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开发用于时间序列趋势检测的软件包:在圣劳伦斯河的水质和工业废水质量数据中的应用。

Development of a software package for trend detection in temporal series: Application to water and industrial effluent quality data for the St. Lawrence River.

机构信息

INRS-Eau, Box 7500, G1V 4C7, Ste. Foy, QC, Canada.

出版信息

Environ Monit Assess. 1989 Nov;13(2-3):429-41. doi: 10.1007/BF00394243.

DOI:10.1007/BF00394243
PMID:24243186
Abstract

Despite the considerable amount of effort and resources involved in monitoring water quality, water quality assessment and environmental follow-up are sometimes carried out with simple statistics, the main reason being the lack of appropriate statistical methods adapted to the nature of sampled water quality data.A survey of the classical methods used for trend detection and of their limitations is first presented, including the most recent non-parametric techniques adapted to the structure of the sampled data and to the possible types of trends occuring. This paper then presents an interactive user-friendly software package developed for microcomputers making use of these latest adapted techniques. Afterwards, some applications of the software are described pertaining to the concentrations measured at long-term stations on the St. Lawrence River and to the mass loadings discharged by regulated industries. Finally, conclusions are drawn about the assumptions, performance and limitations of the package as well as about the research needs to improve the usefulness and applicability of the software.

摘要

尽管在监测水质方面投入了相当多的精力和资源,但水质评估和环境跟踪有时仅采用简单的统计方法,主要原因是缺乏适用于抽样水质数据性质的适当统计方法。本文首先调查了用于趋势检测的经典方法及其局限性,包括最近适应抽样数据结构和可能出现的趋势类型的非参数技术。然后,本文介绍了一个为微机开发的具有交互功能且用户友好的软件包,该软件包利用了这些最新的适应性技术。之后,描述了该软件的一些应用,涉及圣劳伦斯河长期监测站的浓度和受监管行业排放的质量负荷。最后,对软件的假设、性能和局限性以及提高软件的有用性和适用性的研究需求进行了总结。

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本文引用的文献

1
Visual techniques for the detection of water quality trends: Double-mass curves and cusum functions.水质趋势检测的视觉技术:双质量曲线和累积和函数。
Environ Monit Assess. 1983 Jun;3(2):173-84. doi: 10.1007/BF00398846.
监测加拿大/美国酸雨控制项目的结果:一些湖泊的响应情况。
Environ Monit Assess. 2003 Oct-Nov;88(1-3):3-19. doi: 10.1023/a:1025563400336.