The accuracy of interpolation models applied to groundwater depends, among other factors, on the interpolation method chosen. Therefore, it is necessary to compare different approaches. For this, different methods of interpolation of nitrate concentrations were contrasted in sixty-seven wells in an aquifer in Aguascalientes, Mexico. Four general interpolation methods were used in ArcGIS 10.5 to make the maps: IDW, Kriging, Natural Neighbor and Spline. In the modeling, only method type was varied. The input parameters (location, temporality, and nitrate concentration) were the same in the four interpolations; despite this, different maximum and minimum values were obtained for each interpolation method: for IDW, 0.2 to 22.0 mg/l, for Kriging, 3.5 to 16.5 mg/l, for Natural Neighbor, 0.3 to 21.7 mg/l and for Spline ?30.8 to 37.2 mg/l. Finally, an assessment of the maps obtained was conducted by comparing them with the Official Mexican Standard (OMS), where 24 of the 67 wells were found outside the 10 mg/l that the OMS establishes as maximum permissible limit for human consumption. Taking as a starting point the measured values of nitrates (0.25 to 22.12 mg/l), as well as the spatial distribution of the interpolated values, it was determined that the Krigging method best fitted the data measured in the wells within the studied aquifer.
References
[1]
Bouwer, H. (2002). Artificial Recharge of Groundwater: Hydrogeology and Engineering. HydrogeologyJournal,10, 121-142. https://doi.org/10.1007/s10040-001-0182-4
[2]
Chang, K. T. (2022). Introduction to Geographic Information Systems (10th ed.). McGraw Hill.
[3]
CONAGUA (2015). Actualización de la disponibilidad media anual de agua en el acuífero Chihuahua-Sacramento (0830), Estado de Chihuahua. Diario Oficial de la Federación, 1, 1-31.
[4]
CONAGUA (2019). Estadísticas del Agua en México. In SEGOB (pp, 1-32). SEMARNAT. https://www.gob.mx/conagua
[5]
CONAGUA (2024). Actualización de la disponibilidad media anual de agua en el acuífero Valle de Chicalote (0102), estado de Aguascalientes.
[6]
DOF (1995). Norma Oficial Mexicana NOM-127-SSA1-1994, Salud ambiental, agua para uso y consumo humano-Límites permisibles de calidad y tratamientos a que debe som-eterse el agua para su potabilización.
[7]
Fallahzadeh, R. A., Almodaresi, S. A., Dashti, M. M., Fattahi, A., Sadeghnia, M., Eslami, H. et al. (2016). Zoning of Nitrite and Nitrate Concentration in Groundwater Using Geografic Information System (GIS), Case Study: Drinking Water Wells in Yazd City. JournalofGeoscienceandEnvironmentProtection,4, 91-96. https://doi.org/10.4236/gep.2016.43008
[8]
Fetter, C. W. (2014). Applied Hydrogeology (4th ed). Pearson.
[9]
Fetter, C. W. (2018). Contaminant Hydrogeology. In C. W. Fetter, T. Boving, & D. Kreamer (Eds.), Contaminant Hydrogeology, Third Edition (pp. 1-663). Waveland Press Inc.
[10]
Ghayoumian, J., Mohseni Saravi, M., Feiznia, S., Nouri, B., & Malekian, A. (2007). Application of GIS Techniques to Determine Areas Most Suitable for Artificial Groundwater Recharge in a Coastal Aquifer in Southern Iran. JournalofAsianEarthSciences,30, 364-374. https://doi.org/10.1016/j.jseaes.2006.11.002
[11]
Heywood, I., Cornelius, S., & Carver, S. (2010). An Introduction to Geographical Information Systems (3rd ed). Pearson.
[12]
Huang, G. (2013). Characterization of Nitrate Contamination in an Arid Region of China. JournalofEnvironmentalProtection,4, 46-52. https://doi.org/10.4236/jep.2013.47a006
Liu, Y., & Zhu, H. (2011). Notice of Retraction: Feasibility Study on Artificial Recharge of Groundwater for Sustainability in Jinghui Irrigation District. In 2011 5th International Conference on Bioinformatics and Biomedical Engineering (pp. 1-3). IEEE. https://doi.org/10.1109/icbbe.2011.5780898
[15]
Richey, A. S., Thomas, B. F., Lo, M., Reager, J. T., Famiglietti, J. S., Voss, K. et al. (2015). Quantifying Renewable Groundwater Stress with GRACE. WaterResourcesResearch,51, 5217-5238. https://doi.org/10.1002/2015wr017349
SGM (2001). Carta Geológico Minera Rincón de Campos F13-B89 1:50,000.
[18]
Singh, A., Panda, S. N., Kumar, K. S., & Sharma, C. S. (2013). Artificial Groundwater Recharge Zones Mapping Using Remote Sensing and GIS: A Case Study in Indian Punjab. EnvironmentalManagement,52, 61-71. https://doi.org/10.1007/s00267-013-0101-1
[19]
Slocum, T. A., McMaster, R. B., Kessler, F. C., & Howard, H. H. (2023). Thematic Cartography and Geovisualization. CRC Press. https://doi.org/10.1201/9781003150527
Tutmez, B., & Hatipoglu, Z. (2010). Comparing Two Data Driven Interpolation Methods for Modeling Nitrate Distribution in Aquifer. EcologicalInformatics,5, 311-315. https://doi.org/10.1016/j.ecoinf.2009.08.001
[22]
United Nations (2022). World Population Prospects 2022 Summary of Results.
[23]
Virat, C. (2001). ArtificialRechargeforConjunctiveUseinIrrigation:TheSanJoaquinValley,California(Issue2000). Massachusets Institute of Thechnology.