Risk-based evaluation of wastewater treatment projects: A case study in Niasar city,Iran

For sustainable wastewater management, it is essential to consider social, environmental, geological and technical features as well as economic feature in the decision-making process. A limitation of many of the earlier works on wastewater management is that they take into account only financial criteria to make a decision for a given problem. Moreover, the decision-makers’ (DMs) attitude to risk, or optimism degree, when faced with uncertainty is not considered. In this paper, we study the application of risk-based multi-attribute decision-making (RB-MADM) methods to achieve sustainable wastewater management. Consideration of uncertainty, value tradeoffs, and different risk attitudes of decision makers are the important features of the developed methodology. A case study on the Iranian city of Niasar is presented to illustrate how this methodology can be applied to select the most preferred alternative for wastewater management among a set of options. The most preferred option is selected with respect to nine attributes under different optimism/pessimism degrees, using six different MADM methods. The results show that the ranking of options is sensitive to the optimism degree of the DMs. The proposed approach may help policy makers to achieve sustainable wastewater management.     

Risk communication in emergency response to a simulated extreme flood

Risk communication in flood incident management can be improved through developing hydrometeorological and engineering models used as tools for communicating risk between scientists and emergency management professionals. A range of such models and tools was evaluated by participating flood emergency managers during a 4-day, real-time simulation of an extreme event in the Thamesmead area in the Thames estuary close to London, England. Emergency managers have different communication needs and value new tools differently, but the indications are that a range of new tools could be beneficial in flood incident management. Provided they are communicated large model uncertainties are not necessarily unwelcome among flood emergency managers. Even so they are cautious about sharing the ownership of weather and flood modelling uncertainties.     

Interactions between scientific uncertainty and flood management decisions: Two case studies in Colorado

Flood management policies in the United States rely on scientific information about the frequency and intensity of extreme precipitation and runoff. Yet, the available information is inherently uncertain because of the complexity of meteorological and hydrological processes. In mountainous areas, flood risk can vary greatly even within short distances depending on local climate, topography, soil characteristics, and land use. This paper describes two Colorado cases in which policy makers were presented with conflicting scientific estimates: revision of the Fort Collins floodplain map and modification of the Cherry Creek Dam. The case studies demonstrate that uncertainty can have substantial impacts on regulatory processes, public safety, and costs. The analysis considers the differing perspectives of various participants in the flood management processes, illustrating the interplay between uncertainties attributable to scientific issues and values issues. It suggests that attempts to provide a single “best” estimate do not necessarily meet the decision needs of all stakeholders. Conclusions indicate a need to improve communication about uncertainty when scientific estimates areprovided to decision makers. Furthermore, in highly controversial decisions, it may be necessary to reframe the discussion to consider the values issues raised by scientific uncertainty.     

Handling and updating uncertain information in bow-tie analysis

Bow-tie analysis is a fairly new concept in risk assessment that can describe the relationships among different risk control parameters, such as causes, hazards and consequences to mitigate the likelihood of occurrence of unwanted events in an industrial system. It also facilitates the performance of quantitative risk analysis for an unwanted event providing a detailed investigation starting from basic causes to final consequences. The credibility of quantitative evaluation of the bow-tie is still a major concern since uncertainty, due to limited or missing data, often restricts the performance of analysis. The utilization of expert knowledge often provides an alternative for such a situation. However, it comes at the cost of possible uncertainties related to incompleteness (partial ignorance), imprecision (subjectivity), and lack of consensus (if multiple expert judgments are used). Further, if the bow-tie analysis is not flexible enough to incorporate new knowledge or evidence, it may undermine the purpose of risk assessment.Fuzzy set and evidence theory are capable of characterizing the uncertainty associated with expert knowledge. To minimize the overall uncertainty, fusing the knowledge of multiple experts and updating prior knowledge with new evidence are equally important in addition to addressing the uncertainties in the knowledge. This paper proposes a methodology to characterize the uncertainties, aggregate knowledge and update prior knowledge or evidence, if new data become available for the bow-tie analysis. A case study comprising a bow-tie for a typical offshore process facility has also been developed to describe the utility of this methodology in an industrial environment.     

Uncertainty in epidemiology and health risk and impact assessment

Environmental epidemiology and health risk and impact assessment have long grappled with problems of uncertainty in data and their relationships. These uncertainties have become more challenging because of the complex, systemic nature of many of the risks. A clear framework defining and quantifying uncertainty is needed. Three dimensions characterise uncertainty: its nature, its location and its level. In terms of its nature, uncertainty can be both intrinsic and extrinsic. The former reflects the effects of complexity, sparseness and nonlinearity; the latter arises through inadequacies in available observational data, measurement methods, sampling regimes and models. Uncertainty occurs in three locations: conceptualising the problem, analysis and communicating the results. Most attention has been devoted to characterising and quantifying the analysis—a wide range of statistical methods has been developed to estimate analytical uncertainties and model their propagation through the analysis. In complex systemic risks, larger uncertainties may be associated with conceptualisation of the problem and communication of the analytical results, both of which depend on the perspective and viewpoint of the observer. These imply using more participatory approaches to investigation, and more qualitative measures of uncertainty, not only to define uncertainty more inclusively and completely, but also to help those involved better understand the nature of the uncertainties and their practical implications.     

Uncertainty in the assessment of hazard,exposure and risk

The terminology, concepts and current approaches to uncertainty in the assessment of hazard, exposure and risk are reviewed. Five generic questions are discussed on uncertainty, including sources, levels, when and how it should be dealt with or reduced, what are our gaps in understanding and how they can be addressed. A case study of lead exposure of children in Lavrion, Greece, is used to exemplify these questions and possible answers. Estimation of uncertainty may be improved by the use of interorganisational studies to capture sources of uncertainty that are often overlooked. Gaps identified in our understanding of uncertainty include: a limitation in the availability of basic measurements, a lack of knowledge of the environmental processes, an inability to predict the effects of mixtures, the aetiology of disease and devising procedures for optimal resource allocation in impact assessment.     

Definition and calculation of uncertainty in ecological process models

We present a method of multi-criteria assessment for the analysis of process model uncertainty that combines analysis of model structure, parameters and data requirements. There are three components in calculation and definition of uncertainty.

(1)

Assessment criteria: Uncertainty in a process model is reduced as the model can simultaneously simulate an increased number of assessment criteria selected to test specific aspects of the theory being investigated, and within acceptable limits set for those criteria. This reduces incomplete specification of the model—the characteristic that a model may explain some, but not all, of the observed features of a phenomenon. The calculation required is computation of the Pareto set which provides the list of simultaneously achieved criteria within specified ranges.     

An approach to estimating the individual risk for toxic-gas releases using the load-resistance model

A new approach to quantify the uncertainty of the individual risk for toxic releases is presented in this paper. The individual risk is defined as the probability of fatality per year. The probability of fatality is calculated by a classical load-resistance model based on reliability (survivability) theory. The load effect is defined as the concentration intensity to which a human is exposed. Furthermore, the resistance is defined as the human tolerance to a certain concentration load in this study. The Monte Carlo method is used to obtain the probability distributions of outputs (the load effect and resistance) propagated from the uncertainties of the input variables. The fatality probability exceeding a limit state can then be obtained by comparing pairs of samples from the load effect and the resistance distributions. The separation of sampling from the load and resistance distributions is also proposed to allow more efficient calculation than that achieved by the classical Monte Carlo method. The analytical risk estimates computed by the load-resistance model are compared to conventional risk estimates that correspond to the upper-end percentile of the load-effect distribution. A case study shows that the conventional risk estimates can be directed to wrong decisions when the load-effect distribution has upper-end tail heaviness.     

Embedding the human factor in road tunnel risk analysis

The paper is focusing on road tunnel safety and builds upon the Directive 2004/54/EC launched by the European Commission; the latter sets basic requirements and suggests the implementation of risk assessment in several tunnel cases apart from technical measures imposed on the basis of tunnel structural and operational characteristics. Since the EU Directive does not indicate the method for performing risk assessment, a wide range of methods have been proposed, most of them based on quantitative risk assessment (QRA). Although the majority of current road tunnel QRAs assess physical aspects of the tunnel system and consider several hazards concerning the transportation of dangerous goods through a tunnel, they do not take into account, sufficiently, several organizational and human-related factors that can greatly affect the overall safety level of these critical infrastructures. To cope with this limitation this paper proposes a fuzzy logic system based on CREAM method for human reliability analysis (Hollnagel, 1998) in order to provide more sophisticated estimations of the tunnel operator's performance in safety critical situations. It is deduced that a human reliability analysis component to analyze operator performance, like the fuzzy system proposed here, is important for risk analysts. Consideration of organizational and human factors will enhance risk analysts’ studies and highlight the uncertainty related to human performance variability.     

The social cost of carbon revisited

An estimate of the social cost of carbon (SCC) is crucial to climate policy. But how should we estimate the SCC? A common approach uses an integrated assessment model (IAM) to simulate time paths for the atmospheric CO₂ concentration, its impact on temperature, and resulting reductions in GDP. I have argued that IAMs have deficiencies that make them poorly suited for this job, but what is the alternative? I present an approach to estimating an average SCC, which I argue can be a useful guide for policy. I rely on a survey of experts to elicit opinions regarding (1) probabilities of alternative economic outcomes of climate change, but not the causes of those outcomes; and (2) the reduction in emissions required to avert an extreme outcome, i.e., a large climate-induced reduction in GDP. The average SCC is the ratio of the present value of lost GDP from an extreme outcome to the total emission reduction needed to avert that outcome. I discuss the survey instrument, explain how experts were identified, and present results. I obtain SCC estimates of $200/mt or higher, but the variation across experts is large. Trimming outliers and focusing on experts who expressed a high degree of confidence in their answers yields lower SCCs, $80 to $100/mt, but still well above the IAM-based estimates used by the U.S. government.