## F001

- F005 Solvent

- Contaminated Wipes and Laundry Sludge:

Comparison of Landfill Loading Calculations

and

Risk

- Based Mass Loading Limits

U.S. Environmental Protection Agency Office of Solid Waste and Emergency Response

Office of Conservati on and Resource Recovery

Revised April 201 2

### 1 

## Introduction

The purpose of this document is to compare results from two separate analyses that

evaluate the potential human health risks from landfilling solvent

- contaminated wipes and the sludge produced wh en laundering used wipes. The scope of these analyses covers wipes containing solvents listed in 40 CFR 261.31 (F001

- F005). The two separate analyses are:

1) Estimated annual quantities of solvents remaining in wipes and laundry sludge when disposed in lan dfills within the United States. The document entitled Landfill Loadings

Calculations For Solvent

- Contaminated Wipes (SAIC, 20 1 2 ) describes the method for estimating these annual mass loading estimates. SAIC, 2012 is a revised version of the original calc ulations contained in the docket

. The revisions inc orporate updated data submitted in p ublic comments in response to EPA’s October 27, 2009 Notice of Data Availability

. Th e comparison document has been revised to reflect the updated landfill loading calcul ations in SAIC, 2012

.

2) Estimated annual quantities of the solvents in these waste streams that could be disposed in landfills, with a certain probability of the risk remaining at or below the risk

- based

criteria evaluated by EPA. The Risk

- Based Mass Loadi ng Limits for Solvents in Disposed Wipes and Laundry Sludges Managed in Municipal Landfills (RTI, 200 9 ; docket number EPA

- HQ

- RCRA

- 2003

- 0004

- 043 8 ) describes the method for estimating the quantities. The Risk

- Based Mass Loading Limits ( RB

- MLLs ) were calcula ted for both unlined and composite

- lined landfills. The RB

- MLLs for trichloroethylene and tetrachloroethylene have been updated based on new health

- based toxicity values from

EPA’s IRIS database as described in RTI, 2012

.

See each of the documents referen ced above for a detailed description of the data sources, assumptions, and methods used to develop the estimates compared in this document.

We developed each set of estimates using a combination of data and assumptions as inputs to mathematical models

– one model for the estimated landfill loadings rates (ELLRs) and

a different model for the RB

- MLLs. We used the ELLRs (SAIC , 201 2 ) to determine whether current industry practices could potentially result in mass loading rates that are above the RB

MLLs (R TI

, 2009 and 201 2 ) for the twenty F001

– F005 listed solvents considered in this analysis.

The results for both the ELLRs and the RB

- MLLs are generated from a probabilistic analysis. The results from these two separate calculations are given by a distribut ion of values. The theoretical risk distribution provides the basis for calculating risk

- based mass loading rates for any percentile of that distribution. Based on the risk criteria that EPA established for the wipes analysis, the RB

- MLL was identified at the 50 th 

### and 90 th 

### percentile s of the distribution.

Th ese levels represent the allowable mass loading rate (in kg per year) for management options

of solvent

- containing disposed wipes and laundry sludges in a municipal solid waste landfill (MSWLF) anywhere in the country in any given year.

### 2 

## 1.0 Comparison of ELLRs and RB

- MLLs

We compared the results from the SAIC (2012) and the RTI (200 9 and 201 2 ) documents

to determine whether the ELLRs exceed the estimated RB

- MLLs. If the estimated landfill loading s rates appear to exceed the risk

- based mass loading limits for a solvent, then this solvent could pose a potential risk for people living near landfills.

As described in SAIC (201 2 ) , the purpose of the landfill loadings calculations was to develop estim ates of the quantity of solvent from disposed wipes or laundry sludge generated from wipes washing operations disposed in a MSWLF. To account for uncertainty and variability in the input parameters, a Monte Carlo simulation was run to develop a single

dis tribution of mass loading rates (in kg per year per landfill) for each solvent from the dispose d wipes or laundry sludge. These landfill loadings distributions represent the amount of “wipes

related” solvent in the respective waste streams (i.e., wipes an d sludge).

In the SAIC (2012) document , input values were assigned a distribution based on available empirical data. For parameters lacking sufficient data to assign a distribution, the value was fixed. As pointed out in the report, this was not intende d to mean that no uncertainty exists, but rather that insufficient data were available to characterize the uncertainty and/or variability for that parameter. One hundred thousand (100,000) Monte Carlo simulations were run (i.e., iterations) to create each distribution.

For the disposed wipes, the ELLRs were estimated as a function of six variables, four of which are associated with specific industry sectors (printing, transportation, auto repair, electronics, and other). The product of the four industry

- s pecific terms was summed across all sectors, and multiplied by the solvent

- specific percent of the total mass in wipes and the number of generators using a given landfill.

The sampling design was stratified by state location, accounting for density of lar ge quantity generators and small quantity generators, landfill density, and average landfilled waste fraction. The report presented a sensitivity analysis indicating that modeled outputs were most sensitive to two parameters

— the number of disposed wipes u sed per year in printing and the number of disposed wipes used per year in other industries. These parameters have the most significant influence on the variation in the ELLRs.

For the laundry sludge, the ELLRs were calculated as a function of eight vari ables, five of which are associated with washing operations involving shop towels and printer towels, and three that reflect the partitioning of the solvent to the sludge and disposal in a landfill. This analysis used data collected for the effluent guide lines, which were developed for two types of industrial wipes: shop towels and printer towels.

For both the disposed wipes and laundry sludges, the output of the method is a probability distribution of ELLRs based on the best available data gathered fro m a variety of sources. These two sets of ELLR distributions are intended to represent the frequency of actual mass loading rates expected for the national population of municipal landfills that receive (1)

solvent

- containing wipes and (2) laundry sludge.

### 3 To perform a comparison, EPA chose a protective estimate (90 th 

### percentile value) from the distribution of landfill loading model outputs to compare with a protective estimate (90 th


### percentile value) of risk

- based mass loading limits. We also compared th e central tendency (50 th


### percentile) values for the distributions. A risk estimate above the 90 th 

### percentile of the risk distribution is considered a “high end” descriptor of risk (USEPA, 1995 ). Thus, we compared

the 90 th 

### percentile ELLRs to the 90 th 

### per centile RB

- MLLs to determine whether the ELLRs in

landfills that can be attributed to solvent

- contaminated wipes and laundry sludge exceeds the mass loading limits that correspond to the evaluated risk criteria.

The comparisons of the ELLRs and RB

- MLLs in Tables 1 and 2 are expressed as ratios, i.e., the 90 th 

### percentile ELLRs (kg solvent per year) are divided by the 90 th 

### percentile RB

- MLLs (kg solvent per year) for a specific solvent to yield ratios. The ELLR is an estimate of the mass loading into the la ndfill and the RB

- MLL is an estimate of the mass loading that would correspond to an exposure equivalent to the chosen risk criterion, or risk "target." Therefore, if the ratio exceeds one, this indicates the degree to which the ELLR exceeds the health

- ba sed

criteria used to establish the RB

- MLLs ( i.e., a cancer risk of 1 x 10 - 5 

### and an HQ of 1 for noncarcinogenic risk). Tables 1 and 2 present the ratio of the 90 th 

### percentile ELLR to the 90 th


### percentile RB

- MLL for each solvent for three cases: (1) the rati o for the ELLR to RB

- MLL for the wipes loading alone, (2) the ratio for the sludge alone, and (3) the ratio for the total solvent loading from the combined mass loading rates for the wipes and the sludge.

Table 1 compares the ELLRs for disposed wipes, la undry sludge, and total solvent (disposed wipes plus laundry sludge) managed in an unlined landfill to solvent specific RB

MLLs. The table provides the following information:

• The results suggest that the contribution to the total ELLRs from disposed wipe s is predicted to be higher than the contribution from laundry sludges. For 15 of the solvents, the laundry sludge’s contribution to the total landfill loadings estimate is less than 1

percent ; the contributions for the remaining 5 solvents vary between a pproximately 2 percent and 1 7 percent.

• The ratios between the laundry sludge ELLRs and the RB

- MLLs indicate that, at the 90 th


### percentile, tetrachloroethylene is the only solvent that would be expected to exceed the

10 - 5 

### risk criteria.

• The comparison be tween the 90 th 

### percentile values of total ELLRs and the RB

- MLLs for

the unlined

- scenario shows that 8 of the 20 solvents would exceed the risk criteria used (one of the 8 is benzene, for which the ratios would not exceed 1 if the lower cancer risk

factor w ere used). 1



### The RTI documents ( 2009 and 201 2 ) present the exposure pathways that resulted in the highest risk for the solvents examined, that is, the limiting pathway (pathway/receptor/endpoint) resulting in the maximum 90 th 

### percentile risk (see Table 5

- 1 and 5

- 3 in Section 5). For the

unlined scenarios, the groundwater pathways were always associated with the highest predicted

1 The RB

- MLL values for benzene were calculated using both the hi gh end and low end values of the cancer risk range established for benzene. See Table 3

- 12 in Section 3.6.2 of RTI (2009).

### 4 risks at the 90 th 

### percentile of the distribution (i.e., the upper end). Therefore, for unlined units the groundwater pathway was the critical (i.e., “driving”) pathway for the exceedances of the

RB

- MLLs shown in Table 1 . For solvents evaluated for carcinogenic effects , the highest cancer risks were predicted for adult receptors. T he exceedances for the solvents 1,1,2

- trichloro ethane, benzene, and methylene chloride in the unlined scenario were driven by cancer risks to adults from exposure to the combination of drinking water, shower inhalation, and dermal absorption during showering. S ection 4.1 (Tables 4

- 1 through 4

- 6) in th e RTI ( 2009 ) document , provide s further information as to which specific exposure route from the groundwater pathway was most important for the combined risk for the ca rcinogenic solvents. In all cases, the dermal absorption

route was a relatively minor c omponent of the combined risks. For two of the carcinogenic solvents, tetrachloroethylene and trichloroethylene, the noncancer risks drove the exceedances in

Table 1 (see Table 9 in RTI, 2012)

. In both cases the RB

- MLL values were lower for the

noncancer risks compared to the 1 x 10 - 5 

### cancer

- based risks. The limiting pathway/receptor combination was child drinking water consumption for tetrachloroethylene and adult inhalation during showering for trichloroethylene. For two of the three noncancer

- causing s olvents that

showed exceedances of the RB

- MLLs (chlorobenzene and xylenes), the highest hazard was predicted for adults via inhalation during showering. For the other noncarcinogenic solvent of apparent concern (toluene), the highest risk was predicted fo r ingestion of drinking water by

children.

Table 2 compares the 90 th 

### percentile ELLRs for disposed wipes, laundry sludge, and total solvent (disposed wipes plus laundry sludge) managed in a composite lined landfill to the

solvent

- specific 90 th 

### percentile RB

- MLLs. The general trends shown in Table 1 are also evident

in Table 2 (e.g., the solvent contribution from disposed wipes is higher than the contribution from laundry sludge). In a composite

- lined landfill, t he 90 th 

### percentile ELLR for total solvent

exceed s the corresponding RB

- MLL for one solvent, trichloroethylene (based on the updated

health

- based benchmark s from IRIS ; R TI, 201 2 )

. While the results for trichloroethylene indicate that the ELLR calculated for solvent wipes exceeds the RB

- MLL , the EL LR for sludge for this

solvent is below the RB

- MLL. For the solvent tetrachloroet hylene, using the updated IRIS benchmark yields ratios of ELLR to RB

- MLL that are less than one. For trichloroethylene, the noncancer risks drove the exceedance in Table 2 ( see Table 9 in RTI, 2012) , i.e., t he RB

- MLL value was lower for the noncancer risk compared to the 1 x 10 - 5 

### cancer

- based risk.

As expected, the predicted risks for the unlined analysis were always greater than those for the composite

- liner analysis. Us ing the comparison of the 90 th 

### percentile results in Table 2

, t he potential risks from all solvents examined in the composite

- liner scenario, except trichloroethylene , were below the health

- based criteria used in this analysis. For

t richloroethylene

, the l imiting pathway/receptor combination for Table 2 was adult inhalation during showering.

Table 3 compares the 50 th 

### percentile ELLRs managed in an unlined landfill to the 50 th


### percentile RB

- MLLs (where the RB

- MLLs reflect a cancer risk of 1 x 10 - 5 

### and an HQ of 1 for noncarcinogenic risk). At the 50 th 

### percentile, tri chloroethylene is the only solvent in the unlined scenario that produced a ratio of ELLR to RB

- MLL greater than one. Similar to the 90 th


### percentile results for trichloroethylene, the RB

- MLL va lue was lower for the noncancer risk compared to the 1 x 10 - 5 

### cancer

- based risks. The limiting pathway/receptor combination was

### 5 adult inhalation during showering. For the composite liner scenario, all ratios of the 50 th


### percentile ELLR s and 50 th 

### percentile RB

- MLL s are less than 0.1 , so these results were not

tabulated

.

Table s 4 and 5 illustrate the ratios of ELLR to RB

- MLL calculated using a carcinogenic

risk criterion that varies from 10 - 4 

### to 10 - 6 

### . The ratios from a comparison of the ELLRs and the

RB

- M LLs for the constituents with carcinogenic risk would change if the RB

- MLLs were calculated using a risk criterion different from the 1 x 10 - 5 

### criterion used in Tables 1 and 2 . If a target risk level of 1 x 10 - 4 

### were used for calculating the RB

- MLLs, the carcinogenic risk for the carcinogens (1,1,2

- trichloroethane, benzene, methylene chloride, tetrachloroethylene, and trichloroethylene) would be lower by a factor of ten. Alternatively, if a target risk level of 1 x

10 - 6 

### were used, the cancer risks for the se constituents would be higher by a factor of ten. Note

that the RB

- MLL values for the non

- carcinogenic constituents ( 1,2

- dichlorobenzene, 2

- ethoxyethanol, acetone, butanol, chlorobenzene, cresols, cyclohexanone, ethyl acetate, ethyl benzene, isobutanol, methanol, methyl ethyl ketone, methyl isobutyl ketone, toluene, and xylenes) are not effected by any changes in the cancer risk criteria used.

EPA also calculated RB

- MLLs based on the noncancer risks posed for these five carcinogens. In some cases, t he noncancer risks yielded lower RB

- MLLs such that the noncancer

risks bec a me the limiting factor , e.g., as noted previously for trichloroethylene

. Using target risk

levels of 1 x 10 - 6 

### , the cancer

- based 90 th 

### percentile RB

- MLLs were l ower than the RB

- MLLs

c alculated using the noncancer risk end point. However, w hen using a target risk level of 1 x 10 - 4



### or 1 x 10 - 5 

### , the 90 th 

### percentile RB

- MLLs for some solvents were d riven by the noncancer risk

, as shown in Tables 4 and 5

. For example, the ratios of the 90 t h 

### percentile ELLR to the 90 th


### percentile RB

- MLL in Table 4 for the 1 x10 - 4 

### risk results are driven by the noncancer risks for benzene and methylene chloride. See RTI (2009 and 2012 ) for a full presentation of the cancer

and noncancer risks for all constit uents for both the no liner and composite liner scenarios (Tables 5

- 1 through 5

- 4). Using the updated IRIS health benchmarks for trichloroethylene and tetrachloroethylene

, a t the 10 - 4 

### and 10 - 5 

### cancer risk level, the noncancer risk is also the controlling f actor for these two solvents ( see Table 9 in RTI , 2012 ). At the 10 - 6 

### cancer risk level, the cancer risk is the controlling factor for trichloroethylene and tetrachloroethylene.

As Table 4 shows

, using the 10 - 4 

### risk criterion for the no

- liner scenario w ould have the effect of lowering the ratios, such that the ratio s for benzene at the 90 th 

### percentile (using either risk factor) would be less than one

. Using the 10 - 6 

### risk criterion for the 90 th 

### percentile results would have the effect of raising the rati os in the unlined landfill scenario for carcinogens, such

that the ratios for all of the solvents in Table 4 would exceed one by a wider margin.

As Table 5 indicates for the composite

- liner scenario, the ratio s for all solvents would be

below one at bo th the 90 th 

### and 50 th 

### percentile s using the 10 - 4 

### and 10 - 5 

### risk criteri a , except for trichloroethylene , which is driven by the noncancer risk

. U sing the 10 - 6 

### criterion , only the ratio

for trichloroethylene at the 90 th 

### percentiles is above one

.

### 6 Table 1. Comparison of the 90th percentile ELLR estimates with the 90th percentile RB

- MLLs for the No

- Liner Scenario at a Target Cancer Risk of 10 - 5 and Noncancer Hazard of 1 (in kg solvent per year) a

Solvent 90 th
percentile

RB

- MLL 90 th
percentil e ELLR (wipes) ELLR wipes

RB

- MLL 90 th
percentile ELLR (sludge) ELLR sludge

RB

- MLL 90 th
percentile ELLR (total) ELLR (total )

RB

- MLL
1,1,2

- Trichloroethane 1.1E+01 3.3E+02 30 2.3 E+00 0. 2 3.3E+02 30 1,2

- Dichlorobenz ene 7.1E+02 3.6E+02 0.5 1.2 E+00 < 0.1 3.6E+02 0.5

2

- Ethoxyethanol 2.3E+04 2.6E+03 0.1 2 .3E+00 < 0.1 2.6E+03 0.1

Acetone 2.8E+04 9.1E+03 0.3 2.6 E+01 < 0.1 9. 1 E+03 0.3

Benzene b 5.0E+00 1.2E+01 2.4 2.4 E+00 0.5 1. 4 E+01 2.8

1. 9 E+01 0.7 0.1 0. 7 4

Butanol 1.2E+04 6.1E+02 < 0.1 1.4 E+00 < 0.1 6.1E+02 < 0.1
Chlorobenzene 5.8E+01 4.3E+02 7.4 1.1 E+00 < 0.1 4.3E+02 7.4
Cresols 1.4E+03 5.8E+01 < 0.1 1 .1E+00 < 0.1 5.9 E+01 < 0.1 Cyclohexanone 9.9E+04 1.2E+03 < 0.1 3.7 E+00 < 0.1 1.2E+03 < 0.1 Ethyl acetate 5.3E+ 05 4.1E+02 < 0.1 2.0 E+00 < 0.1 4.1E+02 < 0.1 Ethyl benzene 2.1E+03 1.9E+03 0.9 9.6 E+0 1 < 0.1 2. 0 E+03 1.0

Isobutanol 5.0E+04 2.2E+02 < 0.1 1.2 E+00 < 0.1 2.2E+02 < 0.1

Methanol 4.9E+03 2.4E+03 0.5 5.8E+00 < 0.1 2.4E+03 0.5 Methyl ethyl ketone 1.9E+04 1 .2E+04 0.6 3.3 E+01 < 0.1 1.2E+04 0.6 Methyl isobutyl ketone 6.2E+03 2.1E+03 0.3 1.1 E+01 < 0.1 2.1E+03 0.3 Methylene chloride 1.1E+02 3.3E+03 30 5.1 E+01 0. 5 3.4E+03 3 1 Tetrachloroethylene c 7.7E+01 7.3E+02 9.5 9.3 E+0 1 1.2 8.2 E+02 11

Toluene 1.1E+03 8.9E+ 03 8.1 4.3 E+01 < 0.1 8.9 E+03 8.1 Trichloroethylene c 2.4E+00 1.9E+03 790 2.5 E+00 1.0 1.9E+03 790 Xylene 4.8E+02 8.6E+03 18 2.2E+01 < 0.1 8.6E+03 18

a Bolded solvent names have at least one ELLR/RB

- MLL ratio greater than one; the ratios greater than 1 are also bolded.

b Two RB

- MLL values for benzene were calculated using both the high end and low end values of the cancer risk range established for be nzene. See Table 3

- 12 in Section 3.6.2 of RTI ( 2009 ).

c The RB

- MLL values for tetrachloroethylene and tr ichloroethylene were calculated using the updated risk benchmarks developed for EPA’s Integrated Risk Information System (IRIS). See Table 1 in RTI (201 2 ). The final health assessment for trichloroethylene was posted on IRIS on September 28, 2011 ( http://www.epa.gov/iris/subst/0199.htm ). The final assessment for tetrachloroethylene was posted on February 10, 2012 ( http://www.epa.gov/iris/subst/0106.ht m )

.

### 7 Table 2. Comparison of the 90th percentile ELLR estimates with the 90th percentile RB

- MLLs for the Composite

- Liner
Scenario at a Target Cancer Risk of 10 - 5 and Noncancer Hazard of 1 (in kg solvent per year) a

Solvent 90 th
percentile

RB

- M LL 90 th
percentile ELLR (wipes) ELLR wipes

RB

- MLL 90 th
percentile ELLR (sludge) ELLR sludge

RB

- MLL 90 th
percentile ELLR (total) ELLR (total )

RB

- MLL

1,1,2

- Trichloroethane 1.1E+04 3.3E+02 < 0.1 2.3 E+00 < 0.1 3.3E+02 < 0.1 1, 2

- Dichlorobenzene 1.4E+06 3.6E+02 < 0.1 1.2 E+00 < 0.1 3.6E+02 < 0.1

2

- Ethoxyethanol 8.1E+07 2.6E+03 < 0.1 2 .3E+00 < 0.1 2.6E+03 < 0.1

Acetone 1.1E+08 9.1E+03 < 0.1 2.6 E+01 < 0.1 9. 1 E+03 < 0.1

Benzene b 3.6E+03 1.2E+01 < 0.1 2.4 E+00 < 0.1 1.4 E+01 < 0.1

1.3E+04 <0.1 <0.1 < 0.1

Butanol 1.2E+07 6.1E+02 < 0.1 1.4 E+00 < 0.1 6.1E+02 < 0.1
Chlorobenzene 2.6E+04 4.3E+02 < 0.1 1.1 E+00 < 0.1 4.3E+02 < 0.1
Cresols 1.4E+06 5.8E+01 < 0.1 1 .1E+00 < 0.1 5.9 E+01 < 0.1 Cyclohexanone 7.5E+06 1.2E+03 < 0.1 3.7 E+00 < 0.1 1.2E+03 < 0.1 Ethyl acetate 4.4E+08 4.1E+02 < 0.1 2.0 E+00 < 0.1 4.1E+02 < 0.1 Ethyl benzene 2.6E+06 1.9E+03 < 0.1 9.6 E+0 1 < 0.1 2.0 E+03 < 0.1

Isobutanol 2.5E+07 2.2E+02 < 0.1 1.2 E+00 < 0.1 2.2E+02 < 0.1

Methanol 1.2E+07 2.4E+03 < 0.1 5.8E+00 < 0.1 2.4E+03 < 0.1 Methyl ethyl ketone 3.0E+07 1.2E+04 < 0.1 3.3 E+01 < 0.1 1.2E+04 < 0.1 Methyl isobutyl ketone 1.4E+07 2.1E+03 < 0.1 1.1 E+01 < 0.1 2.1E+03 < 0.1 Methylene chloride 2.4E+05 3.3E+03 < 0.1 5.1 E+01 < 0.1 3.4E+03 < 0.1 Tetrachloroethylene c 3.3E +04 7.3E+02 < 0.1 9.3 E+0 1 < 0.1 8.2 E+02 < 0.1

Toluene 6.1E+05 8.9E+03 < 0.1 4.3 E+01 < 0.1 8.9 E+03 < 0.1 Trichloroethylene c 1.4E+03 1.9E+03 1.4 2.5 E+00 < 0.1 1.9E+03 1.4 Xylene 5.4E+05 8.6E+03 < 0.1 2.2 E+01 < 0.1 8.6E+03 < 0.1

a Bolded solvent names ha ve at least one ELLR/RB

- MLL ratio greater than one; the ratios greater than 1 are also bolded.

b Two RB

- MLL values for benzene were calculated using both the high end and low end values of the cancer risk range established for be nzene. See Table 3

- 12 in S ection 3.6.2 of RTI ( 2009 ).

c The RB

- MLL values for tetrachloroethylene and trichloroethylene were calculated using the updated risk benchmarks developed for EPA’s Integrated Risk Information System ( IRIS ). See Table 1 in RTI (201 2 ). The final health asses sment for trichloroethylene was posted on IRIS on September 28, 2011 ( http://www.epa.gov/iris/subst/0199.htm ). The final assessment for tetrachloroethylene was posted on February 10, 2012 ( http://www.epa.gov/iris/subst/0106.htm )

.

### 8 Table 3 . Comparison of the 50th percentile ELLR estimates with the 50th percentile RB

- MLLs for the No

- Liner Scenario at Target Cancer Risk of 10 - 5 and Noncancer Hazard of 1 (in kg solvent per year) a

Solvent 50 th
percentile

RB

- MLL 50 th
percentile ELLR (wipes) ELLR wipes

RB

- MLL 50 th
percentile ELLR (sludge) ELLR sludge

RB

- MLL 50 th
percentile ELLR (total) ELLR (total )

RB

- MLL

1,1,2

- Trichloroethane 5

. 9 E+0 2 4.0 E+0 1 <0.1 2. 1 E

- 0 1 <0.1 4.0 E+0 1 <0.1 1,2

- Dichlorobenzene 4.2 E+0 4 4.6 E+0 1 <0.1 1. 1 E

- 0 1 < 0.1 4 .6E+0 1 <0.1

2

- Ethoxyethanol 2. 1 E+0 6 2.2 E+0 2 <0.1 2. 1 E

- 0 1 < 0.1 2.2 E+0 2 <0.1

Acetone 8.4 E+0 5 1.0 E+03 <0.1 2.5 E+0 0 < 0.1 1.0 E+03 <0.1

Benzene b 2.9E+02 1.8E+00 <0.1 2. 1 E

- 01 <0.1 2. 0 E+00 <0.1

1. 0 E+03 <0.1 <0.1 <0.1

Butanol 5.9E+05 6.1E+01 <0.1 1. 3 E

- 01 < 0.1 6.1E+01 < 0.1
Chlorobenzene 3.1E+03 5.6E+01 <0.1 8.0 E

- 0 2 < 0.1 5.6E+02 <0.1
Cresols 9.9E+04 9.0E+00 < 0.1 1. 1 E

- 0 1 < 0.1 9. 1 E+00 < 0.1 Cyclohexanone 3. 6 E+06 1.6E+02 < 0.1 3.5 E

- 01 < 0.1 1.6E+02 < 0.1 Ethyl acetate 4.4E+08 5.2E+01 < 0.1 1.7 E

- 01 < 0.1 5.2E+01 < 0.1 Ethyl benzene 1.2E+05 1.8E+02 <0.1 7.8E+00 < 0.1 1.9E+02 <0.1

Isobutanol 2.4E+06 3.1E+01 < 0 .1 1.1 E

- 01 < 0.1 3. 1 E+01 < 0.1

Methanol 2.7E+05 2.3E+02 <0.1 3.2E+00 < 0.1 2.3E+02 <0.1 Methyl ethyl ketone 8.0E+05 1.3E+03 <0.1 1.7 E+00 < 0.1 1.3E+03 <0.1 Methyl isobutyl ketone 3.0E+05 1.5E+02 <0.1 1. 0 E+00 < 0.1 1.5E+02 <0.1 Methylene chloride 5.4E+03 4.7E+02 <0.1 9.2 E+0 0 <0.1 4.8E+02 <0.1 Tetrachloroethylene c 3.3E+03 1.1E+02 < 0.1 2.0 E+01 < 0. 1 1.3E+02 <0.1

Toluene 4.9E+04 1.2E+03 <0.1 6.1 E+00 < 0.1 1.2E+03 <0.1 Trichloroethylene c 1.3E+ 02 3.1E+02 2.4 1.7 E

- 01 <0.1 3.1E+02 2.4 Xylene 2.3E+04 9.6E+02 <0.1 4. 7 E+00 < 0.1 9.6E+02 <0.1

a Bolded solvent names have at least one ELLR/RB

- MLL ratio greater than one; the ratios greater than 1 are also bolded. Note that f or the composite liner scenario, all ratios of the 50 th percentile ELLRs and RB

- MLLs are less than 0.1, so these results were not tabulated.

b Two RB

- MLL values for benzene were calculated using both the high end and low end values of the cancer risk range established for be nzene. See Table 3

- 12 in Section 3.6.2 of RTI ( 2009 ).

c The RB

- MLL values for tetrachloroethylene and trichloroethylene were calculated using the updated risk benchmarks developed for EPA’s Integrated Risk Information System (IRIS). See Table 1 in RTI (201 2 ). The final health assessment for trichloroethylene was posted on IRIS on
Se ptember 28, 2011 ( http://www.epa.gov/iris/subst/0199.htm ). The final assessment for tetrachloroethylene was posted on February 10, 2012 ( http://www. epa.gov/iris/subst/0106.htm )

.

### 9 Table 4. Ratios of ELLR to RB

- MLL for 50 th and 90 th Percentiles for the No

- Liner Scenario Using Carcinogenic Target Risks
of 10 - 4

, 10 - 5 , and 10 - 6 a

Solvent 10 - 4 Target Risk Level 10 - 5 Target Risk Level 10 - 6 Target Risk Lev el

Ratio of 50 th

Percentile ELLR

to 50 th Percentile

RB

- MLL Ratio of 90 th

Percentile ELLR

to 90 th Percentile

RB

- MLL Ratio of 50 th

Percentile ELLR

to 50 th Percentile

RB

- MLL Ratio of 90 th

Percentile ELLR

to 90 th Percentile

RB

- MLL Ratio of 50 th

Percentile ELLR

to 50 th Percentile

RB

- MLL Ratio of 90 th

Percentile ELLR

to 90 th Percentile

RB

- MLL 1,1,2

- Trichloroethane <0.1 3.0 <0.1 30 <0.1 300

Benzene b <0.1 0. 6 c <0.1 2.8 <0.1 28

<0.1 0. 6 <0.1 0.7 <0.1 7.4 Methylene chloride <0.1 4.3 c <0.1 31 0.89 310

Tetra chloroethylene <0.1 11 d <0.1 11 d 0.13 180 Trichloroethylene 2.4 d 790 d 2.4 d 790 d 6.5 4000

a ELLR values used were for the total mass loading for wipes and sludges combined.

b Two RB

- MLL values for benzene were calculated using both the high end and low e nd values of the cancer risk range established for benzene.
See Table 3

- 12 in Section 3.6.2 of RTI ( 2009 ).

c At the 10 - 4 cancer risk level at the 90 th percentile , the noncancer risk becomes the controlling factor for benzene and methylene chloride ; at the

10 - 5 and 10 - 6 cancer risk levels at the 90 th percentile, the cancer risk is the controlling factor for these two chemicals ; see Tables 4

- 4 through 4

- 6 in Section 4.0 of RTI ( 2009 ).

d At the 10 - 4 and 10 - 5 cancer risk level at the 50 th and 90 th percentile, the noncancer risk is the controlling factor for trichloroethylene and

t etrachloroethylene ) . At the 10 - 6 cancer risk level at the 50 th and 90 th percentile, the cancer risk is the controlling factor for trichloroethylene and tetrachloroethylene. S ee Table 9 in RTI ( 2012).

### 10 Table 5

. Ratios of ELLR to RB

- MLL for 50 th and 90 th Percentiles for the Co mposite

- Liner Scenario Using Carcinogenic Target
Risks of 10 - 4 , 10 - 5 , and 10 - 6 a

Solvent 10 - 4 Target Risk Level 10 - 5 Target Risk Level 10 - 6 Target Risk Level

Rati o of 50 th

Percentile ELLR

to 50 th Percentile

RB

- MLL Ratio of 90 th

Percentile ELLR

to 90 th Percentile

RB

- MLL Ratio of 50 th

Percentile ELLR

to 50 th Percentile

RB

- MLL Ratio of 90 th

Percentile ELLR

to 90 th Percentile

RB

- MLL Ratio of 50 th

Percentile ELLR

to 5 0 th Percentile

RB

- MLL Ratio of 90 th

Percentile ELLR

to 90 th Percentile

RB

- MLL 1,1,2

- Trichloroethane <0.1 <0.1 <0.1 <0.1 <0.1 0.3

Benzene b <0.1 <0.1 <0.1 <0.1 <0.1 <0.1

<0.1 <0.1 <0.1 <0.1 <0.1 <0.1 Methylene chloride <0.1 <0.1 <0.1 <0.1 <0.1 0.1 Tetrachloroethylene <0.1 < 0.1 <0.1 <0.1 <0.1 0.1 Trichloroethylene <0.1 1.4 c <0.1 1.4 c <0.1 3.6 c

a ELLR values used were for the total mass loading for wipes and sludges combined.

b Two RB

- MLL values for benzene were calculated using both the high end and low end values of the cancer risk range established for benzene.
See Table 3

- 12 in Section 3.6.2 of RTI ( 2009 ).

c At the 10 - 4 and 10 - 5 cancer risk level at the 90 th percentile, the noncancer risk is the controlling factor for trichloroethylene. At the 10 - 6 cancer risk level, the cancer risk is the controlling factor . S ee Table 9 in RTI (2012 )

.

### 11 

## 2.0 How Does Uncertainty in the ELLRs and the RB

- MLLs Affect the Comparison of the 90 th 

## Percentile Estimates?

The comparison performed in section 1.0 is a si mple comparison of the numerical model outputs from each model system (the landfill loading estimate model described in SAIC (20 12 )

and the RB

- MLL model described in RTI ( 2009 )) at the 50 th 

### and 90 th 

### percentile estimates from each respective model’s outputs . Both sets of model outputs include unquantified uncertainty.

USEPA (1992) describes three general types of uncertainty:

• 

### Scenario uncertainty. This type of uncertainty arises when:

o 

### the scenario of interest is described in a manner that deviates fro m the actual conditions in the real world, due to incorrect or insufficient information,

o 

### the actual conditions are approximated in either space or time, in such a way that distortions of the actual conditions occur,

o 

### incorrect models are used to represent the scenario,

o 

### the risk assessment overlooks/excludes an important exposure pathway, and/or

o 

### the risk assessor’s professional judgment is erroneous.

• 

### Parameter uncertainty. This type of uncertainty arises when:

o 

### imprecise or biased measurements are used for model variables,

o 

### model inputs are based on data from unrepresentative samples, and/or

o 

### surrogate data are used to fill in data/knowledge gaps.

• 

### Model uncertainty. This type of uncertainty arises when:

o 

### the model used includes incorrect inferences about the linkages (correlations) between different model inputs,

o 

### the model used excludes variables that are relevant to the scenario being modeled,

and/or

o 

### the model’s simplifications of real

- world circumstances cannot be independently

verified with measurements o f environmental conditions.

All three types of uncertainty described above can obscure the unknowable, “true” value that the risk assessment attempts to estimate. If risk assessors and risk managers possessed perfect knowledge of actual conditions and t he environmental phenomena that are represented in the risk analysis, and of future conditions, then there would be no uncertainty in the risk

estimates.

Estimates from risk assessments such as that described in RTI ( 2009 ), and estimates of

the waste c haracterization data described in SAIC (2012) include numerous sources of uncertainty. The three types of uncertainty described in the previous paragraph exist for the

numerical values for the risk

- based mass loading limit estimates and the landfill loadin gs calculations shown in Tables 1

, 2 , and 3 . Both analyses

– SAIC (2012) and RTI (200 9 )

– include extensive discussions of the steps that were taken to address those sources of uncertainty that could be addressed through further analysis and described qual itatively in terms of the potential degree and direction of influence on the results. The peer reviewers also reviewed the uncertainty descriptions (RTI, 200 8 ).

### 12 Uncertainty in the ELLRs

Uncertainties in the calculated ELLRs arise from uncertainties in the values used as input parameters and the assumptions inherent in the modeling equation. As described in the SAIC (2012) document (sections 2.4 and 3.4), analyses were conducted to evaluate data uncertainty for the most sensitive input parameters use d to calculate ELLRs. For the disposal of solvent

contaminated wipes in a landfill, these analyses demonstrated that the most sensitive input parameter was typically the concentration of chemical in the solvent. The most significant

effects on results fo r the uncertainty analysis occurred when changes in the assumptions for this input parameter were made.

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 washwater generated from the shop towels. In general, the effect of changing various input data assumptions led to less pronounced changes in the results than those

found for the solvent

- contaminated wi pes 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:

• Data are not always available to characte rize the differences for a wide variety of facilities and industries. 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 indust ry.

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

- day practices. In cases where trends were assumed, it is difficult to quantify the magnitude of these trends. For example, the trends include changing solvents, using less, and ch anging the method of applying the solvent to the wipe. We cannot quantify how these trends would impact the use of solvent with wipes.

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

- site removal of excess solvent) prior to disposal of solvent

- contaminated wipes in a landfill. We based the estimates on a

limited number of field tests.

Uncertainty in RB

- MLLs

The RTI ( 2009 ) document (Section 4.2), disc usses three major sources of uncertainty: (1) the uncertainty associated with the conceptual approach developed for the risk analysis (e.g., how does the structure of the assessment influence the risk estimates?), (2) the uncertainty

associated with the mo dels used (e.g., how well does a model represent reality?), and (3) the uncertainty associated with the distributions and values assigned to input parameters, particularly those parameters to which the model is most sensitive (e.g., what are the true value s of each parameter?).

### 13 As described in RTI ( 2009 ), the limitations and uncertainties were primarily addressed through qualitative narrative that describes potential bias associated with certain model simplifications, data gaps, etc., with respect to the risk estimates (i.e., is risk overestimated?). We developed several quantitative analyses to determine the significance of peer

- review comments and explore the models’ behavior (RTI, 2008). A summary of the most important limitations, uncertainties, and analyses considered in the risk characterization are as follows.

Receptor Wells. For each realization, it is implicitly assumed that a potable well is located somewhere between the edges of the landfill out to 1,600 m. In addition, the groundwater

expo sure pathways are modeled only for hypothetical individuals that rely on receptor wells within the lateral extent of the plume. With respect to the general population, these are conservative assumptions that would tend to over

- predict the groundwater pathw ay risk.

Landfill Locations. The geographic distribution of landfills was based on a sample of 66 offsite Industrial D landfill locations, implying that this population is representative of the population of MSWLFs in the contiguous United States. The v eracity of this assumption is unknown.

Receptor Locations. The receptor locations differed between the ambient air and groundwater exposure pathways. Therefore, we conducted an analysis to determine whether limiting the air receptors to the “lateral ext ent of the air plume” would result in a significant change in the risk estimates for ambient air. Based on that analysis, we found that the risk estimates would increase by less than a factor of 2 if we modeled only hypothetical receptors in the air plume (which

would decrease the RB

- MLL by the same factor).

Ecological Risk. The potential impacts on aquatic life via the groundwater

- to

- surface water pathway were not modeled. Based on available ecological benchmarks, it appears that solvent levels protect ive of human health will also be protective of aquatic life. Given the chemical and physical properties of the constituents, it appears that natural dilution and attenuation mechanisms would result in negligible risks to aquatic biota. Comparison of wate r quality criteria and the drinking water standards for the solvents evaluated suggests that risks to aquatic life will be below levels of concern if human health is adequately protected.

Liner Leakage Rates. The liner leakage rates are based on a relati vely small empirical data set that represents some subset of liner behavior. Thus, the use of these leakage rates is associated with an unquantifiable, and perhaps a significant level of uncertainty.

Cover/Liner Characteristics. The cover/liner character istics have a significant influence on the calculation of diffusion. The range of geomembrane permeation coefficients is based on empirical studies and, although there is no known bias in using the empirical studies, the degree

to which these data are rele vant to the distribution of MSWLFs across the country is unknown.

Landfill Characteristics. The area, height, and operating life of landfills were derived using summary statistics for each parameter from a commercial database obtained by EPA. The degree to which this derived data set represents the actual population of MSWLFs is unknown. However, comparisons with other data sources on landfill size and operations indicate that this data set is an appropriate representation.

### 14 Dermal Contact. In response to peer review comments, dermal exposure to solvents during bathing/showering was modeled. EPA has identified the skin permeability coefficient required to calculate dermal dose as a parameter with a high level of uncertainty. Given the fact that drinking water ingestion drives the risk estimates, we do not consider this uncertainty to be significant for most solvents.

Chemical Properties. Point estimates were used for all chemical properties, including biodegradation rates. EPA notes that the paucity o f data on biodegradation introduces a high level of uncertainty in modeling this process. Because we selected conservative values (i.e., lowest biodegradation rate), we believe that the risks are likely to be over

- predicted for some

solvents.

Health Ben chmarks. Point estimates of human health benchmarks were identified in EPA

approved sources. Although these benchmarks are derived to account for differences in intra

species sensitivity and address the quality and quantity of supporting data, the uncertai nty associated with their use has not been quantified. This uncertainty is illustrated by the range of

cancer risk factors used for benzene. However, the primary goal of EPA actions is protection of human health; accordingly, as an Agency policy, risk as sessment procedures, including default options that are used in the absence of scientific data to the contrary, should be health protective. Therefore, health benchmarks ensure that risks are not underestimated and therefore may lean towards overestimating risks.

REFERENCES

SAIC (Science Applications International Corporation), 2012

. Landfill Loadings Calculations

For Solvent

- Contaminated Wipes. Prepared for U.S. Environmental Protection Agency, Office of Solid Waste, Washington, D.C. June , 20 1 2

.

RTI (Research Triangle Institute), 200 9

. Risk

- Based Mass Loading Limits for Solvents in Disposed Wipes and Laundry Sludges Managed in Municipal Landfills . Prepared for U.S. Environmental Protection Agency, Office of Solid Waste, Washington, D.C. March 2009

.

RTI (Research Triangle Institute), 2008. Response to Peer Review Comments on the Report on

Risk

- Based Landfill Mass Loadings for Solvent

- Contaminated Wipes and Laundry Sludges

. Prepared for U.S. Environmental Protection Agency, Office of Solid Waste, W ashington, D.C. July, 2008.

RTI (Research Triangle Institute), 2012

. Impact of Revised Human Health Benchmarks on Solvent Wipes Risk

- Based Mass Loading Limits (RB

- MLLs)

. Prepared for USEPA

. April , 201 2

.

USEPA, 199 5

. Guidance for Risk Characterization , U. S. Environmental Protection Agency,

1995.

USEPA, 1992. Final Guidelines for Exposure Assessment. U.S. Environmental Protection Agency. Federal Register 57(104):22888

- 22938, May 29, 1992.