Analysis of Ambient Toxicity Measurement 

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 in both the ambient and the effluent toxicity tests. 

When analyzing the data, the performance of the animals at each station downstream is compared 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 of the effluent of concern but also the interactions 

of it with other effluents. 

Where the downstream stations show toxic effect at the concentrations measured as toxic 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 in the absence of further dilution, 

the effluent toxicity is degrading. If the decay rate is rapid (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 of toxicity exist or some factor in the receiving water is reacting 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 is happening to the effluent as it is 

mixed instream and interacts with the constituents of the receiving water. 

Trend analysis in the raw test data is important when interpreting ambient toxicity data. As used in 

this context, trend analysis means observing toxic effect as it occurs in the test itself and relating it 

to what is occurring 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 a pattern of mortality that can be linked to discharge events. For 

example, in the table the data indicate late mortality at downstream stations on Days 6 and 7. Flow 

rates for the river in this example correlated this mortality to the downstream movement of a toxic 

slug illegally discharged upstream. 

c-4 
APPENDIX D 

DURATION AND FREQUENCY 
DURATION AND FREQUENCY 

As discussed on pages 7 through 13 of the Guidelines for Deriving Numerical National Water Quality Criteria for 

the Protection of Aquatic Organisms and Their Uses [1], the format used to express 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 as well as variation in the concentrations of pollutants in the 

effluent and in the upstream receiving water, a simple format, such as specifying a concentration that must not 

be exceeded at any time or place, is not realistic. Furthermore, such a simple format does not take into account 

the fact that aquatic organisms can tolerate higher concentrations of pollutants for short periods of time than 

they can tolerate throughout a complete 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 as overprotective as would be 

necessary if criteria were expressed using a simpler format. In addition, 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]. 

In aquatic life criteria for both individual chemicals and Whole Effluents, the recommended concentrations are 

the criterion maximum concentration (CMC) and the criterion continuous concentration (CCC). For individual 

chemicals the CMC and CCC are derived using the procedures described by Stephan et al. [1]. As described in 

Chapter 3 of this TSD, the CMC and CCC for Whole Effluents can be specified generically in terms of toxic 

units. Alternatively, for a particular effluent the CMC is specified in terms of an acute toxicity endpoint (ATE), 

which is either an LC50 or an EC50, and the CCC is specified in terms of a chronic toxicity endpoint (CTE), 

which is either a no observed effect concentration (NOEC) or an ICxx, if the 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, is expected to cause an unacceptable effect. Due to the four sources of 

variation mentioned above, the concentration in the receiving water will not be constant. Because organisms 

can tolerate higher concentrations for short periods of time, it is expected that the concentration of a pollutant 

in a body of water 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 is below the CCC. These goals are accomplished by specifying a duration of an 

averaging period over which the average concentration should not exceed the CCC. For example, if the 

concentration is twice the CCC for one-half the specified averaging period, it must be zero for the rest of the 

averaging period if the average concentration is not to exceed the CCC. Thus, both the magnitude and 

duration of an exceedance are limited and there must be a compensating period of time during the averaging 

period when the concentration is below the CCC. Because exceedences are defined to be due to usual variation, 

most exceedences will be small, with larger exceedances becoming increasingly rare [1, 2]. 

Although an exceedance is defined to occur whenever the instantaneous concentration is above the CCC, an 

excursion is defined to occur only when the average concentration over the duration of the averaging period is 

above the CCC. It is expected that excursions can occur without causing unacceptable effects if (a) the 

frequency of such excursions is appropriately 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 of excursions. Because excursions are the highest average concentrations that occurred due to usual 

variation, all other average concentrations will be less than the CCC. As for exceedances, excursions that are 

defined to be due to usual variation will be small, with larger excursions becoming increasingly rare. The 

duration of the averaging period is intended to limit the impact of exceedances, 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 1 and 2.) 

Although spills can impact aquatic communities, they are not considered exceedances or excursions because 

they are not part of the usual variation in the concentrations of pollutants in receiving water. In the 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 of these studies [4, 51 spills of pollutants resulted in acute toxicity. This suggests that the impacts 

caused by spills might be as important as impacts caused by variation in the compositions and flows of the 

effluent and the receiving water. 

The primary purpose of this appendix is to 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 is based on data from laboratory toxicity tests, whereas the recommended frequency is based on field 

data. With the concurrence of the 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. A secondary purpose of this appendix is to discuss rationales that might be used as a 

basis for selecting alternative durations of averaging periods and average frequencies of allowed 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 of exceedances and provide compensating periods of time 

during which the concentration is below the CCC. 

Even though only a few tests have compared the effects of a constant concentration with the effects of the 

same average concentration resulting from a fluctuating concentration, nearly all the available comparisons 

have shown that substantial fluctuations result in increased adverse effects [6-161. Thus, the duration of the 

averaging period must be shorter than the duration of the chronic tests on which the CCC is based so that the 

averaging period does not allow substantially more adverse effect than would have been caused by a continuous 

exposure to the same average concentration. Life-cycle tests with species such as mysids and daphnids and early 

life-stage tests with warmwater fishes usually last for 20 to 30 days, whereas life-cycle tests with Ceriodaphnids 

usually last for 7 days. If the duration of the averaging period is too 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 a day, an averaging period of 24 hours 

would effectively mean that for most effluents each individual sample that was above the CCC would be 

considered an excursion. 

For the following reasons, a 4-day averaging period is recommended for application of the CCC in aquatic-life 

criteria for both individual pollutants and Whole Effluents: 

l It is substantially shorter than the 20- to 30-day duration of most chronic tests and is somewhat 

shorter than the 7-day duration of the Ceriodaphnio life-cycle test. 

l The results of some chronic tests apparently are due to an acute effect on a sensitive life stage that 

occurs at some time during the test, rather than being caused by either long-term stress or long-term 

accumulation of the test material in the organisms. Horning and Neiheisel [17] documented one such 

situation, and others are probably the cause of at least some of the acute-chronic ratios that are not 

much greater than unity. 

l For both endrin and fenvalerate, Jarvinen et al. [18] found that a 72-hour exposure caused about the 

same amount of effect on the growth of fathead minnows in early life-stage tests as did a 30-day 

exposure to the same concentration. 

l In some life-cycle tests on effluents with Ceriodaphnids, concentrations of effluents that were a 

factor of 1.8 greater than the CCC caused unacceptable effects in 4 or 5 days [S, 19, 201. 

l It is not so short as to effectively defeat the purpose of the concept of the averaging period. 

As discussed below, other averaging periods might be acceptable on a site-specific or pollutant-specific basis. 

D2 
Just as the concept of exceedances can be applied to the CCC, it also can be applied to the CMC. As with the 

CCC, the CMC averaging period should be substantially less than the lengths of the tests on which the CMC is 

based, i.e., substantially less than 48 to 96 hours. Because 4- to 8-hour LC5gs are about the same as the 96-hour 

LC50 for some materials [21-271, the duration of the averaging period for the CMC should be less than 4 hours. 

One hour is probably an appropriate duration of the averaging period for the CMC because concentrations of 

some materials that are only a factor of two higher than the 96-hour LC50 cause death in one to three hours 

[25]. Even when organisms do not die within the first hour or so, it is not known how many organisms might 

have died due to the delayed effects of the short exposure [28-311. If the l-hour average does not exceed the 

CMC, it is unlikely 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 a sitespecific or pollutant-specific basis. A possible site-specific justification for increasing the duration of the 

CCC averaging period would be that the variation in the concentration of the pollutant in the receiving water 

is low. Where variation is demonstrated to be consistently low, a longer CMC averaging would be acceptable 

because the magnitudes and durations of exceedances above the CCC would be limited. A possible pollutantspecific justification for a longer averaging period would be that the LC50 decreases substantially as the 

length of the exposure increases. For example, an B-hour averaging period might be justified for the CMC if it 

were shown that 24-hour exposures of a variety of sensitive species resulted in 96-hour LC5gs that were 

substantially above the 96-hour LCjos obtained from continuous exposure to a constant concentration for 96 

hours. 

In some situations the duration of the averaging period does not have to be stated explicitly because one can 

be implicitly defined using an uptake rate and a depuration rate. For example, if it is known that a specific 

concentration of a pollutant in the whole body or in a particular tissue of an important aquatic species will 

result in an unacceptable effect on the survival, growth, and/or reproduction of that species, and if applicable 

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 A record of daily flows of the body of water, preferably for more than 10 years 

l A maximum acceptable concentration in the whole body or in a particular tissue of an aquatic species 

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

species 

l An allowed frequency of exceedances of the maximum acceptable concentration. 

This approach is likely to be especially useful when an exposure causes delayed effects that are considered 

unacceptable. For example, it might be found in a test that no fish die during a 2-day exposure of rainbow trout 

to a pollutant but 50 percent of the fish die within 4 weeks of being transferred to clean water, whereas no 

comparable control fish die. If values are available for the concentration of the pollutant in the fish at the 

end of the 2-day exposure and for the uptake and depuration rates, these data could be used with a flow record 

for a river to determine how often a specified constant daily input of the pollutant to the river would have 

resulted in exceedances of this concentration and therefore the death of rainbow trout. 

Regardless of what averaging periods are used, exact calculation of the number of excursions would require 

continuous monitoring of the concentration in the receiving water, which is not feasible in most cases. A 

valid alternative would be to use a statistically designed monitoring program and a statistical 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 a time-weighted average. If information 

is available concerning the discharge pattern of a particular effluent, it might be possible to design a 

monitoring program that is specifically appropriate for that effluent. 

D3 
Unless 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. 

In addition, special care should be exercised when many outfalls exist in a small segment of a receiving water, 

because if low flow causes an excursion for one discharge, that same low flow will probably also cause 

excursions for other discharges at the same time. Several “minor” excursions might thus add up to a “major” one. 

Frequency 

The purpose of the average frequency of allowed excursions is to provide an appropriate average period of time 

during which the aquatic community can recover from the effect of an excursion and then function normally for 

a period of time before the next excursion. The average frequency is intended 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. 

6emral Cunsideratlons for Settln# Frequency wttb Wbicb Excursions of Criteria May Occur 

Not long ago ecological communities were thought to be largely in equilibrium 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 is that external factors, including disturbances, often 

play a major role in the structure of communities [32, 331. The frequency of disturbance affects a community 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 as floods and droughts are common in lotic systems [32] and vary in intensity 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; it only implies that 

they are predisposed to recover quickly once the disturbance is gone. The question then is how 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 case studies of freshwater systems in which some aspect of recovery from the impact of 

a disturbance was reported. A case study was used only if the disturbance caused a death or displacement of 

organisms. This restriction was necessary because it was rarely possible to determine if an event was outside the 

normal intensity range (a common alternate definition of disturbance), mainly because it is usually difficult to 

define the normal intensity range. It also permitted the inclusion of natural as well as anthropogenic events. 

Approximately 80 percent of these 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 of endpoints greatly exceeded the number of case 

studies. For short-term (nonpersistent) disturbances, approximately 85 percent of all macroinvertebrate 

endpoints indicated recovery in less than 2 years. Macroinvertebrate biomass, density, and taxonomic richness 

recovered in less than 1 year 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. Fishes recovered in 2 years or less for over 85 percent 

of reported endpoints. However, as discussed below, important exceptions did occur. 

Most excursions of criteria will be minor and their impacts will therefore be difficult to detect. Although most 

disturbances in the above case studies caused more severe impacts than most criteria excursions are expected to 
cause, CMC excursions will result in death of some organisms. These data indicate that as a general rule, the 

purpose of the average frequency of allowed excursions will be achieved if the frequency is set at once every 3 

years on the average. Excursions of the CCC are more difficult to evaluate because nonlethal excursions could 

not be evaluated from the data used by Niemi et al. [38]. It is reasonable to expect, however, that cumulative 

effects from too frequent excursion of the CCC also will result in unacceptable degradation of lotic 

communities. 

Considerations fer PK~OS/R# Sit@-specific Incmases or Decreases in tb8 Average Frequency of Allowed 

Excursions 

Although an average frequency of one criterion excursion every 3 years should usually be protective of lotic 

communities, more frequent excursions might be acceptable in certain situations. Sedell et al. [39] have shown 

that lotic systems with refugia (areas of refuge) such as well-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 of these 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 be grounds for demonstrating that an excursion frequency of more than once every 3 years on 

the average is acceptable. 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 in a year or even 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. For example, discharges to lower-order streams sometimes 

constitute a large fraction of the stream flow for most of the year. 

Although lower-order streams are naturally highly variable and can therefore tolerate higher disturbance 

frequencies, the converse is true for higher-order lotic streams for at least two partially related reasons: (1) 

segments with tributaries draining a large watershed will be buffered from the effects of localized droughts in a 

portion of the watershed, and will therefore experience a less severe natural disturbance regime, and (2) 

organisms inhabiting these segments will therefore not be adapted to disturbances that are as frequent or severe 

as those in lower-order segments. Fish in 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 in larger rivers might take 20 to 25 years to re-establish the predisturbance age 

and size structure of their component populations after a severe disturbance such as a major drought or spill. 

Extreme cases in which recovery has taken much longer than 3 years usually involve spills of persistent 

chemicals or severe habitat modification, such as stream channelization or clear-cutting of a watershed [38]. If 

the chemical contaminant is not widespread, recovery is limited primarily by the rate of disappearance of the 

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 of streams in forested watersheds and also provides woody debris 

and snags that serve as refugia. Channelization and dredging reduce the in-stream habitat diversity and thereby 

decrease refugia. In addition to these anthropogenic disturbances, multiple excursions during a drought, 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 as plant 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 a large area. If maintenance of long-lived fish species in these segments 

is desired, recovery periods up to 25 years may be necessary. 

Based on the above considerations, recovery periods longer than 3 years may be necessary after multiple minor 

excursions or after a single major excursion or spill during a low-flow period in medium-to-large rivers, and up 

to 25 years where long-lived fish species are to be protected. Even longer times may be necessary as the size of 

the affected area or the persistence of the pollutant increases. 
Calculation of Design Conditions 

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 of the specified concentrations, 

durations, and frequencies. If dynamic models cannot be used, then an alternative is steady-state modeling. 

Because steady-state modeling is based on various simplifying assumptions, it is less complex, and might be 

less realistic, than dynamic modeling. 

An important step in the application of steady-state modeling to streams is calculating the design flow. The 

procedures outlined in the EPA document Technical Guidance Manual for Petforming Waste Load Allocation, Book 6, 

Design Conditions: Chapter I, Steam Design Flow for Steady-State Modeling. (U.S. EPA 1986) are recommended for 

calculating design flows for rivers and streams. States may use other methods so long as the 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 

CCC and CMC for both methods. 

The hydrologically based design flow method is presently used by many States. It is based on selecting and 

identifying an extreme value, e.g., the 7QlO flow. The underlying assumption of this method is that the 

design flow will occur X number of times in Y years. Thus, this method limits the number of years in which one 

or more excursions below the design flow can occur. The method has two 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 is that it is 

essentially independent of biological considerations. Design flows calculated using this method might allow 

more or fewer excursions than once every 3 years on the average. tn addition, it is difficult to use site-specific 

durations and frequencies with this method. For toxic wasteload allocation studies in which the 

hydrologically based method is used, EPA recommends the use of the 1 QlO flow as the design flow for the 

CMC and the 7410 as the design flow for the CCC. 

The biologically based design flow method was developed by the U.S. EPA Office of Research and 

Development and directly uses the averaging periods and frequencies specified in the 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 of excursions. The method makes exact use of whatever duration and frequency are 

specified for the CMC and CCC. These might be 1 day and 3 years for the CMC and 4 days and 3 years for the 

CCC or site-specific durations and frequencies. 

The two methods were used on approximately 60 different rivers to compare the hydrologically based 1 QlO 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 in more than the allowed number of excursions. For some 

of the rivers, the 1 QlO and 7410 allowed substantially more or fewer 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, it always provides the maximum allowed number of excursions and never 

provides more excursions than allowed. 

EPA provides 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 a simplified 

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 are easy to use and the user simply needs to know 
the 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 of Water Regulations and Standards (WH-553) 

401 M Street, S.W. 

Washington, D.C. 20460 

(202) 382-7030 

The methods described above use daily flow data to determine design ftow, but they do not consider any other 

physical or chemical condition that might affect toxicity. EPA has prepared a supplementary method and a 

software tool named DESCON that incorporate such supplemental water quality parameters as temperature, pH, 

alkalinity, hardness, and dissolved oxygen to determine design conditions. Note that DESCON takes into 

account such things as effluent variability, which DFLOW does not take into account. The method and software 

are described in two documents available from the Assessment and Watershed Protection Division of the Office 

of Water Regulations and Standards-Technical Cuidonce on Supplementary Stream Design Conditions for Steady 

Store Modeling [3] and DESCON Users Manual [40]. 

The supplementary method is consistent with the hydrologically and biologically based methods described 

above. It simply extends them to include other conditions besides streamflow. The advantage of considering 

multiple conditions is that the worst-case conditions necessary to protect water quality criteria might not occur 

when the streamflow is low; e.g., low DO or high temperatures might occur at times 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 is called DESCON. It is on EPA’s IBM mainframe and is available 

through the STORET environment. DESCON accesses the STORET flow file for the daily flow record and the 

water quality file for data on the physical-chemical parameters. Options are available to the user if the area of 

concern has no flow record or if no water quality data are available. 

D7 
1. 

2. 

3. 

4. 

5. 

6. 

7. 

8. 

9. 

10. 

11. 

12. 

13. 

14. 

15. 

16. 

17. 

18. 

APPENDIX D 

REFERENCES 

Stephan, C.E., 0.1. Mount, 0.1. Hansen, J.H. Gentile, C.A. Chapman, and W.A. Brungs. 1985. Guidelines 

for Deriving Numerical National Water Quality Criteria for the Protection of Aquatic Organisms and 

Their Uses. PB85-227049. National Technical Information Service, Springfield, VA. 

U.S. EPA. 1986. Technical Cuidunce Monuol for Performing Waste load Allocution. Book 6. Chapter 1, 

“Stream Design Flow for Steady-State Modefing.” Monitoring and Data Support Division, Office of 

Water Regulations and Standards, Office of Water, Washington, DC. 

U.S. EPA. 1988. Technical Guidunce on Supplementary Stream Design Conditions for Steudy-State Modeling. 

Assessment and Watershed Protection, Office of Water Regulations and Standards, Office of Water, 

Washington, DC. 

Mount, D.I., N.A. Thomas, T.J. Norberg, M.T. Barbour, T.H. Roush, and W.F. Brandes. 1984. Effluent und 

Ambient Toxicity Testing and lnstream Community Response on the Ottawa River, Lima, Ohio. 

EPA/600/3-84/080. National Technical Information Service, Springfield, VA. 

Norberg-King, T.J., and 0.1. Mount, eds. 1986. Validity of Effluent and Ambient Toxicity Tests for Predicting 

Biological Impact, Skeleton Creek, Enid, Oklahoma. EPA/600/8-86/002. National Technical Information 

Service, Springfield, VA. 

Brown, V.M., D.H.M. Jordan, and B.A. Tiller. 1969. The Acute Toxicity to Rainbow Trout of Fluctuating 

Concentrations and Mixtures of Ammonia, Phenol, and Zinc. /. Fish. Biol. l:l-9. 

Coffin, D.L., D.E. Gardner, G.I. Sidorenko, and M.A. Pinigin. 1977. Role of Time as a Factor in the 

Toxicity of Chemical Compounds in Intermittent Exposures. 1. Toxicol. Environ. Health 3:821-28. 

Smith, L.L., jr., S.]. Broderius, D.M. Oseid, G.L. Kimball, W.M. Koenst, and D.T. Lind. 1979. Acute and 

Chronic Toxicity of HCN to Fish and Invertebrates. EPA-600/3-79-009. National Technical Information 

Service, Springfield, VA. 

Thurston, R.V., C. Chakoumakos, and R.C. Russo. 1981. Effect of Fluctuating Exposures on the Acute 

Toxicity of Ammonia to Rainbow Trout (Sulmo guirdneri) and Cutthroat Trout (5. c/or&i). Wat. Res. 

15:911-17. 

Buckley, J.T., M. Roth, J.A. McCarter, C.A. Rendell, and A.T. Matheson. 1982. Chronic Exposure of Coho 

Salmon to Sublethal Concentrations of Copper I. Effect on Growth, on Accumulation and 

Distribution of Copper, and on Copper Tolerance. Comp. Biochem. Physiol. 72C:lS-19. 

Ingersoll, CC., and R.W. Winner. 1982. Effect on Duphnia pulex (De Ceer) of Daily Pulse Exposures to 

Copper or Cadmium. Environ. Toxicol. Chem. 1:321-27. 

Hodson, P.V., B.R. Blunt, U. Borgmann, C.K. Minns, and 5. McGaw. 1983. Effect of Fluctuating Lead 

Exposures on Lead Accumulation by Rainbow Trout (Sulmo guirdnen). 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 or Intermittently 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 of Continuous and Episodic 

Exposure to Acidic, Aluminum-Contaminated Waters of Brook Trout (Salvelinus fontinalis). 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 

Exposures of Four Species of Aquatic Organisms to Chlorine. 

Wisconsin-Milwaukee, WI. 

Center for Great Lake studies, University of 

Horning, W.B., and T.W. Neiheisel. 1979. Chronic Effect of Copper on the Bluntnose Minnow, 

Pimephales notutus (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 or Continuous Exposure. Ecotoxicoi. Environ. Safety 15:78-95. 
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. Vulidity of Effluent ond Ambient Toxicity Jesting 

for Predicting Biological lmpoct on Five Mile 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 of Effluent ond Ambient Toxicity Tests for 

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. Acute Toxicity of Selected Toxicunts to Six 

Species of Fish. 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 (Salmo goirdnero 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 of Organic Chemical to 

Fathead Minnows (Pimephules promelus), volume 7. Center for Lake Superior Environmental Studies, 

University of Wisconsin-Superior, Superior, WI. 

Geiger, D.L., C.E. Northcott, D.J. Call, and L.T. Brooke, eds. 1985. Acute Toxicities of Organic Chemicals to 

fathead Minnows (Pimephules promelus), volume 2. Center for Lake Superior Environmental Studies, 

University of Wisconsin-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 Aquatic Toxicology and Huzord Assessment: Eighth Symposium. 

ASTM STP 891. 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 (Pimepholes promelas), volume 3. Center for Lake Superior Environmental Studies, 

University of Wisconsin-Superior, Superior, WI. 

Geiger, D.L., D.J. Call, and L.T. Brooke. 1988. Acute Toxicities of Organic Chemicals to Fathead Minnows 

(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 on Aquatic 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 Commurus pulex Exposed to Cadmium, 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 of Spatial and Temporal Heterogeneity 

and Disturbance Regime on the Recovery of Animal 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 of Lotic 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 of Lotic Communities ond Ecosystems from Disturbonce: Theory ond Applications. 

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 and Applications. 

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. In 
Recovery 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 of Case Studies on Recovery of Aquatic Systems from Disturbance.” Ed. J.D. Yount and 

C.J. Niemi. In Recovery 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 River Systems.” 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, Lewis A. DFLOW Users Manual. 

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 is divided 

into two sections. The first section gives an overview of permit limits: the derivation of water 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 of each distribution to permit limits. Tables E-1, E-2, and E-3 at the end of the 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 is discussed in the 

subsection, Other Distributions. 

Section 1: Overview of Permit Limits 

Two types of permit limits are contained in the effluent guidelines regulations: daily maximum limits and 

monthly average limits. The daily maximum permit limit is the maximum allowable value for any daily sample. 

The daily maximum limits are usually based on the 99th percentile of the distribution of daily measurements. 

The monthly average permit limit is the maximum allowable value for the average of all daily samples obtained 

during 1 month. Monthly average limits are in most cases based on the 95th percentile of the distribution of 

averages of daily values. 

The following two subsections discuss the derivation of water quality-based limits and the consistency among 

different permit limits. 

Derivation of Water Quality-based limits 

Water quality-based limits are derived 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 for water quality-based limits are the same as those for technologybased effluent limits; the major difference is that the means and standard deviations in those expressions are 

derived from the WLA. This topic is discussed in Chapter 5. 

Consistency Among Different Permit limits 

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 is determined 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 as monitoring frequency increases only because averages become less variable as the number of values 

included in the average increases. For example, an average based on 10 samples is less variable than an average 

based on 4 samples. This phenomenon makes monthly average permit limits based on 10 samples appear to be 

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 of samples. The stringency of the TSD procedures, accordingly, is constant across 

monitoring frequencies. 

E-l 
Section 2: Statistical Methodology 

The statistical procedures that are used in permit limit development involve fitting distributions to effluent 

data. The estimated upper percentiles of the 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. 

In the 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 10 by replacing loglO for In(x) in the 

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 > 10 are 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 is flexible 

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 

easy to use. 

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 a mixture of non-detect measurements with 

measurements that are lognormally distributed. In delta-lognormal procedures, nondetect values are weighted 

in proportion to their occurrence in the data. 

In determining permit limits based on averages (e.g., monthly average permit limits), a distribution 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, 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 is large enough, the 

average of the n sample values will be approximately normally distributed regardless of the distribution of the 

individual measurements. The section on statistical methodology provides procedures and guidance for 

calculating averages for both small and large samples sizes where the individual measurements are lognormally 

distributed. 

E-2 
The shape of the observed data is the key factor in evaluating a distributional model. For environmental data 

the lognormal distribution is usually appropriate. The critical question in a given situation is how well a 

particular distribution models the shape of observed data. Although the lognormal does not provide an exact 

fit in all cases, it usually provides an appropriate and functional fit to observed environmental data. Graphical 

displays and goodness-of-fit tests, as described in the subsection, Other Distributions, may be used as a guide in 

verifying assumptions and selecting a distribution. 

Coefflcimts uf V8ffatiae 

The coefficient of variation (denoted by “CV”) is the ratio of the standard deviation to the mean. Thus, the CV 

is a dimensionless measure of the relative variability of a distribution. Estimates of the CV can be used when 

the actual CV cannot be calculated or if the available data sets for calculating the CV are small. In such cases, 

different values for the CV should be used in the permit calculations to assess the effect of the CV on the final 

permit limit. Typical values of the CV for effluent data usually range from 0.2 to 1.2. The CV is a measure of 

the relative variation in observed data. In many cases, changes in the CV will have little impact on the final 

permit limit. In assessing the sensitivity of the permit limit to the CV, the calculations may include CV = 0.6 

as a conservative estimate (assumes relatively high variability). If the final permit values vary greatly with 

different CV values either of two approaches may be used. The first approach is to use a conservative estimate 

of the CV that assumes relatively high variability (e.g., CV = 0.6) in the final permit limit. The second 

approach is to collect additional data to obtain a more 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 is the ratio of a large concentration level 

of a pollutant 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 be expected to occur only on rare 

occasions in a well-designed and operated treatment system. Such factors are useful in situations where little 

data are available to characterize the long-term performance of a plant. 

In cases where only a small number of observations are available from a plant, EPA has been reluctant to 

estimate a variability factor. In the Organic Chemicals, Plastics, and Synthetic Fibers (OCPSF) rulemaking [l], a 

minimum of seven daily observations from a plant, with at least three of the seven above the detection limit, 

was established for calculation of a plant level priority pollutant variability factor. However, EPA has not 

established a minimum 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. 

Normal Distribution 

The normal distribution plays a central role in the methods described in this appendix. In most cases, the 

normal distribution is not an appropriate model for individual pollutant measurements; however, the normal 

distribution is related to the lognormal distribution that is used 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 as being 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 is described below and is followed by a 

discussion of the lognormal distribution and its relationship to the normal distribution. 

E-3 
Figure E-l. Normal Probability Distribution 

The normal probability distribution is encountered in a number of applications. The bell-shaped curve of the 

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, as for example in Reference 2. 

Only a brief review is given here. 

A sample of independent observations, denoted by xl, x2 ,...,Xk, from a normally distributed population can be 

used to estimate the mean, p, and variance, cr2, according to the formulas below: 

6 = estimated mean 

= z[Xi] / k, 1 I i 5 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 of the normal distribution are the range is defined for positive and negative values, and the 

frequency curve is bell-shaped and symmetric about the mean. In most cases, the normal distribution is not an 

appropriate model for the distribution of individual pollutant measurements. Environmental data rarely are 

symmetric, which is a fundamental property of the normal distribution. In addition, the normal distribution is 

defined over a range that includes negative values while pollutant measurements are restricted to nonnegative 

values. Thus, fitting a data set to a normal 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. 

Daily MaMaa Pem/t LMts Based on tbe Normal Distrihtiun 

For data sets which have the characteristics of the 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 of the standardized normal distribution (i.e., 

normal distribution with mean = 0, and variance = 1). For example, the Z-score for the 95th percentile is 1.645. 

Z-scores are listed in tables for the normal distribution (in most statistical textbooks and references). The pth 

percentile daily maximum limit is estimated by: 

4, = pth percentile daily maximum limit 

= Ji + zp 6. 

E-4 
For example: 

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 by if,) are estimated by: 

Daily maximum 95th percentile VF1 = A.95 / i 

Daily maximum 99th percentile VF1 = A.99 / c 

HWbty Av#ra#o h&f LWfs 8md on fb# #omwl DistrlbutIm 

The normal distribution can be used to model the averages of the individual measurements for a wide range of 

circumstances. Although the normal ,distribution usually is not an appropriate model for individual pollutant 

measurements, the averages of those individual measurements can often be modeled by the 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 is large enough, the average of the n sample values will be approximately normally distributed 

regardless of the distribution of the individual measurements. In determining 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 a sample 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 of n values. The estimates of the n-day average and the variance of the n-day average are denoted by 

fin and $?,, respectively. 

r; = 

62 = 

in = 

= 

.2 

0” = 

= 

i& = 

= 

CGn = 

= 

estimated mean of distribution of X 

estimated variance of distribution of X 

mean of distribution of the n-day monthly average 

3 

variance of distribution of the n-day monthly average 

i52 / n 

standard deviation 

(62,)‘/2 

coefficient of variation 

47 / fin. 

E-5 
The upper percentile limits are: 

A., = pth percentile n-day monthly average limit 

= in + Zp 6n 

where zp is the pth percentage point of the standard normal distribution. 

For example: 

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 by VF,) are estimated by: 

Monthly average 95th percentile VF, = 2.95 / b 

Monthly average 99th percentile VF, = k.99 / $ 

The above discussion of the normal distribution can be modified for data from the lognormal distribution. The 

next subsection explains the modifications. 

Lognormal Distribution 

Experience has shown 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-2 displays the positively skewed shape of the lognormal distribution. 

The distribution fitting methods assume that the daily measurements are independent, uncorrelated 

observations. Although, in general, this assumption is not satisfied exactly, the lognormal distribution has 

been used in the effluent guidelines program primarily because it consistently provides a reasonably good fit to 

observed efftuent data distributions. Figure E-3 shows the lognormal distribution applied to data used in the 

development of the OCPSF effluent guidelines regulation [l]. 

E-6 
4.5 

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 in ugl 

Figure E-3. BOD Frequency Distribution - Plant C 

E-7 
The logarithmic transformation of the random variable X, Y = In(X) results in a random variable Y that 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 Y are oy 

and ;ry’ respectively. These parameters can be estimated 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 of variation for the random variable X that is lognormally distributed. The 

mean, variance, and coefficient of variation of the random variable X may 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. 

Dally Maximmu Petm/t LimMs Bawd on the Lo~nu/ma/ D&tribntion 

The upper percentile limits for the random variable X (which is lognormally distributed) are: 

R +P = pth percentile daily maximum limit 

= exp[ iiy + zp tSy 21 

where zp is the pth percentage point of the standard normal distribution. 

For example: 

k-95 = 95th p ercentile daily maximum limit 

= exp[ fIy + 1 A45 Gy] 

899 = 99th percentile daily maximum limit 

= exp[ ii,, + 2.326 &y]. 

Note: 

Zg5 = 1.645 

Z99 = 2.326. 

E-8 
The daily maximum variability factors (denoted by VFT) are estimated by: 

Daily maximum 95th percentile VFT = X.95 / k(X) 

Daily maximum 99th percentile VFT = X.99 / t(X). 

mkfth/y An?f8#@ Pem?it LhRits Based OR the 10#mn8/ Distfib#tioR 

This subsection contains the formulas required to approximate the distribution of the average of a small number 

of lognormally distributed values with another lognormal distribution. Although, the Central Limit Theorem 

holds that the average of a sample of independent measurements is normally distributed provided that the 

number of measurements, n, is sufficiently large, the minimum value for n required in specific cases may vary 

considerably. In cases where the individual values are lognormally distributed, the minimum required for the 

average to be normally distributed may be quite large. As a consequence, the distribution of the average of a 

small number of lognormally distributed values may be better approximated by another, related lognormal 

distribution [3]. For sample sizes larger than 10 when the data are lognormally distributed, it is recommended 

that the calculations given in Table E-3 should be used. For the purposes of permit writing, monitoring sample 

sizes of 10 or less are recommended to be “small enough” to assume the sample average is approximately 

lognormally distributed. The mean, variance, and coefficient of variation of the distribution of the average of 

n daily 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 of the maximum n-day monthly average are: 

k *P = pth percentile n-day monthly average limit 

= exp[ in + Zp &n] 

where zp is the pth percentage point of the standard normal distribution. 

For example: 

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

= exp[ &, + 1.645 6t-J 

R.99 = 99th p ercentile n-day monthly average limit 

= exp[ tin + 2.326 ;n] 

E-9 
Note: 

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-Lognormal Distribution 

The delta-lognormal distribution is a generalization of the lognormal distribution. The delta-lognormal 

distribution may be used when the data contain a mixture 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 in setting 

daily maximum limits and for setting limits on monthly averages with the recommended number of monitoring 

samples being 10 or less. 

The delta-lognormal distribution models data as the combination of two distributions: the lognormal 

distribution and a distribution 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 set shown in Figure E-4 contains a number of observations that were 

reported as “nondetect.” These detection limit measurements are observations that are censored at the 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 a certain proportion, 6, of values are 

at the detection limit, which is denoted by D. (The estimation procedure when D = 0 is detailed in Reference 4. 

These values set to D are observations that can only by quantified as nondetect (ND) at some minimum level. 

This minimum level is the detection limit as estabtished by the laboratory performing the chemical analysis. 

Let XT ,x2 ,~*.nxpxr+l,-.-, Xk denote a random sample of size k, with r observations recorded as nondetects, and k-r 

observations greater than the detection limit. The k-r positive observations are assumed to follow a lognormal 

distribution. The entire data set is assumed 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) of the observations greater than the detection limit. Let 8 be the sample 

proportion of nondetects. Then the estimates of the mean and variance of the 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($) - (1 - 8 )] + 8 (1 - 8 ) D [ D - 2 exp( fiy + 0.5 c$ )] 

&(X’) = coefficient of variation 

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

E-10 
160 

120 

40 

0 

t10 30 70 110 150 190 230 270 310 350 

Concentration in ugll 

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

E-l 1 
where 

k = number of samples 

D = detection limit 

k-r 

= number of nondetect values in sample 

= number of values greater than the detection limit 

Yi = In(Xi) r+l 5 i 5 k, r < k 

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

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

= r/k 

Dally Maxhum Pemtt limits Dased on the Delta-Lopomal DlstribiMn 

The 95th and 99th upper percentile limits for the random variable X (which is delta-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 [ ] is the mathematical notation for Z-scores. For example, when 8 = 0, then the corresponding value is 

W1 [.99] = Z99 = 2.326. Values of Q-l [ ] are available from tables of the normal distribution (available in most 

statistical textbooks and references). 

The variability factors (denoted by VF) are estimated by: 

Daily maximum 95th percentile VF = R-95 / k(X) 

Daily maximum 99th percentile VF = A.99 / k(X). 

DebLopnmna~ Dlstributlon of Averages 

The derivation of the formulas for the averages computationally is difficult and beyond the scope of this 

appendix. However, the formulas for n-day averages are included in Table E-2. The derivation of 4-day monthly 

averages using the detta-lognormal distribution is available in Appendix VII-F of the Development Document 

for the OCPSF regulation [l]. For the purpose of permit writing, it is recommended that data sets of greater than 

10 samples be assumed to fit the normal distribution and the averages be calculated using the formulas given in 

Table E-3. 

E-l 2 
Checking Distributional Assumptions 

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-Fit Tests 

In some cases, statistical goodness-of-fit tests may indicate that a particular distribution provides a reasonable 

fit to a data set of pollutant measurements. Such cases should be evaluated carefully to verify that the frequency 

curve for the data also show the shape characteristic of the distribution. 

Probability Plots 

Use of probability plots is one method of determining whether a normal distribution is appropriate for 

modeling a population using only a limited set of measurements. The set of measurements should have at least 

20 observations [5]. Consider an independent sample of size k, labeled X1,X2,...,Xk. Let Ul,U2,...,Uk be the 

ordered sample of x-values in ascending order in which U1sU2s,...,<Uk. Now for each Ui, find Zi from the normal 

table (in any statistical reference or textbook) such that P[Z I Zi] = i/(k+l) and plot each pair (Zi,Ui) on linear 

graph paper (or use a computer graphics software package). If the data are from a normal distribution, they will 

fall approximately along a straight line. 

This same method can be adapted to check the assumption of lognormality. Log-probability plots are similar to 

probability plots used for the normal distribution. To construct a log-probability plot, set yf = In(xi) for 

i=l,2,..., k and then prepare a probability plot for the yi, first by ordering the data as described in the previous 

section. If the data are from a lognormal distribution, they will fall approximately along a straight line, as 

illustrated by Figure E-S. 

Other Dlstributfons 

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 a particular 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 is not intended to address correlation between observed pollutant levels 

and plant operating factors that influence and control treatment performance. 

E-l 3 
-1.5 0.0 

2 Score 

1.5 3.0 

Figure E-S. Example of a Log-Probability Plot with a Normal Dlstributlon 

E-14 
In the case of the monthly average limit derivation, the assumption that observed pollutant levels are 

independent can be quite important. If the 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. If the retention time is large relative to the time between effluent samples, then 

those samples will tend to be correlated with each other in most cases. In municipal systems, for example, the 

retention time is frequently a matter of days, and sampling is often conducted on a daily 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 8 hours. Daily effluent levels from these kinds of systems are generally uncorrelated, i.e., 

statistically independent. These general patterns are the same irrespective of the 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 in determining limits. One general approach 

involves time series modeling. Another possible approach is to use a direct computation of the covariance 

among the observed data to adjust the variance of the average used in determining the limit. Help in adjusting 

the sample size for correlation is available from the OW Statistics Section (phone number [202] 382-5397). 

Table E-l. Daily Maximum Permit Limit Calculations 

The daily maximum permit limit is usually the 99th upper percentile value of the pollutant distribution. In 

:ertain cases the 95th percentile value may be allowable. The following gives the formulas: 

JVlTH ALL 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 size of data 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 on delta-lognormal distribution) 

k.95 = 95th percentile daily maximum limit 

rD 8 2 0.95 

495 = I 

L max [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 size of data set 

D = detection limit (as established by the laboratory) 

r = number of nondetects (xl ,x2,...,xr are 2 D) 

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

3’ 

= Ill(Xi) for r+l I i 5 k 

= r/k 

LY = E&i) / (k - 0 r+l 5 i 5 k (exclude values I D from 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-l 6 
Table E-2. Monthly Average Permit Limit Calculations for 

Ten Samples or Less 

The monthly average permit limit is usually based on the estimates of the 95th percentile of the distribution 01 

:he average of the daily effluent values. For sample sizes less than or equal to 10, the data are assumed to br 

ognormally distributed (or delta-lognormally distributed if the data includes nondetects). 

4LL MEASUREMENTS > 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 of data set 

r; 

3 

= C&i) / k l<ilk 

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

Y 1 li<k 

k(X) = exp( jYiy + 0.5 6; ) 

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-l 7 
Table E-2. Monthly Average Permit Limit Calculations for 

Ten Samples or Less (continued) 

,OME MEASUREMENTS < DETECTION LIMIT (based on delta-lognormal distribution) 

k 95 = 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. 

k 99 = 99th percentile n-day monthly average limit 

rD 6 2 0.99 

899 = 1 

L max [D, exp( j$, + z*&r,)] 8 < 0.99 

vith z* = W1 [(0.99 - 8 ) / (1 - 8 )] 

vhere 

xi = daily pollutant measurement i 

k = sample size of data set 

D = detection limit (as established by the laboratory) 

r = number of nondetects (x1,x2,..., xrare<D) 

k - r = number of detects (Xr+l?r+2s-.tXk are > D) 

Yi = In(Xi) for r+l 5 i I k 

8 = r/k 

fiY = Z(yi) / (k - r) r+l 5 i I k (exclude values ID from sum) 

.2 

“Y = Z[(yi-jiy)211(k-r-l) r+l I i I 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 - 2 exp( 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 = (2 8 D) / &X*) - 8’ D) 

fin = In[(l!(X*) - 8” D) / (1 - 8”)] - 0.5 6: 

E-18 
Table E-3. Monthly Average Permit Limit Calculations for More Than Ten Samples 

‘he monthly average permit limit usually is based on the estimates of the 95th percentile of the distribution 

>f the average of the daily effluent values. These daily values are assumed to be lognormally distributed. For 

#ample sizes larger than 10, the averages (represented by the random variable X,) are assumed to be normally 

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 size of data set 

F-Y = Ii / kt l<i<k 

*2 

“Y = Z[(yi - fiyj2] / (k - 1) 1 5 i 5 k 

&)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-l 9 
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. EPA 440/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. 

66:21 l-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, to J. Taft, U.S. EPA, Permits Division, Washington, DC. 

Kahn, H., and M. Rubin. 1989. Use of Statistical Methods in Industrial Water Pollution Control 

Regulations in the United States. Environmental Monitoring ond Assessment 12:129-48. 

E-20 
APPENDIX F 

SAMPLING 
SAMPLING 

The objective of an effluent or instream sampling program is to obtain a sample (or samples) from which a 

representative measure of the parameters of interest can be obtained. Unfortunately, many of the 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, or were 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 is an 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]. As noted by Bender [2], the sources of effluent variability include both 

random and systematic components that influence both daily and annual characteristics of waste 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 be tailored to measure the type of toxicity of importance for 

that discharge: either long-term (chronic) impact, which is a more constant effect, or short-term (acute) impact, 

which is more variable and subject to peaks of intensity. 

There are several chemical parameters for which continuous analysis is possible. These include pH, temperature, 

dissolved oxygen, and other parameters involving instantaneous measurement. All other types of measurement 

involve some time period over which the analysis is conducted. Toxicity tests require an exposure period. 

Chemical tests require sample preparation and analysis. There is no continuous analysis method for toxicity. 

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. 

Sampling Methods 

Continuous Flow Samples 

For toxicity testing, the test organisms may be exposed to serial dilutions of a sample continuously pumped 

from the effluent pipe or ditch. In the case of effluents, if optimum accuracy is desired, 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 of testing or where treatment costs are sufficiently high that 

this type of analysis can be required. A flowthrough exposure method is not a continuous analysis because only 

one result or data point is obtained at the end of the test. However, the continuous exposure does provide some 

measure of time-varying exposure effects. 

Discrete Samples 

Grab or flow composited sampling provide a discrete sample for chemical analysis or toxicity testing. Static or 

renewal toxicity tests using discrete samples result in exposure of test organisms to a constant effluent 

composition over the period of the tests, or for the period between renewals. 

If discrete samples are collected during peaks of effluent toxicity then constant concentration exposure static 

tests provide a measure of maximum effect. 

Depending on the duration of a peak 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-l 
usually appropriate for chronic tests where peak toxicity of short duration is of less concern. The averaging 

effect of compositing 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 a series of grab samples collected over a 24-hour 

period. 

Sampling Frequency 

Nonrandom effluent variability, resulting from batch processing, variable loadings, etc., is often known or can 

be determined. Therefore, the first step in designing a sampling program for chemical analysis or toxicity 

testing is to select the annual sampling frequency based on available site-specific operational information. 

This is important in selecting sampling periods for both continuous flow and discrete sampling methods. 

If discrete sampling methods (grabs or composites) are used, then random variations between and within days 

for each sampling period must be considered. It is important to recognize the tradeoff between the long-term 

(between days) frequency and short-term (within days) frequency of sample collection and analysis for toxics. 

At present, the permit requirements for sampling and analyzing chemical parameters are site specific and 

generally involve a single grab or 24-hour composite sample collected at daily, weekly, or monthly intervals. 

Unfortunately, a sampling scheme involving a single daily grab or a 24-hour composite sample can conceal the 

presence of those daily extreme values that may be of importance. To optimize sampling cost and 

effectiveness, it may be desirable to reduce long-term frequency so that daily frequency can be increased. 

For example, a weekly grab or composite involves 52 analyses per year. It may be more efficient to reduce the 

annual frequency to monthly or bimonthly, but collect and analyze four or eight grabs daily. Either scheme (12 

x 4 or 6 x 8) 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, it might be more efficient to collect eight samples each composited over a 3-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’s recommendations on sampling 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 of the frequency and the method of sampling 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 a predetermined point in 

the receiving water. For regulatory purposes, the critical point is often the edge of the 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 or suspected, flowthrough testing can be 

conducted at a minimum of quarterly intervals for at least 1 year. 

l Grab Sample Methods - Grab samples are recommended for chemical analyses and for acute 

and chronic toxicity tests where site conditions (such as wastewaters 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 a toxics grab-sampling 

program must take into account the tradeoff between long-term and short-term sampling 

intensity. Where there is no ponding of wastes or retention time is insufficient 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 a minimum of four to six daily grabs collected monthly. An option could 

include the use of short-term (4-hour) composites rather than grabs. If site-specific data are 

available to indicate that treatment system retention time is adequate to minimize daily 

variations, then the daily replicates may be omitted in favor 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 in the toxicity evaluation (see Section 3). 

l Composite Sample Methods - If static or renewal methods are used for evaluation of toxicity, 

it is recommended that 24-hour, continuous-flow composite samples be collected. 

Considerations of annual frequency are the same as those for grab 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 of Variations in Effluent Toxicity Tests.” In Environmental 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 OF A BIOLOGICAL INDICATOR 

APPROACH TO WATER QUALITY-BASED HUMAN HEALTH 

TOXICS CONTROL 
THE DEVELOPMENT OF A BIOLOGICAL INDICATOR 

APPROACH TO WATER QUALITY-BASED HUMAN HEALTH 

TOXICS CONTROL 

Current Approach 

With one exception (New Jersey), the chemical-specific approach to protecting human health is currently 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 risk assessment information for 

each individual chemical. Where data are available for such human health toxicants, the chemical-specific 

approach can be used to develop permit limits. 

However, the complex characteristics of effluent mixtures limit the effectiveness of the single-chemical 

approach. When used as the sole basis for 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 of chemicals 

which may exert their effects on human health after a long latency period. 

2. Human health data are limited or lacking for many of the §307(a) “priority” pollutants. Moreover, 

the number of human health toxicants discharged far exceeds 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 in vitro and in vivo tests. Though not yet widely implemented, biological 

indicator test results can be important supplements to a chemical-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 is currently underway within EPA and in the private sector to 

evaluate various biological indicator test batteries for whole effluent analysis. 

Biological Indicator Tests 

Biological indicator tests include in vitro (test tube) and in vivo (whole animal) tests which can help form the 

first tiers of a single chemical evaluation process. A battery 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 a different type of response. The results of these tests can be used to decide whether more definitive 

(and more resource-intensive) testing is needed 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. If an effluent is extremely 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 (a safe level is presumed to exist) chemicals. 

G-1 
Genotoxicity Tests for Non-Threshold Chemicals 

Cenotoxicity is the 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 a change in the DNA structure that could be passed on to offspring as a heritable mutation. Alterations 

in the DNA of somatic cells can result in cancer or other diseases. 

Interpretation of genotoxicity test results assumes that DNA damage in nonhuman cells may be predictive of 

latent diseases in humans such as genetic disorders, birth defects, and cancer. EPA believes that genotoxicity 

tests for point mutations, numerical and structural chromosome aberrations, DNA damage/repair and in vitro 

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 assess genotoxic effects 

(discussed below). 

For example, a correlation has been established between animal carcinogens and positive mutagenic responses 

in the Ames Test. The Ames test is often used to assess point mutation effects. The original correlation study 

revealed that 90% of tested carcinogens were detected as mutagens, while 87% of noncarcinogens were 

identified as nonmutagens. Other studies have determined that between 77% and 91% of tested carcinogens 

produce positive responses in the Ames test. The Ames Test has been used in over 2,000 laboratories worldwide 

for drug and food additive screening, product development, and environmental testing [New jersey DEP, 19831. 

To assess clastogenic effects (chromosomal breakage) either the mammalian sister chromatid exchange test or a 

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’s Office of Toxic Substances and Office of Pesticides Programs also have published 

test methods [U.S. EPA 1982a and 1982b] that are consistent with the OECD tests. 

Most effluent samples need special preparation (for example, concentration) to produce a measurable biological 

indicator test response for human health effects. When samples are concentrated, the response is calculated 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 livers which act as a source of enzymes. The extract enzymes act to 

mimic metabolic activation of procarcinogens and promutagens in humans, providing a more realistic picture 

of potential effects [U.S. EPA, 19791. 

A number of genetic toxicity assay batteries have been suggested in order 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 on mutagenicity [McGeorge, et. al,, 19851. 

Many of the proposed test batteries utilize the Ames Assay as a screening level test because of its relatively 

high degree of sensitivity (i.e. a high percentage of carcinogens are Ames positive) and specificity (i.e. a high 

percentage of noncarcinogens are Ames negative) [Tennant, et. al., 19871. One study of 28 selected industrial 

discharges revealed that 11 of the 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 

G2 
concordance with rodent carcinogenicity results for pure chemicals [Tennant, et. al., 19871. This suggests that 

if a sample causes only one type of endpoint as measured by several screening level tests, its potential to cause 

human health effects should not be disregarded. 

TO assess the potential carcinogen hazard, subsequent tests focusing on effluent-induced malignant changes in 

mammalian cells in vitro can be conducted. Higher levels of testing may include in vivo rodent testing or the 

Medaka (fish) tumor assay, for example. It should be noted that under existing guidelines, in vivo mammalian 

tumor assays are necessary to establish a material as a possible human carcinogen. Results from short term tests 

alone are considered as inadequate to establish human carcinogenicity [U.S. EPA, 1986~1. Guidelines for risk 

assessment of individual compounds are covered in U.S. EPA, 1986b and 1987~. 

In vivo tests on complex mixtures are extremely complicated and expensive given the variability intrinsic to 

effluents. As a result, it is recommended that after each tier of biological indicator testing, the cost of further 

refining the weight of evidence for carcinogenesis or mutagenesis be balanced against the cost of conducting 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 in vivo dose-response data for risk 

characterization if these data are already available in the literature for the specific chemical. 

In addition, 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. 

In weighing 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. As a default assumption, a high dose of a carcinogen received over a short period of time is 

equivalent to a low dose spread over a life-time [U.S. EPA, 1986~1. While effluents which are highly variable 

in their mutagenicity are of concern, they will be more difficult and costly to deal with in subsequent phases of 

study. 

Accordingly, the initial tier of qualitative tests for human health effects assessment can be relatively 

inexpensive, rapid, and have a low rate of false 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 of the causative agent(s) in the effluent. 

Subsequent tiers of testing should focus on a more concise assessment of risk. Such an assessment can be used to 

delineate hazard type; in effect, to separate germ cell mutations (heritable genetic risk) from carcinogen risk. 

Thus, to assess heritable mutation, subsequent testing should focus on mammalian germ cells, ultimately tested 

in vivo [U.S. EPA, 1986b]. To assess potential carcinogen hazard, subsequent tests focusing on effluent-induced 

malignant changes in in vitro mammalian systems should be conducted. Ultimately, testing must result in a 

dose-response assessment to be used with an exposure assessment in characterizing risk [U.S. EPA, 1987a]. 

EPA’s Region V (Chicago), New Jersey, and Environment Canada have been conducting mutagenicity testing at 

selected facilities. In Region V Ames 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 in vivo data) and human 

exposure. Human exposure assessment typically considers the composition and size of the population exposed 

and the types, magnitude, frequency and duration of exposures [U.S. EPA, 1986d]. 

Evaluation of Effluent Genotoxicity Screening Results 

Control of human health hazards depends upon assessment of both the toxicological properties of the 

pollutants and the level of exposure. The permit authority should review the results of a human health toxicant 

c3 
effluent screening program and establish the actions triggered by each level of potential risk indicated. For 

example, a discharger with either a high exposure risk or a high effects risk might automatically be required to 

conduct a detailed assessment or institute controls. A medium risk in both exposure and effects might require 

further review of the data and a case-specific decision about whether to require additional assessment. A 

medium effects risk and a low exposure risk might 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 in defining exposure risk, as well 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 is the dose of a reference agent needed to produce an effect of a given magnitude in a 

particular bioassay, divided by the dose of the effluent needed to produce the same magnitude of the same 

effect in the same bioassay. A predictive battery of several short-term biological tests, when standardized to a 

reference agent, could provide a rank or comparative estimate of the hazard posed by an effluent in the context 

of measures of other known hazards [Glass, 19881. It should 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 is recommended to evaluate and regulate the discharge constituents. The first half 

of this process involves characterizing the composition of the effluent. Typically, only a small fraction of the 

total organic carbon (TOC) can be accounted for as individual 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 is a study 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 is a list 

of compounds or classes of compounds with a high probability of being present in the 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]. IRIS and SAR can 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. 

Summary of Current Blological Indicator Tests for Non-Threshold Human Health Toxicants 

The following tests are currently in use or 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 a solvent 

extract of the 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 is rapid, relatively inexpensive. The Ames Test has been shown to have broad 

application for the assessment of the mutagenic activity of a diversity of industrial effluent types 

(McCeorge, et. al., 19851. Test sensitivity and specificity are documented [Ashby and Tennant, 

19881. 

CA 
Disadvantaaes: Requires metabolic activation and several different strains of Salmonella to detect 

a broad range of compounds, requires extrapolation from prokaryot, use of effluent extract may 

exclude certain types of compounds, epigenetic carcinogens not detected. 

Cost: Approximately $1200 [Lave and Omenn, 19861 

l Escherichia coli SOS Assay (SOS Chromotest) [Quillardet, et.al., 19851. 

Backaround: All cells contain an “SOS” enzymatic system for detecting and correcting errors in 

their genetic material. A strain of E. coli has been genetically engineered so that DNA damage 

ultimately results in production of an enzyme which reacts with test reagents to form a blue 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 of damaging bacterial DNA. 

Endpoint: DNA damage; response measured as the change in optical 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 of compounds, epigenetic carcinogens not detected, measurement 

of effect 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 is replicated during cell 

Recent advances allow the use of cultured rat hepatocytes in detecting SCE formation, 

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 cells are lysed and genetic material fixed in order to count SCEs. 

Endpoint: DNA damage; response measured in SCE per chromosome/L effluent. 

Advantages: Test is rapid, relatively inexpensive, does not require metabolic activation (therefore 

more realistic). Uses mammalian 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 HCPRT Assay 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 of the effluent, with or without added rat liver enzyme. Mutagen interactions with certain 

sections of the DNA make the cell resistant to toxicants like Gthioguanine. Cell survival is used 

to indicate both cytotoxicity (cell death) and genetic mutations resulting from effluent 

components. 

Endpoint: Gene mutation; response measured in % survival/L. 

Advantaaes: Test is rapid and uses a mammalian system. 

Disadvantaaes: Sensitivity, specificity not well documented, use of effluent extract may exclude 

certain types of compounds, 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 is followed by a S-month grow out period in clean water. At six months, fish are sacrificed 

and submitted for histopathological studies. 

Endooint: Tumor formation, response measured in frequency of tumors at a given site/effluent 

concentration. 

Advantaaes: Use of whole effluent, whole organism, oncogenic endpoint 
Disadvantaaes: Carcinogen levels in unconcentrated effluent may not be high enough to produce 

tumors at a detectable frequency in exposed populations, effluent must not be toxic to Medaka, 

requires extrapolation from non-mammalian system, relatively expensive, length of test, endpoint 

requires pathologist experienced in fish cancers, method still in developmental stages. 

Cost: $20,000 [Johnson, 19891. 

Other Human Health Effects 

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 to as “systemic.” 

Formerly, the only means to assess systemic 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 are expensive ($100,000 and over) and beyond the cost constraints 

for most effluent analyses. As an alternative, a number of short-term in vitro 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. 

As with the nonthreshhold assays previously discussed, these in vitro assays only serve to qualify potential 

human health hazards. In the case of positive in vitro results, tests on intact mammals can be pursued in order 

to confirm screening test findings and establish a dose-response relationship. Alternatively, causative agent 

evaluations resulting in either the identity of the toxicant or toxicity treatability data may be pursued. 

Current limitations of the Biological Regulatory Approach 

At present, the use of biological indicator tests as a regulatory tool is limited for a number 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 in vitro (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, as with aquatic organism toxicity tests, a human 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 use of 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 as the continual change in composition typical for most effluents, the need to concentrate 

samples to obtain a dose-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 as screening 

tools, with actual regulation of effluents posing potential health hazards likely to remain on a chemical-bychemical basis. 

CA 
1. 

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. 

In preparation. 

Ashby, J. and R.W. Tennant, 1988. Chemical Structure, Salmonella Mutagenicity and Extent of 

Carcinogenicity as Indicators of Genotoxic 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 in the Aquatic Environment. Plenum Press, New York, 1980. 

Borenfreund, E. and J. Puerner. 1985. Toxicity Determined in vitro by Morphological Alterations and 

Neutral Red Absorption. Toxicology Letters 24. pp. 119-l 24. 

Brown, N.A. and S.E. Fabro. 1982. The in vitro 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 of Sister 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 of Chemicals. 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 Use of 

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 in vitro Teratogenicity 

Testing: Aspects of Validation 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 in Human Genes. Science 243. pp. 737-738. 

McGeorge, Leslie J., 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 Results to Date. Office of Science and Research. August 1983. 

Quillardet, P., C. de Bellecombe, and M. Hofnung. 1985. “The SOS Chromotest, a Calorimetric Bacterial 

Assay for Genotoxins: Validation Study with 83 Compounds”. Mutation Research, 147. pgs. 79-95. 
19. 

20. 

21. 

22. 

23. 

24. 

25. 

26. 

27. 

28. 

29. 

30. 

31. 

32. 

33. 

34. 

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 

Paris CEDEX 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 in vitro 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. EPA 600/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 Tests for Carcinogens, Mutagens, and other 

Genotoxic Agents. Health Effects Research Laboratory, Research Triangle Part. EPA 625/9-79-003. 

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. EPA 600/4-82-068. 

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. EPA 600/4/85-058. 

U.S. Environmental Protection Agency. 1986a. Guidelines for the Health Risk Assessment of Chemical 

Mixtures. Federal Register 51 (185). pp. 34014-34025. 

U.S. Environmental Protection Agency. 1986b. Guidelines for Mutagenicity Risk Assessment. Federal 

Register 51 (185). pp. 34006-34012. 

U.S. Environmental Protection Agency. 1986~. Guidelines for Carcinogen Risk Assessment. 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 #I 8095A). Office of Research and Development, 

Environmental Research Laboratory, Duluth, MN. August 1988. 

U.S. Environmental Protection Agency. 1989a. Draft Guidance on Assessment, Criteria Development, and 

Control of Bioconcen-tratable Contaminants in Surface Waters. 

U.S. Environmental Protection Agency. 1989b. The Medaka Caarcinogenesis Model: A Progress Report 

(Deliverable # 8094A). Office of Research and Development, Environmental Research Laboratory, 

Duluth, MN. February 1989. 

Wilson, J. G. 1978. Survey of in vitro Systems: Their Potential Use in Teratogenicity Screening. In J.G. 

Wilson and F.C Fiaser, eds. Handbook of Teratology. Vol. 4. New York, NY. Plenum Press. pp. 135- 

153. 
APPENDIX H 

REFERENCE DOSE (RFD): DESCRIPTION AND USE IN 

HEALTH RISK ASSESSMENTS 
REFERENCE DOSE (RFD) DESCRIPTION AND USE IN 

HEALTH RISK ASSESSMENTS (REVISED 02/10/88) 

Principal Author: 

Donald Barnes, Ph.D. (OPTS) 

RfD Work Group: 

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) 

Jerry Stara, D.V.M., Ph.D. (ORD) 

Jeanette Wiltse, Ph.D. (OPTS) 

Larry Zaragosa, Ph.D. (OAR) 

H-1 
REFERENCE DOSE (RFD): DESCRIPTION AND USE IN 

HEALTH RISK ASSESSMENTS 

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. By outlining principles and concepts that guide EPA risk assessment for such systemic effects the 

paper complements the new risk assessment 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 a toxic chemical.) 

Backgroaad and Sunnnary 

Chemicals that give rise to toxic endpoints other than cancer and gene mutations are often referred to as 

“systemic toxicants” because of their effects on the function of various organ systems. In addition, chemicals 

that cause cancer and gene mutations also commonly evoke other toxic effects (i.e., systemic toxicity). Based on 

our understanding of homeostatic and adaptive mechanisms, systemic toxicity is treated as if there is an 

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 are often treated as nonthreshold 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. EPA established the Reference Dose (RfD) Work Group to address these concerns. 

In preparing this report, the RfD Work Group has drawn on traditional report on risk assessment (NRC, 1983), to 

more fully articulate the use of noncancer, nonmutagenic experimental data in reaching regulatory decisions 

about the significance of exposures to chemicals. In the process, the Work Group has coined less value-laden 

terminology -- “reference dose (RfD),” “uncertainty factor (UF)“; “margin of exposure (MOE)“; and “regulatory 

dose (RgD)” -- to clarify and distinguish between aspects of risk assessment and risk management. These 

concepts are currently in general use in many parts of U.S. EPA. 

Traditional Approach to Assessing Systemic Toxicity 

The U.S. EPA’s approach to assessing the risks associated with systemic toxicity is different from its approach 

to assessing the risks associated with carcinogenicity, because of the different mechanisms of action thought to 

be involved in the two cases. In the case of carcinogens, the Agency assumes that a small number of molecular 

events can evoke changes in a single cell that can lead to uncontrolled cellular proliferation. This mechanism 

for carcinogenesis is referred to as “nonthreshold,” since there is theoretically no level of exposure for such a 

chemical that does not pose a small, but finite, probability of generating a carcinogenic response. In the case 

of systemic toxicity, however, organic homeostatic, compensating, and adaptive mechanisms exist that must be 

overcome before a toxic endpoint is manifested. For example, there could be a large number of cells performing 

the same or similar function whose population must be significantly depleted before the effect is seen. 

The threshold concept is important in the regulatory context. The individual threshold hypothesis holds that a 

range of exposures from zero to some finite value can be tolerated by the organism with essentially no chance 

of expression of the toxic effect. Further, it is often prudent to focus on the most sensitive members of the 

population; therefore, regulatory efforts are generally made to keep exposures below the population threshold, 

which is defined as the lowest of the thresholds of the individuals within a population. 

H-2 
&script/on of the Tradifioaal Amroach 

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 as the amount of a chemical to which a person can be exposed on a 

daily basis over an extended period of time (usually a lifetime) without suffering a deleterious effect. The ADI 

concept has often been used as a tool in reaching risk management decisions (e.g., establishing allowable 

levels of contaminants in foodstuffs and water.) 

A NOAEL is an 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 as the highest experimentally 

determined dose without a statistically or biologically significant adverse effect. The NOAEL for the criticaf 

toxic effect is sometimes referred to simply as the NOEL. This usage, however, invites ambiguity in that there 

may be observable effects that are not of toxicological significance (i.e., they are not “adverse”). For the sake 

of precision, this document uses the 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)” is used. 

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 a safety factor (SF), as follows: 

ADI (human dose) = NOAEL (experimental dose)/SF (Equation 1) 

Generally, the SF consists of multiples of 10, each factor representing a specific area of uncertainty inherent in 

the available data. For example, a factor of 10 may be introduced to account for the possible differences in 

responsiveness between humans and animals in prolonged exposure studies. A second factor of 10 may be used 

to account for variation in susceptibility among individuals in the human population. The resultant SF of 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 a SF of 1000) might be judged to be more appropriate. For certain other chemicals, 

based on well-characterized responses in sensitive humans (as in the effect of fluoride on human teeth), an SF as 

small as 1 might be selected. 

While the original selection of SFs appears to have been rather arbitrary (Lehman and Fitzhugh, 1954), 

subsequent analysis of data (Dourson and Stara, 1983) lends theoretical (and in some instances experimental) 

support for their selection. Further, some scientists, but not all, within the EPA interpret the absence of 

widespread effects in the exposed human populations as evidence of the adequacy of the SFs traditionally 

employed. 

Same Diffice/t/es in Ut/Mn# the Traditimwl Approach 

Scientific Issues 

While the traditional approach has performed well over the years and the Agency has sought to be consistent in 

its application, observers have identified scientific shortcomings of the approach. Examples include the 

following: 

a. Too narrow a focus on the NOAEL means that information on the shape of the dose-response curve is 

ignored. Such data could be important in estimating levels of concern for public safety. 

b. As scientific 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 the fact that some studies have used larger 

(smaller) numbers of animals and, hence, are generally more (less) reliable than other studies. 

H-3 
These and other “scientific issues” are not susceptible to immediate resolution, since the database needed is not 

yet sufficiently developed or analyzed. U.S. EPA work groups are presently considering these issues. 

Management-related Issues 

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 is a conceptual basis for believing in the existence of a 

threshold and “absolute safety” associated with certain chemicals, in the majority of cases a firm experimental 

basis for 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 is viewed by many (including risk managers) as an 

“acceptable” level of exposure, and, by inference, any exposure greater than the ADI is seen as “unacceptable.” 

This strict demarcation between what is “acceptable” and what is “unacceptable” is contrary to the views of 

most toxicologists, who typically interpret the ADI as a relatively crude estimate of a level of chronic exposure 

which is not likely to result in adverse effects to humans. The ADI is generally viewed by risk assessors as a 

“soft” estimate, whose bounds of uncertainty can span an order of magnitude. That is, within reasonable limits, 

while exposures somewhat higher than the ADl are associated with increased probability of adverse effects, that 

probability is not a certainty. Similarly, while the ADI is seen as a level at which the probability of adverse 

effects is low, the absence of all risk to all people cannot be assured at this level. 

Possible limitations imposed on risk management decisions - Awareness of the “softness” of the ADI 

estimate, as discussed above, argues for careful case-by-case consideration of the toxicological implications of 

individual situation, so that ADls are not given a degree of significance that is scientifically unwarranted. In 

addition, the ADI is only one factor in a risk 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 in Table 1. For example, if the toxic endpoint for a 

chemical in experimental animals is the same as that which has been established for a related chemical in 

humans at similar doses, one could argue for an SF of less than the traditional 100. On the other hand, if the 

total toxicologic data base is incomplete, one could argue that an additional SF should be included, both as a 

matter of prudent public policy and as an incentive to others to generate the appropriate data. 

Such practices, as employed by a number 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 of them is more “wrong”--or “right”--than the rest. It is more nearly a reflection of 

the honest difference in scientific judgment. 

However, on occasion, these differences in judgment of the scientific data, can be interpreted as differences 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 of affairs arises, at least in part, from treating the ADI as an absolute measure of safety. 

EPA Assessment of Risks Assoclatsd with Systemic Toxlclty 

The U.S. EPA approach 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 is made during the risk assessment process, which consists of hazard identification, doseresponse assessment, exposure assessment, and risk characterization. Having been apprised by the risk assessor 

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 be gained and costs to be incurred). All of these considerations 

go into the determination of the 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 is intended to account for the 

variation in sensitivity among the members of the human population and is referenced as 

“1 OH”. 

Use an additional IO-fold factor when extrapolating from valid results of long-term studies 

on experimental animals when results of studies 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 is referenced as “1 OA”. 

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 less than chronic 

NOAELs to chronic NOAELs and is referenced as “10s”. 

Use an additional lo-fold factor when deriving an RfD from a LOAEL, instead of a NOAEL. 

This factor is intended to account for the uncertainty involved in extrapolating from 

LOAELs to NOAELs and is referenced as “1OL”. 

ModlfyinRg Factor (MF): 

Use professional judgment to determine the MF, which is an additional uncertainty factor 

that is greater than zero and less than 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 MF is 1. 

*Source: Adapted from Dourson and Stara, 1983 

Hazard fdmtlficatlon 

Evidence 

Type of effect - Exposure to a given chemical, depending on the dose employed, may result in a variety of toxic 

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 a chemical, 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 an assessment can be made. 

Principal studies - Pnncrpal studies are those that contrtbute most srgnrfrcantly to the qualrtatrve assessment 

of whether or not a particular chemical is potentially a systemic toxicant in humans. In addition, they may be 
Figure 1. 

Conceptual Framework for Risk Assessment and Risk Management* 

Risk Assessment Risk Management 

Hazard Identification Control Options 

Dose-Response Assessment 

(e.g., RfD) 

Exposure Assessment 

Non-risk Analyses 

V V V V V 

Risk Characterization 

(e.g., Criterion) 

>Regulatory Decision 

(e.g., RgD, Standard) 

*Source: Adapted from NRC, 1983 

used in the quantitative dose-response assessment phase of the risk assessment. These studies are of two 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 in exposed human populations. When there is information 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. 

In epidemiologic studies, confounding factors that are recognized can be controlled and measured, 

within limits. Case reports and acute exposures resulting in severe 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 of establishing 

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. In such cases, the principal studies are drawn from experiments conducted on nonhuman 

mammals, most often the rat, mouse, rabbit, guinea pig, hamster, dog, or monkey. 

Supporting studies - These studies provide supportive, rather than definitive, information and can include 

data from a wide variety of sources. For example, metabolic and other pharmacokinetics studies can provide 

insights into the mechanism of action of a particular compound. By comparing the metabolism of the chemical 

exhibiting the toxic effect in the animal with the metabolism found in humans, it may be possible to assess the 

potential for toxicity in humans or to 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 other 
structurally related compounds can provide clues to the test chemical’s possible toxicity. More reliable in vitro 

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 of toxic processes in humans and nonhumans. 

Route of exposure - The U.S. EPA often approaches the investigation of a chemical with a particular route of 

exposure in mind (e.g., an oral exposure for a drinking water contaminant or an 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 in factors such as mechanism-of-action and 

bioavailability. In general, the U.S. EPA’s position is that the potential for toxicity manifested via one route 

of exposure is relevant 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 of these differences on the risk assessment are delineated. 

Length of exposure - The U.S. EPA is concerned about the potential toxic effects in humans associated with 

all possible exposures to chemicals. The magnitude, frequency, and duration of exposure 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 of subtle neurological changes 

seen in low-dose chronic studies. Special attention is given to studies involving low-dose, chronic exposures, 

since such exposures can elicit effects absent in higher dose, shorter exposures, through mechanisms such as 

accumulation of toxicants in the 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 is given to many other factors, including chemical 

characterization of the compound(s) under study, the type of test species, similarities and differences between 

the test species and humans (e.g., chemical absorption and metabolism), the number of individuals in the study 

groups, the number of study groups, the spacing and choice of dose levels tested, the types of observations and 

methods of analysis, the nature of pathologic changes, the alteration in metabolic responses, the sex and age 

of test animals, and the route and duration of exposure. 

Weight-of-Evidence Determinatlon 

As the 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 a consistent, plausible picture of 

toxicity emerges. For example, the following factors add to the weight of the evidence that the chemical poses 

a hazard to humans: similar results in replicated animal studies by different investigators; similar effects across 

sex, strain, species, and route of exposure; clear evidence of a dose-response relationship; a plausible 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 of the effect of 

concern in humans. 

Dose-Response Assessment 

Concepts and Problems 

Empirical observations have generally revealed that as the dosage of a toxicant is increased, the toxic response 

(in terms of severity and/or incidence of effect) also increases. This dose-response relationship is well- founded 

in the theory and practice of toxicology and pharmacology. Such behavior is observed in the following 

instances: in quanta1 responses, in which the proportion of responding individuals in a population increases 

with dose; in graded responses, in which the severity of the toxic response within an individual increases 

H-7 
with dose; and in continuous responses, in which changes in a biological parameter (e.g., body or organ 

weight) vary with dose. 

In evaluating a dose-response relationship, certain difficulties arise. For example, one must decide on the 

critical endpoint to measure as the “response.” One must also decide on the correct measure of “dose.’ tn 

addition to the interspecies extrapolation aspects of the question of the appropriate units for dose, the more 

fundamental question of administered dose versus absorbed dose versus target organ dose should be considered. 

These questions are the subject of much current research. 

Selection of the Critical Oata 

Critical study - Data from experimental studies in laboratory animals are often selected as the governing 

information when performing quantitative risk assessments, since available human data are usually insufficient 

for this purpose. These animal studies typically reflect situations in which exposure to the toxicant has been 

carefully controlled and the problems of heterogeneity of the exposed population and concurrent exposures to 

other toxicants have been minimized. In evaluating animal data, a series 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 risk assessor first seeks to identify the animal model that is most relevant to humans, 

based on the most defensible biological rationale (e.g., for instance using comparative pharmacokinetics data). 

In the absence of a clearly most relevant species, the most sensitive species (i.e., the species showing a toxic 

effect at the lowest administered dose) is used by risk assessors at U.S. EPA, since there is no assurance that 

humans are not at least as innately sensitive as the 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 risk assessor 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 to other 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). Risk assessments based on human data have the advantage of avoiding the problems 

inherent in interspecies extrapolation. In many instances, use of such studies, as is the 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, a well-designed and well-conducted epidemiologic study that shows no 

association between known exposures and toxicity can be used to directly project an RfD (as has been done in 

the case of fluoride). 

Critical data - In the simplest terms, an experimental exposure level is selected 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, is the primary basis for the scientific evaluation of the risk posed to humans by systemic 

toxicants. This approach is based on the assumption that if the critical toxic effect is prevented, then all 

toxic effects are prevented. 

More formally, the NOAEL is defined in this discussion as the highest experimental dose of a chemical at which 

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 be sound professional differences of opinion in judging whether or not a particular response is 

adverse. In addition, the NOAEL is a function of the size of the population under study. Studies with a small 

number of subjects are less likely to detect low-dose effects than studies using larger numbers of subjects. Also, 

if the interval between doses in an experiment is large, it is possible that the experimentally determined 

NOAEL is lower than that which would be observed in a study using intervening doses. 

Critical endpoint - As noted under “Traditional Approach to Assessing Systemic Toxicity”, a chemical 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). In general, NOAELs for these effects will differ. The 

critical endpoint used in the dose- response assessment is the effect exhibiting the lowest NOAEL. 

Ha 
Reference Doss (RfD) 

The reference dose (RfD) and uncertainty factor (UF) concepts have been developed by the RfD Work Group in 

response to many of the problems associated with ADls and SFs, as outlined under “Traditional Approach to 

Assessing Systemic Toxicity” above. The RfD is a benchmark 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. For example, a valid chronic animal NOAEL is normally divided by an UF of 

100. In addition, a modifying factor (MF), is sometimes used which is based on a professional judgment of 

the entire data base of the chemical. These factors and their rationales are presented in Table 1. 

The RfD is determined by use of the following equation: 

RfD = NOAEL / (UF x MF) 

which is the functional equivalent of Equation 1. In general, the RfD is an estimate (with uncertainty 

spanning perhaps an order-of-magnitude) of a daily exposure to the human population (including sensitive 

subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime. The RfD is 

generally expressed in units of milligrams per kilogram of body weight per day (mg/kg/day). 

The RfD is useful as a reference point from which to gauge the potential effects of the chemical at other doses. 

Usually, doses less than the RfD are not likely to be associated with adverse health risks, and are therefore less 

likely to be of regulatory concern. As the 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 RfD are “acceptable” (or will be risk-free) and that all doses 

in excess of the RfD are “unacceptable” (or will result in adverse effects). 

The U.S. EPA is attempting to standardize its approach to determining RfDs. The RfD Work Group has 

developed a systematic approach to summarizing its evaluations, conclusions, and reservations regarding RfDs 

in a “cover sheet” of a few pages in length. The cover sheet includes a statement on the confidence (high, 

medium, or low) the evaluators have in the stability of the RfD. High confidence indicates the judgment that 

the RfD is unlikely to change in the future because there is consistency among the toxic responses observed in 

different sexes, species, study designs, or in dose-response relationships, or that the reasons for existing 

differences are well understood. High confidence is often given to RfDs that are based on human data for the 

exposure route of concern, since in such cases the 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 in a change in the RfD. 

The third step in the risk assessment process focuses on exposure issues. For a full discussion of exposure 

assessment, consult U.S. EPA’s guidelines 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 of exposure, as well as evaluation of the nature of the exposed populations. 

R/s& Cbafact8rlzathm 

Risk characterization is the final step in the risk assessment process and the first input to the risk management 

(regulatory action) process. The purpose of risk characterization is to present the risk manager with a synopsis 

and synthesis of all the data that should contribute to a conclusion with regard to the nature and extent of the 

risk, including: 

a. The qualitative (“weight-of-evidence”) conclusions as to the likelihood that the chemical may pose 

a hazard to human health. 

b. A discussion of the dose-response information considered in deriving the WD, including the UFs 

and MFs used. 

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 of the observed effects. 

l+9 
d. Estimates of the nature and extent of the exposure and the number and types of people exposed. 

e. Discussion of the overall uncertainty in the analysis, including the major assumptions made, 

scientific judgments employed, and an estimate of the degree of conservatism involved. 

In the risk characterization process, a comparison is made between the RfD and the estimated (calculated or 

measured) exposure dose (EED). The EED should include all sources and routes of exposure involved. If the EED 

is less than the RfD, the need for regulatory concern is likely to be small. 

An alternative measure that may be useful to some risk managers is the margin of exposure (MOE), which is the 

magnitude by which the NOAEL of the critical toxic effect exceeds the estimated exposure dose (EED), where 

both are expressed in the same units: 

MOE = NOAEL (experimental dose) / EED (human dose). 

When the MOE is equal to or greater than UF x MF, the need for regulatory concern is likely to be small. 

“Hypothetical, Simplified Example of Determining and Using WD” contains an example of the use of the 

concepts of NOAEL, UF, MF, RfD, EED, and MOE. 

Application In Risk Management 

Once the risk characterization is completed, the focus turns to risk management. In reaching 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 -- risk management decisions must be made on a case-by-case basis. For example, 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 is entirely 

possible and appropriate that a chemical with a specific RfD may be regulated under different statutes and 

situations through the use of different “regulatory doses (RgDs).” 

That is, after carefully considering the various risk and nonrisk factors, regulatory options, and statutory 

mandates in a given case (i), the risk manager selects the appropriate statutory alternative for arriving at an 

“ample” or “adequate” margin of exposure [MOE(i)]. As shown in Equation 2 below, this procedure establishes 

the regulatory dose, RgD(i) (e.g., a tolerance under FIFRA or 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 a given chemical with a single RfD. Note also that comparing the RfD 

to a particular RgD(i) is equivalent to comparing the MOE(i) with the UF x MF: 

RfD/RgD(i) = MOE(i) / (UF x MF). 

In assessing the significance of a case in which the RgD is greater (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 in order to arrive at a responsible and clearly articulated final decision on the RgD. 

It is generally useful to the risk manager to have information regarding the contribution to the RfD from various 

environmental media (e.g., air, water and food). Such information can provide insights that are helpful in 

H-10 
choosing 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. In such cases, the exposure assessor’s role is particularly important. 

For instance, at a given site, consumption of fish may clearly dominate the local exposure routes, while, on a 

national basis, fish consumption may play a minor role compared to ingestion of treated crops. 

Work is underway in the U.S. EPA to apportion the RfD among the various environmental media. For example, 

consider the case of a food-use pesticide which is a contaminant in drinking water. In selecting among risk 

management actions under the Safe Drinking Water Act, it might be prudent to assume an RfD for drinking water 

purposes which is some fraction of the total RfD. Such an apportionment would explicitly acknowledge the 

possible additional exposure from ingestion of treated crops. The apportionment of the RfD would, in general, 

provide additional guidance for risk managers of the various media- specific programs. 

Other Directions 

In addition to the development of reference doses, the U.S. EPA is pursuing other lines of investigation for 

systemic toxicity. For example, the Office of Air Quality Planning and Standards is using probabilistic risk 

assessment procedures for criteria pollutants. In this procedure, the population at risk is characterized, and the 

likelihood of the occurrence of various effects is predicted through the use of available scientific literature and 

of scientific experts’ rendering their judgments concerning dose-response relationships. This dose-response 

information is then combined with the results of the exposure analysis to generate population risk estimates for 

alternative standards. Through the use of these procedures, decisionmakers are presented with ranges of risk 

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 risk and cost. In addition, scientists in the Office of Research and Development have initiated 

a series of studies designed to increase the reliability of risk assessments. 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 is also 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 Example of Determining and Using RfD 

Experimenttal Results 

Suppose the U.S. EPA had a sound 90-day subchronic gavage study in rats with the data in Table 2. 

Analysis 

Determination of the Reference Dose (RfD) 

Using the NOAEL - Because the study is on animals and of subchronic duration, 

UF = 10H x 10A x 10s = 1000 (Table 1) 

In addition, there is a subjective adjustment (MF) based on the high number of animals (250) per dose group: 

MF = 0.8. These factors then give UF x MF = 800, so that 

RfD = NOAEL/(UF x MF) = S/800 = 0.006 (mg/kg/day). 

H-l 1 
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* in body 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* in body 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 - If the NOAEL is not available, and if 25 mg/kg/day had been the lowest dose tested that 

showed adverse effects, 

UF = 10H x 10A x 10s x 1OL = 10,000 (Table 1). 

Using again the subjective adjustment of MF = 0.8, one obtains 

RfD = LOAEL/(UF x MF) = ZS/SOOO = 0.003 (mg/kg/day). 

Risk Characterization Considerations 

Suppose the estimated exposure dose (EED) for humans exposed to the chemical under the proposed use pattern 

were 0.01 mg/kg/day (i.e., the EED is greater than the RfD). Viewed alternatively, the MOE is: 

MOE = NOAEL/EED = S (mg/kg/day) / 0.01 (mg/kg/day) = 500. 

Because the EED exceeds the RfD (and the MOE is less than the UF x MF), the risk manager will need to look 

carefully at the data set, the assumptions for both the RfD and the exposure estimates, and the comments of the 

risk assessors. In addition, the risk manager will need to weigh the benefits associated with the case, and other 

non-risk factors, in reaching a decision on the regulatory dose (RgD). 

H-l 2 
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. Risk Assessment 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-l 3 
APPENDIX I 

CHEMICALS AVAILABLE IN IRIS 
Page No. 1 

09/28/90 

CAS Chemical RfD q1* 

number Name mg/kg/day /mg/kg/day 

USE ONLY FOR SCREENING 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 RAC in 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-1 
Page No. 2 

09/20/90 

CAS Chemi ccl RfD 41* 

m&w Name m/kg/day /ng/kg/day 

USE DNLY FOR SCREENING 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 RAC in 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) RAC C&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-2 
Page No. 3 

09/28/90 

CAS Chemical RfD ql* 

~rName ng/kg/day /ng/kg/day 

USE ONLY FOR SCREENING 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 RAC in 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 l thylglycolate 3 

64742 Dibutyt p)lthalate 0.1 

855007 Diquat 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-3 
Page No. 4 

W/28/90 

USE DNLY FOR SCREENING 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 RAC in Regulatory Action. 

CAS Chemical 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 

1-4 
Page No. 5 

W/28/90 

CAS Chemical RfD v 

nu&erNam mgfkgfday fmgfkgfday 

USE DNLY FOR SCREENING 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 RAC in 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-5 
Page No. 6 

W/28/90 

CAS Chemical RfD 41. 

rrrkrla w/kg/&y /ng/kg/&y 

USE DNLY FOR SCREENING 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 RAC in 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 

6 
Page No. 7 

W/28/90 

USE ONLY FOR SCREENING 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 RAC in Regulatory Action. 

CAS Chemical RfD v 

nuber Name WWday /Wkg/day 

Estimated RAC (tag/L) RAC tmg/\) 

BCF RL: lOE-6 RL: lOE-6 

3% Lipid 0.0065 kg/day 0.020 kg/day 

Consumption Consurpt i on 

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 f luoranthene 0.04 

298DDD Methyl 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 

1-7 
Page No. 8 

09/28/90 

USE ONLY FDR SCREENING 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 RAC in Regulatory Action. 

CAS Chemical RfD 91. 

mm&r Naae w/kg/day /mg/kg/dw 

Estimated RAC (@t/l) RAC OWL) 

BCF RL: lOE-6 RL: lOE-6 

3% Lipid 0.0065 kg/&y 0.020 kg/day 

Conslnpt i on 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-8 
Page No. 9 

W/28/W 

US Chrical 

nnkrName 

USE DNLY FOR SCREENING 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 RAC in Regulatory Action. 

RfD ql* Estimatad RAC EmgIl) RAC tmp/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-9 
Page No. 10 

W/28/90 

CAS Chmaical 

nt&arName 

USE tMLy FOR SCREENING 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 RAC in 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 Mi rex 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 S i I ver 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-10 
Page No. 11 

W/28/90 

USE ONLY FUR SCREENING 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. 

CAS Chamical 

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-11 
Page No. 12 

w/28/90 

CAS Chemical 

nu&erNw 

USE DULY FOR SCREENING 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 RAC in 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) RAC O&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-12 
Page No. 13 

W/28/90 

CAS Chemical 

nu&erNme 

USE DNLY FOR SCREENING 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 RAC in 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 Dani tot 

39638329 Bis(Z-chloroisopropyl) ether 

LD888479 letrabromodiphenyl ether 

40487421 Pendimethalin (Proul) 

41851507 Chlorocyclopentadiene 

42874033 Oxyfluorfen 

43121433 Bayleton 

43222486 Di f enmquat 

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-13 
Page No. 14 

W/28/90 

CAS Chemical 

nnkrName 

USE ONLY FDR SCREENING 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 RAC in Regulatory Action. 

RfD ql* Estimsted RAC (W/l) RAC fmg/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-14 
Page No. 15 

w/tam 

CAS Chemical 

ru&erName 

USE ONLY FOR SCREENING 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 RAC in 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) RAC OWL) 

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-15 
United States 

Environmental Protection 

Agency 

Office Of Water 

EPA Technical Support Document 

For Water Quality-based 

Toxics Control 

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 CETTP studies. EPA evaluated the 

results of the CETTP studies, 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 TSD and 

referenced. The TSD now includes a discussion of Parkhurst's 

major criticisms of the CETTP studies and why EPA feels 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 

NPDES permits. 

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. Raw precision 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. EPA is 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. 

The biologicalcriteria/bioassessment approach is not yet part of EPA's water quality regulations and should not yet be used 

in the regulatory process. For the best assessment of sources 

and causes, EPA should use a g9weight-of-evidence approach." 

EPA Response: 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 TSD has been revised to read: 

"To better protect the biological integrity of aquatic 

communities, EPA recommends 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 a biosurvey alone is insufficient evidence 

to waive or relax a permit 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 TSD was 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. 

EPA Response: The revised draft TSD now addresses 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°C whereas 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. EPA should 

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 seems contrary to the site specific nature of the permitting process. 

EPA Response: To address concerns with the algal test and to 

allow more flexibility in testing, the recommendation in the 

TSD has been changed to "fish, invertebrate, and plant." EPA 

will not modify its recommendation to include only 2 phyla. EPA's objective in requiring 3 species from 3 different 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 EPA makes 

such a broad ranging recommendation, there must be sufficient 

data to establish an overall relationship. 

EPA Response: 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 NOEC calculated using the IC25 

is comparable to an NOEC calculated using hypothesis testing, and that the relationship is statistically sound. 

4 
7. The data in the TSD shows that whole effluent toxicity is not 

additive. 

EPA Response: The cited article in the TSD and 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. EPA Response: The field studies referenced in Chapter 1 for 

chemical specific criteria investigations were conducted over 

twenty years ago. The field investigators dosed a stream 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. 

EPA Response: 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 EPA requests 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 how to prohibit lethality within the 

mixing zones? 

EPA Response: The TSD has 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 a mixing 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 CCC and CMC cannot be correctly interpreted without accounting for the duration of exposure. 

3. Th8 TSD is flawed because it assumes that mixing zones exist. 

The Great Lakes Water Quality Agreement pr8ClUd8S us8 of flow 

augmentation as a substitute for adequate treatment. 

EPA Response: 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 bioaccumulative toxicants. The Clean Water Act also has a goal of zero 

discharge of pollutants. 

EPA Response: The TSD continues to note that EPA regulations 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 may also 

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 TMDL for 

a water body as a whole is exceeded. Consequently, EPA does 

not consider mixing zone restrictions to be the 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 WQ based 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 standards are 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 may at that time consider the feasibility 

of different acute averaging periods for different chemicals. 

The rationale for the I-day averaging period is weak. Chronic 

toxicity tests for most species require much longer than four 

days. The field studies presented in Chapter 1 indicate that 

longer term excursions are needed to produce ecological effects. EPA inappropriately picked examples of pollutants with low acute-chronic ratios to justify the r-day averaging period. A do-day averaging period (per the freshwater ammonia 

oriteria document) should be acceptable, particularly in cases 

where concentrations do not change rapidly. 

EPA Response: 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, EPA recognizes 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. EPA agrees 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 EPA still 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. 

EPA should present data on the ecological differences between 

sites with different excursion frequencies. EPA should 

develop guidance on how to establish site-specific allowable 

frequencies. 

9 
EPA Response: EPA has used Appendix D to 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. 

EPA believes that the 3-year return interval can be justified by the Appendix D data if one makes the assumption that the 

type of ecological impact shown in Appendix D could 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. EPA also recognizes that the probability of large excursions can be calculated to be extremely small compared 

to the probability of marginal excursions. 

EPA does 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, EPA recommends 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 a calculable 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 on the 

total period of recovery from a full range of possible events, 

compared with the sum of return intervals for such events, the 

allowable frequency for the marginal criteri'a excursion could 

be established. 

12. The once in three year excursion frequency does not take into 

consideration the likelihood of apparent excursions caused by the inherent variability of the analytical tests. 

EPA Response: 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. The IRIS (Integrated Risk Information System) data base should 

be peer reviewed before it is used to update and generate RACS. The TSD should not imply that a State can update its 

standards simply by inserting the latest XRIS information 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 RACs are unrealistically high for many waters. 

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 mean flow should be used. 

EPA Response: For carcinogens it is appropriate to determine 

the long-term arithmetic mean exposure concentration. Because 

flow is not normally distributed, using the arithmetic mean 

flow for design purposes will underestimate the mean 

concentration. 

Using the downstream harmonic mean flow 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 on such criteria. 

EPA Response: The biological criteria and sediment criteria 

sections have been modified. EPA has 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. EPA also 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 be more limiting on dischargers than water quality 

concerns. 

17. Aquatic life protection as measured by whole 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 RAC calculation 

should be deleted because it is contrary to measured levels 

and BCF estimations. 

EPA Response: 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. EPA believes 

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 CFR 122.44(d)); revised the discussion on effluent 

bioconcentration evaluation to conform with the new draft document. 

1. Determining the need for an effluent limit in the absence of 

effluent monitoring data does not address the required factors 

of 40 CFR 5 lzz.rr(d)(l)(ii). 

EPA Response: 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 TSD was 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 much more 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. 

EPA Response: EPA agrees with this comment. The draft of 

chapter 3 did not draw a clear enough distinction between the 

terms @*causelV and l'contributes to" in the context of the 

reasonable potential determination. Chapter 3 now states that 

where multiple discharges collectively are causing or show the 

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. 

EPA Response: EPA agrees 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 3 did 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 WET limits 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: EPA does 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, EPA only recommends establishing effluent limits where toxic 

impacts are projected to occur. Estimates of toxic impact should be made assuming 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 as a result of this comment. However, EPA has added 

a short discussion that suggests that the regulatory authority 

may choose to assess reasonable potential using a stochastic 

dilution model which incorporates both ambient dilution and 

effluent variability to project toxic impact. 

6. EPA should not recommend a 3 species minimum. The algae test 

and the marine tests are not sufficiently studied. 

EPA Response: 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 WET limits. 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. EPA rejects the assertion that the algal tests 

are not sufficiently studied. For both freshwater and marine 

14 
waters, the use of 3 species is more protective than 2 species 

since a wider range of species sensitivity can be measured. 

7. EPA should not recommend that ambient toxicity tests be 

conducted at worst case low flow conditions. 

EPA Response: EPA disagrees 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 a weight of 

evidence approach. 

EPA Response: We disagree. As discussed in the response to 

comment no. 2 in Chapter 1, EPA considers 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. 

EPA Response: EPA disagrees. EPA's position is that where 

even one data point shows that an excursion of a state water 

quality standard is projected, the Director may determine that 

permit limits for whole effluent toxicity or for specific 

chemicals are necessary. In making such a determination, 

NPDES regulations 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 and Wasteload 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 how to 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. 

EPA Response: The revised TSD states 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 a drifting 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 CMC is 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 human health 

impacts and/or aquatic life impacts. 

EPA Response: 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 TSD was 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. EPA should present u valid modeling approaches and determine the best water quality model for each water quality condition. 

EPA Response: 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. 

EPA Response: 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. 

EPA Response: The TSD already contained a sufficient explanation or referenced the documents which contained the 

rationale. 

6. The water quality model "DYNTOX1# and the software program RRD 

FLOW are not available. 

EPA Response: DYNTOX is not currently available but should 

be accessible by early 1991. HHD FLOW is not available, but 

DFLOW is available, and the TSD has been changed to reference 

this software package. 

7. Guidance on regulating nonpoint sources should be included in 

the TSD. 

EPA Response: It is beyond the scope of the document to 

include information on regulating non-point sources. 

8. Guidance should be given in the discussion on design flow for 

persistent pollutants. 

EPA Response: Persistent pollutants should be assessed in 

the same way as bioaccumulative pollutants. 

9. The human health section does not provide direction regard- ing the percentage of fish that are taken from a given area. 

EPA Response: The percentage of fish should be determined 

based on site-conditions. The WLA criteria are conservative 

estimates based on heavy consumption of fish or a potentially large contaminated area. 

17 
10. The TSDs discussion on mixing Zone8 contribution to human 

health is overemphasized. 

EPA Response: The TSDs discussion on mixing zones has been 

revised and does not overemphasize the contributions to human 

health. 

11. EPA should revise its discussion of modeling WLAs for 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 humans or criterion established for Deriving Numerical National Water Quality Criteria. 

EPA Response: 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 a pollutant 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 a water 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 WLA is 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 now a recommended 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. 

EPA Response: The discussion of below detection level limits 

in the draft TSD was 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 a minimum 

level (ML) to ascertain compliance with limits set below 

detection levels. The TSD was revised to follow the dioxin 

guidance (May 21', 1990 Memorandum from LaJuana Wilcher, 

"Strategy for the Regulation of Discharges of PHDDs and 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 CFR 136.) 

3. EPA's criteria recommend that 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 TSD should 

address this issue and give guidance on developing limits only for bioavailable forms of pollutants. 

EPA Response: 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 do not allow permit limits based on one day 

maximum concentrations 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 a permit condition to respond to a violation of a whole effluent toxicity limit. The proper 

response by the permitting authority is through use of 

enforcement mechanisms. 

EPA Response: The discussion on TREs was changed to not 

recommend that the TRE or 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 a single event violation of 

a limit and the need for multiple violations before a TRE 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. 

EPA Response: EPA disagrees with the proposal. A discussion 

was added to explain how EPA considers 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), and that the variability 

can go both ways (higher or lower), that EPA does 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 WLAs using the same CV that 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 same basis. 

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. 

EPA R8SpOnS8: EPA partially 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. EPA is not 

convinced that a daily 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 a permit 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 WLA established 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. EPA should allow adequate time for facilities to come into 

compliance with water quality-based permit limits. 

EPA Response: EPA is 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 CJO ruled that compliance schedules 

for water quality-based effluent limits may not be included 

in NPDES permits 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 CJO granted a stay of the ruling to allow EPA and 

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 a State 

decision which may ultimately need to be expressed in water 

quality standards. 

12. Effluent limits should b8 set within the ability of treatment 

t8ChnOlOgy. 

EPA R8SpOnS8: 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 a treatment 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 mean effluent concentration. 

EPA Response: 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 WLA every month. The statistical 

procedures for permit limit derivation in the TSD are 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 TSD procedures 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 

WLA onlv 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 a year. 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 may not always demonstrate a log-normal distribution. The TSD should present procedures for using Other 

distributions. 

EPA R8SpOnS8: EPA believes, 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 b8 mad8 to delay punitive enforcement action 

Wh8r8 legitimate efforts are being made; permittees should not 

be held in violation of their toxicity limit While conducting 

a TRE. 

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 

United States 

Environmental Protection Agency 

EN-336 

Washington, DC 20460 

Official Business 

Penalty for Private Use 

$300