Revision of sediment quality triad indicators in Puget Sound (Washington, USA): I. a Sediment Chemistry Index and targets for mixtures of toxicants

Authors

  • Edward R Long,

    Corresponding author
    1. Environmental Assessment Program, Washington State Department of Ecology, PO Box 47600, Olympia, Washington 98504-7600, USA
    • Environmental Assessment Program, Washington State Department of Ecology, PO Box 47600, Olympia, Washington 98504-7600, USA
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  • Margaret Dutch,

    1. Environmental Assessment Program, Washington State Department of Ecology, PO Box 47600, Olympia, Washington 98504-7600, USA
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  • Valerie Partridge,

    1. Environmental Assessment Program, Washington State Department of Ecology, PO Box 47600, Olympia, Washington 98504-7600, USA
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  • Sandra Weakland,

    1. Environmental Assessment Program, Washington State Department of Ecology, PO Box 47600, Olympia, Washington 98504-7600, USA
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  • Kathy Welch

    1. Environmental Assessment Program, Washington State Department of Ecology, PO Box 47600, Olympia, Washington 98504-7600, USA
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Abstract

The Washington State Department of Ecology annually conducts sediment quality monitoring in Puget Sound as a component of the Puget Sound Ecosystem Monitoring Program. Sediment samples are analyzed to determine the concentrations of about 170 chemical and physical variables. A Sediment Chemistry Index (SCI) was derived using the State of Washington Sediment Management Standards to account for the presence and concentrations of mixtures of toxicants. Mean Sediment Quality Standard quotients (mSQSq) were calculated as the basis for the SCI and compared to the incidence and degree of toxicity in laboratory tests and to metrics of the diversity and abundance of resident benthic assemblages in a database consisting of as many as 664 samples. These data were evaluated with co-occurrence analyses to identify “cut points” (i.e., thresholds) in the index below which the frequency and magnitude of biological effects were relatively low and above which they occurred with increasing frequency or magnitude. Iterative trials of different sets of cut points established the final cut points in mSQSq of 0.1, 0.3, and 0.5. They defined 4 ranges in chemical exposure: Minimum (<0.1), Low (0.1– < 0.3), Moderate (0.3– < 0.5), and Maximum (≥0.5). Across these 4 exposure ranges both the incidence and magnitude of toxicity in some laboratory tests increased, the abundance of most stress-sensitive benthic taxa decreased, and the abundance of most stress-tolerant taxa increased. The mSQSq cut point of 0.1 appears to be the target value for protection of benthic resources, the value below which the probability and magnitude of adverse effects either in the laboratory or the field are the lowest. The mSQSq values are rescaled from 0 to 100 to form the SCI, used by the Puget Sound Partnership and environmental managers as a Dashboard Indicator, with biologically relevant targets selected to monitor ecosystem recovery. Integr Environ Assess Manag 2013; 9: 31–49. © 2012 SETAC

INTRODUCTION

Initial surveys of sediment quality in Puget Sound demonstrated that sediments and demersal fishes in the industrialized harbors and maritime waterways were contaminated with mixtures of potentially toxic metals and organic compounds (Chapman et al. 1982; Malins et al. 1984). Laboratory tests carried out with invertebrates and fish cell lines established that exposure to contaminated sediments resulted in toxicity with multiple endpoints ranging from mortality to anomalies in mitosis, reduced metabolic rates, and reduced reproductive success (Chapman et al. 1982; Swartz et al. 1982). The incidence of histopathological disorders, including liver neoplasms, was highest in demersal fish collected in the urban bays and harbors (Malins et al. 1984).

After decades of source control and cleanup of chemicals discharged into Puget Sound's most contaminated industrial and municipal sites, monitoring has shown, in part, decreasing levels of some toxic chemicals in urban sediments (Partridge et al. 2009, 2010) and improvement in health of some resident biota (e.g., decreasing liver disease in English sole from Eagle Harbor [Myers et al. 2008] and Elliott Bay [Puget Sound Action Team 2007]). However, high levels of some toxic chemicals (e.g., PCBs, PAHs, metals) are still being measured in species throughout different trophic levels in Puget Sound, including southern resident killer whales (Ross et al. 2000; Ross 2006), harbor seals (Ross et al. 2004; Hickie et al. 2007), Chinook salmon (O'Neill and West 2009), Pacific herring (West et al. 2008), and blue mussels (Kimbrough et al. 2008). Contaminants of emerging concern (CECs), including phthalates, pharmaceuticals, and others, may also be harming biota. Xenoestrogens are suspected in reproductive anomalies observed in English sole from Elliott Bay (Johnson et al. 2008).

Environmental indicators and recovery targets for the Puget Sound ecosystem

In 2007, the Washington State legislature established the Puget Sound Partnership (PSP), a state agency, with the mission to work collaboratively with all levels of government, tribes, businesses, and citizen groups to protect and restore Puget Sound and its diversity of life by 2020 (www.psp.wa.gov). Building on the Puget Sound conservation actions of its predecessor agencies, the PSP adopted an Action Agenda, a comprehensive list of strategies and actions to drive the changes identified as critical for ecosystem recovery (PSP 2008, 2011).

Over the past several years, the PSP has worked with regional experts and the public to identify key ecosystem indicators that will help identify whether implementation of the Action Agenda is resulting in progress toward restoring the Sound. Although hundreds of indicators were initially identified as important measures of the health of the Puget Sound ecosystem (O'Neill et al. 2008), 20 were selected in 2010 and incorporated into a “Dashboard of Vital Signs” of the recovery of Puget Sound's health (www.psp.wa.gov/vitalsigns/index.php).

In 2011, recovery targets were developed for each of the Dashboard Indicators based on scientific understanding of the ecosystem parameters. The targets were adopted as policy statements. They will be used by the PSP and regional environmental managers to prioritize funding for specific recovery and restoration strategies and actions and to measure the progress of ecosystem recovery in the Sound.

A Marine Sediment Quality indicator for Puget Sound: the Sediment Quality Triad Index approach

The PSP selected “Marine Sediment Quality” as 1 of the 20 “Dashboard Indicators” for Puget Sound. This indicator was selected, in part, based on the importance of sediments as habitat for benthic invertebrates and the epibenthic fauna that rely on the benthos as a food source. Additionally, an extensive set of Puget Sound-wide sediment quality data is available, and is still being collected and analyzed as part of the Washington State Department of Ecology (Ecology) Marine Sediment Monitoring Program. Ecology has monitored sediments in Puget Sound annually since 1989 for the Puget Sound Ecosystem Monitoring Program (PSEMP) (formerly the Puget Sound Assessment and Monitoring Program) (www.ecy.wa.gov/programs/eap/psamp/index.htm). This monitoring is carried out to determine spatial status and temporal trends in sediment quality with an approach commonly referred to as the Sediment Quality Triad (SQT).

The SQT approach consists of and integrates data from chemical analyses to determine potential exposure of benthic organisms to toxicants, laboratory toxicity tests to determine relative response, and analyses of the composition of resident benthic assemblages to determine possible in situ adverse effects. The data from the 3 elements of the SQT are treated separately and also integrated together for an overall assessment of sediment quality. The SQT was initially developed in Puget Sound in the 1980s (Chapman et al. 1984; Long and Chapman 1985) and subsequently applied in San Francisco Bay (Chapman et al. 1987) and elsewhere in the United States and other countries (Long and Sloane 2005; Word et al. 2005). The approach was adopted for long-term status and trends sediment monitoring in Puget Sound with the inception of the PSEMP sediment component in 1989 (Puget Sound Water Quality Authority 1988).

The SQT methods that were initially developed for classifying relative sediment quality relied on binary, presence-absence criteria for the chemistry, toxicity, and benthic data. Benthic metrics often were compared to reference area conditions. This approach does not take into account relative toxicological risks of mixtures of chemicals or incremental increases in probabilities of ecotoxicity with increasing degree of contamination. Recently, other approaches have been pursued that provide the data analyst with multiple lines of evidence along continuous scales that lead to numerical scores along with narrative classifications or categories and account for the presence of mixtures of toxicants (Long et al. 2006; Bay and Weisberg 2008, 2010).

The PSEMP sediment data analyses used 4 categories of sediment quality (high to degraded) based on a modification of this binary approach to develop and report a Sediment Quality Triad Index (SQTI) value for stations and study areas (Long et al. 2003, 2005). Using the PSEMP sediment component's probabilistic, random, stratified sampling design (Dutch et al. 2009), the percent of each category of sediment quality has been determined and reported with an SQTI for regional and urban bay study areas sampled throughout Puget Sound (Long et al. 2003, 2005, 2008, 2010; Partridge et al. 2009, 2010, 2012).

In response to the PSP's need for a Marine Sediment Quality indicator and target that track our progress in restoring Puget Sound sediments to a healthy condition, Ecology is currently revising its existing SQTI to provide a wider, more sensitive range of information (Long et al. 2005). The PSEMP sediment quality triad data will be calculated as a suite of 3 separate component indices including a Sediment Chemistry Index (SCI), a Sediment Toxicity Index (STI), and a Sediment Benthos Index (SBI). These component indices will then be combined using a “Multiple Lines of Evidence” approach adapted from Bay and Weisberg (2008, 2010) to develop a SQTI. Each component index and the revised SQTI will be reported both as a single number on a continuous scale of 0 (poorest quality) to 100 (highest quality) and as a series of 4 to 6 quality categories. These categories are defined as ranges of values along the continuous scale, determined by biologically relevant critical values determined for each scale.

An SCI for Puget Sound

The objective of this study was to develop and describe an effects-based SCI applicable to mixtures of substances in Puget Sound. This index is based on the mean Sediment Quality Guideline quotient (mSQGq) approach. The strengths, weaknesses, underlying assumptions, derivation methods, limitations, and potential applications of mSQGq were reviewed previously (Long et al. 2006). In the mSQGq approach the concentrations of multiple chemicals are normalized to (i.e., divided by) their respective effects-based guidelines or criteria, and a single unitless index is derived by calculating the mean of the ratios (Long et al. 2006). This basic effects-based approach avoids issues that arise from using a ratio-to-reference area approach (Bay and Weisberg 2008).

A continuous scale of SCI values, from 0 to 100, was generated. Three critical points associated with changes in toxicity test results and measures of benthic community health were determined on this scale, resulting in the definition of 4 chemistry quality categories. Recovery target values to be achieved by sediments in Puget Sound regions and bays by 2020 were also selected from this scale. These target values, adopted as policy statement by the PSP, will guide revisions to the Action Agenda and identify and prioritize management strategies and actions that will be taken to achieve target conditions by 2020.

METHODS

Guiding principles

The SCI was intended to serve 2 purposes: first, to provide an index with improved sensitivity compared to the binary approach, which would enhance the ability to distinguish among intermediate levels of exposure, and second, to fulfill the need of the PSP for ecosystem indicators. For the PSP, the index would be prepared on a continuous scale from 0 to 100 to be comparable to other sediment quality indicators developed for the Sound. Also, critical cut points along this scale would be used to classify samples in 1 of 4 categories to ensure comparability with 4 categories of toxicity and adverse benthic effects in Ecology's triad evaluations. The 4 categories were intended to define relative levels of chemical exposure (Minimum, Low, Moderate, and Maximum), loosely following the basic framework developed for California estuaries and saltwater bays (Bay and Weisberg 2008, 2010).

It is noteworthy that our intent was to apply the SCI to only our monitoring program data. This index was not intended to supplant, revise, or replace the State's regulatory criteria.

Derivation of mean SQG quotients

The mSQGq typically are calculated by dividing chemical concentrations in the sediments by their respective SQG values, then calculating the mean of the quotients (Long et al. 1998, 2000, 2006; Fairey et al. 2001). Any set of effects-based guidelines or criteria can be used as the denominators. The mSQGq take into account both the presence of mixtures of potentially toxic chemicals and their relative concentrations. The mSQGq can be calculated with all of the individual values in a set of guidelines or criteria or as averages for several classes of chemicals, such as metals, pesticides, and polynuclear aromatic hydrocarbons (PAHs).

In this study, the denominators were the Washington State Sediment Quality Standards (SQS) developed by Ecology (1995) for use primarily in Puget Sound (Table 1). The SQS were derived with the apparent effects threshold (AET) approach (USEPA 1992) for metals, PAHs, chlorinated benzenes, phthalates, total PCBs, phenols, and several other organic compounds. Each of the SQS for 47 chemicals was derived as the highest concentration in a sorted, ascending database associated with the lack of adverse effects. Therefore, the AET value was the highest concentration for a particular chemical above which adverse effects were always recorded based on measures of toxicity and benthic assemblage indices.

Table 1. Washington State marine sediment quality standards (Washington State Department of Ecology 1995)
Chemical parameterConcentration
  • a

    Excluded from mSQSq calculations.

 mg/kg dry wt
As57
Cd5.1
Cr260
Cu390
Pb450
Hg0.41
Ag6.1
Zn410
 mg/kg organic C normalized
Sum LPAHa370
Naphthalene99
Acenaphthylene66
Acenaphthene16
Fluorene23
Phenanthrene100
Anthracene220
2-Methylnaphthalene38
Sum HPAHa960
Fluoranthene160
Pyrene1000
Benz(a)anthracene110
Chrysene110
Total benzofluoranthenes230
Benzo[a]pyrene99
Indeno[1,2,3-cd]pyrene34
Dibenzo[a,h]anthracene12
Benzo[g,h,i]perylene31
1,2-Dichlorobenzene2.3
1,4-Dichlorobenzene3.1
1,2,4-Trichlorobenzene0.81
Hexachlorobenzene0.38
Dimethyl phthalate53
Diethyl phthalate61
Di-n-butyl phthalate220
Butyl benzyl phthalate4.9
Bis(2-ethyl hexyl) phthalate47
Di-n-octyl phthalate58
Dibenzofuran15
Hexachlorobutadiene3.9
n-Nitrosodiphenylamine11
Total PCBs12
 µg/kg dry wt
Phenola420
2-Methylphenola63
4-Methylphenola670
2,4-Dimethylphenola29
Pentachlorophenol360
Benzyl alcohola57
Benzoic acida650

The mSQSq were calculated with the formula mSQSq = 1/n ∑([chemi]/SQSi), where [chemi] = concentration of chemical…i, = 1, 2,…, n = 39 and SQSi is the SQS value for chemical i, for 39 of the 47 chemicals and chemical classes for which there are standards. Six organic compounds (benzoic acid, benzyl alcohol, phenol, 2-methylphenol, 4-methylphenol, and 2,4-dimethylphenol) were omitted from the calculations for multiple technical reasons outlined in Partridge et al. (2009, 2010). Total low molecular weight polycyclic aromatic hydrocarbons (LPAH) and total high molecular weight polycyclic aromatic hydrocarbons (HPAH) also were omitted to avoid double-counting these compounds. Nondetected values were treated as missing as per the protocols of the State Sediment Management Standards (Ecology 1995).

The incidence and degree of response (e.g., survival) in laboratory toxicity tests with marine amphipods relative to ranges in mSQGq were previously reported with data compiled in a national database (Long et al. 1998, 2000). A variety of different methods for deriving mSQGq and the resulting matches with amphipod survival were reported in both a national database and data from California (Fairey et al. 2001).

Matching sediment chemistry and benthic data from southeastern US estuaries were examined to identify adverse benthic effects over ranges in mSQGq (Hyland et al. 2003). The latter study demonstrated that adverse benthic effects occurred in estuarine samples that were not toxic to amphipods and provided possible explanations for these observations. A book on SQGs was published by the Society of Environmental Toxicology and Chemistry (SETAC) with several chapters that described their predictive abilities (Batley et al. 2005; Ingersoll et al. 2005; Word et al. 2005). The same basic methods previously reported were used in these analyses of the Puget Sound data.

Collection and analyses of samples

The database that was analyzed in this study consisted of matching chemistry, toxicity, and benthic data for up to 664 samples collected during the period of 1997 through 2009 throughout all regions of Puget Sound for the PSEMP. All methods were previously described (Long et al. 2003, 2005; Dutch et al. 2009); therefore, only brief summaries are provided here. Surface samples were collected with a double 0.1-m2 van Veen grab sampler, composited at each site, homogenized, and divided for chemical analyses and toxicity tests. The entire contents of 1 side of the grab were retained with a 1-mm sieve for the benthos. Therefore, the “matching” data were derived from portions of the same samples, collected at the same locations on the same dates.

Chemical analyses followed Ecology's standard procedures for metals, Hg, butyl tins, base/neutral/acid compounds, polynuclear aromatic hydrocarbons, chlorinated pesticides, and polychlorinated biphenyls (Long et al. 2003, 2005; Dutch et al. 2009). All analyses were performed in-house at Ecology's Manchester Environmental Laboratory.

Matching biological measures included a combination of 4 laboratory toxicity tests and traditional taxonomic methods for the benthos (Long et al. 2003, 2005; Dutch et al. 2009). The 4 selected toxicity tests followed the methods used nationwide by the National Oceanic and Atmospheric Administration (NOAA) in the National Status and Trends Program (Long 2000a, 2000b) and, therefore, provided a means for comparing Puget Sound with other US estuaries and marine bays.

The toxicity tests included 10-d tests of survival of the amphipods Ampelisca abdita (Thursby et al. 1997) and Eohaustorius estuarius (DeWitt et al. 1989) exposed to solid phase sediments (ASTM 1993). Acceptable results were obtained from the contract labs for 564 samples. Porewater was extracted from the sediments and tested with the gametes of the sea urchin Strongylocentrotus purpuratus for the endpoint of percent fertilization success in all 664 samples (Carr and Chapman 1995; Carr and Nipper 2003).

Organic solvent extracts were tested in 2 separate assays, targeting high molecular weight organic compounds. Microtox™ tests were carried out to determine relative metabolic activity as measured with 50% losses (i.e., EC50s) in bioluminescence (Johnson and Long 1998). Human reporter gene system (HRGS) assays were performed to measure elevated cytochrome P-450 induction in cultured liver tissues (Anderson, Jones, et al. 1999; Anderson, Zeng, et al. 1999). Both tests were performed on 300 samples during 1997 to 1999.

All contract laboratories were required to meet minimum performance standards (e.g., nontoxic outcomes in negative controls) issued by Ecology to potential vendors in annual Quality Assurance Program Plans. All except 1 contractor report met or exceeded these standards. The data from the 1 exception were rejected and not included in this study.

Data analyses

The chemistry, physical, toxicity, and benthic data were assembled in 1 spreadsheet and arranged in order of ascending mSQSq. The mSQSq ranged from 0.01 to 1.34 among the 664 samples.

Following the basic methods and terminology of Hyland et al. (2003), multiple steps were taken to establish the cut points (i.e., “thresholds) in mSQSq. The intent was to find an initial cut point below which results of the toxicity tests and the benthic indices indicated the lowest incidence and degree of effects and higher cut points that showed incremental increases in effects.

These steps were pursued in “co-occurrence analyses” (Long et al. 1995; Hyland et al. 2003) of the matching chemical and biological data. Co-occurrence analyses can take into account and illustrate the nonlinear model of responses that commonly occurs as stresses to the benthos increase in sediment (Pearson and Rosenberg 1978; Swartz et al. 1986; Thompson and Lowe 2004). The co-occurrence analyses involved compiling and comparing means, medians, and ranges in results of toxicity tests and benthic community indices among ranges in mSQSq. Also, the percentages of samples in each category that were classified as “toxic” were compared as measures of the incidence of toxicity (Long et al. 1998, 2000).

Numerous iterative trials were conducted in the co-occurrence analyses, using different ranges and intervals in mSQSq as in Fairey et al. (2001), until visual examinations and statistical analyses of the data indicated which of the cut points were most consistent. Although many trials were run, only the results of the final trial with the selected cut points are presented. These cut points identified 4 ranges and categories in chemical exposure, generally following the framework adopted by the State of California (Bay and Weisberg 2008, 2010): (1) Minimum, (2) Low, (3) Moderate, and (4) Maximum. Co-occurrence analyses cannot be used to establish causality, and the objective of this study did not include the identification of causality.

With the exception of the HRGS assays, samples were defined as “toxic” if the mean response was significantly lower than in the negative control and the mean outcome was less than 80% of the control, a critical value derived in power analyses (Thursby et al. 1997). This definition of toxicity has been used nationwide by both NOAA (Long et al. 1996) and the United States Environmental Protection Agency (USEPA) (2006) and used throughout all of Puget Sound (Long et al. 2005). Samples were defined as toxic in the HRGS assay if the mean response was significantly greater than the negative control and greater than the 90% upper prediction limit of 37.1 µg benzo[a]pyrene/g (i.e., µgB[a]p/g). A response greater than 37.1 µgB[a]p/g was empirically determined to be highly significant in the initial baseline database (n = 300) for Puget Sound (Long et al. 2003, 2005).

The composition of benthic assemblages in Puget Sound reference areas differ among regions, respond in different ways to stressors such as chemical contamination and hypoxia (Long et al. 2003, 2010), and significantly change on approximately decadal scales (Nichols 2003). Therefore, to avoid problems in understanding adverse benthic effects by relying on a single index (Washington 1984; Chapman 2011; Green and Chapman 2011), multiple indices of benthic assemblage composition were calculated. The indices were selected based on our previous experience working with the benthos throughout Puget Sound (Long et al. 2003, 2005) to form a weight of evidence (Ritter et al. 2011).

Currently, there is no multimetric benthic health index for Puget Sound. Therefore, co-occurrence analyses were carried out with the data for multiple benthic indices, including those that are typically reported for the annual PSEMP surveys (Long et al. 2003, 2005, 2010). The indices included total abundance, total taxa richness, Pielou's evenness (Pielou 1966, 1974), and Swartz's dominance (Swartz et al. 1985, 1986). The abundance of 4 macrobenthic phyla, 6 smaller taxonomic groups, and 1 indicator species were selected and included based on previous experience working in the Sound. Benthic data from 581 samples were available and analyzed. Collectively, the data provided information on a wide range in biological organization including cellular (Microtox and HRGS), reproductive products (sea urchin gametes), adult individuals (amphipods), populations, and assemblages of resident benthos.

For each biological or toxicological measure, the data co-occurring with the 4 mSQSq chemical exposure categories were compared using 95% confidence intervals (CI) for the medians, a statistical method equivalent to conducting nonparametric sign tests at α = 0.05 to compare the data (Hettmansperger and Sheather 1986). Confidence intervals that did not overlap indicated that the medians were statistically significantly different. Data from selected toxicity tests and benthic indices are displayed with boxplots including medians and CI (Supplemental Data Figures S1–S10). The boxplots depict central tendency, variability, and shape of the data distributions.

In addition to the PSEMP data, sediment chemistry data maintained in Ecology's environmental information management (EIM) system were analyzed. The EIM is the repository for sediment quality data generated from regulated sediment cleanup sites and other monitoring projects conducted in Puget Sound. The EIM database primarily consists of data from only chemical analyses without accompanying benthic or toxicological data. The number of SQS values exceeded in each of the 4 categories of mSQSq was evaluated for 7614 samples.

RESULTS

In multiple trials, different sets of cut points and different resulting ranges in values were evaluated. Ultimately, 3 cut points in the mSQSq were observed that most consistently showed increasing adverse effects with increasing mSQSq. The 3 cut points of 0.1, 0.3, and 0.5 defined 4 ranges in mSQSq and exposure.

Adverse effects rarely occurred either in the laboratory toxicity tests or in the benthos in samples with mSQSq less than 0.1. Increases in either incidence or degree of effects or both were apparent above 0.1 and again, although less consistently, above 0.3 and 0.5. Therefore, the ranges in mSQSq of less than 0.1, 0.1 to less than 0.3, 0.3 to less than 0.5, and greater than or equal to 0.5 were established as category 1 for Minimum, category 2 for Low, category 3 for Moderate, and category 4 for Maximum Exposure, respectively.

Evaluations of mSQSq with laboratory tests of toxicity

Results of 4 toxicity tests were compared across the 4 ranges in mSQSq from the database of 300 to 664 samples (Tables 2 and 3). They were expressed as both magnitude (or degree) of response and incidence of toxicity (i.e., percentages of total samples tested). As is typical of monitoring data (Long et al. 2000; Fairey et al. 2001; Ritter et al. 2011), the sample sizes in each category decreased considerably from the least contaminated (mSQSq <0.1) to the most contaminated (mSQSq ≥0.5). Also, they differed among the 4 toxicity tests.

Table 2. Distribution of toxicity test results within Puget Sound sediments over 4 exposure categories
Summary statisticsAmphipod survival (%)Sea urchin fertilization (%)Microtox EC50 in organic solvent extractHRGS cytochrome P-450 induction (µgB[a]p/g)
  • 95% CI = 95% confidence interval about the median; SD = standard deviation about the mean.

  • a

    Significantly different from category 1.

  • b

    Significantly different from categories 1 and 2.

1. Minimum exposure (mSQSq <0.10)
 n399469197197
 Median97.7100.84.65.2
 95% CI97.0–98.0100.6–101.13.9–5.64.2–6.6
 Mean96.498.211.310.3
 SD5.422.422.117.6
2. Low exposure (mSQSq 0.10– < 0.30)
 n13115173131
 Median97.9100.72.1a27.8a
 95% CI96.7–99.0100.3–101.01.7–3.423.9–29.7
 Mean95.691.711.135.4
 SD9.930.225.633.9
3. Moderate exposure (mSQSq 0.30– < 0.50)
 n23302121
 Median96.0100.14.796.6b
 95% CI95.2–10198.5–101.42.7–17.937.4–136.8
 Mean97.296.716.7114.2
 SD5.514.522125.3
4. Maximum exposure (mSQSq ≥0.50)
 n111499
 Median97.886.2b3.6132.2b
 95% CI93.4–100.880.0–100.01.0–26.2110.0–221.6
 Mean97.479.514.4347.0
 SD4.129.619.9620.3
Total n564664300358
Table 3. Number and percent of sediment samples within Puget Sound that were toxic in laboratory tests over 4 exposure categories
Toxicity testsTotal nr of samples testedmSQSq Ranges
1. Minimum exposure - mSQSq <0.102. Low exposure - mSQSq 0.10– < 0.303. Moderate exposure - mSQSq 0.30– < 0.504. Maximum exposure - mSQSq ≥0.5
Nr of samples testedSamples that were toxic (%)Nr of samples testedSamples that were toxic (%)Nr of samples testedSamples that were toxic (%)Nr of samples testedSamples that were toxic (%)
  • a

    Samples were classified as toxic when mean survival was significantly different from controls and <80% of controls.

  • b

    Samples were classified as toxic when mean fertilization was significantly different from controls and <80% of controls.

  • c

    Samples were classified as toxic when mean EC50 was significantly different from controls and <80% of controls.

  • d

    Samples were classified as toxic when cytochrome P-450 induction was significantly higher than controls and >37.1 µg benzo[a]pyrene equivalents/g.

Amphipod mortalitya5643993.81316.1230.0110.0
Sea urchin fertilizationb6644698.115115.23013.31421.4
Organic Microtox EC50c3001970.5738.2210.0911.1
HRGS >37.1 µgB[a]p/gd3581972.57326.02171.49100.0

Both mean and median, control-adjusted, percent amphipod survival were similar among the 4 categories (Table 2). The 95% CI about the medians overlapped. Therefore, this test did not show a trend that corresponded with the increasing degree of contamination.

Both mean and median, control-adjusted, percent sea urchin fertilization indicated a decline in category 4 with a median of 86.2% and a mean of 79.5% compared to medians of greater than 100% and means of 98.2% and 91.7% in categories 1 and 2 (Table 2, Figure S1). Median percent fertilization in categories 1–3 exceeded that of the controls, thereby giving values greater than 100%. The median percent fertilization success in category 4 was significantly different from those in either category 1 or 2.

Typically, Microtox EC50s decline with increasing chemical contamination because smaller amounts of the sample are required to cause a 50% loss in bioluminescence as the concentrations of the toxicants increase (Johnson and Long 1998). The median EC50 in category 2 was significantly lower than in category 1 (Table 2). The median EC50s in categories 3 and 4, however, were not significantly different from those in either category 1 or 2.

Cytochrome P-450 induction often increases in the HRGS assay with increasing exposure to polynuclear aromatic hydrocarbons (PAHs) in sediments (Anderson, Jones, et al. 1999; Anderson, Zeng, et al. 1999). The Puget Sound data indicated a significant incremental increase in response with increasing contamination (Table 2, Figure S2). The median responses in categories 2, 3, and 4 were significantly greater than in category 1 by factors of about 6-fold, 19-fold, and 26-fold, respectively. The median and mean responses in categories 3 and 4 exceeded the critical value of 37.1 µgB[a]p/g. The medians in categories 3 and 4 also significantly exceeded those in categories 1 and 2.

The incidence of toxicity in the 4 laboratory tests followed a pattern similar to that of the degree of toxicity (Table 3). The incidence of toxicity to amphipods increased from category 1 to category 2, then fell to 0 in category 3 and 4. The incidence of toxicity to sea urchin gametes increased from category 1 to 4 by a factor of about 3. The incidence of toxicity in the Microtox tests increased from category 1 to category 2, decreased to zero in category 3, and peaked in category 4. In contrast, the incidence of toxic samples in the HRGS assays increased incrementally from 2.5% to 26%, 71%, and 100% from category 1 to 4, respectively, an overall increase of 40-fold.

Evaluations of mSQSq with benthic community indices

Mean and median values for 4 calculated indices of benthic community abundance and diversity were compared among the 4 exposure categories (Table 4). There was a median of 439 and a mean of 588 animals in the category 1 samples as a measure of total abundance. Both the medians and the means increased in categories 2, 3, and 4. The medians in categories 3 and 4 were significantly greater than in category 1 but not category 2.

Table 4. Distribution of calculated benthic invertebrate indices within Puget Sound sediment samples over 4 exposure categories
Summary statisticsTotal abundanceTaxa richnessPielou's evenness indexSwartz's dominance index
  • 95% CI = 95% confidence interval about the median; SD = standard deviation about the mean.

  • a

    Significantly different from category 1.

  • b

    Significantly different from categories 1 and 2.

1. Minimum exposure (mSQSq <0.10)
 n413413413413
 Median439490.739.0
 95% CI380–50846–510.72–0.748.0–10.0
 Mean588540.7011.1
 SD651310.137.8
2. Low exposure (mSQSq 0.10– < 0.30)
 n124124124124
 Median583530.66a7.0
 95% CI506–83346–590.62–0.696.0–9.0
 Mean835560.638.9
 SD829280.176.7
3. Moderate exposure (mSQSq 0.30– < 0.50)
 n30303030
 Median768a70b0.629.5
 95% CI557–91660–790.58–0.736.0–13.5
 Mean908710.6411.3
 SD597210.147.2
4. Maximum exposure (mSQSq >0.50)
 n14141414
 Median770a65a0.709.5
 95% CI622–113756–880.53–0.785.0–15.3
 Mean890700.6411.0
 SD452190.147.2
Total n581581581581

There was a median of 49 and a mean of 54 taxa in the category 1 samples as a measure of taxa richness (Table 4). The medians increased slightly in category 2 and significantly in categories 3 and 4 (Table 4 and Figure S3). The median of 70 in category 3 was greater than the medians in both categories 1 and 2. The median of 65 in category 4 was significantly greater than category 1, but not category 2.

The median value of Pielou's index of evenness was significantly lower in category 2 relative to category 1 (Table 4). There were slightly lower medians for evenness in categories 3 and 4, but the decreases were not significant. Generally, the evenness index values were within the range of 0.6 to 0.8 in each category. The median value of Swartz's dominance index in category 2 was slightly, but not significantly lower than in the other categories.

Evaluations of mSQSq with benthic phyla abundance

The mean and median abundance of 4 phyla of benthic invertebrates indicated different trends across the 4 ranges of mSQSq (Table 5). The abundance of annelids increased significantly in categories 2, 3, and 4 relative to category 1 (Figure S4). Differences between median annelid abundance in categories 3 and 4 were not significant relative to category 2. Mollusk abundance was significantly greater in category 3 relative to both categories 1 and 2 and greater in category 4 relative to category 1.

Table 5. Abundance of 4 benthic invertebrate phyla within Puget Sound sediment samples over 4 exposure categories
Summary statisticsAnnelidaMolluscaArthropodaEchinodermata
  • 95% CI = 95% confidence interval about the median; SD = standard deviation about the mean.

  • a

    Significantly different from category 1.

  • b

    Significantly different from categories 1 and 2.

1. Minimum exposure (mSQSq <0.10)
 n413413413413
 Median15890432.0
 95% CI147.0–179.972.0–107.036.1–53.02.0–3.0
 Mean271.4158.1118.528.2
 SD410.8216.9233.071.7
2. Low exposure (mSQSq 0.10– < 0.30)
 n124124124124
 Median269a12450.53.5
 95% CI219.6–335.794.0–146.734.6–71.11.0–6.0
 Mean500.2182.4109.335.5
 SD654.2198.5174.797.9
3. Moderate exposure (mSQSq 0.30– < 0.50)
 n30303030
 Median338.5a237b692.5
 95% CI214.7–472.1148.0–415.137.9–87.10.2–6.8
 Mean483.7312.196.19.0
 SD469.5223.9130.314.6
4. Maximum exposure (mSQSq ≥0.50)
 n14141414
 Median416a169a470.5
 95% CI307.0–821.0144.6–233.331.7–115.90.0–3.1
 Mean589.1199.191.23.2
 SD424.4138.7103.46.1
Total n581581581581

The mean and median abundance of the arthropods did not change appreciably or significantly. The abundance of the echinoderms decreased, but not significantly, in category 4 relative to categories 1 to 3 (Table 5 and Figure S5). There was a median of 2.0 echinoderms in category 1 compared to a median of 0.5 in category 4. There was a mean of 28.2 in category 1 compared to 3.2 in category 4, a loss by a factor of about 9-fold.

Evaluations of mSQSq with selected benthic taxonomic groups

The abundance of several taxonomic groups that are known or suspected to be highly tolerant, or conversely, highly sensitive to environmental stressors (Diaz and Rosenberg 1995) over the 4 ranges in mSQSq are compared in Table 6. Generally in Puget Sound, many of the annelids and mollusks are known to be relatively tolerant of either near-bottom hypoxia or chemical contamination of the sediments, while many echinoderms and arthropods are relatively sensitive (Long et al. 2003). For example, in the Hood Canal region of the Sound there were significant losses of some echinoderms and arthropods and increases in the abundance of some annelids and mollusks as near-bottom dissolved O2 decreased down the length of the fjord (Long et al. 2010).

Table 6. Abundance of selected benthic invertebrates within Puget Sound sediment samples over 4 exposure categories
Summary statisticsCirratulid polychaetesSpionid polychaetesAxinopsida serricataOther mollusksAll amphipodsPhoxocephalid amphipodsOther amphipods
  • 95% CI = 95% confidence interval about the median; SD = standard deviation about the mean.

  • a

    Significantly different from category 1.

  • b

    Significantly different from categories 1 and 2.

1. Minimum exposure (mSQSq <0.10)
 n413413413413413413413
 Median4.021.09.053.013.02.08.0
 95% CI3.0–5.019.0–23.06.1–14.045.0–63.011.0–15.01.0–3.06.0–9.0
 Mean41.330.743.0115.152.49.143.3
 SD138.039.475.1201.9146.017.7138.0
2. Low exposure (mSQSq 0.10–<0.30)
 n124124124124124124124
 Median19.0a23.019.064.07.0a0.05.0
 95% CI7.0–51.018.6–30.07.0–35.448.6–84.74.0–10.00.0–1.02.6–7.0
 Mean211.740.269.2113.227.15.821.3
 SD426.246.5115.6145.881.419.765.7
3. Moderate exposure (mSQSq 0.30– < 0.50)
 n30303030303030
 Median34.0a37.5a67.5b98.0a3.0a0.02.5a
 95% CI19.0–107.925.7–53.944.2–231.381.5–156.72.0–5.00.0–1.01.0–4.8
 Mean231.857.3176.1136.09.51.97.6
 SD438.667.8199.196.019.54.315.6
4. Maximum exposure (mSQSq ≥0.50)
 n14141414141414
 Median45.5a46.0a28.5a118.06.0a0.04.0
 95% CI15.7–529.124.6–127.618.0–96.043.9–153.14.0–8.10.0–1.13.0–7.2
 Mean274.468.881.4117.79.40.98.5
 SD383.256.6115.696.410.41.410.7
Total n581581581581581581581

Throughout the Sound, there were incremental and significant increases in the abundance of relatively stress-resistant taxonomic groups, including the cirratulid and spionid worms, as the mSQSq increased (Table 6 and Figure S6). The median abundance of the cirratulids was 4 in category 1 and increased to 19, 34, and 45 in categories 2, 3, and 4, respectively, a 10-fold change. The median abundance of the cirratulids was not significantly different in categories 3 and 4 relative to category 2.

The median abundance of the spionid worms increased from 21 to 23, 37, and 46 in categories 1 to 4, respectively, with the latter 2 increases being significant relative to category 1. Mean abundance increased about 2-fold from 31 to 69 (Table 6).

The benthic clam Axinopsida serricata increased significantly in median abundance from 9 in category 1 to 19, 67, and 28 in the subsequent categories. The change in category 3 was significant relative to both categories 1 and 2 and the change in category 4 was significant relative to category 1 (Table 6 and Figure S7). The median abundance of other mollusks also increased from category 1 to categories 2, 3, and 4 (Table 6). The increase in category 3 was significant relative to category 1.

The median abundance of 3 benthic amphipod groupings known or suspected to be among the most sensitive taxa (Diaz and Rosenberg 1995) tended to decrease, sometimes significantly, as the degree of chemical contamination increased (Table 6). The median abundance of the amphipods was 13 in category 1 and decreased significantly in categories 2, 3, and 4, an overall decrease of about 50% (Figure S8). Their abundance in categories 3 and 4 was not significantly different from category 2.

The median abundance of the phoxocephalid amphipods was 2 in category 1 and decreased to zero in categories 2, 3, and 4; however, none of these decreases were significant (Table 6 and Figure S9). The median abundance of other (i.e., nonphoxocephalid) amphipods decreased from 8 in category 1 to 5, 2.5, and 4 in categories 2, 3, and 4, respectively (Figure S10). The decline in abundance in category 3 was significant.

Evaluation of additional sediment chemistry data from Puget Sound

Analysis of Ecology's entire EIM sediment quality database (n = 7614; August, 2011), indicated that 2612 of 2626 samples (99.5%) with mSQSq less than 0.1 had no chemical concentrations exceeding any SQS values (Table 7). There were 14 samples (0.5% of 2626) in this category that had 1 SQS exceeded and none with 2 or greater than or equal to 3 exceeded. In contrast, among 1252 samples in the category with mSQSq of greater than or equal to 0.5, there were none in which zero SQS were exceeded. In the samples with mSQSq greater than or equal to 0.5, the percent of samples with greater than or equal to 3 SQS exceeded was 69% (868/1252). These data, therefore, tend to confirm that samples in the Low Exposure category were nearly always uncontaminated and samples in the Maximum Exposure category were often contaminated relative to the Washington State criteria.

Table 7. A comparison of the number and percentage of Puget Sound sediment samples exceeding 0, 1, 2, or ≥3 SQS values for each of 4 exposure categories
Exposure categoryNumber of SQS values exceeded in each exposure categoryTotal nr samples
012≥3
Samples Nr (%)Samples Nr (%)Samples Nr (%)Samples Nr (%)
  1. Sediment data examined are from Washington Department of Ecology, Environmental Information Management database (n = 7614).

1. Minimum exposure (mSQSq <0.10)2612 (99.5)14 (0.5)0 (0.0)0 (0.0)2626
2. Low exposure (mSQSq 0.10– < 0.30)2003 (70.8)752 (26.6)74 (2.6)2 (0.1)2831
3. Moderate exposure (mSQSq 0.30– < 0.50)69 (7.6)382 (42.2)284 (31.4)170 (18.8)905
4. Maximum exposure (mSQSq >0.50)0 (0.0)198 (15.8)186 (14.9)868 (69.3)1252

DISCUSSION

The issue of additivity

One of the key assumptions in the use of mSQGq is that the contributions of each chemical to toxicity are additive. Because sets of commonly used SQGs include a wide variety of chemicals, including those with differing modes of toxicity, it is possible that the assumption of additivity does not apply in all situations (Batley et al. 2005). For example, antagonism between different chemicals in clean spiked sediments has been reported in laboratory experiments (Oakden et al. 1984).

However, there is evidence of additivity in bioassays of clean sediments spiked with individual chemicals and combinations of these chemicals (McLeese and Metcalfe 1980; McLeese et al. 1982; Sundelin 1984; Plesha et al. 1988; Swartz et al. 1988). Additivity among priority chemicals has been reported in whole effluent toxicity tests (Sarakinos et al. 2000). Models have been developed for estimating risks of chemical mixtures in tissue residues and water (Dyer et al. 2010) and in sediments (Field et al. 2002). Some organic compounds indicated synergistic toxicity (i.e., more than additive) in various combinations (Pape-Lindstrom and Lydy 1997). Van Gestel et al. (2011) recently summarized the state of the science in evaluations of chemical mixtures and predicting their effects.

Evidence of Ecotoxicological effects in Puget Sound

Evidence from both laboratory toxicity tests and various benthic metrics indicate that biological conditions changed as mSQSq increased incrementally between the 4 chemical categories (Table 8). Clearly some biological metrics did not change with increasing contamination, for example, amphipod survival in laboratory toxicity tests. Conflicting results among the biological metrics underscores the need for multiple metrics. The weight of evidence from the multiple analyses suggests that there are 3 cut points in the mSQSq (i.e., 0.1, 0.3, and 0.5) where most biological measures changed. Different kinds of adverse effects occurred within the 3 elevated ranges in mSQSq.

Table 8. Summary of statistically significant increases (+) and decreases (−) in biological measures from either the Minimum exposure or both the Minimum and Low exposure categories
Median biological measure1. Minimum exposure (mSQSq <0.10)2. Low exposure (mSQSq 0.10– < 0.30)3. Moderate exposure (mSQSq 0.30– < 0.50)4. Maximum exposure (mSQSq >0.50)
  • ns = not significantly different from Minimum exposure category.

  • a

    Significantly different from Minimum exposure category.

  • b

    Significantly different from both Minimum exposure and Low exposure categories.

Toxicity tests
 Amphipod survival (%)nsnsnsns
 Sea urchin fertilization (%)nsnsnsb
 Microtox EC50nsansns
 Cytochrome P-450 inductionns+a+b+b
Calculated benthic indices
 Total abundancensns+a+a
 Taxa richnessnsns+b+a
 Pielou's evenness indexnsansns
 Swartz's dominance indexnsnsnsns
Abundance of benthic phyla
 Annelidans+a+a+a
 Molluscansns+b+a
 Arthropodansnsnsns
 Echinodermatansnsnsns
Abundance of selected species or taxa groups
 Cirratulid polychaetesns+a+a+a
 Spionid polychaetesnsns+a+a
 Axinopsida serricatansns+b+a
 Other mollusksnsns+ans
 All amphipodsnsaaa
 Phoxocephalid amphipodsnsnsnsns
 Other amphipodsnsnsans

Samples in the Minimum Exposure category 1 were the least toxic in terms of both the percent incidence and degree of toxicity. Mean and median amphipod percent survival were relatively high. Median sea urchin percent fertilization was highest and the incidence of toxicity was the lowest. Microtox EC50s were relatively high. Both the incidence and degree of response in the HRGS assays were the lowest in this category. Samples in this category had the lowest abundance of stress-tolerant benthic phyla and taxa, and the highest abundance of stress-sensitive phyla and taxa. The median abundance of benthic mollusks, including the stress-tolerant bivalve A. serricata, was lowest. Median abundance of the annelids, including the stress-tolerant cirratulids and spionids, was lowest. Median abundance of amphipods, including the stress-sensitive phoxocephalids, was the highest. Therefore, there is evidence that mSQSq values less than 0.1 are highly protective of marine benthic resources in Puget Sound. About 71% of the 664 samples were in this category.

Relative to the samples in category 1, samples in the Low Exposure category 2 had significantly higher cytochrome P-450 induction, significantly reduced Microtox EC50s, and increased incidence of toxicity in all 4 laboratory tests. The benthic evenness index was significantly reduced. There were significant increases in abundance of stress-tolerant annelids and cirratulid worms. There were significant decreases in the abundance of all amphipods and a nonsignificant trend of decreased abundance of phoxocephalid amphipods. The median abundance of the phoxocephalids was zero.

In Moderate Exposure category 3 samples, median and mean cytochrome P-450 induction increased 19-fold relative to category 1, and the incidence of toxicity in the HRGS assays increased to 71.4%. Total benthic abundance and total benthic taxa richness increased as a result of increased abundance of stress-tolerant annelids, cirratulids, spionids, A. serricata, and other mollusks. In contrast, the abundance of stress-sensitive amphipods decreased significantly relative to category 1. The mean abundance of echinoderms was about one-third of that in category 1. The median abundance of the phoxocephalids was zero.

In the Maximum Exposure category 4 samples, median sea urchin fertilization decreased significantly, median cytochrome P-450 induction increased significantly (a 25-fold increase relative to category 1), all of the samples were classified as toxic in the HRGS assays, and the incidence of toxicity to the sea urchins was the highest. The incidence of toxicity in the Microtox tests was the highest. Median total benthic abundance and taxa richness were significantly elevated as a result of the increased abundance of the stress-tolerant annelids, mollusks, cirratulids, spionids, A. serricata, and other mollusks. The mean abundance of stress-sensitive arthropods was about one-half of that in category 1. Median abundance of the amphipods was reduced significantly. Median and mean abundance of benthic echinoderms were reduced, but not significantly. The median abundance of the phoxocephalids was zero.

We speculate that some relatively tolerant animals are attracted to the organic matter as food in the soft, depositional sediments that tend to also accumulate higher chemical concentrations, but that are not lethal to them. This relationship has been reported previously for areas of Santa Monica Bay, California (Bergen et al. 2001) and Puget Sound (Nichols 2003). Their changes in abundance generally correspond to the nonlinear empirical model developed along stress gradients in Scandinavian fjords (Pearson and Rosenberg 1978). The Spearman rank correlation coefficient (ρ) for mSQSq and percent total organic carbon (TOC) was 0.31, indicating a weak trend of covariance.

Comparisons with previous evaluations of toxicity

This study is the first of its kind performed with the Puget Sound SQS (Ingersoll et al. 2005; Word et al. 2005). It was conducted using methods previously reported with other kinds of sediment quality guidelines (SQG) and field validation data sets (Long et al. 2000; Fairey et al. 2001; Ingersoll et al. 2005). All have been carried out with either Effects Range Median (ERM) values, Probable Effects Level (PEL) values, or consensus freshwater Probably Effect Concentrations (PEC). None were previously performed with the Washington SQS.

The SQS and ERM values were derived with different statistical methods carried out with different kinds of data. Concentrations of organic compounds were treated on a dry weight basis in the ERMs, whereas they were normalized to organic C in the SQS. There are 47 SQS (Ecology 1995), whereas there are 28 ERM values (Long et al. 1995). The SQS were derived for many more organic compounds (e.g., phenols, phthalates, chlorinated benzenes) than the ERM values.

Despite these differences, the distributions of mSQSq and mERMq calculated for the same 664 Puget Sound samples were similar or the same within each of the 4 exposure categories (Table 9). For example, the medians of the calculated mERMq and mSQSq were 0.12 and 0.16, respectively, in the Low Exposure category and they were 0.51 and 0.69 in the Maximum Exposure category. Also, the numbers of ERMs and SQSs exceeded in the samples within each category were similar or exactly the same. Therefore, because the mERMq and mSQSq track well with each other, it appears reasonable to compare the predictive abilities of the mSQSq in Puget Sound with previous evaluations elsewhere of the predictive abilities of mERMq.

Table 9. Distribution of mERMq and mSQSq values within Puget Sound sediment samples over 4 exposure categories
Summary statisticsmERMqmSQSqNr ERMs exceededNr SQSs exceeded
  1. na = not applicable.

1. Minimum exposure (mSQSq <0.10)
 n469469469469
 Median0.050.050.000.00
 95% CI0.04–0.450.05–0.05nana
 Mean0.050.050.000.00
 SD0.030.020.100.20
2. Low exposure (mSQSq 0.10– < 0.30)
 n151151151151
 Median0.120.160.000.00
 95% CI0.11–0.150.15–0.17nana
 Mean0.150.170.400.50
 SD0.150.051.200.70
3. Moderate exposure (mSQSq 0.30– < 0.50)
 n30303030
 Median0.330.391.002.00
 95% CI0.19–0.390.37–0.430.00–1.001.00–2.00
 Mean0.300.401.501.80
 SD0.150.062.801.40
4. Maximum exposure (mSQSq ≥0.50)
 n14141414
 Median0.510.693.006.50
 95% CI0.33–1.050.59–0.781.00–9.203.00–11.00
 Mean0.860.765.806.90
 SD0.850.266.104.80
Total n664664664664

Ingersoll et al. (2005) compiled matching chemistry, toxicity, and benthic data from numerous studies to evaluate the predictive abilities of the various sets of SQGs. They assembled data from more than 8000 samples in peer-reviewed published reports for both saltwater and freshwater to evaluate how well SQGs accurately predicted either the absence or the presence of toxicity and/or adverse benthic effects. Most of the data compiled to evaluate predictive abilities of SQGs focused on tests of amphipod survival as the biological measure (Long et al. 2000; Fairey et al. 2001; Ingersoll et al. 2005). Note that Long et al. (2000) evaluated both the PELs and ERMs with the same database. As is typical of data encountered from ambient monitoring programs, the numbers of samples invariably decreased as the degree of contamination increased. These data are compared with those from Puget Sound (Table 10).

Table 10. Numbers of samples, average percent survival, and percent incidence of toxic samples in amphipod survival tests in the Puget Sound database and in 3 national databases
StudyNr samplesTotals (%)Average % survivalIncidence of toxicity (%)
  1. n/a = not available or analyzed.

Present study (ranges in mSQSq, test species were A. abdita and E. estuarius)
 < 0.1039970.796.43.8
 0.10– < 0.3013123.295.66.1
 0.30– < 0.50234.197.20.0
 ≥0.50112.097.40.0
 Total564   

Ingersoll et al. 2005

(ranges in mERMq, test species unknown)
 < 0.10139250.3n/a9.0
 0.10–0.50104537.8n/a32.0
 0.50–1.001756.3n/a56.0
 > 1.001555.6n/a61.0
 Total2767   

Long et al. 2000

(ranges in mERMq, test species were A. abdita and R. abronius)
 ≤0.1062841.593.09.0
 0.11–0.5030019.886.021.0
 0.51–1.501127.470.049.0
 > 1.50281.941.076.0
 Total1513   

Long et al. 2000

(ranges in mPELq, test species were A. abdita and R. abronius)
 ≤0.1045730.293.08.0
 0.11–1.5053435.386.021.0
 1.51–2.30322.168.049.0
 > 2.30453.046.073.0
 Total1513   

Fairey et al. 2001

(ranges in mSQGQ1, test species were A. abdita, R. abronius, E. estuarius)
 0–0.1072442.8954.0
 0.10–0.2538923.08319.0
 0.25–0.5026215.57633.0
 0.50–0.751136.77632.0
 0.75–1.00774.67052.0
 1.00–1.25442.65368.0
 1.25–1.50211.25681.0
 1.50–2.00211.24567.0
 2.00–2.50110.73691.0
 2.50–3.00100.635100
 3.00–3.50100.620100
 > 3.50100.613100
 Total1692   

The incidence of toxicity to amphipods in samples with mSQSq less than 0.1 invariably was less than 10% in Puget Sound and in 3 national databases (Table 10). In the national databases, the incidence of toxicity invariably increased to about 20% to 30% in mSQGq ranges of 0.1 to 0.5 or 0.1 to 1.5, whereas in Puget Sound the incidence remained low (i.e., 6.1%). The incidence of toxicity increased again to about 40% to 50% in mSQGq ranges of 0.5 to 1.0, 0.51 to 1.5, and 1.51 to 2.3, whereas in Puget Sound it dropped to zero. The incidence of toxicity peaked at 61% to 100% in the highest ranges in the national databases, whereas it remained at zero in Puget Sound.

These comparisons also demonstrate that the degree of toxicity, expressed as average percent survival, decreased incrementally in the national databases, but not in Puget Sound (Table 10). In both Puget Sound and in the national databases, average percent survival was greater than 90% in the lowest ranges in mSQSq, usually less than 0.1. However, in the national databases the average percent survival decreased immediately in the next highest range in mSQGq and continued to decrease in the subsequent higher ranges, whereas in Puget Sound it remained at greater than 90%.

These data provide evidence that the initial cut point of less than 0.1 is highly protective against acute toxicity and nearly a universal safe level. These comparisons also demonstrated that for unknown reasons the amphipod survival tests did not respond to higher levels of contamination in Puget Sound, whereas they consistently did so elsewhere. In contrast, the HRGS assay and sea urchin test responded to the higher levels of contamination in the Sound.

All of these national analyses were performed with data gathered from ambient monitoring programs in U.S. waters. None of them involved data collected specifically to test the predictive abilities of SQGs. Invariably, sample sizes decreased as the degree of chemical contamination increased. In contrast, the results of a study designed specifically to test predictive abilities of SQGs was reported for Sydney Harbor, Australia (McCready et al. 2006). Analyses of tests of 106 samples showed that the incidence of toxicity in laboratory tests was very low in samples with mERMq less than 0.1 (i.e., 0% with amphipods alone and 11% in any 1 of 4 tests). The incidence of toxicity to amphipods peaked at 40% with amphipods alone and 73% in any test where the mERMq values were greater than 1.5. Therefore, the Australian data were similar to the national US data but not the Puget Sound data.

Comparisons with previous evaluations of adverse benthic effects

The 3 cut points identified for mSQSq and the resulting ranges in values identified with a combination of toxicity and benthic data for Puget Sound agree remarkably well with cut points identified elsewhere with other kinds of SQGs and biological data (Table 11). For example, the cut points for Minimum Exposure categories ranged from 0.013 to 0.15, thereby bracketing the Puget Sound mSQSq value of 0.10. The cut points for the Maximum Exposure category ranged from 0.062 to 1.362, also bracketing the Puget Sound mSQSq value of 0.50.

Table 11. Comparisons among US saltwater regions in ranges in mSQGq within narrative categories of sediment contamination as established with adverse benthic effects
Study areaPublicationUnit of measureExposure categories
MinimumLowModerateMaximum
  1. n/a = not applicable or analyzed.

Puget SoundPresent studymSQSq<0.10.1– < 0.30.3– < 0.5≥0.5
Southern California

Ritter et al. 2011

mERMq<0.060.06– < 0.130.13– < 0.40≥0.40
  Consensus-based SQGs<0.150.15– < 0.300.30– < 0.68≥0.68
Virginia province

Hyland et al. 2003

mERMq≤0.022>0.022–0.098>0.098–0.473>0.473
Carolina province

Hyland et al. 2003

mERMq≤0.018>0.018–0.057>0.057–0.196>0.196
Louisiana province

Hyland et al. 2003

mERMq≤0.013>0.013–0.036>0.036–0.062>0.062
San Francisco Bay, CA

Thompson and Lowe 2004

mERMq<0.0510.051–0.1460.147–0.635>0.742
San Diego Bay, CA

Thompson et al. 2009

mERMq<0.054n/an/a>1.362
Biscayne Bay and Miami River, FL

Long et al. 2002

mERMq<0.030.03–0.2n/a>0.2–2.0

Matching chemistry, toxicity, and benthic data from 441 samples collected in marine bays of southern California were analyzed to develop an effects-based sediment chemistry score index (Ritter et al. 2011). Five toxicity-based sets of SQGs and a newly developed benthic index were evaluated as candidate methods. One of the many outcomes of this study was to identify thresholds in the 6 candidate index methods, using the results of toxicity tests with marine amphipods and a benthic health index. Thresholds of low, moderate, and high were identified as mERMq of 0.06, 0.12, and 0.38 with the toxicity data and 0.06, 0.13, and 0.40 with the benthic data. With consensus-based SQGs, the cut points were 0.15, 0.30, and 0.68 with the benthic data (Table 11).

Evaluations of matching mERMq and benthic assemblage data from the Virginia, Carolina, and Louisiana estuarine provinces showed a pattern of increasing incidence of adverse effects with increasing contamination in samples that were not toxic in amphipod survival tests (Hyland et al. 2003). In the 3 provinces in order, the incidence of degraded benthos was lowest (i.e., 9%, 7%, 30%) in samples with mERMq of less than or equal to 0.022, less than or equal to 0.018, and less than or equal to 0.013, respectively. The incidence increased to 31%, 43%, and 52% with mERMq of greater than 0.022 to 0.098, greater than 0.018 to 0.057, and greater than 0.013 to 0.036, respectively. The incidence increased incrementally in 2 subsequent ranges calculated for each province, reaching a maximum at 85% to 100% among the 3 provinces. The cut points for the maximum exposure category (i.e., 0.473, 0.196, and 0.062) differed among the 3 regions (Table 11).

In San Francisco Bay (CA), the incidence of benthic impacts numerically corresponded with increasing mERMq in a regional monitoring data set (Thompson and Lowe 2004). No benthic impacts were observed in 13 samples with mERMq less than 0.051. Impacts were recorded in 9.4% of 106 samples with values between 0.051 and 0.146, in 63.2% of 19 samples with values between 0.147 and 0.635, and in all 3 samples with values greater than 0.742 (Table 11). Whereas only 8.4% of samples with mERMq values below 0.146 were impacted, 68.2% were impacted with values above 0.146. The authors concluded that a mERMq value of 0.146 was reasonably predictive and protective.

In a database composed of 161 samples assembled from 4 previous surveys of San Diego Bay (CA), adverse benthic impacts were correlated with indices of sediment contamination, including mERMq (Thompson et al. 2009). Samples with mERMq less than 0.054 were never impacted, whereas samples with mERMq greater than 1.362 were always impacted (Table 11).

In data from Biscayne Bay and the lower Miami River (FL) (Long et al. 2002), average amphipod survival decreased from 96% to 93% and 41% in mERMq ranges of less than 0.03, 0.03 to 0.2 and greater than 0.2 to 2.0, respectively (Table 11). Over these same 3 ranges, average numbers of species in the benthos decreased from 91 to 58 and 6 per sample and average numbers of arthropods decreased from 179 to 96 and 10 per sample, respectively.

Meta-analyses of ranges in amphipod survival in laboratory toxicity tests and matching benthic assemblage data from surveys conducted along all 3 coastlines of the United States have empirically demonstrated a trend of losses of or absence of relatively sensitive amphipods, other crustaceans, and echinoderms with decreasing percent survival (Long et al. 2001). Similar outcomes, albeit with differing benthic metrics, were reported for studies conducted in estuarine Rhode Island (Kuhn et al. 2002), Elliott Bay in Puget Sound (Ferraro and Cole 2002), and Baltimore Harbor and vicinity in Chesapeake Bay (McGee et al. 2004).

In our present study of samples from Puget Sound, amphipods were present in the minimum exposure category samples, but median abundance of all amphipods decreased significantly and the phoxocephalids were absent in the low, moderate, and maximum exposure categories. However, mortality to amphipods in the laboratory tests did not change significantly across these 4 ranges. We speculate that, along with the increasing degree of chemical contamination, other natural factors, such as low dissolved O2 concentrations, changes in depth, changes in sediment particle sizes, changes in organic C content among the sediment samples had an influence on the benthos, but not in the toxicity tests.

Calculation of SCI values

All of the mSQSq values fell within a scale of 0.0 to 1.5 (highest to poorest sediment quality), three SCI values (93.3, 80.0, and 66.6) were selected as the cut points for 4 exposure categories. These values, along with the most frequent number of chemicals exceeding SQS values (Table 7), are summarized in Table 12.

Table 12. Mean SQS quotient and Sediment Chemistry Index values, and the most frequent number of chemicals exceeding WA State Sediment Quality Standards, for 4 sediment chemistry exposure categories
Sediment chemistry categoryMean SQS quotientSCIMost frequent number of chemicals exceeding WA State SQS
  1. SCI = sediment chemistry index; SQS = sediment quality standard; WA = Washington.

Minimal exposure<0.1>93.equation image–100.00
Low exposure0.1– < 0.3>80.0–93.equation image0–1
Moderate exposure0.3– < 0.5>66.equation image–80.01–3
High exposure≥ > 0.50–66.equation image≥3

Implementing SCI-based recovery targets for Puget Sound

The development of mSQSq and their associated SCI values, biologically and toxicologically-relevant cut points, and the 4 associated ranges in sediment quality on the 0 to 100 scale, have enabled the PSP leadership to adopt 2 of 3 recovery targets for the Marine Sediment Quality Dashboard Indicator. These 2 target values were based on the SCI range associated with the minimal exposure category. They include the following (PSP 2011):

“By 2020, all Puget Sound regions and bays should:

  • Have sediment chemistry measures reflecting ‘minimum exposure’, as defined by having a Sediment Chemistry Index (SCI) score of >93.3.

  • Have no measurements exceeding the Sediment Quality Standard values set in Washington State.”

These 2 targets are derived directly from the analysis of the data described in this report. A third is derived from the derivation of the revised SQTI, and will be described in a later article (in preparation).

Environmental managers implementing the Action Agenda will make use of the Dashboard Indicators and their associated targets as they identify, design, prioritize, and fund strategies and activities that contribute to ecosystem recovery. The indicators and targets will then be used to evaluate the effects of these investments and activities toward the recovery of Puget Sound by 2020.

CONCLUSIONS

Taken together, data from these Puget Sound Sediment Quality Triad analyses indicated that either the incidence or magnitude of toxicity in sensitive laboratory tests or adverse effects to sensitive benthic taxonomic groups or the abundance of stress-tolerant taxa increased with incremental increases in mSQGq. In many other regions, the incidence and magnitude of toxicity to sensitive test animals was universally lowest in samples with mSQGq less than 0.1. In most cases, the incidence and degree of adverse benthic effects also was the lowest with mSQGq less than 0.1. It appears that mSQGq values less than one-tenth of the set of SQGs is a common “safe” level of contamination.

Also, it appears that, except in Puget Sound, the majority of samples with mSQGq values greater than 1.0 are toxic in amphipod survival tests. In Puget Sound, the majority of samples with mSQSq greater than or equal to 0.3 were toxic in sensitive tests of organic solvents. In most regions, the Maximum Exposure mSQGq thresholds were less than 1.0 for adverse benthic effects, therefore, less than sediment guideline unity. This observation indicates that the toxicity of the mixtures was perhaps more than additive or that other unmeasured factors contributed to adverse benthic effects. In all of these data sets, it cannot be construed that the co-occurrence analyses proved causality. These analyses only show statistical relationships, not the cause (or causes) of either toxicity or adverse benthic effects.

It is noteworthy that laboratory tests of amphipod survival were less sensitive than several metrics of benthic community composition in Puget Sound and in southeastern US estuaries (Hyland et al. 1998, 2003). In both regions, significant decreases in the abundance of relatively sensitive benthic taxa occurred in samples that were not toxic to amphipods.

The patterns in the biological responses relative to the incremental increases in the degree of contamination clearly differed among different regions nationwide. These differences probably are attributable to differences in the composition of the chemical mixtures, differences in the kinds of toxicity tests, and geographic differences in the composition of the resident benthos and how they, therefore, responded to gradients in stresses.

The mSQSq values were calculated using a method that takes into account the presence of mixtures of toxicants in the sediments and their additivity in effects. This approach, in addition, provides an index along a continuous scale, instead of only a binary, presence-absence classification. It has been applied elsewhere in both regional and national data sets and in Australia with other kinds of sediment quality guidelines. Despite these differences, the national mERMq and Puget Sound mSQSq tracked with each other and the critical cut points observed in Puget Sound were within the same ranges as observed in other regions.

Many of the more responsive biological endpoints responded immediately in the low exposure category samples and incrementally increased in response in the subsequent categories, tracking with each other. This pattern was apparent in the toxicity tests of the organic solvent extracts, in the increased abundance of stress-tolerant annelids and mollusks, and in the decreased abundance of resident amphipods and other arthropods.

Coordinated by the PSP, current ecosystem management practices for Puget Sound include use of the newly revised SCI and SQTI Dashboard Vital Signs indicators, and their associated ecosystem recovery targets, as part of the effort for Puget Sound recovery by 2020. PSEMP sediment monitoring will continue to cycle through Puget Sound regions and urban embayments to spatially characterize these sampling areas based on these indicators. Change in SCI and SQTI values over time will be examined to determine the extent of recovery of sediment quality by 2020. Descriptions of the associated sediment quality indicators of toxicity, condition of the benthos, and a summary of the sediment quality triad will be developed in the near future.

SUPPLEMENTAL DATA

Figures S1-S10

Acknowledgements

We thank the following individuals for their assistance in reviewing previous versions of this article: Dr. Jeff Hyland, NOAA (Charleston, SC), Dr. Gavin Birch, Sydney University (Sydney, NSW, Australia), Mr. Don MacDonald, MESL (Nanaimo, BC, Canada), and Dr. Steve Bay, SCCWRP (Costa Mesa, CA). This work is funded by the Washington State Department of Ecology to provide sediment quality data for the Puget Sound Ecosystem Monitoring Program. Any use of product or firm names in this publication is for descriptive purposes only and does not imply endorsement by the author or the Department of Ecology.

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