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Impacts of Low Levels of Residual Oils on Toxicity Assessment of Oil Spills using the Target Lipid Model |
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Joy McGrath and Dominic Di Toro
It is assumed that KLW can be related to KOW using a linear free energy relationship of the form
Figure 1. Schematic Diagram of log(LC50) versus log(Kow) equal to 0, the (Kow) is equal to 1 meaning that chemical concentration in the water is equal to the chemical concentration in octanol. (adopted from Di Toro et al. 2000).
For the current CRRC-funded project, CTLBBs for nine additional species, not included in the model development, were computed if acute toxicity data were available. A total of 47 CTLBBs are available and provided in Appendix A. If the acute toxicity data for a particular species included more than four data points, corresponding standard errors were computed. If less than two data points were available, the associated standard errors could not be calculated. Also, for the two species that had three data points, Hyalella azteca and Chironomus tentans, standard errors were not computed because the three data points were all for the same compound. 3.2 Aqueous Chronic Toxicity Prediction The critical aqueous concentrations computed from Equation 13 are acute concentrations that produce an effect in a short-term test (i.e., 96-h LC50). To convert the acute critical concentration to a chronic effect concentration, the TLM adopts the acute-to-chronic ratio (ACR) methodology used by the U.S. EPA in deriving water quality criteria (Stephan et al. 1985). The ACR is computed from paired acute and chronic toxicity data for a particular chemical to a specific organism as follows
The ACR is a means of comparing the acute and chronic toxicities. It does not assume that the toxic mode of action is the same for acute and chronic toxicity. For the application of the TLM, the mechanism for acute effects is assumed to be narcosis. For chronic effects, the mechanism is unknown. In the TLM development ACRs were provided for 34 paired acute and chronic data sets from 6 different species and several Type I narcotic chemicals (Di Toro et al. 2000). The ACRs ranged from 1.2 to 23 with a geometric mean value of 5.09. Since the ACRs were similar for narcotic chemicals, an average ACR was appropriate. For the current CRRC-funded project, a similar approach was taken using ACRs computed from toxicity data for MAHs and PAHs. 3.3 Acute and Chronic Sediment Toxicity Prediction The EqP model (Di Toro et al. 1991) is used to convert the critical aqueous concentration computed from Equation 13 to the equivalent critical sediment concentration. EqP theory is based on two concepts: (1) non-ionic chemicals in sediments will partition between sediment organic carbon, pore water and benthic organisms; and (2) sediment toxicity can be predicted by comparing the pore water concentration to the critical concentration determined in a water-only exposure as shown by Adams et al. (1985). EqP rationalizes that for both of these concepts to be true, the pore water and the organic carbon phase of the sediment must be in equilibrium. At equilibrium, if the concentration in any one phase is known, then the concentrations in the other phases can be predicted. Assuming that the critical aqueous concentration computed using the TLM (Equation 13) is equivalent to the critical pore water concentration, the equivalent critical sediment concentrations producing the same effect on the same organism is
3.4 Uncertainty in Toxicity Predictions
Toxicity data from aqueous or sediment tests using mixtures of chemicals, are presented graphically as percent observed effect (usually mortality) as a function of the predicted total TUs. The observed effects are graphed on an arithmetic scale and the predicted total TUs are graphed on a base ten logarithmic scale. An example of this type of graph for acute toxicity data is shown in Figure 3. Ideally 50% effects will be observed at a total toxic unit of 1.0 as computed from Equations 20 and 21. These indices will be shown as solid lines and are shown for illustrative purposes only. However, the indices of 50% effect and a total TU of 1.0 are not absolute. The HC5 and HC95 values represent the uncertain bounds for when effects are expected and are a function of the uncertainty associated with the species-specific CTLBB. These indices will be shown as dashed lines (Figure 3) and will vary for each species. Effects are expected to be low (less than 50%) for predicted total TUs to the left of the HC5 bound. Effects are expected to be high (greater than 50%) for predicted total TUs to the right of the HC95 bound. The area of uncertainty lies between the HC5 and HC95 bounds. Within this area of uncertainty effects may or may not occur. The larger the uncertainty associated with the CTLBB, the larger the area of uncertainty. For chronic toxicity data, the chronic prediction is assumed to the similar to an EC10. Therefore, the effects threshold is not 50%, but 10%. So with chronic predictions, the effects index would be 10% on the graph. However, it should be noted that control mortality is typically around 5 to 10% and can be as high as 24% (Berry et al. 1996). When comparing the percent of chronic effects observed, it is important to subtract out the percent of effects observed in the control mortality. For chronic toxicity, the area of uncertainty will be larger due to the additional uncertainty associated with the ACR. To estimate the toxicity from exposure to MAH and PAH mixtures, such as those in WSFs prepared from oil or those that result from an oil spill, ideally measurements for all of the components in the mixture, whether in the water column or sediment, are needed. These measurements are then used in Equations 20 and 21 to estimate the total toxicity, expressed as TU, from the mixture. If measurements are not provided for all components, the total TU of the mixture can be underestimated. Even chemicals with high log (KOW) values will contribute TUs, with the maximum TU at their water solubility (Di Toro et al. 2007). Given the complex nature of oils, it is impractical to measure for the presence of every chemical that may be sufficiently water soluble to find its way into the environment. Since MAHs and PAHs are assumed to be the causative agents in oils, sufficient characterization of the MAHs and PAHs is desirable.
Figure 3. Diagram of how acute toxity data from aqueous and sediment tests using mixtures of chemicals will be displayed. Percent effect as a function of predicted total TU's. Dashed Lines represent HC5 and HC95 values. A reasonable question to pose is: What characterization data are needed to compute a total TU? The U.S. EPA addresses this issue for sediments (see Section 3.6), but not for water column exposures. In this research, for water column exposures, at a minimum measurement for parent PAHs and some representation of the alkylated homologs of parent PAHs are required. Alkylated homologs are the parent PAHs substituted with carbon groups (e.g., methyl, ethyl, propyl, etc.). For example, C1-naphthalene represents parent naphthalene with one carbon (a methyl) substitution and C2-naphthalene represents parent naphthalene with two carbon (two methyls or one ethyl) substitutions. C1-naphthalene has two structural isomers, 1-methylnaphthalene and 2-methylnaphthalene. C2-naphthalene has twelve structural isomers. The number of structural isomers increases with the degree of alkylation. This pragmatic requirement for PAH measurements for water column exposures is necessary to capture some of the alkylated PAHs that are prevalent in petrogenic PAH sources and to eliminate data sets that have too few measurements where the toxicity can be severely underestimated. 3.6 Computing Total PAH TUs in Sediment The U.S. EPA defined total PAHs in sediments to be the sum of the TUs from a minimum of 34 PAHs (18 parent PAHs and 16 alkylated PAHs) (U.S. EPA, 2003). These 34 PAHs were selected because they represented the maximum number of PAHs that were routinely measured in the U.S. EPA Environmental Monitoring and Assessment Program in estuaries of the Virginian Province (1990-1993) and Louisianan Province (1991-1993). The U.S. EPA recognized that different sediment monitoring programs require varying PAH characterization. Among the available sediment data sets, there were 13 and 23 common subsets of PAHs. Rather than requiring measurements for the 34 PAHs, adjustment factors were computed from the EMAP data sets to convert the commonly measured 13 or 23 to be equivalent to the 34 PAHs from a toxicity perspective. The mean adjustment factors for the 13 and 23 PAHs were 2.75 and 1.64, respectively. For sediments that had measurements for 13 PAH, the total (summed) TUPAH13 from the 13 PAHs would be computed and then multiplied by 2.75 to convert the TUPAH13 to TUPAH34 , which is equivalent to TUPAHTOT. A listing of the PAHs that comprise the 13 PAHs and 23 PAHs subsets, as well as the 34 PAHs, is provided in Table 1. The application of adjustment factors puts all of the sediment data on the same footing and reduces the uncertainty associated with the unmeasured PAHs. However, the U.S. EPA encourages the measurement of the 34 PAHs. 3.7 Literature Review The literature was reviewed for water column and sediment effect data resulting from exposure to MAHs or PAHs, both as single compounds and as mixtures. The main search engine was Dialog, an on-line data retrieval system that services many fields including science and engineering. For data to be used in the TLM analysis, the following criteria had to be met: A CTLBB must be available for the test organism. For water column exposures, the concentration of the chemical(s) must be below its water solubility. If a chemical is tested at a concentration above its water solubility, then the chemical is present as a pure-phase which may exert a different toxic mode of action. For single chemical exposures, the solid solubility of the chemical is used. For mixtures of chemicals that are liquids, such as those in oils, the sub-cooled liquid solubility the solubility of the component if it were a liquid at the temperature of interest is used. For single chemical-spiked sediment exposures, the EqP-based chemical concentration in the pore water (computed using Equation 15 where the measured concentration normalized to organic carbon content is used instead of , the critical concentration) must be below the spiked chemicals water solubility, again because pure phase may exert a different mode of toxic action. For single chemical exposures, the solid solubility of the chemical is used. For mixtures of individual spiked chemicals, the appropriate solubility is the sub-cooled liquid solubility. The exposure concentrations must be constant with time, particularly for long-term exposures where chronic effects are being observed. Laboratory studies that show diminishing or varying concentrations over time are difficult to interpret. One cannot assign a specific concentration to the observed effect. For exposures to mixtures of chemicals, such as water-soluble fractions prepared from oils or fuels or sediment contamination from an oil spill, the concentrations of the individual chemicals must be measured. Ideally, to determine the toxicity from a chemical mixture, the concentration of all components in the mixture should be measured because each component could potentially contribute toxicity. For sediments, the toxicity from unmeasured PAHs can be estimated from the concentration of select PAHs following guidelines established by the U.S. EPA (see Section 3.6). Using this methodology, the toxicity of total PAH can be estimated. A similar normalization to total PAH toxicity is not available for water column exposures. Therefore, for water exposures, the majority of expected water-soluble constituents should be measured. For a fresh petroleum source (i.e., non-weathered), the majority includes the BTEX, naphthalene, phenanthrene and some of their alkylated homologs. For a weathered petroleum source, the concentration of the heavier PAHs (i.e., chyrsene and fluoranthene homologs) must also be measured. For sediment exposures, the total organic carbon content in the exposure sediment must be reported. 3.8 Physicochemical Properties of Alkanes, MAHs and PAHs A listing of chemicals that appeared in available data sets is provided in Table 1. Types of chemicals include: aliphatic alkanes, cyclic alkanes, MAHs and PAHs. In some data sets, concentrations of alkylated homologs were reported for MAHs and PAHs. These are represented with a C# where the C represents a carbon and the number represents the number of carbon substitutions. For example, C3 represents three carbon substitutions (3 methyls, 1 methyl plus 1 ethyl, 1 propyl). [Section 3.6 contains a more detailed discussion.] The chemical properties include molecular weight, log (KOW), solid solubility and sub-cooled liquid solubility. SPARC was used to compute the molecular weight and log (KOW). Recommended solid solubility and sub-cooled solubility values were taken from MacKay et al. (1992a, 1992b, 1993 and 1995). Relationships between log (solubility) and log (KOW) were used to compute solid solubility and sub-cooled liquid solubility for chemicals that were not listed in MacKay et al. These relationships are provided in Table 1. 4.0 Results 4.1 Literature Review The literature was reviewed for data sets where the effects on aquatic organisms exposed to oil-related chemicals were observed. For water column exposures, 141 references were reviewed of which 80 contained data that met the criteria specified in Section 3.7. For sediment exposures, 64 references were reviewed of which 21 contained data. In total, 205 references were reviewed of which 101 (approximately 49%) contained data deemed acceptable. A summary of the references reviewed and brief explanations for not accepting data are provided in Appendix B. 4.2 TLM Validation - Water Column In this section, toxicity predictions using the TLM and TU methodologies are presented and compared to observed values. For large data sets, graphical summaries are presented. Data for these large data sets are provided in Appendix C. 4.2.1 Acute Effects (Lethality) - Single Compound Exposures In this section, the TLM is applied to predict the acute lethal effects from exposure to single compounds. Acute toxicity data were considered if a CTLBB was available for the test organism. In addition to BTEX, other MAHs are included in the comparison. Most of these additional MAHs have a higher degree of alkylation (i.e., three methyl group substitutions) and include compounds such as trimethylbenzenes and propylbenzenes. Based on structure, the toxic mode of action for these compounds should be similar to BTEX. For MAHs, there are a total of 164 data points from 28 different species Appendix C (Table C1). For PAHs, there are a total of 139 data points from 20 different species. Toxicity data are summarized in Table 2, which also provides information relevant to the exposure condition (e.g.test type, organism life-stage). Note that Table 2 also contains data from eight tests where the observed LC50s were greater than the aqueous solubility of the test chemical. These data were not included in any analysis and are only shown for completeness. The predicted LC50 values for each exposure are also presented in Table 2. Example calculations are provided in Appendix E. The observed and predicted LC50s are compared in the top panel of Figure 4. The solid line represents the 1:1 relationship (i.e., where the observed and predicted LC50s are equal). The dashed line represents the 90% confidence interval. The confidence limits were computed as the 5th and 95th percentiles of the residuals where the residuals were the difference in the observed and predicted effect concentrations. The confidence limits are not symmetrical indicating that the distribution is not exactly log normal. The model tends to underestimate the toxicity. Based on this data analysis, with a 90% confidence, the TLM is able to predict acute toxicity to within a factor of approximately 7. 4.2.2 Acute Effects (lethality) Mixtures In this section, the TLM is applied to predict the acute effects, meaning lethality, from aqueous exposure to a mixture of oil-related compounds. Data sets were considered if the CTLBB of the test organism was available, the concentrations of individual components in the mixture were measured and the concentrations of the chemicals were relatively constant over time (See Section 3.7). Three data sets were available that met the selection criteria. Two data sets were laboratory investigations of the toxicity of WSFs prepared from oils. The other data set was a laboratory experiment using a prepared PAH mixture. All MAHs and PAHs measured in the mixture were included in the analysis. Figure 4. Acute Exposures-Signle Compounds - TLM predicted acute aqueous LC50 versus LC50 for MAHs and PAHs (0). Solid line respresents 1:1 relationship. Dashed lines represent 90% confidence interval. The toxicities of WSFs prepared from neat (unweathered) and naturally weathered Exxon Valdez Alaska North Slope crude oil (EVCO) were measured (ENSR, 2001). Neat oil was collected from the Exxon Valdez oil tanker seven days after the tanker ran aground in Prince William Sound, Alaska. Naturally weathered oil was collected approximately five months after the tanker grounded. WSFs were prepared from 10:1 (water:oil) solutions for the neat and weathered oils. Each WSF was analyzed for BTEX, biphenyl, 19 parent PAHs and 21 alkylated homologs of parent PAHs. These concentrations are provided in Appendix C (Table C2). Six dilutions of the WSFs were used in toxicity testing. The mortality to fathead minnows (Pimephales promelas) after a 48-h exposure in the various dilutions was recorded. The TLM total TU for the 100% WSF from neat and weathered oil were 0.62 and 0.28, respectively. The TLM computed toxic units associated with each chemical are provided in the Appendix C (Table C2). For the neat oil, BTEX contributes significantly to the TU (approximately 60% of the computed TU). This is not the case for weathered oil, where the BTEX account for less than 10% of the TUs. This analysis suggests that BTEX are important contributors to the toxicity of neat oil compared to their toxic contribution in weathered oil, as was demonstrated by Neff et al. (2000). For a discussion of the effect of weathering on the toxicity of oils, the reader is referred to Di Toro et al. (2007). The observed mortality as a function of the total measured concentration (mg TMC/L) in each treatment is shown in Figure 5A. The open and closed symbols represent the neat and weathered oil treatments, respectively. Greater than 50% mortality was only observed in the highest WSF exposure (100% WSF) using neat oil, indicating that the neat oil was more toxic than the weathered oil. All other dilutions resulted in less than 30% mortality. For each dilution, the observed mortality normalized to total TUs is presented in Figure 5B. Solid lines at a TU of 1.0 and 50% mortality are shown for guidance. The dashed lines represent the 5% and 95% uncertainties in the TLM predictions for P. promelas and are computed using Equations 18 and 19 without the terms for ACR. For this data set, the dose-response pattern was correctly predicted by the TLM. The one data point where greater than 50% mortality occurred falls within the uncertainty limits of the TLM. All of the other data points that have low observed mortality and corresponding low total TU fall to the left of the lower uncertainty bound where low mortality is expected. States et al. (1982) also investigated the acute toxicity of PAH mixtures. In this study, WSFs were prepared from No. 6 fuel oil, No. 2 fuel oil and a solvent refined coal liquid. Acute toxicity to Daphnia magna (48 hour immobilization) was determined under static conditions. The major chemical constituents were provided for No. 2 fuel oil and the coal liquid only. No chemical analysis was provided for No. 6 fuel oil; therefore, the TU computation could not be performed and the TLM could not be applied to predict effects from the No. 6 fuel oil. Measurements were provided for aromatic hydrocarbons, which included, indan, tetralin, naphthalene and alklyated homologs of benzene and naphthalene. The chemical concentrations and computed TUs are provided in Appendix C (Table C3). The total computed TUs in the 100% WSF for No. 2 fuel oil were 0.36, suggesting that no toxic effects are expected. This result was in agreement with the reported no observed effects for the 100% WSF (States et al. 1982). The total TUs computed for the 100% WSF from the coal liquid were 8.4 indicating that the 100% WSF would be predicted to be toxic. Reported data indicated that 0.25% WSF from the coal liquid was toxic. The equivalent TU at this dilution is 0.021 and at this level no toxicity would be predicted fromthe aromatic hydrocarbons. In this case, the TLM predictions were not in agreement with the observed effects (low TU, high effect levels). However, States et al. (1982) attributed the toxicity of the coal liquid to phenolic compounds, which are not type I narcotic chemicals and are not included in the TLM analysis. The phenolic compounds were present at significantly higher levels on the coal liquid WSF (1360 mg/L) compared to the No. 2 fuel oil WSF (1.7 mg/L). If the phenolic compounds are the main contributors to the toxicity, it is not surprising that the TLM did not predict the effects correctly because they are not included in the TLM and TU calculation. Barata et al. (2005) tested the acute toxicity of a mixture of 9 PAHs. The PAHs included naphthalene, 1-methylnaphtalene, 1,2-dimethylnaphthalene, phenanthrene, pyrene, fluorene, 1-methylphenanthrene, dibenzothiophene and fluoranthene. The mixture was tested at six dose levels (0.25x, 0.5x, 1x, 1.5x, 2x, 2.5x); however, chemical measurements were only provided for three exposures (0.5x, 1x, 1.5x). Mortality of the adult copepod, Oithona davisae, was measured after 48 h of exposure. The measured chemical concentrations and computed TU units are provided in Appendix C (Table 4C). The observed mortality as a function of the total measured concentration (mg TMC/L) and normalized to total TUs is shown in Figures 5C and 5D, respectively. The dashed lines are the 5 and 95 percent uncertainties for O. davisae based on variation in species-specific CTLBB. For O. davisae, the dose-response was correctly predicted by the TLM where 50% mortality occurs around 1.0 TU.
Acute Exposures - Mixtures - Percent mortality as a function of total measured concentration (mg/L) (top panels) and predicted aqueous TUs (bottom panels). Data on the right is for Pimephales promelas exposure to WSFs prepared from neat and weathered Exxon Valdez crude oil (ENSR, 2000). Data on the left is for Oithona davisae exposure to WSF prepared from a mixture of 9 PAHs (Barata et al. 2005). Solid lines represent 50% mortality (horizontal) and 1 TU (vertical). Dashed vertical lines represent HC5 and HC95 for each species. The data analyses presented above demonstrated that the TLM and TU concept (i.e., theory of additivity) correctly predicted the acute effects from exposure to a mixture of oil-related compounds. The benefit of normalizing the concentration data to TU can also be realized through the data analysis. If the toxicity of the PAH mixture is expressed on a mass per unit volume of total measured concentration, then 50% observed mortality resulted from exposure of approximately 1 mgTMC/L of a mixture of 9 PAHs (Figure 5C) and 10 mgTMC /L of water soluble compounds in neat EVCO (Figure 5A). There is an order of magnitude difference in the concentration that resulted in similar effects. However, a comparison based on mass/volume concentration does not consider chemical differences in the mixtures or differences in organism sensitivity. Once normalized to TUs, 50% mortality occurs in the range of 0.6 to 1.0 TUs, within a factor of 2. Normalizing to TUs allows direct comparison among different studies. This illustrates that the total measured hydrocarbon concentration should NOT be used to assess toxicity, while TUs can be applied across different sources and organisms to express toxicity. 4.2.3 Chronic Effects (Growth, Reproduction and Mortality) - Single Compound Exposures The analysis presented previously suggests that the TLM, which was developed for chemicals that have a narcotic mode of toxic action, can be used to predict the acute toxicity of BTEX (and other MAHs) and PAHs. To convert the acute TLM endpoint to a chronic endpoint, an acute-to-chronic ratio (ACR) is applied (Equation 14). Di Toro et al. (2000) demonstrated that the ACR is independent of chemical and species and can therefore be applied to any chemical and any species in their analysis. A mean ACR of 5.09 was computed from a database that included BTEX and PAHs as well as other chemicals, such as chlorinated alkanes and MAHs (i.e., 1,2-dichloroethane, 1,2,4-trichlorobenzene). More than half of the acute and chronic paired data sets were chlorinated compounds. Since petroleum products do not contained halogenated components, an analysis of the ACRs from non-halogenated compounds is more appropriate. In this section, the distribution of ACRs for aliphatic hydrocarbons, MAHs and PAHs were compared. A total of 29 paired data sets were available (Table 3), of which 17 were PAHs, 6 were MAHs and 6 were aliphatic hydrocarbons. The distributions for each chemical class are shown in Figures 6A-C. The distributions are similar, spanning an ACR range of approximately 1 to 11. Since the distributions were similar, the data sets were combined (Figure 6D). The geometric mean ACR from the combined data sets is 3.83. Although this research focuses on MAHs and PAHs, aliphatic hydrocarbons were included in the ACR computation because: (1) based on structure, aliphatic hydrocarbons are not expected to have a different mode of action; and (2) the ACRs for aliphatic hydrocarbons fell on the high end of the distribution and including them in the computation resulted in a higher average ACR and is therefore the more conservative approach for computing a chronic endpoint. The use of an ACR to convert an acute endpoint to a chronic endpoint does not mean that the toxic mode of actions for acute toxicity and chronic toxicity are the same. Rather, an ACR is a means of relating the acute toxicity of a chemical to its chronic toxicity. In addition, the toxic modes of action are not necessarily the same for different chemical classes that have similar ACRs (i.e., PAHs, MAHs and aliphatic hydrocarbons). The fact that the distributions are similar supports the application of an average ACR. If the ACRs were orders of magnitude different, then perhaps the use of an average ACR would not be appropriate.
Figure 6. Chronic Effects Single Compounds - Distribution of acute to chronic ratios (ACRs) for aliphatic hydrocarbons (A), PAHs (B), BTEX (C) and the combined data set (D) 4.2.4 Chronic Effects (Growth, Reproduction and Mortality)- Mixtures One study that satisfied all of the criteria investigated the chronic effects from exposure to No. 2 fuel oil. Anderson et al. (1977) investigated the hatching success of three marine species: Cyprinodon variegates (Sheepshead minnow), Fundulus heteroclitus (Mummichog) and Fundulus similus (Longnose killifish). A CTLBB is only available for C. variegates and therefore only the effects on this organism can be evaluated. Embryos were exposed to various dilutions of a WSF prepared for No. 2 fuel oil. The WSF was renewed daily. Concentrations of 31 hydrocarbons (11 alkanes, 8 MAHs, 12 PAHs) in the WSF were provided in Anderson et al. (1974a). With the exception of the lowest dilution, 100% mortality was observed in all exposures. The observed mortality as a function of total concentration in the WSF (mg/L) (left panel) and predicted aqueous TUs (right panel) is shown in Figure 7. For this data set, the TLM correctly predicted the observed effects, 100% mortality occurred at greater than 1 TU and low effects occurring at less than 1 TU.
Figure 7. Chronic Effects Mixtures Observed mortality as a function of total measured concentration in WSF (mg/L) (left panel) and predicted aqueous TUs (right panel). Data are for Cyprinodon variegates embryos exposed to WSF prepared from No. 2 fuel oil (Anderson et al. 1977). Dashed vertical lines represent 5th and 95th percentiles based on variations in CTLBB and ACR. Moles (1998) compared the sensitivity of 10 aquatic species to long-term exposure to crude oil. In this study, WSFs were prepared from Cook Inlet crude oil. Organisms were exposed to various dilutions of the WSFs for 4 and 28 days. Although the concentrations of individual components in the WSFs were not measured, the 4-d and 28-d LC50s were reported and used to compute ACRs. The computed ACRs ranged from 1 to 2.5 (data for which 4-d LC50 could not be computed were omitted from analysis). These ACRs for crude oil are similar to the ACRs computed for individual chemicals that comprise oil and further support the use of a mean ACR of 3.8 for oil-related components. 4.2.5 Sub-Lethal Effects There is a significant amount of recent literature that suggests exposure to PAHs during a fishs early life-stage can result in a variety of sub-lethal effects (e.g., yolk sac edema, pericardial edema, hemorrhaging, craniofacial and spinal deformities, lesions, defects in cardiac function and reduced growth) (Carls et al. 1999; Heintz et al. 1999; Brinkworth et al. 2003; Incardona et al. 2004; Rhodes et al. 2005). Many of these symptoms are similar to those of blue-sac disease, which is related to exposure to planar, halogenated aromatic compounds such as 2,3,7,8-tetrachlorodibenzo-p-dioxin (Hornung et al. 1999). These sub-lethal effects were not included in the development of the ACRs and so, the ACRs may not be protective of these types of effects. However, since recent literature is focusing on these types of effects and exposure to PAHs causes these effects, it is appropriate to determine if the TLM is protective of these effects. This section presents the application of the TLM and ACR methodology to determine if it is protective of these types of effects (i.e., whether the methodology predicts chronic endpoints that are lower than concentrations observed to cause these effects). 4.2.5.1 Single Compound Exposures The literature was reviewed to identify data sets where early life-stage organisms were exposed to single compounds (BTEX and PAHs) and sub-lethal effects were observed. These data sets were then screened to meet the four main criteria of (1) available CTLBB, (2) exposure concentrations below compound solubility constant exposure, (3) constant exposure concentrations (i.e., flow-through vs. static test conditions), and (4) measured concentrations. Fourteen data sets were identified for analysis; however; only 6 datasets satisfied all four criteria. In all data sets, the chemical exposure concentrations were below the chemicals water solubility and species-specific CTLBBs were available for all test organisms. For some of the exposures, the reported test concentrations were nominal rather than measured values and the exposure conditions were static (i.e., not constant) rather than flow-through or static renewals (i.e., constant). In following the U.S. EPA water quality criteria guidelines (Stephan et al.1985) credence is given to measured data generated under flow-through or static renewal conditions. Due to the scarcity of data, all data were analyzed. To put some perspective on the conditions under which the data were generated, they were given a ranking number determined as follows: 1 = Nominal concentration 2 = Static test conditions, measured concentrations 3 = Static renewal conditions, measured concentrations 4 = Flow-through conditions, measured concentrations Data that have a ranking number of 3 or 4 are more comparable for use in the TLM since the effect concentrations were based on measured concentrations and the test concentrations were relatively stable throughout the test duration. Six data sets were given a ranking number of at least 3 (see Table 4 for a summary of the data). For each exposure, the organism, chemical, relevant test conditions, observed effects and reported effect concentration are listed. The TLM chronic endpoint and HC5 and HC95 values are also provided for comparison. The TLM chronic endpoint is computed from Equations 13 and 14 using the average ACR of 3.83. The HC5 and HC95 are the 5th and 95th percentiles and include variability in ACR and CTLBB. Example calculations are provided in Appendix E. Data were available for five fish species, Japanese medaka (Oryzias latipes), fathead minnow (Pimephales promelas), rainbow trout (Oncorhynchus mykiss), Inland silverside (Menidia beryllina) and zebra danio (Brachydanio rerio). The compounds tested included toluene, naphthalene, phenanthrene, dibenzothiophene, retene, benzo(a)pyrene, benzo(a)anthracene, 4,6-dimethyldibenzothiophene, 7,12-dimethylbenzo(a)anthracene and benzo(k)fluoranthene. The reported concentrations included lowest observed effect concentrations (LOEC), no observed effect concentrations (NOEC) and observed effect concentrations (OEC). Ideally, the TLM chronic endpoint should fall between the reported LOEC and NOEC. In two exposures, no effects were reported at concentrations tested below the chemicals water solubility (Table 4 - benzo(a)anthracene and 4,6-dimethyldibenzothiophene exposure to Japanese medaka). Based on the TLM, effects should have been observed at the concentrations tested. The fact that no effects were observed at levels predicted by the TLM suggests that the TLM is overly protective and conservative. For eight data points, the average TLM chronic endpoint was above the LOEC. However, it is more appropriate to compare the HC5 value to the observed effect concentration when determining if a criterion is protective. For the majority (13 out of 15) of the early life stage exposures, the TLM methodology was protective of sub-lethal effects. There were two data points for which the TLM chronic endpoints were above the observed effect concentrations (i.e., not protective). In one test, the reported 27-d LC50 for mortality (grossly deformed larvae counted as dead) for rainbow trout exposed to naphthalene was 120 ?g/L (Black et al. 1983) compared to the TLM HC5 of 170 µg/L. The reported 36-d LOEC for abnormalities to rainbow trout exposed to phenanthrene was 0.21 ?g/L (Hannah et al 1982; Hose et al 1984) compared to the TLM HC5 of 0.36 ΅g/L. Both of these exposures had a ranking number of 3 or 4, indicating that the test conditions were optimum and that the data should be of high quality. A graphical presentation comparing the early life stage data to the TLM predictions is shown in Figure 8. The lower and upper bars around the TLM chronic endpoint represent the HC5 and HC95, respectively. 4.2.5.2 PAH Mixtures Several laboratory studies demonstrated that long-term exposure to oil results in various sub-lethal effects in early life stage pink salmon (Oncorhynchus gorbuscha) and pacific herring (Clupea pallasi) (Marty et al. 1997; Carls et al. 1999; Heintz et al. 1999). Data from these studies could not be analyzed because the chemical exposure concentrations were not constant and drastically decreased (by orders of magnitude) during the exposure period. Due to the variable exposure concentrations, linking the observed effects to the exposure concentrations was not possible. Rhodes et al. (2005) determined the effects of PAH mixtures on embryonic development. In this study, early-life stage O. latipes were exposed for 18 days to three different mixtures of PAHs. One mixture contained three parent PAHs (phenanthrene, dibenzothiophene and benzo(a)anthracene). Another mixture contained three dimethylated PAHs (3,6-dimethylphenanthrene, 4,6-dimethyldibenzothiophene and 7,12-dimethylbenzo(a)anthracene). The last mixture was an oil sands extract. The exposure system was static renewal and therefore the exposure concentrations were fairly constant. Nominal concentrations were reported for the two mixtures prepared from three PAHs. For the extract, the concentrations of 16 U.S. EPA priority pollutant PAHs and their alkylated homologs were measured. The endpoints evaluated were prevalence of BSD (blue sac disease) symptoms, % hatch, time to hatch and % normal larvae. For each mixture, the NOEC and LOEC values were reported when effects were observed. For the parent PAH mixture, the NOEC and LOEC values for % hatch and % normal larvae were 100 and 200 µg TPAH/L, respectively. The TLM chronic endpoint for the parent mixture was 80 µgTPAH/L. The only observed effect from the dimethylated PAH mixture was % hatch, where the NOEC and LOEC values were 25 and 50 µgTPAH/L, respectively. The TLM chronic endpoint for the dimethylated parent mixture was 15 µgTPAH/L. The oil sands extract was the only mixture that induced BSD symptoms. The NOEC and LOEC values for BSD and % normal larvae were 8.8 and 22 µgTPAH/L, respectively. Hatch length was affected at lower concentrations of the oil sands extract with NOEC and LOEC values of 0 and 2.2 ΅gTPAH/L, respectively. The TLM chronic endpoint for the oil sands mixture was 10 µg/L. A comparison of the observed and predicted effect concentrations for the three mixtures is shown in Figure 9. All data are provided in Appendix C (Tables C6 and C7). For the TLM predicted concentrations, the symbol represents the predicted concentration. The vertical bars represent the 5th and 95th percentiles and are based on variation in CTLBB for a species and variation in ACR. For all three mixtures, the TLM chronic endpoint was below the LOEC and in some cases below the reported NOEC, indicating that the method is protective. The one exception was for the oil sands extract where the LOEC for hatch length was 2.2 µg/L, which was below the average TLM endpoint of 10 µg/L. However, the HC5 of 0.2 µg/L, is below the LOEC. This is another dataset that supports the use of the HC5 as the criterion value. It should be noted that the large uncertainty in predictions for Japanese medaka (i.e., wide range in HC5 and HC95) is due to the large kz value of 4.47. The dataset used to compute the CTLBB only consisted of 5 data points. With such a high kz value, it is almost certain that the HC5 will be lower than the observed effect concentration. Medaka is a commonly used test organism and additional acute toxicity data are needed to better quantify the statistics of the CTLBB. ![]() Figure 8. Sub-lethal effects Single Compounds Comparison of OEC/LOEC/NOEC observed from early life stage fish exposures to single compounds to TLM chronic effect concentrations. The effects are those associated with sub-lethal endpoints such as abnormal larvae development and blue sac disease-like symptoms. The symbols represent the TLM chronic endpoint. The lines associated with the TLM chronic endpoints represent the 5th and 95th percentiles based on variations in CTLBB and ACR. The number located above the reported effect concentration is the assigned ranking number. See Table 4 for references. ![]() Figure 9. Sub-lethal effects Mixtures Comparison of 18-d NOEC and LOEC from early life stage toxicity tests exposing Oryzias latipes to three prepared mixtures of PAHs to TLM chronic endpoints. The circles represent the TLM effect concentration. The bars represent the 5th and 95th percentiles based on variations in CTLBB and ACR. Data are from Rhodes et al. 2005. This section presented several comparisons of toxicity data resulting from laboratory experiments to toxicity predictions made by the TLM for water column exposures. For acute exposures, it was demonstrated that the TLM reliably predicts the toxicity, particularly for single chemical exposures. For chemical mixtures, the results were not as supportive due to a limitation of available and acceptable data. For data that were available the combination of the TLM and the TU concept produced results that were in agreement with the observed data. It was also shown that normalizing the toxicity data to a TU metric allowed for comparisons of toxicity between data sets, which was not possible when the toxicity data are presented on a mass basis. For evaluations of chronic toxicity, it was shown that the use of an average ACR was appropriate to convert acute toxicity values to chronic values. These chronic endpoints included growth, reproduction and mortality. Recent investigations have indicated that chronic exposure of low levels of PAHs to developing embryos causes sub-lethal effects, such as edemas, heart abnormalities and deformities, which were not included in the derivation of the ACR. The TLM methodology was evaluated to determine if it was protective of these types of endpoints, meaning that the TLM predicted toxicity value was lower than the observed endpoint. It was shown that within the uncertainty of the model (i.e., the variation associated with the parameters) the TLM was protective of these types of endpoints and in some cases it was over protective. 4.3 TLM Validation- Sediment In this section, the TLM and equilibrium partitioning theory are coupled to predict the effects of organisms exposed to oil-related contaminants in sediments. The equations for computing the sediment effect concentrations were provided in Section 3.3 Graphical comparisons of the toxicity predictions and observed values are presented. 4.3.1 Acute Effects Single Compound Exposures For sediment toxicity, 40 data points were found for acute exposures to single PAH compounds. Interestingly, sediment toxicity data for BTEX were not available. Since BTEX are fairly volatile compounds, they are not expected to partition to the sediment. As a result, investigations of their sediment toxicity are not commonly done. Data were available for six different species: Rhepoxynius abronius, Eohaustorius estuaries, Leptocheirus plumulosus, Hyalella azteca, Schizopera knabeni and Coullana sp., and ten different PAHs. The measured data and the corresponding TLM acute predictions are presented in Table 5. The observed and TLM predicted LC50 values are compared in Figure 10. Dashed lines represent the 90% confidence intervals (see Section 4.2.1). The two data points that fall far to the right were considered outliers and not used in the computation of the 90% confidence limits. The majority of the data fall within a factor a three. Based on this analysis, the TLM methodology, coupled with EqP theory, can be used to predict the toxicity of sediment-associated PAHs.
Figure 10. Acute Sediment Exposures Single Compound - Comparison of observed and TLM predicted sediment effect concentrations (see Table 5 for data). Solid line is 1:1 relationship. The dashed lines are 90% confidence intervals. 4.3.2 Acute Effects PAH Mixtures There were 12 data sets available that had acute toxicity for PAH mixtures (Table 6). Three data sets were for laboratory prepared exposures where the contaminants were spiked into the sediment. Two of the laboratory tests were various mixtures of PAHs. The other laboratory exposure involved spiking diesel fuel into sediment. Nine data sets involved a variety of field sediments where PAHs were expected to be the major contaminants of concern. Toxicity data were available for five sediment dwelling organisms, with the amphipod R. abronius being the most commonly used sediment bioassay organism. For each of these data sets, the concentrations of PAH in the mixtures and the corresponding TOC concentrations were provided. The field data sets were not consistent with respect to the number of PAHs measured. Some data sets measured only 13 PAHs (the U.S. EPA defined as priority pollutants) (see Table 1). Other data sets had measurements for all parent PAHs and their alkylated homologs (greater than 30 PAHs). For PAH contamination from petrogenic sources, the alkylated component is known to be large and if the toxicity from those alkylated PAHs is not considered, the toxicity from PAHs can be underestimated. For data sets that only have 13 PAHs measured, the TU from 13 PAH was normalized to total PAHs TU via adjustment factors provided in the U.S. EPA Equilibrium Partitioning Sediment Benchmarks for PAH mixtures (U.S. EPA, 2003) (see Section 3.6). The measured concentrations of PAHs, TOC, sample ID, effect data and TLM toxic units are provided in Appendix D, Tables D1 through D12. The 10-d mortality data for R. abronius are presented in Figure 11. The top panel presents the percent mortality as a function of measured PAH (mg/kg dry weight basis). In the bottom panel, the sediment concentration data are normalized to total PAH toxic units. On a mg/kg basis, relatively low mortality occurs below a measured PAH concentration of 3 mg/kg and 100 % mortality occurs above 500 mg/kg. The area of uncertainty the area where a mixture of low and high effects occurs at similar levels - is 3 to 500 mg/kg. At this concentration range, effects may or may not be observed. Normalizing to total PAH TU, slightly reduces that uncertainty. On a total PAH TU basis, low mortality occurs below a TU of 0.1 and 100% mortality occurs at a TU of greater than 5.0. This comparison indicates that dry weight normalization works almost as well as PAH TU. This is not unexpected for a particularly species. The TLM predicted species-specific sediment LC50s for PAHs are similar on an organic carbon basis. For R. abronius, the LC50s range from 19.7 µmol/goc for naphthalene to 27.8 µmol/goc for benzo(a)pyrene (see Table D4). Since the sediment effect concentrations are similar, a comparison of measured total PAH concentration on an organic carbon basis to the average sediment LC50 on an organic carbon basis is equivalent to the TU analysis (see Di Toro and McGrath, 2000 for equations). If the organic carbon concentration is similar for different sediments, then the dry weight normalization will work as well as the organic carbon normalization and the TU analysis. The advantages of the organic carbon normalization and TU approach are that sorption is considered, species sensitivity is considered and the data are normalized to a total PAH basis. On a TU basis, the area of uncertainty ranges from 0.1 to 5, which is slightly lower than the uncertainty range determined on a mass concentration basis, suggesting that TUs are the better metric for relating concentration to effects. This uncertainty is slightly larger than the range bracketed by the HC5 and HC95 (0.23 to 4.3 TU) and could be attributed to the high degree of scatter commonly observed in field-collected data, compared to laboratory data. In addition, this analysis assumes that PAHs are the primary causative agent, which may not be the case for all field data sets. If other constituents are present in the sediments and contributing to the toxicity, higher than expected mortality would occur at low TUs. Figure 11. Acute Sediment Exposures Mixtures Percent mortality of R. abronius as a function of PAH concentraton (mg/kg) (top panel) and normalized to total PAH sediment toxic units (bottom panel). All data are 10-d exposures. Solid lines at a toxic unit of 1.0 and 50 % mortality are shown for guidance. Dashed lines represent 5th and 95th percentiles based on variation in CTLBB. ( Swartz et al. 1997; Boese et al. 1999; Tay et al. 1992; Techratech, 1982; Swartz et al. unpublished; Swartz et al. 1989; Ozetich et al. 2000; Page et al. 2000. There are four data sets that have acute mortality data for species other than R. abronius. Comparisons of percent mortality as a function of measured PAH (mg/kg) and total PAH TU are shown in Figure 12. DeWitt et al. (1992b) determined the mortality to two species (E. estuaries and L. plumulosus). Since the CTLBB for these species are very similar (41.4 and 43.1 mmol/g octanol), the computed total PAH TUs are almost identical. Therefore, the observed range of mortality for the two species is shown as a function of the sediment concentration (Figure 12, panels E and F). Due to limited data available to compute CTLBB for sediment organisms, HC5 and HC95 values could not computed for E. estuaries, Chironomus riparius, Schizopera knabeni and Ampelisca abdita. For all four data sets, the dose-response is as expected with low total PAH TUs corresponding to low mortality and around 1 TU corresponding to about 50 % mortality.
The 95% confidence sample size-dependent extrapolation factor (kz = 2.3) was based on the number of ACRs (29) rather than the number of CTLBBs (47) to ensure that the calculations were conservative. A summary of chronic HC5 values for MAHs and PAHs is provided in Table 10. EqP theory was used to convert the aquatic HC5s to sediment HC5 values (Equation 15). The values are presented on a molar basis (i.e., µmol/L, mmol/KgOC) and a mass basis (i.e., µg/L, mg/KgOC). Details of the calculations are provided in Appendices E and F.
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Please click here for pdf of Appendix B1 Please click here for pdf of Appendix B2
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