安全等级特征量及其计算方法

指出了目前用模糊评价法确定系统的安全等级所存在的问题和不足之处。分别运用模糊随机变量理论和模糊集理论而提出了安全等级模糊随机特征量和安全等级模糊特征量的概念及其计算方法。安全等级特征量及安全等级变量,均为安全等级取值论域上的模糊子集,而并非是一个确定的点。还给出了安全等级的绝对可能性和相对可能性的计算方法。实例表明,笔者所提出的安全等级特征量及可能性的计算方法是科学的、合理的     

环境污染损害赔偿诉讼中的举证责任转移制度

环境污染损害赔偿案作为特定的民事侵权诉讼案,适用举证责任转移制度,它得到了法律的规定和司法实践的支持。文章深入阐述了举证责任转移制度的定义和内涵,以及在世界各国立法推行的情况。同时,根据我国国情,探讨了将举证责任转移制度纳入立法的必要性和可能性,以及当前在我国立法上存在的缺陷。     

可持续发展中的绿色会计

论述了在可持续发展中，绿色会计的确认、计量和披露，并分析了绿色会计的作用及其在我国实施的可能性．     

动电技术去除城市污泥中重金属的可行性探讨

本文简要介绍了我国城市污泥的处理处置和利用现状及动电技术去除重金属的基本原理,分析动电技术处理土壤中重金属的国内外的试验研究和应用情况,对动电技术处理城市污泥中重金属的可行性进行探讨,认为动电技术去除城市污泥中的重金属具有可行性,并提出了今后研究的方向.     

试论保险制度在环境管理中的适用

通过对美国推行环境保险制度的理论和实践的分析,论述了保险制度在我国环境管理中应用的可行性及其意义。     

A multivariable model for estimation of vapor cloud explosion occurrence possibility based on a Fuzzy logic approach for flammable materials

In this paper, a new method based on Fuzzy theory is presented to estimate the occurrence possibility of vapor cloud explosion (VCE) of flammable materials. This new method helps the analyst to overcome some uncertainties associated with estimating VCE possibility with the Event Tree (ET) technique. In this multi-variable model, the physical properties of the released material and the characteristics of the surrounding environment are used as the parameters specifying the occurrence possibility of intermediate events leading to a VCE. Factors such as area classification, degree of congestion of a plant and release rate are notably affecting the output results. Moreover, the proposed method benefits from experts' opinions in the estimation of the VCE possibility. A refrigeration cycle is used as the case study and the probability of VCE occurrence is determined for different scenarios. In this study, sensitivity analysis is performed on the model parameters to assess their effect on the final values of the VCE possibility. Furthermore, the results are compared with the results obtained using other existing models.     

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.