Tratamento de dados censurados em estudos ambientais

Christófaro, Cristiano; Leão, Mônica Maria Diniz

doi:10.1590/s0100-40422014000100019

Cited by 9 publications

(16 citation statements)

References 27 publications

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“…These “not measured data” receive, in the literature, the designation of “censored data”. The authors 116 discuss four different procedures for solving the censured data: substitution, parametric methods, robust methods and non-parametric methods, all of them, presenting advantages and limitations. They say that, the simplest method to replace the undetected values is using a constant value below the detection limit.…”

Section: Resultsmentioning

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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Section: Resultsmentioning

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

View full text Add to dashboard Cite

show abstract

“…Several approaches can be used to compute descriptive statistics of data sets with left-censored data. According to Christofaro and Leão (2014), these approaches can be subdivided into at least four classes: substitution, parametric, robust, and nonparametric methods. Due to its simplicity, the substitution method is the most commonly used (HELSEL, 2006).…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, nonparametric methods do not require that data fit a previously known probability density function. These methods can be applied fordata setswith a reduced amount of data and tend to be less affected by outliers (CHRISTOFARO and LEÃO, 2014;ANTWEILER and TAYLOR, 2008). Kaplan-Meier method is one of the most widely usednonparametric methods (ANTWEILER and TAYLOR, 2008).…”

Section: Introductionmentioning

confidence: 99%

“…Applying various statistical methods, several authors have been assessed the occurrence of chemical substancesin water bodies (MARIMON et al, 2013;CHRISTOFARO and LEÃO, 2014;SABINO et al, 2014;MELO GURGEL et al, 2016;CASSANEGO and DROSTE, 2017;DALZOCHIO et al, 2017;STAPLES et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…As reported by Helseland Hirsch (2002), environmental datasets frequently do not follow the Gaussian distributionand usually have outliers and left-censored data. Neglecting these aspects canseriously affect the reliability of the estimation of descriptive parameters, hypothesis tests, regression models, and trend analyses (HELSEL, 2006;HELSEL and HIRSCH, 2002;SINGH et al, 2006;CHRISTOFARO and LEÃO, 2014).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Occurrence of chemical substances in water supply systems of Brazil: a nonparametric approach for statistical analysis of Sisagua data

Gomes

Assunção

Nicolau

et al. 2022

CeN

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The objective of this work was to develop a methodology for statistical analysis of monitoring data of chemical compounds in drinking water supply systems in Brazil, using data from Sisagua (Drinking Water Quality Surveillance Information System). Initially, the inconsistencies in the database were identified and adjusted. Then, the descriptive statistics were estimated using the Kaplan-Meier (KM) method, evaluating its applicability in different censored data sets. The descriptive parameters were compared with the substitution method. The substitution method showed susceptibility to biased estimates, especially for groups of compounds containing high percentage of censored data and with high limits of quantification and detection, leading to higher descriptive parameters compared to KM method. This work reinforces the need to use appropriate methods for analyzing environmental data and evidences that the analysis of this type of data may be complex. The methods proposed here can help environmental scientists to deal with this issue, providing a systematic procedure to check and solve consistency problems, as well as presenting a nonparametric approach for computing descriptive statistics for environmental monitoring data.

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Spatio-temporal study of water quality variables in the Rio de Ondas Hydrographic Basin, west of Bahia, Brazil using multivariate analysis

do Rego,

Portela,

de Lima Ribeiro

et al. 2023

Environ Monit Assess

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Water bodies are containers that receive a large load of pollutants through the release of domestic, industrial, and agricultural e uents. With this focus, this work aimed to conduct a temporal-spatial variability study in the Rio de Ondas Hydrographic Basin through multivariate statistical analysis. For this, seventeen collection sites were established in four stations along the Rio de Ondas and its tributaries between 2017 and 2018. Ionic chromatography with suppressed conductivity was used for ions determination, while ICP-OES determined metals' total concentrations. The land use and occupation assessment between 1985 and 2015 was conducted using images from Landsat 5 and 8 satellites and the descriptive and multivariate analysis of the data using version 10 of the Statistica software. The results showed that, in thirty years, there was a growth of 569% of agricultural activities in the watershed area, with signi cant suppression of native vegetation, favoring the transport of pollutants to rivers. Ca 2+ , PO 4 2− , Al, Cu, and Zn concentrations showed a statistically signi cant difference between the seasons, with higher medians in the rainy season. Rainy season in uenced the formation of three groups in the PCA, consisting of electrical conductivity, salinity, TDS, and PO 4 2− (group 1); temperature, Fe, SO 4 2− and Cl − (group 2); and Ca 2+ , Mg 2+ , Na + , and HCO 3 − (group 3). The strong correlation between parameters of each group indicates anthropic in uence on the watershed's water quality. However, levels are within the potability standard.

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Tratamento de dados censurados em estudos ambientais

Cited by 9 publications

References 27 publications

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

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

Occurrence of chemical substances in water supply systems of Brazil: a nonparametric approach for statistical analysis of Sisagua data

Spatio-temporal study of water quality variables in the Rio de Ondas Hydrographic Basin, west of Bahia, Brazil using multivariate analysis

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