A novel Dempster-Shafer theory-based approach with weighted average for failure mode and effects analysis under uncertainty

Risk priority number (RPN) is a commonly-used prioritization method in failure mode and effects analysis (FMEA) for systemic reliability and safety study. However, conventional RPN confronts wide criticisms, due to the neglect of the uncertainties of experts' opinions. It is important to handle the conflicts among experts' multi-opinions which could be described in imprecise, incomplete or crisp forms in lack of knowledge. In this paper, a novel method is proposed to alleviate and/or eliminate counter-intuitive behaviors against conflicts among multi-opinions under Dempster-Shafer theory. Firstly, the abnormity test for experts’ opinions is undertaken to identify and discard those distinct judgments. Then, an approach to determining the subjective weights of experts is proposed, which is used in weighted average for multi-opinions before calculating RPNs. Finally, the risk priority evaluation on main engine crankcase explosion failure on-board ship is implemented to verify the feasibility of the proposed approach.     

How to select the best tree planting locations to enhance air pollution removal in the MillionTreesNYC initiative

Highest priority zones for tree planting within New York City were selected by using a planting priority index developed combining three main indicators: pollution concentration, population density and low canopy cover. This new tree population was projected through time to estimate potential air quality and carbon benefits. Those trees will likely remove more than 10 000 tons of air pollutants and a maximum of 1500 tons of carbon over the next 100 years given a 4% annual mortality rate. Cumulative carbon storage will be reduced through time as carbon loss through tree mortality outweighs carbon accumulation through tree growth. Model projections are strongly affected by mortality rate whose uncertainties limit estimations accuracy. Increasing mortality rate from 4 to 8% per year produce a significant decrease in the total pollution removal over a 100 year period from 11 000 tons to 3000 tons.     

Habitat evaluation of wild Amur tiger (Panthera tigris altaica) and conservation priority setting in north-eastern China

The Amur Tiger (Panthera tigris altaica) is one of the world’s most endangered species. Recently, habitat fragmentation, food scarcity and human hunting have drastically reduced the population size and distribution areas of Amur tigers in the wild, leaving them on the verge of extinction. Presently, they are only found in the north-eastern part of China. In this study, we developed a reference framework using methods and technologies of analytic hierarchy process (AHP), remote sensing (RS), geographic information system (GIS), GAP analysis and Natural Break (Jenks) classification to evaluate the habitat and to set the conservation priorities for Amur tigers in eastern areas of Heilongjiang and Jilin Provinces of northeast China. We proposed a Habitat Suitability Index (HSI) incorporating 7 factors covering natural conditions and human disturbance. Based on the HSI values, the suitability was classified into five levels from the most to not suitable. Finally, according to results of GAP analysis, we identified six conservation priorities and designed a conservation landscape incorporating four new nature reserves, enlarging two existing ones, and creating four linkages for Amur tigers in northeast China. The case study showed that the core habitats (the most suitable and highly suitable habitats) identified for Amur tigers covered 35,547 km², accounting for approximately 26.71% of the total study area (1,33,093 km²). However, existing nature reserves protected only (7124 km² or) 20.04% of the identified core habitats. Thus, enlargement of current reserves is necessary and urgent for the tiger’s conservation and restoration. Moreover, the establishment of wildlife corridors linking core habitats will provide an efficient reserve network for tiger conservation to maintain the evolutionary potential of Amur tigers facing environmental changes.     

首都水源地——洋河流域人为源多环芳烃(PAHs)排放清单估算及其影响分析

洋河流域位于京西北上风向,是北京重要水源地和生态保护屏障,我国北方典型的农牧交错带和生态脆弱敏感区,也是北京-张家口2022年冬奥会的申办地,具有极重要的战略地位.建立洋河流域大气PAHs排放清单,分析其可能来源及影响,通过气团后向轨迹辨识其区域PAHs传输途径,对于北京及张家口地区环境PAHs的污染控制具有重要的指导意义.本研究收集和分析了洋河流域大量工、农业生产和居民生活等相关数据资料,评估了流域各行业、各县市PAHs排放因子,单体排放量及其影响因素,通过气团后向轨迹模型分析了PAHs的传输轨迹.结果表明,洋河流域大气PAHs排放量为4.4×102t.从排放行业看,煤炭燃烧源、秸秆燃烧源是洋河流域大气PAHs的重要排放源,其贡献率分别为76%和16%.从排放地区看,宣化县的排放量最大,约49 t;其次分别为:兴和县、天镇县、怀来县、万全县,排放量分别约为:36、32、24和15 t.从排放谱看,低环(2~3环)与高环分子(4~6环)PAHs的排放量相差不大,分别约占PAHs排放总量的50%左右.洋河流域单位PAHs总排放量与各县市的工业生产总值(r=0.96,P<0.05)、居民收入(r=0.94,P<0.05)、人口密度呈正相关(r=0.92,P<0.05),与单位国土面积呈负相关(r=-0.9,P<0.05),与农业生产总值没有显著相关(r=0.026,P>0.01).流域内PAHs的较高排放与该地以煤炭为主的能源结构和较高的居民消费水平有关.基于气团后向轨迹模拟和洋河流域PAHs排放,可以推断洋河流域已成为PAHs的高污染风险区,通过西北气流可将流域高浓度的PAHs输送至北京,对北京的生态环境和人体健康造成潜在风险.     

淮安某县农村饮用水源中优控污染物的筛选研究

在对淮安某县农村饮用水源中的污染物检测分析的基础上,采用潜在危害指数法与综合评分法对水源中检出的包括重金属在内的59种污染物进行优先排序.研究结果显示,潜在危害指数法与综合评分法各有利弊,可根据实际情况选择合适的筛选方法.综合两种方法的筛选结果,淮安某县农村饮用水源中优控污染物为Cd、Hg、Pb、Ni、Cr、As、Cu、苯并(a)芘、五氯联苯、邻苯二甲酸二丁酯、邻苯二甲酸二(2-乙基己基)酯、四氯化碳,研究结果为该地区农村饮用水源的管理提供了指导方向.     

水源地土壤环境保护优先区划分方法与实例研究

本研究综合考虑水源地沿岸土壤环境质量、水体污染现状、环境脆弱性和污染源压力这4个方面,探索建立了河流型水源地土壤环境保护优先区划分的技术方法,并以广东省佛山市顺德水道作为实证案例,结合当地自然-社会-经济条件,选取土壤污染程度、沉积物污染程度、土壤特性、地下水敏感性、植被覆盖度、污染源类型、污染源分布作为评价因子,构建了水源地土壤环境保护优先区分等定级评价指标体系.将实地采样分析与遥感影像解译相结合获取数据,采用层次分析法(AHP)确定指标权重,通过加权求和与图层叠加完成空间分析与制图,编制了顺德水道土壤环境保护优先区分级图.其中,污染区、风险区、安全区的面积分别占区域总面积的6.37%、60.90%、32.73%,且污染区和风险区集中在河道两岸的乐从镇、龙江镇和勒流街道,分布于该区域的家具制造、塑料建材、纺织服装等产业的长期发展,以及水产养殖等农业活动应是主要的污染来源,并据此提出了有针对性的差异性管控措施.本研究对水源地土壤环境保护和可持续开发利用具有重要指导意义.     

基于污染评价的地下水中优控污染物筛选

优控污染物的筛选是环境管理和环境质量保护的有效技术手段,快速高效地筛选识别出地下水中需要优先控制的污染物,对地下水污染防控具有重要的指导意义.为了探究一套适用于基础资料薄弱地区的优控污染物筛选体系,本文在地下水检测分析数据的基础上,运用指标分类污染评价方法,结合污染程度频率评分叠加,建立了基于污染评价的优控污染物筛选体系.将该体系应用于格尔木地区地下水中优控污染物的筛选,运用基于风险评价的优控污染物筛选体系对结果进行验证,并探讨两种体系的差异性.结果表明:格尔木地区地下水中优控污染物为TDS、总硬度、氯离子、硫酸根、钠、氟、总铁、芘、菲、铝共3类10种,与基于风险评价的地下水中优控污染物筛选结果基本一致,筛选结果具有一定的可靠性.基于风险评价的体系过程复杂,对基础资料要求较多,但具有一定的前瞻性,适用于基础资料丰富的地区;基于污染评价的体系过程简便,对基础资料要求少,但易受背景值计算的影响,更适用于污染源基础资料薄弱的地区地下水中优控污染物的筛选.     

气溶胶与降尘中多环芳烃的含量分布研究

通过广东省茂名市区四个不同功能点大气气溶胶和降尘中多环芳烃的含量分布研究发现 :1、气溶胶中优控多环芳烃大大高于降尘中的含量 ,为降尘的 5.97～ 1 9.3倍 ;以石化厂区为例 ,非优控多环芳烃在气溶胶中的相对含量更高 ,为降尘的 2 4 .7倍。2、气溶胶中优控多环芳烃和非优控多环芳烃的分布为随分子量增加而含量增高的趋势 ,但降尘中优控多环芳烃的高含量相对集中于萤蒽至苯并 (b)萤蒽之间。3、不同功能区由于排放源的差别所表现出的气溶胶和降尘中优控多环芳烃总量及总量比值、部分强致癌和致癌物含量及含量比值均存在差异。4、对气溶胶和降尘中多环芳烃研究可以对降尘中非优控多环芳烃降解和溶解量进行估算。以石化厂区为例 ,降尘中非优控多环芳烃比原始含量已减少76%。     

计算机辅助故障模式影响分析与事件序列图综合分析方法研究

故障模式影响分析(Failure Mode Effect Analysis, FMEA)和事件序列图(Event Sequence Diagram, ESD)是分析系统故障因果关系的两种常用技术,在分别独立应用时,既有各自的优点,也存在着一定的缺陷和不足,主要体现为: FMEA不能展示故障模式到故障后果的推演过程; ESD模型的合理性很大程度上取决于初始事件的确定,而该方法本身不能实现这一功能.本文综合FMEA与ESD的优点,阐述了复杂动态系统FMEA与ESD综合分析的思路,对现有FMEA进行了一定的改进,并进一步分析计算机辅助FMEA和ESD综合方法建模的原理和实现技术.     

“There is nothing political in it”: triggers of local political leaders' engagement in climate adaptation

Local government is attributed a vital role in climate-change adaptation. Previous studies contend that conflicting priorities, insufficient institutional incentives and knowledge of risks, and inadequate resources all impede local climate adaptation. Though the importance of local political support in enabling climate adaptation is widely acknowledged, the views of local politicians have rarely been analysed. Drawing on semi-structured interviews with local politicians in Sweden, we explore what affects their engagement in climate adaptation. The study claims that climate adaptation contrary to mitigation is not viewed as political beyond directing attention and sanctioning guidelines set by officials. A limited number of interviewees claim a more strategic political role in adaptation. The combined effect of institutional incentives (e.g. fragmented national guidelines, unappealing goals, and lack of funding), relative weight in local politics, and ability to exercise political leadership (e.g. campaign value, public and media pressure, and lack of ideology) is perceived as too insignificant to trigger strong political engagement. In less-populous municipalities, adaptive measures were highly valued for demonstrating political action.