太湖湖心区的痕量金属历史沉积过程研究

以太湖湖心区的沉积物为研究对象,解析了湖心区痕量金属的沉积通量年变化,探明了湖心区痕量金属的历史沉积情况.结果表明:湖心区各样点沉积物的沉积速率从1950年以前的(0.032±0.023)g·cm-2·a-1缓慢递增到1980前后的(0.076±0.030)g·cm-2·a-1,1980年后突增明显,在2000年后达到(0.185±0.132)g·cm-2·a-1.湖心区沉积物中8种痕量金属(钒、铬、镍、铜、锌、砷、镉和铅)的含量年变化没有明显递增趋势,在20世纪80年代(对应沉积柱4~9 cm深度处)前后相差不大(变幅小于20%).但8种金属元素的沉积通量随时间推进而递增,与1980年前相比,金属沉积通量在1980年后突增倍数达到11倍以上.金属沉积通量与沉积物的沉积速率显著正相关,并与20世纪20年代以来太湖流域社会经济发展形势相一致.1980年后8种痕量金属沉积总量在湖心区西部最大,而沉积物沉积速率对湖心区南部的痕量金属沉积总量贡献最大.通过金属沉积通量可以更全面地研究太湖重金属的历史沉积过程和人为活动的干扰效应.     

Risk assessment of rare events

Rare events often result in large impacts and are hard to predict. Risk analysis of such events is a challenging task because there are few directly relevant data to form a basis for probabilistic risk assessment. Due to the scarcity of data, the probability estimation of a rare event often uses precursor data. Precursor-based methods have been widely used in probability estimation of rare events. However, few attempts have been made to estimate consequences of rare events using their precursors. This paper proposes a holistic precursor-based risk assessment framework for rare events. The Hierarchical Bayesian Approach (HBA) using hyper-priors to represent prior parameters is applied to probability estimation in the proposed framework. Accident precursor data are utilized from an information theory perspective to seek the most informative precursor upon which the consequence of a rare event is estimated. Combining the estimated probability and consequence gives a reasonable assessment of risk. The assessed risk is updated as new information becomes available to produce a dynamic risk profile. The applicability of the methodology is tested through a case study of an offshore blowout accident. The proposed framework provides a rational way to develop the dynamic risk profile of a rare event for its prevention and control.     

江西遂川林权改革环境影响Logit模型研究

为了了解林权改革后当地环境受到的影响及影响的主要因素,研究采用问卷调查的方式,对江西遂川县两个乡镇的林农、农民和少数外出务工人员进行了随机抽样调查。以322份有效调查问卷为基础进行统计分析,得到环境影响的Logit模型,可以用于林改对环境产生影响的分析研究,有利于当地政府更好地了解林权改革对环境的影响情况,完善相关改革措施。     

基于贝叶斯网络情景分析的含水土质埋压救援现场监测体系研究*

为提高含水土质埋压救援现场救援效率,基于贝叶斯网络进行情景推演分析,明确典型不利情景演化路径和关键节点,构建多方位多因素耦合的含水土质埋压监测体系。研究结果表明:基于贝叶斯网络情景分析的含水土质埋压救援现场监测体系将灾害环境监测、救援人员状态监测、埋压人员状态监测、救援设备状态监测、整体救援进展跟踪监测划分为18个监测单元,明确土质压力等11个关键监测参数及其技术手段。研究结果可有效提高救援行动安全性,为含水土质埋压救援现场安全监测系统研发提供理论支撑。     

Integrating flare gas with cogeneration systems: Operational risk assessment

Flare gas utilization in a cogeneration plant is an attractive proposition considering its environmental and economic incentives. Evaluation of the operational risk of integrating flare gas with cogeneration is complex due to the uncertainty in flare gas quality and process conditions. The current study delves into the change in operational risk after modifying the existing cogeneration process with the addition of fuel from flare gas. Based on the process hazards evaluation, the current study identified two critical loss control events (or top events) - boiler gas temperature exceeding operating design temperature and rich fuel mixture in the boiler firebox. The underlying causes that may contribute and lead to these loss control events were identified using fault trees and were updated to the existing cogeneration scenarios. Similarly, different consequential events that may arise from the loss control events were analysed using event trees with existing system safeguards. A Bayesian network model with its explanatory power mapped all the identified dangerous scenarios from the fault trees and event trees to predict integrated systems reliability and diagnose causal factors. Bayesian Network analysis illustrates the dynamic cause-effect relationship and determines the risk escalation due to the changes in the composition of flare gas that is fed to the boiler. The presence of a higher percentage of hydrogen (above 40 mol%) in the flare gas escalates the risk of lean air to fuel ratio in the boiler firebox and increases boiler radiation zone duty. These conditions are detrimental to the boiler firebox operation and can result in critical scenarios such as flame impingement and tube rupture. Additionally, other consequences-a steam explosion and boiler stack explosion were also investigated. However, their probability of occurrence was relatively insignificant with the given frequency of flare gas utilization in the cogeneration system.     

基于贝叶斯随机评价方法的小城镇灾害易损性分析

为了准确评价小城镇灾害易损性所处的状态,进而为制定小城镇防灾减灾规划提供科学依据,提出了基于贝叶斯公式的小城镇灾害易损性随机评价方法。该方法通过计算小城镇灾害易损性单个指标属于某个评价级别的概率,由最大似然分类原则确定单个易损性指标的评价级别,进而采用最大加权概率原则推求其综合评价级别。通过实例计算分析,并与模糊评估方法、可拓物元评价方法进行比较验证,说明了该方法的可行性及其简单、实用的特点。     

Bayesian spatial prediction

This paper presents a complete Bayesian methodology for analyzing spatial data, one which employs proper priors and features diagnostic methods in the Bayesian spatial setting. The spatial covariance structure is modeled using a rich class of covariance functions for Gaussian random fields. A general class of priors for trend, scale, and structural covariance parameters is considered. In particular, we obtain analytic results that allow easy computation of the predictive distribution for an arbitrary prior on the parameters of the covariance function using importance sampling. The computations, as well as model diagnostics and sensitivity analysis, are illustrated with a set of precipitation data.     

Bayesian estimation of European sulphur emissions using monitoring data and an acid deposition model

A statistical method for estimating national emissions of acidifying air pollutants in Europe is presented. The method uses an acid deposition model to match official emissions data from European countries and measured depositions from a monitoring network. An application to 1990 sulphate data demonstrates the method and suggests some tendencies in the reported emissions. The proposed framework may prove useful for verifying national compliance with emissions standards, and the method should be applicable also to other substances than sulphur dioxide. The problem of designing an optimal monitoring network may also be assessed within the proposed statistical framework.     

中国环境影响评价制度四方信号博弈分析

针对中国环境影响评价执行率逐步提高的同时,环境质量却持续恶化的现状,从环境经济学的理论出发,揭示了参与方之间的关系是由其经济关系决定的,取决于参与方之间的交易成本和经济收益.选用经典博弈论中的信号博弈模型对中国的环评过程进行描述,分析参与四方(政府、企业、环评单位、公众)的行动,得出博弈均衡解,按照博弈的转化条件,获得将现有均衡转变为最优均衡状态的博弈方法.分析发现,模型存在2个均衡解.目前的环境影响评价处在片面追求经济收益的状态,而追求经济和环境保护协调发展的状态,显然更符合社会发展的要求.在将公众作为参与方加入到模型后,通过计算模型总收益和总成本的变化,表明完善的公众参与体制可以实现模型均衡转化的目标.     

Detecting pattern in biological stressor response relationships using model based cluster analysis

Environmental monitoring of aquatic systems is needed to estimate the quality of the systems, to evaluate standards and to study stressor–response relationships. Monitoring programs often focus on the collection of biological, chemical and physical measures of the system. An important concern is the effect of chemical and physical stressors on the biological community. Evaluation of relationships may be difficult as the extent of the relationship is not known. From a management perspective, interest is on what factors affect the biological community and where these factors have an influence. The focus of this paper is on the use of regression based cluster analysis as a tool for finding relationships between a single biological response and a suite of environmental stressors. The approach to cluster analysis uses a penalized regression classification likelihood and Markov Chain Model Composition Monte Carlo. This approach allows for simultaneous development of regression models and clustering of the regression models. The method is applied to the analysis of a data set describing stressors/response relationship in Ohio.