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A comparative assessment of Australia's Lower Lakes water quality under extreme drought and post-drought conditions using multivariate statistical techniques

机译:使用多元统计技术对极端干旱和干旱后条件下澳大利亚下湖水质的比较评估

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摘要

Drought generally results in a decline to freshwater quality, but the spatial nature of these impacts and recovery processes in large lakes systems remain poorly understood. This study applied multiple statistical methods such as cluster analysis (CA), discriminant analysis (DA), principal component analysis (PCA) and factor analysis (FA), to assess spatial and temporal variations of water quality in the Lower Lakes (Australia) during drought (April 2008 September 2010) and post-drought (October 2010 October 2013) periods. The comprehensive analysis of water quality from 22 locations and including 22 key parameters showed that Lower Lakes were eutrophic in both drought and post-drought periods with higher nutrient and algae concentrations than guideline levels for aquatic ecosystem. The Lower Lakes were identified three distinct spatial zones, i.e., (1) low eutrophication for the southeast of Lower Lakes (SE), (2) moderate eutrophication for northeast of Lower Lakes (NE), and (3) high eutrophication for northwest of Lower Lakes (NW) in the drought as well as low eutrophication for NW and high eutrophication for SE in the post-drought. DA allowed a better reduction in the dimensionality of the large dataset during post-drought than during the drought period with better results for spatial analysis rather than for temporal analysis regardless of hydrological periods. PCA/FA reflected three major factors of mineral dissolution, erosion and anthropogenic sources accounting for water constituents. Our results demonstrate the powerful utility of multivariate statistical techniques for revealing the persistent and spatially complex nature of drought-induced impacts on lake water quality and highlight that optimal utilization of water resources in the upper catchment of Lower Lakes are urgently needed for the sustainable lake ecosystems. (C) 2018 Elsevier Ltd. All rights reserved.
机译:干旱通常会导致淡水水质下降,但是对于大型湖泊系统中这些影响和恢复过程的空间性质仍然知之甚少。这项研究应用了多种统计方法,例如聚类分析(CA),判别分析(DA),主成分分析(PCA)和因子分析(FA),以评估下湖区(澳大利亚)水质在时间和空间上的变化。干旱(2008年4月,2010年9月)和干旱后(2010年10月,2013年10月)时期。对22个地点的水质进行了综合分析,包括22个关键参数,结果表明,下湖在干旱和干旱后时期都是富营养化的,其营养和藻类浓度高于水生生态系统的指导水平。下湖区被确定为三个不同的空间区域,即(1)下湖区东南部(SE)的低营养化,(2)下湖区东北部(NE)的中营养化和(3)西湖西北部的高营养化。干旱后的下湖(NW)以及干旱后西北的富营养化程度较低和SE的富营养化程度较高。与干旱时期相比,DA可以更好地减少大数据集的维数,而与干旱时期相比,无论水文时期如何,空间分析结果都优于时间分析结果。 PCA / FA反映了矿物溶解,侵蚀和人为来源的三个主要因素,这些因素构成了水的成分。我们的结果证明了多元统计技术在揭示干旱引起的湖泊水质影响的持久性和空间复杂性方面的强大效用,并强调了为可持续发展的湖泊生态系统迫切需要对下湖上游流域的水资源进行最佳利用。 (C)2018 Elsevier Ltd.保留所有权利。

著录项

  • 来源
    《Journal of Cleaner Production》 |2018年第20期|1-11|共11页
  • 作者单位

    Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, 266 Fangzheng Ave,Shuitu High Tech Pk, Chongqing 400714, Peoples R China;

    Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, 266 Fangzheng Ave,Shuitu High Tech Pk, Chongqing 400714, Peoples R China;

    Sichuan Univ, Coll Water Resource & Hydropower, State Key Lab Hydraul & Mt River Engn, Chengdu 610065, Sichuan, Peoples R China;

    Univ Newcastle, Int Ctr Balanced Land Use, Newcastle Inst Energy & Resources, Callaghan, NSW 2308, Australia;

    Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Hubei, Peoples R China;

    Southwest Univ, Chongqing Key Lab Karst Environm, Chongqing 400715, Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Cluster analysis; Principal component analysis; Discriminant analysis; Sustainable development; Water quality decline;

    机译:聚类分析;主成分分析;判别分析;可持续发展;水质下降;

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