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

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.