A Framework for Comprehensive,Integrated, Watershed Monitoring in New York City

The International Life SciencesInstitute (ILSI) Risk Science Institute (RSI) convenedan expert panel of scientists to developrecommendations for a comprehensive monitoring programfor the Croton and Catskill/Delaware watersheds, whichprovide drinking water to New York City's residents. This effort was conducted as part of efforts topreserve and enhance the quality of New York City'sreservoir system through a watershed protectionprogram. The panel developed recommendations for astrategic framework on which to construct a monitoringprogram. As part of this activity, the paneldetermined whether existing monitoring activities weredeficient and, where activities were deficient, thepanel developed recommendations for additionalinformation that should be collected.The panel recommended the development and use of anintegrated approach to watershed monitoring, whichdraws on modeling, risk-based planning and analysis,statistical sampling and design, and basic compliancemonitoring. The approach should be designed toprovide an assessment of natural and anthropogenicsources of stress to the system as well as anassessment of water quality trends in response tostresses acting in concert, both over the long termand over the five-year New York City Memorandum ofAgreement (MOA) assessment time frame. It should alsoprovide an assessment of the human health andenvironmental risks posed by a variety of stressors,and the impact of management actions implemented toameliorate stressors.     

An air quality forecasting system in Beijing - Application to the study of dust storm events in China in May 2008

An air pollution forecast system, ARIA Regional, was implemented in 2007-2008 at the Beijing Municipality Environmental Monitoring Center, providing daily forecast of main pollutant concentrations. The chemistry-transport model CHIMERE was coupled with the dust emission model MB95 for restituting dust storm events in springtime so as to improve forecast results. Dust storm events were sporadic but could be extremely intense and then control air quality indexes close to the source areas but also far in the Beijing area. A dust episode having occurred at the end of May 2008 was analyzed in this article, and its impact of particulate matter on the Chinese air pollution index (API) was evaluated. Following our estimation, about 23 Tg of dust were emitted from source areas in Mongolia and in the Inner Mongolia of China, transporting towards southeast. This episode of dust storm influenced a large part of North China and East China, and also South Korea. The model result was then evaluated using satellite observations and in situ data. The simulated daily concentrations of total suspended particulate at 6:00 UTC had a similar spatial pattern with respect to OMI satellite aerosol index. Temporal evolution of dust plume was evaluated by comparing dust aerosol optical depth (AOD) calculated from the simulations with AOD derived from MODIS satellite products. Finally, the comparison of reported Chinese API in Beijing with API calculated from the simulation including dust emissions had showed the significant improvement of the model results taking into account mineral dust correctly.     

宁夏地区光伏发电环境效益分析及建模

在阐述了宁夏地区光伏发电发展的优势及发展现状的基础上,分析了光伏发电开发利用对宁夏生态环境产生的积极和消极的影响,并为降低消极影响提出了相应的对策;为了定量直观的评估光伏发电积极影响和消极影响所产生的环境效益,分别建立了节能减排、防风固沙和土地增值、景观效应、植被恢复费用、噪声污染超标罚款、光污染费用等价值模型及光伏发电总的生态环境效益价值模型;最后以中电投宁夏能源铝中卫香山光伏电站为例进行计算验证,结果表明模型的合理性和准确性,同时光伏电站的节能减排效益显著。相对于传统的火力发电,表现了优越的环境效益。     

基于灰建模的瓦斯含量多变量预测模型研究

研究了GM(1,N)、GM(0,N)瓦斯含量预测模型的数学原理,收集郑煤集团告成矿地质勘探期间及生产期间的瓦斯含量实测资料,获得16个可靠点,选取基岩厚度、新生界厚度、煤层厚度、煤层水分、煤层灰分、50m顶板含砂率6个因素作灰色建模预测的指标,分别建立了GM(1,6)和GM(0,6)瓦斯含量多变量预测模型。根据计算和评价结果,GM(1,6)和GM(0,6)瓦斯含量预测模型精度均能够满足工程精度的要求,说明利用灰色模型来预测瓦斯含量是可行的。由于前者精度略高于后者,故建议告成矿采用GM(1,6)模型来进行未知地区煤层瓦斯含量的预测。需要注意,由于模型没有考虑构造的影响,在实际预测时,还应根据构造对待预测区的影响关系和影响程度对模型的预测结果进行修正。     

滑行车类游乐设施动力学建模与仿真

根据滑行车类游乐设施运行速度高、加速度大、载荷工况复杂,发生事故后果非常严重的现状,认为深入的动力学分析、结构强度校核和寿命分析预测对保证其安全性尤为必要。因而分析滑行车的结构原理,提出其多体系统拓扑构型,包括轮轨关系模型、各零部件之间约束模型。运用图形学方法研究了滑行车轨道建模参数提取和坐标变换技术。通过仿真分析,在设计阶段就可获得滑行车在不同工况下运行时各部件的速度、加速度及载荷时间历程,为结构设计、安全性评估提供可靠依据。实例表明,分析结果与实测数据非常接近,所提出的建模方法是可行的。     

基于设备网(DeviceNet)总线技术的矿井瓦斯传感器的设计

介绍瓦斯传感器的国内外研究现状,给出以设备网(DeviceNet)总线构成的瓦斯传感器检测网络结构。简要分析了DeviceNet总线协议特点以及其关键技术,提出具有DeviceNet总线规范的矿井瓦斯传感器的设计及实现方案。硬件电路主要由信号调理电路、数字信号处理器,以及能实现DeviceNet网络通信的接口电路构成,用对象建模的方法构建了矿井瓦斯传感器的对象模型,并用模块化编程技术实现了矿井瓦斯传感器作为一个网络从设备的应用层的软件设计。该传感器作为从设备和带有PCI-CAN网卡的计算机组成主/从连接网络,能对多个矿井瓦斯传感器的数据实现远距离的控制。     

The predictive validity of safety climate

PROBLEM: Safety professionals have increasingly turned their attention to social science for insight into the causation of industrial accidents. One social construct, safety climate, has been examined by several researchers [Cooper, M. D., & Phillips, R. A. (2004). Exploratory analysis of the safety climate and safety behavior relationship. Journal of Safety Research, 35(5), 497-512; Gillen, M., Baltz, D., Gassel, M., Kirsch, L., & Vacarro, D. (2002). Perceived safety climate, job Demands, and coworker support among union and nonunion injured construction workers. Journal of Safety Research, 33(1), 33-51; Neal, A., & Griffin, M. A. (2002). Safety climate and safety behaviour. Australian Journal of Management, 27, 66-76; Zohar, D. (2000). A group-level model of safety climate: Testing the effect of group climate on microaccidents in manufacturing jobs. Journal of Applied Psychology, 85(4), 587-596; Zohar, D., & Luria, G. (2005). A multilevel model of safety climate: Cross-level relationships between organization and group-level climates. Journal of Applied Psychology, 90(4), 616-628] who have documented its importance as a factor explaining the variation of safety-related outcomes (e.g., behavior, accidents). Researchers have developed instruments for measuring safety climate and have established some degree of psychometric reliability and validity. The problem, however, is that predictive validity has not been firmly established, which reduces the credibility of safety climate as a meaningful social construct. The research described in this article addresses this problem and provides additional support for safety climate as a viable construct and as a predictive indicator of safety-related outcomes. METHODS: This study used 292 employees at three locations of a heavy manufacturing organization to complete the 16 item Zohar Safety Climate Questionnaire (ZSCQ) [Zohar, D., & Luria, G. (2005). A multilevel model of safety climate: Cross-level relationships between organization and group-level climates. Journal of Applied Psychology, 90(4), 616-628]. In addition, safety behavior and accident experience data were collected for 5 months following the survey and were statistically analyzed (structural equation modeling, confirmatory factor analysis, exploratory factor analysis, etc.) to identify correlations, associations, internal consistency, and factorial structures. RESULTS: Results revealed that the ZSCQ: (a) was psychometrically reliable and valid, (b) served as an effective predictor of safety-related outcomes (behavior and accident experience), and (c) could be trimmed to an 11 item survey with little loss of explanatory power. IMPACT ON INDUSTRY: Practitioners and researchers can use the ZSCQ with reasonable certainty of the questionnaire's reliability and validity. This provides a solid foundation for the development of meaningful organizational interventions and/or continued research into social factors affecting industrial accident experience.     

基于BP神经网络的煤层自燃预测

在全面分析影响煤层自燃因素的基础上,建立了煤层自燃预测的人工神经网络模型.应用该模型对某煤田的多个煤层样本进行了训练和预测,网络经过10次训练后,误差达到设定的最小值,6次预测测试中最大误差仅为0.027 8,最小的为0.000 1.研究表明,该模型精度较高,可用于预测煤层自燃的实际应用.     

Machine learning based quantitative consequence prediction models for toxic dispersion casualty

Incidental release of toxic chemicals can pose extreme danger to life in the vicinity. Therefore, it is crucial for emergency responders, plant operators, and safety professionals to have a fast and accurate prediction to evaluate possible toxic dispersion life-threatening consequences. In this work, a toxic chemical dispersion casualty database that contains 450 leak scenarios of 18 toxic chemicals is constructed to develop a machine learning based quantitative property-consequence relationship (QPCR) model to estimate the affected area caused by toxic chemical release within a certain death rate. The results show that the developed QPCR model can predict the toxic dispersion casualty range with root mean square error of maximum distance, minimum distance, and maximum width less than 0.2, 0.4, and 0.3, which indicates that the constructed model has satisfying accuracy in predicting toxic dispersion ranges under different lethal consequences. The model can be further expanded to accommodate more toxic chemicals and leaking scenarios.