Extension of sensitivity and uncertainty analysis for long term dose assessment of high level nuclear waste disposal sites to uncertainties in the human behaviour

Biosphere dose conversion factors are computed for the French high-level geological waste disposal concept and to illustrate the combined probabilistic and deterministic approach. Both ¹³⁵Cs and ⁷⁹Se are used as examples. Probabilistic analyses of the system considering all parameters, as well as physical and societal parameters independently, allow quantification of their mutual impact on overall uncertainty. As physical parameter uncertainties decreased, for example with the availability of further experimental and field data, the societal uncertainties, which are less easily constrained, particularly for the long term, become more and more significant. One also has to distinguish uncertainties impacting the low dose portion of a distribution from those impacting the high dose range, the latter having logically a greater impact in an assessment situation. The use of cumulative probability curves allows us to quantify probability variations as a function of the dose estimate, with the ratio of the probability variation (slope of the curve) indicative of uncertainties of different radionuclides. In the case of ¹³⁵Cs with better constrained physical parameters, the uncertainty in human behaviour is more significant, even in the high dose range, where they increase the probability of higher doses. For both radionuclides, uncertainties impact more strongly in the intermediate than in the high dose range. In an assessment context, the focus will be on probabilities of higher dose values. The probabilistic approach can furthermore be used to construct critical groups based on a predefined probability level and to ensure that critical groups cover the expected range of uncertainty.     

Uncertainties in human health risk assessment of environmental contaminants: A review and perspective

Addressing uncertainties in human health risk assessment is a critical issue when evaluating the effects of contaminants on public health. A range of uncertainties exist through the source-to-outcome continuum, including exposure assessment, hazard and risk characterisation. While various strategies have been applied to characterising uncertainty, classical approaches largely rely on how to maximise the available resources. Expert judgement, defaults and tools for characterising quantitative uncertainty attempt to fill the gap between data and regulation requirements. The experiences of researching 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) illustrated uncertainty sources and how to maximise available information to determine uncertainties, and thereby provide an ‘adequate’ protection to contaminant exposure. As regulatory requirements and recurring issues increase, the assessment of complex scenarios involving a large number of chemicals requires more sophisticated tools. Recent advances in exposure and toxicology science provide a large data set for environmental contaminants and public health. In particular, biomonitoring information, in vitro data streams and computational toxicology are the crucial factors in the NexGen risk assessment, as well as uncertainties minimisation. Although in this review we cannot yet predict how the exposure science and modern toxicology will develop in the long-term, current techniques from emerging science can be integrated to improve decision-making.     

From comparative risk assessment to multi-criteria decision analysis and adaptive management: recent developments and applications

Environmental risk assessment and decision-making strategies over the last several decades have become increasingly more sophisticated, information-intensive, and complex, including such approaches as expert judgment, cost-benefit analysis, and toxicological risk assessment. One tool that has been used to support environmental decision-making is comparative risk assessment (CRA), but CRA lacks a structured method for arriving at an optimal project alternative. Multi-criteria decision analysis (MCDA) provides better-supported techniques for the comparison of project alternatives based on decision matrices, and it also provides structured methods for the incorporation of project stakeholders' opinions in the ranking of alternatives. We argue that the inherent uncertainty in our ability to predict ecosystem evolution and response to different management policies requires shifting from optimization-based management to an adaptive management paradigm. This paper brings together a multidisciplinary review of existing decision-making approaches at regulatory agencies in the United States and Europe and synthesizes state-of-the-art research in CRA, MCDA, and adaptive management methods applicable to environmental remediation and restoration projects. We propose a basic decision analytic framework that couples MCDA with adaptive management and its public participation and stakeholder value elicitation methods, and we demonstrate application of the framework to a realistic case study based on contaminated sediment management issues in the New York/New Jersey Harbor.     

Cytogenetic dose-response and adaptive response in cells of ungulate species exposed to ionizing radiation

In the studies reported here, the micronucleus assay, a common cytogenetic technique, was used to examine the dose-responses in fibroblasts from three ungulate species (white-tailed deer, woodland caribou, and Indian muntjac) exposed to high doses of ionizing radiation (1-4 Gy of (60)Co gamma radiation). This assay was also used to examine the effects of exposure to low doses (1-100 mGy) typical of what these species experience in a year from natural and anthropogenic environmental sources. An adaptive response, defined as the induction of resistance to a stressor by a prior exposure to a small "adapting" stress, was observed after exposure to low doses. This work indicates that very small doses are protective for the endpoint examined. The same level of protection was seen at all adapting doses, including 1 radiation track per cell, the lowest possible cellular dose. These results are consistent with other studies in a wide variety of organisms that demonstrate a protective effect of low doses at both cellular and whole-organism levels. This implies that environmental regulations predicated on the idea that even the smallest dose of radiation carries a quantifiable risk of direct adverse consequences to the exposed organism require further examination. Cytogenetic assays provide affordable and feasible biological effects-based alternatives that are more biologically relevant than traditional contaminant concentration-based radioecological risk assessment.     

Development of quantitative estimates of uncertainty in environmental risk assessments when the scientific data base is inadequate

The reasons for developing quantitative estimates of uncertainty in environmental risk assessments are discussed along with a method for developing them which involves scientific judgement. In the situation considered here the regulatory needs are ahead of the science, which makes the development of the estimates on uncertainty more difficult, but not impossible. Quantitative estimates for all uncertainties involved in the estimation of risk resulting from exposure to volatile organic compounds (VOCs) in drinking water are developed and tabulated. By far the largest contribution to the uncertainty in the risk estimates for VOCs in drinking water are due to uncertainty in the extrapolation of the dose-response curve to low levels. The uncertainty due to extrapolation is on the order of 10⁴ and 10⁶. Other components of the analysis may contribute uncertainties of a few orders of magnitude. In general the largest uncertainties are in the toxicological data base and the manipulation of it needed to estimate risk. The data base and manipulations needed to estimate exposure due to VOCs in drinking water were at the more an order to magnitudes in uncertainty.     

A dynamic model for assessing radiological consequences of tritium routinely released in rivers. Application to the Loire River

A dynamic model for assessing the transfer of tritium in a food chain was applied to the Loire River, where 14 nuclear power plants situated on five different sites operate. The model considers several potential exposure pathways in the aquatic and terrestrial ecosystems: transfer of tritium through the aquatic food chain (especially fish); use of river water for agricultural purposes (irrigation) and transfer of radionuclides through the terrestrial food chain (vegetables, meat, milk); subsequent internal exposure of humans due to ingestion of contaminated foodstuffs. For biological environmental compartments, the transfer of tritium to organic matter (i.e. OBT) was simulated. For each of the parameters introduced in this model, a probability density function, allowing further uncertainty and sensitivity analyses, was proposed. Uncertainty/sensitivity analyses were performed to determine a confidence interval for the mean annual dose to critical groups and to identify the parameters responsible for the uncertainty and subsequent research priorities.     

Risk assessment at hazardous waste-contaminated sites with variability of population characteristics

Risk assessment is considered to be an effective scientific tool which enables decisionmakers to manage hazardous waste-contaminated sites in a cost-effective manner while preserving public health. However, the current risk assessment framework proposed by the US Environmental Protection Agency (US EPA) has limitations in addressing the true variability of population characteristics. This study proposed a methodology that is different from the existing framework by accounting for the true variability of population characteristics. The key differences of the proposed methodology and the existing framework are the (1) use of the transient exposure concentration; (2) use of the entire population rather than a representative ideal individual; (3) use of age- and gender-based population subgroups to represent population characteristics; (4) use of a population growth model to represent growth dynamics; and (5) presentation of risk through a risk profile with risk summarized through a single indicator, potential cancer incidences (PCI). The proposed methodology was applied in a ground water contamination scenario due to benzene to determine its applicability. The results of the study showed that age-based variability of population characteristics is important in predicting the population risk while gender played a small role. The existing US EPA methodology and its variation using age-independent variability of population characteristics overestimate the risk given by PCI substantially, and therefore, the decisions can lead to costly cleanup goals. Population risk is not a single value but a distribution due to the contribution from ditferent individuals of the exposed population. Hence, the decision criterion proposed in this study, PCI, is found to be a useful indicator to describe population carcinogenic risk to the society under a variety of conditions and scenarios.     

Scenario-model-parameter: a new method of cumulative risk uncertainty analysis

The recently developed concepts of aggregate risk and cumulative risk rectify two limitations associated with the classical risk assessment paradigm established in the early 1980s. Aggregate exposure denotes the amount of one pollutant available at the biological exchange boundaries from multiple routes of exposure. Cumulative risk assessment is defined as an assessment of risk from the accumulation of a common toxic effect from all routes of exposure to multiple chemicals sharing a common mechanism of toxicity. Thus, cumulative risk constitutes an improvement over the classical risk paradigm, which treats exposures from multiple routes as independent events associated with each specific route. Risk assessors formulate complex models and identify many realistic scenarios of exposure that enable them to estimate risks from exposures to multiple pollutants and multiple routes. The increase in complexity of the risk assessment process is likely to increase risk uncertainty. Despite evidence that scenario and model uncertainty contribute to the overall uncertainty of cumulative risk estimates, present uncertainty analysis of risk estimates accounts only for parameter uncertainty and excludes model and scenario uncertainties. This paper provides a synopsis of the risk assessment evolution and associated uncertainty analysis methods. This evolution leads to the concept of the scenario-model-parameter (SW) cumulative risk uncertainty analysis method. The SMP uncertainty analysis is a multiple step procedure that assesses uncertainty associated with the use of judiciously selected scenarios and models of exposure and risk. Ultimately, the SMP uncertainty analysis method compares risk uncertainty estimates determined using all three sources of uncertainty with conventional risk uncertainty estimates obtained using only the parameter source. An example of applying the SMP uncertainty analysis to cumulative risk estimates from exposures to two pesticides indicates that inclusion of scenario and model sources.     

Causation in risk assessment and management: models, inference, biases, and a microbial risk-benefit case study

Causal inference of exposure-response relations from data is a challenging aspect of risk assessment with important implications for public and private risk management. Such inference, which is fundamentally empirical and based on exposure (or dose)-response models, seldom arises from a single set of data; rather, it requires integrating heterogeneous information from diverse sources and disciplines including epidemiology, toxicology, and cell and molecular biology. The causal aspects we discuss focus on these three aspects: drawing sound inferences about causal relations from one or more observational studies; addressing and resolving biases that can affect a single multivariate empirical exposure-response study; and applying the results from these considerations to the microbiological risk management of human health risks and benefits of a ban on antibiotic use in animals, in the context of banning enrofloxacin or macrolides, antibiotics used against bacterial illnesses in poultry, and the effects of such bans on changing the risk of human food-borne campylobacteriosis infections. The purposes of this paper are to describe novel causal methods for assessing empirical causation and inference; exemplify how to deal with biases that routinely arise in multivariate exposure- or dose-response modeling; and provide a simplified discussion of a case study of causal inference using microbial risk analysis as an example. The case study supports the conclusion that the human health benefits from a ban are unlikely to be greater than the excess human health risks that it could create, even when accounting for uncertainty. We conclude that quantitative causal analysis of risks is a preferable to qualitative assessments because it does not involve unjustified loss of information and is sound under the inferential use of risk results by management.     

Assessing the risk of an excess fluoride intake among Swedish children in households with private wells — Expanding static single-source methods to a probabilistic multi-exposure-pathway approach

It is often assumed that water consumption is the major route of exposure for fluoride and analysis of water fluoride content is the most common approach for ensuring that the daily intake is not too high. In the present study, the risk of excess intake was characterized for children in households with private wells in Kalmar County, Sweden, where the natural geology shows local enrichments in fluorine. By comparing water concentrations with the WHO drinking water guideline (1.5 mg/L), it was found that 24% of the ca. 4800 sampled wells had a concentration above this limit, hence providing a figure for the number of children in the households concerned assessed to be at risk using this straightforward approach. The risk of an excess intake could, alternatively, also be characterized based on a tolerable daily intake (in this case the US EPA RfD of 0.06 mg/kg-day). The exposure to be evaluated was calculated using a probabilistic approach, where the variability in all exposure factors was considered, again for the same study population. The proportion of children assessed to be at risk after exposure from drinking water now increased to 48%, and when the probabilistic model was adjusted to also include other possible exposure pathways; beverages and food, ingestion of toothpaste, oral soil intake and dust inhalation, the number increased to 77%. Firstly, these results show how the risk characterization is affected by the basis of comparison. In this example, both of the reference values used are widely acknowledged. Secondly, it illustrates how much of the total exposure may be overlooked when only focusing on one exposure pathway, and thirdly, it shows the importance of considering the variability in all relevant pathways.     

Empirical analysis of the variability of the maximum likelihood estimates of benzene cancer risks

This paper presents results from an on-going study of the carcinogenicity of benzene at low doses. It focuses on the statistical variability of the parametric maximum likelihood estimator (MLE) of the parameters of the linearized multistage (LMS) cancer dose-response model. This paper describes: 1) how pharmacokinetic conversions from animal to human were made to convert administered benzene to estimated internal doses of its metabolites; 2) the exact form of the LMS; 3) likelihood surfaces for fitting the exact form to data; and 4) results of bootstrap and Monte Carlo simulations that assess the variability of the parameters of the dose-response model. This paper suggests that the cubic form of the LMS dose-response for animal tumors is robust to sampling variability. For environmental policy, the hypothesis that linearity governs low dose benzene carcinogenicity is probably incorrect.     

An approach for environmental risk assessment of engineered nanomaterials using Analytical Hierarchy Process (AHP) and fuzzy inference rules

The usage of Engineered Nanoparticles (ENPs) in consumer products is relatively new and there is a need to conduct environmental risk assessment (ERA) to evaluate their impacts on the environment. However, alternative approaches are required for ERA of ENPs because of the huge gap in data and knowledge compared to conventional pollutants and their unique properties that make it difficult to apply existing approaches. This study aims to propose an ERA approach for ENPs by integrating Analytical Hierarchy Process (AHP) and fuzzy inference models which provide a systematic evaluation of risk factors and reducing uncertainty about the data and information, respectively. Risk is assumed to be the combination of occurrence likelihood, exposure potential and toxic effects in the environment. A hierarchy was established to evaluate the sub factors of these components. Evaluation was made with fuzzy numbers to reduce uncertainty and incorporate the expert judgements. Overall score of each component was combined with fuzzy inference rules by using expert judgements. Proposed approach reports the risk class and its membership degree such as Minor (0.7). Therefore, results are precise and helpful to determine the risk management strategies. Moreover, priority weights calculated by comparing the risk factors based on their importance for the risk enable users to understand which factor is effective on the risk. Proposed approach was applied for Ag (two nanoparticles with different coating) and TiO₂ nanoparticles for different case studies. Results verified the proposed benefits of the approach.     

A dynamic model for assessing radiological consequences of routine releases in the Loire river: parameterisation and uncertainty/sensitivity analysis

A dynamic model for assessing the transfer of several radionuclides ((58)Co, (60)Co, (110 m)Ag, (134)Cs, (137)Cs, (54)Mn and (131)I) in a food-chain was applied on the Loire river, where 14 nuclear power plants situated on five different sites operate. The model considers the following potential exposure pathways: (i) transfer of radionuclides through the aquatic food chain and the subsequent internal exposure of humans due to ingestion of contaminated water and/or fish; (ii) use of river water for agricultural purposes (irrigation), transfer of radionuclides through the terrestrial food chain and the subsequent internal exposure of humans due to ingestion of contaminated foodstuffs; (iii) internal exposure due to inhalation of dust originating from resuspension of contaminated soil particles; (iv) external exposure from radionuclides present in the river or deposited on the river sediments or the soil. For each of the parameters introduced in this model, a probability density function, allowing further uncertainty and sensitivity analysis, was proposed. Uncertainty/sensitivity analysis were performed to: (i) compare calculations to empirical data; (ii) determine a confidence interval for the mean annual dose to critical groups; and (iii) identify the parameters responsible for the uncertainty and subsequent research priorities.     

Dealing with uncertainty in the assessment of human exposure to radioactivity in food and the environment

This paper presents an overview of the approach used to assess radiation dose and risk to members of the public from radioactivity in food and the environment. It describes uncertainties in the process and suggests ways of dealing with them to improve the risk assessment process. It also explains how uncertainty in the assessed dose/risk can be communicated to non-expert audiences such as members of the public. The issues covered in this paper apply to risk assessment of any contaminant and not only radioactivity.     

Precaution,uncertainty and causation in environmental decisions

What measures of uncertainty and what causal analysis can improve the management of potentially severe, irreversible or dreaded environmental outcomes? Environmental choices show that policies intended to be precautionary (such as adding MTBE to petrol) can cause unanticipated harm (by mobilizing benzene, a known leukemogen, in the ground water). Many environmental law principles set the boundaries of what should be done but do not provide an operational construct to answer this question. Those principles, ranging from the precautionary principle to protecting human health from a significant risk of material health impairment, do not explain how to make environmental management choices when incomplete, inconsistent and complex scientific evidence characterizes potentially adverse environmental outcomes. Rather, they pass the task to lower jurisdictions such as agencies or authorities. To achieve the goals of the principle, those who draft it must deal with scientific casual conjectures, partial knowledge and variable data. In this paper we specifically deal with the qualitative and quantitative aspects of the European Union's (EU) explanation of consistency and on the examination of scientific developments relevant to variability and uncertain data and causation. Managing hazards under the precautionary principle requires inductive, empirical methods of assessment. However, acting on a scientific conjecture can also be socially unfair, costly, and detrimental when applied to complex environmental choices. We describe a constructive framework rationally to meet the command of the precautionary principle using alternative measures of uncertainty and recent statistical methods of causal analysis. These measures and methods can bridge the gap between conjectured future irreversible or severe harm and scant scientific evidence, thus leading to more confident and resilient social choices. We review two sets of measures and computational systems to deal with uncertainty and link them to causation through inductive empirical methods such as Bayesian Networks. We conclude that primary legislation concerned with large uncertainties and potential severe or dreaded environmental outcomes can produce accurate and efficient choices. To do so, primary legislation should specifically indicate what measures can represent uncertainty and how to deal with uncertain causation thus providing guidance to an agency's rulemaking or to an authority's writing secondary legislation. A corollary conclusion with legal, scientific and probabilistic implications concerns how to update past information when the state of information increases because a failure to update can result in regretting past choices. Elected legislators have the democratic mandate to formulate precautionary principles and are accountable. To preserve that mandate, imbedding formal methods to represent uncertainty in the statutory language of the precautionary principle enhances subsequent judicial review of legislative actions. The framework that we propose also reduces the Balkanized views and interpretations of probabilities, possibilities, likelihood and uncertainty that exists in environmental decision-making.     

Imprecision of dose predictions for radionuclides released to the environment: An application of a Monte Carlo simulation technique

An evaluation of the imprecision in dose predictions has been performed using current dose assessment models and present knowledge of the variability or uncertainty in model parameter values. The propagation of parameter uncertainties is demonstrated using a Monte Carlo technique for elemental ¹³¹I transported via the pasture-cow-milk-child pathway. The results indicate that when site-specific information is not available the imprecision inherent in the predictions for this pathway is potentially large. Generally, the 99th percentile in thyroid dose for children was predicted to be approximately an order of magnitude greater than the median value. The potential consequences of the imprecision in dose for radiation protection purposes are discussed.     

Economic and Social Impact Assessment of the Alternatives to DDT Usage for Antifouling Paint Production in China

China is the only nation that uses DDT in antifouling pain at present, approximately 5% of DDT is applied as the additive of the antifouling paint production. Therefore, actions shall be taken urgently for banning the use of DDT and substituting with non-POPs alternatives in antifouling paints. The paper researches the social and economic backgrounds of DDT booster antifouling paint production and usage, analyzes the social and economic impact assessment of the alternatives to DDT usage for antifouling paint. The implementation of the project of alternatives will completely eliminate the adverse impact of DDT booster antifouling paint on terrestrial, marine ecosystem and human health. The broad use of alkali silicate and pepper alkali as substitutes will be feasible if appropriate measures will be taken to encourage their development, and the social and economic risk will be reduced to accepted levels.     

Overview and definition of risk: Its assessment and management

Risk assessment includes both risk estimation (identifying hazards and estimating their outcomes and probabilities) and risk evaluation (determining the significance or value of risks to those concerned with or affected by the decision). Risk estimation is about situations, and risk evaluation about the effect on people. Few situations are absolutely safe. Risks need to be estimated, and for many kinds of risk (e.g., exposures to potentially toxic substances or to potentially catastrophic situations) an expert view has to be formed, which must take account of associated uncertainties. Different sections of the public perceive risk in different ways, and regard some risks more seriously than the expert estimates. Thus studies of risk perception form a further input into the decision-making process. Another input is the benefit of the activity. A regulatory framework consistent with the reality of risk estimation has been proposed. Since risks and benefits fall on different parts of the community, the decision process is essentially political, though it must be informed scientifically. How the decision is taken will depend on national governmental practices.