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 Conservation and Resource Recovery

Revised April 2012
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 when 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 landfills within the United States. The document entitled Landfill Loadings 

Calculations For Solvent-Contaminated Wipes (SAIC, 2012) describes the method for 

estimating these annual mass loading estimates. SAIC, 2012 is a revised version of the 

original calculations contained in the docket. The revisions incorporate updated data 

submitted in public comments in response to EPA’s October 27, 2009 Notice of Data 

Availability. The comparison document has been revised to reflect the updated landfill 

loading calculations 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 Loading Limits for Solvents in 

Disposed Wipes and Laundry Sludges Managed in Municipal Landfills (RTI, 2009; 

docket number EPA-HQ-RCRA-2003-0004-0438) describes the method for estimating 

the quantities. The Risk-Based Mass Loading Limits (RB-MLLs) were calculated 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 referenced 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, 2012) to determine whether 

current industry practices could potentially result in mass loading rates that are above the RBMLLs (RTI, 2009 and 2012) 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 distribution 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 50th and 90th percentiles of the distribution. 

These 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 (2009 and 2012) documents

to determine whether the ELLRs exceed the estimated RB-MLLs. If the estimated landfill 

loadings 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 (2012), the purpose of the landfill loadings calculations was to 

develop estimates 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 

distribution of mass loading rates (in kg per year per landfill) for each solvent from the disposed

wipes or laundry sludge. These landfill loadings distributions represent the amount of “wipesrelated” solvent in the respective waste streams (i.e., wipes and 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 intended 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-specific 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 large 

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 used 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 variables, 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 guidelines, 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 from 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 (90th percentile value) from 

the distribution of landfill loading model outputs to compare with a protective estimate (90th

percentile value) of risk-based mass loading limits. We also compared the central tendency (50th

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

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

the 90th percentile ELLRs to the 90th percentile 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 1and 2 are expressed as ratios, 

i.e., the 90th percentile ELLRs (kg solvent per year) are divided by the 90th 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 landfill 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-based 

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 90th percentile ELLR to the 90th

percentile RB-MLL for each solvent for three cases: (1) the ratio 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, laundry sludge, and total solvent 

(disposed wipes plus laundry sludge) managed in an unlined landfill to solvent specific RBMLLs. The table provides the following information:

• The results suggest that the contribution to the total ELLRs from disposed wipes 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 approximately 2

percent and 17 percent. 

• The ratios between the laundry sludge ELLRs and the RB-MLLs indicate that, at the 90th

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

10-5 risk criteria.

• The comparison between the 90th 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 were used).1 

The RTI documents (2009 and 2012) 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 90th 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 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).
4

risks at the 90th 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. The exceedances for the solvents 1,1,2-trichloroethane, 

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. Section 4.1 (Tables 4-1 through 4-6) in the RTI (2009) document, provides

further information as to which specific exposure route from the groundwater pathway was most 

important for the combined risk for the carcinogenic solvents. In all cases, the dermal absorption 

route was a relatively minor component 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 solvents 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 for ingestion of drinking water by 

children.

Table 2 compares the 90th 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 90th 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, the 90th percentile ELLR for total solvent 

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

health-based benchmarks from IRIS; RTI, 2012). While the results for trichloroethylene indicate 

that the ELLR calculated for solvent wipes exceeds the RB-MLL, the ELLR for sludge for this 

solvent is below the RB-MLL. For the solvent tetrachloroethylene, 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., the 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. Using the comparison of the 90th percentile results in Table 2, 

the potential risks from all solvents examined in the composite-liner scenario, except 

trichloroethylene, were below the health-based criteria used in this analysis. For 

trichloroethylene, the limiting pathway/receptor combination for Table 2 was adult inhalation 

during showering.

Table 3 compares the 50th percentile ELLRs managed in an unlined landfill to the 50th

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 50th percentile, trichloroethylene is the only solvent in the unlined 

scenario that produced a ratio of ELLR to RB-MLL greater than one. Similar to the 90th

percentile results for trichloroethylene, the RB-MLL value 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 50th

percentile ELLRs and 50th percentile RB-MLLs are less than 0.1, so these results were not 

tabulated.

Tables 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-MLLs 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 these 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, the noncancer risks yielded lower RB-MLLs such that the noncancer 

risks became the limiting factor, e.g., as noted previously for trichloroethylene. Using target risk 

levels of 1 x 10-6, the cancer-based 90th percentile RB-MLLs were lower than the RB-MLLs 

calculated using the noncancer risk end point. However, when using a target risk level of 1 x 10-4

or 1 x 10-5, the 90th percentile RB-MLLs for some solvents were driven by the noncancer risk, as 

shown in Tables 4 and 5. For example, the ratios of the 90th percentile ELLR to the 90th

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 constituents 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, at the 10-4 and 10-5 cancer risk level, the noncancer risk is also the 

controlling factor 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 would have the 

effect of lowering the ratios, such that the ratios for benzene at the 90th percentile (using either 

risk factor) would be less than one. Using the 10-6 risk criterion for the 90th percentile results 

would have the effect of raising the ratios 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 ratios for all solvents would be 

below one at both the 90th and 50th percentiles using the 10-4 and 10-5 risk criteria, except for

trichloroethylene, which is driven by the noncancer risk. Using the 10-6 criterion, only the ratio

for trichloroethylene at the 90th 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

 90th

percentile

 RB-MLL

 90th

percentile

ELLR (wipes)

ELLR wipes

RB-MLL

 90th

 percentile

ELLR (sludge)

 

 ELLR sludge

 RB-MLL

 90th

percentile

ELLR (total)

 

 ELLR (total)

 RB-MLL

1,1,2-Trichloroethane 1.1E+01 3.3E+02 30 2.3E+00 0.2 3.3E+02 30

1,2-Dichlorobenzene 7.1E+02 3.6E+02 0.5 1.2E+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 2.6E+01 < 0.1 9.1E+03 0.3

Benzeneb 5.0E+00 1.2E+01 2.4 2.4E+00 0.5 1.4E+01 2.8

1.9E+01 0.7 0.1 0.74

Butanol 1.2E+04 6.1E+02 < 0.1 1.4E+00 < 0.1 6.1E+02 < 0.1

Chlorobenzene 5.8E+01 4.3E+02 7.4 1.1E+00 < 0.1 4.3E+02 7.4

Cresols 1.4E+03 5.8E+01 < 0.1 1.1E+00 < 0.1 5.9E+01 < 0.1

Cyclohexanone 9.9E+04 1.2E+03 < 0.1 3.7E+00 < 0.1 1.2E+03 < 0.1

Ethyl acetate 5.3E+05 4.1E+02 < 0.1 2.0E+00 < 0.1 4.1E+02 < 0.1

Ethyl benzene 2.1E+03 1.9E+03 0.9 9.6E+01 < 0.1 2.0E+03 1.0

Isobutanol 5.0E+04 2.2E+02 < 0.1 1.2E+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 3.3E+01 < 0.1 1.2E+04 0.6

Methyl isobutyl ketone 6.2E+03 2.1E+03 0.3 1.1E+01 < 0.1 2.1E+03 0.3

Methylene chloride 1.1E+02 3.3E+03 30 5.1E+01 0.5 3.4E+03 31

Tetrachloroethylenec 7.7E+01 7.3E+02 9.5 9.3E+01 1.2 8.2E+02 11

Toluene 1.1E+03 8.9E+03 8.1 4.3E+01 < 0.1 8.9E+03 8.1

Trichloroethylenec 2.4E+00 1.9E+03 790 2.5E+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 benzene. 

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 (2012). 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.htm ).
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

 90th

percentile

 RB-MLL

 90th

percentile

ELLR (wipes)

 ELLR wipes

 RB-MLL

 90th

percentile

ELLR (sludge)

ELLR sludge

RB-MLL

 90th

percentile

ELLR (total)

ELLR (total)

 RB-MLL

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

1,2-Dichlorobenzene 1.4E+06 3.6E+02 < 0.1 1.2E+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 2.6E+01 < 0.1 9.1E+03 < 0.1

Benzeneb 3.6E+03 1.2E+01 < 0.1 2.4E+00 < 0.1 1.4E+01 < 0.1

1.3E+04 <0.1 <0.1 < 0.1

Butanol 1.2E+07 6.1E+02 < 0.1 1.4E+00 < 0.1 6.1E+02 < 0.1

Chlorobenzene 2.6E+04 4.3E+02 < 0.1 1.1E+00 < 0.1 4.3E+02 < 0.1

Cresols 1.4E+06 5.8E+01 < 0.1 1.1E+00 < 0.1 5.9E+01 < 0.1

Cyclohexanone 7.5E+06 1.2E+03 < 0.1 3.7E+00 < 0.1 1.2E+03 < 0.1

Ethyl acetate 4.4E+08 4.1E+02 < 0.1 2.0E+00 < 0.1 4.1E+02 < 0.1

Ethyl benzene 2.6E+06 1.9E+03 < 0.1 9.6E+01 < 0.1 2.0E+03 < 0.1

Isobutanol 2.5E+07 2.2E+02 < 0.1 1.2E+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 3.3E+01 < 0.1 1.2E+04 < 0.1

Methyl isobutyl ketone 1.4E+07 2.1E+03 < 0.1 1.1E+01 < 0.1 2.1E+03 < 0.1

Methylene chloride 2.4E+05 3.3E+03 < 0.1 5.1E+01 < 0.1 3.4E+03 < 0.1

Tetrachloroethylenec 3.3E+04 7.3E+02 < 0.1 9.3E+01 < 0.1 8.2E+02 < 0.1

Toluene 6.1E+05 8.9E+03 < 0.1 4.3E+01 < 0.1 8.9E+03 < 0.1

Trichloroethylenec 1.4E+03 1.9E+03 1.4 2.5E+00 < 0.1 1.9E+03 1.4

Xylene 5.4E+05 8.6E+03 < 0.1 2.2E+01 < 0.1 8.6E+03 < 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. 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 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 (2012). 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.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

 50th

percentile

 RB-MLL

 50th

percentile

ELLR (wipes)

ELLR wipes

RB-MLL

 50th

 percentile

ELLR (sludge)

 

 ELLR sludge

 RB-MLL

 50th

percentile

ELLR (total)

 

 ELLR (total)

 RB-MLL

1,1,2-Trichloroethane 5.9E+02 4.0E+01 <0.1 2.1E-01 <0.1 4.0E+01 <0.1

1,2-Dichlorobenzene 4.2E+04 4.6E+01 <0.1 1.1E-01 < 0.1 4.6E+01 <0.1

2-Ethoxyethanol 2.1E+06 2.2E+02 <0.1 2.1E-01 < 0.1 2.2E+02 <0.1

Acetone 8.4E+05 1.0E+03 <0.1 2.5E+00 < 0.1 1.0E+03 <0.1

Benzeneb 2.9E+02 1.8E+00 <0.1 2.1E-01 <0.1 2.0E+00 <0.1

1.0E+03 <0.1 <0.1 <0.1

Butanol 5.9E+05 6.1E+01 <0.1 1.3E-01 < 0.1 6.1E+01 < 0.1

Chlorobenzene 3.1E+03 5.6E+01 <0.1 8.0E-02 < 0.1 5.6E+02 <0.1

Cresols 9.9E+04 9.0E+00 < 0.1 1.1E-01 < 0.1 9.1E+00 < 0.1

Cyclohexanone 3.6E+06 1.6E+02 < 0.1 3.5E-01 < 0.1 1.6E+02 < 0.1

Ethyl acetate 4.4E+08 5.2E+01 < 0.1 1.7E-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 1.1E-01 < 0.1 3.1E+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 1.7E+00 < 0.1 1.3E+03 <0.1

Methyl isobutyl ketone 3.0E+05 1.5E+02 <0.1 1.0E+00 < 0.1 1.5E+02 <0.1

Methylene chloride 5.4E+03 4.7E+02 <0.1 9.2E+00 <0.1 4.8E+02 <0.1

Tetrachloroethylenec 3.3E+03 1.1E+02 <0.1 2.0E+01 <0.1 1.3E+02 <0.1

Toluene 4.9E+04 1.2E+03 <0.1 6.1E+00 < 0.1 1.2E+03 <0.1

Trichloroethylenec 1.3E+02 3.1E+02 2.4 1.7E-01 <0.1 3.1E+02 2.4

Xylene 2.3E+04 9.6E+02 <0.1 4.7E+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 for the 

composite liner scenario, all ratios of the 50th 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 benzene. 

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 (2012). 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.htm ).
9

Table 4. Ratios of ELLR to RB-MLL for 50th and 90th 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 Level

Ratio of 50th

Percentile ELLR 

to 50th Percentile 

RB-MLL 

Ratio of 90th

Percentile ELLR 

to 90th Percentile 

RB-MLL

Ratio of 50th

Percentile ELLR 

to 50th Percentile 

RB-MLL

Ratio of 90th

Percentile ELLR 

to 90th Percentile 

RB-MLL

Ratio of 50th

Percentile ELLR 

to 50th Percentile 

RB-MLL

Ratio of 90th

Percentile ELLR 

to 90th Percentile 

RB-MLL

1,1,2-Trichloroethane <0.1 3.0 <0.1 30 <0.1 300

Benzeneb <0.1 0.6c <0.1 2.8 <0.1 28

<0.1 0.6 <0.1 0.7 <0.1 7.4

Methylene chloride <0.1 4.3c <0.1 31 0.89 310

Tetrachloroethylene <0.1 11d <0.1 11d 0.13 180

Trichloroethylene 2.4d 790d 2.4d 790d 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 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 cancer risk level at the 90th 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 90th 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 50th and 90th percentile, the noncancer risk is the controlling factor for trichloroethylene and 

tetrachloroethylene). At the 10-6 cancer risk level at the 50th and 90th percentile, the cancer risk is the controlling factor for trichloroethylene and 

tetrachloroethylene. See Table 9 in RTI (2012).
10

Table 5. Ratios of ELLR to RB-MLL for 50th and 90th Percentiles for the Composite-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

Ratio of 50th

Percentile ELLR 

to 50th Percentile 

RB-MLL 

Ratio of 90th

Percentile ELLR 

to 90th Percentile 

RB-MLL

Ratio of 50th

Percentile ELLR 

to 50th Percentile 

RB-MLL

Ratio of 90th

Percentile ELLR 

to 90th Percentile 

RB-MLL

Ratio of 50th

Percentile ELLR 

to 50th Percentile 

RB-MLL

Ratio of 90th

Percentile ELLR 

to 90th Percentile 

RB-MLL

1,1,2-Trichloroethane <0.1 <0.1 <0.1 <0.1 <0.1 0.3

Benzeneb <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 1.4c <0.1 1.4c <0.1 3.6c

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 90th percentile, the noncancer risk is the controlling factor for trichloroethylene. At the 10-6 cancer risk 

level, the cancer risk is the controlling factor. See Table 9 in RTI (2012).
11

2.0 How Does Uncertainty in the ELLRs and the RB-MLLs Affect the 

Comparison of the 90th Percentile Estimates?

The comparison performed in section 1.0 is a simple comparison of the numerical model 

outputs from each model system (the landfill loading estimate model described in SAIC (2012) 

and the RB-MLL model described in RTI (2009)) at the 50th and 90th 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 from 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 of 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 the 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 characterization 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 loadings 

calculations shown in Tables 1, 2, and 3. Both analyses – SAIC (2012) and RTI (2009) – include 

extensive discussions of the steps that were taken to address those sources of uncertainty that 

could be addressed through further analysis and described qualitatively in terms of the potential 

degree and direction of influence on the results. The peer reviewers also reviewed the 

uncertainty descriptions (RTI, 2008). 
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 used to calculate ELLRs. For the disposal of solventcontaminated 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 for 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 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:

• Data are not always available to characterize 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 industry.

• 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 changing 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), discusses 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 models 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 values 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 

exposure 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 pathway 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 veracity 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 extent 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 protective 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 water 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 relatively 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 characteristics 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 relevant 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 of 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 Benchmarks. Point estimates of human health benchmarks were identified in EPAapproved sources. Although these benchmarks are derived to account for differences in intraspecies sensitivity and address the quality and quantity of supporting data, the uncertainty 

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 assessment 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, 2012.

RTI (Research Triangle Institute), 2009. 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, Washington, 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, 2012.

USEPA, 1995. 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.