United States
Environmental Protection
Agency
Office of Water
(4303)
EPA821-R-97-006
November 1997
Statistical Support Document for
Proposed Pretreatment Standards
for Existing and New Sources for
the Industrial Laundries Point
Source Category

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          Statistical Support Document for
        Proposed Pretreatment Standards for
          Existing and New Sources for the
     Industrial Laundries Point Source Category

                 (EPA-821-R-97-006)
                    Prepared for:

         U.S. Environmental Protection Agency
Office of Water, Engineering and Analysis Division (4303)
                  401 M Street SW
               Washington, DC  20460
                    Prepared by:

      Science Applications International Corporation
       Environmental and Health Sciences Group
  Health and Environment Studies and Systems Division
               11251 Roger Bacon Drive
                  Reston, VA 20190   j
                   November 1997

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ACKNOWLEDGMENTS AND DISCLAIMER
       This report has been reviewed and approved for publication by the Engineering and Analysis
Division, Office of Science and Technology.  This report was prepared with the support of Science
Applications International Corporation (contract 68-C4-0046) under the direction and review of the
EPA's Office of Science and Technology. Neither the United States Government nor any of its
employees, contractors, subcontractors, or then* employees makes any warranty, expressed or
implied, or assumes any legal liability or responsibility for any third party's use of, or the results
of such use of, any information, apparatus, product, or process discussed in this report, or represents
that its use by such third party would not infringe on privately owned rights.

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ABSTRACT
       This document describes the sample design and development of survey weights for the
Industrial Laundries questionnaires.  It also provides the statistical analyses used in developing
the proposed pretreatment standards.  A list of the data used in calculating long-term averages,
variability factors, and limitations is included.

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                                  Table of Contents
1.

2.
Overview and Organization
3.
4.
 5.
Survey Design                                   ;
       2.1.   Trade Association Population
             2.1.1.  Trade Association Screerier Questionnaire
             2.1.2.  Trade Association Detailed Questionnaire
       2.2.   Dun and Bradstreet Population
             2.2.1.  Dun and Bradstreet Detailed Questionnaire
             2.2.2.  Dun and Bradstreet Screener Questionnaire
       2.3.   Hotels, Hospitals, and Prisons Screener Questionnaire
       2.4.   Industrial Laundries Population -
               Final Detailed Questionnaire Design  •

Estimation Methodology for National Estimates
       3.1.   Detailed Questionnaire
             3.1.1.  Estimation from Complete Data
             3.1.2.  Estimation with Item-Level Non-Response
             3.1.3.  Estimation for Domains with Complete Response
             3.1.4.  Estimation for Domains with Item-Level Non-Response
       3.2.   National Estimates

Analytical Data Collection Efforts and Definition of Options
       4.1.   Detailed Monitoring Questionnaire
       4.2.   EPA Wastewater Sampling Program
       4.3.   Definition of Proposed Options

Description of Data Conventions
       5.1.   Data Review  .
       5.2.   Data Types
       5.3.   Data Aggregation
             5.3.1.  Data Aggregation Across Multiple Grabs
             5.3.2.  Data Aggregation for Field Duplicates

Statistical Methodology                          '
       6.1.   Basic Overview of Adapted Delta-Lognormal Distribution
       6.2.   Modifications to the Adapted Delta-Lognormal Model

Estimation under the Modified Delta-Lognormal Model
       7.1.   Facility-specific Estimates           :
              7.1.1.  Estimation of Facility-specific Long-Term Averages
              7.1.2.  Estimation of Facility-specific Variability Factors
                7.1.2.1  Estimation of Facility-specific 1-day Variability Factors
                7.1.2.2  Estimation of Facility-specific 4-day Variability Factors
Page
 1-1

 2-1
 2-1
 2-1
 2-3
 2-9
 2-9
2-10
2-12
2-14
 3-1
 3-1
 3-1
 3-2
 3-4
 3-5
 3-7

 4-1
 4-1
 4-1
 4-1

 5-1
 5-1
 5-1
 5-2
 5-2
 5-2

 6-1
 6-1
 6-4

 7-1
 7-2
 7-2
 7-2
 7-3
 7-4

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              7.2.   Pollutant-specific Estimates
                    7.2.1.  Estimation of Pollutant-specific Long-Term Averages
                    7.2.2.  Estimation of Pollutant-specific Variability Factors
                      7.2.2.1 Estimation of Pollutant-specific 1-day Variability Factors
                      7.2.2.2 Estimation of Pollutant-specific 4-day Variability Factors

8.     Derivation of the Proposed Limitations

9.     Raw Wastewater Concentration Comparisons
       9.1    Comparison of Industrial Laundry Influent to Linen Supply Influent
       9.2    Comparison of Linen Supply Influent to Denim Pre-wash Influent

Appendices
A.     Listing of Daily Data
       A. 1    Listing of Daily Data for Dissolved Air Flotation
       A.2    Listing of Daily Data for Chemical Precipitation
B.
C.
D.
E.
Listing of Summary Statistics for Regulated Pollutants
B. 1    Listing of Summary Statistics for Regulated Pollutants for Dissolved Air
       Flotation
B.2    Listing of Summary Statistics for Regulated Pollutants for
       Chemical Precipitation

Listing of Facility-Level Long-Term Averages and Variability Factors
C.I    Listing of Facility-Level Long-Term Averages and Variability Factors for
       Dissolved Air Flotation
C.2    Listing of Facility-Level Long-Term Averages and Variability Factors for
       Chemical Precipitation
                                                                             7-7
                                                                             7-7
                                                                             7-7
                                                                             7-7
                                                                             7-7

                                                                             8-1

                                                                             9-1
                                                                             9-1
                                                                             9-4
 A-l
 A-l
A-10

 B-l
 B-l

 B-5
 C-l
 C-l

 C-4
Listing of Pollutant-Level Long-Term Averages, Variability Factors and Limitations D-l
D.I   Listing of Pollutant-Level Long-Term Averages, Variability Factors, and     D-l
      Limitations for Dissolved Air Flotation
D.2   Listing of Pollutant-Level Long-Term Averages, Variability Factors and     D-5
      Limitations for Chemical Precipitation

Episode, Sample Point and Data Source Used in Raw Wastewater Concentration     E-1
Comparisons
E.I   Industrial Laundry and Linen Comparisons                               E-l
E.2   Linen and Denim Comparisons                                          E-2

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                                       CHAPTER 1
            OVERVIEW OF ORGANIZATION AND CONTENTS OF DOCUMENT

This document describes the statistical analyses performed for the Effluent Limitations Guidelines and
Pretreatment Standards for the Industrial Laundries Point Source Category. These statistical analyses
were used in estimating the number of industrial laundry facilities, estimating the number of industrial
laundry facilities with particular characteristics of interest, developing the proposed effluent guideline
standards and comparing influent pollutant concentrations of linen, industrial laundry, and denim
prewash facilities.                                          :

This document is organized into nine chapters and five appendices,  The following list summarizes the
content of each chapter and appendix:

Chapter 1: Overview of Organization and Contents of Document
    - Describes the organization of the document and summarizes the contents of each chapter and
    appendix.

Chapter 2: Survey Design
    - Describes the development of the sample frame and selection of facilities to receive the detailed
    and screener questionnaires.

Chapter 3: Estimation Methodology for National Estimates   :
    - Describes the methodology used in calculating national estimates from the detailed questionnaire
    and provides some national estimates.                      !

Chapter 4: Analytical Data Collection Efforts and Definition of Options
    - Provides an overview of the analytical data collection efforts and defines the technology options.

Chapter 5: Description of Data Conventions
    - Describes data conventions and how the data were treated, including aggregation and review.

Chapter 6: Statistical Methodology
    - Describes the modified delta-lognormal distribution that was used to derive the proposed
    limitations.

Chapter 7: Estimation under the Modified Delta-Lognormal Distribution
    - Describes the estimation of long-term averages and variability factors at the facility and pollutant
    levels.                                                i

ChapterS: Derivation of the Proposed Limitations           •
    - Describes the derivation of the proposed limitations.

Chapter 9: Raw Wastewater Concentration Comparisons    ;
    - Describes the comparison of raw wastewater for facilities washing mostly linen items versus
    facilities washing mostly industrial items and a comparison between facilities washing mostly linen
    items versus facilities washing mostly denim pre-wash items.
                                             1-1

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Appendix A: Listing of Daily Data
    - Provides a listing of the concentration data from each facility used to characterize the treatment in
    the options.

Appendix B: Listing of Summary Statistics for Regulated Pollutants
    - Provides summary statistics for the data from each facility used to characterize the treatment in
    each option.

Appendix C: Listing of Facility-Level Long-Term Averages and Variability Factors
    - Provides a summary of the facility-specific long-term averages and variability factors for the
    proposed option.

Appendix D: Listing of Pollutant-Level Long-Term Averages, Variability Factors and Limitations
    - Provides the pollutant-level long-term averages, variability factors and the proposed limitations.

Appendix E: Episode, Sample Point and Data Source Used in Industrial Laundry and Linen
Comparisons
    - Provides the episode, sample point and data source used in the industrial laundry raw wastewater
    comparisons.
                                             1-2

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                                          CHAPTER 2
                                        SURVEY DESIGN
The industrial laundry industry screener and detailed questionnaires were sent to a random selection of
facilities that were identified from two sources. These two sources of population information were
trade association listings and information obtained from Dun & Bradstreet.  The trade association
listings were compiled from Uniform and Textile Service Association (UTSA, formerly IIL) customer
and prospective customer lists, the Textile Rental Service Association (TRSA) mailing list, and the
Occupational Safety and Health Administration's (OSHA) list of violations for industrial laundries.
Industrial laundry facilities were identified in the Dun & Bradstreet listing by their reported Standard
Industry Classification (SIC) codes.  Facilities with primary SIC codes of 7218 (industrial laundering)
or 7213 (linen supply servicing), and facilities with a secondary SIC code of 7218 were considered to
be industrial laundries.
                                                             i
The original screener questionnaires were sent to all facilities in the trade association listing.  Detailed
questionnaires were sent to a random selection of facilities that reported generating wastewater from the
trade association screener responses.  After the frame was developed from the trade  association listings,
it was realized that the entire population of industrial laundries was not covered.  Therefore, the Dun &
Bradstreet information was used to supplement the trade association listings. Additional screener and
detailed questionnaires were sent to a selection of facilities from the Dun & Bradstreet listing in order
to capture industrial laundry facilities in the nation that were not originally identified in the trade
association listing.                                            :

The two population listings initially contained duplicate facilities, due to an overlap between the trade
association and Dun & Bradstreet listings. Extensive efforts were used to select only facilities from the
Dun & Bradstreet listings that did not appear in the trade association lists.  However, due to
inconsistent recording of addresses and ownership status, some facilities were included in both
sampling frames. After removing duplicate facilities from these two listings, the two populations are
mutually exclusive. Different sample selection methods were used to randomly sample facilities within
each of these two populations.  Because the two populations are mutually exclusive,  national estimates
are generated within each population separately,  and then combined to characterize the entire
population of industrial  launderers in the nation.

The development of the sampling frames, the sample selection process, and the resulting survey
weights is summarized below.  The survey weights were developed independently for the screener and
detailed questionnaires within  each population (trade association and Dun & Bradstreet).
2.1  Trade Association Population                            \

2.1.1  Trade Association Screener Questionnaire             :

The final mailing list from the trade association listings contained 1,751 industrial laundry facilities.
Screener questionnaires were mailed to all 1,751 facilities.  Therefore, a census was taken from the
trade association listing with a frame size (N) and a sample size (n) equal to 1,751.
                                               2-1

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Of the 1,751 screeners that were matted, 1,543 were returned. In addition, three facilities that were
not on the original mailing list received copies of the screener from their parent company and
completed and returned them to the EPA. Therefore, the frame (N) and sample size (n) are increased
to 1,754, and the number of respondents is 1,546.  However, 46 of the 1,546 respondents were
duplicate facilities (i.e., 46 facilities were sent two screeners and returned both).  Two copies of the
questionnaire were mistakenly sent to each of these facilities due to inconsistent documentation of the
addresses, facility names, and/or ownership status. Despite efforts to remove duplicate facilities before
the mailing lists were established, differences in the recorded mailing addresses and physical locations
and owner and operator names caused some facilities to be listed twice. After removing the
duplications, the frame and sample size is 1,708 and the number of unique respondents is 1,500. There
were 208 nonrespondents to the screener questionnaire.

Among the 1,500 screener questionnaires that were returned, 1,127 of the facilities were identified as
"in-scope".  In-scope facilities are defined as facilities that generated laundry wastewater in 19931.  An
additional two facilities were later identified as in-scope facilities, which brought the total number of
in-scope respondent facilities to 1,129.

During the development of the detailed questionnaire frame from the list of in-scope screener
respondents (as documented in Section 2.1.2), there were 15 facilities for which information regarding
1992 revenue, wastewater treatment type, or items laundered was not available.  Subsequent telephone
calls to these facilities were used to  obtain this information.  Prom the responses to these telephone
calls, one facility that was originally identified as in-scope was found to be out-of-scope.  Therefore,
the number of in-scope respondents to the screener questionnaire was reduced by  one to 1,128.

The number of in-scope facilities among the 208  nonrespondents to the screener questionnaire is not
known because scope was determined from the response to the screener question regarding  wastewater
generation.  The EPA conducted an assessment to characterize the 208 nonrespondents to determine the
likeliness that these facilities were in-scope.  Characteristics assessed include mail delivery, business
status,  and laundry wastewater generation.

Efforts to obtain more information about these facilities resulted in the identification of 86 of the 208
nonrespondents as out-of-scope.  This was because 65 screener questionnaires were returned by the
post office and new addresses were  not available, implying that the facilities were out of business, and
21 facilities were excused by the EPA from completing the screener questionnaire because they did not
generate laundry wastewater, were out of business, or were duplicates of other facilities.  The
remaining 122 nonrespondents to  the screener questionnaire are possibly in-scope, but the status has not
been verified as of this writing. Of these 122 facilities, 3 were excused by the EPA from completing
the screener questionnaire but are likely to be in-scope and 119 were not returned by the post office,
implying that the facility received the questionnaire, but no completed screener was returned from the
facility.

Among the 208 nonrespondents to the screener questionnaire from the trade association listing, five
facilities also were sent a screener questionnaire from the Dun & Bradstreet listing (as documented in
Section 2.2.2). This duplication was discovered after the screener questionnaire was mailed from the
         "In-scope"  at this time includes denim prewash facilities, linen facilities, and all facilities generating laundry
wastewater, regardless of production amount.

                                              2-2

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Dun & Bradstreet listing.  These five facilities were retained in the Dun & Bradstreet frame to maintain
the probability structure of the Dun & Bradstreet sample.  (The Dun & Bradstreet sample was a
probability sample, whereas the trade association sample was a census.) Therefore, these five facilities
were removed from the trade association frame, and accounted for in the Dun & Bradstreet population
only. Of these five facilities, one was from the set of 86 nonrespohdents that are believed to be out-of-
scope because the screener was returned by the post office and a new address was unavailable. The
other four facilities were from the set of 122 nonrespondents that are possibly in-scope because the
screeners were not returned by the post office.  By removing these four facilities from the trade
association population, there were  118 nonrespondents to the screener questionnaire that were possibly
in-scope.

Because it was not known if the nonrespondents were in-scope, and because auxiliary information was
not available for these facilities, the EPA estimated the number of in-scope nonrespondents in the
following way. The EPA assumed that the proportion of the  118 nonrespondents that were estimated to
be in-scope is equivalent to the proportion of respondents that were identified as in-scope. There were
1,128 in-scope facilities among the 1,500 respondents, so it was estimated that 89 of the 118
nonrespondents also were in-scope.                            :

Therefore, the estimated total number of in-scope facilities from the trade association population was
1,217 (i.e. the 1,128 in-scope respondents plus the estimated  89 in-scope nonrespondents).

After the trade association list was established, the five largest industrial launderers in the nation were
examined to identify facilities that may not have been included in the trade association list. There were
48 facilities identified as belonging to these five industrial  launderers (Aratex, Cintas, Omni, Unifirst,
and Unitog).  Also, mailing addresses were identified for four additional facilities that were not
originally included in the trade association list due to lack  of address information.  Abbreviated
versions of the screeners were sent to these 52 facilities to obtain information regarding their operating
practices and status.  From this information, 29 facilities were identified as being in-scope and did not
duplicate facilities originally in the trade association list. These 29 facilities were added to the trade
association population.

Therefore, the total number of in-scope industrial laundry  facilities in the trade association population
was  1,246, of which, information from the screener questionnaire ;was available from 1,128 in-scope
respondents.

2.1.2 Trade Association Detailed Questionnaire

The  original trade association frame, from which a random sample of facilities was selected to receive
the detailed questionnaire, was based upon the list of in-scope facilities that responded to the screener
questionnaire.  The original list of in-scope screener respondents contained 1,127 facilities (see Section
2.1.1). Only the in-scope respondent facilities to the screener were used as the sampling frame because
information collected from the screener questionnaire responses was used to construct the detailed
questionnaire sampling frame strata.  The stratification scheme wa£ based on items laundered, 1992
revenues, and wastewater treatment processes, for a total of 48 strata (see Table 2-1).
                                              2-3

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                                           Table 2-1
                         Trade Association Detailed Questionnaire Strata

A:
B:
C:
D:

1:
2:
3:
4:

I:
H:
HI:
-- - "" Items Laundered
,-j > ,
i 1 I \V
5% or more printer towels (and possibly anything else)
5% or more shop towels (and possibly less than 5%
anything else)
10% or more industrial garments (and possibly less
less than 5% shop towels or anything else)
Anything not covered by A, B, or C
. „ '", "" 1992 Revenues
Less than $1 million
Greater than or equal to $1 million and less than $3
printer towels or
then 5% printer towels or

" ' * ilt i Oft***" <• f "" *'
*• ', i"1 ; . ' J%J"',. -t^HH

.5 million
Greater than or equal to $3.5 million and less than $7 million
Greater than or equal to $7 million
i > » * _, i f ,
Wastewater .Treatment
- • • -, , V., i 4. IH

! ^ a >. f 1 k S-W ,^i * I "^
- f' J rf * '
Biotreatment, air stripper, centrifuge, membrane filtration, pressure •
filtration, and/or media filtration, and/or carbon adsorption (and possibly
anything else)
Dissolved air flotation, oil/water separation, and/or
anything else)
Anything not covered by I or n
clarifier (and possibly

At the time that the sampling frame was developed, stratification information was not available for 15
of the in-scope respondent facilities. Therefore, the sampling frame contained 1,112 facilities, divided
into the 48 strata.  These facilities and strata are summarized .in Column (a) of Table 2-2.

From the sampling frame of 1,112 facilities, a sample of 214 facilities was randomly selected within the
48 strata. Among the 214 facilities that were randomly sampled, five were facilities that received a
pre-test of the detailed questionnaire.  Two of these pre-test facilities were replaced by redrawing from
the sampling frame within the respective strata.  One additional facility, which was not a pre-test
facility, was identified during the selection process as being closed, so it was replaced by a facility
within the same stratum that was not originally sampled.  The other three pre-test facilities were in
strata from which all of the facilities were sampled, so there were no alternative facilities to use as
replacements.  For these three facilities, responses from the pre-test were incorporated into the detailed
questionnaire response database wherever possible. Therefore, the sample size remained at 214.  All
214 of the sampled facilities for the detailed questionnaire were defined to be in-scope.
                                               2-4

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Following the selection of the random sample of 214 facilities to receive the detailed questionnaire, 17
additional facilities were selected deliberately by the EPA to receive the detailed questionnaire based
upon their wastewater treatment processes. Because these 17 facilities were not randomly selected,
they could not be included as part of the random sample.  But, because detailed questionnaires were
sent to these facilities and responses were available to be used in the calculation of national estimates,
they were included in the detailed questionnaire sample. To accommodate this situation, the 17
deliberately-sampled facilities were removed from the appropriate strata in the sampling frame, and
were placed in a separate stratum from which it is assumed that all 17 facilities were selected. This
adjusted sampling frame, with the additional stratum, is presented in Column (b) of Table 2-2.

After the establishment of the sampling frame, the selection of the detailed questionnaire sample, and
the mailing of the questionnaires, information was obtained regarding the stratification of the 15 in-  .
scope facilities that were excluded from the sampling frame due to lack of stratum information in the
screener responses. One of these facilities was men found to be out-of-scope from the additional
information obtained.  Therefore, the sampling frame was increased by 14 to a total of 1,126. These
additions to the appropriate strata in the sampling frame are reflected in Column (c) of Table 2-2.

After the list of in-scope respondents was established for the detailed questionnaire sampling frame, two
additional screener respondents were classified as in-scope. Because these two facilities were not
included in the original sampling frame and, thus, were not available for selection to receive the
.detailed questionnaire,  the population was increased by two facilities.

The population also was increased to account for the estimated number of in-scope facilities that did not
respond to the screener and, thus, were not included in the original sampling frame for the detailed
questionnaire. Of the 208 screener nonrespondents, 122 were identified as possibly being in-scope
through follow-up telephone calls.  Of these 122 facilities, 3 facilities that are likely to be in-scope were
excused by the EPA from completing the screener questionnaire,  and  119 screener questionnaires were
not returned by the post office,  implying that the facilities received the questionnaires, but completed
screeners were not returned from the facilities. There were 1,128 in-scope facilities among the 1,500
screener respondents, so it is estimated that 92 of the 122 nonrespondents also are in-scope. This
assumes that the proportion of the 122 nonrespondents that are estimated to be in-scope is  equivalent to
the proportion of respondents that were identified as in-scope.    :

The population was adjusted for these  estimated 92 in-scope facilities plus the two facilities that were
declared to be in-scope following the establishment of the original sampling frame.  Because
stratification information was not known for these facilities, the stratum frames were increased in
proportion to the frame sizes. For example, the sample frame for stratum A-l-DI contained 31
facilities and the total frame size was 1,109 (excluding the 17 deliberately-sampled facilities).
Therefore, three (94*31/1109) of the 94 in-scope facilities were apportioned to stratum A-l-m.  These
adjusted stratum frames, totaling 1,220 facilities, are presented in Column (d) of Table 2-2, and were
calculated based on the stratum frames under Column (c).        i

It should be noted that  the screener population (in Section 2.1.1) was adjusted by only 89 of the
nonrespondents because four of the nonrespondents were duplicated in the Dun & Bradstreet screener
sample. However, the detailed questionnaire frame is not affected by these four duplicates because the
Dun & Bradstreet screener frame was  developed after the Dun & Bradstreet detailed questionnaire
frame (as documented  in Section 2.2).
                                               2-5

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Two of the possibly in-scope nonrespondents to the trade association screener questionnaire were also
selected to receive a Dun & Bradstreet detailed questionnaire (i.e., the trade association list and the
Dun & Bradstreet list duplicated these two facilities, as documented in Section 2.1).  These two
facilities were accounted for in the Dun & Bradstreet detailed questionnaire sample to retain the
probability structure, because they did not respond to the trade association screener questionnaire.
Thus, they had no stratification information for the trade association detailed questionnaire sample.
Therefore, the estimated number of in-scope facilities from the screener nonrespondents was calculated
from only 120 facilities, rather than 122.  The revised estimate is 90 in-scope facilities, dictating an
increase in the detailed questionnaire of 92 in-scope facilities, rather than the 94 facilities that were
added to create Column (d).  These adjusted stratum frame sizes, based on Column (c) and the
additional 92 in-scope facilities, are presented under Column (e) of Table 2-2 and result in a total
frame size of 1,218 facilities.

After the trade association list and the subsequent detailed questionnaire sampling frame were
established, the five largest industrial launderers in the nation were examined to identify facilities that
may not have been included in the trade association list.  There were 48 facilities identified as
belonging to these five industrial launderers (Aratex, Cintas, Omni, Unifirst, and Unitog).  Also,
mailing addresses were identified for four additional facilities that were not originally included in the
trade association list due to lack of address information.  Abbreviated versions of the screeners were
sent to these 52 facilities to obtain information regarding then* operating practices and status. From this
information, 29 facilities were identified as being in-scope and  did not duplicate facilities originally hi
the trade association list.  These 29 facilities were added to the adjusted sampling frame into the
appropriate strata that were identified from the  abbreviated screener responses.  These final adjusted
stratum frames are presented hi Column (f) of Table 2-2.

The final adjusted frame size is 1,247 facilities, from which 231 facilities were sent detailed
questionnaires. The final frame sizes for each stratum are listed under Column (f) of Table 2-2 and the
sample sizes for each stratum are listed in Column (g).
                                               2-6

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                         Table 2-2
Trade Association Detailed Questionnaire Sampling Frame Report
>, } K~? >• ''•
Stratum
Items
A
A
A
A
A
A
A
A
A
A
A
A
B
B
B
B
B
B
B
B
B
B
B
B
C
C
Revenue
1
1
1
2
2
2
3
3
3
4
4
4
1
1
1
2
2
2
3
3
3
4
4
4
1
1
Treatment
I
n
m
I
n
m
i
n
m
i
n
m
i
n
m
i
n
m
i
n
m
i
n
m
i
n
Original
Rrame
(a) I
2
7
31
12
22
71
8
35
44
9
42 .
30
4
6
57
15
35
109
5
42
90
5
25
21
1
1
C l K J _^
i
-1 - - - '""••/
Intermediate Frames ** ' „ *'
-W'
2
7
31
12
22
69
8
33
42
9
40
28
4
6
56
15
35
108
5
41
90
5
25
21
1
1
<$_
2
7
31
12
22
71
8
34
42
9
40
28
4
6
57
15
36
109
5
41
91 ; •
5
25
21
1
1
'(d)
2
! 8
:34
13
'24
77
9
:37
46
; 10
! 43
30
4
1 7
62
16
:39
118
5
45
: 99
5
27
; 23
' 1
1
» -
2
8
34
13
24
77
9
37
45
10
43
30
4
7
62
16
39
118
5
44
99
5
27
23
1
1
final
Frame" k
. «3>:-
(f) •
2
8
34
13
24
77
9
37
45
10
43
31
4
7
62
16
41
121
5
50
106
5
29
26
1
1
Sample
Size
(Oh)
(g)
2
7
4
12
4
4
8
4
4
9
4
4
4
6
4
15
4
4
5
4
4
5
4
4
1
1
                            2-7

-------
                               Table 2-2
Trade Association Detailed Questionnaire Sampling Frame Report (Continued)
SF
Stratum
Items
C
C
C
C
C
C
C
C
C
C
D
D
D
D
D
D
D
D
D
D
D
D
Revenue
1
2
2
2
3
3
3
4
4
4
1
1
1
2
2
2
3
3
3
4
4
4
Treatment
m
I
n
m
I
n
m
i
n
m
i
n
m
i
n
m
i
n
m
i
n
m
Deliberate-sample
Total
Original
Frame
Ca)
21
1
3
34
3
9
26
1
3
15
5
2
80
4
6
69
8
9
51
5
9
19

1112
V
i i
Intermediate Frames :.
0>>
21
1
3
34
3
9
26
1
3
14
.5
2
80
4
6
69
8
9
49
5
9
18
17
1112
(c)
21
1
3
36
3
10
26
1
3
14
5
2
81
4
6
71
8
9
50
5
9
18
17
1126
(d)
23
1
3
39
3
11
28
1
3
15
5
2
88
4
7
77
9
10
54
5
10
20
17
1220
.
23
1
3
39
3
11
28
1
3
15
5
2
88
4
7
77
9
10
54
5
10
20
17
1218
Final
Frame
/'<&>'..,
.",/*-*
'#-,*
23
1
3
39
3
11
28
1
3
16
5
2
88
4
7
79
9
10
55
5
10
21
17
1247
l, Sample
- Size "
\
-------
2.2 Dun & Bradstreet Population

The Dun & Bradstreet listing was used to increase the population of industrial laundry facilities to
include facilities that were not captured by the trade association lists.  Additional screener and detailed
questionnaires were sent to a random sample of the facilities that were identified as industrial laundry
facilities, but were not included in the trade association lists. From the Dun & Bradstreet listing, 24
facilities were selected to receive the additional detailed questionnaires.  Following the selection of the
sample for the additional detailed questionnaire, 200 of the facilities in the Dun & Bradstreet listing
were chosen to receive additional screener questionnaires.  Therefore, the development of the detailed
questionnaire frame is discussed prior to the discussion of the additional screener questionnaire frame
in this section.

2.2.1  Dun & Bradstreet Detailed Questionnaire              :

Industrial laundry facilities were identified in the Dun & Bradstreet listing as facilities with primary SIC
codes of 7218 (industrial laundering) or 7213  (linen supply servicing), and facilities with a secondary
SIC code of 7218. These three SIC code categories were used to define three strata for the sampling
design: (1) primary SIC code 7218, (2) primary SIC code 7213, and (3) secondary SIC code 7218.

The Dun & Bradstreet listing was compared with the facilities that responded to the trade association
screener questionnaire to avoid duplication of the facilities.  Duplicate facilities within the Dun &
Bradstreet listing and between the Dun & Bradstreet listing and the trade association respondents were
removed from the Dun & Bradstreet sampling frame.  This resulted in a sampling frame of 2,249
facilities (714 in D&B Stratum 1; 1,372 in D&B Stratum 2; 163 in D&B Stratum 3).
                                                             i
Twenty-four in-scope facilities were selected by randomly sampling facilities from the sample frame
and calling each facility to verify that it was in-scope (i.e., generated wastewater). If a selected facility
was hot in-scope, another facility was  randomly selected. This process was continued until 24 in-scope
facilities were identified. To gain 24 in-scope facilities (12 from D&B Stratum 1; 7 from D&B Stratum
2; 5 from D&B Stratum 3), a total of 66 facilities were selected (36 from D&B Stratum 1; 19 from
D&B Stratum 2; 11 from D&B Stratum 3). In order to develop survey weights, this selection process
is treated as a stratified random sample of 66 facilities, of which 24 were found to be in-scope.  During
the sampling process, one of the 24 in-scope facilities was identified as a management facility, so it was
replaced by a randomly selected facility from the same stratum, thus increasing the total number of
facilities sampled to 67. But, because the replacement facility was randomly selected from the
sampling frame, the survey weights (66 facilities selected, of which, 24 are in-scope) are not affected;
that is, each facility had the same probability of being selected in the sample.

During the development of the Dun & Bradstreet screener frame (as documented in Section 2.2.2),
duplicate facilities were found between the Dun & Bradstreet listing and the respondents to the trade
association screener questionnaire.   The revised sampling frame, adjusted to remove these additional
duplicates, is 1,977.   However, this adjusted sampling frame also removed the 67 facilities that were
selected  for the detailed questionnaire, in addition to the duplicate facilities. Therefore, the 66 detailed
questionnaire facilities (not including the one management facility that was replaced) should not be
removed from the frame.  The adjusted sampling frame, after removing only the additional duplicate
facilities, is 2,043 facilities (631 in D&B  Stratum 1; 1331 in D&B, Stratum 2; 81 in D&B Stratum 3).
                                               2-9

-------
Duplicate facilities also were found between the Dun & Bradstreet listing and the nonrespondents to the
trade association screener questionnaire. There were 73 duplicates found with the trade association
nonrespondents that should be removed from the Dun & Bradstreet frame.  However, two of these
facilities had been previously selected to receive the Dun & Bradstreet detailed questionnaire.
Therefore, the Dun & Bradstreet detailed questionnaire frame was reduced by only 71 facilities. The
two duplicate facilities that were included in the Dun & Bradstreet detailed questionnaire sample were
removed from the trade association detailed questionnaire frame (as documented in Section 2.1.2). The
final sampling frame for the Dun & Bradstreet detailed questionnaire is 1,972 (605 in D&B Stratum 1;
1286 in D&B Stratum 2; 81 in D&B Stratum 3).

The final Dun & Bradstreet detailed questionnaire sampling frame (N), sample sizes (n), and the
number of sampled facilities that are in-scope (n') are summarized in Table 2-3.

2.2.2 Dun & Bradstreet Screener Questionnaire

The sampling frame for the Dun & Bradstreet screener questionnaire was established after the Dun &
Bradstreet detailed questionnaire was administered. During the development of this frame, duplicate
facilities were found between the Dun & Bradstreet listing and the respondents to the trade association
screener questionnaire. This resulted in a different sampling frame than was used for the Dun &
Bradstreet detailed questionnaire.  This sampling frame contained 1,977 facilities (595 in D&B Stratum
1; 1312 in D&B Stratum 2; 70 in D&B Stratum 3).

This frame does not include the facilities that were selected for the Dun & Bradstreet detailed
questionnaire. • These facilities should be included in the screener questionnaire frame because they are
known to be in the population. Therefore, the frame was adjusted to include the 66 detailed
questionnaire facilities (not including the one management facility that was  replaced). The resulting
frame contains 2,043 facilities (631 in D&B Stratum 1; 1331 in D&B Stratum 2; 81 in D&B Stratum
3).

Duplicate facilities were also found between the Dun & Bradstreet sampling frame for the screener
questionnaire and the nonrespondents to the trade association screener questionnaire. There were 60
duplicates found with the trade association nonrespondents that should be removed from the Dun &
Bradstreet frame. However, five of these facilities also were selected to receive the Dun & Bradstreet
screener questionnaire. Therefore, the Dun & Bradstreet screener questionnaire frame was reduced by
only 55 facilities. The five duplicate facilities that were included in the Dun & Bradstreet screener
questionnaire sample were removed from the trade association screener questionnaire frame (as
documented in Section 2.1.1). The adjusted sampling frame for the Dun & Bradstreet screener
questionnaire is 1,988 (613 in D&B Stratum 1; 1294 in D&B Stratum 2; 81 in D&B Stratum 3).

From the Dun & Bradstreet screener questionnaire frame, 200 facilities were randomly selected. This
sample was selected from the three SIC code strata and included  100 facilities from D&B Stratum 1, 60
facilities from D&B Stratum 2, and 40 facilities from D&B Stratum 3.

From the 200 screener questionnaires that were mailed, responses were received from 133 facilities.
Among these responses, 6 facilities were identified as duplicates because they received a previous
screener questionnaire. Therefore, these 6 facilities were removed from the sampling frame and the
sample. The final sampling frame for the Dun & Bradstreet screener questionnaire is 1,982 (608 in
                                            2-10

-------
D&B Stratum 1; 1293 in D&B Stratum 2; 81 in D&B Stratum 3). The final Dun & Bradstreet screener
questionnaire sampling frame (N), sample sizes (n), number of respondents, and number of in-scope
respondents are summarized in Table 2-4.

Among the 127 unique respondents, 11 facilities were identified as out of scope, because they were
sold, out of business, or not an industrial laundry facility. Therefore, the estimated number of in-scope
facilities within each stratum has been estimated. Table 2-3a summarizes the estimated in-scope
sampling frame (N') and number of in-scope respondents (n').    :

                                          Table 2-3
                   Dun & Bradstreet Detailed Questionnaire Sampling Frame
»"V - <- -„ *•":."•.
* ^ ^_-= ^&*z -?,
* L - •" .:A--*
: * '~ " ",-!, -,.--"' f •*""" j* ^
- ,.«' - ^rJSjfccatum , rj /
1. Primary SIC 7218
2. Primary SIC 7213
3. Secondary SIC 7218
Total
Sample Frame ""
'-';(& * '":
605
1268
81
1972
"± * .A
Sample Size
: ' '-.&. \
36
19
11
66
t 'i! js Ol 'I
JSfumber of
In-Scope Sampled
^acililies (H!>
12
7
5
24
                                         Table 2-3a
        Dun & Bradstreet Detailed Questionnaire Sampling Frame for In-scope Facilities
- '-* -- -„ ~ ^~",-«*~ . ., * .
•i, * -K, J<-"^K -<• ^ K
, „ ^ /"f - / _ ' •
; *- . tstratuin " ,
1. Primary SIC 7218
2. Primary SIC 7213
3. Secondary SIC 7218
Total
Sample Frames
. ^ en ---i
202
474
37 i
713
1 Number of
In-Scope Sampled
Facilities (n') *
12
7
5
24
                                            2-11

-------
                                           Table 2-4
                   Dun & Bradstreet Screener Questionnaire Sampling Frame
Stratum
1. Primary SIC 7218
2. Primary SIC 7213
3. Secondary SIC 7218
Total
Sample
Frame
(N)
608
1293
81
1982
t-
i fc
t uV
Sample Size -
(n)"
95
59
40
194
Number of
Respondent
Facilities
58
41
28
127
i k "> t,
Number of
In-scope ;
Respondent *
Facilities
51
39
26
116
2.3 Hotels, Hospitals and Prisons Screener Questionnaire

In response to comments from industrial laundry and linen supply trade associations, the EPA mailed
out screener questionnaires to facilities such as hospitals, hotels, and prisons (HHP's).  The trade
associations indicated that the HHP's may generate revenues by accepting laundry from off-site,
thereby reducing the profits of more traditional industrial laundries and linen supply facilities.

The EPA mailed 100 screener questionnaires to HHP's. HHP's are not traditional industrial laundry
facilities, but generate wastewater from laundering. To obtain the 100 facility addresses, the EPA
randomly selected 25 facility addresses from each of four lists (see Table 2-5). The results of this
survey effort cannot be used to estimate a national number of these types of facilities or national
estimates of any characteristics of these types of facilities.  The purpose of the sampling was to get a
snapshot of the  activities of nontraditional laundries to help determine whether these facilities should be
considered within the scope of the regulation.
                                             2-12

-------
                                          Table2-5
                         HHP Screener Questionnaire Sampling Frame
List Name
Textile Rental Services
Association of
America
(TRSA)
Uniform and Textile
Service Association
(UTSA)
Responses to Question
25(Q25)inthe
Industrial Laundries
Detailed Questionnaire
(as of 11/18/94)
National Association
of Institutional Linen
Management (NAILM)
Members
Total Addresses
2,416
208
312
1,504
Complete Addresses
none
(0)
all
(208)
some
some
(1,057) i
Incomplete Addresses
All
(2,416)
none
(0)
some
some
(447)
The estimated number of facilities in the mailing that were anticipated to be industrial laundries,
prisons, health care facilities, hotels, and miscellaneous industrial facilities are displayed in Table 2-6.

                                          Table 2-6
                Estimated number of HHP facilities in the mailing by Facility type
, List
^Name
TRSA
UTSA
Q25
NAILM
Total
-•" ""^L •4< V""^" l?acifity'J^Pe " * *- '"
Prisons
* " »%
v w
3
0
3
0
6
' Hef ttf>_
'^•"
8
14
7
23
52
t. *•
Hotels -
6
1
7
1
15
Industrial
Laundries
1
4
2
0
7:
Misc.
Industrial
7
6
6
1
20
"$ j,
•* jjv/*'!
, Total* J
m ,
Mailing
25
25
25
25
100
                                             2-13

-------
 2.4 Industrial Laundries Population

 The industrial laundries industry was characterized through the use of a screener questionnaire and was
 distributed a detailed questionnaire.  The detailed questionnaire was distributed to a stratified simple
 random sampling of facilities from two mutually exclusive populations of industrial launderers
 identified through two sources; trade association listings and information obtained from Dun &
 Bradstreet.

 2.4.1  Final Detailed Questionnaire Design

 Through the process of generating national estimates for the industrial laundries industry, an issue was
 identified with regard to the stratification of the trade association population listing. One basic
 motivation for designing a stratified sampling design is to reduce variability through the identification
 of homogeneous strata. That is, stratification is based on the grouping of like sampling elements.  The
 industrial laundries industry trade association population listing was stratified by types of items
 laundered, revenue range, and type of wastewater treatment.

 Table 2-7 displays the trade association detailed questionnaire sampling frame previously described hi
 Section 2.1.2.  Notice that four strata, C-l-I, C-l-E, C-2-I, and C-4-I contain only one facility each.
 Additionally, notice that within several strata all facilities within each stratum eligible to receive a
 detailed questionnaire were sent a detailed questionnaire. Many of these strata have small sample sizes.
 Due to the small sample sizes within these strata, it is difficult to assess the presence of homogeneity
 within strata or heterogeneity between strata.

 In addition, since sample unit nonresponse occurs (addressed later in this section) within these small
 strata, all strata in which all eligible facilities received detailed questionnaires were collapsed into one
 stratum. By collapsing all of these strata into a single stratum, sample unit nonresponse may be
 distributed over a larger number of facilities. This will eliminate any one facility or few facilities
 "over-representing" or "misrepresenting" the nonrespondents.

 As indicated, sample unit nonresponse occurred hi this sampling effort. Sample unit nonresponse is
 defined by a sampling unit, i.e., a facility, not returning a detailed questionnaire or not providing
 enough information hi the detailed questionnaire responses to adequately identify a facility as a
 respondent. If the response rate is considered to be random, then the sample of respondents is
 considered to be a simple random sample, and it is assumed that there are no differences between the
 set of respondents and nonrespondents.  Due to the presence of sample unit nonresponse, an adjustment
 was made to the detailed questionnaire sampling design as described below.

 Stratum B-l-n contains only one respondent to the detailed questionnaire, as indicated in Table 2-7.
 Since there is only one respondent in stratum B-l-n, this stratum was collapsed with stratum A-l-II.
 Although the original stratum weights are not identical, the difference is rninimal.  By collapsing these
strata, the sample unit nonresponse adjustment will be distributed across the seven sites hi the collapsed
stratum (W'h=2.14) versus retaining the original B-l-II stratum with an adjusted weight of 7.00.

Therefore, all censused strata were collapsed into a single stratum and strata A-l-II and B-l-II were
collapsed into a single stratum. Once these strata were collapsed, weights were adjusted for sample
                                              2-14

-------
unit nonresponse in the following manner:
                                               n.
where rh = the number of sampled units in stratum h responding to the detailed questionnaire and Nh
and % are as defined in Table 2-7.

Table 2-8 displays the final detailed questionnaire sample design for the combined trade association and
Dun & Bradstreet frames with sample unit non-response adjustment.  For each stratum, the number of
facilities in the population, the number of facilities sampled, the number of respondent facilities, the
original weight, and the adjusted weights are presented.          ,
                                              2-15

-------
                     Table 2-7
Trade Association Detailed Questionnaire Sampling Frame
-Stratum ' , -
Items
A
A
A
A
A
A
. A
A
A
A
A
A
B
B
B
B
B
B
B
B
B
B
B
B
C
Revenue
1
1
1
2
, 2
2
3
3
3
4
4
4
1
1
1
2
2
2
3
3
3
4
4
4
1
Treatment
I
n
m
I
n
ffl
I
n
m
i
n
m
i
n
m
i
n
in
i
n
m
i
n
m
i
Final
Frame

2
7
4
12
4
4
8
4
4
9
4
4
4
6
4
15
4
4
5
4
4
5
4
4
1
J Number of
Respondents
' , >h> '^
2
6
3
10
4
3
8
2
4
9
4
4
3
1
2
12
3
4
4
4
4
4
4
4
0
i i
Stratum '
Weight
J (WhT1
1.00
1.14
8.50
1.08
6.00
19.25
1.13
9.25
11.25
1.11
10.75
7.75
1.00
1.17
15.50
1.07
10.25
30.25
1.00
12.50
26.50
1.00
7.25
6.50
1.00
                       2-16

-------
                           Table 2-7
Trade Association Detailed Questionnaire Sampling Frame (Continued)
->. ~
~ \ i *
Stratum
Items
C
C
C
C
C
C
C
C
C
C
C
D
D
D
D
D
D
D
D
D
D
D
D
Revenue
1
1
2
2
2
3
3
3
4
4
4
1
1
1
2
2
2
3
3
3
4
4
4
Treatment
n
m
I
n
m
i
n
m
i
n
m
i
n
m
i
n
m
i
n
m
i
n
m
Deliberate-sample
Total
Final
Frame
(¥*)
i
23
1
3
39
3
11
28
1
3
16
-5
2
88
4
7
79
9
10
55
5
10
21
17
1247
Sample
Size
- 
-------
                           Table 2-8
Industrial Laundries Proposed Detailed Questionnaire Sampling Frame
with Sample Unit Nonresponse Adjustment and Single PSU Adjustment
Stratum '-
Items
Revenue
Treatment
Collapsed Census Stratum
A,B
A
A
A
A
A
A
A
A
A
A
B
B
B
B
B
B
B
B
C
C
C
C
1
1
2
2
2
3
3
3
4
4
4
1
2
2
2
3
3
4
4
1
2
3
3
n
m
I
n
m
i
n
m
i
n
m
m
i
n
m
n
m
n
in
m
m
n
m
, final
•JFrame^
(N*)
62
15
34
13
24
77
9
37
45
10
43
31
62
16
41
121
50
106
29
26
23
39
11
28
Sample
Size
"(Dh)
62
13
4
12
4
4
8
4
4
9
4
4
4
15
4
4
4
4
4
4
4
4
4
4
Number of,
Respondents-
fey "
45
7
3
10
4
3
8
2
. 4
9
4
4
2
12
3
4
4
4
4
4
4
3
4
4
i
Stratum
•Weight
(W
1.00
1.15
8.50
1.08
6.00
19.25
1.13
9.25
11.25
1.11
10.75
7.75
15.50
1.07
10.25
30.25
12.50
26.50
7.25
6.50
5.75
9.75
2.75
7.00
Adjusted
Stratum
Weight
, <&*> :
1.38
2.14
11.33
1.30
6.00
25.67
1.13
18.50
11.25
1.11
10.75
7.75
31.00
1.33
13.67
30.25
12.50
26.50
7.25
6.50
5.75
13.00
2.75
7.00
                            2-18

-------
                                Table 2-8
      Industrial Laundries Proposed Detailed Questionnaire Sampling Frame
with Sample Unit Nonresponse Adjustment and Single PSU Adjustment (Continued)
Stratum
Items
C
D
D
D
D
D
D
D
D
Revenue
4
1
2
2
3
3
3
4
4
Treatment
m
m
n
m
i
n
m
n
m
D&B: P7213
D&B: P7218
D&B: S7218
Total
Final
JErame
 *
16
88
7
79
9
10
55
10
21
474
202
37
1960
Sample
- Size
&a
4
4
4
4
8
4
4
4
4
7
12
5
255
Number of
Respondents
4 ~
\ &K
4
:4
:4
,2
:7
'4
3
4
,4
!6
i9
'2
208
Stratum
Weight
' (w*)
4.00
22.00
1.75
19.75
1.13
2.50
13.75
2.50
5.25
67.68
16.81
7.36

Adjusted
Stratum
Weight,
(WV
4.00
22.00
1.75
39.50
1.29
2.50
18.33
2.50
5.25
78.96
22.41
18.41

                                  2-19

-------

-------
                                         CHAPTER 3
                               ESTIMATION METHODOLOGY
This section presents the general methodology and equations for calculating estimates from the
Industrial Laundries detailed questionnaire sampling efforts.
3.1 Detailed Questionnaire

A stratified random sample of 255 industrial laundry facilities (231 in Trade Association, 24 in Dun
and Bradstreet) was selected from the 1,960 facilities in the population. Of the 255 facilities that
received detailed questionnaires, 208 facilities responded.

3.1.1 Estimation from Complete Data

Many characteristics of interest estimated from the detailed questionnaire responses were provided by
every detailed questionnaire respondent (PSU respondent). Therefore, based on a stratified simple
random sample with complete response, stratum weights are used to obtain mean estimates from a
continuous response variable. The stratum weights are the proportion of available facilities in each
stratum (Wh = Nh/N), where Nh is the total number of available facilities for the sample from stratum h
and N is the total number of available facilities for the sample (N=1,960). The sampling fraction,
which is used to estimate totals from a continuous response or the; total number of facilities with a given
characteristic within each stratum, is the fraction of facilities within each stratum that are sampled (fj, =
The stratum weights, Wh = Nh/N, are used to estimate means according to the following formula:
N
                                       'h . 7=1
                                       N
                                                                                         (3.1)
    where,     N = total number of facilities (N= 1,960)
               Nh = total number of facilities in stratum h
               HJ, = number of facilities sampled in stratum h
               y^ = response from i"1 facility in stratum h

The variance of the estimated mean is:
                                             n.
N2  h
                                                                                         (3.2)
    where
                                              3-1

-------
The estimated total number of facilities with a given attribute, or the estimated total from a continuous
response is:
                                                                                          (3.3)
    where,     Nh = total number of facilities in stratum h
               % = number of facilities sampled hi stratum h
               yu = response from 1th facility in stratum h

The estimated number of facilities, within stratum h, with a given attribute assumes
               VM =   1 if the 1th facility has the given characteristic
                       0 if the 1th facility does not have the given characteristic.

The variance of the estimated total is:
                                                                                          (3.4)
    where
3.1.2 Estimation with Item-level Non-Response

If responses are available from only .mh of the % sampled facilities, then the population can be
considered to be divided into two domains:  respondents and non-respondents. The estimated mean for
the domain of respondents can be used as an estimate of the population mean, assuming that the non-
respondent facilities operate at the mean of the responding facilities.2

The estimated mean for the respondents is:
  1 Cochran, W. G., Sampling Techniques, 3rd ed., New York: John Wiley and Sons, Inc., 1977.

                                               3-2

-------
                     Y =
                                N
                                n
(3-5)
    where,     Nh = total number of facilities in stratum h       ;
               i^ = number of facilities sampled in stratum h    i
               mh = number of respondent facilities to the characteristic of interest in stratum h
       Yhi = response from 1th facility in stratum h.               ;

The estimated variance is:                                       i
                     m
                                       n.
                                                                                            (3.6)
If responses are available from only m^ of the n,, sampled facilities, then the estimated total is:
              7 =
(3.7)
    where,     N = total number of facilities (N=1960)         :
               Nh = total number of facilities in stratum h
               % = number of facilities sampled in stratum h    :
               mh = number of respondent facilities to the characteristic of interest  in stratum h
               Vhi = response from i"1 facility in stratum h.       '

This assumes that the proportion of facilities with the given attribute, or the average response, is the
same in the set of non-respondents as in the set of respondents.    ;

The estimated variance is:
                                                3-3

-------


                                                                                   (3.8)
3.1.3 Estimation for Domains with Complete Response

If estimates are to be calculated for a specific subset (domain) of the data other than the strata used in
the sample design, then the formulae must be adjusted. An example of a domain estimate would be the
estimated number of facilities within ranges of daily water flow, where the ranges of daily water flow
are the domains. If there is complete response within each domain, the estimated mean is similar to
equation (3.1), except that the responses, y^, and the number of facilities sampled, are restricted to the
j* domain. The estimated domain mean is:
-    v^ f Nh   }
r'-?bN"
                                    •"A . »'=1

                                    N
                                                                                  (3.9)
    where,     N = total number of facilities (N=1960)
              Nh= total number of facilities hi stratum h
              HJ, «= number of sampled facilities hi stratum h
              By = number of sampled facilities in stratum h of domain j
              Vhjj = response from i* facility hi stratum h of domain j.

The variance of the estimated domain mean is:
rvrj)  = -T _
   1    N2  h
                         -.,>••-E
                                              n.
                                                                     (3.10)
    where     N = total number of facilities (N=1960)
              Nh = total number of facilities hi stratum h
              nj, = number of facilities sampled hi stratum h
              y^ = response from i* facility hi stratum h of domain j.
The estimated total number of facilities in a domain with a given attribute, or the estimated domain total
from a continuous response is:
                                          3-4

-------
                                                                                        (3.11)
    where,     Nh = total number of facilities in stratum h
               % = number of facilities sampled in stratum h
               ny = number of facilities sampled in stratum h of domain j
               yuj = response from 1th facility in stratum h of domain j.

The estimated number of facilities, within stratum h of domain j, with a given attribute assumes
                Vhij = 1 if the 1th facility has the given characteristic
                      0 if the i* facility does not have the given characteristic.
The variance of the estimated total is:
                      = E
                                         »
                                                          (3,12)
    where
3.1.4 Estimation for Domains with Item-level Non Response

When there are responses from only m^ of the n^ sampled facilities, each domain is divided into
respondents and non-respondents. If, however, a facility does not provide a response to .the domain of
interest, it is not possible to characterize that facility. Therefore, facilities are excluded from estimates
based on a domain to which that facility did not respond. The estimated mean for a given domain is
calculated from the set of respondents within that domain. This assumes that the mean of the non-
respondents in each domain is equivalent to the mean of the respondents in each domain. The estimated
domain mean when item-level non response exists is:             ;
                                           •
YJ =
                                            •"m.
                                          n.
                                                                                        (3.13)
                                              3-5

-------
where, Nh =  number of facilities in stratum h of the sample frame
       % =   number of sampled facilities in stratum h
       my =  number of respondent facilities to characteristic of interest in stratum h of
              domain j
       Dy* =  number of respondents to domain identification question(s) in domain j
       y^ SB  response from 1th facility in stratum h of domain j.

The variance of the estimated domain mean is:
                    i	.v_l_M.
                                                         -—
                                                          _y-v2
                                                                                    (3.14)
       where
The estimated total is calculated from the estimated mean, as hi equation (3.13), except that the
population size (N) is adjusted for the estimated population size of the jm domain (Nj).
where, Nh =
I                                       AT  ^M    I
                                       %•£*,]
                                                                                    (3.15)
                                     h  \  n
                     number of facilities in stratum h of the sample frame
                     number of sampled facilities in stratum h
                     number of respondent facilities to characteristic of interest in stratum h of
                     domain j
                     number of respondents to domain identification questions) in domain j
                     response from 1th facility in stratum h of domain j.
The estimated variance for the domain total assumes that the population size for domain j is known.
The calculation is similar to equation (3.14). It is noted that this estimate may contain bias due to the
assumption that the population size of the domain is known, when, in practice,  it must be estimated
from the known size of the sample and the fraction of respondents in domain j.
                                            3-6

-------
  V(YJ)  =
                                  I —
(3.16)
3.2    National Estimates

Using the estimation methodology described above, national estimates were calculated to determine the
estimated number of facilities by different domains of interest.  National estimates are based on facility
responses to the detailed questionnaire in 1993. The following tables present the estimated number of
facilities in 1993 by revenue range, total production per year, items laundered, employee range, and
annual flow.  The number of respondents to the domain of interest, estimated total number of facilities,
estimated standard error, and 95% confidence intervals are presented separately for facilities processing
less than 100% linen (193 responding facilities), facilities processing at least one million pounds of total
laundry per year and/or at least 255,000 pounds of printer and  shop towels (172 responding facilities),
and facilities processing less than one million pounds total laundry per year and less than 255,000
pounds of printer and shop towels.
                                               3-7

-------
                                           Table 3-1
                    Estimated Number of Facilities in 1993 by Revenue Range
   [ Revenue RJangeH
                               Facilities Processing <  100% Linen
< $1,000,000
$1,000,000 - $3,499,999
$3,500,000 - $6,999,999
$7,000,000 - $10,499,999
Overall*
22
51
69
51
193
198
564
664
321
1,747
36.57
124.63
114.68
84.85
93.79
126 - 269
320 - 809
440 - 889
155 - 488
1,564-1,931
                  Facilities Processing at least 1 Million Ibs Total Laundry per Year
                        and/or at least 255,000 Ibs Printer and Shop Towels
< $1,000,000
$1,000,000 - $3,499,999
$3;500,000 - $6,999,999
$7,000,000 - $10,499,999
Overall*
11
44
66
51
, 172
118
508
659
321
1,606
24.56
122.16
• 114.67
84.85
95.63
69 - 166
268 - 747
434 - 884
155 - 488
1,418-1,793
                    Facilities Processing < 1 Million Ibs Total Laundry per Year
                           and < 255,000 Ibs Printer and Shop Towels
< $1,000,000
$1,000,000 - $3,499,999
$3,500,000 - $6,999,999
Overall*
11
7
3
21
80
56
5
141
32.99
31.19
2.18
39.79
. 15 - 145
0-117
1-10
63 - 219
* Overall estimates may not equate to the sum of the domains due to rounding
                                              3-8

-------
                                          Table 3-2
               Estimated Number of Facilities in 1993 by Total Production Range
                              Facilities Processing < 100% Linen
< 1,000,000
1,000,000-1,999,999
2,000,000 - 2,999,999
3,000,000 - 3,999,999
4,000,000 - 4,999,999
5,000,000 - 5,999,999
6,000,000 - 6,999,999
7,000,000-7,999,999 .
^10,000,000
Overall*
24
25
13
22
18
15
17
24
35
193
167
264
211
231
254
144
116
116
245
1,747
42.70
87.17
92.42
; 56.41
96.37
84.01
1 35.83
45.26
; 84.75
93.79
83 - 250
93 - 435
30-392
120 - 342
65-443
0-309
45 - 186
27 - 204
79 - 412
1,564-1,931
                 Facilities Processing at least 1 Million Ibs Total Laundry per Year
                        and/or at least 255,000 Ibs Printer and Shop Towels
< 1,000,000
1,000,000-1,999,999
2,000,000 - 2,999,999
3,000,000-3,999,999
4,000,000 - 4,999,999
5,000,000 - 5,999,999
6,000,000 - 6,999,999
7,000,000 - 7,999,999
z 10,000,000
Overall*
3
25
13
22
18
15
17
24
35
172
25
264
211
231
254
144
116
116
245
1,606
i 21.92
87.17
, 92.42
; 56.41
96.37
; 84.01
35.83
45.26
84.75
: 95.63
0-68
93 - 435
30 - 392
120 - 342
65-443
0-309
45 - 186
27-204
79 - 412
1,418 - 1,793
                   Facilities Processing <  1 Million Ibs Total Laundry per Year
                           and < 255,000 Ibs Printer and Shop Towels
< 1,000,000
Overall*
21
21
141
141
39.79
': 39.79
63 - 219
63 - 219
* Overall estimates may not equate to the sum of the domains due to rounding
                                              3-9

-------
                                       Table 3-3
                Estimated Number of Facilities in 1993 by Items Laundered
(Item
-•,;-; s-si
 Estimated
                           Facilities Processing < 100% Linen
Industrial Garments
Shop Towels, Wipers, etc.
Printer Towels
Floor Mats
Mops, Dust Cloths, etc.
Linen Supply Garments
Linen Flatwork/Flat Dry
Health Care Item Types
Fender Covers
Continuous Roll Towels
Clean Room Garments
Other Item Types
Laundry Bags
Family Laundry
New Item Types
Executive Wear
Miscellaneous
Rewash Item Types
Filters
Buffing Pads
165
141
71
179
162
110
129
78
75
98
9
2
3
6
9
5
2
5
2
1
1,462
1,332
480
1,654
1,529
942
1,364
649
687
928
28
31
28
84
74
43
14
39
7
6
103.85
107.60
57.81
99.62
95.24
134.01
109.05
126.66
117.18
128.59
12.50
29.75
25.17
44.21
38.22
23.89
12.51
26.10
5.52
5.48
1,258 - 1,666
1,121 - 1,543
367 - 594
1,459-1,850
1,342 - 1,716
679 - 1,205
1,150-1,577
400-897
458 - 917
676-1,180
4-53 '
0-90
0-77
0-171
0-149
0-90
0-39
0-90
0-18
0-17
                                         3-10

-------
                             Table 3-3
Estimated Number of Facilities in 1993 by Items Laundered (Continued)
Item' ~£ V'r^/^l
ltem -« ^X J
« kV v,,f>-O^ .
•> * „ " % 1 '<• ?
r* c ^ -^ "s
Number of ,>
Respondents
"% , *4< (s
f'*"
Estimated
To|al <
J/ V-
t. X ^
Estimated "^
Standard
Error
f l - 95%£X
,- ^ "~ "'v\
";s,!H /-V H X-"
     Facilities Processing at least 1 Million Ibs Total Laundry per Year
          and/or at least 255,000 Ibs Printer and Shop Towels
Industrial Garments
Shop Towels, Wipers, etc.
Printer Towels
Floor Mats
Mops, Dust Cloths, etc.
Linen Supply Garments
Linen Flatwork/Flat Dry
Health Care Item Types
Fender Covers
Continuous Roll Towels
Clean Room Garments
Other Item Types
Laundry Bags
Family Laundry
New Item Types
Executive Wear
Miscellaneous
Rewash Item Types
Filters
Buffing Pads
150
129
67
164
153
104
120
72
72 •
93.
5
2
3
4
9
3
2
5
1
1
1,380
1,270
464
1,564
1,472
925
1,283
607
675
903
22
31
28
77
74
15
14
39
6
6
105.61
107.48
! 57.82
97.11
96.97
133.87
; 106.73
124.71
'i 117.02
128.45
• 12.30
' 29.75
25.17
43.89
38.22
; 9.03
12.51
. ; 26.10
5.48
; 5.48
1,173 - 1,587
1,060 - 1,481
351 - 577
1,374-1,755
1,282 - 1,662
663 - 1,188
1,073 - 1,492
362-851
445-904
651 - 1,155
0-46
0-90
0-77
0-163
0-149
0-33
0-39
0-90
0-17
0-17
                                3-11

-------
                             Table 3-3
Estimated Number of Facilities in 1993 by Items Laundered (Continued)
1 Item
Number of ^
Respondents
Estimated
Total
Estimated, .
Standard
Error
95%Cy£ ,- "^
'', " ^/r'"vy *',
k '"HI"'
      Facilities Processing < 1 Million Ibs Total Laundry per Year
              and < 255,000 Ibs Printer and Shop Towels
Industrial Garments
Shop Towels, Wipers, etc.
Printer Towels
Floor Mats
Mops, Dust Cloths, etc.
Linen Supply Garments
Linen Flatwork/Flat Dry
Health Care Item Types
Fender Covers
Continuous Roll Towels
Clean Room Garments
Family Laundry
Executive Wear
Filters
15
12
4
15
9
6
9
6
3
5
4
2
2
1
82.
61
16
90
57
17
81
42
13 •
26
7
7
28
1
29.10
25.09
10.98
27.18
25.06
6.19
27.09
22.14
6.08
12.43
2.28
5.28
22.12
0.72
25 - 139
12-110
0-38
37 - 143
8-106
5-29
28 - 134
0-85
1-25
1-50
2-11
0-17
0-71
0-3
                               3-12

-------
                                           Table 3-4
                   Estimated Number of Facilities in 1993 by Employee Range
Employee Range '

- ' .*> \.
NO: of t*-
Respondents
.. .. *.-:**•'-
Estimated
Total i^
** f
Estimated :••
Standard
Error^
f* 95% CJ. ll.
L
*" i J> A V
                               Facilities Processing < 100% Linen
<10
10-29
30-64
65-99
100 - 199
*200
Overall*
3
33
47
52
49
9
193
39
316
491
583
296
23
1,747
; 24.98
88.78
| 106.49
; 128.26
85.88
8.02
93.79
0-88
142 - 490
282 - 700
331 - 834
127 - 464
7-39
1,564-1,931
                 Facilities Processing at least 1 Million Ibs Total Laundry per Year
                        and/or at least 255,000 Ibs Printer and Shop Towels
10-29 '
30-64
65-99
100 - 199
;>200
Overall*
21
41
52
49
9
172
244
461
583
296
23
1,606
84.57
; 104.27
128.26
85.88
8.02
95.63
78 - 409
257 - 665
331 - 834
127 - 464
7-39
1,418-1,793
                   Facilities Processing < 1 Million Ibs Total Laundry per Year
                           and < 255,000 Ibs Printer and Shop Towels
<10
10-29
30-64
Overall*
3
12
6
21
39
72
30
141
; 24.98
; 31.21
21.62
. 39.79
0-88
11 - 133
0-72
63 - 219
* Overall estimates may not equate to the sum of the domains due to rounding
                                              3-13

-------
                                           Table 3-5
        Estimated Number of Facilities in 1993 by Annual Flow Ranges (Gallons per Year)
1 Annual Flow
No. of
Respondents
Estimated
Total
Estimated s
Standard
Error
» ^95%C.L;^3 *'
i *>, \ *,
. < , * "X
1 "'V
                               Facilities Processing < 100% Linen
< 1,000,000
1,000,000 - 4,999,999
5,000,000 - 9,999,999
10,000,000 - 19,999,999
20,000,000 - 29,999,999
*30,000,000
Overall*
4
35
41
59
26
28 .
193
31
319
471
502
244
181
1,747
22.54
97.70
108.57
106.99
95.88
81.34
93.79
0-75
128-511
258 - 684
292-711
56 - 432
21 - 340
1,564 - 1,931
                 Facilities Processing at least 1 Million Ibs Total Laundry per Year
                        and/or at least 255,000 Ibs Printer and Shop Towels
1,000,000 - 4,999,999
5,000,000 - 9,999,999
10,000,000 - 19,999,999
20,000,000 - 29,999,999
^30,000,000
Overall*
18
41
59
26
28
172
209
471
502
244
181
1,606
92.38
108.57
106.99
95.88
81.34
95.63
27- 390
258 - 684
292-711
56 - 432
21 - 340
1,418-1,793
                   Facilities Processing < 1 Million Ibs Total Laundry per Year
                           and < 255,000 Ibs Printer and Shop Towels
< 1,000,000
1,000,000-4,999,999
Overall*
4
17
21
31
111
141
22.54
35.35
39.79
0-75
41 - 180
63 - 219
* Overall estimates may not equate to the sum of the domains due to rounding
                                              3-14

-------
                                        CHAPTER 4
     ANALYTICAL DATA COLLECTION EFFORTS AND DEFINITION OF OPTIONS
Description of Data Sources

The data used to calculate the proposed pretreatment standards for existing sources (PSES) and
pretreatment standards for new sources (PSNS) limitations were collected from the following two
sources: (1) the EPA wastewater sampling effort and (2) the self-monitoring data submitted by the
facilities in response to the detailed monitoring questionnaire. The EPA wastewater sampling effort
resulted in a database containing the results of intensive sampling efforts conducted between February
1993 and April  1997 at 8 facilities. The self-monitoring data were supplied by 37 sites in the 1995
Detailed Monitoring Questionnaire (DMQ).

A listing of the data used to support PSES and PSNS standards development can be found in
Appendices A.I and A.2.
4.1 EPA Wastewater Sampling                             ;

The EPA wastewater sampling effort consisted of five 24-hour composite samples collected at each of
the 8 facilities.  For most EPA-sampled facilities, five analytical data values were available for each
pollutant at each sampling point. Extensive documentation of the data quality reviews can be found in
Chapter 9 of the Technical Development Document for Proposed Pretreatment Standards for Existing
and New Sources for the Industrial Laundries Point Source Category (EPA Report No.  EPA-821-R-97-
007, DCN L04197).

4.2 Detailed Monitoring Questionnaire

The EPA requested industrial laundry (IL) facilities to submit wastewater monitoring data in the form
of individual daily data points, henceforth referred to as DMQ data. These data were reviewed and,
where sufficient requirements were met, included in the calculation of LTAs, VFs, and limitations.
Because the EPA labs did not analyze these samples, data points in which inconsistencies existed
among, the detection limit were excluded from calculations.      ,
4.3 Definition of Proposed Options

During the site visit and field sampling phases of the rule development, and during follow-up to
responses in the detailed questionnaire, three major technologies were identified for further evaluation
for use in developing regulatory options.  These major technologies are Chemical Emulsion Breaking
(CEB), Dissolved Air Flotation (DAF), and Chemical Precipitation (CP).

CEB is used primarily to remove oil and grease, as well as other related pollutants, from process
wastewater streams.  CEB is effective in treating wastewater streams having stable oil-in-water
emulsions. CEB is also used for the treatment of heavy industrial wastewater, which consists of
wastewater from the washing of heavily soiled items (e.g., shop towels) and wastewater from certain
breaks (i.e., wash water, first rinse, etc.) in the washing cycles for other items that contain high

                                             4-1           ;

-------
 concentrations of pollutants. The treatment consists of lowering the pH of the wastewater to break the
 emulsions, adding chemical flocculents, and skimming the surface of the water to remove the floating
 substances.  Under this option, the heavy industrial wastewater is treated by CEB, combined with the
 untreated wastewater from the rest of the facility, and discharged.

 DAF is used to remove suspended solids, oil, and some dissolved pollutants from process wastewater.
 DAF treatment involves coagulating and agglomerating the solids and oil and grease and then floating
 the resulting flocculents to the surface using pressurized air injected into the unit. Then, the floating
 material is removed. Some DAF systems also have the means to remove material which settles to the
 bottom of the tank without shutting down for maintenance.

 CP is used to remove dissolved pollutants from process wastewater. Precipitation aids, such as lime,
 work by reacting with the ions (e.g., metals) and some anions to convert them into an insoluble form
 (e.g., metal hydroxides).  The pH of the wastewater also affects how much pollutant mass is
 precipitated, as pollutants precipitate more efficiently at different pH ranges.  Coagulation and
 flocculation aids are usually added to facilitate the formation of large agglomerated particles that settle
 more readily and can be removed from the bottom of the clarifiers.

 Along with these major technologies, regulatory options were developed utilizing stream splitting, a
 common practice at some facilities.  Stream splitting provides a means of treating a portion of the total
 wastewater generated at industrial laundries. Stream splitting may be used to isolate and treat a stream
 with a higher pollutant load, while a stream  with a lower load is either recycled and reused or
 discharged to the Publicly-Owned Treatment Works (POTW) without treatment.  A divided trench and
 sump system is used to split process wastewater streams.

 EPA evaluated these technology options (along with splitting the streams for these options) and
 proposed PSES and PSNS pretreatment standards based on the CP technology option.  The proposed
 technology options are discussed in greater detail in Chapter 9 of the Technical Development Document
for Proposed Pretreatment Standards for Existing and New Sources for the Industrial Laundries Point
 Source Category (EPA Report No. EPA-821-R-97-007, DCN L04197)  along with the justification for
 selection of this option.  Pretreatment standards based on DAF are included here since the EPA is
 soliciting comment on a combined option where facilities currently using DAF would receive
 limitations developed using DAF data. All other facilities would receive limitations based on CP.

 EPA proposed pretreatment standards for the following pollutants:

 •   Non Conventional -  Silica Gel Treated - Hexane Extractable Material (SGT-HEM)
 •   Metals - Copper, Lead, Zinc
 •   Organics - Bis(2-Ethylhexyl) Phthalate, Ethylbenzene, Naphthalene, Tetrachloroethene,
    Toluene, M-Xylene, O&P-Xylene
                                              4-2

-------
                                        CHAPTERS
                         DESCRIPTION OF DATA CONVENTIONS
This section discusses the types of data in the IL analytical database and the hierarchy and procedures
for aggregating multiple sampling observations within a sampling day.
5.1 Data Review                                            ;

The EPA wastewater sampling data hi the analytical database were thoroughly reviewed and validated
by the EPA's Sample Control Center (further discussions of this data are at times referred to as the
"SCC" data for this reason). During this review, the integrity of each sample was assessed to ensure
that all specifications of the sampling protocol were met. The reviewers determined that some samples
should be excluded from the analyses.  Samples with flags of "EXCLUDE" or "DETECTED," which
indicate a value was detected but the concentration value was not recorded, were excluded from the
analyses.                                                    '.

Also during the data review, several samples were qualified with a, greater than (>) sign, indicating the
reported concentration value is considered a lower limit of the actual value.  This is because the
reported concentration was outside the range of the analytical method. When possible, these samples
are diluted and reanalyzed. Otherwise these samples were handle4 as right-censored samples and
excluded from all calculations.                                 !

An engineering review of the database was also conducted and a few additional data values were
excluded from the analyses for the reasons summarized in Chapter 9 of the Technical Development
Document for Proposed Pretreatment Standards for Existing and New Sources for the Industrial
Laundries Point Source Category (EPA Report No. EPA-821-R-97-007). One reason for such an
exclusion would be if a pollutant was not detected in sufficient concentrations to evaluate treatment
effectiveness.                                                :
5.2 Data Types

The IL analytical database (from the SCC and DMQ data) contains the following three different types
of samples delineated by certain qualifiers in the database:        :

•  Non-censored (NC): a measured value, i.e., a sample measured above the level at which the
    detection decision was made.

•  Non-detect (ND):  samples for which analytical measurement ;did not yield a concentration above
    the sample-specific detection limit.

•  Right-censored (RC):  samples qualified with a greater than (>) sign, signifying that the reported
    value is considered a lower limit of the actual concentration. All RC values were excluded from
    the analyses because these values could not be quantified with certainty.
                                              5-1

-------
5.3  Data Aggregation

Data aggregation for the IL analytical data was performed at two levels. This section discusses the
different levels and approaches for data aggregation, including multiple grab samples (one or more
samples collected for a particular sampling point over time,  assigned different sample numbers, and
not physically composited) and field duplicates (one or more samples collected for a particular sampling
point at approximately the same time, assigned different sample numbers, and flagged as duplicates for
a single episode number).

5.3.1 Data Aggregation Across Multiple Grab Samples

The first type of data aggregation performed was for multiple grab samples. Within the SCC database,
SGT-HEM was reported as concentrations of multiple grab samples taken during one-day sampling
periods.  Since long-term averages (LTAs) and limitations were based on daily concentrations, multiple
observations on a single day at the same sample point were averaged.  When all of the samples in a set
were NC, i.e., detected samples,  the arithmetic average of the samples was straightforward. However,
when one or more  of the samples were censored, or ND, multiple grab samples were aggregated within
each sampling day/sample point combination using the methods identified in Table 5-1.

                                          Table 5-1
                         Method for Averaging Multiple Grab Samples
If observations are:
AUNG
A11ND
NCandND
1. Max. NC >
Max. Detection Limit
2. Max. NC s
Max. Detection Limit
J Lalbefof
"average"
NC
ND

NC


ND
, Value of "average** fet '
SNQ/n
Maximum Detection Limit

(SNC; +SND;)/n


Max. Detection Limit
          n=number of grab samples per day.

5.3.2 Aggregation of Field Duplicates

Another type of data aggregation for the IL SCC data was performed due to the identification of field
duplicates in the database.  The field duplicates are defined as one or more samples collected for a
particular sampling point at approximately the same time, assigned different sample numbers, and
flagged as duplicates for a single episode number/sampling point.  Duplicates were collected for
purposes of quality assurance/quality control. Table 5-2 presents the methods used to aggregate
duplicates.  Note that within the DMQ data no field duplicates were labeled, but for a few sample days,
two concentrations were reported.   Since there were only two concentrations reported within sample
day, the aggregation method would be the same regardless of whether they were treated as grab
samples or duplicate samples. Thus, these concentrations were classified as duplicate samples and were
aggregated according to the methods outlined hi Table 5.2.
                                             5-2

-------
Listings of summary statistics following aggregation of grabs and field duplicates are presented in
Appendix B.I for all regulated pollutants in DAF (Option 3A), and in Appendix B.2 for all regulated
pollutants in CP (Option 3B).
                                          Table 5-2
                        Method for Averaging Field Duplicate Samples
* ff. *!»**,<• **,{*• „.*->, 1
I , , •> f *•
* «*• V, J I' ' * '
If observations are: * * ^ * '- -
BothNC
BothND
NCandND
1. NC > Detection Limit
2. NC <; Detection Limit
^ " ^*> ? ^ s^ -s * ^
Label of ^ .
^average"
NC
ND
NC
ND
•v-~~ * ;r " .-.V " - ^i^ - '
~ > * ^j-1--"^ ^ ^ _ ^"
Value of^ayerage^ist ^ ^^.
SNQ/2
Maximum Detection Limit
(NC + ND)/2
Detection Limit
  NC = non-censored values                                 ;
  ND = non-detected values

  If a sample had both multiple grabs and field duplicates, the multiple grabs were aggregated first.
                                              5-3

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                                        CHAFFER 6
       STATISTICAL METHODOLOGY - MODIFIED DELTA-LOGNORMAL MODEL
6.1 Basic Overview of Delta-lognormal Distribution

The lognormal distribution is often appropriate for modeling effluent data.  However, the presence of
ND and very low concentration measurements in the IL effluent data led to the consideration of a
modification to the lognormal distribution in modeling such data for several reasons.  First, the
lognormal model assumes that all concentration values are positively-valued.  Second, the actual values
of NDs are not known, though each ND has a concentration somewhere between zero and the reported
detection limit.  In this sense, ND measurements represent, in statistical terms, what are known as
censored samples.                                          ''

In general, censored samples are measurements for which the exact value is not known but are bounded
either by an upper or lower numerical limit. Non-detects qualify in this framework as left-censored
samples,  which have an upper bound at the detection limit and a lower bound at zero. To model NDs
as left-censored samples under a strictly lognormal density model, it is necessary to assume that the
exact (but unknown) values of these measurements follow the same lognormal distributional pattern as
the rest of the detected measurements and that they are positively-valued (i.e., greater than zero).

Therefore, two reasonably simple modifications to the lognormal density model have been used by the .
EPA for several years. The first modification is known as the classical delta-lognormal model (Figure
6-1), first used in economic analysis to model income and revenue patterns (see Atchison and Brown,
1955). In this adaptation of the simple lognormal density, the model is expanded to include zero
amounts.  To do this, all positive (dollar) amounts are grouped together and fit to a lognormal density.
Then all zero amounts are segregated into another group of measurements representing a discrete
distributional  "spike" at zero.  The resulting mixed distribution, combining a continuous density
portion with a discrete-valued spike, is known as the delta-lognormal distribution.  The delta in the
name refers to the percentage of the overall distribution contained in the spike at zero, that is, the
percentage of zero amounts.                                 :

                                         Figure 6-1       ;
                                   Delta-lognormal Model
     Non-Detects
                                                 Detects
                                             6-1

-------
 Researchers at the EPA (see Kahn and Rubin, 1989) further adapted the classical delta-lognormal
 model ("adapted model") to account for ND measurements in the same fashion that zero measurements
 were handled in the original delta-lognormal.  Instead of zero amounts and non-zero (positive)
 amounts, the data consisted of NDs and detects.  Rather than assuming that NDs represented a spike of
 zero concentrations, these samples were allowed to have a single positive value, usually equal to the
 minimum level of the analytical method (Figure 6-2).  Since each ND was assigned the same positive
 value, the distributional spike in this adapted model  was located not at zero, but at the minimum level.
 This adaptation is appropriate since it is known that  the NDs are some value greater than zero.  This
 adapted model was used in developing limitations for the Organic Chemicals, Plastics,  and Synthetic
 Fibers (OCPSF) and pesticides manufacturing rulemaking.

                                          Figure 6-2
                                Adapted Delta-lognormal Model
                                                    Detects
       Non-Detects
             0 S101S20
In the adapted delta-lognormal model, the delta again referred to those measurements contained in the
discrete spike, this time representing the proportion of ND values observed within the data set. By
using this approach, computation of estimates for the population mean and variance could be done
easily by hand, and NDs were not assumed to follow the same distributional pattern as the detected
measurements. The adapted delta-lognormal model can be expressed mathematically as follows:
               Pr (Uzu) =
(1-6) 3> [(log(M) - ^)/a]      if  o< M < D
 6 + (1 -8) $ [(logCD)  - (i)/qj    if  u  = D
 6 + (1-6) $ [(log(w)  - n)/o]    if  u>  D
(6.1)
where 6 represents the true proportion of NDs (or the probability that any randomly drawn
measurement will be a ND), D equals the minimum level value of the discrete spike assigned to all
NDs, <£(•) represents the standard normal cumulative distribution function, and p and o are the
parameters of the lognormal density portion of the model. This model assumes that all non-detected
values have a single detection limit D.

It is also possible to represent the adapted delta-lognormal model in another mathematical form, one in
which it is particularly easy to derive formulas for the expected value (i.e., LTA) and variance of the
model. In this case, a random variable distributed according to the adapted delta-lognormal distribution
can be represented as the stochastic combination of three other independent random variables.  The
first of these variables is an indicator variable, !„, equal to one when the measurement u is a ND and

                                             6-2

-------
equal to zero when u is a detected value.  The second variable, XD, represents the value of a ND
measurement (discrete). In the adapted delta-lognormal, this variable is always a constant equal to the
concentration value assigned to each ND (i.e., equal to D in the adapted delta-lognormal model).  In
general, however, XD need not be a constant, as will be seen below in the modified delta-lognormal
model. The final random variable, Xc, represents the value of a detected measurement, and is
distributed according to a lognormal distribution (continuous) with parameters /* and o.

Using this formulation, a random variable from the adapted delta-lognormal model can be written as:

                                U = IaXD +(1-/UKC        ;                             (6.2)
and the expected value of U is then derived by substituting the expected value of each quantity in the
right-hand side of the equation.  Because the variables !„, XD, and Xc are mutually independent, this
leads to the expression
             E(U) =
                                                   (l-6)exp(|i + 0.5 o2)
     (6.3)
where again 8 is the probability that any random measurement will be ND and the exponentiated
expression is the familiar mean of a lognormal distribution.  In a similar fashion, the variance of the
adapted delta-lognormal model can be established by squaring the expression for U above, taking
expectations, and subtracting the square of E(U) to get:
Var(U) =
                                             -&)Var(Xc)
    (6.4)
Since, in the adapted delta-lognormal formulation, XD is a constant, this expression can be reduced to
the following:                                                \
     Var(U) = (l-8)exp(2|i+CF2)[exp(a2)-(l-8)] + 8(1-8)£>[D -2exp(|i +O.S02)].
                                                                                   (6:5)
In order to estimate the adapted delta-lognormal mean and variance from a set of observed sample
measurements, it is necessary to derive sample estimates for the parameters 8, /*, and o. 6 is typically
estimated by the observed proportion of NDs in the data set.  \i and a are estimated using the log values
of the detected samples where ^ is estimated using the arithmetic mean of the log detected
measurements and a is estimated using the standard deviation of these same log values; NDs are not
included in the calculations. Once the parameter estimates are obtained, they are used in the formulas
above to derive the estimated adapted delta-lognormal mean and viariance.

To calculate effluent limitations and/or standards, it is also necessary to estimate upper percentiles from
the underlying data model. Using the delta-lognormal formulation above in equation (6.1), letting U0
represent the lOO*^ percentile of random variable U, and adopting the standard notation of zs for the
8th percentile of the standard normal distribution, an arbitrary delta-lognormal percentile can be
expressed as the following:
U«  =
 exp(n +0"
      D
exp(n+a i
                                     if
                                     if
                                     if
                                            8+(l-S)((log(D)-n)/0)
(6.6)
                                               6-3

-------
The daily maximum limitations are established on the basis of an estimated upper 99th percentile from
the underlying data model, so that 0.99 would be substituted for a in the above expression. To derive
the daily VF for the 99th percentile based on the adapted delta-lognormal model, divide U-99 in the
expression above by the previous formula for the LTA, namely U-99/E(U).
6.2 Motivations for Modifications to the Adapted Delta-Lognormal Model

While the adapted delta-lognormal model has been used successfully for years by the EPA in a variety
of settings, the model makes two key assumptions about the observed data that are not fully satisfied
within the IL analytical database. First, the discrete spike portion of the adapted delta-lognormal model
is a fixed, single-valued probability mass associated (typically) with all ND measurements.  If all ND
samples in the IL database had roughly the same reported detection limit, this assumption would be
adequately satisfied.  However, the detection limits reported are sample specific and, therefore, varied
as a result of factors such as dilution. Because of this variation in detection limits, a single-valued
discrete spike could not adequately represent the set of ND measurements observed  in the IL database
and a modification to the model was considered.

In addition, the adapted delta-lognormal model sets all NC values below the detection to the minimum
level of the analytical method. For example, if the minimum level for Toluene  was  .10 mg/1, then any
NC samples reported below .10 mg/1 were set to .10 mg/1.  There were a few instances in the IL
analytical studies where a NC value was reported below the minimum level of the analytical method.

6.2.1 Modification of the Discrete Spike

To appropriately modify the adapted delta-lognormal model for the observed IL database, a
modification was made to the discrete, single-valued spike representing ND measurements.  Because
ND samples have varying detection limits, the spike of the delta-lognormal model has been replaced by
a discrete distribution made up of multiple spikes.  Each spike in this modification is associated with a
distinct detection limit observed in the IL database.  Thus, instead of assigning all NDs to a single,
fixed value, as hi the adapted model, NDs can be associated with multiple values depending on how the
detection limits vary (Figure 6-3).

                                          Figure 6-3
                           Modified Adapted Delta-lognormal Model
                                                Detects
   Non-Detects
         \
         0 5101520
                                              6-4

-------
. In particular, because the detection limit associated with a ND sample is considered to be an upper
 bound on the true value, which could range conceivably from zero up to the detection limit, the
 modified delta-lognormal model used here assigns each ND sample to its reported detection limit.

 Once each ND has been associated with its reported detection limit, the discrete "delta" portion of the
 modified model is estimated in a way similar to the adapted delta-lognormal distribution, where
 multiple spikes are constructed and linked to the distinct detection Jimits observed in the data set. In
 the adapted model, the parameter 6 is estimated by computing the proportion of NDs. In the modified
 model, 8 again represents the proportion of NDs, but is divided into the sum of smaller fractions, 6;,
 each representing the proportion of NDs associated with a particular and distinct detection limit. This
 can be written as:                                             ;
                                          v-\ *
                                                                                           (6.7)
 If Dj equals the value of the i* smallest distinct detection limit in the data set, and the random variable
 X represents a randomly chosen ND sample, then the discrete distribution portion of the modified
 delta-lognormal model can be mathematically expressed as:

                                                                                            (6.8)
 The mean and variance of this discrete distribution can be calculated using the following formulas:

                                  and     Var(XD)  = -?-£ £  i6/6/^/ " D¥-                (6.9)
 It is important to recognize that, while replacing the single discrete spike in the adapted delta-lognormal
 distribution with a more general discrete distribution of multiple spikes increases the complexity of the
 model, the discrete portion with multiple spikes plays a role in limitations and standards development
 identically parallel to the single spike case and offers flexibility for handling multiple observed
 detection limits.
                                                6-5

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-------
                                        CHAPTER 7
          ESTIMATION UNDER THE MODIFIED DELTA-LOGNORMAL MODEL
Once the modifications to the adapted delta-lognormal distribution!are made, it is possible to fit a wide
variety of observed effluent data sets to the modified model. Multiple detection limits for NDs can be
handled.  The same basic framework can be used even if there are no ND values or censored data.
                                                          I
Combining the discrete portion of the model with the continuous portion, the cumulative probability
distribution of the modified delta-lognormal model can be expressed as follows, where Dn denotes the
largest distinct detection limit observed among the NDs, and the first summation is taken over all those
values, Dj, that are less than u.                               :
                                           [(log(w)-u)/a)]   if  u
-------
7.1 Facility-Specific Estimates

7.1.1 Estimation of Facility-Specific LTAs

For the purposes of estimating facility-specific LTAs (equal to the expected value in the equation
(7.3)), the EPA chose to divide the IL data sets into two groups based on their size (number of samples)
and the type of samples in the subset because the computations differ for each group. The groups were
defined as follows:

    Group 1:  Less than 2 NC samples or less than 4 total samples.

    Group 2:  Two or more NC samples or 4 or more total samples.

For Group 1, the LTAs were calculated as the arithmetic average of the samples, since the sample sizes
for either the discrete portion or the continuous lognormal portion of the data were too small to allow
distributional assumptions to be made. Specifically, Group 1 contained all data subsets with all NDs or
only one detect.  Sample-specific detection limits were substituted as the values associated with non-
detectable samples.

For Group 2, the LTAs were calculated using the procedures outlined in the preceding section using
equation (7.3) and the Maximum Likelihood Estimates (MLEs) for \L and a.

7.1.2 Estimation of Facility-Specific VFs

After determining estimated LTA values for each pollutant, facility, and option combination, the EPA
developed 1-day variability factors (VF1) and/or 4-day variability factors (VF4) depending on the
proposed frequency of monitoring,  as outlined in Table 7-4..

                                          Table 7-1
                            EPA Proposed Monitoring Frequencies
' ' - "- ^ -^- ,, ?,yX
Pollutant Category
Metals, Organics
Classicals
I ' „ "" - " I If *!"
Frequency of Monitoring , •
Monthly (VF1)
Weekly (VF1, VF4)
Similar to the calculations for the LTAs, the data were divided into the same two computation groups
based on the number and type of samples in each data subset for purposes of estimating variability
factor. These computation groups are defined as follows:

    Group 1:  Less than 2 NC samples or less than 4 total samples.  Upper percentiles and VFs could
              not be computed using the modified delta-lognormal methodology.

    Group 2:  Two or more NC samples and 4 or more total samples. The estimates of the
              parameters for the modified delta-lognormal distribution of the data were calculated
              using maximum likelihood estimation in the log-domain. Upper percentiles and VFs
              were calculated using these estimated parameters.
                                             7-2

-------
Several data subsets belong in Group 1, and therefore have missing 99th percentiles and VFs.
7.1.2.1 Estimation of Facility-Specific VF1

The VF1 are a function of the LTA, E(U), and the 99th percentile.  An iterative approach was used in
finding the 99th percentile of each data subset using the modified delta-lognormal methodology by first
defining D0=0, 50=0, and Dk+1 = «> as boundary conditions, where D; equals the i* smallest detection
limit, and 8; is the associated proportion of NDs at the r* detection limit. A cumulative distribution
function, p, for each data subset was computed as a step function ranging from 0 to 1.  The general
form, for a given value c, is
              = E V. 0.99, was determined and labeled as PJ. If no such m
      existed, steps 3 and 4 were skipped and step 5 was computed instead.
                                                           i
  3.  Computed p* = pj - 6j.                                          .

  4.  If p* < 0.99, thenPj, = Dj?
      else if p* .>_ 0.99, then
            P99=exp
                                    (1-6)
                                                                                         (7.6)
  5.  If no suchmexists, such thatpmj>. 0.99 (m=l,...k), then
                                                           !
                                   0.99-6
                                    (1-6)
                                                                                         (7.7)
The daily variability factor, VF1, was then calculated as

                                 P99
                          VF1 =
                                 E(U)
(7.8)
                                             7-3

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7.1.2.2 Estimation of Facility-Specific VF4
Since the EPA is assuming for costing purposes that the Classical Pollutant, SGT-HEM, will be
monitored weekly (approximately 4 times a month), the EPA calculated a VF for monthly averages
based on the distribution of 4-day averages.  In order to calculate the VF4, the assumption was made
that the approximating distribution of U4, the sample mean for a random sample of 4 independent
concentration values, is also derived from this modified delta-lognormal distribution, with the same
mean as the distribution of the concentration values. The mean of this distribution of 4-day  averages is

                                                                                         (7.9)
where (X^ denotes the mean of the discrete portion of the distribution of the average of four
independent concentration values (i.e., when all observations are not detected), and (X4)c denotes the
mean of the continuous lognormal portion of the distribution.

First, it is assumed that the probability of detection (8) on each of the four days is independent of that
on the other days, since these samples are not taken on consecutive days and are therefore not
correlated such that 84 = S4. Also, since

                                          ~4)0 = E(XD)
then

                                                                                        (7.10)
and since E(04) = E(U), then



                         V-4 = log
                                            7=1
                                       d-64)
                                                    -0.502,.
                                                                                        (7.11)
The expression for o?4 was derived from the following relationship:
                                                                                        (7.12)
Since
                                                          and
                                                                                        (7.13)
then
                                              7-4

-------
= 54
                       64(1 -
                                                                                  (7.14)
This further simplifies to
482
              •64(l-64)
                        7=1
and furthermore,
exp(024)-l =
Then, from (7.10) above,
 exPGi4+0.5o24)=-
                                       (l-84)exp(2u4
                      k



                      j=l
                       (1-84)
and letting
                    -64)
                                    then,  exp^+O.So2,)  =
                            -,   since E(U.) =E(U)

Furthermore,
1 + •
*££w,-*
4 4
(1
(1
92 82(1 84)(^6Z> 6T» I'
[w ' '' (1-84)J
-8V
-54)2
                                                                                  (7.15)
*EEa,»/J>,-^
4 4
S2n frh
u V.* u J
* I2
^ A exp(u4+ . 04)
                                                                                  (7.16)
                                                                                  (7.17)
                                                        (7.18)
                                                                                  (7.19)
                                          7-5

-------
Since Var(04) = Var(U)/4, then, by rearranging terras,
 024 =  log
4T12

                                                 4T12
                                                                         /=!
                                                                                          (7.20)
Thus, estimates of /*4 and 04 were derived by using estimates of S^..^ (sample proportion of NDs at
observed detection limits D^.-.Dk), p. (MLE of logged values), and o2 (MLE logvariance with sample
bias adjustment) in the equations above.

In finding the estimated 95th percentile of the average of four observations (four NDs, not all at the
same detection limit), an average can be generated that is not necessarily equal to Dj, D2,..., or Dk.
Consequently, more than k discrete points exist in the distribution of the 4-day averages.  For example,
the average of four NDs at k=2 detection limits are at the following discrete points with the associated
probabilities:
         1
         2
         3
         4
         5
                                       (3£V
                                       (2Z>,+22>2)/4
                                                            V
In general, when all four observations are not detected, and when k detection limits exist, the
multinomial distribution can be used to determine associated probabilities, that is,
                        Pr
     ^4 =
                                1=1
                           4!
                                                                                          (7.21)
The number of possible discrete points, k*, for k= 1 ,2,3 ,4, and 5 are given below:
        1
        2
        3
        4
        5
                                     1
                                     5
                                     15
                                     35
                                     70
                                               7-6

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To find the estimated 95th percentile of the distribution of the average of four observations, the same
basic steps (described in Section 7.1.2.1) as used for the 99th percentile of the distribution of daily
observations were followed with the following changes:          \

  1.  Change Pj, to P9S, and 0.99 to 0.95.
  2.  Change Dm to Dm*, the weighted averages of the detection limits.
  3.  Change 8; to 8;*.
  4.  Change k to k*, the number of possible discrete points based on k detection limits.
  5.  Change the estimates of 8, p, and 0 to estimates of 84,  jtt4, and a4, respectively.
Then, the estimate of the 95th percentile 4-day mean VF is:

                             P95
                     VF4 =
                                         since
                             E(U)
j) = E(U).
(7.22)
Appendices C.I and C.2 display LTAs, VF1, and VF4 by analyte and facility for DAF and CP,
respectively.


7.2 Pollutant-Specific Estimates                             j

7.2.1 Estimation of Pollutant-Specific LTAs

After estimating the facility-specific LTA for each pollutant and option, as described in section 7.1.1,
pollutant-specific LTAs were calculated.  Within each option, the pollutant specific LTAs were
calculated as the median of the facility-specific LTAs for that pollutant.

7.2.2 Estimation of Pollutant-Specific VFs                   \

7.2.2.1 Estimation of Pollutant-Specific VF1

After the facility-specific VF1 were estimated for each pollutant and option, as described in section
7.1.2.1,  the pollutant-specific VF1 was calculated. The pollutant-specific daily VF was the median of
the facility-specific daily VFs for that pollutant in the option.

7.2.2.2 Estimation of Pollutant-Specific VF4

After the facility-specific VF4 were estimated for each pollutant and option, as described in section
7.1.2.2,  the pollutant-specific VF4 was calculated. The pollutant-specific VF4 was the median of the
facility-specific VF4 for that pollutant in the option.
                                              7-7

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                                       CHAPTERS
                     DERIVATION OF THE PROPOSED STANDARDS
The proposed daily maximum limitations for each pollutant were calculated as the product of the
pollutant-specific LTA and the pollutant-specific daily VF.  Similarly, the proposed 4-day limitation for
SGT-HEM was calculated as the product of the pollutant-specific LTA and the pollutant-specific VF4.
Limitations for CP are presented in Table 8-1.

                                         Table 8-1
                        Daily and 4-day Limitations for CP Option 3B
Pollutant , , ' ^ ^
?• i- t
'- *--^ >. , _ ' '*.>/> „
1 =• ' *•?,< f t a-- ..•. •>
„-„ ^ ^, >t \, rf
SGT-HEM
Copper
Lead
Zinc
O+P Xylene
M-Xylene
Ethylbenzene
Naphthalene
Bis (2-Ethylhexyl) Phthalate
Tetrachloroethane
Toluene
Daily Limit ;
,(mg/L) ^ ;
"" v^
27.5
.24
.27
.61
.95
1.33
1.64
.23
.13
1.71
2.76
Monthly
"A-Terage ^
(nfgflb)* ^
12.7
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
              *Based on an assumption for costing purposes of 4 days of sampling per month.

Appendices D.I and D.2 present pollutant-specific LTAs, VF1 and VF4, and daily and 4-day
limitations for DAF and CP, respectively.                      i
                                            8-1

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                                         CHAPTER 9       !
                 RAW WASTEWATER CONCENTRATION COMPARISONS
9.1 Comparison of Industrial Laundry Influent and Linen Influent

Statistical analyses were conducted to assess whether the mean influent concentrations of 98 pollutants
of concern differed significantly by type of laundry facility. The EPA wanted to compare linen influent
concentrations to industrial laundry influent concentrations. Unfortunately, none of the raw wastewater
data available was for 100% linen items. Therefore, the EPA used facilities that were mostly linen (i.e.
between 60% and 99%  linen). The data from these facilities will be referred to as linen wastewater and
the wastewater from facilities doing mostly industrial laundry items will be referred to as industrial
laundry (IL). Appendix E.I lists the facility, sample point, and data source information used in this
analysis.

It was observed that not all of the 98 pollutants of concern had both IL and linen information.  Also for
several pollutants, only one linen facility was reported. Furthermore, only those facilities which
reported at least 3 concentrations were included in the  analyses.  The following pollutants had sufficient
data for analyses (at least 2 linen and IL facilities with at least 3 reported concentrations):  BOD,
Cadmium, COD,  Chromium, Copper, Lead, Nickel, Silver, Total Recoverable Oil and Grease, Total
Suspended Solids, Zinc, and pH.

For each of the pollutants with sufficient data, a comparison was made between wastewater
concentrations reported by facilities within IL and linen supply, respectively.  Differences in pollutant
wastewater concentrations within IL  and linen facilities were observed for some pollutants. These
pollutants were not considered further in the analysis to determine if IL wastewater concentrations
differ significantly from linen wastewater concentrations. Thus, the list of pollutants for which
comparisons would be made was reduced from 14 to 8.

Table 9-1 displays results from the analysis of variance (ANOVA) which was used to compare the
mean log concentration (log(conc)) between the linen facilities. Results from this analysis indicated that
the mean log(conc) between linen facilities differed significantly for pollutants BOD, COD, Lead,
Silver, and Nickel at  a=0.01.                                 •
                                              9-1

-------
                                          Table 9-1
                           ANOVA Results for Linen Comparisons
Analyte . " ".^
$, ^ 4» " ~"
BOD
COD
TPH (as SGT-HEM)
Total Recoverable
Oil and Grease
Total Suspended Solids
pH
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Silver
Zinc
K *^
Number
of J
Facilities
3
2
2
3
3
4
4
4
4
2
4
4
3
4
Number,of ;
Concentration
Values
9
7
5
8
9
10
15
15
15
5
15
15
13
17
p-value
"" ^
0.0013
0.0068
0.2732
0.0202
0.1345
0.0713
0.2733
0.1567
0.4553
0.1776
0.0054
0.0017
0.0041
0.1070
Signn1<-apt;at >f" >
f -a=0.dl f'X
Yes
Yes
No
No
No
No
No
No
No
No
Yes
Yes
Yes
No
Table 9-2 displays results from the ANOVA analysis which was used to compare the mean log(conc)
between the IL facilities. Results from this analysis indicated that the mean log(conc) between IL
facilities differed significantly for pollutants pH, Nickel, and Silver at a=0.01.
                                             9-2

-------
                                          Table 9-2
                             ANOVA Results for EL Comparisons
ABaayte^ • - *- *"*'*
» * > ' ' ' * •»*••« 4
,, M. '•* " 5 .« -.' > '"*''
, s ' - . ,. ~ V*' **?,-*
.Jk>- ,! _ H „ _ >• „•>•
BOD
COD
TPH (as SGT-HEM)
Total Recoverable
Oil and Grease
Total Suspended Solids
pH
Cadmium
Chromium
Copper
Iron
Lead
Nickel
Silver
Zinc
f. V". ifcJSj«- C j^
rNumfter-'af . ,-•
_ «_»!» W1 ' -
^Facilities
• *. -^S^,1"
u, » ^ , C'
6
6
5
2
6
6
6
6
6
6
6
6
6
6
rf 's i. ?* —
Nppberof ;
Coiicentration
•^ ^ 'i )• M
"Values •# «t
33
34
30
8
34
33
34
34
34
34
34
34
34
34
p-fahie"
.' !*', •-
. ^ *^ i
\
i0.0252
b.1084
0.3625
0.4317
0.1543
0.0002
0.5284
0.0364
0.1385
0.1971
0.1945
0.0065
0.0001
0.7447
** «* h v -jp-
i Significant " '»
at -«=OJi« j,
j, k e N> Vf^fei, ^
^ & "'-•^^ £ * •> ^
No
No
No
No
No
Yes
No
No
No
No
No
Yes
Yes
No
Pollutants in which the significance level exceeded a=0.01 for either linen facilities or IL facilities
were excluded from further analysis. Thus, comparisons of wastewater concentrations between IL
facilities and linen facilities were conducted for the following pollutants: Total Petroleum Hydrocarbon
(TPH), Total Suspended Solids, Total Recoverable Oil and Grease, Cadmium, Chromium, Copper,
Iron, and Zinc.

Table 9-3 displays results from the t-test analysis which was used to compare the mean log(conc) of the
linen daily wastewater concentrations to the mean log(conc) of the IL daily wastewater concentrations.
Results from this analysis indicated that the mean log(conc) of linen wastewater differed significantly
from the mean log(conc) of IL wastewater for pollutants Total Petroleum Hydrocarbon, Oil and
Grease, Total Suspended Solids, Cadmium, Chromium, Copper, Iron, and Zinc at a=0.01.
                                             9-3

-------
                                           Table 9-3
                      Comparison of Mean Pollutant Log Concentrations in
                                Linen Facilities vs. EL Facilities
Analyte
TPH (as SGT-HEM)
Total Recoverable Oil
and Grease
Total Suspended Solids
Cadmium
Chromium
Copper
Iron
Zinc
TvpeofFacSiiy~
j »»* k jt
"" „' s _
Industrial
Linen
Industrial
Linen
Industrial
Linen
Industrial
Linen
Industrial
Linen
Industrial •
Linen
Industrial
Linen
Industrial
Linen
Sample
Size
30
5
8
8
34
9
34
15
34
15
. 34
15
34
5
34
17
Mean' ^
log(conc)
6.05
2.64
7.18
4.56
7.10
5.08
-2.66
-4.33
.-1.47
-3.19
0.85
-1.54
3.23
1.00
1.47
1.15
Mean
Cone
425
14
1310
96
1206
161
.070
.013
.230
.041
2.32
0.21
25.2
2.71
4.16
0.32
p-value
' , f , ' "
0.0001

0.0012

< 0.000

0.0001

< 0.000

•< o.ooo

< 0.000

< 0.000

SignifiKwt
atla^O.ftL
Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Table 9-3 illustrates that for each of the analytes listed, IL wastewater concentrations are significantly
different from linen wastewater concentrations.  Also, note that the IL mean wastewater concentration
is consistently higher than the linen mean wastewater concentration.  Although the linen facilities used
were not 100% linen, EPA assumes that these results would hold if the proportion of linen items at
these facilities were even greater.


9.2  Comparison of Linen Influent to Denim Pre-Wash Influent

Statistical comparisons were conducted between denim prewash wastewater and linen wastewater to
determine if pollutant concentrations in untreated denim prewash wastewater were similar to the
pollutant concentrations in linen wastewater. Appendix E.2 lists the facility, sample point, and data
source information used in this analysis. Prior to comparing pollutant concentrations from denim
facilities to pollutant concentrations from linen facilities, it was first determined whether the pollutant
                                              9-4

-------
concentrations differed significantly across facilities among the linen facilities.  For each pollutant,
ANOVA was used to compare the mean log(conc) in each facility that reported two or more
concentrations among untreated linen facilities.                 :

Results indicated that the mean log(conc) for each linen facility differed significantly  at the a=0.01
significance level for pollutants BOD, COD, Nickel, and Lead.  Thus, the only pollutants that were
used in further analyses included: Cadmium, Chromium, Copper, Iron, Oil and Grease, Total
Suspended Solids, and Zinc. Note that the concentrations reported for Total Petroleum Hydrocarbon
did not differ significantly among linen facilities, but the denim prewash facility did not report
wastewater concentrations for this pollutant.

Influent concentrations were available for only one denim prewash facility. Because  of this, EPA was
unable to compare concentrations between denim prewash facilities to determine if there were
significant differences between influent concentrations for denim prewash facilities. Therefore, the
following results from the t-test analysis represent the comparison between linen facilities and the
sampled denim facility.

Table 9-4 displays results from the t-test analysis which was used to compare the mean log(conc) from
the untreated linen facilities to the mean log(conc) from the untreated denim facility.  Results indicated
that the mean log(conc) from untreated linen wastewater differed significantly from the mean log(conc)
from untreated denim wastewater for pollutants Cadmium, Chromium, and Copper (p < .01).  There
was no significant difference in the mean log(conc) from linen vs. denim wastewater  for Oil and
Grease, Total Suspended Solids, Iron, and Zinc.

                                           Table 9-4
                      Comparison of Mean Pollutant Log Concentrations in
                         Linen Facilities vs. Untreated Denim Facilities
^ \*r
Oil and Grease

Total Suspended
Solids
Cadmium

Chromium

Copper
Iron

Zinc

Type of lacfllfy^
: v >> i f ,t *
( *, „ -
Linen
Untreated Denim
Linen
Untreated Denim
Linen
Untreated Denim
Linen
Untreated Denim
Linen
Untreated Denim
Linen
Untreated Denim
Linen
Untreated Denim
Sample
^Size
8
7
9
15
15
13
15
13
15
13
5
12
17
8
Mean *
log(conc){.
4.56 :
2.96
5.08
6.15 '
-4.33
-5.68 :
-3.19 :
-4.47 :
-1.54 !
-2.85
1.00 !
-0.69
-1.15 I
-2.87 :
"Mean/
cone ^
95
19
161
470
0.013
0.003
0.04
0.01
0.21
0.06
2.71
0.50
0.32
0.06
p-value
-v '«il*
.018

.021
.0001

.0014

.001
.027

.114

Significant
Jat ceMUH
No

No
Yes

Yes

Yes
No

No

                                              9-5

-------
Thus, it was observed that the pollutant log(conc) for the analytes Cadmium, Chromium, and Copper
was significantly higher in untreated linen wastewater than in untreated denim prewash wastewater at
a—0.01. Similar concentrations (0.01) were reported in untreated linen wastewater and untreated
denim prewash wastewater for the analytes Oil and Grease, Total Suspended Solids, Iron, and Zinc.
                                             9-6

-------
APPENDICES A.I and A.2

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