Industrial sulfur demand: Analysis and prospect

Weak growth in industrial (nonagricultural) U.S. sulfur demand has slowed the growth of total sulfur demand. Evaluating prospects for trend reversal is hampered by inadequate detailed data published by industry, and the lack of an easily measured demand component that reliably depicts total demand. In this article, I try to overcome this data deficiency by constructing an industry-by-industry breakdown of published data on total industrial sulfur consumption. Industrial sulfur consumptions were estimated from data on consuming industry activity, characteristics of relevant industrial processes, and analyses of the conditions affecting choice among alternative industrial processes. Subjective projections of the industry detail data are presented.In addition to cyclical factors, past declines in industrial sulfur demand were due to replacement of sulfur-using industrial processes with alternative processes that use less or no sulfur to achieve production cost economies. In some, but not all, cases, changeovers of process were in response to impositions of environmental-related requirements. The demand for industrial sulfur has good prospects for positive growth as shrinkage of some markets reaches natural limits and total market growth comes increasingly to be dominated by markets with positive growth prospects. Positive growth has been generated by environmentally related regulation in several markets, such as petroleum alkylation and copper production.     

A general framework for error analysis in measurement-based GIS Part 2: The algebra-based probability model for point-in-polygon analysis

This is the second paper of a four-part series of papers on the development of a general framework for error analysis in measurement-based geographic information systems (MBGIS). In this paper, we discuss the problem of point-in-polygon analysis under randomness, i.e., with random measurement error (ME). It is well known that overlay is one of the most important operations in GIS, and point-in-polygon analysis is a basic class of overlay and query problems. Though it is a classic problem, it has, however, not been addressed appropriately. With ME in the location of the vertices of a polygon, the resulting random polygons may undergo complex changes, so that the point-in-polygon problem may become theoretically and practically ill-defined. That is, there is a possibility that we cannot answer whether a random point is inside a random polygon if the polygon is not simple and cannot form a region. For the point-in-triangle problem, however, such a case need not be considered since any triangle always forms an interior or region. To formulate the general point-in-polygon problem in a suitable way, a conditional probability mechanism is first introduced in order to accurately characterize the nature of the problem and establish the basis for further analysis. For the point-in-triangle problem, four quadratic forms in the joint coordinate vectors of a point and the vertices of the triangle are constructed. The probability model for the point-in-triangle problem is then established by the identification of signs of these quadratic form variables. Our basic idea for solving a general point-in-polygon (concave or convex) problem is to convert it into several point-in-triangle problems under a certain condition. By solving each point-in-triangle problem and summing the solutions, the probability model for a general point-in-polygon analysis is constructed. The simplicity of the algebra-based approach is that from using these quadratic forms, we can circumvent the complex geometrical relations between a random point and a random polygon (convex or concave) that one has to deal with in any geometric method when probability is computed. The theoretical arguments are substantiated by simulation experiments.This project was supported by the earmarked grant CUHK 4362/00H of the Hong Kong Research grants Council.     

Potential Implications of PCM Climate Change Scenarios for Sacramento–San Joaquin River Basin Hydrology and Water Resources

The potential effects of climate change on the hydrology and water resources of the Sacramento–San Joaquin River Basin were evaluated using ensemble climate simulations generated by the U.S. Department of Energy and National Center for Atmospheric Research Parallel Climate Model (DOE/NCAR PCM). Five PCM scenarios were employed. The first three were ensemble runs from 1995–2099 with a `business as usual' global emissions scenario, eachwith different atmospheric initializations. The fourth was a `control climate'scenario with greenhouse gas emissions set at 1995 levels and run through 2099. The fifth was a historical climate simulation forced with evolving greenhouse gas concentrations from 1870–2000, from which a 50-yearportion is taken for use in bias-correction of the other runs. From these global simulations, transient monthly temperature and precipitation sequences were statistically downscaled to produce continuous daily hydrologic model forcings, which drove a macro-scale hydrology model of theSacramento–San Joaquin River Basins at a 1/8-degree spatial resolution, and produceddaily streamflow sequences for each climate scenario. Each streamflow scenario was used in a water resources system model that simulated current and predicted future performance of the system. The progressive warming of the PCM scenarios (approximately 1.2 °C at midcentury, and 2.2 °C by the 2090s), coupled with reductions in winter and spring precipitation (from 10 to 25%), markedly reduced late spring snowpack (by as much as half on average by the end of the century). Progressive reductions in winter, spring, and summer streamflow were less severe in the northern part of the study domain than in the south, where a seasonality shift was apparent. Results from the water resources system model indicate that achieving and maintaining status quo (control scenario climate) system performance in the future would be nearly impossible, given the altered climate scenario hydrologies. The most comprehensive of the mitigation alternatives examined satisfied only 87–96% of environmental targets in the Sacramento system, and less than 80% in the San Joaquin system. It is evident that demand modification and system infrastructure improvements will be required to account for the volumetric and temporal shifts in flows predicted to occur with future climates in the Sacramento–San JoaquinRiver basins.     

遥感影像配准误差传递模型及模拟分析

在遥感影像配准过程中,通常假设控制点是“完美的”。然而,在实际情况中,由于控制点本身不可避免的带有一定的误差导致这种假设在一定情况下并不成立,并且将会影响遥感影像几何校正的精度。普通最小二乘方法OLS(O rd inary Least Square)是遥感影像配准常用的校正估计模型,令人遗憾的是,在控制点存在误差的情况下,它的估计是有偏的,并且不能够正确传递和估计校正影像的误差大小。引入一致校正最小二乘方法CALS(ConsistentAd justed Least Squares),在此基础上提出的一个改进的方法,称之为松弛一致校正最小二乘方法RCALS(Relaxed ConsistentAd justed Least Squares)。这类回归模型具有改正控制点(解释变量)中的误差和跟踪回归模型中的误差传递的能力。为了验证CALS和RCALS模型的有效性,本文利用模拟影像进行分析。这里着重分析OLS,CALS和RCALS模型在几何校正过程中的比较。结果表明,RCALS和CALS的结果优于OLS估计结果。     

Weather and traffic accidents in Hong Kong

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Mass consistent models for wind distribution in complex terrain — Fast algorithms for three dimensional problems

Mass consistent models for wind distribution in complex terrain are extremely useful and readily applied to many practical situations, such as the siting of wind turbines, or as input in the estimation of diffusion and transport of pollutants in complex terrain. These models are based on the numerical solution of the steady state three dimensional continuity equation for the mean wind components. The momentum and energy equations are not solved explicitly, but considered indirectly using parametric relations and wind data. In practical applications the equations must be solved numerous times (e.g. for each time interval). Standard techniques for numerical solution of three dimensional problems are frequently very expensive and thus not suitable for practical needs. In the present work, great emphasis is given to the development of fast algorithms, and techniques based on the multigrid approach are shown. Two mass consistent programs are described, the first based on the parametric representation of one of the wind components, and the second based on a few wind measurements and a variational principle. To verify the reliability of the variational approach, a measurement program related to a project of wind energy, is being performed at Har-Ahim, a site located in the Galilee.     

X-ray absorption fine structure determination of pH-dependent U-bacterial cell wall interactions

X-ray absorption fine structure (XAFS) measurements was used at the U L3-edge to directly determine the pH dependence of the cell wall functional groups responsible for the absorption of aqueous UO₂²⁺ to Bacillus subtilis from pH 1.67 to 4.80. Surface complexation modeling can be used to predict metal distributions in water-rock systems, and it has been used to quantify bacterial adsorption of metal cations. However, successful application of these models requires a detailed knowledge not only of the type of bacterial surface site involved in metal adsorption/desorption, but also of the binding geometry. Previous acid-base titrations of B. subtilis cells suggested that three surface functional group types are important on the cell wall; these groups have been postulated to correspond to carboxyl, phosphoryl, and hydroxyl sites. When the U(VI) adsorption to B. subtilis is measured, observed is a significant pH-independent absorption at low pH values (<3.0), ascribed to an interaction between the uranyl cation and a neutrally charged phosphoryl group on the cell wall. The present study provides independent quantitative constraints on the types of sites involved in uranyl binding to B. subtilis from pH 1.67 to 4.80. The XAFS results indicate that at extremely low pH (pH 1.67) UO₂²⁺ binds exclusively to phosphoryl functional groups on the cell wall, with an average distance between the U atom and the P atom of 3.64 ± 0.01 Å. This U-P distance indicates an inner-sphere complex with an oxygen atom shared between the UO₂²⁺ and the phosphoryl ligand. The P signal at extremely low pH value is consistent with the UO₂²⁺ binding to a protonated phosphoryl group, as previously ascribed. With increasing pH (3.22 and 4.80), UO₂²⁺ binds increasingly to bacterial surface carboxyl functional groups, with an average distance between the U atom and the C atom of 2.89 ± 0.02 Å. This U-C distance indicates an inner-sphere complex with two oxygen atoms shared between the UO₂²⁺ and the carboxyl ligand. The results of this XAFS study confirm the uranyl-bacterial surface speciation model.     

Effects of Wormhole Configurations on Performance of Cyclic Solvent Injection in Heavy Oil Systems

Natural Resources Research - In this study, the effect of three different wormhole configurations: single-linear, single-branched, and double-branched wormholes on the performance of cyclic solvent...     

Simulating vegetation effects on flows in 3D using an unstructured grid model: model development and validation

Ocean Dynamics - Prevalence of vegetation (either submerged or emergent) in shallow water significantly affects the flow and turbulence structure in this environment. In this paper, we develop a...     

IMAGE: a multivariate multi-site stochastic weather generator for European weather and climate

Capturing the spatial and temporal correlation of multiple variables in a weather generator is challenging. A new massively multi-site, multivariate daily stochastic weather generator called IMAGE is presented here. It models temperature and precipitation variables as latent Gaussian variables with temporal behaviour governed by an auto-regressive model whose residuals and parameters are correlated through resampling of principle component time series of empirical orthogonal function modes. A case study using European climate data demonstrates the model’s ability to reproduce extreme events of temperature and precipitation. The ability to capture the spatial and temporal extent of extremes using a modified Climate Extremes Index is demonstrated. Importantly, the model generates events covering not observed temporal and spatial scales giving new insights for risk management purposes.