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中国秦岭南麓金水河流域水质的时空变化。

Temporal and spatial variations of water quality in the Jinshui River of the South Qinling Mts., China.

机构信息

Key Laboratory of Aquatic Botany and Watershed Ecology, Wuhan Botanical Garden, the Chinese Academy of Sciences, Wuhan 430074, China.

出版信息

Ecotoxicol Environ Saf. 2010 Jul;73(5):907-13. doi: 10.1016/j.ecoenv.2009.11.007. Epub 2010 Jan 4.

Abstract

Water pollution has become a growing threat to human society and natural ecosystems in recent decades, increasing the need to better understand the spatial and temporal variabilities of pollutants within aquatic systems. This study sampled water quality at 12 sampling sites from October 2006 to August 2008 in the Jinshui River of the South Qinling Mts., China. Multivariate statistical techniques and gridding methods were used to investigate the temporal and spatial variations of water quality and identify the main pollution factors and sources. Two-way analysis of variance (ANOVA) showed that 25 studied water quality variables had significant temporal differences (p<0.01) and spatial variability (p<0.01). Using cluster analysis, the 12 sampling sites were classified into three pollution level groups (no pollution, moderate pollution, and high pollution) based on similarity of water quality variables. Factor analysis determined that 80.4% of the total variance was explained by five factors, that is, salinity, trophicity, organic pollution, oxide-related process, and erosion. The gridding methods illustrated that water quality progressively deteriorated from headwater to downstream areas. The analytical results suggested that the water pollution primarily resulted from domestic wastewater and agricultural runoff, and provided critical information for water resource conservation in mountainous watersheds of the South Qinling Mts., China.

摘要

近几十年来,水污染已成为人类社会和自然生态系统面临的一个日益严重的威胁,这使得人们更加需要深入了解水体内污染物的时空变化。本研究于 2006 年 10 月至 2008 年 8 月期间,在中国秦岭南部金水河流域的 12 个采样点采集水质样本。采用多元统计技术和网格化方法,研究了水质的时空变化,识别了主要的污染因子和来源。双向方差分析(ANOVA)表明,25 个研究的水质变量具有显著的时间差异(p<0.01)和空间变异性(p<0.01)。基于水质变量的相似性,采用聚类分析将 12 个采样点分为三组污染水平(无污染、中度污染和高度污染)。因子分析确定,总方差的 80.4%由五个因子解释,即盐度、营养水平、有机污染、氧化相关过程和侵蚀。网格化方法表明,水质从源头到下游地区逐渐恶化。分析结果表明,水污染主要是由生活污水和农业径流造成的,为中国秦岭南部山区流域的水资源保护提供了重要信息。

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