Human Interindividual Variability in Parameters Related to Health Risks

This paper reviews existing data on the variability in parameters relevant for health risk analyses. We cover both exposure-related parameters and parameters related to individual susceptibility to toxicity. The toxicity/susceptibility data base under construction is part of a longer term research effort to lay the groundwork for quantitative distributional analyses of non-cancer toxic risks. These data are broken down into a variety of parameter types that encompass different portions of the pathway from external exposure to the production of biological responses. The discrete steps in this pathway, as we now conceive them, are:Contact Rate (Breathing rates per body weight; fish consumption per body weight)Uptake or Absorption as a Fraction of Intake or Contact RateGeneral Systemic Availability Net of First Pass Elimination and Dilution via Distribution Volume (e.g., initial blood concentration per mg/kg of uptake)Systemic Elimination (half life or clearance)Active Site Concentration per Systemic Blood or Plasma ConcentrationPhysiological Parameter Change per Active Site Concentration (expressed as the dose required to make a given percentage change in different people, or the dose required to achieve some proportion of an individual's maximum response to the drug or toxicant)Functional Reserve Capacity–Change in Baseline Physiological Parameter Needed to Produce a Biological Response or Pass a Criterion of Abnormal FunctionComparison of the amounts of variability observed for the different parameter types suggests that appreciable variability is associated with the final step in the process–differences among people in functional reserve capacity. This has the implication that relevant information for estimating effective toxic susceptibility distributions may be gleaned by direct studies of the population distributions of key physiological parameters in people that are not exposed to the environmental and occupational toxicants that are thought to perturb those parameters. This is illustrated with some recent observations of the population distributions of Low Density Lipoprotein Cholesterol from the second and third National Health and Nutrition Examination Surveys.     

Human Interindividual Variability–A Major Source of Uncertainty in Assessing Risks for Noncancer Health Effects

For noncancer effects, the degree of human interindividual variability plays a central role in determining the risk that can be expected at low exposures. This discussion reviews available data on observations of interindividual variability in (a) breathing rates, based on observations in British coal miners; (b) systemic pharmacokinetic parameters, based on studies of a number of drugs; (c) susceptibility to neurological effects from fetal exposure to methyl mercury, based on observations of the incidence of effects in relation to hair mercury levels; and (d) chronic lung function changes in relation to long-term exposure to cigarette smoke. The quantitative ranges of predictions that follow from uncertainties in estimates of interindividual variability in susceptibility are illustrated.     

Evaluation of the Uncertainty in an Oral Reference Dose for Methylmercury Due to Interindividual Variability in Pharmacokinetics

An analysis of the uncertainty in guidelines for the ingestion of methylmercury (MeHg) due to human pharmacokinetic variability was conducted using a physiologically based pharmacokinetic (PBPK) model that describes MeHg kinetics in the pregnant human and fetus. Two alternative derivations of an ingestion guideline for MeHg were considered: the U.S. Environmental Protection Agency reference dose (RfD) of 0.1 g/kg/day derived from studies of an Iraqi grain poisoning episode, and the Agency for Toxic Substances and Disease Registry chronic oral minimal risk level (MRL) of 0.5 g/kg/day based on studies of a fish-eating population in the Seychelles Islands. Calculation of an ingestion guideline for MeHg from either of these epidemiological studies requires calculation of a dose conversion factor (DCF) relating a hair mercury concentration to a chronic MeHg ingestion rate. To evaluate the uncertainty in this DCF across the population of U.S. women of child-bearing age, Monte Carlo analyses were performed in which distributions for each of the parameters in the PBPK model were randomly sampled 1000 times. The 1st and 5th percentiles of the resulting distribution of DCFs were a factor of 1.8 and 1.5 below the median, respectively. This estimate of variability is consistent with, but somewhat less than, previous analyses performed with empirical, one-compartment pharmacokinetic models. The use of a consistent factor in both guidelines of 1.5 for pharmacokinetic variability in the DCF, and keeping all other aspects of the derivations unchanged, would result in an RfD of 0.2 g/kg/day and an MRL of 0.3 g/kg/day.     

Assessment of Interindividual and Geographic Variability in Human Exposure to Fine Particulate Matter in Environmental Tobacco Smoke

Environmental tobacco smoke (ETS) is a major contributor to indoor human exposures to fine particulate matter of 2.5 μm or smaller (PM_2.5). The Stochastic Human Exposure and Dose Simulation for Particulate Matter (SHEDS‐PM) Model developed by the U.S. Environmental Protection Agency estimates distributions of outdoor and indoor PM_2.5 exposure for a specified population based on ambient concentrations and indoor emissions sources. A critical assessment was conducted of the methodology and data used in SHEDS‐PM for estimation of indoor exposure to ETS. For the residential microenvironment, SHEDS uses a mass‐balance approach, which is comparable to best practices. The default inputs in SHEDS‐PM were reviewed and more recent and extensive data sources were identified. Sensitivity analysis was used to determine which inputs should be prioritized for updating. Data regarding the proportion of smokers and “other smokers” and cigarette emission rate were found to be important. SHEDS‐PM does not currently account for in‐vehicle ETS exposure; however, in‐vehicle ETS‐related PM_2.5 levels can exceed those in residential microenvironments by a factor of 10 or more. Therefore, a mass‐balance‐based methodology for estimating in‐vehicle ETS PM_2.5 concentration is evaluated. Recommendations are made regarding updating of input data and algorithms related to ETS exposure in the SHEDS‐PM model. Interindividual variability for ETS exposure was quantified. Geographic variability in ETS exposure was quantified based on the varying prevalence of smokers in five selected locations in the United States.     

Modeling Human Interindividual Variability in Metabolism and Risk: The Example of 4-Aminobiphenyl

We investigate, through modeling, the impact of interindividual heterogeneity in the metabolism of 4-aminobiphenyl (ABP) and in physiological factors on human cancer risk: A physiological pharmacokinetic model was used to quantify the time course of the formation of the proximate carcinogen, N-hydroxy-4-ABP and the DNA-binding of the active species in the bladder. The metabolic and physiologic model parameters were randomly varied, via Monte Carlo simulations, to reproduce interindividual variability. The sampling means for most parameters were scaled from values developed by Kadlubar et al. (Cancer Res., 51 : 4371, 1991) for dogs; variances were obtained primarily from published human data (e.g., measurements of ABP N-oxidation, and arylamine N-acetylation in human liver tissue). In 500 simulations, theoretically representing 500 humans, DNA-adduct levels in the bladder of the most susceptible individuals are ten thousand times higher than for the least susceptible, and the 5th and 95th percentiles differ by a factor of 160. DNA binding for the most susceptible individual (with low urine pH, low N-acetylation and high N-oxidation activities) is theoretically one million-fold higher than for the least susceptible (with high urine pH, high N-acetylation and low N-oxidation activities). The simulations also suggest that the four factors contributing most significantly to interindividual differences in DNA-binding of ABP in human bladder are urine pH, ABP N-oxidation, ABP N-acetylation and urination frequency.     

Integrating Uncertainty and Interindividual Variability in Environmental Risk Assessment

An integrated, quantitative approach to incorporating both uncertainty and interindividual variability into risk prediction models is described. Individual risk R is treated as a variable distributed in both an uncertainty dimension and a variability dimension, whereas population risk I (the number of additional cases caused by R) is purely uncertain. I is shown to follow a compound Poisson-binomial distribution, which in low-level risk contexts can often be approximated well by a corresponding compound Poisson distribution. The proposed analytic framework is illustrated with an application to cancer risk assessment for a California population exposed to 1,2-dibromo-3-chloropropane from ground water.     

Human Variability in Susceptibility to Toxic Chemicals— A Preliminary Analysis of Pharmacokinetic Data from Normal Volunteers

The tenfold "uncertainty" factor traditionally used to guard against human interindividual differences in susceptibility to toxicity is not based on human observations. To begin to build a basis for quantifying an important component of overall variability in susceptibility to toxicity, a data base has been constructed of individual measurements of key pharmacokinetic parameters for specific substances (mostly drugs) in groups of at least five healthy adults. 72 of the 101 data sets studied were positively skewed, indicating that the distributions are generally closer to expectations for log-normal distributions than for normal distributions. Measurements of interindividual variability in elimination half-lives, maximal blood concentrations, and AUC (area under the curve of blood concentration by time) have median values of log10 geometric standard deviations in the range of 0.11-0.145. For the median chemical, therefore, a tenfold difference in these pharmacokinetic parameters would correspond to 7-9 standard deviations in populations of normal healthy adults. For one relatively lipophilic chemical, however, interindividual variability in maximal blood concentration and AUC was 0.4--implying that a tenfold difference would correspond to only about 2.5 standard deviations for those parameters in the human population. The parameters studied to date are only components of overall susceptibility to toxic agents, and do not include contributions from variability in exposure- and response-determining parameters. The current study also implicitly excludes most human interindividual variability from age and illness. When these other sources of variability are included in an overall analysis of variability in susceptibility, it is likely that a tenfold difference will correspond to fewer standard deviations in the overall population, and correspondingly greater numbers of people at risk of toxicity.     

Differences in Pharmacokinetics Between Children and Adults—II. Children''s Variability in Drug Elimination Half-Lives and in Some Parameters Needed for Physiologically-Based Pharmacokinetic Modeling

In earlier work we assembled a database of classical pharmacokinetic parameters (e.g., elimination half-lives; volumes of distribution) in children and adults. These data were then analyzed to define mean differences between adults and children of various age groups. In this article, we first analyze the variability in half-life observations where individual data exist. The major findings are as follows. The age groups defined in the earlier analysis of arithmetic mean data (0-1 week premature; 0-1 week full term; 1 week to 2 months; 2-6 months; 6 months to 2 years; 2-12 years; and 12-18 years) are reasonable for depicting child/adult pharmacokinetic differences, but data for some of the earliest age groups are highly variable. The fraction of individual children's half-lives observed to exceed the adult mean half-life by more than the 3.2-fold uncertainty factor commonly attributed to interindividual pharmacokinetic variability is 27% (16/59) for the 0-1 week age group, and 19% (5/26) in the 1 week to 2 month age group, compared to 0/87 for all the other age groups combined between 2 months and 18 years. Children within specific age groups appear to differ from adults with respect to the amount of variability and the form of the distribution of half-lives across the population. The data indicate departure from simple unimodal distributions, particularly in the 1 week to 2 month age group, suggesting that key developmental steps affecting drug removal tend to occur in that period. Finally, in preparation for age-dependent physiologically-based pharmacokinetic modeling, nationally representative NHANES III data are analyzed for distributions of body size and fat content. The data from about age 3 to age 10 reveal important departures from simple unimodal distributional forms-in the direction suggesting a subpopulation of children that are markedly heavier than those in the major mode. For risk assessment modeling, this means that analysts will need to consider "mixed" distributions (e.g., two or more normal or log-normal modes) in which the proportions of children falling within the major versus highweight/fat modes in the mixture changes as a function of age. Biologically, the most natural interpretation of this is that these subpopulations represent children who have or have not yet received particular signals for change in growth pattern. These apparently distinct subpopulations would be expected to exhibit different disposition of xenobiotics, particularly those that are highly lipophilic and poorly metabolized.     

Variability in Physical Constant Parameter Values from Standard Data Sources and the Implication of This Variability for Risk Analysis

Physical property values are used in environmental risk assessments to estimate media and risk-based concentrations. However, considerable variability has recently been reported with such values. To evaluate potential variability in physical parameter values supporting a variety of regulatory programs, eight data sources were chosen for evaluation, and chemicals appearing in at least four sources were selected. There were 755 chemicals chosen. In addition, chemicals in seven environmentally important subgroups were also identified for evaluation. Nine parameters were selected for analysis-molecular weight (MolWt), melting point (MeltPt), boiling point (BoilPt), vapor pressure (VP), water solubility (AqSOL), Henry's law constant (HLC), octanol-water partition coefficient (Kow), and diffusion coefficients in air (Dair) and water (Dwater). Results show that while 71% of constituents had equal MolWts across data sources, <3% of the constituents had equivalent parameter values across data sources for AqSOL, VP, or HLC. Considerable dissimilarity between certain sources was also observed. Furthermore, measures of dispersion showed considerable variation in data sets for Kow, VP, AqSOL, and HLC compared to measures for MolWt, MeltPt, BoilPt, or Dwater. The magnitude of the observed variability was also noteworthy. For example, the 95th percentile ratio of maximum/minimum parameter values ranged from 1.0 for MolWt to well over 1.0E + 06 for VP, and HLC. Risk and exposure metrics also varied by similar magnitudes. Results with environmentally important subgroups were similar. These results show that there is considerable variability in physical parameter values from standard sources, and that the observed variability could affect potential risk estimates and perhaps risk management decisions.     

Uncertainty and Variability in Human Exposures to Soil Contaminants Through Home-Grown Food: A Monte Carlo Assessment

This paper presents a general model for exposure to homegrown foods that is used with a Monte Carlo analysis to determine the relative contributions of variability (Type A uncertainty) and true uncertainty (Type B uncertainty) to the overall variance in prediction of the dose-to-concentration ratio. Although classification of exposure inputs as uncertain or variable is somewhat subjective, food consumption rates and exposure duration are judged to have a predicted variance that is dominated by variability among individuals by age, income, culture, and geographical region. Whereas, biotransfer factors and partition factors are inputs that, to a large extent, involve uncertainty. Using ingestion of fruits, vegetables, grains, dairy products, and meat and soils assumed to be contaminated by hexachlorbenzene (HCB) and benzo(a)pyrene (BaP) as cases studies, a Monte Carlo analysis is used to explore the relative contribution of uncertainty and variability to overall variance in the estimated distribution of potential dose within the population that consumes homegrown foods. It is found that, when soil concentrations are specified, variances in ratios of dose-to-concentration for HCB are equally attributable to uncertainty and variability, whereas for BaP, variance in these ratios is dominated by true uncertainty.     

EPA Underestimates,Oversimplifies, Miscommunicates,and Mismanages Cancer Risks by Ignoring Human Susceptibility

If exposed to an identical concentration of a carcinogen, every human being would face a different level of risk, determined by his or her genetic, environmental, medical, and other uniquely individual characteristics. Various lines of evidence indicate that this susceptibility variable is distributed rather broadly in the human population, with perhaps a factor of 25‐ to 50‐fold between the center of this distribution and either of its tails, but cancer risk assessment at the EPA and elsewhere has always treated every (adult) human as identically susceptible. The National Academy of Sciences “Silver Book” concluded that EPA and the other agencies should fundamentally correct their mis‐computation of carcinogenic risk in two ways: (1) adjust individual risk estimates upward to provide information about the upper tail; and (2) adjust population risk estimates upward (by about sevenfold) to correct an underestimation due to a mathematical property of the interindividual distribution of human susceptibility, in which the susceptibility averaged over the entire (right‐skewed) population exceeds the median value for the typical human. In this issue of Risk Analysis, Kenneth Bogen disputes the second adjustment and endorses the first, though he also relegates the problem of underestimated individual risks to the realm of “equity concerns” that he says should have little if any bearing on risk management policy. In this article, I show why the basis for the population risk adjustment that the NAS recommended is correct—that current population cancer risk estimates, whether they are derived from animal bioassays or from human epidemiologic studies, likely provide estimates of the median with respect to human variation, which in turn must be an underestimate of the mean. If cancer risk estimates have larger “conservative” biases embedded in them, a premise I have disputed in many previous writings, such a defect would not excuse ignoring this additional bias in the direction of underestimation. I also demonstrate that sensible, legally appropriate, and ethical risk policy must not only inform the public when the tail of the individual risk distribution extends into the “high‐risk” range, but must alter benefit‐cost balancing to account for the need to try to reduce these tail risks preferentially.     

On the Treatment of Uncertainty and Variability in Making Decisions About Risk

Much attention has been paid to the treatment of dependence and to the characterization of uncertainty and variability (including the issue of dependence among inputs) in performing risk assessments to avoid misleading results. However, with relatively little progress in communicating about the effects and implications of dependence, the effort involved in performing relatively sophisticated risk analyses (e.g., two‐dimensional Monte Carlo analyses that separate variability from uncertainty) may be largely wasted, if the implications of those analyses are not clearly understood by decisionmakers. This article emphasizes that epistemic uncertainty can introduce dependence among related risks (e.g., risks to different individuals, or at different facilities), and illustrates the potential importance of such dependence in the context of two important types of decisions—evaluations of risk acceptability for a single technology, and comparisons of the risks for two or more technologies. We also present some preliminary ideas on how to communicate the effects of dependence to decisionmakers in a clear and easily comprehensible manner, and suggest future research directions in this area.     

Variability in Physical Constant Parameter Values from Standard Data Sources and the Implication of this Variability for Risk Analysis

Physical property values are used in environmental risk assessments to estimate media and risk-based concentrations. Recently, however, considerable variability has been reported with such values. To evaluate potential variability in physical parameter values supporting a variety of regulatory programs, eight data sources were chosen for evaluation, and chemicals appearing in at least four sources were selected. There were 755 chemicals chosen. In addition, chemicals in seven environmentally important subgroups were also identified for evaluation. Nine parameters were selected for analysis--molecular weight (MolWt), melting point (MeltPt), boiling point (BoilPt), vapor pressure (VP), water solubility (AqSOL), Henry's law constant (HLC), octanol-water partition coefficient (Kow), and diffusion coefficients in air (Dair) and water (Dwater). Results show that while 71% of constituents had equal MolWts across data sources, <3% of the constituents had equivalent parameter values across data sources for AqSOL, VP, or HLC. Considerable dissimilarity between certain sources was also observed. Furthermore, measures of dispersion showed considerable variation in data sets for Kow, VP, AqSOL, and HLC compared to measures for MolWt, MeltPt, BoilPt, or Dwater. The magnitude of the observed variability was also noteworthy. For example, the 95th percentile ratio of maximum/minimum parameter values ranged from 1.0 for MolWt to well over 1.0 x 10(6) for VP and HLC. Risk and exposure metrics also varied by similar magnitudes. Results with environmentally important subgroups were similar. These results show that there is considerable variability in physical parameter values from standard sources, and that the observed variability could affect potential risk estimates and perhaps risk management decisions.     

Propagation of Uncertainty in Risk Assessments: The Need to Distinguish Between Uncertainty Due to Lack of Knowledge and Uncertainty Due to Variability

In quantitative uncertainty analysis, it is essential to define rigorously the endpoint or target of the assessment. Two distinctly different approaches using Monte Carlo methods are discussed: (1) the end point is a fixed but unknown value (e.g., the maximally exposed individual, the average individual, or a specific individual) or (2) the end point is an unknown distribution of values (e.g., the variability of exposures among unspecified individuals in the population). In the first case, values are sampled at random from distributions representing various "degrees of belief" about the unknown "fixed" values of the parameters to produce a distribution of model results. The distribution of model results represents a subjective confidence statement about the true but unknown assessment end point. The important input parameters are those that contribute most to the spread in the distribution of the model results. In the second case, Monte Carlo calculations are performed in two dimensions producing numerous alternative representations of the true but unknown distribution. These alternative distributions permit subject confidence statements to be made from two perspectives: (1) for the individual exposure occurring at a specified fractile of the distribution or (2) for the fractile of the distribution associated with a specified level of individual exposure. The relative importance of input parameters will depend on the fractile or exposure level of interest. The quantification of uncertainty for the simulation of a true but unknown distribution of values represents the state-of-the-art in assessment modeling.     

The Impact of Climate Variability on Flood Risk in Poland

This article examines the role of climatic and hydrological variability in assessing the cumulative risk of flood events in Poland over a T-year period. In a broad sense flood-risk estimation combines a frequency analysis of extreme hydrological phenomena with an evaluation of flood-induced damages. The damage from floods depends on the critical values of the river discharges. The probabilistic flood analysis usually includes an estimation of the expected annual probability of the critical discharge Qcr being exceeded and the equivalent long-term risk of it being exceeded over the next T years. If, however, the process is nonstationary, the T-year risk of flood damage may depend importantly on the variation of hydrological processes. As a possible explanation for the variations observed in snowmelt-induced floods in Polish rivers, this article investigates the possible impact of the North Atlantic Oscillation (NAO) on surface air temperature T and precipitation P. The spatial distribution of the correlation coefficients between NAO and T, as well as NAO and P, show very significant differences in the NAO impact on meteorological variables in various parts of Europe. To assess the implications of NAO variations on spring flood discharges, a simple model of Snow Cover Water Equivalent (SCWE) was applied to selected Polish river catchments. The conclusion of this analysis is that the yearly maximum of SCWE values significantly decreases with increasing NAO. This leads to a temporal redistribution of winter and spring runoff. The question of spring flood characteristics being stationary or nonstationary may therefore be linked with stochastic properties of the NAO index time series.     

Uncertainty and Variability Analysis in Multiplicative Risk Models

Currently, there is a trend away from the use of single (often conservative) estimates of risk to summarize the results of risk analyses in favor of stochastic methods which provide a more complete characterization of risk. The use of such stochastic methods leads to a distribution of possible values of risk, taking into account both uncertainty and variability in all of the factors affecting risk. In this article, we propose a general framework for the analysis of uncertainty and variability for use in the commonly encountered case of multiplicative risk models, in which risk may be expressed as a product of two or more risk factors. Our analytical methods facilitate the evaluation of overall uncertainty and variability in risk assessment, as well as the contributions of individual risk factors to both uncertainty and variability which is cumbersome using Monte Carlo methods. The use of these methods is illustrated in the analysis of potential cancer risks due to the ingestion of radon in drinking water.     

Bayesian Hierarchical Structure for Quantifying Population Variability to Inform Probabilistic Health Risk Assessments

Human variability is a very important factor considered in human health risk assessment for protecting sensitive populations from chemical exposure. Traditionally, to account for this variability, an interhuman uncertainty factor is applied to lower the exposure limit. However, using a fixed uncertainty factor rather than probabilistically accounting for human variability can hardly support probabilistic risk assessment advocated by a number of researchers; new methods are needed to probabilistically quantify human population variability. We propose a Bayesian hierarchical model to quantify variability among different populations. This approach jointly characterizes the distribution of risk at background exposure and the sensitivity of response to exposure, which are commonly represented by model parameters. We demonstrate, through both an application to real data and a simulation study, that using the proposed hierarchical structure adequately characterizes variability across different populations.     

Uncertainty and Variability in Health-Related Damages from Coal-Fired Power Plants in the United States

The health‐related damages associated with emissions from coal‐fired power plants can vary greatly across facilities as a function of plant, site, and population characteristics, but the degree of variability and the contributing factors have not been formally evaluated. In this study, we modeled the monetized damages associated with 407 coal‐fired power plants in the United States, focusing on premature mortality from fine particulate matter (PM_2.5). We applied a reduced‐form chemistry‐transport model accounting for primary PM_2.5 emissions and the influence of sulfur dioxide (SO₂) and nitrogen oxide (NO_x) emissions on secondary particulate formation. Outputs were linked with a concentration‐response function for PM_2.5‐related mortality that incorporated nonlinearities and model uncertainty. We valued mortality with a value of statistical life approach, characterizing and propagating uncertainties in all model elements. At the median of the plant‐specific uncertainty distributions, damages across plants ranged from $30,000 to $500,000 per ton of PM_2.5, $6,000 to $50,000 per ton of SO₂, $500 to $15,000 per ton of NO_x, and $0.02 to $1.57 per kilowatt‐hour of electricity generated. Variability in damages per ton of emissions was almost entirely explained by population exposure per unit emissions (intake fraction), which itself was related to atmospheric conditions and the population size at various distances from the power plant. Variability in damages per kilowatt‐hour was highly correlated with SO₂ emissions, related to fuel and control technology characteristics, but was also correlated with atmospheric conditions and population size at various distances. Our findings emphasize that control strategies that consider variability in damages across facilities would yield more efficient outcomes.     

Variability in PAH-DNA Adduct Measurements in Peripheral Mononuclear Cells: Implications for Quantitative Cancer Risk Assessment

Biomarkers such as DNA adducts have significant potential to improve quantitative risk assessment by characterizing individual differences in metabolism of genotoxins and DNA repair and accounting for some of the factors that could affect interindividual variation in cancer risk. Inherent uncertainty in laboratory measurements and within-person variability of DNA adduct levels over time are putatively unrelated to cancer risk and should be subtracted from observed variation to better estimate interindividual variability of response to carcinogen exposure. A total of 41 volunteers, both smokers and nonsmokers, were asked to provide a peripheral blood sample every 3 weeks for several months in order to specifically assess intraindividual variability of polycyclic aromatic hydrocarbon (PAH)-DNA adduct levels. The intraindividual variance in PAH-DNA adduct levels, together with measurement uncertainty (laboratory variability and unaccounted for differences in exposure), constituted roughly 30% of the overall variance. An estimated 70% of the total variance was contributed by interindividual variability and is probably representative of the true biologic variability of response to carcinogenic exposure in lymphocytes. The estimated interindividual variability in DNA damage after subtracting intraindividual variability and measurement uncertainty was 24-fold. Inter-individual variance was higher (52-fold) in persons who constitutively lack the Glutathione S-Transferase M1 (GSTM1) gene which is important in the detoxification pathway of PAH. Risk assessment models that do not consider the variability of susceptibility to DNA damage following carcinogen exposure may underestimate risks to the general population, especially for those people who are most vulnerable.     

Importance of Uncertainty and Variability to Predicted Risks from Trophic Transfer of PCBs in Dredged Sediments

Biomagnification of organochlorine and other persistent organic contaminants by higher trophic level organisms represents one of the most significant sources of uncertainty and variability in evaluating potential risks associated with disposal of dredged materials. While it is important to distinguish between population variability (e.g., true population heterogeneity in fish weight, and lipid content) and uncertainty (e.g., measurement error), they can be operationally difficult to define separately in probabilistic estimates of human health and ecological risk. We propose a disaggregation of uncertain and variable parameters based on: (1) availability of supporting data; (2) the specific management and regulatory context (in this case, of the U.S. Army Corps of Engineers/U.S. Environmental Protection Agency tiered approach to dredged material management); and (3) professional judgment and experience in conducting probabilistic risk assessments. We describe and quantitatively evaluate several sources of uncertainty and variability in estimating risk to human health from trophic transfer of polychlorinated biphenyls (PCBs) using a case study of sediments obtained from the New York-New Jersey Harbor and being evaluated for disposal at an open water off-shore disposal site within the northeast region. The estimates of PCB concentrations in fish and dietary doses of PCBs to humans ingesting fish are expressed as distributions of values, of which the arithmetic mean or mode represents a particular fractile. The distribution of risk values is obtained using a food chain biomagnification model developed by Gobas by specifying distributions for input parameters disaggregated to represent either uncertainty or variability. Only those sources of uncertainty that could be quantified were included in the analysis. Results for several different two-dimensional Latin Hypercube analyses are provided to evaluate the influence of the uncertain versus variable disaggregation of model parameters. The analysis suggests that variability in human exposure parameters is greater than the uncertainty bounds on any particular fractile, given the described assumptions.