Regional Risk Assessment for Point Source Pollution Based on a Water Quality Model of the Taipu River,China

Point source pollution is one of the main threats to regional environmental health. Based on a water quality model, a methodology to assess the regional risk of point source pollution is proposed. The assessment procedure includes five parts: (1) identifying risk source units and estimating source emissions using Monte Carlo algorithms; (2) observing hydrological and water quality data of the assessed area, and evaluating the selected water quality model; (3) screening out the assessment endpoints and analyzing receptor vulnerability with the Choquet fuzzy integral algorithm; (4) using the water quality model introduced in the second step to predict pollutant concentrations for various source emission scenarios and analyzing hazards of risk sources; and finally, (5) using the source hazard values and receptor vulnerability scores to estimate overall regional risk. The proposed method, based on the Water Quality Analysis Simulation Program (WASP), was applied in the region of the Taipu River, which is in the Taihu Basin, China. Results of source hazard and receptor vulnerability analysis allowed us to describe aquatic ecological, human health, and socioeconomic risks individually, and also integrated risks in the Taipu region, from a series of risk curves. Risk contributions of sources to receptors were ranked, and the spatial distribution of risk levels was presented. By changing the input conditions, we were able to estimate risks for a range of scenarios. Thus, the proposed procedure may also be used by decisionmakers for long‐term dynamic risk prediction.     

Risk Perception and the Value of Safety

This paper examines the relationship between perceived risk and willingness-to-pay (WTP) for increased safety from technological hazards in both conceptual and empirical terms. A conceptual model is developed in which a given household's WTP for risk reductions is a function of traditional socioeconomic variables (i.e., income and base level of risk) and perceived characteristics of the hazards (i.e., dread, knowledge, and exposure). Data to estimate the model are obtained through a combined contingent valuation and risk perception survey that considers 10 technological hazards, five of which are well-defined (e.g., death rates are known and the risks are relatively common) and five are less well-defined. Econometric results, using TOBIT estimation procedures, support the importance of both types of variables in explaining WTP across all 10 hazards. When the risks are split into two groups, the results show that WTP for well-defined hazards is most influenced by perceived personal exposure, while WTP for less well-defined risks is most influenced by levels of dread and severity.     

A Scale of Risk

This article proposes a conceptual framework for ranking the relative gravity of diverse risks. This framework identifies the moral considerations that should inform the evaluation and comparison of diverse risks. A common definition of risk includes two dimensions: the probability of occurrence and the associated consequences of a set of hazardous scenarios. This article first expands this definition to include a third dimension: the source of a risk. The source of a risk refers to the agents involved in the creation or maintenance of a risk and captures a central moral concern about risks. Then, a scale of risk is proposed to categorize risks along a multidimensional ranking, based on a comparative evaluation of the consequences, probability, and source of a given risk. A risk is ranked higher on the scale the larger the consequences, the greater the probability, and the more morally culpable the source. The information from the proposed comparative evaluation of risks can inform the selection of priorities for risk mitigation.     

Absolute Risk or Relative Risk? A Study of Intraspecies and Interspecies Extrapolation of Chemical‐Induced Cancer Risk

We have used the CBDS database of the National Toxicology Program to study the difference between absolute risk and relative risk models for interspecies and intersex predictions of cancer risk. For no combination (class) of tumor and site is the prediction good for all chemicals. The variation in predicted risk between chemicals exceeds the difference in risks resulting from application of these two models. On the whole, it appears that relative risk is a better model.     

A Bayesian Approach to Integrated Ecological and Human Health Risk Assessment for the South River,Virginia Mercury‐Contaminated Site

We conducted a regional‐scale integrated ecological and human health risk assessment by applying the relative risk model with Bayesian networks (BN‐RRM) to a case study of the South River, Virginia mercury‐contaminated site. Risk to four ecological services of the South River (human health, water quality, recreation, and the recreational fishery) was evaluated using a multiple stressor–multiple endpoint approach. These four ecological services were selected as endpoints based on stakeholder feedback and prioritized management goals for the river. The BN‐RRM approach allowed for the calculation of relative risk to 14 biotic, human health, recreation, and water quality endpoints from chemical and ecological stressors in five risk regions of the South River. Results indicated that water quality and the recreational fishery were the ecological services at highest risk in the South River. Human health risk for users of the South River was low relative to the risk to other endpoints. Risk to recreation in the South River was moderate with little spatial variability among the five risk regions. Sensitivity and uncertainty analysis identified stressors and other parameters that influence risk for each endpoint in each risk region. This research demonstrates a probabilistic approach to integrated ecological and human health risk assessment that considers the effects of chemical and ecological stressors across the landscape.     

Cryptosporidiosis Susceptibility and Risk: A Case Study

Regional estimates of cryptosporidiosis risks from drinking water exposure were developed and validated, accounting for AIDS status and age. We constructed a model with probability distributions and point estimates representing Cryptosporidium in tap water, tap water consumed per day (exposure characterization); dose response, illness given infection, prolonged illness given illness; and three conditional probabilities describing the likelihood of case detection by active surveillance (health effects characterization). The model predictions were combined with population data to derive expected case numbers and incidence rates per 100,000 population, by age and AIDS status, borough specific and for New York City overall in 2000 (risk characterization). They were compared with same-year surveillance data to evaluate predictive ability, assumed to represent true incidence of waterborne cryptosporidiosis. The predicted mean risks, similar to previously published estimates for this region, overpredicted observed incidence-most extensively when accounting for AIDS status. The results suggest that overprediction may be due to conservative parameters applied to both non-AIDS and AIDS populations, and that biological differences for children need to be incorporated. Interpretations are limited by the unknown accuracy of available surveillance data, in addition to variability and uncertainty of model predictions. The model appears sensitive to geographical differences in AIDS prevalence. The use of surveillance data for validation and model parameters pertinent to susceptibility are discussed.     

A Risk Analysis Model in Concurrent Engineering Product Development

Concurrent engineering has been widely accepted as a viable strategy for companies to reduce time to market and achieve overall cost savings. This article analyzes various risks and challenges in product development under the concurrent engineering environment. A three‐dimensional early warning approach for product development risk management is proposed by integrating graphical evaluation and review technique (GERT) and failure modes and effects analysis (FMEA). Simulation models are created to solve our proposed concurrent engineering product development risk management model. Solutions lead to identification of key risk controlling points. This article demonstrates the value of our approach to risk analysis as a means to monitor various risks typical in the manufacturing sector. This article has three main contributions. First, we establish a conceptual framework to classify various risks in concurrent engineering (CE) product development (PD). Second, we propose use of existing quantitative approaches for PD risk analysis purposes: GERT, FMEA, and product database management (PDM). Based on quantitative tools, we create our approach for risk management of CE PD and discuss solutions of the models. Third, we demonstrate the value of applying our approach using data from a typical Chinese motor company.     

Risk Perception in Context: The Savannah River Site Stakeholder Study

Environmental managers are increasingly charged with involving the public in the development and modification of policies regarding risks to human health and the environment. Involving the public in environmental decision making first requires a broad understanding of how and why the public perceives various risks. The Savannah River Stakeholder Study was conducted with the purpose of investigating individual, economic, and social characteristics of risk perceptions among those living near the Savannah River Nuclear Weapons Site. A number of factors were found to impact risk perceptions among those living near the site. One's estimated proximity to the site and relative river location surfaced as strong determinants of risk perceptions among SRS residents. Additionally, living in a quality neighborhood and demonstrating a willingness to accept health risks for economic gain strongly abated heightened risk perceptions.The Consortium for Risk Evaluation with Stakeholder Participation (CRESP)The Consortium for Risk Evaluation with Stakeholder Participation (CRESP)The Consortium for Risk Evaluation with Stakeholder Participation (CRESP)     

Risk Analysis of Dust Explosion Scenarios Using Bayesian Networks

In this study, a methodology has been proposed for risk analysis of dust explosion scenarios based on Bayesian network. Our methodology also benefits from a bow‐tie diagram to better represent the logical relationships existing among contributing factors and consequences of dust explosions. In this study, the risks of dust explosion scenarios are evaluated, taking into account common cause failures and dependencies among root events and possible consequences. Using a diagnostic analysis, dust particle properties, oxygen concentration, and safety training of staff are identified as the most critical root events leading to dust explosions. The probability adaptation concept is also used for sequential updating and thus learning from past dust explosion accidents, which is of great importance in dynamic risk assessment and management. We also apply the proposed methodology to a case study to model dust explosion scenarios, to estimate the envisaged risks, and to identify the vulnerable parts of the system that need additional safety measures.     

长期利润质量分析

长期利润质量反映了企业各期利润持续、稳定发展的能力，是企业所有者、潜在投资者和其他关注企业长期经营和发展能力的关系人非常关心的问题。分析和评价企业的长期利润质量需要考虑以下因素：会计政策的稳健程度、配比原则的运用、利润构成、财务状况以及一些特殊费用项目等。为了全面、准确地比较和分析企业的长期利润质量，在计算和评价企业盈利能力和风险水平的比率指标时，要充分地考虑和分析以上各种因素对长期利润质量及其发展变动趋势的影响。     

A Quantitative Risk Assessment of Waterborne Cryptosporidiosis in France Using Second-Order Monte Carlo Simulation

A pragmatic quantitative risk assessment (QRA) of the risks of waterborne Cryptosporidium parvum infection and cryptosporidiosis in immunocompetent and immunodeficient French populations is proposed. The model takes into account French specificities such as the French technique for oocyst enumeration performance and tap water consumption. The proportion of infective oocysts is based on literature review and expert knowledge. The probability of infection for a given number of ingested viable oocysts is modeled using the exponential dose-response model applied on published data from experimental infections in immunocompetent human volunteers challenged with the IOWA strain. Second-order Monte Carlo simulations are used to characterize the uncertainty and variability of the risk estimates. Daily risk of infection and illness for the immunocompetent and the immunodeficient populations are estimated according to the number of oocysts observed in a single storage reservoir water sample. As an example, the mean daily risk of infection in the immunocompetent population is estimated to be 1.08 x 10(-4) (95% confidence interval: [0.20 x 10(-4); 6.83 x 10(-4)]) when five oocysts are observed in a 100 L storage reservoir water sample. Annual risks of infection and disease are estimated from a set of oocyst enumeration results from distributed water samples, assuming a negative binomial distribution of day-to-day contamination variation. The model and various assumptions used in the model are fully explained and discussed. While caveats of this model are well recognized, this pragmatic QRA could represent a useful tool for the French Food Safety Agency (AFSSA) to define recommendations in case of water resource contamination by C. parvum whose infectivity is comparable to the IOWA strain.     

Risk Perceptions Toward Drinking Water Quality Among Private Well Owners in Ireland: The Illusion of Control

In rural areas where no public or group water schemes exist, groundwater is often the only source of drinking water and is extracted by drilling private wells. Typically, private well owners are responsible for the quality and testing of their own drinking water. Previous studies indicate that well owners tend to underestimate the risks of their well water being contaminated, yet little is known about why this is the case. We conducted a qualitative study by interviewing private well owners in Ireland to investigate their beliefs surrounding their water quality, which, in turn, inform their risk perceptions and their willingness to regularly test their water. Based on our findings we designed a theoretical model arguing that perceived control is central in the perceived contamination risks of well water. More specifically, we argue that well owners have the illusion of being in control over their water quality, which implies that people often perceive themselves to be more in control of a situation than they actually are. As a result, they tend to underestimate contamination risks, which subsequently impact negatively on water testing behaviors. Theoretical and practical implications are highlighted.     

Recognizing Structural Nonidentifiability: When Experiments Do Not Provide Information About Important Parameters and Misleading Models Can Still Have Great Fit

In the quest to model various phenomena, the foundational importance of parameter identifiability to sound statistical modeling may be less well appreciated than goodness of fit. Identifiability concerns the quality of objective information in data to facilitate estimation of a parameter, while nonidentifiability means there are parameters in a model about which the data provide little or no information. In purely empirical models where parsimonious good fit is the chief concern, nonidentifiability (or parameter redundancy) implies overparameterization of the model. In contrast, nonidentifiability implies underinformativeness of available data in mechanistically derived models where parameters are interpreted as having strong practical meaning. This study explores illustrative examples of structural nonidentifiability and its implications using mechanistically derived models (for repeated presence/absence analyses and dose–response of Escherichia coli O157:H7 and norovirus) drawn from quantitative microbial risk assessment. Following algebraic proof of nonidentifiability in these examples, profile likelihood analysis and Bayesian Markov Chain Monte Carlo with uniform priors are illustrated as tools to help detect model parameters that are not strongly identifiable. It is shown that identifiability should be considered during experimental design and ethics approval to ensure generated data can yield strong objective information about all mechanistic parameters of interest. When Bayesian methods are applied to a nonidentifiable model, the subjective prior effectively fabricates information about any parameters about which the data carry no objective information. Finally, structural nonidentifiability can lead to spurious models that fit data well but can yield severely flawed inferences and predictions when they are interpreted or used inappropriately.     

Water Quality Failures in Distribution Networks—Risk Analysis Using Fuzzy Logic and Evidential Reasoning

The evaluation of the risk of water quality failures in a distribution network is a challenging task given that much of the available data are highly uncertain and vague, and many of the mechanisms are not fully understood. Consequently, a systematic approach is required to handle quantitative-qualitative data as well as a means to update existing information when new knowledge and data become available. Five general pathways (mechanisms) through which a water quality failure can occur in the distribution network are identified in this article. These include contaminant intrusion, leaching and corrosion, biofilm formation and microbial regrowth, permeation, and water treatment breakthrough (including disinfection byproducts formation). The proposed methodology is demonstrated using a simplified example for water quality failures in a distribution network. This article builds upon the previous developments of aggregative risk analysis approach. Each basic risk item in a hierarchical framework is expressed by a triangular fuzzy number, which is derived from the composition of the likelihood of a failure event and the associated failure consequence . An analytic hierarchy process is used to estimate weights required for grouping noncommensurate risk sources. The evidential reasoning is proposed to incorporate newly arrived data for the updating of existing risk estimates. The exponential ordered weighted averaging operators are used for defuzzification to incorporate attitudinal dimension for risk management. It is envisaged that the proposed approach could serve as a basis to benchmark acceptable risks in water distribution networks.     

Project risk management using multiple criteria decision-making technique and decision tree analysis: a case study of Indian oil refinery

This study proposes an integrated analytical framework for effective management of project risks using combined multiple criteria decision-making technique and decision tree analysis. First, a conceptual risk management model was developed through thorough literature review. The model was then applied through action research on a petroleum oil refinery construction project in the Central part of India in order to demonstrate its effectiveness. Oil refinery construction projects are risky because of technical complexity, resource unavailability, involvement of many stakeholders and strict environmental requirements. Although project risk management has been researched extensively, practical and easily adoptable framework is missing. In the proposed framework, risks are identified using cause and effect diagram, analysed using the analytic hierarchy process and responses are developed using the risk map. Additionally, decision tree analysis allows modelling various options for risk response development and optimises selection of risk mitigating strategy. The proposed risk management framework could be easily adopted and applied in any project and integrated with other project management knowledge areas.     

Appraisal of Individual Radiation Risk in the Context of Probabilistic Exposures

There exists a growing desire to base safety criteria in different fields on the same principles. The current approach by the international Commission on Radiological Protection (ICRP) to control radiation exposure touches many aspects such as social, psychological, or economic factors that are important for such principles. This paper attempts to further explore possible ways of defining a common basis for dealing with radiation risks and other safety problems. Specifically, it introduces the following issues: (1) different types of risk are judged differently. To account for this, the concept of risk categories is introduced. (2) The dimension of time may play an important role. There is a difference between an immediate death and a death occurring 20 years after exposure to radiation. Effects such as reduced quality of life after exposure and reduction of lifetime expectancy are discussed. The paper suggests to introduce an individual risk equivalent which allows to compare risks as defined in various fields. Furthermore, it suggests the use of risk acceptance criteria which depend on the different categories of risk.     

Perception of Ecological Risk to Water Environments

This paper examines lay and expert perceptions of the ecological risks associated with a range of human activities that could adversely affect water resource environments. It employs the psychometric paradigm pioneered in characterizing perceptions of human health risks, which involves surveys to obtain judgments from subjects about risk items in terms of several important characteristics of the risks. The paper builds on a previous study that introduced ecological risk perception. This second study employs a larger, more diverse sample, a more focused topic area, and comparisons between lay and expert judgments. The results confirm that a small set of underlying factors explain a great deal of variability in lay judgments about ecological risks. These have been termed Ecological Impact, Human Benefits, Controllability , and Knowledge. The results are useful in explaining subjects' judgments of the general riskiness of, and need for regulation of, various risk items. The results also indicate several differences and areas of agreement among the lay and expert samples that point to potential key issues in future ecological risk management efforts for water resources.     

Model uncertainty and choices made by modelers: lessons learned from the International Atomic Energy Agency model intercomparisons.

The treatment of uncertainties associated with modeling and risk assessment has recently attracted significant attention. The methodology and guidance for dealing with parameter uncertainty have been fairly well developed and quantitative tools such as Monte Carlo modeling are often recommended. However, the issue of model uncertainty is still rarely addressed in practical applications of risk assessment. The use of several alternative models to derive a range of model outputs or risks is one of a few available techniques. This article addresses the often-overlooked issue of what we call "modeler uncertainty," i.e., difference in problem formulation, model implementation, and parameter selection originating from subjective interpretation of the problem at hand. This study uses results from the Fruit Working Group, which was created under the International Atomic Energy Agency (IAEA) BIOMASS program (BIOsphere Modeling and ASSessment). Model-model and model-data intercomparisons reviewed in this study were conducted by the working group for a total of three different scenarios. The greatest uncertainty was found to result from modelers' interpretation of scenarios and approximations made by modelers. In scenarios that were unclear for modelers, the initial differences in model predictions were as high as seven orders of magnitude. Only after several meetings and discussions about specific assumptions did the differences in predictions by various models merge. Our study shows that parameter uncertainty (as evaluated by a probabilistic Monte Carlo assessment) may have contributed over one order of magnitude to the overall modeling uncertainty. The final model predictions ranged between one and three orders of magnitude, depending on the specific scenario. This study illustrates the importance of problem formulation and implementation of an analytic-deliberative process in risk characterization.     

Bayesian Treatment of Model Uncertainty for Partially Applicable Models

This article discusses how analyst's or expert's beliefs on the credibility and quality of models can be assessed and incorporated into the uncertainty assessment of an unknown of interest. The proposed methodology is a specialization of the Bayesian framework for the assessment of model uncertainty presented in an earlier paper. This formalism treats models as sources of information in assessing the uncertainty of an unknown, and it allows the use of predictions from multiple models as well as experimental validation data about the models’ performances. In this article, the methodology is extended to incorporate additional types of information about the model, namely, subjective information in terms of credibility of the model and its applicability when it is used outside its intended domain of application. An example in the context of fire risk modeling is also provided.     

Why RDAs and ULs Are Incompatible Standards in the U‐Shape Micronutrient Model: A Philosophically Orientated Analysis of Micronutrients' Standardizations

Risk assessments of micronutrients are carried out in the customary deficiency‐excess model. It is regarded as straightforward and unambiguous. Nevertheless, it is a problematic amalgamation of two different and to a certain extent contrasting perspectives on risk and science that we will criticize in this contribution. Our critique is framed in a conceptual scheme of opposing perspectives highlighted by the rival characteristics of RDAs and SULs and the role of science therein. The one part of our scheme holds the typically modern approach that centers on risks that can be scientifically assessed more or less confidently. Subsequent policies are aimed at preventing major health problems that affect the majority of the population from early on in life. The RDAs are the ideal type‐case here. The other part of our scheme holds a much more postmodern approach in which health risks are explicitly recognized as “uncertain.” Dealing with those risks has little to do with major health problems from early on in life. Here, we encounter the scientific quandary of disentangling complex factors and impacts that may relate to some extra quality of life later on in life. SULs are exemplarily thereof. We will show that RDAs originally spawned from the scientific aim of securing objective knowledge “to lay down the requirements of an adequate” diet. SULs, conversely, are the upshot of generating acceptable outcomes driven by ever‐increasing safety requirements. This shift from securing objective knowledge to generating acceptable outcomes will be addressed in relation to precautionary culture.