地震危险性概率分析方法:存在的问题和纠正

地震危险性概率分析(PSHA)是目前最广泛应用于地震灾害与风险性评估的方法。然而它在计算中却存在着一个错误:把强地面运动衰减关系(一个函数)的条件超越概率等同于强地面运动误差(一个变量)的超越概率。这个错误导致了运用强地面运动误差(空间分布特征)去外推强地面运动的发生(时间分布特征)或称之为遍历性假设,同时也造成了对PSHA理解和应用上的困难。本文推导出新的灾害计算方法(称之为KY-PSHA)来纠正这种错误。     

Organotin contamination in seafood and its implication for human health risk in Hong Kong

Organotins (OTs) have caused widespread adverse effects on marine organisms, while they can also induce health problems to humans via consumption of contaminated seafood. This study aimed to quantify the tissue concentrations of OTs in 11 seafood species in Hong Kong, and assess the human health risk for consuming these species. The tongue sole Paraplagusia blochii had the highest concentration of total OTs. Triphenyltin (TPT) accounted for 56–97% of total OTs. The highest hazard quotient (HQ) for TPT was 1.41 in P. blochii, while the HQs for butyltins were much less than 1. The results indicated that it is likely to have certain health risks for consuming P. blochii due to its high TPT contamination. Therefore, TPT should be a priority pollutant of concern. Appropriate management actions should be taken to control its use and release in the region in order to safeguard the marine ecosystem and human health.     

Health Risk Assessment for Arsenic in Water Sources of Cankiri Province of Turkey

To investigate the levels of arsenic (As) in the water sources of Cankiri Province, the samples were collected from the stations of central Cankiri (n = 27) and Kursunlu town (n = 12) during 2009 and 2010. The concentrations of As were analyzed with an atomic absorption spectrophotometer, and then compared with permissible limit, 10 µg/L in drinking water, by Turkish legislation and World Health Organization (WHO). The As levels were higher than this limit (mean value 10–30 µg/L in 26 stations), whereas, they were found to be >30 µg/L in 12 sampling points. The water sources were categorized for health risk assessment such as reservoir, tap, well, and spring, and then chronic daily intake for oral and dermal exposure to As via drinking water, hazard quotient (HQ), and hazard index were calculated by using indices. The HQ values were found to be >1 in all samples of Cankiri Province. The effects of As on human health were then evaluated using carcinogenic risk (CR). CR values for As were also estimated to be >10^?5 in drinking water samples of Cankiri Province and might exert potential CR for people. These assessments would point out required drinking water treatment strategy to ensure safety of consumers.     

Vector-valued fragility functions for seismic risk evaluation

This article presents a method for the development of vector-valued fragility functions, which are a function of more than one intensity measure (IM, also known as ground-motion parameters) for use within seismic risk evaluation of buildings. As an example, a simple unreinforced masonry structure is modelled using state-of-the-art software and hundreds of nonlinear time-history analyses are conducted to compute the response of this structure to earthquake loading. Dozens of different IMs (e.g. peak ground acceleration and velocity, response spectral accelerations at various periods, Arias intensity and various duration and number of cycle measures) are considered to characterize the earthquake shaking. It is demonstrated through various statistical techniques (including Receiver Operating Characteristic analysis) that the use of more than one IM leads to a better prediction of the damage state of the building than just a single IM, which is the current practice. In addition, it is shown that the assumption of the lognormal distribution for the derivation of fragility functions leads to more robust functions than logistic, log-logistic or kernel regression. Finally, actual fragility surfaces using two pairs of IMs (one pair are uncorrelated while the other are correlated) are derived and compared to scalar-based fragility curves using only a single IM and a significant reduction in the uncertainty of the predicted damage level is observed. This type of fragility surface would be a key component of future risk evaluations that take account of recent developments in seismic hazard assessment, such as vector-valued probabilistic seismic hazard assessments.     

Heavy Metal Contamination in Arable Soils and Vegetables around a Sulfuric Acid Factory,China

This study was designed to investigate heavy metal (Tl, Pb, Cu, Zn, and Ni) contamination levels of arable soils and vegetables grown in the vicinity of a sulfuric acid factory in the Western Guangdong Province, China. Health risks associated with these metals by consumption of vegetables were assessed based on the hazard quotient (HQ). The soils show a most significant contamination of Tl, followed by Pb, Cu, Zn, and Ni. The heavy metal contents (µg/g, dry weight basis) in the edible parts of vegetables range from 5.60 to 105 for Tl, below detection limit to 227 for Pb, 5.0–30.0 for Cu, 10.0–82.9 for Zn, and 0.50–26.0 for Ni, mostly exceeding the proposed maximum permissible level in Germany or China. For the studied vegetables, the subterranean part generally bears higher contents of Tl and Zn than the aerial part, while the former has lower contents of Cu and Ni than the latter. In addition, the results reveal that Tl is the major risk contributor for the local people since its HQ values are mostly much higher than 1.0. The potential health risk of Tl pollution in the food chain and the issue of food safety should be highly concerned and kept under continued surveillance and control.     

Uncertainty Analysis and Expert Judgment in Seismic Hazard Analysis

The large uncertainty associated with the prediction of future earthquakes is usually regarded as the main reason for increased hazard estimates which have resulted from some recent large scale probabilistic seismic hazard analysis studies (e.g. the PEGASOS study in Switzerland and the Yucca Mountain study in the USA). It is frequently overlooked that such increased hazard estimates are characteristic for a single specific method of probabilistic seismic hazard analysis (PSHA): the traditional (Cornell?CMcGuire) PSHA method which has found its highest level of sophistication in the SSHAC probability method. Based on a review of the SSHAC probability model and its application in the PEGASOS project, it is shown that the surprising results of recent PSHA studies can be explained to a large extent by the uncertainty model used in traditional PSHA, which deviates from the state of the art in mathematics and risk analysis. This uncertainty model, the Ang?CTang uncertainty model, mixes concepts of decision theory with probabilistic hazard assessment methods leading to an overestimation of uncertainty in comparison to empirical evidence. Although expert knowledge can be a valuable source of scientific information, its incorporation into the SSHAC probability method does not resolve the issue of inflating uncertainties in PSHA results. Other, more data driven, PSHA approaches in use in some European countries are less vulnerable to this effect. The most valuable alternative to traditional PSHA is the direct probabilistic scenario-based approach, which is closely linked with emerging neo-deterministic methods based on waveform modelling.     

Uncertainty in floodplain delineation: expression of flood hazard and risk in a Gulf Coast watershed

This paper investigates the development of flood hazard and flood risk delineations that account for uncertainty as improvements to standard floodplain maps for coastal watersheds. Current regulatory floodplain maps for the Gulf Coastal United States present 1% flood hazards as polygon features developed using deterministic, steady‐state models that do not consider data uncertainty or natural variability of input parameters. Using the techniques presented here, a standard binary deterministic floodplain delineation is replaced with a flood inundation map showing the underlying flood hazard structure. Additionally, the hazard uncertainty is further transformed to show flood risk as a spatially distributed probable flood depth using concepts familiar to practicing engineers and software tools accepted and understood by regulators. A case study of the proposed hazard and risk assessment methodology is presented for a Gulf Coast watershed, which suggests that storm duration and stage boundary conditions are important variable parameters, whereas rainfall distribution, storm movement, and roughness coefficients contribute less variability. The floodplain with uncertainty for this coastal watershed showed the highest variability in the tidally influenced reaches and showed little variability in the inland riverine reaches. Additionally, comparison of flood hazard maps to flood risk maps shows that they are not directly correlated, as areas of high hazard do not always represent high risk. Copyright © 2012 John Wiley & Sons, Ltd.     

Seismic Hazard Assessment: Issues and Alternatives

Seismic hazard and risk are two very important concepts in engineering design and other policy considerations. Although seismic hazard and risk have often been used interchangeably, they are fundamentally different. Furthermore, seismic risk is more important in engineering design and other policy considerations. Seismic hazard assessment is an effort by earth scientists to quantify seismic hazard and its associated uncertainty in time and space and to provide seismic hazard estimates for seismic risk assessment and other applications. Although seismic hazard assessment is more a scientific issue, it deserves special attention because of its significant implication to society. Two approaches, probabilistic seismic hazard analysis (PSHA) and deterministic seismic hazard analysis (DSHA), are commonly used for seismic hazard assessment. Although PSHA has been proclaimed as the best approach for seismic hazard assessment, it is scientifically flawed (i.e., the physics and mathematics that PSHA is based on are not valid). Use of PSHA could lead to either unsafe or overly conservative engineering design or public policy, each of which has dire consequences to society. On the other hand, DSHA is a viable approach for seismic hazard assessment even though it has been labeled as unreliable. The biggest drawback of DSHA is that the temporal characteristics (i.e., earthquake frequency of occurrence and the associated uncertainty) are often neglected. An alternative, seismic hazard analysis (SHA), utilizes earthquake science and statistics directly and provides a seismic hazard estimate that can be readily used for seismic risk assessment and other applications.     

CyberShake: A Physics-Based Seismic Hazard Model for Southern California

CyberShake, as part of the Southern California Earthquake Center??s (SCEC) Community Modeling Environment, is developing a methodology that explicitly incorporates deterministic source and wave propagation effects within seismic hazard calculations through the use of physics-based 3D ground motion simulations. To calculate a waveform-based seismic hazard estimate for a site of interest, we begin with Uniform California Earthquake Rupture Forecast, Version 2.0 (UCERF2.0) and identify all ruptures within 200?km of the site of interest. We convert the UCERF2.0 rupture definition into multiple rupture variations with differing hypocenter locations and slip distributions, resulting in about 415,000 rupture variations per site. Strain Green Tensors are calculated for the site of interest using the SCEC Community Velocity Model, Version 4 (CVM4), and then, using reciprocity, we calculate synthetic seismograms for each rupture variation. Peak intensity measures are then extracted from these synthetics and combined with the original rupture probabilities to produce probabilistic seismic hazard curves for the site. Being explicitly site-based, CyberShake directly samples the ground motion variability at that site over many earthquake cycles (i.e., rupture scenarios) and alleviates the need for the ergodic assumption that is implicitly included in traditional empirically based calculations. Thus far, we have simulated ruptures at over 200 sites in the Los Angeles region for ground shaking periods of 2?s and longer, providing the basis for the first generation CyberShake hazard maps. Our results indicate that the combination of rupture directivity and basin response effects can lead to an increase in the hazard level for some sites, relative to that given by a conventional Ground Motion Prediction Equation (GMPE). Additionally, and perhaps more importantly, we find that the physics-based hazard results are much more sensitive to the assumed magnitude-area relations and magnitude uncertainty estimates used in the definition of the ruptures than is found in the traditional GMPE approach. This reinforces the need for continued development of a better understanding of earthquake source characterization and the constitutive relations that govern the earthquake rupture process.     

Assessment of ground motion variability and its effects on seismic hazard analysis: a case study for iceland

Probabilistic seismic hazard analysis (PSHA) generally relies on the basic assumption that ground motion prediction equations (GMPEs) developed for other similar tectonic regions can be adopted in the considered area. This implies that observed ground motion and its variability at considered sites could be modelled by the selected GMPEs. Until now ground-motion variability has been taken into account in PSHA by integrating over the standard deviation reported in GMPEs, which significantly affects estimated ground motions, especially at very low probabilities of exceedance. To provide insight on this issue, ground-motion variability in the South Iceland Seismic Zone (SISZ), where many ground-motion records are available, is assessed. Three statistical methods are applied to separate the aleatory variability into source (inter-event), site (inter-site) and residual (intra-event and intra-site) components. Furthermore, the current PSHA procedure that makes the ergodic assumption of equality between spatially and temporal variability is examined. In contrast to the ergodic assumption, several recent studies show that the observed ground-motion variability at an individual location is lower than that implied by the standard deviation of a GMPE. This could imply a mishandling of aleatory uncertainty in PSHA by ignoring spatial variability and by mixing aleatory and epistemic uncertainties in the computation of sigma. Station correction coefficients are introduced in order to capture site effects at different stations. The introduction of the non-ergodic assumption in PSHA leads to larger epistemic uncertainty, although this is not the same as traditional epistemic uncertainty modelled using different GMPEs. The epistemic uncertainty due to the site correction coefficients (i.e. mean residuals) could be better constrained for future events if more information regarding the characteristics of these seismic sources and path dependence could be obtained.     

Spatio-temporal modelling of hydro-meteorological derived risk using a Bayesian approach: a case study in Venezuela

Extreme environmental events have considerable impacts on society. Preparation to mitigate or forecast accurately these events is a growing concern for governments. In this regard, policy and decision makers require accurate tools for risk estimation in order to take informed decisions. This work proposes a Bayesian framework for a unified treatment and statistical modeling of the main components of risk: hazard, vulnerability and exposure. Risk is defined as the expected economic loss or population affected as a consequence of a hazard event. The vulnerability is interpreted as the loss experienced by an exposed population due to hazard events. The framework combines data of different spatial and temporal supports. It produces a sequence of temporal risk maps for the domain of interest including a measure of uncertainty for the hazard and vulnerability. In particular, the considered hazard (rainfall) is interpolated from point-based measured rainfall data using a hierarchical spatio-temporal Kriging model, whose parameters are estimated using the Bayesian paradigm. Vulnerability is modeled using zero-inflated distributions with parameters dependent on climatic variables at local and large scales. Exposure is defined as the total population settled in the spatial domain and is interpolated using census data. The proposed methodology was applied to the Vargas state of Venezuela to map the spatio-temporal risk for the period 1970–2006. The framework highlights both high and low risk areas given extreme rainfall events.     

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.     

Probabilistic evaluation of seismically induced permanent deformation of slopes

A probabilistic approach that can systematically model various sources of uncertainty involved in the assessment of seismically induced permanent deformations of slopes is presented. This approach incorporates probabilistic concepts into the classical-limit equilibrium and Newmark-type deformation analysis and the risk of damage is then computed by Monte Carlo simulations. The spatial variability of the material properties and the uncertainty arising from insufficient information are treated in the framework of random fields. The uncertainty of seismic loading is modeled by generating a large series of hazard-compatible artificial motions. This approach provides a consistent level of risk within the time period of interest. The results of the case analyses show that the uncertainty of the soil properties can have a significant impact on the computed risk of failure for a slope with spatially correlated soil properties exposed to relatively low levels of seismic hazard (RMS<0.1–0.2 g); however, it appears to have little impact on the computed risk if the slope is exposed to relatively high hazard levels (RMS>0.1–0.2 g).     

Assessment of pollution levels and human health risk of heavy metals in dust deposited on Yerevan's tree leaves(Armenia)

The total concentrations of Cd,As,Pb,Cr,Ni,Co,Zn,Cu,Ag,Hg,and Mo were determined in the atmospheric dust of the city of Yerevan by atomic absorption spectrometry(AAnalyst PE 800).Heavy metal pollution levels were evaluated by calculating geo-accumulation(I_(geo)) and summary pollution(Z_c) indices.Potential human health risk was assessed using the United States Environmental Protection agency's human health risk assessment model.The results show that mean contents of all elements tested except Ni and Cr were substantially higher than local geochemical background values.According to the I_(geo),Yerevan territory is strongly-to-extremely polluted by As,Ag,Hg,Mo,and Cd.The Z_c assessment indicated that very high pollution was detected in 36%of samples,high in 32%,average in 12%,and low in 20%.The health risk assessment revealed a noncarcinogenic risk(HI 1) for children at 13 samplings sites and for adults at one sampling site.For children the risk was due to elevated levels of Mo,Cd,Co,and As,while for adults,only Mo.Carcinogenic risk(1:1,000,000) of As and Cr via ingestion pathway was observed in 25 and 14 samples,respectively.This study,therefore,is the base for further detailed investigations to organize problematic site remediation and risk reduction measures.     

Probabilistic performance‐based optimum design of seismic isolation for a California high‐speed rail prototype bridge

Previous comparison studies on seismic isolation have demonstrated its beneficial and detrimental effects on the structural performance of high‐speed rail bridges during earthquakes. Striking a balance between these 2 competing effects requires proper tuning of the controlling design parameters in the design of the seismic isolation system. This results in a challenging problem for practical design in performance‐based engineering, particularly when the uncertainty in seismic loading needs to be explicitly accounted for. This problem can be tackled using a novel probabilistic performance‐based optimum seismic design (PPBOSD) framework, which has been previously proposed as an extension of the performance‐based earthquake engineering methodology. For this purpose, a parametric probabilistic demand hazard analysis is performed over a grid in the seismic isolator parameter space, using high‐throughput cloud‐computing resources, for a California high‐speed rail (CHSR) prototype bridge. The derived probabilistic structural demand hazard results conditional on a seismic hazard level and unconditional, i.e., accounting for all seismic hazard levels, are used to define 2 families of risk features, respectively. Various risk features are explored as functions of the key isolator parameters and are used to construct probabilistic objective and constraint functions in defining well‐posed optimization problems. These optimization problems are solved using a grid‐based, brute‐force approach as an application of the PPBOSD framework, seeking optimum seismic isolator parameters for the CHSR prototype bridge. This research shows the promising use of seismic isolation for CHSR bridges, as well as the potential of the versatile PPBOSD framework in solving probabilistic performance‐based real‐world design problems.     

2D Monte Carlo versus 2D Fuzzy Monte Carlo health risk assessment

Risk estimates can be calculated using crisp estimates of the exposure variables (i.e., contaminant concentration, contact rate, exposure frequency and duration, body weight, and averaging time). However, aggregate and cumulative exposure studies require a better understanding of exposure variables and uncertainty and variability associated with them. Probabilistic risk assessment (PRA) studies use probability distributions for one or more variables of the risk equation in order to quantitatively characterize variability and uncertainty. Two-dimensional Monte Carlo Analysis (2D MCA) is one of the advanced modeling approaches that may be used to conduct PRA studies. In this analysis the variables of the risk equation along with the parameters of these variables (for example mean and standard deviation for a normal distribution) are described in terms of probability density functions (PDFs). A variable described in this way is called a second order random variable. Significant data or considerable insight to uncertainty associated with these variables is necessary to develop the appropriate PDFs for these random parameters. Typically, available data and accuracy and reliability of such data are not sufficient for conducting a reliable 2D MCA. Thus, other theories and computational methods that propagate uncertainty and variability in exposure and health risk assessment are needed. One such theory is possibility analysis based on fuzzy set theory, which allows the utilization of incomplete information (incomplete information includes vague and imprecise information that is not sufficient to generate probability distributions for the parameters of the random variables of the risk equation) together with expert judgment. In this paper, as an alternative to 2D MCA, we are proposing a 2D Fuzzy Monte Carlo Analysis (2D FMCA) to overcome this difficulty. In this approach, instead of describing the parameters of PDFs used in defining the variables of the risk equation as random variables, we describe them as fuzzy numbers. This approach introduces new concepts and risk characterization methods. In this paper we provide a comparison of these two approaches relative to their computational requirements, data requirements and availability. For a hypothetical case, we also provide a comperative interpretation of the results generated.     

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.     

Estimation of flood inundation probabilities using global hazard indexes based on hydrodynamic variables

In this paper a new procedure to derive flood hazard maps incorporating uncertainty concepts is presented. The layout of the procedure can be resumed as follows: (1) stochastic input of flood hydrograph modelled through a direct Monte-Carlo simulation based on flood recorded data. Generation of flood peaks and flow volumes has been obtained via copulas, which describe and model the correlation between these two variables independently of the marginal laws involved. The shape of hydrograph has been generated on the basis of a historical significant flood events, via cluster analysis; (2) modelling of flood propagation using a hyperbolic finite element model based on the DSV equations; (3) definition of global hazard indexes based on hydro-dynamic variables (i.e., water depth and flow velocities). The GLUE methodology has been applied in order to account for parameter uncertainty. The procedure has been tested on a flood prone area located in the southern part of Sicily, Italy. Three hazard maps have been obtained and then compared.     

The Global Earthquake Vulnerability Estimation System (GEVES): an approach for earthquake risk assessment for insurance applications

For the insurance and reinsurance industries, earthquake loss estimation is crucial not only to adequately price its product but also to manage the accumulation risk in the face of the ever-increasing exposure in highly seismic regions. Changes in the built environment and a continuously evolving earthquake science make it a necessity for the industry to constantly refine earthquake loss estimation models. In particular, it has been recognized for a long time that the vulnerability of buildings to ground shaking is a key parameter in any earthquake risk model. Current methods tend either to rely on the limited historical damage and loss data or on the numerical simulation of the response of individual buildings to the ground-shaking produced by earthquakes. Although both methods have their advantages and pitfalls, we are proposing here a simple solution, using transparent input data, that can be realistically used for the needs of the insurance and reinsurance industry, whether detailed information about the insured structures is available or not. The resulting product is known as GEVES (Global Earthquake Vulnerability Estimation System). It is primarily intended for evaluating the mean damage ratio (MDR) suffered by a portfolio of buildings classified by use, under the action of a given earthquake scenario (i.e. an earthquake of given size at a given distance from the portfolio of buildings). A key assumption was that macroseismic intensity rather than spectral displacement would be the basis of loss estimation. The paper describes the model with emphasis on its structure and the justification for the assumptions made. In addition to a new set of earthquake vulnerability functions, the paper also provides recommendations on some aspects of the earthquake hazard, in particular about how to define macroseismic intensity at the site of interest, for a given earthquake scenario. This paper also discusses validation of the GEVES model against calculated vulnerability approaches, and the treatment of uncertainty within the model.     

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.