Perfluoroalkyl Contaminants in Lake Ontario Lake Trout: Detailed

May 3, 2012 - Perfluoroalkyl Contaminants in Lake Ontario Lake Trout: Detailed Examination of Current Status and Long-Term Trends. Sarah B. Gewurtz†...
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Perfluoroalkyl Contaminants in Lake Ontario Lake Trout: Detailed Examination of Current Status and Long-Term Trends Sarah B. Gewurtz,§,†,∥ Amila O. De Silva,*,†,§ Sean M. Backus,† Daryl J. McGoldrick,† Michael J. Keir,† Jeff Small,† Lisa Melymuk,‡ and Derek C. G. Muir† †

Environment Canada, Water Science and Technology Directorate, 867 Lakeshore Road, Burlington, Ontario, Canada L7R 4A6 Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada M5S 1A4

‡

S Supporting Information *

ABSTRACT: Perfluoroalkyl contaminants (PFCs) were determined in Lake Ontario Lake Trout sampled annually between 1997 and 2008 in order to assess how current trends are responding to recent regulatory bans and voluntary phase-outs. We also combined our measurements with those of a previous study to provide an updated assessment of long-term trends. Concentrations of PFCs generally increased from the late 1970s until the mid-1980s to mid-1990s, after which concentrations either remained unchanged (perfluorooctane sulfonate (PFOS) and perfluorocarboxylates) or declined (perfluorodecanesulfonate (PFDS)). The temporal trends were assessed using three models, quadratic, exponential rise to maximum, and two-segment linear piecewise function, and then evaluated for best fit using Akaike Information Criteria. For PFOS and perfluorocarboxylates, the exponential rise to maximum function had the best fit. This is particularly interesting for PFOS as it suggests that although concentrations in Lake Ontario Lake Trout may have stopped increasing in response to voluntary phase-outs in 2000−2002, declines have yet to be observed. This may be due to continuing input of PFOS from products still in use and/or slow degradation of larger precursor molecules. A power analysis of PFOS suggested that 15 years of data with a within-year sample size of 10 is required to obtain sufficient power (80%) to detect a 5% decreasing trend. However, the length of the monitoring program had a greater influence on the ability to detect a trend compared to within-year sample size. This provides evidence that additional sampling years are required to detect a response to bans and phase-outs, given the variability in the fish data. The lack of observed declines of perfluorocarboxylate residues in fish may be expected as regulations for these compounds were only recently enacted. In contrast to the other compounds, the quadratic model had the best fit for PFDS. The results of this study emphasize the importance of long-term monitoring for assessing the effectiveness of bans and phase-outs on PFCs in the environment.

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perfluorooctane sulfonyl fluoride (PFOSF, used to make PFOS and its precursors), started production in 1949.7,8 PFOSF was voluntarily phased-out by 3M between 2000 and 2002.9 In contrast to PFOSF, production of PFOA and its known precursors continues. However, major manufacturers have voluntarily agreed to reduce emissions by 99% in 2015 (http://www.epa.gov/oppt/pfoa/pubs/stewardship/index. html). Regulation of the longer-chained perfluoroalkyl sulfonates and perfluorocarboxylates and their precursors is also now in progress in Canada10 and the US (http://www.epa. gov/oppt/existingchemicals/pubs/actionplans/pfcs.html). However, despite these regulatory efforts, there continues to be fresh sources into the environment. For example, PFOSF-based production continues in China11,12 and use exceptions exist in

INTRODUCTION For the past decade, perfluoroalkyl contaminants (PFCs) have attracted scientific and regulatory scrutiny due to their widespread environmental occurrence1−3 and toxicity.4,5 PFCs comprise a large group of chemicals that have been used in repellents for paper and packaging, carpets, and fabrics, as well as in aqueous film forming foam (AFFF) for fighting fuel fires.6,7 The most commonly monitored and regulated PFCs include perfluoroalkyl sulfonates (especially perfluorooctane sulfonate, PFOS) and perfluorocarboxylates (particularly perfluorooctanoate, PFOA). However, most current monitoring programs also include the longer-chain perfluorocarboxylates (C9−C15) because they are more bioaccumulative and are frequently detected at higher concentrations in biota compared with PFOA.2,3 The history of PFC production is not well established due to proprietary issues, industry responses to regulations, and changing product lines.8 Nonetheless, it is known that the 3M Company, which was the major manufacturer of © 2012 American Chemical Society

Received: Revised: Accepted: Published: 5842

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several countries.13,14 In addition, PFCs are released from PFCcontaining products still in use and through wastewater treatment plants and landfills.7,15,16 Monitoring data is critical for determining if PFC levels in the environment are responding to regulatory action.3 Most temporal trend studies for PFCs quantify the probability of making a type I error, which is defined as rejection of a true null hypothesis and calculated by the statistical test selected.17 Type II error and statistical power (defined as the probability that a monitoring program will detect a trend in sample measurements when the trend is occurring, despite the variability in the data) are less frequently evaluated.17,18 However, power analysis can provide critical information on the number of years of data that are required before a significant trend of a given magnitude will likely be detected.18 Power analysis can also be used to identify a within-year sample size that would optimize trend detection.19 Many recent PFC monitoring programs have relied on short-term time trend data, whereby samples are not collected annually,2,3,20 which reduces statistical power compared with annually collected data.3,18 Lake Ontario is an ideal system to study the recent time trends of PFCs because it contains elevated levels of many contaminants, including PFCs.3,21 Lake Trout (Salvelinus namaycush) is among the most frequently used biomonitor for assessing contaminant trends in Lake Ontario as well as the other Great Lakes.17 Martin et al.22 previously evaluated the temporal trends of PFOS in Lake Ontario Lake Trout collected every three years between 1980 and 2001. A log−linear model applied to the entire data set showed that PFOS concentrations increased on average by 5.7% per year. However, on closer examination of the data, it was evident that the increase was not linear over the time period examined. PFOS concentrations increased between 1980 and 1989, decreased slightly between 1989 and 1995, and subsequently increased until 2001. Furdui et al.20 measured perfluoroalkyl sulfonates and perfluorocarboxylates in Lake Ontario Lake Trout every 4 or 5 years between 1979 and 2004. They found that concentrations increased until the late 1980s/early 1990s. However, after this time, temporal patterns were difficult to distinguish because PFC concentrations were not measured every year. The temporal trends of PFCs were also recently determined in Herring Gull (Larus argentatus) eggs collected in 1990 and annually from 1997 to 2010 from the Great Lakes.23 In Lake Ontario Herring Gull eggs, there was a marginally significant increasing trend for PFOS (p = 0.06) but no significant trend (p > 0.05) between 2000 and 2010. Perfluorocarboxylates also generally increased (p < 0.05) during the time period. The objective of this study was to provide a detailed examination of PFC concentrations in Lake Ontario Lake Trout collected annually between 1997 and 2008. We also combined our data with those of Furdui et al.20 in order to assess long-term trends. The influence of possible covariates (i.e., fish lipid, length, weight, age, growth, sex, and stable isotopes of nitrogen (δ15N) and carbon (δ13C)) on the trends were evaluated. Finally, we used power analyses to determine the optimal number of years and within-year sample size needed to detect future trends of PFOS in Lake Ontario Lake Trout, given the variability associated with the data. To our knowledge, this study is among the first to evaluate required sample size (both between- and within-year) for optimizing detection of regulatory actions/production phase-outs of PFOS in a biological monitoring program.

Article

METHODS

Sample Collection. Lake Trout samples were collected annually between 1997 and 2008 (n = 7, 8, 6, 8, 3, 9, 7, 6, 6, 12, 5, and 10 individuals per year, respectively) from the western basin of Lake Ontario as part of Environment Canada’s National Fish Contaminants Monitoring and Surveillance Program. The specific sites included Niagara-On-The-Lake (43.333°, −79.083°), Port Credit (43.500°, −79.583°), and Port Hope - Cobourg (43.917°, −78.167°) (Figure S-1). The fish from the three sites were combined to provide data representative of overall concentrations in western Lake Ontario. However, we also evaluated site-specific differences in 2006 (the only year where sample size was greater than 3 for each location) to determine the possible influence of spatial variation on overall results. Fish were caught using bottom set gill nets in late summer or early fall (August to October). Collection, storage, and processing methods are described elsewhere.24,25 Following processing, samples were stored in Canada’s National Aquatic Biological Specimen Bank at −80 °C until being selected for inclusion in this current retrospective investigation. Chemical Analysis of PFCs. PFCs were analyzed according to De Silva et al.26 using a methanolic extraction followed by cleanup using activated carbon SPE. Recovery standards consisting of 40 μL of isotopically labeled surrogate PFCs (Table S-1, Supporting Information) were added to the samples prior to extraction. Once extracted, the samples were reconstituted in 1 mL of 50:50 methanol:HPLC grade water and analyzed by liquid chromatography tandem mass spectrometry (LC-MS/MS) using an Agilent 1200 LC coupled to an AB Sciex 2000 mass spectrometer. Wet weight concentrations were determined using calibration curves based on relative response between native and isotopically labeled surrogate standards (Table S-1). For perfluorotridecanoate (PFTrA) and perfluorotetradecanoate (PFTeA), calibration curves were based on relative response of the analyte to 13 C1,2-perfluorododecanoate (PFDoA). Spike and recovery tests (Table S-2) were conducted by spiking known concentrations of standards into samples (n = 7) that were left for 12 h prior to extraction. In general, recoveries were >80% with the exception of PFTrA which had a mean recovery of 70%. Routine method blanks were assessed by extracting blanks using surrogate standards and all reagents to determine method detection limits (MDL), defined as the concentration yielding the mean blank response plus 3 times the standard deviation (SD) of the blank response. PFOA and perfluorononanoate (PFNA) were the only analytes detected in method blanks with corresponding MDLs of 0.21 ng/g and 0.35 ng/g expressed on a per wet weight basis, respectively. PFNA concentrations in fish were above the MDL in all samples. In contrast, PFOA was typically not detected in fish extracts or was present at concentrations below the MDL. However, this may be expected because PFOA is not appreciably bioaccumulative in fish.27 For all other analytes, MDL was defined by instrument detection limits corresponding to 0.10 ng/g based on the lowest calibration standard yielding a response with 10 times the signal-to-noise. Nondetect values were substituted with one-half of the analyte specific MDL. Based on good recoveries and low blanks, concentrations reported herein were not corrected for recoveries, blank signal, or matrix effects, consistent with previous studies from our 5843

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laboratory.26 PFOA was the only exception, and therefore fish concentrations for this chemical are not reported in this paper. Stable Isotope Analysis. Stable isotopes (δ15N and δ13C) were determined on subsets of individual whole fish samples at the Environmental Isotope Lab, University of Waterloo (Waterloo, ON, Canada) as described previously.28 Prior to analysis, samples (∼1.0−1.5 mg) were dried at 60 °C for ∼48 h and then ground to a fine power using a ball mill (MM 400, Retsch, Haan, Germany). Samples were run on an Isochrom continuous-flow, stable-isotope mass spectrometer (GVInstruments/Micromass, Manchester, UK) coupled to a Carlo Erba Elemental Analyzer (CHNS-O EA 1108, Milan, Italy). Standard materials used were Peedee Belemnite formation for carbon and atmospheric air for nitrogen. Fish were not pre-extracted to remove lipids prior to the stable isotope analysis, and lipid has the potential to influence δ13C results.29,30 Post et al.29 recommended the following equation for correcting δ13C for lipid content

Temporal trends of individual PFCs were evaluated using three models Quadratic: Ct = a + bt + ct 2 Exponential rise to maximum: Ct = a + b(1 − exp−ct ) (3)

Two‐segment linear piecewise: f ⎧ a(T − t ) + b(t − tmin) , tmin ≤ t ≤ T ⎪ T − tmin ⎪ =⎨ ⎪ b(tmax − t ) + c(t − T ) , T ≤ t ≤ tmax ⎪ tmax − T ⎩

(4)

where Ct is concentration at time t (year from start of the available record), and a, b, and c are estimated parameters. In the two-segment piecewise model, T is the breakpoint year (estimated by the model), tmin is the first year of the data set (1979), and tmax is the last year of the data set (2008). Percent annual change for this model was estimated from the slopes of a log−linear regression applied separately to data before and after the breakpoint year, as done previously.32 The models were applied to annual geometric mean data as is a common practice in many contaminant trend studies.17 For each analyte, the best fitting model was identified using Akaike Information Criteria (AIC).33 AIC (corrected for small sample size) was calculated for each equation as follows: AIC = nlog(σ2) + 2K + (2K(K + 1)/(n − K − 1))where n is sample size, σ2 is the residual sum of squares/n, and K is the total number of estimated parameters in the model including σ2 (K = 4 for the quadratic and exponential rise to maximum models and K = 5 for the piecewise model). The best fitting model had the lowest AIC. Akaike weights (wi) were determined as wi = exp(−ΔAICi/2)/ΣM i=1exp(−ΔAICi/2), where M is the number of models. The wi values indicate the probability that a given model is the best among all models evaluated in this study. A power analysis was conducted to determine if the sampling regime utilized here was optimal to detect decreases in PFOS concentrations. Following Fryer and Nicholson,19 power can be calculated from a noncentral F-distribution on 1 and T-2 degrees of freedom, with the noncentrality parameter (δ) determined as follows

δ13C lipid normalized = δ13Cuntreated − 3.32 + 0.99 × C:N (1)

where δ Clipid normalized is an estimate of δ C that is normalized for the effects of lipid concentration, and C:N was determined from the %C and %N content of each sample. However, as the use of this equation has recently been questioned,30 we present results for both δ13Clipid normalized and δ13Cuntreated. Data Treatment and Statistical Analysis. Prior to analysis, data were log-transformed to meet assumptions of normality and homogeneity of variance. Compounds detected in more than 80% of the samples (i.e., all PFCs except for perfluoroheptane sulfonate (PFHpS), PFOA, and PFTeA) were statistically evaluated. Our data set shared three common years, namely 1998, 2001, and 2004, with previous studies on PFCs in Lake Ontario Lake Trout.20,21 For each of these three years, significant differences between studies were tested using the nonparametric Kruskal−Wallis test because the assumptions of normality (Shapiro-Wilk test) and homogeneity of variance (Levene’s test) were not met in all cases even after logtransformation. When the assumptions of parametric tests were met, we also applied ANOVA, and the results were consistent with the nonparametric test. As presented below, this analysis showed that although there were significant differences present between the two data sets for some of the years/compounds, the overall trends were comparable between studies. Therefore, we combined our measurements with Furdui et al.20,21 data from 1979, 1983, 1988, and 1993 for further temporal trend assessment. PFTrA was an obvious exception, as discussed below, and perfluorohexane sulfonate (PFHxS) was not evaluated by Furdui et al.20,21 Therefore, PFTrA and PFHxS were excluded from the time trend modeling discussed below. The biological variables used as covariates (i.e., fish lipid, length, weight, age, growth (evaluated as length at age and weight at age), δ15N, and δ13C) are summarized in Table S-3. The relationships between each PFC compound and covariate were analyzed for all years together and each year separately using a log−linear regression (logCCV = log(a) + b*CV), as done previously,28,31 where CV represents a given covariate, CCV is the concentration at covariate value CV, and a and b are constants representing the intercept and slope of the relationship. Significant differences in PFC concentrations between sexes were evaluated using the Kruskal−Wallis tests because assumptions of parametric tests were not consistently met. 13

(2)

13

2 ⎧ ⎛ q ⎞⎫ ⎧ (T − 1)T (T + 1) ⎫ ⎜ ⎟ ⎬ ⎨ ⎨ ⎬ δ = log 1 + ⎩ ⎝ 100 ⎠⎭ ⎩ 12ψ 2 ⎭

(5)

where q is the minimum annual percent change that can be detected, T is the number of sampling years, and ψ2 is the total residual variance of the model. ψ2 can be determined as19 ψ 2 = σ2y +

σ2w R

(6)

where σ2y and σ2w are the random between-year and within-year variances of the model, respectively, and R is the within-year sample size. σ2y and σ2w were estimated using a linear mixed model that was fit using the restricted maximum likelihood method. Power can then be calculated as power = 1 − FDist(F1 − α , 1, T − 2, δ)

(7)

th

where F1‑α is the 100(1−α) percentile of a central Fdistribution on 1 and T − 2 degrees of freedom, and α is the type I error rate. In this study, we used eqs 5−7 to determine 5844

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Figure 1. Temporal trends of PFC concentrations (geometric mean ±95% confidence interval) in Lake Ontario Lake Trout. Red circles represent data from this present study (1997−2008) and blue squares represent data from Furdui et al.20,21 “NS” and “*” indicate no significant differences and significant differences, respectively, between our data and Furdui et al. data in 1998, 2001, and 2004.

shown in Figure 1 and Table S-4. Data from Furdui et al.20,21 (1979−2004) are also shown in Figure 1 for comparison. The difference in geometric mean PFC concentrations between the two studies ranged from 1- to 4-fold; however, such differences in PFCs are expected given that the fish from the two studies were analyzed in different laboratories.20 Furthermore, the differences between studies were not significant (p > 0.05) for more than half of the years/PFCs evaluated and the trends between the two studies were similar (Figure 1). PFTrA was an obvious exception, as concentrations reported by Furdui et al.20,21 were consistently higher than our study (although only significantly higher in 2001) and increased from 1998 to 2004 (whereas our data showed no relationship with time, log−linear regression on 1997−2008 data, p > 0.05). It is noteworthy that data from both Martin et al.22 and Furdui et al.20,21 suggested a dip in PFOS and perfluorocarboxylate concentrations in the mid-1990s, followed again by increases. Martin et al.22 attributed this dip to shifts in food web processes perhaps as a result of the zebra mussel invasion. Examination of stable isotopes of nitrogen and carbon does not appear to support this hypothesis. The trophic position of Lake Ontario Lake Trout, as measured by δ15N, remained relatively

the minimum percent change (q) that could be detected for an 80% power level and 5% type I error rate (α) as a function of T and R. PFOS data collected as part of this study only (1997 to 2008) were used to calculate σ2y and σ2w. These data were collected annually, which allows for a better estimation of σ2y. We assumed that random within- and between-year variance components of future time trends would be comparable to the 1997 to 2008 time range. In addition, it was assumed that future decreases in PFOS concentrations would follow a log− linear trend and that a power of 80% was adequate, consistent with previous power analyses.18 Statistica 9.0 (StatSoft, Tulsa, OK, USA) was used for comparison of our data with those of Furdui et al.,20,21 the analysis of covariates, and the power analysis. SigmaPlot 11.0 (Systat Software Inc.) was used for the temporal trend model assessment. A type I error rate of α = 0.05 was used in all statistical analyses.

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RESULTS AND DISCUSSION Overall Temporal Trends and Patterns. The temporal trends (1997−2008) of PFCs detected in greater than 80% of samples (all PFCs except for PFHpS, PFOA, and PFTeA) are 5845

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Table 1. Comparison of Three Models Used To Evaluate the PFC Concentration versus Year Relationship in Lake Ontario Lake Trout Using Akaike’s Information Criterion (AIC) Corrected for Small Sample Sizee chemical PFOS

PFNA

PFDA

PFUnA

PFDoA

PFDS

model piecewise exponential quadratic piecewise exponential quadratic piecewise exponential quadratic piecewise exponential quadratic piecewise exponential quadratic piecewise exponential quadratic

rise to max

rise to max

rise to max

rise to max

rise to max

rise to max

p-value

r2

RSSa

Kb

AICc

wid

0.005 0.001 0.002