supplemental manual on the development and … development and implementation of local discharge...

EPA Supplemental Manual On

The Development And

Implementation Of Local

Discharge Limitations

Under The Pretreatment

Program

Residential And Commercial

Toxic Pollutant Loadings And

POTW Removal

Efficiency Estimation

DISCLAIMER

This project has been funded, at least in part, with Federal

funds from the U.S. Environmental Protection Agency (EPA) Office

of Water Enforcement and Compliance under Contract No. 68-C8-

0066, WA Nos. C-1-4 (P), C-1-37 (P), and C-2-4 (P). The mention

of trade names, commercial products, or organizations does not

imply endorsement by the U.S. Government.

ACKNOWLEDGEMENTS

This document was prepared under the technical direction of Mr.

John Hopkins and Mr. Jeffrey Lape, Program Implementation Branch,

Office of Wastewater Enforcement and Compliance, U.S.

Environmental Protection Agency. Assistance was provided to EPA

by Science Applications International Corporation of McLean,

Virginia, under EPA Contract 68-C8-0066, WA Nos. C-1-4 (P),

C-1-37 (P), and C-2-4 (P).

iii

PART 1

RESIDENTIAL AND COMMERCIAL SOURCES OF TOXIC POLLUTANTS

TABLE OF CONTENTS

Section

1.0 RESIDENTIAL AND COMMERCIAL SOURCES OF TOXIC POLLUTANTS . .

1.1 SUMMARY OF DATA RECEIVED .................

1.2 DATA ANALYSIS AND LIMITATIONS ..............

1.3 RESIDENTIAL AND COMMERCIAL MONITORING DATA ........

1.4 SPECIFIC COMMERCIAL SOURCE MONITORING DATA ........

1.5 SEPTAGE HAULER MONITORING DATA ..............

1.6 LANDFILL LEACHATE MONITORING DATA ............

1.7 SUMMARY .........................

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1-29

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V

PART 1

LIST OF TABLES

Table

1.

2.

3.

4.

5.

6.

7.

8.

9.

10.

11.

12.

13.

14.

MUNICIPALITIES WHICH PROVIDED RESIDENTIAL/COMMERCIAL DATA

RESIDENTIAL/COMMERCIAL TRUNK LINE MONITORING DATA . .

COMPARISON OF RESIDENTIAL/COMMERCIAL TRUNK LINE MONITORING DATA WITH TYPICAL DOMESTIC WASTEWATER LEVELS FROM THE 1987 LOCAL LIMITS GUIDANCE . . . . . . . . . . . . . . .

SPECIFIC COMMERCIAL SOURCE WASTEWATER MONITORING DATA - HOSPITALS . . . . . . . . . . .

SPECIFIC COMMERCIAL SOURCE WASTEWATER MONITORING DATA - RADIATOR SHOPS .

SPECIFIC COMMERCIAL SOURCE WASTEWATER MONITORING DATA - CAR WASHES

SPECIFIC COMMERCIAL SOURCE WASTEWATER MONITORING DATA - TRUCK CLEANERS

SPECIFIC COMMERCIAL SOURCE WASTEWATER MONITORING DATA - DRYCLEANERS.....................

SPECIFIC COMMERCIAL SOURCE WASTEWATER MONITORING DATA - LAUNDRIES . . . . . . . . . . . . . . . . . .

SEPTAGE HAULER MONITORING DATA . . .

LANDFILL LEACHATE MONITORING DATA . . . . . . .

OVERALL AVERAGE ORGANIC POLLUTANT LEVELS . . .

OVERALL AVERAGE INORGANIC POLLUTANT LEVELS . . . .

OVERALL AVERAGE NONCONVENTIONAL POLLUTANT LEVELS .

. .

.

.

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vi

PART 2

REMOVAL EFFICIENCY ESTIMATION FOR LOCAL LIMITS

TABLE OF CONTENTS

Section

2.0 REMOVAL EFFICIENCY ESTIMATION GUIDANCE . . . . 2-1

2.1 DEFINITIONS . . . . . . . . . . . . . . . . . . 2-2

2.1.1 Daily Removal Efficiency ................. 2-2 2.1.2 Mean and Average Daily Removal Efficiencies ........ 2-4 2.1.3 Decile Removal Efficiency ................. 2-6

2.2 ILLUSTRATIVE DATA AND APPLICATIONS . . . . . . . . . . . . . 2-7

2.2.1 Daily Influent, Daily Effluent, and Daily Removal Data . 2-7 2.2.2 Average Daily and Mean Removals . . . . . . . . . . . . 2-12 2.2.3 Decile Estimates . . . . . . . . . . . . . . . . . . 2-14

2.3 USE OF REMOVAL ESTIMATES FOR ALLOWABLE HEADWORKS LOADINGS . 2-18

2.4 EXAMPLE ZINC AND NICKEL DATA SETS . . . . . . . . . . . . . . 2-22

2.4.1 Zinc Sample Data ..................... 2-22 2.4.2 Nickel Sample Data .................... 2-30

2.5 OTHER DATA PROBLEMS . . . . . . . . . . . . . . . . . . . . 2-36

2.5.1 Remarked Data ....................... 2.5.2 Seasonality ........................

2.6 NONCONSERVATIVE POLLUTANTS . . . . . . . . . . . . . . . . . . 2-39

2.7 SUMMARY REMARKS . . . . . . . . . . . . . . . . . . . . . . . . . 2-41

2-38 2-39

vii

PART 2

LIST OF TABLES

1. COPPER MASS VALUES (LBS/DAY) AND DAILY REMOVALS .......

2. ORDERED COPPER REMOVALS ...................

3. DECILE ESTIMATION WORKSHEET FOR COPPER DATA .........

4. ZINC MASS VALUES (LBS/DAY) AND DAILY REMOVALS ........

5. ORDERED ZINC REMOVALS ....................

6. DECILE ESTIMATION WORKSHEET FOR ZINC DATA ..........

7. NICKEL MASS VALUES (LBS/DAY) AND DAILY REMOVALS .......

8. ORDERED NICKEL REMOVALS ...................

9. DECILE ESTIMATION WORKSHEET FOR NICKEL DATA .........

1.

2.

3.

4.

5.

6.

7.

8.

9.

10.

11.

12.

PART 2

LIST OF FIGURES

Figures

INFLUENT COPPER MASS VALUES ..................

EFFLUENT COPPER MASS VALUES ..................

DAILY PERCENT REMOVALS FOR COOPER ...............

INFLUENT COPPER vs. EFFLUENT COPPER ..............

INFLUENT ZINC MASS VALUES ...................

EFFLUENT ZINC MASS VALUES ...................

INFLUENT ZINC vs. EFFLUENT ZINC ................

DAILY PERCENT REMOVALS FOR ZINC ................

INFLUENT NICKEL MASS VALUES ..................

EFFLUENT NICKEL MASS VALUES ..................

INFLUENT NICKEL vs. EFFLUENT NICKEL ..............

DAILY PERCENT REMOVALS FOR NICKEL ...............

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viii

APPENDICES

APPENDIX A - ADDITIONAL RESIDENTIAL/COMMERCIAL DATA

. A-1 RESIDENTIAL/COMMERCIAL TRUNK LINE MONITORING DATA

. A-2 COMMERCIAL SOURCE MONITORING DATA

. A-3 SEPTAGE HAULER MONITORING DATA SUMMARIES

A-4 LANDFILL LEACHATE DATA

APPENDIX B - DECILE ESTIMATION WORKSHEET

ix

INTRODUCTION

The National Pretreatment Program as implemented under the Clean Water

Act (CWA) and General Pretreatment Regulations [40 Code of Federal Regulations

(CFR) Part 403] is designed to control the introduction of nondomestic wastes

to Publicly Owned Treatment Works (POTWs). The specific objectives of the

Program are to protect POTWs from pass through and interference, to protect

the receiving waters and to improve opportunities to recycle sludges. To

accomplish these objectives, the program relies on National categorical

standards, prohibited discharge standards and local limits.

Control Authorities are required to develop and enforce local limits as

mandated by 40 CFR 403.5 and 40 CFR 403.8. In December 1987, the U.S.

Environmental Protection Agency (EPA) published a technical document entitled

Guidance Manual on the Development and Implementation of Local Discharge

Limitations (referred to as the "1987 local limits guidance" in the remainder

of this document). That guidance addressed the key elements in developing

local limits such as identifying all industrial users, determining the

character and volume of pollutants in industrial user discharges, collecting

data for local limits development, identifying pollutants of concern,

calculating removal efficiencies, determining the allowable headworks loading,

and implementing appropriate local limits to ensure that the Maximum Allowable

Headworks Loadings (MAHLs) are not exceeded. This manual is intended to

supplement the 1987 local limits guidance and assumes that the reader has a

thorough understanding of local limits development; it builds on information

contained in the 1987 local limits guidance. This is a two-part document

which provides information on toxic pollutant loadings from residential and

commercial sources (Part 1) and calculation of removal efficiencies achieved

by municipal wastewater treatment plants (Part 2).

Part 1 of this document provides background information on pollutant

levels in residential wastewater and in wastewaters from commercial sources,

and characterizes toxic pollutant discharges from these sources. Residential

and commercial source monitoring data summarized in Part 1 are intended to

supplement similar data found in the 1987 local limits guidance.

Xi

The monitoring data provided in Part 1 demonstrate the importance of

accurately characterizing all sources of toxic pollutants during the local

limits development process. While the monitoring data summarized in this

guidance and in the 1987 local limits guidance can be used to estimate

pollutant loadings from specified sources, collection of site-specific

monitoring data is always preferred.

Part 2 of this guidance expands on the 1987 local limits guidance

methodology for calculating POTW removals of toxic pollutants. Calculation

of removal efficiencies for local limits development is necessary to determine

the portion of a given pollutant loading that is discharged to the receiving

stream and the portion that is removed to sludge. The mean approach to

calculating removal efficiencies is probably the most familiar calculation.

The decile approach is a statistical method which allows POTWs to select, with

a particular level of confidence, removal efficiencies for the development of

local limits which will protect the POTW from interference and pass through.

These methods are clearly defined and illustrated with examples and actual

POTW sampling and analysis data. A “worksheet” format is included to simplify

the decile approach. In addition, difficulties that can be encountered (e.g.

negative removals) when applying the calculations to analytical sampling data

are discussed.

Xii

PART 1

RESIDENTIAL AND COMMERCIAL SOURCES OF TOXIC POLLUTANTS

1.0 RESIDENTIAL AND COMMERCIAL SOURCES OF TOXIC POLLUTANTS

In the local limits development process, the Maximum Allowable Headworks

Loading (MAHL) of a particular toxic pollutant is allocated to both

residential and industrial sources. Thus, the POTW classifies each site-

specific source as either a residential or an industrial user. This

classification depends on the size of the facility, and on the toxic pollutant

concentrations and loadings discharged to the POTW. To make informed

decisions regarding this classification, the POTW must have a clear

understanding of toxic pollutant contributions from all sources, including

households, commercial establishments (e.g., radiator shops, car washes,

laundries, etc.), and heavy industries.

Occasionally, a POTW may find that the loadings of a toxic pollutants

exceed the MAHL. Elevated loadings from nonindustrial sources may be

attributable to:

. Nonpoint sources (e.g., runoff) discharging to combined sewers

. Elevated pollutant levels in water supplies

. Household disposal of chemicals into sanitary sewers

. Toxic pollutant discharges from commercial sources.

The first three sources listed above can be controlled through the

implementation of various management practices/programs outside the scope of

local limits development. Nonpoint sources of pollutants are addressed

through combined sewer overflow abatement programs and urban and agricultural

chemical management practice programs. The POTW can address elevated

pollutant levels in water supplies by interacting with the City Water

Department. For example, elevated metals levels in water supplies often arise

from leaching in water distribution pipes; the City Water Department may be

able to reduce such leaching by adjusting the pH and/or alkalinity of the

water supply. The POTW can encourage proper disposal of household chemicals

by instituting public education programs and establishing chemical and used

oil recovery stations.

Elevated pollutant levels in discharges from commercial sources are most

effectively addressed through local limits. Commercial sources such as

radiator shops, car washes, and laundries are often not considered as

1-1

significant sources of toxics due to their small size and generally low flows,

and/or an assumption of insignificant pollutant levels or loadings. These

commercial sources, often discharge at surprisingly high pollutant levels and

should not be overlooked during local limits development. Some of these

commercial sources may warrant consideration as significant industrial users,

including routine monitoring and regulation through local limits.

In addition to commercial sources, other wastewater sources should be

considered when establishing local limits, (e.g., septage haulers’ loads and

landfill leachates).

Given the importance of characterizing wastewaters from these sources,

the purpose of Part 1 of this guidance is to provide data on observed

pollutant levels in residential wastewater, wastewaters from specific types of

commercial sources, septage haulers’ loads, and landfill leachates accepted by

POTWs. The wastewater characterization data provided will enable the POTW to:

. Compare pollutant loadings in its system with those found at other POTWs

. Estimate pollutant loadings from these sources as a supplement to, or in the absence of, pollutant loadings derived from actual site- specific monitoring data. These estimated loadings can be used in local limits calculations when site-specific monitoring data are not available.

. Identify toxic pollutant sources and determine which sources warrant consideration during local limits development, routine monitoring, and regulation under the local pretreatment program.

While the data provided can be used to derive reasonable estimates of

pollutant loadings from specified sources, collection of site-specific data is

preferable.

The monitoring data summarized in this guidance were obtained from a

variety of POTWs. It was summarized by various statistics, including range,

mean, and median pollutant levels. Section 1.1 describes this monitoring

data. While the procedures for data analysis are detailed in Section 1.2.

Sections 1.3-1.6 present and discuss the monitoring data summaries. A summary

of the conclusions is provided in Section 1.7.

1-2

1.1 SLIIMARY OF DATA RECEIVED

To obtain the residential and commercial source monitoring data presented

in this guidance, POTWs were requested to submit the following types of

monitoring data:

. dent&l/commerclol tru mow data - Pollutant levels and flow monitoring flata for trunk lines receiving entirely or primarily residential wastewaters

. c c-1 so- - Pollutant levels and flow monitoring data for specific types of commercial sources (i.e., hospitals, radiator shops, car washes, truck cleaners, dry cleaners, and commercial laundries)

. eotuhauler q oniforinn dau - Pollutant levels in septage haulers’ loads

l Monitorinn data - Pollutant levels in landfill leachates accepted by POTWS.

The monitoring data provided by POTWs did not predate 1986.

Table 1 summarizes the types of residential and commercial source

monitoring data received from POTWs and incorporated into this guidance. As

can be seen from Table 1, 38 POlWs located in all 10 EPA Regions provided

monitoring data.

1.2 DATA ANALYSIS AND LIMITATIONS

Pollutant monitoring data provided by POTWs were summarized by

calculating the following statistics:

0 Hean pollutant level

l Minimum reported pollutant level

l Maximum reported pollutant level

l Median pollutant level.

The number of pollutant detections versus the number of monitoring events

(e.g., a pollutant detected 5 times in 7 monitoring events) was tracked for

each pollutant. Pollutant levels reported as below specified detection limits

were considered in the data analysis and, for the purpose of statistical

l-3

TABLE 1. MUNlClPALmES Wt-4~ PfW/KED RE!XM3llAU~MERCIAL DATA

r nAu1 CCMMERUAL !3OURCE DATA I SEPTAQE lLEAcH*lEl COMMERCIAL

MUNCIPAUTV

analysis, were considered equal to the detection limit. Pollutant levels

reported below detection were incorporated into the statistical analysis as

follows :

. of w now levek - The mean pollutant levels presented in this guidance are based on the use of detection limits (as specified by the POTWs) as surrogates for pollutant levels reported belov detection. For example, the mean of the following data set would be reported as 4 milligrams per liter (mg/l) (assuming a 2 mg/l detection limit).

6 mg/l 4 w/l

< 2 mg/l

. etfl oollutant levels - The use of specified detection limits as surrogates in the determination of minimum and maximum reported pollutant levels is demonstrated as follows:

Set 1: < 2 q g/l Set 2: 1 mg/l 4 w/l < 2 mg/l

< 6 mg/l 5 w/l

minimum - < 2 mg/l minimum - 1 mg/l maximum - < 6 q g/l maximum - 5 mg/l

. on of median Dollutant leveh - Specified detection limits were also used as surrogates in calculating median pollutant levels:

Set 1: 1 w/l Set 2: 1 q g/l < 2 mg/l < 2 mg/l

5 w/l 3 w/l 5 w/l

median - < 2 mg/l mean - 3.25 mg/l

median - < 2 mg/l mean - 2 w/l

In lieu of averaging two detection limits to obtain a median, the lower of the two detection limits was selected as the median:

1 w/l < 2 mg/l < 3 mg/l

5 w/l

median - < 2 mg/l mean - 2.25 q g/l

l-5

Some POTWs reported no pollutant levels below specified detection limits.

For these facilities, the number of monitoring events for each pollutant

equals the corresponding number of pollutant detections and no detection

limits appear as minimum, maximum, or median pollutant levels.

The monitoring data provided by POTWs are assumed to adequately represent

the types of discharges to their systems indicated (i.e., residential trunk

line, specific commercial source, hauled septage, or landfill leachate).

Associated sampling and laboratory quality assurance/quality control data and

protocols were not requested of the municipalities nor reviewed during the

survey; therefore, the assumption of representative monitoring data has not

been verified. This verification was not deemed essential in providing

estimates of pollutant levels in residential/commercial source discharges. It

should be emphasized again that accurate data may only be ensured through the

implementation of site-specific monitoring programs.

The POTWs had obtained their monitoring data through a variety of local

sampling programs, instituted for a variety of purposes, including local

limits development, industrial user compliance monitoring, and industrial user

self-monitoring. The POTUs indicated that both grab and composite sampling

techniques had been employed, depending on the specifics of the local

monitoring program and the nature of the discharges being monitored.

Consistent sampling techniques were not employed by all respondent POTWs. For

a given wastewater source discharging to a given POTU, both grab and composite

monitoring data were often submitted. Due to such variation in sampling

technique, no attempt has been made in this report to resolve monitoring data

in accordance with sample type.

The commercial source and landfill leachate monitoring data submitted by

respondent POTUs were obtained by sampling at the facilities’ sewer

connections, downstream of any installed pretreatment units. The submitted

monitoring data therefore reflect the level of pretreatment, if any, installed

at the time of monitoring. The nature and efficiency of pretreatment units

depend upon the particular discharge being considered, and no attempt has been

made in this document to classify pollutant levels as either raw or treated

l-6

levels. The pollutant levels provided in this document should be considered

as neither raw nor treated pollutant levels, but rather as reflective of the

discharge levels currently being received by the various POTWs.

The types of commercial sources considered in this document (e.g.,

radiator shops, hospitals, etc.) were defined on the basis of the services

they provide, rather than on any similarities in process operations. Process

flowcharts for individual industries were not requested or reviewed to

identify similarities in process operations or wastewater treatment

technologies and practices. The assumption should be made that facilities may

perform a diversity of process operations and may or may not pretreat

wastewaters prior to discharge. Also, as indicated previously, the accuracy

and representativeness of the commercial source monitoring data provided in

this report can only be verified through site-specific monitoring of

individual facilities.

Since process flowcharts were not reviewed while developing this

guidance, it is not known whether the individual industries considered in this

study perform any operations regulated by Federal categorical pretreatment

standards. For example, a radiator shop performing acid etching or phosphate

coating would be subject to the electroplating/metal finishing categorical

standards (40 CFR 413/40 CFR 433). POTWs should be aware that consideration

of a type of commercial source, such as radiator shops, in this document does

not preclude the applicability of Federal categorical pretreatment standards.

Each POTW should review process flowcharts for each of its industrial users,

to determine the applicability of Federal categorical pretreatment standards

on a case-by-case basis.

1.3 RESIDENTIAL AND COMMERCIAL MONITORING DATA

As discussed in the introduction, POTWs should establish total pollutant

loadings from residential sources as part of the local limits development

process. The recommended procedure in the 1987 local limits guidance for

determining residential pollutant loadings is through a site-specific

monitoring program. Such a program entails the periodic collection and

l-7

analysis of samples from trunk lines receiving wastewater from residential and

commercial sources. Site-specific total residential loadings are calculated

from pollutant level and wastewater flow monitoring data resulting from a

residential/commercial trunk line monitoring program.

Many POTUs have established residential/commercial trunk line monitoring

programs. Monitoring data provided by 15 POTWs is presented in this section.

Of these POTUs, nine reported that their residential/commercial trunk line

programs were established specifically to support local limits development.

Table 2 summarizes residential/commercial trunk line monitoring data

provided by 15 POTUs located in 7 EPA Regions. Average, minimum, and maximum

pollutant levels; number of detections; and number of observations are

provided for each pollutant. The monitoring data summarized in Table 2 were

obtained through monitoring of sewer trunk lines which receive wastewaters

exclusively from residences and small commercial sources. The pollutant

monitoring data provided in Table 2 have been sorted by average pollutant

level.

The pollutants identified in Table 2 at highest average levels are

ammonia, phosphate, iron, zinc, and copper. The most frequently detected

pollutants are cadmium, chromium, copper, lead, nickel, and zinc.

The monitoring data provided in Table 2 can be used by POTWs in

estimating total pollutant loadings from residential/commercial sources, for

the purpose of calculating local limits. As previously discussed,

municipalities should also establish residential/commercial monitoring

programs to obtain site-specific data for use in local limits calculations.

The monitoring data summarized in Table 2 are intended to supplement

existing sumaries of residential/commercial wastewater monitoring data, such

as those provided in the 1987 local limits guidance. Table 3 presents a

comparison of the Table 2 monitoring data with typical residential/commercial

wastewater levels presented in the 1987 local limits guidance. The 1987 local

limits guidance provides levels for nine metals and cyanide, based on

compilations of monitoring data from four POTWs.

l-8

TABLE 2. RESIDENTIAUCOMMERCIAL TRUNKLINE MONITORING DATA

POLLUTANT NUMBER OF NUMBER OF MIN. CONC. MAX. CONC. AVG. CONC:

1 DETECTIONS , SAMPLES 6-W F-W @WU

INORGANICS

I PHOSPHATE 2 2 27.4 30.2 IRON 18 18 0.0002 3.4 TOTAL PHOSPHOROUS 1 1 0.7 0.7 BORON 4 4 0.1 0.42 FLUORIDE 2 2 0.24 0.27 BARIUM 31 31 0.04 1 MANGANESE I 31 31 0.04 I ~CYANIDE I 71 71 0.01 I 0.37 I 0.082 1 1 NICKEL I 313 I 540 I <O.ool I 1.6 1 0.047 I I LITHIUM I 21 21 0.03 I 0.031 I 0.031 1 CADMIUM I 361 1 538 1 0.00076 1 0.11 I 0.008 1 I ARSENIC I 140 I 205 1 o.ooo4 I 0.088 I 0.007 I :CHROMIUM (Ill) 1 2 <o.OOs 0.007 ‘CHROMIUM(T) 311 522 <O.Ool 1.2 ,.- -. MERCURY 218 235 <0.oool 0.054 SILVER I 181 I 224 1 0.0007 I 1.052 I 0.019 I

ORGANICS

METHYLENE CHLORIDE

1 :c 1 2

l Parameters are tanked by concenttattons from htgh to low.

l-9

TABLE 2. RESIDENTIAUCOMMERCIAL TRUNKLINE MONITORING DATA (Continued)

POLLUTANT NUMBER OF NUMBER OF MIN. CONC. MAX. CONC. AVG. CONC: DETECTIONS SAMPLES WW) (m94 @WI)

ORGANICS

‘Parameters are ranked by concentrations from high lo low.

l-10

TABLE 3. COMPARISON OF RESIDENTIAL/COMMERCIAL TRUNKLINE MONITORING DATA WITH TYPtCAL DOMESTIC WASTEWATER LEVELS

FROM THE 1987 LOCAL LIMITS GUIDANCE

Local Limits Guidance Overall Average Typical Domestic Average Pollutant Levels Wastewater Level (mg/l) from Table 2 (mg.4)

‘From Guidance Manual on the Development and Implementation of Local Discharge Limitations Under the Pretreatment Program, United States Environmental Protection Agency Office of Water Enforcement and Permits, December 1987, p. 3-59

l-11

As shown in Table 3, the greatest differences in pollutant levels are for

mercury and silver. The average mercury level from Table 2 is 0.002 mg/l,

nearly seven times the mercury level of 0.0003 mg/l reported in the 1987 local

limits guidance. The average silver level from Table 2 is 0.019 q g/l, nearly

five times the silver level of 0.004 mg/l reported in the local limits

guidance. For all other pollutants listed in Table 3 except chromium, the

Table 2 average pollutant level is higher than the 1987 local limits guidance

level by at least a factor of two.

The average residential/commercial trunk line pollutant levels for metals

and cyanide provided in Table 2 are higher than those provided in the 1987

local limits guidance and hence, are more conservative. Also, they are based

on monitoring data from more POTWs, and as such, may more adequately

characterize residential/commercial wastewaters received by most POTWs. Site-

specific monitoring data should always be used in preference to reliance on

any literature data.

Appendix A, Table A.l, provides residential/commercial trunk line

monitoring data summaries for each of the 15 PO'lWs. Average, median, minimum,

and maximum pollutant levels; number of detections; number of observations;

the combined total residential/commercial flow to the POTW; and the

residential/commercial percent of the POTW’s total flow are provided for each

POTW .

The residential/commercial trunk line monitoring data provided in this

section can be used as a supplement to, or in the absence of, actual site-

specific monitoring data in the calculation of local limits. As pollutant

levels in residential/commercial trunk lines can depend on site-specific

factors such as the size of the municipality, it is important to recognize

that the literature data serve only as surrogates for actual site-specific

monitoring data. Rather than continuing to rely exclusively on u literature

data, POTUs in the process of establishing local limits should consider

instituting appropriate residential/commercial trunk line monitoring programs

to establish accurate site-specific data.

1-12

1.4 SPECIFIC COMMERCIAL SOURCE MONITORING DATA

Commercial source monitoring data are useful to POTWs in identifying

sources of toxic pollutants, and in determining which commercial sources

should be considered as regulated sources for the purpose of calculating local

limits, Such data are also helpful in determining which commercial sources

warrant routine monftoring. Data for various types of commercial source are

presented and discussed. The monitoring data provided in this section are

intended to assist the POTW in characterfzing those pollutants most frequently

discharged, and those pollutants discharged at elevated levels by various

types of commercial facilities. This information can be used by the POlW to

better understand the sources of toxic pollutants and in determining

compliance and monitoring priorities.

Specific commercial source monltoring data were provided by 21 PO-TVs.

These POTWs are located in nine EPA Regions. Monitoring data were provided

for six types of commercial sources:

. Hospitals

. Automobile radiator shops

. Car washes

. Truck cleaners

. Dry cleaners

. Commercial laundries.

Table A.2 in Appendix A provides commercial source monitoring data

summaries for each of the 21 POTWs and 6 commercial source types. Average,

median, minimum, and maximum pollutant levels; number of detections; number of

observations ; number of commercfal sources; and total commercial source flow

are provided for each POTW.

As discussed above, specific commercial source monitoring data should be

used in establishing commercial facilities warranting regulation through local

limits. Of the 21 POTWs which submitted data, 14 indfcated that they issue

discharge permits (or other control mechanisms) to commercial facilities

belonging to the above categories. The discharge permits issued by these

municipalities required compliance with the municipalities’ local limits.

l-13

Four of the municipalities reported establishing local Total Toxic Organics

(TTO) limits to address organic solvents known to be discharged by industrial

users, including the above commercial. One municipality reported establishing

a TTO limit specifically for laundries, owing to concern regarding solvent

discharges from these facilities.

Fourteen POlUs required commercial sources belonging to the categories

listed above to be routinely monftored for local limits compliance. Reported

compliance monitoring frequencies ranged from quarterly to once every 2 years,

with annual monitoring being typical. Five municipalities required commercial

sources to self-monitor, usually on a quarterly basis.

The monitoring data in this section can be used to determine those types

of commercial sources which may be of concern. The criteria by which this

evaluation is conducted will vary from POTS to POTW and will depend on such

issues as POlW size, POTW permitting and monitoring resources, and the

magnitude of pollutant loadings currently received by the PO’IW relative to the

maximum allowed. Specific commercial sources identified by the POTW to be of

potential concern should be surveyed, routinely monitored, and/or issued

discharge permits, as determined by site-specific considerations.

Monitoring data obtained for each of the six types of commercial

facilities listed above are discussed and evaluated in the following

subsections. Each subsection addresses a particular type of commercial

facility.

Hospital wastewater monitoring data are summarized in Table 4 for a total

of 42 sources discharging to 7 POTWs. Pollutants present in hospital

wastewaters at the highest average levels included total dissolved solids,

Chemical Oxygen Demand (COD), phosphate, surfactants, formaldehyde, phenol,

and fluoride. Metals at the highest average levels included lead, iron,

barium, copper, and zinc. POTWs may assume that these pollutants are

characteristic of hospital wastewaters. Based on Table 4, the most frequently

detected pollutants in hospital wastewaters were COD, phenol, silver, lead,

copper, and zinc.

l-14

TABLE 4. SPECIFIC COMMERCIAL SOURCE WASTEWATER MONITORING DATA HOSPITALS

POLLUTANT NUMBER OF NUMBER OF DETECTIONS SAMPLES

MIN. CONC. MAX. CONC. AVG. CONC. ’

O-Wl) O-WU (Wl)

INORGANICS

16 1

NONCONVENTIONALS

-~__ TDS 12 12 331 SSO

COD 96 96 20 1345 -~ SURFACTANTS 11 11 0.52 4.6 -__~ __

‘Parameters are ranked by concentrations from high to low.

1-15

TABLE 4. SPECIFIC COMMERCIAL SOURCE WASTEWATER MONITORING DATA HOSPITALS (Continued)


MIN. CONC. MAX. CONC. AVG. CONC:

(mgcl) WW) PIY)

ORGANICS

FORMALDEHYDE 19 35 co. 1 1.4 0.58 PHENOL 38 38 ,025 0.698 0.2


1-16

Table 5 summarizes automobile radletor shop mnltoring data for a total

of 32 sources discharging to 7 PCTWs. Pollutants discharged at highest

average Levels included zinc, lead, and copper. The ImISt ftXquently &+tSctSd

pollutants were also zinc, lead and copper. Based on the data provided in

Table 5, POTWs should consider radiator shop wastewaters to contain elevated

levels of these metals.

Table 6 summarizes car wash IIXInitOrflIg &ta provided for 11 facilities

discharging to 3 POT%. Pollutants discharp at highest levets included COD

and the metals zinc, lead, and copper. The met& zinc, lead, and copper are

the most frequently identified polLutantS.

Duck Cl-

Table 7 provides monitoring data for sfx truck cleming facfliti.es

discharging to 2 POTUs . Pollutants detected at highest average Levels

included COD, total dissolved solids, cyanide, phosphate, phenol, zinc, and

aluminum. The roost frsquently detected pollutants were chromium, Lead,

copper, zinc, COD, and phenol. POTUs should anticipate that truck cleaning

wastewaters may contain a variety of organic and/or inorganic pollutant%,

potentially at elevated levels.

Table 8 sumurises monitoring datr for 31 dry cleaning facilities

discharging to 3 POTUs. Pollutxnta et highest average levels were tote

dissolved solid*, COD, phosphate, iron, zinc, and copper, as well 4s t’ organic solvents butyl cellosolve and N-butyl benzene sulfonamide. TI

frequently fdentifled pollutants in the dry cleaners’ wastewaters vex

Phosphate.

Table 9 presents a sumary of monitoring data for 59 cow

discharging to Lb POTUs. Organic pollutants found at higher

were COD, ethyl toluene, n-propyl alcohol, isopropyl alcohr

ethylbenzene, and bis (2-ethylhexyl) phthalate. Metals I

1-17

TABLE 5. SPECIFIC COMMERCIAL SOURCE WASTEWATER MONITORING DATA RADIATOR SHOPS

POLLUTANT NUMBER OF NUMBER OF MIN. CONC. MAX. CONC. AVG. CONC: DETECTIONS SAMPLES PW) @WI) (mgll)

INORGANICS

MFRCURY

NONCONVENTIONALS

[coo 21 31 <3.7 11.3 1 7.667 ]


1-18

TABLE 6. SPECIFIC COMMERCIAL SOURCE WASTEWATER MONITORING DATA CAR WASHES

rPOLLUTANT NUMBER OF NUMBER OF MIN. CONC. MAX. CONC. AVG. CONC.’

L DETECTIONS SAMPLES (Wl) (fw4 WW)

INORGANICS

ZINC 37 37 0.02 3’ 0.543 !LEAD 29 34 0.002 0.99 0.162 ‘COPPER 29 33 0.03 0.39 0.139 !NiCKEL 17 26 0.02 0.25 0.08 jCHROMlUM (T) 16 29 0.01 0.24 0.074 SILVER 3 12 <O.ool <.05 0.018 ,CADMlUM 21 33 I <.002 0.07 I 0.017

NONCONVENTIONALS

[COD 31 31 341 250 ] 126.33 ]

‘Parameters are ranked by concentrations from high to low

1-19

TABLE 7. SPECIFIC COMMERCIAL SOURCE WASTEWATER MONITORING DATA TRUCK CLEANERS

‘POLLUTANT NUMBER OF NUMBER OF DETECTIONS SAMPLES

MIN. CONC. 1 MAX. CONC. ] AVG. CONC:

ImW I @-@I) I 0-W)

lNORGANlCS

NONCONVENTIONALS

ORGANICS

l Parameters are ranked by concentrations from high to low.

l-20

TABLE 8. SPECIFIC COMMERCIAL SOURCE WASTEWATER MONITORING DATA DRY CLEANERS


MIN. CONC. 1 MAX. CONC. 1 AVG. CONC: ImaIl) Imalll Imall

INORGANICS

NONCONVENTIONALS -_ ~. -- 1 1 625'

87 I.!!----- 1

625 625 _--.-~--- --____ 82 3865 315.565

ORGANICS

r BUTYL CELLOSOLVE


1-21

TABLE 8. SPECIFIC COMMERCIAL SOURCE WASTEWATER MONITORING DATA DRY CLEANERS (Continued)


MIN. CONC. MAX. CONC. AVG. CONC. ’

owl) (mW OWl)

ORGANICS

DI-N-OCTYT-PHTHALTE 1 1 0.042 0.042 0.042 STYRENE 1 1 0.02 0.02 0.02 TOLUENE 1 1 0.016 0.016 0.016

*Parameters are ranked by concentrations from high to low.

1-22

TABLE 9. SPECIFIC COMMERCIAL SOURCE WASTEWATER MONITORING DATA LAUNDRIES


MIN. CONC. 1 MAX. CONC. 1 AVG. CONC: ImaIl I ImalO I fmdl)

INORGANICS

PHOSPHATE I 51 51 4.4 I 18.4 1 13.2 1 SULFIDE I 1 I 31 co.2 I 14 I 4.8 1 IRON I 431 I 441 I 4.01 I 145 I 3.796 1 ZINC I 1166 I 1264 I co.005 I 234 I 1.873 I LEAfI I 953 I 1212 I 0.01 1 150 I 1.514 I MANGANESE I 31 31 0.26 1 0.83 1 0.553 I BARIUM 1 37 I 37 I 0.089 1 1.1 I 0.506 1 COPPER 1038 1063 0.01 14.6 0.452 CHROMIUM (T) 572 908 0.003 36.8 0.216 NICKEL 332 863 <O.ool 1 2.93 0.14 StLVER I 50 I 76 1 c.0002 I 0.017 I 0.123 1 CYANIDE I 124 I 125 1 0.002 I 3.4 I 0.101 I ARSENIC I 30 I 43 I <.002 I co.81 1 0.034 I CADMIUM 525 905 <.002 1 0.518 1 0.034 SELENIUM 17 41 <.002 0.021 1 0.016 -- -_

NONCONVENTIONALS

ICOD 274 1 274 1 60 [ 20000 1 1421.409 ---_

*Parameters are ranked by concentrations from hrgh to low.

1-23

TABLE 9. SPECIFIC COMMERCIAL SOURCE WASTEWATER MONITORING DATA LAUNDRIES (Continued)

POLLUTANT 7

NUMBER OF NUMBER OF MIN. CONC. MAX. CONC. AVG. CONC: DETECTIONS SAMPLES OWl) VW) PW)

ORGANICS

-- ~ 1 -ETHYL-4-METHYL BENZENE 2 3 <150 150 150 1 -ETHYL-3-METHYL BENZENE 3 4 <150 150 150 1 -ETHYL-2-METHYL BENZENE 3 4 <150 150 150 n-PROPYL ALCOHOL 1 1 74 74 74

I ISOPROPYL ALCOHOL 2 2 12 39 25.5 1 1

M-XYLENE 1 4 <1.47 22.57 6.744 TOLUENE 6 10 0.014 16 4.032 P-XYLENE 1 4 CO.96 11.29 3.543 ETHYLBENZENE 4 9 0.033 3.16 0.95 BIS (2-ETHYLHEXYL) PHTHALATE 1 1 0.35 1.1 0.725 NAPTHALENE 1 1 0.310 0.31 0.31 PHENOL 214 231 <O.Ol 6.51 0.244 TETRACHLOROETHENE 5 5 0.096 0.32 0.163 CHLOROFORM 6 10 <O.ool 0.62 0.141 1 ,1,2,2-TETRACHLOROETHANE 2 5 <O.tml 0.43 0.099 DI-N-OCTYL PHTHALATE 1 1 0.057 0.057 0.057 DI-N-BUNL PHTHAtATE 2 2 0.012 0.07 0.941 BUTYL BENZYL PHTHALATE 2 2 0.02 0.046 0.033 TRANS-1,2-DICHLOROETHENE 3 10 <O.ool 0.18 0.026 _ BROMOFORM 1 5 <O.OOl 0.074 0.026 1 ,l ,l -TRICHLOROETHANE 1 5 <O.ool 0.09 0.025 CARBON TETRACHLORIDE 1 5 <O.OOl co.025 0.01 CHLOROBENZENE 1 5 <O.ool <0.025 0.009


1-24

TABLE 9. SPECIFIC COMMERCIAL SOURCE WASTEWATER MONITORING DATA LAUNDRIES (Continued)



@WI) Ow34 @KIN

ORGANICS

BROMODICHLOROMETHANE 2 5 <O.OOl co.025 0.009 METHYLENE CHLORIDE 1 5 0.011 0.011 0.006


1-2s

levels included iron, lead, zinc, and copper. Other inorganics identified in

laundry wastewaters included phosphate and sulfide. The most frequently

detected pollutants were the metals zinc, lead, copper, and chromium. POWS

should anticipate that laundries may discharge a variety of organic solvents

as well as metals, and that organic pollutant levels in laundry wastewaters

may be elevated.

The monitoring data provided in Table 9 provide a basis for POTWs to

determine the significance of various commercial sources and the need for

regulation through local limits.

1.5 SEPTACE HAULER MONITORING DATA

Existing septage hauler monitoring data are useful to the POTU in

evaluating the need for monitoring septage haulers’ loads to verify compliance

with local limits. In this section of the document, septage hauler monitoring

data obtained from POTWs are summarized and discussed.

Table A.3 of Appendix A provides septage hauler monitoring data summaries

for each of nine POTWs. The monitoring data were obtained through periodic

spot sampling of septage haulers’ loads discharged to these POTWs. Average,

median, minimum, and maximum pollutant levels; number of detections; number of

observations; and total septage hauler flows are provided for each POTW.

Table 10 summarizes septage hauler monitoring data provided by the nine

POTWS. Metals identified at highest average levels in septage haulers’ loads

included iron, zinc, copper, lead, chromium, and manganese. The most

frequently identified metals were copper, nickel, chromium, and lead.

Organics identified at highest average levels were COD, acetone,

isopropyl alcohol, methyl alcohol, and methyl ethyl ketone. Based on these

data, POTWs should anticipate that hauled septage may contain relatively high

levels of heavy metals and organic solvents. POlWs should periodically

monitor septage haulers’ loads to verify compliance with applicable local

limits for the metals listed above, as well as for common organic solvents

(especially ketones and alcohols) and for COD.

l-26

POLLUTANT

TABLE 10. SEPTAGE HAULER MONITORING DATA

NUMBER OF NUMBER OF DETECTIONS SAMPLES


hndl) Ondl) Imdl)

INORGANICS

IIRON I 464 1 464 1 0.2 I 2740 1 39.287 IZINC I 959 I 967 1 co.001 I 444 I 9.971 IMANGANESE I 51 51 0.55 I 17.05 I 6.088 I BARIUM I 128 I 128 I 0.002 I 202 I 5.758 ICOPPER I 963 1 971 I .Ol I 260.9 I 4.835 I LEAD I 962 I 1067 I co.025 1 118 1 1.21 INICKEL I 813 1 1030 I 0.01 I 37 I 0.526 ICHROMIUM tn I 931 I 1019 I 0.01 I 341 0.49 ICYANIDE I 575 I 577 I 0.001 I 1.53 I 0.469 ICOBALT I 16 1 32 1 co.003 I 3.45 I 0.406 I ARSENIC I 144 I 145 I 01 3.5 I 0.141 ISILVER I 237 1 272 1 co.003 I 51 0.099 ICADMIUM I 825 1 1097 I 0.005 I 8.1 I 0.097 ITIN I 11 I 25 1 co15 I 1 I 0.076 IMERCURY I 562 1 703 I 0.0001 I 0.742 1 0.005

NONCONVENTIONALS

[COD 1631 - 1. 183 1 so 1 117500 ] 21247.951 --- - -- ]


l-27

TABLE 10. SEPTAGE HAULER MONITORING DATA (Continued)

POLLUTANT

ORGANICS

NUMBER OF NUMBER OF DETECTIONS SAMPLES


@WI) @Wl) (mgll)


l-28

1.6 LANDFILL LEACHATE MONITORING DATA

Landfill leachate monitoring data were obtained from eight POTWs which

accept landfill leachates for treatment. Four of these eight POTWs indicated

that discharge permits are issued to landfill leachate dischargers that

require compliance with the POTWs' local limits. Reported compliance

monitoring frequencies varied from weekly to annually. Most of the POTIJs

reported that routine compliance monitoring was for metals only; however, Ollt?

POTW reported conducting periodic Polychlorinated Biphenols (PCB) analyses,

and another POTW indicated requiring full priority pollutant scans on an

annual basis.

Table A.4 of Appendix A provides landfill leachate monitoring data

summaries for each of the eight POTWs. Average, median, minimum, and maximcm

pollutant levels; number of detections; and number of observations are

provided for each POTW.

Table 11 summarizes landfill leachate monitoring data submitted by the

eight POTWs. Table 11 indicates that such wastewaters may contain a variety

of organic pollutants as well as metals. Metals identified at highest average

levels included iron, manganese, and zinc. Organics identified at highes:

average levels include COD, methyl ethyl ketone, acetone, phenols, and

1,2-dichloroethane (ethylene dichloride). The most frequently detected

pollutants were the metals cadmium, chromium, copper, lead, nickel, and zi:lc

Based on these data, POTWs should anticipate that landfill leachates may

contain a wide variety of metals and organic pollutants.

1.7 SUMMARY

To characterize the composition of wastewaters from residential ar:d

commercial sources, monitoring data provided by 24 POlWs. located in all 10

EPA Regions, have been summarized (by POW) and discussed. Based on a re\vitiv

of the monitoring data summaries provided in Tables 12, 13, and 14,

wastewaters from residential and commercial sources may be characterized as

follows:

l-29

TABLE 11. LANDFILL LEACHATE MONITORING DATA”

POLLUTANT

INORGANICS


(mgrl) (mgrl) O-WU

ORGANICS

METHYL ETHYL KETONE 5.3 29 13.633

ACETONE 2.8 2.8 2.8

1,2-DICHLOROETHANE co.005 6.8 1.136

PHENOL 0.008 2.9 1.06

TOLUENE 0.0082 1.6 0.735


l ‘Number of detections/number of observations could not be determlned from data prowded.

l-30

TABLE 11. LANDFILL LEACHATE MONITORING DATA” (Continued)

POLLUTANT

ORGANICS

MIN. CONC. MAX. CONC. AVG. CONC. l

P-WI) P?Y) (WI)

0.16 1

<O.OOl I <O.l 1 0.021 J

#NE I 0.011 1 0.022 1 0.019

IL 0.018 1 0.018 1 0.018 0.016 1 0.016 1 0.016 1

IINE I 0.011 I 0.011 I 0.011 1

JE I 0.011 1 0.011 1 0.011

‘HTHALATE 0.0049 I 0.0049 I 0.005

_ PHTHALATE I 0.0044 1 0.0044 0.004

ROETHANE <O.OOl I 0.052 0.002


l ‘Number of detections/number of observations could not be determined from data provided. 1-31

. Of the six categories of commercial facilities considered in this guidance, radiator shops, truck cleaning facilities, and industrial laundries were identified as discharging the highest average levels of metals. Average levels of the metals zinc, nickel, chromium, cadmium, lead, iron, and manganese for these three categories of commercial facilities were at least three times the corresponding average residential/commercial trunk line levels for these pollutants.

. Truck cleaners and industrial laundries were identified as discharging elevated levels of organics. The average COD concentration for truck cleaners was 36,500 tug/l, and the average COD for industrial laundries was 1,400 mg/l. Industrial laundries were identified as discharging a number of organic solvents, including aromatics (toluene and xylene) and alcohols.

. Truck cleaning facilities were identified as discharging elevated levels of cyanide and total dissolved solids.

. Inorganic pollutants characteristic of hospital wastewaters included total dissolved solids, barium, lead, silver, and fluoride.

. Inorganic pollutants characteristic of dry cleaners’ wastewaters included total dissolved solids and phosphate

l Metals levels in septage haulers’ loads were considerably higher than in residential/commercial trunk line wastewater. Average levels of arsenic, barium, cadmium, chromium, copper, iron, lead, manganese, nickel, and zinc for hauled septage were at least 10 times the corresponding average resfdentfal/commercial trunk Line levels for these pollutants.

. Septage haulers were identified as discharging elevated levels of COD; the average concentration of COD in hauled septage was 21.250 mg/L.

. Solvents identified in septage haulers’ loads included methyl alcohol, acetone, and methyl ethyl ketone.

1 Leachetes:

. Average levels of the metals manganese, zlr:c, iron, chromium, and nickel in landfill leachates were at Least 10 times the corresponding average residential/commercial trunk line levels for these pollutants.

. Solvents identified in Landfill Leachates included methyl ethyl ketone and acetone.

l-32

Tables 12, 13, and 14 present a summary of the overall, average,

inorganic, organic, and nonconventional pollutant levels for residential and

commercial sources as well as septage haulers and landfill leachates. From

these tables the following pollutants have been identified as characteristic

of the wastewater sources indicated:

Residential/commercial trunk lines - Phosphate, ammonia, and the metals cadmium, chromium, copper, lead, nickel, and zinc

Hospitals - Total dissolved solids, fluoride, and the metals barium, lead, and silver

Radiator shor?? - Zinc, Lead, and copper

Car washes - Zinc, lead, and copper

Truck cleaners - COD, total dissolved solids, cyanide, phenol and the metals lead, zinc, chromium, and copper

Dry cleaners - Total dissolved solids and phosphate

Laundries - COD, ethyl toluene, propanol, xylene, toluene, and the metals iron, Lead, zinc, and copper

mne hauu - COD, methyl alcohol, acetone, methyl ethyl ketone, arsenic, and the metals cadmium, chromium, copper, lead, nickel, zinc, barium, iron, and manganese

Landfill leachates - Methyl ethyl ketone, acetone, and the metals manganese, zinc, iron, chromium and nickel.

The data provided in this guidance may be used in deriving reasonable

estimates of pollutant loadings from the above listed wastewater sources.

Each municipality should determine which of the above listed sources are of

concern on a site-specific basis and should establish residential/commercial

trunk line and specific commercial source monitoring programs to determine

actual pollutant Loadings received from those sources.

l-33

TABLE 12. OVERALL AVERAGE MGANIC POLLUTANT LEVELS (MGIL)

POLLUTANT RES. SEPTAGE LEACHATE AVERAGE AVERAGE AVERAGE

I

lrCETONE 1 10.588 2.8 BENZENE I 0.062 0.025 BENZOIC ACID I I I 0.19 BlS(2-ETHYLHEXYL)PHTHALATE 0.006 BROMODICHLOROMETHANE BROMOFORM Z-BUTANONE I I 1 13.633

4 --- i I~ICHLOROBENZENE

-.-JO3 0.101

1 .l DICHLOROETHANE 0.026 0.575 -.- --- -- -- 1,l DICHLORC%THENE DIETHYL PHTHALATE DIMETHYL PHTHAIATE 2,4 DIMETHYLPHENOL DI-N-OCTYL PHTHALATE I I I ETHYL BENZENE I 0.067 0.171 1 -ETHYL-2-METHYL BENZENE

COMM

CAR DRY WASH CLEANER

AVERAGE AVERAGE

.RCIAL FACILITIES

HOSPITAL INDUSTRIAL RADIATOR TRUCK 2

0.033

0.010 0.009 -- 0.141 v-- 1

1 I 1

----t----i-+ --! 1

---+--I--+-

1-34

TABLE 12. OVERALL AVERAGE ORGANIC POLLUTANT LEVELS (MG/L) (Continued)

POLLUTANT RES. SEPTAGE LEACHATE COMMERCIAL FACILITIES AVERAGE AVERAGE AVERAGE

CAR WASH3RYCLEANER HOSPITAL INDUSTRIAL RADIATOR TRUCK AVERAGE AVERAGE AVERAGE LAUNDRIES SHOP CLEANERS

AVERAGE AVERAGE AVERAGE

1 -ETHYL-4-METHYL BENZENE 150 -_ FLUORANTHENE 0.001 FORMALDEHYDE 0.58 2-HEXANONE 0.094 36478.502 ISOPROPYL ALCOHOL 14.055 METHYL ALCOHOL 15.84 METHYL ETHYL KETONE 3.650 METHYLENE CHLORIDE 0.027 0.101 0.310 0.006 4-METHYLPHENOL 4-METHYL-2-PENTANONE M-XYLENE

0.065 0.43

6.744

1-35

TABLE 12. OVERALL AVERAGE ORGANIC POLLUTANT LEVELS (MGIL) (Continued)

1,2,4-TRkHLOROBENZENE 0.013 1 ,I ,l -TRlCHLOROETHANE 0.019 0.025 TRICHLOROETHENE 0.028 TRlCHLOROETHYLENE 0.018 I VINYL ACTRATE [ 0.250 [ VINYL CHLORIOE 0.067 I XYLENE I 0.051 0.317 I

1-36

TABLE 13. OVERALL AVERAGE INORGANIC POLLUTANT LEVELS (MGIL)

IALUMINUM 0.34 ANTIMONY 0.142

‘ARSENIC 0.007 0.141 0.042

BARIUM 0.115 5.758 0.201 BERYLLIUM BORON 0.3

I -. 0. - 0.

1.13 I 7.7 ) 018 1

__. ! 0.09

026 0.034 0.012 1 0.068

! 1.779 0.506 ! I I I 1 1 0.013

I POLLUTANT 1 -F

I ___ i

CAR DRY HOSPITAL INDUSTRIAL RADIATOR TRUCK WASH CLEANER AVERAGE IAUNDRIES SHOf CLEANERS

AVERAGE AVERAGE AVERAGE AVERAGE AVERAGE -- .- I

1

CADMIUM 0.008 0.097 0.030 j 0.017 0.c

CHROMIUM 0.034 0.490 0.633 1 0.074 0.c CHROMIlJM(III1 0.006 I

K)8 0.018 0.034 0.165 0.027 122 0.117 0.216 0.128 0.120

- I

39 1 0.086 0.452 0.552 22.218 0.233 0.101 0.030 55.587

I 0.637 I I I I -.-_. I

1 2.249 3.796 64.430

62 1 0.032 0.881 1.514 69.210 0.353

0.553 1.23 0.002 0.004 o.ooo4

0.009 0.060 0.140 0.300 0.177 0.011 0.016 0.012 0.098 0.123 0.024 0.114

f 0.042

StLVER 0.019 0.099 0.019 0.018 THALLIUM TIN 0.076 :ZlNC 0.212 9.971 12.c lo61 0.543 j 0.174 0.563 1.8;s 145.295 4.416

1-37

I IAMMONIA COO PHOSPHATE SULFIOE SURFACTANTS TOS TOTAL PHOSPHORUS

TABlE 14. OVERAU AVERAGE NONCONVENTIONAL POLLUTANT LEVELS (MGJL)

28.8 1

I

0.7 I

LEACHATE AVERAGE

34.545

COMMERCUL FACIUTIES

CAR DRY ‘HOSPITAL lNOUSTRlAL RAO1ATO-R TRUCK WASH CLEANER AVERAGE LAUNDRIES SHOP CLEANERS

AVERAGE AVERAGE AVERAGE AVERAGE AVERAGE

126.333 315.565 346.721 1421.409 7.667 25.719 4.465 13.2 7.85

4.800 0.02 1.791 625 426.583

1-38

PART 2

REMOVAL EFFICIENCY ESTIMATION FOR LOCAL LIMITS

2.0 REMOVAL EFFICIENCY ESTIMATION GUIDANCE

This guidance was produced to describe further the determination and

application of removal efficiencies using methods discussed in Chapter 3 of

the 1987 local limits guidance, specifically the mean removal efficiency and

decile Another method for removal efficiency estimation, called

the average daily removal, is also presented here.

Each of these methods for removal efficiency determination is defined and

illustrated with examples and actual POTW sampling and analysis data. Step-

by-step procedures for performing the calculations, together with

computational formats, are also provided. This document discusses and

illustrates difficulties, such as handling nondetections in the calculations,

that may be encountered in applying these methods to analytical sampling data

on POW influent and effluent.

Both the mean removal efficiency and average daily removal methods

provide a single point measure of removal efficiency. That is, the removal

efficiency is described by a single number that is an average removal

efficiency. The actual removal efficiency of a POTW varies from day to day.

On some days it will exceed an average value and on other days it will be less

than that average, although neither of these two methods indicates how often

the actual efficiency is above or below the single number efficiency value.

Such information can be critical because the objective of local limits is to

protect water and sludge quality. If, during a period of time, the actual

removal efficiency is very high, sludge quality may deteriorate during that

period. During those times when the removal efficiency is low, receiving

water quality may be adversely impacted.

The decile approach, however, yields the frequency distribution of daily

removal efficiencies, providing estimates based on the available data of how

frequently the actual daily removal efficiency will be above or below a

specified value. Thus, even though the decile approach is somewhat more

tedious to implement, it provides the POTW with the ability to determine how

often it attains an average removal or other specified removal rate. The 1987

local Limits guidance contains an illustrative example of the decile approach

2-1

and the use of a frequency plot to display the deciles (see pages 3-18 to 3-

21 of the 1987 local limits guidance). Also, EPA’s PRELIH Version 4.0

computer program calculates both the mean and decile values.

The three methodologies and their applications are discussed using

sampling data for copper, zinc, and nickel. The copper data are used to

illustrate the overall approach that would be applied following the

methodologies found in the 1987 local limits guidance. The other two data

sets were selected to provide examples of the types of problems and questions

that are likely to be experienced when determining removal efficiencies. For

each of the pollutants, a review of the data is provided to determine which

values, if any, should be considered for exclusion. Data exclusion should be

performed only if a technical justification exists to support such action

(e.g., poor removals due to maintenance or operational problems or known

sampling problems). Once the data to be used have been determined, mean

removals are calculated and a guided worksheet designed to assist in the

calculation of the nine decide values is provided. The individual decile

values can be used to assess how often a POTU attains a specific removal

efficiency value, as well as to compare the allowable headworks loadings

obtained from an average removal value to that based on a selected decile

removal.

2.1 DEFINITIONS

Before illustrating the steps needed to apply the removal estimation

procedures outlined in the 1987 local limits guidance, the following terms are

defined in this section:

l Daily removal efficiency

0 Mean removal efficiency

l Decile removal efficiency.

2.1.1 DAILY REMOVAL EFFICIENCY

A daily removal efficiency is defined as the percent change of a

pollutant’s mass values for samples taken before and after a treatment system

or a stage of treatment, such as primary or secondary treatment. The “before”

treatment samples are typically influent sample values and the “after”

2-2

treatment values are usually effluent sample values. For example, suppose the

mass level for copper in an influent wastewater sample taken on a specific day

was calculated to be 100 lbs/day, and the mass level of copper in an effluent

wastewater sample taken on the same day might have been 7 lbs/day. The daily

removal efficiency corresponding to those two samples is the percent change

between the two sample values [(loo) x (100 - 7)/100 - 93X1. That is, the

treatment system is assumed to have reduced the influent sample's mass value

of copper by 93 percent from 100 lbs/day to 7 lbs/day. (Sometimes an influent

sample value is less than the corresponding effluent sample value for the same

day). In such cases, the daily removal efficiency is expressed as a negative

percent change. For example, if the mass of the influent sample was

calculated at 20 lbs/day and the corresponding effluent sample at 35 lbs/day,

then the daily removal efficiency would be expressed as (100) x (20 - 35)/20 -

-75%; that is, the mass value for the effluent sample was 75 percent higher

than the mass value of the influent sample.

Daily removal efficiency (expressed as a percent)

following equation:

is exemplified by the

Daily Removal Efficiency - 100 x (Influent - Effluent)/Influent

where:

Influent - Specific value for a daily sample taken prior to treatment or prior to some stage (e.g., secondary effluent) of treatment

and

Effluent - A pollutant-specific value for a daily sample taken after some particular stage of treatment.

It is important to realize that 93 percent removal for a metal means

that 93 percent of the mass went to the sludge, while 7 percent remained in

the effluent. Mass balances are readily determined for metals and

conservative pollutants. However, it is difficult to estimate the mass

balance for organics because of volatility and biodegradability. (For

additional discussion on this topic, refer to Section 2.6 of this document.)

2-3

2.1.2 HEAN AND AVERAGE DAILY RFJiOVhL EFFICIENCIES

A mean (or average) removal efficiency can be calculated in more than

one way. One method is to calculate the arithmetic average of individual

daily removal values. In this document, this type of average will be referred

to as the m dailv r &.

Average Daily Removal - (Daily Removal Efficiency for day 1 + . . . +

Daily Removal Efficiency for day n)/n

where :

-n* is the number,of paired daily influent and effluent sample values that are available.

For example, consider the following set of influent and effluent mass

values for three daily samples containing a pollutant X:

SAMPLE DAY

1 2 3

AVERAGE

INFLUENT EFFLUENT Mhss MASS

(lbs/daa (lbs/davl

20 5 10 3 40 8

23.3 5.3

DAILY REMOVAL

EfFICIENCY (Xl

75 70 80

75%

Average Daily Removal

The mean removal could be calculated by taking the average of the three

individual daily removal values [i.e., (75% + 70% + 80X)/3 - 75X]. Extreme

daily removals (i.e., isolated, small or large removals or negative removals)

can have a substantial effect on the average daily removal, especially in the

case of small sample sizes.

Another way to compute a mean removal would be to determine the averages

of the influent and effluent samples, and then determine a removal efficiency

based on the percent change between the average influent and average effluent

values. This removal estimate is the statistic that is presented and defined

in the 1987 local limits guidance. In this document, it will be called the

remov~cie~ and is calculated as follows:

2-4

Mean Removal Efficiency - (100) x (Average Influent - Average

Effluent)/hverage Influent

where:

Average Influent * Mean influent value for the daily sample values

and

Average Effluent - Mean effluent for the daily sample values.

In the previous example, the average influent level is (20 + 10 + 40)/3 -

23.3 lbs/day the average effluent level is (5 + 3 + 8)/3 - 5.3 lbs/day; thus,

the mean removal is (100) x (23.3 - 5.3)/23.3 - 77%. Whereas the average

daily removal efficiency required individual, paired influent and effluent

sample values, the mean removal efficiency could be based on influtnt and

effluent sample values that are not always paired. (For example, an effluent

sample may have been lost or destroyed; therefore, the average effluent value

could be based on one less effluent sample value. However, the influtnt

sample value might be used for calculating an average influent value.)

Caution should be exercised in constructing influent and effluent averages in

this way to avoid calculating meaningless measures of removal.

As defined in Section 2.1.1 of this document, each of the individual

daily removals receive the same weight in calculating the average daily

removal. If the individual daily removals are weighted by their corresponding

daily influent mass (expressed as a proportion of their summed influent mass),

then the average daily removal and mean removal estimates are equivalent.

In many cases, the two averaging procedures (i.e., average daily removal

and mean removal) will provide different estimates of removal efficiency. The

PO'IW can produce both of the average removal estimates and then decide whether

either of the estimates is reasonable for use in determining the allowable

headworks loading. The decile approach provides a basis for evaluating

whether either the average daily or mean removal can be used, as well as

alternative removal estimates. PRELIM Version 4.0 calculates all three of

these values and allows the user to choose the most appropriate removal

efficiency value.

2-5

2.1.3 DECILE REMOVAL EFFICIENCY

The two average removal efficiencies described previously are

specifically defined estimates of removal. An individual POTW may not know

how often it meets that level of average removal. For that reason, an

alternative approach was recommended by EPA, which it has called the decile

aoproach. The method involves ordering the daily removal efficiencies and

identifying nine decile values. In other words, after the daily removals have

been calculated, the removal values are arranged in ascending order, and an

individual daily removal value (below which 10 percent of the daily removals

fall) is identified. This value is called the first decile. Similarly, the

second decile is the daily removal value below which 20 percent of the daily

removals fall. The third through ninth deciles are defined in a similar way.

The removal value below which half of the daily removals fall is the fifth

decile or median.

The value of the decile approach is that the average daily removal

efficiency and the mean removal efficiency values can be located within the

set of nine deciles, thereby allowing the estimation of how often a POTW could

expect to exceed either of the average removal values. For example, suppose

that the average daily removal was determined from a set of daily removal

values to be 43 percent and the mean removal from the same set of values was

calculated to be 61 percent. What percentage of the time will the POTW have

removals above either 43 or 61 percent? Suppose the 9 estimated deciles

(first decile through the ninth decile, respectively) are: 8 percent, 15

percent, 30 percent, 45 percent, 48 percent, 55 percent, 60 percent, 81

percent, and 87 percent. The average daily removal of 43 percent lies between

the third and the fourth deciles (30 percent and 45 percent, respectively);

therefore, the POTW exceeds a level of 43 percent removal between 60 percent

and 70 percent of the time.

On the other hand, the mean removal value of 61 percent lies between the

seventh and eighth deciles (60 percent and 81 percent, respectively);

therefore, the POTW exceeds a level of 61 percent removal about 20 percent to

30 percent of the time. If a POTW requires a removal estimate for use in

calculating allowable headworks loadings that is not exceeded more than 50

percent of the time, the average daily removal of 43 percent would be

unacceptable because it is exceeded between 60 percent to 70 percent of the

2-6

time. However, if a POTW required a removal value to be exceeded no more than

10 percent of the time, clearly neither the average daily removal nor the mean

removal value would be acceptable.

To apply the decile approach as described in the 1987 local limits

guidance, a minimum of nine daily removal values are required. If only nine

removal values are available, then the nine estimated deciles are simply the

nine ordered daily removals. If 10 or more daily removals are available, then

some arithmetic must be performed to produce the nine decile estimates. To

assist in the process of estimating the decfles, a decile estimation worksheet

has been designed. The use of that worksheet will be demonstrated using the

example data sets. Also EPA’s PRELIH Version 4.0 computer program calculates

deciles, from influent, effluent, and flow data.

2.2 ILLUSTRATIVE DATA AND APPLICATIONS

In this section, the methods intended to assist POTWs in developing

removal efficiency estimates (either mean removal, average daily removal, or

deciles) will be illustrated. In general, the overall approach will encompass

the following steps:

l Displaying the influent, effluent, and daily removal data

b Deciding which data, if any, are candidates to exclude

l Calculating daily average and mean removals

l Ordering (i.e., sorting) the individual daily removal values

l Using the decile worksheet to estimate the nine decfle removals.

The data that will be examined are daily influent and effluent sample

values (reported in lbo/day) from a single POTW for 51 days covering the

period July 1, 1987, through June 21, 1988.

2.2.1 DAILY INFLUENT, DAILY EFFLUENT, AND DAILY REHOVAL DATA

Table 1 presents the first example data set- -a set of 51 influent and

effluent sample pairs for copper. A good, first step in examining any set of

data is to graph the data. Removals are based on influent and effluent values

that are collected over time; therefore, it makes sense to plot daily

2-7

TABLE I. COPPER KASS VALUES (LBS/DAY) AND DAILY itEMOVALS

2-8

influent, daily effluent, and daily removal over time. Figures 1, 2, and 3

display plots of influent copper mass, effluent copper mass, and copper

removal over time.

The influent data contained no influent concentration values reported as

below the detection limit or as zero. Whenever a daily influent sample is

zero (or it was reported as below the detection limit and was assigned a value

of zero), it is impossible to calculate a daily removal, regardless of the

effluent level. Influent and effluent sample pairs for which the influent

level is reported as zero are useless for purposes of calculating daily or

average removals. Such data pairs will be eliminated from the data set and

are not included in any subsequent arithmetic. For the most part, influent

levels in Figure 1 appear to be between 40 and 140 lbs/day, with a few values

occasionally reaching 160 to 180 lbs/day, and a few falling in the 240 to 260

lbs/day range. No extremely high or low copper influent values are apparent

from this graph, however.

The effluent copper mass values in Figure 2 reveal an isolated effluent

copper value around 110 lbs/day. There are formal statistical procedures that

can be applied to evaluate whether a value can be classified as an “outlier”

or extreme value relative to the rest of the data values. The primary

intention here, however, is to identify any values that might be candidates

for exclusion. The final decision to exclude data should rest on technical

justification. An examination of Figures 1 and 2 simultaneously shows that

one of the three high influent values occurred at the same time as the high

effluent value. By referring to Table 1, it is noted that the largest copper

effluent value (103.9 lbs/day) was associated with the third largest influent

value (247.85 lbs/day). The occurrence of corresponding extreme influent and

effluent values should be investigated to determine whether the data values

can be explained by technical or operational problems not related to treatment

system performance (e.g., maintenance, repair, or sampling problems). If this

is the case, dropping the data pair from the data set might be considered.

Another characteristic displayed in Figure 2 is that there appears to be a

pattern showing increasing effluent values over time; a similar pattern was

not observed for the influent copper values in Figure 1. Because daily

influent and effluent values enter into the calculation of the daily removal

2-9

2801 + 260

240

220

200

180

160

140

120

PIGmu 1. INFLU??iNT COPPER MASS VALUES

20 1 1 I I 0 50 100 150 200 250 300 350 400

SAMPLE DAY

120

z 100

3 z 80

!I 60

il 40 7

c

I,

$ 20. 4

‘00° worn

0 00

0.f I 1 I I I t 0 50 100 150 200 250 300 350 400

SAWLEDAY

FIGURE 2. EFFLUENT COPPER MASS VALUES

2-10

1004 &

go: a* a a . 80. a :. *a.

a a

d 70. a a

.**.' l e* *a

P 1

l s l 60. a a *a

50 4 l d! 4

40. a

30.

20. 4

101 I I I I I 1 r 0 50 100 150 200 250 300 350 400

SAMPLE DAY

IIGUBE 3. MILYPBRCBHY -AL8 PO1 COPPBB

2-11

efficiency, if the influent values tend to be fairly constant over time and

the effluent values display an increasing pattern over time, the daily

removals will likely show a decreasing pattern over time.

Figure 3 is a plot of the daily removal values over time. A general

pattern of decreasing daily removal over time is evident. In addition, the

plot shows that there is one low removal at approximately 20 percent. Such

unusual data values warrant review. For example, the laboratory quality

control samples could be checked to determine whether blank or duplicate

samples indicated anything out of the ordinary. This might explain unusual

data values.

Another plot that can provide assistance in the search for data values

that might be considered for exclusion is presented in Figure 4. In this

figure, influent sample values are plotted against their corresponding

effluent sample values. Again, the isolated influent and effluent data pair

(of 247.85 lbs/day and 103.9 lbs/day, respectively) are evident. There are

also two other influent values of approximately 250 lbs/day. These inf luent

values, however, had effluent levels more in line with the rest of the

effluent data. Thus, this plot provides some evidence that the treatment

system has reduced influent copper levels around 250 lbs/day to effluent

copper levels substantially below 100 lbs/day.

For this example, it is assumed that the data were reviewed and

justification did not exist for excluding any of the data pairs identified for

review. That is, the sample data are assumed to reflect the range of influent

and effluent levels that are reasonable for that treatment system.

2.2.2 AVERAGE DAILY AND HEAN REMOVALS

In this section, the copper data set is used to calculate the average

daily removal and mean removal values described earlier. Table 1 lists the

daily influent, daily effluent, and daily removal values for these data. The

average daily removal is calculated by adding the individual daily removal

values and dividing the total by 51 the number of values added). That is,

using Table 1, the average daily removal for copper is (76.36% + 93.42% + ,,,

+ 77.781 + 71.28X)/51 - 72.0%.

2-12

1

80. 80.

60. 60.

0, ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., , , , , . . It 4 25 25 50 50 75 100 75 100 125 125 150 150 175 175 200 200 225 225 250 250 275 275

INFLUENT COPPER (L&DAY)

PIclJuE 4. INFLLmlT COPPER V8. lwFLulsHT COPPUX

2-13

The mean removal efficiency for copper is the percent change between the

average influent value (i.e., the sum of the 51 influent values divided by 51)

and the average effluent value (i.e., the sum of the 51 effluent values

divided by 51). For these data, the average influent value is 108.09 lbs/day

[i.e., (68.85 lbs/day + 95.13 lbs/day + . . . + 135.19 lbs/day + 117.66 lbs/day)

/51 - 108.09 lbs/day] and the average effluent value is 27.51 lbs/day [i.e.,

(16.27 lbs/day + 6.26 lbs/day + . . . + 30.04 lbs/day + 33.80 lbs/day)/51 -

27.51 lbs/day]. Therefore, the mean removal efficiency is calculated by

subtracting the effluent average from the influent average and dividing that

difference by the influent average [i.e., (100) x (108.09 lbs/day - 27.51

lbs/day)/ 108.09 lbs/day - 74.5X].

In summary, the average daily removal for copper was calculated as 72.0

percent, and the mean removal was calculated as 74.5 percent. Note that the

1s

two averages yield slightly different results for this particular data set

(Later, another pollutant data set will show that substantially different

results can exist when using the two averaging methods.) Both of these

individual values can be evaluated to determine how often the daily remova

exceed each of those values.

2.2.3 DECILE ESTIMATES

The set of 51 daily removal values will be used to estimate how often

POTW will exceed a specific level of removal, such as 72.0 percent or 74.5

the

percent. The nine decile removals discussed previously will be developed from

the set of 51 daily removals.

The first step in estimating the deciles is to take the set of 51 daily

removal values and order the values from smallest to largest. Table 2

presents the same information as Table 1 except that the information is sorted

or ordered on percent removal (daily removal) value from smallest to largest.

Table 2 will be used to fill in Table 3 (Decile Estimation Worksheet for

Copper Data). The columns contain general instructions for completing the

worksheet . The worksheet will be filled in column by column, from left to

right. The entries for the Column /8 provide the estimated deciles.

(Appendix B contains a blank decile estimation worksheet for copying

purposes. )

2-14

TABLE 2. COPPER MASS VALUES (LBS/DAY) AND ORDERED REMOVALS

1 cu 4 3

2 cu s 22 3 CU 11 29 a cu 3 29 1 5 cu 4 241 6 cu 10 221 7 CU 3 61

10 10 cu cu 11 11 22 22 11 11 cu cu 11 11 11 11 12 12 cu cu 5 5 15 15 13 13 CU CU 12 12 9 9

I I I 1

81 cu 1 121 20) 91 GUI 21 251

31 cu 0 22 32 cu 0 30

316- I I

341 Cul 71 151 171.491 36.30[ 76.65

71 62.59 I 12.521 00.66 Cu[ 91 2 Cul 01 Cul 101 2

2-15

TMLK 3. DECILE ESTIIIATION U0RKStlEF.t POP COPPER DATA

I I ca. n I m. R m.n 1 m.u 1 m.n I m. w m. n ( m. a 1 I I I 1 I I I I I I

m. u

EUTIY

Ml*55

I I I I LILl TM

I DEClluL

I - I I CDL. n I

mJlllRY I m I ca. n 1 m. n \

ENTRY I EIIYRIES I

BY I I

ax. 16 I EsTIlu1F.D I

WIRY I DCCIL~S I .._..__..._.- ___..__...._..__. I I

cldttr In calm defined as ultfplcs of (#+1)/10. *Iwrr a = the u&et of data pairs used.[i.e. (51+1/10=5.2). (2x5.2=10.1) etc.] -lJser the Ilrt of o&red raorals.

2-16

l SteD- The entries for the first column are obtained by performing the calculations described in the footnote (referenced in the column heading at the bottom of the worksheet). The footnote defines the starting location for the first decile; and then, calculations for the next eight multiples of that number for the second through ninth deciles are made. For example, the copper data set contains 51 influent and effluent data pairs that are used. Thus ) the location of the first decile in the ordered list of removals is (N + l)/lO - (51 + l)/lO - 5.2. The location of the second decile is 2 x 5.2 - 10.4; the location of the third decile is 3 x 5.2 - 15.6, etc.; and the location of the ninth decile is 9 x 5.2 - 46.8. Therefore, the nine entries for Column 61 (proceeding from the first through the ninth decile) are 5.2, 10.4, 15.6, 20.8, 26.0, 31.2, 36.4, 41.6, and 46.8. See the entries for Column #l.

l Step 2 - For the entries in Column #2, the whole number part of each of the nine values listed in Column #l is used. For example, the first decile had a value of 5.2 in Column i/l; therefore, the entry for the first decile in Column 112 is the whole number part of 5.2 (i.e., 5). Similarly, the other eight whole number values are 10, 15, 20, 26, 31, 36. 41, and 46.

9 Ster, 3 - The entries for Column {I3 require the use of Table 2 that contains the ordered list of daily removal values. (Note the footnote marked **.) Entries for Column 113 are the ordered removal values corresponding to the locations specified in Column f/2. For example, the first entry for Column 113 will be the ordered removal for the Column #2 entry of five. That is, the first entry in Column 113 will be the fifth ordered, daily removal value from Table 2. which is 51.47 percent. Similarly, the second entry for Column lj3 will be the ordered removal for the Column #2 entry of 10, which is the 10th ordered daily removal in Table 2 (61.40 percent). The remaining entries for Column #3 are selected from the ordered list of daily removals based on the values specified in Column i/2.

. SteD 4 - The entries for Column #4 are also obtained from the ordered list of daily removals presented in Table 2. The Column #4 entries are the daily removals in Table 2, which immediately follow the Column #3 entries. For example, the first entry in Column 13 is 51.47 percent; the daily removal value immediately following 51.47 percent in Table 2 is 56.25 percent. Similarly, for the second entry in Column #4, the daily removal value in Table 2 (immediately after 61.40 percent) is 64.56 percent.

l Ster, !j - The entries for Column I5 are determined by subtracting Column 13 from Column 114 for a specified decile. For example, for the first decile, the Column #3 entry of 51.47 percent is subtracted from the Column #4 entry of 56.25 percent, producing a result of 4.78 percent for the first entry in Column 115. The rest of the column is obtained by performing the same subtraction process for the decile row of interest.

l SteD- The entries for Column 116 are the decimal part of the entries specified in Column 111. For example, the first entry in Column I/l is 5.2, which has a decimal part of .2; therefore, the first entry for Column #6 is .2.

2-17

0 $teD 7 - The entries for Column 117 are obtained by multiplying the entries of Column #5 by the entries of Column 116. For example, the first entry in Column 117 is 4.78X x .2 - .956X.

l SteD- The entries for Column #8 are obtained by adding the entries of Column 13 and the entries of Column i/7. For example, the first entry in Column #8 is 51.471 + .956X - 52.426%.

Column 18 provides the following nine estimated decile removals (rounded to

the nearest tenth):

l 1st decile - 52.4 percent

l 2nd decile - 62.7 percent

l 3rd decile - 66.5 percent

l 4th decile - 69.6 percent





l 9th decile - 87.6 percent.

Thus, it can be seen from the nine deciles that the average daily removal of

72.0 percent and the mean removal of 74.5 percent both fall between the fifth

and sixth decfles. Based on the decile estimates, between 40 to 50 percent of

the daily removals exceed the specified individual removals.

2.3 USE OF REMOVAL ESTIMATES FOR ALLOWABLE HEADWORKS LOADINGS

In this section, the use of the average removals and decile removals for

calculation of allowable headworks loadings will be demonstrated. In general,

allowable headworks loading equations are expressed in a number of ways,

including:

Effluent quality headworks loading (lbs/day) -

I(8.34) x (Cm,) x (Q,,>>/(l - b,,,,)],

where:

8.34 - conversion factor which takes into account the density of water

C cm11 - NPDES permit limit, mg/l

2-18

Q PON - PQl'w average flow, MGD

&ON - Removal efficiency across the POlW, decimal

The quantfty [(8.34) x (C carr) x (QPON)] fs a National Pollutant Discharge Elimination System (NPDES)-based maximum permissible mass discharge limit and R is an estimated removal efficiency expressed as a decimal (for example, see page 3-3 of the 1987 local limits guidance).

Sludge quality headworks loading (Lbs/day) -

((8.34) X (CsLcn~,) X (PS/lOO) X (Q~LD~)/RoONI v

where :

8.34 - conversion factor which takes into account the density of water

C SLCPJT - sludge disposal criterion, mg/kg dry sludge

PS - percent solids of sludge to disposal

Q SLOG - sludge flow to disposal, MGD

RQoN - removal efficiency across the POTW, decimal

The quantity I((8.34) x (C,,,,*,) x (PS/lOO) x QSLDG)] is a maximum permissible mass sludge loading and R is an estimated removal efficiency expressed as a decimal (for example, see page 3-11 of the 1987 local limits guidance).

The nine decile estimates, the average daily removal estimate, and the

mean removal estimate can be used to examine the effect that each has on the

two allowable headworks loading equations specified above. The headworks

loadings corresponding to the nine deciles, mean value, and average daily

removal efficiencies are displayed on the following pages.

In developfng local limits, appropriate removal efficiencies must be

selected for calculation of an allowable headworks loading for each pollutant.

The typical procedure is for the POTU to select the pollutant’s average

removal efficiency for this purpose. This procedure, however, does not

account for variabilities in removal efficiencies which occur over time. An

alternative procedure, which does account for removal efficiency variability,

is the decile approach. The decile approach entails calculation of allowable

2-19

headworks loadings based on judiciously selected removal efficiency decides

rather than average removals. The decile approach is illustrated by the

following example.

The following effluent quality-based MAHLs for copper to a POTW have been

previously calculated assuming the NPDES-based maximum permissible mass

discharge is 10 lbs/day.

Remova 1 Allowable Headworks Loading lbs/day -U fl ent Oualitv-based)

L 3 4 5

Average Daily Mean Removal

6 7 8 9

52 62 66 69 71 72 74 78 83 84 87

4 21 .o 26 8 29 :9 32 .9 34 .8 35 .7 39 .2 45 .7 58 .8 66 .2 80 .6

The typical procedure is for the POTW to establish MAHLS based on a

chosen removal rate. In this case, the effluent quality-based allowable

headworks loading for copper would then be 35.7 lbs/day, corresponding to the

average removal of 72.0 percent. The POTW might choose to establish local

limits based on this MAHL, and assume that industrial user complfance with the

local limits will ensure POTW compliance with its effluent quality

limitations.

Suppose, however, that the POTW actually receives 30 lbs/day copper at

its headworks. Comparing this copper loading with the allowable copper

loadings listed above, we find that the copper MAHLs for the first, second,

and third deciles are less than the 30 lbs/day copper being received. It can

be concluded that for 30 percent of the year (three deciles), the POW will be

unable to comply with its effluent quality limitations. At the same time, the

POTU’s industrial users may all be in compliance with local limits, since the

30 Ibs/day copper currently received is well below the 35.7 lbs/day allowable

loading established by the POTW based on average removal,

2-20

In using the decile approach, the POTW can establish a more stringent

copper local limit by taking into account variability in copper removal

efficiencies over time. For example, the POTW can base its copper allowable

headworks loading on the second decile removal of 62.7 percent. The copper

allowable headworks loading would then be 26.8 lbs/day, which is considerably

more stringent than the 35.7. lbs/day allowable headworks loading based on

average removal. The 30 lbs/day copper loading currently received exceeds

this allowable headworks loading, implying that the industrial user would be

in noncompliance with the local limit. Once the industrial user achieves

compliance with the limit, the POTW can be reasonably certain it will maintain

compliance with its effluent quality limitations.

A similar procedure is followed in applying the decile approach to

establishing sludge quality-based MARLS. In this regard, the following

removal efficiency deciles and sludge quality-based UAHLs of copper have been

calculated assuming the maximum permissible sludge loading is 20 lbs/day.

Removal Sludge Quality-based fiowable Headworks Loedinn lbs/day

; 3 4 5

Average Daily Mean Removal

6 7 8 9

52.4 38 .2 62.7 31 .9 66.5 30 .1 69.6 28 .7 71.3 28 1 72.0 27 :8 74.5 26 .8 78.1 25 .6 83.0 24 .1 84.9 23 .6 87.6 22 .8

From the above information, it can be seen that allowable headworks

loadings of copper decrease with increasing removal efficiency deciles. Thus,

in order to establish a MARLS more stringent than the allowable loading based

on the average removal (27.8 lbs/day), a decile higher than the fifth decile

must be selected. The POTW may elect to establish a sludge quality-based

allowable headworks loading corresponding to the eighth decile; from the above

information, this loading would be 23.6 lbs/day.

2-21

The final step in the decile approach is to choose a percent removal that

results in an allowable headworks loading that will be met most of the time

2nd compare selected effluent quality and sludge quality-based allowable

headworks loadings and select the most stringent.

Load-

Effluent quality Sludge quality

Allowable Headworks Loadubs/day

2 26.8 8 23.6

From the above information, it can be seen that the POlW should base its

copper local limits on an allowable headworks loading of 23.6 lbs/day. The

resultant local limits will be protective of both the POTW's effluent quality

and sludge quality.

2.4 EXAMPLE ZINC AND NICKEL DATA SETS

In this section, more complicated data sets than the ones previously used

will be examined. The data sets illustrate some of the problems (e.g.,

negative removals) that might be encountered in using individual influent and

effluent values to determine removal efficiency.

2.4.1 ZINC SAMPLING DATA

First, zinc data will be reviewed using the figures discussed earlier.

Table 4 presents the 51 influent and effluent samples for zinc. Figure 5 is a

plot of the influent zinc values over time. All of the influent values are

above 0; 49 of the 51 influent values are above 100 lbs/day. There are a few

high influent values. Table 4 shows the four highest influent values have

daily removals of at least 70 percent. Based on examination of the influent

zinc values it would not be suspected that these data values would be

candidates for elimination from the data set.

Figure 6 is a plot of the effluent zinc values over time showing 2

effluent values that are noticeably above the other 49 effluent values. Table

4 shows that one of the 2 pairs (lines 25 and 26 of Table 3) with the highest

effluent values was noted in review of the influent values. The other pair

has a negative removal. The occurrence of these results on successive days

(December 19, 1987, to December 20, 1987) may indicate that the POW treatment

2-22

I I I 1 I 0 50 100 150 200 250 300 350

SAMPLE DAY 400

PItxrm 5. INFLUEHT ZINCWS VALUES

400.

350.

300.

250.

200.

150,

0

OS8 o OO

0

OO 0 O-QQPOB

0 0 50 100 150 200 250 300 350 400

SAWLE DAY

PIGII'IU 6. KFFLUlS.NT ZINC MSS VALWS

2-23

TABLE 4. ZINC MASS VALUES (LIE/DAY) AND DAILY REMOVALS

system was experiencing some operational difficulties or interference at the

time. Inquiries should be made to determine whether there are valid reasons

for dropping these data for purposes of calculating removals.

Influent zinc levels versus effluent zinc levels are plotted in Figure 7.

The removal efficiency on December 19, 1987, (72.23 percent with an associated

influent value of 1,750 lbs/day) contrasts sharply with the removal efficiency

on September 27, 1987 (95.25 percent with an associated influent value of

2,500 lbs/day). Thus, the data show that the POTW was capable of treating

influent zinc considerably above 1,750 lbs/day.

Figure 8 is a plot of the daily removals over time. The three negative

removals are quite apparent from the plot. It is asswned for this example

that justification to discard any of these data was not possible. Negative

daily removals should not automatically result in data elimination; such

values may be visible evidence of treatment system variability. Based on the

51 daily influent, effluent, and removal values, the summary removals were

calculated: the average daily removal was 53.4 percent and the mean removal

was 69.5 percent. Note that the two removal averages are considerably

different. (Had the influent and effluent data for the negative removals been

discarded, the removal averages would still have been considerably different;

average daily removal would have been 59.6 percent, and the mean removal would

have been 72.4 percent.)

The decile approach can now be used to evaluate these removal averages

with respect to the nine decile estimates. Table 5 presents the ordered daily

removals for use with the decile estimation worksheet.

Table 6 presents the results of using the worksheet. Since the number of

influent and effluent zinc data pairs is 51, the entries for Column #l are,

again, multiples of 5.2 (see the first footnote at the bottom of the

worksheet) Likewise, the entries for Column #2 are the whole numbers of

Column fl. The ordered removal entries for Columns #3 and 84 are taken from

2-25

5004 L

450. d

.

$ 400.

0 0 a

O+ . . . 1 t 0 500 1000 1500 2000 2500 3000

INFLUENT ZINC (LBSDAY)

?IGms 7. INIWJKNT ZINC VI. KIVLCKNT ZINC

2-26

120

100

60

d

P

:: c

20 0 % -20

-40

-60

150 200 SAMPLE DAY

?ICURX 8. DAILY PERCENT RlQ!OVALS FOR ZINC

2-27

TABLE 5. ZINC HASS VALUES (LBS/DAY) AND ORDERED RICHOVALS

2-28

TABIJi 6. DECILE ESTI)(ATION WiU3lIXT FOR ZINC DATA

a.65 1

AI0 I

m.n 1

fnTRlEE 1

-n +r calm defined IS ultlplts of (ll+l)/lO. rlwre a = the ntder of data pairs used.[i.c. (Sl~~/llkS.2). (2x5.2=10.4) etc.] -uses the list of ordered rmuv~lr.

2-29

Table 5. Column /}5 is obtained by subtracting Column 83 from Column #4.

Column #6 is the decimal part of the entries in Column i/l. Column //7 is

obtained by multiplying Columns H5 and 16. The estimated deciles, Column I/S,

are obtained by adding the entries of Column 13 to those of Column /7. The

nine estimated deciles for the zinc data are:

l 1st decile - 18.0 percent

l 2nd decile - 36.3 percent

l 3rd decile - 46.2 percent






l 9th decile - 83.0 percent.

The decile estimates then can be used to estimate how often the POlW’s daily

removals of zinc exceed the average daily removal of 53.4 percent and the mean

removal of 69.5 percent. The former lies between the third and fourth decile,

and therefore is exceeded between 60 and 70 percent of the time. The latter

lies between the sixth and seventh decile, and therefore is exceeded between

30 and 40 percent of the time.

2.4.2 NICKEL SAMPLING DATA

The last example involves working initially with a data set of 51 daily

fnfluent and effluent nickel mass values. Table 7 presents reported influent

and effluent values and, when possible, their daily removals. The table shows

that a number of the daily removals cannot be determined because of reported

zero influent levels. J’hese reoorted zero levels more than likelv indicate

In this section,

the reported zero levels are treated as measurements having the value of zero.

For discussion of this practice and alternate approaches, refer to Section

2.6.

Figure 9 is a plot of the 51 influent nickel mass values over time. The

large number of zero influent values is apparent along the horizontal axis

(sample day) ; the zero values are spread out over the sampling period. An

2-30

1404 b

120. l . 1

100.

80. 80. l + l + l l 4 4

60. 60. l l

l l l l 4 4 l l 4 4

40. 40. l l l l 4 4 l :* l l :* l l l

l l l a l * l a l * 20. 20.

4 4 o+w.* o+w.* #A #A .-*.* .-*.* v--a v--a . . 9 9

0 0 50 50 100 100 150 200 250 300 350 150 200 250 300 350 400 400 SAMPLE DAY SAMPLE DAY

?ItxJu 9. IllhunT IIICXL MASS VALmsS

801 t

70 0 0

60

0 0

00 0 0 0 0

0 O 0

O 0

IImRn 10. JImLmmI IIcKxL HAM VALUES

2-31

TABLE 7. NI(SICEL IGUS vALDl!S (LBS/DAY) AND DAILY pwOvALS

isolated influent nickel value around 120 lbs/day also exists. Table 7 shows

that the daily removal for that influent value is 100 percent because the

corresponding effluent value is zero.

Figure 10 plots the 51 daily eff-luent nickel mass values over time. The

effluent nickel values also show a number of zeroes, many of which will not be

used because their corresponding influent value was also zero.

Figure 11 plots influent nickel mass values versus the effluent nickel

mass values. The horizontal axis shows that there are a number of influent

nickel values above 0 (ranging from about 25 to 120 lbs/day) that have

effluent levels of 0 (that is, 100 percent removal). On the vertical axis,

four influent and effluent sample pairs for which the influent was zero and

the effluent mass level was greater than zero exist. Since daily removals

cannot be calculated from influent values of zero, any influent or effluent

data pair (regardless of effluent level) having an influent value of zero will

be excluded.

Figure 12 plots the daily removal values over time. The figure shows

that the POTU displays some treatment variation. The positive daily removals

vary from about 10 percent to 100 percent. The figure also shows 4 negative

removals ; 3 of the 4 negative removals are similar in magnitude, about -15

percent. The other negative removal corresponds to an influent nickel mass of

32.55 lbs/day and an effluent mass of 65.09 lbs/day on March 16, 1988. These

sample pairs should be investigated to determine whether the data should be

retained. Except for the influent data values of zero, it is assumed that

justification for removing the data from the process of calculating average or

decile removals was not possible.

Table 8 presents the 24 influent and effluent nickel values that were

used to determine individual daily removals (i.e., 27 influent and effluent

sample pairs were excluded because the influent nickel level was 0). The 24

influent and effluent values are ordered on the daily removal values. The

average daily removal based on the 24 daily removals is 61.6 percent; the mean

removal value determined from the influent effluent data is 63.0 percent. (If

2-33

70.1 II

60.

I

= )I II

I 1

20.

1 0.

40 INFLUE:: NICKEL $S/DAY)

100 120 140

- 5 0.

-7 5.

-100. l

-1254 I r I I a ? 0 50 100 150 200 250 300 350

SAMPLE DAY

PIcnm 12. DAILY PERCRMT RlWOVALS FOR IIICKKL

2-34

TABLE 8. ilIclcEL MASS VALUES (LBS/DAY) Arm ORDEBLD imlovALs

2-35

the 4 negative removals had been excluded from the data set, then the average

daily removal, based on the remaining 20 influent and effluent nickel values,

would have been 81.4 percent and the mean removal would have been 76.5

percent. )

The 24 ordered daily removals of Table 8 are used in the decile

estimation worksheet presented in Table 9. (The entries for Column {/l are

multiples of 2.5. Column #2 uses the whole numbers of Column 111. Columns #3

and //4 use the ordered removals from Table 8. Entries for Column {I5 are

obtained by subtracting Column //3 from Column #4. Column #6 is the decimal

part of the entries in Column #l. Column #7 is produced by multiplying the

entries of Columns #5 and #6. Finally, the estimated deciles are produced by

adding the entries of Columns 113 and 117.) The nine estimated deciles for the

nickel data are:

l 1st decile - -

l 2nd decile -

l 3rd decile -

l 4th decile -

4 5th decile -

l 6th decile -

l 7th decile -

l 8th decile -

l 9th decile -

17.0 percent

13.8 percent

47.7 percent

57.5 percent

100 percent

100 percent

100 percent

100 percent

100 percent.

The average daily removal of 61.6 percent and the mean removal of 63.0 percent

both lie between the fourth and fifth deciles. That is, based on the 24 daily

removals, these average removal values are exceeded between 50 percent and 60

percent of the time.

2.5 OTHER DATA PROBLEMS

Some of the difficulties that can be encountered when examining sampling

data used for removal efficiency calculations (e.g., extreme values for

influent, effluent, or daily removal; or negative removals) were previously

illustrated. In this section, two other data characteristics are discussed

that may require special consideration in determining removal efficiency.

2-36

TABLE 9. DECILE ESTIMTION WORKSHEET FOR NICKEL DATA

I nwwhl 1 m.rr 1

FalQIute 1 EWRY I LIST WE I TIE I nlun I #CIW

I CQ. n 1 rm.n 1 II I WNF 1 Wflll I ml. n

I wl.n 1 a. n 1 I tr1n I WNIES I I m I I I m.c6 1 WTIMTW 1 I WNT I DECIKS I

I 7th I /w I 17 I . . . . . . . .._ I I . . . ..-.-......- *..----.----.----

I 9th

I

Clukrs in colrrn &fined as mltiplcr of (11+1)/10. here II r the nuder of data pairs used.[i.e. (51+1/10-5.2). (2x5.2=10.4) etc.)

-uses the list of orb-d NkmrJrals.

2-37

2.5.1 REMARKED DATA

Sometimes influent and affluent concentration values are mt rtou

m. For example, some sample values may be reported as Not

Detected (ND), or Below Detection Limit (BDL), or less than some specified

value. These types of values can occur for either influent or effluent

samples. For example, assume that the following influent effluent sample

values were obtained:

SAMPLE DAY

INFLUENT EFFLUENT DAILY LEVEL LEVEL REMOVAL

1) -clludL EFFICIENCIO

1 100 40 60 2 200 ND 7 3 240 60 80

The remarked data values result from limftatlons in the analytical

methodology used for the chemical analysis. How should such data be dealt

vfth? A common approach applied to remarked data is to substitute a specific

quantity for it. For example, suppose that some effluent samples were

reported as ND and the analytical method that was used has a detection limit

of 10 mg/l. A substitute value of 10 rig/l for each ND might be provided and

then any calculations using that data value performed. Variations on this

approach are to substitute half the detection limit (e.g, 10 x .5 - 5 mg/l),

or even 0 for the not detected value. For the above example, substituting 10

mg/l, 5 mg/l, and 0 q g/l for the ND value uould result in comparable daily

removals of 95 percent, 97.5 percent, and 100 percent, respectively. However,

if the influent concentration value associated with the effluent concentration

value of ND were smaller, say 40 rig/l (instead of the 200 mg/l), then

substituting 10 mg/l, 5 rig/l,, and 0 mg/l for the ND would result in daily

removals of 75 percsnt, 87.5 percent, and 100 percent, respectively. These

latter removals demonstrate that the daily removals can be affected by the

choice of value that is substituted. When replacing remarked data with

quantitative values, it is important to determine whether the various

substitute values produce substantially different mean or decile removals.

The most obvious way to determine this is to perform the necessary

calculations using the different substituted values and then to compare the

final results.

2-38

2.5.2 SEASONALI’N

Seasonal treatment performance variability can be monitored using the

time plots of influent, effluent, and daily removal values. Variations in the

removal efficiencies that can be traced to seasonal patterns may suggest that

average or decile removal efficiencies for specific time periods be determined

or that treatment performance be improved for specific time periods.

2.6 NONCONSERVATIVE POLLUTANTS

In the 1987 local limits guidance, a distinction is drawn between

conservative pollutants, which are not degraded or volatilized within the unit

processes of a treatment plant and nonconservative pollutants, which are, to

some degree, biologically/chemically transformed and/or volatilized by

wastewater aeration/turbulence within the POTW’s unit processes. Conservative

pollutants exit the POTW solely through the POTW’s effluent and sludge

streams, whereas nonconservative pollutants are also destroyed by chemical

reaction (e.g., microbially mediated oxidation) and/or undergo a phase

transformation from wastewater to ambient air.

Removal efficiencies considered to this point have been solely for

conservative pollutants, such as metals. Conservative pollutant removal

efficiencies are determined by pollutant concentrations in the POTW influent

and effluent streams. The presumption applied to conservative pollu

tants, that removal pollutants are exclusively transferred to the POlW’s

sludge screams, cannot be extended to nonconservative pollutants. Losses

through degradation and volatilization do not contribute to pollutant loadings

in sludge. As a consequence, nonconservative pollutant removal efficiencies

cannot be used in deriving allowable headworks loadings from criteria/

standards applicable to the POTW’s sludge streams* (e.g., digester inhibition

data, sludge disposal criteria/standards). An alternative procedure should be

used.

* Removal efficiencies for nonconservative pollutants m be used to calculate allowable headworks loadings based on pass critu (e.g., biological process inhibition data, NPDES permit limits, and water quality standards). The removal efficiency guidance provided in this document can be directly applied to nonconservative pollutant removal efficiencies obtained for this purpose.

2-39

The 1987 local limits guidance provides the following equation for

deriving nonconservative pollutant allowable headworks loadings from sludge-

based criteria/standards:

C CIIIT L IY - L**r x

C SLOG

or C CR11

L IN - GLDchkF

where :

L IN - Allowable headworks loading, lbs/day

L IW - POTU influent loading, lbs/day

C CR11 - Sludge criterion/standard, q g/l

C SLOG - Pollutant level in sludge, q g/l.

In the above expression, the factor CILoJLIMc is a partitioning factor relating

the pollutant level in the POTU sludge (C,,, ) to the headworks loading of the

pollutant (L,,) . The partitioning factor enables calculation of an allowable

headworks loading (L,,) from a sludge criterion/standard (C,,.,) for a

nonconservative pollutant. To determine the partitioning factor for a

particular pollutant, the POTW’s influent and sludge must be routinely

monitored for that pollutant.

It is important to recognize that the factor C,dL,,, expresses

nonconservative pollutant removals to sludge. Nonconservative pollutant

removals to sludge are highly variable, and are dependent on such factors as

wastewater temperature, ambient air temperature, biodegradation rates (which

are temperature dependent), aeration rates, and POTU fnfluent flow. Since

nonconseNative pollutant removals to sludge are highly variable, the

resulting variability in nonconservative pollutant sludge partitioning factors

should be addressed as part of the local limits development process.

2-40

The procedures and recommendations provided in this manual for addressing

removal efficiency variability for conservative pollutants (e.g., the

calculation of mean removals and the decile approach) can be extended without

modification to addressing variability in nonconservative pollutant sludge

partitioning factors. In calculating sludge quality headworks Loadings (see

Section 2.4), the sludge partitioning factor should be used in place of the

removal efficiency for nonconservative pollutants. This sludge partitioning

factor can be entered into .

2.7 SUMMARY REMARKS

In this document the following three methods for removaL efficiency

estimation have been defined and illustrated:

. Average daily removal efficiency

4 Mean removal efficiency

. Dee ile approach.

The first two methods provide single point estimates of POTW removal

efficiency. The average daily removal is simply the average over available

daily removal efficiencies derived from paired influent and effluent

wastewater samples. The mean removal efficiency is the sum of effluent

loadings divided by the sum of the influent Loadings. The mean removal

efficiency weights influent/effluent pairs with a higher flow more than

influent/effluent pairs with a lower flow.

In general, these two methods of estimating removal efficiencies yield

different results. Of the two, the mean removal efficiency is preferred

because it is less sensitive to extreme daily removal efficiencies.

The decile approach is more comprehensive than the first tvo methods

because it yields an estimate of the entire frequency distribution of daily

removal efficiencies, Using the decile approach permits the explicit

incorporation of the variability of daily removal efficiencies into the local

limits development process. Actual removal efficiencies derived from actual

paired influent and effluent wastewater sampling data demonstrate that daily

2-41

removal efficiencies are not constant over time. Daily removal efficiencies

demonstrate considerable variability; a single value approach to estimation of

removal efficiency can only provide an individual measure of the actual

process.

Computationally, the decide approach is more data intensive than both of

the other two methods. For example, the decile approach requires a minimum of

nine daily removal efficiencies; whereas the other two methods can be applied

to less data. From the standpoint of statistical precision (difference

between the estimated removal efficiency and the unknown true value), the mean

removal efficiency is the most precise. Decile approach estimates can be less

precise than either of the mean value sstinates. These statements regarding

statistical precision apply to the respective estimates derived from the same

number of daily removal efficiencies.

In cases for which removal efficiencies are consistently large (e.g.,

greater than 80 percent) or are consistently small (e.g., less than 20

percent), the acceptable statistical precision can be obtained with a small

number of daily removal efficiency values. Even in these instances, no less

than five daily removal efficiency values should be applied. The data set

size should, however, be increased to a larger number whenever the daily

removal efficiencies exhibit more variation. In most cases, more than the

minimum number of daily values should be used in the estimation process.

2-42

APPENDIX A

ADDITIONAL RESIDENTIAL/COMMERCIAL DATA

A.1 RESIDENTIAL/COMMERCIAL TRUNK LINE MONITORING DATA

I 11 b

n n OOWO DOW 0.27J 0.002 n n OObbO OOY 079 0077 n Jo 0.W 0001 0.276 0014 n n 0.m 0001 0.076 0.017s n 1 0.0160 OQol 0.21a om7 1) n O.ooO OWl 0.124 O.QoI n Jo 0.0020 0001 0.01 O.Wl

,

2 2 0.1= 0 120 0 144 0 1Y 2 2 OCOOQ 0.S 0 11 0.1 2 2 OOWO 001 0 07 000 1 2 O.OOW co.006 0011 0.001

2 lb0

. b 4 4 . J J 2 3 2 2 J 2

b b b b J b b . . . 3 J b J J 2 J 2

O.TOOO 00011 0007 0 0474 OWJb 0 0130 OOOQ OWW 000&l 00062 00072 OOOW 0 0010 Ooo(y OOOW OW

O.OOW3 OWOOI OoooOl

01 006 OW

00076 00016 coo02 OOWb

OOWW 0m 00002

OWOOl 0002 0001

000001 OOOQZI 0-1 OOWO2

OOOOOOb OOOWI

07 0 1676

001 0.1 0.1

Oojl 0.W

Oop 0 01 0 01

OOOI 0002 0001

<owl O.ooo1

co- OOQXD Omool OoWOl

07 oon

oom 0.01 001

a002 0.01

O.oQ1 OWbJ 00062 0.0024 0002 0001

do001 O.WWO Omool

0.- O.wml OOoool

A-1

A.1 RESlDENTlAL/COMMERCIAL TRUNK LINE MONITORING DATA

u U O.OIQ Oop 0.2 0.0. u 0 0.W 001 0.01 0.W 7 U 0.U a.wb 0.12 0.m

u u O.Om 0.01 0.m a.- 14 U 0.0001 a.w7 l .a aa

L

N

76 u a bl

76 U a W

0.3144 0. Ia la Oa, 0. 1w 0.0, 0 a7 0 0 Ootlb OOOU 00, OOlU 0.W O.ooOM O.Ol@ OOQZW

b

10 10 OIOO 03 I4 OI 10 10 OIIOO 0.1 00 01 10 IO 0.1600 01 0.4 01 10 IO aBow 0.1 0.1 0.1 1 1 0.W 0.1 0.1 0.1

b

12 I2 0. 1oI I 7 0.1017

12 12 0.07n 4 8 O.Ol#a 10 10 0 @loa B l 0.0117 2 2 0.016 b b 00014 2 2 0.003

0.01 0 01 001

0007 0007 OOOI owl 0001

OoQ)s

0.27 0 a7

0 11a 0.011 0.01

O.op O.OW

O.op O-o005

01n 0.06

OOU 0.0106 OOlY OOO

O.Wlb 0.013 O.ooo1

A-2

A.1 RESIDENTIAUCOMMERCIAL TRUNK LINE MONITORING DATA

Ml b

5, 5, m a 21 21

0. 1w O.W7# O.ooL 0.W 0.W 0.m

0046 0011

awl do01 Q.001 Q.001

0.,7# 0.182 O.wI 0.072 O.Nb 0.001 r.on O.NOb O.Dy O.oDp 0.007 0.002

b 0.01

140 140 140 1yI 140 140

001 0.2laa 0. la2 0 0610 0.0117 o.ooy

O.oI a1

a006 co.06

0.06 do.006

140 01

130 12

7 00

0.76 0 61 0 01 0 12 0 14 ON

02 0.18 0 12

-306 coa

ro.006

007lo 0010) 0.W OOUO 0.010 0-1 ow21 O.ooo1

Oo(8 4006

a02 002

OOOZ COW1 OWO2 OQ101

008 42

Oo(l 0 07

a.w co.01

<o.oN OWW

0.W O.QT QOL 003

a.006 owl2 00021

00002

1.2 80

1.m lb 07OW ON OW OU

0.4.W 022 0m 01

0.1100 001 00171 co oml

00176 OQY 0 0166 0 014

24 1.u 0.m 07 0.N 07 0.74 0.0

OS 02 0. la 0 11

OOU 002ll 0m OOl?b 0017 00166

A-3

A. 1 RESlDENTlAUCOMMERClAt TRUNK LINE MONITORING DATA

-~ - _--___ _-----__- - ____ - _____ --- __---_.------ ---

2 2 0 0076 owr 0006 0 Qo7b 1 2 0.00 a006 0007 O.W. 2 2 OONO 0.001 o.w7 0.004 1 2 OQOIO OWO cow6 0.004 2 2 O.ooY 0.002 O.Wb O.OWb

l-23 u

2 2 2 2 2 2 1 1 2 1

2 2 2 2 2 2 1 1 2 1

am OIIW OOOO OayO OO#W 0.W O.OW!O a.090 0.M 0-W

2r4 m.2 a.0 O.o( 0.78 041 OW 0.42 04 0.Y 027 0.1y 006 0.11 O.Q( 0.W 0.0 O.oW

O.OU mu 0.W 0.04 0.01 0.N 0.03 0.4)1 OOWb

O.oa, o.aam O.OU

a (0

OWN 0.016 l.IY 0.10 0.1* 0.001 204 0.076 0.M 0.01 0.Y 0.07 O.WlO O.OW 1.0 B.oL 0.M co.Wl4 1.2 0.a 0 OlW Q.007 low 0.007 0.0127 coo01 0 11 DOOI OW OWN OOW OW3 O.WlT Ooool 003 OOOW

187 164 IY 181 In 214

2? 13 23 I7 .

27 u 111, ? 11. n a 007n 003 0 lb 007 26 oom COO1 021 004 2s 00x)? CUooQt 00 002 27 00301 0011 0066 0 0276 2a oomo 002% 0m 0028 27 00163 OOW Om? 0006 26 0 01m COO01 006 cowb

1 11

A-4

A. 1 RESIDENTIAUCOMMERCIAL TRUNK LINE MONITORING DATA

LxmNmD TOIM MIyanc MlJueEROF NJuaERoF AVERUY YNNlm YAXMLN UEDbM -8TlC FLOW cowlmrw MEClKMZ3 ONEFWATy*(B RXLUTAM POLLUlbNl pocLulAm POLLUTW

ClN STATE fEavm wm F) LEutL LML LML LML

(uan) (uonl NW (ucu)

--- ----- --__--- ---- --__-I__ -- _-___ -__ ___- ______ _

1 a OOlP 0013 OOlJ 0013 17 2a OOIW <o 001 001) OON

2 n OWU 0006 0.008 0.W.b . 26 OWW cuow 001 a.002 2 26 OOOY .s.ow 0.01 +o.om

on 10

3 ¶ loo@7 00 1Y 12

1 1 0.2100 0 210 0.21# 0.216

s 3 O.ocQ 0.036 001 0.W

J 3 OQ3u 0 014 0017 0.W

1 3 0 0516 0.0164 4001 a.04 I 3 owm Q.wb OW cow

1 J mm8 0.003. a.w <o.w

~ -___- ____-- - - - - - - - -_ I_ ~-- I__--

A-5

A.2 COMMERCIAL SOURCE WASTEWATER MONITORING DATA

1OlM WROF WUYWAOC WUYYROF AWXM3E ylww MAXYUU UEmM

ITAlE RO**I SJUWXFLOW ma CMZIECTW aPWN*Im FWLLU~NCV LEVEL FQLLUlAHT LML po(LUlU4l LML KXLUl*JIT LEWiL

WV (uon) (uanl wow (UMJ -______-_ -____ ___ - .______ - -_-_ ___-__.- -.-_-- ------ -------------- --------------- .---------------- -------------- --------

OWN

0 0714

oob.1

OWbb

<o 03

COO6

co 001

soool

0 17

OP

01

002

?A a

OWW

OWW

002W

OOloe

0 07

awl

002

cow7

00

0 18

002

002

001

00)

002

001

PA 5 17mw 3

0 64la7 01 07 -TE

17 VA 5

10

u

0

1u

22

11

2m

a

10 3OOlWO

w 242m

61 OCUI

1Y 0 L)?.

62 0 1w

67 0-

.w Owol

61 OrmS

20

006

coo2

co.01

coo6

0012

a01

-00s

a07 40.

a. 0 lb

IO l 0 1.

a. 0.

1U 406

OY <ow

IQ cow

OY 0.001

KY 4

W e2

67 W

Q 02

3 J

W P

Y W

W P

u W

u P

40 82

Y a

46 m

u W

22404

I mr

OloI

024.J

0 21n

0 law

OOUO

OObM

00304

00117

00072

OWW

owl?

022

OW

0 07I

0 la

00%

owl

000.

co 03

OWl

0 0027

0003

coo02

co ow2

361 101

I76 OCM

lb 0 on

011 020.

10 0.141

224 00)

2b 0 01a

oa 004

0a 001

ow 001

006 OW

0 01. OW

0022 OOOOO

A-6


TOTAL HlJuHRW -0c NUMBER OF AbtFwx ww WlmJu maw4

WATE IIEaca -FLOW WUIUSS DzILcTm WFWAlm FOLLUTAM LEEl F’CXLUTAW LML PocLuTAMl LEN3 WUTMfl LEXL

(opq WI (eon) ww (UCYI) --- - _-_-_- __-- _-__ ______ -__-- -_- ______._ - -------- - _.-_ ---__-_ _--_- _-__ -___ _-____

12

Y

10

1,

a

1,

I1

10

20

a

26

7

I

b

2

3

uI.ban

m.aa2

32mo

llw8

O.-T

O.bn?

O.plT

OllDI

OOTab

0.0101

OOU

OW

oot.4

0010~

00100

00016

331

ZQ

1Y

0.62

0.0

00)

0001

002

0.002

0.001

0001

0006

OWI

0003

0004

owl

baa 40?

1W a4

a.6 a.3

40 10

2? 0 II

4.6 0 1s

1.3 0 ib

OQ 0.01

OLOZ 00s)

OW 001

0602 0 01

01 0a

001 002

OW Orn?

OW 0 05

OOOZ 0m

A-7


na

163

J.al

1.?4

13

123

1 ia

0 11a

oQ27

Y

483

¶.Jl

1 ?4

13

I23

1 1J

0 11a

0.027

12

442

44

*u

4a

n

146

005

006

002

OOC

003

OWb

ra

Jx)

622

OW

2.w

a4

0076

0011

Py)

J2a

OIID

.

.

1

IO 0

I@

0 *I

al

-3

I1 I

421

n 76

3.4?

b

<o 2

01

006

002

001

001

001

0 014

OWOI

tra

??O

346

126

1.

01

062

OOOI

ow12

703

10 11

la84

O&4

-32

004

001

0022

cooQxI1

2

1

.

0m

044

OOWb 2

A-8

FoRTclooOE


m I no0 a

20 20 1~.1180 008 110 a.2

10 a b2.m a02 (12 3

P 20 21 mm 0.12 II, 0.Y

217.1&M

ballu

20.2774

0.2Ub

O.lY7

0.1tm

0.001

0.01-a

O.oQ

22oa

1.m

2121a

O.oU

o.ola

o.ola

O.Oll3

0.01.

O.OODI

0al.M

WLW

87.ama

0-m

0.1)tO

O.dZN

O.oU

O.osl

O.all

3.ba

so.au

rU.1

0-1

0.w

l .W O.oY

0.M 0.M

--

A-9


TOTAL IaJuuiRoF MuuwFloF NUWEROF Akmmx MAXYUY -

OTATE WW3N -FLOW m OETECTM OOWIWAT- POLLU7AwTLML POLLUlMLfM3 POUUTAMLfVEL PQLIK LEML

(oral W@U W) (UOA) WW _I____ ------- -~---- --__- - ------ ---- -___---- _-_-_-_-_ ____

m 1 am

PA a

a1

2b

2a

I7

14

a

21

51

P

27

a

2b

12

n

0 ml4

0 lrn?

0 14a

0.0013

OOb2b

0.0170

O.Olb7

1a.n

0.2Iao

0.W

0.W

0 la

OOQ2

004

002

a02

CO.001

a.002

Y

004

0.070

a.ob

a

08b

03

02b

0%

a06

0.07

110 ab

0.2. 0.211

0. I2 O.Obb

0.0 O.oY

0.0

0.078b

0 1P

o.on

a.00

0.01

a.01

-- - ___-- ----

A- 10

A.2 COMMERCIAL SOURCE WASTEWATER MONfTORlNG DATA

VA

u

a

moo

27

.

a

Y

Y

b2

Y

P

u

b

b

28

1

b

11

20

18

zo

a

Y

22

0

I

2

1I

1

b

aI IlIl4.ma2

4 7.w

a au

Y 2llO

b7 OAIO

Y 0.m

Y O.Itp

bl O.lZI

0 0.m

b

II

0

I

a,

a

10

W

Y

a

W

17

P

I4

2b

lb

22

1oy.moo

l41o.m

YY

7.a

loo,

1.m

0. Ion

0.W

0 1lW

0. I la

O.lOSJ

O.ooO

O.oQ

O.Mlb

ootn

0.01a1

0.0124

00

4.8

0.0

001

a.-

am

a.0

a.02

a.ab

YI.oQo

aana

0.000

000

O.DoL

0. IW

o.eto

0.007

0001

0.004

0006

OQIO

O.oQ)

O.oo(

0.W

0.001

OOOI

1MoQ

I%.\

m.n

a

a4

1.Y

I.o(

0.a

0.427

11700.004

474o.noo

2wooo

Y.zw

l ooo

am

0.W

1.100

2.100

0.470

010

OIIO

OIIO

0 la0

0.2S

0 10

0.W

a.40

au

2.OT

0.U

0.12

l .U

0.1

a.46

0.00

lUhoo0

121aLoo

0.010

2.ooo

8.110

0.Y

o.on

O.QD

OOOI

aom

O.oI

0.W

o.oIo

O.op

a.010

0.01

0.010

-- -- - ---- ------- -

A-11


TOTAL 6aJMeEROF WJYYROF NlNaEFloF A- MAxmlN MEW

8TATE Roo1 OOUFCEFLOW DUNbXI rNm%Tm -ATII(Q PCUUTM LEXL POUUT~ LEXL POUUTMT W POUUTAMT IENL

WV NM) -1 W=) Cwu) - -_ __I_ ---_- -_---_-- _I-_--_- _____---_ ______ ____ -_

m 1

MLEN-lM

zwc 6 6 0 1740 0 01 026 017

-A 6 6 OW 0.06 0 12 000

LEAD 2 6 00270 cocQ6 0.m a026

-0l 6 6 OOWU 002 003 0.02

NKXEL 3 6 OOOQO coca7 0 01 001

COIAlT 5 6 OOM4 Co415 0.01 -so43

1X40

Imoo

OWOO

OWW

DO420

00200

0 Old0

13

12

0.Y

0.3?

0042

0.02

0.011

13

12

OW

0 3?

ooI2

002

0 010

13

12

OUI

037

OM2

002

0 010

PA a

TDB

coo

PHOOPHATE

ubcw

UXI

2

1

I 1 dzdoooo e6 m6 a6

b2 6? 316.6647 1 na 1W

cm 31 2&7lso 0.1 w7 1

1 1 0 6100 O.bl 061 0.61

8 b 0 1170 0m OU OOb26

I 2 00660 a.o( OOb 0.W

1 2 0m DOW ,301 0.W

---- ---- ------ ---- _----- - ------- ---- _-_- ---- --_---- ---- --------- - __------ - -----_- --_-- ------~

A- 2


TOTAL IaMeEROF l8lmBLR OF IluMaiRoF A- wyw MAxYlJM w3IwI

BTATE fW3lOU -FLOW &W@UXI rsmxxlaa OOlJEfWATm RJLLUTMT LEWL PouuTm LEVEL muuTA)(r LEwil mLLuTMf7LEmL

(CQq (uQI1) -1 (UOA) (uan) ___- ~ __ --__ ------ -- ___-_--- ---_-- - ---- -_--- - ----- --_--- -------- ___-___--

1 1

6

b

1 a7.0

0 4100

0m

O.twD

0 2lW

0.1m

0006.

0.W

0.W

0.M

O.OIY

O.OIW

o.oia 0.0010

0.e

0.n

OW

OS

0.2

00.3

O.DI

00.2

O.op

aola

002

0.01. o.ot2

O.OS¶

0011

0011

22

011

036

0.U

0.Q

OP

0.N

0.061

oow

O.o?

0.W

0.012

0.05)

O.OIb

0.011

I.5

0.Y

0.S

03p

OObO

0 I2

0.M

0.W

o.Qaa

0.02

O.OO(

0.012

O.OOb

OW

OOOI

b

b

b

b

b

b

1

b

1

b

b

b

sub 2

W

*

16

n

ia

11

lb

OlDI

0 0182

0.*7

oa27

0 1Qy

OW72

ooaoo

3207 OISY

42 002S

2.0.7 023

lyI? 0 11

02b. 0 01)

021 COOL

0 1. DOlW

UFMO 2

6

*

e

0

b

b

b

b

b

b

b

b

131000

2m

I tom

0 rm

OOU

OOU2

0O.b.

002b4

0m

OW

owe2

OOOU

4.

02

Ob.

0 1.

COO01

corn1

COW1

COO01

<oool

co 001

COO01

<o WI

18 4 172

168 03

2.76 on

1* 02

00 ~001

0 1.4 -zoo01

0 I# a001

001. a002

00 COOI

aw2b a001

ao2b a001

a026 cow1

1

1

2

A-13

A.2 COMMERCIAL SOURCE WASTEWATEA MONITORING DATA

20

MO

*p

4a

147

lob

140

111

b

Y

2M

Ml

Ml

4n

na

m

277

m

Ia

II

ln4m20

2.a

I.aza

0.01.

0.M

o.a410

0.011

o.opn

0.014.

a.014

?I

4.w

0.03

-no2

0.01

a.01

a04

Qmb

a.001

d.owz

zmm

i-a4

au

14 b

nb

l .bl

OY

0 bib

001

O.ooO

lmo

108

0.4

O!dO

OW

000

004

OOlb

001

O.amb?

4

1

I

II

I

n

Y

14

z!b

21

Y

1 74.mm

1 aL1ooo

a lb.&?10

1 lo.oooo

n 2a

Y l.alz

ab oe1s

a0 OIDI

71 014n

1 o.obn

I4

12

Co.01

IO

OZU

0 lb

CD1

coob

a.04

a.01

74

n

Mb

14

bab

700

bm

on

040

0.a

?4

a&b

l Y

la

l?n

001

0.4b

0 10

0.12

OQU

KV 0

37

a?

a4

a?

a?

a0

aa

P

n

lb

a

n

a7

a?

a?

3?

31

17

a?

J?

a?

ar

ar

ar

l .?mo

l.J.1#

0.M

0.m

OMM

O.zY

0 101

OMOb

0.09

001a

0.0100

O.On

27.1 l .42

442 OI

1 74 OU

Ib O.b?b

1 1 048

b.1. 0 162

2w 0071

011 0.021

CU.1 001

001 a.01

0.05 O.oO?

0.017 0 Ootb

own

0 low

OOOO

o-7

OOm

0 Ol?b

OU

0 I1

003

coo¶

COO3

-301

no

oa

0 I4b

DO1

coob

0027

Ob

0 ta

OQOL

045

003

coo1

A-14


L b 121oooo 14

on b

b

a4# %b

2M %4

2s au

3 %b

2n %l

I#2 Yl

117 117

b2 aM

1n aab

l.UB4

0.41n

oztn

0. la0

O.lYI

0.019

0.010)

O.Qp

O.OWO

01

0.1

0.01

0.1

0.01

0.01

0.001

0.01

O.WOl

Ml 0.7

lb 7 co.2

2.44 0.00

or 0.2

12.b 0.0)

b.Ya *o.aY

OAO? 0.0%

0.P co.02

0.004. am

10 10 7.a

10 10 21am

10 10 1109

10 10 0.7S42

lb 10 O.l4?4

13 10 0.1r

7 10 O.M%

1

0.2

0.1

0.1

CO.1

a.1

a.01

20.0 &b

7.3 1.7

4.2 03

2.2 0.7

0.b 0.1

03 0.1

0.1 a.01

77 77

II b4

w 67

n 81

a7 70

bo bl

2.1071 0.14 b.4

1.W 0.n a

0.n O.Oa bb

O.Y7b o.on 0.72

0. lua CO1 0.00

0.W O.Olb 0.23

2

0.a

0.78

02

co.lab

0.017

4aooo

a

2

a

1

a

1

a

a

a

a

7

bo

22

4b

4 1lQ.m

a l(o.ooO

4 lLo.oQD

4 l .rur

4 l .w

4 a.wI

a0 UOZl

4 2.12m

w l.WM

a 1 on7

0 0 MT1

m 0 P?lb

P 0 llra

u 0.110

<lrn

<lW

<lW

(1.47

(12

c0.a

0.U

<i a

CO.1

OY

a01

OW

0 01

OOlb

l&O

lb0

W.b?

12.#7

1l.b

la

a10

b.47

20

a4

I Ia

043

00

lb0

1Lo

ILO

(I.47

bO28

4.a

2.Y

1.Y

1.4

000

OQ

0 21b

OQ

OOII

A-15


LA 0 LOOQ 2

tb

11

4

I

11

a

lo

0

a

4

I2

2

a

2

1

lb

la

4

a

I2

a

12

0

a

0

IO

0

0

4

1

mm2n8

O.Wl

am70

4.M

2.107

0.W

O.Yl7

0.W

0.W

0.0707

0.001

0.M

O.OU7

0.007b

O.Wl4

0

a.00

1-U

a.2

a.01

01)

a.1

0.n

0.10

0.01

0.0)

a.00

a.Qo

a.QQ

O.Wl4

inn

1LD

I4

I4

Lb

10

O.LI

0.0

0.Y

0.17

0.07

(0.01

O.OM

0.021

O.OOl4

nb

0.4

hm

0.2

2yY

0%

0.Y

0.17

0.)

0.00

0.01

0.01

0.012

0.00%

0 4014

u 7 .

22

3

22

%

1.W

0.07W

0 17

0 01

an

aa

1164

0.3)

10

1

I7

lb

1

a

16

1

18

1

17

0

1

10

18

1

1

l.oan

LlQQI

o.ba12

0.4U7

o.aioo

O.llDL

QOm

o.om

0.Obm

0.M

0.0421

00370

0.W

OUO

1 loo

0 la

0 loo

0 al0

OQDl

Qma

0070

0047

0040

OOOI

0006

0005

4.u

1.100

(.@lO

l.Lo

0 310

0704

a 1u

oom

0047

OOY

0 lim

0 la@

OOQ

12M

1 100

0.410

0.8

0 210

0.010

OQl

0.079

oQI7

O.oY

0.007

0.001

OoZl

1

I2

0

10

lb

11

10

A-16

A.3 SEPTAGE HAULER MONITORING DATA

AMfLUX NtmaER OF NlmlYR OF AVWUOE WNWUY w(Yuu MEOHW

aw STATE tmwJu FLoWfOPQ LETECTaM C%OERvAT*))o =UTMtT Ll3EL POUUTAM LEVEL POLLUlN(T LEXL RTLUT~LEWiL

(uan) (uonl (uan) IWM)

- --_____ _-___- - -------- --- ---___-- - _----- -_I--- - ---_----_- -_- --_-_----- _____ __________

m 1 7ow

01 01 21 01 01 70 #I

01 1.114b 21 O.Ylob 01 l.b4a 01 O.%OO 01 0.1421 01 0.W #I O.OUW

0037 642 bU 0. IO %.7b 4.0 O.o( Js.0 0.01 0.00 a 0 27 0.02 073 0. I

aaob 0.14 0.02 0.01 0.78 0.00

NV 2

21 21 21 21 21 21 21 10 10

21 a7.n2s 21 tomb7 21 l7.ana 21 4.0142 21 4.b4ob PI l.Wl 21 O.uo1 21 0.m 21 0.039

2b.m 2744 m 0.7 10 4.1 0.b bb 2.4

1 s.7 2 0.W 10.2 2.0 076 0.2 1.b 0% 2.1 Ob

X0.01 11 O.lb O.ma? O.lY 0.01

PA a

S 32 P.071, 20 27 11.w P % a.= W P 1.n15 a2 P 1.N l@ 22 0.a 11 20 o.om 2 2b O.Qw

0.0 IQ.0 r.rn a001 U.1 4n

O.&b la a.#b a.020 7.1 1276

0.00 1.n O.O?b aoQo3 24b OOZI QOlb I <o.Olb Q.om 0.4 aom

a

162 10 2la7.W l&I ma 11.0078 I.1 1.1 21-7 ma IN o.mc 103 103 0.27-M 10 183 0.2111 IN 183 0.m

bl0 1lllOO ON llb.0) 0.m 0.8

0.1 W.0 004 2.4 000 2.01

O.QQI 0.40#

b.oI O.o(

0.2 0.14 0.m

0.010

434 2h.w Ul 2.7100 442 O.uD Y 0.4110 w 0.47Q

02 0.1

0.01 0.1

001

171 lb.lb ma 4 2.0 0.N

at a.2 2.2 0.14

A-17


0

13t $17 II7 1 I# 121 lJ0 lib 1P 01 ial 12s 1w iia 121 I?# 11) Ilb I12 bJ

la,

tan 117 I17 114 tat 1% lib 1m tat Iat 121 1W 111 II 1W llb lib 112 47

la,

l&b10 l&u00 14ouT lO.yI1

o-7 &?Ul ¶.oo( 2.m 0 0744 ObS% Olop OluI 0.1101 0.1400 01240 0 101 O.OUJ OQIO o.ob1t o.ol42

001 1

cu m 31 210 p1 a Ju lib Y aJ

42 81

1.1 Jb

b 22 1 I a1

OR 0742

a.2 1 1

0 0 01

OoOa 1

ON 001 001

0001 001

0.001 0

001 OOOI 0.001 0006 OOOI O.oQI

OY O.&M

1

0.Y 0.12 0.20

OJ 0a 001 ON 001 0 01 0.01

0 01 oat

0.002

IO 7

w57a aooa h.00 b..uDo 1.W 0.m 0 lJ% OOJM

OY 0.U 002 0.Y 0.n 00 OOb 0.01

n.2 170( 1I 12

II.2 a 21 1.47 0 21

0.1

II a&? 037 1.47

?.a2 0.44 0.14

0.02

J am0

0 Y YROO Y Y l&mm b7 Y sub b? 60 O.JUO

202 On a2

QD1

1w W 110 l b 21 21

bU OY

A-18


1 tarm San

a4444 2na7 au44 0.4344 l wa 0.0111

Y 20

21 1.1

037 0.2

0.a 0.02

au 40 u

u a# O.#

0.u 0.a

loo m

8 I.? 0.b

am ..t1 am

A-19

A.4 LANDFILL LEACHATE MONITORING DATA

--

ME 1

MA

lam

2

$87

174

1.3

140

2

IS)

1

a

.

7

2

0

7

.

b

7

2

b

2

2

a

.l

I

2

b

0

a

3

I

b

1

2

7

1

2

a

2

Ia

2

187

1u

lbb

in

2

101

1

12.7u

2w

0.7210

O.b772

O.UW

0 la71

OW

o-1

0.W

a W.W b7.m

4 awn0 a.wo

7 1O.U 0010

0 l.Wl7 aoob

7 1 .a.11 0.001

7 0.W O.Pg

0 0.W am1

I OW 0.100

8 0.W d)l

. 0. la0 OWO

a 0 loo0 aw

7 o.ta14 aw

b 0 I2a a006

0 O.llP a01

11 0 1012 comb

I 0.0740 -coo001

I OW OOW

2 OW 004

B 0.01) a.003

* OW cow

0 0 mu am2

7 o-7 -3001

b OW77 COO01

b O.WTb ao.W

0 0.021) caool

0 OWW co 01

l 0 Olaa coooo1

7 0 010 dm2

10 o.oow co.001

I OW Co001

7 0.W cow

oow

17m

0 010

0003

a 01

COOI

0031

c-0001

O.OOb

baom

aom

12ooo

12m

toow

2 110

0 1s

12M

0000

11 I

2w

0287

0.42

0 11

000

0011

OQ*

OOOI

Mao

7a.m

lO.LQ)

08

22

tmo

I 100

OUO

OMO

a.4

03y1

Or)(l

-34

co4

a*

0 210

001

001

02.0

0 110

om1

-31

co1

0060

co1

0010

0-

s-004

OWZ

0010

0.002

a7 Qoo

7 1)

0 IO

do1

0.b

ON

OS

044

0 lb

c-02

0 IO

co2

0 010

OW

ow

e. 1

OQL

OOU

001

003

002

owl

cooQo1

a001

Co01

a01

0 01~

~.oob

a.ow

aab

OW

A-20

A.4 LANDFILL LEACHATE MONITORING DATA

NumBLROF MJmaER OF A- mlwmw mAJlYW mEma

arv OTTATE Rwcm OETLCTIW CWSRVAT- RXLUTMtT LEXL POLLUTM LEWL POUUlbM LEHL POLLUTN#T LMI

WW W@fU WW wm - ______ _----___ __-- -------- -- ------ ~____

.

1

2

1 .

m 2

1 1 zoo00 2 2 2

1 1 1.W 1.b 1.b 1.b

1 1 l.lOOO lb 1.b 1.b

1 1 0.4000 0.4 0.4 0.4

1 1 0.1200 0.27 027 0.27

1 1 0.W 02 02 0.2

1 1 007w 0.07s o.on o.on

1 1 O.OlY O.OlY O.OlY @.OlY

1 1 0.0110 0011 0111 0.011

1 1 O.OlW 0011 l .01 1 a.011

1 1 l .W 0.W aw e.w

. ~.oooo

3 12.a . Jsmo

5 2.2aoQ

5 0.W . 0.4100 . 0.W

a 0.3100

3 0.W . 0l.m

1 0.1mo

a 0.W . 0.0700

J 0.0400

5 0.W

a 0.0310

a 0.0220

0 0.W

3 O.OlW

1 0 OlW

3 O.OlW

1 O.OlW

1 0.0110 . 0.0100

1 O.OOOO

.l 0.007S

3 Ooobl

1 OW

1 00044

x)

b.J

0.10

2.b

001

0.14

0.21

02b

0.04

0.11

O.W8

0.w

0.044

O.OU

0014

Oop

0.01

000

000

0.017

0 010

00~1

00000

00078

0.0011

0.W

0.0044

w

29

10

2b

0 74

eb

!

0.42

02b

1.0

0.11

0.10

041

0.04J

0.04b

O.oU

O.OW

0.04

0.010

0 010

0.017

0 010

0.011

0.S

0.W

OOQW

O.mbl

OW

0.W

0.0 .

2b

0.0 .

0.3

021

011

O.oU

OMO

0.011

00x)

OOW .

0.010

0 010

0.017

o.ow

0011 .

0.W

O.OOW

O.mbl

OW

0.W

A-21

A.4 UNDFILL LEACHATE MONlTORlNG DATA

m 2

“ILoo 0100

0.W 0.W 0.W 0.W o.e1w o.oow 0.W 0.W

E

E

11 0 a 2 a 4 1 0 2

J

a a a a 1 a a a a a

11 to 11 11 11 11 11 u \I I1 1,

b.)ss) LY 0.W a0 0.W 0.H 0.0407 O.U2 O.YW KY1

o.ola am7 O.WW 0408 0.W *.o( 0.W 0.Y 0.8.Y 0.W

0.27W Olooo 0.0100 0.W 0.W 0.W 0.w 0.w

+c.bub l .W 0.1-1 l .W 0.w 0.W ool.4 eela 01127 o.ola o.m.1

a.0 a.1

a.w 0.01 0.Y

4.Q a.a b.W

121 0.n

04 O.OM au1 o.ola 4.a 0.287

0.m7b 0.8012

1.07 01

0.12 01

0.07 0.03 ow 0.w

7.26 am l .u

O.Wl O.Yl

0.01 0.001 0.001 O.OU

0.W

0 va 4.1

a.01 0.Q 0.01 0.0

a.ot a.w

A-22

APPENDIX B

DECILE ESTIMATION WORKSHEET

I

I 0oL.u 1 EMlIT I LJSl TIE ItlU I MCIW

wL.m 1 11 urn t ml. n

MO a.10 t

w I ODI. 87 EUIIIES i

-IuI- t ESIlIYIE8 1 MCILES I

.-_.._._-_.._____ t I

. .._._._..-.____. I I

. ..___..-.--_____ I

B-1

supplemental manual on the development and … development and implementation of local discharge...

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