| 引用本文: | 蔡启铭,高锡芸,陈宇炜,马生伟,MartinDokulil.太湖水质的动态变化及影响因子的多元分析.湖泊科学,1995,7(2):97-106. DOI:10.18307/1995.0201 |
| Cai Qiming,Gao Xiyun,Chen Yuwei,Ma Shengwei,Martin Dokulil.Dynamic variations of water quality in Taihu Lake and multivariate analysis of its influential factors. J. Lake Sci.1995,7(2):97-106. DOI:10.18307/1995.0201 |
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| 摘要: |
| 利用1991~1992年的监测资料,分析了太湖梅梁湾和局部西太湖水域水质的动态变化。用主成分分析方法揭示出各因子间的相互关系。结果表明:水质主要因子有明显的时空变化特征,沿南北方向从湾内向湾外,TP、TN、COD、Chl-a和电导率明显递减,且TP和TN含量有季节变化,冬季高于夏季,梁溪河口处更是如此。Chl-a含量与TP、TN含量密切相关,其变化基本与水温同步,时间上略后滞。主成分分析说明,TP、TN、SS(或SD)、水温与Chl-a是这一水域最重要的影响因子,Chl-a与SS的含量共同决定着太湖的透明度。 |
| 关键词: 太湖 水质动态变化 主成分分析 |
| DOI：10.18307/1995.0201 |
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| 基金项目:国家自然科学基金资助项目(编号:49171017) |
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| Dynamic variations of water quality in Taihu Lake and multivariate analysis of its influential factors |
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Cai Qiming1, Gao Xiyun1, Chen Yuwei1, Ma Shengwei1, Martin Dokulil2
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1.Nanjing Institute of Geography & Lmnology, Chinese Acidemy of Sciences, Nanjing 210008;2.Institute of Limanology, Austria
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| Abstract: |
| Dynamic variation of water quality in Meiliang Bay and part of west Taihu Lake has been analysed based on the data from 1991 to 1992. Principal Component Analysis is used to reveal the mutual relationships of various factors. It is shown that there exists aa obvious spatial and temporal variation in the main factors of water quality. Annual values of TP, TN, CON. Chl-a and conductivity decrease evidently from inner Meiliang Bay to the outer from north to south. TP and TN fluctuate seasonally with much higher value in winter. This is particularly true for the mouth of Liangxi River. In addition, the Chl-a has a synchronous variation with water temperature, although being lagged a little, and closely relates to TP and TN. Finally, the results from Principal Component Analysis bhow that TP. TN, SS (or SD'), water temperature and Chl-a are the most Influential factors to water quality in this area, and both suspensions and algae can contribute to the transperancy in Taihu Lake. |
| Key words: Taihu Lake dynamic variation of water quality principal component analysis |
