# Landfill Loadings Calculations

For Solvent

- Contaminated Wipes

January 2012

Prepared for

U.S. Environmental Protection Agency Ariel Rios Building 1200 Pennsylvania Avenue, N. W. Washington, DC 20460

Prepared by

SAIC

121 00 Sunset Hills Road
Reston, VA 20190

EPA Contract No.: EP

- W

- 05

- 060, WA No. 3

- 06

### Table of Contents

Executive Summary

................................ ................................ ................................ .............................. i

1.0 Introduction to Landfill Loa ding Calculations

................................ ................................ ............... 1 1.1 How Does the Analysis Address Parameter Variability and Uncertainty?

................................ 1

1.2 What Chemicals are Considered ?

................................ ................................ .............................. 1 1.2.1 Chemical Use Restrictions of Ozone Depleting Substances

................................ ............... 3 1.2.2 Chemicals Not Known to be Used as Solvents in Wipe App lications

................................ 4 1.3 Input Parameter Assumptions

................................ ................................ ................................ .... 5 2.0 Solvent Loadings Calculations: Disposables

................................ ................................ ................. 6 2.1 How Can Solvents Enter the Environment from Disposable Wipes?

................................ ........ 6 2.2 Overview of Calculation Methodology

................................ ................................ ...................... 6 2.3 Data Used for Assessing Individual Variables

................................ ................................ ........... 9 2.3.1 Number of Towels or Wipes Used by a Single Generator

................................ .................. 9

2.3 .2 Quantity of Solvent in Disposed Wipe

................................ ................................ .............. 10 2.3.3 Percent of Wipes Containing Constituent

................................ ................................ ......... 12 2.3.4 Estimated Count of Spent Solve nt Disposable Wipes Generators

................................ .... 16

2.3.5 Percent of Constituent in the Solvent

................................ ................................ ................ 20 2.3.6 Number of Generators Disposing Towels or Wipes in a Single Landfill

......................... 21

2.4 Results

................................ ................................ ................................ ................................ ...... 21 2.4.1 Loading Estimates

................................ ................................ ................................ ............. 21 2.4.2 Sensitivity Analysis

................................ ................................ ................................ ........... 23 2.4.3 Effects of Input Parameter Distribution Assumptions on Results

................................ ..... 26

2.4.4 Uncertaintie s Associated with the Analysis

................................ ................................ ...... 29 2.4.5 Numerical Stability for Disposable Wipe Analysis

................................ .......................... 31 2.5 Solvent Extraction Technologies

................................ ................................ ............................. 31 3.0 Solvent Loadings Calculations: Laundries

................................ ................................ ................... 32 3.1 How Can Solvents Enter the Environment from Reusable Wipes?

................................ ......... 32 3.2 Overview of Calculation Methodology

................................ ................................ .................... 32 3.3 Data Used for Assessing Individual Variables

................................ ................................ ......... 34 3.3.1 Quantity of Shop and Printer Towels Washed by Each Laundry

................................ ..... 34 3.3.2 Fraction of Laundries Processing Printer Towels Versus Shop Towels

........................... 35

3.3.3 Concentration in Washwater

................................ ................................ ............................. 35 3.3.4 Quantity of Washwater Generated

................................ ................................ .................... 38 3.3.5 Sludge P artitioning

................................ ................................ ................................ ............ 39 3.3.6 Number of Laundries Disposing Sludge in a Single Landfill

................................ ........... 40

3.3.7 Estimates of Methanol Sent to Industrial Laundries

................................ ......................... 40

3.4 Results

................................ ................................ ................................ ................................ ...... 41 3.4.1 Loading Estimates

................................ ................................ ................................ ............. 41

3.4.2 Se nsitivity Analysis

................................ ................................ ................................ ........... 43 3.4.3 Effects of Input Parameter Distribution Assumptions on Results

................................ ..... 44

3.4.4 Uncertainties Associated wit h the Analysis

................................ ................................ ...... 47 3.4.5 Numerical Stability for Laundry Sludge Analysis

................................ ............................ 48

4.0 References

................................ ................................ ................................ ................................ .... 49 Appendix A. Supporting Data

................................ ................................ ................................ ........... 53 Appendix B. Data Values Used in Disposables Analysis

................................ ................................ . 64 Appendix C . Industry

- Specific Use of F

- Listed Solvents

– NESHAPs

................................ ............ 71 Appendix D. 2003 Biennial Report Data

................................ ................................ .......................... 75 Appendix E. Federal and St ate Right

- to

- Know Data for Evaluating Solvent Use

............................ 82

### Appendix F. Industrial Laundry Data

................................ ................................ ................................ 89 Appendix G. Review of Other Indust ry Data for Estimated Number of Generators

...................... 107 Appendix H. Input Parameter Distributions for Disposable Wipes Analysis

................................ . 109 Appe ndix I. Input Parameter Distributions for Laundry Sludge Analysis

................................ ...... 127 Appendix J. Bootstrap Estimates of 95% Confidence Intervals of Estimated Landfill Loadings

.. 146

### i Executive Summary

Purpose

A wide variety of industries use solvent containing wipes for cleaning and degreasing. The wipes may be used once or several times before they are thrown away, while other wipes are used, laundered, and reused. When d iscarded, spent wipes are considered hazardous waste under the federal hazardous waste regulations if the wipes exhibit a hazardous waste characteristic under 40 CFR 261, Subpart C or contain a solvent listed in 40 CFR 261.31.

To support a rulemaking to e xclude the wipes from the definitions of hazardous waste and solid waste, the Environmental Protection Agency (EPA) conducted a risk analysis. The analysis determines the potential risks from two solvent

- contaminated wipes management scenarios: the disposa l of solvent

- contaminated wipes at municipal solid waste landfills (MSWLFs), and the disposal of solvent

- contaminated wastewater treatment sludge from industrial laundries at

MSWLFs.

This report presents estimates of the quantities of solvent present in solvent

- contaminated wipes and industrial laundry sludge that could be disposed in MSWLFs. A draft of this report was externally peer reviewed by four independent experts in accordance with EPA’s peer review policy. This report incorporates revisions ma de in response to the peer reviewers. The peer review record (i.e., the charge, profile of peer reviewers, the peer review comments, and a written response to the peer review addressing each comment) is available in the docket for this rule.

Chemicals E valuated

For this analysis, 30 chemicals were initially considered; the 30 chemicals correspond to those listed in 40 CFR 261.31 (EPA hazardous waste numbers F001, F002, F003, F004, and F005). These 30

chemicals are identified in Exhibit ES

- 1. A screeni ng evaluation was conducted to determine the likelihood that each chemical would be used in conjunction with wipes, based on solvent use patterns and current regulations affecting solvent use. As a result of this evaluation, 10 of the 30

chemicals were el iminated from this evaluation (i.e., loadings were estimated as zero); further quantitative assessment was conducted for the 20 remaining chemicals.

Approach

This report estimates landfill loadings (i.e., the quantities of solvents disposed in a MSWLF) u nder two management scenarios: the disposal of solvent

- contaminated wipes in a MSWLF, and the disposal of solvent

- contaminated laundry sludge in a MSWLF. The report identifies the evaluation approach, data collection activities, and assumptions used to es timate landfill loadings for the

solvent chemicals in each of the two scenarios.

For each of the two management scenarios, a model equation was developed. Input parameters (discussed below) were assigned empirical data

- based probability distributions to account for the uncertainty and variability in the parameter values. The available data exhibits inherent uncertainty resulting from empirical data that is not specifically representative of all operations within all generator facilities. In addition, th ere is a wide range of variability in possible solvent combinations

and how solvents are used.

### ii The landfill loading results for both scenarios are presented as distributions from a probabilistic (Monte Carlo) analysis method. EPA’s Risk Assessment Guid ance for Superfund Volume III Part A, Process for Conducting Probabilistic Risk Assessment was used to guide the assessment process with regard to selecting probability distributions, evaluating sensitivity, and assessing the numerical stability of the res ults.

Evaluation of Disposal of Solvent

- Contaminated Wipes in a Landfill

Many different types of facilities (or sectors) generate solvent

- contaminated wipes. The use patterns (including the number of facilities, number of wipes used by each facility, qu antities of solvent used, and types of solvents used) vary between sectors as well as among facilities in a single sector. In addition, disposal patterns such as the number of facilities sending waste to a single landfill will also affect the quantities o f chemicals present in solvent

- contaminated wipes disposed

in MSWLFs.

The evaluation quantitatively considered differences among principal solvent use sectors (e.g., the printing sector vs. the automobile repair sector) to the extent data were available. The principal input parameters considered were as follows:

▪ Number of disposable wipes generated by a single facility

▪ Quantity of solvent on a disposed wipe

▪ Percent of wipes in a sector containing each evaluated chemical

▪ Number of generators

▪ F raction of each constituent in solvent, and

▪ Number of generators using a single landfill for disposal.

Evaluation of Disposal of Laundry Sludge in a Landfill

Some solvent

- contaminated wipes are sent to industrial laundries, where the wipes are cleane d and reused. When laundered, the wipes contribute pollutants (i.e., solvent contaminants) to a laundry’s pretreatment system. Laundries may be required to control these pollutants prior to discharge to a publicly owned treatment works (POTW). Once remo ved from the pretreatment system, any generated sludges, semi

- solids and solids are managed as solid wastes using management methods such as disposal in MSWLFs.

I nput data used in the laundry sludge evaluation are from Textile Rental Services Association of America (TRSA) 2005 to 2009 survey data and from effluent guidelines development for industrial laundries, finalized in 2000. D ata were available for two types of industrial wipes: shop towels and printer towels. These differences were incorporated in to the evaluation of this scenario. As noted above, uncertainty and variabi lity of the input parameters were addressed in most cases by assigning probability distributions. The principal input parameters considered and the data sources used are

discussed below:

▪ Quantity and distribution of wipes washed at each laundry

▪ Chemical concentrations of solvent in washwater

▪ Sludge partitioning factor (i.e., the fraction of solvent in the washwater that will be present in the disposed sludge), and

▪ Number o f laundries using a single landfill for disposal.

### iii Results

For each of the 20 chemicals in each of the two scenarios, calculations were performed using both deterministic (point

- value) and Monte Carlo methods. The deterministic calculations were

condu cted by setting all input parameters to their average values. The Monte Carlo results for each

of the two scenarios are shown in Exhibit ES

- 1. The values in Exhibit ES

- 1 represent the quantity of each chemical estimated to be disposed in a single landfil l, for each scenario. Based on the results of conducting multiple Monte Carlo analyses on the same set of input parameters, the probabilistic results for each of the two management scenarios were found to be numerically stable.

A separate sensitivity ana lysis was conducted to identify the input parameter assumptions that have the greatest influence affecting the final results. Based on these results, additional analyses were conducted to evaluate data uncertainty for the most sensitive input parameters. Uncertainty was evaluated with respect to the assumption of the type of probability distribution used for the input data, and to evaluate possible outliers within the input data set. Results from the sensitivity and uncertainty analyses include the follo wing:

• For the disposal of solvent

- contaminated wipes in a landfill, sensitivity was evaluated for 16 input parameters for each of the 20 chemicals. The most sensitive parameter was typically the concentration of chemical in the solvent. The most signifi cant effects on results for the uncertainty analysis occurred when changes in the assumptions for this input parameter were made. Six input parameters (which included solvent concentration as well as five other input parameters) were found to be consisten tly more sensitive than the others evaluated.

• For the disposal of laundry sludge in a landfill, two input parameters were identified to be the most sensitive: the number of shop towels washed by each laundry, and the chemical

concentration in the washwate r generated from the shop towels. In the uncertainty analysis, the effect of changing various input data assumptions was that the subsequent changes in the results were less pronounced than those found for the solvent

- contaminated wipes disposal scenario.

Uncertainty in the results is affected by uncertainties or limitations in the input data, and certain assumptions made to simplify the analysis. These uncertainties include the following:

• High variability is expected for several parameters due to the w ide variety of facilities and industries generating solvent

- contaminated wipes. Data are not always available to fully

characterize these differences.

• Some of the data are somewhat dated and may or may not reflect present

- day practices. Most input param eters are based on ‘present day’ characteristics or data which was current at the time it was collected. Even in cases where trends can be assumed, it is difficult to quantify the magnitude of these trends.

• In general, conservative assumptions were used in estimating the quantity of solvent which may escape from the wipe (due to evaporation and on

- site removal of excess solvent) in the scenario assessing disposal of solvent

- contaminated wipes in a landfill.

### iv • Insufficient data were available to evaluate va riability within certain input parameters. For example, the concentration of a chemical (e.g., toluene) in a solvent used by the printing industry may be different from the concentration in solvents used by the automotive industry.

Exhibit ES

- 1. Quantit ies of Chemicals Present in Solvent

- Contaminated Wipes and Industrial Laundry Sludge Disposed in MSW Landfills

Constituent Landfill Loading Results for Disposal of Wipes

Result per Landfill (kg/yr) 

### Landfill Loading Results for Disposal of Laundry Sludge

Re sults per Landfill (kg/yr) 

### 50 th



### Percentile 90 th



### Percentile 95 th



### Percentile 50 th



### Percentile 90 th



### Percentile 95 th



### Percentile

Carbon disulfide

Carbon tetrachloride

Dichlorodifluoromethane Ethyl ether

Nitrobenzene

2

- Nitropropane Pyridine
1,1,1

- Trichloroeth ane 1,1,2

- Trichlorotrifluoroethane

Trichlorofluoromethane 0 0 0 0 0 0

Acetone 1,033 9,120 16,224 2.53 26.40 53.02

Benzene 1.8 12.1 20.2 0.21 2.36 4.53

n

- Butanol 61 607 1,146 0.13 1.36 2.61

Chlorobenzene 56 431 752 0.08 1.06 2.17

Cresols 9 58 103 0.11 1.14 2.16 Cyclohexanone 159 1,207 2,102 0.35 3.70 7.07
1,2

- Dichlorobenzene 46 364 631 0.1 1 1.22 2.34 Ethyl acetate 52 414 733 0.17 1.96 3.89 Ethyl benzene 184 1,857 3,446 7.81 96.37 193.92

2

- Ethoxyethanol 222 2,606 4,974 0.21 2.34 4.59 Isobutyl al cohol 31 220 376 0.11 1.21 2.35

Methanol 225 2,431 4,747 3.23 5.77 6.57 Methyl ethyl ketone 1,307 11,676 20,532 1.72 32.96 77.87 Methyl isobutyl ketone 150 2,073 4,184 1.00 10.52 19.54 Methylene chloride 465 3,268 5,583 9.25 51.02 78.46

Tetrachloroeth ylene 107 727 1,226 19.71 93.00 139.35

Toluene 1,215 8,918 15,180 6.10 43.05 69.22
1,1,2

- Trichloroethane 40 330 581 0.2 1 2.33 4.53 Trichloroethylene 308 1,911 3,146 0.17 2.54 5.26 Xylenes (total) 958 8,556 15,184 4.65 21.98 33.26

For 10 chemicals in T able ES

- 1

, results are zero because no use was assumed.

### 1 1.0 Introduction to Landfill Loading Calculations

This report describes the approach used in evaluating the landfill loading from the management of

solvent

- contaminated wipes in a landfill. The pu rpose of these calculations is to develop

constituent

- specific estimates of the quantity of chemical disposed in a landfill under two scenarios: (1) disposal of wipes in a municipal solid waste (MSW) landfill; and (2) disposal of industrial laundry sludge in a MSW landfill, generated from the washing of wipes. These results are used as inputs to a risk analysis to assess the potential risks posed by these chemicals in the landfill

environment.

1.1 How Does the Analysis Address Parameter Variability and Unc ertainty?

There are a large number of possible combinations of solvent blends used by generating facilities for cleaning and degreasing operations. Some generators use blends consisting of three or four constituents, others use pure solvents. Also, many generators use very few wipes per day while many others use thousands. In effect, no two generators are the same in terms of their solvent use practices. In addition, the available data will exhibit uncertainty as a result of empirical data that

are not completely representative either of present day operations or of all facilities.

To account for these effects, variability is assessed using probability distributions in a Monte Carlo analysis. The analysis was conducted using CrystalBall ® 

### , user

- friendl y software that facilitates the performance of Monte Carlo

- type analyses by linking data to Excel spreadsheets. Input values were assigned a distribution based on available empirical data. In cases where there was no basis for estimating variability or u ncertainty of a specific parameter, the value was constant (this is not intended to mean that no uncertainty exists, but instead means that uncertainty was not assessed). Each iteration used different input values based on these input distributions. The resulting calculations produce a range (distribution) of results to reflect the uncertainty of the input values. All Monte Carlo analyses were conducted with 100,000 iterations.

In addition, a separate sensitivity analysis is conducted. The sensitivity analysis assesses the influence that each input parameter has on the final result. These results identify which assumptions are the most important or least important. Because many assumptions are used in the calculations, the sensitivity analysis is usef ul for identifying which of these assumptions have the greatest effect on influencing the results.

1.2 What Chemicals are Considered?

For this analysis, 30 chemicals were initially considered, corresponding to the constituents

identified in the F001 to F0 05 listing. These chemicals are identified in Exhibit 1

- 1. These constituents were selected because they are expected to be present in mixtures of hazardous waste spent solvent (e.g., F001 to F005) and wipes.

Exhibit 1

- 1 identifies that quantitative loa ding estimates were subsequently made for 20 of these 30 chemicals. The 30 chemicals were carefully evaluated to identify instances where the chemical would potentially be used in conjunction with a towel or wipe. This led to the elimination of certain

c hemicals from further consideration. These chemicals include:

### 2 • Ozone depleters (five chemicals).

• Chemicals not found to be used as solvents in conjunction with towels or wipes (five chemicals).

Sections 1.2.1 and 1.2.2 below present the data and informat ion used for eliminating these chemicals from the assessment. These chemicals may still be used as solvents, or may have been used as solvents in conjunction with towels or wipes in the past. However, no information is available to suggest that these che micals are presently used in applications where contact with a towel or wipe is likely. There is uncertainty with this step because there is never a way to ‘prove’

that a constituent is absent from formulations.

Exhibit 1

- 1 does not identify the followin g types of additional constituents potentially present on soiled wipes; these following types of chemicals were not assessed:

• Other hazardous solvents (such as P

- and U

- listed commercial chemical products) or non

- F

listed ignitable

- only solvents.

• Other co ntaminants present on a soiled wipe (e.g., metals).

Exhibit 1

- 1. F

- List Constituents

CAS No. Constituent RCRA Waste

Code Loading Estimate Made?

56

- 23

- 5 Carbon tetrachloride F001 No (ODS)

75

- 71

- 8 Dichlorodifluoromethane a 

### F001 No (ODS)

108

- 90

- 7 Chlor obenzene F002 Yes

95

- 50

- 1 1,2

- Dichlorobenzene F002 Yes

75

- 09

- 2 Methylene chloride F001, F002 Yes

127

- 18

- 4 Tetrachloroethylene F001, F002 Yes

79

- 01

- 6 Trichloroethylene F001, F002 Yes

71

- 55

- 6 1,1,1

- Trichloroethane F001, F002 No (ODS)

79

- 00

- 5 1,1, 2

- Trichloroethane F002 Yes

76

- 13

- 1 1,1,2

- Trichlorotrifluoroethane a 

### F002 No (ODS)

75

- 69

- 4 Trichlorofluoromethane a 

### F002 No (ODS)

67

- 64

- 1 Acetone F003 Yes

71

- 36

- 3 Butanol F003 Yes

108

- 94

- 1 Cyclohexanone F003 Yes

141

- 78

- 6 Ethyl acetate F003 Yes

1 00

- 41

- 4 Ethyl benzene F003 Yes

60

- 29

- 7 Ethyl ether F003 No (use with wipes is unlikely)

67

- 56

- 1 Methanol F003 Yes

108

- 10

- 1 Methyl isobutyl ketone F003 Yes

1330

- 20

- 7 Xylenes (total) F003 Yes

1319

- 77

- 3 Cresols F004 Yes

98

- 95

- 3 Nitrobenzene F004 No (use with wipes is unlikely)

71

- 43

- 2 Benzene F005 Yes

75

- 15

- 0 Carbon disulfide F005 No (use with wipes is unlikely)

110

- 80

- 5 2

- Ethoxyethanol F005 Yes

78

- 83

- 1 Isobutyl alcohol F005 Yes

### 3 CAS No. Constituent RCRA Waste

Code Loading Estimate Made?

78

- 93

- 3 Methyl ethyl ketone F005 Yes

79

- 46

- 9 2

- Nitropropan e F005 No (use with wipes is unlikely)

110

- 86

- 1 Pyridine F005 No (use with wipes is unlikely)

108

- 88

- 3 Toluene F005 Yes
a. The F001 listing identifies ‘chlorinated fluorocarbons.’ Dichlorodifluoromethane was selected as one such compound. Other chl orofluorocarbons are explicitly identified in the F002 listing.

ODS: ozone

- depleting substance.



### 1.2.1 Chemical Use Restrictions of Ozone Depleting Substances

Some F001 to F005 listed chemicals have been heavily restricted in their production or use becaus e they are ozone

- depleting substances (ODS) or present other serious hazards. These include the following chemicals:

• Carbon tetrachloride

• 1,1,1

- Trichloroethane (methyl chloroform)

• Trichlorofluoromethane (CFC

- 11)

• Dichlorodifluoromethane (CFC

- 12)

• 1,1,2

- Tri chlorotrifluoroethane (CFC

- 113)

As a result of these restrictions, it is expected that present

- day use of these chemicals as components of solvents used in conjunction with towels or wipes is negligible. Note that carbon tetrachloride

has restrictions wi th regard to both the Consumer Product Safety Commission and the Clean Air Act (described below).

Consumer Product Safety Commission Restrictions

The Consumer Product Safety Commission regulations, 16 CFR 1500.17(a)(2) states the following concerning the use of carbon tetrachloride:

‘Under the authority of section 2(q)(1)(B) of the act, the Commission declares as banned hazardous substances the following articles because they possess such a degree or nature of hazard that adequate cautionary labeling can not be written and the public health and safety can be served only by keeping such articles out of

interstate commerce: ...Carbon tetrachloride and mixtures containing it (including carbon tetrachloride and mixtures containing it used in fire extinguishers) , excluding unavoidable manufacturing residues of carbon tetrachloride in other chemicals that under reasonably foreseeable conditions of use do not result in an atmospheric concentration of carbon tetrachloride greater than 10 parts per million.’

### 4 Ozone Depleting Substances

The Montreal Protocol identifies ozone

- depleting substances and provides a set of agreements for participating countries to follow in eliminating their use of these chemicals (Montreal, 2000). Many

of these conditions were written in to Title VI of the U.S. Clean Air Act (EPA, 1990). The ultimate goal of these restrictions is the complete elimination of these chemicals with replacement products

and a reduction in demand.

The Clean Air Act requires EPA to phase out the production and consumption of ODS, subject to certain exemptions. The phase

- out schedule is in Exhibit 1

- 2.

Exhibit 1

- 2. Clean Air Act Use Restrictions

Chemical CAA Due Date
Carbon tetrachloride, CFC

- 11, CFC

- 12, CFC

- 113 By January 1, 2000, production must cease
1,1, 1

- Trichloroethane By January 1, 2002, production must cease

1.2.2 Chemicals Not Known to be Used as Solvents in Wipe Applications

Several chemicals identified in Exhibit 1

- 1 are not known to be used as solvent mixtures in conjunction with towels or wipes . While it is difficult to ‘prove’ that a constituent is absent from solvent formulations, material contained in the RCRA

- 2003

- 0004 docket, EPA’s Biennial Reporting system, Toxic Reduction Inventory (TRI) reporting system, internet search, as well as

mate rials from related EPA rulemaking activities (primarily NESHAPs) were reviewed to identify if

some F

- listed chemicals can be eliminated from further consideration.

All chemicals in Exhibit 1

- 1 were used as solvents at one time. However, some chemicals ma y have been used strictly as industrial (process) solvents where contact with towels and wipes was not typical. In addition, solvent formulations may have changed over the years to reduce their toxicity, hazardous air pollutant (HAP) content, or volatile organic compounds (VOC)

- content.

One purpose of the data collection activities identified throughout this report (including review of resources identified in the Appendices) was to verify if in fact the constituents listed in Exhibit 1

- 1 are present in formulations likely to be used in conjunction with wipes. For five chemicals listed below, no use in conjunction with wipes could be found. Information for these chemicals is limited to identification of solvent use information, which may be old or outda ted. The subsequent review of more recent information (e.g., from NESHAP support data discussed in Section 2.3.3) did not confirm that these chemicals were still used as solvents in conjunction with wipes. As a result, the following five chemicals are as sumed to be absent from present

- day applications of solvents used in conjunction with wipes and were not assessed in this report:

• Carbon disulfide . Carbon disulfide has been used as a solvent in extraction processes such as preparing fats, lacquers, and camphor. Even as early as the 1970’s, however, carbon disulfide was identified as having been replaced by other solvents for many applications (ATSDR, 1996).

Carbon disulfide is a HAP. 1

1 Solvents classified as HAPs are more lik ely to be discussed in NESHAP technical documents such as those identified in Section 2.3.3 and Appendix C. These chemicals were not mentioned in these documents.

### 5 • Ethyl ether . Ethyl ether is identified as an industrial solvent in applications such as the production of waxes, nitrocellulose, fats, and other products (Spectrum, 2001). Ethyl ether is

not a HAP.

• Nitrobenzene . The primary use of nitrobenzene is in the captive production of aniline.

Nitrobenzene is also used as a so lvent in petroleum refining, in the manufacture of cellulose ethers and acetate, in the synthesis of other organic compounds including acetaminophen (Tylenol ® 

### ), and as a solvent for shoe dyes (ATSDR, 1990). Nitrobenzene is a HAP.

• 2

- Nitropropane . 2

- Nitro propane is identified as a solvent in coating (e.g., printing inks and adhesives), paint and varnish removal, and a chemical intermediate (Spectrum, 2001). 2

- Nitropropane is a HAP.

• Pyridine . Pyridine is a solvent in paint and rubber preparation and as a solvent in research laboratories. Pyridine is a chemical intermediate for various end products (ASTDR, 1992). Pyridine is not a HAP.

Information for other chemicals was used to determine that their use as solvents in conjunction with wipes is more like ly. This information is presented in Sections 2.3 and 3.3. Two additional chemicals, cyclohexanone and 1,1,2

- tichloroethane, are also assumed to be used as solvents in conjunction with wipes although very little quantitative data were available. They ha ve been identified as used in printing.

1.3 Input Parameter Assumptions

For both the disposable wipes and laundry sludge analyses, assumptions are made regarding the distribution of the values of the input parameters; these assumptions are incorporated in to the inputs to the probabilistic (Monte Carlo) analysis described in Section 1.1. These assumptions allow the evaluation of input parameter variability in the analysis. The underlying data for these parameters are detailed in the subsequent sections of this report.

For each parameter, one of the following methods was used for assigning input parameter distributional assumptions:

• Fixed values. These input parameters were constant in all iterations. This is not necessarily

intended to mean that no u ncertainty exists, but instead means that uncertainty was not assessed because there was no basis for estimating uncertainty. For example, in the disposable wipes analysis (Exhibit 2

- 1), 24 percent of all generators are assumed to be printers; this percen tage
was used for all chemicals.

• Continuous distribution: Some input variables use a continuous probability distribution based on empirical data (examples of distribution types include normal, lognormal, and gamma). For example, multiple empirical data p oints are available for identifying the number of disposable wipes used at printer sites. A ‘goodness of fit’ is performed to determine the best fit between these data points and standard probability distributions. 2

2 The single best

- fit distribution for each data set was selected within the CrystalBall ® s oftware based on the Anderson

Darling ranking method (Decision Engineering, 1996).

### 6 In most cases, the distribution with the highest

- ranking fit was selected and subsequently used. The reason for this is that, for most input parameters, no mechanistic basis exists to assess which distribution assumption is most appropriate. The impact of this assumption, when two or

more d istribution assumptions would be appropriate for the data, is evaluated in Sections 2.4.3 and 3.4.3 for certain input parameters in the disposable wipes and laundry sludge analyses, respectively. Appendices H and I presents the distributions used for each parameter and relevant statistics in the disposable wipes and laundry sludge analyses, respectively.

For the concentration terms used in the laundry sludge analyses, the distribution with the highest

- ranking fit was not necessarily used; the choice of di stribution for this parameter is

discussed in Section 3.3.3.

• Uniform distributions. In some cases, two empirical data points are available for an input parameter. Variability is assessed by assuming that all values between these extremes have an equal ( uniform) probability. For example, as indicated in Section 2.3.3, the percent of all printers assumed to use acetone is estimated as a uniform range of 16 to 26 percent. The analysis assumes that any percentage between these extremes is equally probable, and that there is no probability for a value outside this range. As with the fixed values discussed above, no data are available to form a basis for expanding the range.

• Triangular distributions. In cases where three empirical data points are available , a triangular distribution (based on the minimum, mean, and maximum values) is developed when no other information concerning the distribution is available. These cases are identified in Sections 3 and

4.

2.0 Solvent Loadings Calculations: Disposables

2 .1 How Can Solvents Enter the Environment from Disposable Wipes?

This assessment considers potential risks resulting from the disposal of solvent

- contaminated wipes in a MSW landfill. Potential risks may result from the release of solvent components from the wipes while in the landfill. As with other analyses conducted in this report, only the compounds that conform to the definition of the F001 through F005 solvent listing are considered (i.e., other

non

- listed solvents and co

- contaminants are not consi dered).

The purpose of this section is to describe the methodology used to generate landfill loading estimates resulting from the disposal of wipes in a municipal solid waste landfill, and present those results. These landfill loading estimates serve as the basis for subsequent fate and transport modeling.

2.2 Overview of Calculation Methodology

The purpose of the calculation is to estimate the quantity of chemical disposed in a single landfill per year. The overall calculation approach is presented bel ow in Exhibit 2

- 1. As shown in Exhibit

2

- 1, the analysis distinguishes between different types of generators (e.g., printers) when possible. This is because different industries use different quantities of wipes, different quantities of solvents,

or diff erent types of solvent chemicals.

### 7 The values listed in Exhibit 2

- 1 (in the column labeled ‘Avg Value’) are the values used in the deterministic analysis. The column labeled ‘Distributional Assumption’ in Exhibit 2

- 1 refers to how input parameter variabili ty is addressed in the probabilistic (Monte Carlo) analysis described in Section 1.1. Refer to Section 1.3 for discussion of these assumptions. The calculation approach includes the following underlying assumptions:

• Consistent with the Economic Analysis (Document No. RCRA

- 2003

- 0004

- 0004), 75 percent of states are assumed to adopt the exclusion and all disposable wipes generated by large quantity generators (LQGs) and small quantity generators (SQGs) in these states are assumed to enter the

nonhazardous waste stream. 3 

### This is a conservative assumption because it is possible that some generators that presently manage these materials as a hazardous waste will continue to do so in the future, regardless of the regulatory status of the waste.

• No additional solvent removal technologies are considered (e.g., centrifuging) beyond present

day practices. In general, data were obtained from site visits and other data collection efforts, which account for ‘status quo’ solvent reduction techniques such as screen bo ttom drums, hand wringing, and low application rates. The current use of centrifuge and similar solvent removal techniques for towels and wipes is not known and was not included in the calculation. Similarly, projected future use of this technique was no t included in the calculation. A qualitative discussion on this management method is included in Section 2.5.

The analysis only accounted for wipes generated by LQGs and SQGs. It does not account for the potential introduction of the constituent by cond itionally exempt small quantity generators (CESQGs), household hazardous waste, or applications unrelated to solvent wipes.

3 States are not obligated to adopt an exclusion for wipes (if finalized). In their comments to the proposed rule, the

Association of State and Territorial Solid Waste Ma nagement Officials (ASTSWMO), “five of the twelve States represented on the Task Force do not anticipate that such an exclusion from the definition of hazardous waste for

solvent

- contaminated disposable wipes would be adopted in their respective States” (D ocument No. RCRA

- 2003

- 0004

- 0325). This does not precisely match the percentage used in the Economic Analysis but supports why a percentage less than 100 percent was chosen.

### 8 Exhibit 2

- 1. Sample Calculation for Acetone

Parameter Avg Value Distributional Assumption Printing (EA Sector # 1, 10, 11)

A Nu mber of disposables used per site per year 129,990 Gamma distribution

B Solvent Loading, g/towel 37 Weibull distribution

C % wipes containing chemical 21% Chemical

- dependent, uniform distribution

D % Generators in sector 24% Constant Transportation (EA Sector # 7)

A Number of disposables used per site per year 73,819 Lognormal distribution

B Solvent Loading, g/towel 39 Weibull distribution

C % wipes containing chemical 21% Chemical

- dependent, uniform distribution

D % Generators in sector 1% Constant Auto repair (EA Sector # 12)

A Number of disposables used per site per year 12,964 Triangular distribution

B Solvent Loading, g/towel 32 Uniform distribution

C % wipes containing chemical 21% Chemical

- dependent, uniform distribution

D % Generators in sector 14% Constant Electronics (EA Sector # 6)

A Number of disposables used per site per year 237,000 Uniform distribution

B Solvent Loading, g/towel 6 Uniform distribution

C Fraction wipes containing chemical 21% Chemical

- dependent, uniform distribu tion

D % Generators in sector 0.5% Constant Other (EA Sector # 2, 3, 4, 5, 8, 9, 13)

A Number of disposables used per site per year 66,436 Lognormal distribution

B Solvent Loading, g/towel 39 Weibull distribution

C % wipes containing chemical 21% Chem ical

- dependent, uniform distribution

D % Generators in sector 61% Constant

Common for all sectors

E % constituent in solvent 44% Chemical

- dependent; distribution depends on the chemical (refer to Appendix H)

F Number of generators/landfill 19.0 Gamma d istribution Total chemical per landfill (kg/yr) 4,966 [Σ (A× B× C× D)] × (E×F)/1000
“EA Sector” refers to the sector numbers in the Economic Analysis (EPA, 2003), further discussed in Section 2.3.4.



### Exhibit 2

- 1 identifies that data for different indust ries were used as available; these sectors correspond to those evaluated in the Economic Analysis (EPA, 2003). Exhibit 2

- 2 identifies the data used in selecting parameter values for the variables referenced in the above equation. These

variables and sour ce data are discussed in the following sections.

### 9 Exhibit 2

- 2. Source of Data for Disposables Calculations

Variable Report

Section Primary Sources How is variability assessed?

A

- Number of towels or wipes used by single

generator per year 2.3.1 Compile d from site visits, comments, and correspondence. See Table A

- 1. Empirical data were sorted into each of five industries (based on data availability) and best fit to distributions.

B

- Quantity of solvent per towel or wipe 2.3.2 Compiled from site visits , comments, and correspondence. See Table A

- 2. Empirical data were sorted into each of five industries (based on data availability) and best fit to distributions.

C

- % wipes containing

chemical 2.3.3 2003 Biennial Report, NESHAP development data, and 20 04 TRI data. Input is a range reflecting two data

sources. Different values were used for

each of the five industries based on data availability.

D

- % Generators

in sector 2.3.4 Economic Analysis (EPA, 2003). Values were fixed.

E

- % constituent

in sol vent 2.3.5 Data from 175 sites used in developing EPA's Alternative Control Techniques

Document

- Industrial Cleaning Solvents (EPA, 1994a). Empirical data for the percent of

chemical found in formulations were

best fit to distributions.

F

– Number of

gen erators using a single landfill for disposal 2.3.6 Derived from Economic Analysis

- for number of generators (EPA, 2003),

Simmons

- for number of landfills by state (Simmons, 2006), and 2002

Census

- for distribution of generating industries by state (Cens us, 2002). Empirical averages were developed for

each state and best fit to a distribution.



### 2.3 Data Used for Assessing Individual Variables

2.3.1 Number of Towels or Wipes Used by a Single Generator

The number of disposable wipes used by individual site s varies based on facility size and industry.

Much anecdotal data are available in the RCRA

- 2003

- 0004 docket from site visits, public comments, and other materials. These data are tabulated in Exhibit A

- 1 of Appendix A and Exhibit

B

- 1 of Appendix B. The data are summarized below by sector:

• Auto body shops: 7,200 to 24,000/ year (3 values from available sources).

• Transportation (includes aerospace and new vehicle assembly): 7,500 to 300,000/ year (13 values from available sources).

• Printing: 1,000 to 600 ,000/ year (21 values from available sources).

• Electronics: 24,000 to 450,000/ year (2 values from available sources).

• All other manufacturing: 480 to 400,000/ year (23 values from available sources).

These data are intended to represent the quantity of w ipes generated as a result of use with solvents (including F

- listed and other solvents such as nonhazardous solvents). When available, data specific to the quantity of disposable wipes and reusable wipes were used. An assumption was

made that similar fac ilities (i.e., printers) would use approximately the same number of wipes per facility whether the wipes were disposable or reusable.

### 10 2.3.2 Quantity of Solvent in Disposed Wipe

As with the quantity of wipes used by a site, the quantity of solvent in a d isposed wipe will vary.

Exhibit A

- 2 of Appendix A and Exhibit B

- 2 of Appendix B include data regarding the amount of solvent on wipes based on anecdotal data. The available data are summarized below by sector:

• Printing: 9

- 86 grams (9 values from availab le sources)

• Electronics: 6

- 7 grams (2 values from available sources)

• Transportation: No data; assume similar to ‘all other manufacturing’ 4



### • Auto Repair: 21

- 42 grams (2 values from available sources)

• All other manufacturing: 10 to 97 grams (7 values from ava ilable sources)

The available data are obtained from various activities, such as the quantity applied, the quantity disposed, or the quantity recovered from a solvent recovery operation (and therefore which would

have remained with the towels if not recov ered). Where information clearly indicates that the data refers to the quantity of solvent applied to the wipe, allowances were made to account for typical solvent removal processed, evaporation, and removal (e.g., ‘hand wringing’) prior to disposal.

Exh ibit B

- 2 in Appendix B indicates the use of these assumptions.

To account for evaporation in these instances, it was conservatively assumed that 78 percent of the solvent applied would remain on the wipe, using data from EPA (1997). This data source meas ured evaporation rates of five solvents, plus methyl ethyl ketone (MEK), under various simulated conditions and included differences in containers and wipes. The data from EPA (1997) are presented in Exhibit B

- 6 in Appendix B. The value of 78 percent is a calculated average evaporation rate for the five solvents, excluding MEK. The solvents in Exhibit B

- 6 are intended to represent the variation in physical properties of various solvents containing F001

- F005 constituents.

The reason that the MEK data wer e not used is because the results from this study indicated 100 percent evaporation. All wipers used in conjunction with MEK were KimWipes. The KimWipes

used with MEK retained 0% of the solvent, probably because of the volatility of the solvent and the

r elatively low adsorption of the wiper (EPA, 1997). For the analysis presented in the present report, assuming that all the solvent evaporates would not be conservative and not representative of other types of wipes. The low evaporation rates for the rema ining solvents may be attributed to the limited exposure of the wipe surface to ambient air, and /or limited air circulation.

In actuality, evaporation is influenced by multiple factors including solvent properties (e.g., vapor pressure), wipe properties, storage and handling practices, use, and temperature (particularly indoor/ outdoor). Different solvents (and different components within these solvents) will evaporate at

different rates.

4 Appendix A identifies data on application rates used in the transportation sect or from Document No. RCRA

- 2003

- 0004

- 0119; however, these data are not used. This source, a 1999 letter from the Aerospace Industries Association, provides somewhat contradictory information. They state that ‘typically small amounts of solvent are used re lative to the rag's saturation capacity’ with a ‘few milliliters (50

- 250ml) per application.’ The quantity range of 50 to 250 ml (approximately 45 to 225 grams, assuming a solvent density of 0.9 g/ml) is much higher than expected from these qualitative statements. The range of 45 to 225 grams per wipe is an outlier value when compared to the other data listed

here.

### 11 Exhibit 2

- 3 summarizes the volatility of the 20 evaluated solvent chemicals (a higher vapor pressure indicates a more volatile chemical). These data should be used as a guide for evaluating the effect of the evaporation assumption. This assumption is expected to be a conservative approach for

volatile solvents such as pure MEK, or pure chemical solvents such as acetone which are more volatile than MEK. As noted above, many factors, including other components in the solvent, will influence the actual evaporation rate.

Exhibit 2

- 3. Vapor Pressures and Specific Graviti es of Solvent Chemicals

CAS No. Constituent Vapor Pressure of Pure Substance @25C, mm Hg Specific Gravity

1319

- 77

- 3 Cresols 0.17 1.030

- 1.04

95

- 50

- 1 1,2

- Dichlorobenzene 1.36 1.3

108

- 94

- 1 Cyclohexanone 4.33 0.94

110

- 80

- 5 2

- Ethoxyethanol 5.31 0.93

71

- 36

- 3 n

- Butanol 6.7 0.81

1330

- 20

- 7 Xylenes (total) 7.99 0.86

100

- 41

- 4 Ethyl benzene 9.6 0.87

78

- 83

- 1 Isobutyl alcohol 10.5 0.80

108

- 90

- 7 Chlorobenzene 12 1.11

127

- 18

- 4 Tetrachloroethylene 18.5 1.63

108

- 10

- 1 Methyl isobutyl ketone 19.9 0.80

79

- 00

- 5 1,1, 2

- Trichloroethane 23 1.435

108

- 88

- 3 Toluene 28.4 0.86

79

- 01

- 6 Trichloroethylene 69 1.47

78

- 93

- 3 Methyl ethyl ketone 90.6 0.81

141

- 78

- 6 Ethyl acetate 93.2 0.90

71

- 43

- 2 Benzene 94.8 0.88

67

- 56

- 1 Methanol 127 0.8

67

- 64

- 1 Acetone 232 0.79

75

- 09

- 2 Methy lene chloride 435 1.33
Chemicals are arranged in increasing order of vapor pressure. A chemical with a higher vapor pressure is more volatile than a chemical with a lower vapor pressure.



### To further account for solvent removal from a process such as hand wringing, a conservative value of 5 percent removal was used, based on data in EPA (1997). Exhibit 2

- 4 illustrates a sample calculation. This particular calculation was used to estimate the disposal quantity from one site in the electronics industry, an d is subsequently used in Exhibit B

- 2 of Appendix B. As indicated in

Exhibit B

- 2 of Appendix B, a total of two data points are available from the electronics industry (i.e., the value calculated in Exhibit 2

- 4 and a second value), which formed the basis o f the assigned distribution in Appendix H.

Exhibit 2

- 4. Sample Calculation for Quantity of Solvent in Disposed Wipe

Parameter Value Discussion/ Basis

Quantity of solvent applied “<10 mL” per wipe as applied Table A

- 1, Reference Document

No. 0118 Specif ic gravity of solvent 0.9 g/mL Assumed (refer to Exhibit 2

- 3) Weight fraction of applied solvent which does not

evaporate 0.78 From Exhibit B

- 6

Weight fraction of solvent which is not removed from wipe as a result of hand wringing, etc. 0.95 From text

### 12 Parameter Value Discussion/ Basis

S olvent remaining on wipe which reaches the

landfill 6.7 mg per wipe = 10 mL x 0.9 mg/mL x (0.78) x (0.95)



### The various assumptions used in Exhibit 2

- 4 (e.g., specific gravity, evaporation rate, removal efficiency) reflect single point values that are wit hin the range of actual chemical properties. In the case of specific gravity, the specific gravity of the evaluated solvents ranges from 0.79 to 1.63; the selection of 0.9 represents a value within this range. Similarly, the selected evaporation rate

app roximates the range of solvent properties. As noted before, these assumptions are used in cases identified in Appendix B only when the quantity of solvent on the disposed wipe was not measured (i.e., only when the application rate was provided). Based on Exhibit B

- 2, 20 data points are used in estimating solvent quantity loading. For nine cases, the quantity on the disposed wipe was estimated using the assumption methodology in Exhibit 2

- 4. For the remaining 11 cases, the quantity on the disposed solven t was measured so that the assumptions in Exhibit 2

- 4 were not required. As noted in Exhibit 2

- 1, the quantity of solvent applied was assumed to be independent of the type of solvent.

2.3.3 Percent of Wipes Containing Constituent

A facility will have on e or more of the following use patterns with regard to F

- listed constituents in

solvents:

• A facility may not use an F

- listed solvent at all.

• A facility may use an F

- listed solvent, but may not use it in conjunction with disposable towels or wipes.

• A facil ity may use an F

- listed solvent in conjunction with disposable wipes.

• A facility may use an F

- listed solvent in some applications but not others.

• A facility may use combinations of the above practices.

Because of these factors, there is uncertainty with t he fraction of wipes containing the constituent. To account and evaluate this uncertainty, two fractions were calculated using available data sources, forming endpoints for use in the probabilistic analysis.

The following data sources were used in develo ping these estimates and are discussed in detail

below:

• 2003 Biennial Report.

• Industrial Cleaning Solvents Development Data (EPA, 1994a; ERG, 2003).

• 2004 Toxics Release Inventory (TRI) and a similar Massachusetts program, 2004 Toxics Use Reduction Act (TU RA).

• Other sources.

The specific data used for each chemical are identified in Exhibit B

- 3 of Appendix B.

One of the following sources was used as the basis for one endpoint of the estimated usage range (listed in decreasing preference): EPA Biennial Rep ort data, historical industrial cleaning solvents development data, and other sources. The Biennial Report data is a preferred data source because it is recent and includes many facilities.

### 13 The fraction of wipes containing a chemical is expected to chang e from one industry sector to another (e.g., one sector may use acetone with greater frequency than another sector). To account for these effects, industry

- specific data are used as available.

Data Source: 2003 Biennial Report

The 2003 Biennial Report p rovides hazardous waste generation data for large quantity generators (LQG); however, many small quantity generators (SQG) also enter data voluntarily or because of state requirements. In part, the data show the numbers of facilities generating F001 to F0 05 spent solvents and F001 to F005 spent solid materials (which include wipes). Advantages of these data are their reflection of a large number of sites represented and the relatively recent collection of the data. Disadvantages are that specific chemica ls are not identified and there is uncertainty with respect to whether the solvents are used in conjunction with disposable wipes.

The 2003 Biennial Report data were used directly for 10 of the most frequently used solvents (Exhibit B

- 3 identifies the dat a sources used for each chemical). ‘Frequently

- used solvents’ include nine chemicals identified in NESHAP rules for targeted industries as evaluated in Appendix C (i.e., cresols, ethylbenzene, glycol ethers 5 

### , methanol, methylene chloride, methyl ethyl ket one (MEK), methyl isobutyl ketone (MIBK), toluene, and xylene ). In addition, acetone (which is not a HAP) is included in this list because it is identified as extensively used in printing. 6 

### A complete discussion of the Biennial Report data is included in Appendix D.

For these chemicals, the fraction of facilities using the chemical was assumed to be equal to the fraction of facilities reporting the corresponding F

- listed waste. This is conservative because each F001 to F005 waste listing definition incl udes multiple chemicals. For illustration, Exhibit 2

- 5 includes a sample calculation for estimating the number of printing facilities using acetone.

The data in Exhibit 2

- 5 reflect the sites using the chemical as a fraction of only those LQG and SQG

gene rators, which generate F001

- F005 solvent

- contaminated wipes. This fraction is much higher than the fraction of the entire population of industry sites, but is consistent with the basis used in Sections 2.3.6 for identifying the number of generators. For example, the 85 percent value for acetone refers to the percentage of only those LQG and SQG sites assumed to generate F001 to F005 wipes (this total population is approximately 30,000, as indicated in Section 2.3.4).

Exhibit 2

- 5. Example Calculation for Estimating the Percent of Wipes Containing Acetone, Using 2003 Biennial Report Data

Parameter Source Result
Number of printing facilities generating F001

- F005 waste 2003 Biennial Report (Exhibit

D

- 4 of Appendix D) 254

Number of printing facilities

gener ating F003 waste 2003 Biennial Report (Exhibit

D

- 4 of Appendix D) 217

5 Solvent use profiles identified the use of ‘glycol ethers’; 2

- ethoxyethanol is a glycol ether but may or may not be part

of the formulati on. To be conservative, it is assumed to be present when the use of glycol ethers is known.

6 Based on a survey (SGIA, 1996) of used wipe disposal practices for printers, 18 percent of facilities indicated that they used methyl ethyl ketone, 27 percent reported using acetone, 20 percent reported using xylene, and 20 percent reported using toluene.

### 14 Parameter Source Result

Fraction of Sites Generating F003 Calculated from above two data points 85%

Fraction of Sites Using Acetone (acetone is one of the F003 solvents) Assume equal to F003 generation rate 8 5%

Fraction of wipes at these sites which

contain the chemical Kimberly

- Clark (1997) 30% of wipes

Overall calculation for fraction of wipes containing the chemical Of the total number of wipes used at these sites, approximately 26% are assumed to

contain acetone (e.g., 85% × 30% = 26%). This forms one of the ‘usage endpoints’.

This calculation method was used for 10 constituents as discussed in the text.



### Data Source: Industrial Cleaning Solvents Development Data

For six chemicals, frequency of use was obtained from data collected in support of EPA’s Alternative Control Techniques Document

- Industrial Cleaning Solvents (EPA

- 453/R

- 94

- 015) (EPA, 1994a). Detailed data for cleaning solvent use by 175 sites are presented in ERG (2003).

This information al lows for identification of particular chemicals used by facilities. Limitations of these data include their age (1990’s) and that they do not identify use of wipes or towels. However, in general, cleaning solvents are likely to be at least occasionally u sed with wipes.

Exhibit B

- 3 identifies the data sources used for each chemical. Further information regarding this data source, including the frequency that certain chemicals were reported as used is in Section 2.3.5.

Data Source: Other Data

For four c hemicals (i.e., four chemicals not discussed in relation to the TRI data below or the industrial cleaning solvents data above), information is available to suggest they may be used as solvents which potentially may contact towels and wipes. Two chemicals, cyclohexanone and
1,1,2

- trichloroethane, have been identified as used in printing. 7 

### Two other chemicals, 1,2

- dichlorobenzene and chlorobenzene, were detected in industrial laundry wastes (see Section 3.3.3).

Usage for the three chlorinated solvents was estimated using Document No. RCRA

- 2003

- 0004

- 0106, which identifies that ‘trichloroethane’ is used in the printing industry by 2 of 63 respondents (3 percent) circa 1996. This percentage was applied to 1,1,2

- trichloroethane for all sites, as well as

to the two other chlorinated chemicals (1,2

- dichlorobenzene and chlorobenzene) for which no other usage data were available.

A usage estimate for cyclohexanone is based only on the TRI/ Massachusetts TURA data below

because no other data source is available.

7 According to Dave Salman, EPA (Salman, 2006), cyclohexanone is used in some digital printing; the 1993 Printing Ink Manual published by the Society of Brit ish Printing Ink Manufacturers identifies cyclohexanone as "used in special

screen inks". Cyclohexanone is also identified as used in the aerospace industry (Document No. RCRA

- 2003

- 0004

- 0119; Appendix A). Appendix C to EPA PrintStep Printers' Plain Langu age Workbook Template ( Printstep, 1999 ) identifies 1,1,2

- trichloroethane as potentially used in some printing processes.

### 15 D ata Source: TRI and TURA

To account for uncertainty with chemical use, data obtained from 2004 TRI and a similar Massachusetts program, 2004 TURA, were used as the basis for another end of the assumed range of use (Exhibit B

- 3 identifies the data sources used for each chemical). Most of the F

- listed constituents are identified in the 2004 TRI or 2004 TURA toxic chemical reporting results. These results, in part, show the frequency that certain chemicals are reported by facilities. While the

results do n ot identify whether the chemical is used as a solvent in conjunction with towels or wipes, or even if it is used as a solvent at all, the data show the frequency that the chemical is reported as present above the threshold reporting quantities.

Because of its nationwide scope, data from TRI were used preferentially over the Massachusetts TURA data. The Massachusetts TURA data were used for those constituents, such as acetone, that are not included in the list of chemicals for which TRI reporting is requir ed. See Exhibit E

- 3 in Appendix E for the TRI and TURA data.

Resulting Use of Data

Each of these sources is used to identify or estimate the percentage of facilities using a particular type of chemical. However, not all wipes used at a site will be use d in conjunction with F

- listed solvents, and a site may use multiple types of solvents. Kimberly

- Clark (1997) has estimated that 30 percent of disposables generated by a facility contacts ‘hazardous solvents’ (this percentage applies to sites in which wip es contact ‘hazardous fluids’ at any amount). This 30 percent value is

used in the calculation to estimate the fraction of wipes that contain a particular chemical. 8



### Values used in the analysis for the printing sector are summarized in Exhibit 2

- 6 (value s for this sector are shown as an example). Data for other industries are provided in Exhibit B

- 3 in Appendix

B.

Exhibit 2

- 6. Data Used for Estimating the Percent of Wipes Generated by the Printing Sector Containing Certain Constituents

Chemical F

Listi ng Range / Average for Printing

Sector Are Industry

Dependent Data

Used for Other

Sectors? Data Sources Used (see table note)

Min Max

Acetone F003 16

- 26% (avg. 21%) Yes (Biennial Report) 5 1

Benzene F005 0.2

- 2.1% (avg. 1.1%) No

– same for all 2 6

n

- Butanol F003 2.1

- 5.4% (avg. 3.7%) No

– same for all 2 6

Chlorobenzene F002 0.4

- 0.9% (avg. 0.6%) No

– same for all 6 3

Cresols F004 0

- 0.3% (avg. 0.2%) Yes (Biennial Report) 1 6 Cyclohexanone (note 4) F003 1.8% No

– same for all 5 5
1,2

- Dichlorobenzene F002 0.2

- 0.9% (avg. 0.5%) No

– same for all 6 3 Ethyl acetate F003 2.4

- 6.6% (avg. 4.5%) No

– same for all 2 5 Ethyl benzene F003 5.7

- 26% (avg. 16%) Yes (Biennial Report) 6 1

2

- Ethoxyethanol F005 0.1

- 20% (avg. 10%) Yes (Biennial Report) 6 1 Isobutyl alc ohol F005 0.3

- 0.9% (avg. 0.6%) No

– same for all 2 5

8 The Alliance of Automobile Manufacturers (AAM, 2006) similarly estimates that not all wipes at a facility are used

with F

- listed s olvents. AAM estimates five to ten percent of wipes at its facilities contain F

- listed solvents. As a conservative assumption, and because the Kimberly

- Clark estimate is based on a variety of industries, the calculation uses the higher Kimberly

- Clark est imate of 30 percent.

### 16 Chemical F

Listi ng Range / Average for Printing

Sector Are Industry

Dependent Data

Used for Other

Sectors? Data Sources Used (see table note)

Min Max

Methanol F003 10

- 26% (avg. 18%) Yes (Biennial Report) 6 1 Methyl ethyl ketone F005 8.4

- 20% (avg. 14%) Yes (Biennial Report) 6 1 Methyl isobutyl ketone F003 4.2

- 26% (avg. 15%) Yes (Biennial Report) 6 1

Methylene chloride F001,

F002 2.1

- 3.5% (avg. 2.8%) Yes (Biennial Report) 6 1

Tetrachloroethylene F001,

F002 0.7

- 0.9% (avg. 0.8%) No

– same for all 2 6

Toluene F005 13

- 20% (avg. 16%) Yes (Biennial Report) 6 1
1,1,2

- Trichloroethane F002 0.1

- 0.9% (avg. 0 .5%) No

– same for all 6 3

Trichloroethylene F001,

F002 1.7

- 1.8% (avg. 1.8%) No

– same for all 2 6

Xylene F003 12

- 26% (avg. 19%) Yes (Biennial Report) 6 1 1. 2003 Biennial Report. Assume chemical use (e.g., acetone) in the industry is equal to overall use of corresponding F

listed solvent (e.g., F003). Different usage percentages are used for different industries. 2. Miscellaneous Metal Parts NESHAP. The same percentages are used for all industries due to lack of data to

differentiate.

3. Chlorinated solvent usage assumption for printing industry (Document No. RCRA

- 2004

- 0004

- 0106). This same percentage is used for all industries due to lack of data to differentiate. 4. Except as indicated, no other data are available for cyclohexanone to form a basis of estimate. 5. 2004 TURA. The same percentages are used for all industries. 6. 2004 TRI. The same percentages are used for all industries.



### 2.3.4 Estimated Count of Spent Solvent Disposable Wipes Generators

This term is intended to reflect the number o f facilities using disposable wipes in conjunction with solvents. The population is limited to RCRA LQG and SQG facilities (smaller CESQG facilities are already exempt from RCRA regulations for disposable wipes management) and RCRA solvents.

As a result of the methodology described in this section, a single point estimate of the number of generators is developed. There is uncertainty associated with this point estimate because of uncertainties in the contributing data terms. The overall methodology used for developing the point estimate is presented below. Each of the contributing terms, as well as the associated uncertainties, is described in the following text.

• Step 1: Estimate the number of affected facilities in a subset of defined sectors. (a) There a re 445,010 total facilities in 13 evaluated sub

- sectors. (b) 33.5 percent of the facilities use RCRA solvents on their wipes; (c) 67.1 percent of the facilities are either a RCRA LQG or SQG regulated facility (d) 8.1 percent of the facilities use primarily disposable wipes rather than reusable wipes.

• Step 2: Expand the estimate from the defined sectors to across all industries. Of sites impacted, 56 percent are estimated to be included in the 13 sub

- sectors evaluated above.

• Step 3: Account for the potential increased use of disposables in the future, if a regulatory exclusion is implemented. In an earlier economic analysis prepared by EPA (2003), disposable use may increase up to 17%.

• The resulting number of generators is estimated from the above data, as illustrated in Exhibit 2

- 7.

### 17 Exhibit 2

- 7. Sample Calculation for Estimating Number of Generators of Disposable Wipes

Value Parameter
445,010 Facilities in 13 evaluated sub

- sectors.
x 33.5% % Facilities using RCRA solvents on their wipes.

x 67.1% % Facilities wh ich are either a RCRA LQG or SQG (i.e., subject to RCRA hazardous waste management regulations).

x 8.1% % Facilities using primarily disposable wipes rather than reusable wipes.

/ 0.56 ‘Scale up’ from the 13 evaluated sectors to all sectors.

x 17%/8.1% Potential future impact of increased use of disposable wipes in

future.
= 30,380 facilities Result differs slightly from expression due to rounding.

The resulting estimate of 30,380 facilities is used in subsequent calculations, and is intended to

repre sent a ‘most likely estimate’ of this parameter. It is intended to take into account facilities from all industries using disposable wipes, as well as potential increased usage in the future. A number of different data sources were used to develop this e stimate, which are described below.

Step 1: Estimate the Number of Affected Facilities in a Subset of Defined Sectors

As a starting point, EPA’s July 2003 Economic Impact Analysis for the November 2003 wipes proposed rule (EPA, 2003) was used in identify ing the number of potentially impacted generators (i.e., generator facilities) of spent solvent disposable wipes in each of 13 evaluated industrial sub

sectors. Based on the framework of the July 2003 economic analysis (EPA, 2003), EPA developed for the p urpose of these landfill loading calculations an updated estimate of 8,106 facilities (EPA, 2006a); this update resulted from using the more recent 2002 Economic Census data (http://www.census.gov/econ/census02) in place of the 1997 Economic Census data us ed in EPA (2003). The updated wipes generator count reflects three filtering (screening) assumptions applied in the economic analysis to the respective Economic Census counts of industrial facilities:

1) 33 percent of the 445,010 total facilities in these 1 3 sub

- sectors (source: 2002 Economic Census) use RCRA solvents on their wipes; 2) of which 67 percent are either a RCRA LQG or SQG regulated facility excluding CESQGs which are already exempt from RCRA regulations; 3) of which 8 percent use primarily disposable wipes rather than reusable wipes.

This updated estimate is 8,106 sites (i.e., 445,010 x 33.5% x 67.1% x 8.1% = 8,106). These filtering percentages are overall average values from EPA (2003). The sources and computations involving these facility count f iltering percentages are provided in EPA's 2003 Economic Analysis for the proposed rule (EPA, 2003).

Step 2: Expand the Estimate from the Defined Sectors to Across All Industries

A potential limitation of the estimated count from EPA (2006a) is that not all industries using solvent wipes may be accounted for (because more than these 13 industries may use disposable wipes), and therefore the number of facilities and wipe usage may be underestimated in EPA's 2003 economic analysis. Data from EPA’s 2003 Bie nnial Report were used to identify and account for these possible additional facilities in additional industries.

### 18 The following methodology provides additional detail regarding how the 2003 Biennial Report data were used in developing the estimated number of wipe generators:

• Results from the WR Form of the 2003 Biennial Report were used for estimating the number of generators. This form is completed by companies receiving hazardous waste for management (e.g., Treatment, Storage, and Disposal (TSD) facili ties); these companies report the sites from whom waste was received (e.g., generators). According to these data, F001

- F005 hazardous waste was received from 63,845 facilities in 2003. These were assumed to be hazardous waste generators, but are of all s izes (i.e., LQGs, SQGs, and CESQGs) and include all forms of F001

- F005 wastes, not just wipes.

• NAICS codes were obtained for these generators from EPA’s RCRA Info database. NAICS codes were not available for a significant number of sites (32,382 of 63,84 5, or 51%). This high percentage of missing data could be because: (a) there are typographic errors between the Biennial Report data and the RCRA Info data used in looking up the NAICS codes; (b) the generator identified by the TSD is a CESQG and not incl uded in RCRA Info; or (c) the facility was in RCRA Info but no NAICS data were available for the facility.

• Approximately 56 percent of the facilities reported one or more NAICS codes ‘covered’ as the originally evaluated 13 industries, which means that ap proximately 44 percent are not. This does not include the significant number of facilities with blank NAICS codes. Facilities can report more than one NAICS code. This percentage (56 percent) is applied above.

Additional information was used to assess the reasonableness of the estimated present

- day number of wipes generators (from above equation, this is equivalent to 8,106/0.56 = 14,475 sites). Results

are summarized below:

• EPA evaluated available data to identify ten industries, in addition to the 13 previously evaluated, which may use RCRA

- regulated solvent wipes. Details are in Appendix G. Applying the same filtering (screening) percentages as summarized above for the initial 13

industrial sub

- sectors (e.g., 33.5% of facilities use RCRA solvents on wipes), provides an estimate of 6,920 additional RCRA

- regulated spent solvent disposable wipes generators. When added to the 8,106 count for the 13 sub

- sectors, the resulting estimate is 15,026 (6,920 + 8,106) generators of spent solvent disposable wi pes.

There is uncertainty with this estimate because the percentage of RCRA solvent use, wipe use, etc. in these additional industries was assumed to be the same as those for the 13 sectors initially evaluated. EPA does not have wipes

- related informati on for these other 10 candidate industries. Nevertheless, the resulting estimate using this method (15,026 sites) is very close to the estimate using the Biennial Report data (14,475 sites).

• The number of sites calculated using the above method (14,475 s ites) was compared to several other estimates derived from the Biennial Report data:

o As noted above, according to the WR Form of the 2003 Biennial Report, 63,845 facilities generate F001

- F005 wastes in 2003. These are LQGs, SQGs, and CESQGs and include a ll forms of these wastes, not just wipes.

### 19 o Data from the GM Form (generally these are LQGs) show that 9,877 facilities generated

F001

- F005 wastes in 2003. This includes wastes other than shop towels and wipes. Refer

to Exhibit D

- 2 in Appendix D.

As shou ld be expected, the estimated number of (present

- day) facilities of 14,475 is lower than the estimated 63,845 LQGs, SQGs, and CESQGs generating F001

- F005 wastes.

Step 3: Account for the Potential Increased Use of Disposables in the Future

In addition to estimating potential direct impacts of the November 2003 industrial wipes proposed rule, EPA’s July 2003 Economic Impact Analysis (EPA 2003) estimated the potential “induced impacts” of the proposed exclusions in the form of potential effects on relative p rices and national market share of disposable and reusable wipes. Specifically, this affects the assumption of the percentage of facilities primarily using disposable wipes. Precise forecasts affecting the degree of this change in market share are depend ent, in part, on the regulatory option selected for the final rule (which does not exist at the time of this writing). As an approximation, the forecasts used in the Economic Analysis for the proposed rule (page 37 of EPA, 2003) will be used where the rel ative national market share for disposable wipes increases to 17 percent (for a ‘most likely’ scenario).

The following calculation shows the estimated number of facilities using disposable wipes that is applied in this landfill loading analysis:

8,106 Up dated estimate of the number of sites in the initial 13 industrial sub

- sectors identified as using disposable wipes, from EPA (2006a).

/ 0.56 Factor developed from a query of EPA’s 2003 RCRA Biennial Report data (as explained below), to expand the estimat ed number of facilities to include other industries not identified in EPA (2003).

x (17%/8.1%) Potential “induced impacts” multiplier to increase the relative market share of disposable wipes (based on EPA, 2003) which projects a net nationwide

increase i n the use of disposable wipes to 17% of facilities as a result of the proposed regulatory exclusion. 9



### 30,380 Resultant count of industrial facilities estimated to use disposable wipes following a final exclusion, projected for all industries.

These data are summarized in Exhibit 2

- 8. The facility counts from the Economic Analysis, used in deriving the data in Exhibit 2

- 8, are reproduced in Appendix B (Exhibit B

- 3). The number of sites

in each sector listed in Exhibit 2

- 8 is derived by multiplying the p ercentages (in Exhibit B

- 4 of Appendix B and obtained from the Economic Analysis data) by the 'scaled up' total of 30,380 sites.

Exhibit 2

- 8. Percent of Generators in Each Sector

Sector EA Sector # Number of Sites Percent of Total
Printing 1, 10, 11 7,2 62 24%

9 The percent of facilities estimated to use disposables in the future is estimated to be 17% using EPA (2003). This new value is used to essentially replace the previously used ‘present day’ value of 8.1% noted earlier.

### 20 Sector EA Sector # Number of Sites Percent of Total
Transportation 7 334 1%

Auto repair 12 4,252 14%

Electronics 6 147 0.5%

Other 2, 3, 4, 5, 8, 9, 13 5,018 61% All Other

--- 13,367

Total 30,380 100%

EA Sector number: Numbering convention as used in the Economic Analysis (EPA, 2003).

Sum of numbers may differ very slightly from the total due to rounding.



### 2.3.5 Percent of Constituent in the Solvent

An F

- listed chemical may be present in a solvent in a 99+ percent pure form, or may be present in a trace amount. For example, benzene is often p resent only as a trace ingredient of petroleum

- based solvents. The percent of constituent present in a solvent is assumed different for each chemical. Data for these formulations are obtained from development data used for the Miscellaneous Metal

Part NE SHAP (ERG, 2003). This source identifies the cleaning solvents used at a large number of sites (175), but the source is somewhat dated (from the early 1990’s). Data were available for 16

chemicals and are included in Exhibit 2

- 9. For four other evaluate d chemicals not identified in ERG (2003) (i.e., chlorobenzene, cyclohexanone, 1,2

- dichlorobenzene, and 1,1,2

- trichloroethane), a range of 1 to 100% was assumed (in the probabilistic analysis, the concentration is assumed to vary uniformly over this range). This step of the calculations only accounts for chemicals present in a

solvent at non

- zero concentrations, so the lower bound of the range for this parameter is not zero for any chemical.

Exhibit 2

- 9. Source of Data Used for Percent of Constituent in S olvent

Chemical Data Source % of

Sites A 

### Number

of Sites Distributional Assumption

1,1,1

- Trichloroethane B NESHAP (ERG, 2000) 1% 2

--- Acetone NESHAP (ERG, 2000) 10% 17 Exponential

Benzene NESHAP (ERG, 2000) 1% 1 Constant

Chlorobenzene Assumption

--- - -- Uniform

Cresols NESHAP (ERG, 2000) 1% 2 Uniform Cyclohexanone Assumption

--- --- Uniform
1,2

- Dichlorobenzene Assumption

--- --- Uniform Ethyl acetate NESHAP (ERG, 2000) 8% 14 Lognormal Ethyl benzene NESHAP (ERG, 2000) 16% 28 Exponential

2

- Ethoxy e thanol / Glycol ethers C NESHAP (ERG, 2000) 7% 12 Lognormal

Isobutanol NESHAP (ERG, 2000) 1% 2 Uniform

Methanol NESHAP (ERG, 2000) 15% 26 Lognormal Methyl ethyl ketone NESHAP (ERG, 2000) 43% 75 Beta Methylene chloride NESHAP (ERG, 2000) 6% 10 Weibull

MIBK NESHAP (ERG, 2000) 18% 32 Weibull

n

- Butanol NESHAP (ERG, 2000) 7% 12 Lognormal Tetrachloroethylene NESHAP (ERG, 2000) 2% 4 Weibull

Toluene NESHAP (ERG, 2000) 31% 55 Extreme Value
1,1,2

- Trichloroethane Assumption

--- --- Uniform Trichloroethylene NESHAP (ERG, 2000) 6% 10 Weibull Xylene NESHAP (ERG, 2000) 38% 66 Exponential ERG, 2003 is from Miscellaneous Metal Parts NESHAP Development. The 2003 ERG memo identifies that 175 facilities reported using cleaning solvents. The memo lists the solvent s used by each of these 175 facilities.

### 21 A. Percent is of the 175 sites reporting cleaning solvent use (ERG, 2003); these data are used, in part, in estimating the percent of wipes containing some chemicals as identified in Section 2.3.3. B. ERG (2003) incl udes data for 1,1,1

- trichloroethane; as noted in Section 1.2.1, landfill loading estimates are not developed for this chemical. C. Data for glycol ethers in ERG (2003) were used for 2

- ethoxy ethanol.



### Exhibit 2

- 9 represents a summary of the data used in t he evaluation. The columns ‘% of Sites’ and ‘Number of Sites’ identifies the number of data points used for developing the input parameter distribution assumption (i.e., one data point for each site). For example, for methanol, 26 data points were used t o generate the input parameter distributional assumption using a best fit of the data. Much less data were available for isobutanol, where only two data points were available.

2.3.6 Number of Generators Disposing Towels or Wipes in a Single Landfill

As i dentified in Section 2.3.4, approximately 30,000 generators are assumed to generate F001 to

F005 solvent

- contaminated disposable wipes; these generators are assumed to dispose their spent wipes as a nonhazardous waste. Nationwide, there are approximately 1,600 MSW landfills. Therefore, the analysis accounts for the use of one landfill by multiple generators. Neither the landfills nor the generators are spread uniformly across the country; the following factors were

assumed to affect this distribution:

• T he number of generators per state. This is approximated by using Department of Census state distribution data for six target industries (Census, 2002), and applying this distribution to the approximately 30,000 generators to estimate the number of generat ors per state.

• The number of landfills per state (using Simmons, 2006 data). For example, there are a lower number of landfills in Northeastern states as compared to the rest of the country.

• For each state, the percentage of MSW, which is landfilled as opposed to being combusted (using Simmons, 2006 data). Wipe disposal is assumed to follow this percentage. For example, Northeastern states rely on combustion more so than Midwestern states, and therefore Northeastern states were assumed to dispose less of their wipes into landfills.

The average number of generators per landfill was calculated for each state using the above data (with a minimum of one generator per landfill). This distribution (which ranges from 2 to 67 generators per landfill) was used as part of the analysis; the data are included as Exhibit B

- 5 in Appendix B. As indicated in Exhibit 2

- 1, the empirical data were ‘best fit’ to a probability

distribution.

2.4 Results

2.4.1 Loading Estimates

The above data were used as inputs for each o f 20 chemicals (i.e., 10 chemicals identified in Section 1.2 were not assessed). The calculations were conducted using Microsoft Excel ® 

### . Calculations were conducted using both deterministic (point

- value) and Monte Carlo methods. The deterministic

calcul ation was conducted by setting all inputs to their average values.

### 22 The Monte Carlo analysis was conducted with Excel ® 

### spreadsheets using Crystal Ball ® 

### . As discussed above, either a distribution or a fixed value is used for each input parameter (based on data availability). A separate calculation is conducted for each of the 20 chemicals. Certain input parameters are not affected by the evaluated chemical (e.g., number of towels used by a generator), and are assessed using the same probability distributi on for all chemicals. For each of 100,000 iterations, all parameters are assessed independently. The output of a Monte Carlo analysis results in a distribution of values. A sample graphical Monte Carlo output is presented in Exhibit 2

- 10.

Exhibit 2

- 10. Monte Carlo Results for Acetone in Disposable Wipes

The values in Exhibit 2

- 11 represent landfill loading estimates for each chemical (i.e., the quantity of chemical that a single landfill will receive from large and small q uantity generators of hazardous waste). The Monte Carlo analysis provides percentile results from 0 to 100; for summary purposes the mean, median (50 th 

### percentile), 90 th 

### percentile, and 95 th 

### percentile values are shown in Exhibit

2

- 11. The deterministic value, assessed by setting all input parameters to their mean values, is also presented (the sample calculation for this deterministic result was previously presented in Exhibit 2

- 1).

One use of the deterministic results is to provide a reference in revie wing the Monte Carlo results. For each chemical, the deterministic result is between the 50 th 

### and 90 th 

### percentiles of the Monte Carlo distribution, and close to the mean of the Monte Carlo result. For example, Exhibit 2

- 11

identifies a deterministic resu lt for acetone of 4,966 kg/landfill/year, a mean Monte Carlo result of 4,041 kg/landfill/year, a 50 th 

### percentile Monte Carlo result of 1,033 kg/landfill/year, and a 90 th


### percentile Monte Carlo result of 9,120 kg/site/year.

The deterministic result (using mean values as input) and the mean of the probabilistic result are very close, indicating good consistency. The reason that the deterministic results are generally higher than the 50 th 

### percentile Monte Carlo results is due to the shape of the input distri butions (e.g., lognormal distributions); a characteristic of this assumption is that the average value is higher than

the median value. This is shown in Exhibit 2

- 12 for several input parameters. Frequency Chart

kg/yr .000 .075 .151 .226 .301

0 30128

0 10,000 20,000 30,000 40,000 

### 100,000 Trials 1,311 Outliers Forecast: Disposables (kg/yr) - Acetone

### 23 Exhibit 2

- 11. Annual Landfill Loading Results for Disposa ble Wipes

Constituent Result per Landfill (kg/yr)

Deterministic (Mean Inputs) Probabilistic

Mean 50 th



### Percentile 90 th



### Percentile 95 th



### Percentile

Carbon disulfide

Carbon tetrachloride

Dichlorodifluoromethane Ethyl ether

Nitrobenzene

2

- Nitropropane Py ridine
1,1,1

- Trichloroethane
1,1,2

- Trichlorotrifluoroethane

Trichlorofluoromethane 0 0 0 0 0

Acetone 4,966 4,041 1,033 9,120 16,224

Benzene 4.7 5.4 1.8 12.1 20.2

n

- Butanol 298 291 61 607 1,146

Chlorobenzene 164 193 56 431 752

Cresols 28 29 9 58 103

C yclohexanone 493 549 159 1,207 2,102
1,2

- Dichlorobenzene 137 162 46 364 631 Ethyl acetate 172 192 52 414 733 Ethyl benzene 774 861 184 1,857 3,446

2

- Ethoxyethanol 2,083 1,242 222 2,606 4,974 Isobutyl alcohol 90 98 31 220 376

Methanol 1,161 1,208 225 2,431 4,747 Methyl ethyl ketone 5,140 5,052 1,307 11,676 20,532 Methyl isobutyl ketone 1,129 1,015 150 2,073 4,184 Methylene chloride 1,441 1,505 465 3,268 5,583 Tetrachloroethylene 353 323 107 727 1,226

Toluene 3,932 3,920 1,215 8,918 15,180
1,1,2

- Trichloroethane 137 149 40 330 581 Trichloroethylene 900 842 308 1,911 3,146 Xylenes (total) 5,106 3,835 958 8,556 15,184 Deterministic result is calculated using the mean (average) value of all input parameters. For 10 chemicals, no use was assumed; th erefore, results are zero.



### Exhibit 2

- 12. Comparison of Average and Median Values for Input Parameters

Parameter Average Value Median Value
Number of disposables used per site per year (printing) 129,990 41,350 Number of disposables used per site per y ear (transportation) 73,819 37,500 Number of disposables used per site per year (auto repair) 12,964 7,692 Number of disposables used per site per year (other) 66,436 15,500 Number of generators/landfill 18.9 16 Median input values are from the raw emp irical data, and not from the parameters of the fitted distribution.



### 2.4.2 Sensitivity Analysis

Several techniques were used to identify which input parameters contribute most to variation in risk

estimates. As reflected in Exhibit 2

- 1, landfill loading results for a single chemical requires 21 input parameters. Sensitivity analyses were conducted for those input parameters where sufficient information is available to identify a distribution or range of values.

### 24 Inspection of Exhibit 2

- 1 shows that the percent of generators in each sector (e.g., printers) is assumed to be constant. The percentage of generators is highest for ‘other industries’ and ‘printers’, and lowest for electronics and transportation. These percentages affect the sensitivity of ind ustry

specific parameters (such as number of wipes used per site per year in each sector). As a result, input parameters which affect the printing and ‘other’ sectors are much more likely to impact the overall result than input parameters which only affec t the remaining three sectors.

To further evaluate sensitivity, rank correlation coefficients for each parameter were calculated using CrystalBall ® 

### software. Further information on this method is provided in Appendix A to

EPA’s Risk Assessment Guidance for Superfund Volume III Part A, Process for Conducting

Probabilistic Risk Assessment (EPA 2001). In this method, variations in each input parameter are correlated to the output. The higher the correlation, the higher is the dependence of the particular input parameter on the result (the landfill loading).

The results, shown in Exhibit 2

- 13, are expressed as the square of the rank correlation coefficients

and normalized to 100% (i.e., the results in this column add to 100%) . The results serve two

purpo ses. First, the most sensitive parameter is indicated with the highest sensitivity result. Second, the results provide an indication of relative sensitivity. Results that are close to one another (e.g., the input parameters identified in the top two row s) indicate that one parameter is only slightly

more sensitive than the other.

Exhibit 2

- 13 presents the parameters evaluated, the result for one chemical (acetone) arranged from most sensitive to least sensitive, and discussion of results for all 20 chem icals. The most sensitive parameter is indicated with the highest sensitivity result. Exhibit 2

- 13 shows that the number of disposable wipes used per year in printing and the number of disposable wipes used per year in

other industries are the two most s ensitive parameters for the majority of chemicals evaluated and can be considered the most important with respect to their influence on variability of results. From

Exhibit 2

- 13, the most sensitive parameters are as follows:

• Number of wipes used per site per year, in printing and ‘other’

• Solvent loading per wipe, in printing and ‘other’

• Percent of chemical in solvent

• Number of generators per landfill

The following are limitations to this sensitivity analysis:

• Sensitivity was not evaluated for ‘constant’ parameters such as the percent of generators in each sector (e.g., printers), which is directly related to the number of sites in the sector. This does not imply that the result is insensitive to the input parameter. Estimates for these parameters were primarily developed from Census data (described in Section 2.3.4) in which variability was not assessed, and insufficient information is available to develop a plausible distribution. Nevertheless, the estimate used in the analyses (i.e., that printer sit es make up the largest single sector, from Exhibit 2

- 8) is supported by observations that the printing sector has historically been a principal stakeholder in EPA’s solvent wipes activities.

• Data limitations affect the results of the sensitivity analysis, when the range of a given parameter is not fully characterized. For example, the fraction of wipes containing the chemical

### 25 is estimated over a fairly limited range for most parameters (described in Section 2.3.3) from two data points. Again, insufficien t information is available to develop a more extensive distribution, and instead the input parameter was assumed to vary uniformly between these two points.

Exhibit 2

- 13. Sensitivity Analysis for Disposables Evaluation

Parameter Result for Acetone Result for All 20 Chemicals

Percent constituent in solvent 41.1% This parameter was the most sensitive for 14 of 19

chemicals.

Number of disposable wipes used per site per year in Other

Industries 18.1% This parameter was the second

- most sensitive for

13 of t he 20 chemicals; for six of the remaining chemicals it was the most sensitive parameter.

Number of disposable wipes used per site per year in Printing 16.4% For all 20 chemicals this was the third

- or fourth

most sensitive parameter.

Number of generators per landfill 16.1% For 13 of 20 chemicals this was the third

- or fourth

- most sensitive parameter; for the remaining

seven chemicals it was the second

- most sensitive

parameter.

Solvent Loading, g/wipe in

Other Industries 5.4% For 17 of 20 chemicals this w as the fifth

- most sensitive parameter.

Solvent Loading, g/wipe in Printing 2.5% For 17 of 20 chemicals this was the sixth

- most sensitive parameter. Percent wipes containing chemical in Printing 0.1%

These parameters are the least sensitive. They are

les s sensitive than the six parameters above for 17

of 20 chemicals. Percent wipes containing

chemical in Other Industries 0.1%

Percent wipes containing chemical in Transportation 0.0%

Percent wipes containing chemical in Auto Repair 0.0%

Percen t wipes containing

chemical in Electronics 0.0%

Solvent Loading, g/wipe in Transportation 0.0%

Solvent Loading, g/wipe in Auto Repair 0.0%

Solvent Loading, g/wipe in

Electronics 0.0%

Number of disposable wipes used per site per year in

Electronics 0.0%

Number of disposable wipes used per site per year in Transportation 0.0%

Number of disposable wipes used per site per year in Auto Repair 0.0%

The sensitivity results for each parameter are in units of ‘percent’ and total to 100%.

### 26 2.4.3 Effec ts of Input Parameter Distribution Assumptions on Results

The results presented in Section 2.4.1 are based, in part, on assumptions made with regard to the distribution of each input parameter. To assess the significance of changes in the input parameter distribution assumptions on the results, several ‘what

- if’ analyses are shown in this section.

Procedure

As described in Section 1.3, most of the assumptions were selected using a ‘goodness of fit’ test: the assumption with the best fit was selected. T he purpose of this section is to describe the impact on results when changing these assumptions. The following conditions are used in this section as a basis for selecting a different distribution assumption for an input parameter:

• When two or more distr ibutions provide a ‘good fit’ to the data. As discussed in Section 1.3, generally the assumption providing the best fit is used in the analysis because there is typically no information to assess which distribution assumption would be most appropriate for the population from a purely mechanistic perspective. This section examines the impact that an alternative assumption would have on the results. Appendix H summarizes the results for six input parameters and includes graphs comparing the selected distri butions to the empirical data.

• When data outliers may exist. Potential outliers in the data inputs are identified in this section and an alternative distribution, without the outlier, is developed. When an outlier exists, it does

not mean it should nece ssarily be removed, because it may be representative of a tail of a population. Instead, the purpose of this analysis is to evaluate the effects that such an assumption may have on the results. This evaluation is conducted, in part, to investigate impact s of uncertainties in the underlying data. Appendix H summarizes the outlier evaluation results for three input parameters.

• To adjust for unrealistic simulations. A 2010 public comment by INDA identified that it would be appropriate to include an upper l imit on the amount of solvent present on a wipe, because under all possible exclusion scenarios a wipe would be required to contain no free liquids. As an alternative assumption, all distributions of the amount of solvent on a wipe ( described in

Section 2 .3.2) are truncated at 100 grams per wipe; this value was chosen as an upper bound

because no observations were above this value. 10 

### Specifically, the input distributions for the following three parameters were adjusted to truncate at 100 grams per wipe: so lvent loading for printing, solvent loading for transportation, solvent loading for other industries. All three of these parameters use a Weibull distribution (i.e., an unbounded probability distribution). Input

distributions for two other solvent loadin g parameters, auto repair and electronics, did not require adjustment because they were bounded probability distributions with the maximum value less than 100 grams per wipe.

• In this section, most or all analyses are limited to those input parameters iden tified as most

sensitive in Section 2.4.2:

o Number of wipes used per site per year, in printing

o Number of wipes used per site per year, in ‘other’

10 Some jud gment is used here, due to limitations in source data, with regard to the precise value to use as a plausible upper bound. It is intended to reflect ‘plausible’ and not ‘absolute’ conditions.

### 27 o Solvent loading per wipe, in printing

o Solvent loading per wipe, in ‘other’

o Fraction of chemical in solvent

o Num ber of generators per landfill

Results

In these analyses, the assumption regarding the shape of the input distributions is changed, one at a time, for several input parameters. This analysis does not include all possible combinations of the

different as sumptions. The procedure and results are shown in Exhibits 2

- 14 through 2

- 16

.

Exhibit 2

- 14. Effect of Changes in Input Parameter Distributions on Results for Methylene

Chloride

Parameter How was

Distribution

Changed? Reason for Change in Assumption Resu lt, 50 th



### Percentile Result, 90 th



### Percentile Result, 95 th



### Percentile

“Baseline” No change No change 465 3,268 5,583 Number of wipes used per site per year, in printing From gamma to lognormal Per Exhibit H

- 1 in Appendix H, both gamma and lognormal

distrib utions provide good fits

to the data. 459 3,673 6,794

Number of wipes used per site per year, in ‘other’ From lognormal

to gamma 473 3,348 5,527

Number of wipes used per site per year, in ‘other’ Eliminate potential outlier;

re

- evaluate with lognormal P er Exhibit H

- 2, the maximum value is a potential outlier. A lognormal distribution is a good fit for the new data set. 429 2,837 4,806

Solvent loading per wipe, in printing From Weibull to

normal Per Exhibit H

- 1, the normal distribution also provides a

reasonable fit to the data. 485 3,289 5,632

Solvent loading per wipe, in ‘other’ From Weibull to

normal 469 3,247 5,621

Number of

generators per
landfill From gamma to lognormal The distribution is described almost equally well as a gamma distribution o r a lognormal distribution; per

Exhibit H

- 1, both provide good

fits. 458 3,230 5,718

Number of

generators per
landfill Eliminate potential outlier;

re

- evaluate with

gamma Per Exhibit H

- 2, the maximum value is a potential outlier. A lognormal distribution is a good fit for the new data set. 451 3,081 5,217

Results for ‘baseline’ case are identical to those in Exhibit 2

- 11.

### 28 Exhibit 2

- 15. Effect of Changes in Concentration Input Parameter Distributions

Parameter How was

Distribution

Changed? Reason for Ch ange Result, 50 th



### Percentile Result, 90 th



### Percentile Result, 95 th



### Percentile

“Baseline” for 2

- ethoxy ethanol No change No change. 222 2,606 4,974

2

- ethoxy ethanol Lognormal to

Normal Per Exhibits H

- 16 and H

- 28, both provide good fits. 701 5,915 10,583

“ Baseline” for

acetone No change No change. 1,033 9,120 16,224

Acetone Lognormal to

Normal Per Exhibits H

- 13 and H

- 27, both provide good fits. 1,750 12,955 22,001

“Baseline” for methylene chloride No change No change. 465 3,268 5,583

Methylene chloride W eibull to

Normal Per Exhibits H

- 19 and H

- 26, both provide good fits. 428 3,023 5,223

Results for ‘baseline’ case are identical to those in Exhibit 2

- 11.



### Exhibit 2

- 16. Effect of Changes in Solvent Loading Input Parameter Distributions

Parameter How was

Distribution

Changed? Reason for Change Result, 50 th



### Percentile Result, 90 th



### Percentile Result, 95 th



### Percentile

“Baseline” for

acetone No change No change. 1,033 9,120 16,224

Acetone Truncated upper

end of solvent loading at 100

gram/wipe Response to 201 0 INDA public comment [ wipes with

too much solvent would not pass free liquid test and could not be disposed in landfill ]

. 973 8,527 15,099

“Baseline” for tetrachloroethylene No change No change. 107 727 1,226

Tetrachloroethylene Truncated upper

end of s olvent loading at 100

gram/wipe Respo nse to 2010 INDA public comment [ wipes with

too much solvent would not pass free liquid test and could not be disposed in landfill ]

. 102 670 1,117

Results for ‘baseline’ case are identical to those in Exhibit 2

- 11.



### Co nclusions

The most significant effects on results are in Exhibit 2

- 15, where changes in input parameter assumptions on concentrations are made. This is consistent with the sensitivity analyses in Section 2.4.2, where concentration was typically the most sensitive parameter. In Exhibit 2

- 14, variation in the results is less pronounced, indicating that changes in these input parameter assumptions have

minor effects on results.

Exhibit 2

- 16 shows that the inclusion of a plausible upper bound of 100 grams p er wipe had little effect on results. As expected, this change resulted in lower loadings than the original analysis using unbounded input distributions. The magnitude of this difference was approximately 6 to 7 percent for acetone , and approximately 8 t o 9 percent for tetrachloroethylene, at the 90 th 

### and 95 th


### percentiles. Differences at the 50 th 

### percentile were less.

### 29 The results presented in this section demonstrate the effect of various uncertainties regarding the input parameter distributional assump tions on the results presented in Section 2.4.1.

2.4.4 Uncertainties Associated with the Analysis

Uncertainties in the results presented in Exhibit 2

- 11 include the following sources:

• Uncertainties in the values used as input parameters.

• Assumptions inh erent in the modeling equation (discussed in Section 2.2).

• Many aspects of uncertainty are not easily quantifiable. There are also many ways to look at uncertainty. An additional method we considered was a “bootstrapping” technique. The results

from th is technique are discussed in Appendix J.

Due to their importance in contributing to the total quantity of each chemical assumed to be disposed in a landfill, further information on the six most sensitive parameters identified in Exhibit

2

- 13 is summarize d below:

• Number of disposable wipes used per site per year in ‘Other Industries,’ and number of disposable wipes used per site per year in printing. The source of input data for these parameters is discussed in Section 2.3.1. While the number of data po ints available for describing these parameters is higher than for the other industries, high variability is expected for this parameter (e.g., due to differences in sizes of sites). Because of this, further evaluation of the effect of the input distributi on assumption is conducted in Section 2.4.3.

• Number of generators per landfill. The source of input data for these parameters is discussed in Section 2.3.6. The input data depend on the estimated number of generators nationwide, the

number of landfills nationwide, and regional (i.e., state to state) variations to account that generators and landfills are not distributed uniformly across the country. Evaluating variations at the state level allows for incorporation of regional variability; any further ev aluations below the state level would require much more extensive data analysis as well as knowledge of the distribution and waste management practices of generators, which is unavailable or highly

uncertain.

• Percent constituent in solvent. The source of input data for this parameter is discussed in Section 2.3.5. As with some of the other input data, high variability is expected for this parameter (e.g., due to differences in solvent formulations). Because of this, further evaluation

of the effect of t he input distribution assumption is conducted in Section 2.4.3.

• Solvent loading (grams/wipe) in ‘Other Industries’ and solvent loading in printing. The source of input data for these parameters is discussed in Section 2.3.2. The following are some of th e uncertainties associated in developing these estimates:

o The quantity of solvent applied is variable, both in terms of variations at a single facility and variations from one facility to another.

### 30 o Notwithstanding the above, taken together most data (as c ollected from site visits and industry correspondence) reveals that the quantity of solvent applied spans one order of magnitude (10 to 100 grams) in most cases. Other input parameters (e.g., number of wipes used per site) have a higher range of variabili ty. As a result, data limitations associated with this parameter are expected to have less impact on the variability of results than data limitations associated with other input parameters where the input parameter values are

more variable.

o Further uncer tainty results from assumptions used in estimating the quantity of solvent that escapes from the wipe, such as a result of evaporation or liquid removal. In general, conservative assumptions were used to describe these effects.

Further assumptions are in herent in the modeling equation shown in Exhibit 2

- 1 and discussed in Section 2.2. Certain assumptions are made to simplify the analysis, or because of data limitations:

• It is likely that there is some degree of correlation or stratification of the vario us parameters. For example, facilities that use larger quantities of wipes may at the same time use smaller quantities of solvent per wipe (e.g., compare the data for the Electronics industry in Exhibit 2

- 1 to other sectors). As data permitted, these eff ects were incorporated into the present analysis by making different assumptions for the different industry sectors. However, data limitations generally prevented making further assumptions.

• Insufficient data were available to assess if chemical concentr ation in solvent is likely to differ by industry. For example, the chemical concentration of one chemical (e.g., toluene) in a solvent used by the auto repair industry may be different from the chemical concentration in solvents used by the printing indus try.

• The results of the analysis do not explicitly identify the probabilities that different chemicals are disposed in a single landfill. Exhibit 2

- 12 provides the results for each chemical; further assumptions are required in using these results to esti mate the quantities of multiple chemicals

at a landfill.

• Other than estimates made for the number of generators (Section 2.3.4), the input parameters do not account for future changes in generation or management practices. Most input parameters

are based on ‘present day’ characteristics or data that was current at the time it was collected. Trends in the estimates are either unknown or difficult to quantify:

o As an example, it is not known if the quantity of wipes used by generators will increase or

decr ease in the future.

o The use of some specific chemicals can be reasonably expected to increase or decrease in the future. CalEPA (2004) investigated the use of alternative cleanup solvents in the printing industry. Acetone is exempt from VOC regulations and for that reason project sponsors tested its use as a substitute for existing solvents; as a result the use of acetone may increase in the future. Conversely, some chemical use data used in developing the input distributions were collected in preparati on of NESHAP regulations (discussed in Appendix C); it is likely that use has since declined for some of these chemicals.

### 31 2.4.5 Numerical Stability for Disposable Wipe Analysis

Due to the nature of a probabilistic assessment, no two analyses using the sam e input data will yield identical results. This section assesses the numerical stability of the results presented in Exhibit 2

- 11. Numerical stability refers to “the observed numerical changes in parameters of the output distribution (e.g., median, 95 th 

### percentile) from a Monte Carlo simulation as the number of iterations increases” (EPA, 2001).

To assess stability, duplicate analyses using identical input data (i.e., identical input parameter distributions) and identical numbers of iterations (identifie d in Section 1.1) were conducted for one constituent, methylene chloride. R esults are shown in Exhibit 2

- 17. Exhibit 2

- 17 shows that the selected number of iterations gives stable results.

Exhibit 2

- 1 7 . Results of Multiple Monte Carlo Analyses for Meth ylene Chloride in Disposable Wipes

Note Result (kg/yr)

50 th



### Percentile 90 th



### Percentile 95 th



### Percentile

From Exhibit 2

- 6 465 3,268 5,583

Run 2 466 3,256 5,660

Run 3 467 3,232 5,549

Run 4 466 3,251 5,590

Run 5 466 3,240 5,608

Run 6 467 3,226 5,568

A verage 466 3,246 5,593

Minimum 465 3,226 5,549

Maximum 467 3,268 5,660

Relative % Difference A 0.4% 1.3% 2.0% A. Relative percent difference is calculated as the (maximum minus minimum)/average.



### 2.5 Solvent Extraction Technologies

Advanced solvent ex traction technologies, such as centrifuging, will remove significant quantities of solvent from a towel or wipe. Various data are available regarding the performance of these technologies on towels and wipes. These data were not used in the landfill load ing calculations, but are briefly discussed here and in Appendix A (in particular, Exhibit A

- 2) for potential use in further calculations. EPA (undated) includes data for a series of experiments conducted using an industrial centrifuge. Removal efficienc ies are calculated for various towels and wipes and various solvents including isopropanol, methyl ethyl ketone, petroleum solvent, and press wash. Average extraction efficiencies range from 75 to 99 percent. Additional resources for evaluating solvent e xtraction technology performance are included in Appendix A.

### 32 3.0 Solvent Loadings Calculations: Laundries

The purpose of this section is to describe the methodology used to generate landfill loading estimates resulting from the disposal of industrial lau ndry sludge in a municipal solid waste landfill, and present those results. These landfill loading estimates serve as the basis for subsequent fate and transport modeling.

3.1 How Can Solvents Enter the Environment from Reusable Wipes?

Generators of shop and printer towels send these materials to industrial laundries. Some of these towels contain F001 to F005 solvents. When towels are laundered, their contaminants are removed and transferred to air, water, or solid media. When solid media (sludge) is d isposed in a landfill, the contaminants in the sludge can be transferred to landfill leachate and be transported to a receptor.

Shop and printer towels, including solvent

- contaminated towels, contribute contaminants to a laundry’s pretreatment system. To control these pollutants prior to a publicly owned treatment works (POTW) discharge, laundries conduct treatment practices such as oil separation, dissolved air flotation, or settling; alternatively, no treatment is conducted at all. The generated solids are managed as solid wastes using management methods such as landfilling or energy recovery. 11



### 3.2 Overview of Calculation Methodology

Data characterizing quantities of contaminants present in laundry sludges from the washing of printer or shop towels are not available. 12 

### As a result, the quantity of hazardous chemicals present in sludge was estimated from wastewater characterization and/or wipes loading assumptions. As with other analyses conducted in this report, only the compounds that are included in the definition of the F001 through F005 solvent listing were considered (e.g., other non

- listed solvents and co

contaminants are not considered). Landfill loading estimates were developed for the same 20 chemicals as considered in the disposable wipes ana lysis (see Section 1.2 for discussion of chemicals for which loading estimates are not made).

The purpose of the calculation is to estimate the quantity of chemical disposed in a single landfill per year. The overall calculation approach is presented bel ow in Exhibit 3

- 1. This approach was used in estimating landfill loading quantities for 19 chemicals. Due to data availability, an alternate method (identified in Exhibit 3

- 2) is used for methanol. Exhibits 3

- 1 and 3

- 2 also identify the

distributional a ssumptions used in the Monte Carlo analysis for each variable.

Exhibit 3

- 1. Sample Calculation for Estimating Landfill Loading of Acetone from Laundry Sludge

11 Only the chemicals present in the laundry sludge are assesse d in this report. There are other potential release points of chemicals from laundries which are not assessed in this report. These potential release points include volatilization to air at the laundry and the discharge of effluent.

12 As discussed in Se ction 3.3.3

, limited TCLP data for the sludges are available. Shop towel contribution Avg

Value Distributional Assumption

A1 Shop towels washed at a site, lb /yr 312

, 893 Gamma distribution

C1 Shop towel water concentration, mg/L 3.98 Lognormal distribution; values depend on the

### 33 Exhibit 3

- 2. Calculation for Estimating Landfill Loading of Methanol from Laundry Sludge

Parameter Avg

Value Distributional Assumption

G Methanol to laundry, lb/yr 40.5 Uniform dis tribution

E Sludge partitioning (fraction) 0.108 Step (constant)

F Number of Laundries/landfill 1.7 Logistic Distribution (minimum fixed at 1)

Unit conversion 0.454 Result, kg chemical/yr in one landfill 3.38 (product of above fo ur terms)



### Exhibit 3

- 3 identifies the data used and its source in selecting parameter values for the variables referenced in the above equations. The results (mass chemical per landfill per year) from the equations are different for each chemical.

Exhib it 3

- 3. Source of Data for Laundry Sludge Calculations

Reference Variable Source How is Variability Assessed?

A1 & A2 Shop Towels

and Printer

Towels washed (pounds) TRSA Survey (TRSA, 2010 ). Approximately 570 data points are available for shop towels an d approximately 120 data points are available for printer towels (each data point represents a facility/ year). The empirical data for the quantities of shop towels and printer tow els processed were best

- fit to gamma distributions. These input towel

proce ssing quantities span several orders of magnitude.

B % Laundries processing printer towels/ shop towels TRSA, 2010

. Value fixed at 21 % (= 120/ 570) based

on th e TRSA data

.

C1 & C2 Concentration

in washwater See detailed discussion below. Generally used Effluent Guidelines Development Data (ERG, 1999) with separate concentration data for shop towels and printer towels. Assumed lognormal distributions when appropriate.

D Gallons

washwater generated per pound of towels TRSA Survey (TRSA, 2010 )

. The empiric al data w ere best

- fit to a lognormal distribution

. chemical (refer to Appendix I)

Printer towel contribution

A2 Printer towels washed at a site, lb/yr 1 9 6

, 605 Gamma distribution

C2 Printer towel water concentration, mg/L 49.7 Lognormal distribution; values depend on the chemical (refer to Appendix I)

B Probability that laundry manages printer towels in addition to shop towels 21 % Step (Constant)

Other variables

D Gallon washwat er/ lb towels 2. 06 Lognormal distribution

E Sludge partitioning (fraction) 0.235 Step (Constant); values depend on the

chemical

F Number of laundries/landfill 1.7 Logistic distribution (minimum fixed at 1)

Unit conversion 3.78E

- 06

Result, kg chemic al/yr in one landfill 10

. 3 =[(A1×C1)+(A2×C2×B)]×D×E×F×[ Unit conversion]

### 34 Reference Variable Source How is Variability Assessed?

E Sludge partitioning • Used results of partitioning equations developed from 2000 WATER8 modeling simulation (ERG, 2000).

• Used frequency of DAF wastewater treatment (WWT) technology as estimated for the

2 006 Effluent Guidelines Plan (EPA, 2005) Separate partitioning factors were developed for two different WWT systems. These were used in conjunction with the population estimate of each type of WWT technology.

F Number of

Laundries disposing sludge in a

s ingle landfill Derived from Simmons, 2006 (for landfills by state) and Effluent Guidelines Development Data (for laundries by state). Empirical averages were developed

for each state and best

- fit to a

distribution.

G Quantity of

methanol sent to laundry ( methanol only) 2004 TRI reporting for generators that identify sending methanol to an industrial laundry. Narrow range was used based on

available TRI data.



### 3.3 Data Used for Assessing Individual Variables

3.3.1 Quantity of Shop and Printer Towels Washed by Each Laundry

As part of its public comments submitted in response to EPA’s October 27, 2009 Notice of Data Availability , TRSA submitted survey data collected from its membership. The data represented the years 2005 to 2009, providing data from 11 com panies, 271 laundries, and 569 location

- years of data points. TRSA identified that the 271 laundries represent almost 90 percent of all facilities engaged in processing shop towels or printer towels. TRSA provided data regarding the quantity (pounds) of shop towels washed per year per facility, and the quantity (pounds) of printer towels washed per year per facility. Data used in this analysis are presented in Exhibit F

- 1, Appendix F. Graphical output comparing the assumed distributions to the source da ta are presented in Exhibits I

3 and I

- 5 of Appendix I. TRSA’s data included approximately 570 data points for shop towels and approximately 120 data points for printer towels.

The TRSA data were fit to distributions. For each parameter, a gamma distrib ution was selected

because it was either the best fit or the second best fit.

These data from TRSA were used preferentially to similar data from Effluent Guidelines development (ERG, 1999). The TRSA data has advantages and drawbacks in comparison to the Effluent Guidelines data. A primary advantage is that it is more recent (2005 to 2009) than the 1990s Effluent Guidelines data. It also appears to include more facilities (271 as identified by the commenter), versus approximately 141 where data are avail able in the Effluent Guidelines data. A significant drawback to the TRSA data is that it appears to include data for multiple years for a single facility, with no method to distinguish which data are from a single facility. Therefore, it would provide bi as towards those facilities providing multiple years of data over a facility providing a single year of data. Nevertheless, the advantages outweigh the disadvantages in this instance.

Additional data are available from the public comments to the OSW prop osed rule for solvent wipes (RCRA

- 2003

- 0004); these include comments from several laundries identifying the quantity of

### 35 towels processed by each. (A list of the laundry commenters with related information is found in

Exhibit F

- 2, Appendix F.) However, th e information/ data were not in a format compatible with the methods used in this analysis. The size of the data set is smaller than the TRSA data, and the public comments provided the number of towels rather than their weight. Pounds of towels processed are a more convenient measurement for the calculations (see Section 3.3.4 below, where washwater generation is expressed as gallons of water generated per pound of textile washed).

3.3.2 Fraction of Laundries Processing Printer Towels versus Shop Towels

As noted in Section 3.3.1, TRSA (2010) provided approximately 570 data points for shop towels and approximately 120 data points for printer towels. Based on these data it is reasonable to assume that more facilities process shop towels than process printe r towels. As an approximation for this analysis, it is assumed that the fraction of facilities washing printer towels is 21 percent (120/ 570) of those washing shop towels. The TRSA data is not a true reflection of facility count, because a single facili ty can be represented by multiple data points (multiple years of data, as identified in Section 3.3.1). To provide consistency with the use of this data in Section 3.3.1, however, this estimate of 21 percent is used in the calculations.

The assumption ma de from the TRSA results is consistent with similar data from the Effluent Guidelines Technical Development Document (EPA, 2000), Table 4

- 3 . This table identified the numbers (and percentages) of laundries which process shop towels and printer towels. Th is source showed that 1,332 laundries managed shop towels and 480 laundries manage printer towels (36%). Further, Effluent Guidelines data from ERG (1999) showed that the vast majority of laundries which process printer towels also process shop towels.

For simplicity, the analysis assumes the following:

• The analysis ignores laundries that do not manage shop or printer towels.

• Of laundries managing shop towels and/or printer towels, it is assumed that all laundries manage shop towels

.

• There is a 21 perc ent probability that the laundry also manages printer towels, using the

distrib ution described in Exhibit 3

- 1

.

• At the remaining laundries (i.e., 79 percent), there are no contributions from printer towels.

3.3.3 Concentration in Washwater

Exhibit 3

- 4 summ arizes the sources of data used in assigning concentrations to constituents in laundry washwater. Primary reliance was placed on Office of Water sampling data. However,

Office of Water data are not available for all 30 F

- list constituents, and therefore additional data sources and assumptions are required.

### 36 Exhibit 3

- 4. Data Sources for Estimating Levels of Constituents in Laundry Washwater

Constituent Data Source for Concentration of Chemical in Shop

Towel and Printer Towel Washwater

Acetone

Chlorobenz ene Cresols Ethyl benzene Methyl ethyl ketone Methyl isobutyl ketone Methylene chloride Tetrachloroethylene

Toluene Trichloroethylene Xylenes (total) Office of Water sampling results

1,2

- Dichlorobenzene
1,1,2

- Trichloroethane Identified as not detected in any Office of Water sample (Effluent Guidelines Technical Background Document); assume equal to Office of

Water detection limit

Benzene

Butanol Cyclohexanone Ethyl acetate

2

- Ethoxyethanol Isobutyl alcohol No information in Effluent Guidelines Technical Background Document regarding analysis; assume equal to Office of Water detection limit

Methanol Estimated from TRI data; Section 3.3.7

Carbon disulfide

Nitrobenzene Pyridine Assume zero (ND by Office of Water; no use assumed in Section 1.2)

Carbon tet rachloride

Dichlorodifluoromethane Ethyl ether

2

- Nitropropane
1,1,1

- Trichloroethane
1,1,2

- Trichlorotrifluoroethane

Trichlorofluoromethane Assume zero (no use assumed in Section 1.2)



### Office of Water Data

The primary data source for identifying concentrat ions of chemicals in industrial laundry washwater is EPA’s Industrial Laundry effluent characterization study (EPA, 2000). EPA analyzed industrial laundry wastewater including wastewater from the washing of shop towels and printer towels. This

washwater is representative of the washing of shop towels (or printer towels) when washed alone. In practice, laundries do not exclusively wash only shop towels and printer towels (see for example Appendix M of Comment No. RCRA

- 2003

- 0004

- 0348). By only accounting for the quantity of shop towels and printer towels processed, and not the entire throughput of the laundry (e.g., other materials such as mats and uniforms), this analysis accounts only for the quantity of chemical in the washwater and resulting sludge whi ch originates from the shop and printer towels.

The Office of Water conducted sampling at numerous industrial laundries as part of effluent guidelines development for the 1997 proposed rule. The supporting record includes one sample of washwater generate d from the washing of printer towels from each of three different industrial laundries (three samples total). Similarly, six samples of washwater generated from the washing of shop towels are available. Data for levels in shop towel washwater and printer towel washwater are

available from Table DC

- 3 of the Office of Water Development Document (EPA, 2000). This is

### 37 washwater directly from the washer with no pretreatment or dilution. The quantity in washwater may be less than the quantity in the towels rec eived by the laundry due to evaporation or due to solvent recovery steps (if any) practiced by the laundry.

Of the 30 F

- list constituents, Office of Water data directly attributable to shop towel and printer towel washwater were available for 12 (i.e., the 11 identified in Exhibit 3

- 4 plus 1,1,1

- trichloroethane). Although 1,1,1

- trichloroethane was detected in the Office of Water sampling, a concentration of zero is used for the calculation. For the reasons discussed in Section 1.2, it is

assumed that u se of this chemical in conjunction with shop towels or printer towels has fallen off dramatically between the time of the Office of Water sampling (in the mid

- 1990s) and the current day.

The Office of Water sampling results in EPA (2000) are presented as minimum, maximum, and average concentrations. These three values for each chemical were used for fitting the data to a lognormal distribution for all chemicals except methylene chloride and tetrachloroethylene in shop towel washwater. The lognormal distr ibution was assumed because this is a common distribution

for environmental media 13 

### . The data are presented in Exhibit 3

- 5. Exhibits I

- 7 through I

- 28 in Appendix I present a graphical comparison of the data to the assumed distributions. For methylene

chl oride and tetrachloroethylene in shop towel washwater, a triangular distribution was used (corresponding to minimum, maximum, mean) because a lognormal distribution is not a reasonable representation of the input data at high concentrations (see Exhibits I

- 11 and I

- 13 in Appendix I; the mean of an assumed lognormal distribution is much higher than the maximum observed value).

Exhibit 3

- 5. Concentrations in Laundry Washwater

Chemical Shop Towel Washwater Concentration, mg/L Printer Towel Washwater

Concentr ation, mg/L

Minimum Maximum Mean Minimum Maximum Mean

Chlorobenzene 0.01 1 0.252 0.1 1 0.467 Ethylbenzene 0.556 36 5.27 0.521 29.2 13.2 Methylene Chloride 0.01 39.9 4.22 0.14 1.54 0.614 Tetrachloroethylene 0.17 55.5 8.92 2.4 6.16 3.92

Toluene 1.11 1 1.6 5.33 14.1 33.2 20.5 Trichloroethylene 0.01 1 0.247 0.1 1 0.476

MEK 0.0898 15.8 5.4 2.05 5 3.09

Acetone 1 5.95 3.98 23.4 96.6 49.7

MIBK 0.132 5 1.88 0.5 5 2.07 Xylene 1.366 6.26 3.59 0.1 4.84 2.52

Cresol 0.02 1 0.305 0.1 1 0.433
Source: EPA, 200 0.



### Results for cresol in the effluent guidelines technical development document were shown as not detected for both shop towels and printer towels. This result was used as an estimate for a number of other constituents (as identified in Exhibit 3

- 4) whic h were either not detected or not analyzed during the Office of Water sampling. EPA (2000) identified that two chemicals (1,2

- dichlorobenzene and 1,1,2

- trichloroethane) were not detected in any laundry wastewater (not just from shop towels and printer tow els). For the remaining chemicals used as solvents, alternative data such as TRI were investigated as a basis for estimate; such data were available for methanol. For the remaining six constituents (benzene, n

- butanol, cyclohexanone, ethyl acetate, 2

- eth oxyethanol, isobutyl alcohol), the concentration data for cresol (i.e., the Office of Water

13 RAGS (EPA, 2001), Appendix B, ‘Selection and Fitting of Distributions,’ Table B

- 2.

### 38 detection limit) were used. This value approximating the detection limit was selected because (1) no other data are available; (2) the detection limit is intended to represent a conservative but reasonable approximation of what levels could be present; and (3) based on usage data in Exhibit B

1, Appendix B, the constituents are not widely present in solvents ; therefore the concentrations in washwater would be expect ed to be less than concentrations of high

- use solvents identified in

Exhibit 3

- 4, such as methyl ethyl ketone and acetone.

Table DC

- 3 of EPA (2000) evaluates non

- detect values as equal to the detection limit. For transparency, no attempt was made to modi fy the way that non

- detects are used in the landfill loading calculations presented here. This assumption is conservative, because the actual

concentration is less than the detection limit.

Ten chemicals were assumed to have zero concentration; these che micals are discussed in Section

1.2. Note that Exhibit 3

- 4 shows that three of these chemicals were analyzed and not detected in any of the laundry wastewater samples; one chemical (1,1,1

- trichloroethane) was detected in the shop or printer towel analyses but is assumed not to be present in current

- day washwater.

TCLP Data

UTSA /TRSA submitted results of TCLP analysis of laundry sludge to EPA, representing 22 samples from 16 facilities analyzed for organics from the period 1993 to 2005 ( UTSA , 2006).

Dete cted constituents included the following:

• Chlorobenzene (detected in one sample )

• Cresol (detected in 4 samples )

• 1,2

- Dichlorobenzene (detected in 2 sample s )

• Ethyl benzene (detected in 1 sample )

• Tetrachloroethylene (detected in 6 samples )

• 1,1,1

- Trichloroeth ane (detected in 1 sample )

• Xylenes (detected in 1 sample )

These data were not used quantitatively, but were used in conjunction with other information to identify the reasonableness that chemicals detected in the TCLP extract of the sludge would be

expect ed to be present in the washwater. The data are presented in Exhibit F

- 3, Appendix F.

3.3.4 Quantity of Washwater Generated

Wastewater is generated from the washing of towels or other textiles. TRSA (2010) provided 147 data points for the quantity of wa shwater generated per pound of textile. Similar advantages and limitations apply to the TRSA survey data for this parameter as was described in Section 3.3.1.

The calculation methodology (Exhibit 3

- 1) assigns these values to a distribution. A lognormal distribution was fit to the data because it was a second best fit and because TRSA suggests that a lognormal distribution would be reasonable. Data are presented in Exhibit F

- 1, Appendix F. Graphical output s are presented in Exhibit I

- 33, Appendix I.

### 39 3.3 .5 Sludge Partitioning

Once in the washwater, chemicals will be treated onsite if a laundry has a wastewater treatment system. The chemical in the washwater will partition to air or sludge, or remain in water. Because sludge samples were not collected, an air/water/solid partitioning model (WATER8) was used to estimate sludge loadings. The assumptions and results of this portioning model are provided in ERG (2000). The results are expressed as fraction of chemical in incoming washwater, which partition s to the sludge. Calculations were conducted with process assumptions that included the following:

• Reduction of volatility due to surfactant. The result is less chemical partitions to the air and more chemical partitions to water and sludge. The effect of reduced volatility due to surfactant (e.g., laundry cleaning detergent) was suggested during the effluent guidelines public comment period as an assumption yielding a more realistic scenario (ERG, 2000).

• Consideration of two separate wastewater manage ment systems: (1) pit and screen; or (2) pit, screen, equalization, and dissolved air flotation (DAF unit). Each system results in different partitioning results. No other wastewater treatment processes were evaluated.

For these calculations, it is assu med that all laundries generate sludge/ solids for landfill disposal, with 55 percent using a pit and screen wastewater treatment method and 45 percent using a pit/ screen/ equalization/ DAF sequence. This may be conservative because some

laundries do not pre

- treat their wastewater (EPA, 2005) and presumably would not generate any sludge. The assumption that 45 percent use a pit/ screen/ equalization/ DAF sequence is based

on information from the 2006 Effluent Guidelines Plan. 14



### Partitioning will vary dep ending on solvent

- specific properties. The values used in the analysis are presented in Exhibit 3

- 6. Detailed calculations of this partitioning are presented in ERG (2000).

Partitioning was also calculated using the quantity of solvent received by the laundry as the input quantity. This is different from the quantity of solvent in the washwater (discussed above) because some of the solvent will evaporate prior to entering the washwater. Specifically, ERG (2000) estimates that 36 percent evaporates pr ior to entering the washwater. This approach was only used for methanol where the quantity of chemical present in incoming laundry textiles was known.

Exhibit 3

- 6. Fraction of Chemical Partitioning to Sludge in Laundry Washwater

Chemical Scenario: WWT

S ettling pit + screen Scenario: Settling pit + screen + EQ Basin + DAF Weighted Average

Acetone 0.133 0.36 0.235

Benzene 0.212 0.504 0.343

Butanol 0.13 0.275 0.195

Chlorobenzene 0.115 0.184 0.146

Cresol 0.123 0.211 0.163 Cyclohexanone 0.43 0.646 0.527
1,2

- Dichlorobenzene 0.126 0.234 0.175

14 From EPA, 2005: “Based on the Uniform and Textile Service Association (UTSA) and the Textile Rental Servic e Association (TRSA) survey conducted in 1998, 55 percent of the industrial laundries do not pre

- treat their wastewater. Of the remaining 45 percent of the facilities, the primary method of wastewater pre

- treatment is dissolved air flotation (DAF).”

### 40 Chemical Scenario: WWT

S ettling pit + screen Scenario: Settling pit + screen + EQ Basin + DAF Weighted Average
Ethyl acetate 0.147 0.464 0.290 Ethyl benzene 0.368 0.606 0.475

2

- Ethoxyethanol 0.209 0.504 0.342 Isobutyl alcohol 0.122 0.237 0.174

Methanol 0.119 0.219 0.164 Methyl ethyl ketone 0.141 0.419 0.266 Methyl isob utyl ketone 0.157 0.47 0.298 Methylene chloride 0.167 0.476 0.306 Tetrachloroethylene 0.259 0.536 0.384

Toluene 0.273 0.545 0.395
1,1,2

- Trichloroethane 0.204 0.506 0.340 Trichloroethylene 0.222 0.512 0.353 Xylenes (total) 0.37 0.606 0.476

Source: ER G, 2000. Tables 3

- 7 to 3

- 12.



### 3.3.6 Number of Laundries Disposing Sludge in a Single Landfill

All laundry sludge is assumed to be disposed as a nonhazardous waste. Data from 2003 Biennial Reporting (Appendix D, in particular Exhibit D

- 6) and from public comments (UTSA, 2006) support that the vast majority of sludge is managed as nonhazardous.

Nationwide, there are approximately 1,600 MSW landfills. Approximately 1,300 laundries process shop towels and approximately 500 laundries process printer towels. Across the U.S., laundries and landfills are not distributed uniformly and therefore it is assumed that the number of laundries sending sludge to a single landfill can be greater than one (however, it will never be less than one). The following factors w ere assumed to affect this distribution:

• The number of laundries per state (using Office of Water data)

• The number of landfills per state (using Simmons, 2006 data)

• For each state, the percentage of MSW that is landfilled as opposed to being combusted.

L aundry sludge disposal is assumed to follow this percentage.

The average number of laundries per landfill was calculated for each state using the above data (with a minimum of one laundry per landfill). This distribution (which ranged from 1 to 3) was

us ed as part of the analysis. The values used in the analysis are presented in Exhibit F

- 4, Appendix

F. Graphical output s are presented in Exhibit I

- 34, Appendix I.

3.3.7 Estimates of Methanol Sent to Industrial Laundries

Methanol levels in washwater were not estimated using the techniques identified in Section 3.3.3. Instead, 2004 TRI data were used to determine that three generator sites report sending methanol to two different industrial laundries (it is assumed that the methanol is in the soiled texti les). These receipt quantities were used directly as the input quantity to the laundries. A fraction of this quantity was assumed to partition to the sludge, using the assumptions and data in Section 3.3.5.

Exhibit 3

- 2 summarizes the data used in the me thanol analysis.

### 41 3.4 Results

3.4.1 Loading Estimates

The above data were used as inputs for each of 20 chemicals (i.e., 10 chemicals identified in Section 1.2 were not assessed). The calculations were conducted using Microsoft Excel®. Calculations

were conducted using both deterministic (point

- value) and Monte Carlo methods. The deterministic calculation was conducted by setting all inputs to their average values.

The Monte Carlo analysis was conducted with Excel® spreadsheets using Crystal Ball®. As discussed above, either a distribution or a fixed value is used for each input parameter (based on data availability). A separate calculation is conducted for each of the 20 chemicals. Certain input parameters are not affected by the evaluated chemical ( e.g., pounds of shop towels processed), and are assessed using the same probability distribution for all chemicals. For each of 100,000 iterations, all parameters are assessed independently (e.g., a facility accepting a large quantity of shop towels in on e of the iterations may not necessarily also accept a large quantity of printer towels, or generate water with a high concentration of chemical). The output of a Monte Carlo analysis results in a distribution of values. A sample graphical Monte Carlo out put is presented in

Exhibit 3

- 7.

Exhibit 3

- 7. Monte Carlo Results for Acetone in Laundry Sludge

The values in Exhibit 3

- 8 represent landfill loading estimates for each chemical (i.e., the quantity of chemical that a single landfill will receive from la rge and small laundries). The Monte Carlo analysis provides percentile results from 0 to 100; for summary purposes the mean, median (50 th


### percentile), 90 th 

### percentile, and 95 th 

### percentile values are shown in Exhibit 3

- 8. The deterministic value, assessed by setting all input parameters to their mean values, is also presented (the sample calculation for this deterministic result was previously presented in Exhibit 3

- 1).

One use of the deterministic results is to provide a reference in reviewing the Monte Carlo results. For each chemical, the deterministic result is between the 50 th 

### and 90 th 

### percentiles of the Monte Carlo distribution, and less than the mean of the Monte Carlo results. For example, Exhibit 3

- 8

identifies a deterministic result for acetone of 10.26 kg/landfill/year, a mean Monte Carlo result of

### 42 12.34 kg/landfill/year, a 50 th 

### percentile Monte Carlo result of 2.53 kg/landfill/year, and a 90 th


### percentile Monte Carlo result of 26.40 kg/site/year . There are several reasons that the deterministi c results are greater than the 50 th 

### percentile Monte Carlo results and less than the mean Monte Carlo

result:

• In the probabilistic analysis, most laundries are assumed to accept only shop towels, with a small percentage (i.e., 21 percent) accepting both s hop and printer towels. In the results for the probabilistic analysis, results towards the median are expected to be principally reflective of laundries washing only shop towels, while those towards the high end are expected to be

reflective of laundries washing larger quantities of shop and printer towels. This assumption could not be precisely duplicated in the deterministic analysis, and results in the deterministic analysis results being greater than the 50 th 

### percentile probabilistic analysis.

• The sh ape of the input distributions additionally influences the results. This is due to the skewed shape of much of the input data (e.g., lognormal distributions); a characteristic of this assumption is that the average value is higher than the median value. This is shown in Exhibit

3

- 9 for two input parameters. The deterministic analysis uses average values of the input parameters, and is another reason for the deterministic analysis results being greater than the

50 th 

### percentile.

Exhibit 3

- 8. Landfill Loa ding Results for Laundry Sludge Evaluation

Constituent Result (kg/yr)

Deterministic (Mean Inputs) Probabilistic

Mean 50 th



### Percentile 90 th



### Percentile 95 th



### Percentile

Carbon disulfide

Carbon tetrachloride

Dichlorodifluoromethane Ethyl ether

Nitrobenze ne 2

- Nitropropane Pyridine
1,1,1

- Trichloroethane
1,1,2

- Trichlorotrifluoroethane

Trichlorofluoromethane 0 0 0 0 0

Acetone 10.26 12.34 2.53 26.40 53.02

Benzene 0 .51 1.22 0.2 1 2.36 4.53

n

- Butanol 0.29 0 .67 0.13 1.36 2.61

Chlorobenzene 0.19 0.63 0. 08 1. 06 2.17

Cresols 0.24 0.58 0.1 1 1.14 2.16 Cyclohexanone 0.79 1.90 0. 35 3.70 7.07
1,2

- Dichlorobenzene 0.26 0.61 0.1 1 1.22 2.34 Ethyl acetate 0.43 1.03 0. 17 1.96 3.89 Ethyl benzene 13.8 0 51.47 7.81 96.37 193.92

2

- Ethoxyethanol 0 .51 1.20 0.21 2.34 4.59 Isobutyl alcohol 0.26 0.63 0.1 1 1.21 2.35

Methanol 3.38 3.60 3.23 5.77 6.57 Methyl ethyl ketone 6.4 0 29.50 1.72 32.96 77.87 Methyl isobutyl ketone 2.66 5.12 1.00 10.52 19.54 Methylene chloride 5.45 20.74 9. 25 51.02 78.46 Tetrachloroethylene 15.01 38.7 0 19.71 93.00 139.35

Toluene 13.15 17.30 6.10 43.05 69.22

### 43 Constituent Result (kg/yr)

Deterministic (Mean Inputs) Probabilistic

Mean 50 th



### Percentile 90 th



### Percentile 95 th



### Percentile

1,1,2

- Trichloroethane 0 .5 1 1.23 0.2 1 2.33 4.53 Trichloroethylene 0.45 1.54 0. 17 2.54 5.26 Xylenes (total) 7.73 9.57 4.65 21.98 33.26 Deterministic result is calculated using the mean (average) value of all input parameters. For 10 chemicals, input concentration was set to zero; therefore, results are zero.



### Exhibit 3

- 9. Comparison of Average and Median Values for Input Parameters

Parameter Average Value Median Value Shop towels washed at a si te, lb/yr 312

, 893 2 26

, 116 Printer towels washed at a site, lb/yr 196

, 605 1 40

, 270

Number of laundries/landfill 1.7 2
Median input values are from the raw empirical data, and not from the parameters of the fitted distribution. See Appendix F for result.



### 3.4.2 Sensitivity Analysis

As with the analysis for disposable wipes, a sensitivity analysis was conducted for each of the input variables where a distribution or range of values was assigned. The approach used for estimating landfill loading from laundr y sludge (shown in Exhibit 3

- 1) uses fewer input parameters than the approach for disposable wipes (Section 2), and therefore the sensitivity analysis procedure and results are slightly different.

Landfill loadings from laundry sludge result from shop tow els and printer towels. The contributions from the washing of shop towels and the washing of printer towels are each significant, and are highly dependent on the chemical

- specific concentration term. For example, contributions for

acetone and MIBK are sh own in Exhibit 3

- 10, using the deterministic equation from Exhibit 3

- 1.

Exhibit 3

- 10. Relative Contributions from the Washing of Shop Towels and Printer Towels

Contribution Acetone MIBK kg/landfill/yr % kg/landfill/yr % Shop Towels 3

. 9 38 % 2

. 3 8 7%

Pr inter Towels 6.4 62 % 0

. 3 13 %

Total 10

. 3 100% 2 .7 100%
Calculation is conducted using deterministic analysis.



### To further evaluate sensitivity, rank correlation coefficients for each parameter were calculated using CrystalBall ® 

### software, using the same me thods described in Section 2.4.2. Results of the sensitivity analyses in Exhibit 3

- 11 presents the parameters evaluated, the result for one chemical (methyl isobutyl ketone) arranged from most sensitive to least sensitive, and discussion of the

results fo r the other chemicals evaluated. The comparisons for 19 chemicals (i.e., all except methanol, which was evaluated using different input parameters) are summarized in Exhibit 3

- 11.

From Exhibit 3

- 11, the two parameters associated with shop towels

– quanti ty of shop towels washed and shop towel water concentration

– are the two most sensitive parameters for the majority

### 44 of chemicals evaluated and can be considered the most important with respect to their influence on

results.

Exhibit 3

- 11. Sensitivity Ana lysis for Laundry Sludge Analysis

Parameter Result for MIBK Typical Order of Sensitivity for 19

Chemicals A

Shop towel water concentration, mg/L 5 8 % This parameter was the most sensitive for 14 of 19 chemicals, and second

- most sensitive for four others.

Shop towels washed, lb/yr 25 % This parameter was the second

- most sensitive for 14 of 19 chemicals, and most sensitive for the remaining

five chemicals.

Sludge partitioning (fraction) 6.7 % This parameter w as neither the most nor the least

sensitive.

Proba bility that laundry manages printer towels in conjunction with shop towels 3.7 % This parameter was the third

- most sensitive for 10 of

19 chemicals.

Gallons washwater/lb towels 3.4% These parameters were neither the most nor the least

sensitive. Number of laundries/landfill 2.3 % Printer towels washed, lb/yr 0.6 % The s e parameters were the least sensitive for all of

19 chemicals. Printer towel water concentration, mg/L 0.3 %

The sensitivity results for each parameter are in units of ‘percent’ and total t o 100%. A. Sensitivity analyses for methanol are not presented in this exhibit. As described in Section 3.3, methanol was assessed using a different calculation than the other 19 chemicals.



### 3.4.3 Effects of Input Parameter Distribution Assumptions on Res ults

The results presented in Section 3.4.1 are based, in part, on assumptions made with regard to the distribution of each input parameter. To assess the significance of changes in the input parameter distribution assumptions on the results, several ‘wh at

- if’ analyses are shown in this section.

As described in Section 1.3, most of the assumptions were selected using a ‘goodness of fit’ test: the assumption with the best fit was selected. The purpose of this section is to describe the impact on

results when changing these assumptions. The following conditions are used in this section as a basis for selecting a different distribution assumption for an input parameter:

• When two or more distributions provide a ‘good fit’ to the data. As discussed in Sect ion 1.3, generally the assumption providing the best fit is used in the analysis because there is typically no information to assess which distribution assumption would be most appropriate for the population from a purely mechanistic perspective. This sec tion examines the impact that an alternative assumption would have on the results. Appendix I summarizes the results for four input parameters and includes graphs comparing the selected distributions to the empirical data.

• When data outliers may exist. Potential outliers in the data inputs are identified in this section and an alternative distribution, without the outlier, is developed. When an outlier exists it does

not mean it should necessarily be removed, because it may be representative of a tail o f a population. Instead, the purpose of this analysis is to evaluate the effects that such an assumption may have on the results. This evaluation is conducted, in part, to investigate impacts of uncertainties in the underlying data. Appendix I summarize s the results for two input

parameters.

### 45 • In this section, analyses are limited to those input parameters where sufficient data exists to evaluate alternate assumptions:

o Quantity of shop towels washed per laundry per year.

o Quantity of printer towels washed per laundry per year.

o Chemical concentration in shop towels.

o Chemical concentration in printer towels.

To assess the significance of changes in the input parameter distribution assumptions on the results,

‘what

- if’ analyses are presented in this section. In these analyses, the assumption regarding the shape of the input distributions is changed, one at a time, for several input parameters. This analysis does not include all possible combinations of the different assumptions. The procedure and

results a re shown in Exhibits 3

- 12 and 3

- 13 for two chemicals, acetone and toluene, respectively.

For the concentration terms for the laundry sludge analysis, the input parameter is assumed to be described by a lognormal distribution (lognormal distributions of en vironmental concentrations are a common assumption in other analyses). 15 

### An evaluation was conducted to investigate the impact of this assumption, by assuming that triangular distributions describe the distribution due to the availability of data described in Section 3.3.3. Exhibits 3

- 12 and 3

- 13 show how results are impacted by this assumption change.

Appendix I includes graphs comparing the selected distributions to the empirical data for each of the above parameters.

15 RAGS Volume 3 Part A, Appendix B, ‘Selection and Fitting of Distributions,’ Table B

- 2.

### 46 Exhibit 3

- 12. Effect of Changes i n Input Parameter Distributions on Results for Acetone

Parameter How was

Distribution

Changed? Reason for Change Result,

50 th



### Percentile Result,

90 th



### Percentile Result, 95 th



### Percentile

“Baseline” No change 2.53 26.40 53.02

Scenario 1: Quantity of shop

t owels washed per laundry, lb/year Gamma to Lognormal Per TRSA (2010), lognormal was expected to provide a good fit for the

data

. 2.38 28.68 56.38

Scenario 2: Quantity of printer towels washed per laundry, lb/year Gamma to Lognormal P er TRSA (2010), lognor mal was expected to provide a good fit for the

data

. 2.54 25.44 54.47

Scenario 3: Quantity of shop towels washed per laundry, lb/year Eliminate potential outlier; re

evaluate with

gamma Per Table I

- 2, the maximum value is a potential outlier. A gamma dis tribution is the

best fit for the new data set. 2.51 26.09 52.07

Scenario 4: Quantity of printer towels washed per laundry, lb/year Eliminate potential

out lier; re

evaluate with

beta Per Table I

- 2, the maximum value is a potential outlier.

A beta distrib ution is a good

fit for the new data set. 2.53 27.62 52.67

Scenario 5:

Washwater

concentration for shop towels, mg/L Lognormal to triangular Assess effect of a simplifying assumption. 2.94 24.11 49.99

Scenario 6:

Washwater

concentration for printer towel s, mg/L Lognormal to triangular Assess effect of a simplifying assumption. 2.56 28.15 55.35

Results for ‘baseline’ case are i dentical to those in Exhibit 3

- 8

.

### 47 Exhibit 3

- 13. Effect of Changes in Input Parameter Distributions on Results for Toluene

Para meter How was

Distribution

Changed? Reason for Change Result,

50 th



### Percentile Result,

90 th



### Percentile Result, 95 th



### Percentile

“Baseline” No change 6.10 43.05 69.22

Scenario 1: Quantity of shop towels washed per laundry, lb/year Gamma to lognormal Per TR SA (2010), lognormal was expected to provide a good fit for the

data

. 5.74 45.70 76.94

Scenario 2: Quantity of printer towels washed per laundry, lb/year Gamma to lognormal Per TRSA (2010), lognormal was expected to provide a good fit for the

data

. 6.02 4 3.50 74.19

Scenario 3: Quantity of shop towels washed per laundry, lb/year Eliminate potential outlier; re

evaluate with

gamma Per Table I

- 2, the maximum value is a potential outlier. A gamma distribution is the

best fit for the new data set. 6.10 42.81 69.77

Scenario 4: Quantity of printer towels washed per laundry, lb/year Eliminate potential outlier; re

evaluate with

beta Per Table I

- 2, the maximum value is a potential outlier.

A beta distribution is a good

fit for the new data set. 6.16 42.64 67.07

Scenario 5:

Washwater

concentration for shop towels, mg/L Lognormal to triangular Assess effect of a simplifying assumption 7.93 38.21 58.37

Scenario 6:

Washwater

concentration for printer towels, mg/L Lognormal to triangular Assess effect of a simplifyi ng assumption 6.09 43.22 69.41

Results for ‘baseline’ case are identical to those in Exhibit 3

- 8.



### Most of the results in Exhibits 3

- 12 and 3

- 13 are similar, indicating that changes in input parameter assumptions have minor effec ts on results.

The result s presented in this section demonstrate the effect of various uncertainties regarding the input parameter distributional assumptions on the results presented in Section 3.4.1.

3.4.4 Uncertainties Associated with the Analysis

Uncertainties in the results p resented in Exhibit 3

- 8 include the following:

• Data for one of the most sensitive parameters, quantity of shop towels washed, are based on a large sample size from a recent TRSA survey . As a result, a high degree of confidence in th i s parameter is expect ed. Nevertheless, the data include some bias in that some facilities are represented in the data set for multiple years. I n addition, i t is not known if the outliers identified in Section 3.4.3 are representative of the population.

• Data for another sens itive parameter, c hemical concentration data for shop towels, are available for a limited number of samples (six or less), and the source material provides only the summary

### 48 statistics (i.e., minimum, maximum, mean). Due to the combination of these factors , the results

in Exhibit 3

- 8 may not reflect the full range of variability associated with this input parameter.

• Number of laundries per landfill. As with the dis posable wipes analysis, data were evaluated at the state level to evaluate regional variabil ity. Any further evaluations below the state level would require much more extensive data analysis as well as knowledge of the distribution and waste management practices of laundries that is unavailable or highly uncertain.

• The results of the analysis d o not explicitly identify the probabilities that different chemicals are disposed in a single landfill. Exhibit 3

- 8 provides the results for each chemical; further assumptions are required in using these results to estimate the quantities of multiple chem icals

at a landfill.

• As with the disposable wipes analysis, the input parameters do not account for future changes in generation or management practices. Most input parameters are based on ‘present day’

characteristics or data that was current at the tim e it was collected. Similar uncertainties discussed in Section 2.4.4 are applicable here.

3.4.5 Numerical Stability for Laundry Sludge Analysis

Due to the nature of a probabilistic assessment, no two analyses using the same input data will yield

identica l results. This section assesses the numerical stability of the results presented in Exhibit 3

- 8. Numerical stability refers to “the observed numerical changes in parameters of the output distribution (e.g., median, 95 th 

### percentile) from a Monte Carlo si mulation as the number of iterations increases” (EPA, 2001).

To assess stability, duplicate analyses using identical input data (i.e., identical input parameter distributions) and identical numbers of iterations (identified in Section 1.1) were conducted for one constituent, tetrachloroethylene. Results are shown in Exhibit 3

- 14. Exhibit 3

- 14 shows that the selected number of iterations gives stable results particularly at the upper percentiles of most

concern.

Exhibit 3

- 14. Results of Multiple Monte C arlo Analyses for Tetrachloroethylene in Laundry Sludge

Note Result (kg/yr)

50 th



### Percentile 90 th



### Percentile 95 th



### Percentile

From Exhibit 3

- 8 19.71 93.00 139.35

Run 2 19.85 93.15 137.76

Run 3 19.78 92.55 138.19

Run 4 19.73 93.35 137.67

Run 5 19.67 93 .55 139.98

Run 6 19.80 92.65 138.44 Average 19.76 93.04 138.57

Minimum 19.67 92.55 137.67

Maximum 19.85 93.55 139.98

Relative % Difference A 0.9 % 1.1 % 1.7 % A. Relative percent difference is calculated as the (maximum minus minimum)/average.

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. BioCycle. Volume 47, No. 4, pages 26

- 43. April 2006. http://www.jgpress.com/archives/2006_04.htm l

Spectrum, 2001. Chemical Information from Spectrum Laboratories. http://www.speclab.com/compound/

TRSA, 2010. Letter from TRSA to EPA Re: Docket ID No. EPA

- HQ

- RCRA

- 2003

- 0004. February
26, 2010. Doc ument No. RCRA

- 2003

- 0004

- 0461.

UTSA , 2006. Letter from UTSA /TRSA to EPA Re: EPA's Revised Risk Assessment for the 2003 Shop Towel Proposal. September 12, 2006. Document No. RCRA

- 2003

- 0004

- 0396

.

### 53 Appendix A. Supporting Data

### 54 Data in Exhibit A

- 1 we re used, in part, to develop input parameters including towel and wipe usage rates for the disposable towel and wipe analysis. The final column identifies if the data were used in the evaluation; a (

--- ) indicates the data were not used, typically

because use rates were not available. Appendix B presents the actual values used in the analysis.

Exhibit A

- 1. Shop Towel and Wipe Usage Rates

Reference A Site/Location Business Type Year Total Usage Management F

- Listed Solvents Used Apply Wipe Usage
to Sub

- se ctor

Docket Item No.

0052 --- ‘Small sites’ 1995 2,000 to 3,000 lb/year Disposed Other manufacturing

Docket Item No.

0099 --- 4 automobile manufacturing

sites 1994 --- Laundered Petroleum naphtha

---

--- Disposed Petroleum naphtha/ xylene mix

---

--- Laundered 50% Petroleum naphtha/ 50%

IPA mix ---

--- --- 26% acetone/ 9% toluene/ 65% petroleum naphtha mix ---

Docket Item No.

0102 Member

survey Flexible packaging manufacturing
facilities 2001 100

- 1,000/day Disposable and

Laundered Acetone Butyl alcohol Ethyl acetate

MEK

MIBK

Toluene Also, petroleum napthas which may contain low levels of BTEX, etc. Other manufacturing

Docket Item No.

0104 --- Kimberly

- Clark Corporation 1995 Wiper sales 2,500

- 50,000/ month. But only 30% of facilities

used wipers with

solvents Disposable Acetone Methyl ethyl ketone

Toluene Xylene ---

Docket Item No.

0105 --- Printing 1995 “Common” printing solvents

include: XCELLO contains 15% MEK;

2

- ethoxy ethanol 10%;

Toluene 65%
REPROKLENE 104 contains Xylene 30%; met hylene chloride 20%; ethyl benzene

6% ---

Docket Item No.

0106 63 respondents Printing (Flexible packaging & others) 1996 10

- 100+/day 87% use Laundered;

~30% use disposable # of sites using solvents:

Acetone: 5%

Toluene: 6%

MEK: 5% Methylene chloride: 3%

Trichloroethane: 3%

Acetates: 19% Printing

### 55 Reference A Site/Location Business Type Year Total Usage Management F

- Listed Solvents Used Apply Wipe Usage
to Sub

- se ctor

Docket Item No.

0107 13% of membership (5200 of 40000 sites) Screen & Graphic Imaging

Association

International:

50% textile screen printing; 50% graphics 1996 1

- 25/day: 17%

25

- 50/day: 17%

50

- 100/day: 17% Disposa ble and

Laundered # of sites using solvents (Not exhaustive, but represents principal products):

MEK: 18%

Acetone: 27% Xylene: 19.5%

Toluene: 20% Also, Lacquer thinner or mineral spirits which may contain low levels of BTEX,

etc.: 25% Printing

Docket Item No.

0113 Member

survey SGIA: Screen printing sites 1998 1,108 reusables/ week; 3,164 disposables/ week 60% of screen printing & flexographic use laundered; 40% use disposables --- Printing

Docket Item No.

0113 Member

survey SGIA: Flexographic

sites 1998 2,145 reusables/ week; 1,217 disposables/ week --- Printing

Docket Item No.

0113 Member

survey Screen & Graphic Imaging

Association

International

- Lithographic 1998 827 reusables/ week 99% of lithographic printers use

laundered --- Printing

Docket It em No.

0115 1 site Printer 1995 12,000/week Disposables

--- Printing

Docket Item No.

0116 1 site Auto
manufacturer 1999 Concentration in the solvent

blend raw material : Xylene 30

- 50%;
Methanol <2% Ethyl benzene <6% Also petroleum distillates ---

Doc ket Item No.

0118 Member survey (7 sites) Semiconductor manufacturing Industry 1999 1,500/day Primarily Disposed Primarily isopropanol (not an

F

- list solvent) Less frequently: acetone,

methanol < 10 ml/application Electronics

Docket Item No.

0119 Member

s urvey Aerospace

Industries

Association 1999 Range from 25/ day to >1000/ day Primarily Disposal (60

- 100%) MEK, TCE, acetone, ethyl acetate, MIBK, toluene, xylene, methanol, cyclohexanone, perchloroethylene, Also mineral spirits, naptha, paint thinner, sto ddard solvent.
Use of non

- hazardous solvents range from 0

- 80%

50

- 250 ml/application Transportation

### 56 Reference A Site/Location Business Type Year Total Usage Management F

- Listed Solvents Used Apply Wipe Usage
to Sub

- se ctor

Docket Item No.

0120 1 site (medium

- sized printer) Printing 1999 420,000/year Reusable

--- Printing

Docket Item No.

0122 5 sites Various sites:
Paint/ ink manufacturing;

auto manufacturing; general machining 2000 16 to 32 drums/ month (Docket Item

0120 uses 800 towels/ drum; using this conversion gives

13,000 to 25,000/ month) Solvent extraction (not

identified as
laundered/ disposable) Typical: toluene, xyl ene, MEK, butyl alcohol, acetone,

methanol General manufacturing

Docket Item No.

0123 1 site? 5 sites? Printing 1999 Used 109,000 pounds solvent/year Disposables

--- ---

Cal/EPA, 2004 10 California printers Cross section of lithographic

printing 2004

--- Reusables All use solvents for cleanup;

some have F

- listed constituents per MSDSs: Acetone (6%) Benzene (<10 ppm) Glycol ether (10

- 12%) 1
Xylene (1.5%

- 3.5%) Mineral spirits or other HCs (with trace quantities of benzene, toluene, xylene) Project tested ac etone as potential replacement, to meet purpose of lowering VOC ---

Beyer, 2003

- Average from 137 auto shops 1996 641 shop towels per

month Laundered

--- Auto Repair

Beyer, 2003

- Average from 53 manufacturing

sites 1996 Less than auto shop

use Laundere d

--- Other Manufacturing

Beyer, 2003 M&M Precision

– Dayton, OH Precision Measuring Equipment c. 2003 200/ week Laundered

--- Other Manufacturing

Beyer, 2003 CSS

International Corporation

– Philadelphia,

PA MFR Machine Shop c. 2003 300/ week Laundered

- -- Other Manufacturing

Beyer, 2003 Avibank

– N. Hollywood, CA Aerospace c. 2003 2000/ week Laundered

--- Transportation

Beyer, 2003 Tie Down Engineering

–

Atlanta, GA Steel Fabrication c. 2003 500/ week Laundered

--- Other Manufacturing

### 57 Reference A Site/Location Business Type Year Total Usage Management F

- Listed Solvents Used Apply Wipe Usage
to Sub

- se ctor

Beyer, 2003 Sele ct Tool &

Die Corporation

–

Dayton, OH Manufacturing c. 2003 5,000/ year Laundered

--- Other Manufacturing

Beyer, 2003 Ames Safety Envelope

– Somerville, MA Printing c. 2003 400,000/ year Laundered

--- Printing

Beyer, 2003 Integrated Aerospace

– Santa An a, CA Aerospace c. 2003 350/ week Laundered

--- Transportation

Beyer, 2003 North Star
Steel Company

– Wilton, IA Steel Fabrication c. 2003 600/ week Laundered

--- Other Manufacturing

Beyer, 2003 Johnson
Controls

– West Union,
SC Injection molding c. 2003 250/ week Laundered

--- Other Manufacturing

Beyer, 2003 Hydroform
USA

– Carson,
CA Aerospace c. 2003 1,000/ week Laundered

--- Transportation

Beyer, 2003 PAK 2000

– Lancaster, PA Specialty packaging c. 2003 75,000/ year Laundered

--- Other Manufacturing

Beyer, 2003 Porter Engineered Systems

– Westfield, IN Automotive c. 2003 375/ week Laundered

--- Transportation

Beyer, 2003 AW North
Carolina Inc.

–

Durham, NC Automotive bearings c. 2003 1,000/ week Laundered

--- Other Manufacturing

Beyer, 2003 Interm et

– Minneapolis,

MN Metal fabrication c. 2003 4,000 to 5,000/ week Laundered

--- Other Manufacturing

Beyer, 2003 Minnesota
National Guard

– St. Paul, MN Aircraft c. 2003 500 to 1,000/ week Laundered

--- Transportation

Beyer, 2003 Briggs &
Stratton

– Aub urn, AL Small engine manufacturing c. 2003 1,000/ week Laundered

--- Other Manufacturing

Beyer, 2003 Boral Bricks

– Phoenix City,

AL Brick manufacturing c. 2003 1,000/ week Laundered

--- Other Manufacturing

Beyer, 2003 Marvin Engineering

– Inglewood, CA Manufacturing c. 2003 200/ week Laundered

--- Other Manufacturing

### 58 Reference A Site/Location Business Type Year Total Usage Management F

- Listed Solvents Used Apply Wipe Usage
to Sub

- se ctor

Beyer, 2003 HXM Motor Express

– Harrisonburg,

VA Transportation c. 2003 500/ week Laundered

--- Transportation

Beyer, 2003 Parker

– Manitowoc, WI Hydraulics c. 2003 8,000/ week Laundered

- -- Other Manufacturing

Docket Item No.

0130 John Roberts Company

– Minneapolis,

MN Commercial

Printer 1991 7,000 wipers per

week Laundered Solvent: acetone, toluene,

MEK Solvent: Toluene, 24% 1,1,1

- trichloroethane (to be discontinued by 1993) Printing

Do cket Item No.

0280 Los Álamos

National Laboratory

– Los Alamos,

NM R&D c. 2003 252 kilograms per

year Disposable

--- ---

Docket Item No.

0292 - colleges and

universities - 0 to 40/ daily (Average of <15

wipes/ daily) - --- ---

Docket Item No.

0387 Deerfi eld

– Ohio Large manufacturing 1999 376/ week Laundered

--- ---

310/ week Disposable

--- Other Manufacturing

Docket Item No.

0387 Aeroblaze

– Ohio Large manufacturing 1999 297/ week Laundered

--- ---

227/ week Disposable

--- Other Manufacturing

EPA, 2006b Corporate

Press

– Landover, MD Commercial printer 2006 1,500 /week (down

from historical 4,000/week) Laundered

--- Printing

EPA, 2006c Exceptional Engraving Company

– Minneapolis,

MN Printing/ engraving 2006 400 to 500/ month Laundered

--- Pri nting

PNEAC, 1996 Keller
Crescent Company

– Evansville, IN Commercial printer c. 2000 210,000/ year Laundered

--- Printing

EPA, 2006e McArdle Printing Company, Inc.

– Upper Marlboro, MD Commercial printer 2006 2,200 to 3,000/ week Laundered

--- Printing

EPA, 2006f Nilfisk

Advance

– Plymouth, MN Equipment assembly: All

operations 2006 9,000/ month (down

from historical 30,000/week) Laundered

--- Other Manufacturing

### 59 Reference A Site/Location Business Type Year Total Usage Management F

- Listed Solvents Used Apply Wipe Usage
to Sub

- se ctor
Plastics only 2006 200

- 300/week Laundered methyl ethyl ketone Other Manufacturing

Pri nting only 2006 125/month Laundered

--- Printing

EPA, 2006g Royal

Business

Forms & Printing

– Brooklyn Park,

MN Commercial printer 2006 2,500 to 4,000/ week Laundered

--- Printing

EPA, 2006h SpecialMade
Goods &
Services

– Winchester,

VA Printing 2006 1,0 00/ year (down from historical 2,000

- 3,000/year) Disposable Glycol ethers 16 Printing

EPA, 2006i Wipaire

– Inver Grove Heights,

MN Manufactures
floats for seaplanes 2006 3,000lb/ year

--- Methyl ethyl ketone Other Manufacturing

EPA, 1997 Site visit Printin g 1997 2,400 wipes/month reusable; 0 disposable Laundered Printing

EPA, 1997 Site visit Printing 1997 320 wipes/month reusable; 1200

disposable Laundered & disposed Printing

EPA, 1997 Site visit Auto body 1997 250 wipes/month reusable; 2000

disposable Laundered & disposed Auto repair

EPA, 1997 Site visit Auto
maintenance 1997 100 lbs/month reusable; 50 lbs/month disposable Laundered & disposed Auto repair

EPA, 1997 Site visit Aircraft
maintenance 1997 Reusable not avail; 1,200 disposable

wipes/ mont h Laundered & disposed Transportation

EPA, 1997 Site visit Circuit Board 1997 0 Reusable; 2,000 disposable Disposed Electronics

EPA, 1997 Site visit Aerospace instrument mfg 1997 480 wipes/month reusable; 40 disposable wipes/

month Laundered & disposed Other Manufacturing

A. All numbered docket items are referenced from the RCRA

- 2003

- 0004 Docket.

16 Precise glycol ether not specified. Although 2

- ethoxyethanol is a glycol ether, it would likely be uniquely listed on the MSDS due to TRI reporting requirements.

### 60 Data in Exhibit A

- 2 were used, in part, to develop input parameters including solvent application rates for the disposable towel and wipe analysis. The f inal column (labeled “*C*”) identifies if the data were used in the evaluation; a “*” indicates the data were used. A blank in the final column indicates the data were not used, typically because data were not available to estimate a solvent quantit y per wipe. Appendix B presents the actual values used in the analysis.

Exhibit A

- 2. Solvent Application to Shop Towels and Wipes

Reference Type of Towel/ Wipe & Industry Activity Weight of Dry Towel/ Wipe Weight of Wet Towel/ Wipe Notes *C*

Docket Item No. 0110 Customer base 10 grams solvent per wipe *

Docket Item No. 0118 Disposable/ Semiconductor Manufacturing Use quantity <10 mL solvent per wipe *

Docket Item No. 0127 Laundered shop towel Hand wringing until no drips 29 grams 85 grams Avg of 34 sam ples, using

acetone *

EPA, 2006b Laundered / Printing Use quantity 49 grams 135 grams Also sampled used rags
from screen bottom drum. Top: 161 g & 94 g. Bottom: 140 g. Variation is due, in part, on amount
of ink on towel. *

EPA, 2006c Laundered / Prin ting Use quantity 7

- 18 grams solvent added/ wipe (calculated from use data) Uses 1

- 2 quarts pet naphtha/ week; 400

- 500 wipes/month *

PNEAC, 1996 Laundered / Printing Use quantity 28 grams solvent removed via recovery process Process recovers 5 gallon

s olvent/ 600 towels *

EPA, 2006d Laundered / Manufacturing Disposed Quantity

- 30 gallons/ week of

solvent recovered via centrifuge

EPA, 2006e Laundered wipe / Printing Disposed Quantity 64 grams 107 grams (95

- 122 grams) Variation is due, in part, on am ount of ink on

towel. Average weight of 8 towels throughout screen

bottom drum *

EPA, 2006f Laundered/ Assembly Use quantity 12

- 18 grams/ towel Solvent: MEK * EPA, 2006i

- / Floats for seaplanes

- 76 grams

- -

EPA, 2006h Disposable/ Printing Use quanti ty

- 102 grams/ towel (calculated) Not all solvent may be applied to rag; calculated use may be

overestimated EPA, 2006g Laundered/ Printing Disposed Quantity

- 34

- 426 grams/ towel

-

Docket Item No. 0062 (1997)

- UTSA Laundered shop towels Solvent Disp osed 28.4 g 62.5 to 135 lb VOC/ 1000

lb towels Disposable paper towels

--- 6.7 g

---
Docket Item No. 0090 (2001)

- Joe Mattson @ Industrial Towel and Uniform (ITU) Laundered shop towels ITU’s New Berlin plant recovers 10,000 gallons of solvent per

m onth --- ---

### 61 Reference Type of Towel/ Wipe & Industry Activity Weight of Dry Towel/ Wipe Weight of Wet Towel/ Wipe Notes *C*

Docket Item No. 0099 (1994)

- American Automobile
Manufacturers Association Auto manufacturing single
site/ Laundered Solvent Disposed 42.6 g 51,434 lb/yr in spent towel/ wipe

Solvent used> solvent disposed, indicating evaporation during us e Auto manufacturing single site/ Disposable Solvent Disposed 15.5 g 4,544 lb/yr in spent towel/ wipe Auto manufacturing single
site/ Laundered Solvent Disposed 31.2 g 1,448 lb/yr in spent towel/ wipe Auto manufacturing single site/ (reusable or disposable not specified) Solvent Disposed 30.5 g 5,547 lb/yr in spent towel/ wipe

Docket Item No. 0108 (No date)

- Jeffrey Adrian @ The

John Roberts Company Shop towels (reusable or disposable not specified) Centrifuging for solvent recovery 1.3 oz 3 oz (before centrifuge) & 1.64 oz (after centrifuge) At printing site *

Docket Item No. 0115 (1995)

- Paul Wavrock @

MARA TEK Environmental Technolologies, Inc. Disposable & reusable shop

towels Solvent quantity in

used towels --- 1 gal solvent/ 100 rags In dustry average/ ‘typical’ *

Docket Item No. 0116 (1999)

- John Clement @ Bock Engineered Products, Inc. --- Centrifuging test for solvent recovery

– not

usage Centrifuge solvent recovery: 95%, 96%, 98%,

100% Four test runs

Docket Item No. 0119 Aerospac e/ Primarily disposables Use quantity 50

- 250 mL applied per wipe

Docket Item No. 0120 (1999)

- Kent Moore @ MicroChem,

L.L.C. Printing/ Laundered towels Solvent recovered

from screen bottom drum following actual

use --- 4725 gallons recovered/ 420,000 towels Screen

- bottom drum

Actual use

--- 6,300 gallons/ 420,000

towels --- *

Docket Item No. 0122 (2000)

- Ed :Jackson @ Fierro Technologies Various sites: Paint/ ink manufacturing; auto manufacturing; general machining Actual use

--- 20

- 22 gram/wipe

--- *

Docket Item No. 0123 (1999)

- Serigraph Printing wipes Centrifuging for solvent recovery following actual use --- 28.5 lb solvent recovered/ 902 wipes (22 lb wipes) *

Docket Item No. 0124 (2000)

- Christine Clement @ Bock

Engineered Products, Inc. Laundered shop towels Centrifuging for solvent recovery following actual use --- 2.5

- 3.5 gallons of solvent recovered for every 255 wipes centrifuged. . *

Docket Item No. 0125 (2001)

- Joe Mattson @
ITU/AbsorbTech Laundered shop towels Typical/ estimate for solvent recovery firm --- 4.15 gallons/ 100 lb load

---

Cal/EPA, 2004

- Cal/EPA,

2004

- PIP, CA Lithographic printing Solvent use quantity for cleanup (in conjunction with wipes) --- 5 gal/ month

--- Cal/EPA, 2004

- Press Ink,
CA 23 gal/month

Cal/EPA, 2004

- Castle 104 gal/month

### 62 Reference Type of Towel/ Wipe & Industry Activity Weight of Dry Towel/ Wipe Weight of Wet Towel/ Wipe Notes *C*

Press, CA Cal/EPA, 2004

- Nelson, CA 75 gal/ month

Cal/EPA, 2004

- DOT. CA 2600 gal/ yr Cal/EPA, 2004

- Paluch, CA 80 gal/ yr Cal/EPA, 2004

- Donnelley,
CA 13,000 gal/ yr

EPA, 1997 Printin g (Laundered) Application Solvent use: 36 gal/month Application rate of 51 g/wipe estimated using

towel use in Table A

- 1 *

EPA, 1997 Printing ( Disposal + Laundered) Application Solvent use: 12 gal/month Application rate of 27 g/wipe estimated using

laun dered+disposed towel

use in Table A

- 1 *

EPA, 1997 Auto body ( Disposal +

Laundered ) Application Solvent use: 37.5 gal/month Application rate of 57 g/wipe estimated using laundered+disposed towel

use in Table A

- 1 *

EPA, 1997 Auto maintenance ( Disposal + L aundered) Application Solvent use: 15 gal/month Application rate of 28 g/wipe estimated using laundered+disposed towel

use in Table A

- 1 *

EPA, 1997 Circuit board ( Disposed) Application Solvent use: 5 gal/month Application rate of 9 g/wipe estimated usin g towel use in Table A

- 1 *

EPA, 1997 Aerospace instr. Mfg ( Disposal + Laundered) Application Solvent use: 20 gal/month Application rate of 131 g/wipe estimated using laundered+disposed towel

use in Table A

- 1 *

The final column (labeled “*C*”) identifies if the data were used in the evaluation; a “*” indicates the data were used. All numbered docket items are referenced from the RCRA

- 2003

- 0004 Docket

.

### 63 Additional Miscellaneous Model Input Data from Comments and Other Background Sources in

Docket RCRA

- 2003

- 0004

Docket item No. 0109 : Kimberly Clark survey of sites in various SIC codes, 1997 [survey details such as sample size and SIC codes covered are not available]:

• Laundered towels with hazardous solvents

o Percent of survey sites using laundered towels: 10 0% (a condition of the survey)

o Percent of survey sites that use hazardous solvents in conjunction with laundered shop towels: 59% (note: ‘hazardous solvent’ not defined)

o Percent of laundered shop towels which actually contact the hazardous solvent: 38%

o Ove rall percent of laundered shop towels which contact hazardous solvents, at sites using laundered towels: 22% (i.e., 59% x 38%).

• Disposable towels/ wipes with hazardous solvents

o Percent of survey sites using disposable towels/ wipes: not given

o Percent of si tes using disposable towels/ wipes that use hazardous solvents in conjunction with disposable towels/ wipes: 47% (note: ‘hazardous solvent’ not defined)

o Percent of disposable towels/ wipes which actually contact the hazardous solvent: 30%

o Overall percent o f laundered shop towels which contact hazardous solvents, at sites using laundered towels: 14% (i.e., 47% x 30%).

Docket item No. 0110 : Letter by Kimberly Clark, 1997

• Non

- printing

o Percent of laundered shop towel users (non

- printing) that use RCRA solvents in conjunction with laundered shop towels: 15.8% (note: ‘RCRA solvent’ not defined)

o Percent of laundered shop towels which actually contact the RCRA solvent (non

printing): 22%

o Overall percent of laundered shop towels which contact hazardous solvents, at non

printing sites using laundered towels: 3.5% (i.e., 15.8% x 22%).

• Printing

o Percent of laundered shop towel users (printing) that use RCRA solvents in conjunction with laundered shop towels: 8.2% (note: ‘RCRA solvent’ not defined)

o Percent of laundered sh op towels which actually contact the RCRA solvent (printing):

80%

o Overall percent of laundered shop towels which contact hazardous solvents, at printing sites using laundered towels: 6.6% (i.e., 8.2% x 80%).

Docket item No. 0112 : Letter from Maratek, 1998

• The printing industry purchases 10 to 15 million gallons of solvents annually. This does not include the publishing industry.

o 10% of this is listed hazardous solvents

o 80% are characteristically hazardous

o 10% are nonhazardous

### 64 Appendix B. Data Value s Used in Disposables Analysis

### 65 Exhibit B

- 1. Use of Disposable Wipes in Five Industry Sectors

Reference Apply Wipe Usage to Sub

- sector Towels/ year

Gradient

– Beyer, 2003 Auto Repair 7,692

EPA 1997 Auto Repair 24,000

EPA 1997 Auto Repair 7,200

No. 011 8 Electronics 450,000

EPA 1997 Electronics 24,000

No. 0122 Other manufacturing 228,000

No. 0052 Other manufacturing 10,000

No. 0102 Other manufacturing 165,000

Gradient

– Beyer, 2003 Other Manufacturing 7,692

Gradient

– Beyer, 2003 Other Manufacturin g 10,000

Gradient

– Beyer, 2003 Other Manufacturing 15,000

Gradient

– Beyer, 2003 Other Manufacturing 25,000

Gradient

– Beyer, 2003 Other Manufacturing 5,000

Gradient

– Beyer, 2003 Other Manufacturing 30,000

Gradient

– Beyer, 2003 Other Manufacturing 12,500

Gradient

– Beyer, 2003 Other Manufacturing 75,000

Gradient

– Beyer, 2003 Other Manufacturing 50,000

Gradient

– Beyer, 2003 Other Manufacturing 225,000

Gradient

– Beyer, 2003 Other Manufacturing 50,000

Gradient

– Beyer, 2003 Other Manufacturing 50,000

Gradient

– Beyer, 2003 Other Manufacturing 10,000

Gradient

– Beyer, 2003 Other Manufacturing 400,000

No. 0387 Other Manufacturing 15,500

No. 0387 Other Manufacturing 11,350
EPA, 2006 Site visit Other Manufacturing 108,000
EPA, 2006 Site visit Other Manufacturing 12,500
EPA, 2006 Site visit Other Manufacturing 12,000

EPA 1997 Other manufacturing 480

No. 0106 Printing 3,000

No. 0106 Printing 30,000

No. 0107 Printing 3,900

No. 0107 Printing 11,400

No. 0107 Printing 22,500

No. 0113 Printing 158,200

No. 0113 Printing 60,850

No. 0113 Printing 41,350

No. 0115 Printing 600,000

No. 0120 Printing 420,000

Gradient

– Beyer, 2003 Printing 400,000

No. 0130 Printing 350,000
EPA, 2006 Site visit Printing 75,000
EPA, 2006 Site visit Printing 5,40 0
PNEAC, 1996 Printing 210,000
EPA, 2006 Site visit Printing 130,000
EPA, 2006 Site visit Printing 1,500

### 66 Reference Apply Wipe Usage to Sub

- sector Towels/ year
EPA, 2006 Site visit Printing 162,500
EPA, 2006 Site visit Printing 1,000

EPA 1997 Printing 28,800

EPA 1997 Printing 14,400

No. 0119 Transporta tion 7,500

No. 0119 Transportation 300,000

Gradient

– Beyer, 2003 Transportation 100,000

Gradient

– Beyer, 2003 Transportation 17,500

Gradient

– Beyer, 2003 Transportation 50,000

Gradient

– Beyer, 2003 Transportation 18,750

Gradient

– Beyer, 2003 Tra nsportation 37,500

Gradient

– Beyer, 2003 Transportation 25,000

No. 0099 Transportation 270,000

No. 0099 Transportation 67,000

No. 0099 Transportation 11,000

No. 0099 Transportation 41,000

EPA 1997 Transportation 14,400

All numbered docket items are referenced from the RCRA

- 2003

- 0004 Docket.



### Exhibit B

- 2. Solvent Application Rates on Disposable Wipes in Five Industry Sectors

Reference Industry Grams/wipe Note

No. 0118 Electronics 7 Estimated from application rate using evaporation assumption

EPA 1 997 Electronics 6 Estimated from application rate using evaporation assumption

No. 0110 General 10

No. 0127 General 56

EPA 2006 Site

visit General 11 Estimated from application rate using evaporation assumption

No. 0115 General 34

No. 0122 General 21

No. 0124 General 44

EPA 1997 General 97 Estimated from application rate using evaporation assumption

EPA 2006 Site

visit Printing 86

EPA 2006 Site

visit Printing 9 Estimated from application rate using evaporation assumption

EPA 2006 Site

visit Printing 28

EPA 2006 Site

visit Printing 43

No. 0108 Printing 48

No. 0120 Printing 51

No. 0123 Printing 14

EPA 1997 Printing 38 Estimated from application rate using

### 67 Reference Industry Grams/wipe Note evaporation assumption

EPA 1997 Printing 20 Estimated from application rate usi ng evaporation assumption

EPA 1997 Auto Repair 42 Estimated from application rate using evaporation assumption

EPA 1997 Auto Repair 21 Estimated from application rate using evaporation assumption

All numbered docket items are referenced from the RCRA

- 20 03

- 0004 Docket.



### Exhibit B

- 3. Percent of Disposable Wipes Containing Solvent Chemicals in Five Industry

Sectors

Chemical Range and Average for Each Sector

Data

Sources Printing Trans

portation Auto Repair Elec

tronics Other

Acetone 1, 5 16

- 26% (avg. 21 %) Same as printing 16

- 25% (avg. 21%) Same as printing Same as auto repair

Benzene 2, 6 0.2

- 2.1% (avg. 1.1%) Same for all

n

- Butanol 2, 6 2.1

- 5.4% (avg. 3.7%) Same for all

Chlorobenzene 3, 6 0.4

- 0.9% (avg. 0.6%) Same for all

Cresols 1, 6 0

- 0.3% (avg. 0. 2%) 0.3

- 0.4% (avg. 0.3%) 0.3

- 0.5% (avg. 0.4%) 0.3

- 0.5% (avg. 0.4%) 0.3

- 0.6% (avg. 0.5%) Cyclohexanone 4, 5 1.8% Same for all
1,2

- Dichlorobenzene 3, 6 0.2

- 0.9% (avg. 0.5%) Same for all

Ethyl acetate 2, 5 2.4

- 6.6% (avg. 4.5%) Same for all

Ethyl benzene 1 , 6 5.7

- 26% (avg. 16%) Same as printing 5.7

- 25% (avg. 16%) Same as printing Same as auto repair

2

- Ethoxyethanol 1, 6 0.1

- 20% (avg. 10%) 0.1

- 22% (avg. 11%) 0.1

- 26% (avg. 13%) 0.1

- 14.5% (avg. 7.3%) 0.1

- 20.5% (avg. 10%)

Isobutyl alcohol 2, 5 0.3

- 0.9% (avg. 0.6%) Same for all

Methanol 1, 6 10

- 26% (avg. 18%) Same as printing 10

- 25% (avg. 18%) Same as printing Same as auto repair Methyl ethyl

ketone 1, 6 8.4

- 20% (avg. 14%) 8.4

- 22% (avg. 15%) 8.4

- 26% (avg. 17%) 8.4

- 14% (avg. 11%) Same as printing Methyl isobu tyl

ketone 1, 6 4.2

- 26% (avg. 15%) Same as printing 4.2

- 25% (avg. 15%) Same as printing Same as auto repair Methylene

chloride 1, 6 2.1

- 3.5% (avg. 2.8%) 2.1

- 6.6% (avg. 4.4%) 2.1

- 5.2% (avg. 3.6%) 2.1

- 8.7% (avg. 5.4%) 2.1

- 8.0% (avg. 5.1%)

Tetrachloro

ethyl ene 2, 6 0.7

- 0.9% (avg. 0.8%) Same for all

Toluene 1, 6 13

- 20% (avg. 16%) 13

- 22% (avg. 17%) 13

- 26% (avg. 19%) 13

- 14% (avg. 14%) 13

- 20% (avg. 17%)
1,1,2

- Trichloroethane 3, 6 0.1

- 0.9% (avg. 0.5%) Same for all

Trichloro

ethylene 2, 6 1.7

- 1.8% (avg. 1.8%) S ame for all

Xylene 1, 6 12

- 26% (avg. 19%) Same as printing 12

- 25% (avg. 19%) Same as printing Same as auto repair

### 68 Data sources:

1. For NESHAP

- identified chemicals, assume chemical use = overall F

- solvent use because these will be the most frequently used chemicals (industry

- dependent). 2. For others, use frequency from EPA Miscellaneous metal parts NESHAP. Use same percent for all industries. 3. For three chlorinated solvents, Document No. RCRA

- 2003

- 0004

- 0106 identifies trichloroethane is used in printin g industry by 3% of sites. Assume similar usage for other chlorinated chemicals and other industries. 4. For cyclohexanone, use is identified in Document No. RCRA

- 2003

- 0004

- 0119 (aerospace) and in printing. However it is not widely mentioned. Use freque ncy data from TURA. 5. Frequency of reporting is from 2004 TURA.
6. Frequency of reporting is from 2004 TRI



### .

Exhibit B

- 4. Percent of Generators in Each of Five Industry Sectors

EA Sector Sector name NAICS Count Percent A

Printing subtotal 3,460 24%

1 P rinting mfg 323 1,976

10 Publishing (printed matter) 5111 1176

11 Business Services (copy shops) 561439 308 Transportation subtotal 159 1%

7 Transportation Equip Mfg 336 159 Auto Repair subtotal 2,026 14%

12 Auto Repair & Maintenance Services 811 1 2,026

Electronics subtotal 70 0.5%

6 Electronics & Computers Mfg 3344 70

Other subtotal Remaining 61%

2 Chemical & Allied Products Mfg 325 169

3 Plastics & Rubber Products Mfg 326 192

4 Fabricated Metal Products Mfg 332 764

5 Industrial Machi nery & Equip Mfg 333 345

8 Furniture & Fixtures Mfg 337 275

9 Auto Dealers (Retail trade) 4411 638

13 Military Bases 92812 8

TOTAL from EA 8,106 TOTAL accounting for industries beyond EA 8,106/0.56 = 14,475 100%

Data from Economic Analysis ( EPA, 2003) A. Percent calculated as percent of 14,475 (see Section 2.3.4).



### Exhibit B

- 5. Number of Disposable Wipes Generators Per Landfill

State % Generators
in State A Number of

Landfills in
State B Landfilled as
% of all

Discards C Generators

per Landf ill D

Alabama 1.5% 96.94

Alaska 0.1% 96.97

Arizona 1.5% 40 100.00 12

Arkansas 0.9% 98.38

California 11.4% 153 97.52 23

Colorado 1.7% 72 100.00 8

Connecticut 1.6% 2 14.38 35

Delaware 0.2% 3 100.00 23

Florida 5.0% 54 66.58 20 Georgia 2. 6% 57 98.80 15

Hawaii 0.2% 63.97

Idaho 0.5% 26 100.00 7

Illinois 5.0% 52 100.00 30

### 69 State % Generators
in State A Number of
Landfills in
State B Landfilled as
% of all

Discards C Generators

per Landf ill D

Indiana 2.5% 36 92.31 21

Iowa 1.3% 59 97.85 7

Kansas 1.1% 52 99.88 7 Kentucky 1.3% 27 99.74 15

Louisiana 1.0% 95.16

Maine 0.5% 10 70.84 12 Maryland 1.5% 31 71.43 12

Massachusetts 2.4% 21 44.11 16 Michigan 4.4% 51 91.66 25

Minnesota 2.4% 23 63.56 21
Mississippi 0.8% 17 100.00 16

Missouri 2.2% 22 99.18 31

Montana 0.4% 29 99.06 5

Nebraska 0.7% 23 100.00 10

Nevada 0.6% 23 100.00 8
New Hampshire 0.6% 8 7 8.50 19 New Jersey 2.9% 12 76.44 57

New Mexico 0.5% 39 100.00 5

New York 5.8% 26 65.79 45

North Carolina 3.1% 41 98.89 24

North Dakota 0.3% 14 100.00 7

Ohio 4.7% 42 100.00 35

Oklahoma 1.2% 31 91.68 12 Oregon 1.5% 28 93.36 16
Pennsylvania 4.6% 48 7 5.59 23

Rhode Island 0.4% 2 99.77 67

South Carolina 1.3% 18 93.39 21

South Dakota 0.4% 15 100.00 8

Tennessee 2.0% 35 100.00 18

Texas 6.4% 189 99.87 11

Utah 0.9% 34 94.87 8

Vermont 0.4% 4 87.55 24 Virginia 2.1% 60 82.33 10 Washington 2.0% 19 94.13 31 West Virginia 0.5% 18 100.00 9

Wisconsin 2.8% 36 96.15 23 Wyoming 0.2% 52 100.00 2

TOTAL 100.0% 1,654 89.65 17 Avg

-- > 18.9

Median

-- > 16 A. Distribution of 7 NAICS wipe

- generating industries from 2002 Dept of Census.

Pretreatment Standards for the Industrial Laundries Point Source Category (Revised March 2000). Table 4

- 1. Sites for District of Columbia are combined with Maryland. B. Simmons. The State of Garbage in America. Biocycle. April 2006. Pages 26

- 43. Table 9. Data not availab le for

some states. C. Simmons. The State of Garbage in America. Biocycle. April 2006. Pages 26

- 43. Table 3. This column represents the percent landfilled for discarded MSW only, versus that destined for combustion (i.e., excludes recycling).

D. Min imum 1 generator per landfill. Accounts only for SQG/LQG generating disposable RCRA towels; accounts that waste is landfilled or combusted according to overall MSW percentage. Uses total of 30,380 generators.

### 70 Exhibit B

- 6. Evaporation Rates of Solvents from Wipes

Scenario % Solvent Remaining Solvent Reference
Open Container (18

- 24 hours) 49 BP

- 308 EPA, 1997
Open Container (18

- 24 hours) 92 BP

- 308 EPA, 1997
Open Container (18

- 24 hours) 85 BP

- 303 EPA, 1997
Open Container (18

- 24 hours) 52 Plastic EPA, 1 997
Open Container (18

- 24 hours) 70 Plastic EPA, 1997
Open Container (18

- 24 hours) 59 DX330 EPA, 1997
Open Container (18

- 24 hours) 75 DX330 EPA, 1997
Open Container (18

- 24 hours) 97 BP

- 308 EPA, 1997
Closed Container (18

- 24 hours) 106 Lacquer EPA, 1997
Closed Container (18

- 24 hours) 92 Lacquer EPA, 1997
Closed Container (18

- 24 hours) 93 Lacquer EPA, 1997
Closed Container (18

- 24 hours) 72 Lacquer EPA, 1997
Closed Container (18

- 24 hours) 99 Lacquer EPA, 1997
Closed Container (18

- 24 hours) 45 Lacquer EPA, 1997
Closed Container (18

- 24 hours) 0 MEK EPA, 1997
Closed Container (18

- 24 hours) 0 MEK EPA, 1997
Closed Container (18

- 24 hours) 0 MEK EPA, 1997
Closed Container (18

- 24 hours) 0 MEK EPA, 1997
Closed Container (18

- 24 hours) 0 MEK EPA, 1997

Avera ge (excluding MEK) 78 %
Average (MEK only) 0 % EPA, 1997. Use and Management Practices of Solvent Contaminated Industrial Shop Towels. Final Document.

Exhibits 1

- 3a and 1

- 3b. With the exception of methyl ethyl ketone (MEK), no further informat ion is available regarding the properties or composition of the solvents used in EPA (1997).



### The data in Exhibit B

- 6 labeled as ‘open container’ came from facilities where the spent wipers were stored in the open (e.g., on a shelf, in open containers, etc. ). The data in Exhibit B

- 6 labeled as ‘closed container’ came from facilities where the spent wipers were stored in closed containers. All of the wipers stored in open containers retained in excess of 49% of the solvent added to the wiper. With the exce ption of data points for MEK, the same is true for wipers stored in closed containers. There are no clear differences between storage in open containers and closed containers, in terms of the percentage of solvent remaining on the wiper after 18

- 24 hours. The vast majority of the wipers retained in excess of 50% of the solvent (EPA, 1997).

All wipers used in conjunction with MEK were KimWipes. The KimWipes used with MEK retained 0% of the solvent; probably because of the volatility of the solvent and th e relatively low adsorption of the wiper (EPA, 1997).

### 71 Appendix C. Industry

- Specific Use of F

- Listed Solvents

– NESHAPs

### 72 Through its air toxics program, EPA has finalized National Emission Standards for Hazardous Air Pollutants (NESHAPs). EPA has investi gated the use and emissions of specific chemicals when developing these regulations. These regulations cover many industries, which use solvents in conjunction with towels or wipes, and therefore the data are useful in assessing the overall use pattern of specific solvents. These data were used to assess solvent use patterns in various industries. Some of the benefits and limitations in using these data for purposes of this solvent wipes project include the following:

Benefits

• Useful for identifying whe ther or not a chemical is present in an industry.

• Based on detailed site and industry study by EPA.

• Solvents used in conjunction with wipes are a common source of investigated air emissions.

• Of the 30 F

- listed solvents, the majority (21) is HAPs and likely to be assessed in detail in NESHAP development materials. Chemicals, which are not HAPs, include acetone, butanol, ethyl acetate, ethyl ether, 2

- ethoxyethanol, isobutyl alcohol, pyridine, CFC

- 113, CFC

- 11, and methyl ethyl ketone. 17



### Limitations

• The source data are typically old (e.g., 1990’s) and may not reflect current use patterns.

• Sources of air emissions include operations, which do not necessarily involve the use of wipes in conjunction with solvents. This is particularly true of surface coating NESH APs, where the majority of emissions are likely the result of painting where wipes are not typically used. Applications of wipes in surface coating operations are most likely in surface preparation where

solvent use and emissions are smaller.

• Data and reg ulations are applicable to large facilities. These practices may or may not reflect all

sites.

Chemicals representing the most significant HAP emissions were identified from the NESHAPs development materials for 11 industries, predominantly as indicated in the Federal Register notices.

Nine F

- listed chemicals were identified from these materials as follows:

• Cresols

• Ethylbenzene

• Glycol ethers (does not identify individual glycol ethers such as 2

- ethoxyethanol)

• Methanol

• Methylene chloride

• Methyl ethyl ket one

• Methyl isobutyl ketone (MIBK),

• Toluene

• Xylene

The above list represents the most predominant chemicals or the chemicals emitted in the greatest amounts. Additional chemicals, including non

- HAP chemicals, may be emitted from these

industries as well. Industry: Semiconductor manufacturing

17 Methyl ethyl ketone was delisted as a HAP in 2004 but NESHAPs developed prior to 2004 address this chemical. The list of HAPs includes glycol ethers as a category but does not identify individual glycol ethers such as 2

- ethoxyethanol.

### 73 Organic HAPs emitted from semiconductor manufacturing facilities include methanol, glycol ethers, and xylene. Five chemicals (i.e., these three plus two additional chemicals, which are not F

listed solvents) are estim ated to comprise 90 percent of the total nationwide HAP emissions of 636 tons per year in this industry (67 FR 30851).

Aerospace

Organic HAPs emitted from aerospace manufacturing and rework facilities include methylene chloride, toluene, xylene, methyl e thyl ketone, and glycol ethers (60 FR 45948).

Automotive Assembly Surface Coating

Organic HAPs emitted from surface coating of automobiles and light

- duty truck manufacturing (assembly plants) are toluene, xylene, glycol ethers, methyl ethyl ketone (MEK), methyl isobutyl ketone (MIBK), ethylbenzene, and methanol. These chemicals comprise over 95 percent of nationwide HAP emissions from the source category. Emissions are primarily associated with coating and drying. Surface preparation sources were estim ated to be smaller by comparison (69 FR 22602).

Appliance Surface Coating

Organic HAPs emitted from surface coating of appliance manufacturing are toluene, xylene, glycol ethers, and methyl ethyl ketone (MEK). Five chemicals (i.e., these four plus one a dditional

chemical that is not an F

- listed solvent) are estimated to comprise 80 percent of the total nationwide HAP emissions (baseline 2,839 tons) in this source category (67 FR 48253).

Metal Can Surface Coating

Primary organic HAPs emitted from surfac e coating of metal can manufacturing are xylene, glycol ethers, methyl isobutyl ketone (MIBK), and methyl ethyl ketone (MEK). Other organic HAPs identified include ethyl benzene and toluene. Total baseline nationwide HAP emissions are 9,600 tons (include s contributions from these and other chemicals which are not F

- listed solvents) in this source category (68 FR 64431).

Metal Coil Surface Coating

Organic HAPs emitted from surface coating of metal coil are toluene, xylene, glycol ethers, and methyl ethyl ketone (MEK) (67 FR 39793).

Metal Furniture Surface Coating

Organic HAPs emitted from surface coating of metal furniture manufacturing are toluene, xylene, glycol ethers, ethyl benzene, and methyl ethyl ketone (MEK) (68 FR 28605).

### 74 Surface Coating of Mi scellaneous Metal Parts and Products

Organic HAPs emitted from this source category are toluene, xylene, ethyl benzene, methyl ethyl ketone (MEK), cresols, glycol ethers, and methyl isobutyl ketone (MIBK). These chemicals, plus

others identified which ar e not F

- listed solvents, are estimated to comprise 90 percent of the total nationwide HAP emissions in this source category. The majority of emissions are associated with the application, drying, and curing of coatings; smaller emissions are associated wi th cleaning operations (69 FR 130).

The source of cresol is identified in a 1998 Background Document as a solvent for a coating used in the production of magnet wire, a product made by up to 30 large sites (EPA, 2002).

Printing and Publishing

Organic HA Ps emitted from this source category are toluene, xylene, ethyl benzene, methyl ethyl ketone (MEK), methanol, glycol ethers, and methyl isobutyl ketone (MIBK). Total baseline nationwide HAP emissions are 23,900 tons in this source category. The source ca tegory includes rotogravure and flexographic printing (61 FR 27132).

Ship Building and Ship Repair (Surface Coating)

Organic HAPs emitted from this source category are toluene, xylene, ethyl benzene, methyl ethyl ketone (MEK), glycol ethers, and methyl i sobutyl ketone (MIBK). Total baseline nationwide HAP emissions are 1,500 tons per year in this source category (60 FR 64330).

Wood Furniture (Surface Coating)

Organic HAPs emitted from this source category are toluene, xylene, methanol, methyl ethyl ket one (MEK), glycol ethers, and methyl isobutyl ketone (MIBK). Total baseline nationwide HAP emissions are 1,500 tons per year in this source category (60 FR 62930).

### 75 Appendix D. 2003 Biennial Report Data

### 76 Introduction, Purpose, and General Limitations

Th e Hazardous Waste Report (or Biennial Report) is completed by certain hazardous waste generators and hazardous waste treatment, storage, or disposal facilities. Data collection is done on

a two

- year cycle as required by Sections 3002 and 3004 of the Resou rce Conservation and Recovery Act (RCRA). The information is collected via a mechanism known as the Hazardous Waste Report for the required reporting year. Facilities provide their completed forms to the state

from which EPA creates a national database. As of August 2006, the most recent national database is for the 2003 reporting year.

Facilities are required to report if they meet the definition of a RCRA Large Quantity Generator (LQG) during the reporting year (e.g., the site generated at least 2,200 lbs (1,000 kg) of RCRA hazardous waste in any single calendar month), or they are a TSD facility (treated, stored, or disposed of RCRA hazardous wastes). Some states have additional requirements or, for example, require small quantity generators to repor t as well. These state

- by

- state variances are reflected in

the national database.

The national database for the 2003 reporting year is available from an EPA ftp site. 18 

### For this project, the June 10, 2005 versions of the files for the 2003 reporting year were used.

Principal advantages and limitations of the 2003 Biennial Report as they relate to this project are as

follows:

• The biennial report is only required (at the federal level) for LQGs.

• For those facilities required to report, the facility must report all of its hazardous wastes, regardless of quantity. For example, if an LQG facility generates 1 pound of spent F001 solvent it must report this waste. A separate waste report form (Form GM) is completed for each generated RCRA hazardous waste. A s a result, a single facility will typically report the generation of many different hazardous wastes. However, facilities are permitted to combine and report similar hazardous wastes on Form GM. Facilities generating solvent

- contaminated wipes, for exam ple, may combine reporting with similar material such as adsorbents, PPE, filters, etc.

• Only hazardous wastes are reported. Generators will not report shop towels sent to a laundry if they are not shipped as a hazardous waste. Generators will not report disposable wipes placed in the general trash if they are not shipped as a hazardous waste.

• Industrial laundries are not hazardous waste TSDs and therefore will not report quantities of shop towels received from customers. However, they may be generators of hazardous wastes such as from (1) co

- located dry cleaning operations; (2) treatment residues, which are characteristically hazardous.

• In 2003, the Hazardous Waste Report used the NAICS industry classification. This provides consistency with U.S. Cens us data and the solvent wipe economic analysis (EPA, 2003).

18 ftp://ftp.epa.go v/rcrainfodata/ar/br_2003/

### 77 2003 Biennial Report Fields Obtained

EPA’s biennial reporting database contains approximately 100 different fields divided among more

than a dozen individual tables. The available fields and ta bles were carefully reviewed to obtain data likely to be relevant. Fields from three separate tables were obtained to generate relevant data for targeted facilities, using several Microsoft Access queries and the source files from the EPA ftp

site. These fields are identified in Exhibit D

- 1.

Throughout the Microsoft Access process, strict attention was made to identify consistent record counts when combining records from different tables, etc. This provided assurance that duplicate

records were not inad vertently added or other records inadvertently deleted.

Exhibit D

- 1. Data Extracted for Biennial Report Evaluation

File Field Name Description Source File Name

RCRA ID Facility RCRA ID Number USSI6, USGM1, USGM2

Handler Name Facility Name USSI1

NAICS Code Facility NAICS Code USSI6
Page Number (used for internal linking only) USGM1, USGM2

Form Code Identifies Waste Type USGM1

Source Code Identifies Waste Source USGM1

Qty Quantity Reported by Facility USGM1

UOM Unit of Measure for Reported Quantity USGM1
Density Density or Relative Density of Waste Given When UOM is Gallons or Specific Gravity USGM1

Density UOM Unit of Measure for Density USGM1

Waste Desc A user

- provided description of the Waste USGM1

Notes Any additional information provided by the facility USGM1

HW Codes Waste Code (As many as needed to identify each waste) USGM2



### Summary of Results

– Shop Towel Generators

The following process was used to assess solvent wipes generators using Biennial Report data:

• All reporting facilities m ust report their NAICS code. Data was evaluated for generators in those NAICS industry classifications used in the economic analysis. Relevant NAICS codes are

as follows:

o 323: Printing Manufacturing

o 325: Chemical & Allied Products Manufacturing

o 326: Pla stics & Rubber Products Manufacturing

o 332: Fabricated Metal Products Manufacturing

o 333: Industrial Machinery & Equipment Manufacturing

o 3344: Electronics & Computers Manufacturing

o 336: Transportation Equipment Manufacturing

o 337: Furniture & Fixtures Manufac turing

o 4411: Auto Dealers (retail trade)

### 78 o 5111: Publishing (printed matter)

o 561439: Business Services (copy shops)

o 8111: Auto Repair & Maintenance Services

o 92812: Military Bases

A facility may report more than one NAICS code and more than one waste code. Therefore, reported percentages will not be additive to 100%. This factor does not reduce the accuracy of the data in any way, but it must be considered when comparing data between different NAICS codes or waste types.

• The generation of F001 to F005 haza rdous wastes in the above

- targeted NAICS code industries was assessed. This identifies the percentage of facilities that generate F001 to F005 wastes within any chosen industry.

• The generation of F001 to F005 wastes that result from the use of contaminat ed wipes or similar materials was assessed. This shows the percentage of facilities within each industry that report as generators of solvent contaminated wipes (or similar waste). A facility indicates the general physical properties and origin of the wa ste using form codes and source codes. The most

relevant indicator is form code W002: Contaminated debris: paper, clothing, rags, wood, empty fiber or plastic containers, glass, piping, other solids (usually from construction, demolition, cleaning, or rem ediation).

The form and source code data, while accurate and representative of the Biennial Report, contains uncertainties regarding how facilities define their wastes at the time of generation. This information is somewhat qualitative and descriptive an d therefore different facilities may describe similar wastes differently. In addition, heterogeneous waste shipments containing solvent

- contaminated rags may not be described using the above codes. Because of this uncertainty, data analysis has

been cond ucted with and without consideration of these form codes.

Information for a total of 18,786 generators is available in the 2003 biennial report. Of these:

• 9,048 facilities (48%) have an NAICS code corresponding to one of the 13 NAICS codes

identified ab ove (and used in the economic analysis).

• 9,877 facilities (53%) report the generation of F001

- F005 wastes.

• 3,813 facilities (20%) report the generation of wastes with form code W002 (spent rags and other materials).

Exhibits D

- 2 and D

- 3 summarize the gene rated hazardous wastes. Exhibits D

- 4 and D

- 5 provide

similar information as Exhibits D

- 2 and D

- 3 for specific industries.

Exhibit D

- 2. Number of Sites Generating Wastes in 2003 Biennial Report

All wastes Any of F001

- F005 F001 F002 F003 F004 F005

Sit es in 13 NAICS Only 9,048 5,660 ( 63%) 848 ( 9% ) 1,420 ( 16% ) 4,812 ( 53% ) 98 ( 1% ) 3,847 ( 43% )

All BR Sites 18,786 9,877 ( 53%) 1,784 ( 9% ) 3,338 ( 18% ) 8,325 ( 44% ) 363 ( 2% ) 6,689 ( 36% ) Percentages are of the number of sites reporting all wastes (e.g., 9,048 si tes in the 13 NAICS codes).

### 79 Exhibit D

- 3. Number of Sites Generating Shop Towels and Similar Wastes (Form Code W002) in 2003 Biennial Report

All

wastes Any

W002

waste Any of

F001

- F005 F001 F002 F003 F004 F005

Sites in 13 NAICS Only 9,048 1,873 1,504 ( 17% ) 282 ( 3% ) 545 ( 6% ) 1,328 ( 15% ) 48 ( 1% ) 1,131 ( 13% )

All BR Sites 18,786 3,813 2,754 ( 15% ) 657 ( 3% ) 1,229 ( 7% ) 2,434 ( 13% ) 174 ( 1% ) 2,102 ( 11% ) Percentages are of the number of sites reporting all wastes (e.g., 9,048 sites in the 13 NAICS codes).

Form Code W002 includes, but is not limited to, shop towels and wipes.



### Exhibit D

- 4. Number of Sites Generating Wastes in 2003 Biennial Report for Specific

Industries

INDUSTRY NAICS Any

waste Any of F001

- F005 F001 F002 F003 F004 F005
Printing Manufacturing 323 475 239 10 20 203 0 153
Chemical & Allied Products Manufacturing 325 2,669 1,918 229 747 1,718 63 1,328
Plastics & Rubber Products Manufacturing 326 634 491 56 95 424 5 309
Fabricated Metal Products Manufacturing 332 2,534 1,118 256 179 831 11 715
Industrial Machinery & Equipment Manufacturing 333 571 375 63 89 294 2 259
Electronics & Computers Manufacturing 3344 701 390 77 113 332 6 188
Transportation Equipment Manufacturing 336 1,286 994 177 219 874 12 739
Furniture & Fixtures Manufacturing 337 286 251 2 6 239 0 217
Auto Dealers (retail trade) 4411 42 27 3 1 21 0 23
Publishing (printed matter) 5111 24 4 0 2 3 0 3
Business Services (copy shops) 561439 15 11 7 8 11 0 10
Auto Repair & Maintenance Services 8111 119 64 7 11 54 1 56
Military Base s 92812 3 2 0 1 1 0 1
Any of above 13 NAICS 9,048 5,660 848 1,420 4,812 98 3,847
Total (all facilities in Biennial Report) 18,786 9,877 1,784 3,338 8,325 363 6,689

Overall Printing 7% 12% 85% 0% 65%
Transportation 18% 22% 88% 1% 74%

A uto repair 11% 17% 84% 2% 88%

Electronics 20% 29% 85% 2% 48%

Other 15% 27% 84% 2% 68% Numbers in rows and columns are not additive because a site can report multiple NAICS codes or multiple waste types.

### 80 Exhibit D

- 5. Number of Sites Generating Shop Towels and Similar Wastes (Form Code W002) in 2003 Biennial Report for Specific Industries

INDUSTRY NAICS All

wastes Any

W002

waste Any of

F001

- F005 F001 F002 F003 F004 F005
Printing Manufacturing 323 475 71 11% 0% 2% 10% 0% 8%

Chemical & Allied Products Manufacturing 325 2,669 641 20% 3% 10% 19% 1% 15%

Plastics & Rubber Products Manufacturing 326 634 153 22% 3% 5% 19% 0% 17%

Fabricated Metal Products Manufacturing 332 2,534 379 10% 3% 2% 8% 0% 7%

Industrial Machinery & Equipment Manufacturing 333 571 107 15% 2% 5% 13% 0% 11%

Electronics & Computers Manufacturing 3344 701 195 22% 4% 7% 19% 0% 12%

Transportation Equipment Manufacturing 336 1,286 379 26% 7% 9% 23% 1% 22%
Furniture & Fixtures Manufacturing 337 286 28 9% 0% 0% 8% 0% 8%

Auto Deal ers (retail trade) 4411 42 1 2% 0% 0% 2% 0% 2%
Publishing (printed matter) 5111 24 1 4% 0% 4% 4% 0% 4%
Business Services (copy shops) 561439 15 0 NA NA NA NA NA NA

Auto Repair & Maintenance Services 8111 119 3 1% 0% 1% 1% 0% 1%
Military Bases 92812 3 1 0% 0% 0% 33% 0% 33%
Any of above 13 NAICS 9,048 1,873 17% 3% 6% 15% 1% 13%
Total (all facilities in Biennial Report) 18,786 3,813 15% 3% 7% 13% 1% 11% Percentages are of the number of sites reporting all wastes (e.g., 475 sites in NAICS code 323).

Fo rm Code W002 includes, but is not limited to, shop towels and wipes.



### Conclusions regarding the use of the Biennial Report for evaluating towels and wipes are as

follows:

• The Biennial Report primarily collects data for large quantity generators (LQGs) of hazardous wastes. The total number of sites in each industry is greater than this. The Biennial Report data are most useful for industries that have a relatively high number of LQGs (e.g., NAICS 336 Transportation Equipment Manufacturing). The data are less useful for industries, which predominantly are not LQGs (e.g., NAICS 8111 Auto Repair & Maintenance Services).

• The statistics for the 13 NAICS codes (taken together) are similar to all Biennial Report generators. This is shown in Exhibits D

- 2 and D

- 3

.

• Exhibits D

- 2 and D

- 4 are useful for assessing the use of certain types of solvents. Exhibit D

- 2 shows that F003 wastes are most common while F004 wastes are least common. By extension, it is expected that the use of F003 solvents is much more prevalent than the use of F004 solvents. This information does not identify if disposable, reusable, or neither type of towels or wipes is used with these solvents.

• Waste generation for individual NAICS codes have significant variation. For example, Exhibit

D

- 4 s hows that 68% of sites in transportation equipment manufacturing (NAICS 336) generate F003 wastes, while 33% of sites in Fabricated Metal Products Manufacturing (NAICS 332) generate F003 wastes.

• There is high uncertainty with the use of data in Exhibits D

- 3 and D

- 5 as to whether they represent shop towels. Facilities report the form code most closely approximating the physical

### 81 state of the waste; therefore, there is some subjectivity on the part of the reporting facility. The form code category W002 also includes many items, which are not shop towels, or wipes. Finally, facilities may report shipments of shop towels or wipes as some other form code.

• Shop towels or wipes used with F001 to F005 solvents would not be reflected in the Biennial Report data if: (1) they are disposed as nonhazardous waste; or (2) they are sent to an industrial laundry.

Summary of Results

– Laundries

The Biennial Report data were used to assess laundries processing shop towels. Information for a total of 18,786 generators is av ailable in the 2003 biennial report. Of these, 45 facilities have an NAICS code corresponding to laundries:

• 81233: Linen and Uniform Supply

• 812331: Linen supply

• 812332: Industrial launderers

Not all of these sites process shop towels. It is difficult t o identify comprehensively which sites accept shop towels and which do not. Instead, each waste generated by the above facilities was reviewed to estimate wastes associated with shop towel use. These 45 facilities reported generating

128 wastes:

• Wastes retained for assessment (a total of 29 wastes from 19 facilities were retained):

o Some wastes were described (by the generating sites) as solvent recovered from shop towel processing. These are relevant to the assessment.

o Some wastes were identified as was tewater treatment sludges (source code G23) 19



### . These are relevant to the assessment even though shop towels may or may not be processed at this particular site.

• Wastes eliminated from assessment (a total of 99 wastes were eliminated):

o Some wastes are descr ibed as spent tetrachloroethylene. These are most likely associated with dry cleaning operations and were eliminated from assessment.

o Some wastes have no description or are designated in some other manner. These were

eliminated from the assessment.

Exhi bit D

- 6 summarizes the hazardous wastes reported as generated by laundries.

Exhibit D

- 6. Biennial Report Data for Laundries Waste Type Number of Sites Generating Qty Generated,

lb Avg Per Site,

lb

WWT Sludge 17 10,141,935 600,000

Solvent recovered from rags 2 305,047 150,000

19 Source Code G23: Wastewater treatment (sludge, filter cake, etc., including wastes from treatment before discharge by NPDES or POTW or by UIC disposal).

### 82 Appendix E. Federal and State Right

- to

- Know Data for Evaluating Solvent Use

### 83 The availability of Toxics Release Inventory (TRI) data was reviewed for each of the F

- listed chemicals. Data from one state program, Massachusetts, w ere also investigated because this state provides data for additional chemicals and industries, which are not addressed in the federal TRI program. A comprehensive, state

- by

- state evaluation was not performed to identify other states with requirements bey ond those at the federal level. 20



### TRI data is available from EPA’s Envirofacts website ( www.epa.gov/enviro/ ). Massachusetts data (Toxics Use Reduction Act or TURA) are found at http://www.mass.gov/dep/toxics/priorities/priorities.htm#tura . As of August 2006, the most recent data for both programs are for 2004. In neither case was historical data assessed.

Data was evaluated with res pect to the generating industries (e.g., facilities that may generate solvent wipes/ towels) as well as industrial laundries (e.g., chemicals received or discharged by industrial laundries).

Data Relevant to Generators

The primary purpose of this revie w was to identify the frequency that chemicals are reported by targeted industries (i.e., industries most likely to generate solvent towels or wipes). Quantitative data (e.g., pounds of chemicals) were not evaluated because the use or source of the chemic al at the site is not known. For example, a facility may report that 500 pounds of a TRI chemical is sent to a landfill. It is not possible to estimate reasonably the types of wastes, which contribute to this total,

or whether solvent

- contaminated wipes are in fact generated at all.

What is the universe of affected industries?

Neither TRI nor TURA require facilities with less than 10 full

- time employees to report. Facilities are subject to TRI reporting if they have a primary Standard Industrial Classi fication (SIC) code of 20 to 39 (manufacturing), 7389 (facilities primarily engaged in solvents recovery services on a contract or fee basis), and certain other industries such as mining, electric utilities, hazardous waste management facilities, and oil a nd chemical terminals. TURA regulates a broader range of facilities (i.e., SIC codes) than EPCRA. Facilities conducting activities described in the following SIC codes must report: 10

– 14, 20

– 39, 40, 44

– 51, 72, 73, 75, and 76. Occasionally, facilit ies outside these SIC codes report to TRI or TURA on a voluntary basis.

Exhibit E

- 1 summarizes the TRI and TURA reporting requirements for 13 targeted industries (as evaluated in EPA, 2003).

20 New Jersey’s pollution prevention reporting requirements provide some additional information beyond federal requirements with respect to chemical use quantities. The industries and chemicals covered are the same as the federal program. The New Jersey information was not reviewed, in part, due to expected difficulties of r elating the available information to solvent and towel/ wipe use.

### 84 Exhibit E

- 1. Coverage of Industries Using Solvent Wipes/ Sho p Towels in TRI and State Programs

INDUSTRY NAICS SIC Subject to

TRI? Subject to

TURA? Printing Manufacturing 323 275 to 279 Yes Yes

Chemical & Allied Products Manufacturing 325 280 Yes Yes

Plastics & Rubber Products Manufacturing 326 300 Yes Yes

Fabri cated Metal Products Manufacturing 332 340 Yes Yes

Industrial Machinery & Equipment Manufacturing 333 352 to 356 Yes Yes

Electronics & Computers Manufacturing 3344 367 Yes Yes

Transportation Equipment Manufacturing 336 370 Yes Yes

Furniture & Fixtures Manufacturing 337 250 Yes Yes

Auto Dealers (retail trade) 4411 5511 + 5521 No No Publishing (printed matter) 5111 271 to 274 Yes Yes Business Services (copy shops) 561439 7334 No Yes Auto Repair & Maintenance

Services 8111 753 No Yes

Military Bases 92 812 9721 No No

Total 9 of 13 11 of 13
NAICS codes and SIC equivalents are from Economic Analysis (EPA, 2003), page 70.



### What chemicals are required to be reported?

Exhibit E

- 2 identifies the chemicals required for TRI and Massachusetts reporting, with respect to
the F

- listed solvents. Starting in Reporting Year 2004, methyl ethyl ketone was removed from the list of TRI reportable chemicals. Reporting for this chemical was required for reporting year 2003 and earlier. For reference, Exhibit E

- 2 inclu des Hazardous Air Pollutant (HAP) designations. Of

the 30 F

- listed chemicals, 25 are subject to TRI reporting and all are subject to Massachusetts reporting.

For TRI and for Massachusetts, chemical reporting is required only if the facility manufactures, processes, or otherwise uses the chemical in quantities greater than the threshold during the year. Most chemicals used as solvents would be subject to the 10,000 pounds otherwise use threshold. This is equivalent to the use of approximately two 55

- gall on drums of solvent per month, in the case of a pure solvent chemical. Only chemicals used over the threshold quantity are required to be reported. In the case of a solvent mixture containing various individual chemicals (such as a solvent containing 10 percent toluene), calculations and reporting is conducted for each chemical rather

than for the mixture as a whole.

### 85 Exhibit E

- 2. Evaluation of F

- Listed Solvents in TRI and State Programs

CAS No. Constituent EPRCA Status MA Status HAP Status

67

- 64

- 1 Ace tone Reportable

71

- 43

- 2 Benzene Reportable Reportable HAP

71

- 36

- 3 Butanol Reportable Reportable

75

- 15

- 0 Carbon disulfide Reportable Reportable HAP

56

- 23

- 5 Carbon tetrachloride Reportable Reportable HAP

108

- 90

- 7 Chlorobenzene Reportable Reportable H AP

108

- 94

- 1 Cyclohexanone Reportable HAP

1319

- 77

- 3 Cresols Reportable Reportable HAP

75

- 71

- 8 Dichlorodifluoromethane Reportable Reportable HAP

95

- 50

- 1 1,2

- Dichlorobenzene Reportable Reportable HAP

141

- 78

- 6 Ethyl acetate Reportable

100

- 41

- 4 Ethyl benzene Reportable Reportable HAP

60

- 29

- 7 Ethyl ether Reportable

110

- 80

- 5 2

- Ethoxyethanol Reportable Reportable

78

- 83

- 1 Isobutyl alcohol Reportable

67

- 56

- 1 Methanol Reportable Reportable HAP

78

- 93

- 3 Methyl ethyl ketone * Reportable *

108

- 10

- 1 Me thyl isobutyl ketone Reportable Reportable HAP

75

- 09

- 2 Methylene chloride Reportable Reportable HAP

98

- 95

- 3 Nitrobenzene Reportable Reportable HAP

79

- 46

- 9 2

- Nitropropane Reportable Reportable HAP

110

- 86

- 1 Pyridine Reportable Reportable

127

- 18

- 4 Tetra chloroethylene Reportable Reportable HAP

108

- 88

- 3 Toluene Reportable Reportable HAP

71

- 55

- 6 1,1,1

- Trichloroethane Reportable Reportable HAP

79

- 00

- 5 1,1,2

- Trichloroethane Reportable Reportable HAP

79

- 01

- 6 Trichloroethylene Reportable Reportable HAP

76

- 13

- 1 1,1,2

- Trichlorotrifluoroethane Reportable Reportable

75

- 69

- 4 Trichlorofluoromethane Reportable Reportable

1330

- 20

- 7 Xylenes (total) Reportable Reportable HAP

* Effective for the 2004 reporting year, methyl ethyl ketone was removed from the EPCRA and HAP lists.



### Results

Exhibit E

- 3 summarizes the reporting results for each of the 30 chemicals from the 2004 TURA and TRI data. The purpose of Exhibit E

- 3 is to show the prevalence of reporting across the 13 different

industries as identified in Exhi bit E

- 1. This table does not identify how the chemicals are used by

the facilities in the industries.

### 86 Exhibit E

- 3. Evaluation of F

- Listed Solvents in EPCRA and State Programs

Chemical Reporting in 2004 TRI Reporting in 2004 TURA

# of industries repres ented # of sites in 13

industries # of industries represented # of sites in 13

industries Any of 30 chemicals in 13 targeted industries 10 of 13 3,904 sites 6 of 13 65 sites

Acetone N/A

--- 6 of 13 35 sites (54%)

Benzene 5 of 13 286 sites (7%) 0 of 13 0

Butanol 7 of 13 705 sites (18%) 3 of 13 4 sites (6%)

Carbon disulfide 2 of 13 (Chemical, Plastics) 69 sites (2%) 0 of 13 0

Carbon tetrachloride 1 of 13 (Chemical) 33 sites (0.8%) 0 of 13 0

Chlorobenzene 2 of 13 (Chemical, Electronics) 51 sites (1.3%) 1 of 13 (Chemical) 1 site (2%)

Cyclohexanone N/A

--- 1 of 13 (Chemical) 4 sites (6%)

Cresols 2 of 13 (Chemical, Plastics) 44 sites (1.1%) 0 of 13 0

Dichlorodifluoromethan

e 1 of 13 (Chemical) 20 sites (0.5%) 1 of 13 (Chemical) 1 site (2%)

1,2

- Dichlorobe nzene 3 of 13 22 sites (0.6%) 0 of 13 0 Ethyl acetate N/A

--- 3 of 13 14 sites (22%) Ethyl benzene 8 of 13 727 sites (19%) 1 of 13 (Chemical) 6 sites (9%) Ethyl ether N/A

--- 1 of 13 (Chemical) 1 site (2%)

2

- Ethoxyethanol 3 of 13 14 sites (0.4%) 0 of 13 0

Isobutyl alcohol N/A

--- 2 of 13 (Chemical, Furniture) 2 sites (3%)

Methanol 9 of 13 1,328 sites (34%) 4 of 13 23 sites (35%)

Methyl ethyl ketone 9 of 13 * 1,088 sites (28%) 3 of 13 14 sites (22%)

Methyl isobutyl ketone 9 of 13 534 sites (14%) 1 o f 13 (Chemical) 5 sites (8%) Methylene chloride 8 of 13 270 sites (7%) 1 of 13 (Chemical) 10 sites (15%)

Nitrobenzene 1 of 13 (Chemical) 16 sites (0.4%) 0 of 13 0

2

- Nitropropane 1 of 13 (Chemical) 2 sites (0.1%) 0 of 13 0 Pyridine 1 of 13 (Chemical) 35 sites (0.9%) 1 of 13 (Chemical) 1 site (2%)

Tetrachloroethylene 7 of 13 135 sites (3%) 2 of 13 (Chemical, Electronics) 2 sites (3%)

Toluene 10 of 13 1,635 sites (42%) 3 of 13 33 sites (51%)

1,1,1

- Trichloroethane 4 of 13 13 sites (0.3%) 0 of 13 0
1,1,2

- Trichloroethane 1 of 13 (Chemical) 15 sites (0.4%) 0 of 13 0 Trichloroethylene 7 of 13 249 sites (6%) 4 of 13 7 sites (11%)
1,1,2

- Trichlorotri

fluoroethane (Freon 113) 3 of 13 12 sites (0.3%) 0 of 13 0

Trichlorofluoromethane 2 of 13 (Chemical,

Plastics ) 14 sites (0.4%) 1 of 13 (Chemical) 1 site (2%)

Xylenes (total) 10 of 13 1,577 sites (40%) 4 of 13 21 sites (32%)

N/A: Chemical is not reported in TRI. * TRI data for methyl ethyl ketone is for 2003; effective for the 2004 reporting year, methyl ethyl k etone was removed

from the EPCRA list.

### 87 Data Relevant to Industrial Laundries

Industrial laundries are not required to report to TRI. However, when a non

- laundry facility submits a report for a chemical, it must identify all offsite locations where the c hemical is managed. Several facilities identified that they send TRI chemicals to industrial laundries. TRI chemicals may be present in various textiles (not limited to shop towels) which are serviced by the laundry.

TRI data from Envirofacts was used t o identify industrial laundries receiving TRI chemicals. Data was selected from the ‘Chemical Transfer to Outside Locations’ table of the Envirofacts EZ query tool. A search was conducted for 45 industrial laundry facilities (these 45 industrial laundrie s were identified from the 2003 Biennial Report), using the facility’s RCRA ID number. These 45 sites were not expected to represent the entire universe of industrial laundries, but instead were expected to include the largest facilities and the sites mos t likely to be identified in the TRI. Using a much longer list of industrial laundries would require a much greater level of effort for a possibly diminishing return. The search was limited to include only those reports submitted for 2004 (or 2003, in th e case of methyl ethyl ketone).

TRI data relevant to industrial laundries are summarized in Exhibit E

- 4. These data identify the quantities of chemicals sent to industrial laundries (as estimated by the generator, not by the laundry). Five industrial la undries were identified. These laundries most likely collect textiles from other sites that are not reflected in this table for one or more of the following reasons: (1) other generating facilities were not required to report to TRI, or (2) other generati ng facilities who report to TRI did not estimate the quantities of chemicals present in these textiles. It is also significant to note that the TRI data, as reflected in Table E

- 4, do not identify the fate of the chemicals at the industrial laundry (e.g., solvent recovery practices, if any, are not known).

As stated above, Exhibit E

- 4 likely understates the universe of laundries in the TRI because a search

was not conducted for all laundries. The source of the chemicals in the textiles sent to laundries is not known (e.g., uniforms, mats, shop towels), but it is reasonable to assume that at least some of this quantity is the result of shop towel use.

Exhibit E

- 4. Solvent Chemicals Sent to Industrial Laundries, 2004 TRI

Chemical Name Number of Generating Sites Transfer Amount, lb Receiving Laundry RCRA

ID Number

Ethyl benzene 2 1 OHD981784960

110 OHD981784960

Total: 111 1 site

Methanol 3 43 CTD134204916

0 OHD981784960

38 OHD981784960

Total: 81 2 sites

Methyl ethyl ketone * 2 0 OHD9817849 60

16 OHD981784960

Total: 16 1 site

Toluene 6 32 CTD134204916

29205 OHD981784960

2558 OHD037507282

1 OHD981784960

### 88 Chemical Name Number of Generating Sites Transfer Amount, lb Receiving Laundry RCRA

ID Number

8405 PAD014631246

22 OHD981784960

Total: 40,223 4 sites

Trichloroethylene 1 1673 OHD037507282

Total: 1,673 1 site

Xylene (mixed isomers) 5 250 NYD013250618

45 OHD981784960

1 OHD981784960

50 PAD014631246

300 OHD981784960

Total: 646 4 sites

TOTAL: 6 chemicals 7 unique generating

sites Total: 42,750 5 unique laundries * Data for methyl ethyl ketone are for 2003. All other data are 2004.



### Industrial laundries are subject to reporting under Massachusetts TURA (if they otherwise meet the reporting criteria) and several reported in 2004. However, only one laundry facility reported any

one of the 30 F

- list ed chemicals. This facility reported tetrachloroethylene, which is most likely from its associated dry cleaning operation. Therefore, the data in Massachusetts TURA were not used in estimating F

- listed solvents received or managed at industrial laundries

.

### 89 Appendix F. Industrial Laundry Data

### 90 Exhibit F

- 1. Towel Production and Washwater Generation Quantities at Industrial

Laundries

Shop towel production, lb/yr Printer towel production, lb/yr Washwater (gal/lb)

785,783 345,455 2.05

847,220 287,182 2.1

847,841 243,380 5.6

779,715 202,197 1.92

738,666 375,742 1.49

726,302 401,300 1.44

856,501 367,466 1.57

642,804 324 ,236 2.19

241,567 344,200 2.02

620,992 385,840 1.72

445,718 298,200 1.71

308,951 285,880 1.82

330,060 140,000 2.51

440,226 225,000 2.45

374,177 225,000 2.81

354,075 148,000 3.46

283,752 83,000 1.74

188,343 182,391 1.81

1,107,482 168,686 1.8

1,165,925 260,530 2.64

1,184,754 321,968 1.77

1,190,317 255,370 1.76

1,111,143 318,511 2.19

204,675 262,617 2.17

408,855 36,513 2.14

388,785 6,628 2.64

181,840 31,336 2.4

570,713 11, 982 2.25

417,125 14,699 1.96

447,210 18,770 2.58

648,728 26,869 2.76

654,407 2,578 1.19

261,749 285,604 1.43

284,483 364,304 1.4

29 8,687 378,876 1.48

313,875 423,562 1.48

15,997 452,315 2.2

414,318 279,569 2.3

396,143 284,299 2.2

377,231 18,016 2.2

409,672 19,462 2.1

458,811 21,661 1.89

553,085 19,200 1.91

### 91 Shop towel production, lb/yr Printer towel production, lb/yr Washwater (gal/lb)

396,496 7,025 1.942

381,499 75,045 2.013

369,898 258,850 2.183

458,738 94,684 1.457

424,346 86,457 1.857

428,436 128,316 1.906

404,900 65,703 1.35

206,653 86,250 2.011

447,753 191,108 1.65

835,520 256,239 1.7

263,225 270,095 1.6

4 05,495 214,379 1.52

356,253 42,073 1.64

366,815 271,217 1.72

256,437 376,666 1.35

396,900 315,821 1.93

352,829 268,425 1.32

296,249 297,450 1.23

305,390 62,076 1.53

459,650 56,970 2.711

137,782 61,339 2.241

1,106,177 50,833 2.224

967,976 69,609 2.194

684,425 6,840 2.509

579,938 7 ,150 1.375

592,650 8,800 2.03

208,466 9,240 1.289

254,105 9,550 1.999

239,674 52,370 1.687

235,111 59,495 1.606

229,097 67,860 1.56

658,980 68,650 1.506

853,130 80,815 1.862

956,824 72,625 1.443

987,872 21,900 1.564

837,080 33,975 1.313

85,305 44,625 1.474

87,485 140,360 1.37

88,750 231,150 1.523

90,455 319,490 1.894

89,760 467,677 1.644

275,898 525,741 1.825

289,830 12,500 1.173

358,440 89,630 1.872

366,360 93,604 1.461

### 92 Shop towel production, lb/yr Printer towel production, lb/yr Washwater (gal/lb)

414,000 58,314 0.827

898,135 68,264 3.949

967,555 1,000 1.571

1,015,900 387,900 2.44

1,002,680 449,100 1.722

844,215 524,700 1.925

96,000 371,700 2.045

156,000 146,013 1.694

225,300 120,022 2.407

208,200 125,495 0.468

1,211,190 116,629 0.666

1,205,682 176,266 1.497

1,036 ,810 48,995 2.4

877,834 273,159 1.766

810,151 122,120 2.084

331,617 44,650 2.499

438,953 51,918 1.156

507,690 54,320 1.7

5,594 61,884 1.593

403,053 5,949 1.858

472,030 291,493 2.029

24,199 473,315 0.857

613,722 445,271 2.033

501,543 308,842 2.047

300,309 172,770 1.996

492,6 02 481,080 1.566

394,556 114 1.497

390,308 180,353 1.497

324,657 140,180 1.497

871,392 2,318,550 1.497

8,231 1.497

599,451 2.77

1,433,778 2.8

34,031 2.41

429,974 2.26

515,234 1.72

419,030 2.97

934,666 3.14

724,241 2.67

637,122 2.48

372,196 2.77

554,248 2.12

553,057 3

593,564 3.19

485,498 3.83

### 93 Shop towel production, lb/yr Printer towel production, lb/yr Washwater (gal/lb)

979,336 3.75

104,128 2.2

776,647 3.94

221 ,191 3.24

607,947 2.53

379,240 3

613,590 3

662,879 2.67

422,969 3.35

208,478 3.38

383,188 2.93

398,421 3.29

595,078 2.58

546,068 2.76

462,571

368,368

310,823

276,235

264,404

346,920

3 77,609

175,117

678,467

406,335

156,795

240,265

348,927

445,411

577,722

774,7 27

609,533

459,292

580,652

9,420

378,580

366,441

184,999

440,695

891,549

489,839

284,868

718,155

585,300

223,501

890,139

### 94 Shop towel production, lb/yr Printer towel production, lb/yr Washwater (gal/lb)

521,590

612,348

2,137,665

264,010

310,460

600,627

209,447

318,640

215,172

239,355

245,294

120,294

190,747

692,472

221,105

460,898

411,542

532,633

336,353

638,985

436,430

248,777

317,907

779,101

598,655

860,398

654,967

207,740

187,989

256,499

192,338

173,993

306,139

473,943

405,841

65,455

481,069

585,560

358,663

2 04,417

255,380

197,292

562,342

244,485

249,692

### 95 Shop towel production, lb/yr Printer towel production, lb/yr Washwater (gal/lb)

2,330,436

123,985

710,097

360,88 7

368,275

264,598

343,472

176,285

467,758

314,355

429,080

350,686

398,695

76,007

385,334

171,306

309,286

234,662

563,694

455,192

812,963

271,927

315,746

1,198,806

552,778

319,048

469,306

5,446

5,214

3,418

3,000

2,900

627,430

343,216

291,570

584,040

1,600

377,222

439,931

482,890

599,382

486,500

144,840

132,840

100,320

### 96 Shop towel production, lb/yr Printer towel production, lb/yr Washwater (gal/lb)

90,000

345,600

153,024

176,556

132,048

182,016

823,368

214,056

197,376

230,844

630,924

32,400

57,648

57,816

75,600

344,268

205,440

246,972

230,280

280,656

1,139,604

166,608

19 8,972

226,932

188,664

754,644

63,792

71,376

71,016

308,640

124,800

137,22 0

128,640

145,500

650,400

81,228

90,972

89,952

445,044

116,160

122,184

117,600

122,400

498,000

113,040

### 97 Shop towel production, lb/yr Printer towel production, lb/yr Washwater (gal/lb)

117,600

105,840

141,000

511,200

62,820

61,116

106,944

113,352

267,504

71,460

43,776

102,288

100,212

131,412

144,756

538,860

60,492

76,380

79,692

99,504

37,260

78,816

85,608

68,220

86,280

388,404

121,104

119,808

144,192

100,380

584,400

61,356

67,560

319,500

112,548

111,576

87,528

140,472

789,744

46,428

47,736

4 8,540

53,676

280,404

63,468

### 98 Shop towel production, lb/yr Printer towel production, lb/yr Washwater (gal/lb)

62,664

61,752

59,184

195,132

48,960

63,44 4

61,272

79,380

138,372

61,548

68,664

77,064

107,988

402,396

86,268

92,124

116,544

502,092

121,992

142,572

169,872

480,420

122,880

127,728

78,732

52,140

246,996

101,952

79,848

60,468

44,796

59,940

32,316

61,284

58,572

82,176

518,628

101,412

116,400

160,980

390,000

79,536

86,460

89,820

61,356

### 99 Shop towel production, lb/yr Printer towel production, lb/yr Washwater (gal/lb)

282,600

68,064

84,420

71,316

66,108

61,740

70,164

249,372

95,052

104,244

110,532

118,488

459,456

90,792

109,464

13 9,944

114,408

419,940

101,280

112,728

96,168

106,776

569,400

77,124

70,17 6

51,096

53,232

169,188

67,824

79,488

72,168

70,584

60,540

50,820

62,412

70,512

82,428

292,800

50,196

59,196

49,464

55,956

234,324

116,400

128,364

### 100 Shop towel production, lb/yr Printer towel production, lb/yr Washwater (gal/lb)

129,336

135,780

377,076

38,640

40,836

39,816

60,708

198,900

18,948

27,684

31,056

31,992

115,644

102,180

90,900

85,980

102,948

391,380

42,504

37,824

52,260

41,856

177,252

92,304

91,860

77,892

77,460

454,440

133,248

152,196

166,272

148,092

511,920

87,804

10 2,132

130,956

134,124

470,880

159,732

154,836

174,840

763,320

154,272

175,33 2

176,940

### 101 Shop towel production, lb/yr Printer towel production, lb/yr Washwater (gal/lb)

169,728

636,960

56,220

50,280

44,844

63,576

209,040

69,960

79,800

78,912

273,684

34,320

124,932

102,804

104,124

75,480

163,356

172,776

99,552

103,260

88,176

106,560

102,072

94,236

73,752

73,596

102,396

7,944

13,704

15,936

13,548

91,944

120,864

96,024

87,804

438,650

456,750

456,750

307,700

115,375

143,875

197,121

173,436

150,326

33,934

### 102 Shop towel production, lb/yr Printer towel production, lb/yr Washwater (gal/lb)

328,212

137,986

156,257

141,169

146,322

146,847

30,655

352,169

3 53,070

323,796

396,193

108,892

402,300

477,900

124,200

334,800

199,800

342,9 00

517,320

313,200

664,200

410,400

480,840

348,820

275,400

529,200

378,000

261,900

1,191,920

1,104,000

Source: TRSA

, 2010

.

### 103 Exhibit F

- 2. Industrial Laundry Shop Towel Data from Public Comments

Comment

Number A 

### Name & Location Percent of Company’s

Overall Revenue Number of

S hop Towels

Delivered Annually Number of Shop

Towels at

One Time

199 Roscoe Company

– Chicago, IL 15 10,000,000 1,500,000

307 RentWear Inc.

– North Canton, OH 4



# - - 308 Huebsch Services

– Eau Claire, WI 5 2,500,000



# - 309 Budget Uniform Rental Supply

– G ardena, CA 

# - - -

314 Alltex Uniform Rental Services, Inc.

– Manchester NH “significant’ 13,000,000 (“250,000/week ”) 

# -

315 Clean

– St. Louis, MO 12 5,245,188



# - 317 Miller’s Textile Services

– Wapakoneta, OH 4 (4% of volume) 

# - -

326 Walker Towel and Unif orm

– Kansas City, MO 35 20,000,000 900,000

327 Uniforms By Star

– 2 Locations: Dallas, TX and Fort Worth, TX 20 26,000,000 45,000,00

0

333 Clean

– Highland, IL 40 8,900,000 15,000,00

0

334 Clean

– O’Fallon, MO 14 6,576,804



# - 335 Clean

– Webb City, MO 15 4,500,000 200,000

339 Eagle Uniform & Linen Supply

– Lander, WY 8 2,500,000 6,000,000

343 Ace Uniform

– Baltimore, MD 10 4,000,000 400,000 to 600,000

344 Overall Laundry Services, Inc.

– Unknown number of sites in Washington and Oregon 10 35,000,000 1,7 00,000

361 Band Box Cleaners & Laundry Inc.

– Tomah, WI “important” 2,000,000 125,000

- : No information in public comments.

A. All documents are from Docket RCRA

- 2003

- 0004. Comments dated March to April 2004.

### 104 Exhibit F

- 3. TCLP Results for Laundry S ludge

TCLP Concentration, mg/L

Location Date F

- Listed

chemical

detected? Cresol Tetrachloro

ethylene Chloro

benzene Ethyl

benzene 1,1,1

- trichloro

ethane Xylenes 1,2

- Dichloro

benzene

Allentown 12/17/1997 no
Allentown 11/15/1995 yes 0.23

Allentown 11/12/1993 no
Allentown 11/20/1992 no
Auburn 7/8/1998 no
Bedford 2/24/1999 yes 0.08 Birmingham 10/7/1999 yes 0.017 0.079 0.163 0.009 0.181 0.08 Birmingham 10/30/1997 yes 0.056

Griffin 7/24/1986 no

L akeland 4/9/1997 yes 0.28
Morgantown 1/30/1997 yes 1.88
Morgantown 10/6/1993 yes 0.004

Normal 3/10/1996 yes 0.678 Oklahoma City 10/22/1998 no
Pittsfield 6/9/1999 yes 0.11

Rock Hill 9/19/1999 no
Tulsa 1/29/ 1999 no
Plant 1 Sep

- 05 no
Plant 2 Jan

- 06 yes 0.06

Plant 3 Nov

- 04 yes 0.1

Plant 3 Dec

- 05 no
Plant 4 Mar

- 05 no
Maximum 1.88 0.08 0.079 0.163 0.009 0.181 0.678

Average 0.623 0.055 0.079 0.163 0.009 0.181 0.37 9 # of Samples 4 6 1 1 1 1 2
Source: UTSA, 2006. Blanks indicate that the constituent was either not detected or not analyzed (source data did not indicate distinction).



### Exhibit F

- 4. Number of Laundries Using a Single Landfill

State Number of

Laundri es in State A Number of

Landfills B Landfilled as %

of all Discards C Laundries D
per Landfill

Alabama 14 96.94

Alaska 4 96.97

Arizona 14 40 100.00 1

Arkansas 18 98.38

California 102 153 97.52 2

Colorado 16 72 100.00 1

Connecticut 11 2 14.3 8 2

Delaware 4 3 100.00 2

Florida 42 54 66.58 2 Georgia 28 57 98.80 2

Hawaii 8 63.97

Idaho 8 26 100.00 1

Illinois 42 52 100.00 2

Indiana 33 36 92.31 2

### 105 State Number of

Laundri es in State A Number of

Landfills B Landfilled as %

of all Discards C Laundries D
per Landfill

Iowa 14 59 97.85 1

Kansas 8 52 99.88 1
Kentucky 27 27 99.74 2

Louisiana 16 95.16

Maine 4 10 70.84 1
Maryland 20 31 71.43 2

Massachusetts 29 21 44.11 2
Michigan 36 51 91.66 2

Minnesota 17 23 63.56 2
Mississippi 6 17 100.00 1

Missouri 24 22 99.18 2

Montana 3 29 99.06 1

Nebraska 11 23 100.00 2

Nevada 9 23 100.00 1
New Hampshire 6 8 78 .50 2
New Jersey 19 12 76.44 3

New Mexico 10 39 100.00 1

New York 51 26 65.79 3

North Carolina 35 41 98.89 2

North Dakota 1 14 100.00 1

Ohio 56 42 100.00 3

Oklahoma 15 31 91.68 2 Oregon 14 28 93.36 2
Pennsylvania 49 48 75.59 2

Rhode Island 4 2 99 .77 3

South Carolina 13 18 93.39 2

South Dakota 4 15 100.00 1

Tennessee 16 35 100.00 2

Texas 72 189 99.87 1

Utah 6 34 94.87 1

Vermont 1 4 87.55 1
Virginia 21 60 82.33 1
Washington 13 19 94.13 2
West Virginia 7 18 100.00 1

Wisconsin 19 36 96.15 2
Wyoming 6 52 100.00 1

TOTAL 1006 1,654 89.65 2
Average 1.7

A. EPA 821

- R

- 00

- 006 Technical Development Document for the Final Action Regarding Pretreatment Standards for the Industrial Laundries Point Source Category (Revised March 2000). Table 4

- 1. Sites for District of Columbia are combined with Maryland. B. Simmons. The State of Garbage in America. Biocycle. April 2006. Pages 26

- 43. Table 9. Data not available for

some states. C. Simmons. The State of Garbage in America. Biocycle. April 2006. Pages 26

- 43. Table 3. This column represents the percent landfilled for discarded MSW only, versus that destined for combustion (i.e., excludes recycling).

### 106 D. Minimum 1 laundry per landfill. Accounts only for laundries processing shop/ prin ter towels; accounts that waste is landfilled or combusted according to overall MSW percentage.



### Exhibit F

- 5. Statistics Generated from Exhibit F

- 1: Towel Production and Washwater Generation Quantities at Industrial Laundries

Shop towels, lb/yr Printer t owels, lb/yr Washwater, gal/lb

Mean 312,893 196,605 2.06


### Median 226,116 140,270 1.93 

### Range

Minimum 1,600 114 0.47 

### Maximum 2,330,436 2,318,550 5.6 

### Count 568 118 147
Note: TRSA (2010) included statistics for the data included in Exhibit F

- 1. The s tatistics for the shop towels and the washwater matched the values here. The statistics for the printer towels differed from those calculated by TRSA.

### 107 Appendix G. Review of Other Industry Data for Estimated Number of Generators

### 108 In 2006, EPA conducted additional research by reviewing (a) the references EPA collected in support of the 2003 economic analysis, (b) information on institutional users of wipes from EPA's Comprehensive Procurement Guidelines website ( http://www.epa.gov/cpg/products/tissue.htm ) (EPA, 2006j), and (c) data contained in EPA's 1994 “Evaluation of Volatile Organic Emissions

Data for Non

- process Solvent Use in 15 Commercial and Industrial Business Categories” (EPA

600

- SR

- 94

- 0 19) (EPA, 1994b). This research identified 10 additional industries totaling 380,062 establishments, a fraction of which may use RCRA

- regulated solvent wipes (2002 Economic Census establishment counts in parentheses):

(1) NAICS 238220 HVAC service (87 ,936) (2) NAICS 23839 other building finishers (building renovation) (3,776) (3) NAICS 48819 aircraft maintenance & repair services (3,342) (4) NAICS 54138 laboratories (5,955) (5) NAICS 5417 scientific research (15,334) (6) NAICS 561720 jan itorial services (50,984) (7) NAICS 61 schools & universities (49,319) (8) NAICS 721 hotels (60,949) (9) NAICS 8112 electrical equipment maintenance (14,567) (10) NAICS 92 public administration government agencies (87,900)

Applying the same f iltering (screening) percentages as summarized in Section 2.3.4 for the initial

13 industrial sub

- sectors, provides an estimate of 6,920 additional RCRA

- regulated spent solvent disposable wipes generators (i.e., 380,062 x 33.5% x 67.1% x 8.1% = 6,920); add ed to the 8,106

count for the 13 sub

- sectors yields 15,026 spent solvent disposable wipes generators.

There is uncertainty with this estimate because the percentage of RCRA solvent use, wipe use, etc.

in these additional industries was assumed to be the s ame as those for the 13 sectors initially evaluated. EPA does not have wipes

- related information for these other 10 candidate industries. Nevertheless, the resulting estimate using this method (15,026 sites) is very close to the estimate using the Bienni al Report data (14,475 sites).

### 109 Appendix H. Input Parameter Distributions for Disposable Wipes Analysis

### 110 Exhibit H

- 1. Distribution Assumptions for Input Parameters Input parameter Number

of Data

Points Distribution Selected, and Basis D istributions Providing Good Fit Relevant Exhibits

Number of wipes used per site per year in

Other Industries 23 Lognormal (Best Fit) Lognormal

Weibull

Gamma H

- 3 and H

- 4

Number of wipes used per site per year in Printing 21 Gamma (Best Fit) Gamma

Beta Log normal

Weibull H

- 5 and H

- 6

Quantity of solvent used per wipe in Other

Industries 7 Weibull (Best Fit) All H

- 7 and H

- 8

Quantity of solvent used per wipe in Printing 9 Weibull (Best Fit) All H

- 9 and H

- 10

Number of Generators per landfill 45 Gamma (Best Fi t) Lognormal

Gamma

Extreme value

Weibull

Beta Logistic H

- 11 and H

- 12

Fraction of Chemical (others) in solvent See Section 2.3.5 of report

--- H

- 13 to H

- 28

‘Best Fit’ indicates highest rank fit according to the Anderson

- Darling ranking method.

‘Good Fit’ indicates distributions that result in an Anderson

- Darling value less than 1.5 (this value is provided in the Crystal Ball® documentation). ‘All’ indicates all 11 evaluated distributions (e.g., lognormal, normal, gamma) indicated a good fit.

‘None’ indicat es that none of the distributions, including the ‘best fit’, provided a good fit.



### Exhibit H

- 2. Summary of Outlier Tests Input parameter Number of Data Points/

Outlier Test Method Are Outliers Potentially

Present? Number of wipes used per site per year in Other

Industries 23

- Dixon Yes

- Highest value at 5% significance level

Number of wipes used per site per year in Printing 21

- Dixon No

– No outliers

Number of Generators per

landfill 45

- Rosner Yes

– Highest value at 5% significance level

Outlie rs are evaluated at the 1%, 5%, or 10% levels using the Dixon or Rosner methods.



### See Exhibits H

- 29 to H

- 34 for additional detail regarding the outlier tests.

### 111 Exhibit H

- 3. Number of wipes used in Other Industries: Lognormal distribution

Exhibit H

- 4. Number of wipes used in Other Industries: Gamma distribution (alternate assumption)

### 112 Exhibit H

- 5. Number of wipes used in Printing: Gamma distribution

Exhibit H

- 6. Number of wipes used in Printing: Lognormal distribution (alternate assumptio n)

### 113 Exhibit H

- 7. Loading per Wipe, Other Industries: Weibull distribution

Exhibit H

- 8. Loading per Wipe, Other Industries: Normal distribution (alternate assumption)

### 114 Exhibit H

- 9. Loading per Wipe, Printing: Weibull distribution

Exhibit H

- 10. Loading per Wipe, Printing: Normal distribution (alternate assumption)

### 115 Exhibit H

- 11. Number of Generators per Landfill: Gamma distribution

Exhibit H

- 12. Number of Generators per Landfill: Lognormal distribution (alternate distribution)

### 116 Exhibit H

- 13. Concentration of Acetone in Solvent: Exponential distribution

Exhibit H

- 14. Concentration of Ethyl Acetate in Solvent: Lognormal distribution

### 117 Exhibit H

- 15. Concentration of Ethyl Benzene in Solvent: Exponential distribution

Exhibit H

- 16. Concentration of 2

- Ethoxy Ethanol in Solvent: Lognormal distribution

### 118 Exhibit H

- 17. Concentration of Methanol in Solvent: Lognormal distribution

Exhibit H

- 18. Concentration of Methyl Ethyl Ketone in Solvent: Beta distribution

### 119 Exhibit H

- 19. Concentration of Methylene Chloride in Solvent: Weibull distribution

Exhibit H

- 20. Concentration of MIBK in Solvent: Weibull distribution

### 120 Exhibit H

- 21. Concentration of n

- Butanol in Solvent: Lognormal distribution

Exhibit H

- 22. Concentration of Tetrachloroethylene in Solvent Weibull distribution

### 121 Exhibit H

- 23. Concentration of Toluene in Solvent: Extreme Value distribution

Exhibit H

- 24. Concentration of Trichloroethylene in Solvent Weibull distribution

### 122 Exhibi t H

- 25. Concentration of Xylene in Solvent: Exponential distribution

Exhibit H

- 26. Concentration of Methylene Chloride in Solvent: Normal distribution (alternate assumption)

### 123 Exhibit H

- 27. Concentration of Acetone in Solvent: Normal distributio n (alternate assumption)

Exhibit H

- 28. Concentration of 2

- Ethoxy ethanol in Solvent: Normal distribution (alternate assumption)

### 124 Exhibit H

- 29. Rosner's Outlier Test for Number of Generators Per Landfill

Number of data: 45 Number of suspected o utliers: 1

Mean sd Potential

outlier Test

value Critical value (5%) Critical value (1%)


### 18.98 13.19 67.00 3.64 3.09 3.44

For 5% Significance Level, there is 1 Potential Outlier Therefore, Observation 67 is a Potential Statistical Outlier

For 1% Signif icance Level, there is 1 Potential Outlier Therefore, Observation 67 is a Potential Statistical Outlier

Exhibit H

- 30. Number of Generators per Landfill

### 125 Exhibit H

- 31. Dixon's Outlier Test for Number of Wipes in Other Industries

Number of data = 23

1 0% critical value: 0.374

5% critical value: 0.421

1% critical value: 0.505

1. Data Value 400000 is a Potential Outlier (Upper Tail)?

Test Statistic: 0.446

For 10% significance level, 400000 is an outlier. For 5% significance level, 400000 is an outlier

. For 1% significance level, 400000 is not an outlier.

2. Data Value 480 is a Potential Outlier (Lower Tail)?

Test Statistic: 0.032

For 10% significance level, 480 is not an outlier. For 5% significance level, 480 is not an outlier. For 1% significance l evel, 480 is not an outlier.

Exhibit H

- 32. Number of Wipes in Other Industries

### 126 Exhibit H

- 33. Dixon's Outlier Test for Number of Wipes in Printing

Number of data = 21

10% critical value: 0.391

5% critical value: 0.44

1% critical value: 0.524

1. Dat a Value 600000 is a Potential Outlier (Upper Tail)?

Test Statistic: 0.335

For 10% significance level, 600000 is not an outlier. For 5% significance level, 600000 is not an outlier. For 1% significance level, 600000 is not an outlier.

2. Data Value 1000 i s a Potential Outlier (Lower Tail)?

Test Statistic: 0.005

For 10% significance level, 1000 is not an outlier. For 5% significance level, 1000 is not an outlier. For 1% significance level, 1000 is not an outlier.

Exhibit H

- 34. Number of Wipes in Printing

### 127 Appendix I. Input Parameter Distributions for Laundry Sludge Analysis

### 128 Exhibit I

- 1 summarizes the distributional assumptions used for some of the input parameters in the laundry sludge analysis. Exhibit I

- 1 shows the distribution selected, possible alternate distributions, and references to graphs showing how the source data compare to the assumed distributions.

Exhibit I

- 2 summarizes the basis for modifying the input parameter data for two parameters as part of an uncertainty analysis described in Section 3.4.3.

Exhibit I

- 1. Distribution Assumptions for Input Parameters

Input parameter Number

of Data

Points Distribution Selected, and

Basis Distributions Providing

Good Fit Relevant

Exhibits

Quantity of shop towels washed per laundry, lb/year 568 G amma (Best Fit) None I

- 3, I

- 4

Quantity of printer towels washed per laundry, lb/year 118 Gamm a (Best Fit) Gamma I

- 5, I

- 6

Washwater concentration for shop towels, mg/L (acetone) 3 Lognormal (Assumption) All

Exhibits

I

- 7 to I

28 for all

11

chemicals

with d ata Washwater concentration for shop towels, mg/L (toluene) 3 Lognormal (Assumption) All

Washwater concentration for printer towels, mg/L (acetone) 3 Lognormal (Assumption) All

Washwater concentration for printer towels, mg/L (toluene) 3 Lognormal (As sumption) All

Quantity of washwater generated, gal/lb 147 Lognormal (good fit) Extreme value, lognormal, gamma, beta I

- 33

Number of Laundries Disposing Sludge in a Single Landfill 46 Logistic (best fit) None I

- 34

‘Best Fit’ indicates highest rank fit a ccording to the Anderson

- Darling ranking method. For concentrations, see Section 3.3.3 for the basis for selecting a lognormal distribution.

‘Good Fit’ indicates distributions which result in an Anderson

- Darling value less than 1.5 (this value is provided in the Crystal Ball® documentation). ‘All’ indicates all 11 evaluated distributions (e.g., lognormal, normal, gamma) indicated a good fit. ‘None’ indicates that none of the distributions, including the ‘best fit’, provided a good fit.

### 129 Exhibit I

- 2. Summar y of Outlier Tests

Input parameter Number of Data Points/

Outlier Test Method Are Outliers Potentially

Present?

Quantity of shop towels washed per laundry, lb/year 568

- Rosner Yes

- Highest value at 5% significance level (see

Exhibits I

- 29, I

- 30)

Quanti ty of printer towels washed per laundry, lb/year 1 18

- Rosner Yes

- Highest value at 5% significance level (see

Exhibit I

- 31, I

- 32)

### 130 Exhibit I

- 3. Quantity of Shop Towels Laundered: Gamma distribution

Exhibit I

- 4. Quantity o f Shop Towels Launde red: Lognormal distribution (alternate assumption)

### 131 Exhibit I

- 5. Quantity of Printer Towels Laundered: Gamma distribution

Exhibit I

- 6. Quantity of Printer Towels Laundered: Lognormal distribution (alternate assumption)

### 132 Exhibit I

- 7. Chlorob enzene Concentration in Shop Towel Washwater

Exhibit I

- 8. Chlorobenzene Concentration in Printer Towel Washwater

### 133 Exhibit I

- 9. Ethylbenzene Concentration in Shop Towel Washwater

Exhibit I

- 10. Ethylbenzene Concentration in Printer Towel Wa shwater

### 134 Exhibit I

- 11. Methylene Chloride Concentration in Shop Towel Washwater (distribution not used)

Exhibit I

- 12. Methylene Chloride Concentration in Printer Towel Washwater

### 135 Exhibit I

- 13. Tetrachloroethylene Concentration in Shop Towel Wash water (distribution not used)

Exhibit I

- 14. Tetrachloroethylene Concentration in Printer Towel Washwater

### 136 Exhibit I

- 15. Toluene Concentration in Shop Towel Washwater

Exhibit I

- 16. Toluene Concentration in Printer Towel Washwater

### 137 Exhibi t I

- 17. Trichloroethylene Concentration in Shop Towel Washwater

Exhibit I

- 18. Trichloroethylene Concentration in Printer Towel Washwater

### 138 Exhibit I

- 19. MEK Concentration in Shop Towel Washwater

Exhibit I

- 20. MEK Concentration in Printer To wel Washwater

### 139 Exhibit I

- 21. Acetone Concentration in Shop Towel Washwater

Exhibit I

- 22. Acetone Concentration in Printer Towel Washwater

### 140 Exhibit I

- 23. MIBK Concentration in Shop Towel Washwater

Exhibit I

- 24. MIBK Concentration in Prin ter Towel Washwater

### 141 Exhibit I

- 25. Xylene Concentration in Shop Towel Washwater

Exhibit I

- 26. Xylene Concentration in Printer Towel Washwater

### 142 Exhibit I

- 27. Cresol Concentration in Shop Towel Washwater

Exhibit I

- 28. Cresol Concentration in Printer Towel Washwater

### 143 Exhibit I

- 29. Rosner's Outlier Test for Quantity of Shop Towels Washed per Laundry

Number of data: 568 Number of suspected outliers: 1

Mean sd Potential

outlier Test

value Critical value (5%) Critical value (1%)

312

, 893 289

, 957 2

, 330

, 436 6 .958

- 3.869 4.249



### For 5% Significance Level, there is 1 Potential Outlier Therefore, Observation 2,330,436 is a Potential Statistical Outlier

For 1% Significance Level, there is 1 Potential Outlier Therefore, Observation 2,330,436 is a Potential Statistical Outlier



### Exhibit I

- 30. Quantity of Shop Towels Washed per Laundry

### 144 Exhibit I

- 31. Rosner's Outlier Test for Quantity of Printer Towels Washed per Laundry

Number of data: 118 Number of suspected outliers: 1

Mean s d Potential

outlier Test

value Critical value (5%) Critical value (1%)

196

, 605 244

, 668 2

, 318

, 550 8.673 3.43 3.8



### For 5% Significance Level, there is 1 Potential Outlier Therefore, Observation 2,318,55 0 is a Potential Statistical Outlier

For 1% Significa nce Level, there is 1 Potential Outlier Therefore, Observation 2,318,55 0 is a Potential Statistical Outlier

Exhibit I

- 32. Quantity of Printer Towels Washed per Laundry

### 145 Exhibit I

- 33. Quantity of Washwater Generated (gallon/pound towel): Lognormal

d istribution

Exhibit I

- 3 4 . Number of Laundries Disposing Sludge in a Single Landfill: Logistic

distribution

### 146 Appendix J. Bootstrap Estimates of 95% Confidence Intervals of Estimated Landfill Loadings

### 147 As described in Section 2.4 of this report, the e stimated landfill loadings for the disposable wipe scenario are numerical values that are uncertain. There are different types of uncertainty associated with these landfill loading calculations, including uncertainty in the values used as input parameters (Section 2.4.4), uncertainty in the model (Section 2.2), and uncertainty in the scenario. The uncertainty that exists arises from several places, including the analyzed scenario’s potential

deviation from real

- world conditions, lack of perfect knowledge of the values that should be used in the landfill loadings calculations, and the lack of ability to predict future conditions.

If we were able to quantify these uncertainties, a decision maker could gain some sense of the magnitude of the influence of t hese uncertainties on the numbers in Exhibit 2

- 11 (for disposable wipes) and Exhibit 3

- 8 (for laundry sludge). In the absence of uncertainty, these numbers would either increase or decrease. Examples of ways that we could decrease the uncertainty would b e:

• if more/better information were available to decrease parameter uncertainty, or

• alternative analyses of other plausible scenarios were performed in order to decrease scenario uncertainty, or

• the effects of the model’s approximation of reality could be estimated numerically, to reduce model uncertainty.

Many aspects of uncertainty are not easily quantifiable. There are also many ways to look at uncertainty. An additional method we considered was a “bootstrapping” technique, which is

statistical techni que that develops more information with which to interpret the possible magnitude of uncertainty that is associated with the parameter uncertainty in the landfill loadings calculations. (Efron and Tibshirani (1993)). This technique allows a risk assessor to use a distribution of values, such as the model outputs from the landfill loadings calculations, to develop a statistical descriptor of that sample, via a “resampling” technique. An example is the use of this technique to develop

estimates of the conf idence interval surrounding a 90th percentile landfill loading estimate. The bootstrap simulation is an attempt to quantify the range of estimates that are plausible alternative

90 th 

### percentile estimates, given that the values used for each variable in th e analysis are a limited representation of the actual situation.

Bootstrapping is a computational technique that involves “re

- sampling” a distribution of values in order to estimate certain statistics associated with that distribution (such as the distrib ution’s estimated mean, standard error, or confidence intervals around percentile estimates). Typically, when described in textbooks that explain the basis for the bootstrapping technique, the distribution in question is a representative sample of empiric al data, or observations of some type (e.g., test scores, hormone levels, or measurements of strength using some defined methodology). The distribution is “resampled,” with replacement, repeatedly in order to generate various estimators

such as estimated standard errors, or estimated confidence intervals (Efron and Tibshirani (1993)).

Extending the approach described above to a data set such as the outputs from the landfill loadings calculation model described in Section 2.4 could potentially be a viable method for developing

estimated confidence intervals around the 90 th 

### percentile landfill loadings estimates. Although bootstrap techniques generally should be applied at the input level of a model, we applied this approach to the output distributions usi ng an implicit assumption that uncertainty in inputs was the major source of variation in the model output. Specifically, we used a bootstrap simulation to develop 95 percent confidence intervals (CI) around the 50th and 90th percentile estimated landfill loadings of solvents on disposed wipes.

### 148 Typically, bootstrap simulations are repeated a number of times to evaluate the numerical stability of the output distribution, by comparing results among the multiple bootstrap simulations. For the solvent specifi c landfill loadings distribution for disposed wipes (which is larger than the contribution from laundry sludge, based on a comparison of Exhibits 2

- 11 and 3

- 8), we performed the following steps for bootstrapping:

1. A random sample, referred to as the “boots trap sample,” was generated from the cumulative probability distribution functions at sample sizes of 5, 10, 50, and 100.

2. The maximum likelihood estimation approach was used to fit a lognormal probability distribution to the bootstrap samples, assuming tha t a lognormal distribution was an appropriate choice for solvent concentrations on disposed wipes.

3. We further sampled 10,000 values from each lognormal distribution and estimated the 50th and 90th percentiles.

4. We repeated steps 2 and 3 above 1,000 times to generate a sampling distribution around the selected percentiles.

5. We estimated the 95 percent confidence intervals (i.e., values between 2.5 th 

### and 97.5 th


### percentiles) for the selected percentiles. These ranges represent uncertainty associated with

estim ates of those percentiles.

If we could quantify the uncertainty, a decision maker would be able to discern by how much the results would increase or decrease. The methodology developed to generate the estimated landfill loadings distributions included a sensitivity analysis (Section 2.4.2) and a semi

- quantitative uncertainty characterization (various parts of Section 2.4). However, the uncertainty that results from lack of perfect knowledge of the model input variable values is not represented quantitati vely in the model outputs since the uncertainty was not carried through to the model output stage. As a result, the estimated landfill loadings at selected percentiles represent a best estimate of the actual value at each percentile. An example of the so urce of this uncertainty

– lack of perfect knowledge of the values that should be used for the model inputs

– can be shown with the model input “solvent loading per wipe” in the “printing” sector. The variable “solvent loading per wipe” in the printing

se ctor was represented as ranging from 9 to 86 grams, based on nine values from available data sources (Section 2.3.2). If the nine values of solvent loading per wipe used in this analysis were not representative of all printing solvent loadings per wipe, t hen the impact of using these non

representative data on the 90 th 

### percentile estimated landfill loading is left unstated when only that

90 th 

### percentile estimated landfill loading is presented. Thus, the 90 th 

### percentile estimated landfill loadings shown in Exhibits 2

- 11 and 3

- 8 are simply “best” estimates, and do not reflect (in a quantified way) any uncertainty that they contain.

Exhibits J

- 1 through J

- 20 present the following information. For illustration, the results for acetone (Exhibit J

- 1) are discu ssed although the same type of information is presented in each graph:

• The curve presents the full range of percentile values reflected in the results summarized in

Exhibit 2

- 8 (50 th 

### percentile value of approximately 1,000 kg and 90 th 

### percentile value of approximately 9,000 kg for acetone).

• The dot

- with

- error

- bar in the graph presents the estimated 95% confidence intervals for the 90 th



### and 50 th 

### percentile estimated landfill loadings shown in Exhibit 2

- 8.

• The estimated 95% confidence intervals stated below each graph show the values of the error bar reflected in the graph, rounded to one significant figure.

### 149 o The higher of the two dot

- with

- error

- bar combination represents the 90 th 

### percentile estimated landfill loading together with its bootstrap

- estimated 95% confidence interval (i.e., 9,000 to 40,000 kg for acetone).

o The lower dot

- with

- error

- bar combination represents the 50 th 

### percentile estimated landfill loading together with its bootstrap

- estimated 95% confidence interval (i.e., 700 to 2,000 kg

for acetone ).

Exhibit J

- 1. Estimated 95% confidence intervals for 90 th 

### and 50 th 

### percentile estimated landfill loadings for acetone

Acetone

0.00 0.20 0.40 0.60 0.80 1.00

0 1 1,000 1,000,000

Estimated Mass Loading Rate (kg/Yr)Cumulative Probability

Estimated lower bound 95% confidence interval on 90 th percentile estimate: 9,000 kg/yr Estimated upper bound 95% confidence in terval on 90 th percentile estimate: 40,000 kg/yr

Estimated lower bound 95% confidence interval on 50 th percentile estimate: 700 kg/yr Estimated upper bound 95% confidence interval on 50 th percentile estimate: 2,000 kg/yr

### 150 Exhibit J

- 2. Estimat ed 95% confidence intervals for 90 th 

### and 50 th 

### percentile estimated landfill loadings for benzene

Benzene

0.00 0.20 0.40 0.60 0.80 1.00

0.0 1.0 1,000.0 1,000,000.0

Estimated Mass Loading Rate (kg/Yr)CumulativeProbability

Estimated lower bound 95% confidence interval on 90 th percentile estimate: 10 kg/yr Estimated upper bound 95% confidence interval on 90 th percentile estima te: 50 kg/yr

Estimated lower bound 95% confidence interval on 50 th percentile estimate: 2 kg/yr Estimated upper bound 95% confidence interval on 50 th percentile estimate: 3 kg/yr



### J

- 3. Estimated 95% confidence intervals for 90 th 

### and 50 th 

### percentil e estimated landfill loadings for n

- butanol

Butanol

0.00 0.20 0.40 0.60 0.80 1.00

0 1 1,000 1,000,000

Estimated Mass Loading Rate (kg/Yr)Cumulative Probability

Estimated lower bound 95% confidence interval on 90 th percentile estimate: 500 kg/yr Estimated upper bound 95% confidence interval on 90 th percentile estimate: 3,000 kg/yr

Estimated lower bound 95% confi dence interval on 50 th percentile estimate: 50 kg/yr Estimated upper bound 95% confidence interval on 50 th percentile estimate: 100 kg/yr

### 151 J

- 4. Estimated 95% confidence intervals for 90 th 

### and 50 th 

### percentile estimated landfill loadings for chl orobenzene

Chlorobenzene

0.00 0.20 0.40 0.60 0.80 1.00

0.001 1 1000 1000000 Estimated Mass Loading Rate (kg/Yr)Cumulative Probability

Estimated lower bound 95% confidence interval on 90 th percentile estimate: 400 kg/yr Estimated upper bound 95% confidence interval on 90 th percentile estimate: 2,000 kg/yr

Estimated lower bound 95% confidence interval on 50 th percen tile estimate: 40 kg/yr Estimated upper bound 95% confidence interval on 50 th percentile estimate: 90 kg/yr



### J

- 5. Estimated 95% confidence intervals for 90 th 

### and 50 th 

### percentile estimated landfill loadings for cresols

Cresols

0.00 0.20 0.40 0.60 0.80 1.00

0 1 1,000 1,000,000

Estimated Mass Loading Rate (kg/Yr)Cumulative Probability

Estimated lower bou nd 95% confidence interval on 90 th percentile estimate: 50 kg/yr Estimated upper bound 95% confidence interval on 90 th percentile estimate: 300 kg/yr

Estimated lower bound 95% confidence interval on 50 th percentile estimate: 10 kg/yr

Estimated upper bound 95% confidence interval on 50 th percentile estimate: 20 kg/yr

### 152 J

- 6. Estimated 95% confidence intervals for 90 th 

### and 50 th 

### percentile estimated landfill loadings for cyclohexanone

Cyclohexanone

0.00 0.20 0.40 0.60 0.80 1.00

0 1 1,000 1,000,000

Estimated Mass Loading Rate (kg/Yr)Cumulative Probability

Estimated lower bound 95% confidence interval on 90 th per centile estimate: 1,000 kg/yr Estimated upper bound 95% confidence interval on 90 th percentile estimate: 10,000 kg/yr

Estimated lower bound 95% confidence interval on 50 th percentile estimate: 100 kg/yr Estimated upper bound 95% confidence interval on 50 th percentile estimate: 300 kg/yr



### J

- 7. Estimated 95% confidence intervals for 90 th 

### and 50 th 

### percentile estimated landfill loadings for 1,2

- dichlorobenzene

1, 2-Dichlorobenzene

0.00 0.20 0.40 0.60 0.80 1.00

0 1 1,000 1,000,000

Estimated Mass Loading Rate (kg/Yr)Cumulative Probability

Estimated lower bound 95% confidence interval on 90 th percentile estimate: 300 kg/yr Estimated upper bound 95% confidence interval on 90 th percentile estimate: 2,000 kg/yr

Estimated lower bound 95% confidence interval on 50 th percentile estimate: 30 kg/yr Estimated upper bound 95% confidence interval on 50 th perc entile estimate: 80 kg/yr

### 153 J

- 8. Estimated 95% confidence intervals for 90 th 

### and 50 th 

### percentile estimated landfill loadings for 2

- ethoxyethanol

2-Ethoxyethanol

0.00 0.20 0.40 0.60 0.80 1.00

0 1 1,000 1,000,000

Estimated Mass Loading Rate (kg/Yr)Cumulative Probability

Estimated lower bound 95% confidence interval on 90 th percentile estimate: 2,000 kg/yr

Estimated upper bound 95% confidence interval on 90 th percentile estimate: 10,000 kg/yr

Estimated lower bound 95% confidence interval on 50 th percentile estimate: 200 kg/yr Estimated upper bound 95% confidence interval on 50 th percentile estimate: 400 kg/yr



### J

- 9. Estimated 95% confidence intervals for 90 th 

### and 50 th 

### percentile estimated landfill loadings for ethyl acetate

Ethyl Acetate

0.00 0.20 0.40 0.60 0.80 1.00

0 1 1,000 1,000,000

Estimated Mass Loading Rate (kg/Yr)Cumulative Probability

Estimated lower bound 95% confidence interval on 90 th percentile estimate: 400 kg/yr Estimated upper bound 95% confiden ce interval on 90 th percentile estimate: 2,000 kg/yr

Estimated lower bound 95% confidence interval on 50 th percentile estimate: 40 kg/yr Estimated upper bound 95% confidence interval on 50 th percentile estimate: 100 kg/yr

### 154 J

- 10. Estimated 9 5% confidence intervals for 90 th 

### and 50 th 

### percentile estimated landfill loadings for ethylbenzene

Ethyl Benzene

0.00 0.20 0.40 0.60 0.80 1.00

0 1 1,000 1,000,000 Estimated Mass Loading Rate (kg/Yr)Cumulative Probability

Estimated lower bound 95% confidence interval on 90 th percentile estimate: 2,000 kg/yr Estimated upper bound 95% confidence interval on 90 th percenti le estimate: 10,000 kg/yr

Estimated lower bound 95% confidence interval on 50 th percentile estimate: 100 kg/yr Estimated upper bound 95% confidence interval on 50 th percentile estimate: 300 kg/yr



### J

- 11. Estimated 95% confidence intervals for 90 th 

### and 50 th 

### percentile estimated landfill loadings for isobutyl alcohol

Isobutyl alcohol

0.00 0.20 0.40 0.60 0.80 1.00

0 1 1,000 1,000,000

Estimated Mass Loading Rate (kg/Yr)Cumulative Probability

Estimated lower bound 95% confidence interval on 90 th percentile estimate: 200 kg/yr Estimated upper bound 95% confidence interval on 90 th percentile estimate: 800 kg/yr

Estimated lower bound 95% confidence interval on 50 th percentile estimate: 20 kg/yr Estimated upper bound 95% confidence interval on 50 th percentile estimate: 50 kg/yr

### 155 J

- 12. Estimated 95% confidence intervals for 90 th 

### and 50 th 

### percentile estima ted landfill loadings for methanol

Methanol

0.00 0.20 0.40 0.60 0.80 1.00

0 1 1,000 1,000,000

Estimated Mass Loading Rate (kg/Yr)Cumulative Probability

Estimated lower bound 95% confidence interval on 90 th percentile estimate: 2,000 kg/yr Estimated upper bound 95% confidence interval on 90 th percentile estimate: 10,000 kg/yr

Estimated lower bound 95% confidence interval on 50 th percentile estimate: 200 kg/yr Estimated upper bound 95% confidence interval on 50 th percentile estimate: 400 kg/yr



### J

- 13. Estimated 95% confidence intervals for 90 th 

### and 50 th 

### percentile estimated landfill loadings for methyl ethyl ketone

Methyl Ethyl Ketone

0.00 0.20 0.40 0.60 0.80 1.00

0 1 1,000 1,000,000

Estimated Mass Loading Rate (kg/Yr)Cumulative Probability

Estimated lower bound 95% confidence interval on 90 th percentile estimate: 10,000 kg/yr Estimated upper bound 95% confidence interval on 90 th percentile estimate: 70,000 kg/yr

Estimated lower bound 95% confidence interval on 50 th pe rcentile estimate: 1,000 kg/yr Estimated upper bound 95% confidence interval on 50 th percentile estimate: 2,000 kg/yr

### 156 J

- 14. Estimated 95% confidence intervals for 90 th 

### and 50 th 

### percentile estimated landfill loadings for methylene chloride

Methylene Chloride

0.00 0.20 0.40 0.60 0.80 1.00

0 1 1,000 1,000,000

Estimated Mass Loading Rate (kg/Yr)Cumulative Probability

Est imated lower bound 95% confidence interval on 90 th percentile estimate: 3,000 kg/yr Estimated upper bound 95% confidence interval on 90 th percentile estimate: 10,000 kg/yr

Estimated lower bound 95% confidence interval on 50 th percentile estimate: 400 kg/yr Estimated upper bound 95% confidence interval on 50 th percentile estimate: 800 kg/yr



### J

- 15. Estimated 95% confidence intervals for 90 th 

### and 50 th 

### percentile estimated landfill loadings for methyl isobutyl ketone

Methyl Isobutyl Ketone

0.00 0.20 0.40 0.60 0.80 1.00

0 1 1,000 1,000,000

Estimated Mass Loading Rate (kg/Yr)Cumulative Probability

Estimated lower bound 95% confidence interval on 90 th percentile estimate: 2,000 kg/yr Estimated upper bound 95% confidence interval on 90 th percentile estimate: 8,000 kg/yr

Estimated lower bound 95% confidence interval on 50 th percentile estimate: 100 kg/yr Estimated upper bound 95% confidence interval on 50 th percentile estimate: 300 kg/yr

### 157 J

- 16. Estimated 95% confidence intervals for 90 th 

### and 50 th 

### percentile estimated landfill loadings for tetrachloroethylene

Tetrachloroethylene

0.00 0.20 0.40 0.60 0.80 1.00

0 1 1,000 1,000,000

Estimated Mass Loading Rate (kg/Yr)Cumulative Probability

Estimated lower bound 95% confidence in terval on 90 th percentile estimate: 700 kg/yr Estimated upper bound 95% confidence interval on 90 th percentile estimate: 5,000 kg/yr

Estimated lower bound 95% confidence interval on 50 th percentile estimate: 100 kg/yr Estimated upper bound 9 5% confidence interval on 50 th percentile estimate: 200 kg/yr



### J

- 17. Estimated 95% confidence intervals for 90 th 

### and 50 th 

### percentile estimated landfill loadings for toluene

Toluene

0.00 0.20 0.40 0.60 0.80 1.00

0 1 1,000 1,000,000

Estimated Mass Loading Rate (kg/Yr)Cumulative Probability

Estimated lower bound 95% confidence interval on 90 th percentile estimate: 8,000 kg/yr Estimated upper bound 95% confidence interval on 90 th percentile estimate: 40,000 kg/yr

Estimated lower bound 95% confidence interval on 50 th percentile estimate: 1,000 kg/yr Estimated upper bound 95% confidence interval on 50 th p ercentile estimate: 2,000 kg/yr

### 158 J

- 18. Estimated 95% confidence intervals for 90 th 

### and 50 th 

### percentile estimated landfill loadings for 1,1,2

- trichloroethane

1,1,2-Trichloroethane

0.00 0.20 0.40 0.60 0.80 1.00

0 1 1,000 1,000,000 Estimated Mass Loading Rate (kg/Yr)Cumulative Probability

Estimated lower bound 95% confidence interval on 90 th percentile estimate: 300 kg/yr Estimated upper bound 95% confidence interval on 90 th percentile estimate: 1,000 kg/yr

Estimated lower bound 95% confidence interval on 50 th percentile estimate: 30 kg/yr Estimated upper bound 95% confidence interval on 50 th percentile estimate: 70 kg/yr



### J

- 19. Estimated 95% confidence intervals for 90 th 

### and 50 th 

### percentile estimated landfill loadings for trichloroethylene

Trichloroethylene

0.00 0.20 0.40 0.60 0.80 1.00

0 1 1,000 1,000,000

Estimated Mass Loading Rate (kg/Yr)Cumulative Probability

Estimated lower bound 95% confidence interval on 90 th percentile estimate: 2,000 kg/yr Estimated upper bound 95 % confidence interval on 90 th percentile estimate: 7,000 kg/yr

Estimated lower bound 95% confidence interval on 50 th percentile estimate: 200 kg/yr Estimated upper bound 95% confidence interval on 50 th percentile estimate: 500 kg/yr

### 159 J

- 20. Es timated 95% confidence intervals for 90 th 

### and 50 th 

### percentile estimated landfill loadings for xylenes

Xylene

0.00 0.20 0.40 0.60 0.80 1.00

0 1 1,000 1,000,000

Estimated Mass Loading Rate (kg/Yr)Cumulative Probability

Estimated lower bound 95% confidence interval on 90 th percentile estimate: 8,000 kg/yr Estimated upper bound 95% confidence interval on 90 th perce ntile estimate: 40,000 kg/yr

Estimated lower bound 95% confidence interval on 50 th percentile estimate: 700 kg/yr Estimated upper bound 95% confidence interval on 50 th percentile estimate: 2,000 kg/yr