基于FITNET FFS模型的腐蚀管道失效概率敏感性分析

为了获取影响腐蚀管道失效概率的关键因素及敏感性规律,基于FITNET FFS模型,采用可靠性理论对国内某腐蚀管道的失效概率进行计算和分析。通过全寿命方法计算了腐蚀增长速率,从而得到了与时间相关的腐蚀管道损伤概率模型,并采用蒙特卡罗模拟算法进行求解,得出了不同年限下腐蚀管道的失效概率;采用变异系数法对各影响因素进行参数敏感性分析。研究结果表明:管道直径、壁厚及径向腐蚀速率的分散性对管道失效概率具有双向扰动作用,其机理在于随机变量的分散性和腐蚀速率同时影响失效概率的波动,开始阶段随机变量分散性起主导作用,两者在管道失效概率达到50%会趋于一个平衡状态,之后腐蚀速率起主要支配作用;另外,管材的抗拉强度对腐蚀管道失效概率的影响较屈服强度的影响更大,可靠性分析时采用只考虑屈服强度的强度模型将存在一定的局限性,建议同时考虑管材抗拉强度的影响。     

Optimal descriptor as a translator of eclectic information into the prediction of membrane damage by means of various TiO2 nanoparticles

The increasing use of nanomaterials incorporated into consumer products leads to the need for developing approaches to establish “quantitative structure–activity relationships” (QSARs) for various nanomaterials. However, the molecular structure as rule is not available for nanomaterials at least in its classic meaning. An possible alternative of classic QSAR (based on the molecular structure) is the using of data on physicochemical features of TiO₂ nanoparticles. The damage to cellular membranes (units L⁻¹) by means of various TiO₂ nanoparticles is examined as the endpoint.     

Assessment of human exposure to benzene through foods from the Belgian market

Benzene is a volatile organic compound known to be carcinogenic to humans (Group 1) and may be present in food. In the present study, 455 food samples from the Belgian market were analyzed for benzene contents and some possible sources of its occurrence in the foodstuffs were evaluated. Benzene was found above the level of detection in 58% of analyzed samples with the highest contents found in processed foods such as smoked and canned fish, and foods which contained these as ingredients (up to 76.21 μg kg⁻¹). Unprocessed foods such as raw meat, fish, and eggs contained much lower concentrations of benzene. Using the benzene concentrations in food, a quantitative dietary exposure assessment of benzene intake was conducted on a national representative sample of the Belgian population over 15 years of age. The mean benzene intake for all foods was 0.020 μg kg bw d⁻¹ according to a probabilistic analysis. These values are below the minimum risk level for oral chronic exposure to benzene (0.5 μg kg bw d⁻¹).     

Uncertainty Analysis of Hydrologic and Water Quality Predictions for a Small Watershed Using SWAT2000

Hydrologic and water quality (H/WQ) models are being used with increasing frequency to devise alternative pollution control strategies. It has been recognized that such models may have a large degree of uncertainty associated with their predictions, and that this uncertainty can significantly impact the utility of the model. In this study, ARRAMIS (Advanced Risk & Reliability Assessment Model) software package was used to analyze the uncertainty of the SWAT2000 (Soil and Water Assessment Tool) outputs concerning nutrients and sediment losses from agricultural lands. ARRAMIS applies Monte Carlo simulation technique connected with Latin hypercube sampling (LHS) scheme. This technique is applied to the Warner Creek watershed located in the Piedmont physiographic region of Maryland, and it provides an interval estimate of a range of values with an associated probability instead of a point estimate of a particular pollutant constituent. Uncertainty of model outputs was investigated using LHS scheme with restricted pairing for the model input sampling. Probability distribution functions (pdfs) for each of the 50 model simulations were constructed from these results. Model output distributions of interest in this analysis were stream flow, sediment, organic nitrogen (organic-N), organic phosphorus (organic-P), nitrate, ammonium, and mineral phosphorus (mineral-P) transported with water. Developed probability distribution functions for the model provided information with desirable probability. Results indicate that consideration of input parameter uncertainty produces 64% less mean stream flow along with approximately 8.2% larger sediment loading than obtained using mean input parameters. On the contrary, mean of outputs regarding nutrients such as nitrate, ammonia, organic-N, and organic-P (but not mineral-P) were almost the same as the one using mean input parameters. The uncertainty in predicted stream flow and sediment loading is large, but that for nutrient loadings is the same as that of the corresponding input parameters. This study concluded that using a best possible distribution for the input parameters to reflect the impact of soils and land use diversity in a small watershed on SWAT2000 model outputs may be more accurate than using average values for each input parameter.     

Probabilistic fugacity analysis of Lake Pontchartrain pollution after Hurricane Katrina

After Hurricane Katrina passed through the US Gulf Coast in August 2005, floodwaters covering New Orleans were pumped into Lake Pontchartrain as part of the rehabilitation process in order to make the city habitable again. The long-term consequences of this environmentally critical decision were difficult to assess at the time and were left to observation. In the aftermath of these natural disasters, and in cases of emergency, the proactive use of screening level models may prove to be an important factor in making appropriate decisions to identify cost effective and environmentally friendly mitigation solutions. In this paper, we propose such a model and demonstrate its use through the application of several hypothetical scenarios to examine the likely response of Lake Pontchartrain to the contaminant loading that were possibly in the New Orleans floodwaters. For this purpose, an unsteady-state fugacity model was developed in order to examine the environmental effects of contaminants with different physicochemical characteristics on Lake Pontchartrain. The three representative contaminants selected for this purpose are benzene, atrazine, and polychlorinated biphenyls (PCBs). The proposed approach yields continuous fugacity values for contaminants in the water, air, and sediment compartments of the lake system which are analogous to concentrations. Since contaminant data for the floodwaters are limited, an uncertainty analysis was also performed in this study. The effects of uncertainty in the model parameters were investigated through Monte Carlo analysis. Results indicate that the acceptable recovery of Lake Pontchartrain will require a long period of time. The computed time range for the levels of the three contaminants considered in this study to decrease to maximum contaminant levels (MCLs) is about 1 year to 68 years. The model can be implemented to assess the possible extent of damage inflicted by any storm event on the natural water resources of Southern Louisiana or similar environments elsewhere. Furthermore, the model developed can be used as a useful decision-making tool for planning and remediation in similar emergency situations by examining various potential contamination scenarios and their consequences.     

基于健康风险的三氯杀螨醇生产设备表面污染物筛选值推算的初步研究

运用确定性和概率性人体健康风险评估的方法,推算一般工业暴露情景和拆卸清理暴露情景下三氯杀螨醇生产设备表面污染物的筛选值.结果表明:p,p'-滴滴涕、p,p'-滴滴滴和p,p'-滴滴依基于确定性风险评估的设备表面筛选值在一般工业暴露情景下分别为0.224 mg/m2、0.214 mg/m2和0.151 mg/m2,在拆卸...     

平流层硫酸盐气溶胶辐射效应的模拟研究

平流层中的硫酸盐气溶胶在地球能量循环和全球气候变化中发挥着关键性作用.基于自主开发的矢量辐射传输模型,重点研究对流层气溶胶类型、平流层气溶胶光学厚度(AOD)、太阳天顶角(SZA)和地表反照率等对平流层硫酸盐气溶胶辐射强迫和大气加热速率等辐射效应的影响.结果表明,对流层无气溶胶时,平流层气溶胶在大气顶层(TOA)的辐射强迫为-15.80 W·m^-2,地气系统的冷却效应最大.对流层气溶胶为黑碳时,平流层气溶胶在大气底层(BOT)的辐射强迫最小,为-47.53 W·m^-2,地表冷却最大.同时,平流层硫酸盐的辐射强迫导致对流层降温,平流层升温,在模拟条件下,最大升温可达0.6 K·d^-1.此外,结果还表明,平流层硫酸盐辐射强迫对AOD、SZA和地表反照率均具有很高的敏感性.平流层气溶胶在TOA和BOT的辐射强迫随AOD的增大呈线性减小趋势,但随地表反照率的增大呈线性增大趋势.AOD和SZA的增大会强化辐射强迫的作用效果,但地表反照率的增大可能会改变辐射强迫的正负,导致平流层硫酸盐对地气系统的作用效果从冷却变为加热.     

OPTIMAL LONG-TERM SCHEDULING OF STORMWATER DRAINAGE REHABILITATION1

ABSTRACT: A mathematical model is developed to optimally schedule long-term stormwater infrastructure rehabilitation activities. The model is capable of considering multiple rehabilitation projects and is driven by overall cost eensiderations. Rehabilitation activities are scheduled based on perceived reliabilities and future deterioration expected within the specified planning horizon. Future growth within the stormwater drainage basin is incorporated using chance constraints that limit the likelihood that a stormwater discharge exceeds system conveyance capacity. Model structure and development are discussed, and a hypothetical example using a drainage network is presented.     

Nonparametric Monte Carlo Simulation for Flood Frequency Curve Derivation: An Application to a Korean Watershed1

Abstract: A mix of causative mechanisms may be responsible for flood at a site. Floods may be caused because of extreme rainfall or rain on other rainfall events. The statistical attributes of these events differ according to the watershed characteristics and the causes. Traditional methods of flood frequency analysis are only adequate for specific situations. Also, to address the uncertainty of flood frequency estimates for hydraulic structures, a series of probabilistic analyses of rainfall‐runoff and flow routing models, and their associated inputs, are used. This is a complex problem in that the probability distributions of multiple independent and derived random variables need to be estimated to evaluate the probability of floods. Therefore, the objectives of this study were to develop a flood frequency curve derivation method driven by multiple random variables and to develop a tool that can consider the uncertainties of design floods. This study focuses on developing a flood frequency curve based on nonparametric statistical methods for the estimation of probabilities of rare floods that are more appropriate in Korea. To derive the frequency curve, rainfall generation using the nonparametric kernel density estimation approach is proposed. Many flood events are simulated by nonparametric Monte Carlo simulations coupled with the center Latin hypercube sampling method to estimate the associated uncertainty. This study applies the methods described to a Korean watershed. The results provide higher physical appropriateness and reasonable estimates of design flood.     

A note on data augmentation and iterative simulation for survival rate estimation

In this paper we examine the use of data augmentation techniques for simplifying iterative simulation in the context of both Bayesian and classical statistical inference for survival rate estimation. We examine two distinct model families common in population ecology to illustrate our ideas, ring-recovery models and capture–recapture models, and we present the computational advantage of this approach. We discuss also the fact that problems associated with identifiability in the classical framework can be overcome using data augmentation, but highlight the dangers in doing so under both inferential paradigms.