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Statistical analysis of long-term monitoring data for persistent organic pollutants in the atmosphere at 20 monitoring stations broadly indicates declining concentrations Deguo Kong, Matthew MacLeod, Hayley Hung, and Ian T. Cousins Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/es502909n • Publication Date (Web): 08 Oct 2014 Downloaded from http://pubs.acs.org on October 16, 2014

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Environmental Science & Technology

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Statistical analysis of long-term monitoring data for persistent organic pollutants in the atmosphere at 20

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monitoring stations broadly indicates declining concentrations

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Deguo Konga, Matthew MacLeoda, Hayley Hungb, and Ian T. Cousinsa,*

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a

Department of Applied Environmental Science (ITM), Stockholm University, SE-106 91, Stockholm, Sweden

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b

Air Quality Processes Research Section, Environment Canada, 4905 Dufferin Street, Toronto, Ontario M3H

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5T4, Canada

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*

Corresponding author phone: +46-8164012; fax: +46-86747638; e-mail: [email protected].

1 ACS Paragon Plus Environment


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Abstract

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During recent decades concentrations of persistent organic pollutants (POPs) in the atmosphere have been

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monitored at multiple stations worldwide. We used three statistical methods to analyse a total of 748 time series

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of selected POPs in the atmosphere to determine if there are statistically significant reductions in levels of POPs

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that have had control actions enacted to restrict or eliminate manufacture, use and emissions. Significant

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decreasing trends were identified in 560 (75%) of the 748 time series collected from the Arctic, North America

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and Europe, indicating that the atmospheric concentrations of these POPs are generally decreasing, consistent

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with overall effectiveness of emission control actions. Statistically significant trends in synthetic time series

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could be reliably identified with the improved Mann-Kendall (iMK) test and the digital filtration (DF) technique

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in time series longer than 5 years. The temporal trends of new (or emerging) POPs in the atmosphere are often

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unclear because time series are too short. A statistical detrending method based on the iMK test was not able to

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identify abrupt changes in the rates of decline of atmospheric POP concentrations encoded into synthetic time

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series.

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Keywords: persistent organic contaminants, temporal trend, climate change, control strategy effectiveness


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1. Introduction

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The combination of persistence, bioaccumulation potential, and toxicity (so-called PBT properties) together

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with the potential for long-range transport makes persistent organic pollutants (POPs) global threats to wildlife

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and humans.1,2 National, regional and global control actions have been undertaken to restrict and eliminate the

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manufacture, use and emission of POPs. These include the POPs Protocol to the Convention on Long-Range

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Transboundary Air Pollution (CLRTAP) and the Stockholm Convention (SC) on POPs, among others (see

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Table S1). Temporal trends of POP concentrations identified in systematically collected long-term time series of

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abiotic3 and biotic4 samples have shown that the environmental levels of some POPs have declined following

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the implementation of control measures. For example, the atmospheric concentrations of polychlorinated

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biphenyls (PCBs), dichlorodiphenyltrichloroethane (DDT), and polycyclic aromatic hydrocarbons (PAHs) have

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been reported to be decreasing in the Arctic.3,5,6

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The rate of decline of POP concentrations in the atmosphere over the long term in response to control measures

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has often been characterized using a disappearance halving time that implies an exponential loss rate. However,

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even when seasonal and inter-annual variations are accounted for, time trends do not always follow an

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exponential declining trend. The rate of declining concentrations may change gradually or abruptly for a number

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of reasons, e.g., (i) emissions may not decline in a consistent manner in response to control measures and even

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new emissions can be introduced, (ii) a disequilibrium of POPs between surface compartments and the

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atmosphere can buffer declining atmospheric levels if the surface compartments begin to act as a secondary

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source to the atmosphere, and (iii) climate change (CC) driven processes (e.g. changing large-scale wind

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patterns, increasing sea water temperature, loss of ice cover etc.) may influence temporal trends of POPs. There

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has been particular recent interest in how CC-driven processes affect the emissions,2,7,8 fate2,9-11 and time

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trends3,12 of POPs in the environment. For example, Hung et al.3 analysed the temporal trends in time series of

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organic pollutants in the Arctic atmosphere, and noted that CC-driven processes might jointly affect the

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temporal and spatial patterns of POPs in the Arctic atmosphere. Notable attempts also have been made using

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statistical methods to determine if temporal trends of POPs can be related to CC.13,14

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In this study, three research questions are addressed: (i) How prevalent are statistically significant decreasing

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time trends in long-term atmospheric monitoring data for POPs that have had global control actions

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implemented? (ii) Which statistical methods are most appropriate for analysing time trends in concentrations of

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POPs in the atmosphere and what information do the different methods provide? And, (iii) is it possible to use

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statistical methods (e.g. “detrending”) to identify when the rates of declining time trends change?

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2. Methods

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2.1 Statistical methods

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In this study, three statistical methods that have been used to identify temporal trends of atmospheric POPs were

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applied and critically evaluated: 1) log-linear regression (LLR), 2) digital filtration (DF),15 and 3) the improved

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Mann-Kendall (iMK) test.13

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2.1.1 LLR method

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The most direct way to identify and characterize a temporal trend in a time series of POP concentrations is to fit

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a linear regression line to the natural logarithm (ln) form of that time series.16,17 The slope of the linear



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regression line is then used to estimate the halving/doubling time, i.e., t1/2 = -ln(2) / slope, for concentration drop

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(increase) by a factor of 2. A positive (negative) t1/2 indicates a decreasing (increasing) trend.

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2.1.2 DF technique

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The DF technique has been used in a number of studies to identify temporal trends in time series of atmospheric

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POPs.3,18,19 Nakazawa et al.15 described this method in detail. Briefly, in an iterative manner the DF method fits

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a nonlinear trend line to the ln-transformed time series as the long-term trend.15 The slope of this long-term

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trend over a specified time interval in the time series can be used to estimate the halving/doubling time as -ln(2)

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/ slope. Here, the DF method is applied using short- and long-term cut-off periods set to 4 and 48 months,

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respectively.18

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2.1.3 iMK test

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A detailed description of the iMK test can be found in Ma et al.13 Briefly, this test derives the iMK statistic |Z|

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to indicate the direction and significance of an identified temporal trend. A negative Z value indicates a

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decreasing trend, whereas a positive Z indicates an increasing trend. The trend is considered statistically

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significant at the 95% significance level when |Z| is ≥ 1.96. Here, the iMK test is adopted to identify the

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existence and direction of temporal trends in time series that are statistically significant at the 95% confidence

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level.

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2.2 Time series

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2.2.1 Synthetic time series

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Each of the three statistical methods described above have been applied to measured time series of POPs in the

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past.3,13,18 Some concerns about applying a relevant method and the interpretation of results of the analysis have

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been previously discussed in the literature. For example, it has been noted that different methods could perform

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differently with respect to one time series,18 the performance of a specific method could be affected by the

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length of measured time series,3 and unchecked methods applied directly to measured time series could overlook

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important factors.20 Efforts have been made to constrain the performance of methods applied in most studies,

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and the results of statistical analyses have been interpreted as representing evidence of different aspects of

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natural or anthropogenic variability in measured time series.13 However, a transparent strategy to investigate and

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constrain the performance of different methods for trend detection has not been demonstrated.

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In this study, we develop a strategy to investigate the interpretation of results produced by the LLR, iMK and

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DF methods, namely, applying them to synthetic time series with known properties. There are a number of

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advantages to this strategy, including: (i) the trends encoded in synthetic time series are known, so the trend-

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detection performance of different methods is clearly demonstrated and constrained, (ii) the applicability of

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methods to achieve specific goals (e.g., separate short-term changes in temporal trends from long-term

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overwhelming trends) can be checked, and (iii) the strategy is general, and can be applied to any statistical

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method. Here, synthetic time series (TS) were created according to the following equation:

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() = , × e

(

()

1/2,

×)

× sin 2π × − + constant& × noise( (or + noise+ ) '


Eq. 1

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where C(t)i represents the concentration of chemical i in the atmosphere, t is time in years which was assumed to

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range between 0 and 15, C0,i indicates the initial concentration of chemical i in the atmosphere, and t1/2,i (years)

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represents the halving/doubling time of chemical i. The exp() function was used to create an exponential

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concentration profile. The {sin()}j function creates seasonal cycles. Irregular signals are introduced by either

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multiplying (noisek) or adding (noisem) to the concentrations (see Figure S1). We used different

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parameterizations of Equation 1 to create several families of synthetic time series with different characteristics,

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e.g., varied seasonality and noise (see Figures S2 and S3). Each family comprises a group of 15 synthetic time

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series with varying duration of 1 to 15 years, increasing in one-year increments (see Figure S1). They are used

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for investigating the performance of adopted statistical methods in terms of identifying and quantifying temporal

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trends.

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Specifically, one family of synthetic time series (TSL,1; L indicates the length of a time series) was created to

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approximately mimic concentrations of α-hexachlorocyclohexane (α-HCH) measured at Eagle Harbor,

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Michigan, United States obtained through request to the program data manager of the Integrated Atmospheric

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Deposition Network (IADN). The DF method estimates the halving time of α-HCH monitored between 1991

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and 2008 (inclusive) at Eagle Harbor to be 4.0 years. Therefore, the halving time of TSL,1 is assumed to be 4.0

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years. The variation coefficients of the monitored α-HCH and the longest synthetic time series in this family

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(TS15,1) are 1.3 and 1.4, respectively. Based on this family of time series we further created a family of time

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series, i.e. TSL,2, in which the rate of decline of concentrations changes abruptly to zero at a specified time point

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(see Figure S1). This abrupt change in the decay rate of atmospheric POP concentrations is incorporated in TSL,2

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after 8.5 years, assuming that some unknown processes (e.g. CC-induced volatilization, new emissions etc.)

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keep concentrations in air unchanged after this time point. The halving time of TSL,2 is assumed to be 2.0 years

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during the first 8.5 years of the time series, then the halving time is changed to infinitely long. For the whole

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time period, the halving time of TS15,2 estimated using the DF method is 4.6 years. The halving time of TSL,2 is

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selected such that the initial and final values of TS15,1 and TS15,2 are equal. Under each scenario, a group of 15

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synthetic time series with varying duration of 1 to 15 years is also created, i.e., from TS1,1 to TS15,1, and from

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TS1,2 to TS15,2 (see Figure S1).

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2.2.2 Measured time series

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The Arctic Monitoring and Assessment Programme (AMAP) database (http://ebas.nilu.no/) operated by the

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Norwegian Institute for Air Research (NILU) has global data coverage, and we believe it is the most

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comprehensive database of atmospheric POPs monitoring data available. To query the AMAP database, the

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instrument type was set to high volume sampler because various monitoring programs have recommended and

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adopted this technique.21 For the purpose of comparability between sampling stations, only high volume

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sampling data were statistically analysed in this study, but in future studies it may be desirable to also include

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measurements generated by passive sampling techniques. As this study focuses on POPs, especially those

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prioritized by the SC and CLRTAP, the component (i.e., the type of monitored chemicals) was specified as a

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number of well-known POPs to further limit the query. Twenty monitoring stations from seven countries in

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Europe, North America and the Arctic were identified to have long-term monitoring data for atmospheric POPs

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in the AMAP database (see Table S2 and Figure S4). In total, 969 time series were collected: 322 from the

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AMAP database and 647 from our data request to the IADN program data manager. No attempt was made to



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group congeners into suites, unless they were reported as such (e.g. the IADN suite total PCBs). At Alert, there

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was a laboratory change in 2002 that corresponds with a discontinuity in reported PCB concentrations, therefore,

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each time series of PCB congeners is treated as two time series: one before 2002 and one after 2002, and the

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data from the year 2002 are excluded from trend analysis.3 Details of quality control and quality assurance on

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sampling and analytical procedures can be found in the corresponding references listed in Table S2. The time

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series were separated into three groups: organochlorine pesticides (OCPs, 378), industrially and commercially

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used PCB and polybrominated diphenyl ethers (PBDE; PPs, 481), and combustion product PAH (110) datasets.

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2.3 Application of Statistical Methods

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The three statistical methods were first applied to the synthetic time series to compare and evaluate their

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performance in terms of identifying and quantifying temporal trends with varied seasonality, noise and length.

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Specifically, the LLR- and DF-based halving/doubling time (t1/2) and the iMK statistic |Z| were derived and

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compared. Then, the DF and iMK methods were applied to the measured time series. Finally, we examined the

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suitability of the iMK and DF methods to determine if these “detrending” approaches could be used to identify

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abrupt changes in temporal trends.

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3. Results and discussion

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3.1 Constraints on trend identification using t1/2 and Z

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Figure 1 presents the iMK statistic |Z| values and the linear trend lines fitted with the LLR and DF methods for

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TSL,1 and TSL,2. For brevity and clarity, results for the other families of synthetic time series are presented in

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Figures S7 to S13. In general when time series are shorter than 4 or in some cases, 5 years the linear trend lines

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fitted to the ln-transformed synthetic time series using the LLR method indicate increasing trends that contradict

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the overwhelming decreasing trends in the synthetic time series (Figures 1a and 1b, and (a-j) in S7 to S13). In

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contrast, all of the trend lines fitted to the DF-based long-term trends capture the decreasing trends encoded in

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the synthetic time series (Figures 1A and 1B, and (A-J) in S7 to S13). With increasing length of the synthetic

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time series, the DF method always gives more accurate estimates of the trend encoded in the synthetic time

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series compared to the LLR method and both types of linear trend lines eventually reach the same slope. This

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difference in performance is mainly caused by the iterative outlier rejection mechanism possessed by the DF

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method.18 The DF method rejects data lying outside three standard errors of a fitted curve, whereas the LLR

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method does not reject any data. In terms of identifying temporal trends, the performance of the iMK method

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can vary greatly with the changing seasonality, noise and halving times (see details in Supporting Information

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II). The magnitude of iMK |Z| has sometimes been interpreted as indicating the rate of change of concentrations

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in a temporal trend, i.e., a bigger |Z| is interpreted as a more rapid rate of change and a smaller |Z| as a slower

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rate.13,14 Our results for the synthetic time series do not support this interpretation. Instead, the magnitude of the

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iMK statistic |Z| becomes larger with increasing length of the synthetic time series (Figures 1A and 1B, and (A-J)

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in S7 to S13).

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In summary, this comprehensive analysis of synthetic time series indicates that the DF method is always more

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reliable than the LLR method for identifying temporal trends regardless of the characteristics of time series.

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When the length of a time series is 5 years or longer, the DF and iMK methods are similarly reliable for

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identifying temporal trends. Therefore, we selected only the iMK and DF method to apply to monitored time


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series, and restricted the analysis to time series longer than 4 years. It should be noted that the halving/doubling

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time can only be used to indicate the average long-term temporal trend in a time series. It does not indicate

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consistent decreases or increases throughout a monitoring period.3

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3.2 Application to measured time series

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3.2.1 Comparison of estimated t1/2 and Z

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Out of 969 time series collected, 748 are ≥ 5 years and time trends have been derived. Of these 748 time trends,

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659 (88%) estimates of t1/2 are positive (indicating a decreasing trend) and 89 (12%) are negative (indicating an

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increasing trend). The iMK statistic |Z| values suggest that 560 (75%) and 47 (6.3%) of the analysed time series

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show significantly decreasing and increasing trends, respectively. Trends in 9 time series indicated by t1/2 and Z

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values cannot be reconciled (Table S3). Inspection of the 9 time series indicates that the outlier rejection

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mechanism of the DF method is responsible for the difference in trend compared to the iMK test, which

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considers all data. The iMK statistic |Z| values suggest that 132 of the 748 time series show neither a

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significantly decreasing trend nor a significantly increasing trend (Table S3). Inspection of the estimated t1/2

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values shows that many of these time series have negligible slopes and thus very long |t1/2|.

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3.2.2 Temporal trends

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Of the 560 measured time series of atmospheric POPs with significantly decreasing time trends according to the

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iMK test, 257 are OCP time series, 253 are PBDE and PCB time series (PPs) and 50 are PAH time series

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(Figure 2). The decreasing time series include data from all of the 20 monitoring stations, indicating a wide-

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spread decreasing trend. Among them 66% and 92% have halving time estimates shorter than 10 and 20 years

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(Figure 2a). Similar fractions of time series of OCPs and PPs have halving time estimates shorter than 10 and 20

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years (Figures 2b and 2c). However, the fractions of decreasing PAH time series are smaller, i.e., 26% and 78%

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(Figure 2d). The generally shorter atmospheric halving times of OCPs and PPs indicate that their atmospheric

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levels are falling more rapidly relative to PAHs.

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Significantly increasing trends are found in only 19 OCP time series, 24 PCB time series, and 4 PAH time series

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(Figure S5). These numbers are much smaller than the numbers of time series showing significant decreasing

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trends, i.e., 19 compared to 257 OCP decreasing time series, 24 compared to 253 PP decreasing time series, and

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4 compared to 50 PAH decreasing time series (Figures 2 and S5). 81% (38 out of 47) of the increasing time

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series are from the Great Lakes and Alert. The percentages of time series with doubling times shorter than 10

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years are 74%, 63%, and 25% for OCPs, PCBs and PAHs (Figure S5). Thus, the analysis of long-term

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monitoring data broadly demonstrates that levels of POPs in the atmosphere are declining, with increases at a

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few sites for some substances. Details for specific POP groups are discussed below.

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3.2.2.1 PBDEs and PCBs

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All of the Swedish monitoring stations and the Pallas monitoring station in Finland reported the atmospheric

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concentrations of BDE-47, -99, and -100. However, these POPs were only regularly detected at Råö and Pallas.

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Råö is closer to urban areas than Pallas. The iMK statistic |Z| values suggest all of the six time series for BDEs

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at Råö (2003-2012) and Pallas (2004-2012) show significantly decreasing trends. The DF-based halving times

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range from 3.0 to 7.3 years, indicating rapid decreases in atmospheric levels. The significantly decreasing trends

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in the atmospheric levels of BDEs should be mainly attributed to the ban on the relevant commercial penta- and



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octa-BDE technical products since 2004 in the EU,22 by the end of 2004 in North America,23 and since 2009

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globally.24

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As one of the most well studied groups of POPs, PCBs have been monitored across all the studied regions. In

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total, 332 time series of PCBs have been collected, of which 247 show significantly decreasing trends and 24

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show significantly increasing trends (Figures 2c and S5c). Both the iMK |Z| and DF t1/2 suggest that from 1993

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to 2001 the atmospheric levels of the 9 PCB congeners monitored at Alert were all declining.3 From 2003 to

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2009 PCB-28, -31 and -138 were generally not detectable in the atmosphere. The iMK |Z| values suggest no

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significant trends in the time series of PCB-52 and -101, and the DF t1/2 values suggest that their levels were

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increasing very slowly. These findings may indicate that their atmospheric levels started to reach a steady-state.

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For PCB-105, -118, -153 and -180 both the iMK |Z| and DF t1/2 suggest increasing trends (Table S4). With four

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more years (2003-2009) of data, a significant increasing trend in the time series of PCB-105 was observed

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which is opposite to the decreasing trend (2003-2005) found by Hung et al.3 It has been speculated that the

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increases found by Hung et al. could be attributed to increased revolatilizations as a result of CC-driven increase

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in temperature and reduction in sea ice.3

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At Storhofdi, the iMK |Z| and DF t1/2 values suggest that the atmospheric levels of 8 of 10 PCB congeners are

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declining; the exceptions are PCB-52 and -101. At Aspvreten, Košetice, Pallas, Råö, Rörvik and Zeppelin the

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iMK |Z| and DF t1/2 consistently suggest that all of the congeners monitored are significantly decreasing. More

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than eighty PCB congeners were regularly monitored at Burnt Island, Egbert, and Point Petre in the Great Lakes

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region, Canada. The two methods identify significantly decreasing trends in 171 time series out of 236, no

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significant trends in 50 time series, and significant increasing trends in 16 time series. No pattern of specific

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sites, congeners or congener groups is evident among the 16 increasing time series.

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3.2.2.2 OCPs

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Organochlorine pesticides have been regularly monitored at all of the 20 stations. The iMK |Z| test found 257

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time trends for OCPs that are declining significantly in the atmosphere, and only 19 that are increasing

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significantly. The prevalence of decreasing trends should be mainly attributed to the previous ban and controls

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in most western industrialized countries.3 Specifically, at Košetice, Lista, Pallas, Storhofdi, Råö, and Zeppelin

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the atmospheric concentration of monitored OCPs are generally decreasing. Increasing trends can only be

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observed for p,p’-DDT and p,p’-DDE monitored at Rörvik from 1993 to 2001. However, these time series are

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incomplete which may provide incorrect information. The t1/2 of the time series of atmospheric HCB collected

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from Birkenes was estimated as 57 years indicating near steady-state conditions. Increasing trends are identified

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in the atmospheric concentrations of eight OCPs (16 time series) monitored at Alert and some Great Lakes’ sites.

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Hung et al.3 have already noted these inclines (actually observed from 2002 onwards) and attributed them to two

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potential factors: (i) continued use and (ii) increased revolatilization of previously deposited OCPs from ocean

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as a result of reduced sea ice coverage, and enhanced volatilization from forest soil as a result of biomass

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burning. Nevertheless, our results suggest a wide-spread decreasing trend in OCPs in the global atmosphere.

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3.2.2.3 PAHs

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Ninety-five time series of PAHs were collected from six monitoring stations including Aspvreten, Košetice,

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Pallas, Råö, Rörvik, and Zeppelin. Four time series show significantly increasing trends, i.e., benzo(ghi)perylene


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and benzo(b)fluoranthene monitored at Košetice, and benzo(a)fluorene and 3-methylphenanthrene monitored at

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Zeppelin (Figure S6). The time series of benzo(ghi)perylene is incomplete which may provide incorrect trend

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information. Monitored data indicate that the atmospheric concentration of benzo(b)fluoranthene is approaching

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steady-state. The iMK |Z| and DF t1/2 suggest that 50 time series show significantly decreasing trends.

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Regardless of PAH type and monitoring station a strong decreasing trend can be observed between the 1990s

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and early 2000s followed by a weak decreasing or levelling off trend. Notably, in the year 2010 the atmospheric

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concentrations of most PAHs were all elevated at Zeppelin (but not at other stations). The elevated

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measurements in 2010 are the cause of the identified increasing trends of benzo(a)fluorene and 3-

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methylphenanthrene monitored at Zeppelin. We speculate that there may have been local contaminant sources or

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episodic long-range transport influxes from other regions to Zeppelin in 2010.

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3.3 Using detrending to identify abrupt changes in temporal trends

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An attempt has been previously made to find evidence that CC-driven processes are influencing the temporal

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trend in long-term monitoring data for ten POPs (including α-HCH) collected from the Arctic monitoring station

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at Mount Zeppelin.13 The approach was to use the iMK test to remove a linear downward trend from the

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measured atmospheric time series. The detrended data were then correlated against surface air temperature and

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sea ice extent. Strong correlations were observed between detrended data and both climate variables, and this

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was considered evidence that CC-driven processes were influencing time trends of atmospheric POPs.13 It was

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hypothesized that CC-induced revolatilization of POPs was the reason for an apparent abrupt change in the POP

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time trends.13 However, Roberts20 commented that the results could also be explained by other non-CC factors,

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i.e., a disequilibrium of POPs between the surface compartments and the atmosphere and/or nonlinear

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decreasing global emissions. We agree with the conclusions of Roberts, but wanted to further examine if linear

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detrending could be used to determine any abrupt change in POP concentration time series irrespective of its

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underlying mechanism.

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We applied the iMK detrending method to the 748 time series of POP concentrations in air that are ≥ 5 years

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long, and inclines were observed in 459 time series (61%) of the detrended data (see Figure S6 for examples).

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We decided to use synthetic concentration time series to investigate the reasons for this high occurrence of

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inclines in linearly detrended measurement data, and to examine the suitability of the iMK test to identify abrupt

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changes in temporal trends of POPs. The synthetic time series of POPs describe concentration decay profiles

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with and without changes in their rate of decline during the time series, and with varied seasonality, noise and

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halving times. Inclines in the linearly detrended data are observed in all of the synthetic time series except for

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those that decrease perfectly linearly. Notably, inclines are generated when linearly detrending simple

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exponential decay profiles (see Figures 3, S6, S14 to S26; also compare with Figure 1 in Ma et al.13). We

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conclude that inclines in linearly detrended time series of atmospheric POPs are expected to be observed in

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most time series, and are not evidence of a climate change-related influence on POP concentrations, as was

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suggested by Ma et al.13

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We also examined the suitability of the nonlinear DF technique to determine abrupt changes in temporal trends

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of POPs using the synthetic time series. In an iterative manner, the DF technique fits the best-fit seasonal cycles

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and long-term trends to a time series, and it does identify abrupt changes in the rate of decline of serial



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concentrations encoded in synthetic time series (see Figure S6). When the nonlinear trend is removed using the

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DF technique only the seasonal cycles remain.

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4. Implications for research on POPs and policy

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The application of the iMK and DF methods to synthetic time series suggests that statistically significant trends

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in monitoring data can be identified in representative time series longer than 4 or 5 years. Analysis of real

291

monitoring data found significantly decreasing trends in 75% (560) of the analysed time series that are 5 years

292

or longer during a period after control actions were instigated to restrict and eliminate the manufacture, use and

293

emissions of POPs. The halving times for PAHs were generally longer than those for the other POPs, which is

294

likely attributable to lower overall effectiveness of control measures. The temporal trends in new (or emerging)

295

POPs in the atmosphere are unclear because of scarcity of data (not monitored at all, or shorter than 5 years).

296

Furthermore, the measured time series analysed in the present study are generally from three regions, i.e., the

297

Arctic, North America and Europe. Unfortunately, long-term monitoring data for atmospheric POPs are

298

generally missing from the other parts of the world, e.g., Asia where two of the world's largest developing

299

economies exist. Similar to the Arctic, CC is expected to be pronounced in the Antarctic where POPs have not

300

been produced and used, but recently measured. In 2007, the first long-term monitoring program for selected

301

atmospheric POPs was initiated at the Antarctic Troll station (Dronning Maud land, Antarctic).25 Unfortunately,

302

the first batch of available data for measured time series of atmospheric POPs are only 4 years (2007-2010

303

inclusive) long. Though the atmospheric concentrations measured during these four years are higher than data

304

collected in the Antarctic from the early 1990s, they cannot provide information on the long-term trends due to

305

the use of different sampling and analytical techniques in the 1990s.

306

The broadly declining trends in the levels of POPs in the atmosphere are a strong indication that control actions

307

on emissions have been effective to reduce concentrations in the environment (see Table S1). However, even

308

after emission reductions, temporal trends in concentrations may in some cases continue to show no significant

309

declines, begin to plateau or even increase. Primary industrial emission sources are often relatively easy to

310

control compared to diffuse emissions, e.g., PCB leakage from old buildings or landfills.2 Emissions occur

311

throughout the lifecycle of a chemical and the use and disposal phases of the lifecycle for some chemicals can

312

result in a long time lag before emissions are finally eliminated. Discontinuing the manufacture of a chemical

313

often does not result in an immediate reduction in atmospheric levels for many years after a control action. This

314

time lag occurs if the emissions are primarily from emissions in the use and disposal phases and the chemical

315

has long use and disposal lifetimes.2 In addition to problems with eliminating all emissions, remobilization of

316

POPs (i.e. revolatilization) from surface compartments that have acted as long-term reservoirs of POPs can also

317

affect time trends of atmospheric POP concentrations, and these might be enhanced by CC-driven processes.26

318

The halving and doubling times calculated for the POP time series herein are for the entire length of the time

319

series and within each time series the rates of decline or increase may be variable. It is possible to calculate rates

320

of decay at different time points by breaking up time series into smaller time sections if the time series is

321

sufficiently long. There may be a good motivation for breaking a time series into smaller components (e.g.

322

knowledge of a policy intervention). However, based on the findings of this study, longer time series would

323

need to be broken into sections of at least five years, so that statistical methods (e.g. iMk and DF) could reliably


Page 10 of 18

Page 11 of 18


324

identify time trends. An obvious problem is that it is rare to find long, data rich time series to perform this kind

325

of detailed analysis.

326

It has been hypothesized that the temporal trends in time series of POPs in the Arctic atmosphere might be

327

affected by CC-driven processes.13,14 The attempts to link these atmospheric time trends to CC-driven processes

328

with statistical methods appear to be flawed according to the analysis performed here. Even if statistical analysis

329

of POP concentration time series shows correlations to parameters linked to climate variability or change, a

330

mechanistic link cannot be established. It should also be noted that the long-term CC-induced alterations in

331

levels of POPs in key environmental media have been estimated to be modest using multimedia environmental

332

fate models, even under extreme CC scenarios.27 Therefore, it could be anticipated that CC-driven influences on

333

contemporary monitored time series of POPs are weak. It could also be expected that these CC-driven effects

334

are challenging to detect given the large variability in inter-annual and inter-decadal climate,3 as well as other

335

factors influencing POP levels, especially changing emissions.8 We believe that comparing results of

336

mechanistic process-based modelling and long and continuous monitoring data is the best approach to

337

eventually demonstrate the relationships between climate change and/or climate variability and POPs.8,13,26 One

338

such study was conducted by MacLeod et al.,28 who used results of a process-based global fate and transport

339

model to predict relationships between climate variability and concentrations of PCBs in the atmosphere, and

340

then analysed monitoring data for evidence of the relationships predicted by the model. They concluded that

341

longer time series of monitoring data than currently available are required to increase statistical power and allow

342

for meaningful comparisons between measured time series and model predictions that might provide evidence

343

of controlling processes.

344 345

Supporting Information

346

Supplementary information on synthetic time series, representative POP-related agreements and treaties, 20

347

monitoring stations, estimated t1/2 and Z, and the application of the iMK and DF methods to real and synthetic

348

POP concentration time series. This material is available free of charge via the Internet at http://pubs.acs.org.

349 350

Corresponding Author

351

*Phone: +46-8164012; e-mail: [email protected].

352 353

Acknowledgements

354

The Ph.D. studies of Deguo Kong are funded by ArcRisk, which is a Collaborative Project supported under the

355

Seventh Framework Program of the European Community for research, technological development and

356

demonstration activities (FP7-ENV-2008-1, Grant Agreement Number: 226534). We thank Jianmin Ma for

357

useful advice and the computer code to apply the improved Mann-Kendall test. The authors thank all air

358

monitoring programs and teams who provide air monitoring data through AMAP and other databases.



359

References

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(1) Macdonald, R. W.; Mackay, D.; Li, Y. F.; Hickie, B. How Will Global Climate Change Affect Risks from Long-Range Transport of Persistent Organic Pollutants? Human & Ecological Risk Assessment 2003, 9, 643. (2) UNEP/AMAP Climate change and POPs: predicting the impacts. Report of the UNEP/AMAP expert group. Secretariat of the Stockholm Convetion.; Geneva, Switzerland. http://www.pops.int (accessed June 15, 2014), 2011. (3) Hung, H.; Kallenborn, R.; Breivik, K.; Su, Y.; Brorström-Lundén, E.; Olafsdottir, K.; Thorlacius, J. M.; Leppänen, S.; Bossi, R.; Skov, H.; Manø, S.; Patton, G. W.; Stern, G.; Sverko, E.; Fellin, P. Atmospheric monitoring of organic pollutants in the Arctic under the Arctic Monitoring and Assessment Programme (AMAP): 1993-2006. Sci. Total Environ. 2010, 408, 2854-2873. (4) Riget, F.; Bignert, A.; Braune, B.; Stow, J.; Wilson, S. Temporal trends of legacy POPs in Arctic biota, an update. Sci. Total Environ. 2010, 408, 2874-2884. (5) Becker, S.; Halsall, C. J.; Tych, W.; Kallenborn, R.; Su, Y.; Hung, H. Long-term trends in atmospheric concentrations of alpha- and gamma-HCH in the Arctic provide insight into the effects of legislation and climatic fluctuations on contaminant levels. Atmos. Environ. 2008, 42, 8225-8233. (6) Becker, S.; Halsall, C. J.; Tych, W.; Hung, H.; Attewell, S.; Blanchard, P.; Li, H.; Fellin, P.; Stern, G.; Billeck, B.; Friesen, S. Resolving the Long-Term Trends of Polycyclic Aromatic Hydrocarbons in the Canadian Arctic Atmosphere. Environ. Sci. Technol. 2006, 40, 3217-3222. (7) Lamon, L.; von Waldow, H.; MacLeod, M.; Scheringer, M.; Marcomini, A.; Hungerbuhler, K. Modeling the global levels and distribution of polychlorinated biphenyls in air under a climate change scenario. Environ. Sci. Technol. 2009, 43, 5818-5824. (8) Wöhrnschimmel, H.; MacLeod, M.; Hungerbuhler, K. Emissions, Fate and Transport of Persistent Organic Pollutants to the Arctic in a Changing Global Climate. Environ. Sci. Technol. 2013, 47, 23232330. (9) Kong, D.; MacLeod, M.; Cousins, I. T. Modelling the influence of climate change on the chemical concentrations in the Baltic Sea region with the POPCYCLING-Baltic model. Chemosphere 2014, 110, 31-40. (10) Dalla Valle, M.; Codato, E.; Marcomini, A. Climate change influence on POPs distribution and fate: A case study. Chemosphere 2007, 67, 1287-1295. (11) Lamon, L.; MacLeod, M.; Marcomini, A.; Hungerbühler, K. Modeling the influence of climate change on the mass balance of polychlorinated biphenyls in the Adriatic Sea. Chemosphere 2012, 87, 1045-1051. (12) Wöhrnschimmel, H.; Tay, P.; von Waldow, H.; Hung, H.; Li, Y.-F.; MacLeod, M.; Hungerbuhler, K. Comparative Assessment of the Global Fate of α- and β-Hexachlorocyclohexane before and after Phase-Out. Environ. Sci. Technol. 2012, 46, 2047-2054. (13) Ma, J.; Hung, H.; Tian, C.; Kallenborn, R. Revolatilization of persistent organic pollutants in the Arctic induced by climate change. Nature Clim. Change 2011, 1, 255-260. (14) Gao, H.; Ma, J.; Cao, Z.; Dove, A.; Zhang, L. Trend and climate signals in seasonal air concentration of organochlorine pesticides over the Great Lakes. J. Geophys. Res. 2010, 115, D15307. (15) Nakazawa, T.; Ishizawa, M.; Higuchi, K. A. Z.; Trivett, N. B. A. Two curve fitting methods applied to CO2 flask data. Environmetrics 1997, 8, 197-218. (16) Salamova, A.; Pagano, J. J.; Holsen, T. M.; Hites, R. A. Post-1990 Temporal Trends of PCBs and Organochlorine Pesticides in the Atmosphere and in Fish from Lakes Erie, Michigan, and Superior. Environ. Sci. Technol. 2013, 47, 9109-9114. (17) Cortes, D. R.; Basu, I.; Sweet, C. W.; Hites, R. A. Temporal Trends in and Influence of Wind on PAH Concentrations Measured near the Great Lakes. Environ. Sci. Technol. 1999, 34, 356-360. (18) Venier, M.; Hung, H.; Tych, W.; Hites, R. A. Temporal Trends of Persistent Organic Pollutants: A Comparison of Different Time Series Models. Environ. Sci. Technol. 2012, 46, 3928-3934. (19) Hung, H.; Blanchard, P.; Halsall, C. J.; Bidleman, T. F.; Stern, G. A.; Fellin, P.; Muir, D. C. G.; Barrie, L. A.; Jantunen, L. M.; Helm, P. A.; Ma, J.; Konoplev, A. Temporal and spatial variabilities of atmospheric polychlorinated biphenyls (PCBs), organochlorine (OC) pesticides and polycyclic aromatic hydrocarbons (PAHs) in the Canadian Arctic: Results from a decade of monitoring. Sci. Total Environ. 2005, 342, 119-144. 12 ACS Paragon Plus Environment

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(20) Roberts, T. J. Arctic contaminants and climate change. Nature Clim. Change 2012, 2, 829-829. (21) UNEP Global monitoring report under the global monitoring plan for effectiveness evaluation; Conference of the Parties of the Stockholm Convention on Persistent Organic Pollutants Fourth Meeting. Geneva, Switzerland. http://www.pops.int (accessed June 15, 2014), 2009. (22) Cox, P.; Efthymiou, P. Directive 2003/11/EC of the European Parliament and of the Council of 6 February 2003 amending for the 24th time Council Directive 76/769/EEC relating to restrictions on the marketing and use of certain dangerous substances and preparations (pentabromodiphenyl ether, octabromodiphenyl ether); http://www.europa.eu (accessed June 15, 2014), 2003. (23) Wilford, B. H.; Shoeib, M.; Harner, T.; Zhu, J.; Jones, K. C. Polybrominated Diphenyl Ethers in Indoor Dust in Ottawa, Canada: Implications for Sources and Exposure. Environ. Sci. Technol. 2005, 39, 7027-7035. (24) UNEP Stockholm Convention on POPs. Governments unite to step-up reduction on global DDT reliance and add nine new chemicals under international treaty; Geneva, Switzerland. http://www.pops.int (accessed June 15, 2014), 2009. (25) Kallenborn, R.; Breivik, K.; Eckhardt, S.; Lunder, C. R.; Manø, S.; Schlabach, M.; Stohl, A. Long-term monitoring of persistent organic pollutants (POPs) at the Norwegian Troll station in Dronning Maud Land, Antarctica. Atmos. Chem. Phys. 2013, 13, 6983-6992. (26) Macdonald, R. W.; Harner, T.; Fyfe, J. Recent climate change in the Arctic and its impact on contaminant pathways and interpretation of temporal trend data. Sci. Total Environ. 2005, 342, 5-86. (27) Gouin, T.; Armitage, J. M.; Cousins, I. T.; Muir, D. C. G.; Ng, C. A.; Reid, L.; Tao, S. Influence of global climate change on chemical fate and bioaccumulation: The role of multimedia models. Environ. Toxicol. Chem. 2013, 32, 20-31. (28) MacLeod, M.; Riley, W. J.; McKone, T. E. Assessing the Influence of Climate Variability on Atmospheric Concentrations of Polychlorinated Biphenyls Using a Global-Scale Mass Balance Model (BETR-Global). Environ. Sci. Technol. 2005, 39, 6749-6756.

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4

10

4

TS₁₅̦̦̦₁̦̦̦̦̦̦

0 0 Z

lnC

-10

lnC

0

-4

-4 -20 -8

-8

-30 00

05

1

year

10

00

15

4

05

(A)

10

15

(a)

year

10

4 TS₁₅̦̦̦₂̦̦̦̦̦̦

0 0 lnC

lnC

-10

Z

0

-4

-4 -20 -8

-8

-30 00

05

10 year

2

00

15 (B)

05

10 year

15

(b)

3

Figure 1. (A & B) Trend lines determined with the DF method for (A) TS1,1 to TS15,1, and (B) TS1,2 to TS15,2.

4

Also shown are the iMK Z values (●) for TS1,1 to TS15,1 and TS1,2 to TS15,2 on the alternate y-axis of panel (A)

5

and (B), respectively. (a & b) Linear trend lines fitted to the ln-transformed form of (a) TS1,1 to TS15,1, and (b)

6

TS1,2 to TS15,2 using the LLR method. TS15,1 and TS15,2 are shown in the background of (a) and (b) as the light

7

lines for reference.

8



100%

92%

75%

(a)

66%

50% All (560) 25%

0% 20

1

t1/2

40

71% 50% OCPs (257) 25%

60

100%

0

20

t1/2

40

60

100%

(c)

93% 75%

(d) 78%

68%

50% PPs (253) 25%

Cumulative frequency


75%

0% 0

0%

75%

50% PAHs (50)

26% 25%

0% 0

2

(b)

93% Cumulative frequency


100%

Page 16 of 18

20

t1/2

40

60

0

20

t1/2

40

60

3

Figure 2. Cumulative frequency of the DF-estimated halving time [t1/2 (years); only for time series with iMK Z ≤

4

-1.96 (significantly decreasing trends)]: (a) for all 560 time series; (b) for 257 time series of OCPs; (c) for 253

5

time series of PBDEs and PCBs (PPs); (d) for 50 time series of PAHs.

6


Page 17 of 18


D_TS₁₅̦̦̦₁̦̦̦̦̦̦

D_TS₁₅̦̦̦₂̦̦̦̦̦̦

6

8 6

Detrendded C

4

4 2 2 0

0

-2

-2 00

1

05

year

10

15

2

Figure 3. Results of applying the iMK test to the two 15-year synthetic time series without (TS15,1) and with

3

(TS15,2) an abrupt change in the decay rate of POP concentrations in the atmosphere. (prefix ‘D_’ indicates a

4

time series has been detrended.)

5



1 2

TOC Abstract art

3

(Created using ArcGIS)


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Statistical Analysis of Long-Term Monitoring Data ... - ACS Publications

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