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利用多元统计分析对大堡礁集水区水质的空间变异性进行特征描述。

Characterisation of spatial variability in water quality in the Great Barrier Reef catchments using multivariate statistical analysis.

机构信息

Department of Infrastructure Engineering, The University of Melbourne, Parkville, VIC 3010, Australia.

Department of Infrastructure Engineering, The University of Melbourne, Parkville, VIC 3010, Australia.

出版信息

Mar Pollut Bull. 2018 Dec;137:137-151. doi: 10.1016/j.marpolbul.2018.10.019. Epub 2018 Oct 11.

Abstract

Water quality monitoring is important to assess changes in inland and coastal water quality. The focus of this study was to improve understanding of the spatial component of spatial-temporal water quality dynamics, particularly the spatial variability in water quality and the association between this spatial variability and catchment characteristics. A dataset of nine water quality constituents collected from 32 monitoring sites over a 11-year period (2006-2016), across the Great Barrier Reef catchments (Queensland, Australia), were evaluated by multivariate techniques. Two clusters were identified, which were strongly associated with catchment characteristics. A two-step Principal Component Analysis/Factor Analysis revealed four groupings of constituents with similar spatial pattern and allowed the key catchment characteristics affecting water quality to be determined. These findings provide a more nuanced view of spatial variations in water quality compared with previous understanding and an improved basis for water quality management to protect nearshore marine ecosystem.

摘要

水质监测对于评估内陆和沿海水质的变化非常重要。本研究的重点是提高对时空水质动态空间组成部分的理解,特别是水质的空间变异性以及这种空间变异性与集水区特征之间的关系。通过多元技术对在大堡礁集水区(澳大利亚昆士兰州) 32 个监测点收集的九个水质成分数据集进行了评估,该数据集跨越了 11 年(2006-2016 年)。两个聚类与集水区特征密切相关。两步主成分分析/因子分析揭示了具有相似空间模式的四个成分分组,并确定了影响水质的关键集水区特征。这些发现与以前的认识相比,更细致地描述了水质的空间变化,为保护近岸海洋生态系统的水质管理提供了更好的基础。

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