xvEPA
United States
Environmental Protection
Agency
Office Of Water
(EN-336)
21W-4002
May 1991
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
'nnted on Recycled Paper
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DISCLAIMER
This project has been funded, at least in part, with Federal
funds from the U.S. Environmental Protection Agency (EPA) Office
I
of Water Enforcement and Compliance under Contract No. 68-C8-
0066, WA Nos. C-l-4 (P), C-l-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.
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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-l-4 (P),
C-l-37 (P), and C-2-4 (P).
iii
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PART 1
RESIDENTIAL AND COMMERCIAL SOURCES
OF TOXIC POLLUTANTS
TABLE OF CONTENTS
Section Page
1.0 RESIDENTIAL AND COMMERCIAL SOURCES OF TOXIC POLLUTANTS 1-1
1.1 SUMMARY OF DATA RECEIVED 1-3
1.2 DATA ANALYSIS AND LIMITATIONS 1-3
1.3 RESIDENTIAL AND COMMERCIAL MONITORING DATA 1-7
1.4 SPECIFIC COMMERCIAL SOURCE MONITORING DATA 1-13
1.5 SEPTAGE HAULER MONITORING DATA 1-26
1.6 LANDFILL LEACHATE MONITORING DATA 1-29
1.7 SUMMARY 1-29
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PART 1
LIST OF TABLES
Table Page
1. MUNICIPALITIES WHICH PROVIDED RESIDENTIAL/COMMERCIAL DATA .... 1-4
2. RESIDENTIAL/COMMERCIAL TRUNK LINE MONITORING DATA 1-9
3. COMPARISON OF RESIDENTIAL/COMMERCIAL TRUNK LINE MONITORING
DATA WITH TYPICAL DOMESTIC WASTEWATER LEVELS FROM THE 1987 LOCAL
LIMITS GUIDANCE 1-11
4. SPECIFIC COMMERCIAL SOURCE WASTEWATER MONITORING DATA -
HOSPITALS 1-15
5. SPECIFIC COMMERCIAL SOURCE WASTEWATER MONITORING DATA -
RADIATOR SHOPS 1-18
6. SPECIFIC COMMERCIAL SOURCE WASTEWATER MONITORING DATA - 1-19
CAR WASHES
7. SPECIFIC COMMERCIAL SOURCE WASTEWATER MONITORING DATA - 1-20
TRUCK CLEANERS
8. SPECIFIC COMMERCIAL SOURCE WASTEWATER MONITORING DATA -
DRY CLEANERS 1-21
9. SPECIFIC COMMERCIAL SOURCE WASTEWATER MONITORING DATA -
LAUNDRIES 1-23
10. SEPTAGE HAULER MONITORING DATA 1-27
11. LANDFILL LEACHATE MONITORING DATA 1-30
12. OVERALL AVERAGE ORGANIC POLLUTANT LEVELS 1-34
i
13. OVERALL AVERAGE INORGANIC POLLUTANT LEVELS 1-37
14. OVERALL AVERAGE NONCONVENTIONAL POLLUTANT LEVELS 1-38
vi
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PART 2
REMOVAL EFFICIENCY ESTIMATION
FOR LOCAL LIMITS
TABLE OF CONTENTS
Section Page
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
k
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 2-38
2.5.2 Seasonality 2-39
2.6 NONCONSERVATIVE POLLUTANTS 2-39
2.7 SUMMARY REMARKS 2-41
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PART 2
LIST OF TABLES
Table Page
1. COPPER MASS VALUES (LBS/DAY) AND DAILY REMOVALS 2-8
2. ORDERED COPPER REMOVALS 2-15
3. DECILE ESTIMATION WORKSHEET FOR COPPER DATA 2-16
4. ZINC MASS VALUES (LBS/DAY) AND DAILY REMOVALS 2-24
5. ORDERED ZINC REMOVALS . . 2-28
*
6. DECILE ESTIMATION WORKSHEET FOR ZINC DATA 2-29
7. NICKEL MASS VALUES (LBS/DAY) AND DAILY REMOVALS 2-32
8. ORDERED NICKEL REMOVALS 2-35
9. DECILE ESTIMATION WORKSHEET FOR NICKEL DATA 2-37
PART 2
LIST OF FIGURES
Figures Page
1. INFLUENT COPPER MASS VALUES 2-10
2. EFFLUENT COPPER MASS VALUES 2-10
3. DAILY PERCENT REMOVALS FOR COOPER 2-11
4. INFLUENT COPPER vs. EFFLUENT COPPER 2-13
5. INFLUENT ZINC MASS VALUES 2-23
6. EFFLUENT ZINC MASS VALUES 2-23
7. INFLUENT ZINC vs. EFFLUENT ZINC 2-26
8. DAILY PERCENT REMOVALS FOR ZINC 2-27
9. INFLUENT NICKEL MASS VALUES 2-31
10. EFFLUENT NICKEL MASS VALUES 2-31
11. INFLUENT NICKEL vs. EFFLUENT NICKEL 2-34
12. DAILY PERCENT REMOVALS FOR NICKEL 2-34
viii
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APPENDICES
APPENDIX A - ADDITIONAL RESIDENTIAL/COMMERCIAL DATA
A-l RESIDENTIAL/COMMERCIAL TRUNK LINE MONITORING DATA
A-2 COMMERCIAL SOURCE MONITORING DATA
l
A-3 SEPTAGE HAULER MONITORING DATA SUMMARIES
A-4 LANDFILL LEACHATE DATA
APPENDIX B - DECILE ESTIMATION WORKSHEET
ix
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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.
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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 POTUs 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
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PART 1
RESIDENTIAL AND COMMERCIAL SOURCES
OF TOXIC POLLUTANTS
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1.0 RESIDENTIAL AND COMMERCIAL SOURCES OF TOXIC POLLUTANTS
In the local limits development process, the Maximum Allowable Headworks
Loading (MAHL) of a particular toxic pollutant is allocated to both
residential and industrial sources. Thus, the POTW classifies each site-
specific source as either a residential or an industrial user. This
classification depends on the size of the facility, and on the toxic pollutant
concentrations and loadings discharged to the POTW. To make informed
decisions regarding this classification, the POTW must have a clear
understanding of toxic pollutant contributions from all sources, including
households, commercial establishments (e.g., radiator shops, car washes,
laundries, etc.), and heavy industries.
Occasionally, a POTW may find that the loadings of a toxic pollutants
exceed the MAHL. Elevated loadings from nonindustrial sources may be
attributable to:
Nonpoint sources (e.g., runoff) discharging to combined sewers
Elevated pollutant levels in water supplies
Household disposal of chemicals into sanitary sewers
Toxic pollutant discharges from commercial sources.
The first three sources listed above can be controlled through the
implementation of various management practices/programs outside the scope of
local limits development. Nonpoint sources of pollutants are addressed
through combined sewer overflow abatement programs and urban and agricultural
chemical management practice programs. The POTW can address elevated
pollutant levels in water supplies by interacting with the City Water
Department. For example, elevated metals levels in water supplies often arise
from leaching in water distribution pipes; the City Water Department may be
able to reduce such leaching by adjusting the pH and/or alkalinity of the
water supply. The POTW can encourage proper disposal of household chemicals
by instituting public education programs and establishing chemical and used
oil recovery stations.
Elevated pollutant levels in discharges from commercial sources are most
effectively addressed through local limits. Commercial sources such as
radiator shops, car washes, and laundries are often not considered as
1-1
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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
i
pollutant levels in residential wastewater, wastewaters from specific types of
commercial sources, septage haulers' loads, and landfill leachates accepted by
POTWs. The wastewater characterization data provided will enable the POTW to:
Compare pollutant loadings in its system with those found at other
POTWs
Estimate pollutant loadings from these sources as a supplement to, or
in the absence of, pollutant loadings derived from actual site-
specific monitoring data. These estimated loadings can be used in
local limits calculations when site-.specific monitoring data are not
available.
Identify toxic pollutant sources and determine which sources warrant
consideration during local limits development, routine monitoring, and
regulation under the local pretreatment program.
While the data provided can be used to derive reasonable estimates of
pollutant loadings from specified sources, collection of site-specific data is
preferable.
The monitoring data summarized in this guidance were obtained from a
variety of POTWs. It was summarized by various statistics, including range,
mean, and median pollutant levels. Section 1.1 describes this monitoring
data. While the procedures for data analysis are detailed in Section 1.2.
Sections 1.3-1.6 present and discuss the monitoring data summaries. A summary
of the conclusions is provided in Section 1.7.
1-2
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1.1 SUMMARY 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:
Residential/commercial trunk line monitoring data - Pollutant levels
and flow monitoring data for trunk lines receiving entirely or
primarily residential wastewaters
Specific cpminpfqial source monitoring data - 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)
Septage hauler monitoring data - Pollutant levels in septage haulers'
loads
Monitoring 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 POTWs 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:
Mean pollutant level
Minimum reported pollutant level
Maximum reported pollutant level
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
1-3
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TABLE 1. MUNICIPALITIES WHICH PROVIDED RESIDENTIAL/COMMERCIAL DATA
MUNICIPALITY
REGION 1
BANQOR.UE
LAWRENCE. UA
UEnrdMACK. NH
PORTLAND. ME
WARWICK, Rl
REGION 2
AUBURN, NY
BUFFALO, NY
ONONDAQA COUNTY, HV
ONEDA COUNTY. NY
TONAWANDA.NY
REGIONS
ALLENTOWN.PA
ALTOONA.PA
HAMPTON ROADS, VA
WB9C.MO
REGION 4
BOWUNQOREEN,KY '
LOLHSMUE.KV
NORTH CHARLESTON, 80
W.CAROUNA.9C
REGIONS
CMCAOO.I.
COLUMBUS. OH
HOLLAND. Ml
MDIANAPOUa,M
UIWMUKEE.WI
ROCKFORD.L
ST. PAUL, UN
REGION g
BATON ROUQE. LA
DALLAS, TX
FORTOODQE.IA
MMTERLOO.IA
MKHrTA.IC8
REGIONS
QREELEY.CO
LOU9VUEOO
REGION 0
LOSAN3ELE8.CA
ORANQE COUNTY. CA
SANFRANCaCO,CA
SANTA ROSA, CA
REQIOH10
f %
UNFED
SEWER AUTHORTTY. OR
RESIDENTIAL/
COMMERCIAL
DATA
COMMERCIAL SOURCE DATA
HOSPITALS
RADIATOR
SHOPS
CAR
WASHES
TRUCK
CLEANERS
DRY
CLEANERS
LAUNDRIES
SEPTAGE
HAULER
DATA
LEACHATE
DATA
1-4
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analysis, were considered equal to the detection limit. Pollutant levels
reported below detection were incorporated into the statistical analysis as
follows:
Calculation of mean pollutant levels - 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 below detection. For example, the mean of the following data
set would be reported as 4 milligrams per liter (mg/1) (assuming a 2
mg/1 detection limit).
6 mg/1
4 mg/1
< 2 mg/1
Determination of minimnqi and maximum pollutant 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 mg/1 Set 2: 1 mg/1
4 mg/1 < 2 mg/1
< 6 mg/1 5 mg/1
minimum - < 2 mg/1 minimum - 1 mg/1
maximum < 6 mg/1 maximum 5 mg/1
Calculation of median pollutant levels - Specified detection limits
were also used as surrogates in calculating median pollutant levels:
Set 1: 1 mg/1 Set 2: 1 mg/1
< 2 mg/1 < 2 mg/1
5 mg/1 3 mg/1
5 mg/1
median - < 2 mg/1 median - < 2 mg/1
mean - 3.25 mg/1 mean - 2 mg/1
In lieu of averaging two detection limits to obtain a median, the lower
of the two detection limits was selected as the median:
1 mg/1
< 2 mg/1
< 3 mg/1
5 mg/1
median - < 2 mg/1
mean - 2.25 mg/1
1-5
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Some POTtfs 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 POTWs 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 FOTW, 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 POTWs 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
1-6
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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 GFR 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
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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 POTWs have established residential/commercial trunk line monitoring
programs. Monitoring data provided by 15 POTWs is presented in this section.
Of these POTWs, 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 POTWs 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 summaries 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.
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INORGANICS
TABLE 2. RESIDENTIAL/COMMERCIAL TRUNKLINE MONITORING DATA
POLLUTANT
NUMBER OF
DETECTIONS
NUMBER OF
SAMPLES
MIN. CONG.
(mg/l)
MAX. CONC.
(mg/l)
AVG. CONC.*
(mg/l)
PHOSPHATE
IRON
TOTAL PHOSPHOROUS
BORON
FLUORIDE
BARIUM
MANGANESE
CYANIDE
NICKEL
LITHIUM
CADMIUM
ARSENIC
CHROMIUM (III)
CHROMIUM (T)
MERCURY
SILVER
2
18
1
4
2
3
3
7
313
2
361
140
1
311
218
181
2
18
1
4
2
3
3
7
540
2
538
205
2
522
235
224
27.4
0.0002
0.7
0.1
0.24
0.04
0.04
0.01
<0.001
0.03
0.00076
0.0004
<0.005
<0.001
<0.0001
0.0007
30.2
3.4
0.7
0.42
0.27
0.216
0.16
0.37
1.6
0.031
0.11
0.088
0.007
1.2
0.054
1.052
28.8
0.989
0.7
0.3
0.255
0.115
0.087
0.082
0.047
0.031
0.008
0.007
0.006
0.0034
0.002
0.0019
ORGANICS
METHYLENE CHLORIDE
TETRACHLOROETHENE
1 ,2,4-TRICHLOROBENZENE
TRANS-1 ,2-DICHLOROETHENE
PHENOLS
7
5
1
1
2
30
29
3
28
2
0.00008
0.00001
<0.002
0.013
0.00002
0.055
0.037
0.035
0.013
0.00003
0.027
0.014
0.013
0.013
0.01
"Parameters are ranked by concentrations from high to low.
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TABLE 2. RESIDENTIAL/COMMERCIAL TRUNKLINE MONITORING DATA (Continued)
POLLUTANT
NUMBER OF
DETECTIONS
NUMBER OF
SAMPLES
MIN. CONC.
(mg/l)
MAX. CONC.
(mg/0
AVG. CONC.*
(mg/l)
ORGANICS
CHLOROFORM
1,1-DICHLOROETHENE
1,1-DICHLOROETHANE
BISJ2-ETHYLHEXYL) PHTHALATE
TOTAL ENDOSULFAN
FLUORANTHENE
TOTAL BHC
4,4-DDD
PYRENE
21
2
1
5
3
2
3
3
2
30
29
28
5
3
5
3
3
3
<0.002
0.005
0.026
0.00002
0.002
L O.DQ001
0.001
0.00026
0.00001
0.069
0.008
0.026
0.022
0.002
<0.001
0.001
0.0004
<0.005
0.009
0.007
0.007
0.006
0.002
0.001
0.001
0.0003
0.0002
I
1-1
o
'Parameters are ranked by concentrations from high to low.
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TABLE 3. COMPARISON OF RESIDENTIAL/COMMERCIAL TRUNKLINE
MONITORING DATA WITH TYPICAL 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/l)
Cadmium 0.003
Chromium 0.05
Copper 0.061
Lead 0.049
Nickel 0.021
Zinc 0.175
Arsenic 0.003
Mercury 0.0003
Silver 0.004
Cyanide 0.041
0.008
0.034
0.109
0.116
0.047
0.212
0.007
0.002
0.019
0.082
* 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
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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/1,
nearly seven times the mercury level of 0.0003 mg/1 reported in the 1987 local
limits guidance. The average silver level from Table 2 is 0.019 mg/1, nearly
five times the silver level of 0.004 mg/1 reported in the local limits
guidance. For all other pollutants listed in Table 3 except chromium, the
Table 2 average pollutant level is higher than the 1987 local limits guidance
level by at least a factor of two.
The average residential/commercial trunk line pollutant levels for metals
and cyanide provided in Table 2 are higher than those provided in the 1987
local limits guidance and hence, are more conservative. Also, they are based
on monitoring data from more POTWs, and as such, may more adequately
characterize residential/commercial wastewaters received by most POTWs. Site-
specific monitoring data should always be used in preference to reliance on
any literature data.
Appendix A, Table A.I, provides residential/commercial trunk line
monitoring data summaries for each of the 15 POTWs. Average, median, minimum,
and maximum pollutant levels; number of detections; number of observations;
the combined total residential/commercial flow to the POTW; and the
residential/commercial percent of the POTW's total flow are provided for each
POTW.
The residential/commercial trunk line monitoring data provided in this
section can be used as a supplement to, or in the absence of, actual site-
specific monitoring data in the calculation of local limits. As pollutant
levels in residential/commercial trunk lines can depend on site-specific
factors such as the size of the municipality, it is important to recognize
that the literature data serve only as surrogates for actual site-specific
monitoring data. Rather than continuing to rely exclusively on any literature
data, POTWs 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 monitoring. 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 characterizing 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 POTW to
better understand the sources of toxic pollutants and in determining
compliance and monitoring priorities.
Specific commercial source monitoring data were provided by 21 POTWs.
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 commercial 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 indicated 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.
1-13
-------
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 POTWs required commercial sources belonging to the categories
listed above to be routinely monitored 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 POTtf to FOTW and will depend on such
issues as POTU size, POTW permitting and monitoring resources, and the
magnitude of pollutant loadings currently received by the POTU 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.
Hospitals
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, arid 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.
1-14
-------
TABLE 4. SPECIFIC COMMERCIAL SOURCE WASTEWATER MONITORING DATA
HOSPITALS
POLLUTANT
NUMBER OF
DETECTIONS
NUMBER OF
SAMPLES
MIN. CONC.
(mg/l)
MAX. CONC.
(mg/l)
AVG. CONC.*
(mg/l)
INORGANICS
PHOSPHATE
IRON
BARIUM
LEAD
FLUORIDE
ZINC
COPPER
CHROMIUM (T)
SILVER
NICKEL
ARSENIC
CADMIUM
ANTIMONY
SELENIUM
MERCURY
16
62
57
127
9
222
126
355
384
83
64
76
1
42
56
16
62
62
183
9
224
129
586
635
132
97
130
5
70
69
0.5
0.22
0.065
<.001
0.06
<.001
<0.02
0.001
0.001
0.005
0.001
<0.001
0.001
0.0027
<.0002
9.7
35.1
17.5
34
2.7
6.4
10.6
2.24
4.9
0.86
0.502
0.658
0.04
0.02
0.022
4.465
2.249
1.779
0.881
0.637
0.563
0.452
0.117
0.098
0.06
0.026
0.018
0.018
0.011
0.002
NONCONVENTIONALS
TDS
COD
SURFACTANTS
12
96
11
12
96
11
331
20
0.52
580
1345
4.6
426.583
346.721
1.791
* Parameters are ranked by concentrations from high to low.
-------
TABLE 4. SPECIFIC COMMERCIAL SOURCE WASTEWATER MONITORING DATA
HOSPITALS (Continued)
POLLUTANT
NUMBER OF
DETECTIONS
NUMBER OF
SAMPLES
MIN. CONC.
(mg/l)
MAX. CONC.
(mg/l)
AVG. CONC.'
(mg/l)
ORGANICS
FORMALDEHYDE
PHENOL
19
38
35
38
<0.1
.025
1.4
0.698
0.58
0.2
* Parameters are ranked by concentrations from high to low.
-------
Radiator Shops
Table 5 summarizes automobile radiator shop monitoring data for a total
of 32 sources discharging to 7 POTWs. Pollutants discharged at highest
average levels included zinc, lead, and copper. Th^ most frequently detected
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.
Car Washes
Table 6 summarizes car wash monitoring data provided for 11 facilities
discharging to 3 POTWs. Pollutants discharged at highest levels included COD
and the metals zinc, lead, and copper. The metals zinc, lead, and copper are
the most frequently identified pollutants.
Truck Cleaners
Table 7 provides monitoring data for six truck cleaning facilities
discharging to 2 POTWs . Pollutants detected at highest average levels
included COD, total dissolved solids, cyanide, phosphate, phenol, zinc, and
aluminum. The most frequently detected pollutants were chromium, lead,
copper, zinc, COD, and phenol. POTWs should anticipate that truck cleaning
wastewaters may contain a variety of organic and/or inorganic pollutants,
potentially at elevated levels.
Dry Cleaners
Table 8 summarizes monitoring data for 31 dry cleaning facilities
discharging to 3 POTWs. Pollutants at highest average levels were total
f
dissolved solids, COD, phosphate, iron, zinc, and copper, as well as the
organic solvents butyl cellosolve and N-butyl benzene sulfonamide. The most
frequently identified pollutants in the dry cleaners' wastewaters were COD and
phosphate.
Laundries
Table 9 presents a summary of monitoring data for 59 commercial laundries
discharging to 14 POTWs. Organic pollutants found at highest average levels
were COD, ethyl toluene, n-propyl alcohol, isopropyl alcohol, toluene, xylene,
ethylbenzene, and bis (2-ethylhexyl) phthalate. Metals at highest average
1-17
-------
TABLE 5. SPECIFIC COMMERCIAL SOURCE WASTEWATER MONITORING DATA
RADIATOR SHOPS
POLLUTANT
NUMBER OF
DETECTIONS
NUMBER OF
SAMPLES
MIN. CONC.
(mg/l)
MAX. CONC.
(mg/l)
AVG. CONC.*
(mg/l)
INORGANICS
IRON
ZINC
LEAD
COPPER
MANGANESE
NICKEL
CHROMIUM (T)
CADMIUM
CYANIDE
SILVER
ARSENIC
MERCURY
21
494
455
503
1
104
22
128
11
5
5
16
21
503
486
504
1
144
26
141
11
5
5
25
0.1
<0.02
0.02
0.03
1.23
0.01
0.01
0.005
0.014
0.011
.0018
0.0001
770
1720
2280
395
1.23
3.29
0.95
1.3
0.098
0.044
0.0351
0.0012
64.43
22.17
21.408
9.34
1.23
0.18
0.14
0.052
0.03
0.024
0.012
0.0004
NONCONVENTIONALS
COD
<3.7
11.3
7.667
* Parameters are ranked by concentrations from high to low.
-------
TABLE 6. SPECIFIC COMMERCIAL SOURCE WASTEWATER MONITORING DATA
CAR WASHES
POLLUTANT
NUMBER OF
DETECTIONS
NUMBER OF
SAMPLES
MIN. CONC.
(mg/l)
MAX. CONC.
(mg/l)
AVG. CONC.*
(mg/l)
INORGANICS
ZINC
LEAD
COPPER
NICKEL
CHROMIUM (T)
SILVER
CADMIUM
37
29
29
17
18
3
21
37
34
33
26
29
12
33
0.02
0.002
0.03
0.02
0.01
<0.001
<.002
3
0.99
0.39
0.25
0.24
<.05
0.07
0.543
0.162
0.139
0.08
0:074
0.018
0.017
NONCQNVENTIONALS
[COD
34
250
126.33
* Parameters are ranked by concentrations from high to low.
-------
TABLE 7. SPECIFIC COMMERCIAL SOURCE WASTEWATER MONITORING DATA
TRUCK CLEANERS
POLLUTANT
NUMBER OF
DETECTIONS
NUMBER OF
SAMPLES
MIN. CONC.
(mg/l)
MAX. CONC.
(mg/l)
AVG. CONC.*
(mg/l)
INORGANICS
CYANIDE
PHOSPHATE
ALUMINUM
ZINC
LEAD
COPPER
NICKEL
CHROMIUM (T)
ANTIMONY
ARSENIC
THALLIUM
CADMIUM
BERYLLIUM
SELENIUM
5
5
4
83
56
72
53
46
6
9
2
59
1
5
9
5
4
83
85
74
65
79
17
23
14
71
15
22
0.005
0.09
4.8
0.09
0.005
0.007
0.01
0.004
0.01
0.002
0.005
0.001
0.001
0.001
250
34.2
13.1
80.98
6.4
1.8
1.05
0.98
0.64
0.85
0.13
0.427
0.1
0.05
55.587
7.85
7.7
4.416
0.353
0.233
0.177
0.12
0.09
0.068
0.042
0.027
0.013
0.012
NONCONVENTIONALS
COD
TDS
63
5
63
5
35.3
361
17850000
11700
36478.502
3364
ORGANICS
PHENOL
78
83
0.005
62
1.881
'Parameters are ranked by concentrations from high to low.
-------
TABLE 8. SPECIFIC COMMERCIAL SOURCE WASTEWATER MONITORING DATA
DRY CLEANERS
POLLUTANT
NUMBER OF
DETECTIONS
NUMBER OF
SAMPLES
MIN. CONC.
(mg/l)
MAX. CONC.
(mg/l)
AVG.CONC.*
(mg/l)
INORGANICS
PHOSPHATE
IRON
ZINC
COPPER
LEAD
CHROMIUM (T)
NICKEL
CADMIUM
COBALT
30
1
5
5
3
5
3
1
1
31
1
5
5
7
5
5
2
5
0.1
0.51
0.07
0.05
<.025
0.02
<.007
0.006
<0.003
297
0.51
0.25
0.12
0.05
0.03
0.01
<0.01
0.01
25.719
0.51
0.174
0.086
0.032
0.022
0.009
0.008
0.004
NONCONVENTIONALS
TDS
COD
1
82
1
87
625
1
625
3865
625
315.565
ORGANICS
BUTYL CELLOSOLVE
N-BUTYL BENZENESULFONAMIDE
2-(2-BUTOXYETHOXY) ETHANOL
BIS(2-ETHYLHEXYL) PHTHALATE
PHENOL
1
1
1
1
6
1
1
1
1
8
1.3
1.2
0.59
0.37
0.006
1.3
1.2
0.59
0.37
0.53
1.3
1.2
0.59
0.37
0.117
* Parameters are ranked by concentrations from high to low.
-------
TABLE 8. SPECIFIC COMMERCIAL SOURCE WASTEWATER MONITORING DATA
DRY CLEANERS (Continued)
POLLUTANT
NUMBER OF
DETECTIONS
NUMBER OF
SAMPLES
MIN. CONC.
(mg/l)
MAX. CONC.
(mg/l)
AVG. CONC.*
(mg/l)
ORQANICS
DI-N-OCTYL PHTHALTE
STYRENE
TOLUENE
1
1
1
1
1
1
0.042
0.02
0.016
0.042
0.02
0.016
0.042
0.02
0.016
Parameters are ranked by concentrations from high to low.
-------
TABLE 9. SPECIFIC COMMERCIAL SOURCE WASTEWATER MONITORING DATA
LAUNDRIES
POLLUTANT
NUMBER OF
DETECTIONS
NUMBER OF
SAMPLES
MIN. CONC.
(mg/l)
MAX. CONC.
(mg/l)
AVG. CONC.*
(mg/l)
INORGANICS
PHOSPHATE
SULFIDE
IRON
ZINC
LEAD
MANGANESE
BARIUM
COPPER
CHROMIUM (T)
NICKEL
SILVER
CYANIDE
ARSENIC
CADMIUM
SELENIUM
5
1
431
1166
953
3
37
1038
572
332
50
124
30
525
17
5
3
441
1264
1212
3
37
1063
908
863
76
125
43
905
41
4.4
<0.2
<01
<0.005
0.01
0.26
0.089
0.01
0.003
<0.001
<.0002
0.002
<.002
<.002
<.002
18.4
14
145
234
150
0.83
1.1
14.6
36.8
2.93
0.017
3.4
<0.81
0.518
0.021
13.2
4.8
3.796
1.873
1.514
0.553
0.506
0.452
0.216
0.14
0.123
0.101
0.034
0.034
0.016
NONCONVENTIONALS
[COD
274
274
60
20000
1421.409
* Parameters are ranked by concentrations from high to low.
-------
TABLE 9. SPECIFIC COMMERCIAL SOURCE WASTEWATER MONITORING DATA
LAUNDRIES (Continued)
POLLUTANT
NUMBER OF
DETECTIONS
NUMBER OF
SAMPLES
MIN. CONC.
(mg/l)
MAX. CONC.
(mg/l)
AVG. CONC.*
(mg/l)
ORGANICS
1-ETHYL-4-METHYL BENZENE
1-ETHYL-3-METHYL BENZENE
1-ETHYL-2-METHYL BENZENE
n-PROPYL ALCOHOL
ISOPROPYL ALCOHOL
M-XYLENE
TOLUENE
P-XYLENE
ETHYLBENZENE
BIS (2-ETHYLHEXYL) PHTHALATE
NAPTHALENE
PHENOL
TETRACHLOROETHENE
CHLOROFORM
1 ,1 ,2,2-TETRACHLOROETHANE
DI-N-OCTYL PHTHALATE
DI-N-BUTYL PHTHALATE
BUTYL BENZYL PHTHALATE
TRANS-1 ,2-DICHLOROETHENE
BROMOFORM
1,1,1-TRICHLOROETHANE
CARBON TETRACHLORIDE
CHLOROBENZENE
2
3
3
1
2
1
6
1
4
1
1
214
5
6
2
1
2
2
3
1
1
1
1
3
4
4
1
2
4
10
4
9
1
1
231
5
10
5
1
2
2
10
5
5
5
5
<150
<150
<150
74
12
<1.47
0.014
<0.96
0.033
0.35
0.310
<0.01
0.096
<0.001
<0.001
0.057
0.012
0.02
<0.001
<0.001
<0.001
<0.001
<0.001
150
150
150
74
39
22.57
16
11.29
3.16
1.1
0.31
6.51
0.32
0.62
0.43
0.057
0.07
0.046
0.18
0.074
0.09
<0.025
<0.025
150
150
150
74
25.5
6.744
4.032
3.543
0.95
0.725
0.31
0.244
0.163
0.141
0.099
0.057
0.041
0.033
0.026
0.026
0.025
0.01
0.009
* Parameters are ranked by concentrations from high to low.
-------
TABLE 9. SPECIFIC COMMERCIAL SOURCE WASTEWATER MONITORING DATA
LAUNDRIES (Continued)
POLLUTANT
NUMBER OF
DETECTIONS
NUMBER OF
SAMPLES
MIN. CONC.
(mg/l)
MAX. CONC.
(mg/l)
AVG. CONC/
(mg/l)
ORGANICS
BROMODICHLOROMETHANE
METHYLENE CHLORIDE
2
1
5
5
<0.001
0.011
<0.025
0.011
0.009
0.006
I
IsJ
Ln
'Parameters are ranked by concentrations from high to low.
-------
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. POTWs
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 SEPTAGE HAULER MONITORING DATA
Existing septage hauler monitoring data are useful to the POTW 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. POTWs 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.
1-26
-------
TABLE 10. SEPTAGE HAULER MONITORING DATA
POLLUTANT
NUMBER OF
DETECTIONS
NUMBER OF
SAMPLES
MIN. CONG.
(mg/l)
MAX. CONG.
(mg/l)
AVG. CONG.*
(mg/l)
INORGANICS
IRON
ZINC
MANGANESE
BARIUM
COPPER
LEAD
NICKEL
CHROMIUM (T)
CYANIDE
COBALT
ARSENIC
SILVER
CADMIUM
TIN
MERCURY
464
959
5
128
963
962
813
931
575
16
144
237
825
11
582
464
967
5
128
971
1067
1030
1019
577
32
145
272
1097
25
703
0.2
<0.001
0.55
0.002
.01
<0.025
0.01
0.01
0.001
<0.003
0
<0.003
0.005
<.015
0.0001
2740
444
17.05
202
260.9
118
37
34
1.53
3.45
3.5
5
8.1
1
0.742
39.287
9.971
6.088
5.758
4.835
1.21
0.526
0.49
0.469
0.406
0.141
0.099
0.097
0.076
0.005
I
10
NONCONVENTIONALS
COD
183
183
510
117500
21247.951
'Parameters are ranked by concentrations from high to low.
-------
I
to
00
ORGANICS
TABLE 10. SEPTAQE HAULER MONITORING DATA (Continued)
POLLUTANT
NUMBER OF
DETECTIONS
NUMBER OF
SAMPLES
MIN. CONC.
(mg/l)
MAX. CONC.
(mg/l)
AVG. CONC/
(mg/l)
METHYL ALCOHOL
ISOPROPYL ALCOHOL
ACETONE
METHYL ETHYL KETONE
TOLUENE
METHYLENE CHLORIDE
ETHYLBENZENE
BENZENE
XYLENE
117
117
118
115
113
115
115
112
87
117
117
118
115
113
115
115
112
87
1
1
0
1
.005
0.005
0.005
0.005
0.005
396
391
210
240
1.95
2.2
1.7
3.1
0.72
15.84
14.055
10.588
3.65
0.17
0.101
0.067
0.062
0.051
* Parameters are ranked by concentrations from high to low.
-------
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 POTWs
reported that routine compliance monitoring was for metals only; however, one
POTW reported conducting periodic Polychlorinated Biphenols (PCS) 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 maximum
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 highest
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 zinc.
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 and
commercial sources, monitoring data provided by 24 POTWs, located in all 10
EPA Regions, have been summarized (by POTW) and discussed. Based on a review
of the monitoring data summaries provided in Tables 12, 13, and 14,
wastewaters from residential and commercial sources may be characterized as
follows:
1-29
-------
TABLE 11. LANDFILL LEACHATE MONITORING DATA* *
I
u>
o
POLLUTANT
MIN. CONC.
(mg/l)
MAX. CONC.
(mg/l)
AVG. CONC.*
(mg/l)
INORGANICS
IRON
MANGANESE
ZINC
CHROMIUM (T)
NICKEL
COPPER
BARIUM
LEAD
ANTIMONY
ARSENIC
CADMIUM
CYANIDE
SILVER
SELENIUM
MERCURY
1.5
0.63
<01
0,007
0.003
0.007
<0.1
0.005
0.008
0.002
<0.001
.04
<0.01
<.002
<0002
4500
73.2
58
12.1
12.09
10.87
0.55
9.8
0.3
0.13
1.25
0.05
0.05
0.02
0.002
33.8
13.224
12.006
0.633
0.55
0.395
0.201
0.156
0.142
0.042
0.03
0.029
0.019
0.01
0.001
QRGANIGS
Parameters are ranked by concentrations from high to low.
METHYL ETHYL KETONE
ACETONE
1,2-DICHLOROETHANE
PHENOL
TOLUENE
5.3
2.8
<0.005
0.008
0.0082
29
2.8
6.8
2.9
1.6
13.633
2.8
1.136
1.06
0.735
* "Number of detections/number of observations could not be determined from data provided.
-------
TABLE 11. LANDFILL LEACHATE MONITORING DATA'
(Continued)
POLLUTANT
MIN. CONG.
(mg/l)
MAX. CONG.
(mg/l)
AVG. CONG/
(mg/l)
ORGANICS
VINYL ACETATE
BENZOIC ACID
ETHYLBENZENE
NAPTHALENE
DIETHYL PHTHALATE
2,4-DIMETHYL PHENOL
1 ,4-DlCHLOROBENZENE
METHYL BUTYL KETONE
VINYL CHLORIDE
4-METHYLPHENOL
BENZENE
TRICHLOROETHENE
CHLOROETHANE
1 ,1 ,1-TRICHLOROETHANE
P-CHLORO-M-CRESOL
PENTACHLOROPHENOL
N-NITROSODIPHENYLAMINE
CHLOROBENZENE
DIMETHYL PHTHALATE
DI-N-BUTYL PHTHALATE
1,1-DICHLOROETHANE
0.25
0.020
0.017
<0.01
0.11
0.005
<0.005
0.028
<0.002
0.065
<0.002
<0.001
<0.001
0.011
0.018
0.016
0.011
0.011
0.0049
0.0044
<0.001
0.25
<0.4
0.54
<0.4
0.11
<0.4
<0.4
0.16
0.21
0.065
0.031
<0.1
<0.1
0.022
0.018
0.016
0.011
0.011
0.0049
0.0044
0.052
0.25
0.19
0.171
0.113
0.11
0.107
0.101
0.094
0.067
0.065
0.025
0.025
0.021
0.019
0.018
0.016
0.011
0.011
0.005
0.004
0.002
* Parameters are ranked by concentrations from high to low.
'Number of detections/number of observations could not be determined from data provided.
-------
Commercial Sources:
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 mg/1, and the
average COD for industrial laundries was 1,400 mg/1.
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.
Septage Haulers:
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
residential/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/1.
Solvents identified in septage haulers' loads included methyl
alcohol, acetone, and methyl ethyl ketone.
Landfill Leachates:
Average levels of the metals manganese, zinc, 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.
1-32
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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 shops - 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
Septage haulers - 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
i
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.
1-33
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TABLE 12. OVERALL AVERAGE ORGANIC POLLUTANT LEVELS (MG/L)
POLLUTANT
ACETONE
BENZENE
BENZOIC ACID
BIS(2-ETHYLHEXYL)PHTHALATE
BROMODICHLOROMETHANE
BROMOFORM
2-BUTANONE
2-(2-BUTOXYETHOXY) ETHANOL
BUTYL BENZYL PHTHALATE
BUTYL CELLOSOLVE
CARBON TETRACHLORIDE
CHLOROBENZENE
CHLOROETHANE
CHLOROFORM
4,4'-DDD
1 ,4-DICHLOROBENZENE
1,1 DICHLOROETHANE
1 ,1 DICHLOROETHENE
DIETHYL PHTHALATE
DIMETHYL PHTHALATE
2.4 DIMETHYLPHENOL
DI-N-OCTYL PHTHALATE
ETHYL BENZENE
1-ETHYL-2-METHYL BENZENE
RES.
AVERAGE
0.006
0.009
0.0003
0.026
0.007
SEPTAGE
AVERAGE
10.588
0.062
-
0.067
LEACHATE
AVERAGE
2.8
0.025
0.19
13.633
0.011
0.021
0.101
0.575
0.030
0.11
0.005
0.107
0.171
COMMERCIAL FACILITIES
CAR
WASH
AVERAGE
DRY
CLEANER
AVERAGE
0.37
0.59
1.3
0.042
HOSPITAL
AVERAGE
NDUSTRIAL
LAUNDRIES
AVERAGE
0.725
0.009
0.026
0.033
0.010
0.009
0.141
0.057
0.950
150
RADIATOR
SHOP
AVERAGE
TRUCK
CLEANERS
AVERAGE
-------
TABLE 12. OVERALL AVERAGE ORGANIC POLLUTANT LEVELS (MG/L) (Continued)
POLLUTANT
1-ETHYL-4-METHYL BENZENE
FLUORANTHENE
FORMALDEHYDE
2-HEXANONE
ISOPROPYL ALCOHOL
METHYL ALCOHOL
METHYL ETHYL KETONE
METHYLENE CHLORIDE
4-METHYLPHENOL
4-METHYL-2-PENTANONE
M-XYLENE
NAPHTHALENE
N-BUTYL BENZENESULFONAMIDE
N-NITROSODIPHENYLAMINE
PENTACHLOROPHENOL
PHENOLS
2-PROPANOL
1-PROPANOL
PYRENE
P-CHLORO-M-CRESOL
P-XYLENE
1 ,1 ,2,2 TETRACHLOROETHANE
TETRACHLOROETHENE
RES.
AVERAGE
0.001
14.055
0.027
0.010
0.0002
0.015
SEPTAGE
AVERAGE
15.84
3.650
0.101
LEACHATE
AVERAGE
0.094
0.310
0.065
0.43
0.113
0.011
0.016
0.710
0.018
COMMERCIAL FACILITIES
CAR WASH
AVERAGE
DRYCLEANER
AVERAGE
-
1.2
0.117
HOSPITAL
AVERAGE
0.58
0.201
INDUSTRIAL
LAUNDRIES
AVERAGE
150
0.006
6.744
0.310
0.244
25.5
74
3.543
0.099
0.163
RADIATOR
SHOP
AVERAGE
TRUCK
CLEANERS
AVERAGE
36478.502
1.881
-------
TABLE 12. OVERALL AVERAGE ORGANIC POLLUTANT LEVELS (MG/L) (Continued)
POLLUTANT
TETRACHLOROETHYLENE
TOLUENE
TOTAL BHC
TOTAL ENDOSULFAN
TRANS-1 ,2-DICHLOROETHENE
1 ,2,4-TRICHLOROBENZENE
1 .1 .1-TRICHLOROETHANE
TRICHLOROETHENE
TRICHLOROETHYLENE
VINYL ACTETATE
VINYL CHLORIDE
XYLENE
RES.
AVERAGE
0.00001
0.001
0.002
0.013
0.013
SEPTAGE
AVERAGE
0.170
0.051
LEACHATE
AVERAGE
0.735
0.019
0.028
0.018
0.250
0.067
0.317
COMMERCIAL FACILITIES
:AR WASH
AVERAGE
DRYCLEANER
AVERAGE
0.016
HOSPITAL
AVERAGE
INDUSTRIAL
LAUNDRIES
AVERAGE
4.032
0.026
0:025
RADIATOR
SHOP
AVERAGE
TRUCK
CLEANERS
AVERAGE
-------
TABLE 13. OVERALL AVERAGE INORGANIC POLLUTANT LEVELS (MG/L)
POLLUTANT
ALUMINUM
ANTIMONY
ARSENIC
BARIUM
BERYLLIUM
BORON
CADMIUM
CHROMIUM
CHROMIUM(III)
COBALT
COPPER
CYANIDE
FLUORIDE
IRON
LEAD
LITHIUM
MANGANESE
MERCURY
NICKEL
SELENIUM
SILVER
THALLIUM
TIN
ZINC
RES.
AVERAGE
0.007
0.115
0.3
0.008
0.034
0.006
0.109
0.082
0.255
0.989
0.116
0.031
0.087
0.002
0.047
0.004
0.019
0.212
SEPTAGE
AVERAGE
0.141
5.758
0.097
0.490
0.406
4.835
0.469
39.287
1.210
6.088
0.005
.526
0.099
0.076
9.971
.EACHATE
AVERAGE
0.34
0.142
0.042
0.201
0.030
0.633
0.395
0.029
33.8
0.156
13.224
0.001
Occn
.OOU
0.010
0.019
12.006
COMMERCIAL FACILITIES
CAR
WASH
AVERAGE
0.017
0.074
0.139
0.162
Onan
.uou
0.018
0.543
DRY
CLEANER
AVERAGE
0.008
0.022
0.004
0.086
0.51
0.032
0009
0.174
HOSPITAL
AVERAGE
0.018
0.026
1.779
0.018
0.117
0.452
0.637
2.249
0.881
0.002
0060
0.011
0.098
0.563
INDUSTRIAL
LAUNDRIES
AVERAGE
0.034
0.506
0.034
0.216
0.552
0.101
3.796
1.514
0.553
0.004
0.140
0.016
0.123
1.873
RADIATOR
SHOP
AVERAGE
1 1^
0.012
0.165
0.128
22.218
0.030
64.430
69.210
1.23
0.0004
0.300
0.024
145.295
TRUCK
CLEANERS
AVERAGE
7 7
0.09
0.068
0.013
0.027
0.120
0.233
55.587
0.353
0.177
0.012
0.114
0.042
4.416
-------
TABLE 14. OVERALL AVERAGE NONCONVENTIONAL POLLUTANT LEVELS (MG/L)
I
U)
00
POLLUTANT
AMMONIA
COD
PHOSPHATE
SULFIDE
SURFACTANTS
TDS
TOTAL PHOSPHORUS
RES.
AVERAGE
43.111
28.8
0.7
SEPTAGE
AVERAGE
21247.951
LEACHATE
AVERAGE
34.545
-
~ COMMERCIAL FACILITIES
CAR
WASH
AVERAGE
126.333
DRY
CLEANER
AVERAGE
315.565
25.719
0.02
625
HOSPITAL
AVERAGE
346.721
4.465
1.791
426.583
INDUSTRIAL
LAUNDRIES
AVERAGE
1421.409
13.2
4.800
RADIATOR
SHOP
AVERAGE
7.667
TRUCK
CLEANERS
AVERAGE
7.85
3364
-------
FART 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 approaches. 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 FOTW sampling and analysis data. Step-
by-step procedures for performing the calculations, together with
computational formats, are also provided. This document discusses and
illustrates difficulties, such as handling nondetections in the calculations,
that may be encountered in applying these methods to analytical sampling data
on POTW 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
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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 PRELIM 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 decile values is provided. The individual decile
values can be used to assess how often a POTV 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:
Daily removal efficiency
Mean removal efficiency
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 Ibs/day, and the mass level of copper in an effluent
wastewater sample taken on the same day might have been 7 Ibs/day, The daily
removal efficiency corresponding to those two samples is the percent change
between the two sample values [(100) x (100 - 7)/100 - 93%]. That is, the
treatment system is assumed to have reduced the influent sample's mass value
of copper by 93 percent from 100 Ibs/day to 7 Ibs/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 Ibs/day and the corresponding effluent sample at 35 Ibs/day,
then the daily removal efficiency would be expressed as (100) x (20 - 35)/20 -
-75Z; 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) is exemplified by the
following equation:
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
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2.1.2 MEAN AND AVERAGE DAILY REMOVAL 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 average daily removal.
Average Daily Removal - (Daily Removal Efficiency for day 1 4- ... +
Daily Removal Efficiency for day n)/n
where:
"n" is the numberiof 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:
INFLUENT EFFLUENT DAILY
SAMPLE MASS MASS REMOVAL
DAY (Ibs/dav) (Ibs/dav) EFFICIENCY
1 20 5 75
2 10 3 70
3 40 8 80
AVERAGE 23.3 5.3 75Z
Average Daily
Removal
The mean removal could be calculated by taking the average of the three
individual daily removal values [i.e., (75X + 70X + 80X)/3 - 75Z]. 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
mean removal efficiency and is calculated as follows:
2-4
-------
Mean Removal Efficiency - (100) x (Average Influent - Average
Effluent)/Average 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 Ibs/day the average effluent level is (5 + 3 + 8)/3 - 5.3 Ibs/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 influent 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 influent
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
POTW 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
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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
approach. 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 FOTW 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 deciles, a decile estimation worksheet
has been designed. The use of that worksheet will be demonstrated using the
example data sets. Also EPA's PRELIM 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:
Displaying the influent, effluent, and daily removal data
Deciding which data, if any, are candidates to exclude
Calculating daily average and mean removals
Ordering (i.e., sorting) the individual daily removal values
Using the decile worksheet to estimate the nine decile removals.
The data that will be examined are daily influent and effluent sample
values (reported in Ibs/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 REMOVAL 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 1. COPPER MASS VALUES (LBS/DAY) AHD DAILY REMOVALS
1
2
3
T
5
'6
^7
"6
-5
TTi1
TT
"T5
T3
"14
is
16
17
16
Id
2o
21
22
23
24
25
26
27
26
29
36
61
62
66
64
35
66
67
66
6S
4o
41
42
46
44
46
46
KM
KU
iu
tu
111
POLLUTANT
Cu
CU
CU
CU
CU
CU
CU
CU
Cu
Cu
Cu
CU
CU
Cu
Cu
Cu
CU
cu
Cu
CU
Cu
CU
Cu
CU
cu
Cu
cu
Cu
Cu
Cu
Cu
Cu
cu
CU
cu
CU
Cu
CU
cu
Cu
CU
Cu
Cu
Cu
CU
Cu
Cu
Cu
Cu
Cu
| Cu
MONTH
7
7
7
7
7
8
8
8
8
8
9
9
9
9
10
10
10
10
11
11
11
11
11
12
12
12
12
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
DAY
1
6
15
25
29
8
9
22
26
66
10
16
21
27
9
14
22
25
4
11
21
22
29
9
19
20
29
5
12
23
24
6
7
16
25
6
16
21
29
5
11
18
24
2
11
15
22
1
6
14
21
YEAR
87
87
87
87
87
87
87
87
67
67
67
87
67
67
87
87
87
87
87
87
87
87
87
87
87
87
87
88
88
88
88
88
88
88
88
88
88
88
88
88
88
88
63
88
88
88
8
88
88
88
88
INFLUENTMASS
68.85
95.13
62.59
82.62
88.87
116.41
95.13
73.85
5537
62.62
T72774"
153.97
81.36
62.59
77.61
1 61 .48
60.08
1 79.00
122.67
98.89
87.62
71.35
41.31
123.92
92.63
247.85
72.60
96.38
95.13
111.41
60.08
116.41
1 07.65
255.36
85.12
81.36
171.49
145.20
75.10
58.83
85.12
93.88
85.12
113.91
256.61
81.36
76.36
185.26
96.38
135.19
117.66
EFFLUENT-MASS
16.27
T55
12.52
12.52
10.01
18.78
16.2/
16.27
!_2~
TsS
ST2T
15.02
15.02
10.01
22.53
22.53
26.29
30.04
20.03
35.05
30.04
27.54
22.53
42.56
30.04
103.90
22.53
12.52
28.79
11.27
20.03
35.05
31.29
32.55
35.05
35.05
36.30
42.56
37.55
46.31
28.79
30.04
41.31
35.05
38.80
28.79
45.06
23.78
25.03
30.04
33.80
% REMOVAL
,
76.36
93.42
80.00
84.85
88.73
83.87
82.89
77.97
78.79
STTBT
90.24
81.54
84.00
70.97
86.05
56.25
83.22
83.67
64.56
65.71
61.40
45.45
65.66
67.57
58.08
68.97
87.01
69.74
89.89
66.67
69.89
70.93
87.25
58.82
56.92
78.83
70.69
50.00
21.28
66.18
68.00
51.47
69.23
84.88
64.62
40.98
87.16
74.03
77.78
71.28
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 Ibs/day, with a few values
occasionally reaching 160 to 180 Ibs/day, and a few falling in the 240 to 260
Ibs/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 Ibs/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 Ibs/day) was associated with the third largest influent
value (247.85 Ibs/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
-------
2804
260.
5 240.
§ 220.
g 200.
OC 180.
of 160.
§ 14a
£ 120.
uf 80
S 60.
40.
20.
0
* * *
\ * ^** »
* * * * * *A ****** «*
'I***
^
50 100 150 200 250 300 350 4C
SAMPLE DAY
,
.
.
,
.
,
>0
FIGD1E 1. INFLUENT COPPER MASS VALUES
^ 100.
d. 80.
5 60.
Q 40.
ui 20.
o.
C
0
0 0Q° o 0°
6rf>° °° o ° ° o ^
OQ) ^ ^Q o o
O " w
.
I 50 100 150 200 250 300 350 400
SAMPLE DAY
FIGURE 2. EFFLUENT COPPER MASS VALUES
2-10
-------
g
1
s
#
90.
80.
70.
60.
50.
40.
30.
20.
1 0.
-**.-. 4
V* *v*«
>***. *
* ^ ^
* * *
* *
*
,
'
.
0 50 100 150 200 250 300 350 400
SAMPLE DAY
FIGDU 3. DAILY POCEHT REMOVALS FOR COPPER
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 Ibs/day and 103.9 Ibs/day, respectively) are evident. There are
also two other influent values of approximately 250 Ibs/day. These influent
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 Ibs/day to effluent
copper levels substantially below 100 Ibs/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 MEAN 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 + 71.28Z)/51 - 72.OX.
2-12
-------
?
1
of
ID
ft
5
LU
LL
it
^^
LU
120
100.
80.
60.
40.
20.
o.
2
O
3 9
OjJo oa* ° o £
-_ /% ^I^B'^^H. ^^ ^^ ^^
3 » /^.a /* »0 *
3 a00 9 *b a °
» <*&
5 50 75 100 125 150 175 200 225 250 27
INFLUENT COPPER (LBS/DAY)
.
'5
FIGURE 4. INFLUENT COPPER vs. EFFLUENT COPPER
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 Ibs/day
[i.e., (68.85 Ibs/day + 95.13 Ibs/day + ... + 135.19 Ibs/day + 117.66 Ibs/day)
/51 - 108.09 Ibs/day] and the average effluent value is 27.51 Ibs/day [i.e.,
(16.27 Ibs/day + 6.26 Ibs/day + ... + 30.04 Ibs/day + 33.80 lbs/day)/51 -
27.51 Ibs/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 Ibs/day - 27.51
Ibs/day)/ 108.09 Ibs/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
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 removals
exceed each of those values.
2.2.3 DECILE ESTIMATES
The set of 51 daily removal values will be used to estimate how often the
POTW will exceed a specific level of removal, such as 72.0 percent or 74.5
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. COFFER MASS VALUES (LBS/DAY) AND ORDERED REMOVALS
1
2
T
4
5
"T
y
1 a
T
1o
n
1?
-TIT
14
15
1fi'
T7
T*
T5
?n
?1
00
"?3
?4
"M
-ft
~*7
?fl
ra
No
Ni
h*
h*r
-36"
"37
TB1
3$
35
TT
12
43
44
"is
46
71
48
"4l
-5B
51
POLLUTANT
Cu
Cu
Cu
""" Cu
Cu
Cu
Cu
Cu
CU
Cu
Cu
Cu
Cu
Cu
Cu
Cu
"" Cu
1 Cu
Cu
Cu
Cu
Cu
Cu
Cu
" Cu
Cu
| Cu|
Cu
Cu
Cu
1 Cu
| Cu
1 Cu
1 Cu
1 Cu
Cu
Cu
cu
Cu
cu
cu
CU
c"u
Ci
CU
cu
Cu
cu
1 Cu
MONTH
4
5
11
3
4
10
3
12
2
11
11
5
12
11
4
1
12
4
fs
5
1
2
3
2
To
*
8"
6
I ?
£
&
8
W
8
To"
TT
5
t
t
ilC
i
^
EEE>
DAY
5
22
25"
29
24
22
6
20
25
22
11
15
9
21
11
24
15
id
2d
2
12
6
2
7
9
51
23
6
"' 14
' 22
30
16
5T
9"
25
T
I 27
25
if
u
5
1
16
10
25
1?
g
m^^^^~
YEAR
86T
88
S7
88
88
87
88
87
88
87
87
88
87
87
88
88
67
66
87
88
88
88
88
88
87
66
67
88
87
88
67
87
66
5T
57
57
§7
57
57
87
88
87
88
88
88
87
87
88
87
67
^^MB^*
INFLUENT-MASS
58.83
76.36
75.10
85.12
60.08
81.36
247.85
65.12
71.35
96.69
81.36
123.92
87.62
85.12
60.08
92.33
9T58"
' ~~~ 72.60
113.91
95.13
116.41
145.20
107.65
7776T
11776?
96.38
68.85
135.19
73.85
82.62
71.49
5T36"
" 95713"
179.00
122.67
11641
eTST
82.62
256.61
161.46
96.38
1 85.26
255.36
1 72.74
1 1 1 .41
153.57
EFFLUENT-MASS
46.31
45.06
22.53
37.55
41.31
26.29
35.05
103.90
35765
5757
5.05
28.79
42.56
30.04
55779
56T53"
30.04
30.04
35.05
28.79
35.05
42.56
31.29
33.80
25.03
16.27
TI727
12.52
dd.do
12.52
23.78
32.55
21.26
10.01
11.27
10. vc
6.26
% REMOVAL
21.28
35T9T
50.00
51757
56.25
§57§2
58.08
§8752
61.40
64.56
64.62
65.66
65.71
66.67
69.23
69.89
84.85
84.88
86.05"
87\01
87.16"
87.25
87.68"
88.73"
89.89"
93.42"
2-15
-------
TABLE 3. DECILE ESTIMATION WORKSHEET FOR COPPER DATA
NJ
PECILES
1st
2nd
3rd
4th
Sth
Ath
7th
8th
9th
CO.. il
CALOUUTI
DECILE
POSITION
FOR
OMtCftCn
LIST Of
REMOVALS*
....££.
10.4
&.(*
Jo.f
...*.,«
3/,7-
J.&
y/, 6
"ji.'ir
COL. n
MUTE
THE
WHOLE
Mftf
CIVEN IN
COL. 01
£
/O
K
2.0
24
J/
36
«//
%
COL. »
RECORD
THE
ORDERED
REMOVAL
~ ffflA TUP
COL. §2
EMTRY^
S141
L(.<4o
&(,, ig
^f.v3
....7':.^. .
...V'V..
<2i.«f
*i«ar
?n.ir
COL. M
RECORD
THE
ORDERED
REMOVAL
FOLLOWING
COL. n
EKTRT"
sTi.ar
tt.Sb
U.61
6f.7/
71-43
7«.7f
ii.vi-
w.ri'
ifTS
COL. 15
COL. M
ENTRY
COL. «
ENTRY
in
3./t>
.Vf
'$
,/r
.?>
33
,03
".-/j
COL. 16
LIST THE
ncrtMAl
IN
COL. »1
i
.y
,L
,1
,0
,2-
't
,0i/
4t.STV
M. t ^
Tl.T^
is.i^H
^.OTV-
..^m.
«n.^*f
*NuriN»rs In col urn defined as Multiples of (N+l)/10. where N - the maber of data pairs used.[1.e. (51+1/10=5.2), (2x5.2=10.4) etc.]
**Uses the list of ordered ranvals.
-------
Step 1 - 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 + 1)/10 - (51 +
1)/10 - 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 #1 (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 #1,
Step 2 - For the entries in Column #2, the whole number part of each
of the nine values listed in Column #1 is used. For example, the
first decile had a value of 5.2 in Column #1; therefore, the entry for
the first decile in Column #2 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.
Step 3 - The entries for Column #3 require the use of Table 2 that
contains the ordered list of daily removal values. (Note the footnote
marked **.) Entries for Column #3 are the ordered removal values
corresponding t;o the locations specified in Column #2. For example,
the first entry for Column #3 will be the ordered removal for the
Column #2 entry of five. That is, the first entry in Column #3 will
be the fifth ordered, daily removal value from Table 2, which is 51.47
percent. Similarly, the second entry for Column #3 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 #2.
Step 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 #3 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.
Step 5 - The entries for Column #5 are determined by subtracting
Column #3 from Column #4 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 #5. The rest of the column is
obtained by performing the same subtraction process for the decile row
of interest.
Step 6 - The entries for Column #6 are the decimal part of the entries
specified in Column #1. For example, the first entry in Column #1 is
5.2, which has a decimal part of .2; therefore, the first entry for
Column #6 is .2.
2-17
-------
Step 7 - The entries for Column #7 are obtained by multiplying the
entries of Column #5 by the entries of Column #6. For example, the
first entry in Column #7 is 4.78X x .2 - .956X.
Step 8 - The entries for Column //8 are obtained by adding the entries
of Column #3 and the entries of Column #7. For example, the first
entry in Column //8 is 51.47* -I- .956X - 52.426*.
Column #8 provides the following nine estimated decile removals (rounded to
the nearest tenth):
1st decile - 52.4 percent
2nd decile - 62.7 percent
3rd decile - 66.5 percent
4th decile - 69.6 percent
5th decile - 71.3 percent
6th decile - 78.1 percent
7th decile - 83.0 percent
8th decile - 84.9 percent
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 deciles. 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 (Ibs/day) -
[(8.34) x (CCRIT) x (QporJVd - RPOTW)],
where:
8.34 conversion factor which takes into account the density of
water
CCRIT - NPDES permit limit, mg/1
2-18
-------
QPOTW - POTW average flow, MGD
RPOTW - Removal efficiency across the POTW, decimal
The quantity [(8.34) x [CCRIT) x (QPOTW) ] la'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 (Ibs/day) -
[(8.34) x (CSLCRIT) x (PS/100) x (QSLOG)/RPOTW ],
where:
8.34 - conversion factor which takes into account the density of
water
CSLCRIT - sludge disposal criterion, mgAg dry sludge
PS - percent solids of sludge to disposal
QSLDG - sludge f^ow to disposal, MGD
RPOTW - removal efficiency across the POTW, decimal
The quantity [((8.34) x (CSLCHIT) x (PS/100) x QSLOG) ] 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 developing local limits, appropriate removal efficiencies must be
selected for calculation of an allowable headworks loading for each pollutant.
The typical procedure is for the POTW 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 deciles
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 NFDES-based maximum permissible mass
discharge is 10 Ibs/day.
Removal Allowable Headworks Loading Ibs/day
Decile Efficiency X (Effluent Quality-based)
1 52.4 21.0
2 $2.7 26.8
3 66.5 29.9
4 69.6 32.9
5 71.3 34.8
Average Daily 72.0 35.7
Mean Removal 74.5 39.2
6 78.1 45.7
7 83.0 58.8
8 84.9 66.2
9 87.6 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 Ibs/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 compliance with the
local limits will ensure POTW compliance with its effluent quality
limitations.
Suppose, however, that the POTW actually receives 30 Ibs/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 Ibs/day copper being received. It can
be concluded that for 30 percent of the year (three deciles), the POTW will be
unable to comply with its effluent quality limitations. At the same time, the
POTW'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 Ibs/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 Ibs/day, which is considerably
more stringent than the 35.7, Ibs/day allowable headworks loading based on
average removal. The 30 Ibs/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 MAHLs. In this regard, the following
removal efficiency deciles and sludge quality-based MAHLs of copper have been
calculated assuming the maximum permissible sludge loading is 20 Ibs/day.
Removal Sludge Quality-based
Decile Efficiency X Allowable Headworks Loading Ibs/day
38.2
31.9
30.1
28.7
28.1
27.8
26.8
25.6
24.1
23.6
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 MAHLs more stringent than the allowable loading based
on the average removal (27.8 Ibs/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 Ibs/day.
1
2
3
4
5
Average Daily
Mean Removal
6
7
8
9
52.4
62.7
66.5
69.6
71.3
72.0
74.5
78.1
83.0
84.9
87.6
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
and compare selected effluent quality and sludge quality-based allowable
headworks loadings and select the most stringent.
Loading Basis
Effluent quality
Sludge quality
Decile
2
8
Allowable Headworks Loading Ibs/dav
26.8
23.6
From the above information, it can be seen that the POTW should base its
copper local limits on an allowable headworks loading of 23.6 Ibs/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 Ibs/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 POTW treatment
2-22
-------
3000.
p2500.
& 2000.
O
2 1500.
N
(-
in
D 1000.
u.
Z
500.
0.
»
^
*
* ^ *** ***.*V *
»*« » «»»*: *»«*.*»**
0 50 100 150 200 250 300 350 400
SAMPLE DAY
, r
FIGURE 5. INFLUENT ZINC MASS VALUES
500
450.
5 400.
55 350.
o 3oa
R 250.
S 200.)
G? 150-
u.
"J 100.
50.
0
50 100 150 200 250 300
SAMPLE DAY
350
400
FIGURE 6. EFFLUENT ZINC MASS VALUES
2-23
-------
TABLE 4. ZINC MASS VALUES (LBS/DAY) AND DAILY REMOVALS
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
16
IS
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
3?
38
3d
4o
41
42
43
44
46
46
47
48
49
56
51
POLLUTANT
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
MONTH
7
7
7
7
7
a
8
8
8
8
9
9
9
9
10
10
10
10
11
11
11
11
11
12
12
12
12
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6,
6
6
6
DAY
1
6
15
25
29
8
9
22
23
30
10
16
21
27
9
14
22
25
4
11
21
22
29
9
19
20
29
5
12
23
24
6
7
16
25
6
16
21
29
5
11
18
24
2
11
15
22
1
6
14
21
YEAR
87
87
87
87
87
87
87
87
87
87
87
87
87
87
87
87
87
87
87
87
87
87
87
87
87
87
87
88
88
88
88
88
88
88
88
88
88
88
88
88
88
88
88
88
88
88
88
88
88
88
88
INFLUENT-MASS
1 96.52
216.55
168.99
1 85.26
172.74
528.24
229.07
172.74
85.12
93.88
393.05
473.16
1266.77
2501 .00
160.22
349.24
948.83
449.38
533.25
345.48
155.22
1 06.40
100.14
215.30
1739.93
166.48
582.06
231.57
330.46
390.55
163.98
133.94
331.71
230.32
399.31
490.69
314.19
272.88
166.48
105.15
195.27
239.08
131.43
234.08
473.16
148.96
24 ..59
518.22
306.68
246.59
235.33
EFFLUENT-MASS
72.60
52.57
67.59
58.83
71.35
62.59
50.07
76.36
61.34
60.08
120.17
122.67
103.90
93.88
103.90
103.90
108.90
91.38
88.87
93.88
116.41
65.09
83.87
96.38
474.41
320.45
106.40
92.63
207.79
191.52
173.99
185.26
171.49
131.43
116.41
91.38
148.96
96.38
105.15
97.64
93.88
97.64
92.63
95.13
96.38
86.37
96.38
111.41
132.69
91.38
78.86
% REMOVAL
63.06
75.72
60.00
68.24
58.70
88.15
78.14
55.80
27.94
36.00
69.43
74.07
91.80
96.25
35.16
70.25
88.52
79.67
83.33
72.83
25.00
38.82
16.25
55.23
72.73
-92.48
81.72
60.00
37.12
50.96
-6.11
-38.32
48.30
42.93
70.85
81.38
52.59
64.68
36.84
7.14
51.92
59.16
29.52
59.36
79.63
42.02
60.10
78.50
56.73
62.94
66.49
2-24
-------
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 Ibs/day) contrasts sharply with the removal efficiency
on September 27, 1987 (95.25 percent with an associated influent value of
2,500 Ibs/day). Thus, the data show that the POTW was capable of treating
influent zinc considerably above 1,750 Ibs/day.
Figure 8 is a plot of the daily removals over time. The three negative
removals are quite apparent from the plot. It is assumed 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 #1 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 #1. The ordered removal entries for Columns #3 and #4 are taken from
2-25
-------
5004
450.
^ 400.
§ 350.
o 3oa
jq 250.
g 200^
H 150.
ft 100^
50.
o.
C
0)
o
^
^
^»
,
.
I 500 1000 1500 2000 2500 3000
INFLUENT ZINC (IBS/DAY)
i
FIGOU 7. IHFLOK1T ZINC v». EFFLUENT ZINC
2-26
-------
120.
100.
80.
60.
40.
20.
0.
-20.
-40.
-60.
-80.
-100.
-50 0 50 100 150 200 250 300 350 400
SAMPLE DAY
FIGUKE 8. DAILY PERCENT REMOVALS FOR ZINC
2-27
-------
TABLE 5. ZINC MASS VALUES (LBS/DAY) AND ORDERED REMOVALS
1
2
&
4
5
6
7
8
8
16
11
ii
13
U
15
16
17
IS
IS
2o
21
&
2s
24
"55
"55
27
"25
55
30
31
"32
33
3*
35
35
"37
"33
na
To
*T
"47
43
-44
-55?
-4*
-4T
"4*
-4?
-3?
"SI
POLLUTANT
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
zn
zn
zn
Zn
Zn
zn
zn
Zn
Zn
Zn
Zn
Zn
zn
zn
Zn
Zn
Zn
Zn
Zn
Zn
Zn
ZI1
Zn
MONTH
12
2
1
4
11
11
8
4
10
8
3
1
11
5
2
2
1
4
3
12
8
6
7
4
5
1
7
5
6
7
3
6
7
9
10
2
12
11
9
7
8
6
5
10
3
12
11
8
10
9
4
DAY
20
6
24^
5
29
21
23
24
9
30
29
12
22
15
16
7
23
11
16
9
22
6
29
16
2
5
15
22
14
1
21
21
2£
16
14
25
19
11
16
6
d
1
11
25
6
29
4
8
22
21
27
YEAR
87
88
88
88
87
87
87
88
87
87
88
88
87
88
88
88
88
88
88
87
87
88
87
88
S3
66
87
88
88
87
88
88
87
67
87
88
87
87
87
87
87
8fi
66
87
88
87
87
8?
87
87
87
INFLUENT-MASS
166.48
133.94
163.98
105.15
100.14
155.22
85.12
131.43
160.22
93.88
1 66.48
330.46
1 06.40
148.96
230.32
331 .71
390.55
195.27
314.19
215.30
1 72.74
306.68
1 72.74
239.08
254.68
231.57
168.99
241.59
246.59
196.52
272.88
235.33
1 65.26
393.6$
349.24
399.31
1739.93
345.48
473.16
216.55
22§.07
518.22
473.16
449.38
490.69
582.06
533.25
528.24
948.83
155577
2501.00
EFFLUENT-MASS
320.45
1 85.26
173.99
97.64
83.87
116.41
61.34
92.63
103.90
60.08
105.15
207.79
65.09
86.37
131.43
171.49
191.52
93.88
148.96
96.38
76.36
132.69
71.35
97.64
957f5
92.63
67.59
96.38
91.38
72.60
96.38
78.86
120.17
103.90
116.41
474.41
" 9318
122.671
52.57
soToT
11.41
96.38
91.38
91.38
106.40
88.87
62.59
108.90
103.90
93.88
% REMOVAL
-92.48
-38.32
-6.11
7.14
16.25
25.00
27.94
29.52
35.16
36.00
36.84
37.12
38.82
42.02
42.93
48.30
50.96
51.92
52.59
55.23
55.80
56.73
58.70
59.16
§5!35
60.00
60.00
60.10
62.94
63.06
64.68
66.49
55153
69.43
7o5§
75TJ5
72.73
75183
74.07
75/72
7514
.50
79.63
79/67
81.38
81.72
§333
88.15
88.52
91.80
2-28
-------
TABLE 6. DECILE ESTIMATION WORKSHEET FOR ZINC DATA
I
NJ
DECILES
1»t
2nd
3rd
4th
5th
6th
7th
8th
9th
COL. 01
CALCULATE
DECILE
POSITION
FOR
flftftctcn
LIST OF
REMOVALS*
$A
..../P:Y
/5*,£
J*.?
£(..&
K.I.
....?M
//.£
^.8
COL. 02
WRITE
THE.
WHOLE
MIBMUEft
GIVEN IN
COL. 01
£T
ID.
f?
2o
l(o
*/
H
H
It,
COL. 03
RECORD
THE
ORDERED
REMOVAL
COL. 02
ENTRY**
/fc.#*
3t>. tit?
Vi^3*
^2-3
&0.00
iY:.fi .
70.^
'79.'^
?f.7Z.
COL. M
RECORD
THE
ORDERED
REMOVAL
FOLLOUING
COL. «
ENTRY**
Af.Oto
......3fes.«rf .
Y^-3o
5r,*o
&Q.O&
.. $L *i
72,73
7g.ro
^,3i
COL. *5
COL. M
ENTRY
COL. «
ENTRY
^,75'
0.^
^37
0.67
D
/«/ .
in
(>}(?
l.bt
COL. 06
LIST THE
IN
COL. 01
0.2-
?.!
6,6
o.i
O.o
b,T^
0Y
.e-i
fi.g
COL. 07
MULTIPLY
COL. 05
ENTRY
COL. M
ENTRY
/-7*~
0.3*6
3, J,^-"^
o.WL
0^0
b.^T^
0,7f7~
0.1 f I?
i,*rt
COL. 08
ADO
COL. 03
AND
COL. 07
ENTRIES
ESTIMATED
DECILES
/&00
Jt.^
y<./ft.
j-/. ^t
...AQ-.O....
t,f,0<{^
J/.fr-L,
Jt.lK
$*>. QOf
*Nuri>ers In col urn defined as Multiples of (N+l)/10. where N - the nutber of data pairs used.[i.e. (51+1/10=5.2), (2x5.2=10.4) etc.]
**l)ses the list of ordered removals.
-------
Table 5. Column #5 is obtained by subtracting Column #3 from Column #4.
Column #6 is the decimal part of the entries in Column #1. Column #7 is
obtained by multiplying Columns #5 and #6. The estimated deciles, Column #8,
are obtained by adding the entries of Column #3 to those of Column #7. The
nine estimated deciles for the zinc data are:
1st decile - 18.0 percent
2nd decile - 36.3 percent
3rd decile - 46.2 percent
4th decile 55.7 percent
5th decile - 60.0 percent
6th decile - 65.0 percent
7th decile -71.6 percent
8th decile - 78.4 percent
9th decile - 83.0 percent.
The decile estimates then can be used to estimate how often the POTW'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
influent 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. These reported zero levels more than likely indicate
nondetections or below detection limit concentration values. 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
-------
p
1
jj
SB
o
z
2Jj
UJ
u.
120.
100.
80.
60.
40.
20.
(
,
** _
* » * « A
A
^
^:» *^ * »» »
) 50 100 150 200 250 300 350 4
SAMPLE DAY
30
FIGURE 9. INFLUENT NICKEL MASS VALUES
^j
i
53
^7
0
z
1_
^^
i
70.
,
60.
50.
40.
30
20
10
o
O
O
o
0
00 0
Q
-_ O
O ° r» °
^^ ^?
0 0
i r- -- -l i- -i i»nnni|nmri "t"
1
" 50 "100 150 200 250 300 350 400
° SAMPLE DAY
FIGURE 10. EFFLUENT NICKEL MASS VALUES
2-31
-------
TABLE 7. NICKEL HASS VALUES (LBS/DAY) AMD DAILY REMOVALS
1
2
3
4
5
6
7
8
9
16
11
lit
13
14
IS
16
17
IS
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
3d
4o
41
42
43
44
4s
46
47
48
49
56
51
POLLUTANT
Ni
Ni
Ni
Ni
Ni
Ni
Ni
Ni
Ni
Ni
Ni
Ni
Ni
Ni
Ni
Ni
Ni
Ni
NI
Ni
Ni
Ni
Ni
Ni
Ni
Ni
Ni
Ni
Ni
Ni
Ni
Ni
Ni
Ni
Ni
Ni
Ni
Ni
Ni
Ni
NI
Ni
Ni
Ni
Ni
Ni
Ni
Ni
Ni
Ni
Ni
MONTH
7
7
7
7
7
8
8
8
8
8
9
9
9
9
10
10
10
10
11
11
11
11
11
12
12
12
12
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
DAY
1
6
15
25
29
8
9
22
23
30
10
16
21
27
9
14
22
25
4
11
21
22
29
9
19
20
29
5
12
23
24
6
7
16
25
6
16
21
29
5
11
18
24
2
11
15
22
1
6
14
21
YEAR
87
87
87
87
87
87
87
87
87
87
87
87
87
87
87
87
87
87
87
87
87
87
87
87
87
87
87
88
88
88
88
88
88
88
88
88
88
88
88
88
88
88
88
88
88
88
88
88
88
88
88
INFLUENT-MASS
0
0
0
26.29
0
28.79
37.55
31.29
30.04
0
67.59
0
30.04
0
0
87.62
0
82.62
36.30
0
76.36
26.29
0
0
58.83
0
0
0
33.80
50.07
0
0
66.34
27.54
28.79
45.06
32.55
0
28.79
0
61.34
0
0
0
53.83
0
118.92
0
0
0
0
EFFLUENT-MASS
0
0
0
0
0
33.80
0
0
0
0
35.05
0
0
0
0
43.81
0
43.81
31.29
0
27.54
0
70.10
33.80
25.03
0
0
0
38.80
0
0
48.82
35.05
0
0
52.57
65.09
41.31
0
0
0
0
0
0
0
0
0
0
0
0
0
% REMOVAL
100.00
-17.39
100.00
100.00
100.00
48.15
100.00
50.00
46.97
13.79
63.93
100.00
57.45
-
-14.81
100.00
47.17
100.00
100.00
-16.67
-100.00
100.00
100.00
100.00
100.00
2-32
-------
isolated influent nickel value around 120 Ibs/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 effluent 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 Ibs/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 POTW 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 Ibs/day and an effluent mass of 65.09 Ibs/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
-------
I
17
UJ
tJU
1
70.
60.
50.
40.
30.
20.
10.
o.
0
125.
100.
75.
50.
0.
-25.
-50.
-75.
-100.
-12&
(
X
X
X X
x
* X
X «
20 40 60 80 100 120 140
INFLUENT NICKEL (IBS/DAY)
FIGURE 11. INFLUENT NICKEL vs. EFFLUENT NICKEL
+ * « +
« * *
* * «
*
-
) 50 100 150 200 250 300 350
SAMPLE DAY
FIGURE 12. DAILY PERCENT REMOVALS FOR NICKEL
2-34
-------
TABLE 8. NICKEL MASS VALUES (LBS/DAY) AND ORDERED REMOVALS
i
-?
3
4
5
F
7
ft
~*
1o
TT
1?
13
-ft
TR
is
"f7
"Ifl
~TC
?ri
?1
"W
-?3
M
POLLUTANT
Ni
Ni
Ni
Kli
Ni
Ni
Ni
Ni
Ni
-Ni
- Ni
Ni
Ni
Ni
Ni
N!
Ni
Ni
R7
Ni
Ni
Ni
Ni
Ni
MONTH
3
§
3
1
11
10
2
9
10
12
11
3
4
11
8
2
8
2-
7
9
5
1
8
5
DAY
16
8
6
12
4
25
7
10
14
1d
21
29
11
22
22
16
d
25
25
21
11
23
23
22
YEAR
88
87
88
88
87
87
88
87
87
87
87
88
88
87
87
88
87
88
87
8?
88
88
87
88
INFLUENT-MASS
32.55
28.7S
45.06
33.80
36.30
82.62
66.34
eT'.sg
~57.T>2
58.83
76.3S
28.79
61.34
"5519
31.29
57141
37.55
26.79
551T
30.04
53.83
50.07
30.04
118.92
EFFLUENT-MASS
65.09
33.80
52.57
38.80
31.2S
43.81
35.05
35.05
41TBT
25.03
27.54
0
0
51
_
0
0
" ~~ o"
0
0
0
0
% REMOVAL
-100.00
73?
-16.67
-14.81
T5~79"
4TT97
37T7
48.15
5006"
SOS
100.00
100.00
100.00
100.00
100.00
100.00
100.00
100.00
100.00
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 #1 are
multiples of 2.5. Column #2 uses the whole numbers of Column #1. Columns #3
and #4 use the ordered removals from Table 8. Entries for Column //5 are
obtained by subtracting Column #3 from Column #4. Column #6 is the decimal
part of the entries in Column #1. 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 #3 and #7.) The nine estimated deciles for the
nickel data are:
1st decile - -17.0 percent
2nd decile - 13.8 percent
3rd decile - 47.7 percent
4th decile - 57.5 percent
5th decile - 100 percent
6th decile - 100 percent
7th decile - 100 percent
8th decile - 100 percent
9th decile - 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.S 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 ESTIMATION WORKSHEET FOR NICKEL DATA
NJ
I
U)
--J
DECILES
1»t
2nd
3rd
4th
Sth
6th
7th
ftth
9th
OOt. fl
CALCULATE
DECILE
POSITION
MR
flMkUEn
LIST OF
REMOVALS*
*$
6",0
l.f
lO.o
11.4
"if.v
n,f
3o.o
22*
coc. n
MITE
Ttt
WHOLE
MMBft
QIVEN IN
on. ti
oZ
f
7
10
/a
If
n
2*
H
COL. a
RECORD
THE
ORDERED
REMOVAL
MB THJT
COL. f2
CMTRV** ^
/7,3J
/3,7^
..J.7f.(2...
. ^r^^r
. /^..' .
....<#*
/ffT>
/
/(TO
COL. M
RECORD
THE
ORDERED
REMOVAL
rOLLOUIHfi
OX. fl
ENTRT^
-/f^3
JOQ
/(n>
/
-------
2.5.1 REMARKED DATA
Sometimes influent and effluent concentration values are not reported
Quantitatively. 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
LEVEL
100
200
240
EFFLUENT
LEVEL
40
ND
60
DAILY
REMOVAL
EFFICIENCY(Z)
60
80
1
2
3
The remarked data values result from limitations in the analytical
methodology used for the chemical analysis. How should such data be dealt
with? 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/1. A substitute value of 10 mg/1 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/1),
or even 0 for the not detected value. For the above example, substituting 10
mg/1, 5 mg/1, and 0 mg/1 for the ND value would 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 mg/1 (instead of the 200 mg/1), then
substituting 10 mg/1, 5 mg/1, and 0 mg/1 for the ND would result in daily
removals of 75 percent, 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 SEASONALITY
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 POTW's
sludge streams, 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 can be used to
calculate allowable headworks loadings based on pass through criteria
(e.g., biological process inhibition data, NPDES permit limits, and water
quality standards). The removal efficiency guidance provided in this
document can be directly applied to nonconservative pollutant removal
efficiencies obtained for this purpose.
2-39
-------
The 1987 local limits guidance provides the following equation for
deriving nonconservative pollutant allowable headworks loadings from sludge-
based criteria/standards:
CSLDG
or
where:
LJH Allowable headworks loading, Ibs/day
L1NF - FOTW influent loading, Ibs/day
CCHIT ~ Sludge criterion/standard, mg/1
CSL06 - Pollutant level in sludge, mg/1.
In the above expression, the factor CSLOG/LINF is a partitioning factor relating
the pollutant level in the FOTW sludge (CSLOe) to the headworks loading of the
pollutant (LINF). The partitioning factor enables calculation of an allowable
headworks loading (LjN) from a sludge criterion/standard (CCRIT) 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 CSLOG/LINF 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 POTW influent flow. Since
nonconservative 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 nonconseryative 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
Mean removal efficiency
Decile 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 two 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 decile 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 estimates. 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
COMBINED TOTAL DOMESTIC NUMBER OF NUMBER OF AVERAOE MINIMUM MAXIMUM MEDIAN
DOMESTIC FLOW CONTRIBUTION DETECTIONS OBSERVATIONS POLLUTANT POLLUTANT POLLUTANT POLLUTANT
CITY STATE REOION (MOD) (*) LEVEL LEVEL LEVEL LEVEL
(MQ/L) (MQ/L) (MQ/L) (MQ/L)
PORTLAND
ME
11.6
94
ZINC
COPPER
LEAD
SILVER
CHROMHM(T)
NKKEL
CADMIUM
38
38
36
38
38
38
38
38
38
38
38
38
38
38
0.0940
O.OUO
0.0360
0.0230
0.0180
0.0080
0.0020
0.063
0.036
0.001
0.001
0.001
0.001
0.001
0.273
0.29
0.276
0.078
0.218
0.124
0.01
0.082
0.077
0.014
0.0176
0.007
0.003
0.001
WARWICK
ZINC
COPPER
NICKEL
CADMIUM
0.1300
0.1000
0.0800
0.0080
0.128
0.09
0.06
<0.006
0.144
0.11
0.07
0.011
0.138
0.1
0.08
0.008
BUFFALO
TOTAL PHOSPHORUS
ZINC
COPPER
LEAD
NICKEL
1.2,4-TRKHLOROBENZENE
CHROMIUM (T)
Btt(2-ETHYLHEXYL)PHTHALATE
CADMIUM
SILVER
CHLOROFORM
TOTAL END08ULFAN
TOTAL BHC
FLUORANTHENE
4.4'-DDD
PYREME
PHENOLS
METHYLEME CHLORIDE
TETRACHLOROETHENE
NY
180
1
6
6
6
6
3
6
6
4
4
4
3
3
6
3
3
2
3
2
0.7000
0.0911
0.0807
0.0474
0.0436
0.0130
0.0099
0.0086
0.0063
0.0062
0.0022
0.0020
0.0010
0.0006
0.0003
0.0002
0.00003
0.00001
0.00001
0.7
0.06
0.03
0.0078
0.0016
<0.002
0.0046
0.00002
0.0008
0.0002
0.00001
0.002
0.00 1
0.00001
0.00028
0.00001
0.00002
0.000008
0.00001
0.7
0.1676
0.06
0.1
0.1
0.036
0.02
0.022
0.01
o.ot
0.004
0.002
0.001
C0.001
0.0004
<0.0006
0.00003
0.00002
0.00001
0.7
0.078
0.0736
0.01
o.ot
<0.002
0.01
0.006
0.0063
0.0062
0.0024
0.002
0.001
C0.001
0.00028
0.00001
0.000026
0.00001
0.00001
-------
A.1 RESIDENTIAL/COMMERCIAL TRUNK LINE MONITORING DATA
cnv
STATE
REOJON
COMBINED TOTAL
DOMESTIC FLOW
(MOO)
DOMESTIC
OONTRBUTION
NUMBER OF
DETECTIONS OBSERVATIONS
AVERAOE
POLLUTANT
LEVEL
(MO/L)
ihMkUUM
POLLUTANT
LEVEL
(MO/L)
MAXIMUM
POLLUTANT
LEVEL
-------
A.1 RESIDENTIAL/COMMERCIAL TRUNK LINE MONITORING DATA
CITY
STATE
COMBINED TOTAL
DOMESTIC FLOW
REGION (MOO)
DOMESTIC NUMBER OF NUMBER OF AVERAGE MINIMUM
CONTRWUTION DETECTIONS OBSERVATIONS POLLUTANT POLLUTANT
(%) LEVEL LEVEL
(wan.)
MAXIMUM MEDIAN
POLLUTANT POLLUTANT
LEVEL LEVEL
(MO/L) (MO/L)
HOLLAND
1.3
ZINC
COPPER
NICKEL
LEAD
CHROMIUM (T)
CADMIUM'
38
38
33
23
21
21
30
39
40
30
39
36
0.1946
0.0679
0.0096
0.0049
0.0049
0.0024
0.046
0.011
<0.001
C0.001
<0.001
-------
A.1 RESIDENTIAL/COMMERCIAL TRUNK LINE MONITORING DATA
>
COMBINED TOTAL DOMESTIC NUMBER OF NUMBER OF AVERAGE MINIMUM MAXIMUM MEDIAN
DOMESTIC FLOW CONTRIBUTION DETECTIONS OBSERVATIONS POLLUTANT POLLUTANT POLLUTANT POLLUTANT
CITY STATE REGION (MOD) (%) LEVEL LEVEL LEVEL LEVEL
(MO/L) (MOW.) (MG/L) (MO/L)
CHROMIUM (T)
CHROMIUM (M)
CAPMnjy
ARBEMC
SELENIUM
LOS ANGELES CA " 72.3
PHOSPHATE
IRON
BORON
FLUOMDE
zmc
BARIUM
COPPER
MANGANESE
LITHIUM
PHENOLS
SAN FRANCISCO CA 0 63
zmc
LEAD
COPPER
NICKEL
CHROMIUM (T)
SILVER
CADMIUM
ARSENIC
MERCURY
ORANGE COUNTY CA 0
AMMONIA
COPPER
zmc
LEAD
METHYLENE CHLORIDE
1 , 1 -DtCHLOROETHANE
TETRACHLOROETHENE
NICKEL
2
1
2
1
2
64
2
2
2
2
2
2
1
1
2
1
60
242
202
244
167
164
134
161
130
214
27
23
23
17
4
1
3
11
2
2
2
2
2
2
2
2
2
2
2
1
1
2
1
242
206
246
233
216
177
226
203
220
27
26
26
26
27
26
27
26
0.0076
0.0060
0.0040
0.0040
0.0036
26.6000
0.4100
0.4000
0.2660
0.0600
0.0660
0.0620
0.0400
0.0306
0.0200
0.2204
0.1304
0.0626
0.0616
0.0372
0.0102
0.0127
0.0060
0.0017
43.1111
0.0732
0.0724
0.0307
0.0303
0.0200
0.0153
0.0163
0.007
<0.006
0.001
0.003
0.002
27.4
0.06
0.36
0.24
0.06
0.04
0.062
0.04
0.03
0.020
0.016
0.006
0.01
0.003
C0.0014
<0.0007
<0.006
0.0004
0.0001
7
0.03
<0.01
<0.001
0.011
0.026
0.004
<0.006
0.006
0.007
0.007
<0.006
0.006
30.2
0.76
0.42
0.27
0.11
0.00
0.062
0.04
0.031
0.020
1.166
2.04
0.66
1.6
1.2
1.062
0.11
0.066
0.036
114
0.16
0.26
0.00
0.066
0.026
0.037
0.06
0.0076
0.000
0.004
0.004
0.0036
26.6
0.41
0.4
0.266
0.06
0.066
0.062
0.04
0.0306
0.020
0.10
0.076
0.07
0.06
0.02
0.007
0.0006
0.003
0.0006
36
0.07
0.04
0.02
0.0276
0.026
0.006
-------
A.1 RESIDENTIAL/COMMERCIAL TRUNK LINE MONITORING DATA
COMBINED TOTAL DOMESTIC
NUMBER OF NUMBER OF
DOMESTIC FLOW CONTRIBUTION DETECTIONS OBSERVATIONS
CITY STATE REGION (MOD) (%)
THANS-1.2-OICHLOROETHENE
CHLOROFORM
1,1-DtCHLOROETHENE
CHROMIUM (T)
CADMIUM
UNIFIED SEWER AUTHORITY OR 10
IRON
BARIUM
zmc
COPPER
LEAD
CHROMIUM (T)
NICKEL
1
17
2
4
2
3
1
3
3
1
t
t
26
26
28
- 26
26
3
1
3
3
3
3
3
AVERAGE
POLLUTANT
LEVEL
(MQ/L)
0.0130
0.0100
0.0086
0.0040
0.0036
1.0967
0.2160
0.0662
0.0366
0.0318
0.0070
0.0066
MINIMUM
POLLUTANT
LEVEL
(MQ/L)
0.013
<0.002
0.006
<0.002
<0.003
0.6
0.216
0.036
0.018
0.0166
<0.006
0.0036
MAXIMUM
POLLUTANT
LEVEL
(MO/L)
0.013
0.08»
0.008
0.01
0.01
1.49
0.216
O.OB8
0.0667
<0.04
O.OO8
<0.006
MEDIAN
POLLUTANT
LEVEL
(MQrt.)
0.013
0.004
0.0066
<0.002
<0.003
1.2
0.216
0.0366
0.022
<0.04
C0.006
<0.008
-------
A.2 COMMERCIAL SOURCE WASTEWATER MONITORING DATA
SOURCE/CITY
STATE REGION
TOTAL
SOURCE FLOW
NUMBER Of NUMBER OF NUMBER OF AVERAGE MINIMUM MAXIMUM MEDIAN
SOURCES DETECTIONS OBSERVATIONS POLLUTANT LEVEL POLLUTANT LEVEL POLLUTANT LEVEL POLLUTANT LEVEL
(MO/L) (MO/L) (MO/L) (MO/L)
HOSPITALS
BANQOR
ME
104000
SILVER
CHROMIUM (T)
LEAD
CADMIUM
17
17
18
17
0.0963
0.0718
0.0441
0.0086
<0.03
ALTOONA
PHOSPHATE
PA
172000
C.4197
0.6
9.7
0.66
HAMPTON ROADS
VA
17
COO
LEAD
COPPER
ZINC
CHROMIUM 0)
NICKEL
SILVER
CADMIUM
10
42
4S
146
22
21
2(3
28
10
64
(1
146
62
67
403
61
300.1000
2.4220
0.8426
0.0874
0.1828
0.0880
0.0801
0.0388
20
0.06
<0.02
<0.01
<0.06
0.012
<0.01
<0.006
687
34
10.8
6.4
1.66
0.86
4.8
0.868
484
0.16
0.14
0.4
<0.06
C0.04
<0.06
0.007
LOUISVILLE
743000
IRON
BARIUM
ZINC
PHENOLS
COPPER
SILVER
CHROMIUM (T)
LEAD
NICKEL
SELENIUM
ARSENIC
CADMIUM
MERCURY
62
67
62
3
62
60
60
46
62
40
36
46
63
62
62
62
3
62
62
62
62
62
62
62
62
62
2.2484
1.7787
0.2808
0.2443
0.2186
0.1683
0.0880
0.0636
0.0308
0.0117
0.0072
0.0040
0.0017
0.22
0.066
0.076
0.168
0.038
0.001
0.004
<0.03
0.0081
0.0027
0.003
<0.002
C0.0002
36.1
17.6
1-6
0.301
1.62
2.24
2.6
0.63
0.66
0.02
0.06
0.014
0.022
1.08
0.834
0.1876
0.204
0.141
0.08
0.013
0.04
0.02
0.01
0.006
0.003
0.0008
-------
A.2 COMMERCIAL SOURCE WASTEWATER MONITORING DATA
TOTAL NUMBER OF NUMBER OF NUMBER OF AVERAGE MINIMUM MAXIMUM MEDIAN
SOURCE/COY STATE REGION SOURCE FLOW SOURCES DETECTIONS OBSERVATIONS POLLUTANT LEVEL POLLUTANT LEVEL POLLUTANT LEVEL POLLUTANT LEVEL
(OPD) (MO/L) (MO/L) (MO/L) (MQ/L)
NORTH CHARLESTON
FORMALDEHYDE
PHENOL
SILVER
8C
6000
19
36
16
36
36
36
0.6*00
0.1366
0.0869
<0.10
0.026
<0.03
1.4
o.«sa
1.17
0.38
0.108
<0.06
BATON ROUOE
LA
20000
tt
T06
000
PHOSPHATE
SURFACTANTS
FLUORUE
ZINC
PHENOL
COPPER
SILVER
ARSENIC
LEAD
NICKEL
ANTIMONY
CHROMIUM (T)
SELENIUM
MERCURY
12
66
10
11
9
11
71
10
20
29
36
7
1
6
2
3
12
M
. JO
11
9
11
64
11
28
36
41
8
6
8
7
428.6833
340.9302
3.2MO
1.7909
0.6387
0.6387
0.2287
0.1309
0.0788
0.0906
0.0638
0.0280
0.0184
0.0181
0.0100
0.0016
331
20
1.60
0.62
0.09
0.03
0.001
0.02
0.002
0.001
0.001
0.006
0.001
0.003
0.006
0.001
680
1346
6.6
4.0
2.7
4.66
1.3
0.98
0.602
0.602
0.602
0.1
0.04
0.064
0.02
0.002
407
284
3.3
1.8
0.17
0.13
0.16
0.06
0.033
0.01
0.01
0.0096
0.02
0.007
0.01
0.002
-------
A.2 COMMERCIAL SOURCE WASTEWATER MONITORING DATA
SOURCE/CITY
TOTAL NUMBER OF NUMBER OF NUMBER OF AVERAGE MINIMUM MAXIMUM MEDIAN
STATE REGION SOURCE FLOW SOURCES DETECTIONS OBSERVATIONS POLLUTANT LEVEL POLLUTANT LEVEL POLLUTANT LEVEL POLLUTANT LEVEL
(OPO) (MO/L) (MOM.) (MOfl.) (MO/L)
RADIATOR SHOPS
ONONOAOACOUNTY
OHC
COPPER
LEAD
IRON
CADMIUM
MANOANEBE
MOXEL
CHROMIUM (T)
66.0000
8.6300
3.3100
1.7400
1.3000
1.2300
1.1300
0.1130
0.0270
660
.63
3.31
1.74
1.3
1.23
1.13
0.113
0.027
866
.63
3.31
1.74
1.3
1.23
1.13
0.113
0.027
66
.63
3.31
1.74
1.3
1.23
1.13
0.113
0.027
HAMPTON ROADS
VA
12
>
00
COPPER
CHROMIUM
ZINC
LEAD
MKX.EL
CADMIUM
462
84
446
424
69
106
462
118
462
462
118
116
7.8616488726
0.1431366032
8.2627433826
16.6972123694
0.1536440678
0.0274434783
0.03
0.06
0.02
0.08
0.03
0.006
183
3.33
688
2280
3.29
0.410
6.22
0.06
2.08
3.4
0.076
0.016
W98C
4100
LEAD
ZINC
COPPER
79.7000
22.1000
4.9876
10.6
1.6
0.91
224
39.3
11.7
42.1
23.76
3.67
CHICAGO
ZINC
IRON
COPPER
LEAD
NICKEL
CHROMIUM (T)
CADMIUM
CYANIDE
MERCURY
19
20
19
17
9
18
17
11
11
20
20
20
20
20
20
20
11
20
196.2360
67.6860
29.4236
18.4730
0.3306
0.1366
0.1160
0.0302
0.0003
<02
0.1
0.06
0.02
0.01
0.01
0.01
0.014
o.ooot
1720
770
396
126
1.4
0.96
0.62
0.098
0.0012
103
10.16
1.346
0.68
<0.2
0.04
0.04
0.022
C0.0003
BATON ROUGE
LA
COD
LEAD
ZINC
COPPER
7.M67
1.9666
0.4000
0.0*96
<3.7
0.17
0.4«
0.049
11.3
7.06
0.4«
0.13
8
0.303
0.44
00885
-------
A.2 COMMERCIAL SOURCE WASTEWATER MONITORING DATA
TOTAL NUMBER Of NUMBER OF NUMBER OF AVERAGE MINIMUM MAXIMUM MEDIAN
SOURCE/CITY STATE REGION SOURCE FLOW SOURCES DETECTIONS OBSERVATIONS POLLUTANT LEVEL POLLUTANT LEVEL POLLUTANT LEVEL POLLUTANT LEVEL
(QPO> (MG/L) (MOA.) (MG/L) (MG/L)
FORT DODGE
IA
3000
LEAD
ZINC
COPPER
20
IB
20
20
20
20
133.1 ISO
(2.7380
21.1360
0.08
<0.02
0.12
11BO
612
3.2
38
0.»4
SAN FRANCISCO
CA
3781
ZMC
LEAD
COPPER
CHROMIUM (T)
SILVER
217.1636
3.114*
20.2774
0.2140
0.1347
0.1183
0.0230
0.0120
0.0000
3.20(0
1.60(0
2.1213
0.0660
0.0100
0.0180
0.0110
0.001*
0.0003
(31.3200
320.6640
7.0800
0.3330
0.3310
0.4270
0.0440
0.0361
0.0011
a».eo»
30.0067
20.M01
0.261
0.043
0.048
0.024
0.0006
0.0000
-------
SOURCE/CITY
A.2 COMMERCIAL SOURCE WASTEWATER MONITORING DATA
TOTAL NUMBER OF NUMBER Of NUMBER OF AVERAGE MINIMUM MAXIMUM MEDIAN
STATE RECHON aOURCCFLOW SOURCES DETECTIONS OBSERVATIONS POLLUTANT LEVEL POLLUTANT LEVEL POLLUTANT LEVEL POLLUT .LEVEL
-------
A.2 COMMERCIAL SOURCE WASTEWATER MONITORING DATA
SOURCE/CITY
TOTAL NUMBER OF NUMBER OP NUMBER OF AVERAGE MINIMUM MAXIMUM MEDIAN
STATE REGION SOURCE FLOW SOURCES DETECTIONS OBSERVATIONS POLLUTANT LEVEL POLLUTANT LEVEL POLLUTANT LEVEL POLLUTANT LEVEL
(QPO) (MCUL) (MO/L) (MOA.) (MOM.)
TRUCK CLEANERS
HAMPTON ROADS
VA
COO
ALUMINUM
ZINC
PHENOL
LEAD
COPPER
NICKEL
CHROMIUM (0
CADMIUM
37
4
«3
66
34
62
36
22
43
37
4
63
66
67
64
40
61
46
1114.3243
7.7000
6.4366
2.1109
0.4780
0.2696
0.1722
0.1236
0.0321
480
4.6
0.00
0.04
<0.06
<0.02
<0.03
<0.02
40.006
1766000
13.1
60.96
02
0.4
1.04
1.06
0.96
0.427
3640
0.46
2.07
0.46
0.12
O.14
0.1
00.06
0.013
BATON ROUQE
LA
7000
TD6
000
CYANIDE
PHOSPHATE
PHENOL
ZINC
NICKEL
COPPER
SILVER
CHROMIUM (T)
LEAD
ANTIMONY
ARSENIC
THALLIUM
CADMIUM
BERYLLIUM
SELENIUM
6
26
6
6
23
20
16
20
6
24
22
9
2
1«
1
6
6
26
9
6
26
20
19
20
24
26
26
17
23
14
26
16
22
3364.0000
1419.6306
66.6606
7.6600
1.4309
1.2000
0.1699
0.1006
0.1139
0.1129
0.1033
0.0900
0.0662
0.0419
0.01*6
0.0131
0.0124
361.000
36.300
0.006
0.090
0.006
0.130
0.010
0.007
0.001
0.004
0.006
0.010
0.002
0.006
0.001
0.001
0.001
11700.000
4740.000
260.000
34.200
8.000
6.600
0.940
1.600
2.400 '
0.670
0.990
0.640
0.660
0.130
0.230
0.100
0.060
1646.000
1210.600
0.010
2.000
0.170
0.406
0.076
0.060
0.006
0.060
0,030
0.060
0.010
0.023
0.010
0.002
0.010
-------
A.2 COMMERCIAL SOURCE WASTEWATER MONITORING DATA
SOURCE/CITY
STATE
TOTAL NUMBER Of NUMBER OF NUMBER OF AVERAGE MINIMUM MAXIMUM MEDIAN
REGION SOURCE FLOW SOURCES DETECTIONS OBSERVATIONS POLLUTANT LEVEL POLLUTANT LEVEL POLLUTANT LEVEL POLLUTANT LEVEL
(OPD) (MOA) (M
-------
A.2 COMMERCIAL SOURCE WASTEWATER MONITORING DATA
i
i-1
U!
TOTAL NUMBER OF NUMBER OF NUMBER OF AVERAGE MINIMUM MAXIMUM
SOURCE/CITY STATE REGION SOURCE FLOW SOURCES DETECTIONS OBSERVATIONS POLLUTANT LEVEL POLLUTANT LEVEL POLLUTANT LEVEL POL
(OPO) (MQ/L) (MQ/L) (MO/L)
LAUNDRIES
BANQOR ME 1 19000 1
ZINC
LEAD
B»(2~ETHYLHEXYL)PHTHALATE
COPPER
CHLOROFORM
TETRACHLOROETHEME
NICKEL
CHROMIUM (T)
CADMIUM
BUTYL BENZYL PHTHALATE
TOLUENE
DMMMfTVL PHTHALATE
ETHYLBENZENE
TRANS-1.2-OICHLOHOETHENE
METHYLENE CHLORIOE
PORTLAND ME 1 30126 2
ZINC
SILVER
COPPER
LEAD
CHROMIUM (I)
NICKEL
CADMIUM
BUFFALO NY 2 t
PHOSPHATE
LEAD
ZINC
COPPER
1.1,2,2-TETRACHLOROETHANE
CHLOROFORM
TflAN8-1,2-OCHLOHOETHENE
BROMOFORM
1,1,1-TRICHLOROETHANE
CARBON TETRACHLORIDE
CHLOROBENZENE
BROMOOICHLOROMETHANE
6
6
1
6
4
6
2
6
6
1
2
t
t
1
1
20
«
1$
20
6
IS
6
e
0
e
2
2
2
1
1
1
1
2
6
6
t
6
6
S
6
6
6
1
C
t
6
6
6
20
ia
23
13
IS
1S
6
6
6
1.3740
0.4100
0.3600
0.3380
0.212*
0.1832
0.0044
0.066*
0.0240
0.0200
0.0194
0.0120
o.otoe
0.0070
0.0002
0.0230
0.0162
0.3M7
0.2627
0.100*
0.0872
0.0300
13.2000
2.6000
1.1068
0.8776
0.0004
0.00*2
0.0464
0.026*
0.0264
0.000»
0.0002
0.00*6
0.77
0.26
0.36
0.2
0.043
0.008
0.042
0.032
0.013
0.02
0.0)4
0.012
0.033
0.016
0.011
0.16
<0.006
0.00
<0.02
<0.01
<0.001
<0.006
4.4
0.2
0.64
0.14
-CO.OOI
<0.001
<0.001
<0.001
<0.001
-------
A.2 COMMERCIAL SOURCE WASTEWATER MONITORING DATA
HAMPTON ROAM
000
ZINC
LEAD
COPPER
CHROMIUM (I)
PHENOL
MOKEL
CADMIUM
M.VER
MERCURV
BOVMJNQOREEN
8TATC
VA
KY
HOPROPVL ALCOHOL
IRON
TOLUENE
ZINC
COPPER
LEAD
CHROMIUM (I)
LOUMMLLE
IRON
ZINC
LEAD
COPPER
BARIUM
CHROMIUM (T)
MCKEL
CADMIUM
KV
LVER
MERCURY
NORTH CHARLESTON
zmc
COPPER
LEAD
MCKEL
CHROMIUM 0)
CADMIUM
8C
TOTAL NUMBER OF NUMBEROF NU
REOION MURCEFLOW SOURCES DETECTIONS OB6C
CQPD)
3 300000 a
286
660
422
424
147
20ft
140
22*
1
24
4 2
1
2
26
1
28
24
16
26
21
24
4 980200 8
37
37
34
37
37
38
33
32
27
16
23
33
4 1
8
8
8
2
1
2
USER OF
JWATONS POL
288
6*1
Ml
428
2*3
222
277
27*
13
28
1
2
28
1
28
24
26
28
26
28
37
37
37
37
37
37
37
37
37
37
37
37
AVERAGE
LUTANT LEVEL POD
(MOD
1384.0(20
2.823*
£4023
0.8318
0.3*01
0.2410
0.08*1
0.027*
0.0148
0.0014
74.0000
26.6000
lt.6218
18.0000
13*96
1.2242
0.8132
0.2088
0.148*
0.0477
.7048
1.2816
0.8*24
0.8786
0.6066
0.28*0
0.1837
0.06*6
0.0382
0.0186
0.0100
0.0023
2.2233
0.18*3
O.OMO
0.0387
0.0333
0.012*
MINIMUM
.UTAMT LEVEL POU
(MO/l)
76
<0.006
0.03
<0.02
0.04
40.01
<0.04
<0.006
<0.006
<0.0002
74
12
-------
A.2 COMMERCIAL SOURCE WASTEWATER MONITORING DATA
TOTAL NUMBER OF NUMBER OF NUMBER OF AVERAGE MINIMUM MAXIMUM MEDIAN
SOURCE/CITY STATE REGION SOURCE FLOW SOURCES DETECTIONS OBSERVATIONS POLLUTANT LEVEL POLLUTANT LEVEL POLLUTANT LEVEL POLLUTANT LEVEL
(QPO) (MQrt.) (MQ/L) (MOM.) (MO/L)
CHKAQO H. 6 1210000 IS
IRON
zmc
COPPER
NICKEL
LEAD
CHROMIUM (T)
CYANIDE
CADMIUM
MERCURY
ROCKFORO H. 6 226000 6
IRON
ZINC
LEAD
COPPER
NICKEL
CHROMIUM (T)
CADMIUM
COLUMBUS OH 6 4
ZMC
LEAD
COPPER
CHROMIUM (T)
MCKEL
CADMIUM
ST. PAUL MN 6 424000 4
1-ETHYL-3-METHYL BENZENE
t-ETHYL-4-METHYL BENZENE
1-ETHYL-2-METHYL BENZENE
M-XYLENE
TOLUENE
P-XYLENE
ZINC
ETHYL BENZENE
LEAD
COPPER
CYANOE
CHROMIUM (T)
NICKEL
CADMIUM
346
2W
333
36
236
1S3
"ll7
82
186
19
19
19
19
16
13
7
77
61
67
79
37
M
3
2
3
1
3
1
66
3
6t
69
7
49
22
44
366
364
362
364
361
361
117
366
336
19
19
19
19
19
19
19
77
64
67
11
79
at
4
3
4
4
4
4
66
4
69
69
1
60
22
44
1.6634
0.4196
0.2176
0.1966
0.1666
0.0632
0.0703
0.0209
0.0006
7.2386
2.1366
1.2032
0.7642
0.1474
0.1366
0.0432
2.1671
1.0666
0.9996
0.2476
0.1643
0.0660
160.00OO
160.0000
160.0000
6.7437
6.0660
3.6426
3.0621
2.1260
1.6464
1.0797
0.6S71
0.2716
0.1173
0.1109
0.1
0.1
0.01
0.1
0.01
0.01
0.002
0.01
0.0001
1
0.2
0.1
0.1
<0.1
<0.1
<0.01
0.14
0.39
0.023
0.073
<0.1
0.016
<160
060
060
O.47
<1.2
<0.96
0.64
0.3
-------
A.2 COMMERCIAL SOURCE WASTEWATER MONITORING DATA
SOURCE/COY
BTATE RKMON
BATON ROUOE
coo
LEAD
ZINC
MCKEL
CHROMIUM (T)
SILVER
MERCURY
WICHITA K8
ZINC
LEAD
OREELEY CO
ZINC
BMC2-ETHYLHEXYL) PHTHA1ATE
COPTER
LEAD
NAPHTHALENE
MERCURY
CHROMIUM (T)
DUN-BUTYL PHTHALATE
DM4-OCTVL PHTHALATE
BUTYL BENZYL PHTHALATE
NICKEL
SILVER
CADMIUM
TOTAL NUMBER OF NUMBER OF NUMBER OF AVERAOE MINIMUM MAXIMUM MEDIAN
SOURCE FLOW SOURCE* DETECTIONS OBSERVATIONS POLLUTANT LEVEL POLLUTANT LEVEL POLLUTANT LEVEL POLLUTANT LEVEL
(OPD) tttOA) (MOIL) OMUL) (MO/L)
6000 2
16
11
4
1
11
3
10
3
4
12
2
3
2
1
4
22
30
26626 1
16
1
17
16
1
3
16
1
1
1
12
0
16
16
10
4
3
12
3
12
0
3
6
10
6
6
4
1
22
30
16
1
17
16
1
10
16
1
1
1
16
11
16
1062*770
0.0431
16076
4.6000
2.1467
0.6633
0.3417
0.3267
0.3067
0.0707
0.0463
0.0200
0.0167
0.0076
0.0014
1.0460
0.6760
1.0430
1.1000
0.6312
0.4667
0.3100
0.1206
0.0702
0.0700
0.0670
0.0400
0.0421
0.0370
0.0260
00
<0.06
1.14
<0.2
<0.01
0.20
<0.1
0.06
0.16
0.04
0.003
<0.06
<0.002
<0.002
0.0014
0.17
0.01
0.630
1.100
0.160
0.100
0.310
0.001
0.003
0.070
0.067
0.04«
0.006
0.006
0.003
13660
160
14
14
6.6
1.00
0.63
0.0
0.46
0.17
0.07
<0.01
0.024
0.021
0.0014
3.66
3.3
4.060
1.100
1.010
1.600
0.310
0.704
0.140
0.070
0.067
0.040
0.120
0.136
0.062
aw
0.4
6.026
<0.2
2.3466
0.34
0.366
0.17
0.20
0.06
0.01
0.01
0.012
0.0036
0.0014
1.166
0.36
1.306
1.100
0.410
0.326
0.310
0.010
0.061
0.070
0.067
0.04«
0.037
0.031
0.021
-------
A.3 SEPTAGE HAULER MONITORING DATA
CITY
STATE
REQION
AVERAGE
FLOW(QPO)
NUMBER OF NUMBER OF AVERAGE MINIMUM MAXIMUM MEDIAN
DETECTIONS OBSERVATIONS POLLUTANT LEVEL POLLUTANT LEVEL POLLUTANT LEVEL POLLUTANT LEVEL
(MOA) (MQA.) (MO/L) (MOA)
7000
ZINC
COPPER
LEAD
NICKEL
CHROMIUM (T)
SILVER
CADMIUM
01
01
01
01
70
01
01
01
01
01
01
01
01
7.1146
6.2106
1.6462
0.3600
0.1421
0.0036
0.0020
0.037
0.16
0.08
0.06
0.02
«T006
0.01
66.2
30.76
30.6
3
0.73
0.14
0.76
6.66
4.8
0.01
0.27
0.1
0.02
0.06
>
I
ONONDAOACOUNTY
IRON
ZINC
COPPER
LEAD
CHROMIUM 0)
NICKEL
CADMIUM
CYANIDE
MERCURY
NY
21
21
21
21
21
21
21
10
10
21
21
21
21
21
21
21
21
21
207.3420
10.0667
17.3096
4.6143
4.6406
1.0681
0.6406
0.2400
0.0332
M.O
0.7
0.6
1
0.06
0.76
0.26
<0.01
0.0037
2740
120
86
36.7
10.2
0.2
2.1
1.1
0.164
4.1
2.4
2
2.0
1.6
0.6
0.16
0.01
ALLENTOWN
PA
COPPER
ZINC
CHROMIUM (T)
LEAD
NICKEL
COBALT
TIN
SILVER
32
26
32
30
32
16
It
2
32
27
32
32
32
32
26
26
22.6710
11.3640
3.3022
1.0613
1.3369
0.4062
0.0764
0.0246
0.9
<0.001
0.66
C0.026
0.06
<0.003
<0.016
<0.003
260.9
48.1
13
7.6
8.66
3.46
1
0.4
6.476
4.66
2.86
1.276
0.676
0.0266
O.016
<0.003
HAMPTON ROADS
VA
COD
zmc
LEAD
NICKEL
CHROMIUM (T)
183
183
161
183
183
183
183
183
163
161
183
183
183
183
21247.0B08
11.0378
2.1627
0.7781
0.2722
0.2311
0.0366
610
0.03
0.02
0.1
0.04
0.06
0.006
117600
118.02
60.6
30.6
2.4
2.61
0.406
17340
6.06
0.04
0.2
0.14
0.08
0.010
CHCAQO
IRON
ZINC
COPPER
NICKEL
LEAD
434
436
434
282
434
434
441
442
490
636
26.1400
3.7100
0.6630
0.4760
0.4740
0.2
0.1
0.01
0.1
0.01
171
16.3
3.03
6.2
3.2
16.16
4
0.62
<0.2
0.14
-------
A.3 SEPT AGE HAULER MONITORING DATA
omr
STATE
AVERAGE NUMBER OF NUMBER OF AVERAOE MINIMUM MAXIMUM MEDIAN
FLCW(OPD) DETECTIONS OBSERVATIONS POLLUTANT LEVEL POLLUTANT LEVEL POLLUTANT LEVEL POLLUTANT LEVEL
(MO/L) (MO/L) (MOA.) (Mart.)
oo
CYAMOE
CHROMIUM (T)
CADMIUM
MERCURY
DALLAS TX
ZMC
METHYL ALCOHOL
OPROPYL ALCOHOL
ACETONE
cmttH
nniTjM
METHYL ETHYL KETONE
LEAD
CHROMIUM (T)
NICKEL
CYAMOE
CADMIUM
TOLUENE
ARSENIC
SILVER
METHYLENE CHLORJOE
ETHYL BENZENE
BENZENE
XYLENE
MERCURY
WICHITA KB
ZMC
UANflAtfFflf
CHROMIUM (T)
COPPER
LEAD
NICKEL
CADMIUM
SILVER
WATERLOO IA
ZMC
COPPER
LEAD
CHROMIUM (T)
436
434
327
434
6
131
117
117
116
131
126
116
132
131
131
121
130
113
128
120
116
116
112
67
129
7
7 3300
69
69
67
67
436
620
663
131
117
117
116
131
126
116
132
131
131
121
130
113
126
129
116
116
112
67
129
69
69
69
69
0.4710
0.1690
0.0720
0.0022
16.6109
16.6400
14X1647
10.6663
8.9067
6.7681
3.6604
2.4646
0.6744
0.6436
0.6022
0.1868
0.1704
0.1460
0.1249
0.1009
0.0673
0.0610
0.0611
0.0142
16.3740
6.0860
6.6060
6.4200
1.6660
0.6800
0.1320
0.0340
34.7298
16.1463
3.6816
0.7049
0.007
0.01
0.01
0.0001
0.06
1
1
0
0.01
0.002
1
0.03
0.01
0.01
0.001
0.01
0.006
0
0.01
0.006
0.006
0.006
0.006
0.001
0.66
0.66
0.02
0.46
0.24
0.09
0.06
0.01
2.02
0.39
<0.2
<0.06
1.633
0.67
0.07
0.0636
444
306 -
301
210
202
202
240
116
34
37
4.2
6.1
1.06
3.6
6
2.2
1.7
3.1
0.72
0.742
66.2
17.06
16.12
21.2
3.21
1.67
0.21
0.1
130
160
21
6.88
0.6
0.13
<0.02
0.0007
3.2
1
1
1
O.S4
0.836
1
0.24
0.12
0.26
0.3
0.06
0.06
0.02
0.06
0.01
0.01
0.01
0.01
0.002
6.66
3.62
0.37
1.47
2.33
0.44
0.14
0.02
20
9.6
2.3
0.34
-------
A.3 SEPTAGE HAULER MONITORING DATA
cnv
STATE
AVERAGE NUMBER Of NUMBER OF AVERAGE MINIMUM MAXIMUM MEDIAN
FLOW(QPO) DETECTIONS OBSERVATIONS POLLUTANT LEVEL POLLUTANT LEVEL POLLUTANT LEVEL POLLUTANT LEVEL
(MQA) (MOA) (MO/L) (MOW.)
NICKEL
CADMIUM
SILVER
ARSENIC
W
CO
13
10
50
M
13
17
0.7741
0.1M1
O.OM6
O.M61
0.07
0.03
0.01
0.004
2.6
1
0.28
0.28
0.58
0.12
0.06
0.06
SANTA ROSA
CA
It 000
IRON
zmc
COPPER
LEAD
CHROMIUM (I)
CADMIUM
SILVER
110.3333
.4444
2.0087
0.6744
0.4344
0.1007
0.0411
34
26
3.6
1.1
0.37
0.2
0.00
0.02
246
48
16
3.6
0.0
0.8
0.18
0.08
100
36
6
1.7
0.6
0.38
0.11
0.03
>
I
VO
-------
A.4 LANDFILL LEACHATE MONITORING DATA
K)
O
CITY STATE
PORTLAND ME
ZINC
PHENOLS
CHROMIUM (T)
NICKEL
COPPER
LEAD
ARSENIC
CADMIUM
AMTMONY
LAWRENCE MA
ttN
MANOANESE
ZMC
1 9, LAICHI OROETMAME
i («ivVPw*Jii>^^mj^c i vmnE
PHENOL
TOLUENE
XYLENE
BARIUM
ETHYLBENZENE
BENZOICACtO
NICKEL
ANTIMONY
2,4-ttMETHYlPHENOL
NAPHTHALENE
1.4-DICHLOROBENZENE
VINYL CHLORIDE
4-METHYLPHENOL
CYANIDE
LEAD
COPPER
BENZENE
1,2-OCHLOHETHENE
TRKHLOROETHENE
CHROMIUM (T)
CHLOROETHANE
SILVER
ARSENIC
8ELENHJM
CADMIUM
1.1-OICHLOROETHANE
MERCURY
NUMBER OF NUMBER OF AVERAGE MINIMUM MAXIMUM MEDIAN
REGION DETECTIONS OBSERVATIONS POLLUTANT LEVEL POLLUTANT LEVEL POLLUTANT LEVEL POLLUTANT LEVEL
(MQrt.) (MQ/L) (MO/L) (MO/L)
1
189
2
167
174
163
140
2
139
1
1
3
4
7
2
7
4
(
7
2
6
2
1
2
a
3
1
2
6
6
' 6
3
1
6
1
2
7
1
2
3
2
109
2
167
166
166
178
2
191
1
3
4
7
0
7
7
6
6
9
4
6
7
6
6
11
6
1
2
9
9
9
7
6
8
6
a
9
7
10
6
7
13.7406
2.8600
0.7210
0.6772
0.4476
0.1671
0.0606
0.0331
0.0060
70.0333
22.6260
1.6643
1.7017
1.0818
0.6366
0.4636
0.4040
0.2336
0.1900
0.1800
0.1814
0.1264
0.1132
0.1012
0.0740
0.0860
0.0460
0.0382
0.0366
0.0316
0.0297
0.0277
0.0276
0.0213
0.0200
0.0196
0.0109
0.0070
0.0066
0.0004
0.070
1.700
0.010
0.003
<0.0t
<0.01
0.031
<0.001
0.006
67.300
3.040
0.060
-------
A.4 LANDFILL LEACHATE MONITORING DATA
CfTY
NUMBER OF NUMBER OF AVERAGE MINIMUM MAXIMUM MEDIAN
STATE REGION DETECTIONS OBSERVATIONS POLLUTANT LEVEL POLLUTANT LEVEL POLLUTANT LEVEL POLLUTANT LEVEL
(MOA) (MOW.) (MO/L) (MOM.)
I
to
ONEIDA COUNTY NY
IRON
METHYL ETHYL KETONE
ZINC
ACETONE
METHYL I8OBUTYL KETONE
LEAD
NICKEL
METHYLENE CHLORIDE
VINYL ACTETATE
COPPER
DCTHYL PHTHALATE
METHYL BUTYL KETONE
CHROMIUM (I)
VINYL CHLORIDE
XYLENE8
1.1-WCHLOROETHAME
1,1.1-TRKHLOROETHANE
WLVER
TRICHLOROETHENE
P-CHLORO-M-CRESOL
ETHYLBENZENE
PENTACHLOROPHENOL
N-MTROSODIPHENYLAMINE
CADMIUM
2.4-OMETHYLPHENOL
BENZENE
1.2-OICHLOROETHANE
DIMETHYL PHTHALATE
OMMNITYL PHTHALATE
ONONDAQA COUNTY NY
PHENOLS
ZMC
TON
COPPER
MCKEL
LEAD
ETHYLKMZENC
KNZEMc
CHUMOKNZCM1
TOLUENE
2
3
1
3
*
3
1
*
1
2
1
1
2
1
6
1
4
1
1
1
1
1
1
1
1
2
3
*
3
3
*
3
3
i
3
3
3
3
3
6
3
3
1
1
1
3
3
1
t
309.0000
13.6333
3.6300
2.6000
0.4300
0.4100
0.3800
0.3100
0.2600
0.1800
0.1100
0.00*0
0.0700
0.0460
0.0460
0.0330
0.0220
0.0200
0.0160
0 O1IU)
W.V1OU
0.0170
0.0100
0.0110
0.0100
0.0068
0.0078
0.0061
0.0048
0.0044
2.0000
1.6000
1.6000
0.4000
0.2700
0.2000
0.0760
0.0160
0.0110
0.0110
0.0062
30
6.3
0.10
2.6
0.02
*
0.14
0.21
0.26
O.O4
0.11
0.026
0.02
0.046
0.046
0.014
0.022
0.01
0.016
A A IK
W.UIO
0.017
0.016
0.011
0.0060
0.0078
0.0061
0.0048
0.0044
2
1.6
1.6
0.4
0.27
0.2
0.076
A100
0.011
0.011
0.0002
4600
20
10
2.6
0.74
8.6
1
0.42
0.26
1.6
0.11
0.10
0.61
0.046
0.046
0.062
0.022
0.04
0.016
n Am
U.V1O
0.017
0.016
0.011
0.00
0.0060
0.0070
0.0061
0.0040
0.0044
2
1.6
1.6
0.4
0.27
0.2
0.07*
0.0100
0.011
A %
9J919
0.0002
0.0
2.6
0.63
0.3
0.26
0.11
0.004
*
0.046
0.046
0.033
0.022
0.018
A nm
U.II1O
0.017
0.016
0.011
0.0068
0.0070
0.0061
0.0040
0.0044
2
1.6
1.6
0.4
0.27
U
0.076
0.0100
0.011
A AVI -
.»
0.0002
- Could not ho MttmiM* Horn 4ou pmfcMd
-------
A.4 LANDFILL LEACHATE MONITORING DATA
NUMBER OF NUMBER OF AVERAGE UMMUM MAXIMUM MEDIAN
STATE REGION DETECTIONS OMERVATK9N8 POLLUTANT LEVEL POLLUTANT LEVEL POLLUTANT LEVEL POLLUTANT LEVEL
0MML)
AUBURN
NY
IRON
ZMO
MCKEL
LEAP
coppcn
LVER
CHROMIUM 0)
MERCURY
7.6000
o.iaoo
0.0800
0.0400
0.0300
0.0200
0.0100
0.0070
0.0020
0.0020
TONAWWMDA TREATMENT PLANT
TON
MANQANESE
NY
IS)
NJ
OYAMOE
COPPER
CHROMIUM (T)
CADMIUM
LEAD
MERCURY
.3333
0.0000
0.3400
0.0407
0.0410
0.0100
0.0087
0.0000
O.OOM
0.0010
.0*
0.03
0.14
0.042
0.041
0.007
0.00*
0.006
O.OOM
12.1
0.70
0.0
0.004
0.041
0.013
0.001
0.007
0.0076
0.0012
r.M
0.06
0.14
0.043
0.041
0.01
0.006
0.000
0.006
0.0000
W.CAROUNA SEWER AUTHORITY
CHROMIUM (T)
LEAD
ZMC
COPPER
NICKEL
6W.VER
CYAMOE
0.2770
0.1000
0.0700
0.0000
0.0600
0.0300
0.0200
0.0200
40.02
<0.1
«0.02
0.01
0.04
<0.03
00.02
<0.02
1.07
0.1
0.12
0.1
0.07
0.03
0.02
0.02
0.13
<0.1
<0.02
0.06
0.06
0.03
<0.02
<0.02
NORTH CHARLESTON
000
ZMC
SARNJM
LEAD
CHROMIUM (T)
PHENOLS
COPPER
SILVER
CADMIUM
SELENIUM
It
3
2
4
1
0
2
1
3
10
It
11
11
11
11
10
11
11
11
34.6466
0.2020
0.1001
0.0027
0.0636
0.0427
0.0104
0.0140
0.0127
0.0100
0.0001
-------
APPENDIX B
DECILE ESTIMATION WORKSHEET
-------
KCILE ESTIMATION UOMCSNEET
DECILES
1st
2nd
3rd
4th
Sth
oth
7th
Bth
9th
COL. il
CALCULATE
DECILE
POSITION
F01
ORDERED
LIST OF
REMOVALS*
COL. 02
URITE
THE
MULE
NUNKI
filVCM IN
COL. ft
....
COL. a
RECORD
THE
ORDERED
REMOVAL
KM I ME
COL. 02
ENIRY**
COL. ft
RECORD
THE
ORDERED
REMOVAL
FOLLOWING
THE
COL. 13
ENTRY**
""
COL. 05
COL. 04
ENTRY
MINUS
COL. 03
ENTRY
COL. 06
LIST THE
DECIMAL
IN
COL. 01
COL. «7
MULTIPLY
COL. «5
ENTRY
V
COL. 06
ENTRY
COL. 0a
ADD
COL. 03
AND
COL. 0T
ENTRIES
mmmmmmmmmmmmmMMmm
ESTIMATED
DECILES
tu
I
In colum dtfinwl M oultiplM of (N*1)/10. uhw* N th* nxfctr of
------- |