Analysis and Portrayal of Uncertainty in a Food-Web Exposure Model

The results of quantitative risk assessments are key factors in a risk manager's decision of the necessity to implement actions to reduce risk. The extent of the uncertainty in the assessment will play a large part in the degree of confidence a risk manager has in the reported significance and probability of a given risk. The two main sources of uncertainty in such risk assessments are variability and incertitude. In this paper we use two methods, a second-order two-dimensional Monte Carlo analysis and probability bounds analysis, to investigate the impact of both types of uncertainty on the results of a food-web exposure model. We demonstrate how the full extent of uncertainty in a risk estimate can be fully portrayed in a way that is useful to risk managers. We show that probability bounds analysis is a useful tool for identifying the parameters that contribute the most to uncertainty in a risk estimate and how it can be used to complement established practices in risk assessment. We conclude by promoting the use of probability analysis in conjunction with Monte Carlo analyses as a method for checking how plausible Monte Carlo results are in the full context of uncertainty.     

Studies on the use of uncertainty factors in deriving RfDs

Risk assessment of exposure to chemicals having a toxic end point routinely uses the reference dose (RfD) approach based on uncertainty factors of 10. RfD model can be used with widely different databases. However, the quality of individual risk assessment is unequal among chemicals, often resulting in either an over‐ or underestimation of adverse health risk. The purpose of this investigation was to evaluate whether the magnitude of the 10X uncertainty factors has scientific merit against data from recent human and animal experimental studies. Although we assessed the use of uncertainty factors for representative chemicals from various classes of compounds, such as volatile organics, alcohols, gasoline components, and pesticides, we are presenting our findings for 24 chemicals.

A compilation and comparison of ratios between LOAEL/NOAEL (Lowest Observed Adverse Effect Level/No Observed Adverse Effect Level), and subchronic/chronic values were made. Although a 10X uncertainty factor is most commonly used in the risk assessment processes, an examination of the datasets which have been used to calculate RfDs suggests different values which are scientifically justifiable.     

Relevance of the 10X Uncertainty Factor to the Risk Assessment of Drugs Used by Children and Geriatrics

Conventional risk assessment practices utilize a tenfold uncertainty factor (UF) to extrapolate from the general human population to sensitive subgroups, such as children and geriatrics. This study evaluated whether the tenfold UF can be reduced when pharmacokinetic and pharmacodynamic data for pharmaceuticals used by children and geriatrics are incorporated into the risk assessment for human sensitivity. Composite factors (kinetics X dynamics) were calculated from data-derived values for bumetanide, furosemide, metoprolol, atenolol, naproxen, and ibuprofen. For the compounds examined, all of the composite factors were lower than 10. Furthermore, 8 of the 12 composite factors were less than 5.5. Incorporation of human kinetic and dynamic data into risk assessment can aid in reducing the uncertainties associated with sensitive subgroups and further study is encouraged.     

Risk Analysis,Uncertainty Factors,and the Susceptibilities of Children

This article examines the use of uncertainty factors (UFs) by the Agency for Toxic Substances and Disease Registry (ATSDR) when developing health guidance values known as minimal risk levels (MRLs) in environmental risk analysis as it applies to children. Improvements in the chemical-specific databases often reveal new information and thereby reduce uncertainty or alternatively raise new concerns. As a result, MRLs can and will change. Children, in particular, are not “small adults” and in some instances demonstrate greater risks of exposure to environmental toxicants and greater susceptibility for adverse health effects once exposed. Recent experience with risk analysis for three toxicants (organic mercury, dioxin, and manganese) is recounted to demonstrate how ATSDR has revised MRLs as the emerging science generates greater knowledge and awareness of children's special vulnerabilities to toxic substances in the environment.     

The Use of Probabilistic Risk Assessment in Establishing Drinking Water Quality Objectives

There has been a trend in recent years toward the use of probabilistic methods for the analysis of uncertainty and variability in risk assessment. By developing a plausible distribution of risk, it is possible to obtain a more complete characterization of risk than is provided by either best estimates or upper limits. We describe in this paper a general framework for evaluating uncertainty and variability in risk estimation and outline how this framework can be used in the establishment of drinking water quality objectives. In addition to characterizing uncertainty and variability in risk, this framework also facilitates the identification of specific factors that contribute most to uncertainty and variability. The application of these probabilistic risk assessment methods is illustrated using tetrachloroethylene and trihalomethanes as examples.     

Selected applications of reduced uncertainty factors in noncancer risk assessment

Guidelines have been developed within the Environmental Protection Agency (EPA) for applying uncertainty factors to noncancer risk assessments. In many recent cases, reduced values for uncertainty factors of less than 10 have been used. The specific circumstances that justify reduced uncertainty are: partial definition of the sensitive subpopulation among humans, partial database limitations, use of a minimal LOAEL, risk assessment for an essential nutrient, and risk assessments based upon studies in nonhuman primates. Details of the rationale for each of these circumstances are provided.     

Extrapolation in Risk Assessment: Improving the Quantification of Uncertainty,and Improving Information to Reduce the Uncertainty

Risk assessments inevitably extrapolate from the known to the unknown. The resulting calculation of risk involves two fundamental kinds of uncertainty: uncertainty owing to intrinsically unpredictable (random) components of the future events, and uncertainty owing to imperfect prediction formulas (parameter uncertainty and error in model structure) that are used to predict the component that we think is predictable. Both types of uncertainty weigh heavily both in health and ecological risk assessments. Our first responsibility in conducting risk assessments is to ensure that the reported risks correctly reflect our actual level of uncertainty (of both types). The statistical methods that lend themselves to correct quantification of the uncertainty are also effective for combining different sources of information. One way to reduce uncertainty is to use all the available data. To further sharpen future risk assessments, it is useful to partition the uncertainty between the random component and the component due to parameter uncertainty, so that we can quantify the expected reduction in uncertainty that can be achieved by investing in a given amount of future data. An example is developed to illustrate the potential for use of comparative data, from toxicity testing on other species or other chemicals, to improve the estimates of low-effect concentration in a particular case with sparse case-specific data.     

The 3MRA Risk Assessment Framework—A Flexible Approach for Performing Multimedia,Multipathway, and Multireceptor Risk Assessments Under Uncertainty

A flexible framework for conducting nationwide multimedia, multipathway and multireceptor risk assessments (3MRA) under uncertainty was developed to estimate protective chemical concentration limits in a source area. The framework consists of two components: risk assessment and uncertainty analysis. The risk component utilizes linked source, fate/transport, exposure and risk assessment models to estimate the risk exposures for the receptors of concern. Both human and ecological receptors are included in the risk assessment framework. The flexibility of the framework is based on its ability to address problems varying in spatial scales from site-specific to regional and even national levels; and its ability to accommodate varying types of source, fate/transport, exposure and risk assessment models. The uncertainty component of the 3MRA framework is based on a two-stage Monte Carlo methodology. It allows the calculation of uncertainty in risk estimates, and the incorporation of the effects of uncertainty on the determination of regulatory concentration limits as a function of variability and uncertainty in input data, as well as potential errors in fate and transport and risk and exposure models. The framework can be adapted to handle a wide range of multimedia risk assessment problems. Two examples are presented to illustrate its use, and to demonstrate how regulatory decisions can be structured to incorporate the uncertainty in risk estimates.     

城市滑坡灾害生态风险不确定性分析及风险管理——以深圳市为例

城市化的快速推进加剧了建设活动强度,强烈改变自然地表,破环原本稳定的地质环境,导致滑坡灾害日益频繁。本研究以深圳市为研究区,基于"危险性-脆弱性-潜在损失"三维评价框架评价深圳市街道尺度滑坡灾害生态风险、运用"三基色"原理可视化风险结构,并采用蒙特卡洛模拟进行不确定性分析,进而提出生态风险管理措施。结果表明:(1)不确定性与街道面积呈显著的负相关关系,随着街道面积增加,生态风险源不确定性减少;(2)以风险源为不确定性主体的生态风险评价中,不确定性由生态风险源不确定性和"源外"因子大小共同主导,"源外"因子大小是生态风险评价不确定的敏感因素;(3)深圳市滑坡灾害生态风险从西到东呈现"低-高-低-高"交错结构,风险结构主要为"高脆弱-高潜在损失"型和"高危险-高潜在损失"型。分析生态风险结构,建立滑坡灾害生态风险管理体系,制定明确风险管理目标,有利于高效地进行生态风险管理。     

长三角平原区县域土壤磷素流失风险及其空间不确定性的快速评估

磷素是水体富营养化的关键限制因子,其中从农田土壤中流失的磷往往是水体磷素的主要来源。然而,土壤磷素的流失风险不仅与土壤磷素水平直接相关,其他环境因子,如距受纳水体距离、磷肥施用量、地表径流潜力等也强烈影响其流失风险。同时,基于有限样本预测得到的流失风险必然具有一定的空间不确定性。以长三角典型县域金坛区为研究案例,首先结合多个环境因子构建快速磷指数(RPI)评估模型,再利用稳健地统计学方法识别土壤全磷的空间离群值,并利用序贯高斯模型(SGS)模拟土壤全磷可能的空间分布格局,最后将其多个可能的模拟结果及上述主要因子输入到RPI模型,用以快速评估土壤磷素流失风险及其空间不确定性。结果显示,金坛区土壤磷素流失的高风险区和土壤全磷高值区分布格局在研究区北部、中部具有一定的相似性,而在中西部的旱地区两者出现差异性。高风险区主要沿着河流呈现条带状及斑块状分布,较高及以上风险区(快速磷指数值大于0.93)的面积占金坛区面积的65.88%。概率阈值分别设定为0.50、0.75、0.85、0.95时,其超标面积占金坛区总面积分别达到16.71%、5.74%、2.84%、1.04%。引入多个相关环境因子并结合经稳健处理的SGS进行流失风险指数的空间模拟和不确定性评估,可以快速评估区域农田土壤磷素流失风险及不确定性,进而为区域土壤磷素调控提供必要的空间决策支持。     

Evaluation of Sampling-Based Methods for Sensitivity Analysis: Case Study for the E. coli Food Safety Process Risk Model

This article evaluates selected sensitivity analysis methods applicable to risk assessment models with two-dimensional probabilistic frameworks, using a microbial food safety process risk model as a test-bed. Six sampling-based sensitivity analysis methods were evaluated including Pearson and Spearman correlation, sample and rank linear regression, and sample and rank stepwise regression. In a two-dimensional risk model, the identification of key controllable inputs that can be priorities for risk management can be confounded by uncertainty. However, despite uncertainty, results show that key inputs can be distinguished from those that are unimportant, and inputs can be grouped into categories of similar levels of importance. All selected methods are capable of identifying unimportant inputs, which is helpful in that efforts to collect data to improve the assessment or to focus risk management strategies can be prioritized elsewhere. Rank-based methods provided more robust insights with respect to the key sources of variability in that they produced narrower ranges of uncertainty for sensitivity results and more clear distinctions when comparing the importance of inputs or groups of inputs. Regression-based methods have advantages over correlation approaches because they can be configured to provide insight regarding interactions and nonlinearities in the model.     

稳定氢氧同位素定量植物水分来源的不确定性解析

稳定氢氧同位素技术被广泛运用于生态系统、特别是干旱区生态系统中植物水分来源的研究,其理论假设为"水分被植物根系吸收并向木质部运输过程中不发生氢氧同位素分馏"。生态系统中不同水源的氢氧同位素组成普遍存在显著差异,为从水源混合体中区分出各水源的贡献率提供了前提条件。但在实际应用过程中,诸多因素导致稳定氢氧同位素技术定量植物水分来源的结果具有不确定性。综合已有研究并加以分析,举证说明植物吸收水分相对于水源同位素变化的滞后性、水源同位素的季节性变化、蒸发作用和水源之间的混合作用对水源同位素的影响等导致植物水分来源定量结果不确定性的几个因素,以期为今后稳定氢氧同位素技术在植物水分来源领域的应用提供参考。     

Identifying Uncertainty in Environmental Risk Assessments: The Development of a Novel Typology and Its Implications for Risk Characterization

Environmental risk analysts need to draw from a clear typology of uncertainties when qualifying risk estimates and/or significance statements about risk. However, categorizations of uncertainty within existing typologies are largely overlapping, contradictory, and subjective, and many typologies are not designed with environmental risk assessments (ERAs) in mind. In an attempt to rectify these issues, this research provides a new categorization of uncertainties based, for the first time, on the appraisal of a large subset of ERAs, namely 171 peer-reviewed environmental weight-of-evidence assessments. Using this dataset, a defensible typology consisting of seven types of uncertainty (data, language, system, extrapolation, variability, model, and decision) and 20 related sub-types is developed. Relationships between uncertainties and the techniques used to manage them are also identified and statistically evaluated. A highly preferred uncertainty management option is to take no action when faced with uncertainty, although where techniques are applied they are commensurate with the uncertainty in question. Key observations are applied in the form of guidance for dealing with uncertainty, demonstrated through ERAs of genetically modified higher plants in the European Union. The presented typology and accompanying guidance will have positive implications for the identification, prioritization, and management of uncertainty during risk characterization.     

What Can Be Done to Ensure the Usefulness of Epidemiologic Data for Risk Assessment Purposes?

Epidemiologic studies can play a central role in risk assessments. They are used in all risk assessment phases: hazard identification, dose-response, and exposure assessment. Epidemiologic studies have often been the first to show that a particular environmental exposure is a hazard to health. They have numerous advantages with respect to other sources of data which are used in risk assessments, the most important being that they do not require the assumption that they are generalizable to humans. For this reason, fewer and lower uncertainty factors may be appropriate in risk characterization based on epidemiologic studies. Unfortunately, epidemiologic studies have numerous problems, the most important being that the exposures are often not precisely measured. This article presents in detail the advantages of and problems with epidemiologic studies. It discusses two approaches to ensure their usefulness, biomarkers and an ordinance which requires baseline and subsequent surveillance of possible exposures and health effects from newly sited potentially polluting facilities. Biomarkers are biochemical measures of exposure, susceptibility factors, or preclinical pathological changes. Biomarkers are a way of dealing with the problems of poor measures, differential susceptibility and lack of early measures of disease occurrence that inherent in many environmental epidemiologic studies. The advantages of biomarkers is they can provide objective information on exposure days, months or even years later and evidence of pathology perhaps years earlier. The ordinance makes possible the use of a powerful epidemiologic study design, the prospective cohort study, where confounder(s) are best measured, and exposures, pathological changes, and health effects can be detected as soon as possible.     

Prioritizing risks and uncertainties from intentional release of selected Category A pathogens

This paper synthesizes available information on five Category A pathogens (Bacillus anthracis, Yersinia pestis, Francisella tularensis, Variola major and Lassa) to develop quantitative guidelines for how environmental pathogen concentrations may be related to human health risk in an indoor environment. An integrated model of environmental transport and human health exposure to biological pathogens is constructed which 1) includes the effects of environmental attenuation, 2) considers fomite contact exposure as well as inhalational exposure, and 3) includes an uncertainty analysis to identify key input uncertainties, which may inform future research directions. The findings provide a framework for developing the many different environmental standards that are needed for making risk-informed response decisions, such as when prophylactic antibiotics should be distributed, and whether or not a contaminated area should be cleaned up. The approach is based on the assumption of uniform mixing in environmental compartments and is thus applicable to areas sufficiently removed in time and space from the initial release that mixing has produced relatively uniform concentrations. Results indicate that when pathogens are released into the air, risk from inhalation is the main component of the overall risk, while risk from ingestion (dermal contact for B. anthracis) is the main component of the overall risk when pathogens are present on surfaces. Concentrations sampled from untracked floor, walls and the filter of heating ventilation and air conditioning (HVAC) system are proposed as indicators of previous exposure risk, while samples taken from touched surfaces are proposed as indicators of future risk if the building is reoccupied. A Monte Carlo uncertainty analysis is conducted and input-output correlations used to identify important parameter uncertainties. An approach is proposed for integrating these quantitative assessments of parameter uncertainty with broader, qualitative considerations to identify future research priorities.     

The Value of the Value of Information

An important application of decision analysis is determining the value that information has to a decision maker. The expected value of information (EVOI) is the expected increase in the value (or decrease in the loss) associated with obtaining more information about quantities relevant to the decision process. The EVOI can be thought of as a measure of the importance of the uncertainty about a quantity in terms of the expected improvement in the decision that might be obtained from having additional information about it. Examples of EVOI quantities useful in risk management situations include the expected value of including uncertainty (EVIU), the expected value of perfect information (EVPI), and the expected value of sample information (EVSI). Value of information (VOI) analysis is useful because it makes the losses associated with decision errors explicit, balances competing probabilities and costs, helps identify the decision alternative that minimizes the expected loss, prioritizes spending on research, quantifies the value of the research to the decision maker, and provides an upper bound on what should be spent on getting information.     

Critical factors in assessing risk from exposure to nasal carcinogens

Anatomical, physiological, biochemical and molecular factors that contribute to chemical-induced nasal carcinogenesis are either largely divergent between test species and humans, or we know very little of them. These factors, let alone the uncertainty associated with our knowledge gap, present a risk assessor with the formidable task of making judgments about risks to human health from exposure to chemicals that have been identified in rodent studies to be nasal carcinogens. This paper summarizes some of the critical attributes of the hazard identification and dose–response aspects of risk assessments for nasal carcinogens that must be accounted for by risk assessors in order to make informed decisions. Data on two example compounds, dimethyl sulfate and hexamethylphosphoramide, are discussed to illustrate the diversity of information that can be used to develop informed hypotheses about mode of action and decisions on appropriate dosimeters for interspecies extrapolation. Default approaches to interspecies dosimetry extrapolation are described briefly and are followed by a discussion of a generalized physiologically based pharmacokinetic model that, unlike default approaches, is flexible and capable of incorporating many of the critical species-specific factors. Recent advancements in interspecies nasal dosimetry modeling are remarkable. However, it is concluded that without the development of research programs aimed at understanding carcinogenic susceptibility factors in human and rodent nasal tissues, development of plausible modes of action will lag behind the advancements made in dosimetry modeling.     

Emission sources and probabilistic health risk of volatile organic compounds emitted from production areas in a petrochemical refinery in Hainan,China

Abstract

The emission sources and health risks of volatile organic compounds (VOCs) were analyzed and evaluated in a typical petrochemical refinery in Hainan, China. The sources and levels of 9 VOCs in five production areas were identified and qualified, and the probabilistic risk assessment method was employed to obtain more reasonable and scientific outcomes specifically realized by inhalation risk model and Monte Carlo simulation. Sensitivity and uncertainty analysis were also conducted to determine the influential factors in the risk evaluation process. The results indicated that for the refinery benzene, toluene, ethyl benzene, and xylene were the primary pollutants in these production areas, where the aromatic hydrocarbon extraction device (AHED) and xylene fractionation device areas are main contributors. In terms of non-carcinogenic risk, the largest hazard index existed in AHED area. The non-carcinogenic risk values for all production areas were not more than 1. The risk value of substances such as benzene still exceed the carcinogenic risk value of 10^?6, indicating these substances existed potential carcinogenic risk to workers. Meanwhile, the findings can help to accumulate basic data for VOCs research in different installations of refineries, and provide evidence for VOCs pollution prevention and control.