Seeing,believing, and behaving: Heterogeneous effects of an information intervention on household water treatment

Providing information about environmental health risks only sometimes induces protective action. This raises questions about whether and how risk information is understood and acted upon, and how responses vary across contexts. To characterize such variation, we stratified a randomized experiment related to household water quality across two periurban areas in Cambodia. When we showed specific evidence of water contamination to lower-SES households who were initially more optimistic about water safety, they altered their beliefs about health risk and increased their demand for a treatment product. However, demand for the treatment product among higher and initially more pessimistic SES households did not change significantly. These findings highlight the importance of better understanding heterogeneity in the specific drivers of responses to health risk information.     

酚类化合物结构与醇/水分配系数(logK_(ow))关系研究

将不同非氢原子自身及非氢原子之间的关系参数化并构建出新的结构描述符,对部分酚类化合物分子结构进行了参数化表达。采用逐步回归(SMR)与多元线性回归(MLR)相结合的方法建立了化合物结构与醇/水分配系数(log Kow)之间的关系模型,模型的建模相关系数(r)为0.988,标准偏差(SD)为0.121;"留一法"交互检验的相关系数(Q2)为0.966,标准偏差(SDCV)为0.148。结果表明结构描述符能较好地表征化合物分子结构特征,所建模型稳定性好,预测能力强,对于酚类化合物QSPR研究具有一定的参考价值。     

水环境中镍毒性的机制研究：有害结局路径(AOP)分析

目前对镍(Ni)生态风险评估和水质法规采用了基于机制的预测工具,如生物配体模型(BLMs)。然而,尽管有许多具体细致的研究,Ni对水生生物毒性的精确机制仍然难以确定。对Ni行为机制的不确定性导致了大家在一些管理设置中应用像BLM这样的工具时,有所顾虑。为了解决这一认知差距,作者使用了有害结局路径(AOP)分析,也是第一个用于金属的AOP分析,来确定Ni毒性的多种潜在机制以及它们与淡水水生生物的相互作用。在金属的分子起始事件(molecular initiating events)在分类群中可能存在的前提下,在许多不同种水生和陆生生物数据的基础上,考虑建立一种潜在的行为机制。通过分析,作者识别了Ni可能对水生生物产生毒性的5个潜在的分子起始事件：Ca²⁺体内平衡的破坏,Mg²⁺体内平衡的破坏,Fe²⁺/³⁺体内平衡的破坏,活性氧类物质引起的氧化损伤,以及呼吸上皮细胞的过敏反应。在生物组织的器官水平,这5个潜在的分子起始事件会分解成3个可能的途径：支持外骨骼、外壳和骨骼生长的Ca²⁺减少;呼吸受损;细胞毒性和肿瘤形成。在整个生物体的水平,器官水平的反应将可能导致生长、繁殖的降低和/或能量代谢的改变,并且在每个通路之间有几个潜在的反馈回路。总体来说,目前的AOP分析可指导Ni的毒性机制研究,并为其他金属开发AOPs提供了有效的参考。
精选自Brix, K. V., Schlekat, C. E. and Garman, E. R. (2017), The mechanisms of nickel toxicity in aquatic environments: An adverse outcome pathway analysis. Environmental Toxicology and Chemistry, 36: 1128–1137. doi: 10.1002/etc.3706
详情请见http://onlinelibrary.wiley.com/wol1/doi/10.1002/etc.3706/full
     

环境风险评价的现状问题及建议初探

在介绍环境风险评价发展概况的基础上,简要探讨了环境风险评价存在的问题,包括环境风险评价与安全评价在技术方法上协调不足,环境风险评价与环境风险评估衔接不够到位,环境风险评价方法体系与国外尚存在差距,部分环境风险防范措施的标准指导有待完善。针对以上问题,建议不同职能部门加强沟通和合作,推动环境风险评价、安全评价技术标准和手段更好地统一;将环境风险评价和环境风险评估有效衔接或合并;加强相关基础研究和数据积累工作;在相关技术导则或评估指南中明确事故应急池等环境风险防范措施的设置依据。     

企业环境合规指数的构建与发展

随着我国对企业环保要求的逐渐严格化,企业对环境合规越来越重视。同时,可持续发展理念的深化、配套政策标准、环保法律法规的健全使得企业环境合规成为企业参与国际竞争的关键因素。基于此,本文旨在建立企业的环境合规评价指标体系,提出企业环境合规指数的概念,站在企业管理者、监管部门、利益相关方、金融机构、公众等多方使用者的角度,来反映企业的环境合规管理水平和环境合规风险的管控能力,并可通过它的目标程度,使其可成为评价企业环境合规能力的重要依据。     

水工环地质灾害危险性评估的策略研究

随着我国各方面持续的发展,工程建设中的基础建设也不断深入,在后续的使用过程中,因水利工程、矿山开采等对地质环境造成了极其严重的影响,如果不及时改变这些不良的地质环境问题,很有可能对我们以后的生活产生影响。所以,我们要在施工前对工程建设的水利地质灾害进行危险评估,通过科学并且合理的评估,分析当前我国水工环地质灾害的危险性,希望可以对改善我国不良环境提出一些针对性的解决方案。     

A multinomial logit model of motorcycle crash severity at Australian intersections

Introduction: Motorcyclists are exposed to more fatalities and severe injuries per mile of travel as compared to other vehicle drivers. Moreover, crashes that take place at intersections are more likely to result in serious or fatal injuries as compared to those that occur at non-intersections. Therefore, the purpose of this study is to evaluate the contributing factors to motorcycle crash severity at intersections. Method: A data set of 7,714 motorcycle crashes at intersections in the State of Victoria, Australia was analyzed over the period of 2006–2018. The multinomial logit model was used for evaluating the motorcycle crashes. The severity of motorcycle crashes was divided into three categories: minor injury, serious injury and fatal injury. The risk factors consisted of four major categories: motorcyclist characteristics, environmental characteristics, intersection characteristics and crash characteristics. Results: The results of the model demonstrated that certain factors increased the probability of fatal injuries. These factors were: motorcyclists aged over 59 years, weekend crashes, midnight/early morning crashes, morning rush hours crashes, multiple vehicles involved in the crash, t-intersections, crashes in towns, crashes in rural areas, stop or give-way intersections, roundabouts, and uncontrolled intersections. By contrast, factors such as female motorcyclists, snowy or stormy or foggy weather, rainy weather, evening rush hours crashes, and unpaved roads reduced the probability of fatal injuries. Practical Applications: The results from our study demonstrated that certain treatment measures for t-intersections may reduce the probability of fatal injuries. An effective way for improving the safety of stop or give-way intersections and uncontrolled intersections could be to convert them to all-way stop controls. Further, it is recommended to educate the older riders that with ageing, there are physiological changes that occur within the body which can increase both crash likelihood and injury severity.     

Drowsiness and distraction while driving: A study based on smartphone app data

Introduction: Due to the negative impact on road safety from driver drowsiness and distraction, several studies have been conducted, usually under driving simulator and naturalistic conditions. Nevertheless, emerging technologies offer the opportunity to explore novel data. The present study explores retrospective data, which was gathered by an app designed to monitor the driver, which is available to any driver owning a smartphone. Method: Drowsiness and distraction alerts emitted during the journey were aggregated by continuous driving (called sub-journey). The data include 273 drivers who made 634 sub-journeys. Two binary logit models were used separately to analyze the probability of a drowsiness and distraction event occurring. Variables describing the continuous driving time (sub-journey time), the journey time (a set of sub-journeys), the number of breaks, the breaking duration time and the first sub-journey (categorical variable) were included. Additionally, categorical variables representing the gender and age of the drivers were also incorporated. Results: Despite the limitations of the retrospective data, interesting findings were obtained. The results indicate that the main risk factor of inattention is driving continuously (i.e., without stopping), but it is irrelevant whether the stop is long or short as well as the total time spent on the journey. The probability of distraction events occurring during the journey is higher than drowsiness events. Yet, the impact of increasing the driving time of the journey and stopping during the journey on the probability of drowsiness is higher than the probability of distraction. Additionally, this study reveals that the elderly are more prone to drowsiness. The data also include a group of drivers, who did not provide information on gender and age, who were found to be associated to drowsiness and distraction risk. Conclusions: The study shows that data gathered by an app have the potential to contribute to investigating drowsiness and distraction. Practical applications: Drivers are highly recommended to frequently stop during the journey, even for a short period of time to prevent drowsiness and distraction.     

Self healing databases for predictive risk analytics in safety-critical systems

Assuring the quality, consistency and accuracy of safety data repositories is essential in safety-critical systems. In many systems, however, significant effort is required to identify, address, clean and repair data errors and inconsistencies, and to integrate safety data sets and repositories, particularly for risk analyses. Although some self healing and self repairing capabilities leveraging machine learning and predictive analyses have been employed to identify anomalies and monitor quality in structured safety-critical data sets, little attention has been focused on addressing shortcomings in heterogeneous—structured and unstructured—safety data sets, the focus of this work. The text mining and classification analysis employed in this research indicates that machine learning techniques can be employed to improve the accuracy and robustness of large-scale structured and unstructured database repositories, and to improve the effectiveness and efficiency of safety data repository maintenance. Hybrid machine learning approaches, leveraging machine learning, text mining and natural language processing, offer additional promise in future work.     

Application of risk analysis in the liquefied natural gas (LNG) sector: An overview

In recent years, the global demand for liquefied natural gas (LNG) as an energy source is increasing at a very fast rate. In order to meet this demand, a large number of facilities such as platforms, FPSO (floating production, storage and offloading), FSRU (floating storage and regasification unit) and LNG ships and terminals are required for the storage, processing and transportation of LNG. Failure of any of these facilities may expose the market, companies, personnel and the environment to hazards, hence making the application of risk analysis to the LNG sector a very topical issue throughout the world. To assess the risk of accidents associated with LNG facilities and carriers, various risk analysis approaches have been employed to identify the potential hazards, calculate the probability of accidents, as well as assessing the severity of consequences. Nonetheless, literature on classification of the risk analysis models applied to LNG facilities is very limited. Therefore, to reveal the holistic issues and future perspectives on risk analysis of LNG facilities, a systematic review of the current state-of-the-art research on LNG risk analysis is necessary. The aim of this paper is to review and categorize the published literature about the problems associated with risk analysis of LNG facilities, so as to improve the understanding of stakeholders (researchers, regulators, and practitioners). To achieve this aim, scholarly articles on LNG risk analysis are identified, reviewed, and then categorized according to risk assessment methods (qualitative, semi-qualitative or quantitative; deterministic or probabilistic; conventional or dynamic), tools (ETA, FTA, FMEA/FMECA, Bayesian network), output/strategy (RBI, RBM, RBIM, facility siting, etc.), data sources (OREDA handbook, published literature, UK HSE databases, regulatory agencies' reports, industry datasets, and experts’ consultations), applications (LNG carriers and LNG fuelled ships, LNG terminals and stations, LNG offshore floating units, LNG plants), etc. Our study will not only be useful to researchers engaged in these areas but will also assist regulators, policy makers, and operators of LNG facilities to find the risk analysis models that fit their specific requirements.