Survival time models quantitatively can predict lethal effects

of pulsed and different duration exposures to water-accommodated fraction PAH from spilt oil

A Final Report Submitted to

The Coastal Response Research Center

Submitted by

Dr. Michael C. Newman Dr. Michael A. Unger

Department of Environmental and Aquatic Animal Health Virginia Institute of Marine Science

College of William and Mary P.O. Box 1346

Gloucester Point, VA 23062

Project Period: February 1, 2005 to January 31, 2007

Submitted: August 10, 2007

This project was funded by a grant from NOAA/UNH Coastal Response Research Center. NOAA Grant Number(s): V710640. Project Number: 05-956

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Abstract The CRRC 2004 RFP identified the need to quantitatively predict injury from realistic conditions including pulsed, short term and long term exposures. The research described herein addresses this 2004 priority area. Toxicity data derived from conventional concentration-effect test designs predict effect at a single exposure time. A few test durations might be used in some tests to coarsely predict how mortality changes with exposure time. Even in tests employing several exposure times, the selected concentration treatments generate optimal data for only one of the exposure durations. Predictions of effect at different durations are unavoidably gross because mortality information was not collected optimally during the entire exposure. Also, the conventional concentration-effect approach does not quantify mortality that potentially could occur after exposure stops. Such post-exposure mortality can be quite high. These shortcomings can be avoided by noting mortality in test treatments through time including post-exposure mortality, and applying survival time modeling to the resulting data. Survival time models allow inclusion of covariates such as exposure concentration, resulting in models that include both exposure concentration and duration. We parameterized survival time models with data generated for six representative polycyclic aromatic hydrocarbons (PAH) associated with the water-accommodated fraction of oil. Survival models incorporating exposure concentration and duration were produced for the grass shrimp, Palaemonetes pugio, a common test species and an ecologically important one in salt marshes and other coastal environments. The absence of post-exposure mortality allowed use of models based only on mortality during the actual exposure. The results were then used to demonstrate a QSAR approach to predicting survival time model parameters for untested PAH. Keywords: PAH, survival, intermittent exposure, grass shrimp

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Acknowledgments Funding was provided by the NOAA/UNH Coastal Response Research Center (V710640). Help with animal maintenance and tests provided by John Carriger and Kyle Tom is much appreciated.

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Table of Contents

1.0 Introduction …………………………………………………………………………………..6 2.0 Objectives …………………………………………………………………………………….7 3.0 Methods ………………………………………………………………………………………8 4.0 Results ………………………………………………………………………………………..9 5.0 Discussion and Importance to Oil Spill Response/Restoration ……………………………..14 6.0 Technology Transfer ………………………………………………………………………...10 7.0 Achievement and Dissemination ..……………………………………………………….…15 8.0 References ...…………………………………………………………………………………16 Appendices ………………………………………………………………………………………17

Appendix A. ……………………………………………………………………………..18 Appendix B ……………………………………………………………………………...44

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List of Figures and Tables

FIGURES Figure 1. Cumulative mortality for amphipods (Hyalella azteca) exposed for 48 hours to four copper concentrations (Zhao and Newman 2004). Substantial post-exposure mortality occurred, reflecting damage incurred during exposure. Conventional toxicity test designs and associated metrics do not quantify post-exposure mortality, leading to the possibility of a downward inaccuracy in predicted mortality for field exposures. Figure 2. Exposure tanks used in all shrimp survival time experiments. Figure 3. Survival curves (bars indicate the estimated standard errors) from the phenanthrene tests. With the termination of exposure (60 hr), mortality quickly dropped to a minimum level in all concentration treatments. Greenwood’s formula was used to produce the standard errors shown as vertical bars for each exposure time. Figure 4. An illustration of the ability of survival time models to predict the mortality with different combinations of exposure time and concentration. In this figure, contours for percentage of exposed shrimp expected to die are shown. The 48 hr LC50 estimate is also shown for comparison. Less than 5% of the mortality during the entire test was post-exposure mortality. Figure 5. QSAR models for 48 hr LC50 (top panel) and parameter estimates (b1 and b2) for the survival time models (middle and bottom panel). Figure 6. Concentration-effect (log normal) model slopes for the six compounds estimated with the Microtox7 toxicity assay. Brackets indicate 95% confidence intervals. TABLES Table 1. Concentration-effect models (48 hr exposure duration)(95% CI = 95% confidence interval, SE = standard error) Table 2. Parameter estimates for survival models. (95% CI = 95% confidence interval, SE = standard error)

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1.0 Introduction

The CRRC 2004 RFP identified the need to quantitatively predict injury from realistic exposures including pulsed, short term and long term exposures. The research described here addresses this priority area, illustrating an approach to combining exposure duration and concentration into lethality predictions, and generating information applicable nationwide to oil spills. The general approach might also be relevant to predicting injury when confounding factors change through time, e.g., changes in PAH-induced mortality due to diurnal changes in UV light intensity or seasonal temperature changes. Most toxicity data are derived from conventional concentration-effect test designs that produce an effect metric, such as the 96 hr LC50, at one set exposure time. Occasionally, a few test durations might be used to coarsely predict how mortality changes with exposure time. Gross prediction is inevitable if mortality information was not collected throughout the exposure. As a further complication in the conventional concentration-effect approach, mortality potentially occurring after exposure stops is not considered in predictions. In the few studies that quantified such post-exposure (latent) mortality, it was found to vary widely and could be quite high. Mosquitofish exposed to a concentration of sodium chloride that killed 15% of exposed fish by 144 hr, experienced an additional 44% mortality in the days following the pulsed exposure (Newman and McCloskey 1996, 2002). Zhao and Newman (2004) measured high (15 to 35%; copper sulfate; e.g., Figure 1) to minimal (2 to 5%; sodium pentachorophenol) latent mortality of amphipods. The magnitude of latent mortality appeared to depend on the mode of action, consequent residual damage, and recuperative capacity of the exposed individual. The conventional approach is limited relative to predicting all mortality from pulsed exposures differing in duration from conventional tests. These shortcomings are weighty impediments to accurately predicting effects from spilt oil exposures that vary in both duration and concentration through time, and that require prediction of all mortality resulting from exposure.

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0.1

0.2

0.3

0.4

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0.6

0.7

0.8

0.9

0 20 40 60 80 100 120

Prop

ortio

n D

ead

Time (hours)

Exposure Post Exposure

Control

0.2 mg/L

0.3 mg/L

0.4 mg/L

0.6 mg/L

Figure 1. Cumulative mortality for amphipods (Hyalella azteca) exposed for 48 hours to four copper concentrations (Zhao and Newman 2004). Substantial post-exposure mortality occurred, reflecting damage incurred during exposure. Conventional toxicity test designs and associated metrics do not quantify post-exposure mortality, leading to the possibility of a downward inaccuracy in predicted mortality for field exposures.

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2.0 Objectives The shortcomings just described can be avoided by noting mortality in test treatments through time including any potential post-exposure mortality, and applying survival time models to the resulting data. We applied survival time methods to data generated for representative PAH1 associated with weathered oil. Survival models capable of incorporating exposure concentration and duration, and post-exposure mortality were produced using widely accepted methods from health science, social sciences, engineering, and ecology (Allison 1995, Cox and Oakes 1984, Kalbfleisch and Prentice 1980, Muenchow 1986, Miller 1981, Nelson 1972). Although underutilized in ecotoxicology, the PI (Newman and Dixon 1996, Newman and McCloskey 1996, 2002, Zhao and Newman 2004) and others (Crane et al. 2002, Lee et al., 2002) applied this approach successfully to other ecotoxicity test data and risk assessment information. We also began addressing issues germane to predicting joint effects of the PAH in the WAF mixture. As described in the references cited above, survival time models can predict mortality during and after exposures of different durations and concentrations. Models were developed for the grass shrimp, Palaemonetes pugio, exposed to key PAH associated with weathered oil. By conducting toxicity tests with these PAH, we were later able to incorporate compound qualities (e.g., log Kow) into predictive models. The selected PAH were 1-ethylnaphthalene, 2,6-dimethylnaphthalene, dibenzothiophene, fluorene, naphthalene, and phenanthrene. The grass shrimp was chosen because it is a common test species, and is an ecologically important one in salt marshes and other coastal environments. Because of the areas it inhabits, it would also be subject to periodic exposures of a pulsed nature, e.g., compounds released from deposited weathered oil during tidal cycles, or varying in exposure concentration and duration due to spill characteristics or dispersant application. Specific objectives were the following:

1. Produce predictive survival time models for grass shrimp exposed to a pulse of six representative PAH in weathered oil WAF. Models included predictions of pulse and post-pulse mortality under realistic ranges of exposure duration and concentration. This objective was met by generating models for each compound. The ease of model generation was greatly improved by the observation of minimal post-exposure mortality for shrimp exposed to these narcotics.

2. Produce survival time models that incorporate molecular qualities (e.g., log Kow) of the six PAH. Such a model will allow a level of prediction by interpolation to other untested PAH in spilt oil. Several chemical properties allowed generation of QSAR models for conventional LC50 and survival time model parameters. In this report, we use the conventional lipophilicity metric, log Kow, to illustrate the QSAR models.

3. Produce predictive survival time models for grass shrimp exposed to pulses of a PAH mixture. Models include predictions of pulse and any potential post-pulse mortality at a range of realistic exposure concentrations and durations. Several experimental designs were assessed and a final design employing a mixture of two substituted PAH

1 The term, PAH, is used for clarity throughout this report; however, one compound (dibenzothiophene) was a heterocyclic aromatic hydrocarbon.

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(ethylnaphthalene and dimethylnaphthalene) and the heterocyclic aromatic hydrocarbon, dibenzothiophene, was executed. The difficulties of simultaneously maintaining the dosing concentrations for these three compounds resulted in concentrations producing too low mortality in the single compound treatments so the test of combining effects could not be done with the planned conceptual approach.

3.0 Methods

Because specific methodological details are provided in the attached manuscripts (Appendices A and B), only general descriptions will be provided here. Grass shrimp were exposed (Figure 2, 26 shrimp/tank) to a series of concentrations of each of six PAH. The range of concentrations was determined during pilot studies with the intent of encompassing concentrations above the threshold of apparent lethal effect and below the PAH solubility in saltwater. Shrimp were kept in separate glass tubes with free exchange of exposure tank solutions. Triplicate tanks were randomly placed on two adjacent water tables for each of five (including controls) exposure concentration treatments. Tanks were not aerated to reduce PAH volatilization. Exposure solutions were renewed every 12 hours.

Figure 2. Exposure tanks used in all shrimp survival time experiments. At death or the end of the experiment, shrimp were removed from tanks, weighed, and frozen. Body concentrations of the exposure PAH were determined in subsets of shrimp with the intent of estimating body burdens upon death, and fortuitously, estimating elimination rate constants during the post-exposure period. Exposure data for each PAH were fit to a log logistic survival model,

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)()(lnln 321 WbionConcentratbbDeathtoTime ++=−− (1) where b1 = the model intercept parameter estimate, b2 = model parameter estimate for the effect of PAH concentration, and b3 = a scaling parameter estimate. The W is associated with a specific proportion dying. In this case of a log logistic model, W is the log odds, i.e., ln(P/(1-P)) where P is the proportion of exposed individuals dying. As an example, W for predicting median time-to-death (P=0.5) produces a W = ln(0.5/(1-0.5) = 0. The units of concentration are ug/L. Under the intense and relatively brief exposure conditions, the mode of action for all six compounds was judged to be general narcosis. Consequently, one could begin by assuming that models for joint similar action could be applied. Such models assume a common slope for all similar acting compounds so that the proportion of exposed individuals dying during exposure to a specified mixture of these compounds can be estimated with the separate probit models for the individual toxicants:

)(log)(Pr AAA ionConcentratSlopeInterceptPobit += (2)

)(log)(Pr BBB ionConcentratSlopeInterceptPobit += (3)

For the conventional concentration-effect design, the log of relative potency can be calculated.

SlopeInterceptInterceptLog ABB

−=

(ρ (4)

The combined effect of two similar acting toxicants (PA+B) would then be predicted as the following,

))(log(log BBAABA ionConcentrationConcentratSlopeInterceptP ρ++=+ (5) The assumption can be assessed by projecting this reasoning to survival time models that similar acting PAH should have similar b2 values. Also the survival time models for each PAH in a mixture could be used together to predict the total proportion dying at any time.

4.0 Results The survival time tests for the six PAH produced 48 hr LC50 estimates in addition to parameterized survival time models (Tables 1 and 2). The conventional concentration-effect model (with natural mortality) used to estimate 48 h LC50 values was the following:

)()1( 1021 ionConcentratLogaaPPP BBDead +Φ−+= (6)

where PDead = predicted proportion dying by 48 h, PB = estimated proportion dead at 48 h due to background mortality, a1 and a2 = estimated model intercept and slope parameters, and M() = the normal cumulative distribution function.

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Table 1. Concentration-Effect Models (48 hr exposure duration). (95% CI = 95% confidence

interval, SE = standard error)

The 48 hr LC50 estimates and associated 95% confidence intervals are provided in the left side of Table 2. These 48 H LC50 estimates were consistent with the literature as described below in discussions of QSAR models. The background mortality in the exposure experiments averaged 4%: roughly 1 of 25 shrimp that died in an exposure tank died due to nontoxicant-related reasons during the 48 to 60 hour experiments. Table 2. Parameter estimates for survival time models (95% CI = 95% confidence interval, SE =

standard error)

Initial survival modeling was designed to either model survival during exposure, or survival during and after (48 h post-exposure) exposure if warranted. Because of the narcotic mechanism for action at these PAH concentrations and the test durations, latent effects were minimal. This permitted the most parsimonious modeling to occur: latent mortality was minimal for the

Compound PB (SE) a1 (SE) 95% CI a2 (SE) 95% CI Naphthalene 0.01

(0.01) -60.3 (8.2)

-76.5 - -44.2 18.2 (2.5)

13.3 – 23.1

Fluorene 0.03 (0.02)

-33.3 (3.6)

-40.3 – -26.2 11.9 (1.3)

9.4 – 14.4

Dibenzothiophene 0.06 (0.02)

-35.4 (5.3)

-45.8 – -25.0 14.8 (2.2)

10.6 – 19.1

Ethylnaphthalene 0.06 (0.05)

-31.4 (10.7)

-52.4 – -10.4 12.7 (4.2)

4.4 – 21.0

Dimethylnaphthalene 0.06 (0.03)

-20.4 (3.6)

-27.5 – -13.8 7.6 (1.3)

4.9 – 10.2

Phenanthrene 0.02 (0.01)

-14.5 (2.4)

-19.2 – -9.8 5.7 (1.0)

3.8 – 7.5

Compound LC50 (µg/L)

95% CI

b1 (SE) 95% CI

b2 (SE)

95% CI

b3 (SE) 95% CI

Naphthalene 2111 2057-2161

52.2 (2.1)

48.0-56.3

-6.3 (0.3)

-6.8- -5.8

0.64 (0.03)

0.60-0.70

Fluorene 616 593-637

22.1 (1.6)

18.9-25.2

-2.8 (0.2)

-3.3- -2.3

0.41 (0.02)

0.37-0.46

Dibenzothiophene 242 228-253

17.4 (0.6)

16.1-18.7

-2.4 (0.1)

-2.6- -2.2

0.31 (0.02)

0.28-0.35

Ethylnaphthalene 295 162-331

15.3 (1.0)

13.4-17.3

-2.0 (0.2)

-2.4- -1.7

0.26 (0.01)

0.23-0.29

Dimethylnaphthalene 500 463-535

14.9 (1.2)

12.5-17.3

-1.8 (0.2)

-2.2- -1.4

0.34 (0.03)

0.30-0.40

Phenanthrene 360 333-402

13.0 (1.1)

10.7-15.2

-1.5 (0.2)

-1.9- -1.2

0.38 (0.03)

0.33-0.45

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modeled toxicant exposure scenarios and only mortality during exposure required estimation. Figure 3 demonstrates this using the phenanthrene survival data.

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153 µg/l

224 µg/l

302 µg/l

389 µg/l

Post-exposureExposure

Figure 3. Survival curves (bars indicate the estimated standard errors) from the phenanthrene tests. With the termination of exposure (60 hr), mortality dropped quickly to a minimum level in all concentration treatments. Greenwood’s formula was used to produce the standard errors shown as vertical bars for each exposure time. The models defined in Table 1 predict the amount of mortality expected with a specified pairing of exposure concentration and duration as illustrated for phenanthrene in Figure 4. The first objective of the project was completed successfully with the generation of these models.

TTD PHEN = e12.9659 e- 1.5409 ln C e0.3828W0

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300 350 400 450 500 550 600

Water concentration (µg/L)

Tim

e to

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ours

)

5%10%

30%50%

70%

90%

Figure 4. An illustration of the ability of survival time models to predict the mortality with different combinations of exposure time and concentration. In this figure, contours for percentage of exposed shrimp expected to die are shown. The 48 hr LC50 estimate is also shown for comparison. Less than 5% of the mortality during the entire test was post-exposure mortality.

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The second goal of assessing whether QSAR models could be generated for these data was also successfully achieved (see Figure 5).

y = 4E+06e-2.1064x

R2 = 0.9379

1

10

100

1,000

10,000

100,000

2 2.5 3 3.5 4 4.5 5

LC 5

0 (u

g/L)

Unger et al, in prep

Tatem, 1975

Unger et al, 2007

y = -33.211x + 162.63

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stim

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b 1

Unger et al, in prep

Unger et al, 2007

y = -3.9292x + 19.381

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2

3

4

5

6

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2 2.5 3 3.5 4 4.5 5

Log KOW

Sur

viva

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el E

stim

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b 2

Unger et al, in prep

Unger et al, 2007

Figure 5. QSAR models for estimates of the 48 hr LC50 (top panel) and survival time model parameter estimates (b1 and b2)(middle and bottom panel). The LC50 data generated from this study were consistent with those of Tatem (1975) (Figure 5, top panel). Toxicity increased for these oil-related PAH with increasing lipohilicity (as quantified with the log Kow). Similarly, the decrease in survival model parameter estimates (b1 and b2) with increasing lipophilicity indicates that time-to-death decreases with increasing

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lipophilicity. The ability to accurately predict survival time model parameters based on data from these six compounds could be greatly enhanced by adding estimates for more key PAH (3.5

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intent of the authors to continue exploring alternative models to better analyze these data, e.g., a survival model using all data and log Kow values for the six compounds to predict survival during exposure to mixtures. Another option is to combine the relative potency approach embedded in Equations 4 and 5 for the concentration-effect approach into survival time models. Relative potencies would be included and the resulting predictions compared to those generated during the three-compound mixture experiment. A final option is to explore the models described by Olmstead and LeBlanc (2005) and Barata et al. (2005).

5.0 Discussion and Importance to Oil Spill Response/Restoration

The survival time modeling approach was successful in that models were produced that can be applied immediately to predict lethal consequences of PAH exposures differing in concentration and duration. Such a predictive capacity extends well beyond that afforded by the conventional LC50 approach. It is also needed to make predictions about realistic exposures that will vary in concentration and duration. Clearly, post-exposure mortality can be ignored during general predictions. This statement is based on the minimal post-exposure mortality seen in the six compound toxicity exposure tests. This greatly simplifies modeling and consequent predicting of lethal consequences. The minimal post-exposure mortality experienced by shrimp exposed to these general narcotics is similar to that reported for mosquitofish exposed to sodium pentachlorophenol, a toxicant with a reversible mode of action (uncoupling of oxidative phosphorylation). The minimal post-exposure contrasts with that associated with sodium chloride exposed mosquitofish (Newman and McCloskey 2000) or the copper-exposed amphipods shown in Figure 1 that experienced extensive damage during exposure. The extensive damage required significant time and energetic resources to repair. Like sodium pentachlorophenol, the six compounds used in these tests also were eliminated very quickly when shrimp were moved to clean water (see Appendix A for elimination rate constants). Likely, this also contributed to the rapid recovery and minimal post-exposure mortality. In the absence of direct evidence for each relevant PAH, it is a reasonable assumption at this point that minimal post-exposure mortality will occur for untested PAH components under exposure scenarios in which narcosis is the dominant mode of lethal action. Relative to a sequence of pulsed exposures, one additional factor emerges as relevant, the individual tolerance or individual effective dose theory (Zhao and Newman 2007). Briefly, this theory holds that an individual has a unique or characteristic dose (or concentration) below which it will survive but at or above which it will die: the distribution of effective doses/concentrations are assumed to be log normally distributed in populations of exposed individuals. This theory is pervasive in the ecotoxicology literature but it remains poorly tested. The alternative, stochasticity theory (Newman and McCloskey 2000) assumes that all individuals are equally sensitive and which die during an exposure is a matter of chance. The stochastic process can be modeled with a skewed (e.g., log normal) distribution. With repeated exposures as might happen with a tide-dominated movement of PAH into and out of a marsh, which theory is correct becomes important to predictions of population consequences (Zhao and Newman 2007). Based on the research of Zhao and Newman (2007), and Newman and McCloskey (2000) the conservative assumption of the stochastic theory seems most advisable at this time. The

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methods of Zhao and Newman (2007) can also be applied to assessing whether shrimp sensitivity to the lethal effects of PAH exposure changes significantly with repeated exposures.

6.0 Technology Transfer

The results of this study were presented annually at NOAA CRRC meetings and, upon request, at NOAA-related workshops. For example, three presentations were made in 2006 at NOAA’s request. One (August 15-16) was given at the CRRC PAH Toxicity Summit in Seattle, WA. Another was given in September at the CRRC Coastal Modeling for Decision Makers meeting in Durham, NH. A final presentation was made at another Durham meeting, CRRC Submerged Oil Workshop (December). These opportunities allowed for researchers and oil spill responders to interact and discuss the utility and applications of the CRRC research projects. Much discussion at the Toxicity and Modeling workshops was centered on time dependence as a factor influencing PAH toxicity and how dispersants and physical factors may affect the exposure duration and concentration of toxicants in the water-soluble fraction from oil. The time to death approach of evaluating PAH toxicity was of great interest to managers as a mechanism to better understand and predict population mortality under changing conditions to help facilitate oil spill response. Correctly so, these models were also seen as a viable approach to couple chemical fate and toxicity models, a long-term goal of NOAA oil spill responders.

7.0 Achievement and Dissemination Beyond the contributions to NOAA sponsored workshops, the results of this study have been disseminated in a variety of formats including national and international meetings, and peer-reviewed publication. Presentation at national and international (The Hague, The Netherlands) meetings provided wide exposure of the environmental science community to the techniques and results. Written transfer of the study techniques and findings was achieved primarily in the peer-reviewed literature. The first part of the study was published in 2007 (Unger et al. 2007) as cited in the reference section of this report. The second part of the research project is appended to this final report as an “in preparation” manuscript that will soon be submitted to the international journal, Environmental Toxicology and Chemistry. The third part of the study involving mixture effects requires further analysis and possibly more experimentation. Although it is difficult to anticipate the outcome, the results will minimally be presented at Society of Environmental Toxicology and Chemistry annual meetings in combination with the rest of the CRRC-funded study.

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References Allison, P.D. 1995. Survival Analysis Using the SAS System. A Practical Guide. SAS Institute,

Cary, NC. Barata, C., A. Calbet, E. Saiz, L. Ortiz, and J.M. Bayona. 2005. Predicting single and mixture

toxicity of petrogenic ploycyclic aromatic hydrocarbons to the copepod Oithona davisae. Environ. Toxicol. Chem. 24: 2992-2999.

Cox, D.R. and D. Oakes. 1984. Analysis of Survival Data. Chapman & Hall, London. Crane, M., M.C. Newman, P. Chapman and J. Fenlon. 2002. Risk Assessment with Time-to-Event

Models. CRC Press LLC, Boca Raton, FL. Kalbfleisch, J.D. and R.L. Prentice. 1980. The Statistical Analysis of Failure Time Data. John

Wiley and Sons, New York. Lee, J-H, P.F. Landrum and C-H. Koh. 2002. Prediction of time-dependent PAH toxicity in

Hyalella azteca using a damage assessment model. Environ. Sci. Technol. 36: 3131-3138. Muenchow, G. 1986. Ecological use of failure time analysis. Ecology 67: 246-250. Miller, Jr., R.G. 1981. Survival Analysis. John Wiley & Sons, New York. Nelson, W. 1972. Theory and applications of hazard plotting for censored failure data.

Technometrics 14: 945-966. Newman, M.C. and P.H. Dixon. 1996. Ecologically meaningful estimates of lethal effect on

individuals. In: Newman, M.C. and C.H. Jagoe (Eds.), Ecotoxicology: A Hierarchical Treatment. CRC/Lewis Publishers, Inc., Boca Raton, FL.

Newman, M.C. and J.T. McCloskey. 1996. Time-to-event analysis of ecotoxicity data. Ecotoxicology 5: 187-196.

Newman, M.C. and J.T. McCloskey. 2000. The individual tolerance concept is not the sole explanation for the probit dose-effect model. Environ. Toxicol. Chem. 19:520-526.

Newman, M.C. and J.T. McCloskey. 2002. Applying time-to-event methods to assess pollutant effects to populations. In: Crane, M., M.C. Newman, P. Chapman, and J. Fenlon (Eds.) Risk Assessment with Time-to-event Models. CRC Press LLC

Olmstead, A.W. and G.A. LeBlance. 2005. Joint action of polycyclic aromatic hydrocarbons: predictive modeling of sublethal toxicity. Aquat. Toxicol. 75: 253-262.

Tatem, HE. 1975. Ph.D. Thesis, Texas A&M University, College Station, TX : 133 pp. Unger M.A., Newman M.C., Vadas G.G. 2007. Predicting survival of grass shrimp

(Palaemonetes pugio) during ethylnaphthalene, dimethylnaphthalene, and phenenathrene exposures differing in concentration and duration. Environ. Toxicol. Chem. 26:528–534.

Zhao, Y. and M.C. Newman. 2004. Shortcomings of the laboratory derived LC50 for predicting mortality in field populations: exposure duration and latent mortality. Environ. Toxicol. Chem. 23: 2147-2153.

Zhao, Y. and M.C. Newman. 2006. Effects of exposure duration and recovery time during pulsed exposures. Environ. Toxicol. Chem. 25:1298-1304.

Zhao, Y. and M.C. Newman. 2007. The theory underlying dose-response models influences predictions for intermittent exposures. Environ. Toxicol. Chem. 26:543-547.

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Appendices

Appendix A. Unger, M.A., M.C., Newman, and G.G. Vadas. In prep. Predicting survival of grass shrimp (P. pugio) for naphthalene, fluorine and dibenzothiophene exposures differing in concentration and duration. Environ. Toxicol. Chem. Appendix B. Unger, M.A., M.C. Newman, and G.G. Vadas. 2007. Predicting survival of grass shrimp (P. pugio) during ethylnaphthalene, dimethylnaphthalene, and phenanthrene exposures differing in concentration and duration. Environ. Toxicol. Chem. 26: 528-534.

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PAH TOXICITY TO GRASS SHRIMP

Michael A. Unger

Department of Environmental and Aquatic Animal Health

Virginia Institute of Marine Science

College of William and Mary

P.O. Box 1346, Gloucester Point, VA 23062-1346, USA

804-684-7187

804-684-7793 FAX

munger@vims.edu

Total words: 4,535

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PREDICTING SURVIVAL OF GRASS SHRIMP (P. pugio) EXPOSED TO AROMATIC

COMPOUNDS DERIVED FROM OIL

Michael A. Unger*, Michael C. Newman, and George G. Vadas

*Virginia Institute of Marine Science, College of William and Mary, P.O. Box 1346, Gloucester

Point, VA 23062-1346, USA

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* To whom correspondence may be addressed (munger@vims.edu)

This paper is contribution XXXX of the Virginia Institute of Marine Science, College of

William and Mary.

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Abstract- The composition and persistence of dissolved polycyclic aromatic hydrocarbons

(PAH) released to the water column during oil spills are altered by weathering, tidal transport

and the addition of dispersants. Conventional toxicity effect metrics such as the LC50 are

inaccurate predictors of mortality from all toxicant exposure duration/concentration

combinations likely to occur during spills. In contrast, survival models can predict the

proportions of animals dying as a consequence of exposures differing in durations and

intensities. Extending previous work with ethylnaphthalene, dimethylnaphthalene and

phenanthrene, survival time models were developed that include exposure duration and

concentration to predict time-to-death for grass shrimp, Palaemonetes pugio, exposed to two

additional PAH (naphthalene, fluorene) and a heterocyclic aromatic hydrocarbon

(dibenzothiophene). Preliminary explorations of these models confirmed that QSAR models

were possible for predicting survival model parameters from compound characteristics.

Conventional 48h LC50 values were also calculated for the compounds and were combined with

published LC50 values to predict relative PAH toxicity to P. pugio based on octanol:water

partitioning.

Keywords- Survival analysis, Oil spill, Polycyclic aromatic hydrocarbons, Toxicity, Grass

shrimp

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INTRODUCTION

Tides, currents, weathering, and dispersant application alter both the exposure

concentration and duration an organism is exposed to water-soluble compounds from an oil spill

[1-3]. Conventional toxicity tests use a concentration-effect design to produce a metric such as

an LC50 at a set exposure time, e.g., 96 h, that is not well suited to predict toxic effects for

varied exposure scenarios. Although underutilized by environmental toxicologists, survival

models can include both exposure duration and concentration. Survival models have been

applied to predict the effects of a variety of contaminants [4-8] and potentially provide managers

a tool with which to make more effective response and remediation decisions about the potential

impacts from spills. In addition to predicting mortality under varying exposure time and toxicant

concentrations, the models are easily expanded to include post-exposure (latent) mortality effects

or other cofactors, such as organism size or sex, temperature or salinity [9].

The main objectives of this study were to add three more parametrized models to the

three built earlier [8] and then explore the possibility of building QSAR models for survival

model parameters. QSAR interpolation would permit prediction for exposures to untested oil

spill-related aromatic compounds. Exposed shrimp were also analyzed post mortem for

contaminant body burdens to determine if accumulated contaminants were consistent with a

critical body burden at time of death.

Acute toxicity experiments were done with the grass shrimp, Palaemonetes pugio,

exposed to two PAH (naphthalene, fluorene) and the heterocyclic aromatic compound

dibenzothiophene. Combined with the previous models for dimethylnaphthalene,

ethylnaphthalene and phenanthrene [8], the resulting six models represent the range of aromatic

compounds thought to be important contributors to the toxic effects of oil spills [2]. Results from

23

these exposure experiments were modeled with survival time methods [10] to produce models

for each compound that predicted the proportion of individuals dying during, and potentially,

after an exposure of specified duration and concentration. Survival models for six compounds,

representative of those found in the water soluble fraction derived from oil, were used to

examine the relationship between the toxicant octanol:water partitioning behavior and its

toxicity.

MATERIALS AND METHODS

Grass shrimp collection and maintenance

Grass shrimp collected locally from the York River (Virginia, USA) were maintained in

the laboratory in filtered York River water (salinity 19-20 0/00) for at least 2 weeks prior to being

used in the exposures. Shrimp were fed Tetramin® Tropical flake food (Tetra Holding,

Blacksburg, VA) daily. Individual shrimp with no outward signs of damage or disease were

gently placed into glass tubes and the exposure aquaria one day before exposures began.

PAH survival analysis experiments

The survival analysis experiments were conducted in August (naphthalene 1 and

dibenzothiophene), September (fluorene), and October (naphthalene 2) of 2006. The first

naphthalene exposure resulted in more mortality than anticipated by 24 hours so an additional

exposure experiment was conducted in October at lower concentrations. After graphical

24

comparison for consistency, the data from both tests were combined to develop a single

naphthalene survival model and to estimate the 48 h LC50. For each experiment, three replicates

of four PAH concentrations and a control were prepared from saturated solutions generated with

filtered York River water by techniques described in detail previously [8]. Briefly, a 7.5 cm x

59.2 cm generator column was used to produce the large volume of saturated PAH solutions

required to avoid the use of solvent carriers for the experiments.

To minimize toxicant volatilization, exposure chambers were not aerated during the experiment

and solutions were renewed every 12 h. All experiments were conducted under constant light

from standard fluorescent light fixtures that were approximately 1.5 m above aquaria.

Experimental chambers were constructed from 25 cm x 50 cm x 58 cm glass aquaria with glass

lids and were randomly assigned to positions on a wet table. A water-tight glass partition was

installed down the center of each aquarium to create tandem, 30 L exposure chambers 25 cm x

25 cm x 58 cm tall. This design reduced the surface area to volume ratio to minimize volatile

losses of the PAH. One chamber of each aquarium was used to expose the test organisms and the

other was used to prepare the new test solution every 12 h. The tandem 30 L test chambers

permitted the filling of one side with new exposure water and transferring of the shrimp in racks

from old to renewed water with minimal disturbance to the test organisms.

Individual grass shrimp (26 shrimp/replicate) were placed in 2.8 cm x 10.8 cm (40 ml)

glass vials with an open ended screw cap fitted with a stainless steel mesh screen. Shrimp were

not sexed but berried females were excluded from the tests. The vials were suspended in

randomly assigned exposure chambers on aluminum racks to facilitate monitoring the test

organism’s condition and to allow easy transfer of the test organisms to newly prepared solutions

every 12 h. Shrimp were monitored for mortality every 4 h and scored as dead if unresponsive to

25

repeated prodding and no appendage movement was apparent. All dead shrimp were removed,

weighed, and frozen. Shrimp surviving the exposure period (naphthalene 1, 24 h; naphthalene 2,

36 h; fluorene 60 h; and dibenzothiophene, 48 h) were transferred to filtered and aerated York

River water. The water was renewed every 12 h and shrimp were routinely monitored for post-

exposure mortality for 48 h, or until no further mortality was apparent. All shrimp were weighed

and frozen at the completion of the test (48 h post-exposure).

Water chemistry

Old and fresh test solution water chemistries were measured every 12h and included

temperature, dissolved oxygen, salinity, and pH. A HydrolabTM Surveyor 4a (Hydrolab Corp.,

Austin, TX) was used for these measurements. Unfiltered water samples were also collected and

frozen for ammonia analyses (phenol method).

PAH Analysis of water and tissue samples

Exposure waters were analyzed for PAH concentrations using a HPLC with a

fluorescence detector by methods described in detail previously [8]. Briefly, calibration

standards were made in York River water from stock solutions of internal standard (1-methyl

naphthalene) and the PAH analyte of interest. Calibration standards were injected on the HPLC

and the method calibrated for the specific analyte over a range from its quantitation limit to near

its aqueous solubility prior to running samples. The HPLC calibration was verified again with

fresh standards once the analyses for a particular exposure were completed. Shrimp tissues were

26

analyzed by GC-MS using selective ion monitoring (GCMS-SIM). Individual shrimp were

weighed, rinsed with DI water and placed into a 50 mL Teflon centrifuge tube containing 2.0 mL

of concentrated HCL and 500 ng of deuterated PAH surrogate standards. The shrimp were

homogenized with a spatula and ultrasonicated for 10 m. The aqueous homogenate was

sequentially extracted with two aliquots of hexane (2.0 mL each), centrifuging between

extractions to separate the layers. The combined hexane extracts were reduced to 0.1 mL under

dry nitrogen and 0.6 ug of p-terphenyl internal standard was added before analysis on a Varian

CP-3800 Gas Chromatograph with a Saturn 2000 GC/MS/MS ion trap mass spectrometer

operated in electron ionization mode (EI). Analytes and ions monitored were: p-terphenyl I.S.

[152+230], naphthalene-d8 [108+135+136], acenaphthene-d10 [160-165], phenanthrene-d10

[187-189], naphthalene [128+127+102], fluorene [163-166] and dibenzothiophene [184+139].

Six point calibration curves were generated for each analyte and identifications were based on

retention time and matches to library spectra. Shrimp tissue concentrations were corrected for

surrogate recovery relative to the internal standard (p-terphenyl). Surrogate standards were

naphthalene (naphthalene-d8), fluorene (acenaphthene-d10), and dibenzothiophene

(phenanthrene-d10).

Calculating LC50 values

The measured PAH exposure concentrations, number of dead shrimp at 48 h and total

exposed shrimp were fitted by maximum likelihood estimation (MLE) to a log normal model

with the PROBIT procedure in the SAS software (SAS Corp., Cary, NC). Spontaneous mortality

was included in the log normal model because low levels of mortality (≤ 6%) occurred in the

27

control aquarium shrimp during exposures. The 48 h LC50 values and associated 95% fiducial

limits were estimated.

Survival models

Survival time was modeled as a function of PAH concentration using mean

concentrations in each exposure tank and the SAS LIFEREG procedure (SAS Corp., Cary, NC).

The general approach was that described in detail in previous publications, e.g., [8, 10]. Survival

times noted at 4 h intervals were used directly in the model instead of incorporating interval

censoring because the fineness of the sampling used in these experiments minimized any

inaccuracies arising from the discreteness of the sampling [10]. The log logistic model was

chosen to predict survival based on exposure concentration (see [8] for details of model

selection),

TTD e e eb b Concentration b W= 1 2 3(ln )

where TTD = the predicted time-to-death for a specified proportion of the exposed shrimp, b1 =

intercept (MLE estimate) , b2 = the coefficient for the influence of ln of PAH concentration on

time-to-death (MLE estimate), b3 = the scale parameter (MLE estimate), and W = the response

metameter for the model distribution associated with the proportion dying (P) of the exposed

shrimp for which prediction is being made. The W can be generated by special functions within

most statistical or spreadsheet software, or taken from tables such as Appendix Table 7 in [9].

By changing W, the various combinations of exposure concentration and duration can easily be

found that result in P of the exposed shrimp dying. However, prediction is only recommended

28

within the range of concentrations and durations used in the tests from which these data were

generated.

RESULTS

Water chemistry

Temperature, salinity, dissolved oxygen, pH and ammonia were monitored during the

three exposure experiments for both freshly prepared solutions and 12 h-exposed test solutions.

Measured parameters remained within a narrow range for all exposure experiments. During the

two naphthalene exposure experiments temperature in the various treatments averaged 19.6-21.8

°C, salinity 18.8-21.2 ppt, dissolved oxygen 6.8-7.2 mg/L, pH 7.76-7.95, and ammonia 0.14-0.17

mg/L. During the fluorene exposure experiment temperature averaged 19.4-20.6 °C, salinity

18.4-19.8 ppt, dissolved oxygen 6.7-8.7 mg/L, pH 7.8-8.9, and ammonia 0.14-0.90 mg/L. During

the dibenzothiophene exposure experiment, temperature averaged 20.7-21.2 °C, salinity 21.3-

21.7 ppt, dissolved oxygen 6.6-7.1 mg/L, pH 7.6-7.7, and ammonia 0.14-0.17.

Toxicant concentrations

The measured toxicant concentrations are summarized in Table 1. Saturated solutions

prepared by the generator column technique had the following mean concentrations: naphthalene

20,100 ug/L, fluorene 884 ug/L and dibenzothiophene 504 ug/L. These values are approximately

50-60% of the published values for the same compounds in fresh water at 25 oC [11, 12]. The

29

decreased solubility at lower temperature (20 oC) and high salinity (20 ppt) is expected based on

results with similar PAH and conditions [8, 13]. The PAH concentrations varied because they

were measured in both the newly prepared and the 12 h old solutions. Although tanks were not

aerated in order to minimize surface exchange losses, there was still some decrease of the lightest

compound, naphthalene. Biodegradation during the 12 h period might have contributed because

the rate of loss increased as the experiment progressed. Control PAH concentrations were below

detection limits (1 ug/L) during all exposures.

Survival analysis

Shrimp rapidly revived when placed in clean water and there was very little apparent

latent mortality. Conventional LC50 values and 95% fiducial limits were calculated,

naphthalene 2111 µg/L (2057-2161), fluorene 616 µg/L (593-637) and dibenzothiophene 242

µg/L (228-253). Survival data were fitted to accelerated failure time models with the log logistic

distribution [8]. Contours were developed from the models to depict shrimp mortality for various

combinations of exposure times and concentrations (Figure 2A-2C). Compound-specific models

and conventional LC50 values with 95% fiducial limits are also included in Figure 2. Estimated

values for model parameters (b1,b2,b3) for each compound tested are presented in Table 2 along

with those from previous work with three other PAH [8].

Tissue concentrations Toxicant concentrations (wet weight) were measured in select whole shrimp that died

during the exposure experiments. Body burden concentrations at time of death spanned a

30

relatively wide range during each experiment. Mean concentrations are presented in Table 3. The

large range in concentrations was, in part, likely due to increasing body burdens with increasing

dose. Tissue concentrations measured in individual shrimp from the 300 µg/L and 500 µg/L

dibenzothiophene treatments are presented in Figure 2 for comparison. There is a trend of

increasing body burdens with increasing time of exposure and with increasing exposure

concentration.

Naphthalene and fluorene were eliminated rapidly from the shrimp after the exposures

ended. The dibenzothiophene was two-fold slower than the PAH to decrease in concentration. In

our previous study [8], phenanthrene body burdens were measured in exposed shrimp that died

during the depuration phase and they showed an exponential decrease in concentration with time.

Based on this earlier work with similar compounds, we assumed a first order decrease and

calculated elimination rate constants for each compound from the slope of the log transformed

concentration data. The elimination rate constants are shown in Table 3 with PAH data from the

previous study [8] included for comparison. Corresponding half-lives for the six compounds

ranged from 3.7 -17 h.

DISCUSSION

Conventional LC50 values calculated for P. pugio were naphthalene 2111 µg/L (2057-

2161), fluorene 616 µg/L (593-637) and dibenzothiophene 242 µg/L (228-253). Tatem [14]

reported a 48 h LC50 concentration of 2600 µg/L (2340-2890) for P. pugio exposed to

naphthalene that is in general agreement with our results for naphthalene (2111 µg/L). There was

a trend of increasing toxicity with increasing molecular weight or lipophility. While individual

31

PAH toxicity data for P. pugio are not numerous in the literature, enough data were available

from two additional studies [8, 15] to examine the relationship between octanol-water

partitioning (Kow) and LC50 for the grass shrimp (Figure 3a). Data include LC50 values for

diverse compounds ranging form the single ring aromatic compound, benzene, to the three ring

compound, phenanthrene, and includes alkylated PAH as well as the heterocyclic compound,

dibenzothiophene. The results indicate good relationship between log Kow and LC50 over a range

of log Kow that includes the most abundant aromatic components found in the water-soluble

fraction of oil. Similarly, we looked at the relationship between log Kow and the estimates of the

survival model parameters (b1,b2) (Figure Xb, Xc). While survival data is currently too limited to

develop a robust relationship, the available data suggests that a similar approach could be used to

develop models for untested PAH compounds. Due to the increased predictive capability of

survival models over standardized LC50 values, additional work is warranted to develop the

QSAR approach further.

The PAH concentrations measured in whole shrimp at time of death showed a great deal

of variability and appears dependent on exposure concentration and duration. This is consistent

with the results from our earlier study examining the acute toxicity of dimethylnaphthalene,

ethylnaphthalene and phenanthrene to grass shrimp [8]. The high toxicant concentrations and

short exposure durations used in these acute experiments likely contribute to the variability in

body burdens at time of death. Lower doses with long-term exposures result in steady state body

burdens and less variance in tissue concentrations at time of death. Landrum et al [16] have

shown that critical body residues of various PAH are similar on a molar basis (7.5 ± 2.6 µmol g-

1) when amphipods (Diporia sp.) were exposed for 28 days. When the mean lethal body burden

concentrations for P. pugio are compared on a molar basis there is a range of critical body

32

residues from 0.41 -1.2 µmol g-1 (Figure 4). There is much better agreement for the four

unsubstituted compounds tested (1.0 ± 0.19 µmol g-1) and this may be the result of the higher

toxicity of akylated PAH resulting in lower body burdens at time of death

Unlike the single LC50 value, the survival time models developed here can predict what

proportion of an exposed population would be killed for different exposure scenarios generated

from oil spill field measurements or computer simulations. The toxicants tested were eliminated

rapidly from the organisms post-exposure and little post-exposure mortality was observed,

allowing simplified survival models that just included mortality occurring during the exposure

period. Additional work is needed to better understand how the sub-lethal narcotic effects of

PAH exposure can affect survival of prey organisms due to increased predation under natural

conditions. The acute toxicity (LC50) of PAH to P. pugio is proportional to the log Kow of the

compound and provides the ability to predict, by interpolation, the toxicity of untested

compounds which may be present in the water soluble fraction derived from oil. Preliminary data

suggests that survival models can also be constructed for untested compounds based on their

partitioning behavior.

ACKNOWLEDGMENT

We thank John Carriger, Kyle Tom, Chris Prosser, J. Greene, and E. Harvey, for laboratory

assistance. Funding for this research provided by the Coastal Response Research Center

(www.crrc.unh.edu).

33

REFERENCES

1. Wang Z, Fingas MV. 2003. Development of hydrocarbon fingerprinting and identification

techniques. Mar Pollut Bull 47:423-452.

2. Barron MG, Podrabsky T, Ogle S, Ricker RW. 1999. Are Aromatic hydrocarbons the

primary determinant of petroleum toxicity to aquatic organisms? Aquat Toxicol 46:253-268.

3. Neff JM, Stout SA, Gunster, DG. 2005. Ecological risk assessment of polycyclic aromatic

hydrocarbons in sediments: identifying sources and ecological hazard. Integrated

Environmental Assessment and Management1:22-33.

4. Newman, M.C. and P.H. Dixon. 1996. Ecologically meaningful estimates of lethal effect on

individuals. In: Newman, M.C. and C.H. Jagoe (Eds.), Ecotoxicology: A Hierarchical

Treatment. CRC/Lewis Publishers, Inc., Boca Raton, FL, pp. 225-253.

5. Lingtian Xie and Klerks PL. 2003. Responses to selection for cadmium resistance in the least

killifish, Heterandria Formosa. Environ Toxicol Chem 22: 313-320.

6. Sildanchandra W and Crane M 2000. Influence of sexual dimorphism in Chironomus riparius

MEIGEN on toxic effects of cadmium. Environ Toxicol Chem 19:2309–2313.

34

7. Widianarko B and Van Straalen N. 1996. Toxicokinetics-based survival analysis in bioassays

using nonpersistent chemicals. Environ Toxicol Chem 15:402–406.

8. Unger MA, Newman MC, Vadas GG. 2007. Predicting survival of grass shrimp

(Palaemonetes pugio) during ethylnaphthalene, dimethylnaphthalene, and phenenathrene

exposures differing in concentration and duration. Environ Toxicol Chem 26:528–534.

9. Newman MC. 1995. Quantitative Methods in Aquatic Ecotoxicology. CRC/Lewis

Publications, Boca Raton, FL, USA. 137-153.

10. Dixon, PM and MC Newman. 1991. Analyzing toxicity data using statistical models for

time-to-death: an introduction. In: Newman MC and McIntosh AW (Eds.). Metal

Ecotoxicology. Concepts & Applications. CRC/Lewis Publishers, Boca Raton, FL, pp. 207-

242.

11. Lide DR. 2005, CRC Handbook of Chemistry and Physics, Internet version.

http://www.hbcpnetbase.com, CRC Press, Boca Raton, FL, USA.

12. Pearlman, RS, Yalkowsky, SH, Banerjee S. 1984. Water solubilities of polynuclear aromatic

and heteroaromatic compounds. J. Phys. Chem. Ref. Data, Vol. 13, No. 2. 555-562.

35

13 . Gordon JE, Thorne RL. 1967. Salt effects on non-electrolyte activity coefficients in mixed

aqueous electrolyte solutions-II. Artificial and natural sea waters. Geochem Cosmochem Acta

31:2433-2443.

14. Tatem, HE, Anderson JW. 1973. The toxicity of four oils to Paleomonetes pugio (Holthuis)

in relation to uptake and retention of specific petroleum hydrocarbons. American Zoologist

13, 261 (abstracts, 1307-1308).

15. Tatem, HE. 1975. Ph.D. Thesis, Texas A&M University, College Station, TX : 133 pp.

16. Landrum, PF, Lotufo GR, Gossiaux DC, Gedeon ML, Lee JH. 2003. Bioaccumulation and

critical body residue of PAHs in the amphipod, Diporeia sp. : additional evidence to support

toxicity additivity for PAH mixtures. Chemosphere. 51:481-489.

36

Figure legends

Figure 1. Response surfaces predicting mortality levels for P. pugio exposed to two polycyclic

aromatic hydrocarbons (PAH), A) naphthalene, B)fluorene, and the heterocyclic C)

dibenzothiophene. The survival models from which predictions are made are also given in the

figure. Lines indicate different proportions dying predicted with the models for different

combinations of exposure concentration and duration. The 48-h median lethal concentrations

(LC50) and the 95% fiducial limits are shown for comparison. TTD= time to death,

NAP=naphthalene, FLUOR=fluorene, DBT=dibenzothiophene.

Figure 2. Individual whole body concentrations (µg/g wet weight) of dibenzothiophene measured

in P. pugio at time of death. Shrimp from two exposure concentrations (300 µg/L and 550 µg/L)

are shown for comparison.

Figure 3. Acute toxicity of PAH to P. pugio predicted from compound log Kow, A) LC50, B)

survival model parameter b1= the maximum likelihood estimation (MLE)-estimated intercept, C)

survival model parameter b2 = the estimated coefficient for the influence of ln of PAH

concentration on time-to-death.

Figure 4. Mean molar body burden concentrations measured in P. pugio at time of death.

Average unsubstituted compound concentrations (1.0 ± 0.19 µmol g-1) compared to akyl-

substituted PAH. NAP=naphthalene, FLUOR=fluorene, DBT=dibenzothiophene,

PHEN=phenenthrene, DMN=dimethylnaphthalene, ENAP=ethylnaphthalene.

37

Table 1. Measured toxicant concentrations in exposure aquariaa

Toxicant concentration (µg/L)

Treatment Replicate Naphthalene 24h Naphthalene 48h Fluorene

Dibenzo-thiophene

Conc. 1A 2430 ± 260, n=7 1430 ± 650, n=10 520 ± 110, n=8 180 ± 20, n=8

Conc. 1B 2330 ± 250, n=7 1400 ± 710, n=10 520 ± 140, n=10 190 ± 20, n=8

Conc. 1C 2430 ± 250, n=7 1330 ± 680, n=10 520 ± 130, n=8 190 ± 20, n=8

Conc. 2A 2710 ± 210, n=7 1600 ± 670, n=10 590 ± 110, n=9 250 ± 30, n=8

Conc. 2B 2540 ± 290, n=5 1590 ± 740, n=10 570 ± 120, n=7 240 ± 40, n=8

Conc. 2C 2620 ± 210, n=4 1550 ± 730, n=10 620 ± 150, n=8 280 ± 40, n=8

Conc. 3A 3050 ± 220, n=5 1630 ± 720, n=10 720 ± 110, n=8 330 ± 60, n=8

Conc. 3B 3030 ± 130, n=6 1700 ± 650, n=10 730 ± 90, n=9 330 ± 40, n=8

Conc. 3C 2950 ± 180, n=5 1700 ± 710, n=10 690 ± 80, n=8 330 ± 50, n=8

Conc. 4A 3290 ± 280, n=3 1870 ± 590, n=10 780 ± 120, n=9 430 ± 70, n=8

Conc. 4B 3390 ± 270, n=3 1770 ± 810, n=10 830 ± 110, n=9 440 ± 100, n=8

Conc. 4C 3310 ± 240, n=3 1930 ± 520, n=10 810 ± 80, n=7 440 ± 70, n=8 a values presented as the mean ± standard deviation, number of samples)

38

Table 2. Survival models to predict time to death for P. pugio

Survival Models TTD=eb1eb2 (ln Conc)eb3W

Compound b1 (SE) b2(SE) b3 (SE) Naphthalene 52.2 (2.1) -6.3 (0.3) 0.64 (0.03)Fluorene 22.1 (1.6) -2.8 (0.2) 0.41 (0.02)Dibenzothiophene 17.4 (0.6) -2.4 (0.1) 0.31 (0.02)Ethylnaphthalenea 15.3 (1.0) -2.0 (0.2) 0.26 (0.01)Dimethylnaphthalenea 14.9 (1.2) -1.8 (0.2) 0.34 (0.03)Phenanthrenea 13.0 (1.1) -1.5 (0.2) 0.38 (0.03)TTD = the predicted time-to-death for a specified proportion of the exposed shrimp, b1 = MLE-

estimated intercept, b2 = the estimated coefficient for the influence of ln of PAH concentration

on time-to-death, b3 = the estimated scale parameter, and W = the response metameter for the

model distribution associated with the proportion dying (P) of the exposed shrimp for which

prediction is being made. (SE) standard error for the calculated parameter

adata from Unger et al [8]

39

Table 3. Body burdens of toxicants in P.pugio at time of death

Compound Cb mean ± s.d. (µg/g)

Cb mean ± s.d. (µm/g)

Kelim (SE) T1/2 (hours)

Naphthalene 110 ± 50, n=12 0.82 ± 0.4, n=12

0.19(0.012), n=8

3.7

Fluorene 150 ± 50, n=19 0.91 ± 0.3, n=19

0.090(0.005), n=7

7.7

Dibenzothiophene 220 ± 150, n=15 1.2 ± 0.8, n=15

0.041(0.021), n=7

17

Ethylnaphthalenea 65 ± 50, n=12 0.41 ± 0.3, n=12

0.093(0.0066), n=6

7.8

Dimethylnaphthalenea 70 ± 30, n=11 0.45 ± 0.2, n=11

0.13(0.017), n=10

5.3

Phenanthrenea 210 ± 180, n=15 1.2 ± 1.0, n=15

0.12(0.0087), n=6

5.8

Cb measured body burden concentration on a wet weight basis ± standard deviation, number of individuals analyzed Kelim calculated elimination rate constant (standard error), number of individuals analyzed T1/2 half-life for body burden concentration aData from Unger et al [8]

40

TTD NAP = e.52.2 e- 6.31 ln C e0.643W

0

10

20

30

40

50

60

1800 2000 2200 2400 2600 2800 3000

5%10%

30%50%

70%

90%

95%

TTD FLUOR = e22.078 e- 2.816 ln C e0.4103W

0

10

20

30

40

50

60

500 550 600 650 700 750 800

Tim

e to

dea

th (h

ours

)

5%10%

30%

50%

70%

TTD DBT = e17.399 e- 2.405 ln C e 0.3138W

0

10

20

30

40

50

60

200 250 300 350 400 450 500

5%10%

30%

50%

70%

90%

Concentration (µg/L)

A

B

C

41

Dibenzothiophene

0

100

200

300

400

500

600

700

12 24 36 48 72 96

Time (H)

Bod

y B

urde

n C

once

ntra

tion

(ug/

g)

Post-exposureExposure

300 ug/L500 ug/L

42

y = 4.4 x 106e-2.1x

R2 = 0.94

100

1,000

10,000

100,000

2 2.5 3 3.5 4 4.5 5

log Kow

LC 5

0 (u

g/L)

This study

Tatem [15]Unger et al [8]

y = -33.211x + 162.63

0

10

20

30

40

50

60

2 2.5 3 3.5 4 4.5 5

Toxi

city

(b1)

This study

Unger et al [8]

y = -3.9292x + 19.381

0

1

2

3

4

5

6

7

2 2.5 3 3.5 4 4.5 5Log KOW

Toxi

city

(-b 2

)

This studyUnger et al [8]

A

B

C

43

0

0.5

1

1.5

2

2.5

120 130 140 150 160 170 180 190Molecular Weight

Tiss

ue C

onc.

(µm

/g w

et w

eigh

t) This StudyUnger et al [8]

NAP

DMNENAP

FLUOR

PHEN DBT

44

Appendix B

45

46

47

48

49

50

51