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河流水质监测数据的环境计量建模与解读

Environmetric modeling and interpretation of river water monitoring data.

作者信息

Simeonov V, Einax J W, Stanimirova I, Kraft J

机构信息

Department of Analytical Chemistry, Faculty of Chemistry, University of Sofia St. Kl. Okhridski, 1164 Sofia, J. Bourchier Blvd. 1, Bulgaria.

出版信息

Anal Bioanal Chem. 2002 Nov;374(5):898-905. doi: 10.1007/s00216-002-1559-5. Epub 2002 Oct 15.

Abstract

This environmetric study deals with modeling and interpretation of river water monitoring data from the basin of the Saale river and its tributaries the Ilm and the Unstrut. For a period of one year of observation between September 1993 and August 1994 a data set from twelve campaigns at twenty-nine sampling sites from the Saale river and six campaigns from the river Ilm at seven sampling sites and from river Unstrut at ten sampling sites was collected. Twenty-seven chemical and physicochemical properties were measured to estimate the water quality. The application of cluster analysis, principal components analysis, and apportioning modeling on absolute principal components scores revealed important information about the ecological status of the region of interest:identification of two separate patterns of pollution (upper and lower stream of the rivers);identification of six latent factors responsible for the data structure with different content for the two identified pollution patterns; anddetermination of the contribution of each latent factor (source of emission) to the formation of the total concentration of the chemical burden of the river water. As a result more objective ecological policy and decision making is possible.

摘要

这项环境计量学研究涉及对萨勒河及其支流伊尔姆河和昂斯特鲁特河流域河水监测数据的建模与解读。在1993年9月至1994年8月为期一年的观测期内,收集了来自萨勒河29个采样点的12次采样活动、伊尔姆河7个采样点的6次采样活动以及昂斯特鲁特河10个采样点的数据。测量了27种化学和物理化学性质以评估水质。通过聚类分析、主成分分析以及对绝对主成分得分进行分配建模,揭示了有关感兴趣区域生态状况的重要信息:识别出两种不同的污染模式(河流上下游);识别出六个对数据结构负责的潜在因素,这两种已识别的污染模式具有不同的含量;以及确定每个潜在因素(排放源)对河流水化学负荷总浓度形成的贡献。结果是可以实现更客观的生态政策和决策。

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