Joint propagation of variability and imprecision in assessing the risk of groundwater contamination

Estimating risks of groundwater contamination often require schemes for representing and propagating uncertainties relative to model input parameters. The most popular method is the Monte Carlo method whereby cumulative probability distributions are randomly sampled in an iterative fashion. The shortcoming of the approach, however, arises when probability distributions are arbitrarily selected in situations where available information is incomplete or imprecise. In such situations, alternative modes of information representation can be used, for example the nested intervals known as “possibility distributions”. In practical situations of groundwater risk assessment, it is common that certain model parameters may be represented by single probability distributions (representing variability) because there are data to justify these distributions, while others are more faithfully represented by possibility distributions (representing imprecision) due to the partial nature of available information. This paper applies two recent methods, designed for the joint-propagation of variability and imprecision, to a groundwater contamination risk assessment. Results of the joint-propagation methods are compared to those obtained using both interval analysis and the Monte Carlo method with a hypothesis of stochastic independence between model parameters. The two joint-propagation methods provide results in the form of families of cumulative distributions of the probability of exceeding a certain value of groundwater concentration. These families are delimited by an upper cumulative distribution and a lower distribution respectively called Plausibility and Belief after evidence theory. Slight differences between the results of the two joint-propagation methods are explained by the different assumptions regarding parameter dependencies. Results highlight the point that non-conservative results may be obtained if single cumulative probability distributions are arbitrarily selected for model parameters in the face of imprecise information and the Monte Carlo method is used under the assumption of stochastic independence. The proposed joint-propagation methods provide upper and lower bounds for the probability of exceeding a tolerance threshold. As this may seem impractical in a risk-management context, it is proposed to introduce “a-posteriori subjectivity” (as opposed to the “a-priori subjectivity” introduced by the arbitrary selection of single probability distributions) by defining a single indicator of evidence as a weighted average of Plausibility and Belief, with weights to be defined according to the specific context.     

The impact of heterogeneity on the distribution of CO2: Numerical simulation of CO2 storage at Ketzin

Numerical modelling of multiphase flow is an essential tool to ensure the viability of long-term and safe CO₂ storage in geological formations. Uncertainties arising from the heterogeneity of the formation and lack of knowledge of formation properties need to be assessed in order to create a model that can reproduce the data available from monitoring. In this study, we investigated the impact of unknown spatial variability in the petrophysical properties within a sandy channel facies of a fluviatile storage formation using stochastic methods in a Monte Carlo approach. The stochastic method has been applied to the Ketzin test site (CO₂SINK), and demonstrates that the deterministic homogeneous model satisfactorily predicts the first CO₂ arrival time at the Ketzin site. The equivalent permeability was adjusted to the injection pressure and is in good agreement with the hydraulic test. It has been shown that with increasing small-scale heterogeneity, the sharpness of the CO₂ front decreases and a greater volume of the reservoir is affected, which is also seen in an increased amount of dissolved CO₂. Increased anisotropy creates fingering effects, which result in higher probabilities for earlier arrival times. Generally, injectivity decreases with increasing heterogeneity.     

基于风量-风压复合特征的通风系统阻变型故障诊断

为了避免风量单一特征进行故障位置诊断的不适定性,提出基于风量-风压复合特征的故障位置诊断方法,实现特征信息的多维互补,提高故障位置诊断的准确度。利用蒙特卡洛方法生成大致满足实际故障风阻值分布的故障仿真样本,为了避免不同变量之间不同量纲、不同数量级造成的数据损失,对原始风量、风压数据进行标准化处理,并分别以风量单一特征、风压单一特征、风量-风压复合特征作为支持向量机（SVM）的输入,构建通风系统阻变型故障位置诊断模型。通过故障模拟实验研究表明:风量、风压单一特征进行故障位置诊断的准确度分别为89.80%,90.34%,风量-风压复合特征进行故障位置诊断的准确度为98.23%,说明风量-风压复合特征进行故障诊断可以消除风量、风压单一特征进行故障诊断的不适定性,提高故障诊断的准确度。     

铁路事故致因与风险分析

为保障铁路运输安全、预防铁路事故的发生,全面系统地分析各种铁路安全影响因素。基于美国2005年铁路事故的相关统计资料,采用灰色系统理论分析铁路事故致因,确定不同事故类型中各安全影响因素的主次关系;并以蒙特卡罗风险分析方法为基础,运用@Risk软件从人-机-环及时间的角度出发,对各因素的风险概率进行拟合。结果表明:人员-设备因素是导致事故的主要因素,针对人员-设备因素采取适当的预防措施便能有效减少铁路事故的发生。     

Estimation of the impact probability in domino effects due to the projection of fragments

Despite the remarkable severity of domino effects in activities at major hazard, a complete methodology analysing such events has not been developed and integrated within Quantitative Risk Analysis (QRA). Such a deficiency appears to be particularly remarkable for domino effects triggered by the projection of fragments. The aim of the present work is therefore to propose a systematic procedure for the quantification of domino effects due to fragments projection within QRA. To achieve this objective, the deterministic approach for the estimation of the realistic trajectory of fragments is entirely reviewed. In order to incorporate such a reviewed approach within the standard QRA, a probabilistic model for the impact probability of the fragments is developed by applying a Monte-Carlo method to the trajectory equations. The validation of the proposed framework is carried out by using the data related to an accident occurred in 1993 in the oil refinery of Milazzo (Italy).     

How wrong can you be? Implications of incorrect utility function specification for welfare measurement in choice experiments

Despite the vital role of the utility function in welfare measurement, the implications of working with incorrect utility specifications have been largely neglected in the choice experiments literature. This paper addresses the importance of specification with a special emphasis on the effects of mistaken assumptions about the marginal utility of income. Monte Carlo experiments were conducted using different functional forms of utility to generate simulated choices. Multi-Nomial Logit and Mixed Logit models were then estimated on these choices under correct and incorrect assumptions about the true, underlying utility function. Estimated willingness to pay measures from these choice modeling results are then compared with the equivalent measures directly calculated from the true utility specifications. Results show that for the parameter values and functional forms considered, a continuous-quadratic or a discrete-linear attribute specification is a good option regardless of the true effects the attribute has on utility. We also find that mistaken assumptions about preferences over costs magnify attribute mis-specification effects.     

Methods for estimating the uncertainty in emergy table-form models

Emergy studies have suffered criticism due to the lack of uncertainty analysis and this shortcoming may have directly hindered the wider application and acceptance of this methodology. Recently, to fill this gap, the sources of uncertainty in emergy analysis were described and analytical and stochastic methods were put forward to estimate the uncertainty in unit emergy values (UEVs). However, the most common method used to determine UEVs is the emergy table-form model, and only a stochastic method (i.e., the Monte Carlo method) was provided to estimate the uncertainty of values calculated in this way. To simplify the determination of uncertainties in emergy analysis using table-form calculations, we introduced two analytical methods provided by the Guide to the Expression of Uncertainty in Measurement (GUM), i.e., the Variance method and the Taylor method, to estimate the uncertainty of emergy table-form calculations for two different types of data, and compared them with the stochastic method in two case studies. The results showed that, when replicate data are available at the system level, i.e., the same data on inputs and output are measured repeatedly in several independent systems, the Variance method is the simplest and most reliable method for determining the uncertainty of the model output, since it considers the underlying covariance of the inputs and requires no assumptions about the probability distributions of the inputs. However, when replicate data are only available at the subsystem level, i.e., repeat samples are measured on subsystems without specific correspondence between an output and a certain suite of inputs, the Taylor method will be a better option for calculating uncertainty, since it requires less information and is easier to understand and perform than the Monte Carlo method.     

参数不确定性对隧道火灾人员安全时间的影响研究

为分析参数不确定性对隧道火灾人员安全时间的影响,根据烟气失能指数建立人员安全时间预测模型。并将所建立的模型与Monte Carlo抽样方法相结合,用概率密度函数描述火灾增长系数、最大热释放速率等参数的不确定性,提出考虑参数不确定性的隧道火灾人员安全时间分析方法。计算分析结果表明:应用该方法可得到隧道火灾人员安全时间的上下限、均值、标准差等;就10 MW以下中小型客车火灾而言,火灾增长系数的不确定性对人员安全时间影响不大;随着热释放速率的增大,人员安全时间的均值逐渐减小,标准差逐渐增大,分布区间逐渐变宽;当热释放速率超过120 MW时,人员安全时间的分布区间、均值、标准差基本趋于稳定。在热释放速率给定条件下,随着通风风速的增加,人员安全时间上下限有所提高且分布区间增宽,同时均值和标准差均有所增加。     

Spatial variations of human health risk associated with exposure to chlorination by-products occurring in drinking water

During disinfection, chlorine reacts with organic matter present in drinking water and forms various undesirable chlorinated by-products (CBPs). This paper describes a study of the spatial variability of human health risk (i.e., cancer effects) from CBP exposure through drinking water in a specific region. The region under study involves nine drinking water distribution systems divided into several zones based on their characteristics. The spatial distribution of cancer risk (CR) was estimated using two years of data (2006-2008) on various CBP species. In this analysis, trihalomethanes (THMs) and haloacetic acids (HAAs) served as surrogates for CBPs. Three possible routes of exposure (i.e., via ingestion, inhalation and dermal contact) were considered for each selected compound. The cancer risk assessment involved estimating a unit risk (R(T)) in each zone of the selected distribution systems. A probabilistic analysis based on Monte Carlo simulations was employed. Risk assessment results showed that cancer risk varied between systems, but also within individual systems. As a result, the population of the same region was not exposed to the same risk associated with CBPs in drinking water. Unacceptable levels (i.e., R(T) > 10(-4)) for the estimated CR were determined for several zones in the studied region. This study demonstrates that a spatial-based analysis performed to represent the spatial distribution of risk estimates can be helpful in identifying suitable risk management strategies. Suggestions for improving the risk analysis procedure are also presented.     

居民经手口途径摄入含PAHs颗粒物的致癌风险评价

为评价居民经手口途径暴露于被PAHs污染室外土壤和室内灰尘颗粒的致癌风险以及与之相关各个参数的重要程度,本文构建了经手口途径暴露于这两种颗粒物的概率风险模型,运用蒙特卡罗模拟方法评价了居民的致癌风险,探究了主要的风险来源及关键的暴露参数.结果表明,幼儿、儿童和青少年所承受的风险较大;婴儿、成年人和老年人较小.婴儿致癌风险超过10~(-6)的概率为2%左右;幼儿和青少年超过10~(-6)的概率在5%左右,但不超过10~(-4);儿童超过10~(-6)的概率在15%左右,有0.1%左右的概率超过10~(-4);成年人和老年人超过10~(-6)的概率分别在1%和0.1%左右.室内灰尘颗粒是主要的风险来源,占91%左右;室内硬表面灰尘是室内致癌风险的主要来源,占65%左右.与致癌风险相关性最大的因素为:手口途径发生的频次(FQ)、灰尘颗粒中PAHs的等效斜率(PEFj)、灰尘在室内硬表面的含量(DSLHS)、颗粒物从手掌皮肤表面到口腔的转移率(SE)和灰尘在室内软表面的含量(DSLSS).