Page 1 of 19



## Appendix D. Extrapolation Factors for Derivation of Acute Aquatic Life HC 5 

## s, with Emphasis on Acetylcholinesterase Inhibitors

Russell J Erickson National Health and Environmental Research Laborato ry Mid-Continent Ecology Division Duluth, MN

December 20, 2011Page 2 of 19

1.0 INTRODUCTION Risk assessments of chemicals to aquatic life ofte n involve consideration of the statistical distribution of toxicity test results ( e.g. , median lethal concentrations, or LC 50 s) across various taxa of organisms (Posthuma et al. 2002). These di stributions are commonly referred to as species sensitivity distributions (SSDs), because t oxicity test results are often summarized at the biological species level, although other taxonomic levels can be used. A set of available toxicity test results is used to estimate the SSD, and, in s o doing, this set is considered to be a representative sample of a larger set of possible t oxicity tests results that define the SSD. Risk is characterized based on one or more percentiles in t he SSD, generically referred to here as HC p s, "HC" standing for "hazard concentration" and "p" re ferring to the percentile of interest. The nature of the risk characterized by SSDs depen ds on various requirements and assumptions in data selection and processing, inclu ding: the taxonomic composition of the data set; the nature of the toxicity test results; the a ssumed shape for the SSD; the percentile that is a level of concern; and how this percentile is calcul ated. For example, U.S.EPA Office of Water (OW) aquatic life criteria (ALC) for acute toxicity (Stephan et al. 1985) require median concentrations causing mortality or immobilization (LC/EC 50 s) for eight specific taxonomic groups ("minimum data requirements", or MDRs); impo se various requirements about the nature of the toxicity tests; assume the distribution is l ocally log-triangular; and calculate the HC 5 (termed the Final Acute Value, or FAV) from Genus M ean Acute Values (GMAVs) by specified procedures (Erickson and Stephan 1988). Other juri sdictions/entities have different characteristics for SSD development. Directly characterizing an SSD from a set of toxic ity test results requires sufficient data if HC p estimates are to be estimated well (see Appendix C of this document). The task described here concerns what can be done when there are insuf ficient data (too few tests, insufficient taxonomic diversity, etc.) to characterize an SSD f or a chemical well enough based just on toxicity data available for that chemical. The gen eral strategy employed here for doing this is the "extrapolation factor" (EF):

,  	



 
 


 where f (toxicity data set) is a summary statistic for the set of available toxicity test results ( e.g. , the lowest toxicity test result) that can be comput ed regardless of the data set size and HC p,cl refers to an estimate of the HC p at a designated confidence level ( e.g. , the maximum likelihood estimate or some lower confidence limit). An EF is specific to the nature of the toxicity da ta, the summary statistic ( f ) that is used, the percentile of interest, and the desired confide nce level. EFs are developed by examining the relationship between f

(toxicity data set) and the HC p for data sets for which the HC p is known or can be reliably estimated. These EFs can then be a pplied to more limited data sets for chemicals for which the HC p is unknown and cannot be otherwise estimated.
The effort here will focus on HC 5 estimation for acute LC/EC 50 s (hereafter referred to as acute values or AVs), although the methodology can be readily extended to other percentiles and endpoints. Furthermore, it will focus on HC 5 s that reflect the taxonomic and data requirements of OW ALCs, although again the methodology can be r eadily extended to other data requirements.Page 3 of 19

This kind of empirically-based factor was pioneere d by Host et al. (1995), who developed "Final Acute Value Factors" (FAVFs) as part of U.S. EPA Great Lakes Initiative efforts to provide Tier II (screening) values for toxic chemic als without actual ALCs. Host et al. (1995) demonstrated two ways for deriving EFs: (1) "Resampling Approach". Using data for a single chemical for which there are a sufficient number of acute values to assess the SSD and to est imate an HC 5 ("data-rich" chemical), multiple subsets of these data of a specified size and taxonomic composition are generated by repeated random sampling ("resampling") from the di screte acute values. For each subset, the ratio (EF) between the lowest acute value and the k nown HC 5 is calculated. A large number of EFs so generated for a specific size and compositio n provide information on the central tendency and variability of EFs for that subset size/composi tion and chemical. EFs from multiple datarich chemicals can be combined to provide a more ge neral distribution of EFs. This provides the basis for selecting an EF to apply to other chemica ls that are "data-poor". (2) "Distributional Approach". Rather than using discrete acute values for a spe cific chemical, a probability distribution for acute values is spec ified with a shape and dispersion relevant to a chemical of interest. This distribution, rather th an the discrete acute values for data-rich chemicals, is used to generate random samples with the specified size and taxonomic composition. EFs are calculated based on the lowes t acute value in each sample and the known HC 5 for the distribution. A sufficient number of samp les are generated to describe the central tendency and variability of the EFs, which again ca n be applied to data-poor chemicals. The current task expands on Host et al. (1995) in the following ways: (1) It uses a larger database of toxicity test res ults, reflecting more recent data and data for chemicals in addition to those that have published ALC (Host et al.1995 only used data for published ALC). (2) It conducts separate analyses for different me chanisms of action (MOAs). This report addresses acetylcholinesterase (AChE) inhibitors as an initial example, with limited comparison to chemicals with narcotic action to illustrate how MOA might affect EFs. (3) It pays specific attention to EFs for minimum data requirements in certain U.S.EPA Office of Pesticide Programs (OPP) assessment procedures ( i.e., acute tests for rainbow trout, bluegill sunfish, and Daphnia magna ). (4) It extends the distributional approach by usin g 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).

2.0 METHODS
This section will first discuss the issue of "refe rence" HC p s, which are a necessary part of any definition/evaluation/use of EFs. It will nextdescribe the database used in this effort, including some needs regarding further database dev elopment before any EFs are finalized. It will then present the protocols for the "resampling approach" of using subsets of acute toxicity values for data-rich chemicals (with established re ference HC 5 s) to determine how EFs should be developed to apply to acute toxicity values for dat a-poor chemicals. It will finally present protocols for basing EFs on the "distributional app roach" of randomly generating samples fromPage 4 of 19

acute value distributions inferred from all relevan t chemicals, rather than sampling from discrete tests results for individual data-rich chemicals as in the resampling approach.
2.1 Reference HC 5 s Given a set of acute LC/EC 50 s for different aquatic species considered to be representative of a large distribution of species t o be protected, the basic question being addressed here is to how to use this smaller set to estimate the HC 5 for the larger distribution. Any EF methodology needs to start with defining wha t the nature of this distribution is and what percentile is of interest, which, as noted above, i nvolves a variety of decisions regarding data requirements and analysis. For the effort here thi s "reference HC 5 " was the OW ALC FAV (U.S.EPA 1985), but the EF methodology will be read ily adaptable to other percentiles or other taxa/endpoint compositions. The ALC methodology requires a GMAV (and thus at l east one acute toxicity test) for each of the MDRs in Table 1:

Table 1. Minimum acute toxicity data requirements for derivation of final acute value for aquatic life criteria. MDR # MDR Description 1 A salmonid fish 2 A second family of fish 3 A third family of fish or an amphibian 4 A planktonic crustacean 5 A benthic crustacean 6 An insect 7 A third phylum (other than chordata or arthropoda ) 8 A second family of insects or a fourth phylum The resampling analyses conducted here also will o nly address datasets large enough that the FAV is near or above the lowest GMAV. 1 For datasets with a number of genera <15, the FAV often is substantially below the lowest GMAV ( e.g. , for N=9, the expected value for the percentile of the lowest GMAV is 10%, so that the f ifth percentile represents a substantial extrapolation below this value). This extrapolatio n allows for the likelihood of more sensitive, untested taxa, which is to be expected if the datas et is a random sample of a larger distribution. However, the purpose of this analysis is to determi ne whether, and to what degree, such extrapolation is appropriate for a particular MOA a nd dataset composition, so datasets were set to be large enough to eliminate or minimize extrapo lation. Although the ALC methodology requires representati on from a diversity of taxonomic groups and thus provides some degree of coverage of the taxa occurring in aquatic communities, it has no further requirements about the proportion al representation from different taxa for datasets larger than the minimum of 8 GMAVs. Thus, for large datasets, some variability in the meaning of the FAV is introduced when the relative frequencies of major taxonomic groups vary

1 This is a difference from the approach taken in Ho st et al. (1995). Using acute toxicity value datab ases for existing ALC, these authors (a) randomly sampled the databas e for each chemical to generate multiple datasets o f 8 toxicity values meeting the ALC MDRs, (b) calculated the FAV (HC5) for each of these minimum ALC datasets, (c) sampled from these minimum ALC datasets to generate subsets of different sizes and taxonomic compositi on, and (d) calculated EFs as the ratio of the smallest tox icity value in each subset type to the FAV. Thus t heir reference HC 5 for each subset was the FAV for the N=8 dataset fr om which subset was drawn, not the overall database

.Page 5 of 19

among the datasets and differ from the relative fre quencies represented by the MDRs. However, current ALC derivations assume that such variations are not significant for defining the FAV, and this same assumption is also followed for this effort. Furthermore, in the analyses here, the effects of such differences on EFs will be (a) mode rated in the resampling approach by using data sets for several chemicals and (b) addressed i n the distributional approach by defining the reference HC 5 using equal representation of the MDRs. 2.2 Toxicity Database The acute toxicity database used here was develope d by the U.S.EPA's Gulf Ecology Division (GED) to support their Interspecies Correl ation Estimations (ICE) efforts (Raimondo et al. 2010 ) . The database associated with version 3.1 of Web- ICE was obtained from GED as a Microsoft Excel spreadsheet (Sandy Raimondo, U.S.EP A GED, personal communication). This database was further modified to support queries to generate subsets satisfying certain criteria regarding chemical, test conditions, and test speci es (U.S.EPA 2010). 2.2.1 Datasets for Resampling Approach The resampling approach requires individual datase ts for chemicals with sufficient data for defining their individual reference HC 5 s. Because the Web-ICE database contained duplicat e records (due to the use of multiple secondary sourc es covering the same primary sources) that could not be removed with the query builder, indivi dual spreadsheets for AChE-inhibiting chemicals were extracted from the database using th e query builder and the duplicate records were manually removed ( Acute tox data – AChE Inhibitors – OPs.xlsx, Acute tox data - AChE Inhibitors – carbamates.xlsx; Kristina Garber, U.S.EPA OPP/EFED, personal communi cation). The spreadsheet for each of these individual chemic als was substituted for the master record set in the query builder to support further queries for each chemical, without the problem of duplicate records. Insufficient data were availabl e to do this for the narcosis MOA, which therefore was not part of the resampling analyses. 2.2.2 Datasets for Distributional Approach The distributional approach requires conducting an analysis of variance using data for all the chemicals in an MOA, to provide the variances a nd mean differences for the various MDRs. This was conducted on a subset of the Web-ICE datab ase of records specified as meeting data requirements for ICE model development, this subsetalso eliminating duplicate records. This subset of the database supported distributional ana lysis of acute values for both the AChEinhibition and narcosis MOAs. 2.2.3 Data Quality Issues
Some errors and anomalies were discovered in these spreadsheets, such that the analyses in this report should only be considered to illustr ate the procedures for developing EFs, and not to provide final EFs. This included some cases of LC/EC 50 results entered in the database as mg/L rather than μg/L (and vice-versa) or reported erroneously in the original publication, and some records involving misidentified chemicals. Italso included some instances of LC/EC 50 s ranging by 2-3 orders of magnitude within the same species (including some tests from the same laboratory) where there were no obvious errors in d ata transcription errors or in the original publication. For example, the bulk of the cladocer an data for carbaryl indicate acute LC/EC 50 s ≤ 10 ug/L and that this taxa is orders-of-magnitude m ore sensitive than species in other phyla, but a few reports for D. magna have LC/EC 50 s>1 mg/L, indicating similar sensitivity to these o therPage 6 of 19

phyla (e.g., Johnson et al. 1993). This is indicat ive of either large variation among strains of this species (with consequences to how EFs should be der ived), or possible reporting errors that need to be evaluated. In another example, two separate papers from the same laboratory reported the sensitivity to methylparathion of different species from the same mollusc genus to be several μg/L in one case and several mg/L in another (Sukum ar and Rao 1985; Padmaja et al. 1988). There are also cases where data are present for onl y a subset of the species or chemicals reported in the original publication, the reasons for which need to verified/documented before any finalization of the EFs. Although the rate of such errors is very low, if usable and defensible EFs are to be developed, it is imperative that this acu te toxicity database be further validated to assure that its quality be that which would be acceptable for aquatic risk assessments. It would also be useful to update this database by adding the most r ecent data. 2.3 Extrapolation Factors by Resampling Toxicity T ests Results for Data-Rich Chemicals The edited spreadsheets for AChE-inhibiting chemica ls (Kristina Garber, U.S.EPA OPP/EFED, personal communication) included nine ACh E inhibitors for which there was sufficient data to satisfy the 8 MDRs for OW freshw ater acute aquatic life criteria (organophosphates: chlorpyrifos, dichlorvos, fenitr othion, malathion, methylparathion, parathion; carbamates: carbaryl, methomyl, propoxur ). This is subject to possible change contingent on further modifications of the database noted above. These spreadsheets were subject to the following actions: (1) Check for data anomalies ( e.g. , unusually wide variation within species or a broa der taxa) and for compliance with ALC test requirements ( e.g. , test duration, lifestage). Delete/modify records as justified, reviewing original publications as ne eded. (2) Create separate datasets for each chemical for (a) all species and (b) species resident to the continental United States and Canada. Although the ALC methodology specifies the use of the "resident-only" dataset, the "all-species" dataset was included to assess how much effect using non-resident species might have on EFs and to provi de an alternative dataset if the resident-only dataset did not have sufficient data. (3) Compute the GMAVs and FAV (=reference HC 5 ) for each dataset. To avoid appreciable extrapolation below the lowest GMAV ( i.e., extrapolating to untested taxa that are possibly mo re sensitive), require that there be at least 15 gener a and that the reference HC 5 be >80% of the lowest GMAV. Also, to provide a variety of choices for the most sensitive acute value (so that no single value dictates an inordinate percentage o f the EFs), require that there be at least 20 individual arthropod tests. These requirements rem oved diazinon (resident-species only), propoxur, methomyl, and dichorvos from consideratio n. (4) Analyze each dataset ( i.e., each chemical, with separate analyses for resident- only and allspecies) as follows: (a) Randomly generate 999 samples with 8 toxicity t est results each (one for each MDR). (b) Generate a random subset of each N=8 sample mee ting the specifications in Table 2. (c) For each subset specification in Table 2, compu te the EF as the ratio of the lowest AV to the reference HC 5 , and describe the distribution of these EFs across all 999 samples.Page 7 of 19

Table 2: Taxonomic requirements and dataset sizes used in generating EFs (MDRs per Table 1) Taxonomic Requirement Dataset Size
All MDRs 8 MDR 1 or 2; MDR 4; Other MDRs Random 2
3

4
5

6 MDR 1, MDR 2, MDR 4, MDR 5 or 6; Other MDRs Random 4
5

6 Rainbow Trout, Bluegill Sunfish, Daphnia magna 3 MDR1, MDR2, MDR4 3 MDR1 or 2, MDR 4, MDR 5 or 6 3
2.4 Extrapolation Factors by Simulated Samples Bas ed on Distributions of Toxicity Values One limitation of the resampling approach describe d in the previous section is that the finite amount of data results in distributions with a limited number of discrete data ( e.g. , only 2 or 3 acute values for some MDRs for some chemicals) , thereby increasing uncertainties of some percentile estimates. Also, only a limited number of chemicals contribute to the analyses, ignoring relevant information from other chemicals on variability of toxicity within and between taxa. An alternative is to use the available toxic ity tests, not as a source of discrete points in a resampling analyses, but rather as a source of info rmation on the distribution of acute values within and between MDRs. These distributions can b e based on data from all the chemicals within an MOA, not just a few data-rich chemicals. The individual MDR distributions in aggregate define a composite distribution from whic h a reference HC 5 can be specified and samples drawn from a continuum rather than a limite d number of toxicity test results. Host et al. (1995) did some theoretical calculations based on a simple overall distribution, but did not consider composite distributions that address varia bility both within and between MDRs. The Web-ICE database was used to conduct an analys is of variance of the LC 50 s for various MOAs by EMR, Inc. The pooled variance (acr oss chemicals) of log 10 (LC 50 )s within each MDR was determined, as well as the differences of mean log 10 (LC 50 )s between the different MDRs. The distribution for MDR 8 was not directly assessed, but rather specified to be a composite of MDRs 6 and 7 based on the relative fre quency with which a second insect family or a fourth phylum appears in the database. This a nalysis was done on the subset of the WebICE database deemed acceptable by GED for supportin g ICE modeling efforts, which had duplicate records removed and some additional data screening requirements used for ICE model development (Vivian et al. 2009). However, this su bset of the database is still subject to some of the same uncertainties noted above, and also to the question of whether the ICE model data screening requirements eliminated some data that wo uld be suitable for the current analysis. Therefore, there is also a need to update this anal ysis of variance based on a better qualityassured and filtered subset of the database before finalizing any EFs. There is also a need toPage 8 of 19

expand the analysis of variance to address uncertai nties in the composite distribution. However, the data and analysis used are still useful for ill ustrating the approach and providing preliminary results. Composite distributions for AChE-inhibiting and narcosis chemicals were defined based on equal representation of the log 10 (LC 50 ) distributions for each MDR. The fifth percentile of each composite distribution was used to define its reference HC 5 . It should be noted that the HC 5 is thus based on the assumption that distributions based on individual acute toxicity test results are a suitable surrogate for the distributions of G MAVs that are the foundation for aquatic life criteria FAVs. This is a limitation in the efforts to date which also merits attention in further development of this effort. Extrapolation factor development was based on 9999 samples of N=8 generated by randomly sampling one point from each of the eight MDR distributions. Analogous to the resampling of discrete acute values for the data-ri ch chemical datasets in the previous section, random subsamples of each N=8 sample was generated per Table 2, and used to generate EFs and describe their distributions.

3.0 RESULTS 3.1 Extrapolation Factors by Resampling Toxicity T ests Results for Data-Rich Chemicals
3.1.1 Datasets used in the analyses
Table 3 summarizes attributes of each dataset anal yzed. The unshaded rows indicate those datasets for which results are used in furthe r development and description of EFs. Preference was given to the resident-only datasets in accordance with OW ALC procedures (Stephan et al. 1985), but the all-species datasets were also considered to assess how much effect using non-residents species might have and to allow for an alternative if the resident-only dataset did not meet the requirements of the analysis. Suc h an alternative was needed for diazinon because the FAV (0.23 μg/L) for the resident-only d ataset was nearly two-fold below the lowest GMAV (0.40 μg/L). The all-species diazinon datasetwas appropriate for this analysis because it had a taxonomic sensitivity distribution similar to the resident-only dataset and had enough additional data that the FAV did not have this unde sired extrapolation. In contrast, the allspecies fenitrothion dataset included a species who se toxicity test result was 50-fold more sensitive than any other test, resulting in a low F AV of questionable utility for these analyses. For carbaryl, chloropyrifos, malathion, and methylp arathion, using resident-only or all-species had little impact on the analyses – the FAVs were w ithin 20% of each other and the acute values for the sensitive taxa were similar.
3.1.2 EFs vs. # of MDRs, with a minimum requirement of on e fish and one cladoceran tes t For the selected datasets, Figure 1 summarizes EFs as a function of the number of ALC MDRs satisfied (one acute test per MDR) when there is a requirement of at least one fish test (either MDR 1 or 2) and a planktonic crustacean tes t (MDR 4), with the other MDRs being randomly selected. For each chemical, the symbols denote the geometric mean extrapolation factor over the 999 subsamples. The lines denote c omposite results across all chemicals, including not only the geometric mean, but also the 75 th and 90 th percentiles.Page 9 of 19

Figure 1: Geometric mean extrapolation factors (fo r 999 samples) for six acetylcholinesterase-inhibit ing chemicals as a function of the number of MDRs satis fied, given a minimum requirement of one fish test and one planktonic crustacean test, with remaining tests be ing randomly selected to satisfy other MDRs for ALC derivation. Lines denote the geometric mean and th e 75 th and 90 th percentiles for composite results across all chemicals.



### Number of MDRs Satisfied

2 3 4 5 6 7 8Extrapolation Factor

2 5 20

1 10 Carbaryl Chlorpyrifos Diazinon Fentirothion Malathion Methylparathion

Composite

90th Percentile

Composite 75th Percentile
Composite Geometric Mean Table 3. Attributes of acute toxicity datasets an alyzed regarding the generation of extrapolation fa ctors by repeated subsampling of toxicity test results for d ata-rich chemicals. Nonshaded rows denote datasets used for reporting extrapolation factors.

Chemical # of Genera # of Total Test Results # of Test Results

for Arthropoda Reference HC 5

(μg/L) Carbaryl – Resident-Only 44 264 39 4.8 μg/L Carbaryl – All Species 53 281 42 5.3 μg/L Chlorpyrifos – Resident-Only 23 86 43 0.066 μg/L Chlorpyrifos – All Species 30 97 45 0.075 μg/L Diazinon – Resident-Only 17 57 22 0.230 μg/L Diazinon – All Species 28 78 30 0.55 μg/L Fenitrothion – Resident-Only 17 83 20 4.0 μg/L Fenitrothion – All Species 27 108 29 0.1 μg/L Malathion – Resident-Only 43 132 34 1.54 μg/L Malathion – All Species 50 140 34 1.80 μg/L Methylparathion – Resident-Only 22 45 22 0.75 μg/L Methylparathion – All Species 28 67 24 0.94 μg/LPage 10 of 19

Figure 2. Geometric mean extrapolation factors (for 999 samples) for six ac etylcholinesterase-inhibiting chemicals as a function of the number of MDRs satis fied, given a minimum requirement of one salmonid and one nonsalmonid fish test, one planktonic crustacea n test, and one other arthropod test, with remainin g tests being randomly selected to satisfy other MDRs for A LC derivation. Lines denote the geometric mean and the 75 th and 90 th percentiles for composite results across all chemi cals.



### Number of MDRs Satisfied 2 3 4 5 6 7 8Extrapolation Factor

2 5 20

1 10 Carbaryl Chlorpyrifos Diazinon Fentirothion Malathion Methylparathion

Composite

90th Percentile

Composite 75th Percentile Composite Geometric Mean There is substantial variability both across chemi cals and across subsamples, but the EFs generally are modest, exceeding a factor of 10 only at the 90 th percentile for N=3. For all but one chemical, the geometric mean factor is <2 if there are ≥ 4 tests and is only slightly above 1 when there are 8 tests. For the other chemical (methylp arathion) the geometric mean EF stays above 2 because a low GMAV (0.37 ug/L) for one cladoceran genus based on one toxicity test results in an FAV (0.75 ug/L) below most of the other cladocer an tests (geometric mean of 13 tests = 2.7 ug/L), so that the EFs based on a single planktonic crustacean test are often above 2.
3.1.3 EFs vs. # of MDRs with a minimum requirement of two fish and two arthropod tests Figure 2 summarizes the EFs as a function of the n umber of MDRs satisfied when there is a requirement of including, at minimum, a salmon id fish test (MDR 1), a nonsalmonid fish test (MDR 2), a planktonic crustacean test (MDR 4), and a test with a benthic crustacean or an insect (MDR 5 or 6), with the rest of the MDRs for ALC der ivation being randomly selected. Again, the symbols denote geometric mean EFs for the indiv idual chemicals and the lines denote the geometric mean and the 75 th and 90 th percentiles of the composite results across all ch emicals. Because of requiring a minimum of four tests that included two taxa that are typically sensitive (arthropods), the EFs are moderately lowe r at N=4 than in Figure 1 and slightly lower at N=5 and N=6. Again, the notable result is that the EFs are relatively modest (because of the requirement to include taxa sensitive to AChE inhib itors), but have a large uncertainty (because of the variability of results within the sensitive taxa).Page 11 of 19

3.1.4 EFs for N=3 under various sampling schemes
Table 4 summarizes the EFs for the four different sampling schemes with N=3 described in Table 2. Again, the mean factors are relativelysmall but show considerable variability. Interestingly, for most of the individual chemicals and for the composite results, specifically requiring D. magna results in slightly largerfactors, because, on average, other planktonic crustaceans are more sensitive based on the availab le data. Requiring two arthropods in the set of 3 results in lower and less variable EFs, as exp ected given the sensitivity of most arthropods to AChE-inhibiting chemicals ( i.e., including two arthropods increases the chance of a result nearer the reference HC 5 ). However, despite some effects of the taxonomic req uirements, the differences between the four sampling schemes are not great. The compo site geometric mean EF is reduced by less than two-fold by requiring a benthic arthropod in a ddition to a planktonic one. The reduction in uncertainty is also less than two-fold – with the t wo arthropod requirement still having a 10-fold ratio between the 10 th and 90 th composite percentiles, compared to an average of 1 8-fold for the other schemes. Again, the notable result is relati vely modest EFs due to requiring at least one arthropod, but considerable variability because of the variability among individual tests even within the sensitive taxa.
Table 4. Geometric means of extrapolation factors for samples of size 3 with different taxonomic requ irements from datasets for selected acetylcholinesterase-inhibiti ng chemicals

Taxonomic Requirements Rainbow Trout

Bluegill Sunfish Daphnia Magna Salmonid Fish

Nonsalmonid Fish Planktonic Crustacean Any Fish Planktonic Crustacean Benthic Arthropod Any Fish

Planktonic Crustacean Any Other MDR
Carbaryl 3.83 2.19 1.46 1.78
Chlorpyrifos 6.07 1.91 1.36 1.70

Diazinon 2.25 1.16 1.09 1.12

Fenitrothion 2.74 5.57 1.36 2.94

Malathion 1.07 1.28 1.07 1.21
Methylparathion 3.83 3.07 2.73 2.93
Composite Total 2.89 2.19 1.43 1.81 Composite Total 10 th -90 th percentile 1.04-16.0 0.64-12.1 0.49-4.84 0.53-11.0
3.2 Extrapolation Factors by Simulated Samples fro m Distributions of Toxicity Values
3.2.1 Definition of composite distributions Table 5 provides the standard deviations within an d the mean differences between the MDRs for log 10 (logLC 50 )s for AChE-inhibition and narcosis MOAs, using MDR 4 as the reference for mean differences. (This choice of re ference is arbitrary, because EFs will depend only on relative differences, not absolute values.) Table 5 also includes the median acute value on an arithmetic scale (relative to MDR 4=1.0) to b etter illustrate the relative differences between MDRs. The MDR 8 distribution is a composit e consisting of 80% MDR 6 and 20% MDR 7, these percentages being based on the relativ e frequency with which a second order ofPage 12 of 19

Figure 3: Estimated distributions of acute values w ith each MDR, relative to the median value for MDR4 , for chemicals with AChE-inhibiting and narcosis MOAs.

AV for Designated MDR/Median AV for MDR 4 0.01 0.1 1 10 100Frequency

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 Narcosis

1 10 100 1000 10000Frequency

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 MDR 1 MDR 2 MDR 3 MDR 4 MDR 5 MDR 6 MDR 7 MDR 8 ACHE Inhibition insect and a fourth phylum appeared in the database . Figure 3 provides a graphical depiction of these distributions, assuming log normal distributi ons.

Table 5. For chemicals with AChE-inhibition and na rcosis MOAs, standard deviations of logLC 50 within each MDR, mean logLC 50 for each MDR relative to that for MDR4, and median LC 50 for each MDR relative to MDR4.

MDR Description AChE Inhibition Narcosis log 10 (LC 50 ) Median

LC 50 (μg/L) log 10 (LC 50 ) Median LC 50 (μg/L) Std Dev Mean Std Dev Mean
1. A salmonid fish 0.32 2.14 138 0.25 0.02 1.0
2. A 2 nd family of fish 0.67 2.23 170 0.26 0.15 1.4
3. A 3 rd family of fish or an amphibian 0.67 2.23 170 0.26 0.15 1.4 4. A planktonic crustacean 0.40 0.00 1.0 0.43 0.00 1.0 5. A benthic crustacean 0.78 1.02 10.5 0.56 0.00 1.0 6. An insect 0.67 0.55 3.5 0.50 -0.05 0.9
7. A 3 rd phylum (not chordata or arthropoda) 0.80 3.20 1580 0.32 0.21 1.6
8. A 2 nd family of insects or a 4 th phylum Composite of 80% MDR 6 and 20% MDR 7

The distributions vary markedly between the MOAs. For the AChE MOA, the arthropod MDRs are 1-3 orders of magnitude more sensitive on average than the MDRs for other phyla. As would be expected, the variability for narrow ta xonomic groups (MDR 1 and MDR 4) is smaller than for the more diverse MDRs. In contras t, for the narcosis MOA, there is no significant mean difference between the MDRs, and t he variability within MDRs on average is smaller than for the AChE MOA. This represents a g reater similarity of this mode of action across taxa. Assuming normal distributions for each component d istribution, the HC 5 of the composite distribution for AChE-inhibiting chemical s, is -0.39 (base ten log scale), or 0.41 after antilog transformation. For the narcosis MOA, the respective numbers are -0.69 and 0.21.Page 13 of 19

Figure 4: Extrapolation factors for 9999 simulated samples drawn from a composite distribution based on an analysis of the variance within and between MDRs fo r the LC50s of AChE-inhibiting chemicals, as a func tion of the number of ALC MDRs satisfied and given a minimu m requirement of one fish test and one planktonic crustacean , with remaining MDRs being randomly sel ected. Thin lines denote the composite across all chemicals and samples from resampling analyses in F igure 1.



### Number of MDRs Satisfied 2 3 4 5 6 7 8Extrapolation Factor

2 5 20

1 10

90th Percentile

75th Percentile

Geometric Mean Again, note that these are on scale relative to MDR 4 within each MOA. The higher numbers for the AChE MOA do not reflect absolute sensitivity, b ut rather the use of a reference point (MDR 4) that is highly sensitive within the AChE MOA and thus is closer to the HC 5 . As noted earlier, one uncertainty here is defining the HC 5 based on distributions with an assumed shape that are parameterized from toxicity tests, in contrast to the datasets of GMAVs used for the resampling effort which are more close ly related to the ALC process. The reference HC 5 (0.41) for AChE simulations here represents an FAV 60% below the mean for MDR 4. This is, in fact, similar to the FAVs for the six d atasets used in the resampling effort, which varied from 22% to 68% (average 56%) below the mean for MDR 4. However, this agreement might be fortuitous for this case and should receiv e continuing attention in any further method development.
3.2.2 EFs vs. # of MDRs with a minimum requirement of one fish and one cladoceran tes t For the AChE MOA, Figure 4 summarizes EFs as a fun ction of the number of ALC MDRs satisfied (one acute test per MDR) when there is a requirement of at least one fish test (MDR 1 or 2) and a planktonic crustacean test (MDR 4), and when the other MDRs are randomly selected. The bold lines denote the simul ation results from the composite distribution, using the geometric average and the 75 th and 90 th percentile EFs from the 9999 simulations. As for the resampling effort, the EFs are relatively m odest but highly variable. The thin lines in Figure 4 provide a comparison to the composite results from the resampling analyses (Figure 1) of the discrete test s for individual chemicals. The resampling-Page 14 of 19

Figure 5: Extrapolation factors for 9999 simulated samples drawn from a composite distribution based on an analysis of the variance within and between MDRs fo r the LC50s of narcosis chemicals, as a function of the number of ALC MDRs satisfied and given a minimum re quirement of one fish test and one planktonic crustacean , with remaining MDRs being randomly sel ected. Thin lines denote simulation results for AC hEinhibiting chemicals from Figure 4.



### Number of MDRs Satisfied

2 3 4 5 6 7 8Extrapolation Factor

2 5 20

1 10

90th Percentile
75th Percentile

Geometric Mean based EFs are usually lower and vary somewhat more with subsample size. This can be attributed in large part to species from MDRs 5 and 6 being somewhat more sensitive relative to MDR4 for the six chemicals used in the earlier anal ysis than for the overall analysis of variance of all the AChE-inhibiting chemicals (Table 5); i.e., as a greater number of sensitive arthropods are added, the EFs are more likely to drop to lower levels. However, the differences in the EFs are not great, the geometric mean EFs never differi ng by more than 30% and no difference being more than a factor of 2. Figure 5 provides the same EF summary for the narc osis MOA, with the thin lines providing a comparison to the AChE MOA simulation r esults in Figure 4. The nearly identical mean EFs at N=2 reflects low EFs that arise (a) forthe AChE MOA from inclusion of the AChEsensitive MDR 4 even at this lowest subset size and (b) for the narcosis MOA from the low variability of toxicity test results, causing most of the results to be relatively near the HC 5 . This low variability for the narcosis MOA results in low er EFs for the narcosis MOA at higher percentiles and values for N. However, the EFs for the narcosis MOA are at most a factor of 2 lower from those for the AChE MOA, and usually much less. Despite the markedly different distributions (Table 5), the simple requirement tha t an AChE-sensitive taxon be included in EF evaluations resulted in the EFs for both MOAs not b eing extremely different. However, the differences are large enough, especially at the hig her percentiles, to be of potential importance for assessments. Furthermore, because the limited differences are the result of rather specific circumstances, this comparison does not support mor e general conclusions about how EFs vary among MOAs.Page 15 of 19

Figure 6. Extrapolation factors for 9999 simulated samples drawn from a composite distribution based on an analysis of the variance within and between MDRs fo r the LC50s of acetylcholinesterase-inhibiting chem icals, as a function of the number of ALC MDRs satisfied a nd given a minimum requirement of one salmonid and one nonsalmonid fish test, one planktonic crustacean te st, and one other arthropod test, with remaining te sts being randomly selected to satisfy the MDRs for ALC deriv ation. Thin lines denote the composite across all chemicals and samples from resampling analyses in F igure 2.



### Number of MDRs Satisfied 2 3 4 5 6 7 8Extrapolation Factor

2 5 20

1 10

90th Percentile

75th Percentile

Geometric Mean 3.2.3 EFs vs. MDRs with a minimum requirement of two fish and two arthropod tests Figures 6 and 7 summarize the EFs for both MOAs as a function of the number of MDRs satisfied when there is a requirement of including, at minimum, a salmonid fish test, a nonsalmonid fish test, a planktonic crustacean test , and a test with a benthic crustacean or an insect, with the rest of the MDRs for ALC derivatio n being randomly selected. Again, the bold lines denote the geometric mean and the 75 th and 90 th percentiles for the simulation results for the AChE MOA (Figure 6) and the narcosis MOA (Figur e 7). The thin lines in Figure 6 allow comparison of the AChE simulation results to the re sampling results of Figure 2, and the thin lines in Figure 7 allow comparison of the simulatio n results for the two MOAs. For both MOAs, the lines in Figures 6 and 7 are ne arly identical to those in Figures 4 and 5. For the AChE MOA, this is because requiring a b enthic arthropod in the first four data does not usually add a test more sensitive than just req uiring a planktonic crustacean and randomly picking from other MDRs (which usually results in i ncluding a benthic arthropod). For the narcosis MOA, all the MDRs are so similar that requ iring a second fish and a benthic arthropod to the first four data does not have much differenteffect than randomly selecting MDRs. Figure 6 again illustrates some modest differences between the two approaches – the resampling approach producing EFs that are a factor of 1.3-2.0 lower than the distributional approach. This reflects the more limited informati on data that is used in the resampling approach, but also includes some effects introduced by the assumed shapes in the distributional approach. Figure 7 again illustrates how narcosis EFs are moderately lower than those for AChE-inhibition, especially at the high percentiles , due to the lower variability among acute values for this MOA.Page 16 of 19

Figure 7: Extrapolation factors for 9999 simulated samples drawn from a composite distribution based on an analysis of the variance within and between MDRs fo r the LC50s of narcosis chemicals, as a function of the number of ALC MDRs satisfied and given a minimum re quirement of one salmonid and one nonsalmonid fish test, one planktonic crustacean test, and one otherarthropod test, with remaining tests being randoml y selected to satisfy the MDRs for ALC derivation. Thin lines denote simulation results for AChE-inhibiting chem icals from Figure 6.



### Number of MDRs Satisfied

2 3 4 5 6 7 8Extrapolation Factor

2 5 20

1 10

90th Percentile

75th Percentile

Geometric Mean

4.0 Comparison of Current Results to those of Host et al. (1995)
Given that this work was intended as an extension o f that of Host et al. (1995), it is worthwhile comparing EFs from these two efforts. T his comparison does not pertain to the final recommendations in the GLWQG. Rather, the concern here is of the issue of MOA-specific versus generalized EFs and other methodological dif ferences between the current work and that of Host et al. (1995). Table 6 summarizes EFs (by the resampling approach and for the same overall percentiles), for the analysis of AChE-inhi biting chemicals addressed in the current effort to the freshwater ALC chemicals analyzed by Host etal. (1995). In addition to differences in the chemical set and toxicity data, these efforts diffe r slightly regarding taxonomic requirements. The numbers from Host et al. (1995) required a daph nid test and a rainbow trout test while those from the current effort required a planktonic crust acean test (almost always a daphnid) and a test with any fish species. Because fish are tolerant t o AChE-inhibiting chemicals, this taxonomic difference would have little or no effect on the EF s for the AChE-inhibitors. The EFs from Host et al. (1995) range from 40% to 260% higher than the current effort. Although such differences might seem to substantiat e some importance of considering MOAspecific EFs, these differences are confounded by t he methodological differences regarding the definition of the reference HC 5 noted earlier. The current analyses based EFs on FAVs for large datasets from which the subsets were drawn, whereas Host et al. (1995) first sampled minimal (N=8) datasets from the larger datasets, calculated FAVs for each N=8 dataset, and based the EFs for smaller subsets on the FAVs for these N=8 d atasets, not the FAV for the original, largerPage 17 of 19

dataset. Current efforts did include calculating h ow the FAVs for such N=8 datasets would differ from the FAV of the larger dataset, which in dicated that this might increase EFs by a factor of 2 or more for the AChE-inhibiting chemica ls, but by very little for the narcosis chemicals. Efforts are needed to further resolve t his issue. Table 6. Extrapolation factors (EFs) by resampling analyses from current efforts for AChE-inhibiting chemicals and from Host et al. (1995) for all freshwater ALC chemicals. Percentiles are for overall results acro ss all samples and chemicals.

MDRs Satisfied Median EF 75 th Percentile EF 90 th Percentile EF

Current Effort Host et al. Current Effort Host et a l. Current Effort Host et al.
2 2.19 2.93 4.0 7.9 12.1 25.5
3 1.81 2.47 2.7 6.6 11.0 20.5
4 1.55 2.28 2.4 5.1 7.6 18.3
5 1.37 2.16 2.2 3.9 4.9 12.6
6 1.26 2.02 1.8 3.4 4.4 8.8
8 1.13 1.86 1.5 2.7 3.5 4.9
To fully address the issue of MOA-specific versus generalized EFs, some other issues are also of concern to further efforts. Host et al. (1 995) included 28 freshwater chemicals with available ALCs at the time, including 14 inorganic metals, 3 other inorganic chemicals (ammonia, chloride, chlorine), 7 industrial organic s, 2 insecticides (parathion and methylparathion), and 2 other biocides (trybutyltin , pentachlorophenol). This dataset needs to be increased with new and additional data, and be targ eted to generalized EFs for MOAs of interest for which there is insufficient data to develop MOA -specific EFs. In addition, because of intraand inter chemical (and MOA) variability, more info rmation is needed on how this affects percentile selection to guide risk management decis ions. Finally, consideration needs to be given to applying the distributional approach to th e development of generalized EFs, in order to allow the use of more toxicity data.

5.0 SUMMARY AND CONCLUSIONS The analyses described here demonstrated the follow ing: (1) As described by Host et al. (1995), it is possi ble to use existing toxicity data to derive extrapolation factors to derive useful estimates of HC 5 s based on data sets with 3 to 8 toxicity values, and to provide useful information regarding aspects of the uncertainty of such estimates. (2) EFs will depend to some degree on adverse outco me pathways (toxicity mechanisms) which affect the variability of toxicity within and diffe rences between taxonomic groups. For the two MOAs examined here (AChE-inhibition and narcosis), EFs were similar for the median result at N=2, but differences increased with percentile and sample size so that the narcosis EF was up to 2-fold lower. Furthermore, this degree of similari ty was in part due to different attributes of these toxicity sets that might not be true for othe r MOAs. This warrants further evaluation of MOA effects. (3) EFs can be developed by (a) resampling discrete toxicity test results from individual "data rich" chemicals within an MOA or (b) sampling from a composite distribution of the relativePage 18 of 19

toxicity of different taxa, estimated by analysis o f variance of toxicity tests for all chemicals within an MOA. The latter approach has the advanta ge of using a greater amount of data across more chemicals, but has uncertainties in assumption s concerning distributional shapes. For the analyses here, EFs were similar by either approach, but do have some differences which merit further resolution before finalizing an approach. (4) A critical aspect of this process is the defini tion of the desired reference HC 5 . This involves the definition of the composite distribution used i n the simulation approach and sample size/composition issues in the resampling approach. In particular, it involves risk management judgments regarding assumptions about the possibili ty of untested, sensitive taxa. Further efforts to resolve this issue are needed. (5) When insufficient data are available for develo ping MOA-specific EFs, further efforts are warranted to update the generalized EFs of Host et al. (1995) to include new and additional data, consideration of the distributional approach, and a dditional information on percentiles. As noted earlier, the analyses and results present ed here are preliminary because of a need to further refine the database upon which theyare based. This database needs to be expanded with additional available data, and record s for taxa sensitive to chemicals important to an analysis need to be thoroughly reviewed. The da tasets for the resampling approach should include more chemicals, including those that have s ufficient amounts of data for sensitive taxa, but might lack data for taxa reasonably expected to be tolerant and which would thus not affect results. The composite distribution for the simula tion approach should be re-evaluated to address issues of uncertainty in the relative sensitivities of different taxa and better definition of the reference HC 5 . A final area of needed attention is to expand/re fine the conditions to which EFs apply regarding taxonomic requirements, multiple te sts per MDR, etc. to apply more broadly to different dataset compositions. Although these lim itations make the results presented here illustrative and tentative, they still provide good information regarding the utility of the approach and the general magnitude of extrapolation factors appropriate for risk characterizations based on toxicity datasets with <8 toxicity values.

6.0 REFERENCES Erickson, R.J. and C.E. Stephan. 1988. Calculation of the final acute value for water quality criteria for aquatic organisms. U.S. Environmental Protection Agency. Duluth, MN. EPA/600/3-88-018. Host GE, Regal RR, and Stephan CE. 1995. Analyses of acute and chronic data for aquatic life. U.S. Environmental Protection Agency, Duluth, MN. Johnson IC, Keller AE, and Zam SG. 1993. A method for conducting acute toxicity tests with the early life stages of freshwater mussels. in Landis WG, Hughes JS, and Lewis MA (eds) Environmental Toxicology and Risk Assessment. ASTM STP 1179. American Society of Testing and Materials, Philadelphia, PA. Posthuma L, Traas TP, and Suter GW. 2002. General introduction to species sensitivity distributions. in L Posthuma, GW Suter, and Traas TP (eds.) Species Sensitivity Distributions in Ecotoxicology. Lewis. Boca Raton, FL. USA.Page 19 of 19

Raimondo S, Vivian DN, and Barron MG. 2010. Web-b ased Interspecies Correlation Estimation (Web-ICE) for Acute Toxicity: User Manua l. Version 3.1. EPA/600/R10/004. Office of Research and Development, U. S. E nvironmental Protection Agency. Gulf Breeze, FL. Sukumar RV and Rao MB. 1985. Toxicity of γ -HCH, methyl parathion and carbaryl to two varieties of a tropical frshwater gastropod, Bellam ya bengalensis. in Dalela TC and Mane UF (eds). Proceeding of the 5 th National Symposium on the Assessment of Environmental Pollution, 1984, Aurangabad, India. Padmaja K, Nagalakshmi P, and Rao MB. 1988. A com partive evaluation of the toxicity of Metacid (methyl parathion), quinalphos, and Nuvan ( DDVP) to the freshwater snail Bellamya dissimilis. Indian J Comp Anim Physiol 6: 34-38. USEPA. 1985. Guidelines for deriving numerical nati onal water quality criteria for the protection of aquatic organisms and their uses. EPA Report 822 /R-85-100 (NTIS Report PB85- 227049. USEPA. 2010. Query Builder ( Web-ICE CQB v20100820.xls ). US Environmental Protection Agency, Office of Research and Development, Nationa l Health and Environmental Research Laboratory, Mid-Continent Ecology Division , Duluth, MN. Vivian, D.N., S. Raimondo, and M.G. Barron. 2009. Web-ICE Aquatic Database Documentation. US Environmental Protection Agency, Office of Research and Development, National Health and Environmental Rese arch Laboratory, Gulf Ecology Division, Gulf Breeze, FL. 36 p.