Stochastic multimedia risk assessment for a site with contaminated groundwater

 There exist many sites with contaminated groundwater because of inappropriate handling or disposal of hazardous materials or wastes. Health risk assessment is an important tool to evaluate the potential environmental and health impacts of these contaminated sites. It is also becoming an important basis for determining whether risk reduction is needed and what actions should be initiated. However, in research related to groundwater risk assessment and management, consideration of multimedia risk assessment and the separation of the uncertainty due to lack of knowledge and the variability due to natural heterogeneity are rare. This study presents a multimedia risk assessment framework with the integration of multimedia transfer and multi-pathway exposure of groundwater contaminants, and investigates whether multimedia risk assessment and the separation of uncertainty and variability can provide a better basis for risk management decisions. The results of the case study show that a decision based on multimedia risk assessment may differ from one based on risk resulting from groundwater only. In particular, the transfer from groundwater to air imposes a health threat to some degree. By using a methodology that combines Monte Carlo simulation, a rank correlation coefficient, and an explicit decision criterion to identify information important to the decision, the results obtained when uncertainty and variability are separate differ from the ones without such separation. In particular, when higher percentiles of uncertainty and variability distributions are considered, the method separating uncertainty and variability identifies TCE concentration as the single most important input parameter, while the method that does not distinguish the two identifies four input parameters as the important information that would influence a decision on risk reduction.     

Test application of Bayesian Programming: Adaptive water quality management under uncertainty

A new method, Bayesian Programming (BP), developed by Harrison [Harrison KW. Multi-stage decision-making under uncertainty and stochasticity: Bayesian Programming. Adv Water Resour, submitted for publication] is tested on a case study involving optimal adaptive management of a river basin. The case study considers anew the process of permitting pulp mills on the Athabasca River in Alberta, Canada. The problem has characteristics common to many environmental management problems. There is uncertainty in the water quality response to pollutant loadings that will not be completely resolved with monitoring and the resolution of this uncertainty is impeded by the stochastic behavior of the water quality system. A two-stage adaptive management process is optimized with BP. Based on monitoring data collected after implementation of the first-stage decision, the uncertainties are updated prior to the second decision stage using Bayesian analysis. The worth of this two-stage adaptive management approach to this problem and the worth of monitoring are evaluated. Conclusions are drawn on the general practicality of BP for adaptive management. Potential strategies are outlined for extending the BP approach to secure further benefits of adaptive management.     

Risk tolerance measure for decision-making in fuzzy analysis: a health risk assessment perspective

In risk assessment studies it is important to determine how uncertain and imprecise knowledge should be included into the simulation and assessment models. Thus, proper evaluation of uncertainties has become a major concern in environmental and health risk assessment studies. Previously, researchers have used probability theory, more commonly Monte Carlo analysis, to incorporate uncertainty analysis in health risk assessment studies. However, in conducting probabilistic health risk assessment, risk analyst often suffers from lack of data or the presence of imperfect or incomplete knowledge about the process modeled and also the process parameters. Fuzzy set theory is a tool that has been used in propagating imperfect and incomplete information in health risk assessment studies. Such analysis result in fuzzy risks which are associated with membership functions. Since possibilistic health risk assessment studies are relatively new, standard procedures for decision-making about the acceptability of the resulting fuzzy risk with respect to a crisp standard set by the regulatory agency are not fully established. In this paper, we are providing a review of several available approaches which may be used in decision-making. These approaches involve defuzzification techniques, the possibility and the necessity measures. In this study, we also propose a new measure, the risk tolerance measure, which can be used in decision making. The risk tolerance measure provides an effective metric for evaluating the acceptability of a fuzzy risk with respect to a crisp compliance criterion. Fuzzy risks with different membership functions are evaluated with respect to a crisp compliance criterion by using the possibility, the necessity, and the risk tolerance measures and the results are discussed comparatively.     

Risk management of BTEX contamination in ground water--an integrated fuzzy approach

An integrated fuzzy simulation-assessment method (FSAM) was developed for assessing environmental risks from petroleum hydrocarbon contamination in ground water. In the FSAM, techniques of fuzzy simulation and fuzzy risk assessment were coupled into a general framework to reflect a variety of system uncertainties. A petroleum-contaminated site located in western Canada was selected as a study case for demonstrating applicability of the proposed method. The risk assessment results demonstrated that system uncertainties would significantly impact expressions of risk-level outputs. A relatively deterministic expression of the risks would have clearer representations of the study problem but may miss valuable uncertain information; conversely, an assessment under vaguer system conditions would help reveal potential consequences of adverse effects but would suffer from a higher degree of fuzziness in presenting the modeling outputs. Based on the risk assessment results, a decision analysis procedure was used to calculate a general risk index (GRI) to help identify proper responsive actions. The proposed method was useful for evaluating risks within a system containing multiple factors with complicated uncertainties and interactions and providing support for identifying proper site management strategies.     

Multi-objective optimal design for flood risk management with resilience objectives

In flood risk management, the divergent concept of resilience of a flood defense system cannot be fully defined quantitatively by one indicator and multiple indicators need to be considered simultaneously. In this paper, a multi-objective optimization (MOO) design framework is developed to determine the optimal protection level of a levee system based on different resilience indicators that depend on the probabilistic features of the flood damage cost arising under the uncertain nature of rainfalls. An evolutionary-based MOO algorithm is used to find a set of non-dominated solutions, known as Pareto optimal solutions for the optimal protection level. The objective functions, specifically resilience indicators of severity, variability and graduality, that account for the uncertainty of rainfall can be evaluated by stochastic sampling of rainfall amount together with the model simulations of incurred flood damage estimation for the levee system. However, these model simulations which usually require detailed flood inundation simulation are computationally demanding. This hinders the wide application of MOO in flood risk management and is circumvented here via a surrogate flood damage modeling technique that is integrated into the MOO algorithm. The proposed optimal design framework is applied to a levee system in a central basin of flood-prone Jakarta, Indonesia. The results suggest that the proposed framework enables the application of MOO with resilience objectives for flood defense system design under uncertainty and solves the decision making problems efficiently by drastically reducing the required computational time.     

An inexact fuzzy-random-chance-constrained air quality management model

An inexact fuzzy-random-chance-constrained programming model (IFRCCMM) was developed for supporting regional air quality management under uncertainty. IFRCCMM was formulated through integrating interval linear programming within fuzzy-random-chance-constrained programming framework. It could deal with parameter uncertainties expressed as not only fuzzy random variables but also discrete intervals. Based on the stochastic and fuzzy chance-constrained programming algorithms, IFRCCMM was solved when constraints was satisfied under different satisfaction and violation levels of constraints, leading to interval solutions with different risk and cost implications. The proposed model was applied to a regional air quality management problem for demonstration. The obtained results indicated that the proposed model could effectively reflect uncertain components within air quality management system through employing multiple uncertainty-characterization techniques (in random, fuzzy and interval forms), and help decision makers analyze trade-offs between system economy and reliability. In fact, many types of solutions (i.e. conservative solutions with lower risks and optimistic solutions with higher risks) provided by IFRCCMM were suitable for local decision makers to make more applicable decision schemes according to their understanding and preference about the risk and economy. In addition, the modeling philosophy is general and applicable to many other environmental problems that may be complicated with multiple forms of uncertainties.     

Mapping the environmental risk of a tourist harbor in order to foster environmental security: Objective vs. subjective assessments

A new definition of environmental security gives equal importance to the objective and subjective assessments of environmental risk. In this framework, the management of tourist harbors has to take into account managers’ perceptions. The subject of the present study is a tourist harbor in southern Italy where six different managers are present. This paper aims to assess subjectively and objectively the environmental risks associated with the harbor, and to compare the results to provide estimates of environmental security. Hereby managers have been interviewed and a simple model is used for making preliminary assessment of environmental risks. The comparison of the results highlighted a common mismatch between risk perception and risk assessment. We demonstrated that the old part of the harbor is less secure than the new part. In addition, one specific manager representing a public authority showed a leading role in ensuring the environmental security of the whole harbor.     

Risk-based environmental decision-making using fuzzy analytic hierarchy process (F-AHP)

Environmental risk management is an integral part of risk analyses. The selection of different mitigating or preventive alternatives often involve competing and conflicting criteria, which requires sophisticated multi-criteria decision-making (MCDM) methods. Analytic hierarchy process (AHP) is one of the most commonly used MCDM methods, which integrates subjective and personal preferences in performing analyses. AHP works on a premise that decision-making of complex problems can be handled by structuring the complex problem into a simple and comprehensible hierarchical structure. However, AHP involves human subjectivity, which introduces vagueness type uncertainty and necessitates the use of decision-making under uncertainty. In this paper, vagueness type uncertainty is considered using fuzzy-based techniques. The traditional AHP is modified to fuzzy AHP using fuzzy arithmetic operations. The concept of risk attitude and associated confidence of a decision maker on the estimates of pairwise comparisons are also discussed. The methodology of the proposed technique is built on a hypothetical example and its efficacy is demonstrated through an application dealing with the selection of drilling fluid/mud for offshore oil and gas operations.     

Risk analysis for water resources management under dual uncertainties through factorial analysis and fuzzy random value-at-risk

Water resources systems are associated with a variety of complexities and uncertainties due to socio-economic and hydro-environmental impacts. Such complexities and uncertainties lead to challenges in evaluating the water resources management alternatives and the associated risks. In this study, the factorial analysis and fuzzy random value-at-risk are incorporated into a two-stage stochastic programming framework, leading to a factorial-based two-stage programming with fuzzy random value-at-risk (FTSPF). The proposed FTSPF approach aims to reveal the impacts of uncertainty parameters on water resources management strategies and the corresponding risks. In detail, fuzzy random value-at-risk is to reflect the potential risk about financial cost under dual uncertainties, while a multi-level factorial design approach is used to reveal the interaction between feasibility degrees and risk levels, as well as the relationships (including curvilinear relationship) between these factors and the responses. The application of water resources system planning makes it possible to balance the satisfaction of system benefit, the risk levels of penalty and the feasibility degrees of constraints. The results indicate that decision makers would pay more attention to the tradeoffs between the system benefit and feasibility degree, and the water allocation for agricultural section contributes most to control the financial loss of water. Moreover, FTSPF can generate a higher system benefit and more alternatives under various risk levels. Therefore, FTSPF could provide more useful information for enabling water managers to identify desired policies with maximized system benefit under different system-feasibility degrees and risk levels.     

Issues in sediment toxicity and ecological risk assessment

This paper is based on a facilitated Workshop and Roundtable Discussion of key issues in sediment toxicology and ecological risk assessment (ERA) as applied to sediments that was held at the Conference on Dredged Material Management: Options and Environmental Considerations. The issues addressed included how toxicity is defined and perceived, how it is measured, and how it should be used within the context of ERA to support management decisions. The following conclusions were reached regarding scientific considerations of these issues. Toxicity is a measure of hazard and not a risk per se. Thus, toxicity testing is a means but not the end to understand risks of sediments. Toxicity testing cannot presently be replaced by chemical analyses to define hazard. Toxicity test organisms need to be appropriate to the problem being addressed, and the results put into context relative to both reference and baseline comparisons to understand hazard. Use of toxicity tests in sediment ERAs requires appropriate endpoints and risk hypotheses, considering ecological not just statistical significance, and recognizing that hazard does not equate to risk. Toxicity should be linked to population and community response to support decision-making, assessing possible genotypic adaptations that can influence risk estimates, and addressing uncertainty. Additionally, several key scientific issues were identified to improve future sediment ERAs, including the need to improve basic understanding of ecological mechanisms and processes, recognition of variability in the assessment process, and an improved focus and ability to assess risks to populations and communities.     

Stochastic environmental risk analysis: an integrated methodology for predicting cancer risk from contaminated groundwater

Stochastic environmental risk assessment considers the effects of numerous biological, chemical, physical, behavioral and physiological processes that involve elements of uncertainty and variability. A methodology for predicting health risks to individuals from contaminated groundwater is presented that incorporates the elements of uncertainty and variability in geological heterogeneity, physiological exposure parameters, and in cancer potency. An idealized groundwater basin is used to perform a parametric sensitivity study to assess the relative impact of (a) geologic uncertainty, (b) behavioral and physiological variability in human exposure and (c) uncertainty in cancer potency on the prediction of increased cancer risk to individuals in a population exposed to contaminants in household water supplied from groundwater. A two-dimensional distribution (or surface) of human health risk was generated as a result of the simulations. Cuts in this surface (fractiles of variability and percentiles of uncertainty) are then used as a measure of relative importance of various model components on total uncertainty and variability. A case study for perchloroethylene or PCE, shows that uncertainty and variability in hydraulic conductivity play an important role in predicting human health risk that is on the same order of influence as uncertainty of cancer potency.     

A bilevel groundwater management model with minimization of stochastic health risks at the leader level and remediation cost at the follower level

Traditional single-objective programs cannot deal with the tradeoffs between the decision makers who represent different perspectives and have inconsistent decision goals. Multi-objective ones can hardly represent a complex dominant-subordinate relationship between the leader and the follower. This study presents a new bilevel programming model with considering leader–follower-related health-risk and economic goals for optimal groundwater remediation management. The bilevel model is formulated by integrating health-risk assessment and environmental standards (the leader or the environmental concern) and remediation cost (the follower or the economic concern) into a general framework. In addition, stochastic uncertainty in health risk assessment is considered into the decision-making process. The developed bilevel model is then applied to a petroleum-contaminated aquifer in Canada. Results indicate that the performance of bilevel programming can not only meet the low remediation cost as the expectation from the follower but also simultaneously conform to the low contamination level as the expectation from the leader. Furthermore, comparative analyses show that the bilevel model with two-level concerns has the advantage of maximizing the interests and satisfaction degrees of decision makers, which can avoid the extreme results generated from the single-level models.     

Dam overtopping risk assessment considering inspection program

Safety inspection of large dams in Taiwan is conducted every 5 years. The practice does not take into consideration uncertainty of dam conditions. The goal of this study is to determine the optimal dam inspection interval under the consideration of overtopping risk incorporating uncertainty gate availability. In earlier studies, assessment of overtopping risk only considered the uncertainties in reservoir properties and natural randomness of hydrologic events without giving much thought to the availability of spillway gates. As a result, the overtopping risk could be underestimated. In this study, an innovative concept is proposed to evaluate dam overtopping by taking into account spillway gate availability. The framework consists of three parts: (1) evaluation of conditional overtopping risk for different numbers of malfunctioning spillway gates; (2) evaluation of spillway gate availability; and (3) dam inspection scheduling. Furthermore, considerations are given to overtopping risk, inspection cost, and dam break cost for determining the optimal inspection schedule. The methodology is applied to the Shihmen Reservoir in Taiwan and to evaluate its time-dependent overtopping risk. Results show that overtopping risk considering the availability of the spillway gates is higher than the one without considering the availability of the spillway gates.     

Methodology of disaster risk assessment for debris flows in a river basin

“Disaster risk assessment” is important in the planning of risk management strategies that reduce societal losses. However, governmental agencies in Taiwan generally assess risks that emerge from debris flows without adequately considering risk management and taking a systems approach. This work proposes an approach to thoroughly consider the interactive influence mechanism of debris flow disaster risk. Additionally, a systematic method for assessing disaster risks is developed. This proposed method can be used in the current risk assessment and as a basis for management strategy planning. Based on systems thinking, the components and attributes of a conceptual system of disaster risk management associated with debris flows in a river basin are identified. Subsequently, a conceptual mitigation–hazard–exposure–resistance framework and an indicator system for assessing the debris flow disaster risks in a river basin are identified. The disaster risks for each exposed community in each drainage zone can be systematically calculated based on the current status or plans of prevention and evacuation measures using the proposed indicator system. A case study of implementing the proposed methodology that involves the Chishan River Basin is presented, in which disaster risk according to the current status of prevention and evacuation measures is assessed. Drainage zones and communities with a significant debris flow disaster risk are located; this risk is associated with a lack of adequate prevention and evacuation measures that have been planned of government agencies. Analytical results indicate that the proposed methodology can systematically and effectively assess the disaster risks of a river basin. The proposed methodology provides a valuable reference for governmental agencies that must manage disaster risk associated with debris flows.     

A robust risk analysis method for water resources allocation under uncertainty

A robust risk analysis method (RRAM) is developed for water resource decision making under uncertainty. This method incorporates interval-parameter programming and robust optimization within a stochastic programming framework. In the RRAM formulation, penalties are exercised with the recourse against any infeasibility, and robustness measures are introduced to examine the variability of the second stage costs which are above the expected levels. In this study, a number of weighting levels are considered which correspond to the robustness levels of risk control. Generally, a plan with a higher robust level would better resist from system risk. Thus, decision with a lower robust level can correspond to a higher risk of system failure. There is a tradeoff between system cost and system reliability. The RRAM is applied to a case of water resource management. The modeling results can help generate desired decision alternatives that will be particularly useful for risk-aversive decision makers in handling high-variability conditions. The results provide opportunities to managers to make decisions based on their own preferences on system stability and economy, and ensure that the management policies and plans be made with reasonable consideration of both system cost and risk.     

Probabilistic-fuzzy health risk modeling

Health risk analysis of multi-pathway exposure to contaminated water involves the use of mechanistic models that include many uncertain and highly variable parameters. Currently, the uncertainties in these models are treated using statistical approaches. However, not all uncertainties in data or model parameters are due to randomness. Other sources of imprecision that may lead to uncertainty include scarce or incomplete data, measurement error, data obtained from expert judgment, or subjective interpretation of available information. These kinds of uncertainties and also the non-random uncertainty cannot be treated solely by statistical methods. In this paper we propose the use of fuzzy set theory together with probability theory to incorporate uncertainties into the health risk analysis. We identify this approach as probabilistic-fuzzy risk assessment (PFRA). Based on the form of available information, fuzzy set theory, probability theory, or a combination of both can be used to incorporate parameter uncertainty and variability into mechanistic risk assessment models. In this study, tap water concentration is used as the source of contamination in the human exposure model. Ingestion, inhalation and dermal contact are considered as multiple exposure pathways. The tap water concentration of the contaminant and cancer potency factors for ingestion, inhalation and dermal contact are treated as fuzzy variables while the remaining model parameters are treated using probability density functions. Combined utilization of fuzzy and random variables produces membership functions of risk to individuals at different fractiles of risk as well as probability distributions of risk for various alpha-cut levels of the membership function. The proposed method provides a robust approach in evaluating human health risk to exposure when there is both uncertainty and variability in model parameters. PFRA allows utilization of certain types of information which have not been used directly in existing risk assessment methods.     

Developing an Event Tree for probabilistic hazard and risk assessment at Vesuvius

Probabilistic characterizations of possible future eruptive scenarios at Vesuvius volcano are elaborated and organized within a risk-based framework. In the EXPLORIS project, a wide variety of topics relating to this basic problem have been pursued: updates of historical data, reinterpretation of previous geological field data and the collection of new fieldwork results, the development of novel numerical modelling codes and of risk assessment techniques have all been completed. To achieve coherence, many diverse strands of evidence had to be unified within a formalised structure, and linked together by expert knowledge. For this purpose, a Vesuvius ‘Event Tree’ (ET) was created to summarise in a numerical-graphical form, at different levels of detail, all the relative likelihoods relating to the genesis and style of eruption, development and nature of volcanic hazards, and the probabilities of occurrence of different volcanic risks in the next eruption crisis. The Event Tree formulation provides a logical pathway connecting generic probabilistic hazard assessment to quantitative risk evaluation. In order to achieve a complete parameterization for this all-inclusive approach, exhaustive hazard and risk models were needed, quantified with comprehensive uncertainty distributions for all factors involved, rather than simple ‘best-estimate’ or nominal values. Thus, a structured expert elicitation procedure was implemented to complement more traditional data analysis and interpretative approaches. The structure of the Vesuvius Event Tree is presented, and some of the data analysis findings and elicitation outcomes that have provided initial indicative probability distributions to be associated with each of its branches are summarized. The Event Tree extends from initiating volcanic eruption events and hazards right through to human impact and infrastructure consequences, with the complete tree and its parameterisation forming a quantitative synoptic framework for comprehensive hazard evaluation and mapping of risk impacts. The organization of the Event Tree allows easy updating, as and when new information becomes available.     

Multiple hazard incidents lifecycle cost assessment of structural systems considering state‐dependent repair times and fragility curves

The performance and serviceability of structural systems during their lifetime can be significantly affected by the occurrence of extreme events. Despite their low probability, there is a potential for multiple occurrences of such hazards during the relatively long service life of systems. This paper introduces a comprehensive framework for the assessment of lifecycle cost of infrastructures subject to multiple hazard events throughout their decision‐making time horizon. The framework entails the lifecycle costs of maintenance and repair, as well as the salvage value of the structure at the end of the decision‐making time horizon. The primary features of the proposed framework include accounting for the possibility of multiple hazard occurrences, incorporating effects of incomplete repair actions on the accumulated damage through damage state‐dependent repair times, and requiring limited resources in terms of input data and computational costs. A dynamic programming procedure is proposed to calculate the expected damage condition of the structure for each possibility of the number of hazard incidents based on state‐dependent fragility curves. The proposed framework is applied to a moment‐frame building located in a region with high seismicity, and lifecycle costs are evaluated for six retrofit plans. The results displayed variation in the ranking of the retrofit actions with respect to decision‐making time horizon. Furthermore, the sensitivity analyses demonstrated that disregarding repair time in the lifecycle cost analysis can result in false identification of unsafe retrofit actions as optimal and reliable strategies. Copyright © 2016 John Wiley & Sons, Ltd.     

Integration degree of risk in terms of scene and application

Risk is a scene in the future associated with some adverse incident. Scene means something seen by a viewer, or felt by individuals or various societal groups. Any risk assessment is to model some aspects of the scene for risk. Different aspects for assessment leads to different scene. In this paper, we suggest the integration degree of risk to distinguish characters of risks with respect to the aspects. The total number of factors of a risk system determines the macro degree and the granulation scale for measuring a risk reflects the micro degree. A simple framework depends on the degrees provides an explanation of the integrated risk. The most common model for risk assessment is available for the two-freedom-degree serial risk. A case studying flood risk shows the application to explain what the risk is, where the information is incomplete and we use the information diffusion technique to estimate the risk. Project 40771007 supported by National Natural Science Foundation of China.     

A model for assessing and managing the risks of environmental lead emissions

In response to recent activity and legislation concerning lead and its role in electric vehicle development, a model has been developed to assess the health risks to residents from environmental lead emissions. This model may be used to predict the risks to residents in the vicinity of facilities discharging lead into the air. This model is also important for risk management, allowing for risk-based regulations regarding limits on lead emissions. The model is comprehensive, linking together a source term, air dispersion model, household exposure model, physiologically-based pharmakokinetic blood-lead model, and a determination of reference dose. Parameters are treated as distributions, and are considered either uncertain or variable. A range of physiological and behavioral parameters are used to distinguish between various age and gender groups, to reflect the variability in risk of adverse effect to these subsets of the exposed population. A sensitivity study is performed, including a case considering the uncertainty in reference dose which is compared to the case of a deterministic reference dose. Different types of variability are investigated, the variability across sensitive sub-populations of age and gender, and the individual variability within these populations. We found that the differentiation between uncertainty and variability in predicting non-cancer risk human health risk was important, and that methods that combined uncertainty and variability were not expected to be protective to sensitive individuals within a sub-population.