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@article{182370,
        author = {Mr. Pappu Kumar Yadav and Dr. PAPPU KUMAR},
        title = {Assessment of Drinking Water Suitability Using Water Quality Index in Patna District, Bihar, India},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {1617-1621},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=182370},
        abstract = {Anthropogenic activities-related degradation of groundwater quality has emerged as a major concern. By creating a water quality index as a single value, several water quality variables are used to explain the overall state of the water quality. The following water quality factors make up the index: calcium, magnesium, chloride, sulphate, fluoride, nitrate, manganese, pH, EC, total alkalinity, and total hardness. Using a drinking water quality index approach, the current study attempts to evaluate the drinking water quality of the study area in and around urban Patna. Ten criteria related to water quality have been chosen for assessment. The quality of the water samples is assessed using an arithmetic and geometric index using a data set of twenty ground water samples that were taken from the study area in and around urban Patna, Patna. framework. The groundwater quality data collected from 67 sites spread throughout the city were subjected to cluster analysis (CA), principal component analysis (PCA), and discriminant analysis (DA). The data was measured on 10 different parameters. The 67 sampling stations were divided into two groups using hierarchical cluster analysis (CA), with cluster 1 exhibiting high pollution and cluster 2 exhibiting lower pollution. To identify the most significant characteristics that account for the temporal and spatial fluctuations in the groundwater quality of the research area, discriminant analysis (DA) was used. The most significant parameter found by Temporal DA is pH, which accounts for the seasonal assignment of cases and distinguishes between water quality during the pre- and post-monsoon seasons. Mg, Cl, and NO3 were found by spatial DA to be the three most significant factors that differentiated between two clusters and accounted for 89% of the differences.},
        keywords = {principal component analysis, cluster analysis (CA), water Quality Index, Groundwater},
        month = {July},
        }