Mapeamento dos aspectos hidrogeoquímicos de águas subterrâneas a partir de estatística multivariada e redes neurais artificiais / Mapping of groundwater hydrogeochemistry aspects through multivariate statistics and artificial neural networks

RESUMO O objetivo central deste trabalho foi realizar o mapeamento dos aspectos hidrogeoquímicos de águas subterrâneas usando a estatística multivariada e redes neurais artificiais como subsídio para identificação de padrões espaciais. Para tal, foi executado um estudo de caso em aquíferos no município de Lençóis, Bahia, na região da Chapada Diamantina, nordeste do Brasil. Foram realizadas campanhas de campo para coleta de coordenadas geodésicas e amostras de águas subterrâneas. Após análise laboratorial e determinação de dados analíticos, foi feita a interpretação dos processos ambientais com o uso da análise de agrupamentos e mapas auto-organizáveis, além de classificação das águas pela Resolução do Conselho Nacional do Meio Ambiente nº 396/2008. Para fins de mapeamento dos dados analisados, foram usadas técnicas de geoprocessamento no Sistema de Informação Geográfica. Os principais constituintes físicos e químicos analisados em dois períodos climáticos foram mapeados e divididos em sete agrupamentos. Foram identificadas quatro zonas no município, que apresentam diferentes contextos hidrogeoquímicos. As zonas dos setores leste/sudeste, sul (área urbana) e extremo sul apresentam as mais significativas alterações na hidrogeoquímica e qualidade das águas. O mapeamento, subsidiado pela estatística multivariada e redes neurais artificiais, se apresentou potencialmente útil em contribuir com as ações de gestão dos recursos hídricos subterrâneos, como delimitação de áreas prioritárias, monitoramento de zonas de riscos de contaminação, além de intervenções de engenharia que eventualmente busquem o saneamento ambiental das águas subterrâneas.

ABSTRACT The main objective this paper was to map the hydrogeochemistry aspects of groundwater using multivariate statistics and artificial neural networks as a subsidy to identify spatial patterns. For this, a case study was carried out in aquifers in the municipality of Lençóis (BA), in the region of Chapada Diamantina, Northeastern Brazil. Field campaigns were carried out to collect geodetic coordinates and groundwater samples. After laboratorial analysis and determination of analytical data, the environmental processes were interpreted by cluster analysis and self-organizing maps, as well as the waters classification through CONAMA Resolution no. 396/2008. For the purpose of mapping the analyzed data, geoprocessing techniques were used in GIS. The main physical and chemical constituents analyzed in two climatic periods were mapped and divided into seven clusters. Four zones that present different hydrogeochemical contexts were identified in the municipality. The zones of the east/southeastern, south (urban area) and south end sectors present the most significant changes in hydrogeochemistry and water quality. The mapping, supported by multivariate statistics and artificial neural networks, was potentially useful in contributing to the management actions of groundwater resources as delimitation of priority areas, monitoring of contamination risk zones and engineering interventions that eventually seek environmental groundwater sanitation.

Waterborne toxoplasmosis investigated and analysed under hydrogeological assessment: new data and perspectives for further research

We present a set of data on human and chicken Toxoplasma gondiiseroprevalence that was investigated and analysed in light of groundwater vulnerability information in an area endemic for waterborne toxoplasmosis in Brazil. Hydrogeological assessment was undertaken to select sites for water collection from wells for T. gondiioocyst testing and for collecting blood from free-range chickens and humans for anti-T. gondiiserologic testing. Serologic testing of human specimens was done using conventional commercial tests and a sporozoite-specific embryogenesis-related protein (TgERP), which is able to differentiate whether infection resulted from tissue cysts or oocysts. Water specimens were negative for the presence of viable T. gondiioocysts. However, seroprevalence in free-range chickens was significantly associated with vulnerability of groundwater to surface contamination (p < 0.0001; odds ratio: 4.73, 95% confidence interval: 2.18-10.2). Surprisingly, a high prevalence of antibodies against TgERP was detected in human specimens, suggesting the possibility of a continuous contamination of drinking water with T. gondiioocysts in this endemic setting. These findings and the new proposed approach to investigate and analyse endemic toxoplasmosis in light of groundwater vulnerability information associated with prevalence in humans estimated by oocyst antigens recognition have implications for the potential role of hydrogeological assessment in researching waterborne toxoplasmosis at a global scale.

Fluoride characterization by principal component analysis in the hydrochemical facies of Serra Geral Aquifer System in Southern Brazil

Principal component analysis is applied to 309 groundwater chemical data information from wells in the Serra Geral Aquifer System. Correlations among seven hydrochemical parameters are statistically examined. A four-component model is suggested and explains 81 percent of total variance. Component 1 represents calcium-magnesium bicarbonated groundwaters with long time of residence. Component 2 represents sulfated and chlorinated calcium and sodium groundwaters; Component 3 represents sodium bicarbonated groundwaters; and Component 4 is characterized by sodium sulfated with high fluoride facies. The components' spatial distribution shows high fluoride concentration along analyzed tectonic fault system and aligned on northeast direction in other areas, suggesting other hydrogeological fault systems. High fluoride concentration increases according to groundwater pumping depth. The Principal Component Analysis reveals features of the groundwater mixture and individualizes water facies. In this scenery, it can be determined hydrogeological blocks associated with tectonic fault system here introduced.

A Análise de Componentes Principais foi aplicada em 309 dados químicos de águas subterrâneas de poços do Sistema Aqüífero Serra Geral. Correlações entre sete parâmetros hidroquímicos foram examinadas através da estatística. O modelo de quatro componentes foi utilizado por explicar 81 por cento da variância total. A Componente 1 é representada por águas cálcio-magnesianas com longo tempo de residência, a Componente 2 representa águas bicarbonatadas sulfatadas e cloretadas, a Componente 3 representa águas bicarbonatadas sódicas e a Componente 4 é caracterizada por águas de fácies sódica e sulfatada com alto fluoreto. A distribuição espacial das componentes mostra águas com concentrações anômalas ao longo dos sistemas tectônicos de falhas, analisados e alinhados a NE em algumas áreas, sugerindo outros sistemas de falhas hidrogeológicos. As concentrações de fluoreto aumentam de acordo com a profundidade de bombeamento das águas. A Análise de Componentes Principais revelou feições de mistura e individualizou diferentes fácies de águas subterrâneas. Neste cenário, é possível determinar blocos hidrogeológicos associados com os sistemas tectônicos de falhas introduzidos no presente trabalho.