环境DNA技术在环境监测与动物生产管理中的应用进展

概述了环境DNA技术目前的研究方向和分析原理,介绍了该技术在水生、土壤和植物生态系统中的应用进展,以及在监测畜舍环境、促进畜禽粪污无害化处理、检测动物营养与健康状况、调查动物群体遗传多样性、监测和预防畜禽疾病等动物生产管理中的应用前景。分析了环境DNA技术的优势与局限,并在此基础上提出了建立标准操作流程、提高检测精确度、积极推广应用环境DNA技术等建议。     

基于蒙特卡罗模拟的土壤环境健康风险评价:以PAHs为例

为获得更为合理的健康风险评价结果,并辨识对健康风险影响最大的因素,基于蒙特卡罗随机模拟,运用概率风险评价模型,定量评估了中国上海某居民区土壤中16种PAHs对居民的健康风险水平,并对各参数进行敏感性分析.结果表明,土壤中PAHs造成的健康风险服从对数正态分布,总的致癌风险为3.43×10~(-5)±2.63×10~(-5),最小值为8.10×10~(-7),最大值为2.39×10~(-4),超过10-6的概率为95%,超过10~(-5)的概率为75%,超过10~(-4)的概率小于5%;总的危害商为4.74×10~(-2)±3.42×10~(-2),不超过1,风险较小;在7种具有致癌效应的PAHs中,苯并(a)芘、二苯并(a,h)蒽和苯并(a)蒽是总致癌风险的主要贡献物质,贡献率分别占60.41%、26.84%和6.56%;3种暴露途径中,经口途径是造成致癌风险的主要途径,贡献率为73.22%;对于总致癌风险,人体暴露参数中每日土壤摄入量、暴露周期、暴露皮肤面积敏感度较大,分别为58.35%、50.21%和20.51%;体重具有负敏感性,敏感度为-11.66%.     

累积影响评价:中国内地与香港的问题与实践探讨

将过去、现在和将来的其它活动包括在建议活动的评价范围 ,是累积影响评价区别于传统EIA的重要特征和关键环节 ,也是EIA进一步发展完善的重要趋势 .从法规要求、文本格式、时空范围、重点内容、减缓措施等方面 ,分析了内地和香港在环评实践过程中对累积影响的考虑状况及存在问题 .建议制定累积影响评价的技术方法导则 ,以法规的形式明确提出在项目、区域及策略环评中增加累积影响评价的内容 ,累积影响应在环评报告的摘要、目录及结论中专门列出 ,对累积影响时空范围的界定及重要性的判断应专门提供支持依据 ,评价的重点应较多地关注生态完整性、社会经济影响及全球性环境影响等 ,以更有效地预防累积环境影响问题 .     

家用空调碳足迹及其关键影响因素分析

我国居民家庭空调拥有量迅速增加,其生命周期中产生的温室效应也日益受到关注.本文依据国际标准PAS 2050,采用RCEES 2012和Ecoinvent 2.1数据库,并运用SimaPro 7.1软件计算了中国典型家用空调的碳足迹.主要结论为:家用空调生命周期中使用阶段用电产生的碳足迹最大,占67%;制冷剂的泄漏是除电力使用外第二大碳足迹贡献因素,产生了23%的碳足迹;生产制造阶段和废物处理阶段的碳足迹分别占16%和-6%.敏感性分析表明,空调日使用时间、空调年使用季节和制冷剂的泄漏比例是家用空调碳足迹的关键影响因素.     

环境监测机构体制改革设想

指出了环境监测机构存在的问题，提出环境监测机构体制改革应从3方面考虑：机构改革应实行垂直管理，政，事分开，职能改革应体现环境监测站以环境质量监测为基本职能；内部管理改革需建立以聘任制为基础的用人制度，岗位管理和竞争上岗制度，适当的分配激励机制。     

爆炸超压场景下危化品社会风险对允许规划人口密度的影响分析*

为研究危化品重大危险源基于社会风险基准的规划管控影响,采用我国标准规定的定量风险评价方法,TNT当量炸药简化方法,针对最大TNT当量炸药、事故发生总累计频率、人口密度分布控制参数等不同工况条件,对比分析国土开发强度的允许人口密度受社会风险约束影响的变化规律。研究结果表明:人口密度指数分布控制参数Nk与Nb的允许取值随最大TNT当量炸药和事故发生总累计频率降低而提高;确定工况下,允许总人口规模受最大TNT当量炸药影响很小;在最大TNT当量炸药大于100 t或事故发生总累计频率小于1×10-8次/a时可按最大事故场景进行规划控制分析。     

A new pre-assessment model for failure-probability-based-planning by neural network

At present, the prediction of failure probability is based on the operation period for laid pipelines, and the method is complicated and time-consuming. If the failure probability can be predicted in the planning stage, the risk assessment system of gas pipeline will be greatly improved. In this paper, the pre-laying assessment model is established to minimize risk of leakage due to piping layout. Firstly, Fault Tree Analysis (FTA) modeling is carried out for urban natural gas pipeline network. According to expert evaluation, 84 failure factors, which can be determined in the planning stage, are selected as the input variables of the training network. Then the FTA model is used to calculate the theoretical failure probability value, and the failure probability prediction model is determined through repeated trial calculation based on BP (Back Propagation Neural Network) and RBF (Radial Basis Function), for obtaining the optimal network parameter combination. Finally, two prediction models are used to calculate the same example. By comparing our pre-assessment model with the theoretical prediction consequences of the fault tree, the results show that the error of RBF prediction model can be close to 3%, which proves the validity and correctness of the method.     

Dynamic risk assessment for underground gas storage facilities based on Bayesian network

Loss of the underground gas storage process can have significant effects, and risk analysis is critical for maintaining the integrity of the underground gas storage process and reducing potential accidents. This paper focuses on the dynamic risk assessment method for the underground gas storage process. First, the underground gas storage process data is combined to create a database, and the fault tree of the underground gas storage facility is built by identifying the risk factors of the underground gas storage facility and mapping them into a Bayesian network. To eliminate the subjectivity in the process of determining the failure probability level of basic events, fuzzy numbers are introduced to determine the prior probability of the Bayesian network. Then, causal and diagnostic reasoning is performed on the Bayesian network to determine the failure level of the underground gas storage facilities. Based on the rate of change of prior and posterior probabilities, sensitivity and impact analysis are combined to determine the significant risk factors and possible failure paths. In addition, the time factor is introduced to build a dynamic Bayesian network to perform dynamic assessment and analysis of underground gas storage facilities. Finally, the dynamic risk assessment method is applied to underground gas storage facilities in depleted oil and gas reservoirs. A dynamic risk evaluation model for underground gas storage facilities is built to simulate and validate the dynamic risk evaluation method based on the Bayesian network. The results show that the proposed method has practical value for improving underground gas storage process safety.     

Dynamic-risk-informed safety barrier management: An application to cost-effective barrier optimization based on data from multiple sources

An integrated approach for performance assessment and management of safety barriers in a systemic manner is needed concerning the prevention and mitigation of major accidents in chemical process industries. Particularly, the effects of safety barriers on system risk reduction should be assessed in a dynamic manner to support the decision-making on safety barrier establishments and improvements. A simulation approach, named Simulink-based Safety Barrier Modeling (SSBM), is proposed in this paper to conduct dynamic risk assessment of chemical facilities with the consideration of the degradation of safety barriers. The main functional features of the SSBM include i) the basic model structures of SSBM can be determined based on bow-tie diagrams, ii) multiple data (periodic proof test data, continuous condition-monitoring data, and accident precursor data) may be combined to update barrier failure probabilities and initiating event probabilities, iii) SSBM is able to handle uncertainty propagation in probabilistic risk assessment by using Monte Carlo simulations, and iv) cost-effectiveness analysis (CEA) and optimization algorithms are integrated to support the decision-making on safety barrier establishments and improvements. An illustrative case study is demonstrated to show the procedures of applying the SSBM on dynamic risk-informed safety barrier management and validate the feasibility of implementing the SSBM for cost-effective safety barrier optimization.     

Risk assessment of safety level in university laboratories using questionnaire and Bayesian network

Accidents in university laboratories not only create a great threat to students’ safety but bring significant negative social impact. This paper investigates the university laboratory safety in China using questionnaire and Bayesian network (BN) analysis. Sixteen influencing factors for building the Bayesian net were firstly identified. A questionnaire was distributed to graduate students at 60 universities in China to acquire the probability of safe/unsafe conditions for sixteen influencing factors, based on which the conditional probability of four key factors (human, equipment and material, environment, and management) was calculated using the fuzzy triangular theory and expert judgment. The determined conditional probability was used to develop a Bayesian network model for the risk analysis of university laboratory safety and identification of the main reasons behind the accidents. Questionnaire results showed that management problems are prominent due to insufficient safety education training and weak management level of management personnel. The calculated unsafe state probability was found to be 65.2%. In the BN analysis, the human factor was found to play the most important role, followed by equipment and material factor. Sensitive and inferential analysis showed that the most sensitive factors are personnel incorrect operation, illegal operation, and experiment equipment failure. Based on the analysis, countermeasures were proposed to improve the safe management and operation of university laboratories.