GeoSpatial Exposure Modeling in Ecological Risk Assessment: Whitewood Creek Site

1
GeoSpatial Exposure Modeling in Ecological Risk
Assessment Whitewood Creek Site William
Thayer1, Dale Hoff2, Philip Goodrum1, Janet
Burris3, Lynn Woodbury3 Environmental Science
Center - Syracuse Research Corp., North
Syracuse1, and Denver, CO3 and U.S. EPA Region
8, Denver, CO2
Introduction A major source of uncertainty in
risk assessment is often the exposure point
concentration (EPC) the chemical concentration
to which a receptor may be exposed over a
toxicologically relevant time period within a
geographic area called an exposure unit (EU).
For terrestrial ecological risk assessments,
important factors in defining the EPC include
1) the spatial distribution of concentrations
within the EU and 2) the movement of the
receptor. Biased sampling methods are often
employed during site characterization to identify
potential hotspots. This, along with the
assumption that concentrations are lognormally
distributed, may contribute to overly
conservative estimates of the EPC. In addition,
the assumption of equal and random access to all
areas of the site may not be appropriate,
especially if a receptors home range is smaller
than the site. By using the spatial information
present in the sample data, geostatistics can
provide a more reliable measure of uncertainty in
the EPC. We developed the GeoSpatial Exposure
Model (GeoSEM), a software tool that employs
different geostatistical methods (Thiessen
Polygons, Kriging, Sequential Gaussian
Simulation), in a GIS-based application, within a
Microsoft Windows environment. This poster
illustrates an application of GeoSEM to an
ecological risk assessment for shrews exposed to
arsenic in soil at the Whitewood Creek site in
Lead, SD. An individuals movement is simulated
as a random walk over a specified foraging area
(Hope, 2000). The choice of geospatial method
used to estimate concentrations at explicit
locations is shown to be a major source of model
uncertainty. Additional documentation is
available at http//esc.syrres.com/geosem/.
Risk Equation Risks are expressed as a hazard
quotient based on exposure via ingestion of soil
and invertebrates. The assessment endpoint is
growth and survival of the shrew population. An
individual-based exposure modeling approach was
used. The TRV is based on a NOAEL of 0.12
(rather than the LOAEL of 0.36).
Geospatial Methods The concentration of lead in
soil can be estimated at unsampled locations by
employing a variety of geostatistical methods.
Figure 2 illustrates screen captures from GeoSEM
for Ordinary Kriging.
Table 1. Point estimate input values used to
calculate HQ.
HQ hazard quotient
Csoil arsenic concentration in soil mg/kg
AUF area use factor (portion of home range that overlaps with impacted area) 1.0
IR ingestion rate (food plus soil) 0.4 grams/day
AF absorption fraction 1.0
BW body weight 5.3 grams
TRV toxicity reference value for arsenic 0.12 mg/kg-day
Application of GeoSEM to the Whitewood Creek
Site An ecological risk assessment was
completed as part of a required five year review
of the record of decision for the Whitewood Creek
Superfund Site in Lead, South Dakota.
Site-specific data were collected to investigate
arsenic concentrations in soil, earthworms,
plants (grasses and clover), grasshoppers,
aquatic invertebrates, fish, and small mammals.
In addition, field studies were implemented to
collect site-specific data on bioavailability and
toxicity. Masked shrew (Sorex cinereus) was
chosen as the representative receptor to assess
exposures and risk to small insectivorous
mammals, and to illustrate the effect of
assumptions regarding habitat area and foraging
patterns. The shrew has a relatively high
metabolic rate and, thus, a larger foraging area
than other potential surrogate species. Home
ranges for shrews may range from 0.5 to 3.5 acres
(Choate and Fleharty, 1973). For this analysis,
a home range of approximately 1 acre (0.39 ha
3885 sq meters) was assumed.
Figure 3. Examples of foraging areas simulated
for individual receptors. Results presented in
this poster reflect simulations for a population
size of n200 shrews, each with approximately 1
acre home ranges of irregular shapes.
Figure 2. Overview of Zone 1 showing sampling
locations (circles) and the spatial distribution
of predicted (kriged) arsenic concentrations in
soil. Higher concentrations are located nearer
the flood plain.
Other exposure pathways (water, other dietary
sources, inhalation) were assumed to be
relatively minor for this scenario.
Table 2. Arsenic concentrations in surface soil.
Weighted estimates are based on Thiessen
Polygons.
Receptor Movement Exposures to an individual
receptor (shrew) are modeled by choosing a random
starting point within the site and continuing a
random walk until the foraging area is achieved.
An example of random walks for individual
receptors is illustrated in Figure 3. At each
location, an arsenic concentration is either
estimated based on the kriged surface or a
measured concentration is used if a sample
location coincides with the receptor location.
For each individual, the EPC can be estimated
from the mean concentration encountered during
the random walk. Collectively, the simulations
yield a measure of inter-individual variability
in the EPC among a potentially exposed
population. Alternative scenarios could be
simulated to reflect habitat suitability (Hope,
2000).
Sample Statistic Arsenic (ppm) Un-weighted (Fig. 2) Weighted Thiessen Polygons
n 810 810
mean 243 342
median 94 100
Standard deviation 455 614
minimum 1 1
maximum 6228 6228
Figure 1. Location of Whitewood Creek in Lead,
SD.
2
Sequential Gaussian Simulation (SGS)

• Conclusions
• GeoSEM provides a tool for applying a variety of
  geostatistical approaches to data in which the
  exposure unit can assume any shape and the
  receptors can move randomly throughout the site.
• In this example, all of the measured arsenic
  concentrations predict an HQ gt 1 for the shrew.
  Use of un-weighted or area-weighted (Thiessen
  polygon) approaches yield different results for
  the arithmetic mean (243 and 342 ppm,
  respectively). Both approaches suggest that if
  the EU is defined as the entire site, risks to
  the shrew would be high. Yet, the results also
  suggest that geostatistical methods may improve
  upon un-weighted estimates given the distance
  between sampling transect along the flood basin.
• When the home range is taken into consideration
  and an individual-based modeling approach is
  used, kriging and SGS yield similar estimates for
  the mean concentration among the exposed
  population. However, when characterizing
  uncertainty, the two approaches will differ
  significantly due to the difference estimates of
  prediction variance.
• Using a hypothetical example of 500 ppm as a
  level of concern for arsenic in soil, SGS allows
  for a quantitative uncertainty analysis of the
  EPC. Risk managers could be provided information
  about not only the probability of exceeding an
  LOC on average, but also the likelihood that
  specific fractions of the populations will be
  exposed to concentrations exceeding the LOC.

Kriging Results Variability or Uncertainty in
EPC?
Comparison of Kriging and SGS Results For this
analysis, HQ for each simulation is expected to
exceed 1 (HQ gt 1), suggesting adverse impacts to
insectivorous mammals associated with the
incidental ingestion of soil at the Whitewood
Creek Site. Therefore, we have focused the
comparison of the geostatistical methods on the
EPC rather than the risk characterization.
Figure 5 gives the results of the SGS simulations
for n200 individual receptors. The average EPC
for each receptor is approximately the same for
the kriging and SGS approaches (compare Figures 4
and 5).
A clear distinction needs to be made between
modeling variability and modeling uncertainty in
the EPC in exposure assessments. When estimating
risks to a population, the statistic of interest
is typically a measure of the mean, or
uncertainty in the mean concentration within the
EU. Figure 4 illustrates variability in the EPC,
rather than uncertainty in the EPC. Essentially,
kriging produces a single map of the best local
estimates (using mean squared prediction error as
the criterion), and the estimates are smoothed
such that low values will tend to be
overestimated and high values will tend to be
underestimated. The effect of smoothing is
greatest in areas furthest from sample locations.
Deleterious consequences for risk assessment
include 1) underestimate exposures by failing
to reproduce areas of extreme high
concentrations 2) provides unreliable estimates
of the probability that a given number of EUs
exceed a risk-based action level or soil
concentration.

1. Model uncertainty by generating a set of R
   realizations (or maps) of the spatial
   distribution of arsenic concentrations. For this
   analysis, we generated 100.
2. Each realization is conditional to the original
   data and approximately reproduces the spatially
   weighted sample frequency distribution and the
   spatial autocorrelation structure (i.e.,
   variogram). Therefore, the set of mean
   concentrations generated by SGS will be similar
   to that of kriging (Figure 2).
3. The SGS algorithm used here assumes the data are
   normally distributed, although other probability
   models (including nonparametric) could be
   selected.
4. For each individual receptor, generate a unique
   pattern of movement within the site (see Figure
   3) and calculate a set of exposure concentrations
   (yielding a mean concentration). Repeat the
   estimates of concentration (not the random
   movement), thereby yielding a distribution of
   mean concentrations for each individual receptor.
5. For each individual receptor, calculate the
   distribution of HQs that corresponds to the
   distribution of EPCs. Assuming a level of
   concern for HQ is 1.0, calculate PAHQ (the
   probability that HQ exceeds 1).
6. Two outcomes can be gained from this analysis
   a) a proportion of the population expected to
   have HQ gt 1 and b) a likelihood that a
   proportion of the population will have HQ gt 1.
   How likely is it that more than 10 of the
   population will be adversely affected?

Figure 6. Histogram of exceedence probabilities
for a hypothetical level of concern (LOC) of 500
ppm for arsenic in soil. Using SGS, we can
evaluate this criterion for each individual
receptor based on the uncertainty in the soil
concentrations within each foraging area in order
to make inferences about potential population
effects.
Figure 5. Histogram of SGS results showing the
distribution of average EPC for each of 200
individual receptors. Results for the mean are
similar to kriging (Figure 4).
Variance in Arsenic Concentrations Variance can
be used to estimate uncertainty in the EPC. One
of the limitations of kriging variance is that it
does not consider the sample concentrations which
are used in the prediction. It relies solely on
the geographic configuration of the observations.
Non-constant variance (heteroscedasticity),
together with sample clustering in areas with
high concentrations, results in the
overestimation of the variance at short distances
(Goovaerts,1997). Kriging variances can be an
order of magnitude greater than those of
simulation.
Using SGS, for each individual, 100 realizations
were simulated, yielding 200x100 estimates of
EPC. This approach allows for a unique
calculation of the probability that the EPC
exceeds a level of concern (LOC) such as a
preliminary remediation goal. Thus, for each
individual, we can obtain an estimate of P(EPCgt
LOC). Figure 6 illustrates results for a
hypothetical scenario in which the LOC was set at
500 ppm for arsenic. SGS provides a quantitative
measure of uncertainty in the estimates of
exceedence probabilities based on uncertainty in
the soil concentrations within each individuals
foraging area. For example, there is a 75
likelihood that 10 percent of the shrew
population will contact soils with concentrations
exceeding the LOC of 500 ppm. Similarly, there
is a 10 likelihood that greater than 50 percent
of the population will contact soils exceeding
the this LOC.
Acknowledgements The authors would like to thank
Edzer J. Pebesma for the use of the gstat
program. The development of GeoSEM has been
funded by Syracuse Research Corporation.
Figure 4. Histogram of average arsenic
concentrations for 200 receptors each exposed to
39 cell locations. Arsenic concentrations at
each cell were estimated from the kriged surface
and measured concentrations (see Figures 2 and 3).
References Burger, H. and Birkenshake, F. 1994.
Geostatistics and the Polygonal-Method A
Re-Examination. International Association for
Mathematical Geology Annual Conference, Mount
Tremblant, Quebec, Canada, October 3-5, Papers
and Etended Abstracts, pp. 50-55. Choate, J.R.
and Fleharty, E.D. 1973. Habitat preference and
spatial relations of shrews in a mixed grassland
in Kansas. Southwestern Naturalist. 18
110-112. Goovaerts, P. 1997. Geostatistics for
Natural Resources Evaluation. New York Oxford
University Press. Hope, B. 2000. Generating
probabilistic spatially-explicit individual and
population exposure estimates for ecological risk
assessments. Risk Anal. 20 (5)
573-589. Journel, A.G. and Huijbregts, C.J.
1978. Mining Geostatistics. Academic Press, New
York.
Should kriging be used to estimate uncertainty in
the EPC? With GeoSEM, kriging can be applied to
exposure units with any shape. Some software
packages employ point or block kriging, where an
estimate for the mean concentration within an EU
can be calculated by averaging the block
estimates. However, the kriging variances for
the blocks cannot be simply summed to assess
uncertainty in the mean EU concentration because
the estimates for the block variances are not
independent (Journel and Huijbregts, 1978
Burger and Birkenshake, 1994). An approach that
avoids some of the restrictions of kriging is
geostatistical simulation.