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1.
Probabilistic-fuzzy health risk modeling   总被引：3，自引：2，他引：1  
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.  相似文献   

2.
High concentrations of ammonia in a river can cause fish kills and harms to other aquatic organisms. A simple water quality model is needed to predict the probability of ammonia concentration violations as compared to the US Environmental Protection Agencys ammonia criteria. A spreadsheet with Random Monte Carlo (RMC) simulations to model ammonia concentrations at the mixing point (between a river and the effluent of a wastewater treatment plant) was developed with the use of Microsoft Excel and Crystal Ball add-in software. The model uses effluent and river ammonia, alkalinity, and total carbonate data to determine the probability density functions (PDFs) for the Monte Carlo simulations. Normal, lognormal, exponential and uniform probability distributions were tested using the Chi-square method and p-value associated with it to choose the best fit to the random data selected from the East Burlington wastewater treatment plant in North Carolina and the Clinch River in Tennessee. It is suggested that different options be tested with a minimum of three classes and a maximum of n/5 classes (n = number of data points) and the highest probability (p-value) for the PDF being tested be chosen. The results indicted that six violations to the EPA criterion for maximum concentration (CMC) were predicted when using 2000 RMC simulations and PDFs fitted to the available data, which violate the current criterion of no more than one violation over 3 years. All violations occur when the pH of the blend ranges from 8.0 to 9.0. No violations were found to the criteria of chronic concentration (CCC) using RMC.  相似文献   

3.
The ability to describe variables in a health risk model through probability theory enables us to estimate human health risk. These types of risk assessment are interpreted as probabilistic risk assessment (PRA). Generally, PRA requires specific estimate of the parameters of the probability density of the input variables. In all circumstances, such estimates of the parameters may not be available due to the lack of knowledge or information. Such types of variables are treated as uncertain variables. These types of information are often termed uncertainty which are interpreted through fuzzy theory. The ability to describe uncertainty through fuzzy set theory enables us to process both random variable and fuzzy variable in a single framework. The method of processing aleatory and epistemic uncertainties into a same framework is coined as hybrid method. In this paper, we are going to talk about such type of hybrid methodology for human health risk assessment. Risk assessment on human health through different pathways of exposure has been attempted many a times combining Monte Carlo analysis and extension principle of fuzzy set theory. The emergence of credibility theory enables transforming fuzzy variable into credibility distribution function which can be used in those hybrid analyses. Hence, an attempt, for the first time, has been made to combine probability theory and credibility theory to estimate risk in human health exposure. This method of risk assessment in the presence of credibility theory and probability theory is identified as probabilistic-credibility method (PCM). The results obtained are then interpreted through probability theory, unlike the other hybrid methodology where the results are interpreted in terms of possibility theory. The results obtained are then compared with probability-fuzzy risk assessment (PFRA) method. Generally, decision under hybrid methodology is made on the index of optimism. An optimistic decision maker estimates from the \(\alpha\)-cut at 1, whereas a pessimistic decision maker estimates from the \(\alpha\)-cut at 0. The PCM is an optimistic approach as the decision is always made at \(\alpha\)=1.  相似文献   

4.
Uncertainty plagues every effort to model subsurface processes and every decision made on the basis of such models. Given this pervasive uncertainty, virtually all practical problems in hydrogeology can be formulated in terms of (ecologic, monetary, health, regulatory, etc.) risk. This review deals with hydrogeologic applications of recent advances in uncertainty quantification, probabilistic risk assessment (PRA), and decision-making under uncertainty. The subjects discussed include probabilistic analyses of exposure pathways, PRAs based on fault tree analyses and other systems-based approaches, PDF (probability density functions) methods for propagating parametric uncertainty through a modeling process, computational tools (e.g., random domain decompositions and transition probability based approaches) for quantification of geologic uncertainty, Bayesian algorithms for quantification of model (structural) uncertainty, and computational methods for decision-making under uncertainty (stochastic optimization and decision theory). The review is concluded with a brief discussion of ways to communicate results of uncertainty quantification and risk assessment.  相似文献   

5.
The present model permits simulation of any geographic region and the symmetrical or random positioning of any number of rain gauges. The operator has the option of entering precipitation parameters: rain cell diameter, duration, rain swath length, vector angle, and precipitation amount for any number of discrete showers. In a series of computations the model generates (1) a random first echo location and resulting rain swath, which is superimposed on a specific grid of rain gauges; (2) the number of rain gauge receiving a hit; and (3) the number of undetected rain events within an area.By use of a portion of the Iowa climatological rain gauge network and parameters derived from radar and rain gauge observations the model shows that only 7% of single cell showers are detected by the existing sampling grid.Journal Paper No. J-10378 of the Iowa Agriculture and Home Economics Experiment Station, Ames, IA 50011. Project 2449.A portion of this paper was presented at the 93rd Annual Meeting of the Iowa Academy of Science, April 1981.  相似文献   

6.
Soil heterogeneity and data sparsity combine to render estimates of infiltration rates uncertain. We develop reduced complexity models for the probabilistic forecasting of infiltration rates in heterogeneous soils during surface runoff and/or flooding events. These models yield closed-form semi-analytical expressions for the single- and multi-point infiltration-rate PDFs (probability density functions), which quantify predictive uncertainty stemming from uncertainty in soil properties. These solutions enable us to investigate the relative importance of uncertainty in various hydraulic parameters and the effects of their cross-correlation. At early times, the infiltration-rate PDFs computed with the reduced complexity models are in close agreement with their counterparts obtained from a full infiltration model based on the Richards equation. At all times, the reduced complexity models provide conservative estimates of predictive uncertainty.  相似文献   

7.
This paper proposes an approach to estimating the uncertainty related to EPA Storm Water Management Model model parameters, percentage routed (PR) and saturated hydraulic conductivity (Ksat), which are used to calculate stormwater runoff volumes. The methodology proposed in this paper addresses uncertainty through the development of probability distributions for urban hydrologic parameters through extensive calibration to observed flow data in the Philadelphia collection system. The established probability distributions are then applied to the Philadelphia Southeast district model through a Monte Carlo approach to estimate the uncertainty in prediction of combined sewer overflow volumes as related to hydrologic model parameter estimation. Understanding urban hydrology is critical to defining urban water resource problems. A variety of land use types within Philadelphia coupled with a history of cut and fill have resulted in a patchwork of urban fill and native soils. The complexity of urban hydrology can make model parameter estimation and defining model uncertainty a difficult task. The development of probability distributions for hydrologic parameters applied through Monte Carlo simulations provided a significant improvement in estimating model uncertainty over traditional model sensitivity analysis. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

8.
Artificial open channels being costlier infrastructure, their design should ensure reliability along with optimality in project cost. This paper presents reliability analysis of composite channels, considering uncertainty associated with various design parameters such as friction factors, longitudinal slope, channel width, side slope, and flow depth. This study also considers uncertainties of watershed characteristics, rainfall intensity and drainage area to quantify the uncertainty of runoff. For uncertainty modeling, the advanced first order second moment method and Monte Carlo simulation are used and it is found that the results by both approaches show good agreement. Then, a reliability index that can be used to design a composite channel to convey design discharge for a specified risk or probability of failure is presented, and its sensitivity with different channel design parameters are analyzed. To validate the effectiveness of the present approach, the reliability values and safety factors for variable system loading scenario are obtained under static and dynamic environment. The sensitivity analysis shows that flow depth and bed width are the most influencing parameters that affect the safety factor and reliability.  相似文献   

9.
Prestack wave‐equation migration has proved to be a very accurate shot‐by‐shot imaging tool. However, 3D imaging with this technique of a large field acquisition, especially one with hundreds of thousands of shots, is prohibitively costly. Simply adapting the technique to migrate many superposed shot‐gathers simultaneously would render 3D wavefield prestack migration cost‐effective but it introduces uncontrolled non‐physical interference among the shot‐gathers, making the final image useless. However, it has been observed that multishot signal interference can be kept under some control by averaging over many such images, if each multishot migration is modified by a random phase encoding of the frequency spectra of the seismic traces. In this article, we analyse this technique, giving a theoretical basis for its observed behaviour: that the error of the image produced by averaging over M phase encoded migrations decreases as M?1 . Furthermore, we expand the technique and define a general class of Monte‐Carlo encoding methods for which the noise variance of the average imaging condition decreases as M?1 ; these methods thus all converge asymptotically to the correct reflectivity map, without generating prohibitive costs. The theoretical asymptotic behaviour is illustrated for three such methods on a 2D test case. Numerical verification in 3D is then presented for one such method implemented with a 3D PSPI extrapolation kernel for two test cases: the SEG–EAGE salt model and a real test constructed from field data.  相似文献   

10.
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.  相似文献   

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