Atmospheric Environment 43 (2009) 1730–1736
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Identifying source regions for the atmospheric input of PCDD/Fs to the Baltic Sea Ulla Sellstro¨m*, Anna-Lena Egeba¨ck, Michael S. McLachlan Department of Applied Environmental Science (ITM), Stockholm University, SE-106 91 Stockholm, Sweden
a r t i c l e i n f o
a b s t r a c t
Article history: Received 17 September 2008 Received in revised form 4 December 2008 Accepted 8 December 2008
PCDD/F contamination of the Baltic Sea has resulted in the European Union imposing restrictions on the marketing of several fish species. Atmospheric deposition is the major source of PCDD/Fs to the Baltic Sea, and hence there is a need to identify the source regions of the PCDD/Fs in ambient air over the Baltic Sea. A novel monitoring strategy was employed to address this question. During the winter of 2006–2007 air samples were collected in Aspvreten (southern Sweden) and Pallas (northern Finland). Short sampling times (24 h) were employed and only samples with stable air mass back trajectories were selected for analysis of the 2,3,7,8-substituted PCDD/F congeners. The range in the PCDD/F concentrations from 40 samples collected at Aspvreten was a factor of almost 50 (range 0.6–29 fg TEQ/m3). When the samples were grouped according to air mass origin into seven compass sectors, the variability was much lower (typically less than a factor of 3). This indicates that air mass origin was the primary source of the variability. The contribution of each sector to the PCDD/F contamination over the Baltic Sea during the winter half year of 2006/2007 was calculated from the average PCDD/F concentration for each sector and the frequency with which the air over the Baltic Sea came from that sector. Air masses originating from the south–southwest, south–southeast and east segments contributed 65% of the PCDDs and 75% of the PCDFs. Strong correlations were obtained between the concentrations of most of the PCDD/F congeners and the concentration of soot. These correlations can be used to predict the PCDD/F concentrations during the winter half year from inexpensive soot measurements. Ó 2008 Elsevier Ltd. All rights reserved.
Keywords: PCDD/F Baltic Sea Monitoring Ambient air Trajectory
1. Introduction The Baltic Sea ecosystem is contaminated with polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs). The concentrations of these chemicals in many of the commercially harvested fish from the Baltic Sea exceed the limits for dioxins and dioxin-like compounds in food set by the Commission of the European Communities (2006). As a result, the European Commission has placed restrictions on the marketing of fish from the Baltic Sea. The sources of the PCDD/Fs in the Baltic Sea are not yet fully understood. The pulp and paper industry and wood preservatives are suspected to have made a significant historical contribution. However, analysis of the PCDD/F congener patterns in Baltic Sea sediments indicates that the impacts of these sources have been largely local, while atmospheric deposition is responsible for the bulk of the PCDD/Fs that have accumulated in the Baltic Sea (Rappe et al., 1989; Verta et al., 2007). This conclusion is supported by mass balance modeling of PCDD/Fs to the Baltic Sea (Armitage et al., 2008). Thus, a good understanding of the concentrations and
* Corresponding author. Tel.: þ46 8 674 7181; fax: þ46 8 674 7637. E-mail address:
[email protected] (U. Sellstro¨m). 1352-2310/$ – see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2008.12.014
sources of PCDD/Fs in the atmosphere is necessary in order to develop strategies to reduce the contamination of the Baltic Sea ecosystem. To date, few measurements have been made of PCDD/Fs in air in the Baltic region (Broman et al., 1991; Egeba¨ck et al., 1991; Tysklind et al., 1993; McLachlan et al., 1998; Hovmand et al., 2007; Swedish Dioxin Survey Database, 2008). PCDD/F concentrations have been shown to have a pronounced seasonality, with average levels during the coldest winter months that are more than an order of magnitude greater than during the summer (McLachlan et al., 1998; Hovmand et al., 2007). A similar, albeit not quite as pronounced seasonality in bulk deposition of PCDD/Fs was observed in the same studies. Furthermore, analysis of 14 samples from southern Sweden, each collected over 3–5 days, and 20 samples from the Swedish island of Gotland, each collected over 2 days, provided strong indications that air mass origin plays an important role in determining the magnitude of the PCDD/F concentrations and the congener pattern (Egeba¨ck et al., 1991; Tysklind et al., 1993; Swedish Dioxin Survey Database, 2008). A strong influence of air mass origin on PCDD/F concentrations in air has also been observed in other areas (Lohmann et al., 1999). This hypothesis is supported by atmospheric dispersion and fate modeling of PCDD/Fs conduct by the EMEP modeling center (EMEP/MSC-E), which predicts
¨ m et al. / Atmospheric Environment 43 (2009) 1730–1736 U. Sellstro
strong gradients in average PCDD/F concentrations in air around the Baltic Sea (Gusev et al., 2008). The EMEP/MSC-E modeling work does provide some insight into the potential source regions of PCDD/Fs to the Baltic Sea. However, this tool is dependent on emissions estimates provided by the EMEP member states, which are unreliable and often not produced in a consistent manner. Hence, there is a need for empirical studies to aid in identifying the major source regions. The goal of this study was to improve understanding of the current PCDD/F concentrations in air around the Baltic Sea and to identify their major source regions. One methodological approach for identifying source regions of atmospheric contaminants that has found widespread use in recent years is post-measurement interpretation using air mass back trajectory. Egeba¨ck et al. (1991) and Tysklind et al. (1993) applied it in their exploratory work on PCDD/F levels in the Baltic Sea, and it has been used successfully in addressing relatively simple questions, for instance in demonstrating that toxaphene was being transported to the Great Lakes from the south rather than the north (James and Hites, 2002). However, in less qualitative or more complex situations, air mass back trajectory analysis is hampered by difficulties in assigning air mass origins to the samples (Hafner and Hites, 2005). It is common for air mass trajectories to have changed significantly during the sampling period, either because the weather systems were moving rapidly or because the sampling period was too long. In this study we took a different approach, combining short sampling times with pre-measurement screening of the samples to identify those that had a stable air mass origin over the whole sampling period. Only these samples were analysed for PCDD/Fs, providing a dataset containing clear air mass origin information. 2. Materials and methods 2.1. Samples Air samples were collected at two locations, both well established atmospheric monitoring stations, located about 1000 km apart (Fig. 1). Aspvreten is located to the south of Stockholm, Sweden, and Pallas is located in northern Finland close to the Swedish border. Both of these sites are remote and should not be influenced by local sources. Since PCDD/Fs are subject to long-range atmospheric transport, it is not important that the stations be located on the Baltic Sea itself. Pallas gives information on air masses impacting the northern part of the Baltic Sea (i.e. the Bothnian Bay), while the results from Aspvreten are more directly applicable to deposition to the central and southern Baltic Sea. Using back trajectories to identify contaminant source regions is hampered by the dynamics of atmospheric circulation, which frequently results in rapidly changing back trajectories. This was accounted for in the sampling strategy, using a short sampling time of 24 h. Sampling was conducted during the winter season 2006–2007, as studies in Denmark and Germany have demonstrated a clear seasonality in ambient air levels of PCDD/Fs, with levels during the coldest month that were typically one order of magnitude higher than during the warmest month (McLachlan et al., 1998; Hovmand et al., 2007). Since the particle bound fraction was expected to make the greatest contribution to the PCDD/F flux to the Baltic Sea, a greater emphasis was placed on analyzing this fraction. As the air masses coming from the south were expected to be the source of the large majority of the PCDD/Fs to the Baltic Sea, a greater emphasis was put on analyzing samples from Aspvreten. In Aspvreten, four HiVol samplers were run almost continuously, one after the other, from mid October 2006 to mid April 2007. The samplers, which employed an inverted sampling head with
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a glass fiber filter (MG 160, 293 mm diameter, Munktell, Sweden) to collect the PCDD/Fs associated with atmospheric particles, and two polyurethane foam plugs (PUFs, 78 75 mm, r ¼ 22–25 kg/m3, DP Sunde, Norway) in series, to collect the PCDD/Fs in gaseous form, were operated at w30 m3 h1. The station was visited twice a week to change the sampling materials and check sampler performance. Field blanks were obtained using an extra HiVol sampler loaded with PUFs and a filter and left for a whole week without air sampling. In Pallas the sampling capacity was lower with two HiVol samplers from Nov 2006 to mid April 2007. These samplers were turned on and off with a remote control according to the back trajectory forecasts, with prolonged sampling time when the wind conditions were very stable. Field blanks from Pallas were obtained from the two HiVol samplers after the exposure of PUFs and filter to ambient air for 7–13 days without air sampling. The glass fiber filters were pre-baked at 450 C for 24 h, and the PUFs pre-cleaned with toluene in an accelerated solvent extractor (three cycles, temperature 100 C, pressure 100 bars, cell size 100 ml) before use. The sample volume was measured with a flow meter for air, calibrated for atmospheric pressure and a working temperature of 10 C, and was corrected for the average outdoor temperature during the sampling period. Only samples with stable air mass back trajectories during the 24 h sampling period were selected for analysis. The back trajectories (96 h) were produced with the NOAA HYSPLIT model (http://www.arl.noaa.gov/ready.html) at three different heights (20, 100 and 500 m). Of the samples with stable back trajectories, a sub-set was selected to give good representation of different air mass origin. The air mass origin was categorized using several compass sectors (Fig. 1). Air from the north–northwest (NNW) sector had passed over northern Sweden/Norway/Greenland and Svalbard, while air from the northwest (NW) sector had passed over southern Norway/Greenland and Iceland. Air from the southwest (SW) compass sector had passed over the British Isles, while the south–southwest (SSW) sector covered western Europe except the British Isles. The boundary between the NW and SW compass sectors was drawn through Scotland as many years of atmospheric particle measurements at Aspvreten have shown that this represents a dividing line between the more polluted air to the south and the cleaner air to the north (HC Hansson, ITM, Stockholm University, personal communication). Air from easterly directions was divided into three compass sectors: south–southeast (SSE) for air that had passed over eastern Europe, east (E) covering the major part of the former Soviet Union, and north–northeast (NNE) for air that had passed over Finland and northern Russia including the Kola Peninsula. In total, 60 different air samples were analyzed, 45 from Aspvreten and 15 from Pallas (Tables S1–S4 in the Supplementary Materials). The particle bound fraction was analyzed in all of the samples and the gaseous fraction was analyzed in 36 of them. The aim was to select about the same number of samples from each sector, but due to prevailing meteorological conditions during the sampling campaign this was not possible. 2.2. Analysis The analyses were performed at a German commercial labora¨ kometric GmbH), accredited for the analysis of PCDD/Fs tory (O according to DIN EN ISO/IEC 17025:2005. In brief, the samples were extracted using pressurized fluid extraction (Dionex model ASE 300) employing the following parameters: solvent toluene, temperature 125 C (filters) or 100 C (PUFs), pressure 100 bar, three static cycles of 5 min each. A suite of 13C-labeled PCDD/Fs was added after the extraction. The extracts were cleaned up on a mixed silica column (successive layers of H2SO4/silica gel, silica gel, and
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U. Sellstro¨m et al. / Atmospheric Environment 43 (2009) 1730–1736
Fig. 1. Location of the field stations at Aspvreten (58 800 N, 17400 E), Sweden, and Pallas (68 000 N, 24 140 E), Finland. Dashed lines indicate the compass sector division applied when grouping samples due to air mass origin (the labels of the sectors for Pallas given in brackets, see also Fig. S1 in Supplementary Materials). The locations used for estimation of wind direction frequencies over the Baltic Sea are indicated with a star: (57 100 N, 19 000 E) for the southern and central parts, (64 800 N, 23 000 E) for the Bothnian Bay.
3. Results and discussion The concentrations of all measured congeners in the filters and PUFs from Aspvreten and Pallas are given in Tables S1–S4 in the Supplementary Materials, together with sampling date, sampled air volume, average temperature, precipitation and average soot concentration. 35 30 25 20 15 10 5 0
Gas phase Particle phase
<
*< r1
1, ar M
,2
20
00
7
07
7 00 ,2 b1 Fe
Jan
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1,
00
20 07
6
00 6 ,2
<
<
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<
* v1
Six field blanks from Aspvreten and three from Pallas were analyzed. The field blanks (both filters and PUFs) were handled the same way as the sample materials for the real samples, except for the sampling itself. The sample identities (field blank or air sample) were not known to the analytical laboratory. Filters and PUFs were always analyzed separately. The recoveries of the internal standards were always within the method specifications (60–120%). Only a few PCDD/F congeners were present in the field blanks, and mostly at levels close to the detection limits (1,2,3,6,7,8HxCDD, one filter, one PUF, Aspvreten; TCDF, one filter, one PUF, Aspvreten and two PUFs, Pallas; 1,2,3,4,6,7,8-HpCDF and OCDF, two PUFs, Aspvreten; 1,2,3,4,6,7,8-HpCDD, five/six filters and PUFs, Aspvreten). OCDD was present in similar amounts in all field blank samples, both filters and PUFs. No correlation was found between levels in field blanks and time of exposure to ambient air (Pallas) or sample location (Pallas/Aspvreten). The limits of quantification (LOQ) were set to three times the signal to noise (S/N) ratio, or, if the analyte was present in the field blanks, to the average blank value plus three times the standard deviation. Samples above this value were blank corrected using the mean field blanks. The 2,3,7,8-TCDD toxicity equivalents (TEQs) were calculated using the WHO 2005 toxicity equivalency factors (van den Berg et al., 2006). The calculation of TEQs is problematic when the concentrations of some congeners are below the LOQ. To circumvent this problem, missing values were estimated based on the assumption of congener pattern similarity between the air samples. The missing value was calculated with respected to the most
No
2.3. QA/QC
similar quantified congener in the same sample (e.g. 2,3,7,8-TCDD using 1,2,3,7,8-PeCDD), employing the average ratio of the concentrations of these congeners from other similar samples in which the two congeners had been quantified. When an estimated value was higher than the LOQ, the latter was considered as the best estimate. When most of the congeners in a sample were below the LOQ, the procedure was not employed and the sample was not included in the data interpretation. Although this estimation procedure is fraught with uncertainty, it was judged to be superior to the more commonly used approaches of replacing missing values by either zero or half the limit of quantification, which is completely arbitrary. In Table S5 (Supplementary Materials) the percentage of estimated data for each congener is shown.
fg TEQ/m3
NaOH/silica gel). They were then fractionated on an aluminum oxide column which separated PCBs and other non-polar constituents (first fraction) from the PCDD/Fs (second fraction). The analysis was conducted on a MAT 90/95 sector field high resolution mass spectrometer coupled to a Varian 3400 gas chromatograph, which was equipped with a Gerstel cold injection system and a DB Dioxin column (30 m, 0.25 mm i.d., and 0.15 mm film thickness).
Fig. 2. Total PCDD/F concentrations (fg WHO-TEQ/m3) in air samples from Aspvreten. Concentrations in the gas phase (when analyzed) were added on top of the concentrations in the particle phase. < and * means that all congeners were below the detection limit (gas and particle phase, respectively).The X-axis is a time scale and the samples are arranged in the order that they were sampled.
¨ m et al. / Atmospheric Environment 43 (2009) 1730–1736 U. Sellstro
The average concentration (filter þ PUF) measured in this study (w10 fg TEQ m3) is almost identical to what was found in winter samples collected in 1990–1993 (11 samples) at the Swedish island of Gotland in the Baltic Sea (Swedish Dioxin Survey Database, 2008). This is somewhat lower than what was been reported for other air samples collected in winter in this region (45 fg TEQ m3 in three samples from 1989 (Tysklind et al., 1993), 33 fg TEQ m3 in six samples from 1995/96 (McLachlan et al., 1998), w40 fg TEQ m3 in 11 samples from 2002/03 (Hovmand et al., 2007)). The lower levels may be a consequence of Aspvreten and Gotland being more remote from the major industrial centers in Europe than the sampling stations used in the other studies and hence having a lower frequency of exposure to air masses that have passed over
the major source areas (see below). The congener patterns observed in this work were generally consistent with the earlier measurements. The particle/gas partitioning of the PCDD/Fs was also similar to what has been reported earlier (Eitzer and Hites, 1989; Hippelein et al., 1996), namely increasing gaseous fractions with decreasing chlorine number and increasing ambient temperature. A salient feature of the dataset is the day to day variation in the atmospheric concentrations of PCDD/Fs in Aspvreten (Fig. 2). The bars show the total PCDD/F-TEQ concentrations in the filter fraction, with the PUF fraction (when analyzed), added on top. Most of the PCDD/Fs were associated with particles – as was expected during the cold season of the year (Hippelein et al., 1996).
PCDD-TEQ
A
1733
6. NNW
PCDF-TEQ (fg/m3)
5. NNE
20 15 10
7. NW
5 0
# 1. SW
#
*
* 2. SSW
4. E 3. SSE PCDD-TEQ
B
6. NNW
PCDF-TEQ (fg/m3) 3
5. NNE
2.5 2 1.5 1
7. NW
0.5
#
0
#
* 1. SW
*
2. SSW
4. E 3. SSE
Fig. 3. Concentrations of PCDDs and PCDFs in air from Aspvreten, grouped according to air mass origin. Summer samples are marked *, and winter samples taken 1 year earlier are marked #. a) Particle phase, b) gas phase.
U. Sellstro¨m et al. / Atmospheric Environment 43 (2009) 1730–1736
In Fig. 3 the samples are grouped according to the compass sector through which the air mass primarily passed before reaching the sampling station. The variability in particle phase concentrations was much lower within a sector than it was between the sectors (Fig. 3a). The highest concentrations were found in samples that had passed over the European continent (SSW, SSE and E). In air that had passed over the British Isles and air from northerly directions (SW, NW, NNW and NNE), the concentrations were low. A few winter samples taken 1 year earlier were also analyzed (marked with #) and had similar TEQ levels to the samples from this year. This could indicate fairly similar year-to-year seasonal concentrations. Two air samples from the summer of 2006 (marked with *) had lower TEQ levels than the winter samples from the same sector. This was expected, since there is a strong seasonal component to the variations in PCDD/F concentration in air in northern Europe, with much lower levels during the summer (Ko¨nig et al., 1993; Hippelein et al., 1996). The proportion of PCDF-TEQs to PCDD-TEQs was higher in air from the SSW, SSE, E and NNE sectors, while in the air from westerly directions the proportion of PCDD-TEQs was higher (Fig. 3a). Differences in congener patterns depending on the air mass origin were also observed by Egeba¨ck et al. (1991) and Tysklind et al. (1993). Since only three of the 14 samples reported in (Tysklind et al., 1993) were collected during the winter, it is difficult to make comparisons. However, they did report an increased dominance of OCDD in samples with low concentrations, which is consistent with our observations (see Table S1, Supplementary Materials). For the gaseous phase the directional trends were not as clear as for the particle phase (Fig. 3b). The concentrations were more similar between the transects. The difference in the directional trends between Figs. 3a,b is partially due to the higher soot concentrations in the SSW, SE and E sectors (see Table S2, Supplementary Materials), which resulted in higher PCDD/F concentrations in the particle fraction of the air samples from those sectors. Temperature is another factor that could influence the directional trends, as lower temperatures result in a smaller fraction of the PCDD/Fs partitioned into the gas phase. Lower temperatures were observed in several of the samples from the E sector, but this effect was compensated by lower soot concentrations. It is important to note that the fraction of the PCDD/Fs in the gas phase was small and that the total PCDD/F concentrations in air therefore show the same directionality as the particle phase concentrations. The concentrations in Aspvreten were higher than in Pallas, regardless of air mass origin. Fig. S1 (Supplementary Materials) shows the estimated average PCDD/F-TEQs in air from different compass sectors. Higher concentrations in Aspvreten, also in air coming from the north, indicate a net addition of PCDD/Fs to the air as it travels from north to south over Scandinavia. There are, however, uncertainties, especially due to the few samples from for the northeast in Aspvreten and Pallas. The contribution of each compass sector to the PCDDs and PDCFs present in the air over the Baltic Sea during the 6 month sampling period was estimated by multiplying the average concentrations for each sector by the frequency with which the air originated from that sector. The wind frequencies were estimated by studying wind back trajectories (72 h) at three different heights (20, 100 and 500 m), every 8 h for the whole sampling campaign. Because the air circulation over the northernmost part differs from that over the rest of the Baltic Sea (HC Hansson, ITM, Stockholm University, Sweden, personal communication), this was done for two locations (Fig. 1). The average concentrations for each sector, the air mass frequency for each sector, and the estimated averaged concentrations (sum of each sector’s contribution) over the Baltic Sea are listed in Tables S6 and S7 (Supplementary Materials).
The relative contributions of each compass sector to the estimated averaged particle phase PCDD- and PCDF-TEQ concentrations over the southern Baltic Sea are shown in Fig. S2a in the Supplementary Materials. The large majority originates from the European continent and the former Soviet Union (SSW, SSE and E sectors). The SSW sector contributes about 25% of both PCDDand PCDF-TEQs. The SSE and E sectors each contribute a little less (20%) of the PCDD-TEQs but about the same fraction (25%) of the PCDF-TEQs. The SW and NW sectors together contribute about 25% of the PCDD-TEQs and less than 15% of the PCDF-TEQs. Little originates from the northern sectors (NNW and NNE). The gas phase contributions were more evenly distributed among the sectors (Fig. S2b, Supplementary Materials). There is however considerable uncertainty in the contribution from the SW, NW, NNW and NNE sectors due to the small number of data points for these sectors. The results from this study clearly indicate that the levels of PCDD/Fs in the air over the Baltic Sea are determined by the sources of the air masses. As these can vary from one year to another, the results from this study might not be representative for periods with different wind conditions. In order to better be able to extrapolate the results from this study in space and time, correlations between the PCDD/F concentrations and the concentrations of a routinely determined atmospheric parameter, soot, were explored. A strong correlation was found for the particle bound PCDDs and PCDFs (Fig. 4). An explanation for this correlation is that both soot and PCDD/Fs are primarily formed during combustion. Generally,
PCDDs (r2=0.68)
-1.5
-1.0
1.0
0.0
-0.5
0.5
-0.5 -1.0 1.5
PCDFs (r2=0.79) log WHO-TEQ (fg/m3)
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1.0 0.5 0.0 -1.5
-1.0
-0.5
0.5
-0.5 -1.0 1.5
PCDD/Fs (r2=0.79)
0.5
-1.5
-1.0
0.0
-0.5
0.5
-0.5 -1.0
log soot concentration (µg/m3) Fig. 4. Correlations between concentrations of PCDDs and PCDFs and concentrations of soot in air from Aspvreten (particle phase).
¨ m et al. / Atmospheric Environment 43 (2009) 1730–1736 U. Sellstro Table 1 Parameters from the linear regression of the logarithm of the concentrations of PCDD and PCDF congeners in the particle phase against the logarithm of the concentrations of soot in air from Aspvreten.
2,3,7,8-TCDD 1,2,3,7,8-PeCDD 1,2,3,4,7,8-HxCDD 1,2,3,6,7,8-HxCDD 1,2,3,7,8,9-HxCDD 1,2,3,4,6,7,8-HpCDD OCDD 2,3,7,8-TCDF 1,2,3,7,8-PeCDF 2,3,4,7,8-PeCDF 1,2,3,4,7,8-HxCDF 1,2,3,6,7,8-HxCDF 1,2,3,7,8,9-HxCDF 2,3,4,6,7,8-HxCDF 1,2,3,4,6,7,8-HpCDF 1,2,3,4,7,8,9-HpCDF OCDF
Slope
Intercept
r2
0.63 0.80 0.69 0.79 0.79 0.62 0.52 0.94 1.0 0.99 1.1 1.1 1.1 1.2 1.1 1.1 1.0
0.72 0.11 0.79 0.29 0.44 0.37 1.6 0.60 0.98 0.27 0.10 0.077 0.97 0.006 0.44 1.4 2.1
0.48 0.73 0.60 0.66 0.63 0.45 0.34 0.72 0.75 0.74 0.78 0.76 0.80 0.78 0.84 0.86 0.84
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the correctness of their algorithms and emissions estimates. This would be a logical next step in identifying the sources of PCDD/F input to the Baltic Sea. Acknowledgements We thank Hans Karlsson (ITM) for assistance with sampling at Aspvreten and Peter Tunved (ITM) for helping with the calculation of the back trajectories. We are particularly grateful to Jussi Paatero and Juha Hatakka at the Finnish Meteorological Institute, and Eveliina Pa¨a¨kko¨la¨ and Ahti Ovaskainen at the Finnish Forest Research Institute for their generous help with sampling in Pallas. Stimulating discussions with Hans-Christen Hansson (ITM) were very useful in planning the project. This work was financed by the Swedish Environmental Protection Agency under the guidance of Niklas Johansson. Appendix A. Supplemental material
Number of samples ¼ 38.
the correlations were higher for the PCDFs (r2 ¼ 0.72–0.86 for the individual congeners) than for the PCDDs (r2 ¼ 0.34–0.73) (Table 1 and Fig. S3 (Supplementary Materials)). HpCDD and OCDD correlated to a lesser extent (r2 ¼ 0.45 and 0.34, respectively). The correlations for TCDD and 1,2,3,4,7,8-HxCDD were also fairly low (r2 ¼ 0.48 and 0.60, respectively), whereby most of the TCDD data used were estimates as only in a few air samples were the measured concentrations above detection limit (Table S5, Supplementary Materials). For the other congeners it is possible to estimate the concentrations with fairly high accuracy using soot data. Note that the most reliable correlations are likely those for congeners that had a low percentage of estimated values (Table S5). The correlation of the total PCDD/F concentrations (particle bound and gaseous) with the soot concentrations was also evaluated. The sample size was smaller, as the gas phase was not analyzed for all samples. The correlation coefficients were similar to those between soot and the particle bound PCDD/F only (see Table S8, Supplementary Materials). Exceptions to this were TCDD and TCDF for which the correlation with particle phase only was higher (r2 ¼ 0.54 and 0.76, respectively) than with the total air concentrations (r2 ¼ 0.25 and 0.53, respectively). Other authors have reported a relationship between PCDD/F concentrations and the levels of inorganic pollutants. On the basis of a principle component analysis, Tysklind et al. (1993) found that higher PCDD/F concentrations were associated with higher levels of SO2, NO2, NO 3 , and soot. Hovmand et al. (2007) reported a good correlation between PCDD/F concentrations and SO2 þ SO4 in winter samples (r2 ¼ 0.81). In summer samples, however, no correlation was observed. Thus there is considerable evidence that PCDD/F concentrations in the Baltic region can, at least in winter, be predicted from other parameters which are frequently available and much less expensive to determine. 4. Conclusions In conclusion, the methodology employed in this study, namely pre-measurement screening of the samples to identify those that had a stable air mass origin, proved to be very successful. It could potentially be used in contaminant monitoring programs for substances for which air mass origin is a principle determinant of concentration to generate data with greater information content at less cost. Data collected in this manner should be particularly useful for evaluating atmospheric dispersion and fate models to check
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