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.     

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.     

Variability and Uncertainty in Swedish Exposure Factors for Use in Quantitative Exposure Assessments

Information of exposure factors used in quantitative risk assessments has previously been compiled and reported for U.S. and European populations. However, due to the advancement of science and knowledge, these reports are in continuous need of updating with new data. Equally important is the change over time of many exposure factors related to both physiological characteristics and human behavior. Body weight, skin surface, time use, and dietary habits are some of the most obvious examples covered here. A wealth of data is available from literature not primarily gathered for the purpose of risk assessment. Here we review a number of key exposure factors and compare these factors between northern Europe—here represented by Sweden—and the United States. Many previous compilations of exposure factor data focus on interindividual variability and variability between sexes and age groups, while uncertainty is mainly dealt with in a qualitative way. In this article variability is assessed along with uncertainty. As estimates of central tendency and interindividual variability, mean, standard deviation, skewness, kurtosis, and multiple percentiles were calculated, while uncertainty was characterized using 95% confidence intervals for these parameters. The presented statistics are appropriate for use in deterministic analyses using point estimates for each input parameter as well as in probabilistic assessments.     

Mlonte Carlo Techniques for Quantitative Uncertainty Analysis in Public Health Risk Assessments

Most public health risk assessments assume and combine a series of average, conservative, and worst-case values to derive a conservative point estimate of risk. This procedure has major limitations. This paper demonstrates a new methodology for extended uncertainty analyses in public health risk assessments using Monte Carlo techniques. The extended method begins as do some conventional methods--with the preparation of a spreadsheet to estimate exposure and risk. This method, however, continues by modeling key inputs as random variables described by probability density functions (PDFs). Overall, the technique provides a quantitative way to estimate the probability distributions for exposure and health risks within the validity of the model used. As an example, this paper presents a simplified case study for children playing in soils contaminated with benzene and benzo(a)pyrene (BaP).     

The Role of Quantitative Risk Assessments for Characterizing Risk and Uncertainty and Delineating Appropriate Risk Management Options, with Special Emphasis on Terrorism Risk

The purpose of this article is to discuss the role of quantitative risk assessments for characterizing risk and uncertainty and delineating appropriate risk management options. Our main concern is situations (risk problems) with large potential consequences, large uncertainties, and/or ambiguities (related to the relevance, meaning, and implications of the decision basis; or related to the values to be protected and the priorities to be made), in particular terrorism risk. We look into the scientific basis of the quantitative risk assessments and the boundaries of the assessments in such a context. Based on a risk perspective that defines risk as uncertainty about and severity of the consequences (or outcomes) of an activity with respect to something that humans value we advocate a broad risk assessment approach characterizing uncertainties beyond probabilities and expected values. Key features of this approach are qualitative uncertainty assessment and scenario building instruments.     

Statistical Quantification of the Sources of Variance in Uncertainty Analyses

A statistical method using linear regression is shown for quantifying each variable's contribution to the uncertainty analysis in environmental health risk assessments. The method suggests that uncertainty analyses can be significantly simplified when a linear relationship can be established between risk or log(risk) and the independent variables.     

Consumption of Bovine Spongiform Encephalopathy (BSE) Contaminated Beef and the Risk of Variant Creutzfeldt‐Jakob Disease

To date, the variant Creutzfeldt‐Jakob disease (vCJD) risk assessments that have been performed have primarily focused on predicting future vCJD cases in the United Kingdom, which underwent a bovine spongiform encephalopathy (BSE) epidemic between 1980 and 1996. Surveillance of potential BSE cases was also used to assess vCJD risk, especially in other BSE‐prevalent EU countries. However, little is known about the vCJD risk for uninfected individuals who accidentally consume BSE‐contaminated meat products in or imported from a country with prevalent BSE. In this article, taking into account the biological mechanism of abnormal prion PrP^res aggregation in the brain, the probability of exposure, and the expected amount of ingested infectivity, we establish a stochastic mean exponential growth model of lifetime exposure through dietary intake. Given the findings that BSE agents behave similarly in humans and macaques, we obtained parameter estimates from experimental macaque data. We then estimated the accumulation of abnormal prions to assess lifetime risk of developing clinical signs of vCJD. Based on the observed number of vCJD cases and the estimated number of exposed individuals during the BSE epidemic period from 1980 to 1996 in the United Kingdom, an exposure threshold hypothesis is proposed. Given the age‐specific risk of infection, the hypothesis explains the observations very well from an extreme‐value distribution fitting of the estimated BSE infectivity exposure. The current BSE statistics in the United Kingdom are provided as an example.     

Assessments of Direct Human Exposure—The Approach of EU Risk Assessments Compared to Scenario-Based Risk Assessment

The awareness of potential risks emerging from the use of chemicals in all parts of daily life has increased the need for risk assessments that are able to cover a high number of exposure situations and thereby ensure the safety of workers and consumers. In the European Union (EU), the practice of risk assessments for chemicals is laid down in a Technical Guidance Document; it is designed to consider environmental and human occupational and residential exposure. Almost 70 EU risk assessment reports (RARs) have been finalized for high-production-volume chemicals during the last decade. In the present study, we analyze the assessment of occupational and consumer exposure to trichloroethylene and phthalates presented in six EU RARs. Exposure scenarios in these six RARs were compared to scenarios used in applications of the scenario-based risk assessment approach to the same set of chemicals. We find that scenarios used in the selected EU RARs to represent typical exposure situations in occupational or private use of chemicals and products do not necessarily represent worst-case conditions. This can be due to the use of outdated information on technical equipment and conditions in workplaces or omission of pathways that can cause consumer exposure. Considering the need for exposure and risk assessments under the new chemicals legislation of the EU, we suggest that a transparent process of collecting data on exposure situations and of generating representative exposure scenarios is implemented to improve the accuracy of risk assessments. Also, the data sets used to assess human exposure should be harmonized, summarized in a transparent fashion, and made accessible for all risk assessors and the public.     

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.     

Integration of Probabilistic Exposure Assessment and Probabilistic Hazard Characterization

A method is proposed for integrated probabilistic risk assessment where exposure assessment and hazard characterization are both included in a probabilistic way. The aim is to specify the probability that a random individual from a defined (sub)population will have an exposure high enough to cause a particular health effect of a predefined magnitude, the critical effect size ( CES ). The exposure level that results in exactly that CES in a particular person is that person's individual critical effect dose ( ICED ). Individuals in a population typically show variation, both in their individual exposure ( IEXP ) and in their ICED . Both the variation in IEXP and the variation in ICED are quantified in the form of probability distributions. Assuming independence between both distributions, they are combined (by Monte Carlo) into a distribution of the individual margin of exposure ( IMoE ). The proportion of the IMoE distribution below unity is the probability of critical exposure ( PoCE ) in the particular (sub)population. Uncertainties involved in the overall risk assessment (i.e., both regarding exposure and effect assessment) are quantified using Monte Carlo and bootstrap methods. This results in an uncertainty distribution for any statistic of interest, such as the probability of critical exposure ( PoCE ). The method is illustrated based on data for the case of dietary exposure to the organophosphate acephate. We present plots that concisely summarize the probabilistic results, retaining the distinction between variability and uncertainty. We show how the relative contributions from the various sources of uncertainty involved may be quantified.     

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.     

Framework for Microbial Food‐Safety Risk Assessments Amenable to Bayesian Modeling

Regulatory agencies often perform microbial risk assessments to evaluate the change in the number of human illnesses as the result of a new policy that reduces the level of contamination in the food supply. These agencies generally have regulatory authority over the production and retail sectors of the farm‐to‐table continuum. Any predicted change in contamination that results from new policy that regulates production practices occurs many steps prior to consumption of the product. This study proposes a framework for conducting microbial food‐safety risk assessments; this framework can be used to quantitatively assess the annual effects of national regulatory policies. Advantages of the framework are that estimates of human illnesses are consistent with national disease surveillance data (which are usually summarized on an annual basis) and some of the modeling steps that occur between production and consumption can be collapsed or eliminated. The framework leads to probabilistic models that include uncertainty and variability in critical input parameters; these models can be solved using a number of different Bayesian methods. The Bayesian synthesis method performs well for this application and generates posterior distributions of parameters that are relevant to assessing the effect of implementing a new policy. An example, based on Campylobacter and chicken, estimates the annual number of illnesses avoided by a hypothetical policy; this output could be used to assess the economic benefits of a new policy. Empirical validation of the policy effect is also examined by estimating the annual change in the numbers of illnesses observed via disease surveillance systems.     

An ecological risk assessment for chlorpyrifos in an agriculturally dominated tributary of the San Joaquin River.

Single-species toxicity testing of ambient water samples and national-scale probabilistic risk assessment have implicated the organophosphorous (OP) insecticide chlorpyrifos (O, O-diethyl O-(3,5,6-trichloro-2-pyridyl)-phosphorothioate) as a potential chemical stressor of aquatic organisms residing in the lower San Joaquin River basin. This site-specific aquatic ecological risk assessment was conducted to determine the probability of adverse effects occurring from exposure to chlorpyrifos in an agriculturally dominated tributary of the San Joaquin River and to assess the ecological significance of such effects. Assessment endpoints were fish population persistence and invertebrate community productivity. Daily chemical measurements collected over a period of one year were analyzed temporally for frequency, duration, and spacing between events for acute and chronic exposure episodes. Effects thresholds for fish and freshwater lotic invertebrates were determined from single-species laboratory toxicity tests. Potential risk was characterized by the degree of overlap of distributions of exposure events and effects, with consideration given to additive toxicity of other OP insecticides, recovery periods, and duration of chronic exposure (> or = 21 d). Ecological significance was determined by analysis of fish assemblage dietary and reproductive habits in relation to the surrogate invertebrate taxa judged at risk. Results of analysis indicated no direct effects on fish, and indirect effects on fish through elimination of invertebrate food items were considered unlikely. Biological survey information will be necessary to address uncertainty in this risk conclusion, especially as it relates to the benthic invertebrate community. Results of this site-specific risk analysis suggest that fish population persistence and invertebrate community productivity were not adversely affected by measured chlorpyrifos residues during a year-long monitoring period.     

Building a Human Health Risk Assessment Ontology (RsO): A Proposed Framework

Over the last decade the health and environmental research communities have made significant progress in collecting and improving access to genomic, toxicology, exposure, health, and disease data useful to health risk assessment. One of the barriers to applying these growing volumes of information in fields such as risk assessment is the lack of informatics tools to organize, curate, and evaluate thousands of journal publications and hundreds of databases to provide new insights on relationships among exposure, hazard, and disease burden. Many fields are developing ontologies as a way of organizing and analyzing large amounts of complex information from multiple scientific disciplines. Ontologies include a vocabulary of terms and concepts with defined logical relationships to each other. Building from the recently published exposure ontology and other relevant health and environmental ontologies, this article proposes an ontology for health risk assessment (RsO) that provides a structural framework for organizing risk assessment information and methods. The RsO is anchored by eight major concepts that were either identified by exploratory curations of the risk literature or the exposure‐ontology working group as key for describing the risk assessment domain. These concepts are: (1) stressor, (2) receptor, (3) outcome, (4) exposure event, (5) dose‐response approach, (6) dose‐response metric, (7) uncertainty, and (8) measure of risk. We illustrate the utility of these concepts for the RsO with example curations of published risk assessments for ionizing radiation, arsenic in drinking water, and persistent pollutants in salmon.     

Disparity in Quantitative Risk Assessment: A Review of Input Distributions

Monte Carlo simulations are commonplace in quantitative risk assessments (QRAs). Designed to propagate the variability and uncertainty associated with each individual exposure input parameter in a quantitative risk assessment, Monte Carlo methods statistically combine the individual parameter distributions to yield a single, overall distribution. Critical to such an assessment is the representativeness of each individual input distribution. The authors performed a literature review to collect and compare the distributions used in published QRAs for the parameters of body weight, food consumption, soil ingestion rates, breathing rates, and fluid intake. To provide a basis for comparison, all estimated exposure parameter distributions were evaluated with respect to four properties: consistency, accuracy, precision, and specificity. The results varied depending on the exposure parameter. Even where extensive, well-collected data exist, investigators used a variety of different distributional shapes to approximate these data. Where such data do not exist, investigators have collected their own data, often leading to substantial disparity in parameter estimates and subsequent choice of distribution. The present findings indicate that more attention must be paid to the data underlying these distributional choices. More emphasis should be placed on sensitivity analyses, quantifying the impact of assumptions, and on discussion of sources of variation as part of the presentation of any risk assessment results. If such practices and disclosures are followed, it is believed that Monte Carlo simulations can greatly enhance the accuracy and appropriateness of specific risk assessments. Without such disclosures, researchers will be increasing the size of the risk assessment "black box," a concern already raised by many critics of more traditional risk assessments.     

An Application of the Holistochastic Human Exposure Methodology to Naturally Occurring Arsenic in Bangladesh Drinking Water

The occurrence of arsenic in drinking water is an issue of considerable interest. In the case of Bangladesh, arsenic concentrations have been closely monitored since the early 1990s through an extensive sampling network. The focus of the present work is methodological. In particular, we propose the application of a holistochastic framework of human exposure to study lifetime population damage due to arsenic exposure across Bangladesh. The Bayesian Maximum Entropy theory is an important component of this framework, which possesses solid theoretical foundations and offers powerful tools to assimilate a variety of knowledge bases (physical, epidemiologic, toxicokinetic, demographic, etc.) and uncertainty sources (soft data, measurement errors, etc.). The holistochastic exposure approach leads to physically meaningful and informative spatial maps of arsenic distribution in Bangladesh drinking water. Global indicators of the adverse health effects on the population are generated, and valuable insight is gained by blending information from different scientific disciplines. The numerical results indicate an increased lifetime bladder cancer probability for the Bangladesh population due to arsenic. The health effect estimates obtained and the associated uncertainty assessments are valuable tools for a broad spectrum of end-users.     

Uncertain Numbers and Uncertainty in the Selection of Input Distributions—Consequences for a Probabilistic Risk Assessment of Contaminated Land

Risks from exposure to contaminated land are often assessed with the aid of mathematical models. The current probabilistic approach is a considerable improvement on previous deterministic risk assessment practices, in that it attempts to characterize uncertainty and variability. However, some inputs continue to be assigned as precise numbers, while others are characterized as precise probability distributions. Such precision is hard to justify, and we show in this article how rounding errors and distribution assumptions can affect an exposure assessment. The outcome of traditional deterministic point estimates and Monte Carlo simulations were compared to probability bounds analyses. Assigning all scalars as imprecise numbers (intervals prescribed by significant digits) added uncertainty to the deterministic point estimate of about one order of magnitude. Similarly, representing probability distributions as probability boxes added several orders of magnitude to the uncertainty of the probabilistic estimate. This indicates that the size of the uncertainty in such assessments is actually much greater than currently reported. The article suggests that full disclosure of the uncertainty may facilitate decision making in opening up a negotiation window. In the risk analysis process, it is also an ethical obligation to clarify the boundary between the scientific and social domains.     

Hot Potato: Expert-Consumer Differences in the Perception of a Second-Generation Novel Food

Novel foods have been the object of intense public debate in recent years. Despite widespread efforts to communicate the outcomes of risk assessments to consumers, public confidence in risk management has been low. Social scientists have identified various reasons for this, including a disagreement between technical experts and consumers over the nature of the hazards on which risk assessments should focus. The aim of this study was to identify and compare the ways in which experts and consumers understand the benefits and risks associated with a genetically modified example crop. Two qualitative studies were conducted. In Study 1, mental models were elicited from 24 experts by means of a three-wave Delphi procedure. In Study 2, mental models were elicited from 25 consumers by means of in-depth interviews. As expected, the expert mental models were focused on the types of hazards that can realistically be addressed under current regulatory frameworks, whereas the consumers were often more concerned about issues outside the scope of current legislation. Moreover, the experts tended to define risk and benefit in terms of detailed chains of cause-effect relationships between variables for which clear definitions and measurement rules exist. The concepts the consumers used when reasoning about biological processes were very abstract, suggesting that the participants had, at most, a holistic understanding. In line with this, issues of uncertainty played a prominent role for the consumers.     

Quantitative Uncertainty Analysis for a Weed Risk Assessment System

Weed risk assessments (WRA) are used to identify plant invaders before introduction. Unfortunately, very few incorporate uncertainty ratings or evaluate the effects of uncertainty, a fundamental risk component. We developed a probabilistic model to quantitatively evaluate the effects of uncertainty on the outcomes of a question‐based WRA tool for the United States. In our tool, the uncertainty of each response is rated as Negligible, Low, Moderate, or High. We developed the model by specifying the likelihood of a response changing for each uncertainty rating. The simulations determine if responses change, select new responses, and sum the scores to determine the risk rating. The simulated scores reveal potential variation in WRA risk ratings. In testing with 204 species assessments, the ranges of simulated risk scores increased with greater uncertainty, and analyses for most species produced simulated risk ratings that differed from the baseline WRA rating. Still, the most frequent simulated rating matched the baseline rating for every High Risk species, and for 87% of all tested species. The remaining 13% primarily involved ambiguous Low Risk results. Changing final ratings based on the uncertainty analysis results was not justified here because accuracy (match between WRA tool and known risk rating) did not improve. Detailed analyses of three species assessments indicate that assessment uncertainty may be best reduced by obtaining evidence for unanswered questions, rather than obtaining additional evidence for questions with responses. This analysis represents an advance in interpreting WRA results, and has enhanced our regulation and management of potential weed species.     

A Resilience-Based Approach to Risk Assessments—Building Resilient Organizations under Arctic Conditions

Reliability and higher levels of safety are thought to be achieved by using systematic approaches to managing risks. The assessment of risks has produced a range of different approaches to assessing these uncertainties, presenting models for how risks affect individuals or organizations. Contemporary risk assessment tools based on this approach have proven difficult for practitioners to use as tools for tactical and operational decision making. This article presents an alternative to these assessments by utilizing a resilience perspective, arguing that complex systems are inclined to variety and uncertainty regarding the results they produce and are therefore prone to systemic failures. A continuous improvement approach is a source of reliability when managing complex systems and is necessary to manage varieties and uncertainties. For an organization to understand how risk events occur, it is necessary to define what is believed to be the equilibrium of the system in time and space. By applying a resilience engineering (RE) perspective to risk assessment, it is possible to manage this complexity by assessing the ability to respond, monitor, learn, and anticipate risks, and in so doing to move away from the flawed frequency and consequences approach. Using a research station network in the Arctic as an example illustrates how an RE approach qualifies assessments by bridging risk assessments with value-creation processes. The article concludes by arguing that a resilience-based risk assessment can improve on current practice, including for organizations located outside the Arctic region.