PCB in soils and estimated soil–air exchange fluxes of selected PCB congeners in the south of Sweden

Environmental Pollution 128 (2004) 59–72 www.elsevier.com/locate/envpol

PCB in soils and estimated soil–air exchange fluxes of selected PCB congeners in the south of Sweden Cecilia Backea,*, Ian T. Cousinsb, Per Larssona a

Chemical Ecology and Ecotoxicology, Department of Ecology, Lund University, Ecology Building, S-223 62 Lund, Sweden b Institute for Applied Environmental Research (ITM), Stockholm University, 106 91 Stockholm, Sweden Received 14 July 2003; accepted 11 August 2003

‘‘Capsule’’: Soil–air exchange of PCBs is investigated and modelled across Sweden. Abstract PCB concentrations were studied in different soils to determine the spatial variation over a region of approximately 11 000 km2. PCB congener pattern was used to illustrate the spatial differences, as shown by principal component analysis (PCA). The relationship to different soil parameters was studied. PCB concentrations in soil showed a large variation between sampling-areas with median concentrations ranging between 2.3 and 332 ng g
1 (dw). Highest concentrations were found at two sites with sandy soils, one with extremely high organic carbon content. Both sites were located on the west coast of southern Sweden. Soils with similar soil textures (i.e. sandy silt moraine) did not show any significant differences in PCB concentrations. PCB congener composition was shown to differ between sites, with congener patterns almost site-specific. PCB in air and precipitation was measured and the transfer of chemicals between the soil and air compartments was estimated. Soil–air fugacity quotient calculations showed that the PCBs in the soil consistently had a higher fugacity than the PCBs in the air, with a median quotient value of 2.7. The gaseous fluxes between soil and air were estimated using standard modelling equations and a net soil–air flux estimated by subtracting bulk deposition from gaseous soil–air fluxes. It was shown that inclusion of vertical sorbed phase transport of PCBs in the soil had a large effect on the direction of the net soil–air exchange fluxes. # 2003 Elsevier Ltd. All rights reserved. Keywords: Polychlorinated biphenyls; Soil; Spatial variation; Air–soil fluxes; Fugacity

1. Introduction It has been hypothesized that for certain persistent organic pollutants (POPs) such as the polychlorinated biphenyls (PCBs), soils have acted as a significant repository or ‘‘buffer’’ of the chemical, initially absorbing the substance from the atmosphere during periods of increased discharge and then releasing it slowly back to the atmosphere during a later period of reduced discharge (Harner et al., 1995). The present levels of several types of POP in soil and air are controlled mainly by air-soil exchange (Jones, 1994). This will play an important role in controlling the regional and global transport and redistribution of POP (Wania and Mackay, 1993). The compounds are * Corresponding author. Tel.: +46-46-52028; fax: +46-40-6411417. E-mail address: [email protected] (C. Backe). 0269-7491/$ - see front matter # 2003 Elsevier Ltd. All rights reserved. doi:10.1016/j.envpol.2003.08.038

thought to recycle continuously between the atmosphere and the terrestrial environment over long periods of time as they move toward equilibrium between the environmental compartments. One way to describe the equilibrium partitioning processes is the fugacity concept (Mackay, 1991). Fugacity is defined as the tendency of a compound to escape from one compartment to another. Fugacities can be calculated from the compound concentrations in the compartments and the fugacity capacities. Equal fugacity values in two compartments mean equilibrium. If there is a large difference in fugacities there will be a tendency for the chemical to move from one compartment to the other in order to establish equilibrium conditions. A useful way of studying air–soil exchange is to calculate fugacity quotients, and this concept has been applied previously by several authors (McLachlan, 1996; Duarte-Davidson et al., 1997; Cousins and Jones, 1998; Harner et al., 2001).

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Transfer of POPs from the atmosphere to the soil occurs via dry- and wet deposition. Indirect deposition of compounds can occur via vegetation (Thomas et al., 1998), where chemicals taken up by vegetation may enter the soil as plant material falls to the ground and decays. Concurrently, volatilisation of chemicals from the vegetation will also occur. It has been shown by McLachlan and Horstmann (1998) that decaying leaf litter will increase the supply of POP in forest soils, which may be more effective sinks than agricultural soils. The most important pathway for the transfer of POP from the soil to the atmosphere is diffusive gaseous transport (Harner et al., 1995), while the transport of dust to the atmosphere seems to be of minor importance (DuarteDavidson et al., 1997). Other removal processes of POP from soil can be biodegradation and leaching to ground water (van Leeuwen and Hermens, 1995). Leaching is mainly important for chemicals with low Kow. Variation in soil properties such as soil organic matter (Borisover and Graber, 1997), moisture content (Hippelein and McLachlan, 2000) and texture, structure and porosity of the soils (Cousins et al., 1999a) strongly affects soil/air partitioning. But the physical-chemical properties of the compound are also of importance for the exchange processes. Volatilisation of organic compounds from the soil is controlled by the compound vapour pressure and water solubility (Walker et al., 1996). Steric factors of the compounds can also be of importance for volatilisation (Haque and Kohnert, 1976). Temperature has a fundamental effect on the vapour/particle partitioning of POP and thus on the relative and absolute importance of the various deposition pathways. Additionally, volatilisation rates are strongly influenced by both soil temperature and ambient air temperature, mainly affecting vapour pressure (Jury et al., 1987). Consequently low temperature will cause an increased magnitude of the deposition processes and reduce re-volatilisation, whereas increased temperature has the opposite effect. Obviously, atmospheric conditions and heterogeneity of soil properties will result in great spatial and temporal variation in concentrations of POP in the various compartments. It has been shown that there are large differences in PCB concentrations in air (Backe et al., 2000) and in soil (Creaser and Fernandes, 1986) between urban and rural locations, with substantially higher levels in the urban areas. Polynuclear aromatic hydrocarbons (PAH) have been found to have a spatial variation of orders of magnitudes in soil, depending on proximity to point sources and soil properties (Jones et al., 1989). The aim of this study was to investigate the spatial variation of PCB concentration in soils and to determine whether spatial differences for PCB in air and precipitation were reflected in soils. In addition, patterns of PCB congeners were compared between different sampling sites in an attempt to define similarities

between groups and to suggest emission sources. Finally, fugacity quotients were calculated in order to estimate the gaseous exchange of PCBs between soil and the air. Estimating gaseous fluxes from fugacity quotients using modelling techniques has become usual practice because the fluxes are often low and difficult to measure experimentally (Cousins et al., 1999a). Net soil– air fluxes were estimated by subtracting measured bulk deposition fluxes from estimated gaseous soil–air fluxes.

2. Material and methods 2.1. Sample collection Soil samples were collected from 11 different areas, labelled A to K, in the southernmost province of Sweden (Fig. 1). Sampling was done in combination with an air- and precipitation study of POP, using the same sampling-sites. The sampling-locations were chosen at random, with an attempt to get them evenly distributed over the region (approximately 11 000 km2, Fig. 1). The region comprises both rural and urban areas and has a population of about 1 million. In the south-western parts industrialisation is pronounced while the central, southern and south-eastern parts are dominated by farmland. Forest dominates in the northern parts. Station A and I are coastal stations, placed within 50 m of the shoreline. The area of station A consists of a ridge of gneiss and is located at 88.5 m above sea level while site I is located in the direct vicinity of a sandy beach, nearly sea level. Those stations are also weather stations, run by the Swedish Meteorology and Hydrology Institute (SMHI). The stations B, C, F, H, and K are all rural sites surrounded either by woodland or farmland. Station G is located in an area of fields on a grassy plain. Stations D, E and J are all located close to urban areas. All sampling areas were located free of high buildings and trees, and not directly exposed to industrial or municipal emissions. Soil samples were collected at the end of the air- and precipitation sampling programme, during September 1993. Six soil samples from each of the sampling-sites were taken within an area of approximately 100 m2. The samples were taken with a stainless steel soil cylinder (f 0.7 cm) from the upper 5 cm of the soil, after vegetation had been cut off. Samples were stored in dense plastic bags and were frozen immediately on return to the laboratory prior to being analysed. Before analysis, soil samples were sieved through a 2 mm sieve and homogenised. Five samples from each sampling-site were analysed and one sample was archived frozen. Air samples (n=260) and rain samples (n=206 ) were collected simultaneously from June 1992 to September 1993. Air was sampled with air pumps, 2 m above ground. Approximately 1000 m3 of air was drawn

C. Backe et al. / Environmental Pollution 128 (2004) 59–72

61

Fig. 1. Locations of the 11 sampling areas (A–K, longitude and latitude within parentheses) and the meteorological stations (marked with x).

through two polyurethane foam (PUF) plugs in series, at a rate of 40 l min
1 (Backe et al., 2000). Precipitation was collected with a 1 m2 funnel of stainless steel mounted in a wooden stand, 1.3 m above the ground. Precipitation was run through two polyurethane foam (PUF) plugs, connected in series (2740 mm and with a density of 30 kg m
3). The accumulated water was measured in a calibrated jar inside the box. If the temperature was below 0  C, a heater within the box was started, thus melting the snow. The sampling equipment was open during the whole sampling period and the measured deposition is therefore the sum of both dry(particulate) and wet (particulate and dissolved) deposition. The filters were changed monthly or when approximately 40 l of precipitation had passed through them. The sampling equipment was regularly controlled

and cleaned from debris (leaves, twigs, etc.). PUFs were kept in the freezer until analysis. Further descriptions of air- and rain-sampling and analyses are given in Backe et al. (2000) and Backe et al. (2002). 2.2. Analytical procedure Soil water content was determined by drying soil samples in an oven (105  C for 48 h). Organic matter was determined by ignition at 550  C for 2 h. For PCB analysis 5–15 g soil, wet weight, were weighed out into small aluminium trays (rinsed in hexane) and freezedried. The whole sample was transferred to a glass extraction vial and extracted in a modified Soxhlet apparatus for 2 h, using acetone (Merck, 12 p.a.z.r.): n-hexane (Merck, 4371 p.a.z.r.), 25:16 v/v. Octachloronaphthalene

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C. Backe et al. / Environmental Pollution 128 (2004) 59–72

was used as recovery standard and was added prior to the extraction. Copper powder (ca 0.2 g, Merck, > 230 mesh ASTM) was added to eliminate sulphur. The soil extracts were subjected to water equilibrium, to separate the acetone and polar impurities, and treated with sulphuric acid. The hexane phase was isolated and evaporated in a vacuum centrifuge (Savant). To clean the samples, an open column step was performed according to Bremle et al. (1995). Samples were sealed in glass vials with copper granulate (Aldrich Chem Co) to further eliminate sulphur prior to analysis. Samples were analysed for PCBs by capillary gas chromatography/ECD on a Varian Star 3400 CX equipped with an on-column injector. The PCB components were separated on a 30 m DB-5 quartz capillary column (id. 0.25 mm) with H2 (1.5 ml min
1) as carrier gas and N2 (50 ml min
1) as make up gas. The following temperature program was used: 80  C for 3 min, 10  C min
1 to 110  C, 4  C min
1 to 320  C, hold at 320  C for 17 min. The injector and the detector temperatures were 300 and 340  C respectively. PCB congeners were identified and quantified according to Mullin et al. (1984) and Schulz et al. (1989). We used a quantification programme where identification standard compounds were made up from Aroclor 1242 and Chlopen A60. Concentration of total PCB was calculated from the sum of 44 identified peaks. These were IUPAC no: 28/31, 52, 49, 47/48/75, 44, 37/ 59/42, 41/64, 40, 74, 70, 66/95, 91, 92, 90/101, 99, 83, 97, 87/115, 136, 77/110, 82/151, 135, 118/123/149, 146, 153/ 132/105, 141/179, 138/160/158, 129/126/178, 187, 183, 128, 174, 177, 171/156/202, 172, 180, 191, 200, 170/190, 199, 203/196, 189, 208/195, 194. For every five samples processed, a chemical blank was run. The average blank for
PCB was 2.5% of the field samples, except for congener 47/48/75 which was 30.7% and congener 203/196 which constituted 37.6%. These two congeners were therefore excluded from the data. Samples were not corrected further for blanks. The average recovery for soil samples and standard deviation of octachloronaphtalene was 86 10%. PCB concentrations were not corrected for recovery. 2.3. Data presentation and statistical procedures Prior to statistical analysis, concentration data were transformed to natural logarithmic scale, owing to their skewed distribution (Sokal and Rohlf, 1995). For the report on central estimates, median or geometric mean values have been presented. One-way analysis of variance (ANOVA) was used for multiple comparison of means, and the Tukey–Kramer procedure (Dunett, 1980) to identify means that differed from others. Principal Component Analysis (PCA) was used for analysing differences and similarities in PCB congener composition of samples from the different sampling-

sites (Zitko, 1989). PCA is a useful technique for analysing patterns and trends in large and complex data sets. In short, PCA is a means of simplifying data by reducing the number of variables, in this case PCB congener concentrations, to a smaller number of principal components. The principal components can then be plotted to show the relative similarities between samples. Data were normalised to unit concentrations to avoid the influence of absolute concentrations and lntransformed to obtain log-normal distribution. Missing values were replaced by station-specific weighed mean values. The PCA were carried out using an SPSS 6.1 computer package. PCB congeners included in the PCA was 28/31, 49, 44, 66/95, 92, 90/101, 99, 83, 97, 136, 77/ 110, 82/151, 135, 118/123/149, 146, 153/132/105, 141/ 179, 138/160/158, 129/126/178, 187, 183, 128, 174, 177, 171/156/202, 172, 180, 170/190, 199, 189, 194. 2.4. Fugacity calculations The fugacity of a compound in a particular compartment were calculated from the concentration C (g m
3) using the equation f ¼ C=ZM

ð1Þ

(Mackay, 1991) where M is the molecular mass (g mol
1) and Z is the fugacity capacity of the compartment for the compound (mol m
3 Pa
1). Concentrations in soil were calculated from the geometric mean value of the five soil samples at the different sampling sites. Concentration in air samples was represented by the last sampling period carried out, and was linked with the soil sampling. The fugacity capacity of air, Za, is defined by Za ¼ 1=RT

ð2Þ

where R is the gas constant (8.314 J mol
1 K
1) and T is the absolute temperature. The fugacity capacity of soils Zs can be estimated by Zs ¼ foc ; Zs Koc zw

ð3Þ

where foc is the fraction of organic carbon. In this study foc was estimated from the organic matter content (fom) by dividing by 1.7 according to van Leuween and Hermens (1995) and Cousins et al. (1999a). s is the soil density (g cm
3), Koc is the organic carbon/water partition coefficient, defined according to Karickhoff (1981) as Koc ¼ 0:41Kow

ð4Þ

where Kow is the octanol/water partition coefficient. zw is the fugacity capacity of water and is calculated from Zw ¼ 1=H

ð5Þ

where H is the Henry’s law constant (Pa m3 mol
3) at 25  C. The fugacity capacities were adjusted to the

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C. Backe et al. / Environmental Pollution 128 (2004) 59–72

temperature measured in the air overlying the sampling sites. Air fugacity capacities were adjusted by simply altering T in Eq. (2). Temperature corrected values of H were calculated to adjust the soil fugacity capacities for temperature. KOW values were not adjusted for temperature, as they are not usually very temperature-sensitive. Temperature correction of H was undertaken by using the integrated van’t Hoff equation, in the same way as discussed in Cousins and Jones (1998) lnðH1 =H2 Þ ¼
DHaw ð1=T1
1=T2 Þ=R

ð6Þ

where H1, H2 are Henry’s Law constants at two temperatures; T1, T2 are temperatures (K), and Haw is the enthalpy of air–water exchange (in J mol
1). Values for Haw for PCBs were derived from Burkhard et al. (1985) and Ten Hulscher et al. (1992). Haw for PCBs is in the range 50 to 75 KJ mol
1 and based on the homologue specific data by Burkhard et al. (1985), Haw increases by approximately 2.5 KJ mol
1 with every additional chlorine atom. The fugacity quotients were calculated as fs =fa

1=Dv ¼ 1=De þ ð1=ðDa þ Dw þ Ds ÞÞ

ð9Þ

where De is the D-value for transport across the soil–air boundary layer (mol Pa
1 h
1), Da is the D-value for diffusion in the soil–air (mol Pa
1 h
1), Dw is the D-value for transport in the soil-water and Ds is the D-value for vertical bioturbation of soil-solids (Cousins et al., 1999b,c; Sweetman et al., 2002; McLachlan et al., 2002). Descriptions of the equations for estimating these Dvalues are well documented (see Mackay, 1991; Sweetman et al., 2002). The mass transfer coefficients for sorbed-phase diffusion, diffusion across the soil–air boundary layer, diffusion in soil–air and diffusion in soil–water were set at 1.0910
6, 90, 4.3 and 4.310
4 m d
1, respectively (typical values taken from McLachlan et al., 2002). The incorporation depth was assumed to be 10 cm. An arbitrary area of 1 m2 was used, which means that the flux N actually has units of mol/m2/h. To calculate net soil–air exchange fluxes, bulk deposition fluxes were subtracted from estimated volatilisation fluxes.

ð7Þ

where fs is the fugacity in soil, and fa is the fugacity in air. Ambient air temperatures were obtained at five weather stations in the region, operated by the Swedish Meteorology and Hydrology Institute (SMHI). Sites A and I were situated at two of these weather stations. 2.5. Air–soil flux calculations The net flux N (mol/h) from soil to air is then determined through (Mackay, 1991) N ¼ Dv ðfs
fa Þ

ð8Þ

where Dv (mol Pa
1 h
1) is the overall D-value for transport across the soil/air interface and fs and fa are the fugacities (Pa) in soil and air, respectively, and which have been previously calculatated. Dv is calculated through

3. Results 3.1. Soil property variability Soil properties within the 11 sites varied widely (Table 1). A majority of the soils were coarse-textured and fell into the sandy, silt, moraine textural class. The remaining soils were sandy sediment, clayey sediment, muddy silt moraine and sandy soil with high organic matter. The majority of the sites had a soil organic matter (SOM) content between 5 and 15%. Site A had much higher SOM, with a median of 45%. The water content in the 11 soils varied between 16 and 40% (median value). The variation in soil texture within sites was minor. For a majority of the sites the within-site variation of SOM was less than 20% (coefficient of variation, c.v.).

Table 1 Soil properties of samples (geometric means), and average air temperature during the sampling-period (range within brackets) Sampling site

Soil type

Organic matter (%)

Water content (%)

Density (g cm
3)

Air temperature ( C)

A B C D E F G H I J K

Sandy (+ organic matter) Sandy, silty moraine Sandy, silty moraine Sandy, silty moraine Muddy, silty moraine Sandy, loamy moraine Sandy, silty moraine Sandy, silty moraine with some clay Sandy sediment Clayey sediment Sandy, silty moraine

40.3 (23.1–51.7) 7.1 (5.1–10.5) 12.3 (8.8–20.4) 5.5 (4.9–7.5) 10.3 (9.6–12.1) 7.6 (6.8–8.5) 4,0(3.1–4.8) 17.7 (12.0–25.2) 10.3(8.1–11.5) 3.0 (2.5–3.4) 7.8 (6.8–10.5)

25.0 (20.6–32.1) 20.6 (14.3–25.6) 26.4 (20.2–28.3) 16.5 (14.3–20.1) 34.2 (31.8–62.8) 23.9 (23.4–24.4) 16.8 (13.6–17.8) 41.4 (33.0–45.2) 20.6(13.7–22.8) 18.3 (17.5–20.9) 24.0 (22.2–27.5)

0.77 1.21 0.89 1.22 1.75 1.40 1.36 1.08 1.05 1.63 1.11

12.6 10.0 12.9 13.6 13.9 12.5 13.9 10.9 12.5 13.9 11.9

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C. Backe et al. / Environmental Pollution 128 (2004) 59–72

Sites A, B, C and H had c.v. around 30%. The water content variation within sites was below 20% (c.v.), except for site E which varied between 31.8 and 62.8% (c.v.=33%).

Tukeys test it was shown that sites A and I were responsible for the differences. Further, the variation in
PCB concentration among sites with similar soil types (site B, C, D, F, G and K) was tested by ANOVA. No significant differences were found among these sites.

3.2. PCB concentration variability 3.3. PCB congener pattern in soil
PCB concentrations varied within and between sites with an overall range from 2.3 to 986 ng PCB g
1 dry weight (dw) soil, with a median of 7.1 ng g
1 (Fig. 2). Highest concentration of
PCB was found at site A with a median of 332 ng g
1 and a range from 237 to 986 ng g
1. In contrast, at the less contaminated site (J) the median concentration of
PCB was 2.6 ng g
1, with a range from 2.3 to 3.2 ng g
1. The within-site variation of
PCB was greatest at site D, where the concentration ranged from 2.9 to 55 ng g
1. The
PCB concentrations (ng g
1 dw) were weakly related to the SOM (regression of ln transformed data, r2=0.18, n=30, P < 0.05). Calculations were based on data from sampling-sites with similar soil texture. Due to the great variation in SOM between the different sampling-sites concentrations were normalised with respect to SOM (Fig. 3), resulting in reduced variation in median
PCB concentration between sites. For sites A and I the median concentrations remained high; 11 and 5 times higher respectively than the overall median concentration (88 ng g
1 SOM
1). There was no significant relationship between
PCB and soil water content (Pearson correlation, P > 0.05). The variation in
PCB concentration (SOM normalised) among sites was tested by analysis of variance (ANOVA). Significant differences were found between the 11 sampling-sites (r2=0.6, n=55, P < 0.001). Using

To investigate the spatial difference of congener patterns in soil, principal component analysis (PCA) was used. One benefit of using PCA for analysing large data sets is that only a small number of principal components (PCs) are usually needed to represent most of the information in the data. In this study, 71.7% of the variability in 31 variables (PCB congeners) was explained by 5 PCs. The results suggest a different pattern between soils from the sites, with 45% of the variance in the underlying data explained by the first two principal components (PCs 1 and 2). The relationships among the variables are shown in the loading plot (Fig. 4a), whereas the relationships among samples (sites) are displayed in the score plot (Fig. 4b). A separation of the PCB congeners in the soil was shown for the different sampling sites on the first principal component. Most samples from the same site clustered together, indicating unique PCB fingerprints for each sampling-site, although there were outliers among the sites, and at site D the congener pattern variation among soil samples was obvious. Samples from site A and I had similar patterns, dominated by hexa- to octa-chlorinated congeners (118/149, 138, 174,177, 180, 189 and 170/190). It was also noted that soil samples from site D and one sample from site K clustered with samples from site A and I. Samples from site E and H, on the other hand,

Fig. 2. Box-plots showing the variation of the
PCB concentration (ng g
1 dw) in soil, within and among the sampling-sites (A–K). Each box displays the 25th to the 75th percentile with the median represented by a horizontal line. The lines from the boxes show the values within 1.5 times the box length and outlyer values are marked with

Fig. 3. Box-plots showing the variation of the
PCB concentration in soil, normalised to soil organic matter (SOM) (ng g
1 SOM), within and among the sampling-sites (A–K). Each box displays the 25th to the 75th percentile with the median represented by a horizontal line. The lines from the boxes show the values within 1.5 times the box length and outlyer values are marked as stars.

C. Backe et al. / Environmental Pollution 128 (2004) 59–72

had a larger fraction of tri-, tetra- and penta-chlorinated congeners, separated at the positive side of PC 1. Most of the samples from sites B, F and G were influenced mainly by mid-molecular weight PCBs. Samples from the sites D, K and J had a congener pattern dominated by hexa- and hepta-chlorobiphenyls (i.e. 146, 153/132/ 105, 141/179, 129/126/178, 187, 183, 128) separated in PC 2.

65

3.4. Relationships between PCBs in the atmosphere and in soil Fugacity quotients were calculated using the PCB air and soil concentrations. When fugacities from the different compartments are more or less equal then the compartments are close to equilibrium, but if there is a large disparity then there will be a tendency for the

Fig. 4. Principal component analysis (PCA) of 44 different PCB congeners in the soil samples from site A to K. The relationships between the variables are shown in the loading plot (a). High scores on the PC 1 refer to a relatively less chlorinated pattern and low scores refer to a more highly chlorinated pattern. Relationships between sites are displayed in the score plot (b). The first principal component (PC 1) explained 27% of the variation and the second (PC2) explained 18%.

Bulk deposition, yearly

Estimated soil–air gaseous flux (with bioturbation)

Estimated soil–air gaseous flux (without bioturbation)

Estimated net soil–air flux (with bioturbation)

Estimated net soil–air flux (without bioturbation)

Soil, median concentrations

(ng m
2 year
1)

(ng m
2 year
1)

(ng g
1)a

(ng g
1 SOM
1)b

(ng dm
3)c

1.1 10 42 34 32 332 0.03 0.28 0.72 0.55 0.44 8.03 0.03 0.27 0.94 0.84 0.58 11 0.04 0.05 0.43 0.41 0.20 4.3 0.07 0.27 0.68 0.59 0.38 7.1 0.02 0.14 0.51 0.44 0.24 4.5

2.1 32 137 109 99 1043 0.45 3.7 11 9.0 6.3 112 0.34 1.8 9.9 7.7 5.9 83 0.63 1.0 8.8 8.3 4.1 87 0.62 2.7 6.8 5.5 3.7 67 0.26 1.8 6.4 5.9 3.0 61

607 6150 21,410 17,087 16,426 168,839 29 239 662 513 438 7392 27 145 710 557 416 6059 41 56 477 454 222 4741 78 278 721 661 389 7217 22 130 508 481 264 4658

Site

Congener (IUPAC)

(pg m
3)

(ng m
2 year
1)

(ng m
2 year
1)

(ng m
2 year
1)

A

PCB28 PCB101 PCB138 PCB153 PCB180
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PCB

3.08 3.58 2.72 2.70 0.78 76.35 2.92 3.31 1.99 2.38 0.56 68.75 3.20 6.07 3.84 3.54 1.23 111.00 3.47 7.58 6.33 6.12 2.15 145.00 2.95 11.50 15.95 14.50 5.63 192.49 3.46 3.32 2.48 2.32 0.72 68.50

16 74 135 103 64 1382 18 37 51 45 33 1049 33 39 39 32 19 854 20 92 131 96 59 1563 17 2600 6284 4954 4416 53,998 35 60 104 73 49 1510

1759 4501 4216 3885 1284

133 189 160 146 47

1744 4428 4081 3781 1221

117 115 25 42
16

123 441 312 251 79

20 25 13 10 3

105 405 261 206 46

2
11
38
35
30

54 93 119 135 21

7 5 5 5 1

21 54 80 103 2


26
35
34
26
18

170
79 67 102
18

33
5 3 4
1

150
170
63 5
77

13
96
128
92
60

303 125
271
208
126

34 6
11
8
5

286
2475
6555
5162
4542

18
2594
6295
4962
4421

60 171 182 182 27

9 9 7 7 1

25 111 78 109
22


26
51
96
65
48

B

C

D

E

F

(continued on next page)

C. Backe et al. / Environmental Pollution 128 (2004) 59–72

Air, yearly median concentrations

66

Table 2 Measured PCB concentrations in air and soil, measured deposition fluxes and estimated soil–air gaseous fluxes at the 11 sampling-sites. Values for the partition koefficient for octanol/water (Kow), Henry’s law constant (H), Enthalpy of air–water exchange (DHaw), liquid vapor pressure (PL) and molecular weight (M) are given

Table 2 (continued) Air, yearly median concentrations

Bulk deposition, yearly

Estimated soil–air gaseous flux (with bioturbation)

Estimated soil–air gaseous flux (without bioturbation)

Estimated net soil–air flux (with bioturbation)

Estimated net soil–air flux (without bioturbation)

Soil, median concentrations

(ng m
2 year
1)

(ng m
2 year
1)

(ng m
2 year
1)

(ng m
2 year
1)

(ng g
1)a

Congener (IUPAC)

(pg m
3)

(ng m
2 year
1)

G

PCB28 PCB101 PCB138 PCB153 PCB180
PCB PCB28 PCB101 PCB138 PCB153 PCB180
PCB PCB28 PCB101 PCB138 PCB153 PCB180
PCB PCB28 PCB101 PCB138 PCB153 PCB180
PCB PCB28 PCB101 PCB138 PCB153 PCB180
PCB

3.53 4.39 3.27 3.57 0.92 89.40 3.18 4.02 2.75 2.77 0.68 82.00 2.02 4.50 2.90 2.79 0.81 99.85 3.97 4.11 3.96 4.32 1.42 86.20 2.24 2.78 2.48 2.38 0.56 51.40

27 94 349 190 118 2444 31 60 76 56 38 1429 31 1731 5058 4039 3185 40,835 40 73 110 92 58 1519 44 63 78 61 39 1345

PCB28 4.68E+05 31 1.96E
02
57.5 257.5

PCB101 2.40E+06 27 1.84E
03
62.5 326.40

PCB138 6.76E+06 15 2.68E
04
62.5 360.9

H

I

J

K

Kow (25 C) H (25 C) PL (25 C) DHaw M a b c

29 15 12 12 2

100 120
74 89
59

73 26
423
101
177

192 204 153 177 29

19 9 6 7 1

161 144 77 121
9

130 84 1 64
47

495 2430 1910 1671 650

76 135 78 68 24

464 699
3148
2368
2535

433
1032
8206
6408
5720

172 146 207 172 27

42 11 9 8 1

131 73 97 80
31

91 0
13
12
89

62 39 94 89 20

10 2 4 4 1

18
24 16 27
19


25
87
62
34
58

PCB153 8.32E+06 23 3.57E
04
65 360.9

PCB180 2.29E+07 22 6.63E
05
67.5 395

Units – Pa m3 mol
1 Pa kJ mol
1 K
1 (g mol
1)

0.03 0.11 0.41 0.38 0.22 3.7 0.08 0.37 1.2 1.1 0.59 12 0.14 1.7 4.7 3.6 3.8 46 0.03 0.07 0.29 0.23 0.15 2.6 0.02 0.07 0.38 0.31 0.20 3.8

0.57 2.5 9.6 10 5.5 93 0.42 1.8 5.3 4.9 2.7 57 1.3 15 42 33 35 419 1.0 2.2 10 7.5 5.2 92 0.29 0.99 5.2 4.5 2.9 53

(ng dm
3)c 28 132 416 450 256 4461 46 202 629 596 325 6997 123 1465 4036 3164 3339 40,187 35 85 376 307 198 3434 19 61 388 320 196 3870

Reference Beyer et al. (2002) Beyer et al. (2002) Falconer and Bidleman (1994) Burkhard et al. (1985)

67

Median of five soil samples, expressed on dry weight basis. Median of five soil samples, SOM normalised. Median of five soil samples, expressed on basis of volume.

127 214 275 279 59

(ng g
1 SOM
1)b

C. Backe et al. / Environmental Pollution 128 (2004) 59–72

Site

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C. Backe et al. / Environmental Pollution 128 (2004) 59–72

compound to move from one compartment to the other in order to establish equilibrium conditions. We calculated the fugacity quotients for 5 different PCB congeners (PCB 28, 101, 138, 153, and 180) from the 11 sampling-sites. The soil properties from Table 1 and the concentrations listed in Table 2 (for the geometric mean soil concentrations were used) were applied for Eq. 1–5 to calculate the fugacities. The fugacity quotients Eq. (6) for the different congeners at the 11 sampling-sites are displayed in Fig. 5. Soil/air fugacitiy ratios ranged from 0.3 to nearly 40 for the different congeners and sampling sites with a median value of 2.7. This suggests that the soil has become oversaturated with PCB with respect to the atmosphere and has not approached an equilibrium partitioning condition. There was an inverse relationship between fugacity quotients and biphenyl content, although this relationship was not significant when regressed on biphenyl vapour pressure (P > 0.05). At site E the fugacity quotients for congeners 138, 153 and 180 were below 1, suggesting that there is net absorption of PCBs to soil from the atmosphere. 3.5. Estimated air–soil fluxes Table 2 contains measured bulk deposition fluxes, estimated net soil–air gaseous fluxes and estimated overall air–soil exchange fluxes for each of the 11 sampling sites. The general pattern is that the lighter congeners are estimated to be outgassing from most of the sites, whereas the heavier congeners are outgassing at some sites and exhibiting a net absorption from the atmosphere at others. For example, at all sites there is an estimated net flux of PCB-28 from soil to air, and at all but two sites (D and K), there is an estimated net flux of PCB 101 from soil to air. PCB-180, however, has an estimated net absorption from the atmosphere at 8 of 11 sites.

Fig. 5. Calculated soil/air fugacity quotients (fs/fa) for five selected PCBs, for the 11 sampling-sites (A–K). Quotients are expressed on a logarithmic scale. Fugacity quotients near one show equilibrium between air and soil, fugacity quotients greater than one indicate a volatilisation tendency from soil, whereas fugacity quotients of less than one indicate a tendency for the substances to remain in the soil and a capacity for the air to supply the soil with more substances.

It is noteworthy that the inclusion of vertical-sorbed phase transport or bioturbation in the estimation of soil–air gaseous fluxes has a large influence on the magnitude of the flux and thus on the estimated net air– soil exchange fluxes. Table 2 shows that if this transport process is excluded, PCB congeners are mostly estimated to have a net air to soil flux at all 11 sites.

4. Discussion Soil is a heterogeneous environmental compartment, with a range of properties controlling the fate of organic chemicals. For example the texture of the soil influences the moisture holding capacity and the temperature of the soil (Jackson and Jackson, 1996) which further strongly affects the volatilisation of organic compounds (Iwata et al., 1973, Hippelein and McLachlan, 2000). The fraction of SOM is an important factor for sorption of organic compounds (Karickhoff, 1981; van Leeuwen and Hermans, 1995; Cousins et al., 1999a). SOM is a mixture of partially decayed plant and animal remains and material synthesised by micro-organisms (Jackson and Jackson, 1996). Except for the soil texture (soil type), the most striking variability between soil properties in this study was the organic matter content, where site A showed a higher level than the other sites. Site A was located on a ridge of primary rock (gneiss) with a thin soil layer at the seaward edge of Scania’s northwestern coast, 88.5 m above sea level. The high level of organic matter at this site can be explained by the fact that the rock has a great heat-storage capacity and has, therefore, a desiccating effect on the thin layer of soil which, in turn depresses the break-down of organic matter (Nihlga˚rd pers comm.). The large variation in PCB concentration in the soil between different sites (ranging between median 2.6 and 332 ng g
1) could partly be explained by differences in soil properties, i.e. soil texture and organic matter content, but other environmental conditions such as proximity to secondary sources and wind direction may also play a major role. A large variation in PCB concentration in soils (ranging between 2.3 and 444 mg kg-1) was also found by Creaser and Fernandes (1986) in rural and urban locations in Great Britain. They found a trend toward higher concentrations of PCB in 95 soil samples in the more industrial Midlands, although other more isolated rural areas also showed elevated levels. They deduced that the variation in PCB concentration between sample sites was due to local conditions such as proximity to sources of pollutants, land use, wind direction, soil type and soil organic content. The majority of the PCB concentrations in the study were below 20 mg kg
1. We found a positive relationship between PCB concentration and SOM. To eliminate the influence of

C. Backe et al. / Environmental Pollution 128 (2004) 59–72

organic matter covariation in the comparison of PCB concentrations between different soils we expressed the data on the basis of organic matter (Schwarzenbach et al., 1993). By this transformation, variations in median
PCB concentrations between sites were reduced. However, the deviation for site A and I was still maintained. The within-site variation was slightly reduced by organic matter normalisation. Although there was a reduction of variability in the data by compensation for SOM, both within and among sites, this variable alone could not explain the variability in the data. Influences from local contaminant sources as well as various environmental conditions probably affect variations as well. Further, variation in both quantity and quality of SOM could create differences in the effect of SOM normalisation. The quantity of SOM can vary widely between different soil samples as an effect of, for example, different land use history (Pulleman et al., 2000) resulting in differences in the adsorption capacity of organic chemicals (Haque and Schmedding, 1976). Further, the SOM quality has been shown to be of importance in the degree of adsorption capacity of organic compounds. Fulvic acids are generally more oxidized and acidic than humic acid and thus more water soluble (Weed and Weber, 1974) and consequently contain fewer sites for the adsorption of hydrophobic organics. Humic acids are more lipophilic in nature, insoluble in water and could be expected to adsorb a higher proportion of hydrophobic organics. In a discussion about movements of DDT through a forest soil, Ballard (1971) discovered that 91% of the applied DDT was recovered in the humic acid fraction and only 9% in the fulvic acid fraction of the SOM. Vegetation is an effective scavenger of both particleand gaseous-phase organic compounds from the atmosphere (Simonich and Hites, 1995, McLachlan and Hortsmann, 1998), and it has been shown that there is a variation in accumulation from one plant species to another (Ko¨mp and McLachlan, 1997; Bo¨hme et al., 1999). For example plant surface area (Schreiber and Scho¨herr, 1992) and the quality of the lipophilic material in the plants (Ko¨mp and McLachlan 1997) are important factors. Consequently, vegetation that is more effective in uptake of PCBs from the atmosphere will also give rise to a higher PCB load and probably also a different composition of PCBs to the underlying soil. Decaying plant material can be an accountable input of PCBs in the soil. Simonich and Hites (1994) assume that most of the PAHs sorbed by vegetation are incorporated into the soil once a year, when leaves fall to the ground and decays at the end of the growing season. Once the compounds have been incorporated into the soil compartment they may be degraded or stored permanently or they may volatilise back to the atmosphere. The heavier PCBs, deposited by decaying vegetation, will probably remain in the soil while the

69

lighter PCBs are subjected to volatilisation. The variation in soil PCB concentration and composition between sites and within sites, i.e. site D, may partly be explained by local differences in rate of decay. The composition of PCB congeners was seen to be useful for explaining spatial differences of these compounds in soil. It was shown that congeners with more than five chlorine atoms dominated the lower range of PC1, while congeners with three to five chlorine atoms were located at the upper range of PC1, indicating that PCA may be useful in discriminating between samples based on the compound’s physical-chemical properties. For example, the lighter congeners have higher vapour pressures than the heavier congeners and therefore volatilise more easily from the soil. Recently Cousins and Jones (1998) showed that the loss of PCBs from a spiked soil was slower for the heavier PCBs than for the lighter congeners, and volatilisation was thought to be the dominant loss mechanism. Alcock et al., 1994 and Lead et al., 1996 have demonstrated that exposure of samples to air can cause either contamination or loss of PCBs, depending on the equilibrium position of the soil. Local contaminant sources, degradation processes and nondiffusive processes such as deposition in association with particles, tend to cause non-equilibrium conditions. In this study it was shown that areas with the highest soil concentrations of PCBs (i.e. A and I) had a higher degree of heavier congeners. This could partly be explained by faster volatilisation rates of lighter congeners and a larger fraction of heavier congeners left in the soil. Soil texture has also been shown to be important in the sorption/volatilisation process of different PCB congeners. Iwata et al. (1973) found that in loamy sand, the majority of the tetra-chlorinated congeners in Arochlor 1254 had volatilised within 12 months. In a silty loam no preferential losses were detected over this period. Further, Wilcke and Zech (1998) showed, in a study of PCB in bulk soil and particle size separation, that after normalisation to organic carbon, the fine sand fraction had the highest PCB concentration. At site I the PCB deposition was high, and the fraction of heavy congeners was large, which probably has a substantial influence on the PCB content and composition in the soil. Finally, it is also possible that the large fraction of heavy PCB congeners at site A and I originate from leakage of transformer fluid oil used within the lighthouses that were present at both sites. Transformer oils (for example Aroclor 1260, manufactured by Monsanto, USA) had a PCB composition dominated by penta- to octa-chlorinated congeners (Hutzinger et al., 1974), which were also the dominating PCB congeners in the soil at site A and I. Today, usage of transformer oils including PCB have ceased in Sweden and dielectric fluids are replaced by other products. The composition of PCB congeners in air and precipitation at site E were dominated by penta- to hepta-

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C. Backe et al. / Environmental Pollution 128 (2004) 59–72

chlorinated congeners (Backe et al., 2000; Backe et al., 2002), while the composition in soil was dominated by tri- to penta- chlorinated congeners. One would expect a higher degree of heavy congeners in the soil at this site as a result of air–soil exchange. Possible explanations for the observations could be a recent undefined local input of PCBs to the atmosphere, not yet expressed in the soil compartment, or/and effective soil fate processes such as microbial degradation at this site. Areas with or near background concentrations of PCB in the soil have probably been subjected to longrange transport of PCBs rather than short-range transport from local sources. The long-range transport mechanism may result in a preference for lighter congeners due to the increased atmospheric residence time (Wania and Mackay, 1993). These areas are probably closer to air–soil equilibrium as the processes are not affected by recent high input of PCBs. In theory, a strong relationship exists between POP in the atmosphere and POP in the soil compartment (Cousins and Jones, 1998; Lee et al., 1998; Sweetman and Jones, 2000). The main processes contributing to the air–soil exchange of these compounds are dry deposition, wet deposition and volatilisation from the soil (Cousins et al., 1999a). In the field the variation in these processes together with the chemical and physical characters of the substances, combined with different soil variables and air variables (e.g. temperature) directs the scale of the transport (Grundy et al., 1996; Hippelein and McLachlan, 2000). If the transfer of the chemicals between soil and air is close to equilibrium it means that little or no net diffusive transfer between soil and air is occurring. Several recent studies of the air/soil transfer processes of PCBs have shown the compartments to be close to equilibrium as a consequence of the use restrictions of these compounds (Cousins and Jones, 1998; Sweetman and Jones, 2000). By comparing the fugacities of the PCBs in the compartments, the fugacity quotients provide a snapshot of the current state of whether compartments are in equilibrium with each other or not. Fugacity quotients approaching a value of one show equilibrium between air and soil, since fugacity for the two compartments are equal when the phases are in equilibrium. However, it is expected that the soil/air fugacity ratio will at steady state approach a value slightly in excess of 1.0 (Harner et al., 1995). This is because there is an appreciable nondiffusive transport (e.g. wet and dry deposition) from air to soil, with only relatively slow return to the atmosphere by volatilisation. The ratio fs/fa will thus approach a steady state value above 1.0 according to the equation fs =fa ¼ 1 þ ðks
a Þ=ðka
s Þ

ð10Þ

where ks
a and ka
s are the overall rate constants (/h) for soil to air and air to soil transport, respectively. In this case reaction is ignored.

There was a large variation in the soil/air fugacity ratio between sites, although at many sites (C, D, E, F, H and K, Fig. 5) the ratio is only slightly above 1.0, suggesting that the soil and air may indeed be reaching a steady state condition for some congeners. The flux estimates indicted that lighter PCB congeners had a stronger tendency to move from soil to air than heavier congeners. Furthermore, the direction and magnitude of the soil-air exchange flux was greatly affected by the inclusion of vertical sorbed phase transport. For compounds such as PCBs, which primarily bind to soil solids biturbation is the most important transport process in the soil (Cousins et al., 1999b). Without vertical sorbed-phase transport, the rate limiting process in gaseous soil–air transport of PCBs is the soil-side resistance, whereas when it is included the rate limiting process is the air-side resisitance (McLachlan et al., 2002). In this current study it is not known how fast the soil was turned over by earthworms, but it is clear from the model calculations that this process needs to be better characterised in future studies if air–soil exchanges are to be accurately estimated.

Acknowledgements We wish to thank Siv Billberg, Maj-Lis Gernersson and Rangnhild Ohlin at the Department of Plant Ecology for help with the soil analyses and Professor Bengt Nihlga˚rd for helping with classification of the soil samples. Further we are greatful to Marco Tiden for helping with the soil sampling, to Dr. Andy Sweetman for professional support with the fugacity calculations, to Dr. Jep Agrell for help with statistics and to Prof. Terry Bidleman, Dr. Cecilia Agrell and Dr. Go¨ran Ewald for constructive comments on this manuscript. Parts of the project were financed by Ska˚nes Luftva˚rdsfo¨rbund together with the county administrative boards and the county councils in the region.

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