The use of multivariate statistical methods for optimization of the surface water quality network monitoring in the Paraopeba river basin, Brazil

Abstract

This study sought to evaluate and propose adjustments to the water quality monitoring network of surface freshwaters in the Paraopeba river basin (Minas Gerais, Brazil), using multivariate statistical methods. A total of 13,560 valid data were analyzed for 19 water quality parameters at 30 monitoring sites, over a period of 5 years (2008-2013). The cluster analysis grouped the monitoring sites in eight groups based on similarities of water quality characteristics. This analysis made it possible to detect the most relevant monitoring stations in the river basin. The principal components analysis associated with non-parametric tests and the analysis of violation of the standards prescribed by law, allowed for identifying the most relevant parameters which must be maintained in the network (thermotolerant coliforms, total manganese, and total phosphorus). The discharge of domestic sewage and industrial wastewater, that from mining activities and diffuse pollution from agriculture and pasture areas are the main sources of pollution responsible for the surface water quality deterioration in this basin. The BP073 monitoring site presents the most degraded water quality in the Paropeba river basin. The monitoring sites BP094 and BP092 are located geographically close and they measure similar water quality, so a possible assessment of the need to maintain only one of the two in the monitoring network is suggested. Therefore, multivariate analyses were efficient to assess the adequacy of the water quality monitoring network of the Paraopeba river basin, and it can be used in other watersheds.

A Proposal for Redesigning the Water Quality Network of the Tunjuelo River in Bogotá, Colombia through a Spatio-Temporal Analysis

“…First there were significant (p < 0.05) differences among the stations for each of the 10 variables measured, indicating that water quality declined in the Tunjuelo River as it passed through Bogotá. Calazans et al [45] found a similar result and they concluded that the reduction in water quality along the series of stations was statistically significant for the variables they measured. The month and hour of measurement also significantly (p < 0.05) affected each of the measured variables except fecal coliform, which was not temporally influenced by month or weather.…”

“…Only TSS and TP significantly contributed to the second component (Figure 4). It is common to observe a relationship between the first [45,47,48] or second [49] principal components and organic matter variables (COD and BOD) and an inverse relationship with DO in PCA analyses of basins contaminated by urban processes. However, as Gigues et al [50] and Pejman et al [51] demonstrate, different behaviors are found in rural basins.…”

Primena Multivarijabilne Analize Za Ocenu Kvaliteta Vode Jezera

“…The goal of this paper was to establish fitted equations (prediction models) for determining two water quality parameters, namely chlorophyll-a and ammonia-nitrogen, using other water quality parameters that can be determined easily (temperature, dissolved oxygen, power of hydrogen, suspended solids and electric conductivity). For this purpose a number of statistical methods have been used throughout this research [1,2,3,4,5,6].…”

Analiza Kvaliteta Vode Jezera Palić Postupkom Analize Glavnih Komponenti

“…Стандардне процедуре истраживања мерења се често заснивају на анализи великог броја података, покушају идентификације потенцијалних корелација, карактеристичних показатеља, евентуалних груписања података и сличних тенденција. Једна од метода која се често користи као средство за редукцију димензионалности посматраног проблема је анализа главних компонената (АГК), [1,2,3,4,5,6]. Поред примене за редукцију димензионалности посматране проблематике, ова helps to improve the overall interpretation of the gathered information [5].…”

“…Standard data assessment relies on the investigation of a large number of data, finding various correlations among them, characteristic behaviors, possible clustering and similar tendencies. One of the standard methods often utilized for the reduction of the analyzed sample size is the principal component analysis (PCA), [1,2,3,4,5,6]. In addition to the reduction of the dimensionality, PCA allows enhanced interpretation of the water quality data by helping the identification of spatial and/or temporal trends within the evaluated water quality measurements.…”