Vertical Distribution and Potential Risk of Particulate Polycyclic Aromatic Hydrocarbons in High Buildings of Bangkok, Thailand

Polycyclic aromatic hydrocarbons, particularly benzo[a]pyrene (B[a]P), have been widely acknowledged as a group of persistent organic pollutants (POPs) to be responsible for cancer, endocrine disruption, reproductive and developmental effects (Hoyer, 2001; Matsui, 2008; Liao et al., 2011; Wickramasinghe et al., 2012). In addition to several experimental studies in rat lungs on the carcinogenicity and dose-response of PAHs (Rosenkranz, 1996; Müller et al., 2004; Borm et al., 2005), the role of meteorological parameters on the fate of PAHs has been comprehensively investigated in various places during the past few years (Hong et al., 2007; Tasdemir and Esen, 2007; Tham et al., 2008; Zhang and Tao, 2008; Akyüz and Çabuk, 2009; Amodio et al., 2009; Hanedar et al., 2011; Lee et al., 2011; Choi et al., 2012; Hu et al., 2012; Massei et al., 2003; Tan et al., 2011). Since PAHs are commonly considered as carcinogenic and mutagenic compounds, it is therefore important to comprehend the factors influencing


Introduction
Polycyclic aromatic hydrocarbons, particularly benzo[a]pyrene (B[a]P), have been widely acknowledged as a group of persistent organic pollutants (POPs) to be responsible for cancer, endocrine disruption, reproductive and developmental effects (Hoyer, 2001;Matsui, 2008;Liao et al., 2011;Wickramasinghe et al., 2012). In addition to several experimental studies in rat lungs on the carcinogenicity and dose-response of PAHs (Rosenkranz, 1996;Müller et al., 2004;Borm et al., 2005), the role of meteorological parameters on the fate of PAHs has been comprehensively investigated in various places during the past few years (Hong et al., 2007;Tasdemir and Esen, 2007;Tham et al., 2008;Zhang and Tao, 2008;Akyüz and Çabuk, 2009;Amodio et al., 2009;Hanedar et al., 2011;Lee et al., 2011;Choi et al., 2012;Hu et al., 2012;Massei et al., 2003;Tan et al., 2011). Since PAHs are commonly considered as carcinogenic and mutagenic compounds, it is therefore important to comprehend the factors influencing RESEARCH ARTICLE

Vertical Distribution and Potential Risk of Particulate Polycyclic Aromatic Hydrocarbons in High Buildings of Bangkok, Thailand
Siwatt Pongpiachan the spatial distribution and temporal variation of PAH contents in the Atmospheric Environment. It is worth stressing that PAHs have complicated emission sources and thus numerous studies focused on characterization of source profiles and source apportionment by using field measurement of particulate PAHs collected from network monitoring stations around the world (Okuda et al., 2002;Yang et al., 2002;Tsapakis and Stephanou, 2005;Pongpiachan, 2006;Wan et al., 2006;Vasconcellos et al., 2010;Oanh et al., 2011;Rajput et al., 2011;Riva et al., 2011). In spite of innumerable publications in the field regarding the diurnal and seasonal variation of PAHs, its vertical distribution in tropical aerosols are still unclear, in particular in Southeast Asian countries where air pollution is a daily concern.
Bangkok city captures an administrative area of 1,569 square kilometers in the Chao Phraya River delta in Central Thailand, and possesses 50 districts under the authority of Bangkok Metropolitan, making it the 73 rd largest city in the world. A rapid increase in the number 1866 of vehicles in Bangkok appears to be responsible for the deterioration of air quality, causing adverse respiratory health effect (Muttamara and Leong, 2002). While the atmospheric concentration of trace gaseous species in residential zones of Bangkok are basically at a satisfactory level, the roadside detection of NO x and CO frequently exceeds the national air quality standard as issued by the Pollution Control Department (PCD), Ministry of Natural Resources and Environment. For this reason, numerous research studies attempted to clarify the fate of other traffic-originated organic compounds like PAHs in airborne particles in Bangkok's atmosphere (Chetwittayachan et al., 2002;Ruchirawat et al., 2002;Boonyatumanond et al., 2007). Since the literature is sparse concerning the atmospheric contents of particulate PAHs in the capital and/or other cities in Thailand (Chetwittayachan et al., 2002;Boonyatumanond et al., 2007;Pengchai et al., 2009;), it appears problematic to perform the assessment of adverse respiratory health impact caused by exposure to PAHs, particularly in higher altitudes. To the best of our knowledge, there have not been any studies associated with monitoring of PAH contents in different atmospheric layers. Overall, the major aims of this research are to i) study the diurnal variations and vertical distributions of PAHs in PM 10 , ii) interpret the influence of meteorological factors on behaviors of PAH compositions in PM 10 and iii) perform a risk assessment of airborne PAHs at three different atmospheric layers at the heart of Bangkok Metropolitan.

Sampling sites
Two monitoring sites, explicitly at Baiyoke Suit Hotel (13°45'10.65'' N 100°32'24.92'' E) and Baiyoke Sky Hotel (13°45'15.69'' N 100°32'29.73'' E), were located in the heart of Bangkok Metropolitan, surrounded by tourism spots and shopping streets. Three sampling sites were prudently selected at specific altitudes. Site-1 and Site-2 were placed at Baiyoke Suit Hotel at 38 m and 158 m above ground level respectively, whilst site-3 was situated at Baiyoke Sky Hotel at 328 m above the building basement. It is noteworthy and should be emphasized that Baiyoke Sky Hotel is the highest building in Southeast Asia, and the fourth-highest all-hotel structure in the world. Intensive monitoring campaigns were performed at all monitoring sites simultaneously from February 18 th to 21 st 2008 in the winter season. PM 10 samples were taken every three hours sequentially from 2100 h February 18 th to 2100 h February 21 st by employing Graseby-Andersen High Volume Air Sampler PM 10 TE-6001 with the flow rate of 1.132 m 3 min -1 . A more comprehensive explanation of the air sampling method was given in "Compendium Method IO-2.2. Sampling of Ambient Air for PM 10 using an Andersen Dichotomous Sampler".

Filter sample collection and meteorological data
Three Graseby-Anderson high volume air samplers TE-6001 were employed to achieve unmanned three-hour and six-hour samplings for PM 10 . The high volume air samplers were controlled by time, namely, operated from 2100 to midnight, midnight to 0300 and so on. A total of 72 samples (i.e. 24×3=72) were acquired using high volume yielding sample volumes of approximately 180 m 3 for each 3 h sample. Aerosol particles were collected on 20×25 cm Whatman quartz microfibre filters (QMFs) at a flow rate of about 1.133 m 3 min -1 (i.e. 40 cfm): Sample air flow rate was calibrated for standard temperature and pressure conditions. After sampling, each of the QMF samples were wrapped by DCM rinsed aluminum foil, sealed in plastic bags and preserved in refrigerator at 4 o C until chemical analysis. To estimate potential contamination over field sampling and analytical process, several filed and method blank samples were collected and treated in the same manner as actual aerosol sampling during the observation. QMF samples were cleaned up using DCM by Soxhlet extraction for 8 h prior to use, to remove any potential contamination of PAHs. A versatile sensor suite, Davis Vantage Pro2, was used to record all meteorological parameters such as barometric pressure, temperature, humidity, rainfall, wind speed, wind direction and UV/ solar radiation every hour. In addition, PM 10 mass loadings were measured gravimetrically operated by electronic microbalance Mettler Toledo AB204-S (Columbus (Ohio), USA).

PAHs analysis
All  (B[g,h,i]P); each 100 μg mL -1 in toluene: unit: 1×1 mL) and a mix of recovery Internal Standard PAHs (d 12 -perylene (d 12 -Per), d 10 -fluorene (d 10 -Fl); each 100 μg mL -1 in xylene: unit: 1×1 mL) were supplied by Chiron AS (Stiklestadveine 1, N-7041 Trondheim, Norway). Standard stock solutions of 4 μg mL -1 of deuterated PAHs (used as internal standard) and 100μg mL -1 of native PAHs were prepared in nonane. Working solutions were obtained by appropriate dilution in n-cyclohexane. All solutions were stored in amber colored vials at -20 o C. Silica gel (0.040-0.063 mm), which were purchased from Merck. All materials used (silica gel, glass and cotton wool etc.) were Soxhlet extracted with DCM for 24 h, and kept dry (in desiccator) until use. The fractionation/cleanup and blow-down process followed the method reported by Gogou et al. (1996).
The analysis, calibrations and QA/QC procedures were performed at the laboratory of the Inter-Department of Environmental Science, Faculty of Graduate Studies, Chulalongkorn University, Bangkok, Thailand. The samples were analyzed for PAHs using Varian GC/MS-MS system comprising a CP-3900 gas chromatograph (Walnut Creek, CA, USA) with a 1077 universal injector and a three dimensional quadrupole ion-trap selected ion storage mass Asian Pacific Journal of Cancer Prevention, Vol 14, 2013 1867 DOI:http://dx.doi.org/10.7314/APJCP.2013.14.3.1865 Vertical Distribution andPotential Risk of Particulate PAHs in High Buildings of Bangkok, Thailand spectrometer (Varian Saturn 2200). The target compounds were separated on a 60 m length×0.25 mm i.d. capillary column coated with a 0.25 μm film thickness (phase composition: cross-linked/surface bonded 5% phenyl, 95% methylpolysiloxane. Specified in EPA methods 207, 508, 515, 515.2, 524.2, 525, 548.1, 680, 1625EPA methods 207, 508, 515, 515.2, 524.2, 525, 548.1, 680, , 1653 and 8280) stationary phase (Agilent JW Scientific DB-5 GC columns). Helium (99.999%) was employed as carrier gas at a constant column flow of 1.0 mL min -1 and a pressure pulse of 25 psi with duration of 0.50 min. The chromatographic conditions coupled with the quantification and identification of PAHs was clearly described in a previous study . Quantification of the compounds is based upon the Internal Standard (IS) method. One of the fundamental requirements of using an IS is that it displays similar physiochemical properties or the same type of substitution as the analytes because be similar to each other. A relative response factor (RRF) for each native analyte was first determined. This is used for quantification, as the relative response between the internal standard (IS) and the native analyte should remain constant. It is a convenient method because recovery losses of the compound during extraction and analysis are assumed to match those of the IS. The calculation of relative response factor (RRF) is described as follows;

RRF=(A nat /A is )×(C is /C nat ) Equation 1
Where A nat =Peak area of the native compound in the standard; C nat =Concentration of the native compound in the standard; A is =Peak area of internal standard; C is =Concentration of the internal standard. The RRF STD used for quantifying samples are the mean of those calculated for the two quantification standards run on the same day. Concentration (C) of analytes in sample extracts is calculated using the following formula:

C=(A nat /A is )×(1/RRF STD )×(W s /W is ) Equation 2
Where W is =weight of IS added to the sample, W s =weight or volume of the sample analyzed. Analytical precisions and accuracies were calculated using the standard SRM 1941b. Mean recovery (based on extraction of matrix-matched certified reference materials, (n=8) was in range of 77-119%. The precision of the procedure, calculated as relative standard deviation on the duplicate samples, was less than 15%. All sample concentrations were calculated using standardized relative response factors run with each batch ). In addition, six field blanks from all sampling sites were analyzed to determine the background contamination from the analytical procedure, filter storage and transportation. Pre-cleaned QMFs were taken to the sampling site, exposed to the ambient air by using DCM cleaned tongs for a few seconds, then placed QMFs on the top of precleaned filter media holder. At this stage, QMFs were left inside PM 10 head high volume air sampler for 3 h. Most individual compounds had blank levels less than 5% of the mean concentrations before correction for blanks level were made and hence blank values for all PAHs detected were subtracted for all samples. 1868 Trace gaseous species and water-soluble ionic species analysis A chemiluminescence NO/NO 2 /NO x Analyzer: Model 200E coupled with stateof-the-art microprocessor technology (Teledyne Technologies Incorporated, USA) was used to detect NO/NO 2 /NO x in this study. The analytical capability of Model 200E ranges from 50 ppb to 20 ppm with the instrumental detection limit of 0.4 ppb. A CO Analyzer: Model 300E (Teledyne Technologies Incorporated, USA) was used for monitoring CO in this study. The instrumental sensitivity for CO determination ranges from 0-1 ppm to 0-1,000 ppm with the instrumental detection limit of 0.04 ppm. Three-hour water-soluble ionic species in PM 10 were also sampled over the monitoring campaign. The operation and QA/QC of instrument and extraction procedure have been previously described (Lai et al., 2007;Tao et al., 2007). For more details, the reader is referred to the aforementioned studies. Seven water-soluble ionic species (WSIS), namely Cl -, NO 3 -, SO 4 2-, Na + , NH 4 + , K + and Ca 2+ , in PM 10 were also measured during the monitoring campaign. The Metrohm IC system which comprise 819 Advance IC Pump, 819 Advance IC Detector, 820 Advance IC Separation Center, 833 Advance IC Liquid Handling Suppressor Unit and 830 Advance IC Interface, was employed for the analysis of seven WSIS. The setup, quality control and quality assurance of instrument and analytical method have been previously explained (Lai et al., 2007;Tao et al., 2007). For more details the reader is referred to these publications.

Meteorological parameters and statistical analysis
A versatile sensor suite, Davis Vantage Pro2, was employed to record all meteorological parameters, namely barometric pressure, temperature, humidity, rainfall, wind speed, wind direction and UV/solar radiation, every hour. In addition, PM 10 mass loadings were measured gravimetrically operated by electronic microbalance Mettler Toledo AB204-S (Columbus (Ohio), USA). In addition, the vertical atmospheric temperature, pressure, relative humidity and wind speed profiles were monitored every six hours, by using weather balloon coupled with radiosonde and carefully analyzed by research staff from the Meteorological Department of Thailand. All statistical analysis, such as t-Test, ANOVA and PCA were conducted by using SPSS software version 13.0.

Results
All atmospheric PAHs in PM 10 were identified successfully from February 18 th to 21 st 2008 (n=72). Table 1 summarizes the concentrations of selected 15 PAHs measured in the 72 samples taken at Baiyoke Suit Hotel and Baiyoke Sky Hotel, which are encompassed by Phetchaburi Road and Rachaprarop Road. These two roads pass from east to west and north to south through Bangkok City respectively, and therefore form main arteries for traffic into and out of the city center on a daily basis. The percentage contributions of 5-6 ring PAHs are 46%, 70% and 56% for Site-1, Site-2 and Site-3 respectively. Statistical description of PAH compositions along with its percentage contributions in each sampling height is displayed in Table  1. PAH mass concentrations at Site-1 ranged from 39.0±60.8 pg m -3 (11H-B[b]F) to 472±495 pg m -3 (An), and at Site-2 they ranged from N.D. (Ind and B[g,h,i]P) to 2,023±2,638 pg m -3 (B[b]F) whilst at Site-3 they varied from 128±83.0 (B[k] F) to 742±932 pg m -3 (An). The average concentrations of ΣPAHs (i.e. sum of 15 PAHs) at Site-1, Site-2 and Site-3 were 6,076±6,479 pg m -3 , 5,951±7,957 pg m -3 and 2,776±2,573 pg m -3 respectively.
The average 15 PAH concentration was significantly lower at Site-3 (185±126 pg m -3 ) in comparison with those detected at Site-1 (405±178 pg m -3 , p<0.001) and Site-2 (458±560 μg m -3 , p<0.1). As previously mentioned, the distributions of PAHs at Site-2 are mainly dominated by 5-6 ring PAHs such as B[b]F and B[e]P with a mean concentrations of 2,023±2,638 pg m -3 and 1,287±2,633 pg m -3 in that order, followed by relatively high contributions of 11H-B[a]F and B[a]P also making an important contribution of 411±530 pg m -3 and 358±262 pg m -3 respectively. It is crucial to mention that for Site-2, B[b]F alone, with the mean concentration of 2,023±2,638 pg m -3 comprises 34% of the ΣPAHs.
On the other hand, the 3-4 ring PAHs are found mainly at Site-1 with percentage contribution of 54%, for instance. An (742±932 pg m -3 ), Phe (571±565 pg m -3 ) and  Fluo (550±1,039 pg m -3 ) constructs 12.2%, 9.40% and 9.06% of the ΣPAHs respectively. B[a]P concentrations in all sites ranged from 135 pg m -3 to 374 pg m -3 with an average of 289 pg m -3 , this value is approximately four times lower than the value of the guideline limits of annual B[a]P concentration (i.e. 1 ng m -3 or 1,000 pg m -3 ) proposed by World Health Organization (WHO). However, the average value of 289 pg m -3 is slightly higher than the proposed value of 250 pg m -3 by the UK Expert Panel of Air Quality Standard (UK EPAQS, 1998). In addition, the atmospheric concentrations of B[a]P explained 6.16%, 6.02% and 4.85% of the ΣPAHs, determined gravimetrically at level-1, level-2 and level-3 in that order.

Diurnal variation of PAHs
Little information is known about the possible variation of PAHs in the tropical atmosphere between day and night. Many factors such as fluctuation of source strength, ambient temperature variation, chemical reactions with trace gaseous species and OH radicals could change the concentrations of particulate PAHs. It should be emphasized that PAHs taken from 24-hour samples cannot describe those factors, which govern the diurnal fluctuations. Thus it is important and interesting to acquire such data, especially for a very large city like Bangkok where air pollutants are known to be mainly a mixture of traffic emissions combined with industrial and cooking activities. Since all sampling sites are situated in "Pratunam (or Watergate in Thai)", one of the largest night market areas in Bangkok, it appears reasonable to consider the daily ΣPAHs mass concentration maximum at 2100-0000 (Day1-Site-1, Day3-Site-2, Day2-Site-3), 0000-0300 (Day3-Site-1) and 0300-0600 (Day2-Site-1, Day1-Site-3) as 1870 consequences of continuous nighttime traffic emissions coupled with lower nocturnal mixing layer, which can appreciably increase atmospheric chemical compositions, as documented by Wilson and Stockburger (1990) and Beyrich (1997). It is worth mentioning that the maximum peak detected during the period of 0900-1200 at Day1-Site-2, Day2-Site-2 and Day3-Site-3 reflects the crucial impact of morning rush hours on enhancements of particulate ΣPAHs. The decreased wind speed and stabilized nocturnal boundary layer around midnight influenced by vertical temperature profile detected by weather balloon coupled with radiosonde, can decelerate atmospheric dispersion and hence increase the contents of nighttime ΣPAHs. By contrast, the midday minimum ΣPAHs concentrations during the period of 1200-1500 (Day3-Site-1, Day1-Site-2, Day2-Site-2, Day3-Site-2, Day1-Site-3) and 1500-1800 (Day1-Site-1, Day2-Site-1, Day2-Site-3, Day3-Site-3) can be governed by i) the relatively low vehicle density during the daytime, ii) the expansion of mixing layer in midday which causes the dilution effects, iii) the greater dispersion triggered by relatively high wind speed in midday and iv) the fact that noodle stalls and barbecue peddlers were for the most part closed during the period of 1200-1500. Apart from meteorological conditions that might govern both vertical distributions and diurnal variations, it is also crucial to address the issue of positive sampling artifacts, particularly during the nighttime with relatively low temperature. Several factors govern the sampling artefacts in both particulate and gaseous three-to-four ring PAHs (i.e. Phe, An, Fluo, Pyr, 11H-B[a]F, 11H-B[b] F, B[a]A, Chry), which are categorized as semi-volatile organic compounds (SVOCs). The adsorption of gaseous SVOCs onto a filter can cause positive biases in the measured particle-phase concentrations, and negative biases in the measured gas-phase concentrations (McDow and Huntzicker, 1990;Hart and Pankow, 1994;Turpin and Huntzicker, 1994). In order to investigate the sampling artefact effect, gas/particle partitioning can be parameterised using the coefficient K p (m 3 μg -1 ):

K p =c p /c g Equation 3
Where c p is the measured particle-phase concentration (μg μg -1 ) and c g is the measured gas-phase concentrations (μg m -3 ) respectively. The length of time over which sampling take place, the filter type of interest, the ambient temperature are the main factors which control the K p values and therefore the sampling artefacts (Mader and Pankow, 2000a;2000b).
To minimize the sampling artefacts, the monitoring time should be kept to a minimum to avoid fluctuations in temperature and concentration during sampling and thus the observatory period of this study was selected as three hours. The selection of filter type is also one of the most significant factors in controlling the measured gas/ particle partitioning in ambient air. Pankow (2000a and2000b) investigated the sampling artefacts in Teflon membrane filters (TMFs) and quartz fibre filters (QFFs). It is well known that the TMFs have less surface area than the QFFs, thus the QFFs can be expected to be more absorptive than the TMFs. It was therefore assumed that the proportion of SVOCs adsorbed on QFFs are large compared to that adsorbed to TMFs (Mander and Pankow, 2000a;2000b). By normalizing a gas/filter Kp value by the surface area a f (m 2 g -1 ) of the filter yields.

K p,s [m]=[K p (m 3 μg -1 )]/[10 -6 (gμg -1 )×a f (m 2 g -1 )] Equation 4
Log K p,s values for PAHs on QFFs were on average a factor of two higher than those for PAHs found on TMFs. This result can also be explained by the larger surface area and higher energy surface of the QFFs resulting in a greater adsorptive affinity. The effects of temperature T (K) on the measured K p,s values were studied over the range 285-299 K. A 10º increase in T resulted in decreases in the K p,s values of PAHs, PCDFs and PCDDs by factors of 2.4, 3.1 and 3.4 respectively (Mader and Pankow, 2000). Since QFFs was selected for this study and hence it is important to take the "filter type" effect into consideration when compare the PAH data with those collected by TMFs and/or GFFs (Glass Fibre Filters).

T-Test and ANOVA
T-Test was conducted to investigate if there are any significant differences between the average daytime (i.e. 0600-1800) and nighttime (i.e. 1800-0600) PAH concentrations in PM 10 collected at Site-1, Site-2 and Site-3. As illustrated in Table 3, the average nighttime ΣPAHs at Site-1 was approximately two times higher than those of daytime with the confidence level of 95%, whilst no significant differences between the average daytime and nighttime PAHs were observed in other sampling sites. In particular, there are only four PAH congeners at Site-1 namely 11H-B[b]F, B[b]F, B[a]P and Ind that are significantly higher during nighttime. Therefore, it seems rationale to interpret the relatively high PAH contents from 1800-0600 at Site-1 as results of heavy traffic congestion, charcoal burning from noodle stalls, barbecue peddlers and the reduction of mixing layer growth rate during nighttime. As the altitude increases within urban boundary layer, the mixing rate of air pollutants enhances and thus triggers the dilution effects. Hence, there are no significant differences between the average daytime and nighttime of any PAH congeners detected at Site-2 and Site-3.
Further statistical examinations on average PAH contents at eight different sampling periods (i.e. 0600-0900, 0900-1200, 1200-1500, 1500-1800, 2100-0000, 0000-0300, 0300-0600, 1800-2100) were performed by using PAH data at all sampling sites and analyzed by ANOVA technique as illustrated in Table  4. Although the highest and lowest levels of ΣPAHs were detected at the period of 2100-0000 (6,286±5,308 pg m -3 ) and 1200-1500 (1,896±1,237 pg m -3 ) respectively, there is no significant difference among average PAH levels at eight different sampling periods. This reflects that PAHs remain fairly homogeneous throughout the air mass at all sampling heights during the monitoring period. The fact that the maximum of average ΣPAHs content was observed at 2100-0000 raises public concern over the potential human health effects of exposures to PAHs through nighttime activities at the heart of Bangkok Metropolitan. In addition, the vertical expansion of air mass during 1200-1500 appears responsible for the minimum value of average ΣPAHs from midday to afternoon.
In order to investigate the effect of photodecomposition on PAH contents at different altitudes, PAHs were compared and defined as sum of 3-4 ring PAHs PAHs (5,6)) for medium and high MW PAHs respectively. In spite of the fact that PAHs (5,6) contents were higher than those of PAHs (3,4) at all sampling altitudes, no statistically significant differences were observed during daytime (i.e. 0600-1800) at the confidence level of 95% (see Table 5). Hence, it seems logical to assume that photolysis plays a minor role in removal process of particulate PAHs during the monitoring period, particularly at the air mass adjacent to ground level. Interestingly, the average contents of PAHs (5,6) were significantly higher than those of PAHs (3,4) at Site-2 and Site-3 during nighttime (i.e. 1800-0600) at the confidence level of 90%. Since there are only two major PAH emission sources close to the observatory sites, namely vehicular traffic exhausts and cooking emissions, it appears reasonable to consider noodle stalls and barbecue peddlers from night markets as major contributors of PAHs at nighttime.

Evaluation of traffic contribution on PAHs level at high altitude
The impact of traffic emission to particulate PAH concentrations at high altitude can be investigated by using a similar concept as conducted by Nielsen et al. (1996). Since site-3 locates only 290 meters higher than Site-1, it appears reasonable to ignore the contributions to PAH aerosol concentrations caused by non-traffic sources and atmospheric chemical and/or physical processes. Thus the only difference in particulate PAH concentrations between Site-1 and Site-3 could be attributed to traffic emissions. A similar concept had been introduced to Copenhagen and Birmingham as investigated by Nielsen et al. (1996), Lim et al. (1999), then followed by Laurie (2002). In order to apply Nielsen method, firstly the significance of B[e]P as an indicator of PAH pollutions was tested by calculating correlation coefficients of individual particulate PAHs against B[e]P. Secondly, the traffic emission profiles of PAHs were produced by using the method of Nielsen et al. (1996). Finally, the evaluations of PAH traffic contributions   coming from vehicular exhaust gases were estimated by using the methods of B[g,h,i]P/B[e]P ratios (Lim et al., 1999;Laurie, 2002).
Several studies suggested that B[e]P can be used as an indicator of PAH pollutions due to its high stability in atmospheric environment Menichini et al., 1999;Wan et al., 2006). In this study, B[e]P was also selected as a profile index because of its relative stability in the atmosphere, which ensures that the differences in PAH profile between two sites were the result of traffic and not caused by atmospheric chemical reaction. The average of correlation coefficients and its significance levels of individual PAHs with B[e]P for both Site-1 and Site-3 were calculated. Most of individual PAHs showed appreciable correlations with B[e]P, supporting the concept of using B[e]P as an indicator of PAHs pollution in urban air. For instance, the correlation coefficients between B[e]P and B[a]P, Ind, D[a,h]A, B[g,h,i]P were higher than 0.7, whilst those of Phe, An, Fluo, Pyr were lower than 0.2. Threeto-four ring PAHs were not significantly correlated with B[e]P at both sites, as would be reflected to the different atmospheric loss mechanism (e.g. chemical reactions with OH and NO 3 radicals, photo-degradation and wet/dry depositions). The poor correlation between low molecular weight PAHs and B[e]P had been previously reported at the University of Birmingham monitoring sites, namely Bristol Road Observatory Site (BROS) and Elms Road Observatory Site (EROS), by Lim (1999) and Laurie (2002) indicated another potential source/sink of these compounds.
To obtain a PAH traffic emission profile, delta (δ)PAHs was plotted against δB[e]P where δPAHs and δB[e]P are the differences of individual PAH concentrations and B[e] P taken at two sampling sites respectively. Traffic emission profiles were established by slopes of linear regression analysis of the δPAHs values against B[e]P values with the y-axis forced to zero. Nielsen (1996) and Lim (1999) had made the assumption of this method that i) appreciable positive correlation between δPAHs and B[e]P should be observed and ii) traffic emissions are the major sources of atmospheric PAHs in urban air. By contrast to the study from an individual vehicle in tunnel, which provided only PAHs data originated from an individual car, this method provides practical information related to more realistic traffic emission sources in urban air. For the current data an alternative index of high MW PAHs, B[g,h,i]P, is considered to be a suitable indicator and was used in two methods for the evaluation of traffic emission. This assumption can be supported by the high δB[g,h,i]P/δB[e] P ratio of 1.52 coupled with high B[g,h,i]P-Site-1 /B[g,h,i] P-Site-3 ratio of 1.79. Moreover, several studies supported the assumption that the majority of particulate B[g,h,i] P is related to traffic emissions (Greenberg et al., 1985;Lodovici et al., 2003;Kalaiarasan et al., 2009). In order to estimate the influence of vehicular emissions on level of PAH contents at both sampling sites, calculations were performed using Method 1 and Method 2 as described below.

input the B[g,h,i]P/B[e]P traffic ratio of 1.52, the B[g,h,i]P/B[e]P non-traffic ratio of 0.8 as mentioned above and the B[g,h,i]P/B[e]P site
ratio of 1.30 and 1.46 for Site-1 and Site-3 into the equation, the fractional traffic contribution were calculated as 69% and 91% for Site-1 and Site-3 in that order. The percentage PAH traffic calculated from Method 2 using data set of Laurie, Lim and Nielsen were 109%, 81% and 84% for the traffic site (BROS) and 79%, 62% and 29% for the urban background site (EROS) respectively. The difference from those values reported by Nielsen, Lim and Laurie can be explained by the differences in sampling locations, vehicle fleet composition and the early years of monitoring in 1990, when the regulations of using catalytic converters in passenger cars were not introduced. Despite these discrepancies observed in previous studies, the results in both methods emphasize the fact that most of PAH aerosols at Site-1 and Site-3 was dominated by vehicular exhausts. It is also worth mentioning that the estimated values of Site-3 were higher than those of Site-1 about 10% and 22% for Method 1 and Method 2 respectively. This can be explained by more complicated PAH emission sources at ground level (e.g. cooking emissions from noodle stalls and barbecue peddlers) overwhelmed air mass at Site-1 whilst Site-3 was plausibly governed by traffic-originated PAHs from all mobile exhausts around Bangkok City.

Occupational exposure to PAHs
In spite of numerous investigations implying that indoor PM 10 concentrations, PAHs and VOCs were gradually lower than outdoor concentrations, the adequate positive correlations between indoor and outdoor concentrations were detected, indicating that indoor concentrations were to some extent dependent on ambient concentrations (Colome et al., 1992;Ohura et al., 2009;Masih et al., 2010). To the best of our knowledge, there is no available study related to the assessment of health risk associated with the exposure to particulate PAHs of workers and residents living in high buildings of Thailand. Hence, it is crucial to conduct the health risk assessment by using the concept of B[a]P equivalent concentration and incremental individual lifetime cancer risk (ILCR) as previously reported by Xia et al. (2010) and Chen et al. (2006), respectively. It is common to express the carcinogenic risk of a PAH mixture in form of its B[a]P equivalent concentration (B[a]P eq ) by using the toxicity equivalency factors (TEFs) as developed by Nisbet and Lagoy (1992). TEFs were applied to assess the carcinogenic potency of PAH mixtures in PM 10 . The B[a]P eq of PM 10 (BEC) was computed according to Equation (8).
Where: C i =concentration of PAH congener i in PM 10 ; for Site-1, Site-2 and Site-3, respectively. Surprisingly, the total B[a]P eq results showed the potential health risk to cancer due to inhalation exposure is of concern for residents living in Site-2 and Site-3. Two main factors, namely building configurations and wind direction, can be attributed to these relative high B[a]P eq results at higher altitudes. It is also worth mentioning that the total B[a]P eq concentrations for both observatory sites were close to, or vaguely below the maximum permissible risk level of 1,000 pg m -3 of B[a]P.
Further estimations on incremental individual lifetime cancer risk (ILCR) were conducted by using the model proposed by Chen et al. (2006), which can be defined as follows:
The calculated ILCR levels in three different altitudes are summarized in Table 6. The estimated ILCR of the sum of B[a]P, B[a]A, Chry, B[b]F, B[k]F, Ind and D[a,h]A were constantly highest at Site-2 followed by Site-1 and Site-3 with values of 2.30×10 -6 ±2.99×10 -6 , 2.11×10 -6 ±1.83×10 -6 and 1.14×10 -6 ±1.27×10 -6 respectively. These results are also in good agreement with those average values of ILCR detected at Site-2, Site-1 and Site-3 with the values of 3.28×10 −7 ±4.28×10 −7 , 3.01×10 −7 ±2.62×10 −7 and 1.62×10 −7 ±1.81×10 −7 respectively. It should be emphasized that both the average and sum of predicted ILCR of seven PAHs display the maximum value at Site-2, which is consistent with those trend of the estimated B[a]P eq of PM 10 (i.e. 13 PAHs) calculated by using Equation 8. Again, the building configuration coupled with wind directions appears responsible for this phenomenon. According to the USEPA, a one in a million chance of additional human cancer over a 70-year lifetime (ILCR=10 −6 ) is the level of risk comparable to daily risk exposure (e.g. diagnostic X-rays, fishing, etc.) and thus considered acceptable or negligible (Asante-Duah, 2002). Supplementary lifetime cancer risk of one in ten thousand or larger (ILCR=10 −4 ) can be considered as harmful and may request elevated priority for paying attention to inhalation respiratory health problems (Xia et al., 2010). The average values of ILCR for all sampling sites fell within the range of 10 −7 -10 −6 , being close to the acceptable risk level (10 −6 ) but much lower than the priority risk level (10 −4 ).

Principal component analysis
As illustrated in Table 7, the principal component patterns for Varimax rotated components of three observatory sites composed of three components, which account for 43.2%, 37.2% and 19.6% for the total of variances of PC1, PC2 and PC3 respectively. The contribution of PC1 and PC2 explains 80% of total variance, and moreover PC1 (43.2% of variation) is two times higher than PC3 (19.6%). The main cluster (PC1) contains dominantly PAHs (3,4)  It is well known that both cooking emissions and vehicular exhausts are major sources of PAHs and thus the strong correlation coefficients of PAHs (5,6) observed in PC1 can be attributed to those emissions derived from transportation, noodle stalls and barbecue peddlers. The strong positive loading on secondary aerosols (i.e. Ca 2+ , K + , NH 4+ ) implied that dust particles from nearby building construction and agricultural activities related particles, which were conveyed to Site-2 by long-range transportation, are probably two main sources found in PC2. There are also moderate loadings of O 3 as well as Na + and Cl -, which can be explained as a production of sea spray aerosols from the Gulf of Thailand. PC3 displays high loadings of Ph and An, which are present in high concentrations in vehicular emissions, and moderate loadings of CO. The positive correlations of these three parameters suggest that fuel combustion from transportation was a dominated source in PC3. Finally, the negative loadings of solar radiation can be attributed to higher boundary layer and photochemical activities in daytime, whilst the positive loading of wind speed suggests that wind velocity during nighttime promotes re-suspension of soil dust.
In conclusion, all selected 15 PAHs in PM 10 measured in the 72 samples were quantified successfully from February 18 th to 21 st 2008. The nighttime traffic emissions coupled with lower nocturnal mixing layer was responsible for the highest peak of PAHs observed during midnight, whilst the lowest peak observed during midday can be attributed to the relatively low vehicle density, the expansion of mixing layer and the greater dispersion triggered by relatively high wind speed. Besides these meteorological conditions, there are "sampling artefacts" and "filter type" that can significantly influence the variation of particulate PAHs. Nielsen's method highlights the importance of traffic contribution on PAH content levels, particularly at higher altitude. The average values of ILCR for all sampling sites fell within the range of 10 −7 -10 −6 , being close to the