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.     

车载巡测在区域射频电磁环境质量监测与表征中的应用分析

对北京中央电视塔周围25 km²区域电磁环境质量分别进行了射频的网格法手工监测和车载巡测自动监测，通过SPSS软件等对两种监测方法获取的数据进行了统计对比分析，发现两组数据在总体水平及数值分布特征上较为接近，因此车载巡测监测可以替代网格法监测。以车载巡测数据为基础，绘制了实测数据的道路电磁地图，利用插值法绘制了区域电磁地图，对区域电磁环境质量进行了直观表征。从回应公众关注和城市电磁规划出发，建议今后可利用车载巡测监测加强时域和频域的监测。     

宁夏总量减排与环境质量改善的相关性分析

利用SPSS统计软件分析宁夏“十一五”期间主要污染物总量控制指标与环境质量数据的关联程度,并建立回归模型.结果表明:宁夏总量减排与环境质量改善存在高度关联,拟合曲线均为一元线性回归曲线.随着SO2排放总量降低,重点城市空气质量稳步改善,两者呈现同向变化的趋势;随着COD排放总量降低,黄河宁夏段水环境质量稳步改善,两者亦呈现同向变化的趋势.宁夏总量减排在环境质量改善上成效已显现,污染防治政策较为成功,环保成绩显著.     

藻华聚集的环境效应:对漂浮植物水葫芦光合作用的影响

利用适应性强、生物量大的水生植物来净化污染水体,已成为目前水体生态修复的一种快捷有效的方法.然而,在夏季蓝藻水华严重聚集的水体,藻华聚集后对水生植物的生理生态影响及其环境效应,尚缺乏系统研究.本研究以水葫芦为代表,模拟在高温阶段(水温WT25℃)、水华严重聚集时,对水葫芦的光合作用的影响进行研究,以揭示蓝藻水华聚集后造成的浅水生态系统中水生植物消亡的深层机制,并为减轻藻华聚集对水生植物的不良影响、充分发挥水生植物的水体净化功能提供理论依据.结果表明,藻华聚集会很快消耗掉水生植物根区内的溶氧,呈现缺氧状态(DO0.2 mg·L-1);植物根区内ORP出现明显下降现象,实验进行1 d后低于-100 m V,实验结束时达-200 m V,水体呈现强还原环境.与对照相比,根区内p H值低0.7个单位.藻华细胞在死亡、分解后释放大量的无机营养盐于水体中,植物根区内的NH+4-N含量比对照实验中高102倍;较高的NH+4-N含量(平均为45.6 mg·L-1)加之缺氧的协迫作用,导致植物机体受到破坏,植物的光合作用能力严重下降.叶片的平均净光合速率仅为对照的0.6倍,实验结束时其光合速率Pn为3.96μmol·(m2·s)-1,而同期对照实验的叶片净光合速率Pn为22.0μmol·(m2·s)-1;叶片蒸腾速率仅为对照的0.55倍,至实验结束时其蒸腾速率为1.38 mmol·(m2·s)-1,同期对照实验的叶片蒸腾速率为7.61 mmol·(m2·s)-1,表明藻华长期的聚集对植物产生了不可逆的伤害作用.在实际生产中,要避免蓝藻的严重堆积和快速消亡,以减轻藻华暴发对植物的伤害,充分发挥植物的水体净化功能.     

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.     

An integrated EDIB model for probabilistic risk analysis of natural gas pipeline leakage accidents

Natural gas pipeline construction is developing rapidly worldwide to meet the needs of international and domestic energy transportation. Meanwhile, leakage accidents occur to natural gas pipelines frequently due to mechanical failure, personal operation errors, etc., and induce huge economic property loss, environmental damages, and even casualties. However, few models have been developed to describe the evolution process of natural gas pipeline leakage accidents (NGPLA) and assess their corresponding consequences and influencing factors quantitatively. Therefore, this study aims to propose a comprehensive risk analysis model, named EDIB (ET-DEMATEL-ISM-BN) model, which can be employed to analyze the accident evolution process of NGPLA and conduct probabilistic risk assessments of NGPLA with the consideration of multiple influencing factors. In the proposed integrated model, event tree analysis (ET) is employed to analyze the evolution process of NGPLA before the influencing factors of accident evolution can be identified with the help of accident reports. Then, the combination of DEMATEL (Decision-making Trial and Evaluation Laboratory) and ISM (Interpretative Structural Modeling) is used to determine the relationship among accident evolution events of NGPLA and obtain a hierarchical network, which can be employed to support the construction of a Bayesian network (BN) model. The prior conditional probabilities of the BN model were determined based on the data analysis of 773 accident reports or expert judgment with the help of the Dempster-Shafer evidence theory. Finally, the developed BN model was used to conduct accident evolution scenario analysis and influencing factor sensitivity analysis with respect to secondary accidents (fire, vapor cloud explosion, and asphyxia or poisoning). The results show that ignition is the most critical influencing factor leading to secondary accidents. The occurrence time and occurrence location of NGPLA mainly affect the efficiency of emergency response and further influence the accident consequence. Meanwhile, the weight ranking of economic loss, environmental influence, and casualties on social influence is determined with respect to NGPLAs.