Use of water quality index and multivariate statistical methods for the evaluation of water quality of a stream affected by multiple stressors: A case study.
Use of water quality index and multivariate statistical methods for the evaluation of water quality of a stream affected by multiple stressors: A case study.
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DOI：
10.1016/j.envpol.2020.115417
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发表时间：
2020-08
影响因子：
8.9
通讯作者：
M. Varol
中科院分区：
文献类型：
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作者：
M. Varol
苏尔古河位于土耳其幼发拉底河流域，是饮用水水源、农业灌溉和虹鳟鱼生产的主要水源。因此，河流的水质非常重要。在这项研究中，多变量统计技术（MSTs）和水质指数（WQI）被应用到评估的多个压力源，如未经处理的生活污水，从养鱼场，农业径流和河岸侵蚀的影响流的水质。为此，16个水质参数在5个站点沿着流每月进行监测，在一年。大部分参数表现出明显的空间变异，表明人为活动的影响。由于水温和流量具有很强的季节性，除总氮（TN）外的所有参数均表现出显著的季节差异。水质指数的空间变异显著（p< 0.05），平均值介于87.6至95.3之间，表明该溪流的水质为“良好”至“优良”。聚类分析将5个样点分为3类，即清洁区、低污染区和非常清洁区。逐步时间判别分析（DA）表明，pH、WT、Cl−、SO 42 −、COD（化学需氧量）、TSS（总悬浮物）和Ca 2+是影响季节间变化的参数;逐步空间判别分析表明，DO（溶解氧）、EC（电导率）、NH 4-N、TN（总氮）和TSS是影响区域间变化的参数。主成分分析/因子分析表明，负责水质变化的参数主要与悬浮固体（自然和人为），可溶性盐（自然）和营养物质和有机物（人为）。
The Sürgü Stream, located in the Euphrates River basin of Turkey, is used for drinking water source, agricultural irrigation and rainbow trout production. Therefore, water quality of the stream is of great importance. In this study, multivariate statistical techniques (MSTs) and water quality index (WQI) were applied to assess water quality of the stream affected by multiple stressors such as untreated domestic sewage, effluents from fish farms, agricultural runoff and streambank erosion. For this, 16 water quality parameters at five sites along the stream were monitored monthly during one year. Most of parameters showed significant spatial variations, indicating the influence of anthropogenic activities. All parameters except TN (total nitrogen) showed significant seasonal differences due to high seasonality in WT (water temperature) and water flow. The spatial variations in the WQI were significant (p< 0.05) and the mean WQI values ranged from 87.6 to 95.3, indicating “good” to “excellent” water quality in the stream. Cluster analysis classified five sites into three groups, that is, clean region, low polluted region and very clean region. Stepwise temporal discriminant analysis (DA) identified that pH, WT, Cl−, SO42−, COD (chemical oxygen demand), TSS (total suspended solids) and Ca2+are the parameters responsible for variations between seasons, and stepwise spatial DA identified that DO (dissolved oxygen), EC (electrical conductivity), NH4–N, TN (total nitrogen) and TSS are the parameters responsible for variations between the regions. Principal component analysis/factor analysis revealed that the parameters responsible for water quality variations were mainly associated with suspended solids (both natural and anthropogenic), soluble salts (natural) and nutrients and organic matter (anthropogenic).