Quantifying the Risks of Unexploded Ordnance at Closed Military

Dec 19, 2008 - Some 1,976 sites at closed military bases in the United States are contaminated with unexploded ordnance (UXO) left over from live-fire...
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Environ. Sci. Technol. 2009, 43, 259–265

Quantifying the Risks of Unexploded Ordnance at Closed Military Bases J A C Q U E L I N E A . M A C D O N A L D , * ,† MITCHELL J. SMALL,‡ AND M. GRANGER MORGAN‡ Department of Environmental Sciences and Engineering, University of North Carolina, Chapel Hill, North Carolina 27599, and Department of Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213

Received May 21, 2008. Revised manuscript received November 8, 2008. Accepted November 11, 2008.

Some 1,976 sites at closed military bases in the United States are contaminated with unexploded ordnance (UXO) left over from live-fire weapons training. These sites present risks to civilians who might come into contact with the UXO and cause it to explode. This paper presents the first systems analysis model for assessing the explosion risks of UXO at former military training ranges. We develop a stochastic model for estimating the probability of exposure to and explosion of UXO, before and after site cleanup. An application of the model to a 310-acre parcel at Fort Ord, California, shows that substantial risk can remain even after a site is declared clean. We estimate that risk to individual construction workers of encountering UXO that explodes would range from 4 × 10-4 to 5 × 10-2, depending on model assumptions, well above typical Occupational Safety and Health Administration (OSHA) and U.S. Environmental Protection Agency (EPA) target risk levels of 10-4 to 10-6. In contrast, a qualitative UXO risk assessment method, the Munitions and Explosives of Concern Hazard Assessment (MEC HA), developed by an interagency work group led by the EPA, indicates that the explosion risk at the case study site is low and “compatible with current and determined or reasonably anticipated future risk.” We argue that a quantitative approach, like that illustrated in this paper, is necessary to provide a more complete picture of risks and the opportunities for risk reduction.

Introduction This paper presents the first systematic method for quantifying the risks of UXO on former military training land. In total, an estimated 1,976 sites totaling 40,000 km2 of land at closed military bases in the United States are suspected of containing UXO (1, 2). Estimates of the costs of cleaning up this acreage have ranged from tens of billions of dollars to more than $100 billion (2, 3). UXO can pose two kinds of risks to civilians: (1) explosion risks, with the potential to cause immediate physical harm, and (2) toxicity-related risks, due to the leakage of explosives and other munitions constituents into the surrounding soil and/or water. This paper addresses explosion risksthe chance that a person will encounter UXO and cause it to detonate, possibly resulting in bodily injury or death. * Corresponding author phone: [email protected]. † University of North Carolina. ‡ Carnegie Mellon University. 10.1021/es8014106 CCC: $40.75

Published on Web 12/19/2008

(919)966-7892;

 2009 American Chemical Society

e-mail:

Although reliable statistics on the frequency of civilian encounters with UXO are lacking, incidents involving UXO periodically are reported in the news. As an example, UXO was discovered on the grounds of the Odyssey Middle School in Orlando, Florida, this year (4, 5). The school was built in 2001 atop the former Pinecastle Jeep Range. A worker digging a hole for a footer in the long-jump pit of the running track encountered an igniter for an incendiary bomb, accidentally set it off, and was treated for smoke inhalation. Previously, at this same site, a construction worker had set off a firebomb, and three children had been injured (two lost legs and one lost an eye) when they accidentally detonated a piece of UXO they had brought home as a souvenir (4). This paper describes a method for quantifying the risk of civilian exposure to and detonation of UXO at former military training ranges. It then presents the results of an application of the method at a case study site. Finally, it compares the quantitative risk assessment method to a qualitative approach that is currently under development by an interagency task force led by the EPA. Magnitude of the UXO Problem in the United States. The potential for civilian contact with UXO left over from military training has emerged as an important environmental problem in the United States, even though no major battles have been fought here since the Civil War (1, 3, 6, 7). As a result of the end of the Cold War and the changing nature of national security threats, since 1988 the United States has closed 112 major domestic military installations and dozens of smaller ones under four rounds of a “Base Realignment and Closure” (BRAC) program authorized by Congress (8). The Department of Defense (DOD) will close an additional 22 major bases, with at least 25 training ranges containing UXO, under a further round of BRAC recently approved by Congress (9). In addition to the BRAC bases, a large number of smaller properties from installations closed before 1988 and no longer under military management also contain UXO; these properties are known as “Formerly Utilized Defense Sites” (FUDS) (10). Limitations of available technologies for UXO cleanup pose major impediments to reusing UXO-contaminated land once the military departs. In fact, a number of UXO accidents have occurred at former bases that were certified as clean (11). Cleanup of a UXO site is accomplished by sweeping the area with a metal detector and digging up metallic objects that the detector signals (1, 6, 7). Numerous field studies have demonstrated that even when the best-performing instruments are used, typically a significant percentage of UXO remains behind (12-14). Existing UXO Risk Assessment Methods. The federal government has undertaken a number of efforts to develop a comprehensive UXO risk assessment method. The four primary methods are 1 the Ordnance and Explosives Cost Effectiveness Risk Tool (OECert) (15) 2 the Ordnance and Explosives Risk Impact Analysis (OERIA) method (16) 3 the Interim Range Rule Risk Methodology (IR3M) (17) 4 the MEC HA (18). Each of these methods was intended to inform decisions about UXO remediation. However, none estimates the probability of harm occurring or the amount of risk reduction achievable with different approaches to site remediation (e.g., different metal detectors and UXO excavation methods). The first method (OECert) seeks to quantify the number of people directly exposed to UXO but does not compute the probability of harm occurring due to such exposures. The DOD VOL. 43, NO. 2, 2009 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 1. Stages in the quantitative risk model simulation process. abandoned the method due to criticism of its approach to calculating exposures; the approach was deemed inaccurate after application at a number of sites with very different environmental characteristics and surrounding population demographics yielded very similar exposure estimates (19). The other three methods do not quantify exposure or the probability of harm but rather rank sites on an ordinal scale (A-D, A-E, and 1-4, respectively), with different values along the risk scale intended to represent different levels of risk. Currently, none of the methods is widely used (19).

The risk model presented here estimates the probabilities that an individual will be exposed to UXO and that a detonation will occur. The purpose of the model is to enable quantitative analysis of the risks that might remain after a UXO site is cleaned up and how those risks change with different combinations of cleanup technology and land-use plan. The risk model has five components that successively simulate the process of UXO deposition and burial, detection during cleanup, and exposure (Figure 1) (20). Table 1 summarizes the model input parameters, sources of information that could be used to estimate these parameters, and values (or probability distributions) used for each in a case study application of the method. The input parameters, processes for determining parameter values, and case study site are described in detail below. Stage 1: Estimate Horizontal Distribution. The first stage of the model involves estimating the total number of UXO in the area of concern before cleanup. The area of concern might be defined based on the planned future land use or on the previous use of the site for military training. If based on future land use, the area may need to be divided into parcels based on prior uses for military training in order to develop appropriate models of the spatial distribution of UXO. Since the locations of the UXO items are not known before cleanup and thus the total number cannot be determined with certainty, the model represents this number as a random variable, N (N ) 0, 1, 2, . . .). The model requires a probability mass function indicating the chance that the total number of UXO within the area of concern will equal each of the possible values of N. It represents this probability mass function as (1)

N can be simulated using statistical models available in the spatial point-process literature (21-24). In previously 260

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(

tp ) 0.833W f1⁄3kplog 1 + 4.65

Quantitative Risk Assessment Method

Pr(N ) n) ) h(N)

published research, we demonstrated that at two former Army artillery ranges, h(N) is well represented by a point-process model known as the “Poisson cluster process” (24). This model represents “events” (in this case, UXO locations) as grouped around “cluster centers” (in this case, presumably targets at which soldiers aimed during firing practice). The model has three parameters: (1) F, the expected number of cluster centers per unit area; (2) R, the expected number of UXO associated with each cluster center; and (3) σ, the standard deviation of the distance from the UXO to their associated cluster centers. MacDonald and Small (24) provide information about how to estimate these parameters from limited site survey data or historical information. No analytical expression is available to represent the probability function h(N) directly for the Poisson cluster process. However, the distribution is easily estimated through simulation using built-in functions within the S+ Spatial Stats software package (or its free counterpart, known as R) (25). Stage 2: “Bury” the UXO. Given N ) n, the next task is to estimate how the UXO are distributed with depth. Our risk model divides the subsurface into discrete layers and estimates the probability that a given UXO will be contained in each layer using site data from previous excavations of UXO. If such data are not available, then depth data from similar sites could be used, or, alternatively, Army Corps of Engineers empirical functions that estimate the penetration depth of different kinds of ordnance could be used (26). For example, one such equation estimates the penetration depth of a projectile based on the projectile’s weight and velocity, along with the soil type, as follows:

( )) Vs

10

2

3

(2)

where tp ) penetration depth, cm; Wf ) projectile weight, g; kp ) constant depending on soil type (refer to ref 26); and Vs ) projectile striking velocity (refer to ref 26). If the soil is partitioned into j depth layers, then the distribution of the N ) n UXO items among the depth layers can be described according to a multinomial distribution with parameters n and p ) (p1, . . ., pj), where each pi is the fraction of UXO items at the site expected within the ith depth layer. Let Xi represent the number of UXO in the ith depth layer. Then Pr(Xi ) xi|N ) n) )

(

)

n! p xi(1 - pi)n-xi xi ! (n - xi)! i

(3)

Stage 3: Estimate UXO Remaining After Cleanup. As explained above, no existing metal detector can provide consistent detection of all UXO types with 100% probability. Site field-testing data or data from national test sites (see Table 1) can be used to estimate a probability of detection for each kind of instrument that may be used for site cleanup. This probability will vary with the depth of the UXO item, so that the result will be a vector of detection probabilities, (pd1, . . ., pdj) for each of the j depth layers. Let Xci represent the number of UXO items remaining in depth layer i after cleanup. Representing Xci as a binomial distribution conditional on Xi gives Pr(Xci ) xci|Xi ) xi) )

(

)

xi ! xi-xci (1 - pdi)xcip di (4) xci ! (xi - xci)!

Stage 4: Estimate the Number of Exposures to UXO. The first three modeling stages determine the three-dimensional distribution of UXO remaining after site remediation. The next stage is to estimate the probability of encountering residual UXO. The model makes the simplifying assumption that if the land-use footprint overlaps with a location containing UXO and the UXO is within the depth of any

TABLE 1. Input Parameters for Quantitative Risk Model model stagea

description

information sources

F

expected number of firing targets per unit area

9.9 × 10-7/ft2 (3.2 × 10-6/m 2)

σ

standard deviation of distance from UXO to target

R

expected number of UXO items per target

historical information; data from similar, previously surveyed training ranges; site characterization data data from similar sites; theoretical information about accuracy of firing; site-characterization data historical information (e.g., duration of site use, number of rounds fired, expected failure rate); data from similar sites; site-characterization data

2

p1, p2, . . ., pn

expected percentage of total UXO within each subsurface layer of soil

Army Corps of Engineers estimates of UXO penetration depths (e.g., ref 26; data from similar sites; site-characterization data

(0.01, 0.27, 0.33, 0.34, 0.025, 0.025) for the following depth intervals: surface, 0-0.5 ft (0-0.15 m), 0.5-1.0 ft (0.15-0.3 m), 1.0-2.0 ft (0.30-0.61 m), 2.0-3.0 ft (0.61-0.91 m), 3.0-4.0 ft (0.91-1.2 m)

3

pd

vector of detection probabilities corresponding to depth intervals used in stage 2

data from Standardized UXO Technology Demonstration Program, available online at http://aec.army.mil/usaec/ technology/uxo03.html; site-specific test data

GA-52CX detector:b pd ) (1.0, 0.91, 0.99, 1.0, 0.75, 0.75) EM61 detector: pd ) (0.68, 0.42, 0.40, 0.28, 0.75, 0.75) MTADS detector: pd ) (0.21, 0.054, 0.052, 0.053, 0, 0)

4

pexposure

vector of exposure probabilities corresponding to depth intervals used in stage 2

land-use plans; observational data on human behavior at similar areas (for example, playgrounds, campgrounds)

p

5

pexplosion

explosion probability under expected exposure scenario

ref 24; test model results using different explosion probability assumptions to determine effect on decision

average expert: pexplosion ∼ uniform (0, 1) pessimistic expert: pexplosion ∼ beta (26, 3.9) optimistic expert: pexplosion ∼ beta (2.0, 295)

1

parameter

value used in case study

σ ) 330 ft (101 m)

R ) 57/target

exposure ) 1 if UXO is within planned excavation depth for construction

a “Stage” refers to the model stages shown in Figure 1. b Vector elements are the probabilities of detection corresponding to the depth intervals shown above. So, for example, pd (2) ) 0.91 indicates a 0.91 probability of detecting UXO buried between 0 and 0.5 ft (0.15 m).

excavation expected for that land use, then the probability of exposure equals 1. For example, if the land-use plan calls for construction of a building, then the model assumes that the exposure probability equals 1 for any UXO that is (1) buried under the building’s footprint and (2) within the planned depth to which the building’s foundation will be excavated. Although the model currently assumes that the land-use footprint is fully specified at the time of the risk assessment and to date has been applied only to scenarios involving exposure of construction workers during excavation, more complex models to represent uncertain land-use footprints could be developed. As an example, if the land-use plan called for the area to be converted to a nature preserve with hiking trails, then observational studies of human behavior at similar preserves could be carried out to estimate the frequency of activities such as off-trail excursions and excavation. Stage 5: Estimate the Probability of Explosion. The final stage of modeling is to estimate the probability that a UXO item will explode, given that a human encounters it. Experimental data on the explosion probability of UXO are not available, and we lacked the resources for physical experiments involving UXO. Therefore, we developed probability distributions for estimating the chance of explosion of different types of UXO under different handling scenarios by conducting structured surveys of 25 explosive ordnance disposal (EOD) experts (27). All of the experts had received formal training in the safe handling and disposition of UXO. They had an average of 24 years (range 3 months to 44 years; standard deviation 12 years) of field experience in UXO

clearance for the active military and/or civilian contractors. Among the experts, 64% had battlefield experience in UXO clearance, and 84% personally knew an EOD officer who had been injured or killed during a UXO clearance operation. The survey results indicate that the experts display a wide range of views about the probability of explosion of UXO, even when presented with identical scenarios and photographs of the UXO. Our research showed that for all but a few extremely stable types of UXO, the distribution of expert estimates of the mean probability of UXO detonation is statistically equivalent to the uniform (0, 1) distribution (27). In light of the lack of expert consensus, and in the absence of experiments to provide empirical information, we recommend running the risk model using three different scenarios for the probability of explosion: (1) the uniform (0, 1) distribution; (2) the distribution elicited from the most pessimistic of the experts in our previous research (a beta distribution with parameters 26 and 3.9); and (3) that elicited from the most optimistic expert (a beta distribution with parameters 2.0 and 295). The median probability of explosion for encountered UXO is 0.5 for case 1, 0.88 for case 2, and 0.006 for case 3. Model Implementation. As currently designed, the UXO risk model is implemented with the programming package S+. The model successively simulates each of the five stages described above, using the input parameters relevant to each stage. Through multiple (e.g., 20,000) iterations of these simulations, the model estimates means and probability functions for various risk measures (e.g., probability of VOL. 43, NO. 2, 2009 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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encountering one or more UXO, probability of one or more detonations, and so on). Case Study Site. Data and land-use plans for a parcel of land at the former Fort Ord (near Monterey, California) provided the basis for a case-study test of the quantitative risk model. Fort Ord served as an Army base from 1917 until it was closed in 1994 under the BRAC program. Over its history, some 12,000 acres on the base were used for firing practice and thus, at the time of closure, were known to contain or suspected of containing UXO. The case study focused on a 310-acre land parcel, Site OE-53. Historical records indicated that the area was used for firing shoulderlaunched projectiles and a variety of other types of ammunition (28). Remediation of OE-53 commenced in June 1998. Locations of all UXO and other anomalies encountered during remediation were recorded in a database, to which we were given access. Site OE-53 is slated for transfer to Monterey Peninsula College. The college plans to convert OE-53 and some of the surrounding lands to a training facility for emergency vehicle operators. The Fort Ord Reuse Authority provided a photocopy of the planned building locations for this facility during an August 2006 site visit. We overlaid these plans on a map of Site OE-53 to determine the kinds and locations of construction activities that will occur once the land is transferred (see Supporting Information for a map showing planned construction). Here, we report on the results of applying the risk assessment method to areas of the site that are slated for paving. The Supporting Information reports the results of a separate application of the risk assessment method for areas where buildings will be constructed. Four paved areas will be constructed within the boundaries of Site OE-53; these are (1) a motorcycle training track, (2) a skid pad, (3) a slowspeed maneuver and accident avoidance training pad, and (4) a parking lot. Altogether, these paved areas will cover 1.65 × 105 m2 (1.77 × 106 ft2). The exposure scenario for the case study involves workers encountering UXO while excavating and grading the land prior to paving it. Table 1 lists the model input values used in the case study and the basis for the choice of input values (see ref 20 for details).

Case Study Results Figure 2 shows the expected values of the probability distributions for the outcome that construction workers will be exposed to UXO. The figure shows the results for different assumptions about (1) the metal detector used for remediation and (2) the depth to which excavation will occur during construction on the remediated site. As mentioned above, Site OE-53 already has been cleared. We ran the risk model using performance information for the specific detector that actually was used at the site, the Schoenstedt GA-52CX, and for two other detectors: (1) the Geonics EM61 (which has replaced the GA-52CX as the most commonly used detector at Fort Ord) and (2) the Blackhawk Multisensor Towed Array Detection System (MTADS), which was the poorest performing instrument in a field study of metal detectors at Fort Ord (14). Probabilities of detection for all three instruments were estimated from this study. As shown in Figure 2, construction workers could face significant risks of exposure to UXO at Site OE-53. For example, assuming cleanup with the GA-52CX metal detector and that 6 in. (15 cm) of soil will be graded prior to paving, the probability of at least one UXO surfacing during construction is 0.86. The probability of exposure is approximately 0.97 for both the EM61 and MTADS detectors. Figure 3 shows the probability that the construction crew will be exposed to at least one UXO explosion in areas slated for paving, assuming that the GA-52CX detector was used for 262

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FIGURE 2. Probability that a construction crew will encounter one or more UXO items during excavation and grading of land at Site OE-53 in preparation for paving. The horizontal axis shows the planned depth of the grading and excavation activities. Results are shown for four different remediation scenarios: (1) no previous UXO remediation, (2) prior cleanup using the MTADS detector, (3) cleanup with the EM61 detector, and (4) cleanup with the GA52CX detector (see text for detector descriptions).

FIGURE 3. Probability that one or more explosions will occur in areas of Site OE-53 that will be paved, assuming the site has been cleaned up with the GA52CX detector. The horizontal axis shows the planned depth of the grading and excavation activities. Results are shown for three different estimates of the sensitivity of UXO to detonation: estimates of (1) the average of 25 explosive ordnance disposal experts surveyed, (2) the most optimistic of the experts (i.e., the expert who estimated the lowest chance of detonation for any one UXO), and (3) the most pessimistic of the experts. cleanup. The figure shows how the risk predictions vary with assumptions about the sensitivity of UXO to detonation. The detonation sensitivities represent the average of the expert opinions from the survey of EOD experts discussed above, the predictions of the most pessimistic expert, and the predictions of the most optimistic expert (see Table 1 for the assumed probability distributions used to represent each of these three opinions). The scenario considered in these estimates is the case where a UXO with sensitivity to explosion similar to that of a Stokes mortar (which accounts for 60% of the UXO at Site OE-53) is struck with a backhoe. As shown, the risks of detonation are high under all three assumptions about explosion probability. As an example, if 6 in. of soil is excavated, then the predicted mean probability of at least one UXO detonation is 0.62 using the average expert probability of explosion; 0.02 if the most optimistic expert’s

TABLE 2. MEC HA Hazard Categories

a

total MEC HA score

hazard level

840-1,000

1

725-835

2

530-720

3

125-525

4

potential for UXO explosiona highest potential for an explosive event under current conditions potential for an explosive event under current conditions current use associated with the site is considered consistent with the current state of MEC [munitions and explosives of concern] on the site low potential for an explosive event under current and reasonably anticipated and appropriate future use conditions

Potential for explosion as described in ref 18.

estimate of the probability of detonation is used; and 0.83 if the most pessimistic expert’s estimate is used. The risk output from the model can also be expressed in terms of an annual risk to each construction worker. This form of output requires assumptions about the number of workers who will be employed in excavation, the duration of their on-site work, and the number of workers affected by an encounter with UXO. As an illustration, consider the case in which 50 construction workers are employed for a year and in which only one worker is affected by each UXO exposure or explosion. We can compute the annual risk to an individual worker as follows: worker risk )

E(exposures or detonations) 50 workers × 1 year

(5)

Computations using this equation for a scenario in which excavation and grading are restricted to the top 6 in. of soil yield an annual exposure probability of 0.06 for each worker. The probability of exposure to a detonation of a UXO is 0.03 using the average expert prediction; 0.05 using the pessimistic prediction, and 0.0004 using the optimistic expert’s prediction. Based on these results, the risk levels are likely to be substantially higher than those allowed by either the OSHA or the EPA, which generally aim for individual risk levels of 10-4 to 10-6 (29-31). The predicted annual risk levels for construction workers at Site OE-53 are commensurate with or higher than the observed rates of fatal accidents in the highest-risk domestic occupations (32). As an example, the risk of a fatal accident in the logging industrysthe highestrisk occupational categorysis approximately 0.1%; in the oil and gas extraction industry, which is among the riskiest of employment categories, the annual fatality risk is 0.02% (32). Thus, the quantitative risk model indicates that construction workers could face significant risks in the redevelopment of Site OE-53.

Discussion As noted in the introduction, the DOD and EPA have long sought to develop a method for assessing explosion risks at UXO-contaminated sites. This effort is motivated by the need to comply with an EPA-DOD agreement that when cleaning up closed military bases contaminated with UXO, the DOD will adhere to the requirements of the Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA) (33). CERCLA regulations mandate the quantitative assessment of baseline risks at contaminated sites and how those risks would change under different remediation scenarios (34). The three risk assessment methods (OECert, OERIA, and IR3M) developed by the DOD have been essentially abandoned due to technical concerns expressed by EPA and other organizations (including the military branches) involved in decision-making at UXO sites. The process for formulating the most recent method, the MEC HA, was intended to address these stakeholder concerns by involving the stake-

holders in the method’s design. The MEC HA is the product of a working group on UXO hazard assessment led by the EPA and including representatives from the DOD, Department of the Interior, Association of State and Territorial Solid Waste Management Officials, and Tribal Association for Solid Waste and Emergency Response (18). In conceiving the approach for the MEC HA, members of the interagency working group concluded that quantitative estimation of explosion risks at UXO sites is infeasible because of uncertainties inherent in several critical variables required for risk assessment (35). The variables mentioned include the spatial locations of UXO, future human behaviors that might lead to encounters with UXO, and the stability of the UXO. The working group opted for a purely qualitative approachsone that contains no measure of the probability of harm occurring and no quantitative estimate of the level of damage that could result. Based on a simple sum of scores on nine site attributes, the MEC HA assigns a site a categorical ranking of 1 through 4, with 1 intended to represent the highest risk (18). (For a complete list of the attributes and scoring rules for each, see ref 18.) The maximum score on all attributes is 1,000, and the minimum score is 125. The scores for each attribute and the breakpoints for the four categories were determined in round-table meetings of the MEC HA working group. Table 2 shows the four categories and the total MEC HA scores to which they correspond, along with the working group’s qualitative description of the level of risk that each category is intended to represent. To illustrate the application of the MEC HA method, consider the outcome for the case study site. Table 3 shows the input factor values and total scores for the construction scenario described above. The third column shows the input factor values that the MEC HA method assigns for this scenario, based on look-up tables in the MEC HA guidance document (18). The fourth column shows the scores that would be applied under current conditions, since a subsurface cleanup already was completed. Note that in contrast to the quantitative method, the MEC HA method does not provide the ability to estimate how the use of different metal detectors or other alternative cleanup strategies would affect residual risks. As illustrated in Table 3, the total score of 490 for the current (postcleanup) condition in the case study corresponds to a hazard level of 4sthe lowest risk category. The MEC HA guidance document states that a site “scored in Hazard Level 4 is compatible with current and determined or reasonably anticipated future use.” Thus, the MEC HA result suggests that proceeding with construction as planned is safe at this site, without any special risk reduction measures. This conclusion is not in accordance with the results of the quantitative risk assessment, which estimates that the probability of a UXO detonation is more than 60% after cleanup with the GA-52CX metal detector if one uses the average of the explosion probabilities predicted by the experts VOL. 43, NO. 2, 2009 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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TABLE 3. MEC HA Applied to Site OE-53a scores input factor energetic material type distance of additional human receptors site accessibility potential contact hours amount of munitions and explosives of concern (MEC) minimum MEC depth relative to the maximum intrusive depth migration potential MEC classification MEC size total score MEC HA hazard class a

input factor category

100

100

30

30

55 70

55 20

180

30

MEC located surface and subsurface; intrusive depth overlaps with subsurface MEC possible UXO small

240

95

30 110 40 855 1

10 110 40 490 4

Scores are determined directly from the instructions for assigning factor values in ref 18.

(see Figure 3, for the cases in which the depth of excavation is greater than zero). As is evident from this example, an advantage of the MEC HA method is its simplicity. Assessment can proceed with the type of site data typically available following a review of historical records (required at UXO sites and called an “archives search report”). Further, no special mathematical training, programming knowledge, or other skills are needed to apply the method. The analyst simply selects values for each input factor from look-up tables in the MEC HA guidance document. These values are then summed, and the final score of 1-4 is determined from the break-points shown in Table 2. The only math required is addition. Another significant advantage is that the MEC HA scoring method represents the consensus of a diverse committee that included environmental regulators, DOD personnel, state and local authorities, and other interest groups. Since a diverse group developed the input factors and scoring method, the method might be viewed as credible by the assorted stakeholders who need to agree on UXO cleanup and land reuse plans. However, the MEC HA method might not address the concerns of local stakeholders who were not part of the development process. Despite its strengths, the MEC HA method has limitations. One important limitation is that the use of an additive, linear equation to compute the total score from the input factors may violate fundamental assumptions needed for such a linear equation to be mathematically valid. In essence, the MEC HA is a multiattribute utility (MAU) function. A MAU function is a mathematical equation that summarizes how a decision-maker values an outcome having certain attributes. In order for a linear MAU model form to accurately represent preferences in the output, the input factors must be independent (36, 37). The MEC HA input factors clearly are not mutually independent. For example, two of the MEC HA input factors are “size” and “migration potential,” where migration potential refers to the potential for the UXO to surface due to frost heave. Smaller UXO would be more likely to surface than larger items, but the MEC HA model does not account for this dependence. Perhaps even more important than such technical shortcomings, the usefulness of the MEC HA method may be limited by its lack of consistency with the requirements for risk assessment under CERCLA. CERCLA regulations stipulate that the choice of a remedy for site contamination must be based in part on the “magnitude of residual risk remaining from untreated waste or treatment residuals remaining at the conclusion of the remedial activities” (34). CERCLA also 264

baseline condition after subsurface cleanup

high explosives and low explosive fillers in fragmenting rounds inside the munitions response site or inside the explosives safety quantity-distance arc moderate accessibility (some barriers to entry) some hours (50 workers × 40 h/week/worker × 50 weeks/year ) 100,000 h) target area

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requires identification of the “type and quantity of residuals that will remain following treatment” (34). The MEC HA method does not compute the magnitude of the residual risk (except in a relative sense), nor does it quantify the type and quantity of UXO remaining after cleanup. The MEC HA output also does not reflect the potential risk differences that may result from different cleanup approaches, using, for example, different metal detectors. The output is the same regardless of the cleanup tools and protocol employed, as long as removal of buried UXO is involved. If the MEC HA method becomes the primary tool for UXO site risk assessment, then an exception to the CERCLA policy of quantifying risks will have to be made. In contrast, the quantitative risk assessment method presented herein provides a systematic model of the process by which UXO is deposited, later encountered, and then possibly detonated. The quantitative method thus allows for quantitative estimates of residual risks under different remediation scenarios, as CERCLA requires. Quantitative risk assessment, even in the face of substantial uncertainties, is standard practice across federal regulatory agencies, and the Supreme Court has upheld its use to support regulatory decision-making (30). This paper has illustrated that mathematical methods and data are available to quantify the risks of UXO explosion and to express the level of uncertainty in the risk estimates. Additional case study tests of the method described in this paper should be undertaken. In addition, several steps could be taken to improve the quantitative model: 1 Analyze data from well-characterized UXO sites to develop a set of model parameters representing the horizontal distribution of UXO under a variety of conditions. Separate parameter estimates could be developed for different range types, such as artillery ranges, rocket ranges, and multipurpose ranges. 2 Conduct physical experiments to understand the sensitivity to explosion of different types of UXO. Tools and methods used to test the stability of unfired ammunition for transport could be employed for this purpose. 3 Explore methods for developing land-use scenarios for consideration when a specific land-use plan is not available. Sets of scenarios could be developed that could be ported to different sites, so that the scenario development process would not need to be repeated each time the risk assessment model is used. 4 Consolidate the model’s computer code into a software package that would be easy to employ, allow flexibility in the choice of model input parameters, and provide for automation of the process of sensitivity and un-

certainty analysis. The model could be converted to a format with a graphical user interface, so that no programming knowledge would be required of the user. In conclusion, we believe that our model (even in its current state of development) would provide important technical information to support decisions about land reuse at former military bases that are contaminated with UXO. It can express the quantitative change in risk that occurs after cleanup, the differences in risk reduction provided by different remediation alternatives, differences in risk for alternative land-use scenarios, and uncertainties in the risk estimates.

Acknowledgments Special thanks to Gail Youngblood, Dave Eisen, Clinton Huckins, and all of the other personnel at the Fort Ord Base Realignment and Closure Office for their support of this research. This work would not have been possible without their assistance. In addition, thanks to the National Science Foundation Graduate Research Fellowship Program for providing the funding that made this research possible.

Supporting Information Available Additional information about the case study site, including a map and a risk analysis of areas where buildings will be constructed, is available free of charge via the Internet at http://pubs.acs.org.

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