Page 1 of 40



### Review of Methods for Characterizing Effects of Pesticides and Other Chemical Stressors to Aquatic Organisms

United States Environmental Protection Agency

December 20 , 2011Page 2 of 40

Project Team:

Office of Pesticide Programs , Environmental Fate and Effects Division Mark Corbin, Kristina Garber, Anita Pease, Sujatha Sankula, Thomas Steeger

Office of Water , Office of Science and Technology Joe Beaman, Charles Delos, Diana Eignor, Wade Lehmann, Lisa Huff

Office of Research and Development

Russell Erickson, Matthew Etterson, Dale Hoff, Michael Lewis, David Mount, Sandy Raimondo, Cindy Roberts, Chris Russom, Charles Stephan, Glen Thursby

Region 9

Patti TenBrookPage 3 of 40

1. Executive Summary

The mission of United States Environmental Protection Agency (USEPA) is to protect human

health and the environment. Consistent with the USEPA‟s mission, the Office of Pesticide Programs (OPP) and the Office of Water (OW) are both responsible for scientif ically evaluating potential effects of chemicals on aquatic life. This is accomplished by OPP through risk assessment and OW through aquatic life criteria (ALC) development. For pesticides, data are

available to inform deterministic

, and in some cases, pro babilistic risk assessment used in pesticide registration by OPP; however, in many cases the data bases used by OPP contain fewer

studies than OW uses to derive ALC according to OW‟s 1985 Guidelines 1

. Recent surface water monitoring reports ( e.g., by the U nited States Geological Survey‟s National Water

- Quality Assessment Program) for certain chemicals, including many pesticides, have prompted

stakeholders to raise questions as to the possible risk s these chemicals pose to aquatic

communities . N o ALC have been established for many of these chemicals, which impedes the ability of stakeholders to interpret the potential effects on aquatic communities where the

chemicals are detected . OPP‟s Aquatic Life Benchmarks

, which are based on toxicity values

used in OPP‟s most recent risk assessments developed as part of the decision

- making process for pesticide registration

, allow for comparison of measured concentrations to acute and chronic endpoints for fish, invertebrates and plants; however, they are n ot designed to represent community level benchmarks.

This effort involves exploration of several approaches that may be considered with varying

amounts of data for OPP and OW to characterize the effects of chemicals on aquatic organisms

. Emphasis is placed on situations where data that OPP would regard as sufficient are available to conduct risk assessments, but OW would not have a data set that, according to the 1985 Guidelines, would support derivation of ALC. This paper analyzes the pot ential uses of predictive tools, i.e., Quantitative Structure Activity Relationships (QSARs) and Interspe cies Correlation Estimation (ICE ), for estimating the acute toxicity of aquatic animals exposed to pesticides . This paper also explores the potential a pplications of species sensitivity distributions (SSDs) and extrapolation factors (EFs) for characterizing the effects of chemicals within taxa and communities of aquatic animals and plants. In addition, this paper investigates the uses of

a cute

- chronic ra tios (ACRs) to estimate chronic effect thresholds for aquatic animals lacking chronic toxicity data. Particular emphasis is placed on the ability of these approaches to reliably predict the fifth percentiles of sensitivity distribution s of toxicity data ( termed the “HC5”), which are intended to be representative o f more sensitive species in aquatic communities and serve as

the assessment endpoint used by OW for derivation of ALC. Potential influences of pesticidal

mode s of action (MOA) on the utility of these methods are also considered.

This white paper describes analyses of some readily available QSAR tools that may be used to predict acute toxicity values for sensitive taxa. Three publicly available QSAR models wer e examined, including: Ecological Structure Activity Relationships (ECOSAR), Toxicity Estimation Software Tool (TEST), and the Organization for Economic Cooperation and Development (OECD) QSAR Toolbox. These tools are increasingly used by USEPA risk

asses sors to determine the potential toxicity of various chemicals, including pesticide degradates.

1 Guidelines for Deriving Numerical National Water Quality Criteria for the Protection of Aquatic Organisms and Their Uses (USEPA 1985), referred to as the 1985 GuidelinesPage 4 of 40

The white paper‟s analyses of ECOSAR, TEST and the OECD Toolbox suggest that, at this time, 1) the chemical domains for most models within these QSAR tools do no t include sufficient data for pesticide active ingredients and therefore these models are not currently robust estimators of the toxicity for modes of action (MOAs) that are unique to pesticides ( e.g., acetylcholinesterase (AChE) inhibitors), 2) current models are generally populated with data for species for which toxicity data are typically submitted under the Federal Insecticide, Fungicide Rodenticide Act ( FIFRA ) , and generally, could not be used to fulfill other taxonomic data requirements defined in the 1985 guidelines, and 3) more toxicologically relevant molecular descriptors and structural alerts need to be identified for use in building models for MOAs associated with pesticide active ingredients.

The work involved investigat ion of the use of We b

- based Interspecies Correlation Estimation models (Web

- ICE) to predict acute toxicity values. The review of the current version of Web

ICE suggests that, when u sing the acute toxicity data for aquatic animals that are typically submitted to fulfill requi rements under FIFRA ( i.e., data for Daphnia magna , rainbow trout ( Oncorhynchus mykiss ) and bluegill sunfish ( Lepomis macrochirus ) ), Web

- ICE appears to generate the most precise predictions of toxicity for closely related fish species ( e.g., within the same family). If toxicity data are available for either rainbow trout or bluegill sunfish, Web

- ICE may be useful for predicting the toxicity of a chemical to some species of fish. The current

review of this tool indicates that Web

- ICE does not have sufficient data to fulfill the minimum data requirements across all taxa defined in the 1985 Guidelines when there are only acute surrogate data available for rainbow trout, bluegill and D. magna

.

For this effort, USEPA explored the uses of SSDs wi th varying amounts of empirical data for acute exposures to aquatic animals to estimate the HC5. Analyses performed by USEPA for this effort suggest that SSDs have limited utility where biological diversity is not sufficient to represent the distribution o f taxa sensitivity. The analyses further suggest that SSDs applied to datasets that satisfy existing taxonomic requirements with extrapolation constants (not extrapolation factors) would likely result in lower and yet reasonable approximations of the HC5.

Several potential avenues for future work to improve the utility of SSDs are described, including i) extension of methods for ordered data to all continuous distributions ii) development of stepwise methods, including decision points, for data analysis to incorporate information on the relationship of MOA and taxa sensitivity, and iii) the use of random effects methods for handling cases in which more than one test result is available for a given taxon.

Of particular interest to this effort is the set of EFs developed by Host et al. (1995) and incorporated into the Great Lakes Water Quality Guidance (GLWQG: USEPA 1995), specifically for deriving Tier II values. The analysis by Host et al. (1995 ) included toxicity test datasets used in freshwater ALC that existed at that time; these analyses involved repeatedly subsampling (resampling) these datasets to assess the relationship of the lowest toxicity values in datasets of a specified size and composition to Final Acute Values (FAVs; approximation of an HC5 that is derived according to the methods described in the 1985 Guidelines) based on larger datasets. The analyses desc ribed in this white paper build upon the analysis conducted by Host

et al. (1995) by ( a) utilizing more recent aquatic toxicity data and data for chemicals that do not have an ALC, (b) developing EFs for different MOAs to reduce uncertainty, (c) paying specific attention to EFs for OPP minimum data requirements for acute toxicity testing of aquaticPage 5 of 40

animals, and (d) generating EFs using statistical distributions descriptive of acute toxicity values for different taxonomic groups and based on multiple chemicals, rather than resampling toxicity data for individual chemicals. These analyses est ablished the feasibility of establishing EFs for certain MOAs, demonstrated how a distributional rather than a resampling approach can more effectively exploit data to develop MOA

- specific EFs, and documented the relationship between

OPP and OW benchmarks. In addition, the current efforts considered how EF applicability and meaning might be affected, relative to the GLWQG, by different strategies for assigning reference HC5 and for summarizing EF distributions.

Chronic toxicity data are generally availa ble for fewer species than are tested to determine acute toxicity. ACRs are a common tool used to estimate chronic effect thresholds for taxa lacking chronic toxicity data. Both OPP and OW currently use ACRs developed for specific chemicals

to estimate c hronic effect levels. ACR distributions have been developed for broad groups of chemicals without consideration of MOA or taxa. This white paper considers the application of ACRs developed for chemicals within the same MOA to estimate chronic effects for chemicals with the same MOA and lacking any chemical

- specific ACRs. Analysis of ACR data

, specifically for AChE inhibitors

, identified patterns in the ACR distributions. Most notably, ACRs for invertebrates were smaller than those for fish. Although fis h had larger ACRs, this did not translate to higher chronic sensitivity of fish to AChE inhibitors, because of their very low acute sensitivity. In fact, analyses showed that concentrations protective of acute toxicity to

invertebrates would simultaneousl y protect fish from chronic effects. By extension, for AChE inhibitors, applying an invertebrate

- based ACR to the acute HC5 should provide a chronic HC5 protective of both fish and invertebrates. When comparing ACRs developed for broad groups of

chemical s and taxa to ACRs for aquatic invertebrates exposed to AChE inhibitors, they were approximately 2 fold different.

This white paper explores potential uses of EFs and SSDs to estimate the HC5 relevant to aquatic plants. The primary analyses investigated the use of the standard aquatic plant species submitted to fulfill FIFRA data requirements in estimating the HC5. These species, which are referred to in the white paper as “the FIFRA

- 5” include: Pseudokirchneriella subcapitata (a freshwater green alga), A nabaena flos

- aquae (a freshwater cyanobacterium), Navicula pelliculosa (a freshwater diatom), Skeletonema costatum (a saltwater diatom) and Lemna gibba (the aquatic vascular plant, duckweed). There was no pattern among the FIFRA

- 5 species for their relati ve sensitivity, and there was no species that was consistently the most sensitive. Of ten chemicals, the most

sensitive of the FIFRA

- 5 ranged from the lower 4 th to the lower 31 st percentile of the SSDs for

those chemicals. The analysis suggested that estim ating the HC5 using the FIFRA

- 5 will result in a reasonable approximation of the HC5 of an aquatic plant community. Although use of EF

value s consistently generate s conservative estimates of these HC5 values, the EF approach is limited for predicting the H C5 for aquatic plant communities because it is based on a limited number of chemicals and, unlike EF approaches involving aquatic animals, the magnitude of the EF does not change with addition of other test species.Page 6 of 40

Table of Contents

1. Executive Summary

................................ ................................ ................................ ................ 3

2. List of Acronyms Used in this White Paper

................................ ................................ ........... 8

3. Introduction

................................ ................................ ................................ ............................. 9

3.1. Current OW and OPP Procedures for Characterizing Effects of Chemical Stressors on Aquatic Organisms

................................ ................................ ................................ .................... 10

3.1.1. OPP

................................ ................................ ................................ ......................... 10

3.1.2. OW

................................ ................................ ................................ .......................... 12

3.2. Common Effects Project

................................ ................................ ................................ 13

3.3. Great Lakes Water Quality Guidance

................................ ................................ ............ 15

3.3.1. Extrapolation Factors

................................ ................................ .............................. 15

3.3. 2. ACRs

................................ ................................ ................................ ....................... 16

3.4. Purpose and Organization of this Document

................................ ................................ .. 17

4. Methods for Predicting the Toxicity of Chemicals to Species of Aquatic Animals

............. 18

4.1. QSARs

................................ ................................ ................................ ............................ 18

4.2. ICE

................................ ................................ ................................ ................................ . 19

5. Methods for Estimating the HC5 of Sensitivity Distributions of Acute Toxicity Data for

Animals

................................ ................................ ................................ ................................ ......... 21

5.1. HC5 Estimation Based on Characterization of Species Sensitivity Distribution

........... 21

5.2. HC5 Estimation Based on Extrapolation Factors

................................ ........................... 23

5.2.1. EFs for AChE Inhibition by Resampling Approach

................................ ............... 24

5.2.2. EFs by Resampling Approach versus Distributional Approach

............................. 26

5.2.3. Comparison of AChE

- Inhibition and Narcosis MOAs

................................ ........... 26

5.2.4. Comparison of AChE

- inhibitor EFs to GLWQG EFs

................................ ............ 27

6. Use of ACR for Characterizing the Chronic Toxicity of Chemicals to Aquatic Animals

.... 27

7. Methods for Estimat ing the HC5 for Aquatic Plants Using Data Submitted to Fulfill FIFRA Data Requirements

................................ ................................ ................................ ........................ 31

7.1. Use of FIFRA

- 5 species to Estimate HC5

................................ ................................ ..... 32

7.1.1. SSDs

................................ ................................ ................................ ........................ 32

7.1.2. EFs

................................ ................................ ................................ .......................... 34

7.2. Standardization of endpoints

................................ ................................ .......................... 35

8. Conclusions

................................ ................................ ................................ ........................... 35

8.1. Methods for Predicting the Acute Toxicity of Chemicals to Aquatic Animal Species 35

8.2. Methods for Estimating the HC5 of Sensitivity Distributions of Acute Toxicity Data

for Animals

................................ ................................ ................................ ............................... 36Page 7 of 40

8.3. Use of ACR for Characterizing the Chronic Toxicity of Chemicals to Aquatic Animals

36

8.4. Methods for Estimating the HC5 for Aquatic Plants using Data Typically Submitted to Fulfill FIFRA Data Requirements

................................ ................................ ............................ 37

9. References

................................ ................................ ................................ ............................. 37

Appendices:

A. Review of predictive approaches for use in common effects framework: QSAR Tools

B. The Use of Interspecies Correlation Estimation (ICE) in Interpreting Chemical Risks to Aquatic Species

C. Analysis of Sensitivity Distributions for Estimation of Acute Hazard Concentrations to Aquatic Animals

D. Extrapolation Factors for Derivation of Acute Aquatic Life HC 5s, with Emphasis on Acetylcholinesterase Inhibitors

E. Analysis of Chronic Toxicity Data and Acute Chronic Ratios (ACRs) in Support of Deriving Chronic HC5s: Acetylcholinesterase Inhibitors

F. Estimating Aquatic Plant Community Hazard Concentrations for Pestici de EffectsPage 8 of 40

2. List of Acronyms Used in this White Paper

AChE Acetylcholinesterase

ACR Acute

- Chronic Ratio

AIC Akaike‟s Information Criterion

AIC

c Akaike‟s Information Criterion, corrected for small sample size

ALC Aquatic Life Criteria

CCC Criterion Continuous Concentration

CMC Criterion Maximum Concentration

CWA Clean Water Act

EC50 M edian effects concentration

ECOSAR Ecological Structure

- Activity Relationships

EEC Estimated Environmental Concentration

EF Extrapolation Factor

FACR Final Acute Chronic Ratio

FAV Final Acute Value

FCV Final Chronic Value

FIFRA Federal Insecticide, Fungicide and Rodenticide Act

FPV Final Plant Value GLWQG Great Lakes Water Quality Guidance

GMAV Genus Mean Acute Value

GMCV Genus Mean Chronic Value

HC5 The 5 th percentile o f a sensitivity distribution , HC stands for “hazard

concentration”

ICE Interspecies Correlation Estimation

LC50 median lethal concentration

LOC Level of Concern

LOEC Lowest Observable Effect Concentration

MDR Minimum Data Requirement

MOA Mode of Action

NOE C No Observable Effect Concentration

OECD Office of Economic Cooperation and Development

OW Office of Water

OPP Office of Pesticide Programs QSAR Quantitative Structure

- Activity Relationship RQ Risk Quotient

SACR Secondary Acute Chronic Ratio

SMAV Species Mean Acute Value

SSD Species Sensitivity Distribution

TEST Toxicity Estimation Software Tool

USEPA United States Environmental Protection AgencyPage 9 of 40

3. Introduction

The mission of the USEPA is to protect human health and the environment. Consistent with the USEPA‟s mission, OPP and OW are both responsible for scientifically evaluating potential effects of chemicals on aquatic life. This is accomplished by OPP through risk assessment and

OW through ALC development. For pesticides, data are available to inform deterministic, and in some cases, probabilistic risk assessment used in pesticide registration by OPP; however, in many cases the data bases used by OPP contain fewer studies than OW use s to derive ALC according to OW‟s 1985 Guidelines (USEPA 1985). The data and methods used by the two

offices for risk assessment and ALC development are described in the next section of this

introduction.

Currently, OW has 47 established national recommen ded ALC 2 . Of these, 16 are pesticides that are currently registered for use in the US. Meanwhile, OPP has registered over 1000 active ingredients that may be used in the US

. Surface water monitoring reports ( e.g., by the United States Geological Survey‟s National Water

- Quality Assessment Program) for certain chemicals, including many pesticides, have prompted stakeholders to raise questions as to the possible risks these chemicals pose to aquatic communities. No AL C have been established for many of these chemicals, which impedes the ability of stakeholders to interpret the potential effects on aquatic

communities where the chemicals are detected. Stakeholders have express ed a need for guidance

from the USEPA to all ow them to gauge whether these chemicals represent a concern for aquatic

life. To that end, OPP‟s Aquatic Life Benchmarks 3 allow for comparison of measured concentrations to acute and chronic endpoints for fish, invertebrates and plants; however, they

are not designed to represent community level benchmarks. Although some ALC have been developed for pesticides ( e.g., diazinon, acrolein), a major impediment to the development of

ALC often exists: the amount of toxicity data that is generally available for most chemicals is insufficient to meet the minimum data requirements (MDRs) that are defined in the 1985 Guidelines for developing ALC . For pesticides, the only available data may be from the studies required under FIFRA for the registration of pesticides. Data defined by the 1985 Guidelines for the development of ALC are more extensive than the data require d under FIFRA for aquatic animals, not plants

.

The purpose of this white paper is to explore approaches that may be considered with varying amounts of data to characterize the effects of chemicals on aquatic organisms. Emphasis is placed on situations where data that OPP would regard as sufficient are available to conduct risk

assessmen ts

, but OW would not have a data set that, according to the 1985 Guidelines , would support derivation of ALC . Particular emphasis is placed on the ability of these approaches to reliably predict the HC5

, which is intended to be representative o f sensitive species in aquatic

communities and serve s as the assessment endpoint used by OW for derivation of ALC

. Potential influences of pesticidal MOA on the utility of these methods are also considered.

2 Current National Recommended Water Quality Criteri a are available online at: http://www.epa.gov/waterscience/criteria/wqctable/index.html

3 Available online at: http://www.epa.gov/oppefed1/ecorisk_ders/aquatic_life_benchmark.htmPage 10 of 40

3.1. Current OW and OPP Procedures for Characterizi ng Effects of Chemical Stressors on Aquatic Organisms

3.1.1. OPP

The objective of FIFRA is to provide Federal control of pesticide distribution, sale and use. All pesticides used in the US must be registered by USEPA. Registration assures that pesticides will be properly labeled and that, if used in accordance with label specifications, will not cause unreasonable adverse effects on human health and the environment. Each registration specif ies the crops or sites on which a pesticide may be applied, and each use must be supported by data that the pesticide registrant is required to submit to the USEPA . Categories of required data include product chemistry, environmental fate, residue chemistr y, dietary and non

- dietary hazards to humans, and hazards to domestic animals and non

- target organisms. All studies submitted by registrants must be conducted under conditions that meet Good Laboratory Practice regulations.

For ecological risk assessments of pesticides, OPP relies upon toxicity data submitted by registrants of pesticides and any data available from the open literature . Toxicity data are submitted in order to provide the USEPA with an understanding of the tox icity of a pesticide to animals and plants (non

- target) that inhabit aquatic and terrestrial habitats. Since the focus of this effort is on aquatic habitats, only the data relevant to aquatic animals and plants will be discussed

further.

Most baseline da tasets for chemicals submitted by a registrant are comprised of acute toxicity data for two freshwater fish, which are most commonly represented by rainbow trout and bluegill sunfish and one freshwater invertebrate ( D. magna ). Chronic toxicity data are ge nerally available for a freshwater fish (rainbow trout or fathead minnow ( Pimephales promelas ) ) and invertebrate ( D. magna ). Acute d ata for estuarine/marine species typically include one estuarine/marine fish, which is most commonly represented by sheepshe ad minnow ( Cyprinodon variegatus ) and two estuarine/marine invertebrates, which are most commonly represented by mysid shrimp (

Americamysis bahia ) and Eastern oyster ( Crassostrea virginica ). In addition, chronic toxicity data are often available for the s heepshead minnow and mysid shrimp. For aquatic plants, data are submitted for one vascular plant, typically duckweed and four species of nonvascular plants, including blue

- green algae, green algae an d freshwater and marine diatoms. These plant species are referred to in this white paper as the “FIFRA

- 5”. It should be noted that these are the test species of animals and plants that are typically tested in registrant

- submitted studies. For some pesticides, a broader range of data for other test species may be available from registrant

- submitted studies and ope n literature; however, for new pesticides, typically available data are often registrant

- submitted studies that fulfill the requirements under FIFRA 4

.

OPP‟s aquatic risk assessments evaluate effects of pesticides to individual taxa and are based on

the most sensitive taxa

- specific endpoints for freshwater and estuarine/marine species. Reliance on the most sensitive species is intended to be conservative and does not reflect intra

- and inter

-

4 OPP's data requirements are set forth in Title 40 of the Code of Fede ral Regulations (CFR) Part 158. US EPA has authority to ask for additional data or waive requirements, as appropriate. FIFRA section 3(c)(2)(B) provides the authority to require additiona l data on currently registered products.Page 11 of 40

s pecies variability (USEPA 2004) . OPP‟s risk assessments are based on an integration of exposure and ecological effects characterization s to determine the potential taxa

- specific ecological risk s from the use s of pesticides and the likelihood of direct and indirect effects to

non

- target organisms in aquatic habita ts. E xposure and toxicity data for pesticides are integrated in order to evaluate the risks of adverse ecological effects on non

- target species . In this approach, risk is determined using risk quo tients (RQs) which are calculated as the ratio of exposure to effect

, i.e., e stimated environmental concentrations (EECs) are divided by acute and chronic toxicity values. For acute exposures, the toxicity endpoint is the median lethal

concentration (LC 50 ) or median effect concentration (EC 50 ) and for chronic exposures, the toxicity value is the No Observable Effects Concentration (NOEC). The resulting RQs are then compared to the Agency‟s LOCs (USEPA 2004; Table 1 ). Th is method is used to indicate when the use of a pesticide, as directed on the label, may have the potential to cause adverse direct or

indirect effects to non

- target organisms.

Table 1. Agency risk quotient (RQ) metrics and leve ls of concern (LOC) for aquatic animals and plants.

Risk Class Risk Description RQ LOC

Acute

- Animal Potential for effects to non

- listed animals

from acute exposures Peak EEC/LC 50 0.5

Acute

- Listed

Animal Federally

- l isted endangered and

threatened (listed) species may be potentially affected by acute exposures Peak EEC/LC 50 0.05

Chronic

- Animal Potential for effects to non

- listed and

listed animals from chronic exposures 60

- day EEC/NOEC (fish)

1 21

- day EEC/NOEC (invertebrates)

Non

- Listed

Plant Potential for effects to non

- target, non

listed plants from exposures EEC/ EC 50 1

Listed Plant Potential for effects to non

- target, listed

plant species from exposures EEC/ NOEC or EC05 1

OPP routinely uses ACRs in ecological risk assessments for pesticides to predict chronic toxicity for aquatic animals when chronic toxicity data are not available for a species of interest, for example, when the acute toxicity profile indicates that the mo st sensitive surrogate aquatic species was not tested in a chronic study. The ACR is calculated using toxicity values from valid acute (LC50s or EC50s) and chronic studies (NO ECs), ideally from the same test species. This ACR is then applied to species within a similar group ( e.g., freshwater fish).

When sufficient data exist, assessments may be refined using probabilistic methods, including

SSDs to provide a description of the range of sensitivities within and between taxa ( e.g., USEPA

2002 , 2007 ) . OPP ‟s risk conclusions have not typically relied on SSDs to quantitatively characterize the potential effects of pesticides, but rather to qualitatively characterize the conservatism of the most sensitive toxicity endpoints.

In response to recommendations a nd input from stakeholders, OPP developed a webpage of non

regulatory taxa

- specific endpoints referred to as “OPP Aquatic Life Benchmarks

. ” OPP‟sPage 12 of 40

benchmarks may be useful for interpreting the potential effects of pesticides in surface water in

cases where there is no existing ALC. These Benchmarks are based on the most sensitive acute and chronic toxicity test results for fish and invertebrates (considering registrant

- submitted studies and the scientific literature) from OPP‟s ecological risk assessments of specific pesticides. In the context of OPP‟s risk assessment, any water concentrations exceeding a benchmark for a specific taxon have potential to cause adverse effects to that taxon.

3.1.2. OW

The objective of the Clean Water Act ( CWA ) is to restore and maintain the chemical, physical, and biological integrity of the nation's waters. CWA section 101(a)(2) sets an interim national goal of achieving, wherever attainable, water quality which provides for the protection and propagation of f

ish, shellfish, and wildlife and provides for recreation in and on the water (which is often referred to simply as "fishable and swimmable"). CWA section 304(a) requires USEPA to develop and publish ALC recommendations in the Federal Register to be used as guidance that can be adopted by States into water quality standards by a rulemaking process to protect designated uses. These criteria must reflect the latest scientific knowledge and do not consider the social and economic impacts, or the technological feasibility, of achieving that concentration

of a chemical in the surface water. ALC define water column concentrations to protect against adverse ecological effects to aquatic life as a result of exposure from direct contact or ingestion

of the chemical o f concern in the water column. US EPA interprets ALC that protect the designated use for "fishable" waters as criteria that protect the aquatic assemblage in a water body

, i.e ., not the protection and preservation of every individual of every species in a w ater body

. US EPA published the 1985 Guidelines (USEPA 1985) to provide uniformity and transparency to this process. ALC are usually derived from laboratory toxicity tests using specific standard procedures described in the 1985 Guidelines , but the 1985 Guidelines also have general provisions for deviating from these procedures as warranted by the available science.

ALC consist of two concentrations : the Criterion Maximum Concentration (CMC) that is intended to protect against severe acute effects, and the Criterion Continuous Concentration (CCC) that is intended to protect against longer

- term effects on survival, growth, and reproduction. The acute c riterion limits peak exposures by generally requiring that 1

- hour averages of exposure concentrations not exceed the CMC more often than once in three years on

average . The chronic criterion limits more prolonged exposures by requiring that 4

- day averages of exposure concentrations not exceed the CCC more often than once in three years on average

.

The CMC is based on available acute toxicity values for aquatic animals. These values are median lethal concentrations (LC 50 ) or median effect concentrations (E C 50 for detrimental acute effects such as immobilization) from acute toxicity tests (48

- to 96

- hrs duration) meeting certain data quality requirements. To compute the CMC, the 1985 Guidelines require that acceptable acute toxicity values be available for a t least eight families to address a range of taxonomic diversity. For freshwater aquatic communities, these minimum data requirements (MDRs) include three vertebrates (a salmonid, another family of bony fish, and an amphibian or third family of fish) and f

ive invertebrates (a plankt on ic crustacean, a benthic crustacean, an insect, a species from a phylum other than Chordata or Arthropoda, and a species from another order of insect or another phylum not already represented). Similarly, a saltwater CMC can be derivedPage 13 of 40

using two vertebrates, a shrimp (Mysidae or Penaeidae; note that this is an arthropod), three non

vertebrates, a non

- vertebrate species that is not an arthropod and a species from any other family that is not already represented. For both freshwat er and saltwater criteria, acute toxicity values are averaged within species and genera to provide genus mean acute values (GMAVs). The fifth percentile of these GMAVS is estimated by procedures specified in the 1985 Guidelines and designated the FAV. The CMC is set equal to half of the FAV to represent a low level of toxicity (rather than 50%). It should be noted that the data that are required for pesticides (described in Section 2.1.1) fulfill 3 of the 8 acute MDRs for both freshwater and saltwater CMC s.

The 1985 Guidelines specify that the CCC be set to the lower of either a final chronic value (FCV) for animals or a final plant value (FPV). The FCV is based on effects concentration determined for survival, growth, or reproduction of aquatic anima ls in chronic tests meeting certain data requirements. If the 8 MDRs are met, the FCV is calculated as the 5 th percentile of Genus Mean Chronic Values (GMCVs) by the same methodology as for the FAV. If fewer MDRs are met, the FCV can be calculated by di viding the FAV by a final acute

- chronic ratio (FACR), which is an average (or other extraction) of ACRs from paired acute and chronic tests meeting certain data quality criteria

. The 1985 Guidelines specify that the FPV be the lowest toxicity values for i mportant aquatic plant species and endpoints from tests meeting certain data quality criteria , but do not specify MDRs for the variety of test species or the magnitude of the

effect

. Aquatic plant toxicity data available for use in a criterion derivation are frequently absent, and no criteria to date have been published that use plant data in the calculation.

3.2. Common Effects Project

This white paper is part of the development of a common effects characterization methodology that may be used by USEPA‟s OW and OPP in ecological assessments of chemicals, in particular of pesticides. In April 2009, USEPA developed a document entitled “ Toward a Common Effects Characterization Methodology Scoping Document 5 ” (hereafter referred to as the Scoping Document). The Scoping Document provided background for a proposal to develop a common effects characterization methodology for use in ecological assessments of chemicals, specifically pesticides, by USEPA. The res ulting methodology was intended to integrate the USEPA‟s aquatic effects characterization methods and provide a common basis for achieving the water quality protection goals established under the CWA and FIFRA statutes. The common effects

characterization methodology focuses on situations where data that OPP would regard as sufficient are available to conduct risk assessments, but OW would not have a data set that, according to the 1985 Guidelines, would support derivation of ALC

. Once sufficiently validat ed and vetted, the methods could then be used by state, local and Tribal water management agencies to interpret aquatic ecological risks associated with chemical exposure information, e.g ., water monitoring data. In order to allow stakeholders to provide i nput on the Common Effects Project throughout the process, the USEPA opened a docket (ID: EPA

- HQ

- OPP

- 2009

- 0773) where comments could be submitted and later considered by USEPA.

The approaches articulated in the Scoping Document were subsequently explored by USEPA and discussed in greater detail at stakeholder meetings in six regions throughout the country in

5 Available online at: http://www.epa.gov/oppefed1/cwa_fifra_effects_methodology/scope.htmlPage 14 of 40

January 2010 6

. The purpose of those meetings was to solicit input on methods being evaluated as part of the common effects project to characterize e ffects from pesticides on fish, other aquatic organisms, and aquatic plants in aquatic ecosystems.

In December 2010, USEPA hosted a national stakeholder meeting, where three white papers that

buil t on the topics discussed at the regional stakeholder meet ings were discussed. 7 The first

paper , titled “ Predicting the Toxicity of Chemicals to Aquatic Animal Species ” (USEPA 2010 a ), discussed different methods and tools available to predict the toxicity of a chemical to aquatic animals. The second white paper , “ Exploration of Methods for Characterizing Effects of Chemical Stressors to Aquatic Animals, ” (USEPA 2010 b ) identified possible means through

which available empirical data sets could be either enhanced using predictive tools or employed along with adjustment factors to characterize the lower range of sensitivities to specific chemicals that may exist in an aquatic community. Additionally, a third white paper, “ Deriving Plant Aquatic Life Screening Values for Pesticide Effects, ” (USEPA 2010 c ) was d eveloped to explore the uncertainties associated with and methods for characterizing adverse effects to the aquatic vascular and nonvascular plant community from chemical exposure.

At this time, OPP and OW , with consultation from the Office of Research and Development, are in the process of developing a common set of guidelines for reviewing and categorizing studies for their utility in ecological effect assessments and ALC development. These guidelines are intended to enable scientists in both OPP and O W to have a consistent method for evaluating studies used in populating chemical

- specific databases with empirical toxicity data. The guidelines for evaluating open literature identify critical study elements that should be considered by OPP and OW scienti sts in determining whether a study can be used quantitatively ( i.e., for deriving risk quotients or ALC), qualitatively ( e.g., for characterization of uncertainty), or to identify those studies where there is sufficient uncertainty such that the study is c onsidered scientifically unsound and classified as unacceptable for use in risk assessment or ALC. OPP‟s

Environmental Fate and Effects Division‟s current Guidance for evaluating studies from open literature is available online (USEPA 2011 c ) as a reference for informational purposes; however,

this is not intended as the focus of this white paper.

The current white paper builds upon the approaches described in the scoping document and provides the results of USEPA‟s reviews of many of the methods described in USEPA 2010 a, b

and c

. One of these methods included the Tier II methodology incorporated into the Great Lakes Water Quality Guidance (GLWQG)

. In 1995

, as part of the Great Lakes Initiative , USEPA published an alternate method (also known as the Tier II methodology) for calculating Tier II values when the minimum data requirements for deriving national recommended CWA section 304(a) ALC are not met (40 CFR part 132, App. A. XII). These Tier II values represent estimates of ambient chemical concentrations that would likely be at or below ALC values had the ALC data requirements been fully met. These Tier II values may be used as a basis for State or Tribal water quality standards in the Great Lakes States. The Tier II methods incorporate the use of EFs (for acute values) and ACRs (for chronic values). These methods are described in the following

section.

6 See http://water.epa.gov/scitech/swguidance/standards/criteria/aqlife/cem.cfm#meeting1 for more information.

7 See http://water.epa.gov/scitech/swguidance/standards/criteri a/aqlife/cem.cfm for more information.Page 15 of 40

3.3. Great Lakes Water Quality Guidance

3.3.1. Extrapolation Factors

In regulatory applications, EFs generally include default values applied to acute toxicity test

results to derive the desired assessment endpoint . Of particular interest to this effort is the set of EFs developed by Host et al. (1995) and incorporated into the Great Lakes Water Quality Guidance (GLWQG: USEPA 1995), specifically for deriving Tier II values. The GLWQG was developed by USEPA with participation from the eight Great Lakes states ( i.e ., Illinois, Indiana, Michigan, Mi nnesota, New York, Ohio, Pennsylvania and Wisconsin) as part of the Great Lakes Water Quality Initiative. The purpose of this guidance is to help establish consistent, enforceable, long

- term protection with respect to all types of pollutants. The guidance includes a two

- tiered approach to derive guidance values , including: 1) Tier I values for which data meet the minimum data requirement in the 1985 Guidelines , and 2) Tier II values where there is an absence of the

full set of data meet ing Tier I data requi rements. Due to limited data used to derive Tier II

values , these values are likely to be more uncertain compared to Tier I values . Tier II values are intended to be more conservative , on average, than what Tier I values would be for the same

chemicals if sufficient data were available. Discussed below are the Tier II values incorporated into the GLWQG, which are derived using EFs as well as additional work on EFs by Host et al.

1995.

The GLWQG EF s constitute an existing methodology that can be used for pr edicting the HC5 using limited data because they 1) are designed to conservatively approximate the FAV (which is an estimate of the HC5) that wou ld be generated using the 1985 G uidelines; 2) can be used for

chemicals with various amounts of empirical data ; 3) have been used by states to derive Tier II values, which are similar in nature to the estimates of the HC5 desired here ; 4) are derived from a variety of chemicals with several MOAs, some of which are pesticides; 5) have been adapted

into methods used by several different regulatory agencies, including states ( e.g., MI, CA and OH) and the Department of Energy; and 6) are derived from data sets that meet the acceptability standards employed by US EPA OW.

Host et al. (1995) developed EFs for acute toxicity by using toxicity datasets for chemicals with established ALC, generating 199 samples of 8 tests from these datasets that still satisfied the MDRs, computing an FAV for each of these samples, generating subsamples o f these samples of various sizes and taxonomic compositions, and calculating EFs as the ratio of the lowest datum in each of these subsamples to the FAV for its respective sample. This produced 199 EFs for each chemical and subsample composition, from whi ch distributions for each chemical and for

the chemicals overall could be described. Table 2 provides some relevant summary EFs from

Host et al. (1995) based on 29 freshwater ALC datasets, which included metals ( e.g., cadmium, copper), inorganic chemicals ( e.g., ammonia) and organic chemicals, including pesticides ( e.g., parathion). Each EF in Table 2 represents the median across chemicals of the 95 th percentile EF within each chemical, for the specified dataset composition. Comparison of the EFs in the

s econd column, which were generated without consideration of taxonomic requirements, and the EFs in the third column, which was generated by assuming that at least one Daphnid species ( i.e., Ceriodaphnia sp.

, Daphnia sp. , or Simocephalus sp. ) was present, indicate that there is a large influence of requiring an acute toxicity result f or at least one Daphnid species. This is likelyPage 16 of 40

because these species are often sensitive to chemical stressors. Comparison of the third and

fourth columns of Tab le 2 indicate s that when a daphnid species is required, the additional requirement of a salmonid species some additional, but limited impact on the magnitude of the

EF for the smallest sample sizes . The EFs specified in the GLWQG for deriving Tier II valu es were those from the middle column of Table 2 , requiring an acute toxicity test for a daphnid species.

Table 2 . Extrapolation factors derived by Host et al . (1995). Values represent median of

95 th percentiles. EFs where daphnid is required (*) were incorporated into GLWQG for deriving Tier II values.

Number of

MDRs met Any MDR Daphnid required* Both salmonid and daphnid required

1 426 21.9 Not applicable

2 65 13.0 7.9

3 43 8.0 7.1

4 28 7.0 7.1

5 20 6.1 6.1

6 13 5.2 5.0

7 7.5 4.3 4.3

The utility of these EFs can be demonstrated using three recent ALC for pesticides, including: acrolein (aquatic herbicide; USEPA 2009), carbaryl (carbamate insecticide; USEPA 2011) and diazinon (organophosphate insecticide; USEPA 2005). For this evaluation, the HC5 for each

chemical was estimated using only the minimum FIFRA acute toxicity data set for freshwater animals ( i.e., D. magna , bluegill sunfish and rainbow trout). The lowest acute toxicity data value

from these three s pecies was divided by the GLWQG EF for N = 3 ( i.e., 8.0). The estimated HC5 for each chemical was then compared to the FAV calculated for the full quantitative data set reported in the ALC. The FAVs calculated in the ALC for these three pesticides are included in

Table 3 as well as the means of the acute toxicity data (SMAVs) for the typical FIFRA test species. For all three chemicals, the estimated HC5 is lower than the FAV by a factor varying

from 2.5 to 4.5 , indicating that the EF approach provides a conservative estimate of the HC5.

Table 3 . SMAVs ( in ppb) for typical OPP test species from criteria for pesticides. Also included are FAVs from criteria and HC5 values estimated using lowest SMAV and

G LWQG EF for N=3 ( i.e., 8.0).

Chemical SMAV s Estimated

HC5 FAV D. magna Rainbow Trout Bluegill sunfish

Acrolein 51 16 27 2.0 5.9 Carbaryl 7.5 860 6970 0.94 4.2

Diazinon 1.05 426 460 0.13 0.34

3.3.2. ACRs

The GLWQ G also outlines an approach to calculate a chronic guidance value when fewer than 3 appropriate Acute to Chronic Ratios (ACR) are available, by using a default value of 18 is usedPage 17 of 40

to replace each missing ACR. This default value is based on an analysis conducted by Host et al. (1995). The default value represents the 80 th percentile of the distribution of FACRs from existing ALC. Under the GLWQG, a minimum of three ACRs are required to calculate a Secondary Acute

- Chronic Ratio (SACR); if fewer than three experimentally

- determined ACRs are available, values of 18 are added as needed to bring the total ACRs to 3, then the SACR is

calculated as the ge ometric mean of those values; if no experimentally determined ACRs are available, the SACR is 18.

3.4. Purpose and Organization of this Document

The purpose of this white paper is to describe efforts to better characterize the fifth percentiles of toxicity en dpoint distributions (HC5s) for acute and chronic toxicity to aquatic animals and for aquatic plants. Emphasis is placed on situations where data that OPP would regard as sufficient are available to conduct risk assessments, but OW would not have a data set that, according to the 1985 Guidelines, would support derivation of ALC. Consideration is also given to the uncertainty of HC5 estimates to allow designation of a desired level of conservatism.

For acute toxicity to aquatic organisms, efforts are bei ng made in three areas: (1) Because HC5 estimates would be improved by a greater number of toxicity values across more diverse taxa ( e.g., meeting more of the ALC MDRs), tools to predict toxicity values

in the absence of measured values were evaluated. Thi s included QSARs and ICE

. These efforts are summarized in Section 4 and described in detail in Appendices A and

B

. (2) Methods for characterizing SSDs were evaluated regarding how well HC5s are estimated as a function of the number and composition of data, with particular emphasis on the number of toxicity values being between 3 and 8. These efforts are described in Section

5 .1 and Appendix C

. (3) EFs to estimate HC5s based on the lowest of available measured toxicity values were

evaluated. This work built o n EFs developed by Host et al. (1995) for the GLWQG ( US EPA 1995) to provide conservative estimates of FAVs in the absence of sufficient data to derive FAVs according to the 1985 Guidelines. T he current work expands on the

efforts of Host et al. (1995) by considering new data, new analysis approaches, and how EFs might vary with MOA . The efforts are described in Section 5 .2 and Appendix D

.

Initial efforts have focused on AChE inhibiting chemicals as an important class of pesticides with a large amount of aquatic toxicity data for variety of chemicals and taxonomic groups.

For chronic toxicity to aquatic organisms, efforts focused on applying ACRs to estimate a

chronic HC5 from an acute HC5. ACR data for ACh E inhibitors were compiled and evaluated to

asse ss whether MOA

- specific ACRs might reduce uncertainty in estimating a chronic HC5, compared to generalized ACR approaches from the literature. These efforts are summarized in

Section 6 and Appendix E.

For plant toxicity, the use of SSDs and EFs to estimate HC5 from typical test species submitted to fulfill FIFRA‟s requirements were investigated. In addition, the utility of standardizing toxicity endpoints was explored. These efforts are summarized in Section 7 and Appendix F

.Page 18 of 40

4. Methods for Predicting the Toxicity of Chemical s to Species of Aquatic Animal s

To supplement available empirical data using predicted acute toxicity values, USEPA investigated the use of some available predictive tox icology tools, including 3 QSAR applications and ICE. Through the use of algorithms, QSARs attempt to quantify relationships between an aspect of chemical structure and an activity ( e.g. , toxicological response) imparted by

that structure. A detailed description of the methods and results of the USEPA‟s analysis of some available QSAR applications is provided in Appendix A

. ICE uses empirical toxicity data for a given test species to predict the toxicity of that chemical to another species. This is done by correlating toxicity results ( e.g

. , LC50s values) for two species for as many chemicals as are available for the species combination ( e.g.
, rainbow trout vs. bluegill) rega rdless of chemical‟s type, use or MOA. A detailed description of th e methods and results of the analysis of an application of ICE ( i.e., Web

- ICE) is provided in Appendix B . Information on additional methods and tools that may be used to predict the toxicity of chemicals to aquatic animals can be

found in USEPA 2010 a

.

4.1. QS ARs

In theory, if acceptable robust models and data to build reliable models exist, the use of QSAR

methods could be used to supplement empirical data to a larger toxicity set ( e.g., 3 acute toxicity

values to fulfill FIFRA requirements or t he 8 MDRs for ALC )

. In order to determine whether a QSAR estimate for a chemical and species of interest is appropriate, a number of characteristics

of the model need to be considered

. These characteristics include the applicability of the model to the endpoint or para meter of interest; applicability to the chemical class, MOA , group and structure of the chemical of interest; the requirement for a quantitative or qualitative prediction;

and whether the tool has inherent biases or limitations (OECD 2007)

. Many of the ava ilable predictive tools are qualitative and not suitable for deriving predicted values. Although qualitative tools may be useful in other aspects of the risk assessment, they are not considered in the current analysis. Conversely, quantitative models, whi ch predict values of interest ( e.g ., LC 50 , EC 50 ), are available, but the use of these tools may be limited when statistical rigor, predictive performance, domain of applicability, and the level of validation of the statistical algorithm/model are considere d.

As described in detail in Appendix A , USEPA evaluated the ability of three QSAR tools to predict the acute toxicity of AChE inhibitors to fish and aquatic invertebrates. Since a critical

evaluation was not conducted on all existing QSAR model s and tools as well as MOAs, the

limitations of QSARs that are discussed here and in Appendix A are based on the restricted

evaluations that were undertaken. In this analysis, three publicly available tools were examined,
i.e., existing QSAR models f or fish and daphnids within ECOSAR ( USEPA 2011a ), TEST ( USEPA 2011b ) applications, and the OECD QSAR Toolbox (OECD 2011) , which was used to develop new QSAR models. These tools were selected because they are freely accessible, regulatory authorities frequently rely on these tools when chemical

- specific empirical data are not available, and they have undergone some level of validation as described by the OECD validation principles (OECD 2007)

. These tools were evaluated by comparing their predictedPage 19 of 40

values to empirical ly based toxicity data. The purpose of this evaluation was to address the following questions as the y related to the common effects framework:

W hat is the potential utility for each model/tool in developing quantitative toxicity values?
For each model, what are the strengths, weaknesses and uncertainties as they relate to use in predicting toxicity of a chemical to specific taxa ? How do model predictions compare to an independently compiled empirical data collection?

What future research could improve th e utility of the predictive tools considered in this

review ?

The analyses of ECOSAR, TEST and the OECD Toolbox would suggest that, at this time, (a) the chemical domain for most models does not include pesticide active ingredients and therefore

models a re insufficient for estimating the toxicity for modes of action that are unique to pesticides ( e.g., AChE inhibitors), (b) existing validated QSAR models provide estimates of chemical toxicity for species that typically have sufficient empirical data sets ( i.e. , fish and daphnids), and (c) the development of reliable QSARs to fill these model gaps will require the availability of empirical toxicity data that meet rigorous acceptability requirements.

Future work will require the development of methods to consistently identify empirical test data that are acceptable for use in QSAR model development. This acceptability evaluation should take into account the relevance ( i.e. , applicability and appropriateness) and the quality ( i.e.

, reliability, validity, c redibility, trustworthiness, accuracy, repeatability, and reproducibility) of the study. In addition, QSAR models within existing tools need to be expanded to better include pesticide active ingredients within their domain of applicability. Improvement o f the reliable predictability of these tools will likely require development of specific models based on MOA and inclusion of parameters other than the octanol

- water partition coefficient ( log P ) to describe toxicity. It would be helpful to have informati on related to molecular descriptors that work best in describing toxicity for specific chemical classes of pesticide structures. These descriptors should have some linkage to key events within the MOA, and would then provide a means to develop reliable, c onsistent, and toxicologically

- defensible QSAR models.

4.2. ICE

The Web

- ICE provides ICE models for acute toxicity estimation for aquatic and wildlife species with known certainty. Almost 3000 ICE models are available for wildlife (birds and mammals), and aquatic (fish and invertebrates) species, genera, and families to predict t oxicity in three separate modules. ICE calculator modules predict single toxicity values for a species, genus (aquatic only) or family.

The Web

- ICE user‟s manual ( USEPA 2010d ) provides recommendations for evaluating the quality of model regressions. Thes e recommendations were used to evaluate the application of

Web

- ICE for estimating toxicity data for specific species. Termed as “Rules of Thumb”, they are recommendations for selecting models with low uncertainty. For best estimates, the authors

recommend to select models that possess the following attributes: 1. Relatively low mean square error (<0.22) 2. Close taxonomic distance (≤ 3 ; i.e., species are within the same genus, family or order ) 3. High cross

- validation success rate (> 85%)Page 20 of 40

4. High degrees of freedom ( > 8 when N > 10) 5. High R 2 value (> 0.6) 6. Low p

- values (<0.01) 7. Narrow confidence bands on the graph

When consid ering these rules of thumb, Web

- ICE‟s predictions function best when estimating toxicity for closely related specie s ( e.g., same genus or family) for which empirical data are available. Using the acute toxicity data for aquatic animals that are typically submitted to fulfill requirements under FIFRA ( i.e., data for D. magna , rainbow trout and bluegill sunfish), this to ol appears to generate the most precise predictions of the toxicity of closely related fish species. Specifically, when using data for rainbow trout and bluegill sunfish, predictions are most precise for the following species: yellow perch ( Perca flavesce ns ), largemouth bass ( Micropterus

salmoides ), brown trout ( Salmo trutta ), lake trout ( Salvelinus namaycush ), cutthroat trout ( O.

clarkia ) and Coho salmon ( O. kisutch ). The current version of Web

- ICE does not have sufficient data to fulfill all of the MDRs of the 1985 Guidelines when there are only acute surrogate data available for rainbow trout, bluegill and D. magna

. With future updates, the tool has the potential

to u se the typical data submitted under FIFRA to predict the toxicity of a chemical to species that may fulfill the MDRs of the 1985 Guidelines. If toxicity data are available for only one fish species that is typically submitted under FIFRA ( i.e., rainbow tro ut or bluegill sunfish) , it appears that this tool could be useful for predicting the toxicity of a chemical to the missing test species.

The influence of chemical MOA, relative to taxa relatedness, in aquatic species ICE modeling (Raimondo et al 2010) h as been tested using Web

- ICE. Taxa relatedness proved to be a more important determinant in the precision of the ICE models than MOA up to the phylum taxonomic level. When species pairs were co rrelated across different phyla , MOA had a significant influence on the precision of ICE estimates. At this time, updates to Web

- ICE are underway. One major update to the application involves the development of MOA

- specific models. MOA specific models are intended to improve the accura cy of predictions for two species of greater taxonomic distance (Raimondo et al. 2010). While MOA

- specific models may improve toxicity predictions, fewer species pairs will be available using the smaller datasets of specific MOAs.

In addition to predicti ng the toxicities of individual species, the Web

- ICE tool allows the user to
derive an HC5 (from a logistic distribution) using a combination of empirical and predicted values. The predicted values may be derived for many species of fish and invertebrates; however,

the user has the choice of which values to include in the SSD. The Web

- ICE guidance suggests

that the user consider the “rules of thumb” in interpreting the reliability of the results of the tool.

If all of the Web

- ICE rules of thumb are applie d, the SSD application will be very limited in its ability to predict the HC5 . For example, if the typical FIFRA acute toxicity data set for diazinon (an organophosphate insecticide) is used to derive a distribution and the rules of thumb are applied, the HC5 would be based on 9 species (8 fish and 1 invertebrate; see Table 4 of Appendix B ). The user may expand the data set by relaxing the reliance on these rules of thumb and the data set will likely contain a mixture of data on fish and a greater number of invertebrates, with a predominance of data for fish.Page 21 of 40

For insecticides, which are generally more toxic (by orders of magnitude) to aquatic invertebrates compared to fish, the predominance of reliable predictions of toxicity endpoints for fish are likely to result in an overestimation of the HC5 (less conservative value) by the Web

ICE tool. Based on this, the application of Web

- ICE for estimating the HC5 appears to be limited

for insecticides. For chemicals that do not have a marked difference in effects on fish and invertebrates ( e.g., chemicals with a narcotic MOA , including many herbicides and fungicides)

the Web

- ICE tool may provide a more reliable estimate of the HC5

.

5. Methods for Estimating the HC5 of Sensitivity Distributions of Acute Toxicity Data for

Animals

The statistical distribution of toxicity endpoints ( e.g., LC50s) for different biological taxa to the same stressor is a common tool used in the decision process for setting safe limits on toxic

chemical concen trations in surface waters (US EPA 1985, Posthuma et al. 2002, Suter 2002, Chapman et al. 2007). These distributions are generically referred to as species sensitivity distributions (SSDs) because the toxic endpoints are typically summarized at the species level, although other taxonomic levels might be used. Risk is characterized based on one or more percentiles in the SSD, generically referred to here as HC p s, "HC" standing for "hazard concentration" and "p" referring to the percentile of interest. Efforts here are concerned with HC5 estimation for acute toxicity (EC/LC50s) to aquatic animals, at a specified confidence level,

for datasets with 3

- 8 species.

Two types of HC5 estimation are considered here. Section 5 .1 concerns the direct

characterization of SSDs from an individual dataset, evaluating how well HC5s are thereby estimated as a function of various attributes of the dataset and methodology. Section 5 .2

concerns the use of EFs , defined here as the ratio of the smallest toxicity value in the dataset to the HC5. EFs are developed through analysis of acute toxicity data for chemicals with robust data sets for which the HC5 is known or can be reliably estimated; these EFs can then be used to estimate the HC5 for smaller data sets by dividing the lowest to xicity value by the EF.

5.1. HC5 Estimation Based on Characterization of Species Sensitivity

Distribution

Although methods for analysis and interpretation of SSDs vary widely, the basic methodology is quite general and can be summarized as a three

- step procedure. First, all available acute toxicity data for a given chemical are compiled. Second, a distribution to which the test results are thought to conform is chosen and fit to the data. Third, the fitted distribution is used to infer a

concentration that will be protective of a desired proportion of species in an (hypothetical) aquatic community ( e.g., the HC5). As part of this effort, the direct analysis of SSDs for AChE inhibitors was evaluated in light of the following eight questions, which are discussed briefly

here and in detail in Appendix C :Page 22 of 40

1. How does sample size influence accuracy (bias and variance) of the estimated HC 5 ?

The SSD analysis for AChE inhibitors indicates that the reliable calculation of a distribution‟s HC5 requires more than 3 to 8 data points (due to large bias and variance)

. Within this range, estimates of the HC5 may be affected by both bias and substanti al uncertainty, when constraints on data composition, such as taxonomic requirements (see below), are not imposed. When data requirements are imposed (see below) estimates of the HC5 at sample sizes of 3 to 8 data points improve considerably

.

2. How do minim um data requirements influence accuracy of the estimated HC 5 ?

The imposition of MDRs (either the 3 test species typically available to fulfill FIFRA data requirements or the 8 MDRs included in the 1985 Guidelines) improved precision and tended to produce conservative estimates of the HC5 ( i.e ., estimates that, on average, would be lower than the „true population‟ HC5). This result is due to MDRs forcing inclusion of sensitive species (typically arthropods in the case of AChE inhibitors) in all

data subsets , and thus the eliminating data subsets that contain only insensitive species.

3. How do different estimation methods influence accuracy of the estimated HC 5 ?

For all SSD estimation methods considered, bias decreased with increasing sample size.

Within th e range of sample sizes of interest ( i.e ., 3 ≤ N ≤ 8), moment estimators ( e.g., estimating distribution parameters from the sample mean and variance, see Section 2.2.4

of Appendix C ) were the least biased. Conversely, graphical methods produced the most conservative estimates of the HC5 in this range. Estimation methods did not differ much in precision.

4. Are measures of fit (goodness

- of

- fit, Akaike‟s Information Criterion) reliable indicators of performance?

Within the range of n=3 to 8, neither AIC nor goodness

- of

- fit measures were reliable indicators of the performance of a distribution, though both methods performed well at large sample sizes (N ≥ 50). In analyses of simulation data, AIC did not consistently identify the distribution used to simulate the data as the best distribution to fit the data with any sample sizes less than N = 50. Similarly, goodness

- of

- fit tests rarely showed sufficient power to reject alternative distributions with greater than 50% probability with sample sizes less than N = 50.

5. What are actual coverage rates of 95% confidence limits for subsamples?

Lower 95% confidence limits (calculated only for normal, logistic, and triangular distributions at N = 3 and N = 8) generally showed nominal coverage rates ( i.e., the true

or r eference HC5 is above the confidence limit in 95% of data subsets) with simulated data. With empirical data, lower 95% confidence limits showed higher than nominal coverage rates when MDRs were imposed, but usually showed lower than nominal rates

when MDR s were not imposed.Page 23 of 40

6. How do different candidate distributions perform relative to each other?

No single distribution was preferred for either simulation data or empirical data. Performance of each distribution, based on AIC

c was very evenly distributed i n the long run, with the Burr III distribution being disfavored compared to the other six distributions considered, probably due to the larger number of estimated parameters for the Burr distribution. Judging by bias at small sample sizes, distributions on log

- transformed data (normal, logistic, triangular, Gumbel) generally outperformed distributions on untransformed data (Pareto, Weibull, and Burr III ) and of the former, the log

- normal, log

logistic, and log

- triangular showed very similar performance.

7. Do es model

- averaging across distributions improve estimates of the HC 5 ?

Model averaging substantially reduced bias in estimates of the HC5 in empirical analyses, and thus shows some promise for application to data when sample sizes are limited. However, in theoretical analyses on simulated data, this pattern of improved performance was not observed. This topic is one among several identified for future research on the application of SSD for the conservative prediction of HC5 values using

limited data sets (refer to future research section of Appendix C ).

8. What future research is suggested by these results?

The results of these analyses suggest that additional work on sensitivity distributions could be valuable. In particular, extension of methods for ordered data to all continuous distributions might alleviate some of the large positive biases with limited data for other distributions than those currently amenable to graphical estimation. Similarly, development of Bayesian methods for hierarchical data analysis using information on

MOAs and phylogenetic trees as underlying hypotheses for the distribution of sensitivities could help to extend the benefits of MDR imposition to other, as yet unexplored MOAs that may not be as well covered by existing MDRs. Another potential improvement in SSD analyses might be to incorporate random effects methods for handling cases in which more than one test result is available for a given taxon.

5.2. HC5 Estimation Based on Extrapolation Factors

When there are insufficient data to derive ALC in accordanc e with the 1985 Guidelines,

US EPA's GLWQG (USEPA 1995) specified extrapolat ion factors for deriving conservative estimates of the FAV for use in Tier II values (screening/advisory concentrations). These EFs require that the toxicity data include a test with a daphnid, and range from a value of 21.9 when just the daphnid test is available to 4.3 when all but one of the ALC MDRs are met.

The GLWQG EFs were based on analyses by Host et al. (1995). Using acute toxicity value databases for existing ALC, these authors (a) randomly sampled the database for each chemical to generate multiple datasets of 8 toxicity values meeting the ALC MDRs, (b) calculated the FAV (HC5) for each of these minim um ALC datasets, (c) sampled from these minimum ALCPage 24 of 40

datasets to generate subsets of different sizes and taxonomic composition, and (d) calculated EFs as the ratio of the smallest toxicity value in each subset type to the FAV. For freshwater acute toxicity , this resulted in 199 EFs for each of 29 chemicals for each subset type, providing a composite distribution of EFs reflecting both intra

- and inter

- chemical variability. The GLWQG

used the median value of the 95 th percentiles EFs for the individual chemi cals. In addition to this repeated sampling of discrete toxicity values ("resampling approach"), Host et al. also developed EFs by sampling from a statistical distribution representing toxicity values ("distributional approach").

To provide EFs more appl icable to conservative estimates of acute toxicity HC5s for pesticides and to better defining the relationship of OW and OPP assessment procedures, the efforts in the current project expand on those of Host et al. (1995) as follows:

(1) Using a larger database of toxicity test results, reflecting more recent data and data for chemicals other than those that actually have ALC. (2) Conducting separate analyses for different MOAs. Results reported here are just for AChE inhibitors as an initial example, with limited comparison to chemicals with narcotic action to illustrate how MOA might affect EFs. (3) Paying specific attention to EFs for certain minimum data requirements in OPP assessment procedures ( i.e., acute tests for rainbow trout, bluegill sun fish, and Daphnia magna ). (4) Extending the distributional approach by using a composite distribution reflecting the relative sensitivities of different taxonomic groups, rather than a single simple distribution as was

the case for Host et al. (1995).

In addition, this effort addresses how the reference (known) HC5 used to calculate EFs is

defined. For Host et al. (1995) the reference HC5 was the FAV calculated for each of the N=8 datasets satisfying the ALC MDRs, rather than the FAV for the complete d ataset for a chemical. For the current effort, analyses use the HC5 of complete datasets as a more appropriate reference point for EFs.

Work to date is summarized here and discussed in more detail in Appendix D . As noted in this appendix , there is a nee d for further improvements in the toxicity database used in these analyses and in assessing its distributional properties. Because of data uncertainties, the specific EFs calculated should be considered provisional, but they still are informative regardin g the methodology and general outcomes. Overall, this work supports the approach of Host et al. (1995), in that it is possible to use existing toxicity data to derive EFs that can be used to provide useful estimates of HC5s for datasets not meeting the AL C MDRs, and to address aspects of the uncertainty of such estimates. Summaries of more specific issues follow.

5.2.1. EFs for AChE Inhibition by Resampling Approach

Available toxicity data for six AChE

- inhibiting pesticides satisfied the ALC MDRs and had

suffi cient data for sensitive taxa to support the analyses. Random resampling was used to establish EF distributions for datasets satisfying from 2 to 8 of the MDRs (one test result per MDR) and various taxonomic requirements, including the OPP acute test mini mums. Figure 1 summarizes the effects of dataset size when there was requirement of a test on a planktonicPage 25 of 40

crustacean (daphnid) and any species of fish. Evaluations for other taxonomic requirements are

included in Appendix D

.

Figure 1. Geometric mean extrapolation factors (for 999 samples) for six AChE

- inhibiting chemicals as a function of the number of MDRs satisfied, given a minimum requirement of one fish test and one planktonic crustacean test, with remaining tests being randomly selected to satisfy other MDRs for ALC derivation. Lines denote the geometric mean and the 75 th and 90 th percentiles for composite results across all chemicals.

By requiring a test from a highly sensitive taxon (daphnids) in the da tasets, composite median EFs (across all chemicals and samples) were small and did not change grea tly with dataset size ( Figure 1 ). However, due to the variability of toxicity values within sensitive taxa, the uncertainty on EFs was substantial, with the composite 90 th percentile being more than 5

- fold higher than the median for N=2 and 3. There was also considerable variability among chemicals, especially at the lowest sample sizes. This variability among chemicals highlights how the composite EF distri bution contains both intra

- and inter

- chemical variability, so that the composite 90 th percentile includes more than 90% of the EFs for some chemicals and less for others. This is an important factor in the selection of any regulatory EFs, and was conside red in

the work of Host et al. (1995) and its application to the GLWQG.

For the typical data submitted to OPP under FIFRA ( i.e., Daphnia magna , rainbow trout, and bluegill sunfish ) , the composite median EF was determined to be 2.9. This is greater than the composite results for N=3 (and even for N=2) in Figure 1 , because, on average, other daphnids

that were tested were more sensitive than D. magna and because the OPP data requirem ents specify data for a second fish species, which are comparatively insensitive to AChE chemicals.Page 26 of 40

Results to date indicate that:

(1) Useful MOA

- specific EFs can be developed by the resampling approach when there are

sufficient data for several chemic als with that MOA; these EFs can be tailored to the taxonomic composition of the datasets to which they are applied. (2) A data requirement that includes a taxon that is highly sensitive to AChE inhibitors ( i.e., daphnids) leads to small EFs even for the small est datasets of these chemicals

. (3) Care must be taken in selecting and expressing what percentile in the composite EF distribution (= the probability with which the HC5 estimate exceeds the desired value),

because the EF distributions involve both intra

- and inter

- chemical variability.

5.2.2. EFs by Resampling Approach versus Distributional Approach

The resampling approach requires that a sufficient number of chemicals within the MOA have sufficient data to support resampling, a requirement few MOAs are likely to meet. The distributional approach has the potential to support EF derivations for more MOAs because data from all chemicals within the MOA can contribute to the analysis, not just those that meet all ALC MDRs and are large enough for resampling. To compa re these two approaches, the toxicity data for all chemicals with an AChE

- inhibition MOA were analyzed to estimate the

standard deviation of EC/LC50s within each MDR and the mean differences between MDRs. EFs were then generated as for the resampling appr oach, except that sampling was from these distributions rather than from the discrete toxicity test values.

Despite the markedly different toxicity databases used for the two approaches, the resultant EFs from the resampling and distributional approaches are similar. For analyses requiring one daphnid and one fish, the median EFs differed by no more than a factor of 1.3 and the 90 th
percentiles were within a factor of 1.4. This work established a basic framework for the distributional approach and demons trated its utility. Because the distributional approach could apply to more MOAs and include more data, it would be advantageous to standardize on it, using the resampling analysis only as part of uncertainty evaluations. To do this, work is ongoing to

i mprove the distributional analysis, especially with regard to parameter uncertainty and distributional shape.

5.2.3. Comparison of AChE

- Inhibition and Narcosis MOAs

Although only one narcosis chemical had data sufficient for a resampling analysis, there were

su fficient data across all chemicals with a narcosis MOA to estimate distributions for the MDRs. The composite of 8 MDR distributions differed greatly between the MOAs

– for narcosis, the 8 MDR distributions were very similar, while for AChE

- inhibition, art hropods were 2

- 3 orders of magnitude more sensitive than other phyla and variability within MDRs was higher than for narcosis. Despite the large differences in the composite distributions for these two MOAs,

differences between their median EFs were actua lly rather small

– differing by no more than a factor of 1.2. At higher percentiles, the narcosis EFs were lower, especially at higher N, by up to

a factor of 2.Page 27 of 40

The similarity of median EFs for the two MOAs can be attributed to each MOA having characteristics that lead to a high probability that the lowest toxicity value is near the reference

HC5. For AChE

- inhibiting chemicals, requiring a planktonic invertebrate even w hen overall N

was low ensured that a highly sensitive species was included i n the dataset. For narcosis chemicals, the low variability and similar mean sensitivity across MDRs make it likely that at least one sampled value will be near the HC5. This lower variability for the narcosis chemicals also results in less variability of the EFs, and thus the lower 90 th percentile EFs, especially at N=8. Because these underlying characteristics are not necessarily true for other MOAs, it would be inappropriate to presume EFs are widely generalizable based solely on analysis of these two

MOAs.

5.2.4. Comparison of AChE

- inhibitor EFs to GLWQG EFs

Host et al. (1995) also reported overall percentiles for EFs based on a requirement of a daphnid and a rainbow trout test, which is similar enough to the requirement of a daphnid and a fish in the current work to warrant some comparison. The Host et al. (1995) media n EFs range from 35 to 65% greater than those for AChE

- inhibitors reported above for the resampling approach, and the

90 th percentiles range from 4 0 to 1 6 0% higher.

Although such differences might seem to substantiate an importance of considering MOA

sp ecific EFs, these differences are confounded by the methodological differences regarding the

definition of the reference HC 5 noted earlier. The effects of these different references were investigated for datasets with N=8 and it was determined that the ap proach of Host et al. (1995) increased EFs by more than two

- fold for AChE

- inhibitors, but by very little for narcosis

chemicals

. Efforts are needed to resolve this issue to better establish the efficacy of MOA

specific EFs. Additionally

, future work coul d be undertaken to address updating the work of

Host et al. (1995) on broader groups of chemical for when MOA

- specific EFs are not available.

Issues that this work could address include the aforementioned reference HC5s, the implications of percentile sel ection, the use of the distributional approach, new data, and chemical selection

relative to assessment needs.

6. Use of ACR for Characterizing the Chronic Toxicity of Chemicals to Aquatic Animals

As discussed in Section 3

, ACRs are currently used by both OPP and OW to extrapolate from acute effect concentrations to chronic effect thresholds. In current practice, this is generally a “within chemical” extrapolation, meaning that an ACR developed for a chemical tested with one

or more species is used to estimate chronic effect thresholds for other organisms exposed to the same chemical. In this section, we discuss applying the ACR concept to estimate chronic effect

thresholds in cases were little or no chronic data are available f or that chemical. The generalized scenario contemplated in this analysis is where an acute HC5 has been calculated or estimated, and an ACR will be used to estimate the chronic HC5 from the acute HC5; this ACR is termed

the ACR HC5 . The ACR HC5 is parallel in concept to the Final Acute

- Chronic Ratio (FACR) under the 1985 Guidelines, which is used to relate the FAV to the FCV. While not a focus of this analysis, some of the same approaches could also be used to make taxa

- specific predictions of

chronic thre sholds when chemical

- specific ACRs are not available.Page 28 of 40

The ACR is calculated as the ratio of the acute effect concentration to a chronic effect concentration. In all formulations used by OPP or OW, the acute effect concentration is defined

as the LC 50 or EC 50 . The chronic effect concentration is either the NOEC , the geometric mean of the NOEC and the Lowest Observed Effect Concentration (LOEC), or the chronic EC20 (concentration reducing measures of survival, growth, development, or reproduction by 20%), depending on the context. For purposes of this discussion, ACRs calculated using different chronic effect concentrations will be noted with the appropriate subscript: ACR NOEC , ACR GM , and ACR EC20 for ACRs calculated using the NOEC, geometric mean of NOEC and LOEC, and EC20, respectively.

Several authors have evaluated ACRs in an attempt characterize their distributions and/or define “default” values to use in the absence of empirical data for chemicals or organisms of interest ( e.g., Raimondo et al. 2007, Ahlers et al. 2006, TenBrook et al. 2010). Despite the limited success of literature analyses to refine ACR prediction based on the chemical class or MOA, it seems a logical extension of the MOA concept that chemicals acting through the same toxicity

pat hway should show greater similarity in the expression of toxicity and, by extension in the relationship between acute and chronic effects ( i.e., the ACR). Put differently, particular toxicity pathways should logically lead to consistently higher or lower ACRs compared to averages obtained across multiple mechanisms (as in the GLWQG ). The idea of MOA

- specific ACRs

would plausibly extend also to the possibility of particular taxa having higher or lower ACRs because of the intersection of their physiology an d the toxic mechanism of the chemical.

Toward that end, the analyses described in Appendix E were undertaken, focusing specifically on data for pesticides whose primary mode of action is AChE inhibition ( i.e., carbamates and organophosphates). While Appendix E deals solely with AChE inhibitors, this analysis was also intended to be demonstrative of approaches that might be used for developing MOA

- specific ACRs for other groups of chemicals in the future.

Initial evaluation of these data indicated tha t ACRs for fish were larger and somewhat more variable than those for invertebrates (arthropod invertebrates specifically; Figure 2 ). Comparisons of data for carbamate and organophosphate chemicals did not suggest systematic

differences in ACRs between tho se chemical groups so they were combined for all subsequent analyses. ACRs for freshwater and saltwater fish were well distributed across the overall range of values. In the case of invertebrates, saltwater ACRs (exclusively from tests with mysids) were more common in the upper tail of the combined invertebrate distribution; however, it is not clear that this necessarily indicates that mysid ACRs are intrinsically higher than daphnid ACRs for AChE inhibitors (see Appendix E for more discussion).Page 29 of 40

Figure 2 . Distribution of ACR GM s for fish and invertebrates derived from acute and chronic toxicity data involving exposures of animals to AChE inhibitors.

While fish had generally higher ACRs, they are far less acutely sensi tive to AChE inhibitors than are invertebrates. As a consequence, the higher ACRs for fish do not translate into greater chronic sensitivity

– in fact, the opposite is true. To put this in perspective, the NOEC values

from chronic studies with fish were compared to the acute LC50 for D. magna ( Figure 3 ) . A substantial majority of chronic fish NOECs were higher than the D. magna acute LC50 for the same chemical. If the applicable EFs for AChE inhibitors ( Appendix D ) are applied to the D.

magna acute values, one sees that the resulting 50th or 90 th percentile EF

- based estimates of the acute HC5 for AChE inhibitors would be lower than all, or nearly all chronic fish NOEC values. This suggests it is the chronic sensitivity of invertebrates that w ould be the primary determinant of the chronic HC5, and that fish ACRs are not relevant to the chronic HC5 for AChE inhibitorsPage 30 of 40

Figure 3 . Comparison of chronic fish NOEC values to acute D. magna LC50 and acute HC5

estimates for AChE inhibitors.

Accordingly, an ACR HC5 for AChE inhibitors would logically be based on invertebrate ACRs. As indicated above, there is some uncertainty as to whether mysid ACRs are inherently higher than those for daphnids, which influences the decision of which invertebrate ACRs would be

mos t appropriate to defining the ACR HC5 . At present, the suggested approach is to select the

ACR HC5 from the combined invertebrate ACR distribution. This suggestion stems from the precedent established in the 1985 Guidelines, under which ACRs for daphnids a nd mysids would likely be averaged to derive a FACR, if both ACRs are available, and both species are acutely sensitive (as is the case of AChE inhibitors). However, separating the distributions could also be

considered.

As the FACR values underlying the Host et al. (1995) and TenBrook et al. (2010) ACRs are tied to the 1985 guidelines and therefore to ACR GM or ACR EC20 values, those same ACRPage 31 of 40

distributions are the most appropriate points of comparison between generalized and AChE

inhibitor

- specific ACRs. As shown in Table 4 , the 50 th and 80 th percentile ACR GM and ACR EC20 values derived specifically for AChE inhibitors are up to 2

- fold lower than the Host et al. (1995) values, and less different from the TenBrook et al. (2010) 80 th percentile value of 12.4. The selection of a specific ACR HC5 value from among these values (or other points in their distribution) would be dependent on both the definition of the chronic toxicity threshold and the desired level of conservatism/uncertainty in the assessment.

Ta ble 4 . ACR distributional values from Appendix E and literature sources

ACR Distribution Source/Type Percentile

50th 80th Appendix E AChE inhibitors, invertebrates only ACR NOEC 7.98 17.9 AChE inhibitors, invertebrates only ACR GM 5.91 13.6 AChE inhibitors, invertebrates only ACR EC20 3.88 8.63

Host et al. (1995)
Combined chemicals, freshwater FACR 7.1 17.9

TenBrook et al. (2010) Mixed pesticides, freshwater FACR NR 12.4

Building on the experience of evaluating ACR data for AChE inhibitors, there are additional studies that might help advance the overall approach. Within AChE chemicals, it may be useful to explore whether additional data screening to more carefully “match” ( e.g. , same lab, same water, same time frame) th e acute and chronic data would decrease variability in ACRs. In addition, conducting similar analyses for other MOA would help define the degree to which developing MOA

- specific ACR distributions can reduce uncertainty relative to generalized ACR

distribu tions. Finally, selecting default ACR values from within a distribution raises the issue of potential under

- protection in cases where the “true” ACR lies higher in the distribution than the selected default value. Better understanding the circumstances t hat lead to unusually high ACRs might inform the development of computational or other approaches useful to flagging chemicals that might have unusually high ACRs ( e.g. , structural alerts proposed by Ahlers et al. , 2006).

7. Methods for Estimating the HC5 for Aquatic Plants Using Data Submitted to Fulfill FIFRA Data Requirements

The current guidance for derivation of ALC for aquatic plants is to use the lowest toxic effect value of the available data. Data acceptable for ALC are described only as “the result of a 96

- hr test conducted with an alga or a chronic test conducted with an aquatic vascular plant” (US EPA 1985). Although the establishment of a plant effects value is not req uired for the derivation of

AL C, if such a value is desired, then the mini mum data required is one acceptable test with a freshwater alga or vascular aquatic plant for the freshwater criterion. Similarly the result of only one saltwater species is required to establish a saltwater plant criterion. In the event that plants

are li kely to be more sensitive than animals to a given compound ( e.g., herbicides), then the “results with a plant in another phylum (division) should also be available”.Page 32 of 40

The lack of specificity for plant toxicity data is due largely to the fact that at the t ime of the writing of the 1985 Guidelines the “procedures for conducting tests with plants and interpreting the results of such tests [were] not as well developed”. Current scientific understanding on how to conduct and interpret plant toxicity tests has i mproved. Nevertheless, standardization of procedures and selection of relevant endpoints are still issues. In addition, there is no published guidance on how many plant species and from which taxonomic groups they should be selected in order to reasonably represent the sensitivity of the aquatic plant community. The current analysis (detailed in Appendix F ) seeks to investigate the relevance of some of these uncertainties, and to explore alternative approaches to characterizing effects of chemical

stressor s on aquatic plant communities when limited data are available

.

As noted in the introduction, this effort focuses on the HC5 of a SSD of aquatic plant data. As with the acute animal toxicity data, two different methods were investigated for estimating the HC5, i.e., EFs and SSDs. For this effort, the EC50 value is used to represent effects of chemical stressors on plants. The primary a nalyses investigated the relative sensitivities of the standard aquatic plant species submitted to fulfill FIFRA data requirements ( i.e., the FIFRA

- 5) to the HC5 of a log normal distribution of toxicity data including other aquatic plant species. Detailed descriptions of the methods and results are provided in Appendix F

.

7.1. Use of FIFRA

- 5 species to Estimate HC5

7.1.1. SSDs

The relative sensitivities of the FIFRA

- 5 species were compared to those of other aquatic plant test species using log normal SSDs. This d istribution was deemed appropriate since the SSD analysis with acute animal toxicity data indicated that distributions of log

- transformed data (normal, logistic, triangular, Gumbel) performed better than distributions with untransformed data (Pareto, Weibu ll and Burr) but no one distribution stood out as the best when considering

various amounts of data . For this analysis, toxicity data for nine herbicides (atrazine, diquat, diuron, irgarol, linuron, metolachlor, metribuzin, metsulfuron methyl and prometryn ) and one fungicide (pentachlorophenol) were used. These chemicals were selected because they had relatively large toxicity datasets ( i.e., number of test species ≥ 10), that included the FIFRA

- 5 species. The median and lower 25 th percentile estimates of t he HC5 values from the larger datasets were usually within a factor of 5 of the HC5 values of determined for the FIFRA

- 5 test species ( Table 5 ). For the majority of these chemicals (7 of 10), the HC5 value estimated from

the FIFRA

- 5 data set was lower than the HC5 of the full data set, suggesting that estimating the HC5 using the FIFRA

- 5 will usually be a conservative method for approximating th e HC5.Page 33 of 40

Table 5 . Median and 25 th percentile HC5 values from full data sets and FIFRA

- 5 for 9 herbicides and 1 fungicide. Units are ppb.

Chemical Full Data Set FIFRA 5

N Median HC5 HC5 25 th

percentile Median

HC5 HC5 25 th

percentile

Atrazine 54 15.30 14.51 12.66 9.99

Diquat 13 0.60 0.43 0.359 0.172

Diuron 32 1.57 1.40 5.77 4.96

Irgarol 38 0.066 0.055 0.034 0.027

Linuron 10 3.41 2.87 9.33 8.29

Metolachlor 17 22.98 20.73 14.08 10.13

Metribuzin 18 7.65 7.21 1.81 1.1

Metsulfuron 19 0.064 0.044 0.010 0.0024

Pentachlorophenol 27 12.26 9.73 16.61 14.02

Prometryn 13 1.25 1.05 0.53 0.34

There was no pattern among the FIFRA

- 5 species for their relative sensitivity, and there was no

one species (including the FIFRA

- 5 and all others) that was consistently the most sensitive. Toxicity data for Myriophyllu m sp. were available for the majority of the chemicals. The SSDs for the full data sets were used to estimate the proportion of species expected to be more

sensitive than the most sens itive FIFRA

- 5 species. These proportions of the SSD ranged from 0.04 to 0.31 ( Table 6 ). Nine of the ten FIFRA

- 5 most sensitiv e values are greater than the HC5.

Table 6 . Lowest FIFRA

- 5 EC50 values and their relative positions ( i.e., proportion) on the

SSD for the full data sets.

Chemical Lowest FIFRA

- 5 EC50 (μg/L) Proportion of SSD

Atrazine 31.46 0.19

Diquat 4 0.17

Diuron 7.09 0.31

Irgarol 0.1 0.08

Linuron 13.7 0.31

Metolachlor 61 0.13

Metribuzin 8.09 0.06

Metsulfuron 0.41 0.25

Pentachlorophenol 35.26 0.13

Prometryn 1.0 0.04Page 34 of 40 7.1.2. EFs

The ratios of the lowest FIFRA

- 5 values to the respective HC5 were greatest for diquat and metsulfuron (6.6 and 6.4, respectively). The average of these values could potentially be used to derive an extrapolation factor to adjust the lowest FIFRA

- 5 values to minimize under

- protection. Use of this extrapolation factor would result in a conservative estimate ( i.e., lower) of the HC5

that is within a factor of 1.4

- 8.1 of the HC5 values from the large data sets ( Table 7 ). Division by an EF, which is commonly done, results in the FIFRA

- 5 data being conservative relative to

those from the full data sets.

Table 7 . HC5 values estimated by dividing lowest FIFRA

- 5 value by 6.5 (EF), and median

HC5 va lues from full data sets. Units are ppb. Chemicals which were used to derive EF are

not included in this table.

Chemical Lowest

FIFRA

EC50 Estimate

HC5 (from EF) Median

HC5 Factor

difference

between

HC5s

Atrazine 31.46 4.8 15.3 3.2

Diuron 7.09 1.1 1.57 1.4

Irgarol 0.1 0.015 0.066 4.3

Linuron 13.7 4.02 3.41 1.6

Metolachlor 61 9.4 22.98 2.4

Metribuzin 8.09 1.2 7.65 6.1

Pentachlorophenol 35.26 5.4 12.26 2.3

Prometryn 1.0 0.15 1.25 8.1

There are a couple of cautions with using these data to establish an EF. Since only ten pesticides were used, the maximum value that could be recommended is uncertain. Another issue is that there is insufficient information on how to adjust the EF when more phytotoxicity data than

those for the FIFRA

- 5 are available. Th e analyses presented in this section considered a full data set to contain results for a minimum of ten species. Based on this, an EF would not be needed when similar numbers of toxicity results are available. However, it is uncertain what should be

done w hen toxicity data are available for more than five species, but less than ten species.

One notable uncertainty associated with these analyses is that there is no established minimum data requirement for aquatic plants for deriving ALC . Because of this, th e relationship between the range of sensitivities within the current “full” data sets to that from a more taxonomically diverse representation o f an aquatic plant community is unknown. It is assumed here that the test species incorporated into the data sets for each of the ten chemicals considered in this analysis are representative of the range of sensitivities of aquatic plants. Until aquatic pla nt minimum data requirements are established, the accuracy of this assumption cannot be fully evaluated.Page 35 of 40

7.2. Standardization of endpoints

There have been some question s about whether or not aquatic plant toxicity results should be standardized to only growth rate. Sufficient toxicity data were available for three herbicides (atrazine, diuron and irgarol) to investigate the impact of standardizing endpoints to growth on

e stimates of the HC5. For each of these herbicides, two distributions were compared. The first distribution used to estimate the HC5 was a log normal distribution of all available EC50 values. For each chemical, this distribution incorporated all toxicity i nformation for aquatic plant species and all endpoints, such as biomass, counts, length, weight, area, chlorophyll a concentration, photosynthetic rate or growth rate. If multiple endpoints were available for a given species, only

the result for the most s ensitive endpoint was included in the SSD. The second distribution constructed for each chemical was a log normal distribution using EC50 values based on growth

rate alone.

The HC5 values for the herbicides are provided in Table 8

. The HC5 values from th e full data sets were within a factor of 3 of the HC5 values from the distributions based on growth rate only. Although this suggests that standardization of endpoints to growth rate may not have a great impact on the estimate of the HC5, it should be note d that these three compounds all have the same mode of action (photosystem II inhibition). Different modes of action may have different conclusions. One limitation to this approach is that it may result in much smaller data sets for a

chemical. For the mai n analyses, data sets for ten chemicals were considered (see Table 5 for full list); however, only three had sufficient data to derive distributions for growth rate.

Table 8 . Median HC5 estimates (in ppb) from SSDs based on full data sets and normalized to growth rate for atrazine, diuron and irgarol.

Chemical HC5

(full data set) HC5

(growth rate) Factor

different

atrazine 15 17 1.1

diuron 1.6 3.4 2.1
irgarol 0.066 0.024 2.8

8. Conclusions

8 .1. Methods for Predicting the Acute Toxicity of Chemicals to Aquatic Animal Species

The analysis of several predictive tools suggests that, at this time, QSAR models are insufficient to confidently estimate the toxicity of pesticide active ingredients, and that models do not exist for species associated with missing MDR s defined by the 1985 Guidelines . It is expected that, as chemical domains within existing QS AR models expand, these tools may become more robust, which could increase their potential use in predicting the toxicity of a chemical to desired species and taxa ( e.g., MDRs of 1985 Guidelines)

. Proper data curation, involving the evaluation of open li terature and registrant submitted data could result in reliable data sets for use in expanding predictive QSAR models both from a chemical and a taxa perspective. Even with the compilation of reliable empirical data sets to build models, it is unlikely th at sufficient highPage 36 of 40

quality taxa specific data will exist to develop rigorous QSAR models to predict the toxicity of a chemical to species that may fulfill all of the MDR defined in the 1985 Guidelines without further animal testing. Nevertheless, the prop er curation and evaluation of open literature and registrant submitted data could result in reliable data sets for use in expanding predictive models for use in estimating the toxic effects of degradate products which typically have little or no

available empirical data, and possibly FIFRA requirements for fish and invertebrate species.

The current version of Web

- ICE does not have sufficient data to fulfill all of the MDRs of the 1985 Guidelines when there are only acute surrogate data available for rain bow trout, bluegill

and D. magna. This may change with future updates. The precision of Web

- ICE predictions are best for closely related species ( e.g

. , within the same family ) , suggesting that this tool may be useful for predicting the toxicity of a chemi cal to some species of fish when empirical toxicity data are available for a species of fish, especially one of the typical test species submitted to fulfill FIFRA data requirements ( i.e., rainbow trout or bluegill sunfish). The precision of Web

ICE is bet ter for chemicals that do not target specific taxa in a distribution of species.

8 .2. Methods for Estimating the HC5 of Sensitivity Distributions of Acute Toxicity Data for Animals

A review of the use of available SSD methods in predicting HC5 values with limited empirical data indicate that these methods may generate reasonable estimates of HC5 values, but are likely to suffer from considerable uncertainty. The review suggested that the use of SSDs with limited

data should be restricted to the log

- normal, log

- logistic, and log

- triangular distributions, should employ graphical methods for parameter estimation, and should impose data requirements to ensure that sensitive taxa are included in the analysis. The review also suggested that the use of published extrapolation constants (in conjunctions with the previous constraints) would likely provide at least nominal coverage of 95% confidence limits around the HC5, but that these extrapolated limits were likely to be very conservative ( i.e., on avera ge much lower than the true HC5).

Extrapolation factors can provide an effective approach for estimating HC5s, with a defined level of uncertainty, for toxicity datasets with less than 8 values. The acute EFs re commended in the GLWQG (US EPA 1995) for a br oad set of chemicals appear to provide conservative estimates for HC5s, but need to be further evaluated with regard to the reference HC5s used in their development, the chemical selection, and definition of uncertainties. Where sufficient data are

availab le, useful MOA

- specific EFs can be developed, and the number of MOAs for which this is possible can be improved by further development of the distributional approach for EF development rather than the approach of resampling of discrete values from toxicity datasets.

8.3. Use of ACR for Characterizing the Chronic Toxicity of Chemicals to Aquatic Animals

Chronic toxicity data are generally available for fewer species than are tested to determine acute toxicity. ACRs are a common tool used to estimate chronic e ffect thresholds for taxa lacking chronic toxicity data. Both OPP and OW currently use ACRs developed for specific chemicals to estimate chronic effect levels. ACR distributions have been developed for broad groups ofPage 37 of 40

chemicals without consideration of M OA or taxa ( e.g., mixed pesticides by TenBrook et al. 2010; all chemicals by Host et al. 1995). This analysis considers the application of ACRs developed for chemicals within the same MOA to estimate chronic effects for chemicals with the same MOA and lacking any chemical

- specific ACRs. Analysis of ACR data specifically for

AChE inhibitors indicated that ACRs fo r invertebrates were smaller than those for fish. Although fish had larger ACRs, this did not translate to higher chronic sensitivity of fish to AChE inhibitors, because of their very low acute sensitivity. In fact, analyses showed that concentrations pr otective of acute toxicity to invertebrates would simultaneously protect fish from chronic effects. By extension, for AChE inhibitors, applying an invertebrate

- based ACR to the acute HC5 ( i.e., fifth percentile of a sensitivity distribution) should provid e a chronic HC5 protective of both fish and invertebrates. When comparing ACRs developed for broad groups of chemicals and taxa to ACRs for aquatic invertebrates exposed to AChE inhibitors, they were approximately 2 fold different.

8.4. Methods for Estimati ng the HC5 for Aquatic Plants using Data Typically Submitted to Fulfill FIFRA Data Requirements

There was no pattern among the FIFRA

- 5 species for their relative sensitivity, and there was no species that was consistently the most sensitive. The SSDs for the full data sets were used to estimate the proportion of species expected to be more sensitive than the most sensitive FIFRA

- 5 species, which ranged from 0.04 to 0.31 for the ten chemicals considered.

EFs were generated from the ratio of the most sensi tive of the FIFRA

- 5 test species and the HC5. The highest of these EFs was approximately 6.5. Although this value consistently generates conservative estimates of these HC5 values, it is limited for predicting the HC5 for aquatic plant

communities because it is based on a limited number of chemicals and, unlike EF approaches involving aquatic animals, the magnitude of the EF does not change with addition of other test species. This is because, due to a lack of defined species requirements (outside of the FI FRA

- 5), the magnitude of different EFs cannot be evaluated.

The HC5 values generated using an SSD approach and the FIFRA

- 5 data set were generally within a factor of 5 and were more conservative than the HC5 from the larger data set. This

method seems to be reasonably predictive and has the ability to incorporate the FIFRA

- 5 as well as any additional test species. Normalization of endpoints to growth rate does not appear to greatly improve the predictability of SSDs in predicting the HC5 for aquatic plant communities.

9. References

Ahlers, J., C. Riedhammer, M. Vogliano, R. Ebert, R. Kuhne, and G. Schuurman. 2006. Acute to chronic ratios in aquatic toxicity

-- variation across trophic levels and relationship with chemical

structure. Environ. Toxicol. Chem. 25:2937

- 2945.

Chapman, P.F., M. Reed, A. Hart, et al. (2007). Methods of Uncertainty Analysis. Work Package 4 in EUFRAM: Concerted action to develop a European framework for probabilistic riskPage 38 of 40

assessment of the environmental impacts of pesticides. Versio n 3, March 2007. http://www.eufram.com/ , accessed 8 August 2011.

Host, G.E., R.R. Regal, and C.E. Stephan. 1995. Analyses of acute and chronic data for aquatic life. Draft report. United States Environmental Protectio n Agency. Washington, D.C. March 16,

1995.

OECD. 2007. Guidance document on the validation of (Quantitative)Structure

- Activity Relationships [(Q)SAR] models. OECD Series on Testing and Assessment No. 69. ENV/JM/MONO(2007)2: 154 p.

OECD. 2011. OECD Toolb ox. Version 2.2. http://www.oecd.org/document/54/0,3746,en_2649_34379_42923638_1_1_1_1,00.html#Downl oad_qsar_application_tool box

Posthuma, L, T.P. Traas, and G.W. Suter. 2002. General introduction to species sensitivity distributions. Pp. 3

- 10 in L. Posthuma, G.W. Suter, and T.P. Traas (Eds.) Species Sensitivity Distributions in Ecotoxicology. Lewis. Boca Raton, FL. USA.

Raimo ndo, S., B.J. Montague and M.G. Barron. 2007. Determinants of variability in acute to chronic ratios for aquatic invertebrates and fish. Environ. Toxicol. Chem. 26:2019

- 2023.

Raimondo, S., C.R. Jackson and M.G. Barron. 2010. Influence of taxonomic r elatedness and chemical mode of action in acute interspecies estimation models for aquatic species. Environ.

Sci. Technol. 44. 7711

– 7716.

Spjuth, O., E.L. Willighagen, R. Guha, M. Eklund, and J.E.S. Wikberg. 2010. Towards interoperable and reproduc ible QSAR analyses: Exchange of datasets. J. Chemoinf. 2(5): 1

- 7.

Suter, G.W. 2002. North American history of species sensitivity distributions. Pp. 11

- 18 in L. Posthuma, G.W. Suter, and T.P. Traas (Eds.) Species Sensitivity Distributions in Ecotoxico logy. Lewis. Boca Raton, FL. USA.

TenBrook PL, Palumbo AJ, Fojut TL, Hann P, Karkoski J, Tjeerdema RS. 2010. The University

of California

- Davis Methodology for Deriving Aquatic Life Pesticide Water Quality Criteria.

Reviews of Environmental Contamination and Toxicology. Volume 209.

USEPA. 1985. Guidelines for Deriving Numerical National Water Quality Criteria for the Protection of Aquatic Organisms and their Uses. United States Environmental Protection Agency. Stephan, C.E., D.I. Mount, D.J. Hansen, J.H. Gentile, G.A. Chapman and W.A. Brungs.

PB85

- 227049. National Technical Information Service, Springfield, VA.

USEPA. 1994. Pesticide Reregistration Rejection Rate Analysis. Ecological Effects. Prevention, Pesticides, and Toxic Substances. EPA 738

- R

- 94

- 035.Page 39 of 40

USEPA. 1995. Part III

– 40 CFR 9, 122, 131, and 132. Final Water Quality Guidance for the Great Lakes System; Final Rule. Federal Register 60(56): 15365

- 15425.

USEPA 2002. Environmental Fate and Ecological Risk Assessment for the Reregistration

Eli gibility Decision on Endosulfan. DP Barcode D238673. U.S. Environmental Protection Agency, Office of Pesticide Programs, Environmental Fate and Effects Division, Washington,

DC.

USEPA. 2004. Overview of the Ecological Risk Assessment Process in the Offi ce of Pesticide Programs. USEPA Environmental Fate and Effects Division. Office of Pesticide Programs.

Available at: http://www.epa.gov/espp/consultation/ecorisk

- overview.pdf

.

USE PA. 2005 Aquatic life ambient water quality criteria for Diazinon. EPA

- 822

- R

- 05

- 006.

USEPA. 2007. Risks of Diazinon Use to the Federally

- Listed California Red Legged Frog ( Rana aurora draytonii ): Pesticide Effects Determination . Environmental Fate and Effects Division, Office of Pesticide Programs, Washington, D.C. http://www.epa.gov/oppfead1/endanger/litstatus/effects/redleg

- f rog/diazinon/analysis.pdf

USEPA. 2009. Ambient aquatic life water quality criteria for acrolein. http://water.epa.gov/scitech/swguidance/standards/criteria/aqlife/pollutants/acrolein/upload/aq

final.pdf

USEPA. 2011. Draft aquatic life ambient water qu ality criteria for carbaryl. Available online at:

USEPA. 2010a. Predicting the toxicities of chemicals to aquatic animal species. United States Environmental Protection Agency.

USEPA 2010b. Web

- ICE v. 3.1. United States Environmental Protection Agency, Gulf Breeze,

FL. Available online at: http://www.epa.gov/ceampubl/fchain/webice/

.

USEPA. 2010c. Exploration of Methods for Characterizing Effects of Chemical Stressors to Aquatic Animals. Draft Report. United States Environmental Protection Agency. November 2,

2010.

USEPA . 2010d. Web

- based Interspecies Correlation Estimation (Web

- ICE) for Acute Toxi city:

User Manual. Version 3.1. EPA/600/R

- 10/004. Office of Research and D evelopment, U. S.

Environmental Protection Agency. Raimondo, S., D.N. Vivian, and M.G. Barron. Gulf Breeze,

FL.

USEPA. 2011a. Ecological Structure Activity Relationships (ECOSAR). http://www.epa.gov/oppt/newchems/tools/21ecosar.htm

USEPA. 2011b. Toxicity Estimation Software Tool (T.E.S.T.). http://www.epa.gov/nrmrl/std/cppb/qsa r/#TESTPage 40 of 40

USEPA. 2011c. Evaluation Guidelines for Ecological Toxicity Data in the Open Literature: procedures for screening, reviewing and using published open liteartaute toxicity data in ecological risk assessments. United States Environmental Proteciton Agency, Office of Pesticide Programs, Environmental Fate and Effects Division, May 9, 2011. Available online at: http://www.epa.gov/pesticides/science/efed/policy_guidance/team_authors/endangered_species_ reregistration_workgroup/esa_evaluation_open_literature.pdf

.