Joy McGrath and Dominic Di Toro

Abstract
A method is presented for developing scientifically defensible numeric guidelines for oil-related constituents, specifically monocyclic aromatic hydrocarbons (MAH) and polycyclic aromatic hydrocarbons (PAHs), in the water column and sediment. The guidelines are equivalent to a HC5, a hazard concentration value that protects 95 percent of the test species. The model of toxicity used in this evaluation is the target lipid model (TLM) that was developed for assessing the toxicity of Type I narcotic chemicals (Di Toro et al. 2000). Structurally the aromatic components of oil should exert an acute narcotic mode of action. This research focused on validating the TLM for its appropriateness in assessing the toxicity of these oil components, both on an acute and chronic basis. The methodology was determined to be effective at predicting the toxicity of MAHs and PAHs. The resulting HC5 guidelines were found to be protective of sub-lethal effects commonly associated with blue sac disease resulting from early life stage exposure to PAHs. The use of toxic units (TUs) as the metric for expressing the toxicity of mixtures of oil-related components, or mixtures of hydrocarbons in general, is demonstrated to be an effective means of normalizing toxicity data across different sources and different species. The toxicity of the mixture depends on which hydrocarbons are present because the toxicity of the individual components in the mixture varies. A concentration of 1 TU from the components in the mixture implies toxicity. A concentration of 1 µg/L of total measured compounds in a mixture may or may not be toxic and depends on which components are present, the concentration of those components and the species exposed. The use of TUs eliminates this confusion and normalizes the data across sources. The methodology presented can be used by the oil spill community, which includes the regulators and the regulated industries, to compare residual concentrations of MAHs and PAHs against defensible numeric guidelines to assess potential ecological impacts.

Keywords: Polycyclic Aromatic Hydrocarbons, Target Lipid Model, Model Validation, Oil Toxicity, Guidelines

Acknowledgements
Funding for this project was provided by the NOAA/UNH Coastal Response Research Center (Grant number NA170Z2607). John Sondey at HydroQual, Inc. was responsible for producing final graphical displays.

Table of Contents

1.0	Introduction	1
2.0	Objectives	1
3.0	Methods	4
3.1	Aqueous Acute Toxicity Prediction - Target Lipid Model	4
3.2	Aqueous Chronic Toxicity Prediction	9
3.3	Sediment Toxicity Prediction	9
3.4	Uncertainty in Toxicity Predictions	9
3.5	Application to Mixtures	11
3.6	Computing Total PAH Toxic Units in Sediment	13
3.7	Literature Review	14
3.8	Physical Chemical Properties	15
4.0	Results	15
4.1	Literature Review	15
4.2	TLM Validation - Water Column	16
4.3	TLM Validation - Sediment	27
5.0	Discussion and Importance to Oil Spill Response/Restoration	34
6.0	Technology Transfer	37
7.0	Achievement and Dissemination	37
References		38

List of Figures and Tables

Figure 1	Schematic Diagram of log(LC50) versus log(KOW). At 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)	7
Figure 2.	Log(LC50) Versus log(KOW) for the Indicated Species. (Redrawn from Di Toro et al., 2000).	8
Figure 3.	Diagram of how acute toxicity data from aqueous and sediment tests using mixtures of chemicals will be displayed. Percent effect as a function of predicted total TUs. Dashed lines represent HC5 and HC95 values	13
Figure 4.	Acute Exposures- Single Compounds - TLM predicted acute aqueous LC50 versus observed LC50 for MAHs and PAHs. Solid line represents 1:1 relationship. Dashed lines represent 90% confidence interval	17
Figure 5.	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	19
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).	21
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.	22
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.	25
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.	26
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	28
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)	30
Figure 12	Acute Sediment Exposures – Mixtures – Percent mortality as a function of PAH concentraton (mg/kg) (top panel) and normalized to total PAH sediment TUs (bottom panel). See Table 6 for exposure information. Solid vertical and horizontal lines at a TU of 1.0 and 50 % mortality, respectively, are shown for guidance.	31
Figure 13	Chronic Sediment Exposures – Single Compounds - Comparison of reported OEC/LOEC/NOEC and TLM effect concentrations from long-term exposures of fluoranthene and phenanthrene to the marine copepods Coullana sp. and Schizopera knabeni. See Table 8 for references.	33
Figure 14	Chronic HC5 concentration for PAHs versus log (KOW). The HC5 concentration is based on the 5th percentile CTLBB for all species in the database. The observed NOECs for PAHs are denoted with a 0 (Table 10).	36
Table 1	Chemicals and their properties (at 25C) measured in various data sets.
Table 2	Observed water-only acute LC50/EC50 values for PAHs and TLM predictions
Table 3	Acute and chronic values used in the development of ACRs. Chronic endpoints are those effecting growth, reproduction or mortality of an organism.
Table 4	Comparison of target lipid model chronic endpoints to NOECs, LOECs, OECs (sub-lethal effects) from early life stage tests for single PAH exposures.
Table 5	Observed sediment acute LC50 values for PAHs and TLM predictions.
Table 6	Summary of sediment data sets for acute toxicity from PAH mixtures.
Table 7	Example demonstrating benefit of using adjustment factor to convert TU from 13PAH to Total PAH TU. Data are from Swartz et al. unpublished (Table D5).
Table 8	Chronic effects from single PAH sediment exposures.
Table 9	Summary of CTLBBs for different chemical classes.
Table 10	Chronic HC5 values for MAHs and PAHs for aqueous and sediment toxicity.
Table 11	PAH NOEC toxicity data for various species (measured data considered only).

1.0 Introduction
This research project is directed at evaluating the impacts of low levels of residual oils based on assessment of the toxicity of oil-related constituents and their exposure risks. The primary compounds of interest in this research are the monocyclic aromatic and polycyclic aromatic hydrocarbons (MAHs and PAHs). Structurally, these compounds are Type I narcotic chemicals and their acute toxic mode of action is narcosis. The toxicity of such compounds is additive and therefore a methodology is needed that accounts for exposure to a mixture of hydrocarbons. The target lipid model (TLM) and the concept of toxic units (TUs) are used to assess the toxicity of oil-related chemicals. The TLM is a method of computing water criteria for Type I narcotic chemicals (Di Toro et al. 2000). Equivalent sediment criteria were derived using the equilibrium partitioning (EqP) model (Di Toro and McGrath, 2000). Aqueous and sediment criteria were derived using methodology based on narcotic chemicals in general, rather than MAHs and PAHs specifically. The United States Environmental Protection Agency (U.S. EPA) developed sediment benchmarks (water-only benchmarks were not presented) for PAH mixtures based on the TLM (U.S. EPA, 2003). These benchmarks were derived with the objective of protecting sediment organisms from long-term effects of mortality, growth and reproduction. Recent literature suggests that exposure to PAHs during an organism’s early life stage results in various sub-lethal effects similar to those of blue-sac disease (Carls et. 1999; Heintz et al. 1999; Incardona et al. 2004). These sub-lethal effects were not addressed in the EPA’s sediment benchmarks for PAH mixtures and therefore they may not be protective of these types of effects.

For this research, the appropriateness of the TLM for predicting the toxicity of MAHs and PAHs as single chemical exposures and in mixtures is investigated. The TLM is validated for predicting the toxicity of these chemicals in water-column and sediment exposures, both on an acute and chronic basis. Additionally, the TLM is evaluated to determine if computed endpoints are protective of sub-lethal endpoints such as yolk sac edema, hemorrhaging and larvae abnormalities. Once it has been demonstrated that the TLM methodology can be used to assess whether effects are expected at a certain concentration of a chemical or from a mixture of chemicals, the methodology is used to derive defensible chemical-specific guidelines. HC5 values, the ecological hazard concentrations that protect 95% of species, are presented on an aqueous basis and a sediment basis as a function of log octanol-water partitioning coefficient, KOW.

The beneficiaries of this research include regulators and the regulated communities because they will have an innovative scientifically defensible method to estimate toxicity of oil components in water and sediment.
Moving a research oriented model like CDOG into operational use requires more than adding a Graphical User Interface (GUI). Particularly for emergency response, the user has a task to accomplish as part of the response. That “task” is not running the model, though the task may involve running the model. For example, NOAA/HAZMAT provides trajectory analysis during a spill response. The GNOME model may be run, but the GNOME output is post-processed to add additional information from the trajectory forecaster to the Federal On Scene Coordinator (FOSC).


2.0 Objectives
The overall objective of this project is to evaluate the impacts of low levels of residual oils based on the toxicity assessment of oil-related constituents and their exposure risks. Generally, the short-term toxicity of petroleum is attributed to the water-soluble MAHs (i.e., benzene, toluene, ethylbenzene and xylene, commonly referred to as BTEX) and PAHs. BTEX are not expected to persist in the environment because they are volatile compounds and evaporate quickly and are fairly biodegradable. PAHs are less volatile and the heavier PAHs (those with 4 or 5 rings) are known to persist and they can potentially have a long-term impact on the aquatic environment. Specific objectives and how they were met include:

• Identify key components of residual oil that contribute to toxicity – The literature was reviewed for toxicity data resulting from exposure to single chemicals and mixtures of chemicals expected to be present in oils (i.e., MAHs and PAHs) both in the water column and in sediment. Toxicity endpoints reflected both short-term (acute) and long-term (chronic) effects.
• Provide a synthesized and reviewed effects database to use as a screening tool to evaluate risk to aquatic resources from petrogenic sources of MAHs and PAHs.
• Validate that the TLM of toxicity can predict the aqueous toxicity of oil related compounds – Toxicity predictions based on the TLM methodology were performed on the data sets identified in the literature review. Predictions were made for single chemical exposures for both acute and chronic effects. The predicted toxicity was compared to the observed toxicity.
• Establish that the TU can be used as a universal endpoint for expressing the toxicity from mixtures of compounds such as those resulting from different oil sources – The toxicity of different mixtures of compounds that result from various oil sources were normalized to TU and compared to mass based (i.e., mg/L) endpoints.
• Demonstrate that EqP theory is appropriate for converting the TLM-predicted aqueous effect concentrations for oil-related compounds to equivalent sediment effect concentrations – Sediment toxicity predictions based on the TLM and EqP methodologies were performed on the data sets identified in the literature. Predictions were made for single chemical exposures and mixtures of chemicals for both acute and chronic effects. Sediment toxicity predictions were compared to observed effects data.
• The TLM in conjunction with EqP theory was used to derive guidelines for oil-related compounds that may be protective of aquatic and benthic species from long-term sub-lethal effects.

These project objectives are directly related to the work of the oil spill community. A scientifically defensible methodology is presented for computing guidelines to judge what levels of oil-related compounds should not have an adverse impact on the aquatic community. The methodology is simple to use, only the log octanol-water partitioning efficients (KOW) of the chemicals in the mixture are needed. The method is versatile in that it allows the user to compute protective guidelines for a particular species or guidelines that protect 95% of all species considered in the method development (see Section 3.0 Methods).

In addition, the use of the TU as the toxicity metric for mixtures normalizes differences in toxicity among chemicals. This is of critical importance to the oil spill community because it allows data from one location/test to be directly comparable to data for another location/test. Historically, the measured concentrations of chemicals in the water phase have been summed to assess the aqueous toxicity of crude oils. Water-soluble fractions (WSFs) are prepared from different sources of oil and although the compositions of the WSFs vary and different compounds are measured, the interpretation of the toxicity is based on summed concentrations. The effects are often expressed as total petroleum hydrocarbons on a mass per volume basis (i.e., mg TPH/L) (Anderson et al. 1974; Rossi and Anderson, 1976; Moles et al. 1979; Brodersen, 1987) or total PAH concentrations (i.e., mg TPAH/L) (Anderson, 1977; Neff and Stubblefield, 1995; Carls et al.1999; Heintz et al. 1999). The fundamental problem with reporting toxicities of hydrocarbon mixtures on a total concentration basis is that it assumes all of the compounds have equal toxicities. The toxicity of hydrocarbons varies widely and has been related to the log KOW (Konemann, 1981; Veith et al. 1983). The toxicities of individual chemicals in a mixture are normalized by expressing the toxicity of each chemical in terms of a TU (Hermens, 1989; Peterson, 1994). Once the toxicities are normalized, comparisons of different oils can be made.

This research effort for determining impacts of low levels of residual oil components focused on meeting the objectives described above. It is impractical and probably impossible to develop guidelines that address every situation that may occur in the environment. The following is a listing of conditions and assumptions of the TLM that can impact the toxicity of oil-related components, which were NOT addressed in this research, but are important to consider when determining the effects from oil.

• The TLM assumes that octanol is a good surrogate for organism lipid and that the KOW describes the partitioning of the chemical between the water and the organism lipid – Other solvents or physical-chemical properties may be better descriptors for the partitioning.
• Some data suggest that a toxicity cut-off exists for compounds with log KOW greater than approximately 5.5 (i.e., compounds with log KOW values of 5.5 or greater have similar toxicity). The TLM ignores a cut-off and relies on the KOW to be the descriptor for the toxicity. The KMW, membrane-water partition coefficient, has been shown to be a better surrogate than KOW for describing the partitioning of a chemical into the organism lipid membrane. (Gobas et al. 1988; Vaes et al 1998; Verbruggern 2004). The log(KMW)-log(KOW) relationship is linear until a log KOW of approximately 5.5 where the log KMW no longer increases linearly with log KOW (Verbruggern 2004). However, the KOW is used in the TLM because a cut-off point for PAHs has not been confirmed or identified and toxicity from PAHs with log KOW values greater than 5.5 has been observed. The TLM would not have correctly predicted the toxicity if a cut-off or the KMW was used in the analysis.
• The TLM assumes that metabolites have the same mode of action as the parent compound – Recent literature suggests that some metabolites may be more toxic than parent compounds.
• Effects of photoactivation of PAHs – Some PAHs exert photoenhanced toxicity after accumulation in the tissues of organisms and exposure to ultraviolet radiation. Photoinduced toxicity can be orders of magnitude greater than baseline toxicity (i.e., narcosis) (Ankley et al. 1994; Spehar et al. 1999; Lyons et al. 2002).
• Other water-soluble components of oil – This research focused on MAHs and PAHs, the most commonly measured oil components, as being the causative agents of oil toxicity. There may be other water-soluble components that exert toxicity. Note that this methodology can be applied to any chemical, but the toxicity computed will be the chemical’s “baseline” or minimum toxicity.
• Time-variable concentrations – This methodology assumes that the exposure concentrations are constant with time or that equilibrium conditions have been achieved, particularly for sediment exposures. Time-variable concentrations are not addressed and it is difficult to assign observed effects to a concentration if the concentration is varying with time.
• Non-standard conditions – The physicochemical properties used in this research were determined at 25 ° C. These parameters vary with temperature. For example, organic compounds are usually less soluble in colder temperatures. Therefore, the computed guidelines would be conservative for temperature conditions less than 25 ° C.
• Other sediment partitioning phases – Equilibrium partitioning theory assumes organic carbon is the only partitioning phase, described by KOC, for non-polar organic contaminants. Some field data sets indicate that other partitioning phases may be present and include soot carbon or coal. PAHs seem to have a higher affinity for these other carbon phases than for organic carbon as described by KOC. Therefore, the PAHs will be less bioavailable than predicted using KOC as the sole partitioning coefficient. Ignoring other possible partitioning phases is conservative since the maximum toxicity will be predicted.



3.0 Methods
3.1 Aqueous Acute Toxicity Prediction - Target Lipid Model

Based on chemical structure, the MAHs and PAHs should exhibit a narcotic mode of action (Verhaar et al. 1992). The TLM (Di Toro et al. 2000) will be used to assess the toxicity of MAHs and PAHs resulting from exposure to low levels of oil in the water column. The TLM is for Type I narcotic chemicals, which are non-ionic organic chemicals with a similar mode of toxic action, namely narcosis. Narcosis is a reversible state of arrested activity. The toxic mode of action is non-specific and is caused by a variety of chemicals, such as gases, aliphatic and aromatic hydrocarbons, alcohols, chlorinated compounds, ethers and ketones (Veith et al. 1983; Verhaar et al. 1992). With narcosis, it is assumed that an equilibrium exists between the chemical concentration in the organism and the chemical concentration in the external media phase, such as the water phase.

The TLM model is based on the inverse relationship between toxicity in water-only exposures, as defined by the LC50, and the K_OW. Numerous quantitative-structure activity relationships (QSARs) of log(LC50) versus log(KOW) have been developed for narcotic chemicals (Konemann 1981; Veith et al., 1983; Van Leeuwen et al., 1992). The TLM is an extension of these models. It is based on a single universal slope for the log (LC50)-log (K_OW) relationship, independent of species. It includes corrections for chemical classes that are slightly more potent than baseline narcotics in particular MAHs and PAHs. And it interprets the y-intercepts as the species-specific critical body burdens for a particular endpoint. A complete presentation of the narcosis TLM is available (Di Toro et al. 2000). A brief description is provided below.

This result can be understood by examining Figure 1. For a chemical with a log(K_OW) equal to zero or K_OW equal to 1.0, the y-intercept is the LC50. At a K_OW equal to one, the chemical concentration in the water is equal to the chemical concentration in the octanol. Assuming octanol is a good surrogate for lipid, then the chemical concentration in the water is equal to the chemical concentration in the lipid (target tissue) of the organism. Therefore, the y-intercept is equal to the target lipid concentration producing an effect (i.e., 50% mortality, LC50) for that organism. In the TLM, the y-intercept is the CTLBB and has units of µmol/g octanol = µmol/g lipid). The CTLBB varies for each species. The slope of the line is a chemical property of the lipid and should be the same regardless of the species tested. The slope of line is referred to as the universal narcosis slope. Figure 2 shows the relationship of log(LC50)-log(K_OW) for several species in the database. The symbols represent many chemicals from different chemical classes. The line drawn through the data is the universal narcosis slope (i.e., it is the same for all species).

Figure 1. Schematic Diagram of log(LC50) versus log(K_ow) equal to 0, the (K_ow) 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).

The TLM has been extensively validated using a large database to predict the aquatic toxicity of classic Type I narcotic chemicals. CTLBBs for 33 aquatic species (Di Toro et al. 2000) and 5 algae (McGrath et al. 2004) were computed with the TLM using the K_OW values determined from a 1997 version of SPARC, a U.S. EPA sponsored computer program that estimates numerous physicochemical properties for chemicals based on chemical structure (Karickoff et al. 1991). Since 1997, SPARC has undergone enhancements in its programming. Therefore, K_OW values computed using the 1997 version may be different than those values computed using the enhanced on-line version. To take advantage of the enhancements to SPARC, new K_OW values were determined for all chemicals in the narcosis toxicity database and revised coefficients for the TLM were computed (see Appendix A for coefficients)

Figure 2 Log (LC50) Versus log (k_ow) for the Indicated Species. (Redrawn from Di Toro 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.





Figure 12. Acute Sediment Exposures – Mixtures – Percent mortality as a function of PAH concentraton (mg/kg) (top panel) and normalized to total PAH sediment TUs (bottom panel). See Table 6 for exposure information. Solid vertical and horizontal lines at a TU of 1.0 and 50 % mortality, respectively, are shown for guidance.


It should be noted that the measured concentrations in the field datasets represent different numbers of PAHs measured ranging from 13 to 40. For example, a value of 10 mg/kg in a sediment sample contaminated from the Exxon Valdez oil spill (Page et al. 2002) that was computed as the sum of 40 PAHs is not necessarily equivalent to a value of 10 mg/kg in a sediment sample collected in Eagle Harbor, WA (Swartz et al. 1989) that was computed as the sum of 13 PAHs. In addition, the bioavailability of the sediment-associated PAHs is a function of the organic carbon concentration of the sediment (Di Toro et al. 1991). Two sediment samples that have 10 mg/kg of the same 40 PAHs, may have different toxicities if the organic carbon concentrations are different. Therefore, concentrations of measured PAHs in different sources should not be compared nor should effects be assigned based on mass based concentrations. Using the summed TU approach for sediments (i.e., converting individual sediment PAH concentrations to individual PAH TU, summing the individual PAH TU and then applying a factor to convert to total PAH TU) considers the toxicity from unmeasured PAHs and considers the difference in organic carbon concentration and therefore reduces the uncertainty associated with PAHs measurements across different sources and allows for comparisons to be made.

The importance of applying the adjustment factor to account for unmeasured PAHs is demonstrated using a San Diego Bay sediment dataset (Swartz et al. unpublished). In this dataset, 13 PAHs were measured in 54 sediment samples. The test organism was R. abronius and the endpoint was 10-d mortality. Table 7 presents a comparison of the TUs computed from the measured 13 PAH and from Total PAH as they relate to the observed mortality. A correction prediction was one where the observed mortality was greater than or equal to 50% and the computed TUs were greater than or equal to 0.4 (the uncertainty based on the ratio of the HC5 and the TLM endpoint for R. abronius) or where the observed mortality was less than 50% and the computed TUs were less than 0.4. When the TUs were computed from the 13 PAH only, there were 42 correct predictions. When the adjustment for unmeasured PAHs was considered the number of correct predictions increased to 45. In addition to the number of correct predictions, one can compare the number of times that the criterion was protective, meaning high mortality was observed and correctly predicted. The observed mortality was greater than or equal to 50% in 13 samples. Based on TUs from 13 PAHs, one sample would have been predicted to be toxic (station STA28 100% with TU of 0.608 from 13 PAH). In comparison, the TUs from Total PAHs were greater than 0.4 in eight of the samples with high mortality. This analysis demonstrates the importance of adjusting for the toxicity from unmeasured PAHs. The overall number of correct predictions (toxic and non-toxic) increased. Furthermore, the number of correct toxic predictions increased from 1 to 8 with the incorporation of the adjustment factor.

4.3.3 Chronic Effects – Single Compound Exposures
There are several data sets available that report chronic effects from exposure to sediment spiked with single PAHs. The chronic effects are growth and reproduction endpoints. The tests species are Coullana sp. and S. knabeni (Figure 13). The CTLBBs for these species were measured on a lipid basis (Lotufo, 1998). The effect of fluoranthene on grazing and reproduction for Coullana sp. was reported (Lotufo, 1998). The 10-d LOECs ranged from 3133 to 8800 mg/goc for grazing and reproduction, respectively. The 10-d NOECs ranged from 1200 to 3111 mg/goc. The TLM predicted chronic effect concentration of 1410 mg/goc was below the observed LOECs indicating that the TLM correctly predicted the effects. The chronic effects of phenanthrene and fluoranthene on grazing and reproduction of S. knabeni were determined (Lotufo, 1997; Fleeger and Lotufo, 1999). Phenanthrene had a reported 14-d IC25 (inhibition concentration - concentration causing 25% reduction of measured endpoint in relation to control) of 1730 µg/g_oc for reproduction effects. The reported 10-d NOEC and LOEC were 1470 and 3000 µg/g_oc, respectively, for hatching success. The TLM predicted chronic effect concentration of 3560 µg/g_oc was slightly higher than the reported OEC/LOEC. Fluoranthene had reported 10-d LOECs of 1200 and 3133 µg/g_oc for grazing and reproduction, respectively (Lotufo, 1998). The TLM predicted chronic effect concentration of 4130 µg/g_oc was slightly higher than the reported LOEC. These data are summarized in Table 8 and Figure 13.


The CTLBBs for S. knabeni and Coullana are measured values based on a single compound (fluoranthene) and the uncertainty associated with them could not be determined. In addition, Lotufo (1998) stated that the lipid content was based on an initial value, which could have changed during the exposure time. The variability in the reported CTLBB for these species is unknown. Therefore, these data are presented for informational purposes only and the results of the analysis should not be used to assess the performance of the TLM.




Figure 13. Chronic Sediment Exposures – Single Compounds - Comparison of reported OEC/LOEC/NOEC and TLM effect concentrations from long-term exposures of fluoranthene and phenanthrene to the marine copepods Coullana sp. and Schizopera knabeni. See Table 8 for references.


4.3.4 Chronic Effects – PAH Mixtures
Fleeger and Lotufo (1999) investigated the chronic effects on S. knabeni from exposure to a sediment spiked with diesel fuel. They measured the concentrations of 20 PAHs and TOC in the sediment. The sediment was spiked with the diesel fuel and dilutions were prepared for use in the bioassay. The resulting total PAH concentrations in the sediment were 19, 45, 93, 130, 185 and 370 mg/kg. The LOEC and NOEC for reproductive effects were 93 and 45 mg/kg, respectively. The equivalent TLM Total PAH TU for the LOEC and NOEC were 1.7 and 0.8, respectively (Table 10D). Based on these equivalent TUs, effects should have been have observed at 45 mg/kg. While the TLM was overprotective (i.e., effects were predicted at concentrations where no effects were observed), these data cannot be used to assess the performance of the TLM because of the uncertainty in the CTLBB (See Section 4.3.3).



5.0 Discussion and Importance to Oil Spill Response/Restoration
This research focused on developing a methodology that can be used to assess the toxicity of low levels of residual oil, focusing on the MAH and PAH components. The methodology has to be able to predict the toxicity of these components in the aqueous phase and in the sediment, on acute and chronic bases. The methodology had to include the derivation of a metric that enabled the toxicity from one location to be comparable to the toxicity at another.

It has been demonstrated that the TLM can be used to assess the acute and chronic toxicity of oil-related components on an acute basis in the water column and sediments. It has been shown that the use of an ACR to relate the acute toxicity to an equivalent chronic toxicity is appropriate without consideration of the toxicity mechanism. Within the uncertainty of the model, the TLM has been shown to be protective of sub-lethal effects that result from exposure to low levels of PAHs during an organism’s early life stages. The TLM coupled with the theory of additivity via the concept of TUs can be used to predict the toxicity of mixtures of chemicals present in oils. TUs are a means of normalizing the toxicity of different chemicals. A total TU of 1.0, implies toxicity. A total concentration of 1µg/L is not necessarily toxic. Rather, its effect is dependent on the specific chemical(s) to which the organism is exposed and the sensitivity of the organism. It should be noted, that the available toxicity data for mixtures that met the criteria for inclusion, both on an acute and chronic basis, are limited. There is research need to determine the toxicity of mixtures of chemical resulting from exposure to oil for species where a CTLBB is available. These data would further validate that the TLM and TU concept are appropriate for predicting the toxicity.

For sediment assessment, the TLM aqueous effect concentrations are converted to sediment effect concentrations via EqP methodology. In the sediment, the chemical concentration can be related to the bioavailability by normalizing the chemical concentration to the organic carbon concentration. The TU metric allows for the consideration of the toxicity from unmeasured PAHs by incorporating the U.S.EPA adjustment factors, which cannot be done on a mass basis (i.e., mg/kg). Organic carbon normalization and TU conversion allow for the generation of a toxicity metric that is applicable across a broad range of sediment types and representative of toxicity from Total PAH.

In addition to being used as a tool that can assess whether effects are expected at a certain concentration of a chemical or from a mixture of chemicals, the methodology can be used to derive defensible chemical-specific guideline values. When the statistics (geometric mean and variance) of the CTLBB of all test species are used in Equation 18, the computed HC5 is the aqueous concentration that protects 95% of the species. These HC5s can be used as decision-making values to determine the concentration of a particular chemical that will not likely have an adverse impact on the aquatic community. The geometric mean CTLBB of all tests species for baseline chemicals (i.e., aliphatic hydrocarbons, alcohols, see Appendix A) is 123 µmol/g octanol. The geometric mean CTLBB for specific chemical classes is computed as

	C*L(geometicmean, Chemicalclassi) = C*L(geometicmean,baseline)XCi	(23)
whereC is the chemical class correction factor for chemical class i (see Appendix A). The CTLBBs for the various chemical classes are provided in Table 9. The resulting equation to compute the chronic HC5 values for PAHs is:
		(24)
		(25)

The 95% confidence sample size-dependent extrapolation factor (k_z = 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.

To assess the management implications of adopting chronic HC5s derived via the TLM for decision-making, a comparison was made of the aqueous HC5 values to NOECs values for PAHs. The NOECs values used in this analysis are presented in Table 11 and Figure 14. A total of 27 NOECs were compiled for 7 different PAHs. Two NOECs fell below the HC5 line. Both of these NOECs were for phenanthrene and ranged from 5 to 5.5 µg/L, which were considerably lower than NOECs for other compounds. The excursion of two data points below the HC5 is consistent with the 95% protection level goal (i.e., 92.6% (25/27)). The HC5 derived from the TLM is very close to the expected level of protection. Therefore, these values are appropriate for use as numeric chemical-specific benchmarks and can be used to assess the ecological risk of contaminated sediments and/or establish safe levels for cleanup activities. Of course, one needs to consider the assumptions of the TLM and the conditions that can impact the toxicity of MAHs and PAHs (refer to Section 2.0) to determine the appropriateness for applying these guidelines to a specific location.




Figure 14. Chronic HC5 concentration for PAHs versus log (KOW). The HC5 concentration is based on the 5th percentile CTLBB for all species in the database. The observed NOECs for PAHs are denoted with a 0 (Table 10).

6.0 Technology Transfer
The results of this research effort on the application of the TLM to predict acute and chronic effects on oil-related compounds and the derivation of HC5 will be submitted to Environmental Chemistry and Toxicology. An abstract focusing on the application of TLM to be protective of sub-lethal effects from exposure to PAHs has been submitted to the Society of Environmental Chemistry and Toxicology (SETAC) for presentation at the Fall 2006 North America meeting. Intermediate results of this research effort were presented at a March 2005 workshop entitled “Emerging Research in Oil Spill Response and Restoration”, at a September 2006 summit for the PAH Toxicology Working Group, at a September 2006 workshop entitled “Innovative Coastal Modeling: Integrating Physical, Biological and Toxicological Models”, all sponsored by CRRC, and at the 2005 SETAC North America meeting.


7.0 Achievement and Dissemination
Workshops and Conferences

Workshop on Emerging Research in Oil Spill Response and Restoration. 2005. “ Impacts of Low Levels of Residual Oil: Predicting the Acute and Chronic Toxicity of Residual Oil”. Office of Response and Restoration of NOAA. Silver Spring, Maryland. March 2005.

26th Annual SETAC North America Meeting. 2005. “Predicting the Acute and Chronic Effects of PAHs using the Target Lipid Model of Narcotic Toxicity: Is a New Paradigm Needed? Baltimore, Maryland. November 2005.

PAH Toxicology Working Group Summit. 2006. “ Impacts of Low Levels of Residual Oil: Predicting the Acute and Chronic Toxicity of MAHs and PAHs”. CRRC. Seattle, Washington. August 2006.

Workshop on Innovative Coastal Modeling: Integrating Physical, Biological and Toxicological Models. 2006. “ Impacts of Low Levels of Residual Oil: Predicting the Acute and Chronic Toxicity of MAHs and PAHs”. CRRC. Durham, New Hampshire. September 2006.

27th Annual SETAC North America Meeting. 2006. “Predicting the Chronic Effects of PAHs using the Target Lipid Model of Narcotic Toxicity”. Montreal, Canada. November 2005.
7.0 Achievement and Dissemination

Table 1. Chemicals and their properties (at 25 o C) measured in various data sets
		Molecular Weight (g/mol)		Subcooled Liquid Solubility ( m g/L) b,d	Key PAHs e
	Log K ow a		Solid Solubility ( m g/L) b
ethane	1.730	30.00	1350000	1350000
benzene	1.943	78.11	1780000	1780000
propane	2.370	44.09	248000	248000
toluene	2.438	92.14	515000	515000
butane	2.868	58.12	61400	61400
isobutane	2.869	58.12	48900	48900
o-xylene	2.946	106.17	220000	220000
cyclopentane	2.991	70.00	156000	156000
ethylbenzene	3.006	106.17	152000	152000
m-xylene	3.032	106.17	160000	160000
p-xylene	3.051	106.17	215000	215000
9,10-Anthracenedione	3.080	208.22	116000	130000
naphthalene	3.256	128.19	31000	110000	A,B,C
isopentane	3.335	72.15	13800	13800
acenaphthylene	3.436	152.20	16100	75000	A,B,C
C3-benzenes c	3.455	120.00	19700	19700
pentane	3.471	72.15	38500	38500
9-fluorenone	3.510	180.20	25000	48000
methylcyclopentane	3.571	84.00	42000	42000
1-methylnaphthalene	3.781	142.20	28000	28000	B
C1-naphthalenes c	3.788	142.20	7900	22000	C
2-methylnaphthalene	3.789	142.20	25000	31100	B
acenaphthene	3.878	154.21	3800	19200	A,B,C
fluorene	3.930	166.20	1900	15100	A,B,C
biphenyl	3.936	154.21	7000	20000
2-chloronaphthalene	3.940	162.64	5500	18000
1-chloronaphthalene	3.950	162.64	5400	5400
methylcyclohexane	3.963	98.19	14000	14000
hexane	4.053	86.00	9500	9500
C2-naphthalenes c	4.244	156.23	1970	9400	C
1,3-dimethylnaphthalene	4.257	156.23	8000	8000
1-methylfluorene	4.370	180.25	1090	4270
C1-fluorenes c	4.370	180.25	1510	8400	C
anthracene	4.546	178.20	45	3500	A,B,C
2,3,5-trimethylnaphthalene	4.570	170.20	580	5200	B
2,3,6-trimethylnaphthalene	4.570	170.20	740	5200
phenanthrene	4.584	178.23	1100	6210	A,B,C
n-heptane	4.584	100.20	2930	2930
dimethylbiphenyl c	4.692	182.00	530	4400
C3-naphthalenes c	4.730	170.25	440	3800	C
C2-fluorenes c	4.819	194.27	380	3600	C
C1-Dibenzothiophene c	4.859	198.30	340	3400
9-methylanthracene	4.996	192.26	261	945
1-methylphenanthrene	5.036	192.26	270	2520	B
C1-phenanthrene/anthracene c	5.037	192.26	180	2300	C
2-methylphenanthrene	5.040	192.26	180	2300
pyrene	5.126	202.26	132	2610	A,B,C
fluoranthene	5.190	202.26	260	1700	A,B,C
C4-naphthalenes c	5.220	184.28	97	1500	C
C1-fluoranthene/pyrene c	5.257	216.28	101	1600	C
C3-fluorenes c	5.318	208.30	80	1400	C
C2-Dibenzothiophene c	5.332	212.30	78	1400
3,6-dimethylphenanthrene	5.340	206.29	73	1300
4,6-dimethyldibenzothiophene	5.450	212.30	53	1100
C2-phenanthrene/anthracene c	5.455	206.29	51	1000	C
C2-fluoranthene/pyrene c	5.557	230.31	40	950
Triphenylene	5.630	228.30	43	2260
benzo(a)anthracene	5.744	228.29	11	240	A,B,C
chyrsene	5.782	228.29	2.0	376	A,B,C
C3-Dibenzothiophene c	5.810	226.30	17	560
C4-phenanthrene/anthracene c	6.357	234.34	3.0	190	C
benzo(b)fluoranthene	6.341	252.32	1.5	38.9	A,B,C
C3-fluoranthene/pyrene c	6.384	244.34	2.9	190
benzo(k)fluoranthene	6.400	252.32	0.80	63.6	A,B,C
benzo(a)pyrene	6.409	252.31	3.8	116	A,B,C
7,12-dimethylbenzo(a)anthracene	6.420	256.35	50.00	455
Benzo(e)pyrene	6.447	252.30	4.0	130	B,C
perylene	6.447	252.31	0.40	124	B,C
C2-Chyrsenes/benzo(a)anthracene c	6.593	256.34	1.5	130	C
C4-fluoranthene/pyrene c	6.687	258.35	1.1	110
C5-phenanthrene/antracene c	6.700	248.37	1.1	99.5
C1-benzofluoranthene c	6.743	266.11	1.0	97.6
benzo(ghi)perylene	6.886	276.34	0.26	83.2	B,C
C3-Chyrsenes/benzo(a)anthracene c	6.972	270.36	0.47	62	C
dibenz(a,h)anthracene	7.129	278.35	0.60	148	B,C
C2-benzofluoranthene c	7.200	280.13	0.23	40.5
C4-Chyrsenes/benzo(a)anthracene c	7.421	284.38	0.12	26	C
a Log K ow computed from SPARC
b Solubility data from Mackay et al. 1992a, 1992b, 1993 and 1995 or computed from relationships below
Solid solubility computed from Log S = (-1.4136)(LogK ow ) + 7.1022 where solubility has units of m mol/L
Sub-cooled solubility computed from Log S = (-0.8857)(LogK ow ) + 5.5367 where solubility has units of m mol/L
c Mixture of isomers
d For compounds that are liquid at room temperature, the solid solubility and sub-cooled solubilitiy are equal.
e A = PAH is one of key 13 EPA priority PAHs; B= PAH is one of key 23 PAHs; C = PAH is one of 34 PAHs

Table 2. Observed water-only acute LC50/EC50 values for PAHs and TLM predictions.
Species	PAH	Life Stage	Test Type b	Observed LC50 ( m g/L)	Predicted LC50 ( m g/L)	Reference
Artemia salina	naphthalene	larvae	S,U	10600	9000	Abernethy et al. 1986
	naphthalene	larvae	S,M	11070	9000	Maclean and Doe, 1989
	1-methylnaphthalene	larvae	S,U	2560	3500	Abernethy et al. 1986
	2-methylnaphthalene	larvae	S,U	4740	3500	Abernethy et al. 1986
	2-chloronaphthalene	larvae	S,U	2330	1300	Abernethy et al. 1986
	1-chloronaphthalene	larvae	S,U	1840	1300	Abernethy et al. 1986
	phenanthrene	larvae	S,U	680	790	Abernethy et al. 1986
Chlamydomonas angulosa	naphthalene	not applicable	S,U	9600	16000	Hutchinson et al. 1980
	1-methylnaphthalene	not applicable	S,U	1700	6100	Hutchinson et al. 1980
	2-methylnaphthalene	not applicable	S,U	4480	6000	Hutchinson et al. 1980
	phenanthrene	not applicable	S,U	945	1400	Hutchinson et al. 1980
Chlorella vulgaris	naphthalene	not applicable	S,U	19200	21000	Hutchinson et al. 1980
	1-methylnaphthalene	not applicable	S,U	5100	8200	Hutchinson et al. 1980
	2-methylnaphthalene	not applicable	S,U	8900	8000	Hutchinson et al. 1980
Cyprinodon variegatus	naphthalene	unknown	M	2400	5300	Anderson et al. 1974 a
	1-methylnaphthalene	unknown	M	3400	2100	Anderson et al. 1974a
	2-methylnaphthalene	unknown	M	2000	2000	Anderson et al. 1974a
	acenaphthene	juvenile	S,U	2200	1800	Heitmuller et al. 1981
	acenaphthene	adult	FT,M	3100	1800	Ward et al. 1981
	1-chloronaphthalene	juvenile	S,U	2400	760	Heitmuller et al. 1981
	1-chloronaphthalene	juvenile	F,M	690	760	Ward et al. 1981
	phenanthrene	juvenile	FT,M	429.4	460	Battelle Ocean Sciences, 1987
	phenanthrene	juvenile	R	478	460	Moreau et al. 1999
Daphnia magna	naphthalene	juvenile	S,U	4723	5400	Abernethy et al. 1986
	naphthalene	unknown	S,U	8600	5400	U.S. EPA, 1978
	naphthalene	neonate	S,M	11800	5400	Maclean and Doe, 1989
	naphthalene	unknown	S,U	3400	5400	Crider et al. 1982
	naphthalene	unknown	S,U	4100	5400	Crider et al. 1982
	naphthalene	unknown	S,U	22600	5400	Eastman et al. 1984
	naphthalene	unknown	S,M	2160	5400	Millemann et al. 1984
	1-methylnaphthalene	juvenile	S,U	1420	2100	Abernethy et al. 1986
	2-methylnaphthalene	juvenile	S,U	1491	2100	Abernethy et al. 1986
	acenaphthene	unknown	S,M	320	1900	EG&G Bionomics, 1982
	acenaphthene	unknown	S,M	1300	1900	EG&G Bionomics, 1982
	acenaphthene	unknown	FT,M	120	1900	EG&G Bionomics, 1982
	acenaphthene	unknown	S,U	3450	1900	Randall and Knopp, 198
	fluorene	unknown	S,U	430	1800	Finger et al. 1985
	2-chloronaphthalene	juvenile	S,U	1640	780	Abernethy et al. 1986
	1-chloronaphthalene	unknown	S,U	1600	770	LeBlanc, 1980
	phenanthrene	juvenile	S,U	207	470	Abernethy et al. 1986
	phenanthrene	unknown	S,U	843	470	Eastman et al. 1984
	phenanthrene	neonate	S,M	700	470	Millemann et al. 1984
	phenanthrene	unknown	FT,M	117	470	Call et al. 1986
	9-methylanthracene	juvenile	S,U	124.8	210	Abernethy et al. 1986
	pyrene	juvenile	S,U	90.9	170	Abernethy et al. 1986
	fluoranthene	juvenile	S,M	45	140	Oris et al. 1991
	fluoranthene	unknown	S,M	100	140	EG&G Bionomics, 1982
	fluoranthene	unknown	S,M	105.7	140	Suedel and Rogers, 1996
	benzo(a)pyrene	unknown	S	250 a	13	Atienzar et al. 1999
Daphnia pulex	naphthalene	unknown	S,U	4663	4210	Smith et al. 1988
	fluorene	unknown	S,U	212	1400	Smith et al. 1988
	1,3-dimethylnaphthalene	unknown	S,U	767	650	Smith et al. 1988
	1,3-dimethylnaphthalene	neonate	S,U	1280	650	Passino and Smith, 1987
	2,6-dimethylnaphthalene	unknown	S,U	193	630	Smith et al. 1988
	phenanthrene	neonate	S,U	734	370	Passino and Smith, 1987
	phenanthrene	unknown	S,M	100	370	Trucco et al. 1983
	phenanthrene	unknown	S,U	350	370	Smith et al. 1988
Ictalurus punctatus	acenaphthene	juvenile	FT,M	1720	1180	Holcombe et al. 1983
	fluoranthene	juvenile	S,M	37.4	91	Gendusa, 1990
Lepomis macrochirus	acenaphthene	juvenile	S,U	1700	2100	Buccafusco et al. 1981
	fluorene	unknown	S,U	910	2000	Finger et al. 1985
	1-chloronaphthalene	juvenile	S,U	2300	870	Buccafusco et al. 1981
	phenanthrene	juvenile	FT,M	234	540	Call et al. 1986
	fluoranthene	juvenile	FT,M	44	160	Spehar et al. 1999
Leptochirus plumulosus	acenaphthene	adult	FT,M	589.4	690	Swartz, 1991
	phenanthrene	adult	FT,M	198.4	180	Swartz, 1991
	pyrene	juvenile	FT,M	66.49	62	Champlin and Poucher, 1992
	fluoranthene	unknown	R,M	51	54	Boese et al. 1997
Menidia beryllina	acenaphthene	juvenile	R,U	5564 a	4700	Thursby, 1991
	acenaphthene	unknown	S,U	2300	4700	Horne et al. 1983
	fluoranthene	unknown	R,U	616 a	360	Spehar et al. 1999
Mysidopsis bahia	acenaphthene	juvenile	R,U	1190	550	Thursby et al. 1989a
	acenaphthene	juvenile	S,U	970	550	Ward et al. 1981
	acenaphthene	juvenile	FT,M	460	550	Thursby et al. 1989b
	acenaphthene	juvenile	S,M	160	550	EG&G Bionomics, 1982
	acenaphthene	juvenile	FT,M	190	550	EG&G Bionomics, 1982
	acenaphthene	juvenile	FT,M	466.1	550	Horne et al. 1983; Thursby, 1991
	acenaphthene	juvenile	FT,M	271.9	550	Horne et al. 1983; Thursby, 1991
	1-chloronaphthalene	juvenile	S,U	370	230	U.S. EPA, 1978
	phenanthrene	juvenile	FT,M	17.7	140	Battelle Ocean Sciences, 1987
	phenanthrene	juvenile	FT,M	27.1	140	Kuhn and Lussier, 1987
	fluoranthene	juvenile	S,U	40	43	U.S. EPA, 1978
	fluoranthene	juvenile	FT,M	87	43	EG&G Bionomics, 1982
Neanthes arenaceodentata	naphthalene	immature young adult	S,U	3800	8500	Rossi and Neff, 1978
	acenaphthene	unknown	S,U	3600	2900	Horne et al. 1983
	phenanthrene	immature young adult	S,U	600	740	Rossi and Neff, 1978
	fluoranthene	immature young adult	S,U	500 a	230	Rossi and Neff, 1978
Oithona davisae	naphthalene	adult	S,M	7195	6600	Barata et al. 2005
	1-methylnaphthalene	adult	S,M	2652	2600	Barata et al. 2005
	fluorene	adult	S,M	1800	2200	Barata et al. 2005
	1,2-dimethylnaphthalene	adult	S,M	617	990	Barata et al. 2005
	dibenzothiophene	adult	S,M	551 a	1010	Barata et al. 2005
	phenanthrene	adult	S,M	522	580	Barata et al. 2005
	1-methylphenanthrene	adult	S,M	931 a	230	Barata et al. 2005
	pyrene	adult	S,M	154 a	200	Barata et al. 2005
	fluoranthene	adult	S,M	196	180	Barata et al. 2005
Oncorhynchus mykiss	naphthalene	juvenile	FT,M	1600	3100	DeGraeve et al. 1982
	naphthalene	unknown	FT,M	2300	3100	DeGraeve et al. 1980
	naphthalene	pre SU	S,U	1800	3100	Edsall, 1991
	naphthalene	pre SU	S,U	6100	3100	Edsall, 1991
	naphthalene	pre SU	S,U	2600	3100	Edsall, 1991
	naphthalene	pre SU	S,U	4400	3100	Edsall, 1991
	naphthalene	pre SU	S,U	5500	3100	Edsall, 1991
	2-methylnaphthalene	unknown	R	1456	1200	Kennedy, 1990
	acenaphthene	juvenile	FT,M	670	1100	Holcombe et al. 1983
	fluorene	juvenile	S,U	820	1020	Finger et al. 1985
	1,3-dimethylnaphthalene	pre SU	S,U	1700	480	Edsall, 1991
	phenanthrene	juvenile	FT,M	375	270	Call et al. 1986
	fluoranthene	unknown	S,M	187	82	Horne and Oblad, 1983
	fluoranthene	juvenile	FT,M	26	82	Spehar et al. 1999
Palaemonetes pugio	naphthalene	unknown	S,M	2350	2700	Tatum et al. 1978
	naphthalene	unknown	M	2600	2700	Anderson et al. 1974 a
	2-methylnaphthalene	unknown	M	1700	1000	Anderson et al. 1974a
	acenaphthene	larvae	R,U	1697	920	Thursby et al. 1989a
	acenaphthene	unknown	S,U	676	920	Horne et al. 1983; Thursby, 1991
	dimethylnaphthalene	unknown	M	700	400	Anderson et al. 1974a
	phenanthrene	adult	R,U	201	230	Battelle Ocean Sciences, 1987
	phenanthrene	adult	FT,M	145.4	230	Battelle Ocean Sciences, 1987
	fluoranthene	juvenile	S,U	142	71	Spehar et al. 1999
Pimephales promelas	naphthalene	juvenile	FT,M	6080	5700	Holcombe et al. 1984
	naphthalene	juvenile	FT,M	7900	5700	DeGraeve et al. 1982
	naphthalene	unknown	FT,M	4900	5700	DeGraeve et al. 1980
	naphthalene	juvenile	FT,M	8900	5700	DeGraeve et al. 1980
	naphthalene	juvenile	S,M	1990	5700	Millemann et al. 1984
	1-methylnaphthalene	juvenile	S,U	9000	2200	Mattson et al. 1976
	acenaphthene	juvenile	FT,M	1600	2000	Holcombe et al. 1983
	acenaphthene	juvenile	FT,M	608	2000	Cairns and Nebeker, 1982
	acenaphthene	juvenile	S,M	1500	2000	EG&G Bionomics, 1982
	acenaphthene	adult	R,U	3700	2000	Academy of Natural Sciences, 1981
	acenaphthene	juvenile	S,M	3100	2000	Marine Bioassay Lab, 1981
	acenaphthene	juvenile	FT,M	1730	2000	Geiger et al. 1985
	fluoranthene	juvenile	S,M	95	150	Horne and Oblad, 1983
	fluoranthene	juvenile	S,M	77.1	150	Gendusa, 1990
Tanytarsus dissimilis	naphthalene	larvae	S,U	12600	8500	Darville and Wilhm, 1984
	naphthalene	larvae	S,U	20700	8500	Darville and Wilhm, 1984
Tetrahymena elliotti	1-methylnaphthalene	not applicable	S,M	9400	5000	Rogerson et al. 1983
Xenopus laevis	naphthalene	larvae	FT,M	2100	7600	Edmisten and Bantle, 1982
	benzo(a)pyrene	unknown	R	13400 a	18	Propst et al. 1997
Rhepoxynius abronius	2,3,5-trimethylnaphthalene	unknown	unknown	156	100	Ozretich et al. 1997
	fluoranthene	unknown	unknown	24	39	Ozretich et al. 1997
	naphthalene	unknown	unknown	10346	1400	Ozretich et al. 1997
	2,6-dimethylnaphthalene	unknown	unknown	192	220	Ozretich et al. 1997
	1-methylfluorene	unknown	unknown	45	200	Ozretich et al. 1997
	2-methylphenanthrene	unknown	unknown	67	51	Ozretich et al. 1997
	9-methylanthracene	unknown	unknown	32	56	Ozretich et al. 1997
a Observed LC50 greater than solubility. These data were not used in the comparison.
b S= static; R = renewal; FT= flow-through; U = unmeasured or nominal; M = measured

Table 3. Acute and chronic values used in the development of ACRs. Chronic endpoints are those effecting growth, reproduction or mortality of an organism.
Species	Chemical	Chemical Code	Acute LC50/EC50 (µg/L)	Chronic Endpoint	Chronic (µg/L)	ACR	Source
Cyprinodon variegatus	Acenaphthene	PAH	3,100	survival	710	4.36	Ward et al. 1981
Daphnia magna	Fluoranthene	PAH	117	growth	24.5	4.78	Spehar et al. 1999
	Phenanthrene	PAH	117	survival and reproduction	96.4	1.21	Call et al. 1986
	Biphenyl	MAH	362		322	1.12	Gersich et al. 1989
	Methyltertbutylether	ALI	472,000	not specified	42,000	11.2	Mancini et al. 2002
Mysidopsis bahia	Acenaphthene	PAH	466	reproduction	286	1.63	Horne et al. 1983
	Acenaphthene	PAH	460	reproduction	64.0	7.19	Thursby et al. 1989b
	Fluoranthene	PAH	30.5	survival and reproduction	14.4	2.11	Spehar et al. 1999
	Fluoranthene	PAH	40.0	survival and reproduction	15.9	2.52	U.S. EPA, 1978
	Phenanthrene	PAH	27.1	survival	8.13	3.33	Kuhn and Lussier, 1987
	Pyrene	PAH	28.3	reproduction	4.53	6.24	Champlin and Poucher, 1992a and b
	Methyltertbutylether	ALI	187,000	not specified	36,000	5.2	Mancini et al. 2002
Onchorhyncus mykiss	Phenanthrene	PAH	50.0	survival and growth	6.32	7.91	Call et al. 1986
Paratanytarsus species	Acenaphthene	PAH	2,040	survival, growth and reproduction	411	4.96	Northwestern Aquatic Sciences, 1982
	Acenaphthene	PAH	2,040	survival and growth	227	8.97	Northwestern Aquatic Sciences, 1982; Thursby, 1991
Pimephales promelas	Acenaphthene	PAH	608	growth	405	1.50	Cairns and Nebeker, 1982; Thursby 1991
	Acenaphthene	PAH	608	growth	419	1.45	Cairns and Nebeker, 1982
	Naphthalene	PAH	7,900	survival and growth	620	12.7	DeGraeve et al. 1982
	Fluoranthene	PAH	69.0	survival and growth	15.0	4.60	Spehar et al. 1999
	Methyltertbutylether	ALI	980,000	not specified	289,400	3.39	Mancini et al. 2002
Brachionus calyciflorus	Xylene	MAH	248,500		40,300	6.17	Snell and Moffat, 1992
Ceriodaphnia dubia	Toluene	MAH	3,750		2,780	1.35	Niederlehner et al. 1998
	Ethylbenzene	MAH	3,200		1,680	1.90	Niederlehner et al. 1998
Selenastrum capricornutum	Benzene	MAH	100,000	biomass growth	8,300	12	Mayer et al. 2001
	Cyclohexane	ALI	9,317	biomass growth	952	9.8	Exxon Biomedical Sciences, 1998
	Methyltertbutylether	ALI	491,000	biomass growth	103,000	4.8	European Commission, 2000
	Pentane	ALI	10,700	biomass growth	2,040	5.2	Exxon Biomedical Sciences, 1997
Skeletonema costatum	Ethylbenzene	MAH	7,700		4,500	1.7	Boeri, R. 1987
Onchorhyncus gorbuscha	Naphthalene	PAH	1,200	growth	380	3.2	Moles and Rice, 1983
Geometric mean						3.8
MAH: Monoaromatic hydrocarbon
PAH: Polycyclic aromatic hydrocarbon
ALI: Aliphatic hydrocarbon

Please click here for Table 4

Please click here for Table 5

Table 6. Summary of sediment data sets for acute toxicity from PAH mixtures
Contaminant Source	PAH Measurement	Number of Samples	Species	Endpoint	Reference
Laboratory prepared mixtures	4 Parent	7	Rhepoxynius abronius	10 day mortality	Swartz et al. 1997
PAH contaminated sediment (Halifax Harbor, Nova Scotia)	13 Parent PAHs	11	Rhepoxynius abronius	10 day mortality	Tay et al. 1992
PAH contaminated sediment (Curtis Creek, Virginia)	13 Parent PAHs	6	Leptocheirus plumulosus	10 day mortality	DeWitt et al. 1992
			Eohaustorius estuarius
PAH contaminated sediment (Eagle Harbor, Washington)	13 Parent PAHs	10	Rhepoxynius abronius	10 day mortality	Tetratech, 1986
PAH contaminated sediment (San Diego Bay, California)	13 Parent PAHs	54	Rhepoxynius abronius	10 day mortality	Swartz et al. unpublished data
PAH contaminated sediment (Eagle Harbor, Washington)	13 Parent PAHs	8	Rhepoxynius abronius	10 day mortality	Swartz et al. 1989
PAH contaminated sediment (Elliott Bay, Washington)	40 PAHs (parent and alkylated)	30	Rhepoxynius abronius	10 day mortality	Ozretich et al. 2000
			Leptocheirus plumulosus
Laboratory prepared mixtures	2 to 6 PAHs	12	Rhepoxynius abronius	10 day mortality	Boese et al. 1999
PAH contaminated sediment (Asphalt) (Estonia)	11 Parent PAHs	8	Chironomus riparius	10 day mortality and growth	Huuskonen et al. 1998
Dilutions of Spiked Diesel fuel	20 PAHs (parent and alkylated)	4	Schizopera knabeni	4 day mortality	Fleeger and Lotufo, 1999
PAH contaminated sediment (Exxon Valdez Crude Oil Spill)	39 PAHs (parent and alkylated)	470	Rhepoxynius abronius	10 day mortality	Page et al. 2002
PAH contaminated sediment (North Cape Oil Spill)	33 PAHs (parent and alkylated)	12	Ampelisca abdita	4 day mortality	Ho et al. 1999

Table 7. Example demonstrating benefit of using adjustment factor to convert TU from 13PAH to Total PAH TU. Data are from Swartz et al. unpublished (Table D5).
		No Adjustment factor for unmeasured PAHs
	Observed Mortality (%)	13 PAH TU	Toxic based on 13 PAH TU	Correct prediction? 1	Was Criterion Protective? 2	Total PAH TU	Toxic based on Total PAH TU	Correct prediction? 1	Was Criterion Protective? 2
STATIONS
STA 15-1	15.0	0.058	no	yes		0.159	no	yes
STA 15-2	25.0	0.060	no	yes		0.164	no	yes
STA 15-3	15.0	0.074	no	yes		0.202	no	yes
STA 15-4	5.0	0.050	no	yes		0.137	no	yes
STA 27-1	40.0	0.159	no	yes		0.437	yes	no
STA 27-2	85.0	0.203	no	no	no	0.559	yes	yes	yes
STA 27-3	80.0	0.201	no	no	no	0.553	yes	yes	yes
STA 27-4	35.0	0.250	no	yes		0.689	yes	no
STA 27-5	50.0	0.179	no	no	no	0.491	yes	yes	yes
STA 28-1	100.0	0.103	no	no	no	0.283	no	no	no
STA 28-2	95.0	0.151	no	no	no	0.417	yes	yes	yes
STA 28-3	100.0	0.089	no	no	no	0.244	no	no	no
STA 28-4	100.0	0.123	no	no	no	0.337	no	no	no
STA 28-5	100.0	0.092	no	no	no	0.253	no	no	no
STA 28-6	90.0	0.150	no	no	no	0.414	yes	yes	yes
STA 28X-1	15.0	0.084	no	yes		0.232	no	yes
STA 28X-2	20.0	0.083	no	yes		0.227	no	yes
STA 28X-3	35.0	0.111	no	yes		0.304	no	yes
STA 28X-4	25.0	0.099	no	yes		0.273	no	yes
STA 28X-5	25.0	0.100	no	yes		0.276	no	yes
STA 33-1	0.0	0.007	no	yes		0.020	no	yes
STA 33-2	5.0	0.006	no	yes		0.017	no	yes
STA 33-3	10.0	0.005	no	yes		0.014	no	yes
STA 33-4	5.0	0.006	no	yes		0.016	no	yes
STA 33-5	30.0	0.021	no	yes		0.057	no	yes
STA 40-1	10.0	0.014	no	yes		0.038	no	yes
STA 40-2	0.0	0.012	no	yes		0.033	no	yes
STA 40-3	15.0	0.016	no	yes		0.044	no	yes
STA 40-4	15.0	0.012	no	yes		0.033	no	yes
STA 40-5	15.0	0.016	no	yes		0.044	no	yes
STA 13-1	10.0	0.076	no	yes		0.209	no	yes
STA 13-2	20.0	0.173	no	yes		0.476	yes	no
STA 13-4	0.0	0.098	no	yes		0.270	no	yes
STA 13-5	5.0	0.090	no	yes		0.247	no	yes
STA 13-6	5.0	0.244	no	yes		0.672	yes	no
STA 11-1	10.0	0.023	no	yes		0.065	no	yes
STA 11-2	10.0	0.021	no	yes		0.058	no	yes
STA 11-3	15.0	0.020	no	yes		0.055	no	yes
STA 11-4	0.0	0.016	no	yes		0.044	no	yes
STA 11-5	25.0	0.018	no	yes		0.049	no	yes
STA 11-6	15.0	0.021	no	yes		0.057	no	yes
STA 44-1	0.0	0.053	no	yes		0.145	no	yes
STA 44-2	0.0	0.046	no	yes		0.128	no	yes
STA 44-3	10.0	0.061	no	yes		0.167	no	yes
STA 44-4	0.0	0.040	no	yes		0.110	no	yes
STA 44-5	0.0	0.045	no	yes		0.125	no	yes
STA 44-6	5.0	0.051	no	yes		0.141	no	yes
YAQ BAY	0.0	0.001	no	yes		0.004	no	yes
STA28 0%	14.0	0.031	no	yes		0.086	no	yes
STA28 13%	32.0	0.070	no	yes		0.194	no	yes
STA28 22%	61.0	0.101	no	no	no	0.279	no	no	no
STA28 36%	81.0	0.149	no	no	no	0.410	yes	yes	yes
STA28 60%	93.0	0.268	no	no	no	0.738	yes	yes	yes
STA28 100%	98.0	0.608	yes	yes	yes	1.672	yes	yes	yes
Number of overall correction predictions:				42				45
Number of incidences criterion was not protective					1				8
1 A correction prediction is where the observed mortality was greater then 50% and the computed TU was greater than 0.4.
2 yes = greater than 50% mortality observed and greater than 0.4 TU computed
no = greater than 50% mortality observed and less than 0.4 TU computed

Table 8. Chronic effects from single PAH sediment exposures
			Effect		TLM Predicted chronic (mg/g oc )
Species	Chemical	Method a		Observed Effect (mg/g oc )		Reference
Schizopera knabeni	fluoranthene	S,M	10-d LOEC (grazing)	1200	4310	Lotufo, 1998
			10-d NOEC (grazing)	330
			10-d LOEC (offspring prod.)	3133
			10-d NOEC (offspring prod.)	1200
Schizopera knabeni	fluoranthene	S,M	14-d IC25 realized reproduction	1230		Fleeger and Lotufo, 1999; Lotufo, 1997
	phenanthrene	S,M	14-d IC25 realized reproduction	1730	3560
	phenanthrene	S,M	10-d LOEC (hatching success)	3000
	phenanthrene	S,M	10-d NOEC (hatching success)	1470
Coullana sp.	fluoranthene	S,M	10-d LOEC (grazing)	3133	1410	Lotufo, 1998
			10-d NOEC (grazing)	1200
			10-d LOEC (offspring prod.)	8800
			10-d NOEC (offspring prod.)	3133
a S = static; M = measured

Table 9. Summary of CTLBBs for different chemical classes
	Chemical class correction
Class of Compounds			(C L * )
			( m mol/g octanol )
Baseline	0		119
Halogenated	-0.339	0.458	54.3
Monoaromatic hydrocarbon (MAH)	-0.109	0.778	92.3
Halogenated MAH	-0.448	0.356	42.3
Polycyclic aromatic hydrocarbon (PAH)	-0.352	0.445	52.7
Halogenated PAH	-0.701	0.199	23.6
a see Appendix A

Table 10. Chronic HC5 values for MAHs and PAHs for aqueous and sediment toxicity
		Aqueous		Sediment
		HC5	HC5	HC5	HC5
Chemical	log K ow	(mmol/L)	(mg/L)	(mmol/kg oc )	(mg/kg oc )
benzene	1.943	30.892	2413	2.51	196
toluene	2.438	10.600	977	2.64	244
o-xylene	2.946	3.534	375	2.78	295
ethylbenzene	3.006	3.104	330	2.80	297
m-xylene	3.032	2.935	312	2.81	298
p-xylene	3.051	2.816	299	2.81	299
naphthalene	3.256	1.033	132	1.64	210
acenaphthylene	3.436	0.700	107	1.67	254
C3-benzenes	3.455	1.175	141	2.93	351
1-methylnaphthalene	3.781	0.332	47.2	1.73	246
C1-naphthalenes	3.788	0.327	46.4	1.73	246
2-methylnaphthalene	3.789	0.326	46.3	1.73	246
acenaphthene	3.878	0.269	41.5	1.75	269
fluorene	3.93	0.240	39.9	1.75	292
biphenyl	3.936	0.415	64.0	3.07	474
2-chloronaphthalene	3.94	0.108	17.5	0.80	131
1-chloronaphthalene	3.95	0.105	17.1	0.81	131
C2-naphthalenes	4.244	0.122	19.0	1.81	283
1,3-dimethylnaphthalene	4.257	0.118	18.5	1.81	283
methylbiphenyl	4.259	0.206	34.7	3.17	533
2,6-dimethylnaphthalene	4.27	0.115	18.0	1.81	283
1,2-dimethylnaphthalene	4.27	0.115	18.0	1.81	283
dibenzothiophene	4.34	0.099	18.2	1.83	336
1-methylfluorene	4.37	0.093	16.7	1.83	330
C1-fluorenes	4.37	0.093	16.7	1.83	330
anthracene	4.546	0.063	11.3	1.86	332
2,3,5-trimethylnaphthalene	4.57	0.060	10.2	1.87	318
2,3,6-trimethylnaphthalene	4.57	0.060	10.2	1.87	318
phenanthrene	4.584	0.058	10.4	1.87	333
dimethylbiphenyl	4.692	0.081	14.7	3.31	602
C3-naphthalenes	4.73	0.042	7.23	1.90	323
C2-fluorenes	4.819	0.035	6.80	1.91	371
C1-Dibenzothiophene	4.859	0.032	6.37	1.92	381
9-methylanthracene	4.996	0.024	4.59	1.94	374
1-methylphenanthrene	5.036	0.022	4.21	1.95	375
C1-phenanthrene/anthracene	5.037	0.022	4.20	1.95	375
2-methylphenanthrene	5.04	0.022	4.17	1.95	376
pyrene	5.126	0.018	3.64	1.97	398
fluoranthene	5.19	0.016	3.17	1.98	401
C4-naphthalenes	5.22	0.015	2.70	1.99	366
C1-fluoranthene/pyrene	5.257	0.014	2.93	1.99	431
C3-fluorenes	5.318	0.012	2.47	2.01	418
C2-Dibenzothiophene	5.332	0.012	2.44	2.01	426
3,6-dimethylphenanthrene	5.34	0.011	2.33	2.01	415
4,6-dimethyldibenzothiophene	5.45	0.0089	1.89	2.03	431
C2-phenanthrene/anthracene	5.455	0.0088	1.82	2.03	419
C2-fluoranthene/pyrene	5.557	0.0071	1.63	2.05	472
Triphenylene	5.63	0.0060	1.38	2.07	472
benzo(a)anthracene	5.744	0.0047	1.08	2.09	477
chyrsene	5.782	0.0043	0.99	2.10	478
C3-Dibenzothiophene	5.81	0.0041	0.92	2.10	475
C3-phenanthrene/anthracene	5.907	0.0033	0.729	2.12	467
Retene	6.12	0.0021	0.488	2.16	507
indeno(1,2,3-cd)pyrene	6.158	0.0019	0.530	2.17	600
C1-Chyrsenes/benzo(a)anthracene	6.19	0.0018	0.434	2.18	527
C4-phenanthrene/anthracene	6.357	0.0012	0.292	2.21	518
benzo(b)fluoranthene	6.341	0.0013	0.325	2.21	557
C3-fluoranthene/pyrene	6.384	0.0012	0.287	2.22	541
benzo(k)fluoranthene	6.4	0.0011	0.286	2.22	560
benzo(a)pyrene	6.409	0.0011	0.281	2.22	560
7,12-dimethylbenzo(a)anthracene	6.42	0.0011	0.278	2.22	570
Benzo(e)pyrene	6.447	0.0010	0.258	2.23	562
perylene	6.447	0.0010	0.258	2.23	562
C2-Chyrsenes/benzo(a)anthracene	6.593	0.0007	0.191	2.26	579
C4-fluoranthene/pyrene	6.687	0.0006	0.157	2.28	589
C5-phenanthrene/antracene	6.7	0.0006	0.147	2.28	567
C1-benzofluoranthene	6.743	0.0005	0.143	2.29	609
benzo(ghi)perylene	6.886	0.0004	0.109	2.32	641
C3-Chyrsenes/benzo(a)anthracene	6.972	0.0003	0.089	2.34	632
dibenz(a,h)anthracene	7.129	0.0002	0.065	2.37	660
C2-benzofluoranthene	7.2	0.0002	0.056	2.39	669
C4-Chyrsenes/benzo(a)anthracene	7.421	0.0001	0.035	2.43	692

Table 11. PAH NOEC toxicity data for various species (measured data considered only)
Chemical	Species	Endpoint	Endpoint (µg/L)	Reference
Naphthalene	Daphnia magna	21-d NOEC - Reproduction	600	European Commission, 2003
	Oncorhynchus kisutch	40-d NOEC - Larval growth	370	Moles et al. 1981
	Pimephales promelas	30-d NOEC - Larval growth	450	DeGraveve et al. 1982
Acenaphthene	Paratanytarsus sp.	26-d NOEC - Survival/Growth/Repro	295	Northwestern Aquatic Sciences, 1982
	Paratanytarsus sp.	26-d NOEC - Survival/Growth/Repro	164	Northwestern Aquatic Sciences, 1982
	Pimephales promelas	32-d NOEC - Growth	109	Academy of Natural Sciences, 1981
	Pimephales promelas	32-d NOEC - Growth	50	Academy of Natural Sciences, 1981
	Pimephales promelas	32-35-d - Growth	332	Cairns and Nebeker, 1982
	Pimephales promelas	32-35-d - Growth	345	Cairns and Nebeker, 1982
	Pimephales promelas	32-d NOEC - Survival	64	ERCO, 1981
	Americamysis bahia	35-d NOEC Survival	240	Horne et al. 1983
	Americamysis bahia	25-d NOEC Survival/Reproduction	44.6	Thursby et al. 1989
	Cyprinodon variegatus	28-d NOEC Survival	520	Ward et al. 1981
Phenanthrene	Daphnia magna	21-d NOEC - Reproduction	180	Hooftman and Evers-de Ruiter, 1992b, c
	Daphnia magna	21-d NOEC - Survival	56	Hooftman and Evers-de Ruiter, 1992b, c
	Daphnia magna	21-d NOEC - Growth	32	Hooftman and Evers-de Ruiter, 1992b, c
	Daphnia magna	21-d NOEC - Reproduction	57	Call et al. 1986
	Brachydanio rerio	42-d NOEC - Growth	56	Hooftman and Evers-de Ruiter, 1992b, c
	Oncornhynus mykiss	90-d NOEC - Survival/Growth	5	Call et al. 1986
	Americamysis bahia	32-d NOEC Survivial	5.5	Kuhn and Lussier, 1987
Fluoranthene	Brachydanio rerio	41-d NOEC - Growth	22	Hooftman and Evers-de Ruiter, 1992d
	Daphnia magna	21-d NOEC - Growth	17	Spehar et al. 1999
	Pimephales promelas	32-d NOEC - Survival/Growth	10.4	Spehar et al. 1999
	Americamysis bahia	31-d NOEC - Survival/Reproduction	11.1	Spehar et al. 1999
Pyrene	Americamysis bahia	28-d NOEC Reproduction	3.82	Champlin and Poucher, 1992b
Benzo(k)fluoranthene	Brachydanio rerio	42-d NOEC - Growth	0.48	Hooftman and Evers-de Ruiter, 1992a
Benzo(a)pyrene	Brachydanio rerio	42-d NOEC - Growth	6.3	Hooftman and Evers-de Ruiter, 1992c

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