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101.
James R Payne John R Clayton Jr. Bruce E Kirstein 《Spill Science & Technology Bulletin》2003,8(2):201-221
The interactions of physically dispersed oil droplets with suspended particulate material (SPM) can be important for the transport of bulk quantities of spilled crude oil and polycyclic aromatic hydrocarbons (PAH) to subtidal sediments. The literature regarding oil/SPM interactions is reviewed, and results from whole-oil droplet/SPM interaction kinetics and pure-component (Prudhoe Bay crude oil distillate cut) equilibrium partitioning experiments are presented. The effects of oil type, SPM characteristics, and salinity on the interaction rates are examined, and the importance of whole-oil droplet/SPM interactions on particle agglomeration and settling behavior are discussed. Whole-oil droplet/SPM interactions are retarded as oil droplet dispersion into the water column is inhibited by oil viscosity increases due to evaporation weathering and water-in-oil emulsification. Compared to whole oil droplet/SPM interactions, dissolved-component/SPM adsorption is not as significant for transport of individual components to sediments. The information presented in this paper can be used to augment computer-based models designed to predict oil-spill trajectories, oil-weathering behavior, and spilled oil impacts to the marine environment. 相似文献
102.
层化效应是自然河口水体的典型特征,本文采用TELEMAC三维水动力数学模型,研究了不同层化强度下温排水在理想河口的输移扩散规律。结果表明：层化强度最大的小潮,温排水的垂向扩散受到显著抑制,导致中层水体的温度及其包络面积显著高于表层和底层,且小潮期间较强的重力环流导致中下层温度包络范围存在净向上游的分布趋势;层化强度最弱但潮汐混合作用最大的大潮,温排水整体影响范围最小;中潮工况下的温度包络线分布与小潮类似,但影响范围有所减小。未考虑层化效应的中潮计算结果显示：温排水的表层影响范围显著高于其他水层,中层以下温排水影响范围很小,且未出现净向上游的分布趋势。由此可见,在河口层化水体中,温排水的输移扩散规律与非层化水体存在显著差异,因此,在取排水设计时应重点考虑层化效应的影响。
相似文献103.
104.
Nobuhisa Kashiwagi 《Environmetrics》2004,15(8):777-796
A chemical mass balance method is proposed for the case where the existence of an unknown source is suspected. In general, when the existence of an unknown source is assumed in statistical receptor modeling, unknown quantities such as the composition of an unknown source and the contributions of assumed sources become unidentifiable. To estimate these unknown quantities avoiding the identification problem, a Bayes model for chemical mass balance is constructed in the form of composition without using prior knowledge on the unknown quantities except for natural constraints. The covariance of ambient observations given in the form of composition is defined in several ways. Markov chain Monte Carlo is used for evaluating the posterior means and variances of the unknown quantities as well as the likelihood for the proposed model. The likelihood is used for selecting the best fit covariance model. A simulation study is carried out to check the performance of the proposed method. Copyright © 2004 John Wiley & Sons, Ltd. 相似文献
105.
When CO2 is injected into aquifers in a hydrocarbon basin, either for CO2 geological storage or enhanced oil recovery (EOR), these aquifers are most likely saturated with CH4. Dissolving CO2 into the aqueous phase will cause CH4 to come out of the aqueous phase and partitioning into the CO2‐rich non‐aqueous phase under static conditions. It is not well understood how this process develops under flow conditions, which is the subject of this paper. Although CH4 solubility in brine is low under the subsurface conditions, the effects of CH4 dissolved in saline aquifers cannot be ignored for CO2 sequestration projects. This is due to the changes of the phase behavior, capillary pressure, and relative permeability which has already been recognized by previous studies. This work couples a recently developed thermodynamic model of CO2‐CH4‐brine system with an industrial reservoir simulator and applies it in the simulation of CO2 injection into a CH4 saturated aquifer. Simulation results are compared to field observations from U‐tube sampling and used to provide reasonable explanations. From the simulations, the compositions in U‐tube samples are influenced by U‐tube inlet position, perforation and the reservoir (geological) properties. With the explanations, the U‐tube observations of gas samples can be well matched by numerical simulation results.© 2014 Society of Chemical Industry and John Wiley & Sons, Ltd 相似文献
106.
Marie Chavent Herv Gugan Vanessa Kuentz Brigitte Patouille Jrme Saracco 《Environmetrics》2009,20(8):928-942
Air pollution is a wide concern for human health and requires the development of air quality control strategies. In order to achieve this goal pollution sources have to be accurately identified and quantified. The case study presented in this paper is part of a scientific project initiated by the French Ministry of Ecology and Sustainable Development. For the following study measurements of chemical composition data for particles have been conducted on a French urban site. The first step of the study consists in the identification of the sources profiles which is achieved through principal component analysis (PCA) completed by a rotation technique. Then the apportionment of the sources is evaluated with a receptor modeling using positive matrix factorization (PMF) as estimation method. Finally the joint use of these two statistical methods enables to characterize and apportion five different sources of fine particulate emission. Copyright © 2008 John Wiley & Sons, Ltd. 相似文献
107.
D. G. Chen 《Environmetrics》2009,20(1):101-109
The isobologram model is a commonly used and powerful graphical and statistical tool for analyzing the joint action for simple chemical mixtures. Substantial research has been done for the quantitative response and the amount of research in the qualitative framework is minuscule. In this paper, isobologram model is proposed to analyze the joint action of chemical mixtures for quantal dose‐response relationship based on the generalized linear model technique to estimate the associated parameters by the maximum likelihood estimation and then to be used to construct the isobologram so that the joint action from the chemicals can be identified both by the isobologram and the statistical inference for interaction parameter. A real dataset is used to illustrate the application of the developed approach. Copyright © 2008 John Wiley & Sons, Ltd. 相似文献
108.
Catherine A. Calder 《Environmetrics》2008,19(1):39-48
Elevated levels of particulate matter (PM) in the ambient air have been shown to be associated with certain adverse human health effects. As a result, monitoring networks that track PM levels have been established across the United States. Some of the older monitors measure PM less than 10 µm in diameter (PM10), while the newer monitors track PM levels less than 2.5 µm in diameter (PM2.5); it is now believed that this fine component of PM is more likely to be related to the negative health effects associated with PM. We propose a bivariate dynamic process convolution model for PM2.5 and PM10 concentrations. Our aim is to extract information about PM2.5 from PM10 monitor readings using a latent variable approach and to provide better space‐time interpolations of PM2.5 concentrations compared to interpolations made using only PM2.5 monitoring information. We illustrate the approach using PM2.5 and PM10 readings taken across the state of Ohio in 2000. Copyright © 2007 John Wiley & Sons, Ltd. 相似文献
109.
The exact distributions of the ratio X/Y are derived when X and Y are independent random variables and come from type I, type II or type III extreme value distributions. A detailed application of the results is provided to extreme rainfall data from Korea. Copyright © 2005 John Wiley & Sons, Ltd. 相似文献
110.
Over the past few years, Bayesian models for combining output from numerical models and air monitoring data have been applied to environmental data sets to improve spatial prediction. This paper develops a new hierarchical Bayesian model (HBM) for fine particulate matter (PM2.5) that combines U. S. EPA Federal Reference Method (FRM) PM2.5 monitoring data and Community Multi‐scale Air Quality (CMAQ) numerical model output. The model is specified in a Bayesian framework and fitted using Markov Chain Monte Carlo (MCMC) techniques. We find that the statistical model combining monitoring data and CMAQ output provides reliable information about the true underlying PM2.5 process over time and space. We base these conclusions on results of a validation exercise in which independent monitoring data were compared with predicted values from the HBM and predictions from a standard kriging model based solely on the monitoring data. Copyright © 2009 John Wiley & Sons, Ltd. 相似文献