r/EPA United States Office of Scienco ard Tecnrciogy EPAxxx'x-xx-wx Envircnmental Protection Agency Health and Ecological Criteria Division August 1991 Office of Water Washington. DC 20460 Proposed Technical Basis for Establishing Sediment Quality Criteria for Nonionic Organic Chemicals Using Equilibrium Partitioning ------- PROPOSED TECHNICAL BASIS FOR ESTABLISHING SEDIMENT QUALITY CRITERIA FOR NONIONIC ORGANIC CHEMICALS USING EQUILIBRIUM' PARTITIONING ------- CONTENTS Section Water quality criteria (WQC) concentrations versus colonization experiments 85 Conclusions •. 87 GENERATION OF SQC 89 Parameter Values 89 Example Calculations 92 Sediment quality criteria (SQC) uncertainty 94 SITE SPECIFIC SEDIMENT CRITERIA MODIFICATIONS 99 Site-specific criteria modification based on species sensitivity 99 CONCLUSIONS , 102 Research needs 103 REFERENCES 106 ------- CONTENTS Section FIGURE iii TABLES vii INTRODUCTION 1 OVERVIEW 1 Toxicity and bioavailability of chemicals in sediments 1 Partitioning of nonionic organic chemicals 9 Effects concentration 12 BACKGROUND 16 Rationale for selecting the EqP method 17 Relationship to WQC methodology 18 Applications of Sediment Quality Criteria 20 TOXICITY AND BIOAVAILABILITY OF CHEMICALS IN SEDIMENTS 22 Toxicity experiments 22 Bioaccumulation 29 Conclusion 30 SORPTION OF NONIONIC ORGANIC CHEMICALS 34 Partitioning in particle suspensions 34 Particle concentration effect 35 Organic carbon fraction 40 Dissolved organic carbon (DOC) complexing 43 Phase distribution in sediments 44 Bioavailability of DOC complexed chemicals 48 Field observations of partitioning in sediments 51 Organic carbon normalization 51 Sediment/pore water partitioning 55 Organic carbon normalization of biological responses 58 Toxicity and bioaccumuLation experiments 60 Bioaccumulation and organic carbon normalization 64 Determination of the route of exposure 71 APPLICABILITY OF WQC AS THE EFFECTS LEVELS FOR BENTHIC ORGANISMS 73 Method - relative acute ser--. : : ivi :y 73 Comparison of the sensici.•;• : benthic and water' column species 77 Most Sensitive Species • 77 All species 80 Benthic community coloniza: . . --r: Tents 82 ------- FIGURES Page 7 Comparison of percent mortality of H. azteca to DDT (left) and endrln (right) concentrations in bulk sediment (top) and pore water (bottom) for sediments with varying organic carbon concentrations [21,22] 25 8 Comparison of percent mortality of A. abdita [24] (left) and R. abronius [23] (right) to concentrations of cadmium in bulk sediment (top) and pore water (bottom). Also presented is water-only exposure data, identified with open circles 26 9 Comparison of C_. tentans body burden of cypermethrin (left) and permethrin (right) versus concentration in bulk sediment (top) and pore water (bottom) for sediments with varying organic carbon concentrations [26] 31 10 Comparison of observed reversible component partition coefficient to calculated partition coefficient using Equation 10 [31] '....' 37 11 Comparison of the adsorption (left) and reversible component (right) organic carbon normalized partition coefficient, Koc, to the octanol/water partition coefficient, Kow, for experiments with low solids concentrations: m foc Kow < 1. The line represents equality [31] 38 12 Comparison of the normalized partition coefficients for adsorption (left) and reversible component sorption (right) to sediment organic carbon. The data are restricted so that particle effects are not expected to be significant: ra foc Kow < 1. The line represents perfect agreement [31] 42 13 Partition coefficients of chemicals to particulate organic carbon (POC), Aldrich humic acid, and natural DOC. Benzo[a]pyrene (BaP); 2,2',414',5,5' hexachlorobiphenyl (HCBP); DDT; 2,2',5,5' tetrachlorobiphenyl (TCBP); pyrene (PYR); 4 monochlorobiphenyl (MCBP) . (Data: [43]) 45 14 Phase distribution of a chemical in the three-phase system: water, sediment, and DOC (Eqns. 18, 19, and 20). Koc - KDQC ~ KOW - 106 L/kg, foc - 2.0%, and m - 0.5 kg/L 47 15 - Average uptake rate of chemicals by Pontoporeia hovi with (filled) and without (hatched) DOC present. Benzo[a]pyrene (BaP); 2,2',4,4' tetrachlorobiphenyl (TCBP); Pyrene; Phenanthrene. Data: [46].. ^9 16 Comparison of the DOC partition coefficient calculated from the suppression of chemical uptake versus the GI% reverse. phase HPLC column estimate. Circles are Aldrich humic acid; triangles are interstitial water DOC. Chemicals are listed in Figure 15 caption (also anthracene and benzo[a]anthracene).... ------- FIGURES Figure Page 1 Diagram of the organism exposure routes for a water-only exposure (left) and a sediment exposure (right). Equilibrium partitioning refers to the assumption that an equilibrium exists between the chemical sorbed to the particulate sediment organic carbon and the pore water. The partition coefficient is Koc 4 2 Mortality versus predicted pore water toxic units for five chemicals and three sediments per chemical. Sediment types are indicated by the single hatching (lowest organic carbon content), cross hatching (intermediate organic carbon content), and filled symbols (highest organic carbon content). See Tables 1 and 2 for data sources. Predicted pore water toxic units are the ratio of the pore water concentration to the water-only LC50 (Eqn. 1) 5 3 Mortality versus predicted sediment toxic units. Predicted sediment toxic units are the ratio of the organic carbon normalized sediment chemical concentration to the predicted sediment LC50 (Eqn. 8). Sediment types are indicated by the single hatching (lowest organic carbon content), cross- hatching (intermediate organic carbon content), and filled symbols (highest organic carbon content). See Tables 1 and 2 for data sources. Koc values are computed from Kow for DDT (5.84), endrin (4.80), and fluoranthene (5.30) with Equation 11. These are log averages of the reported values in the Log P data base [71]. The Kepone Koc is the log mean of the ratio of organic carbon normalized Kepone concentration to pore water Kepone concentration from the toxicity data set 13 4 A comparison of the minimum LC50 for water column versus benthic organisms. Each data point represents a particular chemical in either a freshwater or a saltwater exposure. The data are from the WQC or draft criteria documents. See Table 4 for data sources 15 5 Comparison of percent mortality (left) and growth rate reduction (right) of C. tentans to Kepone concentration in bulk sediment (top) and pore water (bottom) for three sediments with varying organic carbon concentrations [17] 23 6 Comparison of percent mortality of R. abronius to fluoranthene [19] (left) and cadmium [20] (right) concentration in bulk sediment (top) and pore water (bottom) for sediments with varying organic carbon concentrations J- i i. i ------- FIGURES Figure 25 Plots of the BSF (ratio of organism-to-sediment concentration) for three sediments for a series of PCB congeners versus the logio Kow for that congener. The dry weight normalization for both organism and sediment (left panels); organic carbon normalization for the sediment (middle panels); and organic carbon and lipid normalization (right panels) as indicated. The organisms are Yoldia (top) and Macoma (bottom). Data from [58] 68 26 Plots of the BSF (ratio of organism lipid to sediment organic carbon concentration) for a series of PCB congeners and other chemicals versus logio Kow. Data for oligachaetes [53] and polychaetes [58 ] 69 27 Comparison of LC50 or EC50 acute values for the most sensitive benthic (abscissa) and water column (ordinate) species for chemicals listed in Table 5. Benthic species are defined as infaunal species (habitat types 1 and 2, left panel) or infaunal and epibenthic species (habitat types 1 - 4); see Table 6 78 28 LCSOs versus rank for nickel in seawater. Infaunal organisms (left) and infaunal and epibenthic (right) are identified by the solid symbols. The plot illustrates the distribution of benthic organisms in the overall species sensitivity distribution 81 29 Histograms of the proportion of saltwater and freshwater benthic organisms in 10 percentile groups of all normalized LCSOs. If benthic organisms were as equally sensitive as water column organisms, the histograms should be of uniform height as indicated by the dashed line, the overall percentage of benthic species in the data set. Top panels include only infaunal organisms as benthic. The bottom panel includes infaunal and epibenthic as benthic organisms 83 30 Comparison of the dieldrin LC50 probability distributions for water column and benthic freshwater species. Lognormal probability plot (left panel) and '.he empirical cumulative distribution functions (right panel) with the maximum difference used in the Kolmogorov - Smirnov test indicated.... 91 31 Logio SQC versus logio Kow- The diagonal lines indicate the FCV values. The criteria are computed from Equation 34. Koc is obtained from Kow with Equation 11. The symbols indicate SQCOC for Cne freshwater (filled) and saltwater (hatched) criteria for the listed chemicals. The vertical line connects symbols for the same chemical. The FCVs are from the WQC or draft criteria documents (Table 4). The octanol/water partition coefficients are the log mean of the values reported in the Log P data base [71] vi ------- FIGURES Figure 17 The organic carbon fractions (X dry weight) in the low-density fraction 53 18 Comparison of PAH concentrations of the sand-sized and low- density-fraction sediment particles (ordinate) to the clay/silt fraction (abscissa) (Stations 4, 5, 7). Top panels are for dry weight normalization; bottom panels are for organic carbon normalization. Data from Prahl [48] 54 19 Observed partition coefficient versus the product of organic carbon fraction and octanol/water partition coefficient. The line represents equality. The partition coefficients are computed by using total dissolved PCB (squares), and free PCB (circles) which is computed Equation 26 with KDOC " Kow. (Data from [50]) 57 20 Observed apparent partition coefficient to organic carbon versus the octanol/water partition coefficient. The lines represent the expected relationship for DOC concentrations of 0, 1, 10, and 100 mg/L and KDOC " Kow- Data from [53] for PCB congeners and other chemicals and from [52] for phenanthrene, fluoranthene, and perylene 59 21 Comparison of percent survival (left) and growth rate reduction (right) of C. tentans to Kepone concentration in pore water (top) and in bulk sediment, using organic carbon normalization (bottom) for three sediments with varying organic carbon concentrations [ 17 ] 61 22 Comparison of percent survival of H. azteca to DDT (left) and endrin (right) concentration in pore water (top) and in bulk sediment using organic carbon normalization (bottom) for three sediments with varying organic carbon concentrations [21,22]. 62 23 Comparison of percent survival of R. abronius to fluoranthene concentration in pore water (cop) and bulk sediment, using organic carbon normalization (bottom) for sediments with varying organic carbon concentrations [19] 63 24 Plots of the BSF (ratio of organism-to-sediment concentration) for three sediments for a series of PCB congeners versus the log^Q Kow for that congener. The dry weight normalization for both organism and sediment (left panels); organic carbon normalization for the sediment (middle panels); and organic carbon and lipid normalization (right panels) as indicated. The organisms are Nereis (top) and Nephtvs (bottom). Data from [58] 67 ------- TABLE Table Page 1 SEDIMENT TOXICITY DATA AND BIOACCULUMATION DATA 6 2 LC50 AND EC50 FOR SEDIMENT DRY WEIGHT AND SEDIMENT ORGANIC CARBON NORMALIZATION AND FOR PORE WATER AND WATER ONLY EXPOSURES 7 3 BIOACCUMULATION FACTORS 32 4 DRAFT OR PUBLISHED WQC DOCUMENTS AND NUMBER OF INFAUNAL (HABITAT 1 AND 2) EPIBENTHIC (HABITATS 3 AND 4), AND WATER COLUMN (HABITATS 5 TO 8) SPECIES TESTED ACUTELY FOR EACH SUBSTANCE 74 5 HABITAT CLASSIFICATION SYSTEM FOR LIFE SAGES OR ORGANISMS 76 6 ANALYSIS OF VARIANCE FOR DERIVATION OF SEDIMENT QUALITY CRITERIA CONFIDENCE LIMITS 86 7 COMPARISON OF WQC, FCVs AND CONCENTRATION AFFECTING (LOEC) AND NOT AFFECTING (NOEC) BENTHIC COLONIZATION 104 ------- OVERVIEW This report presents the technical basis for establishing sediment quality criteria (SQC) for nonionic organic chemicals using the equilibrium partitioning (EqP) method. An overview is presented first that summarizes the evidence and the major lines of reasoning. The references are cited in the body of the report. Sediment quality criteria, as used herein, refers to numerical concentrations for individual chemicals that are applicable across the range of sediments encountered in practice. Sediment quality criteria are intended to be predictive of biological effects. As a consequence they could be used in much the same way as the final chronic value water quality criteria - as the concentration of a chemical that is protective of benthic aquatic life. The specific regulatory uses of SQC have not been established. However, the range of potential applications is quite large as the need for the evaluation of potentially contaminated sediments arises in many contexts. Sediment quality criteria are meant to be used with direct toxicity testing of sediments as a method of evaluation. They provide a chemical by chemical specification of what sediment concentrations would be protective of benthic aquatic life. Toxicity and bioavailability of chemicals in sediments Establishing SQC requires a detenu i: ..- ion of the extent of the bioavailability of sediment associated • • r. :>• .Is It has frequently been observed that similar concentrations o: . :. nral., in units of mass of ------- INTRODUCTION This report presents the technical basis for establishing sediment quality criteria using equilibrium partitioning. This methodology was selected because it addresses the two principal technical issues that must be resolved: the varying bioavailability of chemicals in sediments and the appropriate biological effects concentration. Equilibrium partitioning assumes that the partitioning of the chemical between sediment organic carbon and pore water is at equilibrium Sediment quality criteria are numerical concentrations for individual chemicals - in this case, nonionic organic chemicals - that can be applied across the range of sediments encountered. Intended to predict biological effects, sediment quality criteria can be used much as final chronic value water quality criteria are used: as the concentration of a chemical that protects benthic aquatic life. Sediment quality criteria for nonionic organic chemicals are based on the chemical concentrations in sediment organic carbon; these criteria are designed for use with direct toxicity testing of sediments as an evaluation method. The final validation of sediment quality criteria will come from field studies that are designed to evaluate the extent to which biological effects can be predicted from these criteria. A review paper based on this report is published by the Society for Environmental Toxicity and Chemistry in volume 10 (1991) of their journal, Environmental Toxicicy and Chemistry. ------- Page 3 effects concentration found for the pore water is essentially equal to that found in water-only exposures. Organism mortality, growth rate, and bioaccumulation data are used to demonstrate this correlation, which is a critical part of the logic behind the EqP approach to developing SQC. For nonionic organic chemicals, it is shown that the concentration - response curves correlate equally well with the sediment chemical concentration on a sediment organic carbon basis. These observations can be rationalized by assuming that the pore water and sediment carbon, are in equilibrium and that the concentrations are related by a partition coefficient, Koc, as shown in Figure 1 (right). The name equilibrium partitioning (EqP) describes this assumption of partitioning equilibrium. The rationalization for the equality of water-only and sediment- exposure-effects concentrations on a pore water basis is that the sediment - pore water equilibrium system (right) provides the same exposure as a water- only exposure (left). The reason is that the chemical activity is the same in each system at equilibrium. It should be pointed out that the EqP assumptions are only approximately true, and, therefore, the predictions from the model have an inherent uncertainty. The data presented below illustrate the degree to which EqP can rationalize the observations. Figure 2 presents mortality data for various chemicals and sediments compared to pore water concentrations when normalized on a toxic unit basis. Three different sediments are tested for each chemical as indicated. Predicted pore water toxic units are the racio of the measured pore water concentration to the LC50 from water only toxic icy :escs. The EqP model predicts that the pore water LC50 will equal the wacer only LC50 which is obtained from a separate water only exposure toxicicy ;cs; ------- Page 2 chemical per mass of sediment dry weight (e.g. micrograms chemical per gram sediment), can exhibit a range in toxicity in different sediments. If the purpose of SQC is to establish chemical concentrations that apply to sediments of differing types, it is essential that the reasons for this varying bioavailability be understood and be explicitly included in the criteria. Otherwise the criteria cannot be presumed to be applicable across sediments of differing properties. The importance of this issue cannot be overemphasized. For example, if 1 Mg/g of kepone is the LC50 for an organism in one sediment and 35 ^g/g is the LC50 in another sediment, then unless the cause of this difference, can be associated with some explicit sediment properties it is not possible to decide what would be the LC50 of a third sediment without performing a toxicity test. The results of toxicity tests used to establish the toxicity of chemicals in sediments would not be generalizable to other sediments. Imagine the situation if the results of toxicity tests in water depended strongly on the particular water source - for example, Lake Superior versus well water. Until the source of the differences was understood, ic would be fruitless to attempt to establish water quality criteria (WQC). It is for this reason that the issue of bioavailability is a principal focus of this report. The observations that provided :he key insight to the problem of quantifying the bioavailability of chemicals in sediments were that the concentration-response curve for the biological effect of concern could be correlated not to the total sediment • • r'icaL concentration (micrograms chemical per gram sediment), but to : • .: -erscitial water (i.e., pore water) concentration (micrograms chemical p. : : ;<•:'<.' water). In addition, the ------- Pore Water Normalization o 5 100 BO 60 40 20 0 1 i i i i i 1 1 1 i i i i i i i i a - ENDRIN A - ACENAPHTHENE c v - PHENANTHRENE ° 0 - FLUORANTHENE D o - KEPONE + - CADMIUM o D I ° D c 0 ^ o <*"• „ ° n a QxA .„ V V V^ V v «j^°X D"^ O do«naxxxnB o a § V* a - 0 A vv V O v v + A ) V V O^ A °v A i ^V - A V — VA - 1 1 1 | 1 1 1 1 1 1 1 1 1 1 1 1 1 0.01 0.1 10 100 Predicted Pore Water Toxic Units l-'ij-urt; ?. Mortality versus predicted pore water toxic units for five chemicals and three sediments per (h.-mio.il. Sediment types are indicated by the single hatching (lowest organic carbon content), cross hatching ( iiitcmicdiiiti- organic carbon content), and filled symbols (highest organic carbon content). See Tables 1 and 2 ioi data sources. Predicted pore water toxic units are the ratio of the pore water concentration to the water-only LC50 (Eqn. 1). ------- Water Only Exposure Sediment - Pore Water Exposure Biota Biota Water Sediment Carbon K oc Pore Water Equilibrium Partitioning iguri- 1. Diagram of the organism exposure routes for a water-only exposure (left) and a sediment exposure i if,hi ) Kijui 1 ibriuin partitioning refers to the assumption that, an equilibrium exists between the chemical .»------- Table 2. LC50 and EC50 for sediment dry weight and sediment organic carbon normalization and for pore water and water only exposures LC50 and Chemical End point Kepone (mortality) Kepone (growth) Fluoranthene (mortal ity) DDT (mortal i ly ) DDT (mortali ty) Endrin (mortality) Endrin (mortality) Cadmium (mo it al ity) foe % 0 1 12 0 1 12 0 0 0 3 7 10 3 3 11 3 6 11 3 11 11 0 0 1 .09 .50 .0 .09 .50 .0 .2 .3 .5 .0 .2 .5 .0 .0 .0 .0 .1 .2 . 0 .0 .0 .0 .25 .0 Total Sediment USL/K. 0 6 35 0 9 37 3 6 10 10 17 44 1 4 10 3 5 5 4 18 10 22 20 10 .90 (0.73 .9 (5.85 .2 (30.6 .46 (0.42 .93 (7.74 .3 (31.5 .2 (2.85 .4 (5.56 .7 (8.34 .3 (8.74 .5 (12.5 .9 (36.7 .54 (1.18 .16 (3.91 .95 (9.34 .39 (2.61 .07 (4.05 .91 (4.73 .76 (3.70 .9 (13.6 .5 (8.29 .5 (18.7 .8 (16.7 .2 (7.02 - 1.10) - 8.12) - 40.5) - 0.51) - 12.8) - 44.2) - 3.59) - 7.27) - 13.7) - 12.2) - 24.3) - 55.0) - 2.00) - 4.42) - 12.9) - 4.41) - 6.36) - 7.37) - 6.13) - 26.3) - 12.7) - 27.1) - 26.0) - 14.7) 29 31 18 17 48 20 21 30 22 0 1 0 1 1 1 2 3 2 2 1 Pore water ue./L .9 (25.3 .3 (25.7 .6 (15.7 .1 (15.7 .5 (34.6 .1 (16.7 .9 (19.6 .9 (27.0 .2 (17.5 .74 (0.67 .45 (1.20 .77 (0.67 .80 (1.44 .92 (1.55. .74 (1.37 .26 (1.67 .75 (2.72 .81 (2.44 .50 (2.19 .76 (1.48 - 35.6) - 38.1) - 21.9) - 18.7) - 67.8) - 24.1) - 24.4) - 35.4) - 29.3) - 0.82) - 1.75) - 0.89) - 2.24) - 2.36) - 2.20) - 3.05) - 5.19) - 3.23) - 2.87) - 2.09) EC50 Organic Carbon Water Only ug/E OC ue./L References 1000. 460. 293. 511. 662. 311. 1600. 2130. 2140. 344. 243. 428. 51.3 139. 99.6 113. 83.1 52.8 159. 172. 95.8 (811. (390. (255. (467. (516. (262. (1430. (1850. (1670. (291. (174. (350. (39.3 (130. (84.9 (87.0 (66.4 (42.2 (123. (124. (75.4 - 1220.) 26.4 (22.7 - 30.6) (17| -541.) - 337.) - 567.) 16.2 (15.0 - 17.5) [17] - 1050.) - 368.) - 1800.) (19) - 2420.) - 2740.) - 405.) 0.45 (0.38 - 0.53) [211 - 338.) 0.48 (0.42 - 0.55) - 524.) 0.52 (0.45 - 0.60) - 66.7) [22] - 147.) - 117.) - 147.) 4.81 (4.46 - 5.20) [21] -104.) 3.39 (3.10 - 4.98) - 65.8) 3.71 (3.11 - 4.44) -204.) [22] - 239.) - 115.) 1.6 (1.4 - 1.8) [23] . .mil KOUs and the ay I confidence limits in parentheses are computed using the modified Spearman - Karber ------- Table 1. Sediment toxicity data and bioaccuroulation data Chemical Kepone Kepone Cadmium Fluoranthene DDT Endr in Cadmium Cadmium Cypermethrin Permethrin Kepone Organism Chironomus tentans Chironomus tentans Rhepoxvnius abronius Rhepoxvnius abronius Hvalella azteca Hval lei la azteca Rhepoxvnius abronius Ampelisca abdita Chironomus tentans Chironomus tentans Chironomus tentans Exposure Sediment duration source (davs) Soil Soil Yaquina Bay, OR Yaquina Bay, OR Soap Creek, Mercer Lake Soap Creek Mercer Lake Yaquina Bay, OR Long Island Sound River and pond River and pond Soil 14 14 4 10 10 10 4 10 1 1 14 Biological end point Mortality Growth Mortality Mortality Mortality Mortality Mortality Mortality Body burden Body burden Body Burden Reference [17] (17) (20) [19] 121.22] [21,22] [23] [24] [26] [26] [17.28] Figure 5 5 6 6 7 7 8 8 9 9 ------- Page 9 In addition, if it were true that benthic organisms are as sensitive as, water column organisms - and the evidence to be presented appears to support this supposition - then SQC could be established using the final chronic value (FCV) from WQC documents as the effects concentration for benthic organisms. The apparent equality between the effects concentration as measured in pore water and in water-only exposures (Fig. 2) supports using an effects concentration derived from water only exposures. The calculation procedure for establishing SQC is as follows. If FCV is the final chronic WQC for the chemical of interest, then the SQC sediment) are computed using the partition coefficient Kp (L/kg sediment) between sediment and pore water: SQC - Kp FCV This is the fundamental equation from which SQC are generated. Its utility depends on the existence of a methodology for quantifying partition coefficients. Partitioning of nonionic organic chemicals The partitioning of nonionic organic chemicals to soil and sediment particles is reasonably well understood, and a standard model exists for describing the process. The hydrophobia icy of the chemical is quantified by using the octanol/water partition coefficient, Kow. The sorption capacity of ------- Page 8 To examine this prediction .. . . (pore water concentration) .,. predicted pore water toxic unit - : : T_InN (1) ^ * (water only LC50) v ' Therefore, a toxic unit of one occurs when the pore water concentration equals the water-only LC50, at which point it would be predicted that SOX mortality would be observed. The correlation of observed mortality to predicted pore water toxic units in Figure 2 demonstrates (a) the efficacy of using pore water concentrations to remove sediment to sediment differences and (d) the applicability of the water-only effects concentration and, by implication, the validity of the EqP model. By contrast, as shown below, the mortality versus sediment chemical concentration on a dry weight basis varies dramatically from sediment to sediment. The equality of the effects concentration on a pore water basis suggests that the route of exposure is via pore water. However, the equality of the effects concentration on a sediment organic carbon basis, which is demonstrated below, suggests that the ingestion of sediment organic carbon is the primary route of exposure. It is important to realize that if the sediment and pore water are in equilibrium, then the effective exposure concentration is the same regardless of exposure route. Therefore, it is not possible to determine the primary route of exposure from equilibrated experiments. Whatever the route of exposure, the correlation to pore water suggests chat if it were possible to either measure the pore water chemical concentration, or predict it from the total sediment concentration and the relevant sediment properties such as the sediment organic carbon concentration, then that concentration could be used to quantify che exposure concentration for an organism. Thus, the partitioning of chemicals between the solid and the li-j'.iivl phase in a sediment becomes a necessary component for establishing SQC. ------- Page 11 If we define SQC SQCoc -— (6) oc as the organic carbon normalized SQC concentration (microgram chemical per kilogram organic carbon, then SQC - K FCV oc oc (7) Hence we arrive at the following important conclusion: For a specific chemical having a specific Koc> the organic carbon normalized sediment concentration, SQCOC, is independent of sediment properties. Hydrophobic chemicals also tend to partition to colloidal-sized organic carbon particles that are commonly referred to as dissolved organic carbon, or DOC. Although DOC affects the apparent pore water concentrations of highly hydrophobic chemicals, the DOC-bound fraction of the chemical appears not to be bioavailable and Equation 7 for SQCOC still applies. Therefore, we expect that toxicity in sediment can be predicted from the water-only effects concentration and the Koc of the chemical. The utility of these ideas can be tested with the same mortality data as these in Figure 2 but restricted to nonionic organic chemicals for which organic carbon normalization applies. The concept of sediment coxic units is useful in this regard. These are computed as the ratio of the organic carbon-normalized sediment concentrations, Cs/foc, and the predic-.-.i sediment LC50 using Koc and the water-only LC50. That is: ------- Page 10 the sediment is determined by the mass fraction of organic carbon for the sediment, foc. For sediments with foc > 0.2Z by weight, the organic carbon appears to be the predominant phase for chemical sorption. The partition coefficient, Kn, the ratio of sediment concentration, Cs, to pore water concentration, C^, is given by K -~ -f K (3) p C. oc oc where Koc is the partition coefficient.for sediment organic carbon. The only other environmental variable that has a dramatic effect on partitioning appears to be the particle concentration in the suspension in which Kp is measured. There is considerable controversy regarding the mechanism responsible for the particle concentration effect, and a number of explanations have been offered. However, all the interpretations yield the same result for sediment/pore water partitioning, namely that Koc =• Kow for sediments. A detailed review of the arguments is presented below. Using Equations 2 and 3, an SQC is calculated from SQC - f K FCV oc oc (4) This equation is linear in the organic carbon fraction, foc. As a consequence, the relationship can be expressed as SQC - K FCV ,,. f oc (5) oc ------- Page 12 C /f s oc predicted sediment toxic unit - (8) Koc (water only LC50) Figure 3 presents the percent mortality versus predicted sediment toxic units. The correlation is similar to that obtained using the pore water concentrations in Figure 2. The cadmium data are not included because partitioning is not determined by sediment organic carbon. The predicted sediment toxic units for each chemical follow a similar concentration-response curve independent of sediment type. The data demonstrate that 50% mortality occurs at about one sediment toxic unit, independent of chemical, species or organism or sediment type, as expected if the EqP assumptions are correct. If the assumptions of EqP were exactly true and there were no experimental variability or measurement error, then the data in Figures 2 and 3 should all predict SOX mortality at one toxic unit. There is an uncertainty of approximately a factor of two in the results (also see Table 2 below). This variation reflects inherent variability in these experiments as well as phenomena that have not been accounted for in the EqP model. This appears to be the limit of the accuracy and precision to be expected. Effects concentration The development of SQC requires an effects concentration for benthic organisms. Because many of the organisms used to establish the WQC are benthic, perhaps the WQC are adequate estimates of the effects concentrations for benthic organisms. To examine this possibility, the acute toxicity data ------- Organic Carbon Normalization co h. o 5 100 BO 60 40 20 0 I i I I I I 1 1 1 l l I l l l 1 1 a - ENDRIN A - ACENAPHTHENE a c v - PHENANTHRENE ° o - DIELDRIN - 0 ~ FLUORANTHENE o A a oQ0 o I A 1 av « AV AR ofc v VX Q " V W V i i i i 1 1 1 ii i i i i 1 1 1 1 i i i i i 1 1 1 1 i i i i i 1 1 1 ^A ^ ^d!D3 B fT flD OO •" a A ^ o o v v - 3 V V ° A 0 V A V - A 1 1 | 1 1 1 1 1 1 1 1 1 1 1 | 1 1 0.01 0.1 10 100 Predicted Sediment Toxic Units Figure 3. Mortality versus predicted sediment toxic units. Predicted sediment toxic units are the ratio of the organic carbon normalized sediment chemical concentration to the predicted sediment LC50 (Eqn. 8). Sediment types are indicated by the single hatching (lowest organic carbon content), cross-hatching (intermediate organic carbon content), and filled symbols (highest organic carbon content). See Tables 1 and 2 for data sources. Koc values are computed from Kow for DDT (5.84), endrin (4.80), and fluoranthene (5.30) with Equation 11. These are log averages of the reported values in the Log P data base [71]. The Kepone Koc the log mean of the ratio of organic carbojM^ormalized Kepone concentration to pore from the toxicity data set. ------- Page 14 base, which is used to establish the WQC is segregated into benthic and water column species, and the relative sensitivities of each group are compared. Figure 4 compares the acute values for the most sensitive benthic (epibenthic and infaunal) species to the most sensitive water column species. The data are from the 40 freshwater and 30 saltwater U.S. Environmental Protection Agency (EPA) criteria documents. Although there is considerable scatter, these results, a more detailed analysis of all the acute toxicity data and the results of benthic colonization experiments, presented below, support the contention of equal sensitivity. ------- Comparison of Most Sensitive Species O 10 O c E 3 "5 O *. 9 +* CO 0> o -1 -3 Freshwater Saltwater -3 -1 Log 1O Benthic LC5O (ug/L) Figure 4. A comparison of the minimum LC50 for water column versus benthic organisms. Each data point represents a particular chemical in either a freshwater or a saltwater exposure. The data are from the WQC or di.iIt criteria documents. See Table 4 for data sources. ------- Page 16 BACKGROUND Under the Clean Water Act (CWA), the EPA is responsible for protecting the chemical, physical, and biological integrity of the nation's waters. In keeping with this responsibility, EPA published WQC in 1980 for 64 of the 65 priority pollutants and pollutant categories listed as toxic in the CWA. Additional water quality documents that update criteria for selected consent decree and new chemicals have been published since 1980. These WQC are numerical concentration limits that are protective of human health and aquatic life. Although these criteria play an important role in assuring a healthy aquatic environment, they alone are not: sufficient to ensure appropriate levels of environmental and human health protection. Toxic contaminants in bottom sediments of the nation's lakes, rivers, wetlands, and coastal waters create the potential for continued environmental degradation even where water column contaminant levels comply with established WQC. The absence of defensible SQC makes it difficult to accurately assess the extent of sediment contamination. As a result of the need to assist regulatory agencies in making decisions concerning contaminated sediment, the EPA's Office of Water Regulations and Standards, Criteria and Standards Division, established a research team to review alternative approaches to assess sediment contamination. These and related problems were the subject of a conference [1]. Alternative approaches to establishing SQC [2] and their merits and deficiencies were discussed [3]. Additional efforts to identify the scope of national sediment contamination [4] ------- Page 17 and to review proposed approaches for addressing contaminated sediments [5,6] were undertaken. The EqP method was selected because it appeared to provide the most practical, scientifically defensible, and effective regulatory tool for addressing individual chemicals associated with contaminated sediments on a national basis [7]. Rationale for selecting the EqP method The principal reasons for the selection of the EqP method were: 1. It was likely that the EqP method would yield sediment criteria that were predictive of biological effects in the field and would be defensible when used in a regulatory context. These criteria directly address the issue of bioavailability and are founded on the extensive biological effects data base used to establish national WQC. 2. Sediment criteria could be readily incorporated into existing regulatory operations because a unique numerical sediment specific criterion can be established for any chemical and compared to field measurements to assess the likelihood of significant adverse effects. 3. Sediment criteria could provide a simple and cost-effective means of screening sediment measurements to identify areas of concern and could provide regulators with information in a short period of time. ------- Page 18 4. The method takes advantage of the large amount of data and expertise that went into the development of the national WQC. 5. The methodology could be used as a regulatory predictive tool to ensure uncontaminated sites would, be protected from attaining unacceptable levels of contamination. Relationship to WQC methodology Perhaps the first question to be answered is: Why not use the already existing procedure for the development of WQC to develop SQC? A detailed methodology has been developed that presents the supporting logic, establishes the required minimum toxicological data set, and specifies the numerical procedures to be used to calculate the criteria values [8]. Furthermore, WQC developed with this methodology are routinely used in the regulation of effluent discharges. A natural extension would be to apply these methods directly to sediments. The WQC are based on total chemical concentration, and the transition to using dissolved chemical concentration for those chemicals that partition to a significant extent would not be difficult. The experience with site-specific modifications of the national WQC has demonstrated that the water-effect ratio, the ratio of chemical concentrations in site water to laboratory water that produces the same effect has averaged 3.5 [9,10]. The implication is that s- differences of this magnitude due to variations in site-specific water chemistry are not an overwhelming impediment to nationally applicable numerical WQC. . ------- Page 19 The WQC are based on using the total chemical concentration as a measure of bioavailable chemical concentration. However, the use of total sediment chemical concentration as a measure of bioavailable - or even potentially bioavailable - chemical concentration is not supported by the available data [11]. A summary of recent experiments is presented in the two sections that follow. The results of these experiments indicate that different sediments can differ in toxicity by factors of 100 or more for the same total chemical concentration. This is a significant obstacle. Without a quantitative estimate of the bioavailable chemical concentration in a sediment it is impossible to predict a sediment's toxicity on the basis of chemical measurements. This is true regardless of the methodology used to assess biological impact - be it laboratory toxicity experiments or field data sets comprising benthic biological and chemical sampling [12,13,14,15]. Without a unique relationship between chemical measurements and biological end points that applies across the range of sediment properties that affect bioavailability, the cause and effect linkage is not supportable. If the same total chemical concentration is 100 times more toxic in one sediment than it is in another, how does one set universal SQC that depend only on the total sediment chemical concentration? Any SQC that are based on total sediment concentration have a potential uncertainty of this order of magnitude. Thus, it appears that bioavailability must be explicitly considered for any sediment evaluation methodology that depends on chemical measurements and, in particular, in establishing defensible SQC. ------- Page 20 Applications of Sediment Quality Criteria Sediment quality criteria are designed to protect benthic infauna. These criteria are designed to supplement Ambient Water Quality Criteria in assessing the hazards to water bodies. For example, discharges of low concentrations of a hazardous waste over many years may not indicate hazard to aquatic organisms (as ambient water concentrations of chemicals do not exceed AWQC) but may result in adverse effects to benthic infauna in the resulting contaminated sediments. Because sediment criteria identify additional impacts to aquatic systems they are expected to address an important void. As such, it is expected that these criteria may be used in identification and monitoring of contaminated sediments and determining the potential for adverse effects to benthic infauna. EPA recommends that SQC be applied only in the following situations: • the organic carbon concentration of the sediment is greater than or equal to 0.2% on a dry weight basis • the sediments are bedded and not suspended to sediments that are either permanently inundated with water, or the sediments are in an intertidal zone which is inundated periodically for durations sufficient to permit development of benthic assemblages. They do not apply to occasionally inundated soils containing terrestrial organisms. ------- Page 21 In spills where chemical equilibrium between water and sediment has not been reached, sediment chemical concentrations in excess of the SQC indicate benthic organisms may be at risk. This is because for spills the concentrations in interstitial and overlying water may be higher relative to the equilibrium in sediment concentrations. In spills, sediments having concentrations less than SQC may also pose risks to benthic organisms. ------- Page 22 TOXICITY AND BIOAVAILABILITY OF CHEMICALS IN SEDIMENTS The observation chat provided a key insight into the problem of quantifying the bioavailabilicy of chemicals in sediments was that the concentration- response curve for the biological effect of concern could be correlated, not to the total sediment chemical concentration (raicrograms chemical per gram dry sediment) but to the pore water concentration (micrograms chemical per liter pore water) [17]. In retrospect ic has become clear that these results do not necessarily imply that pore water is the primary route of exposure. This is because all exposure pathways are at equal chemical activity in an equilibrium experiment (see Fig. 1) and the route of exposure cannot be determined. Nevertheless, this observation was the critical first step in understanding bioavailability of chemicals in sediments. Toxicity experiments A substantial amount of data has been assembled that addresses the relationship between toxicity and pore water concentration. Table 1 lists the sources and characteristics of these experiments. The data are presented in a uniform fashion on Figures 5 to 8. The biological response - mortality or growth rate suppression - is plotted versus the total sediment concentration in the top panel, and versus the measured pore water concentration in the bottom panel. Table 2 summarizes the LC50 and EC50 estimates and 95X confidence limits for these data on a total sediment and pore water basis, as well as the water-only values. ------- Dry Weight Normalization Kepone - Mortality Kepone - Growth 100 S? 80 * eo I 40 | 20 0 foe (X) • O.O9 • 1.6 • 12 I 0.1 1.O 1O.O 100.O Dry Weight Concentration (ug/g) O.I 1.O 1O.O 100.O Dry Weight Concentration (ug/g) 100 a •« x ** •<> 4° 20 0 Pore Water Normalization Kepone - Mortality 1OO X BO \ 6O 5 4O 2O O 1 1O 10O 100O Pore Water Concentration (ug/L) Kepone - Growth 1 1O 10O 1OOO Pore Water Concentration (ug/L) Figure 5. Comparison of percent mortality (left) and growth rate reduction (right) of C. tentans to Kepone concentration in bulk sediment (top) and pore water (bottom) for three sediments with varying organic carbon concentrations |17). ------- Dry Weight Normalization Fluoranthene Cadmium foe C/.) • 0.2 • 0.3 1 • 0.5 foe (X) • o.o • 0.25 • 1.0 O 5 1O 15 2O Dry Weight Concentration (ug/g) 0 2O 40 6O 80 Dry Weight Concentration (ug/g) Pore Water Normalization Fluoranthene Cadmium ^ x ss 3 5 o 20 4O oo so Pore Water Concentration (ug/L) O 2OOO 4000 6000 Pore Water Concentration (ug/L) . Comparison of percent mortality of R. abronius to fluoranthene [19] (left) and cadmium [20] (right) \it ion in bulk sediment (top) and pore water (bottom) for sediments with varying organic carbon at i ons . ------- Dry Weight Normalization DDT Endrln foe (X) • 3.0 e 10.5 3 "5 o 100 8O 80 40 2O o1 foe (X) _^__ • 3.0 J§ ^w^^^^ • 11.2 • * * ••" • * ' feSf •• SO 1OO ISO 20O Dry Weight Concentration (ug/g) 0.1 1.O .1O.O 1OO.O Dry Weight Concentration (ug/g) Pore Water Normalization DDT Endrln ~ ^ 3 i 100 80 80 40 20 o1 / **— > • • • 4> • •• * :*••*•"• 012345 Pore Water Concentration (ug/L) O.I 1.O 1O.O 1OO.O Pore Water Concentration (ug/L) l-'i^ure 7 hulk i Comparison of percent mortality of H. azteca to DDT (left) and endrin (right) concentrations, in MI (top) and pore water (bottom) for sediments with varying organic carbon concentrations [21,22]. ------- Dry Weight Normalization o Cadmium - Ampelisca 10 10J 10 10 10 10 Dry Weight Concentration (ug/g) o 2 Cadmium - Rhepoxynius i mm i MIIII 11 mrn—i 11 nnii 10° IO1 102 IO3 IO4 105 Dry Weight Concentration (ug/g) Pore Water Normalization 100 BO 60 40 20 0 Cadmium - Ampelisca I 1 HATER ONLY o SEDIMENT EXPOSURE • I I HIM • Illl io~4 io"3 io"2 io~* 10° 10* io2 io3 io4 Pore Water Concentration (mg/L) 100 BO 60 40 20 0 Cadmium - Rhepoxynius HIM I Him MATER ONLY O SEDIMENT EXPOSURE • 10~4 10"310"210~* 10° 101 IO2 103 IO4 Pore Water Concentration (mg/L) Figure 8. Comparison of percent mortality of A. abdita [24] (left) and R. abronius [23) (right) to concentrations of cadmium in bulk sediment (top) and pore water (bottom). Also presented is water-only exposure data, identified with open circles. ------- Page 27 The results from Kepone experiments (Fig. 5) are particularly dramatic [17,18]. For the low organic carbon sediment (foc - 0.09X), the 50th percentile total Kepone concentration for both Chironomus tentans mortality (LC50) and growth rate reduction from a life cycle test (EC50) are <1 /*g/g. By contrast, the 1.52 organic carbon sediment EC50 and LC50 are approximately 7 and 10 Mg/gi respectively. The high organic carbon sediment (12X) exhibits still higher LC50 and EC50 values on a total sediment kepone concentration basis (35 and 37 Mg/g, respectively). However, as shown in the bottom panels, essentially all the mortality data collapse into a single Curve and the variation in growth rate data is significantly reduced when the pore water concentrations are used as the correlating concentrations. On a pore water basis, the biological responses as measured by LC50 or EC50 vary approximately less than a factor of two, whereas when they are evaluated on a total sediment Kepone basis they exhibit an almost 40-fold range in Kepone toxicity. The comparison between the pore water effects concentrations and the water-only results indicates that they are similar. The pore water LC50s are 19 to 30 Mg/L, and the water-only exposure LC50 is 26 Mg/L- The pore water EC50s are 17 to 49 Mg/L. and the water-only EC50 is 16 Mg/L (Table 2). Laboratory experiments have also been performed to characterize the toxicity of fluoranthene [19] and cadmium [20] to the sediment-dwelling marine amphipod Rhepoxynius abronius. Figure 6 presents the R. abronius mortality data for the fluoranthene and cadmium experiments. The results of the fluoranthene experiments parallel those for Kepone. The sediment with the lowest organic carbon content (0.2%) exhibits the lowest LC50 on a total ------- Page 28 sediment concentration basis (3.2 Mg/g) and as the organic carbon concentration increases (0.3 and 0.5%) the LC50 increases (6.4 and 10.7 Mg/g)• On a pore water basis, the data again collapse to a single concentration-response curve and the LC50s differ by less than 50%. The cadmium experiments [20] were done with constant pore water concentrations and a sediment amended with varying quantities of organic carbon. The unamended and 0.25% additional organic carbon exhibit essentially similar responses. However, the IX amended sediment had a much different LC50 based on the total sediment: concentration. Using the pore water concentrations as the correlating variable again collapses the data into one concentration- response curve. Figure 7 presents mortality data for DDT and endrin using the freshwater amphipod Hvalella azteca [21,22]. The responses for DDT [21] are similar to those observed for Kepone, cadmium, and fluoranthene. On a total sediment concentration basis the organism responses differ for the various sediments (LC50s are 10.3 to 45 /*g/L), but on a pore water basis the responses are again similar (LCSOs are 0.74 to 1.4 Mg/L) and comparable to the water-only LCSOs of approximately 0.5 Mg/L- The DDT data in [22] is more variable. By contrast, the organism survival for endrin exposures varies by a factor of almost six among the six sediments. The LCSOs are 3.4 to 18.9 Mg/g- The pore water LCSOs were less variable, 1.7 to 3.8 Mg/L and comparable to than the water-only exposure LC50 of approximately 4 ^g/L (Table 2). ------- Page 29 Additional cadmium toxicity data are compared on Figure 8. The responses of R. abronius [23] and Ampelisca abdita [24] to cadmium in seawater exposures without sediment and to the measured pore water concentrations in sediment exposures (lower panels) demonstrate again that the survival responses are similar with or without the sediment. The concentration response curves using total cadmium concentrations are also shown (top panels). It is interesting to note that these two organisms exhibit similar sensitivity to cadmium in water- only exposures (0.34 mg/L for A. abdita and 1.6 mg/L for R. abronius - bottom panels) yet the total sediment cadmium LC50s differ by almost two orders of magnitude (25 and 2,000 pg/g, respectively) for the different sediments. These dramatic differences demonstrate the need to explicitly consider bioavailability of sediment cadmium, and by implication, any toxicant of concern in developing SQC. It has been demonstrated that the variation in bioavailability of cadmium and nickel in various freshwater and marine sediments can be related to the acid-volatile sulfide concentration of the sediment [24,25]. Bioaccumulation A direct measure of chemical bioavailability is the amount of chemical retained in organism tissues. Hence, tissue bioaccumulation data can be used to examine the extent of chemical bioavailability. Chironomus tentans was exposed to two synthetic pyrethroids cypermechrin and permethrin that were ------- Page 30 added to three sediments, one of which was laboratory grade sand [26]. The bioaccumulation from the sand was approximately an order of magnitude higher than it was from the organic carbon-containing sediments for both cypermethrin and permethrin (Fig. 9, top panels). On a pore water basis, the bioaccumulation appears to be approximately linear and independent of sediment type (bottom panels). The mean bioaccumulation factor (BAF) for cypermethrin (and permethrin) varies from 6.2 to 0.6 (4.0 to 0.23) (^g/g organism//ig/g sediment) as sediment foc Increases (Table 3). By contrast the mean BAFs on a pore water basis vary by less than a factor of two. Bioaccumulation was also measured by Adams et al. [17,27,28] in the £_,. tentans - Kepone experiments presented previously (Fig. 3). The body burden variation on a total sediment basis is over two orders of magnitude (BAF - 600 to 3.3 Mg/g organism//jg/g sediment), whereas the pore water bioaccumulation factor is within a factor of four (5,200 to 17,600 MgAg organism/^g/L), with the very low organic carbon sediment exhibiting the largest deviation (Table 3). Conclusion These observations - that organism concentration response and bioaccumulation from different sediments can be reduced to essentially one curve if pore water is considered as the concentration that quantifies exposure - can be interpreted in a number of ways. However, it has become clear that these results do not necessarily imply chat pore water is the primary route of ------- Dry Weight Normalization Cypermethrln 1 1O 1OO 1OOO 100OO Dry Weight Concentration (ng/g) Permethrln 1WWVW 'o> \ 1OOO 01 | 100 a £ 10 o a 1 foe C/.} • « 0.1 • m • « 2.9 /~ //^ \ -•' *_"— 4 * 1 UUU.U "ai •^ 100.0 | 10.0 I $1.0 & 01 • • • _ •- " * ^ ^'x^ tx ^ : 1 1O 10O 10OO Dry Weight Concentration (ng/g) Pore Water Normalization Cypermethrln Permethrin 1UWUW *^ \ 10OO 01 j ,00$ 10 2 0.4 foe {'/.) \ • - 0.1 j^m * 2.9 s^ I •£ .-^ 1 e*-*' y jt. -* •^^ 91 O.1O 1.OO 1O.OO IOC 1 UWW.U -5 N 1OO.O 01 S I 10.0 £ 1.0 2 LOO CM foe (%) • * 0.1 /*<-. :" F+*S r~ i / -^ ^r^ • )1 O.1O 1.OO 1O. Pore Water Concentration (ug/L) Pore Water Concentration (ug/L) Figure 9. Comparison of C. tentans body burden of cypermethrin (left) and permethrin V^B--/ vCl;,Ui, concentration in bulk sediment (top) and pore water (bottom) for sediments with varying organic carbon concentrations [26]. ------- Table 3. Bioaccumulation factors Bioaccumulation foe Chemical Cypermethrin Permethrin Kepone Total Sediment (ug/g organism) Factors3 Pore water (ug/kg organism) (X) (uz/e. sediment) <0 2 3 <0 2 3 1 12 .1 .3 .7 .1 .3 .7 .09 .50 • 6. 0. 0. 4. 0. 0. 600 20 3. 21 50 60 04 38 23 3 (4.41 (0.30 (0.37 (2.89 (0.17 (0.18 ( 308 ( 4.8 ( 0.3 - 8.01) - 0.71) - 0.83) - 5.20) - 0.59) - 0.28) - 892) - 35.2) - 6.3) 80. 51. 92. 39. 52. 29. 17, 5, 5, 1 3 9 7 5 7 600 180 790 (ug/L) (73 (43 (87 (25 (22 (15 (6, (1. (2, .5 - .8 - .0 - .0 - .6 - .6 - 540 970 890 Reference 86.7) [26] 58.8) 98.8) 54.3) [26] 82.4) 43.7) - 28,600) [17,27,28] - 8,390) - 8,700) a 95X confidence limits shown in parentheses ------- Page 33 i exposure. This is because all exposure pathways are at equal chemical activity^ in an equilibrium experiment. Hence the route of exposure cannot be determined. This can be seen by comparing the concentration-response correlation's to pore water and organic carbon normalized sediment concentrations. As shown below, both are equally successful at correlating the data. This suggests that neither the pore water nor the sediment exposure pathway can be implicated as the primary exposure route. However, in order to relate pore water exposure to sediment carbon exposure, it is necessary that the relationship between these two concentrations be established. Thus, an examination of the state of the art of predicting the partitioning of chemicals between the solid and the liquid phase is required. This is examined in the following section. ------- Page 34 SORFTION OF NONIONIC ORGANIC CHEMICALS Partitioning in particle suspensions For nonionic hydrophobic organic chemicals sorbing to natural soils and sediment particles, a number of empirical models have been suggested (see Karickhoff [29] for an excellent review). The chemical property that indexes hydrophobicity is che octanol/water partition coefficient, Kow. The important particle property is the weight fraction of organic carbon, foc. Another important environmental variable appears to be the particle concentration itself [30]. In many experiments using particle suspensions, the partition coefficients have been observed to decrease as the particle concentration used in the experiment is increased [30]. Unfortunately very few experiments have been done on settled or undisturbed sediments. Therefore the correct interpretation of particle suspension experiments is of critical importance. It is not uncommon for the partition coefficient to decrease by two to three orders of magnitude at high particle concentrations. If this partitioning behavior is characteristic of bedded sediments, then quite low partition coefficients would be appropriate. This would result in lower sediment chemical concentrations for SQC. However, if this phenomenon is an artifact or is due to a phenomenon that does not apply to bedded sediments, then a quite different partition coefficient would be used. The practical importance of this issue requires a detailed discussion of the particle concentration effect. ------- Page 35 Particle concentration effect. For the reversible (or readily desorbable) component of sorption, a particle interaction model (PIM) has been proposed that accounts for the particle concentration effect and predicts the partition coefficient of nonionic hydrophobia chemicals over a range of nearly seven orders of magnitude with a logio prediction standard error of 0.38 [31]. The reversible component partition coefficient, Kp, is the ratio of reversibly bound chemical concentration, Cs Og/kg dry weight), to the dissolved chemical concentration, C^ C - K* C.. (9) s p d The PIM model for Kp is: f K K* - oc oc (10) P 1 + mf K /v OC OC X where: Kp - reversible component partition coefficient (L/kg dry weight) Koc - particle organic carbon partition coefficient (L/kg organic carbon) foc - particle organic carbon weight fraction (kg organic carbon/kg dry weight) m - particle concentration in the suspension (kg dry weight/L) i/x - 1.4, an empirical constant (unitless) . ------- Page 36 The regression of Koc to the octanol/water coefficient, KOWl yields lo*10Koc ' °-00028 + °'983 lo6lOKow which is essentially Koc approximately equals Kow. Figure 10 presents the observed versus predicted reversible component partition coefficients using this model [31]. A substantial fraction of the data in the regression is at high particle concentrations (m foc Kow > 10), where the partitioning is determined only by the solids concentration and t/x. The low particle concentration data (m foc Kow < 1) are presented on Figure 11 for the conventional adsorption (left) and reversible component (right) partition coefficient, Kp, normalized by foc, that is Koc - Kp/foc. The relationship K------- Reversible Component Partition Coefficient 6 O) J* O) o -2 -2 6 Predicted Log 10 Kp (L/kg) Figure 10. Comparison of observed reversible component partition coefficient to calculated partition coefficient using Equation 10 (31). ------- Partition Coefficient - m foe Kow < 1 Adsorption Reversible Component o o o> s .* \ 8 3 O) 1 o -1 AMtearb CarfoofurMi Unuron Fkiomctron Carbaryl Dluron Itotteyt ParatMen ParalMon HCH ppDOT O O O) 5 X g 3 -1 1 35 Log 1O Kow -1 -i 135 Log10 Kow Figure 11 partition coefficient concentrations: Comparison of the adsorption (left) and reversible component (right) organic carbon normalized efficient, Koc, to th.e octanol/water partition coefficient, Kow, for experiments with low solids Loc arption (lertj and reversible component vngnt; organic caroon normalized .... octanol/water partition coefficient, Kow, for experiments with low solids < 1. The line represents equality [31]. ------- Page 39 Sorpcion by nonseparated particles or complexing by dissolved organic carbon can produce an apparent decrease in partition coefficient with increasing particle concentration if the operational method of measuring dissolved chemical concentration does not properly discriminate the truly dissolved or free chemical concentration from the complexed or colloidally sorbed portion. However, the question is not whether improperly measured dissolved concentrations can lead to an apparent decrease in partition coefficient with increasing particle concentrations. The question is whether these third-phase models explain all (or most) of the observed partition coefficient - particle concentration relationships. An alternate possibility is that the particle concentration effect is a distinct phenomena that is a ubiquitous feature of aqueous-phase particle sorption. A number of experiments have been designed to explicitly exclude possible third-phase interferences. Both the resuspension experiment for polychlorinated biphynels (PCBs) [39] and metals [40,41] in which particles are resuspended into a reduced volume of supernatant and the dilution experiment [39] in which the particle suspension is diluted with supernatant from a parallel vessel display particle concentration effects. It is difficult to see how third-phase models can account for these results because the concentration of the colloidal particles is constant while the concentration of the sediment particles varies substantially. The model (Eqn. 10) is based on the hypothesis that particle concentration effects are due to an additional desorption reaction induced by particle- particle interactions [31]. It has been suggested that actual particle collisions are responsible [42]. This interpretation relates i/x to the collision efficiency for desorption and demonstrates that it is independent of the chemical and particle properties, a face that has been experimentally observed [31,40]. ------- Page 40 It is not necessary to decide which of these mechanisms is responsible for the effect if all the possible interpretations yield the same result for sediment/pore water partitioning. Particle interaction models would predict that Koc =« Kow because the particles are stationary in sediments. Third-phase models would also relate the free (i.e., uncomplexed) dissolved chemical concentration to particulate concentration via the same equation. As for kinetic effects, the equilibrium concentration is again given by the relationship Koc =• Kow. Thus there is unanimity on the proper partition coefficient to be used in order to relate the free dissolved chemical concentration to the sediment concentration, that is, Koc <* Kow. Organic carbon fraction. The unifying parameter that permits the development of SQC for nbnionic hydrophobic organic chemicals that are applicable to a. broad range of sediment types is the organic carbon content of the sediments. This can be shown as follows: The sediment/pore water partition coefficient, Kp, is given by K - f K - f K (12) p oc oc oc ow and the solid phase concentration is given by C - f K C. (13) s oc oc d where Cs is the concentration on sediment particles. An important observation can be made that leads to the idea of organic carbon normalization. Equation 12 indicates that the partition coefficient for any nonionic organic chemical is linear in the organic carbon fraction, foc. The partitioning data examined ------- Page 4L in Figure 11 can be used to examine the linearity of Kp to foc. Figure 12 compares Kp/KOw co foe f°r boch the adsorption and the reversible component partition coefficients. The data are restricted to m foc KQW < 1 to suppress particle effects. The line indicates the expected linear relationship in Equation 12. These data and an analysis presented below appear to support the linearity of partitioning to a value of foc - 0.2X. This result and the toxicity experiments examined below suggest that for foc > 0.2X, organic carbon normalization is valid. As a consequence of the linear relationship of Cs and foc, the relationship between sediment concentration, Cs, and free dissolved concentration, Cd, can be expressed as oc If we define C C - — (15) s,oc f oc as the organic carbon normalized sediment concentration (/ig chemical/kg organic carbon), then from Equation (14): C - K C , (16) s,oc oc d ------- Partition Coefficient - m foe Kow < 1 Adsorption Reversible Component o * a O) o -1 -2 -3 -4 Aldtearb Carbofuran LJnuron FluofiMtron Carbaryl Oluron Itothyl ParatMon PcratNon HCH O a X o T- 0> o O.O1 O.1O 1.0O 1O.OO 1OO.OO foe (%) O.1O 1.00 1O.OO 10O.OO foe (%) i.ui. 1^. Comparison of the normalized partition coefficients for adsorption (left) and reversible component •"I'1 ion ------- Page 43 Therefore, for a specific chemical with a specific Koc, the organic carbon normalized tatal sediment concentration, Cs,oc, is proportional to the dissolved free concentration, C^, for any sediment with foc > 0.2X. This latter qualification is judged to be necessary because at foc < 0.2X the other factors that influence partitioning (e.g., particle size and sorption to nonorganic mineral fractions) become relatively more important [29]. Using the proportional relationship given by Equation 16, the concentration of free dissolved chemical can be predicted from the normalized sediment concentration and Koc. The free concentration is of concern as it is the form that is bioavailable. The evidence is discussed in the next section. Dissolved organic carbon (DOC) complexing In addition to partitioning to particulate organic carbon (POC) associated with sediment particles, hydrophobic chemicals can also partition to the organic carbon in colloidal sized particles. Because these particles are too small to be removed by conventional filtration or centrifugation they are operationally defined as DOC. Because sediment interstitial waters frequently contain significant levels of DOC, it must be considered in evaluating the phase distribution of chemicals. A distinction is made between che free dissolved chemical concentration, Cd, and the DOC-complexed chemical. CDOc The partition coefficient for DOC, KDOC> *-s analogous to Koc as i.: ;...r.:Lfies the ratio of DOC-bound chemical, CDOC• to cne f^6® dissolved : .-entration, C^: CDOC " mDOC KDOC Cd ------- Page 44 where mDOC ^s c^e DOC concentration. The magnitude of KDQC aru* c^e DOC concentration determine the extent of DOC complexation that takes place. Hence it is important to have estimates of these quantities when calculating the level of free dissolved chemicals in sediment pore waters. A recent compilation of KDQC together with additional experimental determinations is available [43]. A summary that compares the partitioning of six chemicals to POC, natural DOC, and Aldrich humic acid (HA) is shown on Figure 13. The magnitude of the partition coefficients follow the order: POC > AHA > natural DOC. The upper bound on KDQC would appear to be KDQC " Koc, the POC partition coefficient. Phase distribution in sediments Chemicals in sediments are partitioned into three phases: free chemical; chemical sorbed to POC and chemical sorbed to DOC. To evaluate the partitioning among these three phases, consider the mass balance for total concentration C^: C - *C + mf K C, H- 0m KC (18) T d oc oc d DOC DOC d where ^ is the sediment porosity (volume of water/volume of water plus solids) and m is the sediment solids concentration (mass of solids/volume of water plus solids). The three terms on the right side of the equation are the concentration of free chemical in the interstitial water, and that sorbed to the POC and DOC, respectively. Hence, from Equation 18 the free dissolved concentration can be expressed as ------- o o 8 2 "o 6 0. o CD O POC Humlc Acid Pore Water DOC BaP DDT HCBP MCBP PYR TCBP Chemicals Figure 1 '3. Partition coefficients of chemicals to particulate organic carbon (POC), Aldrich humic acid, and iKituial UOC . Benzo[alpyrene (BaP); 2 , 2 ' , 4,4' .5,5' hexachlorobipheny1 (HCBP); DDT; 2,2',5,5' ii-t r.u l.lorobiphcMiyl (TCBP); pyrene (PYR); 4 monochlorobiphenyl (MCBP). (Data: [43]). ------- Page 46 CA ' TT—T~Z——; r~ ' d * + mfocKoc * *mDOCKDOC The concentration associated with the particle carbon (Eqn. 16) and DOC (Eqn. 17) can then be calculated. The total pore water concentration is the sum of the free and DOC complexed chemical, so that Cpore " Cd + CDOC " Cd(L + mDOCKDOC} (20) Figure 14 illustrates the phase partitioning behavior of a system for a unit concentration of a chemical with the following properties: Koc - KDOC ™ 10*> L/kg, foc -2.OX, m - 0.5 kg solids/L sediment, and mooc varies from 0 to 50 mg/L, a reasonable range for pore waters [44]. With no DOC present the pore water concentration equals the free concentration. As DOC increases, the pore water concentration increases due to the increase in complexed chemical, CDOC- Accompanying this increase in CDOC ^s a slight - in fact, insignificant - decrease in C^ (Eqn. 19) and a proportional decrease in Cs (Eqn. 16). It is important to realize that the free chemical concentration, C^, can be estimated directly from Cs,oc, the organic carbon normalized sediment concentration, using Equation 16, and chat the estimate is independent of the DOC concentration. However, to estimate Cj from the pore water concentration requires that the DOC concentration and KDOC be known. The assumption Cpore - Cd is clearly not warranted for very hvdrophobic chemicals. For these cases CS.QC gives a more direct estimate of rht- free dissolved bioavailable concentration, C^, than does the port- •-.'• r . .uicentration. ------- 1.0000 o a »- o.iooo - o *3 <0 i. 4* c o o c o o 10 20 30 4O 50 6O DOC Concentration (mg/L) Figure 14. Phase distribution of a chemical in the three-phase system: water, sediment, and DOC (Eqns. 18 19, and 20). Koc - KnQC - Kow - 106 L/kg, foc - 2.OX, and m - 0.5 kg/L. ------- Page 48 Bioavailability of DOC complexed chemicals The proportion of a chemical in pore water that is complexed to DOC can be substantial (Fig. 14). Hence, the question of bioavailability of DOC-complexed chemical can be important in assessing toxicity directly from measured pore water concentrations. A significant quantity of data indicates that DOC- complexed chemical is not bioavailable. Fish [45] and amphipod [46] uptake of polycyclic aromatic hydrocarbons (PAHs) are significantly reduced by adding DOC. An example is shown in Figure 15 for a freshwater amphipod [46]. For a highly hydrophobic chemical such as benzo[a]pyrene (BaP) the effect is substantial, whereas for less hydrophobic chemicals (e.g., phenanthrene) the reduction in uptake rate is insignificant. This is the expected result because, for a fixed amount of DOC, the quantity of DOC-complexed chemical decreases with decreasing KHQC (Eqn. 17). The quantitative demonstration that DOC-complexed chemicals are not bioavailable requires an independent determination of the concentration of complexed chemical. Landrum et al. [46] have developed a C^g reverse-phase HPLC column technique that separates the complexed and free chemical. Thus it is possible to compare the measured DOC-complexed chemical to the quantity of complexed chemical inferred from che uptake experiments, assuming that all the complexed chemical is not bioavailable [46,47]. As shown on Figure 16, although the KnQC inferred from uptake suppression is larger than that inferred from the reverse phase separation for HA, these data support the assumption that the DOC-complexed fraction, CQQC • >-s noc bioavailable. Hence the bioavailable form of dissolved chemical is Cj, the free uncomplexed component. This is an important observation because ic is C^ that is in equilibrium with CSjOC, the organic carbon normalized sediment concentration (Eqn. 15). ------- 300 I O) £ "a DC ------- DOC Partition Coefficient o o O) o '55 CO Q) h. a 5 a> .* CO 4-» a 3 E o o o 1000O 1OOO 100 10 • Humic Acid A Pore Water 10 10O 1OOO 1OOOO Koc from Reverse Phase (L/g oc) Figure 16. Comparison of the DOC partition coefficient calculated from the suppression of chemical uptake versus the C\Q reverse phase HPLC column estimate. Circles are Aldrich humic acid; triangles are interstitial water DOC. Chemicals are listed in Figure 15 caption (also anthracene and benzo[ajanthracene). ------- Page 51 Field observations of partitioning in sediments There exists an enormous quantity of laboratory data for partitioning in particle suspensions. However, pore water and sediment data from field samples are scarce. Two types of data from field samples are examined. The first is a direct test of the partitioning equation CSiOC - Koc C^, which is independent of the DOC concentration. The second examines the sediment and pore water concentrations and accounts for the DOC that is present. Organic carbon normalization. Consider a sediment sample that is segregated into various size classes after collection. The particles in each class were in contact with the pore water. If sorption equilibrium has been attained for each class, then, letting Cs(j) be the particle chemical concentration of the jc^ size class, it is true that Cs(J> -foc(J) Koc Cd where f0c(J) i-s c^e organic carbon fraction for each size class j. On an organic carbon normalized basis this equation becomes C (j) - K C. s,oc J oc d (22) where CSiOC(j) - Cs(j)/foc(j). This result indicates that the organic carbon normalized sediment concentration of a chemical should be equal in each size class because Koc and C^ are the same for each size class. Thus a direct test of the validity of both organic carbon normalization and EqP would be to examine whether Cs,oc(j) is constant across size classes in a sediment sample. ------- Page 52 Data from Prahl [48] can be used to test this prediction. Sediment cores were collected at three stations near the Washington State coast (Stations 4, 5 and 7). These were sieved into a silt-and-clay sized fraction (<64 pm), and a sand sized fraction (>64 pm). This latter fraction was further separated into a low density fraction (<1.9 g/cra^) and the remaining higher density sand-sized particles. The concentrations of 13 individual PAHs were measured in each size fraction. It is important to realize that these size fractions are not pure clay, silt, or sand but are natural particles in the size classes denoted by clay, silt, and sand. The organic carbon fractions, shown on Figure 17, range from 0.2X for the high-density sand-sized fraction to greater than 30X for the low density fraction. This exceeds two orders of magnitude and essentially spans the range usually found in practice. For example, 90X of the estuarine and coastal sediments sampled for the National Status and Trends program exceed 0.2X organic carbon [49]. Figure 18 (top) compares the dry weight normalized clay/silt sized fraction sediment PAH concentrations, Cs(j), to the sand-sized high and low density PAH concentrations on a dry weight basis. The dry weight normalized data have distinctly different concentrations - the low density high organic carbon fraction is highly enriched, whereas the sand-sized fraction is substantially below the clay/silt fraction concentrations. Figure 18 (bottom) presents the same data but on an organic carbon normalized basis, Cs Oc(J)- *n contrast to dry weight normalization, the PAH concentrations are essentially the same in each size class, as predicted by Equation 22. ------- Organic Carbon Fractions 100.0 10.0 |r o o 1.0 |r 0.1 LOW SAND SILT/CLAY Sediment Fraction Figure 17. The organic carbon fractions (% dry weight) In the low-density fraction 64 ^m <1 9 g/cm3- the sand sized fration >64 Mn, > 1.9 gm/cra3; the silt/clay sized fraction <64 Mm. Numbered stations as indicated. Data from Prahl [48]. ------- Dry Weight Normalization 1000 too * •x TJ in n 3 10 x a. Sand vs Clay/Silt Low Density vs Clay/Silt 10000 1000 3 too a. 10 100 PAH (ug/g dry wt) 10OO 1O ^^^^P^^^T^^^PH 10 too PAH (ug/g dry wt) 100O Organic Carbon Normalization Sand vs Clay/Silt 100000 o 10000 a 3 x 1OOO 100 Low Density vs Clay/Silt 100000 o 10000 1OOO 100 100 1OOOOO 1OOO 1OOOO 1OOOOO 1OO 10OO 1OOOO PAH (ug/g oc) PAH (ug/g oc) l-'i j'.uri; 18. Comparison of PAH concentrations of the sand-sized and low-density-fraction sediment particles (01 (1 i ii.-i i i-) t.o the clay/si.lt fraction (abscissa) (Stations 4, 5, 7). Top panels are for dry weight normalization; bottom panels are for organic carbon normalization. Data from Prahl [48]. ------- Page 55 Ic is concluded from these data that the organic carbon normalized PAH concentrations are relatively independent of particle size class and that organic carbon is the predominant controlling factor in determining the partition coefficient of the different sediment size particles in a sediment sample. The organic carbon concentration of the high-density sand-sized fraction (0.2 to 0.3X) suggests that organic carbon normalization is appropriate at these low levels. Sediment/pore water partitioning. . Normally when measurements of sediment chemical concentration, Cs , and total pore water chemical concentrations, Cpore* are made, the value of the apparent partition coefficient is calculated directly from the ratio of these quantities. As a consequence of DOC complexation, the apparent partition coefficient, Kp, defined as Kp -^-J- (23) pore is given by K f K B - - 2C_0£ - 2 l + "DOC^OC l + mDOCKDOC As mj)oc increases, the quantity of DOC-coraplexed chemical increases and the apparent partition coefficient approaches f K 0C (25) DOCDOC ------- Page 56 which is just the ratio of sorbed to complexed chemical. Because the solid- phase chemical concentration is proportional to the free dissolved portion of the pore water concentration, C------- 00 X ------- Page 58 and to compute the actual partition coefficient: Kp - Cs/Cd. The data indicate that if KDOC " Kow i-s used, the results, shown on Figure 19, agree with the expected partition equation, namely that Kp - foc Kow. A similar three-phase model has been presented by Brownawell and Farrington [51). Other data with sediment/pore water partition coefficients for which the DOC concentrations have not been reported [52,53] are available to assess the significance of OOC partitioning on the apparent sediment partition coefficient. Figure 20 presents thes.e apparent organic carbon normalized partition coefficients, that is Koc - Kp/foc versus Kow. The expected relationship for DOC concentrations of 0, 1, 10, and 100 mg/L is also shown. Although there is substantial scatter in these data, reflecting the difficulty of measuring pore water concentrations, the data conform to DOC levels of 1.0 to 10 mg/L, which is well within the observed range for pore waters [44,50]. Thus these results do not refute the hypothesis that Koc =• Kow in sediments but show the need to account for DOC complexing in the analysis of pore water chemical concentrations. Organic carbon normalization of biological responses The results discussed above suggest that if a concentration-response curve correlates to pore water concentration, it should correlate equally well to organic carbon normalized total chemical concentration, independent of sediment properties. This is based on the parcicioning formula Cs oc - Koc CQ< (Eqn. 16), which relates the free dissolved concentration to the organic carbon ------- o o O> X \ o o 0> o 8 7 6 5 - 4 3 A Ollvar (Various) Socha (PAHa) 100.0 6 8 Log1O Kow if.uif '20. Observed apparent partition coefficient to organic carbon versus the octanol/water partition in-1 t if iriit . The lines represent the expected relationship for DOC concentrations of 0, 1, 10, and 100 mg/L itl KIJOC - Kow. Dai;* 1 rom (53) for PCB congeners and other chemicals and from [52] for phenanrtirene, praiiiheue , and perylene. -^*^^ ' i ------- Page 60 normalized particle concentration. This applies only to nonionic hydrophobic organic checricals because the rationale is based on a partitioning theory for this class of chemicals. Toxicity and bioaccumulation experiments. To demonstrate this relationship, concentration-response curves for the data presented in Figures 5 and 7 are used to compare results on a pore water-normalized and organic carbon-normalized chemical concentration basis. Figures 21 to 23 present these comparisons for Kepone, DDT, endrin, and fluoranthene. The mean and 95Z confidence limits of the LC50 and EC50 values for each set of data are listed in Table 2. The top panels repeat the response - pore water concentration plots shown previously in Figures 5 to-7, while the lower panels present the response versus the sediment concentration, which is organic carbon normalized (micrograra chemical per gram organic carbon). The general impression of these data is that there is no reason to prefer pore water normalization over sediment organic carbon normalization. In some cases, pore water normalization is superior to organic carbon normalization, for example, Kepone - mortality data (Figure 21) whereas the converse sometimes occurs, for example Kepone - growth rate (Fig. 21). A more quantitative comparison can be made with the LC50s and EC50s in Table 2. The variation of organic carbon normalized LC50s and EC50s between sediments is less than a factor of two to three and is comparable to the variation in pore water LC50s and EC50s. A more comprehensive comparison has baen presented in Figures 2 and 3, which also examine the use of the water-only LC50 to predict the pore water and sediment organic carbon LC50s. ------- Pore Water Normalization Kepone - Mortality Kepone - Growth N« -\ v^ .«• 2 100 8O 60 40 20 O foe ('/.) ~ • 0.09 . • 1.6 • 12 - A ^^. ""^ r 1 T.| " -------I •^*~~* // ' / / / f // ^l/ ' / 3*Srf J? c _o «« o 1 1C 10 10O 1000 Pore Water Concentration (ug/L) 100 80 60 40 20 0 ""• • ••••••-, —^*e / '' / / / '' x* /'•'/ F f * ' / "dW*^"^*> ^^^— — *V . .......i . • 1 1O 1OO 100O Pore Water Concentration (ug/L) Organic Carbon Normalization Kepone - Mortality ^ 7[ | o 100 80 60 40 20 0 foe ('/.} • 0.09 . • 1.6 • 12 • , «__ ^ e- 10 ff^— • •' / / • • 1 1 ?! 1 * i / * 1 • * » A/ / - *^»»^ •• ^ t •••. >^ ^ 3 O e 100 100O 1000O Organic Carbon Normalized (ug/g oc) 21 Comparison Kepone - Growth 100 80 60 40 20 0 • •**-•• • --»•»-•! fa^* f W Is .**' , / ' » '* ' • ' / v*^* % -v 10 10O 100O 1OOO< Organic Carbon Normalized (ug/g oc) of percent survival (left) and growth rate reduction (rieht) of C. tentans concentration in pore water (top) and in bulk sediment, using organic carbon normalization (bottom) for three sediments with varying organic carbon concentrations [17]. ------- Page 66 performed by Rubinstein and co-workers [58]. The uptake of various PCB congeners was moni^red until steady-state body burdens were reached. Sediment organic carbon and organism lipid content were measured. Figures 24 and 25 present the log mean of the replicates for the ratio of organism-to-sediment concentration for all measured congeners versus KOW for each organism. Dry weight normalization for both organism and sediment (left panels), organic carbon normalization for the sediment (center panels), and both organic carbon and lipid normalization (right panels) are shown. The results for each sediment are connected by lines and separately identified. The BSFs basc.1 on dry weight normalization are quite different for each of the sediments with the low carbon sediment exhibiting the largest values. Organic carbon normalization markedly reduces the variability in the BSFs from sediment to sediment (center panels). Lipid normalization usually further reduces the variability. Note that the BSFs are reasonably constant for the polychaetes, although some suppression at logio Kow > 7 is evident. The clams, however, exhibit a marked declining relationship. Results of a similar though less extensive experiment using one sediment and oligochaete worms have been reported [53]. A plot of the organic carbon and lipid-normalized BSF versus Kow from this experiment is shown on Figure 26, together with the averaged polychaece data (Fig. 24). There appears to be a systematic variation with respect co Kow, which suggests that the simple lipid equilibration model with a constant lipid-octanoi solubility ratio is not descriptive for all chemicals. This suggests that a more detailed model of benthic organism uptake is required co describe chemical body burdens for all nonionic chemicals as a function of Kow [55]. However, for a specific chemical ------- Dry Weight § O o 3.0 2.0 1.0 0.0 -1.0 5.5 Ner/es Organic Carbon | U 3 1.O 0.0 -1.0 -2.0 «.6 7.5 LoglO Kow 8.6 -3 "*6.5 6.5 7* Log10 Kow C 5 o Organic Carbon, Llpld 3.0 1.0 0.0 8.5 8.5 7* Log 10 Kow 8.5 Dry Weight Nephtys Organic Carbon u a O O 3.0 2.0 QJQ -1 foe (Y.) •°6 o o ! o 1.O O.O -1.0 -2.0 8.5 7.6 LoglO Kow 8.6 -3.0 Organic Carbon, Llpld 5.5 3.0 | U 3 o 5 1.0 8.5 7.6 Log 10 Kow 8.5 -1.0 66 O5 7.6 Log 10 Kow 8.5 Figure 24. Plots of the BSF (ratio of organism-to-sediment concentration) for three sediments for a series of PCB congeners versus the loglo Kow for that congener. The dry weight normali2ation for both organism and ^ediment (left panels); organic carbon normalization for the sediment (middle panels); and organic ca^n and id normalization (right; panels) as indicated. ^^ organisms are Nereis (top) and Nephtvs m [58]. \ " ------- Page 64 Bioaccumulation factors calculated on the basis of organic carbon normalized chemical concentrations are listed in Table 3, for permethrin, cypermethrin, and kepone. Again, the variation of organic carbon normalized BAFs between sediments is less than a factor of two to three and is comparable to the variation in pore water BAFs. Bioaccumulation and organic carbon normalization. Laboratory and field data also exist for which no pore water or DOC measurements are available but for which sediment concentration, organic carbon fraction, and organism body burden have K°.en determined. These data can be used to test organic carbon normalization for sediments and to examine organism normalization as well. The use of organism lipid fraction for this normalization has become conventional (see references in Chiou [54]). If Cfc is the chemical concentration per unit wet weight of the organism, then the partitioning equation is S-KL'L'CI (27) where: KL - lipid/water partition coefficient (L/kg lipid) fL - weight fraction of lipid (kg lipid/kg organism) C(j - free dissolved chemical concentration (/ig/L) The lipid-normalized organism concentration, CD>L, is C h T - ~T~ " KT CH b , L E L d ------- Page 65 The lipid-normalized body burden and the organic carbon normalized sediment concentration can be used to compute a bioaccuraulation ratio, which can ue termed the BSF [55]: C K K BSF-T^ -IT -? <29> s,oc oc ow The second equality results from using the partitioning Equations 16 and 28 and the third from the approximation that Koc = Kow. The BSF is the partition coefficient betwr .rganism lipid and sediment organic carbon. If ..lie equilibrium assumptions are valid for both organisms and sediment particles, the BSF should be independent of both particle and organism properties. In addition, if lipid solubility of a chemical is proportional to its octanol solubility, KL « Kow, then the lipid normalized - organic carbon normalized BSF should be a constant, independent of particles, organisms, and chemical properties [54,56,57]. This result can be tested directly. The representation of benthic organisms as passive encapsulations of lipid that equilibrate with external chemical concentrations is clearly only a first- order approximation. Biomagnification effects, which can occur via ingestion of contaminated food and the dynamics of internal organic carbon metabolism, can be included in a more comprehensive analysis [55]. Nevertheless it is an appropriate initial assumption because deviations from the first-order representation will point to necessary refinements, and for many purposes this approximation may suffice. A comprehensive experiment involving four benthic organisms two species of deposit-feeding marine polychaetes, Nere is and Nephtys. and two species of deposit-feeding marine clams, Yolda and Macoma and five sediments has been ------- Pore Water Normalization Fluoranthene 0 20 40 60 80 Pore Water Concentration (ug/L) Organic Carbon Normalization Fluoranthene 0 2OOO 4OOO 6000 800O Organic Carfaon Normalized (ug/g oc) Figure 23. Comparison of percent survival of R. abronius to fluoranthene concentration in pore water (top) and bulk sediment, using organic carbon normalization (bottom) for sediments with varying organic carbon concentrations [19]. ------- Pore Water Normalization DDT Endrin ,_. X J *• 0 100 80 6O 40 20 0 **»• a • • • foe M • 3.0 - f •" * * 7 2 . • 10.5 1OO X 80 £ 60 1 40 I 20 O foe (y.) • 3.0 • 11.2 0.01 O.1O 1.OO 1O.OO 100.OO Por« Water Concentration (ug/L) O.1 1.0 1O.O 10O.O Pore Water Concentration (ug/L) Organic Carbon Normalization DDT Endrin 1OO 8O 60 40 20 0 • • • • • •• • w * • * ' * • .-«*.« I* • * .*••%'* 1OO 7 8O ^ 6O 1 40 | 20 0 rt «•»— • M mf . " |ef* •*• . . i i i • - - - - 1 10 10O 100O 1000O Organic Carbon Normalized (ug/g oc) 1 1O 1OO 1OOO 10OOO Organic Carbon Normalized (ug/g oc) F'i^un 17. Comparison of percent survival of H. azteca to DDT (left) and endrin (right) concentration in pore w.itri (top) and in bulk sediment using organic carbon normalization (bottom) for three sediments with varying organic carbon concent; rat ions [ 21,22). ------- Dry Weight Yo/d/a Organic Carbon Organic Carbon, Llpid a O 3.0 2.0 1.0 0.0 -1.0 foe (X) * 8.2 • 3.0 • 1.0 o 2- S 1.0 0.0 -1.0 -2.0 *JS 0.5 7.5 LoglO KOW M -3J tt 3.0 2.0 1.0 0.0 0.5 7.5 LoglO Kow 8.5 -1.0 5.5 0.5 7.5 Log10 Kow 8.5 2.O 0.0 S -, Dry Weight Macoma Organic Carbon Organic Carbon, Upld 1.0 0.0 -1.0 -2.0 U^ S^u 1—* 8.6 7.5 8.6 LoglO KOW 2.0 s 8.6 7.5 8.6 LoglO Kow -2.0. T^^V* 8^ 7^ 8.6 LoglO Kow Figure '5. Plots of the BSF (ratio of organism-to-sediment concentration) for three sediments for a series of PCB congeners versus the logio Kow f°r that congener. The dry weight normalization for both organism and sedime u. (left panels); organic carbon normalization for the sediment (middle panels); and organic carbon and lipid normalization (right panels) as indicated. The organisms are Yoldia (top) and Hacoma (bottom). Data from IJ8]. ------- Oligochaete - Polychaete BSF 100.0 £ 10.0 CD o o o o vt O a o 0.1 • Ollgocha«t« • Polychaate 3.0 4.0 11 i 5.0 6.0 7.3 Log 10 Kow 8.0 9.0 i/.m'f 26. Plots of the BSF (ratio of organism lipid to sediment organic carbon concentration) for a series >l l'i H n.uj'.eners and other chemicals versus logjo Kow. Data for oligachaetes [531 a"d polychaetes (58]. ------- Page 70 and a specific organism, for example Nereis and any PCB congener (Fig. 24) organic carbon normalization reduces the effect of the varyirg sediments. This demonstrates the utility of orjanic carbon normalization and supports its use in generating SQC. A further conclusion can be reached from these results. It has been pointed out by Bierman [59] that the fact that the lipid- and carbon-normalized BSF is in the range of 0.1 to 10 (Fig. 24 to 26) supports the contention that the partition coefficient for sedimetxts is Koc - Kow and that the particle concentration effect does not: appear to be affecting the free concentration in sediment pore water. The reason is that the lipid- and carbon-normalized BSF B is the ratio of the solubilities of the chemical in lipid and in particle carbon (Eqn. 29). Because the solubility of nonionic organic chemicals in various nonpolar solvents is similar [60], it would be expected that the lipid- organic carbon solubility ratio should be of order one. If this ratio is taken to be approximately one, then the conclusion from the BSF data is that, indeed, Koc is approximately equal to Kow for sediments [59]. A final observation can be made. The data analyzed in this section demonstrate that organic carbon normalization accounts for much of the reported differences in bioavailability of chemicals in sediments for deposit-feeding polychaetes, oligochaetes and clams. The data presented in previous sections are for amphipods and midges. Hence these data provide important additional support for organic carbon normalization as a determinant of bioavailability for different classes of organisms. ------- Page 71 Determination of the route of exposure The exposure route by which organic chemicals are accumulated has been examined in some detail for water column organisms (e.g. [61]). It might be supposed that the toxicity and bioaccuraulation data presented above can be used to examine the question of the route of exposure. The initial observations were that biological effects appear to correlate to the interstitial water concentration, independent of sediment type. This has been interpreted to mean that exposure is primarily via pore water. However, the data Correlate equally well with the organic carbon normalized sediment concentration (see Fig. 2 and 3). This suggests that the sediment organic carbon is the route of exposure. In fact, neither of these conclusions necessarily follow from these data. The reason is that an alternate explanation is available that is independent of the exposure pathway. Consider the hypothesis that the chemical potential or, as it is sometimes called, the fugacity [62], of a chemical controls its biological activity. The chemical potential, /i------- Page 72 where CS(OC is che weight fraction of chemical in organic carbon. If the pore water is in equilibrium with the sediment organic carbon then "d-"oc- (32) The chemical potential that the organism experiences from either route of exposure (pore water or sediment) is the same. Hence, so long as the sediment is in equilibrium with the pore water, the route of exposure is immaterial. Equilibrium experiments cannot distinguish between different routes of exposure. The data analysis presented above, which normalizes biological response to either pore water or organic carbon normalized sediment concentration, suggests that biological effects are proportional to chemical potential or fugacity. The issue with respect to bioavailability becomes: In which phase is p most easily and reliably measured? Pore water concentration is one option. However, it is necessary that the chemical complexed to DOC be a small fraction of the total measured concentration or that the free concentration be directly measured, perhaps by the C]_g column technique [46]. Total sediment concentration normalized by sediment organic carbon fraction is a second option. This measurement is not affected by DOC complexing. The only requirement is that sediment organic carbon be the only sediment phase that contains significant amounts of the chemical. This appears to be a reasonable assumption for most aquatic sediments. Hence, sediment quality criteria are based on organic carbon normalization because pore water normalization is complicated by DOC complexing for highly hydrophobic chemicals ------- Page 73 APPLICABILITY OF WQC AS THE EFFECTS LEVELS FOR BENTHIC ORGANISMS The EqP method for deriving SQC utilizes partitioning theory to relate the sediment concentration to the equivalent free chemical concentration in pore water and in sediment organic carbon. The pore water concentration for SQC should be the effects concentration for benthic species. This section examines the validity of using the EPA WQC concentrations to define the effects concentration f~r benthic organisms. This use of WQC assumes cha (a) the sensitivities of benthic species and species tested to derive WQC predominantly water column species, are similar and (b) the levels of protection afforded by WQC are appropriate for benthic organisms. This section examines the assumption of similarity of sensitivity in two ways. First, a comparative toxicological examination of the acute sensitivities of benthic and water column species, using data compiled from the published EPA WQC for nonionic organic chemicals as well as metals and ionic organic chemicals, is presented. Then a comparison of the FCVs and the chronic sensitivities of benthic saltwater species in a series of sediment colonization experiments is made. Method - relative acute sensitivity The relative acute sensitivities of benthic ana water column species are examined by using LC50s for freshwater and saltwater species from draft or published WQC documents that contain minimum data base requirements for calculation of final acute values (Table 4). These data sets are selected ------- Table 4. Draft or published (<3C documents and number of infaunal (habitats 1 and 2), epibenthic (habitats 3 and *). and water column (habitats 5 to 8) species tested acutely for each substance Number of saltwater species Chemical Acenaphthene Acrolein Aldrin Aluminum Ammonia Antimony III Arsenic III Cadmium Chlordane Chloride Chlorine Chlorpyrifos Chromium III Chromium VI Copper Cyanide DDT Dieldnn 2. 4-dimethylphenol Endosulfan Endrin Heptachlor Hexachlorocyclohexane Lead Mercury Nickel Parathion Parathion - Methyl Pentachlorophenol Phenanthrene Phenol Selenium IV Selenium VI Silver Thallium Toxaphene "r:butyltin 1.2, 4 -Trichlorobenzene 2 . <• , 5-Trichlorophenol iir.c Date of Publication 9/37b 9/87b 1980 1986 1985; 1989 9/870 1985 1985 1980 1988 1985 1986 1385 1985 1985 1985 1980 1980 6/do~ 1980 1980 1980 1980 1985 1985 1966 1986 10/880 1986 9/870 5/88° 1987 1987 9/870 11/88° 1986 9/87 9/88° 9/870 1987 Total' . - 16 - 20 11 12 38 8 - 23 15 - 23 25 9 17 21 9 12 21 19 19 13 33 23 - 19 10 - 16 - 21 . 15 19 15 11 33 Infaunal . - 0 - 2 3 2 10 1 - 2 2 • 3 6 1 1 1 2 2 1 1 2 2 10 7 - 7 4 - 1 . 1 - 2 1 7 4 10 Epibenthic . - 11 - 7 6 3 18 7 - 9 8 - 9 5 4 11 15 2 3 1* 14 14 3 7 10 - 7 6 - 5 - 6 - 9 3 7 5 9 Water Column . - 12 - 16 5 8 18 7 - 15 10 - 9 18 5 12 15 6 a 16 13 12 10 18 9 - 11 4 - 13 - 16 . - 11 15 4 5 17 Number of freshwater species Total* 10 12 21 IS 48 9 16 56 1* IS 33 18 17 33 57 17 42 19 12 10 28 18 22 14 30 21 37 36 9 32 23 12 19 8 37 9 14 10 45 Infaunal .' 1 2 - 2 1 1 13 1 3 1 2 3 1 8 1 3 1 1 1 3 2 1 - 11 2 7 1 9 2 6 2 1 1 1 5 1 2 1 5 Epibenthic 3 5 10 5 17 2 6 16 4 6 9 8 8 10 15 6 15 9 3 4 12 3 4 4 8 7 14 9 •11 1 9 6 4 9 3 13 1 5 2 12 Hater Co Luinn 7 7 15 11 33 6 13 31 10 3 26 i * 12 21 36 12 29 12 7 7 17 12 13 i 1 12 13 -^ 4 2: 1 3 • -\ 13 3 22 5 ; 3 23 a The total numbers of tested species may not be the same as the sum of the number of species from each habitat -.ype because a species may occupy more than one habitat. - Draft aquatic life criteria document. U.S. Environmental Protection Agency, Office of Water Regulator.! i-.i Standards. Criteria and Standards Division, Washington, D.C. ------- Page 75 because exposures were via water, durations were similar, and data and test conditions have been scrutinized by reviewing the original references. For each of the 2,887 tests conducted in fresh water, using 208 species with 40 chemicals, and the 1,046 tests conducted in salt water, using 118 species with 30 chemicals, the chemical, species, life stage, salinity, hardness, temperature, pH, acute value, and test condition (i.e., static, renewal, flow- through, nominal, or measured) were entered into a data base. If necessary, original references were consulted to determine the tested life stage and any other missing information. Each life stage of the tested species was classified according to habitat (Table 5). Habitats were based on degree of association with sediment. A life stage that occupied more than one habitat was assigned to both of the appropriate habitats. For each chemical, if a life stage was tested more than once or more than one life stage was tested, data were systematically sorted in a three-step process to arrive at the acute value based on the most experimentally sound testing methodology and the most sensitive life stages. First, if a life stage for a soecies was tested more than once, flow-through tests with measured concentrations had precedence, and data from other tests were omitted. When there were no flow-through tests with measured concentrations, all acute values for that life stage were given equal weight. If the remaining acute values for that life stage differed by greater :han a factor of four, the higher values were omitted and the geometric mean of nhe lower acute values was calculated co derive the acute value for that life s:age. Second, life stages were classified as either "benthic" (infaunal species [habitats 1 and 2] or infaunal and epibenthic species [habitats 1. 2 i. and 4j), or "water column" (habitats ------- TABLE 5. Habitat classification system for life stages of organisms Habitat Type Description 1 Life stages that usually live in the sediment and whose food consists mostly of sediment or organisms living in the sediment: infaunal nonfilter feeders. 2 Life stages that usually live in the sediment and whose food consists mostly of plankton and/or suspended organic matter filtered from the water column: infaunal filter feeders. 2 Li e stages that usually live on the surface of sediment and whose food consists mostly of organic matter in sediments and/or organisms living in or on the sediment: epibenthic bottom feeders. 4 Life stages that usually live on the surface of sediment and whose food is mostly from the water column, including suspended detritus, plankton, and larger prey: epibenthic water column feeders. 5 Life stages that usually live in the water column and whose food consists mostly of organisms that live on or in the sediment. 6 Life stages that usually live in. and obtain their food from, the water column but have slight interaction with sediment because they occasionally rest or sit on the sediment and/or occasionally consume organisms that live in or on the sediment. 7 Life stages that live in or on such inorganic substrates as sand, rock, and gravel, but have negligible contact with sediment containing organic carbon. 8 Life stages that have negligible interactions with sediment because they spend essentially all cheir time in the water column and rarely consume organisms in direct contact with the sediment; that is fouling organisms on pilings, ships, and so on, and zooplankton, pelagic fish, and so on. ------- Page 77 5 to 8). Third, if two or more life stages were classified as either benthic or water column and their acute values differed by a factor of four, the higher values were omitted and the geometric mean of the lower acute values was calculated to derive the acute value for that life stage of the benthic or water column species. This procedure is similar to that used for WQC [8]. Comparison of the sensitivity of benthic and water column species Most Sensitive Species. The relative acute sensitivities of the most sensitive benthi" and water column species were examined by comparing the lowest acute LC50 concentration for the benthic and water column organisms, using acute values from the 40 freshwater and the 30 saltwater WQC documents. When benthic species were defined as only infaunal organisms (habitat types 1 and 2) and water column species were defined as all others (habitat types 3 :o 8), the water column species were typically the most sensitive. The results are cross-plotted on Figure 27 (left). The line represents perfect agreement. In most instances where acute values for saltwater benthic and water column species are identical, it is because penaeid shrimp are most sensitive to insecticides and are classified as both infaunal (benthic) and epibenthic (water column). Unfortunately data on the sensitivities of benthic infaunal species are limited. Of the 40 chemicals for which WQC for freshwater organisms are available, two or fewer infaunal species were tested with 28 (70X) of the chemicals, and five or fewer species were tested with 34 (85X) of the chemicals. Of the 30 chemicals for which WQC for saltwater organisms are available, 2 or fewer infaunal species were cesced with 19 (63Z) of the ------- Comparison of Most Sensitive Species Infaunal Infaunal & Eplbenthic o m o o -1 -3 Fraahwatar Saltwater O> 3 O IO o o *- o» o -1 -3-1 1 3 5 Log 10 LC50 (ug/L) -3 Fraahwatar Saltwatar -3-11 3 Log 10 LC50 (ug/L) Figure- 27. Comparison of I.CM) or EC'iO acute values tor the most sensitive benthic (abscissa) and water column (ordi naie) species for i-lu-inir.i Is listed in Table '» . Hentbic species are defined as infaunal species (habitat ^•^•s I and 2, li-ft p.nu-l) or iiit.iunal and i-p ibeni Iijg^tpec ies (habitat types 1 - lt) , see Table 6. ------- Page 79 chemicals, and 5 or fewer species were nested with 23 (77X) of the chemicals. Of these chemicals only zinc in salt water has been tested using infaunal species from, three or more phyla and eight or more families, the minimum acute toxicity data base required for criteria derivation. Therefore, it is probably premature to conclude from the existing data that infaunal species are more tolerant than water column species. A similar examination of the most sensitive benthic and water column species, where the definition of benthic includes both infaunal and epibenthic species (ha': icat types: 1 to 4) , is based on more data and suggests a similarity in sensitivity (Fig. 27, right). In this comparison, the number of acute values for freshwater benthic species for each chemical averaged nine, with a range of 2 to 27; the number of acute values for saltwater benthic species for each chemical substance averaged 11, with a range of 4 to 26. The variability of these data is high, suggesting that for some chemicals, benthic and water column species may differ in sensitivity, that additional testing would be desirable, or that this approach to examining species sensitivity is not sufficiently rigorous. Examination of individual criteria documents in which benthic species were markedly less sensitive than water column species suggests that the major factor for this difference is that benthic species phylogenetically related to sensitive water column species have not been tested. Apparent differences in sensitivity, therefore, may reflect an absence of appropriate data. Data that are available suggest that, on the average, benthic and water column species are similarly sensitive and support che use of WQC to derive SQC for the protection of infaunal and epibenthic species. ------- Page 80 All species. A more general comparison of the species sensitivities can be made if all the LC50 data are used. One approach examines the relative location of benthic species in the overall species sensitivity distribution. For each chemical in either fresh or salt water, one can examine the distribution of benthic species in a rank-ordering of all the species' LC50s. If benthic species were relatively insensitive, then they would predominate in ranking among the larger LC50 concentrations. Equal sensitivity would be indicated by a uniform distribution of species within the overall ranking. Figure 28 presen' -.he results for te.'ts of nickel in salt water. .; LCSOs are plotted in rank order, and the benthic species are indicated. Infaunal species are among the most tolerant (left panel), whereas infaunal and epibenthic species are uniformly distributed among the species (right panel). This comparison can be done chemical by chemical. However, in order to make the analysis more robust, the LC50 data for each chemical - water type can be normalized to zero log mean and unit log variance as follows: log(LC50 ) u. LC50 .. - U-: k (33) n.ij OL where i indexes the chemical - wacer type, m is the log mean and a^ is che log standard deviation, j indexes the LC50s within the ic^ class, and LC50n jj is the normalized LC50. This places all rhe LC50s from each set of chemical - water type on the same footing. Thus rhe data can now be combined and che uniformity of representation of ben:hic species can be examined in th~ combined data set. ------- Species Sensitivity for Ni in Seawater Infaunal Infaunal & Epibenthic ^ 10000O o IO 1OOOO 1000 100 Water Column Banthlc •e 10OOOOO 1OOOOO 1000O 100O O.O 0.2 O.4 O.6 Rank 0.8 i.o 10O O Wafer Column • Banthic •© o.o 0.2 0.4 0.6 Rank 0.8 1.0 Kip.mr ?H l.i:')l)s vt-rsus I'.ink for nickt-l in soawater. Infaunal organisms (left) and infaunal and epibenthic (it(,',lii) .u«- ulriii i t i oil by i Ju- solid symbols Tin- plot illustrates the distribution of benthic organisms in ibt- twt-i.ill ;,()»-ri«-s sriib > i i v i i y d;i b t r i but i on ------- Page 82 The comparison is made in Figure 29. If che sensitivity of benthic species is noc unique, then a constant percentage of benthic species normalized LC50s, indicated by the dashed line, should be represented in each 10-percentile (decile) interval of data for all species. That is, the 10 rectangles in each histogram should be identical in height. The infaunal species (top panel) display a tendency to be underrepresented in the lowest deciles. However, the infaunal and epibenthic species (bottom panels) more closely follow this idealized distribution. Infaunal and epibenthic freshwater species are nearly uniformly distr'v -.ed, whereas che saltwater benthic species are s"?.ewhat underrepresented in the lowest ranks. Given the limitations of these data, they appear to indicate that, except for possibly freshwater infaunal.species, benthic species are not uniquely sensitive or insensitive and that SQC derived by using the FCV should protect benthic species. Benthic community colonization experiments Toxicity tests that determine the effects of chemicals on the colonization of communities of benthic saltwater species [64-70] appear to be particularly sensitive at measuring the impaccs of chemicals on benthic organisms. This is probably because the experiment exposes the most sensitive life stages of a wide variety of benthic saltwater species, and they are exposed for a sufficient duration to maximize response. The test typically includes three concentrations of a chemical and a control, each with 6 to 10 replicates. The test chemical is added to inflowing ambient seawater containing planktonic ------- Infaunal Saltwater 0 16 26 *6 46 66 66 76 86 98 Itom of Parcanttta Rang* 50 S 40 •5 3O a o 2O 5 • 1O o Freshwater m n 6 15 26 35 45 55 65 75 85 95 Maan of Parcantlla Range (%) Infaunal & Epibenthic too Saltwater 8 16 26 86 46 68 66 76 66 98 Maan of Parcantlto Rang* (%) 1OO 80 80 o -«° S 2O Freshwater 6 1.. 26 36 46 68 65 75 65 95 Maan of Parcantlla Rang* (%) Figure ^9. Histograms of the proportion of saltwater and freshwater benthic organisms in 10 percentile groups oi all normalized LC50s. If benthic organisms were as equally sensitive as water column organisms, the histogiams should be of uniform height as indicated by the dashed line, the overall percentage of benthic specie: in the data set. Top panels include only infaunal organisms as benthic. The bottom panel includes intaunal arid epibenthic as benthic organisms. ------- Page 84 larvae and other life stages of marine organisms chat can settle onto clean sand in each replicate aquarium. The test typically lasts from two to four months, and the number of species and individuals in aquaria receiving the chemical are enumerated and compared to controls. If this test is extremely sensitive and if concentrations in interstitial water, overlying water, and the sediment particles reach equilibrium, then the effect and no-effect concentrations from this test can be compared with the FCV from the saltwater WQC documents to examine the applicability of WQC to protect benthic organisms. *.n FCV is the concentration, derived from acute and chronic toxicity data, that is predicted to protect organisms from chronic effects of a chemical [8]. In addition, similarities in sensitivities of taxa tested as individual species and in the colonization experiment can indicate whether the conclusion of similarity of sensitivities of benthic and water column species is reasonable. The benthic colonization experiment is consistent with the assumptions used to derive SQC. The initially clean sandy sediment will rapidly equilibrate with the inflowing overlying water chemical concentration as ~he pore water concentrations reach the overlying water concentration. The production of sedimentary organic matter should be slow enough to permit its equilibration as well. As a consequence the organisms will be exposed to an equilibrium system with a unique chemical potential. Thus the assumption of the EqP is met by this design. In addition, the experimental design guarantees that the interstitial water - sediment - overlying water is at the chemical potential of the overlying water. Hence there is a direct correspondence between the exposure in the colonization experiment and the water-only exposures from which WQC are derived, namely the overlying water chemical concentration. This allows a direct comparison. ------- Page 85 Water quality criteria (WQC) concentrations versus colonization experiments Comparison of the concentrations of six chemicals that had the lowest- observable-effect concentration (LOEC) and the no-observable-effect concentration (NOEC) on benthic colonization with the FCVs either published in saltwater portions of WQC documents or estimated from available toxicity data (Table 6) suggests that the level of protection afforded by WQC to benthic organisms is appropriate. The FCV should be lower than the LOEC and larger than the NOEC. The FCV from the WQC document for pentachlorophenol of 7.9 ng/L is less than the LOEC for colonization of 16.0 ng/L. The NOEC of 7.0 Mg/L is less than the FCV. Although no FCV is available for Aroclor 1254, the lowest concentration causing no effects on the sheepshead minnow (Cvorinodon variegatus) and pink shrimp (Penaeus duorarum) as cited in the WQC document is about 0.1 A»g/L- This concentration is less than the LOEC of 0.6 Mg/L and is similar to the NOEC of 0.1 Mg/L based on a nominal concentration in a colonization experiment. The lowest concentration tested with chlorpyrifos (0.1 Mg/L) and fenvalerate (0.01 Mg/L) affected colonization of benthic species. Both values are greater chan either the FCV estimated for chlorpyrifos (0.005 ^g/L) or the FCV estimated from acute and chronic effects data for fenvalerate (0.002 jig/L) . The draft water quality criteria document for 1,2,4-trichlorobenzene suggests that the FCV should be 50.0 A»g/L. This v?.lue is slightly above the LOEC from j colonization experiment (40.0 Mg/L) suggesting that the criterion might be somewhat underprotective for benthic species. Finally, a colonization sxperiment with toxaphene provides the only evidence from these tests that the FCV iu§ht be overprotective for benthic species; the FCV is 0.2 A*g/L versus :.-.e NCEC for colonization of 0.8 ------- TABLE 6. ANALYSIS OF VARIANCE FOR DERIVATION OF SEDIMENT QUALITY CRITERIA CONFIDENCE LIMITS Source of Uncertainty Parameter Value Exposure media aa 0.39 Replication a£ 0.21 Sediment Quality Criteria <7SQCa 0.39 - on ------- Page 87 The taxa most sensitive to chemicals, as indicated by their LCSOs and the results of colonization experiments, are generally similar, although, as might be expected, differences occur. Both the WQC documents and the colonization experiments suggest that Crustacea are most sensitive to Aroclor 1254, chlorpyrifos, fenvalerate, and toxaphene. Colonization experiments indicated that molluscs are particularly sensitive to three chemicals, an observation noted only for pentachlorophenol in WQC documents. Fish, which are not tested in colonization experiments, are particularly sensitive to four of the six chemicals. Conclusions Comparative toxicological data on the acute and chronic sensitivities of freshwater and saltwater benthic species in the ambient WQC documents are limited. Acute values are available for only 34 freshwater infaunal species from four phyla and only 28 saltwater infaunal species from five phyla. Only seven freshwater infaunal species and 24 freshwater epibenruic species have been tested with five or more of the 40 WQC chemicals. Similarly, nine saltwater infaunal species and 20 epibenthic species have been tested with five or more of the 30 substances for which saltwater criteria are available. In spite of the paucity of acute toxicity data on benthic species, available data suggest that: benthic species are not uniquely sensitive and chat SQC can be derived from WQC. The data suggest that the most sensitive infaur.jL species are typically less sensitive than the most sensitive water column (epibenthio and water column) species. When both infaunal and epibenthic species are classed as benthic, the sensitivities of benthic and water col-.m ------- Page 88 species are similar, on average. Frequency distributions of the sensitivities of all species to all chemicals indicate that infaunal species may be relatively insensitive but that infaunal and epibenthic species appear almost evenly distributed among both sensitive and insensitive species overall. Finally, in experiments to determine the effects of chemicals on colonization of benthic saltwater organisms, concentrations affecting colonization were generally greater, and concentrations not affecting colonization were generally lower, than estimated or actual saltwater WQC FCVs. ------- Page 89 GENERATION OF SQC Parameter Values The equation from which SQC are calculated is SQCOC - Koc FCV (34) (see Eqns. 2 ~" and associated text). Hence, the SQC concsntr ion depends only on these two parameters. The Koc of the chemical is calculated from the Kow of the chemical via the regression Equation 11. The reliability of SQCOC depends directly on the reliability of Kow. For most chemicals of interest, the available Kows (e.g. [71]) are highly variable - a range of two orders of magnitude is not unusual. Therefore the measurement methods and/or estimation methodologies used to obtain each estimate must be critically evaluated to ensure their validity. The technology for measuring Kow has improved in recent years. For example, the generator column [72] and the slow stirring [73] method appear to give comparable results, whereas earlier methods produced more variable results. Hence, it is recommended that literature values for KOWS not be used unless they have been measured using these newer techniques. The FCV is used as the appropriate end point for the protection of benthic organisms. Similarly, its applicability to benthic species for each chemical should be verified. The analysis presented in the previous section indicated that this is not an unreasonable assumption across all the criteria chemicals. To test this assumption for a particular chemical, the Kolmogorov - Smirnov ------- Page 90 test [74], which tests whether two samples came from the same population, can be applied to the distribution of LCSCs for the water column and benthic species. The Kolmogorov-Smirnov test is based on the maximum difference between the two empirical cumulative probability distributions. The test will reject the hypothesis that the samples come from the same probability distribution if the difference is so large, given the number of samples in each of the two distributions, that chance alone cannot account for the difference. An example for dieldrin is shown in Figure 30, which presents the probability distributions oT :he freshwater species' LC50s for the water, column and benthic species. The left panel is a log probability plot of the two distributions. It presents the LC50s on a log scale versus the rank order on a normal probability scale. The natural way to judge the equality of these distributions is to compare the LC50s at a particular probability, for example at SOX probability, which is a comparison of the medians. The Kolmogorov-Smirnov test compares another difference. This is illustrated in the right panel, which presents the same data but in a slightly different way. The rank order, as a percentage, is plotted versus the LC50s The points are connected with straight lines to form the empirical cumulative distribution functions for the two daca sets. The Kolmogorov-Smirnov test is based on the maximum difference in probability between these two distributions. as indicated in the figure. Note that this difference is the horizontal distance on the log probability ploc in Figure 30 if the probability scale were linear. The test depends on the number of LC50s in each distribution (12. 9) and the maximum difference in probability (0.333). The probability that a value of this magnitude or less can occur, given that these two samples came from the same distribution (0.573), can be calculated [74]. Because this ------- Test of Equality of Specie t Sensitivity Lognormal Probability Plot Cumulative Distribution 10000 1000 1 mmm \ 0» 100 o 10 10 o 1 04 ^IIH 1 Illlll T 1 1 1 1 I 1 Milli II SHiiE - 0 - HATER = - N - BENTHIC 1 * * * | ; * o ° - 0 -- * o - - * « oo : * no° = * *b° " -_ *o = o I = = = E IIIIM i n mm i i I I 1 1 1 IIIIM" ' •"»«' 100 90 BO ^*. V A X 70 *"^ X 60 == 50 jQ M ^ 40 O £ 30 ••• 20 10 O i i iHilii i i till MAXIMUM DIFFERENCE ~™ ^^ "l"™ ^ - ,^ e ft_ ,J- ^k u ist m ^ t! ! IliliB m !i!SS 1 !!!!!» r J oJ i rn 1 „ •T^ : 1 • ^•1 J f hJ r I J J r : r - e— ' i ii mm i iimiu i ii mm i iimiu i it mil 0.11 10 50 90 99 99 . 9 0.1 1 10 100 1000 100 Probability (%) LC50 (ug/L) 30 Comparison ol tin- dit-ldriii l.CbO probability distributions for water column and benthic freshwater ;>pt.-i irs l.of,norinal probability plot ( 11-1 t paiu 1 ) and t be empirical cumulative distribution functions (rigbt p.nn-1) with tin- maximum il i t 11-1 i-nri- ust:d in tin- Ko 1 uio£urov - Smiriiov test, indicated. ------- Page 92 probability is less than 0.95, the hypothesis that the samples came from the same distribution is accepted at a 95X confidence level. A similar test for the saltwater species yields a probability of 0.0617 a value that is much less than 0.95, which would cause the hypothesis of equality to be rejected. The conclusion from this analysis is that the benthic and water column species that have been tested with dieldrin come from the same probability distribution of LCSOs for both freshwater and saltwater organisms. Therefore they have the same distribution of acute sensitivity. This suggests that the freshwater ..d saltwater FVCs for dieldrin are appropriate effects concentrations for benthic species and should provide a similar level of protection for benthic organisms and water column organisms. This analysis should be performed for any chemical for which SQC are developed. Example Calculations Equation 34 can be used to compute SQCOC for a range of Kows and FCVs. The results for several chemicals are shown in Figure 31 in the form of a nomograph. The diagonal lines are for constant FCVs as indicated. The abscissa is logio Kow. For example, if a chemical has an FCV of 1.0 Mg/L and a loS10 Kow of 4, so that KQW - 104, the logic SQCOC is approximately 1 and the SQC - IQl - 10.0 Mg chemical/g organic carbon. As can b« seen, the relationships between SQCOC and the parameters chat determine its magnitude, KQW and FCV, are essentially linear on a log-log basis. For a constant FCV, a 10-fold increase in KQW (one log unit) increases the SQCOC by approximately 10-fold (one Log unit) because Koc also increases approximately 10-fold. Thus, chemicals with similar FCVs will have larger SQCocs if their Kows are larger ------- Sediment Quality Criteria o o 0) o x 4* "5 O 4-* C 0 E TI CO o 6 5 - 4 - 3 2 1 * 0 - -1 -2 -' -3 100O 1OO FCV (ug/L) 1O - 1 - 0.1 0.01 O.O01 • M«thyl Parathlon A Toxaph«n« T Chtordan* <4 Parathlon ^ EndoMiltan * Endrln • Ph«iMnthr«n« * Chlorpyrifos • DUMrin Figure 31. Log10 SQC versus loglo Kow. The diagonal lines indicate the FCV values. The criteria are computed from Equation 3A. Koc is obtained from Kow with Equation 11. The symbols indicate SQCOC for the freshwater (filled) and saltwater (hatched) criteria for the listed chemicals. The vertical line connects symbols for the same chemical. The FCVs are from the WQC or draft criteria documents (Table A) The octanol/water partition coefficients are the log mean of the values reported in the Log P data base [71|. ------- Page 94 The chemicals listed in Figure 31 have been chosen to illustrate the SQCOC concentrations that result from applying the EqP method. The water quality concentrations used are the FCVs (not the final residue values).from draft or published EPA WQC documents (see Table 4). The Kows are the log averages of the values reported in the Log P data base [71]. These values are used for illustrative purposes only because final SQC when published, should reflect the best current information for both FCV and Kow, as discussed above. The FCVs that are available for nonionic organic insecticides range from approximately 0.01 ^g/L to 0.3 Mg/L, a factor of 30. The SQCocs range from approximately 0.01 Mg/g organic carbon to in excess of 10 Mg/g organic carbon, a factor of over 1,000. This increased range in values occurs because the K<,ws of these chemicals span over two orders of magnitude. Hence the most stringent SQCOC in this example is for chlordane, a chemical with the lowest Kow among the chemicals with an FCV of approximately 0.01 Mg/L. By contrast, the PAHs included in this example have a range of FCVs and Kows of approximately one-half order of magnitude. But these values vary inversely: The chemical with the larger FCV has a smaller Kow. The result is chat the SQCocs are approximately the same, 200 Mg/g organic carbon. Classes of chemicals for which the effects concentrations decrease logarithmically with increasing KOW*> for example, chemicals that are narcotics [75], will have SQC that are mor* nearly constant. Sediment quality criteria (SQC) uncertainty The SQC methodology relies on an empirical partitioning model lo relate the pore water exposure concentration (actually the chemical potential) to the ------- Page 95 equivalent sediment organic carbon exposure concentration. As a consequence there is an uncertainty associated with the use of the model. Some of the uncertainty in the calculation of the sediment quality criteria can be estimated from the degree to which the equilibrium partitioning model, which is the basis for the criteria, can rationalize the available sediment toxicity data. The EqP model asserts that (1) the bioavailability of non-ionic organic chemicals from sediments is equal on an organic carbon basis, and (2) that the effects concentration (Mg/goc) can be estimated from the product of the effects .oncentration from water only exposures (Mg/L) and the partition coefficient Koc (L/kg). The uncertainty associated with the sediment quality criteria can be obtained from a quantitative estimate of the degree to which the available data support these assertions. The data used in the uncertainty analysis are the water-only and sediment toxicity tests that have been conducted in support of the sediment criteria development effort. These freshwater and saltwater tests span a range of chemicals and organisms; they include both water-only and sediment exposures and they are replicated within each chemical-organisms-exposure media treatment. These data are analyzed using an analysis of variance (ANOVA) to estimate the uncertainty (i.e. the variance) associated with varying the exposure media and that associated with experimental error. If the EqP model were perfect, then there would be only experimental error. Therefore, the uncertainty associated with the use of EqP is the variance associated with varying exposure media. ------- Page 96 The data used in the uncertainty analysis are the LCSOs from the water-only and sediment exposures for endrin [21,22], dieldrin [76], acenaphthene [77], phenanchrene [7/j and fluoranthene [19] as used in the individual chemical sediment quality criteria documents in preparation for these chemicals [78,79,80,81,82]. The EqP model can be used to normalize the data in order to put it on a common basis. The LC50 for sediment on an organic carbon basis, LC50S|OC, is related to the LC50 obtained from a water only exposure, LC50V via the partitioning equation: LC50S>OC - KOCLC50W . (35) Therefore, Koc can be used to define the equivalent sediment toxicity based on free concentration in pore water: LC50 LC50 - „ S|0------- Page 97 In order Co perform an analysis of variance, a model of the random variations is required. As discussed above, experiments that seek to validate equation 37 are subject to various sources of random variations. A number of chemicals and organisms have been tested. Each chemical • organism pair was tested in water-only exposures and in different sediments. Let a represent the random variation due to this source. Also, each experiment is replicated. Let f represent the random variation due to this source. If the model were perfect, there would be no random variations other than that due to experimental er'-or which is reflected in the replications. Hence a represents the uncertainty due to the approximations inherent in the model and « represents the experimental error. Let (<7Q)2 and (a€)2 be the variances of these random variables. Let i index a specific chemical - organism pair. Let j index the exposure media: water-only, or the individual sediments. Let k index the replication of the experiment. Then the equation that describes this relationship is: ln(LC50i_j )jc) - Mi + <*j + «j,k (38> where ln(LC50j_tjtk) are either ln(LC50w) or ln(LC50SiOC) corresponding to a water-only or sediment exposure; Mi are the population ln(LC50) for chemical - organism pair i. The error structure is assumed to be lognormal which corresponds to assuming that the errors are proportional to the means, eg a 20X error, rather than absolute quantities, e.g. 1 mg/L. The statistical problem is: estimate ^i and the variances of the model error, (aQ)' and :he measurement error, (c/£)2. The maximum likelihood method is used to make :he-,f estimates [83]. The results are shown Ln Table 6. ------- Page 98 The last line of the Table 6 Ls Che uncertainty associated with the sediment quality criteria: i.e. the variance associated with the exposure media variability. The confidence limits for the sediment quality criteria are computed using this estimate of uncertainty for sediment quality criteria. For the 95X confidence interval limits, the significance level is 1.96 for normally distributed errors. ln(SQCoc)UPPER - i.n(SQCoc) + 1.96aSQC (39) In(SQCOC)LOWER - ln(SQCoc) + 1.96aSQC (40) These are the 95% confidence limits for the sediment quality criteria. ------- Page 99 SITE SPECIFIC SEDIMENT CRITERIA MODIFICATIONS Modifications of SQC to improve their applicability to specific sites may be desirable. As with site-specific modification of WQC, the SQC modification procedure should consider site-specific differences in species sensitivity and the biological availability of specific nonionic chemicals. The following discussion serves only to highlight issues associated with derivation of site- specific SQC. It must not be used as guidance to modify national SQC at this time. EPA intends to provide specific guidance, including testing -°thods for public comment following a workshop on this topic. Site-specific criteria modification based on species sensitivity. Site-specific modification of SQC for nonionic organic chemicals may be possible by a modification of the WQC using the "Recalculation Option" (U.S. EPA, 1982). In the derivation of water quality criteria, minimum database requirements ensure that species from several families and a variety of phyla are tested. Therefore, the range of acute sensitivities to a chemical of a diverse group of organisms is determined. For some chemicals for which WQC are are developed, one of the species tested nay be significantly more sensitive than other tested species. The most sensitive species may not be benthic or the sensitive species may not be resident at a specific site of concern. Any or all of these three factors may result in WQC that may not be applicable to benthic species or to the site of concern. In these situations, the site- specific guidelines (U.S. EPA, 1982) may be appropriate for recalculation o: the WQC following deletion of inappropriate data. ------- Page 100 Ic can be argued chac this approach has merit because daca on non-resident or non-benthic species are not relevant to derivation of SQC for sp~~ific sites. Alternatively, it can be argued that acute toxicity data on tested species are surrogates for the acute sensitivities of untested, but equally sensitive, phylogenetically related species. If true, deletion of data will result in underprotective site-specific SQC. One compromise position might be to require tests of resident benthic species phylogenetically related to, and the same life-stage as, the species deleted from the database. Given these arguments, the technical defensibility and"specific guidance for site-specific SQC modifications need to be developed. Three ----.lens which should be considered are: Option I: Allow data from non-benthic and non-resident species to be deleted. Require no additional testing as long as the minimum data requirements are .T.e: If minimum data requirements are not met following deletion, resting should be required to meet data needs. Option II: Allow no modification because deletion excludes data from species which T.J . be surrogates for similarly sensitive un:ested species. The national SQC does not change and, from a species sensi: :VL:Y standpoint, it is appropriate to t'.'. sites. ------- Page'101 Option III: Allow data from non-resident and non-benthic species Co be deleted from the criteria database, but require replacement with other acute toxicity data. The required acute toxicity tests should emphasize selection of species or life- stage most likely to be sensitive to that specific chemical and local benthic taxa not represented in the database. EPA guidance needs to be developed on the quality and quantity of additional data required for this approach. Site-specific criteria modification based on bioavailability. Modification of SQC because of site-specific differences in bioavailability are probably not necessary for sediments having > 0.2X organic carbon. This is because almost all toxicological and bioaccumulation data demonstrate that organic carbon is the dominant sediment phase controlling bioavailability of non-ionic organic chemicals. However, site-specific modification will be considered on a case-by-case basis if data demonstrate thrt biological availability of a specific nonionic organic chemical in sediments from the specific site differs from the EqP prediction. Experiments should be designed Co test the precision and accuracy of the EqP prediction. The acceptabilicy of various differences in biological availability need to be formulated. It is anticipated that designs similar to, or as robust as, those described in SQC document Section 4.3 will be appropriate for providing positive control sediments, appropriate test species, statistical analyses, and other factors co be included in the experiment design. ------- Page L02 CONCLUSIONS The cechnical basis and data chat support the use of the EqP method to generate SQC have been presented for nonionic organic chemicals. The use of organic carbon normalization is equivalent to using pore water normalization as a means of accounting for varying bioavailability (Figs. 2, 3, 5 - 9, 21 - 23). The variation in organism body burden across sediments can also be significantly reduced if organic carbon and lipid normalization are used (Figs. 24 - 26). For naturally contaminated sediments, particle si c' effects are removed if organic carbon normalized concentrations are compared (Fig. 18). The reason is that organic carbon is the proper normalization for partitioning between free dissolved chemical and sediment bound chemical (Fig. 12). Using pore water normalization for highly hydrophobic chemicals is complicated by chemical complexing to DOC (Fig. 14). Partitioning between pore water and sediment organic carbon from field-collected sediments can be rationalized if DOC complexing is taken into account (Figs. 19 and 20). However, the complexed chemical appears not to be bioavailable (Fig. 16). These observations are consistent with the EqP model, which assumes the equivalence of water-only exposure and the exposure from pore water and/or sediment organic carbon. Sediment quality criteria are based on organic carbon normalization because pore water normalization is complicated by DOC complexing for highly hydrophobic chemicals. The justification for using the FCV from the WQC to define the effects level for benthic organisms has also been discussed. Water column and benthic organisms appear to have similar sensicivicies for both the most sensitive species tested (Fig. 27) and all tested species (Fig. 29). Benthic ------- Page 103 colonization experiments also demonstrate that WQC can be used to predict effects concentrations for benthic organisms. A direct statistic^ test of the equality of the distributions can be used to confirm or refute this assumption for individual chemicals (Fig. 30). Equilibrium partitioning cannot remove all of the observed variation from sediment to sediment. It does reduce the much larger sediment-to-sediment variation that exists if no corrections for bioavailability are made (Figs. 5 - 9). A variation factor of approximately a factor of two to three remains (Figs. 2 and 3) which includes measurement and other sources of variability. This is not unexpected as EqP is an idealization of the actual aquation. Other factors that are not considered in the model play roles in determining biological effects. Hence, it is recognized that a quantification of the uncertainty will accompany the SQC that reflect these additional sources of variation. Research needs The final validation of SQC will come from field studies that are designed to evaluate the extent to which biological effects can be predicted from SQC. The colonization experiments (Table 7) are a laboratory simulation of a field validation. Sediment quality criteria can possibility be validated more easily than WQC because determining the organism exposure is more straightforward. The benthic population exposure is quantified by the organic carbon normalized sediment 'concentration. ------- Table 7. Comparison of WQC FCVs and concentrations affecting (LOEC) and not affecting (NOEC) benthic colonization Substance Pentachlorophenol Aroclor 1254 Chlorpyrifos h • nv.i Lerate 1,2,4- Trichlorobenzene Toxaphene Colonization Cone. versus FCVa ng/L Colonization LOEC 16.0 FCV 7.9 Colonization NOEC 7.0 Colonization LOEC 0.6 Estimated FCV -0.1 Colonization NOEC <0.1 Colonization LOEC 0.1 FCV 0.005 Colonization NOEC Colonization LOEC 0.01 Estimated FCV -0.002 Colonization NOEC Estimated FCV 50. Colonization LOEC 40. Colonization NOEC Colonization LOEC 11.0 Colonization NOEC 0.8 FCV 0.2 Sensitive taxa Molluscs, Abundance Molluscs, Crustacea, Fish Crustacea Crustacea, Fish Crustacea, Molluscs, Species Richness Crustaceans Crustacea, Chordates Crustacfa Crustacea, Fish Molluscs, Abundance Crustacea, Species Richness Crustacea, Fish Reference 165.66] [67] [64] [68] [69] 70[ :64[ aSix day exposure to established benthic community ------- Page 105 Ic has been suggested chat the kinetics of PAH desorption from sediments control the chemical body burden of a benthic amphipod [84], The extent to which kinetics can be important in field situations is unknown at present, and field studies would be an important component in examining this question. In addition, more laboratory sediment toxicity tests, particularly chronic tests involving multiple sediments, would also be helpful. In a typical practical application of SQC mixtures of chemicals are involved. The extension of EqP methodology to mixtures would be of great practical value. Initial experiments indicate that it should be possible [85]. The EqP method is presently restricted to computing effects-based criteria for the protection of benthic organisms. The direct extension of this methodology for computing sediment criteria that are protective of human health, wildlife, and marketability of fish and shellfish requires that the equilibrium assumption be extended to the water column and to water column organisms. This is, in general, an untenable assumption. Water column concentrations can be much lower than pore water concentrations if sufficient dilution flow is present. Conversely, upper-trophic-level organisms are at concentrations well above equilibrium values [86]. Hence, the application of the final residue values from the WQC for the computation of SQC, as was done for certain interim criteria [87], is not technically justifiable. 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