This study was aimed to evaluate the water quality and pollution sources in Sapanca Lake and its tributaries by applying multivariate statistical techniques to physicochemical parameters and toxic metals. For this purpose, the multivariate statistical methods such as principal component analysis (PCA) and absolute principal component score-multiple linear regression (APCS-MLR) model have been employed. It was tried to determine the seasonal pollution sources of physicochemical parameters and toxic metals obtained from 22 different sampling points between the years of 2015 and 2017. PCA was applied to the datasets, and 6 varimax factors describing 84%, 80%, 76%, and 79% of the total variance for each season were extracted. The obtained factors were analyzed using the APCS-MLR model for the apportionment of various pollution sources affecting physicochemical parameters and toxic metals. The results show that the natural soil structure, municipal-industrial wastewater, agricultural-atmospheric runoff, highways, and seasonal effects are the major pollution sources for toxic metals and physicochemical parameters. The material contribution of pollutant sources to toxic metals and physicochemical parameters was calculated and verified by the concentrations analyzed. Consequently, multivariate statistical techniques are useful to determine the physicochemical parameters and toxic metals through reciprocal correlation and assess the seasonal impact of pollutant sources in the basin. This study also provides a basis for the creation of measurement programs, determination of pollution sources, and provision of sustainable watershed management regarding other water resources.

中文翻译：

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重金属的季节性分配及其理化参数：以萨潘卡湖流域为例

这项研究旨在通过将多元统计技术应用于理化参数和有毒金属来评估萨潘卡湖及其支流的水质和污染源。为此，采用了多元统计方法，例如主成分分析（PCA）和绝对主成分评分-多元线性回归（APCS-MLR）模型。尝试确定2015年至2017年之间从22个不同采样点获得的理化参数和有毒金属的季节性污染源。将PCA应用于数据集，并使用6个方差因子描述了84％，80％，76％，并提取每个季节总变异的79％。使用APCS-MLR模型分析获得的因素，以分摊影响物理化学参数和有毒金属的各种污染源。结果表明，天然土壤结构，市政工业废水，农业大气径流，公路和季节影响是有毒金属和理化参数的主要污染源。计算并通过分析浓度验证了污染物源对有毒金属和理化参数的物质贡献。因此，多元统计技术可用于通过相互关系确定理化参数和有毒金属，并评估盆地中污染物源的季节性影响。这项研究还为创建测量程序提供了基础，