Assessing Spatial, Temporal, and Analytical Variation of Groundwater Chemistry in a Large Nuclear Complex, USA

Statistical analyses were applied at the Hanford Site, USA, to assess groundwater contamination problems that included (1) determining local backgrounds to ascertain whether a facility is affecting the groundwater quality and (2) determining a ‘pre-Hanford' groundwater background to allow formulation of background-based cleanup standards. The primary purpose of this paper is to extend the random effects models for (1) assessing the spatial, temporal, and analytical variability of groundwater background measurements; (2) demonstrating that the usual variance estimate s ², which ignores the variance components, is a biased estimator; (3) providing formulas for calculating the amount of bias; and (4) recommending monitoring strategies to reduce the uncertainty in estimating the average background concentrations. A case study is provided. Results indicate that (1) without considering spatial and temporal variability, there is a high probability of false positives, resulting in unnecessary remediation and/or monitoring expenses; (2) the most effective way to reduce the uncertainty in estimating the average background, and enhance the power of the statistical tests in general, is to increase the number of background wells; and (3) background for a specific constituent should be considered as a statistical distribution, not as a single value or threshold. The methods and the related analysis of variance tables discussed in this paper can be used as diagnostic tools in documenting the extent of inherent spatial and/or temporal variation and to help select an appropriate statistical method for testing purposes.     

Quantification and reduction of erroneous differences between images in remote sensing

The reliability of image data, along with the difficulty of accurately comparing images acquired at different times or from different sensors, is a generic problem in remote sensing. Measurement repeatability errors occur frequently and can substantially reduce the system’s ability to reliably quantify real spectral and spatial change in a target. This paper outlines methodologies for quantifying and reducing erroneous differences between monochrome and multispectral or hyperspectral images. [Each of these image types is acquired by an instrument that collects light photons across a variable range of the electromagnetic spectrum (often referred to as an image band). The hyperspectral image is often referred to as a hyperspectral cube with XY spatial dimensions and many Z bands (spectral demotions)]. In this paper, we specifically discuss the Pixel Block Transform (PBT), the Spectral Averaging Transform (SAT), and the Wavelet Transform (WAVEL). We briefly address sensor fusion. Results indicate that the PBT is a powerful cross-noise and repeatability error reducing tool, applicable to monochrome, multispectral, and hyperspectral images. The SAT is as powerful as the PBT in reducing error but is suitable only for hyperspectral imagery. WAVEL can reduce some of the finer scale noise, but it is not as powerful in reducing cross-noise as PBT or SAT and it requires some trial and error for selecting the appropriate wavelet function. It is important that those involved in developing statistically sound relationships between remotely sensed imagery and other data sources understand the problems and their solutions to prevent wasting time or developing relationships that are statistically insignificant or unstable, or that lead to faulty conclusions.     

A simulation model of rhizome networks for Fallopia japonica (Japanese knotweed) in the United Kingdom

Fallopia japonica (Japanese knotweed) is an aggressively invasive herbaceous perennial that causes substantial economic and environmental damage in the United Kingdom (UK). As such, it is of considerable concern to councils, environmental groups, private landowners and property developers. We construct a 3D correlated random walk model of the development of the subterranean rhizome network for a single stand of F. japonica. The formulation of this model uses detailed knowledge of the morphology and physiology of the plant, both of which differ in the UK to that of its native habitat due to factors including a lack of predation and competition, longer growth seasons and favourable environmental conditions in the UK. Field data obtained as a part of this study are discussed and used in the model for parameterisation and validation. The simulation captures the field data well and predicts, for example, quadratic growth in time for the stand area. Furthermore, the role of a selection of parameters on long-term stand development are discussed, highlighting some key factors affecting vegetative spread rates.     

Combining mapped and statistical data in forest ecological inventory and monitoring – supplementing an existing system

A forest ecological inventory and monitoring system combining information derived from maps and samples is proposed based on ecosystem regions (Bailey, 1994). The system extends the design of the USDA Forest Service Region 6 Inventory and Monitoring System (R6IMS) in the Pacific Northwest of the United States. The key uses of the information are briefly discussed and expected results are illustrated with examples. The system is flexible, allowing regions based on ecological considerations to be modified. Sampling intensities that are affordable are likely to be insufficient to provide meaningful estimates for key parameters relating to rare and endangered species, watersheds, and other ecological units. Methods are proposed for collecting additional information in follow-up surveys and combining it with relevant information obtained in R6IMS. Near-continuous information on weather and possible pollution variables recorded by instruments at sampling sites is needed to develop meaningful models to understand what is happening in the ecoregions. R6IMS and the proposed additions constitute a dynamic system which will be modified further as data are analyzed.     

航空维修差错分析及其管理

航空维修差错是诱发或直接导致飞行事故最重要的原因之一 ,对维修差错进行分类和分析有助于航空安全。笔者在分析航空维修环境变化的基础上 ,基于Reason模型构建了维修差错分类与诱因分析的框架 ,并结合机务维修的实际情况 ,对框架所包括的不安全行为、不安全行为的先兆、不安全的管理及组织因素进行了初步编码。文章还简要论述了维修差错的管理技术 ,指出借助框架编制详细的差错分类与分析编码系统是发展的方向。     

考虑人因差错影响的结构可靠度计算方法

通过对单个或任意多个人因差错影响下构件截面失效概率计算方法的分析讨论,推导出在理想已知条件下的数学解析求解公式;在考虑人因差错对结构参数影响程度的空间离散化后,得出了相应的简化计算方法;并以一个单筋梁的正截面为例进行了失效概率计算和人因差错影响程度分析.在目前人因差错的发生及其影响规律的研究不完善、缺乏必要的数学模型的现实条件下,该方法对人因差错影响下构件截面失效概率的分析计算有一定的实用价值.     

战略导弹核武器系统人因工程应用研究

以战略导弹核武器系统的设计、生产、使用与维修为背景，分析了人为失误的定义、原因及分类，并根据模糊图理论，提出了一种评估人员可靠性的双向模糊图模型，并对人因工程学在战略导弹核武器系统的应用，提出了一些看法和建议。     

街区建筑物对城市交通隧道废气排放的影响

采用大气边界层模式和随机游动扩散模式相连接的模拟方法,对上海市拟建的交通隧道排气口附近街道建筑物区域的气流分布和废气排放物浓度场进行了数值模拟分析,设计了6种方案,并按不同的废气排放形式,分别分析了街区的地面污染物质量浓度分布.结果表明,在建筑物存在的情况下,排风口造成的地面污染物质量浓度的最高值会很大,可达0.44 mg/m3,若换成排风塔,则为0.13 mg/m3; 没有建筑物的情况下,由排风口和排风塔造成的地面污染物最高质量浓度分别为0.11 mg/m3,0.4 mg/m3.当风速增大,质量浓度会降低,最大值分别从0.44 mg/m3降为0.2 mg/m3,和从0.13 mg/m3降为0.1mg/m3.分析表明,建筑物附近的气流特征对污染物扩散会起引导作用: 垂直方向上,导致污染物从高空被带入地面; 水平方向上,使得污染物在下风向堆积; 当风速增大时,地面污染物质量浓度值降低.同时研究表明,对排风塔污染物散布起主要作用的是水平方向的气流结构,而对排风口的污染物散布起主要作用的则是其附近建筑物的背风侧的气流下洗效应和水平流场,因此建筑物背风侧有可能成为重污染区.     

混凝土结构施工中人因差错的模拟

以施工现场人因差错真实准确的调查实测数据为依据,笔者采用人的可靠性分析方法建立了混凝土结构施工中人因差错发生及其对混凝土强度影响规律的数学模型,并编制了相应的模拟计算程序。将该模拟计算程序所得的考虑人因差错的混凝土强度模拟计算结果与实测的混凝土强度调查结果进行比较,二者具有很好的一致性,说明该数学模型和模拟计算程序是比较符合实际的,也是比较合理的。上述模拟计算结果为考虑人因差错的结构安全性分析以及人因差错的有效控制措施研究提供了参考依据。     

安全等级特征量及其计算方法

指出了目前用模糊评价法确定系统的安全等级所存在的问题和不足之处。分别运用模糊随机变量理论和模糊集理论而提出了安全等级模糊随机特征量和安全等级模糊特征量的概念及其计算方法。安全等级特征量及安全等级变量,均为安全等级取值论域上的模糊子集,而并非是一个确定的点。还给出了安全等级的绝对可能性和相对可能性的计算方法。实例表明,笔者所提出的安全等级特征量及可能性的计算方法是科学的、合理的