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 receptor’s 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 individual’s 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 EquationRisks 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 MethodsThe 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. 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 n=200 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). Figure 1. Location of Whitewood Creek in Lead, SD.

2. Kriging Results – Variability or Uncertainty in EPC? Sequential Gaussian Simulation (SGS) Comparison of Kriging and SGS Results For this analysis, HQ for each simulation is expected to exceed 1 (HQ > 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 n=200 individual receptors. The average EPC for each receptor is approximately the same for the kriging and SGS approaches (compare Figures 4 and 5). • 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 > 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. 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. • Model uncertainty by generating a set of R realizations (or maps) of the spatial distribution of arsenic concentrations. For this analysis, we generated 100. • 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). • The SGS algorithm used here assumes the data are normally distributed, although other probability models (including nonparametric) could be selected. • 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. • For each individual receptor, calculate the distribution of HQ’s that corresponds to the distribution of EPC’s. Assuming a level of concern for HQ is 1.0, calculate PA[HQ] (the probability that HQ exceeds 1). • Two outcomes can be gained from this analysis: a) a proportion of the population expected to have HQ > 1; and b) a likelihood that a proportion of the population will have HQ > 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(EPC> 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 individual’s 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.