Ambient sediment quality conditions in Minnesota lakes, USA: Effects of watershed parameters and aquatic health implications

Science of the Total Environment 607–608 (2017) 1320–1338

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Ambient sediment quality conditions in Minnesota lakes, USA: Effects of watershed parameters and aquatic health implications Judy L. Crane Minnesota Pollution Control Agency, 520 Lafayette Road North, St. Paul, MN 55155-4194, USA

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Information on ambient sediment quality has been lacking in the state of Minnesota. • Surficial sediments were collected from 54 lakes for sediment chemistry parameters. • Overall sediment quality ranged from good (43%) to moderate (57%). • Some contaminants varied significantly in lakes from different watershed land uses. • Ecological health impacts were assessed for benthic invertebrates and fish.

a r t i c l e

i n f o

Article history: Received 2 April 2017 Received in revised form 25 May 2017 Accepted 26 May 2017 Available online xxxx Editor: Kevin V. Thomas Keywords: Sediment chemistry Metals Polycyclic aromatic hydrocarbons Persistent organic pollutants Land use Fish tissue

E-mail address: [email protected].

http://dx.doi.org/10.1016/j.scitotenv.2017.05.241 0048-9697/Published by Elsevier B.V.

a b s t r a c t Surficial sediments were collected from 50 randomly selected Minnesota lakes, plus four a priori reference lakes, in 2007. The lakes encompassed broad geographic coverage of the state and included a variety of major land uses in the surrounding watersheds. Sediment samples were analyzed for a suite of metals, metalloids, persistent organic pollutants, total organic carbon, and particle size fractions. In addition, a small fish survey was conducted to assess PBDEs in both whole fish and fish tissues. Sediment quality in this set of lakes ranged from good (43%) to moderate (57%) based on an integrative measure of multiple contaminants. On an individual basis, some contaminants (e.g., arsenic, lead, DDD, and DDE) exceeded benchmark values in a small number of lakes that would be detrimental to benthic invertebrates. The sediments in two developed lakes tended to be more contaminated than sediments in lakes from other major watershed land uses. These differences were often statistically significant (p b 0.05), particularly for lakes with developed versus cultivated land uses for arsenic, lead, zinc, and numerous PAH compounds. Multivariate statistical approaches were used on a subgroup of contaminants to show the two urban lakes, as well as a few northeastern Minnesota lakes, differed from the rest of the data set. Background threshold values were calculated for data with b 80% nondetects. Source apportionment modeling of PAHs revealed that vehicle emissions and coal-related combustion were the most common sources. A general environmental forensic analysis of the PCDD/F data showed that ubiquitous combustion sources appeared to be important. BDE-209, a decaBDE, was detected in 84% of lake sediment samples, whereas fish at the top of the food chain (i.e., predator trophic group) had significantly higher (p b 0.05) mean lipid-normalized concentrations of BDEs-47, 100, and 153 than lower trophic fish. These results will be used for future status and trends work. Published by Elsevier B.V.

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1. Introduction Sediment quality is important for healthy aquatic ecosystems. The bed sediments provide vital habitat for benthic invertebrates and fish, as well as nearshore vegetation. Depositional sediments are also a repository for particle-associated contaminants that enter waterways through surface water runoff, leaking septic systems, atmospheric deposition, and historical dumping of waste material and effluent (Taylor and Owens, 2009). The accumulation of contaminants over time can contribute to beneficial use impairments (BUIs) in waterways, including: 1) tumors and other deformities in bottom-dwelling fish, 2) degraded benthic communities, which results in less food for fish and some wildlife, 3) degraded fish and wildlife habitats, 4) bioaccumulation of contaminants up the food chain, which may result in fish and wildlife consumption advisories for humans, 5) potential human health risks from exposure to sediment-derived contaminants, 6) aesthetic impairments, and 7) restrictions on navigational dredging and beneficial use of dredged material (Zarull et al., 2001; Crane and Hennes, 2016a). Contaminated sediments were a leading contributor of BUIs in the initial designation of 43 Great Lakes Areas of Concern (AOCs) by the International Joint Commission (IJC, 1989). Worldwide, contaminated sediments are of concern in coastal ports and other urban areas, particularly where past or current commercial and industrial operations have contaminated receiving waters (Taylor and Owens, 2009). Although developed countries have implemented point source controls on contaminant sources, reductions in nonpoint sources are a more challenging problem for environmental agencies to address. The diffusive nature of nonpoint sources can have widespread impacts on water and sediment quality, as well as human and ecological health. Local, regional, or national product bans are one way to reduce these sources. Examples of bans in the U.S. include: organochlorine pesticides like DDT (USEPA, 2000a), polychlorinated biphenyls (PCBs; Johnson et al., 2006), and coal tar-based sealants in local and state jurisdictions (including Minnesota) to reduce environmental contamination by polycyclic aromatic hydrocarbons (PAHs; Crane, 2014). Voluntary or negotiated restrictions on producing certain chemicals have also been utilized, such as when the Great Lakes Chemical Corporation, the sole U.S. manufacturer of certain flame retardants [i.e., penta-brominated diphenyl ether (BDE) and octaBDE], voluntarily ceased production in 2004 (USEPA, 2009). Implementation of stormwater best management practices (Minnesota Stormwater Steering Committee, 2017), improved farming techniques (Centner et al., 1999; Wortmann et al., 2011), use of mass transit (IAGLR, 2002), and pollution prevention efforts (Daughton, 2004) can be effective ways to reduce nonpoint sources of contamination to waterways. However, the physical/chemical properties of persistent organic pollutants (POPs) can result in continued cycling in the environment (Jones and de Voogt, 1999; Lohmann et al., 2007; Wöhrnschimmel et al., 2013), even after source control measures have been implemented. Thus, these contaminants may continue to persist in surficial sediments for some time. Ambient sediment quality pertains to chemical concentrations derived from natural and/or widespread diffuse anthropogenic sources (e.g., atmospheric deposition). Surficial sediments provide the most recent record by which to assess ambient sediment quality conditions, such as from legacy organic contaminants. The U.S. Geological Survey (USGS) identified 686 chemicals of highest priority for ambient monitoring of sediment quality for national- and regional-scale studies (Olsen et al., 2013). Determining ambient sediment quality is important for assessing current conditions, as well as for future status and trends work. These types of investigations should be conducted using a randomized statistical design for the selection of sample sites. The purpose of this study was to collect and evaluate ambient sediment chemistry and particle size data from a subset of Minnesota lakes located in the upper Midwest, U.S. Surficial sediments were collected from 50 randomly selected lakes, plus four a priori reference

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lakes, during the summer of 2007. The sediments were analyzed for a large suite of metals, metalloids, persistent organic pollutants, total organic carbon (TOC), and particle size. These data were used to provide an indication of the statistical range of more recently deposited analytes and to determine if these concentrations were influenced by major watershed land uses. The sediment quality data were evaluated using chemical indices, making comparisons to benchmark ecological health values, conducting statistical analyses (including multivariate methods), and incorporating environmental forensic techniques to identify sources for certain POPs. To further assess biological impacts from one class of contaminants, a fish survey of five study lakes was conducted to evaluate BDE congeners in up to three trophic classes of fish. 2. Methods 2.1. Sampling design The selection of study lakes for sediment samples was the same as those selected for the Minnesota component of the 2007 National Lake Assessment (NLA) study (Monson and Heiskary, 2008; Crane and Hennes, 2016a, 2016b). Fifty randomly selected lakes, and four a priori reference lakes, were selected within five size classes of lake surface areas (Table A-1). The reference lakes were selected by the U.S. Environmental Protection Agency (EPA) in consultation with state agencies, as well as by evaluation of aerial photographs (Herlihy et al., 2013). The lake watersheds encompassed a variety of land uses (Table A-2) and ecoregions within Minnesota (Fig. 1). Due to either drought conditions during the summer of 2007 or the inaccessibility of some lakes that did not have public access, some of the original draw lakes were replaced by the oversample list of randomly selected lakes. 2.2. Sample collection 2.2.1. Sediment samples The NLA field crews collected sediment samples during June 26, 2007 through August 22, 2007. The sediment sampling and processing procedures are described elsewhere (Crane and Hennes, 2016a). Briefly, a modified K-B corer with Lexan core tubes was used to collect surficial sediment samples from the deepest location of each lake. The upper 15 cm of the sediment profile was collected and extruded into a large, pre-cleaned 1 L wide-mouthed glass jar with a Teflon-lined lid. For most lakes, two cores were collected in close proximity to each other, and each core was extruded into a separate labelled jar. Four lakes in the Boundary Waters Canoe Area Wilderness (BWCAW) [i.e., Alruss (#2), Becoosin (#6), Lamb (#17), and Vesper (#47)] were sampled by canoe since motorized boats are generally not allowed there. Only one sediment core sample was collected from each BWCAW lake due to more difficult field conditions. Field replicates, consisting of two cores, were collected in close proximity to the original sample to assess field precision for five lakes [i.e., Fairy (#12), Flat (#15), Long (#19), Nokomis (#27), and Pine Mountain (#35)]. The samples were stored in ice-filled coolers and transported to St. Paul, MN. The sediment samples were composited and processed further at the Minnesota Pollution Control Agency's (MPCA's) St. Paul, MN office before subsamples were stored and transported to analytical laboratories. The samples were processed within one to 23 days of sample collection. 2.2.2. Fish tissue samples Five NLA lakes [i.e., August (#5), Cass (#7), Jennie (#16), Mayo (#24), and Nokomis (#27)] were sampled for fish by Minnesota Department of Natural Resources (MDNR) staff during June to September 2007. The field sampling methods are described elsewhere (Crane and Hennes, 2016a). Briefly, fish were collected with gill nets or trap nets from each of the following trophic guilds: predator (i.e., northern pike or walleye), omnivore (pan fish; i.e., bluegill or yellow perch), and

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Fig. 1. Map of random and reference lake sediment samples in Minnesota.

benthic fish (i.e., brown bullhead, yellow bullhead, or white sucker). However, benthic fish from Mayo (#24) and Nokomis (#27) were inadvertently not collected. Five additional predator fish (i.e., northern pike) from August (#5) were collected for the field replicate. Weight and length measurements were taken in the field, the fish were wrapped in aluminum foil and stored on ice in coolers during transport to St. Paul, MN, and the samples were stored in a walk-in freezer at the MPCA. Five comparable fish for a particular species from each guild were selected and shipped overnight on dry ice to AXYS Analytical Services Ltd., located in Sidney, BC, Canada, for processing and analysis. The samples were transferred to secure storage and kept at −20 °C prior to extraction. The length, sex, and age of the individual fish species were determined.

2.3. Analytical methods 2.3.1. Sediment A large suite of sediment chemistry and particle size parameters were analyzed on the sediment samples. In order to maximize available resources, three groups of parameters [i.e., PCB congeners, organochlorine pesticides/Total PCBs, and polychlorinated dibenzo-p-dioxin/furan (PCDD/F) congeners] were limited to a subgroup of 23–24 lakes (Crane and Hennes, 2016a). All other parameters were analyzed on the full group of 54 lakes. A detailed description of the analytical methods is provided elsewhere (Crane and Hennes, 2016a). Briefly, the Minnesota Department of Health (MDH) analyzed 18 metals and metalloids using inductively

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coupled plasma – mass spectrometry (ICP-MS). MDH also analyzed mercury by cold-vapor atomic absorption (CVAA). All other chemical and physical analyses of sediment samples were performed by TDIBrooks International (College Station, TX); their subcontract laboratory (Columbia Analytical Services in Houston, TX) analyzed sediment samples for PCDD/F congeners. The analysis of 43 parent and alkylated PAHs, as well as biphenyl, was performed using gas chromatography/ mass spectrometry (GC/MS) in selected ion monitoring (SIM) mode. The analysis of 28 organochlorine pesticides and metabolites, as well as Total PCBs and 114 individual and coeluting groups of PCB congeners, were performed using a high resolution GC with electron capture detection (ECD). Total PCBs was an analytical parameter included with the organochlorine pesticide analyses, and it is hereafter referred to as Total PCBspesticide scan. The analysis of 17 PCDD/F congeners that had chlorine atoms located in the 2,3,7,8 positions, as well as several homolog groups, was conducted using high resolution GC (HRGC) – high resolution MS (HRMS). Table A-3 provides the abbreviations for individual PCDD/F congeners and homolog groups. Fifty-five individual and coeluting homolog groups of polybrominated diphenyl ether (PBDE) compounds were quantified, including BDE-209, by GC/MS-Negative Chemical Ionization (GC/MS-NCI) and GC/MS-SIM. The analysis of TOC was conducted by the Lloyd Kahn method (Kahn, 1988). Particle size was determined using a combination of sieves and settling tubes on homogenized subsamples mixed with a deflocculant solution to disaggregate the samples. Organic contaminants were quantified different ways. Quantitation of PAHs, PCB congeners, Total PCBspesticide scan, organochlorine pesticides, and PBDE concentrations were determined using the internal standard method, and analyte concentrations were corrected for surrogate recovery. Quantitation of individual PCDD/F congeners, total PCDDs, and total PCDFs was achieved in conjunction with the establishment of a multipoint (five-point) calibration curve for each homolog, during which each calibration solution was analyzed once. Percent moisture was determined so that all results were reported as dry weight measurements. A variety of quality control (QC) samples and quality assurance (QA) procedures were used by the analytical laboratories (TDI-Brooks International, Inc., 2009; Crane and Hennes, 2016a). For metals and metalloids, the QC samples included: mixed calibration standard solutions, analytical blanks, post-digestion spike additions, and one analytical duplicate per batch of 20 samples. For organic parameters, the QC samples for each analytical batch included: a method blank, analytical duplicate, matrix spike, and matrix spike duplicate. For PAHs, PCBs, and organochlorine pesticides, calibration check standards were interspersed throughout an analytical batch in order to insure the instrument's integrity. A diluted oil standard was used as a retention index solution for PAH compounds not found in the calibration solution (e.g., alkylated PAH homolog groups). An Aroclor mixture was used as a retention index solution for individual PCBs not found in the calibration solution, and individual PCB congener retention times were based on pattern recognition. Identification of PCDD/F congeners and PBDEs is described in detail elsewhere (Crane and Hennes, 2016a). The QC samples for TOC and particle size included analytical duplicates. The analytical QA/QC results were determined to be acceptable for most parameters, with a few exceptions (Crane and Hennes, 2016a). Analytical duplicate anomalies were noted for C3-chrysene (lake #19), C3-phenanthrenes/anthracenes (lake #19R field replicate), and C4phenanthrenes/anthracenes (lake #31). In each case, the analytical duplicates were below the reporting limits. The analytical laboratory used one-half the reporting limit in their calculation of the relative percent differences (RPD) between each sample and analytical duplicate, and the RPDs were artificially elevated. The spike recoveries of BDE-85 and BDE-154 in two samples (#5 and 28, respectively) were high, and the results may be biased high. The spike recoveries for beta-endosulfan were low for two samples (#7 and 28), and the results may be biased low. For these four spike anomalies, each of these analytes was below

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the reporting limit for the corresponding sediment samples. Additional QA/QC results are provided elsewhere (TDI-Brooks International, Inc., 2009; Crane and Hennes, 2016a). 2.3.2. Fish tissue The fish samples were processed different ways, depending on trophic guild and species type (Crane and Hennes, 2016a). Predator fish were processed as skin-on fillets (removed scales). Omnivore fish were processed as skin-on whole fish (removed scales). Benthic fish were processed as skin-on whole fish for bullheads and skin-on whole fish (removed scales) for white suckers. For each lake, five fish for each trophic guild collected were homogenized and composited together. The percentage of lipids in each composite sample was determined. The extraction and analysis of BDEs in fish tissue followed EPA Method 1614 (USEPA, 2007), with some modifications developed by AXYS (Crane and Hennes, 2016a). Analysis of the extracts was performed on a HRMS coupled to a HRGC equipped with a DB-5HT chromatography column (30 m × 0.25 mm ID and 0.10 μm film thickness). The isotope dilution/internal standard quantification procedure was used, and the data were recovery corrected for possible losses during extraction and cleanup. The analytical QC samples for fish included three procedural blanks, a lab-generated reference sample known as the ongoing precision and recovery sample, a matrix spike, and matrix spike duplicate. The fish tissue samples were re-run after an internal source of contamination was found in some of the blank and fish tissue samples. The final analyses passed the laboratories QA/QC parameters. However, the MPCA considered six of the composite sample results for BDE-99 and four of the composite sample results for BDE-153 as estimated values due to the detection and quantification of these congeners in the procedural blanks (Crane and Hennes, 2016a, 2016b). 2.4. Watershed information Lake, ecoregion, and watershed land use information was obtained for the study lakes. Five size classes of lakes, as used by the NLA program (Crane and Hennes, 2016a, 2016b), were represented in the study lakes, including: A) 4–10 ha: four lakes; B) 10–20 ha: three lakes; C) 20–50 ha: 11 lakes (including two reference lakes); D) 50–100 ha: 13 lakes; and E) N100 ha: 23 lakes (including two reference lakes; Table A-1). The September 2008 ecoregion classifications used by the MDNR were utilized (Crane and Hennes, 2016a). The study lakes encompassed five ecoregions, most of which were located in the North Central Hardwood Forest and the Northern Lakes and Forest ecoregions. Land use type information for each lake watershed was obtained from other MPCA staff involved in the NLA project (Crane and Hennes, 2016a, 2016b). Land use categories included developed, cultivated (i.e., agriculture), pasture and open land, forest, and lakes and wetlands (which included the surface area of the lake). The number of feedlots in each watershed was noted, too. The major land use categories for the study lakes included: developed (two lakes); cultivated (20 lakes); forested (24 lakes, including the four reference lakes); and lakes and wetlands (eight lakes; Table A-2). 2.5. Calculated benchmarks Ecological benchmark values were calculated for groups of chemical classes, as well as for a compilation of different chemicals. The calculation methods, including how censored (i.e., nondetect) data were treated, is provided elsewhere (Crane and Hennes, 2016a). Briefly, the U.S. EPA's PAH Equilibrium Partitioning Sediment Benchmark (ESB) Toxic Units (TU) model was used for evaluating ecological risk to aquatic invertebrates. It was calculated for ∑PAH34-TU (Table A-4) using critical concentrations of PAHs in sediment that are related to final chronic values (USEPA, 2003; Burgess, 2009; Table A-5) and TOC data. For PCDD/Fs, aquatic life toxic equivalents (TEQs) were calculated by

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multiplying the 17 PCDD/F congener concentrations by their respective World Health Organization (WHO) toxic equivalency factors (TEFs; Table A-3) and summing the results. Because TEF values for benthic invertebrates have not been developed, TEF values based on fish were used (Van den Berg et al., 1998). Mean probable effect concentration quotients (PEC-Qs) were calculated on a dry weight basis to distill data from a mixture of contaminants into one unitless index (Crane and Hennes, 2007). Briefly, individual PEC-Qs [i.e., chemical concentration divided by the corresponding PEC value (USEPA, 2000b)] were calculated for chemicals with reliable PECs [i.e., seven metals, ∑PAH13 (Table A-4), and Total PCBspesticide scan; MacDonald et al., 2000]. The PAH and PCB concentrations were not normalized to TOC (Crane and Hennes, 2007). Next, the mean PEC-Q for metals with reliable PECs (i.e., arsenic, cadmium, chromium, copper, lead, nickel, and zinc) were calculated. Finally, the mean PEC-Q values for the three main classes of chemicals with reliable PECs were calculated. In some cases, the mean PEC-Qs were only calculated using the mean PEC-Qmetals and PEC-Q∑PAH13 data for samples lacking Total PCBspesticide scan data. 2.6. Comparisons to sediment quality targets (SQTs) Sediment chemistry data and benchmark values, on a dry weight basis, were compared to analogous Level I and Level II sediment quality target (SQT) values for benthic invertebrates (Crane et al., 2000, 2002; Crane and Hennes, 2007). The SQTs include a suite of eight metals and metalloids, 13 low molecular weight and high molecular weight PAHs, ∑PAH13, ∑PCBs, 10 legacy organochlorine pesticides, mean PEC-Qs (Table A-6), and PCDD/F TEQs (Table A-7). Most of the SQTs were adopted from consensus-based sediment quality guidelines (MacDonald et al., 2000), for which most of the Level I SQTs correspond to threshold effect concentrations (TECs) and most of the Level II SQTs correspond to PEC values. The Level I SQTs are intended to identify contaminant concentrations below which harmful effects on benthic invertebrates are unlikely to be observed, whereas the Level II SQTs are intended to identify contaminant concentrations above which harmful effects on benthic invertebrates are likely to be observed (Crane et al., 2000, 2002; Crane and Hennes, 2007). The SQTs adopted from other sources are identified in Tables A-6 and A-7. 2.7. Statistical analyses An in-depth description of the statistical methods used to evaluate the analytical data and calculated benchmarks is presented in Crane and Hennes (2016a). Summary statistics were run in either Microsoft Excel 2010 (Microsoft Corporation, Redmond, WA), SigmaPlot 13.0 (Systat Software, Inc., San Jose, CA), or the U.S. EPA's ProUCL 5.0 software. Shapiro-Wilk normality tests, Pearson product moment correlations, Spearman rank order correlations, equal variance tests, pairwise Student's t-tests, Mann-Whitney rank-sum tests, one-way Analysis of Variance (ANOVA), Kruskal-Wallis ANOVA on ranks, Holm-Sidak and Dunn's method multiple pairwise comparisons, and principal components analysis (PCA) were run in SigmaPlot 13.0. Hierarchical cluster analysis (HCA) was conducted using PAleontological STatistics (PAST) version 3.0 software. Box plots, quantile-quantile (Q-Q) plots, outlier tests at the 5% significance level (i.e., Dixon's outlier test for b25 detected samples and Rosner's outlier test for ≥25 detected samples), goodness of fit tests, and background threshold values were determined using ProUCL 5.0 software for both detected parameters and those with b 80% nondetects. The Kaplan-Meier method (Helsel, 2012) was used in ProUCL 5.0 to calculate total values of PAH compounds and PCB congeners containing censored data with b80% nondetects (i.e., the Kaplan-Meier mean was multiplied by the number of PAH compounds or PCB congeners, respectively, to determine total values). The Kaplan-Meier method (Helsel, 2012) was also used in ProUCL 5.0 to calculate summary statistics and background threshold values for

censored data sets with b80% nondetects. For some other statistical methods (e.g., correlation analyses), substitution at the full reporting limit was used when there were b5% nondetects. This low percentage was selected to reduce bias from substitution methods. The data were not weighted to make general classifications for statewide lakes, although the study design could allow this to be done. PCA is a multivariate statistical technique used to reduce the complexity of data sets to its most important components. PCA takes a large number of interrelated (i.e., correlated) variables and transforms the data to a new set of uncorrelated reference variables known as principal components. The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible. If most of the variability between samples can be accounted for by a small number of principal components (e.g., two or three), then relationships between multivariate data can be assessed by a two- or three-dimensional plot of the principal component scores. Since PCA is sensitive to variance in data sets, all of the detected and calculated data selected for PCA analyses underwent a range transformation (also known as the maximum-minimum transformation; Johnson et al., 2004). This step resulted in unitless data so that a covariance matrix could be run for each PCA. The significance level for hypothesis testing was set at 0.050 with a confidence level of 95%. The selection method for components was either selected for the average eigenvalue, minimum eigenvalue (e.g., 1.0), or number of components (e.g., three). The Henze-Zinker normality test was used with a p value to reject of 0.050. Scree plots, component loading plots, and component score plots were produced for each PCA analysis. In addition, separate 3D plots of the component scores were made using SigmaPlot 13.0. Similar groups of samples were noted on the 3D plots based on the HCA results of the PCA component scores. The best HCA option for identifying discrete groups was the Paired Group (UPGMA) algorithm with the Gower similarity index (Crane and Hennes, 2016a). ProUCL 5.0 was used to estimate several types of background threshold values of the ambient data. In order to perform these analyses, outliers and/or separate populations of data from the dominant population of raw data were first removed. While the outlier tests provided a statistical basis for selecting outliers, visual observation of the Q-Q plots (Crane and Hennes, 2016a) was also used to select breaks in the data that might be indicative of additional outliers or a separate population of data. The 95% one-sided upper tolerance limit (UTL) with 95% coverage (hereafter referred to as the UTL or UTL95-95) was the background threshold value of greatest interest. The UTL95-95 represents the value below which 95% of the population values are expected to fall with 95% confidence. This approach resulted in no more than a 5% false positive error rate (i.e., the chance of falsely classifying a true background concentration as a contaminated concentration). When the data were not normally distributed and the gamma distribution was used, the gamma UTL upper limits were estimated using the Wilson Hilferty (WH) and Hawkins Wixley (HW) methods. For censored data with b80% nondetects, the Kaplan-Meier versions of the UTL95-95 calculations were performed. 2.8. Environmental forensics 2.8.1. PAHs Environmental forensic approaches for determining sources of PAHs in the study lakes are described in detail in Crane and Hennes (2016a). Briefly, qualitative (i.e., PAH histogram plots), semi-quantitative (i.e., PAH source ratio plots), and quantitative (i.e., chemical mass balance model) approaches were used. Histogram plots of parent and alkylated PAH concentrations (generated in SigmaPlot 13.0) were used to assess patterns associated with petrogenic (i.e., oil-based) and/or pyrogenic (i.e., combustion-based) sources of PAHs. In addition, the following diagnostic source ratios were used to identify petrogenic and pyrogenic sources of PAHs: fluoranthene:pyrene (F/P) and

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phenanthrene:anthracene (P/A; Budzinski et al., 1997). The following source ratios were used for determining a range of major emission sources from automobiles: benzo[b]fluoranthene:benzo[k]fluoranthene (BbF/BkF) vs. benzo[a]pyrene:benzo[e]pyrene (BaP/BeP; Dickhut et al., 2000). Double-ratio plots were generated in SigmaPlot 13.0 for these combinations of PAH source ratios. The U.S. EPA's Chemical Mass Balance (CMB) 8.2 receptor model (Coulter, 2004) was used to provide a quantitative assessment of important sources of PAHs to the study lakes. This air model has been adapted for use in the source apportionment of PAHs in sediment (Li et al., 2003; Van Metre and Mahler, 2010; Crane, 2014; Baldwin et al., 2016). The model uses an effective variance weighted least square solution to the CMB equations, provided several model assumptions are met (Li et al., 2003; Coulter, 2004). Similar methods to Crane (2014) were followed for this data set. Briefly, the model included ≤ 12 parent PAHs (∑PAHCMB) identified in Table A-4. A group of published PAH source profiles listed in Crane (2014) were considered for inclusion in the model, including the general categories of coal tar-based sealant (CTsealant) dust and particulate runoff, asphalt sealant pavement dust, vehicle-related sources, coal combustion, fuel oil combustion, fireplace combustion of wood products, and used motor oil. The model included several source inputs. Each run of the CMB8.2 model attempted to fit the source data to the ambient sediment data in ≤20 iterations using source elimination. The source profile uncertainty was set at 40% (Li et al., 2003; Van Metre and Mahler, 2010; Crane, 2014; Baldwin et al., 2016), and the minimum source projection was set at the default value of 0.95. The uncertainty values of the ambient PAH data from the lake sediment samples were set at 20%. Output files were generated as comma-separated value files that were imported into Microsoft Excel 2010 for further analysis. Several statistical procedures were used to prepare the measured ∑PAHCMB data and for evaluating source profile data for inclusion in the CMB8.2 model. PAH proportional values (i.e., individual PAH concentrations divided by ∑PAHCMB) were calculated and assessed for normality using the Shapiro-Wilk test. The linear dependence between PAH proportional values of normally distributed sources and sediment data were assessed using Pearson product moment correlations (r) and the statistical significance (p) of the correlations. A Spearman rank order correlation was run when the data were not normally distributed. The sediment data were evaluated several different ways to select the best sources to include in the model. Due to the widespread geographic coverage of the samples in Minnesota, the best approach was to run each sample independently through the model with an assortment of source options that were significantly correlated to the sediment data. Graphs comparing the PAH proportional values for the sources and groups of lakes using those sources were prepared in SigmaPlot 13.0. Several statistical measures were used to assess the performance of each model run. These measures included Chi square, coefficient of determination (R 2 ) values, the percent mass estimated by the model, and the T-statistic (Coulter, 2004). Chi square is the weighted sum of squares of the differences between the calculated and measured fitting species concentrations; values less than one were preferred (indicating a very good fit to the data), whereas values between one and two were also acceptable. R2 values near 1.0 indicated that the source contribution estimates explained the measured concentrations very well, but other R2 values N0.8 were acceptable, too. Percent mass represented the percent ratio of the sum of the modelcalculated source contribution estimates to the measured mass concentration, and ratios approaching 100% were ideal; ratios between 80 and 120% were also acceptable. The T-statistic that appeared with the source contribution estimates provided additional information to assess model performance because it could be indicative of collinearity among the source profiles when it was b 2.0 (Coulter, 2004). Finally, mean RPD values between measured and modeled ∑PAHCMB concentrations were calculated.

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2.8.2. PCDD/Fs The weight percentage pattern of PCDD/F congeners and its homologs creates a unique “fingerprint” that can be used to identify sources of these compounds. The weight percentages of 17 PCDD/F congeners were calculated, and the mean and standard deviation (SD) values were plotted in a histogram plot. The results were then compared to published literature values for likely sources. The percent homolog patterns of PCDD/Fs were calculated, and the mean (SD) values were plotted in a histogram plot. The data were further broken out into different lake groups and assessed for differences due to source type. Another common forensic technique is to compare patterns in the ratios of 2,3,7,8-TCDD (i.e., PCD2378) concentrations with the homolog of total TCDDs (i.e., PCD_T4; Quadrini et al., 2015). A plot of this ratio for detected compounds in individual lakes were compared to published literature values for sources. All plots were prepared in SigmaPlot 13.0.

3. Results and discussion 3.1. Lake sediment quality 3.1.1. Summary statistics Summary statistics for SQT-compatible parameters with b80% nondetects are provided in Table 1. For the other chemical and particle size parameters, summary statistics are provided in Table A-8 along with additional data interpretation in the Supplemental Information. Data from the four a priori reference lakes were pooled with the random lakes, because a preliminary evaluation of the PAH data showed that the contaminant concentrations were not statistically significantly different (p N 0.05) between the reference lakes and random lakes in the same major land use class (i.e., forested; Crane and Hennes, 2016a). Based on the small sample size of the a priori reference lakes (n = 4) and random lakes (n = 50), it was also advantageous to pool these data to increase the power of the statistical tests. Most of the SQT-compatible metals, the metalloid arsenic, and PAHs were detected in all sediment samples (Table 1). However, mercury was not detected in 59% of samples due to higher reporting limits than expected, and cadmium was not detected in 11% of lake sediments (Table 1). The highest mercury concentrations were found in lakes located in northeast Minnesota, which was consistent with other research (Engstrom et al., 2007). Sediment accumulation rates are lowest in northeast Minnesota lakes where the local geology is characterized by mostly thin, noncarbonated glacial drift and crystalline bedrock (Engstrom et al., 2007). Thus, these lakes have lower watershed sources of sediment to dilute mercury resulting from atmospheric inputs. Most of the mercury found in Minnesota's lakes and rivers is due to atmospheric deposition, of which 90% comes from other states and countries (MPCA, 2013). Although the mean mercury concentrations were low compared to other metals and metalloids (Tables 1 and A-8), this element is of high concern because of its potential to transform to methylmercury. Certain bacteria can convert mercury to methylmercury in aquatic sediments, where it can build-up in the food chain and accumulate in fish tissue (Hsu-Kim et al., 2013). Twenty-four of the 54 study lakes have fish advisories for mercury (Crane and Hennes, 2016a). SQT-compatible PAHs were ubiquitous in all 54 lakes. The two urban lakes from the Minneapolis-St. Paul, MN metropolitan area [i.e., Nokomis (#27) and Snail (#38)] had the highest ∑PAH13, ∑PAH17, and ∑PAH34 concentrations of the study lakes (Fig. A-1), indicating urban watershed sources of PAHs were important. Mean concentrations of SQT-compatible PAHs in the 54 lakes varied by two orders of magnitude, ranging from 4.5 μg/kg dry wt. for dibenzo[a,h]anthracene to 109.7 μg/kg dry wt. for fluoranthene (Table 1). All PAH ESB Toxic Unit values were below 1.0 (Table 1), indicating that benthic organisms in these lakes are not likely to encounter adverse effects through narcosis (i.e., resulting in the alteration of cell membrane function; Burgess, 2009).

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Table 1 Summary statistics for SQT-compatible parameters with b80% nondetects in sediment. Parameter

N

% Nondetects

Meana

SDa

Metal or metalloid (mg/kg dry wt.) Arsenic Cadmium Chromium Copper Lead Mercury Nickel Zinc

54 54 54 54 54 54 54 54

0 11.1 0 0 0 59.3 0 0

10.5 0.52 35 25.4 34.1 0.1 16 61.4

12.6 0.33 19.8 35.6 30.9 0.1 10.9 29.2

PAHs (μg/kg dry wt.) 2-Methylnaphthalene Acenaphthene Acenaphthylene Anthracene Fluorene Naphthalene Phenanthrene Benzo[a]anthracene Benzo[a]pyrene Chrysene Dibenzo[a,h]anthracene Fluoranthene Pyrene ∑PAH13

54 54 54 54 54 54 54 54 54 54 54 54 54 54

0 0 0 0 0 0 0 0 0 0 5.6 0 0 0

9.8 16.3 8.7 11.7 41.1 16.4 70.4 31.1 34.2 57.1 4.5 109.7 78.4 489.3

6.5 10.6 28 30.5 30.4 11.3 103.1 77.8 96.4 128.4 12.6 282 193.2 980.5

PAH ESB Toxic Units

54

0

0.017

0.019

Total PCBs (μg/kg dry wt.) Total PCBspesticide scan ∑PCBcongeners

24 24

8.3 50

16.7 18.8

19.6 19.7

PEC-Qs Metal PEC-Q Mean PEC-Q

54 54

0 0

0.23 0.11

0.13 0.062

70.8 62.5 62.5 29.2 16.7 54.2 4.2 62.5 4.2 29.2 45.8

0.36 0.38 0.1 2.2 8 0.36 12.5 0.38 25 0.24 0.31

0.63 0.91 0.083 5.8 20.6 0.84 24.8 0.47 53.7 0.3 0.42

0

3.8

4.1

Pesticide or metabolite (μg/kg dry wt.) alpha-Chlordane 24 gamma-Chlordane 24 Dieldrin 24 o,p′-DDD 24 p,p′-DDD 24 o,p′-DDE 24 p,p′-DDE 24 p,p′-DDT 24 Total DDT 24 cis-Nonachlor 24 trans-Nonachlor 24 PCDD/F TEQs (ng TEQ/kg dry wt.)

23

SQT = sediment quality target; N = number of samples; SD = standard deviation; PAHs = polycyclic aromatic hydrocarbons; PCBs = polychlorinated biphenyls; PEC-Q = probable effect concentration quotient; DDD = dichlorodiphenyldichloroethane; DDE = dichlorodiphenyldichloroethylene; DDT = dichlorodiphenyltrichloroethane; TEQ = toxic equivalent. a The Kaplan-Meier method was used to calculate mean and SD values for censored data sets that had b80% nondetects.

Total PCBs, determined two different ways, had similar mean (SD) concentrations (Table 1). Total PCBspesticide scan were detected in 22 of 24 lakes, and ∑PCBcongeners were estimated in 12 of 24 lakes from PCB congener data (Table 1). The highest concentrations were observed in Nokomis (#27) and Snail (#39). The estimated ∑PCBcongener values were highly correlated (r = 0.998) to Total PCBspesticide scan values (Fig. A-2). Further work is needed to determine if the pesticide scan Total PCBs could provide a cost-effective screen for congener-specific ∑PCBs. The coplanar PCBs were also included in a different version of the PCDD/F TEQs, but they contributed little to this calculation (Crane and Hennes, 2016a). As an integrative indicator, the mean PEC-Qs demonstrated that metals were the most important component. When calculated with metals alone, the metal PEC-Q was twice as high as the mean PEC-Q (Table 1). The inclusion of ∑PAH13 and Total PCBspesticide scan data in

the calculation of mean PEC-Qs reduced the contribution of the metal PEC-Q values. The detection frequency of organochlorine pesticides and metabolites varied widely in 24 study lake sediments (Tables 1 and A-8). Dieldrin, which is also a common degradate of aldrin, was detected in nine lakes (Table 1). The half-life of dieldrin in temperate soils is about five years (ATSDR, 2002). Dieldrin had the lowest mean pesticide concentration (0.1 μg/kg dry wt.) in Table 1. Total DDT (which included all DDT metabolites) and isomers of three DDT metabolites (i.e., p,p′-DDE, p,p′-DDD, and o,p′-DDD) were detected the most frequently, ranging from 70.8 to 95.8% detects (Table 1). These compounds also had the highest mean pesticide concentrations (Table 1), despite the ban on DDT in 1972 (USEPA, 1975). These persistent DDT metabolites, along with o,p′-DDE, were also frequently detected in historical sediment cores from the Duluth-Superior Harbor, MN (Schubauer-Berigan and Crane, 1997). DDMU, a breakdown product of DDE (Wetterauer et al., 2012), was detected in 66.7% of samples (Table A-8). The DDT isomers (i.e., p,p′-DDT and o,p′-DDT) were detected less frequently (Table 1). Sediment quality data on ambient DDT and DDT metabolite concentrations in freshwater, surficial sediments are lacking for much of the world (Ricking and Schwarzbauer, 2012). A recent study of DDTs in Polish lake sediments also found p,p′-DDE in nearly all surficial samples (n = 522 samples) followed by p,p′-DDD (n = 502 samples); o,p′-DDT and o,p′-metabolites were not evaluated (Bojakowska et al., 2014). DDT and its metabolite isomer ratios can be used to assess the environmental fate of these compounds (Ricking and Schwarzbauer, 2012). Five different ratios were calculated, as described further in the Supplemental Information and Table A-9. Interestingly, the presence or absence of feedlots in the lake watershed was associated with a significant difference (p b 0.05) in one particular ratio. The median value of o,p′-DDX (i.e., o,p′-DDT + o,p′-DDE + o,p′-DDD) as a percentage of Total DDT was over three times higher in lakes with feedlots than those lacking feedlots. Additional work would be needed to determine if historical or current feedlot operations are a potential source of residual DDT metabolites (particularly o,p′-DDD) to nearby lakes. 3.1.2. Comparisons to guidelines The sediment chemistry data were compared to ranges of Level I and Level II SQT values (Crane et al., 2000, 2002; Crane and Hennes, 2007). The percentage of most parameters (e.g., cadmium, mercury, and benzo[a]pyrene) were below the analogous Level I SQT values (Table 2). Based on the mean PEC-Q values, nearly 43% of lakes were below the Level I SQT value of 0.1. This meant that conditions were protective of sediment-dwelling organisms for these chemicals, and sediment quality was good. A short list of chemicals were more highly elevated between the Level I and Level II SQT values than other SQT ranges, including: acenaphthene, mean PEC-Qs, Total DDT, and PCDD/ F TEQs (Table 2). In particular, approximately 57% of lakes had mean PEC-Qs within the range of N0.1 to ≤0.6, which indicated moderate sediment quality. Previous work in the St. Louis River AOC, MN showed that sediment toxicity increased as the mean PEC-Q ranges increased from ≤0.1, N0.1 to ≤0.5, and three higher ranges (Crane et al., 2002). Thus, lake sediment samples between the Level I and II SQT values for mean PEC-Qs (Table 2) could show a higher incidence of sediment toxicity compared to those below the Level I SQT value. Other physicalchemical factors in sediments, such as particle size, TOC, acid volatile sulfide, and ammonia, can also influence the health of benthic invertebrates (Crane et al., 2000). The Level II SQT values were exceeded in a small number of sediment samples for four metals, arsenic, acenaphthylene, sum DDD, and sum DDE (Table 2). Harmful effects on sediment-dwelling organisms were likely in these samples. The two developed lakes (i.e., lakes #27 and 38) exceeded the Level II SQT value for lead, which was likely due to urban contamination from stormwater runoff and other sources. The Level II SQT exceedances for chromium (lake #3), copper (lake #6), and nickel (lakes #5 and 6) occurred in lakes from northeast

J.L. Crane / Science of the Total Environment 607–608 (2017) 1320–1338 Table 2 Percentage of parameter results within designated SQT ranges. Parameter

Percent of Samples ≤Level I SQT

NLevel I SQT to ≤ Level II SQT

NLevel II SQT

Metal or metalloid (n = 54) Arsenic Cadmium Chromium Copper Lead Mercury Nickel Zinc

75.9 88.9 70.4 87.0 64.8 81.5 88.9 94.4

20.4 11.1 27.8 11.1 31.5 18.5 7.4 5.6

3.7 0 1.8 1.8 3.7 0 3.7 0

PAHs (n = 54) 2-Methylnaphthalene Acenaphthene Acenaphthylene Anthracene Fluorene Naphthalene Phenanthrene Benzo[a]anthracene Benzo[a]pyrene Chrysene Dibenzo[a,h]anthracene Fluoranthene Pyrene ∑PAH13

96.3 16.7 74.1 98.1 90.7 100 96.3 94.4 94.4 94.4 98.1 96.3 96.3 96.3

3.7 83.3 24.1 1.9 9.3 0 3.7 5.6 5.6 5.6 1.9 3.7 3.7 3.7

0 0 1.8 0 0 0 0 0 0 0 0 0 0 0

Total PCBs (n = 24) Total PCBspesticide scan ∑PCBcongeners

91.7 91.7

8.3 8.3

0 0

Mean PEC-Q (n = 54)

42.6

57.4

0

Pesticide or metabolite (n = 24) Chlordanea Dieldrin Sum DDD Sum DDE Sum DDT Total DDT (measured)b Endrin Heptachlor Epoxide gamma-Hexachloro-cyclohexane (Lindane) Toxaphenec

91.7 100 75 50 100 41.7 100 100 100

8.3 0 16.7 37.5 0 58.3 0 0 0

0 0 8.3 12.5 0 0 0 0 0 0

87

0

PCDD/F TEQs (n = 23)

100 13

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organisms showed that sodium arsenite increased in use in Minnesota in 1954, and it was used to eradicate aquatic vegetation through 1969 (Crane and Hennes, 2016a). Arsenic is likely to remain high in Snail Lake for some time. For a lake in Michigan that was treated with sodium arsenite in 1957, Siami et al. (1987) developed a model that predicted it would take N 100 years for arsenic levels to reach pretreatment concentrations in the surficial sediments. The source of the high arsenic concentration of 67.5 mg/kg dry wt. in Elk Lake (#51) is less certain. Elk Lake is a “super-sentinel” deep-water lake located in Itasca State Park (O'Hara et al., 2011), and it is included in a group of 24 lakes being studied by the MDNR to understand the effects of environmental stressors such as climate change (http://www.dnr.state.mn.us/fisheries/slice/ sentinel.html; accessed 3/30/2017). Further sampling is warranted to evaluate if other areas of Elk Lake have elevated arsenic concentrations in the sediment. 3.1.3. Multivariate statistical analyses PCA analysis of the 54 lakes was performed to reduce a set of chemical variables to the ones that accounted for most of the variance. In turn, similiarites and differences among lakes were evaluated using HCA analysis. The PCA analysis was done for a subset of chemicals that also have SQT values, including: five metals (i.e., chromium, copper, lead, nickel, and zinc), the metalloid arsenic (hereafter lumped with the other metals), ∑PAH13, and mean PEC-Qs (Fig. 2). The scree plot, component loading plots, and component score plots are provided in the Supplemental Information (Figs. A-3 and A-4). Three principal components accounted for 80.1% of the variance in the data. Each principal component (PC) is a linear combination of the original variables, after each original variable has been centered about its mean. The Eigenvectors in PC1 were highest for zinc, mean PEC-Qs, and lead, while nickel and arsenic contributed to PC2. Arsenic, copper, and zinc contributed the greatest Eigenvectors to PC3. Chromium and ∑PAH13 were reduced from the first three PCs. The unexplained variance ranged from 5.3% for mean PEC-Qs to 48.1% for chromium. Four significant outliers (p b 0.05) were noted from the component scores, including: lakes #6 (Becoosin),

SQT = sediment quality target; PAHs = polycyclic aromatic hydrocarbons; PCBs = polychlorinated biphenyls; PEC-Q = probable effect concentration quotient; DDD = dichlorodiphenyldichloroethane; DDE = dichlorodiphenyldichloroethylene; DDT = dichlorodiphenyltrichloroethane; PCDD/Fs = polychlorinated dibenzo-p-dioxins/dibenzofurans; TEQs = toxic equivalents. a Chlordane = cis-Nonachlor + trans-Nonachlor + alpha-Chlordane + gammaChlordane. b When Total DDT was calculated as Sum DDD + Sum DDE + Sum DDT, it yielded 45.8% of samples ≤ Level I SQT and 54.2% of samples in the category of NLevel I to ≤ Level II SQT. c The reporting limits for toxaphene exceeded the Level I SQT value of 0.1 μg/kg dry wt., but were less than the Level II SQT value of 32 μg/kg dry wt. Values can only be expressed as bLevel II SQT.

Minnesota where these metals are naturally high in geological minerals. Lake #6 (Becoosin) is located in the protected BWCAW, and would only receive air-derived anthropogenic inputs of contaminants besides naturally derived runoff from the local watershed. For acenaphthylene, the Level II SQT value was exceeded in lake #27. The developed lakes (#27 and 38) exceeded the Level II SQT values for Sum DDD and Sum DDE, as did lake #32 (Pebble) for Sum DDE. Arsenic exceeded the Level II SQT value in lakes #38 (Snail) and 51 (Elk). The high arsenic concentration of 73.2 mg/kg dry wt. in Snail Lake (#38) was due to past treatments of arsenic compounds to control aquatic macrophytes and algae (Crane and Hennes, 2016a). A review of historical lake permits to destroy or control aquatic vegetation and

Fig. 2. PCA plot of three principal components for six metals, mean PEC-Q values, and ∑PAH13 that have corresponding SQT values. The significant outliers (p b 0.05) identified in the PCA analysis included lakes #6, 27, 38, and 51.

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Table 3 Multiple pairwise comparisons by land use categories for selected parameters. Pairs noted with gray shading are significantly different (p b 0.05) from each other.

p < 0.05 (gray shading) Parameter

Dev vs. cul

Dev vs. wet

Dev vs. for

For vs. cul

For vs. wet

Wet vs. cul

Metal or metalloid Arsenica,b Bariuma,b Chromiuma,b Coppera,b Leadb,c Zincb PAHs Acenaphtheneb,c Acenaphthylened

DNT

DNT

DNT

Benzo[a]anthracened

DNT

DNT

DNT

Dibenzo[a,h]anthracenea,b Benzo[a]pyrened

DNT

DNT

DNT

DNT

DNT

DNT

Benzo[b]fluoranthenea,b Benzo[g,h,i]perylenea,b Benzo[k]fluoranthened C1-chrysenesa,b C1-dibenzothiophenesb,c C1-fluorenesb,c C1-fluoranthenes/pyrenesd

DNT

DNT

DNT

DNT

DNT

DNT

DNT

DNT

C1-naphthalenesb C1-phenanthrenes/anthracenesd C2-fluorenesb,c C2-phenanthrenes/anthracenesd C3-naphthalenesb Chrysened

DNT

DNT

DNT

Dibenzothiophened

DNT

DNT

DNT

Fluoranthened

DNT

DNT

DNT

Pyrened

DNT

DNT

DNT

∑PAH13d

DNT

DNT

DNT

∑PAH17d

DNT

DNT

DNT

∑PAH34d

DNT

DNT

DNT

Indeno[1,2,3-cd]pyrenea,b 1-Methylnaphthaleneb 2-Methylnaphthaleneb 1-Methylphenanthrenea,b 1,6,7-Trimethylnaphthalenea,b Naphthaleneb,c Phenanthreneb,c

Biphenylb,c PEC-Qs Metal PEC-Qa,b Mean PEC-Qa,b TOCb Dev = developed; cul = cultivated; for = forested; wet = lakes and wetlands; PAHs =polycyclic aromatic hydrocarbons; DNT = do not test; PEC-Qs = probable effect concentration quotients; TOC = total organic carbon. a

Based on natural log transformation of data. The Holm-Sidak method was used for these pairwise multiple comparisons. c Based on square root transformation of data. d Dunn's method was used for these pairwise multiple comparisons. b

27 (Nokomis), 38 (Snail), and 51 (Elk; Fig. 2). This result also matched the dissimilarity of these lakes from each other and the other lakes as determined by HCA analysis (Figs. 2 and A-5). All four of these outlier lakes exceeded the Level II SQT values for metals that were important in one or more PCs. In addition, the northeast Minnesota lakes of #3 (Arthur), 4 (Aspen), and 5 (August) clustered together (Fig. 2) These lakes tended to have higher nickel and elevated chromium concentrations

compared to the other lakes. All other lakes, encompassing cultivated, forested, and lakes and wetlands land uses, were clustered together in one group (Fig. 2). 3.1.4. Major land use comparisons Statistical comparisons were run on highly detected sediment data (i.e., ≤5% nondetects) collected from all the study lakes to test the null

J.L. Crane / Science of the Total Environment 607–608 (2017) 1320–1338

hypothesis of no significant differences between major watershed land use groups. Either one-way ANOVAs or Kruskal-Wallis one-way ANOVAs on ranks were run on either raw or transformed data (Table A-10). Fifteen parameters (e.g., aluminum, manganese, and vanadium) were similar (p N 0.05) across land use groups (Table A-10). Parameters with statistically significant results (p b 0.05) underwent multiple pairwise comparisons by major land use categories (Table 3). The mean or median values of thirty-four of 41 parameters (Table 3) showed statistically significant differences (p b 0.05) between lakes with developed watersheds (n = 2) versus lakes with cultivated watersheds (n = 20 lakes). In all cases, higher concentrations were observed in the developed lakes for arsenic, lead, zinc, 25 PAHs, ∑PAH13, ∑PAH17, ∑PAH34, biphenyl, metal PEC-Qs, and mean PEC-Qs. Lead, zinc, and PAHs are common urban contaminants (Callender and Rice, 2000; Van Metre and Mahler, 2005; Jartun et al., 2008). The significant result for arsenic was due to past treatment of an urban lake (i.e., Snail—#38) with arsenic compounds to reduce aquatic macrophytes (Crane and Hennes, 2016a). Biphenyl occurs naturally in coal tar, crude oil, and natural gas, and it is released to the environment by the incomplete combustion of mineral oil and coal in addition to vehicle emissions (World Health Organization, 1999). Thus, biphenyl has several common urban sources. The elevated urban PEC-Q values are reflective of the metals and/or ∑PAH13 values used to calculate them. For lakes in developed watersheds versus those in watersheds dominated by lakes and wetlands, as well as forests, there were 19 and 18 parameters, respectively, with statistically significant (p b 0.05) results (Table 3). In all cases, mean or median concentrations were highest in lakes surrounded by developed land uses. Some other PAH compounds (e.g., acenaphthylene) had “Do Not Test” results for these comparisons when the nonparametric Dunn's method was used for the pairwise comparisons (Table 3). This result occurs for a comparison when no significant difference (p N 0.05) is found between the two rank sums that enclose that comparison. A result of “Do Not Test” should be treated as if there is no significant difference between the rank sums, even though one may appear to exist. The other multiple pairwise comparisons had a lower number of statistically significant (p b 0.05) results. For lakes in forested versus cultivated watersheds, 13 parameters had statistically significant (p b 0.05) differences (Table 3). These parameters included barium, lead, zinc, seven PAH compounds, metal PEC-Q, mean PEC-Q, and TOC (Table 3). In all cases, except barium, the mean or median concentrations were

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higher in lakes with forested watersheds. Only a few statistically significant (p b 0.05) results were noted for lakes in cultivated versus lakes and wetlands watersheds (Table 3). For other parameters measured in a subset of lakes (n ≤ 24), statistical comparisons were run between the developed lakes (#27 and 38) and the remaining samples if there were ≤ 5% nondetects. The results are provided in the Supplemental Information. Briefly, statistically significant (p b 0.05) differences were observed for Total DDT, p,p′-DDE, ∑PCBcongeners, Total PCBspesticide scan, ∑PCDD/Fs, and most PCDD/F congeners and homologs (except PCD_T4; Table A-11). For these parameters, higher mean or median values were observed in the developed lakes. These results, in addition to the majority of the results for parameters measured in all of the study lakes, demonstrate how urban environments have impacted sediment quality in developed lakes compared to lakes surrounded by other major watershed land uses. 3.1.5. Lake surface area comparisons For sediment quality parameters measured in each study lake, statistical comparisons were run between different surface area categories. The analyses were limited to parameters with ≤ 5% nondetects. The null hypothesis was no significant differences between the five surface area categories. Either one-way ANOVAs or Kruskal-Wallis one-way ANOVAs on ranks were run on either raw or transformed data (Table A-12). The only parameters with statistically significant (p b 0.05) differences included: TOC, manganese, six low molecular weight PAHs, and biphenyl (Table A-12). Multiple pairwise comparisons were then run. A limited number of statistical differences were observed between surface area groups. For manganese, surface area categories E and A were not statistically different (p N 0.05) from each other, while the other nine pairwise comparisons were given a “Do Not Test” classification (Table 4). The surface area categories D versus E were significantly different (p b 0.05) for five of the six naphthalene and alkylated naphthalene compounds, as well as biphenyl (Table 4). In all cases, mean values were higher in lakes included in surface area category D (i.e., 50–100 ha). This category included the two developed lakes (#27 and 38), for which one or both of them were statistical outliers at the 5% significance level for these six compounds. For TOC, two pairwise comparisons were statistically significant (p b 0.05; Table 4). Mean TOC values were significantly greater (p b 0.05) in class A versus class

Table 4 Multiple pairwise comparisons of selected parameters by lake surface area categories. Pairs shaded gray are significantly different (p b 0.05) from each other.

p < 0.05 (gray shading) Parameter

A vs. E

C vs. E

D vs. E

Seven other pairwise comparisons

DNT

DNT

DNT

Metal Manganesea PAHs C1-naphthalenesb,c C2-naphthalenesc,d 1-Methylnaphthaleneb,c 2-Methylnaphthaleneb,c 1,6,7-Trimethylnaphthalenec,d Naphthaleneb,c Biphenylc,d TOCc DNT = do not test; PAHs = polycyclic aromatic hydrocarbons; TOC = total organic carbon. Surface Area Categories: A = 4-10 ha; C = 20-50 ha; D = 50-100 ha; E = >100 ha. a

Dunn's method was used for this pairwise multiple comparison. Based on square root transformation of data. c The Holm-Sidak method was used for these pairwise multiple comparisons. d Based on natural log transformation of data. b

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the 12 PAH compounds included in ∑PAHCMB (Crane and Hennes, 2016a). For the final model runs, no samples were excluded from the CMB8.2 model, residual uncertainty was minimized, the T-statistic that appeared with the source contribution estimates was N 2.0 for the major source(s), and the model performance [mean (SD)] was acceptable for R2 [0.957 (0.027)], Chi Square [0.46 (0.22)], and percent mass [96.8% (5.5%); Fig. A-7]. The mean RPD between measured and calculated ∑PAHCMB concentrations (Fig. A-7) was 4.7% with a SD of 4.8%; the median RPD was 2.7%. The source apportionment of PAHs in each study lake was effectively accomplished with the CMB8.2 model. Twelve source profiles (Table A13) were considered for the entire lake data set, for which each lake typically used two or three sources (Crane and Hennes, 2016a, 2016b). Two lakes used one source, four lakes used four sources, and one lake used five sources. Lakes that had a common source were grouped together so that the mean and SD of PAH proportional values for the 12 PAHs used in the calculation of ∑PAHCMB could be compared to the analogous PAH proportional values of the common source profile. Those plots with the strongest correlations are provided in Fig. 4, while the remaining plots are given in Fig. A-8. In particular, the mean PAH proportional values for lakes were highly correlated to both the CT-sealant dust: Austin, TX #2 source (Mahler et al., 2010) and the coal emissions average source (Li et al., 2003; Fig. 4). For vehicle emission sources, the PAH proportional values for diesel vehicle particulate emissions (Li et al., 2003) were similar to the higher molecular weight PAH proportional values from lakes using this source, while the traffic tunnel air source (Li et al., 2003) more closely aligned with lower molecular weight PAHs from lakes using this source (Fig. 4). For the pine wood soot particles #1 source (Schauer et al., 2001), it was similar to the PAH proportional values of lakes using this source, except for fluoranthene and pyrene. The CMB8.2 model considered the best mix of source profiles to approximate the ambient sediment data so it was not necessary for all source PAH compounds to be in sync with the ambient data. The source apportionment results were further generalized into overarching source categories of CT-sealant pavement dust, vehicle emissions, coal-related combustion, and wood combustion. Most study lakes had two to three results in these broad source categories. Vehicle emissions and coal-related combustion were tied as the most prevalent sources of PAHs to sediments in 41 lakes. CT-sealant pavement dust was a source of PAHs to sediment in 32 lakes. Van Metre et al. (2012) estimated that PAH emissions from new CT-sealant applications each year (~1000 Mg) are larger than annual vehicle emissions of PAHs for the U.S. Thus, there could be long-range transport of CTsealant dust particles that could be deposited via wet and dry deposition to Minnesota lakes outside of urban areas. Wood combustion was a source of PAHs in 12 lakes. Most of the study lakes, outside of the

E lakes, as well as in class C versus class E lakes. In general, assessing lakes by surface area class was less useful than by major land use categories in the lake watersheds. 3.1.6. Environmental forensics 3.1.6.1. PAHs. The use of several types of environmental forensic procedures demonstrated the importance of pyrogenic sources of PAHs to the study lakes. Visual observations of PAH histogram plots indicated pyrogenic sources of PAHs were dominant due to higher concentrations of most parent PAHs than their alkylated homologs (Crane and Hennes, 2016a, 2016b). In addition, high concentrations of perylene in some lakes comprised a major proportion of ∑PAH34 (Fig. A-6), which is indicative of a diagenic source for this PAH compound. In many sediment studies, perylene does not associate well with pyrogenic PAHs resulting from atmospheric deposition (Slater et al., 2013). Perylene is produced naturally in anoxic sediments, but its precursor has not been identified. Some researchers think that perylene originates mainly from in situ biogenic diagenesis under anoxic conditions and that this process is microbially mediated (Silliman et al., 2000; Han et al., 2015). Christensen et al. (2007) observed that watersheds dominated by peat and bogs can lead to high levels of perylene in lake sediments. North central and northeast Minnesota have the greatest concentration of peatland in the U.S. (N6 million acres) after Alaska (Wright et al., 1992). Perylene was high in other parts of Minnesota, so there are likely other organic material sources important for its formation in sediments. PAH source ratios were used for semi-quantitative analysis of the data. A double-ratio plot of P/A versus F/P (Fig. 3a) indicated that the samples were dominated by pyrogenic sources since the F/P ratios were N 1 and the P/A ratios were b10 for most samples (Budzinski et al., 1997). For P/A ratios between 10 and 15 in Fig. 3a, these values are considered indeterminate relative to source (Gao et al., 2007). A double-ratio plot was also generated for BbF/BkF versus BaP/BeP (Fig. 3b), and it was compared to a range of major emission sources from automobiles (Dickhut et al., 2000). The two urban lakes (#27 and 38) were not within the range of vehicle emissions being a major source of PAHs. Four lakes completely intersected the major vehicle emission ranges of both source ratios: #1 (Allen), #37 (Richey), #39 (South), and #40 (South Drywood). These lakes represented a wide geographic range of Minnesota, which would indicate atmospheric transport of vehicle emissions provide a widespread source of PAHs to Minnesota lake sediments. Since double-ratio plots do not provide a complete understanding of all sources of PAHs, quantitative modeling was used to provide a rigorous assessment of PAH sources. Two-hundred and seventy (270) runs of the CMB8.2 model were made using different combinations of source profiles (Table A-13) for

Range of major emission sources from automobiles

14

Benzo[b]fluoranthene:Benzo[k]fluoranthene

Phenanthrene:Anthracene (P/A)

16 Petrogenic F/P <1 P/A >10

12 10 8 6 Pyrogenic F/P >1 P/A <10

4 2 0.8

a)

1.0

1.2

1.4

1.6

1.8

Fluoranthene:Pyrene (F/P)

2.0

4.0 3.5 3.0 2.5 2.0 1.5

Range of major emission sources from automobiles

1.0 0.5

2.2

0.0

b)

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

Benzo[a]pyrene:Benzo[e]pyrene

Fig. 3. a) Double-ratio plot of phenanthrene:anthracene (P/A) vs. fluoranthene:pyrene (F/P) to distinguish between petrogenic and pyrogenic sources, and b) double-ratio plot for PAH compounds indicative of major emission sources from vehicles.

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Fig. 4. Comparison of PAH proportional values (i.e., individual PAH concentration normalized to ∑PAHCMB concentration) between sources used in the EPA's CMB8.2 model (open circles) and the mean profile for NLA lakes (closed circles; uncertainty bars indicate one SD) for which that source was used in the model results. PAHs range from low molecular weight to high molecular weight compounds along the x-axis. Either Pearson's r or Spearman's rho values, associated p values, and number of lakes in each group are given in parentheses; p values b0.05 are significant.

urban core of the Minneapolis-St. Paul, MN metropolitan area, had low concentrations of ∑PAH13 (i.e., below the Level I SQT value). While PAH concentrations in most of these lake sediments were of low concern, it was helpful to go through this modeling exercise to create a baseline source apportionment that could be used for evaluating the results of federal and state actions in the future (e.g., clean air regulations, improvements in vehicle performance). Sediments provide a historical record of contaminant fluxes and radioisotope dating of cores, coupled with source apportionment modeling, could provide stronger evidence of changes because of regulations or management actions. The source apportionment of PAHs, by general source categories, was also displayed visually for each lake. CT-sealant dust and coalrelated combustion were the dominant sources of PAHs in surficial sediments of the three lakes with the highest ∑PAHCMB concentrations (i.e., #27, 38, and 32; Fig. 5). In comparison, CT-sealant dust and

particulate wash off were dominant sources of PAHs to stormwater pond sediments in the Minneapolis-St. Paul, MN metropolitan area, accounting for 67.1% of modeled ∑PAHCMB concentrations (Crane, 2014). Atmospheric deposition was probably the main transport pathway of PAHs to the four BWCAW lakes (i.e., #2, 6, 17, 47) where motorized vehicles and boats are prohibited from most of the park. Vehicle emissions comprised the major source to each BWCAW lake (Figs. 5 and A-9), demonstrating long-range transport of emission source particles. In addition, coal-related combustion also contributed small amounts of PAHs to Alruss (#2) and Becoosin (#6) lakes (Figs. 5 and A-9). Although the CMB8.2 model provided a rigorous source apportionment of ∑PAHCMB in lake sediments throughout Minnesota, this model has several important limitations. These limitations include: 1) the model results are sensitive to uncertainty in the input data, which is often not well defined; 2) the source profiles are limited by a

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Fig. 5. General source apportionment of PAHs in each lake.

common number of PAHs (i.e., 12 parent PAHs), and a broader suite of PAHs would allow more detailed and unique source profiles to be developed; 3) source profiles were obtained from several literature sources, which may have varying data quality objectives and adherence to QA/ QC protocols; 4) no source profiles were available for ethanol plants (21 in Minnesota; http://www.eia.gov/state/analysis.cfm?sid=MN, accessed 3/31/2017) or for the combustion of biofuels, which may be more important in the future as these fuels increase in use in Minnesota; and 5) source results can be misinterpreted if important sources are excluded from the model (Li et al., 2003; Van Metre and Mahler, 2010; Crane, 2014). Another limitation with interpreting the results is that the surficial sediment core samples were not dated, and the sample lakes are likely to have different sedimentation rates. Thus, the historical record of PAH accumulation in the top 15 cm of each lake is likely to vary. A shorter depth interval (e.g., 0–2 cm or 0–5 cm) would provide a better assessment of more recent sources of PAHs. 3.1.6.2. PCDD/Fs. Multiple, semi-quantitative environmental forensic methods were used to determine broad source categories of PCDD/Fs. On a weight percentage basis, OCDD comprised 72.3 ± 3.5% of ∑PCDD/F congeners followed by PCD1234678 at 12.9 ± 1.5% (Fig. 6a). These two highly chlorinated congeners are indicative of ubiquitous combustion sources, such as wood fires and vehicle exhaust (Cleverly et al., 1997; Barabas et al., 2004; Shields et al., 2006; USEPA, 2006; Towey et al., 2012). These two congeners also dominated regional background sources in soils collected near the Tittabawassee River, MI, for which sections of the river downstream of Midland, MI were contaminated with PCDD/Fs from the Dow Chemical Company (Towey et al., 2010). OCDD comprised 60–80% of ∑PCDD/F concentrations in flood-plain soils and sediment of the Tittabawassee River upstream of Midland, MI, and 30–60% in downstream samples (Hilscherova et al., 2003). Thus, the upstream percentages of OCDD in the Tittabawassee River flood-plain soils and sediments were consistent with the percentages of OCDD observed in the study lake sediments. OCDF comprised the third highest weight percentage of PCDD/Fs in this subset of study lakes at 5.5 ± 2.5% (Fig. 6a). OCDF is a common nonpoint source congener (Shields et al., 2006; USEPA, 2006; Towey et al., 2012). Because of

these highly chlorinated ubiquitous congeners, the fractions of less chlorinated PCDD/F congeners are often more useful for distinguishing unique sources in contaminant site investigations (Towey et al., 2010; Quadrini et al., 2015). The other PCDD/F congeners each comprised b3% of the weight percentage of ∑PCDD/F congeners. Since only one sediment sample was collected from each lake over a wide geographic area in Minnesota, forensic use of less chlorinated PCDD/F congeners is more limited. Another common forensic technique is to compare patterns in ratios of PCD2378 concentrations to PCD_T4. The ratio of detected PCD2378 to PCD_T4 for individual lakes is provided in Fig. 6b. The ratios ranged from 0.010 in lake #11 [Eagle (North)] to 0.21 in lake #37 (Richey). Ratios on the order of 0.06 or less may be indicative of wastewater and atmospheric sources (Chaky, 2003). Quadrini et al. (2015) noted high ratios between 0.6 and 1.0 in lower Passaic River, NJ sediments where 2,3,7,8-TCDD was a by-product of the 2,4,5-trichlorophenol manufacturing process. The ratios in the study lakes were far below those observed in the lower Passaic River. PCDD/Fs emitted from residential burn barrels and open burning of yard waste is the number one source of these compounds in the U.S. EPA's national emissions inventory (USEPA, 2006). As such, the PCDD/F “fingerprint” can vary depending on the type of waste being burned (e.g., plastics and paper treated with chemicals, coatings, and inks). Although most burning of household garbage is illegal in Minnesota, 45% of respondents in a Minnesota survey burned household wastes (https://www.pca.state.mn.us/ living-green/dont-burn-your-garbage, accessed 3/31/2017). The percent homolog patterns of PCDD/Fs provided another forensic technique for three groups of lakes: lake #36, the two developed lakes, and the remaining 20 lakes. Compared to the other study lakes, lake #36 (Red Rock) had very low PCDD/F homolog concentrations and a unique particle size distribution (i.e., very sandy). This lake had a higher percentage of lower and medium chlorinated PCDD homologs (i.e., PCD_T4, PCD_T5, and PCD_T6), and a lower percentage of highly chlorinated PCDD homologs (i.e., PCD_T7 and OCDD) than the other lakes (Fig. 6c). PCF_T6 was the only PCDF homolog detected in Red Rock, for which the homolog weight percentage was lower than in the other lakes (Fig. 6c). A consistent source profile was observed for the

70 15

10

5

0

a) Ratio of PCD2378 to PCD_T4

0.25

0.20

0.15

0.10

0.05

0.00 1

7 11 14 15 20 27 32 37 38 39 50 52 53

b)

Lake #

Homolog Weight Percentage

60 PCDF Homologs

PCDD Homologs

50 40

#36-Red Rock Developed Mean 20 Lake Mean

30 20 10

OCDF

PCF_T7

PCF_T6

PCF_T5

PCF_T4

OCDD

PCD_T7

PCD_T6

PCD_T5

PCD_T4

0

c)

1333

developed lakes, while the mean weight percentage of PCF_T7 was significantly higher (p b 0.05) in the developed lakes than the other 20 lakes (Fig. 6c). These significant differences may be attributed to different sources. For example, residential trash burning (which could contribute PCD_T6) should be less in the Minneapolis-St. Paul, MN metropolitan area where residents have access to commercial trash haulers compared to outstate Minnesota where these services may be lacking. The Minneapolis-St. Paul, MN metropolitan area also has more vehicle and commercial/industrial emissions than most outstate areas, which could contribute to higher weight percentages of PCF_T7.

80

OCDD PCD1234678 OCDF PCF1234678 PCD123789 PCD123678 PCF2378 PCF123478 PCF234678 PCF123678 PCF23478 PCF12378 PCD123478 PCD12378 PCF1234789 PCD2378 PCF123789

PCDD/F Congener Weight Percentage

J.L. Crane / Science of the Total Environment 607–608 (2017) 1320–1338

Fig. 6. a) Mean (SD) weight percentages of PCDD/F congeners, b) the ratio of detected PCD2378 concentrations to PCD_T4 plotted for each lake, and c) mean (SD) weight percentages of PCDD/F homologs in lake #36 (Red Rock), the developed lakes, and the other 20 lakes. For c), the ^ symbol represents four nondetect PCDF homologs in lake #36, and statistically significant (p b 0.05) differences between the developed lakes and 20 lake group are noted with an asterisk.

mean weight percentages of PCDD/F homologs in 20 study lakes, where OCDD N PCD_T7 N PCD_T6 N PCF_T4 N PCF_T5 (Fig. 6c). OCDD and PCD_T7 also dominated the homolog weight percentages in the developed lakes (i.e., #27, 38). Interestingly, OCDD also dominated the PCDD/F homolog profile from residential and industrial areas in central South Africa (Nieuwoudt et al., 2009).The results of either t-tests or Mann-Whitney rank sum tests showed that the weight percentages of two PCDD/F homolog groups had statistically significant differences (p b 0.05). The mean homolog weight percentage of PCD_T6 was significantly higher (p b 0.05) in the other 20 lakes compared to the

3.1.7. Background threshold values The UTL95-95 values were determined for SQT-compatible chemicals (Tables 5 and 6), as well as other parameters (Tables A-14 and A-15), to represent ambient sediment quality conditions in this group of Minnesota lakes. For organic parameters, one or both developed lakes (i.e., #27 and 38) were the most common outliers removed from the UTL95-95 calculations. Potential outliers, or separate distributions of data, were more variable for the metals and metalloids. Environmental lake sediment data above the UTL95-95 values would be more unusual due to natural geologic and/or anthropogenic (e.g., point source) influences on the surrounding watershed or in-lake processes. Since certain metals (e.g., copper, nickel) are naturally elevated in portions of northeast Minnesota, it would be useful to collect more randomly determined surficial sediment data from this area in order to calculate regional UTL95-95 values. In addition, the UTL95-95 values can be expanded as more randomly collected surficial sediment data are collected throughout the state. The SQT-compatible UTL95-95 values were compared to their corresponding Level I and Level II SQT values. Several UTL95-95 values exceeded the corresponding Level I SQT values, but were less than the corresponding Level II SQT values. This list included: arsenic, cadmium, chromium, lead, mercury, nickel, zinc, acenaphthene, acenaphthylene, mean PEC-Qs, sum chlordane isomers, Total DDT, sum DDD isomers, sum DDE isomers, and PCDD/F TEQs. None of the UTL95-95 values exceeded the corresponding Level II SQT values. The MPCA is lacking SQT values for several pesticides (e.g., heptachlor, mirex) so the UTL values are a useful benchmark to assess exceedances of ambient background concentrations. With time, the UTL95–95 values will become more robust as additional, randomly collected surficial sediment data are used to calculate them. In addition, the SQT and UTL95-95 values have different purposes, and it is not necessary for the UTL95–95 values to be less than the Level I SQT values. However, this finding may point out the widespread anthropogenic contamination or natural enrichment of certain parameters. This issue should be taken into consideration for remediation decisions at small, uncomplicated sediment sites where individual SQTs or mean PEC-Qs may be used to develop remediation targets (Crane and MacDonald, 2003). 3.2. Fish trophic class comparisons of BDEs Two of the more commonly detected BDE congeners in sediment samples (i.e., BDEs-47 and 99; Table A-8) were also the dominant congeners in most composite fish tissue samples from lakes #5, 7, 16, 24, and 27. In addition, BDEs-49, 100, and 153 were commonly detected in these fish. For each of these five BDE congeners, there were no significant differences (p N 0.05) in the mean wet weight concentrations between the trophic guilds of fish (i.e., predator, omnivore, benthic). The mean concentrations (pg/g wet wt.) were highest for BDE-47, followed by BDEs-99, 49, 100, and 153 (Table A-16). BDE-47 is usually found more frequently than other BDE congeners in humans, fish, and other biota, followed by BDEs-99, 100, 153, and 154 (USEPA, 2009). The most common BDEs in the fish tissue samples were also prominent in other environmental studies. BDEs-47, 49, 99, and 100 were also prevalent in the atmosphere over Lake Superior and showed a net flux into the lake near Duluth, MN during the summer of 2011 (Ruge et al.,

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Table 5 UTLs of detected SQT-compatible parameters and index values with potential outliers removed at the 5% significance level. UTL units are provided with each chemical group. Parameter

N

Potential outliers removed at 5% significance level (lake ID numbers)

Normal distributiona

Gamma distributiona 95% Approximately gamma UTL with 95% coverage

95% UTL with 95% Coverage

WH

HW

18.2 73.1

18.6 75.2

42.3 10.9

44.7 11.4

41.0 43.5

43.8 46.0

162.3

172.1

147.7 106.7

156.3 113.7

0.48 0.21

0.48 0.22

Metal or metalloid (mg/kg dry wt.) Arsenic Chromium Copper Lead Nickel Zinc

52 53 47 52 51 54

38, 51 3 4, 6, 9, 25, 37, 38, 47 27, 38 4, 5, 6

– – 27.5 67.2 25.1 121

PAHs (μg/kg dry wt.) 2-Methylnaphthalene Acenaphthene Acenaphthylene Anthracene Benzo[a]anthracene Benzo[a]pyrene Chrysene Fluoranthene Fluorene Naphthalene Phenanthrene Pyrene ∑PAH13

52 53 51 52 49 51 49 51 50 53 52 49 51

27, 38 27 24, 27, 38 27, 38 24, 27, 32, 38, 47 27, 32, 38 24, 27, 32, 38, 47 27, 32, 38 24, 27, 34, 49 27 27, 38 24, 27, 32, 38, 47 27, 32, 38

17.2 – – 14.9 – – 64.3 – 69.9 32.8 – – 636

PAH ESB Toxic Units

49

27, 31, 32, 38, 46

0.023

Metal PEC-Q Mean PEC-Q

53 51

38 6, 27, 38

– –

PCDD/F TEQs (ng TEQ/kg dry wt.)

21

27b, 38

6.2

UTL = Upper Tolerance Limit; N = number of samples after removing outliers; ID = identification; WH = Wilson Hilferty; HW = Hawkins Wixley; PAHs = polycyclic aromatic hydrocarbons; ESB = equilibrium partitioning sediment benchmark; PEC-Q = probable effect concentration quotient; PCDD/F = polychlorinated dibenzo-p-dioxins/dibenzofurans; TEQ = toxic equivalent. a UTLs for normally distributed data were preferred over those derived from gamma distributed data. When the data were not normally distributed, the gamma distribution was used to estimate UTL values. Gamma UTLs were calculated two different ways, for which professional judgment can be used to select the value of interest. b Potential outlier was based on professional judgment; the other outlier was based on Dixon's outlier test at the 5% significance level. This test can only select one outlier when there are b25 samples.

2015). Similarly, BDEs-47 and 99 (along with BDE-209) were the most abundant congeners in water column samples from all five Great Lakes (Venier et al., 2014). The prevalence of BDEs-47, 99, and 100 in fish tissues from the study lakes was consistent with other studies of fish tissues in western Lake Superior (MPCA, 2006), Lake Michigan (Kuo et al., 2010), all five Great Lakes for lake trout (Crimmins et al., 2012), in fillets of 18 species of Great Lakes fish in Canadian waters (Gandhi et al., 2017), and in European high mountain lakes and Greenland for liver and muscle tissues of trout (Vives et al., 2004). Concentrations of BDE congeners in lake trout throughout the Great Lakes, in general, are declining as a result of the elimination of the commercial production of pentaBDE in the U.S. during 2004 (Crimmins et al., 2012). Gandhi et al. (2017) found that major lower brominated BDEs declined by 46–74% between 2006/07 and 2012 in Great Lakes fish fillets from Canadian waters. BDE-209 was detected in only one composite fish tissue sample, for which a concentration of 575 pg/g wet wt. was observed in bluegills (an omnivore species) from Lake Nokomis (#27). Stapleton et al. (2006) showed that juvenile rainbow trout and common carp have a metabolic pathway for the debromination of BDE-209, particularly in fish tissue as opposed to the liver where it accumulates. Gandhi et al. (2017) also observed that deca-BDEs in panfish, like bluegill and pumpkinseed, comprised a major contribution to ∑PBDE. The bluegill composite sample from Lake Nokomis (#27) was based on skin-on whole fish with the scales removed so BDE-209 likely accumulated in the liver of these fish. Some of the other fish species examined in this study were based on skin-on fillets or whole fish, and the fillets would be expected to

have lower concentrations of BDE-209 based on the work of Stapleton et al. (2006). Kuo et al. (2010) also observed the concentration of BDE-209 decreased at higher trophic levels, whereas BDEs-47 and 100 biomagnified in a Lake Michigan food web of plankton, Diporeia, lake whitefish, lake trout, and Chinook salmon. Kuo et al. (2010) suggested that partial uptake or biotransformation of BDE-209 could explain their observations. The fish tissue data were normalized for percent lipids to enable all species to be treated similarly (Randall et al., 1998). For three trophic classes of fish, summary statistics were generated for the lipidnormalized concentrations of five BDE compounds (Table A-17). Oneway ANOVAs were run of the trophic class data. The statistical results were significant (p b 0.05) for all five BDE congeners (Table A-18), indicating the differences in the lipid-normalized mean values for the trophic groups were greater than would be expected by chance. Next, the Holm-Sidak method was used for all pairwise multiple comparisons. For BDEs-47, 100, and 153, there were significant differences (p b 0.05) between the predator and omnivore trophic groups, as well as between the predator and benthic trophic groups (Table 7). In all cases, the corresponding lipid-normalized mean BDE concentrations were higher in the predator fish (Table A-17). There was also a significant difference (p b 0.05) between the predator and benthic trophic groups for BDE-49 (Table 7), where the mean value was higher in the predator fish (Table A-17). These differences were apparent despite various sample processing procedures. The predator fish analyses were based on skinon fillets (removed scales), which excluded the other tissues and organs included in the skin-on whole fish of the omnivore and benthic fish

J.L. Crane / Science of the Total Environment 607–608 (2017) 1320–1338

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Table 6 UTLs of detected and censored SQT-compatible parameters (b80% nondetects) with outliers removed at the 5% significance level. UTL units are provided with each chemical group. Parameter

N (# Nondetects)

% Nondetects

Potential outliers removed at 5% significance levels (lake ID numbers)

Normal distribution and KM estimatesa

Gamma distribution and KM estimatesa 95% Approximately gamma UTL with 95% coverage

95% UTL with 95% coverage

WH

HW

– 0.26

1.5

1.6

47

5.9

27, 32, 38

5.2

22 (2) 22 (12)

9.1 54.6

27c, 38 27c, 38

33.7 36.6

Pesticide or metabolite (μg/kg dry wt.) alpha-Chlordane 24 (17) gamma-Chlordane 23 (15) o,p′-DDD 22 (7) o,p′-DDE 22 (13) p,p′-DDD 22 (4) p,p′-DDE 21 (1) p,p′-DDT 24 (15) Total DDT 21 (1) Dieldrin 24 (15) trans-Nonachlor 23 (11) cis-Nonachlor 22 (7)

70.8 65.2 31.8 59.1 18.2 4.8 62.5 4.8 62.5 47.8 31.8

0.89

0.90

0.36 13.9

0.36 15.9

30.8

35.0

Metal (mg/kg dry wt.) Cadmium Mercury

54 (6) 53 (32)

11.1 60.4

PAH (μg/kg dry wt.) Dibenzo[a,h]anthracene

51 (3)

Total PCBs (μg/kg dry wt.) Total PCBspesticide scan ∑PCBcongenersb

38 27c, 38 27, 38c 27c, 38 27, 32c, 38c 27c, 32c, 38 38 27c, 38

1.8 – 2.1 – – 10.7 1.5 – 0.29 0.70 0.46

UTL = Upper Tolerance Limit; N = number of samples after removing outliers; ID = identification; KM = Kaplan-Meier; WH = Wilson Hilferty; HW = Hawkins Wixley; PAH = polycyclic aromatic hydrocarbon; PCBs = polychlorinated biphenyls; DDD = dichlorodiphenyldichloroethane; DDE = dichlorodiphenyldichloroethylene; DDT = dichlorodiphenyltrichloroethane. a UTLs for normally distributed data were preferred over those derived from gamma distributed data. When the data were not normally distributed, the gamma distribution was used to estimate UTL values. Gamma UTLs were calculated two different ways, for which professional judgment can be used to select the value of interest. b The Kaplan-Meier method was used to determine means for 12 lake sediment samples with b80% nondetects, which were then multiplied by the number of congeners to yield estimated ∑PCBcongeners. For samples with N80% nondetects, the detected congener values and reporting limits for other congeners were summed and expressed as less than ∑PCBcongener values used in the UTL calculation. c Potential outlier was based on professional judgment; all other outliers were based on statistical tests at the 5% significance level.

species. All other pairwise comparisons were not significant (p N 0.05) (Table 7). 4. Implications The results of this study will be used to help prioritize government agency activities related to ambient sediment quality in Minnesota, and for future status and trends work. For the first time, sediment chemistry (i.e., metals and metalloids, PAHs, PCB congeners, organochlorine pesticides, TOC, and particle size) will be added to the 2017 NLA suite of lakes nationwide. The Minnesota component of the 2017 NLA sediment data will provide an opportunity to reassess some of the UTL9595 values with additional data collected from a shorter depth interval (i.e., 0–5 cm). The 2017 data set will include 12 of the lakes sampled in 2007. If continued long-term, this national status and trends work

can help track the effectiveness of government programs and regulations pertaining to a suite of contaminants. In addition, the nationwide sediment contaminant data set to be collected from the 2017 NLA suite of lakes will be freely available for others to use (e.g., calculation of chemical specific UTL95-95 values by state or region). As urban development expands in Minnesota, degradation of sediment quality in those areas is likely to occur. Incorporation of best management stormwater practices can alleviate some of these issues. In addition, pollution prevention efforts can reduce the release of contaminants into the environment. For example, expansion of Project Green Fleet, from the Minneapolis-based Environmental Initiative, is retrofitting vehicles and equipment with diesel engines (e.g., school buses) to reduce emissions (Crane and Hennes, 2016a). In addition, statewide clean energy initiatives can result in lower emissions of PAHs in both urban and rural areas of Minnesota (Crane and Hennes,

Table 7 Multiple pairwise comparisons by fish trophic classes for lipid-normalized BDEs with significant one-way ANOVA results (Table A-18). Pairs shaded gray are significantly different (p b 0.05) from each other as determined by the Holm-Sidak method.

p < 0.05 (gray shading) BDE congener

Predator vs. omnivore

BDE-47a BDE-49a BDE-99 BDE-100a BDE-153 BDE = brominated diphenyl ether; ANOVA = analysis of variance. a

Based on natural log transformation of the data.

Predator vs. benthic

Omnivore vs. benthic

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2016a). In turn, these efforts can reduce local sources of PAHs to lakes, although emission sources from outside Minnesota will not be affected. The BDE congener results in sediment and fish tissue provided additional relevance for Minnesota's legislative actions to prohibit products with pentaBDE and octaBDE in 2008, as well as an upcoming ban in 2018 on decaBDE. With recent declines in BDE congeners in Great Lakes fish, Gandhi et al. (2017) recommend switching regular fish monitoring in the Great Lakes to other in-use flame retardants and targeted surveillance of BDE congeners. A similar approach may be helpful in Minnesota lakes, too. Bans on persistent organic chemicals may take decades to result in contaminant reductions in surficial sediments. Several banned organochlorine pesticides and their metabolites were still detected in a subgroup of study lake sediments, even after being banned over 30– 40 years ago. Although PCBs were banned in 1979 (Johnson et al., 2006), these legacy contaminants are still an important issue in urban sediments. These recalcitrant compounds continue to cycle through the environment, and nonlegacy PCBs (such as PCB 11 used in commercial paint pigments; Hu and Hornbuckle, 2010) are an emerging concern. Although Minnesota's 2014 ban on coal tar-based sealants reduced a source of PAHs to urban waterbodies from new sealant applications (Crane, 2014), old sealant will continue to abrade and be transported from parking lots and driveways to waterbodies. A ban on coal tar-based sealants in Austin, TX in 2006 resulted in decreases of ∑PAH16 sediment concentrations by nearly one-half in 6–8 years in Lady Bird Lake (Van Metre and Mahler, 2014). Pavlowsky (2013) estimated that it could take ≥20 years to result in an 80–90% decrease in PAH concentrations in stream and pond sediments in Springfield, MO if a local ban on coal tar-based sealants was enacted. Incorporation of buffer strips along public agricultural waterbodies may help reduce sediment and contaminant loads to agricultural waterways. Minnesota's 2015 landmark Buffer Law, which was amended by the Legislature and signed into law by Governor Mark Dayton on April 25, 2016, designates an estimated 110,000 acres of land for water quality buffer strips statewide (Crane and Hennes, 2016a). This new law is very timely, because row crop farming has expanded substantially in central Minnesota during the past decade due to higher crop prices (Lark et al., 2015). Other jurisdictions may compare their ambient sediment quality data to this study. The most relevant comparisons may be for neighboring states and provinces. The addition of other ecosystem health indicators, such as sediment toxicity tests and benthic invertebrate community structure, would provide a well-rounded weight-of-evidence approach for fully assessing ambient sediment quality. Acknowledgements The author appreciates the assistance of many individuals responsible for collecting, processing, and analyzing sediment and fish tissue samples, in addition to internal staff who provided database, GIS, and management support. Preliminary discussions about the sediment quality results with Steve Hennes (MPCA—retired) were helpful. This project was funded by internal state funds from the MPCA. In addition, a joint powers agreement with the U.S. Fish and Wildlife Service allowed most of the sediment samples to be analyzed by their contract laboratory at a significant cost savings to the MPCA. Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.scitotenv.2017.05.241. References ATSDR, 2002. Toxicological profile for aldrin/dieldrin. Agency for Toxic Substances and Disease Registry. Atlanta, GA http://www.atsdr.cdc.gov/toxprofiles/tp1.pdf.

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