A multi-step approach to evaluate the sustainable use of groundwater resources for human consumption and agriculture

The rapid decline in both quality and availability of freshwater resources on our planet necessitates their thorough assessment to ensure sustainable usage. The growing demand for water in industrial, agricultural, and domestic sectors poses significant challenges to managing both surface and groundwater resources. This study tests and proposes a hybrid evaluation approach to determine Groundwater Quality Indices (GQIs) for irrigation (IRRI), seawater intrusion (SWI), and potability (POT), finalized to the spatial distribution of groundwater suitability involving water quality indicator along with hydrogeological and socio-economic factors. Mean Decrease Accuracy (MDA) and Information Gain Ratio (IGR) were used to state the importance of chosen factors such as level of groundwater above the sea, thickness of the aquifer, land cover, distance from coastline, silt soil content, recharge, distance from river and lagoons, depth to water table from ground, distance from agricultural wells, hydraulic conductivity, and lithology for each quality index, separately. The results of both methods showed that recharge is the most important parameter for GQIIRRI and GQIPOT, while the distance from the coastline and the rivers, are the most important for GQISWI. The spatial modelling of GQIIRRI and GQIPOT in the study area has been achieved applying three machine learning (ML) algorithms: the Boosted Regression Tree (BRT), the Random Forest (RF), and the Support Vector Machine (SVM). Validation results showed that RF has the highest prediction for GQIIRRI, while the SVM model has the highest prediction for the GQIPOT index. It is worth to mention that the future utilization and testing of new algorithms could produce even better results. Finally, GQIIRRI and GQIPOT were combined and compared using two combine and overlay methods to prepare a hybrid map of multi-GQIs. The results showed that 69% of the study area is suitable for irrigation and potable use, due to both geogenic and anthropogenic activities which contribute to make some water resources unsuitable for either use. Specifically, the northern, western, and eastern portions of the study area are in the "very high and high quality" classes while the southern portion shows "very low and low quality" classes. In conclusion, the developed map and approach can serve as a practical guide for enhancing groundwater management, identifying suitable areas for various uses and pinpointing regions requiring improved management practices.

Agrawal, P., Sinha, A., Kumar, S., Agarwal, A., Banerjee, A., Villuri, V.G.K.,
Pasupuleti, S., 2021. Exploring artificial intelligence techniques for groundwater
quality assessment. Water 13 (9), 1172. https://doi.org/10.3390/w13091172.
Alamne, S.B., Assefa, T.T., Belay, S.A., Hussein, M.A., 2022. Mapping groundwater
nitrate contaminant risk using the modified DRASTIC model: a case study in
Ethiopia. Environ. Syst. Res. 11 (1), 8. https://doi.org/10.1186/s40068-022-00253-
9.
Aller, L., 1985. DRASTIC: a Standardized System for Evaluating Ground Water Pollution
Potential Using Hydrogeologic Settings. Robert S. Kerr Environmental Research
Laboratory, Office of Research and Development, US Environmental Protection
Agency.
Allocca, V., Celico, F., Celico, P., De Vita, P., Fabbrocino, S., Mattia, S., Monacelli, G.,
Musilli, I., Piscopo, V., Scalise, A.R., Summa, G.M., Tranfaglia, G., 2007. Illustrative
JOURNAL of MAPS 573 Notes of the Hydrogeological Map of Southern Italy, vol.
211. Istituto Poligrafico e Zecca Dello Stato, 88- 448-0215-5.
Amorosi, A., Pacifico, A., Rossi, V., Ruberti, D., 2012. Late quaternary incision and
deposition in an active volcanic setting: the Volturno valley fill. southern Italy.
Sediment. Geol. 282, 307–320. https://doi.org/10.1016/j.sedgeo.2012.10.00.
Ascott, M.J., Gooddy, D.C., Wang, L., et al., 2017. Global patterns of nitrate storage in the
vadose zone. Nat. Commun. 8, 1416. https://doi.org/10.1038/s41467-017-01321-
w.
Babiker, I.S., Mohamed, M.A.A., Hiyama, T., 2007. Assessing groundwater quality using
GIS. Water Resour. Manag. 21, 699–715. https://doi.org/10.1007/s11269-006-
9059-6.
Batjes, N.H., Ribeiro, E., van Oostrum, A., Leenaars, J., Hengl, T., Mendes de Jesus, J.,
2017. WoSIS: providing standardised soil profile data for the world. Earth Syst. Sci.
Data 9, 1–14. https://doi.org/10.5194/essd-9-1-2017.
Barzegar, R., Razzagh, S., Quilty, J., Adamowski, J., Pour, H.K., Booij, M.J., 2021.
Improving GALDIT-based groundwater vulnerability predictive mapping using
coupled resampling algorithms and machine learning models. J. Hydrol. 598,
126370 https://doi.org/10.1016/j.jhydrol.2021.126370.
BaSeLiNe, 1999. Natural Baseline Quality in European Aquifers, a Basis for Aquifer
Management. https://nora.nerc.ac.uk/id/eprint/512162.
Bedi, S., Samal, A., Ray, C., Snow, D., 2020. Comparative evaluation of machine learning
models for groundwater quality assessment. Environ. Monit. Assess. 192, 1–23.
https://doi.org/10.1007/s10661-020-08695-3.
Benaafi, M., Yassin, M.A., Usman, A.G., Abba, S.I., 2022. Neurocomputing modelling of
hydrochemical and physical properties of groundwater coupled with spatial
clustering, GIS, and statistical techniques. Sustainability 14 (4), 2250. https://doi.
org/10.3390/su14042250.
Bordbar, M., Neshat, A., Javadi, S., 2019. A new hybrid framework for optimization and
modification of groundwater vulnerability in coastal aquifer. Environ. Sci. Pollut.
Res. 26, 21808–21827. https://doi.org/10.1007/s11356-019-04853-4.
Bordbar, M., Neshat, A., Javadi, S., Pradhan, B., Dixon, B., Paryani, S., 2021. Improving
the coastal aquifers’ vulnerability assessment using SCMAI ensemble of three
machine learning approaches. Nat. Hazards 1–22. https://doi.org/10.1007/s11069-
021-05013-z.
Boufekane, A., Maizi, D., Madene, E., Busico, G., Zghibi, A., 2022. Hybridization of
GALDIT method to assess actual and future coastal vulnerability to seawater
intrusion. J. Environ. Manag. 318, 115580 https://doi.org/10.1016/j.
jenvman.2022.115580.
M. Bordbar et al.
Journal of Environmental Management 347 (2023) 119041
10
Braca, G., Bussettini, M., Gaf`
a, R.M., Monti, G.M., Martarelli, L., Silvi, A., La Vigna, F.,
2022. The nationwide water budget estimation in the light of the new permeability.
Map of Italy AS/IT JGW 11 (3), 31–39. https://doi.org/10.7343/as-2022-575.
Busico, G., Kazakis, N., Colombani, N., Khosravi, K., Voudouris, K., Mastrocicco, M.,
2020a. The importance of incorporating denitrification in the assessment of
groundwater vulnerability. Appl. Sci. 10 (7) https://doi.org/10.3390/app10072328.
Busico, G., Kazakis, N., Cuoco, E., Colombani, N., Tedesco, D., Voudouris, K.,
Mastrocicco, M., 2020b. A novel hybrid method of specific vulnerability to
anthropogenic pollution using multivariate statistical and regression analyses. Water
Res. 171, 115386 https://doi.org/10.1016/j.watres.2019.115386.
Busico, G., Kazakis, N., Colombani, N., Mastrocicco, M., Voudouris, K., Tedesco, D.,
2017. A modified SINTACS method for groundwater vulnerability and pollution risk
assessment in highly anthropized regions based on NO3− and SO42−
concentrations. Sci. Total Environ. 609, 1512–1523. https://doi.org/10.1016/j.
scitotenv.2017.07.257.
Busico, G., Cuoco, E., Kazakis, N., Colombani, N., Mastrocicco, M., Tedesco, D.,
Voudouris, K., 2018. Multivariate statistical analysis to characterize/discriminate
between anthropogenic and geogenic trace elements occurrence in the Campania
Plain. Southern Italy. Environ. Pollut. 234, 260–269. https://doi.org/10.1016/j.
envpol.2017.11.053.
Busico, G., Mastrocicco, M., Cuoco, E., Sirna, M., Tedesco, D., 2019. Protection from
natural and anthropogenic sources: a new rating methodology to delineate “nitrate
vulnerable zone”. Environ. Earth Sci. 78 (4), 1–13. https://doi.org/10.1007/s12665-
019-8118-2.
Busico, G., Buffardi, C., Ntona, M.M., Vigliotti, M., Colombani, N., Mastrocicco, M.,
Ruberti, D., 2021. Actual and forecasted vulnerability assessment to seawater
intrusion via GALDIT-SUSI in the Volturno river mouth (Italy). Rem. Sens. 13 (18),
3632. https://doi.org/10.3390/rs13183632.
Chachadi, A.G., Lobo-Ferreira, J.P., 2001. Sea water intrusion vulnerability mapping of
aquifers using GALDIT method. Coastin 4, 7–9.
Cuoco, E., Darrah, T.H., Buono, G., Verrengia, G., De Francesco, S., Eymold, W.K.,
Tedesco, D., 2015. Inorganic contaminants from diffuse pollution in shallow
groundwater of the Campanian plain (southern Italy). Implications for geochemical
survey. Environ. Monit. Assess. 187 (2), 46. https://doi.org/10.1007/s10661-015-
4307-y.
Danielopol, D.L., Griebler, C., Gunatilaka, A., Notenboom, J., 2003. Present state and
future prospects for groundwater ecosystems. Environ. Conserv. 30 (2), 104–113.
https://doi.org/10.1017/S0376892903000109.
Durov, S.A., 1948. Natural waters and graphic representation of their composition. Dokl.
Akad. Nauk SSSR 59 (3), 87–90.
Elbeltagi, A., Pande, C.B., Kouadri, S., Islam, A.R.M.T., 2022. Applications of various
data-driven models for the prediction of groundwater quality index in the Akot
basin, Maharashtra, India. Environ. Sci. Pollut. Res. 1–15 https://doi.org/10.1007/
s11356-021-17064-7.
Famiglietti, J.S., Rodell, M., 2013. Water in the balance. Science 340 (6138), 1300–1301.
https://doi.org/10.1126/science.1236460.
Fan, Y., Li, H., Miguez-Macho, G., 2013. Global patterns of groundwater table depth.
Science 339 (6122), 940–943. http://doi:10.1126/science.1229881.
Farmani, R., Henriksen, H.J., Savic, D., 2009. An evolutionary Bayesian belief network
methodology for optimum management of groundwater contamination. Environ.
Model. Software 24 (3), 303–310. https://doi.org/10.1016/j.envsoft.2008.08.005.
Fiorillo, F., Guadagno, F.M., 2011. Long karst spring discharge time series and droughts
occurrence in Southern Italy. Environ. Earth Sci. 65 (8), 2273–2283. https://doi.org/
10.1007/s12665-011-1495-9.
Gaiolini, M., Colombani, N., Busico, G., Rama, F., Mastrocicco, M., 2022. Impact of
boundary conditions dynamics on groundwater budget in the Campania region
(Italy). Water (Switzerland) 14 (16). https://doi.org/10.3390/w14162462.
Ghosal, S., Ruj, C., 2023. Societal impact analysis of community-managed potable water
supply system in rural India (2023. J. Appl. Soc. Sci. 17 (1), 148–167. https://doi.
org/10.1177/19367244221119140.
Giaccio, B., Hajdas, I., Isaia, R., Deino, A.L., Nomade, S., 2017. High-precision 14C and
40Ar/39Ar dating of the Campanian Ignimbrite (Y-5) reconciles the timescales of
climatic-cultural processes at 40 ka. Sci. Rep. 7, 45940 https://doi.org/10.1038/
srep45940.
Goodarzi, M.R., Niknam, A.R.R., Jamali, V., Pourghasemi, H.R., 2022. Aquifer
vulnerability identification using DRASTIC-LU model modification by fuzzy analytic
hierarchy process. Model. Earth Syst. Environ. 8 (4), 5365–5380. https://doi.org/
10.1007/s40808-022-01408-4.
Kazakis, N., Voudouris, K.S., 2015. Groundwater vulnerability and pollution risk
assessment of porous aquifers to nitrate: modifying the DRASTIC method using
quantitative parameters. J. Hydrol. 525, 13–25. https://doi.org/10.1016/j.jh.
Kazakis, N., Busico, G., Colombani, N., Mastrocicco, M., Pavlou, A., Voudouris, K., 2019.
GALDIT-SUSI a modified method to account for surface water bodies in the
assessment of aquifer vulnerability to seawater intrusion. J. Environ. Manag. 235,
257–265. https://doi.org/10.1016/j.jenvman.2019.01.069.
Khan, Q., Liaqat, M.U., Mohamed, M.M., 2022. A comparative assessment of modeling
groundwater vulnerability using DRASTIC method from GIS and a novel
classification method using machine learning classifiers. Geocarto Int. 37 (20),
5832–5850. https://doi.org/10.1080/10106049.2021.1923833.
Khosravi, K., Barzegar, R., Golkarian, A., Busico, G., Cuoco, E., Mastrocicco, M.,
Kazakis, N., 2021a. Predictive modeling of selected trace elements in groundwater
using hybrid algorithms of iterative classifier optimizer. J. Contam. Hydrol. 242
https://doi.org/10.1016/j.jconhyd.2021.103849.
Khosravi, K., Bordbar, M., Paryani, S., Saco, P.M., Kazakis, N., 2021b. New hybrid-based
approach for improving the accuracy of coastal aquifer vulnerability assessment
maps. Sci. Total Environ. 767, 145416 https://doi.org/10.1016/j.
scitotenv.2021.145416.
Konikow, L.F., 2011. Contribution of global groundwater depletion since 1900 to sea level rise. Geophys. Res. Lett. 38 (17), L17401 https://doi.org/10.1029/
2011GL048604.
Lee, H., Park, S., Hang, V., Nguyen, M., Shin, H.S., 2023. Proposal for a new
customization process for a data-based water quality index using a random forest
approach. Environ. Pollut. 121222 https://doi.org/10.1016/j.envpol.2023.121222.
Mace, R.E., 2023. The importance of groundwater sustainability. In: Groundwater
Sustainability: Conception, Development, and Application. Springer International
Publishing, Cham, pp. 1–20. https://doi.org/10.1007/978-3-031-13516-3_1.
Machiwal, D., Jha, M.K., Singh, V.P., Mohan, C., 2018a. Assessment and mapping of
groundwater vulnerability to pollution: current status and challenges. Earth Sci. Rev.
185, 901–927. https://doi.org/10.1016/j.earscirev.2018.08.009.
Machiwal, D., Cloutier, V., Güler, C., Kazakis, N., 2018b. A review of GIS-integrated
statistical techniques for groundwater quality evaluation and protection. Environ.
Earth Sci. 77, 681. https://doi.org/10.1007/s12665-018-7872-x.
Mastrocicco, M., Colombani, N., 2021. The issue of groundwater salinization in coastal
areas of the mediterranean region: a review. Water (Switzerland) 13 (1). http://do
i:10.3390/w13010090.
Mastrocicco, M., Busico, G., Colombani, N., 2019. Deciphering interannual temperature
variations in springs of the Campania region (Italy). Water 11 (2), 288. https://doi.
org/10.3390/w11020288.
Milia, A., Torrente, M.M., 2015. Tectono-stratigraphic signature of a rapid multistage
subsiding rift basin in the Tyrrhenian-Apennine hinge zone (Italy): a possible
interaction of upper plate with subducting slab. J. Geodyn. 86, 42–60. https://doi.
org/10.1016/j.jog.2015.02.005.
Mishra, N., Sharma, A.K., 2021. Groundwater storage analysis in changing land use/land
cover for haridwar Districts of upper Ganga canal command (1972–2011). In:
Advances in Civil Engineering and Infrastructural Development: Select Proceedings
of ICRACEID 2019. Springer Singapore, pp. 233–241. https://doi.org/10.1007/978-
981-15-6463-5_22.
Mohammed, M.A., Szabo, ´ N.P., Szucs, ˝ P., 2022. Multivariate statistical and
hydrochemical approaches for evaluation of groundwater quality in north Bahri city Sudan. Heliyon 8 (11), e11308. https://doi.org/10.1016/j.heliyon.2022.e11308.
Molinari, A., Guadagnini, L., Marcaccio, M., Guadagnini, A., 2018. Geostatistical
multimodel approach for the assessment of the spatial distribution of natural
background concentrations in large-scale groundwater bodies. Water Res. https://
doi.org/10.1016/j.watres.2018.09.049.
Mountrakis, G., Im, J., Ogole, C., 2011. Support vector machines in remote sensing: a
review. ISPRS J. Photogrammetry Remote Sens. 66 (3), 247–259. https://doi.org/
10.1016/j.isprsjprs.2010.11.001.
Najafzadeh, M., Homaei, F., Mohamadi, S., 2022. Reliability evaluation of groundwater
quality index using data-driven models. Environ. Sci. Pollut. Res. 29 (6), 8174–8190.
https://doi.org/10.1007/s11356-021-16158-6.
Nieto, P., Custodio, E., Manzano, M., 2005. Baseline groundwater quality: a European
approach. Environ. Sci. Pol. 8 (4), 399–409. https://doi.org/10.1016/j.
envsci.2005.04.004.
Norouzi, H., Moghaddam, A.A., 2020. Groundwater quality assessment using random
forest method based on groundwater quality indices (case study: Miandoab plain
aquifer, NW of Iran). Arabian J. Geosci. 13, 1–13. https://doi.org/10.1007/s12517-
020-05904-8.
Norouzi, H., Moghaddam, A.A., Celico, F., Shiri, J., 2021. Assessment of groundwater
vulnerability using genetic algorithm and random forest methods (case study:
Miandoab plain, NW of Iran). Environ. Sci. Pollut. Res. 28, 39598–39613. https://
doi.org/10.1007/s11356-021-12714-2.
Peters, N.E., Meybeck, M., 2000. Water quality degradation effects on freshwater
availability: impacts of human activities. Water Int. 25 (2), 185–193. https://doi.
org/10.1080/02508060008686817.
Pham, Q.B., Tran, D.A., Ha, N.T., Islam, A.R.M.T., Salam, R., 2022. Random forest and
nature-inspired algorithms for mapping groundwater nitrate concentration in a
coastal multi-layer aquifer system. J. Clean. Prod. 343, 130900 https://doi.org/
10.1016/j.jclepro.2022.130900.
Piper, A.M., 1944. A graphic procedure in the geochemical interpretation of water analyses. Eos, Transactions American Geophysical Union 25 (6), 914–928. https://
doi.org/10.1029/TR025i006p00914.
Rama, F., Busico, G., Arumi, J.L., Kazakis, N., Colombani, N., Marfella, L.,
Mastrocicco, M., 2022. Assessment of intrinsic aquifer vulnerability at continental
scale through a critical application of the drastic framework: the case of south
America. Sci. Total Environ. 823 https://doi.org/10.1016/j.scitotenv.2022.153748.
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). Sci. Total Environ. 476, 189–206.
https://doi.org/10.1016/j.scitotenv.2014.01.001.
Rokhshad, A.M., Khashei Siuki, A., Yaghoobzadeh, M., 2021. Evaluation of a machine based learning method to estimate the rate of nitrate penetration and groundwater
contamination. Arabian J. Geosci. 14, 1–11. https://doi.org/10.1007/s12517-020-
06257-y.
Rufino, F., Busico, G., Cuoco, E., Darrah, T.H., Tedesco, D., 2019. Evaluating the
suitability of urban groundwater resources for drinking water and irrigation
purposes: an integrated approach in the Agro-Aversano area of Southern Italy.
Environ. Monit. Assess. 191, 1–17. https://doi.org/10.1007/s10661-019-7978-y.
Rufino, F., Busico, G., Cuoco, E., Muscariello, L., Calabrese, S., Tedesco, D., 2022.
Geochemical characterization and health risk assessment in two diversified
M. Bordbar et al.
Journal of Environmental Management 347 (2023) 119041
11
environmental settings (southern Italy). Environ. Geochem. Health 44 (7),
2083–2099. http://doi:10.1007/s10653-021-00930-1.
Sajedi-Hosseini, F., Malekian, A., Choubin, B., Rahmati, O., Cipullo, S., Coulon, F.,
Pradhan, B., 2018. A novel machine learning-based approach for the risk assessment
of nitrate groundwater contamination. Sci. Total Environ. 644, 954–962. https://doi.
org/10.1016/j.scitotenv.2018.07.054.
Shahabi, A., Malakouti, M., Fallahi, E., 2005. Effects of bicarbonate content of irrigation
water on nutritional disorders of some apple varieties. J. Plant Nutr. 28, 1663–1678.
http://doi:10.1080/01904160500203630.
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.
Sorichetta, A., Ballabio, C., Masetti, M., Robinson Jr., G.R., Sterlacchini, S., 2013.
A comparison of data-driven groundwater vulnerability assessment methods.
Groundwater 51 (6), 866–879. https://doi.org/10.1111/gwat.12012.
Steichen, J., Koelliker, J., Grosh, D., Heiman, A., Yearout, R., Robbins, V., 1988.
Contamination of farmstead wells by pesticides, volatile organics, and inorganic
chemicals in Kansas. Ground Water Monit. Remediat 8 (3), 153–160. https://doi.
org/10.1111/j.1745-6592.1988.tb01092.x.
Sullivan, T.P., Gao, Y., 2017. Development of a new P3 (Probability, Protection, and
Precipitation) method for vulnerability, hazard, and risk intensity index assessments
in karst watersheds. J. Hydrol. 549, 428–451. https://doi.org/10.1016/j.
jhydrol.2017.04.007.
Taghavi, N., Niven, R.K., Paull, D.J., Kramer, M., 2022. Groundwater vulnerability
assessment: a review including new statistical and hybrid methods. Sci. Total
Environ. 153486 https://doi.org/10.1016/j.scitotenv.2022.153486.
Tesoriero, A.J., Voss, F., 1997. Predicting the probability of elevated nitrate
concentrations in the Puget Sound Basin: implications for aquifer susceptibility and
vulnerability. Groundwater 35 (6), 1029–1039. https://doi.org/10.1111/j.1745-
6584.1997.tb00175.x.
Tomaszkiewicz, M., Abou Najm, M., El-Fadel, M., 2014. Development of a groundwater
quality index for seawater intrusion in coastal aquifers. Environ. Model. Software 57,
13–26. https://doi.org/10.1016/j.envsoft.2014.03.010.
Tufano, R., Allocca, V., Coda, S., Cusano, D., Fusco, F., Nicodemo, F., De Vita, P., 2020.
Groundwater vulnerability of principal aquifers of the Campania region (southern
Italy). J. Maps 16 (2), 565–576. https://doi.org/10.1080/17445647.2020.1787887.
Vasavi, M., Bhavana, M., 2021. Ground water quality assessment in Guntur district GIS
data using data mining techniques. PalArch’s J. Archaeol. Egypt/Egypt 18 (4),
2758–2767. https://archives.palarch.nl/index.php/jae/article/view/6708.
Wei, A., Bi, P., Guo, J., Lu, S., Li, D., 2021. Modified DRASTIC model for groundwater
vulnerability to nitrate contamination in the Dagujia river basin, China. Water
Supply 21 (4), 1793–1805. https://doi.org/10.2166/ws.2021.018.
Worrall, F., Besien, T., Kolpin, D.W., 2002. Groundwater vulnerability: interactions of
chemical and site properties. Sci. Total Environ. 299 (1–3), 131–143. https://doi.
org/10.1016/S0048-9697(02)00270-X.
Yamazaki, D., Ikeshima, D., Neal, J.C., O’Loughlin, F., Sampson, C.C., Kanae, S., Bates, P.
D., 2017. Merit DEM: a new high-accuracy global digital elevation model and its
merit to global hydrodynamic modeling. In: AGU Fall Meeting Abstracts, H12C-04.
Yamazaki, D., Ikeshima, D., Sosa, J., Bates, P.D., Allen, G.H., Pavelsky, T.M., 2019.
MERIT Hydro: a high-resolution global hydrography map based on latest topography
dataset. Water Resour. Res. 55 (6), 5053–5073. https://doi.org/10.1029/
2019WR024873.
Zhang, Y., Li, X., Luo, M., Wei, C., Huang, X., Xiao, Y., Pei, Q., 2021. Hydrochemistry and
entropy-based groundwater quality assessment in the Suining area, Southwestern
China. J. Chem. 2021, 1–11. https://doi.org/10.1155/2021/5591892