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Analysis of aquifer vulnerability and water quality using SINTACS and geographic weighted regression
Authors:Jose A. Ramos Leal  Felipe O. Tapia Silva  Ismael Sandoval Montes
Affiliation:1. Instituto Potosino de Investigaci??n Cient??fica y Tecnol??gica, A.C. IPICYT, Camino a la Presa de San Jos?? # 2055, Lomas 4ta. Secci??n, CP 78216, San Luis Potos??, SLP, Mexico
2. Centro de Investigaci??n en Geograf??a y Geom??tica ??Ing. Jorge L. Tamayo?? A.C. Contoy No.137, Col. Lomas de Padierna Delegaci??n Tlalpan, CP 14740, Mexico, DF, Mexico
4. Universidad Aut??noma Metropolitana Campus Iztapalapa, Av. San Rafael Atlixco No.186, Col. Vicentina, Del. Iztapalapa, CP 09340, Mexico, DF, Mexico
3. Instituto Nacional de Estad??stica y Geograf??a, INEGI, Coordinaci??n Estatal Oaxaca, Calle: Emiliano Zapata N??m. 316, Col. Reforma, CP 68050, Oaxaca de Ju??rez, OAX, Mexico
Abstract:Aquifer vulnerability and water quality were assessed in the Central Valleys of Oaxaca (Mexico) using the SINTACS method, based on a geographic information system. SINTACS layers were prepared using data such as climate (rainfall and temperature), water table, hydraulic conductivity, geology, soil type and topographic model. Maps for water quality index (WQI), contamination index and pollution sources index (PSI) were also obtained by this work. Groundwater quality in the Central Valleys may be affected by two factors, those with an anthropogenic origin and those with natural origin. High vulnerability values are located in the valleys of the basin, where granular sediments are exposed. Low vulnerability values are distributed in the basin??s ranges, where metamorphic rocks are found. Given that many of the zones with the highest groundwater vulnerability values correspond to zones with the greatest PSI values, there is great risk of groundwater contamination for the area of study because external (indicated by PSI) and internal (indicated by SINTACS) factors that cause pollution can be frequently observed in the same place. Geographic weighted regression (GWR) is used to test the dependency between WQI as dependent variable and SINTACS, PSI, Urban localities, Agriculture, Pastures and Rivers as predictors. The results indicate the non-stationary behavior of the dependent variable with respect to the predictors. While the obtained GWR models used to model WQI cannot be used in practical situations to predict the behavior of said variable, they can be used to estimate the degree to which the predictors influence the variable of interest.
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