河南省人类活动净氮输入量与参数影响研究

为探究平原地区粮食主产区人类活动净氮输入量特征及其参数对估算结果的影响,以河南省为研究区,收集整理1990~2015年县级统计数据及NANI模型参数,对NANI的时空分布特征、变化趋势以及参数对估算结果的影响进行分析.结果表明：（1）时间尺度上看,1990~2015年河南省NANI呈升高趋势,1990、1995、2000、2005、2010、2015年NANI分别为14347、19146、21466、24251、23711、26156kg/（km²·a）,化肥施用为主要贡献因子,占比为63.56%,其次是食品/饲料净氮输入量,占比14.81%;空间尺度上看,河南省NANI较高的县市主要分布在中部和东部的平原地区,而西部山地丘陵地区县市NANI较低.（2）NANI模型输入组分中受参数影响最大的是食品/饲料净氮输入量,与选用适宜参数估算结果相比较,该项变化范围在-23.1%~71.3%,作物固氮量变化范围在-31.2%~41.2%,化肥氮输入量变化范围在-2.8%~4.5%.     

基于贝叶斯公式的不确定性水环境容量研究——以北运河为例

针对水环境的随机不确定性,构建了基于贝叶斯公式的不确定性水环境容量核算方法,并以北运河为实证案例对所建立的核算方法进行验证：利用贝叶斯公式,通过对水环境容量核算涉及的流量、流速、背景浓度、污染物综合衰减系数这4个参数和水环境容量本身5个随机变量的分布特征进行分析,最后得到水环境容量的概率分布.结果表明：北运河流域10个河段的水环境容量都服从瘦尾偏态分布,并且河段流量与长度对其分布影响最大;与传统设计条件下的确定性水环境容量相比较,不确定性水环境容量分布不仅能够体现前者的信息,并且利用各参数的随机性以及实际监测数据进一步对前者进行了“修正”,能够更为全面准确的描述水环境.     

紫外活化过硫酸盐降解二苯甲酮-4的动力学影响及降解机理与风险评价

活化PS（过硫酸盐）氧化工艺对于降解水中新兴微污染物具有潜在应用价值.为研究活化PS体系对BPs（二苯甲酮类）有机防晒剂的降解性能,以BP4（二苯甲酮-4）为研究对象,采用UV/PS（紫外活化过硫酸盐）工艺降解BP4,比较单一UV、单一PS和UV/PS 3种工艺对BP4的去除效果,考察各因素对UV/PS工艺去除BP4动力学的影响,同时探究BP4降解机理并进行风险评价.结果表明:BP4降解过程符合准一级反应动力学模型;最佳PS投加量为1.0 mmol/L,反应30 min后BP4去除率可达94%,增加PS投加量或降低初始c（BP4）均可促进BP4降解,无机阴离子（HCO₃-和Cl-）对BP4降解均有抑制作用,酸性条件有利于BP4降解;基于HPLC-MS/MS鉴定出8种中间产物,并提出降解路径,费氏弧菌毒性试验和ECOSAR v1.10软件预测表明,UV/PS工艺降解BP4过程中生成的中间产物比母物质毒性更高.研究显示,UV/PS工艺可有效去除BP4,但其中间产物可能会造成潜在的生态风险,后续需进一步深入研究.      

A method for simplifying the analysis of traffic accidents injury severity on two-lane highways using Bayesian networks

Introduction

This study describes a method for reducing the number of variables frequently considered in modeling the severity of traffic accidents. The method's efficiency is assessed by constructing Bayesian networks (BN).

Method

It is based on a two stage selection process. Several variable selection algorithms, commonly used in data mining, are applied in order to select subsets of variables. BNs are built using the selected subsets and their performance is compared with the original BN (with all the variables) using five indicators. The BNs that improve the indicators’ values are further analyzed for identifying the most significant variables (accident type, age, atmospheric factors, gender, lighting, number of injured, and occupant involved). A new BN is built using these variables, where the results of the indicators indicate, in most of the cases, a statistically significant improvement with respect to the original BN.

Conclusions

It is possible to reduce the number of variables used to model traffic accidents injury severity through BNs without reducing the performance of the model.

Impact on Industry

The study provides the safety analysts a methodology that could be used to minimize the number of variables used in order to determine efficiently the injury severity of traffic accidents without reducing the performance of the model.     

Bayesian approach to estimating margin of safety for total maximum daily load development

A Bayesian-updating approach is presented to the estimation of total uncertainty-based Margin of Safety (MOS) for Total Maximum Daily Load (TMDL) calculations. Probability distributions are presented to construct the likelihood function, the prior probability distribution, and the posterior (total uncertainty) probability distribution. The Bayesian-updating approach is demonstrated through a case study for the Lower Amite River, Louisiana. The posterior probability distribution-based on the Bayesian approach updates the standard deviation of summer dissolved oxygen in the Amite River from 1.88 mg/L to 2.10 mg/L when the total uncertainty is considered. Results from the Bayesian-updating approach are compared with two conventional methods. The dissolved oxygen reserve based on a conventional margin of safety of 20% is estimated to be 45,682.26 kg/Day. The second conventional method, where we consider the standard deviation of 1.88 mg/L, produces a dissolved oxygen reserve of 40,516.09 kg/Day. The Bayesian approach yields the dissolved oxygen reserve of 38,614.43 kg/Day with the first level (μ-σ) of MOS, producing a deficit of 5606.65 kg/Day in dissolved oxygen. The dissolved oxygen reserve deficit increases to 23,895.13 kg/Day when the second level (μ-2σ) of MOS is used, which escalated to 42,383.52 kg/Day when the highest level (μ-3σ) of MOS is used. While the total uncertainty-based Bayesian approach is demonstrated for a TMDL development on the Amite River, the overall approach could be applied in any river system with similar available data.     

亿元GDP死亡率与区域发展水平的相关性分析

为探讨亿元GDP死亡率与区域发展水平之间的关系,选择能够反映区域整体发展水平,又与安全生产密切相关的影响因素,通过统计年鉴获取大量数据的基础上,从时间和空间2个维度,采用SPSS数据统计软件对亿元GDP死亡率和13个定量影响因素、5个定性影响因素之间的Spearman相关系数进行计算,排除区域发展水平的极度差异,在总体趋势上,亿元GDP死亡率与各相关因素的相关性在时间和空间2个维度上保持较好的一致性。计算结果表明,各地区可从提高经济发展水平、优化产业结构、大力发展科学教育事业、提高医疗卫生水平和健全法制体系等方面降低亿元GDP死亡率,改善当地的安全生产水平,该结论能为各区域控制亿元GDP死亡率,对安全生产进行宏观调控提供科学依据。     

基于PSR与贝叶斯网络的非常规突发事件情景分析

基于情景分析在非常规突发事件应急决策中的重要性,为了提高情景分析的有效性、全面性,在非常规突发事件"情景"界定和演变规律分析的基础上,基于PSR模型构建非常规突发事件的"压力-状态-响应"网络表达方式;利用贝叶斯网络理论,构建非常规突发事件的情景演变分析模型;以简化后的大连输油管道爆炸事件为例,示范基于PSR模型与贝叶斯网络的非常规突发事件情景分析方法的具体流程,并对推理结果进行分析。分析结果与现实情况基本一致,证明方法的可行性和有效性。实际应用中可针对具体的事件进行"多响应"、"多路径"的推理分析,以全面、系统地分析事件情景的演变情况。     

基于贝叶斯网络的尾流事故机理分析

针对事故树分析法的局限性,在尾流事故树的基础上,建立贝叶斯网络(BN)。运用推理运算对BN进行定量分析,得出:空中交通密度太大、空中交通管制(ATC)间隔判断错误和短期冲突告警(STCA)被忽略是事故的关键致因。将针对致因提出的改进措施引入到BN中,评价相关措施的有效性。应用BN进行尾流事故的机理分析,能够以比逻辑门更好的形式表达变量间的不确定性关系,从而更加方便地找到导致事故发生的关键因素。     

Value of information analysis for adequate monitoring of carbon dioxide storage in geological reservoirs under uncertainty

As monitoring is essential for the proper management of geological storage of carbon dioxide (CO₂), the ability to value information from monitoring is indispensable to adequately design a monitoring program. It is necessary to judge whether the expected improvement in management is worth the cost of monitoring. The value of information (VOI) is closely related to the possible increase in expected utility gained by gathering the information, the concept of which can be applied to such judgement. Although VOI analysis has been extensively studied in the context of decision analysis, its application to the management of carbon dioxide capture and storage (CCS) operations is rare. This paper introduces and discusses the methodology of VOI analyses in the context of monitoring CO₂ storage. A motivating problem with discrete probabilities is used to illustrate the concept of VOI. It is demonstrated that information is not always of value; for information to be worthwhile, monitoring under uncertainty must satisfy certain conditions. This concept is then extended to continuous probability distributions. The effects of prior uncertainty and information reliability on the VOI are examined. It is shown that an excessive improvement in information accuracy yields little value and that the optimal level of reliability can be inferred. VOI analyses provide quantitative insights into the value of information-gathering activities and therefore can be an objective means to adequately design and impartially justify a monitoring program.     

Skew and attribute non-attendance within the Bayesian mixed logit model

We investigate non-attendance to choice set attributes and the accommodation of preference heterogeneity within the mixed logit model. We propose a generalisation of the mixed logit enabling the degree of skew of marginal utility distributions to be estimated. The implementation is Bayesian with the marginal likelihood used as an arbiter of model performance. We find strong evidence of skew in the distributions of marginal utilities for most attributes. Models incorporating skew are preferred in all cases. The irrelevance of an attribute to significant numbers of respondents is a possible cause of such skew. We test alternative empirical accommodations of self-reported attribute non-attendance (ANA) and continue to find strong evidence of skew in the distributions of marginal utilities even having accounted for ANA. We find that, contrary to some recent findings, respondents who report having ignored an attribute typically do indeed have a zero marginal utility for that attribute.