JSEM: A Framework for Identifying and Evaluating Indicators

There are two issues in indicator development that have not been adequately addressed: (1) how to select an optimal combination of potentially redundant indicators that together best represent an endpoint, given cost constraints; (2) how to identify and evaluate indicators when the endpoint is unmeasured. This paper presents an approach to identifying and evaluating combinations of indicators when the mathematical relationships between the indicators and an endpoint may not be quantified, a limitation common to many ecological assessments. The approach uses the framework of Structural Equation Modeling (SEM), which combines path analysis withmeasurement models, to formalize available informationabout potential indicators and to evaluate their potential adequacy for representing an endpoint. Unlike traditional applications of SEM which require data on all variables, our approach – judgement-based SEM (JSEM) – can utilize expert judgement regarding the strengths and shapes of indicator-endpoint relationships. JSEM is applied in two stages. First, a conceptual model that relates variables in a network of direct and indirect linkages is developed, and is used to identify indicators relevant to an endpoint. Second, an index of indicator strength – i.e., the strength of the relationship between the endpoint and a set of indicators – is calculated from estimates of correlation between the modeled variables, and is used to compare alternative sets of indicators. The second stage is most appropriate for large, long-term assessments. Although JSEM is not a statistical technique, basing JSEM on SEM provides a structure for validating the conceptual model and for refining the index of indicator strength as data become available. Our main objective is to contribute to a rigorous and consistent selection of indicators even when knowledgeabout the ability of indicators to represent an endpoint is limited to expert judgement.     

Limitations of multimedia models for use in environmental decision making

The United States currently is engaged in a complex,multi-billion dollar effort to cleanup a legacy ofboth privately- and federally-owned hazardous wastesites. Decisions regarding the best approach forremediation of these sites often are based on theanalysis of potential risks to human health and theenvironment. A cornerstone of such analysis is thefrequent use of computerized multimedia environmentaltransport models, to evaluate the large quantities ofinformation necessary to understand the present andfuture implications of contamination at a site. Onebarrier to wide-spread use of this analyticalprocedure is the view that results obtained usingcomputer models are highly dependent on user input,and therefore, subject to manipulation. It is widelyrecognized that for decisions to be both credible andimplementable, the public must have confidence in boththe scientific basis for judgments involved and thedecision processes employed (NRC, 1983). Our purposein this article is to overview the difficultiesassociated with application of multimedia models toreal world problems and the contribution these modelscan make to technically sound estimates of exposure and risk.     

国外城市非点源径流水质模型简介

随着人们环保意识的提高。城市中的点源污染已大部分得到了控制，于是非点源污染变得越来越突出,越来越严重．并引起了城市环保者的关注。借此。本文介绍了国外四种最主要的城市非点源污染的管理模型,以供我国城市环保人员在对非点源污染进行管理时作为参考。     

Learning Machines Applied to Potential Forest Distribution

The clearing of forests to obtain land for pasture and agriculture and the replacement of autochthonous species by other faster-growing varieties of trees for timber have both led to the loss of vast areas of forest worldwide. At present, many developed countries are attempting to reverse these effects, establishing policies for the restoration of older woodland systems. Reforestation is a complex matter, planned and carried out by experts who need objective information regarding the type of forest that can be sustained in each area. This information is obtained by drawing up feasibility models constructed using statistical methods that make use of the information provided by morphological and environmental variables (height, gradient, rainfall, etc.) that partially condition the presence or absence of a specific kind of forestation in an area. The aim of this work is to construct a set of feasibility models for woodland located in the basin of the River Liébana (NW Spain), to serve as a support tool for the experts entrusted with carrying out the reforestation project. The techniques used are multilayer perceptron neural networks and support vector machines. Their results will be compared to the results obtained by traditional techniques (such as discriminant analysis and logistic regression) by measuring the degree of fit between each model and the existing distribution of woodlands. The interpretation and problems of the feasibility models are commented on in the Discussion section.     

WATERSHED AND INSTREAM IMPACTS ON THE FISH POPULATION IN THE SOUTH FORK OF THE CLEARWATER RIVER,IDAHO1

ABSTRACT: This paper presents the results of a statistical analysis performed for the watershed and stream corridor in the South Fork of the Clearwater River (SFCR) basin, in north central Idaho. The analysis was performed for 61 six‐field hydrological unit codes (HUCs) of the SFCR basin using an extensive record (up to 100 years) for 50 watershed and in‐stream parameters, including hydrologic, flow, fish, anthropogenic, and natural activity data. The objective of this research was twofold: first, the development of quantitative relations that describe the Index of Fish Density (IFD) of particular fish species as a function of watershed and instream parameters; and second, to provide a robust confirmation for the effects of some of these parameters, previously recorded by the fisheries profession, by using well established statistical techniques. The uniqueness of this work is the compilation and statistical analysis of large data sets to quantitatively describe the impacts of watershed and instream parameters on the IFD of all salmonids and specific fish species. Factor analysis was employed to regroup parameters that are highly correlated to each other into a set of single factors and to relate the IFD to these factors. Using factor extraction, 12 factors were developed from the 50 watershed and instream parameters. Multiple regression diagnostic tests indicated that only 7 of the 12 factors are strong predictors offish indicators. The strongest predictors are longitude, latitude, elevation, watershed gradient, and water temperature. The analysis indicated that the present model has reasonable predictive power, considering the uncertainty involved in estimating the interdependence of IFD with watershed parameters.     

对几种大气环境预测方法的评估

本文根据石家庄市的常规气象资料和混合层高度,用不同的计算模式和计算方法对本市特征污染因子SO_4的长期平均浓度进行计算,把计算结果与实测浓度比较并进行误差分析,对每种计算方法进行评价,最后讨论了各种计算方法的特点及在城市大气环境预测中的实用性.     

Evaluation of the effect of meteorological data resolution on Lagrangian particle dispersion simulations using the ETEX experiment

This paper presents results from a series of numerical experiments designed to evaluate operational long-range dispersion model simulations, and to investigate the effect of different temporal and spatial resolution of meteorological data from numerical weather prediction models on these simulations. Results of Lagrangian particle dispersion simulations of the first tracer release of the European Tracer Experiment (ETEX) are presented and compared with measured tracer concentrations. The use of analyzed data of higher resolution from the European Center for Medium-Range Weather Forecasts (ECMWF) model produced significantly better agreement between the concentrations predicted with the dispersion model and the ETEX measurements than the use of lower resolution Navy Operational Global Atmospheric Prediction System (NOGAPS) forecast data. Numerical experiments were performed in which the ECMWF model data with lower vertical resolution (4 instead of 7 levels below 500 mb), lower temporal resolution (12 h instead of 6 h intervals), and lower horizontal resolution (2.5° instead of 0.5°) were used. Degrading the horizontal or temporal resolution of the ECMWF data resulted in decreased accuracy of the dispersion simulations. These results indicate that flow features resolved by the numerical weather prediction model data at approximately 45 km horizontal grid spacing and 6 h time intervals, but not resolved at 225 km spacing and 12 h intervals, made an important contribution to the long-range dispersion.     

持久性有机污染物环境多介质空间分异模型研究进展

环境多介质空间分异模型能够对持久性有机污染物(POPs)在环境多个介质中空间尺度上的迁移转化和分配过程进行准确、细致和接近真实的描述,是进行POPs的环境多介质归趋模拟和环境风险评价的重要工具.将环境多介质空间分异模型分为环境多介质质量平衡空间区划模型和大气化学传输模型,对目前几种常用的环境多介质空间分异模型GLOBO...     

Quantifying The Rainfall-Water Level Fluctuation Process in a Geologically Complex Lake Catchment

Abstact Simulating hydrologic processes in geologically complex environments is a difficult scientific task since it incorporates high level of uncertainty. Many studies have attempted to accurately quantify the rainfall-water level elevation relationship in freshwater bodies so as to predict flooding and drought events. For this purpose several types of models have been implemented including distributed, black box and conceptual models that often provide efficient results, depending on the availability of reliable data as well as on the level of understanding of the system. Nevertheless, in the particular effort, three different models have been used to describe the relationship between rainfall and water level elevation in Trichonis Lake during the period 1951–1997. A Transfer Function model, a Dynamic Linear Regression and a physically based model, consisting of the lake's water budget equation, its Digital Bathymetric Model and GIS algorithms. These models have been tested to assess their efficiency and applicability in a karstic environment and the aim of the study was to find the best modeling option for developing sustainable water management plans and establishing a flooding/drought warning system in the particular lake catchment. The results indicated that in areas with geologically complex conditions, simple, physically-based models operate better than mechanistic models which usually cannot describe adequately the complexity of the system