摘要
浙江省临安市青山湖主要用于旅游业、农业灌溉及饮用水源等多种用途,而周边旅游业、工业等方面的发展可能使得它存在受污染的风险,通过水质评价来了解青山湖水环境质量状况具有重要的意义。以2009-2013年临安市青山湖水质采样数据为基础,采用传统的综合水质标识指数法对青山湖水质进行综合评价,并结合3种赋权法对指标权重进一步优化处理,通过污染成因分析初步得出各支流对青山湖水库水质污染贡献情况。结果表明:青山湖化学需氧量、氨氮的单因子水质标识指数平均达到Ⅱ类水质标准,符合青山湖水质功能区划分等级要求;总氮、总磷、叶绿素a的单因子水质标识指数平均大于Ⅲ类水质,为主要污染物;基于主成分分析赋权的综合水质标识指数法和传统的综合水质标识指数法的计算结果一致,2009-2013年青山湖水质都达到Ⅳ类,且2009年青山湖水质最差,综合水质标示指数为4.331,2013年最好,为4.131,呈U型变化趋势;锦溪的总氮、叶绿素a污染贡献最高,南苕溪次之,灵溪的总磷污染贡献最高。
Qingshan Lake used mainly for tourism, agricultural irrigation, and drinking water and other purposes and development of tourism, industry may make the existence of the risks contamination. So this study was conducted to help understand the lake's water quality by using a water quality assessment. Based on sampled water quality data from Qingshan Lake in Lin'an from 2009 to 2013, water quality was evaluated using the water quality index method with a Principal Component Analysis along with a coefficient of variation for standard multiple weight and average weight. The water pollution contribution of each into the reservoir was obtained by analyzing the causes of pollution. Results showed that the single factor chemical oxygen demand index and NH3-N in Qingshan Lake reached level Ⅱ, in accordance with function grade of Qingshan Lake. Single factor labeling indexes revealed that total phosphorus, total nitrogen, and chlorophyll-a were the main pollutant. The comprehensive water quality identification index method based on the principal component analysis and the av-erage weight, confirmed these results. From 2009 to 2013 the lake's water quality reached level Ⅳ. The maximum WQI was 4.331 in 2009, and the minimum was 4.131 in 2013 with a U-shaped pattern. The pollution contribution from the Jinxi Stream was highest for TN and Chl-a. The South Tiaoxi Stream took the second place. The pollution contribution from Linxi Stream was highest for TP. These results will help provide a scientific basis for management and protection of Qingshan Lake.
出处
《浙江农林大学学报》
CAS
CSCD
北大核心
2016年第5期890-898,共9页
Journal of Zhejiang A&F University
基金
浙江省自然科学基金资助项目(Y14C130046)
浙江农林大学科研发展基金人才启动基金资助项目(2013FR035)
浙江省林业智能监测与信息技术研究重点实验室资助项目
浙江省临安市环境保护局资助项目(2014-2)