Mechanistic sediment quality guidelines based on contaminant bioavailability: Equilibrium partitioning sediment benchmarks

Authors

  • Robert M. Burgess,

    1. U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Atlantic Ecology Division, Narragansett, Rhode Island
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  • Walter J. Berry,

    1. U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Atlantic Ecology Division, Narragansett, Rhode Island
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  • David R. Mount,

    1. U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, Duluth, Minnesota
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  • Dominic M. Di Toro

    1. Department of Civil and Environmental Engineering, University of Delaware, Newark, Delaware, USA
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Abstract

Globally, estimated costs to manage (i.e., remediate and monitor) contaminated sediments are in the billions of U.S. dollars. Biologically based approaches for assessing the contaminated sediments which pose the greatest ecological risk range from toxicity testing to benthic community analysis. In addition, chemically based sediment quality guidelines (SQGs) provide a relatively inexpensive line of evidence for supporting these assessments. The present study summarizes a mechanistic SQG based on equilibrium partitioning (EqP), which uses the dissolved concentrations of contaminants in sediment interstitial waters as a surrogate for bioavailable contaminant concentrations. The EqP-based mechanistic SQGs are called equilibrium partitioning sediment benchmarks (ESBs). Sediment concentrations less than or equal to the ESB values are not expected to result in adverse effects and benthic organisms should be protected, while sediment concentrations above the ESB values may result in adverse effects to benthic organisms. In the present study, ESB values are reported for 34 polycyclic aromatic hydrocarbon, 32 other organic contaminants, and seven metals (cadmium, chromium, copper, nickel, lead, silver, zinc). Also included is an overview of EqP theory, ESB derivation, examples of applying ESB values, and considerations when using ESBs. The ESBs are intended as a complement to existing sediment-assessment tools, to assist in determining the extent of sediment contamination, to help identify chemicals causing toxicity, and to serve as targets for pollutant loading control measures. Environ. Toxicol. Chem. 2013;32:102–114. © 2012 SETAC

INTRODUCTION

Of the ecological risks recognized to occur in aquatic systems, contaminated sediments are arguably among the most prevalent and technically challenging. For example, in the United States monitoring programs coordinated by the U.S. Environmental Protection Agency, the National Oceanic and Atmospheric Administration, and other organizations have documented that vast quantities of freshwater and marine sediments are moderately to severely contaminated with anthropogenic pollutants 1–8. Of course, the risks associated with contaminated sediments are not limited to the United States; several other countries around the world also wrestle with related issues (e.g., Australia, New Zealand, the Netherlands, China, the United Kingdom 9, 10). According to some surveys conducted in the United States, the quantities approach billions of metric tons of sediment representing potentially significant ecological and human health risks 4–6. Furthermore, extensive surveys of these sediments have shown many to be toxic to benthic marine organisms 11, 12. Estimated costs associated with managing these sediments in terms of remediation (e.g., dredging and capping) and postoperational monitoring are in the billions of U.S. dollars 13.

In the more than 40 years that contaminated sediments have been recognized as a source of ecological risk 14–20, several decision-making tools for addressing the magnitude of the threat have been developed 21, 22. One of the earliest approaches was the sediment quality triad, which combined sediment toxicity, sediment contaminant concentrations, and benthic community data to assess the amount of risk associated with sediments of interest 23–26. However, the projected costs associated with assessing contaminated sediment for ecological risk using approaches dependent on toxicity testing, bioaccumulation studies, benthic community, or other data-intensive tools fueled the development of alternative approaches that use relatively simple and inexpensive measures to predict contaminated sediment risk. Among the predominant approaches was development of sediment-quality guidelines (SQGs) 25. In principle, this approach relies on the relatively inexpensive measure of sediment contaminants and related parameters (e.g., sediment organic carbon) to predict adverse toxicological effects. The currently developed SQGs can be categorized into two general forms: empirical and mechanistic. The empirical form uses a database of matched sediment chemistry and biological effects to derive sediment concentrations with varying likelihoods of causing adverse effects (e.g., low or high) to benthic organisms. Examples of empirical SQGs include the apparent effects threshold 27, effects range low/effects range median 28–31, threshold effects level/probable effects level 32, 33, and a logistical model 34, 35 as well as others that evolved from these 36–42.

The mechanistic approach for the SQGs is based on understanding the bioavailability of anthropogenic contaminants in sediments and determining whether these bioavailable contaminants will be present in quantities sufficient to cause adverse effects 43–45. While fundamentally different, the empirical and mechanistic approaches can be used to complement each other 46 based on the recognition that they address different questions. In most cases, empirical SQGs attempt to predict the likelihood of observing adverse benthic effects, often measured as sediment toxicity, as related to measured sediment contaminant concentrations. In contrast, mechanistic SQGs seek to estimate the likelihood of individual contaminants or classes of contaminants causing an adverse benthic effect. While this distinction between SQGs may appear subtle, it is significant: Empirical SQGs seek to predict whether a sediment will be toxic, while mechanistic SQGs determine if a specific contaminant or mixture of contaminants may be sufficiently bioavailable to cause toxic effects. In recent years, there has been a concerted effort to use a weight-of-evidence approach for performing contaminant assessments that would include both empirical and mechanistic SQGs, along with other end points (e.g., bioaccumulation, benthic community analysis, and toxicity identification evaluations [TIEs]), as complementary tools 22.

Within mechanistic SQG methods, the physical–chemical concept of equilibrium partitioning (EqP) 43, 45 has been dominant. Simply put, the approach asserts that a contaminant's bioavailability is directly proportional to its chemical activity in sediment. In this context, chemical activity is a contaminant's availability in an environmental phase relative to its availability in a reference state 43. Often, with environmental contaminants, the chemical's solubility in water is used as the reference state and activity is expressed as a unitless ratio of the environmental and reference state concentrations. Therefore, as a contaminant's dissolved concentration in an environmental phase (e.g., interstitial water) approaches the concentration in the reference state (i.e., aqueous solubility), the contaminant's activity increases and so does its bioavailability. Furthermore, the approach states that the bioavailability and subsequent adverse effects of anthropogenic contaminants ranging from nonionic organic pesticides to cationic metals can be predicted by understanding the phases in sediments sequestering the contaminants and controlling their chemical activity, as chemical activity is essentially the free or uncomplexed dissolved phase concentration in interstitial water. Equilibrium partitioning asserts that chemical activity is an accurate predictor of bioavailability within an equilibrated aquatic system. It is important to note that this is not the same as asserting that all exposure to sediment contaminants is via the interstitial water. Equilibrium partitioning asserts only that any simultaneous exposure through ingested sediment reflects the same degree of chemical activity (i.e., bioavailability) indicated by the concentration in interstitial water, assuming that no transformations occur within the gut that significantly change chemical activity. Thus, EqP predicts bioavailability using partition coefficients between sediment particles (including binding phases contained therein) and the interstitial water. With this information, an accurate estimate of a sediment contaminant's bioavailable concentration can be generated and the likelihood of adverse effects due to that chemical can be predicted.

The mechanistic approach can be used to predict effects with many end points as long as the corresponding water-only effect concentration (i.e., dissolved) is known. Most often, acute toxicity (mortality) or a common chronic end point (e.g., reproduction, growth) for a representative benthic organism(s) is used because these types of data are readily available in the scientific literature. However, any water-only effect concentration can be used in the mechanistic approach; for example, the concentration of a chemical that causes significant degradation of the benthos or acute effects to a commercially important benthic species. Under ideal circumstances, the effect concentration used in the mechanistic approach is the measured dissolved concentration because that is the most accurate measure of the bioavailable concentration. However, as a result of some circumstances, a measurement of the dissolved concentration may not be possible (e.g., analytical detection conditions). In these instances, a total measurement of the contaminant effect concentration can serve as an adequate substitute. It should be noted that the mechanistic approach does not seek to predict bioaccumulation or trophic transfer. As a result, the broader ecological risks of bioaccumulative chemicals such as polychlorinated biphenyls and mercury are not well addressed by mechanistic SQGs based on direct sediment exposure. This is because these contaminants most often and seriously affect toxicologically upper trophic–level organisms (e.g., wildlife, birds, humans). Benthic organisms can be adversely affected by mercury and polychlorinated biphenyls, but the most ecological risk occurs with the upper trophic–level organisms. To address these risks adequately, other approaches are required 47.

The present study summarizes a multiyear effort to develop tools to understand and predict the bioavailability and adverse effects of contaminants in sediments and to incorporate this understanding into mechanistically based SQGs. The specific type of mechanistic SQGs discussed are known as equilibrium partitioning sediment benchmarks (ESBs) and have been developed for a number of common sediment contaminants, including 34 polycyclic aromatic hydrocarbons (PAH), 32 other nonionic organic chemicals (NOCs), and metal mixtures (e.g., cadmium, chromium, copper, nickel, lead, silver, and zinc). A primary objective of the present study was to improve the ease of using the ESBs by providing a centralized tabulation of the ESB values for these common nonionic organic and metal sediment contaminants. In addition, the present study briefly describes ESB theory and development, discusses examples that demonstrate the derivation and interpretation of ESBs, including the use of conventional and narcosis end points, and describes some of the considerations when applying the ESBs. The U.S. Environmental Protection Agency documents that discuss the ESBs in far more detail 48–52 are available at http://www.epa.gov/nheerl/publications.html or directly from the authors.

EQUILIBRIUM PARTITIONING THEORY

The first demonstration of the potential of EqP for mechanistically predicting the bioavailability of anthropogenic contaminants was provided by Adams et al. 53. In this classic study, Adams et al. 53 illustrated that the toxicity of the pesticide decachloro-octa-hydro-1,3,4,-metheno-2H-cyclobuta[cd]-pentalene-2-on (Kepone) amended into three different freshwater sediments and exposed to midge larvae was unpredictable, varying by two orders of magnitude when the pesticide sediment concentrations were expressed on the conventional basis of chemical per mass of dry sediment (e.g., micrograms of Kepone per gram dry). However, when toxicity was expressed based on the interstitial water concentration of Kepone (micrograms of Kepone per liter of interstitial water), the toxicity range compressed to within approximately a factor of 2. These observations suggested that to understand the toxicity and ultimately bioavailability of nonionic organic chemicals in sediments, it was necessary to know the interstitial water concentrations. A few years later, Di Toro et al. 43 showed, based on the findings of Adams et al. 53 and other data, that the bioavailability of nonionic organic chemicals was a function of their distribution between sediment phases (e.g., organic carbon and interstitial water). This understanding was the foundation for using EqP to derive mechanistic sediment-quality guidelines.

Based on the concept discussed above, in the EqP procedure, when the exposure exceeds an established concentration, a biological effect may be expected to occur, while an exposure below or equal to the concentration is unlikely to have a biological effect. In sediments, this EqP-based procedure is performed by predicting the bioavailable concentration of a contaminant of interest and relating that concentration to an established toxic effect concentration. A frequently used and established effect concentration is the final chronic value (FCV) used to derive water-quality criteria (WQC) in the United States 54. However, because of the stringent data requirements for the development of WQC 54, sufficient data are not always available to derive an FCV, and a secondary chronic value (SCV) or other water-only value may need to be used (see discussion of tier-1 and tier-2 ESBs below). Di Toro et al. 43 demonstrated the similarity in sensitivities of pelagic and benthic organisms, supporting the use of FCVs, SCVs, and potentially other water-only data for predicting toxicological effects in the sediments. For the exposure component of EqP, the organic carbon–water partition coefficients (KOCs), based on the octanol–water partition coefficients (KOWs), of the NOCs are used to predict the interstitial water concentration to estimate the bioavailable sediment concentration.

As described previously, the EqP approach is based on the concept that the dissolved phase contaminant concentration in the sediment interstitial water reflects a chemical's activity and is a good surrogate for the bioavailable concentration 43, 55. To be clear, the interstitial water concentration is not the only exposure an organism living in the sediment experiences. This is because in an equilibrated aquatic system, the organism is exposed simultaneously to multiple sources in the sediment and the exposure may be dynamic. For example, the organism is exposed to chemicals dissolved in the interstitial water, but it is also exposed to chemicals on the surface of particles it physically contacts. Furthermore, as an organism accumulates contaminants from the interstitial waters, contaminants desorb from the sediment organic carbon and replace the bioaccumulated chemical, thus maintaining the interstitial water exposure concentration. However, contaminants associated with particulate and related solid phases (e.g., colloids) are poor surrogates of bioavailable concentrations, in part because they are dependent on the complexities of the rate of contaminant desorption into the dissolved phase (i.e., interstitial water). Therefore, by understanding the phases in the sediment that control the amount of contaminant in the dissolved phase, the EqP approach can be used to make accurate predictions of exposure and therefore adverse effects. The distributions of NOCs in sediment can be expressed as

equation image(1)

where the sum of the concentrations of a chemical X in the dissolved phase (DissolvedX) and the particulate phase (ParticulateX) are equivalent to the total amount of chemical (TotalX) in the sediment 43, 45. In sediments, DissolvedX is equivalent to the interstitial water concentration and, as discussed above, a good surrogate for what is bioavailable.

Equation 1 illustrates two important points. First, total concentrations of a contaminant in a sediment do not necessarily reflect what is bioavailable, as demonstrated by the Adams et al. 53 experiments. Second, to understand what is bioavailable, it is necessary to know what is controlling the binding and distribution of contaminants to sediments and the total contaminant concentration. Fortunately, TotalX can be measured analytically relatively easily and inexpensively. Determining what is controlling the binding of contaminants to sediments is more complicated and will be discussed below.

Binding of the two classes of sediment contaminants for which ESBs have been developed (i.e., NOCs and metals) revolves around two principal phases. For NOCs, the binding phase is particulate organic carbon (fOC in grams of organic carbon per gram of dry sediment) 43. Given this conceptual understanding of contaminant geochemistry for organic contaminants, Equation 1 can be refined to

equation image(2)

where TotalX is now equivalent to the dissolved concentration (DissolvedX) added to the concentration of contaminant associated with sediment organic carbon (Organic CarbonX). As discussed further below, the relationship between the particulate and dissolved phase concentrations of contaminants can be measured and used to predict either phase's contaminant concentration via a partition coefficient. Figure 1A shows a conceptual model of the partitioning relationship between these phases that will be discussed later.

Figure 1.

Conceptual models of the partitioning of (A) nonionic organic contaminants and (B) cationic metals between primary sediment phases. KOC = organic carbon–water partition coefficient; KSP = solubility product.

For cationic metals, including cadmium, copper, lead, nickel, silver, and zinc, sediment acid volatile sulfide (AVS) is the principal binding phase in reduced sediments 45, 56, 57. However, sedimentary organic carbon is also important to metal bioavailability 58 (Fig. 1B). For metals, Equation 1 can be conceptually rewritten as

equation image(3)

where DissolvedY is the dissolved phase concentration of metal Y, AVSY is the concentration of metal Y associated with AVS, and Organic CarbonY is the concentration of metal Y associated with organic carbon, which are combined to equal TotalY. The bioavailability of the anionic metal chromium is addressed differently from the cationic metals (see discussion below). Both NOCs and cationic metals, when associated with their respective binding phases, can be visualized as being distributed between the sediment particles or, when dissolved, present in the interstitial waters (Fig. 1B).

Using these fundamental conceptual models of contaminant partitioning and bioavailability, quantitative models of bioavailability have been derived, allowing for the calculation and prediction of bioavailable concentrations of contaminants in sediments. These bioavailable contaminant concentrations can then be compared to known effect values to make predictions of sediment effects. The following sections discuss these mechanistic models.

Nonionic organic chemicals

The freely dissolved interstitial water concentration is predicted using the organic carbon–water partition coefficient (KOC in liters per kilogram organic carbon [OC])

equation image(4)

where COC is the organic carbon normalized sediment concentration (micrograms per kilogram OC) and Cd is the dissolved concentration (micrograms per liter). By rearranging Equation 4,

equation image(5)

where the 1/1,000 converts the kilograms OC to grams OC and substituting a known effect concentration (e.g., the FCV or SCV for the dissolved concentration [Cd]), the ESB (micrograms per gram OC) is calculated as

equation image(6)

This is the organic carbon normalized particulate concentration for NOCs below which or equal to which adverse effects are unlikely to occur and above which effects may occur. In Equation 6, the FCV was used as the effects concentration; however, as discussed above, any effect concentration can be used as long as the corresponding water-only effect concentration is known.

It should be noted that a chemical's KOC is not necessarily easily measured. As a consequence, linear free energy relationship models using KOW (liters per kilogram octanol, which is more easily measured) have been developed to estimate KOC. For the work discussed here, the model of Di Toro et al. 43 will be used, in which

equation image(7)

Finally, over the last several years, the universal applicability of the EqP model described in Equation 2 has come under some critical scrutiny. As discussed, Equation 5 is based on the concept that nonionic organic contaminants are primarily associated with the organic carbon fraction of sediment (i.e., fOC in grams of organic carbon per gram of dry sediment); that is, the diagenic organic carbon formed by the decomposition of plant and animal biomass. Several recent studies have shown that some organic contaminants are also associated with another form of sediment carbon called “black carbon” (fBC in grams black carbon per gram dry sediment) 59–63. Black carbon is formed via the incomplete combustion of diagenic organic carbon and fossil fuels 60, 63. The effect of black carbon is to increase the field partition coefficient (KOC,field) compared to the value computed in Equation 7 above. The result is that the ESB will be larger than actually required to protect benthic organisms. The implications of black carbon on deriving ESBs will be discussed in a second article on site-specific sediment benchmarks (Burgess et al., unpublished data).

Cationic metals

As discussed above, cationic metals including cadmium, copper, nickel, lead, silver, and zinc behave in some ways like NOCs as they associate with various sediment phases. The specific phases include carbonates and alumino- and silicaoxides 64; however, the phases that relate most directly to the bioavailable concentration of metals are sulfides and, to a lesser extent, organic carbon 45, 56–58. Sulfides are a significant sedimentary phase, particularly in reduced anoxic sediments 56. Primarily composed of amorphous monosulfide (FeS), AVS is the particulate sulfide fraction (micromoles per gram dry sediment) released from sediment during a weak acid extraction. Cationic metals liberated from the sediment during the extraction, collection, and measurement of AVS are called the simultaneously extracted metals (SEM, micromoles per gram dry sediment). The SEM is the metal either associated with sulfides or adsorbed to other phases that are in equilibrium with the interstitial water. When there is an excess of AVS, toxic metals will outcompete the Fe, as a function of their solubility product (KSP), to form an insoluble and nonbioavailable metal sulfide (e.g., NiS, ZnS, CdS, PbS, CuS, and Ag2S) rather than partition into the dissolved phase and become bioavailable to aquatic organisms. Therefore, if the concentration of the SEM exceeds the AVS, it is possible for bioavailable metals to be present in the sediment interstitial waters

equation image(8)

Conversely, in sediments with an excess of AVS, metals are not present at bioavailable concentrations sufficient to cause toxic effects

equation image(9)

To incorporate the effects of sediment organic carbon on metal bioavailability, we can consider the metal bound to organic carbon explicitly:

equation image(10)

This equation can be rearranged to provide a more accurate measure of the bioavailable metal that includes interactions with both AVS and organic carbon

equation image(11)

Rearranging and substituting an effect concentration, for example, the FCV for a given metal (e.g., copper), into Equation 11 for Cd results in

equation image(12)

It has been shown empirically that Equation 12 can be used to derive an ESB for multiple metals (e.g., Cd, Cu, Ni, Pb, Zn) 51.

Chromium

Unlike the NOCs and cationic metals, the ESB for chromium is not based on the dissolved or bioavailable concentrations. Rather, the ESB is based on the speciation of chromium as a function of the oxidation-reduction status of the sediment. This oxidation-reduction status can be determined using the presence or absence of AVS 51, 65. The speciation of chromium in aqueous systems depends on the redox state of the system and is dominated by two forms: Cr(III) and Cr(VI) 64.

The trivalent form of chromium, Cr(III), is not very soluble or toxic, while the hexavalent form of chromium, Cr(VI), is much more soluble and toxic to aquatic organisms 65. Under the reducing anoxic conditions that occur in many contaminated sediment environments, where AVS is abundant, very little Cr(VI) is present and the nontoxic Cr(III) form is dominant. The ESB for chromium is based on this relationship between the speciation of chromium between toxic, Cr(VI), and nontoxic, Cr(III), forms and the presence or absence of AVS 51. Simply stated, if AVS is detected, then it is unlikely that Cr will be present in the toxic Cr(VI) form.

Types of ESBs: tier 1 and tier 2

As noted above, sufficient toxicological data are not always available to derive FCVs based on the WQC requirements 54. Consequently, SCVs or other water-only toxicity values are derived using smaller data sets. Similarly, when developing KOWs for calculating KOCs (see Eqn. 7), the SPARC Performs Automated Reasoning in Chemistry (SPARC) model is the recommended method 66 (www.epa.gov/athens/research/projects/sparc/index.html). Because of the different amounts and quality of data available to derive ESBs and the resulting uncertainty, two tiers of ESBs have been defined: the more scientifically rigorous and data-intensive tier 1 and the less rigorous tier 2. Characteristics of tier 1 are as follows: (1) effect endpoint uses an FCV based on the Stephan et al. 54 data requirements; (2) for NOCs SPARC-based KOWs are used 66; (3) for NOCs KOC is based on Equation 7 43; and (4) confirmation tests are performed to validate ESB predictions 67–69. Tier-2 characteristics are as follows: (1) effect endpoint uses published or draft FCVs, SCVs, or other water-only effect concentrations 70–72; (2) for NOCs, SPARC is highly recommended for consistency, but slow-stir, shaker flask, or other methods can be used for deriving KOW 73; and (3) confirmation tests validating ESB predictions are recommended but not required.

Using these definitions, tier-1 ESBs have been derived for the pesticides endrin and dieldrin 48, 49, PAH mixtures 50, and metal mixtures 51. Tier-2 ESBs are available for 32 NOCs 52.

ESB DERIVATION AND VALUES

Tier-1 ESBs for nonionic organic chemicals

Pesticides

The chlorinated pesticides endrin and dieldrin are among the most frequently detected contaminants found on sediment inventories including the National Sediment Inventory 4–8. Tier-1 ESBs for dieldrin and endrin were calculated with the KOWs and effect concentrations in Table 1. The ESBs for the two pesticides were derived using Equation 6 and are reported in Table 1 48, 49.

Table 1. Log octanol—water partition coefficients (KOWs; L/Kg), conventional and narcosis chronic toxicity values (µg/L; final and secondary chronic values), log KOCs (L/Kg organic carbon), and conventional and narcosis equilibrium partitioning sediment benchmark (ESB) values (µg/gOC) for a selection of nonionic organic chemicals
ChemicalLog KOW (L/kg)Conventional FCV or SCV (µg/L)Narcosis SCV (µg/L)Log KOC (L/kg organic carbon)Conventional ESB (µg/gOC)Narcosis ESB (µg/gOC)
FreshwaterMarineFreshwaterMarine
  • a

    Conventional value should be used.

  • NA = not available; FCV = final chronic value; SCV = secondary chronic value; KOC = organic carbon––water partition coefficient.

Ethers
 4-Bromophenyl phenyl ether5.00SCV = 1.5SCV = 1.5194.921201201,600
Low–molecular weight compounds
 Benzene2.13SCV = 130SCV = 1305,3002.091616660
 Chlorobenzene2.86SCV = 64SCV = 648802.814141570
 1,2-Dichlorobenzene3.43SCV = 14SCV = 143303.373333780
 1,3-Dichlorobenzene3.43SCV = 71SCV = 713303.37170170780
 1,4-Dichlorobenzene3.42SCV = 15SCV = 153403.363434780
 Ethylbenzene3.14SCV = 7.3SCV = 7.37903.098.98.9970
 1,1,2,2-Tetrachloroethane2.39SCV = 610SCV = 61037002.35140140830
 Tetrachloroethene2.67SCV = 98SCV = 9820002.624141840
 Tetrachloromethane2.73SCV = 240SCV = 24016002.68120120770
 Toluene2.75SCV = 9.8SCV = 9.816002.705.05.0810
 Tribromomethane (Bromoform)2.35SCV = 320SCV = 32060002.3165651,200
 1, 1, 1-Trichloroethane2.48SCV = 11SCV = 1124002.443.03.0660
 Trichloroethene2.71SCV = 47SCV = 4714002.662222650
 m-Xylene3.20SCV = 67SCV = 677003.159494980
Pesticides
 Alpha-, Beta-, Delta-BHC3.78SCV = 2.2NAa3.7211NANA
 Gamma-BHC, Lindane3.73FCV = 0.08NAa3.670.37NANA
 Biphenyl3.96SCV = 14SCV = 141903.891101101,500
 Diazinon3.70FCV = 0.1699FCV = 0.8185a3.640.743.6NA
 Dibenzofuran4.07SCV = 3.7SCV = 3.71704.0037371,700
 Dieldrin5.37FCV = 0.06589FCV = 0.1469a5.2812 (5.4–27)28 (12–62)NA
 Endosulfan mixed isomers4.10FCV = 0.056FCV = 0.0087a4.030.60.093NA
 Alpha-Endosulfan3.83FCV = 0.056FCV = 0.0087a3.770.330.051NA
 Beta-Endosulfan4.52FCV = 0.056FCV = 0.0087a4.441.60.24NA
 Endrin5.06FCV = 0.05805FCV = 0.01057a4.975.4 (2.4–12)0.99 (0.44–2.2)NA
 Hexachloroethane4.00SCV = 12SCV = 121603.931001001,400
 Malathion2.89SCV = 0.097FCV = 0.1603a2.840.0670.11NA
 Methoxychlor5.08SCV = 0.019NAa4.991.9NANA
 Pentachlorobenzene5.26SCV = 0.47SCV = 0.47115.1770701,600
 Toxaphene5.50FCV = 0.039FCV = 0.2098a5.411054NA
 1, 2, 4-Trichlorobenzene4.01SCV = 110SCV = 1101203.949609601,100
Phthalates        
 Butyl benzyl phthalate4.84SCV = 19NAa4.761100NANA
 Diethyl phthalate2.50SCV = 270NAa2.4677NANA
 Di-n-butyl phthalate4.61SCV = 35NAa4.531200NANA
Polycyclic aromatic hydrocarbons
 Naphthalene3.356NANA193.53.299NANA385
 C1-naphthalenes3.8NANA81.693.736NANA444
 Acenaphthylene3.223NANA306.93.168NANA452
 Acenaphthene4.012NANA55.853.944NANA491
 C2-naphthalenes4.3NANA30.244.227NANA510
 Fluorene4.208NANA39.34.137NANA538
 C3-naphthalenes4.8NANA11.14.719NANA581
 Anthracene4.534NANA20.734.457NANA594
 Phenanthrene4.571NANA19.134.494NANA596
 C1-fluorenes4.72NANA13.994.64NANA611
 C4-naphthalenes5.3NANA4.0485.21NANA657
 C1-phenanthrene/anthracenes5.04NANA7.4364.955NANA670
 C2-fluorenes5.2NANA5.3055.112NANA686
 Pyrene4.922NANA10.114.839NANA697
 Fluoranthene5.084NANA7.1094.998NANA707
 C2-Phenanthrene/anthracenes5.46NANA3.1995.367NANA746
 C3-fluorenes5.7NANA1.9165.603NANA769
 C1-pyrene/fluoranthenes5.287NANA4.8875.197NANA770
 C3-phenanthrene/anthracenes5.92NANA1.2565.82NANA829
 Benz[a]anthracene5.673NANA2.2275.577NANA841
 Chrysene5.713NANA2.0425.616NANA844
 C4-Phenanthrenes/anthracenes6.32NANA0.55946.213NANA913
 C1-Benzanthracene/chrysenes6.14NANA0.85576.036NANA929
 Benzo[a]pyrene6.107NANA0.95736.003NANA965
 Perylene6.135NANA0.90086.031NANA967
 Benzo[e]pyrene6.135NANA0.90086.031NANA967
 Benzo[b]fluoranthene6.266NANA0.67746.16NANA979
 Benzo[k]fluoranthene6.291NANA0.64156.184NANA981
 C2-benzanthracene/chrysenes6.429NANA0.48276.32NANA1,008
 Benzo[ghi]perylene6.507NANA0.43916.397NANA1,095
 C3-benzanthracene/chrysenes6.94NANA0.16756.822NANA1,112
 Indeno[1,2,3-cd]pyrene6.722NANA0.2756.608NANA1,115
 Dibenz[a,h]anthracene6.713NANA0.28256.599NANA1,123
 C4-benzanthracene/chrysenes7.36NANA0.070627.235NANA1,214

PAH mixtures

Because of their formation or release during the use of fossil fuels by developing and industrialized societies, PAHs are arguably the most widely distributed NOCs globally 74. Also, PAHs demonstrate several modes of toxicity, including narcosis, carcinogenicity, mutagenicity, as well as photoenhanced toxicity 50. For benthic organisms and for some contaminants, the narcosis endpoint has been demonstrated to describe adequately observed sensitivity and the effect concentrations are identical for both freshwater and marine species 50 (Table 1). Furthermore, the additive narcotic toxicity of PAHs allows for prediction of the toxicity caused by mixtures of PAHs present in a sediment. This mixture approach is used because PAH molecules very seldom occur in sediments individually. The magnitude of toxicity is determined based on the target lipid model of narcosis toxicity 21, 50, 75, 76. Currently, the approach evaluates the toxic contribution of 34 PAHs.

Narcosis-specific FCV (FCV) or SCVN values (millimoles per liter, later converted to micrograms per liter) for PAHs were derived with the following general expression 76

equation image(13)

where, equation image is the critical target lipid concentration predicted to cause 50% mortality (for PAHs, 9.31 µmol/g octanol), ACR is the acute to chronic ratio (4.16 for PAHs), Δcl is the chemical class–specific correction (for PAHs, −0.263), and −0.945 is the universal narcosis slope. The KOW is specific to the chemical being investigated 50, 76 (Table 1). For PAHs, this equation can be simplified to the following 50

equation image(14)

Because Equation 14 was developed using only PAHs, no Δcl was necessary. It is worth noting that the EqP approach used to derive these ESBs was developed by applying multiple classes of narcotic chemicals and the Δcl corrections were necessary to remove systematic errors. A later version of the target lipid model, not discussed here, proposed that using a polyparameter model to estimate lipid–water partition coefficients did not require these corrections and is applicable to both polar and nonpolar narcosis 77. With FCVPAHi values from Equation 14 and Equation 6, PAH ESBs (ESBPAH, micrograms per gram OC) were calculated.

Especially for chemicals occurring in mixtures, toxic units can be used to evaluate toxicity. A toxic unit is defined as the bioavailable concentration in the sediment of a specific PAH divided by the effect concentration of that PAH. The ESB for PAH mixtures is calculated by summing the toxic units of individual PAHs predicted to be present in a sediment

equation image(15)

where ∑ESBTUFCV (unitless) is the sum of the individual PAH toxic units based on the FCV, CPAHi (micrograms per gram OC) is the organic carbon normalized PAH concentration in the sediment of interest, and ESBPAHi (micrograms per gram OC) is the ESB for each PAH (Table 1). In sediments in which ∑ESBTUFCV is >1.0, sediment toxicity due to PAH mixtures may occur. Conversely, if the ∑ESBTUFCV is ≤ 1.0, sediment toxicity due to PAHs is unlikely to occur. Table 1 provides the narcosis-based toxicity values using FCV for individual PAHs and, when combined, PAH mixtures.

Tier-2 ESBs for nonionic organic chemicals

Two approaches were used to derive tier-2 ESBs for 32 organic non-PAH chemicals that are found frequently in contaminated sediments 2–8. One approach used conventional aquatic toxicity values (e.g., FCVs, SCVs) similar to the approach used with the pesticides endrin and dieldrin 48, 49, and the other approach used the narcosis model applied to the PAH mixtures (Eqn. 15) 50, 52, 76. All ESB values were calculated using Equation 6. For maximum protection of the benthos, the conventional ESB for a given chemical should always be used, if available, because it includes the effects of other modes of action (e.g., neurotoxicity), rather than the sometimes higher (i.e., less sensitive) narcosis mode of action ESB. Exceptions to this recommendation include having a preponderance of compelling evidence indicating, for example, that narcosis is the primary mode of action for a given chemical. Conventional aquatic toxicity values (i.e., FCVs and SCVs) were derived using either the procedures detailed in the Great Lakes Initiative 70–72 or taken from existing or draft WQC (Table 1). For example, marine ESBs for pesticides were based only on FCVs from existing or draft WQC, while freshwater ESBs for pesticides were derived using both WQC and Great Lakes Initiative toxicity values. Similarly, ESBs for phthalates were derived only for freshwater species using the Great Lakes Initiative approach because WQC values were not available. To identify which of the 32 chemicals had a narcotic mode of action, the quantitative structural activity relationship Assessment Tools for the Evaluation of Risk (ASTER) 78 was applied. As noted above, ESB values for narcotic chemicals are applicable to both freshwater and marine species based on the concept that these organisms show similar sensitivity to narcotic chemicals 50, 52. Figure 2 illustrates the relationship between predicted toxicity (expressed as median lethal concentrations) based on narcosis and observed toxicity 52. However, it is critical to note that this relationship is applicable only to narcotic chemicals and may not apply to chemicals with other modes of action. For example, pesticides and phthalates have modes of action with greater toxicity than narcosis (e.g., neuro- and diester toxicity). The SCVNs for narcotic chemicals were derived with Equation 13, using a equation image of 35.3 µmol/g octanol and an ACR of 5.09 (quotient = 6.94) 76. For chlorinated chemicals, Equation 13 is changed to the following

equation image(16)

using a Δcl of −0.244 (the other chemicals did not require an adjustment). As with the tier-1 ESBs, using the calculated SCVNs and Equation 6, ESB values for each chemical were calculated. Furthermore, when using Equation 15 for tier-2 chemicals, toxic units can be calculated for mixtures of narcotic chemicals by applying their narcotic ESB values (Table 1).

Figure 2.

Comparison of observed median lethal concentration (LC50) values used in the calculation of secondary chronic values (SCVs) and LC50 values predicted using narcosis theory as described by Di Toro et al. 76 for 20 tier II narcotic chemicals. Plot shows data for all species that had both measured LC50 values in the SCV derivation and species-specific sensitivity data as calculated by Di Toro et al. 76. See U.S. Environmental Protection Agency 52 for more details. The solid line is the one-to-one line, and the dashed lines show ± a factor of 2. Chemicals potentially having more specific modes of action (e.g., pesticides and phthalates) are not shown.

Tier-1 ESBs for metals

Cationic metal mixtures

The cationic metals cadmium, copper, nickel, lead, silver, and zinc as well as the anionic metal chromium (discussed below) are common sediment contaminants 2–8 that are known to be toxic to benthic organisms 45, 51. Two approaches are available for deriving mechanistic metal mixtures ESBs: AVS and interstitial water 51. The AVS approach discussed above relates the dissolved and bioavailable concentrations of i metals to the SEM exceeding the AVS

equation image(17)

where

equation image(18)

Note, the silver SEM is multiplied by 0.5 because this metal is monovalent and forms Ag2S, unlike the divalent metals which form CdS, CuS, NiS, PbS, and ZnS. For the divalent metals, there is a one-to-one molar relationship achieved between sulfide and each metal. For silver, the AVS to metal molar relationship is 2:1; consequently, the quantity of sulfide bound to silver is one-half the quantity of silver. If the ESB for cationic metals is >0, toxicity due to cationic metals may occur. Conversely, if the ESB is ≤0, toxicity resulting from metals is unlikely.

As discussed above, organic carbon is also an important phase affecting the bioavailability of metals in sediments and soils 58. To consider the importance of this effect on metal bioavailability, Equation 17 is modified to include organic carbon in the calculation of the ESB for metal mixtures 51

equation image(19)

where the ESBAVS OC is interpreted using the following conditions: (1) if >3,000 µmol/gOC adverse effects may occur, (2) if 130 to 3,000 µmol/gOC adverse effects are uncertain, and (3) if <130 µmol/gOC adverse effects are unlikely.

An advantage of the incorporation of organic carbon normalization into predictions of metal mixture toxicity is a reduction in uncertainty. For example, using the SEM–AVS relationship (Eqn. 17) alone results in an uncertainty factor (i.e., ratio of concentrations above which adverse effects are likely and below which adverse effects are unlikely) of approximately 70, while the uncertainty factor for the conditions above in Equation 19 is approximately 23 51. Therefore, whenever possible, use of ESBAVS OC is encouraged during the performance of contaminated sediment assessments.

For the interstitial water approach, the ESB is expressed in interstitial water toxic units (IWTUs) and assumes metal toxicity additivity

equation image(20)

in which

equation image(21)

where [Mi,d] is the dissolved concentration of metali and FCVi,d is the effect concentration for a given metali (Table 2). In sediments, when the metal mixture interstitial water ESB > 1.0, sediment toxicity due to metal mixtures may occur 51, while in cases where the ESB value is ≤ 1.0, toxicity due to metals is unlikely.

Table 2. Final chronic values (FCVs) used to calculate interstitial water-based equilibrium partitioning sediment benchmarks (ESBs) for six cationic metals and one anionic metal
MetalFreshwater FCVi,d (µg/L)Saltwater FCVi,d (µg/L)
  • a

    CF = conversion factor to calculate dissolved FCV for cadmium from the total FCV for cadmium: CF = 1.101672−[(ln hardness)(0.041838)].

  • b

    For this table, the hardness-based freshwater FCV for copper is presented. The biotic ligand model approach is also recommended for calculating a site-specific copper FCV 94.

  • c

    CF = conversion factor to calculate dissolved FCV for lead from the total FCV for lead: CF = 1.46203−[(ln hardness)(0.145712)].

  • From 50 and http://water.epa.gov/scitech/swguidance/standards/current/index.cfm.

  • NA = not available.

CadmiumCF[e0.7409[ln(hardness)]−4.719]a8.8
Copper0.960[e0.8545[ln(hardness)]−1.702]b3.1
LeadCF[e1.273[ln(hardness)]−4.705]c8.1
Nickel0.997[e0.8460[ln(hardness)]+0.0584]8.2
SilverNANA
Zinc0.986[e0.8473[ln(hardness)]+0.884]81
Chromium  
 III0.860[e0.8190[ln(hardness)]+0.6848]NA
 VI1150

Chromium

Like the cationic metal mixtures ESB, the chromium ESB uses both AVS and interstitial water approaches. The AVS approach simply states that in sediments where AVS > 0.0, toxicity due to chromium is unlikely because very little Cr(VI) will be present. If AVS ≤ 0.0, Cr(IV) could be present at toxic concentrations 51. In the interstitial water approach, interstitial water benchmark units are calculated

equation image(22)

and

equation image(23)

where [MCr(III),d] and [MCr(VI),d] are the concentrations of dissolved chromium III or VI in the interstitial water, respectively, and FCVCr(III),d and FCVCr(VI),d are the effect concentrations of chromium III and VI, respectively, from Table 2. As noted for cationic metals, in sediments where either form of chromium interstitial water ESBIW > 1.0, sediment toxicity due to chromium may occur and toxicity is unlikely if the ESBIW ≤ 1.0.

EXAMPLES OF ESB APPLICATION AND INTERPRETATION

In this section, three examples are provided to illustrate the application of ESBs: two examples use sediment contaminated with NOCs, while the other sediment is contaminated with metals. These examples illustrate how to apply the ESB values using routinely collected sediment chemistry data and their interpretation. The number of analytes in the examples is small and not meant to be a realistic set of contaminants that would need to be collected to adequately assess sediment toxicity at a site. The three example sediments (i.e., A, B, C) have the same geochemical characteristics: 4.5% organic carbon (fOC = 0.045 g OC/g dry sediment), 13.2 µg AVS/g dry sediment (0.4 µmol AVS/g dry sediment), and interstitial water hardness is 25 mg/L. To enhance interpretation in Table 3, for sediments A and B, the toxic units (TUs) of normalized sediment concentrations and ESB values for each chemical i are calculated as

equation image(24)

where if TUi is ≤ 1, adverse effects are unlikely to occur and if TUi > 1, adverse effects may occur. Furthermore, as in Equation 15, the sum of TUs for narcotic chemicals is also calculated.

Table 3. Example applications of equilibrium partitioning sediment benchmark (ESB) values using three versions of a freshwater sediment (A, B, C) with the following geochemical characteristics: 4.5% organic carbon (fOC = 0.045 g organic carbon/g dry sediment), 13.2 µg AVS (0.4 µmol AVS/g dry sediment), and interstitial water hardness of 25 mg/L
Sediment A
ChemicalConventional ESB (µg/gOC)Narcosis ESB (µg/gOC)Sediment concentration (µg/kg dry sediment)Normalized sediment concentration (µg/gOC)Conventional ESB toxic units (unitless)Narcosis ESB toxic units (unitless)
Ethylbenzene8.9970100.220.020.00
Dieldrin28––541.200.04 
Alpha-endosulfan0.051––0.0450.000.02 
Hexachloroethane1001,4003006.670.070.00
Malathion0.11––0.450.010.09 
1,2,4-Trichlorobenzene9601,1002004.440.000.00
Anthracene––5941002.22 0.00
Phenanthene––5961403.11 0.01
Pyrene––697110.24 0.00
Chrysene––844541.20 0.00
Perylene––9674.40.10 0.00
Sum narcosis ESB toxic units<0.1
Sediment B
ChemicalConventional ESB (µg/gOC)Narcosis ESB (µg/gOC)Sediment concentration (µg/kg dry sediment)Normalized sediment concentration (µg/gOC)Conventional ESB toxic units (unitless)Narcosis ESB toxic units (unitless)
Ethylbenzene8.9970450101.120.01
Dieldrin28NA0.4500.00 
Alpha-endosulfan0.051NA0.04500.02 
Hexachloroethane1001,4005,0001111.110.08
Malathion0.11NA0.900.18 
1,2,4-Trichlorobenzene9601,10021050.000.00
AnthraceneNA5942,50056 0.09
PhenantheneNA59611,000244 0.41
PyreneNA6978,500189 0.27
ChryseneNA84441,000911 1.08
PeryleneNA96740,000889 0.92
Sum narcosis ESB toxic units2.90
Sediment C
ChemicalSEMi (µg/g dry sediment)SEMi (µmol/g dry sediment)AVS (µg/g dry sediment)AVS (µmol/g dry sediment)Dissolved interstitial water concentration (µg/L)Hardness (mg/L)Detection limits (µg/L)CFFCV (µg/L)IWTU (unitless)
  1. NA = not available; fOC = fraction organic carbon; AVS = acid volatile sulfide; SEM = simultaneously extracted metal; CF = conversion factor; FCV = final chronic value; IWTU = interstitial water toxic unit; ESBAVS = ESB based on AVS; ESBIW = ESB based on interstitial water; ND = not detected.

Nickel1051.7913.20.4123250.8 167.6
Zinc4236.4713.20.467255 361.8
Cadmium970.8713.20.42.3250.20.96700.0924.5
Lead1770.8613.20.4ND250.70.99300.540
Copper3054.8013.20.45.9250.6 2.72.2
Silver00.0013.20.4ND250.9 NA 
Chromium III00.0013.20.4ND250.9 240
ΣSEM14.8 
ESBAVS = SEM–AVS14.4 
equation image320 
ESBIW = ΣIWTU36.1

Sediment A

In this sediment (Table 3), all measured organic chemicals including low–molecular weight compounds, pesticides, and PAHs, ranged between 0.05 and 300 µg/kg dry sediment and were well below their conventional and narcosis ESB values. Furthermore, all TUs, conventional and narcotic, are < 1. In addition, the sum of the narcosis TUs was < 0.1, far below a value of 1, which would indicate concern for a narcotic effect caused by a mixture of chemicals. Note, the sum of the TUs for the conventional ESBs is not calculated because there is no underlying assumption that their toxicity is additive as is the case for narcosis. While these results themselves indicate no reason to suspect adverse effects to benthic organisms from these chemicals, it must be remembered that this conclusion is limited to the effects of these specific measured chemicals. It is, of course, still possible that other chemicals could be present in the sediment at concentrations that could cause adverse effects. Conducting whole-sediment or interstitial water toxicity tests would be one way to address the potential for adverse effects caused by unmeasured chemicals.

Sediment B

This sediment, like sediment A, is contaminated with many organic contaminants; however, it is more severely contaminated with concentrations ranging from 0.05 to 41,000 µg/kg dry sediment. Several of the organic contaminants exceed their conventional and narcosis ESB values (Table 3). Starting with conventional ESB values, ethylbenzene and hexachloroethane each have concentrations that exceed their respective conventional ESBs, with TUs of 1.12 and 1.11, respectively. This suggests the sediment could be toxic due to these chemicals. Furthermore, the concentrations of PAHs are also elevated, and the sum of the narcosis TUs is equivalent to 2.90. These results would suggest that sediment toxicity due to these narcotic chemicals may occur. In summary, sediment B may be expected to be toxic due to both conventional and narcotic toxicity. As noted above, unmeasured chemicals could also be present in the sediment at concentrations resulting in even more toxicity.

Sediment C

Metals are the primary contaminant concern for sediment C, with concentrations of some, like copper and zinc, exceeding 300 mg/kg dry sediment (Table 3). Based on Equation 17, ESBAVS is 14.4 µmol/g dry sediment and greatly exceeds 0.00, indicating that cationic metals are bioavailable and the sediments may be toxic. As discussed above, the organic carbon normalized ESB (ESBAVS OC) is a refinement of the ESBAVS. However, the ESBAVS OC calculated using Equation 19 is equivalent to 320 µmol/g OC, which puts sediment C into the category of toxicity is uncertain. Finally, the ESBIW derived with Equation 20 results in approximately 36 TUs, suggesting relatively strongly that sediment C may cause toxicity. In summary, for the metals contaminating sediment C, two of the lines of evidence (i.e., ESBAVS and ESBIW) strongly suggest that sediment C will be toxic as a result of elevated measured metal concentrations.

CONSIDERATIONS WHEN USING ESBS

The ESBs and associated methodology presented in the present study provide mechanistic means to estimate the concentration of a contaminant or mixture of contaminants that may be present in sediment, while still protecting benthic organisms from adverse effects. Conversely, if the ESBs are exceeded, contamination may result in adverse effects. The ESBs are applicable to a variety of freshwater and marine sediments because they are based on the biologically available concentration of the contaminant. This mechanistic approach is intended for use with NOCs with log KOWs > 2.00 and sediments containing organic carbon ≥ 0.2% dry weight (i.e., fOC = 0.002). This report, shown in Table 1, supplies ESB values for several contaminants but also provides guidance for deriving ESBs for chemicals for which water-only effects data are available with different modes of action. The types of contaminants for which ESB values are provided for in the present article are primarily legacy pollutants, which have been present in the environment for several decades. More recent contaminants (e.g., current-use pesticides) are not well represented. However, as discussed above, with a relevant water-only toxicity value and KOW, using Equations 6 and 7 an ESB value can be derived for an array of recent NOCs. In general, the ESBs do not intrinsically consider the antagonistic, additive, or synergistic effects of other sediment contaminants in combination with the sediment contaminants discussed in the present study or the potential for bioaccumulation and trophic transfer of these chemicals to aquatic life, wildlife, or humans. However, as shown above, for narcotic NOCs and cationic metals, additivity may be used to predict the toxicity of mixtures of contaminants. The ESBs are useful as a complement to other existing sediment-assessment tools, to assist in determining the extent of sediment contamination, to help identify chemicals causing toxicity, and to serve as targets for pollutant loading control measures. To this end, application of the ESBs may function most effectively in a framework that uses these values to screen contaminated sediments for potential toxic effects followed by more rigorous assessments if determined to be necessary (e.g., site-specific assessments of bioavailability, whole-sediment toxicity testing, TIEs).

It is recognized that site-specific considerations may reduce the accuracy of the ESBs, and another report addresses some of those circumstances 79. It should be noted that in general, however, the reductions in ESB accuracy by site-specific considerations like the presence of black carbon tend to result in the ESBs being more protective of benthic organisms. This is because many site-specific considerations, like the presence of black carbon, reduce contaminant bioavailability in the field compared to bioavailability as predicted based only on organic carbon (Fig. 3) 63. Conversely, if an unusual form of organic carbon with a low sorptive affinity (e.g., hair, wood chips, hide fragments) is present in a sediment, an ESB would be underprotective. Furthermore, in a chemical spill situation where chemical equilibrium between water and sediment has not yet been reached, sediment chemical concentrations less than an ESB may pose risks to benthic organisms. This is because for spills, disequilibrium concentrations in interstitial and overlying water may be proportionally higher relative to sediment concentrations. These site-specific considerations demonstrate the need to apply ESBs carefully while considering site conditions.

Figure 3.

Revised conceptual model of the partitioning of nonionic organic contaminants between sediment phases including black carbon. KOC = organic carbon–water partition coefficient; KBC = black carbon–water partition coefficient.

Finally, in recent years, our ability to measure directly interstitial water concentrations of NOCs has improved vastly, and several studies have been published demonstrating the use of passive sampling for making these measurements 80–88. When SQGs were first being developed in the 1980 and 1990 s, making interstitial water measurements was challenging and came with many potential artifacts 89. Recent developments allow for the direct comparison of interstitial water concentrations to the effect concentrations discussed above (e.g., FCV, SCV). Specifically, Equation 24 can be altered to the following form

equation image(25)

where ITWi is the directly measured interstitial water concentration of a contaminant and the FCVi or SCVi for a specific chemical can be used as the effects concentration. However, the cost for making these measurements continues to exceed the cost for performing a standard sediment contaminant analysis, which provides the raw data for conducting the mechanistic analyses described in this report. Differences between the costs for direct interstitial water and sediment measurements are likely to diminish with time, and the direct measurement approaches will become more prevalent. Furthermore, along with costs, it is likely the uncertainties associated with the direct measurement of interstitial water concentrations of contaminants will ultimately be smaller than those uncertainties currently related to the mechanistic ESBs. One reason for this is that the partition coefficients used to calculate the dissolved interstitial water concentrations of a wide range of contaminants (at least for NOCs) using passive sampling are not overly difficult to determine experimentally. Conversely, the use of the linear free energy relationship in Equation 7 to estimate KOC based on KOW is likely to generate greater uncertainties for chemicals not used to develop the original linear free energy relationship. Furthermore, a recent study by Hawthorne et al. 90 illustrated large uncertainties in some empirically derived KOCs. As time goes by and the direct measurement of interstitial water contaminant concentrations using passive sampling is performed more accurately, less expensively, and with smaller uncertainties, passive sampling will more frequently be used with the ESBs described here as complementary lines of evidence in contaminated sediment assessments. However, challenges remain before the direct measurement of interstitial water concentrations is widely accepted in performing sediment assessments. For example, the ability to measure directly interstitial metal concentrations is still technically challenging and expensive 91–93. These remaining challenges to measuring interstitial water concentrations of NOCs and metals highlight the power of the mechanistically based bioavailability models discussed above and their continuing utility for making contaminated sediment risk assessments and environmental management decisions.

Acknowledgements

The information summarized here is based on the scientific advancements made by many people including G.T. Ankley, W.S. Boothman, L.D. DeRosa, D.J. Hansen, T.K. Linton, J.A. McGrath, R.J. Ozretich, R.L. Spehar, F.E. Stancil, and R.C. Swartz as well as support from the U.S. Environmental Protection Agency (U.S. EPA) Program Offices and Regions including the Office of Water and the Office of Superfund Remediation and Technology Innovation, specifically H.E. Bell, S.J. Ells, L. Evison, D.S. Ireland, F.J. Keating, M.C. Reiley, and C.S. Zarba. We also appreciate the insightful comments on the draft manuscript by the internal reviewers D.J. Cobb, K.T. Ho, J. LiVolsi, W.R. Munns, and M.M. Perron. Finally, we acknowledge the years of constructive input from countless colleagues on the development of mechanistic guidelines and enthusiastic debate surrounding their application. This is NHEERL Contribution AED-12-021. Mention of trade names or commercial products does not constitute endorsement or recommendation for use. This report has been reviewed by the U.S. EPA's Office of Research and Development, National Health and Environmental Effects Research Laboratory, Atlantic Ecology Division, Narragansett, RI, and approved for publication. Approval does not signify that the contents necessarily reflect the views and policies of the agency.

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