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Gas-particle partitioning and behavior of dioxin-like PCBs in the urban atmosphere of Gyeonggi-do, South Korea
Atmospheric Research 101 (2011) 386–395
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Atmospheric Research j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / a t m o s
Gas-particle partitioning and behavior of dioxin-like PCBs in the urban atmosphere of Gyeonggi-do, South Korea Dong–Gi Kim a, Ki–In Choi b, Dong–Hoon Lee c,⁎ a b c
Department of Atmospheric Research, Gyeonggi-do Institute of Health & Environment, 324-1 Pajang-dong, Jangan-gu, Suwon, Gyeonggi-do, 440-290, Republic of Korea Test and Standard Center, Korea Institute of Ceramic Engineering and Technology, 233-5 Gasan-dong, Geumcheon-gu, Seoul, 153-801, Republic of Korea Department of Environmental Engineering, The University of Seoul, 90 Jeonnong-dong, Dongdaemun-gu, Seoul 130-430, Republic of Korea
a r t i c l e
i n f o
Article history: Received 8 September 2010 Received in revised form 17 March 2011 Accepted 28 March 2011
Keywords: dl-PCBs Partitioning Particle bound fraction Junge–Pankow model KOA-based model
a b s t r a c t To evaluate their behavior and gas-particle partitioning, the concentrations of dioxin-like polychlorinated biphenyls (dl-PCBs) in both gas and particle phases in ambient air were measured at urban sites in Gyeonggi-do, South Korea. The predominance of gas-phase PCBs in the atmosphere was more significant during summer at all sites. In July, approximately 92% of the total toxic equivalent quantity (TEQ) was in the gaseous phase, whereas in January, approximately 96% of the total TEQ was in the particle phase. The dl-PCBs can be transported over long ranges and accumulate rapidly in plants at warm temperatures. Partial vapor pressures of dl-PCBs were well correlated with temperature and the steep slopes obtained from Clausius–Clapeyron plots indicated that the volatilization of dl-PCBs from surfaces in the local surroundings of the sampling sites dominated the atmospheric concentrations. The gas-particle distribution was also examined through several different approaches such as the log KP–log PoL, log KP–log KOA, Junge–Pankow, and KOA-based models. The slopes obtained from regressing log KP versus log PoL (log KP versus log KOA) were smaller than −1 (or 1). The particlebound fraction (φ) and KP of the dl-PCB congeners were estimated using the Junge–Pankow and KOA-based models. Both models tended to overestimate the φ and KP values of the dl-PCB congeners compared to those obtained from field measurements. Nevertheless, the coefficients of determination (r2) between the experimental and modeled KP values were 0.74 to 0.84 and 0.76 to 0.84 for the Junge–Pankow and KOA-based models, respectively. © 2011 Elsevier B.V. All rights reserved.
1. Introduction In the past, polychlorinated biphenyls (PCBs) were widely used in many applications, particularly as dielectric fluids in transformers, capacitors, and coolants (Cindoruk and Tasdemir, 2007). Due to the toxicity of PCBs, their persistence, and longrange transport, their production was banned in most countries in the 1970s (Manodori et al., 2006), and they were classified as persistent organic pollutants (POPs) at the Stockholm Convention. Although banned for more than 30 years, PCBs are still detected globally in all environmental materials. Today, a major source of PCBs existing in the atmosphere is considered to be ⁎ Corresponding author. Tel.: + 82 2 2210 2377; fax: + 82 2 2217 8550. E-mail address: [email protected] (D.–H. Lee). 0169-8095/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.atmosres.2011.03.010
due to their volatilization from sites where they have been disposed off or stored, air–water/soil exchange, and incineration of PCBs-containing materials (Mandalakis et al., 2002). Therefore, urban/industrial areas are major sources of atmospheric PCBs found in the surrounding regions (Brunciak et al., 2001; Ozcan and Aydin, 2009). PCBs are mainly present in the atmosphere in the gas phase, and they reach gas-particle partitioning equilibrium depending on the ambient temperature, vapor pressure, and concentration of total suspended particles (TSPs) (Pankow, 1994). Gasparticle partitioning influences the fate, transport, atmospheric residence time, and removal processes of semi-volatile organic compounds (SOCs) in atmospheric environments (Matsumoto et al., 2010). The atmospheric concentrations of PCBs, their gasparticle partitioning, and the relationship with meteorological
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Table 1 Summary of the sampling and meteorological data for this study. Site
A
Location
Suwon
Anyang
Sungnam
Ansan
Buchen
Siheung
Sampling 1 (winter) Temp. a, TSP b, RF c, WS d Sampling 2 (spring) Temp. a, TSP b, RF c, WS d Sampling 3 (summer) Temp. a, TSP b, RF c, WS d Sampling 4 (late summer) Temp. a, TSP b, RF c, WS d Sampling 5 (late autumn) Temp. a, TSP b, RF c, WS d
28/1–31/1, 2005 1.1, 137, 0, 1.3 17/5–21/5, 2005 16.6, 102, 19.5, 1.5 19/7–22/7, 2005 26.2, 72, 1, 1.8 20/9–23/9, 2005 18.9, 55, 15, 1.5 19/11–22/11, 2005 7.3, 98, 1.5, 1.7
26/1–29/1, 2005 − 2.1, 89, 0, 1.3 24/5–27/5, 2005 18.9, 98, 0, 1.5 20/7–23/7, 2005 27.1, 74, 1, 1.8 20/9–23/9, 2005 18.7, 61, 15, 1.5 19/11–22/11, 2005 8.0, 113, 1.5, 1.6
28/1–31/1, 2005 1.1, 107, 0, 1.2 17/5–21/5, 2005 17.4, 96, 19.5, 1.7 19/7–22/7, 2005 27.2, 79, 1, 1.5 20/9–23/9, 2005 17.7, 65, 15, 1.4 19/11–22/11, 2005 5.7, 117, 1.5, 1.2
26/1–29/1, 2005 − 3.6, 101, 0, 2.0 17/5–21/5, 2005 16.1, 142, 19.5, 3.0 20/7–23/7, 2005 26.6, 79, 1, 2.6 21/9–24/9, 2005 19.2, 110, 44, 2.5 19/11–22/11, 2005 6.8, 167, 2, 2.0
28/1–31/1, 2005 2.7, 90, 0, 1.9 24/5–27/5, 2005 20.9, 107, 0, 2.6 20/7–23/7, 2005 27.2, 56, 1, 2.4 21/9–24/9, 2005 19.8, 63, 44, 2.5 19/11–22/11, 2005 7.9, 84, 2, 1.9
28/1–31/1, 2005 2.3, 151, 0, 2.0 24/5–27/5, 2005 19.9, 113, 0, 3.0 20/7–23/7, 2005 26.6, 82, 1, 2.6 21/9–24/9, 2005 19.9, 88, 44, 2.5 19/11–22/11, 2005 8.0, 121, 2, 2.0
a b c d
B
C
D
E
F
Mean temperature (°C). Mean total suspended particle (μg m− 3). Mean rainfall (mm). Mean wind speed (m s− 1).
parameters have been well studied to predict the behavior of PCBs under various environmental conditions and to investigate the significance of local emissions (Mandalakis and Stephanou, 2007; Cindoruk et al., 2007). Among the 209 PCB congeners, dioxin-like PCBs (dl-PCBs), which include non-ortho-PCBs (PCB-77, 81, 126, and 169) and mono-ortho-PCBs (PCB-105, 114, 118, 123, 156, 157, 167 and 189; IUPAC numbers), have relatively higher toxicity. Each homologue group of PCBs should have lower vapor pressure and should present a higher fraction in the particle phase than multi-ortho PCBs (Falconer and Bidleman, 1994). However, only a few studies have been conducted on the behavior of dl-PCBs in the atmosphere (Ogura et al., 2004; Helm and Bidleman, 2005). The distribution of dl-PCBs toxic quantity existing in the gas and particle phases can be changed by varying the ambient temperature. In order to evaluate the partitioning process of dl-PCBs, this study focused on the gas-particle partitioning of twelve dl-PCB congeners in the urban atmospheric air. Behavior and gasparticle partitioning of dl-PCBs was studied by (i) measuring the gas- and particle-phase concentrations of atmospheric dl-PCBs in urban sites at Gyeonggi-do, (ii) examining the temperature dependence of the gas-phase concentration to investigate the significance of local emissions, and (iii) describing the process using the Junge–Pankow and octanol– air partitioning (KOA) models. The gas-particle partitioning phenomena and results measured from the field versus that predicted by the models for each site are discussed in this paper. 2. Experimental section 2.1. Air sampling Table 1 gives a summary of sampling information and fundamental meteorological data obtained at each site. Air samples were taken every second month from January (Sampling 1) to November (Sampling 5) in 2005, with the exception of March. The sampling program was conducted in six cities of Gyeonggi-do, South Korea; Suwon (A), Anyang (B), Sungnam (C), Ansan (D), Buchen (E) and Siheung (F). Sites A and B were located in old residential areas surrounded by two or three-story houses. Site C was in the commercial district surrounded by highrise apartments and buildings that had been constructed by the
new town project in the early 1990s. Sites D, E and F were located in the industrial area. The D was located in the center of the Banwol industrial complex, one of the largest national industrial district (area, 15.37 km2), and the F was 1 km away from the Sihwa industrial complex (area, 16.43 km2). Site E was 300 m away from a municipal solid waste incineration facility (incineration capacity, 200 tons day−1). Ambient air was collected using a high volume air sampler (HV-1000 F, SIBATA, Japan) equipped with a quartz filter (QF) (20 cm× 25 cm) for particle-phase dl-PCBs, connecting to two polyurethane foam (PUF) plugs (length 5.0 cm, diameter 9.0 cm) for the gas-phase. All samples were collected with a suction flow of 0.4 m3 min−1 over a 72 h period, resulting in a sample volume of approximately 1700 m3. Prior to sampling, 37 the Cl-2,3,7,8-T4CDD standard (ED-2522, CIL, USA) was spiked on the PUF to estimate the sampling performance and extraction efficiency. 2.2. Analytical method The QFs were pre-combusted at 450 °C for 12 h, the PUFs were pre-cleaned by soxhlet with acetone, and they were sealed and stored at 4 °C until sampling. The QF and PUF samples were extracted and analyzed separately in order to study the gas-particle partitioning of dl-PCBs. Sample analysis was performed according to Korean standard testing method for persistent organic pollutants (NIER, 2010). The samples were extracted in soxhlet apparatus with toluene for 24 h. The 13 samples were spiked with the C12-labeled internal standards (WP-LCS, Wellington Laboratories Inc., Canada) before the clean-up process. The extracts were cleaned on multilayer silica gel columns composed of 2 g Na2SO4, 1 g neutral silica, 3 g KOH silica (2%, w/w), 1 g silica, 4 g H2SO4 silica (44%, w/w), 6 g H2SO4 silica (22%, w/w), 1 g neutral silica, 4 g AgNO3 silica (10%, w/w) and 2 g Na2SO4 from the bottom, which was followed by disposable aluminum oxide column chromatography (FMS, USA) using the Power-Prep system (FMS, USA). Finally, the purified extracts were concentrated to approximately 50 μl and 13 spiked with the C12-labeled injection internal standard (WP-ISS, Wellington Laboratories Inc., Canada) to examine the recovery of the internal standards. All samples were analyzed by HRGC/HRMS (Agilent 6890 and Autospec NT, Micromass, UK) operated in EI mode at a resolving power of
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other Asian cities: 0.002–0.014 pg-TEQ m−3 in Yokohama, Japan (Kim and Masunaga, 2005); 0.003–0.008 pg-TEQ m−3 in Taipei City, Taiwan (Chi et al., 2008); an average of 0.007–0.011 pgTEQ m−3 at three urban sites in Hong Kong (Choi et al., 2008b); and 0.017–0.065 pg-TEQ m−3 in Busan, Korea (Jeong et al., 2009). On the contrary, concentrations of dl-PCBs at sites D, E and F determined to be 6.66, 7.73 and 6.37 pg m−3 on average (0.036, 0.031 and 0.049 pg-TEQ m−3), respectively, were higher than those measured at the other sites for all sampling periods. Differences in the surrounding environment may influence this noticeable increase. It is possible that ambient air of industrial areas like sites D, E and F is affected by contaminated emission gas from manufacturing plants or waste incinerators in the vicinity, which are known to be potential contamination sources of PCDD/DF and dl-PCBs (Alcock et al., 1998; Choi et al., 2008a). Similar to the data obtained for dl-PCBs, average concentrations of PCDD/DF at sites D, E and F (0.451, 0.370 and 0.538 pgTEQ m−3) were higher than those at sites A, B and C (0.195, 0.184 and 0.115 pg-TEQ m−3), and were in good agreement with the classification of sampling sites reported by Lohmann and Jones (1998). While specific partitioning between gas and particle in atmospheric air will differ according to the site location (distance from the contaminant source), ambient temperature, target congeners, and so on, PCBs are usually in the gas-phase which accounts for 85–99% of total concentrations (Mandalakis et al., 2002; Tasdemir et al., 2004; Cindoruk et al., 2007). Average concentrations of each dl-PCB congener in gas and particle phases throughout the sampling period are shown in Fig. 1. Average gas-phase percentages for each sampling (Samplings 1–5) were 34± 14%, 76 ± 15%, 98 ± 1%, 88± 6%, and 54± 8%, respectively (Fig. 1a), and 4 ± 3%, 80 ± 13%, 92± 3%, 73± 13%, and 32± 3%, as TEQ (Fig. 1b). As noted below, the predominance of gas-phase PCBs in the atmosphere was more significant during summer at all sites. The average percentage of gas-phase dl-PCBs was in the range of 23–62% (0–14% as TEQ) during Sampling 1 (winter), but increased to 97–99% (56– 95% as TEQ) during Sampling 3 (summer). Among the congeners examined, PCB-118 had the highest concentration
R ≥ 10,000 (10% valley). The congeners of the dl-PCBs were quantified using a DB-5MS column (60 m × 0.2 mm × 0.25 μm, J&W Scientific). The toxic equivalency factors (TEFs) according to the WHO (2006) (Van den Berg et al., 2006) were used to quantify the toxic equivalent (TEQ). 2.3. Quality control Pre-cleaned quartz filter and PUF plugs were used for the particle and gas phases, respectively. Field blanks were analyzed for every sampling set, and trace amounts of the highly-chlorinated congeners were detected in the blanks, which were subtracted from the measured sample values. The method detection limit (MDL) for the dl-PCBs was below 0.1 pg m−3 per congener. This was derived from the blanks and quantified as the mean concentration in the blank plus three times the standard deviation of the mean concentration. The recoveries were similar for both QF and PUF samples 13 analyzed. The average values for the C12-labeled internal standards were 84±16% for PCB-77, 78±13% for PCB-81, 88±11% for PCB-105, 85±11% for PCB-114, 85±14% for PCB118, 85±13% for PCB-123, 95±13% for PCB-126, 91±11% for PCB-56, 93±13% for PCB-157, 88±12% for PCB-167, 94±15% for PCB-169 and 86±10% for PCB-189. The recovery rates of 37 the Cl-2,3,7,8-T4CDD spiked in the PUF plugs as a sampling standard ranged from 91% to 102%. 3. Results and discussion 3.1. Atmospheric concentrations of dl-PCBs Concentrations of gas- and particle-phase dl-PCBs in ambient air from six sampling sites are shown in Table 2. The lowest average concentration of 3.52 pg m−3 (0.008 pg-TEQ m–3) was determined for site C. Concentrations at sites A and B located in residential areas were similar to each other; i.e., 4.14 pg m−3 (0.015 pg-TEQ m−3) and 4.10 pg m−3 (0.015 pg-TEQ m−3), respectively. In fact, concentration levels in sites A, B and C were in the same range as those in residential or commercial areas in
Table 2 Gas and particle phase concentrations of dl-PCBs in this study (unit: pg m− 3). A
Sampling 1 Sampling 2 Sampling 3 Sampling 4 Sampling 5 Average ∑ PCDD/FS a
B
C
D
E
F
Particle
Gas
Particle
Gas
Particle
Gas
Particle
Gas
Particle
Gas
Particle
Gas
3.00 (0.030) 1.70 (0.002) 0.06 (0.001) 0.12 (0.001) 1.06 (0.009) 1.19 (0.008) 3.922 (0.195)
1.46 (0.002) 2.73 (0.004) 6.08 (0.015) 2.59 (0.005) 1.88 (0.005) 2.95 (0.006)
2.83 (0.022) 1.61 (0.002) 0.12 (0.001) 0.20 (0.002) B (0.011) 1.31 (0.008) 3.494 (0.184)
1.34 (0.000) 3.12 (0.010) 4.70 (0.011) 2.19 (0.011) 2.58 (0.005) 2.79 (0.008)
1.67 (0.006) 1.63 (0.000) 0.10 (0.001) 0.12 (0.001) 1.30 (0.012) 0.96 (0.004) 2.121 (0.115)
2.69 (0.001) 3.09 (0.003) 4.43 (0.008) 1.14 (0.003) 1.42 (0.004) 2.55 (0.004)
5.89 (0.053) 0.70 (0.014) 0.26 (0.003) 1.30 (0.016) 2.43 (0.021) 2.11 (0.021) 12.837 (0.451)
2.13 (0.001) 5.11 (0.021) 7.20 (0.027) 5.87 (0.018) 2.42 (0.009) 4.55 (0.015)
5.48 (0.064) 0.17 (0.001) 0.22 (0.011) 1.68 (0.005) 2.92 (0.025) 2.10 (0.021) 4.726 (0.370)
1.90 (0.002) 8.41 (0.013) 8.71 (0.014) 6.92 (0.009) 2.21 (0.010) 5.63 (0.010)
5.92 (0.086) 2.12 (0.009) 0.20 (0.003) 0.45 (0.003) 1.27 (0.018) 1.99 (0.024) 9.256 (0.538)
1.79 (0.003) 7.42 (0.043) 6.63 (0.059) 4.29 (0.011) 1.77 (0.010) 4.38 (0.025)
(): TEQ value calculated with WHO-TEF (2006). a Average concentrations for 2,3,7,8-subsituted PCDD/Fs over the sampling period.
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Fig. 1. Average concentrations of dl-PCB congeners in gas and particle phases: (a) concentrations with pg m−3 and (b) concentrations with pg TEQ m−3.
(52 ± 3% contribution to gas-phase ∑dl-PCBs concentrations and 35 ± 7% contribution to particle-phase ∑dl-PCBs concentrations), followed in descending order by PCB-105 and PCB-77. In addition, PCB-126 had the highest TEQ value, accounting for more than 90% of the dl-PCBs TEQ in all samples. These results are consistent with those reported in other studies on dl-PCBs (Helm and Bidleman, 2005; Choi et al., 2008b; Castro-Jiménez et al., 2008). 3.2. Temperature dependence of dl-PCBs Atmospheric PCB concentrations increase with emissions from primary sources (i.e., incinerators and thermal treatment facilities) and volatilization from secondary sources such as soils, vegetation, and water surfaces, and should be related to the ambient temperature. The relationship between gas-phase
dl-PCB concentrations and ambient temperature can be described by the Clausius–Clapeyron equation shown below: ln P =
ΔHSA 1 + const; T R
ð1Þ
where P is the partial pressure (atm), T is the temperature (K), ΔHSA is the enthalpy of the surface–air exchange (J mol−1) and R is the ideal gas constant (8.314 J mol−1 K−1). Partial pressures of the dl-PCBs were calculated from their gas-phase concentrations and molecular masses using the ideal gas law, and the slope m and intercept b were determined by carrying out linear regression using Eq. (2). 1 ln P = m +b T
ð2Þ
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The Clausius–Clapeyron plots drawn using our data are shown in Fig. 2. Values of the slope m for the urban residential (sites A and B) and industrial sites (sites D, E and F) were −3,782 and −5,152, respectively, consistent with various reported values for different sites (mean value at inland temperature sites: −5149 ± 2463) (Carlson and Hites, 2005). Wania et al. (1998) suggested that high temperature dependence (a steep slope) indicates that the air concentration is controlled by re-volatilization from the surfaces in the local surroundings of the sampling sites, whereas low temperature dependence (a shallow slope) indicates that the atmospheric transport of air mass controls the air concentration. Seen from this perspective, volatilization of dl-PCBs from surfaces at these sites would dominate the atmospheric concentrations. In the case of site C, however, there was no statistical correlation (r2 = 0.10, p = 0.6) between the ambient temperature and gasphase concentrations (−1426 of slope m value). Its shallow slope resulted from the relatively higher portion in the gas phase at site C during Sampling 1 (in winter), which shows a large discrepancy when compared with the gas-particle partitions at the other sites. At site C, the gas-particle ratio of dl-PCBs during Sampling 1 was 1.6, which was much higher than the average ratio of 0.39 (ranging from 0.3 to 0.49) at the other sites during the same sampling period. At the moment, it is still unclear why an unusually high portion of gas-phase dl-PCBs was obtained at site C; factors that may contribute to this anomaly include temporary contamination caused by passing emission sources or atmospheric transport during the day (Cindoruk et al., 2007), a narrow temperature range during the sampling period (Tasdemir et al., 2004), or variations in PCBs partitioning (Carlson and Hites, 2005). In addition, unavoidable physico-chemical reactions that may occur during sampling (using a high volume air sampler) could influence the results; e.g., adsorption or volatilization of semi-volatile organic
compounds, and/or loss of particulate matters by "blow-off", as discussed in the literature (Volckens and Leith, 2003; Tsapakis and Stephanou, 2003; Helm and Bidleman, 2005). Furthermore, the 72-hour sampling period in this study seems too rigorous to prevent blow-off losses or adsorption/volatilization of gasphase dl-PCBs during air collection. Gas-particle partitioning of dl-PCBs at site C during Sampling 1 presents further questions that must be reserved for a more extensive study. 3.3. Gas-particle partitioning of dl-PCBs Gas-particle partitioning is an important mechanism that affects the fate and transport of SOCs. To understand the partitioning phenomena of dl-PCBs in our sites, partitioning of the measured dl-PCBs between the gas and particle phases in the atmosphere was evaluated. The gas-particle partitioning coefficient KP (m3 μg−1) for each compound was calculated using the following equation: KP =
F = TSP A
ð3Þ
where F is the particle-phase concentration for the compound of interest (pg m−3), A is the gas-phase concentration (pg m−3), and TSP is the amount of total suspended particulate matter (μg m−3) (Pankow, 1994). 3.3.1. Log KP versus log PoL correlation Partitioning between the particle and gas phases was evaluated by regression of KP with the sub-cooled vapor pressure PoL (Pa) using Eq. (4): o
log KP = mr logPL + br :
Fig. 2. Clausius–Clapeyron plots of ln P versus 1/T for Σdl–PCBs at the urban residential, commercial and industrial sites.
ð4Þ
D.–G. Kim et al. / Atmospheric Research 101 (2011) 386–395
Useful information pertaining to the gas-particle partitioning can be extracted from the slope mr and intercept br of the trend line. It has been suggested that at equilibrium, the regression slope between log KP and log PoL should be close to −1 (Pankow, 1994). However, other researchers argued that a deviation from −1 is not always indicative of non-
391
equilibrium, and proposed reasons for the variability in the slopes and intercepts (Pankow and Bidleman, 1992; Simcik et al., 1998; Wang et al., 2011). In this study, the KP values were calculated using the field data, and the correlation between log KP and log PoL was subsequently evaluated. Temperature-dependent vapor
Fig. 3. Regression plots of log KP with log PoL (r2 = 0.47–0.96, p b 0.0001) and log KP with log KOA (r2 = 0.47–0.96, p b 0.0001) at sites A, C and D.
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pressures for dl-PCB congeners were calculated using mL and bL (log PoL = mL/T + bL) data given by Falconer and Bidleman (1994) and adjusted to the average ambient temperature during the sampling period. Regression plots of log KP versus log PoL are shown in Fig. 3a. Three sites (A, C, and D) were selected and compared to set the data points of the regression plots. Correlations of the plots were highly significant (r2 = 0.47–0.96, p b 0.001), with the average slopes mr for sites A, C, and D found to be −0.88 ± 0.15, −0.70± 0.14, and −0.85 ± 0.11, respectively. The slopes obtained in this study were very similar to those obtained in previous studies, which reported values ranging between −0.51 and −0.97 for different urban sites (Harner and Bidleman, 1998; Falconer and Harner, 2000; Lohmann et al., 2000; Kim and Masunaga, 2005; Cindoruk et al., 2007). Emission of PCBs into the atmosphere primarily occurs when gasses are volatilized from areas where they have been used, stored, spilled, or atmospherically deposited. Gas-phase dl-PCB concentrations increase with increasing ambient temperature and slow re-equilibrium of gas-phase PCBs may be responsible for the shallow slopes obtained from our sites. Shallow slopes observed in this study may indicate that partitioning of dl-PCBs between the gas and particle phases in the atmosphere takes place under non-equilibrium conditions. There were variations in slopes mr with respect to the sampling period; i.e., slopes mr measured during Sampling 3 (−0.66 to −0.70) slightly deviated from −1, whereas the slopes obtained from Samplings 1 (−0.69 to −1.11) and 5 (−0.92 to −1.01) were closer to −1 (Table 3). Significant correlation between slope values and ambient temperature was found at sites A and D (p= 0.04, r2 = 0.42) but there was no significant correlation at site C (p= 0.3, r2 = 0.32), as has been noted above. The result is related to the distribution of gasparticle concentrations for dl-PCBs in the atmosphere: dl-PCBs remained mostly in the gas phase during Sampling 3 (summer), whereas they diverged into the gas and particle phases for Samplings 1 and 5 (winter and late autumn, respectively). These results are similar to those obtained in a study on the gasparticle partitioning of n-alkanes (C17–C24); these alkanes were emitted and remained mostly in the gas phase, and their slopes ranged from −0.7 to −0.79 during summer and from −0.80 to −1.01 in winter (Pankow and Bidleman, 1992).
Table 3 Slope (mr) and intercept (br) values for log log KP versus log PoL for dl-PCBs in the atmosphere of A, C and D. Sites A
C
D
Sampling 1 Sampling 2 Sampling 3 Sampling 4 Sampling 5 mr − 1.11 br − 6.62 r2 0.92
− 0.87 − 5.49 0.68
− 0.70 − 5.41 0.78
− 0.71 − 5.38 0.78
− 0.92 − 5.88 0.89
mr − 0.69 br − 5.11 r2 0.52
− 0.57 − 4.63 0.47
− 0.66 − 5.25 0.5
− 0.65 − 4.96 0.49
− 0.93 − 5.96 0.96
mr − 0.90 br − 6.34 r2 0.76
− 0.73 − 4.84 0.58
− 0.70 − 5.33 0.86
− 0.82 − 5.5 0.93
− 1.01 − 6.14 0.87
Regression of log KP versus log PoL was highly significant (p b 0.001).
3.3.2. Log KP versus log KOA correlation The octanol–air partitioning coefficient KOA has been used as a key descriptor for predicting the environmental fate of SOCs as an alternative to PoL . Finzio et al. (1997) suggested that KOA is an excellent descriptor of gas-to-aerosol partitioning. They described the relationship between KP and KOA as follows: log KP = m log KOA + b:
ð5Þ
KOA was calculated from Eq. (6) using the octanol–water partitioning coefficient KOW and Henry's law constant H (Cindoruk et al., 2007). KOA =
KOW RT ; H
ð6Þ
where KOA is dimensionless and R is the ideal gas constant. The correlation obtained from the log KP versus log KOA plots was highly significant (r2 = 0.44–0.96, p b 0.001), and the average slopes m and intercepts b for sites A, C, and D were 0.71 ± 0.10 and −10.39 ± 1.2, 0.57 ± 0.11 and −8.76 ± 1.19, and 0.67± 0.08 and −9.83 ± 1.07, respectively (Fig. 3b). The average slopes determined for our results were smaller than 1, and they were in line with those reported for urban sites (Lohmann et al., 2000; Cindoruk et al., 2007). The shallow slopes can be attributed to the lower KP values depending on the particle-bound PCB. Nevertheless, the trend of gas-particle partitioning based on the KOA values in the same sites was consistent with the results obtained from that based on the PoL values. The correlations of slopes obtained from log KP–log PoL plots and those from log KP–log KOA plots were significant (r2 = 0.91, p b 0.0001), and those for the intercepts were also significant (r2 = 0.90, p b 0.0001). 3.4. Gas-particle partitioning model Partitioning between the gas and particle phases was further examined using the Junge–Pankow and KOA-based models. Both the Junge–Pankow and KOA-based models can be used as descriptors to predict not only the particle-bound fraction φ but also the gas-particle partitioning coefficients KP with SOCs. In this study, KP and φ of dl-PCBs were calculated using the Junge–Pankow and KOA-based models and compared with those obtained from field measurements at sites A, C, and D. According to the Junge–Pankow model, φ of a specific chemical is defined as follows: φ=
log
Cp cθ = ∘ PL + cθ Cg + Cp
Cp cθ ∘ −log PL ; = log Kp = log TSP Cg
ð7Þ
ð8Þ
where Cp (pg μg−1) and Cg (pg m−3) are the atmospheric concentrations associated with the particle and gas phases, respectively. θ is the total suspended particulate surface area concentration (m2 aerosol m−3 air), and c is a parameter that depends on the heat of condensation for the chemical and surface properties.
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The values for model calculations were assumed to be c = 0.172 Pa m − 1 , θ = 1.1 × 10 − 3 m 2 m − 3 for urban, 1.5×10−3 m2 m−3 for rural, and 4.2×10−3 m2 m−3 for clean background air (Cotham and Bidleman, 1992; Pankow et al., 1993; Falconer and Harner, 2000). The KOA-based model is given in Eqs. (9) and (10) (Harner and Bidleman, 1998; Falconer and Harner, 2000): log KP = log KOA + log ƒOM –11:91 ϕ=
Cp KP ðTSP Þ : = 1 + KP ðTSP Þ Cg + Cp
ð9Þ ð10Þ
393
The organic matter fraction of a particle (ƒOM) varies according to local factors and the particulate characteristics. The value of φ was also calculated from the measured particleand gas-phase dl-PCB concentrations. Fig. 4 compares the φ values of dl-PCB congeners predicted by the Junge–Pankow and KOA-based models with the measured data for sites A, C, and D. The measured φ values of dl-PCB congeners obtained from field data were between the rural and urban lines of the predicted plots obtained from the Junge–Pankow model. However, dl-PCB congeners with low φ (φ b 20%) were close to the rural line of the predicted plots. For the KOA-based model, the measured φ values of dl-PCB
Fig. 4. Comparison between the measured results with the prediction from Junge–Pankow model (a) and KOA-based model (b).
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congeners were smaller than those predicted by the KOA-based model consisting of 10% organic matter in the particulates. On the other hand, Fig. 5 compares the KP values of dl-PCB congeners predicted by the Junge–Pankow and KOA-based models with the measured data for sites A, C, and D. The KP values predicted using both the models were larger than those measured from field data for dl-PCB congeners. Mandalakis and Stephanou (2007) reported that the Junge–Pankow model overestimates the KP coefficients of PCB congeners with lower vapor pressures than PCB-110 (vapor pressure, 1.8 × 10−3 Pa at 25 °C), and Cindoruk et al. (2007) obtained similar results for
the modeled φ values. The observed difference between the measured and modeled KP values has been suggested to largely be due to non-equilibrium partitioning of PCBs in the gas and particulate phases in the atmosphere. In the present study, despite the uncertainties in the model parameters, the correlations between the experimental and modeled KP values were significant at p b 0.001: r2 = 0.74–0.84 for the Junge–Pankow model and r2 = 0.76–0.84 for the KOA-based model. Therefore, both the models are good indicators for explaining the gas-particle partitioning of dl-PCBs in the urban atmospheric air of Gyeonggi-do.
Fig. 5. Correlation of field experimental KP with modeled KP calculated by Junge–Pankow model (a) and KOA-based model (b). Bold solid line is 1:1 trend line for the model predicted KP. The modeled KP was calculated using PLθ = 1.1 × 10−3 m2 m−3 for Junge–Pankow model and ƒOM = 0.1 for KOA-based model.
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Acknowledgment This work was performed as a part of the annual survey program for dioxins in Gyeonggi-do. This work was supported by the 2010 Research Fund of the University of Seoul. References Alcock, R.E., Behnisch, P.A., Jones, K.C., Hagenmaier, H., 1998. Dioxin-like PCBs in the environment - human exposure and the significance of sources. Chemosphere 37 (8), 1457–1472. Brunciak, P.A., Dachs, J., Gigliotti, C.L., Nelson, E.D., Eisenreich, S.J., 2001. Atmospheric polychlorinated biphenyl concentrations and apparent degradation in coastal New Jersey. Atmospheric Environment 25, 3325–3339. Carlson, D.L., Hites, R.A., 2005. Temperature dependence of atmospheric PCB concentrations. Environmental Science and Technology 39 (3), 740–747. Castro-Jiménez, J., Mariani, G., Eisenreich, S.J., Christoph, E.H., Hanke, G., Canuti, E., Skejo, H., 2008. Atmospheric input of POPs into Lake Maggiore (Northern Italy): PCDD/F and dioxin-like PCB profiles and fluxes in the atmosphere and aquatic system. Chemosphere 73, S122–S130. Chi, K.H., Hsu, S.C., Wang, S.H., Chang, M.B., 2008. Increase in ambient PCDD/F and PCB concentrations in Northern Taiwan during an Asian dust storm episode. Science of the Total Environment 401, 100–108. Choi, K.I., Lee, S.H., Lee, D.H., 2008a. Emission of PCDDs/DFs and dioxin-like PCBs from small waste incinerators in Korea. Atmospheric Environment 42, 940–948. Choi, M.P.K., Ho, S.K.M., So, B.K.L., Cai, Z., Lau, A.K.H., Wong, M.H., 2008b. PCDD/F and dioxin-like PCB in Hong Kong air in relation to their regional transport in the Pearl River Delta region. Chemosphere 71, 211–218. Cindoruk, S.S., Tasdemir, Y., 2007. Deposition of atmospheric particulate PCBs in suburban site of Turkey. Atmospheric Research 85, 300–309. Cindoruk, S.S., Esen, F., Tasdemir, Y., 2007. Concentration and gas/particle partitioning of polychlorinated biphenyls (PCBs) at an industrial site at Bursa, Turkey. Atmospheric Research 85, 338–350. Cotham, W.E., Bidleman, T.F., 1992. Laboratory investigations of the partitioning of organochlorine compounds between the gas phase and atmospheric aerosols on glass fiber filters. Environmental Science and Technology 26 (3), 469–477. Falconer, R.L., Bidleman, T.F., 1994. Vapor pressures and predicted particle/ gas distributions of PCB congeners as functions of temperature and ortho-chlorine substitution. Atmospheric Environment 28 (3), 547–554. Falconer, R.L., Harner, T., 2000. Comparison of the octanol–air partition coefficient and liquid phase vapor pressure as descriptors for particle/gas partitioning using laboratory and field data for PCBs and PCNs. Atmospheric Environment 34, 4043–4046. Finzio, A., Mackay, D., Bidleman, T.F., Harner, T., 1997. Octanol–air partition coefficient as a predictor of partitioning of semi volatile chemical to aerosols. Atmospheric Environment 31 (15), 2289–2296. Harner, T., Bidleman, T.F., 1998. Octanol–air partition coefficient for describing particlr/gas partitioning of aromatic compounds in urban air. Environmental Science and Technology 32, 1494–1502. Helm, P.A., Bidleman, T.F., 2005. Gas-particle partitioning of polychlorinated naphthalene and non- and mono ortho-substituted polychlorinated biphenyls in arctic air. Science of the Total Environment 342, 161–173. Jeong, S.R., Jang, E.H., Kim, D.H., Kim, K.S., 2009. Characteristics of congener distribution for persistent organic pollutant (POPs) in air, Busan. The Annual report of Busan Metropolitan City Institute of Health & Environment, 19, pp. 207–224. Kim, K.S., Masunaga, S., 2005. Behavior and source characteristic of PCBs in urban ambient air of Yokohama, Japan. Environmental Pollution 138, 290–298.
395
Lohmann, R., Jones, K.C., 1998. Dioxins and furan in air and deposition: a review of levels, behavior and processes. The Science of the Total Environment 219, 53–81. Lohmann, R., Harner, T., Thomas, G.O., Jones, K.C., 2000. A comparative study of the gas particle partitioning of PCDD/Fs, PCBs and PAHs. Environmental Science and Technology 34, 4943–4951. Mandalakis, M., Tsapakis, M., Tsoga, A., Stephanou, E.G., 2002. Gas-particle concentrations and distribution of aliphatic hydrocarbons, PAHs, PCBs and PCDD/Fs in the atmosphere of Athens (Greece). Atmospheric Environment 36, 4023–4035. Mandalakis, M., Stephanou, E.G., 2007. Atmospheric concentration characteristics and gas-particle partitioning of PCBs in a rural site of eastern Germany. Environmental Pollution 147, 211–221. Manodori, L., Gambaro, A., Moret, I., Capodaglio, G., Cairns, W.R.L., Cescon, P., 2006. Seasonal evolution of gas-phase PCB concentrations in the Venice Lagoon site. Chemosphere 62, 449–458. Matsumoto, K., Matsumoto, K., Mizuno, R., Igawa, M., 2010. Volatile organic compounds in ambient aerosols. Atmospheric Research 97, 124–128. NIER, 2010. Korean Standard Testing Method for Persistent Organic Pollutants. Ministry of Environment. ES 10309.1. Ogura, I., Masunaga, S., Nakanishi, J., 2004. Quantitative source identification of Dioxin-like PCBs in Yokohama, Japan, by temperature dependence of their atmospheric concentrations. Environmental Science and Technology 38, 3279–3285. Ozcan, S., Aydin, M.E., 2009. Polycyclic aromatic hydrocarbons, polychlorinated biphenyls and organochlorine pesticides in urban air of Konya, Turkey. Atmospheric Research 93, 715–722. Pankow, J.F., Bidleman, T.F., 1992. Interdependence of the slopes and intercepts from log–log correlations of measured gas-particle partitioning and vapor pressure —1. Theory and analysis of available data. Atmospheric Environment 26A, 1071–1080. Pankow, J.F., Storey, J.E., Yamasaki, H., 1993. Effects of relative humidity on gas/ particle partitioning of semivolatile organic compounds to urban particulate matter. Environmental Science and Technology 27 (10), 2220–2226. Pankow, J.F., 1994. An absorption model of gas/particle partitioning of organic compounds in the atmosphere. Atmospheric Environment 28 (2), 185–188. Simcik, M.F., Franz, T.P., Zhang, H., Eisenrech, S/.J., 1998. Gas/particle partitioning of PCBs and PAHs in the Chicago urban and adjacent coastal atmosphere: states of equilibrium. Environmental Science and Technology 32, 251–257. Tasdemir, Y., Vardar, N., Odabasi, M., Holsen, T.M., 2004. Concentrations and gas/particle partitioning of PCBs in Chicago. Environmental Pollution 131, 35–44. Tsapakis, M., Stephanou, E.G., 2003. Collection of gas and particle semi-volatile organic compounds: use of an oxidant denuder to minimize polycyclic aromatic hydrocarbons degradation during high-volume air sampling. Atmospheric Environment 37, 4935–4944. Van den Berg, M., Birnbaum, L.S., Denison, M., Vito, M.D., Farland, W., Feeley, M., Fiedler, H., Hakansson, H., Hanberg, A., Haws, L., Rose, M., Safe, S., Schrenk, D., Tohyama, C., Tritscher, A., Tuomisto, J., Tysklind, M., Walker, N., Peterson, R.E., 2006. The 2005 World Health Organization re-evaluation of human and mammalian toxic equivalency factors for dioxins and dioxin-like compounds. Toxicological Sciences 93 (2), 223–241. Volckens, J., Leith, D., 2003. Comparison of methods for measuring gas-particle partitioning of semivolatile compounds. Atmospheric Environment 37, 3177–3188. Wang, W., Simonich, S.L.M., Wang, W., Giri, B., Zhao, J., Xue, M., Cao, J., Lu, X., Tao, S., 2011. Atmospheric polycyclic aromatic hydrocarbon concentrations and gas/particle partitioning at background, rural village and urban sites in the North China Plain. Atmospheric Research 99, 197–206. Wania, F., Haugen, J.E., Lei, Y.D., Mackay, D., 1998. Temperature dependence of atmospheric concentrations of semivolatile organic compounds. Environmental Science and Technology 32, 1013–1021.
Contents lists available at ScienceDirect
Atmospheric Research j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / a t m o s
Gas-particle partitioning and behavior of dioxin-like PCBs in the urban atmosphere of Gyeonggi-do, South Korea Dong–Gi Kim a, Ki–In Choi b, Dong–Hoon Lee c,⁎ a b c
Department of Atmospheric Research, Gyeonggi-do Institute of Health & Environment, 324-1 Pajang-dong, Jangan-gu, Suwon, Gyeonggi-do, 440-290, Republic of Korea Test and Standard Center, Korea Institute of Ceramic Engineering and Technology, 233-5 Gasan-dong, Geumcheon-gu, Seoul, 153-801, Republic of Korea Department of Environmental Engineering, The University of Seoul, 90 Jeonnong-dong, Dongdaemun-gu, Seoul 130-430, Republic of Korea
a r t i c l e
i n f o
Article history: Received 8 September 2010 Received in revised form 17 March 2011 Accepted 28 March 2011
Keywords: dl-PCBs Partitioning Particle bound fraction Junge–Pankow model KOA-based model
a b s t r a c t To evaluate their behavior and gas-particle partitioning, the concentrations of dioxin-like polychlorinated biphenyls (dl-PCBs) in both gas and particle phases in ambient air were measured at urban sites in Gyeonggi-do, South Korea. The predominance of gas-phase PCBs in the atmosphere was more significant during summer at all sites. In July, approximately 92% of the total toxic equivalent quantity (TEQ) was in the gaseous phase, whereas in January, approximately 96% of the total TEQ was in the particle phase. The dl-PCBs can be transported over long ranges and accumulate rapidly in plants at warm temperatures. Partial vapor pressures of dl-PCBs were well correlated with temperature and the steep slopes obtained from Clausius–Clapeyron plots indicated that the volatilization of dl-PCBs from surfaces in the local surroundings of the sampling sites dominated the atmospheric concentrations. The gas-particle distribution was also examined through several different approaches such as the log KP–log PoL, log KP–log KOA, Junge–Pankow, and KOA-based models. The slopes obtained from regressing log KP versus log PoL (log KP versus log KOA) were smaller than −1 (or 1). The particlebound fraction (φ) and KP of the dl-PCB congeners were estimated using the Junge–Pankow and KOA-based models. Both models tended to overestimate the φ and KP values of the dl-PCB congeners compared to those obtained from field measurements. Nevertheless, the coefficients of determination (r2) between the experimental and modeled KP values were 0.74 to 0.84 and 0.76 to 0.84 for the Junge–Pankow and KOA-based models, respectively. © 2011 Elsevier B.V. All rights reserved.
1. Introduction In the past, polychlorinated biphenyls (PCBs) were widely used in many applications, particularly as dielectric fluids in transformers, capacitors, and coolants (Cindoruk and Tasdemir, 2007). Due to the toxicity of PCBs, their persistence, and longrange transport, their production was banned in most countries in the 1970s (Manodori et al., 2006), and they were classified as persistent organic pollutants (POPs) at the Stockholm Convention. Although banned for more than 30 years, PCBs are still detected globally in all environmental materials. Today, a major source of PCBs existing in the atmosphere is considered to be ⁎ Corresponding author. Tel.: + 82 2 2210 2377; fax: + 82 2 2217 8550. E-mail address: [email protected] (D.–H. Lee). 0169-8095/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.atmosres.2011.03.010
due to their volatilization from sites where they have been disposed off or stored, air–water/soil exchange, and incineration of PCBs-containing materials (Mandalakis et al., 2002). Therefore, urban/industrial areas are major sources of atmospheric PCBs found in the surrounding regions (Brunciak et al., 2001; Ozcan and Aydin, 2009). PCBs are mainly present in the atmosphere in the gas phase, and they reach gas-particle partitioning equilibrium depending on the ambient temperature, vapor pressure, and concentration of total suspended particles (TSPs) (Pankow, 1994). Gasparticle partitioning influences the fate, transport, atmospheric residence time, and removal processes of semi-volatile organic compounds (SOCs) in atmospheric environments (Matsumoto et al., 2010). The atmospheric concentrations of PCBs, their gasparticle partitioning, and the relationship with meteorological
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387
Table 1 Summary of the sampling and meteorological data for this study. Site
A
Location
Suwon
Anyang
Sungnam
Ansan
Buchen
Siheung
Sampling 1 (winter) Temp. a, TSP b, RF c, WS d Sampling 2 (spring) Temp. a, TSP b, RF c, WS d Sampling 3 (summer) Temp. a, TSP b, RF c, WS d Sampling 4 (late summer) Temp. a, TSP b, RF c, WS d Sampling 5 (late autumn) Temp. a, TSP b, RF c, WS d
28/1–31/1, 2005 1.1, 137, 0, 1.3 17/5–21/5, 2005 16.6, 102, 19.5, 1.5 19/7–22/7, 2005 26.2, 72, 1, 1.8 20/9–23/9, 2005 18.9, 55, 15, 1.5 19/11–22/11, 2005 7.3, 98, 1.5, 1.7
26/1–29/1, 2005 − 2.1, 89, 0, 1.3 24/5–27/5, 2005 18.9, 98, 0, 1.5 20/7–23/7, 2005 27.1, 74, 1, 1.8 20/9–23/9, 2005 18.7, 61, 15, 1.5 19/11–22/11, 2005 8.0, 113, 1.5, 1.6
28/1–31/1, 2005 1.1, 107, 0, 1.2 17/5–21/5, 2005 17.4, 96, 19.5, 1.7 19/7–22/7, 2005 27.2, 79, 1, 1.5 20/9–23/9, 2005 17.7, 65, 15, 1.4 19/11–22/11, 2005 5.7, 117, 1.5, 1.2
26/1–29/1, 2005 − 3.6, 101, 0, 2.0 17/5–21/5, 2005 16.1, 142, 19.5, 3.0 20/7–23/7, 2005 26.6, 79, 1, 2.6 21/9–24/9, 2005 19.2, 110, 44, 2.5 19/11–22/11, 2005 6.8, 167, 2, 2.0
28/1–31/1, 2005 2.7, 90, 0, 1.9 24/5–27/5, 2005 20.9, 107, 0, 2.6 20/7–23/7, 2005 27.2, 56, 1, 2.4 21/9–24/9, 2005 19.8, 63, 44, 2.5 19/11–22/11, 2005 7.9, 84, 2, 1.9
28/1–31/1, 2005 2.3, 151, 0, 2.0 24/5–27/5, 2005 19.9, 113, 0, 3.0 20/7–23/7, 2005 26.6, 82, 1, 2.6 21/9–24/9, 2005 19.9, 88, 44, 2.5 19/11–22/11, 2005 8.0, 121, 2, 2.0
a b c d
B
C
D
E
F
Mean temperature (°C). Mean total suspended particle (μg m− 3). Mean rainfall (mm). Mean wind speed (m s− 1).
parameters have been well studied to predict the behavior of PCBs under various environmental conditions and to investigate the significance of local emissions (Mandalakis and Stephanou, 2007; Cindoruk et al., 2007). Among the 209 PCB congeners, dioxin-like PCBs (dl-PCBs), which include non-ortho-PCBs (PCB-77, 81, 126, and 169) and mono-ortho-PCBs (PCB-105, 114, 118, 123, 156, 157, 167 and 189; IUPAC numbers), have relatively higher toxicity. Each homologue group of PCBs should have lower vapor pressure and should present a higher fraction in the particle phase than multi-ortho PCBs (Falconer and Bidleman, 1994). However, only a few studies have been conducted on the behavior of dl-PCBs in the atmosphere (Ogura et al., 2004; Helm and Bidleman, 2005). The distribution of dl-PCBs toxic quantity existing in the gas and particle phases can be changed by varying the ambient temperature. In order to evaluate the partitioning process of dl-PCBs, this study focused on the gas-particle partitioning of twelve dl-PCB congeners in the urban atmospheric air. Behavior and gasparticle partitioning of dl-PCBs was studied by (i) measuring the gas- and particle-phase concentrations of atmospheric dl-PCBs in urban sites at Gyeonggi-do, (ii) examining the temperature dependence of the gas-phase concentration to investigate the significance of local emissions, and (iii) describing the process using the Junge–Pankow and octanol– air partitioning (KOA) models. The gas-particle partitioning phenomena and results measured from the field versus that predicted by the models for each site are discussed in this paper. 2. Experimental section 2.1. Air sampling Table 1 gives a summary of sampling information and fundamental meteorological data obtained at each site. Air samples were taken every second month from January (Sampling 1) to November (Sampling 5) in 2005, with the exception of March. The sampling program was conducted in six cities of Gyeonggi-do, South Korea; Suwon (A), Anyang (B), Sungnam (C), Ansan (D), Buchen (E) and Siheung (F). Sites A and B were located in old residential areas surrounded by two or three-story houses. Site C was in the commercial district surrounded by highrise apartments and buildings that had been constructed by the
new town project in the early 1990s. Sites D, E and F were located in the industrial area. The D was located in the center of the Banwol industrial complex, one of the largest national industrial district (area, 15.37 km2), and the F was 1 km away from the Sihwa industrial complex (area, 16.43 km2). Site E was 300 m away from a municipal solid waste incineration facility (incineration capacity, 200 tons day−1). Ambient air was collected using a high volume air sampler (HV-1000 F, SIBATA, Japan) equipped with a quartz filter (QF) (20 cm× 25 cm) for particle-phase dl-PCBs, connecting to two polyurethane foam (PUF) plugs (length 5.0 cm, diameter 9.0 cm) for the gas-phase. All samples were collected with a suction flow of 0.4 m3 min−1 over a 72 h period, resulting in a sample volume of approximately 1700 m3. Prior to sampling, 37 the Cl-2,3,7,8-T4CDD standard (ED-2522, CIL, USA) was spiked on the PUF to estimate the sampling performance and extraction efficiency. 2.2. Analytical method The QFs were pre-combusted at 450 °C for 12 h, the PUFs were pre-cleaned by soxhlet with acetone, and they were sealed and stored at 4 °C until sampling. The QF and PUF samples were extracted and analyzed separately in order to study the gas-particle partitioning of dl-PCBs. Sample analysis was performed according to Korean standard testing method for persistent organic pollutants (NIER, 2010). The samples were extracted in soxhlet apparatus with toluene for 24 h. The 13 samples were spiked with the C12-labeled internal standards (WP-LCS, Wellington Laboratories Inc., Canada) before the clean-up process. The extracts were cleaned on multilayer silica gel columns composed of 2 g Na2SO4, 1 g neutral silica, 3 g KOH silica (2%, w/w), 1 g silica, 4 g H2SO4 silica (44%, w/w), 6 g H2SO4 silica (22%, w/w), 1 g neutral silica, 4 g AgNO3 silica (10%, w/w) and 2 g Na2SO4 from the bottom, which was followed by disposable aluminum oxide column chromatography (FMS, USA) using the Power-Prep system (FMS, USA). Finally, the purified extracts were concentrated to approximately 50 μl and 13 spiked with the C12-labeled injection internal standard (WP-ISS, Wellington Laboratories Inc., Canada) to examine the recovery of the internal standards. All samples were analyzed by HRGC/HRMS (Agilent 6890 and Autospec NT, Micromass, UK) operated in EI mode at a resolving power of
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other Asian cities: 0.002–0.014 pg-TEQ m−3 in Yokohama, Japan (Kim and Masunaga, 2005); 0.003–0.008 pg-TEQ m−3 in Taipei City, Taiwan (Chi et al., 2008); an average of 0.007–0.011 pgTEQ m−3 at three urban sites in Hong Kong (Choi et al., 2008b); and 0.017–0.065 pg-TEQ m−3 in Busan, Korea (Jeong et al., 2009). On the contrary, concentrations of dl-PCBs at sites D, E and F determined to be 6.66, 7.73 and 6.37 pg m−3 on average (0.036, 0.031 and 0.049 pg-TEQ m−3), respectively, were higher than those measured at the other sites for all sampling periods. Differences in the surrounding environment may influence this noticeable increase. It is possible that ambient air of industrial areas like sites D, E and F is affected by contaminated emission gas from manufacturing plants or waste incinerators in the vicinity, which are known to be potential contamination sources of PCDD/DF and dl-PCBs (Alcock et al., 1998; Choi et al., 2008a). Similar to the data obtained for dl-PCBs, average concentrations of PCDD/DF at sites D, E and F (0.451, 0.370 and 0.538 pgTEQ m−3) were higher than those at sites A, B and C (0.195, 0.184 and 0.115 pg-TEQ m−3), and were in good agreement with the classification of sampling sites reported by Lohmann and Jones (1998). While specific partitioning between gas and particle in atmospheric air will differ according to the site location (distance from the contaminant source), ambient temperature, target congeners, and so on, PCBs are usually in the gas-phase which accounts for 85–99% of total concentrations (Mandalakis et al., 2002; Tasdemir et al., 2004; Cindoruk et al., 2007). Average concentrations of each dl-PCB congener in gas and particle phases throughout the sampling period are shown in Fig. 1. Average gas-phase percentages for each sampling (Samplings 1–5) were 34± 14%, 76 ± 15%, 98 ± 1%, 88± 6%, and 54± 8%, respectively (Fig. 1a), and 4 ± 3%, 80 ± 13%, 92± 3%, 73± 13%, and 32± 3%, as TEQ (Fig. 1b). As noted below, the predominance of gas-phase PCBs in the atmosphere was more significant during summer at all sites. The average percentage of gas-phase dl-PCBs was in the range of 23–62% (0–14% as TEQ) during Sampling 1 (winter), but increased to 97–99% (56– 95% as TEQ) during Sampling 3 (summer). Among the congeners examined, PCB-118 had the highest concentration
R ≥ 10,000 (10% valley). The congeners of the dl-PCBs were quantified using a DB-5MS column (60 m × 0.2 mm × 0.25 μm, J&W Scientific). The toxic equivalency factors (TEFs) according to the WHO (2006) (Van den Berg et al., 2006) were used to quantify the toxic equivalent (TEQ). 2.3. Quality control Pre-cleaned quartz filter and PUF plugs were used for the particle and gas phases, respectively. Field blanks were analyzed for every sampling set, and trace amounts of the highly-chlorinated congeners were detected in the blanks, which were subtracted from the measured sample values. The method detection limit (MDL) for the dl-PCBs was below 0.1 pg m−3 per congener. This was derived from the blanks and quantified as the mean concentration in the blank plus three times the standard deviation of the mean concentration. The recoveries were similar for both QF and PUF samples 13 analyzed. The average values for the C12-labeled internal standards were 84±16% for PCB-77, 78±13% for PCB-81, 88±11% for PCB-105, 85±11% for PCB-114, 85±14% for PCB118, 85±13% for PCB-123, 95±13% for PCB-126, 91±11% for PCB-56, 93±13% for PCB-157, 88±12% for PCB-167, 94±15% for PCB-169 and 86±10% for PCB-189. The recovery rates of 37 the Cl-2,3,7,8-T4CDD spiked in the PUF plugs as a sampling standard ranged from 91% to 102%. 3. Results and discussion 3.1. Atmospheric concentrations of dl-PCBs Concentrations of gas- and particle-phase dl-PCBs in ambient air from six sampling sites are shown in Table 2. The lowest average concentration of 3.52 pg m−3 (0.008 pg-TEQ m–3) was determined for site C. Concentrations at sites A and B located in residential areas were similar to each other; i.e., 4.14 pg m−3 (0.015 pg-TEQ m−3) and 4.10 pg m−3 (0.015 pg-TEQ m−3), respectively. In fact, concentration levels in sites A, B and C were in the same range as those in residential or commercial areas in
Table 2 Gas and particle phase concentrations of dl-PCBs in this study (unit: pg m− 3). A
Sampling 1 Sampling 2 Sampling 3 Sampling 4 Sampling 5 Average ∑ PCDD/FS a
B
C
D
E
F
Particle
Gas
Particle
Gas
Particle
Gas
Particle
Gas
Particle
Gas
Particle
Gas
3.00 (0.030) 1.70 (0.002) 0.06 (0.001) 0.12 (0.001) 1.06 (0.009) 1.19 (0.008) 3.922 (0.195)
1.46 (0.002) 2.73 (0.004) 6.08 (0.015) 2.59 (0.005) 1.88 (0.005) 2.95 (0.006)
2.83 (0.022) 1.61 (0.002) 0.12 (0.001) 0.20 (0.002) B (0.011) 1.31 (0.008) 3.494 (0.184)
1.34 (0.000) 3.12 (0.010) 4.70 (0.011) 2.19 (0.011) 2.58 (0.005) 2.79 (0.008)
1.67 (0.006) 1.63 (0.000) 0.10 (0.001) 0.12 (0.001) 1.30 (0.012) 0.96 (0.004) 2.121 (0.115)
2.69 (0.001) 3.09 (0.003) 4.43 (0.008) 1.14 (0.003) 1.42 (0.004) 2.55 (0.004)
5.89 (0.053) 0.70 (0.014) 0.26 (0.003) 1.30 (0.016) 2.43 (0.021) 2.11 (0.021) 12.837 (0.451)
2.13 (0.001) 5.11 (0.021) 7.20 (0.027) 5.87 (0.018) 2.42 (0.009) 4.55 (0.015)
5.48 (0.064) 0.17 (0.001) 0.22 (0.011) 1.68 (0.005) 2.92 (0.025) 2.10 (0.021) 4.726 (0.370)
1.90 (0.002) 8.41 (0.013) 8.71 (0.014) 6.92 (0.009) 2.21 (0.010) 5.63 (0.010)
5.92 (0.086) 2.12 (0.009) 0.20 (0.003) 0.45 (0.003) 1.27 (0.018) 1.99 (0.024) 9.256 (0.538)
1.79 (0.003) 7.42 (0.043) 6.63 (0.059) 4.29 (0.011) 1.77 (0.010) 4.38 (0.025)
(): TEQ value calculated with WHO-TEF (2006). a Average concentrations for 2,3,7,8-subsituted PCDD/Fs over the sampling period.
D.–G. Kim et al. / Atmospheric Research 101 (2011) 386–395
389
Fig. 1. Average concentrations of dl-PCB congeners in gas and particle phases: (a) concentrations with pg m−3 and (b) concentrations with pg TEQ m−3.
(52 ± 3% contribution to gas-phase ∑dl-PCBs concentrations and 35 ± 7% contribution to particle-phase ∑dl-PCBs concentrations), followed in descending order by PCB-105 and PCB-77. In addition, PCB-126 had the highest TEQ value, accounting for more than 90% of the dl-PCBs TEQ in all samples. These results are consistent with those reported in other studies on dl-PCBs (Helm and Bidleman, 2005; Choi et al., 2008b; Castro-Jiménez et al., 2008). 3.2. Temperature dependence of dl-PCBs Atmospheric PCB concentrations increase with emissions from primary sources (i.e., incinerators and thermal treatment facilities) and volatilization from secondary sources such as soils, vegetation, and water surfaces, and should be related to the ambient temperature. The relationship between gas-phase
dl-PCB concentrations and ambient temperature can be described by the Clausius–Clapeyron equation shown below: ln P =
ΔHSA 1 + const; T R
ð1Þ
where P is the partial pressure (atm), T is the temperature (K), ΔHSA is the enthalpy of the surface–air exchange (J mol−1) and R is the ideal gas constant (8.314 J mol−1 K−1). Partial pressures of the dl-PCBs were calculated from their gas-phase concentrations and molecular masses using the ideal gas law, and the slope m and intercept b were determined by carrying out linear regression using Eq. (2). 1 ln P = m +b T
ð2Þ
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The Clausius–Clapeyron plots drawn using our data are shown in Fig. 2. Values of the slope m for the urban residential (sites A and B) and industrial sites (sites D, E and F) were −3,782 and −5,152, respectively, consistent with various reported values for different sites (mean value at inland temperature sites: −5149 ± 2463) (Carlson and Hites, 2005). Wania et al. (1998) suggested that high temperature dependence (a steep slope) indicates that the air concentration is controlled by re-volatilization from the surfaces in the local surroundings of the sampling sites, whereas low temperature dependence (a shallow slope) indicates that the atmospheric transport of air mass controls the air concentration. Seen from this perspective, volatilization of dl-PCBs from surfaces at these sites would dominate the atmospheric concentrations. In the case of site C, however, there was no statistical correlation (r2 = 0.10, p = 0.6) between the ambient temperature and gasphase concentrations (−1426 of slope m value). Its shallow slope resulted from the relatively higher portion in the gas phase at site C during Sampling 1 (in winter), which shows a large discrepancy when compared with the gas-particle partitions at the other sites. At site C, the gas-particle ratio of dl-PCBs during Sampling 1 was 1.6, which was much higher than the average ratio of 0.39 (ranging from 0.3 to 0.49) at the other sites during the same sampling period. At the moment, it is still unclear why an unusually high portion of gas-phase dl-PCBs was obtained at site C; factors that may contribute to this anomaly include temporary contamination caused by passing emission sources or atmospheric transport during the day (Cindoruk et al., 2007), a narrow temperature range during the sampling period (Tasdemir et al., 2004), or variations in PCBs partitioning (Carlson and Hites, 2005). In addition, unavoidable physico-chemical reactions that may occur during sampling (using a high volume air sampler) could influence the results; e.g., adsorption or volatilization of semi-volatile organic
compounds, and/or loss of particulate matters by "blow-off", as discussed in the literature (Volckens and Leith, 2003; Tsapakis and Stephanou, 2003; Helm and Bidleman, 2005). Furthermore, the 72-hour sampling period in this study seems too rigorous to prevent blow-off losses or adsorption/volatilization of gasphase dl-PCBs during air collection. Gas-particle partitioning of dl-PCBs at site C during Sampling 1 presents further questions that must be reserved for a more extensive study. 3.3. Gas-particle partitioning of dl-PCBs Gas-particle partitioning is an important mechanism that affects the fate and transport of SOCs. To understand the partitioning phenomena of dl-PCBs in our sites, partitioning of the measured dl-PCBs between the gas and particle phases in the atmosphere was evaluated. The gas-particle partitioning coefficient KP (m3 μg−1) for each compound was calculated using the following equation: KP =
F = TSP A
ð3Þ
where F is the particle-phase concentration for the compound of interest (pg m−3), A is the gas-phase concentration (pg m−3), and TSP is the amount of total suspended particulate matter (μg m−3) (Pankow, 1994). 3.3.1. Log KP versus log PoL correlation Partitioning between the particle and gas phases was evaluated by regression of KP with the sub-cooled vapor pressure PoL (Pa) using Eq. (4): o
log KP = mr logPL + br :
Fig. 2. Clausius–Clapeyron plots of ln P versus 1/T for Σdl–PCBs at the urban residential, commercial and industrial sites.
ð4Þ
D.–G. Kim et al. / Atmospheric Research 101 (2011) 386–395
Useful information pertaining to the gas-particle partitioning can be extracted from the slope mr and intercept br of the trend line. It has been suggested that at equilibrium, the regression slope between log KP and log PoL should be close to −1 (Pankow, 1994). However, other researchers argued that a deviation from −1 is not always indicative of non-
391
equilibrium, and proposed reasons for the variability in the slopes and intercepts (Pankow and Bidleman, 1992; Simcik et al., 1998; Wang et al., 2011). In this study, the KP values were calculated using the field data, and the correlation between log KP and log PoL was subsequently evaluated. Temperature-dependent vapor
Fig. 3. Regression plots of log KP with log PoL (r2 = 0.47–0.96, p b 0.0001) and log KP with log KOA (r2 = 0.47–0.96, p b 0.0001) at sites A, C and D.
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pressures for dl-PCB congeners were calculated using mL and bL (log PoL = mL/T + bL) data given by Falconer and Bidleman (1994) and adjusted to the average ambient temperature during the sampling period. Regression plots of log KP versus log PoL are shown in Fig. 3a. Three sites (A, C, and D) were selected and compared to set the data points of the regression plots. Correlations of the plots were highly significant (r2 = 0.47–0.96, p b 0.001), with the average slopes mr for sites A, C, and D found to be −0.88 ± 0.15, −0.70± 0.14, and −0.85 ± 0.11, respectively. The slopes obtained in this study were very similar to those obtained in previous studies, which reported values ranging between −0.51 and −0.97 for different urban sites (Harner and Bidleman, 1998; Falconer and Harner, 2000; Lohmann et al., 2000; Kim and Masunaga, 2005; Cindoruk et al., 2007). Emission of PCBs into the atmosphere primarily occurs when gasses are volatilized from areas where they have been used, stored, spilled, or atmospherically deposited. Gas-phase dl-PCB concentrations increase with increasing ambient temperature and slow re-equilibrium of gas-phase PCBs may be responsible for the shallow slopes obtained from our sites. Shallow slopes observed in this study may indicate that partitioning of dl-PCBs between the gas and particle phases in the atmosphere takes place under non-equilibrium conditions. There were variations in slopes mr with respect to the sampling period; i.e., slopes mr measured during Sampling 3 (−0.66 to −0.70) slightly deviated from −1, whereas the slopes obtained from Samplings 1 (−0.69 to −1.11) and 5 (−0.92 to −1.01) were closer to −1 (Table 3). Significant correlation between slope values and ambient temperature was found at sites A and D (p= 0.04, r2 = 0.42) but there was no significant correlation at site C (p= 0.3, r2 = 0.32), as has been noted above. The result is related to the distribution of gasparticle concentrations for dl-PCBs in the atmosphere: dl-PCBs remained mostly in the gas phase during Sampling 3 (summer), whereas they diverged into the gas and particle phases for Samplings 1 and 5 (winter and late autumn, respectively). These results are similar to those obtained in a study on the gasparticle partitioning of n-alkanes (C17–C24); these alkanes were emitted and remained mostly in the gas phase, and their slopes ranged from −0.7 to −0.79 during summer and from −0.80 to −1.01 in winter (Pankow and Bidleman, 1992).
Table 3 Slope (mr) and intercept (br) values for log log KP versus log PoL for dl-PCBs in the atmosphere of A, C and D. Sites A
C
D
Sampling 1 Sampling 2 Sampling 3 Sampling 4 Sampling 5 mr − 1.11 br − 6.62 r2 0.92
− 0.87 − 5.49 0.68
− 0.70 − 5.41 0.78
− 0.71 − 5.38 0.78
− 0.92 − 5.88 0.89
mr − 0.69 br − 5.11 r2 0.52
− 0.57 − 4.63 0.47
− 0.66 − 5.25 0.5
− 0.65 − 4.96 0.49
− 0.93 − 5.96 0.96
mr − 0.90 br − 6.34 r2 0.76
− 0.73 − 4.84 0.58
− 0.70 − 5.33 0.86
− 0.82 − 5.5 0.93
− 1.01 − 6.14 0.87
Regression of log KP versus log PoL was highly significant (p b 0.001).
3.3.2. Log KP versus log KOA correlation The octanol–air partitioning coefficient KOA has been used as a key descriptor for predicting the environmental fate of SOCs as an alternative to PoL . Finzio et al. (1997) suggested that KOA is an excellent descriptor of gas-to-aerosol partitioning. They described the relationship between KP and KOA as follows: log KP = m log KOA + b:
ð5Þ
KOA was calculated from Eq. (6) using the octanol–water partitioning coefficient KOW and Henry's law constant H (Cindoruk et al., 2007). KOA =
KOW RT ; H
ð6Þ
where KOA is dimensionless and R is the ideal gas constant. The correlation obtained from the log KP versus log KOA plots was highly significant (r2 = 0.44–0.96, p b 0.001), and the average slopes m and intercepts b for sites A, C, and D were 0.71 ± 0.10 and −10.39 ± 1.2, 0.57 ± 0.11 and −8.76 ± 1.19, and 0.67± 0.08 and −9.83 ± 1.07, respectively (Fig. 3b). The average slopes determined for our results were smaller than 1, and they were in line with those reported for urban sites (Lohmann et al., 2000; Cindoruk et al., 2007). The shallow slopes can be attributed to the lower KP values depending on the particle-bound PCB. Nevertheless, the trend of gas-particle partitioning based on the KOA values in the same sites was consistent with the results obtained from that based on the PoL values. The correlations of slopes obtained from log KP–log PoL plots and those from log KP–log KOA plots were significant (r2 = 0.91, p b 0.0001), and those for the intercepts were also significant (r2 = 0.90, p b 0.0001). 3.4. Gas-particle partitioning model Partitioning between the gas and particle phases was further examined using the Junge–Pankow and KOA-based models. Both the Junge–Pankow and KOA-based models can be used as descriptors to predict not only the particle-bound fraction φ but also the gas-particle partitioning coefficients KP with SOCs. In this study, KP and φ of dl-PCBs were calculated using the Junge–Pankow and KOA-based models and compared with those obtained from field measurements at sites A, C, and D. According to the Junge–Pankow model, φ of a specific chemical is defined as follows: φ=
log
Cp cθ = ∘ PL + cθ Cg + Cp
Cp cθ ∘ −log PL ; = log Kp = log TSP Cg
ð7Þ
ð8Þ
where Cp (pg μg−1) and Cg (pg m−3) are the atmospheric concentrations associated with the particle and gas phases, respectively. θ is the total suspended particulate surface area concentration (m2 aerosol m−3 air), and c is a parameter that depends on the heat of condensation for the chemical and surface properties.
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The values for model calculations were assumed to be c = 0.172 Pa m − 1 , θ = 1.1 × 10 − 3 m 2 m − 3 for urban, 1.5×10−3 m2 m−3 for rural, and 4.2×10−3 m2 m−3 for clean background air (Cotham and Bidleman, 1992; Pankow et al., 1993; Falconer and Harner, 2000). The KOA-based model is given in Eqs. (9) and (10) (Harner and Bidleman, 1998; Falconer and Harner, 2000): log KP = log KOA + log ƒOM –11:91 ϕ=
Cp KP ðTSP Þ : = 1 + KP ðTSP Þ Cg + Cp
ð9Þ ð10Þ
393
The organic matter fraction of a particle (ƒOM) varies according to local factors and the particulate characteristics. The value of φ was also calculated from the measured particleand gas-phase dl-PCB concentrations. Fig. 4 compares the φ values of dl-PCB congeners predicted by the Junge–Pankow and KOA-based models with the measured data for sites A, C, and D. The measured φ values of dl-PCB congeners obtained from field data were between the rural and urban lines of the predicted plots obtained from the Junge–Pankow model. However, dl-PCB congeners with low φ (φ b 20%) were close to the rural line of the predicted plots. For the KOA-based model, the measured φ values of dl-PCB
Fig. 4. Comparison between the measured results with the prediction from Junge–Pankow model (a) and KOA-based model (b).
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congeners were smaller than those predicted by the KOA-based model consisting of 10% organic matter in the particulates. On the other hand, Fig. 5 compares the KP values of dl-PCB congeners predicted by the Junge–Pankow and KOA-based models with the measured data for sites A, C, and D. The KP values predicted using both the models were larger than those measured from field data for dl-PCB congeners. Mandalakis and Stephanou (2007) reported that the Junge–Pankow model overestimates the KP coefficients of PCB congeners with lower vapor pressures than PCB-110 (vapor pressure, 1.8 × 10−3 Pa at 25 °C), and Cindoruk et al. (2007) obtained similar results for
the modeled φ values. The observed difference between the measured and modeled KP values has been suggested to largely be due to non-equilibrium partitioning of PCBs in the gas and particulate phases in the atmosphere. In the present study, despite the uncertainties in the model parameters, the correlations between the experimental and modeled KP values were significant at p b 0.001: r2 = 0.74–0.84 for the Junge–Pankow model and r2 = 0.76–0.84 for the KOA-based model. Therefore, both the models are good indicators for explaining the gas-particle partitioning of dl-PCBs in the urban atmospheric air of Gyeonggi-do.
Fig. 5. Correlation of field experimental KP with modeled KP calculated by Junge–Pankow model (a) and KOA-based model (b). Bold solid line is 1:1 trend line for the model predicted KP. The modeled KP was calculated using PLθ = 1.1 × 10−3 m2 m−3 for Junge–Pankow model and ƒOM = 0.1 for KOA-based model.
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Acknowledgment This work was performed as a part of the annual survey program for dioxins in Gyeonggi-do. This work was supported by the 2010 Research Fund of the University of Seoul. References Alcock, R.E., Behnisch, P.A., Jones, K.C., Hagenmaier, H., 1998. Dioxin-like PCBs in the environment - human exposure and the significance of sources. Chemosphere 37 (8), 1457–1472. Brunciak, P.A., Dachs, J., Gigliotti, C.L., Nelson, E.D., Eisenreich, S.J., 2001. Atmospheric polychlorinated biphenyl concentrations and apparent degradation in coastal New Jersey. Atmospheric Environment 25, 3325–3339. Carlson, D.L., Hites, R.A., 2005. Temperature dependence of atmospheric PCB concentrations. Environmental Science and Technology 39 (3), 740–747. Castro-Jiménez, J., Mariani, G., Eisenreich, S.J., Christoph, E.H., Hanke, G., Canuti, E., Skejo, H., 2008. Atmospheric input of POPs into Lake Maggiore (Northern Italy): PCDD/F and dioxin-like PCB profiles and fluxes in the atmosphere and aquatic system. Chemosphere 73, S122–S130. Chi, K.H., Hsu, S.C., Wang, S.H., Chang, M.B., 2008. Increase in ambient PCDD/F and PCB concentrations in Northern Taiwan during an Asian dust storm episode. Science of the Total Environment 401, 100–108. Choi, K.I., Lee, S.H., Lee, D.H., 2008a. Emission of PCDDs/DFs and dioxin-like PCBs from small waste incinerators in Korea. Atmospheric Environment 42, 940–948. Choi, M.P.K., Ho, S.K.M., So, B.K.L., Cai, Z., Lau, A.K.H., Wong, M.H., 2008b. PCDD/F and dioxin-like PCB in Hong Kong air in relation to their regional transport in the Pearl River Delta region. Chemosphere 71, 211–218. Cindoruk, S.S., Tasdemir, Y., 2007. Deposition of atmospheric particulate PCBs in suburban site of Turkey. Atmospheric Research 85, 300–309. Cindoruk, S.S., Esen, F., Tasdemir, Y., 2007. Concentration and gas/particle partitioning of polychlorinated biphenyls (PCBs) at an industrial site at Bursa, Turkey. Atmospheric Research 85, 338–350. Cotham, W.E., Bidleman, T.F., 1992. Laboratory investigations of the partitioning of organochlorine compounds between the gas phase and atmospheric aerosols on glass fiber filters. Environmental Science and Technology 26 (3), 469–477. Falconer, R.L., Bidleman, T.F., 1994. Vapor pressures and predicted particle/ gas distributions of PCB congeners as functions of temperature and ortho-chlorine substitution. Atmospheric Environment 28 (3), 547–554. Falconer, R.L., Harner, T., 2000. Comparison of the octanol–air partition coefficient and liquid phase vapor pressure as descriptors for particle/gas partitioning using laboratory and field data for PCBs and PCNs. Atmospheric Environment 34, 4043–4046. Finzio, A., Mackay, D., Bidleman, T.F., Harner, T., 1997. Octanol–air partition coefficient as a predictor of partitioning of semi volatile chemical to aerosols. Atmospheric Environment 31 (15), 2289–2296. Harner, T., Bidleman, T.F., 1998. Octanol–air partition coefficient for describing particlr/gas partitioning of aromatic compounds in urban air. Environmental Science and Technology 32, 1494–1502. Helm, P.A., Bidleman, T.F., 2005. Gas-particle partitioning of polychlorinated naphthalene and non- and mono ortho-substituted polychlorinated biphenyls in arctic air. Science of the Total Environment 342, 161–173. Jeong, S.R., Jang, E.H., Kim, D.H., Kim, K.S., 2009. Characteristics of congener distribution for persistent organic pollutant (POPs) in air, Busan. The Annual report of Busan Metropolitan City Institute of Health & Environment, 19, pp. 207–224. Kim, K.S., Masunaga, S., 2005. Behavior and source characteristic of PCBs in urban ambient air of Yokohama, Japan. Environmental Pollution 138, 290–298.
395
Lohmann, R., Jones, K.C., 1998. Dioxins and furan in air and deposition: a review of levels, behavior and processes. The Science of the Total Environment 219, 53–81. Lohmann, R., Harner, T., Thomas, G.O., Jones, K.C., 2000. A comparative study of the gas particle partitioning of PCDD/Fs, PCBs and PAHs. Environmental Science and Technology 34, 4943–4951. Mandalakis, M., Tsapakis, M., Tsoga, A., Stephanou, E.G., 2002. Gas-particle concentrations and distribution of aliphatic hydrocarbons, PAHs, PCBs and PCDD/Fs in the atmosphere of Athens (Greece). Atmospheric Environment 36, 4023–4035. Mandalakis, M., Stephanou, E.G., 2007. Atmospheric concentration characteristics and gas-particle partitioning of PCBs in a rural site of eastern Germany. Environmental Pollution 147, 211–221. Manodori, L., Gambaro, A., Moret, I., Capodaglio, G., Cairns, W.R.L., Cescon, P., 2006. Seasonal evolution of gas-phase PCB concentrations in the Venice Lagoon site. Chemosphere 62, 449–458. Matsumoto, K., Matsumoto, K., Mizuno, R., Igawa, M., 2010. Volatile organic compounds in ambient aerosols. Atmospheric Research 97, 124–128. NIER, 2010. Korean Standard Testing Method for Persistent Organic Pollutants. Ministry of Environment. ES 10309.1. Ogura, I., Masunaga, S., Nakanishi, J., 2004. Quantitative source identification of Dioxin-like PCBs in Yokohama, Japan, by temperature dependence of their atmospheric concentrations. Environmental Science and Technology 38, 3279–3285. Ozcan, S., Aydin, M.E., 2009. Polycyclic aromatic hydrocarbons, polychlorinated biphenyls and organochlorine pesticides in urban air of Konya, Turkey. Atmospheric Research 93, 715–722. Pankow, J.F., Bidleman, T.F., 1992. Interdependence of the slopes and intercepts from log–log correlations of measured gas-particle partitioning and vapor pressure —1. Theory and analysis of available data. Atmospheric Environment 26A, 1071–1080. Pankow, J.F., Storey, J.E., Yamasaki, H., 1993. Effects of relative humidity on gas/ particle partitioning of semivolatile organic compounds to urban particulate matter. Environmental Science and Technology 27 (10), 2220–2226. Pankow, J.F., 1994. An absorption model of gas/particle partitioning of organic compounds in the atmosphere. Atmospheric Environment 28 (2), 185–188. Simcik, M.F., Franz, T.P., Zhang, H., Eisenrech, S/.J., 1998. Gas/particle partitioning of PCBs and PAHs in the Chicago urban and adjacent coastal atmosphere: states of equilibrium. Environmental Science and Technology 32, 251–257. Tasdemir, Y., Vardar, N., Odabasi, M., Holsen, T.M., 2004. Concentrations and gas/particle partitioning of PCBs in Chicago. Environmental Pollution 131, 35–44. Tsapakis, M., Stephanou, E.G., 2003. Collection of gas and particle semi-volatile organic compounds: use of an oxidant denuder to minimize polycyclic aromatic hydrocarbons degradation during high-volume air sampling. Atmospheric Environment 37, 4935–4944. Van den Berg, M., Birnbaum, L.S., Denison, M., Vito, M.D., Farland, W., Feeley, M., Fiedler, H., Hakansson, H., Hanberg, A., Haws, L., Rose, M., Safe, S., Schrenk, D., Tohyama, C., Tritscher, A., Tuomisto, J., Tysklind, M., Walker, N., Peterson, R.E., 2006. The 2005 World Health Organization re-evaluation of human and mammalian toxic equivalency factors for dioxins and dioxin-like compounds. Toxicological Sciences 93 (2), 223–241. Volckens, J., Leith, D., 2003. Comparison of methods for measuring gas-particle partitioning of semivolatile compounds. Atmospheric Environment 37, 3177–3188. Wang, W., Simonich, S.L.M., Wang, W., Giri, B., Zhao, J., Xue, M., Cao, J., Lu, X., Tao, S., 2011. Atmospheric polycyclic aromatic hydrocarbon concentrations and gas/particle partitioning at background, rural village and urban sites in the North China Plain. Atmospheric Research 99, 197–206. Wania, F., Haugen, J.E., Lei, Y.D., Mackay, D., 1998. Temperature dependence of atmospheric concentrations of semivolatile organic compounds. Environmental Science and Technology 32, 1013–1021.