ENVIRONMENTAL POLICY ANALYSIS

REMEDIATION

Improving Remediation Decisions at Hazardous Waste Sites with Risk-based Geostatistical Analysis MICHAEL E. GINEVAN M. E. Ginevan & Associates Washington, DC 20036

DOUGLAS E. SPLITSTONE Splitstone and Associates Murrysville, PA 15668

The soil-related cancer risk of a hazardous waste site is usually bounded by selecting an upper confidence limit on the mean concentra­tion value for each carcinogen and using these levels to define exposures, which are then com­bined with cancer potency factors to define risk. We suggest it is more appropriate to define can­cer risk for each sample taken at the site. This sample-specific risk can be used in a geostatis­tical procedure to produce maps of block-spe­cific risk at the site. When combined with mod­els of site utilization, a quantity, which we call "riskiness," can be developed to define the risk contribution of the /r*h site location to a particu­lar exposure scenario. We demonstrate the deri­vation of block-specific risk and show how both this quantity and riskiness can be displayed graphically to aid communication with stake­holders and decision making regarding actual remediation strategies.

Typical remedial investigation and feasibility stud­ies of hazardous waste sites suffer from several short­comings. First, initial site-sampling efforts, con­ducted to find out what toxic materials might be present at the site, often focus on worst-case "hot spots" and are thus likely to yield contaminant con­centrations that exaggerate actual levels at the site. Risk assessors then ask the question, "Given my in­formation on levels and extent of contamination, how high could exposures, and thus risks, reasonably be?" Reasonable upper bound exposures are usually based on UDDer 95% confidence bounds on the mean concentration of each contaminant at the site. This procedure is efficient as a screening exercise be­cause if our selection of samples correctly identi­fies the contaminated areas any contaminants that do not undue risks to human health or the

vironment can be confidently excluded from fur­ther consideration

However, because such a screening exercise fo­cuses on bounding possible site risks rather than characterizing actual site risks, it does not provide an accurate idea of what risks might actually be posed by the site. Having identified a set of contaminants of interest, one defines "bright-line" cleanup stan­dards for these toxicants that are designed to re­move all or most of the material that presents an un­acceptable risk. At this point, additional sampling may take place to better define the spatial extent of con­tamination that exceeds the bright line and re-quires soil removal or groundwater treatment. But these decisions are divorced from the question of how much risk reduction is actually achieved because the risk assessment process is essentially

This approach works against cost-efficient allo­cation of remediation resources. First, because the initial site characterization effort is focused on sup­porting a screening-type risk assessment, it yields up­per bound exposure estimates that are often much too high relative to the actual level of contamina­tion at the site (i). This is compounded by remedi­ation planning that typically does not assume any de­tailed "likely use" scenario for the site and that typically does not take into account the spatio-temporal distribution of toxicant levels. Finally, be­cause the risk assessment does not really describe dis­tribution of risk at the site, it does not allow a site manager to ask questions like "What is the most risk reduction I can buy for $1 million?" or "What is the least costly way of reducing the risk of a typical (or

95%) site user to less than 10"5?" The focus of our approach is soil-related cancer

risk, but it is generally applicable to other risk sources. For a number (iV) of carcinogens in soil, the risk of the site is usually bounded by selecting an upper bound on the average concentration value for each

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carcinogen at the site. Risk is assessed using the equation

where SRC is soil-related cancer risk, D, is the as­sumed exposure based on the upper bound concen­tration of the fi1 carcinogen, and q"{ is the cancer po­tency for that carcinogen. We suggest that a more appropriate expression is

where k is a sample collected from a specific site lo­cation and Dik is the assumed exposure resulting from the measured concentration of the z-th carcinogen in the fc* sample. This sample-specific risk can be used in a geostatistical procedure to produce maps of block-specific risk at the site. That is, one can use the k calculated SRCk values to estimate a soil-related cancer risk for each of the B potential remediation blocks at the site, SRCb. When combined with mod­els of site utilization, a quantity, which we term "risk­iness," can be developed as

where Rbs is the estimated riskiness of the bth site location or block for exposure scenario, $[,]. SRCb is as before the soil-related cancer risk of the bth block and UbiSi she probability oo fontact with block b given the Xth site utilization scenario.

In our definition of the problem, the block-specific site utilization probabilities are mutually ex­clusive and exhaustive. That is, if there are B total blocks at the site,

The total soil-related cancer risk for the site, SRCT;

is given by

That is, total site risk is given by the sum of riski­ness over all potential remediation blocks.

Geostatistical analysis of sample-specific risks Geostatistical analysis is used to convert sample-specific risks to estimates of the risk associated with each potential remediation block at the site. Point es­timates of risk for each potential remediation block are of limited use in addressing the questions that need to be answered. What are desired are esti­mates of various quantiles of the distribution of risk for each block. Most algorithms for using the obser­

vation points to interpolate characteristics at points where observations are not made are parametric in the sense that a model for the distribution of inter­polation errors is assumed. Frequentiy that model is assumed to be normal, or at least symmetric. Such models are often useful for describing measure­ment errors in well-controlled laboratory or manu­facturing studies. However, experience has shown that such models are rarely applicable to spatial inter­polation errors in environmental investigations (2-7). Therefore, we emphasize modeling the uncer­tainty of the estimate of risk within 3. block rather than the derivation of an "optimal" estimator of the risk characteristic of the block. To achieve this ob­jective the nonparametric geostatistical interpola­tion technique Icnown a. s "probability kriging" is employed

Probability kriging uses "indicator" transforma­tions rather than actual sample-specific values of risk, SRCk. The "indicators values" are zero or one de­pending on whether or not a sample exceeds a given fixed level of risk, or cutpoint. That is, if the cut-point is 1(T6, for a risk less than 10"6, the indicator equals zero, and for a risk greater than or equal to 10"6, it equals one. This transformation plus a rank order transformation of the sample-specific risk data is used to obtain a nonparametric estimate of the probability that the block-specific risk of each po­tential remedial unit exceeds the cutpoint. When this process is repeated for several different cutpoints, the

FIGURE 1

Estimating conditional distribution of risk Five fixed levels of risk, or outpoints (10 ,10 ,10 ,10 ,1Q ) were selected, and the respective probabilities of the risk exceeding these outpoints were determined to be 0.01,0.15,0.35,0.75, and 0.96 for a particular block. This figure shows this information viewed as a conditional cumulative distribution function (CCDFK

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result is a nonparametric estimate of the condi­tional distribution of risk for each potential reme­dial unit.

Assume that we select a set of five cutpoints— 10"3, 10"4, 10"5, 10"6, and 10"7—and determine that the respective probabilities of the risk exceeding these cutpoints are 0.01, 0.15, 0.35, 0.75, and 0.96 for a par­ticular block. Figure 1 shows this information viewed as a conditional cumulative distribution function (CCDF). Note that by using linear (or curvilinear; we use the former) interpolation, one can get an esti­mate for any percentile of this CCDF, and that this CCDF is not restricted to a particular distributional form. (Further discussion of the procedure and of the advantages of using probability kriging for hazard­ous waste investigations is given in References 2-6; discussions of geostatistics in 26116 T3.1, t i re provided in References 8 and 9.)

Our example is based on an actual nine-acre haz­ardous waste site, which has been disguised to pro­tect the interests of our client. Surface soil contam­ination used for purposes of illustration has been characterized by a total of 43 surface samples that were not collected with an a priori view toward char­acterizing site risk. Review of the data showed that cancer risk was determined by four toxic materials. Sample-specific risks were calculated using Equa­tion 2 with k = 4 (Figure 2). Here, the Dik values are taken as the measured concentration for each car­cinogen (exposure is, for convenience, assumed equal to concentration), and the a values tency factors taken from EPA's IRIS system {10) It should be noted that the choice of exposure and risk models is not feature of our analytic ap­proach which will accommodate a variety of mod­eling approaches

Generally, the results of geostatistical estimation are presented as a series of contour maps. How­ever, the estimation process is performed on dis­crete remedial units or blocks. The block size is gen­erally chosen to correspond to a meaningful excavation unit and to provide for conditional cu­mulative distribution estimation that provides the

percentiles desired for decision making. The empha­sis for all practical purposes should be focused on the remedial block because this is the basic reme­dial unit (8). A 10 x 10 x 2-foot block of surface soil has been defined as a convenient remedial unit. The 7.4 cubic yards of soil represented by such a unit are easily handled by most excavation and cleanup tech­nologies. In addition, this unit is small enough that the estimated conditional distribution of risk at the centroid of the block reasonably describes the dis­tribution of individual sample risks within the block (4).

Five indicator levels of risk were selected for our probability kriging procedure. These are 2 x 10"7, 1 x 10"6, 1 x 10"5, 2 x 10"4 and 4.2 x 10~4. These par­ticular indicator levels of risk were chosen to facil­itate estimation of quantiles and/or expected risk lev­els from the block CCDF resulting from probability kriging.

Geostatistical techniques such as probability krig­ing require the investigation and characterization of the spatial correlation among the SRCk for the k sam­ple locations. The basic tool used for this investiga­tion and characterization is the semivariogram, which describes the variation among observations as a func­tion of their distance apart as well as any direc­tional dependence. Semivariogram models were es­timated for each indicator variable as well as for the rank transformation. The nugget (unexplained vari­ation), sill (maximum variation), and range (sepa­ration distance to reach the sill) were estimated for each semivariogram model using the Variowin pro­gram {11). Because these data exhibited no change in spatial correlation with different directions an om­nidirectional spherical variogram model was used in

Probability kriging resulted in the estimation of CCDFs for each of 3911 conceptual remedial blocks defining the site's surface soil. In actuality, the esti­mation is performed for all conceptual blocks in 2-foot increments of soil depth. However, only the surface soil is considered here for illustration.

Planning site remediation Figure 3 shows median (A) and 95% upper percen­tile (B) risks for each of the 3911 potential remedi­ation blocks at the site. The shape of these maps re­flects, for the most part, site boundaries, but some potential blocks near the site border were not esti­mated because the estimation algorithm had insuf­ficient data. The "V"-shaped feature near the north side of the site represents a rocky outcrop that was not a remediation candidate. The median block-specific risk map (A) suggests that high-risk blocks are confined to the center of the site but the 95% up­per percentile map (B) suggests that a much more extensive region of high-risk blocks cannot be def­initely refuted.

With this description of the site in hand, we turned to the question of how this information should be used to plan site remediation. One simple ap­proach would be to remove all blocks with an un­acceptable risk. This is conceptually equivalent to the practice of remediating all blocks with contami­nant concentration exceeding a particular bright line. If we wished to eliminate all blocks with risks in ex-

FIGURE 2 Sample-specific risk by location Sample-specific risks for the 43 soil samples collected at the hazardous waste site under investigation were calculated using Equation 2.

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cess of 10"5, we would simply remove all blocks in Figure 3A or B that are red or orange. Obviously, the consequences of using medians versus 95% upper bounds are substantial: Almost four times as many blocks would have to be remediated using the up­per bound map.

This approach, however, bases remediation de­cisions on lifetime risks assuming exposure to a sin­gle block, when in fact the risk of the site is an av­erage of the risks of all blocks assuming some use scenario, embodied by our Ubs values. These val­ues determine which blocks will be most often con­tacted and which thus dictate likely exposures. This view is supported by the practice of moving toxic ma­terials to secure landfills. The relocation does not re­duce their toxicity, but does ensure a utilization pat­tern resulting in essentially zero risk because exposure probabilities 3xc zero

Figure 4 shows remediation strategies devel­oped from Equation 5, assuming median block-specific risks, equal utilization probabilities for all blocks, and desired remaining site risks of 10"5 (A) or 10"6 (B). Defining the blocks to be remediated is a simple process. Riskiness of a block is given by block-specific risk times 3911_1 (because there are 3911 potential surface remediation blocks, and uti­lization probabilities, Ubs, are equal for all blocks). The data are sorted by riskiness, from smallest to larg­est, and by spatial coordinates, which helps to de­fine contiguous remediation blocks. A new vari­able, C, is generated which describes the cumulative sum of riskiness. All blocks with values of C exceed­ing the desired risk threshold are candidates for cleanup That is we bc2in by rem.ccii3.ting the blocks with highest riskiness and continue until the de­sired level of site risk is achieved The preference for contiguous blocks is based on the premise that this will minimize cleanup cost

Figure 4A shows that even if one has a relatively low risk threshold, a modest cleanup will reduce the

overall site risk to 10 . However, as Figure 4B shows, basing decisions on upper 95% risks results in greatly increased cleanup costs, even if a 10-fold higher risk threshold is selected.

Modeling spatial distribution of risk The approach presented here breaks with current practice in several ways. First, we emphasize mod­eling the spatial distribution of risk, not the spatial distribution of hazardous materials. We believe that this is appropriate because the object of site reme­diation is risk reduction, not mining hazardous ma­terials. Our approach also differs in its emphasis on a nonparametric approach. This diminishes the sen­sitivity of the statistics, and thus the decisions based on these statistics, to anomalous features of the data. Finally, our graphical displays are simple scatter plots with each potential remediation block represented with 0. symbol that describes its attribute with re -spect to a particular view of the data This focuses attention on the actual units of the decision mak­er's concern and clearly shows the consequences of basing decisions on different views of the data More­over portraying a complex analysis as a series of at­tribute-specific site maps provides a much better ba­sis for discussion among the various stakeholder grouDS than does a report with hundreds of tables that are difficult to interpret

There is more flexibility in this approach than might be apparent at first. For example, differ­ences in, say, bioavailability can be reflected by the function that converts measured concentration of a contaminant to exposure (Ds k). Similarly, differ­ent site use scenarios can be reflected by different sets of Ubs values. This last is, in our view, quite im­portant. That is, the nice grassy area along the river may have a higher riskiness than the fenced off area around the sludge pit because, even though the lat­ter is undeniably dirtier, its Ub are arguably much smaller.

FIGURE 3

Probability kriging results for the site Median and 95% upper percentile risks for each of the 3911 potential remediation blocks at the site are shown. The median block-specific risk map (A) suggests that high-risk blocks are confined to the center of the site, but the upper 95% percentile map (B) suggests that a much more extensive region of high-risk blocks cannot be definitely refuted.

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One might ask what is the correct way to view the data to support decision making. We can recom­mend a graphical view without hesitation, but would argue that there is no "optimal perspective." Fig­ures 3 and 4 focus on different aspects of the data and allow a decision maker to see a variety of per­spectives. From a pure decision support view, Fig­ure 4 is the most important because it offers a clear suggestion as to which blocks should be remedi­ated. We favor the limited cleanup option sug­gested by Figure 4A rather than the more extensive cleanup suggested by Figure 4B. It is extremely un­likely that all of the remediation blocks would actu­ally be at or above their upper 95th percentiles.

However, this question could be answered more definitely by a couple of extensions of our proce­dure. First, the form of the output of our procedure is ideally suited to subsequent economic analyses wherein the cost of excessive cleanup is compared with the cost of leaving the site in an excessively risky condition (7). One could also use the block-specific CCDFs of the unremediated blocks, together with some assumed use scenario and Monte Carlo sim­ulation methods, to develop an empirical risk distri­bution for the hypothetical remediated site. It should also be noted that these extensions are complemen­tary in that the risk distribution for the remediated site would provide a starting point for the calculation of loss functions for the economic analysis.

References (1) Ginevan, M. E.; Putzrath, R. M. In Cost-efficient Acqui­

sition and Utilization of Data in the Management of Haz­ardous Waste Sites; Air & Waste Management Associa­tion: Pittsburgh, PA, 1994; pp. 259-67.

(2) Flatman, G. T. et al. In Proceedings of the 6th National Conference eo Management of Uncontrolled Hazardous Waste; Hazardous Materials Research Institute: Wash­ington, DC, 1985; pp. 442-48.

(3) Isaaks, E. H. Master's thesis, Stanford University, Stan­ford, CA, 1984.

(4) Journel, A. G. Geostatistics for Natural Resources Char­acterization; Reidel: Dordrecht, Netherlands, 1984, pp. 307-35.

(5) Journel, A. G. Indicator Approach to toxic Chemical Sites; report of Project No. CR-811235-02-0; U.S. Environmen­tal Protection Agency: EMSL-Las Vegas, 1984.

(6) Journel, A. G. In Principles of Environmental Sampling, Keith, L., Ed.; American Chemical Society: Washington, DC, 1988.

(7) Splitstone, D. E. In Cost-efficient Acquisision and dtili­zation of Data in the Management of Hazardous Waste Sites; Air & Waste Management Association: Pittsburgh, 1994; pp. 185-97.

(8) Isaaks, E. H.; Srivastava, R. M. An Introduction to Ap­plied Geostatistics; Oxford University yress: New York, 1989.

(9) Deutsch, C. V; Journel, A. G. GSLIB, Geostatistical Soft­ware Librarrynd User's Guide; Oxford University Press: New York, 1992.

(10) U.S. Environmental Protection Agency. Risk Assessment Guidance for Superfund: Human Health Evaluation Man­ual - Part A; ;nterim Finall EPA Office oo Emergency and Remedial Response: Washington, 1989.

(11) Pannatier, Y. Variowin: Software for Spatiat Data Analy­sis in 2-D; Sprrnger-Verlag: New York, 1996.

FIGURE 4

Two cleanup scenarios Distribution of cleanup blocks for the site assuming median block-specific risks, equal utilization probabilities for all blocks, and desired remaining site risks of I0 (A) or 10 (B).

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