What type of study is this?

This is a Quantitative Study study.

October 18, 2025

Assessment of water quality using multivariate statistical techniques: Case study of the man Sagar lake, Jaipur, Rajasthan, India

Puntos clave

The study found low dissolved oxygen levels (3.39–3.42 mg/L), indicating severe oxygen stress in Man Sagar Lake.
Principal Component Analysis revealed a pollution gradient that accounted for 92.6% of the total variance in water quality data.
Elevated biochemical oxygen demand and chemical oxygen demand at Site 1 indicate heavy organic pollution affecting water quality.
Correlation analysis showed significant relationships between dissolved oxygen and alkalinity, highlighting critical physicochemical interdependencies.

Resumen

The present study aimed to assess the water quality of Man Sagar Lake using multivariate statistical techniques. Water samples were collected over two years (2021–2023) from four sampling sites, and ten critical physicochemical parameters, namely temperature, pH, electrical conductivity (EC), dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), alkalinity, hardness, nitrate, and phosphate, were analyzed following APHA standard methods. To interpret the complex dataset, Principal Component Analysis (PCA), Factor Analysis (FA), and Cluster Analysis (CA) were applied to identify major pollution sources, underlying factors, and site groupings. The results indicated relatively stable surface water temperatures (~26 °C), consistently alkaline pH (~8.95), and high EC (~2.38 mS/cm), while critically low DO (3.39–3.42 mg/L) reflected severe oxygen stress. Elevated BOD and COD, particularly at Site 1, signified heavy organic pollution, while nitrate (~4.2 mg/L) and phosphate (~1.19 mg/L) suggested eutrophication risk. PCA revealed a dominant pollution gradient accounting for 92.6% of total variance, mainly driven by organic and nutrient enrichment. FA extracted four latent factors linked to organic decomposition, chemical buffering, ion–nutrient imbalance, and seasonal variability. CA classified the sites into two distinct clusters, separating highly polluted zones (Sites 1 and 3) from relatively cleaner areas (Sites 2 and 4). Correlation analysis showed DO positively correlated with alkalinity (r = 0.738), COD (r = 0.562), BOD (r = 0.514), and hardness (r = 0.555). COD was strongly related to BOD (r = 0.505) and alkalinity (r = 0.657). pH correlated positively with alkalinity (r = 0.504) and COD (r = 0.398), but negatively with phosphate (r = –0.156). Both DO and BOD were weakly but significantly negatively correlated with phosphate. This study demonstrates the effectiveness of multivariate statistical techniques in diagnosing pollution drivers and provides a comprehensive framework for water quality assessment and restoration of stressed urban lakes.

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