While some persistent organic pollutants (POPs) have
been declining globally due to their worldwide ban since the 1980s, the
declining trends of many of these toxic chemicals become less significant
and in some cases their ambient air concentrations, e.g., polychlorinated
biphenyls (PCBs), showed observable increase during the 2000s, disagreeing
with their declining global emissions and environmental degradation. As part
of the efforts to assess the influences of environmental factors on the
long-term trend of POPs in the Arctic, step change points in the time series
of ambient POP atmospheric concentrations collected from four arctic
monitoring sites were examined using various statistical techniques. Results
showed that the step change points of these POP data varied in different
years and at different sites. Most step change points were found in
2001–2002 and 2007–2008. In particular, the step change points
of many PCBs for 2007–2008 were coincident with the lowest arctic sea ice
concentration occurring during the 2000s. The
perturbations of air concentration and water–air exchange fluxes of several
selected POPs averaged over the Arctic, simulated by a POP mass balance
perturbation model, switched from negative to positive during the early 2000s,
indicating a tendency for reversal of POPs from deposition to volatilization
which coincides with a positive to negative reversal of arctic sea ice
extent anomalies from 2001. Perturbed ice–air exchange flux of PCB 28 and
153 showed an increasing trend and a negative to positive reversal in
2007, the year with the lowest arctic sea ice concentration. On the other
hand, perturbed ice–air exchange flux of
A number of studies have been carried out to examine temporal trends of persistent organic pollutants (POPs) in the Arctic (Hung et al., 2005, 2010; Becker et al., 2008; Ma et al., 2011; Wöhrnschimmel et al., 2013). Due to worldwide ban and restrictions of these toxic chemicals, most legacy POPs have been declining in the Arctic over the last several decades. Fluctuations of many POPs on interannual or longer timescales, however, have been observed in POP time series collected from arctic monitoring stations. The long-term trends of POPs in the Arctic have been attributed to the changes in their primary emissions, use patterns, retreating sea ice, degradation, and other complex natural and anthropogenic activities (Macdonald et al., 2005; UNEP/AMAP, 2010; Armitage et al., 2011; Kallenborn et al., 2012). The fluctuations of monitored POP atmospheric concentrations have been also associated with interannual climate change at several POP monitoring sites where the longest atmospheric monitoring programs have been operated, notably the Great Lakes region and the Arctic (Ma et al., 2004a; MacLeod et al., 2005; Wang et al., 2010). The notable interannual climate change influencing interannual changes in elevated atmospheric level of POPs in the Northern Hemisphere are the North Atlantic Oscillation (NAO), the El Niño–Southern Oscillation (ENSO), and the Arctic Oscillation (Ma et al., 2004a, b; Ma and Li, 2006; MacLeod et al., 2005; Macdonald, 2005; Becker et al., 2008; Gao et al., 2010). These studies have revealed abundant evidence for the associations between the interannual climate change and ambient atmospheric concentrations of POPs in the Great Lake and arctic regions.
Previous trend assessments for POP concentration time series in the Arctic
have revealed changes in these time series induced potentially by climate
change. The relationship between ambient POP air concentrations and rapid
change in the arctic environment has, however, not been proven
statistically. Recently, there has been ongoing debate on a climate “tipping
point” (Lenton, 2011; Duarte et al., 2012; Livina and Lenton, 2013;
Holland et al., 2006) in the Arctic. The tipping point has been
connected to an abrupt increase in the amplitude of seasonal variability of
sea ice area in 2007 that has been persistent since then, indicating the
likelihood of rapid arctic climate change (Livina and Lenton, 2013). It is
worthwhile to point out that arctic climate change occurs on a much longer timescale than the lifetime of POPs and it might not be appropriate to link short-term
changes in POP environmental level with long-term climate change. However,
the rapid change in arctic environments would change the environmental fate
and temporal trend of POPs, together with their primary emissions and use
patterns in the globe. Yet, the response of the monitored POP long-term
time series in the Arctic to the rapid change in the arctic environment has
not been investigated intensively. POPs releasing from seasonal melting snow
pack and mountain glaciers has been demonstrated to alter significantly the
atmospheric levels of POPs (Stocker et al., 2007; Bogdal et al., 2009; Meyer and Wania, 2008). Except for permanent ice and glaciers, Arctic sea ice as a temporal storage reservoir for POPs
undergoes seasonal changes. The sea
ice melting and aging may increase air concentrations of POPs. It was
observed that an abrupt increase in
To identify decadal or longer timescale climate change (e.g., global
warming) signals, a time series of climate data should not be shorter than
30 years (the classical climate change period; Le Treut et al., 2007). This
raises the question of whether currently available POP observational
data sets are long enough to address climate change influence on their
environmental fate. Several recent modeling investigations and sensitivity
analysis to the long-term trend of PCBs and
Since the Arctic is warming at a rate of almost twice the global average, which leads to strong sea ice melt since the 2000s (Steele et al., 2008), the measured POP atmospheric concentrations in the Arctic might provide the best data sets to discern the signals of climate change in monitored POP data. The increasing trend of PCBs appeared to coincide to the strong sea ice melt in the Arctic, characterized by rapid decline in arctic sea ice from 2000 (Duarte et al., 2012). Since the sea ice decline took place over a short period of time, the monitored POP air concentrations data sets in the Arctic, though short, would likely respond to rapid sea ice decline and increasing air temperature, which may provide further field evidence to the association between temporal trend of POPs and climate warming. The present study examined the statistics for step changes in monitored atmospheric concentrations of POPs at several arctic monitoring stations. The association between the statistically significant step change points of POP concentrations and arctic climate change was quantitatively assessed to identify arctic climate change signals in measured POP time series.
Monitored ambient atmospheric concentrations of selected PCBs and OCPs
(organochlorine pesticides) in the present study were collected from four
Arctic monitoring sites representing the longest time series of POPs across
the Arctic. These are the Zeppelin Mountain air-monitoring station
(Svalbard/Norway, 1992–2012), Alert (Canada, 1993–2012), Pallas (Matorova,
Finland; 68
Searching for a step change (also referred to abrupt change or
abrupt discontinuities) in a time series is often conducted by the
detection of a point year as a sign of step change in the time series. It is
the process of finding step changes (or shifts) in the mean level of the
time series. In the cases of POP atmospheric concentration time series, the
step changes can be considered as a statistically significant abnormal
increasing or decreasing from their long-term trend, extending to a certain
period of time. In climate and hydrological studies, three statistical
methods have been widely used to identify abrupt climate change points.
These are the Mann–Kendall (MK) test (Mann, 1945; Kendall, 1955), the moving
The MK test is a nonparametric statistical test (Kendall, 1955) which has
been used to find trend and step change points of hydrological stream flows
and air temperatures (Moraes et al., 1998; Gan, 1998). Under the null
hypothesis (no step change point), the normally distributed statistic
From these two equations one can derive a normalized
The idea of the moving
The Yamamoto method is somewhat similar to the MTT approach, defined by a signal-to-noise ratio (
A step point of a concentration time series is inferred when
Since the changes in atmospheric concentrations of POPs are often driven by the first-order processes that scale multiplicatively with the concentrations (Meijer et al., 2003), all concentrations in the MTT and Yamamoto statistics are log-transformed (in natural logarithm) before they are used in the statistical analysis. Because the MK method is a rank-based test, the log-transformed time series make no difference from original time series for the step change results.
These methods each have their advantages and disadvantages. For example,
while the MK test has been successfully used in detecting step change points
(Moraes et al., 1998; Gan, 1998), it failed to discern statistically
significant signals in step changes in some cases. To increase confidence of
the
statistical test for potential step change point in the selected POP time
series, the present study applied simultaneously the MK test, MTT technique,
and Yamamoto method. Although the step change points of a time series from
the MK test may occur in a certain year, this year is often regarded as the
onset year of the step change. The year immediate after the onset year can
be also included in the period of the step change in the time series. We
also applied monthly and seasonal mean air concentrations data at the four
arctic sites to examine the step change points for monitored POP
concentrations. The monthly and seasonal mean concentrations were compiled
by averaging weekly (Zeppelin, Alert, Pallas) or bi-weekly (Storhofdi)
sampled air concentrations. Using monthly or seasonally averaged time series
can increase sample size. However, periodic variations in monthly and
seasonal POP concentration time series, characterized by higher
concentrations in warmer months (or season) and lower concentrations in
colder months (or season), overwhelmed the changes in annually averaged
concentration time series. Our results showed that the monthly and seasonal
averaged data could not yield step changes for most POP data. Figure S1 in the Supplement displays the
The coupled air–surface perturbation model for POPs was developed by
Ma and Cao (2010) and Ma et al. (2011) to simulate and
predict perturbations of POP concentrations in various environmental media
under projected climate change scenarios. This approach defined the
concentration (
The global emission inventory of selected POPs employed in the perturbation
modeling
The instantaneous water–air exchange flux is calculated by the Whitman
two-film model (Bidleman and McConnell, 1995):
Calculations of mean and perturbed
The number of PCB congeners and OCPs measured at different sites differs
from each other. We have calculated the step change points for all monitored
PCBs and OCPs at each monitoring site. The presence of these points in the
monitored PCBs and OCPs was not identical but varied with different
chemicals at different monitoring sites. It is impossible to illustrate the
step change points for all POP time series at all monitoring sites. In the
present study, only those chemicals whose forward and backward sequences
(
Figure 1 displays
Mann–Kendall (MK) testing statistics for PCBs and OCPs
collected from the Alert station (1993–2012). The blue solid line is the
forward sequence
PCB congeners and OCP isomers having step change points at four arctic monitoring stations detected by the MK test.
For tri-PCBs, three step change points were found between 2000 and 2005
(PCB 16, 25, and 26) and two found in 1998 (PCB 18 and 25).
The step change points in PCB 44, 49, 105, 106, and 209 were also found
after 2000 but more step change points in tetra-, penta-, hexa-, and
deca-PCBs were detected before 2000. On the other hand, the step change
points in all six DDT (dichlorodiphenyldichloroethane) isomers were found
after 2000 and the four of these six DDT isomers showed step change points
before 2000. It can be also observed that, though
Figure 2 illustrates
There are only 10 coeluting PCB congeners reported by Storhofdi station
(Hung et al., 2010). The
There are seven coeluting PCB congeners reported by Pallas station. The
Same as Fig. 1 but for the Zeppelin station (1992–2012).
Moving
Considering that in some cases the MK test failed to yield step change point
for a time series (Yamamoto et al., 1985), the MTT and Yamamoto methods were
further employed in the same data sets of PCBs and OCPs at the four arctic
monitoring sites to verify the MK test results and to increase the
confidence of detected step change points by the MK test. Figures S4 and S5 in the Supplement show the MTT and Yamamoto statistics for PCB
and OCP time series at Storhofdi and Pallas monitoring stations,
respectively. Compared with the results from the MK statistics, both the MTT
and Yamamoto methods did not detected statistically significant step change
points in 2007 for most PCBs and OCPs at Pallas. The MTT method detected the
step change point around 2000 for penta-PCB (PCB 101 and 118) and
hexa-PCB (PCB 138 and 153) and from 2007 to 2008 for penta-PCB. Step
changes in
Figure 3 shows the MTT statistics for 30 PCBs at Zeppelin station. The MTT statistics for these PCBs illustrate a “V” pattern except for several heavier PCBs. All tri-PCBs exhibited the step change year in 2008. The same step change year was also found for PCB 52, 74, 101, 138, 149, 170, and 180. The step change year 2008 for these PCBs derived from the MTT method lagged 1 year behind the step change year (2007) detected by the MK test (Fig. 1 and Table 1). However, this step change year (2008) can be regarded as an extension of the step change year 2007 because the step change year 2007 by the MK test is an onset year of step change. Other step change years were detected in 2000, 2002, and 2005 but for only several PCBs out of 30 PCB congeners. The MTT statistics for OCPs did not show any well-organized pattern like for PCBs (figures not shown). The step change points of OCPs varied with different chemicals, but 1999 and 2003 appeared to be the most detected step change years among these OCPs. The Yamamoto statistics also displayed peak values and step changes in 2008 for many PCBs, followed by 2000 and 2002 (results not shown). Tables S4 and S5 in the Supplement present the step change years for PCBs and OCPs at the Zeppelin site computed by the MTT and Yamamoto methods.
Moving
The results from the MTT and Yamamoto statistics for 20 PCBs at Alert are
illustrated in Fig. 4 and also presented in Tables S4 and
S5 in the Supplement. Both the MTT and Yamamoto methods yielded a step change point in 2006
for several PCBs, notably PCB 16A, 25, 44, 118, 174, and 209. The MTT method
detected the step change year in 2005 for both
To summarize the step change points for selected chemicals at the four arctic
atmospheric monitoring sites, we first selected those PCBs and OCPs whose
step point years were identified by the MK test and confirmed by one of the
other two methods. Results show that for Alert, the statistically
significant step change year detected by the MK test and the MTT or Yamamoto
method was only found in 2005–2006 for PCB 16 and 44, respectively. While
the MK test also identified the same step change in PCB 49, 105, and 110
during this period of time (Fig. 1), this step change was not detected
by the other two methods (Fig. 4). The step change years in OCPs
detected by the MK test were not confirmed by the MTT and Yamamoto method
either. In the MK test,
The step change years detected by the MK test and the MTT or Yamamoto method
at Storhofdi include 2007 for PCB 52 and 2003 for PCB 105, respectively
(Fig. S2 and S4). Among the measured PCBs, the
PCB congeners with step change points at four arctic
monitoring stations detected by the MK test and moving
At Pallas site, the MK test and MTT or Yamamoto method found the step change
year 2001 for PCB 118 and 138 and 2007 for PCB 101 and 108, respectively
(Figs. S3 and S5). The
The MTT method confirmed the step change year in most PCBs in 2007 at
Zeppelin calculated by the MK test, except for PCB 47, 153, and 180 (Figs. 2 and 3). Another step change year in 2002–2003 for several
PCBs detected by the MTT method was also consistent, to some extent, with
that derived from the MK test. As shown by Fig. 2, the
Table 2 summarized the step change years for PCBs and OCPs at the four
arctic atmospheric monitoring sites discerned simultaneously by the MK test
and the MTT or Yamamoto method. Overall, although the step changes years
varied with different chemicals and monitoring sites, these step change
points all took place in the 2000s. Furthermore, although individual statistical
methods did identify the step changes in OCPs, these changes were not
detected simultaneously by two out of the three statistical methods used in the
present study. Among the three periods of 2001–2003, 2005–2006, and
2007–2008 listed in Table 2, the step change point was found in the
highest number of monitored chemicals for the period of 2007–2008, followed
by 2001–2003 and 2005–2006. Although 2005–2006 and 2007–2008
are two adjacent periods, the step changes in POP concentrations during
these two periods might show their distinct response to marked decline of
sea ice concentrations in 2005 and 2007, as shown in Fig. 5. Another common feature from the MK test was that the forward sequence
Mean summer temperature (K; July–September, 1981–2012)
averaged over the Arctic as the departures from 1950 to 2010 mean (NCEP
reanalysis; Kalney et al., 1996) and sea ice extent (July–September,
10
The causes for the existence of those step change points in monitored POP
time series at the different arctic sites are complex. They depend on
locations of the monitoring sites, chemical–physical properties of
individual chemical, changes in arctic sea ices and air temperatures which
are nonuniform across the Arctic, and others. Our statistical tests showed
that the step change points were mostly detected in PCBs. This is likely
related to their relatively higher Henry's law constants, ranging from 4.4 Pa m
Results presented on Tables 1 and 2 also show more PCB congeners with statistically significant step change points at Zeppelin than at Alert. Compared with the Zeppelin monitoring site, which is located on the western coast of Spitsbergen (Svalbard, Norway), the Alert station experiences lower temperatures, is surrounded by rugged hills and valleys, and hence is less affected by sea ice retreat and open waters. In addition, as shown by Fig. 1 and Table 1, the step change points in several PCBs and DDTs were detected in 2001–2003 by the MK test. Due to laboratory switching in 2002, which led to increasing air concentrations of monitored POPs after 2002 (Hung et al., 2010), the step changes in POPs from 2001 to 2003 at Alert might be also subject to laboratory switching.
The both temporal and spatial patterns of POPs in arctic air have been
attributed to various processes driven by climate-induced changes in the
arctic environment, such as reduced ice cover, increasing air and seawater
temperatures, and biomass burning in boreal regions (Hung et al., 2010; Ma
et al., 2011; Becker et al., 2012). Since the step changes in POPs are
unlikely to be associated with interannual climate variability (e.g., NAO,
ENSO) and biomass burning (which should affect primarily the seasonal or
interannual alteration in POPs), these step changes were anticipated to be
fluctuations in the long-term trend in POP time series. The statistically
significant step change point years for PCBs in the present study appeared
to coincide with those years during which arctic sea ice exhibited marked
decline. Figure 5 shows the mean summer temperature and sea ice extent
anomalies from 1981 to 2012 averaged over the Arctic as the departures from
their means over 1950 to 2010 and 1979 to 2010, respectively. It can be seen
that the mean sea ice extent declined in the summer of 2002, 2005, and 2007.
In particular, the mean summer sea ice extent exhibited a decreasing trend
and has been negative since 2001. This result agreed with a previous report
which showed that, during the 2000s, the arctic sea ice September minimum
extent (i.e., area with at least 15 % sea ice coverage, 10
Since sea ice melting is a crucial factor for controlling the environmental fate
of POPs in the Arctic under a warming climate (Becker et al., 2012; Grannas
et al., 2013) and the associations between the step changes in ambient
atmospheric concentrations and sea ice concentrations, it is worthwhile to
elucidate the response of POPs to arctic warming and sea ice fluctuation and
melting. We simulated perturbed air concentration (pg m
Modeled perturbed air concentration (
Compared with the measured ice concentration used in the present study (0.1 ng L
The rapid decline of arctic sea ice in 2007 has triggered the debate that
abrupt climate change in the Arctic and low summer sea ice area were likely
persistent on a decadal (climate change) timescale. Given that the
environmental fate of the selected POPs in the Arctic has been dominated by
their primary emission and outgassing from their reservoirs accumulated from
past use, deposition, and long-range transport from atmosphere and oceanic
currents (Macdonald et al., 2005; Gioia et al., 2008; Hung et al., 2010;
Kallenborn et al., 2012; Wöhrnschimmel et al., 2013), atmospheric levels
of many POPs in the Arctic have been declining over the last decades after
their regulation and phaseout. Previous modeling investigations (Lamon et
al., 2009; Wöhrnschimmel, et al., 2013) have revealed that the maximum
changes in POP atmospheric concentrations induced by climate change were
driven mostly by climate-warming-forced potential changes in primary
emission. This appeared to suggest that the POP outgassing from their arctic
repositories associated with arctic warming and sea ice retreat would not
change their long-term declining trend because the emission and degradation
overwhelmed the POP long-term declining trend. However, as previously mentioned,
the measured ambient POP air concentrations in the mid-2000s did not follow
the declining trend driven by primary emissions and degradations. The
perturbation modeling was aimed at assessing major processes contributing to
concentration anomalies as the departure from the mean POP concentrations
driven mostly by their primary emission and degradation. We have demonstrated
that the temperature-dependent emission (Eqs. 8 and 9) could alter the
magnitude of modeled concentration perturbations but not change long-term
trend and interannual variation of the perturbed concentrations (Ma and Hung,
2012). The perturbed (rather than measured) POP air concentrations were,
therefore, forced largely by the outgassing from their reservoirs in melting
ice (snow) and the Arctic oceans. In this context, a reversal of many POPs
from deposition to volatilization associated potentially with arctic warming
and sea ice retreat would likely take place. To examine this argument, we
estimated perturbed water–air and ice–air exchange flux
(ng m
Simulated water–air exchange flux perturbations (ng m
The modeled annual ice–air exchange flux perturbations of two PCBs
averaged over the Arctic also showed similar trend and interannual
variation. The exchange fluxes of PCB 28 were positive throughout the
modeling period (1995–2012), indicating outgassing. The fluxes of the other
two chemicals were negative. To illustrate ice–air exchange, Fig. 8
displays perturbed ice–water exchange fluxes (ng m
Simulated ice–air exchange flux perturbation (ng m
After their phaseout, many legacy POPs have been and will be still declining in the Arctic environment in forthcoming years. An increasing trend of POP atmospheric concentrations under strong warming and sea ice melt across the Arctic is unlikely to take place as comparing with continuously increasing trend of arctic temperature. However, there is likely a “turning point” for these POPs in the context of climate change. From this year onward, arctic warming influence on POP trend may become relatively stronger. In other words, there would be stronger arctic climate change “signals” in monitored ambient POP concentrations data after this turning point. Our statistical test identified two major step change points in the measured PCB time series, coincident with the onset of rapid arctic sea ice melting after 2001. Our perturbation modeling suggested that the periods of 2001–2002 and 2007–2008 were likely the “turning points” for PCBs in arctic air, as demonstrated by the reversal of deposition to outgassing of the perturbed water–air exchange flux and negative to positive transition in the modeled air concentration perturbations of the selected chemicals. In particular, from the second step change point year (2007–2008) onward identified in the present study, we would expect that the effect of arctic climate change on POP environmental fate would become more detectable. This is supported partly by the increasing air concentrations of many PCBs measured at the Zeppelin and Alert sites (Hung et al., 2010). The finding from the present study not only discerned the abrupt climate change signatures in measured time series of POP atmospheric concentrations but also provided additional evidence for the response of arctic environment and ecosystem to arctic climate change.
We would like to acknowledge all site and laboratory operators and students of the air monitoring programs that make long-term data available. This research was supported by the National Science Foundation of China (grants 41371478 and 41371453). Edited by: L. Zhang