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Water Res. 2012 Jun 1;46(9):3110-22. doi: 10.1016/j.watres.2012.03.028. Epub 2012 Mar 21.
Comprehensive water quality investigations to characterize large watersheds include collection of surface water samples over time at various locations within the watershed and analyses of the samples for multiple chemical and biological constituents. The size and complexity of the resulting dataset make overall evaluations difficult, and as a result, multivariate statistical methods can be useful to evaluate environmental patterns and sources of contamination. The most commonly applied multivariate method in watershed studies is principal components analysis (PCA), which uses correlation among multiple water quality constituents to effectively reduce the number of variables. The reduced set of variables may assist in the identification and description of spatial patterns in water quality that result from hydrologic and geochemical processes and from sources of contamination. The utility of PCA for identifying important environmental factors in a given study is obviously affected by sampling design, constituents analyzed, data quality, data treatment prior to PCA, methods of interpreting PCA results, and other factors. Unfortunately no comprehensive evaluations have been performed and no standard procedures exist for dealing with these issues. This paper examines and evaluates the current state-of-the-science by review of 49 published papers dealing with multivariate (typically PCA) techniques to evaluate watershed water quality. Additionally an example PCA for a surface water quality study in the Illinois River Watershed (IRW) is provided to illustrate methods to address the above issues and to evaluate the sensitivity of results to alternative methods. The example PCA evaluations were consistent with two dominant sources of surface water contamination in the IRW: 1) discharge to the streams from municipal wastewater treatment plants and 2) runoff and infiltration from fields with land applied poultry waste.
全面的水质调查包括在流域内的不同位置定期采集地表水样本，并对这些样本进行多种化学和生物成分的分析。由此产生的数据集的规模和复杂性使得全面评估变得困难，因此，多元统计方法可以用于评估环境模式和污染来源。在流域研究中最常用的多元方法是主成分分析（PCA），它利用多个水质成分之间的相关性来有效地减少变量的数量。减少的变量集可能有助于识别和描述由于水文和地球化学过程以及污染来源而导致的水质空间模式。PCA 用于识别给定研究中重要环境因素的效用显然受到采样设计、分析成分、数据质量、PCA 前的数据处理、PCA 结果解释方法以及其他因素的影响。不幸的是，没有进行全面评估，也没有标准程序来处理这些问题。本文通过审查 49 篇涉及多元（通常是 PCA）技术评估流域水质的已发表论文，检查和评估了当前的科学状况。此外，还提供了伊利诺伊河流域（IRW）地表水质量研究的 PCA 示例，以说明解决上述问题的方法，并评估结果对替代方法的敏感性。该 PCA 示例评估与 IRW 地表水的两个主要污染来源一致：1）从城市污水处理厂排放到溪流中，2）从施用过禽粪肥的农田中径流和渗透。
