UNITED STATES ENVIRONMENTAL PROTECTION AGENCY

^ s u ^ REGION 4

\Wjh I 61 Forsyth Street Break: ' ' ^ ^ " i ^ ^ m i ^ / Atlanta, Georgia 30303-3104 Ot*»f: < i ^ C .

Febmary 17, 2009

4SD-TSS

MEMORANDUM

SUBJECT: Hipps Road Landfill, Jacksonville, Florida

FROM: William N. O'Steen, Environmental Scientist Technical Services Section, Superfund Division

THROUGH: Glenn Adams, Chief H ^ ^ Technical Services Section, Superflind Division '•

TO: Scott Miller, Remedial Project Manager Superfimd Remedial Branch

This memorandum report responds to your request for a review of the Forty-Ninth Operation, Monitoring and Maintenance Report for the Hipps Road Landflll, Jacksonville, Florida. This document is herein referred to as "the Report." You may consider this review as a follow up to my March 27, 2007 memorandum to you on the Forty-Fourth Operation, Monitoring and Maintenance Report. In that review, I focused on your concem about the assertion that the source of vinyl chloride and benzene in certain downgradient monitoring well samples is household septic tank effluent, rather than the contamination being derived from the Hipps Road Landfill. In this review memorandum, that issue is again considered. Additionally, I have done a statistical analysis on some of the monitoring data in the Report. My analysis differs from the statistical evaluation presented in Appendix F to the Report. A statistical analysis was not included in my previous 2007 review.

Figures referenced in this memorandum are at the end of the memorandum. If you have any questions about this memorandum report or need additional hydrogeologic technical assistance on this project, please contact me.

The Report presents the results of sampling at the three monitoring wells (termed MNA monitoring wells) where benzene and vinyl chloride have persisted in concentrations slightly higher than their respective performance standards. Data for the full TCL list of volatile organic compounds are presented for these wells, and although some additional VOCs are present in some of the samples from the MNA monitoring wells, only benzene and vinyl chloride remain as VOCs of concem. The MNA monitoring wells are TMW-91, TMW-IOI, TMW-13I, and RW-2 (Report Appendix B). Several additional wells

10636076

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closer to the landfill are also monitored for VOC contamination (Appendix C), although no organic contaminants have recently been observed in any of these wells. Figure 1 shows the monitoring wells where volafile organic contaminants continue to be monitored, an4d.4entifies the three wells where the benzene and/or vinyl chloride concentrationsecontinue to exceed performance standards (in at least one sample obtained

.m,, • sinGe-m-y-f)revic?us review of the Site monitoring data).

Figures 2 and 3 show the benzene and vinyl chloride concentrations observed in the three highlighted monitoring wells downgradient of the landfill. Figure 2 shows that for benzene, concentrations increased somewhat in all three monitoring wells once the ground-water remedy transitioned from a pump and treat to a monitored natural attenuation remedy. Since that time, benzene concentrations have apparently stabilized but are too few and too variable to be able to determine if they are decreasing over time. Benzene concentrations are only slightly higher than the 1 ug/L Florida MCL. Figure 3 shows that for vinyl chloride, recent concentrations at all three monitoring wells are less than the I ug/L Florida MCL: Vinyl chloride concentrations apparently increased somewhat at TMW-13I and RW-2 after pumping stopped in 2004. The less dramatic response of vinyl chloride than benzene to cessation of pumping is consistent with the higher organic carbon partitioning coefficient of benzene relative to vinyl chloride.

The Report does not include a discussion of any comprehensive MNA indicator parameter monitoring results. As indicated on page 7, the Forty-Eighth Operation, Monitoring and Maintenance Report included a detailed MNA evaluation. Some information related to MNA can be obtained from data presented in the Report. In particular, the very low concentrations of organic contaminants indicates there is probably limited biodegradation of either benzene or vinyl chloride occurring; such a conclusion is perhaps supported by the monitoring data shown on Figure 2, where there is no clear decrease of benzene over time since the pump and treat operations ended.

There are field monitoring data and some metals data included in the Report that indicate there are no dramatic changes in ground-water chemistry across the monitored area. Figure 4 shows August 2008 pH, dissolved oxygen, and Eh (oxidation-reduction potential) data. From Figure 4, it appears that the ground-water pH in both the shallower and deeper monitoring zones increases in the vicinity of the landfill. Another observation is that the Eh apparently decreases beneath part of the landfill, as suggested by results from MW-11 and MW-18. The information shown on Figure 4 and presented in the Report does not allow for a comprehensive evaluation of geochemical conditions in the monitored ground water.

One issue that was paramount when I reviewed the Forty-Fourth Operation, Monitoring and Maintenance Report was whether or not the observed benzene and vinyl chloride in the ground water may have come from altemative contaminant sources, such as domestic wastewater discharged to septic tank drain fields. I included a partial evaluafion of this issue in the above-referenced March 2007 memoranduiii. That memorandum recommended additional data collection that might be useful in evaluation of the source(s) of the benzene and vinyl chloride. The Report does not provide any further information regarding this issue. •

As noted above, the benzene and vinyl chloride monitoring data demonstrate there are

. • . . - 3 -

source of the benzene contamination may be contaminant back-diffusion out of low hydraulic conductivity layers. Desorption of benzene from aquifer materials may also be a factor. In either case, there seems to be little rationale for attempting to enhance contaminant mass removal through any active remedial measures. Thus, although there is no clear downward trend in benzene concentrations in the post-pumping period, the continuance of an MNA remedy seems to be reasonable, based on the current monitoring data and the low contaminant concentrations.

To summarize my review of the remaining issues of benzene and vinyl chloride ground-water contamination, the 2007 and 2008 data obtained since my last review indicate that vinyl chloride contamination may have effectively been removed from the ground water, although more data are needed to confirm this preliminary conclusion. Benzene concentrations may also be declining over time, although the recent monitoring data indicate that benzene concentrations are decreasing at only one of three monitoring wells. However, given the very low benzene concentrations remaining in the ground water, there is little reason for attempting to enhance benzene mass removal.

I have another concem about the Report's evaluation of ground-water metals monitoring data. Appendix F to the Report presents a statistical analysis of the monitoring data for metals, comparing the concentrations of several monitored metals in ground-water samples obtained from wells near the landfill to the concentrations of those metals in ground water from wells upgradient of the landfill. This analysis considers as background valiies the data from monitoring wells TMW-IS, TMW-II, and MW-3. An analysis is done in order to determine if the shallow and deeper monitoring data from, respectively, TMW-IS and TMW-II are from two different sample populations. This evaluation indicates that for aluminum, chromium, iron, and lead, there is no difference between the data for the two wells; there is apparently some difference for manganese. This comparative analysis is probably valid for metals where there are predominantly detectable concentrations for the metals (e.g. iron). For data sets with almost entirely nondetects (e.g. TMW-II lead), the highly censored data set renders this analysis meaningless from a statistical perspective, although the problems with the statistical analysis of these data sets may be irrelevant in practical terms. Chromium and lead have ground-water remediation goals (Table 1, Record of Decision Amendment, EPA, 1990), and so comparison of background concentrations to downgradient concentrations of these metals has potential relevance. However, none of the recent samples from near-landfill downgradient wells have contained these metals at concentrations in excess of their respective performance standards, so there are no practical issues about what constitutes a valid background data set for these metals.

The statistical analysis does have other problems that may be more of a practical concem. In particular, the analysis of the iron background data set uses the concept of an upper tolerance limit to establish that the background concentration of iron could be as high as 22 mg/L, and thus, all downgradient samples with iron concentrations less than this value are within what is anticipated as "normal." The difficulty is that the 22 mg/L background iron concentration is an extreme value compared to all of the other background values observed. The statistical analysis cites EPA guidance on how to treat "outlier" values, of which the 22 mg/L iron concentration is clearly an example (reference page 3 of Appendix F, which establishes the 22 mg/L observation as a statistical outlier). EPA guidance establishes that if no error can be found in an outlier value, then it must be treated as an extreme but valid observation (EPA, 1989, Section 8.2). The question arises as to whether or not the 22 mg/L iron concentration is an erroneous value.

The 22 mg/L iron concentration was observed in the May 1994 sample from monitoring well TMW-IS. This sample also contained an aluminum concentration of 9.9 mg/L and a chromium concentration of 1.7 mg/L. The second highest iron, aluminum, and chromium concentrations were

observed in the following September 1994 sample from this monitoring well (respectively, 2.5 mg/L, 5.8 mg/L, and 0.31 mg/L). The third highest concentration of aluminum was 0.57 mg/L and the third highest chromium concentration was 0.03 mg/L. Later iron concentrations were more similar to the 2.5 mg/L September 1994 concentration but were generally less than 1 mg/L.

if the 22 mg/L iron concentration is not a reliable value, then the available data imply that it is high because of excessive suspended solids in the May 1994 sample. However, the iron concentration could a:lso be naturally high. The 1.7 mg/L chromium concentration sufficiently resolves the two possible explanations for the high iron concentration. This 1.7 mg/L observation greatly exceeds the maximum reported surficial aquifer total chromium value for the Florida DEP's statewide water-quality monitoring network (maximum chromium =276 ug/L; N=392; 1994-1997 data) (Florida DEP, 2008). As such, it is considered to be highly improbable that the 1.7 mg/L chromium concentration is a valid representation of the mobile fraction of this metal in the ground water. Thus, it is concluded herein that the associated 22 mg/L iron concentration is suspect. This comment also likely applies to the 2.5 mg/L iron concentration from September 1994, as the reported chromium in that sample was also higher than the maximum surficial aquifer chromium concentration reported in the extensive state-wide network of shallow background monitoring wells.

If the two highest iron concentrations in the Site background data set are removed, an exploratory data analysis (EDA) comparison shows a probable difference between the background iron concentrations (MW-3, TMW-II, TMW-IS data combined) and the iron concentration in samples from downgradient, near-landfill monitoring well MW-18. EDA begins with a simple plotting ofthe observed iron concentrations in the background data set versus the MW-18 data set (Figure 5). Figure 5 graphically shows that most of the MW-18 iron concentrations greatly exceed most of the background iron concentrations.

A more traditional means of EDA is boxplot analysis. A boxplot is a simple graphical means of showing the nature of a sample population distribution. Figure 7 shows the fundamentals of a boxplot. Note that beyond the two stems "whiskers" bounding the box, any observation is defined as an outlier. Typically, in boxplot analysis, one-sided outliers (as shown on Figure 6) are defined as being outside the sum of the 75̂ *̂ percentile value (75% of all sample observations are lower) plus 1.5A- the interquartile range (\.5x the difference in values between the 75'*̂ percentile concentration and the 25*** percentile concentration). For example, if the 75'*' percentile value is 10 and the 25* percentile value is 4, then an outlier would be detennined as any value greater than lO-i- [1.5»(10-4)] = 10-i-(1.5»6) = 19.

For the iron data from the Hipps Road Landfill monitoring, the background concentrations (excluding the two already-determined TMW-IS suspect concentrations, informally termed "outliers") and the MW-18 iron data set are shown on boxplots (Figure 7).

Figure 7 shows that the statistical program has identified several additional statistical outliers in both data sets, using the outlier criterion as specified above. If these outliers are also removed from the data sets, the boxplots of Figure 8 are generated.

Note that in Figure 8, the lower 95% confidence limit on the median for the MW-18 data set (1.8 mg/L) is greater than the upper 95% confidence limit for the median of the background data set (0.51 mg/L). Thus, without subjecting these data sets to a formal statistical analysis, the MW-18 iron data show an average concentration that is higher than the iron concentration in the background data set.

If a formal statistical analysis is done, the same conclusion is reached. In order to decide how to

formally statistically compare the two sample populations, normality testing was first done on the data sets. Figures 9a and 9b show the results of this evaluation. The figures indicate that while the MW-18 data (with statistical outliers removed) may be normally distributed, the background data (with statisfical outliers removed) are probably not normally distributed.

Given the likely non-normality of at least one of the data sets, a nonparametric comparison of the data sets was done. The Maim-Whitney U test was selected to compare the two data sets. In order to be comprehensive, testing was done using all sample data and then on the outlier-censored data sets. The following information summarizes the results of the statistical analysis.

Mann-Whitney Test and Cl: All upgradient data versus MW-18, outliers included

N Median All upgradient data 82 0.470 MW-18 (downgradient, near If) 30 2.200 Point estimate for ETA 1-ETA2 is-1.778 95.0 Percent CI for ETA1-ETA2 is (-2.240,-1.490) W = 3494.0 Test of ETAl = ETA2 vs ETAl not = ETA2 is significant at 0.0000 The test is significant at 0.0000 (adjusted for ties)

Mann-Whitney Test and Cl: All upgradient data versus MW-18, outliers removed

N Median All upgradient data 74 0.4450 "MW-18 27 2.2000 Point estimate for ETAl-ETA2 is-1.7500 95.0 Percent CI for ETA1-ETA2 is (-2.1199,-1.4797) W = 2785.5 - • Test of ETAl = ETA2 vs ETAl not = ETA2 is significant at 0.0000 The test is significant at 0.0000 (adjusted for ties)

The Report uses the concept of an upper tolerance limit in the background data set to define any potential observations in downgradient monitoring wells outside of what might reasonably be expected in the background data. EPA (1992) guidance defines how tolerance limits are used in data analysis. In that guidance document, the text states

A Tolerance interval is designed to contain a designated proportion ofthe population (e.g., 95 percent of all possible sample measurements). Since the interval is constructed from sample data, it also is a random interval. And because of sampling fluctuations, a

. Tolerance interval can contain the specified proportion of the population only with a certain confidence level. Two coefficients are associated with any Tolerance interval. One is the proportion of the population that the interval is supposed to contain, called the coverage. The second is the degree of confidence with which the interval reaches the specified coverage. This is known as the tolerance coefficient. A Tolerance interval with coverage of 95 percent and a tolerance coefficient of 95 percent is constructed to contain, on average, 95 percent ofthe distribution with a probability of 95 percent.

The guidance then states

When the assumptions of Normality and Lognormality cannot be justified, especially when a significant portion of the samples are nondetect, the use of non-parametric

•tolerance intervals should be considered. The upper Tolerance limit in a non-parametric setting is usually chosen as an order statistic ofthe sample data (see Guttman, 1970), commonly the maximum value or maybe the second largest value observed. As a consequence, non-parametric intervals should be constructed only from wells that are not contaminated. Because the maximum sample value is often taken as the upper Tolerance limit, non-parametric Tolerance intervals are very easy to construct and use.

As noted in Figure 9a above, the background data set is probably not normally distributed. The background data are not converted to a log-normal data set, but rather, the highest concentration in the background data set is taken as the upper tolerance limit (UTL) (with the two iron concentrations in the TMW-IS data removed, as discussed above). Any iron results from MW-18 that exceed that UTL concentration are assumed to represent ground water that is impacted by iron, probably from some ongoing release of contamination from the landfill (Table I, which follows the text of this memorandum). Note that although the MW-18 iron concentrations generally exceed concentrations in the background data set, only about half of the MW-18 iron concentrations exceed the maximum observed background concentration.

This Statistical analysis concludes that there is probably a localized area of above-background iron ground-water contamination downgradient of one part of the Hipps Road Landfill. There is no obvious trend in the iron concentration (Figure 10). Also, the two-sided 95% confidence interval on the mean iron concentration (t-test) analysis, including oufiier concentrations, is 2.17 mg/L to 3.99 mg/L. The 95% UCL is less than any risk-based concentration of concem, using EPA's Regional Screening Levels for Chemical Contaminants at Superfund Sites (EPA, 2008)

One-Sample T: MW-18 (downgradient, near landfill)

V a r i a b l e MW-18

References

N 30

Mean 3 . 0 8 0 6 7

S t D e v 2 . 4 3 6 3 8

SE Mean 0 . 4 4 4 8 2

95% C I ( 2 . 1 7 0 9 1 , 3 . 9 9 0 4 2 )

EPA, 1989, Statistical Analysis of Ground-Water Monitoring Data at RCRA Facilities Interim Final Guidance, Office of Solid Waste.

EPA, 1992, Statistical Analysis of Ground-Water Monitoring Data at RCRA Facilities Addendum to Interim Final Guidance, Office of Solid Waste.

EPA, 2008, Regional Screening Levels for Chemical Contaminants at Superfund Sites, available online at address http://www.epa.gov/reg3hwmd/risk/humanyrb-concentration table/index.htm.

Florida DEP, 2008 (update), Florida Ground Water Quality Monitoring Network Summary 1994-1997 by Jay Silvanima, Paul Hansard, & David Ouellette, available online at address http://tlhdwf2.dep.state.fl.us/ambient/triennial/.

cc: Glenn Adams, Chief, TSS (electronic copy)

-7

Table l.Tolerance Limit Evaluation Background Data

0.025 0.025

0.05 0.069

0.16 0.18 0.21 0.26 0.27 0.28

0.3 0.3 0.3

0.31 0.32 0.33 0.33 0.33 0.34 0.35

0.35 0.35 0.35 0.35 0.35 0.38 0.38 0.39

0.4 0.4

0.41 0.41 0.42 0.42 0.42 0.44, 0.44 0.45 0.46 0.46

0.46 0.48 0.48 0.51 0.51 0.51 0.53

. 0.54 0.55 0.56 0.56 0.56 0.56 0.58 0.59 0.59 0.61 0.61 0.61 0.63

0.63 0.64 0.65 0.66 0.69 0.72 0.73 0.82 0.93 0.93 0.93 0.94 0.99

1 1.1 1.2 1.4 1.5 1.6 2.4

MW-18 Data 0.89 0,9.3 1,4 1.4 l.,S

1.5 1.6 1.8 1:8 . !.9 2,1 2.1 2,1 "> • • >

2.2 2,2 2.6 2.7 2.8 2,8 2.9 3.1 3.3 3.6 3.9 4.6 4.6 6.1 9.8 12

Bold MW-18 data exceed the 95% nonparametric upper tolerance limit for the background data set

Figure 1. Monitoring Wells with Recent VOC Data

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Figure 3. Vinyl Chloride Concentrations in Monitoring Wells Downgradient ofthe Landfill

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Figure 5. Comparison of MW-18 Iron Concentrations to Background Iron Concentrations

(outlier data from TMW-IS removed; see text for discussion)

Iron Concentrations, mg/L

OAII Background Iron Concentrations

^ MW-18 Iron Concentrations

Figure 6. Fundamentals of a Boxplot

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Figure 10. TMW-18 Iron Concentration Over Time

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