ESTIMATION OF PRE- AND POST-DEVELOPMENT NONPOINT WATER QUALITY LOADINGS1

ABSTRACT: A simple procedure for estimating pre- and post-development water quality loadings from residential communities is discussed. The procedure deals with: (a) gathering basic water quality loading numbers observed by others at several watersheds with various land uses; (b) obtaining the breakdown of proposed land uses at various phases of the community development; and (c) estimating pre- and post-development water quality loading numbers by taking the weighted average of the basic loading numbers in terms of areal coverages of different land uses at various phases of development. Results of this simplified procedure have been verified indirectly by comparing them with the estimates derived independently through a more fundamental but time-consuming approach. The procedure was used to evaluate the anticipated water quality impact of two future residential communities in South Florida by analyzing four water quality parameters: Suspended Soils (SS), Total Nitrogen (TN), Total Phosphorus (TP), and Biochemical Oxygen Demand (BOD₅). Although computation of loading numbers with mixed land uses is not an exact science at the present time, the recommended approach appears to be the best available technique to analyze quantitatively the water quality-quantity-land use interactions.     

Development and Operational Testing of a Super‐Ensemble Artificial Intelligence Flood‐Forecast Model for a Pacific Northwest River

Coastal catchments in British Columbia, Canada, experience a complex mixture of rainfall‐ and snowmelt‐driven contributions to flood events. Few operational flood‐forecast models are available in the region. Here, we integrated a number of proven technologies in a novel way to produce a super‐ensemble forecast system for the Englishman River, a flood‐prone stream on Vancouver Island. This three‐day‐ahead modeling system utilizes up to 42 numerical weather prediction model outputs from the North American Ensemble Forecast System, combined with six artificial neural network‐based streamflow models representing various slightly different system conceptualizations, all of which were trained exclusively on historical high‐flow data. As such, the system combines relatively low model development times and costs with the generation of fully probabilistic forecasts reflecting uncertainty in the simulation of both atmospheric and terrestrial hydrologic dynamics. Results from operational testing by British Columbia's flood forecasting agency during the 2013‐2014 storm season suggest that the prediction system is operationally useful and robust.     

应用概率物种敏感度分布法研究太湖重金属水生生物水质基准

目前广泛使用的水质基准推导方法—物种敏感度分布法存在曲线拟合模型不确定、曲线拟合效果不佳、种内差异欠考虑、基准值不准确等诸多问题,概率物种敏感度分布法可有效解决上述问题。应用概率物种敏感度分布法构建了太湖水体中5种重金属Ag、Pb、Cd、Hg和Zn的概率物种敏感度分布曲线,在此基础上得到了保护水生生物的急性水质基准分别为1.079μg·L~(-1)、637.973μg·L~(-1)、19.465μg·L~(-1)、8.729μg·L~(-1)和105.506μg·L~(-1),慢性水质基准分别为0.108μg·L~(-1)、63.797μg·L~(-1)、1.947μg·L~(-1)、2.340μg·L~(-1)和52.753μg·L~(-1);不同类群间生物对重金属的敏感度存在差异,不同重金属对同一类群生物的毒性也存在差异;通过与国内外已有的重金属水质基准值比较,发现水质基准具有明显的区域性,目前基于国外水质基准或我国整体水域特点来制定的太湖水质标准,往往造成对太湖水生生物欠保护或过保护的状况。     

Pipeline leakage detection and isolation: An integrated approach of statistical and wavelet feature extraction with multi-layer perceptron neural network (MLPNN)

Leakage diagnosis of hydrocarbon pipelines can prevent environmental and financial losses. This work proposes a novel method that not only detects the occurrence of a leakage fault, but also suggests its location and severity. The OLGA software is employed to provide the pipeline inlet pressure and outlet flow rates as the training data for the Fault Detection and Isolation (FDI) system. The FDI system is comprised of a Multi-Layer Perceptron Neural Network (MLPNN) classifier with various feature extraction methods including the statistical techniques, wavelet transform, and a fusion of both methods. Once different leakage scenarios are considered and the preprocessing methods are done, the proposed FDI system is applied to a 20-km pipeline in southern Iran (Goldkari-Binak pipeline) and a promising severity and location detectability (a correct classification rate of 92%) and a low False Alarm Rate (FAR) were achieved.     

Development of a probabilistic blood lead prediction model

A partially probabilistic blood lead prediction model has been developed, based on the US Environmental Protection Agency integrated exposure-uptake-biokinetic blood lead model (IEUBK model). This study translated the IEUBK model into a spreadsheet format. The uptake submodel incorporates uncertainty distributions for exposure and bioavailability parameters. The biokinetic submodel is duplicated with a table incorporating partitioning and decay of lead levels in the body. As a case study, the probabilistic model is applied to a lead exposure scenario involving a former smelter site in Sandy, Utah. The probabilistic model produces less biased estimates of means and standard deviations than the deterministic model. Parameter uncertainty is propagated in the model by the use of Monte Carlo simulation. Thus, sensitivity analysis is possible, and driving variables can be determined.     

经手口途径摄入室内含PAHs灰尘颗粒的致癌风险评价

为了探究经手口途径摄入被PAHs污染室内灰尘的致癌风险,构建了居民经手口途径摄入被污染室内灰尘颗粒的概率风险模型,运用蒙特卡罗模拟方法评价了中国上海地区居民由PAHs引起的致癌风险,分析了致癌风险的主要来源及手口途径中各参数的重要程度。结果表明:幼儿和儿童所承受的风险最大,婴儿和成年人次之,青少年和老年人较小;对于致癌风险,幼儿和儿童超过10-6的概率都为80%,但超过10-4的概率都小于5%,婴儿和成年人超过10-6的概率都为50%,但婴儿超过10-4的概率小于5%,成年人的小于0.1%,青少年和老年人超过10-6的概率分别为10%和15%,不会高于10-4;室内硬表面灰尘引起的致癌风险远大于软表面灰尘引起的致癌风险;与致癌风险相关性最大的因素为灰尘颗粒中PAHs的等效斜率、灰尘在室内软硬表面的浓度、手到口途径发生的频率和颗粒物从手掌皮肤表面到口腔的转移率。     

基于概率物理模型的贮存可靠性评估方法

针对小样本高截尾试验信息贮存可靠性评估问题,提出了基于概率物理模型的贮存可靠性评估方法.通过对概率物理模型与传统指数模型的贮存可靠性评估方法进行对比分析,分别利用2种评估方法进行模型参数拟合、无失效数据可靠性评估和系统可靠性评估,发现无论是从拟合精度,还是小样本数据评估方面,概率物理模型评估方法都更符合工程实际.该模型...     

PSA中人因失误模型化研究

主要研究PSA模型如何考虑人因失误的影响,系统地提出如何在电厂系统模型中建立相对应的人因失误分析模型。利用事件树把系统故障和人因失误相结合的方法,探讨如何最大可能地真实描述事故后的操纵员行为,确定重要人因事件发展序列以及根据系统响应确定合理可分析的人因题头,建立完整的人因失误模型化的体系,并以实例说明具体分析过程。此项研究能够较好地描述硬件可靠性和人因可靠性之间的关联关系,降低HRA出力并满足PSA对于事故后人员行为的概率分析需求。     

概率统计在矿井通风系统可靠性评估中的应用

构建一个随机非稳定地下矿山通风系统,此通风系统有三个机组,机组的随机运行状态构成了一个完整的“系统的概率结构”。对此通风系统的运行状态进行概率统计分析,探讨了该随机系统的特征。由此推导出了通风系统故障的平均时间是一个显式的概率表达式。同时,假设系统失效时间和更换时间符合指数分布的情况下,得出计算系统故障平均时间的一个五阶多项式公式。通过验证,证明了系统故障平均时间的大小依赖于系统维修和更换的比率,而且是检验系统可靠性的重要指标,有利于提高矿山安全水平。     

Effects of soil-structure interaction on fragility and seismic risk; a case study of power plant containment

Safety-related structures are designed to provide a safe environment for the occupants and equipment during and after earthquakes. This is due to the fact that any damage imposed to the systems might lead to catastrophic consequences. Seismic probabilistic risk assessment (SPRA) is a systematic approach for the quantification of the seismic risk. One of the crucial steps in this assessment is to determine the seismic capacity of the structures by fragility method. After a review of available methodologies, this article analyzes the seismic fragility for a typical power plant containment considering the effects of soil-structure interaction (SSI). The structure and underneath soil profile are analyzed as a unified model by the subtraction method. Two steps are considered for the assessment of seismic response: In the first step, a fixed-base hypothesis framework is implemented to the computational problem. The second step covers computations taking into account the SSI effects. Using the results of seismic response analysis and safety factor method, seismic fragility of the structure is computed and related fragility curves are developed. Finally, by comparing the fragility curves, the effects of SSI are quantified on the overall seismic risk.