# AnalysisofAmbientToxicityMeasurement

When used in screening, the ambient toxicity data can identify areas in receiving waters where ambient toxicity exists instream. Attributing such impact to specific point sources (particularly where several sources discharge) may require effluent toxicity testing. Except when used for screening purposes, ambient toxicity measurements must be interpreted with effluent toxicity test data if conclusions are to be drawn concerning changes in toxic effect after discharge. The same species must be used inboth the ambient and the effluent toxicity tests. When analyzing the data, the performance ofthe animals ateach station downstream iscompared to that oftbe animals exposed to receiving water without the effluent of concern in it but containing all other upstream additions. The result is an integration of effects from all contaminants and components and represents not only the toxicity ofthe effluent of concern but also the interactions of itwith other effluents. Where the downstream stations show toxic effect at the concentrations measured astoxic in the effluent toxicity tests, effluent toxicity can be considered to be occurring instream, after discharge. Where the toxic effect decreases from station to station downstream inthe absence offurther dilution, the effluent toxicity isdegrading. Ifthe decay rate israpid (e.g., no toxicity at the closest instream station to the discharge point), the effluent has a nonpersistent toxicity. Where the decay rate is more gradual, toxicity is more persistent. The rate of decay of toxicity together with mixing data allows the regulatory authority to approximate a receiving water toxicity impact area. That impact area can then be compared to the appropriate State water quality standards when establishing control requirements. In some cases, ambient toxicity may increase in relation to effluent toxicity measurements. Either upstream sources oftoxicity exist orsome factor inthe receiving water isreacting with the effluent to increase its toxicity. Again, the pattern and magnitude of change in toxicity should be analyzed. Differences in toxicity levels between stations will reveal what ishappening to the effluent asit is mixed instream and interacts with the constituents ofthe receiving water. Trend analysis inthe raw test data isimportant when interpreting ambient toxicity data. Asused in this context, trend analysis means observing toxic effect asit occurs in the test itself and relating it to what isoccurring instream (plug flow, intermittent discharge, peak toxicity of effluents). Using time-of-travel data or receiving water flow rates and patterns, observe effects on the test organisms from day to day. There may be apattern of mortality that can be linked to discharge events. For example, inthe table the data indicate late mortality at downstream stations on Days 6and 7. Flow rates for the river in this example correlated this mortality to the downstream movement of atoxic slug illegally discharged upstream.

c-4

# APPENDIX 

# D
DURATION AND FREQUENCY

# DURATION AND FREQUENCY

Asdiscussed on pages 7through 13ofthe Guidelines for Deriving Numerical National Water Quality Criteria for theProtection ofAquatic Organisms and TheirUses[1], the format usedtoexpress water quality criteria for aquatic life should take into account toxicological and practical realities. Because of variation in the flows of the effluent and the upstream receiving water aswell asvariation in the concentrations of pollutants in the effluent and inthe upstream receiving water, asimple format, such asspecifying aconcentration that must not be exceeded atany time orplace, isnot realistic. Furthermore, such asimple format does not take into account the fact that aquatic organisms can tolerate higher concentrations of pollutants for short periods oftime than they can tolerate throughout acomplete life cycle. The format that was selected for expressing water quality criteria for aquatic life consists of recommendations concerning concentrations, durations of averaging periods, and average frequencies of allowed excursions. Use of this concentration-duration-frequency format allows water quality criteria for aquatic life to be adequately protective without being asoverprotective aswould be necessary ifcriteria were expressed using asimpler format. Inaddition, this format can be applied directly to hydrological data and to the flow of, and concentrations of pollutants in, effluents using both dynamic and steady-state modeling [2, 3].
Inaquatic life criteria for both individual chemicals and Whole Effluents, the recommended concentrations are the criterion maximum concentration (CMC) and the criterion continuous concentration (CCC). Forindividual chemicals the CMC and CCC are derived using the procedures described by Stephan et al. [1]. Asdescribed in Chapter 3of this TSD, the CMC and CCC for Whole Effluents can be specified generically in terms of toxic units. Alternatively, for aparticular effluent the CMC isspecified in terms ofan acute toxicity endpoint (ATE), which iseither an LC50 oran EC50, and the CCC isspecified in terms of achronic toxicity endpoint (CTE), which iseither ano observed effect concentration (NOEC) or an ICxx, ifthe LC50, EC50, NOEC, and ICxx, were obtained from appropriate toxicity tests conducted on the effluent with sensitive species.
The CCC is intended to be the highest concentration that could be maintained indefinitely in a receiving water without causing an unacceptable effect on the aquatic community or its uses. Any concentration above the CCC, if maintained indefinitely, isexpected to cause an unacceptable effect. Due to the four sources of variation mentioned above, the concentration inthe receiving water will not be constant. Because organisms can tolerate higher concentrations for short periods oftime, itisexpected that the concentration ofapollutant in abody ofwater can exceed the CCC without causing an unacceptable effect if(a) the magnitudes and the durations of exceedances are appropriately limited and (b) there are compensating periods of time during which the concentration isbelow the CCC. These goals are accomplished by specifying aduration of an averaging period over which the average concentration should not exceed the CCC. For example, if the concentration istwice the CCC for one-half the specified averaging period, it must be zero for the rest ofthe averaging period if the average concentration is not to exceed the CCC. Thus, both the magnitude and duration ofan exceedance are limited and there must be acompensating period oftime during the averaging period when the concentration isbelow the CCC. Because exceedences aredefined to bedue to usual variation, most exceedences will besmall, with larger exceedances becoming increasingly rare [1, 2]. Although an exceedance isdefined to occur whenever the instantaneous concentration isabove the CCC, an excursion isdefined to occur only when the average concentration over the duration ofthe averaging period is above the CCC. It is expected that excursions can occur without causing unacceptable effects if (a) the frequency of such excursions isappropriately limited and (b) all other average concentrations are below the CCC. The recommended average frequency of allowed excursions is intended to appropriately limit the frequency ofexcursions. Because excursions arethe highest average concentrations that occurred due to usual variation, all other average concentrations will be lessthan the CCC. Asfor exceedances, excursions that are defined to be due to usual variation will be small, with larger excursions becoming increasingly rare. The duration ofthe averaging period isintended to limit the impact ofexceedances, whereas the average frequency of allowed excursions is intended to limit the impact of excursions. (Note: The words “exceedance” and “excursion” are used slightly differently here than in References 1and 2.) Although spills can impact aquatic communities, they are not considered exceedances or excursions because they are not part ofthe usual variation inthe concentrations of pollutants in receiving water. Inthe Complex Effluent Toxicity Testing Program, eight field studies were conducted to evaluate the use of toxicity tests to diagnose the cause of biological impact. Ambient toxicity measurements were taken over a 7-day period.During two ofthese studies [4, 51spills of pollutants resulted inacute toxicity. This suggests that the impacts caused by spills might be as important asimpacts caused by variation in the compositions and flows of the effluent and the receiving water.
The primary purpose of this appendix isto present the rationale for the recommendations of the U.S. EPA concerning duration and frequency in national water quality criteria for aquatic life. The recommended duration isbased on data from laboratory toxicity tests, whereas the recommended frequency isbased onfield data. With the concurrence ofthe U.S. EPA,States may adopt site-specific criteria, rather than national criteria, in their State standards. Such site-specific criteria may include not only site-specific concentrations, but also site-specific, and possibly pollutant-specific, durations of averaging periods and average frequencies of allowed excursions. If adequate justification is provided, site-specific and/or pollutant-specific concentrations, durations, and frequencies may be higher or lower than those given in national water quality criteria for aquatic life. Asecondary purpose ofthis appendix isto discuss rationales that might be used asa basis for selecting alternative durations of averaging periods and average frequencies ofallowed excursions.



# Duration

In order for this concentration-duration-frequency format to allow water quality criteria for aquatic life to be adequately protective without being unnecessarily overprotective, the duration of the averaging period must allow some exceedances above the CCC without allowing unacceptable effects. Thus, the averaging period must appropriately limit the magnitude and duration ofexceedances and provide compensating periods oftime during which the concentration isbelow the CCC.
Even though only afew tests have compared the effects of aconstant concentration with the effects ofthe same average concentration resulting from afluctuating concentration, nearly all the available comparisons have shown that substantial fluctuations result in increased adverse effects [6-161. Thus, the duration ofthe averaging period must beshorter than the duration ofthe chronic tests onwhich the CCC isbased sothat the averaging period does not allow substantially more adverse effect than would have been caused byacontinuous exposure tothe same average concentration. Life-cycle testswith species such asmysids and daphnids and early life-stage tests with warmwater fishes usually last for 20 to 30days, whereas life-cycle tests with Ceriodaphnids usually last for 7days. Ifthe duration of the averaging period istoo short, however, it will not allow any meaningful exceedances and will, in effect, defeat the purpose of the concept of the averaging period. For example, because few effluents are monitored more often than once aday, an averaging period of24 hours would effectively mean that for most effluents each individual sample that was above the CCC would be considered anexcursion.
Forthe following reasons, a4-day averaging period isrecommended for application of the CCC in aquatic-life criteria for both individual pollutants and Whole Effluents:

l It issubstantially shorter than the 20- to 30-day duration of most chronic tests and issomewhat shorter than the 7-day duration ofthe Ceriodaphnio life-cycle test.

l The results of some chronic tests apparently are due to an acute effect on asensitive life stage that occurs atsome time during the test, rather than being caused by either long-term stressor long-term accumulation ofthe test material inthe organisms. Horning and Neiheisel [17] documented one such situation, and others are probably the cause ofat least some ofthe acute-chronic ratios that are not much greater than unity.

l Forboth endrin and fenvalerate, Jarvinen et al. [18] found that a72-hour exposure caused about the same amount of effect on the growth of fathead minnows in early life-stage tests asdid a 30-day exposure tothe same concentration.

l In some life-cycle tests on effluents with Ceriodaphnids, concentrations of effluents that were a factor of 1.8greater than the CCC caused unacceptable effects in4or5days [S, 19, 201.

l Itisnot soshort asto effectively defeat the purpose ofthe concept ofthe averaging period.

Asdiscussed below, other averaging periods might be acceptable on asite-specific or pollutant-specific basis.

D2Justasthe concept ofexceedances can beapplied to the CCC, italso can be applied to the CMC. Aswith the CCC, the CMC averaging period should besubstantially lessthan the lengths ofthe tests on which the CMC is based, i.e., substantially lessthan 48 to 96hours. Because 4-to8-hour LC5gs areabout the same asthe 96-hour LC50 for some materials [21-271, the duration ofthe averaging period for the CMC should be lessthan 4 hours. One hour isprobably an appropriate duration ofthe averaging period for the CMC because concentrations of some materials that are only afactor oftwo higher than the 96-hour LC50 cause death in one to three hours [25]. Evenwhen organisms do not die within the first hour orso, it isnot known how many organisms might have died due to the delayed effects ofthe short exposure [28-311. Ifthe l-hour average does not exceed the CMC, it isunlikely that the concentration of the pollutant in the receiving water can fluctuate rapidly enough during the hour to cause additional adverse effect. Thus, it seems inappropriate to apply the CMC to instantaneous concentrations.
With adequate justification, the CMC and/or CCC averaging periods may be increased or decreased on asitespecific or pollutant-specific basis. A possible site-specific justification for increasing the duration of the CCC averaging period would be that the variation inthe concentration ofthe pollutant inthe receiving water islow. Where variation isdemonstrated to be consistently low, alonger CMC averaging would be acceptable because the magnitudes and durations of exceedances above the CCC would be limited. Apossible pollutantspecific justification for a longer averaging period would be that the LC50 decreases substantially as the length ofthe exposure increases. Forexample, an B-hour averaging period might bejustified for the CMC ifit were shown that 24-hour exposures of avariety of sensitive species resulted in 96-hour LC5gs that were substantially above the 96-hour LCjos obtained from continuous exposure to aconstant concentration for 96 hours.
Insome situations the duration ofthe averaging period does not have to be stated explicitly because one can be implicitly defined using an uptake rate and adepuration rate. Forexample, if it isknown that aspecific concentration of a pollutant in the whole body or in aparticular tissue of an important aquatic species will result inan unacceptable effect on the survival, growth, and/or reproduction ofthat species, and ifapplicable that species or tissue, the only additional information needed to allow calculation of an excessively high estimate of the total maximum daily load from the record of daily flows is the allowed frequency of exceedances of the concentration in the aquatic species. Thus, this approach can be used whenever the following are available:

l Arecord ofdaily flows ofthe body ofwater, preferably for more than 10years

l Amaximum acceptable concentration inthe whole body or inaparticular tissue ofanaquatic species

l Uptake and depuration rates that are applicable to that pollutant in the whole body or tissue ofthat species

l Anallowed frequency ofexceedances ofthe maximum acceptable concentration.

This approach islikely to be especially useful when an exposure causes delayed effects that are considered unacceptable. Forexample, itmight befound inatestthat nofish die during a2-day exposure ofrainbow trout to apollutant but 50 percent of the fish die within 4weeks of being transferred to clean water, whereas no comparable control fish die. Ifvalues are available for the concentration of the pollutant in the fish at the end ofthe 2-day exposure and for the uptake and depuration rates, these data could be usedwith aflow record for a river to determine how often aspecified constant daily input of the pollutant to the river would have resulted inexceedances ofthis concentration and therefore the death of rainbow trout. Regardless ofwhat averaging periods are used, exact calculation of the number of excursions would require continuous monitoring of the concentration in the receiving water, which isnot feasible in most cases. A valid alternative would be to useastatistically designed monitoring program and astatistical interpretation of the measured concentrations. The l-hour averaging period for the CMC would imply that the samples analyzed should be l-hour composites; the 4-day averaging period would imply that concentrations in all samples obtained within any 4-day period should be averaged, preferably using atime-weighted average. Ifinformation is available concerning the discharge pattern of a particular effluent, it might be possible to design a monitoring program that isspecifically appropriate for that effluent.

D3Unless critical species are especially sensitive to particular toxicants, most excursions of criteria should have minor impacts on aquatic communities. However, whereas excursions above the CCC will probably reduce growth and reproduction, excursions above the CMC will probably cause death and other severe acute effects. Inaddition, special care should be exercised when many outfalls exist in asmall segment ofareceiving water, because if low flow causes an excursion for one discharge, that same low flow will probably also cause excursions forother discharges atthe sametime. Several “minor” excursions might thus add uptoa“major” one. 

# Frequency

The purpose ofthe average frequency ofallowed excursions isto provide anappropriate average period oftime during which the aquatic community can recover from the effect ofanexcursion and then function normally for aperiod oftime before the next excursion. The average frequency isintended to ensure that the community is not constantly recovering from effects caused by excursions of aquatic-life criteria. Because most regulated discharges are to flowing water (lotic) systems, this discussion will emphasize discharges to rivers and streams rather than to lakes, ponds, reservoirs, and estuaries.


## 6emralCunsideratlons forSettln#Frequency

## wttbWbicbExcursions ofCriteriaMayOccur Not long ago ecological communities were thought to be largely inequilibrium and their structure and function determined primarily by internal interactions between species, such as competition and predation. Communities were considered to be analogous to “super-organisms,” with close parallels to organisms in their response to stress and in “health.” Current understanding isthat external factors, including disturbances, often play amajor role inthe structure ofcommunities [32, 331. Thefrequency ofdisturbance affects acommunity not only by decreasing the fitness of component species, but also by causing a natural selection of species or phenotypes having characteristics that allow them to tolerate or even thrive under the disturbance regime. Natural disturbances such asfloods and droughts arecommon inlotic systems [32] and vary inintensity not only between headwater streams and large rivers, but also between similar sized lotic communities in different climatic regions. Rather than requiring more time to recover from the effects of additional anthropogenic disturbances, lotic communities with high natural background disturbance frequencies are actually predisposed to recover more rapidly because only species that are able to recolonize and reproduce quickly, or perhaps to avoid disturbances, can persist there [34-371. This does not imply that they also are more resistant to novel anthropogenic disturbances with which they have had no previous evolutionary experience; itonly implies that they are predisposed to recover quickly once the disturbance isgone. The question then ishow frequently can aquatic communities experience these additional disturbances (excursions of criteria) without being unacceptably affected.
In an extensive review of the published literature, Niemi et al. [38] reviewed the published literature and identified more than 150 casestudies offreshwater systems inwhich some aspect ofrecovery from the impact of adisturbance was reported. Acasestudy was used only ifthe disturbance caused adeath or displacement of organisms. This restriction was necessary because itwas rarely possible to determine ifanevent was outside the normal intensity range (acommon alternate definition of disturbance), mainly because it isusually difficult to define the normal intensity range. Italso permitted the inclusion of natural aswell asanthropogenic events. Approximately 80 percent ofthese systems were lotic, and the remainder were lentic (lakes and ponds). The impacts were due to such disturbances as persistent and nonpersistent chemicals, logging, flooding, channelization, dredging, and drought. Reported endpoints for recovery were sparse for phytoplankton, periphyton, and macrophytes, but were numerous for macroinvertebrates and fishes. Because more than one recovery endpoint was reported for most studies, the number ofendpoints greatly exceeded the number ofcase studies. For short-term (nonpersistent) disturbances, approximately 85 percent of all macroinvertebrate endpoints indicated recovery in lessthan 2years. Macroinvertebrate biomass, density, and taxonomic richness recovered in lessthan 1year for approximately 95 percent of reported endpoints. Dipterans (flies, mosquitos, midges, etc.), which generally have short generation times or high dispersal ability, recovered most rapidly, whereas stoneflies and caddisflies recovered least rapidly. Fishesrecovered in2years or lessfor over 85 percent ofreported endpoints. However, asdiscussed below, important exceptions did occur.
Most excursions of criteria will be minor and their impacts will therefore be difficult to detect. Although most disturbances inthe above casestudies caused more severe impacts than most criteria excursions areexpected tocause, CMC excursions will result indeath ofsome organisms. These data indicate that asageneral rule, the purpose ofthe average frequency ofallowed excursions will be achieved ifthe frequency issetat once every 3 years on the average. Excursions ofthe CCC are more difficult to evaluate because nonlethal excursions could not be evaluated from the data used by Niemi et al. [38]. Itisreasonable to expect, however, that cumulative effects from too frequent excursion of the CCC also will result in unacceptable degradation of lotic communities.


## ConsiderationsferPK~OS/R# Sit@-specificIncmasesorDecreasesintb8Average

## FrequencyofAllowed Excursions Although an average frequency of one criterion excursion every 3years should usually be protective of lotic communities, more frequent excursions might be acceptable incertain situations. Sedell et al.[39] have shown that lotic systems with refugia (areas of refuge) such aswell-developed riparian zones, connected flood plains and meanders, snags, etc., recover more rapidly from disturbances than segments without such refugia, because organisms are better able to avoid disturbances and return or repopulate. However, many ofthese refugia are likely to be most restricted and vulnerable during the low-flow periods when criteria excursions also are most likely to occur. Evidence of action to preserve refugia, particularly during low-flow periods, or to create or restore them, might begrounds fordemonstrating that anexcursion frequency ofmore than once every 3years on the average isacceptable. Schlosser [36] found that lower-order (i.e., headwater) streams, because of their natural high variability, contain communities consisting of species that have short life cycles and/or high dispersal ability and can recover from major disturbances inayear oreven less. Thus, many lower-order streams, particularly those for which refugia are available, may be able to tolerate somewhat higher excursion frequencies, unless other considerations are important. Forexample, discharges to lower-order streams sometimes constitute alarge fraction ofthe stream flow for most ofthe year. Although lower-order streams are naturally highly variable and can therefore tolerate higher disturbance frequencies, the converse istrue for higher-order lotic streams for at least two partially related reasons: (1) segments with tributaries draining alarge watershed will be buffered from the effects of localized droughts ina portion of the watershed, and will therefore experience a less severe natural disturbance regime, and (2) organisms inhabiting these segments will therefore not beadapted todisturbances that are asfrequent orsevere asthose in lower-order segments. Fishin particular will be larger and have longer generation times in larger streams and rivers. Consequently, it will take longer for these populations to reproduce and regain predisturbance densities and size class distributions. Schlosser [36] suggests that, based on such life-history characteristics, fish communities inlarger rivers might take 20 to 25years to re-establish the predisturbance age and sizestructure oftheir component populations after asevere disturbance such asamajor drought orspill.
Extreme cases in which recovery has taken much longer than 3years usually involve spills of persistent chemicals orsevere habitat modification, such asstream channelization or clear-cutting ofawatershed [38]. If the chemical contaminant isnot widespread, recovery islimited primarily by the rate of disappearance ofthe chemical rather than by strictly ecological processes. Widespread contamination can affect recovery by increasing the distance over which recolonizers must travel. Watershed clear-cutting reduces the input of organic matter that provides the food base ofstreams inforested watersheds and also provides woody debris and snags that serve asrefugia. Channelization and dredging reduce the in-stream habitat diversity and thereby decrease refugia. Inaddition tothese anthropogenic disturbances, multiple excursions during adrought, due to low-flow conditions, can result in a severe cumulative impact on sensitive species even if the individual excursions are small. Special measures, such asplant shutdowns, might be required in extreme cases. Finally, severe chemical spills, which cannot be regulated but which will occur in any highly industrialized river segment, will affect aquatic life over alarge area. Ifmaintenance of long-lived fish species inthese segments isdesired, recovery periods upto 25years may benecessary.
Based on the above considerations, recovery periods longer than 3years may be necessary after multiple minor excursions or after asingle major excursion orspill during alow-flow period in medium-to-large rivers, and up to 25years where long-lived fishspecies areto be protected. Even longer times may be necessary asthe sizeof the affected area orthe persistence ofthe pollutant increases.

# CalculationofDesignConditions

The use of aquatic-life criteria for developing water quality-based permit limits and for designing waste treatment facilities requires the selection of an appropriate wastetoad allocation model. Dynamic models are preferred for the application of aquatic-life criteria in order to make best use ofthe specified concentrations, durations, and frequencies. Ifdynamic models cannot be used, then an alternative issteady-state modeling. Because steady-state modeling isbased on various simplifying assumptions, it islesscomplex, and might be less realistic, than dynamic modeling.
An important step in the application of steady-state modeling to streams iscalculating the design flow. The procedures outlined inthe EPAdocument Technical Guidance Manual forPetforming WasteLoadAllocation, Book6, Design Conditions: Chapter I, Steam Design FlowforSteady-State Modeling. (U.S. EPA1986) are recommended for calculating design flows for rivers and streams. States may use other methods so long asthe methods are technically defensible. The document discusses and recommends two methods for determining design flows, the hydrologically based method and the biologically based method, and the flows that should be used for the CCCand CMC for both methods.
The hydrologically based design flow method ispresently used by many States. It isbased on selecting and identifying an extreme value, e.g., the 7QlO flow. The underlying assumption of this method isthat the design flow will occur Xnumber oftimes inYyears. Thus, this method limits the number ofyears inwhich one ormore excursions below the design flow canoccur. The method hastwo advantages: (1)the log-Pearson Type III flow estimating technique or other extreme value analytical techniques that are used to calculate flow statistics from daily flow data are consistent with past engineering and statistical practice, and (2)the U.S. Geological Survey provides technical support for this method. The disadvantage of this method isthat it is essentially independent of biological considerations. Design flows calculated using this method might allow more orfewer excursions than once every 3years on the average. tnaddition, it isdifficult to usesite-specific durations and frequencies with this method. For toxic wasteload allocation studies in which the hydrologically based method isused, EPArecommends the use of the 1QlO flow asthe design flow for the CMC and the 7410 asthe design flow forthe CCC.
The biologically based design flow method was developed by the U.S. EPA Office of Research and Development and directly usesthe averaging periods and frequencies specified inthe aquatic-life water quality criteria for individual pollutants and Whole Effluents for determining design flows. The method is an empirical iterative convergence procedure that includes the calculation of harmonic means of the flow to determine the total number ofexcursions. The method makes exact useofwhatever duration and frequency are specified forthe CMC and CCC. These might be 1dayand 3years forthe CMC and4days and 3 years forthe CCC orsite-specific durations and frequencies.
Thetwo methods were used on approximately 60different rivers tocompare the hydrologically based 1QlO and 7410 design flows with the biologically based 1-day/3-year and 4-day/3-year design flows. For most of the rivers the hydrologically based design flows resulted inmore than the allowed number ofexcursions. Forsome ofthe rivers, the 1QlO and 7410 allowed substantially more orfewer excursions than the intended number of excursions. Because the biologically based method calculates the design flow directly from the national or site-specific duration and frequency, italways provides the maximum allowed number of excursions and never provides more excursions than allowed.
EPAprovides software tools to calculate both types of design flows via the STORET environment on its NCCIBM mainframe. Biologically based design flows can be calculated using the program DFLOW [40]. The hydrologically based design flows can be calculated using FLOSTAT or DFLOW; the latter uses asimplified version of the log-Pearson Type III method. Both programs access the STORET Flow file that contains daily flow records for U.S.Geological Survey gaging stations. They areeasyto useand the usersimply needs to knowthe identification number of the gaging station. To obtain further information on the STORET environment and the programs, contact:
Mr. Thomas Pandolfi U.S. Environmental Protection Agency Office ofWater Regulations and Standards (WH-553) 401 MStreet, S.W. Washington, D.C. 20460 (202) 382-7030
The methods described above usedaily flow data to determine design ftow, but they do not consider any other physical or chemical condition that might affect toxicity. EPAhas prepared asupplementary method and a software tool named DESCON that incorporate such supplemental water quality parameters astemperature, pH, alkalinity, hardness, and dissolved oxygen to determine design conditions. Note that DESCON takes into account such things aseffluent variability, which DFLOW does not take into account. The method and software are described intwo documents available from the Assessment and Watershed Protection Division ofthe Office ofWater Regulations and Standards-Technical Cuidonce on Supplementary Stream Design Conditions forSteady StoreModeling [3] and DESCON UsersManual [40].
The supplementary method isconsistent with the hydrologically and biologically based methods described above. Itsimply extends them to include other conditions besides streamflow. The advantage of considering multiple conditions isthat the worst-case conditions necessary to protect water quality criteria might not occur when the streamflow islow; e.g., low DO orhigh temperatures might occur attimes other than when the flow is low.
This supplementary method can be used for five pollutant categories with the physical-chemical parameters described above. The pollutant categories are general toxicant, ammonia, heavy metals (Cd, Cr+3, Cu, Pb, Ni, Zn), pentachlorophenol, and ultimate oxygen demand.
The software tool to facilitate this method iscalled DESCON. It ison EPA’s IBM mainframe and isavailable through the STORET environment. DESCON accesses the STORETflow file for the daily flow record and the water quality file for data on the physical-chemical parameters. Options are available to the user ifthe area of concern hasnoflow record or ifnowater quality data areavailable.

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### (Sulmoguirdnen). Environ.Toxicol.Chem.2:225-38. Seim, W.K., L.R. Curtis, S.W. Glenn, and C.A. Chapman. 1984. Growth and Survival of Developing Steelhead Trout (Salmo 

### guirdneri) Continuously orIntermittently Exposed to Copper. Can.1.Fish.Aquat. Sci.41:433-38. Curtis, L.R., W.K. Seim, and C.A. Chapman. 1985. Toxicity of Fenvalerate to Developing Steelhead Trout Following Continuous or Intermittent Exposure. 

### /.Toxicol.Environ.Heulth 15:445-57. Siddens, L.K.,W.K. Seim, L.R.Curtis, and C.A. Chapman. 1986. Comparison ofContinuous and Episodic Exposure to Acidic, Aluminum-Contaminated Waters of Brook Trout 

### (Salvelinusfontinalis). Can.I.fish. Aquat.Sci.43:2036-40. Brooks, A.S., D.C. Szmania, and M.S. Goodrich. 1989. A

### Comparison of Continuous 

### and Intermittent ExposuresofFourSpeciesofAquaticOrganismstoChlorine. Wisconsin-Milwaukee, WI. Center for Great Lakestudies, University of
Horning, W.B., and T.W. Neiheisel. 1979. Chronic Effect of Copper on the Bluntnose Minnow, 

### Pimephalesnotutus (Rafinesque). 

### Arch.Environ.Contam.Toxicol. 8545-52. Jarvinen, A.W., O.K. Tanner, and E.R.Kline. 1988. Toxicity of Chlorpyrifos, Endrin, or Fenvalerate to Fathead Minnows Following Episodic orContinuous Exposure. 

### Ecotoxicoi.Environ.Safety15:78-95.19.

20.

21.

22.

23.

24.

25.

26.

27.

28.

29.

30.

31.

32.

33.

34.

35.

36.

37. Mount, D.I., A.E. Steen, and T.J.Norberg-King, eds. 1985. 

### VulidityofEffluentondAmbient Toxicity 

### Jesting for Predicting Biological 

### lmpoct on

### FiveMile Creek, 

### Birmingham,Alabama. EPA/600/8-85/015. National Technical Information Service, Springfield, VA. Mount, D.I., T.J.Norberg-King, and A.E.Steen, eds. 1986. Vulidity ofEffluent

### ondAmbient Toxicity Testsfor Predicting Biological Impact, 

### Nuugutuck River, 

### Waterbury, Connecticut. EPA/600/8-86/005. National Technical Information Service, Springfield, VA. Cardweli, R.D., D.G. Foreman, T.R. Payne, and 0.1.Wilbur. 1976. 

### AcuteToxicity 

### ofSelectedToxicuntstoSix SpeciesofFish. EPA-600/3-76-008. National Technical Information Service, Springfield, VA. Thurston, R.V., G.R. Phillips, R.C. Russo, and S.M. Hinkins. 1981. Increased Toxicity of Ammonia to Rainbow Trout 

### (Salmogoirdnero Resulting from Reduced Concentrations of Dissolved Oxygen. Can. 1. Fish.Aquat. Sci.38:983-88. Brooke, L.T., 0.1.Call, D.L. Geiger, and C. E.Northcott, eds. 1984. Acute Toxicities ofOrganic 

### Chemicalto Fathead Minnows 

### (Pimephulespromelus), volume 7. Center for Lake Superior Environmental Studies, University ofWisconsin-Superior, Superior, WI. Geiger, D.L., C.E.Northcott, D.J.Call, and L.T. Brooke, eds. 

### 1985. AcuteToxicities 

### ofOrganicChemicalsto fathead Minnows (Pimephules 

### promelus), volume 2. Center for Lake Superior Environmental Studies, University ofWisconsin-Superior, Superior, WI. Bailey, H.C., D.H.W. Liu, and H.A. Javitz. 1985. “Time/Toxicity Relationships in Short-Term Static, Dynamic, and Plug-Flow Bioassays.” In 

### AquaticToxicologyand 

### HuzordAssessment: Eighth Symposium. ASTM STP891. Ed. R.C. Bahner and 0.1. Hansen. American Society for Testing and Materials, Philadelphia, PA. Geiger, D.L., S.H. Poirier, L.T. Brooke, and D.J.Call, eds. 1986. Acute Toxicities of

### Organic Chemicals to Fathead Minnows 

### (Pimepholespromelas), volume 3. Center for Lake Superior Environmental Studies, University ofWisconsin-Superior, Superior, WI. Geiger, D.L., D.J.Call, and L.T. Brooke. 

### 1988. Acute Toxicities 

### ofOrganic Chemicals 

### toFatheadMinnows (Pimephales promelas), volume 4. Center for Lake Superior Environmental Studies, Univerisity of Wisconsin-Superior, Superior, WI. Abel, P.D. 1980. Toxicity of Hexachlorocyclohexane (Lindane) to 

### Commorus pulex: Mortality in Relation to Concentration and Duration of Exposure. freshwater Biol. lo:251 -59. Abel, P.D. 1980. A New Method for Assessing the Lethal Impact of Short-Term, High-Level Discharges of Pollutants onAquatic Animals. 

### Prog.Wut.Tech.13:347-52. Abel, P.D., and S.M. Garner. 1986. Comparisons of Median Survival Times and Median Lethal Exposure Times for

### Commuruspulex Exposed toCadmium, Permethrin and Cyanide. Wt. Res.20:579-82. Heming, T.A., A. Sharma, and Y. Kumar. 1989. Time-Toxicity Relationships in Fish Exposed to the Organochlroinepesticide Methoxychlor. Environ. Toxicol. Chem. 8:923-32. Resh, V.H., A.V. Brown, A.P. Covich, M.E. Gurtz, H.W. Li, C.W. Minshall, S.R. Reice, A.L. Sheldon, J.B. Wallace, and R.C.Wissmar. 1988. The Role of Disturbance in Stream Ecology. /.North Am. Benlhol. sot. 7:433-55. Reice, S.R.,R.C.Wissmar, and R.J.Naiman. 1989. “The Influence ofSpatial and Temporal Heterogeneity and Disturbance Regime onthe Recovery ofAnimal Communities in Lotic Ecosystems.” Ed. I.D. Yount and G.J. Niemi. In Recovery of Lotic Communities and Ecosystems from Disturbonce: Theory and 

### Applicutions. Environ. Management (submitted). Steinman, A.D. 1989. “Recovery of Lotic Periphyton Communities After Disturbance. Ed. 1.0.Yount and G.J.Niemi. In Recovery ofLotic Communities ond Ecosystems from 

### Disturbonce: Theory and Applications. Environ. Management (submitted). Wallace, J.B. 1989. “Recovery of Lotic Invertebrate Communities from Disturbance.” Ed. 1.0. Yount and G.J.Niemi. In Recovery ofLotic Communities 

### ondEcosystems from Disturbonce: 

### TheoryondApplications. Environ. Management (submitted). Schlosser, I.J. 1989. “Environmental Variation, Life History Attributes, and Community Structure in Stream Fishes: Implications for Environmental Management and Assessment.” Ed. J.D.Yount and G.J. Niemi. In Recovery of Lotic Communities and Ecosystems from Disturbance: Theory 

### andApplications. Environ. Management (submitted). Poff, N.L., and J.V.Ward. 1989. “The Physical Habitat Template of Lotic Systems: Recovery in the Context of Historical Pattern of Spatio-Temporal Heterogeneity.” Ed. J.D.Yount and G.J. Niemi. InRecovery of iotic Communities and Ecosystems from Disturbance: Theory and Applications. Environ. Management (submitted). 38. Niemi, C.J., P.DeVore, N. Detenbeck, D.Taylor, I.D. Yount, A. Lima, j. Pastor, and R.J.Naiman. 1989. “An Overview ofCase Studies on Recovery ofAquatic Systems from Disturbance.” Ed.J.D.Yount and C.J.Niemi. InRecovery of Lotic Communities and Ecosystems from Disttirbance: Theory and Applications. Environ. Management (submitted). 39. Sedell, J.R.,F.R.Hauer, C.P. Hawkins, and j.A. Stanford. 1989. “The Role of Refugia in Recovery from Disturbance: Modern Fragmented and Disconnected RiverSystems.” Ed. J.D.Yount and C.J.Niemi. In Recovery of Lotic Communties and Ecosystems from Disturbance: Theory and Applications. Environ. Management (submitted). 40. Rossman, LewisA. DFLOWUsersManual.

DlO

# APPENDIX 

# E
LOGNORMAL DISTRIBUTION AND PERMIT LIMIT DERIVATIONS

# LOGNORMAL DISTRIBUTION AND PERMIT LIMIT DERIVATIONS

Introduction

This appendix provides supporting information for the statistical methodology used in permit limit calculations. The methodology described in this appendix applies to many types of data including data that are used to develop both technology-based and water quality-based permit limits. The appendix isdivided into two sections. The first section gives an overview of permit limits: the derivation ofwater quality-based limits and the consistency among different permit limits. The second section describes the statistical methodology for the normal distribution, the lognormal distribution, the delta-lognormal distribution, methods of checking distributional assumptions, and correlation. This section also provides guidance on the application ofeach distribution to permit limits. Tables E-1, E-2,and E-3atthe end ofthe appendix summarize the permit limit calculations. This appendix describes the statistical methodology for three distributions that are often used in determining permit limits. Other distributions can be used, and this topic isdiscussed inthe subsection, Other Distributions.



# Section1:Overview ofPermitLimits

Two types of permit limits are contained in the effluent guidelines regulations: daily maximum limits and monthly average limits. The daily maximum permit limit isthe maximum allowable value for any daily sample. The daily maximum limits are usually based on the 99th percentile ofthe distribution ofdaily measurements. The monthly average permit limit isthe maximum allowable value for the average ofall daily samples obtained during 1month. Monthly average limits are in most cases based on the 95th percentile ofthe distribution of averages of daily values.
The following two subsections discuss the derivation ofwater quality-based limits and the consistency among different permit limits.


## DerivationofWaterQuality-basedlimits Water quality-based limits arederived from the required treatment system performance necessary to comply with the wasteload allocation (WLA). Technology-based effluent limits are derived from treatment system performance. The mathematical expressions forwater quality-based limits arethe same asthose for technologybased effluent limits; the major difference isthat the means and standard deviations in those expressions are derived from the WLA. This topic isdiscussed in Chapter 5. 

## ConsistencyAmongDifferent 

## Permitlimits The current Technical Support Document for Water Quality-based Toxics Control (TSD) procedures provide consistency among different permit limits. The stringency of permit limits is independent of monitoring frequency and isdetermined entirely by the WLA and permit limit derivation procedures. The daily maximum limit is constant regardless of monitoring frequency. The numerical value of the monthly average limit decreases asmonitoring frequency increases only because averages become lessvariable asthe number ofvalues included inthe average increases. Forexample, anaverage based on 10samples islessvariable than anaverage based on 4samples. This phenomenon makes monthly average permit limits based on 10samples appear tobe more stringent than the monthly limit based on 4 samples. A permittee performing according to the WLA specifications will in fact be equally capable of meeting either of these monthly average limits when taking the corresponding number ofsamples. The stringency ofthe TSD procedures, accordingly, isconstant across monitoring frequencies.

E-l

# Section2:StatisticalMethodology

The statistical procedures that are used in permit limit development involve fitting distributions to effluent data. The estimated upper percentiles ofthe distributions form the basis of the limits. This section describes the statistical methodology applied to permit limits in the following subsections: the normal distribution, the lognormal distribution, the delta-lognormal distribution, methods of checking distributional assumptions, and correlation. Before discussing these topics several definitions are made for notation, assumptions, coefficients of variation, and variability factors.

Inthe calculations in this appendix, natural logarithms (i.e., logarithms to the base e), denoted by In(x), are used. The calculations can be modified to use logarithms to the base 10by replacing loglO for In(x) inthe formulas.


### Assmptlons The distribution fitting methods assume that the daily measurements are independent, uncorrelated observations.
The fundamental assumptions underlying the discussion on calculating limits are:

l Daily pollutant measurements are approximately lognormally distributed for values above the detection limit

l Maximum n-day monthly averages for n 5 10 are approximately lognormally distributed above the detection limit

l Maximum n-day monthly averages for n>10are normally distributed.

Recommendation of the use of the lognormal distribution for daily pollutant measurements is based on practical rather than theoretical consideration. Usually environmental data sets possess the basic lognormal characteristics of positive values and positive skewness. In addition, the lognormal distribution isflexible enough to model a range of nearly symmetric data. Furthermore, in comparison to other positive valued, positively skewed distributions that could be used to model environmental data, the lognormal is relatively easytouse.
When lognormal data are log transformed, the properties of the normal distribution apply to the transformed data. The section on statistical methodology describes the properties of the normal distribution and its relationship to the lognormal distribution. The delta-lognormal distribution is a generalization of the lognormal distribution and may be used to model data that are amixture of non-detect measurements with measurements that are lognormally distributed. Indelta-lognormal procedures, nondetect values are weighted in proportion totheir occurrence inthe data.
In determining permit limits based on averages (e.g., monthly average permit limits), adistribution should be used that approximates the distribution of an average of pollutant measurements. The lognormal distribution can be used for approximating the distribution of averages for small sample sizes where the individual measurements are approximately lognormally distributed. For larger sample sizes, apowerful statistical result, called the Central Limit Theorem, provides theoretical support for determining limits based on averages of individual measurements. According to the Central Limit Theorem, when the sample sizenislarge enough, the average ofthe nsample values will be approximately normally distributed regardless of the distribution ofthe individual measurements. The section on statistical methodology provides procedures and guidance for calculating averages for both small and large samples sizeswhere the individual measurements are lognormally distributed.

E-2The shape ofthe observed data isthe keyfactor inevaluating adistributional model. Forenvironmental data the lognormal distribution is usually appropriate. The critical question in a given situation is how well a particular distribution models the shape ofobserved data. Although the lognormal does not provide anexact fit in all cases, it usually provides an appropriate and functional fit to observed environmental data. Graphical displays and goodness-of-fit tests, asdescribed inthe subsection, Other Distributions, may be used asaguide in verifying assumptions and selecting adistribution.


## CoefflcimtsufV8ffatiae The coefficient ofvariation (denoted by “CV”) isthe ratio ofthe standard deviation to the mean. Thus, the CV isadimensionless measure of the relative variability of adistribution. Estimates ofthe CVcan be used when the actual CV cannot be calculated or ifthe available data setsfor calculating the CVare small. Insuch cases, different values for the CVshould be used inthe permit calculations to assessthe effect ofthe CVon the final permit limit. Typical values ofthe CVfor effluent data usually range from 0.2 to 1.2. The CV isameasure of the relative variation in observed data. In many cases, changes in the CVwill have little impact on the final permit limit. Inassessing the sensitivity ofthe permit limit to the CV, the calculations may include CV =0.6 asa conservative estimate (assumes relatively high variability). Ifthe final permit values vary greatly with different CVvalues either oftwo approaches may be used. The first approach isto useaconservative estimate of the CV that assumes relatively high variability (e.g., CV = 0.6) in the final permit limit. The second approach isto collect additional data to obtain amore definitive value for the CV. 

### viffi8biiitjf 

## Factors An important component of the process used by the Environmental Protection Agency (EPA) for developing technology-based limits are variability factors. The variability factor isthe ratio of alarge concentration level ofapollutant to the average level determined from that particular plant. The ratio expresses the relationship between the average treatment performance level and large values that would beexpected tooccur only on rare occasions in awell-designed and operated treatment system. Such factors are useful insituations where little data areavailable to characterize the long-term performance ofaplant.
In cases where only asmall number of observations are available from a plant, EPAhas been reluctant to estimate avariability factor. Inthe Organic Chemicals, Plastics, and Synthetic Fibers (OCPSF) rulemaking [l], a minimum ofseven daily observations from aplant, with at least three ofthe seven above the detection limit, was established for calculation of a plant level priority pollutant variability factor. However, EPA has not established aminimum number of observations required for calculating variability factors for all pollutants in all industries.
The calculations for variability factors for the daily maximum and the monthly average are included in the discussion of the different distributions below.



# NormalDistribution

The normal distribution plays acentral role in the methods described in this appendix. In most cases, the normal distribution isnot an appropriate model for individual pollutant measurements; however, the normal distribution isrelated to the lognormal distribution that isused to establish many permit limits. In most cases, the simple logarithmic transformation of effluent and water quality data results in data distributions that are normally distributed. Such data are referred to asbeing lognormally distributed. When lognormal data are log transformed, the properties of the normal distribution apply to the transformed data. Since the normal and lognormal distributions are related in a straightforward manner, the methods of analysis for normal and lognormal data also are easily related. The normal distribution isdescribed below and isfollowed by a discussion ofthe lognormal distribution and its relationship to the normal distribution.

E-3Figure E-l. Normal Probability Distribution
The normal probability distribution isencountered in anumber ofapplications. The bell-shaped curve ofthe normal distribution is shown above in Figure E-l. Excellent introductions and reviews of the normal distribution are found in numerous statistical, engineering, and scientific texts, asfor example in Reference 2. Only abrief review isgiven here.
Asample of independent observations, denoted by xl, x2,...,Xk, from anormally distributed population can be usedtoestimate the mean, p,and variance, cr2,according tothe formulas below: 

## 6 = estimated mean
= z[Xi] /k, 1 I i5 k 

### 3 

## = estimated variance

= Z[(xi-b)2]/(k-l),1 <ilk 6 = estimated standard deviation
= (&2)1/2 c^v = estimated coefficient of variation

The characteristics ofthe normal distribution arethe range isdefined for positive and negative values, and the frequency curve isbell-shaped and symmetric about the mean. In most cases, the normal distribution isnot an appropriate model for the distribution of individual pollutant measurements. Environmental data rarely are symmetric, which isafundamental property ofthe normal distribution. Inaddition, the normal distribution is defined over arange that includes negative values while pollutant measurements are restricted to nonnegative values. Thus, fitting adata setto anormal distribution allows for the possibility, however small, of observing negative values. The lognormal distribution, or any positive valued distribution, is not defined for negative values and thus avoids assigning any probability to negative values. 

### DailyMaMaa Pem/tLMts BasedontbeNormalDistrihtiun Fordata setswhich have the characteristics ofthe normal distribution, the daily maximum permit limits can be calculated. The upper percentile daily maximum permit limits for the normal distribution are calculated using the quantity zp, the standardized Z-score for the pth percentile ofthe standardized normal distribution (i.e., normal distribution with mean =0,and variance =1). Forexample, the Z-score for the 95th percentile is1.645. Z-scores are listed intables for the normal distribution (in most statistical textbooks and references). The pth percentile daily maximum limit isestimated by:
4, = pth percentile daily maximum limit

= Ji+zp 6.

E-4Forexample:
g.95 = 95th percentile daily maximum limit

= i+1.645& R.99 = 99th percentile daily maximum limit

= r;+2.326 6
Note:
+,, = 1.645 k = 2.326
The daily variability factors (denoted byif,) are estimated by: Daily maximum 95th percentile VF1=A.95 /i Daily maximum 99th percentile VF1=A.99 /c 

## HWbtyAv#ra#oh&f LWfs8md onfb##omwlDistrlbutIm The normal distribution can be used to model the averages ofthe individual measurements for awide range of circumstances. Although the normal ,distribution usually isnot an appropriate model for individual pollutant measurements, the averages ofthose individual measurements can often be modeled bythe normal distribution. This subsection explains the theory behind using the normal distribution for averages and provides the general formulas.
A powerful statistical result, called the Central Limit Theorem, provides theoretical support for determining limits based on averages of individual measurements. According to the Central Limit Theorem, when the sample size n islarge enough, the average of the nsample values will be approximately normally distributed regardless of the distribution of the individual measurements. Indetermining permit limits, the calculations incorporate the number of samples that will be required for monitoring purposes during the specified time period (usually a month). For the purposes of permit writing, monitoring sample sizes greater than 10 are recommended to be sufficiently “large enough” to assume the sample average is approximately normally distributed. The above formulas can be modified for finding the estimated mean and variance for the average from asample of size n(e.g., for 14-day monthly average, n= 14 samples during the month for monitoring purposes). The parameters p,, and CJ~denote the mean and variance, respectively, of the distribution of the average ofnvalues. The estimates ofthe n-day average and the variance ofthe n-day average aredenoted by fin and$?,, respectively. 

# r; = 62 

# = in= = .2 0” =

= i& =

= CGn =

= estimated mean ofdistribution ofX estimated variance of distribution ofX mean ofdistribution ofthe n-day monthly average 3 variance of distribution ofthe n-day monthly average 

### i52/n standard deviation 

# (62,)‘/2

coefficient of variation 47 /fin.

E-5The upper percentile limits are: A., = pth percentile n-day monthly average limit

= in +Zp6n
where zp isthe pth percentage point ofthe standard normal distribution.

Forexample: 

## R

.95 = 95th percentile n-day monthly average limit

= jIi,,+ 1.645 &, k+99 = 99th percentile n-day monthly average limit

= (l, +2.326 b,,
Note: Zg5 = 1.645 Zgg = 2.326.
The monthly average variability factors (denoted byVF,) areestimated by:
Monthly average 95th percentile VF, =2.95 /b Monthly average 99th percentile VF, =k.99 / $

The above discussion ofthe normal distribution can be modified for data from the lognormal distribution. The next subsection explains the modifications.


# LognormalDistribution
Experience hasshown that daily pollutant discharges are generally lognormally distributed. The distributional fit of the data varies somewhat from application to application, but not enough to alter the conclusion that effluent pollutant discharges are generally lognormally distributed. Ambient water quality data also are often lognormally distributed. Figure E-2displays the positively skewed shape ofthe lognormal distribution.
The distribution fitting methods assume that the daily measurements are independent, uncorrelated observations. Although, in general, this assumption isnot satisfied exactly, the lognormal distribution has been used inthe effluent guidelines program primarily because itconsistently provides areasonably good fit to observed efftuent data distributions. Figure E-3shows the lognormal distribution applied to data used in the development ofthe OCPSF effluent guidelines regulation [l].

E-64.5

4

3.5

3

2.5

2

1.5

1

0.5

0

150

50

0 

### - 0

I

I
3 0.5 1 1.5 2 2.5 3 3.5 4 Value
Figure E-2. Examples of Lognormal Densities

1

9 15 21 27 33 39 45 51 57 63 69 I I I I I I I I I I

Concentration inugl

Figure E-3. BOD Frequency Distribution -Plant C

E-7The logarithmic transformation of the random variable X, Y= In(X) results in a random variable Ythat is normally distributed. Therefore, the analysis procedures for analyzing lognormal data are similar to those for the normal distribution. The mean and variance from the normal distribution of the random variable Yare oy and;ry’respectively. These parameters can beestimated by:

$Y = C&i) / k and .* 

### 2 =Y = C[(yi -jQ2] /(k- l), respectively where - Yi = ln(xi) for i=1,2,...k.
When data are lognormally distributed, these values from the normal distribution can then be used to calculate the mean, variance, and coefficient ofvariation for the random variable Xthat islognormally distributed. The mean, variance, and coefficient of variation of the random variable Xmay be estimated by c(X), V(X), and c;(X), respectively. 

## i?(X)=
= V(X)= = h(X)= = daily average exp( ;ly +6: /2) variance exp(2 jiy +6;) [exp(+ -l] coefficient of variation [exp($) -1]li2. 

## DallyMaximmuPetm/tLimMsBawdontheLo~nu/ma/D&tribntion The upper percentile limits for the random variable X(which islognormally distributed) are: 

# R

+P = pth percentile daily maximum limit

= exp[ iiy +zp tSy 21 where zp isthe pth percentage point ofthe standard normal distribution.
Forexample:



### k-95 = 95th percentile daily maximum limit

= exp[ fIy + 1A45 Gy] 899 = 99th percentile daily maximum limit

= exp[ ii,, +2.326 &y].
Note: Zg5 = 1.645 Z99 = 2.326.

E-8The daily maximum variability factors (denoted byVFT) are estimated by:
Daily maximum 95th percentile VFT=X.95 / k(X) Daily maximum 99th percentile VFT=X.99 /t(X). 

## mkfth/yAn?f8#@ Pem?itLhRitsBasedOR 

## the10#mn8/ Distfib#tioR This subsection contains the formulas required to approximate the distribution ofthe average ofasmall number of lognormally distributed values with another lognormal distribution. Although, the Central Limit Theorem holds that the average of asample of independent measurements isnormally distributed provided that the number of measurements, n, issufficiently large, the minimum value for nrequired in specific cases may vary considerably. In caseswhere the individual values are lognormally distributed, the minimum required for the average to be normally distributed may be quite large. Asaconsequence, the distribution ofthe average ofa small number of lognormally distributed values may be better approximated by another, related lognormal distribution [3]. Forsample sizeslarger than 10when the data are lognormally distributed, it isrecommended that the calculations given inTable E-3should be used. Forthe purposes of permit writing, monitoring sample sizes of 10 or less are recommended to be “small enough” to assume the sample average isapproximately lognormally distributed. The mean, variance, and coefficient of variation of the distribution of the average of
ndaily values are&,, k’,, and&, estimated by:

.2 nn = variance

= In

## (V(X)/

# [n[~(X)121 +1) fin = n-day monthly average

= In&X)) -0.5 &Z,
4-t = standard deviation

= 

### (&y/2
CGn = coefficient of variation

= [exp($) -1]1/2 where
k(X) = exp( by +6; /2) V(X) = exp(2 cy +6;) [exp($ -11.

The upper percentile limits ofthe maximum n-day monthly average are:
k*P = pth percentile n-day monthly average limit

= exp[ in +Zp &n]

where zp isthe pth percentage point ofthe standard normal distribution.

Forexample:
A.95 =95th percentile n-day monthly average limit

=exp[ &, + 1.645 6t-J 

### R.99 =99th percentile n-day monthly average limit

=exp[ tin +2.326 ;n]

E-9Note: Zg5 = 1.645 Zgg =2.326.
The variability factors are: Monthly average 95th percentile VF, =R-95 /j.&, Monthly average 99th percentile VFn =R-99 /in. 

# Delta-LognormalDistribution

The delta-lognormal distribution is a generalization of the lognormal distribution. The delta-lognormal distribution may be used when the data contain amixture of nondetect values and values above the detection limit and can be used to model nondetects in water quality-based limits. In delta-lognormal procedures, nondetect values are weighted in proportion to their occurrence in the data. The values above the detection limit are assumed to be lognormally distributed values. The delta-lognormal distribution can be used insetting daily maximum limits and for setting limits on monthly averages with the recommended number ofmonitoring samples being 10or less.
The delta-lognormal distribution models data as the combination of two distributions: the lognormal distribution and adistribution with discrete probability of obtaining observations at or below the detection limit. The lognormal distribution models the observations above the detection limit. The nondetect values are modeled by the distribution with discrete probability of obtaining observations at or below the detection limit. The organic priority pollutant data setshown in Figure E-4contains anumber of observations that were reported as“nondetect.” These detection limit measurements areobservations that arecensored atthe detection limit and are represented by the left-most bar in the histogram. Data sets of this form are fairly typical of organic chemicals in wastewater. The delta-lognormal distribution often provides an appropriate and computationally convenient model for analyzing such data.
The estimation procedure for the delta-lognormal distribution assumes that acertain proportion, 6,ofvalues are atthe detection limit, which isdenoted by D.(The estimation procedure when D=0isdetailed in Reference 4. These values setto Dare observations that can only by quantified asnondetect (ND) at some minimum level. This minimum level isthe detection limit asestabtished by the laboratory performing the chemical analysis.
LetXT,x2,~*.nxpxr+l,-.-, Xkdenote arandom sample ofsizek,with robservations recorded asnondetects, and k-r observations greater than the detection limit. The k-rpositive observations are assumed tofollow alognormal distribution. The entire data set isassumed to follow the delta-lognormal distribution with censoring point equal to the detection limit D. Let iy and r$ be the sample mean and variance of the distribution of the logarithmic transformation Y=In(X) ofthe observations greater than the detection limit. Let 8bethe sample proportion of nondetects. Then the estimates ofthe mean and variance ofthe delta-lognormal distribution are estimated by: 

# w = daily average

= SD+(1 -8)exp( i$, +0.5 6; ) W) = variance

= (1 -8)exp(2 4 +8: )[exp($) -(1 -8)] +8(1 -8)D[D-2exp( fiy +0.5 c$ )] &(X’) = coefficient of variation

= p(x*)p* /ax*>

E-10160

120

40

0 t10 30 70 110 150 190 230 270 310 350



### Concentration inugll

Figure E-4. Organic Priority Pollutant Frequency Distribution-Plant B

E-l1where
k = number ofsamples D = detection limit
k-r = number ofnondetect values insample

= number ofvalues greater than the detection limit Yi = In(Xi) r+l 5i5k, r<k

= C&i) /(k - r) r+l I iI k, rc k

= C(yi -iiy12 /(k- r -1) r+l <ilk, r<k

= r/k 

## DallyMaxhumPemttlimitsDasedonthe

## Delta-Lopomal DlstribiMn The 95th and 99th upper percentile limits for the random variable X (which isdelta-lognormally distributed) are given by the following formulas:
The estimated 95th percentile daily maximum limit is:
rD k.95* = 1 8 2 0.95

Lmax [D,exp( jiy+~‘6; ] $5<0.95 where z* = o-1 [(0.99 -8)/(1 -8)].
The estimated 99th percentile daily maximum limit is:
I-D 8 2 0.99 keg,* = I

where Lmax [D, exp( iy +z*by)] 8 <0.99

z* =o-1 [(0.99 -s)/(1 -8)I.

W1[ ]isthe mathematical notation forZ-scores. Forexample, when 8=0,then the corresponding value is

W1[.99] =Z99=2.326. Values ofQ-l [ ]are available from tables ofthe normal distribution (available in most statistical textbooks and references).
The variability factors (denoted byVF)are estimated by: Daily maximum 95th percentile VF=R-95 /k(X) Daily maximum 99th percentile VF=A.99 /k(X).


## DebLopnmna~ DlstributlonofAverages The derivation of the formulas for the averages computationally isdifficult and beyond the scope of this appendix. However, the formulas for n-day averages are included inTable E-2. The derivation of4-day monthly averages using the detta-lognormal distribution isavailable in Appendix VII-F of the Development Document forthe OCPSFregulation [l]. Forthe purpose ofpermit writing, itisrecommended that data setsofgreater than 10samples beassumed tofit the normal distribution and the averages becalculated using the formulas given in Table E-3.

E-l2

# CheckingDistributionalAssumptions

Two methods of checking distributional assumptions are goodness-of-fit and probability plots. When checking distributional assumptions, the sample size must be large enough. Small sample sizes may lead to erroneous conclusions.


## 6oodmss-of-FitTests Insome cases, statistical goodness-of-fit tests may indicate that aparticular distribution provides areasonable fit to adata setofpollutant measurements. Such casesshould beevaluated carefully toverify that the frequency curve for the data also show the shape characteristic ofthe distribution. 

## ProbabilityPlots Use of probability plots is one method of determining whether a normal distribution is appropriate for modeling apopulation using only alimited setof measurements. The setof measurements should have at least 20 observations [5]. Consider an independent sample ofsize k, labeled X1,X2,...,Xk. Let Ul,U2,...,Uk bethe ordered sample ofx-values inascending order inwhich U1sU2s,...,<Uk. Now foreach Ui,find Zifrom the normal table (in any statistical reference ortextbook) such that P[Z I Zi]=i/(k+l) and plot each pair (Zi,Ui) on linear graph paper (or useacomputer graphics software package). Ifthe data arefrom anormal distribution, they will fall approximately along astraight line.
This same method can be adapted to check the assumption of lognormality. Log-probability plots aresimilar to probability plots used for the normal distribution. To construct a log-probability plot, set yf = In(xi) for i=l,2,..., kand then prepare aprobability plot for the yi, first by ordering the data asdescribed inthe previous section. Ifthe data are from alognormal distribution, they will fall approximately along astraight line, as illustrated by Figure E-S.



# OtherDlstributfons

If the probability plots or the log-probability plots show serious deviation from straight lines, other distributions should be considered. Nonparametric methods, which do not require the assumption that the data follow aparticular distributional form, are often useful for this type of data. Further details are available in many statistical references (e.g., Reference 6).

Up to this point, we have assumed that all the observed pollutant levels are independent, i.e., uncorrelated with one another. This subsection isnot intended to address correlation between observed pollutant levels and plant operating factors that influence and control treatment performance.

E-l3-1.5 0.0 2Score 1.5 3.0

Figure E-S. Example of aLog-Probability Plot with aNormal Dlstributlon

E-14In the case of the monthly average limit derivation, the assumption that observed pollutant levels are independent can be quite important. Ifthe effluent levels are correlated, the actual monthly average limit can be substantially higher than that derived from the analysis based on the independence assumption. However, correlation has essentially no effect on the calculated daily permit limits. This sub-section provides guidance on determining when levels may be correlated, and adjusting the sample size.
A mafor factor that determines whether effluent levels are highly correlated is the retention time of the wastewater treatment system. Ifthe retention time islarge relative to the time between effluent samples, then those samples will tend to be correlated with each other in most cases. Inmunicipal systems, for example, the retention time isfrequently amatter of days, and sampling isoften conducted on adaily basis. The effluent levels, consequently, may be substantially correlated. However, in many industrial systems, for instance a physical/chemical treatment system for electroplating wastewaters, the treatment system retention time is relatively short 4 to 8hours. Daily effluent levels from these kinds of systems are generally uncorrelated, i.e., statistically independent. These general patterns are the same irrespective ofthe kind of pollutant in question. Significant correlation between observed pollutant levels, when present, should be factored into monthly average permit limits.
Several different methods can be used to account for correlation indetermining limits. One general approach involves time series modeling. Another possible approach isto use a direct computation of the covariance among the observed data to adjust the variance ofthe average used indetermining the limit. Help inadjusting the sample sizefor correlation isavailable from the OW Statistics Section (phone number [202] 382-5397).



### Table E-l. Daily Maximum Permit Limit 

### Calculations

The daily maximum permit limit isusually the 99th upper percentile value ofthe pollutant distribution. In :ertain casesthe 95th percentile value may be allowable. The following gives the formulas:
JVlTHALL MEASUREMENTS >DETECTION LIMIT (based on lognormal distribution)


### 8.95 = = A.99 = =
ivtiere

Xi = 

### Yi = k = by = .2 =Y = 

## k(X) = cl(X) = &(X) = 95th percentile daily maximum limit exp[&+ 1.645 $1 99th percentile daily maximum limit exp[j$ +2.326 $1

daily pollutant measurement i In(Xi> sample sizeofdata set

Uy$ I k 1<ilk Z[(yi-by)‘]/(k-1) 1<irk exp( f$ +0.5 6; ) exp(2 f!iy+6;) [exp($) -l]
[exp(+ -1]li2

E-15

### Table E-l. Daily Maximum Permit Limit 

### Calculations (continued)

VITH SOME MEASUREMENTS <DETECTION LIMIT (based 

### ondelta-lognormal distribution)

k.95 = 95th percentile daily maximum limit rD 8 2 0.95
495 = I Lmax [D,exp( $y+z*$1 8<0.95
vith z’ =a-l [(0.95 -8)/(1-8)]
A.99 = 99th percentile daily maximum limit 

### rD 8 2 0.99 k I

.99 = Lmax [D,exdy +~~$1 8<0.99


### with z*=Q-l [(0.99 -8)/(1-8)] yhere
xi = daily pollutant measurement i 

### k = sample sizeofdata set D = detection limit (asestablished by the laboratory) r = number ofnondetects (xl

### ,x2,...,xr are2D) 

### k- r = number ofdetects (Xr+ltXr+2,...tXk are>W

3’ = Ill(Xi) for r+l I i5k



### = r/k LY = E&i) /(k-0 r+l 5i5k(exclude values I Dfrom sum) .2 =Y 

# =E[(yi-i$21/tk- r -1) r+l <ilk &x*) = 80 +(1 -8)exp( i$, +0.5 6;) 0(x*) = (1 -8)exp(Z i$, +8;) [exp(&$ -(1 -8 )]+8 (1 -8)D[D -2

### exp( i$,+0.5 SC)]

E-l6

### Table E-2. Monthly Average Permit Limit Calculations for Ten Samples or 

### Less

Themonthly average permit limit isusually based on the estimates ofthe 95th percentile ofthe distribution 01 :heaverage ofthe daily effluent values. Forsample sizes lessthan or equal to 10, the data are assumed to br ognormally distributed (or delta-lognormally distributed if the data includes nondetects).
4LLMEASUREMENTS > DETECTION LIMIT (based on lognormal distribution)

A.95 = 95th percentile n-day monthly average limit

= exp[i, +1.645 kn] k-99 = 99th percentile n-day monthly average limit

= exp[j$.,+2.326 ;m]
Nhere

xi = daily pollutant measurement i 

## Yi = Ill(Xi) k = sample size ofdata set r;3 = C&i) / k l<ilk

&Y = E[(yi-b) ]/(k-1) 2 Y 1li<k 

### k(X) = exp( jYiy+0.56; ) 

## V(X) = exp(2 iy +B$[exp($ -I] A2 on = 

# InCOoQ /(n[h12>+1) I& = In&x)) -0.5 62, C"Vn .2 = [exp(o,) -1]lj2

E-l7

### Table E-2. Monthly Average Permit Limit Calculations for Ten Samples or Less (continued)

,OME MEASUREMENTS <DETECTION LIMIT (based on delta-lognormal distribution)
k95 = 95th percentile n-day monthly average limit 

### rD s 2 0.95 

### 495 = 1 Lmax [D,exp( cn+z*&,)] 8 <0.95

vith z* =@-I[ (0.95 -!Zi )/(1-$)I.
k99 = 99th percentile n-day monthly average limit 

### rD 6 2 0.99 

### 899 = 1 Lmax [D,exp( j$,+z*&r,)] 8 <0.99

vith z* =W1[(0.99 -8)/(1-8)]
vhere
xi = daily pollutant measurement i 

### k = sample sizeofdata set 

### D = detection limit (asestablished by the laboratory) r = number ofnondetects 

### (x1,x2,...,xrare<D) 

### k-

### r = number ofdetects (Xr+l?r+2s-.tXk are>D) 

### Yi = In(Xi) 

### for r+l 5iI k 8 = r/k fiY = Z(yi) /(k- r) r+l 5iI k(exclude values ID from sum) .2 “Y 

## =Z[(yi-jiy)211(k-r-l) r+l I iI k ik(X*) = 8 D+(1-8)exp( j?ty+0.56;) v(X*) = (1-8)exp(2 by+6:) [exp( 6;) -(1-8 )]+8(1-8)D[D -2exp( fiy +0.5$1 .2 

### % = In{(l -8”) [l +A+B+C])

vith A 

### = 0(X*) /[n&X*) -8” D)2] 6 = -16”D2(1 -8”)] /&X*) -6” D)2 c = (28 D)/&X*) -8’ D) fin = In[(l!(X*) -8” D)/(1-8”)] -0.56:

E-18

### Table E-3. Monthly Average Permit Limit Calculations for More Than Ten Samples

‘he monthly average permit limit usually isbased on the estimates ofthe 95th percentile ofthe distribution >fthe average ofthe daily effluent values. These daily values are assumed to be lognormally distributed. For #amplesizeslarger than 10,the averages (represented bythe random variable X,) areassumed tobenormally Sstributed.

k.95 = 95th percentile n-day monthly average limit

= ^E(X,)+1.645 fl&)]‘/2 499 = 99th percentile n-day monthly average limit

= i(Xn) +2.326 yC/(Xn)]‘/2
vhere

xi = daily pollutant measurement i Yi = In (Xi) k = sample sizeofdata set F-Y = Ii / kt l<i<k *2 “Y = Z[(yi -fiyj2] /(k-

### 1) 1 5i5k &)o = exp( $y+0.5 ire )

w> 

### = exp(2 jI$+6:) [exp($) -1 ] ‘bn) = E(X) 0(X,) = V(X) / n C”v(Xn) = V(Xn)‘j2 / (Xn)

E-l9

# APPENDIX E REFERENCES

1.

2.

3. U.S. EPA. 1987a. Development Document for Effluent Limitations Guidelines and Standards for the Organic Chemicals, Plastics and Synthetic Fibers. EPA440/l -87/009. Mendenhall, W., 9. Scheaffer, and D. Wackerly. 1981. Mathematical Statistics with Applications. 2d ed. Wadsworth, MA. Barakat, R. 1976. Sums of Independent Lognormally Distributed Random Variables. J.Optical Sot. Am. 66:21l-l 6. 4. 5. Aitchison, J.,and J.Brown. 1963. The Lognormal Distribution. Cambridge University Press. Johnson, R., and D. Wichern. 1982. Applied Multivariate Statistical Analysis. New Jersey: PrenticeHall. 6. Hollander, M., and D.Wolfe. 1973. Nonparametric Statistical Methods. New York: Wiley.


# ADDITIONAL 

# REFERENCES

Kahn, H. 1989. Memorandum: Response to Memorandum from Dr. Don Mount of December 22, 1988. August 30, 1989. U.S. EPA,Washington, DC, toJ.Taft, U.S. EPA,Permits Division, Washington, DC. Kahn, H., and M. Rubin. 1989. Use of Statistical Methods in Industrial Water Pollution Control Regulations inthe United States. Environmental Monitoring ond Assessment 12:129-48.

E-20

# APPENDIX 

# F
SAMPLING

# SAMPLING

The objective of an effluent or instream sampling program isto obtain a sample (or samples) from which a representative measure ofthe parameters of interest can beobtained. Unfortunately, many ofthe industrial and municipal National Pollutant Discharge Elimination System sampling protocols presently in use are carryovers from schemes used for calculating loadings of nutrients and oxygen-demanding substances, orwere developed to evaluate treatment plant operational efficiency. Sampling for individual toxicants and particularly for effluent toxicity can require more specific (and thus different) sampling procedures.
Wastewater variability isan important consideration in selecting the method and frequency of sampling for both chemical analysis and toxicity testing. Industrial waste characteristics have been shown to vary in frequency, intensity, and duration [1]. Asnoted by Bender [2], the sources of effluent variability include both random and systematic components that influence both daily and annual characteristics ofwaste discharges. Although toxic pollutant loading may be of primary concern in assessing human health impact or bioaccumulation, loading may be of lesser importance in toxicity assessment than frequency, intensity, and duration of peak toxic discharge. Sampling must betailored to measure the type oftoxicity of importance for that discharge: either long-term (chronic) impact, which isamore constant effect, orshort-term (acute) impact, which is more variable and subject to peaks of intensity.
There areseveral chemical parameters forwhich continuous analysis ispossible. These include pH, temperature, dissolved oxygen, and other parameters involving instantaneous measurement. All other types ofmeasurement involve some time period over which the analysis isconducted. Toxicity tests require an exposure period. Chemical tests require sample preparation and analysis. There isnocontinuous analysis method fortoxicity.
It should be noted that although it is difficult to design a representative sampling program for toxicity analysis, the problems are of no greater magnitude than similar problems associated with obtaining a representative sample for conventional pollutants. 

# SamplingMethods
ContinuousFlowSamples For toxicity testing, the test organisms may be exposed to serial dilutions of asample continuously pumped from the effluent pipe or ditch. Inthe case of effluents, if optimum accuracy isdesired, then the ratio of effluent flow to test chamber volume can be scaled to simulate the time-varying concentration at the mixing zone boundary. Although flowthrough methods can provide a realistic simulation of time-varying exposure, they are relatively expensive and are usually conducted on site. Therefore, flowthrough methods may only be practical where the goals of the analysis of impact require this type oftesting orwhere treatment costs are sufficiently high that this type ofanalysis can be required. Aflowthrough exposure method isnot acontinuous analysis because only one result ordata point isobtained atthe end ofthe test. However, the continuous exposure does provide some measure oftime-varying exposure effects.


## DiscreteSamples Grab or flow composited sampling provide adiscrete sample for chemical analysis ortoxicity testing. Static or renewal toxicity tests using discrete samples result in exposure of test organisms to a constant effluent composition over the period ofthe tests, orfor the period between renewals.
Ifdiscrete samples are collected during peaks of effluent toxicity then constant concentration exposure static testsprovide ameasure ofmaximum effect. Depending on the duration of apeak and the compositing period, composited samples may not be useful for examining toxicity peaks because the compositing process tends to dilute the peaks. Cornposited samples are

F-lusually appropriate for chronic tests where peak toxicity of short duration isof lessconcern. The averaging effect ofcompositing may be misleading when testing for acute toxicity.
Grab samples must be collected at sufficiently frequent intervals to provide a high probability of sampling daily peaks. Fortunately static toxicity tests are relatively inexpensive and can be done on shipped samples; thus, it may be cost effective to conduct individual tests on aseries ofgrab samples collected over a24-hour period. 

## SamplingFrequency Nonrandom effluent variability, resulting from batch processing, variable loadings, etc., isoften known or can be determined. Therefore, the first step in designing asampling program for chemical analysis or toxicity testing isto select the annual sampling frequency based on available site-specific operational information. This isimportant inselecting sampling periods for both continuous flow and discrete sampling methods.
Ifdiscrete sampling methods (grabs or composites) are used, then random variations between and within days for each sampling period must be considered. Itisimportant to recognize the tradeoff between the long-term (between days) frequency and short-term (within days) frequency ofsample collection and analysis for toxics. At present, the permit requirements for sampling and analyzing chemical parameters are site specific and generally involve asingle grab or 24-hour composite sample collected at daily, weekly, or monthly intervals. Unfortunately, asampling scheme involving asingle daily grab or a24-hour composite sample can conceal the presence of those daily extreme values that may be of importance. To optimize sampling cost and effectiveness, itmay bedesirable to reduce long-term frequency sothat daily frequency can be increased.
Forexample, aweekly grab orcomposite involves 52 analyses per year. It may be more efficient to reduce the annual frequency to monthly or bimonthly, but collect and analyze four oreight grabs daily. Either scheme (12 x4or 6x8)would involve 48 analyses per year versus 52 for the weekly single sample approach. Assuming that daily toxic events of environmentally significant intensity and duration would not be masked by shortterm composites, itmight be more efficient to collect eight samples each composited over a3-hour interval.
If costs or other constraints prohibit satisfactory daily and annual replication of sampling, then a level of uncertainty must be introduced into the calculations used to evaluate waste toxicity (see Section 3,Table 3-l).
Box F-l presents EPA’srecommendations onsampling methods.

F-2

## Box F-l. Recommendations
The initial sampling design step should involve stratification of sampling periods to account for nonrandom sources of variation (e.g., batch processing). The second step includes selection ofthe frequency and the method ofsampling to be conducted within each sampling period. Depending on site-specific considerations, several options are available.

l Flowthrough Methods - Ideally, for both acute and chronic effluent toxicity tests, the exposure of biota should simulate the time-varying concentration at apredetermined point in the receiving water. Forregulatory purposes, the critical point isoften the edge ofthe mixing zone where the waste should exhibit neither acute nor chronic toxicity. Therefore, if warranted by site-specific factors, it is recommended that test biota be exposed to a continuously collected flowthrough sample of serially diluted effluent. If no systematic annual variations (e.g., batch processing) are known orsuspected, flowthrough testing can be conducted ataminimum ofquarterly intervals for at least 1year.

l Grab Sample Methods - Grab samples are recommended for chemical analyses and for acute and chronic toxicity tests where site conditions (such aswastewaters that are known to have relatively constant composition) do not require use of continuous flow methods. Grab samples of effluent or receiving water may be used for static or renewal acute toxicity tests, which may be conducted onsite or at a remote lab. The design of atoxics grab-sampling program must take into account the tradeoff between long-term and short-term sampling intensity. Where there isno ponding ofwastes or retention time isinsufficient for thorough mixing, it is important to collect or 

### analyze a sufficient number of samples to provide a measure of daily spikes. Therefore, to minimize analytical costs where daily fluctuations are known or suspected, the annual sampling frequency should be reduced in favor of more intensive daily sampling. It is recommended that on an annual cycle, grab sampling and analysis include aminimum of four to six daily grabs collected monthly. An option could include the use ofshort-term (4-hour) composites rather than grabs. Ifsite-specific data are available to indicate that treatment system retention time is adequate to minimize daily variations, then the daily replicates may be omitted infavor of more frequent annual sampling (e.g., weekly or semimonthly rather than monthly). If,to minimize costs in screening tests, only single samples are collected at infrequent intervals (e.g., quarterly) an uncertainty factor for variability should be used inthe toxicity evaluation (see Section 3).

l Composite Sample Methods - Ifstatic or renewal methods are used for evaluation oftoxicity, it is recommended that 24-hour, continuous-flow composite samples be collected. Considerations ofannual frequency arethe same

### asthose forgrab samples.

F-3

## APPENDIX F
REFERENCES

1. Nemetz, P.N., and H.D. Dreschler. 1978. The Role of Effluent Monitoring in Environmental Control. /. Water, Air,and Pollution 10:477-97. 2. Bender, E.S. 1984. “Sources ofVariations in Effluent Toxicity Tests.” InEnvironmental Hazard Assessment of fffluents. Ed. H. Bergman, R. Kimerle, and A.W. Maki. Proceedings of the Fifth Pellston Environmental Workshop, Cody, WY,August 1982.

F-4

# APPENDIX 

# G
THE DEVELOPMENT OFABIOLOGICAL INDICATOR APPROACH TOWATER QUALITY-BASED HUMAN HEALTH 

# TOXICSCONTROL

# THE DEVELOPMENT OFA BIOLOGICAL INDICATOR APPROACH TOWATER QUALITY-BASED HUMAN HEALTH TOXICS CONTROL

CurrentApproach

With one exception (New Jersey), the chemical-specific approach to protecting human health iscurrently the only method used to regulate human health toxicants in effluents. The chemical-specific approach identifies the individual chemicals in an effluent and regulates them based upon health riskassessment information for each individual chemical. Where data are available for such human health toxicants, the chemical-specific approach can be usedto develop permit limits. However, the complex characteristics of effluent mixtures limit the effectiveness of the single-chemical approach. When used asthe sole basisfor identifying effluents of human health concern, the chemical-specific approach can overlook wastewaters potentially toxic to humans for the following reasons:
1. Analytical methods may not be sensitive enough to detect extremely small quantities ofchemicals which may exert their effects on human health after along latency period. 2. Human health data are limited or lacking for many ofthe §307(a) “priority” pollutants. Moreover, the number of human health toxicants discharged farexceeds the “priority” pollutants list. 3. The various chemical constituents of an effluent may resulting in synergistic, additive or antagonistic chemical effects.

As a result of these limitations, biological indicator tests have been developed for human health impact effluent screening, including both invitro and invivo tests. Though not yet widely implemented, biological indicator test results can be important supplements to achemical-by-chemical effluent characterization.
Short-term biological indicator tests for human health impact screening are based on cellular-level responses, indicating whether the substances being tested are biologically active, and providing some measure of that activity. While these tests do not quantify the degree of toxicity to humans, they can be used to identify effluents with potential human health impacts, and regulatory priority-setting and targeting of dischargers for further chemical-specific analyses. Research iscurrently underway within EPAand in the private sector to evaluate various biological indicator test batteries for whole effluent analysis.


## Biological IndicatorTests Biological indicator tests include invitro (test tube) and invivo (whole animal) tests which can help form the first tiers of asingle chemical evaluation process. Abattery of simple biological tests can be used to test for the major types of effects which are underlying causes of potential health impact, since each biological test measures adifferent type of response. The results ofthese tests can be used to decide whether more definitive (and more resource-intensive) testing isneeded to identify actual problem pollutants.
Test results can serve as triggers to additional chemical-specific analysis or more sophisticated definitive biological tests. Where results of these screening tools indicate potential health hazards, further characterization of the effluents, and regulation based upon toxicological data and/or chemical structureactivity relationships can proceed. Ifan effluent isextremely variable in other parameters, screening assays should be repeated periodically to ensure that potentially hazardous discharges are detected. Two types of biological indicator tests are discussed below: tests for non-threshold (no safe level exists) chemicals and tests for threshold (asafe level ispresumed to exist) chemicals.

G-1

## GenotoxicityTestsforNon-ThresholdChemicals Cenotoxicity isthe ability of a substance to damage an organism’s genetic material (its DNA). Certain positively-charged compounds tend to bind to DNA and may lead to permanent changes in the genetic information. Such damage to the DNA of reproductive (germ) cells can impair reproductive ability or can produce achange inthe DNA structure that could be passed on to offspring asaheritable mutation. Alterations inthe DNA ofsomatic cells can result incancer orother diseases. Interpretation of genotoxicity test results assumes that DNA damage in nonhuman cells may be predictive of latent diseases in humans such asgenetic disorders, birth defects, and cancer. EPAbelieves that genotoxicity tests for point mutations, numerical and structural chromosome aberrations, DNA damage/repair and invitro transformation provide supportive evidence of carcinogenicity [U.S. EPA, 1979 and 1987~1. In addition, wastewater mutagenicity tests could be used to detect genotoxic activity which can adversely affect aquatic biota [Black, et. al., 19801. Several short-term assays have been developed which can assessgenotoxic effects (discussed below).
Forexample, acorrelation hasbeen established between animal carcinogens and positive mutagenic responses in the Ames Test. The Ames test isoften used to assesspoint mutation effects. The original correlation study revealed that 90% of tested carcinogens were detected as mutagens, while 87% of noncarcinogens were identified asnonmutagens. Other studies have determined that between 77% and 91% oftested carcinogens produce positive responses inthe Ames test. The Ames Test hasbeen used inover 2,000 laboratories worldwide fordrug and food additive screening, product development, and environmental testing [New jersey DEP,19831.
Toassessclastogenic effects (chromosomal breakage) either the mammalian sister chromatid exchange test ora mammalian ceil chromosomal aberrations test can be conducted. Both of these tests typically use Chinese hamster ovary cell cultures and involve cytologic examination after exposure to determine if chromosomal effects are evident. The Organization of Economic Cooperation Development (OECD) test methodology is recommended [OECD]. EPA’sOffice ofToxic Substances and Office of Pesticides Programs also have published test methods [U.S. EPA1982a and 1982b] that areconsistent with the OECDtests.
Most effluent samples need special preparation (for example, concentration) to produce ameasurable biological indicator test response for human health effects. When samples are concentrated, the response iscalculated in terms of the pre-concentration sample. In addition, for genotoxicity tests, because many chemicals are not actively mutagenic in humans until they enter the body and are metabolized, many in vitro tests are supplemented with extracts from mammalian liverswhich act asasource ofenzymes. The extract enzymes actto mimic metabolic activation of procarcinogens and promutagens in humans, providing amore realistic picture of potential effects [U.S. EPA,19791.
Anumber ofgenetic toxicity assay batteries have been suggested inorder to address the many potential effects produced by nonthreshold chemicals (for which no safe level exists) [U.S. EPA, 1979; Lave and Omenn; Environment Canada]. In addition to providing assays that detect different endpoints, a battery of tests can also be structured to minimize effort at the screening level while supplying more definitive data for samples failing the initial tier of testing. Positive results can lead to further effluent characterization, including priority and other pollutant chemical analyses, or mutagenicity testing of specific processes or effluent fractions. Another approach would be to evaluate the effects of various treatment or waste segregation techniques onmutagenicity [McGeorge, et. al,, 19851. Many of the proposed test batteries utilize the Ames Assay asascreening level test because of its relatively high degree ofsensitivity (i.e. ahigh percentage of carcinogens are Ames positive) and specificity (i.e. ahigh percentage of noncarcinogens areAmes negative) [Tennant, et. al., 19871. One study of28 selected industrial discharges revealed that 11ofthe 28, or 39%, produced positive results using the Ames Test (described below). Other test endpoints frequently covered in the initial tier of testing include mammalian cell chromosomal effects, mammalian gene mutation and microbial and mammalian cell DNA damage.
Results of a recent National Toxicology Program project suggest that combinations of four of the most commonly used short-term tests covering these endpoints did not show significant differences in individual

G2concordance with rodent carcinogenicity results for pure chemicals [Tennant, et. al., 19871. This suggests that ifasample causes only one type ofendpoint asmeasured by several screening level tests, its potential to cause human health effects should not bedisregarded.
TOassessthe potential carcinogen hazard, subsequent tests focusing on effluent-induced malignant changes in mammalian cells invitro can be conducted. Higher levels oftesting may include invivo rodent testing or the Medaka (fish) tumor assay,for example. Itshould be noted that under existing guidelines, invivo mammalian tumor assaysare necessary to establish amaterial asapossible human carcinogen. Resultsfrom short term tests alone are considered as inadequate to establish human carcinogenicity [U.S. EPA, 1986~1. Guidelines for risk assessment ofindividual compounds arecovered in U.S. EPA,1986b and 1987~.
Invivo tests on complex mixtures are extremely complicated and expensive given the variability intrinsic to effluents. Asaresult, it isrecommended that after each tier of biological indicator testing, the cost offurther refining the weight ofevidence for carcinogenesis or mutagenesis be balanced against the cost ofconducting a causative agent identification evaluation. Given the identity of the substance leading to positive results in short term in vitro tests, it should not be necessary to generate invivo dose-response data for risk characterization ifthese data are already available in the literature for the specific chemical.
Inaddition, causative agent identification studies may be unnecessary if information on the physical and/or chemical characteristics of the toxicant is obtained. Such information may provide clues to appropriate effluent treatment technologies needed to reduce effluent mutagenicity.
Inweighing the need for more definitive biological assays against causative agent evaluation, the frequency (i.e., how often the effluent tests positive) and intensity (e.g., revertants/liter) of the effluent’s mutagenicity must be considered. Asadefault assumption, ahigh dose ofacarcinogen received over ashort period oftime is equivalent to a low dose spread over alife-time [U.S. EPA, 1986~1. While effluents which are highly variable intheir mutagenicity are ofconcern, they will be more difficult and costly to deal with insubsequent phases of study. Accordingly, the initial tier of qualitative tests for human health effects assessment can be relatively inexpensive, rapid, and have alow rate offalse negative results. Subsequent tests can be designed to increase confidence in the predictive nature of the results. Additional levels of testing may also provide diagnostic information on the characteristics ofthe causative agent(s) inthe effluent. Subsequent tiersoftesting should focus onamore concise assessment ofrisk. Suchanassessment can beused to delineate hazard type; in effect, to separate germ cell mutations (heritable genetic risk) from carcinogen risk. Thus, to assessheritable mutation, subsequent testing should focus on mammalian germ cells, ultimately tested invivo [U.S. EPA,1986b]. To assesspotential carcinogen hazard, subsequent testsfocusing on effluent-induced malignant changes in invitro mammalian systems should be conducted. Ultimately, testing must result in a dose-response assessment to be usedwith anexposure assessment incharacterizing risk[U.S. EPA,1987a].
EPA’sRegion V(Chicago), New Jersey, and Environment Canada have been conducting mutagenicity testing at selected facilities. In Region VAmes test results are used to suggest the need for more intensive chemicalspecific analyses of the effluent. New Jersey has incorporated a prohibition against discharging mutagenic compounds in amounts that are mutagenic into its “New Jersey Administrative Code” [N.J.S.A. Section 7:9-4.5 (a)4, May 19851.
For both types of endpoints (genotoxicity and carcinogenesis), hazard identification should be followed by quantitative risk assessment which includes assessment of dose response (requiring invivo data) and human exposure. Human exposure assessment typically considers the composition and sizeofthe population exposed and thetypes, magnitude, frequency and duration ofexposures [U.S. EPA,1986d].



## EvaluationofEffluentGenotoxicityScreeningResults
Control of human health hazards depends upon assessment of both the toxicological properties of the pollutants and the level ofexposure. The permit authority should review the results ofahuman health toxicant

c3effluent screening program and establish the actions triggered by each level of potential risk indicated. For example, adischarger with either ahigh exposure riskorahigh effects riskmight automatically be required to conduct adetailed assessment or institute controls. Amedium riskinboth exposure and effects might require further review of the data and acase-specific decision about whether to require additional assessment. A medium effects riskand alow exposure riskmight indicate the need for limited testing to ensure that the low is really indicative of the risk. Low risk in both exposure and effects should receive low priority for further assessment. The bioconcentration evaluation procedures can be used to aid indefining exposure risk,aswell as determining receiving water concentration.
One possible tool for evaluating results of biological indicator effluent screening is the “relative potency approach,” a concept used rather widely in radiation biology and chemical pharmacology. The relative potency of an effluent isthe dose of areference agent needed to produce an effect ofagiven magnitude ina particular bioassay, divided by the dose of the effluent needed to produce the same magnitude of the same effect inthe same bioassay. Apredictive battery ofseveral short-term biological tests, when standardized to a reference agent, could provide arank orcomparative estimate ofthe hazard posed by aneffluent inthe context ofmeasures ofother known hazards [Glass, 19881. Itshould be recognized that this approach does not consider exposure through bioaccumulation.
When screening has indicated a high potential for health hazard, further assessment should be required. A chemical-specific approach isrecommended to evaluate and regulate the discharge constituents. The first half of this process involves characterizing the composition ofthe effluent. Typically, only asmall fraction ofthe total organic carbon (TOC) can be accounted for asindividual chemicals. Therefore, effort should be placed on identifying constituents through means other than chemical analysis, such as through a detailed process evaluation and/or toxicant characterization evaluation.
A process evaluation isastudy in which components in the wastewater are determined from an analysis of feedstocks, manufacturing processes, products, by-products, and pollution control in place. The result isalist ofcompounds or classes ofcompounds with ahigh probability of being present inthe wastewater. Chemical analysis can also be conducted for not only the priority pollutants but also nonpriority pollutant peaks and bioconcentratable chemicals [EPA/6OO/XX-XX]. IRISand SARcan be used to determine the likelihood that a given compound is causing positive results in the bioassay. The toxicant characterization evaluation can provide information on the physical/chemical nature of the chemical producing positive bioassay results.


## SummaryofCurrentBlological IndicatorTestsforNon-ThresholdHumanHealthToxicants

The following tests are currently in useor under development for assessing carcinogenicity or mutagenicity:



### . Salmonella tvphimurium Assay(Ames Test) [U.S. EPA,1985 and 19831 Backoround: Strains of Salmonella requiring the amino acid, histidine, are exposed to asolvent extract ofthe effluent. Tests are performed with and without added rat liver enzyme for activation of indirect mutagens. The bacteria are grown on histidine-free medium; colony formation indicates the effluent contains mutagenic compounds capable of genetically altering the bacteria. Endboint: Gene mutation; response measured in revertant colonies/L effluent. Advantaou: Test israpid, relatively inexpensive. The Ames Test has been shown to have broad application for the assessment ofthe mutagenic activity of adiversity of industrial effluent types (McCeorge, et. al., 19851. Test sensitivity and specificity are documented [Ashby and Tennant, 19881.

CADisadvantaaes: Requires metabolic activation and several different strains of Salmonella to detect abroad range of compounds, requires extrapolation from prokaryot, use of effluent extract may exclude certain types ofcompounds, epigenetic carcinogens not detected. Cost: Approximately $1200 [Lave and Omenn, 19861

l Escherichia coli SOSAssay (SOSChromotest) [Quillardet, et.al., 19851. Backaround: All cells contain an “SOS” enzymatic system for detecting and correcting errors in their genetic material. Astrain of E.coli has been genetically engineered sothat DNA damage ultimately results inproduction ofanenzyme which reacts with test reagents to form ablue color. Bacteria are exposed to effluent or an extract of the effluent, with or without added rat liver enzyme for activation indirect mutagens. The intensity of color produced indicates the extent to which the effluent contains mutagenic compounds capable ofdamaging bacterial DNA. Endpoint: DNA damage; response measured asthe change inoptical density. Advantaaes: Simple kit commercially available, test requires <8 hrs to perform, relatively inexpensive. Test sensitivity, specificity documented [Quillardet, et.al., 19851. Disadvantaaes: Requires metabolic activation, extrapolation from prokaryot, use of effluent extract may exclude certain types ofcompounds, epigenetic carcinogens not detected, measurement ofeffect must be referenced to known genotoxic compound. Cost: ??

l Sister-Chromatid Exchange Assay (SCE)[Eckl, et. al., 19871 Backaround: division. Sister chromatid exchange occurs when damaged DNA isreplicated during cell Recent advances allow the use of cultured rat hepatocytes in detecting SCEformation, thus precluding the need to add rat liver enzyme for metabolic activation. Hepatocyte exposure to the sample is effected by using filter sterilized effluent in preparing the cell culture medium. Exposed cellsare lysed and genetic material fixed inorder to count SCEs. Endpoint: DNA damage; response measured inSCEperchromosome/L effluent. Advantages: Test israpid, relatively inexpensive, does not require metabolic activation (therefore more realistic). Usesmammalian cells, therefore results more readily applicable to humans. Disadvantaaes: Sensitivity, specificity not well documented, test more complex relative to prokaryotic systems, filter sterilization may remove some genotoxic compounds from the sample, epigenetic carcinogens not detected. Cost: $5000 [Jirtle, 19891

l HCPRTAssay with Chinese Hamster Ovary Cells (HCPRTKHO) [Hsie, et. al., 19811 Backaround: Strains of Chinese Hamster Ovary cells in culture are exposed to the effluent or an extract ofthe effluent, with orwithout added rat liver enzyme. Mutagen interactions with certain sections of the DNA make the cell resistant to toxicants like Gthioguanine. Cell survival isused to indicate both cytotoxicity (cell death) and genetic mutations resulting from effluent components. Endpoint: Gene mutation; response measured in%survival/L. Advantaaes: Test israpid and usesamammalian system. Disadvantaaes: Sensitivity, specificity not well documented, useof effluent extract may exclude certain types ofcompounds, epigenetic carcinogens not detected, requires metabolic activation. Cost. $6500 -.

l Medaka Tumor Assay [U.S. EPA,1988; U.S. EPA,1989b.l Backaround: Larval fish are exposed to nonlethal concentrations of effluent for one month, this period isfollowed by aS-month grow out period inclean water. At six months, fish aresacrificed and submitted for histopathological studies. Endooint: Tumor formation, response measured in frequency of tumors at agiven site/effluent concentration. Advantaaes: Useofwhole effluent, whole organism, oncogenic endpointDisadvantaaes: Carcinogen levels in unconcentrated effluent may not be high enough to produce tumors at adetectable frequency inexposed populations, effluent must not be toxic to Medaka, requires extrapolation from non-mammalian system, relatively expensive, length of test, endpoint requires pathologist experienced infish cancers, method still in developmental stages. Cost: $20,000 [Johnson, 19891.


## OtherHumanHealthEffects

Toxicants present in effluents may produce a variety of effects in humans besides genotoxicity or carcinogenicity via exposure through ingestion of water and/or contaminated fish and shellfish. Potential health effects could include suppression of the immune system, neurotoxicity, specific organ toxicity, or developmental toxicity. These effects occur after exposure above a presumed safe (threshold) level and are referred toas“systemic.” Formerly, the only means to assesssystemic effects was by using subchronic toxicity procedures designed to determine the effects that may occur with repeated exposure over a part of the average life span of an experimentat animal. However, such studies areexpensive ($100,000 and over) and beyond the cost constraints for most effluent analyses. Asan alternative, anumber ofshort-term invitro tests utilizing mammalian cells have been developed [U.S. EPA, 1978; Wilson, 1978; Kimmel, et. al., 1982; Brown and Fabro, 1982; Borenfreund and Puerner, 19851. Test endpoints include cytotoxicity, effects on cell growth, division, structure, metabolism and function, alterations in enzyme activities, and metabolite formation.
Aswith the nonthreshhold assays previously discussed, these invitro assays only serve to qualify potential human health hazards. Inthe caseofpositive invitro results, tests on intact mammals can be pursued inorder to confirm screening test findings and establish a dose-response relationship. Alternatively, causative agent evaluations resulting in either the identity ofthe toxicant ortoxicity treatability data may be pursued.



## Currentlimitations oftheBiological RegulatoryApproach

At present, the useof biological indicator tests asa regulatory tool islimited for anumber of reasons. First, biological indicator information must be linked to human exposure to wastewater components. To date, no definitive mechanism exists for interpreting the human health hazard implications of the biological test results. While many invitro (i.e. test tube) human health assays provide data about cellular changes relative to the dose delivered to the target tissue, they do not provide the information necessary to correlate environmental exposure to target tissue dose or cellular change to ultimate human health effects (e.g., cancer). The higher animal testing necessary to quantify the dose-response relationship (or “potency” of the effluent) would be extremely costly. Second, aswith aquatic organism toxicity tests, ahuman health hazard test must be capable of dealing with intra- and interspecies sensitivity variability. This concern is particularly relevant for those effluents containing chemicals which only become carcinogenic upon metabolism by mammalian systems (i.e. procarcinogens). The useof cultured human liver cells (hepatocytes), currently being tested, would eliminate the need for interspecies extrapolation. Finally, whole effluent testing to assess potential human health impacts presents several unique practical problems such asthe continual change in composition typical for most effluents, the need to concentrate samples to obtain adose-response curve, and the need to compensate for or eliminate interferences from cytotoxic (toxic to ceils) components of the effluent. Only those components which occur in the relatively nonvolatile, nonpolar organic fraction of the effluent sample are conventionally measured. [AndersonCarnahan, article in preparation].
Until additional research resolves these difficulties, biological indicator tests will be most useful asscreening tools, with actual regulation of effluents posing potential health hazards likely to remain on achemical-bychemical basis.

CA1.

2.

3.

4.

5.

6.

7.

8.

9.

16.

18. 

## APPENDIX G
REFERENCES

Anderson-Carnahan, L.,P.M. Eckl,and R.L.Jirlle. Sister Chromatid Exchange in Screening Wastewaters. Inpreparation. Ashby, J.and R.W. Tennant, 1988. Chemical Structure, Salmonella Mutagenicity and Extent of Carcinogenicity asIndicators ofGenotoxic Carcinogens Among 222 Chemicals Tested in Rodents by Ihe U.S.NCI/NTP. Mutation Research 204:17-l 15. Black, J., P. Dumerski, and Zapisek, W. 1980. “Fish Tumor Pathology and Aromatic Hydrocarbon Pollution in a Great Lakes Estuary.” In B. Afghan and D. MacKay (eds.), Hydrocarbons and Halogenated Hydrocarbons inthe Aquatic Environment. Plenum Press,New York, 1980. Borenfreund, E.and J.Puerner. 1985. Toxicity Determined invitro by Morphological Alterations and Neutral RedAbsorption. Toxicology Letters 24. pp. 119-l 24. Brown, N.A. and S.E.Fabro. 1982. The invitro Approach to Teratogenicity Testing. In: K.Snell. ed. Developmental Toxicology. London, England: Croom-Helm, p.p. 31057. Eckl, P.M., S.C.Strom, C. Michalopoulos, and R.L.Jirtle. 1987. Induction ofSister Chromatid Exchanges in Cultured Hepatocytes by Directly and Indirectly Acting Mutagens/Carcinogens. Carcinogenesis 8:1077-l 083. Environment Canada. 1986. Guidelines on the Use of Mutagenicity Tests in the Toxicological Evaluation ofChemicals. Health and Welfare Canada. Ottawa, Canada Glass, L.R. “Background and Rationale for Relative Potency Framework for Evaluating Hazards Associated with Waste Water Samples.” Appendix B in “Health Hazard Evaluation of Waste Water Using Bioassays: Preliminary Concepts”. C. E.Easterly, et. al. Oak Ridge National Laboratory, Oak Ridge, TN 37831-6101 and U.S. EPA Office of Research and Development, Health Effects Research Laboratory, Cincinnati, Ohio 45268. July 1988. Hsie, A.W., D.A. Casciano, D.B. Coach, D.F. Krahn, J.P. O’Neill and B.L. Whitfield. 1981. The Useof Chinese Hamster Ovary Cells to Quantify Specific Locus Mutation and to Determine Mutagenicity of Chemicals. Mutation Research 86:193-214. Jirtle, R.,Duke University Medical School, Durham, NC. Personal communication, April 24, 1989. Johnson, Rodney. Office of Research and Development, Environmental Research Laboratory, Duluth, MN. Personal communication, June 1, 1989. Kimmel, C.L., K. Smith, D.M. Kochhar, and R.M. Pratt. 1982. Overview of invitro Teratogenicity Testing: Aspects ofValidation and Application to Screening. Teratogenesis. Carcinog. Mutagen. 2. pp. 221-229. Lave, L. 8.and G.5.Omenn. 1986. Cost-Effectiveness of Short-Term Tests for Carcinogenicity. Nature 324, 6:29-34. Note: Costs based on 1981 figures for pure chemicals. Marx, J.L.1989. Detecting Mutations inHuman Genes. Science 243. pp. 737-738. McGeorge, LeslieJ., Judith 8. Louis, Thomas B.Atherholt, and Gerard J.McCarrity. 1985. “Mutagenicity Analyses of Industrial Effluents: Results and Considerations for Integration into Water Pollution Control Programs,” in Short-Term Bioassays in the Analysis of Complex Environmental Mixtures, IV. Edited by Michael D. Waters, Shahbeg 5. Sandhu, Joellen Lewtas, Larry Claxton, Gary Strauss and Stephen Nesnow. (Hearst Publishing Corp., 1985). Miller, ].A., and E.C. Miller. 1977. Ultimate Chemical Carcinogens as Reactive Mutagenic Electrophiles, in H.H. Hiatt, J.D. Watson and J.A.Winston (Eds.), Origins of Human Cancer, Cold Springs Harbor Laboratory, pp. 605-628. New Jersey Department of Environmental Protection. Mutagenicity Analyses of Industrial Effluents: Background and Resultsto Date. Office ofScience and Research. August 1983. Quillardet, P.,C.de Bellecombe, and M. Hofnung. 1985. “The SOSChromotest, aCalorimetric Bacterial Assayfor Genotoxins: Validation Study with 83 Compounds”. Mutation Research, 147. pgs. 79-95.19.

20.

21.

22.

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3.5. Organization of Economic Cooperation and Development (OECD). 1984. Guidelines for Testing Chemicals. Section 4-- Health Effects. Director of Information, OECD, 2, rue Andre-Pascal 75775 ParisCEDEX 16, France. Tennant, R.W., 8.H. Margolin, M.D. Shelby, E.Zeiger, J.K. Haseman, J.Spalding, W.Caspary, M. Resnick, 5.Stasiewicz, 6.Anderson, and R.Minor. 1987. Prediction of Chemical Carcinogenicity in Rodents From invitro Genetic Toxicity Assays. Science 236:943-941. U.S. Environmental Protection Agency. 1978. Directory of Short Term Tests for Health and Ecological Effects. Health Effects Research Laboratory. EPA600/l -78-052. U.S. Environmental Protection Agency. 1979a. Environmental Assessment: Short-term Tests for Carcinogens, Mutagens and other Genotoxic Agents. Health Effects Research Laboratory. Research Triangle Park, N.C. U.S. Environmental Protection Agency. 1979b. Short Term Testsfor Carcinogens, Mutagens, and other Genotoxic Agents. Health Effects Research Laboratory, Research Triangle Part. EPA625/9-79-003. U.S. Environmental Protection Agency. 1982a. Pesticide Assessment Guidelines, Office of Pesticide Programs. EPA/9-82-018 through 028. U.S. Environmental Protection Agency. 1982b. Toxic Substances Test Guidelines, Office of Toxic Substances. EPA/6-82-001 through 003. U.S. Environmental Protection Agency. 1983. Interim Procedures for Conducting the Salmonella/MicrosomaI Mutagenicity Assay -Ames Test. EPA600/4-82-068. U.S. Environmental Protection Agency. 1985. Guidelines for Preparing Environmental and Waste Samples for Mutagenicity (Ames) Testing: Interim Procedures and Panel Meeting Proceedings. Office of Research and Development. EPA600/4/85-058. U.S. Environmental Protection Agency. 1986a. Guidelines for the Health RiskAssessment of Chemical Mixtures. Federal Register 51 (185). pp. 34014-34025. U.S. Environmental Protection Agency. 1986b. Guidelines for Mutagenicity RiskAssessment. Federal Register 51 (185). pp. 34006-34012. U.S. Environmental Protection Agency. 1986~. Guidelines for Carcinogen RiskAssessment. Federal Register 51(185). pp. 33932-34003. U.S. Environmental Protection Agency. 1986d. Guidelines for Exposure Assessment. Federal Register 51(185). pp. 34042-34054. U.S. Environmental Protection Agency. 1988. Validation of the Medaka Assay for Chemical Carcinogens: A Progress Report (Deliverable #I8095A). Office of Research and Development, Environmental Research Laboratory, Duluth, MN. August 1988. U.S. Environmental Protection Agency. 1989a. Draft Guidance onAssessment, Criteria Development, and Control of Bioconcen-tratable Contaminants in Surface Waters. U.S. Environmental Protection Agency. 1989b. The Medaka Caarcinogenesis Model: AProgress Report (Deliverable # 8094A). Office of Research and Development, Environmental Research Laboratory, Duluth, MN. February 1989. Wilson, J. G. 1978. Survey of invitro Systems: Their Potential Use inTeratogenicity Screening. InJ.G. Wilson and F.CFiaser,eds. Handbook ofTeratology. Vol. 4. New York, NY. Plenum Press.pp. 135- 153.

# APPENDIX 

# H REFERENCEDOSE

# (RFD): DESCRIPTIONANDUSEIN HEALTHRISKASSESSMENTS

# REFERENCEDOSE

# (RFD) DESCRIPTIONANDUSEIN HEALTHRISKASSESSMENTS (REVISED

# 02/10/88)

PrincipalAuthor:
Donald Barnes, Ph.D. (OPTS)



## RfDWorkGroup:
Donald Barnes, Ph.D. (OPTS) Judith Bellin, Ph.D. (OSWER) Christopher DeRosa, Ph.D. (ORD) Michael Dourson, Ph.D. (ORD), Co-Chair Reto Engler, Ph.D. (OPTS) Linda Erdreich, Ph.D. (ORD) Theodore Farber, Ph.D. (OPTS) Penny Fenner-Crisp, Ph.D. (OW) Elaine Francis, Ph.D. (OPTS) George Ghali, Ph.D. (OPTS) Richard Hill, M.D., Ph.D. (OPTS) Stephanie Irene, Ph.D. (OPTS) William Marcus, Ph.D. (OW) David Patrick, P.E., B.S.(OAR) Susan Perlin, Ph.D. (OPPE) Peter Preuss,Ph.D. (ORD), Co-Chair Aggie Revesz, B.S.(OPTS) Reva Rubenstein, Ph.D. (OSWER) JerryStara, D.V.M., Ph.D. (ORD) Jeanette Wiltse, Ph.D. (OPTS) Larry Zaragosa, Ph.D. (OAR)

H-1

# REFERENCEDOSE

# (RFD): DESCRIPTIONANDUSEIN HEALTHRISKASSESSMENTS

Introduction

This concept paper describes the U.S. Environmental Protection Agency’s (U.S. EPA)principal approach to and rationale for assessing risk for health effects other than cancer and gene mutations from chronic chemical exposure. Byoutlining principles and concepts that guide EPArisk assessment for such systemic effects the paper complements the new riskassessment guidelines (U.S. EPA,1987), which describe the Agency’s approach to risk assessment in other areas, specifically carcinogenicity, mutagenicity, developmental toxicity, exposure, and chemical mixtures. (In this document the term “systemic toxicity” refers to an effect other than carcinogenicity or mutagenicity induced by atoxic chemical.) 

### BackgroaadandSunnnary Chemicals that give rise to toxic endpoints other than cancer and gene mutations are often referred to as “systemic toxicants” because oftheir effects on the function ofvarious organ systems. Inaddition, chemicals that cause cancer and gene mutations also commonly evoke other toxic effects (i.e., systemic toxicity). Basedon our understanding of homeostatic and adaptive mechanisms, systemic toxicity istreated as ifthere isan identifiable exposure threshold (both for the individual and for populations) below which there are no observable adverse effects. This characteristic distinguishes systemic endpoints from carcinogenic and mutagenic endpoints, which areoften treated asnonthreshold processes. Systemic effects have traditionally been evaluated using such terms as“acceptable daily intake (ADI),” “safety factor (SF),” and “margin of safety (MOS),” concepts that are associated with certain limitations described below. The U.S.EPAestablished the Reference Dose (RfD)Work Group toaddress these concerns.
Inpreparing this report, the RfDWork Group hasdrawn ontraditional report on riskassessment (NRC, 1983), to more fully articulate the use of noncancer, nonmutagenic experimental data in reaching regulatory decisions about the significance ofexposures to chemicals. Inthe process, the Work Group hascoined lessvalue-laden terminology --“reference dose (RfD),” “uncertainty factor (UF)“; “margin ofexposure (MOE)“; and “regulatory dose (RgD)” --to clarify and distinguish between aspects of risk assessment and risk management. These concepts arecurrently ingeneral useinmany parts ofU.S.EPA.



## Traditional ApproachtoAssessingSystemicToxicity

The U.S. EPA’sapproach to assessing the risksassociated with systemic toxicity isdifferent from itsapproach to assessing the risksassociated with carcinogenicity, because ofthe different mechanisms ofaction thought to be involved inthe two cases. Inthe caseofcarcinogens, the Agency assumes that asmall number of molecular events can evoke changes inasingle cell that can lead to uncontrolled cellular proliferation. This mechanism for carcinogenesis isreferred to as“nonthreshold,” since there istheoretically no level ofexposure for such a chemical that does not pose asmall, but finite, probability ofgenerating acarcinogenic response. Inthe case ofsystemic toxicity, however, organic homeostatic, compensating, and adaptive mechanisms exist that must be overcome before atoxic endpoint ismanifested. Forexample, there could be alarge number ofcellsperforming the same orsimilar function whose population must besignificantly depleted before the effect isseen.
The threshold concept isimportant inthe regulatory context. The individual threshold hypothesis holds that a range of exposures from zero to some finite value can betolerated bythe organism with essentially no chance of expression ofthe toxic effect. Further, it isoften prudent to focus on the most sensitive members ofthe population; therefore, regulatory efforts are generally made to keep exposures below the population threshold, which is defined asthe lowest ofthe thresholds ofthe individuals within a population.

H-2

### &script/on oftheTradifioaalAmroach In many cases, risk decisions on systemic toxicity have been made by the Agency using the concept of the “acceptable daily intake (ADI)” derived from an experimentally determined “no-observed-adverse-effect level (NOAEL).” The ADI is commonly defined asthe amount ofachemical towhich aperson can be exposed on a daily basis over an extended period oftime (usually alifetime) without suffering adeleterious effect. The ADI concept has often been used as atool in reaching risk management decisions (e.g., establishing allowable levels of contaminants infoodstuffs and water.)
A NOAEL isan experimentally determined dose at which there was no statistically or biologically significant indication of the toxic effect of concern. In an experiment with several NOAELs, the regulatory focus is normally on the highest one, leading to the common usage of the term NOAEL asthe highest experimentally determined dose without astatistically or biologically significant adverse effect. The NOAEL for the criticaf toxic effect issometimes referred to simply asthe NOEL. This usage, however, invites ambiguity inthat there may be observable effects that are not of toxicological significance (i.e., they are not “adverse”). Forthe sake of precision, this document usesthe term NOAEL to mean the highest NOAEL in an experiment. In cases in which a NOAEL has not been demonstrated experimentally, the term “lowest-observed-adverse-effect level (LOAEL)” isused.
Once the critical study demonstrating the toxic effect of concern has been identified, the selection of the NOAEL results from an objective examination of the data available on the chemical in question. The ADI is then derived by dividing the appropriate NOAEL by asafety factor (SF),asfollows:
ADI (human dose) =NOAEL (experimental dose)/SF (Equation 1) Generally, the SFconsists of multiples of 10, each factor representing aspecific area of uncertainty inherent in the available data. For example, afactor of 10 may be introduced to account for the possible differences in responsiveness between humans and animals inprolonged exposure studies. A second factor of 10may be used to account for variation insusceptibility among individuals inthe human population. The resultant SFof 100 has been judged to be appropriate for many chemicals. For other chemicals, with databases that are less complete (for example, those for which only the results of subchronic studies are available), an additional factor of 10 (leading to aSFof 1000) might bejudged to be more appropriate. Forcertain other chemicals, based onwell-characterized responses insensitive humans (asinthe effect offluoride on human teeth), an SFas small as1might beselected.
While the original selection of SFsappears to have been rather arbitrary (Lehman and Fitzhugh, 1954), subsequent analysis ofdata (Dourson and Stara, 1983) lends theoretical (and in some instances experimental) support for their selection. Further, some scientists, but not all, within the EPAinterpret the absence of widespread effects in the exposed human populations asevidence of the adequacy of the SFstraditionally employed.


### SameDiffice/t/es inUt/Mn# theTraditimwlApproach
ScientificIssues While the traditional approach hasperformed well over the years and the Agency hassought to be consistent in its application, observers have identified scientific shortcomings of the approach. Examples include the following:

a. Too narrow afocus onthe NOAEL means that information onthe shape ofthe dose-response curve is ignored. Such data could be important inestimating levels ofconcern for public safety. b. Asscientific knowledge increases and the correlation of precursor effects (e.g., enzyme induction) with toxicity becomes known, questions about the selection of the appropriate “adverse effect” arise.

C. Guidelines have not been developed to take into account thefact that some studies have used larger (smaller) numbers ofanimals and, hence, are generally more (less) reliable than other studies.

H-3These and other “scientific issues” are not susceptible to immediate resolution, since the database needed isnot yet sufficiently developed or analyzed. U.S. EPAwork groups are presently considering these issues. 

### Management-relatedIssues The use of the term “safety factor” -The term “safety factor” suggests, perhaps inadvertently, the notion of absolute safety (i.e., absence of risk). While there isaconceptual basis for believing in the existence of a threshold and “absolute safety” associated with certain chemicals, in the majority of casesafirm experimental basisfor this notion does not exist.
The implication that any exposure in excess of the ADI is“unacceptable” and that any exposure less than the ADI is “acceptable” or “safe” - In practice, the ADI isviewed by many (including risk managers) asan “acceptable” level ofexposure, and, by inference, any exposure greater than the ADI isseen as“unacceptable.” This strict demarcation between what is“acceptable” and what is“unacceptable” iscontrary to the views of most toxicologists, who typically interpret the ADI asa relatively crude estimate of alevel of chronic exposure which isnot likely to result in adverse effects to humans. The ADI isgenerally viewed by riskassessors asa “soft” estimate, whose bounds of uncertainty can span anorder of magnitude. That is, within reasonable limits, while exposures somewhat higher than the ADl are associated with increased probability ofadverse effects, that probability is not acertainty. Similarly, while the ADI isseen asalevel at which the probability of adverse effects islow, the absence of all riskto all people cannot be assured atthis level.
Possible limitations imposed on risk management decisions - Awareness of the “softness” of the ADI estimate, asdiscussed above, argues for careful case-by-case consideration of the toxicological implications of individual situation, sothat ADls are not given adegree of significance that isscientifically unwarranted. In addition, the ADI isonly one factor inarisk management decision and should not be used to the exclusion of other relevant factors. Development of different ADls by different programs - In addition to occasionally selecting different critical toxic effects, Agency scientists have reflected their best scientific judgments in the final ADI by adopting factors different from the standard factors listed inTable 1. Forexample, ifthe toxic endpoint for a chemical in experimental animals isthe same asthat which has been established for a related chemical in humans atsimilar doses, one could argue for an SFof lessthan the traditional 100. On the other hand, ifthe total toxicologic data base isincomplete, one could argue that an additional SFshould be included, both asa matter ofprudent public policy and asanincentive to others to generate the appropriate data.
Such practices, asemployed by anumber of scientists in different programs/agencies, exercising their best scientific judgment, have in some cases resulted in different ADls for the same chemical. The fact that different ADls were generated (for example, by adopting different SFs) can be a source of considerable confusion when the ADls are used exclusively in risk management decisionmaking. The existence of different ADls need not imply that any ofthem is more “wrong”--or “right”--than the rest. Itis more nearly areflection of the honest difference in scientific judgment. However, on occasion, these differences injudgment ofthe scientific data, can be interpreted asdifferences in the management of the risk. As a result, scientists may be inappropriately impugned, and/or perfectly justifiable risk management decisions may be tainted by charges of “tampering with the science.” This unfortunate state ofaffairs arises, at least inpart, from treating the ADI asanabsolute measure ofsafety.



## EPAAssessmentofRisksAssoclatsdwithSystemicToxlclty

The U.S. EPAapproach to analyzing systemic toxicity data follow the general format set forth by NRC in its description of the risk assessment process (NRC, 1983). The determination of the presence of risk and its potential magnitude ismade during the riskassessment process, which consists of hazard identification, doseresponse assessment, exposure assessment, and riskcharacterization. Having been apprised by the riskassessor that a potential risk exists, the risk manager considers control options available under existing statutes and other relevant non-risk factors (e.g., benefits to begained and costs to be incurred). All ofthese considerations go into the determination ofthe regulatory decision (Figure 1).

### Table 1. Guidelines for the Use of Uncertainty Factors in Deriving Reference Doses and Modifying Factors

Standard 

### Uncertainty Factors(Ws): Use a lo-fold factor when extrapolating from valid experimental results in studies using prolonged exposure to average healthy humans. This factor isintended to account for the variation in sensitivity among the members of the human population and isreferenced 

### as “1OH”.
Usean additional IO-fold factor when extrapolating from valid results of long-term studies on experimental animals when results ofstudies of human exposure are not available or are inadequate. This factor is intended to account for the uncertainty involved in extrapolating from animal data to humans and isreferenced 

### as“1OA”.
Use an additional lo-fold factor when extrapolating from less than chronic results on experimental animals when there are no useful long-term human data. This factor is intended to account for the uncertainty involved in extrapolating from lessthan chronic NOAELs tochronic NOAELs and isreferenced as“10s”.
Use

### anadditional lo-fold factor when deriving an RfDfrom a LOAEL, instead of a NOAEL. This factor is intended to account for the uncertainty involved in extrapolating from LOAELsto NOAELs and isreferenced 

### as“1OL”. 

### ModlfyinRgFactor(MF): Use professional judgment to determine the MF, which isan additional uncertainty factor that isgreater than zero and lessthan or equal to 10. The magnitude of the MF depends upon the professional assessment of scientific uncertainties of the study and data base not explicitly treated above; e.g., the completeness of the overall data base and the number of species tested. The default value for the MFis1.

*Source: Adapted from Dourson and Stara, 1983



### Hazardfdmtlficatlon

Evidence Type of

### effect -Exposure to agiven chemical, depending on the dose employed, may result inavariety oftoxic effects. These may range from gross effects, such as death, to more subtle biochemical, physiologic, or pathologic changes. In assessments of the risk posed by achemical, the toxic endpoints from all available studies are considered, although primary attention usually is given to the effect (the “critical effect”) exhibiting the lowest NOAEL. In the case of chemicals with limited data bases, additional toxicity testing may be necessary before anassessment canbemade. 

### Principal studies - Pnncrpal studies are those that contrtbute most srgnrfrcantly to the qualrtatrve assessment ofwhether or not aparticular chemical 

### ispotentially asystemic toxicant inhumans. Inaddition, they may beFigure 1. Conceptual Framework for RiskAssessment and Risk Management*

RiskAssessment RiskManagement

Hazard Identification Control Options
Dose-Response Assessment (e.g., RfD) Exposure Assessment Non-risk Analyses

V V V V V RiskCharacterization

(e.g., Criterion) >Regulatory Decision (e.g., RgD,Standard)

*Source: Adapted from NRC, 1983

used inthe quantitative dose-response assessment phase ofthe riskassessment. These studies areoftwo types: studies of human populations (epidemiologic investigations) and studies using laboratory animals.

1. Epidemiologic studies - Human data are often useful in qualitatively establishing the presence of an adverse effect inexposed human populations. When there isinformation on the exposure level associated with an appropriate endpoint, epidemiologic studies can also provide the basis for a quantitative dose-response assessment. The presence of such data obviates the necessity of extrapolating from animals to humans; therefore, human studies, when available, are given first priority, with animal toxicity studies serving to complement them. Inepidemiologic studies, confounding factors that are recognized can be controlled and measured, within limits. Case reports and acute exposures resulting insevere effects provide support for the choice of critical toxic effect, but they are often of limited utility in establishing a quantitative relationship between environmental exposures and anticipated effects. Available human studies on ingestion are usually of this nature. Cohort studies and clinical studies may contain exposureresponse information that can be used in estimating effect levels, but the method ofestablishing exposure must be evaluated for validity and applicability. 2. Animal studies - For most chemicals, there is a lack of appropriate information on effects in humans. Insuch cases,the principal studies are drawn from experiments conducted on nonhuman mammals, most often the rat, mouse, rabbit, guinea pig, hamster, dog, ormonkey.
Supporting studies -These studies provide supportive, rather than definitive, information and can include data from awide variety ofsources. Forexample, metabolic and other pharmacokinetics studies can provide insights into the mechanism ofaction ofaparticular compound. Bycomparing the metabolism ofthe chemical exhibiting the toxic effect inthe animal with the metabolism found inhumans, it may be possible to assessthe potential for toxicity inhumans orto estimate the equitoxic dose in humans. Similarly, in vitro studies can provide insights into the chemical’s potential for biological activity; and under certain circumstances, consideration of structure-activity relationships between a chemical and otherstructurally related compounds can provide clues to the test chemical’s possible toxicity. More reliable invitro tests are presently being developed to minimize the need for live-animal testing. There is also increased emphasis on generating mechanism-of-action and pharmacokinetics information as a means of increasing understanding oftoxic processes inhumans and nonhumans.
Route of exposure - The U.S. EPAoften approaches the investigation ofachemical with aparticular route of exposure in mind (e.g., an oral exposure for adrinking water contaminant oran inhalation exposure for an air contaminant). In most cases, the toxicologic data base does not include detailed testing on all possible routes of administration, with their possibly significant differences infactors such asmechanism-of-action and bioavailability. In general, the U.S. EPA’sposition isthat the potential for toxicity manifested via one route of exposure isrelevant to considerations of any other route of exposure, unless convincing evidence exists to the contrary. Consideration is given to potential differences in absorption or metabolism resulting from different routes of exposure, and whenever appropriate data (e.g., comparative metabolism studies) are available, the quantitative impacts ofthese differences on the riskassessment are delineated. Length of exposure -The U.S. EPAisconcerned about the potential toxic effects in humans associated with all possible exposures to chemicals. The magnitude, frequency, and duration ofexposure may vary considerably in different situations. Animal studies are conducted using a variety of exposure durations (e.g., acute, subchronic, and chronic) and schedules (e.g., single, intermittent, or continuous dosing). Information from all these studies is useful in the hazard identification phase of risk assessment. For example, overt neurological problems identified in high-dose acute studies tend to reinforce the observation ofsubtle neurological changes seen in low-dose chronic studies. Special attention isgiven to studies involving low-dose, chronic exposures, since such exposures can elicit effects absent in higher dose, shorter exposures, through mechanisms such as accumulation oftoxicants inthe organisms. Quality of the study - Evaluation of individual studies in humans and animals requires the consideration of several factors associated with a study’s hypothesis, design, execution, and interpretation. An ideal study addresses a clearly delineated hypothesis, follows a carefully prescribed protocol, and includes sufficient subsequent analysis to support its conclusions convincingly.
In evaluating the results from such studies, consideration isgiven to many other factors, including chemical characterization ofthe compound(s) under study, the type oftest species, similarities and differences between the test species and humans (e.g., chemical absorption and metabolism), the number ofindividuals inthe study groups, the number ofstudy groups, the spacing and choice ofdose levels tested, the types ofobservations and methods of analysis, the nature of pathologic changes, the alteration inmetabolic responses, the sex and age oftest animals, and the route and duration ofexposure. 

### Weight-of-EvidenceDeterminatlon Asthe culmination of the hazard identification step, a discussion of the weight-of-evidence summarizes the highlights of the information gleaned from the principal and supportive studies. Emphasis is given to examining the results from different studies to determine the extent to which aconsistent, plausible picture of toxicity emerges. Forexample, the following factors add to the weight ofthe evidence that the chemical poses ahazard to humans: similar results in replicated animal studies by different investigators; similar effects across sex, strain, species, and route of exposure; clear evidence ofadose-response relationship; aplausible relation between data on metabolism, postulated mechanism-of-action, and the effect of concern; similar toxicity exhibited by structurally related compounds; and some link between the chemical and evidence ofthe effect of concern inhumans. 

### Dose-ResponseAssessment ConceptsandProblems Empirical observations have generally revealed that asthe dosage ofatoxicant isincreased, the toxic response (in terms ofseverity and/or incidence ofeffect) also increases. This dose-response relationship iswell- founded in the theory and practice of toxicology and pharmacology. Such behavior is observed in the following instances: in quanta1 responses, inwhich the proportion of responding individuals in apopulation increases with dose; in graded responses, in which the severity of the toxic response within an individual increases

H-7with dose; and in continuous responses, in which changes in a biological parameter (e.g., body or organ weight) vary with dose.
In evaluating adose-response relationship, certain difficulties arise. For example, one must decide on the critical endpoint to measure asthe “response.” One must also decide on the correct measure of “dose.’ tn addition to the interspecies extrapolation aspects ofthe question of the appropriate units for dose, the more fundamental question ofadministered doseversus absorbed doseversus target organ dose should be considered. These questions arethesubject ofmuch current research. 

### SelectionoftheCriticalOata Critical study - Data from experimental studies in laboratory animals are often selected asthe governing information when performing quantitative riskassessments, since available human data are usually insufficient for this purpose. These animal studies typically reflect situations inwhich exposure to the toxicant has been carefully controlled and the problems of heterogeneity ofthe exposed population and concurrent exposures to other toxicants have been minimized. Inevaluating animal data, aseries of professional judgments are made which involve, among others, consideration of the scientific quality of the studies. Presented with data from several animal studies, the riskassessorfirst seeksto identify the animal model that ismost relevant to humans, based on the most defensible biological rationale (e.g., for instance using comparative pharmacokinetics data). Inthe absence of aclearly most relevant species, the most sensitive species (i.e., the species showing atoxic effect at the lowest administered dose) isused by riskassessors at U.S. EPA,since there isno assurance that humans are not at least asinnately sensitive asthe most sensitive species tested. This selection process is more difficult when the routes of exposure in the animal tests are different from those involved in the human situation under investigation. In order to use data from controlled studies of genetically homogeneous animals, the riskassessor must also extrapolate from animals to humans and from high experimental doses to comparatively low environmental exposures, and must account for human heterogeneity and possible concurrent human exposures toother chemicals. Although for most chemicals there is a lack of well-controlled cohort studies investigating noncancer endpoints, in some cases an epidemiologic study may be selected as the critical data (e.g., in cases of cholinesterase inhibition). Riskassessments based on human data have the advantage ofavoiding the problems inherent in interspecies extrapolation. In many instances, use of such studies, as isthe case with animal investigations, involves extrapolation from relatively high doses (such as those found in occupational settings) to the low doses found in the environmental situations to which the general population is more likely to be exposed. In some cases, awell-designed and well-conducted epidemiologic study that shows no association between known exposures and toxicity can be used to directly project an RfD(ashasbeen done in the case offluoride).
Critical data - Inthe simplest terms, an experimental exposure level isselected from the critical study that represents the highest level tested in which “no adverse effect” was demonstrated. This “no-observed-adverseeffect-level” (NOAEL) is the key datum gleaned from the study of the dose-response relationship and, traditionally, isthe primary basis for the scientific evaluation of the risk posed to humans by systemic toxicants. This approach isbased on the assumption that ifthe critical toxic effect is prevented, then all toxic effects are prevented.
More formally, the NOAEL isdefined in this discussion asthe highest experimental dose ofachemical atwhich there is no statistically or biologically significant increase in frequency or severity of an adverse effect in individuals in an exposed group when compared with individuals in an appropriate control group. As noted above, there may besound professional differences ofopinion injudging whether or not aparticular response is adverse. Inaddition, the NOAEL isafunction ofthe size ofthe population under study. Studies with asmall number ofsubjects are lesslikely to detect low-dose effects than studies using larger numbers ofsubjects. Also, if the interval between doses in an experiment is large, it is possible that the experimentally determined NOAEL islower than that which would be observed inastudy using intervening doses.
Critical endpoint - Asnoted under “Traditional Approach to Assessing Systemic Toxicity”, achemical may elicit more than one toxic effect (endpoint), even in one test animal, or in tests of the same or different duration (acute, subchronic, and chronic exposure studies). Ingeneral, NOAELs for these effects will differ. The critical endpoint used inthe dose- response assessment isthe effect exhibiting the lowest NOAEL.

Ha

### ReferenceDoss(RfD) The reference dose (RfD) and uncertainty factor (UF) concepts have been developed by the RfDWork Group in response to many of the problems associated with ADls and SFs,asoutlined under “Traditional Approach to Assessing Systemic Toxicity” above. The RfD isabenchmark dose operationally derived from the NOAEL by consistent application of generally order-of-magnitude uncertainty factors (UFs) that reflect various types of data sets used to estimate RfDs. Forexample, avalid chronic animal NOAEL isnormally divided by an UFof 100. Inaddition, amodifying factor (MF), issometimes used which isbased on aprofessional judgment of the entire data base ofthe chemical. These factors and their rationales are presented inTable 1.
The RfDisdetermined by useofthe following equation:
RfD=NOAEL /(UFxMF)
which is the functional equivalent of Equation 1. In general, the RfD is an estimate (with uncertainty spanning perhaps an order-of-magnitude) of adaily exposure to the human population (including sensitive subgroups) that islikely to be without an appreciable risk of deleterious effects during a lifetime. The RfD is generally expressed in units of milligrams per kilogram ofbody weight per day (mg/kg/day).
The RfDisuseful asareference point from which to gauge the potential effects ofthe chemical at other doses. Usually, doses lessthan the RfDare not likely to be associated with adverse health risks,and are therefore less likely to be of regulatory concern. Asthe frequency and/or magnitude of the exposures exceeding the RfD increase, the probability of adverse effects in a human population increases. However, it should not be categorically concluded that all doses below the RfDare “acceptable” (or will be risk-free) and that all doses inexcess ofthe RfDare “unacceptable” (or will result inadverse effects).
The U.S. EPA is attempting to standardize its approach to determining RfDs. The RfD Work Group has developed asystematic approach to summarizing itsevaluations, conclusions, and reservations regarding RfDs ina“cover sheet” ofafew pages in length. The cover sheet includes astatement on the confidence (high, medium, or low) the evaluators have inthe stability ofthe RfD. High confidence indicates the judgment that the RfDisunlikely to change inthe future because there isconsistency among the toxic responses observed in different sexes, species, study designs, or in dose-response relationships, or that the reasons for existing differences arewell understood. High confidence isoften given to RfDsthat are based on human data for the exposure route of concern, since in such casesthe problems of interspecies extrapolation have been avoided. Low confidence indicates the judgment that the data supporting the RfD may be of limited quality and/or quantity and that additional information could result inachange inthe RfD.

The third step in the risk assessment process focuses on exposure issues. For a full discussion of exposure assessment, consult U.S. EPA’sguidelines on the subject (U.S. EPA, 1987). In brief, the exposure assessment includes consideration of the size and nature of the populations exposed and the magnitude, frequency, duration and routes ofexposure, aswell asevaluation ofthe nature ofthe exposed populations.


### R/s&Cbafact8rlzathm Riskcharacterization isthe final step inthe riskassessment process and the first input to the risk management (regulatory action) process. The purpose of riskcharacterization isto present the risk manager with asynopsis and synthesis ofallthe data that should contribute to aconclusion with regard to the nature and extent ofthe risk, including:

a. The qualitative (“weight-of-evidence”) conclusions asto the likelihood that the chemical may pose ahazard to human health. b. A discussion of the dose-response information considered in deriving the WD, including the UFs and MFsused.

C. Data on the shapes and slopes of the dose-response curves for the various toxic endpoints, toxicodynamics (absorption and metabolism), structure-activity correlations, and the nature and severity ofthe observed effects.

l+9d. Estimates ofthe nature and extent ofthe exposure and the number andtypes ofpeople exposed.
e. Discussion of the overall uncertainty in the analysis, including the major assumptions made, scientific judgments employed, and an estimate ofthe degree ofconservatism involved.

Inthe risk characterization process, acomparison ismade between the RfDand the estimated (calculated or measured) exposure dose (EED). The EEDshould include allsources and routes ofexposure involved. Ifthe EED islessthan the RfD,the need for regulatory concern islikely to besmall.
An alternative measure that may be useful to some riskmanagers isthe margin ofexposure (MOE), which isthe magnitude by which the NOAEL ofthe critical toxic effect exceeds the estimated exposure dose (EED), where both areexpressed inthesame units:
MOE =NOAEL (experimental dose)/EED(human dose).
When the MOE isequal to orgreater than UFxMF,the need for regulatory concern islikely to besmall. “Hypothetical, Simplified Example of Determining and Using WD” contains an example of the use of the concepts ofNOAEL, UF, MF,RfD,EED,and MOE.


## ApplicationInRiskManagement
Once the riskcharacterization iscompleted, the focus turns to riskmanagement. Inreaching decisions, the risk manager utilizes the results of risk assessment, other technological factors, and legal, economic and social considerations in reaching a regulatory decision. These additional factors include efficiency, timeliness, equity, administrative simplicity, consistency, public acceptability, technological feasibility, and nature of the legislative mandate.
Because of the way these risk management factors may impact different cases, consistent --but not necessarily identical --riskmanagement decisions must bemade on acase-by-case basis. Forexample, the Clean Water Act calls for decisions with “an ample margin of safety”; the Federal Insecticide, Fungicide and Rodenticide Act (FIFRA) calls for “an ample margin of safety,” taking benefits into account; and the Safe Drinking Water Act (SDWA) calls for standards which protect the public “to the extent feasible.” Consequently, it isentirely possible and appropriate that a chemical with a specific RfD may be regulated under different statutes and situations through the useofdifferent “regulatory doses (RgDs).”
That is, after carefully considering the various risk and nonrisk factors, regulatory options, and statutory mandates in agiven case (i), the risk manager selects the appropriate statutory alternative for arriving at an “ample” or “adequate” margin ofexposure [MOE(i)]. Asshown in Equation 2 below, this procedure establishes the regulatory dose, RgD(i) (e.g., atolerance under FIFRAor a maximum contaminant level under SDWA), applicable to the case in question: RgD(i) =NOAEL / MOE(i) (Equation 2)
Note that different RgDs are possible for agiven chemical with asingle RfD. Note alsothat comparing the RfD to aparticular RgD(i) isequivalent to comparing the MOE(i) with the UFx MF: RfD/RgD(i) =MOE(i) /(UFxMF).
In assessing the significance of acase in which the RgD isgreater (or less) than the RfD, the risk manager should carefully consider the case- specific data compiled by the risk assessors, as discussed under “Risk Characterization”. In some cases, additional explanation and interpretation may be required from the risk assessors inorder to arrive at aresponsible and clearly articulated final decision on the RgD.
Itisgenerally useful to the riskmanager to have information regarding the contribution to the RfDfrom various environmental media (e.g., air, water and food). Such information can provide insights that are helpful in

H-10choosing among available control options. However, in cases in which site-specific criteria are being considered, local exposures through various media can often be determined more accurately than exposure estimates based upon generic approaches. Insuch cases, the exposure assessor’s role isparticularly important. For instance, at agiven site, consumption offish may clearly dominate the local exposure routes, while, on a national basis,fish consumption may play aminor role compared to ingestion oftreated crops.
Work isunderway inthe U.S. EPAto apportion the RfDamong the various environmental media. Forexample, consider the case of afood-use pesticide which isacontaminant in drinking water. Inselecting among risk management actions under the SafeDrinking Water Act, itmight beprudent to assume an RfDfordrinking water purposes which issome fraction ofthe total RfD. Such an apportionment would explicitly acknowledge the possible additional exposure from ingestion oftreated crops. The apportionment ofthe RfDwould, ingeneral, provide additional guidance for riskmanagers ofthe various media- specific programs.



## OtherDirections

In addition to the development of reference doses, the U.S. EPAispursuing other lines of investigation for systemic toxicity. For example, the Office of Air Quality Planning and Standards isusing probabilistic risk assessment procedures for criteria pollutants. Inthis procedure, the population at risk ischaracterized, and the likelihood ofthe occurrence ofvarious effects ispredicted through the useof available scientific literature and of scientific experts’ rendering their judgments concerning dose-response relationships. This dose-response information isthen combined with the results ofthe exposure analysis to generate population riskestimates for alternative standards. Through the useofthese procedures, decisionmakers are presented with ranges ofrisk estimates in which uncertainties associated with both the toxicity and exposure information are explicitly considered. The Office of Policy, Planning and Evaluation is investigating similar procedures in order to balance health riskand cost. Inaddition, scientists in the Office of Research and Development have initiated aseries ofstudies designed to increase the reliability of riskassessments. They are investigating the use of extrapolation models as a means of estimating RfDs, taking into account the statistical variability of the NOAEL and underlying UFs. ORD isalso exploring procedures for conducting health risk assessments that involve less- than-lifetime exposures. Finally, they are working on approaches to ranking the severity of different toxic effects.


## Hypothetical,Simplified ExampleofDeterminingandUsingRfD
ExperimenttalResults Suppose the U.S.EPAhad asound 90-day subchronic gavage study inratswith the data inTable 2. 

### Analysis

DeterminationoftheReferenceDose(RfD) Using the NOAEL - Because the study isonanimals and ofsubchronic duration,
UF=10H x 10Ax10s =1000 (Table 1)
Inaddition, there isasubjective adjustment (MF) based on the high number ofanimals (250) per dose group: MF=0.8. Thesefactors then give UFxMF=800, sothat
RfD=NOAEL/(UF xMF) =S/800 =0.006 (mg/kg/day).

H-l1

### Table 2. Hypothetical Data to 

### Illustrate the Reference Dose Concept

Dose mg/kg/day Observation Effect Level

0 Control-no adverse effects observed - 1 No statistically or biologically significant differences NOEL between treated and control animals
5 % decrease* inbody weight gain (not considered to be NOAEL of biological significance); increased ratio of liver weight to body weight; histopathology indistinguishable from controls; evaluated liver enzyme levels
25 20% decrease* inbody weight gain; increased’ ratio of liver weight to body weight; enlarged, fatty liver with vacuole formation; increased* liver enzyme levels LOAEL

‘Statistically significant compared to controls.

Using the LOAEL -Ifthe NOAEL isnot available, and if25 mg/kg/day had been the lowest dose tested that showed adverse effects,
UF=10Hx10Ax 10sx1OL=10,000 (Table 1). Using again the subjective adjustment of MF=0.8, one obtains
RfD=LOAEL/(UF xMF) =ZS/SOOO=0.003 (mg/kg/day).


### RiskCharacterization Considerations Suppose the estimated exposure dose (EED)for humans exposed tothe chemical under the proposed usepattern were 0.01 mg/kg/day (i.e., the EEDisgreater than the RfD). Viewed alternatively, the MOE is:
MOE =NOAEL/EED =S(mg/kg/day) / 0.01 (mg/kg/day) =500. Because the EEDexceeds the RfD(and the MOE islessthan the UFxMF), the riskmanager will need to look carefully atthe data set,the assumptions for both the RfDand the exposure estimates, and the comments ofthe riskassessors. Inaddition, the riskmanager will need toweigh the benefits associated with the case,and other non-risk factors, in reaching adecision on the regulatory dose (RgD).

H-l2

## APPENDIX H
REFERENCES

1. Dourson, M.L. and J.F.Stara. 1983. Regulatory Toxicology and Pharmacology. 3:224-238. 2. Lehman, A.J.and O.G. Fitzhugh. 1954. Association of Food Drug Officials. USQ Bull. 18: 33-35. 3. NRC (National Research Council). 1983. RiskAssessment in the Federal Government: Managing the Process. NAS Press,Washington, DC. 4. U.S. EPA. 1987. The Risk Assessment Guidelines of 1986. Office of Health and Environmental Assessment, Washington, DC. EPA/600/8-871045.

H-l3

# APPENDIX I CHEMICALS AVAILABLE IN

# IRISPage No. 1 09/28/90

CASChemical RfD q1* numberName mg/kg/day /mg/kg/day USEONLYFORSCREENING Values from IRIS 9/1/90 Consult IRIS for Update and Whenever Possible, Site Specific

Lipid, Consumption and Bioaccumulation Factors Should be Used in Application of RACin Regulatory Action.

50000 Formaldehyde 0.2 50293 p,p1-DDT 0.0005 50328 Benzo[a]pyrene ** 51285 2,4-Dinitrophenol 0.002 55185 N-Nitrosodiethylamine ** 56235 Carbon tetrachloride 0.0007 56359 Tributyltin oxide 0.00003 56382 Parathion ** 57125 Cyanide, free 0.02 57249 Strychnine 0.0003 57749 Chlordane 0.00006 58899 gamma-Hexachlorocyclohexane 0.0003 58902 2,3,4,6-Tetrachlorophenol 0.03 60297 Ethyl ether 0.2 60515 Dimethoate 0.0002 60571 Dieldrin 0.00005 62384 Phenylmercuric acetate 

### 0.00008 62533 Aniline 

### ** 62737 Dichlorvos 0.0008 62759 N-Nitrosodimethylamine ** 63252 Carbaryl 0.1 64186 Formic acid 2 65850 Benzoic acid 4 67561 Methanol 0.5 67641 Acetone 0.1 67663 Chloroform 0.01 67721 Hexachloroethane 0.001 70304 Hexachlorophene 0.0003 71363 n-Butenol 0.1 **

**

150 0.13

**

1.3

16

0.0057 0.29 51

**

0.014 Estimated RAC(mg/l) RAC(mg/l) BCF RL: 10E-6 RL: 10E-6 3%Lipid 0.0065 kg/day 0.020 kg/day

40000 10960 5.2 0.40 29.72 0.0001 0.00004

4 0.0002 0.003 1 0.00006 0.0009

87.6

0.4 8 3 3804 0.000002 0.0000007 146.8 0.00002 0.000007 416 0.8 0.3 0.77 3000 900 0.4 6 2 32.04 0.00002 0.000007 0.58 2 0.5 0.84 2 0.7 0.4 0.1 0.3 0.4 0.0005 0.0002 12.2 90 30

4.92 0.4 0.4 5.56 700 40000 0.71 9000 14000 3000 4000

20 6 0.001 0.0004 

### 0.00008 0.00002 2000 500 Consumption Consumption



### I-1Page No. 2 09/20/90

CASChemiccl RfD 41* m&w Name m/kg/day /ng/kg/day USEDNLY FORSCREENING Values from IRIS 9/l/90 Consult IRIS for Ufxlate and Uhenever Possible, Site Specific

lipid, Conscnption and BioaccumLation factors Should be Used in Application of RACin Regulatory Action.

71432 Benzene 71556 l,l,l-Trichloroethane 72208 Endrin 72435 Bethoxychlor 72548 p,p’-ODD 72559 p,p’-DDE 74839 Brcmomethane 74906 Nydrogen cyanide 75058 Acetonitrile 75070 Acetaldehyde 75092 Dichloromethane 75150 Carbon disulfide 75252 Brmofon 75274 Bromodichtoromthane 75354 l,l-Dichloroethylem 75694 lrichloromonofluoromethane 75718 Dichlorodifluoromethane 75876 ChloraL 75990 Dalapon, sodium salt 76131 CFC-113 76448 NeptachLor 77474 RexachIorocycLopentadiene 77781 Dimethyl sulfate 78002 Tetraethyl lead 78488 Merphos oxide 78591 lsophorone 78831 lsobutyl alcohol 78864 2-Chlorobutane 78933 Methyl ethyl ketone l *

0.09 0.0003 0.005
l *
l * 0.0014 0.02 0.006
l *

0.06 0.1 0.02 0.02 0.009 0.3 0.2 0.002 0.03 30 0.0005 0.007 l * 0.029

l *
l * 0.24 0.34

l *

0.0075

0.0079

0.6

4.5
l *
l * 0.0000001 0.00003 0.2 0.0041 0.3 l * l * 0.05 l * Estimated RAC(me/l) RACC&VI, BCF RL: lOE-6 RL: lOE-6 3%Lipid 0.0065 kg/day 0.020 kg/&y Consurpt ion Consrnpt ion

7.84 0.05 0.01 14.52 70 20 32.04 0.1 0.03 2564 0.02 0.007 12840 O-O00004 0.000001 coDoo O.ODOOOW o.oODOOo2 1.13 10 4

0.4 0.4 1.54 200 50

0.9 0.3

11.92 7.16 7.44 13.36 6 3.23 2.31 63.2 692 744 0.4 0.1 0.04 30 10 0.002 0.0008 200 80 400 100 7 2 100 50 5000 2000 0.000004 0.000001 0.1 0.03

9 0.56 15.5 0.4 0.3 0.09 6000 2000

1000 400

l-2Page No. 3 09/28/90

CASChemical RfD ql* ~rName ng/kg/day /ng/kg/day USEONLYFORSCREENING Values from IRIS 9/l/90 Consult IRIS for Update and bhenever Possible, Site Specific

lipid, Consmption and Bioaccuwlation Factors Should be Used in Application of RACin Regulatory Action.

79DD5 1,1,2-Trichloroethane 0.004 79016 Trichloroethylene l * 79061 Acrylamide o.DDO2 79107 Acrylic acid 0.08 79221 Methyl chlorocarbonate l * 79345 1,1,2,2-Tetrachloroethane l * 8OD57 Bishpenol A. 0.05 81812 Uarfarin o.ODD3 82688 Pentachloronitrobenzene 0.003 837% Rotemne 0.004 84662 Diethyl phthalate 0.8 8472D Ethylphthalyl 

### lthylglycolate 3 64742 Dibutyt p)lthalate 0.1 855007Diquat 0.0022 85449 Phthalic hydride 2 85687 Butyl bentyl phthalate 0.2 85701 Butylphthalyl butylglycolate 1 86306 N-Nitrosodiphenylain l * 07683 HexachIorobutadiene 0.002 87821 Nexabromobenzene 0.002 87865 Pentachlorophenol 0.03 88062 2,4,6-Trichlorophenol l * 08857 Dinoseb 0.001 91941 3,3g-dichlorobenridine l * 92524 l,l-Biphenyl 0.05 92875 Benridine 0.002 93652 HCPP 0.003 93721 2.4.5-TP 0.008 93765 2,4,5-Trichlorophenoxyacetic acid 0.01 0.057 0.011 4.5

0.20

l *

t*

l *

0.0049 0.078

c.

0.45

230

l t Estimated RAC(awl) RAC(ng/L) BCF RL: lOE-6 RL: lDE-6 

### 3%Lipid 0.0065 kg/day 0.020 kg/day Consuption Consmption

6.64 0.03 9.84 0.1 0.4 D.OD6 0.4 zoo0

19.52 0.003 166.8 3 4.76 0.7 1160 0.03 91.2 0.5 17.2 SW 7.92 4OOD 808 1

1120 372.4 50 397.2 4560 1568 106 239.2 117 243.2 2.8 48 143.2 81.6 2 0.6 30 9 0.04 0.01 0.0004 O.OODl 0.005 0.002 0.2 0.07

0.05 0.01 D.DW2 o.ww7 2 0.7 0.00002 D.WOOO5 0.7 0.2 0.6 0.2 1 0.4



### l-3Page No. 4 W/28/90 USEDNLY FORSCREENING Values from IRIS 9/l/90 Consult IRIS for Update and Uhenever Possible, Site Specific

Lipid, Consumption and Bioaccmulatim Factors Should be Used in Application of RACin Regulatory Action.
CASChemical RfD ql' nmtn?rNmw Wkg/&y 

## /mg/kg/&,y

94746 HCPA 0.0005 27.32 94757 2,4-Dichloro@wnoxyacetic acid 0.01 2o.w 94815 BCPB 0.01 50.4 94826 4-(2,4-Dichlorophenoxytmtyric acid 0.008 38.56 45487 o-Cresol 0.05 l * 7.6 95498 o-Chlorotoluene 0.02 88 95501 1,2-Dichlorobenzene 0.09 l * 103 95578 2-Chlorophenol 0.005 8.8 95658 3,4-Dimethylphenol 0.001 24.72 95943 1,2,4,5-Tetrachlorobenrene 0.0003 1404 95954 2,4,5-Trichlorophenol 0.1 176.4 96184 1,2,3-Trichloropropane 0.006 5.84 98011 Furfural 0.003 0.54 98077 Benzotrichloride l ,t l * 283 98828 cume 0.04 138 98862 Acetophenone 0.1 2.82 98953 Nitrobenzene 0.0005 4.92 W354 1,3,5-Trinitrobenrene O.WDOS 1.93 99650 n-Dinitrobenrene 0.0001 3.08 100414 Ethylbenrene 0.1 l * 66.8 loo425 styrene 0.2 29.24 100647 Benryl chloride ++ 0.17 21.5 100527 Bentaldehyde 0.1 2.42 101213 Chlorprophan (CIPC) 0.2 96.4 101553 p-Bromodiphenyl ether l * tt 2179 101611 44~Hethylenebis(NN~dimethyl)aniline .* 0.046 888 103231 Di-(2-ethylhexylHtdipnte 0.7 40000 103333 Ambenzene l * 0.11 175.2 1056D2 Caprolactam 0.5 0.4 Estimsted RAC(mgfl) RAC(tag/l) BCF RL: lDE-6 RL: lOE-6 3%Lipid 0.0065 kg/day 0.020 kg/day Conslnption Consumption

0.2 0.06 5 2 2 0.7 2 0.7 70 20 2 0.8 9 3 6 2 0.4 0.1 0.002 0.0007 6 2 10 4 60 20

3 1 400 loo 1 0.4 0.3 0.09 0.4 0.1 20 5 80 20 0.003 D.Wl SW 100 20 7

0.0003 O.DWW 0.2 0.06 0.0006 0.0002 10000 4000

1-4Page No. 5 W/28/90

CASChemical RfD v nu&erNam mgfkgfday fmgfkgfday USEDNLY FORSCREENING Values from IRIS 9/l/90 Consult IRIS for Update and Whenever Possible, Site Specific

Lipid, Consumption and Bioaccmlation Factors Should be Used in Application of RACin Regulatory Action.

106376 1,4-Dibranobenzem 106445 p-Cresol 106478 p-Chloroaniline 106898 Epichlorohydrin 106934 1,2-Dibromoethane 106990 1,3-Butadiene 107028 Acrolein 107051 Ally1 chloride 107062 1,2-Dichloroethane 107131 Acrylonitrile 107186 Ally1 alcohol 107211 Ethylene glycol 107302 Chloromethyl methyl ether 108101 Methyl isobutyl ketone 108316 Maleic anhydride lD8394 m-Cresol 108452 n-Phenylenediamine 108883 Toluene 108907 Chlorobenrene 108918 Cyclohexylmine 108941 Cyclohexanone 108952Phenol 109693 1-Chlorobutane 11WW Furan 110543 n-Hexane 110861 Pyridine 111444 Bis(chloroethyl)ether 114261 Baygon 115297 Endosulfan 0.01 0.05 0.004 0.002
l *
*t
l *
l + l *
l * 0.005 2 ** 0.05 0.1 0.05 0.006 0.2 0.02 0.2 5 0.6 l t 0.001 .* 0.001 t* 0.004 0.00005 .*

0.0099 85
t*

l * 910 0.54

l *

l *

t.
l *

l *

*.

1.1 Estimated RAC(mgfl) RAC(Ml) BCF RL: 10E-6 RL: lOE-6 3%Lipid 0.0065 kg/day 0.020 kg/day Consumption Consumption

181.2 7.6 5.32 0.4 3.76 5.08 0.4 2.20 2.26 0.4 0.4 0.4 0.4 1.38 0.6 0.2 70 20 8 2 3 0.9 0.00003 0.00001

0.000005 0.000002 0.05 0.02 100 40 60000 20000

400 100

7.6 0.4 25.52 28 1.92 0.69 2.33 15.5 1.75 179 0.53 0.98 2.81 70 20 200 50 80 30 8 3 1000 400 80000 30000 3000 900

6

20 0.01 20 2

7 0.003 5



### t-5Page No. 6 W/28/90

CASChemical RfD 41. rrrkrla w/kg/&y 

## /ng/kg/&y USEDNLY FORSCREENING Values from IRIS 9/l/90 Consult IRIS for Update and Uhenever Possible, Site Specific

Lipid, Ccmsrnption and Bioaccumlation Factors Should be Used in Application of RACin Regulatory Action.

115322 Dicofol 116063 Aldicarb 117817 BisCZ-ethylhexyl)phthalate 118741 Hexachloro&ntene 118967 2,4,6-Trinitrotoluem 120127 Anthracene 120616 Dilcthyl terephthalate 12D821 1,2,4-Trichlorobenrene 12D832 2,4-Dichloro@wnol 121142 2,4/2,6-Dinitrotoluene mixture 121697 N-N-Dimethylaniline 121755 Malathion 121824 RDX 122349 Simstine 122394 Diphenylmine 122429 Propham 122667 1,2-Diphenylhydrazine 123331 claleic hydraride 123911 1,4-Dioxane 124403 Dinethylamine 124481 Dibromochloromethane 126987 Methacrylonitrile 127184 Tetrachloroethylene 129000 Pyrefw 131113 Dimethyl phthalate 1318% 4,6-Dinitro-o-cyclohexyl phenol 133062 Captan 133073 Folpet 133904 Chloranrbm l *
l * 0.02 0.0008 o.ooo5 0.3 0.1 l * 0.003
l * 0.002 0.02 0.003 0.002 0.025 0.02
l * 0.5
l *
l * 0.02 0.0001 0.01 0.03
l * 0.002 0.013 0.1 0.015 0.44 l * 0.014

l *

l *

0.68

0.11 l *

0.80
0.011
l *

l *
l *

0.0035

t-6 Estimated RAC(mgfl) RN. (no/l) BCF RL: lOE-6 RL: lOE-6 3%Lipid 0.0065 kg/day 0.020 kg/day

%8D 1.21 4oooo 188W 2.27 550 11.56 383.6 42 5.96 11.16 3.83

15.36 115.2 18 35.36 0.4 0.4 0.4 9.24 0.42 38.72 1280 2.51 800

8.24 Consrnption Consuption

o.oODDD3 O.OllWDO8

0.00002 O.DODD% o.olm O.DOOl 2 0.8 6 2 loo 30

0.8 0.3 0.002 D.ODO9 2 0.6 60 20

1 0.5 2 0.8 10 4 0.0004 O.DoOl 1OODO 4OOD 3 0.8

20 8 3 0.8 3 0.9 0.3 0.08

0.03

20 0.009

6Page No. 7 W/28/90 USEONLYFORSCREENING Values from IRIS 9/l/90 Consult IRIS for Update and Whenever Possible, Site Specific

Lipid, Consumption and BioaccuwLation Factors Should be Used in Application of RACin Regulatory Action.
CASChemical RfD v nuber Name WWday /Wkg/day Estimated RAC(tag/L) RACtmg/\) BCF RL: lOE-6 RL: lOE-6 3%Lipid 0.0065 kg/day 0.020 kg/day Consumption Consurpt ion

137268 Thiram 0.005 139402 Propazina 0.02 141662 Bidrin 0.0001 141786 Ethyl acetate 0.9 143339 Sodim cyanide 0.04 145733 Endothall 0.02 148185 Sodim diethyldithiocarbanate 0.03 150505 lkrphos 0.00003 151508 Potassim cyanide 0.05 156605 tram-1.2~Dichloroethylene 0.02 2D6440 fluoranthene 0.04 298DDDMethyl parathion 0.00025 298044 Oisulfoton 0.00004 300765 Waled 0.002 302012 HydrazitWHydrazine sulfate l * 3lWDD2 ALdrin 0.00003 319846 alpha-Herachlorocyclohexen l * 319857 beta-tfexschlorocycloherane l * 319868 deLta-HexachlorocyclohexMw l * 330541 Diuron 0.002 330552 Linuron 0.002 4601% Cyanogen 0.04 SD4245 4-Aminopyridine tt SD6616 Potassius silver cyanide 0.2 506649 Silver cyanide 0.1 506683 Cyanogen bromide 0.09 506774 Chlorine cyanide 0.05 507200 t-Butylchloride l * 510156 Chlorobenzilate 0.02 l *

3.0 17 6.3 1.8 t*

l *

l *

l * 47.2 5 1 0.4 3 0.9 0.54 20000 6000

0.4 600 200

2.5 90 30 128D 0.3 0.1 25.48 0.1 0.03 60 0.007 0.002 65.9 0.3 0.1

1638 0.0000004 0.0000001 146.8 0.00001 O.OODOO4 146.8 0.00004 O.DOOOl 146.8 23.56 0.9 0.3 51.6 0.4 0.1

0.4

12.2 434 0.5 0.2



### 1-7Page No. 8 09/28/90 USEONLYFDRSCREENING Values from IRIS 9/l/90 Consult IRIS for Update and Whenever Possible, Site Specific

Lipid, Consumption and Bioaccurulation factors Should be Used in Application of RACin Regulatory Action.
CASChemical RfD 91. mm&r Naae w/kg/day /mg/kg/dw Estimated RAC(@t/l) RACOWL) BCF RL: lOE-6 RL: lOE-6 3%Lipid 0.0065 kg/&y 0.020 kg/day Conslnpt ion Consunpt ion

541731 1,3-Dichlorobenzene l * et 542621 Bariua cyanide 0.07 542756 1,3-Oichloropropene 0.0003 l + 542881 Bis(chloromethyl)ethcr l * 220 544923 Copper cyanide 0.005 556887 Nitroguanidine 0.1 556887 Nitroguanidine 0.1 l * 557211 Zinc cyanide 0.05 563122 Ethion 0.0005 563688 Thallim acetate 0.00009 l * 576261 2,6-Dimethytphenol 0.0006 592018 Calcius Cyanide 0.04 598776 1,1,2-Trichloropropane 0.005 608731 tech-HexachIorocyclohcxane 0.003 1.8 608935 Pentachtorobenzene 0.0008 615543 1,2,4-Tribromobenrene 0.005 621647 I-Nitrosodi-N-propyLmnine l * 7.0 630104 Selenourea 0.005 630206 1,1,1,2-TetrachLoroethane 0.03 709988 PropaniL 0.005 732116 Phosmet 0.02 759944 S-Ethyl dipropyLthiocarbamate 0.025 765344 GlycidyaMehyck 0.0004 834128 Amtryn 0.009 886500 Terbutryn 0.001 924163 N-Nitroso-di-n-butylamine +* 5.4 930552 N-Nitrosopyrrolidine l * 2.1 944229 fonofos 0.002 950378 Methidathion 0.001 103

2.94 1 0.4 1.05 0.00005 0.00002

191 0.03 0.009

24.72 0.3 0.08

17.4 3 1 146.8 0.00004 0.00001 5120 0.002 0.0005 524 0.1 0.03 1.85 0.0009 0.0003

39.72 8 3 105.2 0.5 0.2 15.2 10 4 68.4 4 1

40.4 2 0.8 83.6 0.1 0.04 12.68 0.0002 o.oooo5 0.4 0.01 0.004 193.6 0.1 0.04

l-8Page No. 9 W/28/W

US Chrical nnkrName USEDNLYFORSCREENING Values from IRIS 9/l/90 ConsuLt IRIS for Update and Mwnever Possible, Site Specific

Lipid, Consrnption and Bioaccuulation Factors Should be Used in Application of RACin Regulatory Action.

RfD ql* Estimatad RACEmgIl) RACtmp/L) Wkg/W /ng/kg/W BCf RL: loE-6 RL: lOE-6 3%Lipid 0.0065 kg/&y 0.020 kg/&y Consrnption Consumption

957517 Diphenamid 0.03 961115 Tetrachlorovinphos 0.03 1024573 HeptachLor epoxide 0.000013 1071836 Glyphosate 0.1 1116547 N-Nitrosodiethanolmine l * 11631% DecabrcuaodiphenyL ether 0.01 1314325 lhallic oxide l * 1314621 Vanadium pentoxide 0.009 1314847 Zinc phosphide o.clOo3 1330207 Xylanes 2 1332214 Asbestos l * 1336363 Polychlorinated biphenyls l c 1445756 DiisopropyL methyl phosphonate 0.08 1563662 Carbofuran 0.005 1582098 Triflurelin 0.0075 1596845 Alar 0.15 1610180 Prometon 0.015 1646884 ALdicarb sulfone 0.0003 1689845 BromoxyniI 0.02 1689992 Bromoxynil octanoate 0.02 1861321 Dacthal 0.5 1861401 Benefin 0.3 1897456 Chlorothalonil 0.015 1910425 Paraquat 0.0045 1912249 Atrazine 0.005 1918009 Dim&a 0.03 1918021 Picloram 0.07 1918167 Propachlor 0.013 1929777 Vernam 0.001 9.1
l * 2.8 +*

l t

l *
t* 

### 7.7



### o.w??

t* 10.72 30 10 64.8 5 2 13.72 0.00009 o.owo3

0.4 40000

0.4 2000 700 14.32 4 1 1784 0.0008 0.0003 0.4 4000 1000 40.8 4 1 0.4 8 3 36.36 6 2 5800 0.04 0.01 327.6 20 5 1784 2 0.6 178.4 0.9 0.3

26.96 2 0.6 14.08 20 

### 7 11.56 70 20 16.72 9 3 179.2 0.06 0.02 0.01 0.003 0.003 O.DDW

l-9Page No. 10 W/28/90

CASChmaical nt&arName USEtMLy FORSCREENING Values from IRIS 9/l/90 Consult IRIS for Llpdate and Uhemver Possible, Site Specific

Lipid, Consumption and Bioacctmutation Factors Should be Used in Application of RACin Regulatory Action.

RfD ql* Estimated RAC(mg/L) RAC(ne/l) Wkg/dsy /ng/kg/day BCF RL: lOE-6 RL: lOE-6 3%Lipid 0.0065 kg/day 0.020 kg/day Conslnption Conslnpticm

1929824 Nitrapyrin O.Wl5 2088415 Butylate 0.1 2050477 p,pl-Dibromodiphenyl ether l * 2104645 EPN O.WWl 2164172 fluometuron 0.013 2212671 Hoalinate 0.002 2303175 Triallate 0.013 2310170 PhosaIone 0.0025 2312350 Propargite 0.02 2385855 Mirex 0.000002 2425861 Captafol 0.002 2439103 Dodine 0.004 2691410 Dctahydro-1,3,5,7-tetranitro-1.3,s. 0.05 2921882 ChLorpyrifoa 0.003 3337711 Asulm 0.05 3689245 Tetraethyldithiopyrophosphate o.wDs 52346a4 Carboxin 0.1 5902512 ferbacil 0.013 6108107 epsilon-HexachLorocycLohexana l * 6533739 ThaLLius carbonate 0.00008 72871% Prometryn 0.004 7439921 Lead and canpovds l * 7439965 Manganese 0.1 7439976 Mercury, (inorganic) l * 7440020 Nickel, soluble salts 0.02 7440144 Radiun 226 and 228 l * 7440144 Rdius 228 (and 226) l * 7440224 SiIver 0.003 7440360 Antimony 0.0004 l *

l

. l *

l *
l *

c*
l * 2-M.S/pCi/L 79.2 292 11882 648 10.08 35.28 3a8 0.2 0.07 4 1

0.0002 o.oooo5 10 5 0.6 0.2 0.4 0.1

752 o.oow3 O.WWW

800 0.4

3.37 5.4 146.8 0.04 0.01 1000 SW

300 loo 30 8

71.2 0.6 0.2



### I-10Page No. 11 W/28/90 USEONLYFURSCREENING Values frun IRIS P/l/M) Consult IRIS for Update and Whenever Possible, Site Specific

Lipid, Consumption and Bioaccmulation Factors Should be Used in Application of RAC in Regulatory Action.

CASChamical tnm&rNam

7440382 Arsenic, inorganic 7440393 Bariu 7440417 Berylliu 7440428 Boron (Boron and Borates only) 7440439 ca&iua 7448473 ChromitmW) 7440508 Copper 7440611 Uraniua, natural 7446186 ThalliuaCl) sulfate 7723140 Wtitc phosphorus 7773868 Amostim sulffmate 7782414 fluorine (sol&le fluoride) 7703008 Selenious acid 7783864 Hydrogen sulfida 7791120 Thatliu chloride 7883512 Phosphina 8001352 Toxapfme 8tM1589 Creosote 8007452 Coke oven emissions aD65483 Demetal 10102439 Nitric oxida 10102440 Nitrogen dioxida 10102451 lhallim nitrate 10265926 Itethaidophos 10453868 Resmethrin 10595956 N-Nitroso-N-methylethylainc 12035722 Nickel s&sulfide 12039520 Thallium selenite 12122677 Zineb RfO 91* Estimated RAC(mg/l) RAC(mg/l) mg/WW /Wkg/cJw BCF RL: W-6 RL: 10E-6 3X Lipid 0.0065 kg/day 0.020 kg/day Conslnption Consumption

l * l c 0.07 0.005 4.3 0.09
l * l *

O-DOS l * *. l *
.* l *

o.woo8 l * o.ww2 l * 0.25 0.06 0.003 0.003

o.oDow l * o.ooo3
l * 1.1 414 o.oooo2 O.DWW8
l * l *
l *

o.ww4 0.1 1

o-woo9 l *
O.WW5 0.4 1 0.4 0.03 11900 0.03 0.009 ** 22 0.4 0.001 0.0004 t. .t

o.ww9 l *
0.05

I-11Page No. 12 w/28/90

CASChemical nu&erNw USEDULY FORSCREENING Values from IRIS 9/l/90 Consult IRIS for Update and Whenever Possible, Site Specific

Lipid, Consusptim and Bioaccwlation Factors Should be Used in Application of RACin Regulatory Action.

12427382 Maneb 0.005 13463393 Nickel carbonyl l * 13593038 Quinalphos 0.0005 13684634 Phemediphan 0.25 14797558 Nitrate l * 14797650 Nitrite 0.1 14059677 Radon 222 l * 15299997 Napropamide 0.1 15972608 Alachlor 0.01 16065831 Chromiu<lIl) 1 16672810 Ethephon 0.005 16752775 Hethoaryl 0.025 17884352 Benaayl 0.05 19044883 Oryzalin .* 19488743 Hexachlorodibenro-p-dioxin mixture l * 19666309 Oxadiaron 0.005 28859738 Aluinm Phosphide 0.0004 21087649 Uetribuzin 0.025 21725462 Cyanazine 0.002 22224926 Fenmiphos 0.00025 22967926 Methyl mercury 0.0003 23135220 oxaryl 0.025 23564858 Thiophanate-methyl 0.08 23958585 Pronamide 0.075 24307264 Wepiquat chloride 0.03 2505789D Bentazon 0.0025 25329355 Pentachlorocyclopentadiene l * 26628228 Sodius aride 0.004 27314132 Norflurazon 0.04 RfD ql* WWdey /Irg/kg/day

l *

1X-b/pCi/L

62.00

l * Estimated RAC(mg/l) RACO&I/L) BCF RL: lDE-6 RL: lDE-6 3%Lipid 0.0065 kg/day 0.020 kg/day Consumption Consumption

42.8 0.1 0.04 124 20 7

156 7 2 226 0.5 0.2

31.1

3.64 6 21.5 0.1

2.92 300 33.92 20 2 0.04

100 8



### l-12Page No. 13 W/28/90

CASChemical nu&erNme USEDNLYFORSCREENING Values from IRIS 9/l/90 Consult IRIS for Update and Uhenever Possible, Site Specific

Lipid, Consumption and Bioaccuwlstion Factors Should be Used in Application of RACin ReguLatory Action.

RfD ql* w/kg/dey /mg/kg/day Estimated RAC(mg/l) RAC(ng/l) BCF RL: lOE-6 RL: lOE-6 3%Lipid 0.0065 kg/day 0.020 kg/day Consrnption Consumption

28249776 Thiobencarb 29232937 Pirimiphos-methyl 30560191 Acephate 32534819 Pentabromodiphmyl ether 32536520 Dctabromodiphenyl ether 33089611 Amitraz 33820530 lsopropalin 34014181 Tebuthiurcm 35367385 Difl&enruron 35554440 lnmralil 36483600 Hexabrcwnodiphenyl ether 36734197 Iprodione (Rovral) 39148248 Fosetyl-al 39515418 Danitot 39638329 Bis(Z-chloroisopropyl) ether LD888479 letrabromodiphenyl ether 40487421 Pendimethalin (Proul) 41851507 Chlorocyclopentadiene 42874033 Oxyfluorfen 43121433 Bayleton 43222486 Difenmquat 49690940 Tribromodiphenyl ether 50471448 Vinclozolin 51218452 Metolachlor 51235042 Hexazinone 51630581 Pydrin 52315078 Cypermethrin 52645531 Permethrin 55285148 Carbosulfan 0.01 0.01
l * 0.002 0.003 0.0025 0.015 0.07 0.02 0.013
l *

0.04 3 0.0005 0.04 l *

0.04 tt 0.003 0.03 0.08
l * 0.025 0.1 0.033 0.025 0.01 0.05 0.01 71.6 2 0.5 66.7 2 0.5 0.0087 0.4 3 1 ** 

### *t

5520 0.03 0.01 0.4 2000 600 44.8 5 2 19.84 7 2 l *

5170 0.001 0.0003 21.9 20 6 l * 3416 0.1 0.04 l *

l * 11040 0.003 0.001 39.28 8 3

252.4 4 1 348.4 1 0.3 35200 0.008 0.002 10320 0.01 0.003 40000 0.01 0.004



### l-13Page No. 14 W/28/90

CASChemical nnkrName USEONLYFDRSCREENING Values from IRIS 9/1/W Consult IRIS for Update and Uhenever Possible, Site Specific

Lipid, Cmslmption and Bioaccutulation Factors Should be Used in Application of RACin Regulatory Action.
RfD ql* Estimsted RAC(W/l) RACfmg/l) m/kg/day /W/kg/day BCF RL: lOE-6 RL: lOE-6 3%Lipid 0.0065 kg/&y 0.020 kg/day Consumption ConsLnption

55290647 Dimethipin 57837191 Metalaxyl 58138082 Tridiphane 59756604 fluridone 60207901 Propiconazole 60568050 furmcyclox 62476599 Sodiw acifluorfm 63936561 Nonabromdiphmyl ether 64902723 Chlorsulfuron 65195553 Avermectin Bl 662152'18 Cyromarine 66332965 flutolanil 66841256 Tralathrin 67485294 Amdro 677470% Prochlorar 68085858 Cyhalothrin/Karate 683593?5 Baythroid 69409945 Flwalinate 69806402 Haloxyfop-methyl 72128020 fcmssafen 72128020 Fomesafen 74051802 Sethoxydim 74115245 Apollo 74223646 Ally 76578148 Assure 76738620 Paclobutrarol 77182822 Glufosirmte-ammniu 77323843 Trichlorocyclopentadiane 77323854 Tetrachlorocyclopentadiemt 0.02 l *

0.06 0.003 0.08 0.013
l * 0.030 0.013 l t l *

0.05
0.004
0.0075
0.06
0.0075 0.0003 0.009 0.15 0.005 0.025 0.01 0.00005 .+ 0.19

l * 0.19 0.w 0.013 0.25 0.009 0.013 0.0004 l * l *
l

. l * 24.1

1856

19.0

0.54

40000

10700 40000 30 9

0.5 0.2

30 9

200 SO

0.002 0.0007

0.005 0.002 0.007 0.002 0.003 o.ww

l-14Page No. 15 w/tam

CASChemical ru&erName USEONLYFORSCREENING Values from IRIS g/l/W Consult IRIS for Update and Whenever Possible, Site Specific

Lipid, Consumption and Bioacclnulation Factors Should be Used in Application of RACin Regulatory Action.

77501634 Lactofen 0.002 78587050 Savey 0.025 fO277273 Harmony 0.013 81335377 lmazsquin o-25 81335725 Pursuit 0.25 l * 82558507 lsoxaben 0.05 82657043 Bipbenthrin 0.015 83055w6LondaX 0.2 a550ww NuStar 0.0007 88671890 Systhane 0.025 90992324 Chlorinuron-ethyl 0.02 101200480 Express 0.008 RfD 41* Estimatad RAC(tag/l) RACOWL) WkgfW /ng/kg/dey BCF RL: lOE-6 RL: lOE-6 3X Lipid 0.0065 kg/day 0.020 kg/day Consumption Cmsurption

40000 0.004 0.001

l-15United States Environmental Protection Agency Office OfWater 

# EPA TechnicalSupport Document ForWaterQuality-based ToxicsControl

RESPONSIVENESS SUMMARY

### RESPONSIVENESS SUMMARY

General
1. The TSD needs to be peer reviewed.
EPA Response: We feel EPA has provided ample opportunity for review of the TSD. The Williamsburg Workgroup met in December, 1988, and was composed of a diverse group of people from EPA Regions, States, environmental groups, trade associations, academia, private industry, and municipalities. This group was formed to determine what changes were needed in the document. Based on the Workgroup's inputs, EPA produced a first draft TSD in November, 1990. This draft was sent out to Workgroup members, and anyone else upon request, for review and comment. EPA considered each comment and made changes to the document. A second revised draft TSD was noticed in the Federal Resister on May 11, 1990. Over 2500 copies of the new draft were sent out. Between the two drafts, we received 120 comments from a diverse group of people. EPA considered each individual comment and made changes to the document where necessary. 2. The TSD doesn't apply to CSO discharges.
EPA Response: We acknowledge that the TSD was written with continuous discharges in mind. However, there is no reason why the general concepts and some of the recommendations of the TSD cannot be extended to CSO or other rainfall related discharges. The dynamic model applications in the TSD have been used over the last 10 years to address water quality problems related to CSOs. EPA believes that this model can also be applied to address toxic problems with CSOs.

Chanter 1: Approaches to Water Quality-based Toxics Control Overview: Added more information and clarification to support EPA's position with regard to the major issues addressed in the comments.
1. The cause and effect relationship between effluent toxicity and instream impacts is not adequately documented, and other factors that may cause instream impacts have not been addressed. EPA's use of the CETTP studies was criticized. EPA Response: The revised draft TSD has been changed to include more documentation to support our position on the cause and effect relationship between effluent toxicity and instream impacts. Supporting information on a study conducted on the Trinity River in Texas was added as well as more 1

### 2.

3. 

## detailed explanation of the CETTPstudies. EPAevaluated the results of the CETTPstudies, the North Carolina studies, and the Trinity River study and found that if toxicity is present after considering dilution, instream impact will also be present. EPA's finding is now clearly stated in the TSDand referenced. The TSDnow 

## includes adiscussion of Parkhurst's major criticisms of the CETTPstudies and why EPAfeels those criticisms are unfounded. The TSD also acknowledges that biological, 

# physical, and chemical 

# factors of the community can influence the actual effects that effluent toxicity may cause in the receiving water. 

# Toxicity test method precision is too variable to be 

## used in 

# NPDESpermits. EPA Response: The revised draft includes all available precision data for both acute and chronic toxicity tests; this includes intralaboratory and all available interlaboratory test results. Rawprecision data is presented in table form in Appendix A and discussed within the chapter. EPA evaluations, as well as published literature reviews which include estimates of whole effluent precision data are presented. EPAis comfortable with the conclusion that whole effluent toxicity tests are no more variable than chemical analytical methods and therefore stands behind the recommendation that toxicity test methods be used in NPDES permits. The same data contained in the TSD has been used by EPA in proposing adoption of the toxicity methods into EPA's regulations at 40 CFR 136. The toxicity methods were proposed for adoption on December 4, 1989. Comments were received and will be answered upon notice of EPA's decision regarding these methods. The decision is expected in April 1991.


# Thebiologicalcriteria/bioassessment approach is not yet part of EPA's water quality regulations and should not yet beused in the regulatory process. For the best 

# assessment of 

# sources and causes, EPAshould use a g9weight-of-evidence approach." EPAResponse: As previously stated in the TSD (per section 131,11(b)(2) 

# of the Water Quality Standards Regulation), biological criteria 

# can supplement existing chemical-specific criteria and provide an alternative to chemical-specific criteria where such criteria cannot be established. To acknowledge the current evolving status of the use of biocriteria, EPA's statement in the TSDhas been revised to read: "To better protect the biological integrity of aquatic communities, EPArecommends that States besin to develop and implement biological criteria in their water quality standards."

2

## EPA does not agree with use of the llweight-of-evidence'M approach in this context because biosurveys are too complex to override the other two methods (i.e., chemical-specific and whole effluent toxicity). The TSD now explains EPA's position that the concept of "independent application" be applied to water quality-based situations. Since each method has unique as well as overlapping attributes, sensitivities, and program applications, no single approach for detecting impact should be considered uniformly superior to any other approach. For example, the inability to detect receiving water impacts using 

# abiosurvey alone is insufficient evidence to 

# waive or relax 

# apermit limit established using either of the other methods. The most protective results from each assessment conducted should be used in the effluent characterization process (Chapter 3). The results of one assessment technique should not be used to contradict or overrule the results of the other(s). However, EPA recognizes that there are instances when the whole effluent, chemical specific, and biological criteria approaches will give disparate results. The TSDwas revised to recommend that permitting authorities use a more complex way to assess excursions beyond standards and establishing permit limits to provide assurance that simplifying assumptions are not the cause of the apparent discrepancy. The TSD also now includes examples of where the whole effluent toxicity test protocols may conflict with critical environmental parameters to lead to an apparent disparity between the whole effluent toxicity and chemical specific approaches. 4. 

# The problem of false positives (i.e., instream impact pre- dicted by toxicity test where none exists) is not addressed. EPAResponse: The revised draft TSDnowaddresses the problem of toxicity test interferences caused by environmental parameters and explains that there may be a few unusual situations where the pH, temperature, hardness, salinity, and solids requirements of the testing procedures differ greatly from the worst environmental conditions for these parameters. In these situations, the effluent toxicity tests may either over or under predict the toxicity in the ambient receiving water. An example of this is where ammonia is present and the highest expected ambient water temperature is 20°Cwhereas the chronic toxicity test must be conducted at 25'C. Since a higher temperature causes more ammonia toxicity, the temperature requirements of the test may induce toxicity not found in the ambient water. In such an instance, the regulatory authority must carefully look at the test protocols and all the data collected to determine if the facility is actually contributing to toxicity in the ambient water. A toxicity identification evaluation (TIE) may be necessary to 3

## make this determination. If this analysis shows a toxicity test result to be artificial due to environmental parameters, then that test should be overridden by subsequent toxicity tests conducted. 

# 5. Current standard algal toxicity test methods lack the ability to provide useful data on the 

# ecological impact of a

# discharge and should not be recommended as a 

# test species. EPAshould recommend three species representing two different phyla be used for toxicity testing. EPA's recommendation to use surrogate (rather than resident) species in toxicity testing as being more protective seemscontrary to the site specific nature of the permitting process. EPAResponse: To address concerns with the algal test and to allow more flexibility in testing, the recommendation in the 

# TSDhas been 

# changed to "fish, invertebrate, and plant." EPA will not modify its recommendation to include only 2 phyla. EPA's objective in requiring 3species from 3different phyla is to be predictively protective. The TSD's discussion of the use of surrogate species was expanded to explain that to use a resident organism, a facility would have to develop a protocol to culture the organism and to assess intra- and inter-laboratory variability. Such testing is more costly, more difficult, and potentially subject to more variability (disease, age, etc.) than standardized testing. In any case, organisms collected directly from the receiving water itself should never be used because their health cannot be assured. 

# 6. More documentation is needed for the statement that the ttIC25 is 

# approximately the analogue of an NOEC.@@ Before EPAmakes such abroad ranging recommendation, there must 

# be sufficient 

# data to establish anoverall relationship. 

# EPAResponse: The language in the revised draft has been clarified to better explain what an IC25 is and how it is calculated. EPA believes that there is sufficient data to support the statement that the "IC25 is approximately the analogue of an NOEC." The data in Appendix A is presented so that both hypothesis testing and IC25 calculations of an NOEC can be compared. A statistical analysis using minimum significant differences is graphically presented in Figure l1. Figure l-l shows that an NOECcalculated using the IC25 is comparable to an NOECcalculated using hypothesis testing, and that the relationship is statistically sound.



# 4

# 7. Thedata in the TSDshowsthat whole effluent toxicity 

# is not 

# additive.
EPA 

# Response: The cited article in the TSDand the TIE data were reviewed. These data support EPA's position that acute toxicity is additive. However, the 

# data do not support additivity of chronic toxicity. Therefore, the TSD was 

# changed to reflect this. 

# 8. There 

# are insufficient chemical-specific field data 

# to support that exceedances of 

# th8 criteria cause in&ream impacts. EPAResponse: The field studies referenced in Chapter 1 for chemical specific criteria investigations were conducted over twenty years ago. The field investigators dosed astream with toxicants to measure the response. This approach is not possible today because it would violate the States water quality standards. Since that time, EPA has developed a method of using laboratory toxicity data on specific chemicals to derive data.


# ChaDter 2: Water Oualitv Criteria and Standards Overview: Revised introduction to summarize key regulatory requirements; reorganized into clearly defined aquatic life and human health discussions; added more information on what should be considered when allowing mixing zones; added more in-depth discussion of criteria for human health protection.
1. 

# Water quality criteria are 

# not reliable due to data gaps or errors in derivation. Site-specific criteria should not be limited to being more stringent than the 

# national criteria. EPAResponse: Changing the general procedure for deriving WQ criteria is not within the scope of the TSD. In the near future EPA expects to re-examine the general procedures for deriving aquatic life criteria, and will request public comment thereon. While EPArequests public comment on all WQ criteria documents before publishing them in final form, EPA 

# accepts comment on criteria at any time and can correct errors through criteria summary documents that it distributes from time to time. Finally, there is no Agency policy, set forth in the TSD or elsewhere, that prevents state-wide or sitespecific criteria from being less stringent than the national criteria. 

# 2. Provide more clarity on howto prohibit lethality within the mixing zones? EPAResponse: The TSDhas been amended to clarify the goals of EPA's recommendations on mixing zones. It now states that mixing zone conditions should not be lethal to 

# organisms 5

## passing through it. Survival of organisms that might wish to reside permanently in amixing zone is not assured by past or current policy. Because the 

# chronic criteria may be exceeded at the end of the pipe there is a potential for lethality to sensitive organisms that attempt to reside permanently in the mixing zone. Part of the intent of the recommendations of the 1985 TSD 

# and 1991 TSD is to protect the 

# survival of organisms 

# passing through the mixing zone. In all cases, exposures to 

# concentrations above the CCCand CMC

# cannot be correctly interpreted without accounting for the duration of exposure. 3. 

# Th8TSDis flawed because it assumesthat mixing zones exist. TheGreat Lakes 

# Water Quality 

# Agreement pr8ClUd8S us8 of flow augmentation as a 

# substitute for adequate treatment. EPAResponse: The TSD recommendations in no way authorize mixing zones where otherwise prohibited. In addition, the recommendations on mixing zones do not advocate management of reservoirs for flow augmentation. 4. TSD 

# should at a minimum be against mixing zones for per- sistent and 

# bioacccumulative toxicants, and the burden of justifying mixing zones for non-persistent and non-bioacccumulative toxicants should be on the discharger. The 

# Great Lakes Water Quality Agreement calls for zero discharge 

# of pollutants and elimination of persistent and bioaccumu

# lative toxicants. TheClean Water Act 

# also has agoal of zero discharge of pollutants. EPAResponse: The TSDcontinues to note that EPAregulations 

# allow mixing zones at the discretion of the State. The TSD also discusses options that should be considered when determining whether to allow mixing zones for aquatic life and human health protection. For protection of aquatic life, a mixing zone may be permitted as long as its size is sufficiently limited that it does not significantly impair the integrity of the water body as a whole, and it does not cause lethality to organisms passing through the mixing zone. For protection of human health, mixing zones should be restricted such that they do not encroach on areas often used by the public for fishing, and particularly where stationary species such as shellfish are harvested; mixing zones mayalso be restricted to compensate for uncertainties in the protectiveness of the water quality criteria or uncertainties in the assimilative capacity (TMDL) of the water body. Bioaccumulative pollutant problems are not fundamentally caused by mixing zones. Bioaccumulation is generally a system-wide problem that occurs when the appropriate TMDLfor a water body as awhole is exceeded. Consequently, EPAdoes not consider mixing zone restrictions to bethe best mechanism 6

### for addressing such problems. However, eliminating mixing zones can be used as a device to further reduce the loading below the TMDL, although the results are not as predictable as a direct reduction of the TMDL would be. Chapter 2 has been now been modified to state the factors that should be considered in judging whether a mixing zone causes significant health or ecological risks.
It should be noted that the TSD deals with WQbased effluent limits (i.e., those needed to protect aquatic life and human health), implemented primarily under the specific requirements of Sections 301, 303, and 304 of the CWA. Under this framework, zero discharge of pollutants is generally required where the water quality standard or the Total Maximum Daily Load is set at zero. 5. The TSD should allow mixing zones for all toxicants including bioaccumulative pollutants. Furthermore, fate processes (such as sedimentation or decay) that occur within mixing zones should be taken into consideration.
EPA Response: The TSD now sets forth specific conditions under which denial of mixing zones would be appropriate. EPA regulations also allow (while neither encouraging nor discouraging) States to use mixing zones. The recommendations of the TSD implicitly discourage consideration of fate processes such as sedimentation or decay. Mixing zones are an allowance for variations in concentration due to incomplete mixing, over small spatial scales, usually too small for fate processes to significantly reduce concentrations. 6. Limiting all dischargers to 0.3 TUa is akin to a technology based approach such as a 30 mg/L BOD or suspended solids limit. The TSD should consider the resulting instream water quality.
EPA Response: The mixing zone discussion has been modified to provide different alternatives for assuring that instream goals and standardsare met. Not exceeding 0.3 TUa at the end of the pipe is one of the recommended ways to assure survival of organisms passing through the mixing zone. Nevertheless, the discussion has been modified to deemphasize the technological requirements and emphasize the attainment of instream goals.
7. EPA should provided more information on the tests it used to arrive at an LC5O/LC1 ratio of 0.3. According to the data presented in the TSD, the 0.3 ratio is overly conservative in most cases. EPA Response: The magnitude of the acute WET criterion is based on data collected from a number of facilities in EPA
7

### Region 4. These data show that 90% of the facilities exhibited an LC1 which was no less than 0.3 times the LC50. This is different from the 0.5 factor used to derive a chemical specific acute criterion.
8. The acute toxicity criterion of 0.3 TUa is below detection. How would it be implemented?
EPA Response: The implementation of this criterion would be identical to that used for specific chemicals. This implementation is expressed in Chapter 5.
9. The l-hour averaging period for the acute criteria (CMC) is overly restrictive, does not correspond to the 48-96 hour toxicity tests, and cannot be modeled with existing EPA WQ models. Elsewhere in the document, EPA indicates that 24 hours is an appropriate averaging period for modeling purposes. Both concentration and exposure time are important, since for many toxicants the 96-hour LC50 is dramatically higher than the l-hour LC50. The 1-hour averaging period is technically unsupported by the limited information presented. EPA appears to have edited the data presented in Appendix D to include only those data that support the l-hour averaging period. EPA should present all available data on the effect of exposure duration on toxicity.
EPA Response: EPA agrees that both concentration and exposure time are important. The TSD has now been modified to note that the l-hour averaging period is based on ammonia, a fastacting toxicant. As the l-hour averaging period was intended to be protective even for the fastest acting toxicants, it may be overly conservative for many pollutants. Consequently, the TSD recommends allowance for site-specific (or chemicalspecific) modification of the averaging periods. Alternative averaging periods can be developed from data on the time course of mortality in acute toxicity tests. EPA expects that for many pollutants, such site-specific or state-wide alternative averaging periods, if developed using adequate data, may be greater than the period recommended for national criteria. Furthermore, EPA recognizes that a 24- hour acute averaging period may be appropriate in some modeling contexts, where concentrations do not change rapidly over short time periods.
While Appendix D of the TSD presents some examples of pollutants for which a short averaging period, on the order of hours, might be appropriate, the TSD was not intended as the mechanism by which EPA would develop a rationale for the acute averaging period. The averaging periods were set forth in 1985 in the *'Guidelines for Deriving...National Criteria". In the near future EPA intends to review and perhaps modify
8

## 10.

11. the Guidelines, and mayat that time consider the feasibility of different 

# acute averaging periods for different chemicals. 

# Therationale for the I-day averaging period is weak. Chronic toxicity tests for most species require muchlonger than four days. 

# Thefield studies presented in Chapter 1indicate that longer term excursions are needed to produce ecological effects. EPAinappropriately picked 

# examples of pollutants with low acute-chronic ratios to justify the r-day averaging period. Ado-day averaging period (per the freshwater ammonia oriteria document) should beacceptable, particularly in cases where concentrations do not change rapidly. EPAResponse: The TSD has been modified to note that EPA selected the 4-day chronic averaging period based on the shortest period that chronic effects may be observed for certain chemicals. As the 4-day period was selected for provide adequate protection in all cases, EPArecognizes that longer averaging periods may be appropriate for many pollutants, and recommends using site-specific or state-wide pollutantspecific alternative averaging periods, where scientifically supported. EPA believes that selection of 

# an appropriate chronic averaging period is technically difficult, with less applicable data than is available for selecting the acute averaging period. EPAagrees that the acute-chronic ratio is a confounding influence in interpreting the duration needed to produce a chronic effect endpoint. Where an appropriate acute criterion is in force, the chronic averaging period 

# need not be shortened simply because the acute-chronic ratio is low. The TSD does not supersede the freshwater ammonia criteria document, and thus EPAstill recommends an averaging period of as long 

# as 30 days for ammonia, where concentrations do not vary excessively. 

# EPA's recommended once in three year return frequency for criteria excursions is overly conservative. Appendix D presents time periods needed for ecological recovery from 

# severe or catastrophic stresses, not slight stresses caused by marginal criteria excursions. The frequency of signif- icant criteria excursions, comparable to those that caused the measurable ecological impacts set forth in Chapter 1, would be much less than for marginal 

# criteria excursions. EPAshould present data onthe 

# ecological differences between sites with different excursion frequencies. EPA should develop guidance on howto establish site-specific allowable frequencies.

9

# EPAResponse: EPAhas used Appendix Dto set forth information on time periods needed for ecological recovery from severe or catastrophic stresses. EPA's recommended 3-year return interval was set forth in the 1985 "Guidelines for Deriving Numeric National Criteria...", and a review or revision of this recommended frequency was not within the scope of the TSD. Nevertheless, EPA intends to address the excursion frequency during the upcoming revision of the Guidelines. EPA expects that criteria exceedances can cause adverse effects and that the magnitude of the effect will depend on many things including the magnitude and duration of the exceedance. EPA believes that all adverse effects are not necessarily unacceptable, but that pollution should not be allowed to subject aquatic communities to long-term or regular short-term adverse effects. All dramatic adverse effects are certainly unacceptable. EPAbelieves that the 3-year return interval can be justified by the Appendix Ddata if one makes the assumption that the type of ecological impact shown in Appendix Dcould be caused by fairly small criteria excursions. The concentrations causing the Appendix D impacts were in fact not known. EPA recognizes that the chemical and ecological field data summarized in Chapter 1 suggest that successive excursions well above the criteria would be needed to cause severe impacts. EPAalso recognizes that the probability of large excursions can be calculated to be extremely small compared to the probability of marginal excursions. EPAdoes not have information to allow direct comparisons of ecological quality versus criteria excursion frequency, except possibly as could be inferred from the field data shown in Chapter 1. EPA does not intend at this time to set forth guidance on developing site-specific allowable frequencies. Nevertheless, in general, EPArecommends that ecosystems not spend 

# a substantial portion of time in a state of recovery from pollution stresses, and that pollution stresses not significantly increase the total stress experienced by organisms in the ecosystem. If the criteria are set appro- priately, a marginal excursion might be expected to have little or no measurable impact, and little or no time period needed for recovery. The probability of a marginal criteria excursion nevertheless has acalculable relationship with the probabilities of severe criteria excursions. Consequently, a scientifically justified site-specific or state-wide frequency could be developed by considering (a) the probability (estimated by simulation or by statistical calculation) of a range of excursions of differing severity, coupled with (b) the estimated ecological recovery period for 10

## the corresponding different degrees of impact. 

# Based onthe total 

# period of recovery from 

# afull 

# range of 

# possible events, compared with the sumof return intervals for such events, the allowable frequency for the marginal criteri'a excursion could be established. 12. 

# Theonce in three year excursion frequency does not take 

# into consideration the likelihood of apparent excursions caused by the inherent variability of 

# the analytical tests. EPAResponse: The allowable frequency for criteria excursions should refer to true excursions of the criteria, not to spurious excursions caused by analytical variability or error. In evaluating data on chemical concentrations or toxicity units, it is desirable to subtract the analytical error log variance from the observed log variance in order to arrive 

# at the true log variance contributing to criteria excursions. 13. 

# TheIRIS (Integrated Risk Information System) 

# data base should be peer reviewed before it is used to 

# update and generate RACS. The TSDshould not imply 

# that a State can update its standards simply by 

# inserting the latest XRISinformation into the equation used to calculate the criteria. EPA Response: EPA's IRIS data base reflects the latest information about the Agency's.health assessments for specific chemicals. While the material in the data base is internally reviewed, the Agency does not plan to have it undergo external peer review. Use of IRIS information for developing state water quality standards or discharge permits in no way relieves the State of applicable requirements for public notice and comment. 14. 

# The fish consumption rates used to derive residue-based criteria and RACsare unrealistically high for manywaters. EPA Response: EPA recommends using site-specific fish consumption rates whenever such information can be obtained. 15. 

# The harmonic mean is not an appropriate design flow. The arithmetic meanflow should be used. 

# EPAResponse: For carcinogens it is appropriate to determine the long-term arithmetic meanexposure concentration. Because flow is not normally distributed, using the arithmetic mean flow for design purposes will underestimate the mean concentration. Using the downstream harmonic meanflow will result in closely estimating the mean concentration, providing that the streamflow is not dominated by the effluent flow, and provided that the effluent input is not correlated to the streamflow.

## 16. 

# Discussion of sediment criteria and biological criteria is premature. The TSD should not advocate that states and regions implement regulatory controls based onsuch criteria. EPAResponse: The biological criteria and sediment criteria sections have been modified. EPAhas undertaken development of biological criteria and sediment criteria with the intent that they would, after development, have regulatory applications. EPA does not .intend to imply that these approaches can or should necessarily be used at this time to implement controls. Nevertheless, EPA believes that the states, the regulated community, and the interested general public need to know how EPA is proceeding with these criteria, and what the future regulatory implications may be. EPA is not advocating that sediment criteria, by themselves, be used to establish remediation target levels. EPAalso recognizes that sediment criteria cannot be used for setting discharge limits without first developing a scientifically sound basis for predicting the effect of effluent quality on sediment quality. EPA is not suggesting that sediment 

# quality concerns would necessarily bemore limiting ondischargers than water quality concerns.
17. 

# Aquatic life protection asmeasured bywhole effluent toxicity and chemical specific criteria 

# are not applicable 

# to waters without aquatic life designated uses. EPA Response: The TSD explains that numeric water quality criteria are developed by States to protect the designated uses within the water quality standards. However, the TSD also reiterated EPA's position, as expressed in the June 2, 1989, Federal Resister preamble on the 304(l) promulgation was added, that the narrative criteria apply to all waters to prohibit acute toxicity. 18. 

# The food chain multiplier factors in the RACcalculation should be deleted because it is 

# contrary to measured levels and BCFestimations. EPAResponse: The differences between bioconcentration and bioaccumulation have been recognized in the scientific literature for a number of years. Data published by Thomann in Environmental Science and Technolosv (June 1989) show that bioaccumulation can be over 100 times 

## higher than bioconcentration. Published critiques on EPA's dioxin criterion have also expressed that bioaccumulation is more important than bioconcentration for pollutants with log water octanol partition coefficients greater than 6. EPAbelieves 12

## that consideration of bioaccumulation through use of the food chain multiplier is consistent with the existing knowledge of bioconcentration 

# factors. Chauter 3: Effluent Characterization Overview: Simplified the chapter 

# organization; cited regulatory requirements (40 CFR122.44(d)); revised the discussion on effluent bioconcentration evaluation to conform with the newdraft document. 1. 

# Determining the need for an effluent limit in the absence of 

# effluent monitoring data does 

# not address the required factors of 40 CFR5 lzz.rr(d)(l)(ii). EPAResponse: EPA maintains the position that regulatory authorities may make a finding of reasonable potential even where effluent monitoring data is not available. EPA disagrees with the commenter on the issue of whether the required factors can be addressed in the absence of effluent monitoring data. Box 3-l of the TSDwas revised to illustrate how the recommended procedure for making reasonable potential determinations in the absence of effluent monitoring data will address each of the 4 required factors. 

# 2. Including bioconcentration and bioaccuntulation recommendations in the TSD 

# is premature. Approaches to this problem 

# require muchmore 

# peer review, input 

# and development. EPA Response: EPA agrees with this comment. The draft bioconcentration guidance that was referenced in the draft chapter 3 has not yet been released for public comment and does require additional peer review. The majority of the chapter 3 discussion on the specifics of this guidance was removed; the remaining sections specifically state that the procedures in the draft guidance should not be used by regulatory authorities until the guidance is finalized by EPA. 

# 3. In determining reasonable potential, the cumulative effluent discharge to a receiving 

# water should be considered instead of single discharges. EPAResponse: EPA agrees with this comment. The draft of chapter 3did not draw aclear enough distinction between the terms @*causelVand l'contributes to" in the context of the reasonable potential determination. Chapter 3nowstates that where multiple discharges collectively are causing or showthe reasonable potential to cause or contribute to an excursion of water quality standards, limits must be developed for each discharger to protect against such collective excursions. This is underscored by adding the exact regulatory language of 122.44(d). Finally, the document now has recommendations on the use of toxicity testing in multiple discharge situations. 13

# 4. Guidance is needed on how to demonstrate that crhemicalspecific limits alone are sufficient to achieve applicable water quality standards, thus obviating the need for a WET limit. EPAResponse: EPAagrees with this comment. Regulations at 122.44(d)(l)(v) 

# provide that WET limits 

# are not necessary where the permitting authority demonstrates that chemicalspecific limits are sufficient to protect water quality standards. The draft of chapter 3did not clearly recognize this point and provided no guidance on how to make this demonstration. Chapter 3 now reiterates this regulatory provision with a new section entitled VJsing a Chemicalspecific Limit to Control Toxicity.11 This new section 

## recommends that the discharger conduct a TIE to identify causative toxicants. Where the 

# causative toxicants are controlled by chemical-specific limits, the permitting authority may make the determination that WETlimits are not necessary. 

# 5. The multiple conservative assumptions in the effluent characterization methodology (the effluent is most toxic to 

# the most sensitive life stage at the time of lowest stream flow and peak design flow) are overkill. EPA

# Response: EPAdoes not agree that multiple conservative assumptions amount to overkill. This comment implies that EPA

## recommends establishing effluent limits to protect against toxic impacts that are never projected to occur. 

# In truth, EPAonly recommends establishing effluent limits where toxic impacts are projected to occur. Estimates of toxic impact should be madeassuming that the effluent is most toxic to the most sensitive species or lifestage at the time of lowest available dilution because these are conditions that can be expected to occur. For the most part chapter 3 remains unchanged 

# asaresult of this comment. However, EPAhas added ashort discussion that suggests that the regulatory authority may choose to assess reasonable potential using 

# astochastic dilution model which incorporates both ambient dilution and effluent variability to project toxic impact. 

# 6. EPAshould not recommenda 3species minimum. Thealgae test and the marine tests are not sufficiently studied. EPAResponse: Chapter 3 continues to recommend as a minimum that 3 species be tested quarterly for a minimum of 1 

# year where toxicity tests are used to make decisions regarding the need for WETlimits. Experience indicates that 

# algal tests can be a highly sensitive test species for some pollutants. Furthermore, using a plant adds another trophic level to the test regimen. EPArejects the assertion that the 

# algal tests are not sufficiently studied. For both freshwater and marine 

# 14

# waters, the use of 3species is more protective than 2species since awider range of species sensitivity 

# can be measured. 

# 7. EPA should not recommend that ambient toxicity tests be conducted at worst case low flow conditions. EPAResponse: EPAdisagrees with this comment. Chapter 3 continues to recommend that ambient toxicity testing 

# be conducted during appropriate low flow or worst 

# case design periods. In order for the results of ambient toxicity tests to form the basis for decisions about whether toxicity controls are needed, the test must reflect the conditions that such controls would be designed to protect. If a regulatory authority's policy is to protect at the 7410 flow, than the ambient tests must be conducted at flows that are very near the 7410. Otherwise, the regulatory authority will learn very little from the ambient test about whether toxicity limits are necessary for a particular discharge. 

# 8. Reasonable potential determinations should 

# not be based on whole effluent toxicity data alone. Toxicity data and instream survey data should be used together in aweight of evidence 

# approach. EPAResponse: Wedisagree. As discussed in the response to comment no. 2 in Chapter 1, EPAconsiders that water quality standards apply independently of each other. Whole effluent toxicity measures a different biological endpoint than do instream survey data. 

# 9. One piece of effluent data projecting an excursion above a water quality standard is insufficient to justify setting an effluent limit. EPAResponse: EPAdisagrees. EPA's position is that where even one data point shows that an excursion of 

# a state water quality standard is projected, the Director maydetermine that permit limits for whole effluent toxicity or for specific chemicals are necessary. In making such 

# a determination, NPDESregulations requires that the Director 

# also account for existing controls on point and nonpoint sources, the variability of the pollutant parameter in the effluent, the sensitivity of the species to toxicity testing (for whole effluent), and where appropriate the dilution 

# of the effluent in the receiving water. In addition, the Director should consider all other available information pertaining to the discharger to assist in making an informed judgement.

15

# Chapter 4: Exnosure Assessment andWasteload Allocation Overview: Clarified terminology for mixing zone 

# and design flow conditions. 1. 

# Clarify or reevaluate Agency's position with respect to key 

# mixing zone issues: including howto establish geographical boundaries of mixing zones and point of application of criterion 

# for persistent bioaccumulative pollutants, and how to prevent lethality in the mixing zone. EPAResponse: The revised TSDstates as it did in previous drafts that the mixing zone size should be minimized and the dimensions should be based on the site-specific conditions. As previously stated, site-specific evaluations should also be conducted by the permitting agency to determine whether to allow a mixing zone for discharge of bioaccumulative pollutants. The TSD was revised to expand the three approaches for preventing lethality in the mixing zone to four approaches. The new approach includes submission of actual data to show that adrifting organism would not be exposed to l-hour average concentrations exceeding the CMC. In addition, clarification 

# was provided on the approach not requiring the use of a high velocity discharge to show that the CMCis met. Approaches for preventing lethality in the mixing zone conform with the position taken in Chapter 2.


# 2. Clarify or reevaluate Agency's position on appropriate critical flows for toxicants which have potential 

# humanhealth impacts and/or aquatic life impacts. 

# EPAResponse: It was determined that the harmonic mean flow was appropriate for evaluation of human 

# health impacts that are of concern due to long-term exposures (e.g., 

# cancer). The TSDwas revised to clarify the use of harmonic mean flow and its appropriateness for use in water quality modeling to evaluate human health impacts (e.g., a step-by-step calculation procedure was added). Additional clarification was provided 

# for the use of hydrologically-based 7410 and lQl0 flows for the evaluation of "worst case" scenarios for determining potential aquatic life impacts. 

# 3. EPAshould present u valid modeling approaches and determine the best water quality model for each water quality condition. EPAResponse: The models described in the text were those EPA considers to be comprehensive 

# to evaluate most 

# water quality conditions. Because of all the potential sitespecific scenarios, it is beyond the scope of the document to present al-J of the valid approaches that could be used 

# for 16

## water quality modeling. The permitting authority needs to evaluate the data available for the site and select the water quality model that is best for calculating the receiving water concentrations and the TMDLs. 4. specific 

# caveats should 

# be added to the model descriptions. EPAResponse: It is beyond the scope of the document to provide all of the caveats that are applicable for each of the models. Although the commenters presented specific 

# caveats for 

# a model, they were not incorporated since it was beyond the scope of revising the document to determine all the cases that the caveat could apply. 5. Commenters want additional explanation 

# or justification for the assumptions and applications for the equations presented in the TSD. EPAResponse: The TSDalready contained a sufficient explanation or referenced the documents which contained the rationale. 

# 6. Thewater quality model 

# "DYNTOX1# and the software program RRD FLOWare not available. EPAResponse: DYNTOXis not currently available but should be accessible by early 1991. HHDFLOWis not available, but DFLOWis available, and the TSDhas been changed to reference this software package. 

# 7. Guidance onregulating nonpoint sources should be included in the TSD. EPAResponse: It is beyond the scope of the document to include information on regulating non-point sources.
8. 

# Guidance should begiven in 

# the discussion ondesign flow for persistent pollutants. 

# EPAResponse: Persistent pollutants should be 

# assessed in the same way as bioaccumulative pollutants. 

# 9. The humanhealth section does not provide direction regard- ing 

# the percentage of fish that are 

# taken from agiven area. EPAResponse: The percentage of fish should be determined based on site-conditions. The WLAcriteria are conservative estimates based on 

# heavy consumption of fish or apotentially 

# large contaminated area.

17

## 10. 

# The TSDsdiscussion on mixing Zone8 

# contribution to human health is overemphasized. EPAResponse: The TSDs discussion on mixing zones has been revised and does not overemphasize the contributions 

# to human health. 11. 

# EPAshould revise its discussion of modeling WLAsfor human health toxicants because it is misleading since it suggests that the method should be 

# used whether the applicable criterion is a 

# drinking water standard intended to prevent acute effects on humansor criterion established for Deriving Numerical National Water Quality Criteria. EPAResponse: The TSD does not discuss 

# acute human health effects; therefore, it was determined that the discussion is not misleading. 

# Chapter 5: Permit Reffuirements Overview: Added clarifications and examples to support the existing text; gave equal weight to developing limits 

# based on a dynamic and steady state wasteload allocations; added discussion on metals, average and maximum permit limits, single dilution tests, variability, and mass-based limits. 1. 

# The two value, steady-state model approach to permit limit derivation is overly conservative with too many built-in safety factors. As a result the 

# limits derived are too stringent. EPA Response: EPA has revised Chapter 5 of the TSD to emphasize the development of permit limits that are 

# as exact as possible to attain and maitain water quality standards. Chapter 5 now recommends the use of the statistical limit derivation procedure which provides for two options. The first option uses a steady state model approach for development of the wasteload allocation (WLA) and long term average (LTA). Since this approach relies on critical condition assumptions regarding effluent characteristics and receiving 

# water characteristics, this approach may derive limits that are more restrictive than the second option. The effluent characteristics of importance are pollutant concentrations, pollutant concentration variation, and effluent flow. The receiving water characteristics of importance are pollutant concentrations and receiving water flow. Where a discharger or permitting authority believes that the steady state model approach results in overly restrictive permit limits, under the revised TSD the discharger or permitting authority has the alternative of using a dynamic 18

## model approach. Under the dynamic model approach worst 

# case assumptions are minimized and more accurate receiving water concentrations of apollutant can be calculated. In general, dynamic models account for the daily variations of and relationships between flow, effluent and environmental conditions and therefore directly determine the probability that awater quality standards exceedance will occur. Because of this, dynamic models can be used to develop wasteload allocations which more exactly maintain the water quality standards at the return frequency requirements of the standards. The WLAis first developed by iteratively running the dynamic model with successively lower LTAs until the model shows compliance with the water quality standards. With this approach nowarecommended option in the TSD, this major issue is resolved. A disadvantage of using dynamic model outputs to develop permit limits is the lack of 

# necessary data for effluent variability and receiving water flows. 2. 

# The discussion of below detection levels is confusing. EPA should not be setting water quality-based limits below detection levels, especially since variability of a test method is greater the closer the 

# results are to the detection level. EPAResponse: The discussion of below detection level limits in the draft TSDwas unclear. Since the time of the draft TSD, EPA issued its guidance on setting permit limits below the detection level for dioxin. This guidance uses aminimum level (ML) to ascertain compliance with limits set below detection levels. The TSDwas revised to follow the dioxin guidance 

# (May 21', 1990 Memorandum from LaJuana Wilcher, "Strategy for the Regulation of Discharges of PHDDsand PHDFs from Pulp and Paper Mills to Waters of the United StateslV). Specific values for the minimum level are found in the description of methods 1624 and 1625 for some organic compounds. (See the appendix to 40 CFR136.) 3. EPA's 

# criteria recommendthat limits be derived for toxicant in the soluble form or biologically available form, yet 

# many permit limits are being written 

# and compliance being based upon the total recoverable form of 

# metals. The TSDshould address this issue andgive guidance on

# developing limits 

# only for bioavailable forms of pollutants. 

# EPAResponse: The TSD has been revised to cite the regulatory requirements at 

# 40 CFR 122.45(c) 

# and to provide three options for use where a state has not developed a method for determining total recoverable permit limits based on 

# a dissolved or acid soluble water quality criterion. The three options are 1) assume complete availability of the total recoverable metali 2) use the method in the EPA 

# wasteload allocation guidance manual for toxics in rivers to relate the 19

## two, and 3) use site specific data to develop a relationship. 

# 4. EPA's regulations donot allow permit limits based on one day 

# maximumconcentrations for POTBS. EPA Response: The NPDES regulation at 40 CFR 122.45(d) require the use of 

# a 7-day average unless impractical. The discussion on the expression of permit limits now states that EPA considers the 7-day average limit for POTWs to be impractical for the purposes of controlling the discharge of toxics. The reason for this statement is that control of the 7-day average in lieu of control of the 

# l-day maximum will allow for unmeasured short-term excursions of an acute water quality standard. 5. TREs should 

# not be required as apermit condition to respond to aviolation of 

# awhole effluent 

# toxicity limit. Theproper response by the permitting authority is through use of enforcement mechanisms. EPAResponse: The discussion on TREs 

# was changed to not recommend that the TREor accelerated monitoring be included in the permit to respond to permit limit violations but rather be part of the enforcement response. This was done to help distinguish between monitoring only provisions and enforcement of permit limits. This should reduce confusion about the difference between enforcement on asingle event violation of a limit and the need for multiple violations before aTRE is warranted. 

# 6. Permit limits should be increased to consider analytical variability. EPA should consider using 

# the approach it proposed in the Amelia River study. EPAResponse: EPAdisagrees with the proposal. Adiscussion was added to explain how EPAconsiders analytical variability in developing permit limits. The discussion states that since this variability is an intrinsic part of all data collection (effluent monitoring, wasteload allocation development, and water quality standards development), andthat the variability can go both ways (higher or lower), that EPAdoes not separate it out from all other variability factors. EPA's Amelia River study is not final; the approach cited may not be included in the final report. Although difficult, it may be possible to determine what proportion of the observed variability can be attributed to sampling error, and what proportion can be attributed to the method of measurement. Regardless, the TSD makes use of a coefficient of variation that includes both sources of variability. This is not unreasonable since sampling for monitoring purposes also results in the inclusion of these 

# 20

## two sources of variability. concentrations Any consideration of upstream variability. in the WLA will also include analytical 

## There is no "true" calculation in the process of developing permit limits because there is analytical uncertainty throughout the process. Instead, LTAs should be calculated from the WLAsusing the same CVthat is also used to calculate permit limits from the most limiting LTA. It is unimportant exactly what CV is used because the most restrictive LTA itself is used only for determining the desired treatment performance level. However, this procedure assures (99 or 95%confident) that the permit limits will be less than or equal to the more limiting LTA. 7. 

# Since permit limits were derived based on 95th and 99th percentile probabilities, that occasional 9XC88danC8S of permit limits should be 

# allowed on the samebasis. EPA

# Response: In statistics, the selection of an acceptable probability level reflects the level of confidence that is desired of the results. As such, an acceptable level must be defined prior to performing any statistical procedure. As stated in the TSD, the probability basis of 0.99 for the daily maximum limit, and 0.95 for the average monthly limit have been used historically in connection with development of the effluent guideline limitations and have been well accepted upheld in legal challenges to the guidelines. These values are tied to monitoring frequencies that are required for each limit. There is no mixing of two probability bases since they are distinct and separate limits. The goal in establishing these levels is to allow the regulatory agency to distinguish between adequately operated wastewater treatment plants with normal variability from poorly operated treatment plants.

8. 

# Permit limits should 

# vary with 

# flow conditions in the receiving waters since exposure is baS8d on dilution with receiving water flow. EPAR8SpOnS8: EPApartially agrees but only to the extent that the limits are seasonally based. The seasonal approach has been used by permitting authorities for setting permit limits to protect against excursions of dissolved oxygen 

# and ammonia standards. However, seasonal limits are different than limits which vary daily based on river flow. EPAis not convinced that adaily variable approach 

# would be universally practicalgivenwastewatertreatmentresponse and performance; for this reason EPA has not included procedures for this approach. In addition, the discharger has the option of using dynamic modeling to develop permit limits. Since dynamic modeling considers all receiving water flows, this option would provide the discharger a less restrictive permit limit than would be obtained by using steady state modeling.

21

# 9. If 

# limits W8r8 

# derived that W8r8 Overly 

# Stringent due to the worst case assumption of the Steady stat8 model, and if in the future the permittee conducted dynamic modeling which r8SUlt8d in less stringent limits , permittees would be bound to the previous limits due 

# to the anti-backsliding require- ments. EPA R8SpOnS8: There is no absolute prohibition. EPA's September 1989 guidance document on antibacksliding contains EPA's interpretation of the Clean Water Act provisions in §402(0) and §303(d)(4). This guidance also does not contain an absolute prohibition. It is EPA's position that the CWA's anti-backsliding provision and EPA's existing regulations do not uniformly prohibit the incorporation into 

# apermit of less stringent limits, standards, or conditions. In 

# certain situations (i.e., under Sections 402(o) and 303(d)(4) of the CWA), less stringent limits or conditions may be permissible. Section 402(o)(l) provides that backsliding from water quality-based limits is prohibited except in compliance with Section 303(d)(4). Section 303(d)(4)(A) only allows establishment of less stringent limits in a permit for discharge into a non-attainment water only if two conditions are met: 1) the existing permit limit must have been based on a TMDL or other WLAestablished under Section 303, and 2) attainment of water quality standards must be assured. Section 303(d)(4)(B) allows establishment of less stringent limits in a permit for discharge into an attained water only where relaxation is consistent with a State's antidegradation policy. Section 402(o)(2) also outlines exceptions to the general prohibition against backsliding from water quality-based permit limitations. Under Section 402(o)(3), backsliding may be allowed: 1) where there have been material and substantial alterations or additions to the facility; 2) where good cause exists due to events beyond the permittee's control and for which there is no reasonably available remedy; 3) where the permittee has installed and properly operated and maintained required treatment facilities; and 4) where new information justifies backsliding from water quality-based permit limitations and other Section 301(b)(l)(C) limitations. 10. EPA does not 

# have guidance on how to conduct 

# a chronic TRE 

# and therefore limits should not b8 derived 

# based upon chronic endpoints. EPA 

# R8SpOnS8: EPA is aware of the need for guidance on conducting TREs for chronic toxicity. EPA's Duluth laboratory is near completion of a draft guidance document. The guidance document will be widely available when finished. Regardless, the lack of a finished guidance document is not 22

## a valid reason to allow for excursions above a narrative standard as measured by chronic whole effluent toxicity. Some dischargers have been able to comply with chronic toxicity limits and identify and control sources of chronic toxicity. EPA experience has shown that portions of the published TRE procedures for solving incidences of acute toxicity (EPA/600/2-88/070, EPA/600/2-88,'062, EPA/600/3- 88/034) can be used for resolving incidences of chronic toxicity. 11. 

# EPAshould allow adequate time for facilities to come into 

# compliance with water quality-based permit limits. EPAResponse: EPAis aware that facilities may need time to comply with newly established effluent limits. This has been accomplished in the past in NPDES permits by allowing compliance schedules within the permit. Regulatory agencies use of compliance schedules for water quality-based effluent limits are governed by recent decisions regarding the StarKist Caribe ruling by EPA's Chief Judicial Officer (CJO). On March 8, 1989, in review of the evidentiary hearing request by Star-Kist Caribe, the CJOruled that compliance schedules for water quality-based effluent limits may not be included in NPDESpermits unless explicitly authorized by the State in its water quality standards or implementation regulations. The ruling was based on an interpretation of section 301(b)(l)(C) of the Clean Water Act. Later, on Septermber 4, 1990, the CJOgranted a stay of the ruling to allow EPAand States to use compliance schedules for water quality-based limits where such schedules are consistent with State policy. In any case, the allowance for compliance schedules is aState decision which may ultimately need to be expressed in water quality standards. 12. Effluent 

# limits should b8 set within 

# the ability of treatment t8ChnOlOgy. EPAR8SpOnS8: EPA is aware that there may be a number of water quality-based permit limits for toxics which may be presently unachievable with existing wastewater treatment technology. However, the NPDES regulations at 40 CFR 122.44(d) require that effluent limits more stringent than those established based on atreatment technology basis must be set to achieve water quality standards. 13. The 

# limit derivation prOC8dUr8S for human health should us8 the same Statistical prOC8dUreS as used for deriving limits 

# for aquatic life. Permit limits should be derived from 

# the harmonic meaneffluent concentration. EPAResponse: Since compliance with permit limitations is by regulation determined on a daily and monthly basis, it is necessary to set permit limitations expressed in these 23

## 14. contexts that meet a given WLAevery month. The statistical procedures for permit limit derivation in the TSDare designed to accomplish this for aquatic life protection where the use of shorter term averaging periods is consistent with two number aquatic life criteria. HOWeVer, if the TSDprocedures were directly used for setting permit limits on bioconcentratable pollutants, both maximum daily and average monthly permit limits could 

# exceed the wasteload allocation necessary to meet the criterion. These two permit limits would assure that the long term average effluent discharge would comply with the human health derived WLAonlv if the 

# assessment of the effluent variability was precise. With bioconcentratable pollutants where exposure duration ranges up to 70 years, EPA believes that effluent variability cannot be reliably estimated from existing data for exposure periods ayear. If the effluent variability was over-estimated when establishing the permit limits, then a facility could be discharging in compliance with the permit limits but would be exceeding the wasteload allocation for human health protection. This approach is clearly unacceptable. This problem does not arise when using the TSD statistical procedure for setting permit limits for protecting against aquatic toxicity. In this case, the monthly average and daily maximum permit limits are more closely related to the four day average and one hour maximum used as exposure periods for the criteria. Any imprecision in assessing effluent variability would therefore not have as great an effect on the permit limits. Effluents 

# maynot always demonstrate 

# a log-normal distribution. The TSD 

# should present procedures for using Other 

# distributions. EPAR8SpOnS8: EPAbelieves, after reviewing the database used to establish effluent guidelines, that the log-normal distribution best characterizes effluents. EPA's analysis of these data are provided in Appendix E. The general characteristics of the lognormal distribution (it is only positive and is skewed towards extreme high values) make it an appropriate distribution for dealing with effluent concentrations. According to Gilbert (1987), the lognormal distribution is the only available 2-parametric distribution that can routinely be applied to environmental data. Since the Agency is not providing derivation procedures for alternate data distributions, it is not necessary to test for lognormality of effluent data. Permitting authorities can develop their own methods using other probabilistic distributions.

24

# Chapter 6: 

# Enforcement oV8rVi8W: No major changes made in recommendations; added clarifications and explanations. 1. 

# One effluent test failure should not equal a violation; provisions should b8mad8 

# to delay punitive enforcement action Wh8r8legitimate efforts are being made;permittees should not be held 

# in violation of 

# their toxicity limit While conducting aTRE. EPA R8SpOnS8: 

## The current draft still emphasizes the principle that any single exceedance is 

# a violation and is subject to 

# a full range of enforcement responses. However, the draft has been caveated with discussion on EPA's guidance which outlines a systematic review of all violations to determine the appropriate level of enforcement. EPA's enforcement guidance is included in an appendix.

25

# &EPA
UnitedStates Environmental ProtectionAgency EN-336 Washington, DC20460
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