ABSTRACT Analyzing the physical-chemical and biological characteristics allows the evaluation of the water quality of a water body. Thus, the objective of this study was to determine the water quality index of the Passaúna and Piraquara rivers, as well as to apply statistical quality control methodologies to evaluate the data resulting from the monitoring of water quality. Therefore, a database with physico-chemical and microbiological parameters of the Passaúna and Piraquara rivers, a watershed of the Iguaçu River, belonging to the cities of Araucária and Piraquara, respectively Paraná, Brazil, was used to carry out the research. The water quality index was determined with the time series and, subsequently, these data were submitted to the statistical control of the process, with the control charts of individual Shewhart, EWMA and CUSUM, in addition to the development of the process capacity index. The WQI detected that the rivers remained in average quality until the year 2000, however, from that year it was possible to see a decreasing trend in the water quality of the evaluated rivers. The control charts of Shewhart, EWMA, CUSUM and the process capability index were able to identify the decreasing trend in water quality, demonstrating that they are fast and efficient techniques for the evaluation of water quality control.

ABSTRACT This study demonstrates using remote sensing, geographic information systems and multivariate statistics to study water quality in an agricultural watershed. The monitoring of water quality in the watershed of Lontras's river in the southwestern region of the State of Paraná had been done in 2012 and 2013 with a multi-parameter probe in ten sites that were defined upstream and downstream watershed, during four different seasons. Mosaicked images were used from Google Earth, Digital Elevation Model and soil types of maps, defined as the explanatory variables. The definition of the areas of influence and multivariate statistical techniques, particularly the Redundancy Analysis (RDA), were used for the correlation between variables. In a spring area, located upstream watershed, the contribution on water quality variation has gotten smaller, when compared with the other monitoring sites. There was interference in water quality in downstream sites that has become greater due to the effects of diffuse pollution. The RDA enabled synthesizes the data variability structure and the relationship of multidimensional variables. These statistical techniques added to products resulting from the GIS contributed to a better understanding of the variation of water quality in the watershed.