| 春季融雪补给后巩乃斯河水物理化学性质空间分布特征研究 |
| 摘要点击 2180 全文点击 1045 投稿时间：2014-08-06 修订日期：2014-10-08 |
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| 中文关键词 水质 空间分布 多元统计分析 融雪补给 巩乃斯河 |
| 英文关键词 water quality spatial distribution multivariate statistical analysis supply of snowmelt Kunes River |
| 作者 | 单位 | E-mail | | 刘翔 | 中国科学院新疆生态与地理研究所, 荒漠与绿洲生态国家重点实验室, 乌鲁木齐 830011
中国科学院大学, 北京 100049 | liuxiang12@mails.ucas.ac.cn | | 郭玲鹏 | 中国科学院新疆生态与地理研究所, 荒漠与绿洲生态国家重点实验室, 乌鲁木齐 830011
中国科学院大学, 北京 100049 | | | 张飞云 | 中国科学院新疆生态与地理研究所, 荒漠与绿洲生态国家重点实验室, 乌鲁木齐 830011
中国科学院大学, 北京 100049 | | | 马杰 | 中国科学院新疆生态与地理研究所, 荒漠与绿洲生态国家重点实验室, 乌鲁木齐 830011
中国科学院大学, 北京 100049 | | | 牟书勇 | 中国科学院新疆生态与地理研究所, 荒漠与绿洲生态国家重点实验室, 乌鲁木齐 830011 | | | 赵鑫 | 中国科学院新疆生态与地理研究所, 荒漠与绿洲生态国家重点实验室, 乌鲁木齐 830011
中国科学院大学, 北京 100049 | | | 李兰海 | 中国科学院新疆生态与地理研究所, 荒漠与绿洲生态国家重点实验室, 乌鲁木齐 830011 | lilh@ms.xjb.ac.cn |
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| 中文摘要 |
| 在春季融雪补给后对巩乃斯河19个采样点8个水物理化学指标进行了监测,运用聚类分析(CA)、判别分析(DA)和主成分分析(PCA)方法分析了巩乃斯河水物理化学性质的空间分布特征. 聚类分析结果表明,按各采样点之间河水物理化学性质的相似性可将巩乃斯河大致分为3个河段,分别代表河流的上游、中游和下游; 判别分析的结果证实了此种分类的可靠性,并表明DO、Cl-以及BOD5是影响这种分类的显著性指标; 主成分分析共提取了3个主成分,对应特征值的累积方差贡献率达到86.90%,表明影响河水物理化学性质的主要指标为EC、ORP、NO3--N、NH4+-N、Cl-和BOD5. 对各采样点的主成分得分进行排序,结果显示DO主要影响上游水质,pH主要影响中游水质,其余指标则是影响下游水质的主要因素. 主成分综合得分的排序则表明上游水质最优,其次为中游,而下游最差,恰好对应聚类分析所划分的3个河段. 人类活动及污染物的沿河积累可能是造成这种空间差异的主要原因. |
| 英文摘要 |
| Eight physical and chemical indicators related to water quality were monitored from nineteen sampling sites along the Kunes River at the end of snowmelt season in spring. To investigate the spatial distribution characteristics of water physical and chemical properties, cluster analysis (CA), discriminant analysis (DA) and principal component analysis (PCA) are employed. The result of cluster analysis showed that the Kunes River could be divided into three reaches according to the similarities of water physical and chemical properties among sampling sites, representing the upstream, midstream and downstream of the river, respectively; The result of discriminant analysis demonstrated that the reliability of such a classification was high, and DO, Cl- and BOD5 were the significant indexes leading to this classification; Three principal components were extracted on the basis of the principal component analysis, in which accumulative variance contribution could reach 86.90%. The result of principal component analysis also indicated that water physical and chemical properties were mostly affected by EC, ORP, NO3--N, NH4+-N, Cl- and BOD5. The sorted results of principal component scores in each sampling sites showed that the water quality was mainly influenced by DO in upstream, by pH in midstream, and by the rest of indicators in downstream. The order of comprehensive scores for principal components revealed that the water quality degraded from the upstream to downstream, i. e., the upstream had the best water quality, followed by the midstream, while the water quality at downstream was the worst. This result corresponded exactly to the three reaches classified using cluster analysis. Anthropogenic activity and the accumulation of pollutants along the river were probably the main reasons leading to this spatial difference. |
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