Probabilistic risk assessment's use of trees and distributions to reflect uncertainty and variability and to overcome the limitations of default assumptions

Probabilistic risk assessment is an emerging approach to exposure assessment and quantitative cancer and non-cancer risk characterizations. The approach is easily extended to other types of risks and outcomes. A tree, like a decision tree or probability tree, encourages the evaluation of not only the default assumptions but also alternatives to those defaults, and reflects the uncertainty in the current state of knowledge. Trees are used in both the characterization of the dose received by individuals in a potential exposure situation and the characterization of the dose-response relationship for a specified response of concern. Probability distributions are used to reflect the variability in exposure, dose, and dose-response relationships among individuals and over time within individuals. Distributions incorporating variabilities, uncertainties, subjective probabilities, and expert judgments are used to characterize the probabilities of observing an individual in a population with a specified dose from exposure, with a specified probability of a certain adverse health effect for a designated dose, and with a specified probability of a certain adverse health effect (i.e., a specified risk). Some suggestions are given on how a risk manager can incorporate a distributional risk characterization into decision making. Some discussion is included concerning sensitivity analyses and path analyses. The major finding is methodology to explicitly incorporate variability, uncertainty, and alternatives to defaults into exposure, dose-response, and risk characterizations.     

Dynamic model for the assessment of radiological exposure to marine biota

A generic approach has been developed to simulate dynamically the uptake and turnover of radionuclides by marine biota. The approach incorporates a three-compartment biokinetic model based on first order linear kinetics, with interchange rates between the organism and its surrounding environment. Model rate constants are deduced as a function of known parameters: biological half-lives of elimination, concentration factors and a sample point of the retention curve, allowing for the representation of multi-component release. The new methodology has been tested and validated in respect of non-dynamic assessment models developed for regulatory purposes. The approach has also been successfully tested against research dynamic models developed to represent the uptake of technetium and radioiodine by lobsters and winkles. Assessments conducted on two realistic test scenarios demonstrated the importance of simulating time-dependency for ecosystems in which environmental levels of radionuclides are not in equilibrium.     

Risk evaluation of natural disasters based on connection function

There are many uncertain factors,such as stochastic,fuzzy and gray information in the risk analysis on natural hazard.The set pair analysis(SPA)deals effectively with the various uncertain factors contributing to evaluation of the risk level of natural disasters.The evaluation indicators and standards of natural disasters risk are analyzed by identity-discrepancycontrary(IDC).The result,the connection numbers,still has uncertainty information.Thus,yielding the risk evaluation model of natural disasters based on connection function,which construct a set pair relation between the indicators of connection numbers and comprehensive evaluation standards,and describe uncertainty of the connection numbers by using connection function.The study showed that the proposed model takes into account only the uncertainty of risk evaluation indicator identification on natural disasters,also the uncertainty of result connection numbers.This approach gives full consideration to the uncertainty of systematic evaluation process along with the actual meaning of comprehensive evaluation functions.Therefore,this means it is able to reduce the uncertainty of final evaluation results and improve the accuracy and reasonability of evaluation results.This model is capable of reflecting actual situation of the risk evaluation on natural disaster affected by various uncertain factors and has a promotional value in the natural disaster risk assessment.     

Addressing uncertainties in the ERICA Integrated Approach

Like any complex environmental problem, ecological risk assessment of the impacts of ionising radiation is confounded by uncertainty. At all stages, from problem formulation through to risk characterisation, the assessment is dependent on models, scenarios, assumptions and extrapolations. These include technical uncertainties related to the data used, conceptual uncertainties associated with models and scenarios, as well as social uncertainties such as economic impacts, the interpretation of legislation, and the acceptability of the assessment results to stakeholders. The ERICA Integrated Approach has been developed to allow an assessment of the risks of ionising radiation, and includes a number of methods that are intended to make the uncertainties and assumptions inherent in the assessment more transparent to users and stakeholders. Throughout its development, ERICA has recommended that assessors deal openly with the deeper dimensions of uncertainty and acknowledge that uncertainty is intrinsic to complex systems. Since the tool is based on a tiered approach, the approaches to dealing with uncertainty vary between the tiers, ranging from a simple, but highly conservative screening to a full probabilistic risk assessment including sensitivity analysis. This paper gives on overview of types of uncertainty that are manifest in ecological risk assessment and the ERICA Integrated Approach to dealing with some of these uncertainties.     

Cancer risk assessment of selected hazardous air pollutants in Seattle

The risk estimates calculated from the conventional risk assessment method usually are compound specific and provide limited information for source-specific air quality control. We used a risk apportionment approach, which is a combination of receptor modeling and risk assessment, to estimate source-specific lifetime excess cancer risks of selected hazardous air pollutants. We analyzed the speciated PM(2.5) and VOCs data collected at the Beacon Hill in Seattle, WA between 2000 and 2004 with the Multilinear Engine to first quantify source contributions to the mixture of hazardous air pollutants (HAPs) in terms of mass concentrations. The cancer risk from exposure to each source was then calculated as the sum of all available species' cancer risks in the source feature. We also adopted the bootstrapping technique for the uncertainty analysis. The results showed that the overall cancer risk was 6.09 x 10(-5), with the background (1.61 x 10(-5)), diesel (9.82 x 10(-6)) and wood burning (9.45 x 10(-6)) sources being the primary risk sources. The PM(2.5) mass concentration contributed 20% of the total risk. The 5th percentile of the risk estimates of all sources other than marine and soil were higher than 110(-6). It was also found that the diesel and wood burning sources presented similar cancer risks although the diesel exhaust contributed less to the PM(2.5) mass concentration than the wood burning. This highlights the additional value from such a risk apportionment approach that could be utilized for prioritizing control strategies to reduce the highest population health risks from exposure to HAPs.     

A spatial risk assessment methodology to support the remediation of contaminated land

When soil and groundwater contaminations occur over large areas, remediation measures should be spatially prioritized on the basis of the risk posed to human health and in compliance with technological and budget constraints. Within this scope, the application of human health risk assessment algorithms in a spatially resolved environment raises a number of methodological and technical complexities. In this paper, a methodology is proposed and applied in a case study to support the entire formulation process of remediation plans, encompassing hazard assessment, exposure assessment, risk characterisation, uncertainty assessment and allocation of risk reduction measures. In the hazard assessment, it supports the selection of Contaminants of Concern (CoC) with regard to both their average concentrations and peak concentrations, i.e. hot spots. In the exposure assessment, it provides a zoning of the site based on the geostatistical mapping of contaminant. In the risk characterisation, it generates vector maps of Risk Factors on the basis of the risk posed by multiple substances and allows the interrogation of most relevant CoC and exposure pathways for each zone of the site. It also supports the Monte Carlo based probabilistic estimation of the Risk Factors and generates maps of the associated uncertainty. In the risk reduction phase, it supports the formulation of remediation plans based on the stepwise spatial allocation of remediation interventions and the on-time simulation of risk reduction performances. The application of this methodology is fully supported by an easy-to-use and customized Geographical Information System and does not require high expertise for interpretation. The proposed methodology is the core module of a Decision Support System (DSS) that was implemented in the DESYRE software aimed at supporting the risk-based remediation of megasites.     

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.     

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.     

Pollutant exposures and health symptoms in aircrew and office workers: Is there a link?

Sensory effects in eyes and airways are common symptoms reported by aircraft crew and office workers. Neurological symptoms, such as headache, have also been reported. To assess the commonality and differences in exposures and health symptoms, a literature search of aircraft cabin and office air concentrations of non-reactive volatile organic compounds (VOCs) and ozone-initiated terpene reaction products were compiled and assessed. Data for tricresyl phosphates, in particular tri-ortho-cresyl phosphate (ToCP), were also compiled, as well as information on other risk factors such as low relative humidity.A conservative health risk assessment for eye, airway and neurological effects was undertaken based on a “worst-case scenario” which assumed a simultaneous constant exposure for 8 h to identified maximum concentrations in aircraft and offices. This used guidelines and reference values for sensory irritation for eyes and upper airways and airflow limitation; a tolerable daily intake value was used for ToCP. The assessment involved the use of hazard quotients or indexes, defined as the summed ratio(s) (%) of compound concentration(s) divided by their guideline value(s).The concentration data suggest that, under the assumption of a conservative “worst-case scenario”, aircraft air and office concentrations of the compounds in question are not likely to be associated with sensory symptoms in eyes and airways. This is supported by the fact that maximum concentrations are, in general, associated with infrequent incidents and brief exposures. Sensory symptoms, in particular in eyes, appear to be exacerbated by environmental and occupational conditions that differ in aircraft and offices, e.g., ozone incidents, low relative humidity, low cabin pressure, and visual display unit work. The data do not support airflow limitation effects. For ToCP, in view of the conservative approach adopted here and the rareness of reported incidents, the health risk of exposure to this compound in aircraft is considered negligible.     

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.     

Fuzzy rule-based modelling for human health risk from naturally occurring radioactive materials in produced water

Produced water, discharged from offshore oil and gas operations, contains chemicals from formation water, condensed water, and any chemical added down hole or during the oil/water separation process. Although, most of the contaminants fall below the detection limits within a short distance from the discharge port, a few of the remaining contaminants including naturally occurring radioactive materials (NORM) are of concern due to their bioavailability in the media and bioaccumulation characteristics in finfish and shellfish species used for human consumption. In the past, several initiatives have been taken to model human health risk from NORM in produced water. The parameters of the available risk assessment models are imprecise and sparse in nature. In this study, a fuzzy possibilistic evaluation using fuzzy rule based modeling has been presented. Being conservative in nature, the possibilistic approach considers possible input parameter values; thus provides better environmental prediction than the Monte Carlo (MC) calculation. The uncertainties of the input parameters were captured with fuzzy triangular membership functions (TFNs). Fuzzy if-then rules were applied for input concentrations of two isotopes of radium, namely (226)Ra, and (228)Ra, available in produced water and bulk dilution to evaluate the radium concentration in fish tissue used for human consumption. The bulk dilution was predicted using four input parameters: produced water discharge rate, ambient seawater velocity, depth of discharge port and density gradient. The evaluated cancer risk shows compliance with the regulatory guidelines; thus minimum risk to human health is expected from NORM components in produced water.     

Health risks analysis of energy systems: Issues and approaches

Health risk analysis of energy systems is affected by various principles of law. In this paper those principles are discussed and linked to measures of risk, such as risk rates and the expected value, and to the cumulative distributions. These measures of risk are related to health decisionmaking, including case law. Uncertainty defeats decisionmaking, although such uncertainty is properly derived from scientific analyses. There is a paradox, in that portraying the uncertainty forestalls decisions. Yet at present, science cannot do better. Risk analysis is the tool to help resolve this problem by identifying irreducible uncertainty, that uncertainty which is present in most human activities.     

Integration of environmental and human health risk assessment for industries using hazardous materials: a quantitative multi criteria approach for environmental decision makers

Environmental management, for which environmental and human health risk assessment is the first stage, is a requirement for industries both before construction and during operation in order to sustain improved quality of life in the ecosystem. Therefore, the aim of this study is to propose an approach that integrates environmental and human health risk assessment for industries using hazardous materials in order to support environmental decision makers with quantitative and directive results. Analytic hierarchy process and fuzzy logic are used as tools to handle problems caused by complexity of environment and uncertain data. When the proposed approach is implemented to a scenario, it was concluded that it is possible to define risk sources with their risk classes and related membership degrees in that classes which enable the decision maker to decide which risk source has priority. In addition, they can easily point out and rank the factors contributing those risk sources owing to priority weights of them. As a result, environmental decision makers can use this approach while they are developing management alternatives for unfounded and on-going industrial plants using hazardous materials.     

Mineralogical,chemical and toxicological characterization of urban air particles

Systematic characterization of morphological, mineralogical, chemical and toxicological properties of various size fractions of the atmospheric particulate matter was a main focus of this study together with an assessment of the human health risks they pose. Even though near-ground atmospheric aerosols have been a subject of intensive research in recent years, data integrating chemical composition of particles and health risks are still scarce and the particle size aspect has not been properly addressed yet. Filling this gap, however, is necessary for reliable risk assessment. A high volume ambient air sampler equipped with a multi-stage cascade impactor was used for size specific particle collection, and all 6 fractions were a subject of detailed characterization of chemical (PAHs) and mineralogical composition of the particles, their mass size distribution and genotoxic potential of organic extracts. Finally, the risk level for inhalation exposure associated to the carcinogenic character of the studied PAHs has been assessed. The finest fraction (< 0.45 μm) exhibited the highest mass, highest active surface, highest amount of associated PAHs and also highest direct and indirect genotoxic potentials in our model air sample. Risk assessment of inhalation scenario indicates the significant cancer risk values in PM 1.5 size fraction. This presented new approach proved to be a useful tool for human health risk assessment in the areas with significant levels of air dust concentration.     

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.     

A critical review of frameworks used for evaluating reliability and relevance of (eco)toxicity data: Perspectives for an integrated eco-human decision-making framework

Considerable efforts have been invested so far to evaluate and rank the quality and relevance of (eco)toxicity data for their use in regulatory risk assessment to assess chemical hazards. Many frameworks have been developed to improve robustness and transparency in the evaluation of reliability and relevance of individual tests, but these frameworks typically focus on either environmental risk assessment (ERA) or human health risk assessment (HHRA), and there is little cross talk between them. There is a need to develop a common approach that would support a more consistent, transparent and robust evaluation and weighting of the evidence across ERA and HHRA. This paper explores the applicability of existing Data Quality Assessment (DQA) frameworks for integrating environmental toxicity hazard data into human health assessments and vice versa. We performed a comparative analysis of the strengths and weaknesses of eleven frameworks for evaluating reliability and/or relevance of toxicity and ecotoxicity hazard data. We found that a frequent shortcoming is the lack of a clear separation between reliability and relevance criteria. A further gaps and needs analysis revealed that none of the reviewed frameworks satisfy the needs of a common eco-human DQA system. Based on our analysis, some key characteristics, perspectives and recommendations are identified and discussed for building a common DQA system as part of a future integrated eco-human decision-making framework. This work lays the basis for developing a common DQA system to support the further development and promotion of Integrated Risk Assessment.     

Framework for the uncertainty assessment in the impact pathway analysis with an application on a local scale in Spain

The estimation of damage estimates due to air emissions gives important basic knowledge for decision-making on the level of environmental politics and business strategies. Nowadays, a frequently applied method to estimate environmental damages is the Impact Pathway Analysis (IPA), which can be easily carried out using models such as EcoSense or PathWays. These models produce results in a relatively short term. However, there is a lack of reliability in the results. As in many other environmental software tools, the uncertainty is the key problem that makes it difficult to convince decision-makers by the outcomes of a study. Therefore, a framework that allows assessing the uncertainties within studies in which the IPA is applied on a local scale has been developed. In this assessment framework, the uncertainties of the used parameters, including their spatial and temporal variability, are taken into account. As the model is processing a huge quantity of data, one step of the assessment consists of a screening procedure to determine the parameters that are supposed to be fixed. For the other data, probability distributions have to be selected and classified into two groups: extensively available data for which average and standard deviation can be calculated and data based on little information. A quantification of the uncertainty can be completed by a stochastic model in the form of Monte Carlo (MC) simulation on the basis of the framework. As an illustration of the framework, we have applied it to a study on the installation of an advanced gas treatment in the municipal waste incinerator of Tarragona. It can be shown that the presented stochastic approach gives a lower geometric deviation than the analytical one and that the new gas treatment reduces the environmental damages without any doubt.     

基于情景分析的区域洪涝灾害风险评价——以巢湖流域为例

洪涝灾害是制约区域粮食安全和社会可持续发展的主要因子之一。在风险识别的基础上,从致灾因子、孕灾环境、承灾体等方面选取评价指标,建立评价指标体系。运用层次分析法确定指标权重,通过情景分析技术从降水、土地利用、人口、GDP等方面构建复合情景;应用GIS空间分析技术构建洪涝灾害风险评价模型,对巢湖流域洪涝灾害风险进行评价。研究结果表明:2020年巢湖流域洪涝灾害危险性由东南部向西北部减小;合肥市区的洪涝灾害易损性最大,和县的易损性最小。巢湖流域东南部洪涝灾害风险最大,西南部的大别山区风险较小,随着重现期的增大,流域的洪涝灾害风险也逐渐增大。模拟灾害发生的情景,并分析不同情景下的洪涝灾害风险,更能体现洪涝灾害的不确定性和变化性,为流域防洪战略决策研究提供科学依据。     

A systemic health risk assessment for the chromium cycle in Taiwan

Health risk assessment (HRA) has been recognized as a useful tool for identifying health risks of human activities. In particular, this method has been well applied to spatially defined units, such as a production plant, a treatment facility, and a contaminated site. However, the management strategies based on the risk information will be more efficient if the comprehensive picture of total risks from all kinds of sources is depicted. In principle, the total risks can be obtained when all risk sources are assessed individually. Apparently, this approach demands huge amount of efforts. This study develops a methodology that combines substance flow and risk estimation to facilitate examination of risk in a systemic way and provide comprehensive understanding of risk generation and distribution corresponding to flows of substances in the anthroposphere and the environment. Substance flow analysis (SFA) and HRA method is integrated to produce a systemic risk assessment method, from which substance management schemes can be derived. In this study, the chromium cycle in Taiwan is used as an example to demonstrate the method, by which the associated substance flow in the economy and the risk caused by the substance in the environmental system is determined. The concentrations of pollutants in the environmental media, the resultant risks and hazard quotients are calculated with the widely-used CalTOX multimedia model.