RESUMOEste trabalho apresenta os mapas de qualidade das águas subterrâneas do Estado da Bahia, utilizando os valores do IQNAS -Índice de Qualidade Natural das Águas Subterrâneas. O IQNAS foi construído à semelhança do Índice de Qualidade das Águas IQA-CETESB que classifica a qualidade das águas superficiais. O cálculo do IQNAS envolve pesos e notas, extraídas de gráficos da concentração do parâmetro hidroquímico, versus a qualidade da água, ou nota, definido pelos autores, com critério semelhante ao estabelecido para o índice IQA-CETESB, ou seja: de 80-100, ótima; de 52-79, boa; de 37-51, aceitável; e de 0-36, imprópria. Os parâmetros utilizados para o cálculo do IQNAS foram: cloreto, pH, sólidos totais, dureza, nitrato e flúor. Foram utilizados os dados consistidos das análises químicas de 1.899 poços cadastrados no Banco de Dados da CERB -Companhia de Engenharia Rural da Bahia, e de 05 amostras de águas minerais, tomadas como padrão, da Bacia Sedimentar do Recôncavo-Tucano. Os dados de IQNAS foram utilizados para construir os mapas de qualidade das águas dos seguintes domínios aqüíferos: Coberturas Detríticas; Bacias Sedimentares; Metassedimentar, Cárstico e Embasamento Cristalino. Verificou-se que os mapas semafóricos de IQNAS, representaram adequadamente a qualidade química natural das águas subterrâneas das regiões estudadas, confirmando positivamente a metodologia adotada. Palavras-chave: Água Subterrânea, Hidroquímica, Índices de Qualidade, IQNAS ABSTRACTThis work presents maps of the State of Bahia groundwater quality, built with values of GNQIGroundwater Natural Quality Index. The GNQI was developed similarly to the Water Quality Index -WQI (IQA-CETESB), to classify the surface water quality. The calculation of GNQI values were based on weights and graphs for the concentration of the hydrochemical parameter versus water quality, or grade, defined by the authors of this work, with the same criteria utilized for the IQA-CETESB, which are: 80-100, exceptional quality; 52-79, good; 37-51, acceptable; and 0-36, improper. The parameters utilized for GNQI calculations were: pH, chloride, total solids, hardness, nitrate and fluorite, obtained from chemical analysis of 1,899 wells, from CERB Data Bank, State of Bahia, Brazil, plus 05 samples of mineral water from the Sedimentary Basin Recôncavo-Tucano, taken as water quality standard. The GNQI data were used to construct the maps of the following aquifer domains: Sedimentary, Metasedimentary, Karst and Crystalline. It was verified that the maps with GNQI values adequately represented the groundwater quality of the studied regions, attesting the usefulness of the adopted methodology.
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.
This work applied the Water Quality Index developed by the Canadian Council of Ministers of the Environment (WQI-CCME), to communicate the water quality per section of the Joanes River basin, State of Bahia, Brazil. WQI-CCME is a statistical procedure that originally requires the execution of at least four monitoring campaigns per monitoring location and the measurement of at least four parameters. This paper presents a new aggregation method to calculate the WQI-CCME because, to apply the original method in Joanes River, a huge loss of information would occur, by the fact that, the number of analyzed parameters varied between the monitoring campaigns developed by the Government Monitoring Program. This work modified the original aggregation method replacing it by a data aggregation for a single monitoring campaign, in a minimum of four monitoring locations per section of the river and a minimum of four parameters per monitoring location. Comparison between the calculation of WQI-CCME for river sections, with the index, WQI-CETESB, developed by the Brazilian Environmental Sanitation and Technology Company-CETESB, proved the applicability of the new aggregation method. The WQI-CETESB has it bases on the WQI from the National Sanitation Foundation and uses nine fixed parameters. As WQI-CCME uses the totality of the analyzed parameters without restrictions, it is more flexible, and the results seem more adequate to indicate the real river water quality. However, the WQI-CCME has a more stringent water quality scale in comparison with the WQI-CETESB, resulting in inferior water quality information. In conclusion, the WQI-CCME with a new aggregation method is adequate for communicating the water quality at a given time, per section of a river, respecting the minimum number of four analyses and four monitoring points. As a result, without a need to wait for other campaigns, it reduces the cost of a monitoring program and the period to communicate the water quality. The adequacy of the WQI-CCME was similar to the finding of others.
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