基于APJE-SLIM的海运人因失误概率的确定

安全是海运行业永恒的主题,调查研究表明,人因失误是造成海事的主要原因。为了对海运人因失误进行研究,探讨引起人因失误的行为形成因子(PSF),确定在PSF影响下的人因失误概率。在调查海上避让行为的人因失误和这些失误的行为形成因子的基础上,采用APJE和SLIM相结合的方法对航海人员避让行为中的可靠性进行分析。结果表明,航海人员疲劳与健康程度、知识、经验与培训水平、任务复杂程度、安全管理水平与组织有效性等PSF对人因失误概率有着不同程度的影响,相应提高PSF水平,可极大地减少人因失误概率。利用APJE确定端点绝对失误概率,解决了SLIM方法中难以获得参考点概率的问题,获得了在不同种类不同水平PSF影响下的海运人因失误概率。     

化工园区泄漏事故安全风险评估模式研究

为减少化工事故频发造成严重人员伤亡及财产损失,在分析危化品泄漏事故类型的基础上,从事故发生概率和事故后果两方面提出危化品泄漏事故风险评估模型.以中毒事故为例,对重庆长寿化工园区内某企业一液氨储罐进行风险评估.基于概率模型计算中毒事故概率,结果表明该储罐发生泄漏引起中毒的概率较小;数值模拟结果显示:影响范围随时间的增加而...     

基于TVR的腐蚀油气管道失效概率及安全寿命研究

为避免因腐蚀导致油气管道失效,针对因管道特性和腐蚀尺寸的不确定性使得管道剩余强度成为概率模型的特点,建立了腐蚀管道强度损失随机模型;借助可靠性理论,通过分析管道腐蚀进程的时变性特点,将管道系统由损伤积累和抗力衰减导致的剩余强度随机化;提出基于穿越率的腐蚀油气管道失效评定及安全寿命预测方法。研究结果表明:腐蚀速率和运行压力对管道失效概率及安全寿命影响显著,管道尺寸影响适中,而相关系数和拉伸强度影响较小;若腐蚀速率Va=0.2 mm/a,VL=10 mm/a或局部腐蚀缺陷半径达到管道壁厚的0.5倍时,建议作为重点风险段监测并检修。所建方法是对腐蚀油气管道运营监控和风险评估的有益补充。     

不同模糊评价方法在水环境质量评价中的应用比较

近年来,运用模糊评价方法对水质和水体营养化程度进行评价逐渐受到重视。目前,对模糊评价方法的研究多集中在实测数据标准化、权重的确定和模糊模式识别模型研究几个方面。以滇池历年营养物质数据为实测数据,通过对几种实测数据标准化方法、权重确定方法和模糊模式识别方法的组合应用,获得水环境质量模糊评价最优组合方法。结果表明,在评价方法中,实测数据标准化方法采用线性内插法,权重的确定采用超标倍数法,模糊模式识别模型采用广义海明距离和加权海明距离法,即采用超标倍数法的广义海明距离和加权海明距离法能够更真实反映水环境质量的实际情况。     

Estimating Trophic State Proportions of a Regional Lake Population: Are Larger Samples Always Better?

During the summers of 1991–1994, the Environmental Monitoringand Assessment Program (EMAP) sampled 344 lakes throughout thenortheastern United States using a proportional stratified sampling design based on lake size. Approximately one-quarter ofthe 344 lakes were sampled each year (4 years) for totalphosphorus to determine the proportion (and associated95% confidence intervals) of the northeast lake population 1ha (11,076 ± 1,699 lakes) that was in oligotrophic,mesotrophic, eutrophic, or heupereutropic (4 classes) conditionaccording to the total phosphorus criteria of the North AmericaLake Manegement Society. Estimates for the second, third, andfourth yr were developed as cumulative of the previous yrsamples and the current yr samples for the northeast as a wholeand for each of its three ecoregions (4 regions). New confidence intervals were computed for each cumulative yrcondition estimate. This produced a total (4 years × 4classes × 4 regions) of 64 cumulative yr tropic conditionestimates. Confidence intervals for 21% of these estimates didnot shorten with increased sample size. This phenomena raisedquestions about the accuracy of estimates based on cumulativesampling procedures. We explain why and how the phenomenon comesabout with both straight random and proportional randomsampling. Further, we present an example of the effects thisphenomenon has on lake tropic state condition estimates in thenortheastern United States.     

天然气管道失效个人生命风险评价技术研究

为研究天然气长输管道失效个人生命风险,提出一种以人员伤亡概率为指标的天然气管道失效后果风险评价方法。基于天然气管道的失效概率和失效致死长度参数,建立天然气长输管道生命风险评价模型。用该模型,对国内某城市住宅小区内带腐蚀缺陷的天然气管线进行定量风险分析。借鉴英国天然气输送公司数据,确定天然气管线个人生命风险值。案例证明,用所建立的天然气管道失效个人生命风险评价模型能够有效地分析带缺陷天然气管道失效后果,实现天然气管道的个人安全生命风险全定量评价。     

川东北低山丘陵区细颗粒物主要相关因素和季节变化分析——以南充主城区为例

基于中国空气质量在线监测分析平台和全球天气精准预报网的大气质量和气象数据,以四川盆地东北低山丘陵区典型城市南充市主城区为例,检验了细颗粒物(PM_(2.5))浓度的概率密度分布,发现其接近对数正态分布,由相关分析确定了PM_(2.5)浓度的主要相关因素为CO、NO_2(相关系数r分别为0.76、0.55,P0.01),再通过对2014年1月—2016年6月的日数据的逐步回归筛选出最优的回归指标和模拟方程(决定系数R_(adj)~2为0.68,P0.05),2016年7月—2017年6月的数据验证表明模拟效果较好(拟合优度为0.64,相对误差15.48%);最后根据时序插值、浓度和IAQI(PM_(2.5))的时段均值发现PM_(2.5)浓度在年际上有降低趋势;在季节上由高到低依次为冬季、春季、秋季、夏季;PM_(2.5)浓度在1月和6月分别呈现出年内的峰值和谷值,5、10月出现了阶段性峰值,尤其是5月;IAQI(PM_(2.5))的季节变化与浓度变化规律相似;且PM_(2.5)与PM_(10)比值的均值为0.67,表明现阶段南充市主城区大气污染物中细颗粒物占有较大比重。     

Probability distributions of arsenic in soil from brownfield sites in Beijing (China): statistical characterization of the background populations and implications for site assessment studies

A probabilistic analysis was performed on soil arsenic concentration data from 4 brownfield sites at Beijing (Chaoyang and Haidian Districts), involved in environmental assessment studies. The available data sets were processed to provide a statistical characterization of the background populations and differentiate “anomalous data” from the natural range of variation of arsenic concentrations in soil. The site-specific background distributions and the existing wide-scale background values defined for the Beijing area were compared, discussing related implications for the definition of metal contamination soil screening levels (SSLs) in site assessment studies. The statistical analysis of As data sets discriminated site-specific background populations, encompassing 88% to 94% of the sample data, from outliers values, associated with either subsoil natural enrichments or possible anthropogenic releases. Upper Baseline Concentration (UBC) limits (+ 2σ level), including most of the site-specific metal background variability, were derived based on the statistical characterization of the background populations. Sites in the Chaoyang South District area had UBC values in the range 10.4–12.6 mg·kg^-1. These ranges provide meaningful SSL values to be adopted for As in local site assessment studies. Using the wide-scale background value for the Beijing area would have erroneously classified most of the areas in the subject sites as potentially contaminated.     

Optimization of capture–recapture monitoring of elusive species illustrated with a threatened grasshopper

Information on population sizes and trends of threatened species is essential for their conservation, but obtaining reliable estimates can be challenging. We devised a method to improve the precision of estimates of population size obtained from capture–recapture studies for species with low capture and recapture probabilities and short seasonal activity, illustrated with population data of an elusive grasshopper (Prionotropis rhodanica). We used data from 5 capture–recapture studies to identify methodological and environmental factors affecting capture and recapture probabilities and estimates of population size. In a simulation, we used the population size and capture and recapture probability estimates obtained from the field studies to identify the minimum number of sampling occasions needed to obtain unbiased and robust estimates of population size. Based on these results we optimized the capture–recapture design, implemented it in 2 additional studies, and compared their precision with those of the nonoptimized studies. Additionally, we simulated scenarios based on thresholds of population size in criteria C and D of the International Union for Conservation of Nature (IUCN) Red List to investigate whether estimates of population size for elusive species can reliably inform red-list assessments. Identifying parameters that affect capture and recapture probabilities (for the grasshopper time since emergence of first adults) and optimizing field protocols based on this information reduced study effort (−6% to −27% sampling occasions) and provided more precise estimates of population size (reduced coefficient of variation) compared with nonoptimized studies. Estimates of population size from the scenarios based on the IUCN thresholds were mostly unbiased and robust (only the combination of very small populations and little study effort produced unreliable estimates), suggesting capture–recapture can be considered reliable for informing red-list assessments. Although capture–recapture remains difficult and costly for elusive species, our optimization procedure can help determine efficient protocols to increase data quality and minimize monitoring effort.     

Value of information in product recovery decisions: a Bayesian approach

There is a widespread recognition of the need for better information sharing and provision to improve the viability of end-of-life (EOL) product recovery operations. The emergence of automated data capture and sharing technologies such as RFID, sensors and networked databases has enhanced the ability to make product information; available to recoverers, which will help them make better decisions regarding the choice of recovery option for EOL products. However, these technologies come with a cost attached to it, and hence the question ‘what is its value?’ is critical. This paper presents a probabilistic approach to model product recovery decisions and extends the concept of Bayes' factor for quantifying the impact of product information on the effectiveness of these decisions. Further, we provide a quantitative examination of the factors that influence the value of product information, this value depends on three factors: (i) penalties for Type I and Type II errors of judgement regarding product quality; (ii) prevalent uncertainty regarding product quality and (iii) the strength of the information to support/contradict the belief. Furthermore, we show that information is not valuable under all circumstances and derive conditions for achieving a positive value of information.