# Comparison of Methodologies to Estimate Intake Dose for Exposure to Soil Contaminants

Authors: Arne E. Olsen; N. Persaud
Journal: Scholarly Research Exchange
 (2008)

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## Abstract

It has been suggested that probabilistic approaches would provide more realistic estimates for human intake dose from exposure to soil contaminants than the commonly-used standard deterministic method. The objective of this study was to compare intake dose estimated by these methods for noncarcinogens and carcinogens in soil from 21 contaminated sites in Pennsylvania, USA. Intake doses by the principal human exposure routes for these contaminants were estimated by the standard deterministic method using fixed input parameter values, and by two emergent probabilistic methods. The probabilistic methods were based (a) on distribution functions for all input parameters, or (b) on some combination of these functions and fixed parameter values. Intake doses were then taken as the 90th, 95th, or 99.9th percentile of the generated cumulative output distribution and compared with the commonly-used deterministic estimates over all contaminant/site combinations. For all exposure routes, the 90th and 95th percentile intake dose estimates were not markedly different from the deterministic values or from each other. The opposite was generally the case for the 99.9th percentile estimates. These results did not indicate clear and definitive advantages in using probabilistic methods over the deterministic method for estimating human intake dose from exposure to soil contaminants.


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## Body

## 1. Introduction

In industrialized countries with
strong enforcement of environmental laws and regulations, many manufacturing facilities
have been abandoned or idled because of real or perceived soil contamination.  These facilities are usually in urban areas,
and costly governmental and private programs have sought to stimulate their redevelopment
in order to regenerate jobs that have been lost due to their closure.  Facilities with economical potential for
redevelopment are called brownfields [1]. The soil contamination associated with
brownfields creates legal liabilities for potential redevelopers. This liability can eliminate the possibility
of obtaining bank loans for the redevelopment of the facility. The financial cost and risk of investing in
brownfields are therefore a direct function of the risk for deleterious health
effects to the surrounding community from potential exposure to contaminants at
the site [1]. The
first step in assessing this risk is to estimate intake dose by the principal
human exposure routes for contaminants present at the brownfield. The intake dose is then combined with
established toxicity values to determine the human health risk for a given soil
contaminant. Greater health risk will
mean higher costs of soil cleanup and lower likelihood of reinvestment in these
properties [1].The algorithms for estimating intake
dose require data on the type and concentration of the contaminant together
with many exposure input parameters [1]. Currently, most algorithms use some fixed upper-percentile
value (usually the 95th percentile) obtained from statistical analysis of the observed
concentrations for a given soil contaminant, together with fixed standard
recommended values for exposure input parameters such as intake rates, exposure time, exposure
duration, and body weight [2]. To obviate any possibility of endangering public health and welfare the fixed input
parameters are often chosen as the maximum (or minimum as appropriate) over the
range of possible values to ensure “erring on the side of safety” [3]. This upper-percentile level of
contamination and these fixed exposure input parameters are then used in the algorithms
to determine a single estimate of the intake dose.  This approach to estimating the intake dose
for human health risk assessment is termed as deterministic.Variability and uncertainty are
always associated not only with the observed contaminant levels but also with the
exposure input parameters and toxicity values, and these are not accounted for
in the deterministic risk assessment approach [2]. Bogen
[4], Burmaster and Anderson [5], and USEPA [6] have strongly
suggested that the deterministic approach overestimates the intake dose, and that
probabilistic-based approaches would provide more realistic estimates.Probabilistic-based methodologies
use probability density distribution functions for the observed contaminant levels
and exposure factors instead of fixed values.  Unlike the deterministic approach that
provides only a single estimate of the intake dose, probabilistic-based methodologies
generate a cumulative distribution of intake doses that would better account
for variability and uncertainty.  An
intake dose value in the 90th to 99.9th percentiles of this distribution is then used for further risk assessment [6]. A
study by Smith [7] indicated that the deterministic approach would give
similar intake dose estimates as the 95th percentile of the intake dose
distribution obtained by such a probabilistic-based approach.In the probabilistic-based
approach, the cumulative intake dose distribution is generated using Monte Carlo simulations. Once the appropriate probability distribution functions for the input
variables have been developed, values are selected at random from these
distributions. These are then used as input
into the algorithms to calculate a single value of the intake dose as is done
for the deterministic approach [8]. This process is then repeated thousands of
times to develop a cumulative distribution of intake doses.This Monte Carlo-based probabilistic
methodology allows for sensitivity analysis to determine which input parameters
would have the least impact on the intake dose estimate of a simulation. Pearson’sr or Spearman's ρ can be used to test for sensitivity of
the intake dose estimates to each parameter [9, 10]. The
nonsignificant input parameters can then be treated as deterministic variables. This hybrid methodology, between the purely
Monte Carlo and deterministic approaches, reduces the number of distributions
that must be developed and the number of Monte Carlo simulations that must be performed.Clearly the need exists to
investigate the applicability and advantage of using Monte Carlo-based probabilistic
methodologies to better capture variability and uncertainty in brownfields risk
assessments vis-à-vis the standard deterministic approach.  It is possible that the Monte Carlo-based
methodologies may not produce significantly different intake dose estimates
compared to the deterministic method. However, if it can be shown that Monte Carlo-based risk assessments can better capture the
uncertainty and variability inherent to the risk assessment process, it will
have far-reaching financial implications for brownfield cleanup and redevelopment.The primary objective of this study
was to compare intake dose estimates for the principal human exposure routes obtained
by the deterministic and Monte Carlo-based probabilistic methodologies using
measured soil levels of noncarcinogens and carcinogens at a number of
randomly-chosen brownfield sites.


## 2. Materials and Methods

A reliable set of brownfield contaminant
data was developed using the Pennsylvania Department of Environmental
Protection (PADEP) Land Recycling Program list of brownfields for which site investigations
were completed. The list at the end of
2000 contained 633 such completed brownfields. These 633 sites were sorted into four groups
based on whether the site was closed to background, statewide generic
standards, site-specific, or industrial standards. Only the sites that were closed to statewide
health, site-specific, and industrial standards were used in this study. From this reduced list, 30 sites were
selected at random. A formal request
was make to PADEP to gain full access to the files on these 30 sites. Ten of these sites did not have sufficient
data or the PADEP could not find the files. One site (Exxon SGH Specialty Products) was listed as one facility but
had two separate site investigations. Each of these was treated separately,
giving a total of 21 brownfield datasets used in this study.Measured contaminant levels in the
soil at these sites were used to estimate exposure concentrations in the air
and groundwater. The concentrations were
calculated using the procedures provided in the “Soil Screening Guidance:
Technical Background Document” [11]. These procedures use several assumptions that
simplify the underlying soil/air and soil/water mass transfer models [11] and reduce the need for large quantities of site-specific data that are
often not available.The calculation
of exposure concentrations for the inhalation route is divided into two
possible mechanisms: inhalation of
vapors for volatile contaminants and inhalation of contaminants adsorbed onto
suspended soil particles for nonvolatile contaminants. The concentration of contaminant in the air
for volatile and nonvolatile contaminants is calculated using the
volatilization factor (VF) or the particulate emission factor (PEF),
respectively. Both the VF and PEF are
based on two models: one that estimates vapor or particulate emissions from the
soil and one that estimates subsequent atmospheric dispersion [11].For the deterministic approach, the
single upper-percentile soil, air, and groundwater exposure concentrations were calculated using
Land's method as outlined “Supplemental Guidance to RAGS: Calculating
the Concentration Term” [12]. Land's formula uses the mean (x¯) and standard deviation (sx) of n log-transformed sample data points to
calculate the 95th percentile upper confidence level (UCL0.95) of
the sample data as [12](1)UCL0.95=exp⁡[x¯+sx+sxh0.95n−1], where h0.95 is the h-statistic
for confidence level 0.95 [12]. The h-statistic is a unique symmetric unbiased estimator for the central
moment of a distribution [13].When there is high standard
deviation or low sample size, Land's formula provides extremely high estimates
for the UCL0.95. In those
cases, the maximum detected or modeled concentration was used for the upper
percentile value [12]. Where
the value of the sample concentration was reported as nondetect or below the
detection limit, the sample was treated as coming from a noncontaminated area
and was removed from further consideration [14].The deterministic intake dose
estimates for individual contaminants were calculated in accordance with the
procedures outlined in “Risk Assessment Guidance for Superfund Volume
I: Human Health Evaluation Manual (Part A)” Chapters 6, 7, and
8 [2]. Exposure parameters
were taken from “The Exposure Assessment Handbook” and “USEPA
Region III Risk-Based Concentration Table: Technical Background Information”
[15].The Monte Carlo-based and hybrid Monte
Carlo/ deterministic-based intake dose distributions for individual contaminants
were calculated using @Risk 4.0, Advanced Risk Analysis for Spreadsheets (Palisade
Corporation, Newfield, NY, USA), a Monte
Carlo simulator that is a plug-in to EXCEL (Microsoft Corporation, Redmond, Wash,
USA). Recommended probability density
functions for the input parameters were obtained from the “Support
Document for the Development of Generic Numerical Standards and Risk Assessment
Procedures” issued by the Ohio Environmental Protection Agency  (Columbus, Ohio 43215, USA) [16],
and from Finley et al. [17].  @Risk 4.0 calls specific function routines to
generate the type of distribution specified for a given input parameter to be
used in the Monte-Carlo simulation.The @Risk 4.0 best fit function was
used to fit probability density functions to the observed soil concentration
data values. The goodness of fit was
determined using the Chi-Squared Statistic, the Kolmogorov-Smirnov statistic,
and the Anderson-Darling statistic. Only
the normal, lognormal, uniform, and triangular distributions were fitted to the
soil concentration data. Ten thousand
iterations of the Monte Carlo and Monte Carlo/deterministic simulations were
run using Latin hypercube sampling to ensure convergence for the tails of the
output distribution of intake doses. The
results were then used to calculate the 90th, 95th, and 99.9th percentiles of
the intake dose distribution.Sensitivity analysis was performed
using the Monte Carlo simulations to determine
which of the parameters could be treated as deterministic. The sensitivity analysis tool in @Risk 4.0
was used to perform the sensitivity analysis. Multivariate stepwise regression was used to determine theR-squared
values. The parameters with the lowest
R-squared values were then treated as deterministic values in the Monte Carlo/deterministic simulations.Seven sets of intake doses were
estimated and compared. These were the
deterministic estimates and the 90th, 95th, and 99.9th percentiles of the
estimated output distributions from the Monte Carlo and the Monte-Carlo/deterministic methods. These estimated intake dose levels were laid
out according to a randomized complete block design where each contaminant/site
combination was a block and each estimating method was a treatment. A one-way analysis of variance (ANOVA) was performed
for this design and the treatment means of the intake dose levels were compared
for significant differences using Tukey’s LSD. Other designs with 2-way ANOVA for the intake dose estimates were
possible. However, analyzing the intake
dose estimates in this manner allowed us (a) to examine overall trends in the
relative performance of the 7 estimation methods over varying contaminants,
sites, and exposure routes, and (b) to use the mean intake dose averaged across
all contaminant/site combinations for each method as a single response variable
for comparison of the 7 methods. All
statistical analyses were performed using SAS (SAS Institute, Cary, NC 27513, USA).Analyses of variance were performed
for estimated noncarcinogenic intake dose levels corresponding to 69
contaminant/site combinations for the soil ingestion, soil dermal absorption,
and groundwater ingestion exposure routes as listed in Table1. Analysis of the noncarcinogenic inhalation
intake dose levels was done for 45 contaminant/site combinations as listed in
Table 1.Table 1
Contaminant/site combinations and
exposure routes for which intake dose for noncarcinogens was estimated.
ContaminantNo. of sitesSoil ingestionDermal absorptionInhalationGroundwater ingestionNaphthalene10XXXXEthylbenzene7XXXXMethylene chloride7XXXXPhenathrene7XX—XToluene6XXXXArsenic6XX—XAcetone5XXXXBarium5XXXXChromium5XXXXFluoranthene5XX—XXylene5XX—XTotals6969694569Intake dose levels of carcinogens
for 30 contaminant/site combinations were estimated for the soil ingestion,
dermal absorption, and groundwater ingestion exposure routes and were analyzed
as for the noncarcinogens. These are
listed in Table2.Table 2
Contaminant/site combinations and exposure routes for which intake dose for carcinogens was estimated.
ContaminantNo. of sitesSoil ingestionDermal absorptionGroundwater ingestionArsenic6XXXBenzo[a]anthracene6XXXBenzo[a]pyrene6XXXChrysene5XXXMethylene chloride7XXXTotals30303030It should be noted that since USEPA
lists methylene chloride to be only a probable human carcinogen it was treated
as both a noncarcinogen and carcinogen and appears as a contaminant in both
Tables1 and 2.


## 3. Results and Discussion

For convenience, in the following
discussion we designated the intake dose estimation methods as deterministic
(DET), the Monte Carlo probabilistic (MC), and the Monte
Carlo probabilistic/deterministic (MCD). In addition, we attached 90, 95, or 99.9 to
MC or MCD as appropriate to indicate that the intake dose for each contaminant/site
combination using the probabilistic methods was taken as the 90th, 95th or the
99.9th percentile of the estimated cumulative intake dose distribution. The mean intake dose (MID) and the standard
error of the mean resulting from the analysis of variance were used to
demonstrate differences between the 7 intake dose estimation methodologies for
the noncarcinogens and carcinogens by each human exposure route (see Tables1
and 2).Figure1 shows the MID by the soil
ingestion exposure route for noncarcinogens. The deterministic MID estimate was not significantly different from
those for the MC 90 and MC 95 or the MCD 90 and MCD 95 treatments. However, the 99.9th percentile MID estimates
for the MC and MCD methods were significantly different from the deterministic
value. For all percentile levels of the
MCD method, there were no significant differences in the MID estimates as
compared to the corresponding MC generated values. In summary, inclusion of deterministic values
in the MCD estimation of the intake dose did not significantly influence the
outcome compared to the MC method.Figure 1
Estimated mean  intake dose (MID) by the soil ingestion exposure route for the noncarcinogens. Values not annotated with the same letter are significantly different atP≤.05. Bars represent the standard error of the mean.Figure2 shows the MID estimates
for the dermal absorption exposure route. The MID estimates by this exposure route are an order of magnitude
greater than those for the direct soil ingestion route. The pattern of the response to the various
estimation methods is very similar to that in Figure 1. The MID estimate calculated using the deterministic
method was not significantly different from those generated by the MC 90 and MC
95 or the MCD method at the 90th, 95th, and 99.9th percentiles. However, the MID estimate for the MC 99.9
method was significantly different from the value for the deterministic
method. For all percentile levels of the
MCD method, there were no significant differences in the MID estimates compared
to the corresponding values generated by the MC method.Figure 2
Estimated mean  intake dose (MID) by the dermal exposure route for the noncarcinogens. Values not annotated with the same letter are significantly different atP≤.05.  Bars represent the standard error of the mean.Figure3 shows the MID estimate for
the inhalation exposure route. These MID
estimates were, respectively, 3 and 2 orders of magnitude higher than those for
noncarcinogens administered by the soil ingestion and dermal exposure
routes. However, the pattern of the
response of the MID estimates to the different methods is quite similar to
those of the other two routes. The MID
estimate calculated by the deterministic method was significantly different
only when compared to the MC 99.9 and MCD 99.9 methods.Figure 3
Estimated mean  intake dose (MID) by the inhalation exposure route for the noncarcinogens.  Values not annotated with the same letter are significantly different atP≤.05.  Bars represent the standard error of the mean.The
MID estimates by the ingestion-of-groundwater exposure route for noncarcinogens
are shown in Figure4. These values were
almost the same as for the inhalation exposure route except that the MID
estimate of the deterministic method was 4 times greater. As for the inhalation exposure route, the
deterministic MID estimate was significantly different from those generated by
the MC and the MCD methods at the 90th and 95th but not for the 99.9th
percentiles. These latter MID estimates
were not significantly different from each other.Figure 4
Estimated mean  intake dose (MID) by the ingestion-of-groundwater  exposure route for the noncarcinogens. Values not annotated with the same letter are significantly different atP≤.05.  Bars represent the standard error of the mean.Regardless of the exposure route,
the MID estimates for noncarcinogens showed a similar response pattern for the
different estimation methods. As would
be expected, the MID estimates for all the probabilistic methods increased
nonlinearly as the cutoff limits on the output probability distribution for the
intake dose were raised from the 90th to the 95th and 99.9th percentiles. For all the exposure routes, the MC 99.9 and
MCD 99.9 methods gave the highest MID estimates.Figure5 shows the MID estimates for
the carcinogens by the soil ingestion exposure route. Similar results are presented in Figures 6
and 7 for the dermal and groundwater ingestion exposure routes. As pointed out earlier, results for the
inhalation exposure route for carcinogens were not generated since the
available data set did not contain enough contaminant/site combinations and toxicity
values for carcinogens were unavailable to adequately perform the
analyses. As shown in Figures 5 to 7,
the MID estimates are 1 or 2 orders of magnitude lower than those for
noncarcinogens for a given exposure route. This is because the measured levels for carcinogens were relatively
lower than those for noncarcinogens at all the 21 brownfields sites. Additionally, for carcinogens, the averaging
time is taken as 70 years, which is greater than the exposure duration. For noncarcinogens, it is the same as the
exposure duration.Figure 5
Estimated mean  intake dose (MID) by the soil ingestion exposure route for the carcinogens. Values not annotated with the same letter are significantly different atP≤.05. Bars represent the standard error of the mean.Figure 6
Estimated mean  intake dose (MID) by the dermal exposure route for the carcinogens. Values not annotated with the same letter are significantly different atP≤.05.  Bars represent the standard error of the mean.Figure 7
Estimated mean  intake dose (MID) by the ingestion-of-groundwater exposure route for the carcinogens. Values not annotated with the same letter are significantly different atP≤.05.  Bars represent the standard error of the mean.The MID estimates for carcinogens
by the various methods follow the overall pattern shown in the results for
noncarcinogens. The highest MID
estimates were obtained with the MC 99.9 and MCD 99.9 methods, although, unlike
the results for noncarcinogens, the latter were consistently lower than the
former. As for noncarcinogens, the MID
estimates for the carcinogens increased nonlinearly as the cutoff limits on the
output intake dose distribution were raised to the higher percentiles. However, as shown in Figures5 and 6, these
increases were much steeper than those for noncarcinogens except for the
groundwater ingestion route (see Figure 7). This would indicate that the tails of the output intake dose
distributions by the MC and MCD methods were much steeper than the
corresponding ones for the noncarcinogens. This is probably caused by the lower variability in the measured
concentrations for the carcinogens.Taken together, the results
presented in Figures1 though 7 were consistent with the findings of Smith
[7]. The MID estimates obtained by
the deterministic method for carcinogens and noncarcinogens for each exposure
route were generally little different from those generated by the probabilistic
methods except at the 99.9th percentile cutoff on the output intake dose
distribution. We observed that several, but
not all, of the fixed input parameter values used in the deterministic
computations fell in the upper tails of their probability density distributions
used in the Monte-Carlo simulations. Therefore, this finding would not appear
to support the idea introduced by Bogen [4] that intake dose is
overestimated if many upper-bound values of the input parameters are used in the
deterministic method. On the other hand,
other input parameter values that were closer to the mean of the distribution
(such as body weight) would tend to offset such possible overestimation of the
intake dose using the deterministic approach.The results in Figures1 through 7
clearly show that for both carcinogens and noncarcinogens the MC and the MCD
methods would tend to produce the same intake doses and risk estimates
regardless of the exposure route. This
is not surprising since using deterministic values in the Monte
Carlo approach for the least sensitive input variables, as
determined by sensitivity analyses, would not be expected to significantly
impact the generated cumulative intake dose distribution.


## 4. Summary and Conclusion

Variability and uncertainty are intrinsic to each input parameter of the intake dose algorithms
used for estimating human health risk from exposure to soil contaminants
[3, 6, 8, 18]. Using fixed input parameters
that represent maximum (or minimum) values over their range to calculate intake
dose is the most common approach to “erring on the side of safety” in
environmental exposure assessment [3]. The deterministic procedure is simple, accessible, and computationally
straightforward [6]. In
addition, the computation can be done in reverse to provide soil concentration
levels for a specified exposure dose or risk, and used to screen site
contaminant levels to decide if they should be considered for further
investigation [18]. On the other hand, it has been
suggested that using many extreme values could result in inflated and
unrealistic exposure dose estimates [4, 18, 19].Probabilistic
exposure assessment makes use of the same basic algorithms but makes use of
probability distribution of the input parameters instead of fixed values. This makes possible generating an output
cumulative probability distribution of intake doses. Monte Carlo techniques are most frequently used to develop the input parameter probability
distributions [18]. However, Monte Carlo-based exposure assessment is computationally more
complicated and time consuming requiring thousands of iterations to stabilize
the tails of the output exposure probability distribution. This makes quality control and calculation
checking more difficult than for the deterministic procedure. These drawbacks of the Monte Carlo-based
approach can be partially overcome by applying sensitivity analysis to reduce
the number of input probability distributions if their simulated impact on the
output distribution is found to be inconsequential.The results of this study in their entirety showed that use of Monte
Carlo-based probabilistic methods to
estimate intake dose at the low to moderate cutoff levels (90th and 95th
percentiles) of the output cumulative distribution would not provide
significantly different estimates as compared to the traditional
fixed-parameter method. Taken together,
these results indicated that there might be little advantage to choose
probabilistic methods over the deterministic method for intake dose estimation
and risk assessment for brownfield redevelopment.



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