Yogeshwaran Venkatraman, Priya Arunkumar
School of Building and Civil Engineering, CETVET, Fiji National University, Suva, Fiji.
Department of Chemical Engineering, KPR Institute of Engineering and Technology, Coimbatore, India.
Water Environ Res. 2025 Mar;97(3):e70055. doi: 10.1002/wer.70055.
Water quality deterioration due to industrialization and urbanization is a growing environmental concern, particularly in developing regions. This study assesses the surface water quality of Kurichi and Big Lakes in the Ukkadam area, Coimbatore, India, using multivariate statistical techniques to identify key pollution sources and evaluate contamination levels. Despite prior research on water quality in urban lakes, limited studies have systematically analyzed multiple contaminants using advanced statistical approaches. A total of 12 water samples were collected between June 2023-June 2024 and analyzed for physicochemical, microbiological, and anionic parameters. Principal Component Analysis (PCA) and Factor Analysis (FA) revealed three dominant components explaining 68.42% and 42.81% of the total variance in Kurichi and Big Lakes, respectively. The Piper plot classified water types, while Cluster Analysis (CA) grouped sampling sites based on contamination levels. The Pearson correlation matrix determined pollutant interdependencies, and the Water Quality Index (WQI) categorized pollution severity against WHO and BIS standards. The results indicate that organic matter, industrial discharge, fertilizer runoff, and untreated wastewater are the primary contributors to water pollution. High pollution levels were detected near industrial zones, with Kurichi Lake exhibiting significantly poorer water quality than Big Lake. The findings highlight the urgent need for improved wastewater management and pollution control policies to safeguard aquatic ecosystems and public health. PRACTITIONER POINTS: Multivariate Statistical Analysis: Applied PCA, FA, Piper plot, CA, and Pearson correlation matrix to assess water quality. Water Quality Index (WQI) Classification: Identified pollution sources and categorized water quality based on WHO and BIS standards. Principal Component Analysis (PCA) Findings: Three major components explained 68.42% and 42.81% of the total variation in Kurichi and Big Lakes, respectively. Major Pollution Sources: Factor analysis identified organic compounds, human activity, fertilizers, chemical waste, and wastewater discharge as primary contaminants. Industrial Area Impact: CA and WQI results highlighted high pollution sensitivity near industrial zones.
工业化和城市化导致的水质恶化是一个日益严重的环境问题，在发展中地区尤为如此。本研究评估了印度哥印拜陀乌卡丹地区库里奇湖和大湖的地表水水质，采用多元统计技术来识别主要污染源并评估污染水平。尽管此前已有关于城市湖泊水质的研究，但使用先进统计方法对多种污染物进行系统分析的研究有限。在2023年6月至2024年6月期间共采集了12份水样，并对其理化、微生物和阴离子参数进行了分析。主成分分析（PCA）和因子分析（FA）分别揭示了三个主要成分，它们分别解释了库里奇湖和大湖总方差的68.42%和42.81%。派珀图对水的类型进行了分类，而聚类分析（CA）则根据污染水平对采样点进行了分组。皮尔逊相关矩阵确定了污染物之间的相互依存关系，水质指数（WQI）根据世界卫生组织（WHO）和印度标准局（BIS）的标准对污染严重程度进行了分类。结果表明，有机物、工业排放、肥料径流和未经处理的废水是水污染的主要原因。在工业区附近检测到高污染水平，库里奇湖的水质明显比大湖差。研究结果凸显了迫切需要改进废水管理和污染控制政策，以保护水生生态系统和公众健康。从业者要点：多元统计分析：应用主成分分析、因子分析、派珀图、聚类分析和皮尔逊相关矩阵来评估水质。水质指数（WQI）分类：识别污染源并根据世界卫生组织和印度标准局的标准对水质进行分类。主成分分析（PCA）结果：三个主要成分分别解释了库里奇湖和大湖总变异的68.42%和42.81%。主要污染源：因子分析确定有机化合物、人类活动、肥料、化学废物和废水排放为主要污染物。工业区影响：聚类分析和水质指数结果凸显了工业区附近的高污染敏感性。
