ABSTRACT
The main purpose of this study was to build multivariate classification models using water quality monitoring data for the hydrographic basin of the Gualaxo do Norte River, Minas Gerais state, Brazil, which was impacted in 2015 by the rupture of a containment structure for iron ore tailings. A total of 27 points were evaluated, covering areas affected and unaffected by the disaster, with monitoring of chemical, physical, and microbiological variables during the period from July 2016 to June 2017. Multivariate classification techniques were applied to the data, with the aim of developing models to determine when the impacted locations would present characteristics equivalent to those existing prior to the rupture. Classification models constructed using PLS-DA and LDA were able to predict three classes: unaffected main river, affected main river, and tributaries. The first technique was able to clearly differentiate the three classes for the data evaluated, achieving averages corresponding to 90% accuracy. The second method was consistent with the first, identifying the chloride content, conductivity, turbidity, and alkalinity as discriminatory variables, among those monitored, with the relationships among the parameters being coherent with the environmental conditions of the region. The model, with a correct classification rate of 91.67%, enabled identification of the behavior of new samples, using only these easily measured variables. In summary, application of the multivariate statistical tools allowed the development of models capable of providing information about the recovery process of an ecosystem impacted by the greatest environmental disaster to have occurred in Brazil.
Subject(s)
Water Pollutants, Chemical , Water Quality , Environmental Monitoring , Ecosystem , Rivers/chemistry , Water Pollutants, Chemical/analysis , BrazilABSTRACT
RESUMO O cerne do presente trabalho consistiu em aplicar ferramentas de análise exploratória multivariada objetivando avaliar o número de estações de monitoramento de qualidade da água e a frequência de amostragem. Para tal, utilizou-se banco de dados disponibilizado pelo Instituto Mineiro de Gestão das Águas (IGAM) referente à Bacia do Rio das Velhas, na região central mais populosa de Minas Gerais. Foram utilizadas as técnicas de análise das componentes principais (ACP) e a rede neural de Kohonen, que culminaram na significativa redução da frequência de amostragem, em alguns casos de mensal para anual ou semestral, e na redução do número de estações de monitoramento de 36 para 33. Os resultados permitem abrir a possibilidade do emprego dos métodos utilizados como ferramentas de gestão de recursos hídricos de bacias hidrográficas visando à otimização dos programas de monitoramento de qualidade de água.
ABSTRACT The core of this work consisted of applying multivariate exploratory analysis tools to evaluate the number of water quality monitoring stations and the sampling frequency. In such way, the database provided by the Minas Gerais Institute of Water Management (IGAM) on the river basin of Rio das Velhas, in the most populous central region of Minas Gerais state, was used. The Principal Components Analysis and the Kohonen neural network techniques were applied, resulting in a significant reduction in sampling frequency, in some cases from monthly to annual or semi-annual, and in the reduction of the number of monitoring stations from 36 to 33. The results open the possibility of using these methods as watershed water resources management tools aimed at optimization of water quality monitoring programs.
ABSTRACT
RESUMO O principal objetivo desta pesquisa consistiu em desenvolver e aplicar um sistema de indicadores de desempenho direcionado a estações convencionais de tratamento de água com base na visão do prestador de serviço. A metodologia abrangeu três etapas principais: (i) definição dos indicadores e justificativa; (ii) formulação e aplicação do sistema de indicadores a um conjunto de cinco estações de pequeno porte (vazões nominais de 20 a 60 L.s-¹) operadas pelo mesmo prestador; (iii) análise estatística a partir dos resultados de cálculo dos indicadores visando identificar eventuais sobreposições. O sistema proposto abarcou 13 indicadores de desempenho, calcados em parâmetros comumente inseridos na rotina operacional das estações de tratamento brasileiras.
ABSTRACT The main objective of this research was to develop and apply a performance indicator system focusing on conventional water treatment plants, based on the service provider's point of view. The methodology comprised three principal steps: (i) definition of indicators and justification; (ii) development and application of the indicator system to five small plants (flow rate from 20 to 60 L.s-1) operated for same provider; (iii) statistical analysis of the results, aiming to identify overlapping among the proposed performance indicators. The system comprised 13 performance indicators whose application is based on parameters usually monitored in the vast majority of Brazilian plants.
ABSTRACT
Manganese recovery from industrial ore processing waste by means of leaching with sulfuric acid was the objective of this study. Experimental conditions were optimized by multivariate experimental design approaches. In order to study the factors affecting leaching, a screening step was used involving a full factorial design with central point for three variables in two levels (2(3)). The three variables studied were leaching time, concentration of sulfuric acid and sample amount. The three factors screened were shown to be relevant and therefore a Doehlert design was applied to determine the best working conditions for leaching and to build the response surface. By applying the best leaching conditions, the concentrations of 12.80 and 13.64 %w/w of manganese for the global sample and for the fraction -44 + 37 µm, respectively, were found. Microbeads of chitosan were tested for removal of leachate acidity and recovering of soluble manganese. Manganese recovery from the leachate was 95.4%. Upon drying the leachate, a solid containing mostly manganese sulfate was obtained, showing that the proposed optimized method is efficient for manganese recovery from ore tailings.
