Epidemiology of alcohol and drug screening among pedestrian fatalities in the United States, 2014–2016

Objective: U.S. pedestrian fatalities increased by 25% between 2010 and 2015. Risk factors include distractions, the built environment, urbanization, economic variables, and weather conditions. Of interest is the role of alcohol and drugs in premature death among pedestrians. This study sought to explore the prevalence of substance use screenings among pedestrian fatalities in the United States between 2014 and 2016.

Methods: Data were collected from the Fatality Analysis Reporting System provided by the NHTSA. Pedestrian crash variables included demographics as well as information regarding alcohol or drug testing status. Frequency and cross-tabulation tables were constructed to assess the prevalence of screening by person, place, and time. Log-linear analyses were completed to explore age, race, and sex differences. A 3-year examination period was used to control for yearly fluctuations and to incorporate an increasing trend in cases.

Results: Pedestrian fatalities accounted for 84% of all deaths among vulnerable road users during the examination period. Those most at risk were white males between the ages of 45 and 64. Over all states, 74.7% of fatalities were tested for alcohol and 67.1% were tested for drugs; further, 66.5% of cases were tested for both alcohol and drugs and 24.8% were tested for neither substance. Cases screened for both alcohol and drugs ranged from 2.9% in North Carolina to 95.7% in Nevada and those testing for neither substance ranged from a high of 68.9% in Indiana to a low of 1.1% in Maryland. Log-linear regression revealed significant differences in alcohol screening by age and race but not by sex. Differences in drug screening were not identified for any demographic variable. Fatalities tested for alcohol were significantly more likely to be tested for drugs; only 8.2% were screened solely for alcohol and 0.05% were screened for drugs alone.

Conclusions: Preventive strategies become more important as pedestrian crashes and fatalities increase. Risk reduction in the form of policy change, alterations to the built environment, or interdisciplinary approaches to injury prevention is dependent upon best evidence supported in part by more deliberate and consistent screening.     

2016—2020年全国化工和危险化学品事故分析研究*

为分析“十三五”期间（2016—2020年）我国化工和危险化学品事故发生情况，统计“十三五”期间化工和危险化学品事故数量及死亡人数，从发生月份、发生区域、行业分布等方面分析事故特征，从事故类别、事故环节、关键作业等方面分析事故原因，总结2011—2020年我国化工和危险化学品事故发展趋势，按照全国4大经济区域研究2013—2020年事故特征。结果表明:“十三五”期间我国化工和危险化学品事故数量呈下降趋势，死亡人数出现波动，总体形势较为平稳。2011—2020年化工和危险化学品事故风险向中西部转移趋势明显，东北、西部地区事故死亡率普遍高于东部地区。研究结果可为相关行业从业者及监管部门提供决策参考。     

A proactive process risk assessment approach based on job hazard analysis and resilient engineering

Complex systems often experience a long period of incubation before accidents occur. Therefore, a proactive risk assessment is essential for process safety. The conventional job hazard analysis (JHA) method has been an effective tool to conduct a process risk assessment in the high-risk industrial field. However, the conventional JHA is inadequate for the proactive risk assessment since it is usually conducted during and before one specific operation process. Operations such as startup and maintenance are performed repeatedly on the lifecycle of a plant. Therefore, the risk reduction measures for the industrial process should include not only preventive actions obtained from the conventional JHA but also recovery ones. Resilience engineering (RE) has proven to be helpful for the recovery analysis of a complex system. The objective of this paper is to propose a proactive and comprehensive process risk assessment approach based on JHA and RE. The mechanism of applying RE to address operation process risk is illustrated. The integrated approach can provide guidelines to establish proactive risk reduction measures as well as maintain a low-risk level. Finally, a gas transmission startup process risk assessment case is presented to demonstrate its applicability.     

Factors associated with driving under the influence of alcohol

Objective: This study examined the risk factors of driving under the influence of alcohol (DUI) among drivers of specific vehicle categories (DSC). On the basis of this research, the variables related to DUI and involvement in traffic crashes were defined. The analysis was conducted for car drivers, bicyclists, motorcyclists, bus drivers, and truck drivers.

Method: The research sample included drivers involved in traffic crashes on the territory of Serbia in 2016 (60,666). Two types of analyses were conducted in this study. Logistic regression established the correlation between DUI and DSC and the The Technique for Order of Preference by Similarity to Ideal Solution (Multi-criteria decision making) method was applied to consider the scoring and explore the potential for the prevalence of DUI on the basis of 2 data sets (DUI and non DUI).

Results: The study results showed that driver error and male drivers were the 2 most significant risk factors for DUI, with the highest scores and potential for prevalence. The nonuse of restraint systems, driver experience, and driver age are the factors with a significant prediction of involvement in an accident and an insignificant prediction of DUI.

Conclusions: Following the development of the logistic prediction models for DUI drivers, testing of the model was conducted for 3 control driver groups: Car, motorcycle, and bicycle. The prediction model with a probability greater than 50% showed that 77% of car drivers were under the influence of alcohol. Similarly, the prediction percentage for motorcyclists and bicyclists amounted to 71 and 67%, respectively. The recommendation of the study is that drivers whose DUI probability is above 50% should be potentially suspected of DUI. The results of this study can help to understand the problem of DUI among specific driver categories and detect DUI drivers, with the aim of creating successful traffic safety policy.     

官方媒体参与下不同风险感知群体竞争模型及其演化规律

为识别公众风险感知的演化规律和官方媒体参与的作用机制，首先，根据公众的行为表现划分不同风险感知群体，并分析群体间的相互竞争和官方媒体的扰动作用；然后，通过扩展Lotka Volterra模型建立群体间的相互竞争模型；在此基础上，通过推导模型平衡点及其稳定性分析模型演化规律；最后，运用数值仿真考查了官方媒体的参与对模型演化的影响。研究结果表明:模型演化具有3种结果情景，官方媒体参与能够显著改变模型演化的速度和结果。     

基于双指标多等级的土壤重金属生态风险评价

采用土壤中重金属的全量和有效态双重指标,建立基于多等级综合评估的土壤中重金属生态风险评价模型,将联合概率曲线法引入土壤评价模型,分析重金属暴露浓度与毒性数据的概率分布,考察重金属对土壤生物的毒害程度,从而确定土壤中重金属对于生态系统的风险。建立从简单到复杂的多等级综合评价方法,表征重金属的污染等级、浓度效应、多种重金属污染物的协同效应、不同重金属的毒性效应和土壤对不同重金属污染物的敏感性。选择典型地区采集有代表性的土壤样品,测定不同重金属的总量和有效态,验证评价模型的实用性和评价分级的合理性。旨在解决土壤重金属风险评价的方法学问题,为土壤环境质量管理提供支持。     

天津市地表水体与沉积物中7种高关注酚类化合物的污染特征与生态风险分析

为探究地表水体与沉积物中酚类化合物的污染分布特征和生态风险,选择天津市3个水源地与6条主要河流,采集了26个地表水样与6个沉积物样品,利用固相萃取与超声萃取、高效液相色谱-串联质谱法(HPLC-MS/MS)测定了水样及沉积物中1-萘酚(1-naphthol)、壬基酚(nonylphenol, NP)、双酚A(bisphenol A, BPA)、2-苯基苯酚(biphenyl-2-ol)、3,4-二氯酚(3,4-dichlorophenol)、四溴双酚A(tetrabromobisphenol A, TBBPA)和对叔丁基苯酚(p-tert-butylphenol, PTBP)等7种高关注酚类化合物的浓度水平,并应用物种敏感性分布(species sensitivity distribution, SSD)法和熵值法(ecological risk quotient, RQ)评估7种酚类化合物水环境和沉积物的生态风险。结果表明,地表水样中7种酚类化合物均全部检出;其中壬基酚的检出浓度最高,其次为四溴双酚A、对叔丁基苯酚、1-萘酚、2-苯基苯酚、3,4-二氯酚和双酚A。沉积物中酚类化合物的污染分布规律与水样相似,除双酚A外的目标物全部检出。其中,壬基酚浓度比其他物质浓度高2个数量级。风险评估结果显示,壬基酚对水环境与沉积物存在不可接受的风险;而四溴双酚A、对叔丁基苯酚、1-萘酚、2-苯基苯酚、3,4-二氯酚和双酚A则对环境具有较低风险或者存在一定的风险。     

数据挖掘在预测组织事故防控效果中的应用*

为进一步探索数据挖据技术在组织事故预防工作中的融入性与适用性，基于24Model构建事故预控基础模型,通过预测准确率数值及接受者操作特性曲线（ROC曲线）对比分析随机森林（RF）、支持向量机(SVM)、决策树（DT）与神经网络（NN）4种方法对组织事故防控效果的预测性能。结果表明:针对事故率控制（Y1）、职业危害预防（Y2）、财产损失3类预测目标（Y3），RF方法均能达到较高的准确率及稳定性，具有较优的预测性能。根据特征重要度（FI）排序，明确对组织事故水平影响最显著的因素为安全实践活动认知（SC5）及安全管理程序文件（SMS3），FI值均大于0.150 0。研究结果可为有效预测组织事故防控效果提供方法依据，同时为企业安全工作的规划设计提供思路。     

基于模糊推理的FRAM事故定量分析方法研究

为有效克服FRAM事故分析中无法进行定量分析的缺陷，提出结合模糊推理技术的Fuzzy FRAM模型。此改进模型基于FRAM识别系统运行状态；依据功能输出要素的时间/精度属性利用Matlab构建2阶模糊推理系统量化功能输出质量；根据通用性能条件(CPC)及功能输入耦合端口构建功能评价体系，针对评价体系中存在的不确定性信息融合及建模问题，采用模糊证据推理技术，通过模糊信度结构建立、数据处理、信息融合测度后获得功能的风险指数;以既有铁路危险品运输事故为例，验证方法的可行性。结果表明:Fuzzy FRAM模型的评估结果较为精确，是FRAM分析方法的有效补充。     

废铅酸蓄电池污染防治技术及相关政策分析

废铅酸蓄电池具有一定的回收价值,但生产过程和回收过程都容易造成污染。为了引导企业对废旧电池进行正确处理,也为了防止环境被破坏,必须采取科学合理的技术和工艺,并遵循国家相关政策,本文就对此进行了分析,希望能给相关工作提供借鉴。