Spatial and temporal variation of water quality in a watershed in center-west Paraná, Brazil

Water quality monitoring is an essential strategy for water resources management. Physicochemical and microbiological parameters play an important role in the characterization of water quality. They are helpful tools for the identi ﬁ cation of pollution in aquatic ecosystems, being of natural sources or because of anthropogenic actions, and contribute to making decisions as well as sustainable development in a hydrographic basin. This study analyzed the water quality variation in a period of 20 years in Piquiri River Watershed. Also, TP concentration was estimated using linear regression model from af ﬂ uent rivers. The Relationship between TN and TP presented a Person ’ s linear correlation of 0.80, while turbidity and TSS presented correlation of 0.79. The relationship between the predicted and observed values for Turbidity and TP presented r² higher than 0.60. Spatial-temporal variation of water quality in Piquiri River Watershed has showed good quality over the years, although, unacceptable values of Escherichia coli, BOD, COD and Total Phosphorus appeared. Most unacceptable values were identi ﬁ ed in af ﬂ uent rivers, suggesting the improvement in the water quality closer to downstream of the Piquiri River. WQI also showed good quality water for all stations.


GRAPHICAL ABSTRACT INTRODUCTION
The importance of water resources for the maintenance of life, through water consumption for living organism's regulation and functioning, food production, electricity generation and industrial processes is unquestionable (Muniz et al. ). Thus, adequate quantities of water of good quality are essential for economic development and ecological integrity (Wu et al. ). However, over the years, changes have affected the amount, distribution and quality of water resources (Benvenutti et al. ). These changes might severely impact the water volume and quality of surface waters in rivers, which are the main source of water for domestic, agricultural and industrial purposes (Shil et al. ). Thereby, there is a need to assess surface water quality (Sȩner et al. ).
An important strategy for water resources management is to monitor frequently the surface water, to characterize spatial and temporal variation of water quality (Stachelek & Madden ). Also, this monitoring plays important roles in the possible identification of sources of pollution.
This pollution could be associated with the discharge of industrial sewage, domestic wastewater and agricultural drainage water, and, also coming from natural phenomena driven by hydrological processes (Bortoletto et al. ; Shil et al. ). According to Fathi et al. (), monitoring and controlling surface waters are of vital importance to ensure the availability of water of good quality for its several uses.
As water quality monitoring is of extreme importance, it is necessary to plan and to define parameters and variables to indicate the quality of an aquatic environment. Thereby, physicochemical and microbiological variables are of great importance to estimate environmental quality of a small region or larger areas, such as hydrographic basins (Oliveira et al. b). Through water quality monitoring, important information might be provided, to help river basin management, predict future environmental outcomes and to contribute to a sustainable development of a region (Benvenutti et al. ).
Long-term monitoring programs produce a large dataset of several water quality parameters, in which it sometimes becomes difficult to interpret (Bortoletto et al. ). Thus, Water Quality Index (WQI) could be an interesting tool for the evaluation of water quality, seeing that it transforms a large amount of water quality parameters data into a single number that describes the water quality qualitatively (e.g., poor, moderate, good) (Benvenutti et al. ; Wu et al. ). Thereby, understanding the importance of water quality for all living organisms, it is essential and it imposes the need to share the water quality monitoring data to take advantage of and to facilitate in the conservation of natural resources with objectives of sustainable development (Benvenutti et al. ).
Complementarily, spatial analyses are also of great importance for water quality studies. However, there are some difficulties in, for instance, collecting many water samples in a watershed. Thus, inverse distance weighted Therefore, this study aimed at evaluating the water quality of Piquiri River Watershed from a dataset of 20 years (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019), comparing the variation of water quality between stations located in the watershed, classifying the water bodies according to Brazilian law through their qualities, identifying possible unacceptable values of water quality parameters, and at evaluating TP and turbidity concentrations in Piquiri River from affluent data. Also, the IDW interpolation method was used to verify the variation of some water quality parameters along Piquiri River and its affluent.

Study area
Piquiri River Watershed is located from center south to west of Paraná state, being the third largest watershed of Paraná (Brazil). Its delimitation represents 12% of the total area of Parana, with approximately 660 km of extension (Pires ).
In addition, the watershed comprises in total 36 municipalities and 32 partially (Araújo et Figure 1 shows the elevation in the Piquiri River Watershed varying from lower than 400 meters above sea level (west) to approximately 1.200 meters above sea level (east) (Figure 1(a)). In addition, Figure 1

Water quality parameters
Eighteen water quality parameters were chosen for analysis of spatial-temporal variation of water quality in Piquiri river  Table 1 shows the maximum allowed value for some water quality parameters and the type of water treatment for possible human consumption for each class. The parameters analyzed in this study were classified according to this classification.

Water quality index (WQI)
Nine of the 18 physical, chemical and microbiological parameters were subjected to the water quality index WQI (Equation (1)  from 51 to 79 good; and from 79 to 100 excellent).
The values predicted using IDW interpolation were calculated in R Software.

RESULTS AND DISCUSSION
Variation of water quality Figure 2 shows a boxplot of water quality parameters for stations located in Piquiri river and its affluent. Ammoniacal Nitrogen presented to have a low concentration, less than 0.2 mgL À1 in almost all campaigns (n ¼ 118). The highest concentration was seen in an affluent of Piquiri river (64775000) with concentration of 0.36 mgL À1 . This parameter is an important indicator of water quality, seen that it could lead water bodies to eutrophication, being toxic for aquatic organisms, and a hazard for public health (Chen et al. ). In addition, this variable affects the dissolved oxygen concentration when oxidized to nitrate form (Nuruzzaman et al. ). In this collect, the dissolved oxygen concentration was of 6.99 mgL À1 below DO mean for this station (8.04 mgL À1 , n ¼ 50). In addition, the boxplot also showed three outliers for station 64785000 and one for station 64830000.
Beyond ammoniacal nitrogen, another crucial parameter to characterization and evaluation of water quality  Generally, the COD concentration stayed below 20 mgL À1 (n ¼ 259).
The highest DO concentrations were found in Piquiri river.
The highest values found of E. coli were seen in Piquiri river affluent: 64785000 (500000 NMP (100 mL) À1 , n ¼ 31) and 64775000 (240000 NMP (100 mL) À1 , n ¼ 29), and in the initial point in Piquiri river, station 64771500 (280000 NMP (100 mL) À1 , n ¼ 30). Another high value found in Piquiri river was at station 64799500 (80000 NMP (100 mL) À1 , n ¼ 28). E. coli could be a biological indicator of anthropogenic organic pollution in aquatic ecosystems, where its quantification could be associated with human fecal contamination, differently from fecal coliforms, which present some species that are not necessarily from a fecal contamination (Amirat et al. ). Complementarily, fecal and total coliforms were also measured, however in lower quantity compared to E. coli (fecal coliforms: n ¼ 30; total coliforms: n ¼ 119). The highest quantification of fecal coliforms was seen at 64799500 (50000 NMP (100 mL) À1 , n ¼ 3). Generally, fecal coliforms stayed below 10000 NMP (100 mL) À1 , however, this parameter has a low number of quantifications. In relation to total coliforms, the highest quantification was seen at 64785000 (1600000 NMP (100 mL) À1 , n ¼ 29). Although, there are outliers in stations located in Piquiri river, the boxplot for affluent presented higher concentrations in higher values of Kjedahl Nitrogen.
Phosphorus is another nutrient of great importance to be monitored in aquatic ecosystems. In this study, total phosphorus was higher in stations 6478500 (0.30, 0.23 mgL À1 , n ¼ 21) and 64790000 (0.20 mgL À1 , n ¼ 12).
Although, all points in Piquiri river presented outliers, in other words, non-common values for these points seen by temporal analysis. The importance of phosphorus is that this element is an essential and limiting nutrient in aquatic ecosystems, controlling, therefore, primary production. Water temperature presented practically the same range for all points from approximately 10-33 C (n ¼ 380). However, it is notable that temperatures in the affluent trend to have lower value than in stations located in Piquiri river. Classification of water quality according to the Brazilian law and water quality index water of good quality for this parameter, where the concentrations stayed below 1.25 mg·L À1 for all stations. The highest concentrations were seen in the affluent, and the most higher values were also seen after July. Total Nitrogen was classified in class 1 and 2, seen that the maximum concentration for this parameter is 2.18 mg·L À1 (both classes) for lotic environment. In this study, all values were lower than 2 mg·L À1 . Differently from Nitrate and Ammoniacal Nitrogen, the highest value was found on Piquiri river.