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Relationships between atmospheric transport regimes and PCB concentrations in air at Zeppelin, Spitsbergen Sandy Ubl, Martin Scheringer, and Konrad Hungerbühler Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b02571 • Publication Date (Web): 18 Jul 2017 Downloaded from http://pubs.acs.org on August 1, 2017
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Environmental Science & Technology
Relationships between atmospheric transport regimes and PCB concentrations in air at Zeppelin, Spitsbergen 1
Sandy Ubl,† Martin Scheringer,∗,†,‡ and Konrad Hungerbühler† †Institute for Chemical and Bioengineering, ETH Zürich, 8092 Zürich, Switzerland ‡RECETOX, Masaryk University, 625 00 Brno, Czech Republic E-mail:
[email protected] 2
Abstract
3
Polychlorinated biphenyls (PCBs) are persistent hazardous chemicals that are still
4
detected in the atmosphere and other environmental media although their production
5
has been banned for several decades. At the long-term monitoring site, Zeppelin at
6
Spitsbergen, different PCB congeners have been continuously measured for more than
7
a decade. However, it is not clear what factors determine the seasonal and inter-annual
8
variability of different (lighter vs. heavier) PCB congeners. To investigate the influence
9
of atmospheric transport patterns on PCB-28 and PCB-101 concentrations at Zeppelin,
10
we applied the Lagrangian Particle Dispersion Model FLEXPART and calculated “foot-
11
prints” that indicate the potential source regions of air arriving at Zeppelin. By means
12
of a cluster analysis, we assigned groups of similar footprints to different transport
13
regimes and analyzed the PCB concentrations according to the transport regimes. The
14
concentrations of both PCB congeners are affected by the different transport regimes.
15
For PCB-101, primarily the origin of air masses from the European continent is related
16
to high concentrations; elevated PCB-101 concentrations in winter can be explained by
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17
the high frequency of this transport regime in winter whereas PCB-101 concentrations
18
are low when air is arriving from the oceans. For PCB-28, in contrast, concentrations
19
are high during summer when air is mainly arriving from the oceans, but low when
20
air is arriving from the continents. The most likely explanation of this finding is that
21
local emissions of PCB-28 mask the effect of long-range transport and determine the
22
concentrations measured at Zeppelin.
23
Introduction
24
Polychlorinated biphenyls (PCBs) are a group of environmentally hazardous chemicals be-
25
cause of their high persistence, bioaccumulation potential, and toxicity 1 . They are included
26
in the Stockholm Convention on Persistent Organic Pollutants (POPs), a global treaty with
27
the aim of protecting human health and the environment from hazardous, highly persistent
28
chemicals 2 .
29
PCBs have been widely used as dielectric and coolant fluids, as transformer and capacitor
30
insulating materials, in paints, and as sealing materials 3 . The majority of PCBs have been
31
used in the Northern Hemisphere, mostly between 30◦ and 60◦ N. Due to the remoteness of
32
the Arctic (i. e., the region north of 60◦ N), it is generally assumed that this region received no
33
direct input of POPs. However, many POPs were detected in various environmental media
34
in the Arctic, such as sediments, soils, and the atmosphere 2 . The atmospheric concentrations
35
of these chemicals have been measured routinely within monitoring programs such as the
36
European Monitoring and Evaluation Programme (EMEP) 2 for more than a decade.
37
The main source of PCBs detected in the Arctic results from long-range atmospheric
38
transport 2 in combination with dry/wet deposition to the surface, and transport via ocean
39
currents 4 . Exceptionally high PCB concentrations at Zeppelin have been linked to boreal
40
forest fires 5 and it has been shown that the 20% highest PCB-28 and PCB-101 concentrations
41
are associated with air masses arriving from regions over Europe with known PCB emission
42
sources 6 . 2 ACS Paragon Plus Environment
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However, temporal trends in PCB concentrations measured in the atmosphere at Zeppelin
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are complex and difficult to explain. A downward trend until 2004 followed by increasing
45
concentrations has been reported for many PCB congeners 7,8 , but different PCB congeners
46
exhibit different seasonal variability. Lower-chlorinated PCBs show the highest concentra-
47
tions in summer, whereas elevated concentrations of medium-chlorinated PCBs are observed
48
in winter 6 . To identify potential sources and source regions of PCBs and to link PCB con-
49
centrations to long-range transport phenomena, it is essential to understand these complex
50
seasonal trends for different PCBs observed at Zeppelin. Therefore, the aims of this study
51
were i) to identify relevant atmospheric transport regimes for air masses arriving at Zep-
52
pelin, ii) to link seasonal concentration patterns of different PCBs to the identified transport
53
regimes, and iii) to evaluate the importance of local PCB sources contributing to the mea-
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surements at Zeppelin. For this purpose we used the Lagrangian Particle Dispersion Model
55
(LPDM) FLEXPART to simulate the atmospheric transport of PCB-28 and PCB-101 for
56
the years 2000–2012. Based on a cluster analysis, we merged similar footprints into distinct
57
transport regimes and related measured PCB concentrations to different transport regimes.
58
We compared the seasonal trends of the PCB concentrations measured at Zeppelin to the
59
trends at Andøya, another Arctic measurement site, to assess the influence of local PCB
60
sources and to explain the seasonal PCB trends observed at Zeppelin.
61
Material and Methods
62
Measurement site
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The Zeppelin observatory is located in the Arctic on Zeppelin Mountain, Spitsbergen, at
64
79◦ N and 12◦ E at 474 meters above sea level. It is generally assumed that the station is
65
above the local inversion layer most of the time and thus is not influenced by local pollution
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from the nearby community, e.g. Ny-Ålesund. For PCB measurements, 1000 m3 of air are
67
sampled over 48 h and measured on a weekly basis. A detailed description of the sampling 3 ACS Paragon Plus Environment
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procedure and the analytically methods is provided by Hung et al. 9 , Bossi et al. 10 , Eckhardt
69
et al. 11 , and available from the UNECE-EMEP website: http://www.emep.int. We selected
70
PCB-28 and PCB-101 for our analysis because both mainly occur in the gas phase and are
71
therefore suitable for modeling with a Lagrangian Particle Model. We considered data from
72
June 2000 to 2012. Before June 2000 the PCB measurements were affected by contamination
73
or samples were taken in Ny-Ålesund and not at Zeppelin mountain 7 and are therefore not
74
representative for a background measurement site.
75
Model description
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To focus on the role of atmospheric transport regarding PCB concentrations in the Arctic we
77
need to employ a model that describes Arctic and mid-latitude transport accurately. LPDMs
78
have a detailed treatment of atmospheric transport (including turbulence and convection),
79
wet and dry deposition, and include simple chemical transformations such as the removal
80
of POPs by hydroxyl (OH) radicals. Additionally, LPDMs can be run backward in time
81
to identify the source regions of chemicals transported in the atmosphere. The LPDM
82
FLEXPART 12–14 has been used successfully to study atmospheric transport patterns in the
83
Arctic 14 as well as to simulate PCB-28 concentrations at Birkenes 11 . Here, FLEXPART was
84
driven by the European Center for Medium Range Weather Forecast (ECMWF) operational
85
analysis data (input) with 91 vertical levels and a horizontal grid spacing of 1◦ × 1◦ . The
86
model was used in backward mode to calculate so-called footprints (output) for Zeppelin.
87
A footprint indicates the potential source region of a pollutant arriving at Zeppelin and
88
consists of residence times expressed in units of seconds per grid cell. The grid cells have
89
a lateral dimension of 1◦ × 1◦ and a height of 100 m. For the model calculation 48 · 104
90
so-called tracer particles (representing infinitesimally small air parcels 13 ) were released from
91
the Zeppelin grid cell during 24 hours 6,11,15 . The tracer particles were traced backward in
92
time for 22 days. FLEXPART considers horizontal and vertical displacements due to the
93
mean atmospheric flow, turbulent mixing, especially in the atmospheric boundary layer, and 4 ACS Paragon Plus Environment
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vertical transport due to deep convection. The removal of PCB due to the reaction with OH
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radicals 16 and due to dry and wet deposition are implemented in the FLEXPART model. A
96
detailed description of these processes and their implementation in the FLEXPART model is
97
given by Seibert and Frank 17 and by Eckhardt et al. 11 . As the partitioning of PCBs between
98
gaseous and particulate phase is not considered in FLEXPART, the model is less suitable
99
for calculating the transport of PCBs that are sorbed strongly to atmospheric particles (log
100
KOA ≈ 10 or higher 18 ). To calculate the losses by dry and wet deposition, Henry’s law
101
constants of 33.1 Pa m3 mol−1 (PCB-28) and 31.4 Pa m3 mol−1 (PCB-101) were used 19 and
102
for the reaction of the PCBs with OH radicals, rate constants of 1.1−12 cm3 s−1 for PCB-28 16
103
and 3.4−13 cm3 s−1 for PCB-101 were used 20 .
104
Cluster analysis
105
For each weekly 48-hour sampling interval we calculated a footprint, resulting in 658 indi-
106
vidual footprints for PCB-28 and PCB-101. To identify the main geographical areas over
107
which air masses arriving at Spitsbergen have been transported and thus may have picked
108
up PCBs, we conducted a cluster analysis to group footprints that represent similar trans-
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port regimes. For the cluster analysis we used the model domain ranging from 35◦ North to
110
85◦ North and treated the residence times in every grid cell as a variable. To account for
111
the non-normally distributed residence time values, we followed the approach described by
112
Sturm et al. 21 and calculated the absolute distance between the ranks of the residence times
113
according to
di,j =
N X
|rank(τk )i − rank(τk )j |, i, j = 1, ..., 658
(1)
k=1 114
where the residence times are indicated as τ , the index k runs over all grid cells from 1
115
to N ), and i and j refer to the sampling interval. The clustering based on the dissimilarity
116
matrix, di,j was then calculated with the k-medoid method 22 . We varied the number of
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clusters between 3 and 8 and applied the silhouette technique 22 and visual analysis to select
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the optimum number of clusters for further analysis. The silhouette value for each point is a
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measure of how similar that point is to points in its own cluster in comparison to points in
120
other clusters. Thus the average of the silhouette values of the entire data set is a measure of
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how well the data have been clustered. The average silhouette value showed a local maximum
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of 0.20 for 5 clusters for PCB-28 and 0.19 for 6 clusters for PCB-101. The number of clusters
123
is a compromise between resolution of different potential source regions (more clusters) and
124
a sufficiently high number of footprints in the individual clusters (fewer clusters).
125
We normalized the cluster footprints according to equation 2, where anorm is the nor-
126
malized footprint of a given cluster, a is the average footprint of this cluster and b is the
127
average of all 658 footprints. This procedure resulted in normalized, average footprints per
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cluster with values ranging from −2 to +2. Normalized cluster footprints with values above
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0 indicate areas where the air masses resided longer than on average (average footprint) and
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clusters with values below 0 represent areas where the air masses resided shorter than on
131
average.
anorm =
a−b (a + b)/2
(2)
132
Results
133
Changes of the concentrations of PCB-28 and PCB-101 from 2000–
134
2012
135
The trends of PCB concentrations measured at Zeppelin are not consistent (Fig.1). For PCB-
136
28, concentrations decrease from the middle of 2000 until 2002. In 2002, the concentrations
137
increase again, especially in summer. In 2003 and 2004 they are less variable, and in 2005
138
they increase again and remain on a higher level until the middle of 2009. This structural
139
change of the PCB-28 concentration trend in 2005 was also identified by Wöhrnschimmel 6 ACS Paragon Plus Environment
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PCB-28
pg/m 3
15
10
5
0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
PCB-101
pg/m 3
1.5 1 0.5 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Figure 1: Time series of PCB-28 and PCB-101 concentrations measured at Zeppelin in 2000– 2012 (source: http://www.emep.int). Black line: raw data; red line: seasonal trend of the PCB concentrations, calculated by the robust locally weighted scatterplot smoothing (rloess) in Matlab. The ticks are set at the beginning of each year. 140
et al. 8 . In 2010, another change with decreasing PCB-28 concentrations is visible in the data
141
(Fig. 1 top); this change has been identified as a step change by Zhao et al. 23 .
142
For PCB-101, the concentrations show a clear seasonal variability with a winter maxi-
143
mum 6 . Generally, PCB-101 concentrations slightly decrease from the middle of 2000 until
144
2005. In 2006 and 2007, the concentrations slightly increase before they start to decrease
145
again in 2008. This change in PCB-101 concentrations has also been identified by Zhao et
146
al. 23 .
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Transport regimes
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The silhouette technique yielded 5 clusters for PCB-28 (Fig. 2) and 6 clusters for PCB-101
149
(Fig. 3), reflecting transport regimes with different origins of air masses arriving at Zeppelin.
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The reason for the different numbers of clusters is the differences between the footprints for
151
PCB-28 and PCB-101, reflecting the chemicals’ different physicochemical properties. For
152
PCB-28 the footprints of cluster 1 represent air masses that arrive from all directions, with
153
residence times slightly enhanced over Russia. Footprints associated with clusters 2 and 5
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represent air originating from the Arctic ocean (cluster 2) and Atlantic ocean (cluster 5). The
155
footprints of cluster 3 represent air masses coming from Europe. The footprints of cluster 4
156
represent air masses with longer-than-average residence times predominantly over Canada,
157
the US and the Pacific ocean. Like PCB-28, the footprints for PCB-101 forming cluster 1
158
have average residence times over Europe, the US and the oceans. Air masses dominantly
159
originating from the oceans are reflected in the footprints of cluster 2 (Arctic ocean) and
160
cluster 3 (Atlantic ocean). Air masses from the continents are mainly represented by cluster
161
4 (Europe, Russia, eastern Asia) and cluster 5 (Europe, Russia and US). The footprints of
162
cluster 6 represent air masses originating from the Pacific ocean but also from Canada and
163
eastern Asia.
164
165
Similar transport regimes were also described by Hirdman et al. 15 and are consistent with general transport pathways to the Arctic 14,24 .
166
To evaluate the seasonal dependence of the transport regimes, the total and seasonal foot-
167
print frequencies per cluster were calculated (Table 1). Ocean transport regimes represented
168
by footprints in clusters 2 and 5 for PCB-28 and clusters 2 and 3 for PCB-101 dominantly oc-
169
cur during the summer months, whereas the footprints representing transport regimes from
170
the continents (cluster 3 for PCB-28 and clusters 4 and 5 for PCB-101) dominate during the
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winter months.
172
Transport of air masses from the Pacific ocean (cluster 4 for PCB-28, cluster 6 for PCB-
173
101) predominantly occurs in spring and autumn for both PCBs. Thus, the occurrence 8 ACS Paragon Plus Environment
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of different transport regimes represented by the footprints of individual clusters strongly
175
depend on the season, which is again in line with the known Arctic transport pattern 14 . Table 1: Frequencies of PCB-28 and PCB-101 footprints per cluster and season in spring (March, April, May), summer (June, July, August), autumn (September, October, November), and winter (December, January, February). cluster PCB-28 1 2 3 4 frequency % 23 24 26 15 all 154 157 174 97 55 16 33 47 spring summer 2 110 0 7 67 31 25 33 autumn winter 30 0 116 10
5 12 76 7 54 14 1
cluster PCB-101 frequency 1 2 3 4 % 18 17 22 13 all 118 113 144 84 spring 32 24 19 29 summer 15 63 80 1 51 20 34 19 autumn winter 20 6 11 35
5 16 109 15 1 22 71
6 14 90 39 1 24 14
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Measured PCB concentrations in relation to transport regimes
177
For each of the 658 time points there is a modeled footprint and a measured PCB concentra-
178
tion. The transport regimes were derived from the clustering of the footprints, see preceding
179
section, and every individual PCB measurement was assigned to its respective transport
180
regime. We then calculated median PCB concentrations for each transport regime to assess
181
whether the different transport regimes are reflected by distinct PCB concentrations.
182
For PCB-28, highest median concentrations were observed when air masses arrived from
183
the Arctic and Atlantic oceans (corresponding to clusters 2 (2.41 pg/m3 ) and 5 (3.20 pg/m3 )
184
(Fig. 4 left)). Transport of air masses from all directions (cluster 1; 1.58 pg/m3 ), Europe
185
and the US (cluster 3; 1.87 pg/m3 ) and Canada and US (cluster 4; 1.66 pg/m3 ) resulted in 9 ACS Paragon Plus Environment
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Cluster: 1
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Cluster: 2 1.5
o
1
Cluster: 3
Cluster: 4
0.5
0
-0.5
Cluster: 5
-1
-1.5
Figure 2: Normalized footprints (PCB-28) for 5 different transport regimes for Zeppelin from 2000–2012 as obtained from FLEXPART simulations and footprint clustering. Values greater than 0 indicate areas where the air masses reside longer than on average (average footprint) and clusters with values below 0 indicate areas where the air masses reside shorter than on average. Values above 1 or below −1 indicate 3 times longer or shorter residence times, respectively, than on average. The pink circle indicates the station location. The maps were generated with the M_Map package of Matlab.
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Cluster: 1
Cluster: 2 1.5
o
1
Cluster: 3
Cluster: 4
0.5
0
-0.5
Cluster: 5
Cluster: 6
-1
o -1.5
Figure 3: Normalized footprints (PCB-101) for 6 different transport clusters for Zeppelin from 2000–2012 as obtained from FLEXPART simulations and footprint clustering. Values greater than 0 indicate areas where the air masses reside longer than on average (average footprint) and clusters with values below 0 indicate areas where the air masses reside shorter than on average. Values above 1 or below −1 indicate 3 times longer or shorter residence times, respectively, than on average. The pink circle indicates Spitsbergen where the measurement station is located. The maps were generated with the M_Map package of Matlab.
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similar PCB concentrations that are all lower than those represented by clusters 2 and 5.
187
For PCB-101, we observed the highest median concentration (0.51 pg/m3 , 0.43 pg/m3 )
188
when air masses were transported over Europe (cluster 5) and Russia (cluster 3 (Fig. 4
189
right)). For air masses coming from all directions and the Arctic ocean (clusters 1 and 3) the
190
median concentrations were 0.37 pg/m3 , 0.36 pg/m3 . Lowest median values were associated
191
with air masses transported over Arctic and Pacific ocean (clusters 2 (0.30 pg/m3 ) and 6 (0.32
192
pg/m3 )). Thus, PCB-101 concentrations were high when air arrived from the continents and
193
PCB-28 concentrations were high when air arrived from the oceans. The Wilcoxon rank-
194
sum test was applied to evaluate whether the concentration distribution related to a specific
195
cluster is significantly different from the concentration distribution in another cluster. A
196
significant difference of the PCB concentration distribution between clusters suggests that the
197
identified transport regimes are responsible for the different PCB concentrations measured
198
at Zeppelin.
199
For PCB-28, the concentrations related to transport of air masses from the ocean (clus-
200
ters 2 and 5) are significantly higher than PCB-28 concentrations related to the remaining
201
transport regimes (clusters 1, 3, and 4) when the air is mainly transported over land. For
202
PCB-101, concentrations related to the pure ocean transport regime (cluster 2) are signif-
203
icantly lower in comparison to PCB-101 concentrations related to the remaining transport
204
regimes represented by clusters 1, 3, 4, 5 and 6. The PCB-101 concentrations related to
205
air masses arriving from Europe (cluster 5) and Russia (cluster 4) are significantly higher
206
compared to PCB-101 concentrations related to all other transport regimes represented by
207
clusters 1, 2, 3, and 6.
208
To investigate whether the PCB concentrations within individual transport regimes ex-
209
hibit seasonal variations, we calculated the median PCB concentration per transport regime
210
per season (colored dots in Fig. 4). The larger the spread of the colored dots (corresponding
211
to the seasons), the stronger are the seasonal variations of the PCB concentrations, which in
212
turn can be interpreted as changing emissions in the respective source regions. For PCB-28,
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23%
24%
26%
15%
18%
12%
17%
22%
13%
16%
14%
8 PCB-101
6
0.8
pg/m 3
pg/m 3
spring
1
PCB-28
4 2
summer 0.6
autumn
0.4 0.2
winter 0
0 1
2
3
4
5
1
cluster number
2
3
4
5
6
cluster number
Figure 4: Boxplot of PCB-28 (left) and PCB-101 (right) concentrations classified by the transport regimes. Boxes show the interquartile range; whiskers extend to the most extreme values. To focus on the difference between the clusters, outliers are not plotted. The colored dots are the median concentrations per season with spring (MAM), summer (JJA), autumn (SON) and winter (DJF). The percent values on top of the panels indicate the frequencies of the transport regimes. Seasons with fewer than 4 footprints in a given cluster are not shown. 213
the general concentration variability as well as the seasonal variation is highest for the ocean
214
transport regimes (clusters 2 and 5). PCB-28 concentrations related to cluster 2 during the
215
summer season were clearly higher than the median and autumn and spring concentrations
216
were clearly lower than the median. PCB-28 concentrations related to the Atlantic trans-
217
port regime (cluster 5) were below the 25th percentile during spring. For the Arctic-Pacific
218
ocean transport regime (cluster 4) the PCB-28 concentrations were below the 25th percentile
219
during the winter season. The transport from Canada, US and Europe (clusters 3) did not
220
reveal any seasonal variations.
221
For PCB-101, a considerable seasonal variability of concentrations was observed for air
222
masses arriving from all directions (cluster 1) and from the Atlantic ocean (cluster 3) with
223
concentrations during summer (cluster 1) and winter (cluster 3) above the 75th percentile.
224
During spring PCB-101 concentrations were above the median for transport regimes repre-
225
sented by clusters 1, 4, 5 and 6.
226
The time series of PCB-28 and PCB-101 (Fig. 1) do not show a clear trend and, therefore, 13 ACS Paragon Plus Environment
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cluster
pg/m 3
4
PCB-28
1 2 3 4 5
3 2 1 2000
2002
2004
2006
2008 cluster
pg/m 3
0.6
PCB-101
0.4 0.2 2000
2002
2004
2006
1 2 3 4 5 6
2008
Figure 5: 4-year running median per cluster of measured PCB-28 (top) and PCB-101 (bottom) concentrations. The different clusters are color coded; the numbers in the legend correspond to the cluster numbers in Figures 2 and 3.
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we did not subtract a background to correct the data. To evaluate whether the median
228
PCB concentration per cluster, considering the years from 2000–2012, is dominated by high
229
PCB concentrations in individual years, we calculated the running median over 4 years for
230
the measured PCB concentration per cluster (Fig. 5). For PCB-28, a decreasing trend of
231
the running medians for all the 5 clusters was observed for the years 2000–2002. From
232
2002 to 2005 the median concentration increased and then decreased again after 2006. The
233
results from the 4-year running medians again indicated that the highest concentrations were
234
associated with air masses arriving from the oceans (clusters 2 and 5), in agreement with the
235
results from the whole time series. In recent years, the difference of the PCB concentrations
236
resulting from the distinct transport regimes became less prominent. For PCB-101, running
237
medians decreased for the ocean transport regimes (cluster 2 and 3). The running medians
238
for the two transport regimes receiving air from Russia and eastern Asia (cluster 4 and 6)
239
are invariant until 2005 and decrease afterwards. For the European transport regime and
240
the all-directions transport regime the running medians decreased from 2000 to 2003 and
241
stayed on a similar level until 2007 when the concentrations start to decrease again.
242
The highest PCB-101 concentrations were associated with air masses arriving from Eu-
243
rope (cluster 5) and Russia (cluster 4) in agreement with the results from the whole time
244
series.
245
PCB concentrations versus residence time over land
246
The different PCB concentrations associated with the distinct transport regimes can be
247
related to (i) varying residence times of the respective air masses in the respective source
248
region and/or (ii) the variable PCB emission rates within the source regions. To disentangle
249
these two effects, we evaluated the median PCB concentrations observed for the different
250
transport regimes as a function of the mean residence time over land and over ocean (Fig.
251
6). The mean residence times over land and ocean were calculated by splitting each footprint
252
into a land and an ocean fraction and then summing and normalizing the respective footprint 15 ACS Paragon Plus Environment
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5
median PCB-101 [pg/m3 ]
median PCB-28 [pg/m3 ]
5 3 R 2 = 0.65
R 2 = 0.11
2
2
2
3 4
3 4 1
1
1 0
10
20
30
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0.5 R 2 = 0.66
4
4
0.4 1
3
31
6 0.3
6
2
2 2
R = 0.71 0.2
40
10
mean surface residence time [s]
20
30
40
mean surface residence time [s]
Figure 6: Median of concentrations of PCB-28 (left) and PCB-101 (right) of the different transport regimes (given by the cluster numbers) as a function of mean residence time over land (black) and over the ocean (blue) per transport regime. 253
fractions per cluster. For PCB-101, the measured concentrations were positively correlated
254
with the mean surface residence times over land (R2 = 0.66) and negatively correlated with
255
mean surface residence times over oceans (R2 = 0.71). Thus, the longer the air masses reside
256
close to the surface over land (high PCB emissions, see Breivik et al. 25 ,26 ), the higher are the
257
PCB-101 concentrations and, vice versa, the longer the air resided over the oceans, where
258
PCB emissions are low, the lower are the PCB-101 concentrations.
259
For PCB-28, in contrast, the measured concentrations were negatively correlated with
260
the mean surface residence time over land (R2 = 0.65) and over the ocean (R2 = 0.11). The
261
negative correlation between residence times over land and the measured PCB-28 concen-
262
trations reveals that the main primary PCB-28 sources do not have a strong influence on
263
PCB-28 concentrations measured at Zeppelin. For the ocean, there is no clear relationship;
264
this finding will be discussed in the next section.
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Discussion
266
The seasonal trends of PCB-101 can be well explained by dominant transport regimes de-
267
livering air masses from distinct source regions to the Arctic, in combination with primary
268
PCB emissions from the respective source regions 3 . High residence times over land coincide
269
with high PCB concentrations measured at Zeppelin.
270
In contrast, PCB-28 concentrations are high when residence times over the main primary
271
source regions (e.g. Europe) are low and thus, the seasonal trends of the PCB concentrations
272
at Zeppelin can neither be explained by the transport regimes nor by the primary PCB
273
emissions. Thus, alternative emission pathways such as re-emissions of PCB from the ocean
274
water and ice or local PCB sources which both may account for the observed discrepancies
275
are discussed in the following sections.
276
Re-emission of PCB from ocean water and ice
277
Air transport from the Arctic and Atlantic ocean (clusters 2 and 5) mainly occurs during
278
summer and thus additional PCB emissions in summer may be responsible for the elevated
279
PCB concentrations. Re-emissions of PCBs from the ocean water and from sea ice have been
280
considered as potential sources of POPs 7,27 . Net volatilization of PCBs to the atmosphere
281
have been reported for the tropical Atlantic 28,29 . However, the potential source regions or
282
the air masses reaching Zeppelin during summer (clusters 2 and 5) are within the Arctic
283
ocean for which net deposition dominates 30,31 . Friedman and Selin 32 also exclude secondary
284
PCB emissions as explanation for elevated PCB concentrations in summer, because sec-
285
ondary emissions would have to be unrealistically high to explain the seasonal discrepancies
286
between measured and modeled PCB. Temperature-dependent changes in the surface area
287
and structure of snow and ice may affect deposition and re-emission patterns of PCBs 33,34 ,
288
but the extent to which these processes may influence the PCB concentrations in the Arctic
289
remains unclear. However, the sharp decrease in the PCB concentrations observed after 2010
17 ACS Paragon Plus Environment
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is inconsistent with such an emission pathway.
291
Local PCB sources
292
A collaborative project (2007–2010) between the Geological Survey of Norway (NGU), the
293
Governor of Spitsbergen and the Climate and Pollution Agency (Klif) (2007–2010) aimed
294
to identify and eliminate local PCB sources in Spitsbergen. Sources of PCBs were detected
295
on land in Barentsburg and Pyramiden 4,35 . Furthermore, elevated PCB concentrations were
296
detected in soils and PCBs were found in paints, in addition to other PCB sources not further
297
listed 4 . The use of PCB-containing fluids in underground mining (especially coal mining at,
298
e.g., Barentsburg) may additionally contribute to local PCB emissions close to Zeppelin, but
299
no quantitative data are available on the amounts of PCB released into the environment
300
by ventilation systems, mine output and pit water 2 , which makes it very challenging to
301
quantitatively account for potential local PCB sources. However, during summer, elevated
302
temperatures favor the emission of volatile PCBs and the atmospheric boundary layer height
303
is above the elevation of the Zeppelin mountain 36,37 . Both factors support the hypothesis
304
that local PCB emissions are responsible for the high PCB concentrations observed during
305
the summer season when air masses dominantly arrive from the oceans and residence times
306
are high close to the station (clusters 2 and 5). This hypothesis is also supported by Fried-
307
man and Selin 32 , who consider local primary emissions a more probable driver for summer
308
concentration maxima compared to re-emissions from surface media because of the greater
309
effect of temperature changes on primary volatilization emissions compared to secondary
310
emissions. Furthermore, the PCB concentrations measured at Zeppelin are substantially
311
higher than the PCB concentrations reported from six other Arctic monitoring stations 4 .
312
Local PCBs sources were removed from Svalbard after 2009 4 , which may explain the sharp
313
decrease of the PCB concentrations observed between 2009 and 2010, but the presence of
314
additional local emissions, such as heavily contaminated soils, likely still persists.
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Comparison of PCB concentrations measured at Spitsbergen and
316
Andøya
317
To evaluate whether local PCB emissions or emissions from ice and open water were dominat-
318
ing the PCB measurements during summer in Spitsbergen, we compared the PCB concentra-
319
tions from Spitsbergen and Andøya (data available since 2009), another Arctic measurement
320
site located 10 degrees south of Zeppelin. PCB-28 concentrations at Zeppelin are consider-
321
ably higher, mostly by more than a factor of 3 compared to Andøya. After 2009, however,
322
the PCB-28 concentrations at Zeppelin and Andøya differ only by a factor of 2. Both stations
323
receive air from the ocean, indicating that re-emissions of PCBs from the open ocean and or
324
from sea ice would influence both stations equally. The time series of PCB-28 measured at
325
Andøya (see Fig. 12 38 ) clearly shows a seasonal cycle with higher concentrations in winter
326
than in summer, consistent with the Arctic transport regime. However, PCB-28 concentra-
327
tions at Zeppelin show an opposite seasonal cycle with elevated concentrations in summer,
328
suggesting that PCB concentrations measured at Zeppelin especially before 2009 and their
329
seasonal variations were dominated by local emissions on Spitsbergen.
330
Environmental Significance
331
For PCB-101, we are able to explain 66% of the concentration variability between the trans-
332
port regimes by the residence time over land (Figure 6). However, PCB-28 concentrations
333
are elevated when residence times over land are low and cannot be explained by primary
334
PCB emissions.
335
PCB re-emissions from ocean water are not a suitable explanation of this finding because
336
this explanation is inconsistent with the opposite seasonal trends reported for PCB-28 at
337
Andøya, another Arctic measurement station that should be equally influenced by PCB
338
re-emissions from ocean waters as Zeppelin. Furthermore the sharp decline in the PCB
339
concentrations observed after a PCB clean-up campaign in 2009 suggests local PCB sources 19 ACS Paragon Plus Environment
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340
rather than general emission patterns from water or ice, which are unlikely to show such
341
abrupt changes. The seasonality of the PCB-28 concentrations at Zeppelin is consistent with
342
PCB emissions from local sources, in combination with a seasonal modulation of the PCB
343
emissions by the temperature. Therefore, linking seasonal trends of PCB-28 concentrations
344
from Zeppelin, especially before the cleanup campaign in 2009, to atmospheric long-range
345
transport and emission inventories may be compromised by local PCB sources masking
346
atmospheric transport patterns.
347
The Zeppelin mountain observatory is an important measurement site because it is con-
348
sidered representative of atmospheric background concentrations of various chemicals in the
349
Arctic. According to our findings this representativeness is questionable for PCB-28 con-
350
centrations measured at Zeppelin. The influence of local emissions from Spitsbergen on the
351
concentrations of PCB-28 measured at Zeppelin mountain should be further clarified and
352
taken into account in any future interpretation of the PCB-28 measurements.
353
Acknowledgement
354
We thank Ralf Kägi for helpful comments. Martin Scheringer acknowledges financial support
355
by the Czech Ministry of Education, Youth and Sports (LM2015051) and Masaryk University
356
(CETOCOEN PLUS project, CZ.02.1.01/0.0/0.0/15_003/0000469).
357
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Zeppelin
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