Surface water quality assessment in the Federal District, Brazil: application of multivariate statistical analysis and water quality indices for human consumption and irrigation

Surface water quality assessment in the Federal District, Brazil: application of multivariate statistical analysis and water quality indices for human consumption and irrigation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Surface water quality assessment in the Federal District, Brazil: application of multivariate statistical analysis and water quality indices for human consumption and irrigation Daphne H. F. Muniz, Juaci V. Malaquias, Eduardo C. Oliveira-Filho This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4329941/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Monitoring and evaluating water quality in urban areas has been emphasized as a fundamental tool in the management of water resources. The Federal District (FD) of Brazil has the third most populous city in the country and has recently faced a significant water crisis, culminating in a deterioration of water quality. The aim of this study was to apply multivariate statistical analysis (MSA) and water quality indices (WQIs) for human supply and irrigation in order to evaluate the quality of surface water in rivers under different land uses and occupations (8 rural, 4 urban and 6 natural). To this end, 29 water quality variables were analyzed in 18 sampling points between 2017 and 2019. The HCA grouped the points into 4 statistically significant clusters, taking into account similar types of sources. PCA explained 59.4% (rural), 66.9% (urban) and 58.7% (natural) of the total data variation in the first two principal components. Factor Analysis identified the key variables for each data matrix through the first three factors. The WQI for supply classified 16 of the 18 sampling points as “good”, demonstrating their suitability for human consumption after simplified treatment. The WQI for irrigation classified 10 points as “good” and eight points as “average”, demonstrating the restriction of points considered “average” for irrigation of raw vegetables and fruits that grow in the soil and are consumed raw without the skin. Data showed that tools applied are promising and have potential for application in surface water quality monitoring and communication programs for the FD. surface water rivers multivariate statistical analysis WQI Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Universal access to water in desirable quantity and quality is one of the great challenges for societies in the 21st century [1]. As a limited resource and endowed with socioeconomic and environmental value, water has received increasing attention in the global sustainability agenda, as a consequence of increasing pressure from factors such as climate change, models of economic development and accelerated population growth [2]. All over the world, large metropolises have faced serious water crises in the last decade. By 2050 the number of large cities that will be exposed to water scarcity is projected to increase from 193 to 284, including 10 to 20 megacities, among them cities in Brazil [3]. This is a country with continental dimensions and an abundance of water resources; about 20% of all the global continental water that flows to the oceans is generated in Brazilian territory and approximately 85% of the country's freshwater needs are supplied by surface waters, such as rivers and lakes [4]. Problems related to water in Brazil stem from contrasting weaknesses, where regions have very different climatic and socio-environmental contexts [5]. While certain areas, like the Northeastern region, have a historical record of droughts, others face water system capacity challenges, driven by population pressures, resulting in demand surpassing available resources, such as the Southeastern region, or due to the well-marked seasonality, with long periods of drought, as seen in the Midwestern region and illustrated in the present article [6,7]. The Federal District (FD), located in the Midwestern region of Brazil, has the smallest territory among the Brazilian states and is located in the Cerrado Biome (Brazilian savanna), harboring a large number of springs and forming an important natural watershed. Ranking third lowest among Federation Units, its surface water availability per capita per year is notably constrained [8]. Added to this, the FD is home to the third most populous city in the country with approximately three million inhabitants. The metropolitan region of the city of Brasília, the federal capital of Brazil, has had the fastest growth of all large Brazilian cities, with a 16.9% increase in population in the last 11 years [9,10]. The FD also experiences a thriving agricultural sector, and irrigated agriculture stands out as the segment that has shown the most significant increase in demand for water consumption in the region in recent years [11]. Between 2016 and 2018, the FD faced a serious water crisis, due to irregular rainfall in subsequent years and accelerated population growth that culminated in an increase in water consumption (with greater demands for public supply and irrigation), changes in land use, water use conflicts, among other factors. This experience revealed concerns about weaknesses in the FD's Integrated Water Resources Management (IWRM) system, which includes improvements in management tools, strategic management, planning and expansion of knowledge, including hydrological and water quality monitoring [12,13]. An important pillar of IWRM, water quality monitoring is a tool that has gained significance over the last few years. As crucial as quantity, the monitoring of water quality offers practical evidence to underpin decisions related to social, economic, health, and environmental matters. Assessments based on monitoring data help policymakers and water resource managers to measure the effectiveness of water policies [14,15]. Water quality management requires the collection and analysis of large data sets that can be difficult to synthesize and evaluate. Consequently, a range of tools has been developed to evaluate data on the quality of water resources [16]. An important instrument in the assessment of water quality, Multivariate Statistical Analysis (MSA) has been increasingly used in many areas of science, due to the increasingly complex nature of research questions [17,18]. Techniques such as Principal Component Analysis (PCA), Factor Analysis (FA), Hierarchical Cluster Analysis (HCA) and Discriminant Analysis (DA) have been widely used as tools in the assessment of water quality of rivers [19,20], groundwater [21,22], lakes [23,24], reservoirs [25,26] and drinking water [27,28]. These works employ MSA with different objectives, such as: analyzing the relationships between water quality, land use and land cover; assessing similarities and differences between periods and sampling points; recognizing variables responsible for spatial and temporal trends in water quality and also selecting variables to compose Water Quality Indexes (WQIs), reducing bias and the need for time and expenditure to monitor a large number of variables. The Water Quality Index (WQI) is a successful method for portraying water quality conditions and evaluating different water quality sets [29], and serves as another widely utilized tool in evaluating water quality. It has proven to be highly efficient and plays a significant role in water resource management, in addition to expressing water quality in a simple and logical way for the general public [30,31]. The WQI seeks to provide a unified assessment of water quality for a source by utilizing a system that condenses various variables and their concentrations found in a sample, into a single value. This allows for the comparison of quality in different samples using their respective index values [32,16]. The value obtained can be used for several purposes, including: allocation of financial resources in water resource management, classification of allocations, application of norms and legislation, analysis of trends (spatial and temporal), public information and scientific research to assess the health of water bodies [33,31]. In Brazil, the US National Sanitation Foundation index (WQI-NSF) was adapted and is the primary index utilized in both national and state water quality assessment programs. The quality parameters comprising this index are closely associated with water quality for public supply purposes [34]. In the context of the FD, this WQI serves as the primary indicator for monitoring surface water quality in lotic environments (rivers and streams) with a monitoring network that does not include all hydrographic units [35]. WQIs have found extensive application in Brazil for assessing water quality in diverse water bodies for various purposes [36–38]. Despite being a widely used tool around the world [30], WQIs present some problems, among the most significant of which is the allocation applied to water. The water resource can be used for different purposes, considering its multiple uses [39]. In this context, aiming to assess the quality of water for irrigation purposes, Muniz et al. [11] proposed a water quality index for the context of the FD (IWQI FD ), considering the regional attributes of water resources and aiding in the evaluation of issues related to soil and irrigated crops. In consideration of the aforementioned, the aim of this article is to assess the quality of surface water of hydrographic units, under different land uses, through the application of tools such as Multivariate Statistical Analysis and Water Quality Indices for public supply and irrigation, aiming to support the IWRM in the Federal District. 2. Material and methods 2.1. Study area and sampling points The FD is located in the Midwestern region of Brazil. It has a climate characterized by strong seasonality and two distinct hydrological periods: dry (from May to September) and rainy (from October to April) with annual rainfall between 1500 and 1700 mm. The natural vegetation covers different types of Cerrado (Brazilian savanna) and features gallery forests lining the entire stretch of the rivers. The main land use is urban, followed by areas of extensive cultivation (soybean and corn), vegetables and fruit, mainly with a focus on local supply [40,11]. The sampling points were defined based on the objective of the study, and preliminary studies, land occupation, and access to the points were considered. Eighteen points (P1 to P18) were defined in strategic locations. The collection points are located in nine hydrographic units (HU) in the FD. For each HU, two sampling points were selected, eight points located in river springs and eight points in places with anthropic influence (Fig. 1 ). In defining the points, the land use or land cover of the hydrographic units was taken into account, and these were classified as HU-RURAL (percentage of agropastoral area including irrigation pivots), HU-URBAN (percentage of built-up area) or HU-NATURAL (percentage of natural formation), according to the Land Cover Mapping of the Federal District, by the Federal District Planning Company [41]. Table 1 presents the description of the sampling points. Table 1 Description of sampling points. Point River Land Use/Cover Watershed Hydrographic Unit (HU) %AA %BA %NF P1 Várzea do Buracão Rural Preto HU 35 - Upper Jardim River 80.3 1.19 17.6 P2 Jardim P3 Buriti Vermelho Rural Preto HU 3 - Upper Preto River 88.2 0.5 11.1 P4 Buriti Vermelho P5 Chapadinha Rural São Bartolomeu HU 4 - Upper São Bartolomeu River 52.3 10.2 34.8 P6 Sarandi P7 Cabeceira Comprida Rural Descoberto HU 26 – Rodeador River 47.6 8.8 30.4 P8 Rodeador P9 Sobradinho Urban São Bartolomeu HU 30 – Sobradinho River 40.8 44.2 12.1 P10 Sobradinho P11 Tamanduá Urban Corumbá HU 25 - Ponte Alta River 24.3 18.9 56.3 P12 Ponte Alta P13 Taquara Natural Paranoá HU 17 – Gama River 1.9 30.8 66.9 P14 Taquara P15 Ouro Natural Maranhão HU 12 – Palma River 4.5 8.4 87.1 P16 Ouro P17 Covancas Natural Maranhão HU 15 – Contagem River 4.4 5.3 87.9 P18 Contagem %AA = percentage of HU agropastoral area (including irrigation pivots) / % BA = percentage of HU built-up area / % NF = percentage of natural formation of the HU. 2.2. Sampling and Analytical Methods The collections occurred bimonthly between December 2017 and October 2019, totaling 12 campaigns. In each sample, analyses of 29 physical, chemical and microbiological variables of water quality were performed. Surface water samples were collected in 500 mL polyethylene bottles. To collect samples to determine the biochemical oxygen demand, 300 mL Winkler flasks were used. For determination of Escherichia coli , samples were collected in sterile flasks containing sodium thiosulfate 0.1 mg per 100mL of sample. The variables dissolved oxygen, water temperature, total dissolved solids, electrical conductivity and pH were determined in the field, on the days of collection, using a portable multiparameter meter model HQ40d (Hach, USA). Turbidity was measured using a portable turbidimeter model 2100P (Hach, USA), and apparent color was measured using a CheckerHC color meter (Hanna, USA). To ensure quality control in sampling and measurements, field blanks, equipment and bottle blanks were used. All reagents and standards used in equipment calibration were analytical grade. Calibration coefficients for all methods were maintained at a level greater than 0.999. The collection, preservation and analysis procedures followed the recommendations of the International Organization for Standardization (ISO), American Society for Testing and Materials (ASTM) and Standard Methods for the Examination of Water and Wastewater (SMEWW) [42–44]. A summary of the analytical methods and methodologies can be seen in Table 2 . Table 2 Variables, abbreviations, units, methods and analytical methodologies for 29 physical, chemical and microbiological water quality variables analyzed. Variable Abbreviation Unit Method Methodology Ammonium NH 4 + mg/L Ion Chromatography ISO 14911:1998 [42] Apparent Color COLOR PCU Spectrophotometric APHA, 2018, 2120 C [44] Bicarbonate HCO 3 − mg/L Alkalinity Calculation APHA, 2018, 2320 B [44] Biochemical Oxygen Demand BOD mg/L I5-day Incubation APHA, 2018, 5210 B [44] Bromide Br − mg/L Ion Chromatography APHA, 2018, 4110 B [44] Calcium Ca 2+ mg/L Ion Chromatography ISO 14911:1998 [42] Chloride Cl − mg/L Ion Chromatography APHA, 2018, 4110 B [44] Dissolved Oxygen DO mg/L O 2 Electrometric ISO 17289:2014 [42] Electrical Conductivity EC µS/cm Electrometric APHA, 2018, 2510 B [44] Escherichia coli ECOLI NMP/100 mL Enzyme Substrate APHA, 2018, 9223 B [44] Fluoride F − mg/L Ion Chromatography APHA, 2018, 4110 B [44] Magnesium Mg 2+ mg/L Ion Chromatography ISO 14911:1998 [42] Nitrate NO 3 − mg/L Ion Chromatography APHA, 2018, 4110 B [44] Nitrite NO 2 − mg/L Ion Chromatography APHA, 2018, 4110 B [44] pH pH - Electrometric APHA, 2018, 4500H + B [44] Phosphate PO 4 3− mg/L Ion Chromatography APHA, 2018, 4110 B [44] Potassium K + mg/L Ion Chromatography ISO 14911:1998 [42] Sodium Na + mg/L Ion Chromatography ISO 14911:1998 [42] Sodium Adsorption Ratio SAR meq/L Calculation Suarez et al., 2008 [45] Sulfate SO 4 2− mg/L Ion Chromatography APHA, 2018, 4110 B [44] Total Alkalinity TA mg/L CaCO 3 Titrimetric APHA, 2018, 2320 B[44] Total Carbon TC mg/L Combustion APHA, 2018, 5310 B [44] Total Dissolved Solids TDS mg/L Electrometric APHA, 2018, 2510 A [44] Total Hardness TH mg/L CaCO 3 Titrimetric EDTA-Na APHA, 2018, 2340 B [44] Total Nitrogen TN mg/L Combustion ASTM D8083–2016 [43] Total Phosphorus TP mg/L Ascorbic Acid APHA, 2018, 4500P E [44] Total Residue TR mg/L Gravimetric APHA, 2018, 2540 B [44] Turbidity TURB NTU Turbidimetric APHA, 2018, 2130 B [44] Water Temperature TEMP °C Electrometric APHA, 2018, 2550 [44] 2.3. Multivariate Statistical Analysis Hierarchical cluster analysis (HCA) is an common approach, widely used in water quality studies, and provides intuitive similarity relationships between any sample and the entire data set, and is typically illustrated by a dendrogram (tree diagram) [46,47]. In this work, the HCA was performed on the standardized data matrix to classify the monitoring sites into different groups. The HCA was obtained using the Ward method with Euclidean distances as a measure of similarity [19,48]. Principal Component Analysis (PCA) is a multivariate analysis technique designed to minimize a matrix containing numerous interrelated variables, keeping the variability present in the data matrix as much as possible [49]. It converts the original variables into new unrelated variables (axes), called Principal Components (PC) [50]. PCs generated by PCA are often not readily interpreted. This purpose can be achieved by rotating the axis defined in the PCA, according to established methods, and building new variables (varifactors), through Exploratory Factor Analysis (EFA) [18]. The use of EFA after PCA aims to reduce the contribution of less significant variables and further simplify the data structure taken from PCA [51]. Before PCA, measurements of sampling adequacy were performed using the Kaiser-Meyer-Olkin (KMO) index and Bartlett's sphericity test (p < 0.05). Values of KMO greater than 0.5 up to 1.0 are deemed suitable for PCA utilization [52]. In this study, the broken-stick model was used to select the main components to be interpreted and the varimax rotation in the EFA. PCA was applied in order to extract important information related to the most significant variables, for the three data matrices (rural, urban and natural) separately. All statistical treatments of the data were performed with the aid of R software version 3.6.3 [53]. 2.4. Water Quality Indices (WQI) To evaluate the water's suitability for supply purposes, a nationwide index (WQI CETESB ) was applied, while for the assessment of water quality for irrigation, a regional index (IWQI FD ) was applied. The WQI CETESB was adapted in 1977 by the São Paulo State Environmental Sanitation Technology Company (CETESB) from the NSF index, USA. In the subsequent decades, additional Brazilian states embraced the WQI CETESB , which today is the water quality index most used in the country [34]. The objective of WQI CETESB is to evaluate the quality of raw water, aiming for it to be used for public supply after conventional treatment, and its variables mainly reflect the pollution caused by the release of domestic sewage [32, 16]. This index is calculated by the weighted product of nine variables that compose it, according to Eq. 1: $$\text{WQI}\text{CETESB}\text{ = }\prod _{\text{i}\text{ =1}}^{n}{qi}^{wi}\text{ (1)}$$ here, qi represents the quality of the i th parameter, ranging from 0 to 100, derived from the respective average quality variation curve based on its concentration or measurement. Meanwhile, wi denotes the weight assigned to the i th parameter, a value between 0 and 1, determined by its significance in shaping the overall quality. Here, n stands for the number of variables involved in computing the WQI CETESB [32]. The Irrigation Water Quality Index (IWQI FD ) was proposed with the objective of using water for irrigation purposes in the FD. This WQI was adapted from Meireles et al. [54] and the selection of variables and weights was performed using multivariate statistical methods (PCA) [11]. This index covers restrictions on the use of water for plants and soil as a measure of quality assessment and is calculated by the sum of the individual quality ( qi ) of each variable weighted by the weight of this variable ( wi ) in the assessment of water quality for irrigation according to Eq. 2: $$\text{} \text{IWQI}\text{FD}\text{ = }\sum _{\text{i}\text{ =1}}^{\text{n}}\text{q}\text{i}\text{w}\text{i}\text{ (2)}$$ Table 3 describes the ranges, classes, variables and their respective weights for the WQI CETESB and IWQI FD . Table 3 Ranges, classes, colors, variables and weights of the two indices. The WQI CETESB classes were divided taking into account the suitability of water for treatment for human supply (Table S1 ). The IWQI DF classes were divided based on existing irrigation water quality indices, taking into account the risk of salinity problems, reduced water infiltration into the soil, as well as contamination by pathogens (Table S2). 3. Results and Discussion 3.1. Multivariate Statistical Analysis (MSA) 3.1.1. Hierarchical Cluster Analysis (HCA) The HCA was applied to the data matrix including the 18 sample points and 28 variables. The variable Br − was removed from the analysis because it presented values ​​below the detection limit in all months analyzed for all points (Tables S4 and S6). The choice of the number of clusters was performed using the Gap statistic method, which uses the output of any hierarchical clustering algorithm, comparing the change in dispersion within the cluster with the expected one, under an appropriate null distribution of reference [55] (Fig. 2 a). From there, a dendrogram was obtained that gathered the 18 sample points into four statistically significant clusters (Fig. 2 b). The generated clusters have similar characteristics and font types. In the first grouping of Cluster 1 (P1, P3, P5, P9 and P13) are the points considered most preserved, including headwaters of HUs with rural, urban and natural occupation. The headwaters of the Jardim River (P1) and Buriti Vermelho Stream (P3) belong to the Preto River watershed, a region with strong agricultural activity in the FD. Despite the intense activity in the region, the samples from these points have similar characteristics, such as low concentration of ions [56]. The headwaters of Chapadinha Stream (P5) and Sobradinho River (P9) belong to the São Bartolomeu River watershed, the first located in HU-RURAL and the second in HU-URBAN. Despite being located in an urban area, the source of Sobradinho River is located in a Permanent Preservation Area (PPA). The source of Taquara Stream (P13), in turn, belongs to the Gama River Basin and is part of an Ecological Reserve. Points P2, P4, P6, P7, P8, P11 and P14 make up the second grouping (Cluster 2). Points P2, P4, P6 and P8 belong to HUs with rural human influence and intense agricultural activity nearby. The other points in Cluster 2 (P7, P11 and P14) have similarities despite being composed of a spring within HU-RURAL (Cabeceira Comprida Stream P7) and HU-URBAN (Tamanduá Stream P11) and a point with anthropic influence in HU-NATURAL (Taquara Stream P14). In Cluster 3, points P15 and P16 (both in Ouro Stream) and points P17 and P18 (source and point with human influence in Contagem River) are grouped. The points belonging to the cluster are located in the Maranhão River watershed and have similar hydrogeological characteristics, with no differentiation between the source and the point with anthropic influence. In this region occur the highest concentrations of minerals and deposits of the FD [57], evidenced in the high levels of ions (Na + , K + , Ca 2+, Mg 2+ , HCO 3 − ) and pH, total hardness, total alkalinity and total carbon above average in relation to the other points (Tables S5 and S6). Cluster 4 corresponds to points P10 (point with human influence on Sobradinho River) and P12 (point with human influence on Ponte Alta River). The two points are located in HUs with urban occupation and both receive effluents from a Sewage Treatment Plant (STP) in three important cities in the FD [56]. These points showed low values ​​of dissolved oxygen, high values ​​of NO 3 − , NH 4 + , total phosphorus, total nitrogen, biochemical oxygen demand and Escherichia coli , in all months sampled (Tables S5 and S6). 3.1.2. Principal Component Analysis (PCA) The PCA was applied to the data matrices separately, according to the land use/cover of the HUs, including 27 variables. The value of the KMO index was 0.708 for the HU-RURAL data matrix, 0.744 for the HU-URBAN and 0.792 for the HU-NATURAL. For all data sets, the p-value in Bartlett ' s sphericity test was considered significant (p < 0.000), evidencing suitability for the application of PCA. The determination of the number of Principal Components (PC) interpreted was performed using the broken-stick model. This method more accurately selects the appropriate number of PCs relative to common rule methods (i.e. eigenvalues ​​>1) and is typically more robust than statistically derived methods [58,59]. Figure 3 presents the graph of the eigenvalue and the broken-stick model for each component of the three different matrices. As can be seen in the figure, for the HU-URBAN matrix, the model selected the first PC. For the HU-RURAL and HU-NATURAL matrices, the first two PCs were selected. The loads of the first two PCs retained for each data matrix (rural, urban, and natural) are shown in Fig. 4 . The principal component loads can be used to determine the relative importance of a water quality variable compared to other variables of the PC, not reflecting the importance of the component itself [51]. For the HU-RURAL, principal component 1 (PC1) explained 48.8% of the total variance and was positively formed by physical variables, minerals and inorganic nutrients (EC, TDS, TH, TA, HCO 3 − , NO 3 − , Ca 2+ and Mg 2+ ) that presented loads greater than 0.7. This indicates that these variables are the most representative in defining the water quality of the analyzed water bodies. Variables with loadings greater than ± 0.70 are those that appropriately contribute to the data variation [60]. PC2 explained 10.6% of the total variance with physical variables related to the load of substances dissolved in water (TURB and COLOR). For these two components, the water quality variables related to the physical and inorganic characteristics predominate in relation to the organic and biological properties of the samples. These two components explained 59.4% of the total variation in the data. In studies that apply PCA in the assessment of water quality, the first two or three main components generated explain a good part of the variation in the original data (50 to 80%), without significant loss of information [61]. At HU-URBAN, the total variation explained for PC1 was 52.7%. In this first component, the variables that most contributed to the total explanation included BOD, TP and TN (as organic contributors) and EC, pH, TH, TA, TR, Cl − and SO 4 2− (as physical and chemical variables related to mineral characteristics and acidity of the water). For PC2, which explained 14.2% of the total variation, ECOLI, Na + , NH 4 + and K + were the variables that contributed to the component, all positively (Fig. 4 ). The PC1 of HU-NATURAL explained 43.5% of the total variance of data with the physical variables, minerals and inorganic nutrients responsible for the contribution in this component (EC, TDS, pH, TH, TA, TR, TC, HCO 3 - , F - , K + , Ca 2+ and Mg 2+ ). As can be seen in Fig. 4 , for the PC2 of this HU (total explained variance of 15.2%), the variables that contributed most were Na + and SO 4 2- . The two components together explained 58.7% of the data variation. These results show that the variables that influence the water quality of a group of water bodies (HUs) may not be important for other groups. The loads of the first two components, for the three matrices, also reveal that variables such as TEMP, DO and SAR were less important in the general variation of water quality, with low eigenvectors for these three variables (< 0.6). As can be seen in Fig. 4 , PC1 and PC2 for all matrices were (positively) influenced by a large number of variables, making it difficult to interpret which variables are most important in the general variation of water quality for a given land use or cover. Thus, Exploratory Factor Analysis (EFA) was applied in order to determine the relative importance of water quality variables. Table 4 presents the correlation coefficients rotated in the EFA for the first three factors in each data matrix. The three factors accounted for 84.3%, 88.7% and 89.1% of the total changes in HU-RURAL, HU-URBAN and HU-NATURAL, respectively. Rotated factors with load above 0.75 are considered strong, loads between 0.75 and 0.5 moderate and loads between 0.5 and 0.3 are considered weak [62]. In this study, only variables with factor loadings considered strong (> 0.75) were considered relevant, contributing to seasonal variations in water quality in each group. Table 4 Key variables for each land use/cover group. Group Factor Key variables Loads* HU-RURAL F1 TH 0.796 TA 0.821 HCO 3 − 0.952 NO 3 − 0.909 F2 TURB 0.856 F3 SAR 0.883 HU-URBAN F1 EC 0.834 TP 0.820 TN 0.925 NH 4 + 0.912 F2 TR 0.903 ECOLI 0.738 F3 BOD 0.761 HU-NATURAL F1 EC 0.954 TDS 0.886 TH 0.976 TC 0.946 HCO 3 − 0.961 F2 Ca 2+ 0.798 Mg 2+ 0.940 F3 F − 0.816 * Only variables with loads > 0.75 The key variables for HU-RURAL, in the first three factors rotated, were TH, TA, HCO 3 - , NO 3 - , TURB and SAR. These variables are important when water use is directed to irrigation. Water hardness refers to the presence of alkaline earth metals, mainly Ca 2+ and Mg 2+ , which are the main ones found in natural waters [11]. Very hard water (> 180.0 mg/L CaCO 3 ) can affect its suitability for certain techniques such as sprinkling or dripping [63,64]. High water hardness can also be limiting for fertigation, where values ​​above 100 mg/L of calcium and 43 mg/L of magnesium increase the risk of precipitation of phosphate fertilizers inside the pipes [65]. At points P1 to P8 (HU-RURAL) the TH values ​​ranged from 1.38 mg/L (P3) to 18.8 mg/L (P8). Calcium ranged from 0.078 mg/L (P1) to 5.992 mg/L (P2), and magnesium had a minimum of 0.005 mg/L (P1) and a maximum of 1.421 (P2) (Tables S3 and S4). TA and HCO 3 - are equally important variables for assessing water quality for irrigation. The total alkalinity of water is the sum of all titratable bases, especially carbonate and bicarbonate (HCO 3 - ). Waters rich in bicarbonates tend to precipitate calcium carbonate and magnesium carbonate when the soil solution is concentrated by evapotranspiration, increasing soil sodicity and consequently SAR. HCO 3 - levels above 518 mg/L in water can damage susceptible crops [66,67]. In this TA study, for the HU-RURAL points, the maximum value was 16.52 mg/L of CaCO 3 in P2 and minimum of 1.28 mg/L of CaCO 3 in P1. As for HCO 3 - , there was a maximum of 20.15 mg/L also in P2 and minimum of 1.562 mg/L in P1 (Tables S3 and S4). The SAR is an important variable for the assessment of water quality for irrigation. This is a relative ratio of Na + ion to Ca 2+ and Mg 2+ ions. It is used to estimate the potential for Na + to accumulate in the soil mainly to the detriment of Ca 2+ , Mg 2+ and K + as a result of the regular use of water with a high concentration of sodium. High SAR values ​​(> 26 meq/L) can influence the percolation time of water in the soil, leading to a decrease in the infiltration rate due to the dispersion and disaggregation of the soil structure [63,68–69]. For the HU-RURAL points, the highest mean values ​​of SAR were found in P2–3.378 meq/L and P8–3.462 meq/L (Table S4). NO 3 - , in turn, is one of the most common pollutants found in surface and groundwater, coming from point and non-point sources. Some non-point sources include agricultural activities such as fertilizer and manure application, leguminous crops and irrigation with groundwater containing nitrogen compounds [70,71]. Excess NO 3 - in irrigation water can affect sensitive crops at concentrations above 5 mg/L. Most other crops are relatively unaffected by up to 30 mg/L nitrate [72]. The maximum content of NO 3 - was found in P4, with a value of 0.962 mg/L and the minimum in P8, a value of 0.001 mg/L (Table S4). For HU-URBAN, the most important variables, with loads rotated above 0.75, were EC, TP, TN, NH 4 + , TR, ECOLI and BOD. These variables are important indicators for water bodies in urban areas, since they can indicate contamination, for example, by effluents from domestic sewage. Electrical conductivity (EC) is extremely useful as a general measure of water quality. Significant changes in conductivity can be an indicator that a discharge or some other source of pollution has reached a given water body, especially freshwater bodies [73,74]. The maximum EC for the HU-URBAN points was 448 µS/cm at P12 (Table S5). Biochemical oxygen demand (BOD) is the amount of dissolved oxygen required to decompose the organic material present in the water sample, by aerobic biological organisms, in a given time at a certain temperature [75]. High BOD values ​​in a water body are generally caused by the release of organic loads, mainly effluents from domestic sewage, and are associated with a decrease in dissolved oxygen in the water, which can lead to the mortality of aquatic organisms [44]. Point P12 showed a maximum of 6.42 mg/L, as can be seen in Table S5 (supplementary). BOD values ​​can vary significantly; in general, unpolluted fresh water has a value below 1 mg/L, moderately polluted water from 2 to 8 mg/L and treated domestic effluent 20 mg/L [76]. Total residue (TR) represents the sum of dissolved solids and suspended solids in water, including colloidal particles. TR analysis in urban surface samples is an important indicator of pollution from domestic sewage or other point sources [44]. High levels of TR can affect the aesthetic quality of water, especially for human consumption, and can also reduce the efficiency of effluent treatment plants [77]. The highest levels of TR in this study were found in the HU-URBAN for P12 with a maximum of 450.9 mg/L (Table S5). Phosphorus (P) and nitrogen (N) compounds are essential for the processes that occur in the aquatic environment. However, in excessive amounts they represent a significant source of water pollution [78]. P is a primary nutrient limiting the growth of algae and phytoplankton in many freshwater bodies, and its source can be either anthropogenic (domestic eluents and fertilizers) or natural (precipitation or geological materials) [79]. Total N is the sum of all forms of nitrogen present in water (organic, ammoniac, nitrite and nitrate). Elevated levels of N and P in water bodies cause nutrient imbalance and induce eutrophication, bringing anoxic conditions to the water [80]. Both TP and TN were key variables in the factor analysis of this study, along with NH4+. The maximum values ​​of TP and TN were found in points with urban influence (HU-URBAN), 0.282 mg/L TP and 41.40 mg/L TN for P12; and 16.01 mg/L of NH 4 + at P10, both receiving effluents from Sewage Treatment Stations. The bacterium of the coliform group, Escherichia coli (ECOLI), is an important indicator of fecal pollution in freshwater bodies, especially in urban environments, considered a simple and economic analysis compared to other pathogens [81,82]. The maximum concentrations of ECOLI detected by the method of enzyme substrates in this study were for P12–48,392 NMP/100 mL and P10–12,200 NMP/100 mL. The sampling points in the area under mostly natural land cover (HU-NATURAL) are located in the Ecological Reserve of the Brazilian Institute of Geography and Statistics (RECOR-IBGE), in the center-south of the FD and in the Environmental Protection Area (APA) of Cafuringa, in the extreme north of the FD. These two regions are characterized by extensive areas of preserved vegetation in the Cerrado biome [83,84]. The key variables of greatest interest to the group were CE, TDS, TH, TC, HCO 3 - , Ca 2+ and Mg 2+ . These variables are closely linked to the natural geological characteristics of these regions, since there is little or no human influence at the sampling points. Points P15, P16, P17 and P18 are located in a region characterized by the presence of Cambisol, originating from predominantly limestone rocks [85]. In the HU-NATURAL group, point P16 (point with anthropic influence in Ouro River) presented the maximum levels for CE (251 µS/cm), TDS (120.5 mg/L), TH (140.51 mg/L CaCO3), TC (32.514 mg/L) and HCO 3 + (170.31 mg/L). P18 (point with anthropic influence in Contagem River) showed maximum Ca 2+ (31.35 mg/L) and P15 (Ouro River headwater) maximum Mg 2+ (13.611 mg/L) (Tables S5 and S6). 3.2. Water Quality Index 3.2.1. Water Quality Index for Human Supply (WQI CETESB ) The WQI CETESB classified the samples from P10 and P12 as “ reasonable ” and the rest of the sampled points as “ good ” (Fig. 5 ). As evidenced in the HCA, points P10 (point with human influence on Sobradinho River) and P12 (point with human influence on Ponte Alta River) have similar characteristics, as both are receivers of sewage effluents from three administrative regions with an estimated population of 275,778 inhabitants [86]. The applied index takes into account the contamination of water bodies caused by the release of domestic sewage, and the variables used in the WQI CETESB are related to the evaluation of the quality of raw surface water for public supply purposes, after conventional treatment [32]. This means that samples from points P10 and P12 are considered unsafe for human consumption. Points P9 (Sobradinho River headwater) and P11 (Tamanduá River headwater), both in Urban HUs, were classified as “ good ”, with the need for pre-treatment so that water can be used for human consumption. The FD and the metropolitan region of Brasília have approximately 97% of the population living in the urban area, almost three million people [23]. In the FD, 99% of the population is served by the regular water supply network. Around 870,000 households are served by five main supply systems, including reservoirs, rivers and underground wells, with a production capacity of more than 11,000 liters of water per second [8]. Despite the high levels of coverage of the urban water supply network, the water crisis that occurred between 2016 and 2018 raised an alert for managers about conflicts over the use and search for new sources of water in sufficient quantity and quality to supply the city’s existing population in the long term [12]. In this context, monitoring and generating water quality data are essential, as they help water resource managers with information on pollution problems and in surveying promising water sources. Points P1 (Várzea do Buracão River headwater), P7 (Cabeceira Comprida River headwater) and P13 (Taquara stream headwater) had the highest medians during the analyzed period (n = 12). These three points have in common the fact that they are springs located in areas considered preserved, even with predominantly rural land use in the case of P1 and P7. Points P1, P7 and P5 (Chapadinha River headwater) are located in headwaters of HU-RURAL. The water samples from these points were classified as " good ", as were P2, P6 and P8 (points with anthropic influence in HU-RURAL), showing that the water from these six points can be used for rural supply purposes (after treatment) for communities located close to water bodies. The FD, like the vast majority of Brazilian municipalities, has most of the population concentrated in urban areas. According to the last agricultural census, the population of the rural area of ​​the FD was 87,950 inhabitants, representing 3.42% of the total population, with a demographic density of the rural population of 18.84 hab/km², a low value when calculated in relation to the totality of the area [10]. The low population density of rural areas makes collective water supply solutions difficult. There are currently 61 independent rural supply systems operated by the Federal District Environmental Sanitation Company (CAESB), in small, more densely populated localities, corresponding to a service of about 15% of the rural population in these areas [8]. The percentage not met by CAESB uses individual sources (wells and direct capture of surface water) for supply, and these have little or no water quality control carried out by the Sanitary Surveillance. The large extension of the rural area, the low population density and the great distance between the operational units of the Environmental Sanitation Company, raises the operational cost of supply through the general network, which makes its expansion difficult [8,87]. Of the points located in HU-NATURAL, P13 and P14 had higher medians, compared to other points in the same HU. These two points are located in the IBGE-RECOR, a protected area known as a Conservation Unit with 1,300 ha designated as a conservation area in 1975 [83]. Points P15, P16, P17 and P18 are located in HUs within the Maranhão River Basin, and they share similar water quality characteristics, as seen in HCA (Fig. 2 b). This region, located in the northern portion of the FD, may be a promising source of water supply for the population located in the northern portion of the FD. 3.2.2. Irrigation Water Quality Index (IWQI FD ) The IWQI FD was developed to assess the quality of water for irrigation purposes in the FD. According to the classification by the index, the samples of P1, P2, P5, P6, P7, P8, P9, P11, P13 and P14 were classified as “ good ”. Points P3, P4, P10, P12, P15, P16, P17 and P18 were classified as “ average ” (Fig. 6 ). In the FD, agriculture is an important economic activity, and irrigated agriculture was the sector that most showed an increase in demand for water consumption in the region, due to the large investment by the private sector and with the incorporation of new areas with aptitude for irrigation [88]. This type of agriculture is characterized by areas for large crops, vegetables and fruits. The more than 20,000 agricultural enterprises produce flowers, grain, vegetables and fruit, having produced more than 700,000 tons of grain in 2021. The cultivation of vegetables reached more than 200,000 tons and that of fruit, more than 30,000 tons [89,90]. It is important to emphasize that local agriculture is developed in small areas, given the territorial dimension of the FD and any factor that affects the cultivation areas, such as climatic effects or conflicts due to water scarcity, generates a great impact on the index of the agricultural sector [91]. Brazil is among the 10 countries in the world with the largest area equipped for irrigation [92]. In the FD, the use of irrigation equipment began in 1986, with strong expansion between 1988 and 1997, with about 12,000 hectares in 2012, 14,000 hectares in 2015, and currently the FD has 34,000 hectares of irrigated area [88,93]. The predominant type of irrigation in the FD is center pivots concentrated mainly in a small strip in the eastern part of its territory. This range, where the pivots are concentrated, corresponds to approximately a quarter of the FD area and is the region where almost all grain production is concentrated. The main products grown in the central pivot-irrigated areas in the FD are beans, corn, wheat, vegetables and coffee [88,94]. In the eastern part of the FD territory, there are the HUs with the largest number of rural properties and equipment that captures and distributes water for irrigation [88]. In the Rio Preto watershed, for example, the distribution of irrigated areas and the water demand for each irrigation system indicate a total of 7,546 L/s of water demand [92]. Points P1, P2, P5 and P6, located in HU-RURAL in the eastern area of ​​the FD, had a median IWQI FD between 71 and 85, being classified as " good ". This means that water can be used for irrigation of grain, cereal, trees and fodder, but its use should be avoided on vegetables that are eaten raw and fruit that grows close to the ground and that is eaten raw without removing the skin. Points P3 and P4, also located in HU-RURAL in the eastern area, were classified as " average ", (median between 41 and 55), which means that their use is inappropriate for irrigation of vegetables and fruit in general, in addition to crops of grain, cereal and fodder [11]. In the western region of the FD, the basin that contributes to irrigation is the Descoberto River watershed. The distribution of irrigated areas and the water demand for each type of irrigation system indicate an estimate for water demand of approximately 2,462 L/s, for this basin, an area of ​​2,052 ha. The main HU ​​that contributes to irrigation in this area is Rodeador River [92]. The water samples from points P7 and P8 (HU-RURAL), located at the source and points with anthropic influence in Rodeador River, were classified as " good " during the analyzed period, showing suitability for irrigation of grain, cereal, trees and fodder. 4. Conclusions In this study, we used multivariate statistical analysis and water quality indices as tools in assessing the quality of water - for supply and irrigation - from a large dataset. This set included 29 physical, chemical and biological variables, in 18 sampling points monitored for 12 months, under different land uses and cover (rural, urban and natural). Through HCA, the sampling points were grouped into four distinct clusters, according to the similar characteristics of the water samples. Through PCA and EFA, it was possible to reduce the number of variables in the original data matrices. The PCA explained 59.4%, 66.9% and 58.7% of the total variation in data for HU-RURAL, HU-URBAN and HU-NATURAL, respectively. Through the EFA, it was possible to remove the key variables for each group of land use and land cover. For HU-RURAL, the key variables were TH, TA, HCO 3 − , NO 3 − , TURB and SAR. These variables are important parameters related to water quality for irrigation. For HU-URBAN, the most important variables were EC, TP, TN, NH 4 + , TR, ECOLI and BOD. All of them are strongly related to the release of effluents from sewage treatment stations at the points sampled. In the HU-NATURAL, the key variables were related to the geological characteristics of the regions where samples were collected from the rivers: CE, TDS, TH, TC, HCO 3 − , Ca 2+ and Mg 2+ . The WQI CETESB , the index used in the assessment for supply purposes, classified 16 of the 18 sample points as " good " (medians between 52 and 79), demonstrating that water from these points is suitable for human consumption after simplified treatment. The exception was points that receive sewage effluents from large metropolitan regions, which were classified as “ reasonable ” (medians between 37and 51). The IWQI FD , an index developed to assess the quality of irrigation in the region, classified 10 points as “ good ” (medians between 71 and 85) and another eight sampling points as “ average ” (medians between 56 and 70). The places where the water samples were considered " good " present quality for irrigation of grain, cereal, trees and fodder, but irrigation should be avoided in vegetables that are consumed raw and fruit that grows close to the ground and that is eaten raw without removing the skin. The findings obtained demonstrate that the tools used were useful in the general assessment of water quality, since it is a large set of data, in a complex area of ​​study. These tools aim to support integrated water resource management actions aimed at mediating conflicts over water use and water security, with great potential in the application of programs to monitor the quality of surface water in the FD. Declarations Ethical Approval Not Applicable Consent to Participate The authors provided consent to participate in this study. Consent to Publish The authors agreed to publish this study. Authors Contributions DHF Muniz: designed the study, performed data collection and laboratory analysis and drafted the manuscript. JV Malaquias: performed the statistical analysis, revised the manuscript. EC Oliveira-Filho: drafted and revised the final manuscript. All authors reviewed the results and approved the final version of the manuscript. Funding This work was supported by the Federal District Research Support Foundation - FAPDF [grant number 193.00002283/2022-91]. Competing Interests The authors declare no competing or conflict interests . Availability of data and materials Data is provided within the manuscript and supplementary information files. References 1. Connor R, Coates D (2021) The state of water resources. In: The United Nations World Water Development Report 2021: Valuing Water. UNESCO, Paris, France, 2021, p. 11–16. 2. Mehmood H (2019) Bibliometrics of Water Research: A Global Snapshot. UNU-INWEH Report Series, Issue 06. United Nations University Institute for Water, Environment and Health, Hamilton, Canada, 2019, 24 p. 3. He C, Liu Z, Wu J, Pan X, Fang Z, Li J, Bryan BA (2021) Future global urban water scarcity and potential solutions. Nat Commun. https://doi.org/10.1038/s41467-021-25026-3 4. Getirana A, Libonati R, Cataldi M (2021) Brazil is in water crisis - it needs a drought plan. Nature. https://doi.org/10.1038/d41586-021-03625-w 5. Gesualdo GC, Sone JS, Galvão CO, Martins ES, Montenegro SMGL, Tomasella J, Mendiondo EM (2021) Unveiling water security in Brazil: current challenges and future perspectives. Hydrol Sci J. https://doi.org/10.1080/02626667.2021.1899182 6. Cunha APMA, Zeri M, Deusdará LK, Costa L, Cuartas LA, et al. (2019) Extreme Drought Events over Brazil from 2011 to 2019. Atmosphere. https://doi.org/10.3390/atmos10110642 7. Pereira V R, Rodriguez DA, Coutinho SMV, Santos DV, Marengo JA (2020) Adaptation opportunities for water security in Brazil. Sustain Debate. https://doi.org/10.18472/SustDeb.v11n3.2020.33858 8. Lima LA, Silva DH. (2020) Um Panorama das Águas no Distrito Federal. CODEPLAN, Brasília. https://www.codeplan.df.gov.br/wp-content/uploads/2020/07/Estudo-Um-Panorama-das-%C3%81guas-no-Distrito-Federal.pdf 9. Strauch M, Lima JEFW, Volk M, Lorz C, Makeschin F (2103) The impact of Best Management Practices on simulated streamflow and sediment load in a Central Brazilian catchment. J Environ Manage. https://doi.org/10.1016/j.jenvman.2013.01.014 10. IBGE (2023). Brasil – Distrito Federal – População (2022) Available at: https://cidades.ibge.gov.br/brasil/df/. 11. Muniz DHF, Malaquias JV, Lima JEFW, Oliveira-Filho, EC (2020) Proposal of an irrigation water quality index (IWQI) for regional use in the Federal District, Brazil. Environ Monit Assess. https://doi.org/10.1007/s10661-020-08573-y 12. Lima JEFW, Freitas GK, Pinto MAT, Salles PSBA (2018) Gestão da crise hídrica 2016–2018: experiências do Distrito Federal. ADASA, CAESB, SEAGRI, EMATER-DF, Brasília. https://www.adasa.df.gov.br/images/banners/alta.pdf 13. Adasa (2012) PGIRH-DF - Plano de Gerenciamento Integrado dos Recursos Hídricos do Distrito Federal. Brasília, DF: Adasa, GDF, Ecoplan. https://www.Adasa.df.gov.br/images/storage/programas/PIRHFinal/PGIRH_relatorio_sintese_versaofinal.pdf Accessed 03 August 2022 14. Damania R, Desbureaux S, Rodella AS, Russ J, Zaveri E (2019) Quality Unknown: The Invisible Water Crisis. Washington DC: World Bank. https://openknowledge.worldbank.org/handle/10986/32245 15. Myers DN (2022) Why monitor water quality? U.S. Geological Survey - USGS. https://water.usgs.gov/owq/WhyMonitorWaterQuality.pdf 16. Uddin MG, Nash S, Olbert AI (2021) A review of water quality index models and their use for assessing surface water quality. Ecol Indic. https://doi.org/10.1016/j.ecolind.2020.107218 17. Fu L, Wang YG (2012) Statistical Tools for Analyzing Water Quality Data. In: Voudouris, K.; Voutsa, D. (Eds) Water Quality Monitoring and Assessment. IntechOpen, London, UK, 2012, pp. 144–168. https://doi.org/10.5772/35228 18. Muniz DH F, Oliveira-Filho EC (2023) Multivariate Statistical Analysis for Water Quality Assessment: a review of research published between 2001 and 2020. Hydrology. https://doi.org/10.3390/hydrology10100196 19. Wang Y, Wang P, Bai Y, Tian Z, Li J, et al. (2013) Assessment of surface water quality via multivariate statistical techniques: A case study of the Songhua River Harbin region, China. J Hydro Environ Res. https://doi.org/10.1016/j.jher.2012.10.003 20. Jung KY, Lee K-L, Im TH, Lee IJ, Kim S, Han K-Y, Ahn, JM (2016) Evaluation of water quality for the Nakdong River watershed using multivariate analysis. Environ Technol Innov. https://doi.org/10.1016/j.eti.2015.12.001 21. Khanoranga A, Khalid S (2019) An assessment of groundwater quality for irrigation and drinking purposes around brick kilns in three districts of Balochistan province, Pakistan, through water quality index and multivariate statistical approaches. J Geochem Explor. https://doi.org/10.1016/j.gexplo.2018.11.007 22. Barbosa-Filho J, de Oliveira IB (2021) Development of a groundwater quality index: GWQI, for the aquifers of the state of Bahia, Brazil using multivariable analyses. Sci Rep. https://10.1038/s41598-021-95912-9 23. Iqbal J, Shah MH (2013) Health Risk Assessment of Metals in Surface Water from Freshwater Source Lakes, Pakistan. Hum Ecol Risk Assess. https://doi.org/10.1080/10807039.2012.716681 24. Han Q, Tong RZ, Sun WC, Zhao Y, Yu JS, Wang GQ, Shrestha S, Jin YL (2019) Anthropogenic influences on the water quality of the Baiyangdian Lake in North China over the last decade. Sci Total Environ. https://doi.org/10.1016/j.scitotenv.2019.134929 25. Siepak M, Sojka M (2017) Application of multivariate statistical approach to identify trace elements sources in surface waters: a case study of Kowalskie and Stare Miasto reservoirs, Poland. Environ Monit Assess. https://doi.org/10.1007/s10661-017-6089-x 26. Golshan A, Evans C, Geary P, Morrow A, Rogers Z, Maeder M (2020) Turning Routine Data into Systems Insight: Multivariate Analysis of Water Quality Dynamics in a Major Drinking Water Reservoir. Environ Model Assess. https://doi.org/10.1007/s10666-020-09700-2 27. Güler C (2007) Characterization of Turkish bottled waters using pattern recognition methods. Chemom Intell Lab Syst. https://doi.org/10.1016/j.chemolab.2006.08.009 28. Felipe-Sotelo M, Henshall-Bell ER, Evans NDM, Read D (2015) Comparison of the chemical composition of British and Continental European bottled waters by multivariate analysis. J Food Compost Anal. https://doi.org/10.1016/j.jfca.2014.10.014 29. Gao Z, Liu Y, Li N (2022) An enhanced beetle antennae search algorithm based comprehensive water quality index for urban river water quality assessment. Water Resour Manag. https://doi.org/10.1007/s11269-022-03169-2 30. Dash S, Kalamdhad AS (2021) Science mapping approach to critical reviewing of published literature on water quality indexing. Ecol Indic. https://doi.org/10.1016/j.ecolind.2021.107862 31. Gupta S, Gupta SK (2021) A critical review on water quality index tool: Genesis, evolution and future directions. Ecol Infom. https://doi.org/10.1016/j.ecoinf.2021.101299 32. Abbasi T, Abbasi SA (2012) Chap. 1 - Why Water-Quality Indices. In: Abbasi T, Abbasi SA (eds) Water quality indices. Elsevier, New York, pp 3–7. https://doi.org/10.1016/B978-0-444-54304-2.00001-4 33. Gitau MW, Chen J, Ma Z (2016) Water Quality Indices as Tools for Decision Making and Management. Water Resour Manag. https://doi.org/10.1007/s11269-016-1311-0 34. Zagatto PA, Lorenzetti ML, Lamparelli MC, Salvador MEP, Menegon-Jr N, Bertoletti E (1999) Aperfeiçoamento de um índice de qualidade de águas. Acta Limnol Bras 11(2): 111–126 35. Adasa (2022) Sistema de Informações sobre Recursos Hídricos – DF. Rede de Monitoramento da Qualidade das Águas Superficiais da ADASA. Índice de Qualidade da Água – IQA. Brasília, DF: Adasa. http://gis.Adasa.df.gov.br/portal/home/ 36. Medeiros AC, Faial KRF, Faial KCF, Lopes IDS, Lima MO, Guimarães RM, Mendonça NM, et al. (2017) Quality index of the surface water of Amazonian rivers in industrial areas in Pará, Brazil. Mar Pollut Bull. https://doi.org/10.1016/j.marpolbul.2017.09.002 37. Cicilinski AD, Virgens-Filho JS (2020) A new water quality index elaborated under the Brazilian legislation perspective. Int J River Basin Manag. https://doi.org/10.1080/15715124.2020.1803335 38. Costa DA, Azevedo JPS, dos Santos MA et al. (2020) Water quality assessment based on multivariate statistics and water quality index of a strategic river in the Brazilian Atlantic Forest. Sci Rep. https://doi.org/10.1038/s41598-020-78563-0 39. Kachroud M, Trolard F, Kefi M, Jebari S, Bourrié G (2019). Water quality indices: challenges and application limits in the literature. Water. https://doi.org/10.3390/w11020361 40. Castro KB, Roig HL, Neumann MRB, Rossi MS, Seraphim APACC, Réquia-Júnior WJ, Costa ABB, Höfer R (2019) New perspectives in land use mapping based on urban morphology: A case study of the Federal District, Brazil. Land Use Policy. https://doi.org/10.1016/j.landusepol.2019.104032 41. CODEPLAN (2017) Mapeamento da cobertura do Distrito Federal: 1984 a 2017 - Relatório Síntese. Brasília, DF. http://coberturadaterra.codeplan.df.gov.br/. 42. ISO (1998). Water quality - Determination of dissolved Li + , Na + , NH 4 + , K + , Mn 2+ , Ca 2+ , Mg 2+ , Sr 2+ and Ba 2+ using ion chromatography - Method for water and waste water (ISO Standard No. 14911:1998). https://www.iso.org/standard/25591.html 43. ASTM (2016) Standard test method for total nitrogen, and Total Kjeldahl Nitrogen (TKN) by calculation, in water by high temperature catalytic combustion and chemiluminescence detection (ASTM D8083-16), ASTM International, West Conshohocken. https://doi.org/10.1520/D8083-16 44. APHA (2018) Standard methods for the examination of water and wastewater (23nd ed.). American Public Health Association, Washington 45. Suarez DL, Wood JD, Lesch SM (2008) Infiltration into cropped soils: effect of rain and sodium adsorption ratio–impacted irrigation water. J Environ Qual. https://doi.org/10.2134/jeq2007.0468 46. Ogwueleka TC (2014) Assessment of the water quality and identification of pollution sources of Kaduna River in Niger State (Nigeria) using exploratory data analysis. Water Environ J. https://doi.org/10.1111/wej.12004 47. Bouguerne A, Boudoukha A, Benkhaled A, Mebarkia AH (2017) Assessment of surface water quality of Ain Zada dam (Algeria) using multivariate statistical techniques. Int J River Basin Manag. https://doi.org/10.1080/15715124.2016.1215325 48. Barakat A, El Baghdadi M, Rais J, Aghezzaf B, Slassi M (2016) Assessment of spatial and seasonal water quality variation of Oum Er Rbia River (Morocco) using multivariate statistical techniques. Inter Soil Water Conserv Res. https://doi.org/10.1016/j.iswcr.2016.11.002 49. Jolliffe IT, Cadima J (2016) Principal component analysis: a review and recent developments. Phil Trans R Soc. https://doi.org/10.1098/rsta.2015.0202 50. Holland SM (2019) Principal Components Analysis (PCA). Department of Geology, University of Georgia, Athens, Greece. http://strata.uga.edu/8370/handouts/pcaTutorial.pdf 51. Ouyang Y, Nkedi-Kizza P, Wu QT, Shinde D, Huang CH (2006) Assessment of seasonal variations in surface water quality. Water Res. https://doi.org/10.1016/j.watres.2006.08.030 52. Hair JFK, Black WC, Babin BJ, Anderson RE (2014) Multivariate data analysis. 7th Edition, Pearson Prentice Hall, Hoboken 53. R Core Team (2021) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/. 54. Meireles ACM, Andrade EM, Chaves LCG, Frischkorn H, Crisostomo LA (2010) A new proposal of the classification of irrigation water. Ciência Agronômica. https://doi.org/10.1590/S1806- 66902010000300005 55. Tibshirani R, Walther G, Hastie T (2001) Estimating the number of clusters in a data set via the gap statistic. J R Stat Soc Ser B Stat Methodol. https://doi.org/10.1111/1467-9868.00293 56. Muniz DHF, Moraes AS, Freire IS, Cruz CJD, Lima JEFW, Oliveira-Filho EC (2011) Evaluation of water quality parameters for monitoring natural, urban, and agricultural areas in Brazilian Cerrado. Acta Limnol Bras. https://doi.org/10.1590/S2179-975X2012005000009 57. Lima JEFW, Oliveira-Filho EC, Silva EM, Farias MFR (2006) Caracterização Hidrológica da APA da Cafuringa. In: Netto PB, Mecenas VV, Cardoso ES (ed). APA da Cafuringa – a última fronteira natural do DF. SEMA-DF, Brasília 58. Olsen RL, Chappell RW, Loftis JC (2012) Water quality sample collection, data treatment and results presentation for principal components analysis e literature review and Illinois River watershed case study. Water Res. https://doi.org/10.1016/j.watres.2012.03.028 59. Sergeant CJ, Starkey EN, Bartz KK, Wilson MH, Mueter FJ (2016) A practitioner’s guide for exploring water quality patterns using principal components analysis and Procrustes. Environ Monit Assess. https://doi.org/10.1007/s10661-016-5253-z 60. Gvozdić V, Brana J, Puntarić D, Vidosavljević D, Roland D (2011) Changes in the lower Drava River water quality parameters over 24 years. Arh Hig Rada Toksikol. https://doi.org/10.2478/10004-1254-62-2011-2128 61. Simeonov V, Stratis JA, Samara C, Zachariadis G, Voutsa D, Anthemidis A, Sofoniou M, Kouimtzis T (2003) Assessment of the surface water quality in Northern Greece. Water Res. https://doi.org/10.1016/S0043-1354(03)00398-1 62. Shrestha S, Kazama F (2007) Assessment of surface water quality using multivariate statistical techniques: A case study of the Fuji river basin, Japan. Environ Model Softw. https://doi.org/10.1016/j.envsoft.2006.02.001 63. Rawat KS, Singh SK, Gautam SK (2018) Assessment of groundwater quality for irrigation use: a peninsular case study. Appl Water Sci. https://doi.org/10.1007/s13201-018-0866-8 64. Malakar A, Snow DD, Ray C (2019) Irrigation Water Quality - A Contemporary Perspective. Water. https://doi.org/10.3390/w11071482 65. Kafkafi U, Tarchitzky J (2011) Fertigation: A Tool for Efficient Fertilizer and Water Management. International Fertilizer Industry Association, Paris. 141 p. 66. Ayers RS, Westcot DW (1999) Water quality for agriculture. Irrigation and Drainage paper No. 29. FAO: Rome 67. Zaman M, Shahid SA, Heng L (2018) Irrigation Water Quality. In: Zaman M, Shahid SA, Heng L (eds) Guideline for Salinity Assessment, Mitigation and Adaptation Using Nuclear and Related Techniques. Springer Cham. https://doi.org/10.1007/978-3-319-96190-3 68. Aboukarima AM, Al-Sulaiman MA, El Marazky MSA (2018) Effect of sodium adsorption ratio and electric conductivity of the applied water on infiltration in a sandy-loam soil. Water SA. https://doi.org/10.4314/wsa.v44i1.12 69. Sattari MT, Feizi H, Colak MS, Ozturk A, Apaydin H, Ozturk F (2020) Estimation of sodium adsorption ratio in a river with kernel-based and decision-tree models. Environ Monit Assess. https://doi.org/10.1007/s10661-020-08506-9 70. Esmaeili S, Thomson NR, Rudolph DL (2020) Evaluation of nutrient beneficial management practices on nitrate loading to groundwater in a Southern Ontario agricultural landscape. Can Water Resour J. https://doi.org/10.1080/07011784.2019.1692697 71. Zhang X, Zhang Y, Shi P, Bi Z, Shan Z, Ren L (2021) The deep challenge of nitrate pollution in river water of China. Sci Total Environ. https://doi.org/10.1016/j.scitotenv.2020.144674 72. Simsek C, Gunduz O (2007) IWQ index: a GIS integrated technique to assess irrigation water quality. Environ Monit Assess. https://doi.org/10.1007/s10661-006-9312-8. 73. Sousa DNR, Mozeto AA, Carneiro RL, Fadini PS (2014) Electrical conductivity and emerging contaminant as markers of surface freshwater contamination by wastewater. Sci Total Environ. https://doi.org/10.1016/j.scitotenv.2014.02.135 74. Rusydi AF (2017) Correlation between conductivity and total dissolved solid in various type of water: a review. IOP Conf Ser: Earth Environ Sci. https://doi.or/10.1088/1755-1315/118/1/012019 75. Li D, Liu S (2019) Chap. 7 - Detection of River Water Quality. In: Li D, Liu S (ed) Water Quality Monitoring and Management: Basis, Technology and Case Studies. Academic Press, London. https://doi.org/10.1016/B978-0-12-811330-1.00007-7 76. Wilhem FM (2009) Pollution of Aquatic Ecosystems I. In: Likens GE (Ed) Encyclopedia of Inland Waters. Academic Press. https://doi.org/10.1016/B978-012370626-3.00222-2 77. Serajuddin MD, Chowdhur AI, Haque MD, Haque E (2019) Using Turbidity to Determine Total Suspended Solids in an Urban Stream: A Case Study. Proceedings of the 2nd International Conference on Water and Environmental Engineering, Dhaka, 19–22 January 2019, 148–154. 78. Shen LQ, Amatulli G, Sethi T, Raymond P, Domisch S (2020) Estimating nitrogen and phosphorus concentrations in streams and rivers, within a machine learning framework. Sci Data. https://doi.org/10.1038/s41597-020-0478-7 79. Abu-Hmeidan HY, Williams GP, Miller AW (2018) Characterizing Total Phosphorus in Current and Geologic Utah Lake Sediments: Implications for Water Quality Management Issues. Hydrology. https://doi.org/10.3390/hydrology5010008 80. Kakade A, Salama E-S, Han H, Zheng Y, Kulshrestha S, Jalalah M, Harraz FA, Alsareii SA, Li X (2021) World eutrophic pollution of lake and river: Biotreatment potential and future perspectives. Environ Tech Innov. https://doi.org/10.1016/j.eti.2021.101604 81. Wen X, Chen F, Lin Y, Zhu H, Yuan F, Kuang D, Jia Z, Yuan Z (2020) Microbial Indicators and Their Use for Monitoring Drinking Water Quality - A Review. Sustainability. https://doi.org/10.3390/su12062249 82. Ma C-Y, Ihara M, Liu S, Sugie Y, Tanaka H (2022) Tracking the source of antibiotic-resistant Escherichia coli in the aquatic environment in Shiga, Japan, through whole-genome sequencing. Environ Adv. https://doi.org/10.1016/j.envadv.2022.100185 83. Silva LCR, Sternberg L, Haridasan M, Hoffmann WA, Miralles-Wilhem F, Franco AC (2008) Expansion of gallery forests into central Brazilian savannas. Glob Chang Biol. https://doi.org/10.1111/j.1365-2486.2008.01637.x 84. Nunes RV, Frizzas MR, Vaz-de-Mello FZ (2012) Scarabaeinae (Coleoptera: Scarabaeidae) of a rupestrian field at Cafuringa, Distrito Federal, Brazil: commented list of species. Biota Neotrop. https://doi.org/10.1590/S1676-06032012000400013 85. Ferreira MC, Rodrigues SB, Vieira DLM (2017) Regeneration through resprouting after clear-cutting and topsoil stripping in a tropical dry forest in Central Brazil. Rev Árvore. https://doi.org/10.1590/1806-90882017000200018 86. CODEPLAN (2018) Pesquisa Distrital por Amostra de Domicílios (PDAD). Brasília, DF: CODEPLAN. https://www.codeplan.df.gov.br/wp-content/uploads/2020/06/Destaques_PDAD_revisado.pdf. 87. GDF (2017) PDSB - Plano Distrital de Saneamento Básico. Brasília, DF: SEMA. 484 p. https://www.sema.df.gov.br/wp-conteudo/uploads/2017/09/Relatorio_S%C3%ADntese.pdf 88. Lima JEFW, Antonini JCA, Borges MM, Andrade SML, Lobato BR, Sousa LLP, Rocha FEC, Carvalho AVV (2017) Demandas relacionadas às Culturas irrigadas no DF e propostas para pesquisa, extensão e política pública. In: Andrade SML, Rocha FEC, Lobato BR. Expedição Safra Brasília – 2016: Soja, milho safrinha e culturas irrigadas: diagnóstico e prospecção de demandas para pesquisa, extensão rural e política pública. SEAGRI, EMATER-DF, CEASA, Embrapa Cerrados, Brasília. https://www.infoteca.cnptia.embrapa.br/infoteca/bitstream/doc/1070037/1/LivroExpedicaoSafraBrasilia2016versaofinal.pdf 89. CONAB (2022) Acompanhamento da safra brasileira de grãos – 9° levantamento, v. 1 (2013–2022) – Brasília: MAPA, CONAB. Available at: https://www.conab.gov.br/info-agro/safras/graos/boletim-da-safra-de-graos/item/download/42837_ 526b4c0d6f83ae8e34bb846683666d92. 90. EMATER-DF (2022) Informações Agropecuárias do Distrito Federal 202. Brasília: GDF, SEAGRI, EMATER-DF. http://emater.df.gov.br/wp-content/uploads/2018/06/Relatorio_Atividades_ Agropecuarias____2021___DF.pdf 91. CODEPLAN (2022) Índice de Desempenho Econômico do Distrito Federal Idecon/DF – 1° Trimestre de 2022. https://www.codeplan.df.gov.br/wp-content/uploads/2018/02/Idecon-DF_1o-Tri_2022.pdf. 92. Borghetti JR, Silva WLC, Nocko HR, Loyola LN, Chianca GK (2017) Agricultura Irrigada Sustentável no Brasil: Identificação de Áreas Prioritárias. FAO: Brasília. https://www.fao.org/3/i7251o/i7251o.pdf 93. Adasa (2020) Mapa de áreas irrigadas para fins agrícolas no Distrito Federal em 2020. Brasília, DF: Adasa. http://gis.Adasa.df.gov.br/portal/home/ 94. ANA (2016) Levantamento da Agricultura Irrigada por Pivôs Centrais no Brasil − 2014: relatório síntese / Agência Nacional de Águas - Brasília: ANA. https://www.ana.gov.br/arquivos/institucional/sge/CEDOC/Catalogo/2016/LevantamentodaAgriculturaIrrigadaporPivosCentrais.pdf. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterialDWManuscript.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4329941","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":312229667,"identity":"f13f3907-c2dd-4ae6-98a4-ee34cbb85b04","order_by":0,"name":"Daphne H. F. Muniz","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYDCCAwxsUBZjA8MHBoYEGJs4LYwzSNTCwMDMQ4wWvuNnjz0uqGCINrjd3PzZpmJbnnn72YMfGHfcw6lF8kxeuvGMMwy5G+4cbDDOOXO7WOZMXrIE45linFoMDuSYSfO2AbXcSGxIzm27nTiDIcdAgrEtAbeW82+AWv5BtBy2/AfUwv/G+AdeLTdAtjSAtTQ2MzYAtUjkmOG1RfLGuzRpnmMSuTNvJDYz9hy7XSwh8S7NIvEMbi1853OPSfPU2OT23Uh//OFHze08Cf7cwzc+7sCthYGBB0RIoIng0wDVQkBkFIyCUTAKRjYAAEHZXT4QUp+1AAAAAElFTkSuQmCC","orcid":"","institution":"Brazilian Agricultural Research Corporation","correspondingAuthor":true,"prefix":"","firstName":"Daphne","middleName":"H. F.","lastName":"Muniz","suffix":""},{"id":312229668,"identity":"411b92cf-b716-4d02-ae11-6eb90cefc355","order_by":1,"name":"Juaci V. Malaquias","email":"","orcid":"","institution":"Brazilian Agricultural Research Corporation","correspondingAuthor":false,"prefix":"","firstName":"Juaci","middleName":"V.","lastName":"Malaquias","suffix":""},{"id":312229669,"identity":"ba26cebb-9dd0-4b72-9b6b-157fed3b5657","order_by":2,"name":"Eduardo C. Oliveira-Filho","email":"","orcid":"","institution":"Brazilian Agricultural Research Corporation","correspondingAuthor":false,"prefix":"","firstName":"Eduardo","middleName":"C.","lastName":"Oliveira-Filho","suffix":""}],"badges":[],"createdAt":"2024-04-26 13:44:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4329941/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4329941/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58173192,"identity":"494261ec-c9bd-4878-a3ac-7b79b342a955","added_by":"auto","created_at":"2024-06-12 04:00:25","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":411884,"visible":true,"origin":"","legend":"\u003cp\u003eFederal District map with of sampling points locations.\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4329941/v1/f8d97a918d836772746937d0.jpeg"},{"id":58171214,"identity":"a1d34472-2f16-4eaa-8a23-c8ba3bd15cb0","added_by":"auto","created_at":"2024-06-12 03:44:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":134369,"visible":true,"origin":"","legend":"\u003cp\u003e(2a) Optimal number of clusters by \u003cem\u003eGap statistic\u003c/em\u003e method. (2b) Dendrogram showing similarities between the sample points.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4329941/v1/0757b175ad236e025a532f2f.png"},{"id":58171216,"identity":"b96dfa33-9ba6-4552-a58a-27142f8e42fd","added_by":"auto","created_at":"2024-06-12 03:44:25","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":41768,"visible":true,"origin":"","legend":"\u003cp\u003eGraphs of the eigenvalues ​​and broken-stick model for each main component.\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4329941/v1/6b56943f1d9497210601013e.jpeg"},{"id":58171215,"identity":"023d63c7-eec0-416a-ae32-52e369cab976","added_by":"auto","created_at":"2024-06-12 03:44:25","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":320857,"visible":true,"origin":"","legend":"\u003cp\u003eLoads of the first two principal components for the three data matrices.\u003c/p\u003e","description":"","filename":"image4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4329941/v1/d6b077ab110db70526f91a04.jpeg"},{"id":58172012,"identity":"fcaaa36a-68b3-4469-84ec-a5f1aa19b409","added_by":"auto","created_at":"2024-06-12 03:52:25","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":143183,"visible":true,"origin":"","legend":"\u003cp\u003eWQI\u003csub\u003eCETESB\u003c/sub\u003e boxplot from the 18 collection points\u003c/p\u003e","description":"","filename":"image5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4329941/v1/8338ce2ad566dc690d34a516.jpeg"},{"id":58171217,"identity":"0b756828-0a68-445d-a8a3-6e494ce2d5fb","added_by":"auto","created_at":"2024-06-12 03:44:25","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":117116,"visible":true,"origin":"","legend":"\u003cp\u003eIWQI\u003csub\u003eFD\u003c/sub\u003e boxplot of collection points.\u003c/p\u003e","description":"","filename":"image6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4329941/v1/2bc34e5ccb31e88e00a9c5aa.jpeg"},{"id":75111801,"identity":"415e6851-30e2-4865-b13e-a324b3b69b73","added_by":"auto","created_at":"2025-01-30 15:24:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2187353,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4329941/v1/04873ce0-b200-4d3a-b79f-fa994d8b8493.pdf"},{"id":58171220,"identity":"c69556dc-8320-4175-b716-5c04e4d823e5","added_by":"auto","created_at":"2024-06-12 03:44:25","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":100938,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialDWManuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-4329941/v1/4e39cba8a1175049dbfe614b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Surface water quality assessment in the Federal District, Brazil: application of multivariate statistical analysis and water quality indices for human consumption and irrigation","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eUniversal access to water in desirable quantity and quality is one of the great challenges for societies in the 21st century [1]. As a limited resource and endowed with socioeconomic and environmental value, water has received increasing attention in the global sustainability agenda, as a consequence of increasing pressure from factors such as climate change, models of economic development and accelerated population growth [2].\u003c/p\u003e \u003cp\u003eAll over the world, large metropolises have faced serious water crises in the last decade. By 2050 the number of large cities that will be exposed to water scarcity is projected to increase from 193 to 284, including 10 to 20 megacities, among them cities in Brazil [3]. This is a country with continental dimensions and an abundance of water resources; about 20% of all the global continental water that flows to the oceans is generated in Brazilian territory and approximately 85% of the country's freshwater needs are supplied by surface waters, such as rivers and lakes [4].\u003c/p\u003e \u003cp\u003eProblems related to water in Brazil stem from contrasting weaknesses, where regions have very different climatic and socio-environmental contexts [5]. While certain areas, like the Northeastern region, have a historical record of droughts, others face water system capacity challenges, driven by population pressures, resulting in demand surpassing available resources, such as the Southeastern region, or due to the well-marked seasonality, with long periods of drought, as seen in the Midwestern region and illustrated in the present article [6,7].\u003c/p\u003e \u003cp\u003eThe Federal District (FD), located in the Midwestern region of Brazil, has the smallest territory among the Brazilian states and is located in the Cerrado Biome (Brazilian savanna), harboring a large number of springs and forming an important natural watershed. Ranking third lowest among Federation Units, its surface water availability per capita per year is notably constrained [8]. Added to this, the FD is home to the third most populous city in the country with approximately three million inhabitants. The metropolitan region of the city of Bras\u0026iacute;lia, the federal capital of Brazil, has had the fastest growth of all large Brazilian cities, with a 16.9% increase in population in the last 11 years [9,10]. The FD also experiences a thriving agricultural sector, and irrigated agriculture stands out as the segment that has shown the most significant increase in demand for water consumption in the region in recent years [11].\u003c/p\u003e \u003cp\u003eBetween 2016 and 2018, the FD faced a serious water crisis, due to irregular rainfall in subsequent years and accelerated population growth that culminated in an increase in water consumption (with greater demands for public supply and irrigation), changes in land use, water use conflicts, among other factors. This experience revealed concerns about weaknesses in the FD's Integrated Water Resources Management (IWRM) system, which includes improvements in management tools, strategic management, planning and expansion of knowledge, including hydrological and water quality monitoring [12,13].\u003c/p\u003e \u003cp\u003eAn important pillar of IWRM, water quality monitoring is a tool that has gained significance over the last few years. As crucial as quantity, the monitoring of water quality offers practical evidence to underpin decisions related to social, economic, health, and environmental matters. Assessments based on monitoring data help policymakers and water resource managers to measure the effectiveness of water policies [14,15].\u003c/p\u003e \u003cp\u003eWater quality management requires the collection and analysis of large data sets that can be difficult to synthesize and evaluate. Consequently, a range of tools has been developed to evaluate data on the quality of water resources [16]. An important instrument in the assessment of water quality, Multivariate Statistical Analysis (MSA) has been increasingly used in many areas of science, due to the increasingly complex nature of research questions [17,18].\u003c/p\u003e \u003cp\u003eTechniques such as Principal Component Analysis (PCA), Factor Analysis (FA), Hierarchical Cluster Analysis (HCA) and Discriminant Analysis (DA) have been widely used as tools in the assessment of water quality of rivers [19,20], groundwater [21,22], lakes [23,24], reservoirs [25,26] and drinking water [27,28]. These works employ MSA with different objectives, such as: analyzing the relationships between water quality, land use and land cover; assessing similarities and differences between periods and sampling points; recognizing variables responsible for spatial and temporal trends in water quality and also selecting variables to compose Water Quality Indexes (WQIs), reducing bias and the need for time and expenditure to monitor a large number of variables.\u003c/p\u003e \u003cp\u003eThe Water Quality Index (WQI) is a successful method for portraying water quality conditions and evaluating different water quality sets [29], and serves as another widely utilized tool in evaluating water quality. It has proven to be highly efficient and plays a significant role in water resource management, in addition to expressing water quality in a simple and logical way for the general public [30,31]. The WQI seeks to provide a unified assessment of water quality for a source by utilizing a system that condenses various variables and their concentrations found in a sample, into a single value. This allows for the comparison of quality in different samples using their respective index values [32,16]. The value obtained can be used for several purposes, including: allocation of financial resources in water resource management, classification of allocations, application of norms and legislation, analysis of trends (spatial and temporal), public information and scientific research to assess the health of water bodies [33,31].\u003c/p\u003e \u003cp\u003eIn Brazil, the US National Sanitation Foundation index (WQI-NSF) was adapted and is the primary index utilized in both national and state water quality assessment programs. The quality parameters comprising this index are closely associated with water quality for public supply purposes [34]. In the context of the FD, this WQI serves as the primary indicator for monitoring surface water quality in lotic environments (rivers and streams) with a monitoring network that does not include all hydrographic units [35].\u003c/p\u003e \u003cp\u003eWQIs have found extensive application in Brazil for assessing water quality in diverse water bodies for various purposes [36\u0026ndash;38]. Despite being a widely used tool around the world [30], WQIs present some problems, among the most significant of which is the allocation applied to water. The water resource can be used for different purposes, considering its multiple uses [39]. In this context, aiming to assess the quality of water for irrigation purposes, Muniz et al. [11] proposed a water quality index for the context of the FD (IWQI\u003csub\u003eFD\u003c/sub\u003e), considering the regional attributes of water resources and aiding in the evaluation of issues related to soil and irrigated crops.\u003c/p\u003e \u003cp\u003eIn consideration of the aforementioned, the aim of this article is to assess the quality of surface water of hydrographic units, under different land uses, through the application of tools such as Multivariate Statistical Analysis and Water Quality Indices for public supply and irrigation, aiming to support the IWRM in the Federal District.\u003c/p\u003e"},{"header":"2. Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Study area and sampling points\u003c/h2\u003e\n \u003cp\u003eThe FD is located in the Midwestern region of Brazil. It has a climate characterized by strong seasonality and two distinct hydrological periods: dry (from May to September) and rainy (from October to April) with annual rainfall between 1500 and 1700 mm. The natural vegetation covers different types of Cerrado (Brazilian savanna) and features gallery forests lining the entire stretch of the rivers. The main land use is urban, followed by areas of extensive cultivation (soybean and corn), vegetables and fruit, mainly with a focus on local supply [40,11].\u003c/p\u003e\n \u003cp\u003eThe sampling points were defined based on the objective of the study, and preliminary studies, land occupation, and access to the points were considered. Eighteen points (P1 to P18) were defined in strategic locations. The collection points are located in nine hydrographic units (HU) in the FD. For each HU, two sampling points were selected, eight points located in river springs and eight points in places with anthropic influence (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eIn defining the points, the land use or land cover of the hydrographic units was taken into account, and these were classified as HU-RURAL (percentage of agropastoral area including irrigation pivots), HU-URBAN (percentage of built-up area) or HU-NATURAL (percentage of natural formation), according to the Land Cover Mapping of the Federal District, by the Federal District Planning Company [41]. Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the description of the sampling points.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescription of sampling points.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePoint\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRiver\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLand Use/Cover\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWatershed\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHydrographic Unit (HU)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%AA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%BA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%NF\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eV\u0026aacute;rzea do Burac\u0026atilde;o\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePreto\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eHU 35 - Upper Jardim River\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e80.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e17.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJardim\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBuriti Vermelho\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePreto\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eHU 3 - Upper Preto River\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e88.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e11.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBuriti Vermelho\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChapadinha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eS\u0026atilde;o Bartolomeu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eHU 4 - Upper S\u0026atilde;o Bartolomeu River\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e52.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e34.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSarandi\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCabeceira Comprida\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eDescoberto\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eHU 26 \u0026ndash; Rodeador River\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e47.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e30.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRodeador\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSobradinho\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eS\u0026atilde;o Bartolomeu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eHU 30 \u0026ndash; Sobradinho River\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e40.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e44.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSobradinho\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTamandu\u0026aacute;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCorumb\u0026aacute;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eHU 25 - Ponte Alta River\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e24.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e18.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e56.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePonte Alta\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTaquara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNatural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eParano\u0026aacute;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eHU 17 \u0026ndash; Gama River\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e30.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e66.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTaquara\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOuro\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNatural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMaranh\u0026atilde;o\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eHU 12 \u0026ndash; Palma River\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e87.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOuro\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCovancas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNatural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMaranh\u0026atilde;o\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eHU 15 \u0026ndash; Contagem River\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e87.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eContagem\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e%AA\u0026thinsp;=\u0026thinsp;percentage of HU agropastoral area (including irrigation pivots) / % BA\u0026thinsp;=\u0026thinsp;percentage of HU built-up area / % NF\u0026thinsp;=\u0026thinsp;percentage of natural formation of the HU.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. Sampling and Analytical Methods\u003c/h2\u003e\n \u003cp\u003eThe collections occurred bimonthly between December 2017 and October 2019, totaling 12 campaigns. In each sample, analyses of 29 physical, chemical and microbiological variables of water quality were performed. Surface water samples were collected in 500 mL polyethylene bottles. To collect samples to determine the biochemical oxygen demand, 300 mL Winkler flasks were used. For determination of \u003cem\u003eEscherichia coli\u003c/em\u003e, samples were collected in sterile flasks containing sodium thiosulfate 0.1 mg per 100mL of sample.\u003c/p\u003e\n \u003cp\u003eThe variables dissolved oxygen, water temperature, total dissolved solids, electrical conductivity and pH were determined in the field, on the days of collection, using a portable multiparameter meter model HQ40d (Hach, USA). Turbidity was measured using a portable turbidimeter model 2100P (Hach, USA), and apparent color was measured using a CheckerHC color meter (Hanna, USA). To ensure quality control in sampling and measurements, field blanks, equipment and bottle blanks were used. All reagents and standards used in equipment calibration were analytical grade. Calibration coefficients for all methods were maintained at a level greater than 0.999.\u003c/p\u003e\n \u003cp\u003eThe collection, preservation and analysis procedures followed the recommendations of the International Organization for Standardization (ISO), American Society for Testing and Materials (ASTM) and Standard Methods for the Examination of Water and Wastewater (SMEWW) [42\u0026ndash;44]. A summary of the analytical methods and methodologies can be seen in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eVariables, abbreviations, units, methods and analytical methodologies for 29 physical, chemical and microbiological water quality variables analyzed.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAbbreviation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnit\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMethod\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMethodology\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAmmonium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIon Chromatography\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eISO\u0026nbsp;14911:1998 [42]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eApparent Color\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOLOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePCU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpectrophotometric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPHA, 2018, 2120 C [44]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBicarbonate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlkalinity Calculation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPHA, 2018, 2320 B [44]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBiochemical Oxygen Demand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBOD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI5-day Incubation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPHA, 2018, 5210 B [44]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBromide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBr\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIon Chromatography\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPHA, 2018, 4110 B [44]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCalcium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCa\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIon Chromatography\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eISO\u0026nbsp;14911:1998 [42]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChloride\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCl\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIon Chromatography\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPHA, 2018, 4110 B [44]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDissolved Oxygen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L O\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElectrometric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eISO 17289:2014 [42]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElectrical Conductivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026micro;S/cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElectrometric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPHA, 2018, 2510 B [44]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eECOLI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNMP/100 mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnzyme Substrate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPHA, 2018, 9223 B [44]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFluoride\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIon Chromatography\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPHA, 2018, 4110 B [44]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMagnesium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMg\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIon Chromatography\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eISO\u0026nbsp;14911:1998 [42]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNitrate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIon Chromatography\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPHA, 2018, 4110 B [44]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNitrite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIon Chromatography\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPHA, 2018, 4110 B [44]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElectrometric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPHA, 2018, 4500H\u003csup\u003e+\u003c/sup\u003e B [44]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhosphate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIon Chromatography\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPHA, 2018, 4110 B [44]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePotassium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eK\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIon Chromatography\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eISO\u0026nbsp;14911:1998 [42]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSodium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNa\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIon Chromatography\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eISO\u0026nbsp;14911:1998 [42]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSodium Adsorption Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emeq/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCalculation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSuarez et al., 2008 [45]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSulfate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIon Chromatography\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPHA, 2018, 4110 B [44]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal Alkalinity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L CaCO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTitrimetric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPHA, 2018, 2320 B[44]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal Carbon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCombustion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPHA, 2018, 5310 B [44]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal Dissolved Solids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElectrometric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPHA, 2018, 2510 A [44]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal Hardness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L CaCO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTitrimetric EDTA-Na\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPHA, 2018, 2340 B [44]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal Nitrogen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCombustion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASTM D8083\u0026ndash;2016 [43]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal Phosphorus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAscorbic Acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPHA, 2018, 4500P E [44]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal Residue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGravimetric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPHA, 2018, 2540 B [44]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTurbidity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTURB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNTU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTurbidimetric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPHA, 2018, 2130 B [44]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWater Temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTEMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElectrometric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPHA, 2018, 2550 [44]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3. Multivariate Statistical Analysis\u003c/h2\u003e\n \u003cp\u003eHierarchical cluster analysis (HCA) is an common approach, widely used in water quality studies, and provides intuitive similarity relationships between any sample and the entire data set, and is typically illustrated by a dendrogram (tree diagram) [46,47]. In this work, the HCA was performed on the standardized data matrix to classify the monitoring sites into different groups. The HCA was obtained using the Ward method with Euclidean distances as a measure of similarity [19,48].\u003c/p\u003e\n \u003cp\u003ePrincipal Component Analysis (PCA) is a multivariate analysis technique designed to minimize a matrix containing numerous interrelated variables, keeping the variability present in the data matrix as much as possible [49]. It converts the original variables into new unrelated variables (axes), called Principal Components (PC) [50]. PCs generated by PCA are often not readily interpreted. This purpose can be achieved by rotating the axis defined in the PCA, according to established methods, and building new variables (varifactors), through Exploratory Factor Analysis (EFA) [18]. The use of EFA after PCA aims to reduce the contribution of less significant variables and further simplify the data structure taken from PCA [51].\u003c/p\u003e\n \u003cp\u003eBefore PCA, measurements of sampling adequacy were performed using the \u003cem\u003eKaiser-Meyer-Olkin\u003c/em\u003e (KMO) index and Bartlett\u0026apos;s sphericity test (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Values of KMO greater than 0.5 up to 1.0 are deemed suitable for PCA utilization [52]. In this study, the broken-stick model was used to select the main components to be interpreted and the varimax rotation in the EFA. PCA was applied in order to extract important information related to the most significant variables, for the three data matrices (rural, urban and natural) separately. All statistical treatments of the data were performed with the aid of R software version 3.6.3 [53].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4. Water Quality Indices (WQI)\u003c/h2\u003e\n \u003cp\u003eTo evaluate the water\u0026apos;s suitability for supply purposes, a nationwide index (WQI\u003csub\u003eCETESB\u003c/sub\u003e) was applied, while for the assessment of water quality for irrigation, a regional index (IWQI\u003csub\u003eFD\u003c/sub\u003e) was applied. The WQI\u003csub\u003eCETESB\u003c/sub\u003e was adapted in 1977 by the S\u0026atilde;o Paulo State Environmental Sanitation Technology Company (CETESB) from the NSF index, USA.\u003c/p\u003e\n \u003cp\u003eIn the subsequent decades, additional Brazilian states embraced the WQI\u003csub\u003eCETESB\u003c/sub\u003e, which today is the water quality index most used in the country [34].\u003c/p\u003e\n \u003cp\u003eThe objective of WQI\u003csub\u003eCETESB\u003c/sub\u003e is to evaluate the quality of raw water, aiming for it to be used for public supply after conventional treatment, and its variables mainly reflect the pollution caused by the release of domestic sewage [32, 16]. This index is calculated by the weighted product of nine variables that compose it, according to Eq. 1:\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\text{WQI}\\text{CETESB}\\text{ = }\\prod _{\\text{i}\\text{ =1}}^{n}{qi}^{wi}\\text{ (1)}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ehere, \u003cem\u003eqi\u003c/em\u003e represents the quality of the \u003cem\u003ei\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e parameter, ranging from 0 to 100, derived from the respective average quality variation curve based on its concentration or measurement. Meanwhile, \u003cem\u003ewi\u003c/em\u003e denotes the weight assigned to the \u003cem\u003ei\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e parameter, a value between 0 and 1, determined by its significance in shaping the overall quality. Here, \u003cem\u003en\u003c/em\u003e stands for the number of variables involved in computing the WQI\u003csub\u003eCETESB\u003c/sub\u003e [32].\u003c/p\u003e\n \u003cp\u003eThe Irrigation Water Quality Index (IWQI\u003csub\u003eFD\u003c/sub\u003e) was proposed with the objective of using water for irrigation purposes in the FD. This WQI was adapted from Meireles et al. [54] and the selection of variables and weights was performed using multivariate statistical methods (PCA) [11]. This index covers restrictions on the use of water for plants and soil as a measure of quality assessment and is calculated by the sum of the individual quality (\u003cem\u003eqi\u003c/em\u003e) of each variable weighted by the weight of this variable (\u003cem\u003ewi\u003c/em\u003e) in the assessment of water quality for irrigation according to Eq.\u0026nbsp;2:\u003c/p\u003e\n \u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$$\\text{} \\text{IWQI}\\text{FD}\\text{ = }\\sum _{\\text{i}\\text{ =1}}^{\\text{n}}\\text{q}\\text{i}\\text{w}\\text{i}\\text{ (2)}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e describes the ranges, classes, variables and their respective weights for the WQI\u003csub\u003eCETESB\u003c/sub\u003e and IWQI\u003csub\u003eFD\u003c/sub\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e Ranges, classes, colors, variables and weights of the two indices.\u003c/p\u003e\n \u003cp\u003e\u003cimg 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aVg8h5k5pGdcMGu6qUR9cuaZZxqoTgPngnj55ZdTP5xxxhmpT/1cRbpTp7fRDTfccECaC89YsLp9+/bUv+CCC1I/ROEf7bF5/iN/3oAT3fxBX6bxma6uzTrT82dI8i7m5zd51oRlMJ5nHZpOqOcKrkjGiSAVaSfdpiP2A+lHn31EugZ+L9rosz/4nO9XDr7YT6T/XN0HDz/8cOpffvnlqd+Ewp8AL8RxkefRXp7RCPlxEMdUvn9YFvuZ/cK+Yh5+a5jPlcfJWl1AyrrwTEQ1r+X5JrY7D7g6rXc+LZfnNTrm4bt1WEakeTVPdxIvaeJ79ON5tZnEyQUVHxVFNW/sKp/ReeaZZ1I/sN3cda2e6HHskdasP9tP+RjbTj+eD8uPYbA80jPSNu9Ih6p8/9KP3+D3rrvuuvT5lFNOSdPneplc9cEHH6R+3UlJpzxc3T9NaRxYVuzrakeeD53yxHzFCSbHGxfiup2ws4+6YX9FeUPferQZaTHoOQzfzcspPveaTuyPTnVx9fiLOoThURMXMTlvqTvWafly6623lkMTIn0ibcmPeR4lnSP9mDfyM3k3f+473x/dlpnLl8l8LLcXB6p8u/rqq1P/kUceSf3w1VdfpT4XK6NsD9Tjl1xySTn0k07nHU1pi17rgBDlFF2e91lG3GiJ78+asjnwwMYL8UntnGknPl75pnF1z68xP22xGU8X89KGG3w/nn+jvTfPJOTj8nbz1bbp9Blm+Tm+G+3C+X7dsuYatpdtJL37EWmYP8cR6Rz7KIbztvakeXwn0jgfZv58P9FeP4bnCtKH7aTrRzyfwDMkcVzE8yTVZwMYV92nHEOkd+Tn2PeMA8uL/Rr7hY7fHPYxwDL5neq+jd/MsV7kA9YfrAvfZxw6rfe//vWv/dPy9IhxkTdjOH4DMY40potlxu/kquUb2Cd1+4qyaqbVbTPrwHpHWZrvU9at+kxkPDddzS/MC8bHuHyf5fsrtj1+M9/2PM1JK4arvwGWzTimz2XV7aSciHGkZ5SrIdKvKQ/n+6dbGsd3Yz/GMca4PJ90yxPzVeTv/DjoRTXdEfvcerQ3kQf7PYeJejjSNdKRcXlZWLePKNPYB+T7KOP4zHxRF7O8WDfyB5+Zh98YRbF9eTqA9KlLe8aRRtU6IPI134v0ZpnUDcxP2jNP/F6eb7stE8zDcpnGcmM5dPl+ZVp1+Qe6fGPdq+tEfop1zdcj8mtVt/OOprTlM+Ni/7J80ppxkR6IdYx9xvdiP9bNN9uGFqzmHRvYdOIWmS1EQuaJG8tkhwSWV52Pgpp5c5HJA9+r7ngyLvPMVkY9EOIgqKZPN9WMDQ6SCCBCdR6G8wKDzB3TY5nV/Znvy7kgtpOuH+TDavqirkBhON+ncVzkhTqqBRkYZpkzKdYnP8bBMZjvf7BdbHecVCDyba7TejMtT484tquVXD5P7Kf8d8E8jM/zcYwLrDPD+bbwmXEznbahmi9Ic7Yl0j4/LuvWiXH5NqJaKdWVA1HB58ct6VydL74bJ3eB3833Q+zrfHlzUWxn3kW+rx4T6CcPd0vjan2Iuv3YS56Yj+L4z/N3L2L/5N+zHu1PHDd5fu4Febla/0S5zbRQt496rYvju3PhHDLSprrdbFtd3urnHL6u7Ij9ms/f6zLZD3n+j/n4fqhb/oEu36JuzvNLrDPrRtrHdlE+0+ViH+XbzmfG5Xm6btt7rQNin1XLl6b5ZtvQmgGPL6sYr1zTw8TjCTvlxUvho48+SreV45b1eeedV06ZKm+69vOf/7z89BNuUX/xxRfl0ITxHVd+mvDaa6+l2+z5be9zzjknTRtmE8i5jIe8eXkLTSaiGULVeEVcXHnllWnfMh9Npni4HDR3HT8wU9ONaLJBs5y8Gex8RprVHS/xQp3qsw458jeqz8euWbMm9Z9++unUD3X7bpjYrxyDNBWJ44vto/lItSkqeYR8xbpG0yGeUarT63rz8hPKItKD3yXPNT0XedRRR5WfJtx0002p/5///Cf167z77rupf9hhh+0vT/gMyr3ZUM0Xjz32WGrORdpT/j755JNpPNs/XlGlz4HmRqwnZWBeJsZzk3v37k39OvESjmi+hLpyOVRf2jHTea/txiv8YvxEIJWFNC39xS9+MeWYQD95uFsaU0cir+v43dx08oR6Zz068yIvH3fcceWYCaQjZSHTqk0kc6R7P3XxL3/5y/LT6CJtxoOoSY+XUF7w2E5d3urnHJ5l96LXZXLM5GVmHBOdnvtvQ/kWdXMc1xy/PAsM8hZpH+cWNBeuvmNiOucdvdQBuW5x14Ey1GdW40F3Ep6CgZ1SxThOStlR//M//1M8++yz5ZT+0Z6ewj/abbMzOEmmwA/fffddet6Eyr/a8Wa/ueroo49O/WowPyjasJ944onp/5NRiFVREbPv77777uJXv/pVmj/f/6T1xRdfnCpiCpwoFOeSI488svzU34UQjpc61WCqDvm7TjV4nU3xzOqjjz6a+uSJ6nMvgYqEky++c+655xYbNmwopwyOioA89ve//z0tj+O/F738jzLyP+rKE7rZEBUZQSn5jIsDUcFQKVJ5kQZcqIjnZQL/Tw0ETnXr3+nEl2WTlgTHkb95MQeVcBtf4NRG7CdeqEGaURaS/+sMmoerOO7IHxyLlMd0PA/FCV7s6+nkibnu+OOPT/0vv/wy9afLerR3g5zDRF6uUw1g60ynLh5lUT+T70B50fQyxGGew4fpLLPugl+uLeVbXBAg8HzjjTf2P5MaL1qiXuVCCmlQ3abpnHf0UgeMgqG/YIkTGnYKVy14a1SOROLElACTAoh5B/0ntuAKNDuWyp8rDSQ+QXL+uueDDz64sbCbywU9aUMG7XYlsRcUIpwY8+r4/F+xVLE/SWsqWx7Yv+KKK8opEwUK3/3vf/9bXHvttelfAVARzyVsI3kfne6G1vnwww/LT1M1vVY+13TSS/6fbeQD8h5vreUY4xityzOsM/+WgJewVf8lxKD4Pa6gvvLKKykfDhK0H3744eWnqWJf1F2MmK3yJPIZxzbH5vnnn19O+enFXvw7FMrbarovXLgw9fO7o4Ft6lZWEDhR4VLWUua+9NJL6V9H9HoFXZMv6l544YVpP+WGkYcD+4Ur99y54Ep8XI3PL9RON0/MZXGxh31S3U/9sh7tz3TOYT755JPy01SR35tMty4eReTFqFO4+UN5QT6sGvY5PIaxTPJJk7aUb1GWEJSSvlGuk/ZcjCRm+t3vfpcu2ldN57yjlzpgFAwtWM0Lcv5/Eycz3OWkgA6ff/552iGcoA4DBxWZlJ3A1QUyex6ogt+Kk7ocJ8pxtWKuiv9hyAHQqaIls9cdBIGgg/1ZvdqTo+lGoLChoo2maxQG0WwpKlsKxnhj9FzCVSzumnCS0RRAgjSJQpK04MS1Wuh89tlnXe9axRt4o3VBiN++8cYbU3+2cSIFTqaa3oxM/mC7TzrppHLM9HE3kTQb5ASft7Py3bjSWSfWtdpyhM///ve/y6GZFxUaJ1b5SQVXSikTKWdvueWWcuxPSBe2kfKwmj+58trpSivbSHDFyTZlLWVuXumqsx9++KH89NNFXeomTtTyvDSdPFzFPuZibtSRdJQzeVk+nTwx13GiR0styql77rmnHDsV+69aBldZj/av33OYKP/yx1ACASxBQafjajp18aiLepq3tHP3vs6wz+ExnWWSJ6gD43yjTlvKN8oSjn+29ZprrinHTojzOPJe3UX76Zx39FIHjITxFR/YeEWbHgxmMdUHgnk5REwbT+A0HC/jGN9h6WFnHt5lGuN4+UA8AM10xuUvmIgHgnmAOIwXPOk36EfHdDrWDTwsHMujzzR+i3nng3hAerwAT58jXUB6jxfOkx48j3RmvwTSmI7vMy/pxzykIePAMN8lven4Pr8JHs7m+/E7rAPT+O25KPI+Hfktz8d8Jv3yfBzHER1phUizSF/E8UPa5Q/BR/6OY5DlMS5PX8YxD8vM88BMYf34rU7HWZ7X2F7ShPkZx3YzrtN6x7Q8PaI8YVl8n7RmOt8n//EdxjMPvxXLjHXJjwWWyXcZn+9D0jV+N/Yl6V1dv5nG79cdQ6wP65fnkRxpy3TShO9HuufbHvPk5UCkG9vK/HTx/Tyf1n030pLfjPXi95iPZfCdfBlzBdsaeTpPDzAtjl36kf695OFIu25pHHkh9hdd5FmWHWKfdcoT8xlpEulImkT60iftmJ6XEdajwxN5k23lc17OkhZsf55P+cz8eZlMmpJ23fYR88d+iuMj0j32EWKd2HeRF+YCtodtbdqmOAeJ8op0iPKK/Mw4vhvlWr5fQhxLpD96WSZi3eJ77Cu+Q5eL78Z8iP3F9/n9A1W+xXrkeRikGevG9jaJdOM4iDKcbc+XVU1b9FIH5Pus7hhh3sB34zf4XN2WmTJwsMoGs8LVLhc7Jjo2ODY0Ml0UDiyPBMvnj+9Uf4thkKjsOLp8Oh3LjAOOPjuRcUwjM8e0+YA0ZvvzdIoMn2e0yNR5B/ZjpB3fiYzNMiJjV/cDaRzL5oBg/lgG/VjOXMW2kZ5RAMR2sx+igMiRVlHI0vG9vCDlOzEturygIT0jfdkPeeHS6bsziXUg7zRhvePYZp1ZJ7aZYban03rXTUOUCQyzbNI10iZfFz7n5QppnxfSiGnR5WnK5/gd9lX1u7OBdaj73TjeOyGdI2+yHXnasNx8u6O8BemUp1ve8b3qd+ma9mO+/9lHc01dWtBFHgb7L45bOtKjWx7uN43Zz3k5VJ0ndMoTmihzyP/5/iK9OI8hnUPd/gHpGd9lf8a+Ia3jOOZz7Hu6+V6PhijT8rThM9sf6ZOLtMrTMS8rm/YRWB7zx3iWk9fFnb476ki3bnUHac42k/96PYcP1bojpnVbJqp5gH6+bFSXz3BoQ/nGtjSlL+Pr8nKO7Y3tZ1vyPN2UtlHO5MdD3rHPq+P4blNaRv3E/pnNNFzAn/EVGUnc1udNotVmHTT/GM/8qQnkfG7CJEnDQnMimjrSNLGK5ok02Ru152DmOpp78bhLvB080HyMl2OB5qSSpLlnrtQBQ3/B0mxhBzQ9L0Xb8PHIf9LbWSVJg+N51bqXP+DXv/51cfrpp5dDagMu2vLMeN1zdjyvxIvEfvOb35RjJElzyVyqA0b2ziovInjwwQfTA8dXXXXV/reH8RILXsvMGzLzF49IkgZDpXfCCSekzw899NCkf+XAy0deffXV4rnnnhu9lzbMYdzt5q3C7DdetpX/z7y//e1v6WUedXfJJUmjby7VASPdDJi7q7w5Md6Wt3jx4hSg8kZW7q5KkoaDgJW3J/L/IXftmvhH5FEJemGwnXiD7J/+9Ke0zzgxARd4fURGkua+uVIHjHSwKkmSJEmam0b2mVVJkiRJ0txlsCpJkiRJah2DVUmSJElS6xisSpIkSZJax2BVkiRJktQ6U94GvGDBgvKTJEntNeyX2Vv/SZI0HMOqo/3XNZIkSZKk1rEZsCRJkiSpdQxWJUmSJEmtYzNgaQA/W1R+mIP2/lB+kCRpBlmXSurGO6uSJEmSpNYxWJUkSZIktY7BqiRJkiSpdQxWJUmSJEmtY7AqSZIkSWodg1VJkiRJUusYrEqSJEmSWsdgVZIkSZLUOgarkiRJkqTWMViVJEmSJLWOwaokSZIkqXUMViVJkiRJrWOwKkmSJElqHYNVSZIkSVLrDBysvvDCC8URRxxRLFiwIPU3btxYTvnJtm3b9s9z/fXXF19//XU5pTu+y3cOOeSQcsxkLIvp8fvMX/Xpp58WK1euTPPQZ3imxO906iKNOk1Dp2kg7ZctW7Z/+qWXXlq8//77aTz96a4Ly6hiXN28dVh23bx5xzrmy2R76vJHdVl8r5fl02nmdcpr7FOO0br8FJhG/o3vcCz3W1bMRYOUXaQZZQDpyXeqKCMjnasd31Pveqn/Qp7H6/ZLnVGr/4Yp1jnvqvJpdfPXdeyHvM6pdpRX1f1YV9fk89QtrxP2G9+vq78ZT7+Kcf2WkdVzhF63LTp+g9+0XBhtdfs21O3/uvyn2UEZHnVKL+dATM/LBT53K+PryoGRMTaAN998c4yvbt68OQ3v3LlzbPHixWNbt25Nw9ixY0caRx9r164dW7FiRfrcDd9hXn6jaRWZzjL37du3//fjt8B4xsU6sm4MM36msB6s74YNG8oxP2FcjGcdWC/Wh/nXrVuXxueWLl06dsIJJ0zZJsbxPbYntoXfXbVq1aTf7nVdwLowb77/msTv7Nq1qxxTj3VjG+r2eexfMB/7kWU25Y/YbrYJzB/7FWwL3w+sG7/dbR2n4/+3cO52/Yq8xn4JpH0cc0yry+PMH8donpfJB4yP/T3fkBaRLui17GJ+jpOmYymOs7qu27L1k17qvxB5nDKq1/w8qvXfMLG+bDvleN06RxkfaQC2le/k6QCmU2/l46MeC6RhHDvVsorvxTT2fRXTmRZp3YR1Zjl0+XLy366ue79lJPOwLNIm/w0+M45peXryuTqePvPHOvFb+XeGra4OmitdG8RxUZd32a9MY1/rwOG4z8twjvu6OjzHPDE/ffZhpzI+zpPzc/9RMlCwSkFfzdwU8HniUgDmhT4JSELWVehNWB6JWxUVWb5T2AH8ZmBHVteR5TF+JnXKDNXxFB7MT7rkgRXzse7VTBfpUa2gAvsl/41e14WMzryR8Tvhe8zbC9Y3zxO56nqxTLqm/ZMvpzpP3TpRQPeyPYOqq5jmSjcI0r8ur5GHyctMzyvL2Gd1FSii4J3JCw5tNd2yi3StO+4YV01PylLKDfWul/oPsR+byutuWF61XEOb679hYl0pA5pUt69TPca4fHxdnUF6Mq4uzTluWJfqb4L80Mt5Dd9lH1XrdUQ5WbeO/ZSR7OOmcrNpG/hONe8G9gHrUHexcVjq6qC50rVBp+MCTGva/5od/cZL7MtqvRL7ue6iGfNy3DO97jxtFAzUDJimSR9//HGxe/fuckxRjBeExQUXXJA+cyua4bPOOisN49BDDy3OPPPM4rXXXivHDI5ljB9caZmBYX4zboPTfOXiiy9OnwPrdyCatdC0gu72228vx0w4++yzi/GMU3z77bfFeIWXxjHfk08+Wbz11luTto/xb7/9djFeeRTHHHNMOXayBx98sPzUrGldZhPNEKq/z/4jDTZt2tS1mcITTzxRfmq2Zs2a4uSTTy6HdKCQh//4xz+mzzfddFPqU27ccccdxXjhmY6BOr///e/TcfHoo4+WY+aPmSi7SPPnnnuuWLJkSTlmAmXpVVddVQ6pF93qP2zZsiWVZU899VRjeT2oUav/BnX55ZenMqBunanDOJ/oBfUJdUG3+iBPzyqOmzvvvDPt97x+iubXNMHrhPzAd++///7a32EcZV4YpIwkneIcoXqcg3FMYz16zQf33XdfMX7SnM4t8vwuaTgGiZcoy6r1SpRv33//fernKEuoi0bZQMHqrbfemgowggsSmoKPCpwAAZ9//nnqH3nkkakfjjvuuOKdd94phwZHYXv88ceXQxNix/3jH/9IhSqF+NFHH53GhaOOOiqNjwp9tvzpT38qP01F0EY6sk20U1+9enXxyiuvTKnQnn/++dQ/99xzU78OlVG3ILTTuswGKu0mf/7zn1PlzIE1SidV6oxCdOnSpftPpt999900vnoynTvxxBNTf77lg5kquygbqmUKz7xQHjedDKtet/oPd911VwogKe94nojpw3pWaNTqv0FRblAf3H333eWYn1AfXnLJJeVQM/I4F397EcEYZVUd6lamPfzww2leln3vvff2dJE4ThTPOOOM1K/DcRgnnIOUkXFim5/0Vv32t79N/aaT4CrKjAjEY53UDuTB9evXp7KFYzqeUR6l59M1vHgpnnGl3slR73ChctgXTWfbQMEqJz6vv/56Ovk87bTTir///e+T7nbt2bMn9asnR6CynC5+96CDDiqHJuOqwt69e9PnRYsWpX7Vjz/+WH6aGQRb8dAz3YsvvlhOqRdBGlfiufJal6miIm3apib9rsuwcaU3//3rrruunDIV+YU7ypwIXnbZZRa4cwgvDgDHXt2Vv6ooO4ZRXoyS2Sy7Xn755a53hDRVt/qPu37kWwJKAtt9+/aldKYs7nSxrldtr/+G6ZZbbknbS5oGTso++uijxpOvU045ZX99c9hhh6Xvd8LyCPi4k0Hdw8XiJk8//XTat+vWrSv+8Ic/FNdcc03KD91wgQF150R1L4gapIz87rvvUr+TyBO9zBt++ctfpn4v66TZw80NLpSw/7lIxTGxc+fO4osvviguvPDCcq6f5MdF3unAGla8xMUkAtW8XOQc+pNPPpl0IXVUDfw24IULF6YKmCuNBFneCfsJTXvHxtLzwKmLJr5NyKRxNfexxx7bf4VkGPpdl2Hj4Ml/f/PmzeWUeqTFe++9l04aOBE0YJVmBnd75kIldiB0qv8++OCD1H/ggQf239EmmGXeRx55JE1Tb0hj6gIu4gYushAkNtmxY8f++oYLBVG31uFknYCWi6PXXnttCio73YHgzidNabnoy12PYRw/XKCNgJpls95SN+SbuIsW+ZC8y7FSvcCD/LjIO40+YgZih+ojclwg5WbYXDBQsEoAwZUbnmfggKGApbCP5zearvqCigfVq4kM9yqWUYff5kSik27Th+3GG28sP9WLZmQEllSWXLGtOvjgg8tP09NtXWZaL5U7Be6zzz6bripxUjLM4F0HBld78bOf/Wx/+dDLlfpOJ5pz0WyVXdFUtNOJuep1q/+acKEwgpL5VP9NF+lLC524cEnAf9FFF6XP3XChgCC0SZywc9L/0ksv9XSXlP2OTk10q2KfNV18jd+Nu5iDlJFxjvDDDz+kfie9bGf48ssvU//www9PfbVbNDWPi2Zqt17ipW7uueeedPE5P65p/st7Quru2I6igYLVhx56KDXrIxHiqjEVcbxA5aSTTkr96pWd7du39/xShE5YBsvKxW/x25yAsZOrByvDjJ/tEzSuxsazKFVUXnFFhGdiaA5M045q2p1//vmp/7e//S31B9VpXZrwXEQvCCp7eS6r23O14PmdrVu3puCdEzkD1tFFXuYknZMqCtOoTDs1SY8T/9luCXCgzVbZxd2p+Za2w9Kt/ouT+nh0I3BS0uvJRyejVv9N19VXX536NKFmO6kj+zkB66W+4eVjlFE0reymn98O0dy+12bgg5SRcY7wxhtvpH6dOH/o9O6LHPVutBpYvnx56qvdIn96cWE0TDde4pz71FNPnVKuc1f1nHPOmXRRNMbzufp7bTdQsFr3vANvlPzmm2/SZxKNE9O8sqTQ+/DDD/cXqFyRzpsiMNwrlsGy8gCG3+I3Y4dROVQrdIaj0jgQyBx5czHWnyv0+bZTOXFCcd555026Cst6x3OtTVdnGd/rSzyq69KEZfbabp4T4F4LSLa9WxDMNnNVnYA1nvnRaGE//+53v0ufed4LBKw888XJYVMefPzxx9NxcPPNN5dj5o/ZKLt46UwEAepPt/ovAg3Kwxx3qGIfzsf6b1CUFwRk8U6HQd9eTd1YvYAQOMHnOeSZeqQp3qrbqf7ODVJGsm85R2D+ut8gv/D73EXu9aVq3LGh/qfVV37XRu0VFzG8uDAaeomXmnCsc86dl+tccKOcy+uX6BCPBvZ70+qAG1/pvo1v7KT/5zNeoKb/4cP4wP/8GS9I0//34X8GjQcdU/6/VzfMz+/w/arxAnf//4yL38r/H1n1/xSxrgyzrjOFbWV96/4nGevB77NedDHMulexriynOp3lM46O70e6xPLGK/Q0jF7XBfF71f/nxm+zD5g/8Bt1+ySWGenL9PEDsHafx3Lj9/gO81aXGeI3mzSt00yq+59qc6XrV+S1OB7BPo08wbQ8DwXmZzr5L/Ydy+LYZjyf5yPSgu2PNKsru0ijumOL75LeddMC6dppujrrpf7jM/ssyu+6fdgNy2wq19pY/80ktpG0YBuasI3MU63HSAv2R57nm+qM2LfUm01p1alu7YTvsf58l9+J5bMO7KcYn+u3jGQetpP6NE8HPkd9nG8zn6vj6TN/5L+8XJ8JdXXQXOlmGvmAfVQti/K8GcdFXR3MvmYa39GBE2U4xzP7hGMu3yeMY3q+X6OsqnYcz02YXi1jRsVAwSrYYBIlEqcuATg4Yh4SPwrDbqJiyrvq8llWVDj8RrWCAjs+ClwO6mrBPkxRaHTqotCvjo8TGtRlQJYd2O487enYxiisMJ11qesoAOv2SbVjnVC3DdUu5q2ua10+YpvrCtO6dcrTaibVVUxzpetHp7zGPut00gfKiHwZ5Au+02tZMVd1K7sYVz0m6o67vGwJpG9eXqh/eRlMv67cYlwEJ3X7sMko1n+zIcqGOnlaNXWUNb3UGQzHtGq659OiqzvGmkT9HfslOpZbF0yg3zKy7jf4XD3mmSemVzt+g3OEfrZtUHV10FzpZlrkC/ID/WpAk+/T6ELd/p+N/a16neIlPuf7NvZ3XZfv/yqmV8u0UbGAP+MbILUazSIGeVZopvysv/8gNFL2dn8/hyRJ02ZdOjje58HLxzyN11w38L+ukWZTmwJVSZIkSTPPYFWSJEkaIfGStfxla9JcZDNgSZIkaUREE+DgqbzmMoNVSZIkSVLr2AxYkiRJktQ63lmVJEnSrPu//p8F5ae55//9vz29lobBO6uSJEmSpNbxzqrUJ68ES5I0fdankrrxzqokSZIkqXUMViVJkiRJrWOwKkmSJElqHYNVSZIkSVLrGKxKkiRJklrHYFWSJEmS1DoGq5IkSZKk1jFYlSRJkiS1jsGqJEmSJKl1DFYlSZIkSa1jsCpJkiRJah2DVUmSJElS6xisSpIkSZJax2BVkiRJktQ60wpWV65cWSxYsGB/t379+nJKUXz66afFsmXL0vhDDjmkuP7664uvv/66nNpZfK/aHXHEEeUck5fP+C1btpRTDrzdu3en7WW9WL/YfsaTZnXef//94tJLL520rZ3SjPEbN27c/xt0LHvbtm3lHBOq+yjvSD9+g9+WBtEpf0VHPq2q5nfy4gsvvJCOkbr5mZaXC3yuzle3LnXLQnU+ujahfIvtoc9wN5QJpBPpyneaVNMpL7fVHWVsnn55R/oH9hlpSxndTxnLfsyPDT7X7X/ydv7bHBNzjXWpVK9Tfoyurv7rt+7V7KC8iTKoU3kVeqknGI7zpn7jsLYZOFglY7/99tvl0IRLLrkk9ZlG8Pjiiy8WY2NjxRNPPJEOhiuuuCJN74Tvfvzxx+XQZKtWrUp95jnttNOKW265JS3/6aefLu66665WBKwUBCeccELxzTffFK+88kpaPz6fe+65aXw1zUAGOu+889I27du3L32H77Kdv/rVr2ozIOO3b9++/zf43gUXXFBceeWVaXnhrbfeKnbu3Jk+r127Ns1Lt2vXrpR+7JdTTjnFE1YNJM9fGzZs2J+/omNcFfmTPHfcccelfMh8lBV79uwpli5dmvJ1oGClsL377ruL3//+9/uXy+cnn3wyTYvCty6v33777Wm4imlbt25Nn+kz3BZsD2UBxzPrtXr16jTcrZJ5+eWXi8ceeyylZZNO5bZ688Ybb5SfpjrjjDPKT0VxzTXXFJs2bUp5vB+chPKdODY4ybjwwgvLqT8h/+f4vbnEulRqNtN1r2YPZR3lDbEM+wTd4qV77rmnuPHGG9P8O3bsSPszP0+gzBs0Dmul8Y0YyHhhPTZ+klcOTcb48QK/HJowfuCwB8bGD65yTL3Nmzenrmr8QNr/XX6bLsfyFy9ePOV3Z9N4ZknrMB5Ul2MmY/2Znot0efPNN8sxk41Xyuk7LBtsH8OMr8NyWN66devKMRMYx29VsTyW1WkdNNn/97ViznaDaspfyMdHfq87xsH4FStWlENj6XOe/3NxvFWPhU7rkhsv4NO89NuEsq26TaRDtcxrwjblaZjrVG6rN6RtNT+SpnXlfuT3XvMYdQTz52UxZXR1Gfxer/lhFFmXzh919dBc6WZDU35EPj6Oj17rXs0e4pu8nImyqamupi6oxlJxPhP7dzpxWBsNdGeViJ0rxlzFp9lA9Yo/t6MPPfTQcmjCb37zm9T/8ccfU7/JSSedVKxZs6YcmsDVz/EdVxxzzDFpmKsDv/zlL9PnMH6QFd9++23xz3/+sxwz+8YzW1qHBx98sBwzGes/foJRDk2k4x133JGuEp999tnl2Mm4g8QyH3300TTM1RSGGV+H5bA81oHld8N++uMf/5g+33TTTakvDQNXC+ni7iblxMMPP5yu4FaP8cD4JUuWpM8c59w94ZiJcTnGMY2WGMw7V7AtF198cTk0gTs9093GbuW2uiMNn3vuuSn58bXXXiuuuuqqcmhwCxcuTP387u1XX32V6r+TTz65HFOklgbvvPNOulPSSzk/aqxLpcFNt+7V7CG+2bVrV3HWWWeVYybKkjPPPDPVK3WoCyIeClE/fP/996k/nTisjQYKVmluBk4SqSDqmtdU/fDDD1Mq3DrVHYC//vWvk5o4UcHEDgnxvf/85z+pP9soDLjdTuXW6YB/4IEHyk9F8e6776Z+9cQ0d+KJJ6Z+nKhGv6lCBpkcsfxu2CcUYhww3faj1Ks//elP5acJ5EeO3WjO34TmKoiCOi/Eq37729+mflOhPmo4KSaNjj766HLMhKOOOiqNn87xOUi5rcko26snAJT9BI6dyuResXwCNS4qxAUF9tV7771XzjFxIkpZTcd8lN1z6WKNdak0PdOtezV7Pv/889Q/8sgjUz/QVJt6pVeUm+DGXZNe47A2GihY5WrNvn37ijfffHP/HU2eqYnEqsOV4jvvvLMc6g8Vcv4sEJVB9bmsuPJ50EEHpf5siwxXPZEJ8RB0dDyXVA2468TySOO830mkQS/LDzzYjVG84qJ24KQ6z+PVY5TnYtDrMfrdd9+Vn5otWrQo9XuZdxTs3bs39WO7qqZzfA5Sbqs7LgJwFXtYCMI4qeR4IsDasGHDpIu4nGiMjU08pxR3Fy+77LI5ExxZl0r9GXbdq9kT+6auvOuljApckKBer7vhF6YThx1oA79giYTliiQPeXMlmCuJTVcfqUQ/+uijxpeddMIbsri6mV9hvfbaa9PvxcsPCFT/8pe/pM80I24jTi62Zi90Id2kuYSTavJ5dN2u4mr29VNuqzdPPfVUY9O6QXFVnUCU/UOroroLCgSt3Anh4gPa9Eb8mWRdKk1m3Tu/UT/weE+nO+PTicPaYOBgNceVYO52xhWCHIn40EMPTalQqq/dZrgOVwLOP//8cmgCic3BSTMevsszKFyBYB06XVWYSfGs0Ycffpj6dX7+859P6vdz1ZZtA7fw0cvdkMMPP7z81N0XX3yR+j/72c9SX5ou3lSX6/eq7sEHH5z6NF3pplNzwVES5UiTbtP70ancVm+i2Xa/9U6n+i8uwnLiwd1TAtam+hFcfCCw7eW5ylFgXSpNz3TrXs2eTvsmyqhueP6ei6ZN50FNcdgoGUqwCu5+1hXoN9xwQ/q3Mk1NejohgQlI8ybAgYCV19hzFem+++5L891///3l1NnHyQqVICcu8SxMN7Fd1SYbufhfb3GlLJqbdbob8tJLL6V+XbrViWegWP+5ctKvA487P/mzEZEfI392ExepOv2rkL/97W+pz7+z6EXbXyxEOUIF9cEHH5RjJjDM+GFfjGsqt9UbmgAP8y4GV7957CVehMHxw/Oq3V4iduqpp86Zstu6VJqe6da9mj3RGpSyI8e/EYpn5jvhnIbyv9O5wXTisLYYWrDKVd1qgc7/GyOBIhE5SYwKggg/b7ZQF/FTidS90SrHMvm/QcwXyz5Q+B9J4E2NvZwQU5lFU7ymSvnxxx9PJ6k333xzGr711lvTMLf8636DDM+JDXeee8mYLON3v/td+hzrLw0TeZL8Hfm904k3+THuLHE885IV5q17Ho95ObHnOY1eXm7D/FQAbS+w2e7q/7tjeCbKt7pyW73jf51effXV5VDvmuq/uuccqT85DjrdNfz3v/9dXH755eXQ6LMulaZv0LpXsycuzuUXqNkXtCyptiqtYl9ysTk/N6i+Ib5THDZKBgpWieTj5JFEIXH4x/VRoEdi8Mr3Y489dn9Tp8MOOyz9g/Ne8YbPpjsm/C47ihdQcCC24S1mXMniGRoqTN60yfqRFmB94y5QjqZ4NOEiDXnmKOYnfWn6RYblynqkLdvKML9BkB77ge/xff4hOsvL26XHPF9++WXqI9KP9aQAY73zK3FSryJ/1Z1Mk8fIkxEQcaLIiTcvhKEcyQtV7nxwBZCTyMBJPAU5LwKKOyPgM8c+0/hXIqFpXfgdls3xE/iXIHm/LWgpwnFP2oHjmuE8XSgbli1bVg79JMqP6Oe6ldvqD2lJsJPnqarIh700ZQdvhGSZBD1xbLD/Ke8vuuiiNEy+iGMhyn3uQs6l8tu6VOou8uNM1L2aPVzc4l8LsT8pf2jWy7lNBJWMI3Yi8AzsQ/YlXf5ICW8QpmzjO8OIw1pjbAArVqxI/1iWbtWqVVP+wez4AbF/erXr9Z9lxz/FrRO/z2/n/yi9LcYrv/QP28cz2/7tZls6re94BTcpXfnuunXrpvxT31D3Gyy/mr75Mqsd+4nfYFnqXd0//54rXb865a/oyKdV5Pe8nOiU3xm3YcOGSfPzufrPzXtZF34XddPahDI1tpftqpaxjGN6jjSqblNe3uTpU1duqz/k16Z/sI98P9CR/r1gv7B/4nvVfZXvZ/JAr3XqKLIunfvq6qG50s2kTvkxuunWvZo97Jcog9hv+f7gM+Ue+wn0Y/9Vu5gn38fVbhTrjAX8GV95ST36v/6fBeWnuef//b8tDiRJs8P6VFI3BqtSn6xcJUmSpJk3tBcsSZIkSZI0LN5ZlSRJkiS1jndWJUmSJEmtY7AqSZIkSWodg1VJkiRJUuv4zKrUt7n7NuCJf8MlSdJssD6V1Jl3ViVJkiRJrWOwKkmSJElqHYNVSZIkSVLrGKxKkiRJklrHYFWSJEmS1DoGq5IkSZKk1jFYlSRJkiS1jsGqJEmSJKl1DFYlSZIkSa1jsCpJkiRJah2DVUmSJElS6xisSpIkSZJax2BVkiRJktQ6BquSJEmSpNYZOFh94YUXikMOOaRYsGBBsXLlyuLrr78up0xYv359mlbX7d69u5yr2fvvvz/le/lv8Jl1uPTSS9PvH2isQ3V96Ugj1vHTTz8t55xQN2+1Iw2a8Hsst5MtW7YURxxxRFrWsmXL0vJIs7rlMo7lxW/zveuvv35SmufbyHLqxPTo6n6LZZIurF+dprSkYzs2bty4f71Yft18dfhe3bx514a8NNfUpXOo2yd1eWa+obyI44B+tfyo061MZHp+jNeVS+pdtZyizquTl629lC+Ucflyo6NMDuxLyucYv23btnLK3BTlfFN+zdOJrprOTfVEpy6WUd3P0TXV7XUo55i/FxzD+W+SH6LeZjmwLpvf8vxM/ojzoVw1j9QdE3l9UHfOp9lDGR7n673sh17rc8ZRN7Fs9vnIGhvAm2++ObZ58+b0eceOHWNLly4dW7FiRRoOjGPx1e6EE04o5+hs7dq1k763atWqcsoEfp9lMa362wfKzp070/ps2LAhDe/bty+tJ+MWL148tmvXrjQ+xDTSMMf32N7q+MBy+B4d89ZhH/GbsQy+wzLrfo+0Zl7WJ5bHtpCujOdz4LuMq1tO2Lp165T9lWM63++UF6ppCbZh3bp1td+NbaumcRXbV5dfwfb0lpc4bOZqNzMir5Mvq9gn3fLDfEJ6xPEIjheGm471wPydykSO8zhm6TNvL8vVVHkZHF1eToYoWynH6qZX1S03Osq+wP5l2ew7lstvNJXHc0GcD+RpkCMdom5oqntIH6bHccV3eq0LqvUR340yjbTvVu/E+VBd+Zdj3Vkex3wclyw7tj+vD/tZ/3arq4fmSjdz2P+RL5r2NfNQzlfLniiXyMORz5iH5TC+l7JKw8Pxmpfh7J9uxy/zxPz0m+rzGE8+iflH0UBHEwVpjgKUxAhkdBKymmiMi4qiEwpnCuFedDpQD4RqhYKoRKvjyThNGYhxTRkrMjLfbUpPptelC+Py5bJOLKepEo2MnlfG8R3G1xVqLL+6rbmoYFlGp0KR6XXLYfuZVrcdvWhKG3Ra75/kldFc62YG+6q6z3JMa9on8w35m+MuR9owvhd1aUm6V4+12Ce9lMmajH1RrQerYj/2c+LHvqjbH5SZsZy42JfXr5RbvdaZoybOB+jy84w6zNMUrLKc6rR+6gLSvDquqW7Psb/4DeZrWjfEspryC/mp+jv9rH971dVDc6WbeeQZuqb6oZo/yBfM3885n2YW5VZ+IY6ynX3QVMf0W5/HPm86/xoFAzUD5nZz7ssvvyzGD5RyaMITTzxRHHrooeXQBJqyXHTRReVQs0cffbT45ptv0u+M9G3r0kEHHVR+6g3NN04++eTUVXHr/5133ineeuutYjwzF4888kg5ZaovvvhiSlOC1atXl5+K1Bz7jjvuKMYLp+Lss88ux072+9//vvj222/TPsmNHxBp/GmnndZXsxH2J783fvCk4aamwAcC6X777beXQ9KBQTl58cUXl0MTLrjggjR+UJQlxxxzTDk0IcqX77//PvXVG8rNTZs2FY899lgqM+rKP8o15nnqqaempHsnJ510UrFmzZpyaALNuCjrYzmvvfZaMX4COql+ZXj85LKnJqmj5uWXXy5uu+221FHndDoOrr322uLFF1+sfdSI5Vx11VXlUGe91gW91O1PP/10qu/GA9XGdWO/Pfjgg+k8qim/3HfffeWn7qzL5g+OffIW5Q37vZPpnPNpZnDsU3afddZZ5Zgile1nnnlmKuvrzMf6fFovWCLj07YaDzzwQOqjrrClPTaJXw1gq6j4qYw4WCjYTznllP2/MaoI5nH44Yenfids/5NPPlkOTUWFS4UMKramE5TTTz89TfvVr341qXLnAkBk6nfffTf1qyfGuRNPPDH1qycIRx11VLF169a0n3gWoteA9U9/+lNx4403pjxCgdnPCTi/ESeBfDe2YxjaFDSPEsoAnofgeSzyYTxvR56YiyfOM4305Jg6+uijyzETON4YP8w0jWOWkx31jjIYH3/8cTrxo4yt7pe77rorpSvlCscDx0e3E0nU1Z1//etfi2uuuaYcmvjd448/vhyaEN/7xz/+kfpzCfUhF7njQnfTCRyuvvrq1K870X7ppZcaT9Bz/dQF3er2yBfsnwiUo97NsY9x7rnnpn4dzp16CUCty+afP//5z+mciPKo0znVdM75NDM+//zz1D/yyCNTPxx33HHpxlSv5np9PnCwyh2ypUuXpsCBBO12B/SZZ54pzj///HKoGQUyd1V37tyZrkZyRZnfGMUCmIqKQJv1J7Cs3pEOBOTxkPRhhx2Wgswm3EmNSvuSSy5J/bq0oVJbt25dOsG97LLL0sPV1cKnlyswcXGB5VSxPewjTp5uuOGGcmwzTsSZN4JMTsBYbqdCkcI3T5vrrrsuHYzcWZ6Ot99+e/9y6Viu+kf+5o4A+5ET5Y8++igdu9zVv/DCC8u5fpLn9bzThL1796b+okWLUr/qxx9/LD9NHycuHEt1AZKaUbbu27evePPNN1P6kffJ63GyQF3IOALKW2+9Nc1LWUlZNkg9Rv1xxhlnlENFqh+a7ujNtavq1A1xkZuu091JMA91Ld/LL6DGxfI6g9QFvdbt7O9ozUSg3NQa6l//+lfq1x33XOTI148uP9+yLhP5Plrbcb7XdFFzkHM+8lrkrabjToPbs2dP6ke652If9GKu1+cDB6sEHGNjY+nuGsHleeed15iRqTQIaJsK9DokOCcFBDfdmru2TQRYxx57bDp556SGZtFNduzYsf8hB05suAhQJyruJUuWpGHSKO5O5hVz4G43yyYDc4JDIdbPXdBesI+orDmB6HYHnKvd999/fzlU7A+6aSbVhGA40oZtYJjKmat/TQVyL0iTWC4dzZrVPypI0hLRfJF8yX5mf1UvYuV5Pe80uygDaMbaqVxSM04sCD7I/1wUJK/HXYsPPvgg9Sl/KauZl3SmXO+3HosgK8r8+ebuu+9OAX+Iu5Nxd7sO5RAneX/5y1/KMRMXy+PiblU/dUE/dTvnQ9XznmgN1e3ifi4ujlDX0/E5b1VkXSZQzrz33nvpfJnHs6ZzfpR7/vnnU76i7rZpcDvNh/p8Ws2AQUH8+uuvp8qhrnkLGF8NVAma4moNXfW12oFKmh1AAT8qIsDi4CbY7gcFTjTzrSKo40punm4sv1PaU6lxQsW6UNER6N1zzz1pWlyd7+VqW1MADfYPy2bdmu6ScjAxjYA51p07pWCderliR16g4qYyJj889NBD5ZTpqz4npumJO0Fx4q7eLFy4sPxUr9v0XlEG8DzlfA2ChomglPIxrpA34a5g1GO91n9vvPHGlBZJnIw26ff9CG1GQEd6kbaRTuecc06a1ulRmbiI+/DDD6dh6haW0+sdh051QT91OwE1v5vvZ1qhgAAgd/DBB6f+Z599lvpVnBfkXSfWZfMXefzZZ59N54S0XKvemBjknC8uFlGXX3755emzhqdTmd2prM/Nh/p82sEquj07SJDV6VmMbpYvX94xWGor0oXK7corr+zrKhcBWVV8P66eRsdVVjI0V1Vy1bucEbSSjhFQRkDBXdEm8f/7ONHqhGVzgkAwWq2IQcV95513Tll/Kn3kV8G7icq403rnKLB7eV6sLt01mDih6uU5bf2Ekw2O52qQzzDjez3h7oRj4dRTTx3KsjSBu5+R16NfvQDHSUmvJx+IC3x5E2DwW9u3by+HJsSdOl7QNFfwfgPqh2qdQZ1KEBh1U51bbrklnbCTftQ9+TO/vehWF/RStxNQUz9X1587oVzYzQOJuCAxrBZk1mXzFy0+aPHIxZRqS7pBzvkIgHiMjFYF3c711b8os6utLSjjmx5dyM2X+nwowWocDNVKFVTYPL9WfbEBwU1egDPc5H//938b7za2HZUGwXbdVa5uyIRxwpM/+5IjKOCuNQVTfnLE52rmZ14KoPjn5BRC0YSt6Y7o448/nk6wbr755nJMPZbNlR3mpSKuyp+1zVH4EUD3GngitovguBecrPQaNLGPmv65v3oXFR55X/3heK4GIwxXW6cMguOcYyFfFhe2qoGV+kP6Rf0X/WpTVV7GE+neS/0XLZKqd9IIbD788MNJ9QkXMyhH58oJC+lJnVZ3chz1CHedm5Bu1EU0I44XNPWrW13QqW7nOONEs7rvEPV43hqK9SWIpS4e1vs5rMvmL/ITTc45hvIWAIOe83EOTwDc1AJEg6PMpuzOL1Bz7FLGd3vPz7yqz8cryr7wv3342oYNG9L/AqIbL2Qn/Y+g3ObNmxun1dmxY8ek/xPE/4Ji+fxOFeNYl/GgpRxzYEXaVLeX9Rw/+NP/Usr/txXbyfz5ODA/6RvbFfPVpQH4fqTDeCGUxpFm/Cb/pym+x3wxLjdeqKXx/E7My7bEMvicY/58H+Xy/AGWx/yd9hHpxXeYL//9GBeYxrqzTkwjr4TxIDyNi++HmD/Shensh7r1YXmMr+6PqSadZ86xrn/kE9I+8gRpTTrmx0Hk4WreA/uEaZ3yyHxCeuTHKWmX52GQ5nXp1SktOSaZVu04HtQ70jHKRPYJZVQ1XzMP+yzKqLp92A1lWlNZxP6PspHfYNndy63RUFd+VDGdvNtUDyHyO+lYp5+6IOqjXur2bvsjjtE8f4DxsV2sc17vsix+g/0e+ln/dqurh+ZKN7M4VsgD5IU6cV5URdlB/uP4ie+S35rO+UKe/zQ8UWaQ7uwP9k9+XDOO6Xn50099HufYo1Ee1Ov7aCLRyLCRMHzulAAkeFPGr8Oy2CmR6OyQOnU7Ki/4Z1ueJnXrw+fq9G4dJ0DV7axuY1M6kDnZV0zP07NpX/Fb+TYwbywj1G1DnVhvVNOlqi7d6sZFx7ZQAEc69JKucfDWpVW1qzvQp2I75mrXv9hfUSBWC9U8faMLTfl3vqPMjBNX0rdahjKuepLaKS1j39R1+b5Sd3n5VA0qcnnZW7cPO4mTkyZMjxPRTuX6KIq0jXSryqc3zQNO5Jlelzb91AX5/o4uL6M61UH8Tq5u3uo81bqYjmOd+aI+7mf9269aB82lbuZU80g1H4H8Uq0nQi/nfLm4MKeZwf5gH7AvSOd8P/A5P6/qpz6vTm8qL9tuAX/GN0AaKpox1DWBmhvm8r9a6b84oGkQL8myKJEk9cf6dCZN51yM5qTjAVT6TJ/mwNKBMJRnVqWquRuoSpIktd90zsV4xpWL0HQGqjqQDFYlTUu8XCT6kiRJ0jDYDFjSwKIJcLA4kSRJ0rAYrEqSJEmSWsdmwJIkSZKk1jFYlSRJkiS1jsGqJEmSJKl1DFYlSZIkSa3jC5akfqyZw//AfItFgSRpFlmnSurCO6uSJEmSpNYxWJUkSZIktY7BqiRJkiSpdQxWJUmSJEmtY7AqSZIkSWodg1VJkiRJUusYrEqSJEmSWsdgVZIkSZLUOgarkiRJkqTWMViVJEmSJLWOwaokSZIkqXUMViVJkiRJrWOwKkmSJElqnYGC1a+//rq49NJLiwULFqSOz59++mk59Sfbtm0rjjjiiDTP9ddfn77XC+ZbuXJl+t4hhxxSvPDCC+WUqZh348aN+3/n/fffL6fMrljfum7ZsmVpHbttP/OwvXXYrrpl0/Edfn/Lli09p7E0DNPJ9+TpvBzhGK6WE/M131OeRtrSrytfq0gDykrSlO/UGWS56mz9+vW16U3aMo183W+9VJfvq3mcYyufzvE2F0T+rOusS6UJnfJx3jXNF6rlCF2/5ZWmb5B4affu3amsinKxDnVQ7Fc+j6qBgtV77rmnuPHGG4uxsbFix44dxa5du4rTTjttUuKS2a+88sri6aefTvPhiiuuSP1uWP4TTzxR7Nu3r1i7dm1x2WWX1R48nAz86le/KrZv3148/vjjaf6TTz65nDq73nrrrWLnzp3p84YNG9I205E2Z555ZnHHHXfUntDknnzyyeLbb79NmbaK7WJ5q1atSsMsN37j9ddfLw4++ODiuuuuK0488URPQDVrBs33FMbnnXdeKjc4bvnOK6+8kgpfjunIw/Mx31OOki4XXHBB2s7Vq1dPKV/rvPzyy8Vjjz1WvPjii+WYyQZdrppRLz344IPl0GTXXHNNsWnTppRn+/X888+XnyaQ/w899NByaAL1RY7fmwusS6Xuvvrqq3R+HPUn5+KgzzDjyePMx/DmzZvT9DfffDMNh9tvvz3NixNOOCFNO1Dn0fPVoPHSo48+WjzyyCPFxx9/XI6ZjAD2rLPOSsvcunVrqquagtq26ztYJVHXrFmzPzPT/+Mf/5gqBk6WAidCHEgx33333Vd8+OGHHe+SgsqFeZcsWZIq5wceeCCN/+yzz1I/UIlwonXnnXemyu3ss8+eUpnPtmOOOab89BO2g20gLchQdUE3SBeuquCZZ55J/TrHHXdc6rPcQBrzfTIjFe9cOWnRaOg331NYchL/7LPPprIkjluWw7G8dOnSdGwTuIb5lO+5WEcakDbgTuny5cvT+E6Y/6OPPiqHphp0uapHkP+73/0upWkd9gX1U7/I9++88046wYiuWm8yTOCWzxP7dS6wLpU627NnT3Hrrbc2nvcy/q677krz4aijjkr9RYsWpX4ulnGgz6Hnq0HjJW7qEeDWoR75zW9+s3+Z1PcrVqwoPvnkkzQ8avoOVtnwakUSifH999+nPoEkBT0RfeAgoHJ97bXXyjH1qkEnCb548eLijDPOKMdMnCRceOGFaXlcFZoLyHBcReZKGHdG8hP1XkVmpCLvlsmlA4F8zZ0RruByrNf5/e9/ny5+cdWwF3Mt37MNF198cTk0gbuh0922mVrufPWHP/yhuOWWW/YHRsNCvv/mm29Svm4KyO6+++4U0NJCYZC6Yi6zLtV8wLlvfqGlDufqc+Ucea6aTrzUCXkjYrPwxRdfFFdddVU5NFqG8oKlaEZG4Y7PP/889Y888sjUD1zJpILtFZUEFc5777036aDkDi47l+VFG2+aBbWxyQ5pQ5ty7iRxgl7NPIj1pmCJjPTuu++mfr+4QoPpZHJpupryfeTratCUo/kd+jlJnCv5nhNrAvWjjz66HDOBq+KMH7SMm6nlzlcEkdRBBDXDxHFDvmefEGidcsopKSDNxW/TcXxxZ3c+BFTWpZLmmmHFS51QZxAj3X///Y03CdpuKMEqlQGBatxxjWYHdU0KqIR7QcLyrCpXNv/617+miiq8+uqrqYLmxIsrBbTR55Y5d1vz+Q4U7hzFA82HHXZYev6F9KGJYx0q4KgYyUjcSaYd+iB+/vOfp/53332X+tJs6SXfR+uLTqLc6LWswFzJ93v37k39uqZa+PHHH8tP/Zmp5c5H1DFU+n/+85/LMcND3ueuKs9scneQuoDgjDoiEKTR7Jd6j6ZjoK6cixccrEul4eICWBxTeacDYxjxUic8dsU+f/vtt1Ork1FtiTPtYJWKm5d60HZ6mKiMuHLMnVUeCqbJVSDRr7322v1XCKi8+X3mH/Qq6jBVXwrBMOtc98IGMg5XT/Ir9JyA8L2mJmBSG/WT76VRRV1EM9y6k4thieZ7XKxtCrii3uOFKcgD2rnCulQarngBU7XT3BQv0Iqyk5hqFE07WOXlHE899dSkZroHHXRQ+WkqKl5w5zS/qlP3dj+WSfMmrqRydbmTeKY1rlK0BdtAZuFNbFSaDz30UDllQjRpztMi3i5ZfSNkL3jzG/L9Ic22pnwfZUMvd1ibXlxTZ67k+4ULF5af6nWb3mSmljvfRNBT1wS1H73UfyA/x4XYJly0JSgb1SvmvbIulTTX9BIvTRcXVik7CVi5ADqKNw+mFaxye/nUU0/d3/w3nHTSSalfvZrJv5jhoeF+nX766eWnCZzEfvnll+XQhLjK3WnHH0jxpsbqv5XgFfvx6vG8iwC932bN8WYw9ot0oFXzfVxUqh4Hufh3E/1cAZwr+Z6ylArqgw8+KMdMYJjx1bK2VzO13PmG5r8EQHlAxNVqOj7PxL8F4I3N3S7ckO/nS1BlXSpprhh2vNQJbwceVQMHq9zxPPzwwyc1uYk3E3LiQ+WanxhRUfBc6fnnn5+GaeabVyhNz6CAuzD573ASy+/nlU9cVc7fGtwmkRF5MURgG8iMdc3J4rmbfpo1szxOmqich/3iD2kQ1XzPCfW6devSHRDyax3+ZzIB1M0331yO6Wyu5Xu2gYoqx/B0t22mljufVOstOvIdHZ+5et2Lfuq///3f/02PvXTy73//u7j88svLobnNulTSXNFLvDQsP/zwQ/qtUbw4PVCwytVjXuhAl19h5nmRuLrLVcmHH3443W4m4eN//HUr+Jm+bNmy/RUSd1m4Ksr/kwpxEssyWTaBKgEsJ8EH8upy3FrP7/qyflR85513Xhrmf9KC7SO4P/fcc9NwVQTdzBNpgfgfSXmTL6aTbuwPKvDnnnuunCLNvH7yPeJ/JZK3ec6OecFyaA5JIc0bwPMTz/mU76v/Y400YjgvA0knysmqSMvo53pZroYrmrtzktAL8nT+7Cn1Hxdvrr766nLMRCAVrQ/Yz8zPizim2zS5TaxLpf589tlnqd9U1sT0aN6e61RvaOZ1i5cYd8ghhxTr169Pw7nY39VHq6gX+E7U9yz7pptuSvXJKOo7WCWxeENfnbzZXrz8gTf08hY/dLp6HLiSwF0X3l7Fv6V54403iv/+97+TglBOYjmZpZJh2VQq/CsMToIPFE4ejz322PSZ4DoCeNaPSpKrvjzYTrpQIbJ9nGCcc845U5qOMT3SjHni3xewvGj6REaO34jKm39kzj+ir7u6LM2EfvJ9jrKBjjd7My/foaw4/vjj0/EeV/44Fpg2n/J9lG+8uI7tJI2q/76rDuVIlBs8l8J385PzQZerwZDG8cwk5XzTc6k5Tjz4R/58l/rvP//5T6o387zNexlYHvOwzF/84hfpWJorrEul/pB3eVM2omzI5dPjJlPoVm9o5g0aL1FWsr8Rj6cEmhcTrLK/6fOM/yuvvDKy/7pmwRhtkCT1Zs0cfsX7FosCSdIssk6V1MW03wYsSZIkSdKwGaxKkiRJklrHYFWSJEmS1Do+sypJkiRJah3vrEqSJEmSWsdgVZIkSZLUOgarkiRJkqTW8ZlVqR+Vf7Y9p1gUSJJmk3WqpC68sypJkiRJah2DVUmSJElS6xisSpIkSZJax2BVkiRJktQ6BquSJEmSpNYxWJUkSZIktY7BqiRJkiSpdQxWJUmSJEmtY7AqSZIkSWodg1VJkiRJUusYrEqSJEmSWsdgVZIkSZLUOgarkiRJkqTWGThY/fTTT4uVK1cWCxYsSH2Ge7Vt27bi+uuvLw455JByzGRff/11cemll6ZlM8/69evTuDaLtMi7qnxa3fx13fvvv5+6uml0y5YtKzZu3Fj+wgSGq/Pl89Qtr6puGdEdccQRaf/t3r27nLte/E5d3ui0Texz0mfLli2t3+/zXaf9mHf9zFedl3zQCeVJPn9TV3estNUg5SvHygsvvJDKTr5TZzrltn4yjHSk/KSsI79XkU+ZRjcqeXZYIl3zriqfVjd/XVdXtuSddalGSS/5vq7sIL/E+TUd+Z56gzxYNz/TmCefvzpf3brULQvV+eg0cR5DeUB6UCb0cryyzzi2m85tKC9i31EW9LrcVhobwL59+8YWL148tnnz5jS8devWNMz4bnbs2DG2YsWKMX666edPOOGEsbVr16bPu3btSsOrVq1Kw21GOrBNS5curU0LtoVpbFtMJw35DumSYzrbnI9nOE+znTt3prRh3Lp168qxE/heTHvzzTfLsT9hOtNiH9ZhHVhf9ldgHN9hf9OxTU3Yzrp1y8U25cth3WI8v892tsb4Os3ZbgDk+Tw/R76KfBv5mPkwSH4nD3QS5QnzB/IM4zZs2JCGyV+RH6NsaSvSYpDylfnjmM+P2TDocjXZsNIx8m31WCDPsh/JsyyTz5GP5wvSlLSxLp0wJ+vSML5uc7abYdV6Lse46vjIR4yPfEKfYcZX8yf5nnyT53s+M45pcewh1qWX+jWOb/qaOE4pA6KMIg3r6vAq5mNf1OUB9ivTYz9HPdXLcttooKOJBCCj5kiAXjJpYH4SuIoEJfFzURnEjmwz0oAM0aSabp22jXH5+ChQchQWjKtLSzIp61L9TVCB9VJQsJ/qMncUNtUDJPDb7Ee6TulRt00hfqNu/Q8Y1nWudgNg/0VhiLr8TCUW+aRuemBcPp7vRDnRlFdZdsxTzYt146Jgb+VJW2m65SvbV3fMDqPc1nDSkSCFZbCv8jzPscS4/OQwjpn8OJsPSE/r0jlclwbWea52s6BT3snHR/5ouqjC+Dx/8pn8VlfuNB0PndYl1+lYnY84tvMLUZRFpG8v5UqkZTXd+W5+MQGRB9p8/tNkoGbANAu4+OKLy6EJF1xwQRo/Xa+99lq6FZ47+eSTi/EdV/ztb38rx7TX5ZdfXnz77be1aUHzizPPPLMc6oxb+mw3XSeHHnpo+WmqJUuWFHfeeWfx8ccfT2oiQHMD0BRkprz88svFbbfdlrqm9OiG9RsvMNP6DyNvafhuv/32lM86OeaYY9J8nTTl95tuuikd+3fffXc5ZjKawKxevboc6i7Klh9//DH122imyteZWu58M910pOnWq6++Wvz+978vx/zk3XffTf2zzz479RH1X0ybL6xLJ1iXql8cH3RR79L08+GHHy7Gg6JizZo1aVwV46MuJ4+8/fbbxdq1a2vrd8Yxzfw0fTTVHQ/+i7POOqscM1EWUb4RDw2KY75apv3mN79J/Taf/zTpO1iloqXAPProo8sxE4466qg0fpBnd3LfffddbZvq5cuXF//617/KofaiQjzhhBNqT66ff/754pJLLimHmrH9Tz75ZDnUWTzrQiFUh8KKaRRUzMuy77333uLBBx8s5+hfPHPMCdRFF11Ujp2M9WdaTB/0oItAZDoHrdqtU35ftGhROkmkMI8Tw0B+fuedd/o6UYyyZeHChanfNjNVvs50uT1fDCMdKTs3bNiQ8nYV36f+qKL+e++998qh+cG6dIJ1qfr1pz/9qfw0gQtdlE+rVq0qx9R74oknUj/ySB5AVf32t79NffPT9Hz++eepf+SRR6Z+OO6449L5zTD98MMPqazpduGujfoOVvfu3Zv6dRUtphuxc8WGqzVRcYQvvviiOPjgg8uhdrvlllvSyTVXtgIV20cffZTuMtU55ZRT9j9sfthhh6Xvd8LyuKLF1Rcy3yuvvFJOmerpp59OBdW6deuKP/zhD8U111xTe7WsCVfYYt3ozjnnnPSwNidPdcuJ9eKqDh0F5Isvvjhln/bi5z//eepzEUNzRz/5/eqrr079xx9/PPXDX/7yl3S3oZPvv/8+9TleOCmkbOEOQ9NxeKDNVPk60+X2fDHddKQlAHdhm/IfZWTTHb75WAZal1qXqrs77rhjUr4ij+T27NmT+gcddFDqd9NLHoky0Pw0PbFv6sp9yppheuONN9LF/1HUun9dQ9MfUChTiURFQoVz2mmnpWltx50eKr3777+/HDPRlIeKrcmOHTt4wCF1+/bta7y6CwojKuHLLrusuPbaa9MJeKeTb66i0GSDAowrNU3NQJpwcp+v29atW4tvvvkm7Q/2TRVXwm+99dZyqCiuuuqq1CcNJPST3ynEyb+c6MVJK+UC+bnpbkRgHpr+cryQV1nOc889V06VZg8BBkFJv+XvfGZdal2q7mipEfmKrtsdVM0/tNrhIl+3R7Laqu9gtVvzOabzTEd+lYeuV1QGb775ZrqiQCVyxRVX7L/ycMYZZ6T+KIiT62gW9sgjj3Q9sQ6cnFNxNokCiYrvpZde6unK7n333Zf61Wet+sW6cQLBSRf7iLtVOYIJLixwghD7nqvH6LU5Vu6rr75K/X6uXmu0dMvviBO2OGnlZI3v1F2NzDEPrTI4XjgppJlTt+8cSL2Ur4OYqeXON72kY1P9x924bk1GO7UeGpWWRcNmXWpdqv7ceOON5acJvd5RDVHW0Gy0G/PT9HTaN1yoGwYu7j/00EPFW2+9VY4ZPX0Hq1x1JAE/+OCDcswEhhnf6apkr3i5RJxgkrjbt29PFdYoHRTRdJFmX1Q6PHvTz0lyL1c/uENEZVat5OoM+wSd/UwFTyWbN9HiWYn8ynZ0XPljXavPHXZDsyuceuqpqa+5qVt+59jPT1r7OWEdJTNVvs5GuT0fDJqOlJHcjcsDD5qrIpqtgjts5PEqxo1Ky6Jhsy61LlV/uOmTP5cYN3q4INOL888/P/VpNtokXnh67rnnpn43XMQjaNJkJ510Uurnxz6Ie3gEYBhuuOGG4q677hp62TWbBmoGzNVAEjLHMONB5VAtYAe1fv36lMHjauao4OSaphibNm1Kd4Oi+U6/OMCbnk8h473++uvpN+qaEM0k9smHH36YPseD4awnzajyQjJEYNGp8KtimzhJoyKPvKW5rVN+j7urF154YSrE5+oV3W7l66BmarnzTbd0rKv/KBOr4whEEAEJ4qQyP3GJz6PUsmiYrEsnsy5Vryg72PccQ7TsIE815W/yYVysIY9wUYh5o0VDjnk5VshP+ZvLmzA/ZeQoB0szhYtVXMTML4BGmRAXDaaDGIpANS6ksuyRLAPGK8m+7av8D6D4x9a7+vg/cOMHQvp/PyyrinH8n7nxAyHNVzfPKBg/CUnbSNo0Ie2YJ/+/emCb+Z9IbH8Yr7Br04z5GM//aWraB/EPm5mnV/zO+EE0ZR+wrrH/+G3wu4zrtPz4Tv5/vmKb8vUm3WJ89bcPuPF1mrPdEDTl59Bvfq/+nzHKhGp+4buMY/4wSH5vC7anW/kaZWNVpEXTtOmW2xpeOkb9QD8XxwG/wzL5PIr5eJisSyeL77DNYeTq0jC+bnO2m2Gd8iLlE8dL7HP6eV7L8wn5kHySj4v5ybf5McXnurzctC4sk2Wvzf4PNevGvFGGznccp+wr0pA0Ja1I38A4ptftZ/ZHXbrznTj2q12+L0bFwEcTiRoZnxMnhnsRlU7eRSGNqCxY9lzIyBzUTZVONR3qOtKgLs1I81ycxNPl6Yl8WnQss5PYD00dy8z3T3VaVT69W8dByUHWyv0/vn5ztpumun2Zq5te7Zrye2Banr/q8nZd1y2/t0238pVxeWWGumO2ut2DltuabBjpGPm8Lm9SZ1AO0lXL8/nKunRCPr1b1+q6NIyv55ztZlBdXqx2dUEJeSHKLro4rvLAMzCO/JvPz+f8Igl6WZfIg3XTNLFf2BekB/st3x9NwWpduod8n1W7/OLDqFjAn/GVl9SL8tmyOcmiQJI0m6xTJXXRun9dI0mSJEmSwaokSZIkqXUMViVJkiRJreMzq5IkSZKk1vHOqiRJkiSpdQxWJUmSJEmtY7AqSZIkSWodg1VJkiRJUusYrEqSJEmSWsdgVZIkSZLUOgarkiRJkqTWMViVJEmSJLWOwaokSZIkqXUMViVJkiRJrWOwKkmSJElqHYNVSZIkSVLrGKxKkiRJklrHYFWSJEmS1DoGq5IkSZKk1jFYlSRJkiS1jsGqJEmSJKl1DFYlSZIkSa1jsCppIO+//36xYMGC1C1btqz4+uuvyyk/2bhx4/556FauXDllXFM3n3366acprUgH+gx3Q/q/8MILxaWXXpq+U2eQ5UqS2iXK8Wp3yCGHpDqgl7Kdupj5O9m9e3daXix7/fr1qa65/vrryzk0DNu2bSuOOOKIlM6kbd35VFUv9Tn7i/3GPJyn8TujyGBV0kBOPvnkYt++fcXatWuLjz/+uLjiiivKKT+5/fbb0zwnnHBCsXPnzuKtt94qvvzyy2Lz5s3F2NhY6jZs2JDmjeFdu3YVS5cuTZXkfEQlddpppxUXXHBBSo/Vq1en4W6V18svv1w89thjxYsvvliOmWzQ5UqS2oW6lDoV1KGU6dS1Dz30UKoDKNu71aFPPvlk8e2333YMYFatWpWCHZb9zTffFP/zP/9T/OpXv5q39fNM4ML/lVdeWTz99NNpP6LufCrXS31OoLpp06bi9ddfT/PccsstxTnnnDOa+258AyRpWihK6MYD13LMZCtWrCg/jU2ZZ7yiTd/NjQezYzt27CiH5hfSZzy4L4cmkH5NaVtFWubpHaa7XElSu1DeU4fm1q1bVzs+t3Xr1lT+M994QFqOnYw6mOnVupjhujpGg1m6dGnaZ2Hfvn1jixcvTvuoSS/1OcvIlwu+0ylftJV3ViVN23ghma7AchWPpkWdPPHEE+WnZmvWrEl3bucjmvJefPHF5dAErp4yfjpmarmSpPY46KCDyk/NuIvHHVnqbe7Edrrb9tlnn5WfJlA3L1mypBzSdNB0l9ZkZ511VjmmKA499NDizDPPLF577bVyzFS91OfcEa+2tOK3OF8bNQarkobiz3/+c2rue8cddxgADYgTBpplHX300eWYCUcddVQaP+gzpjO1XElSu/CoDQ4//PDUr4ry/phjjimuuuqq9Pndd99N/dyRRx5ZLF68uLjuuuvSc6t5QNvLRWd19/nnn6c+aZ077rjjinfeeaccmqzX+vzxxx9PwSn7jubBPAvLfmO/jxqDVUlDwdVAnqOhcrvssssMgAawd+/e1F+0aFHqV/3444/lp/7M1HIlSe1AnUtAQguntWvXpiClzpYtW9Izjjj77LNTnf3II4+k4Rx1+nvvvZfeIcEdOvosv9NdWPVnz549qU9aVxF41um1Pmff8n4Q9h3PGZ966qmNeaLtDFYlDU1UblR+POxvwCpJ0syhNRNvez322GOLjz76qHjzzTcb73wSaHLHLg9aCGy5A8eLfqq4C/fPf/4zNRmmXicQpgWVradGwy9+8Yu0f8GFhlE9JzNYlTRUVG7PPvtsuip4zTXX+LbZPixcuLD8VK/b9CYztVxJ0oFFIDk2Nlbs2LEjvZm/E94aT2BKcBvdgw8+mKY9//zzqV/FRWje7P/f//43/RZ1O62nvMM6fZ2eL+biQJ1e63MuKNAUmAsX5AvujPfylug2MliVNHQ0P9m6dWsqIPn/XwasvSHQp4L64IMPyjETGGb8oM+azNRyJUntwIuPCCb5NyhNd9D4dzX8GxqC27zjpTvcNc3rau605ndQI2ilaSnqnnNVf0466aTUr97V3r59e3rJUp1e63PupJ5++unpMy/E4jEtXrr06KOPpnGjxGBV0oygmRHNTwhYu13t1U9INyqqHMPTfdZkppYrSWoHgsnly5fXtmoi8CQAqns+Mp5hrQagvDW46qKLLkr9Xt46rM4ILLnjmQee7LcPP/ywOP/888sxU/VSn1efeWW/8/Zn/l/uyBmTpGnYtWtX+j9h/G+wOvwPt05FTUxv+v58U/0fa/zPWYZJ58D/U6v+jzXwXdKyaVq35UqSRsPOnTtTeV/9X5pR1lMvv/nmm2kc/xuVcTFcFXUH88T/VaXPOOpofgvUF/wvT+oYvqPpi31DGpOmkb4h9men/8XadJ5Q3Z/58CgxWJU0MApDKrPoNtT8s2kK1brgKSrCvGN5mjgJIc0iTeJEIdQFq6R9NT2rlVK35UqS2q9a91bL+7r6NbpqPV03L/MwnuCWfv57BFMGqsNF0MnFhbr0rQtW0a0+53ssi+8yD/M2XaxouwX8Gd8ISZoxNGupa3okSZIkNTFYlSRJkiS1ji9YkiRJkiS1jsGqJEmSJKl1DFYlSZIkSa1jsCpJkiRJah2DVUmSJElS6xisSpIkSZJax2BVkiRJktQ6BquSJEmSpNYxWJUkSZIktY7BqiRJkiSpdQxWJUmSJEmtY7AqSZIkSWodg1VJkiRJUusYrEqSJEmSWsdgVZIkSZLUOgarkiRJkqTWMViVJEmSJLWOwaokSZIkqXUMViVJkiRJrWOwKkmSJElqHYNVSQNbuXJlsWDBgindsmXLio0bN5ZzFelz3XzVThM+/fTT/WlLn+Fuvv766+KFF14oLr300vSdOoMsV5Ik9W79+vWTzm3quvfff7+ce7h2795dbNmyZcp5WCdtPzcwWJU0sLfeeqvYuXNn+rxhw4ZibGys2LVrVyok77jjjuL6669P07788sti8+bNaTod8yKG+c7SpUtTITvfEXSedtppxQUXXJDSZvXq1WmY8Z28/PLLxWOPPVa8+OKL5ZjJBl2uJEnq3QMPPFCsWLEifd66dev+c53o1q1bV3z11Vdp+rA9+uijxSOPPFJ8/PHH5ZjORuHcwGBV0rQcc8wx5acJS5YsKZ544okUfG7atGn/Fbo1a9akfh2+c9tttxV79+4tx8xf99xzT0q7SC/ulC5fvjyN74T5P/roo3JoqkGXK0mS+vPcc88VixcvLi677LIpdypvvvnmYs+ePeXQcHH+9fTTT5dD3Y3CuYHBqqQZccQRR6T+jz/+mArPbigoTz755HJo/qIp78UXX1wOTeCKJ+OnY6aWK0mSJjv00EOL9957L32+8MILJ92pZNrtt99eDh1Yo3BuYLAqaUZEwbxw4cLUV3c0g/7222+Lo48+uhwz4aijjkrjB32OZKaWK0mS6tHyjEegeNTpiiuuKMc24znW6rOtdd2wnncdlXMDg1VJQ/H999+nPkEqz6ryvATPbFSbCatZNINetGhR6ldxl3oQM7VcSZLUjFZja9euLd5+++2udytpXVZ9vrWuG1YrtFE5NzBYlTQUvNiHpr+HHXZYKpApnHlmQ5Ikab7ioj3nRDwPqv4ZrEoaimuvvbb44osv0lW/b775Jj2nynMZ6l23JtODNqmeqeVKkqRmNKV99dVXi/vuu68c02y2mwGPyrmBwaoktQRXX3l74AcffFCOmcAw4wdtUj1Ty5UkSfV4LOqaa65JrcyqF+/jX/vlZrsZ8KicGxisSlKL0Exo+/bt5dAEhqfbfGimlitJkiYjUOWlSk899dSUQHXbtm0d/9XcbBqFcwODVUnTEm+Lixcs9eKTTz5J/fxV7ppAU6EPP/xw/4sYtmzZkoZvvfXWNIyVK1cWy5YtK4d+EulZl669LFeSJE0f/6eUlyode+yxU5rxnnPOOTP6mNQPP/yQ+nXnZdXzh1E4NzBYlTQwCj0KYjz44INdn6WI5zF4GRN4GRPL0E+owPjfbI899lhKK551YXjJkiXlHPU2btyY0hO8ibm6LwZdriRJ6t369euLTZs2lUP1Zqru5ZyKYBhxXtbJKJwbLBij8bMkSZIkSS3inVVJkiRJUusYrEqSJEmSWsdgVZIkSZLUOgarkiRJkqTWMViVJEmSJLWOwaokSZIkqXUMViVJkiRJrWOwKkmSJElqHYNVSZIkSVLrGKxKkiRJklrHYFWSJEmS1DoGq5IkSZKk1jFYlSRJkiS1jsGqJEmSJKl1DFYlSZIkSa1jsCpJkiRJah2DVUmSJElS6xisSpIkSZJax2BVkiRJktQ6BquSJEmSpNYxWJUkSZIktY7BqiRJkiSpdQxWJQ3s/fffLxYsWNC1e+KJJ6aMW7lyZbmUCQzn06+//vpJw3m3bNmyYuPGjeU3J1S/n3cx/9dff13O3W6ffvrp/u2hz3A3bFuk2RFHHFFs27atnPKTF154IU2LeappKEmS1CYGq5IG9tVXXxVr164t9u3bV4yNjRU7duxI4+kzzPhVq1YVhxxySBpet25dmr5z587irbfeSp8Dw7t27SqWLl2avkeAy3f4PvhMx3dxxx13FOvXr0+fwfdj2oYNG/bPzzLPPPPMNH81QG4jgs7TTjutuOCCC9L6r169Og13C7SvuOKK1CftXnnlleLKK69MFxMCwetll11W3HbbbWm5zPPwww+nAFaSJKmNDFYlDWzPnj3FrbfeWhx66KHlmMkYf9ddd6X5cNBBB6X+Mccck/pVS5YsSXf88uUdd9xx5acJfDcC3QcffDD1Q91yWeYDDzyQguqPP/54UgDXRvfcc08K2NesWZOGL7300mL58uVpfBMCzrfffru47777UtqRDnfeeWcKdMMzzzxTnHDCCfuXyzykydNPP52GJUmS2sZgVdLAbr/99hQMdkJQxHzD1BQczwUEnhdffHE5NIG7rJ3ugL722mvFihUrJqULw9xVjibE3N0mWN+9e3caBtNZtiRJUhsZrEoaORFwcQeyG5rPbtmypdi0aVO6s3jyySeXU9qH7fr222+Lo48+uhwz4aijjkrjm55dJQg9/vjjy6EJcZf5H//4R+pzB3zx4sWpWTXLIfglgI07rZIkSW1jsCppZBB4EmTxDCqBF89d1uH51Hi50mGHHVZcd9116U5j9TnZttm7d2/qL1q0KPWrfvzxx/LTZNwhjSbWVd9//33qcwf89ddfT/PyDOzf//739FywJElSWxmsShoJEXjykqBrr7023U2se0YV1RcsMcwznSeeeGJPb9adqxYuXJiegeWONHeaOzUtliRJOtAMViWNhAg+uUP60ksvdX1WNjAfz8xu3rw5Ba4PPfRQOaV9CCY7aZrOXeYmcceVIP3CCy9ML2HiDjMvVyLwr/sXN5IkSW1gsCpp1sSzmE1v5M1f/tPkueeeS0En/1O0H/Fs5osvvpj6bcSdYgLPDz74oBwzgWHGN91Jpln09u3by6EJkcYnnXRS6hOkx5uW6WgCzPOrN910U5ouSZLUNgarkmbNr3/969SvBmPh5Zdf7vp2WgItnr3stxlrBG+8ZKnNaKZbDTwZZnyT888/v/jwww8n/S9W0pjmvhHgfvfdd6mfu+qqq4pvvvmmHJIkSWoXg1VJQ/PZZ5+l/g8//JD6VTTJXbduXfHwww+nN/RGcEUTVe6U0rz3oosuSuPCJ598kvp5IMYbfXkOlWas69ev339HNp5H/fLLL1Mf8VKm8847Lw3/8Y9/TP22opkugWcE4qQTw7zNN6xcubJYtmxZOTT1f7ESmJPGjz/+eBrG6aefnp7bZXkgze699970/1glSZLaaMEYD4FJ0jTxAqSqpuJl48aNKTDlJUngDiBNWQnU4n+FEnCdcsop6XOovtGXoI0ArBua0LL8G2+8sdX/uiYQdF9zzTUpfdhmAvO8CTDbTRD+0UcflWMmgvIbbrghNXMmPQlUzz777HLqBNL9ySefTM2omYcXVQ37f+BKkiQNi8GqJEmSJKl1bAYsSZIkSWodg1VJkiRJUusYrEqSJEmSWsdgVZIkSZLUOgarkiRJkqTWMViVJEmSJLWOwaokSZIkqXUMViVJkiRJrWOwKkmSJElqHYNVSZIkSVLrGKxKkiRJklrHYFWSJEmS1DoGq5IkSZKk1jFYlSRJkiS1jsGqJEmSJKl1DFYlSZIkSa1jsCpJkiRJah2DVUmSJElS6xisSpIkSZJax2BVkiRJktQ6BquSJEmSpNYxWJU0sPfff79YsGBB167TfEcccURx/fXXF19//XW5VH366afFypUrU/rQZ7gb0o90jDTdtm1bOeUnL7zwQpoW82zcuLGcIkmS1D4Gq5IG9tVXXxVr164t9u3bV4yNjRU7duxI4+kzzPhVq1al+RjevHnzpOm7du0qzjzzzGLTpk0pKNNE0HnaaacVF1xwQUqj1atXp+FuwfwVV1yR+qT5K6+8Ulx55ZXpIkEgeL3sssuK2267LS2XeR5++OEUwEqSJLWRwaqkge3Zs6e49dZbi0MPPbQcMxnj77rrrjQfjjrqqNQPS5YsKZ544onihBNOKD7++ONJwdV8dc899xRLly4t1qxZk4YvvfTSYvny5Wl8EwLOt99+u7jvvvtSmh9zzDHFnXfemQLd8Mwzz6R0juUyDxcann766TQsSZLUNgarkgZ2++23p4CzE4Ii5uukKdidjwg8L7744nJoAndZO90Bfe2114oVK1ZMSkeGuXMdTYgPOeSQdEFg9+7daRhMZ9mSJEltZLAq6YD74osvUv9nP/tZ6s9XBJLffvttcfTRR5djJnBHmvFNz64ShB5//PHl0AQuEuAf//hH6nMHfPHixalZNssh+CWAjTutkiRJbWOwKumA4TlKnlXlDt/WrVu73qWd6/bu3Zv6ixYtSv2qH3/8sfw0Gel30EEHlUOTff/996lP2r7++utpXp6B/fvf/56aYEuSJLWVwaqkWXfKKaekN9Kec845xcEHH1zs3LkzPZupmbVw4cKUzjwTy0utfLmSJElqM4NVSbMu3ga8YcOG4p133klBlCaCyU6aptO8t0nccaXp74UXXphewvTWW2+llyvxduC6f3EjSZLUBgarkg4YXrzEm255jtL/szrxnCmB5wcffFCOmcAw4+M51Cr+/c/27dvLoQnxZuWTTjop9R966KH0v1V5CRMdTYBJ95tuuilNlyRJahuDVUkH1HPPPZeeo+z0r1nmE5rpVgNPhjs1kz7//POLDz/8cFLAT4BLc98IcL/77rvUz1111VXFN998Uw5JkiS1i8GqpKH57LPPUv+HH35I/aq66dzl48U/PEPJy5aa3ng7X9BMl8AznifdsmVLGuZtvoF0WrZsWTk09X+xclf14YcfLh5//PE0jNNPPz39L1aWB948fO+996b/xypJktRGC8Z4cEySpokXJlXlxUu36Rs3bizuuOOO9Jn/EcpzlfMVAfs111yT/iUNacGzvXkTYIJV7qJ+9NFH5ZgiDd9www3Fiy++mO6oEqieffbZ5dQJpPGTTz6Z7mQzz7XXXtv1f+BKkiQdKAarkiRJkqTWsRmwJEmSJKl1DFYlSZIkSa1jsCpJkiRJah2DVUmSJElS6xisSpIkSZJax2BVkiRJktQ6BquSJEmSpNYxWJUkSZIktY7BqiRJkiSpdQxWJUmSJEmtY7AqSZIkSWodg1VJkiRJUusYrEqSJEmSWsdgVZIkSZLUOgarkiRJkqTWMViVJEmSJLWOwaokSZIkqXUMViWpJdavX18sWLCgY/f++++Xcw/X7t27iy1bthTLli0rNm7cWI7t7NNPPy1WrlyZ1os+w5IkScNisCpJLfHAAw8UK1asSJ+3bt1ajI2NTerWrVtXfPXVV2n6sD366KPFI488Unz88cflmM6+/vrr4rTTTisuuOCCtG6rV69Ow4yXJEkaBoNVSWqR5557rli8eHFx2WWXTblTefPNNxd79uwph4briSeeKJ5++ulyqLt77rmnWLp0abFmzZo0fOmllxbLly9P4yVJkobBYFWSWuTQQw8t3nvvvfT5wgsvnHSnkmm33357OXRgvfDCC8XFF19cDk3gLivjJUmShsFgVZJa5phjjik2b95c7Nq1q7jiiivKsc14jrX6bGtdN6znXXm+9dtvvy2OPvrocsyEo446Ko332VVJkjQMBquS1EI0r127dm3x9ttvd71befLJJ095vrWuY75h2Lt3b+ovWrQo9at+/PHH8pMkSdLgDFYlqaW4w0rAyvOgkiRJ843BqiS1EE1pX3311eK+++4rxzSb7WbACxcuLD/V6zZdkiSpFwarktQyvFTpmmuuSW8G5qVKueuvv7789JPZbgbMHV/eWPzBBx+UYyYwzHimS5IkTZfBqiS1CIEqL1V66qmnpgSq27ZtKz766KNy6MCiafL27dvLoQkM22RZkiQNi8GqJLUI/6eUlyode+yxU5rxnnPOOVMC2GH64YcfUv/7779P/dzKlSuLZcuWlUNFap784Ycf7n/505YtW9LwrbfemoYlSZKmy2BVklpi/fr1xaZNm8qhekuWLCk/DRfBKMEwHnzwwRQcdxL/D/axxx5L8/J8LcMztX6SJGn+WTDGg0ySJEmSJLWId1YlSZIkSa1jsCpJkiRJah2DVUmSJElS6xisSpIkSZJax2BVkiRJktQ6BquSJEmSpNYxWJUkSZIktc6U/7Pa7R/BS5LUBsP+N+HWf5IkDcew6ugpwaokSZIkSQeazYAlSZIkSa1jsCpJkiRJah2DVUmSJElS6xisSpIkSZJax2BVkiRJktQ6BquSJEmSpNYxWJUkSZIktY7BqiRJkiSpdQxWJUmSJEmtY7AqSZIkSWqZovj/A3RW4INV64YgAAAAAElFTkSuQmCC\"\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eThe WQI\u003csub\u003eCETESB\u003c/sub\u003e classes were divided taking into account the suitability of water for treatment for human supply (Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). The IWQI\u003csub\u003eDF\u003c/sub\u003e classes were divided based on existing irrigation water quality indices, taking into account the risk of salinity problems, reduced water infiltration into the soil, as well as contamination by pathogens (Table S2).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Multivariate Statistical Analysis (MSA)\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1. Hierarchical Cluster Analysis (HCA)\u003c/h2\u003e \u003cp\u003eThe HCA was applied to the data matrix including the 18 sample points and 28 variables. The variable Br\u003csup\u003e\u0026minus;\u003c/sup\u003e was removed from the analysis because it presented values ​​below the detection limit in all months analyzed for all points (Tables S4 and S6). The choice of the number of clusters was performed using the Gap statistic method, which uses the output of any hierarchical clustering algorithm, comparing the change in dispersion within the cluster with the expected one, under an appropriate null distribution of reference [55] (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). From there, a dendrogram was obtained that gathered the 18 sample points into four statistically significant clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The generated clusters have similar characteristics and font types.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the first grouping of Cluster 1 (P1, P3, P5, P9 and P13) are the points considered most preserved, including headwaters of HUs with rural, urban and natural occupation. The headwaters of the Jardim River (P1) and Buriti Vermelho Stream (P3) belong to the Preto River watershed, a region with strong agricultural activity in the FD. Despite the intense activity in the region, the samples from these points have similar characteristics, such as low concentration of ions [56].\u003c/p\u003e \u003cp\u003eThe headwaters of Chapadinha Stream (P5) and Sobradinho River (P9) belong to the S\u0026atilde;o Bartolomeu River watershed, the first located in HU-RURAL and the second in HU-URBAN. Despite being located in an urban area, the source of Sobradinho River is located in a Permanent Preservation Area (PPA). The source of Taquara Stream (P13), in turn, belongs to the Gama River Basin and is part of an Ecological Reserve.\u003c/p\u003e \u003cp\u003ePoints P2, P4, P6, P7, P8, P11 and P14 make up the second grouping (Cluster 2). Points P2, P4, P6 and P8 belong to HUs with rural human influence and intense agricultural activity nearby. The other points in Cluster 2 (P7, P11 and P14) have similarities despite being composed of a spring within HU-RURAL (Cabeceira Comprida Stream P7) and HU-URBAN (Tamandu\u0026aacute; Stream P11) and a point with anthropic influence in HU-NATURAL (Taquara Stream P14).\u003c/p\u003e \u003cp\u003eIn Cluster 3, points P15 and P16 (both in Ouro Stream) and points P17 and P18 (source and point with human influence in Contagem River) are grouped. The points belonging to the cluster are located in the Maranh\u0026atilde;o River watershed and have similar hydrogeological characteristics, with no differentiation between the source and the point with anthropic influence. In this region occur the highest concentrations of minerals and deposits of the FD [57], evidenced in the high levels of ions (Na\u003csup\u003e+\u003c/sup\u003e, K\u003csup\u003e+\u003c/sup\u003e, Ca\u003csup\u003e2+,\u003c/sup\u003e Mg\u003csup\u003e2+\u003c/sup\u003e, HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e) and pH, total hardness, total alkalinity and total carbon above average in relation to the other points (Tables S5 and S6).\u003c/p\u003e \u003cp\u003eCluster 4 corresponds to points P10 (point with human influence on Sobradinho River) and P12 (point with human influence on Ponte Alta River). The two points are located in HUs with urban occupation and both receive effluents from a Sewage Treatment Plant (STP) in three important cities in the FD [56]. These points showed low values ​​of dissolved oxygen, high values ​​of NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, total phosphorus, total nitrogen, biochemical oxygen demand and \u003cem\u003eEscherichia coli\u003c/em\u003e, in all months sampled (Tables S5 and S6).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2. Principal Component Analysis (PCA)\u003c/h2\u003e \u003cp\u003eThe PCA was applied to the data matrices separately, according to the land use/cover of the HUs, including 27 variables. The value of the KMO index was 0.708 for the HU-RURAL data matrix, 0.744 for the HU-URBAN and 0.792 for the HU-NATURAL. For all data sets, the p-value in Bartlett\u003cem\u003e'\u003c/em\u003es sphericity test was considered significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.000), evidencing suitability for the application of PCA.\u003c/p\u003e \u003cp\u003eThe determination of the number of Principal Components (PC) interpreted was performed using the broken-stick model. This method more accurately selects the appropriate number of PCs relative to common rule methods (i.e. eigenvalues ​​\u0026gt;1) and is typically more robust than statistically derived methods [58,59]. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the graph of the eigenvalue and the broken-stick model for each component of the three different matrices. As can be seen in the figure, for the HU-URBAN matrix, the model selected the first PC. For the HU-RURAL and HU-NATURAL matrices, the first two PCs were selected.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe loads of the first two PCs retained for each data matrix (rural, urban, and natural) are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The principal component loads can be used to determine the relative importance of a water quality variable compared to other variables of the PC, not reflecting the importance of the component itself [51].\u003c/p\u003e \u003cp\u003eFor the HU-RURAL, principal component 1 (PC1) explained 48.8% of the total variance and was positively formed by physical variables, minerals and inorganic nutrients (EC, TDS, TH, TA, HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, Ca\u003csup\u003e2+\u003c/sup\u003e and Mg\u003csup\u003e2+\u003c/sup\u003e) that presented loads greater than 0.7. This indicates that these variables are the most representative in defining the water quality of the analyzed water bodies. Variables with loadings greater than \u0026plusmn;\u0026thinsp;0.70 are those that appropriately contribute to the data variation [60].\u003c/p\u003e \u003cp\u003ePC2 explained 10.6% of the total variance with physical variables related to the load of substances dissolved in water (TURB and COLOR). For these two components, the water quality variables related to the physical and inorganic characteristics predominate in relation to the organic and biological properties of the samples. These two components explained 59.4% of the total variation in the data. In studies that apply PCA in the assessment of water quality, the first two or three main components generated explain a good part of the variation in the original data (50 to 80%), without significant loss of information [61].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAt HU-URBAN, the total variation explained for PC1 was 52.7%. In this first component, the variables that most contributed to the total explanation included BOD, TP and TN (as organic contributors) and EC, pH, TH, TA, TR, Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e and SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e (as physical and chemical variables related to mineral characteristics and acidity of the water). For PC2, which explained 14.2% of the total variation, ECOLI, Na\u003csup\u003e+\u003c/sup\u003e, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e and K\u003csup\u003e+\u003c/sup\u003e were the variables that contributed to the component, all positively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe PC1 of HU-NATURAL explained 43.5% of the total variance of data with the physical variables, minerals and inorganic nutrients responsible for the contribution in this component (EC, TDS, pH, TH, TA, TR, TC, HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e, F\u003csup\u003e-\u003c/sup\u003e, K\u003csup\u003e+\u003c/sup\u003e, Ca\u003csup\u003e2+\u003c/sup\u003e and Mg\u003csup\u003e2+\u003c/sup\u003e). As can be seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, for the PC2 of this HU (total explained variance of 15.2%), the variables that contributed most were Na\u003csup\u003e+\u003c/sup\u003e and SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2-\u003c/sup\u003e. The two components together explained 58.7% of the data variation.\u003c/p\u003e \u003cp\u003eThese results show that the variables that influence the water quality of a group of water bodies (HUs) may not be important for other groups. The loads of the first two components, for the three matrices, also reveal that variables such as TEMP, DO and SAR were less important in the general variation of water quality, with low eigenvectors for these three variables (\u0026lt;\u0026thinsp;0.6).\u003c/p\u003e \u003cp\u003eAs can be seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, PC1 and PC2 for all matrices were (positively) influenced by a large number of variables, making it difficult to interpret which variables are most important in the general variation of water quality for a given land use or cover. Thus, Exploratory Factor Analysis (EFA) was applied in order to determine the relative importance of water quality variables.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the correlation coefficients rotated in the EFA for the first three factors in each data matrix. The three factors accounted for 84.3%, 88.7% and 89.1% of the total changes in HU-RURAL, HU-URBAN and HU-NATURAL, respectively. Rotated factors with load above 0.75 are considered strong, loads between 0.75 and 0.5 moderate and loads between 0.5 and 0.3 are considered weak [62]. In this study, only variables with factor loadings considered strong (\u0026gt;\u0026thinsp;0.75) were considered relevant, contributing to seasonal variations in water quality in each group.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKey variables for each land use/cover group.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKey variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLoads*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eHU-RURAL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.796\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.952\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTURB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.856\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eHU-URBAN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.925\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.903\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eECOLI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.761\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eHU-NATURAL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.954\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.886\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.946\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.961\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCa\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.798\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMg\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.940\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e* Only variables with loads\u0026thinsp;\u0026gt;\u0026thinsp;0.75\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe key variables for HU-RURAL, in the first three factors rotated, were TH, TA, HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e, TURB and SAR. These variables are important when water use is directed to irrigation. Water hardness refers to the presence of alkaline earth metals, mainly Ca\u003csup\u003e2+\u003c/sup\u003e and Mg\u003csup\u003e2+\u003c/sup\u003e, which are the main ones found in natural waters [11]. Very hard water (\u0026gt;\u0026thinsp;180.0 mg/L CaCO\u003csub\u003e3\u003c/sub\u003e) can affect its suitability for certain techniques such as sprinkling or dripping [63,64]. High water hardness can also be limiting for fertigation, where values ​​above 100 mg/L of calcium and 43 mg/L of magnesium increase the risk of precipitation of phosphate fertilizers inside the pipes [65]. At points P1 to P8 (HU-RURAL) the TH values ​​ranged from 1.38 mg/L (P3) to 18.8 mg/L (P8). Calcium ranged from 0.078 mg/L (P1) to 5.992 mg/L (P2), and magnesium had a minimum of 0.005 mg/L (P1) and a maximum of 1.421 (P2) (Tables S3 and S4).\u003c/p\u003e \u003cp\u003eTA and HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e are equally important variables for assessing water quality for irrigation. The total alkalinity of water is the sum of all titratable bases, especially carbonate and bicarbonate (HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e). Waters rich in bicarbonates tend to precipitate calcium carbonate and magnesium carbonate when the soil solution is concentrated by evapotranspiration, increasing soil sodicity and consequently SAR. HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e levels above 518 mg/L in water can damage susceptible crops [66,67]. In this TA study, for the HU-RURAL points, the maximum value was 16.52 mg/L of CaCO\u003csub\u003e3\u003c/sub\u003e in P2 and minimum of 1.28 mg/L of CaCO\u003csub\u003e3\u003c/sub\u003e in P1. As for HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e, there was a maximum of 20.15 mg/L also in P2 and minimum of 1.562 mg/L in P1 (Tables S3 and S4).\u003c/p\u003e \u003cp\u003eThe SAR is an important variable for the assessment of water quality for irrigation. This is a relative ratio of Na\u003csup\u003e+\u003c/sup\u003e ion to Ca\u003csup\u003e2+\u003c/sup\u003e and Mg\u003csup\u003e2+\u003c/sup\u003e ions. It is used to estimate the potential for Na\u0026thinsp;+\u0026thinsp;to accumulate in the soil mainly to the detriment of Ca\u003csup\u003e2+\u003c/sup\u003e, Mg\u003csup\u003e2+\u003c/sup\u003e and K\u003csup\u003e+\u003c/sup\u003e as a result of the regular use of water with a high concentration of sodium. High SAR values ​​(\u0026gt;\u0026thinsp;26 meq/L) can influence the percolation time of water in the soil, leading to a decrease in the infiltration rate due to the dispersion and disaggregation of the soil structure [63,68\u0026ndash;69]. For the HU-RURAL points, the highest mean values ​​of SAR were found in P2\u0026ndash;3.378 meq/L and P8\u0026ndash;3.462 meq/L (Table S4).\u003c/p\u003e \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e, in turn, is one of the most common pollutants found in surface and groundwater, coming from point and non-point sources. Some non-point sources include agricultural activities such as fertilizer and manure application, leguminous crops and irrigation with groundwater containing nitrogen compounds [70,71]. Excess NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e in irrigation water can affect sensitive crops at concentrations above 5 mg/L. Most other crops are relatively unaffected by up to 30 mg/L nitrate [72]. The maximum content of NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e was found in P4, with a value of 0.962 mg/L and the minimum in P8, a value of 0.001 mg/L (Table S4).\u003c/p\u003e \u003cp\u003eFor HU-URBAN, the most important variables, with loads rotated above 0.75, were EC, TP, TN, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, TR, ECOLI and BOD. These variables are important indicators for water bodies in urban areas, since they can indicate contamination, for example, by effluents from domestic sewage. Electrical conductivity (EC) is extremely useful as a general measure of water quality. Significant changes in conductivity can be an indicator that a discharge or some other source of pollution has reached a given water body, especially freshwater bodies [73,74]. The maximum EC for the HU-URBAN points was 448 \u0026micro;S/cm at P12 (Table S5).\u003c/p\u003e \u003cp\u003eBiochemical oxygen demand (BOD) is the amount of dissolved oxygen required to decompose the organic material present in the water sample, by aerobic biological organisms, in a given time at a certain temperature [75]. High BOD values ​​in a water body are generally caused by the release of organic loads, mainly effluents from domestic sewage, and are associated with a decrease in dissolved oxygen in the water, which can lead to the mortality of aquatic organisms [44]. Point P12 showed a maximum of 6.42 mg/L, as can be seen in Table S5 (supplementary). BOD values ​​can vary significantly; in general, unpolluted fresh water has a value below 1 mg/L, moderately polluted water from 2 to 8 mg/L and treated domestic effluent 20 mg/L [76].\u003c/p\u003e \u003cp\u003eTotal residue (TR) represents the sum of dissolved solids and suspended solids in water, including colloidal particles. TR analysis in urban surface samples is an important indicator of pollution from domestic sewage or other point sources [44]. High levels of TR can affect the aesthetic quality of water, especially for human consumption, and can also reduce the efficiency of effluent treatment plants [77]. The highest levels of TR in this study were found in the HU-URBAN for P12 with a maximum of 450.9 mg/L (Table S5).\u003c/p\u003e \u003cp\u003ePhosphorus (P) and nitrogen (N) compounds are essential for the processes that occur in the aquatic environment. However, in excessive amounts they represent a significant source of water pollution [78]. P is a primary nutrient limiting the growth of algae and phytoplankton in many freshwater bodies, and its source can be either anthropogenic (domestic eluents and fertilizers) or natural (precipitation or geological materials) [79]. Total N is the sum of all forms of nitrogen present in water (organic, ammoniac, nitrite and nitrate).\u003c/p\u003e \u003cp\u003eElevated levels of N and P in water bodies cause nutrient imbalance and induce eutrophication, bringing anoxic conditions to the water [80]. Both TP and TN were key variables in the factor analysis of this study, along with NH4+. The maximum values ​​of TP and TN were found in points with urban influence (HU-URBAN), 0.282 mg/L TP and 41.40 mg/L TN for P12; and 16.01 mg/L of NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e at P10, both receiving effluents from Sewage Treatment Stations.\u003c/p\u003e \u003cp\u003eThe bacterium of the coliform group, \u003cem\u003eEscherichia coli\u003c/em\u003e (ECOLI), is an important indicator of fecal pollution in freshwater bodies, especially in urban environments, considered a simple and economic analysis compared to other pathogens [81,82]. The maximum concentrations of ECOLI detected by the method of enzyme substrates in this study were for P12\u0026ndash;48,392 NMP/100 mL and P10\u0026ndash;12,200 NMP/100 mL.\u003c/p\u003e \u003cp\u003eThe sampling points in the area under mostly natural land cover (HU-NATURAL) are located in the Ecological Reserve of the Brazilian Institute of Geography and Statistics (RECOR-IBGE), in the center-south of the FD and in the Environmental Protection Area (APA) of Cafuringa, in the extreme north of the FD. These two regions are characterized by extensive areas of preserved vegetation in the Cerrado biome [83,84]. The key variables of greatest interest to the group were CE, TDS, TH, TC, HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e, Ca\u003csup\u003e2+\u003c/sup\u003e and Mg\u003csup\u003e2+\u003c/sup\u003e. These variables are closely linked to the natural geological characteristics of these regions, since there is little or no human influence at the sampling points. Points P15, P16, P17 and P18 are located in a region characterized by the presence of Cambisol, originating from predominantly limestone rocks [85].\u003c/p\u003e \u003cp\u003eIn the HU-NATURAL group, point P16 (point with anthropic influence in Ouro River) presented the maximum levels for CE (251 \u0026micro;S/cm), TDS (120.5 mg/L), TH (140.51 mg/L CaCO3), TC (32.514 mg/L) and HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e (170.31 mg/L). P18 (point with anthropic influence in Contagem River) showed maximum Ca\u003csup\u003e2+\u003c/sup\u003e (31.35 mg/L) and P15 (Ouro River headwater) maximum Mg\u003csup\u003e2+\u003c/sup\u003e (13.611 mg/L) (Tables S5 and S6).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Water Quality Index\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1. Water Quality Index for Human Supply (WQI\u003csub\u003eCETESB\u003c/sub\u003e)\u003c/h2\u003e \u003cp\u003eThe WQI\u003csub\u003eCETESB\u003c/sub\u003e classified the samples from P10 and P12 as \u0026ldquo;\u003cem\u003ereasonable\u003c/em\u003e\u0026rdquo; and the rest of the sampled points as \u0026ldquo;\u003cem\u003egood\u003c/em\u003e\u0026rdquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). As evidenced in the HCA, points P10 (point with human influence on Sobradinho River) and P12 (point with human influence on Ponte Alta River) have similar characteristics, as both are receivers of sewage effluents from three administrative regions with an estimated population of 275,778 inhabitants [86].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe applied index takes into account the contamination of water bodies caused by the release of domestic sewage, and the variables used in the WQI\u003csub\u003eCETESB\u003c/sub\u003e are related to the evaluation of the quality of raw surface water for public supply purposes, after conventional treatment [32]. This means that samples from points P10 and P12 are considered unsafe for human consumption. Points P9 (Sobradinho River headwater) and P11 (Tamandu\u0026aacute; River headwater), both in Urban HUs, were classified as \u0026ldquo;\u003cem\u003egood\u003c/em\u003e\u0026rdquo;, with the need for pre-treatment so that water can be used for human consumption.\u003c/p\u003e \u003cp\u003eThe FD and the metropolitan region of Bras\u0026iacute;lia have approximately 97% of the population living in the urban area, almost three million people [23]. In the FD, 99% of the population is served by the regular water supply network. Around 870,000 households are served by five main supply systems, including reservoirs, rivers and underground wells, with a production capacity of more than 11,000 liters of water per second [8].\u003c/p\u003e \u003cp\u003eDespite the high levels of coverage of the urban water supply network, the water crisis that occurred between 2016 and 2018 raised an alert for managers about conflicts over the use and search for new sources of water in sufficient quantity and quality to supply the city\u0026rsquo;s existing population in the long term [12]. In this context, monitoring and generating water quality data are essential, as they help water resource managers with information on pollution problems and in surveying promising water sources.\u003c/p\u003e \u003cp\u003ePoints P1 (V\u0026aacute;rzea do Burac\u0026atilde;o River headwater), P7 (Cabeceira Comprida River headwater) and P13 (Taquara stream headwater) had the highest medians during the analyzed period (n\u0026thinsp;=\u0026thinsp;12). These three points have in common the fact that they are springs located in areas considered preserved, even with predominantly rural land use in the case of P1 and P7.\u003c/p\u003e \u003cp\u003ePoints P1, P7 and P5 (Chapadinha River headwater) are located in headwaters of HU-RURAL. The water samples from these points were classified as \"\u003cem\u003egood\u003c/em\u003e\", as were P2, P6 and P8 (points with anthropic influence in HU-RURAL), showing that the water from these six points can be used for rural supply purposes (after treatment) for communities located close to water bodies.\u003c/p\u003e \u003cp\u003eThe FD, like the vast majority of Brazilian municipalities, has most of the population concentrated in urban areas. According to the last agricultural census, the population of the rural area of ​​the FD was 87,950 inhabitants, representing 3.42% of the total population, with a demographic density of the rural population of 18.84 hab/km\u0026sup2;, a low value when calculated in relation to the totality of the area [10]. The low population density of rural areas makes collective water supply solutions difficult. There are currently 61 independent rural supply systems operated by the Federal District Environmental Sanitation Company (CAESB), in small, more densely populated localities, corresponding to a service of about 15% of the rural population in these areas [8].\u003c/p\u003e \u003cp\u003eThe percentage not met by CAESB uses individual sources (wells and direct capture of surface water) for supply, and these have little or no water quality control carried out by the Sanitary Surveillance. The large extension of the rural area, the low population density and the great distance between the operational units of the Environmental Sanitation Company, raises the operational cost of supply through the general network, which makes its expansion difficult [8,87].\u003c/p\u003e \u003cp\u003eOf the points located in HU-NATURAL, P13 and P14 had higher medians, compared to other points in the same HU. These two points are located in the IBGE-RECOR, a protected area known as a Conservation Unit with 1,300 ha designated as a conservation area in 1975 [83].\u003c/p\u003e \u003cp\u003ePoints P15, P16, P17 and P18 are located in HUs within the Maranh\u0026atilde;o River Basin, and they share similar water quality characteristics, as seen in HCA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). This region, located in the northern portion of the FD, may be a promising source of water supply for the population located in the northern portion of the FD.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2. Irrigation Water Quality Index (IWQI\u003csub\u003eFD\u003c/sub\u003e)\u003c/h2\u003e \u003cp\u003eThe IWQI\u003csub\u003eFD\u003c/sub\u003e was developed to assess the quality of water for irrigation purposes in the FD. According to the classification by the index, the samples of P1, P2, P5, P6, P7, P8, P9, P11, P13 and P14 were classified as \u0026ldquo;\u003cem\u003egood\u003c/em\u003e\u0026rdquo;. Points P3, P4, P10, P12, P15, P16, P17 and P18 were classified as \u0026ldquo;\u003cem\u003eaverage\u003c/em\u003e\u0026rdquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the FD, agriculture is an important economic activity, and irrigated agriculture was the sector that most showed an increase in demand for water consumption in the region, due to the large investment by the private sector and with the incorporation of new areas with aptitude for irrigation [88].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis type of agriculture is characterized by areas for large crops, vegetables and fruits. The more than 20,000 agricultural enterprises produce flowers, grain, vegetables and fruit, having produced more than 700,000 tons of grain in 2021. The cultivation of vegetables reached more than 200,000 tons and that of fruit, more than 30,000 tons [89,90]. It is important to emphasize that local agriculture is developed in small areas, given the territorial dimension of the FD and any factor that affects the cultivation areas, such as climatic effects or conflicts due to water scarcity, generates a great impact on the index of the agricultural sector [91].\u003c/p\u003e \u003cp\u003eBrazil is among the 10 countries in the world with the largest area equipped for irrigation [92]. In the FD, the use of irrigation equipment began in 1986, with strong expansion between 1988 and 1997, with about 12,000 hectares in 2012, 14,000 hectares in 2015, and currently the FD has 34,000 hectares of irrigated area [88,93].\u003c/p\u003e \u003cp\u003eThe predominant type of irrigation in the FD is center pivots concentrated mainly in a small strip in the eastern part of its territory. This range, where the pivots are concentrated, corresponds to approximately a quarter of the FD area and is the region where almost all grain production is concentrated. The main products grown in the central pivot-irrigated areas in the FD are beans, corn, wheat, vegetables and coffee [88,94].\u003c/p\u003e \u003cp\u003eIn the eastern part of the FD territory, there are the HUs with the largest number of rural properties and equipment that captures and distributes water for irrigation [88]. In the Rio Preto watershed, for example, the distribution of irrigated areas and the water demand for each irrigation system indicate a total of 7,546 L/s of water demand [92].\u003c/p\u003e \u003cp\u003ePoints P1, P2, P5 and P6, located in HU-RURAL in the eastern area of ​​the FD, had a median IWQI\u003csub\u003eFD\u003c/sub\u003e between 71 and 85, being classified as \"\u003cem\u003egood\u003c/em\u003e\". This means that water can be used for irrigation of grain, cereal, trees and fodder, but its use should be avoided on vegetables that are eaten raw and fruit that grows close to the ground and that is eaten raw without removing the skin. Points P3 and P4, also located in HU-RURAL in the eastern area, were classified as \"\u003cem\u003eaverage\u003c/em\u003e\", (median between 41 and 55), which means that their use is inappropriate for irrigation of vegetables and fruit in general, in addition to crops of grain, cereal and fodder [11].\u003c/p\u003e \u003cp\u003eIn the western region of the FD, the basin that contributes to irrigation is the Descoberto River watershed. The distribution of irrigated areas and the water demand for each type of irrigation system indicate an estimate for water demand of approximately 2,462 L/s, for this basin, an area of ​​2,052 ha. The main HU ​​that contributes to irrigation in this area is Rodeador River [92]. The water samples from points P7 and P8 (HU-RURAL), located at the source and points with anthropic influence in Rodeador River, were classified as \"\u003cem\u003egood\u003c/em\u003e\" during the analyzed period, showing suitability for irrigation of grain, cereal, trees and fodder.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eIn this study, we used multivariate statistical analysis and water quality indices as tools in assessing the quality of water - for supply and irrigation - from a large dataset. This set included 29 physical, chemical and biological variables, in 18 sampling points monitored for 12 months, under different land uses and cover (rural, urban and natural). Through HCA, the sampling points were grouped into four distinct clusters, according to the similar characteristics of the water samples. Through PCA and EFA, it was possible to reduce the number of variables in the original data matrices.\u003c/p\u003e \u003cp\u003eThe PCA explained 59.4%, 66.9% and 58.7% of the total variation in data for HU-RURAL, HU-URBAN and HU-NATURAL, respectively. Through the EFA, it was possible to remove the key variables for each group of land use and land cover. For HU-RURAL, the key variables were TH, TA, HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, TURB and SAR. These variables are important parameters related to water quality for irrigation. For HU-URBAN, the most important variables were EC, TP, TN, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, TR, ECOLI and BOD. All of them are strongly related to the release of effluents from sewage treatment stations at the points sampled. In the HU-NATURAL, the key variables were related to the geological characteristics of the regions where samples were collected from the rivers: CE, TDS, TH, TC, HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, Ca\u003csup\u003e2+\u003c/sup\u003e and Mg\u003csup\u003e2+\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe WQI\u003csub\u003eCETESB\u003c/sub\u003e, the index used in the assessment for supply purposes, classified 16 of the 18 sample points as \"\u003cem\u003egood\u003c/em\u003e\" (medians between 52 and 79), demonstrating that water from these points is suitable for human consumption after simplified treatment. The exception was points that receive sewage effluents from large metropolitan regions, which were classified as \u0026ldquo;\u003cem\u003ereasonable\u003c/em\u003e\u0026rdquo; (medians between 37and 51).\u003c/p\u003e \u003cp\u003eThe IWQI\u003csub\u003eFD\u003c/sub\u003e, an index developed to assess the quality of irrigation in the region, classified 10 points as \u0026ldquo;\u003cem\u003egood\u003c/em\u003e\u0026rdquo; (medians between 71 and 85) and another eight sampling points as \u0026ldquo;\u003cem\u003eaverage\u003c/em\u003e\u0026rdquo; (medians between 56 and 70). The places where the water samples were considered \"\u003cem\u003egood\u003c/em\u003e\" present quality for irrigation of grain, cereal, trees and fodder, but irrigation should be avoided in vegetables that are consumed raw and fruit that grows close to the ground and that is eaten raw without removing the skin.\u003c/p\u003e \u003cp\u003eThe findings obtained demonstrate that the tools used were useful in the general assessment of water quality, since it is a large set of data, in a complex area of ​​study. These tools aim to support integrated water resource management actions aimed at mediating conflicts over water use and water security, with great potential in the application of programs to monitor the quality of surface water in the FD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval \u003c/strong\u003eNot Applicable\u003cstrong\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate \u003c/strong\u003eThe authors provided consent to participate in this study.\u003cstrong\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish \u003c/strong\u003eThe authors agreed to publish this study.\u003cstrong\u003e\u003cbr /\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contributions \u003c/strong\u003eDHF Muniz: designed the study, performed data collection and laboratory analysis and drafted the manuscript. JV Malaquias: performed the statistical analysis, revised the manuscript. EC Oliveira-Filho: drafted and revised the final manuscript. All authors reviewed the results and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e This work was supported by the Federal District Research Support Foundation - FAPDF [grant number 193.00002283/2022-91].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e The authors declare no competing or conflict interests .\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials \u003c/strong\u003eData is provided within the manuscript and supplementary information files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e1. Connor R, Coates D (2021) The state of water resources. In: The United Nations World Water Development Report 2021: Valuing Water. UNESCO, Paris, France, 2021, p. 11\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e2. Mehmood H (2019) Bibliometrics of Water Research: A Global Snapshot. UNU-INWEH Report Series, Issue 06. United Nations University Institute for Water, Environment and Health, Hamilton, Canada, 2019, 24 p.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e3. He C, Liu Z, Wu J, Pan X, Fang Z, Li J, Bryan BA (2021) Future global urban water scarcity and potential solutions. Nat Commun. https://doi.org/10.1038/s41467-021-25026-3\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e4. Getirana A, Libonati R, Cataldi M (2021) Brazil is in water crisis - it needs a drought plan. Nature. https://doi.org/10.1038/d41586-021-03625-w\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e5. Gesualdo GC, Sone JS, Galv\u0026atilde;o CO, Martins ES, Montenegro SMGL, Tomasella J, Mendiondo EM (2021) Unveiling water security in Brazil: current challenges and future perspectives. Hydrol Sci J. https://doi.org/10.1080/02626667.2021.1899182\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e6. Cunha APMA, Zeri M, Deusdar\u0026aacute; LK, Costa L, Cuartas LA, et al. (2019) Extreme Drought Events over Brazil from 2011 to 2019. Atmosphere. https://doi.org/10.3390/atmos10110642\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e7. Pereira V R, Rodriguez DA, Coutinho SMV, Santos DV, Marengo JA (2020) Adaptation opportunities for water security in Brazil. Sustain Debate. https://doi.org/10.18472/SustDeb.v11n3.2020.33858\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e8. Lima LA, Silva DH. (2020) Um Panorama das \u0026Aacute;guas no Distrito Federal. CODEPLAN, Bras\u0026iacute;lia. https://www.codeplan.df.gov.br/wp-content/uploads/2020/07/Estudo-Um-Panorama-das-%C3%81guas-no-Distrito-Federal.pdf\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e9. Strauch M, Lima JEFW, Volk M, Lorz C, Makeschin F (2103) The impact of Best Management Practices on simulated streamflow and sediment load in a Central Brazilian catchment. J Environ Manage. https://doi.org/10.1016/j.jenvman.2013.01.014\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e10. IBGE (2023). Brasil \u0026ndash; Distrito Federal \u0026ndash; Popula\u0026ccedil;\u0026atilde;o (2022) Available at: https://cidades.ibge.gov.br/brasil/df/.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e11. Muniz DHF, Malaquias JV, Lima JEFW, Oliveira-Filho, EC (2020) Proposal of an irrigation water quality index (IWQI) for regional use in the Federal District, Brazil. Environ Monit Assess. https://doi.org/10.1007/s10661-020-08573-y\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e12. Lima JEFW, Freitas GK, Pinto MAT, Salles PSBA (2018) Gest\u0026atilde;o da crise h\u0026iacute;drica 2016\u0026ndash;2018: experi\u0026ecirc;ncias do Distrito Federal. ADASA, CAESB, SEAGRI, EMATER-DF, Bras\u0026iacute;lia. https://www.adasa.df.gov.br/images/banners/alta.pdf\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e13. Adasa (2012) PGIRH-DF - Plano de Gerenciamento Integrado dos Recursos H\u0026iacute;dricos do Distrito Federal. Bras\u0026iacute;lia, DF: Adasa, GDF, Ecoplan. https://www.Adasa.df.gov.br/images/storage/programas/PIRHFinal/PGIRH_relatorio_sintese_versaofinal.pdf Accessed 03 August 2022\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e14. Damania R, Desbureaux S, Rodella AS, Russ J, Zaveri E (2019) Quality Unknown: The Invisible Water Crisis. Washington DC: World Bank. https://openknowledge.worldbank.org/handle/10986/32245\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e15. Myers DN (2022) Why monitor water quality? U.S. Geological Survey - USGS. https://water.usgs.gov/owq/WhyMonitorWaterQuality.pdf\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e16. Uddin MG, Nash S, Olbert AI (2021) A review of water quality index models and their use for assessing surface water quality. Ecol Indic. https://doi.org/10.1016/j.ecolind.2020.107218\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e17. Fu L, Wang YG (2012) Statistical Tools for Analyzing Water Quality Data. In: Voudouris, K.; Voutsa, D. (Eds) Water Quality Monitoring and Assessment. IntechOpen, London, UK, 2012, pp. 144\u0026ndash;168. https://doi.org/10.5772/35228\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e18. Muniz DH F, Oliveira-Filho EC (2023) Multivariate Statistical Analysis for Water Quality Assessment: a review of research published between 2001 and 2020. Hydrology. https://doi.org/10.3390/hydrology10100196\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e19. Wang Y, Wang P, Bai Y, Tian Z, Li J, et al. (2013) Assessment of surface water quality via multivariate statistical techniques: A case study of the Songhua River Harbin region, China. J Hydro Environ Res. https://doi.org/10.1016/j.jher.2012.10.003\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e20. Jung KY, Lee K-L, Im TH, Lee IJ, Kim S, Han K-Y, Ahn, JM (2016) Evaluation of water quality for the Nakdong River watershed using multivariate analysis. Environ Technol Innov. https://doi.org/10.1016/j.eti.2015.12.001\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e21. Khanoranga A, Khalid S (2019) An assessment of groundwater quality for irrigation and drinking purposes around brick kilns in three districts of Balochistan province, Pakistan, through water quality index and multivariate statistical approaches. J Geochem Explor. https://doi.org/10.1016/j.gexplo.2018.11.007\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e22. Barbosa-Filho J, de Oliveira IB (2021) Development of a groundwater quality index: GWQI, for the aquifers of the state of Bahia, Brazil using multivariable analyses. Sci Rep. https://10.1038/s41598-021-95912-9\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e23. Iqbal J, Shah MH (2013) Health Risk Assessment of Metals in Surface Water from Freshwater Source Lakes, Pakistan. Hum Ecol Risk Assess. https://doi.org/10.1080/10807039.2012.716681\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e24. Han Q, Tong RZ, Sun WC, Zhao Y, Yu JS, Wang GQ, Shrestha S, Jin YL (2019) Anthropogenic influences on the water quality of the Baiyangdian Lake in North China over the last decade. Sci Total Environ. https://doi.org/10.1016/j.scitotenv.2019.134929\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e25. Siepak M, Sojka M (2017) Application of multivariate statistical approach to identify trace elements sources in surface waters: a case study of Kowalskie and Stare Miasto reservoirs, Poland. Environ Monit Assess. https://doi.org/10.1007/s10661-017-6089-x\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e26. Golshan A, Evans C, Geary P, Morrow A, Rogers Z, Maeder M (2020) Turning Routine Data into Systems Insight: Multivariate Analysis of Water Quality Dynamics in a Major Drinking Water Reservoir. Environ Model Assess. https://doi.org/10.1007/s10666-020-09700-2\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e27. G\u0026uuml;ler C (2007) Characterization of Turkish bottled waters using pattern recognition methods. Chemom Intell Lab Syst. https://doi.org/10.1016/j.chemolab.2006.08.009\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e28. Felipe-Sotelo M, Henshall-Bell ER, Evans NDM, Read D (2015) Comparison of the chemical composition of British and Continental European bottled waters by multivariate analysis. J Food Compost Anal. https://doi.org/10.1016/j.jfca.2014.10.014\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e29. Gao Z, Liu Y, Li N (2022) An enhanced beetle antennae search algorithm based comprehensive water quality index for urban river water quality assessment. Water Resour Manag. https://doi.org/10.1007/s11269-022-03169-2\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e30. Dash S, Kalamdhad AS (2021) Science mapping approach to critical reviewing of published literature on water quality indexing. Ecol Indic. https://doi.org/10.1016/j.ecolind.2021.107862\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e31. Gupta S, Gupta SK (2021) A critical review on water quality index tool: Genesis, evolution and future directions. Ecol Infom. https://doi.org/10.1016/j.ecoinf.2021.101299\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e32. Abbasi T, Abbasi SA (2012) Chap.\u0026nbsp;1 - Why Water-Quality Indices. In: Abbasi T, Abbasi SA (eds) Water quality indices. Elsevier, New York, pp 3\u0026ndash;7. https://doi.org/10.1016/B978-0-444-54304-2.00001-4\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e33. Gitau MW, Chen J, Ma Z (2016) Water Quality Indices as Tools for Decision Making and Management. Water Resour Manag. https://doi.org/10.1007/s11269-016-1311-0\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e34. Zagatto PA, Lorenzetti ML, Lamparelli MC, Salvador MEP, Menegon-Jr N, Bertoletti E (1999) Aperfei\u0026ccedil;oamento de um \u0026iacute;ndice de qualidade de \u0026aacute;guas. Acta Limnol Bras 11(2): 111\u0026ndash;126\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e35. Adasa (2022) Sistema de Informa\u0026ccedil;\u0026otilde;es sobre Recursos H\u0026iacute;dricos \u0026ndash; DF. Rede de Monitoramento da Qualidade das \u0026Aacute;guas Superficiais da ADASA. \u0026Iacute;ndice de Qualidade da \u0026Aacute;gua \u0026ndash; IQA. Bras\u0026iacute;lia, DF: Adasa. http://gis.Adasa.df.gov.br/portal/home/\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e36. Medeiros AC, Faial KRF, Faial KCF, Lopes IDS, Lima MO, Guimar\u0026atilde;es RM, Mendon\u0026ccedil;a NM, et al. (2017) Quality index of the surface water of Amazonian rivers in industrial areas in Par\u0026aacute;, Brazil. Mar Pollut Bull. https://doi.org/10.1016/j.marpolbul.2017.09.002\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e37. Cicilinski AD, Virgens-Filho JS (2020) A new water quality index elaborated under the Brazilian legislation perspective. Int J River Basin Manag. https://doi.org/10.1080/15715124.2020.1803335\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e38. Costa DA, Azevedo JPS, dos Santos MA et al. (2020) Water quality assessment based on multivariate statistics and water quality index of a strategic river in the Brazilian Atlantic Forest. Sci Rep. https://doi.org/10.1038/s41598-020-78563-0\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e39. Kachroud M, Trolard F, Kefi M, Jebari S, Bourri\u0026eacute; G (2019). Water quality indices: challenges and application limits in the literature. Water. https://doi.org/10.3390/w11020361\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e40. Castro KB, Roig HL, Neumann MRB, Rossi MS, Seraphim APACC, R\u0026eacute;quia-J\u0026uacute;nior WJ, Costa ABB, H\u0026ouml;fer R (2019) New perspectives in land use mapping based on urban morphology: A case study of the Federal District, Brazil. Land Use Policy. https://doi.org/10.1016/j.landusepol.2019.104032\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e41. CODEPLAN (2017) Mapeamento da cobertura do Distrito Federal: 1984 a 2017 - Relat\u0026oacute;rio S\u0026iacute;ntese. Bras\u0026iacute;lia, DF. http://coberturadaterra.codeplan.df.gov.br/.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e42. ISO (1998). Water quality - Determination of dissolved Li\u003csup\u003e+\u003c/sup\u003e, Na\u003csup\u003e+\u003c/sup\u003e, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, K\u003csup\u003e+\u003c/sup\u003e, Mn\u003csup\u003e2+\u003c/sup\u003e, Ca\u003csup\u003e2+\u003c/sup\u003e, Mg\u003csup\u003e2+\u003c/sup\u003e, Sr\u003csup\u003e2+\u003c/sup\u003e and Ba\u003csup\u003e2+\u003c/sup\u003e using ion chromatography - Method for water and waste water (ISO Standard No. 14911:1998). https://www.iso.org/standard/25591.html\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e43. ASTM (2016) Standard test method for total nitrogen, and Total Kjeldahl Nitrogen (TKN) by calculation, in water by high temperature catalytic combustion and chemiluminescence detection (ASTM D8083-16), ASTM International, West Conshohocken. https://doi.org/10.1520/D8083-16\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e44. APHA (2018) Standard methods for the examination of water and wastewater (23nd ed.). American Public Health Association, Washington\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e45. Suarez DL, Wood JD, Lesch SM (2008) Infiltration into cropped soils: effect of rain and sodium adsorption ratio\u0026ndash;impacted irrigation water. J Environ Qual. https://doi.org/10.2134/jeq2007.0468\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e46. Ogwueleka TC (2014) Assessment of the water quality and identification of pollution sources of Kaduna River in Niger State (Nigeria) using exploratory data analysis. Water Environ J. https://doi.org/10.1111/wej.12004\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e47. Bouguerne A, Boudoukha A, Benkhaled A, Mebarkia AH (2017) Assessment of surface water quality of Ain Zada dam (Algeria) using multivariate statistical techniques. Int J River Basin Manag. https://doi.org/10.1080/15715124.2016.1215325\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e48. Barakat A, El Baghdadi M, Rais J, Aghezzaf B, Slassi M (2016) Assessment of spatial and seasonal water quality variation of Oum Er Rbia River (Morocco) using multivariate statistical techniques. Inter Soil Water Conserv Res. https://doi.org/10.1016/j.iswcr.2016.11.002\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e49. Jolliffe IT, Cadima J (2016) Principal component analysis: a review and recent developments. Phil Trans R Soc. https://doi.org/10.1098/rsta.2015.0202\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e50. Holland SM (2019) Principal Components Analysis (PCA). Department of Geology, University of Georgia, Athens, Greece. http://strata.uga.edu/8370/handouts/pcaTutorial.pdf\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e51. Ouyang Y, Nkedi-Kizza P, Wu QT, Shinde D, Huang CH (2006) Assessment of seasonal variations in surface water quality. Water Res. https://doi.org/10.1016/j.watres.2006.08.030\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e52. Hair JFK, Black WC, Babin BJ, Anderson RE (2014) Multivariate data analysis. 7th Edition, Pearson Prentice Hall, Hoboken\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e53. R Core Team (2021) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e54. Meireles ACM, Andrade EM, Chaves LCG, Frischkorn H, Crisostomo LA (2010) A new proposal of the classification of irrigation water. Ci\u0026ecirc;ncia Agron\u0026ocirc;mica. https://doi.org/10.1590/S1806- 66902010000300005\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e55. Tibshirani R, Walther G, Hastie T (2001) Estimating the number of clusters in a data set via the gap statistic. J R Stat Soc Ser B Stat Methodol. https://doi.org/10.1111/1467-9868.00293\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e56. Muniz DHF, Moraes AS, Freire IS, Cruz CJD, Lima JEFW, Oliveira-Filho EC (2011) Evaluation of water quality parameters for monitoring natural, urban, and agricultural areas in Brazilian Cerrado. Acta Limnol Bras. https://doi.org/10.1590/S2179-975X2012005000009\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e57. Lima JEFW, Oliveira-Filho EC, Silva EM, Farias MFR (2006) Caracteriza\u0026ccedil;\u0026atilde;o Hidrol\u0026oacute;gica da APA da Cafuringa. In: Netto PB, Mecenas VV, Cardoso ES (ed). APA da Cafuringa \u0026ndash; a \u0026uacute;ltima fronteira natural do DF. SEMA-DF, Bras\u0026iacute;lia\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e58. Olsen RL, Chappell RW, Loftis JC (2012) Water quality sample collection, data treatment and results presentation for principal components analysis e literature review and Illinois River watershed case study. Water Res. https://doi.org/10.1016/j.watres.2012.03.028\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e59. Sergeant CJ, Starkey EN, Bartz KK, Wilson MH, Mueter FJ (2016) A practitioner\u0026rsquo;s guide for exploring water quality patterns using principal components analysis and Procrustes. Environ Monit Assess. https://doi.org/10.1007/s10661-016-5253-z\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e60. Gvozdić V, Brana J, Puntarić D, Vidosavljević D, Roland D (2011) Changes in the lower Drava River water quality parameters over 24 years. Arh Hig Rada Toksikol. https://doi.org/10.2478/10004-1254-62-2011-2128\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e61. Simeonov V, Stratis JA, Samara C, Zachariadis G, Voutsa D, Anthemidis A, Sofoniou M, Kouimtzis T (2003) Assessment of the surface water quality in Northern Greece. Water Res. https://doi.org/10.1016/S0043-1354(03)00398-1\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e62. Shrestha S, Kazama F (2007) Assessment of surface water quality using multivariate statistical techniques: A case study of the Fuji river basin, Japan. Environ Model Softw. https://doi.org/10.1016/j.envsoft.2006.02.001\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e63. Rawat KS, Singh SK, Gautam SK (2018) Assessment of groundwater quality for irrigation use: a peninsular case study. Appl Water Sci. https://doi.org/10.1007/s13201-018-0866-8\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e64. Malakar A, Snow DD, Ray C (2019) Irrigation Water Quality - A Contemporary Perspective. Water. https://doi.org/10.3390/w11071482\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e65. Kafkafi U, Tarchitzky J (2011) Fertigation: A Tool for Efficient Fertilizer and Water Management. International Fertilizer Industry Association, Paris. 141 p.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e66. Ayers RS, Westcot DW (1999) Water quality for agriculture. Irrigation and Drainage paper No. 29. FAO: Rome\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e67. Zaman M, Shahid SA, Heng L (2018) Irrigation Water Quality. In: Zaman M, Shahid SA, Heng L (eds) Guideline for Salinity Assessment, Mitigation and Adaptation Using Nuclear and Related Techniques. Springer Cham. https://doi.org/10.1007/978-3-319-96190-3\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e68. Aboukarima AM, Al-Sulaiman MA, El Marazky MSA (2018) Effect of sodium adsorption ratio and electric conductivity of the applied water on infiltration in a sandy-loam soil. Water SA. https://doi.org/10.4314/wsa.v44i1.12\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e69. Sattari MT, Feizi H, Colak MS, Ozturk A, Apaydin H, Ozturk F (2020) Estimation of sodium adsorption ratio in a river with kernel-based and decision-tree models. Environ Monit Assess. https://doi.org/10.1007/s10661-020-08506-9\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e70. Esmaeili S, Thomson NR, Rudolph DL (2020) Evaluation of nutrient beneficial management practices on nitrate loading to groundwater in a Southern Ontario agricultural landscape. Can Water Resour J. https://doi.org/10.1080/07011784.2019.1692697\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e71. Zhang X, Zhang Y, Shi P, Bi Z, Shan Z, Ren L (2021) The deep challenge of nitrate pollution in river water of China. Sci Total Environ. https://doi.org/10.1016/j.scitotenv.2020.144674\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e72. Simsek C, Gunduz O (2007) IWQ index: a GIS integrated technique to assess irrigation water quality. Environ Monit Assess. https://doi.org/10.1007/s10661-006-9312-8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e73. Sousa DNR, Mozeto AA, Carneiro RL, Fadini PS (2014) Electrical conductivity and emerging contaminant as markers of surface freshwater contamination by wastewater. Sci Total Environ. https://doi.org/10.1016/j.scitotenv.2014.02.135\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e74. Rusydi AF (2017) Correlation between conductivity and total dissolved solid in various type of water: a review. IOP Conf Ser: Earth Environ Sci. https://doi.or/10.1088/1755-1315/118/1/012019\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e75. Li D, Liu S (2019) Chap.\u0026nbsp;7 - Detection of River Water Quality. In: Li D, Liu S (ed) Water Quality Monitoring and Management: Basis, Technology and Case Studies. Academic Press, London. https://doi.org/10.1016/B978-0-12-811330-1.00007-7\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e76. Wilhem FM (2009) Pollution of Aquatic Ecosystems I. In: Likens GE (Ed) Encyclopedia of Inland Waters. Academic Press. https://doi.org/10.1016/B978-012370626-3.00222-2\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e77. Serajuddin MD, Chowdhur AI, Haque MD, Haque E (2019) Using Turbidity to Determine Total Suspended Solids in an Urban Stream: A Case Study. Proceedings of the 2nd International Conference on Water and Environmental Engineering, Dhaka, 19\u0026ndash;22 January 2019, 148\u0026ndash;154.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e78. Shen LQ, Amatulli G, Sethi T, Raymond P, Domisch S (2020) Estimating nitrogen and phosphorus concentrations in streams and rivers, within a machine learning framework. Sci Data. https://doi.org/10.1038/s41597-020-0478-7\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e79. Abu-Hmeidan HY, Williams GP, Miller AW (2018) Characterizing Total Phosphorus in Current and Geologic Utah Lake Sediments: Implications for Water Quality Management Issues. Hydrology. https://doi.org/10.3390/hydrology5010008\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e80. Kakade A, Salama E-S, Han H, Zheng Y, Kulshrestha S, Jalalah M, Harraz FA, Alsareii SA, Li X (2021) World eutrophic pollution of lake and river: Biotreatment potential and future perspectives. Environ Tech Innov. https://doi.org/10.1016/j.eti.2021.101604\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e81. Wen X, Chen F, Lin Y, Zhu H, Yuan F, Kuang D, Jia Z, Yuan Z (2020) Microbial Indicators and Their Use for Monitoring Drinking Water Quality - A Review. Sustainability. https://doi.org/10.3390/su12062249\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e82. Ma C-Y, Ihara M, Liu S, Sugie Y, Tanaka H (2022) Tracking the source of antibiotic-resistant Escherichia coli in the aquatic environment in Shiga, Japan, through whole-genome sequencing. Environ Adv. https://doi.org/10.1016/j.envadv.2022.100185\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e83. Silva LCR, Sternberg L, Haridasan M, Hoffmann WA, Miralles-Wilhem F, Franco AC (2008) Expansion of gallery forests into central Brazilian savannas. Glob Chang Biol. https://doi.org/10.1111/j.1365-2486.2008.01637.x\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e84. Nunes RV, Frizzas MR, Vaz-de-Mello FZ (2012) Scarabaeinae (Coleoptera: Scarabaeidae) of a rupestrian field at Cafuringa, Distrito Federal, Brazil: commented list of species. Biota Neotrop. https://doi.org/10.1590/S1676-06032012000400013\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e85. Ferreira MC, Rodrigues SB, Vieira DLM (2017) Regeneration through resprouting after clear-cutting and topsoil stripping in a tropical dry forest in Central Brazil. Rev \u0026Aacute;rvore. https://doi.org/10.1590/1806-90882017000200018\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e86. CODEPLAN (2018) Pesquisa Distrital por Amostra de Domic\u0026iacute;lios (PDAD). Bras\u0026iacute;lia, DF: CODEPLAN. https://www.codeplan.df.gov.br/wp-content/uploads/2020/06/Destaques_PDAD_revisado.pdf.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e87. GDF (2017) PDSB - Plano Distrital de Saneamento B\u0026aacute;sico. Bras\u0026iacute;lia, DF: SEMA. 484 p. https://www.sema.df.gov.br/wp-conteudo/uploads/2017/09/Relatorio_S%C3%ADntese.pdf\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e88. Lima JEFW, Antonini JCA, Borges MM, Andrade SML, Lobato BR, Sousa LLP, Rocha FEC, Carvalho AVV (2017) Demandas relacionadas \u0026agrave;s Culturas irrigadas no DF e propostas para pesquisa, extens\u0026atilde;o e pol\u0026iacute;tica p\u0026uacute;blica. In: Andrade SML, Rocha FEC, Lobato BR. Expedi\u0026ccedil;\u0026atilde;o Safra Bras\u0026iacute;lia \u0026ndash; 2016: Soja, milho safrinha e culturas irrigadas: diagn\u0026oacute;stico e prospec\u0026ccedil;\u0026atilde;o de demandas para pesquisa, extens\u0026atilde;o rural e pol\u0026iacute;tica p\u0026uacute;blica. SEAGRI, EMATER-DF, CEASA, Embrapa Cerrados, Bras\u0026iacute;lia. https://www.infoteca.cnptia.embrapa.br/infoteca/bitstream/doc/1070037/1/LivroExpedicaoSafraBrasilia2016versaofinal.pdf\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e89. CONAB (2022) Acompanhamento da safra brasileira de gr\u0026atilde;os \u0026ndash; 9\u0026deg; levantamento, v. 1 (2013\u0026ndash;2022) \u0026ndash; Bras\u0026iacute;lia: MAPA, CONAB. Available at: https://www.conab.gov.br/info-agro/safras/graos/boletim-da-safra-de-graos/item/download/42837_ 526b4c0d6f83ae8e34bb846683666d92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e90. EMATER-DF (2022) Informa\u0026ccedil;\u0026otilde;es Agropecu\u0026aacute;rias do Distrito Federal 202. Bras\u0026iacute;lia: GDF, SEAGRI, EMATER-DF. http://emater.df.gov.br/wp-content/uploads/2018/06/Relatorio_Atividades_ Agropecuarias____2021___DF.pdf\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e91. CODEPLAN (2022) \u0026Iacute;ndice de Desempenho Econ\u0026ocirc;mico do Distrito Federal Idecon/DF \u0026ndash; 1\u0026deg; Trimestre de 2022. https://www.codeplan.df.gov.br/wp-content/uploads/2018/02/Idecon-DF_1o-Tri_2022.pdf.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e92. Borghetti JR, Silva WLC, Nocko HR, Loyola LN, Chianca GK (2017) Agricultura Irrigada Sustent\u0026aacute;vel no Brasil: Identifica\u0026ccedil;\u0026atilde;o de \u0026Aacute;reas Priorit\u0026aacute;rias. FAO: Bras\u0026iacute;lia. https://www.fao.org/3/i7251o/i7251o.pdf\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e93. Adasa (2020) Mapa de \u0026aacute;reas irrigadas para fins agr\u0026iacute;colas no Distrito Federal em 2020. Bras\u0026iacute;lia, DF: Adasa. http://gis.Adasa.df.gov.br/portal/home/\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e94. ANA (2016) Levantamento da Agricultura Irrigada por Piv\u0026ocirc;s Centrais no Brasil \u0026minus;\u0026thinsp;2014: relat\u0026oacute;rio s\u0026iacute;ntese / Ag\u0026ecirc;ncia Nacional de \u0026Aacute;guas - Bras\u0026iacute;lia: ANA. https://www.ana.gov.br/arquivos/institucional/sge/CEDOC/Catalogo/2016/LevantamentodaAgriculturaIrrigadaporPivosCentrais.pdf.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"surface water, rivers, multivariate statistical analysis, WQI","lastPublishedDoi":"10.21203/rs.3.rs-4329941/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4329941/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMonitoring and evaluating water quality in urban areas has been emphasized as a fundamental tool in the management of water resources. The Federal District (FD) of Brazil has the third most populous city in the country and has recently faced a significant water crisis, culminating in a deterioration of water quality. The aim of this study was to apply multivariate statistical analysis (MSA) and water quality indices (WQIs) for human supply and irrigation in order to evaluate the quality of surface water in rivers under different land uses and occupations (8 rural, 4 urban and 6 natural). To this end, 29 water quality variables were analyzed in 18 sampling points between 2017 and 2019. The HCA grouped the points into 4 statistically significant clusters, taking into account similar types of sources. PCA explained 59.4% (rural), 66.9% (urban) and 58.7% (natural) of the total data variation in the first two principal components. Factor Analysis identified the key variables for each data matrix through the first three factors. The WQI for supply classified 16 of the 18 sampling points as \u0026ldquo;good\u0026rdquo;, demonstrating their suitability for human consumption after simplified treatment. The WQI for irrigation classified 10 points as \u0026ldquo;good\u0026rdquo; and eight points as \u0026ldquo;average\u0026rdquo;, demonstrating the restriction of points considered \u0026ldquo;average\u0026rdquo; for irrigation of raw vegetables and fruits that grow in the soil and are consumed raw without the skin. Data showed that tools applied are promising and have potential for application in surface water quality monitoring and communication programs for the FD.\u003c/p\u003e","manuscriptTitle":"Surface water quality assessment in the Federal District, Brazil: application of multivariate statistical analysis and water quality indices for human consumption and irrigation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-12 03:44:20","doi":"10.21203/rs.3.rs-4329941/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d8b0d62e-d538-48fa-828d-30a5bc0059c0","owner":[],"postedDate":"June 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-30T15:23:28+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-12 03:44:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4329941","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4329941","identity":"rs-4329941","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}