Monitoring groundwater quality using principal component analysis

被引:2
作者
Patnaik, Manaswinee [1 ]
Tudu, Chhabirani [2 ]
Bagal, Dilip Kumar [3 ]
机构
[1] Govt Coll Engn Kalahandi, Dept Civil Engn, Bhawanipatna 766003, Orissa, India
[2] Odisha Univ Technol & Res, Dept Civil Engn, Bhubaneswar 751029, Orissa, India
[3] Govt Coll Engn Kalahandi, Dept Mech Engn, Bhawanipatna 766003, Orissa, India
关键词
Correlation; Groundwater; Physiochemical; Principal component analysis; Potable; Variability; WATER-QUALITY; RIVER;
D O I
10.1007/s12518-024-00552-z
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
For areas without perennial surface water sources, groundwater might be considered the second-largest source of drinking water after surface water. However, groundwater is highly prone to contamination as the groundwater reservoir is formed by the movement of surface water into the subsoil; in its due course of motion, it may dissolve any probable contaminants such as agrochemicals, landfill leachates, the oil spill from underground pipelines, and sewer waste and further convey the contaminated water to join some groundwater aquifers from where the water is again pumped out for human consumption. Therefore, prior to its potable applicability, the quality of groundwater should be evaluated for the presence of alkalinity, hardness, and undesirable and heavy minerals. The Central Ground Water Board (CGWB), Bhubaneswar, collects data on 61 stations in the Kalahandi District for 15 physiochemical parameters, including pH, bicarbonate, hardness, sulphate, Cl-, total dissolved solids, Mg++, K+, Na+, total alkalinity, nitrate, fluoride, carbonate, electrical conductivity, and calcium, to assess the quality of the groundwater. The goals were to pinpoint the major elements influencing water quality and comprehend the groundwater quality measures' regional distribution. Data from the Central Groundwater Board (CGWB) were collected as part of our research, and PCA was used to identify the major impacting elements. To further minimize the dataset's multidimensionality, a principal component analysis is used. Together, the first three major components explain 76.64% of the overall variability. The first two principal factors themselves explain about 56.9% of the total variance. The three principal factors indicate salinity, hardness, and relative alkalinity and acidity, respectively, in the groundwater.
引用
收藏
页码:281 / 291
页数:11
相关论文
共 50 条
  • [41] Interpretation of groundwater hydrographs in the West Thessaly basin, Greece, using principal component analysis
    Seferli, S.
    Modis, K.
    Adam, K.
    ENVIRONMENTAL EARTH SCIENCES, 2019, 78 (08)
  • [42] Interpretation of groundwater hydrographs in the West Thessaly basin, Greece, using principal component analysis
    S. Seferli
    K. Modis
    K. Adam
    Environmental Earth Sciences, 2019, 78
  • [43] DOUGH PROPERTIES RELATED TO BAKING QUALITY USING PRINCIPAL COMPONENT ANALYSIS
    Osella, C. A.
    Robutti, J.
    Sanchez, H. D.
    Borras, F.
    de la Torre, M. A.
    CIENCIA Y TECNOLOGIA ALIMENTARIA, 2008, 6 (02): : 95 - 100
  • [44] Water Quality Assessment for Wastewater Reclamation Using Principal Component Analysis
    Hao, R. X.
    Li, S. M.
    Li, J. B.
    Zhang, Q. K.
    Liu, F.
    JOURNAL OF ENVIRONMENTAL INFORMATICS, 2013, 21 (01) : 45 - 54
  • [45] Prediction of Wheat Quality Using GlutoPeak Combined with Principal Component Analysis
    Jiang L.
    Li X.
    Cao Y.
    Ma X.
    Wang M.
    Hao J.
    Zhang D.
    Ji H.
    Shipin Kexue/Food Science, 2022, 43 (14): : 85 - 92
  • [46] River water quality assessment based on principal component analysis
    Li, Guiping
    Yu, Zhongbo
    HYDROLOGICAL CYCLE AND WATER RESOURCES SUSTAINABILITY IN CHANGING ENVIRONMENTS, 2011, 350 : 430 - 435
  • [47] Groundwater quality assessment using water quality index and principal component analysis in the Achnera block, Agra district, Uttar Pradesh, Northern India
    Shahjad Ali
    Sitaram Verma
    Manish Baboo Agarwal
    Raisul Islam
    Manu Mehrotra
    Rajesh Kumar Deolia
    Jitendra Kumar
    Shailendra Singh
    Ali Akbar Mohammadi
    Deep Raj
    Manoj Kumar Gupta
    Phuyen Dang
    Mehdi Fattahi
    Scientific Reports, 14
  • [48] Groundwater quality assessment using water quality index and principal component analysis in the Achnera block, Agra district, Uttar Pradesh, Northern India
    Ali, Shahjad
    Verma, Sitaram
    Agarwal, Manish Baboo
    Islam, Raisul
    Mehrotra, Manu
    Deolia, Rajesh Kumar
    Kumar, Jitendra
    Singh, Shailendra
    Mohammadi, Ali Akbar
    Raj, Deep
    Gupta, Manoj Kumar
    Dang, Phuyen
    Fattahi, Mehdi
    SCIENTIFIC REPORTS, 2024, 14 (01)
  • [49] CLUSTER AND PRINCIPAL COMPONENT ANALYSIS IN THE ASSESSMENT OF FOUNTAIN SOLUTION QUALITY
    Kiurski, Jelena S.
    Oros, Ivana B.
    Ralevic, Nebojsa M.
    Kovacevic, Ilija M.
    Adamovic, Savka Z.
    Krstic, Jelena D.
    Comic, Lidija Lj
    CARPATHIAN JOURNAL OF EARTH AND ENVIRONMENTAL SCIENCES, 2013, 8 (01): : 19 - 28
  • [50] Improving automatic-controlled process quality using adaptive principal component monitoring
    Tsung, F
    QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, 1999, 15 (02) : 135 - 142