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Superfund and Mining Megasites: Lessons from the Coeur d’alene River Basin 6 Human Health Risk Assessment: Lead Exposure and Uptake—Use of the IEUBK Model MODEL DEVELOPMENT BACKGROUND Childhood Lead Exposure and Model Development Needs Lead exhibits a broad range of toxic effects on animal systems, organs, and cellular biochemical and metabolic processes. A National Research Council report (NRC 1993) titled Measuring Lead Exposure in Infants, Children, and Other Sensitive Populations concluded that “lead causes nonspecific, decremental loss of tissue and organ function, with no important pathognomonic manifestations of toxicity.” Furthermore, exposure to lead occurs by multiple pathways and routes. Because many environmental reservoirs are contaminated with lead, it is seldom possible to identify a sole significant source of lead exposure. A primary human exposure pathway to lead is through soil and dust, which children are assumed to incidentally or deliberately ingest. Empirical evidence for this assumption comes from reports of excess amounts of soil tracer elements, especially silicon and aluminum, in the feces of children (Wong et al. 1988; Calabrese et al. 1989; Davis et al. 1990). However, because of the inherent difficulties associated with sampling feces from many children over long periods, available data are limited. As a consequence, actual rates of soil ingestion are somewhat uncertain. Quantitative evidence of hand-to-mouth activity in children has been produced by videography (Zartarian et al. 1997; Reed et al. 1999; Freeman et al. 2001). It is also well established that some fraction of the lead found in soils is
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Superfund and Mining Megasites: Lessons from the Coeur d’alene River Basin absorbable in mammalian gastrointestinal tracts (Casteel et al. 1996a-d, 1997a,b, 1998a-e). Studies generally are consistent in demonstrating that a nonnegligible fraction of lead in soil can be absorbed but that the efficiency of absorption depends on multiple factors including chemical speciation of lead, other dietary components, and particle size of soil ingested. Typically paint-derived lead is relatively available for absorption, whereas lead associated with sulfide minerals is relatively unavailable. Under the environmental health paradigm, preventing injury is the first choice (see Box 6-1). As discussed in Chapter 5, the primary threat presented by lead relates to its ability to cause developmental deficits in children. Although chelation therapy can be applied to reduce body burdens of lead, available information suggests that chelation is not effective in restoring neurological function (Rogan et al. 2001). Hence a “monitor and react” strategy, even if conducted well, cannot prevent injury. The primary prevention strategy (Campbell and Osterhoudt 2000; Rosen and Mushak 2001) is widely recognized as the only truly effective method for eliminating pediatric lead poisoning; this requires a degree of predictive capability for both risk assessment and risk management. The U.S. Environmental Protection Agency (EPA) has adopted a strategy that entails modeling lead exposure rather than biomonitoring as the first line of defense. Existing epidemiological evidence for health effects of lead exposure is anchored to BLLs rather than to dose rates. The relationship between dose and blood level is complicated by the fact that lead is stored in bone. This entails a greater level of modeling sophistication than the standard risk assessment guidance for Superfund (RAGS) paradigm. A primary difference between lead risk assessment and cancer and noncancer risk assessment for other chemicals or compounds is that BLLs can be readily measured in individuals and used to “ground-truth” risk calculations. BLLs provide an integrated picture of lead exposure over the preceding months to years, depending on age and other characteristics of BOX 6-1 Preventing Lead Exposure Children with access to lead-contaminated soils are likely to be exposed to that lead. To establish levels of lead contamination that would not be expected to present unacceptable or unavoidable risk, it is necessary to define the relationship between magnitude of exposure and level of soil contamination. Children exposed to lead who develop elevated blood lead levels (BLLs) may have already been irreversibly damaged by the time they have been identified in screening programs. A primary prevention strategy requires the predictive capability of models for exposure risk assessment and management activities.
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Superfund and Mining Megasites: Lessons from the Coeur d’alene River Basin exposure. In addition, a large body of research exists linking levels of lead in blood to various health effects. As a result, the toxicity and risk characterization steps of a typical risk assessment, as described in the previous chapter, are combined in lead risk assessment into a prediction of BLLs arising from associated lead exposures. Whether risk is deemed acceptable or unacceptable is assessed by comparing the predicted BLLs with target BLLs established by the Centers for Disease Control and Prevention (CDC 1991) and adopted by EPA. EPA uses two predictive blood lead models for risk assessment purposes: the IEUBK model for children up to the age of 7 years (84 months) and the adult lead model for adolescents and adults. In this chapter, we discuss only the integrated exposure uptake biokinetic (IEUBK) model because children are the most susceptible population and residential soil lead cleanup levels generally are set on the basis of childhood lead risk. Predictive Blood Lead Models Lead exhibits a broad range of toxic mechanisms across a variety of target organ systems, and because it has multimedia exposure pathways, the overall dose-response relationships for lead are more complex than those of some other toxic agents. This argues for both biokinetic and pharmacokinetic methods of study to elucidate the concentration and rates of change of lead in various body reservoirs. Mathematical models are particularly useful in this regard because the impacts of lead exposure need to be established on a population-wide basis (NRC 1993). Thus, a variety of predictive blood lead models have evolved for use in lead exposure risk assessment and risk management activities. Two kinds of model development approaches can be used for predicting blood lead values in response to environmental exposure factors. Slope factor models propose a simple linear relationship between BLL and the uptake or intake of lead from environmental media (air, water, food, soil, dust). If uptake is modeled, in contrast to lead intake, the models are sometimes referred to as biokinetic slope factor models. Examples include those developed by Carlisle and Wade (1992), Bowers et al. (1994), Stern (1994, 1996), the Ontario Ministry of Energy and Environment (OMOEE) (1994), and the Agency for Toxic Substances and Disease Registry (ATSDR 1999). The comparative functioning of several of these models and the multicompartment models described below are detailed in a review of adult lead models examined by the technical review workgroup for lead (TRW) (EPA 2001a). Multicompartment predictive blood lead models simulate the movement and concentration of lead in several interconnected tissue compartments with blood or extracellular fluid (plasma) serving as the exchange
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Superfund and Mining Megasites: Lessons from the Coeur d’alene River Basin medium. Rabinowitz (1998) reviewed the early development of this approach, illustrating the usefulness of such models after the experimental application of radioactive tracers showed the relatively short half-life of lead in blood (about 1 month) compared with a 15- to 20-year residence time in skeletal tissue. Models of this type have been developed by Rabinowitz et al. (1976), Marcus (1985), Bert et al. (1989), O’Flaherty (1993), Leggett (1993), and EPA (1994a,b). A simple depiction of a multicompartment model, similar to that of Rabinowitz et al. (1976) is shown in Figure 6-1. Biokinetic and pharmacokinetic models relate exposure dose to the lead concentration in various target tissues; they represent the mathematics of the time course of absorption, distribution, metabolism, and excretion (ADME) of the substance being followed. Biological, physiological, and physicochemical factors all influence the rate and extent of ADME. Several mathematical approaches underlie the pharmacobiokinetic (PBK) model structures: in diffusion-limited models, such as the IEUBK model, rates of change of lead concentration in the various compartments are defined by the rates of transfer across compartment boundaries. The time parameter is represented in the diffusion rate constants. Lead transfers are typically assumed to follow first-order kinetics; exchanges are repre- FIGURE 6-1 Simple model framework illustrating compartments and pathways of exchange for a pharmacobiokinetic model of lead in the human system. SOURCE: Rabinowitz et al. 1976. Reprinted with permission from the American Society for Clinical Investigation.
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Superfund and Mining Megasites: Lessons from the Coeur d’alene River Basin sented by first-order rate constants. However, such “constants,” may take on age-specific values, an important characteristic of PBK models applied to children’s lead exposure. An alternative (O’Flaherty 1993) is a flow-limited model; this approach quantifies the mass transfer of the extracellular fluid to the tissue compartments of the model. Here, the time variable is incorporated in the flow rates of fluid between body compartments. A central feature of the O’Flaherty model is its emulation of bone growth and resorption as a mechanism for controlling plasma lead levels. “Lead is assumed to instantaneously partition between plasma and soft tissues and to achieve an equilibrium (that is, partition coefficient). Therefore the rates of change of lead masses in soft tissues are limited by the rates of delivery of lead to the tissues, given by the product of the plasma concentration of lead and the rate of plasma flow to the tissue, rather than by limiting steps in the transfer of lead across tissue boundaries” (EPA 2001a). Predictive blood lead models generally distinguish between the intake of lead during exposure and its uptake by the body. The fraction of lead that is absorbed and enters the blood by whatever portal-of-entry compared with the total amount of lead acquired is termed the bioavailability. In the simple illustration of a PBK model (Figure 6-1), lead intake is represented as ingestion. Subsequently, a fraction of the lead present in the gastrointestinal tract is taken up into the bloodstream—a process that may vary with the age of the individual; the person’s health, physiological, and/ or nutritional status; and whether ingestion occurred with or without food. Bioavailability of inhaled lead may differ from that of ingested lead. By either route of entry, biokinetic or pharmacokinetic models incorporate a variable for the fraction of total lead that is actually absorbed and define it as the uptake of lead. In the 1999 EPA Guidance Document IEUBK Model Bioavailability Variable (EPA 1999), the following terms are defined and adopted for use in this chapter: Absolute bioavailability is the amount of a substance entering the blood via a particular route of exposure (for example, gastrointestinal) divided by the total amount administered (for example, soil lead ingested). Relative bioavailability is indexed by measuring the bioavailability of a particular substance relative to the bioavailability of a standardized reference material, such as soluble lead acetate. Evolution of EPA’s IEUBK Model Federal agencies documented and summarized extensive research on the toxicological impact of lead exposure (McMichael et al. 1986; Bellinger et al. 1989; Bornschein et al. 1989; Needleman et al. 1990; and others)
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Superfund and Mining Megasites: Lessons from the Coeur d’alene River Basin before development of the IEUBK model (ATSDR 1988; EPA 1989, 1990). As pointed out by Choudhury et al. (1992), epidemiological and behavioral research had not identified a threshold or no-observed-adverse-effect level (NOAEL) that could be used to establish a reference dose for lead—that is, a value that could be used for risk assessment in the manner discussed in Chapter 5 for other metals of concern. Empirical studies showed relationships between children’s BLL and the concentration of lead in a variety of media (Barltrop et al. 1975; Yankel et al. 1977; Angle and McIntire 1982; Stark et al. 1982). These slope factor (SF) models were the foundation for the current modeling structure. The impetus for further development of such tools was to quantify the impact of lead in setting National Ambient Air Quality Standards (NAAQS) (EPA 1986) and National Primary Drinking Water Regulations. However, substantial limitations of SF models were identified, owing to the individual variability of children with respect to factors including ingestion rates and activity patterns, the influence of physiological states and nutritional factors on lead absorption, and physicochemical differences in the distribution and occurrence of lead between sites of exposure. Thus, biokinetic models were developed as an alternative approach, emphasizing the need for a predictive capability in order to implement primary prevention strategies. In 1985, the EPA Office of Air Quality Planning and Standards (OAQPS) began a computer-simulation-model development based on the biokinetic model of Kneip et al. (1983) and Harley and Kneip (1985). These studies brought together a critical mass of biokinetic parameter information. The exposure component for model operation was developed by OAQPS. A 1989 OAQPS staff paper reviewing the NAAQS for lead contained results of model applications to point sources of air lead. Shortly thereafter, the TRW for lead was formed to advise on cleanup at Superfund and Resource Conservation and Recovery Act of 1976 (RCRA) sites; they modified the model for lead risk assessment, calling it the uptake biokinetic (UBK) model. The TRW recognized the desirability of a frequency distribution for BLLs of a population and used a geometric standard deviation based on NHANES II (1986) data. Initial calibration and validation exercises for the developing model were based on the 1983 Helena, Montana, primary lead smelter study, as cited in the 1989 Review of the National Ambient Air Quality Standards for Lead: Exposure Analysis Methodology and Validation (EPA 1989). Further validation of the UBK model was reported by DeRosa et al. (1991) and by Bornschein et al. (1990); whereas the latter study used the Midvale, Utah, data set, the data source for the DeRosa study was not identified. Choudhury et al. (1992) indicated that, for the Midvale exposure data, the UBK default conditions provided an acceptable agreement between observed and calculated values for measures of central tendency but that the upper end of the distribution was not well predicted. Agreement between
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Superfund and Mining Megasites: Lessons from the Coeur d’alene River Basin predictions and empirical results for Midvale data improved when an age-dependent dust/soil ingestion rate was used. The latter are the same as the current default values for the model. Subsequent to the release of the IEUBK model executable in 1994, additional evaluation of the model was conducted by EPA, including an independent validation and verification of the source code (Zaragoza and Hogan 1998) and an evaluation of predictions of BLLs in children for whom environmental levels and BLLs were measured (Hogan et al. 1998). The EPA Clean Air Science Advisory Committee (CASAC) of the Science Advisory Board provided initial review and approval of model structure and functioning in 1989. In 1990, CASAC concluded that the model provided “an adequate scientific basis for EPA to retain or revise primary and secondary NAAQS for airborne lead.” In 1992, the EPA Science Advisory Board reviewed and reported on the UBK model for lead. Suggested modifications also derived from comments on the draft 1992 Office of Solid Waste and Emergency Response (OSWER) Soil Lead Directive proposed using the UBK model in support of lead exposure risk assessments. Since 1991, the TRW has been responsible for model development. Modifications have made it suitable for evaluating exposure from all media, and the product became a stand-alone PC software package. The biokinetic model approach was deemed suitable for assessing total lead exposures and for developing cleanup levels at residential Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA)/RCRA sites. With refinements resulting from comments on early model versions, the model was released in executable form only in 1994 as the IEUBK model. DESCRIPTION OF THE IEUBK MODEL Model Structure and Operation This section presents an overview of the model’s structure and operation. A more detailed summary of the IEUBK model can be found in the work of White et al. (1998). The compartmental structure of the IEUBK model is slightly more complex than that shown previously for the simple PBK example and is illustrated in Figure 6-2 (EPA 1994a). Despite significantly more structure in this version of a multicompartment model, lead accumulation in various model reservoirs still has, as a fundamental control, the time-dependent difference between the uptake and the excretion pathways. When concentrations of lead in environmental media are specified, the model calculates a point estimate of a child’s blood lead values over the age range of 0-84 months. The IEUBK model is defined operationally by EPA’s computer program(s). These programs have been publicly available in object code form
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Superfund and Mining Megasites: Lessons from the Coeur d’alene River Basin FIGURE 6-2 Compartments and functional arrangement of the IEUBK components for prediction of children’s blood lead values. SOURCE: EPA 1994a. (that is, in a form suitable for running on a computer) since 1994 and have been through multiple versions. The latest version is available from EPA’s Superfund Web site (EPA 2004a),1 and that site also contains technical documentation on the model. The source code for the IEUBK model is not linked at this or any other Web site and has never been readily available in this way; rather, it has always been necessary to specifically request it from EPA. The primary technical source describing the model is the Technical Support Document (TSD) (EPA 1994b). Although this is explicitly for version 0.99d of the model, the model specification has not changed in any essential way in the 10 years since then. Examination of the computer code shows that the biokinetic portion of the code is identical in all relevant (and some irrelevant) respects. Notably, the current code contains the same 1 Surprisingly, there appears to be no link to the IEUBK model information from EPA’s “lead in paint, dust, and soil” (EPA 2005).
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Superfund and Mining Megasites: Lessons from the Coeur d’alene River Basin errors2 and redundancies, as described below, that were present in the original version. The essential parts of the IEUBK model3 can be partitioned into four components: an intake component, an uptake component, a biokinetic component, and a probability component. These four components are strictly independent of one another, each feeding into the following one with no feedback. Intake Component The intake component of the model collects information on exposures to lead-contaminated media (air, dust, soil, food, water) and sums the quantities of lead that enter the body from each exposure medium. Within each medium, the intake of lead is obtained as the product of an average concentration or mass fraction4 of lead in the medium and the average intake rate of that medium. For example, the intake of lead from soil is the product of the soil lead concentration (milligrams [mg] of lead per kilogram [kg] of soil) and the ingestion rate for soils (mg of soil ingested per day) to provide an intake rate for lead from soil. The exposure module contains default values for environmental concentrations and ingestion rates should no site-specific information be available. Similarly, default values for absolute bioavailability are programmed for model operation but may be altered by the user. For soil and dust ingestion, default bioavailability values of 30% are assigned. That value is derived from an absolute bioavailability for soluble lead in water and diet constituents of 50%, together with a 60% relative bioavailability for soil and dust lead compared with water (EPA 1999). Table 6-1 summarizes the IEUBK default values. 2 As described in the subsection “Incorrect Model Specifications” below, the committee considers the computer code for the biokinetic part of the model to be in error if it does not solve, in the limit of small time step, the set of algebraic and differential equations and boundary conditions specified in the TSD (EPA 1994b) (which is taken to define the model). The committee has not examined other parts of the code and does not certify that even the examined code is free of other errors. The documentation is considered to be in error if it specifies physical impossibilities or fails to define some element of the model. These definitions are imposed because the committee believes that the model specification should be the standard of comparison (for observations, other implementations, and other models), rather than the computer code itself. 3 The user interface is not considered here because that does not comprise an essential component of the model. The principal changes in the model over the last 10 years have been in the user interface and in the default values that are automatically present in that user interface. 4 We do not subsequently distinguish between concentration and mass fraction, using the first term in the usual colloquial sense to represent both.
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Superfund and Mining Megasites: Lessons from the Coeur d’alene River Basin TABLE 6-1 Default Values for the EPA IEUBK Model 0-1 y 1-2 y 2-3 y 3-4 y 4-5 y 5-6 y 6-7 y Ventilation rate, m3 per day 2 3 5 5 5 7 7 Diet intake, μg lead per day 5.53 5.78 6.49 6.24 6.01 6.34 7.00 Water intake, L per day 0.20 0.50 0.52 0.53 0.55 0.58 0.59 Soil/dust ingestion, total mg per day 85 135 135 135 100 90 85 Water = 4 μg of lead per L, air = 0.1 μg of lead per m3, maternal blood lead = 2.5 μg of lead per dL. Indoor air lead concentration = 30% of outdoor concentration. Soil lead concentration = dust lead concentration = 200 μg lead per gram of soil/dust. Soil = 45% of total ingestion, dust = 55% of total ingestion. Diet and water bioavailability = 50%, soil and dust bioavailability = 30%. NOTE: Bioavailability is not constant. The values cited apply for low lead intake rates. Absolute bioavailability decreases as lead intake increases and uptake saturation is reached. SOURCE: EPA 1994b. Uptake Component The uptake part of the model contains two parts: one deals with absorption in the lung, the other with absorption in the gut. Absorption in the lung is treated as linear; some fixed fraction of the inhaled quantity of lead is assumed to be absorbed. Absorption in the gut is assumed to consist of two fractions: a linear, nonsaturable component and a nonlinear, saturable component. Details of the gastrointestical tract uptake specifications are illustrated in Box 6-2 and Figure 6-3. For each ingested medium (labeled BOX 6-2 Lead Uptake Formulations for the IEUBK Model Description of Model Formulation for Uptake of Lead from the Gastrointestinal Tract Figure 6-3 illustrates the two types of uptake from the gut. Suppose the total lead ingestion intake in medium k is Zk. Then defining (0-1) the linearly absorbed component Ul and nonlinearly absorbed component Un are assumed to be given by (0-2) with the total gut absorption given by the sum Ul + Un. The value p has default value 0.2, and Zsat is estimated by default as 100 μg/day at 24 months, and is scaled with body weight for other ages.
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Superfund and Mining Megasites: Lessons from the Coeur d’alene River Basin FIGURE 6-3 Mathematical treatment of the lead absorption in the IEUBK model. SOURCE: EPA 1994b. here by index k), there is assumed to be a fixed fraction αk (the bioavailability of lead from that medium) that could be absorbed at a low exposure level. The user can override the program default values and specify separate bioavailability values for each exposure medium. Biokinetic Component The biokinetic component of the IEUBK model is a compartment model with seven compartments plus three excretion-only pseudocompartments (URINE, FECES, and SNH) as named and numbered in Table 6-2. The plasma-ECF compartment exchanges lead with all the other compartments, and excretion occurs only to the urine pseudocompartment. The only other connectivity between compartments and pseudocompartments is the excretion of lead from liver to feces and from soft tissues to skin, nails, and hair. The only connection between the uptake and biokinetic components of the model occurs through uptake in the lung and gut. These uptakes are assumed to be independent of the internal state of the body incorporated in the biokinetic component. In theory, there is some dependence—for example because of excretion of lead into the gut (from where it could be re-absorbed) in bile; however, the effect of any such dependencies is expected to be small. Equations describing the transfer of lead between these compartments (equations of motion) are presented in Box 6-3. Transfer between these compartments is described by the time constants Fi and Ti, which denote uptake to plasma or transfer from plasma, respectively. Similarly, Ai is the
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Superfund and Mining Megasites: Lessons from the Coeur d’alene River Basin the O’Flaherty models, showing identical cleanup-level determinations, would have highlighted the critical importance of uncertainty in bioavailability and ingestion rate parameters. Recommendation EPA should promote use and development of both deterministic and probabilistic multipathway uptake and pharmacokinetic models for lead as research tools and provide scientific maintenance for their continued development and improvement. This could substantially improve their application as regulatory instruments. Conclusion 5 The committee finds that EPA guidance concerning specific use of the IEUBK model and additional use of blood lead studies is incomplete. The inherent uncertainties associated with model predictions coupled with the high value placed on the need for predictive capability in the protection of both present and future populations requires a more clear and comprehensive articulation of IEUBK model-use policy. The 1998 OSWER directive fails, as described in this chapter, to give adequate guidance about what to do when BLLs and IEUBK model results disagree by a substantial margin. It states without clear justification that model results are to take precedence in these situations. Significant emphasis in the directive suggests that, where such disagreement exists, the blood lead study may be suspect. It is clear that blood lead observations may not always be representative of the population, may have been conducted at the wrong time of year, or may have been influenced by significant knowledge of lead hazards within a population. However, uncertainties may also exist in the IEUBK model results, where the relationship between soil and dust may not be well understood, the bioavailability of soil and/or dust may be unknown, or where factors, such as lead in paint, may be inadequately addressed in the model input parameter characterizations. Additional information for addressing such uncertainties could be provided by assays of soil and dust bioavailability, determining the presence or absence of lead-based paint, which can serve as a confounder in the model, and by analyses of additional metals such as arsenic, cadmium, and zinc as these metals may co-occur with lead and can improve the estimate of soil transfer to dust. Recommendation EPA’s guidance on use of blood lead studies in conjunction with the IEUBK model needs clarification, especially on protocols for reconciling
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Superfund and Mining Megasites: Lessons from the Coeur d’alene River Basin differences between modeled and observed blood lead values and for objectively considering the uncertainties associated with each. The guidance/ policy should address the following points: Where blood lead observations are available, a systematic protocol for comparison of predicted and observed BLLs should be used for all risk assessments, and an acceptable level of variability between such results should be established to define “significant” differences. Criteria should be established upon which to judge whether or not the extant blood lead observations are representative of the community concerned, covering the full range of lead-exposure potential. If “significant” differences exist between observed and predicted blood lead values, such criteria would establish whether an additional blood lead study effort was required. Definitive guidelines for the conduct of blood lead studies should be established. The focus should be on the coherence of the joint data set covering the full range of lead exposure risks and the collection of blood lead data associated with that range of exposure. When model results and acceptable blood lead study observations do not agree, and when default IEUBK exposure values have been used for some or all of the modeling exercise, additional information should be collected to examine uncertainty in model inputs and to ensure that all exposure sources and lead uptake/intake rates have been adequately established for the specific site in question. Before development of a fully probabilistic IEUBK model, uncertainty in the GSD should be explored with the ISE, lead risk model, or another similar model to understand how it may depart from the default for a particular site. Conclusion 6 The IEUBK model results should not be the sole criterion for establishing health-protective soil concentrations at mining megasites such as OU-3 of the Coeur d’Alene River basin, because model uncertainty and site complexity may interact in unexpected or unknown ways. This chapter details a variety of specific challenges associated with IEUBK application to OU-3. The geographic area defined as OU-3 exhibits a great diversity of topography, land use practice, bedrock geology, ecologic community structure, and hydrologic regime. Consequently, one would expect the nature and extent of natural geochemical mineral alteration, soil digenetic processes, and sediment transport and deposition dynamics to vary accordingly. Such variations are manifest in the IEUBK box model predictions, which suggest regional differences between the upper
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Superfund and Mining Megasites: Lessons from the Coeur d’alene River Basin and lower basin in lead bioavailability and possibly in other model operation parameters as well. By extension, it is likely that similar problems will arise at other sites where ecologic, geomorphological, and sociodemographic complexity of this nature exists. A comprehensive revision of the 1998 OSWER directive on model use, incorporating those issues just outlined, is needed to adequately address issues associated with geographic variability at large geographically heterogeneous sites. Recommendation Incorporate the IEUBK model in a negotiated and carefully communicated HHRA/ROD structure for which the primary prevention paradigm contains the four fundamental elements of Predictive capability (IEUBK or successors) Empirical results (blood lead study results) Economic feasibility Sustainable remediation (long-term remedy maintenance) Each of these key elements is necessary for successful remediation, but the way they are weighted for the mutual satisfaction of all stakeholders may be different across the variety of contiguous spatial elements defined for the OU. Both risk assessment and risk management activities should be structured according to natural environmental system boundaries; they should not represent the aggregation of apparently applicable policies previously found to be successful for smaller, simpler systems. REFERENCES Angle, C.R., and M.S. McIntire. 1982. Children, the barometer of environmental lead. Adv. Pediatr. 29:3-31. Anspaugh, L.R., S.L. Simon, K.I. Gordeev, I.A. Likhtarev, R.M. Maxwell, and S.M. Shinkarev. 2002. Movement of radionuclides in terrestrial ecosystems by physical processes. Health Phys. 82(5):669-679. Aschengrau, A., S. Hardy, P. Mackey, and D. Pultinas. 1998. The impact of low technology lead hazard reduction activities among children with mildly elevated blood lead levels. Environ. Res. 79(1):41-50. ATSDR (Agency for Toxic Substances and Disease Registry). 1988. The Nature and Extent of Lead Poisoning in Children in the United States: A Report to Congress. Agency for Toxic Substances and Disease Registry, U.S. Public Health Service, U.S. Department of Health and Human Services, Atlanta, GA. ATSDR (Agency for Toxic Substances and Disease Registry). 1999. A framework to guide public health assessments decisions at lead sites. Appendix D in Toxicological Profile for Lead. Agency for Toxic Substances and Disease Registry, U.S. Public Health Service, U.S. Department of Health and Human Services, Atlanta, GA.
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Representative terms from entire chapter: