Ref: 8EPR-EP 12/12/11

Amanda Smith

Executive Director Utah Department of Environmental Quality 195 North 1950 West, 4 th Floor

P.O. Box 144810 Salt Lake City, Utah 84114-4810

RE: EPA Action on the Gilbert Bay Selenium Criterion and Footnote (14)

Dear Ms. Smith:

The U.S. Environmental Protection Agency has comple ted its review of the Gilbert Bay selenium criterion and associated Footnote (14) that were ad opted by the Utah Water Quality Board (Board) on November 10, 2008 and submitted to the EPA for revi ew with a letter dated April 8, 2009. A letter from the Office of the Attorney General, certifying that the revisions were adopted pursuant to State law, was enclosed with the submittal letter. Receipt of the revisions on April 29, 2009 initiated the EPA’s rev iew pursuant to Section 303(c) of the Clean Water Act ( CWA or the Act) and the water quality standards regulation. The EPA acted on many of the revisions in a letter dated September 30, 2009. The EPA has now completed its review of the remaining revisions , including the Gilbert Bay selenium criterion and associated Footnote (14). This letter is to notify you of our action.

We commend the Department of Environmental Quality (DEQ) and the Division of Water Quality (Division) for their excellent collaboration with s takeholders when developing the Gilbert Bay seleniu m criterion. Several years of collaboration efforts i ncluding the development of a Steering Committee an d expert Science Panel produced an egg tissue-based s elenium criterion protective of aquatic dependent birds that nest in Gilbert Bay. This criterion is t he first numeric criterion adopted for the Great Sa lt Lake. We thank the DEQ and Division for their commitment to protecting birds that utilize Gilbert Bay and fo r the considerable effort that was put into the devel opment of the new and revised water quality standar ds. UNITED STATES ENVIRONMENTAL PROTECTION AGENCY

REGION 8
1595 Wynkoop Street
DENVER, CO 80202-1129

Phone 800-227-8917 http://www.epa.gov/region08C LEAN W ATER A CT R EVIEW R EQUIREMENTS

CWA § 303(c)(2) requires States and authorized Indi an Tribes 1 to submit new or revised water quality

standards to the EPA for review. The EPA is to revi ew and approve or disapprove the submitted standards. Pursuant to CWA § 303(c)(3), if the EPA determines that any standard is not consistent with the applicable requirements of the Act, the agency shall notify the State or authorized Tribe and spec ify the changes to meet the requirements. If such chang es are not adopted by the State or authorized Tribe

, the EPA shall promulgate the needed standard pursua nt to CWA § 303(c)(4). The EPA’s goal has been, and will continue to be, to work closely with State s and authorized Tribes throughout the standards revision process as a means to develop approvable w ater quality standards revisions.

T ODAY ’ S A CTION

Today, the EPA is approving the Gilbert Bay seleniu m criterion and taking no action on the remaining provisions in the associated Footnote (14). 2 Enclosure 1 presents the rationale for the EPA’s a ction.

E NDANGERED S PECIES A CT R EQUIREMENTS

Section 7(a)(2) of the Endangered Species Act (ESA) requires federal agencies, in consultation with th e U.S. Fish and Wildlife Service and/or the National Oceanic and Atmospheric Administration Fisheries Service (Services), to ensure that actions they aut horize, fund, or carry out are not likely to jeopar dize the continued existence of federally listed endange red or threatened species or result in the destruct ion or adverse modification of designated critical habi tat of such species. 16 U.S.C. § 1536 (a)(2). Howev er, consultation with the Services is not required wher e the action has no effect on listed species or designated critical habitat. 50 CFR § 402.14. Beca use there is no occurrence of listed species or cri tical habitat for listed species in Gilbert Bay, the EPA concludes that its approval of Utah’s Gilbert Bay selenium criterion has no effect on listed species or designated critical habitat.

1 CWA Section 518(e) specifically authorizes the EPA to treat eligible Indian Tribes in the same manner as States for purposes of CWA Section 303. See also 40 CFR Sectio n 131.8.

2 The EPA disapproved the 6.4 mg/kg trigger value in its September 30, 2009 action letter. The remainin g provisions in Footnote (14) do not alter the magnitude, duration, or frequency of the criterion or any of the provis ions that constitute the water quality standards for Gilbert Bay, or are oth erwise not revisions requiring a CWA § 303(c) actio n.C ONCLUSION

The EPA thanks DEQ, the Board, and the Division for their efforts to review and revise Utah’s water quality standards. The EPA looks forward to working with the State to make additional improvements to the water quality standards that apply to the Great Salt Lake ecosystem. If you have any questions concerning this letter, contact Lareina Guenzel on my staff at 303-312-6610.

Sincerely,

Carol L. Campbell Assistant Regional Administrator Office of Ecosystems Protection

and Remediation

Enclosure

cc: Walt Baker, Director, Division of Water Quality1 Enclosure 1

RATIONALE FOR THE EPA’S ACTION ON THE REVISIONS TO UTAH WATER QUALITY

STANDARDS

Today’s EPA action letter addresses the new Gilbert Bay selenium criterion adopted into Utah’s water quality standards by the Water Quality Board (Board ) on November 10, 2008. This enclosure identifies

the new selenium criterion as well as the EPA’s rat ionale for approving the criterion for the purposes of Clean Water Act (CWA) §303(c). The enclosure also a cknowledges certain portions of Footnote (14) upon which the EPA is taking no action.

Gilbert Bay Selenium Criterion and Associated Footn ote (14)

The Board adopted a selenium criterion of 12.5 mg/k g dry weight in bird egg tissue that is a geometric mean over the nesting season to be applied to Gilbe rt Bay of the Great Salt Lake (GSL). The Board also adopted Footnote (14) which states:

The selenium water quality standard of 12.5 (mg/kg dry weight) for Gilbert Bay is a tissue based standard using the complete egg/embryo of aquatic d ependent birds using Gilbert Bay based upon a minimum of five samples over the nesting season. As sessment procedures are incorporated as a part of this standard as follows:

Egg Concentration Triggers: DWQ Responses

Below 5.0 mg/kg: Routine monitoring with sufficient intensity to determine if selenium concentrations within the Great Salt Lake ecosystem are increasing.

5.0 mg/kg: Increased monitoring to address data gap s, loadings, and areas of uncertainty identified from initial Great

Salt Lake selenium studies.

6.4 mg/kg: Initiation of a Level II Antidegradation review by the State for all discharge permit renewals or new disc harge permits to Great Salt Lake. The Level II Antidegradation re view may include an analysis of loading reductions.

9.8 mg/kg: Initiation of preliminary TMDL studies t o evaluate selenium loading sources.

12.5 mg/kg and above: Declare impairment. Formalize and implement TMDL.

Antidegradation Level II Review procedures associated with this sta ndard are referenced at R317-2-3.5.C.

On September 30, 2009, the EPA Region 8 identified the portion of Footnote (14) dealing with the 6.4 mg/kg trigger value (i.e., the text from “6.4 mg/kg ” to “an analysis of loading reductions”) as a new water quality standard. The EPA disapproved that portion of Footnote (14) because it would not adequately maintain and protect assimilative capacity for sele nium in the eggs of aquatic dependent birds using2

Gilbert Bay. In this enclosure, the EPA acknowledge s the remaining portions of Footnote (14), but take s no action upon them as they are not new or revised wat er quality standards.

Background

The Utah Division of Water Quality (UDWQ) establish ed a multi-tiered review process to aid in the development of the Gilbert Bay selenium criterion. This process included the organization of a Steerin g Committee, Science Panel, and public involvement pr ogram that provided guidance, technical expertise and perspectives from a broad range of interests. T he Steering Committee was composed of a wide range of stakeholders and included one or more repr esentatives from the Utah Department of Natural Resources, EPA Region 8, U.S. Fish and Wildlife Ser vice (FWS), U.S. Geological Survey (USGS), Utah Artemia Association, various industries that d ischarge to the GSL, GSL Alliance, University of Utah, the Nature Conservancy, West Side Associated Duck Clubs, and the Utah Department of Environmental Quality. The Science Panel was formed by the Steering Committee to provide technical guidance, oversight, and a review of the required r esearch necessary to develop the criterion. The Science Panel was also tasked with providing recomm endations to the Steering Committee on what would be considered a protective selenium criterion for Gilbert Bay of the GSL. Finally, the public involvement program included a number of stakeholde r workgroup meetings, the development of a website, poster sessions, and public participation in the water quality standards hearing process. The EPA commends UDWQ and the Board for their commitmen t to the protection of wildlife as well as their signification public participation efforts that wen t beyond the 131.20(b) public participation requirements.

Multiple original research projects were organized by the Steering Committee and Science Panel to support the development of the Gilbert Bay selenium criterion. One example is a study that focused on analytical methods for measuring selenium in water samples collected from the GSL. Data collected from earlier studies showed high variability in wat er column selenium concentrations. Results from the recent study indicate that much of the variability in the historic data was due to interferences cause d by the high salinity, total dissolved solids (TDS), an d alkalinity of GSL water when analyzed using inappropriate methods, rather than actual variabili ty of selenium in the water column. Furthermore, a round-robin laboratory study concluded that only tw o methods (hydride generation atomic absorption and graphite furnace atomic absorption) produce rel atively consistent and accurate results. Therefore, only selenium data generated with these two methods were considered acceptable for use in the criteria development.

Additional studies focused on a conceptual model of selenium cycling in the GSL. Development of a conceptual model was necessary to answer questions concerning the sources, fate, transport, and trophi c transfer of selenium in the GSL. The model develope d in this effort focused on five components including: (1) selenium in the upper food chain; (2 ) selenium in the lower food chain; (3) selenium in the shallow layer of the lake; (4) selenium in the deep layer and sediment of the lake; and (5) selenium loading to the lake. Although the version of the mo del available when the standard was adopted was sti ll considered preliminary, it provided information on how selenium cycles in the GSL and was taken into consideration by the Steering Committee and Science Panel when making criteria recommendations to

the Board.

Several scientific studies were conducted to answer questions on the current concentration of selenium in shorebirds and potential effects of selenium on shorebirds of the GSL. These studies focused on bir d species that actively feed and nest on the GSL incl uding American avocet, black-necked stilt, Californ ia gull, eared grebe, and common goldeneye. Data colle cted included selenium concentrations in adult3

blood and liver, food chain invertebrates, water co lumn, and bird eggs, as well as multiple parameters that evaluate the reproductive success of shorebird s (see Appendix C and D of the Final GSL Selenium report). 1 Study results showed egg selenium concentrations o f birds utilizing the GSL were relatively

low. Concentration of selenium in American avocets and black-necked stilt eggs ranged from 1.2 to 9.2 mg/kg, with a mean of 2.7 mg/kg. Concentration of s elenium in California gull eggs ranged from 2.0 to 4.3 mg/kg, with a mean of 2.9 mg/kg. The average co ncentrations are within the range of concentrations that are considered background (Ohlendorf 2003).

Results from the reproductive success studies confi rm that the existing low concentration of selenium in shorebird eggs is not affecting bird populations ut ilizing Gilbert Bay. Hatchability of American avoce t and black–necked stilt eggs ranged from 94-100%. 2 These hatching success rates are within the range of conditions expected to be observed at non-contamina ted sites. Additional results showed that in American avocet eggs, selenium concentrations of ne sts with 100% hatchability were not significantly different from nests with lower hatchability. Final ly, of the 142 eggs that were dissected and observe d for deformities, no malformations were observed and only one American Avocet egg had a possible Type I malposition. 3

Finally, the Science Panel was tasked with developi ng toxicity thresholds for birds that utilize the G SL. To accomplish this task, the panel considered the s ensitivity of multiple aquatic-dependent birds and endpoints protective of reproductive success and bo dy condition. Species that were evaluated included, but were not limited to, the American avocet, black -necked stilt, California gull, and mallard duck. F rom these reviews, it was determined that selenium-rela ted impairment of the open waters of the GSL should be defined by the most sensitive endpoint of the mo st sensitive species. This resulted in the developm ent of a criterion to protect the hatching success of t he mallard duck.

Details on the specific duties and objectives of th e Steering Committee and Science Panel, as well as final reports and criteria recommendations are incl uded in the Final GSL Selenium report available on UDWQ’s website.

Criteria Development

CWA § 101(a)(2) establishes as a national goal the achievement of water quality that provides for the protection and propagation of fish, shellfish, and wildlife and recreation in and on the water. CWA § 304(a)(1) requires the Administrator to develop and publish criteria for water quality that accurately reflect the latest scientific knowledge on the (1) kind and extent of all identifiable effects on heal th and welfare including, but not limited to wildlife, tha t may be expected from the presence of pollutants i n any body of water; (2) concentration and dispersal of pollutants through biological, physical and chemical processes; and (3) effects of pollutants o n biological diversity, productivity, and stability

.

The EPA does not have national guidance for derivin g wildlife criteria. Therefore, the agency has not formally addressed endpoints necessary to protect w ildlife on a national level. However, the Agency ha s taken a position on the level of protection for wil dlife criteria as part of the Great Lakes Initiativ e (40

1 Great Salt Lake Water Quality Studies. Development of a Selenium Standard for the Open Waters of Grea t Salt Lake. Final Report May 2008. Prepared by CH2MHILL for the State of Utah Department of Environmental Quality, Divis ion of Water Quality. http://www.deq.utah.gov/Issues/GSL_WQSC/do cs/GLS_Selenium_Standards/index.htm

2 Hatchability was calculated as the #eggs hatched/# full term eggs in nest.

3 Malposition was classified according to Romanoff a nd Romanoff 1972. Type I malposition is when the he ad is between the thighs, when the head under the right wing is consi dered normal.4

CFR § 132 Appendix D). In this effort, the agency u sed the no observed effect concentration (NOEC) 4

in diet to calculate tissue-based wildlife criteria for DDT, mercury, PCBs and 2,3,7,8-TCDD. The final avian wildlife value was calculated as the geometri c mean of the kingfisher, gull, and eagle speciesspecific thresholds (EPA/820/B-95/008). Utah adopte d a wildlife criterion for selenium using a methodology that fit toxicological data from six la boratory studies to a mathematical equation and the n determined the concentration that would correspond to a hypothetical 10% adverse effect (EC 10 ) for the most sensitive species. The resulting criterion is not significantly distinguishable from a criterion concentration derived from the same data using the NOEC concentration (see below). EC 10 s and NOECs are generally of similar magnitude, but EC 10 s have the advantage of being more reproducible and reliable than NOECs, and are generally recommended as the replacement for NOECs (Crane and Newman 2000; Van der Hoeven 1997; Warne and van Dam 2008). Overall, use of an EC 10 for wildlife criteria is generally less than or equivalent in ma gnitude to a NOEC, but more reliable than a NOEC an d therefore is considered consistent with previous ap proaches approved by the EPA.

Derivation of the Selenium Criterion

The Gilbert Bay criterion was derived from a regres sion meta-analysis of six different laboratory stud ies that evaluated the effect of selenium on mallard eg g hatchability (Heinz et al. 1987, 1989; Heinz and Hoffman 1996, 1998; Stanley et al. 1994, 1996). Hav ing determined that mallards are the most sensitive known species, a logistic curve was fitted to the c ombined, control normalized data from the six malla rd studies. The logistic curve was used to estimate th e egg selenium concentration that corresponds to an

EC 10 for mallard egg hatchability (Figure 1). This valu e was thereby found to be 12.5 mg/kg.

Six of the nine Science Panel Members recommended a doption of the 12.5 mg/kg dry weight in bird egg

tissue criterion to the Board. 5 The Science Panel Members that recommended adoptio n of the 12.5 mg/kg criterion indicated that setting the Gilbert Bay criterion to the mallard EC 10 is an appropriately conservative site-specific criterion since: the mal lard duck is the most sensitive bird species tested to date; egg hatchability is the most sensitive endpoi nt; and the mallard duck is more sensitive to the effects of selenium than aquatic dependent birds th at commonly feed and nest at Gilbert Bay (e.g., black-necked stilt). 6 Based on these recommendations, recommendations fr om the Steering Committee, and public comments, the Board adopted 12.5 mg/kg a s the criterion for Gilbert Bay.

4 A NOEC is the highest tested concentration that did not yield an effect that is statistically signific antly different from the control. The detectability of an effect depends on the quality and size of the experiment and the stat istical procedure.

5 One Science Panel Member recommended adoption of a no effect concentration of 5.0 mg/kg, another Scien ce Panel member recommended adoption of 10.4 mg/kg, and anot her Science Panel member abstained from making a

recommendation.

6 When reviewing the Gilbert Bay criterion, the EPA a lso evaluated the sensitivity of the black-necked s tilt to selenium. An extensive field dataset (n = 673 nests) has reveale d that egg hatchability is also the most sensitive endpoint for black-necked stilt. Similar to the mallard duck, the EC 10 values for stilt are sensitive to the method by wh ich the data are organized and the model used to fit the data. When using a triangular tolerance distribution analysis, the median EC 10 14.6 mg/kg when data are grouped into 10 bins. The stilt egg counting was co nducted on a nest-wise or hen-wise basis (n = numbe r of nests with typically 4 eggs per nest) rather than an egg-wise basis (n = number of eggs), which was used for the mallard duck studies. These counting methods are not directly equivalent, with the nest-wise approach typically producing mo re conservative

results.5

Figure 1: Mallard egg hatchability as a function of selenium concentration in eggs. Figure modified fr om Ohlendorf (2003). Dashed line identifies the 10 percent effec t concentration (EC 10 ). The data were normalized to their respective control hatchability values. LCL = lower confidence limit. UCL = upper confidence limit.

The EPA closely examined the logistic curve model u sed to derive the 12.5 mg/kg EC 10 . Using the data from the six mallard studies, the EPA performed an independent evaluation, fitting the data using a variety of different approaches. In its analysis, t he EPA eliminated certain treatments from the Stanl ey et al. (1994) study because they were potentially conf ounded by arsenic exposure, which was part of the study design. Removal of these data, however, did n ot have a significant influence on the final result s.

Some authors suggest that control normalization is inappropriate because control responses also contai n variability. Normalizing for control response effec tively removes this estimation error from the contr ol values (OECD 2006). To address this concern, the EP A also analyzed the six-study data set without normalizing for control hatchability. Using a trian gular tolerance distribution analysis, the EPA obta ined

an EC 10 of 12.3 mg/kg, which is within 2% of the Gilbert B ay criterion of 12.5 mg/kg. The fit is shown in Figure 2. Using a Gaussian tolerance distributio n analysis, which is more similar to the analysis performed by Ohlendorf (2003), the EPA obtained an EC 10 of 12.0 mg/kg, which is within 4% of the Gilbert Bay criterion.6

Figure 2. Mallard egg hatchability in the six studi es, fitted by triangular tolerance distribution, wi thout normalization

to control values.

Additional review of the model included an evaluati on of the effect of mixing data from studies having substantial differences in control hatchability. To do this, the EPA excluded the two studies with low (<50%) control hatchability (Heinz and Hoffman 1996 , 1998). This produced an EC 10 of 12.7 mg/kg through the triangular tolerance distribution appro ach, which is within 2% for the GSL criterion. Thus reducing the range of control hatchability included in the analysis did not have a significant effect on the resulting criterion value.

The EPA also reviewed a logistic nonlinear regressi on fit to the data. The logistic nonlinear approach yields an EC 10 of 9.7 mg/kg. Although this estimate of an EC 10 is different than the Gilbert Bay EC 10 - based criterion, the EPA can find no scientific bas is for concluding that a logistic nonlinear regress ion fit is more or less appropriate than tolerance distribu tion fit described in the previous paragraph. In th e absence of any meaningful scientific justification to prefer the one approach over the other, the diff erent values derived from the application of these two mo dels to the same data are equally scientifically

defensible.

Protectiveness of the Adopted Criterion

Gilbert Bay is a Class 5A waterbody which includes protection of the following designated uses: frequent primary and secondary contact recreation, waterfowl, shorebirds and other water-oriented wildlife including their necessary food chain. Base d on the scientific literature and available inform ation for selenium specific to the GSL, protection of aqu atic-dependent birds is the most sensitive use assigned to Gilbert Bay, and reproductive success o r egg hatchability is the critical endpoint to be u sed in the selenium criterion that would be protective of that designated use.

The EPA considered multiple lines of evidence to de termine if the adopted selenium criterion is protective of designated uses and approvable under the CWA and the EPA’s implementing regulation at 40 CFR § 131.6. One line of evidence considered by the EPA was an evaluation of effects of selenium in egg tissue below the criterion of 12.5 mg/kg. Fi gure 3 shows the percent hatch in the six control L og of Se (mg/kg) in EggsProportion of Eggs Hatching

-.5 0 .5 1.0 1.5 2.0 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1.0 1.1treatments, one for each of six stud ies, and

mg/kg criterion is represented by the vertical line

. 12.5 mg/kg are within the range o f the controls and

which is show n by the lower horizontal dashed line.

than the criterion yielded hatchability below the lower confidence bound for the control mean and below the hatchability of any control. These data suggest that the

EC 10 -based criterion of 12.5 mg/kg would be

and that selenium concentrations up to hatchability beyond natural conditions.

Figure 3 : Mallard percent hatch v. egg concentration for si x studies values. 1 = Heinz et al. (1989) ; 2 = Heinz et al Hoffman (1996) ; 6 = Heinz and Hoffman

Another line of evidence the EPA considered is a

the NOECs and the lowest observed derive the dose respons e curve presented in Figure

treatment concentrations , not interpolations

selenium treatments. U se of the NOE available data ( Van der Hoeven 1997 selenium treatments for the majority of the tests

criterion is less than the NOECs for three of the four studies 1). In the one study (S tanley et al. 1996

the confidence limits of the EC 10 ( 5% LC Overall, these data are not inconsistent aquatic dependent birds utilizing Gilbert Bay

7 LOEC is the highest tested concentration that yield ed an effect that is statistically significantly di ffe

7 ies, and the selenium exposed treatment s from those studies. mg/kg criterion is represented by the vertical line

. Hatchability at all treatment concentrations less t han

f the controls and the lower 95% confidence range of the control mean, n by the lower horizontal dashed line. By contrast, al l treatment concentrations greater the criterion yielded hatchability below the lower confidence bound for the control mean and below These data suggest that the ex pected hatchability associated with the

would be within the variability of background (control) resp onses, up to the proposed criterion would not lead to additional beyond natural conditions.

: Mallard percent hatch v. egg concentration for si x studies . Raw data without normalization to control

; 2 = Heinz et al (1987); 3 = Stanley et al. (1996); 4 = Stanley et al. ; 6 = Heinz and Hoffman (1998).

considered is a

comparison of the Gilbert Bay selenium effect concentrations (LOECs) 7 of the six individual studies e curve presented in Figure 1. NOECs and LOECs can only be , not interpolations , and therefore are strongly influenced by

se of the NOE Cs and LOECs alone is not a preferred method to analyze the

Van der Hoeven 1997 ; Warne and van Dam 2008), especially given the low number of selenium treatments for the majority of the tests ; however, it is useful to recognize that

three of the four studies for which a NOEC can be derived tanley et al. 1996 ) where the NOEC is less than 12.5 mg/kg, the NOEC is

5% LC L = 6.4 mg/kg; 95% UCL = 16.5 mg/kg ; see Figure

not inconsistent with the concept that an EC 10 -based criterion aquatic dependent birds utilizing Gilbert Bay

.

LOEC is the highest tested concentration that yield ed an effect that is statistically significantly di ffe s from those studies. The 12.5 Hatchability at all treatment concentrations less t han the lower 95% confidence range of the control mean,

l treatment concentrations greater the criterion yielded hatchability below the lower confidence bound for the control mean and below pected hatchability associated with the within the variability of background (control) resp onses, the proposed criterion would not lead to additional reductions in

without normalization to control

al. (1994); 5 = Heinz and

the Gilbert Bay selenium criterion to

individual studies used to NOECs and LOECs can only be a measured strongly influenced by the number of is not a preferred method to analyze the

given the low number of it is useful to recognize that the adopted

a NOEC can be derived (Table where the NOEC is less than 12.5 mg/kg, the NOEC is within ; see Figure 1).

criterion is protective of

LOEC is the highest tested concentration that yield ed an effect that is statistically significantly di ffe rent from the control.8

Table 1: NOEC and LOEC (mg/kg) for six studies eval uating egg hatchability in mallard duck. * Number o f Se

treatments excludes the control. The two Heinz and Hoffman (1996) treatments were fed the same seleniu m dose but differed in the form of selenium (seleno-DL-methion ine vs. seleno-L-methionine). Differences between t he two treatments were not statistically significant.

Study #Se treatments* NOEC LOEC

Heinz et al. 1989 5 36.3 59.4 Stanley et al. 1996 2 11.6 23.4

Heinz et al. 1987 1 >15.2 --

Heinz and Hoffman 1998 1 >25.1 --

Heinz and Hoffman 1996 2 -- <29.4/<30.4 Stanley et al. 1994 1 -- <37

The EPA also considered how the criterion is expres sed (i.e., geometric mean over the nesting season) when evaluating the protectiveness of the Gilbert B ay selenium criterion by estimating the site-specif ic risk associated with the existing selenium in eggs collected from Gilbert Bay. The Society for Risk Analysis (2011) definition of risk is “the potentia l for realization of unwanted adverse consequences to human life, health, property, or the environment; e stimation of risk is usually based on the expected value of the conditional probability of the event o ccurring times the consequence of the event given t hat it has occurred.” For the Gilbert Bay selenium crit erion, the site-specific risk is the combination of the probability of bird eggs having particular concentr ations multiplied by the effects predicted to be ca used by those concentrations. It is the product of two m athematical curves, one describing the distribution of egg concentrations allowed by the criterion, and th e other describing the concentration-response curve

from which Utah derived their EC 10

.

The EPA utilized the egg selenium data presented in the Final GSL Report (Projects 1A and 1B) and a response curve similar to that of Ohlendorf (2003)( see Figure 1) in its analysis. Among the 130 observations, the standard deviation of the natural logs of the egg concentrations is 0.33. (The base 10 log standard deviation is 0.33/ln(10).) In contrast to the arithmetic standard deviation, the log stan dard deviation is a measure of relative variability, ind ependent of the sample mean or the units of

measurement.

The general equation for relating concentrations in the normal (or probit) distribution curve is:

ln x = μ + σ z

In this case x

is the EC 10 ; μ is ln(EC 50 ); σ is the natural log standard deviation (and recipro cal of the log probit curve slope) and z is the normal deviate (fr om normal distribution tables or NORMSINV function) corresponding to 10% (for the EC 10 ). Solving for σ yields a log standard deviation of 0.56, the inverse of which is the probit slope.

The probability, p i , of an individual having concentrations within som e range increment ∆ ln(C i ) is:

p i = φ i ∆ ln(C i )

where φ i is the probability density function for the egg-ov ary concentration C i at the midpoint of each increment. (In Excel or Quattro Pro, φ is given by NORMDIST, selecting for the density fu nction rather than the cumulative function. This is then multipli ed by the increment breadth in natural log units.)

Figure 4 illustrates how the allowed Gilbert Bay eg g concentration distribution is combined with the concentration-response curve. The bell-shaped, logn ormally skewed probability density function is theallowed Gilbert Bay egg concentrations, having median and g eometric mean of 12.5 mg/kg.

shaped concentration- response curve defines potential reduction in hatch ability. predicted potential effect or site- specific risk For Gilbert Bay, the summation of these products Bay egg concentrations to have extremely low variab ility, the bell curve would be a narrow spike at 12 .5 mg/kg, and the site- specific risk would be 10%. the sample concentrations would be below the prevents the reduced effects at concentrations <12. 5 mg/k at concentrations >12.5 mg/kg, thus raising the agg regate above 10%.

Figure 4. Allowed GSL egg concentrations (with current degree of variability) expressed as a probability density function on an arbitrary y- scale, coupled with a concentration

Plotted on the arithmetic x- axis, both curves are skewed

To relate the site-specific risk to the concentration that would be predicted to yield the potential 13.5% reduction response curve similar to that of Ohlendorf (2003)

each of the six studies used to develop

the site- specific risk of 13.5% is 13.8 within the range of the control variability ( exceeds the control variability ( 15.86 mg/kg mg/kg is also within the 95% confidence limit of the mg/kg; see Figure 1). Given the resul criterion as a geometric mean is protective of the designated use given the

selenium concentrations in bird eggs in indicating that such variability has significantly protectiveness of the criterion in light of those new data, consistent with its obl igations under 40 CFR

131.20. The EPA would furthermore expect Utah to the criterion is no longer protective of the existing and designated uses

9 Gilbert Bay egg concentrations, having median and g eometric mean of 12.5 mg/kg.

response curve defines potential reduction in hatch ability. The calculated total

specific risk

is the summation ∑ p i E i for all possible concentrations, C summation of these products yields a site- specific risk of 13.5%. Bay egg concentrations to have extremely low variab ility, the bell curve would be a narrow spike at 12 .5

specific risk would be 10%. Higher variability widens the bell curve. the sample concentrations would be below the EC 10 and half above, the nonlinearity of the S prevents the reduced effects at concentrations <12. 5 mg/k g from counterbalancing the increased effects at concentrations >12.5 mg/kg, thus raising the agg regate above 10%.

Allowed GSL egg concentrations (with current degree of variability) expressed as a probability density

scale, coupled with a concentration

- response curve, expressed as fraction reduced hatch

. axis, both curves are skewed or asymmetric.

protectiveness of the criterion, the EPA determined concentration that would be predicted to yield the potential 13.5% reduction in hatchability using Ohlendorf (2003) and compared this concentration to the raw data for to develop the curve. The median egg tissue concentration associated specific risk of 13.5% is 13.8 mg/kg, which is greater than the highest concentrat ion that is

variability ( 12.07 mg/kg), but less than the lo west concentration that 15.86 mg/kg )(see Figure 3). A bird egg- tissue concentration of 13.8

is also within the 95% confidence limit of the EC 10 (5% LCL = 6.4 mg/kg; 95% UCL = 16.5

Given the resul ts of these analyses, the EPA has determined that expressing the criterion as a geometric mean is protective of the designated use given the relatively low variability of bird eggs in Gilbert Bay. In the event that new data becom significantly increased, the EPA would expect Utah to reevaluate the in light of those new data, consistent with its obl igations under 40 CFR would furthermore expect Utah to revise the criterion if those new data

of the existing and designated uses

. Gilbert Bay egg concentrations, having median and g eometric mean of 12.5 mg/kg. The SThe calculated total for all possible concentrations, C i

. specific risk of 13.5%. Were the Gilbert Bay egg concentrations to have extremely low variab ility, the bell curve would be a narrow spike at 12 .5 Higher variability widens the bell curve. Although half and half above, the nonlinearity of the S -curve g from counterbalancing the increased effects

Allowed GSL egg concentrations (with current degree of variability) expressed as a probability density response curve, expressed as fraction reduced hatch

.

EPA determined the egg in hatchability using a and compared this concentration to the raw data for The median egg tissue concentration associated with mg/kg, which is greater than the highest concentrat ion that is

west concentration that

tissue concentration of 13.8 (5% LCL = 6.4 mg/kg; 95% UCL = 16.5 EPA has determined that expressing the

relatively low variability of

becom e available, would expect Utah to reevaluate the in light of those new data, consistent with its obl igations under 40 CFR §

new data indicate that10

Other Considerations

EC 10 Estimates Using a Biphasic Model

One Science Panel member did not support the logist ic analysis by Ohlendorf (2003) to derive an EC 10

. Rather, he advocated deriving the EC 10 using the biphasic model as described by Beckon et al. (2008) (Skorupa 2008). Beckon et al. (2008) analyzed the d ata of Heinz et al. (1989) using a biphasic model (formerly called a hormetic model) and reported an EC 10 of 7.7 mg/kg. With this approach, data from only one of the six studies included by Ohlendorf ( 2003) was used to calculate an EC 10

.

The EPA’s review of the Gilbert Bay criterion inclu ded a careful evaluation of the Beckon et al. (2008 ) approach to estimating an EC 10 using a biphasic model. The EPA has essentially re produced the Beckon et al. (2008) result, obtaining an EC 10 of 7.76 mg/kg, with the small difference due to th e EPA using individual study values rather than cross-study ave rages in converting egg wet weight concentrations t o dry weight concentrations.

Using the biphasic model as described by Beckon et al. (2008) to derive the Gilbert Bay selenium criterion has several problematic implications. Fir st, the EPA has determined that the recommendation from the majority of the Science Panel Members to u tilize the data from all of the six studies and not only rely on the Heinz et al. (1989) data is reason able. Reducing the number of observations in the da ta set substantially increases the uncertainty in esti mating an effect concentration. When analyzed by logistic regression apart from the rest of the data , the Heinz et al. (1989) data yields a high EC 10 estimate, 29 mg/kg, which would not be considered p rotective. Also, further statistical analysis by th e EPA indicates that the Heinz et al. (1989) dataset, by itself, is too small to support the biphasic mo del. 8

Additionally, Beckon et al. (2008) assumes that the Heinz et al. (1989) control was deficient in selen ium since the percent hatch in the control was less tha n the percent hatch of the first treatment (59.6% v s 70.7%, respectively, see Figure 5). 9 This assumption was accepted by Beckon et al. (200 8) even though the confidence bounds for the control (48.7 - 70.1) fall completely inside the confidence bounds for t he first treatment (44.8 - 91.0). The Beckon et al. (2 008) biphasic model relies on this difference in ha tch not merely reflecting random experimental noise, bu t accurately reflecting a significant reduction in

hatch due to a diet that was deficient in selenium. However, a post hoc statistical analysis of the ra w data from the Heinz et al. (1989) study conducted by the EPA indicates that the difference between the control (59.6 percent hatch) and the first treatmen t (70.7 percent hatch) is not statistically signifi cant at

8 The EPA does not believe a criterion set the EC 10 of 29 mg/kg would be protective. Nevertheless, when the Heinz et al. (1989) data are analyzed by themselves, statistical measures indicate that the logistic fit to the Hei nz et al. (1989) data yielding the EC 10 of 29 mg/kg is generally better than the biphasic fit yielding the EC 10 of 7.7 mg/kg. To some degree this is shown by the higher R 2 (better fit) for the logistic model (0.95) compare d to the biphasic model (0.86). More important are the results for the Akaike Information Criterion, A IC (Burnham and Anderson 2002). The AIC is used to evaluate the tradeoff between model accuracy and complexity. The ACI metric penalizes overfitting by accounting for the number of parameters. The biphasic model uses four parameters and the logistic model uses three. When comparing two models describing the same data, a lower AIC value is better. Uncorrected for finite sample size, the smaller (more negative) AIC of -10.74 for the logistic model, compared to -3.90 fo r the biphasic model indicates that the logistic mo del may be preferred over the biphasic model. Corrected for finite sampl e size, the smaller AIC

c of 1.26 for the logistic compared to 36.10 for the biphasic even more strongly indicates preference fo r the logistic model. These statistical measures in dicate that the Heinz et al. (1989) dataset, by itself, is too small to supp ort the biphasic model.

9 In poultry, dietary Se concentration greater than 0 .3 mg/kg dw are considered adequate for good adult health and Se egg concentrations less than 0.66 mg/kg dw may indicate inadequate Se in maternal diets (Puls 1988, Ohlend orf and Heinz 2011). Heinz et al. (1989) does not report the concentrati on of selenium in the control diet; however, Ohlend orf (2003) indicated that control diets typically contain 0.4 mg/kg selenium. The control egg concentration was 0.6 mg/kg dw.11

either the α =0.05 or the α =0.5 confidence level. 10 That is, whether willing to accept a 5% or a 50% chance that the difference arose by chance, the dif ference is not statistically significant. Therefore , it

cannot be concluded the difference in hatch is due to a selenium deficient diet. 11

Figure 5. Biphasic analysis of Heinz et al. (1989) data assuming background egg concentration correspo nding to the peak response.

Furthermore, when comparing the EC 10 values derived using a logistic curve model (as pr oposed by Ohlendorf (2003) and adopted by the Board) and biph asic model (as proposed by Beckon et al. (2008)), sidestepping the above statistical problems with th e Beckon analysis, the EPA has found that the major difference in the resulting EC 10 is not a reflection of the model used to fit the d ata. Rather, the major difference is a reflection of the differing assumpt ions about the expected background response (percen t hatchability) and background egg selenium concentra tion (collectively, we refer to this as background condition) in the absence of selenium contamination . When background conditions are similarly defined in both models, the difference between the EC 10 values derived using the biphasic model and the lo gistic

curve model is small.

10 Using the Games and Howell (1976) procedure and the Dunnett-Tukey-Kramer pairwise multiple comparison test (Dunnett 1980).

11 The response patterns observed in the five studies not used by Beckon et al. (2008) and Skorupa (2008) also appear to disaffirm the biphasic model predictions. Although none of those studies contain exposures near enough to the biphasic model’s 2.7-2.8 mg/kg predicted optimum concentrati on range to allow visual determination of whether p ercent hatch attains a peak in that area, it is possible to examine the ratio of observed percent hatch at different concen trations within each study on either side of the biphasic model’s predicted op timum range, and compare them to the ratios predict ed by the Beckon et al. (2008) biphasic model and the Ohlendorf (2003) logi stic model. For example, in Stanley et al. (1996) t he observed hatch response at 11.6 mg/kg was 98% of the hatch respons e at 0.89 mg/kg. This can be compared to the biphas ic model’s predicted response ratio of 85% and the logistic mo del’s predicted response ratio of 92%. Comparing su ch response ratios within each of the five studies not used by Beckon et al. (2008) and Skorupa (2008) yields an observed -predicted r 2 of 0.46 for the biphasic model and 0.72 for the logistic mo del. The five studies thus suggest that the monoton ic response pattern of the Ohlendorf (2003) model (shown in Figure 1) is m ore appropriate than the biphasic response pattern of the Beckon et al. (2008) model (shown in Figure 5). Egg Se (mg/kg dw) 0.1 1 10 100Egg Hatchability (proportion)

0.0 0.2 0.4 0.6 0.8 1.0

Se Control

Se Treatments

} 10% 10% { Max Hatchability 2.75 mg/kg

7.76 mg/kg 0.696 mg/kg12

In both cases, 10 percent reduction in hatchability is estimated relative to background percent hatchability. Ohlendorf (2003) calculated the backg round percent hatchability as the average hatchability of the background (control) exposures estimated from the fitted model . In contrast, Beckon et al. (2008) estimates background percent hatchabi lity as the single peak value of hatchability at an optimum egg concentration of 2.75 mg/kg that is est imated by the biphasic model (Figure 5). This difference in how background is defined (i.e., an a verage or range vs. a single peak value) is the pri mary reason for the difference between the Ohlendorf (20 03) EC 10 of 12.5 mg/kg and the Beckon et al. (2008)

EC 10 of 7.7 mg/kg. Neither Beckon et al. (2008) nor Sko rupa (2008) discuss the appropriateness of assigning this single optimum value as the applicab le background condition. This assignment is particularly significant because of its corollary: that all increases or decreases in background selen ium concentration represent a movement away from the op timal selenium concentration.

It is difficult to justify and unrealistic to assum e that a single optimum would apply to all real-wor ld ecosystems. In fact, optimal egg hatchability and b ackground concentration of Se in eggs can range widely; therefore, the EPA believes it is reasonabl e to define the background condition as a range or an average when modeling a response. For the same toxi city data, Figures 6 and 7 illustrate other possibl e applications of the biphasic approach when other, e qually valid, methods are used to estimate background concentrations. In each figure, the hori zontal line represents an alternative background hatchability level from which a 10% reduction in ha tchability can be estimated. In Figure 6 the background response is calculated from the control egg concentrations in four studies included in Ohlendorf (2003). This yields an EC 10 of 10.64 mg/kg. In Figure 7 the background respons e is derived from a log-triangular distribution of egg-concentra tions in the range of 0.1–3.0 mg/kg. It yields an E C 10 of 11.96 mg/kg, less than 5% different from the Ohl endorf (2003) value. Overall, when measured against an average hatchability across a range of p ossible background eggs concentration, the differen ce between the EC 10 derived by the biphasic model and EC 10 s derived by other scientifically defensible

methods decreases.

The EPA review of the biphasic approach suggests th at the value of the biphasic model is contextspecific. The EPA has determined it is not necessar y to assume, as a starting point, that the backgrou nd concentration of selenium is the optimum concentrat ion to derive a protective criterion. Application o f the biphasic approach may well best be confined to (a) studies specifically designed to elucidate the biphasic response – that is, studies designed to ch aracterize both deficiency and toxicity; and (b) situations where the distribution of background con centrations is sufficiently well defined to reliabl y estimate the average background response against wh ich a 10 percent reduction (EC 10 ) is measured. Based on the absence of such studies and data respe cting background concentrations in Gilbert Bay, the EPA concludes that it was scientifically defensible , and consistent with CWA requirements, for Utah to derive the Gilbert Bay selenium criterion without r elying on the biphasic approach, and without discarding the data from five of the six studies no ted above. As described above, the logistic curve model that Utah employed is scientifically defensib le and consistent with CWA requirements.13

Figure 6. Biphasic analysis of Heinz et al. (1989) data, assuming background egg concentration equal t o the average of four study controls in Olhendorf (2003).

Figure 7. Biphasic analysis of Heinz et al. (1989) data, assuming background egg concentration a log-t riangular distribution between 0.1 and 3.0 mg/kg Egg Se (mg/kg dw) 0.1 1 10 100Egg Hatchability (proportion)

0.0 0.2 0.4 0.6 0.8 1.0

Se Control

Se Treatments

} 10% 10% { Average Background Hatch (0.17-1.6 mg/kg)

10.64 mg/kg

0.345 mg/kg

Egg Se (mg/kg dw) 0.1 1 10 100Egg Hatchability (proportion)

0.0 0.2 0.4 0.6 0.8 1.0

Se Control

Se Treatments Average Background Hatch (0.1-3.0 mg/kg)

11.96 mg/kg 0.262 mg/kg } 10% 10% {14

Use of Low-Level Effect Concentrations to Preclude the Possibility of Effects to Individual Migratory

Birds.

The Gilbert Bay selenium criterion was derived usin g a scientifically defensible method for calculatin g a tissue-based selenium criterion (i.e., logistic-cur ve fit to the control normalized six study data set ). The U.S. Fish and Wildlife Service (FWS) has suggested that the criterion should be set to an EC 0 , rather

than an EC 10, to assure not only that Utah’s designated use for w ildlife will protected, but also that no individual migratory bird would ever experience any effect from exposure to selenium so long as

selenium does not exceed the criterion concentratio n. The EPA’s position is that it is not possible to calculate a criterion that would achieve that objec tive (i.e., that would preclude the hypothetical possibility that an individual migratory bird could be affected by selenium, so long as selenium remai ns below that criterion concentration).

With the Ohlendorf (2003) approach for deriving a t issue-based criterion, there is no threshold for determining no effect. The only EC 0 is 0.0 mg/kg. Setting the criterion to zero would not be a valid

criterion because selenium is an essential element that is found in all organic material. While FWS ha s put forward 5 mg/kg as an alternative selenium crit erion for Gilbert Bay (and characterized it as an E C 0 ), such a criterion would actually correspond to an EC 3 by their own calculation, and thus (akin to Utah’s 12.5 mg/kg EC 10 criterion) would not preclude the hypothetical pos sibility that an individual migratory bird could be affected by selenium at the criterion concentration. 12 While there is no criterion value that can preclude the possibility of calculating hypothe tical, individual-level effects on migratory birds by one or the other model that has been proposed, an E C 10 signifies a level of effect that cannot be distinguished from zero effect in real-world ecosys tems. There is greater certainty regarding the midrange levels of effect, between 20% to 50% (that is , EC 20 s to EC 50 s), but a corresponding certainty of effect does not exist with respect to an EC 10

.

The determination of an effect at a concentration a t or less than an EC 10 is more dependent on the selection of curve-fitting approach than the toxici ty data set itself. Thus, any conclusion about whet her

an effect would or would not occur to an individual migratory bird, at a criterion set to a low level effect concentration, is heavily dependent on unverifiable assumptions built into the curve-fitting mathemati cs. Given that there is no objective scientific basis t o preclude or establish that selenium produces individual-level effects on migratory birds at the EC 10 level, the EPA disagrees that the hypothetical possibility of such effects weighs against approvin g Utah’s selenium criterion for Gilbert Bay.

Revisions Upon Which EPA is Taking No Action

The EPA does not consider the remaining portions of Footnote (14) (i.e., the provisions other than the portion the EPA disapproved on September 30, 2009), to be new or revised water quality standards requiring a CWA § 303(c) action. This is because th ey do not describe a desired ambient condition of a waterbody to support a particular designated use. T hey neither alter the magnitude, duration, or frequency of the adopted selenium criterion nor any of the other provisions that constitute the water quality standards for Gilbert Bay. Instead they out line research and monitoring programs that Utah wil l implement if triggered by particular concentrations of selenium in egg tissue.

12 Skorupa (2008) used a biphasic equation with the H einz et al. (1989) data to calculate an EC 10 (see above discussion) and an estimated 3.0 mg/kg as a background concentratio n to justify a NOEC of 5 mg/kg. However, when the 5 mg/kg value is evaluated with the biphasic model, it is the EC 3 , not the EC 0 . Therefore, the 5 mg/kg proposed by Skorupa (2008) as a NOEC

would be modeled as a concentration with effect to individual migratory birds. With use of the biphasi c model, an EC 0 is only equal to the optimal background concentration deter mined by the model.15

Accordingly, the EPA is taking no action on these r emaining portions of Footnote (14). Although the EPA is not acting on these remaining portions of Fo otnote (14), the EPA acknowledges them and commends the Board for adopting them. Footnote (14) requires routine monitoring of selenium in eggs collected from the Great Salt Lake and triggers inc reased monitoring and data collection if selenium concentrations exceed 5.0 mg/kg. These actions will provide early detection of potential selenium problems and allow the State to make proactive deci sions to ensure the criterion will be effectively implemented.

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