--- - - ____- 

3.1.3 General Consiftt?mtions in fffhmt Characterization 

There are two possible ways to characterize an effluent to determine the need for effluent limits for the protection of aquatic life 

and human health. First, an assessment may be made without 

generating effluent data; second, an assessment may be conducted after effluent data have been generated. Regulatory authorities must determine whether a discharge causes, has the 

“reasonable potential” lo cause, or contributes to an excursion 

above an applicable narrative or numeric water quality criterion. 

An analysis of “reasonable potential” determines an effluent’s 

capability to cause such excursions. 

In determining the need for a permit limit for whole effluent 

toxicity or for an individual toxicant, the regulatory authority is 

required to consider, at a minimum, existing controls on point and 

nonpoint sources of pollution, the variability of the pollutant or 

pollutant parameter in the effluent, the sensitivity of the involved 

species to toxicity testing (for whole effluent), and, where appropriate, the dilution of the effluent in the receiving water (40 CfR 

122.44(d)(ii)). 

The regulatory authority is also required by NPDES regulations to 

consider whether technology-based limits are sufficient to maintain State water quality standards. There are two possibilities that 

will need to be assessed. First, if the limits based on appropriate 

treatment technology have already been specified in a previous 

permit, and if the facility is operating at the required level, then 

historical effluent and receiving water information can be used. 

Second, if the facility has yet to achieve the required technology 

performance (best available technology or best conventional technology), the regulatory authority will need to assess the technology-based limit for reasonable potential for causing or contributing to an excursion above the water quality standard. 

In addition, the regulatory authority should consider all other 

available data and information pertaining to the discharger to 

assist in making an informed judgment. Where both effluent 

testing data and important other factors exist, the regulatory 

authority will need to exercise discretion in the determination of 

the need for a limit. The authority should employ the principle of “independent application” of the data and information that characterizes the effluent. In other words, effluent 

data alone, showing toxicity at the RWC, may be adequate to 

demonstrate the need for a limit for toxicity or for individual 

toxicants. Likewise, other factors may form an adequate basis for 

determining that limits are necessary. for example, where available dilution is low and monitoring information shows that toxic 

pollutants are frequently discharged at concentrations that have 

caused toxicity when discharged from similar facilities, the permitting authority may reason that a whole effluent toxicity limit is 

necessary even without whole effluent toxicity data from the 

specific facility. In all cases, the decision must be based upon 

consideration of factors cited in 40 CfR 122.44(d)(l)(ii). The 

regulatory authority will need to prioritize, on a case-by-case 

basis, the importance of all data and information used in making 

a determination. To assist in case-by-case determinations, recommended guidelines for characterizing an effluent for the need 

for a permit limit for whole effluent toxicity or individual toxicants are discussed below and summarized in Boxes 3-l through 

3-3. 

Box 3-1. Determining “Reasonable Potential” for Excursions Above Ambient Criteria Using 

Factors Other than Facility-specific Effluent Monitoring Data 

When determining the “reasonable potential” of a discharge to cause an excursion above a State water quality 

standard, the regulatory authority must consider all the factors listed in 40 CFR 122.44(d)(l)(ii). Examples of the 

types of information relating to these factors are listed below. 

Existina controls on mint and nonDoint sources of Dollution 

l Industry type: Primary, secondary, raw materials used, products produced, best management practices, 

control equipment, treatment efficiency, etc. 

l Publicly owned treatment work type: Pretreatment, industrial loadings, number of taps, unit processes, 

treatment efficiencies, chlorination/ammonia problems, etc. 

Variability of the oollutant or oollutant Darameter in the effluent 

l Compliance history 

l Existing chemical data from discharge monitoring reports and applications. 

Sensitivitv of the sDecies to toxicitv testing 

l Adopted State water quality criteria, or EPA criteria 

l Any available in-stream survey data applied under independent application of water quality standards 

l Receiving water type and designated/existing uses 

Dilution of the effluent in the receivina water 

l Dilution calculations 

49 
3.2 DETERMlWlNG THE NEED FOR PERYlT UMITS 

WITHOUT EFFLUENT MONITORING DATA FOR A 

SPECIFIC FACILITY 

If the regulatory authority so chooses, or if the circumstances 

dictate, the authority may decide to develop and impose a 

permit limit for whole effluent toxicity or for individual toxicants 

without facility-specific effluent monitoring data, or prior to the 

generation of effluent data. Water quality-based permit limits 

can be set for a single toxicant or for whole effluent toxicity based 

on the available dilution and the water quality criterion or the 

State standard in the absence of facility specific effluent monitoring data. However, in doing so, the regulatory authority must 

satisfy all the requirements of 40 CfR 122.44(d)(l)(ii). 

When determining whether or not a discharge causes, has the 

reasonable potential to cause, or contributes to an excursion of a 

numeric or narrative water quality criterion for individual toxicants or for toxicity, the regulatory authority can use a variety of 

factors and information where facility-specific effluent monitoring data are unavailable. These factors also should be considered 

with available effluent monitoring data. Some of these factors are 

the following: 

l Dilution-Toxic impact is directly related to available dilution for the effluent. Dilution is related to the receiving 

stream flow and the size of the discharge. The lower the 

available dilution, the higher the potential for toxic effect. 

If an effluent’s concentration at the edge of a mixing zone 

in a receiving water is expected to reach 1 percent or 

higher during critical or worst-case design periods, then 

such an effluent may require a toxicity limit (see discussion 

in Section 3.3.3). Assessment of the amount of stream 

dilution available should be made at the conditions required by the water quality standards or, if not specified in 

the standards, at the harmonic mean flow and the 7QlO 

flow. Figure 3-3 (Pg. 57) shows that, whereas a majority of 

NPDES permittees nationwide discharge to areas during 

annual mean flow ranging in dilution from 100 to 1,000, 

the majority of dischargers fall into the 1 to 10 dilution 

range during low-flow conditions. 

l Type of industry-Although dischargers should be individually characterized because toxicity problems are sitespecific, the primary industrial categories should be of 

principal toxicity concern. EPA’s treatment technology 

data base generally suggests that secondary industrial categories may have less potential for toxicity than primary 

industries. However, based on experience, it is virtually 

impossible to generalize the toxicity of effluents with any 

certainty. If two plants produce the same type of product, 

one effluent may be toxic while the other may not be toxic 

due to the type and efficiency of the treatment applied, 

general materials handling practices, and the functional 

target of the compound(s) being produced. 

. Type of POTW-POTWs with loadings from indirect dischargers (particularly primary industries) may be candidates for toxicity limits. However, absence of industrial 

input does not guarantee an absence of POTW discharge 

toxicity problems. For example, commercial pesticide applicators often discharge to POT%%, resulting in pesticide 

concentrations in the POTW’s effluent. Household disposal 

of pesticides, detergents, or other toxics may have a similar 

effect, The types of industrial users, their product lines, their 

raw materials, their potential and actual discharges, and 

their control equipment should be evaluated. POlWs should 

also be characterized for the possibility of chlorine and 

ammonia problems. 

l Existing data on toxic pollutants-Discharge monitoring 

reports (DMRs) and data from NPDES permit application 

forms 2C and 2A may provide some indication of the presence of toxicants. The presence or absence of the 126 

“priority pollutants” may or may not be an indication of the 

presence or absence of toxicity. There are thousands of 

“nonpriority” toxicants that may cause effluent toxicity. 

Also, combinations of several toxicants can produce ambient toxicity where the individual toxicants would not. EPA 

regulations at 40 CFR 122.21 (j) require POTWs with design 

flows equal to or greater than 1 MC0 and POTWs with 

approved pretreatment programs, or POlWs required to 

develop a pretreatment program, to submit the results of 

whole effluent toxicity tests with their permit applications. 

These regulations also provide discretion to the permitting 

authority to request such data from other POlWs at the 

time of permit application. 

l History of compliance problems and toxic impact-Reguiatory authorities may consider particular dischargers that 

have had difficulty complying with limits on toxicants or 

that have a history of known toxicity impacts as probable 

priority candidates for effluent toxicity limits. 

l Type of receiving water and designated use-Regulatory 

authorities may compile data on water quality. Examples of 

available data include fish advisories or bans, reports of fish 

kilts, State lists of priority waterbodies, and State lists of 

waters that are not meeting water quality standards. Regulatory authorities should use this information as a means of 

identifying point sources that discharge to impaired 

waterbodies and that thus may be contributing to this 

impairment. One source of this information is the lists of 

waters generated by states to comply with Section 304(l) 

regulations at 40 CFR 130.1 O(d)(6); 50 FR 23897-98, June 2, 

1989: 

1) Waters where fishing or shellfish bans and/or 

advisories are currently in effect or are anticipated; 

2) Waters where there have been repeated fish 

kills or where abnormalities (cancers, lesions, 

tumors, etc.) have been observed in fish or 

other aquatic life during the last ten years; 

3) Waters where there are restrictions on water 

sports or recreational contact; 

4) Waters identified by the state in its most recent state section 305(b) report as either “partially achieving” or “not achieving” designated 

uses; 

so 
5) 

6) 

7) 

8) 

9) 

10) 

11) 

12) 

13) 

Waters identified by the states under section 

303(d) of the Clean Water Act as waters needing water quality-based controls; 

Waters identified by the state as priority water 

bodies; 

Waters where ambient data indicate potential 

or actual excursions of water quality criteria 

due to toxic pollutants from an industry classified as a primary industry in Appendix A of 40 

CFR Part 122; 

Waters for which effluent toxicity test results 

indicate possible or actual excursions of state 

water quality standards, including narrative 

“free from” water quality criteria or EPA water 

quality criteria where state criteria are not available; 

Waters with primary industrial major dischargers where dilution analyses indicate 

exceedances of state narrative or numeric water quality criteria (or EPA water quality criteria 

where state standards are not available) fortoxic 

pollutants, ammonia, or chlorine; 

Waters with POlW dischargers requiring local 

pretreatment programs where dilution analyses indicate exceedances of state water quality 

criteria (or EPA water quality criteria where 

state water quality criteria are not available) 

for toxic pollutants, ammonia, or chlorine; 

Waters with facilities not included in the previous two categories such as major POlWs, and 

industrial minor dischargers where dilution 

analyses indicate exceedances of numeric or 

narrative state water quality criteria (or EPA 

water quality criteria where state water quality 

criteria are not available) for toxic pollutants, 

ammonia, or chlorine; 

Water classified for uses that will not support 

the “fishable/swimmable” goals of the Clean 

Water Act; 

Waters where ambient toxicity or adverse water quality conditions have been reported by 

local, state, EPA or other Federal Agencies, the 

private sector, public interest groups, or universities; 

14) Waters identified by the state as impaired in its 

most recent Clean Lake Assessments conducted 

under 314 of the Clean Water Act; and 

15) Surface waters impaired by pollutants from 

hazardous waste sites on the National Priority 

List prepared under section 105(8)(A) of 

CERCIA. 

16) Waters judged to be impaired as a result of a 

bioassessmenttbiosurvey. 

The presence of a combination of these factors, such as low 

available dilution, high-quality receiving water, poor compliance record, and clustered industrial and municipal discharges, 

could constitute a high priority for effluent limits. 

Regardless, the regulatory authority, if it chooses to impose an 

effluent limit after conducting an effluent assessment without 

facility-specific monitoring data, will need to provide adequate 

justification for the limit in its permit development rationale or 

in its permit fact sheet. A clear and logical rationale for the need 

for the limit covering all of the regulatory points will be necessary to defend the limit should it be challenged. In justification 

of a limit, EPA recommends that the more information the 

authority can acquire to support the limit, the better a 

position the authority will be in to defend the limit if necessary. In such a case, the regulatory authority may well benefit 

from the collection of effluent monitoring data prior to establishing the limit. 

If the regulatory authority, after evaluating all available information on the effluent, in the absence of effluent monitoring data, 

is not able to decide whether the discharge causes, has the 

reasonable potential to cause, or contributes to, an excursion 

above a numeric or narrative criterion for whole effluent toxicity 

or for individual toxicants, the authority should require whole 

effluent toxicity or chemical-specific testing to gather further 

evidence. In such a case, the regulatory authority can require 

the monitoring prior to permit issuance, if sufficient time exists, 

or it may require the testing as a condition of the issued/ 

reissued permit. 

Under these circumstances, the regulatory authority may find it 

protective of water quality to include a permit reopener for the 

imposition of an effluent limit should the effluent testing establish that the discharge causes, has the reasonable potential to 

cause, or contributes to excursion above a water quality criteria. 

A discussion of these options is provided later in this chapter. 

3.3 DETERMINING THE NEED FOR PERMIT 

UMITS WITH EFFLUENT MONITORING DATA 

3.3. I General Conslderatlons 

When characterizing an effluent for the need for a whole effluent toxicity limit, and/or an individual toxicant limit, the regulatory authority should use any available effluent monitoring 

data, together with any information like that discussed under 

Section 3.2 above, as the basis for a decision. The regulatory 

authority may already have effluent toxicity data available from 

previous monitoring, or it may decide to require the permittee 

to generate effluent monitoring data prior to permit issuance or 

as a condition of the issued permit. EPA regulations at 40 Cfi? 

122.21 (j) require POTWs with design flows equal to or greater 

than 1 MCD and POTWs with approved pretreatment programs, or POTWs required to develop a pretreatment program, 

to submit the results of whole effluent toxicity tests with their 

permit applications. These regulations also provide discretion 

to the permitting authority to request such data from additional 

POTWs at the time of permit application. 

51 
----------- __- __-__ ---- __--_ 

In the instance where the permittee is required to generate data in 

advance, data collection should begin 12 to 18 months in advance 

of permit development to allow adequate time for conducting 

toxicity tests and chemical analyses. The type of data, including 

toxicity testing data, should be specified by the regulatory authority at the outset so that decisions on permit actions will not be 

delayed. EPA recommends monitoring data be generated on 

effluent toxicity prior to permit limit development for the 

foIlowing reasons: (1) the presence or absence of effluent 

toxicity can be more clearly established or refuted and (2) 

where toxicity is shown, effluent variability can be more cleady 

defined. Several basic factors that should be considered in generating effluent monitoring data are discussed below. 

All toxic effects testing and exposure assessment parameters, for 

both effluent toxicity and individual chemicals, have some degree 

of uncertainty associated with them. The more limited the amount 

o! test data available, the larger the uncertainty. The least amount 

of uncertainty of an effluent’s impact on the receiving water exists 

where (1) a complete data base is available on the effects of acute 

and chronic toxicity on many indigenous species, (2) there is a 

clear understanding of ecosystem species composition and functional processes, and (3) actual measured exposure concentrations 

are available for all chemicals during seasonal changes and dilution 

situations. The uncertainty associated with such an ideal situation 

would be minimal. However, generation of these data can be very 

resource intensive. 

An example of uncertainty that results from limited monitoring 

data is if a regulatory authority has only one piece of effluent data 

(e.g., an LCso of 50 percent) for a facility. Effluent variability in 

such a case, given the range of effluent toxicity variability seen in 

other effluents, may range between 20 percent and 100 percent 

(see Appendix A). It is impossible to determine from one piece of 

monitoring data where in this range the effluent variability really 

falls. More monitoring data would need to be generated to 

determine the actual variability of this effluent and reduce this 

source of uncertainty. 

To better characterize the effects of effluent variability and reduce 

uncertainty in the process of deciding whether to require an 

effluent limit, EPA has developed the statistical approach described 

below and in Box 3-2. This approach combines knowledge of 

effluent variability as estimated by a coefficient of variation with 

the uncertainty due to a limited number of data to project an 

estimated maximum concentration for the effluent. The estimated 

maximum concentration is calculated as the upper bound of the 

expected lognormal distribution of effluent concentrations at a 

high confidence level. The projected effluent concentration after 

consideration of dilution can then be compared to an appropriate 

water quality criterion to determine the potential for exceeding 

that criterion and the need for an effluent limit. 

The statistical approach has two parts. The first is a characterization of the highest measured effluent concentration based on the 

desired confidence level. The relationship that describes this is the 

following: 

pn = (1 - confidence level)“” 

where pn is the percentile represented by the highest concentration in the data and n is the number of samples. The 

following are some examples of this relationship at a 99 

percent confidence level: 

l The largest value of 5 samples is greater than the 40 

percentile 

l The largest value of 10 samples is greater than the 63 

percentile 

l The largest value of 20 samples is greater than the 79 

percentile 

l The largest value of 100 samples is greater than the 96 

percentile. 

The second part of the statistical approach is a relationship 

between the percentile described above and the selected 

upper bound of the lognormal effluent distribution. EPA’s 

effluent data base suggests that the lognormal distribution 

well characterizes effluent concentrations (see Appendix E). 

For example, if five samples were collected (which represents a 40th percentile), the coefficient of variation is 0.6, 

and the desired upper bound of the effluent distribution is 

the 99th percentile, then the two percentiles can be related 

using the coefficient of variation (CV) as shown below: 

C99 exp(2.326o - 0.50~) = = 4.2 

c40 exp(-0.258o - 0.50~) 

where & = In ((X2+1) and 2.326 and -0.258 are the normal 

distribution values for the 99th and 40th percentiles, respectively. The use of the 99th percentile is for illustrative 

purposes here. Although it does represent a measure of the 

upper bound of an effluent distribution, other percentiles 

could be selected by a regulatory agency. The relationship 

shown above can be calculated for other percentiles and 

CVs by replacing the values in the equation. 

Tables 3-l and 3-2 show the combined effects of both parts 

for a 99percent confidence level and upper bounds of the 

99th and 95th percentiles, respectively. The factors shown 

in the tables are multiplied by the highest concentration in 

an effluent sample to estimate the maximum expected concentration. 

This procedure can be used for both single and multiple 

discharges to the same receiving waterbody. This is accomplished for multiple dischargers by summing the projected 

RWCs for the pollutant or pollutant parameter of concern 

from each individual discharger, and comparing it to the 

water quality standard. This involves an assumption of 

conservative additivity of the pollutant after discharge, which 

may not accurately reflect the true behavior of the toxicant. 

To overcome this, and to further refine the proportional 

contribution of each discharger and the resultant limits, the 

permitting authority should supplement this evaluation with 

multiple source WLA modeling and/or ambient water concentration monitoring. 

52 

A 
Box 3-2. Determining “Reasonable Potential” for Excursions Above 

Ambient Criteria Using Effluent Data Only 

EPA recommends finding that a permittee has “reasonable potential” to exceed a receiving water quality 

standard if it cannot be demonstrated with a high confidence level that the upper bound of the lognormal 

distribution of effluent concentrations is below the receiving water criteria at specified low-flow conditions. 

Step 1 Determine the number of total observations (“n”) for a particular set of effluent data (concentrations or 

toxic units PUS]), and determine the highest value from that data set. 

Step 2 Determine the coefficient of variation for the data set. For a data set where ~10, the coefficient of 

variation (CVJ is estimated to equal 0.6, or the CV is calculated from data obtained from a discharger. 

For a data set where n>lO, the CV is calculated as standard deviation/mean (see Figure 3-l). For less 

than 10 items of data, the uncertainty in the CV is too large to calculate a standard deviation or mean 

with sufficient confidence. 

Step 3 Determine the appropriate ratio from Table 3-l or 3-2. 

Step 4 Multiply the highest value from a data set by the value from Table 3-l or 3-2. Use this value with the 

appropriate dilution to project a maximum receiving water concentration (RWC). 

Step 5 Compare the projected maximum RWC to the applicable standard (criteria maximum concentration, 

criteria continuous concentration [CCC], or reference ambient concentration). EPA recommends that 

permitting authorities find reasonable potential when the projected RWC is greater than an ambient 

criterion. 

Example 

Consider the following results of toxicity measurements of an effluent that is being characterized: 5 TU,, 2 TU,, 9 TU,, 

and 6 TU,. Assume that the effluent is diluted to 2 percent at the edge of the mixing zone. Further assume that the 

CV is 0.6, the upper bound of the effluent distribution is the 99th percentile, and the confidence level is 99 percent. 

Step 1 There are four samples, and the maximum value of the sample results is 9 TU,. 

Step 2 The value of the CV is 0.6. 

Step 3 The value of the ratio for four pieces of data and a CV of 0.6 is 4.7. 

Step 4 The value that exceeds the 99th percentile of the distribution (ratio times xmax) after dilution is calculated as: 

[9 TU, x 4.7 x 0.021 = 0.85 TU,. 

Step 5 0.85 TU, is less than the ambient criteria concentration of 1 .O TU,. There is no reasonable 

potential for this effluent to cause an excursion above the CCC. 

3.3.3 Etfhnl CharacMzatiun tar Whh Efh?nt T~xWty 

Once an effluent has been selected for whole effluent toxicity 

characterization after consideration of the factors discussed above, 

the regulatory authority should require toxicity testing in accordance with appropriate site-specific considerations and the recommendations discussed below. In the past 5 years, significant 

additional experience has been gained in generating effluent 

toxicity data upon which to make decisions as to whether or not 

an effluent will cause toxic effects in the receiving water in both 

freshwater and marine environments. 

General Considerations and Assumptions 

EPA has revised its initial effluent toxicity data generation recommendations based on three observations made over the last 5 

years: 

1) Only rarely have effluents discharged by NPDES permittees 

been observed to have LCsos less than 1 .O percent or no 

observed effect concentrations (NOECs) less than 0.1 percent. However, there is always a chance that an effluent 

could be toxic at such low effluent concentrations. 

53 
-_-___- -- - -- -.- ------ 

Jumber o 

Samples 

1 

2 

3 

4 

5 

6 

7 

a 

9 

10 

11 

12 

13 I 

! :: 

16 I 

17 

18 

I 19 

20 

f 

I 

- 

Table 3-l. Reasonable Potential Multiplying Factors: 99% Confidence Level and 99% Probability Basis 

0.1 0.2 0.3 0.4 0.5 

1.6 2.5 3.9 6.0 9.0 

1.4 2.0 2.9 4.0 5.5 

1.4 1.9 2.5 3.3 4.4 

1.3 1.7 2.3 2.9 3.8 

1.3 1.7 2.1 2.7 3.4 

1.3 1.6 2.0 2.5 3.1 

1.3 1.6 2.0 2.4 2.9 

1.2 1.5 1.9 2.3 2.8 

1.2 1.5 1.8 2.2 2.7 

1.2 1.5 1.8 2.2 2.6 

1.2 1.5 1.8 2.1 2.5 

1.2 1.4 1.7 2.0 2.4 

1.2 1.4 1.7 2.0 2.3 

1.2 1.4 1.7 2.0 2.3 

1.2 1.4 1.6 1.9 2.2 

1.2 1.4 1.6 1.9 2.2 

1.2 1.4 1.6 1.9 2.1 

1.2 1.4 1.6 1.8 2.1 

1.2 1.4 1.6 1.8 2.1 

1.2 1.3 1.6 1.8 2.0 

- 

-I 

- 

Coefficient of Variation 

0.6 0.7 0.8 0.9 1.0 

13.2 18.9 26.5 36.2 48.3 

7.4 9.8 12.7 16.1 20.2 

5.6 7.2 a.9 11.0 13.4 

4.7 5.9 7.2 a.7 10.3 

4.2 5.1 6.2 7.3 8.6 

3.8 4.6 5.5 6.4 7.5 

T 

t 

1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0 

63.3 81.4 102.8 128.0 157.1 

24 9 30.3 36.3 43.0 50.4 

16.0 19.0 22.2 25.7 29.4 

12.2 14.2 16.3 18.6 21.0 

10.0 11.5 13.1 14.8 16.6 

8.6 9.8 11.1 12.4 13.8 

3.6 4.2 5.0 5.8 6.7 I 7.7 8.7 9.7 10.8 12.0 

3.3 3.9 4.6 5.3 6.1 1 6.9 7.8 8.7 9.6 10.6 

3.2 3.7 4.3 5.0 5.7 6.4 7.1 7.9 a.7 9.6 

3.0 3.5 4.1 4.7 5.3 ; 5.9 6.6 7.3 8.0 8.8 

2.9 3.4 3.9 4.4 5.0 5.6 6.2 6.8 7.4 8.1 

2.8 3.2 3.7 4.2 4.7 5.2 5.8 6.4 7.0 7.5 

2.7 3.1 3.6 4.0 4.5 I 5.0 5.5 6.0 6.5 7.1 

2.6 3.0 3.4 3.9 4.3 ) 4.8 5.2 5.7 6.2 6.7 

2.6 2.9 3.3 3.7 4.1 I 4.6 5.0 5.4 5.9 6.4 

2.5 2.9 3.2 3.6 4.0 I 4.4 4.8 5.2 5.6 6.1 

2.5 2.8 3.1 3.5 3.8 1 4.2 4.6 5.0 5.4 5.8 

2.4 2.7 3.0 3.4 3.7 ( 4.1 4.4 4.8 5.2 5.6 

2.4 2.7 3.0 3.3 3.6 4.0 4.3 4.6 5.0 5.3 

2.3 2.6 2.9 3.2 3.5 3.8 4.2 4.5 4.8 5.2 

1 

90.3 227.8 269.9 316.7 368.3 

58.4 67.2 76.6 86.7 97.5 

33.5 37.7 42.3 47.0 52.0 

23.6 26.3 29.1 32.1 35.1 

18.4 20.4 22.4 24.5 26.6 

15.3 16.8 18.3 19.9 21.5 

13.1 14.4 15.6 16.9 18.2 

11.6 12.6 13.6 14.7 15.8 

10.4 11.3 12.2 13.1 14.0 

9.5 10.3 11.0 11.8 12.6 

8.8 9.4 10.1 10.8 11.5 

a.1 8.8 9.4 10.0 10.6 

7.6 8.2 a.7 9.3 9.9 

7.2 7.7 8.2 a.7 9.2 

6.8 7.3 7.7 8.2 8.7 ; 

6.5 6.9 7.3 7.8 8.2 I 

6.2 6.6 7.0 7.4 7.8 1 

5.9 6.3 6.7 7.0 7.4 [ 

5.7 6.0 6.4 6.7 7.1 

5.5 5.8 6.1 6.5 6.8 ; 

Table 3-2. Reasonable Potential Multiplying Factors: 95% Confidence Level and 95% Probability Basis 

INumberof Coefficient of Variation 

Samples 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 

1 1 1.4 1.9 2.6 3.6 4.7 6.2 8.0 10.1 12.6 15.5 

2 1.3 1.6 2.0 2.5 3.1 3.8 4.6 5.4 6.4 7.4 

3 1.2 1.5 1.8 2.1 2.5 3.0 3.5 4.0 4.6 5.2 

4 ; 1.2 1.4 1.7 1.9 2.2 2.6 2.9 3.3 3.7 4.2 

5 Il.2 1.4 1.6 1.8 2.1 2.3 2.6 2.9 3.2 3.6 

6 j 1.1 1.3 1.5 1.7 1.9 2.1 2.4 2.6 2.9 3.1 

ii ; ::: ::; ::: ::: ::t ::i ::: :I': ::: :I", 

9 1 1.1 1.2 1.4 1.5 1.7 1.8 2.0 2.1 2.3 2.4 

10 ( 1.1 1.2 1.3 1.5 1.6 1.7 1.9 2.0 2.2 2.3 

11 I 1.1 1.2 1.3 1.4 1.6 1.7 1.8 1.9 2.1 2.2 

12 I 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.9 2.0 2.1 

:: ) :;; ::; :;; ;'I 1.5 4;;; 1.7 6;:; 1.9 8 2.0 1.9 

15 ' 1.1 1.2 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.8 

16 ' 1.1 1.1 1.2 1.3 1.4 1.5 1.6 1.6 1.7 1.8 c 

17 I 1.1 1.1 1.2 1.3 1.4 

18 I 1.1 7.1 1.2 1.3 1.3 

19 ' 1.1 1.1 1.2 1.3 1.3 

20 I 1.1 1.1 1.2 1.2 1.3 

F 1 

1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0 ' ' 

8.7 22.3 26.4 30.8 35.6 40.7 46.2 52.1 58.4 64.9 

8.5 9.7 10.9 12.2 13.6 15.0 16.4 17.9 19.5 21.1 I 

5.8 6.5 7.2 7.9 8.6 9.3 10.0 10.8 11.5 12.3 ) 

4.6 5.0 5.5 6.0 6.4 6.9 7.4 7.8 a.3 8.8 I 

3.9 4.2 4.5 4.9 5.2 5.6 5.9 6.2 6.6 6.9 ; 

3.4 3.7 3.9 4.2 4.5 I 4.7 5.0 5.2 5.5 5.7 I 

3.1 3.3 3.5 3.7 3.9 ( 4.1 4.3 4.5 4.7 4.9 , 

2.8 3.0 3.2 3.3 3.5 1 3.7 3.9 4.0 4.2 4.3 I 

2.6 2.8 2.9 3.1 3.2 3.4 3.5 3.6 3.8 3.9 / 

2.4 2.6 2.7 2.8 3.0 1 3.1 3.2 3.3 3.4 3.6 ' 

2.3 2.4 2.5 2.7 2.8 ; 2.9 3.0 3.1 3.2 3.3 

2.2 2.3 2.4 2.5 2.6 \ 2.7 2.8 2.9 3.0 3.0 

2.1 2.2 2.3 2.4 2.5 2.5 2.6 2.7 2.8 2.9 ' 

2.0 2.1 2.2 2.3 2.3 ' 2.4 2.5 2.6 2.6 2.7 

1.9 2.0 2.1 2.2 2.2 2.3 2.4 2.4 2.5 2.5 

1.9 1.9 2.0 2.1 2.1 1 2.2 2.3 2.3 2.4 2.4 

1.4 1.5 1.6 1.7 1.7 1.8 1.9 1.9 2.0 2.0 ' 2.1 2.2 2.2 2.3 2.3 

1.4 1.5 1.6 1.6 1.7 1.7 1.8 1.9 1.9 2.0 2.0 2.1 2.1 2.2 2.2 

1.4 1s 1.5 1.6 1.6 , 1.7 1.8 1.8 1.9 

1.4 1.4 1.5 1.5 1.6 1.7 1.7 1.8 1.8 

54 
Long-term average 

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 

Value 

50 

Number 01 Samples 

100 

Figure 3-la. Frequency Distribution of Values for a Figure 3-1~. Relationship Between the Largest Value of n 

Lognormal Distribution with a Mean of 1 .O and a Samples and the Percentile It Exceeds 

Coefficient of Variation of 0.6 with 99 Percent Confidence 

I/ 

Long-term average 

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 

Value 

Figure 3-1 b. Comparison of Relative Frequencies of 

Lognormal Distributions with a Mean of 1 .O for 

Different Coefficients of Variation 

2) With the exception of a small number of “outliers” for 

which confirmation is not possible, acute-to-chronic ratios 

(ACRs) above 20 for effluents discharged by NPDES permittees have not been observed by EPA. The majority of 

observed ACRs are very seldom above 10. However, higher 

ACRs may be found for selected facilities. 

3) The use of the three commonly used freshwater species 

and of three of the five commonly used marine organisms 

has generally been sufficient to measure any effluent’s 

toxicity for the purposes of projecting effluent toxicity 

impact and making regulatory decisions. 

Percentile = (1 - 0.99) 1in 

8 

20 

I I 

40 60 80 100 

Number of Samples 

Figure 3-ld. Example of 90 Percent Confidence 

Intervals Around Coefficient of Variation Estimates for 

Numbers of Samples 

Figure 3-2 is a flow chart of EPA’s recommendations for data 

generation for three different dilution scenarios. It is divided 

into three basic steps: determining initial dilution, developing toxicity testing procedures, and developing decision 

criteria for permit limit. There are certain basic assumptions 

built into this flow chart. The basic principle used in making 

decisions is to compare available dilution to known or projected toxic effect concentrations in order to place an effluent into one of three categories: 

55 
STEP 1 

determination l 

STEP 2 

Conduct toxicity testing* based 

on dilution determination (3 species 

at a minimum of quarterly for 1 year) 

STEP 3 

Acute toxicity data or Chronic toxicity data or 

estimate based on ACR v estimate based on ACR 

YES A Develop permit 4 

limits 

4 NO 

NO 

NO 

/A 

NO 

Require 

C monitoring at -= 

reissuance 

Notes: 

‘Dilution determrnations should be performed for critical flows and any applicable mixing zones. 

2Toxicity testing recommendations 

a. Dilution > 1OOO:l: acute testing, check CMC only. 

b. 100: 1 < Dilution c 1000: 1: acute or chronic testing, check CMC and CCC with data or ACR. 

c. Dilution < 1 0o:l: conduct chronic testing, check CCC with data and CMC using acute data or ACR. 

3Reasonable potential: Use procedures in Box 3-3. 

Figure 3-2. Effluent Characterization for Whole Effluent Toxicity 

1) The effluent causes or contributes to an excursion of a 

numeric or narrative water quality criterion and the permit 

require5 a limit on toxicity. 

2) The effluent has a reasonable potential of causing or contributing to an excursion of a numeric or narrative water 

quality criterion and a limit is required. 

3) The effluent has a very low probability of causing or contributing to an excursion of a water quality standard and 

no limit is required. 

This categorization is accomplished by using dilution estimates in the first step and the results of the toxicity tests in 

the next steps. In addition, all these impact estimates 

assume discharge at critical conditions and imposition of 

any applicable mixing zone requirements. Therefore, a 

conservative assumption is used to determine whether or 

not an impact is projected to occur. Estimates of possible 

toxic impact are made assuming that the effluent is most 

toxic to the most sensitive species or lifestage at the time of 

lowest available dilution. 

56 
dilution toxicity tests are much more variable than multiple dilution tests. Therefore, the use of single concentration toxicity 

tests is strongly discouraged for this data generation process. 

Since the new data generation requirements are much less expensive than the previous requirements, tiered testing (less expensive, 

single-concentration, initial screening followed by increasingly 

expensive definitive data generation, using multiconcentration 

tests, as described in the September 1985 version of the technical 

support document) is unnecessary. However, elimination of the 

requirement to conduct toxicity testing on the basis of projections using dilution alone is not recommended. Although EPA’s 

data review suggests that an LCso of 1 percent and an NOEC of 

0.1 percent are the lower bounds on effluent toxicity, there may 

be other effluents that are presently unmeasured that are more 

toxic. Testing data are always desirable for fully characterizing 

discharges of concern. 

Steps in Whole Effluent Characterization Process 

The following is a detailed description of the major steps presented in Figure 3-2 and the rationale behind each. 

Step 1: Dilution Determination 

The initial step is to determine the dilution of the effluent at the 

edge of the mixing zone, assuming the State allows mixing zones. 

Figure 3-4 shows a schematic representation of typical mixing 

zone requirements for both acute and chronic toxicity. Calculating the dilution at the edges of mixing zones for site-specific 

situations can be complicated. Modeling can be employed using 

either steady-state or dynamic approaches to calculate the dilution (see Chapter 4). However, for complex situations, such as 

marine and estuarine waters or lakes, dye studies (or other techniques used to assess mixing zones) may still be required. 

Some State water quality standards do not allow the use of 

mixing in the control of acute toxicity. For these States, acute 

toxicity is often limited at the end of the pipe. Permit limits 

derived to enforce such requirements would be considered “water quality-based” because they would be based upon an ambient 

criterion (as opposed to an arbitrary test endpoint). Regardless, 

both chronic and acute toxicity must be assessed in these situations. 

Step 2: Toxicity Testing Procedures 

Where toxicity tests are required in order to make decisions 

regarding appropriate next steps in a screening protocol, EPA 

recommends as a minimum that three species (for example, a 

vertebrate, an invertebrate, and a plant) be tested quarterly 

for a minimum of 1 year. As discussed in Chapter 1, the use of 

three species is strongly recommended. Experience indicates that 

marine algae can be a highly sensitive test species for some 

effluents. Using a surrogate species of the plant kingdom adds 

another trophic level to the testing regimen. For both freshwater 

and marine situations, the use of three species is more protective 

than two species since a wider range of species sensitivity can be 

measured. EPA is continuing to develop toxicity test methods 

using additional organisms including plants. In addition, EPA has 

revised the test for Selenastnum, which has improved the test 

precision. 

2,467 2,771 

2,064 

1.626 

1,241 2000 

0 

DiMon (stream/etfluent) 

(a) At Low Flow (7QlO) 

6000 

6000 

e al 

P 

2 

x 

a 

B 4000 

$ 

E 

2 

2oQo 

0 

3.849 

2,477 

l-b 

Dilution (stream/effluent) 

(b) At Annual Mean Flow 

Figure 3-3. National Distribution of NPOES Dilution 

Conditions at 7410 and at Annual Mean Flow 

The changes to the EPA’s data generation recommendations 

eliminate the application of multiple sets of safety margins 

that was proposed in the 1985 version of this document. 

Rather, general observations on effluent toxicity described 

above now allow regulatory authorities to tighten the bounds 

of the initial dilution categorization, eliminate the species 

sensitivity uncertainty factor and target LC~I-JS of 1 percent 

and NOECs of 0.1 percent as the most extreme toxicity 

measurements that can normally be expected for the vast 

majority of effluents discharged by NPDES permittees for 

acute and chronic toxicity, respectively. The observation of 

toxicity was based on multiple dilution tests. The same 

observation may not hold for toxicity measured with single 

dilution tests (pass/fail). As reflected in Chapter 1, single 

57 
--. --. .-. ~ - - -.~ ~.- ~- -_ - - -.-__- - -.-. - --_ 

No initial mlxtng allowed 

ccc 

b 

mlxlng zone 

- I wer width 

flow - - - + 

lnlrlal mlxlng allowed: 

flow - - - + I 

where 

CMC = 0.3 TU, 

CCC = 1 .O TUc 

Figure 3-4. Schematic Representation of Mixing Zone Areas 

Where the CMC and CCC Apply 

EPA recommends against selecting a “most sensitive” species 

for toxicity testing. For one organism to consistently be the 

most sensitive in a battery of toxicity tests, two conditions must 

occur: (1) the toxicants causing toxicity must remain the same, 

and (2) the ratios of the toxicants in the effluent (if more than 

one) must remain the same. Based on EPA’s experience at the 

Duluth research laboratory, neither of these conditions is likely to 

occur. For example, the causes of effluent toxicity in POlWs can 

vary on a seasonal basis. Toxicity in the summer can be caused 

by pesticides to which invertebrates are most sensitive. However, 

the winter toxicity could be caused by ammonia to which fathead 

minnows will respond most sensitively. The most sensitive species for an effluent actually may not exist and at best is difficult to 

identify. 

Conducting toxicity tests using three species quarterly for 1 

year is recommended to adequately assess the variability of 

toxicity obserwd in effluents. Below this minimum, the chances 

of missing toxic events increase. The toxicity test result for the 

most sensitive of the tested species is considered to be the 

measured toxicity for a particular effluent sample. 

The data generation recommendations in Figure 3-2 represent 

minimum testing requirements. Since uncertainty regarding 

whether or not an effluent causes toxic impact is reduced with 

more data, EPA recommends that this test frequency be increased where necessary to adequately assess effluent variability. If less frequent testing is required in the permit, it is 

preferable to use three species tested less frequently than to test 

the effluent more frequently with only a single species whose 

sensitivity to the effluent is not well characterized. 

EPA recommends that a discharger conduct acute toxicity 

testing if the dilution of the effluent is greater than 1OOO:l at 

the edge of the mixing zone [3]. Such a discharger would be 

considered a low priority for chronic toxicity testing. The rationale 

for this is that the effluent concentration would be below 0.1 

percent at the edge of the mixing zone and thus incapable of 

causing an excursion above the CCC. A worst case NOEC of 0.1 

percent translates into 1,000 TU,, which would result in a concentration of less than 1 .O TU, at the edge of the mixing zone for this 

dilution category. The test results would be compared to the CMC 

after consideration of any allowable mixing. 

EPA recommends that a discharger conduct either acute or 

chronic toxicity testing if the dilution of the effluent falls 

between 1OO:l and 1,OOO:l at the edge of the mixing zone. 

Effluents have been shown to be both acutely and chronically toxic 

within this range of receiving water dilution. Under worst-case 

scenarios, LCsos of 1 .O percent and ACRs of 10 will result in 

excursions above both the CCC and CMC at the edge of the 

regulatory mixing zone. 

Although either acute or chronic testing can be required within 

this dilution range, acute testing would be more appropriateat the 

higher end of this dilution range (1,OOO:l or 0.1 percent). At the 

lower end of this dilution range (1OO:l or 1 .O percent), chronic 

tests may be more appropriate. Where other factors are equal, 

chronic testing may be preferable since the interim results in a 

chronic test gives data on acute toxicity as well. The acute 

endpoint data can then be used to compare directly to the CMC 

without the need for an ACR. 

Whichever type of toxicity test (either acute or chronic) is specified, the results from that test should be compared to the criterion 

associated with that type of test. For example, a chronic test 

would be compared to the CCC. Comparisons to the other criteria 

can be made by using the ACR or additional data generated to 

convert a chronic test result to an acute endpoint and vice versa. 

For example, a chronic NOEC of 5 percent effluent (or 20 TU,) 

represents an acute LCso of 50 percent (or 2 TU,) at an ACR of 10. 

EPA recommends that a discharger conduct chronic toxicity 

testing if the dilution of the effluent falls below 1OO:l at the 

edge of the mixing zone. The rationale for this recommendation 

is that chronic toxicity has been observed in some effluents down 

to the 1 .O percent effect concentration. Therefore, chronic toxicity 

tests, although somewhat more expensive to conduct, should be 

used directly in order to make decisions about toxic impact. 

There is a potential for acute toxicity within this dilution range, 

although this is less likely as the 1OO:l dilution level is approached. 

Thus, the recommended screening protocol shown in Figure 3-2 

includes a determination of whether excursions above the CMC 

are projected [4]. This analysis may be performed by assuming an 

ACR, applying this value to the chronic toxicity testing data, and 

allowing for any allowable initial mixing. Alternatively, the regulatory authority may use the interim results in the chronic test to 

calculate the acute toxicity. 

58 
Both the chronic and acute toxicity test data would be compared 

to their respective criterion. The chronic test results would be 

compared to the CCC, and the acute results, regardless of how 

calculated, would be compared to the CMC. 

Step 3: Decision Criteria for Permit Limit Development 

Once the toxicity data have been generated for a discharger, the 

regulatory authority must decide whether or not the results show 

that the permittee causes, has the reasonable potential to cause, or 

contributes to an excursion of an applicable numeric or narrative 

water quality criterion and therefore needs to limit effluent toxicity. To do this, these data should be used to project receiving 

water concentrations, which are then compared to the CCC and 

CMC. One of four outcomes will be reached when following the 

screening protocol shown in Figure 3-2: 

1) 

2) 

Excursion Above CMC or CCC-Where any one data point 

shows an excursion above the State’s numeric or narrative 

criterion for the parameter toxicity, EPA regulations require a 

permit limit be set for whole effluent toxicity (40 CFR 

122.44(d)(l)(iv or v)), unless limits on a specific chemical 

will allow the narrative water quality criterion to be attained 

or maintained. In the absence of a State numeric criterion 

for the parameter toxicity, EPA recommends that 1.0 TU, 

and 0.3 TU, be used as the CCC and CMC,.respectively. 

The decision to develop permit limits based upon an excursion above either the CMC or CCC will lead to protection 

against both acute and chronic toxicity if the permit derivation procedures in Chapter 5 are used to set effluent limits. 

Reasonable Potential for Excursion Above CMC or CCCEPA believes that “reasonable potential” is shown where 

an effluent is projected to cause an excursion above the 

CCC or CMC. This projection is based upon a statistical 

analysis of available data that accounts for limited sample 

size and effluent variability. EPA’s detailed recommendations for making a statistical determination based upon 

effluent monitoring data alone are shown in Box 3-2. Where 

a regulatory authority finds that test results alone indicate a 

“reasonable potential” to cause an excursion above a State 

water quality criterion in accordance with 40 CFR 

122,44(d)(l)(ii), a permit limit must be developed. 

A regulatory authority may select an alternative approach 

for assessing reasonable potential. For example, an authority may opt to use a stochastic dilution model that incorporates both ambient dilution and effluent variability for determining reasonable potential. Such an approach is analogous to the statistical approach shown in Box 3-2. Whatever 

approach selected by the authority, it must use all the 

factors that account for all the factors listed in 40 CFR 

122,44(d)(l)(ii). 

In some cases the statistical analysis of the effluent data may 

not actually project an excursion above the CMC or CCC 

but may be close. Under such conditions, reasonable potential determinations will include an element of judgment on 

the part of the regulatory authority. Other factors will need 

to be considered and given appropriate weight in the 

decisionmaking process, including value of waterbody (e.g., 

high-use fishery), relative proximity to the CCC or CMC, 

existing controls on point and nonpoint sources, information on effluent variability, compliance history of the facility, and type of treatment facility. These factors are 

summarized in Box 3-2 and are discussed in detail in 

Section 3.1. EPA recommends regulatory authorities 

establish a written policy and procedure for making 

determinations of “reasonable potential” under these 

circumstances. 

3) No Reasonable Potential for Excursions Above CMC or: 

a---In these situations, EPA recommends that the 

toxicity tests recommended above be repeated at a 

frequency of at least once every 5 years as a part of 

the permit application. Such testing is required for 

certain POTWs under 40 CFR 122.21 (j). 

4) lnadeauate Information-Where a regulatory authority 

has inadequate information to determine reasonable potential for an excursion of a numeric or narrative water 

quality criterion, there may still be a basis for concern on 

the part of the authority. The permit should contain 

whole effluent toxicity monitoring requirements and a 

reopener clause. This clause would require reopening of 

the permit and establishment of a limit based upon any 

test results, or other new factors, which substantiate that 

the effluent causes, has the reasonable potential of causing, or contributes to an excursion above the CCC or 

CMC. 

3.3.4 Use of TuxMy Tesfhg in Multiple-sourMI glsclrarge 

SItuBtI4RF 

Where more than one discharge to the same receiving waterbody 

contributes, or has the reasonable potential to contribute to an 

excursion of water quality standards, permit limits must be 

developed for each individual discharger on that waterbody. 

For the regulatory authority to make this assessment, additional 

testing may be needed to provide the authority with the information necessary to assess the relative impact of each source. 

For purposes of this discussion, a multiple-source discharge 

situation is defined as a situation where impact zones overlap, or 

where ambient receiving water concentrations of a pollutant 

are elevated due to upstream discharges. In multiple-source 

discharge situations, additivity, antagonism, and persistence of 

toxicity can be of concern. To collect additional data, the permit 

authority should employ the toxicity testing procedures for 

multiple dischargers described in Box 3-3. In addition, ambient 

toxicity testing, as described below, could be used. 

Assuming that screening has been conducted that reveals the 

need for permit limits, two options for controlling the discharges exist. The first option is for the permit authority to 

regulate each source separately using the procedures for individual point sources. In this option, the permitting authority 

would require use of upstream ambient water as a diluent in the 

toxicity test so as to be able to evaluate the contributions of 

upstream sources of toxicity. A second option is to treat each 

discharge as an interactive component of a whole system. In 

this option, the permit writer would determine a total maximum daily load for the receiving waterbody and develop individual wasteload allocations for each discharger using the procedures discussed in Chapter 4. 

59 
Box 3-3. Recommend Multiple-source Toxicity Testing Procedures 

Tests 

Where the combined effluents make up 1 percent or greater of the receiving waters, conduct chronic toxicity 

tests following the testing procedures described in Section 3.3.3. 

Where the combined effluents make up less than 1 percent of the receiving waters, conduct acute toxicity tests 

following the testing procedures described in Section 3.3.3 (see Figure 3-2) to determine if any of the effluents 

are exhibiting toxicity. 

An additional data requirement is the assessment of relative and absolute toxicity of each source so that 

appropriate permit conditions can be set for individual dischargers. The following procedure is suggested. 

1) Conduct one set of toxicity tests on the effluents using a control of reconstituted or uncontaminated dilution 

water. The set of tests will give an absolute toxicity measurement of the effluent. 

2) Run a parallel set of toxicity tests on the effluent using dilution water taken directly upstream from the point of 

discharge or, for estuarine waters, from an area outside of the immediate discharge impact zone (this will have 

to be determined by a dye study). This dilution water may be contaminated with upstream effluents or other 

toxicant sources. The purpose of this test is to project toxic impact of the effluent after it is mixed at its point 

of discharge. This is a relative effluent toxicity measurement. The relative testing procedure could result in a 

change in the standard concentration-effect curve generated by the testing. The dilution water for the relative 

toxicity test may cause significant mortality, growth, or reproductive effects at the lower effluent concentrations (including the 100 percent diluent control concentration) if the diluent from the receiving water is toxic 

(from an upstream discharge). Such mortality does not invalidate the test. Instead, analysis of toxicity trends 

resulting from the relative toxicity tests can be used to assess the effluent’s toxicity in relation to other sources 

and ambient receiving water conditions. However, a control dilution water with no toxicity must be used for 

quality assurance and determination of absolute toxicity of the effluent. 

3) Conduct ambient toxicity tests to (a) determine whether or not the effluent has a measurable toxicity after 

mixing, (b) measure persistence of toxicity from all sources contributing to receiving water toxicity, and (c) 

determine combined toxicity resulting from the mixing of multiple, point, and nonpoint sources of toxicity. 

See Appendix C for a discussion of ambient toxicity testing procedures. 

The ambient testing can be required of each discharger and conducted during low-flow or worst-case design 

periods. 

Frequency for Ambient Testing 

All testing should be conducted simultaneously by each discharger, if possible. At a minimum, the tests should 

be conducted concurrently starting within a short time period (1 to 2 days). Repeated ambient toxicity analyses 

will be desirable when variable effluents are involved. Effluent toxicity data showing variability can be used to 

assess what frequency will be most applicable. The level of repetition for variability analysis should be similar to 

that used in effluent variability analyses. 

Other Considerations 

Dye studies of effluent dispersion for rivers, lakes, reservoirs, and estuaries are strongly recommended. This 

allows analysis of effluent concentration at the selected sampling stations above and below the discharge points. 

The procedures suggested in this multiple source section are based on actual multiple source site investigations 

conducted under the Complex Effluent Toxicity Testing Program. Site reports from that study can be used to 

obtain further description of the toxicity testing procedures used to analyze multiple source toxic impact [l, 21. 

60 
3.3.5 Ambient Toxicity Testing 

Ambient toxicity testing also is useful in screening receiving 

water bodies for existing toxic conditions. The procedure described in Appendix C uses short-term chronic toxicity tests to 

measure the toxicity of samples of receiving water taken above, 

at, and below outfalls. It can be used in freshwater, marine, and 

estuarine systems. The procedure must be conducted during an 

appropriate low-flow or worst-case design period. 

The utility of the ambient toxicity screening approach is that 

actual receiving water toxicity is directly measured. No extrapolation from exposure or ACR is needed. Further, impact from 

multiple source discharge situations, which may not be apparent 

from individual discharger data, is identified. Finally, the technique can provide an assessment of the persistence of effluent 

toxicity. 

3.3.6 Special Considerations fur Dischaqes to Marine and 

Estuarine Environments 

Special problems are encountered when assessing and controlling impacts of toxic pollutants discharged to marine and estuarine waterbodies. These special problems include the following: 

l Determining the physical characteristics of estuaries and 

the complex mixing and effluent dilution situations for 

RWCs of effluents. 

l Generating toxicity data on nonsaline effluents that discharge to brackish or saline waters and establishing causeeffect relationships on that basis. 

l Assessing exposure and controlling impacts from persistent toxicants accumulating in fish and shellfish tissues 

and in sediments. These factors are particularly important 

in estuaries and near coastal waters because of high use of 

estuaries as breeding and fishing areas for important commercial seafood supplies and recreational fishing, and because many estuaries and near coastal waters act as sinks 

for pollutants that accumulate in sediments. 

Where these special problems are encountered, additional information may need to be gathered to better quantify dilution, to 

determine metals partitioning, and to identify potential interferences in whole effluent toxicity tests. 

To characterize the type of whole effluent toxicity that is most 

relevant for a particular discharge to marine and estuarine waters, the following questions should be considered [5]: 

l What is the salinity of the receiving water, and is this 

important in terms of the State standards? 

l What is the appropriate test organism to require for toxicity testing under differing salinity conditions? 

The answers to these questions will enable the permitting authority to determine what type of toxicity testing is most suitable 

for effluent characterization and whole effluent toxicity control. 

For most marine and estuarine discharges the choice of test 

species and dilution water should be made based on the characteristics of the receiving water at the critical conditions for flow, 

mixing, and salinity. Foremost in this determination should be 

the salinity of the receiving water and, to a lesser extent, the 

salinity of the effluent itself. 

The primary objective of whole effluent toxicity tests is to identify 

sources of toxicity that can potentially cause an excursion of a 

State’s narrative or numeric water quality criteria. For this reason, 

the toxicity tests should reflect the natural conditions of the 

receiving water so to be able to measure any effluent characteristic that could contribute to ambient toxicity. The marine toxicity 

test methods identify 1,000 mg/l as the point at which salinity 

begins to exert an effect on freshwater species. As a general 

rule, EPA recommends that freshwater organisms be used 

when the receiving water salinity is less than 1,000 mg/l, and 

that marine organisms be used when the receiving water 

salinity equals or exceeds 1,000 mg/l. 

Saline Effluent Discharges to Saltwater 

The dissolved salts in the effluent are pollutants. These salts may 

or may not be the same as those present in the receiving water. 

Also, the proportion of dissolved salts in the effluent may be 

different from that of the salts in the receiving water. In this case, 

the toxicity test needs to be able to determine if these salts 

contribute to ambient toxicity. For this reason, marine organisms are needed. 

Saline Effluent Discharged to Freshwater 

In this case, the dissolved salts in the effluent is a pollutant that 

does not exist in the receiving water. The toxicity test needs to 

determine whether the dissolved salts can be one of the toxicants 

that contribute to ambient toxicity. For this reason, freshwater 

organisms are needed. 

Freshwater Effluent Discharged to Saltwater 

In this instance, the lack of dissolved salts in the effluent can 

cause an apparent toxic effect to the marine organisms in the 

toxicity test. However, in contrast to the instances presented 

above, the toxicity test does not need to be able to measure this 

effect because a lack of salts is not a pollutant. The marine 

toxicity test methods account for this by requiring that the 

salinity of the effluent be adjusted to approximate the salinity of 

the receiving water. As an alternative to using a marine organism, 

a freshwater organism can be used if the test is being conducted 

only on a loo-percent effluent sample and if State water quality 

standards do not require that a marine organism be used. 

3.3.7 Using a Chemical-specific limit to Control Toxicity 

EPA regulations at 40 CFR 122.44(d)(l )(v) provide that limits on 

whole effluent toxicity are not necessary where the permitting 

authority demonstrates in the fact sheet or statement of basis of 

the NPDES permit that chemical-specific limits for the effluent are 

sufficient to attain and maintain applicable numeric and narrative 

State water quality criteria. To make this demonstration that 

chemical-specific limits are sufficient, additional effluent information will be needed. EPA recommends that the discharger 

conduct a toxicity identification evaluation to identify the 

causative agent(s) in the effluent. Where the permitting authority determines that the demonstration required by 40 CFR 

122.44(d)(l)(v) has been made, limits on whole effluent toxicity 

61 
need not be imposed. Effluent limits on the controlling chemical 

with concurrent whole effluent monitoring will be sufficient. Where 

subsequent whole effluent toxicity testing reveals the presence of 

toxicity in the effluent, the above process will need to be repeated, 

or alternatively a whole effluent toxicity limit will be needed. If 

continued toxicity testing shows that additional chemical-specific 

effluent limits are insufficient to control whole effluent toxicity, 

then toxicity limits may be the only practical way to control 

toxicity. 

3.3.8 Eif/uent Characterizetion ior @edtic Chemkak 

The previous section discussed effluent characterization for whole 

effluent toxicity. This section will describe EPA’s recommendations 

for data generation to determine whether or not permit limits are 

needed to control specific chemical pollutants in effluents. While 

many of the same principles apply when developing chemicalspecific limits, there are some differences based upon regulatory 

and analytical considerations. 

Characterization of impacts due to specific chemicals do not require a determination of the type of testing as is required for whole 

effluent toxicity because there is generally only one type of test for 

specific chemicals. However, there are some antecedent steps that 

are unique to effluent characterization for specific chemicals: determination of the chemicals of concern and determination of 

acceptable ambient levels (RAC, CMC, or CCC) for these pollutants. 

Steps for Chemical-specific Effluent Characterization Process 

Figure 3-5 illustrates EPA’s recommendations for determining 

whether or not permit limits need to be developed according to 

an evaluation of a limited data set. The following discussion 

corresponds to the various activities shown in Figure 3-5. (Refer to 

the human health discussion in Section 3.3.9 for additional details 

on procedures to characterize the bioconcentration potential of 

effluents.) 

Step 7: ldentifj the Po//uCunCs of Concern 

This process should begin with an examination of existing data to 

determine the presence of specific toxicants for which criteria, 

standards, or other toxicity data are available. Sources of data 

include the following: 

l Permit application forms, DMRs, permit compliance systems 

(PCS), and permit files 

l Pretreatment industrial surveys 

l STORET for ambient monitoring data 

l SARA Title Ill Toxic Chemical Release Inventory 

l Industrial effluent guidelines development documents 

l The Treatability Manual [6] 

. Effluent bioconcentration assessment (see Section 3.3.9). 

Data on specific chemicals that are typically submitted with NPDES 

application forms will consist of a limited number of analytical test 

results for many of the reported parameters. Where the regulatory authority has reason to believe that additional data for key 

parameters of concern are needed in order to adequately characterize the effluent, this information should be requested as a part 

of the application or, in some cases, through the use of Section 

308 letters. It is recommended that 8 to 12 samples be analyzed for key parameters of concern. In some cases, special 

analytical protocols will need to be specified in order to gather all 

appropriate information. 

Step 2: Determine the Basis for fstablishing RAG, ChlCs, and 

CCCs for the Pollutants of Concern 

The second step is to identify the appropriate water quality standard, including designated or existing use, and criteria for use. 

Ideally, the State water quality standards include aquatic life and 

human health criteria for the pollutants of concern. If a State does 

not have a numeric water quality criterion for the pollutant of 

concern, then one of three options for using the narrative criterion may be used (40 CFR 122.44(d)(l)(vi)) to determine whether 

a discharge causes, has the reasonable potential to cause, or 

contributes to an excursion above a narrative criteria because of 

an individual pollutant. Although the provisions of 40 CFR 

122.44(d)(l)(vi) are presented in the regulation in the context of 

permit limit development, these same considerations should be 

applied in characterizing effluents in order to determine whether 

limits are necessary. The options available are as follows: 

l Option A allows the regulatory authority to establish limits 

using a “calculated numeric water quality criterion” that 

the regulatory authority demonstrates will attain and maintain applicable narrative water quality criteria and fully 

protect the designated use. This option allows the regulatory authority to use any criterion that protects aquatic life 

and human health. This option also allows the use of sitespecific factors, including local human consumption rates 

of aquatic foods, the State’s determination of an appropriate risk level, and any other current data that may be 

available. 

l Option B allows the regulatory authority to establish effluent limits using EPA’s Water Quality Criteria guidance documents, if EPA has published a criteria document for the 

pollutant supplemented where necessary by other relevant 

information. As discussed earlier, EPA criteria documents 

provide a comprehensive summary of available data on the 

effects of a pollutant. 

l Option C may be used to develop limits for a pollutant of 

concern based on an indicator parameter under limited 

circumstances. An example of an indicator parameter is 

total toxic organics (l-TO>; effluent limits on TTO are useful 

where an effluent contains organic compounds. However, 

use of this option must be justified to show that controls on 

one pollutant control one or more other pollutants to a 

level that will attain and maintain applicable State narrative 

water quality criteria and will protect aquatic life and human health (see 40 CFR 122.44(d)(l)(vi)(C)). Use of this 

option is restricted by regulation to those instances where it 

can be demonstrated that controls on indicator pollutants 

serve to control the toxicant of concern. Using Option A or 

Option B is a more direct and perhaps more defensible 

approach. 

62 
STEP 1 

STEP 2 Determine RAC and/or 

CMUCCC for pollutants 

RAC available CMC and/or 

CCC avarlable 

STEP 3 rhiizkd Dilution determination 

for aquatic life 

NO A NO 

A 

NO 

I I 

NO 

I I 

Require 

monitoring at 

reissuance 

Notes: 

’ RAC and/or CMC/CCC: Use State numeric criterion or interpret State narrative criterion using one of three options specified under 40 CFR 

122.44(d). 

2 Dilution determination: Perform for critical flow and for any applicable mixing zones for aquatic life and human health protection procedures, 

respectively. 

3 Reasonable potential: Use procedures in Boxes 3-2 and 3-4. 

Figure 3-5. Effluent Characterization for Specific Chemicals 

Step 3: Dilution Determination Step 4: Decision Criteria for Permit Limit Development 

The third step is to calculate the effluent dilution at the edge of 

the mixing zone. The pertinent factors for consideration here are 

the same as were previously presented for whole effluent toxicity 

with one difference: there are two levels of dilution analysis for 

chemical data. The first level is to use simple fate models based 

on a dilution analysis and comparison with the RAC, CMC, or 

CCC. The second level of analysis is to use more complex fate 

models, including dynamic models to estimate persistence, and 

may be applied to lakes, rivers, estuaries, and coastal systems 

using a desktop calculator or microcomputer. EPA has supported development of a second level of analysis that estimates 

point source wasteload allocations and nonpoint source allocations and predicts the resulting pollutant concentrations in receiving waters [7]. 

After this dilution analysis has been performed, the projected RWC 

is compared to the RAC, CMC, or CCC (either the State numeric 

criteria or an interpretation of the narrative criteria as described 

earlier). Whereas analysis of aquatic impacts should include evaluations with respect to both the CCC and the CMC, analysis of 

human health impacts will only involve comparisons with the RAC. 

The four possible outcomes discussed above in the triggers for 

permit limit development discussion in Section 3.3.3 also apply 

here: 

l Excursion above the RAC, CMC, or CCC 

l Reasonable potential for excursion above the RAC, CMC, or 

ccc 

63 
- - - -. - - _. - -.- 

l No reasonable potential for excursion above the RAC, CMC, 

ccc 

. Inadequate information. 

If these evaluations project excursions or the reasonable potential 

to cause or contribute to an excursion above the RAC, CMC, or 

CCC, then a permit limit is required (40 CFR 122.44(d)(l)(iii)). 

The statistical approach shown in Box 3-2 or an analogous approach developed by a regulatory authority can be used to determine the reasonable potential. Effluents that are shown not to 

cause or that have a reasonable potential to cause or contribute to 

an excursion above an RAC, CMC, or CCC should be reevaluated 

at permit reissuance. 

Where chemical-specific test results do not show a reasonable 

potential but indicate a basis for concern after consideration of the 

other factors discussed in Section 3.2, or if there were inadequate 

information to make a decision, the permit should contain chemical testing requirements and a reopener clause. This clause would 

require reopening of the permit and establishment of a limit based 

upon any test results that show effluent toxicity at levels that cause 

or have a reasonable potential to cause or contribute to an excursion above the RAC, CCC, or CMC. 

3.3.9 Effluent Characteriwtion tar BiocunceMh? 

Pollutants 

The previous section discussed how to characterize effects of 

specific chemicals, including those that may threaten human health, 

to determine whether or not a discharge causes, has the reasonable potential to cause, or contributes to excursions above an 

water quality criterion. The primary disadvantage of this approach 

is that it does not identify all effluent chemicals of potential cqntern for human health. To help address this gap, EPA is developing a procedure for identifying pollutants with the propensity to 

bioconcentrate in fish tissue. This procedure is presently in draft 

form and should not be used for establishing NPDES permit limits 

until EPA releases the final document on the procedure. This 

section describes the outline of this procedure. 

The overall approach illustrated in Figure 3-6 is a seven-step procedure that starts with collecting samples and ends with developing 

permit effluent limits. The effluent characterization step unique to 

this approach lies in Step 3. There are two alternatives under this 

step: fish tissue residue and effluent assessment. An analytical 

chemistry laboratory with residue chemistry and gas chromatograph/mass spectometer (GUMS) capability is needed to conduct 

the analytical methods for both alternatives. A summary of the 

alternatives follows: 

l Tissue Residue Alternative: This alternative measures the concentrations of organic bioconcentratable chemicals in tissue 

samples of indigenous organisms from the receiving water. 

This analysis involves the collection of fish or shellfish samples, 

the extraction of the organic chemicals from the tissue and 

the analysis of these extracts with CC/MS to identify and 

quantify the bloconcentratable contaminants. The procedure 

provides recommendations to sort the results of this screening 

analysis in order to determine which of the contaminants pose 

a hazard and require regulatory action. The approach recommends that the identity of those contaminants then be confirmed prior to taking subsequent action. 

Select di6chargerr 

biOcOnCentr6tiOn 

etternatlve 

Ti66ue 

n6idUe 

akernative 

Develop 

RACIRTC 

I 

DWdOQ 

WastelOad 

allocalion 

I 

Develop 

permit limit6 

I 

Conduct 

monitodng 

Figure 3-6. Procedure for Assessment and Control of 

Bloconcentratable Contaminants in Surface Waters 

l Effluent Alternative: This alternative measures the concentrations of organic bioconcentratable chemicals in effluent 

samples from point source dischargers. This analysis involves the collection of effluent samples, the extraction of 

the organic chemicals from the effluent sample, and the 

separation of the chemicals that have characteristics known 

to result in bioconcentration from the other chemical components of the effluent sample. This separation is achieved 

by way of an analytical chemistry methodology called high64 
pressure liquid chromatography (HPLC). The HPLC 

also separates (fractionates) an effluent sample into 

three subsamples or “fractions.” These three fractions 

contain chemicals with increasing potential to 

bioconcentrate, with the third fraction containing those 

chemicals with the highest bioconcentration rates. 

Following HPLC fractionation, each fraction is then 

analyzed with CC/MS to identify and quantify the 

bioconcentratable contaminants. The effluent procedure also provides recommendations to sort the results of the initial screening analysis to determine 

which of the contaminants pose a hazard and require 

subsequent regulatory action. The approach then 

recommends that the identity of those contaminants 

then be confirmed prior to taking further regulatory 

action. 

While both of the assessment alternatives described above 

may be used for a given discharger, generally one of these 

alternatives may be preferred by the regulatory authority. 

The regulatory authority would select the assessment approach based on the available site- and facility-specific information and the objectives of the application. 

Although the approach provides a means to identify chemicals 

that can bioconcentrate, it does not identify all bioconcentratable 

chemicals. Chemicals that bioconcentrate include many organic 

compounds, and a small number of metals (e.g., mercury and 

selenium) and organometals (e.g., tributyltin). The new approach 

is limited to nonpolar organic chemicals that produce measurable 

chemical residues in aquatic organisms or that have log octanolwater partition coefficients greater than 3.5. 

3.3.10 Analytical Considerations for Chemicals 

Analysis of discharges for toxic substances requires special quality 

control procedures beyond those necessary for conventional parameters. Toxicants can occur in trace concentrations and are 

frequently volatile or otherwise unstable. An EPA publication entitled, Test Methods-Technical Additions to Methods for Chemical 

Analysis of Water and Wastes [8], contains sampling and handling 

procedures recommended by EPA for a number of toxic and 

conventional parameters. Additional methods for analyses for 

toxicants are described in Standard Methods of Water and Wastewater Analyses (ASTM, 17th edition, 1989, or most recent edition) 

and 40 CFR Part 136. Chapter 5 discusses detection limits and 

sampling requirements. 

65 
CHAPTER 3 

REFERENCES 

1. Mount, D., N. Thomas, M. Barbour, T. Norberg, T. Roush, and 

W. Brandes. 1984. Effluent and Ambient Toxicity Testing and 

Instream Community Response on the Ottawa River, Lima, Ohio. 

Permits Division, Washington, DC, Office of Research and 

Development, Duluth, MN. EPA-600/2-84-080, August 1984. 

2. Mount, D.I., and T.J. Norberg-King, eds. 1985. Validity of 

fffluent and Ambient Toxicity Tests for Predicting Biological 

Impact, Scippo Creek, Circleville, Ohio. U.S. EPA. EPAJ600/3- 

85/044, june 1985. 

3. Peltier, W., and C.I. Weber. 1985. Methods for Measuring the 

Acute Toxicity of Effluents lo Aquatic Organisms, 3d ed. Office of Research and Development, Cincinnati, OH. EPA600/4-85-013. 

4. Weber, C.I., et al., eds. 1989. Short-Term Methods far fstimating the Chronic Toxicity of Effluents and Receiving Waters 

to Freshwater Organisms, 2d ed. Office of Research and 

Development, Cincinnati, OH. EPA-600/4-89/001. 

5. Weber, C.I., et al., eds. 1988. Short-Term Methods for fstimating the Chronic Toxicity of Effluents and Receiving Waters to 

Marine and fstuarine Organisms. Office of Research and 

Development, Cincinnati, OH. EPA-600/4-87/02. 

6. U.S. EPA. 1983. The Treataability Manual, Volume 4. U.S. EPA 

Office of Research and Development. EPA 600/2-82-001 

(revised January 24, 1983). 

7. Mills, W., et al, 1982. Water Quality Assessment: A Screening 

Procedure Toxic and Conventional Pollutants: Parts 1 and 2. 

Office of Research and Development, Athens, CA. EPA 

600/6-82-004 A, September 1982. 

8. U.S. EPA. 1982. Test Methods - Technical Additions to Methods 

for Chemical Analysis of Water and Wastes. Office of Research and Development, Cincinnati, OH. EPA 600/4-82- 

055, December 1982. 

AIIDKIONAL REFERENCES 

Crane, J.L., A. Pilli, and R.C. Russa. 1984. CfT/S: Complex DiToro, D. 1985. Exposure Assessment for Complex 

Effluent Toxicity Information System. CfTiS Retrieval System Effluents: Principles and Possibilities. In Environmental 

User’s Manual. Office of Research and Development, Hazard Assessment of ffflvents. Eds. H. Bergman, R. 

Duluth, MN. EPA-60018-84-030, November 1984. Kimerle, and A. Maki. 

Crane, J.L., A. Pilli, and R.C. Russa. 1984. UT/S: Complex Macek, K. 1985. Perspectives on the Application of the 

Effluents Toxicity Information System. Data Encoding Cuide- Hazard Evaluation Process. In Environmental Hazard 

lines and Procedures. Office of Research and Development, Assessment of fMuents. Eds. H. Bergman, R. Kimerle, 

Duluth, MN. EPA-60018-84-029. November 1984. and A. Maki. 

66 
4. EXPOSURE AND WASTELOAD ALLOCATION 

4.1 INTRODUCTION 

At this point in the toxics control process, a water quality problem 

has been identified. Screening analyses may have been done to 

assess the extent of toxicity, or a wasteload allocation (WLA) 

based on an existing total maximum daily load (TMDL) may 

already have been established. A TMDL is the sum of the individual WLAs for point sources and load allocations (LAs) for 

nonpoint sources of pollution and natural background sources, 

tributaries, or adjacent segments. WLAs represent that portion of 

a TMDL that is established to limit the amount of pollutants from 

existing and future point sources so that surface water quality is 

protected at all flow conditions. 

The TMDL process uses water quality analyses to predict water 

quality conditions and pollutant concentrations. Limits on wastewater pollutant loads are set and nonpoint source allocations are 

established so that predicted receiving water concentrations do 

not exceed water quality criteria. TMDLs and WLAs/LAs should 

be established at levels necessary to attain and maintain the 

applicable narrative and numerical water quality standards, with 

seasonal variations and a margin of safety that takes into account 

any lack of knowledge concerning the relationship between point 

and nonpoint source loadings and water quality. Determination 

of WLAs/LAs and TMDLs should take into account critical conditions for stream flow, loading, and water quality parameters. 

Conditions that will protect the receiving water have been determined from State numeric or narrative water quality criteria. 

This chapter is divided into sections that explain the steps that 

precede establishment of a WLA and then the methods and tools 

(models) that can be used to determine the WLA. Section 4.2 

briefly discusses TMDLs and how they relate to waters identified 

as requiring a water quality-based approach for toxics control. 

The section also discusses different WLA schemes. Sections 4.3 

and 4.4 discuss mixing zones, areas described as allocated impact 

zones where acute and chronic water quality criteria may be 

exceeded. Section 4.3 provides background information on mixing zones and discusses EPA’s mixing zone policy and how this 

policy affects the allowable toxic load that can be discharged from 

a point source. State mixing zone dimensions and the determination of mixing zone boundaries are also discussed. 

Section 4.4 discusses mixing zone analyses for situations in which 

the discharge does not mix completely with the receiving water 

within a short distance. Included in Section 4.4 are discussions of 

outfall designs that maximize initial dilution in the mixing zone, 

critical design periods for mixing zone analyses, and methods to 

analyze and model near-field and far-field mixing. 

Section 4.5 discusses the calculations of the WLA and LA and the 

types of EPA-recommended mathematical models available to 

determine WLAs in completely mixed situations for both aquatic 

life and human health. The WLA models listed in Section 4.5 can 

be used to predict ambient concentrations and to calculate the 

effluent quality required to meet the criteria and protect designated and existing uses of the receiving water. The data requirements of each of these models are also described so that the 

effluent characterization procedures described in Chapter 3 can 

be designed to support the specific types of WLA modeling 

selected by the regulator. Section 4.6 discusses human health 

considerations and how to determine WLAs for human health 

toxicants. 

EPA is currently working on methods to develop sediment criteria. 

Once developed, point source discharges could be further limited 

to prevent accumulation of pollutants in the bed sediment; such 

accumulation impairs beneficial uses. Although the criteria are 

not yet available for this document, they will be addressed in 

future documents. In the meantime, some of the models discussed in Section 4.5 are capable of simulating interactions between 

the water column and sediment and between toxic transport and 

transformation in the sediment. EPA is encouraging the States to 

consider the role of sediments in WLA. 

4.2 TOTAL MAXIMUM DAILY LOADS AND WASTELOAD 

ALLOCATIONS 

4.2.1 Total Maximum daily Loads 

The Federal Clean Water Act (CWA), under Section 303(d), requires the establishment of TMDLs for “water quality limited” 

stream segments. In such segments, water quality does not meet 

applicable water quality standards and/or is not expected to meet 

applicable water quality standards even after the application of 

the technology-based effluent limitations. A TMDL includes a 

determination of the amount of a pollutant, or property of a 

pollutant, from point, nonpoint, and natural background sources, 

including a margin of safety, that may be discharged to a water 

quality-limited waterbody. Any loading above this loading capacity risks violating water quality standards. TMDLs can be expressed 

in terms of chemical mass per unit of time, by toxicity, or by other 

appropriate measures. Permits should be issued based on TMDLs 

where available. 

The establishment of a TMDL for a particular waterbody is dependent on the location of point sources, available dilution, water 

quality standards, nonpoint source contributions, background 

conditions, and instream pollutant reactions and effluent toxicity. 

All of these factors can affect the allowable mass of the pollutant 

in the waterbody. Thus, two issues must be determined in 

conjunction with the establishment of the TMDL: (1) the definition of upstream and downstream boundaries of the waterbody 

for which the TMDL is being determined, and (2) the definition of 

critical conditions. For the following discussion, the waterbody 

boundaries are delineated as the portion of the waterbody be67 
tween the pollutant source (whether point source or nonpoint 

source) that is farthest upstream and the downstream point at 

which water quality has recovered to the background quality 

found above the pollutant source that is farthest upstream. The 

delineation of critical conditions for stream flow, loading, and 

water quality parameters may be specific to the type of waterbody 

and is discussed in Section 4.4. 

TMDLs are established based on water quality criteria pertinent to 

the designated and existing uses for the waterbody in question. 

TMDLs are traditionally calculated using State water quality standards as applied to a specific waterbody. Such a fitting of the 

TMDL to desired water quality criteria requires information concerning the distribution of loadings within the waterbody, namely, 

thelocations and relative contributions of pollutant-specific loadings from point, nonpoint, and background sources during all 

flow conditions (40 CFR 130.2(f)). Low-flow TMDLs, by themselves, will not be adequate in situations where nonpoint source 

loadings (LAs) during high or intermediate flow conditions cause 

excursions above water quality standards (40 CFR 130.2(f)). 

The loading capacity of TMDLs have been determined in many 

ways, but the most common method is to find the pollutant 

loading that will attain and maintain applicable water quality 

criteria. For example, in the Tualatin River Basin in Oregon, 

loading capacity was determined by multiplying stream flow in 

critical flow periods by the pollutant water quality standard [1]. 

Another method of determining a loading capacity is by quantifying instream toxicity. This method was used in developing a 

TMDL for the Amelia River in Florida [2]. 

The allowable TMDL is defined as the sum of the individual WLAs 

and LAs; a margin of safety can be included with the two types of 

allocations to ensure that allocated loads, regardless of source, 

would not produce an excursion above water quality standards. 

The WLAs are those portions of the TMDL assigned to point 

sources; the LAs are those portions of the TMDL assigned to the 

sum of all nonpoint sources and background sources (40 CFR 

130.2(f)). The background sources represent loadings to the 

specified waterbody or stream segment that come from sources 

outside the defined segment. For example, loadings from regions 

upstream of the segment and estimated atmospheric deposition 

of the pollutant would constitute background sources. Sediments 

that are highly contaminated from upstream discharges or historical discharges might also act as a source of toxicants and contribute 

to the background levels; these sediments also may be part of the 

nonpoint sources. 

The TMDL represents a mass loading that may occur over a given 

time period to attain and maintain water quality standards. As a 

result, the design flows under which the TMDL is determined can 

significantly alter its value. This phenomenon results in a somewhat unusual dichotomy. The design flows for aquatic life protection most applicable to point source loadings (WLAs) usually 

involve low-flow events (e.g., 7Q10) because the volumes associated with the point sources generally do not decrease with decreased stream flow. As a result, the highest concentrations 

associated with specific point source loads would be expected 

under low flow conditions. Conversely, elevated nonpoint source 

pollutant loadings (i.e., urban, agricultural) generally correspond 

to storm events. In fact, agricultural and urban runoff are often 

minimal or nonexistent in the absence of precipitation (i.e., nonexistent under low-flow drought conditions), 

The TMDL is a composite of the allowable loads associated with 

point sources and nonpoint sources within the defined boundaries of the waterbody segment and the background loadings to 

that segment from upstream and from in-place sediments. 

Therefore, the TMDL should be evaluated under conditions that 

reflect worst-case (critical) conditions for both point and nonpoint 

source loadings (i.e., low-flow drought and high flow conditions). 

Determination of the TMDL under these two scenarios would 

identify the lower of the two loading capacities of the waterbody. 

This lower capacity is necessary to protect the waterbody in 

question. 

In the case of design flows for human health protection, the 

harmonic mean flow is recommended as the basis for TMDLs for 

carcinogens. Design flows for human health protection should 

consider worst-case conditions for both point and nonpoint source 

loadings under this flow condition (see Section 4.6). 

In many cases, LAs for nonpoint sources are difficult to assess 

because the information needed to describe the runoff associated 

with the high-flow storm events does not exist. This lack of 

information is due to the high variability of the events. Because of 

the importance of estimating the nonpoint contributions to the 

waterbody, site-specific models may be required to estimate 

nonpoint source loadings. Even then, detailed models are difficult 

to calibrate with accuracy without intensive monitoring studies, 

and simplistic correlations between loadings and rainfall can be, 

by their statistical nature, unreliable for estimating low-frequency 

events (e.g., worst 10-year storm). The uncertainties associated 

with nonpoint source loadings and background sources require 

that the TMDL be determined with a sufficient margin of safety to 

allow for significant variability in nonpoint source loadings. 

CWA Section 303(d) and EPA regulations (40 CFR Parts 35 and 130, 

January 11, 1985) require that TMDLs contain a margin of safety 

“which takes into account any lack of knowledge concerning the 

relationship between effluent limitations and water quality.” The 

margin of safety is to take into account any uncertainties related 

to development of the water quality-based control, including any 

uncertainties in pollutant loadings, ambient conditions, and the 

model analysis. The size of the required margin of safety can, of 

course, be reduced by collecting additional information, which 

reduces the amount of uncertainty. The margin of safety can be 

provided for in the TMDL process by one of the following: 

• Reserving a portion of the loading capacity to a separate 

margin of safety. 

• Including a margin of safety within the individual WLAs for 

point sources and within the LAs for nonpoint sources and 

background sources. 

Most TMDLs are developed using the second approach, most 

often through the use of conservative design conditions. 

In addition, all WLAs, LAs. and TMDLs must meet the State 

antidegradation provisions developed prusuant to the Water 

Quality Standards Regulation (Section 137.12 of 40 CFR Part 131, 

68 
November 8, 1983). This regulation establishes explicit procedures that must be followed prior to lowering existing water 

quality to a level that still supports the Section 101 (a)(2) “fishable/ 

swimmable” goal of the Act. WlAs, LAS, and TMDLs that allow 

such a decline in water quality cannot be established unless the 

applicable public participation and intergovernmental review 

requirements of the antigradation provisions have been met and 

all existing uses are fully maintained and protected. 

4.2.2 WastsoMAkation scrbemes 

WLAS for water quality-based toxics permits must be set in accordance with EPA regulations [3, 41. EPA has developed a number 

of WIA guidance documents to assist regulatory authorities in 

developing TMDLs and WtAs. The EPA Office of Water Regulations and Standards, Assessment and Watershed Protection Division, maintains the latest listing of all WLA guidance documents. 

Toxic WLA guidance documents are currently available for rivers 

and streams [5], lakes and reservoirs 161, and estuaries [7]. Cuidante for the determination of critical design conditions for steadystate modeling of rivers and streams also is available [8]. 

Table 4-l lists 19 allocation schemes that may be used by the 

States to develop WLAs. This is not intended to be a complete list 

of approaches; regulatory authorities may use any reasonable 

allocation scheme that meets the antidegradation provisions and 

other requirements of State water quality standards [3]. 

The most commonly used allocation methods have been equal 

percent removal, equal effluent concentrations, and a hybrid 

method. The equal percent removal approach can be applied in 

two ways: the overall removal efficiencies of each pollutant 

source must be equal, or the incremental removal efficiencies 

must be equal. The equal effluent concentration approach also 

can be applied in two acceptable ways-equal final concentrations or equal incremental concentration reductions. This method 

is similar to the equal percent removal method if influent concentrations at all sources are approximately the same. However, if 

one point source has substantially higher influent levels, requiring 

equal effluent concentrations will result in higher overall treatment levels for that source than the equal percent removal appreach . 

The final commonly used method of allocating wasteloads is a 

hybrid method in which the criteria for waste reduction may not 

be the same for each point source. One facility may be allowed to 

operate unchanged, while another may be required to provide 

the entire load reduction. More often, a proportionality rule that 

requires the percent removal to be proportional to the input 

loading can be assigned. In these cases, larger sources would be 

required to achieve higher overall removals. 

4.3 INCIMPLETELY MIXED, DISCHARGE RECEIVING 

WATER SITUATIOWS 

Mixing zones are areas where an effluent discharge undergoes 

initial dilution and are extended to cover the secondary mixing in 

the ambient waterbody. A mixing zone is an allocated impact 

zone where acute and chronic water quality criteria can be exceeded as long as a number of protections are maintained, including freedom from the following: 

l Materials in concentrations that settle to form objectionable deposits 

. Floating debris, oil, scum, and other matter in concentrations that form nuisances 

Table 41. Wasteload Allocation Methods [9] 

1. Equal percent removal (equal percent treatment) 

2. Equal effluent concentrations 

3. Equal total mass discharge per day 

4. Equal mass discharge per capita per day 

5. Equal reduction of raw load (pounds per day) 

6. Equal ambient mean annual quality (mg/l) 

7. Equal cost per pound of pollutant removed 

8. Equal treatment cost per unit of production 

9. Equal mass discharged per unit of raw material used 

10. Equal mass discharged per unit of production 

11 a. Percent removal proportional to raw load per day 

11 b. Larger facilities to achieve higher removal rates 

12. Percent removal proportional to community effective 

income 

13a. Effluent charges (dollars per pound, etc.) 

13b. Effluent charge above some load limit 

14. Seasonal limits based on cost-effectiveness analysis 

15. Minimum total treatment cost 

16. Best availability technology (BAT) (industry) plus some 

level for municipal inputs 

17. hsimilative capacity divided to require an “equal effort 

among all dischargers” 

18a. Municipal: treatment level proportional to plant size 

18b. Industrial: equal percent between best practicable technology (BPT) and BAT, i.e., Allowable wasteload allocation: 

(WIA) = BPT- 1 i. (BPT-BAT) 

19. Industrial discharges given different treatment levels for 

different stream flows and seasons. For example, a plant 

might not be allowed to discharge when stream flow is 

below a certain value, but below another value, the 

plant would be required to use a higher level of treatment than BPT. Finally, when stream flow is above an 

upper value, the plant would be required to treat to a 

level comparable to BPT. 

L 

69 
l Substances in concentrations that produce objectionable 

color, odor, taste, or turbidity 

l Substances in concentrations that produce undesirable 

aquatic life or result in a dominance of nuisance species. 

Acutely toxic conditions are defined as those lethal to aquatic 

organisms that may pass through the mixing zone. As discussed 

in Chapter 2, the underlying assumption for allowing a mixing 

zone is that a small area of concentrations in excess of acute and 

chronic criteria, but below acutely toxic releases, can exist without 

causing adverse effects to the overall waterbody. The State 

regulatory agency can decide to allow or deny a mixing zone on a 

site-specific basis. For a mixing zone to be permitted, the discharger should prove to the State regulatory agency that all State 

requirements for a mixing zone are met. 

When wastewater is discharged into a waterbody, its transport 

may be divided into two stages with distinctive mixing characteristics. Mixing and dilution in the first stage are determined by the 

initial momentum and buoyancy of the discharge. This initial 

contact with the receiving water is where the concentration of the 

effluent will be its greatest in the water column. The design of the 

discharge outfall should provide ample momentum to dilute the 

concentrations in the immediate contact area as quickly as possible. 

The second stage of mixing covers a more extensive area in which 

the effect of initial momentum and buoyancy is diminished and 

the waste is mixed primarily by ambient turbulence. In large 

rivers or estuaries, this second-stage mixing area may extend for 

miles before uniformly mixed conditions are attained. In some 

instances, such as larger lakes or coastal bays, completely mixed 

conditions are never reached in the waterbody. The general 

definition for a completely mixed condition is when no measurable difference in the concentration of the pollutant (e.g., does 

not vary by more than 5 percent) exists across any transect of the 

waterbody. 

This section provides background information on the policy of 

mixing zones and the means to characterize them for use in WLAs 

(Section 4.5). The first subsection discusses the concerns that 

must be addressed when the boundaries and restrictions of a 

mixing zone are determined. The second subsection discusses 

the guidelines for preventing lethal conditions in the mixing zone. 

4.3.1 Detlmnlnatian of Mlxlog Z&w Bmtdiwk 

Allowable mixing zone characteristics should be established to 

ensure the following: 

l Mixing zones do not impair the integrity of the waterbody 

as a whole. 

l There is no lethality to organisms passing through the 

mixing zone. 

l There are no significant health risks, considering likely pathways of exposure (see Section 2.2.2). 

The Water Quality Criteria-l 972 [lo] recommends that mixing 

zone characteristics be defined on a case-by-case basis after it has 

been determined that the assimilative capacity of the receiving 

system can safely accommodate the discharge. This assessment 

should take into consideration the physical, chemical, and biological characteristics of the discharge and the receiving system; 

the life history and behavior of organisms in the receiving system; 

and the desired uses of the waters. Nearly all States require such 

an analysis before they allow a mixing zone [l l]. Further, mixing 

zones should not be permitted where they may endanger critical 

areas (e.g., drinking water supplies, recreational areas, breeding 

grounds, areas with sensitive biota). 

EPA has developed a holistic approach to determine whether a 

mixing zone is tolerable [12]. The method considers all the 

impacts to the waterbody and all the impacts that the drop in 

water quality will have on the surrounding ecosystem and 

waterbody uses. It is a multistep data collection and analysis 

procedure that is particularly sensitive to overlapping mixing 

zones. It includes the identification of all upstream and downstream waterbodies and the ecological and cultural data pertaining to them; the collection of data on all present and future 

discharges to the waterbody; the assessment of relative environmental value and level of protection needed for the waterbody; 

and, finally, the allocation of environmental impact for a discharge 

applicant. Because of the difficulty in collecting the data necessary 

for this procedure and the general lack of agreement concerning 

relative values, this method will be difficult to implement in full. 

However, the method does serve as a guide on how to proceed in 

allocating a mixing zone. 

Most States allow mixing zones as a policy issue, but provide 

spatial dimensions to limit the areal extent of the mixing zones. 

The mixing zones are then allowed (or not allowed) after case-bycase determinations. State regulations dealing with streams and 

rivers generally limit mixing zone widths, cross-sectional areas, 

and flow volumes and allow lengths to be determined on a caseby-case basis. For lakes, estuaries, and coastal waters, dimensions 

are usually specified by surface area, width, cross-sectional area, 

and volume. 

Where a mixing zone is allowed, water quality standards are met 

at the edge of that regulatory mixing zone during design flow 

conditions and generally, (1) provide a continuous zone of passage that meets water quality criteria for free-swimming and 

drifting organisms and (2) prevent impairment of critical resource 

areas. Individual State mixing zone dimensions are designed to 

limit the impact of a mixing zone on the waterbody. Furthermore, 

EPA’s review of State WLAs should evaluate whether assumptions 

of complete or incomplete mixing are appropriate based on 

avaitable data. 

In river systems, reservoirs, lakes, estuaries, and coastal waters, 

zones of passage are defined as continuous water routes of such 

volume, area, and quality as to allow passage of free-swimming 

and drifting organisms so that no significant effects are produced 

on their populations. Transport of a variety of organisms in river 

water and by tidal movements in estuaries is biologically important in a number of ways: food is carried to the sessile filter 

70 
feeders and other nonmobile organisms, spatial distribution of 

organisms and reinforcement of weakened populations are enhanced, and embryos and larvae of some fish species develop 

while drifting [ll], Anadromous and catadromous species must 

be able to reach suitable spawning areas. Their young (and in 

some cases the adults) must be assured a return route to their 

growing and living areas. Many species make migrations for 

spawning and other purposes. Barriers or blocks that prevent or 

interfere with these types of essential transport and movement 

can be created by water with inadequate chemical or physical 

quality. 

As explained above, a State regulatory agency may decide to 

deny a mixing zone in a site-specific case. For example, denial 

should be considered when bioaccumulative pollutants are in the 

discharge. The potential for a pollutant to bioaccumulate in living 

organisms is measured by (1) the bioconcentration factor (BCF), 

which is chemical-specific and describes the degree to which an 

organism or tissue can acquire a higher contaminant concentration than its environment (e.g., surface water); (2) the duration of 

exposure; and (3) the concentration of the chemical of interest. 

While any BCF value greater than 1 indicates that bioaccumulation 

potential exists, bioaccumulation potential is generally not considered to be significant unless the BCF exceeds 100 or more. 

Thus, a chemical that is discharged to a receiving stream, resulting in low concentrations, and that has a low BCF value will not 

create a bioaccumulation hazard. Conversely, a chemical that is 

discharged to a receiving stream, resulting in a low concentration 

but having a high BCF value, may cause in a bioaccumulation 

hazard. Also, some chemicals of relatively low toxicity, such as 

zinc, will bioconcentrate in fish without harmful effects resulting 

from human consumption. 

Another example of when a regulator should consider prohibiting 

a mixing zone is in situations where an effluent is known to attract 

biota. In such cases, provision of a continuous zone of passage 

around the mixing area will not serve the purpose of protecting 

aquatic life. A review of the technical literature on avoidance/ 

attraction behavior revealed that the majority of toxicants elicited 

an avoidance or neutral response at low concentrations [13]. 

However, some chemicals did elicit an attractive response, but the 

data were not sufficient to support any predictive methods. Temperature can be an attractive force and may counter an avoidance 

response to a pollutant, resulting in attraction to the toxicant 

discharge. Innate behavior such as migration may also supersede 

an avoidance response and cause fish to incur a significant exposure. 

4.32 Minimkhg the She of Mixing Zones 

Concentrations above the chronic criteria are likely to prevent 

sensitive taxa from taking up long-term residence in the mixing 

zone. In this regard, benthic organisms and territorial organisms 

are likely to be of greatest concern. The higher the concentrations occurring within an isopleth, the more taxa are likely to be 

excluded, thereby affecting the structure and function of the 

ecological community. It is thus important to minimize the 

overall size of the mixing zone and the size of elevated concentration isopleths within the mixing zone. 

4.3.3 Prevention of Lethalfty to Passing lkgankms 

The Water Quo/ity Standards Handbook [14] indicates that whether 

to establish a mixing zone policy is a matter of State discretion, 

but that any State policy allowing for mixing zones must be 

consistent with the CWA and is subject to approval of the Regional Administrator. The handbook provides additional discussion regarding the basis for a State mixing zone policy. 

Lethality is a function of the magnitude of pollutant concentrations and the duration an organism is exposed to those concentrations. Requirements for wastewater plumes that tend to attract 

aquatic life should incorporate measures to reduce the toxicity 

(e.g., via pretreatment, dilution) to minimize lethality or any 

irreversible toxic effects on aquatic life. 

EPA’s water quality criteria provide guidance on the magnitude 

and duration of pollutant concentrations causing lethality. The 

criterion maximum concentration (CMC) is used as a means to 

prevent lethality or other acute effects. h explained in Appendix 

D, the CMC is a toxicity level and should not be confused with an 

LCso level. The CMC is defined as one-half of the final acute value 

for specific toxicants and 0.3 acute toxic unit (TU,) for effluent 

toxicity (see Chapter 2). The CMC describes the condition under 

which lethality will not occur if the duration of the exposure to the 

CMC level is less than 1 hour. The CMC for whole effluent toxicity 

is intended to prevent lethality or acute effects in the aquatic 

biota. The CMC for individual toxicants prevents acute effects in 

all but a small percentage of the tested species. Thus, the areal 

extent and concentration isopleths of the mixing zone must be 

such that the l-hour average exposure of organisms passing 

through the mixing zone is less than the CMC. The organism 

must be able to pass through quickly or flee the hiqh-concentration area. The objective of developing water quality recommendations for mixing zones is to provide time-exposure histories that 

produce negligible or no measurable effects on populations of 

critical species in the receiving system. 

Lethality to passing organisms can be prevented in the mixing 

zone in one of four ways. The first method is to prohibit concentrations in excess of the CMC in the pipe itself, as measured 

directly at the end of the pipe. As an example, the CMC should 

be met in the pipe whenever a continuous discharge is made to 

an intermittent stream. The second approach is to require that 

the CMC be met within a very short distance from the outfall 

during chronic design-flow conditions for receiving waters (see 

Section 4.4.2). 

If the second alternative is selected, hydraulic investigations 

and calculations indicate that the use of a high-velocity discharge with an initial velocity of 3 meters per second, or 

more, together with a mixing zone spatial limitation of 50 

times the discharge length scale in any direction, should 

ensure that the CMC is met within a few minutes under 

practically all conditions. The discharge length scale is defined 

as the square root of the cross-sectional area of any discharge 

pipe. 

A third alternative (applicable to any waterbody) is not to use a 

high-velocity discharge. Rather the discharger should provide 

71 
- -- 

data to the State regulatory agency showing that the most restrictive of the following conditions are met for each outfall: 

The CMC should be met within 10 percent of the distance 

from the edge of the outfall structure to the edge of the 

regulatory mixing zone in any spatial direction. 

The CMC should be met within a distance of 50 times the 

discharge length scale in any spatial direction. In the case 

of a multiport diffuser, this requirement must be met for 

each port using the appropriate discharge length scale of 

that port. This restriction will ensure a dilution factor of at 

least 10 within this distance under all possible circumstances, including situations of severe bottom interaction, 

surface interaction, or lateral merging. 

The CMC should be met within a distance of five times the 

local water depth in any horizontal direction from any 

discharge outlet. The local water depth is defined as the 

natural water depth (existing prior to the installation of the 

discharge outlet) prevailing under mixing zone design conditions (e.g., low flow for rivers). This restriction will prevent locating the discharge in very shallow environments or 

very close to shore, which would result in significant surface 

and bottom concentrations. 

A fourth alternative (applicable to any waterbody) is for the 

discharger to provide data to the State regulatory agency showing that a drifting organism would not be exposed to l-hour 

average concentrations exceeding the CMC, or would not receive 

harmful exposure when evaluated by other valid toxicological 

analysis, as discussed in Section 2.2.2. Such data should be 

collected during environmental conditions that replicate critical 

conditions. 

For the third and fourth alternatives, examples of such data 

include monitoring studies, except for those situations where 

collecting chemical samples to develop monitoring data would 

be impractical, such as at deep outfalls in oceans, lakes, or 

embayments. Other types of data could include field tracer 

studies using dye, current meters, other tracer materials, or detailed analytical calculations, such as modeling estimations of 

concentration or dilution isopleths. 

The Water Quality Criteria-l 972 [l l] outlines a method, applicable to the fourth alternative, to determine whether a mixing 

zone is tolerable for a free-swimming or drifting organism. The 

method incorporates mortality rates (based on toxicity studies for 

the pollutant of concern and a representative organism) along 

with the concentration isopleths of the mixing zone and the 

length of time the organism may spend in each isopleth. The 

intent of the method is to prevent the actual time of exposure 

from exceeding the exposure time required to elicit an effect [lo]: 

1 T 0-4 < , 

ET(X) at C(n) - 

where T(n) is the exposure time an organism is in isopleth n, and 

ET(X) is the “effect time.” That is, ET(X) is the exposure time 

required to produce an effect (including a delayed effect) in X 

percent of organisms exposed to a concentration equal to C(n), 

the concentration in isopleth n. ET(X) is experimentally determined; the effect is usually mortality. If the summation of ratios of 

exposure time to effect time is less than 1, then the percent effect 

will not occur. 

4.3.4 mvootho of Bio%cc~l~ioo Rvblmos for Muman 

States are not required to allow mixing zones. Where unsafe fish 

tissue levels or other evidence indicates a lack of assimilative 

capacity in a particular water-body for a bioaccumuiative pollutant, care should be taken in calculating discharge limits for this 

pollutant or the additivity of multiple pollutants. In particular, 

relaxing discharge limits because of the provision of a mixing 

zone may not be appropriate in this situation. 

4.4 MIXMG ZOHE ANALYSES 

Proper design of a mixing zone study for a particular waterbody 

requires estimation of the distance from the outfall to the point 

where the effluent mixes completely with the receiving water. 

The boundary is usually defined as the location where the concentrations across a transect of the waterbody differ by less than 5 

percent. The boundary can be determined based on the results of 

a tracer study or the use of mixing zone models. Both procedures, along with simple order-of-magnitude dilution calculations, are discussed in the following subsections. 

If the distance to complete mixing is insignificant, then mixing 

zone modeling is not necessary and the fate and transport models 

described in Section 4.5 can be used to perform the WLA. It is 

important to remember that the assumption of complete 

mixing is not a conservative assumption for toxic discharges; 

an assumption of minimal mixing is the conservative approach. If completely mixed conditions do not occur within a 

short distance of the outfall, the WLA study should rely on mixing 

zone monitoring and modeling. Just as in the case of completely 

mixed models, mixing zone analysis can be performed using both 

steady-state and dynamic techniques. State requirements regarding the mixing zone will determine how water quality criteria are 

used in the TMDL. 

This section is divided into five subsections. The first discusses 

recommendations for outfall designs and means to maximize 

initial dilution. The second provides a brief description of the four 

major waterbody types and the critical design period when mixing zone analysis should be performed for each. The third provides a brief description of tracer studies and how they may be 

used to define a mixing zone. The fourth and fifth subsections 

discuss simplified methods and sophisticated models to predict 

the two stages of mixing (i.e., discharge-induced and ambientinduced mixing). For a detailed explanation of the mechanisms 

involved in estimating both stages of mixing, two references are 

recommended, Halley and Jirka [15] and Fischer et al. [16]. 

Although the models presented in Sections 4.4.4 and 4.4.5 simplify the mixing process, the assessor should have an understanding of the basic physical concepts governing mixing to use these 

72 
models appropriately. (The U.S. EPA Center for Exposure Assessment Modeling [CEAM] in Athens, Georgia, provides an overview 

course that teaches the basics of mixing and how the basics 

should be used for water quality management.) 

It is important to note that the mixing zone models presented 

here attempt to predict the dispersion and dilution of the effluent 

plume. They do not attempt to predict any removal or transformation of the pollutants. In the near field, dispersion and dilution 

caused by discharge-induced mixing and then ambient-induced 

mixing will be the major cause of toxicity reduction. If incomplete 

mixing persists downstream (such as in the case of shore hugging 

plumes), then some far-field processes will become important. 

Some of the models described in Section 4.5 that have sophisticated hydrodynamic simulation routines coupled with fate simulation routines may be used for these far-field, incomplete mixing 

analyses. 

An important factor in maximizing the initial dilution of an effluent is the design of the effluent outfall. There are three major 

types of outfall designs: surface discharge from free flows in a pipe 

or canal, single-port submerged discharge, and multiport submerged discharge. The last type is often referred to as multiport 

diffusers. Of the three, the surface discharge type is the least 

favorable for toxic discharges since it offers the least initial mixing. 

In particular, surface discharges at the shoreline of a waterbody 

usually have an impact along the shoreline when there is significant cross-flow and thus yield high surface concentrations. 

Submerged discharges offer more flexibility in meeting the design 

goals for toxic discharges. Submerged discharges may be in the 

form of a single pipe outlet or of multiport discharges (diffusers) 

giving rise to one or several submerged discharge jets. A typical 

diffuser section is illustrated in Figure 4-1. Submerged discharges 

allow the effluent to be directed at different angles to the ambient 

flow to maximize the initial dilution. Diffusers are particularly 

effective in counteracting the buoyancy of the effluent. However, 

submerged muttiport discharges are only feasible in waterbodies 

that are of sufficient depth and are not subjected to periodic 

dredging or to considerable scour or deposition. 

0.20 m x 0.15 m 

90” reducer elbow 

0.15 m cast-iron pipe \ \ / F’ange 

Top of tremle 

encasement 

‘t7i / Flannta m,nl 

flange with 0.15 m casliron trap at Invert 

0.15 m 90” elbow 

Bottom of tremle / 

encasement 

Figure 4-l. A Typical Diffuser Section [17] 

Many of the complexities of submerged diffusers have been 

summarized by jirka [I 81, Halley and Jirka [15], and Roberts et al. 

[19, 20, 211. Submerged discharges should be designed to avoid 

direct surface impingement and bottom attachment of the submerged jet or jets. Surface and bottom impacts should be 

evaluated at critical design conditions (low flow or high stratification) and at off-design conditions (higher flow or lower stratification) to ensure the best placement and design of the diffuser. 

Diffusers provide more dilution than single outlets, but the alignment of the diffuser with the receiving water flow direction influences how much dilution will be provided. If the outlet structure 

is directed parallel to the direction of flow, dilution under high 

ambient velocities (off-design conditions) may be lower than 

under low velocities (critical design conditions). 

In rivers, the preferred arrangement for a submerged discharge is 

to direct the outlet into the current flow direction or vertically 

upward. To deal with the reversing currents of estuaries and 

coastal bays, the preferred arrangements for offshore discharges 

are parallel diffuser alignment (tee diffuser) and perpendicular 

diffuser alignment (staged diffuser) [18]. In lakes and reservoirs, 

the preferred arrangement for a negatively buoyant discharge is 

to direct the diffuser vertically upward. A positively buoyant, 

vertically directed jet could penetrate stratification, so the preference for this type of discharge is to orient the diffuser at a slight 

angle above the horizontal. For ocean outfalls, initial dilution is 

improved by longer (perpendicular to the shoreline) and deeper 

diffusers. Further, the ports of the diffuser should be sufficiently 

separated to minimize merging of the separate plumes [22]. 

4.4.2 Critical Des&n Periods for Watehdies 

This section provides a brief description of the four major waterbody 

types and defines the critical design periods that should be used 

when performing mixing zone analyses in each of these waterbody 

types. Appendix D provides a further discussion on the appropriate selection of design periods. 

1) Rivers and Run-of-River Reservoirs 

Rivers and run-of-river reservoirs are waterbodies that have a 

persistent throughflow in the downstream direction and do not 

exhibit significant natural density stratification. Recommendations for hydrologically based and biologically based design flows 

for completely mixed, steady-state modeling of rivers are described in Appendix D of this document. The biologically based 

design flows are determined using the averaging periods and 

frequencies specified in water quality criteria [8]. Also, the hydrologically based flows 1QlO and 7410 for the CMC and CCC, 

respectively, have been used traditionally and may continue to be 

used for steady-state modeling. Run-of-river reservoirs with residence times less than 20 days at critical conditions also should be 

analyzed using biologically or hydrologically based design flows 

(see below). Regulated rivers may have a minimum flow in excess 

of these toxicological flows. In such cases, the minimum flow 

should be used in TMDL modeling. 

2) Lakes and Reservoirs 

This receiving water category encompasses lakes and reservoirs 

with residence times in excess of 20 days at critical conditions 

73 
[23]. Seasonal variations in the water level, wind speed and 

direction, and seasonal solar radiation should be determined to 

define the critical period [23]. In the case of long and narrow 

reservoirs, areas above the plunge point (i.e., areas where no 

stream-like flow is present and waters are mixed or stratified by 

density) can be analyzed as rivers. The areas below can be 

analyzed as reservoirs. Since effluent density relative to the ambient water can vary over seasons, no one season or stratification 

condition can be selected as the most critical dilution situation for 

all cases. In general, all four seasons should be analyzed to 

determine the most critical periods for mixing zone analyses. All 

seasonal analyses should assume an ambient velocity of zero 

unless persistent currents have been documented. Special attention should be given to periods of rising water level since pollutants can move back into coves and accumulate under these 

conditions. Location of discharges in coves and dead-end 

embayments should be prevented whenever possible. 

3) Estuaries and Coastal Bays 

This receiving water category encompasses estuaries, which are 

defined as having a main channel reversing flow, and coastal 

bays, which are defined as having significant two-dimensional 

flow in the horizontal directions. For both waterbodies, the 

critical design conditions recommended here are based on astronomical, not meteorotogicat, tides. 

Determining the nature and extent of the discharge plume is 

complicated in marine systems by such conditions as differences 

in tides, riverine input, wind intensity and direction, and thermal 

and saline stratification. Because of the tidal nature of the estuaries and coastal systems and their complex circulation patterns, 

dilution of discharges cannot be determined simply by calculating 

the discharge rate and the rate of receiving water flow (i.e., the 

design flow). For example, tidal frequency and amplitude vary 

significantly in different coastal regions of the United States. 

Furthermore, tidat influences at any specific location have daily 

and monthly cycles. These and additional factors require that 

direct, empirical steps be taken to ensure that basic dilution 

characteristics of a discharge to salt water are determined. 

tn estuaries without stratification, the critical dilution condition 

includes a combination of low-water slack at spring tide for the 

estuary and design low flow for riverine inflow. In estuaries with 

stratification, a site-specific analysis of a period of minimum stratification and a period of maximum stratification, both at lowwater slack, should be made to evaluate which one results in the 

lowest dilution. In general, minimum stratification is associated 

with low river inflows and large tidal ranges (spring tide), whereas 

maximum stratification is associated with high river inflows and 

low tidal ranges (neap tide). 

After either stratified or unstratified estuaries are evaluated at 

critical design conditions, an off-design condition should be 

checked. The off-design condition (e.g., higher flow or lower 

stratification) recommended for both cases is the period of maximum velocity during a tidal cycle. This off-design condition 

results in greater dilution than the design condition, but it causes 

the maximal extension of the plume. Extension of the plume into 

critical resource areas may cause more water quality problems 

than the high-concentration, low-dilution situation. 

Recommendations for a critical design for coastal bays are the 

same as for stratified estuaries. The period of maximum stratification must be compared with the period of minimum stratification 

in order to select the worst case. The off-design condition of 

maximum tidal velocity should also be evaluated to predict the 

worst-case extent of the plume. 

4) Oceans 

Critical design periods for ocean analyses are described in two 

separate documents, the Section 301 (h) Technical Support Document [22] and the Section 301 (h) document, hitid Mixing Characteristics of Municipal Ocean Discharges [24]. The following subsection contains a summary from these documents. Like discharges to estuaries, discharges to ocean waters are subject to 

two-dimensional horizontal flows. Oceanic critical design periods 

must include periods with maximum thermal stratification, or 

density stratification. These periods shorten the distance of vertical diffusion that occurs in the zone of initial dilution. Thus, 

during these periods it is difficult to achieve the recommended 

loo-to-1 dilution that is to occur before the plume begins a 

predominantly horizontal flow as compared to vertical flow. Periods when discharge characteristics, oceanographic conditions 

(spring tide and neap tide currents), wet and dry weather periods, 

biological conditions, or water quality conditions that indicate 

that water quality standards are likely to be exceeded should also 

be noted. The 10th percentile value from the cumulative frequency of each parameter should be used to define the period of 

minimal dilution. 

4.4.3 lbwml Rwmmwht&as for Tracer 3iudk 

A tracer or dye study can be used to determine the areal extent of 

mixing in a waterbody, the boundary where the effluent has 

completely mixed with the ambient water, and the dilution that 

results from the mixing. Analysis of the mixing zone with a dye 

study that is supplemented with modeling should be performed 

at flow conditions that approach critical flow. Some of those 

design conditions are summarized above in the subsections dealing with specific waterbodies. Once the critical design condition 

has been selected for a waterbody, dye studies can be performed 

to provide data on the dimensions and dilution of the wastewater 

plume during this critical period. Tracer studies other than dye 

studies (e.g., chloride, lithium) can be performed for cases in 

which the receiving water is amenable to such tests. 

For WIA studies in which a discharge is already in operation, 

tracer studies can be used to determine specific concentration 

isopleths in the mixing zone that reflect both discharge-induced 

and ambient-induced mixing. The isopleth concentrations, with 

effluent toxic concentrations, should be superimposed over a 

map of Ihe various resource zones of the waterbody. The map 

will illustrate whether the State’s mixing zone dimensions are 

exceeded, whether the required zone of passage is provided, and 

whether the plume avoids critical resource areas. The WLA can 

then be calculated to provide the appropriate zone of passage 

and to prevent detrimental impacts on spawning grounds, nurseries, water supply intakes, bathing areas, and other important 

resource areas. 

Obviously, if the outfall is not yet in operation, it is impossible to 

determine discharge-induced mixing by tracer studies. Tracer 

74 
studies can be used in these situations to determine characteristics 

of the ambient mixing. For ambient mixing studies, the tracer 

release can be either instantaneous or continuous. Instantaneous 

releases are used frequently to measure longitudinal dispersion, 

but can also be used to determine lateral mixing in rivers [15] and 

lateral and vertical mixing in estuaries, bays, reservoirs, and lakes. 

For waterbodies with significant flow velocities, continuous releases of tracer are normally used to determine lateral and vertical 

mixing coefficients. Continuous releases can also be used to 

determine three-dimensional concentration isopleths for steadystate conditions. The tracer study must be made at critical design 

conditions in order to use the results directly for WlAs. If a tracer 

study for ambient mixing is conducted at near-to-design conditions, the observed data can be used to determine dimensionless 

mixing coefficients. These coefficients can then be extrapolated 

to critical conditions using hydraulic parameters [15]. A tracer 

study at near-to-critical conditions also can be used to determine 

the computer model required to predict critical-condition mixing 

and provide the coefficients needed for that TMDL model. 

A number of references provide information concerning the design, conduct, and analysis of tracer studies for mixing analyses. 

Techniques of Water-Resources Investigations of the USC.5 provides 

the best overview of how to conduct tracer studies [25, 26, 271. 

The fluorescent dyes (usually Rhodamine WT), measuring equipment, fluorometers, field and laboratory procedures, and calculation methods are all discussed. The procedures essentially consist 

of adding dye to the waterbody and recording concentrations of 

the dye at various stations at specific time intervals. Examples of 

tracer studies for river systems are presented in Fischer [28]; Kisiel 

[29]; Holley and )irka [l 51; and Yotsukura, Fisher, and Sayre [30]. 

Examples of tracer studies in tidal systems are presented in Wilson, 

Cobb, and Yotsukura [31] and Hetling and O’Connell [32], both 

of which are studies of the Potomac River estuary; Baily [33], a 

study of Suisun Bay in California; Fischer [34], a study of Bolinas 

Lagoon, a coastal bay in Marin County, California; and Cracker et 

al. [35], a study of Corpus Christi Bay, Texas. Methods to perform 

a tracer study in a reservoir are provided in johnson [36]. 

The dye study recommended for obtaining a quick saltwater 

dilution assessment is one in which Rhodamine WT dye is administered to a discharge and monitored in the receiving waters for 

not less than 24 hours. The basic goal of this study is to determine 

the near-field nature of the effluent dilution, not the steady-state 

or far-field dilution. The environmental and discharge conditions 

selected for the study should be those that would elicit “worstcase” conditions (i.e., highest ambient concentrations in the receiving water). These include low wind, neap tide (tide of minimum range occurring during the 1st and 3rd quarters of the 

moon), plume trapping by density stratification, low rainfall and 

low riverine input, and, if possible, high effluent discharge. 

The dye should be administered to the effluent before discharge 

to the receiving water in proportion to effluent flow rate. Dye 

should be maintained at a concentration in the effluent sufficient 

to permit detection of the dilution ratio of interest when the 

amount and variability of background fluorescence in the receiving water are taken into account. Measurements of dye concentration are made using a fluorometer and should be corrected for 

water temperature. 

A survey of background fluorescence and its variability in the 

anticipated mixing zone must be conducted just prior to the 

beginning of the study in order to permit correction of fluorescence data and to determine the dye concentration required in 

the effluent. Since Rhodamine Wl dye is bleached by free chlorine, a preliminary study of the degree of dye bleaching by the 

effluent should precede the study for chlorinated discharges to 

avoid underestimation of the extent of the mixing zone. Dye 

concentrations should be surveyed for two successive slack tides, 

and for any other conditions that could lead to concentration 

maxima. Surveys should extend from the point of discharge to a 

distance at which the effluent dilution ratio of interest is attained. 

The dye fluorescence at this point should be at least twice the 

variability in background fluorescence. 

EPA has completed two TMDL studies to test the procedures 

outlined in the previous version of this document. Both studies 

used dye to determine the mixing zone and the dilution within it. 

The first study was performed on the Amelia River, an estuarine 

system in Florida [2]; the second was performed on the Creenwith Cove, an embayment of Narragansett Bay in Rhode Island 

[37]. In both studies, Rhodamine Wl dye was introduced continuously into the effluent and numerous stations were set up to 

measure the spatial and temporal distribution of the dye. Both 

studies are good examples of how to perform a dye study in 

complex tidal systems. 

4.4.4 Discharge-induced Mixing 

The first stage of mixing is controlled by discharge jet momentum 

and buoyancy of the effluent (see Figure 4-2). This stage generally covers most of the regulatory or near-field mixing zone. It is 

particularly important in lakes and reservoirs and slow moving 

rivers since ambient mixing in those waterbodies is minimal. 

In shallow environments, it is important to determine whether 

near-field instabilities occur. These instabilities, associated with 

surface and bottom interaction and localized recirculation cells 

extending over the entire water depth, can cause buildup of 

effluent concentrations by obstructing the effluent jet flow. There 

are no simple means to estimate dilution in these cases. Criteria 

for these instabilities and specialized predictive models have been 

developed to address these problems [13]. 

In the absence of near-field instabilities, horizontal or nearly horizontal discharges will create a clearly defined jet in the water 

column that will initially occupy only a small fraction of the 

available water depth. The following equations and models are 

designed to describe mixing under stable near-field conditions. 

1) Use of a Simplistic Screening Equation 

A minimum estimate of the initial dilution available in the vicinity 

of a discharge can be made using the following equation derived 

from information in Holley and lirka (1986) [15]: 

s=o.3 ; 

where 

5 = flux-averaged dilution 

X = distance from outlet 

d = diameter of outlet. 

75 
recelvlng waler surface 

/ 

recewmg water bottom 

c) Deep-wafer, high-buoyancy, nonvertical discharge 

recelvlng water surface 

/ 

receiving 

water 

bottom 

d) St-tallow-water. low-buoyancy, nonvertical discharge 

IFigure 42. Example of Discharge-Induced Mixing [7] 

The coefficient 0.3 represents the average of two values derived 

from the literature, 0.28 [16] and 0.32 [38]. 

The equation provides a minimum estimate of mixing because it 

is based on the assumptions that outlet velocity is zero and the 

discharge is neutrally buoyant. Dilution may be underestimated 

for partially fuH pipes because the equation assumes a fully flowing pipe. The equation can be used in inverse form to solve for 

the discharge x at which a desired solution-for example, that 

corresponding to the CMC-has been achieved. The equation is 

valid only close to the discharge, up to a distance corresponding 

to several (two to three) water depths. At longer distances, other 

factors are of increasing importance in jet mixing and must be 

included. 

Mixing graphs that include the effects of discharge buoyancy, 

ambient velocity, and stratification can be found in Halley and 

Jirka [15], Fischer et al. [16], and Wright [39]. They are useful to 

account for these other initial dilution factors and can aid in 

determining whether criteria will be met at the edge of the 

regulatory mixing zone. 

2) Use of Detailed Computer Models 

More detailed design data for the mixing zone can be obtained 

from the use of computer models based on integral jet techniques. It is important to note that most models represent an 

idealization of actual field conditions and must be used with 

caution to ensure that the underlying model assumptions hold for 

the site-specific situation being modeled. In general, these buoyant jet models require the following input data: discharge depth, 

effluent flow rates, density of effluent, density gradients in receiving water, ambient current speed and direction, and outfall characteristics (port size, spacing, and orientation). Model output 

includes the dimensions of the plume at each integration step, 

time of travel to points along the plume centerline, and the 

average dilution at each point. 

Described below are six mixing zone models that are available 

through EPA. All of the models require a user who is well versed in 

mixing concepts and the data necessary to run the models. The 

first model, CORMIX [40, 411, may be the most useful to regulators since it is an expert system that guides the user in selecting an 

appropriate modeling strategy for rivers or estuaries. It is available 

from the National Technical Information Service (NTIS), and user 

support is available from the U.S. EPA CEAM. The other models 

were developed and designed for ocean discharges. All but one 

can be used on rivers, lakes, and estuaries with appropriate input 

modifications; UPLUME is restricted to stagnant water environments where the ambient water current velocity is zero (e.g., 

lakes, reservoirs). 

These five models were designed for submerged discharges in 

oceans. They all report dilution, and all terminate execution 

when the vertical ascent of the plume is zero (e.g., when the 

plume reaches the surface or when plume density is equal to 

ambient density in some stratified systems). With the exception 

of CORMIXl, they all assume that there is a “deep” receiving 

stream (i.e., no bottom interference). They too are available from 

NTIS, and user support is provided by the U.S. EPA Hatfield 

Marine Science Center in Newport, Oregon [24]. These five 

models have been modified such that the user inputs the data 

into a universal data format that allows the user to apply any of 

the five models with only minor input changes. 

l CORMIX is a series of software elements for the analysis and 

design of a submerged buoyant or nonbuoyant discharge 

containing conventional or toxic pollutants and entering 

into stratified or unstratified watercourses, with emphasis 

on the geometry and dilution characteristics of the initial 

mixing zone. Subsystem CORMlXl deals with single-port 

discharges, and subsystem CORMIXZ addresses multiport 

diffusers. The system operates on microcomputers with the 

MS-DOS operating system. CORMlXl can summarize dilution characteristics of the proposed design, flag undesirable 

designs, give dilution characteristics at specified boundaries 

(i.e., legal and toxic mixing zones) and recommend design 

alterations to improve dilution characteristics. The CORMlXl 

program guides the user, based on the user’s input, to 

76 
appropriate analyses of design conditions and mixing zone 

dimensions. 

l UPLUME is an initial dilution model that can be used for 

stagnant waterbodies, such as lakes and reservoirs, where 

the ambient currents can be assumed to be zero. The 

model simulates a submerged single-port discharge. The 

bouyancy between the effluent and ambient water can be 

accounted for, and the discharge can be given a vertical 

angle. UPLUME calculates flux-averaged dilutions and, for 

one output option, a centerline dilution. 

l UOUTPLM can be used in flowing and stagnant waterbodies. 

The user specifies the current speed of the ambient water, 

and this speed is assumed to be constant with depth. The 

model simulates a submerged single-port discharge. Buoyancy between the effluent and ambient water can be modeled, as well as the discharge vertical angle. The ambient 

current is assumed to be perpendicular to the diffuser. 

l UMERCE is a model that can also be used for both flowing 

and stagnant waters. It has capabilities that UOUTPLM 

does not have: it considers multiple submerged ports, and 

the user can specify arbitrary ambient current speed variations with depth. The ports are assumed to be equally 

spaced. The model accounts for adjacent plume interferences over the course of the plume trajectory and in the 

subsequent dilution calculation. Positive buoyancy is accounted for, and the discharge vertical angle can be modified. The ambient current is assumed to be perpendicular 

to the diffuser. 

l UDKHDEN is a three-dimensional model that can be used 

for flowing and stagnant waterbodies. It has all the capabilities of UMERCE plus the ability to simulate instances 

where the ambient current flow is not perpendicular to the 

diffuser. 

l ULINE models a vertical slot jet discharge into a flowing 

waterbody. The discharge angle is assumed to be perpendicular to ambient current. The ambient current may vary 

with depth, and the axis of the diffuser may range from 

parallel to perpendicular to the ambient current. The buoyancy of the effluent can also be modeled. 

An evaluation and comparison of all these models can be found in 

the Technical Guidance Manual for Performing Wastelood Allocations-&ok 3, Estuaries [ 71. 

4.4.5 Ambieut-induced Mixing 

The equations for discharge-induced mixing can be used to predict concentrations in the regulatory mixing zone where strong 

jet mixing predominates over ambient mixing. Beyond this point, 

the mixing is controlled by ambient turbulence. Thus, ambient 

mixing models must be used to predict the pollutant concentration distributions up to the stage of complete lateral mixing to 

provide boundary conditions for the completely mixed fate and 

transport models described in Section 4.5. This information also 

may be needed to estimate concentrations encountered at important resource areas or at subsequent downstream dischargers. 

If there is no discharge-induced vertical mixing associated with 

the jet action of the discharge, then mixing over the depth of the 

waterbody must be accomplished by ambient mixing. For a 

neutrally buoyant, soluble effluent discharged with low velocity at 

the surfaceor at the bed of a stream, the flow distance required to 

achieve complete vertical mixing is on the order of 50 to 100 

times the depth of water in that portion of the channel where the 

effluent is discharged [42]. For a discharge that is either lighter 

(positively buoyant) or heavier (negatively buoyant) than the 

ambient water, but still has no excess momentum, the flow 

distance for mixing over the depth will be greater. In the normal 

case with a high-velocity jet designed to prevent lethality in the 

mixing zone, mixing over the depth will be accomplished primarily by jet action, and the distance required for this vertical mixing 

will be much shorter. 

In general, ambient mixing must also accomplish mixing over the 

width of a waterbody to bring the effluent to the completely 

mixed condition. For situations where the width of the zone that 

is mixed by the discharge-induced mixing is much smaller than 

the width of the river, the flow distance (X,) required to achieve 

the completely mixed condition may be estimated from an equation of the form [16]: 

x,= 

mW2u 

DY 

where 

W = width of the river 

U = flow velocity for the critical design flow 

Dy = lateral dispersion coefficient as discussed below 

m = a parameter whose value depends on the degree of 

uniformity used to define “complete mixing” and 

on the transverse location of the outfall in the 

stream. 

If completely mixed conditions are defined as a S-percent variation in concentration across the stream width, the value of m 

would be approximately 0.1 for a discharge near the center of 

river flow (not the center of river width) and approximately 0.4 for 

a discharge near the edge of the river. If, because of other 

uncertainties, a 25percent variation across the width is accepted 

as being completely mixed, then the corresponding values for m 

would be approximately 0.06 for a discharge near the center of 

river flow and approximately 0.24 for a discharge near the edge of 

the river. For a very small stream, X, may be only a few hundred 

feet; for medium and large streams, X, is normally several miles 

to several tens of miles. 

The lateral dispersion coefficient (Dy) for most rivers can be 

calculated with the following equation [16]: 

where 

Dy = 0.6 du* f 50% 

d = water depth at design flow 

u’ = shear velocity. 

The coefficient (0.6) can vary from 0.3 to above 1 .O depending 

on the type and degree of irregularity of the channel crosssections. The more straight and uniform the flow, the tower the 

77 
value; the more irregular the flow (resulting from curves, sidewall 

interference, etc.), the higher the value. Values approaching and 

exceeding 1 .O are normally associated with significant channel 

meandering [42]. The following equation for shear velocity should 

be used [16]: 

u* = (gds)‘/2 

where 

9 = acceleration due to gravity 

S = slope of the channel 

d = water depth. 

For diffusers that initially spread the discharge across a significant 

part of the river width or for cases where the discharge-induced 

mixing causes mixing across a significant part of the river width, 

the values of m and X, can be smaller than the ones indicated 

here. For distances greater than X,, the models for completely 

mixed effluents discussed in Section 4.5 can be used to calculate 

concentrations at these distances. For shorter distances, maximum concentrations can be much greater than those predicted 

by “completely mixed” models and should be estimated using 

the following equation: 

c, = CeQeW 

where 

C, = maximum pollutant concentration distance x from 

the outlet 

C, = effluent concentration 

Qe = design effluent flow 

Qs = design stream flow 

Dy = lateral dispersion coefficient 

X = distance from the outlet 

W = stream width 

U = flow velocity for the design flow. 

It should be noted that this estimate of C, is a worst-case prediction since the equation assumes no significant discharge-induced 

mixing and a neutrally buoyant effluent. A more accurate way to 

predict concentrations within this second stage of mixing is to use 

the methods of Yotsukura and Sayre [42]. To use this approach, 

however, the value of Dy and pollutant concentrations after discharge-induced mixing must be known from tracer studies and/ 

or from the use of one of the discharge-induced models. 

The PSY model can be used to predict ambient mixing in shallow, 

freshwater streams where water depth is small in proportion to 

the width. PSY is a steady-state, two-dimensional plume model 

that predicts dilution of a surface discharge into a shallow receiving water where the plume attaches to both bottom and nearshore 

[43]. Uniform vertical mixing is assumed to occur at the point of 

discharge. 

Ambient mixing is minor for lakes and reservoirs because flow 

velocity is assumed to be minimal and mixing is accomplished by 

means of the discharge momentum and buoyancy. For estuaries 

that are completely mixed with regard to salinity, the equations 

presented above can be used to estimate concentrations between 

the outlet and the point of complete mixing with a slight modification of shear velocity. The above equations will be applicable to 

only unstratified estuaries since the time required to mix across 

the estuary must be significantly less than the time required for 

the effluent to pass out of the unstratified part of the estuary, the 

time required for the effluent to pass into a segment of greatly 

changed cross-section, or the time required for the substance to 

decay. When the above equations for estuaries are used, the 

velocity of the design flow should include the velocity associated 

with the inflow of freshwater as well as the tidal velocity; thus ut, 

which is based on an average total velocity; is substituted for u in 

the equations and shear velocity becomes 

lJ* = 0.10 u t. 

The CORMlX expert system model can also be used to obtain 

predictions for the ambient-induced mixing. In addition to the 

routines for discharge-induced mixing, this model also includes 

predictiveelements that apply to ambient mixing in riverine, lake, 

or coastal situations. 

4.5 CDMPl.ElElY MIXED DtSCHARGE RECEIVING WATER 

SITIJATIDNS 

At the present time, most States and EPA Regions use steady-state 

models that assume the wastewater is completely mixed with the 

receiving waters in order to calculate WLAS for contaminants. 

This approach is appropriate for conventional contaminants where 

critical environmental effects are expected to occur far downstream from the source. WLAs for toxic chemicals require a 

different approach, however, because critical environmental conditions occur near the discharge before complete mixing with the 

receiving water occurs. Consequently, mixing analyses should be 

performed because many of these toxicants can exert maximal 

toxicity in a variety of regions spanning from the discharge point 

to significant distances downstream. 

If complete mixing occurs near the discharge point, such as in 

effluent-dominated receiving streams, then steady-state models 

may be used to calculate TMDLs. Recent EPA developments in 

the identification of critical design flows based on toxicological 

concerns provide for better use of steady-state models in calculating toxic WLAS. However, if complete mixing does not occur near 

the discharge point and the effluent plume is discernible downriver, 

then modeling techniques that can simulate and predict mixing 

conditions are more appropriate. The mixing zone models presented in the previous section may be used to define the mixing 

zone. However, they only determine the dispersion and dilution 

of the effluent and do not account for chemical or biological 

processes in the mixing zone. TMDL models are available that 

can simulate mixing processes and predict areas of maximal 

concentrations in the receiving stream based on chemical, biological, and physical processes. 

4.5. I Wastelapd Metkli~ Techuhptes 

1) Steady-State Modeling Techniques 

A steady-state model requires single, constant inputs for effluent 

flow, effluent concentration, background receiving water concentration (RWC), receiving water flow, and meteorological conditions (e.g., temperature). The frequency and duration of ambient 

concentrations predicted with a steady-state model must be assumed to equal the frequency and duration of the critical receiv78 
ing water conditions used in the model. The variability in effluent 

flows and concentrations also affects RWCs, but these effects 

cannot be predicted with constant inputs. Steady-state models 

can be improved for toxic WlAs by means of the following: 

l Using design flows that will ensure criteria compliance at 

the appropriate duration and frequency. 

l Calculating both acute and chronic WlAs. 

EPA is encouraging the States to adopt two-number aquatic life 

water quality criteria and is using them in WIA studies. Ambient 

water quality criteria have been established for numerous toxic 

pollutants. These criteria specify an acute concentration (CMC) 

and a chronic concentration (criteria continuous concentration, 

or CCC) for each toxicant, as well as durations and frequencies of 

exposure for the two concentration levels. The design flows used 

in steady-state modeling should be reflective of the CCC and 

CMC durations and frequencies. The duration of the design flow 

is based on the maximum exposure time that will prevent acute 

and chronic effects. The duration of flow is assumed to apply to 

the duration of the allowable effluent concentration or load. For 

example, if the flow used is a 7-day average value, the allowable 

load is considered to be a 7-day average. The return frequency is 

based on the number of years required for biological population 

recovery after criteria have been exceeded. Appendix D describes 

the toxicological basis for selecting receiving stream design flows 

for steady-state modeling and recommends specific design flows 

for CCC and CMC calculation of TMDLs for rivers and streams. 

In summary, there are two types of design flows, hydrologically 

based and biologically based. The hydrologically based design 

flows are those traditionally used by the States, in which the 7410 

flow is used as the CCC design flow and the 1 QlO is used as the 

CMC design flow. The biologically based method uses the 1 -day, 

3-year duration-frequency for determining the CMC design flow 

and the 4day, 3-year duration-frequency for determining the 

CCC design flow. Consequently, the biologically based design 

flows are based on specific toxicological effects of a pollutant and 

biological recovery times from localized stresses [6]. The advantages of both types, as well as how they may be calculated, also 

are described in Appendix D. 

A 4-day, 3-year biological design flow does not equate to a 443 

hydrological design flow. EPA has determined that a 443 design 

flow would result in an excessive number of water quality criteria 

exceedances. As explained in Appendix D, a hydrologically based 

7QlO will, for most streams, be similar to a biologically based 4- 

day, 3-year design flow. 

At the present time, there are no recommended toxicological 

flows for steady-state modeling of lakes, reservoirs, or estuaries. 

The design conditions recommended for these waterbodies in 

Section 4.4.2 are based on hydrological and meteorological conditions rather than on toxicological duration and frequency data. 

These conditions should be used until further guidance is provided. 

Another improvement in steady-state toxics modeling can be 

realized by performing two separate WLAS, one for the CMC and 

one for the CCC. Steady-state WIA models should be used to 

calculate the allowable effluent load that will meet the CMC at the 

acute design flow and the allowable load that will meet the CCC 

at the chronic design flow. Calculation of these values will enable 

the permit writer to calculate the more limiting long-term average 

(LTA) for the treatment system and develop permit limits protective of both WLAs (see Chapter 5). 

In addition to stream design flow, steady-state models require 

design temperature, pH, alkalinity, and hardness, depending on 

the pollutants modeled at site-specific conditions. To determine 

stream design temperature, pH, alkalinity, and hardness, a program called DESCON was developed. (See Appendix D for 

additional information.) DESCON is a computer program that 

estimates design conditions for WIA modeling. These conditions 

are based on maintaining a desired limit on the frequency of 

water quality excursions in a receiving water. DESCON considers 

the effect that daily fluctuations in stream flow and water quality 

conditions, such as temperature and pH, have on the variability of 

the capability of a receiving water to accept pollutant loadings. It 

specifically accounts for the within-year correlations observed 

between such variables as stream flow, temperature, pH, alkalinity, hardness, and dissolved oxygen. DESCON determines design 

conditions using a four-step process (see Figure 4-3): 

1) A long-term record of observed stream flows and pertinent 

water quality data are assembled or synthesized. 

2) The maximum allowable pollutant load that the receiving 

water can accept without causing a water quality excursion 

is computed for each day of this record. 

3) This synthesized record of allowable loads is searched for 

the critical load, i.e., the load whose frequency of not being 

exceeded matches the desired water quality excursion frequency. 

4) Design conditions are then derived from receiving water 

conditions realized during the period of record when the 

computed allowable load was closest to the critical load. 

DESCON provides the same advantages as continuous simulation 

by considering the joint occurrences of stream flow and other 

water quality parameters as observed in the historical record. In 

addition, it is more computationally efficient; it contains a facility 

for extracting and analyzing flow and water quality data from 

STORET; it can use both the extreme value and the biologically 

based methods of calculating of water quality excursions; and it is 

specifically designed to handle such pollutants as ammonia, heavy 

metals, pentachlorophenol, and biochemical oxygen demand 

(BOD) for which water quality criteria are functions of such design 

condition variables as temperature, pH, alkalinity, hardness, and 

dissolved oxygen. The main limitations of DESCON are that it 

requires at least 10 years of historical daily flow data and it can 

only analyze a single discharger, edge-of-mixing zone situations 

(or a simplified Streeter-Phelps dissolved oxygen response for 

BOD). 

2) Dynamic Modeling Techniques 

Steady-state modeling considers only a single condition; effluent 

flow and loading are assumed to be constant. The impact of 

receiving water flow variability on the duration for which and 

frequency with which criteria are exceeded is implicitly included 

79 
Allowable Stream Loading 

Days 

Days 

Design Temperature 

Days 

--- 

Final 

Critical 

Load 

Trial Critical Load 

1 AllOWed 

I number 

I 

Number of Excursions 

Figure 4-3. Computational Scheme for Deriving Design Conditions 

in the design conditions if these conditions reflect the desired 

toxicological effects regime. Dynamic modeling techniques explicitly predict the effects of receiving water and effluent flow and 

of concentration variability. The three dynamic modeling techniques recommended by EPAfor WLAs are continuous simulation, 

Monte Carlo simulation, and lognormal probability modeling. 

These methods calculate a probability distribution for RWCs rather 

than a single, worst-case concentration based on critical conditions. Prediction of complete probability distributions allows the 

risk inherent in alternative treatment strategies to be directly 

quantified. 

The use of probability distributions in place of worst-case conditions has been accepted practice for years in water resource 

engineering, where it was found to produce more cost-effective 

design of bridge openings, channel capacities, floodplain zoning, 

and water supply systems. The same cost-effectiveness can be 

realized for pollution controls if probability analyses are used. 

The dynamic modeling techniques have an additional advantage 

over steady-state modeling in that they determine the entire 

effluent concentration frequency distribution required to produce 

the desired frequency of criteria compliance. Maximum daily and 

monthly average permit limits can be obtained directly from the 

effluent LTA concentration and coefficient of variation (CV) that 

characterize this distribution. Generally, steady-state modeling 

has been used to calculate only a chronic WIA. Steady-state 

modeling generates a single allowable effluent value and no 

information about effluent variability. If the steady-state model is 

used to calculate both acute and chronic wasteloads, limited 

information will be provided and the entire effluent distribution 

will not be predicted. Steady-state WLA values can be more 

difficult to use in permits and enforcement because of the variable 

nature of the receiving waterbody and the effluent. The outcome 

of probabilistic modeling can be used to ensure that permit limits 

are determined based on best probability estimates of RWCs 

rather than a single, worst-case condition. As a result, maximum 

daily and monthly average permit limits, based on compliance 

80 
with water quality criteria over a 3-year period, can be obtained 

directly from the probability distribution. 

Continuous Simulation Models. As shown in Figure 44, a 

continuous simulation model uses daily effluent flows (Q) and 

concentration data (Ce) with daily receiving water flow (QJ and 

background concentration data (C,) to calculate downstream 

RWCs (44). The model predicts these concentrations in chronological order with the same time sequence as the input variables 

(Ch versus time). The daily RWCs can then be ranked from the 

lowest to the highest without regard to time sequence. A probability plot can be constructed from these ranked values, and the 

occurrence frequency of any 1 -day concentration of interest can 

be determined (Cb versus frequency). Running average concentrations for 4 days (i.e., the chronic design flow), or for any other 

averaging period, also can be computed from the daily concentrations (Figure 4-5). 

The probability plot generated by the continuous simulation model 

using existing effluent data will indicate whether criteria are predicted to be exceeded more frequently than desired. Appendix D 

discusses how to select the appropriate allowed frequency of 

excursions based on the biological recovery period required for a 

specific waterbody. If recurrence intervals of 10 or 20 years are 

desired, at least 30 years of flow data should be available to 

provide a sufficient record to estimate the probability of such rare 

events. Of the 30 years of required flow data, at least 20 to 25 

years should be continuous daily data, with the remaining years 

represented with only intermittent data. The data should be 

examined to verify that the receiving stream has not undergone 

significant hydrological modification. The data also should be 

examined to determine if there were any long-term changes due 

to technology-based treatment or periodic changes due to industrial or municipal plant closings or expansions. The same data 

requirements are also true for the lognormal probabilistic and 

Monte Carlo methods. However, except for the continuous 

simulation models, other nonsteady-state models in this section 

I I 

A c I ’ dw / I 

01 : : : : : : : ; : ’ . -4 

99 99.5 100 

Percent of Time Concenlra!~on Is Less Than or EqUal 

IO Concemt~on Plotted 

0.5 1 * 510.2 

I I I w 20 

Figure 4-4. Frequency of Occurrence of Concentrations in 

Receiving Waters and Recurrence Intervals Generated 

by a Continuous Simulation Model 

cannot be used to account for the duration and frequency provision of the two-number water quality criteria. Users are cautioned 

about the specific limitations of some of the dynamic models 

included here. Continuous simulation models have the following 

advantages compared to steady-state formulations: 

l The frequency and duration of toxicant concentrations in a 

receiving water can be predicted. 

l The cross-correlation and interaction of time-varying pH, 

flow, temperature, pollutant discharges, and other parameters are incorporated. 

l The effect that the serial correlation of daily flows and other 

parameters has on the persistence of criteria excursions is 

incorporated. 

l Long-term stream flow records for ungauged rivers using 

precipitation and evapotranspiration data can be synthesized. 

l Long simulation times can prevent the initial conditions 

used in the model from affecting the calibration of fate and 

transport processes. 

Unlike steady-state models, continuous simulation models require 

significantly more data to apply, to calibrate, and/or to verify a 

specific problem and require that input information for the application of the model be time-series data. Also, the model results 

need manipulation to calculate the effluent LTA concentration 

and CV for use in developing effluent limits. 

Monte Carlo Simulation Models. Monte Carlo simulation combines probabilistic and deterministic analyses since it uses a fate 

and transport mathematical model with statistically described 

inputs. Monte Carlo simulations have been the most frequently 

used approach in stochastic water quality studies [45-511. The 

probability distributions of effluent flow, effluent concentration, 

and other model input must be defined using the appropriate 

duration for comparison to the CMC and CCC. If 1 -day average 

RWCs must be predicted for CMC comparisons, probability distributions of daily model input data are needed for Monte Carlo 

simulation. If 4-day average concentrations must be predicted for 

CCC comparisons, the probability distributions of 4-day average 

input data are required. The computer selects input values from 

these distributions using a random generating function. The fate 

and transport model is repetitively run for a large number of 

randomly selected input data sets. The result is a simulated 

sequence of RWCs. These concentrations do not follow the 

temporal sequence that is calculated with the continuous simulation model, but they can be ranked in order of magnitude and 

used to form a frequency distribution. Monte Carlo analyses can 

be used with steady-state or continuous simulation models (521. 

The approach for calculating the allowable pollutant load distribution using Monte Carlo simulation is the same as that described 

for the continuous simulation model. The advantages of Monte 

Carlo simulation are the following: 

l It can predict the frequency and duration of toxicant concentrations in a receiving water. 

81 
It can be used with steady-state or continuous simulation 

models that include fate processes for specific pollutants. 

It can be used with steady-state or continuous simulation 

models that include transport processes for rivers, lakes, 

and estuaries. 

It can be used with steady-state or continuous simulation 

models that are designed for single or multiple pollutant 

source analyses. 

It does not require time series data. 

It does not require model input data to follow a specific 

statistical distribution or function. 

It can incorporate the cross-correlation and interaction of 

time-varying pH, flow, temperature, pollutant discharges, 

and other parameters if the analysis is developed separately 

for each season and the results are combined. 

The primary disadvantages of Monte Carlo simulation are that it 

requires more input, calibration, and verification data than do 

steady-state models, and the model results need manipulation to 

calculate the effluent LTA concentration and CV to develop effluent limits. 

Lomormal Probabilistic Dilution Model. Without resorting to 

the continuous simulation method of computing RWCs in temporal sequence, this probabilistic method uses the lognormal prob 

ability distributions of the input variables to calculate probability 

distributions of output variables [53]. As a result, the method 

requires only the relevant statistical parameters of the input vatiables (medians and coefficients of variation) rather than the actual 

time series data needed for continuous simulation, If l-day 

average RWCs must be predicted for comparisons with the CMC, 

lognormal probability distributions of daily input data are needed. 

If 4-day average concentrations must be predicted, the lognormal 

probability distributions of 4-day average input data are required. 

Because this probabilistic model cannot, as yet, incorporate fate 

and transport processes, it can be used to predict the concentration of a substance only after complete mixing and before degradation or transformation significantly alters the concentration. 

The lognormal probabilistic dilution model has the following 

advantages: 

l It can predict the frequency and duration of toxicant concentrations in riverine environments. 

l It does not require time series data, 

l It can incorporate the cross-correlation and interaction of 

time-varying pH, flow, temperature, pollutant discharges, 

and other parameters if the analysis is developed separately 

for each season and the results are combined. 

The lognormal probability dilution model has the following disadvantages: 

l It requires more input than a steady-state model. 

c b 

Time 

cb 

,- _,-,’ 

,__A-” -- 

,/’ Standard 

/’ 

L---- - - - 

Frequency 

Figure 4-5. Concentration Frequency Curves 

l It does not include instream fate processes. 

l It applies only to rivers and streams. 

l It analyzes multiple pollutant sources inaccurately. 

l It requires model input data to be lognormally distributed. 

4.5.2 Ca/cu/ath# t&s A//uwab/e Etttumt Cuncentrath 

Information concerning effluent concentration means and variabilities can be obtained from data bases on existing treatment 

plants and from development documents for specific industrial 

point source categories. This information is available from the 

Industrial Technology Division of the Office of Water Regulations 

and Standards. These effluent data can be used with dynamic 

models to determine what the effluent concentration distribution 

must be to meet water quality standards. Two possible approaches can be taken to determine this distribution regardless of 

the type of dynamic modeling technique (i.e., continuous, Monte 

Carlo, or lognormal probabilistic). One approach is based on the 

simplifying assumption that treatment will change only the mag82 
nitude of effluent concentrations; no changes are assumed to 

occur in effluent flows or in the relative variability of effluent 

concentrations. With these assumptions, no additional model 

runs are needed to determine the allowable distribution for effluent concentrations. The other approach assumes that the required effluent concentration distribution is the same as the existing distribution except that it is reduced in magnitude by whichever is greater-the percentage necessary for the 1 -day average 

concentrations to meet the CMC, or the 4-day average concentrations to meet the CCC at the desired recurrence interval. 

Chapter 5 includes details on how permit limits are derived from 

the mean and coefficient of variation of effluent concentrations 

determined from this analysis. 

The second approach for determining the allowable effluent concentration distribution is based on the assumption that effluent 

concentrations after treatment will not have the same CV as 

concentrations before treatment. Studies have documented that 

advanced secondary treatment increases the CV of BOD and total 

suspended solids concentrations compared to secondary treatment. Where feasible, investigations should be conducted to 

evaluate how treatment processes for heavy metals, organic chemicals, and effluent toxicity will change the variability of these 

constituents. The development documents mentioned above 

also provide some variability data for treatment processes. To 

account for a change in variability, an alternative approach should 

be used to determine the allowable effluent distribution. Iterative 

model runs can be performed using different concentration means 

with the effluent “future treatment” variance until a mean is 

found that meets the criteria at the desired recurrence intervals. 

These iterative model runs require stochastic generation of effluent input data since daily effluent concentrations will not be 

available for the hypothetical treatment schemes. The required 

“future treatment” mean and CV of effluent concentration can 

then be used to set permit limits (see Chapter 5). 

EPA’s Office of Water Regulations and Standards developed an 

interactive preprocessor for DYNTOX that automatically creates 

input for continuous simulation models, randomly selects the sets 

of input data required for Monte Carlo simulations, and performs 

the numerical integration calculation for the lognormal probabilistic model. DYNTOX is available from the EPA CEAM, Environmental Research Laboratory (ERL) [54]. If the observed data base 

is fairly complete but missing a few points, a linear interpolation 

scheme is used to fill in the missing data. If data are scarce, a lagone Markov method is used to generate daily data stochastically. 

The lag-one Markov method uses the mean, standard deviation, 

and daily correlation coefficient of the observed data to create 

random sequences of data having the same statistical properties. 

The interactive program is written in FORTRAN and is available for 

use on mainframe or IBM PC-compatible computers. 

Two common methods exist to calculate the return period for a 

given concentration from probabilistic modeling: the percentile 

method and the extrema method. The percentile method used 

by DYNTOX ranks a listing of all individual daily concentrations. 

The return period for a concentration is then calculated based on 

the percentile occurrence. In the extrema method, only annual 

extrema values are used in the ranking. The return periods 

calculated from these two methods are equally valid statistical 

representations. When using the percentile method, results express an average return period and multiple occurrences within 

any year. The extrema method describes the return period for an 

annual extreme and includes only the extreme of multiple occurrences within a year. 

4.5.3 6uutwalRec aPnm8twb88 for Model&?ktitm 

The reliability of the predictions from any of the modeling techniques depends on the accuracy of the data used in the analysis. 

The minimum data required for model input include receiving 

water flow, effluent flow, effluent concentrations, and background 

concentrations. In many locations, stream flow data should be 

sufficient for both steady-state and dynamic models. At least 30 

years of flow data should be available if excursions of the CMC 

and CCC must be evaluated at rare frequency of once in 10 or 20 

years. Measurements of effluent toxicity or individual toxicity can 

be much more limited. 

If only a few toxicant or effluent toxicity measurements are available, steady-state assessments should be used. Modeling also 

should be limited to steady-state procedures if a daily receiving 

water flow record is not available; however, in effluent-dominated 

situations, critical flow may be used to characterize the receiving 

stream. Appendix D describes how to select appropriate design 

flows if State regulations do not require a specific design flow for 

river WI-As. Fate and transport models or dilution calculations can 

be used for individual toxicants. At the present time, only dilution 

calculations or first-order decay equations are recommended for 

effluent toxicity analyses. Chapter 1 discusses the conservative/ 

additive assumption for toxicity. 

If adequate receiving water flow and effluent concentration data 

are available to estimate frequency distributions, one of the dynamic modeling techniques should be used to develop more 

cost-effective treatment requirements. If the effluent data exhibit 

significant seasonal differences or batch process trends, the continuous simulation approach may be the easiest dynamic modeling method to use. The best results will, of course, be obtained if 

daily effluent flows and concentrations are available for model 

input for an entire year. The lag-one Markov technique can be 

used to generate daily effluent data for the entire simulation as 

long as adequate measurements for the site-specific facility (or a 

similar one) are available to estimate a day-to-day correlation 

coefficient and to determine when seasonal or batch process 

changes in effluent quality occur. 

If adequate receiving water flow and effluent concentration data 

are available and if effluent data exhibit no seasonal or batch 

process trends, lognormal and Monte Carlo methods may be 

easier and require less computer time than the continuous simulation approach. 

The following section recommends models for toxicity and individual toxicants for each type of receiving water-rivers, lakes, 

and estuaries. Detailed guidelines on the use of fate and transport 

models of individual toxicants are included in the toxic TMDL 

guidance available from the Monitoring Branch of EPA’s Office of 

Water Regulations and Standards [5, 6, 71 and Office of Research 

and Development [55]. These manuals describe in detail the 

83 
-- --. --~.- - - ~. 

transport and transformation processes involved in water quality 

modeling. Transport processes include the dispersion and advection of a contaminant once it enters the receiving stream; its 

volatilization from the water; and its sorption to suspended sediment, eventual settling, and possible resuspension and diffusion 

from the sediment. Transformation processes include the oxidation, hydrolysis, photolysis, biodegradation, and bioaccumulation 

of the chemical. 

Most water quality models were developed with an emphasis on 

the dynamics in the water column and the eventual water column 

concentrations. Several models, including some of those listed 

below (EXAMS-II, WASP4) are now capable of simulating water 

column-sediment interactions (resuspension, settling, and diffusion), however, additional work needs to be completed on the 

mechanisms of sediment-water column exchange before themodels can be validated for predictive applications involving sediments. With the advent of sediment criteria in the next few years, 

it will be necessary to use models that predict concentrations in 

both receiving water and bed sediment. This will be of particular 

importance in areas where the sediments are contaminated to the 

point at which they act as the source of a pollutant to the water 

column. Table 4-2 lists and summarizes models that may be used 

for predicting the fate and transport of toxicants and that are 

supported by the EPA CEAM [56]. All the models, plus two 

bioaccumulation models, briefly are described below. 

l DYNTOX 1541 is a WLA model that uses a probabilistic 

dilution technique to estimate receiving water chemical 

concentrations or whole effluent toxicity fractions. The 

model considers dilution and net first-order loss, but not 

sorption and benthic exchange. The net loss rate must be 

determined empirically on a case-by-case basis and cannot 

be extrapolated to different conditions of flow, temperature, solids, pH, or light. 

. EXAMS-II [57] is a compartment model that can be used as 

either a steady-state or quasi-dynamic model designed for 

evaluation of the behavior of synthetic organic chemicals in 

aquatic ecosystems. It simulates a toxic chemical and its 

transformation products using second-order kinetics for all 

significant organic chemical reactions. EXAMS-II does not 

simulate the solids with which the chemical interacts. The 

concentration of solids must be user-specified for each 

compartment. The model accounts for sorbed chemical 

transport based on solids concentrations and specified transport fields. Sediment exchanges with the water column 

include pore-water advection, pore-water diffusion, and 

solids mixing. The last describes a net steady-state exchange associated with solids that is proportional to porewater diffusion. 

l WASP4 [58] is a generalized modeling framework for contaminant fate in surface waters. Based on the flexible 

compartment modeling approach, WASP4 can be applied 

in one, two, or three dimensions, given the transport of 

fluxes between segments. WASP4 can read output files 

from the link-node hydrodynamic model DYNHYD4, which 

predicts unsteady flow rates in unstratified rivers and estuaries, given variable tides, wind, and inflow. TOX14, a subset 

of WASP4, simulates up to three interacting toxic chemicals 

and up to three sediment size fractions in the bed and 

overlying waters. First- or second-order kinetics can be 

used for all significant organic chemical reactions. Sediment exchanges include pore-water advection, pore-water 

diffusion, and deposition/scour. Net sedimentation and 

burial rates can be specified or calculated. The output can 

be used with the two bioaccumulation models FCETS and 

FCMZ, which are described below. 

HSPF [59] simulates watershed hydrology and water quality 

for both conventional and toxic organic pollutants. HSPF 

incorporates the watershed-scale ARM and NPS models 

into a basin-scale analysis framework that includes transport and transformation in one-dimensional stream channels. The simulation provides a time history of the runoff 

flow rate, sediment load, and nutrient and pesticide concentrations, along with a time history of water quantity and 

quality at any point in a watershed. HSPF simulates three 

sediment types (sand, silt, and clay) in addition to specific 

Table 4-2. Toxicant Fate and Transport Models 

Model 

DY NTOX 

EXAMS-II 

WASP4 

HSPF 

SARAH2 

M lNTEQA2 

Environment 

river 

lake, river, 

estuary 

lake, river, 

estuary 

river 

river 

lake, river, 

estuary 

Time Domain 

dynamic 

steady-state, 

quasi-dynamic 

steady-state, 

dynamic 

dynamic 

steady-state 

steady-state 

Spatial Domain 

far field, 

1 -dimensional 

far field, 

3-dimensional 

far field, 

3-dimensional 

far field 

1 -dimensional 

treatment plant, 

near field, 

2-dimensional 

- 

Chemical 

organic, 

metal 

organic 

organic, 

metal 

organic, 

metal 

organic 

metal 

84 
--- ---- - --- __-----------.--.--.__~____ - -_- - -- -- ------- --.-.- _ -- - - -----___ 

organic chemicals and transformation products of those 

chemicals. The reaction and transfer processes included are 

hydrolysis, oxidation, photolysis, biodegradation, volatilization, and sorption. Sorption is modeled as a first-order 

kinetic process in which a desorption rate and an equilibrium partition coefficient for each of the three solid types 

must be specified. Resuspension and settling of silts and 

clays (cohesive solids) are defined in terms of shear stress at 

the sediment-water interface. For sands, the system’s capacity to transport sand at a particular flow is calculated 

and resuspension or settling is defined by the difference 

between the sand in suspension and the calculated capacity. Sediment exchanges with surficial benthic sediments 

are modeled as sorption/desorption and deposition/scour. 

Underlying sediment and pore water are not modeled. 

l SARAH2 [60] is a steady-state, near-field model for calculating acceptable concentrations of hazardous organic chemicals discharged to land disposal or wastewater treatment 

facilities. Acceptable leachate or treated industrial waste 

discharge constituent concentrations are estimated by a 

“back calculation” procedure starting from chemical safety 

criteria in surface water, drinking water, or fish. For steady 

or batch waste streams, SARAH.2 considers the following 

concentration reductions: dilution and loss during treatment, initial Gaussian mixing at the edge of a stream, 

lateral and longitudinal diffusion in the mixing zone, sorption, volatilization, hydrolysis, and bioaccumulation in fish. 

The user must specify appropriate concentrations for protection of the aquatic community and of humans exposed 

through consumption of fish and water. The benthic community is not presently considered. Treatment loss is handled 

empirically. SARAH2 contains data sets for three disposalwatershed scenarios that can be easily modified and employed. The model is designed for screening analysis and 

contains numerous assumptions that should be verified 

before the model is used in actual cases. 

l MINTEQA2 is an equilibrium metals speciation model for 

dilute aqueous systems [61]. It does not have any transport 

and transformation processes and must be run with one of 

the above models. It can be used to calculate the mass 

distribution at equilibrium among dissolved, absorbed, and 

solid phases and the species distribution within each phase. 

MlNTEQA2 contains a chemical component data set for 

major ions commonly found in aqueous systems (e.g., Ca, 

Fe, and S), trace metals/metalloids of pollution interest 

(e.g., Cd, Cr, Ni, Pb, and Zn), and organic ligands of 

significant affinity for metal complexation. The model can 

be used to calculate the concentrations of adsorbed metals 

via any of seven different adsorption algorithms. 

l FCETS is a toxicokinetic model that simulates the 

bioaccumulation of nonpolar organic chemicals by fish from 

both water and food [62]. Both of these routes of exchange are modeled as diffusion processes that depend 

upon physicochemical properties of the pollutant and morphological/physiological characteristics of the fish. FCETS 

contains a moderately sized data base of allometric relationships for gill morphology with which it can simulate the 

direct gill/water exchange of organic chemicals for essentially any fish species, assuming certain default values. FCETS 

also contains a limited data base of physiological/morphological relationships that are used to set parameters for food 

exchange. In addition to simulating bioaccumulation of 

organic toxicants, FCETS can calculate time to death from 

chemicals whose mode of action is narcosis. This calculation is based on the existence of a single, lethal, internal 

chemical activity for such chemicals. The concentrations of 

toxic chemical to which the food chain is exposed may be 

specified by the user or may be taken directly from the 

values calculated by the exposure concentration model 

WASP4. Thus FCETS may be executed as a separate model 

or as a postprocessor to WASP4. 

l FCM2 is a generalized model of the uptake and elimination 

of toxic chemicals by aquatic organisms (631. It generates a 

mass balance calculation in which the rates of uptake and 

elimination are related to the bioenergetic parameters of 

the species. A linear food chain or a food web may be 

specified. Fish tissue concentrations are calculated as a 

function of time and age for each species included. Exposure to the toxic chemical in food is based on a consumption rate and predator-prey relationships that are specified 

as a function of age. Exposure to the toxic chemical in 

water is functionally related to the respiration rate. Steadystate concentrations also may be calculated. The concentrations of the toxic chemical to which the food chain is 

exposed may be specified by the user or may be taken 

directly from the values calculated by the exposure concentration model WASP4. Thus FCM2 may be executed as a 

separate model or as a postprocessor to WASP4. Migratory 

species, as well as nonmigratory species, may be considered. Separate nonmigratory food chains may be specified, 

and the migratory species is exposed sequentially to each 

food chain based on its seasonal movements. 

To apply the steady-state, continuous simulation, or probabilistic 

methods to effluent toxicity modeling, the percent effluent measurements should be converted to toxic units (TUs). As discussed 

in Chapters 1, 2, and 3, it is necessary to convert toxicity to units 

that can be directly related to mass. When comparing toxicity 

among chemicals, the relationship between toxicity and concentration is inverse; chemicals that have toxic effects at low concentrations have a greater “toxicity” than chemicals that have toxic 

effects at higher concentrations. The modeling of toxic effluents 

is based on mass balance principles; therefore, toxicity needs to 

be in units that increase when the percent of the effluent of the 

receiving stream increases. Thus, a TU is the reciprocal of the 

dilution that produces the test endpoint, i.e., acute toxicity endpoint (ATE) or chronic toxicity endpoint (CTE). An acute toxic 

unit (TU,) is the reciprocal of an ATE. A chronic toxic unit (TU,) is 

the reciprocal of a CTE. The TMDL must ensure that the CMC 

and the CCC are met in the receiving water at the desired 

duration and frequency. The CMC for toxicity is recommended 

as 0.3 TU,. This is a value that should prevent lethality unless the 

duration of exposure exceeds 1 hour. 

The CCC for toxicity measured with chronic tests is recommended 

as the following: 

CCC = 1 .O TU,. 
The first step in the TMDL process is to calculate the allowable 

acute effluent toxicity that meets the CMC in the receiving water 

at the duration and frequency discussed in Appendix D. 

The next step in the TMDL process is to calculate the allowable 

chronic effluent toxicity that meets the CCC in the receiving water 

at the duration and frequency discussed in Appendix D. To 

compare the allowable acute toxicity value to the allowable chronic 

toxicity value, the numbers must be converted to the same units 

as follows: 

TU, = (ACR)(TlJ,) 

where the acute-to-chronic ratio (ACR) is determined from tests 

on the effluent. It is important that the ACR used for TMDL 

purposes be based on actual data and not be assumed to be 10 or 

20, as in the screening procedure (Chapter 3). The value of this 

ratio will influence whether the acute or chronic TMDL is more 

stringent and is used to calculate the permit limit using the 

methods described in Chapter 5. 

At the present time, the fate of effluent toxicity in a receiving 

water is not fully understood. Even if a decay rate for toxicity can 

be measured on a given day in a site-specific situation, there is no 

way as yet to know how this rate is affected by temperature, pH, 

or other environmental conditions. There is also no way to know 

how this rate may change when new treatment is installed. 

lnstream measurements of toxicity should be made at least once 

per season to identify any time-varying trends in site-specific fate 

processes. These monitored decay rates can then be used in 

steady-state or continuous simulation fate and transport models 

to predict receiving water toxicity, assuming that the rates will not 

change with future treatment. 

Without specific information concerning the persistence of toxicity, it is recommended that effluent toxicity be limited to dilution 

estimates and that toxicity be assumed to be additive and conservative. Toxicity is expected to be additive even when the toxicity 

of one effluent affects selected biota while the toxicity of a downstream discharge affects different biota. For rivers and run-of-river 

reservoirs with a detention time of less than 20 days, the following 

ditution equation should be used, assuming completely mixed 

conditions: 

where 

c = 

c, = 

Qs = 

c, = 

Qe= 

c = C@s f C&e 

Qe + Qs 

downstream concentration (TU, or TU, ) 

upstream concentration (TU, or TU, ) 

upstream flow (cfs) 

effluent concentration (TV, or TU, ) and 

effluent Row (cfs). 

For multiple dischargers, this equation must be applied sequentially to find the concentration as a function of distance downstream. The equation can be used for a steady-state analysis if Qs 

is set equal to the design flow, & is set equal to the historical 

plant flow, and C, is calculated to meet the CMC and CCC. This 

equation can also be used with the continuous simulation, lognormal probabilistic, or Monte Carlo methods. For these dynamic analyses, a series of C, Qe, C, and Qs values would be 

used. 

If instream toxicity measurements are available and a first-order 

decay rate for toxicity can be estimated, the following equation 

should be used: 

c = C,e-K(X/U) 

where 

C = downstream concentration (TU, or TU,) 

Co = concentration after the point source discharge has 

mixed completely with the river (TU, or TU,,) 

X = distance downstream of complete mix point 

U z velocity of river 

K = measured decay rate. 

Additional statistical approaches are available that might provide 

better statistical fits to the available data. However, these models 

are somewhat more limited than the example provided above. 

The same equations used for toxicity analyses in rivers can also be 

used in steady-state, continuous simulation, or probabilistic analysis of long, narrow, shallow impoundments with high inflow 

velocities. Wider, deeper lakes require more complicated analyses since prolonged detention times (>20 days) and stratification 

exert a significant impact on water quality. The prolonged detention times make it essential that receiving water measurements of 

toxicity be available to estimate decay factors. These measurements should be made at least once per season to identify any 

time-varying trends in toxicity fate processes. Steady-state or 

continuous simulation fate and transport models for lakes can 

then be run with monitored decay rates for toxicity. A simple 

steady-state analysis can be performed using the following equations [64]: 

where 

T, = 

v = 

Q = 

c = 

Gin = 

K = 

T,V = V/Q 

C = Ci,/(l +TwK) 

mean hydraulic residence time 

lake volume at design conditions 

mean total inflow rate at design conditions 

steady-state lake concentration (TV, or TU,) 

steady-state inflow concentration (TU, or TU,) 

first-order decay rate. 

If effluent is discharged into a stratified lake and mixes only with 

the hypolimnion or epilimnion, the volume of the layer should be 

used only to calculate mean hydraulic residence time (T,,.,). The 

mean total inflow rate (Q) and the inflow concentration (Gin) 

should be calculated as the sum of all sources to the lake, including point source, nonpoint source, and tributary inputs. 

Dilution calculations for effluent toxicity discharges to an estuary 

are complicated by the oscillatory motion of the tides and possible stratification of the estuary. The prolonged detention times 

make it essential that field measurements of toxicity be available 

to estimate decay factors. These measurements should be made 

at least once per season to identify any time-varying trends in 

toxicity rate processes. Steady-state or continuous simulation fate 

and transport models for estuaries can then be run with monitored decay rates for toxicity. A simple steady-state analysis can 

be performed using the following equations for each 

nonconservative pollutant entering from the river at the head of 

an estuary [64]: 

86 
where 

(fi> 

Bi = 1 _ (li$e’kt 

ri = exchange ratio for segment i as defined by modified 

tidal prism method 

t = flushing time 

fi = fraction of freshwater in segment i 

Ci = nonconservative pollutant concentration in segment 

i (TU, or TU,) 

k = decay rate of pollutant. 

The following equations should be used for each nonconservative 

pollutant entering along the side of an estuary: 

For segments downstream of outfall: 

fi ri 

ci = 'O A c 1- (l-ri)e-kt 

i= 1 

For segments upstream of outfall: 

where 

Ci = 

co = 

ri = 

; I 

fb = 

Si = 

so = 

k = 

t = 

nonconservative pollutant mean concentration in 

segment i (TU, or TU,) 

nonconservative pollutant mean concentration in 

segment of discharge 

exchange ratio for segment i as defined by the 

modified tidal prism method 

number of segment away from outfall 

fraction of freshwater in segment i 

fraction of freshwater in segment with discharge 

salinity in segment i 

salinity in segment of discharge 

decay rate 

flushing time. 

The details of how to calculate exchange ratios and flushing times 

for estuaries are included in Part 2 of EPA’s water quality assessment manual [64]. This manual also describes how to perform 

these calculations for stratified estuaries using a two-dimensional 

box model analysis. 

4.6 HUMAN HEALTH 

4.6.1 Mman kal# considerations 

Human exposure to pollutants should be evaluated as completely 

as available information will allow. Exposure information is used 

in calculating the human health reference ambient concentration 

(RAC) from the formulas in Chapter 2, Water Quality Standards. 

This information should be used to estimate exposures due to fish 

consumption and drinking water ingestion, background concentrations, and other exposure routes, such as recreational, occupational, drinking water, dietary (other than fish), and inhalation. 

Factors in the formulas for which information is not available can 

be omitted from the calculation. If States choose, bioaccumulation 

factors also can be modified. 

4.6.2 lbtem?inim.gthe TMDLBasedonlllurnanhaM~ Rdants 

TMDLs are typically necessary only where mixing is allowed. 

Mixing zones are used at the discretion of the States. If a State 

does not allow a mixing zone or the assumption of complete 

mixing, then the RAC is applied at the end of pipe and no TMDL 

determination is typically necessary. 

With persistent or bioconcentlatable pollutants, special mixing 

zone considerations apply. Bioconcentratable pollutant criteria 

exceedances within the mixing zone can potentially result in 

tissue contamination of organisms directly or indirectly through 

contamination of bed sediments with subsequent incorporation 

into the food chain. For discharge situations with incomplete 

mixing (e.g., large rivers, lakes, estuaries, oceans), States need to 

carefully consider whether mixing zones for persistent or 

bioconcentratable pollutants are appropriate. Where a mixing 

zone is allowed, one TMDL should be calculated to achieve the 

RAC or criterion selected above [65]. Because most human health 

criteria are chronic only, a TMDL to protect against acute effects 

will usually not be needed, although EPA’s Office of Drinking 

Water does have acute criteria for some pollutants. 

For the purpose of the following discussion, use of simple, steadystate dilution models is assumed. l-lowever, these models may be 

inappropriate for certain situations where sediments serve as a 

sink for bioconcentratable pollutants and where additional factors 

need to be considered. Dynamic models, where available, are 

useful tools for accounting for an array of variables that may have 

an impact on the fate of bioconcentratable pollutants in the food 

chain. These models may be used by States for surface waters in 

appropriate instances. 

In simple situations, the TMDL is determined from the RAC and 

the design flow of the receiving water. In more complicated 

situations, e.g., where mixing is not rapid or where lakes or 

estuaries are involved, a spatial averaging scale must be chosen. 

Selection of the spatial scale must be consistent with reasonable 

assumptions about the behavior of aquatic organisms and the 

target human population. 

In some cases, it may be necessary to apply the chronic human 

health criterion within a mixing zone if it is reasonable to assume 

that the bioconcentrating aquatic organisms have little mobility, 

thus spending most of their time within the mixing zone; and the 

target human population consistently consumes fish from the 

mixing zone (over a 70-year lifetime, for carcinogenic risks). 

The procedure for developing TMDLs/WLAs generally requires 

determining values for the following parameters, based upon 

water quality considerations: (1) the duration of the averaging 

period applicable to the WlA; (2) design considerations, e.g., 

flow; (3) the discharge &VIA) concentration that will result in 

meeting the ambient water quality criterion during the design 

condition; and (4) the allowable probability (or frequency) of the 

discharge’s exceeding the WlA, averaged over the appropriate 

87 
duration. The technical basis for setting these values is discussed 

in the following sections. 

1) Averaging Periods 

The duration of the averaging period for the WLA should be 

selected to be consistent with the assumptions used to derive the 

water quality criteria. Two categories of pollutants should be 

recognized: carcinogens and noncarcinogens. 

The human health criteria for carcinogens are derived assuming 

lifetime exposure. The upper-bound risk is directly proportional 

to the lifetime arithmetic mean dose. The criteria thus apply to 

the ambient water concentrations averaged over a 70-year period. 

The duration of exposure assumed in deriving criteria for 

noilcarcinogens may be ambiguous, particularly where a criterion 

is derved from animal studies. Furthermore, the duration may be 

highly variable, ranging as high as 20 to 30 years for cadmium. 

2) Dilution Design Conditions 

u) Carcinogens: River and Stream Discharge Situations 

In well-mixed situations, the RWC, C, is determined by the pollutant load, W (mass/time), and the combined receiving water plus 

effluent flow, Q, such that, C = W/Q. 

The long-term harmonic mean flow IS recommended as the 

design flow for carcinogens. The recommendation of long-term 

harmonic mean flow has been derived from the definition of the 

human health criteria (HHC) for carcinogenic pollutants. The 

adverse impact of carcinogenic pollutants is estimated in terms of 

receptors (human) lifetime intakes. To be within the acceptable 

level of life-time body-burden of any carcinogen, such intakes 

should not exceed the HHC during the average life-time of the 

receptor. A life-time for exposure to carcinogenic pollutants is 

defined as 70 years, or approximately 365 (days/year) multiplied 

by 70 years. 

The HHC for carcinogenic pollutants can be numerically expressed 

as: 

HHC = C (design) = (Cl + C2 + C3 + ----- + C, )/n 

where 

n = (365 days/year) x 70 years 

C = concentrations 

Based on an assumption of a constant daily load from a treatment 

facility, the fully mixed instream concentration will go up or down 

inversely with the ups and downs of receiving water flows. Therefore, instream concentration is a function of, and inversely proportional to, the streamflow downstream of the discharge. Using 

this concept, 1 /Q can be substituted for C, as follows: 

1 /Q (design) = (1 /QT + 1 /Q2 + 1 /Q3 + ----- + 1 /Qn)/n. 

The stream design flow (Q design) can then be shown as follows: 

Q (design) = ni(1 /QT + 1 IQ2 + 1 tQ3 + ----- + 1 /Qn) 

The harmonic mean is expressed as follows: 

Q (design) = n/k (1 /Qi) 

i=l 

where 

n = the number of recorded flows. 

The harmonic mean is always less than the arithmetic mean. The 

harmonic mean is the appropriate design flow for determining 

long-term exposures using steady-state modeling of effluents. 

The arithmetic mean flow is not appropriate as the design flow 

since it overstates the dilution available. Extreme value statistics 

(such as 7410 or 30QS) are also not appropriate since they have 

no consistent relationship with the long-term mean dilution. 

However, for situations involving seasonably variable effluent discharge rates, hold-and-release treatment systems, and effluentdominated sites, the harmonic mean may not be appropriate. In 

these cases, the effluent load and downstream flow are not independent (i.e., they are correlated). Modeling techniques that can 

calculate an average daily concentration over a long period of 

time are more appropriate to determine the long-term exposure 

in these cases. 

The harmonic mean flow may be estimated by any of several 

methods [8], assuming that flows are approximately lognormally 

distributed: 

where 

Qhrn = Qgl-l12 

Q 

am 

q, is the geometric mean flow 

Q am is the arithmetic mean flow. 

For U.S. Geological Survey flow records, summaries of the statistical parameters needed to estimate the harmonic mean can be 

quickly obtained from STORET, through a user-friendly procedure 

for permit writers, as described in Appendix D. 

WQA8 DFLOW is a software package available for computation 

of harmonic mean flow. The DFLOW program (as discussed 

below and described in Appendix D) should be used with data 

that are not lognormally distributed. 

To develop some quantitative sense of how a long-term harmonic 

mean flow of any stream compares with its 7410 flow, the 

Assessment and Watershed Protection Division and the Risk Reduction Engineering Laboratory at Cincinnati, Ohio, analyzed 

flow records of 60 streams selected at random throughout the 

United States. These are the same stream flow records that had 

been analyzed for stream design flow condition for aquatic life 

protection as listed in EPA guidance [8]. Based on the long-term 

harmonic flow and 7-day, 1 O-year low-flow estimates for these 60 

streams, the long-term harmonic mean flows of all 60 streams 

were equal to or greater than two times the 7410 low flow. Fiftyfour of the streams’ harmonic mean flows were equal to or 

greater than 2.5 times their 7QlO low flows. Finally, 40 of the 60 

streams’ harmonic mean flows were equal to or greater than 3.5 

times the 7410. 

Based on the above observations, permit authorities may choose 

a multiplication factor of 3 x 7QlO to estimate stream design flow 

for human health protection for carcinogenic pollutants. How88 
ever, it is recommended that the harmonic mean flow be calculated directly from the historical daily flow record, if possible. 

Alternatively, the following equation might be used to estimate 

harmonic mean flow [66]: 

Qhm = [l .194 l (Qam)0.473] l [(7Ql O)“.552], rz = 0.99. 

In this equation, Qam and 7QlO are estimated using the U.S. 

Geological Survey computer program, FLOSTAT. 

b) Noncarcinogens: River and Stream Discharge Situations 

The choice of average period represents a level-of-protection 

consideration inherent in the risk management decision to be 

made by the permitting agency. If a short-term duration of 

exposure is chosen (i.e., 90 days or less), design flows may be 

appropriately based on extreme value statistics. Because the 

effects from noncarcinogens are more often associated with shortened exposures, EPA suggests the use of 3045. However, in the 

comparisons of flows for smaller rivers (i.e., low flow of 50 cfs), the 

3045 flow was, on the average, only 1 .l times that of the 7QlO. 

For larger rivers (i.e., low flow of 600 cfs), the factor was, on the 

average, 1.4 times. If the effects from certain noncarcinogens 

are manifested after a lifetime of exposure, then a harmonic 

mean flow may be appropriate. 

3) Point of Application of the Criteria 

The point at which the chronic criteria are to be met in the 

receiving water may be fixed by existing State standards or may 

be determined by considerations for managing individual and 

aggregate risks. The several possibilities include the following: 

l Where State standards allow no mixing zone and no spatial 

averaging, the criterion would be met at the end of the 

pipe. 

l Where State standards specify that the criterion must be 

met at the end of the mixing zone, the criterion would be 

applied at that point. 

l Where State standards allow consideration of spatial averaging, the criterion may be met as an average within a 

specified area, as appropriate for the individual and aggregate risk scenarios underlying the application. 

89 
- 

CRAMER 4 

REFERENCES 

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Loads (TMDL) (OAR 340-41-006) and Proposed Adoption 

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87-002. 

7. U.S. EPA. 1989. Draft Technical Guidance Manual for Performing Wastelood Allocations. Book 3, Estuaries. U.S. EPA, 

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8. U.S. EPA. 1986. “Stream Design Flow for Steady-State 

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DC. 

9. Chadderton, R.A., A.C. Miller, and A.J. McDonnell. 1981. 

Analyses of Waste Load Allocation Procedures. Water Resources Bulletin 17(5):760-66. 

10. NAS. 1972. Water Quality Criteria, 1972. National Academy 

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12. Brungs, W.A. 1986. Ailocated Impact Zones for Areas of NonCompliance. U.S. EPA, Region 1. Water Management 

Division, Boston, MA. 

13. Versar. 1984. Draft Assessment of lnternationa! Mixing Zone 

Policies. Avoidance/Attraction Characteristics, and Available 

Prediction Techniques. U.S. EPA, Office of Water Regulations and Standards and EPA Office of Pesticides and Toxic 

Substances, Washington, DC. 

14. U.S. EPA. 1983. Water Quality Standards Handbook. U.S. 

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

15. Holley, E.R., and C.H. Jirka. 1986. Mixing in Rivers. Technical 

Report E-86-11. U.S. Army Engineer Waterways Experiment Station, Vicksburg, MS. 

16. Fischer, H.B., E.J. List, C.Y. Koh, J. Imberger, and N.H. Brooks. 

1979. Mixing in Inland and Coastal Waters. New York: 

Academic Press. 

17. Metcalf & Eddy, Inc. 1979. Wastewater Engineering: Treatment, Disposal, Reuse. New York: McGraw-Hill Book Compaw 

18. Jirka, C.H. 1982. Multiport Diffusers for Heat Disposal: A 

Summary. 1. of the Hydraulics Division. Proc. ASCE, 

108(HY12):1425-68. 

19. Roberts, P.J.W., W.H. Synder, and D.J. Baumgartmer. 1989a. 

Ocean Outfalls 1: Submerged Wastefield Formation. AXE. 

I. Hydraulic Engineering 15(1):1-25. 

20. Roberts, P.J.W., W.H. Synder, and D.J. Baumgartmer. 1989b. 

Ocean Outfalls 2: Spatial Evolution of Submerged 

Wastefield. ASCE. j. Hydraulic Engineering, 115(1):26-48. 

21. Roberts, P.J.W., W.H. Synder, and D.I. Baumgartmer. 1989~. 

Ocean Outfalls 3: Effect of Diffuser Design on Submerged 

Wastefield. ASCE. 1. Hydraulic Engineering 115(1):49-70. 

22. U.S. EPA. 1982. Revised Section 301(h) Technica/ Support 

Document. U.S. EPA, Office of Water Program Operations, 

Washington, DC. EPA 430/9-82-011. 

23. Ford, D.E., and L.S. johnson. 1986. An Assessment of Reservoir Mixing Processes. Technical Report 86-7 prepared by 

Ford, Thornton, Norton and Associates, ltd., Little Rock, 

AR, for the U.S. Army Engineer Waterways Experiment 

Station, Vicksburg, MS. 

24. Muellenhoff, W.P., A.M. Soldate, D.J. Baumgartner, M.D. 

Schuldt, L.R. Davis, and W.E. Frick. 1985. Initial Mixing 

Characteristics of Municipal Ocean Discharges: Volume I, 

Procedures and Applications. U.S. EPA, Environmental Research Laboratory, Narragansett, RI. PB 86-l 37478. 

25. Hubbard, E.F., F.A. Kilpatrick, L.A. Martens, and J.F. Wilson. 

“Techniques of Water-Resources investigations of the 

USGS.” Measurement of Time of Travel and Dispersion in 

Streams by Dye Tracing. Book 3, A@‘cation of Hydraulics. 

U.S. Department of the Interior, Reston, VA. 

26. Kilpatrick, F.A., and E.D. Cobb. “Techniques of Water-Resources Investigations of the USGS.“ Measurement of Discharge Using Tracers. Book 3, Application of fiydmulics. U.S. 

Department of the Interior, Reston, VA. 

90 
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27. Wilson, J.F., E.D. Cobb, and F.A. Kilpatrick. “Techniques of 

Water-Resources Investigations of the USGS.” fluorometric 

Procedures for Dye Tracing. Book 3, Application of Hydraulics. 

U.S. Department of the Interior, Reston, VA. 

28. Fischer, H.B. 1968. Methods for Predicting Dispersion Coefficients in Natural Streams with Applications to Lower Reaches 

of the Green and Duwamish Rivers, Washington. U.S. Geological Survey Professional Paper 582-A. U.S. Geological 

Survey, U.S. Department of the Interior, Reston, VA. 

29. Kisiel, CC. 1964. Dye Studies in the Ohio River. Proceedings 

of Symposium on Diffusion in Oceans and Fresh Waters. 

Columbia University. 

30. Yotsukura, N., H.B. Fisher, and W.W. Sayre. 1970. Measurement of Mixing Characteristics of the Missouri River Between 

Sioux City, Iowa, and Plattsmouth, Nebraska. Geological 

Survey Water-Supply Paper 1899-G. U.S. Geological Survey, U.S. Department of the Interior, Reston, VA. 

31, Wilson, J.F., E.D. Cobb, and N. Yotsukura. 1969. Movement 

of o Solute in the Potomac Estuary ut Washington, D.C. ot 

Low Flow Conditions. U.S. Geological Survey Circular 5298. 

U.S. Geological Survey, U.S. Department of the Interior, 

Reston, VA. 

32. Hetling, L.]., and R.L. O’Connell. 1966. A Study of Tidal 

Dispersion in the Potomac River. Water Resources Research 

2(4): 825. 

33. Baily, T.E. 1966. Fluorescent-Tracer Studies of an Estuary. ). 

of the Water Pollution Contra/ Federation 38(12):1988. 

34. Fischer, H.B. 1972. A Longrangian Method for Predicting 

Pollutant Dispersion in Bolinas lagoon, Morin County, California. Dispersion in Surface Water. U.S. Geological Survey 

Professional Paper 582-B. U.S. Geological Survey, U.S. 

Department of the Interior, Reston, VA. 

35. Cracker, P.A., G. Morrison, S.R. Rives, F.S. Shipley, R. Palachek, 

D. Neleigh, and S. Schimmel. 1989. Case Study to 

Evaluate Effluent and Receiving Water Toxicity for an Estuarine Discharger: U.S. Naval Air Station, Corpus Christi, Texas. 

Technical Section, Water Quality Management Branch, 

U.S. EPA, Region 6. 

36. Johnson, M.C. 1984. fluorometric Techniques for Tracing 

Reservoir Inflows. Instruction Report E-84-l. U.S. Army 

Engineer Waterways Experiment Station, Vicksburg, MS. 

37. Dettmann, E.H., J.F. Paul, 1.5. Rosen, and C.J. Strobel. 1988. 

Transport, fate, and Toxic Effects of a Sewage Treatment 

Plant Effluent in a Rhode Island Estuary. U.S. EPA, Environmental Research Laboratory, Narragansett, RI. EPA/600/X87-366. 

38. Albertson, M.L., Y.B. Dai, R.A. Jensen, and H. Rouse. 1950. 

Diffusion of Submerged Jets. Trans. Am. Sot. Civ. Eng. 

(15):639-64. 

39. Wright, S.]. 1977. Mean Behavior of Buoyant Jets in a 

Crossflow. 1. of Ihe Hydruulics Division. Proc. ASCE, volume 103. 

40. Donekar, R.L., and G.H. lirka. 1988. CORMIX 1. An Expert 

System for Mixing Zone Analysis of Toxic and Conventional 

Single Port Aquatic Discharges. U.S. EPA, Environmental 

Research Laboratory, Athens, GA. PB 88-220 S&+/AS. 

41. Akar and Jirka. 1990. An Expert System for Mixing Zone 

Analysis of Toxic and Conventional Single Port Aquatic Discharges (CORMIXZ). Report under peer review. 

42. Yotsukura, N., and W.W. Sayre. 1976. Transverse Mixing in 

Natural Channels. Water Resources Research 12(4): 695- 

704. 

43. Pailey, P.P., and W.W. Sayre. 1978. Model for ShoreAttached Thermal Plumes in Rivers. I. of the Hydraulics 

Division. Proc. ASCE, volume 104. 

44. Danigan, A., and W. Waggy. 1984. Simulation-A Tool for 

Water Resource Management. Water Resources Bulletin 

1 O(2). 

45. Tiwari, J.L., and J.E. Hobbie. 1976. Random Differential 

Equations as Models of Ecosystems: Monte Carlo Simulation Approach. Math Bioscience, volume 28. 

46. Malone, R.F., D.S. Bowles, W.J. Grenney, and M.P. Windham. 

1979. Stochastic Analysis of Water Quality. Utah State 

University. UWRL/Q-79/01. 

47. Hornberger, G.M., and R.C. Spear. 1980. Eutrophication in 

Peel Inlet-l. The Problem-Defining Behavior and a 

Mathmatical Model for the Phosphorous Scenario. Water 

Research 14:29-42. 

48. Ford, D.E., K.W. Thornton, A.S. Lessem, and J.L. Norton. 

1981. A Water Quality Management Model for Reservoirs. 

ASCE Proc. of Symposium on Surface Water Impoundments, June 2-5, 1980, at Minneapolis, MN. 

49. Scavia, D., W.F. Powers, R.P. Canale, and J.L. Moody 1981. 

Comparison of First-order Error Analysis and Monte Carlo 

Simulations in Time-dependent Lake Eutrophication Models. Water Resources Research 17(4):1051-59. 

50. Rose, K.A., J.K. Summers, and R.I. McLean. 1989. A Monte 

Carlo Bioaccumulation Model of Silver Uptake by the Oyster, Crassostrea Virginia. Ecol. Mod. 42:l O-21. 

51. Versar. 1989. Prediction of Uncertainties Associated with 

Water Qua/ity Models. U.S. EPA, Office of Research and 

Development and Office of Toxic Substances. Washington, DC. 

52. Thornton, K.W., D.E. Ford, and J.L. Norton. 1982. Use of 

Monte Carlo Water Qua/iv Simulations to Evaluate Reservoir 

Operational and Managemenl Alternatives. Presented at 

Third International Conference on State-of-the-Art Ecological Modeling, Colorado State University. 

53. DiToro, D. 1984. Probability Model of Stream Quality Due 

to Runoff. /. of Environ. Engineering. Proc. ASCE, vol. 110, 

no. 3. 

54. Limno Tech, Inc. 1985. Dynamic Toxics Wasteload Allocution 

Mode/(DYNTOX): User’s Manual. U.S. EPA. Office of Water 

Regulations and Standards, Washington, DC. 

91 
- 

55. McKeon, T.J., and ].I. Segna. 1987. Selection Criteria for 

Mathematical Models Used in Exposure Assessments Surface 

Water Models. U.S. EPA, Office of Health and Environmental Assessment, Washington, DC. EPA/600/8-87/042. 

56. Ambrose, R.B., and T.O. Barnwell. 1989. Environmental 

Softwore at the U.S. EPA Center for Exposure Assessment 

Modelrng. U.S. EPA, Environmental Research Laboratory, 

Athens, GA. 

57. Burns, L.A., and D.M. Cline. 1985. Exposure Analysis Modeiing System Reference Manual for EXAMS II. U.S. EPA, Environmental Research Laboratory, Athens, GA. EPA 600/3- 

85-038. 

58. Ambrose, R.B., T.A. Wool, J.P. Connolly, and R.W. Shantz. 

1988. WASP4, A Hydrodynamic and Water Quality Mode/- 

Model Theory, User‘s Manual, and Programmer’s Guide. U.S. 

EPA, Center for Exposure Assessment Modeling, Athens, 

GA. EPAf600/3-87f039. 

59. Johansen, R.C., J.C. Imhoff, J.L. Kittle, and AS. Donigian. 

1984. Hydrologic Simulation PrograHORTRAN (HSPF): 

Users Manual for Release 8.0. U.S. EPA, Environmental 

Research Laboratory, Athens, GA. EPA/600/3-84-066. 

60. Vandergift, S.B., and R.B. Ambrose. 1988. SARAH2 A Near 

Field Exposure Assessment Model for Surface Waters. U.S. 

EPA, Environmental Research Laboratory, Athens, GA. EPA/ 

600/3-881020. 

61. Brown, D.S., and 1.0. Allison. 1987. MINTEQAl Equilibrium 

Metal Speciation Model: A Users Manual. U.S. EPA, Environmental Research Laboratory, Athens, GA. EPA/BOO/ 

3-871012. 

62. Barber, M.C., L.A. Suarez, and R.R. Lassiter. 1988. FCETS 

(Food and Gill Exchange for Toxic Substances): A Simulation 

Model for Predicting Bioaccumulation of Nonplor Organic 

Pollutants by fish. U.S. EPA, Center for Exposure Assessment Modeling, Athens, GA. EPA/600/3-87/038. 

63. Connolly, J,P., and R.V. Thomann. 1985. WASTOX, A Fmmework for Modeling the Fate of Toxic Chemicals in Aquatic 

Environments. Port 2: food Chain. U.S. EPA, Environmental 

Research Laboratory, Gulf Breeze, FL. EPA/600/4-85/040. 

64. Mills, W-B., I.D. Dean, D.B. Porcella, S.A. Cherini, R.J.M. 

Hudson, W.E. Frick, G.L. Rupp, and G.L. Bowie. 1982. 

Water Quality Assessment: A Screening Procedure for Toxic 

and Conventional Pollutants. U.S. EPA, Environmental Research Laboratory. Athens, GA. EPA 600/6-82-004 

a and b. 

65. U.S. EPA. 1987. Permit Writers Guide to Water Quo&y-based 

Permitting for Toxic Pollutants. U.S. EPA, Office of Water 

Enforcment and Permits, Washington, DC. EPA/440/4-87/ 

005. 

66. Rossman, L.A. Design Stream Flows Based on Harmonic 

Means. /, of Hydraulic Engineering 116(7):946-50. 

92 
5. PERMIT REQUIREMENTS 

5.1 INTRODUCTION 

As the final step in the “standards-to-permits” process, development of permit requirements is often the culmination of the 

activities discussed in the preceding chapters. This chapter 

describes the basic principles of effluent variability and permit 

limit derivation and provides recommendations for deriving limits 

from various types of wasteload allocation outputs such that 

water quality standards are protected. It also addresses important 

considerations in the expression of limits and other types of 

permit requirements, including toxicity reduction evaluations. 

The first portion of the chapter deals principally with aquatic life 

protection. Permitting for protection of human health is found in 

Section 5.4.4. 

5.1.1 Regulatory Requirements 

There are both mandatory and discretionary elements associated 

with the development of water quality-based permit limits to 

control toxic pollutants and toxicity. The mandatory elements are 

described in the revisions to the National Pollutant Discharge 

Elimination System (NPDES) Surface Water Toxics Control Program regulations (54 FR 23868, June 2, 1989). The regulations at 

40 CFR 122.44(d)(1) require that regulatory authorities first determine whether a discharge causes, has the reasonable potential to 

cause, or contributes to an excursion above water quality standards (narrative or numeric). In making these determinations, 

regulatory authorities must use a procedure that accounts for 

effluent variability, existing controls on point and nonpoint sources 

of pollution, available dilution, and (when using toxicity testing) 

species sensitivity. Each of these regulations were previously 

discussed in Chapter 3. 

There is a degree of flexibility in the specific procedures a regulatory authority uses in determining whether an excursion occurs or 

is reasonably expected to occur and in the weight given to the 

various factors in conducting the evaluation of a specific discharger. The Environmental Protection Agency’s (EPA) guidance 

for making these determinations is contained in the recommendations in Chapter 3. 

There are also several EPA policies that reflect these regulatory 

requirements, including the “National Policy for the Development 

of Water Quality-Based Limits for Toxic Pollutants” (Appendix B2) and EPA’s “Whole Effluent Toxicity Permitting Principles and 

Enforcement Strategy,” (Appendix B-4). This strategy states that 

“all major permits and minors of concern must be evaluated for 

potential or known toxicity (chronic or acute if more limiting).” In 

addition, the strategy states that “[final whole effluent toxicity 

limits must be included in permits where necessary to ensure that 

State Water Quality Standards are met. These limits must properly account for effluent variability, available dilution, and species 

sensitivity.” 

There is an element of judgment inherent in the specific permit 

limit derivation procedures used for an individual discharger once 

a decision has been made to develop a specific type of limit. 

Case-specific considerations will usually dictate the most appropriate approach to be taken in individual situations. Nevertheless, 

the various assumptions used in the permit limit development 

process should be consistent with the assumptions and principles 

inherent in the effluent characterization and exposure assessment 

steps preceding permit limit development. The permit limit 

derivation procedure used by the permitting authority should 

be fully enforceable and should adequately account for effluent variability, consider available receiving water dilution, 

protect against acute and chronic impacts, account for compliance monitoring sampling frequency, and protect the 

wasteload allocation (WLA) and ultimately water quality standards. To accomplish these objectives, EPA recommends that 

permitting authorities use the statistical permit limit derivation procedure discussed in Section 5.4 with the outputs from 

either steady state or the dynamic wasteload allocation modeling. 

5.2 BASIC PRINCIPLES OF EFFLUENT 

VARIABILITY 

An understanding of the basic principles of effluent variability is 

central to water quality-based permitting. Many of the concepts 

are the same as those considered in the development of technology-based limits. However, the process for applying the principles is substantially different, as explained below. 

5.21 Variations in Effluent Quality 

Effluent quality and quantity vary over time in terms of volumes 

discharged and constituent concentrations. Variations occur due 

to a number of factors, including changes in human activity over 

a 24-hour period for publicly owned treatment works (POTWs), 

changes in production cycles for industries, variation in responses 

of wastewater treatment systems to influent changes, variation in 

treatment system performance, and changes in climate. Very few 

effluents remain constant over long periods of time. Even in 

industries that operate continuous processes, variations in the 

quality of raw materials and activities, such as back-washing of 

filters, cause peaks in effluent constituent concentrations and 

volumes. 

If effluent data for a particular pollutant or pollutant parameter for 

a typical POTW are plotted against time, the daily concentration 

variations can be seen (see Figure 5-1, left-hand graphs). This 

behavior can be described by constructing frequency-concentration plots of the same data (see Figure 5-1, right-hand graphs). 

93 
Figure 5-1. Data Relative Frequency Distributions for Ceriodaphnia Toxicity, Daphnia Toxicity, 

and Zinc Concentrations for Three Different Effluents 

94 
5.2.2 Statistical Parameters and Relathnsh(p to Pemdt 

Lhdts 

Based upon the shape of the curve of a frequency-concentration 

plot, the data can be described in terms of a particular type of 

statistical distribution. The choices for statistical distributions 

include normal (bell-shaped), lognormal (positively skewed), or 

other variations on the lognormal distribution. From the vast 

amount of data that EPA has examined, it is reasonable to assume 

(unless specific data show otherwise) that treated effluent data 

follow a lognormal distribution. This is because effluent values 

are non-negative and treatment efficiency at the low end of the 

concentration scale is limited, while effluent concentrations may 

vary widely at the high end of the scale, reflecting various degrees 

of treatment system performance and loadings. These factors 

combine to produce the characteristically positively skewed appearance of the lognormal curve when data are plotted in a 

frequency histogram. Appendix E discusses the basis for concluding that effluent data are typically lognormally distributed, as well 

as recommendations for handling data sets from treatment plants 

that follow some other type of distribution. 

Effluent data from any treatment system may be described using 

standard descriptive statistics, such as the mean concentration of 

the pollutant or pollutant parameter (i.e., the long-term average 

[LTA] and the coefficient of variation [CVj). The CV is a standard 

statistical measure of the relative variations of a distribution or set 

of data, defined as the ratio of the standard deviation to the 

mean. Using a statistical model, such as the lognormal, an entire 

distribution of values can be projected from limited data, and 

limits can be set at a specified probability of occurrence. Figure 5- 

1 shows the frequency-concentration curve and the relative positions of the concentrations corresponding to the mean for the 

data. 

:x----- 

--___---__----__----_ 

, Wasteloed Akxat~on cv=o9 

s 12 

= 10 

!z 0 

Es 

s 4 

2 

n 

Days 

Figure S-2a. Relationship Between a Single Wasteload 

Allocation and Two Long-Term Averages 

for Different Coefficients of Variation 

All permit limits, whether technology-based or water qualitybased, are set at the upper bounds of acceptable performance. 

The purpose of a permit limit is to specify an upper bound of 

acceptable effluent quality. For technology-based requirements, 

the limits are based on proper operation of a treatment system. 

For water quality-based requirements, the limits are based on 

maintaining the effluent quality at a level that will comply with 

water quality standards, even during critical conditions in the 

receiving water. These requirements are determined by the WM. 

The WIA dictates the required effluent quality which defines the 

desired level of treatment plant performance or target LTA. 

In the development of technology-based effluent limits guidelines, the operating records of various wastewater treatment facilities for a particular category of discharger are examined. Based 

on the effluent data for the treatment facilities, a composite mean 

or LTA value for the parameter is determined. This LTA value, 

with relevant estimates of variability, is then used to derive effluent limit guidelines, which lead directly to permit limits. 

In contrast, the process operates in reverse for water quality-based 

permit limits. The WV\, determined from water quality standards, defines the appropriate discharge level, which in turn 

determines the requisite target LTA for the treatment facility in 

order to meet that WIA. Permit limits may then be derived from 

this targeted LTA and CV. Figure 5-2 illustrates the relationship 

among the various statistical parameters. As these figures show, 

highly variable effluents require a much lower targeted LTA to 

meet the WLA and account for the variability that occurs in 

effluent concentration above the LTA. 

It is extremely important to recognize that the various statistical 

principles and relationships discussed above operate in any dis05 10 1.5 20 

Coeffmeni of Vanatlon 

Figure S-2b. Long-Term Average Per Unit Wasteload 

Allocation as a Function of the Coefficient of Variation 

95 
charge situation-whether or not they are specifically recognized 

or accounted for. Where a permit limit derivation procedure does 

not address these principles specifically, the permit writer will be 

implicitly assuming that there are enough conservative assumptions built into other steps in the process (e.g., water quality 

models, “buffer” between permit limits and actual operating 

conditions) to ensure that there will be no reasonable potential for 

excursions above water quality standards. 

5.23 &m?ssho of Pemit lhits 

The NPDES regulations at 40 CFR 122.45(d) require that all permit 

limits be expressed, unless impracticable, as both average monthly 

and maximum daily values for all discharges other than POlWs 

and as average weekly and average monthly limits for POTWs. 

The maximum daily permit limit (MDL) is the highest allowable 

discharge measured during a calendar day or 24-hour period 

representing a calendar day. The average monthly permit limit 

(AML) is the highest allowable value for the average of daily 

discharges obtained over a calendar month. The average weekly 

permit limit (AWL) is the highest allowable value for the average 

of daily discharges obtained over a calendar week. 

EPA believes that a maximum daily permit limit can be directly 

used to express an effluent limit for all toxic pollutants or pollutant 

parameters except chronic whole effluent toxicity. The typical 

toxicity test used to measure chronic toxicity consists of samples 

collected from at least 3 different days over a 7-day period. 

Therefore, the test does not measure toxicity in any given 24-hour 

period or calendar day, but rather measures toxicity over a 7-day 

period. The toxicity could be caused by any one sample or a 

combination of samples. To address this situation, EPA recommends that the permit contain a notation indicating that 

when chronic toxicity tests are required in a permit, the MD1 

should be interpreted as signifying the maximum test result 

for the month. 

Additionally, in lieu of an AWL for POTWs, EPA recommends 

establishing an MDL (or a maximum test result for chronic toxicity) for toxic pollutants and pollutant parameters in water quality 

permitting. This is appropriate for at least two reasons. First, the 

basis for the 7-day average for POTWs derives from the secondary 

treatment requirements. This basis is not related to the need for 

assuring achievement of water quality standards. Second, a 7-day 

average, which could comprise up to seven or more daily samples, 

could average out peak toxic concentrations and therefore the 

discharge’s potential for causing acute toxic effects would be 

missed. A MDL, which is measured by a grab sample, would be 

toxicologically protective of potential acute toxicity impacts. 

5.3 EHSURIWG CONSISTENCY WITH THE 

waslEl.oao au.ocaTloH 

The WLA provides a definition of effluent quality that is necessary 

to meet the water quality standards of the receiving water. The 

WIA is based on ambient criteria and the exposure of the resident 

aquatic community or humans to toxic conditions. Once a WIA 

has been developed, accounting for all appropriate considerations, a water quality-based permit limit may be derived to 

enforce the WLA. The method used to derive the permit limits 

must be consistent with the nature of the WLA. 

The WIA addresses variability in effluent quality. For example, a 

WLA for human health pollutants is typically expressed as a single 

level of receiving water quality necessary to provide protection 

against long-term or chronic effects. On the other hand, a WLA 

for toxic pollutants affecting aquatic life (with corresponding 

duration and frequency requirements) should describe levels necessary to provide protection against both short-term and longterm effects. 

5.3. I 3tat&tlcol &onsi~~os of bus 

Direct use of a WLA as a permit limit creates a significant risk that 

the WLA will be enforced incorrectly, since effluent variability and 

the probability basis for the limit are not considered specifically. 

For example, the use of a steady state WIA typically establishes a 

level of effluent quality with the assumption that it is a value never 

to be exceeded. The same value used directly as a permit limit 

could allow the WLA to be exceeded without observing permit 

violations if compliance monitoring was infrequent, Confusion 

can also result in translating a longer duration WlA requirement 

(e.g., for chronic protection) into maximum daily and average 

monthly permit limits. The permit writer must ensure that permit 

limits are derived to implement a WIA requirement correctly. 

Potential problem areas are as follows: 

The WL4 must be enforced in a regulatory context by 

translating it into MDLs and AMLs; then and only then, will 

compliance monitoring associated with permit limits allow 

the regulatory authority to determine whether or not such 

permit limits are violated. 

The WLA that assumes that the discharge is steady state 

(i.e., not changing over time) requires a limit derivation 

assumption regarding how the effluent may vary. 

MDLs and AMLs average monthly limits must be developed 

so that they are consistent with each other and mandate 

the required level of wastewater treatment facility performance. 

If the acute WLA is used alone directly as the MDL, the limit 

will not necessarily be protective against chronic effects. If 

the acute WLA is used alone directly as the AML, the limit 

can allow excursions above the WIA within each month. 

If the chronic WLA is used alone as an MDL, the limit will be 

protective against acute and chronic effects but at the 

expense of being overly stringent. If the chronic WLA is 

used alone as the AML, the limit may be protective against 

acute and chronic effects depending upon effluent variability. 

The objective is to establish permit limits that result in the effluent 

meeting the WL4 under normal operating conditions virtually all 

the time. It is not possible to guarantee, through permit limits, 

that a WLA will never be exceeded. It is possible, however, using 

the recommended permit limit derivation procedures, to account 

for extreme values and to establish low probabilities of exceedence 

96 
of the WIA in conformance with the duration and frequency 

requirements of the water quality standards. This is not to suggest that permit writers should assume a probability of exceeclence 

of the WIA, but rather, that they should develop limits that will 

make an exceedance a very small likelihood. 

Since effluents are variable and permit limits are developed based 

on a low probability of exceedence, the permit limits should 

consider effluent variability and ensure that the requisite loading 

from the WIA is not exceeded under normal conditions. In effect 

then, the limits must “force” treatment plant performance, which, 

after considering acceptable effluent variability, will only have a 

low statistical probability of exceeding the WIA and will achieve 

the desired loadings. 

figure 5-3 shows a number of important aspects of the relationships among the various statistical parameters. In this illustration, 

the most limiting LTA (after comparing the LTAS derived from 

both acute and chronic WLAs) has been chosen for the chronic 

limiting condition. The more restrictive LTA will automatically 

meet both WIA requirements. If the effluent “fingerprint” for this 

LTA (and associated CV) is projected, it can be seen that the 

distribution of daily effluent values will not exceed the acute or 

chronic wasteload allocations for unacceptable periods of time. 

The duration and frequency requirements of the acute and chronic 

criteria for the pollutant or pollutant parameter will not be exceeded. This figure also illustrates permit limits derived from the 

more limiting LTA. (Note that for the scenario depicted in Figure 

5-3, the MDL is lower than the acute WIA and the average 

monthly limit is lower than the chronic WIA. This scenario will 

occur when a 99-percent probability basis is used to calculate the 

LTA and a 95percent probability basis is used to calculate the 

permit limits from the lower of the acute and chronic LTA. For 

other probability assumptions, these relationships will differ.) 

5.3.2 Tvpes of Water QuaMy Models and ModsI lli@wts 

Each of the two major types of water quality models, steady-state 

and dynamic, and their WlA outputs ha\‘? specific implications 

I I I I I I 

0 10 20 30 40 50 

Days 

Figure 5-3. Relationship Between Daily Concnetrations, 

Long-Term Average, Wasteload Allocations, 

and Permit Limits 

for the subsequent permit limit development process. These 

implications are discussed in detail below. EPA recommends 

that steady-state WLA analyses generally be used by permitting authorities in most cases and especially where few or no 

whole effluent toxicity or specific chemical measurements are 

available, or where daily receiving water flow records are not 

available. Two-value, steady-state models, although potentially 

more protective than necessary, can provide toxicologically protective results and are relatively simple to use. If adequate 

receiving water flow and effluent concentration data are available to estimate frequency distributions, EPA recommends 

that one of the dynamic WLA modeling techniques be used to 

derive WiAs that will more exactly maintain water quality 

standards. 

Steady-State Modeling 

Traditional single-value or two-value steady-state WIA models 

calculate WlAs at critical conditions, which are usually combinations of worst-case assumptions of flow, effluent, and environmental effects. For example, a steady-state model for ammonia 

considers the maximum effluent discharge to occur on the day of 

lowest river flow, highest upstream concentration, highest pH, 

and highest temperature. Each condition by itself has a low 

probability of occurrence; the combination of conditions may 

rarely or never occur. Permit limits derived from a steady-state 

WL4 model will be protective of water quality standards at the 

critical conditions and for all environmental conditions less than 

critical. However, such permit limits may be more stringent than 

necessary to meet the return frequency requirements of the water 

quality criterion for the pollutant of concern. 

On the other hand, a steady-state model approach may involve 

simplifying assumptions for other factors, such as ambient background concentrations of a toxicant, multiple source discharges 

of a toxicant, number of pollutants causing toxicity, incorrect 

effluent variability assumptions, and infrequent compliance monitoring. The effect of these types of factors, especially if unaccounted 

for in the WLA determination, can reduce the level of protectiveness provided by the critical condition assumptions of the steadystate model approach. Therefore, when using a steady-state WIA 

model, the permitting authority should be aware of the different 

assumptions and factors involved and should consider these assumptions and factors adequately consideration when developing permit limits. 

In general, steady-state analyses tend to be more conservative 

than dynamic models because they rely on worst case assumptions. Thus, permit limits derived from these outputs will generally be lower than limits derived from dynamic models. 

o) Sing/e Value From a Steady-Stote Analysis 

Some single-value, steady-state modeling has been used to calculate only chronic WlAs. These models produce a single effluent 

loading value and no information about effluent variability. Single 

value WLAs are typically based upon older State water quality 

standards that do not specify levels for both acute and chronic 

protection but only include one level of protection. Such outputs 

also would be found where a model is based upon protection of 

human health, since only a single long-term ambient value is of 

concern. 

97 
b) Two Values from Steady-State Analysis 

Steady-state modeling for protection of aquatic life can specify 

two sets of calculations-one for protection against acute effects 

and one for protection against chronic effects. These models 

must use water quality criteria specifying two levels of protection. 

In addition, these models include considerations of mixing zones 

when developing WlAs to afford two levels of protection. Like 

the single-value, steady-state models, these models do not produce any information about acceptable effluent variability and 

may require additional calculations to be translated into permit 

limits. 

For complex discharge situations (i.e., multiple dischargers or 

complex environmental factors needing consideration), water quality models and associated WLAS are typically developed by specialized water quality analysts in the regulatory authority. However, the permit writer is often required to develop a water quality 

model and WLA prior to permit limit derivation. In the latter 

situation, water quality modeling usually consists of simple steadystate dilution models using worst-case assumptions. 

Dynamic Modeling 

Dynamic models use estimates of effluent variability and the 

variability of receiving water assimilation factors to develop effluent requirements in terms of concentration and variability. The 

outputs from dynamic models can be used to base permit limits 

on probability estimates of receiving water concentrations rather 

than worst-case conditions. The advantages and disadvantages 

of various types of dynamic models are provided in Chapter 4. 

In general, dynamic models account for the daily variations of and 

relationships between flow, effluent, and environmentat conditions and therefore directly determine theactual probability that a 

water quality standards exceedence will occur. Because of this, 

dynamic models can be used to develop WLAs that maintain the 

water quality standards exactly at the return frequency requirements of the standards. Since this return frequency is usually one 

event in 3 years, Wk developed by dynamic models are typically 

higher than those developed by steady-state models. 

A targeted long-term average performance level and coefficient of 

variation can be derived from each type of dynamic model output, but some of the outputs require some additional manipulation of the data to develop the LTA and the CV. These parameters 

are also the starting point for the statistical permit limit derivation 

procedures discussed in the next section. Continuous Simulation models offer an array of effluent data that require further 

manipulation to develop an LTA and a CV. Both Monte Carlo 

and Lognormal Probabilistic models produce an LTA and CV, 

which can be used directly in developing permit limits. Chapter 4 

details the different dynamic models. Specific instructions for the 

use of dynamic models are available in the references listed at the 

end of Chapter 4. 

5.4 PERMlTlJMlTDERlVATlOW 

There are a number of different approaches currently being used 

by permitting authorities to develop water quality-based limits for 

toxic pollutants and toxicity. Differences in approaches are often 

attributable to the need for consistency between permit limit 

derivation procedures and the assumptions inherent in various 

types of water quality models and WlA outputs. In addition, 

permitting authorities also are constrained by legal requirements 

and policy decisions that may apply to a given permitting situation. In some instances, however, permitting procedures have 

been adopted without careful consideration of the toxicological 

principles involved or the advantages and disadvantages of the 

procedure. 

To avoid this problem, EPA recommends that the statistical 

permit limit derivation procedure described in this chapter be 

used for the derivation of both chemical-specific and whole 

effluent toxicity limits for NPDES permits. The type of WLA 

chosen from which to derive the limits is a matter of case-by-case 

application, as determined by the permitting authority. Although 

there are advantages and disadvantages associated with each of 

the procedures, EPA believes that the statistical derivation procedures will result in the most defensible and protective water 

quality-based permit limits for both specific chemicals and whole 

effluent toxicity. 

The following section explains EPA’s recommended permitting 

procedures and highlights advantages and disadvantages of various other approaches. With this information, permitting authorities will be better informed when deciding on the most appropriate permit limit derivation approach. For example, permitting 

authorities may decide to derive water quality-based permit limits 

for all dischargers using a steady-state WIA model as a baseline 

limit determination. If time and resources are available or if the 

discharger itself takes the initiative (after approval by the regulatory authority), dynamic modeling could be conducted to further 

refine the WV\ from which final permit limits would be derived. 

Box 5-l presents example permit limit calculations for each of the 

principal types of WL4 outputs dlscussed in Section 5.4.1. Permit 

limits derived from dynamic modeling are usually higher than 

those based upon steady-state modeling. The difference is reflected in Box 5-l and has been observed in actual applications [l, 

2, 31. In addition, the case studies in Chapter 7 illustrate how 

water quality-based permit limits are derived and compare the 

results of limits derived from steady state and dynamic wasteload 

allocations. 

54.1 EPA Rec~n~tituts for Pmritthrg for Aquatic 

Uf&? mtRctiun 

Permit Limit Derivation from Two-Value, Steady-State Outputs for Acute and Chronic Protection 

A number of WL4s have two results: acute and chronic requirements. These types of allocations will be developed more often as 

States begin to adopt water quality standards that provide both 

acute and chronic protection for aquatic life. These WLA outputs 

need to be translated into MDLs and AMLs. The following 

methodology is designed to derive permit limits for specific chemicals as well as whole effluent toxicity to achieve these WLAs. 

l A treatment performance level (LTA and CV) that will allow 

the effluent to meet the WLA requirement is calculated. 

98 
Box B-1. Sample Calculations of Permit Limits for Whole Effluent Toxicity 

from Different Wasteload Allocation Data 

r---- ----- .- - - --- L------------ Available Data 

1 

Two Value wasteload Dynamic model Single warteload j ------ --- - ---~-~_ 

allocation ~-----------.- output allocation - ---.--.-.- --A 

, Wastefoad Allocation (WLA) ____ _--_ 14.3 

Acute Wasteload Allocation (WlAa) 2.60 ---_ -__- 

Chronic Wasteload Allocation (WlAc) 14.3 __-_ __-_ 

I Acute-Chronic Ratio 4.62 ____ I-.- 

1 Coefficient of Variation (CV) 0.8 0.8 0.8 

) Number of Samples per Month (n) 4 4 4 

I Long Term Average (LTA) 

I ---- 9.44 -_.- ----- --i 

I- -.- - 

From two-value steady state wasteload allocatlon 

---7 I- - - - - - 

From dynamk model output I 

i I 

WLA,,,= WlA,*ACR = 2.6094.62 = 12.0 

LTA, = WfApe (0.504~-2.326041 = 14.300.440 (from Table 5-1) = 6.29 MDL = LTAc*e [2.326o-O.So*]= 9.4404.01 (from Table 5-2)= 37.9 

LTAa,c = WIAa,c*e [O.S$-2.3260]= 12.0*0.249 

II 

I 

(from Table S-l) = 2.99 

MDL = LTA,,c*e [2.326o-O.Sa*] = 2.99*4.01 (from Table 5-2) = 12.0 II AML = LTApe [2.3260n-O.Son*]= 9.44.2.27 (from Table S-2)= 21.4 

AML = LTAa,,*e [2.326o,-O.So,*~= 2.9902.27 (from Table 5-2) = 6.79 I I 

! ! i__ -~---.- - - - - - - -I 

r---- -- ---- -.--------~ 

From slngk warteload allocation ------- ------------J 

lOntion 1 I 

I . LTA = WLA*e [0.502-2.32601 = 14.3*0.440 (from Table 5-l) = 6.29 ’ 

MDL = LTA-e [2.3260-O.Sa*] = 6.29*4.01 (from Table 5-2) = 25.2 ’ 

Note: All calculations use the 99th 

AML = LTA*e [2.3260,-OSo,*) = 6.29-2.27 (from Table 5-2) = 14.3 percentile z statistic for calculation 

I of long-term averages and permit 

: Option 2 ’ limitsMDL =wlA = 14.3 I 

1 AML = MDLI2 = 7.15 

I - .- - .- - -J 

Where two requirements are specified based on different 

duration periods, two performance levels are calculated 

(Box 5-2, Step 2). 

l For whole effluent toxicity only, the acute WV\ is converted 

into an equivalent chronic WL4 by multiplying the acute 

WLA by an acute-to-chronic ratio (ACR). This ratio should 

optimally be based on effluent data, but also can be estimated as 10, based on the information presented in Chap 

ter 1 and Appendix A. 

l Permit limits are then derived directly from whichever performance level is more protective (Box 5-2, Steps 3 and 4). 

Figure 54 presents a flow chart summarizing the various steps in 

this procedure. In addition, the equations used in Box 5-2 are 

based on the lognormal distribution, which is explained in more 

detail in Appendix E. The principal advantages of this procedure 

are described below. 

l This procedure provides a mechanism for setting permit 

limits that will be toxicologically protective. A steady-state 

WL4 uses a single value to reflect the effluent loading and 

thus is an inherent assumption that the actual effluent will 

not exceed the calculated loading value. If the WIA is 

simply adopted as the permit limit, the possibility exists for 

exceedance of the WLA due to effluent variability. Clearly, 

however, effluents are variable. Therefore, permit limits are 

established using a value corresponding to a percentile of 

the selected probability distribution of the effiuent (e.g., 

95th or 99th percentile). 

. It allows comparison of two independent WLAs (acute and 

chronic) to determine which is more limiting for a discharge. The WLA output provides two numbers for protection against two types of toxic effects, each based upon 

different mixing conditions for different durations. Acute 

effects are limited based upon 1 -hour exposures at critical 

conditions, close to the point of discharge, or where necessary, at the end of the pipe. Chronic effects are limited 

based on 4-day exposures after mixing at critical conditions. These requirements yield different effluent treatment 

requirements that cannot be compared to each other without calculating the LTA performance level the plant would 

need to maintain in order to meet each requirement. Without this comparison (or in the absence of procedures that 

address this comparison), the WLA representing the more 

critical condition cannot be determined. A treatment system will only need to be designed to meet one level of 

99 
Box 5-2. Calculating Permit Limits Based on Two-Value Wasteload Allocation 

To set maximum daily and average 

monthly permit limits based on 

acute and chronic wasteload 

Step 1 (for whole effluent toxicity only) 

allocations, use the following four WLA,, (in TU,) = WLA, (in TU,) l ACR 

steps: 

Convert the acute wasteload 

1 allocation to chronic toxic Step 2 (start here for chemical specific limits) 

units. Skip to Step 2 for 

chemical-specific limits. LTA, c = Wu, c l e [o.52 - “1 

Calculate the long-term where o’ = fn(CV’+l) 

2 average wasteload that will z = 1.645 for 95th percentile probability basis, and 

satisfy the acute and chronic z = 2.326 for 99th percentile probability basis 

wasteload allocations. 

Determine the lower (more 

LTA, = W& l e [“.50z - z”4] 

3 limiting) of the two long-term where 04* = ln(CV2/4 +l) 

averages z = 1.645 for 95th percentile probability basis, and 

z = 2.326 for 99th percentile probability basis 

Calculate the maximum daily 

Step 3 

Term Meaning 

LTA = min (LTA,, LTA,,) 

cv 

0 

w%,c 

w% 

W”c 

LTAsc 

LTA, 

T”a 

T”c 

ACR 

MDL 

AML 

Z 

Coefficient of variation 

Standard deviation 

Acute wasteload allocation 

in chronic toxic units 

Acute wastelaad allocation 

in acute toxic units 

Chronic wasteload 

allocation in chronic toxic 

units 

Acute tong-term average 

wasteload in chrunic units 

Chronic long-term average 

wasteload 

Acute toxic units 

Chronic toxic units 

Acute-to-chronic ratio 

Maximum daily limit 

Average monthly limit 

2 statistic 

Step 4 

MDL = LTA l e iza -o.52l 

where d = In(CV2+1) 

z = 1.645 for 95th percentile probability basis, and 

z = 2.326 for 99th percentile probability basis 

AML = LTA l e Izon - o.50,21 

where on2 = ln(CV*/n +l ) 

z = 1.645 for 95th percentile probability basis, and 

z = 2.326 for 99th percentile probability basis 

‘Full &tails of this procedure are found in Appendix E. 

100 
I 

1 CakulateChmnic 1 

Calculate Maximum 

Dally Umlt 

/ 

I Calculate Avaraga 

Monthly Llmh I 

Figure S-4. Flowchart for Calculating Permit Llmits From 

Two-Value, Steady-State Wasteload Allocatlon 

for Aquatic Life Protection 

treatment for effluent toxicity-treatment needed to control 

the most limiting toxic effect. 

l The actual number of samples can be factored into permit limit 

derivation procedures. The procedure provides the means to 

accurately determine the AML based on the number of observations that will be taken. 

The principal disadvantages of this approach are: 

l Some permit writers have indicated that additional mathematical calculations associated with these procedures increase 

the burden for the permit writer and add what is perceived to 

be an unnecessary step. 

l The use of a steady-state WLA may result in permit limits that 

are more conservative due to the assumption of critical conditions. However, these limits are still protective of water quality 

criteria. The level of conservatism may be necessary in those 

instances where limited data prevent a more precise evaluation 

of a WLA. 

This procedure provides a toxicologically sound approach. To 

help the permit writer, EPA has developed tables (see Tables 5-l 

and 5-2) to be used to quickly determine the necessary values. In 

addition, some permit authorities have developed their own computer programs to readily compute the necessary information 

from the appropriate inputs. 

Permit Limit Derivation From Dynamic Model Outputs 

The least ambiguous and most exact way that a WIA for specific 

chemicals or for whole effluent toxicity can be specified by using 

dynamic modeling from which the WIA is expressed as a required 

effluent performance in terms of the LTA and CV of the daily 

values. When a WLA is expressed as such, there is no confusion 

about assumptions used and the translation to permit limits. A 

permit writer can readily design permit limits to achieve the WLA 

objectives. The types of dynamic exposure analyses that yield a 

WLA in terms of required performance are the continuous simulation, Monte Carlo, and lognormal probabilities analyses. Chapter 

4 provides a general discussion of these models. Guidance manuals for developing WLAS are listed in the references at the end of 

Chapter 4. Once the WL4 is determined, the permit limit derivation procedure which can be used for both whole effluent toxicity 

and specific chemicals, is as follows: 

The WL4 is first developed by iteratively running the dynamic model with successively lower LTAs until the model 

shows compliance with the water quality standards. 

The effluent LTA and CV must then be calculated from the 

model effluent inputs used to show compliance with the 

water quality standards. This step is only necessary for the 

Monte Carlo and continuous simulation methods. 

The permit limit derivation procedures described in Box 5 

2, Step 4 are used to derive MDLs and AMLs from the 

required effluent LTA and CV. Unlike these procedures for 

steady-state WIAS, there is only a single LTA that provides 

both acute and chronic protection, and, therefore, the 

comparison step indicated in Figure 5-4 and Box 5-2 is 

unnecessary. 

The principal advantages of this procedure are: 

l It provides a mechanism for computing permit limits that 

are toxicologically protective. As with the procedure summarized below for two-value, steady-state WLA outputs, 

the permit limit derivation procedures used with this type 

of output consider effluent variability and derive permit 

limits from a single limiting LTA and CV. 

l Actual number of samples is factored into permit limit 

derivation procedures. This procedure has the same elements as discussed for the statistical procedures in Option 2 

below. 

l Dynamic modeling determines an LTA that will be adequately protective of the WLA, which relies on actual flow 

data thereby reducing the need to rely on worst case critical 

flow condition assumptions. 

101 
Table S-l. Back Calculations of Long-Term Average 

---_----_---_ 

WIA Multipliers .-- - --- 

(-J/ I e[o.5 a2-201 I 

i--~-------- 

__ ---I 

95th 

Percentile 

____t----L------! 

0.1 

0.2 

0.3 

0.4 

0.5 

0.6 

0.7 

0.0 

0.9 

1.0 

1.1 

1.2 

1.3 

1.4 

1.5 

1.6 

1.7 

1.8 

1.9 

2.0 

0.853 

0.738 

0.644 

0.571 

0.514 

0.468 

0.432 

0.403 

0.379 

0.360 

0.344 

0.330 

0.319 

0.310 

0.302 

0.290 

0.290 

0.285 

0.2m 

0.277 

0.797 ; 

0.643 ( 

0.527 

0.440 

0.373 

0.321 

0.281 

0.249 

0.224 

0.204 1 

0.187 

0.174 

0.162 

0.153 

0.144 

0.137 

0.131 

0.126 1 

0.121 

0.117 I 

I_ - -.---~ -----------____-- - - -- -- ------ _ - - --- ---- ---, 

AC&? 

LTA,,c = WLA,, l e[“” 02- ’ = ’ 

here u*= /n[CV* + 11. 

2=1.645for95thpercentileoaxrnen~probebili(y,and I 

z= 2.326 for9Qth percentileoccurrerxe probability I 

--- ------------ I ------- T--- - 

---_-------, 

WIA Multipliers I 

Chronic 

(4-day average) 

where u,2= ffl[CV2/4+ 11. 

z = 1.645foc95thpercentileoccunenceprobability,and 

z =2,326for991trpercentile~~eprohabiliOl 

cv 

0.; 

0.2 

0.3 

0.4 

0.5 

0.6 

0.7 

0.8 

0.9 

1.0 

1.1 

1.2 

1.3 

1.4 

1.5 

1.6 

1.7 

1.8 

1.9 

2.0 

j e[o.s u4’- L u4 ] I 

7---- _-- ----- 

0.922 

0.853 

0.791 

0.736 

0.687 

0.644 

0.606 

0.571 

0.541 

0.514 

0.490 

0.460 

0.449 

0.432 

0.417 

0.403 

0.390 

0.379 

0.369 

0.380 

I 0.891 

0.797 

’ 0.715 

’ 0.643 

, 0.581 

/ 0.527 

0.481 

I 0.440 

1 0.404 

’ 

0.373 

’ 

0.345 

0.321 

, 0.300 

0.261 

0.264 

; 0.249 

0.236 

, 0.224 

i 0.214 

0.204 _L_ _--- - 

The principal disadvantages of this procedure are: Permit Limit Derivation From Single, Steady-State Model 

Output 

l Necessary data for effluent variability and receiving water 

flows may be unavailable, which prevents the use of this 

approach. 

Some State water quality criteria and the corresponding WlAs are 

reported as a single value from which to define an acceptable 

l The amount of staff resources needed to explain how the level of effluent quality. For example, “copper concentration 

limits were developed and to conduct the WLA also is a 

must not exceed 0.75 milligrams per liter (mg/l) instream.” Steadyconcern. The permit documentation (i.e., fact sheet) will state analyses assume that the effluent is constant and, therefore, 

need to clearly explain the basis for the LTA and CV and this the WL4 value will never be exceeded. This presents a problem in 

can be resource intensive. deriving permit limits because permit limits need to consider 

effluent variability. 

102 
Table 5-2. Calculation of Permit Limits 

LTA multipliers 

cv eIzo-o.502] 

95th 99th 

Percentile Percentile 

0.1 1.17 1.25 

0.2 1.36 1.55 

0.3 1.55 1.90 

0.4 1.75 2.27 

0.5 1.95 2.66 0.8 2.13 3.11 

0.7 2.31 3.56 

0.8 2.40 4.01 

0.9 2.64 4.46 

1.0 2.78 4.90 

1.1 2.91 5.34 

1.2 3.03 5.76 

1.3 3.13 6.17 

1.4 3.23 6.56 

1.5 3.31 6.93 

1.6 3.38 7.29 

1.7 3.45 7.63 

1.8 3.51 795 

1.9 3.56 6.26 

2.0 360 8.55 

Maximum Daily Limit 

MDL=LTAme[Zu-o.5621 

where u2 = In [ CV2 + 1 1. 

z = 1.645 for 95th percentile occurrence probability, and 

z = 2.326 for 99th percentile occurrence probability 

LTA Multipliers 

e’ 2 a, -0.5 CT"21 

cv . 95th 99th 

Average Monthly Limit Percentile Percentile 

rkl n=2 n=4 n=lO n=30 n=l n=2 n=4 n=iO n=30 

0.1 1.17 1.12 1.06 106 1.03 1.25 1.18 1.12 106 1.04 

0.2 1.36 1.25 1 17 1.12 1.06 1.55 1.37 1.25 116 109 

0.3 1.55 1.38 1.26 1.18 1.09 1.90 159 1.40 124 113 

0.4 1.75 t.52 1.36 1.25 1 12 2.27 1.83 1.55 1.33 1 16 

0.5 

JML=LTAee [zo,-0’5n”21 0.6 

1.95 1.86 1.45 1.31 1.16 2.68 2.09 1.72 1.42 1.23 

2.13 1.60 1.55 1.38 1.19 3.11 2.37 190 152 1.26 

0.7 2.31 1.94 1.65 1.45 1.22 3.56 2.66 2.06 162 1.33 

wherean2=In[CV2/n+1], 0.8 2.48 2.07 1.75 1.52 1.26 4.01 2.96 2.27 173 1.39 

09 2.64 2.20 1.65 1.59 1.29 4.46 3.28 2.48 184 1.44 

z = 1.645 for 95th percentile, 1.0 2.78 2.33 1 95 1.66 1.33 4.90 3.59 2.68 1 96 150 

z = 2.326 for 99th percentile, and t t 2.91 2.45 2.04 '.73 1.36 5.34 3.91 2.90 2.07 156 

n = number of sampletimonth 12 3.03 2.56 2.13 1.80 1.39 5.76 4.23 3.11 2.19 1.62 

1.3 3.13 2.67 2.23 1.87 1.43 6.17 4.55 3.34 2.32 166 

14 3.23 2.77 2.31 1.94 1.47 6 56 4.86 3.56 2.45 1 74 

1.5 3.31 2.66 2.40 2.00 1.50 6.93 5.17 3.78 2.58 160 

1.6 3.38 2.95 2.48 2 07 1.54 7.29 5.47 4.01 2.71 1.67 

17 3.45 3.03 2.56 2.14 1.57 7.63 5.77 4.23 2.84 193 

1.8 3.51 3.10 2.64 2.20 1.61 7.95 6.06 4.46 2.98 2.00 

1.9 3.56 3.17 2.71 2.27 1.64 8.26 6.34 4.68 3.12 2.07 

2.0 3.60 3.23 2.78 2.33 1.66 8.55 6.61 4.90 326 2 14 

The proper enforcement of this type of WIA depends on the 

parameter limited. For nutrients and biochemical oxygen demand (BOD), the WlA value generally has been used as the 

average daily permit limit. However, the impact associated with 

toxic pollutants is more time dependent, as reflected in the 4-day 

average duration for the criteria continuous concentration (CCC) 

(see Chapter 2). Where there is only one water quality criterion 

and therefore only one WL4, permit limits can be developed 

using the following procedure: 

The principal advantages and disadvantages of this procedure are 

similar to those for the two-value permit limit derivation method 

discussed previously except that it does not examine two WL4s. 

5.4.2 Uthor A#mdo.s to Pamittin~ for Aquatfc life 

Other approaches for translating WLA outputs into permit limits 

have been used by some permitting authorities. These methods 

may combine elements of the statistical procedures discussed 

earlier with specific technical and policy requirements of the 

permitting authority to derive limits that may be protective of 

water quality and consistent with the requirements of the WLA. 

Such approaches may use simplified statistical procedures. 

l Consider the single WLA to be the chronic WLA and derive 

an chronic LTA for this WLA using the procedures in Box S2 (Step 2, Part 2). 

l Derive MDLs and AMLs using the procedures ‘in Box 5-2 

(Step 4). 

103 
For example, some permitting authorities assume a value for the 

CV and an acute to chronic ratio above which the chronic WIA 

will always be more limiting. Where such simplifying assumptions 

are used, the need to compare LTAs derived from acute and 

chronic steady-state models is unnecessary. Similarly, for assumed values for n, CV, and exceedence probability, the various 

equations shown in Box S-2 can be simplified further, such that 

the AML will always be a constant fraction of the MDL. 

These approaches allow the permit writer to rapidly and easily 

translate the results of WL4.s into permit limits. However, the 

permit writer clearly should understand the underlying procedures and carefully explain the basis for the chosen assumption. 

Appropriate State or regional guidance documents also should be 

referenced. 

Another approach used by some permit authorities involves the 

direct use of the WIA as a permit limit. This approach sometimes 

involves the following steps: 

l The WlA value for toxic pollutants is used as the MDL. 

. In the absence of other information, permit writers typically 

divide the MDL by 1.5 or 2.0 to derive an AML (depending 

on the expected range of variability). 

The principal advantage of this approach is that it is very straightforward to implement and requires minimal resources. The disadvantage of this option is that the average monthly limits must be 

derived without any information about the variability of the effluent parameter; therefore, the permit writer cannot be sure that 

these procedures are protective of water quality criteria. Conversely, limits derived from this approach may be overly stringent 

and subject to challenge. 

The direct application of both the acute and chronic WLAS as 

permit limits is another approach that has been used. The WLA 

developed for protection against chronic effects becomes the 

average monthly limit and the acute WIA becomes the MDL. 

EPA discourages the use of this approach. Since effluent variability has not been specifically addressed with this approach, 

compliance with the monthly average (30-day) effluent limit 

during critical conditions could exceed the chronic (4-day) WLA. 

Whether standards are violated with excessive frequency under 

such conditions would depend upon whether the conditions 

represented by the worst-case assumptions of the model also 

were occurring at the same time. By contrast, compliance with 

limits that were developed using statistical procedures have a low 

chance of leading to WIA excursions before effluent variability is 

accounted for in deriving the limits (see Figure S-3). 

Another permitting approach is to use a narrative “no toxicity” 

limit that is measured using a toxicity testing method that employs only a control and a single exposure at the receiving water 

concentration (RWC). This is sometimes referred to as a “pass/ 

fail” toxicity test. Although these tests can be less expensive than 

full dilution series testing, they provide no knowledge as to the 

extent of toxicity present during the test and therefore no data 

concerning the seriousness of the impact or the amount of toxicity reduction necessary. The death of a single test animal can 

occur at any concentration level beyond the lethality threshold for 

the test organism; therefore, such a test is much less powerful 

from a statistical standpoint. In addition, it is not possible to 

determine dose-response relationships for the test organisms without using multiple effluent concentrations. Dose-response curves 

are useful in determining quality assurance of the tests and in 

defining threshold dosages for regulatory purposes. Because the 

drawbacks of the approach generally outweigh the benefits, EPA 

recommends that whole effluent toxicity limits be established 

using a statistical derivation procedure that adequately accounts for effluent variability and that monitoring for compliance with whole effluent toxicity limits be conducted using a 

full dilution series. 

When setting a whole effluent toxicity limit to protect against 

acute effects, some permitting authorities use an end-of-pipe 

approach. Typically, these limits are established as an LCsdl OOpercent effluent at the end of the pipe. These limits are routinely 

set without any consideration as to the fate of the effluent and the 

concentrations of toxicant after the discharge enters the receiving water. Limits derived in this way are not water quality-based 

limits and suffer from significant deficiencies since the toxicity of a 

pollutant depends mostly upon concentration, duration of exposure, and repetitiveness of the exposure. This is especially true in 

effluent dominated waters. For example, an effluent that has an 

LCso=lOO percent contains enough toxicity to be lethal to up to 

50 percent of the test organisms. If the effluent is discharged to a 

low-flow receiving waterbody that provides no more than a threefold dilution at the critical flow, significant mortality can occur in 

the receiving water. Furthermore, such a limit could not assure 

protection against chronic effects in the receiving waterbody. 

Chronic effects could occur if the dilution in the receiving water 

multiplied by the acute to chronic ratio is greater than 100 

percent. Therefore, in effluent dominated situations, limits set 

using this approach may be severely underprotective. In contrast, 

whole effluent toxicity limits set using this approach in very high 

receiving water flow conditions may be overly restrictive. Because of these problems, EPA recommends that all whole 

effluent toxicity limits be set as water quality-based limits and 

that to do so, the statistical permit limit derivation procedures 

discussed in Section 5.4.1 be followed. 

5.4.3 special Pemlntin# lh@mmt?& 

Water quality-based permit limit development for discharges to 

marine and estuarine waters follows the same basic steps as the 

water quality-based approach for freshwater discharges. There 

are some differences in the water quality criteria used as the basis 

for protection, the designation of mixing zones, and the water 

quality models used to develop WL4s; however these differences 

are addressed in the WL4. (See discussions of these elements in 

previous chapters.) In addition, there are some special regulatory 

considerations associated with these types of dischargers, including special reviews of permits with such programs as the Coastal 

Zone Management Program. Some discharges also require an 

Ocean Discharge Criteria Evaluation under Section 403(c) of the 

Clean Water Act (CWA). 

5.4.4 EPA ltmmnmmt8tions for PemMting fbr Muman 

Healtlr Ntection 

Permit development to protect against certain routes of exposure 

is another key consideration. Ingesting contaminated fish and 

shellfish is a toxic chemical exposure route of serious potential 

104 
human health concern for which there is no intervening treatment process, unlike the drinking water route of exposure. Effluent limits designed to meet aquatic life criteria for individual 

toxicants and whole effluent toxicity are not necessarily protective 

of toxic pollutant residue formation in fish or shellfish tissue. 

Developing permit limits for pollutants affecting human health is 

somewhat different from setting limits for other pollutants because the exposure period is generally longer than 1 month, and 

can be up to 70 years, and the average exposure rather than the 

maximum exposure is usually of concern. Because compliance 

with permit limits is normally determined on a daily or monthly 

basis, it is necessary to set human health permit limits that meet a 

given WLA for every month. If the procedures described previously for aquatic life protection were used for developing permit 

limits for human health pollutants, both MDLs and AMLs would 

exceed the WIA necessary to meet criteria concentrations. Thus, 

even if a facility was discharging in compliance with permit limits 

calculated using these procedures, it would be possible to constantly exceed the WL4. This approach clearly is unacceptable. In 

addition, the statistical derivation procedure is not applicable to 

exposure periods more than 30 days. Therefore, the recommended approach for setting water quality-based limits for human health protection with statistical procedures is as follows: 

l Set the AML equal to the WLA 

l Calculate the MDL based on effluent variability and the 

number of samples per month using the multipliers provided in Table 5-3. 

This approach ensures that the instream criteria will be met over 

the long-term and provides a defensible method for calculating a 

MDL. Both an MDL (weekly average limit for POTWs) and a 

monthly average limit are required by EPA regulations, unless 

impracticable (40 CfR 122.45(d)) and are applicable for human 

health protection. The MDL sets an upper bound on effluent 

values used to determine the monthly average and provides a 

measure of effluent compliance during operational periods between monthly sampling. 

5.5 SPEClAlCONSlDERATlONSlHlJSEOF 

STATlSTlCAlPERMlTUMfTDERlVATlON 

TECHNIQUES 

The following discussion summarizes the effect of changes in the 

various statistical parameters on the permit limits that are derived. 

An understanding of these relationships is important for the permit writer. Additional considerations of each of these parameters 

with respect to the statistical methods for permit limit derivation 

also are discussed below. 

5.5. I Eflect of Chaqes of 3fotisticol Panmreters on Pmnit 

Lfmlts 

l Effect of changes in CV on derivation of LTA from WLA: 

IG the CV increases, the LTA decreases; and conversely, as 

the CV decreases, the LTA increases (see Figure 5-S). 

Reason: The LTA must be lower relative to the WLA to 

account for the extreme values observed with high CVs. An 

LTA with a zero CV equals the WLA. 

l Effect of changes in CV on derivation of permit limits for 

a fixed probability basis: As the CV increases, the permit 

limits increase (become less stringent); and conversely, as 

the CV decreases, the permit limits decrease (become more 

stringent; see Figure S-6). 

Reason: A higher value for the permit limit is produced for 

the same LTAs as the CV increases in order to allow for 

fluctuations about the mean. Following the steps in Box S2 to derive the LTA will account for such fluctuations. 

l Effect of changes in number of monthly samples on 

permit limits: As the value for “n” (number of observations) increases in the average monthly permit limit derivation equations, the average monthly permit limit decreases 

to a certain point. The effect on the average monthly limit 

is minimal for values of n greater than approximately 10. 

Conversely, as the value for “n” decreases, the AML increases until n=l, at which point the AML equals the MDL 

(see Figure S-7). 

Reason: As n increases, the probability distribution of the 

n-day average values becomes less variable (narrower) 

around the LTA. Therefore, the 95th or 99th percentile 

value for an n-day average decreases in absolute value as n 

increases. (See additional discussion in Section 5.5.3.) 

l Effect of changes in probability basis for permit limits: 

As the probability basis for the permit limits expressed in 

percentiles (e.g., 95 percent and 99 percent) increases, the 

value for the permit limits increases (becomes less stringent). The converse is true as the probability basis decreases (see Figure 5-6). 

0 I 

0.0 0.5 10 1.5 2. 

Coefficient of Vanation 

Figure 5-5. Long-Term Average as a Function of the 

Coefficient of Variation 

105 
Reason: There is a higher probability that any randomly 

chosen effluent sample will be in compliance with its permit 

limits, if those limits are statistically designed to be greater 

than a high percentage (e.g., 99 percent) of all possible 

values for a given LTA and CV. 

illustrates how the CV, number of samples and probability basis 

affect the derivation of the AML. Figure 5-9 illustrates the combined effect of the CV and the probability basis on the derivation 

of the MDL. 

The overall combination of the coefficient of variation, number of 5.52 Coemcknt af vwaml 

samples, and the assumed probability basis for calculating the LTA 

from the WLA, and the most limiting LTA, has different effects on 

the derived limits depending upon the selection made for each. 

To help illustrate the combined effect of these factors, Figure 5-8 

Use of the statistical method of permit limit derivation requires an 

estimate of the CV of the distribution of the daily measurements 

of the parameter after the plant complies with the requirements. 

Table 5-3. Multipliers for Calculating Maximum Daily Permit Limits From Average Monthly Permit Limits 

To obtain the maximum daily permit limit (MDL) for a bioconcentratable pollutant, multiply the average monthly permit limit 

(AML) (the wasteload allocation) by the appropriate value in the following table. 

Each value in the table is the ratio of the MDL to the AML as calculated by the followinq relationship derived from Step 4 of the 

statistically based permit limit calculation procedure. 

MDL = exp [z,o - 0.502] 

AML exp [zaan - 0.50n2] 

where 

un2= In (CV2/n + 1) 

o2 = In(CV2+1) 

CV = the coefficient of variation of the effluent concentration 

n = the number of samples per month 

Z m= the percentile exceedance probability for the MDL 

za = the percentile exceedance probability for the AML. 

Ratio Between Maximum Daily and Average Monthly Permit Limits 

Maximum = 99th percentile Maximum = 99th percentile 

Average = 95th percentile Average = 99th percentile 

cv n=l n=2 n=4 n=a n=30 n=l n=2 n=4 n=8 

0.1 1.07 

0.2 1.14 

0.3 1.22 

0.4 1.30 

0.5 1.38 

0.6 1.46 

0.7 1.54 

0.8 1.61 

0.9 1.69 

1.0 1.76 

1.1 1.83 

1.2 1.90 

1.3 1.97 

1.4 2.03 

1.5 2.09 

1.6 2.15 

1.7 2.21 

1.8 2.27 

1.9 2.32 

2.0 2.37 

.13 

.2s 

.37 

so 

.622 

.73 

.a4 

1.94 

2.03 

2.11 

2.18 

2.25 

2.31 

2.37 

2.42 

2.42 

2.52 

2.56 

2.60 

2.64 

.oo 

1.16 1.18 

.oo 

1.22 1 .oo 

1.33 1.39 

.oo 

1.46 1.00 

1 .so 1.60 

.oo 

1.74 / 1 .oo 

1.67 

.oo 

1.82 2.02 - 1.00 

.oo 

1.84 2.04 2.32 I 1 .OO 

2.01 2.2s 2.62 1.00 

2.16 2.45 2.91 1 .oo 

2.29 2.64 3.19 1 .oO 

2.41 2.81 3.45 , 1 .oo 

2.52 2.96 3.70 1.00 

2.62 3.09 3.93 , 1 .oo 

2.70 3.20 4.13 1.00 

2.77 3.30 4.31 1 .oo 

2.83 3.39 4.47 

2.89 3.46 4.62 

2.89 3.46 4.62 

2.98 3.57 4.85 

3.01 3.61 4.94 

3.05 3.65 5.02 

1.07 

1.3s 

1.12 

1.84 

1.16 

1.13 

2.46 

1.24 

1.34 

1.32 

1.19 

1.83 

1.36 

2.46 

1.49 

1.24 

1.33 

1.46 

1 .a2 2.46 

1.66 

I .2a 

1.32 

1.56 

1.80 

1.81 

1.31 

2.45 

1.64 

1.31 

1.9s 

1.34 

1.78 

1.71 

2.43 

2.08 

1.30 

1.3s 1.76 

1.76 

2.19 

2.41 

1.36 1.80 2.27 

1.37 1.83 2.34 

1.37 1 .a4 2.39 

1.36 1.85 2.43 

1.36 1.85 2.45 

3.07 3.67 5.09 1 .oo 1.29 1.74 2.38 

n=30 j 

\ 

1.20 1 

1.43 , 

1.67 ’ 

1.92 

2.18 

2.43 / 

2.67 1 

2.89 1 

3.09 

3.27 I 

3.43 i 

3.56 1 

3.68 ; 

3.77 I 

3.84 

3.90 

3.94 

3.97 j 

3.99 1 

4.00 ( 

106 
99% ProbabdMy Basrs , 

0.5 1.0 

Coefficient of Variation 

1.5 2.0 

Figure S-6. Maximum Daily Permit Limit as a Function of the 

1 

0.6 

!i 

2 

zi 

06 

z 

.s 2 0.4 

02 

0 

Coefficient of Variation 

10 20 

Number of Samples Per Month 

30 

Figure S-7. Relationship Between Average Monthly Permit 

Limits and Number of Samples Per Month 

If variability is mostly related to production, current data may be 

used to estimate the CV. If future variability is expected to be 

substantially different, the CV must be estimated. Discharges of 

toxic pollutants are generally more variable than discharges of 

conventional pollutants. It is important to use the best estimate of 

the CV that can be reasonably achieved. As explained in Chapter 

3, EPA’s review of the uncertainty associated with effluent variability suggests that a minimum of 10 samples is needed to reasonably quantify the CV. 

One concern with respect to using an appropriate CV in the 

statistical limit derivation procedures is that CVs of regulated 

systems may be quite different from nonregulated systems. In 

other words, after permit limits are in place and the permittee is 

operating to achieve the requisite limits, the variability associated 

with the parameter of concern may change considerably. Where 

the permit writer has reason to believe that the CV of the regulated system may behave differently from the nonregulated system (e.g., where changes in the treatment facility are planned), 

information concerning effluent concentration means and variability can be obtained from effluent guideline documents for 

individual chemical parameters. 

Variability associated with effluent levels of both individual chemicals and whole effluent toxicity is difficult to predict for any 

individual situation. However, it is important to recognize that 

failure to assign any CV to an individual toxicant or the parameter 

toxicity involves an implicit assumption that there is no effluent 

variability present. Based upon analyses of a wide variety of data 

from various types of plants, EPA recommends a value of 0.6 as 

a default CV, if the regulatory authority does not have more 

accurate information on the CV for the pollutant or pollutant 

parameter. Permit limits are usually not extremely sensitive to 

small changes in the 0’. The value of 0.6 is typical of the range of 

variability of effluents measured by EPA (see Appendix A) and 

represents a reasonable degree of relative variability. However, 

wherever possible, it is recommended that data on effluent variability for the pollutant of concern be collected to define a CV 

rather than selecting a default value. 

5.53 Nmbef of samples 

The statistically based method for permit limit derivation results in 

an MDL that does not depend on monitoring frequency. However, the AML decreases as the monitoring frequency increases, 

and a greater number for “n“ is inserted in the relevant equations. 

Some permit writers are concerned with this outcome because 

facilities with more frequent sampling requirements appear to 

receive more stringent permit limits than those with less frequent 

monthly sampling requirements. 

The AML decreases as the number of monthly samples increases 

because an average of 10 samples, for example, is closer to the 

LTA than an average based on 4 samples. This phenomenon 

makes AMLs based on 10 samples appear to be more stringent 

than the monthly limit based on 4 samples. However, the stringency of these procedures is constant across monitoring frequencies because the probability basis and the targeted LTA performance are the same regardless of the number of samples taken. 

Thus, a permittee performing according to the LTA and variability 

associated with the wasteload allocation will, in fact, meet either 

of these AMLs when taking the corresponding number of monthly 

samples. 

For water quality-based permitting, effluent quality is determined 

by the underlying distribution of daily values, which is determined 

by the LTA associated with a particular WLA and by the CV of the 

effluent concentrations. Increasing or decreasing monitoring 

frequency does not affect this underlying distribution or treatment performance, which should, at a minimum, be targeted to 

comply with the values dictated by the WLA. Therefore, it is 

recommended that the actual planned frequency of monitoring normally be used to determine the value of n for calculating the AML. However, in situations where monitoring frequency is once per month or less, a higher value for n must be 

assumed for AML derivation purposes. This is particularly 

applicable for addressing situations such as where a single criterion is applied at the end of the pipe and a single monthly sample 

107 
-.- 

3 

2.5 

2 

1.5 

1 

0.5 

a. LTA at 95% Probability Basis 

AML at 99% Probability Basis 

0.5 1 .o 1.5 2.0 

Coefficient of Variation 

3.5 - 

c. LTA at 95% Probabllity Basis 

3- 

AML at 95% Probabllity Basis 

s 

6 2.5 - 

2 

; 2- 

E 

3 1.5- 

& 

a 

J 5 l0.5 - / 

n=30 

0 “““‘I”“J1’-“’ 

0.0 0.5 1 .o 1.5 2.0 

Coefficient of Variation 

3.5 

b. LTA at 99% Probability Basis 

AML at 95% Probability Basls 

n=l 

0”““““““““” 

0.0 0.5 1.0 1.5 

Coefficient of Variation 

2.0 

-.- ^I d. LTA at 99% Probability Basis 

AML at 99% Probability Basis 

0”““““““““” 

0.0 0.5 1.0 1.5 

Coefficient of Variation 

Figure 5-8. Effect of Coefficient of Variation on Average Monthly Limits 

2.0 

108 
8. LTA at 9f 

o”“““l”““““‘j 

0.0 0.5 1 .o 1.5 2.0 

Coefficient of Variation 

c. LTA at 95% Probabiilty Basis 

3 

MDL at 95% Probablllty Basls 

2.5 

0.5 1.0 1.5 

Coefficient of Variation 

3.5 

3- 

b. LTA at 99% Probability Basis 

MDL at 95% Probability Basis 

2.5 - 

2- 

1.5 

Ol”““I”““““‘IJ 

0.0 0.5 1 .o 1.5 2.0 

Coeff bent of Variation 

3.5 

3 

2.5 

2 

t .5 

1 

0.5 

0 

d. LTA at 99% Probability Basis 

MDL at 99% Probabfllty Basis 

0.0 0.5 1.0 I.5 

Coefficient of Variation 

2.0 

Figure 5-9. Effect of Coefficient of Variation on Maximum Daily Limits 

109 
-- -- - 

is contemplated for compliance monitoring purposes, or where 

monitoring frequency is only quarterly. In this case, both the 

average monthly and the MDL would exceed the criterion. (For 

example, for a CCC of 1 .O chronic toxic unit [TU J applied as a 

WLA at the end of the pipe, both the MDL and AML would be 1.6 

TU,; assuming CV=O.6, n=l, and a 99percent probability basis.) 

A discharger could thus comply with the permit limit but routinely exceed the criterion. Under these circumstances, the 

statistical procedure should be employed using an assumed 

number of samples of at least fm for the AML derivatlon. 

5.5.4 ProQaQlllly nasls 

Selection of the probability basis for use in the equations in Boxes 

5-l and 5-2 is a permitting authority decision necessary for establishing statistically derived permit limits. Where a permitting 

authority does not have specific guidance for the probability 

basis, EPA recommends the following: 

For calculation of the LTAs from the WLAs (Box 5-2): 

l Both acute and chronic WLA-.Ol probability (99th percentile level). 

For calculation of permit limits from the most limiting LTA 

1): 

l MDL-.Ol probability basis (99th percentile level) 

l AML--.05 probability basis (95th percentile level). 

(Box 5- 

The probability levels for deriving permit limits have been used 

historically in connection with development of the effluent limits 

guidelines and have been upheld in legal challenges to the guidelines [4). It is important to note that these levels are statistical 

probabilities used as the basis for developing 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 and to protect water quality criteria. 

The level for the calculation of the LTA from the WLA is based 

upon EPA’s interpretation of the steady state model used to 

develop the WI-A. EPA considers the WLA to produce an effluent 

condition that should never be exceeded whenever the critical 

design conditions occur. To characterize this effluent condition, 

EPA uses the 99th percentile concentration from the upper tail of 

the effluent probabilistic distribution curve. The selection of this 

value is one which can have a significant influence on the level of 

conservatism in the permit limits. Permit authorities should consider Figures 5-8 and 5-9 to understand the effect of this decision 

along with other decisions on the AMls and MDLs. 

5.6 PERMK GOCUMENTATION 

The fact sheet and supporting documentation accompanying the 

permit must clearly explain the basis and the rationale for the 

permit limits. When the permit is in the draft stage, the supporting documentation will serve to explain the rationale and assumptions used in deriving the limits to the permittee and the general 

public in order to allow public comment on the draft permit. 

When the permit is issued, the administrative record for the 

facility (particularly the fact sheet) will be the primary support for 

defending the permit in administrative appeals including 

evidentiary hearings. This information also will serve to alert 

compliance/enforcement personnel to any special considerations 

that were addressed at the time of permit issuance. In addition, 

the accompanying documentation will be extremely important 

during permit reissuance and will assist the permit writer in developing a revised permit. 

In 40 CFR Part 124.56, a fact sheet containing “[a]ny calculations 

or other necessary explanation of the derivation of specific effluent limitations” for many draft permits is required. Accordingly, 

the WlAs along with the required LTA and CV used and the 

calculations deriving them must be included or referenced in the 

fact sheet. The permit limit derivation method used must also be 

explained in the permit documentation. Where a permitting 

authority develops a standardized and simplified method for permit limit development as discussed in Section 5.4.2, the permitting authority may not need to document all of the underlying 

assumptions in the fact sheet, provided that the fact sheet references a written permit limit development protocol. Any other 

guidance used must also be cited. 

5.7 EXPRESSHi LIMITS AND DEVELOPING 

MONlTORING REQUIREMENT!3 

Limits must be expressed clearly in the NPDES permit so that they 

clearly are enforceable and unambiguous. Chapter 6 discusses 

compliance monitoring and enforcement problems that can result from improperly expressed limits. All limits, both chemicalspecific and whole effluent, should appear in Part 1 of the permit. 

Special considerations in the use of both chemical-specific and 

whole effluent toxicity limits are discussed below. 

5.7.1 Atmdawd Eftbtmt Llmtb 

Mass-based effluent limits are required by NPDES regulations at 

40 CFR 122.450. The regulation requires that all pollutants 

limited in NPDES permits have limits, standards, or prohibitions 

expressed in terms of mass with three exceptions, including one 

for pollutants that cannot be expressed appropriately by mass. 

Examples of such pollutants are pH, temperature, radiation, and 

whole effluent toxicity. Mass limitations in terms of pounds per 

day or kilograms per day can be calculated for all chemicalspecific toxics such as chlorine or chromium. Mass-based limits 

should be calculated using concentration limits at critical flows. 

For example, a permit limit of 10 mg/l of cadmium discharged at 

an average rate of 1 million gallons per day also would contain a 

limit of 38 kilograms/day of cadmium. 

Mass-based limits are particularly important for control of 

bioconcentratable pollutants. Concentration-based limits will not 

adequately control discharges of these pollutants if the effluent 

concentrations are below detection levels. For these pollutants, 

controlling mass loadings to the receiving water is critical for 

preventing adverse environmental impacts. 

However, mass-based effluent limits atone may not assure attainment of water quality standards in waters with low dilution. In 

110