<p>This study presents a comprehensive evaluation of groundwater quality in Mahoba district, Uttar Pradesh, using a multi-analytical approach involving hydrochemical, spatial, and statistical techniques. A total of 80 groundwater samples were analyzed for key physicochemical parameters, including Electrical Conductivity (EC), Total Dissolved Solids (TDS), and major ions such as chloride (Cl⁻), sulfate (SO₄<sup>2</sup>⁻), and nitrate (NO₃⁻). Spatial interpolation using Inverse Distance Weighting (IDW) in a GIS environment revealed contamination hotspots, particularly in the southern and southwestern regions, where chloride and nitrate levels reached 694.1&#xa0;mg/L and 429.5&#xa0;mg/L, respectively, indicating potential agricultural runoff as a primary source. The Water Quality Index (WQI) was applied to classify potability, showing that northern areas (e.g., Charkhari block) fall under the ‘good’ category (WQI: 28.2–44.72), whereas southern areas (e.g., Panwari block) exhibit ‘poor’ water quality (WQI &gt; 82.67). Principal Component Analysis (PCA) identified salinity, hardness, and carbonate content as the dominant factors controlling groundwater quality, influenced by both geological conditions and anthropogenic activities such as excessive fertilizer use and unregulated groundwater extraction. The study underscores the urgent need for region-specific groundwater management strategies, including improved agricultural practices, rainwater harvesting, and policy enforcement. The integrated methodology offers a robust and replicable framework for groundwater quality assessment in semi-arid regions facing similar hydrogeological pressures.</p>

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Assessment of Groundwater Quality Using Statistical and Hydro-Chemical Approaches: A Case Study of Mahoba District, India

  • Hemant Kumar Pandey,
  • Vishal Kumar Singh,
  • Sudhir Kumar Srivastava,
  • Ram Pal Singh,
  • Sanjay Gopal Bhartariya

摘要

This study presents a comprehensive evaluation of groundwater quality in Mahoba district, Uttar Pradesh, using a multi-analytical approach involving hydrochemical, spatial, and statistical techniques. A total of 80 groundwater samples were analyzed for key physicochemical parameters, including Electrical Conductivity (EC), Total Dissolved Solids (TDS), and major ions such as chloride (Cl⁻), sulfate (SO₄2⁻), and nitrate (NO₃⁻). Spatial interpolation using Inverse Distance Weighting (IDW) in a GIS environment revealed contamination hotspots, particularly in the southern and southwestern regions, where chloride and nitrate levels reached 694.1 mg/L and 429.5 mg/L, respectively, indicating potential agricultural runoff as a primary source. The Water Quality Index (WQI) was applied to classify potability, showing that northern areas (e.g., Charkhari block) fall under the ‘good’ category (WQI: 28.2–44.72), whereas southern areas (e.g., Panwari block) exhibit ‘poor’ water quality (WQI > 82.67). Principal Component Analysis (PCA) identified salinity, hardness, and carbonate content as the dominant factors controlling groundwater quality, influenced by both geological conditions and anthropogenic activities such as excessive fertilizer use and unregulated groundwater extraction. The study underscores the urgent need for region-specific groundwater management strategies, including improved agricultural practices, rainwater harvesting, and policy enforcement. The integrated methodology offers a robust and replicable framework for groundwater quality assessment in semi-arid regions facing similar hydrogeological pressures.