A Case Study Comparing Static and Spatially Explicit Ecological Exposure Analysis Methods

Exposure to chemical contaminants must be estimated when performing ecological risk assessments. A previous article proposed a habitat area and quality conditioned population exposure estimator, E[HQ]P, and described an individual-based, random walk, Monte Carlo model (SE3M) to facilitate calculation of E[HQ]P. In this article, E[HQ]P was compared with exposure estimates from a baseline risk assessment that evaluated mink and great blue heron exposure to fluoride at a federal Superfund site. Calculation of E[HQ]P took into consideration a receptor's forage area, movement behavior, population size, and the areal extent and quality of suitable habitat. The baseline assessment used four methods that did (total and unit Tier 2) and did not (total and unit Tier 1) consider habitat area or quality; where "total" included all exposure units on site and "unit" only a given exposure unit. Total Tier 1 estimates were consistently higher than E[HQ]P (e.g., 169.1 mg/kg x d versus 21.6 mg/kg x d). Risk managers using total Tier 1 results for decision making would be unlikely to underestimate exposure; however, implementability of correspondingly lower remedial objectives could be challenging. Unit Tier 1 estimates were higher (e.g., 96.5 mg/kg x d versus 61.6 mg/kg x d) or lower (e.g., 3.5 mg/kg x d versus 51.1 mg/kg x d) than E[HQ]P depending on variations in landscape features. Total Tier 2 and E[HQ]P estimates were similar (e.g., 20.7 mg/kg x d versus 21.6 mg/kg x d) when an ecologically questionable average exposure was assumed. Unit Tier 2 estimates were consistently well below E[HQ]P (e.g., 17.8 mg/kg x d versus 61.6 mg/kg x d) when an average exposure was not assumed. Risk managers using unit Tier 1 or 2 results could be basing their decisions on potentially large underestimates of exposure. By forgoing average exposure assumptions, and explicitly addressing landscape heterogeneity, SE3M appears capable of yielding exposure estimates that are not as potentially misleading to risk managers as those produced with traditional averaging methods.     

Application of Geostatistics to Risk Assessment

Geostatistics offers two fundamental contributions to environmental contaminant exposure assessment: (1) a group of methods to quantitatively describe the spatial distribution of a pollutant and (2) the ability to improve estimates of the exposure point concentration by exploiting the geospatial information present in the data. The second contribution is particularly valuable when exposure estimates must be derived from small data sets, which is often the case in environmental risk assessment. This article addresses two topics related to the use of geostatistics in human and ecological risk assessments performed at hazardous waste sites: (1) the importance of assessing model assumptions when using geostatistics and (2) the use of geostatistics to improve estimates of the exposure point concentration (EPC) in the limited data scenario. The latter topic is approached here by comparing design-based estimators that are familiar to environmental risk assessors (e.g., Land's method) with geostatistics, a model-based estimator. In this report, we summarize the basics of spatial weighting of sample data, kriging, and geostatistical simulation. We then explore the two topics identified above in a case study, using soil lead concentration data from a Superfund site (a skeet and trap range). We also describe several areas where research is needed to advance the use of geostatistics in environmental risk assessment.     

Ecological Risks to Fossorial Vertebrates from Volatile Organic Compounds in Soil

The past several years has seen an increased awareness of the need to conduct ecological risk assessments (ERAs) for hazardous waste sites. One technique used in ERAs involves estimating contaminant exposure to individual animals of selected species, which is then compared to a reference dose derived from the literature. Exposure estimates are conducted on those species which are representative of the different trophic levels found at the site. In many terrestrial systems, fossorial (burrowing) vertebrates are found in both lower and upper trophic levels. As part of the ERA conducted for Site 300, Lawrence Livermore National Laboratory's high-explosive test facility, contaminant exposures were estimated for fossorial and nonfossorial vertebrates spanning two trophic levels. The results of the evaluation revealed that a significant pathway by which fossorial vertebrates could be exposed to trichloroethylene in soil was through the inhalation of contaminated subsurface burrow air. This was the first time that the importance of this ecological exposure pathway has been recognized. The results of this analysis suggest that further research into the ecological significance of subsurface burrow air contaminated with volatile organic compounds is warranted.     

Exposure Assessment: Then, Now, and Quantum Leaps in the Future

Health risk assessments have become so widely accepted in the United States that their conclusions are a major factor in many environmental decisions. Although the risk assessment paradigm is 10 years old, the basic risk assessment process has been used by certain regulatory agencies for nearly 40 years. Each of the four components of the paradigm has undergone significant refinements, particularly during the last 5 years. A recent step in the development of the exposure assessment component can be found in the 1992 EPA Guidelines for Exposure Assessment. Rather than assuming worst-case or hypothetical maximum exposures, these guidelines are designed to lead to an accurate characterization, making use of a number of scientific advances. Many exposure parameters have become better defined, and more sensitive techniques now exist for measuring concentrations of contaminants in the environnment. Statistical procedures for characterizing variability, using Monte Carlo or similar approaches, eliminate the need to select point estimates for all individual exposure parameters. These probabilistic models can more accurately characterize the full range of exposures that may potentially be encountered by a given population at a particular site, reducing the need to select highly conservative values to account for this form of uncertainty in the exposure estimate. Lastly, our awareness of the uncertainties in the exposure assessment as well as our knowledge as to how best to characterize them will almost certainly provide evaluations that will be more credible and, therein, more useful to risk managers. If these refinements are incorporated into future exposure assessments, it is likely that our resources will be devoted to problems that, when resolved, will yield the largest improvement in public health.     

Improving Risk Assessment Calculations for Traditional Foods Through Collaborative Research with First Nations Communities

As industrial development is increasing near northern Canadian communities, human health risk assessments (HHRA) are conducted to assess the predicted magnitude of impacts of chemical emissions on human health. One exposure pathway assessed for First Nations communities is the consumption of traditional plants, such as muskeg tea (Labrador tea) (Ledum/Rhododendron groenlandicum) and mint (Mentha arvensis). These plants are used to make tea and are not typically consumed in their raw form. Traditional practices were used to harvest muskeg tea leaves and mint leaves by two First Nations communities in northern Alberta, Canada. Under the direction of community elders, community youth collected and dried plants to make tea. Soil, plant, and tea decoction samples were analyzed for inorganic elements using inductively coupled plasma‐mass spectrometry. Concentrations of inorganic elements in the tea decoctions were orders of magnitude lower than in the vegetation (e.g., manganese 0.107 mg/L in tea, 753 mg/kg in leaves). For barium, the practice of assessing ingestion of raw vegetation would have resulted in a hazard quotient (HQ) greater than the benchmark of 0.2. Using measured tea concentrations it was determined that exposure would result in risk estimates orders of magnitude below the HQ benchmark of 0.2 (HQ = 0.0049 and 0.017 for muskeg and mint tea, respectively). An HHRA calculating exposure to tea vegetation through direct ingestion of the leaves may overestimate risk. The results emphasize that food preparation methods must be considered when conducting an HHRA. This study illustrates how collaboration between Western scientists and First Nations communities can add greater clarity to risk assessments.     

Biological Markers of Environmental and Ecological Contamination: An Overview

An approach, using biomarkers (biological responses) for assessing the biological and ecological significance of contaminants present in the environment is described. Living organisms integrate exposure to contaminants in their environment and respond in some measurable and predictable way. Responses are observed at several levels of biological organization from the biomolecular level, where pollutants can cause damage to critical cellular macromolecules and elicit defensive strategies such as detoxication and repair mechanisms, to the organismal level, where severe disturbances are manifested as impairment in growth, reproduction, developmental abnormalities, or decreased survival. Biomarkers can provide not only evidence of exposure to a broad spectrum of anthropogenic chemicals, but also a temporally integrated measure of bioavailable contaminant levels. A suite of biomarkers are evaluated over time to determine the magnitude of the problem and possible consequences. Relationships between biomarker response and adverse ecological effects are determined from estimates of animal health and population structure.     

Comparative Probabilistic Assessment of Occupational Pesticide Exposures Based on Regulatory Assessments

Implementation of probabilistic analyses in exposure assessment can provide valuable insight into the risks of those at the extremes of population distributions, including more vulnerable or sensitive subgroups. Incorporation of these analyses into current regulatory methods for occupational pesticide exposure is enabled by the exposure data sets and associated data currently used in the risk assessment approach of the Environmental Protection Agency (EPA). Monte Carlo simulations were performed on exposure measurements from the Agricultural Handler Exposure Database and the Pesticide Handler Exposure Database along with data from the Exposure Factors Handbook and other sources to calculate exposure rates for three different neurotoxic compounds (azinphos methyl, acetamiprid, emamectin benzoate) across four pesticide‐handling scenarios. Probabilistic estimates of doses were compared with the no observable effect levels used in the EPA occupational risk assessments. Some percentage of workers were predicted to exceed the level of concern for all three compounds: 54% for azinphos methyl, 5% for acetamiprid, and 20% for emamectin benzoate. This finding has implications for pesticide risk assessment and offers an alternative procedure that may be more protective of those at the extremes of exposure than the current approach.     

Effects of Uncertainties on Exposure Estimates to Methylmercury: A Monte Carlo Analysis of Exposure Biomarkers versus Dietary Recall Estimation

This article presents a general model for estimating population heterogeneity and "lack of knowledge" uncertainty in methylmercury (MeHg) exposure assessments using two-dimensional Monte Carlo analysis. Using data from fish-consuming populations in Bangladesh, Brazil, Sweden, and the United Kingdom, predictive model estimates of dietary MeHg exposures were compared against those derived from biomarkers (i.e., [Hg]hair and [Hg]blood). By disaggregating parameter uncertainty into components (i.e., population heterogeneity, measurement error, recall error, and sampling error) estimates were obtained of the contribution of each component to the overall uncertainty. Steady-state diet:hair and diet:blood MeHg exposure ratios were estimated for each population and were used to develop distributions useful for conducting biomarker-based probabilistic assessments of MeHg exposure. The 5th and 95th percentile modeled MeHg exposure estimates around mean population exposure from each of the four study populations are presented to demonstrate lack of knowledge uncertainty about a best estimate for a true mean. Results from a U.K. study population showed that a predictive dietary model resulted in a 74% lower lack of knowledge uncertainty around a central mean estimate relative to a hair biomarker model, and also in a 31% lower lack of knowledge uncertainty around central mean estimate relative to a blood biomarker model. Similar results were obtained for the Brazil and Bangladesh populations. Such analyses, used here to evaluate alternative models of dietary MeHg exposure, can be used to refine exposure instruments, improve information used in site management and remediation decision making, and identify sources of uncertainty in risk estimates.     

Probability Distributions for Showering and Bathing Water-Use Behavior for Various U.S. Subpopulations

It has been shown that bathroom-type water uses dominate personal exposure to water-borne contaminants in the home. Therefore, in assessing exposure of specific population groups to the contaminants in the water, understanding population water-use behavior for bathroom activities as a function of demographic characteristics is vital to realistic exposure estimates. In this article, shower and bath frequencies and durations are analyzed, presented, and compared for various demographic groups derived from analyses of the National Human Activities Pattern Survey (NHAPS) database and the Residential End Uses of Water Study (REUWS) database as well as from a review of current literature. Analysis showed that age and level of education significantly influenced shower and bath frequency and duration. The frequency of showering and bathing reported in NHAPS agreed reasonably well with previous studies; however, durations of these events were found to be significantly longer. Showering frequency reported in REUWS was slightly less than that reported for NHAPS; however, durations of showers reported in REUWS are consistent with other studies. After considering the strengths and weaknesses of each data set and comparing their results to previous studies, it is concluded that NHAPS provides more reliable frequency data, while REUWS provides more reliable duration data. The shower- and bath-use behavior parameters recommended in this article can aid modelers in appropriately specifying water-use behavior as a function of demographic group in order to conduct reasonable assessments of exposure to contaminants that enter the home via the water supply.     

Probabilistic Health Risk Assessment for Exposures to Estuary Sediments and Biota Contaminated with Polychlorinated Biphenyls, Polychlorinated Terphenyls and Other Toxic Substances

A Latin Hypercube probabilistic risk assessment methodology was employed in the assessment of health risks associated with exposures to contaminated sediment and biota in an estuary in the Tidewater region of Virginia. The primary contaminants were polychlorinated biphenyls (PCBs), polychlorinated terphenyls (PCTs), polynuclear aromatic hydrocarbons (PAHs), and metals released into the estuary from a storm sewer system. The exposure pathways associated with the highest contaminant intake and risks were dermal contact with contaminated sediment and ingestion of contaminated aquatic and terrestrial biota from the contaminated area. As expected, all of the output probability distributions of risk were highly skewed, and the ratios of the expected value (mean) to median risk estimates ranged from 1.4 to 14.8 for the various exposed populations. The 99th percentile risk estimates were as much as two orders of magnitude above the mean risk estimates. For the sediment exposure pathways, the stability of the median risk estimates was found to be much greater than the stability of the expected value risk estimates. The interrun variability in the median risk estimate was found to be +/-1.9% at 3000 iterations. The interrun stability of the mean risk estimates was found to be approximately equal to that of the 95th percentile estimates at any number of iterations. The variation in neither contaminant concentrations nor any other single input variable contributed disproportionately to the overall simulation variance. The inclusion or exclusion of spatial correlations among contaminant concentrations in the simulation model did not significantly effect either the magnitude or the variance of the simulation risk estimates for sediment exposures.     

Multimedia Benchmarking Analysis for Three Risk Assessment Models: RESRAD, MMSOILS, and MEPAS

This paper is one in a series that describes results of a benchmarking analysis initiated by the Department of Energy (DOE) and the United States Environmental Protection Agency (EPA). An overview of the study is provided in a companion paper by Laniak et al. presented in this journal issue. The three models used in the study—RESRAD (DOE), MMSOILS (EPA), and MEPAS (DOE)—represent analytically-based tools that are used by the respective agencies for performing human exposure and health risk assessments. Both single media and multimedia benchmarking scenarios were developed and executed. In this paper, the multimedia scenario is examined. That scenario consists of a hypothetical landfill that initially contained uranium-238 and methylene chloride. The multimedia models predict the fate of these contaminants, plus the progeny of uranium-238, through the unsaturated zone, saturated zone, surface water, and atmosphere. Carcinogenic risks are calculated from exposure to the contaminants via multiple pathways. Results of the tests show that differences in model endpoint estimates arise from both differences in the models' mathematical formulations and assumptions related to the implementation of the scenarios.     

What to Do at Low Doses: A Bounding Approach for Economic Analysis

To quantify the health benefits of environmental policies, economists generally require estimates of the reduced probability of illness or death. For policies that reduce exposure to carcinogenic substances, these estimates traditionally have been obtained through the linear extrapolation of experimental dose-response data to low-exposure scenarios as described in the U.S. Environmental Protection Agency's Guidelines for Carcinogen Risk Assessment (1986). In response to evolving scientific knowledge, EPA proposed revisions to the guidelines in 1996. Under the proposed revisions, dose-response relationships would not be estimated for carcinogens thought to exhibit nonlinear modes of action. Such a change in cancer-risk assessment methods and outputs will likely have serious consequences for how benefit-cost analyses of policies aimed at reducing cancer risks are conducted. Any tendency for reduced quantification of effects in environmental risk assessments, such as those contemplated in the revisions to EPA's cancer-risk assessment guidelines, impedes the ability of economic analysts to respond to increasing calls for benefit-cost analysis. This article examines the implications for benefit-cost analysis of carcinogenic exposures of the proposed changes to the 1986 Guidelines and proposes an approach for bounding dose-response relationships when no biologically based models are available. In spite of the more limited quantitative information provided in a carcinogen risk assessment under the proposed revisions to the guidelines, we argue that reasonable bounds on dose-response relationships can be estimated for low-level exposures to nonlinear carcinogens. This approach yields estimates of reduced illness for use in a benefit-cost analysis while incorporating evidence of nonlinearities in the dose-response relationship. As an illustration, the bounding approach is applied to the case of chloroform exposure.     

Daily Soil Ingestion Estimates for Children at a Superfund Site

Ingestion of contaminated soil by children may result in significant exposure to toxic substances at contaminated sites. Estimates of such exposure are based on extrapolation of short-term-exposure estimates to longer time periods. This article provides daily estimates of soil ingestion on 64 children between the ages of 1 and 4 residing at a Superfund site; these values are employed to estimate the distribution of 7-day average soil ingestion exposures (mean, 31 mg/day; median, 17 mg/day) at a contaminated site over different time periods. Best linear unbiased predictors of the 95th-percentile of soil ingestion over 7 days, 30 days, 90 days, and 365 days are 133 mg/day, 112 mg/day, 108 mg/day and 106 mg/day, respectively. Variance components estimates (excluding titanium and outliers, based on Tukey's far-out criteria) are given for soil ingestion between subjects (59 mg/day)2, between days on a subject (95 mg/day)2, and for uncertainty on a subject-day (132 mg/day)2. These results expand knowledge of potential exposure to contaminants among young children from soil ingestion at contaminated sites. They also provide basic distributions that serve as a starting point for use in Monte Carlo risk assessments.     

Health Risk Characterization of Chlorpyrifos Using Epidemiological Dose‐Response Data and Probabilistic Techniques: A Case Study with Rice Farmers in Vietnam

Various methods for risk characterization have been developed using probabilistic approaches. Data on Vietnamese farmers are available for the comparison of outcomes for risk characterization using different probabilistic methods. This article addresses the health risk characterization of chlorpyrifos using epidemiological dose‐response data and probabilistic techniques obtained from a case study with rice farmers in Vietnam. Urine samples were collected from farmers and analyzed for trichloropyridinol (TCP), which was converted into absorbed daily dose of chlorpyrifos. Adverse health response doses due to chlorpyrifos exposure were collected from epidemiological studies to develop dose‐adverse health response relationships. The health risk of chlorpyrifos was quantified using hazard quotient (HQ), Monte Carlo simulation (MCS), and overall risk probability (ORP) methods. With baseline (prior to pesticide spraying) and lifetime exposure levels (over a lifetime of pesticide spraying events), the HQ ranged from 0.06 to 7.1. The MCS method indicated less than 0.05% of the population would be affected while the ORP method indicated that less than 1.5% of the population would be adversely affected. With postapplication exposure levels, the HQ ranged from 1 to 32.5. The risk calculated by the MCS method was that 29% of the population would be affected, and the risk calculated by ORP method was 33%. The MCS and ORP methods have advantages in risk characterization due to use of the full distribution of data exposure as well as dose response, whereas HQ methods only used the exposure data distribution. These evaluations indicated that single‐event spraying is likely to have adverse effects on Vietnamese rice farmers.     

Estimated Soil Ingestion Rates for Use in Risk Assessment

Assessing the risks to human health posed by contaminants present in soil requires an estimate of likely soil ingestion rates. In the past, direct measurements of soil ingestion were not available and risk assessors were forced to estimate soil ingestion rates based on observations of mouthing behavior and measurements of soil on hands. Recently, empirical data on soil ingestion rates have become available from two sources (Binder et al., 1986 and van Wijnen et al., 1986). Although preliminary, these data can be used to derive better estimates of soil ingestion rates for use in risk assessments. Estimates of average soil ingestion rates derived in this paper range from 25 to 100 mg/day, depending on the age of the individual at risk. Maximum soil ingestion rates that are unlikely to underestimate exposure range from 100 to 500 mg. A value of 5,000 mg/day is considered a reasonable estimate of a maximum single-day exposure for a child with habitual pica.     

A Framework for Estimating the Adverse Health Effects of Contamination Events in Water Distribution Systems and its Application

Intentional or accidental releases of contaminants into a water distribution system (WDS) have the potential to cause significant adverse health effects among individuals consuming water from the system. A flexible analysis framework is presented here for estimating the magnitude of such potential effects and is applied using network models for 12 actual WDSs of varying sizes. Upper bounds are developed for the magnitude of adverse effects of contamination events in WDSs and evaluated using results from the 12 systems. These bounds can be applied in cases in which little system‐specific information is available. The combination of a detailed, network‐specific approach and a bounding approach allows consequence assessments to be performed for systems for which varying amounts of information are available and addresses important needs of individual utilities as well as regional or national assessments. The approach used in the analysis framework allows contaminant injections at any or all network nodes and uses models that (1) account for contaminant transport in the systems, including contaminant decay, and (2) provide estimates of ingested contaminant doses for the exposed population. The approach can be easily modified as better transport or exposure models become available. The methods presented here provide the ability to quantify or bound potential adverse effects of contamination events for a wide variety of possible contaminants and WDSs, including systems without a network model.     

Equation reliability of soil ingestion estimates in mass-balance soil ingestion studies

Exposure to chemicals from ingestion of contaminated soil may be an important pathway with potential health consequences for children. A key parameter used in assessing this exposure is the quantity of soil ingested, with estimates based on four short longitudinal mass-balance soil ingestion studies among children. The estimates use trace elements in the soil with low bioavailability that are minimally present in food. Soil ingestion corresponds to the excess trace element amounts excreted, after subtracting trace element amounts ingested from food and medications, expressed as an equivalent quantity of soil. The short duration of mass-balance studies, different concentrations of trace elements in food and soil, and potential for trace elements to be ingested from other nonsoil, nonfood sources contribute to variability and bias in the estimates. We develop a stochastic model for a soil ingestion estimator based on a trace element that accounts for critical features of the mass-balance equation. Using results from four mass-balance soil ingestion studies, we estimate the accuracy of soil ingestion estimators for different trace elements, and identify subjects where the difference between Al and Si estimates is larger (>3 RMSE) than expected. Such large differences occur in fewer than 12% of subjects in each of the four studies. We recommend the use of such criteria to flag and exclude subjects from soil ingestion analyses.     

Exposure to Persistent Organochlorines in Canadian Breast Milk: A Probabilistic Assessment

Exposure to persistent organochlorines in breast milk was estimated probabilistically for Canadian infants. Noncancer health effects were evaluated by comparing the predicted exposure distributions to published guidance values. For chemicals identified as potential human carcinogens, cancer risks were evaluated using standard methodology typically applied in Canada, as well as an alternative method developed under the Canadian Environmental Protection Act. Potential health risks associated with exposure to persistent organochlorines were quantitatively and qualitatively weighed against the benefits of breast-feeding. Current levels of the majority of contaminants identified in Canadian breast milk do not pose unacceptable risks to infants. Benefits of breast-feeding are well documented and qualitatively appear to outweigh potential health concerns associated with organochlorine exposure. Furthermore, the risks of mortality from not breast-feeding estimated by Rogan and colleagues exceed the theoretical cancer risks estimated for infant exposure to potential carcinogens in Canadian breast milk. Although levels of persistent compounds have been declining in Canadian breast milk, potentially significant risks were estimated for exposure to polychlorinated biphenyls, dibenzo-p-dioxins, and dibenzofurans. Follow-up work is suggested that would involve the use of a physiologically based toxicokinetic model with probabilistic inputs to predict dioxin exposure to the infant. A more detailed risk analysis could be carried out by coupling the exposure estimates with a dose–response analysis that accounts for uncertainty.     

Spatially Representing Vulnerability to Extreme Rain Events Using Midwestern Farmers’ Objective and Perceived Attributes of Adaptive Capacity

Potential climate‐change‐related impacts to agriculture in the upper Midwest pose serious economic and ecological risks to the U.S. and the global economy. On a local level, farmers are at the forefront of responding to the impacts of climate change. Hence, it is important to understand how farmers and their farm operations may be more or less vulnerable to changes in the climate. A vulnerability index is a tool commonly used by researchers and practitioners to represent the geographical distribution of vulnerability in response to global change. Most vulnerability assessments measure objective adaptive capacity using secondary data collected by governmental agencies. However, other scholarship on human behavior has noted that sociocultural and cognitive factors, such as risk perceptions and perceived capacity, are consequential for modulating people's actual vulnerability. Thus, traditional assessments can potentially overlook people's subjective perceptions of changes in climate and extreme weather events and the extent to which people feel prepared to take necessary steps to cope with and respond to the negative effects of climate change. This article addresses this knowledge gap by: (1) incorporating perceived adaptive capacity into a vulnerability assessment; (2) using spatial smoothing to aggregate individual‐level vulnerabilities to the county level; and (3) evaluating the relationships among different dimensions of adaptive capacity to examine whether perceived capacity should be integrated into vulnerability assessments. The result suggests that vulnerability assessments that rely only on objective measures might miss important sociocognitive dimensions of capacity. Vulnerability indices and maps presented in this article can inform engagement strategies for improving environmental sustainability in the region.     

Feasibility Study on the Use of Probabilistic Migration Modeling in Support of Exposure Assessment from Food Contact Materials

The use of probabilistic approaches in exposure assessments of contaminants migrating from food packages is of increasing interest but the lack of concentration or migration data is often referred as a limitation. Data accounting for the variability and uncertainty that can be expected in migration, for example, due to heterogeneity in the packaging system, variation of the temperature along the distribution chain, and different time of consumption of each individual package, are required for probabilistic analysis. The objective of this work was to characterize quantitatively the uncertainty and variability in estimates of migration. A Monte Carlo simulation was applied to a typical solution of the Fick's law with given variability in the input parameters. The analysis was performed based on experimental data of a model system (migration of Irgafos 168 from polyethylene into isooctane) and illustrates how important sources of variability and uncertainty can be identified in order to refine analyses. For long migration times and controlled conditions of temperature the affinity of the migrant to the food can be the major factor determining the variability in the migration values (more than 70% of variance). In situations where both the time of consumption and temperature can vary, these factors can be responsible, respectively, for more than 60% and 20% of the variance in the migration estimates. The approach presented can be used with databases from consumption surveys to yield a true probabilistic estimate of exposure.