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 ......................... 

Page 

. 1-1 

. 1-3 

. . 1-3 

1-7 

. 1-13 

1-26 

1-29 

. 1-29 

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 . 

. . 

. 

. 

Page 

1-4 

1-9 

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1-20 

1-21 

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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 ............... 

Pane 

2-8 

2-15 

2-16 

2-24 

2-28 

2-29 

2-32 

2-35 

2-37 

Pane 

2-10 

2-10 

2-11 

2-13 

2-23 

2-23 

2-26 

2-27 

2-31 

2-31 

2-34 

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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 sitespecific 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 sitespecific 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. Sitespecific 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 sitespecific 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) 

POLLUTANT NUMBER OF NUMBER OF 

DETECTIONS SAMPLES 

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 

‘Parameters are ranked by concentrations from high to low. 

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 ClTable 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 ] 

‘Parameters are ranked by concentrations from high to low. 

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 

POLLUTANT NUMBER OF NUMBER OF 

DETECTIONS SAMPLES 

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 

‘Parameters are ranked by concentrations from high to low. 

1-21 
TABLE 8. SPECIFIC COMMERCIAL SOURCE WASTEWATER MONITORING DATA 

DRY CLEANERS (Continued) 

POLLUTANT NUMBER OF NUMBER OF 

DETECTIONS SAMPLES 

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 

POLLUTANT NUMBER OF NUMBER OF 

DETECTIONS SAMPLES 

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 

‘Parameters are ranked by concentrations from high to low. 

1-24 
TABLE 9. SPECIFIC COMMERCIAL SOURCE WASTEWATER MONITORING DATA 

LAUNDRIES (Continued) 

POLLUTANT NUMBER OF NUMBER OF 

DETECTIONS SAMPLES 

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

@WI) Ow34 @KIN 

ORGANICS 

BROMODICHLOROMETHANE 2 5 <O.OOl co.025 0.009 

METHYLENE CHLORIDE 1 5 0.011 0.011 0.006 

‘Parameters are ranked by concentrations from high to low. 

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 

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

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 1. 1631 - 183 1 so 1 117500 ] 21247.951 

--- - -- 

] 

‘Parameters are ranked by concentrations from high to low. 

l-27 
TABLE 10. SEPTAGE HAULER MONITORING DATA (Continued) 

POLLUTANT 

ORGANICS 

NUMBER OF NUMBER OF 

DETECTIONS SAMPLES 

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

@WI) @Wl) (mgll) 

‘Parameters are ranked by concentrations from high to low. 

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 

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

(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 

‘Parameters are ranked by concentrations from high to low. 

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 

‘Parameters are ranked by concentrations from high to low. 

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 

1 v-- 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. Stepby-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 

60. l s l 

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. 

60. 

0, .,, .,, .,, .,, .,, .,, .,, .,, , , . It 4 

255 500 755 10000 125 150 175 20000 22525 25050 27575 

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 cu 11 22 

11 cu 11 11 

12 cu 5 15 

13 CU 12 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 5th decile - 71.3 percent 

l 6th decile - 78.1 percent 

l 7th decile - 83.0 percent 

l 8th decile - 84.9 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. 

LoadEffluent 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 4th decile - 55.7 percent 

l 5th decile - 60.0 percent 

l 6th decile - 65.0 percent 

l 7th decile - 71.6 percent 

l 8th decile - 78.4 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. l + l 4 

60. l 

l l 

4 l 4 

40. l 

l 4 l :* l l 

l l a l * 

20. 

4 

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

0 50 100 15050 200 250 300 350 400 

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 sludgebased 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 

coOOQXD 

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 

-TE 0 64la7 01 07 

VA 5 17 

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 
A.2 COMMERCIAL SOURCE WASTEWATER MONITORING DATA 

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 
A.2 COMMERCIAL SOURCE WASTEWATER MONITORING DATA 

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 

2 OOWb 

A-8 
FoRTclooOE 

A.2 COMMERCIAL SOURCE WASTEWATER MONITORING DATA 

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 
A.2 COMMERCIAL SOURCE WASTEWATER MONITORING DATA 

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 
A.2 COMMERCIAL SOURCE WASTEWATER MONITORING DATA 

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 
A.2 COMMERCIAL SOURCE WASTEWATER MONITORING DATA 

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 
A.2 COMMERCIAL SOURCE WASTEWATER MONITORING DATA 

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 
A.2 COMMERCIAL SOURCE WASTEWATER MONITORING DATA 

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 
A.3 SEPTAGE HAULER MONITORING DATA 

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 
A.3 SEPTAGE HAULER MONITORING DATA 

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