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首页> 外文期刊>Environmental Monitoring and Assessment >SPATIAL ANALYSIS AND INDICATOR BUILDING FOR METAL ACCUMULATION IN MOSSES
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SPATIAL ANALYSIS AND INDICATOR BUILDING FOR METAL ACCUMULATION IN MOSSES

机译:苔藓中金属积累的空间分析与指标构建

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摘要

Mosses are used as passive accumulation monitors for metal accumulation in terrestrial ecosystems. Under leadership of the Federal Agency for Environmental Protection Germany took part in the previous European-wide moss monitoring campaigns 1990, 1995 and 2000. The inves-tigations accomplished thereby cannot be presented completely in this article. The remarks rather concentrate on methodical aspects of the statistical data analysis. In Chapter 2 the design of data collection is summarized. Chapter 3 treats the geostatistical analysis and transformation of point data to areal information. Chapter 4 describes the aggregation of the element-specific metal concentrations in mosses to a spatially and temporally differentiated indicator of metal accumulation by means of descriptive and multivariate statistics. The work presented is only a small part of geostatistics and multivariate statistics which fit for analysis of moss monitoring data. Taking the results presented here as a basis, the following steps would further be of great importance: cluster-analytic evaluation of the results of the Moss Monitoring 1990 and 1995, detailing the cluster results using additional empirical and location describing information (e.g. moss species, ecoregions, site and species spe-cific variability of metal accumulation) as well as optimizing the indicator buildung by testing of multivariate statistical regression models (e.g. Classification and Regression Trees).
机译:苔藓用作陆地生态系统中金属积累的被动积累监测器。在联邦环境保护局的领导下,德国参加了先前在1990年,1995年和2000年在欧洲进行的青苔监测运动。由此完成的调查不能在本文中完整介绍。这些评论主要集中在统计数据分析的方法方面。在第二章中,总结了数据收集的设计。第3章讨论了地统计分析和将点数据转换为面信息。第4章通过描述性和多元统计方法,将苔藓中特定于元素的金属浓度的聚集,描述为金属积累的时空区分指标。提出的工作只是地统计和多元统计的一小部分,适合于监测青苔监测数据。以此处介绍的结果为基础,以下步骤将变得非常重要:对1990年和1995年的Moss Monitoring的结果进行聚类分析评估,使用其他经验和位置描述信息(例如,moss物种,生态区,地点和物种的金属累积特定变异性),并通过测试多元统计回归模型(例如分类树和回归树)来优化指标的建立。

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