. (2012) might pronounce the gasoline source at this location. 3.4. LLCR of PAHs Table 4 shows the toxic equivalency factor (TEF), or relative

Table 4 The BaPequivalents (BaPeq) and lifetime lung cancer risk (LLCR) of individual and total PAHs. PAHs

NAP ACY ACP FLR ANT PHE FLT PYR B[a]A CYR B[k]F B[a]P B[b]Fth D[h]A B[g]P I[c]P P Total PAHs a b

TEFa

0.001 0.001 0.001 0.001 0.01 0.001 0.001 0.001 0.1 0.01 0.1 1 0.1 0.1 1 0.01 e

Nisbet and LaGoy (1992). WHO (2000).

BaPeq (ng m3)

Unit risk (URBaP)b

Overall

SW

NE

9.6 105 3.4 105 1.3 104 6.3 105 3.5 104 4.0 105 7.2 105 9.0 105 4.1 103 8.5 104 24.6 104 289.1 103 542.7 104 358.5 104 1574.3 104 54.3 104 572.6 103

9.9 105 4.3 105 1.8 104 9.2 105 5.5 104 6.4 105 1.6 104 1.2 104 60.3 104 13.4 104 330.1 104 373.3 103 747.2 104 509.5 104 2176.6 104 80.5 104 766.3 103

8.0 105 2.8 105 6.8 105 3.7 105 1.8 104 2.2 105 4.1 105 3.7 105 26.6 104 4.7 104 175.8 104 227.7 103 420.5 104 223.4 104 1118.4 104 31.0 104 428.2 103

8.7 105

LLCR Overall

SW

NE

8.3 109 3.0 109 1.1 108 5.5 109 3.1 108 3.5 109 6.3 109 7.8 109 3.6 107 7.4 108 2.1 106 2.5 105 4.7 106 3.1 106 1.4 105 4.7 107 5.0 105

8.6 109 3.7 109 1.6 108 8.0 109 4.8 108 5.6 109 1.4 108 1.1 108 5.3 107 1.2 107 2.9 106 3.3 105 6.5 106 4.4 106 1.9 105 7.0 107 6.7 105

7.0 109 2.4 109 5.9 109 3.2 109 1.6 108 1.9 109 3.6 109 3.2 109 2.3 107 4.1 108 1.5 106 2.0 105 3.7 106 1.9 106 9.7 106 2.7 107 3.7 105


potency, BaPeq of individual PAHs and the respective LLCR. The carcinogenic activity of the integrated PAHs was 572.6 103 ng m3, which is two fold higher in the SW monsoon compared to that in the NE monsoon. The BaPeq of individual B[a]P was more than half of that shown by the total PAHs. Other studies also showed that the carcinogenic potency of B[a]P is in the range of 27e67% of the carcinogenic activity of total PAHs (Petry et al., 1996; Castellano et al., 2003). Thus, B[a]P has been regulated in many countries and has a set guideline value. For example, the WHO recommended unit risk of B[a]P is 8.7 105 (ng m3) per year (WHO, 2000) and the target value of B[a]P set by the European Union (EU) is 1 ng m3 per year in PM10 (EU, 2008). The second most potent BaPeq PAH was BgP which poses a carcinogenicity risk of 27% compared to total PAHs. The BaPeq of BgP was also two fold higher in the SW monsoon samples when compared the NE monsoon samples. The LLCR was also estimated based on the BaPeq of each PAH and the inhalation cancer unit risk (UR) of B[a]P. The value of UR advised by WHO is 8.7 105, i.e. 8.7 cases per 100,000 people who experience chronic inhalation exposure to 1 ng m3 B[a]P over a 70 year lifetime (WHO, 2000). The results of LLCR were calculated as 5.0 105, 6.7 105 and 3.7 105 for the overall data, the SW monsoon data and the NE monsoon data respectively. The upper limit of the LLCR values should be less than 106 to 104 per year maximal risk level (European Commission, 2001). Thus, the carcinogenic risk of the total PAHs has shown an acceptable risk level in the present study area in Malaysia. 4. Conclusions This study reports the concentrations of PM2.5-bound PAHs; the averages of the total PAHs were 2.79 (0.21e12.08), 3.85 (0.21e12.08) and 1.85 (0.68e3.80) ng m3 for the overall data, SW and NE monsoon samples, respectively. In general, the total PAHs concentrations at the current study location are comparatively lower than the most of the locations being reviewed. Strong seasonal dependency of the total PAHs was observed in this study, where the SW monsoon showed higher concentrations than the NE monsoon. Molecule-wise, HMW PAHs (5-ring) are significantly predominant in the fine particle-bound aerosol. The LMW PAHs correlate strongly with other LMW PAHs while the HMW PAHs correlate strongly with other HMW PAHs. The sources identified by PMF are consistent to the PAH sources identified by the DRs. Source apportionment analysis of the PAHs using the PMF 5.0 model identified three main sources: gasoline combustion (65%), diesel and heavy oil combustion (19%), natural gas and coal burning (15%). Seasonally, there were almost similar ratios and percentage of PAHs sources, indicating weaker seasonal influence. The DRs of I[c]P/I[c]P þ B[g]P and B[a]P/B[g]P confirm that the HMW PAHs originate from fuel combustion sources. In addition, fuel combustion source was identified based on the DRs of FLT/FLT þ PYR. Thus, the DRs-PMF-MLR, an integration of the three models, was successfully applied and would be chosen in future studies. The carcinogenic activity of the integrated PAHs was 572.6 103 ng m3 which poses a two fold-higher risk in the SW monsoon compared to the NE monsoon. The B[a]Peq of individual B [a]P was more than half of that shown by total PAHs. The study of the LLCR suggests that the carcinogenic risk of the total PAHs is of an acceptable risk level (106 to 104 per year) in the present study area in Malaysia. Acknowledgements The authors would like to thank Universiti Kebangsaan Malaysia for the Iconic Grant (ICONIC-2013-004) and the Ministry of Education for the Fundamental Research Grant (FRGS/1/2013/STWN01/

189

UKM/02/2). Special thanks to Dr Rose Norman for proofreading this manuscript. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.atmosenv.2015.01.077. References Abas, M.R., Oros, D.R., Simoneit, B.R.T., 2004. Biomass burning as the main source of organic aerosol particulate matter in Malaysia during haze episodes. Chemosphere 55, 1089e1095. Abas, M.R., Simoneit, B.R.T., 1996. Composition of extractable organic matter of air particles from Malaysia: initial study. Atmos. Environ. 30, 2779e2793. Akyüz, M., Çabuk, H., 2010. Gaseparticle partitioning and seasonal variation of polycyclic aromatic hydrocarbons in the atmosphere of Zonguldak, Turkey. Sci. Total Environ. 408, 5550e5558. Alam, M.S., Delgado-Saborit, J.M., Stark, C., Harrison, R.M., 2013. Using atmospheric measurements of PAH and quinone compounds at roadside and urban background sites to assess sources and reactivity. Atmos. Environ. 77, 24e35. Anwar, A., Juneng, L., Othman, M.R., Latif, M.T., 2010. Correlation between hotspots and air quality in Pekanbaru, Riau, Indonesia in 2006-2007. Sains Malays. 39, 169e174. Armstrong, B., Hutchinson, E., Unwin, J., Fletcher, T., 2004. Lung cancer risk after exposure to polycyclic aromatic hydrocarbons: a review and meta-analysis. Environ. Health Persp. 112, 970e978. Aydin, Y.M., Kara, M., Dumanoglu, Y., Odabasi, M., Elbir, T., 2014. Source apportionment of polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyls (PCBs) in ambient air of an industrial region in Turkey. Atmos. Environ. 97, 271e285. Azhari, A., Dalimin, M.N., Wee, S.T., 2011. Polycyclic aromatic hydrocarbons (PAHs) from vehicle emission in the vegetation of highway roadside in Johor, Malaysia. Int. J. Environ. Sci. Dev. 2 (6), 465e468. Baumann, K., Jayanty, R.K.M., Flanagan, J.B., 2008. Fine particulate matter source apportionment for the chemical speciation trends network site at Birmingham, Alabama, using positive matrix factorization. J. Air Waste Manage. Assoc. 58, 27e44. Bosetti, C., Boffetta, P., La Vecchia, C., 2007. Occupational exposures to polycyclic aromatic hydrocarbons, and respiratory and urinary tract cancers: a quantitative review to 2005. Ann. Oncol. 18, 431e446. Bourotte, C., Forti, M.-C., Taniguchi, S., Bícego, M.C., Lotufo, P.A., 2005. A wintertime ~o Paulo city, Brazil. Atmos. Enstudy of PAHs in fine and coarse aerosols in Sa viron. 39, 3799e3811. Br€ andli, R.C., Bucheli, T.D., Ammann, S., Desaules, A., Keller, A., Blum, F., et al., 2008. Critical evaluation of PAH source apportionment tools using data from the Swiss soil monitoring network. J. Environ. Monit. 10, 1278e1286. n, M.S., Iturmendi, A., Lo pez, J.M., Mastral, A.M., 2014. Source apportionment of Calle the carcinogenic potential of polycyclic aromatic hydrocarbons (PAH) associated to airborne PM10 by a PMF model. Environ. Sci. Pollut. Res. 21, 2064e2076. n, P.S., Rodríguez, J.S., 2003. Polycyclic aromatic Castellano, A.V., Cancio, J.L., Alema hydrocarbons in ambient air particles in the city of Las Palmas de Gran Canaria. Environ. Int. 29, 475e480. Choi, H., Harrison, R., Komulainen, H., et al., 2010. Polycyclic aromatic hydrocarbons. In: WHO Guidelines for Indoor Air Quality: Selected Pollutants. World Health Organization, Geneva, p. 6. Available from: http://www.ncbi.nlm.nih.gov/books/ NBK138709/. De La Torre-Roche, R.J., Lee, W.Y., Campos-Díaz, S.I., 2009. Soil-borne polycyclic aromatic hydrocarbons in El Paso, Texas: analysis of a potential problem in the United States/Mexico border region. J. Hazard. Mater. 163, 946e958. Dickhut, R.M., Canuel, E.A., Gustafson, K.E., Liu, K., Arzayus, K.M., Walker, S.E., et al., 2000. Automotive sources of carcinogenic polycyclic aromatic hydrocarbons associated with particulate matter in the Chesapeake Bay region. Environ. Sci. Technol. 34, 4635e4640. European Union (EU), 2008. Directive 2008/50/EC of the European Parliament and of the Council of 21 May 2008 on Ambient Air Quality and Cleaner Air for Europe (OJ L 152, 11.6.2008), pp. 1e44. EU, 2005. Commission recommendation 2005/108/EC. Off. J. Eur. Comm. L34, 43. European Commission, 2001. Polycyclic Aromatic Hydrocarbons (PAH) Position Paper (July 2001) (Prepared by the Working Group on Polycyclic Aromatic Hydrocarbons). Fang, G.-C., Wu, Y.-S., Chang, C.-N., Ho, T.-T., 2006. Retracted: a study of polycyclic aromatic hydrocarbons concentrations and source identifications by methods of diagnostic ratio and principal component analysis at Taichung chemical Harbor near Taiwan Strait. Chemosphere 64, 1233e1242. Fraser, M.P., Yue, Z.W., Tropp, R.J., Kohl, S.D., Chow, J.C., 2002. Molecular composition of organic fine particulate matter in Houston, TX. Atmos. Environ. 36, 5751e5758. Galarneau, E., 2008. Source specificity and atmospheric processing of airborne PAHs: implications for source apportionment. Atmos. Environ. 42, 8139e8149. Gao, B., Guo, H., Wang, X.-M., Zhao, X.-Y., Ling, Z.-H., Zhang, Z., Liu, T.-Y., 2012. Polycyclic aromatic hydrocarbons in PM2.5 in Guangzhou, southern China:

190


spatiotemporal patterns and emission sources. J. Hazard. Mater. 239e240, 78e87. Guo, H., Lee, S.C., Ho, K.F., Wang, X.M., Zou, S.C., 2003. Particle-associated polycyclic aromatic hydrocarbons in urban air of Hong Kong. Atmos. Environ. 37, 5307e5317. Guo, Z., Lin, T., Zhang, G., Hu, L., Zheng, M., 2009. Occurrence and sources of polycyclic aromatic hydrocarbons and n-alkanes in PM2.5 in the roadside environment of a major city in China. J. Hazard. Mater. 170, 888e894. Gupta, S., Kumar, K., Srivastava, A., Srivastava, A., Jain, V.K., 2011. Size distribution and source apportionment of polycyclic aromatic hydrocarbons (PAHs) in aerosol particle samples from the atmospheric environment of Delhi, India. Sci. Total Environ. 409, 4674e4680. Hanedar, A., Alp, K., Kaynak, B., Avs¸ar, E., 2013. Toxicity evaluation and source apportionment of Polycyclic Aromatic Hydrocarbons (PAHs) at three stations in Istanbul, Turkey. Sci. Total Environ. 488e489, 437e446. Harrison, R.M., Smith, D.J.T., Luhana, L., 1996. Source apportionment of atmospheric polycyclic aromatic hydrocarbons collected from an urban location in Birmingham, U.K. Environ. Sci. Technol. 30, 825e832. Harrison, R.M., Beddows, D.C.S., Dall'Osto, M., 2011. PMF analysis of wide-range particle size spectra collected on a major highway. Environ. Sci. Technol. 45, 5522e5528. Hedberg, E., Gidhagen, L., Johansson, C., 2005. Source contributions to PM10 and arsenic concentrations in Central Chile using positive matrix factorization. Atmos. Environ. 39, 549e561. Henry, R.C., 1987. Current factor analysis receptor models are ill-posed. Atmos. Environ. 21, 1815e1820. Ho, K.F., Lee, S.C., Chiu, G.M.Y., 2002. Characterization of selected volatile organic compounds, polycyclic aromatic hydrocarbons and carbonyl compounds at a roadside monitoring station. Atmos. Environ. 36, 57e65. Jamhari, A.A., Sahani, M., Latif, M.T., Chan, K.M., Tan, H.S., Khan, M.F., et al., 2014. Concentration and source identification of polycyclic aromatic hydrocarbons (PAHs) in PM10 of urban, industrial and semi-urban areas in Malaysia. Atmos. Environ. 86, 16e27. Jang, E., Alam, M.S., Harrison, R.M., 2013. Source apportionment of polycyclic aromatic hydrocarbons in urban air using positive matrix factorization and spatial distribution analysis. Atmos. Environ. 79, 271e285. Jiang, Y.-F., Wang, X.-T., Wang, F., Jia, Y., Wu, M.-H., Sheng, G.-Y., et al., 2009. Levels, composition profiles and sources of polycyclic aromatic hydrocarbons in urban soil of Shanghai, China. Chemosphere 75, 1112e1118. Juneng, L., Latif, M.T., Tangang, F., 2011. Factors influencing the variations of PM10 aerosol dust in Klang Valley, Malaysia during the summer. Atmos. Environ. 45, 4370e4378. Khalili, N.R., Scheff, P.A., Holsen, T.M., 1995. PAH source fingerprints for coke ovens, diesel and gasoline engines, highway tunnels, and wood combustion emissions. Atmos. Environ. 29, 533e542. Khan, M.F., Hirano, K., Masunaga, S., 2012. Assessment of the sources of suspended particulate matter aerosol using US EPA PMF 3.0. Environ. Monit. Assess. 184, 1063e1083. Koe, L.C.C., Arellano Jr., A.F., McGregor, J.L., 2001. Investigating the haze transport from 1997 biomass burning in Southeast Asia: its impact upon Singapore. Atmos. Environ. 35, 2723e2734. Latif, M.T., Hey, L.S., Juneng, L., 2012. Variations of surface ozone concentration across the Klang Valley, Malaysia. Atmos. Environ. 61, 434e445. Li, P.-H., Wang, Y., Li, Y.-H., Wang, Z.-F., Zhang, H.-Y., Xu, P.-J., Wang, W.-X., 2010. Characterization of polycyclic aromatic hydrocarbons deposition in PM2.5 and cloud/fog water at Mount Taishan (China). Atmos. Environ. 44, 1996e2003. € din, A., Needham, L.L., Lee, S., Russell, A.G., Mulholland, J.A., Li, Z., Porter, E.N., Sjo 2009. Characterization of PM2.5-bound polycyclic aromatic hydrocarbons in Atlantadseasonal variations at urban, suburban, and rural ambient air monitoring sites. Atmos. Environ. 43, 4187e4193. Liu, Y., Chen, L., Huang, Q.-H., Li, W.-Y., Tang, Y.-J., Zhao, J.-F., 2009. Source apportionment of polycyclic aromatic hydrocarbons (PAHs) in surface sediments of the Huangpu River, Shanghai, China. Sci. Total Environ. 407, 2931e2938. Manoli, E., Kouras, A., Samara, C., 2004. Profile analysis of ambient and source emitted particle-bound polycyclic aromatic hydrocarbons from three sites in northern Greece. Chemosphere 56, 867e878. Miguel, A.H., Kirchstetter, T.W., Harley, R.A., Hering, S.V., 1998. On-road emissions of particulate polycyclic aromatic hydrocarbons and black carbon from gasoline and diesel vehicles. Environ. Sci. Technol. 32, 450e455. Naser, T.M., Yoshimura, Y., Sekiguchi, K., Wang, Q., Sakamoto, K., 2008. Chemical composition of PM2.5 and PM10 and associated polycyclic aromatic hydrocarbons at a roadside and an urban background area in Saitama, Japan. Asian J. Atmos. Environ. 2, 90e101. Nisbet, I.C.T., LaGoy, P.K., 1992. Toxic equivalency factors (TEFs) for polycyclic aromatic hydrocarbons (PAHs). Regul. Toxicol. Pharm. 16, 290e300. Norris, G., Duvall, R., Brown, S., Bai, S., 2014. EPA Positive Matrix Factorization (PMF) 5.0 Fundamentals & User Guide. Prepared for the US Environmental Protection Agency, Washington, DC. National Exposure Research Laboratory, Research Triangle Park, USA. Ogulei, D., Hopke, P.K., Wallace, L.A., 2006a. Analysis of indoor particle size distributions in an occupied townhouse using positive matrix factorization. Indoor Air 16, 204e215. Ogulei, D., Hopke, P.K., Zhou, L., Patrick Pancras, J., Nair, N., Ondov, J.M., 2006b. Source apportionment of Baltimore aerosol from combined size distribution

and chemical composition data. Atmos. Environ. 40 (Suppl. 2), 396e410. Okuda, T., Okamoto, K., Tanaka, S., Shen, Z., Han, Y., Huo, Z., 2010. Measurement and source identification of polycyclic aromatic hydrocarbons (PAHs) in the aerosol in Xi'an, China, by using automated column chromatography and applying positive matrix factorization (PMF). Sci. Total Environ. 408, 1909e1914. Oliveira, R., Loyola, J., Minho, A., Quiterio, S., de Almeida Azevedo, D., Arbilla, G., 2014. PM2.5-bound polycyclic aromatic hydrocarbons in an area of Rio de Janeiro, Brazil impacted by emissions of light-duty vehicles fueled by ethanolblended gasoline. Bull. Environ. Contam. Toxicol. 93, 781e786. Omar, N.Y.M.J., Abas, M.R.B., Ketuly, K.A., Tahir, N.M., 2002. Concentrations of PAHs in atmospheric particles (PM10) and roadside soil particles collected in Kuala Lumpur, Malaysia. Atmos. Environ. 36, 247e254. Omar, N.Y.M.J., Mon, T.C., Rahman, N.A., Abas, M.R.B., 2006. Distributions and health risks of polycyclic aromatic hydrocarbons (PAHs) in atmospheric aerosols of Kuala Lumpur, Malaysia. Sci. Total Environ. 369, 76e81. Paatero, P., 1997. Least squares formulation of robust non-negative factor analysis. Chemom. Intell. Lab. 37, 23e35. Paatero, P., Tapper, U., 1994. Positive matrix factorization: a non-negative factor model with optimal utilization of error estimates of data values. Environmetrics 5, 111e126. Park, S.S., Kim, Y.J., Kang, C.H., 2002. Atmospheric polycyclic aromatic hydrocarbons in Seoul, Korea. Atmos. Environ. 36, 2917e2924. Perraudin, E., Budzinski, H., Villenave, E., 2007. Kinetic study of the reactions of ozone with polycyclic aromatic hydrocarbons adsorbed on atmospheric model particles. J. Atmos. Chem. 56, 57e82. Petry, T., Schmid, P., Schlatter, C., 1996. The use of toxic equivalency factors in assessing occupational and environmental health risk associated with exposure to airborne mixtures of polycyclic aromatic hydrocarbons (PAHs). Chemosphere 32, 639e648. Pies, C., Hoffmann, B., Petrowsky, J., Yang, Y., Ternes, T.A., Hofmann, T., 2008. Characterization and source identification of polycyclic aromatic hydrocarbons (PAHs) in river bank soils. Chemosphere 72, 1594e1601. Pleil, J.D., Vette, A.F., Rappaport, S.M., 2004. Assaying particle-bound polycyclic aromatic hydrocarbons from archived PM2.5 filters. J. Chromatogr. A 1033, 9e17. Polissar, A.V., Hopke, P.K., Malm, W.C., Sisler, J.F., 1998a. Atmospheric aerosol over Alaska: 1. Spatial and seasonal variability. J. Geophys. Res. 103, 19035e19044. Polissar, A.V., Hopke, P.K., Paatero, P., Malm, W.C., Sisler, J.F., 1998b. Atmospheric aerosol over Alaska: 2. Elemental composition and sources. J. Geophys. Res. 103, 19045e19057. Rogge, W.F., Hildemann, L.M., Mazurek, M.A., Cass, G.R., Simonelt, B.R.T., 1993. Sources of fine organic aerosol. 3. Road dust, tire debris, and organometallic brake lining dust: roads as sources and sinks. Environ. Sci. Technol. 27, 1892e1904. Sarkar, S., Khillare, P.S., 2013. Profile of PAHs in the inhalable particulate fraction: source apportionment and associated health risks in a tropical megacity. Environ. Monit. Assess. 185, 1199e1213. Sharma, H., Jain, V.K., Khan, Z.H., 2007. Characterization and source identification of polycyclic aromatic hydrocarbons (PAHs) in the urban environment of Delhi. Chemosphere 66, 302e310. Sicre, M.A., Marty, J.C., Saliot, A., Aparicio, X., Grimalt, J., Albaiges, J., 1987. Aliphatic and aromatic hydrocarbons in different sized aerosols over the Mediterranean Sea: occurrence and origin. Atmos. Environ. 21, 2247e2259. Sun, F., Littlejohn, D., Gibson, M.D., 1998. Ultrasonication extraction and solid phase extraction clean-up for determination of US EPA 16 priority pollutant polycyclic aromatic hydrocarbons in soils by reversed-phase liquid chromatography with ultraviolet absorption detection. Anal. Chim. Acta 364, 1e11. Thurston, G.D., Spengler, J.D., 1985. A quantitative assessment of source contributions to inhalable particulate matter pollution in metropolitan Boston. Atmos. Environ. 19, 9e25. United States Environmental Protection Agency priority list (US EPA), 2010. Fed. Regist. van Drooge, B.L., Crusack, M., Reche, C., Mohr, C., Alastuey, A., Querol, X., Prevot, A., Day, D.A., Jimenez, J.L., Grimalt, J.O., 2012. Molecular marker characterization of the organic composition of submicron aerosols from Mediterranean urban and rural environments under contrasting meteorological. Atmos. Environ. 61, 482e489. Venkataraman, C., Friedlander, S.K., 1994. Size distributions of polycyclic aromatic hydrocarbons and elemental carbon. 2. Ambient measurements and effects of atmospheric processes. Environ. Sci. Technol. 28, 563e572. Wang, F., Lin, T., Li, Y., Ji, T., Ma, C., Guo, Z., 2014. Sources of polycyclic aromatic hydrocarbons in PM2.5 over the East China Sea, a downwind domain of East Asian continental outflow. Atmos. Environ. 92, 484e492. Watson, J.G., Robinson, N.F., Chow, J.C., Henry, R.C., Kim, B.M., Pace, T.G., et al., 1990. The sUSEPA/DRI chemical mass balance receptor model, CMB 7.0. Environ. Softw. 5, 38e49. World Health Organization (WHO), 2000. Polycyclic Aromatic Hydrocarbons, second ed. In: Air Quality Guidelines for Europe WHO Regional Office for Europe (WHO Regional Publications, European Series, No. 91), Copenhagen. WHO, 1987. Air Quality Guidelines for Europe e Copenhagen. WHO Regional Office for Europe, Publication No. 23. Yunker, M.B., Macdonald, R.W., Vingarzan, R., Mitchell, R.H., Goyette, D., Sylvestre, S., 2002. PAHs in the Fraser River basin: a critical appraisal of PAH ratios as indicators of PAH source and composition. Org. Geochem. 33, 489e515.