Original Papers
30 September 2025
Vol. 14 No. 3 (2025)
The state of the art on groundwater quality studies: a bibliometric analysis of the topic at a global level
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
121
Views
52
Downloads
Authors
Groundwater is a critical freshwater resource, particularly in regions where surface water availability is limited. This study presents a comprehensive systematic review of global groundwater quality research, aiming to map and analyze the most prevalent and emerging themes. A bibliometric analysis was conducted on articles published in English over the last 25 years (2000–2024) using the Scopus database. The analysis, performed with RStudio and the Bibliometrix package, initially included 1,686 publications. A clear upward trend in scientific production was observed, with the highest annual growth rate (60.9%) occurring in 2020 (140 articles). Before 2018, less than 60 articles were published annually. The leading journals in the field were Science of the Total Environment, Water, and Journal of Hydrology, with 80, 70, and 63 publications, respectively. The top contributing countries were China (1,201), India (1,006), the United States (572), and Iran (533), with Iran having the highest international collaboration rate (MCP_Ratio=0.42) and the greatest groundwater consumption. Cluster analysis and Sankey diagrams showed a significant increase in keywords linking “machine learning,” “groundwater,” and “water quality” after 2014. Prior to this, research focused primarily on the physicochemical characterization of groundwater and its environmental impacts. The results indicate a shift towards interdisciplinary methods, integrating geoprocessing, hydrological modeling, and statistical analysis, alongside growing connections to climate change, water management, and artificial intelligence.
Altmetrics
Downloads
Download data is not yet available.
Citations
Adeniyi, A., & Giwa, O. (2021). Accumulation and health effects of metals in selected urban groundwater. Physical Sciences Reviews. https://doi.org/10.1515/psr-2020-0089. DOI: https://doi.org/10.1515/9783110726145-004
Barrett, M. H., Hiscock, K. M., Pedley, S., Lerner, D. N., Tellam, J. H., & French, M. J. (1999). Marker species for identifying urban groundwater recharge sources: A review and case study in Nottingham, UK. Water Research, 33, 3083-3097. https://doi.org/10.1016/S0043-1354(99)00021-4. DOI: https://doi.org/10.1016/S0043-1354(99)00021-4
Baas, J., Schotten, M., Plume, A., Côté, G., & Karimi, R. (2020). Scopus as a curated, high-quality bibliometric data source for academic research in quantitative science studies. Quantitative Science Studies, 1(1), 377–386. https://doi.org/10.1162/qss_a_00019. DOI: https://doi.org/10.1162/qss_a_00019
Bose, S., Mazumdar, A., & Basu, S. (2023). Evolution of groundwater quality assessment on urban area-a bibliometric analysis. Groundwater for Sustainable Development, 20, 100894. https://doi.org/10.1016/j.gsd.2022.100894. DOI: https://doi.org/10.1016/j.gsd.2022.100894
Chen, W., Li, H., Hou, E., Wang, S., Wang, G., Panahi, M., Li, T., Peng, T., Guo, C., Niu, C., Xiao, L., Wang, J., Xie, X., Ahmad, B. B. (2018). GIS-based groundwater potential analysis using novel ensemble weights-of-evidence with logistic regression and functional tree models. Science of The Total Environment, 634, 853-867. https://doi.org/10.1016/j.scitotenv.2018.04.055. DOI: https://doi.org/10.1016/j.scitotenv.2018.04.055
Chegbeleh, L. P., Akurugu, B. A., & Yidana, S. M. (2020). Assessment of groundwater quality in the Talensi District, Northern Ghana. Scientific World Journal, 2020, 8450860. https://doi.org/10.1155/2020/8450860. DOI: https://doi.org/10.1155/2020/8450860
Chotpantarat, S., Parkchai, T., & Wisitthammasri, W. (2020). Multivariate statistical analysis of hydrochemical data and stable isotopes of groundwater contaminated with nitrate at Huay Sai Royal Development Study Center and adjacent areas in Phetchaburi Province, Thailand. Water, 12, 1127. https://doi.org/10.3390/W12041127. DOI: https://doi.org/10.3390/w12041127
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070. DOI: https://doi.org/10.1016/j.jbusres.2021.04.070
Devic, G., Djordjevic, D., & Sakan, S. (2014). Natural and anthropogenic factors affecting the groundwater quality in Serbia. Science of the Total Environment, 468–469, 933-942. https://doi.org/10.1016/j.scitotenv.2013.09.011. DOI: https://doi.org/10.1016/j.scitotenv.2013.09.011
Foster, S., Chilton, J., Nijsten, G.J., & Richts, A. (2013). Groundwater-a global focus on the ‘local resource’. Current Opinion in Environmental Sustainability, 5(6), 685-695. https://doi.org/10.1016/j.cosust.2013.10.010. DOI: https://doi.org/10.1016/j.cosust.2013.10.010
Fu, Y., Cao, W., Pan, D., & Ren, Y. (2022). Changes of groundwater arsenic risk in different seasons in Hetao basin based on machine learning model. Science of the Total Environment, 817, 153058. https://doi.org/10.1016/j.scitotenv.2022.153058. DOI: https://doi.org/10.1016/j.scitotenv.2022.153058
Graham, J. P., & Polizzotto, M. L. (2013). Pit latrines and their impacts on groundwater quality: a systematic review. Environmental Health Perspectives, 121(5), 521-530. https://doi.org/10.1289/ehp.1206028. DOI: https://doi.org/10.1289/ehp.1206028
Hao, H., Li, P., Li, K., Shan, Y., Liu, F., Hu, N., Zhang, B., Li, M., Sang, X., Xu, X., Lv, Y., Chen, W., Jiao, W. (2024). A novel prediction approach driven by graph representation learning for heavy metal concentrations. Science of the Total Environment, 947, 174713. https://doi.org/10.1016/j.scitotenv.2024.174713. DOI: https://doi.org/10.1016/j.scitotenv.2024.174713
Inam Ullah Ehsan, Ahmad, S., Khokhar, M. F., Khayyam, U., Azmat, M., Arshad, M., & Qaiser, F. U. R. (2024). Analyzing land use land cover (LULC) changes induced by the run-of-river project and respondent survey: a case of Ghazi Barotha Hydropower Project on Indus River, Pakistan. Environmental Research Communications, 6(3), 035002. https://doi.org/10.1088/2515-7620/ad2bb5.
Janardhana Raju, N.; Shukla, U. K.; Ram, P. (2011). Hydrogeochemistry for the assessment of groundwater quality in Varanasi: a fasturbanizing center in Uttar Pradesh, India. Environmental Monitoring and Assessment, v. 173, p. 279–300, 2011. https://doi.org/10.1007/s10661-010-1387-6. DOI: https://doi.org/10.1007/s10661-010-1387-6
Kannazarova, Z., Juliev, M., Abuduwaili, J., Muratov, A., & Bekchanov, F. (2024). Drainage in irrigated agriculture: Bibliometric analysis for the period of 2017–2021. Agricultural Water Management, 305, 109118. https://doi.org/10.1016/j.agwat.2024.109118. DOI: https://doi.org/10.1016/j.agwat.2024.109118
Katz, B. G., Eberts, S. M., & Kauffman, L. J. (2011). Using Cl/Br ratios and other indicators to assess potential impacts on groundwater quality from septic systems: a review and examples from principal aquifers in the United States. Journal of Hydrology, 397(3-4), 151-166. DOI: https://doi.org/10.1016/j.jhydrol.2010.11.017
Lee, S., Kaown, D., Koh, E. H., Lee, H. L., Ko, K. S., & Lee, K. K. (2022). Advanced utilization of multi-learning algorithm: ensemble super learner to map groundwater potential for potable mineral water. Geocarto International, v. 37, n. 25, p. 9897–9916. https://doi.org/10.1080/10106049.2022.2025921. DOI: https://doi.org/10.1080/10106049.2022.2025921
Li, P., Wu, J., Qian, H., Lyu, X., & Liu, H. (2014). Origin and assessment of groundwater pollution and associated health risk: a case study in an industrial park, northwest China. Environmental Geochemistry and Health, v. 36, p. 693–712, 2014. https://doi.org/10.1007/s10653-013-9590-3. DOI: https://doi.org/10.1007/s10653-013-9590-3
Li, C., Xie, L., & Xiong, Y. (2019). Bioelectrochemical systems for groundwater remediation: the development trend and research front revealed by bibliometric analysis. Water, 11(8), 1532. https:// doi.org/10.3390/w11081532. DOI: https://doi.org/10.3390/w11081532
Li, H., Dong, Q., Zhang, M., Gong, T., Zan, R., & Wang, W. (2023). Transport behavior difference and transport model of long- and short-chain per- and polyfluoroalkyl substances in underground environmental media: a review. Environmental Pollution, 327, 121579. https://doi.org/10.1016/j.envpol.2023.121579. DOI: https://doi.org/10.1016/j.envpol.2023.121579
Loftis, J. C. (1996). Trends in groundwater quality. Hydrological Processes, 10(2), 335-355. https://doi.org/10.1002/(SICI)1099-1085(199602)10:2<335::AID-HYP359>3.0.CO;2-T. DOI: https://doi.org/10.1002/(SICI)1099-1085(199602)10:2<335::AID-HYP359>3.3.CO;2-K
Mallick, J., Naikoo, M. W., Talukdar, S., Ahmed, I. A., Rahman, A., Islam, A. R. M. T., Pal, S., Ghose, B., & Shashtri, S. (2021). Developing groundwater potentiality models by coupling ensemble machine learning algorithms and statistical techniques for sustainable groundwater management. Geocarto International, 37(25), 7927–7953. https://doi.org/10.1080/10106049.2021.1987535. DOI: https://doi.org/10.1080/10106049.2021.1987535
Naghibi, S. A., Pourghasemi, H. R., & Dixon, B. (2016). GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran. Environmental Monitoring and Assessment, 188(1), 1-27. DOI: https://doi.org/10.1007/s10661-015-5049-6
Naghibi, S. A., Ahmadi, K., & Daneshi, A. (2017). Application of support vector machine, random forest, and genetic algorithm optimized random forest models in groundwater potential mapping. Water Resources Management, 31(2), 1-15. DOI: https://doi.org/10.1007/s11269-017-1660-3
Oiro, S., Comte, J.-C., Soulsby, C., MacDonald, A., & Mwakamba, C. (2020). Depletion of groundwater resources under rapid urbanisation in Africa: recent and future trends in the Nairobi Aquifer System, Kenya. Hydrogeology Journal, 28(8), 2635-2656. https://doi.org/10.1007/s10040-020-02236-5. DOI: https://doi.org/10.1007/s10040-020-02236-5
Podgorski, J., & Berg, M. (2020). Global threat of arsenic in groundwater. Science, 368(6493), 845-850. https://doi.org/10.1126/science.aba1510. DOI: https://doi.org/10.1126/science.aba1510
Rahaman, E. I. U., Ahmad, S., Khokhar, M. F., Khayyam, U., Azmat, M., Arshad, M., & Qaiser, F. U. R. (2024). Analyzing land use land cover (LULC) changes induced by the run-of-river project and respondent survey: A case of Ghazi Barotha Hydropower Project on Indus River, Pakistan. Environmental Research Communications, 6(3), 035002. https://doi.org/10.1088/2515-7620/ad2bb5. DOI: https://doi.org/10.1088/2515-7620/ad2bb5
Riboli, S. A. & Lindino, C. (2023). Análise de componentes principais (PCA) na discriminação de fontes de água potável. Revista Tecnia, 5(1). https://periodicos.ifg.edu.br/tecnia/article/view/577. DOI: https://doi.org/10.56762/tecnia.v8i2.577
Rodriguez-Galiano, V., Mendes, M. P., Garcia-Soldado, M. J., Chica-Olmo, M., & Ribeiro, L. (2014). Predictive modeling of groundwater nitrate pollution using Random Forest and multisource variables related to intrinsic and specific vulnerability: A case study in an agricultural setting (Southern Spain). Science of The Total Environment, 476–477, 189–206. https://doi.org/10.1016/j.scitotenv.2014.01.001. DOI: https://doi.org/10.1016/j.scitotenv.2014.01.001
Sahoo S., Russo T. A., Elliott J. (2017). Machine learning algorithms for modeling groundwater level changes in agricultural regions of the U.S. Water Resources Research, v. 53, p. 3878–3895, 2017. Disponível em: https://doi.org/10.1002/2016WR019933. DOI: https://doi.org/10.1002/2016WR019933
Sajedi-Hosseini, F., Malekian, A., Choubin, B., Rahmati, O., Cipullo, S., Coulon, F., & Pradhan, B. (2018). A novel machine learningbased approach for the risk assessment of nitrate groundwater contamination. Science of The Total Environment, v. 644, p. 954-962, 2018. https://doi.org/10.1016/j.scitotenv.2018.07.054. DOI: https://doi.org/10.1016/j.scitotenv.2018.07.054
Sakthipriya, E., & Chandrakumar, T. (2024). Weather based paddy yield prediction using machine learning regression algorithms. Journal of Agrometeorology, 26(3), 344–348. https://doi.org/10.54386/jam.v26i3.2598. DOI: https://doi.org/10.54386/jam.v26i3.2598
Singha, S., Pasupuleti, S., Singha, S. S., Singh, R., & Kumar, S. (2021). Prediction of groundwater quality using efficient machine learning technique. Chemosphere, 276, 130265. https://doi.org/10.1016/j.chemosphere.2021.130265. DOI: https://doi.org/10.1016/j.chemosphere.2021.130265
Subba Rao, N. (2006). Seasonal variation of groundwater quality in a part of Guntur District, Andhra Pradesh, India. Environmental Geology, 49, 413–429. https://doi.org/10.1007/s00254-005-0089-9. DOI: https://doi.org/10.1007/s00254-005-0089-9
Vliet, M. T. H. Van, Jones, E. R., Flörke, M., Franssen, W. H. P., & Hanasaki, N. (2021). Global water scarcity including surface water quality and expansions of clean water technologies. Global Water Scarcity. DOI: https://doi.org/10.1088/1748-9326/abbfc3
Wisitthammasri, W., Chotpantarat, S., & Thitimakorn, T. (2020). Multivariate statistical analysis of the hydrochemical characteristics of a volcano sedimentary aquifer in Saraburi Province, Thailand. Journal of Hydrology: Regional Studies, 32, 100745. https://doi.org/10.1016/j.ejrh.2020.100745. DOI: https://doi.org/10.1016/j.ejrh.2020.100745
Wu, Z., Lu, C., Sun, Q., Lu, W., He, X., Qin, T., Yan, L., & Wu, C. (2023). Predicting groundwater level based on machine learning: a case study of the Hebei Plain. Water, 15, 823. https://doi.org/10.3390/w15040823. DOI: https://doi.org/10.3390/w15040823
Wu, J., Cao, Y., Islam, M. S., & Emch, M. (2025). Application of machine learning to identify influential factors for fecal contamination of shallow groundwater. Water, 17, 160. https://doi.org/10.3390/w17020160. DOI: https://doi.org/10.3390/w17020160
Wu, J., Li, P., Wang, D., Ren, X., & Wei, M. (2020). Statistical and multivariate statistical techniques to trace the sources and affecting factors of groundwater pollution in a rapidly growing city on the Chinese Loess Plateau. Human and Ecological Risk Assessment: An International Journal, 26(6), 1603–1621. https://doi.org/10.1080/10807039.2019.1594156. DOI: https://doi.org/10.1080/10807039.2019.1594156
Zhan, X., Liu, W., Chen, S., Yao, R., Yang, C., Zhang, X., Li, J., Wang, Y., & Zhang, Y. (2025). Machine learning approaches to identify hydrochemical processes and predict drinking water quality for groundwater environment in a metropolis. Journal of Hydrology: Regional Studies, 58, 102227. https://doi.org/10.1016/j.jhydrol.2025.102227. DOI: https://doi.org/10.1016/j.ejrh.2025.102227
Zhang, S., Mao, G., Crittenden, J., Liu, X., & Du, H. (2017). Groundwater remediation from the past to the future: a bibliometric analysis. Water Research, 119, 114-125. https://doi.org/10.1016/j.watres.2017.01.029. DOI: https://doi.org/10.1016/j.watres.2017.01.029
Zhou, Y., Yang, F., Wu, X., Jia, C., Liu, S., & Gao, Y. (2020). Bibliometric analysis of research progress on karst groundwater pollution. IOP Conference Series: Earth and Environmental Science, 568, 012040. https://doi.org/10.1088/1755-1315/568/1/012040. DOI: https://doi.org/10.1088/1755-1315/568/1/012040
Zyoud, S. H., & Fuchs-Hanusch, D. (2017). Estimates of Arab world research productivity associated with groundwater: a bibliometric analysis. Applied Water Science, 7(3), 1255-1272. https://doi.org/10.1007/s13201-016-0520-2. DOI: https://doi.org/10.1007/s13201-016-0520-2
Supporting Agencies
This study was funded by the Coordination for the Improvement of Higher Educational Personnel (CAPES Finance Code 001), Brazilian National Research Counsel (CNPq) and the Minas Gerais State Research Foundation (FAPEMIG APQ- 01011-22).How to Cite
The state of the art on groundwater quality studies: a bibliometric analysis of the topic at a global level. (2025). Acque Sotterranee - Italian Journal of Groundwater, 14(3). https://doi.org/10.7343/as-2025-883
Copyright (c) 2025 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
