Multivariate statistical analysis applied to the evaluation of groundwater quality in the central-southern portion of the state of Bahia - Brazil

Gomes, Maria da Conceição Rabelo; Anjos, José Ângelo Sebastião Araújo dos; Daltro, Rafael Ribeiro

doi:10.4136/ambi-agua.2408

Cited by 6 publications

(8 citation statements)

References 12 publications

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“…The values of the correlation matrix were later extracted through the PCA method. Then, the rotation of the factorial load matrix, generated in the extraction, was carried out using the varimax rotation method, which minimizes the number of variables with high loads in different factors, allowing the association of a variable with a single factor (Gomes, Anjos & Daltro 2020;Keita & Zhonghua 2017).…”

Section: Methodsmentioning

confidence: 99%

Multivariate Statistics Applied to the Identification of Compositional Control Parameters for Groundwater

De Araújo,

Freire,

Cavalcante

et al. 2024

Anu. Inst. Geociênc.

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The objective of the present study was to identify the most influent parameters in the composition of groundwater in the municipality of Icapuí, Ceará - Brazil, seeking correlations with the composition of the percolating aquifer formations that can be associated with the sources of these components. For this purpose, multivariate statistical techniques were applied by means of a Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA). The PCA allowed a reduction of physicochemical parameters and determined the two components responsible for approximately 86% of total variance in the data for both sampling periods (rainy and dry). The first component is represented by variables that indicate natural rock weathering processes, and the second comprises seasonality and pollution indicators. Samples were also correlated through HCA according to compositional similarities, which were associated with possible natural or human sources.

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Section: Methodsmentioning

confidence: 99%

Multivariate Statistics Applied to the Identification of Compositional Control Parameters for Groundwater

De Araújo,

Freire,

Cavalcante

et al. 2024

Anu. Inst. Geociênc.

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“…In India 104 , used these techniques to elucidate aspects of the groundwater geochemistry and drinking water suitability in the Kudal region. In Brazil, state of Bahia 105 , applied multivariable analysis for groundwater quality evaluation in the central-southern portion of the state, while 106 , used to classify the groundwater quality in the Salitre river watershed. And, in the state of Ceará 107 , used to explain the processes responsible for the groundwater quality in the city of Fortaleza; while 108 , were searching the similarity of hydrochemical variables in the Salgado river watershed.…”

Section: Methodsmentioning

confidence: 99%

Development of a groundwater quality index: GWQI, for the aquifers of the state of Bahia, Brazil using multivariable analyses

Filho

Oliveira

2021

Sci Rep

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This work elaborated a groundwater quality index—GWQI, for the aquifers of the state of Bahia, Brazil, using multivariable analyses. Data from 600 wells located in the four hydrogeological domains: sedimentary, crystalline, karstic, and metasedimentary, were subjected to exploratory statistical analysis, and 22 out of 26 parameters were subjected to multivariable analysis using Statistica (Version 7.0). From the PCA, 5 factors were sufficient to participate in the index, due to sufficient explanation of the cumulative variance. The matrix of factorial loads (for 1–5 factors) indicated 9 parameters related to water quality and 4 hydrological, with factor loads above ± 0.50, to be part of the hierarchical cluster analysis. The dendrogram allowed to choose the 5 parameters related to groundwater quality, to participate in the GWQI (hardness, total residue, sulphate, fluoride and iron). From the multivariable analyses, three parameters from a previous index—NGWQI, were not selected for the GWQI: chloride (belongs to the hardness hierarchical group); pH (insignificant factor load); and nitrate (significant factor load only for 6 factors), also, not a regionalized variable. From the set of communality values (5 factors), the degree of relevance of each parameter was extracted. Based on these values, were determined the relative weights (wi) for the parameters. Using similar WQI-NSF formulation, a product of quality grades raised to a power, which is the weight of importance of each variable, the GWQI values were calculated. Spatialization of 1369 GWQI values, with the respective colors, on the map of the state of Bahia, revealed good correlation between the groundwater quality and the index quality classification. According to the literature on water quality indexing, the GWQI developed here, using emerging technologies, is a mathematical tool developed as specific index, as it was derived using limits for drinking water. This new index was tailored to represent the quality of the groundwater of the four hydrogeological domains of the state of Bahia. Although it has a regionalized application, its development, using, factor analysis, principal component analysis, and hierarchical cluster analysis, participates of the new trend for WQI development, which uses rational, rather than subjective assessment. The GWQI is a successful index due to its ability to represent the groundwater quality of the state of Bahia, using a single mathematical formulation, the same five parameters, and unique weight for each parameter.

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“…The natural context of the rocks of the municipality of Boquira influences the chemical composition of water resources (Gomes, Anjos & Daltro 2020). However, the highest concentrations of metals such as lead and zinc were located in the waters of the underground mine galleries, but high metal contents were not found in samples of groundwater, spring surface water and cisterns used for human supply.…”

Section: Study Area and Datamentioning

confidence: 99%

“…The climatic adversities of the semi-arid region are characterized by low and irregular rainfall events as well as long cyclical droughts. Moreover, concentrations of heavy metals found in groundwater, springs and cisterns of the municipality are low (Gomes, Anjos & Daltro 2020). These facts contributed to the formulation of the hypothesis that wind erosion and transport would be the main processes causing the dispersion of lead (and other contaminants) originating from the tailings basin across the urban area of Boquira.…”

Section: Introductionmentioning

confidence: 99%

Modeling Urban Atmospheric Lead Dispersion from a Mining Tailings Basin in Bahia, Brazil

Santos

Klammler

Leal

2022

Anu. Inst. Geociênc.

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After the exhaustion of the lead mine in Boquira, Bahia in the 1990s, mining support structures and the tailings basin were abandoned, resulting in one of the largest environmental liabilities from mining in the state. Detailed studies on environmental damages are inexistent and this article aimed to analyze lead contamination in the urban area of the municipality, based on atmospheric dispersion modeling. For this purpose, a model was elaborated and implemented in Matlab R2014a considering houses, streets and air as three coexisting spatial domains. The model uses wind speed and direction as input data and results were calibrated (RMSE = 0.64 on decadal logarithmic scale) against existing measurements taken in 2014 of lead concentrations in street sediments and house dust within the urban area. Results suggested that particulate resuspension from streets and accumulation in houses may explain observed concentration patterns, and that the amount of suspended lead in the air is small compared to that in streets and houses. Additional simulations carried out for a hypothetical future remediation scenario showed that after 200 days the concentrations in the air and in the streets decreased considerably, while they stopped accumulating in houses. Hence, the model may be a potentially useful tool to assist in the evaluation and management of this and other urban areas affected by contamination through atmospheric dispersion of particulates. Consistent with our model formulation, we recommend mass per area for lead concentrations in streets and houses as more meaningful units for exposure risk/intensity than ppm (mass per mass) of street sediment or house dust.

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Multivariate statistical analysis applied to the evaluation of groundwater quality in the central-southern portion of the state of Bahia - Brazil

Cited by 6 publications

References 12 publications

Multivariate Statistics Applied to the Identification of Compositional Control Parameters for Groundwater

Multivariate Statistics Applied to the Identification of Compositional Control Parameters for Groundwater

Development of a groundwater quality index: GWQI, for the aquifers of the state of Bahia, Brazil using multivariable analyses

Modeling Urban Atmospheric Lead Dispersion from a Mining Tailings Basin in Bahia, Brazil

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