Analysis of spatial and temporal variability of water quality parameters and their possible correlations in the Bauru Aquifer System for the years 2016 to 2018. - Lobo, Isadora Vitali [UNESP]; Lima, Cesar Gustavo da Rocha [UNESP]; Tercini, Olavo Tozete [UNESP] (My University, 2024)
Groundwater is a key resource to meet the water demand of communities. This study aimed to analyze the seasonal spatial variability of groundwater quality parameters in the Bauru Aquifer System (SAB) and its linear correlations. Data from 97 monitoring wells for the years 2016, 2017 and 2018 made available by the Environmental Company of the State of São Paulo (CETESB) were used. Five parameters were evaluated: Aluminum (Al), Chloride (Cl), Electrical Conductivity (EC), pH and Total Dissolved Solids (SD). The analysis of descriptive statistics showed a high variability for data with a coefficient of variation (CV) between 13.5% (pH 2018-1) and 121.39% (Cl 2017-1). All parameters presented in accordance with the legislation. The geostatistical analysis indicated spatial dependence for all data analyzed with good semivariographic adjustments, where the values of the spatial dependence evaluator (ADE) were between 55.5% (Al 2018-1) and 99.9% (Cl_18-1, CE 16-2, CE 17-2, Al 17-1, pH 16-2, pH 18-1, pH 18-2), the spatial determination coefficients (R²) ranged between 0.06 (Al 2018-2) and 0.998 (Cl 2017-1 and Cl 2017-2), with a range between 32.39 km (Cl 2017-1) and 253.05 km (Cl 2017-2) resulting in appreciable kriging maps. The results indicated possible changes in the patterns of behavior between the parameters comparing dry and rainy periods. In the linear correlation analyses between the quality parameters, there were positive correlation coefficients (ranging from moderate to strong) between CE and SD (r = 0.92), Cl and SD (r = 0.66), which enabled mathematical modeling by regression with optimal and significant adjustments.
Variabilidade espacial sazonal e correlação entre parâmetros da qualidade das águas no Sistema Aquífero Bauru (2016 a 2018).
Keywords: geostatistics; groundwater; kriging; water management; water resources