Quali-quantitative water behaviour in an intensive swine production catchment in the Atlantic Forest biome, southern Brazil

Quali-quantitative water behaviour in an intensive swine production catchment in the Atlantic Forest biome, southern Brazil | 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 Quali-quantitative water behaviour in an intensive swine production catchment in the Atlantic Forest biome, southern Brazil Aimê Cardozo, Gustavo Antonio Piazza, Thiago Caique Alves, Adilson Pinheiro, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3869871/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 May, 2024 Read the published version in Environmental Monitoring and Assessment → Version 1 posted 9 You are reading this latest preprint version Abstract Agriculture is an essential economic activity in Brazil. However, it is also the main source of water quality degradation. Monitoring catchments with agricultural land use is a way to generate information on a scale to identify causes and sources of water quality degradation. This work used monitoring data derived from hydrology and the quality of surface and underground water in an intensive agricultural catchment in the Atlantic Forest biome. The Fortaleza River catchment is located in the western part of Santa Catarina state in southern Brazil and has 62 km² of drainage area. Hydrological and water quality monitoring was conducted for seven years at two fluviometric stations, three lysimeters, one meteorological station and one piezometer. Data on precipitation, temperature, water flow, surface runoff, drainage, and water quality were used. Statistical analyses were also developed. Precipitation between 2013 and 2019 presented a homogeneous distribution in monthly and annual data, with January and July the months with the highest and lowest values, respectively. Statistical difference in the average and Q 95 flows was found in upstream and downstream fluviometric sections. In terms of quality, statistical differences were identified for ammonium, nitrate and potassium concentrations, which had higher concentrations in lysimeter runoff, indicating direct influence of agricultural activity on water quality. Principal component analysis (PCA) indicated that (i) surface water presented a positive relationship in Component 1 for the magnesium-calcium, sulphate-chloride and acetate-bromide groups and a negative relationship for phosphate-nitrate; (ii) in lysimeters, the positive relationship occurred for Component 2 for the phosphate-chloride and sulphate-nitrate groups and was negative for ammonium-lithium and calcium-potassium-magnesium; and (iii) in piezometer, positive relationships were found for chloride-sodium and phosphate-nitrite pairs, while negative relationships were found for calcium-magnesium. monitoring water quality agricultural catchment principal component analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Water with appropriate quantity and quality conditions is a crucial resource for natural and anthropic ecosystems (Braga et al., 2005). Human activity through land use, however, produces negative impacts on hydrological processes, impacts that include changes in streamflow and water quality (Tucci, 2002). The degradation of water quality is a growing and global issue that influences not only economic and social well-being but also the sustainability of ecosystems and biodiversity (Michalak, 2016). The impacts on water quality from human activity are usually linked to pollution from different sources, such as urban, industrial and agricultural effluents (Merten and Minella, 2002), all of which are modified and contribute to climate change (Gascuel-Odoux et al., 2022). In agricultural catchments, studies are generally focused on agriculture tillage or incorrect land management (Breda, 2011; Sales et al., 2020). According to Rossi et al. (2023), the vast majority of producers in the rural part of Santa Catarina have empirical knowledge, with no technical criteria, about the right dose of pig manure to use as fertilizer in agricultural areas. The problem stems from agriculture because it is the most important economic activity in Brazil (Martinelli et al., 2013). Land use in Brazil is mainly used by pasture (55.79%), followed by agriculture (23.14%), forestry (3.24%), urban areas (1.36 %) and mining (0.13%) (MapBiomas Brasil, 2023). It has to be taken into account that over the course of centuries, in response to increased water and temperature stress, humans have continually adapted their agricultural practices, which has subsequently led to profound impacts on water quality (Merot et al., 2014). Intensive livestock farming in Brazil has grown in recent years due to the global demand for animal protein, as a result of African swine fever and the Covid-19 pandemic (Rossi et al., 2023; Nunes and Zanella, 2020), especially in the South Region of Brazil, in Santa Catarina. Santa Catarina state leads the country’s swine production (ABPA, 2023). Swine farms, however, present high loads of nutrients, e.g. nitrogen and phosphorus (Silva et al., 2015). The impact of agricultural activities on water quality in Brazil is attested to by several studies, such as those conducted by Ribeiro et al. (2014) and Gonçalvez and Rocha (2016) in Paraná state, Simedo et al. (2018) in São Paulo and Pinheiro et al. (2013), Perazzoli, Pinheiro and Kaufmann (2013), Zucco, Pinheiro and Soares (2015) and Piazza et al. (2017) in Santa Catarina. To understand if agricultural activities are changing water quality, monitoring techniques are applied. Monitoring catchments allows us to understand how different processes act in the hydrological cycle and in nutrient transport (Monte-Mor, 2012). Water quality monitoring consists of gauging physical, chemical or biological characteristics and determining their spatial and seasonal variability (Bertossi et al., 2013). With these data, for example, it is possible to estimate whether an increase in population or agricultural production will impact water resources (Granziera, 2006; Palhares, 2016). There are several motivations for monitoring water quality, such as identification of sources, and loads of nutrients (Bezerra, 2017). Furthermore, it must be considered that variability patterns are controlled by physical, climatic, biological and anthropogenic factors, which can vary between catchments (Ebeling et al., 2021) and by intervals of nutrient inputs that can vary from immediate to several decades due to legacy storage (Bouraoui and Grizzetti, 2011; Dupas et al., 2020; Ehrhardt et al., 2020; Meals et al., 2010). Another fact to be considered is that climate changes generally occur on a global and regional scale, but water quality is studied locally (Gascuel-Odoux et al., 2022). Understanding seasonal nutrient and flow patterns and their trends improves the ability to predict response times, locations prone to pollution and priorities for interventions (Frei et al., 2020). This paper evaluated hydrological and water quality behaviour using monitoring data from the Fortaleza River catchment in the western part of Santa Catarina, state in southern Brazil. Materials and Methods Study area The Fortaleza River catchment is located in the western region of Santa Catarina state and has a drainage area of 62 km² (Figure 1). The urban perimeter of São João do Oeste, the municipality fully inserted in the Fortaleza River catchment, has a population of 6,295 inhabitants (IBGE, 2023), of whom approximately 25% are in the urban area and 75% in the rural area. The number of pigs is 129,104 (IBGE, 2023), which leads to a proportion of pigs in the municipality of 20.5 pigs/per inhabitant. The Fortaleza River catchment is located in the phytoecological region of a seasonal deciduous forest in the Atlantic Forest biome. The catchment is composed of cambisols (78.8%) and nitisols (structured purple earth, 21.2%). Land use is predominantly used for agriculture (39.8%) and native forest (23.8%), pastures (10.6%), reforestation (9.3%), urban areas (6.8%), forest fragments (3.7%), regeneration forests (3.0%), eucalyptus (2.4%) and water (0.6%). In the agricultural area, São João do Oeste stands out economically for extensive farming, mainly by family groups, with agriculture and livestock. The usual crops are corn, tobacco and beans, and livestock farming includes chicken, swine and cattle. The main agricultural crops are corn ( Zea mays ) and soybeans ( Glycine max ) during spring/summer and black oat ( Avena strigosa ) during autumn/winter. Before each farming, doses of fertilisers are applied to the soil, with compounds of nitrogen (N), phosphorus (P 2 O 5 ) and triple phosphate (TSP), potassium (K 2 O, KCl), urea (CH 4 N 2 O), ammonium nitrate (NH 4 NO 3 ) and sulphate of ammonia ((NH 4 ) 2 SO 4 ). Environmental monitoring Hydrological and quality monitoring was initiated in 2013 by the postgraduate program in environmental engineering at the Regional University of Blumenau. Meteorological monitoring began in February 2016. The total monitoring period was seven hydrological years (2013–2019). Two fluviometric stations were used for hydrological monitoring. Regarding rainfall, one rain gauge at the meteorological station and three other rain gauges in the catchment (the H-500 from WaterLOG®) were used at 15-minute frequencies. Also, data from the nearest conventional rainfall station of the National Water Agency (ANA), located in Iporã do Oeste, Santa Catarina (ANA code 02753013), were used. In addition, there are also three volumetric lysimeters measuring 1 m³ of undisturbed soil, two on land dedicated to pasture and one on land dedicated to agriculture (corn-fallow). Quantities and quality of surface and drainage runoff were measured. The data were stored in a datalogger at 5-minute intervals. Pig manure was applied to the lysimeters, as recommended by the municipality’s agriculture department (7.3 litres of manure per m², in December/January, March/April and August/September). In the agricultural lysimeter (fallow), glyphosate was applied in periods prior to cultivation. Subsurface water was also monitored using a PVC piezometer equipped with a hydrostatic level sensor and datalogger. Water quality was measured in surface water (river), piezometer and lysimeter (surface and drainage) from September 2013 to October 2019 (seven hydrological years). The water samples were stored in propylene bottles, collected randomly and kept at 4°C until analysis. The chemical species were the anions: chloride (Cl - ), acetate (CH 3 COO), nitrite (NO 2 - ), nitrate (NO 3 - ), phosphate (PO 4 3- ), sulphate (SO 4 2 ), bromide (CH 3 Br); and the cations: ammonium (NH 4 + ), calcium (Ca 2+ ), lithium (Li + ), magnesium (Mg 2+ ), potassium (K + ) and sodium (Na + ). Chemical species were analysed on the with an ion exchange chromatograph (Thermo Scientific, model ICS-90). Data and statistical treatment To check the existence of variability, the Mann-Kendall test (Mann, 1945; Kendall, 1975) was applied; this test is a non-parametric method that does not require normal distribution (Yue et al., 2002). To analyse differences, analysis of variance (ANOVA) was used; this analysis seeks differences between means of two or more independent groups (Maxwell; Delaney and Kelley, 2017). Comparisons in this study were carried out at a significance level of 5% (α = 0.05). As ANOVA does not provide information about which groups are different, a post-hoc Tukey test was performed to identify different groups. Rainfall results were compared using Student’s t test (α = 0.05). The specific flow (Qesp) was calculated with data between April 2013 and November 2020 at the upstream and downstream fluviometric stations of Fortaleza River, which have contribution areas of 14 km² and 48 km², respectively. The minimum specific flows (Q 95 ) and annual averages were calculated. Means, medians, standard deviations and coefficients of variation (CV) were calculated as percentages. Principal component analysis (PCA) was also used on solutes (Cl - , CH 3 COO, NO 2 - , NO 3 - , PO 4 3- , SO 4 2- , Na + , NH 4 - , CH 3, Br, Li + , Na + ). PCA was performed using RStudio software. Annual mass transport was calculated based on the flow formula described by Quilbé et al. (2006). Results and Discussion Hydroclimatology Monthly rainfall is presented in Table 1. As rainfall monitoring began in June 2013, the first five months of 2013 were filled with records from the nearest ANA rainfall gauging station (Iporã do Oeste, Santa Catarina, ANA Code 02753013). Another gap was filled in October 2017, in which the rain gauges were malfunctioned, and it was not possible to obtain average values. Table 1. Average rainfall values (mm/year), intra- and interannual averages (A) of gauges in the Fortaleza River catchment, Brazil. Rainfall (mm) 2013 2014 2015 2016 2017 2018 2019 2020 A montly January 263,8* 185,7 347,3 130,6 134,8 243,8 189,3 194,4 211,2 February 135,7* 124,9 173,1 151,4 128,7 92,6 231,8 99,3 142,2 Mach 376,2* 180,9 73,1 208,8 132,8 261,0 141,9 36,4 176,4 April 170,7* 312,1 134,9 119,3 213,6 15,9 155,8 107,5 153,7 May 80,1* 185,3 210,7 117,0 345,4 116,7 257,9 153,2 183,3 June 120,8 445,8 164,6 46,8 134,6 137,0 51,8 307,3 176,1 July 57,7 125,8 301,2 68,7 17,4 30,3 109,9 129,5 105,1 August 196,7 73,5 45,0 158,5 157,8 81,4 63,3 83,6 107,5 September 142,4 397,1 139,1 81,8 57,6 217,0 51,1 42,7 141,1 October 204,0 61,8 216,8 207,5 408,1* 267,7 246,8 46,9 207,5 November 168,3 172,4 405,2 103,6 212,0 183,5 178,7 148,3 196,5 December 187,1 236,6 351,2 282,2 145,4 96,7 158,1 124,9 197,8 Annual 2103,5 2501,8 2562,2 1676,4 2088,3 1743,6 1836,5 1474,1 *Missing values, filled by Iporã do Oeste meteorological station – Santa Catarina. The t test demonstrated that there is no significant difference between the monthly observations. The absence of difference is consistent with the study by Baptista and Severo (2018), which also found a homogeneous distribution of rainfall in the region over the range of years 1984 to 2014. In relation to annual precipitation, the highest annual rainfall was measured in 2015, with 2,562.2 mm/year, and the lowest in 2020, with 1,474.1 mm/year. Monthly precipitation showed a decreasing trend according to the Mann-Kendall test (z = −2.04, p-value = 0.041). The negative trend was also observed at the Iporã do Oeste station (z = −2.32, p-value = 0.020). This trend differs from other rainfall stations in Santa Catarina during the period from 1940 to 2000 (Pinheiro, Graciano and Severo, 2013). Analysing recent data (1957–2014), Baptista and Severo (2018) also observed a negative trend in precipitation in the western region of Santa Catarina state, associated with intense La Niña events that have occurred since the beginning of the 2000s. In other words, for Santa Catarina state, in general, there is a positive trend, but for the western region, the trend is negative considering recent records. The average and Q 95 specific flow obtained between April 2013 and November 2020 presented a normal distribution, according to the Shapiro-Wilk test, with the majority of p-values above 0.05 (Figure 2). ANOVA demonstrated that there was a significant difference between the upstream and downstream fluviometric stations (Figures 2 and 3). January and July are the months with the highest and lowest rainfalls, respectively, and these months were also responsible for the highest CV of flow, at 163.84% and 90.85%, respectively. This difference between flows can be explained by the influence of different factors, such as land use and even difficulties with measurements carried out by hydrometric equipment in small catchments (Rodrigues et al., 2013; Garbossa and Pinheiro, 2015). A significant difference was also observed between the upstream and downstream fluviometric stations over the years analysed (Figure 4). For the upstream station, all months except January and September presented CVs above 100%. For the downstream, the highest Q 95 flows were in May and February. The Q 95 flow also showed a significant difference between the upstream and downstream fluviometric stations. For Q 95 , the difference between the fluviometric stations is shown by the highest flows in 2015, 2016 and 2020 (Figure 5). Differences between upstream and downstream fluviometric stations can be explained by malfunctioning problems during monitoring and establishment of the key curve and also by other factors, such as variations in land use and flow pathways. According to Rezende, Pires and Mendiondo (2010), modifications in vegetation cover in catchments generate contrasting results in permanence flows. In relation to CV, technical challenges related to monitoring in minor catchments as mentioned by Garbossa and Pinheiro (2015) have to be considered. However, other particularities can also be the cause of high CVs, as cited by Mancuzzo and Simon (2017) in catchments with drainage areas of 4,000 km² and 7,000 km². Water quality The normality test (Shapiro-Wilk) indicated normal distribution in solute concentrations (p-value > 0.05) (Table 2). Among the solutes, ammonium, nitrate and potassium presented a statistical difference between the monitoring points (upstream and downstream fluviometric stations, lysimeters and piezometer). Water quality downstream (river) and in piezometer presented the lowest concentrations (averages). Piezometer was responsible for the lowest concentrations of acetate, bromide, calcium, chloride, potassium and sulphate. The downstream river station had the lowest concentrations of lithium and magnesium; nitrate was also low compared to lysimeters. For ammonium, piezometer and downstream combined the lowest average concentration. For upstream (river), lower concentrations of phosphate, nitrite and sodium were found. Both lysimeters drainage and runoff were responsible for the highest average concentrations of acetate (runoff), ammonium (runoff), bromide (drainage), calcium (runoff), chloride (runoff), phosphate (runoff), magnesium (runoff), nitrate (runoff), nitrite (runoff), potassium (runoff) and sodium (drainage). Surface water (downstream) was responsible for the highest concentrations of sulphate, and piezometer was responsible for the highest concentration of lithium. Table 2. Averages (µ) and standard deviations (SD) of solute concentrations (2013–2018) upstream, downstream and at lysimeters (drainage and flow) and piezometer, Fortaleza River catchment, Brazil. Upstream Downstream Lysimeter (drainage) Lysimeter (runoff) Piezometer mg L -1 µ SD µ SD µ SD µ SD µ SD Acetate 0,01 0,01 0,01 0,01 0,01 0,01 0,02 0,04 0,00 0,00 Ammonium* 0,46 b 0,70 0,55 b 1,60 0,66 ab 1,40 1,47 a 1,86 0,46 b 0,58 Bromide 16,48 34,73 12,49 7,66 38,39 69,57 28,25 27,39 3,51 2,96 Calcium 4,76 10,81 4,54 2,39 4,05 3,77 7,21 8,37 3,68 2,82 Chloride 14,22 49,67 6,34 8,01 14,20 58,46 16,72 54,79 5,80 15,08 Phosphate 9,33 11,86 6,80 8,53 10,56 35,56 14,69 30,19 7,24 8,85 Lithium 0,05 0,08 0,46 1,37 0,06 0,09 0,43 0,74 1,28 2,86 Magnesium 1,54 1,11 1,93 1,11 1,52 1,77 2,49 4,72 1,70 1,22 Nitrate * 11,06 b 10,94 15,97 ab 13,22 37,50 ab 107,50 51,71 a 127,07 11,61 ab 13,59 Nitrite 3,54 4,74 1,65 2,59 2,58 4,18 8,95 13,84 2,63 2,18 Potassium * 0,74 b 0,58 1,39 b 3,61 2,46 b 16,33 22,23 a 65,72 0,51 b 0,82 Sodium 5,36 26,75 1,71 1,71 6,89 21,64 6,31 15,26 2,32 3,57 Sulphate 32,83 232,45 2,42 1,47 2,35 2,91 4,78 7,62 1,21 0,72 *Solutes with significant difference in the ANOVA. Means followed by the same lowercase do not differ from each other (Fisher, p ≤ 0.05). For lysimeters, pig manure is applied at least three times a year to fertilise before the framing. The use of swine manure has a direct effect on the concentration of solutes. According to Maggi et al. (2013), higher levels of potassium, phosphorus and nitrogen were detected in soil when swine manure was applied before farming. Seganfredo (2000) also found high concentrations of organic components, such as nutrients (nitrogen and phosphorus) and bacteria, after application of swine manure in the soil as fertiliser. Cunha et al. (2011) and Taniwaki et al. (2017) discuss concerns related to the excessive use of fertilisers in agriculture, which can lead to degradation of aquatic ecosystems. Statistical differences for ammonium, nitrate and potassium were found. Drainage in lysimeter was the second highest value for ammonium, considering the statistical difference (ab). For nitrate, downstream, drainage (lysimeter) and piezometer stations were the statistical group (ab). Nitrate concentrations in groundwater are widely discussed in the literature due to anthropogenic activities (Santos and Silva, 2021). According to Barreto et al. (2017) and Dinama (2013), higher concentrations of nitrate, ammonium and total phosphorus were also found in the Santa Lucia River catchment in Uruguay, which has a predominance of pastures and agriculture, with cattle, poultry, and pig farms. Higher concentrations of nitrate and phosphate in drainage and runoff lysimeters were found by Pinheiro et al. (2013) in the Concordia River catchment, also in Santa Catarina state. Considering potassium, runoff lysimeter was responsible for the statistical difference, followed by a group of no statistical difference. For Grecco (2019), potassium is normally associated to losses of fertilisers into surface water. This process was also found by Zucco, Pinheiro and Soares (2015) for nitrite, nitrate and phosphate in areas of fertilisation in the Concórdia River catchment. Principal component analysis (PCA) PCA was generated for surface water, lysimeters and piezometer. The surface water PCA (Figure 6) indicated that there was no distinction between upstream and downstream monitoring points. Downstream had a greater range of variability when compared to upstream. Dimensions 1 and 2 together explained 27% of the total variance of surface water PCA. Flow (illustrative variable in PCA) was not related to Dimension 1, indicating that this variable is not responsible for data variability. However, for Dimension 2, flow was positively associated with acetate, bromide, sulphate, chloride and phosphate and negatively associated with nitrate, potassium, calcium and magnesium. Still analysing Dimension 1 (14.5% of variability), there was a group of solutes that stood out: calcium and magnesium (same direction) together with sodium. The calcium-magnesium relation may be associated with minerals, such as limestone rocks or industrial waste (Sperling, 2014). In Dimension 1, only nitrate and phosphate had a negative response. Another finding is the reverse relationship between phosphate and nitrate, presented by Capoane et al. (2015) in Rio Grande do Sul, near Santa Catarina, where nitrate concentrations remained constant while phosphate varied during the rainy season. Considering lysimeter PCA (runoff and drainage), both dimensions were able to explain 47% of the variance (Figure 7). Drainage in the lysimeter was more centralised (yellow) when compared to the runoff (blue), which was responsible for the greater dispersion of the data. For Dimension 1, all solutes showed a positive response, with potassium, magnesium and nitrite being responsible for the greatest variability. In relation to Dimension 2, positive variability was observed for sulphate, phosphate, chloride and nitrate, as opposite to ammonium, lithium and calcium. Relationships between phosphate and chloride and sulphate and nitrate were also observed by Sardinha et al. (2008), whose study results showed that the concentration of these four ions were influenced by rain and laminar erosion of runoff. Piezometer PCA (Figure 8) was able to explain 38% of the data variability. Again, there was the formation of the calcium-magnesium pair, this time negative. The phosphate-nitrite group was also verified, along with sulphate, indicating a common source of contamination. The positive relation of these ions with the drainage in lysimeter and piezometer was also observed by Pinheiro et al. (2013). The calcium-magnesium relationship was observed in groundwater by Gomes and Cavalcante (2017). For chloride-sodium, it is assumed that the solutes have a similar relationship, observed by Mondelli, Giacheti and Hamada (2016) in a study of groundwater in the vicinity of a landfill. Mass transport Mass transport was presented for upstream, downstream and drainage in lysimeter (Table 3). In 2019, mass transport for lithium and upstream was not calculated due to incompatibility of concentrations and samples, respectively. The lowest mass transport was observed in the annual averages of acetate and ammonium, and the highest was observed for bromide, chloride and phosphate, indicating excess of these solutes in the catchment. Martínez-Suller et al. (2008) observed high concentrations of phosphorus in sludge from pig sheds. This may be associated with the phosphorus content in the diet during production phases (Beily et al., 2023). According to Hatfield et al. (1998) and Beily et al. (2023), pigs excrete phosphorus as organic complexes, such as phytic acid, since phosphorus in the form of phytate is not available to non-ruminant animals. Therefore, unabsorbed phosphorus passes through the gastrointestinal tract, increasing its concentration in manure (Hatfield et al., 1998). Considering averages, downstream was not higher than upstream for ammonium and potassium, since the downstream/upstream ratio was above 1 for the other solutes (Table 3). Table 3. Mass transport of different solutes from 2013 to 2019 for upstream, downstream and drainage in lysimeter, Fortaleza River catchment, Santa Catarina, Brazil. Solute Mass transport (kg km -2 ) 2013 2014 2015 2016 2017 2018 2019 Average Acetate Up. 0,5 6,9 20,1 11,5 31,7 9,8 n.a. 13,4 Down. 1,0 12,7 6,6 96,1 14,2 12,4 14,5 22,5 Lys. 1,3 24,1 0,002 7,6 4,9 0,2 n.a. 6,4 Up/Down 2,2 1,8 0,3 8,4 0,4 1,3 n.a. 1,7 Ammonium Up. 380,8 526,8 631,8 149,0 7968,0 143,6 n.a. 1633,3 Down. 1181,2 344,8 301,7 1131,5 292,1 586,2 1214,9 721,8 Lys. 1676,8 1673,3 0,3 1147,4 297,6 201,5 n.a. 832,8 Up/Down 3,1 0,7 0,5 7,6 0,0 4,1 n.a. 0,4 Bromide Up. 2434,6 11950,5 17258,7 9065,8 19983,9 12628,9 n.a. 12220,4 Down. 4849,5 17041,2 12535,5 53887,5 32018,8 13470,7 12818,0 20945,9 Lys. 11212,3 91924,0 15,5 170215,7 14225,5 3191,8 n.a. 48464,1 Up/Down 2,0 1,4 0,7 5,9 1,6 1,1 n.a. 1,7 Calcium Up. 1015,0 7120,2 4908,2 2941,1 6670,6 1273,2 n.a. 3988,0 Down. 3165,2 8136,1 5784,6 16081,8 9940,0 2699,5 43,3 6550,1 Lys. 4364,2 15273,1 1,9 9506,3 4846,9 372,4 n.a. 5727,5 Up/Down 3,1 1,1 1,2 5,5 1,5 2,1 n.a. 1,6 Chloride Up. 780,4 2275,6 5699,3 3666,0 9570,8 2742,5 n.a. 4122,4 Down. 1453,9 6808,2 7136,6 22893,1 46834,2 8058,5 11879,9 15009,2 Lys. 1869,4 3160,8 2,4 8483,2 24873,0 226,3 n.a. 6435,9 Up/Down 1,9 3,0 1,3 6,2 4,9 2,9 n.a. 3,6 Phosphate Up. 359,9 2364,2 14082,3 290,9 7935,3 4818,0 n.a. 4975,1 Down. 2403,0 12288,0 15560,6 18364,2 14133,8 16206,8 9226,6 12597,6 Lys. 748,0 3767,1 1,7 16398,1 35582,5 3614,0 n.a. 10018,6 Up/Down 6,7 5,2 1,1 63,1 1,8 3,4 n.a. 2,5 Magnesium Up. 323,1 2275,9 2531,8 1071,9 2309,8 1180,0 n.a. 1615,4 Down. 819,6 4105,8 2567,2 3140,0 3326,6 1544,5 673,6 2311,0 Lys. 676,5 4170,1 0,7 4128,6 1471,6 471,5 n.a. 1819,8 Up/Down 2,5 1,8 1,0 2,9 1,4 1,3 n.a. 1,4 Nitrate Up. 5840,0 11474,6 24993,9 7100,4 19523,5 7242,1 n.a. 12695,7 Down. 5522,3 27725,5 17812,7 23670,7 17466,2 11990,4 11250,7 16491,2 Lys. 21223,3 43005,9 16,6 52645,3 120064,7 1149,2 n.a. 39684,2 Up/Down 0,9 2,4 0,7 3,3 0,9 1,7 n.a. 1,3 Nitrite Up. 76,7 2465,4 6159,6 2139,2 3404,3 1829,1 n.a. 2679,0 Down. 206,8 4803,0 20668,4 10601,2 1171,3 3520,6 3658,9 6375,8 Lys. 851,7 8919,6 1,5 6670,8 5371,2 1142,6 n.a. 3826,2 Up/Down 2,7 1,9 3,4 5,0 0,3 1,9 n.a. 2,4 Potassium Up. 151,2 10768,5 820,9 601,3 1308,1 594,1 n.a. 2374,0 Down. 435,6 2335,9 1060,6 1712,6 1391,8 542,7 517,9 1142,4 Lys. 1178,3 1440,4 0,7 1135,2 636,7 1529,4 n.a. 986,8 Up/Down 2,9 0,2 1,3 2,8 1,1 0,9 n.a. 0,5 Sodium Up. 222,4 1556,8 1120,1 1850,5 2616,8 1018,0 n.a. 1397,4 Down. 815,8 6087,9 2068,4 24311,3 8713,2 1576,4 2626,9 6600,0 Lys. 238,5 6190,3 3,0 5254,2 6193,9 342,6 n.a. 3037,1 Up/Down 3,7 3,9 1,8 13,1 3,3 1,5 n.a. 4,7 Sulphate Up. 220,6 1873,6 2721,3 1791,4 3630,5 2099,8 n.a. 2056,2 Down. 1159,4 4700,6 5554,8 35833,2 24537,8 3195,8 17212,9 13170,7 Lys. 390,5 8586,4 1,2 6692,0 3313,5 719,9 n.a. 3283,9 Up/Down 5,3 2,5 2,0 20,0 6,8 1,5 n.a. 6,4 Legend: Up.: Upstream; Dow.: Downstream; Lys.: Lysimeter; n.a.: not available. Sulphate presented the greatest overall increase in the upstream/downstream ratio, about 6.4 times higher. According to Odero et al. (2023), sulphate in surface water is a common occurrence for rivers that cross agricultural fields and settlements, it is attributed to fertilisers, such as ammonium sulphate, superphosphate and potassium muriate. In São João do Oeste, before each farming, agricultural farmers apply doses of fertilisers, such as nitrogen (N), phosphorus (P 2 O 5 ) and triple phosphate (TSP), potassium (K 2 O, KCl), urea (CH 4 N 2 O), ammonium nitrate (NH 4 NO 3 ) and ammonium sulphate ((NH 4 )S 2 O 4 ). These applications, if they do not follow recommendations, such as avoiding application prior to a rainy period, can be a source of contamination involving these solutes in rivers. In relation to each year, 2016 was the year with the highest mass transport between the upstream and downstream ratios, in which phosphate downstream was 63 times higher than upstream, followed by sulphate (20 times) and sodium (13 times). It is worth mentioning that 2016 was the second driest year in the period, second only to 2019, which does not have data for comparison. According to Chantal et al. (2022), the reduction in water flow, induced by climate change, reduces the dilution effects, thus increasing solute concentrations. Mass transported in lysimeters was similar or lower than those downstream, except for bromide, nitrate and nitrite. For nitrate, phosphate and bromide, concentrations were significantly higher, possibly due to the application of pig manure (7.3 litres of manure per m²). Lawniczak-Malińska et al. (2023) also found higher values of nitrate concentrations in wells near pig farming sites in Poland. Conclusions Surface and subsurface water quality data were analysed in a 62 km² river catchment. Two fluviometric stations (upstream and downstream), three lysimeters (runoff and drainage) and a piezometer were considered. Monitoring small river catchments is essential, as it allows identification of causes and sources on a proper scale, which is essential for catchment management. A homogeneous distribution of monthly and annual rainfall was found (2013–2019). January and July had the highest and lowest rainfall averages, respectively. There was a decreasing trend in annual precipitation. In relation to flow, there was a significant difference between upstream and downstream considering specific and minimum flows (Q 95 ). The difference can be attributed to differences in land cover or malfunctioning in measurement sensors and key curves. For water quality, significant differences were identified for concentrations of ammonium, nitrate and potassium. These compounds are related to fertilisers used in the region. The highest concentrations of these solutes were also found in runoff using the lysimeter, indicating direct influence of agricultural practices on water quality based on fertilisers used in the catchment. Mass transport analysis confirmed the increase of these solutes when considering upstream and downstream stations. PCA indicated (i) for surface water, a positive relationship (Dimension 1) between magnesium and calcium, sulphate and chloride, and acetate and bromide, and a negative relationship between phosphate and nitrate; (ii) in lysimeters, a positive relationship (Dimension 2) between phosphate and chloride, and sulphate and nitrate, and a negative relationship between potassium and magnesium; and (iii) in piezometer, positive relationships (Dimension 1) between chloride and sodium, and phosphate and nitrite and negative relationships for calcium and magnesium. Water quality and quantity monitoring was essential for generating data, which can also be used for decision-making. As a recommendation, it is suggested to continue monitoring for follow-up research, which will enable an in-depth understanding of processes, climate variability and water quality. Furthermore, it is essential to expand monitoring in more river catchments of similar size and land use. Declarations Funding Declaration This study was funded by CAPES granting master's scholarships (Finance Code 001) and CNPq for the research productivity grant (process 304475/2020-3). Competing Interest Declaration The authors have no competing interests to declare that are relevant to the content of this article. Acknowledgments This work was carried out with the support of the National Council for Scientific and Technological Development (CNPq). The authors thank CAPES for granting master's scholarships (Finance Code 001) and CNPq for the research productivity grant (process 304475/2020-3). Author contributions Aimê Cardozo: Conceptualization, Analysis, Methodology, Writing - review & editing. Gustavo Antonio Piazza: Conceptualization, Maps, Writing - original draft. Thiago Caique Alves: Methodology, Analysis and Figures. Adilson Pinheiro: Funding acquisition, Project administration, Writing - review & editing. Vander Kaufmann: Writing - review & editing. Edson Torres: Writing - review & editing. João André Ximenes Mota: Analysis and Figures. Availability of data and material Not applicable References ABPA - Associação Brasileira de Proteína Animal (2023). Relatório Anual 2021 . São Paulo: Disponível em: https://abpa-br.org/wp-content/uploads/2023/04/Relatorio-Anual-2023.pdf Baptista, G. C. Z.; Severo, D. L. (2018). Variabilidade espacial e temporal da precipitação de Santa Catarina. 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Cite Share Download PDF Status: Published Journal Publication published 27 May, 2024 Read the published version in Environmental Monitoring and Assessment → Version 1 posted Editorial decision: Revision requested 15 Apr, 2024 Reviews received at journal 15 Apr, 2024 Reviews received at journal 10 Apr, 2024 Reviewers agreed at journal 22 Mar, 2024 Reviewers agreed at journal 21 Mar, 2024 Reviewers invited by journal 21 Mar, 2024 Submission checks completed at journal 04 Mar, 2024 Editor assigned by journal 04 Mar, 2024 First submitted to journal 16 Jan, 2024 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. 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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-3869871","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276179043,"identity":"a2db99c5-293e-45fc-9375-8864f15bc76b","order_by":0,"name":"Aimê Cardozo","email":"","orcid":"","institution":"Universidade Regional de Blumenau","correspondingAuthor":false,"prefix":"","firstName":"Aimê","middleName":"","lastName":"Cardozo","suffix":""},{"id":276179044,"identity":"02a25612-fafd-49ff-8166-0bf45b6fa593","order_by":1,"name":"Gustavo Antonio 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1","display":"","copyAsset":false,"role":"figure","size":2717665,"visible":true,"origin":"","legend":"\u003cp\u003eFortaleza River catchment, western part of Santa Catarina state, southern Brazil.\u003c/p\u003e","description":"","filename":"Fig1SJO.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3869871/v1/3afc4947f390edb005aa62ba.jpg"},{"id":52080484,"identity":"783fb92d-90e4-4fc4-81b1-d1c0232dcf66","added_by":"auto","created_at":"2024-03-06 11:17:20","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":299422,"visible":true,"origin":"","legend":"\u003cp\u003eIntra-annual average specific flow between 2013 and 2020, Fortaleza River catchment, Santa Catarina, Brazil.\u003c/p\u003e","description":"","filename":"Fig2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3869871/v1/cfc0fc324871b28b9ef21d0b.jpeg"},{"id":52079894,"identity":"0bdd0bc4-18c9-4120-ba59-1aeb76e44622","added_by":"auto","created_at":"2024-03-06 11:09:20","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":284150,"visible":true,"origin":"","legend":"\u003cp\u003eIntra-annual Q\u003csub\u003e95\u003c/sub\u003e specific flow between 2013 and 2020, Fortaleza River catchment, Santa Catarina, Brazil.\u003c/p\u003e","description":"","filename":"Fig3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3869871/v1/42dd9adb4ed405b1aaf53dc4.jpeg"},{"id":52079892,"identity":"26ae26e0-5fb9-4130-89ee-3882a3ace354","added_by":"auto","created_at":"2024-03-06 11:09:20","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":309054,"visible":true,"origin":"","legend":"\u003cp\u003eAverage specific flow between April 2013 and November 2020, Fortaleza River catchment, Santa Catarina, Brazil.\u003c/p\u003e","description":"","filename":"Fig4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3869871/v1/9c7d46b285d42eaf782fc84f.jpeg"},{"id":52079896,"identity":"239218fc-82a8-49e6-90e8-d1d024d00d78","added_by":"auto","created_at":"2024-03-06 11:09:20","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":287397,"visible":true,"origin":"","legend":"\u003cp\u003eSpecific Q\u003csub\u003e95\u003c/sub\u003e flow between April 2013 and November 2020, Fortaleza River catchment, Santa Catarina, Brazil.\u003c/p\u003e","description":"","filename":"Fig5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3869871/v1/99e3a504ec6cd39d5e2b4db9.jpeg"},{"id":52079898,"identity":"6e44c6ea-61e8-4887-a233-28333e9f08fa","added_by":"auto","created_at":"2024-03-06 11:09:20","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":189475,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component analysis (PCA) for surface water concentrations, Fortaleza River catchment, Santa Catarina, Brazil.\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-3869871/v1/3a07ae965937463aa6613b16.png"},{"id":52080485,"identity":"580353e5-1454-41a2-9a66-57862f38416a","added_by":"auto","created_at":"2024-03-06 11:17:20","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":175287,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component analysis (PCA) for lysimeter concentrations, Fortaleza River catchment, Santa Catarina, Brazil.\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-3869871/v1/e7db2b9bed5a68276b673d7b.png"},{"id":52079897,"identity":"0e1e9e5e-47d2-46c1-80fd-a51b357bf526","added_by":"auto","created_at":"2024-03-06 11:09:20","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":122774,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component analysis (PCA) for piezometer concentrations, Fortaleza River catchment, Santa Catarina, Brazil.\u003c/p\u003e","description":"","filename":"Fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-3869871/v1/db8eccf5bbead51901e384c2.png"},{"id":57303373,"identity":"210a2a1f-05b9-47de-9600-a418eb28661b","added_by":"auto","created_at":"2024-05-29 00:30:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5232939,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3869871/v1/423d4d88-6242-43b5-8b58-2c3e4a99afed.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Quali-quantitative water behaviour in an intensive swine production catchment in the Atlantic Forest biome, southern Brazil","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWater with appropriate quantity and quality conditions is a crucial resource for natural and anthropic ecosystems (Braga et al., 2005). Human activity through land use, however, produces negative impacts on hydrological processes, impacts that include changes in streamflow and water quality (Tucci, 2002). The degradation of water quality is a growing and global issue that influences not only economic and social well-being but also the sustainability of ecosystems and biodiversity (Michalak, 2016). The impacts on water quality from human activity are usually linked to pollution from different sources, such as urban, industrial and agricultural effluents (Merten and Minella, 2002), all of which are modified and contribute to climate change (Gascuel-Odoux et al., 2022). In agricultural catchments, studies are generally focused on agriculture tillage or incorrect land management (Breda, 2011; Sales et al., 2020). According to Rossi et al. (2023), the vast majority of producers in the rural part of Santa Catarina have empirical knowledge, with no technical criteria, about the right dose of pig manure to use as fertilizer in agricultural areas.\u003c/p\u003e\n\u003cp\u003eThe problem stems from agriculture because it is the most important economic activity in Brazil (Martinelli et al., 2013). Land use in Brazil is mainly used by pasture (55.79%), followed by agriculture (23.14%), forestry (3.24%), urban areas (1.36 %) and mining (0.13%) (MapBiomas Brasil, 2023). It has to be taken into account that over the course of centuries, in response to increased water and temperature stress, humans have continually adapted their agricultural practices, which has subsequently led to profound impacts on water quality (Merot et al., 2014). Intensive livestock farming in Brazil has grown in recent years due to the global demand for animal protein, as a result of African swine fever and the Covid-19 pandemic (Rossi et al., 2023; Nunes and Zanella, 2020), especially in the South Region of Brazil, in Santa Catarina. Santa Catarina state leads the country\u0026rsquo;s swine production (ABPA, 2023). Swine farms, however, present high loads of nutrients, e.g. nitrogen and phosphorus (Silva et al., 2015). The impact of agricultural activities on water quality in Brazil is attested to by several studies, such as those conducted by Ribeiro et al. (2014) and Gon\u0026ccedil;alvez and Rocha (2016) in Paran\u0026aacute; state, Simedo et al.\u0026nbsp;(2018) in S\u0026atilde;o Paulo and Pinheiro et al. (2013), Perazzoli, Pinheiro and Kaufmann (2013), Zucco, Pinheiro and Soares (2015) and Piazza et al.\u0026nbsp;(2017) in Santa Catarina.\u003c/p\u003e\n\u003cp\u003eTo understand if agricultural activities are changing water quality, monitoring techniques are applied. Monitoring catchments allows us to understand how different processes act in the hydrological cycle and in nutrient transport (Monte-Mor, 2012). Water quality monitoring consists of gauging physical, chemical or biological characteristics and determining their spatial and seasonal variability (Bertossi et al., 2013). With these data, for example, it is possible to estimate whether an increase in population or agricultural production will impact water resources (Granziera, 2006; Palhares, 2016). There are several motivations for monitoring water quality, such as identification of sources, and loads of nutrients (Bezerra, 2017). Furthermore, it must be considered that variability patterns are controlled by physical, climatic, biological and anthropogenic factors, which can vary between catchments (Ebeling et al., 2021) and by intervals of nutrient inputs that can vary from immediate to several decades due to legacy storage (Bouraoui and Grizzetti, 2011; Dupas et al., 2020; Ehrhardt et al., 2020; Meals et al., 2010). Another fact to be considered is that climate changes generally occur on a global and regional scale, but water quality is studied locally (Gascuel-Odoux et al., 2022). Understanding seasonal nutrient and flow patterns and their trends improves the ability to predict response times, locations prone to pollution and priorities for interventions (Frei et al., 2020).\u003c/p\u003e\n\u003cp\u003eThis paper evaluated hydrological and water quality behaviour using monitoring data from the Fortaleza River catchment in the western part of Santa Catarina, state in southern Brazil.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eStudy area\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Fortaleza River catchment is located in the western region of Santa Catarina state and has a drainage area of 62 km² (Figure 1). The urban perimeter of São João do Oeste, the municipality fully inserted in the Fortaleza River catchment, has a population of 6,295 inhabitants (IBGE, 2023), of whom approximately 25% are in the urban area and 75% in the rural area. The number of pigs is 129,104 (IBGE, 2023), which leads to a proportion of pigs in the municipality of 20.5 pigs/per inhabitant.\u003c/p\u003e\n\u003cp\u003eThe Fortaleza River catchment is located in the phytoecological region of a seasonal deciduous forest in the Atlantic Forest biome. The catchment is composed of cambisols (78.8%) and nitisols (structured purple earth, 21.2%). Land use is predominantly used for agriculture (39.8%) and native forest (23.8%), pastures (10.6%), reforestation (9.3%), urban areas (6.8%), forest fragments (3.7%), regeneration forests (3.0%), eucalyptus (2.4%) and water (0.6%).\u003c/p\u003e\n\u003cp\u003eIn the agricultural area, São João do Oeste stands out economically for extensive farming, mainly by family groups, with agriculture and livestock. The usual crops are corn, tobacco and beans, and livestock farming includes chicken, swine and cattle. The main agricultural crops are corn (\u003cem\u003eZea mays\u003c/em\u003e) and soybeans (\u003cem\u003eGlycine max\u003c/em\u003e) during spring/summer and black oat (\u003cem\u003eAvena strigosa\u003c/em\u003e) during autumn/winter. Before each farming, doses of fertilisers are applied to the soil, with compounds of nitrogen (N), phosphorus (P\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e) and triple phosphate (TSP), potassium (K\u003csub\u003e2\u003c/sub\u003eO, KCl), urea (CH\u003csub\u003e4\u003c/sub\u003eN\u003csub\u003e2\u003c/sub\u003eO), ammonium nitrate (NH\u003csub\u003e4\u003c/sub\u003eNO\u003csub\u003e3\u003c/sub\u003e) and sulphate of ammonia ((NH\u003csub\u003e4\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e).\u003c/p\u003e\n\u003cp\u003eEnvironmental monitoring\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHydrological and quality monitoring was initiated in 2013 by the postgraduate program in environmental engineering at the Regional University of Blumenau. Meteorological monitoring began in February 2016. The total monitoring period was seven hydrological years (2013–2019).\u003c/p\u003e\n\u003cp\u003eTwo fluviometric stations were used for hydrological monitoring. Regarding rainfall, one rain gauge at the meteorological station and three other rain gauges in the catchment (the H-500 from WaterLOG®) were used at 15-minute frequencies. Also, data from the nearest conventional rainfall station of the National Water Agency (ANA), located in Iporã do Oeste, Santa Catarina (ANA code 02753013), were used.\u003c/p\u003e\n\u003cp\u003eIn addition, there are also three volumetric lysimeters measuring 1 m³ of undisturbed soil, two on land dedicated to pasture and one on land dedicated to agriculture (corn-fallow). Quantities and quality of surface and drainage runoff were measured. The data were stored in a datalogger at 5-minute intervals. Pig manure was applied to the lysimeters, as recommended by the municipality’s agriculture department (7.3 litres of manure per m², in December/January, March/April and August/September). In the agricultural lysimeter (fallow), glyphosate was applied in periods prior to cultivation. Subsurface water was also monitored using a PVC piezometer equipped with a hydrostatic level sensor and datalogger.\u003c/p\u003e\n\u003cp\u003eWater quality was measured in surface water (river), piezometer and lysimeter (surface and drainage) from September 2013 to October 2019 (seven hydrological years). The water samples were stored in propylene bottles, collected randomly and kept at 4°C until analysis. The chemical species were the anions: chloride (Cl\u003csup\u003e-\u003c/sup\u003e), acetate (CH\u003csub\u003e3\u003c/sub\u003eCOO), nitrite (NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e), nitrate (NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e), phosphate (PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3-\u003c/sup\u003e), sulphate (SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e), bromide (CH\u003csub\u003e3\u003c/sub\u003eBr); and the cations: ammonium (NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e), calcium (Ca\u003csup\u003e2+\u003c/sup\u003e), lithium (Li\u003csup\u003e+\u003c/sup\u003e), magnesium (Mg\u003csup\u003e2+\u003c/sup\u003e), potassium (K\u003csup\u003e+\u003c/sup\u003e) and sodium (Na\u003csup\u003e+\u003c/sup\u003e). Chemical species were analysed on the with an ion exchange chromatograph (Thermo Scientific, model ICS-90).\u003c/p\u003e\n\u003cp\u003eData and statistical treatment\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo check the existence of variability, the Mann-Kendall test (Mann, 1945; Kendall, 1975) was applied; this test is a non-parametric method that does not require normal distribution (Yue et al., 2002). To analyse differences, analysis of variance (ANOVA) was used; this analysis seeks differences between means of two or more independent groups (Maxwell; Delaney and Kelley, 2017). Comparisons in this study were carried out at a significance level of 5% (α = 0.05). As ANOVA does not provide information about which groups are different, a post-hoc Tukey test was performed to identify different groups. Rainfall results were compared using Student’s t test (α = 0.05).\u003c/p\u003e\n\u003cp\u003eThe specific flow (Qesp) was calculated with data between April 2013 and November 2020 at the upstream and downstream fluviometric stations of Fortaleza River, which have contribution areas of 14 km² and 48 km², respectively. The minimum specific flows (Q\u003csub\u003e95\u003c/sub\u003e) and annual averages were calculated. Means, medians, standard deviations and coefficients of variation (CV) were calculated as percentages.\u003c/p\u003e\n\u003cp\u003ePrincipal component analysis (PCA) was also used on solutes (Cl\u003csup\u003e-\u003c/sup\u003e, CH\u003csub\u003e3\u003c/sub\u003eCOO, NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e, PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3-\u003c/sup\u003e, SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e\u0026nbsp;2-\u003c/sup\u003e, Na\u003csup\u003e+\u003c/sup\u003e, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e\u0026nbsp;-\u003c/sup\u003e, CH\u003csub\u003e3,\u0026nbsp;\u003c/sub\u003eBr, Li\u003csup\u003e+\u003c/sup\u003e, Na\u003csup\u003e+\u003c/sup\u003e). PCA was performed using RStudio software. Annual mass transport was calculated based on the flow formula described by Quilbé et al. (2006).\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003eHydroclimatology\u003c/p\u003e\n\u003cp\u003eMonthly rainfall is presented in Table 1. As rainfall monitoring began in June 2013, the first five months of 2013 were filled with records from the nearest ANA rainfall gauging station (Ipor\u0026atilde; do Oeste, Santa Catarina, ANA Code 02753013). Another gap was filled in October 2017, in which the rain gauges were malfunctioned, and it was not possible to obtain average values.\u003c/p\u003e\n\u003cp\u003eTable 1. Average rainfall values (mm/year), intra- and interannual averages (A) of gauges in the Fortaleza River catchment, Brazil.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRainfall (mm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2013\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2014\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2015\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2016\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2020\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003csub\u003emontly\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003eJanuary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e263,8*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e185,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e347,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e130,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e134,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e243,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e189,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e194,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u003cstrong\u003e211,2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003eFebruary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e135,7*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e124,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e173,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e151,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e128,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e92,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e231,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e99,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u003cstrong\u003e142,2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003eMach\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e376,2*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e180,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e73,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e208,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e132,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e261,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e141,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e36,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u003cstrong\u003e176,4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003eApril\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e170,7*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e312,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e134,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e119,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e213,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e15,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e155,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e107,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u003cstrong\u003e153,7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003eMay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e80,1*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e185,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e210,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e117,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e345,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e116,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e257,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e153,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u003cstrong\u003e183,3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003eJune\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e120,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e445,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e164,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e46,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e134,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e137,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e51,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e307,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u003cstrong\u003e176,1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003eJuly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e57,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e125,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e301,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e68,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e17,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e30,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e109,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e129,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u003cstrong\u003e105,1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003eAugust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e196,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e73,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e45,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e158,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e157,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e81,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e63,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e83,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u003cstrong\u003e107,5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003eSeptember\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e142,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e397,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e139,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e81,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e57,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e217,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e51,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e42,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u003cstrong\u003e141,1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003eOctober\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e204,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e61,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e216,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e207,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e408,1*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e267,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e246,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e46,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u003cstrong\u003e207,5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003eNovember\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e168,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e172,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e405,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e103,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e212,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e183,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e178,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e148,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u003cstrong\u003e196,5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003eDecember\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e187,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e236,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e351,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e282,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e145,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e96,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e158,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e124,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u003cstrong\u003e197,8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnnual\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2103,5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2501,8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2562,2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1676,4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2088,3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1743,6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1836,5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1474,1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Missing values, filled by Ipor\u0026atilde; do Oeste meteorological station \u0026ndash; Santa Catarina.\u003c/p\u003e\n\u003cp\u003eThe t test demonstrated that there is no significant difference between the monthly observations. The absence of difference is consistent with the study by Baptista and Severo (2018), which also found a homogeneous distribution of rainfall in the region over the range of years 1984 to 2014. In relation to annual precipitation, the highest annual rainfall was measured in 2015, with 2,562.2 mm/year, and the lowest in 2020, with 1,474.1 mm/year.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMonthly precipitation showed a decreasing trend according to the Mann-Kendall test (z =\u0026nbsp;\u0026minus;2.04, p-value = 0.041). The negative trend was also observed at the Ipor\u0026atilde; do Oeste station (z =\u0026nbsp;\u0026minus;2.32, p-value = 0.020). This trend differs from other rainfall stations in Santa Catarina during the period from 1940 to 2000 (Pinheiro, Graciano and Severo, 2013). Analysing recent data (1957\u0026ndash;2014), Baptista and Severo (2018) also observed a negative trend in precipitation in the western region of Santa Catarina state, associated with intense La Ni\u0026ntilde;a events that have occurred since the beginning of the 2000s. In other words, for Santa Catarina state, in general, there is a positive trend, but for the western region, the trend is negative considering recent records.\u003c/p\u003e\n\u003cp\u003eThe average and Q\u003csub\u003e95\u003c/sub\u003e specific flow obtained between April 2013 and November 2020 presented a normal distribution, according to the Shapiro-Wilk test, with the majority of p-values above 0.05 (Figure 2).\u003c/p\u003e\n\u003cp\u003eANOVA demonstrated that there was a significant difference between the upstream and downstream fluviometric stations (Figures 2 and 3). January and July are the months with the highest and lowest rainfalls, respectively, and these months were also responsible for the highest CV of flow, at 163.84% and 90.85%, respectively. This difference between flows can be explained by the influence of different factors, such as land use and even difficulties with measurements carried out by hydrometric equipment in small catchments (Rodrigues et al., 2013; Garbossa and Pinheiro, 2015).\u003c/p\u003e\n\u003cp\u003eA significant difference was also observed between the upstream and downstream fluviometric stations over the years analysed (Figure 4). For the upstream station, all months except January and September presented CVs above 100%. For the downstream, the highest Q\u003csub\u003e95\u003c/sub\u003e flows were in May and February. The Q\u003csub\u003e95\u003c/sub\u003e flow also showed a significant difference between the upstream and downstream fluviometric stations. For Q\u003csub\u003e95\u003c/sub\u003e, the difference between the fluviometric stations is shown by the highest flows in 2015, 2016 and 2020 (Figure 5).\u003c/p\u003e\n\u003cp\u003eDifferences between upstream and downstream fluviometric stations can be explained by malfunctioning problems during monitoring and establishment of the key curve and also by other factors, such as variations in land use and flow pathways. According to Rezende, Pires and Mendiondo (2010), modifications in vegetation cover in catchments generate contrasting results in permanence flows. In relation to CV, technical challenges related to monitoring in minor catchments as mentioned by Garbossa and Pinheiro (2015) have to be considered. However, other particularities can also be the cause of high CVs, as cited by Mancuzzo and Simon (2017) in catchments with drainage areas of 4,000 km\u0026sup2; and 7,000 km\u0026sup2;.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWater quality\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe normality test (Shapiro-Wilk) indicated normal distribution in solute concentrations (p-value \u0026gt; 0.05) (Table 2). Among the solutes, ammonium, nitrate and potassium presented a statistical difference between the monitoring points (upstream and downstream fluviometric stations, lysimeters and piezometer).\u003c/p\u003e\n\u003cp\u003eWater quality downstream (river) and in piezometer presented the lowest concentrations (averages). Piezometer was responsible for the lowest concentrations of acetate, bromide, calcium, chloride, potassium and sulphate. The downstream river station had the lowest concentrations of lithium and magnesium; nitrate was also low compared to lysimeters. For ammonium, piezometer and downstream combined the lowest average concentration. For upstream (river), lower concentrations of phosphate, nitrite and sodium were found.\u003c/p\u003e\n\u003cp\u003eBoth lysimeters drainage and runoff were responsible for the highest average concentrations of acetate (runoff), ammonium (runoff), bromide (drainage), calcium (runoff), chloride (runoff), phosphate (runoff), magnesium (runoff), nitrate (runoff), nitrite (runoff), potassium (runoff) and sodium (drainage). Surface water (downstream) was responsible for the highest concentrations of sulphate, and piezometer was responsible for the highest concentration of lithium.\u003c/p\u003e\n\u003cp\u003eTable 2. Averages (\u0026micro;) and standard deviations (SD) of solute concentrations (2013\u0026ndash;2018) upstream, downstream and at lysimeters (drainage and flow) and piezometer, Fortaleza River catchment, Brazil.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"573\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.498257839721255%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.59581881533101%\" colspan=\"2\"\u003e\n \u003cp\u003eUpstream\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.770034843205575%\" colspan=\"2\"\u003e\n \u003cp\u003eDownstream\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.770034843205575%\" colspan=\"2\"\u003e\n \u003cp\u003eLysimeter (drainage)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.59581881533101%\" colspan=\"2\"\u003e\n \u003cp\u003eLysimeter (runoff)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.770034843205575%\" colspan=\"2\"\u003e\n \u003cp\u003ePiezometer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.538461538461538%\"\u003e\n \u003cp\u003emg L\u003csup\u003e-1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e\u0026micro;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e\u0026micro;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e\u0026micro;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e\u0026micro;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e\u0026micro;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.538461538461538%\"\u003e\n \u003cp\u003eAcetate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e0,01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e0,01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e0,01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e0,01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e0,01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e0,01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e0,02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e0,04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e0,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e0,00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.538461538461538%\"\u003e\n \u003cp\u003eAmmonium*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e0,46\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e0,70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e0,55\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e1,60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e0,66\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e1,40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e1,47\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e1,86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e0,46\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e0,58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.538461538461538%\"\u003e\n \u003cp\u003eBromide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e16,48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e34,73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e12,49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e7,66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e38,39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e69,57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e28,25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e27,39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e3,51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e2,96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.538461538461538%\"\u003e\n \u003cp\u003eCalcium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e4,76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e10,81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e4,54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e2,39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e4,05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e3,77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e7,21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e8,37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e3,68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e2,82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.538461538461538%\"\u003e\n \u003cp\u003eChloride\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e14,22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e49,67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e6,34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e8,01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e14,20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e58,46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e16,72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e54,79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e5,80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e15,08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.538461538461538%\"\u003e\n \u003cp\u003ePhosphate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e9,33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e11,86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e6,80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e8,53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e10,56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e35,56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e14,69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e30,19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e7,24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e8,85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.538461538461538%\"\u003e\n \u003cp\u003eLithium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e0,05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e0,08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e0,46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e1,37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e0,06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e0,09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e0,43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e0,74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e1,28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e2,86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.538461538461538%\"\u003e\n \u003cp\u003eMagnesium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e1,54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e1,11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e1,93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e1,11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e1,52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e1,77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e2,49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e4,72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e1,70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e1,22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.538461538461538%\"\u003e\n \u003cp\u003eNitrate *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e11,06\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e10,94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e15,97\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e13,22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e37,50\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e107,50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e51,71\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e127,07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e11,61\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e13,59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.538461538461538%\"\u003e\n \u003cp\u003eNitrite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e3,54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e4,74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e1,65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e2,59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e2,58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e4,18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e8,95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e13,84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e2,63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e2,18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.538461538461538%\"\u003e\n \u003cp\u003ePotassium *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e0,74\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e0,58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e1,39\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e3,61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e2,46\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e16,33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e22,23\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e65,72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e0,51\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e0,82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.538461538461538%\"\u003e\n \u003cp\u003eSodium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e5,36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e26,75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e1,71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e1,71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e6,89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e21,64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e6,31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e15,26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e2,32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e3,57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.538461538461538%\"\u003e\n \u003cp\u003eSulphate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e32,83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e232,45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e2,42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e1,47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e2,35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e2,91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e4,78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e7,62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e1,21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.741258741258742%\"\u003e\n \u003cp\u003e0,72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Solutes with significant difference in the ANOVA. Means followed by the same lowercase do not differ from each other (Fisher, p \u0026le; 0.05).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor lysimeters, pig manure is applied at least three times a year to fertilise before the framing. The use of swine manure has a direct effect on the concentration of solutes. According to Maggi et al. (2013), higher levels of potassium, phosphorus and nitrogen were detected in soil when swine manure was applied before farming. Seganfredo (2000) also found high concentrations of organic components, such as nutrients (nitrogen and phosphorus) and bacteria, after application of swine manure in the soil as fertiliser. Cunha et al. (2011) and Taniwaki et al. (2017) discuss concerns related to the excessive use of fertilisers in agriculture, which can lead to degradation of aquatic ecosystems.\u003c/p\u003e\n\u003cp\u003eStatistical differences for ammonium, nitrate and potassium were found. Drainage in lysimeter was the second highest value for ammonium, considering the statistical difference (ab). For nitrate, downstream, drainage (lysimeter) and piezometer stations were the statistical group (ab). Nitrate concentrations in groundwater are widely discussed in the literature due to anthropogenic activities (Santos and Silva, 2021). According to Barreto et al. (2017) and Dinama (2013), higher concentrations of nitrate, ammonium and total phosphorus were also found in the Santa Lucia River catchment in Uruguay, which has a predominance of pastures and agriculture, with cattle, poultry, and pig farms. Higher concentrations of nitrate and phosphate in drainage and runoff lysimeters were found by Pinheiro et al. (2013) in the Concordia River catchment, also in Santa Catarina state.\u003c/p\u003e\n\u003cp\u003eConsidering potassium, runoff lysimeter was responsible for the statistical difference, followed by a group of no statistical difference. For Grecco (2019), potassium is normally associated to losses of fertilisers into surface water. This process was also found by Zucco, Pinheiro and Soares (2015) for nitrite, nitrate and phosphate in areas of fertilisation in the Conc\u0026oacute;rdia River catchment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrincipal component analysis (PCA)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePCA was generated for surface water, lysimeters and piezometer. The surface water PCA (Figure 6) indicated that there was no distinction between upstream and downstream monitoring points. Downstream had a greater range of variability when compared to upstream. Dimensions 1 and 2 together explained 27% of the total variance of surface water PCA.\u003c/p\u003e\n\u003cp\u003eFlow (illustrative variable in PCA) was not related to Dimension 1, indicating that this variable is not responsible for data variability. However, for Dimension 2, flow was positively associated with acetate, bromide, sulphate, chloride and phosphate and negatively associated with nitrate, potassium, calcium and magnesium.\u003c/p\u003e\n\u003cp\u003eStill analysing Dimension 1 (14.5% of variability), there was a group of solutes that stood out: calcium and magnesium (same direction) together with sodium. The calcium-magnesium relation may be associated with minerals, such as limestone rocks or industrial waste (Sperling, 2014). In Dimension 1, only nitrate and phosphate had a negative response. Another finding is the reverse relationship between phosphate and nitrate, presented by Capoane et al. (2015) in Rio Grande do Sul, near Santa Catarina, where nitrate concentrations remained constant while phosphate varied during the rainy season.\u003c/p\u003e\n\u003cp\u003eConsidering lysimeter PCA (runoff and drainage), both dimensions were able to explain 47% of the variance (Figure 7). Drainage in the lysimeter was more centralised (yellow) when compared to the runoff (blue), which was responsible for the greater dispersion of the data.\u003c/p\u003e\n\u003cp\u003eFor Dimension 1, all solutes showed a positive response, with potassium, magnesium and nitrite being responsible for the greatest variability. In relation to Dimension 2, positive variability was observed for sulphate, phosphate, chloride and nitrate, as opposite to ammonium, lithium and calcium. Relationships between phosphate and chloride and sulphate and nitrate were also observed by Sardinha et al. (2008), whose study results showed that the concentration of these four ions were influenced by rain and laminar erosion of runoff.\u003c/p\u003e\n\u003cp\u003ePiezometer PCA (Figure 8) was able to explain 38% of the data variability. Again, there was the formation of the calcium-magnesium pair, this time negative. The phosphate-nitrite group was also verified, along with sulphate, indicating a common source of contamination.\u003c/p\u003e\n\u003cp\u003eThe positive relation of these ions with the drainage in lysimeter and piezometer was also observed by Pinheiro et al. (2013). The calcium-magnesium relationship was observed in groundwater by Gomes and Cavalcante (2017). For chloride-sodium, it is assumed that the solutes have a similar relationship, observed by Mondelli, Giacheti and Hamada (2016) in a study of groundwater in the vicinity of a landfill.\u003c/p\u003e\n\u003cp\u003eMass transport\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMass transport was presented for upstream, downstream and drainage in lysimeter (Table 3). In 2019, mass transport for lithium and upstream was not calculated due to incompatibility of concentrations and samples, respectively.\u003c/p\u003e\n\u003cp\u003eThe lowest mass transport was observed in the annual averages of acetate and ammonium, and the highest was observed for bromide, chloride and phosphate, indicating excess of these solutes in the catchment. Mart\u0026iacute;nez-Suller et al. (2008) observed high concentrations of phosphorus in sludge from pig sheds. This may be associated with the phosphorus content in the diet during production phases (Beily et al., 2023). According to Hatfield et al. (1998) and Beily et al. (2023), pigs excrete phosphorus as organic complexes, such as phytic acid, since phosphorus in the form of phytate is not available to non-ruminant animals. Therefore, unabsorbed phosphorus passes through the gastrointestinal tract, increasing its concentration in manure (Hatfield et al., 1998).\u003c/p\u003e\n\u003cp\u003eConsidering averages, downstream was not higher than upstream for ammonium and potassium, since the downstream/upstream ratio was above 1 for the other solutes (Table 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3. Mass transport of different solutes from 2013 to 2019 for upstream, downstream and drainage in lysimeter, Fortaleza River catchment, Santa Catarina, Brazil.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" rowspan=\"2\"\u003e\n \u003cp\u003eSolute\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\" rowspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"79.79797979797979%\" colspan=\"8\"\u003e\n \u003cp\u003eMass transport (kg km\u003csup\u003e-2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12%\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.666666666666666%\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.333333333333334%\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.333333333333334%\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.666666666666666%\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16%\"\u003e\n \u003cp\u003eAverage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.578947368421053%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eAcetate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003eUp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e0,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e6,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e20,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e11,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e31,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e9,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e13,4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003eDown.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e1,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e12,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e6,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e96,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e14,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e12,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e14,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e22,5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003eLys.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\"\u003e\n \u003cp\u003e1,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\"\u003e\n \u003cp\u003e24,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\"\u003e\n \u003cp\u003e0,002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e7,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e4,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\"\u003e\n \u003cp\u003e0,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"bottom\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"bottom\"\u003e\n \u003cp\u003e6,4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003eUp/Down\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e2,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e1,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e0,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e8,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e0,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e1,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e1,7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.578947368421053%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eAmmonium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003eUp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e380,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e526,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e631,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e149,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e7968,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e143,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e1633,3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003eDown.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e1181,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e344,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e301,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e1131,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e292,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e586,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e1214,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e721,8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003eLys.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\"\u003e\n \u003cp\u003e1676,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\"\u003e\n \u003cp\u003e1673,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\"\u003e\n \u003cp\u003e0,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e1147,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e297,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\"\u003e\n \u003cp\u003e201,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"bottom\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"bottom\"\u003e\n \u003cp\u003e832,8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003eUp/Down\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e3,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e0,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e0,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e7,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e0,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e4,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0,4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.578947368421053%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eBromide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003eUp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e2434,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e11950,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e17258,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e9065,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e19983,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e12628,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e12220,4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003eDown.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e4849,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e17041,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e12535,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e53887,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e32018,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e13470,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e12818,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e20945,9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003eLys.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\"\u003e\n \u003cp\u003e11212,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\"\u003e\n \u003cp\u003e91924,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\"\u003e\n \u003cp\u003e15,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e170215,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e14225,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\"\u003e\n \u003cp\u003e3191,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"bottom\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"bottom\"\u003e\n \u003cp\u003e48464,1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003eUp/Down\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e2,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e1,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e0,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e5,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e1,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e1,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e1,7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.578947368421053%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eCalcium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003eUp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e1015,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e7120,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e4908,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e2941,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e6670,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e1273,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e3988,0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003eDown.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e3165,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e8136,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e5784,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e16081,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e9940,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e2699,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e43,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e6550,1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003eLys.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\"\u003e\n \u003cp\u003e4364,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\"\u003e\n \u003cp\u003e15273,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\"\u003e\n \u003cp\u003e1,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e9506,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e4846,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\"\u003e\n \u003cp\u003e372,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"bottom\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"bottom\"\u003e\n \u003cp\u003e5727,5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003eUp/Down\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e3,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e1,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e1,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e5,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e1,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e2,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e1,6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.578947368421053%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eChloride\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003eUp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e780,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e2275,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e5699,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e3666,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e9570,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e2742,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e4122,4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003eDown.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e1453,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e6808,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e7136,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e22893,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e46834,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e8058,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e11879,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e15009,2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003eLys.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\"\u003e\n \u003cp\u003e1869,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\"\u003e\n \u003cp\u003e3160,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\"\u003e\n \u003cp\u003e2,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e8483,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e24873,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\"\u003e\n \u003cp\u003e226,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"bottom\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"bottom\"\u003e\n \u003cp\u003e6435,9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003eUp/Down\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e1,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e3,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e1,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e6,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e4,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e2,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e3,6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.578947368421053%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003ePhosphate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003eUp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e359,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e2364,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e14082,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e290,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e7935,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e4818,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e4975,1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003eDown.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e2403,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e12288,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e15560,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e18364,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e14133,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e16206,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e9226,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e12597,6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003eLys.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\"\u003e\n \u003cp\u003e748,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\"\u003e\n \u003cp\u003e3767,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\"\u003e\n \u003cp\u003e1,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e16398,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e35582,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\"\u003e\n \u003cp\u003e3614,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"bottom\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"bottom\"\u003e\n \u003cp\u003e10018,6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003eUp/Down\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e6,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e5,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e1,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e63,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e1,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e3,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e2,5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.578947368421053%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eMagnesium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003eUp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e323,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e2275,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e2531,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e1071,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e2309,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e1180,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e1615,4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003eDown.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e819,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e4105,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e2567,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e3140,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e3326,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e1544,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e673,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e2311,0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003eLys.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\"\u003e\n \u003cp\u003e676,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\"\u003e\n \u003cp\u003e4170,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\"\u003e\n \u003cp\u003e0,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e4128,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e1471,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\"\u003e\n \u003cp\u003e471,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"bottom\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"bottom\"\u003e\n \u003cp\u003e1819,8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003eUp/Down\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e2,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e1,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e1,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e2,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e1,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e1,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e1,4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.578947368421053%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eNitrate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003eUp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e5840,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e11474,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e24993,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e7100,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e19523,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e7242,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e12695,7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003eDown.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e5522,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e27725,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e17812,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e23670,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e17466,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e11990,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e11250,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e16491,2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003eLys.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\"\u003e\n \u003cp\u003e21223,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\"\u003e\n \u003cp\u003e43005,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\"\u003e\n \u003cp\u003e16,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e52645,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e120064,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\"\u003e\n \u003cp\u003e1149,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"bottom\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"bottom\"\u003e\n \u003cp\u003e39684,2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003eUp/Down\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e0,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e2,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e0,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e3,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e0,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e1,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e1,3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.578947368421053%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eNitrite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003eUp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e76,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e2465,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e6159,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e2139,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e3404,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e1829,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e2679,0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003eDown.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e206,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e4803,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e20668,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e10601,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e1171,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e3520,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e3658,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e6375,8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003eLys.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\"\u003e\n \u003cp\u003e851,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\"\u003e\n \u003cp\u003e8919,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\"\u003e\n \u003cp\u003e1,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e6670,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e5371,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\"\u003e\n \u003cp\u003e1142,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"bottom\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"bottom\"\u003e\n \u003cp\u003e3826,2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003eUp/Down\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e2,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e1,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e3,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e5,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e0,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e1,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e2,4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.578947368421053%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003ePotassium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003eUp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e151,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e10768,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e820,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e601,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e1308,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e594,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e2374,0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003eDown.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e435,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e2335,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e1060,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e1712,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e1391,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e542,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e517,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e1142,4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003eLys.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\"\u003e\n \u003cp\u003e1178,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\"\u003e\n \u003cp\u003e1440,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\"\u003e\n \u003cp\u003e0,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e1135,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e636,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\"\u003e\n \u003cp\u003e1529,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"bottom\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"bottom\"\u003e\n \u003cp\u003e986,8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003eUp/Down\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e2,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e0,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e1,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e2,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e1,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e0,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0,5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.578947368421053%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eSodium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003eUp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e222,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e1556,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e1120,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e1850,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e2616,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e1018,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e1397,4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003eDown.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e815,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e6087,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e2068,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e24311,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e8713,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e1576,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e2626,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e6600,0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003eLys.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\"\u003e\n \u003cp\u003e238,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\"\u003e\n \u003cp\u003e6190,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\"\u003e\n \u003cp\u003e3,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e5254,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e6193,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\"\u003e\n \u003cp\u003e342,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"bottom\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"bottom\"\u003e\n \u003cp\u003e3037,1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003eUp/Down\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e3,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e3,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e1,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e13,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e3,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e1,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e4,7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.578947368421053%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eSulphate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003eUp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e220,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e1873,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e2721,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e1791,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e3630,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"top\"\u003e\n \u003cp\u003e2099,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e2056,2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003eDown.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e1159,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e4700,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e5554,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e35833,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e24537,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e3195,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e17212,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e13170,7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003eLys.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\"\u003e\n \u003cp\u003e390,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\"\u003e\n \u003cp\u003e8586,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\"\u003e\n \u003cp\u003e1,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e6692,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\"\u003e\n \u003cp\u003e3313,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\"\u003e\n \u003cp\u003e719,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"bottom\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"bottom\"\u003e\n \u003cp\u003e3283,9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003eUp/Down\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e5,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e2,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e2,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e20,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.904761904761905%\" valign=\"top\"\u003e\n \u003cp\u003e6,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"top\"\u003e\n \u003cp\u003e1,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e6,4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eLegend: Up.: Upstream; Dow.: Downstream; Lys.: Lysimeter; n.a.: not available.\u003c/p\u003e\n\u003cp\u003eSulphate presented the greatest overall increase in the upstream/downstream ratio, about 6.4 times higher. According to Odero et al. (2023), sulphate in surface water is a common occurrence for rivers that cross agricultural fields and settlements, it is attributed to fertilisers, such as ammonium sulphate, superphosphate and potassium muriate. In S\u0026atilde;o Jo\u0026atilde;o do Oeste, before each farming, agricultural farmers apply doses of fertilisers, such as nitrogen (N), phosphorus (P\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e) and triple phosphate (TSP), potassium (K\u003csub\u003e2\u003c/sub\u003eO, KCl), urea (CH\u003csub\u003e4\u003c/sub\u003eN\u003csub\u003e2\u003c/sub\u003eO), ammonium nitrate (NH\u003csub\u003e4\u003c/sub\u003eNO\u003csub\u003e3\u003c/sub\u003e) and ammonium sulphate ((NH\u003csub\u003e4\u003c/sub\u003e)S\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e). These applications, if they do not follow recommendations, such as avoiding application prior to a rainy period, can be a source of contamination involving these solutes in rivers.\u003c/p\u003e\n\u003cp\u003eIn relation to each year, 2016 was the year with the highest mass transport between the upstream and downstream ratios, in which phosphate downstream was 63 times higher than upstream, followed by sulphate (20 times) and sodium (13 times). It is worth mentioning that 2016 was the second driest year in the period, second only to 2019, which does not have data for comparison. According to Chantal et al. (2022), the reduction in water flow, induced by climate change, reduces the dilution effects, thus increasing solute concentrations.\u003c/p\u003e\n\u003cp\u003eMass transported in lysimeters was similar or lower than those downstream, except for bromide, nitrate and nitrite. For nitrate, phosphate and bromide, concentrations were significantly higher, possibly due to the application of pig manure (7.3 litres of manure per m\u0026sup2;). Lawniczak-Malińska et al. (2023) also found higher values of nitrate concentrations in wells near pig farming sites in Poland.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eSurface and subsurface water quality data were analysed in a 62 km\u0026sup2; river catchment. Two fluviometric stations (upstream and downstream), three lysimeters (runoff and drainage) and a piezometer were considered. Monitoring small river catchments is essential, as it allows identification of causes and sources on a proper scale, which is essential for catchment management.\u003c/p\u003e\n\u003cp\u003eA homogeneous distribution of monthly and annual rainfall was found (2013\u0026ndash;2019). January and July had the highest and lowest rainfall averages, respectively. There was a decreasing trend in annual precipitation. In relation to flow, there was a significant difference between upstream and downstream considering specific and minimum flows (Q\u003csub\u003e95\u003c/sub\u003e). The difference can be attributed to differences in land cover or malfunctioning in measurement sensors and key curves.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor water quality, significant differences were identified for concentrations of ammonium, nitrate and potassium. These compounds are related to fertilisers used in the region. The highest concentrations of these solutes were also found in runoff using the lysimeter, indicating direct influence of agricultural practices on water quality based on fertilisers used in the catchment. Mass transport analysis confirmed the increase of these solutes when considering upstream and downstream stations.\u003c/p\u003e\n\u003cp\u003ePCA indicated (i) for surface water, a positive relationship (Dimension 1) between magnesium and calcium, sulphate and chloride, and acetate and bromide, and a negative relationship between phosphate and nitrate; (ii) in lysimeters, a positive relationship (Dimension 2) between phosphate and chloride, and sulphate and nitrate, and a negative relationship between potassium and magnesium; and (iii) in piezometer, positive relationships (Dimension 1) between chloride and sodium, and phosphate and nitrite and negative relationships for calcium and magnesium.\u003c/p\u003e\n\u003cp\u003eWater quality and quantity monitoring was essential for generating data, which can also be used for decision-making. As a recommendation, it is suggested to continue monitoring for follow-up research, which will enable an in-depth understanding of processes, climate variability and water quality. Furthermore, it is essential to expand monitoring in more river catchments of similar size and land use.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by CAPES granting master's scholarships (Finance Code 001) and CNPq for the research productivity grant (process 304475/2020-3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest Declaration\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests to declare that are relevant to the content of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was carried out with the support of the National Council for Scientific and Technological Development (CNPq). The authors thank CAPES for granting master's scholarships (Finance Code 001) and CNPq for the research productivity grant (process 304475/2020-3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAimê Cardozo: Conceptualization, Analysis, Methodology, Writing - review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eGustavo Antonio Piazza: Conceptualization, Maps, Writing - original draft.\u003c/p\u003e\n\u003cp\u003eThiago Caique Alves: Methodology, Analysis and Figures.\u003c/p\u003e\n\u003cp\u003eAdilson Pinheiro: Funding acquisition, Project administration, Writing - review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eVander Kaufmann: Writing - review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eEdson Torres: Writing - review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eJoão André Ximenes Mota: Analysis and Figures.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eABPA - Associa\u0026ccedil;\u0026atilde;o Brasileira de Prote\u0026iacute;na Animal (2023). \u003cem\u003eRelat\u0026oacute;rio Anual 2021\u003c/em\u003e\u003cstrong\u003e.\u003c/strong\u003e S\u0026atilde;o Paulo: Dispon\u0026iacute;vel em: https://abpa-br.org/wp-content/uploads/2023/04/Relatorio-Anual-2023.pdf\u003c/li\u003e\n\u003cli\u003eBaptista, G. 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Journal of Hydrology\u003cstrong\u003e, \u003c/strong\u003ev 259, p 254-271, mar. 2012. https://doi.org/10.1016/S0022-1694(01)00594-7 .\u003c/li\u003e\n\u003cli\u003eZucco, E.; Pinheiro, A.; Soares, P. (2015). Concentra\u0026ccedil;\u0026otilde;es de nutrientes e de carbono transportados por ondas de cheia em uma bacia agr\u0026iacute;cola no estado de Santa Catarina. \u003cem\u003eRevista Brasileira de Recursos H\u0026iacute;dricos\u003c/em\u003e, v. 20, n. 2, p. 369\u0026ndash;378. Dispon\u0026iacute;vel em: https://doi.org/10.21168/rbrh.v20n2.p369-378.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"monitoring, water quality, agricultural catchment, principal component analysis","lastPublishedDoi":"10.21203/rs.3.rs-3869871/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3869871/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAgriculture is an essential economic activity in Brazil. However, it is also the main source of water quality degradation. Monitoring catchments with agricultural land use is a way to generate information on a scale to identify causes and sources of water quality degradation. This work used monitoring data derived from hydrology and the quality of surface and underground water in an intensive agricultural catchment in the Atlantic Forest biome. The Fortaleza River catchment is located in the western part of Santa Catarina state in southern Brazil and has 62 km\u0026sup2; of drainage area. Hydrological and water quality monitoring was conducted for seven years at two fluviometric stations, three lysimeters, one meteorological station and one piezometer. Data on precipitation, temperature, water flow, surface runoff, drainage, and water quality were used. Statistical analyses were also developed. Precipitation between 2013 and 2019 presented a homogeneous distribution in monthly and annual data, with January and July the months with the highest and lowest values, respectively. Statistical difference in the average and Q\u003csub\u003e95\u003c/sub\u003e flows was found in upstream and downstream fluviometric sections. In terms of quality, statistical differences were identified for ammonium, nitrate and potassium concentrations, which had higher concentrations in lysimeter runoff, indicating direct influence of agricultural activity on water quality. Principal component analysis (PCA) indicated that (i) surface water presented a positive relationship in Component 1 for the magnesium-calcium, sulphate-chloride and acetate-bromide groups and a negative relationship for phosphate-nitrate; (ii) in lysimeters, the positive relationship occurred for Component 2 for the phosphate-chloride and sulphate-nitrate groups and was negative for ammonium-lithium and calcium-potassium-magnesium; and (iii) in piezometer, positive relationships were found for chloride-sodium and phosphate-nitrite pairs, while negative relationships were found for calcium-magnesium.\u003c/p\u003e","manuscriptTitle":"Quali-quantitative water behaviour in an intensive swine production catchment in the Atlantic Forest biome, southern Brazil","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-06 11:09:15","doi":"10.21203/rs.3.rs-3869871/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-15T14:03:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-15T13:13:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-10T10:00:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"7d872766-7f6b-400a-a5fc-bd2f988368bc","date":"2024-03-22T08:26:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"9c611816-4872-4204-97cd-6ef106e95f77","date":"2024-03-21T11:11:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-21T10:48:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-04T09:46:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-04T09:46:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Monitoring and Assessment","date":"2024-01-16T13:02:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"01a7a5c9-9d46-4b35-a80a-cfa56027d502","owner":[],"postedDate":"March 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-05-29T00:30:48+00:00","versionOfRecord":{"articleIdentity":"rs-3869871","link":"https://doi.org/10.1007/s10661-024-12737-5","journal":{"identity":"environmental-monitoring-and-assessment","isVorOnly":false,"title":"Environmental Monitoring and Assessment"},"publishedOn":"2024-05-28 00:30:48","publishedOnDateReadable":"May 28th, 2024"},"versionCreatedAt":"2024-03-06 11:09:15","video":"","vorDoi":"10.1007/s10661-024-12737-5","vorDoiUrl":"https://doi.org/10.1007/s10661-024-12737-5","workflowStages":[]},"version":"v1","identity":"rs-3869871","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3869871","identity":"rs-3869871","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}