我国能源消耗对人群PM2.5暴露水平的随机影响效应分析

能源燃烧产物是PM2.5暴露水平提高的重要因素,燃烧不同种类的能源对PM2.5形成的影响机理不同,但各类能源消耗量对人群PM2.5暴露水平的影响程度尚不明确.基于2003-2010年的PM2.5质量浓度与煤炭、焦炭、原油、汽油、煤油、柴油、燃料油、天然气和电力消耗数据组成的面板数据,建立了不同种类能源消耗影响我国人群PM25暴露水平的随机效应模型.结果表明,我国2003-2010年多数省(市、自治区)的年均PM2.5质量浓度超过了世界卫生组织的标准.在研究时间段内,不同种类能源消耗量对人群PM2.5暴露水平的影响具有较大差异,煤炭、焦炭、汽油和煤油消耗对人群PM2.5暴露水平具有正影响,其中,正向影响最大的为焦炭消耗量,表明工业消耗焦炭对形成PM2.5的促进作用比较明显;与焦炭消耗量具有相近的影响效果的因素是汽油消耗,表明改进机动车和航空燃油技术同样非常重要;原油、柴油、燃料油、天然气和电力消耗对人群PM2.5暴露水平具有负影响,其中负向影响最大的为电力消耗量,表明电力作为一种清洁能源,有利于降低人群PM2.5暴露水平.     

随机振动的描述及其试验与仿真

阐述了随机振动的描述方法,随机振动试验条件和相关参数的计算等。并结合实例,介绍了随机振动有限元仿真的流程及其工程意义,以便在深入了解随机振动原理的基础上,指导随机振动试验和有限元仿真的开展。     

基于随机过程的城市应急车辆数量配置模型

针对城市中应急车辆的配置规模问题,运用随机过程理论,获得系统中空闲应急车辆数量期望值模型。在一定的事件需求下,空闲应急车辆数量期望值与应急车辆配置总量、平均的事件发生间隔时间成正比,与平均的事件救援时间成反比。克服了采用马尔可夫过程分析多用于应急车辆繁忙期以及布局研究的局限。以某市火灾统计数据为例,说明了模型的使用,为在宏观上合理确定城市应急车辆的配置规模提供理论依据。     

移动通信基站产品的随机振动试验分析

本文首先对随机振动试验和电动式振动台的原理进行简要分析,然后结合这些分析,对移动通信基站产品的随机振动试验的级别分类,设备选择,问题解决等进行探讨。     

开展新的安全监察模式探讨

安全稽查的运作突破常规的安全检查方式,本文介绍开展安全稽查工作的运做情况,通过安全稽查实时反映出施工的具体实际情况,取得了直接的、第一手资料,纠正施工人员侥幸心理,有效控制习惯性违章的行为,促使其落实好各项安全措施,减少事故隐患,杜绝事故发生.     

AN EVALUATION OF PHYSICAL STREAM HABITAT ATTRIBUTES USED TO MONITOR STREAMS1

ABSTRACT: The last few decades have seen an increased reliance on the use of stream attributes to monitor stream conditions. The use of stream attributes has been criticized because of variation in how observers evaluate them, inconsistent protocol application, lack of consistent training, and the difficulty in using them to detect change caused by management activity. In this paper, we evaluate the effect of environmental heterogeneity and observer variation on the use of physical stream attributes as monitoring tools. For most stream habitat attributes evaluated, difference among streams accounted for greater than 80 percent of the total survey variation. To minimize the effect that variation among streams has on evaluating stream conditions, it may be necessary to design survey protocols and analysis that include stratification, permanent sites, and/or analysis of covariance. Although total variation was primarily due to differences among streams, observers also differed in their evaluation of stream attributes. This study suggests that if trained observers conducting a study that is designed to account for environmental heterogeneity can objectively evaluate defined stream attributes, results should prove valuable in monitoring differences in reach scale stream conditions. The failure to address any of these factors will likely lead to the failure of stream attributes as effective monitoring tools.     

隧道渗涌水量的随机模型预测

渗涌水问题是常见的隧道病害 ,是影响隧道正常使用 ,危及行车安全的重要因素。因此 ,科学地预测隧道涌水量大小 ,是制定最优防治水方案 ,确保安全通车的关键。笔者以大瑶山隧道渗涌水量的实测数据为基础 ,应用随机过程的理论和方法 ,建立了隧道渗涌水量的平稳序列预测模型 ,并将预测值与实测结果进行了对比 ,比较吻合。     

跨河道桥工程防洪风险管理体系研究

针对目前我国河道管理的现状，在原有防洪影响评价的基础上，综合运用广义结构可靠性原理及多种相关学科理论，提出了跨河道桥工程防洪风险的评价理念，建立了跨河道桥工程防洪风险的评价指标体系和管理体系，并结合工程实例验证了方法体系的适用性与可靠性。     

厌氧水解—高负荷生物滤池处理城镇污水的试验研究

厌氧水解－高负荷生物滤池是一种利用附着在塑料模块填料上的微生物系统对城镇污水中的污染物质进行降解处理的绿色环保技术。厌氧水解池和高负荷生物滤池采用的塑料模块填料具有高空隙率、高附着面积、高布水性能和抗堵塞的优异性能,并无须回流。当厌氧水解池水力停留时间为4小时,生物滤池水力负荷为30米^3/米^2。日,该系统处理城镇污水的CODCr去除率达80－86％,BOD5去除率达85％－95％。SS去除率达85－95％,处理后出水上述各项指标均可满足国家二级生物处理排放标准的要求。与广泛运用的活性法处理系统相比,该技术可节约基建投资20％以上,节约能耗50％以上,同时还具有流程简单、管理方便、耐冲击负荷、剩余污泥少等特点。     

A single-chain-based multidimensional Markov chain model for subsurface characterization

Multidimensional Markov chain models in geosciences were often built on multiple chains, one in each direction, and assumed these 1-D chains to be independent of each other. Thus, unwanted transitions (i.e., transitions of multiple chains to the same location with unequal states) inevitably occur and have to be excluded in estimating the states at unobserved locations. This consequently may result in unreliable estimates, such as underestimation of small classes (i.e., classes with smaller than average areas) in simulated realizations. This paper presents a single-chain-based multidimensional Markov chain model for estimation (i.e., prediction and conditional stochastic simulation) of spatial distribution of subsurface formations with borehole data. The model assumes that a single Markov chain moves in a lattice space, interacting with its nearest known neighbors through different transition probability rules in different cardinal directions. The conditional probability distribution of the Markov chain at the location to be estimated is formulated in an explicit form by following the Bayes’ Theorem and the conditional independence of sparse data in cardinal directions. Since no unwanted transitions are involved, the model can estimate all classes fairly. Transiogram models (i.e., 1-D continuous Markov transition probability diagrams) are used to provide transition probability input with needed lags to generalize the model. Therefore, conditional simulation can be conducted directly and efficiently. The model provides an alternative for heterogeneity characterization of subsurface formations.