The D&P analysis used the same risk management criteria as in the DRASv2 evaluation and the IWEM Tier 1 analysis.  The D&P modeling results for a landfill with a composite liner (i.e., a liner consisting of a synthetic liner overlying some type of clay liner) yielded significantly lower risks from potential groundwater releases compared to an unlined landfill.  The D&P analysis calculated allowable levels that were higher (i.e., lower risk) than the allowable levels obtained from modeling an unlined landfill or a landfill with a simple clay liner.  Table 16 illustrates the added protection afforded in a landfill with a composite liner compared to a landfill with no liner for the four inorganic constituents that we modeled in both the DRASv2 and the D&P analysis.

Table 12.  Ratio of D&P analysis composite lined landfill allowable levels

to D&P unlined landfill allowable levels

Constituent	CAS#	Ratio:

	D&P composite liner/

	D&P unlined

Barium	7440393	149

	Copper	7440508	133

	Lead	7439921	269

	Zinc	7440666	183

Data from EPA 2003c, Table 2-1b, page 2-4 for results for no liner (“NL”)

	and synthetic composite liner (“SL”)

5.2.3 Discussion of IWEM and D&P analyses

Comparing the results of the IWEM Tier 1 allowable levels for disposal in an unlined landfill with the results of the high-end DRASv2 allowable levels for disposal in an unlined landfill indicates that some of the IWEM Tier 1 assumptions cause a consistently more stringent interpretation of allowable constituent levels than the DRASv2 assumptions.  It is likely that many differences between the IWEM Tier 1 and DRASv2 models affect the differences in the results.  For example, the DRAS model uses waste volume as a specific input to the model, while the IWEM results rest on an assumption that the entire landfill’s waste volume is composed of the waste under study.  Other examples of reasons for the numerical differences in the ratio of IWEM Tier 1 to DRASv2 are:

- differences in the well location (IWEM Tier 1 models a well located 150 meters from the landfill while DRASv2 models a well located at varying locations within one mile downgradient from the landfill)

- the exposure routes evaluated (IWEM Tier 1 does not evaluate dermal exposure from showering or bathing, while DRASv2 does)

- differences in the inhalation-from-showering equations used

- differences in the inhalation reference concentrations used

For nickel, IWEM Tier 1 indicates that a landfill with a single liner (defined in IWEM as “a layer of compacted clay three feet thick, with a hydraulic conductivity of 1x10-7 meters per second”) affords protection over an unlined landfill by a factor of three.

Table 12 shows that the D&P analysis of certain metal constituents indicates that composite-lined landfills afford a significant factor of protection, compared to unlined landfills.  The table shows that the ratios of the allowable constituent levels for composite-lined landfills to allowable constituent levels for unlined landfills ranged from 133 to 269 for the metals evaluated in both the DRASv2 and the D&P analyses.  In modeling a synthetic composite liner, the D&P analysis used infiltration rates collected from municipal landfills.  The composite liners for these landfills consisted of geomembrane liners overlying a compacted clay liner or a geosynthetic clay liner. A compacted clay liner  SEQ CHAPTER \h \r 1  is composed of natural mineral materials, a bentonite-soil blend, and other materials placed and compacted in layers (typically at least two feet thick).  A geosynthetic clay liner is a relatively thin layer of processed clay (typically bentonite) fixed between two layers of geotextile.  The IWEM Tier 1 model also uses infiltration rates collected from landfills; however, IWEM uses infiltration rates from a more limited set of the composite liner systems, i.e., a geomembrane overlying a geosynthetic clay liner.

While the only metal COPC in the DRASv2 evaluation that was also modeled in the D&P analysis is zinc, the ratios for the other metals are similar and are a further indication of the protective effect resulting from a composite liner.

6.  Potential carcinogenic risk to humans

In order to determine whether there is a potential carcinogenic risk from constituents present in the waste, we use a criterion, or guideline, of what an acceptable exposure is.  We estimate whether a person’s exposure to a particular constituent for thirty years would be likely to result in that person’s developing cancer.  In mathematical terms, we represent this as the probability of an individual developing cancer.  The criterion, or guideline, that we use is to evaluate whether the constituent levels in the waste would cause an individual’s lifetime risk to exceed a probability of one in one hundred thousand. Mathematically, this is shown as 0.00001, 10-5, or sometimes, 1E-05.

6.1   Unlined landfill analysis

To calculate the cancer probability, we use DRASv2 to represent the movement of chemical constituents from the waste in the landfill toward human receptors, and to develop estimates of the doses of those constituents to which nearby human receptors would be exposed.  Appendix B shows the specific model inputs that we used.  If you are interested in running the DRASv2 model to see how we estimated the results shown in this document, you can replicate our methodology by using our model inputs.  If you use the default model inputs in DRASv2, instead of the inputs shown in Appendix B, you will get results that will differ slightly, for some constituents, from the results we present in this document.  You must set the “carcinogen/noncarcinogen” field at “carcinogen” in order to obtain the cancer probability results in the output screens.  Note that for correct results you must run DRASv2 in “forward-calculate” mode.

For cancer effects, the numerical indication of a constituent’s potency as a carcinogen when ingested orally is the oral cancer slope factor, or oral CSF.  When inhaled, the numerical indication of a constituent’s potency as a carcinogen is the inhalation cancer slope factor (sometimes referred to as Inhalation CSF, or Inh CSF).  12 of the Table 3 constituents have either an oral or an inhalation CSF (or both).  Thus, it was only possible to evaluate these 12 constituents for potential cancer risk.

Table 13 shows the oral and inhalation cancer slope factors we used in our evaluation.  The DRASv2 model requires that the cancer potency values be input with the same units that cancer slope factors typically have.  If inhalation unit risk factors (an alternative way of showing cancer potency) are the only numerical indicators of cancer potency available, we convert them to inhalation cancer slope factors using an assumed body weight of 50.6 kilograms and an assumed daily inhalation volume of 12.2 cubic meters of air.

Table 13.  Carcinogen slope factors (CSFs) and Inhalation unit risk factors (URFs)

Constituent	CAS#	Oral CSF	URF	Inhalation CSF

Carcinogens detected in both totals and leachate samples:

	nickel (refinery dust)

	0.24	1

formaldehyde	50000

0.013	0.054

arsenic	7440382	1.5	4.3	18

hexavalent chromium	18540299

0.12	0.5

acrylamide	79061	4.5	1.3	5.4

cadmium	7440439

1.8	7.5

bis(2-ethylhexyl) phthalate	117817	0.014

beryllium	7440417

2.4	10

dichloroethane, 1,2-	107062	0.091	0.026	0.11

methylene chloride	75092	0.0075	0.00047	0.002

Detected only in totals samples (not in leachate samples):

	chloroform	67663	Note 2	0.023	0.095

benzene	71432	0.015 to 0.055	0.0022 to 0.0078	0.021

Note 1: units for Oral CSF are (kilograms body weight - day)/(milligrams of constituent)

Note 2: exposures below the non-cancer Oral Reference Dose not expected to cause

   cancer (see EPA 2005c entry for chloroform)

Note 3: units for URF are (cubic meters air)/(milligrams of constituent)

	Note 4: units for Inhalation CSF are (kilograms body weight - day)/(milligrams of

   constituent).  Inhalation CSF derived by converting URF for 50.6 kg body weight

   individual breathing 12.2 cubic meters air per day.

Using DRASv2, we assess the potential carcinogenic risk to humans from these 12 constituents.   For the high end estimates, we use the total waste volume of 90,000 cubic yards disposed (equivalent to 3000 cubic yards disposed per year, for 30 years, or 4500 cubic yards disposed per year for 20 years), together with the maximum totals and leachate concentrations.

6.1.2   High end results

Using DRASv2 to evaluate the 13 carcinogenic constituents listed in Table 13 yields the high end cancer probabilities shown in Table 14.  According to the model calculations, only three of the 12 constituents appear to have a maximum concentration in the sludge that potentially could cause an exposed person to experience a probability of cancer, due to that exposure, that would be greater than one in one hundred thousand.  These three constituents of initial concern are arsenic, acrylamide, and formaldehyde.

Table 14.  Individual excess cancer risk probabilities calculated

 at 90,000 cubic yards disposed

	Constituent	CAS#	 	Cancer probability

	Carcinogens detected in both totals and leachate samples:

	Nickel

	0.00000005

formaldehyde	50000

0.00005(0.0000003)

Arsenic	7440382

0.0003(0.00003)

hexavalent chromium	18540299

0.000000009

acrylamide	79061

0.0004(0.000001)

Cadmium	7440439

0.000000001

bis(2-ethylhexyl) phthalate	117817

0.0000001

Beryllium	7440417

1E-10

dichloroethane, 1,2-	107062

0.000001

methylene chloride	75092

0.0000002

	Detected only in totals samples (not in leachate samples):

	chloroform	67663

3.00E-12

Benzene	71432

4E-13

Note 1: results rounded to one significant figure.

	Note 2: acrylamide, formaldehyde, and arsenic results presented for

 nominal maximum concentration; parenthetical results are for

 the likely true maximum concentration (see discussion of results in text)

6.1.3   High end results discussion

For the same reasons described in section 5.1.2 for acrylamide, after reviewing the laboratory analytical procedures that were used to quantify the maximum detected values that we used in this analysis, we conclude that acrylamide should be dropped from the list.  For acrylamide, we use the 0.0007 mg/L (higher of two detection limits) value as the maximum value (rather than the 0.29 mg/L value that we use to develop the results shown in Table 14).  The corresponding recalculated high end cancer probability from acrylamide, at this lower waste concentration (at 90,000 cubic yards total disposal volume) is one in one million.

For formaldehyde, Table 9 contains a summary of the analytical results obtained by omitting the data from the wet chemistry method at the two facilities described in section 5.1.2; these results show that the maximum detected “total” formaldehyde was 142 mg/kg.   The corresponding recalculated high end cancer probability from formaldehyde, at this lower waste concentration is three in ten million (3E-7), due to breathing formaldehyde as the waste is being placed in the landfill.  In addition, although we used the IRIS slope factor for formaldehyde, there continues to be considerable controversy in the scientific community regarding formaldehyde's carcinogenicity by the inhalation route.

Therefore, we perform no further analysis of potential cancer risk caused by acrylamide or formaldehyde, leaving arsenic as the only constituent of concern for potential cancer risk.  Using the most reliable data for arsenic, the maximum concentration of arsenic (at 90,000 total cubic yards disposal volume) corresponds to a high end estimated cancer probability of three in one hundred thousand (3E-05).

Adult and child exposure.  The modeled movement for arsenic indicates that the risk from arsenic, if realized, would occur via the groundwater pathway (constituents would leach from the waste into groundwater underneath the landfill, and then be ingested or inhaled by humans using the groundwater as a drinking water or bath/shower water source).

To reflect the potential cancer health effects that children could experience due to ingesting groundwater contaminated with constituents from the F019 waste, the DRASv2 model includes an adjustment factor for body weight and drinking water intake within the cancer probability calculations.  The adjustment factor included within the model represents a child from birth to age six, weighing 15 kilograms and drinking 1 liter of water per day (and then an additional 24 years of exposure as a 72-kilogram adult drinking 2 liters of water per day).  As with the noncancer adjustment factor, different choices of age group, body weight percentile, and/or drinking water percentile could affect the numerical value of the adjustment factor in either an upward or downward direction.  However, the results shown in Table 14 reflect the exposure of a 15 kilogram child exposed from birth to age six who drinks one liter of water per day, and then is exposed an additional 24 years at a body weight of 72 kilograms and drinking water intake of 2 liters per day.

6.1.4   Central tendency results and discussion

We perform our central tendency analysis on only the single constituent identified from the high end analysis (arsenic).  (The constituents with high end estimates below the range of concern would have central tendency estimates that are even lower than the high end estimates, and thus, we need not analyze them.)  We also use the Table 10 analytical value from the method 6020 results, for median leachate concentration (0.011 mg/L).  As with the noncancer central tendency estimates, we use a median value (1,088 cubic yards per year) for the disposed waste volume and an assumed disposal period of 20 years.  The central tendency estimate of an individual’s cancer probability due to exposure to the arsenic in groundwater, from the disposed F019 sludge (running DRASv2 at the central tendency settings of 1088 cubic yards/year for 20 years) is 2E-06, or two in one million.

Application of an adjustment factor for the drinking water intake is more complicated than for the noncancer estimates, because the DRASv2 model includes both the child and adult life stages in the exposure equations.  The DRASv2 model assumes a drinking water intake of 1 liter per day for the child, ages 0-6, and 2 liters per day for the adult, ages 7-30, which results in a higher amount of ingested arsenic from the groundwater than would be ingested at a central tendency drinking water intake.  Thus the central tendency cancer risk in the previous paragraph is actually an overestimate of the central tendency cancer risk.  We do not adjust the central tendency cancer risk estimate to reflect mean drinking water intakes for the child and adult.

6.2 Lined landfill analysis

As with the noncarcinogen assessment described in section 5.2.1, we perform an IWEM Tier 1 evaluation for arsenic (the sole constituent of potential concern for cancer risk).  We reviewed the dyes and pigments rule (D&P) analysis; however, the D&P analysis did not model arsenic as a constituent of potential concern.

Results of IWEM Tier 1 Evaluation

Due to the conservative assumptions in IWEM when compared to the DRAS model, IWEM Tier 1 indicates that neither an unlined landfill nor a single-lined landfill is protective for human cancer risk (at the adjusted maximum arsenic in leachate concentration of 0.048 mg/L) from ingesting arsenic in groundwater that has flowed from underneath the landfill to the well located 150 meters downgradient from the landfill.  IWEM Tier 1 indicates that a composite-lined landfill would be protective for human cancer risk, as a result of exposure to groundwater contaminated with arsenic from the landfill.  IWEM Tier 1’s results for arsenic, based on an individual’s excess lifetime cancer probability of one in one million, indicate that in an unlined landfill, the allowable level of arsenic in leachate would be 0.0002 mg/L; in a single-lined landfill, the allowable level of arsenic in leachate would be 0.001 mg/L, and in a composite-lined landfill, the allowable level of arsenic in leachate would be 5 mg/L.

6.2.2   Discussion of IWEM analysis for cancer risk

The risk management criterion in IWEM Tier 1 (in other words, the target risk level that the model developers set for users) were an individual excess cancer risk not to exceed one in one million.  This risk management criterion is one order of magnitude more protective than the risk management criterion we used in the DRASv2 evaluation (one in one million individual probability of developing cancer in IWEM, versus one in one hundred thousand individual probability of developing cancer in the DRASv2 evaluation).

The individual modeled in IWEM for potential cancer risk from drinking water is a person age 0-29 years (inclusive), whose drinking water intake rate, 25.2 milliliters per kilogram of body weight per day, represents the time-averaged body weight and drinking water intakes of both children and adults up to age 30.

7.  Potential ecological effects

In order to determine whether there is a potential ecological risk from constituents present in the waste, we have evaluated the potential exposures of the ecological receptors described in section 4.3.  The DRASv2 model scenario, and its documentation in the RCRA Delisting Technical Support Document (EPA 2002a), pertains to a community of freshwater aquatic organisms that live in a second-order stream, or surface water body.

 7.1  Unlined landfill analysis

We use the DRASv2 model to estimate the concentrations of a particular constituent in the surface water body.  Those concentrations represent the levels of exposure of the members of that aquatic community.  Then we calculate the ratio of those exposure levels to the level of that particular constituent that should be without adverse effects (the toxicity reference value for that constituent).  If the ratio is less than one, we infer that the aquatic community should be protected.  According to the RCRA Delisting Technical Support Document (EPA 2002a), the analysis afforded by the DRASv2 model can allow inferences about the level of protection of ecosystems from the effects of chemical stressors, but the toxicological data (the toxicity reference values) are insufficient to reflect all of the complexities of an ecosystem’s functioning.

The DRASv2 model does not yield an actual calculation of the ecological hazard ratio we describe in the previous paragraph.  It performs the calculations and only indicates that an ecological hazard is posed when the waste constituent concentrations are estimated to cause the surface water body constituent concentrations to exceed the toxicity reference value.  Thus, in this document, we present the calculated ratio and call it a “toxicity reference value exceedance ratio”, or “TRV exceedance ratio.”  It is similar in concept to the hazard quotient ratio for the human noncancer health effects estimates.

DRASv2 was intended to run in backward-calculation mode, and yield waste concentrations that correspond to a given “acceptable” exposure level.  However, EPA learned that some of the backward calculations in DRASv2 do not operate correctly, and thus, in this analysis (both for human health effects and environmental effects), has used DRASv2 in “forward” calculation mode exclusively.  The practical significance of this, for the ecological hazard analysis, is that model users provide the values for a particular chemical constituent’s concentration in the waste leachate and totals samples, run the model in forward-calculation mode, and review the model outputs to see if the model flagged that concentration as presenting a potential ecological risk.  The model only provides the flag when the concentration in the surface water body exceeds the toxicity reference value.  Thus, DRASv2 users must run the model in forward-calculation mode using varying waste constituent concentrations, to determine the waste constituent concentration that corresponds to the toxicity reference value level (indicating an acceptable exposure level).

The ecological effects modeled are those that occur by surface pathways, not those that occur due to movement of constituents mixed with groundwater.  The DRASv2 model estimates surface pathway effects based on the level of constituent that is present in the “totals” sample.  If there is no detected level of constituent in the totals sample, but it is detected in leachate samples, then EPA can assume that it is present in the totals samples below the detection limit of the totals samples.  For this analysis, EPA is assuming that constituents detected in leachate, but not in totals, are present in the totals samples at one-half of the highest reported detection limit (of the samples taken at the thirteen facilities whose data we used for this analysis).

In general, we use the National Recommended Water Quality Criteria (EPA 2004b) as our toxicity reference values.  In cases in which there were no national water quality criteria, we use corresponding state water quality criteria.  Of the 56 constituents in Table 3, we are unable to model the potential ecological effects of fluoride and sulfide for the reasons explained in section 8.  Of the remaining 54 constituents, five (acetophenone, allyl chloride, cobalt, tin, and n-butanol) do not have national recommended water quality criteria, nor were we able to locate equivalent state water quality criteria for them.  Thus we are only able to evaluate 49 of the 56 constituents known to be present in the waste.

The high end estimates are from running the DRASv2 model in forward-calculation mode using 90,000 cubic yards as the total waste quantity disposed.  We also use the maximum reported value (for totals samples) for each constituent.  The central tendency estimates are from running DRASv2 using 21,760 cubic yards as the total waste quantity disposed, and median totals sample results for detected values.

7.2 High end results

Table 15 shows the results of this analysis.

Table 15.  Results from ecological risk analysis

Constituent	CAS#	 	Concentrations

soil	TRV	maximum

saturation	exceedance	in waste

 	 	(mg/kg)	(mg/kg)	(mg/kg)

Detected in both totals and leachate samples:

Nickel	7440020

400,000	5,700

Zinc	7440666

900,000	18,000

Barium	7440393

30,000	4,300

Copper	7440508

60,000	1,500

Chromium	7440473

1,000,000	1,800

formaldehyde	50000

1,000,000	20,000

Lead	7439921

20,000	11,000

Mercury	7439976

10,000	0.71

Xylenes	1330207	400

40

Vanadium	7440622

140,000	44

Arsenic	7440382

1,000,000	25

Cyanide	57125

40,000	20

ethylbenzene	100414	200

7

Hexavalent chromium	18540299

80,000	22

Toluene	108883	500

20

Acrylamide	79061

1,000,000	6

Cadmium	7440439

2,000	22

bis(2-ethylhexyl) phthalate	117817

300,000	100

Antimony	7440360

200,000	170

Methyl ethyl ketone	78933	30,000

30

Beryllium	7440417

40,000	1.3

Selenium	7782492

40,000	21

Silver	7440224

20,000	5.5

Thallium	7440280

30,000	7.1

butyl benzyl phthalate	85687	900

300

naphthalene	91203

100,000	30

cresol, p- 	106445

900,000	20

Acetone	67641	100,000

6

trichloroethylene	79016	1,000

0.04

dichloroethane, 1,2-	107062	3,000

0.02

Acetonitrile	75058	200,000

5

chlorobenzene	108907	900

0.03

methylene chloride	75092	2,000

2

trichloroethane, 1,1,1-	71556	1,000

0.02

dichloroethane, 1,1-	75343	2,000

0.02

Detected only in totals samples (not in leachate samples):

	di-n-octyl phthalate	117840	10,000

90

Methyl isobutyl ketone	108101	20,000

0.4

tetrachloroethylene 	127184	400

0.2

Styrene	100425	2,000

0.02

Acrolein	107028

2,000	0.1

dichloroethylene, cis-1,2-	156592	1,000

0.3

Methyl chloride	74873	4,000

0.8

Carbon disulfide	75150

100,000	0.06

Chloroform	67663	4,000

0.01

Benzene	71432

300,000	0.01

Detected only in leachate samples (not in totals samples):

	1/2 DL:

Phenol	108952	20,000

300

cresol, o- 	95487

1,000,000	300

cresol, m-	108394

900,000	300

di-n-butyl phthalate	84742	2,000

300

Note 1: We removed fluoride and sulfide from the list of Table 3 constituents; see text.

Note 2: We removed acetophenone, allyl chloride, cobalt, n-butanol, and tin from the

  list of Table 3 constituents due to lack of an available toxicity reference value.

Note 3: results rounded to one significant figure; maximum detected values

  Rounded to two significant figures (metals/metalloids)

	  or one significant figure (carbon-containing).

For 22 of the 49 constituents that we evaluate, the DRASv2 model indicates that the soil becomes saturated at a constituent concentration that is higher than the maximum constituent concentration used in the analysis, and does not provide any results.

For the remaining 27 constituents that we evaluate, the DRASv2 model predicts water concentrations and generates ecological receptor exposure results.

In Table 15, the column labeled “soil saturation” represents the waste concentration that DRASv2 estimated as causing soil saturation conditions, and the column labeled “TRV exceedance” represents the waste concentration that DRASv2 estimates as causing surface water body water concentrations to equal or exceed the toxicity reference value.  We round these model-calculated values to one significant figure.  The column labeled “maximum in waste” shows the maximum concentrations reported as detected in totals samples.  We round the maximum reported waste concentrations to two significant figures (metals/metalloids) or one significant figure (carbon-containing constituents).

7.3 High end results discussion

In most of the 22 cases of soil saturation, the model indicates that soil saturation would occur when the constituent concentration in the disposed sludge is an order of magnitude, or more, higher than the maximum constituent concentration observed in the sludge.  The only exceptions are xylenes, butyl benzyl phthalate, and di-n-butyl phthalate.  For these 22 constituents, the model is unable to predict the water concentrations to which the ecological receptors would be exposed (due to soil saturation conditions uphill from the surface water body).   However, we can use the waste concentration that DRASv2 estimates to result in soil saturation as a surrogate for a waste concentration that we do not want to see exceeded (because soil saturation conditions would result).  Soil saturation conditions mean that “the adsorptive limits of the soil particles and the solubility limits of the available soil moisture have been reached.  Above this concentration, pure or free-phase compound is expected in the soil.”

In the case of xylenes, butyl benzyl phthalate, and di-n-butyl phthalate, the DRASv2 model predicts that soil saturation conditions may occur at waste constituent concentrations that are in the range of a factor of 3 (for butyl benzyl phthalate) to 10 (for xylenes and di-n-butyl phthalate) greater than the maximum waste concentration reported in the data for the 13 facilities that we reviewed for this analysis.

We believe it is unlikely that these conditions would occur, for a number of reasons.  Butyl benzyl phthalate does not appear to be a frequently-occurring constituent in this waste; based on 77 samples reported from 11 facilities that analyzed their samples for it, it was detected in only 10% of totals and leachate samples.  In the case of di-n-butyl phthalate, the model result relies on an assumed totals concentration that is one-half of the greatest detection limit used by any of the facilities whose data we reviewed for this analysis.  Di-n-butyl phthalate was not detected in any of the totals samples that we reviewed.  Xylenes are far more frequently found in the waste (in 74% of totals samples).   However, the maximum reported xylene concentration (at two significant figures, it is 42 mg/kg) is one order of magnitude below the waste concentration predicted to result in soil saturation conditions (at two significant figures, 440 mg/kg), and the average reported xylene concentration (at two significant figures, 5.4 mg/kg) is almost two orders of magnitude below that concentration.  Thus we believe that soil saturation conditions due to xylene present in the waste are unlikely to occur, based on average xylene concentrations in the waste.

To identify the constituents that appear to pose the greatest potential for ecological harm, we convert the DRASv2-calculated waste concentrations that correspond to surface water body exposures that are at the toxicity reference value level into a hazard-quotient type of ratio, using the maximum detected waste constituent concentration in the numerator and the DRASv2-calculated surface water body water concentration in the denominator.  This calculation illuminates those constituents that have maximum detected concentrations that are far below the levels expected to cause problematic exposures in the surface water body, and those that are closer to the level that may cause problematic exposures (ratios between 0.1 and 1).  After performing this calculation, all 27 constituents for which DRASv2 estimates waste concentrations for surface water body ecological receptor exposures show hazard quotient ratios below one (in other words, none of the 27 constituents appears to have maximum detected waste concentration that we infer may cause ecological risk).  Only two of the 27 constituents have a hazard quotient ratio over 0.1 (or one-tenth of the level at which we infer ecological risk).  Lead’s calculated hazard quotient ratio (using the maximum detected value) is 0.6 and barium’s is 0.2.

Table 16 shows the ratio of the maximum detected waste concentrations to the waste concentrations predicted to result in soil saturation (in the column labeled “soil saturation ratio,”) and the ratio of the maximum detected waste concentration to the waste concentrations from which we infer potential ecological harm (in the column labeled “TRV exceedance ratio”).  We show the unrounded DRASv2 results and the unrounded maximum detected values, for the purpose of calculating these ratios, which are shown rounded to one significant figure.  Note that the five constituents for which the unrounded TRV exceedance waste concentrations are shown as 999,000 mg/kg actually represent cases in which the model indicates that a waste constituent concentration of 1,000,000 mg/kg will not result in an exceedance of the toxicity reference value.

Table 16.  Ratios of maximum detected waste concentrations to soil saturation

 and toxicity reference value exceedance concentrations

Constituent	CAS#	 	Concentrations	Ratios

TRV

	TRV

soil	exceed-	maximum	soil	exceed-

saturation	ance	in waste	saturation	ance

 	 	(mg/kg)	(mg/kg)	(mg/kg)	ratio	ratio

Detected in both totals and leachate samples:

Nickel	7440020

370,000	5,720

0.02

Zinc	7440666

850,000	17,600

0.02

Barium	7440393

28,000	4,280

0.2

Copper	7440508

64,000	1,490

0.02

Chromium	7440473

999,000	1,820

0.002

formaldehyde	50000

999,000	19,000

0.02

Lead	7439921

18,000	10,800

0.6

Mercury	7439976

11,000	0.71

0.00006

Xylenes	1330207	430

42	0.1

	Vanadium	7440622

140,000	43.9

0.0003

Arsenic	7440382

999,000	25

0.00003

Cyanide	57125

38,000	18

0.0005

ethylbenzene	100414	230

7.2	0.03

	hexavalent chromium	18540299

78,000	22

0.0003

Toluene	108883	520

18.7	0.03

	acrylamide	79061

999,000	5.8

0.000006

Cadmium	7440439

1,800	21.5

0.01

bis(2-ethylhexyl) phthalate	117817

250,000	100

0.0004

Antimony	7440360

220,000	174

0.0008

Methyl ethyl ketone	78933	34,000

25	0.0007

	Beryllium	7440417

37,000	1.3

0.00004

Selenium	7782492

36,000	21

0.0006

Silver	7440224

23,000	5.5

0.0002

Thallium	7440280

29,000	7.1

0.0002

butyl benzyl phthalate	85687	930

290	0.3

	naphthalene	91203

145,000	34

0.0002

cresol, p- 	106445

860,000	23

0.00003

Acetone	67641	100,000

6	0.00006

	trichloroethylene	79016	1,300

0.044	0.00003

	dichloroethane, 1,2-	107062	2,900

0.02	0.000007

	acetonitrile	75058	190,000

5	0.00003

	chlorobenzene	108907	870

0.025	0.00003

	methylene chloride	75092	2,300

1.7	0.0007

	trichloroethane, 1,1,1-	71556	1,400

0.018	0.00001

	dichloroethane, 1,1-	75343	2,300

0.015	0.000007

	Detected only in totals samples (not in leachate samples):

	di-n-octyl phthalate	117840	10,000

91.5	0.009

	methyl isobutyl ketone 	108101	17,000

0.41	0.00002

	tetrachloroethylene 	127184	370

0.21	0.0006

	Styrene	100425	1,700

0.017	0.00001

	Acrolein	107028

1,600	0.14

0.00009

dichloroethylene, cis-1,2-	156592	1,200

0.33	0.0003

	methyl chloride	74873	4,000

0.84	0.0002

	carbon disulfide	75150

145,000	0.059

0.0000004

chloroform	67663	3,500

0.013	0.000004

	Benzene	71432

330,000	0.01

0.00000003

Detected only in leachate samples (not in totals samples):

	1/2 DL:

Phenol	108952	23,000

294	0.01

	cresol, o- 	95487

999,000	294

0.0003

cresol, m- 	108394

920,000	250

0.0003

di-n-butyl phthalate	84742	2,300

294	0.1

	Note 1: We removed fluoride and sulfide from the list of Table 3 constituents; see text.

Note 2: We removed acetophenone, allyl chloride, cobalt, n-butanol, and tin from the list of Table 3

constituents due to lack of an available ecological toxicity reference value.

Note 3: results rounded to one significant figure.

7.4 Central tendency results and discussion

To compute central tendency estimates of potential ecological hazard, we use the median disposed waste volume of 1088 cubic yards, for 20 years.  We use calculated median, rather than maximum, totals concentrations for barium and lead (neither of which appear to exceed the level of concern in the high end estimates, but both of which were within one order of magnitude of that level of concern in the high end estimates).  Using these central tendency variable settings, we obtain TRV exceedance ratios of 0.004 for barium and 0.006 for lead (in other words, the DRASv2 model estimates that barium and lead concentrations in the nearby stream are at levels more than two orders of magnitude below the levels that could cause harm to the organisms living in the stream).  Thus at central tendency conditions we confirm that there do not appear to be potential adverse effects on aquatic organisms living in a stream downhill from the landfill.

8.  Special cases: fluoride, sulfide, trivalent chromium, lead and cyanide

8.1 Human health effects

Due to DRASv2 model limitations or a lack of health toxicity values, it is difficult to assess the potential noncancer human health hazards that may be posed by exposure to fluoride, sulfide, trivalent chromium, and lead in the F019 sludge.  We are able to evaluate cyanide’s potential to cause noncancer human health effects using DRASv2, and present those results in section 5.

Fluoride

Fluoride is not entered in the database of chemical constituents that DRASv2 models.  We can develop an approximate high end hazard quotient for potential groundwater exposure from fluoride, by performing hand calculations using the same equations in the DRASv2 model, and making an assumption about fluoride’s dilution/attenuation factor.  The specific equations used for this calculation are shown as equations 2-3, 2-5, 2-6 and 4-86 in the RCRA Delisting Technical Support Document (EPA 2002a).   For the high end estimate, we assume a waste disposal volume of 90,000 cubic yards, and a dilution/attenuation factor of 10.  Using equation 2-3, the DAF scaling factor is 1.7956.  Thus the assumed DAF of 10 is adjusted for volume to be 17.956 (volume-adjusted DAF).  We use the maximum detected fluoride in leachate concentration of 1.75 mg/L, the default exposure assumptions in DRASv2, and the IRIS Oral Reference Dose of 0.06 mg/kg-day.  We obtain a hand-calculated approximate high end hazard quotient of 0.04.  Applying the child exposure factor described in section 5.1.1 increases the hazard quotient to 0.08.  Therefore, using this approach, fluoride does not appear to be a constituent of potential concern.

Sulfide

Sulfide is also not entered in the database of constituents that DRASv2 models.  We do not attempt to develop estimates of the noncancer human health hazards potentially posed by sulfide in the F019 sludge.  Sulfide has no toxicity value in the sources of toxicity data that we used, except for air exposures to hydrogen sulfide.  Since the F019 sludge totals and leachate waste concentration data do not include hydrogen sulfide levels, a toxicity value for hydrogen sulfide is not relevant.

Trivalent chromium

Chromium is entered in the database of chemical constituents that DRASv2 models.  We can evaluate chromium in two valence states: trivalent (sometimes written as Cr+3 or Cr (III)) and hexavalent (Cr+6, or Cr (VI)).   The DRASv2 model contains two entries for chromium in its constituent database: hexavalent chromium, listed as “Chromium (+6)” under Chemical Abstract Services Registry Number 18540-29-9, and “Chromium,” listed under CAS # 7440-47-4 (which is in error; it should be listed as 7440-47-3).

In order to run the DRASv2 model for both valence states, we need four types of model inputs:

1)	totals and leachate values for trivalent chromium in the sludge samples

2)	toxicity reference values for trivalent chromium

3)	totals and leachate values for hexavalent chromium in the sludge samples

4)	toxicity reference values for hexavalent chromium

We have the model inputs to satisfy 3) and 4) above, for hexavalent chromium, so we are able to evaluate potential human health effects of hexavalent chromium exposure (described in sections 5 and 6).  Our evaluation showed that potential exposure to hexavalent chromium does not present significant risk.

To satisfy 1) above, for trivalent chromium, we have no laboratory analytical data for trivalent chromium in the sludge samples.  Rather, the analytical results for “chromium” measure the total level of chromium present, regardless of valence state.  When a laboratory reports a chromium result for a sample, and also a hexavalent chromium result for the same sample, we could infer the concentration of trivalent chromium by subtracting the hexavalent chromium value from the corresponding chromium value.  In the chemical constituent concentration spreadsheet/database in the docket, we have numerous instances in which laboratories reported results for chromium, and corresponding results for hexavalent chromium, in “totals” samples.  We have only four instances in which laboratories reported results for chromium, and corresponding results for hexavalent chromium, in “leachate” samples (hexavalent chromium was not detected in the other 24 leachate samples).  Thus we have numerous observations from which we could infer concentrations of trivalent chromium in “totals” samples, and four observations from which we can infer concentrations of trivalent chromium in leachate samples.  Because most of the chromium reported, in either the totals or the leachate samples, is not in hexavalent form, for simplicity (and also to impart further precaution in the risk estimates) we can simply assume that all chromium reported in these laboratory results is in the trivalent, rather than the hexavalent, state (rather than subtracting the hexavalent concentrations from the corresponding reported chromium concentrations, as described above).

To satisfy 2) above, for trivalent chromium, there is a toxicity value for oral ingestion of insoluble salts of trivalent chromium (1.5 mg chromium III insoluble salts per kilogram of body weight per day (EPA 2006)), but no corresponding toxicity value for oral ingestion of soluble salts of trivalent chromium.  Because we expect trivalent chromium present in groundwater to be present in soluble forms, it is unclear how appropriate this insoluble-salts toxicity value is for estimating potential human noncancer health effects from trivalent chromium ingested from groundwater used as a drinking water source.

Currently, there are no cancer slope factors for trivalent chromium due to “inadequate data to determine the potential carcinogenicity of trivalent chromium.” (EPA 2006, entry for “Chromium (III), insoluble salts (CAS # 16065-83-1)”).

Nevertheless, to develop an estimate for potential human health effects from trivalent chromium, we use this toxicity value for oral ingestion of insoluble salts of trivalent chromium in the DRASv2 entry for chromium (CAS # 7440-47-4; note that this entry is in error; the correct CAS # for chromium is 7440-47-3).  We also use the maximum reported concentrations of chromium in totals samples (1820 mg/kg) and the maximum reported concentrations of chromium in leachate samples (0.53 mg/L), and assume that all of the chromium is present in trivalent, rather than hexavalent, form.  For the high end estimate for trivalent chromium, we assume a 90,000 cubic yard disposal volume, and use DRASv2 to estimate a hazard quotient of 0.0000014, well below our level of concern.

Lead

Lead is also entered in the database of chemical constituents that DRASv2 models.  However, at the time of this document (2006), lead does not have a recommended human health noncancer toxicity value (toxicity information that is needed to run the model and develop estimates of potential human health hazards).  Using the same hand-calculation technique that we used for fluoride in order to develop the estimated groundwater lead concentration at the drinking water well, the maximum reported waste leachate concentration of 1.33 mg/L, and the DRASv2 default dilution/attenuation factor (5000), adjusted for the 90,000 cubic yard disposal volume to be 8978, gives an estimated drinking water well concentration of 0.0001 mg/L (using equations 2-3, 2-5 and 2-6 from EPA 2002a).  This estimated drinking water concentration is below EPA’s Maximum Contaminant Level of 0.015 mg/L published as the National Primary Drinking Water Standard at 40 CFR Part 141.  Also, the leachable concentration of lead in the sludge is regulated, because the sludge remains subject to the Toxicity Characteristic limit of 5 mg/L for lead (D008, see 40 CFR 261.24).

8.2 Ecological effects

For the same reasons described above, we were unable to evaluate the potential ecological effects of fluoride and sulfide.  However, we were able to evaluate the potential ecological (non-explosive hazard) effects of chromium, lead and cyanide (described in section 7.2).

8.3 Potential explosive hazard

Sulfide and cyanide present special considerations, because some forms of these compounds can cause an explosion or violent chemical reaction under certain conditions.  This potential for explosions or violent chemical reactions is a basis for the characteristic of reactivity that defines certain wastes as hazardous (see 40 CFR 261.23).  Sulfide and cyanide are present in most samples analyzed either for total sulfide or total cyanide.   However, EPA has granted delistings to many motor vehicle manufacturing facilities, and considered the potential for reactivity as one criterion for granting delistings.  Furthermore, the sludges are generated from wastewater treatment, and such residuals are not expected to be reactive.  We are not aware of any information that suggests that the F019 sludges from motor vehicle manufacturing cause violent reactions or explosions.

9.  Sources of uncertainty in risk and hazard estimates

This risk assessment describes the estimated potential human noncancer health hazard, potential human cancer risk, and potential ecological risk associated with a specific waste management method (landfilling) for the F019 wastewater treatment sludge from motor vehicle manufacturers.

The estimates of human and environmental health effects in this document are not exact or precise predictions of what would happen if F019 waste from motor vehicle manufacturers was disposed in unlined landfills.  Instead, they are estimates that are based on the best available information that we have, using models which, of necessity, represent a simplified version of real-world conditions.  In this section, we describe the sources of uncertainty that we are aware of, along with our assessment of the direction that our imperfect knowledge influences the risk assessment results in sections 5, 6, and 7 (causing either underestimates or overestimates).  In section 9.4 we provide an overall assessment of how we think the risk assessment estimates are affected by these uncertainties.

There are at least three major types of uncertainty: measurement uncertainty; model uncertainty; and the impact of future and past states.  Measurement uncertainty results from errors in measurements.  We also use this term to refer to uncertainty that results from inherent variability in natural systems (when we try to represent that variability with a single value instead of reflecting the entire range of values), and to refer to uncertainty that results from lack of research, or “knowledge gaps.”   Model uncertainty results from using a tool to represent real-world conditions, when that tool (the model) is of necessity a simplified version of those real-world conditions.  The impact of future and past states on our uncertainty results from a lack of perfect knowledge of past or future conditions that influence the estimates we obtain using the model.

9.1 Measurement Uncertainty

We divide our descriptions of sources of measurement uncertainty into four categories: 1) waste characteristics; 2) fate and transport variables; 3) exposure assumptions; and 4) toxicity of chemical constituents.

Waste characteristics.  While we completed an extensive review of waste data, and had a large database of analytical data available, there may be other chemicals in the waste that could be of concern.  We know, from the Material Safety Data Sheet review described in section 3, that it is likely that there is some quantity of titanium and manganese present in many facilities’ sludge due to their use in the original conversion coating formulations.  However, we do not have analytical data for these constituents in our database.  There may also be other chemical constituents present, due to commingling the conversion coating wastewater with other wastewaters.  However, we know neither the identities nor the exact quantities of these constituents.  Thus omitting these constituents from the risk assessment may underestimate total potential health effects.

For the constituents that we do know are present, the analytical procedures used (the sampling and sample preparation techniques, the specific analytical instruments, materials, and methods used) and other constituents or substances present in the sample “matrix” all contribute to measurement errors regarding the exact quantities present in the F019 sludge.  In sections 5 and 6, we describe those situations where we believe that analytical procedure measurement errors may have caused overestimates of the constituent concentrations, and we attempt to account for them when possible.

The “matrix” that the laboratory analytical sample is composed of can affect the accuracy of the laboratory’s analytical results.  In general, samples that are composed of relatively clean water, such as a sample of drinking water, are easier to analyze with greater accuracy than samples that have more contaminants present.  Samples that are composed of difficult-to-analyze matrices, in general, include wastewater treatment sludge samples, especially those with contaminants such as oil and grease present in the sample.

Some of the samples from the thirteen facilities whose wastewater treatment sludge chemical constituent data we present in the spreadsheet/database in the docket have high total oil and grease levels.  As a result, for some of these samples, the laboratories reported elevated detection limits for organic analytes in the totals analysis.  The TCLP leachate samples derived from the same samples used in the totals analysis typically had much lower detection limits, because the matrix, which is primarily water, is much cleaner than the original sludge sample.  In a limited number of cases, this led to the detection of some constituents in leachate samples, but not in the totals analysis. However, the reported detection limits in most samples (especially the TCLP samples that served as the critical component in the risk evaluation in most cases) were adequate, and we believe that the entire data set is sufficiently robust to support our waste characterization.  To the extent that the elevated detection limits in the totals samples may have masked the presence of some constituents, these results may have caused underestimates of the totals concentrations of organic constituents.

Our analysis also assumes that the TCLP test accurately quantifies leaching that occurs in real landfills.  However, the TCLP test only approximates the actual composition of real-world leachate.  In the TCLP test the waste sample is shaken vigorously with a low pH extraction solution for 18 hours.  In real landfills, infiltrating water percolates through the landfill’s contents over many years.  We do not know whether the TCLP test results cause an overestimate or an underestimate of chemical constituents that leach from landfills.  There is research underway to better understand the influences of real-world conditions on landfill leachate.

For the high-end estimates, this analysis uses the maximum constituent concentrations (both TCLP and totals) that were reported in the waste characterization data set located in the docket.  These concentrations may reflect a “worst case” scenario with respect to the quantities of chemical constituents present in the F019 sludge.

Uncertainty associated with the waste characterization also arises from uncertainty in the waste volumes disposed.  For our analysis, we use a total waste volume of either 90,000 cubic yards (for high end estimates) or 22,000 cubic yards (for central tendency estimates).  The 90,000 cubic yard total waste volume is equivalent to disposing 4,500 cubic yards per year for 20 years (the usual disposal time assumed for a landfill in the DRAS model), or 3,000 cubic yards per year for 30 years (a disposal time period used in recent EPA hazardous waste listing determination analyses).  The 22,000 cubic yard total waste volume is equivalent to disposing 1100 cubic yards per year for 20 years.  If the actual volumes of F019 waste from motor vehicle manufacturers are smaller than these cubic yardages, then our analysis may overestimate risk; if the actual volumes are greater, then our analysis may underestimate risk.

The previous paragraph deserves elaboration, since the disposal volume is likely to be one of the key variables influencing the magnitude of the risk estimates.  The volume of sludge disposed, together with the concentration of constituents in the sludge, determines the mass input into the landfill that we model in the risk analysis.  The mass input ultimately drives the estimates of constituent quantities to which both the human and ecological receptors are exposed.  Thus the sludge volume is an important variable for determining the results of the analysis.  The DRASv2 model uses the model input of annual waste disposed, in cubic yards, and multiplies it by the model input of number of years of disposal, and calculates a total waste volume that is the quantity of constituents available for dispersion.  Thus the total quantity of F019 sludge disposed in the landfill is more important for the model results than an annual waste generation rate or the total number of years in which waste is disposed in the landfill.  Reviewing several facilities’ delisting petitions revealed that motor vehicle manufacturers sometimes dispose of different facilities’ F019 sludge in the same landfill (“co-disposal”).  Thus, for the model to reflect actual practices, we represent the total quantity of motor vehicle manufacturers’ F019 sludge disposed in the same landfills.

Appendix C compiles information on known motor vehicle manufacturer F019 sludge generation rates and co-disposal patterns, expressed as F019 cubic yards, either generated or disposed, for a given year.  The data in Appendix C are from delisting petition submissions from several of the same facilities whose sludge analytical data we use for waste characterization purposes, and additional sludge volume data from these facilities from the Alliance of Automobile Manufacturers (in the docket, and reproduced in Appendix C).  We combine F019 sludge generation quantities from different assembly plants for the co-disposal situations in which we are aware that the assembly plants’ F019 sludge was disposed together with other assembly plants’ F019 sludge in the same landfill.

There is an alternate, secondary, source of data for F019 sludge quantities from individual assembly plants.  EPA’s Biennial EPA’s Biennial Report System (  HYPERLINK "http://www.epa.gov/epaoswer/hazwaste/data/biennialreport"  http://www.epa.gov/epaoswer/hazwaste/data/biennialreport ) contains data from RCRA hazardous waste generators on the quantities of hazardous waste generated and managed, on an every-other-year basis.  Motor vehicle manufacturers’ F019 sludge generation quantities are reported in this data system on either a mass or volume unit of measure basis and are converted into consistent units of short tons (2,000 pounds) in the national database (using the reporting facility’s stated waste density when reported in volume units).

This secondary data source presents difficulties with respect to its use as the source of F019 sludge quantity information.  For example, the Biennial Report data cover F019 generation on an every other year basis.  Vehicle production rates at the assembly plants are likely to vary on a year-by-year basis, and so too might F019 sludge generation; thus the delisting petition and supplemental data that we present in Appendix C, and use in the risk analysis, provide a more complete picture of F019 sludge quantities on an annual basis.

In this risk analysis, we use the delisting petition submissions and supplemental data from the Alliance of Automobile Manufacturers, because these data are explicitly identified by the submitters as being the quantities of zinc phosphate chemical conversion coating sludge that are the subject of the delisting petitions.  Note that, for the Economics Background Document prepared for this proposal (EPA 2007, available in the docket), the economic analysis requires zinc phosphate conversion coating sludge quantities to be expressed on a mass basis, because the waste management cost factors are expressed in units of dollars per ton.  Thus, we use the Biennial Report F019 tonnages in the Economics Background Document analysis.

Fate and transport.  Once placed into the landfill, the sludge becomes subject to environmental influences (such as temperature and atmospheric pressure changes, and precipitation) that cause the constituents to begin moving into different environmental “compartments” (air, water and soil).  The default values used in the model for the many different environmental fate and transport variables (such as the solubility, soil saturation level, or Henry’s Law Constant) can contain measurement errors.  We do not know whether these measurement errors cause overestimates or underestimates.  It is likely that, for some constituents, the fate and transport variable measurement errors combined cause an overall overestimate, while for others, the errors cause an overall underestimate.

Exposure assumptions.  For human health effects, the DRASv2 model developers made certain assumptions about the body weights and body surface areas, and intake rates of air, water and soil.  For the groundwater pathway, for example, the DRASv2 model used a drinking water intake set approximately at the 84th percentile for adults, (we used the 90th percentile value for children’s drinking water intake in our adjustment of the results to reflect risk to children). Model inputs for each of these variables are subject to measurement error.  However, the data that these variables are based on are relatively robust, and thus for some variables, the measurement errors are likely to be small relative to other sources of error in the analysis.   For ecological effects, an example of a variable subject to measurement error is the bioaccumulation factor that the model uses to account for the amount of the chemical that a fish takes up either from the water in the stream, or from the food that it takes in.  EPA 2002a (Appendix A-1, pp. A-1-11 to A-1-14) gives a lengthy explanation of how the model developers have addressed this particular area of uncertainty.  We do not know whether the ecological effects exposure assumptions cause overestimates or underestimates.

Another common issue related to exposure assumptions is the duration of exposure – in other words, how long the modeled nearby resident actually lives at that location.  The U.S. Bureau of the Census provides estimates of residential duration that, recently, indicated more than 93% of U.S. residents move from their residence within 30 years.  This statistic indicates that assuming an exposure duration of 30 years would generally overestimate exposures from the landfill source.  The exposure duration issue is only of concern for the cancer probability estimates, since for noncancer health effects, the 30-year exposure duration is averaged over 30 years, effectively canceling out the assumption.

Toxicity.  For both human health and environmental effects, the toxicity information for each constituent of potential concern can vary in its completeness and quality.  Incomplete information on a constituent’s toxicity via a particular exposure route has the practical result that a value of “0” is entered as the model input, and thus the model does not generate any estimates for exposure pathways that rely on that exposure route (in other words, the model’s output is “0” or a blank screen).  Thus incomplete toxicity information can result in an assumed lack of health effects when health effects may actually occur, but were not modeled.  The result is an underestimate of potential health effects.

In this risk assessment, we used a hierarchy of data sources for human health toxicity information.  Most of the human health toxicity values used in this risk assessment are in EPA’s Integrated Risk Information System database (EPA 2005c).  If toxicity values were unavailable in IRIS, our secondary source was EPA’s Provisional Peer Reviewed Toxicity Values database (EPA 2004c).  Our tertiary source was Minimal Risk Levels published by the United States Centers for Disease Control and Prevention’s Agency for Toxic Substances and Disease Registry (ATSDR 2006).  All three data sources develop their toxicity values using the same general approach by groups of toxicologists who review the available toxicity information and arrive at consensus toxicity estimates.  All three sources include peer review as part of the process.  The ATSDR Minimal Risk Levels also have a public comment period.

Regarding the quality of toxicity information for human noncancer health effects, the toxicologists and other scientists at EPA, ATSDR and other organizations who develop toxicity values that represent acceptable exposures to toxic constituents, often present those values together with “uncertainty factors,” “modifying factors,” and assessments of their confidence in the data and/or method underlying the toxicity value.  Sources of uncertainty in toxicity values include one or more of the following: extrapolation from laboratory animal data to humans, variability of response within the human population, extrapolation of responses at high experimental doses under controlled conditions to low doses under variable environmental conditions, and adequacy of the database (number of studies available, toxic endpoints evaluated, exposure routes evaluated, sample sizes, length of study, etc.)  Uncertainty factors are applied to address the limitations of the available toxicological data and are used to ensure that the Reference Dose or Reference Concentration protects individuals in the general population.  The use of uncertainty factors is based on long-standing scientific practice.  Uncertainty factors, when combined, often range from 10 to 1000 depending on the nature and quality of the underlying data.

Tables 22, 23, and 24 show, where available, the uncertainty factors, modifying factors and confidence ratings for the IRIS Reference Doses, IRIS Reference Concentrations, Agency for Toxic Substance and Disease Registry (ATSDR) Minimal Risk Levels, and EPA’s Superfund Provisional Peer Reviewed Toxicity Values (PPRTV) that we used in this analysis.  When values were unavailable for chronic (long-term, such as more than one year) exposures, we used the value that was available for a shorter time period.  For example, we use ATSDR Minimal Risk Levels for subchronic (14 to 365 day) exposures if no chronic exposure values were available.

Table 18.  Uncertainty factors, modifying factors, and confidence ratings

	for IRIS oral Reference Doses

Constituent	CAS#	uncertainty	modifying	confidence

 	 	factor	factor	rating

Detected in both totals and leachate samples:

	Nickel	7440020	300	1	medium

Fluoride	16984488	1	1	high

Zinc	7440666	3	1	medium

Barium	7440393	3	1	medium

Formaldehyde	50000	100	1	medium

Xylenes	1330207	1000	1	medium

Arsenic	7440382	3	1	medium

Cyanide	57125	100	5	medium

Ethylbenzene	100414	1000	1	low

hexavalent chromium	18540299	300	3	low

Toluene	108883	3000	1	medium

butanol, n-	71363	1000	1	low

Acrylamide	79061	1000	1	medium

Cadmium	7440439	10	1	high

bis(2-ethylhexyl) phthalate	117817	1000	1	medium

Antimony	7440360	1000	1	low

methyl ethyl ketone 	78933	1000	1	low

Beryllium	7440417	300	1	low-medium

Selenium	7782492	3	1	high

Silver	7440224	3	1	low

Thallium	7440280	3000	1	low

butyl benzyl phthalate	85687	1000	1	low

Naphthalene	91203	3000	1	low

Acetone	67641	1000	1	medium

Chlorobenzene	108907	1000	1	medium

methylene chloride	75092	100	1	medium

Detected only in "totals" samples (not in leachate samples):

tetrachloroethylene 	127184	1000	1	medium

Styrene	100425	1000	1	medium

Acrolein	107028	100	1	medium-high

carbon disulfide	75150	100	1	medium

Chloroform	67663	1000	1	medium

Benzene	71432	300	1	medium

Detected only in leachate samples (not in "totals" samples):

Phenol	108952	300	1	medium/high

Acetophenone	98862	3000	1	low

cresol, o-	95487	1000	1	medium

cresol, m- 	108394	1000	1	medium

di-n-butyl phthalate	84742	1000	1	low

Note 1: Factors and ratings for nickel are for the IRIS entry for "nickel, soluble salts"

Note 2: Factors and ratings for thallium are for the IRIS entries for the thallium species

Note 3: Factors and ratings for fluoride are for the IRIS entry for "fluorine (soluble fluoride)" with

  CAS# 7782-41-4 (elemental fluorine).  When measured in a laboratory,

	  fluorine species are ionized as fluoride and are given the CAS # 16984-48-8.

	  The IRIS entry indicates that fluorine was measured as fluoride in the ingestion studies

  (that is, the analytical method measures the fluorine used to develop the Reference Dose).

Table 19.  Uncertainty factors, modifying factors, and confidence ratings

for IRIS inhalation Reference Concentrations

Constituent	CAS#	uncertainty	modifying	confidence

 	 	factor	factor	rating

Detected in both totals and leachate samples:

	Mercury	7439976	30	1	medium

Xylenes	1330207	300	1	medium

Ethylbenzene	100414	300	1	low

hexavalent chromium	18540299	300	1	medium

Toluene	108883	10	1	high

methyl ethyl ketone	78933	300	1	medium

Beryllium	7440417	10	1	medium

Naphthalene	91203	3000	1	medium

Acetonitrile	75058	100	10	medium

Detected only in "totals" samples (not in leachate samples):

methyl isobutyl ketone (4-methyl-2-pentanone)	108101	300	1	low-medium

Styrene	100425	30	1	medium

Acrolein	107028	1000	1	medium

methyl chloride (chloromethane) 	74873	1000	1	medium

carbon disulfide	75150	30	1	medium

Benzene	71432	300	1	medium

allyl chloride	107051	3000	1	low

Table 20. Uncertainty factor and confidence rating

for PPRTV reference dose

	Constituent	CAS#	reference	reference

dose	dose

uncertainty	confidence

 	 	factor	rating

	Detected only in "totals" samples (not in leachate samples):

	dichloroethylene, cis-1,2-	156592	3000	low

Table 21.  Uncertainty factors and confidence ratings

	for ATSDR Minimal Risk Levels

Constituent	CAS#	Minimal Risk	Minimal Risk

Level (oral)	Level (inhalation)

uncertainty	uncertainty

 	 	factor	factor

	Detected in both totals and leachate samples:

	nickel	7440020

30

copper	7440508	10

	tin	7440315	100

	formaldehyde	50000

30

cobalt	7440484	100	10

vanadium	7440622	100	100

cresol, p- 	106445	100

	acetone	67641

100

trichloroethylene	79016

300

dichloroethane, 1,2-	107062	300	90

methylene chloride	75092

30

trichloroethane, 1,1,1-	71556	100	100

	Detected only in "totals" samples (not in leachate samples):

di-n-octyl phthalate	117840	100

	tetrachloroethylene 	127184

100

chloroform	67663

100

	Note 1: the oral Minimal Risk Level for copper is based on intermediate

  (>14 to <365 day) exposures

	Note 2: the oral Minimal Risk Level for tin is based on intermediate

	  (>14 to <365 day) exposures

	Note 3: the oral Minimal Risk Level for cobalt is based on intermediate

  (>14 to <365 day) exposures

	Note 4: the oral Minimal Risk Level for vanadium is based on intermediate

  (>14 to <365 day) exposures; the inhalation Minimal Risk level is based

  on acute (14 days or less) exposures

	Note 5: the oral Minimal Risk Level for p-cresol is based on acute

	   (14 days or less) exposures

	Note 6: the inhalation Minimal Risk Level for trichloroethylene is based on intermediate

  (>14 to <365 day) exposures

	Note 7: the oral Minimal Risk Level for 1,2-dichloroethane is based on intermediate

  (>14 to <365 day) exposures

	Note 8: the oral and inhalation Minimal Risk Levels for 1,1,1-trichloroethane are

  based on intermediate (>14 to <365 day) exposures

	Note 9: the oral Minimal Risk Level for di-n-octyl phthalate is based on intermediate

  (>14 to <365 day) exposures

We believe that for most constituents, the general direction of influence is to overestimate the potential for adverse health effects.  The overestimates occur because the Reference Doses, Reference Concentrations, and Minimal Risk Levels are often derived using data from studies of laboratory animals, assumptions that humans are more sensitive to adverse effects caused by a constituent than are the laboratory animals studied, and assumptions that some humans are particularly sensitive.  In general, when EPA’s Reference Doses and Reference Concentrations and ATSDR’s Minimal Risk Levels are based on studies of humans, then the numerical uncertainty factors are lower (and confidence ratings are higher).  However, for antimony, 1,1,1-trichloroethane, trichloroethylene and vanadium (inhalation pathways), and cobalt, copper, p-cresol, 1,2-dichloroethane, di-n-octyl phthalate, tin, 1,1,1-trichloroethane and vanadium (oral pathways), the model’s estimated health effects may be underestimates because we used Reference Doses or Reference Concentrations that were appropriate for shorter-term exposures than the long-term exposures we modeled in the risk assessment.

Regarding the quality of toxicity information for human cancer effects, the toxicologists and other scientists at EPA and other organizations who develop toxicity values for acceptable exposures are faced with the challenge of putting a numerical value on an acceptable exposure when the exact biological mechanisms of cancerous growths are less than fully understood.  Under EPA’s 1986 carcinogen risk assessment guidelines, EPA classified constituents as “known,” “probable,” or “possible” human carcinogens, based on human or animal data, or “unclassified” (due to a lack of data or no evaluation performed) or “no evidence of carcinogenicity.”  Arsenic is classified as a known human carcinogen.  Arsenic’s cancer slope factor is based on studies of humans exposed to arsenic.  Regarding uncertainty about the cancer probability estimates, we cannot state the direction of influence that perfect knowledge of arsenic’s carcinogenic potential would have on the risk assessment results.  We note that numerous issues relating to arsenic’s carcinogenicity are under review by EPA’s Science Advisory Board.

In early 2005, EPA published new guidelines for evaluating potential cancer risk (EPA 2005a and EPA 2005b).  EPA 2005b addresses potential susceptibility to carcinogens due to early-life exposures by advising adjustment of cancer potency factors upward to address early-life susceptibility when carcinogens have a mutagenic mode of action.  Certain forms of arsenic may operate by a mutagenic mode of action.  EPA has not yet evaluated how these new guidelines may impact arsenic cancer risks.

Regarding the quality of toxicity information for ecological effects, EPA’s Current National Recommended Water Quality Criteria, found at   HYPERLINK "http://www.epa.gov/waterscience/criteria/wqcriteria.html"  http://www.epa.gov/waterscience/criteria/wqcriteria.html , give detailed explanations of how the freshwater “CCC” (Criterion Continuous Concentration, for chronic exposures) values (that we used for many of our toxicity reference values) were derived.  Many of these values were derived using methods described in EPA 1985, Guidelines for Deriving Numerical National Water Quality Criteria for the Protection of Aquatic Organisms and Their Uses.  This document indicates that factors such as the water’s hardness, salinity and pH can affect the actual toxicity experienced by an aquatic organism due to exposure to the particular constituent.   We have no quantitative estimates of the uncertainty (such as the uncertainty factors used in IRIS) for the ecological toxicity reference values used in this risk assessment.  For the Illinois water quality criteria used for several constituents’ toxicity reference values, the Illinois Environmental Protection Agency states that some of the water quality criteria “have been derived from a less than ideal data set yet we include these values because we find it desirable to have some numeric indication of toxicity.  These values may not be suitable for use in a regulatory setting, however.”  Similar caveats likely are appropriate for the two toxicity reference values used from the Kansas water quality regulations (Kansas 2004a and Kansas 2004b).

9.2 Model Uncertainty

All models are simplified versions of very complex real-world conditions.  Thus all models are subject to a certain degree of error, because of the simplification of real-world conditions.

One source of model uncertainty arises from the DRASv2 model’s use of variables that are “point estimates” represented by a single numeric value.  DRASv2 is an example of a deterministic model that uses point estimates for variables that, in reality, can possess a range of values under different conditions (such as temperature fluctuations, precipitation conditions, or wind patterns). For example, a constituent’s solubility may vary depending on water temperature.  A constituent’s volatility may vary depending on both air temperature and wind speed.  The model’s inability to reflect variability in the variable values contributes to uncertainty in the model results, since the model does not perform calculations using the entire range that the variable values might encompass.  It is possible that the model’s use of point estimates for variable values results in overestimates of national-scale adverse effects, for some constituents, and results in underestimates of national-scale adverse effects for other constituents.  The difference between “deterministic” and “probabilistic” models explained in section 4.4 illustrates how increasing the complexity of a model (moving from “deterministic” to “probabilistic” modeling) can mitigate this source of model uncertainty, provided the data used in a probabilistic model’s input distributions is accurate.

Another source of model uncertainty results from simplification of the actual disposal conditions.  The model does not account for the impact of other wastes that are “co-disposed” along with the F019 wastewater treatment sludge.  The composition of other waste that is co-disposed along with the F019 waste could have two important influences on the behavior of the constituents in the F019 waste.  First, if the same constituents are present in the other co-disposed waste, in significant enough quantities, their additional effect on emissions to air and groundwater from the landfill is not accounted for in the DRASv2 modeling.  Second, if other constituents are present in the other co-disposed waste, which could either adsorb or react with some of the constituents in the F019 waste, the adsorption or other chemical reactions could affect whether, and if so, how quickly, the F019 constituents are emitted from the landfill.  Third, information from EPA’s Toxics Release Inventory database suggests that, in some situations, motor vehicle manufacturers may send their F019 sludge for “stabilization” prior to landfill disposal.  The information in this database does not reveal what stabilization practices are used, but it is possible that chemical constituents or materials are added to the waste that may serve to adsorb hazardous constituents present in the F019 sludge, and thus alter the chemical reactions that occur within the landfill after disposal.

Regarding co-disposed waste, omitting other waste with the same constituents present could underestimate exposures.  The magnitude of the underestimates probably varies by constituent.  For example, a rough comparison (using a single landfill) of the quantity of mercury present in the F019 with the quantity of mercury present in ordinary municipal solid waste found that the quantity of mercury present in municipal solid waste was far greater than the quantity present due to the F019.   For other constituents, the F019 may be the greatest source of that constituent in the landfill by a large margin, and thus omitting other waste with the same constituents would not underestimate exposures by very much.

Regarding co-disposed waste that may react chemically with the F019, we believe that the model may either overestimate or underestimate exposures.  The state of knowledge of the chemical composition of the waste disposed in a given landfill, over time, is such that predictions of the chemical reactions that could occur would be virtually impossible to make.  However, we can say that some chemical reactions will take place; the waste, once deposited into the landfill, is not inert but will undergo various reactions with other wastes with which it comes into contact.  The result of the reactions that do take place could either increase or decrease the quantity of a specific chemical constituent that is available to be emitted into air or groundwater, relative to what the model assumes.  We can provide bounds on the magnitude of our uncertainty on this point, by using the two theoretical extremes: either no constituent is emitted in a given timeframe (total adsorption), or the entire mass of constituent is emitted in a given timeframe (total release).  The rate of release over time is something that we cannot put similar bounds on, and the rate of release over time governs the exposure that a given receptor (nearby resident, or stream-dwelling organism) would experience.

Another potential source of model uncertainty relates to changes in landfill practices since the original model algorithms were developed.  In the 1990s, requirements imposed on most U.S. municipal solid waste landfills for both leaking and air emissions, caused many changes to landfill design and operation.   Resource Conservation and Recovery Act (RCRA) regulations required most landfills to meet a leak prevention standard; Clean Air Act regulations required most landfills to collect and burn methane to prevent harmful ground-level ozone from forming due to emissions of volatile organic constituents.  Neither of these relatively recent developments in landfill design and operation is reflected in the basic landfill scenario that DRASv2 represents (an unlined landfill with no leachate recovery system, and no landfill gas recovery).  Note, however, that at the federal level, industrial nonhazardous waste landfills are not required to meet either of these standards (and in some cases, municipal waste landfills are not required to meet one or both of these standards).  Thus the DRASv2 modeled scenario does reflect current landfill disposal practices in certain limited situations.

Whether the model adequately reflects environmental effects is another question.  As noted in EPA 2002a (p. 5-7), the equations used to estimate chemical concentrations in surface water bodies may be particularly conservative because they do not account for decay, nor do they account for sorption processes.  These equations do not account for dilution of chemical concentrations in water and sediment.  Thus the DRASv2 model may overestimate aquatic organisms’ exposures to chemicals in surface water.  Also, the model only represents impacts on aquatic organisms and may not fully represent environmental effects.  We believe that aquatic organisms may be those organisms most likely to be adversely affected by the waste (when it is disposed in a landfill near a stream).  However, it is possible that in some situations or localities (for example, the landfill affects habitat of an endangered species), the model does not represent the most significant or important environmental impact.  The risk assessment likely underestimates the impact on organisms that use the landfill itself as habitat (since it does not account for direct exposure of those organisms to the constituents in the waste).  It may also underestimate the impact on sensitive populations such as endangered or threatened species with members whose habitat range includes the landfill.

There also are limitations to the air, surface transport, and groundwater fate and transport models that are used in DRASv2, because of the assumptions that those model developers made.  For example, for groundwater fate and transport, the model should not be used if the soil and aquifer underlying the landfill does not have a uniform porous media, each consisting of a single layer, since the model does not account for preferential groundwater flow pathways such as solution cavities or fractures, or layering in the subsurface saturated or unsaturated zones.  Another example is in situations in which a non-aqueous phase liquid (NAPL) is present.  In these situations significant contaminant migration might occur which is not accounted for by the model.  EPA 2002a and EPA 2002d describe the groundwater model’s limitations in greater detail.  Regarding the limitations of the groundwater model, the two most important limitations could result in an underestimate of risk if the F019 waste were disposed in a landfill that was located in an area with nonhomogeneous subsurface layers, or if a NAPL plume was present in the groundwater underneath the landfill.  The underestimates would result from the fact that the model assumes that the constituents released from the waste spread out in the groundwater underneath the landfill in a fairly uniform way, whereas in nonhomogeneous terrain or in the presence of a NAPL plume, the constituents could move more quickly, and might spread out less (resulting in higher groundwater concentrations).

Our analysis assumes that the residents near the landfill have no other source of drinking water besides the groundwater from their wells.  If the nearby residents use other sources of drinking fluids, such as bottled water or water from other sources, our exposure assumptions may overestimate exposure to constituents via groundwater ingestion.

To better illustrate the way that our use of the DRASv2 model may affect the estimates that we obtain in this risk assessment, we review the equations that represent movement of constituents in groundwater underneath the landfill, and the resulting estimated exposures from groundwater ingestion.  For the noncancer groundwater ingestion risk, there are (effectively) six variables: leachate concentration, drinking water intake, DAF (which includes waste volume and numerous other variables), body weight, number of days per year at home, and toxicity value.   In the high end estimates four of the six are set high – the leachate concentration (set at the maximum value), the drinking water intake (set approximately at the 84th percentile for adult, 90th percentile for child), and the DAF (includes both high end waste volume assumptions and the choice of the 90th percentile well concentration from probabilistic modeling performed using the EPACMTP; see EPA 2002a pp. 2-7 to 2-14 for explanation).  For nearby residents who work or attend school at a location outside the home, the assumption that they are present at the residence 24 hours per day, for 350 days out of 365 is also high.  (For nearby residents who are homebound individuals, however, this may accurately reflect their presence at the residence.)  Setting so many variables near the higher end of the range of possible variable values could very well overstate risk in the high end estimates.

In the central tendency estimates – we set as few as possible at the high end of the range.  A true central tendency analysis would set all variable values at central tendency (such as mean (average) or median values).  However, we are not able to adjust the DAF in DRASv2 without rerunning the EPACMTP model, so for this analysis we have continued to use the default DRASv2 DAFs, which represent a 90th percentile, or high end, DAF.  As with the high end estimates, the assumption of presence at the residence 350 days per year, 24 hours a day, also is a higher-end value for populations that work or attend school outside the residence.  Thus even the central tendency results presented in this risk assessment are likely to overstate the actual central tendency risk and hazard estimates.

Finally, there are additional sources of potential exposure to the constituents of potential concern that we do not include in our model results.  Certain constituents in motor vehicle manufacturers’ F019 sludge may already be present in the environment as a result of both natural processes and anthropogenic activities.  For example, constituents of potential concern that are present in an individual’s diet are separate from the exposures that we model in this analysis, and omitting them may understate risks.  Under these circumstances, receptors potentially receive a “background” exposure that may add to the exposure resulting from release of contaminants from the waste.  For a national analysis like this assessment, the inclusion of background concentrations as part of the analysis is difficult because of the limited data on national background concentrations for each constituent and the potential high variability of background concentrations. In past listing decisions, we have not factored in such background exposures in our hazardous waste assessments.  Not including the background exposure an individual may already have to a constituent of concern does not change the “marginal” increase in risk a person may have as a result of possible exposures to constituents in these wastes, and that our modeled estimates presented in this document represent.

9.3 Past and Future States

Questions 4 and 10 in section 1 pertain to the time period in which the expected effects will occur.  The DRASv2 modeling does not indicate the timeframe in which the risk assessment results apply.  The DRASv2 model estimates that ecological effects occur in approximately the same year in which the disposal occurs, since they result from precipitation coming into contact with the disposed waste when it is exposed to the atmosphere on the day of disposal.  The precipitation runs off of the landfill surface and eventually affects the nearby stream.  Thus, for question 10, the answer is, effectively, “right now.”  However, for question 4, the time period in which the expected effects will occur varies depending on the pathway that the constituents take before they encounter the nearby resident.  The direct air inhalation pathway affects nearby residents with exposures that occur “right now” (since the volatilized constituents would be emitted from the landfill immediately, and be breathed by the nearby resident on the same day that the disposal occurred).  However, for the groundwater pathways (which the model found were responsible for the majority of the potential noncancer effects, and for the arsenic cancer risk), the best answer we can give to question 4 (using DRASv2) is “in the future.”  A more involved and detailed analysis, using a model called EPA Composite Model for Leachate with Transformation Products (EPACMTP) would be needed to estimate the timeframe more precisely.  It is possible that this analysis would result in answers to question 4 ranging from tens to hundreds (and possibly thousands) of years for the various constituents of potential concern.  For example, use of the EPACMTP for modeling in a listing determination for wastes from chlorinated aliphatics production yielded a timeframe of 8,800 years for exposure to arsenic from a landfill release (see 65 FR 67068, November 8, 2000).

There is considerable uncertainty in predicting the movement of contaminants over long periods of time.  The DRASv2, IWEM Tier 1, and dyes and pigments analyses all use the EPACMTP to assess groundwater pathway movement over a time period of up to 10,000 years.  There are likely to be significant changes in environmental conditions over this period of time, yet the modeling methodology maintains constant assumptions over this entire 10,000 year period.

Finally, there is uncertainty regarding the effectiveness of liner systems in containing landfill leachate over long periods of time.  This uncertainty does not affect the DRASv2 model results, since those results are for releases from an unlined landfill.  However,  the IWEM Tier 1 analysis (and the risk analysis performed for the dyes and pigments rulemaking) used infiltration rate data obtained from closed and operating landfills.  Landfill owners and operators in the U.S. have only used liner systems for a relatively short period of time (generally, two to three decades at the most).  Long-term performance of these liner systems in real-world conditions remains to be documented in future years.  Since liner systems are engineered structures, and would be susceptible to chemical degradation as well as the possibility of physical disruptions (from circumstances such as equipment use that disrupts the liner’s continuity, flooding, or burrowing animals), they can be expected to fail eventually at containing the waste leachate inside the landfill.  To the extent that a leachate collection and removal system is installed and operated, over the period that the constituents within the waste remain inside the landfill, then liner failure would not be as significant an issue due to the removal of leachate by the collection and removal system.

9.4 Summary of Overall Uncertainty

Regarding the overall uncertainty in the human noncancer, human cancer, and ecological risk estimates provided in sections 5, 6 and 7, we believe that the risk assessment provides reasonably good information regarding certain constituents that are known to be present in the waste, and that our best available tools indicate could potentially cause adverse human health effects under certain circumstances.  The tool that we use in our primary analysis, the DRASv2 model, probably tends to overestimate potential adverse human health effects due to groundwater ingestion due to the setting of most variables to high end values.  In some situations (nonhomogeneous subsurface conditions at the landfill, or presence of a non-aqueous phase liquid in the groundwater) it might underestimate potential adverse human health effects due to groundwater ingestion.  DRASv2 may overestimate ecological effects on aquatic organisms as well.  However, it does not address adverse ecological effects on nonaquatic organisms that use the landfill as habitat or that are sensitive or vulnerable populations.  The main tool that we use in our additional analysis, the IWEM Tier 1 model, also probably tends to overestimate potential adverse human health effects due to groundwater ingestion (insofar as the IWEM Tier 1 model presumes a groundwater well located only 150 meters from the landfill).  However, like DRASv2, it might underestimate potential adverse human health effects due to groundwater ingestion in situations where the landfill is located above nonhomogeneous subsurface conditions, or in the presence of a non-aqueous phase liquid.

Thus it is apparent that there are numerous assumptions regarding past, present and future conditions that affect the magnitude of the estimates.  Many of the assumptions could cause overestimates, while others could cause underestimates.  We believe that the assumptions that we made that could cause overestimates are necessary because of the incomplete information that we sometimes have, that could cause underestimates.

As a result of these uncertainties, we have rounded all estimates to one significant figure.  Portraying hazard and risk estimates with more than one significant figure could imply a greater degree of precision than is warranted.  Users of these hazard and risk estimates are best served by considering the order of magnitude represented, rather than focusing on the magnitude of a given integer that we present as the estimate.

10.  Conclusions

For the human and ecological receptors whose exposures we model in this risk assessment, we draw the following conclusions:

Human noncancer health effects.  We use DRASv2 to model an unlined landfill scenario receiving motor vehicle manufacturers’ F019 waste.  The high end model results represent conditions that may or may not actually occur at a future point in time, at a given landfill, including disposal of large volumes of F019 waste for many years, at very high concentrations of chemical constituents.  These high end model results indicate that:

future residents near the landfill could experience adverse noncancer health effects from certain constituents present in the waste, due to ingesting groundwater contaminated with constituents from the waste.

the residents most at risk are children with average body weights, ages 6 months to 4 years, who drink greater-than-average quantities of water from a well located within one mile downgradient from the landfill.

the constituents most likely to cause potential adverse health effects for these children are likely to be nickel (present in virtually all samples of F019 from motor vehicle manufacturers; toxicity uncertainty factor of 300).

nearby adult residents with average body weight who drink greater-than-average quantities of well water are potentially at risk due to exposure to nickel in the drinking water.

The central tendency model results represent conditions that are somewhat more likely to occur at a future point in time, at a given landfill, since they represent disposal of median waste volumes at median concentrations of chemical constituents.  These central tendency model results indicate that:

nearby child and adult residents are unlikely to experience adverse health effects from exposure to chemical constituents in their drinking/bathing water, when those chemical constituents have moved from the motor vehicle manufacturers’ F019 waste disposed in the landfill to the nearby residents’ well.

We use the IWEM Tier 1 model to model a landfill receiving motor vehicle manufacturers’ F019 waste, when the landfill has a composite liner system (several layers of material at the bottom of the landfill, installed in order to prevent downward flow of water from the bottom of the landfill into the subsurface, and groundwater, beneath the landfill).  When the composite liner system is made up of a geomembrane flexible membrane layer made from HDPE (high density polyethylene), on top of a geotextile layer which in turn is on top of a geosynthetic (bentonite) clay layer 6 millimeters thick, with another geotextile layer underneath it, the IWEM Tier 1 model indicates that the potential adverse health effects of the high end estimates using the DRASv2 model would be alleviated for nickel.

Finally, we use the analysis from the dyes and pigments listing determination to extend the same logic regarding composite liners that the IWEM Tier 1 modeling shows.  When the composite liner system is made up of geomembrane liners overlying a compacted clay liner or a geosynthetic clay liner, the dyes and pigments analysis also shows similar reductions in constituent concentrations in groundwater.

Human cancer health effects.  We use DRASv2 to model an unlined landfill scenario receiving motor vehicle manufacturers’ F019 waste.  The high end model results represent conditions that may or may not actually occur at a future point in time, at a given landfill, including disposal of large volumes of F019 waste for many years, at very high concentrations of chemical constituents.  These high end model results indicate that:

future residents living near the landfill in which the F019 waste is disposed could experience exposures that may lead to cancer health effects from arsenic present in the waste, due to ingesting arsenic in contaminated groundwater.

the residents at greater risk are children from birth to age six, whose weights average 15 kilograms during that period, who continue to drink the groundwater until age 30.

The central tendency model results represent conditions that are somewhat more likely to occur, at a given landfill, since they represent disposal of average volumes of waste at average concentrations of chemical constituents.  These central tendency model results indicate that:

nearby residents have a probability of approximately two in one million of developing cancer from exposure to arsenic from F019 disposal in groundwater used as their drinking water source.

We use the IWEM Tier 1 model to model a landfill receiving motor vehicle manufacturers’ F019 waste, when the landfill has a composite liner system (several layers of material at the bottom of the landfill, installed in order to prevent downward flow of water from the bottom of the landfill into the subsurface, and groundwater, beneath the landfill).  When the composite liner system is made up of a geomembrane flexible membrane layer made from HDPE (high density polyethylene), on top of a geotextile layer which in turn is on top of a geosynthetic (bentonite) clay layer 6 millimeters thick, with another geotextile layer underneath it, the IWEM Tier 1 model indicates that the potential cancer risk from arsenic exposure is less than one in one million.

Ecological effects.  Under the high end conditions that we model using DRASv2 (large waste volumes and high chemical constituent concentrations), we infer that present and future aquatic organisms living in a stream near the landfill, and downgradient from it, would not experience adverse effects from the constituents known to be present in the F019 waste from motor vehicle manufacturers.   Lead and barium are the two chemical constituents with the highest hazard quotients in the high end results.  Under more typical central tendency conditions, the water concentrations of lead and barium are well below the toxicity reference values, indicating that the motor vehicle manufacturers’ F019 waste would not cause a significant impact on the nearby stream organisms.

Uncertainty in results.

For the human health results, our overall assessment is that the models we use probably overestimate the potential adverse effects of disposing of the F019 waste in either unlined or lined landfills.  The risk analysis evaluates high-end risks, and is designed to be protective of human health and the environment.  In spite of this, the possibility that in some circumstances the models might underestimate potential adverse effects means that landfill liner performance is relevant to mitigating potential risks.  Note that, as engineered structures, landfill liners will eventually cease containing the leachate at their peak efficiencies.  However, even as a less-than-perfectly-efficient leachate containment device, the liner system’s presence (compared to the condition of no landfill liner at all) should serve to retard, or diminish, the downward flow of leachate through the bottom of the landfill, and thus actual exposures that would be experienced by future residents near the landfill should be lower than those estimated using the DRASv2 model.

For both the human health and ecological results, all of the model results described above (DRASv2, IWEM Tier 1, and the results from the dyes and pigments analysis) are subject to uncertainty.  We describe the sources of uncertainty in more detail in section 9.

11.  References

AOAC 1990.  Official methods of analysis of the Association of Official Analytical Chemists, 15th ed. Vol. 2.  Association of Official Analytical Chemists.  Arlington, VA.  1990.

ASTSWMO 1996.  “Non-Municipal, Subtitle D Waste Survey,” Association of State and Territorial Solid Waste Management Officials.  Washington, D.C.  March 1996.

ATSDR 2006.  ATSDR Minimal Risk Levels.  Database updated at varying intervals, accessible at   HYPERLINK "http://www.atsdr.cdc.gov/mrls.html"  http://www.atsdr.cdc.gov/mrls.html .  U.S. Centers for Disease Control and Prevention, Atlanta, GA.  2006.

Commerce 1998.  U.S. Department of Commerce, Census Bureau, Economics and Statistics Administration, Current Population Reports: Household Economics Studies: Seasonality of Moves and Duration of Residence.  Report No. P70-66.  Washington, D.C. October 1998.

EPA 1985.  Guidelines for Deriving Numerical National Water Quality Criteria for the Protection of Aquatic Organisms and Their Uses.   PB85-227049.  U.S. Environmental Protection Agency, Office of Research and Development.  January 1985.

EPA 1994.  Test Methods for Evaluating Solid Waste, Physical/Chemical Methods.  U.S. Environmental Protection Agency, Office of Solid Waste and Emergency Response, Office of Solid Waste.  Publication Number SW-846, Revision 2.  September 1994.

EPA 1996.  Ecotox Thresholds.  ECO Update 3(2):1-12.  EPA/540/F-95/038.  Publication 9345.0-12FSI.  PB95-963324.   U.S. Environmental Protection Agency, Office of Solid Waste and Emergency Response, Office of Emergency and Remedial Response. Washington, D.C.  January 1996.

EPA 1997a.  EPA’s Composite Model for Leachate Migration with Transformation Products EPACMTP: User’s Guide.    U.S. Environmental Protection Agency, Office of Solid Waste and Emergency Response, Office of Solid Waste.  Washington, D.C.  1997.

EPA 1997b.  Exposure Factors Handbook.  U.S. Environmental Protection Agency.  Washington, D.C.  1997.

EPA 2002a.  RCRA Delisting Technical Support Document.  EPA906-D-98-001.  Interim Final.  U.S. Environmental Protection Agency, Office of Solid Waste and Emergency Response, Office of Solid Waste.  Prepared by U.S. Environmental Protection Agency, Region 6, Dallas, TX.  April 2002.

EPA 2002b.  Child-Specific Exposure Factors Handbook (Interim Report).  EPA-600-P-00-002B. U.S. Environmental Protection Agency, Office of Research and Development, National Center for Environmental Assessment, Washington Office.  Washington, D.C.  September 2002.

EPA 2002c.  Industrial Waste Management Evaluation Model (IWEM) User’s Guide.  EPA530-R-02-013.  U.S. Environmental Protection Agency, Office of Solid Waste and Emergency Response, Office of Solid Waste.  Washington, D.C.  August 2002.

EPA 2002d.  Industrial Waste Management Evaluation Model (IWEM) Technical Background Document.  EPA530-R-02-012.  U.S. Environmental Protection Agency, Office of Solid Waste and Emergency Response, Office of Solid Waste.  Washington, D.C.  August 2002.

EPA 2003a.  EPA’s Composite Model for Leachate Migration with Transformation Products (EPACMTP) Technical Background Document.  EPA530-R-03-006.  U.S. Environmental Protection Agency, Office of Solid Waste and Emergency Response, Office of Solid Waste.  Washington, D.C.  April 2003.

EPA 2003b.  EPA’s Composite Model for Leachate Migration with Transformation Products (EPACMTP) Parameters/Data Background Document.  EPA530-R-03-003.  U.S. Environmental Protection Agency, Office of Solid Waste and Emergency Response, Office of Solid Waste.  Washington, D.C.  April 2003.

EPA 2003c.  Risk Assessment Technical Background Document for the Dye and Pigment Industry Hazardous Waste Listing Determination.  U.S. Environmental Protection Agency, Office of Solid Waste.  Washington, D.C.  November 2003.

EPA 2004a.  EPA’s Multimedia, Multipathway, and Multireceptor Risk Assessment (3MRA) Modeling System: A Review by the 3MRA Review Panel of the EPA Science Advisory Board.  EPA-SAB-05-003.  U.S Environmental Protection Agency, Science Advisory Board.  Washington D.C.  November 2004.

EPA 2004b.  National Recommended Water Quality Criteria.  EPA822-R-02-047.  U.S. Environmental Protection Agency, Office of Water.  Washington, D.C. 2004.

EPA 2004c.  Provisional Peer Reviewed Toxicity Values.  Database updated at varying intervals, accessible to EPA and state/tribal environmental agency staff at   HYPERLINK "http://hhpprtv.ornl.gov/"  http://hhpprtv.ornl.gov/ .  U.S. Environmental Protection Agency, Office of Solid Waste and Emergency Response.  Washington, D.C.  2004.

EPA 2005a.  U.S. EPA, Guidelines for Carcinogen Risk Assessment.  EPA/630/P-03/001F.   U.S. Environmental Protection Agency, Office of Research and Development.  Washington, D.C., 2005.

EPA 2005b.  U.S. EPA, Supplemental Guidance for Assessing Susceptibility from Early-Life Exposure to Carcinogens.  EPA/630/R-03/003F.   U.S. Environmental Protection Agency, Office of Research and Development.  Washington, D.C., 2005.

EPA 2005c.  Integrated Risk Information System.  Database updated at varying intervals, accessible at   HYPERLINK "http://www.epa.gov/iris/intro.htm"  http://www.epa.gov/iris/intro.htm .  U.S. Environmental Protection Agency, Office of Research and Development.  Washington, D.C.  2004.

EPA 2007.  U.S. EPA, Economics Background Document: Estimate of Potential Economic Impacts for USEPA’s Proposed Amendment to the RCRA Hazardous Wastecode F019 to Exclude Certain Motor Vehicle Manufacturing Industries.  Washington, D.C., 2007.

Illinois Pollution Control Board 2006.  Title 35, Environmental Protection.  Part 302, Water Quality Standards.  Subpart B.  General Use Water Quality Standards.  Sections 302.208(e) and 302.210(a).  Illinois Pollution Control Board.  Chicago, IL, 2006.

Illinois EPA 2006.  Derived Water Quality Criteria.  Illinois Environmental Protection Agency.  Springfield, IL, 2006.

Kansas 2004a.  Amended Regulation.  Article 16.  Surface Water Quality Standards.  Section 28-16-28(e)(d).  Kansas Department of Health and Environment.  Topeka, KS, 2004.

Kansas 2004b.  Kansas Surface Water Quality Standards: Tables of Numeric Criteria.  Kansas Department of Health and Environment.  Topeka, KS, 2004.

APPENDIX A

Fate, Transport, Exposure and Risk Models

References cited in Appendix A are listed at the end of Appendix A.  Because the references are listed separately from the references cited in the main text, some of the reference numbers here may be different than the reference number for the same document when it is cited in the main text of this document.

Delisting Risk Assessment Software, Version 2.0 (DRASv2)

Model documentation: EPA 2002a

Main DRASv2 assumptions:

Waste quantity (for groundwater pathways)

The quantity of the waste disposed in the landfill, together with the chemical constituent concentrations in that waste, determines the total mass of contaminant that is available for release.  DRASv2 contains equations that represent the relationship between the amount of disposed waste that is the waste being evaluated, and the total quantity of waste in the landfill (see EPA 2002a, pp. 2-14 to 2-18).

Fate and transport in groundwater

The main model assumptions of the groundwater fate and transport part of the DRASv2 model (which is the version of the EPACMTP model that existed in the mid-1990s; see EPA 1996 and EPA 1997a), are excerpted from EPA 2002a, pp. 2-4 through 2-7, and summarized here:

the landfill acts as a “finite source” of contamination.

the landfill is above an unsaturated zone, which is above the water table (aquifer).

contaminants in the waste move downward through the landfill, then move downward through the unsaturated zone to the water table.

the soil and aquifer media are uniform, porous media; flow and transport are described by Darcy’s law and the advection-dispersion equation, respectively.

flow in the unsaturated zone is steady-state, one-dimensional, vertical flow from beneath the landfill toward the water table.  The lower boundary of the unsaturated zone is assumed to be the water table.  The flow in the unsaturated zone is assumed to be predominantly gravity-driven, and, therefore, the vertical flow component accounts for most of the fluid flux between the source and the water table.  The flow rate is assumed to be determined by the long-term average infiltration rate through the landfill.

Flow in the saturated zone is simulated as flow in an unconfined aquifer with constant saturated thickness.  The model assumes regional flow in a horizontal direction with vertical disturbance resulting from recharge and infiltration from the overlying unsaturated zone and landfill, respectively.  The lower boundary of the aquifer is assumed to be impermeable.  Flow in the saturated zone is assumed to be steady-state.  The model assumes that groundwater “mounding” underneath the landfill is small relative to the thickness of the saturated zone.

Contaminant transport in the unsaturated zone is assumed to occur by advection and dispersion.  The unsaturated zone is assumed to be initially contaminant-free, and contaminants are assumed to migrate vertically downward from the landfill.

Contaminant transport in the saturated zone is assumed to be a result of advection and dispersion.  The aquifer is assumed to be initially contaminant-free, and contaminants are assumed to enter the aquifer only from the unsaturated zone immediately beneath the landfill, which is modeled as a rectangular, horizontal plane source.

The model assumes that the dissolved phase is the only mobile phase and disregards interphase mass transfer processes other than adsorption onto the solid phase.

The model does not account for volatilization in the unsaturated zone.

The model does not account for the presence of a nonaqueous-phase liquid (such as oil) or for transport in the gas phase.   When a mobile oil phase is present, significant contaminant migration may occur within it, and the model may underestimate the movement of hydrophobic chemicals.

The model computes chemical reactions involving adsorption.  The model assumes that sorption of organic compounds in the subsurface can be represented by linear adsorption isotherms in both the unsaturated and saturated zones.  It is assumed that adsorption of contaminants onto the soil or aquifer solid phase occurs instantaneously and is entirely reversible.  For metals, the model uses either pH-dependent, empirically-derived isotherms, or sorption isotherms generated by a geochemical speciation model called MINTEQA2 (Allison et al. 1991).

The model accounts for transformation processes as first-order decay processes.

Fate and transport in air

The air fate and transport part of the DRASv2 model consists of several air source estimation techniques and dispersion models.  Particulates from wind erosion of soil-waste surfaces and from vehicular traffic and waste loading and unloading are modeled using Rapid Assessment of Exposure to Particulate Emissions from Surface Contamination Sites (EPA 1985a) and Compilation of Air Pollutant Emission Factors, Volume I: Stationary Point and Area Sources (EPA 1985b).  The DRASv2 model then uses the Ambient Air Dispersion Model (EPA 1985c) for the dispersion of the particulates, with source unit dimensions modified to more closely resemble a landfill’s dimensions.  The main assumptions in the Ambient Air Dispersion Model, listed in EPA 2002a, are:

the emission rate is constant over time

the emissions arise from an upwind virtual point source with emissions occurring at ground level, and

no atmospheric destruction or decay occurs.

Other assumptions that EPA makes with respect to the deposition of particulates from the landfill disposal activities onto the soil of the neighboring residence are described in EPA 2002a, pp. 2-30 through 2-32.

For the fate and transport of volatile constituents, DRASv2 uses a modified version (EPA 1984) of the equations in Land Disposal of Hexachlorobenzene Wastes: Controlling Vapor Movement in Soils (Farmer et al., 1978) together with the AADM model for dispersion (EPA 1985c, modified as described in EPA 1994a).  Other assumptions that EPA makes with respect to the dispersion of volatiles from the landfill disposal activities are described in EPA 2002a, pp. 2-33 to 2-35.

Fate and transport to surface water

For the fate and transport of constituents that erode from the landfill and are washed by precipitation to nearby surface water, DRASv2 uses the Universal Soil Loss Equation, or USLE (Wischmeier and Smith 1965).  Other assumptions that EPA makes with respect to the fate and transport of constituents from the landfill to the nearby surface water body and the resulting dilution in that surface water body, and sorption onto soils and sediments, are described in EPA 2002a, pp. 2-41 to 2-47.

Human exposure, cancer and noncancer effects

The DRASv2 human exposure and hazard/risk equations for soil and water ingestion and direct air inhalation are generally adapted from EPA’s Superfund program risk assessment guidance (EPA 1988 and EPA 1991).  The DRASv2 human exposure/hazard/risk equations for shower inhalation are from EPA 1997b and the DRASv2 human exposure/hazard/risk equations for dermal exposure are from EPA 1992.

Ecological receptor exposure and effects

The DRASv2 ecological receptor exposure equations for the aquatic organisms living in the stream near the landfill are described in EPA 2002a, pp. 2-47 to 2-48, and Appendix A-1 to EPA 2002a.  The hazard equations are essentially a comparison of the estimated surface water concentration with a toxicity reference value that represents a water concentration that should not adversely affect population viability of an entire freshwater aquatic community (EPA 2002a, pp. 4-24 through 4-27).

Industrial Waste Management Evaluation Model (IWEM)

Model user’s guide and documentation: EPA 2002c and EPA 2002d.

One distinction between IWEM Tier 1 and DRASv2 results, for the groundwater ingestion and inhalation (via showering) exposures, is that DRASv2 provides a summed health effects estimate that includes both types of exposure (ingestion and exposure from inhalation of constituents from the groundwater used as shower water).  The DRASv2 model outputs allow the model user to see the relative contribution from groundwater ingestion vs. inhalation via showering. However, IWEM Tier 1 apparently does not sum the two exposures, but rather, separately calculates health effects based on each type of exposure (see EPA 2002d, p. ES-8).

Main IWEM Tier 1 Assumptions:

Waste quantity (for groundwater pathways)

IWEM Tier 1 assumes that the entire quantity of waste in the landfill is composed of the waste being evaluated (see EPA 2002d, p. 4-9).  It does not contain equations that represent the relationship between the disposed quantity being evaluated and the entire quantity of waste in the landfill.

Fate and Transport in Groundwater

IWEM Tier 1 models closed landfills with an assumed 2 foot thick cover layer, then the waste, then one of three liner systems.  The leachate concentration that the model user enters as the model input is the initial leachate concentration that is assumed to leach from the waste; the model assumes that no losses occur during the active life of the landfill (such as volatilization).  The practical effect of these assumptions is that the model assumes that all of the waste in the landfill begins leaching when the landfill closes.  The leaching is modeled as a depleting source scenario (until all waste that is originally present has been depleted).

IWEM uses a more recent version of the EPACMTP model than the 1996/1997 version that DRASv2 uses (see EPA 2003a and EPA 2003b for documentation of the EPACMTP version used in IWEM.  The IWEM User’s Guide and the IWEM Technical Background Document cites 2002 draft versions of the EPACMTP documents that are listed in this Appendix as EPA 2003a and EPA 2003b).

The main model assumptions of the groundwater fate and transport part of the IWEM model are similar to those listed above for DRASv2, since the fundamental design of the EPACMTP model remained the same between the 1996/1997 version and the 2002/2003 version.  However, the IWEM User’s Guide also mentions these other assumptions (see EPA 2002c pp. 3-4, 3-18 to 3-20):

Infiltration rates from the bottom of unlined and single-lined landfills to the vadose zone are calculated using the Hydrologic Evaluation of Landfill Performance (HELP) model (Schroeder et al., 1994).

Groundwater flow is not affected by groundwater sources such as injection wells, or groundwater sinks such as pumping wells.

Matrix diffusion processes are not accounted for (when the aquifer formation is comprised of zones with widely varying permeabilities, and constituents move in and out of low permeability zones by diffusion).

Complex geochemical interactions in soil and groundwater are equilibrium sorption processes (sorption occurs instantaneously).

The soil-water partition coefficient for organic constituents is constant and equal to the product of the mass fraction of organic carbon in the soil or aquifer and a constituent-specific organic carbon partition coefficient.

The soil-water partition coefficient for metal constituents in the unsaturated zone is a nonlinear sorption isotherm developed from the MINTEQA2 geochemical speciation model.

The soil-water partition coefficient for metal constituents in the saturated zone is assumed to be in a range where the isotherm is approximately linear (which may not be valid when the metal concentrations in the leachate are high).

The geochemical environment at a site is constant and not affected by the presence of the leachate plume.

Colloidal transport or other forms of facilitated transport are not accounted for.

Constituent transformation is represented by a first-order degradation reaction that is uniform throughout the unsaturated zone and is uniform (but may be a different value) throughout the saturated zone.

Human Exposure, Cancer and Noncancer Effects

Like DRASv2, the IWEM Tier 1 human exposure and hazard/risk equations for water ingestion are adapted from EPA’s Superfund program risk assessment guidance (EPA 1991).  For cancer effects from inhalation via showering, IWEM Tier 1 also uses risk equations adapted from EPA 1991.  For noncancer effects from inhalation via showering, IWEM Tier 1 uses a simple comparison with a reference air concentration.  IWEM Tier 1 uses the same methodology as DRASv2 for the inhalation via showering, but cites original references (McKone 1987 and Little 1992) and provides a detailed description in Appendix E to EPA 2002d.

EPA Composite Model for Leachate Migration with Transformation Products (EPACMTP)

Model documentation: EPA 2003a and EPA 2003b.

Main EPACMTP assumptions:

Waste quantity (for groundwater pathways)

EPACMTP models the infiltration of the leachate from the bottom of the landfill, into the subsurface.  Waste quantity is accounted for according to assumptions of the particular analysis in question (see above for assumptions for analyses performed using the DRASv2 model, or for assumptions for analyses performed using the IWEM Tier 1 tool).

Fate and transport in groundwater

The main model assumptions of the groundwater fate and transport are described above.  EPA 2003a and EPA 2003b describe the specific model equations, assumptions, and cautions to the user in more detail.

References for Appendix A

Allison et al. 1991.  Allison, J.D., D.S. Brown, and K.J. Novo-Gradac.  MINTEQA2/PRODEFA2, A Geochemical Assessment Model for Environmental Systems: Version 3.0 User’s Manual.  EPA/600/3-91/021.  U.S. EPA Office of Research and Development, Environmental Research Laboratory, Athens, GA. March 1991.

EPA 1984.  Evaluation and Selection of Models for Estimating Air Emissions from Hazardous Waste Treatment, Storage and Disposal Facilities.  EPA-450/3-84-020.  Office of Air Quality Planning and Standards, Research Triangle Park, NC.  December.

EPA 1985a.  Rapid Assessment of Exposure to Particulate Emissions from Surface Contamination Sites.  EPA/600/8-85/002.  Office of Research and Development, Office of Health and Environmental Assessment, Washington, D.C.  February.

EPA 1985b.  Compilation of Air Pollutant Emission Factors, Volume I: Stationary Point and Area Sources.  Fourth Edition.  EPA Publication AP-42.  Office of Air and Radiation, Office of Air Quality Planning and Standards, Research Triangle Park, NC.  September.

EPA 1985c.  “Transport to the Air – Ambient Air Dispersion Model (AADM).”  In: “Hazardous Waste Management System: Identification and Listing of Hazardous Waste; Proposed Exclusions and Proposed Organics Model.”  Federal Register, 50(229): 48963-48967.  November 27.

EPA 1988.  Risk Assessment Guidance for Superfund (RAGS), Volume 1: Human Health Evaluation Manual (Part A).  Office of Emergency and Remedial Response.  Washington, D.C.  EPA/540-1-89/002.

EPA 1991.  Risk Assessment Guidance for Superfund (RAGS), Volume 1: Human Health Evaluation Manual (Part B, Development of Risk-Based Preliminary Remediation Goals).  Office of Emergency and Remedial Response.  Washington, D.C.  Publication Number 9285.7-013.

EPA 1992.  Dermal Exposure Assessment: Principles and Applications, Interim Report.  Office of Health and Environmental Assessment.  Washington. D.C.  EPA/600/8-91/011B.

EPA 1994a.  Docket Report on Evaluation of Contaminant Releases to Air from U.S. DOE Hanford’s Petitioned Waste.  May 27.

EPA 1996.  EPACMTP Background Document.  Office of Solid Waste.  Washington, D.C. September.

EPA 1997a.  EPA’s Composite Model for Leachate Migration with Transformation Products, EPACMTP: User’s Guide.  Office of Solid Waste, Washington, D.C.

EPA 1997b.  Supplemental Background Document: NonGroundwater Pathway Risk Assessment: Petroleum Process Waste Listing Determination.  Office of Solid Waste.  Research Triangle Park, N.C.  March 20.

EPA 2002a.  RCRA Delisting Technical Support Document.  EPA906-D-98-001.  Interim Final.  U.S. Environmental Protection Agency, Office of Solid Waste and Emergency Response, Office of Solid Waste.  Prepared by U.S. Environmental Protection Agency, Region 6, Dallas, TX.  April 2002.

EPA 2002b.  Industrial Waste Management Evaluation Model (IWEM) User’s Guide.  EPA530-R-02-013.  U.S. Environmental Protection Agency, Office of Solid Waste and Emergency Response, Office of Solid Waste.  Washington, D.C.  August 2002.

EPA 2002c.  Industrial Waste Management Evaluation Model (IWEM) Technical Background Document.  EPA530-R-02-012.  U.S. Environmental Protection Agency, Office of Solid Waste and Emergency Response, Office of Solid Waste.  Washington, D.C.  August 2002.

EPA 2003a.  EPA’s Composite Model for Leachate Migration with Transformation Products (EPACMTP) Technical Background Document.  EPA530-R-03-006.  U.S. Environmental Protection Agency, Office of Solid Waste and Emergency Response, Office of Solid Waste.  Washington, D.C.  April 2003.

EPA 2003b.  EPA’s Composite Model for Leachate Migration with Transformation Products (EPACMTP) Parameters/Data Background Document.  EPA530-R-03-003.  U.S. Environmental Protection Agency, Office of Solid Waste and Emergency Response, Office of Solid Waste.  Washington, D.C.  April 2003.

Farmer et al. 1978.  Farmer, W.J., M.S. Yang, and J. Letey.  Land Disposal of Hexachlorobenzene Wastes: Controlling Vapor Movement in Soils.  In: Land Disposal of Hazardous Wastes: Proceedings of the Fourth Annual Research Symposium.  Held at San Antonio, TX on March 6, 7 and 8, 1978.  EPA/600/9-78-016.  U.S. EPA Office of Research and Development, Municipal Environmental Research Laboratory, Cincinnati, OH.  August.

Little 1992.  Little, J.C.  “Applying the two resistance theory to contaminant volatilization in showers.”  Environmental Science and Technology 26(7): 1341-1349.

McKone 1987.  McKone, T.E.  “Human Exposure to Volatile Organic Compounds in Household Tap Water: The Indoor Inhalation Pathway.”  Environmental Science and Technology 21: 1194-1201.

Schroeder et al., 1994.  Schroeder, Paul R., et al.  The hydrologic evaluation of landfill performance model (HELP): Engineering Documentation for Version 3.  EPA/600/R-94/168b.  U.S. Environmental Protection Agency, Cincinnati, OH.  1994.

Wischmeier and Smith 1965.  Wischmeier, W.H. and D.D. Smith.  Predicting Rainfall-Erosion Losses from Cropland East of the Rocky Mountains.  USDA Handbook, No. 282.  Agricultural Research Service.

APPENDIX B

DRASv2 Model Inputs

This Appendix contains the model inputs used in a screening human health and ecological risk assessment performed

	for chemical constituents detected in samples of F019 hazardous waste from the motor vehicle

	manufacturing industry from 1996-2003.

The model is the Delisting Risk Assessment Software (DRAS) Version 2.

We use the default values in DRAS Version 2 for cancer slope factors (CSF (o) and CSF (i)) and reference doses

	(RfD(o) and RfC (i)), except for two instances: 1) the DRASv2 model developers applied a

route-to-route extrapolation to create the default values, in which case we do not use those default values, or 2) a more

	recent value has been published in either EPA's IRIS (Integrated Risk Information System) database, EPA's PPRTV

	(Provisional Peer Reviewed Toxicity Values) database, or ATSDR's (Agency for Toxic Substances and Disease

	Registry) MRL list. When we use a more recent value from a source other than the DRASv2 model

	documentation, we show it in the table below as an 'alternate value.'

DRAS Version 2 default values for ecological effects ("Aquatic TRV") are listed together with alternate values from

	EPA's 2002 National Recommended Water Quality Criteria (NRWQC), criteria continuous concentration for

	freshwater aquatic life (from EPA-822-R-02-047), where available, and where not available, the Illinois (and

	in two cases, Kansas) equivalents.  We use the alternate values from these NRWQC or state sources if

	they are lower than the DRAS Version 2 default values, except for naphthalene (for which we are unable to determine the default value’s source).  The Illinois equivalents are from Illinois Pollution Control Board 2006 and Illinois EPA 2006; the Kansas equivalents are from Kansas 2004a and Kansas 2004b (see references for main text).

Conversions to common units are included in the Model Inputs (example: acetone's

RfC of 13 ppm from ATSDR MRL has been converted into units of mg/m3 required by the model, at an assumed

	temperature of 25 degrees Celsius and 1 atmosphere of pressure.  The formula for this

conversion is X ppm = (Y mg/m3)(24.45)/(molecular weight).

Also shown are the maximum observed leachate and "total" concentrations in waste samples.  Observed values for

	organic constituents are rounded to two significant figures and observed values for inorganic constituents are

	rounded to three significant figures.  In some cases observed values were qualified in laboratory reports as estimates

	or were qualified in some other manner.  Refer to the Summary Totals and Summary TCLP spreadsheets, and

	individual manufacturing plant spreadsheets, for more information on the specific sample that is the basis for the

	value reported on this spreadsheet.  See the original delisting petitions and verification data submissions

	for information on specific sampling procedures and sample preparation/laboratory analyses performed.

The entry under "Carcinogen/Noncarcinogen" reflects the constituent's status as either having carcinogenic human

	health effects, noncarcinogenic human health effects, or both.  For constituents that have both carcinogenic and

	noncarcinogenic human health effects, the DRASv2 model must be run once with the "Carcinogen/Noncarcinogen"

	flag set at "Carcinogen" status with values entered for the cancer slope factors, in order to obtain carcinogenic risk

	results, and must be run once with the "Carcinogen/Noncarcinogen" flag set at "Noncarcinogen" status

	with values entered for the reference doses, in order to obtain noncarcinogenic health effects results.  In two cases

	(chromium and lead) the DRAS Version 2 model is run only to evaluate ecological effects; in these cases the entry

	in the spreadsheet reads "ecological risk" rather than "Carcinogen," "Noncarcinogen," or "Car. & Noncarc."

	Abbreviations

alt.: alternate	Kpw: Skin Permeability Coefficient

	Aquatic TRV: Aquatic Toxicity Reference Value	L: liter

atm: atmosphere	m3: cubic meters

	ATSDR: Agency for Toxic Substances and Disease Registry	MCL: Maximum Contaminant Level

	B: Bunge Coefficient	mg: milligram

BAF: Bioaccumulation Factor 	midpt: midpoint

	BCF: Bio-concentration factor	mol: mole

Car.: Carcinogen	MRL: Minimal Risk Level

	cm2: square centimeters	MW: Molecular Weight

	CSF(i): Carcinogenic Slope Factor - Inhalation	NA: Not analyzed

	CSF(o): Carcinogenic Slope Factor - Oral	Nonc.: Noncarcinogen

	Da: Diffusion Coefficient in Air 	PPRTV: Provisional Peer Reviewed

	DAFLF: Landfill Dilution Attenuation Factor	     Toxicity Value

	DAFSI: Surface Impoundment Dilution Attenuation Factor	protect.: protection

	DRASV2: Delisting Risk Assessment Software, Version 2	refin.: refinery

Dw: Diffusion Coefficient in Water	RFC: Reference Dose - Inhalation

	gm: gram	RfD(o): Reference Dose - Oral

	H: Henry's Law Constant	SOILSAT: Soil Saturation Level

	HEAST: Health Effects Assessment Summary Tables	SOL: Solubility

	hrs: hours	T: Tau