Modelling forest succession in two southeastern Canadian mixedwood ecosystem types using the ZELIG model

The gap model ZELIG was validated for red spruce–balsam fir–yellow birch and yellow birch–sugar maple–balsam fir forest types in southern Quebec, Canada. Long-term historical data originating from the Lake Edward Experimental Forest, La Mauricie National Park, were used. The effect of the variation in plot size, representing the space within which trees uptake site resources, was also examined. Several species were included in both forest types: red spruce (Picea rubens Sarg.), balsam fir (Abies balsamea (L.) Mill.), yellow birch (Betula alleghaniensis Britton), white birch (Betula papyrifera Marsh.), red maple (Acer rubrum L.), sugar maple (Acer saccharum Marsh.), American beech (Fagus grandifolia Ehrh.), eastern hemlock (Tsuga canadensis (L.) Carr.) and northern white cedar (Thuja occidentalis L.). The pattern of change in basal area growth varied among species, ranging from a steady increase to a more or less rapid decline. There was a good agreement between observations and predictions for yellow birch, red spruce, red maple, sugar maple, balsam fir and northern white cedar. Plot size had a significant impact on the dynamics of the different species. Depending on the species, the decline was accelerated, the amplitude of the fluctuations varied, or the maximum basal area reached changed. Predicted regeneration varied among species and the number of seedlings generally increased with increase in plot size. The pattern of development for most species was related to their life characteristics. The results highlighted the fact that there is a critical lack of knowledge and data on the dynamics of regeneration from the seedling to the sapling stages for the two forest types studied, which resulted in poor predictions for some species. As the life characteristics varied among species, the use of only one plot size for all species may not be realistic.     

广州地铁二号线列车控制系统的设计

从用户的角度，介绍了广州地铁二号线列车控制系统的技术参数，特点及其性能，并对其在一号线列车控制基础上所做的改进进行了说明.     

关于确定大气混合层高度的几种方法

本文阐述了大气混合层高度研究在环境保护中的作用，论述了大气混合层高度确定的几种方法，分析了各种方法的特点及应用前景，同时也讨论了目前在我国大气混合层高度研究中提出的一些新方法。     

海水水质评价的人工神经网络模型研究

通过在各类海水水质污染指标浓度区间内生成随机分布样本的方法，生成足够多用于人工神经网络模型训练和检验用的样本，并应用基于误差反传原理的前向多层神经网络建立了用于海水水质评价的人工神经网络模型。并根据海水水质标准给出了区分不同类型水质的模型分界样本和模型输出分界值。讨论了确定合理隐层及其节点数的方法，使得训练后的神经网络模型具有更强的泛化能力，不受网络连接权值初始取值的影响。经训练的评价模型应用于实例的评价结果表明，新的评价模型具有较好的客观性、通用性和实用性。     

Emission inventory on company level: lessons from Russia

Developing a transparent,accurate greenhouse gas (GHG) emissionsinventory is the first step toward buildingan effective GHG management system. Todate, GHG inventories have been conductedprimarily at national levels. Theinternationally accepted inventorymethodology developed by theIntergovernmental Panel on Climate Change(IPCC) is oriented to countrywideinventories. The electricity company RAOUESR is the largest single corporateemitter of GHG in the Russian Federation. The company is responsible for about 1/3 ofRussia's CO₂ emissions; RAO's fossil fuelemissions are comparable to the fossil fuelemissions of the United Kingdom. The GHGinventory prepared by RAO is the first suchcorporate emissions inventory undertaken ina non-OECD country. In this article wepresent a detailed independent examinationof the methodology RAO applied for theinventory. We identify the most importantsources of uncertainty and we estimate theuncertainty. The main conclusion of theindependent review is that the methodologyutilized by RAO and the informationsupporting the methodology are reliable andpresent a reasonably accurate company-widepicture of RAO's CO₂ emissions. The shareof other greenhouse gases is negligiblysmall and we did not focus on this fractionof RAO's GHG emissions. As a next step, RAOmay wish to conduct more precisefacility-by-facility inventories in orderto create a robust GHG emission managementsystem.     

长江水系河流沉积物重金属元素含量的计算方法研究

采用长江水系约２６０个背景样点沉积物原样和小于６３μｍ细粒沉积物中１１种重金属元素（铜、铅、锌、镉、汞、钴、镍、砷、铬、锰和铁）含量的数据，研究了数据的频率分布特征和变异系数，比较了算术均值、几何均值和中位数与由Ｂｏｘ－Ｃｏｘ变换而得到的对称均值间的大小关系，结果表明，大多数重金属元素含量服从对数正态分布；采用算术均值表示均值会有较大的偏差，且这种偏差随变异系数的增大而增大；采用中位数偏差较小，但     

环境监测数据分析和监测网设计中SPSS 10.0的应用

SPSS10.0是目前功能齐全、便于应用的优秀统计软件。文章介绍了该软件在环境监测数据分析和监测网设计中的应用。文中简述了该软件的功能及优点，并通过实例，展示其在环境监测数据分析和监测网设计中的应用，这对于优化监测数据分析结果和监测网的设计均有较好的借鉴作用。     

Assessment of Wet Sulfate Concentration Changes at CAPMoN Sites, Canada

This paper describes a statistical analysis of wet sulfate deposition data sampled by the Canadian Air and Precipitation Monitoring Network (CAPMoN) since 1988 till 1997. The goal of the investigation is to detect presence of prevailing significant changes in the probability distribution of annual samples collected by the network at each site. The considerations are based on a first order autoregression model with second order polynomial trend and methods used for analysis of variance and multiple comparison. Unlike studies suggesting existence of long term trends in the data, methods applied here indicate absence of any systematic changes in the observed annual concentration patterns at most of the sites.     

Uncertainty Assessment of Voc Emissions from Paint in the Netherlands Using the Nusap System

We demonstrate an innovative approach to uncertainty assessment known as the NUSAP system, to assess qualitative and quantitative uncertainty for the case of emissions of volatile organic compounds (VOC) from paint in The Netherlands. Using expert elicitation, we identified key sources of error, critical assumptions, and bias in the monitoring process. We assessed pedigree and probabilistic uncertainty of all input quantities. We used four pedigree criteria to assess the strength of the knowledge base: proxy representation, empirical basis, methodological rigour and degree of validation. Using Monte Carlo analysis, we assessed sensitivity and propagation of uncertainty. Results for sensitivity and pedigree were combined in a NUSAP Diagnostic Diagram, which effectively highlighted the assumption for VOC percentage of imported paint as the weakest spot in the monitoring of VOC emissions. We conclude that NUSAP facilitates systematic scrutinization of method and underlying assumptions and structures creative thinking on sources of error and bias. It provides a means to prioritise uncertainties and focus research efforts on the potentially most problematic parameters and assumptions, at the same time identifying specific weaknesses in the knowledge base. With NUSAP, nuances of meaning about quantities can be conveyed concisely and clearly, to a degree that is not possible with statistic methods only.     

A method to combine non-probability sample data with probability sample data in estimating spatial means of environmental variables

In estimating spatial means of environmental variables of a region from datacollected by convenience or purposive sampling, validity of the results canbe ensured by collecting additional data through probability sampling. Theprecision of the estimator that uses the probability sample can beincreased by interpolating the values at the nonprobability sample points tothe probability sample points, and using these interpolated values as anauxiliary variable in the difference or regression estimator. Theseestimators are (approximately) unbiased, even when the nonprobability sampleis severely biased such as in preferential samples. The gain in precisioncompared to the estimator in combination with Simple Random Samplingis controlled by the correlation between the target variable andinterpolated variable. This correlation is determined by the size (density)and spatial coverage of the nonprobability sample, and the spatialcontinuity of the target variable. In a case study the average ratio of thevariances of the simple regression estimator and estimator was 0.68for preferential samples of size 150 with moderate spatial clustering, and0.80 for preferential samples of similar size with strong spatialclustering. In the latter case the simple regression estimator wassubstantially more precise than the simple difference estimator.