Factors contributing to US chemical plant process safety incidents from 2010 to 2020

Process safety incidents can result in injuries, fatalities, environmental impacts, facility damage, downtime & lost production, as well as impacts on a company's and industry's reputation. This study is focused on an analysis of the most commonly reported contributing factors to process safety incidents in the US chemical manufacturing industry. The database for the study contained 79 incidents from 2010 to 2019, partly investigated by the Chemical Safety Board (CSB). To be included in the study, the CSB archive of incident investigations were parsed to include only incidents which occurred at a company classified as 325 in the North American Industry Classification System (NAICS), assigned to businesses that participate in chemical manufacturing. For each incident, all of the identified contributing factors were catalogued in the database. From this list of identified contributing factors, it was possible to name the ‘top three’ contributing factors. The top three contributing factors cited for the chemical manufacturing industry were found to be: design; preventive maintenance; and safeguards, controls & layers of protection. The relationship between these top contributing factors and the most common OSHA citations was investigated as well. The investigation and citation history for NAICS 325 companies in the Occupational Safety & Health Administration (OSHA) citations database was then analysed to assess whether there was any overlap between the top reported contributing factors to process safety events and the top OSHA citations recorded for the industry. A database consisting of the inspection and citation history for the chemical manufacturing industry identified by NAICS code 325 was assembled for inspections occurring between 2010 and 2020 (August). The analysis of the citation history for the chemical manufacturing industry specifically, identified that the list of the top contributing factors to process safety incidents overlapped with the most common OSHA violations. This finding is relevant to industry stakeholders who are considering how to strategically invest resources for achieving maximum benefit – reducing process safety risk and simultaneously improving OSHA citation history.     

河北省不同盐渍化土壤类型的微生物多样性与种群结构

为了探索盐渍化土壤中微生物多样性及群落构成，有效筛选盐渍土壤中耐盐微生物菌群.采用高通量测序技术对采集的河北省滨海盐渍土（原生盐渍化）、设施盐渍土（次生盐渍化）和高产粮田（健康土壤）3个生境的耕层土壤样本细菌和真菌多样性、群落结构、网络关系及其影响因子进行测定.结果表明，与大田土壤相比，设施土壤中OM、AP、AK、TS和EC显著升高，滨海盐渍土壤的TS和EC显著升高，其他养分指标则显著降低.细菌α多样性依次为：设施盐渍土>高产粮田>滨海盐渍土，真菌α多样性则为高产粮田显著高于设施盐渍土和滨海盐渍土.在门和属水平上分析盐渍化土壤的菌群结构，细菌群落中绿弯菌门（Chloroflexi）及其菌属和真菌群落中子囊菌门（Ascomycota）及其中有益菌Trichocladium和病原菌Fusarium为盐渍化土壤中的优势微生物类群.土壤EC和TS两个盐分因子是对细菌和真菌菌群分布贡献最大的因子，与绿弯菌门中unclassified_A4b和unclassified_Chloroflexi以及变形菌门中unclassified_α-Proteobacteria等细菌菌属和子囊菌门中Trichocladium、unclassified_Chaetomiaceae、Crassicarpon、Cephaliophora和Sodiomyces等真菌菌属呈显著正相关.研究结果为盐渍化土壤修复所需的微生物资源筛选提供了理论依据.     

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.     

Injury severity analysis of rollover crashes for passenger cars and light trucks considering temporal stability: A random parameters logit approach with heterogeneity in mean and variance

Problem: The rollover crash is a serious crash type that often causes higher injury severities. Moreover, factors that contribute to the injury severities of rollover crashes may show instabilities in different vehicle types and time periods, which requires further investigations. This study utilizes the rollover crash data in North Carolina from Highway Safety Information System (HSIS) to study the effect instabilities of factors in vehicle type and time periods in rollover crashes. Methods: The injury severities of drivers are estimated using the random parameters logit (RPL) model with heterogeneity in means and variances. Available factors in HSIS have been categorized into three groups, which are drivers, road, and environment, respectively. This study also justifies the segmentations through transferability tests. The effects of identified significant factors are evaluated using marginal effects. Results: Factors such as FWP (farm, wood, and pasture areas), unhealthy physical condition, impaired physical condition, road adverse, and so forth have shown instabilities in marginal effects among vehicle types and time periods. Practical Applications: The finding of this research could provide important references for policy makers and automobile manufactures to help mitigate the injury severity of rollover crashes.     

民用燃煤源中多环芳烃排放因子实测及其影响因素研究

民用燃煤是我国目前关注较多的多环芳烃(polycyclic aromatic hydrocarbons,PAHs)排放源.根据我国民用燃煤所涉及的煤种、燃烧方式和炉灶类型,利用全流稀释采样系统对5种不同成熟度的煤种,分别以散块煤和蜂窝煤形式在3种炉灶类型中燃烧产生的烟气进行了采样和分析,初步获取了我国民用燃煤烟气中PAHs排放因子(EFPAHs)的实测数据.结果表明,烟煤的EFPAHs在块煤燃烧方式下的变化范围为1.1～3.9 mg.kg-1,在蜂窝煤燃烧方式下的变化范围为2.5～21.1 mg.kg-1,无烟煤的EFPAHs在块煤与蜂窝煤燃烧方式下分别为0.2 mg.kg-1与0.6 mg.kg-1.在民用燃煤的EFPAHs的各种影响因素中,煤的地质成熟度起主导作用,不同成熟度煤种的EFPAHs差异幅度高达1～2个数量级.其次是燃烧形态(块煤/蜂窝煤),主要表现为同一种地质成熟度的煤在蜂窝煤燃烧方式下排放的PAHs比块煤高2～6倍.炉灶类型的影响最小.     

A data-driven narratives skeleton pattern recognition from accident reports dataset for human-and-organizational-factors analysis

Accidents in the process industry involve several interacting factors, including human and organizational factors (HOFs). A long-standing obstacle to HOFs analysis is lack of data. Accident reports are an essential data source to learn from the past and contain HOFs-related data, but they are usually unstructured text in a not standardized format. Some studies have explored the extraction of information automatically from accident reports based on Natural Language Processing (NLP) techniques. However, they were not dedicated to HOFs. Risk communication is considered an essential pillar in safety and risk science. This research develops a HOFs-focused risk communication framework based on the NLP techniques that can support risk assessment and mitigation. The proposed approach automatically extracts the target groups oriented “Who, When, Where, Why” (4Ws) information from accident reports.This framework was applied to explore the eMARS database. The results show that the “4Ws” skeleton of narratives has appreciated performance in pattern recognition and holistic information analysis. The graphical representation interfaces are designed to display the features of HOFs-related accidents, which can better be communicated to the sharp-end operators and decision-makers.     

集成DEMATEL-ISM的古建筑火灾致因因素评价

为深入了解古建筑火灾事故致因因素,提高古建筑消防安全管理水平,基于事故致因理论,采用德尔菲法从人员、古建筑消防系统、古建筑防火能力、安全管理与环境五个方面确定18个古建筑火灾致因因素,并建立古建筑火灾事故评价指标体系。同时运用实验室决策分析法(DEMATEL)构建矩阵对古建筑火灾致因因素进行分析。一方面,通过原因度定量结果对古建筑火灾致因因素的属性进行分类,确定其属于火灾原因因素还是结果因素;另一方面,通过中心度指标评价古建筑火灾各个致因因素的重要程度。进而利用解释结构模型(ISM)将致因因素进行层级划分,构建5层3阶的多级递阶模型,分析致因因素间的耦合和交互关系。结果表明,古建筑火灾事故的发生是近邻原因、过渡原因及本质原因共同作用的结果,并针对这三方面确定古建筑火灾事故重点管控方向。同时,在古建筑消防安全管理中,应重点关注中心度较大的致因因素。     

壳聚糖絮凝机理及絮凝性能的影响因素分析

壳聚糖是一种天然高分子聚合物,被广泛地作为絮凝剂用于水处理过程。探讨了壳聚糖的三种絮凝机理。在水处理中,其絮凝性能的发挥受到pH值、壳聚糖的投加量、壳聚糖的分子量以及壳聚糖的脱乙酰度等因素的影响。     

五日生化需氧量测定的影响因素

五日生化需氧量是一种表间接表示水体中有机物含量,是水质监测的一个重要参数。本文对水样的保存、溶解氧、pH值、温度、接种稀释水和稀释度等影响测定五日生化需氧量成败的因素进行探讨,并提出相应的解决方法。     

阳澄湖浮游植物群落结构与环境因子的典范对应研究

2013年4月（春季）和10月（秋季）对阳澄湖6个点位的浮游植物群落结构进行了调查,共发现浮游植物8门91属184种,其中硅藻门为阳澄湖的主要优势门类,硅藻门中的小环藻属占主要优势种.对六个点位的浮游植物结构组成与9个环境因子的关系进行典范对应分析,结果表明东湖南与中湖北的浮游植物群落结构与其他4个点位差异性较大,pH、高锰酸盐指数、总磷、氨氮、水温是影响浮游植物分布的主要环境因子.