Development and Practical Application of Petroleum and Dispersant

Development and Practical Application of Petroleum and Dispersant Interspecies Correlation Models for Aquatic Species ... U.S. Environmental Protectio...
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Development and Practical Application of Petroleum and Dispersant Interspecies Correlation Models for Aquatic Species Adriana C. Bejarano*,† and Mace G. Barron‡ †

Research Planning, Inc., 1121 Park Street, Columbia, South Carolina 29201, United States U.S. Environmental Protection Agency, Gulf Ecology Division, 1 Sabine Island Drive, Gulf Breeze, Florida 32561, United States

‡

S Supporting Information *

ABSTRACT: Assessing the acute toxicity of oil has generally relied on existing toxicological data for a relatively few standard test species, which has limited the ability to estimate the impacts of spilled oil on aquatic communities. Interspecies correlation estimation (ICE) models were developed for petroleum and dispersant products to facilitate the prediction of toxicity values to a broader range of species and to better understand taxonomic differences in species sensitivity. ICE models are log linear regressions that can be used to estimate toxicity to a diversity of taxa based on the known toxicity value for a surrogate tested species. ICE models have only previously been developed for nonpetroleum chemicals. Petroleum and dispersant ICE models were statistically significant for 93 and 16 unique surrogate-predicted species pairs, respectively. These models had adjusted coefficient of determinations (adj-R2), square errors (MSE) and positive slope ranging from 0.29 to 0.99, 0.0002 to 0.311, and 0.187 to 2.665, respectively. Based on model cross-validation, predicted toxicity values for most ICE models (>90%) were within 5-fold of the measured values, with no influence of taxonomic relatedness on prediction accuracy. A comparison between hazard concentrations (HC) derived from empirical and ICE-based species sensitivity distributions (SSDs) showed that HC values were within the same order of magnitude of each other. These results show that ICE-based SSDs provide a statistically valid approach to estimating toxicity to a range of petroleum and dispersant products with applicability to oil spill assessment.

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INTRODUCTION

and have been shown to produce HC values similar to those used to derive water quality criteria.6 Recent research has shown that existing data from standardized toxicity tests can be used to develop oil-specific SSDs,10−13 but species diversity is extremely limited compared to single compounds.2,4,5 The objectives of the current study were to determine if petroleum and dispersant-specific ICE models could be developed and used to generate SSDs and HC5 values with acceptable uncertainty, and to determine associations between taxonomic relatedness and species sensitivity.4 Previous studies have documented variability in test results attributable to differences in test conditions and exposure media prepared from the same source oil and/or dispersants.14−17 Therefore, this study focused on two distinct categories of studies (spiked and continuous exposure) that reported measured aqueous exposure concentrations of petroleum products (primarily light and medium crude oils) and several oil spill dispersants (primarily Corexits). The models and approach presented here can be used to estimate toxicity to a broad range of species and assemblages which

Assessments of the potential toxicological effects of physically or chemically dispersed oils and dispersants have commonly relied on relatively few toxicity tests for a limited number of aquatic species, primarily standard test species. In most cases, the sensitivity of species of concern is unknown, making informed decisions challenging. One approach to addressing uncertainty in species sensitivity has been the development and application of interspecies correlation estimation models (ICE),1 which are log−linear regressions between the acute toxicity of two species for many paired toxicity data. These models can generate acute toxicity estimates by extrapolating known toxicity values from a surrogate species to species with unknown toxicity values. While these models are available for many chemicals,2−5 these have not been developed for petroleum or dispersant products. ICE models can be used to generate species sensitivity distributions (SSDs),2,6,7 which are cumulative distributions of toxicity data (e.g., median lethal, LC50, and effects concentrations, EC50) that allow for comparisons of the relative sensitivities of across species.8 SSDs have been used to establish protective levels to specific chemicals9 by deriving hazard concentrations (HC) assumed to protect a wide number of species with varying sensitivities. ICEbased SSDs have been developed for a number of chemicals,2,6,7 © 2014 American Chemical Society

Received: Revised: Accepted: Published: 4564

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should facilitate the development of protective environmental concentrations and improved assessment of the potential consequences of oil spills and dispersant use.

core data are provided in the Supporting Information (SI) material. Model Development and Verification. Because of the number of steps involved in the development, selection, verification and application of ICE models, a diagram is provided (SI Figure S1) to facilitate the understanding of the approach presented here. As several statistical methods were used, the readers are encouraged to refer to key statistical references20,21 including those describing the development of ICE models.2,5 Linear regression models were developed for each pair of species in both, the petroleum hydrocarbon and dispersant database, containing at least 4 data points. These linear models are described by Log Pi = β0 + β1 × Log (Si), where Pi is the acute toxicity of the predicted species, β0 and β1 are the intercept and slope, respectively, and Si is the acute toxicity of the surrogate species, where both the independent and dependent variables are random and independent of each other.22 The slope represents change in the response of toxicity values of the predicted species per unit change in the toxicity values of the surrogate species, where a slope of approaching 1 indicates a similar response between two species. Only ICE models (p-value < 0.05) that passed both the F-test for the overall fit of the regression equation and the t test for the slope parameter significantly different from 0, were included in further analyses. Prior to model validation, statistically significant models were evaluated for potential influential data via regression diagnostics analyses.20 The reliability and predictive power of statistically significant ICE models with at least 2 degrees of freedom was assessed using a leave-one-out cross-validation technique.23,24 Briefly, each data set was split into K subsets, equal to the number of pairs within each data set, and models fitted K times, each time leaving out one data subset from the larger training data. For each reduced data set, toxicity values of the predicted species were calculated and responses for the deleted subset predicted from the model. Model uncertainty of the estimated toxicity value was calculated via the cross-validation success rate (or 1-bias corrected misclassification error or sum of the square difference between observed and estimated values), which is an estimate of generalized error. Taxonomic relatedness for each surrogate and predicted species was assigned a numeric distance value4 (from same genus = 1 to same domain = 7), and model prediction accuracy assessed as a function of taxonomic distance. Practical Application of ICE Models. Regression parameters of statistically significant ICE models were used to estimate toxicity values of several predicted species, using the known toxicity of petroleum hydrocarbons or dispersants for one or more surrogate species as the model input. In all instances, input data for surrogate species were independent of the data used to develop ICE models.10,18 Surrogate species included Americamysis bahia (mysid shrimp), Holmesimysis costata (kelp forest mysid), Menidia beryllina (inland silverside) and Atherinops af f inis (topsmelt). ICE-based SSDs were generated for each category of petroleum or dispersant product using the ICE estimated toxicity values, and in all cases the minimum acceptable number of species on an SSD was set to five. The petroleum product categories included light crude oils (>31.1°API) (Forties, South Louisiana, Venezuelan crudes) and medium crudes (22.3−31.1°API) (Alaska North Slope, Arabian Medium, Kuwait and Prudhoe Bay crudes), while the dispersant categories included Corexit 7664, Corexit 9500, and Corexit 9527. Hazard concentrations (HC) assumed to be

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EXPERIMENTAL SECTION Petroleum and Dispersant Toxicity Data Sources. The primary data source used in the development of ICE models came from a recently developed toxicity database,18 which contains quantitative information on the acute toxicity of crude oils and dispersants from publically available literature. This database was developed following a quality assurance and quality control (QA/QC) plan similar to that of a related database.19 QA/QC procedures included an evaluation of each original data source, removal of duplicate information, and an evaluation of currently accepted scientific names. Two types of data sets were queried and used in the development of ICE models: petroleum hydrocarbon and dispersant toxicity data. The petroleum hydrocarbon data set was comprised of toxicity data (LC50 and EC50) for aquatic species (primarily fish and crustaceans) derived from aqueous exposures to physically dispersed (i.e., water accommodated fractions; WAF) or chemically dispersed (i.e., enhanced water accommodated fractions; CEWAF) oil. These toxicity data were generated using a variety of oils under various weathering stages, but most data were from light (e.g., Chirag, Forties, South Louisiana, Venezuelan) and medium (e.g., Alaska North Slope, Forcados, Kuwait, Prudhoe Bay) crude oils (42 oils of 48 total oils). Only studies that reported LC50 and EC50 toxicity values on the basis of measured concentrations of analytes in the aqueous exposure media were included in the development of these models. While rigorous data selection for petroleum hydrocarbons focused on test results reported on the basis of measured total hydrocarbon content (THC; including C6− C36 carbon chains; 72% of the entire data set), other reported metrics (total polycyclic aromatic hydrocarbons, TPAHs; sum of PAHs and alkyl homologue groups) were also included. This data set did not include results from studies reporting effects concentrations for specific THC carbon chains or PAH analytes. Petroleum ICE models were developed using over 1500 paired data points for a total of 136 unique species pairs. The dispersant data set was comprised of toxicity data (LC50 and EC50) for aquatic species exposed to dispersants, almost exclusively Corexit 9500 and Corexit 9527 (36% and 33% of the entire data set respectively). Toxicity data for other dispersants (Corexit 7500; Corexit 7664; Corexit 9552; Nokomis; SlickAWay) were also included. Only studies reporting LC50 and EC50 toxicity values on the basis of measured dispersant concentrations in the exposure media were included in model development. Dispersant ICE models were developed using 286 paired data points for a total of 38 unique species pairs. The majority of data included here were derived from standard toxicity tests, including 96 h (65% of the entire data set) and constant static (76% of the entire data set) laboratory exposures. To develop petroleum and dispersant ICE models, surrogate and predicted species were paired from the same original data source. Pairing of surrogate and predicted species toxicity data was done only when tests for both species were performed under the same exposure conditions (i.e., same oil or dispersant product, exposure regime, analytical methods in chemical characterization), but with data independently collected for each species. A complete list of data sources and 4565

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Figure 1. Diagnostic analyses of homogeneity of variances (left) and normality (right) for petroleum and dispersant ICE models.

The validity of statistically significant models was verified using standard regression diagnostics procedures.20 Visual examination of the standardized residuals of each significant model (Figure 1, left panel) showed that all residuals fell within a horizontal band centered on 0, indicating that the variance of the error term was constant. No obvious outliers were detected (±4 standardized residual values). Diagnostic analyses also indicated that the error term did not depart substantially from a normal distribution (Figure 1, right panel), as the coefficient of determination (R2) between residuals and quantiles for petroleum and dispersant ICE models were greater (0.997 and 0.991, respectively) than those of the critical R2 value (0.985) (α = 0.05; n ≥ 100).26 A moderate departure from normality was noted at the end tails of the distributions, particularly for petroleum ICE models (14 data points) but was considered minimal because removal of these data points did not cause major changes in fitted values. In all cases, the absolute change between fitted values with and without potentially influential points ranged from 4 to 11% indicating no disproportional influence on fitted values. Statistically significant models had an adjusted coefficient of determination (adj-R2) ranging from 0.29 and 0.99, a Mean Square Error (MSE; a measure of fit) ranging from 0.0002 to 0.311, and a positive slope ranging from 0.187 to 2.665 (SI Tables S2 and S3). Examination of model parameter viability shows that 92% of all intercepts were between −0.75 and 0.75, and that 85% of all slopes were between 0.5 and 1.5. This relatively narrow variability suggests similarities across most models. Notable exceptions, where both the slope and intercept of the same model were outside these ranges included (1) the petroleum ICE models for the surrogate-predicted pairs Calanus sinicus- Paracalanus aculeatus (both copepods species), Cyprinodon variegatus (sheepshead minnow)-A. bahia, and Pontogammarus maeoticus (amphipod)-Calanipeda aquae dulcis (copepod); and (2) the dispersant ICE models for the pairs C. variegatus-A. bahia, A. af f inis-H. costata, and A. af f inisMacrocystis pyrifera (giant kelp). In these deviating cases, except pairs involving A. af f inis, the predicted species was generally more sensitivity than the surrogate species, but in all cases MSE were small (below 0.16).

protective of 99% or 95% of the species in the SSD (i.e., HC1 and HC5, respectively) were computed from the modeled SSD function. All SSDs and HC values were derived using an approach described elsewhere.25 Briefly, toxicity values were fitted to a log-normal distribution function, and SSDs randomly resampled 2000 times to derive the HC1 and HC5 values and their associated 95% confidence intervals (95% CI). Only SSDs that passed goodness of fit tests (α = 0.01) (the Anderson− Darling for SSDs with >7 species, and the Kolmogorov− Smirnov test statistics) were included in these analyses. SSDs were also generated using empirical data for the same categories of petroleum products and dispersants, as well as using a combination of empirical data and ICE-based toxicity values. SSDs with empirical data were generated by calculating the geometric mean of all toxicity values available for each unique species. The geometric was used because it gives less weight to influential data or potentially outliers, leading to conservative estimates.8 To determine if ICE-based SSDs are a viable alternative to the estimation of SSDs where toxicity data are limiting, ICE-based SSDs were compared to empirically based SSDS using log-likelihood statistics.21 This approach compares the log-likelihood values of individual SSDs (e.g., M. beryllina ICE-based and empirical data) with the fitted SSDs of the combined (pooled) model (e.g., M. beryllina ICE-based plus empirical data), testing the hypothesis (via the chi-square statistic) that these SSDs are derived from the same fitted lognormal curve. The same approach was used to compare ICEbased SSDs pairs. All the analyses above were performed using the R statistical platform (v. 2.13.2).22

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RESULTS Model Development and Verification. A total of 93 petroleum ICE models for 29 surrogate species, and 16 dispersant ICE models for seven surrogate species were statistically significant (p-value 0.05), and mean fold-difference values were 1.29 and 1.14 for taxonomic distances 6 and 7, respectively. The largest fold difference of 7 occurred in the A. bahia- M. beryllina surrogate- predicted pair. Practical Application of ICE Models. Toxicity data for several petroleum products from an analysis independent of the research presented here10 were used to verify the applicability of ICE models. Two sets of SSD were developed using ICE models for A. bahia and M. beryllina as the surrogate species, with input surrogate concentrations from Barron et al.10 The first set of SSDs used all ICE models regardless of their reliability, while the second set used only ICE models with MSE 0.05). This indicates that exclusion of the least reliable models did not influence the shape of the SSD. No statistically significant differences were also found between A. bahia and M. beryllina ICE-based SSDs (p-value >0.05), or between either of these two ICE-based SSDs and empirical SSDs10 (p-value >0.05; Figure 4). Estimated petroleum hydrocarbon HC5s from ICEbased SSDs were in general agreement with those derived from SSDs using empirical data10 (SI Table S5), though values from M. beryllina as the surrogate were generally larger (up to a 6 fold larger) than those calculated from empirical data. A second verification approach utilized empirical toxicity data for physically and chemically dispersed combined from constant and spiked exposures, as well as toxicity data for Corexit 9500 and Corexit 9727,18 which were used to generate SSDs (Figure 5). The selection of these data was driven by the fact that these SSDs shared data for two surrogate species: H. costata and A. bahia. SSDs were derived using all ICE models

The reliability and predictive power of statistically significant models was further assessed by identifying the MSE cutoff associated with a-priori cross-validation success rates of 85% and 95%,5 assumed to have moderate and high reliabilities, respectively (Figure 2). MSE cutoffs for petroleum and

Figure 2. Assessment of model reliability based on the relationship between model mean square error (MSE) and cross-validation success rate for both, petroleum and dispersant ICE models.

dispersant models were 0.126 and 0.04, respectively. Over half of the petroleum ICE models (54 models) had MSEs ≤0.04, while over 30% (32 models) had MSEs between 0.04 and 0.126. By comparison, dispersant ICE models were equally distributed among MSE cutoffs, suggesting that petroleum ICE models may be more robust. The high cross-validation success rate (a measure of predictive power) was consistent with the agreement between predicted and observed values for both ICE models types (Figure 3). For petroleum ICE models, 98% of the predicted values were within 5-fold difference of the observed data, with most values (92%) being within 2-fold difference of the observed data. There was no influence of taxonomic relatedness on prediction accuracy (p-value >0.05), and mean folddifference values across taxonomic distances ranged from 1.00 to 1.38 (SI Figure S4). Only 0.5% of predicted values (seven

Figure 3. Comparison of observed and ICE-predicted toxicity values for petroleum hydrocarbons and dispersants. The solid line represents the 1:1 line (equal toxicity), while the dotted lines represent a 5-fold difference between these values. Bar chart on the right, display the fold-difference between observed and ICE-predicted toxicity values. 4567

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models for the same surrogate species to generate SSDs for dispersants (five species minimum), predicted ICE model concentrations were combined for H. costata and A. bahia. Curve comparison of empirically and ICE-based SSDs, using log-likelihood, showed no statistically significant differences between A. bahia and H. costata ICE-based SSDs (p-value >0.05), or between either of these two ICE-based SSDs and empirical SSDs10,18 (p-value >0.05), with one exception: the H. costata ICE-based SSDs and the empirical SSDs18 for constant exposures to dispersed oil (p-value = 0.01; Figure 5). In most cases, ICE-based models with H. costata and A. bahia as surrogates, produced smaller HC5s indicative of a slight model bias toward overprotection of aquatic species. In all cases, HC5s from ICE-based SSDs were within the same order of magnitude as HC5s from empirical SSDs. HC5s for Corexit 9500 and Corexit 9727 from ICE-based SSDs were also similar to those reported by Barron et al.10 Hazard concentrations (96 h HC1 and HC5) were estimated for fresh petroleum and dispersant products for which data, on the basis of measured concentrations, were available from constant static and spiked flow-through exposures (Table 1). These hazard concentrations were estimated using ICE-based SSD with models from each of two surrogate species (A. bahia and M. beryllina). An additional set of SSDs was also constructed by combining all available empirical data plus

Figure 4. Comparison between species sensitivity distribution (SSD) curves for all petroleum products from Barron et al.10 (dashed line), and SSDs derived using all ICE models, regardless of their reliability, with A. bahia (black dots and lines) and M. beryllina (blue dots and lines) as the surrogate species. These curves are not significantly different (p-value >0.05) from each other.

for these surrogate species regardless of model reliability. Because there were insufficient paired surrogate-predicted ICE

Figure 5. Comparison between physically and chemically dispersed oil (top), and dispersant (bottom) species sensitivity distribution (SSD) using empirical18 and ICE-based models (red dots and lines; red dashed line from Barron et al.10 for dispersants only). SSDs using empirical data for dispersed oil were developed for constant and spiked exposures separately. SSDs were derived using all ICE models regardless of their reliability with A. bahia (black dots and lines) and H. costata (blue dots and lines) as surrogate species, combining models for these two species for dispersant SSDs (black lines). Within panels, SSDs are not significantly different (p-value > 0.05) from each other, except for the comparison of H. costata ICE-based SSDs and the empirical SSDs for constant exposures to dispersed oil (p-value = 0.01; top left). 4568

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Table 1. Estimated HC1 and HC5 Petroleum Hydrocarbon (THC) Concentrations (mg/L) for Specific Petroleum and Dispersant Products and Exposure Regimesa petroleum/dispersant product

experimental conditions

Alaska North Slope

constant static

surrogate: A. bahia HC1 (first row) | HC5 (second row) (95% CI)

Corexit 7664

flow-through

0.55 0.69 1.03 1.32 NA NA 0.15 0.18 2.32 3.06 0.23 0.28 2.62 3.49 NA NA 2.40 3.19 0.92 1.15 0.13 0.16 2.12 2.78 0.19 0.24 0.84 1.07 0.62 0.71 1.74 2.25 NA

Corexit 9500

constant static

NA

NA

flow-through

NA

NA

constant static

NA

NA

flow-through

NA

NA

flow-through Arabian Medium Forties

flow-through constant static flow-through

Kuwait

constant static flow-through

Prudhoe Bay

constant static flow-through

South Louisiana Venezuelan

constant static constant static flow-through

Light crudesb

constant static flow-through

Medium crudesc

constant static flow-through

Corexit 9527

(0.30−0.92) (0.44−1.02) (0.61−1.60) (0.93−1.86)

surrogate: M. beryllina HC1 (first row) | HC5 (second row) (95% CI) 3.03 3.97 3.72 4.92 0.59 0.71 0.14 0.16 2.10 2.70 0.21 0.23 1.56 1.97 1.94 2.48 3.11 4.09 1.27 1.58 0.19 0.21 0.29 0.33 0.44 0.53 0.64 0.78 1.21 1.50 1.72 2.20 NA

(0.07−0.28) (0.10−0.29) (1.41−3.66) (2.11−4.29) (0.11−0.41) (0.17−0.42) (1.58−4.29) (2.43−4.95)

(1.40−3.90) (2.21−4.50) (0.51−1.49) (0.76−1.68) (0.06−0.24) (0.09−0.25) (1.27−3.38) (1.94−3.93) (0.09−0.35) (0.13−0.4) (0.45−1.44) (0.61−1.74) (0.41−0.89) (0.50−0.98) (1.04−2.85) (1.44−3.55)

(1.96−4.55) (2.93−5.35) (2.44−5.63) (3.69−6.57) (0.34−0.95) (0.49−1.00) (0.06−0.29) (0.08−0.26) (1.36−3.16) (1.98−3.62) (0.10−0.37) (0.14−0.37) (1.01−2.31) (1.45−2.61) (1.24−2.84) (1.82−3.31) (2.01−4.68) (3.03−5.40) (0.80−1.94) (1.16−2.10) (0.09−0.36) (0.12−0.35) (0.14−0.50) (0.20−0.50) (0.24−0.74) (0.31−0.82) (0.37−1) (0.48−1.2) (0.74−1.88) (0.98−2.21) (1.09−2.63) (1.51−3.12)

empirical+ ICE-predicted HC1 (first row) | HC5 (second row) (95% CI) 0.45 (0.24−0.77) 0.63 (0.37−0.99) 1.27 (0.75−2.06) 1.72 (1.09−2.58) NA NA 0.17 (0.07−0.34) 0.18 (0.09−0.36) 1.76 (1.05−2.72) 2.28 (1.50−3.35) 0.23 (0.12−0.41) 0.26 (0.15−0.43) 1.26 (0.77−1.9) 1.57 (1.05−2.23) NA NA 3.54 (2.24−5.41) 4.52 (3.14−6.42) 0.75 (0.43−1.20) 0.97 (0.63−1.49) 0.16 (0.07−0.31) 0.18 (0.09−0.32) 0.36 (0.19−0.61) 0.42 (0.25−0.66) 0.22 (0.11−0.39) 0.29 (0.16−0.47) 0.36 (0.17−0.66) 0.47 (0.24−0.82) 0.27 (0.12−0.49) 0.35 (0.17−0.62) 2.35 (1.52−3.54) 2.98 (2.08−4.14) 375 (182−744) 789 (478−1145) 2.24 (1.25−3.73) 3.33 (2.15−5.13) 27 (16−42) 40 (27−60) 1.09 (0.62−1.80) 1.62 (1.02−2.49) 5.51 (3.44−8.60) 7.65 (5.27−11.02)

HC Values were generated from ICE-based SSDs using surrogate-predicted ICE Models with at least five pairs per unique surrogate, as well as by combining empirical and ICE predicted toxicity data from one or more surrogate species. Known surrogate concentrations include 96 h THC toxicity data from physically dispersed fresh oil and fresh oil chemically dispersed with Corexit 9500, except for Kuwait Oil, which was chemically dispersed with Corexit 9527. Surrogate toxicity data are the geometric mean of all measured values for the same species.14,15,27−37 NA, data not available. bForties + South Louisiana + Venezuelan. cAlaska North Slope + Arabian Medium + Kuwait + Prudhoe Bay.

a

species were similar to those with M. beryllina, and were generally within the same order of magnitude of each other. HC values from SSDs that combined empirical data and predicted toxicity values from one or more surrogate species were also similar to those derived with either A. bahia or M. beryllina. In all cases, spiked flow-through exposures produced HC values up to 12 times greater than HC values from static exposures, with Venezuelan and Alaska North Slope crude oil having the largest and smallest differences, respectively, between these exposure types. SSDs using A. bahia or M. beryllina as surrogate species, and SSDs with empirical plus ICE

ICE toxicity values for one or more surrogate species (A. bahia, H. costata, M. beryllina and A. af f inis), keeping the smallest predicted value for each unique predicted- species (SI Figure S5). Starting concentrations for surrogate species were those of studies reporting measured toxicity data,14,15,27−37 and excluded from model development. HC1s and HC5s for seven fresh oils and by oil category ranged from 0.13 mg THC/L to 3.72 mg THC/L, and from 0.16 to 4.92 mg THC/L, respectively. Venezuelan crude oil had the lowest HC values (more toxic), while Alaska North Slope oil had the highest values (less toxic). HC values from ICE-based SSDs with A. bahia as the surrogate 4569

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associated with petroleum or dispersant products, particularly when data are limited. Moreover, data from concurrent toxicity testing of rarely investigated taxa (e.g., corals, pelagic fish) and a surrogate species for which ICE models are available (e.g., A. bahia or M. beryllina), can be used to construct an ICE-based SSD allowing for the placement in the curve of the species for which little toxicity data, facilitating comparisons of relative sensitivities. As demonstrated here, ICE models could be used to augment estimates of benchmark concentrations from spiked flow-through exposures that may be more applicable to shortduration oil spills, and for which toxicity data are less available in the scientific literature.13 As shown here and elsewhere,2,6,7 ICE-based SSDs can produce HC5 values similar to those generated from empirical SSDs, adding reliability to the use of ICE models to augment toxicity data. Here, HC5s from ICEbased SSDs for petroleum and Corexit dispersants were within 1 order of magnitude HC5s from SSDs with empirical data.10,18 While previous studies have recommended between 7 and 15 species to develop reliable SSDs and associated benchmarks,2,7 the minimum number of species used here was 5. As a result, petroleum ICE-based SSDs could only be developed for 9 surrogate species (including A. bahia and M. beryllina), or by combining data for at least two species as demonstrated with the dispersant ICE-based SSDs. While A. bahia generally appears to be more sensitivity than M. beryllina (as shown here and elsewhere10), SSDs from ICE models with M. beryllina as the surrogate species did not result in significantly larger HC values. Consequently, the surrogate set of models that leads to smaller HC values may be preferred, particularly when protection of especially sensitive species is a concern, or when there are concerns about species in specific microhabitat (e.g., sheltered salt marshes, mangroves or tidal flats) within coastal ecosystems. One of the limitations of the current study is that, because of the nature of the existing toxicity data, models were developed under the assumption that petroleum hydrocarbons represent a single compound with a predominant mode of toxicity (nonpolar narcosis). In reality, petroleum hydrocarbons are a complex mixture of chemicals with more than one mode of toxicity (narcosis, receptor-mediated). Because compounds in these mixtures have different chemical properties and affinities for lipids, it is widely recognized that their composition determine their overall toxicity.43−45 This limitation, however, could be overcome by using quantitative structure activity relationships (QSARs),2 which are relationships based on chemical structure. QSAR toxicity data can provide surrogate values for the development of hydrocarbon-specific ICE models,2 or could be used to develop QSAR surrogatepredicted species ICE models.46 Future refinements of ICE models are dependent upon greater availability of detailed analytical chemistry results (e.g., individual PAH analytes), which are currently lacking in most petroleum and dispersant toxicity data sources.13 In this study petroleum and dispersant ICE models were developed and used to generate ICE-based SSDs. While the development of ICE models was limited by the availability of toxicity data meeting the rigorous standardization criteria, the information presented here could facilitate assessments of the potential toxicological consequences oil and dispersants to aquatic communities, aid in the estimation of concentration associated with low or no effects, and allow for comparisons of the relative sensitivity across test species. ICE-based SSDs could also be used in conjunction with environmental

toxicity values were not statistically different (p-value > 0.05) within petroleum products and by experimental condition. While there were insufficient dispersant ICE models for A. bahia or M. beryllina as surrogate species to produce ICE-based SSDs, combined data from empirical and ICE toxicity values allowed the estimation HC values for three dispersants.

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DISCUSSION One of the greatest challenges in evaluating potential impacts to aquatic communities during oil spills and impact assessment continues to be uncertainties in species sensitivity. The Deepwater Horizon oil spill further highlighted the need to understand the sensitivity of the diversity of aquatic species in the deep ocean, pelagic and coastal areas with generally limited existing toxicity data.38 The current study provides both petroleum and dispersant-specific toxicity estimation models that can be applied to a broad range of aquatic species assemblages using a SSD-based approach. The practical use of existing oil toxicity data has been limited by the general lack of standardized laboratory practices, including differences in media preparation.13,14,39,40 While these issues may have added uncertainty to the development of petroleum and dispersant ICE models, emphasis was placed on rigorously standardizing data selection with the sole intent of reducing the uncertainty introduced by differences in experimental procedures across studies. As a result, and despite inherent limitations with existing data, all statistically significant ICE models had a relatively small MSE (range 0.0002−0.311), indicative of a robust model fit. As indicated elsewhere,6 and applicable here, these robust ICE model relationships suggest that the same mechanisms of action for each species pair may be at play. Furthermore, MSE values associated with 85% and 95% cross-validation success rates of ICE models were smaller (0.126 and 0.04, respectively) than MSE values associated with the same cutoffs (0.22 and 0.15, respectively) for ICE models with wildlife species.5 In addition, predicted values from ICE models were generally within 5-fold difference of the observed data, with most models being within 2-fold of the observed data. These predicted-observed differences are well within the fold difference commonly found across laboratories (fold difference of 3)41 during optimal interlaboratory comparisons with the same species. Taxonomic relatedness has been previously shown to influence model fit and reliability of ICE models developed from chemicals with mixed modes of action.4,5 In contrast, petroleum and dispersant-specific ICE models showed no influence of taxonomic distance on model accuracy. These results suggest that ICE models developed with chemicals with a common nonspecific mode of chemical action such as narcosis can be used to predict toxicity across a broad range of taxa, and can help improve predictions over ICE models from with mixed modes of action. While SSDs have been used in the field of aquatic toxicology and integrated into the regulatory framework,42 their use in oil spill research has been limited.10−12 SSDs and derived benchmarks can be used to protect untested species under the assumption that their sensitivity is within the range of sensitivities captured by the species in the SSD. Although SSDs cannot replace toxicity testing, they can provide additional information when the costs of toxicity testing are prohibitive or species-specific testing is restricted or not feasible (e.g., endangered, rare, deepwater species). Furthermore, both empiric and ICE-based SSDs can help inform resource managers in their assessment of potential acute effects 4570

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concentrations from fate models or environmental monitoring to help characterize, via joint probability distribution curves, the fraction of potentially affected species or the magnitude of adverse effects to aquatic communities.8 The inclusion of additional paired-empirical data for a wider number of species, particularly for sensitive life stages, may allow for further application of ICE models in damage assessments so as to allow comparisons across communities (e.g., epibenthic vs benthic effects assessments) and habitats (e.g., tidal flats vs marshes). While model verification showed promising results, additional toxicity data could help improve existing ICE models and facilitate the development of additional ones. Of special interest is the inclusion of paired standard test-sensitive or rare species toxicity data, which is essential to refine petroleum and dispersant benchmark concentrations protective of the most sensitive or untested species.

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REFERENCES

(1) Asfaw, A., Ellersieck, M. R., Mayer, F. L. Interspecies Correlation Estimations (ICE) for Acute Toxicity to Aquatic Organisms and Wildlife. II. User Manual and Software, EPA/600/R-03/106; United States Environmental Protection Agency: Washington, DC, 2003. (2) Dyer, S. D.; Versteeg, D. J.; Belanger, S. E.; Chaney, J. G.; Mayer, F. L. Interspecies correlation estimates predict protective environmental concentrations. Environ. Sci. Technol. 2006, 40 (9), 3102−3111. (3) Golsteijn, L.; Hendriks, H. W.; van Zelm, R.; Ragas, A. M.; Huijbregts, M. A. Do interspecies correlation estimations increase the reliability of toxicity estimates for wildlife? Ecotoxicol. Environ. Saf. 2012, 80, 238−243. (4) Raimondo, S.; Jackson, C. R.; Barron, M. G. Influence of taxonomic relatedness and chemical mode of action in acute interspecies estimation models for aquatic species. Environ. Sci. Technol. 2010, 44 (19), 7711−7716. (5) Raimondo, S.; Mineau, P.; Barron, M. Estimation of chemical toxicity to wildlife species using interspecies correlation models. Environ. Sci. Technol. 2007, 41 (16), 5888−5894. (6) Dyer, S. D.; Versteeg, D. J.; Belanger, S. E.; Chaney, J. G.; Raimondo, S.; Barron, M. G. Comparison of species sensitivity distributions derived from interspecies correlation models to distributions used to derive water quality criteria. Environ. Sci. Technol. 2008, 42 (8), 3076−3083. (7) Awkerman, J. A.; Raimondo, S.; Barron, M. G. Development of species sensitivity distributions for wildlife using interspecies toxicity correlation models. Environ. Sci. Technol. 2008, 42 (9), 3447−3452. (8) Posthuma, L.; Suter II, G. W.; Traas, T. P. Species Sensitivity Distributions in Ecotoxicology; Lewis: Boca Raton, FL, 2002. (9) De Zwart, D., Observed regularities in species sensitivity distributions for aquatic species. In Species Sensitivity Distributions in Ecotoxicology; Posthuma, L., Suter, G. W., Traas, T. P., Ed. Lewis Publishers: Boca Raton, FL, 2002; pp 133−154. (10) Barron, M. G.; Hemmer, M. J.; Jackson, C. R. Development of aquatic toxicity benchmarks for oil products using species sensitivity distributions. Integr. Environ. Assess. Manage. 2013, 9 (4), 610−615. (11) Bejarano, A. C.; Levine, E.; Mearns, A. Effectiveness and potential ecological effects of offshore surface dispersant use during the Deepwater Horizon oil spill: A retrospective analysis of monitoring data. Environ. Monit. Assess. 2013, 185, 10281−10295. (12) de Hoop, L.; Schipper, A. M.; Leuven, R. S.; Huijbregts, M. A.; Olsen, G. H.; Smit, M. G.; Hendriks, A. J. Sensitivity of polar and temperate marine organisms to oil components. Environ. Sci. Technol. 2011, 45 (20), 9017−9023. (13) Bejarano A. C.; Clark J. R.; Coelho, J. M., Issues and challenges with oil toxicity data and implications for their use in decision making: A quantitative review. In Environ. Toxicol. Chem. DOI: 10.1002/ etc.2501, 2014. (14) Aurand, D.; Coelho, G. Cooperative Aquatic Toxicity Testing of Dispersed Oil and the “Chemical Response to Oil Spills: Ecological Effects Research Forum (CROSERF)”. Technical Report.; EM&A Inc.: Lusby, MD, 2005; p 79. (15) Clark, J. R.; Bragin, G. E.; Febbo, R.; Letinski, D. J. In Toxicity of Physically and Chemically Dispersed Oils under Continuous and Environmentally Realistic Exposure Conditions: Applicability to Dispersant Use Decisions in Spill Response Planning, Proceedings of the 2001 International Oil spill Conference; American Petroleum Institute: Tampa, FL, March 26−29, 2001; pp 1249−1255. (16) Singer, M.; Smalheer, D.; Tjeerdema, R.; Martin, M. Toxicity of an oil dispersant to the early life stages of four California marine species. Environ. Toxicol. Chem. 1990, 9 (11), 1387−1395. (17) Singer, M. M.; George, S.; Jacobson, S.; Lee, I.; Tjeerdema, R. S.; Sowby, M. L. Comparative toxicity of Corexit ® 7664 to the early life stages of four marine species. Arch. Environ. Contam. Toxicol. 1994, 27 (1), 130−136. (18) Bejarano, A. C.; Chu, V.; Farr, J.; Dahlin, J., Development and application of DTox: A quantitative database of the toxicological effects of dispersants and chemically dispersed oil. In Proceedings of the

ASSOCIATED CONTENT

S Supporting Information *

Additional information related to ICE models is available in Supporting Information 1. This information includes a complete list of references of the original sources, One table containing scientific and common names, two tables containing statistically significant ICE model parameters, 1 Table comparing empirical and ICE based HC5s, 1 Figure of the model building scheme, two figures of the fitted models, one figure on taxonomic relatedness, and one figure with representative SSDs. Supporting Information 2 contains core data used in the development of ICE models. This material is available free of charge via the Internet at http://pubs.acs.org.

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Article

AUTHOR INFORMATION

Corresponding Author

*Phone: +1 803 254 0278; fax: + 1 803 254 6445; e-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS Special thanks to C. Jackson and S. Raimondo (U.S. EPA) for comments to an earlier version of this manuscript. This research was made possible by a grant from NOAA and the University of New Hampshire’s Coastal Response Research Center (Contract No. 13-034) to Research Planning, Inc. None of these results have been reviewed by CRRC and no endorsement should be inferred. The views expressed in this article are those of the authors and do not necessarily reflect the views or policies of the U.S. EPA. This publication does not constitute an endorsement of any commercial product.

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ABBREVIATIONS adj-R2 adjusted coefficient of determination HC1 and HC5 1st and fifth percentile hazard concentrations ICE interspecies correlation estimation LC50 and EC50 median lethal and effects concentrations, respectively MSE mean square error SSD species sensitivity distributions THC total hydrocarbon content 4571

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2014 International Oil Spill Conference, American Petroleum Institute: Savanah, GA, 2014, in press. (19) USEPA, ECOTOX. Release 4.0. Ecotoxicology Database; US Environmental Protection Agency: Duluth, MN, 2012. www.epa.gov/ ecotox. (20) Neter, J.; Wasserman, W.; Kutner, M. H., Applied Linear Statistical Models: Regression, Analysis of Variance, And Experimental Designs; McGraw-Hill Higher Education: Boston, MA, 1990. (21) Piegorsch, W. W.; Bailer, A. J., Statistics for Environmental Biology and Toxicology; Chapman & Hall: London, UK., 1997. (22) R: A Language and Environment for Statistical Computing,Version 2.13.2011-09-30; R Development Core Team: Vienna, Austria, 2011. (23) Canty, A.; Ripley, B. boot: Bootstrap R (S-Plus) Functions, R package version 1.3-9, 2013. (24) Davison, A. C.; Hinkley, D. V., Bootstrap Methods and Their Applications; Cambridge University Press: Cambridge, 1997. (25) Bejarano, A. C.; Farr, J. K. Development of short acute exposure hazard estimates: A tool for assessing the effects of chemical spills in aquatic environments. Environ. Toxicol. Chem. 2013, 32 (8), 1918− 1927. (26) Looney, S. W.; Gulledge, T. R., Jr Use of the correlation coefficient with normal probability plots. Am. Stat. 1985, 39 (1), 75− 79. (27) Bragin, G.; Clark, J.; Pace, C. Comparison of Physically and Chemically Dispersed Crude Oil Toxicity to Both Regional and National Test Species under Continuous and Spiked Exposure Scenarios, MSRC Technical Report Series 94-015; Marine Spill Response Corporation: Washington, DC, 1994; pp 1−45. (28) Bragin, G. E.; Clark, J. R. Chemically Dispersed Crude Oils: Toxicity to Regional and National Test Species under Constant and Spiked Exposures; Marine Spill Response Corporation: Washington DC, 1995; pp 1−44. (29) Hemmer, M. J.; Barron, M. G.; Greene, R. M. Comparative toxicity of eight oil dispersants, Louisiana sweet crude oil (LSC), and chemically dispersed LSC to two aquatic test species. Environ. Toxicol. Chem. 2011, 30 (10), 2244−2252. (30) Pace, C. B.; Clark, J. R.; Bragin, G. E. In Comparing Crude Oil Toxicity under Standard and Environmentally Realistic Exposures, Proceedings of the 1995 International Oil Spill Conference; American Petroleum Institute: Washington, DC, February 27 to March 2, 1995, 1995; pp 1003−1004. (31) Perkins, R. A.; Rhoton, S.; Behr-Andres, C. Toxicity of dispersed and undispersed, fresh and weathered oil to larvae of a cold-water species, Tanner crab (C. bairdi), and standard warm-water test species. Cold Reg. Sci. Technol. 2003, 36 (1), 129−140. (32) Perkins, R. A.; Rhoton, S.; Behr-Andres, C. Comparative marine toxicity testing: A cold-water species and standard warm-water test species exposed to crude oil and dispersant. Cold Reg. Sci. Technol. 2005, 42 (3), 226−236. (33) Rhoton, S. Acute Toxicity of the Oil Dispersant Corexit 9500, and Fresh and Weathered Alaska North Slope Crude Oil to the Alaskan Tanner Crab (C. bairdi), Two Standard Test Species, and V. fischeri (Microtox Assay). MSc Thesis, Institute of Northern Engineering, University of Alaska: Fairbanks, AK 2000. (34) Wetzel, D.; Van Fleet, E. S. In Cooperative Studies on the Toxicity of Dispersants and Dispersed Oil to Marine Organisms: A 3-Year Florida Study, Proceedings of the 2001 International Oil spill Conference; American Petroleum Institute: Tampa, FL, March 26−29, 2001; pp 1237−1241. (35) Gardiner, W.; Word, J.; Word, J.; Perkins, R.; McFarlin, K.; Hester, B.; Word, L.; Ray, C. The acute toxicity of chemically and physically dispersed crude oil to key arctic species under arctic conditions during the open water season. Environ. Toxicol. Chem. 2013, 32 (10), 2284−2300. (36) Singer, M.; George, S.; Lee, I.; Jacobson, S.; Weetman, L.; Blondina, G.; Tjeerdema, R.; Aurand, D.; Sowby, M. Effects of dispersant treatment on the acute aquatic toxicity of petroleum hydrocarbons. Arch. Environ. Contam. Toxicol. 1998, 34 (2), 177−187.

(37) Singer, M. M.; Jacobson, S.; Tjeerdema, R. S.; Sowby, M. In Acute Effects of Fresh versus Weathered Oil to Marine Organisms: California Findings, Proceedings of the 2001 International Oil Spill Conference; American Petroleum Institute, Tampa, FL, USA, March 26−29, 2001; pp 1263−1268. (38) Carriger, J. F.; Barron, M. G., Minimizing risks from spilled oil to ecosystem services using influence diagrams: The Deepwater Horizon spill response. Environ. Sci. Technol. 45, (18), 7631-7639. (39) Coelho, G.; Clark, J.; Aurand, D. Toxicity testing of dispersed oil requires adherence to standardized protocols to assess potential real world effects. Environ. Pollut. 2013, 177, 185−188. (40) Singer, M.; Tjeerdema, R.; Aurand, D.; Clark, J.; Sergy, G.; Sowby, M. In CROSERF: Toward a Standardization of Oil Spill Cleanup Agent Ecological Effects Research, Arctic and marine oil spill program technical seminar, 1995; Ministry of Supply and Services: Canada, 1995; pp 1263−1270. (41) Rand, G.; Wells, P.; McCarty, L. Chapter 1. Introduction to Aquatic Toxicology. In Fundamentals of Aquatic Toxicology Effects, Environmental Fate, And Risk Assessment; Rand, G., Ed.; Taylor and Francis Publishers: North Palm Beach, FL, 1995; pp 3−67. (42) Barron, M. G.; Wharton, S. R. Survey of methodologies for developing media screening values for ecological risk assessment. Integr. Environ. Assess. Manage. 2005, 1 (4), 320−332. (43) Di Toro, D. M.; McGrath, J. A.; Hansen, D. J. Technical basis for narcotic chemicals and polycyclic aromatic hydrocarbon criteria. I. Water and tissue. Environ. Toxicol. Chem. 2000, 19 (8), 1951−1970. (44) Di Toro, D. M.; McGrath, J. A.; Stubblefield, W. A. Predicting the toxicity of neat and weathered crude oil: Toxic potential and the toxicity of saturated mixtures. Environ. Toxicol. Chem. 2007, 26 (1), 24−36. (45) USEPA Procedures for the Derivation of Equilibrium Partitioning Sediment Benchmarks (ESBs) for the Protection of Benthic Organisms: PAH Mixtures. EPA-600-R-02-013. http://www. epa.gov/nheerl/download_files/publications/PAHESB.pdf (accessed 8 March 2013). (46) Chicu, S.; Putz, M. Köln-Timişoara Molecular activity combined models toward interspecies toxicity assessment. Int. J. Mol. Sci. 2009, 10 (10), 4474−4497.

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