Birla Institute of Technology and Science, Pilani, Rajasthan, 333031, India.
REVA- University, Bengaluru, India.
Environ Manage. 2024 Oct;74(4):818-834. doi: 10.1007/s00267-024-02020-1. Epub 2024 Jul 29.
In many developed and developing nations, lakes are the primary source of drinking water. In the current scenario, due to rapid mobilization in anthropogenic activities, lakes are becoming increasingly contaminated. Such practices not only destroy lake ecosystems but also jeopardize human health through water-borne diseases. This study employs advanced hierarchical clustering through multivariate analysis to establish a novel method for concurrently identifying significantly polluted lakes and critical pollutants. A systematic approach has been devised to generate rotating component matrices, dendrograms, monoplots, and biplots by combining R-mode and Q-mode analyses. This enables the identification of contaminant sources and their grouping. A case study analyzing five lakes in Bengaluru, India, has been conducted to demonstrate the effectiveness of the proposed methodology. Additionally, one pristine lake from Jammu & Kashmir, India, has been included to validate the findings from the aforementioned five lakes. The study explored correlations among various physical, chemical, and biological characteristics such as temperature, pH, dissolved oxygen, conductivity, nitrates, biological oxygen demand (BOD), fecal coliform (FC), and total coliform (TC). Critical contaminants forming clusters included conductivity, nitrates, BOD, TC, and FC. Factor analysis identified four primary components that collectively accounted for 85% of the overall variance. Following identification of pollution hotspots, the study recommends source-based pollution control and integrated watershed management, which could significantly reduce lake pollution levels. Continuous monitoring of lake water quality is essential for identifying actual contaminant sources. These findings provide practical recommendations for maximizing restoration efforts, enforcing regulations on pollutant sources, and improving water quality conditions to ensure sustainable development of lakes.
在许多发达国家和发展中国家，湖泊是主要的饮用水源。在当前情况下，由于人类活动的迅速动员，湖泊的污染越来越严重。这些做法不仅破坏了湖泊生态系统，还通过水传播疾病威胁人类健康。本研究采用先进的层次聚类多元分析，建立了一种同时识别污染严重湖泊和关键污染物的新方法。通过将 R 模式和 Q 模式分析相结合，设计了一种系统的方法来生成旋转成分矩阵、聚类图、单图和双图。这使得识别污染源及其分组成为可能。对印度班加罗尔的五个湖泊进行了案例研究，以证明所提出方法的有效性。此外，还包括印度查谟和克什米尔的一个原始湖泊，以验证上述五个湖泊的发现。该研究探讨了各种物理、化学和生物特征之间的相关性，如温度、pH 值、溶解氧、电导率、硝酸盐、生物需氧量（BOD）、粪大肠菌群（FC）和总大肠菌群（TC）。形成聚类的关键污染物包括电导率、硝酸盐、BOD、TC 和 FC。因子分析确定了四个主要成分，它们共同占总方差的 85%。在确定污染热点后，该研究建议进行基于源头的污染控制和综合流域管理，这可以显著降低湖泊污染水平。对湖水水质进行持续监测对于识别实际的污染源至关重要。这些发现为最大限度地恢复努力、对污染源实施法规以及改善水质条件以确保湖泊的可持续发展提供了实际建议。
