Modeling the buoyant flow of heated water discharged from surface and submerged side outfalls in shallow and deep water with a cross flow

In this study, a three-dimensional model was used to numerically study the buoyant flow, along with its mixing characteristics, of heated water discharged from the surface and submerged side outfalls in shallow and deep water with a cross flow. Hydraulic experimental data were used to evaluate the applicability of the model. The simulation results agree well with the experimental results, particularly, the jet trajectories, the dimensions of the recirculating zone, and the distribution of the dimensionless excess temperature. The level of accuracy of the simulation results of the present study is nearly identical to that of the results conducted by McGuirk and Rodi (1978). If the heated water is discharged into shallow water where the momentum flux ratio and the discharge densimetric Froude number are high, the submerged discharge method is better than the surface discharge method in terms of the scale of the recirculating zone and the minimum dilution. In deep water, where the momentum flux ratio and discharge densimetric Froude number are low, however, the submerged discharge method had few advantages. In shallow water, the discharge jet is deflected by the ambient cross flow, while forcing the ambient flow to bend towards the far bank for the full depth. For a submerged discharge in shallow water, the recirculating zone is the largest in the lowest layer but becomes smaller in the upper layer. As the water depth increases, the ambient flow goes over the jet and diminishes the blocking effect, thereby decreasing the bending of the jet.     

A set of associated statistical tests for spatial clustering

The purpose of this paper is to develop a set of associated statistical tests for spatial clustering. In particular, a set of three associated tests will be developed; these will correspond to the three types of tests set out by Besag and Newell (general tests, focused tests, and tests for the detection of clustering). The associated tests draw primarily, though not exclusively, upon existing tests and results. The principal contributions are based upon the score statistic for focused tests, which has been an important approach to testing for clustering around environmental hazards. The first contribution consists of the formulation of a global statistic for general tests that corresponds to focused score statistics, along with an assessment of the distribution of the statistic under the null hypothesis of no raised incidence. The local score statistics used for focused tests will have the property of summing to the global statistic used for the corresponding general test. Attention is also given to the maximum local score statistic for the “test for the detection of clustering”. The critical values of this statistic which are required for testing the null hypothesis are described. Application of the methods is made to leukemia data for central New York State.     

Some statistical and conceptual issues in the detection of low-level environmental pollutants

With increasing concern over chemicals that are potential health hazards at low levels, determination of limits of detection have undergone considerable scrutiny. Most traditional detection limit estimators suffer from extensive statistical and/or conceptual limitations. In this paper, traditional detection limit estimators are described and critically evaluated. Using the terminology of Currie (1968), methods are categorized into decision limits versus detection limits. The methods are further categorized into single concentration design versus calibration design methodologies. While the single concentration design methods are useful for fixing ideas and clarifying definitions, they are shown to be extremely limited in practice since dependence of variability on concentration can neither be estimated or incorporated. Calibration-based detection limit estimators are described, compared and contrasted. Generalizations to non-constant variance, multiple future detection decisions and simultaneous control of Type I and II errors are provided. The various calibration-based methods are illustrated using real data and experimental design issues for detection limit studies discussed.     

Genetic Effects of Habitat Fragmentation on Common Species of Swiss Fen Meadows

Abstract:  The area of Caricion davallianae alliance in Switzerland has been considerably reduced and fragmented during the last 150 years. We assessed the genetic variability, inbreeding level, and among-population differentiation of two common habitat-specific plant species, Carex davalliana SM. and Succisa pratensis Moench, in 18 Caricion davallianae fen meadows subjected to fragmentation. We used a spatial field design of fen systems (six systems total), each consisting of one large habitat island and two small habitat islands. We used allozyme electrophoresis to derive standard genetic parameters ( A, P, H_O, H_E, F_IS, F_ST ). In Carex we identified a consistently lower A in isolated habitat islands; furthermore, H_E was lower in small habitat islands than in large habitat islands. In Succisa we identified a lower H_O in small habitat islands than in larger ones. Small habitat islands were marginally significantly differentiated (  F_ST ) from large islands for Succisa . For both species, no effects were evident for F_IS ; therefore, we argue that genetic drift rather than inbreeding is the main cause of the observed differences. The genetic structure of Carex and Succisa in small habitat islands differed from that in large habitat islands, but differences were small. It appears that the observed differences in genetic variability among fen meadows correspond to observed differences in fitness and demographic traits. We show that habitat fragmentation affects not only the rare species in an ecosystem but also reduces the survival probabilities of common species. One of the main goals of conservation should be to mitigate fragmentation of natural habitats in order to increase population sizes and connectivity.     

Detection and delineation of critical areas using echelons and spatial scan statistics with synoptic cellular data

Geographical surveillance for hotspot detection and delineation has become an important area of investigation both in geospatial ecosystem health and in geospatial public health. In order to find critical areas based on synoptic cellular data, geospatial ecosystem health investigations apply recently discovered echelon tools. In order to find elevated rate areas based on synoptic cellular data, geospatial public health investigations apply recently discovered spatial scan statistic tools. The purpose of this paper is to conceptualize a joint role for these together in the spirit of a cross-disciplinary cross-fertilization to accomplish more effective and efficient geographical surveillance for hotspot detection and delineation, and early warning system.     

Bayesian areal wombling via adjacency modeling

Recently, public health professionals and other geostatistical researchers have shown increasing interest in boundary analysis, the detection or testing of zones or boundaries that reveal sharp changes in the values of spatially oriented variables. For areal data (i.e., data which consist only of sums or averages over geopolitical regions), Lu and Carlin (Geogr Anal 37: 265–285, 2005) suggested a fully model-based framework for areal wombling using Bayesian hierarchical models with posterior summaries computed using Markov chain Monte Carlo (MCMC) methods, and showed the approach to have advantages over existing non-stochastic alternatives. In this paper, we develop Bayesian areal boundary analysis methods that estimate the spatial neighborhood structure using the value of the process in each region and other variables that indicate how similar two regions are. Boundaries may then be determined by the posterior distribution of either this estimated neighborhood structure or the regional mean response differences themselves. Our methods do require several assumptions (including an appropriate prior distribution, a normal spatial random effect distribution, and a Bernoulli distribution for a set of spatial weights), but also deliver more in terms of full posterior inference for the boundary segments (e.g., direct probability statements regarding the probability that a particular border segment is part of the boundary). We illustrate three different remedies for the computing difficulties encountered in implementing our method. We use simulation to compare among existing purely algorithmic approaches, the Lu and Carlin (2005) method, and our new adjacency modeling methods. We also illustrate more practical modeling issues (e.g., covariate selection) in the context of a breast cancer late detection data set collected at the county level in the state of Minnesota.     

The National Resources Inventory: a long-term multi-resource monitoring programme

Interest in natural resources and the environment has led to the development of new federal monitoring efforts, the expansion of existing federal inventory programmes, and discussions of inter-agency collaboration for natural resource assessment data collection. As federal programmes evolve, knowledge gained from existing long-term survey programmes can provide valuable contributions to statistical and operational aspects of survey efforts. This paper describes the National Resources Inventory (NRI), which has been conducted by the US Department of Agriculture's Natural Resources Conservation Service in cooperation with the Iowa State University Statistical Laboratory for several decades. The current NRI is a longitudinal survey of soil, water, and related environmental resources designed to assess conditions and trends every five years on non-federal US lands. An historical overview is provided highlighting the development of the survey programme. Sample design, data collection, and estimation procedures used in the 1992 NRI are described, and statistical issues related to long-term monitoring are discussed.     

活性污泥法的多变量最优控制Ⅱ.限制有机物(BOD)的排放总量

着重研究了有限制有机物排放总量时，使其运行费用最低的最优周期控制问题。通过增加新的状态变量和用补偿函数法，将本课题的有约束条件问题化为无约束条件问题，并提出了最优长参数的动态搜索法来修改传统的梯度法，从而较完善地解决了多变量最优周期控制的计算问题。     

窄脉冲电晕等离子体烟气脱硫产物收集研究

对窄脉总电晕等离子体烟气脱硫过程中的产物收集进行了研究与探讨，结果表明正脉冲流注电晕放电烟气脱硫过程中产生的电粉尘以及电晕极肥大等问题，对脱硫产物的收集有很大影响。     

新疆叶尔羌河突发性洪水初步研究

叶尔羌河发源于昆仑山和喀喇昆仑山区,是新疆第三大河。近30年,每2—3年发生一次突发性洪水,给下游人民造成很大灾难。 本文阐述了该洪水的特性,时空分布、演进情势及峰量变化,加之分析气象资料、地质地貌条件,首次提出突发性洪水成因于冰川阻塞湖泄洪,其策源地位于克勒青河上游的克亚吉尔特索湖和特拉木坎力湖。随着冰川的衰退,阻塞湖将逐渐缩小。