Water quality assessment of Bheemasandra Lake, South India: A blend of water quality indices, multivariate data mining techniques, and GIS

Shantha Kumar Dhanush; Mahadeva Murthy; Sathish Ayyappa; Devalapurada Kyari Prabhuraj; Rinku Verma

doi:10.1007/s11356-024-33670-7

Water quality assessment of Bheemasandra Lake, South India: A blend of water quality indices, multivariate data mining techniques, and GIS

Environ Sci Pollut Res Int. 2024 May 16. doi: 10.1007/s11356-024-33670-7. Online ahead of print.

Authors

Shantha Kumar Dhanush¹, Mahadeva Murthy², Sathish Ayyappa³, Devalapurada Kyari Prabhuraj⁴, Rinku Verma²

Affiliations

¹ Department of Forestry and Environmental Science, University of Agricultural Sciences, GKVK, Bangalore, Karnataka, India. academiadocd@gmail.com.
² Department of Forestry and Environmental Science, University of Agricultural Sciences, GKVK, Bangalore, Karnataka, India.
³ Department of Soil Science and Agricultural Chemistry, University of Agricultural Sciences, GKVK, Bangalore, Karnataka, India.
⁴ Karnataka State Remote Sensing Applications Centre, Bangalore, Karnataka, India.

PMID: 38753236
DOI: 10.1007/s11356-024-33670-7

Abstract

An integrated approach combining water quality indices (WQIs), multivariate data mining, and geographic information system (GIS) was employed to examine the water quality of Bheemasandra Lake, located adjacent to a sewage treatment plant (STP) in Tumakuru city, India. The analysis of 22 lake water samples, examined before and after the monsoons, revealed that the physicochemical parameters namely - electrical conductivity, biochemical oxygen demand, turbidity, total dissolved solids, ammoniacal nitrogen, nitrates, phosphates, magnesium, total hardness, total alkalinity, and calcium - exceeded the acceptable limits stipulated by national and international standards. The Canadian Council of Ministers of the Environment WQI (pre-monsoon: 25.3; post-monsoon: 33.9) and weighted arithmetic WQI (pre-monsoon: 3398; post-monsoon: 2093) designated the water as unsafe for drinking. Irrigation WQIs (sodium adsorption ratio, sodium percentage, residual sodium carbonate, magnesium hazard, permeability index, and potential salinity) implied water's suitability for irrigation. However, electrical conductivity indicated otherwise. Industrial WQIs (Larson-Skold Index, Langelier Index, Aggressive Index, and Puckorius Scaling Index) illustrated scaling propensity and the chloride sulfate mass ratio alluded galvanic corrosion potential. Hierarchical cluster analysis gathered 22 sampling points into two clusters (cluster 1: relatively lower polluted regions; cluster 2: highly polluted regions) for each season based on similarities in water features. Principal component analysis extracted four (79.07% cumulative variance) and six (87.14% cumulative variance) principal components before and after the monsoons, respectively. These components identified the primary pollution sources as urban sewage and natural lithological processes. WQI maps, created using the inverse distance weighted interpolation technique, enhanced the visualization of spatial-temporal variations. This study highlights the dire consequences of urbanization, STP pollution, and sewage management failures, necessitating that concerned authorities should implement policies and measures to curb the negative impacts on the environment and public health.

Keywords: Canadian Council of Ministers of the Environment WQI; Hierarchical cluster analysis; Industrial WQI; Irrigation WQI; Principal component analysis; Sewage treatment plant; Tumakuru; Weighted arithmetic WQI.