Treffer: Spatial Prediction Modeling of Geogenic Chromium in Groundwater Using Soft Computing Techniques.
Title:
Spatial Prediction Modeling of Geogenic Chromium in Groundwater Using Soft Computing Techniques.
Authors:
Joodavi A; East Water and Environment Research Institute (EWERI), Mashhad, Iran.; Hydrogeology Department, Technical University of Berlin, Ernst-Reuter Platz 1, 10587, Berlin., Sanikhani H, Majidi M; East Water and Environment Research Institute (EWERI), Mashhad, Iran., Baghbanan P; Department of Geography, Tarbiat Modares University, Tehran, Iran.
Source:
Ground water [Ground Water] 2025 Jul-Aug; Vol. 63 (4), pp. 538-550. Date of Electronic Publication: 2025 May 12.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Blackwell Publishing Country of Publication: United States NLM ID: 9882886 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1745-6584 (Electronic) Linking ISSN: 0017467X NLM ISO Abbreviation: Ground Water Subsets: MEDLINE
Imprint Name(s):
Publication: 2005- : Malden, MA : Blackwell Publishing
Original Publication: Worthington, Ohio : Water Well Journal Pub. Co.,
Original Publication: Worthington, Ohio : Water Well Journal Pub. Co.,
MeSH Terms:
References:
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Barzegar, R., J. Adamowski, and A.A. Moghaddam. 2016. Application of wavelet‐artificial intelligence hybrid models for water quality prediction: A case study in Aji‐Chay River, Iran. Stochastic Environmental Research and Risk Assessment 30, no. 7: 1797–1819. https://doi.org/10.1007/s00477‐016‐1213‐y.
Bertolo, R., C. Bourotte, R. Hirata, L. Marcolan, and O. Sracek. 2011. Geochemistry of natural chromium occurrence in a sandstone aquifer in Bauru Basin, São Paulo state, Brazil. Applied Geochemistry 26, no. 8: 1353–1363. https://doi.org/10.1016/j.apgeochem.2011.05.009.
Bretzler, A., M. Berg, L. Winkel, M. Amini, L. Rodriguez‐Lado, C. Sovann, D.A. Polya, and A. Johnson. 2017. Geostatistical modelling of arsenic hazard in groundwater. In Metals and Related Substances in Drinking Water: Best Practice Guide on the Control of Arsenic in Drinking Water, Vol. 6, ed. P. Bhattacharya, D.A. Polya, and D. Jovanovic, 153–160. London: IWA Publishing.
Burri, N.M., R. Weatherl, C. Moeck, and M. Schirmer. 2019. A review of threats to groundwater quality in the Anthropocene. Science of the Total Environment 684: 136–154. https://doi.org/10.1016/j.scitotenv.2019.05.236.
Coyte, R.M., K.L. McKinley, S. Jiang, J. Karr, G.S. Dwyer, A.J. Keyworth, C.C. Davis, A.J. Kondash, and A. Vengosh. 2020. Occurrence and distribution of hexavalent chromium in groundwater from North Carolina, USA. Science of the Total Environment 711: 135135. https://doi.org/10.1016/j.scitotenv.2019.135135.
Dey, M., M.N. Uddin, and R. Saha. 2021. Assessment of contamination level, pollution risk and source apportionment of heavy metals in the Halda River water, Bangladesh. Heliyon 7, no. 12: e08625. https://doi.org/10.1016/j.heliyon.2021.e08625.
Egbueri, J.C., and J.C. Agbasi. 2022. Data‐driven soft computing modeling of groundwater quality parameters in southeast Nigeria: Comparing the performances of different algorithms. Environmental Science and Pollution Research 29, no. 25: 38346–38373. https://doi.org/10.1007/s11356‐022‐18520‐8.
Esmaeilbeiki, F., M.R. Nikpour, V.K. Singh, O. Kisi, P. Sihag, and H. Sanikhani. 2020. Exploring the application of soft computing techniques for spatial evaluation of groundwater quality variables. Journal of Cleaner Production 276: 124206. https://doi.org/10.1016/j.jclepro.2020.124206.
Fantoni, D., G. Brozzo, M. Canepa, F. Cipolli, L. Marini, G. Ottonello, and M.V. Zuccolini. 2002. Natural hexavalent chromium in groundwaters interacting with ophiolitic rocks. Environmental Geology 42: 871–882. https://doi.org/10.1007/s00254‐002‐0605‐0.
Farokhneshat, F., A.H. Mahvi, and Y. Jamali. 2016. Carcinogenic and non‐carcinogenic risk assessment of chromium in drinking water sources: Birjand, Iran. Research Journal of Environmental Toxicology 10, no. 3: 166–171. https://doi.org/10.3923/RJET.2016.166.171.
Ferreira, C. 2006. Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence, Vol. 21. Berlin, Heidelberg: Springer.
Fijani, E., A.A. Nadiri, A.A. Moghaddam, F.T.C. Tsai, and B. Dixon. 2013. Optimization of DRASTIC method by supervised committee machine artificial intelligence to assess groundwater vulnerability for Maragheh–Bonab plain aquifer, Iran. Journal of Hydrology 503: 89–100. https://doi.org/10.1016/j.jhydrol.2013.08.038.
Friedman, J.H. 1991. Multivariate adaptive regression splines. Annals of Statistics 19, no. 1: 1–67. https://doi.org/10.1214/aos/1176347963.
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Gonzalez, A.R., K. Ndung'u, and A.R. Flegal. 2005. Natural occurrence of hexavalent chromium in the Aromas Red Sands Aquifer, California. Environmental Science & Technology 39, no. 15: 5505–5511. https://doi.org/10.1021/es048835n.
Gray, D.J. 2003. Naturally occurring Cr6+ in shallow groundwaters of the Yilgarn Craton, Western Australia. Geochemistry: Exploration, Environment, Analysis 3, no. 4: 359–368. https://doi.org/10.1144/1467‐7873/03‐012.
Guo, H., C. Liu, S. Yan, J. Yin, and J. Shan. 2024. Source, distribution, and geochemical processes of geogenic high chromium groundwater around the world: A critical review. Journal of Hydrology 131480. https://doi.org/10.1016/j.jhydrol.2024.131480.
Hagan, M.T., and M.B. Menhaj. 1994. Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks 5, no. 6: 989–993. https://doi.org/10.1109/72.329697.
Ham, F.M., and I. Kostanic. 2001. Fundamental neurocomputing concepts. In Principles of Neurocomputing for Science and Engineering. London: Arnold Publishers.
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Hutchinson, M.F. 1989. A new procedure for gridding elevation and stream line data with automatic removal of spurious pits. Journal of Hydrology 106, no. 3–4: 211–232. https://doi.org/10.1016/0022‐1694(89)90073‐5.
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Jafarian, A., and S. Jafarian. 2017. Geogenic and anthropogenic chromium contamination in groundwater in an ophiolitic area, northeastern Iran. Universal Journal of Geoscience 5, no. 6: 183–190. https://doi.org/10.13189/ujg.2017.050603.
Jahanpanah, E., P. Khosravinia, H. Sanikhani, and O. Kisi. 2019. Estimation of discharge with free overfall in rectangular channel using artificial intelligence models. Flow Measurement and Instrumentation 67: 118–130. https://doi.org/10.1016/j.flowmeasinst.2019.04.005.
Joodavi, A. 2018. Effects of Geology on the Quality of Drinking Water Wells in Razavi Khorasan Province. Razavi Khorasan Water and Wastewater Company (in Farsi).
Joodavi, A., R. Aghlmand, J. Podgorski, R. Dehbandi, and A. Abbasi. 2021. Characterization, geostatistical modeling and health risk assessment of potentially toxic elements in groundwater resources of northeastern Iran. Journal of Hydrology: Regional Studies 37: 100885. https://doi.org/10.1016/j.ejrh.2021.100885.
Karunanithi, N., W.J. Grenney, D. Whitley, and K. Bovee. 1994. Neural networks for river flow prediction. Journal of Computing in Civil Engineering 8, no. 2: 201–220. https://doi.org/10.1061/(ASCE)0887‐3801(1994)8:2(201).
Kazakis, N., N. Kantiranis, K. Kalaitzidou, E. Kaprara, M. Mitrakas, R. Frei, G. Vargemezis, P. Tsourlos, A. Zouboulis, and A. Filippidis. 2017. Origin of hexavalent chromium in groundwater: The example of Sarigkiol Basin, Northern Greece. Science of the Total Environment 593–594: 552–566. https://doi.org/10.1016/j.scitotenv.2017.03.128.
Khalilpourfard, S.M., S. Ghaemi, and M. Akbarzadeh. 2017. Development of a hybrid model to predict groundwater contamination using artificial neural networks and multiple linear regression. Environmental Earth Sciences 76, no. 3: 119. https://doi.org/10.1007/s12665‐017‐6405‐5.
Kisi, O., A. Azad, H. Kashi, A. Saeedian, S.A.A. Hashemi, and S. Ghorbani. 2019. Modeling groundwater quality parameters using hybrid neuro‐fuzzy methods. Water Resources Management 33: 847–861. https://doi.org/10.1007/s11269‐018‐2147‐6.
Knoll, L., L. Breuer, and M. Bach. 2019. Large‐scale prediction of groundwater nitrate concentrations from spatial data using machine learning. Science of the Total Environment 668: 1317–1327. https://doi.org/10.1016/j.scitotenv.2019.03.045.
Legates, D.R., and G.J. McCabe Jr. 1999. Evaluating the use of “goodness‐of‐fit” measures in hydrologic and hydroclimatic model validation. Water Resources Research 35, no. 1: 233–241. https://doi.org/10.1029/1998WR900018.
Mishra, A.K., and V.R. Desai. 2006. Drought forecasting using feed‐forward recursive neural network. Ecological Modelling 198, no. 1: 127–138. https://doi.org/10.1016/j.ecolmodel.2006.04.017.
Moosavi, V., M. Vafakhah, B. Shirmohammadi, and M. Ranjbar. 2013. Optimization of wavelet‐ANFIS and wavelet‐ANN hybrid models by Taguchi method for groundwater level forecasting. Arabian Journal for Science and Engineering 39, no. 3: 1785–1796. https://doi.org/10.1007/s13369‐013‐0762‐3.
Nash, J.E., and J.V. Sutcliffe. 1970. River flow forecasting through conceptual models part I—A discussion of principles. Journal of Hydrology 10, no. 3: 282–290. https://doi.org/10.1016/0022‐1694(70)90255‐6.
Podgorski, J., and M. Berg. 2022. Global analysis and prediction of fluoride in groundwater. Nature Communications 13, no. 1: 4232. https://doi.org/10.1038/s41467‐022‐31940‐x.
Podgorski, J., D. Araya, and M. Berg. 2022. Geogenic manganese and iron in groundwater of Southeast Asia and Bangladesh – Machine learning spatial prediction modeling and comparison with arsenic. Science of the Total Environment 833: 155131. https://doi.org/10.1016/j.scitotenv.2022.155131.
Quinlan, J.R. 1992. Learning with continuous classes. In Proceedings of Australian Joint Conference on Artificial Intelligence, Hobart, 16–18 November 1992, 343–348.
Rahimikhoob, A. 2010. Estimation of evapotranspiration based on only air temperature data using artificial neural networks for a subtropical climate in Iran. Theoretical and Applied Climatology 101, no. 1–2: 83–91. https://doi.org/10.1007/s00704‐009‐0204‐z.
Rahimikhoob, A. 2014. Comparison between M5 model tree and neural networks for estimating reference evapotranspiration in an arid environment. Water Resources Management 28: 657–669. https://doi.org/10.1007/s11269‐013‐0506‐x.
Rezaei, A., M.H. Sayadi, R.J. Zadeh, and H. Mousazadeh. 2021. Assessing the hydrogeochemical processes through classical integration of groundwater parameters in the Birjand plain in eastern Iran. Groundwater for Sustainable Development 15: 100684. https://doi.org/10.1016/j.gsd.2021.100684.
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Bretzler, A., M. Berg, L. Winkel, M. Amini, L. Rodriguez‐Lado, C. Sovann, D.A. Polya, and A. Johnson. 2017. Geostatistical modelling of arsenic hazard in groundwater. In Metals and Related Substances in Drinking Water: Best Practice Guide on the Control of Arsenic in Drinking Water, Vol. 6, ed. P. Bhattacharya, D.A. Polya, and D. Jovanovic, 153–160. London: IWA Publishing.
Burri, N.M., R. Weatherl, C. Moeck, and M. Schirmer. 2019. A review of threats to groundwater quality in the Anthropocene. Science of the Total Environment 684: 136–154. https://doi.org/10.1016/j.scitotenv.2019.05.236.
Coyte, R.M., K.L. McKinley, S. Jiang, J. Karr, G.S. Dwyer, A.J. Keyworth, C.C. Davis, A.J. Kondash, and A. Vengosh. 2020. Occurrence and distribution of hexavalent chromium in groundwater from North Carolina, USA. Science of the Total Environment 711: 135135. https://doi.org/10.1016/j.scitotenv.2019.135135.
Dey, M., M.N. Uddin, and R. Saha. 2021. Assessment of contamination level, pollution risk and source apportionment of heavy metals in the Halda River water, Bangladesh. Heliyon 7, no. 12: e08625. https://doi.org/10.1016/j.heliyon.2021.e08625.
Egbueri, J.C., and J.C. Agbasi. 2022. Data‐driven soft computing modeling of groundwater quality parameters in southeast Nigeria: Comparing the performances of different algorithms. Environmental Science and Pollution Research 29, no. 25: 38346–38373. https://doi.org/10.1007/s11356‐022‐18520‐8.
Esmaeilbeiki, F., M.R. Nikpour, V.K. Singh, O. Kisi, P. Sihag, and H. Sanikhani. 2020. Exploring the application of soft computing techniques for spatial evaluation of groundwater quality variables. Journal of Cleaner Production 276: 124206. https://doi.org/10.1016/j.jclepro.2020.124206.
Fantoni, D., G. Brozzo, M. Canepa, F. Cipolli, L. Marini, G. Ottonello, and M.V. Zuccolini. 2002. Natural hexavalent chromium in groundwaters interacting with ophiolitic rocks. Environmental Geology 42: 871–882. https://doi.org/10.1007/s00254‐002‐0605‐0.
Farokhneshat, F., A.H. Mahvi, and Y. Jamali. 2016. Carcinogenic and non‐carcinogenic risk assessment of chromium in drinking water sources: Birjand, Iran. Research Journal of Environmental Toxicology 10, no. 3: 166–171. https://doi.org/10.3923/RJET.2016.166.171.
Ferreira, C. 2006. Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence, Vol. 21. Berlin, Heidelberg: Springer.
Fijani, E., A.A. Nadiri, A.A. Moghaddam, F.T.C. Tsai, and B. Dixon. 2013. Optimization of DRASTIC method by supervised committee machine artificial intelligence to assess groundwater vulnerability for Maragheh–Bonab plain aquifer, Iran. Journal of Hydrology 503: 89–100. https://doi.org/10.1016/j.jhydrol.2013.08.038.
Friedman, J.H. 1991. Multivariate adaptive regression splines. Annals of Statistics 19, no. 1: 1–67. https://doi.org/10.1214/aos/1176347963.
Geological Survey and Mineral Exploration of Iran. 2021. https://gsi.ir/en.
Geological Survey and Mineral Exploration of Iran – East Territory. 2019. The Geochemical Information of Khorasan Razavi, Mashhad.
Ghorbani, M. 2013. Economic Geology of Iran, Mineral Deposits and Natural Resources of Iran. Dordrecht: Springer. https://doi.org/10.1007/978‐94‐007‐5625‐0.
Gonzalez, A.R., K. Ndung'u, and A.R. Flegal. 2005. Natural occurrence of hexavalent chromium in the Aromas Red Sands Aquifer, California. Environmental Science & Technology 39, no. 15: 5505–5511. https://doi.org/10.1021/es048835n.
Gray, D.J. 2003. Naturally occurring Cr6+ in shallow groundwaters of the Yilgarn Craton, Western Australia. Geochemistry: Exploration, Environment, Analysis 3, no. 4: 359–368. https://doi.org/10.1144/1467‐7873/03‐012.
Guo, H., C. Liu, S. Yan, J. Yin, and J. Shan. 2024. Source, distribution, and geochemical processes of geogenic high chromium groundwater around the world: A critical review. Journal of Hydrology 131480. https://doi.org/10.1016/j.jhydrol.2024.131480.
Hagan, M.T., and M.B. Menhaj. 1994. Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks 5, no. 6: 989–993. https://doi.org/10.1109/72.329697.
Ham, F.M., and I. Kostanic. 2001. Fundamental neurocomputing concepts. In Principles of Neurocomputing for Science and Engineering. London: Arnold Publishers.
Haykin, S. 1998. Neural Networks: A Comprehensive Foundation, 2nd ed. Upper Saddle River: Prentice‐Hall.
Hutchinson, M.F. 1989. A new procedure for gridding elevation and stream line data with automatic removal of spurious pits. Journal of Hydrology 106, no. 3–4: 211–232. https://doi.org/10.1016/0022‐1694(89)90073‐5.
Iranian Department of Environment. 2012. Soil Pollution Atlas of Razavi Khorasan Province.
Iran Water Resources Management Company. 2019. Iran Water Resources Management Company [online]. https://www.wrm.ir. Accessed December 23, 2019.
Jafarian, A., and S. Jafarian. 2017. Geogenic and anthropogenic chromium contamination in groundwater in an ophiolitic area, northeastern Iran. Universal Journal of Geoscience 5, no. 6: 183–190. https://doi.org/10.13189/ujg.2017.050603.
Jahanpanah, E., P. Khosravinia, H. Sanikhani, and O. Kisi. 2019. Estimation of discharge with free overfall in rectangular channel using artificial intelligence models. Flow Measurement and Instrumentation 67: 118–130. https://doi.org/10.1016/j.flowmeasinst.2019.04.005.
Joodavi, A. 2018. Effects of Geology on the Quality of Drinking Water Wells in Razavi Khorasan Province. Razavi Khorasan Water and Wastewater Company (in Farsi).
Joodavi, A., R. Aghlmand, J. Podgorski, R. Dehbandi, and A. Abbasi. 2021. Characterization, geostatistical modeling and health risk assessment of potentially toxic elements in groundwater resources of northeastern Iran. Journal of Hydrology: Regional Studies 37: 100885. https://doi.org/10.1016/j.ejrh.2021.100885.
Karunanithi, N., W.J. Grenney, D. Whitley, and K. Bovee. 1994. Neural networks for river flow prediction. Journal of Computing in Civil Engineering 8, no. 2: 201–220. https://doi.org/10.1061/(ASCE)0887‐3801(1994)8:2(201).
Kazakis, N., N. Kantiranis, K. Kalaitzidou, E. Kaprara, M. Mitrakas, R. Frei, G. Vargemezis, P. Tsourlos, A. Zouboulis, and A. Filippidis. 2017. Origin of hexavalent chromium in groundwater: The example of Sarigkiol Basin, Northern Greece. Science of the Total Environment 593–594: 552–566. https://doi.org/10.1016/j.scitotenv.2017.03.128.
Khalilpourfard, S.M., S. Ghaemi, and M. Akbarzadeh. 2017. Development of a hybrid model to predict groundwater contamination using artificial neural networks and multiple linear regression. Environmental Earth Sciences 76, no. 3: 119. https://doi.org/10.1007/s12665‐017‐6405‐5.
Kisi, O., A. Azad, H. Kashi, A. Saeedian, S.A.A. Hashemi, and S. Ghorbani. 2019. Modeling groundwater quality parameters using hybrid neuro‐fuzzy methods. Water Resources Management 33: 847–861. https://doi.org/10.1007/s11269‐018‐2147‐6.
Knoll, L., L. Breuer, and M. Bach. 2019. Large‐scale prediction of groundwater nitrate concentrations from spatial data using machine learning. Science of the Total Environment 668: 1317–1327. https://doi.org/10.1016/j.scitotenv.2019.03.045.
Legates, D.R., and G.J. McCabe Jr. 1999. Evaluating the use of “goodness‐of‐fit” measures in hydrologic and hydroclimatic model validation. Water Resources Research 35, no. 1: 233–241. https://doi.org/10.1029/1998WR900018.
Mishra, A.K., and V.R. Desai. 2006. Drought forecasting using feed‐forward recursive neural network. Ecological Modelling 198, no. 1: 127–138. https://doi.org/10.1016/j.ecolmodel.2006.04.017.
Moosavi, V., M. Vafakhah, B. Shirmohammadi, and M. Ranjbar. 2013. Optimization of wavelet‐ANFIS and wavelet‐ANN hybrid models by Taguchi method for groundwater level forecasting. Arabian Journal for Science and Engineering 39, no. 3: 1785–1796. https://doi.org/10.1007/s13369‐013‐0762‐3.
Nash, J.E., and J.V. Sutcliffe. 1970. River flow forecasting through conceptual models part I—A discussion of principles. Journal of Hydrology 10, no. 3: 282–290. https://doi.org/10.1016/0022‐1694(70)90255‐6.
Podgorski, J., and M. Berg. 2022. Global analysis and prediction of fluoride in groundwater. Nature Communications 13, no. 1: 4232. https://doi.org/10.1038/s41467‐022‐31940‐x.
Podgorski, J., D. Araya, and M. Berg. 2022. Geogenic manganese and iron in groundwater of Southeast Asia and Bangladesh – Machine learning spatial prediction modeling and comparison with arsenic. Science of the Total Environment 833: 155131. https://doi.org/10.1016/j.scitotenv.2022.155131.
Quinlan, J.R. 1992. Learning with continuous classes. In Proceedings of Australian Joint Conference on Artificial Intelligence, Hobart, 16–18 November 1992, 343–348.
Rahimikhoob, A. 2010. Estimation of evapotranspiration based on only air temperature data using artificial neural networks for a subtropical climate in Iran. Theoretical and Applied Climatology 101, no. 1–2: 83–91. https://doi.org/10.1007/s00704‐009‐0204‐z.
Rahimikhoob, A. 2014. Comparison between M5 model tree and neural networks for estimating reference evapotranspiration in an arid environment. Water Resources Management 28: 657–669. https://doi.org/10.1007/s11269‐013‐0506‐x.
Rezaei, A., M.H. Sayadi, R.J. Zadeh, and H. Mousazadeh. 2021. Assessing the hydrogeochemical processes through classical integration of groundwater parameters in the Birjand plain in eastern Iran. Groundwater for Sustainable Development 15: 100684. https://doi.org/10.1016/j.gsd.2021.100684.
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Grant Information:
97008161 Iran National Science Foundation
Substance Nomenclature:
0R0008Q3JB (Chromium)
0 (Water Pollutants, Chemical)
0 (Water Pollutants, Chemical)
Entry Date(s):
Date Created: 20250512 Date Completed: 20250718 Latest Revision: 20250718
Update Code:
20250718
DOI:
10.1111/gwat.13488
PMID:
40353617
Database:
MEDLINE
Weitere Informationen
The presence of chromium (Cr) in groundwater poses a significant threat to human health. However, the lack of testing in many wells suggests that the severity of this issue may be underestimated. In this study, various predictive models, including soft computing techniques such as gene expression programming (GEP), artificial neural networks (ANN), multivariate adaptive regression splines (MARS), and the M5 Tree model, along with random forest (RF) and multiple linear regression (MLR), were employed to estimate geogenic Cr concentrations in groundwater based on geological and geochemical parameters in northeastern Iran. A dataset of 676 Cr concentration measurements was used to train and evaluate the models. Among the methods tested, ANN demonstrated the highest predictive accuracy, followed closely by RF, which provided competitive results. GEP and MARS also showed reasonable performance, while MLR exhibited the weakest accuracy, highlighting the limitations of linear models in addressing complex geochemical processes. The ANN model identified over 600,000 individuals in the central and western regions of the study area as being at significant risk of geogenic Cr contamination in groundwater. The findings underscore the potential of advanced predictive models in groundwater quality management and their applicability in other regions with similar challenges.
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