Seasonal Source Apportionment of Heavy Metals and Physicochemical Parameters: A Case Study of Sapanca Lake Watershed

Asude Ateş,Bülent Şengörür,Hüseyin Altundağ,Hülya Demirel,Şenay Çetin Doğruparmak,Rabia Köklü

doi:10.1155/2020/7601590

Abstract

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.

Highlights

Heavy metals can be classified as toxic elements (Cd, Co, Cr, Ni, Zn, etc.) and metalloids (As, Se, etc.)
Multivariate statistical methods were used in order to interpret the correlation and pollution sources’ contribution to physicochemical parameters and toxic metals in the Sapanca Lake Basin. e principal component analysis (PCA)/factor analysis (FA) method was used to identify the factors responsible for toxic metal pollution and physicochemical effects in the basin. e APCS-MLR model has allowed us to identify the pollution sources contributing to the toxic metal pollution of the lake
E main sources responsible for toxic metal pollution and physicochemical parameters regarding the lake water quality were determined as lithogenic, agricultural activities, industrial and domestic wastewater, traffic load around the lake, and seasonal change. e material contribution of each toxic metal and physicochemical parameter to the contaminant sources was calculated and verified in terms of the measured values

Summary

Introduction

Heavy metals can be classified as toxic elements (Cd, Co, Cr, Ni, Zn, etc.) and metalloids (As, Se, etc.) Since they are toxic and persistent and have bioaccumulative abilities on living organisms in terms of long-time exposure at even low concentrations, they are very dangerous [1, 2]. E heavy metal pollution of aquatic ecosystems is increasing due to the effects stemming from agricultural runoff and industrial and domestic sewage discharge. Lakes and rivers have always played an important role in supplying fresh water for human beings. Lakes are the most sensitive water sources among surface waters in terms of pollution. E quality of water sources is essential for the health of human beings, animals, and plants [5].

Methods

Results

Conclusion