Geochemistry and Natural Background Values of Waters in the Area Surrounding the Guamá Waste Treatment Landfill, Marituba (Pa), Amazon Region

Geochemistry and Natural Background Values of Waters in the Area Surrounding the Guamá Waste Treatment Landfill, Marituba (Pa), Amazon Region | 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 Geochemistry and Natural Background Values of Waters in the Area Surrounding the Guamá Waste Treatment Landfill, Marituba (Pa), Amazon Region Lucas Salles, Luiz Rogério Bastos Leal, Giovana Rebelo Diório Brazil, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8979562/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract The definition of natural background values is essential to distinguish natural geochemical processes from potential anthropogenic influences, thereby supporting the assessment of surface and groundwater quality. This study evaluates the hydrogeochemistry of waters surrounding the Central Waste Processing and Treatment Facility (CPTR) landfill in Marituba, Pará, Brazil, with the objective of establishing Natural Background Levels (NBL) and supporting the development of public policies in landfill-influenced areas. Sampling campaigns and statistical analyses of physicochemical parameters were conducted, including pH, temperature, electrical conductivity, redox potential, and inorganic and organic constituents. The results indicate slightly acidic pH (4.58–7.46), moderate temperatures (25.7–32.3°C), low electrical conductivity (≤ 274 µS/cm), and mildly oxidizing conditions. Elevated concentrations of iron and aluminum are attributed to water–rock interaction and intensified leaching driven by high Amazonian rainfall. Parameters such as phosphorus, color, and biochemical oxygen demand exceed drinking water standards, rendering the water unsuitable for consumption. Although localized variations in ammoniacal nitrogen were observed, systematic monitoring indicates that the CPTR landfill has not significantly altered regional water quality. These findings underscore the importance of continuous monitoring and the technical distinction between natural geochemical conditions and potential contamination sources in landfill-affected environments. Groundwater Surface water Baseline Brazilian Amazon Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 INTRODUCTION In Brazil, 47% of municipalities rely exclusively on surface water for supply, 39% on groundwater, and 14% on both sources (ANA 2010), underscoring the importance of preserving this resource. Both surface and groundwater are subject to natural and anthropogenic processes that may alter their quality (Meyber & Helmer 1992, Bartram & Ballance 1996 ), as they are vulnerable to anthropogenic contamination sources, such as agricultural activities, industrialization, and urbanization (Meyber & Helmer 1992, Bartram & Ballance 1996 , Huang et al. 2014, Alibuyog & Pastor 2015 ). However, aquifers possess a certain degree of natural protection provided by the unsaturated zone, which influences the mobility of contaminants, or in cases of confined aquifers covered by impermeable layers (Aller et al. 1987 , Foster & Hirata 1988 , Civita et al. 1994, Francés et al. 2001 ). The hydrogeochemical characteristics of both water resources are determined by rock composition, soil type, climate, biological activity, land use, residence time, watershed drainage capacity, discharge of domestic and industrial effluents, and dam construction (Trofimov et al. 2009 , Batista & Gastmans 2015 , Dinka 2017 ). The combination of natural and anthropogenic processes tends to create distinct hydrochemical signatures in water bodies (Singh et al. 2006 , Aghazadeh et al. 2016 ). In many cases, both natural mineral dissolution and human-induced pollution coexist, resulting in complex geochemical patterns (Kamel et al. 2013 ). Identifying these patterns is essential for effective water quality management and the development of environmental protection and remediation strategies (Mountassir & Bahir 2023 ). In this context, water contamination represents one of the primary environmental impacts associated with the disposal of solid waste (Bacellar & Catapreta 2010 , Alves & Bertolo 2012 , Siqueira & Aprile 2013 ). Leachate, the liquid effluent generated by waste decomposition, is the principal pollutant affecting both surface and groundwater in the vicinity of landfills during operation, closure and post-closure (Bispo 2004, Gallas et al. 2005 , Bittencourt & Rigoti 2011). This contaminant triggers various biogeochemical processes that vary according to leachate composition, environmental conditions, and the geological, geomorphological, and pedogenetic characteristics of a region (Christensen et al. 2001, Alves & Bertolo 2012 ). Therefore, in order to support sustainable planning and environmental protection in areas with high pollution potential, it is crucial to implement an efficient monitoring plan (Ladeira 2005, Mondelli et al. 2016 ), conduct geochemical and surface water studies (Jalali 2007, Boyacioglu & Boyacioglu 2008 ), monitor water bodies over extended periods and across different seasons (e.g., Horbe & Santos 2009 , Trindade et al. 2017 ), and establish reference values for surface water quality assessment. Determining local reference values (background values) is fundamental to understand a region's geochemical profile, enabling early detection and monitoring of potential geogenic or anthropogenic impacts associated with land use (BRIDGE 2006, 2009, ISPRA 2009). Additionally, these values allow for the identification of chemical elements that naturally occur above potable water standards, as well as the monitoring of elements that lack legally established maximum concentration limits. In Brazil, these reference values are defined by the National Environmental Council (CONAMA) through its resolutions (Brasil 2005, Brasil 2008, Brasil 2009, Brasil 2011). However, given that surface water composition is inherently influenced by the interplay of multiple processes, nationally or internationally established reference values (VROM 1994, USEPA 1996) may not accurately reflect the chemical characteristics of a specific locality. For instance, pH, iron, manganese, and sulfate levels exceeding legal standards have been observed in Criciúma (SC) (Simão et al. 2019 ). Aragão et al. ( 2020 ) showed that the establishment of Natural Background Levels (NBLs) is essential for distinguishing geogenic hydrochemical signatures from anthropogenic inputs in karst aquifers, demonstrating that parameters such as sulfate are predominantly controlled by geological sources (e.g., sulfide oxidation), while localized exceedances of nitrate and phosphate are associated with diffuse land-use pressures in areas of moderate to high intrinsic vulnerability. Thus, defining reference values in any project with potential environmental impacts on water resources is an essential strategy for protecting these resources - particularly in regions with limited geochemical studies, such as the Amazon region, in the northern region of Brazil. The study area is located in the Amazon region of northern Brazil, within the Igarapé Pau-Grande sub-basin (a tributary of the Guamá River) across the municipalities of Benevides, Marituba, and Ananindeua, where the CPTR Marituba sanitary landfill operates in a landscape marked by intense rainfall (≈ 2,500–2,700 mm/year) and year-round hydrological activity. This climatic setting, combined with the local geology dominated by the Barreiras Group (ferruginous sandstones and sandy–clayey units) and Quaternary/post-Barreiras deposits, favors strong leaching, rapid mobilization of iron and aluminum, and naturally acidic waters—processes that can produce concentrations exceeding drinking-water standards even in the absence of anthropogenic contamination. At the same time, landfills represent high pollution potential due to leachate generation, and the regional knowledge of hydrogeochemistry in smaller Amazonian tributaries remains limited, making it difficult to separate natural signals from possible impacts linked to waste disposal and surrounding land use. Establishing Natural Background Levels (NBL) in this context is therefore essential to (i) define a defensible baseline for surface and groundwater quality, (ii) distinguish geogenic enrichment and seasonal dilution effects from contamination fingerprints, and (iii) support monitoring design and public policy decisions in landfill-influenced environments, reducing uncertainty in compliance assessment and environmental management. This study investigates the geochemistry of surface and groundwater in the area surrounding the Sanitary Landfill of the Central Waste Processing and Treatment Facility (CTPR), specifically within the sub-basin of the Igarapé Pau-Grande River, a tributary of the Guamá River, which extends across the municipalities of Benevides, Marituba, and Ananindeua in the state of Pará, Brazil (Fig. 1 a, Fig. 1 b). By analyzing the physicochemical properties of surface water, groundwater, and soil, as well as employing multiple hydrochemical tools and establishing Natural Background Levels (NBL) for surface and groundwater, this study seeks to elucidate the hydrogeochemical, physical, and anthropogenic processes influencing water composition. Such understanding is essential for informing public policy development in areas with landfill sites in northern Brazil. Notably, most studies on surface water geochemistry in the Amazon region focus on the Solimões, Amazon, and Negro Rivers, while knowledge regarding the chemical composition of smaller tributaries remains limited and fragmented. SITE DESCRIPTION The study area is located in the Eastern Amazon region, in the State of Pará, encompassing the surroundings of the CPTR Marituba sanitary landfill and situated within the Igarapé Pau-Grande sub-basin, a tributary of the Guamá River, with drainage distributed across the municipalities of Benevides, Marituba, and Ananindeua in the Metropolitan Region of Belém. The area is characterized by low relief and high hydrological connectivity, marked by a network of small drainage channels (igarapés and streams) that respond rapidly to rainfall events and maintain direct interaction with the shallow aquifer. This hydrodynamic setting is particularly relevant for interpreting seasonal variations in parameters such as pH, Eh, and electrical conductivity. Geologically, the area is dominated by units of the Barreiras Group and Post-Barreiras/Quaternary deposits, composed of sandy to sandy-clayey materials that promote intense leaching under a high annual rainfall regime (≈ 2,500–2,700 mm/year). This combination of characteristics — proximity to a high pollution-potential facility (landfill), presence of low-order watercourses, and strong influence of the hydrological cycle — provides essential context for understanding the controls on local water chemistry.The spatial distribution of lithologies in the study area is presented in Fig. 1 c. Geologically, the Barreiras Group comprises a continental and marine sedimentary cover that extends along Brazil's coastal zone, from the state of Amapá to Rio de Janeiro, characterized by its almost continuous occurrence and geomorphological regularity (Arai 2006 ). Lithologically, it consists of marls; micritic, biohermal, and dolomicritic limestones; as well as biocalcirudites and biocalcarenites from the Pirabas Formation (Menezes 2000). Overlying this unit is the Barreiras Formation, composed of ferruginous sandstones, fine to medium silty and clayey sands. Notably, the CPTR Marituba sanitary landfill was established in an area previously exploited for sand, gravel, and clay extraction, all belonging to this geological Group. Post-Barreiras sediments consist of unconsolidated, yellowish sandy-clayey material that unconformably overlies the Barreiras Group. These deposits were formed by major fluctuations in sea level (transgressive and regressive episodes) associated with climate changes during the Quaternary (Martin et al. 1980; Martin et al. 1981; Esquivel 2006). Finally, modern sediments correspond to alluvial deposits along the Igarapé Pau-Grande River channel, forming the alluvial plains. The region's climate is classified as Tropical, with annual precipitation ranging between 2,500 mm and 2,700 mm, distributed throughout the year without a defined dry season. The highest precipitation levels occur between January and May, with March being the wettest month, while November records the lowest rainfall. From January to September, water surplus conditions prevail, linked to increased precipitation, particularly in February and March. However, in October and November, water withdrawal from the environment leads to a hydrological deficit due to reduced rainfall levels. The mean temperature of the warmest month exceeds 26.7°C, whereas the coldest month typically records temperatures below 24.5°C (INMET 2020). MATERIALS AND METHODS A total of 27 hydrochemical monitoring campaigns were conducted in the study area between May 2018 and May 2021, covering both groundwater and surface water at the sampling points highlighted in Fig. 1 d. Seven groundwater monitoring campaigns were carried out during the rainy season, with a total of 88 collected samples, while eight took place during the dry season, with a total of 90 samples. For surface water, six campaigns were conducted in the dry season (30 samples) and six in the rainy season (25 samples). In total, 233 samples were collected, with 120 obtained during the dry season and 113 during the rainy season. Surface water sampling followed the guidelines established by ABNT NBR 9898, ensuring appropriate sampling and preservation methods for water bodies. For groundwater, a low-flow (minimal drawdown) groundwater sampling procedure was employed, as per ABNT NBR 15516, in shallow wells (up to 15 m) to minimize interference with sample quality. Soil samples were collected and prepared following ABNT NBR 6457, ensuring proper representativeness and storage conditions. All sampling activities were conducted under constant field parameter monitoring and by qualified professionals. As a result, a database was compiled with quarterly chemical analyses. Aliquots intended for analysis were filtered using a Millipore system with 0.45 µm filters, and the sealed containers were kept refrigerated before being sent to the laboratory. Chemical analyses were performed at the LP Analítica Laboratory, following the methods established in Standard Methods for the Examination of Water and Wastewater (Rice et al. 2017). Cation (e.g., Ca²⁺, Mg²⁺, Na⁺, K⁺, Fe²⁺) and anion (e.g., F⁻, Cl⁻, NO₃⁻, NO₂⁻, SO₄²⁻) concentrations were determined using ion chromatography (IC), while alkalinity (HCO₃⁻ and CO₃²⁻) was assessed through titration. To ensure analytical accuracy, blank and duplicate samples were randomly collected during sampling campaigns. Subsequently, groundwater and surface water analysis results underwent basic statistical processing, along with equilibrium diagram construction using Microsoft Excel, to assess and interpret the physical, chemical, and anthropogenic processes influencing the study area. To determine local reference values for surface waters, a statistical treatment was applied, presenting the number of samples, minimum, maximum, mean, median, and upper limit (UL), based on descriptive statistical analysis using box plot diagrams. In this assessment, values exceeding the upper limit were considered anomalies, also referred to as outliers. The term outlier was initially described by Hawkins ( 1980 ) as an observation that significantly deviates from others, raising suspicion that it was generated by a distinct mechanism, which in the context of water quality may indicate contamination. The selection of chemical parameters for reference value determination was based on systematic studies of leachate composition in Brazilian landfills conducted by Souto and Povinelli (2007). The NBL was also calculated for both surface and groundwater, considering cumulative frequency percentiles of 70%, 90%, and 95% for both dry and rainy seasons, following the method of Lucon et al. (2018) and Aragão et al. ( 2020 ). A comprehensive analysis of physicochemical parameters, including temperature, pH, redox potential (Eh), and electrical conductivity (EC), was conducted to assess geochemical processes and the quality of surface and groundwater resources (Hem 1985 ; APHA 2012). These parameters reflect both environmental conditions and potential anthropogenic interferences, encompassing seasonal variations (dry and rainy periods) and episodic contamination events (Stumm & Morgan 1996 ). Finally, maximum values were compared against drinking water standards established by CONAMA Resolution No. 357 (Brasil 2005), Ministry of Health Ordinance No. 36, CONAMA Resolution No. 420 (Brasil 2009), GB-5749–2006, and Ministry of Health Ordinance No. 888 (Brasil 2021). RESULTS Classification and physico-chemical parameters of surface and groundwater Overall, results from monitoring campaigns conducted since 2018 indicate a slightly acidic pH, ranging from 4.58 to 7.46, while temperature remained at moderate levels, between 25.70 and 32.29°C. In terms of salinity, the rivers exhibited low electrical conductivity (EC) values, with a maximum of 274 µS/cm, characterizing a low-salinity environment. Eh varied between slightly oxidizing and mildly reducing conditions, indicating a naturally balanced system, albeit subject to both climatic and anthropogenic influences. Integrated analyses of temperature, pH, Eh, and EC in surface and groundwater (Fig. 2 ) reveal significant seasonal variations that directly affect water quality and availability. The temperature distribution (Fig. 2 a) indicates that surface waters exhibit greater variability, especially during the dry season, whereas groundwater temperatures remain more stable. The concentration of temperature within the 26°C to 30°C range reflects local thermal equilibrium characteristics. Surface waters display a wider temperature range, with higher values during the dry season, likely due to direct exposure to solar heating. Groundwater, on the other hand, shows less variation, maintaining more stable temperatures due to the thermal insulation provided by the aquifer. A comparison of pH distribution by water type and season (Fig. 2 b) reveals that groundwater exhibits less variation, tending to be more acidic. In contrast, surface waters show greater dispersion, with significantly higher pH values during the rainy season. Notably, surface water displays greater pH variability compared to groundwater, particularly in the rainy season, where more alkaline samples are observed. These differences may be attributed to the increased interaction of surface waters with external sources (such as organic matter, waste, and the alkalinity of rainwater), while groundwater is more influenced by geochemical processes and buffering mechanisms within the aquifer. Most Eh values (Fig. 2 c) are concentrated in lower ranges, around 0 to 500 mV, particularly in groundwater, indicating more reducing conditions. Surface water shows a wider range of Eh values, especially during the rainy season, suggesting the influence of oxidative processes due to increased circulation and mixing with the atmosphere. Surface waters generally exhibit higher and more dispersed Eh values, implying greater exposure to oxidation during the rainy period. In contrast, groundwater remains more stable, predominantly controlled by reducing conditions within the aquifer. Finally, most EC values (Fig. 2 d) fall within low ranges (0–500 µS/cm), with a higher density in groundwater, suggesting minimal external influences compared to surface water. Surface water during the rainy season displays greater variability, indicating the influence of surface runoff and mixing. Groundwater exhibits lower EC variation, with outliers potentially linked to localized contamination sources or high mineralization. In contrast, surface waters show greater dispersion, particularly in the rainy season, reinforcing the contribution of external water inputs rich in dissolved salts or particles during this period. Additionally, Piper diagram analysis (Fig. 3 ) reveals differences in the chemical composition of groundwater between dry and rainy seasons. During the rainy season (represented by blue points), samples tend to reflect greater ion dilution due to increased recharge from precipitation, generally exhibiting bicarbonate-dominated waters with lower salt concentrations. In contrast, during the dry season (red points), a higher concentration of dissolved compounds is observed, which may be associated with evapotranspiration processes, longer water residence time within the matrix, or more concentrated anthropogenic inputs. Chemistry, background values, and reference limits The calculated background values, along with the reference limits (upper limit) for surface waters within the area influenced by Guamá Waste Treatment are presented in Table 1 . Many chemical elements do not have maximum allowable values established by CONAMA Resolution No. 357 (Brasil, 2005), making direct comparison with drinking water standards unfeasible. However, certain chemical parameters, such as phosphate, biochemical oxygen demand (BOD), and color, naturally exceed the legally established values. Furthermore, when considering Ordinance No. 36 of the Ministry of Health, it becomes evident that elements such as iron (upper limit = 4.8 mg/L) and aluminum (upper limit = 0.84 mg/L) naturally occur at levels exceeding those deemed suitable for human consumption, which are 0.3 mg/L and 0.1 mg/L, respectively. Table 1 Results of statistical treatment and comparison with reference values for the analyzed data. Parameter Unit Count Minimum Maximum Average Median 3°quartile Upper limit Brasil, 2005 Alkalinity due to bicarbonates mgCaCO 3 /L 20 <LQD 36.00 12.60 13.00 16.00 28.00 Aluminum (al) mg/L 45 <LQD 2.35 0.35 0.24 0.40 0.84 Barium (ba) mg/L 45 <LQD 0.09 0.004 <LQD 0.004 0.01 0.7 Cadmium (cd) mg/L 45 <LQD <LQD <LQD <LQD <LQD <LQD 0.001 Calcium (ca) mg/L 45 <LQD 10.70 1.70 1.63 1.90 3.24 Total organic carbon (toc) mg/L 37 <LQD 5.80 2.38 2.00 3.75 7.58 Lead (pb) mg/L 45 <LQD 0.0041 0.0001 <LQD <LQD <LQD 0.01 Chloride mg/L 37 <LQD 13.06 5.41 5.40 6.30 10.56 250 Copper (cu) mg/L 45 <LQD 6.95 0.16 <LQD 0.001 0.003 Conductivity µS/cm 21 <LQD 150.00 44.19 44.00 54.00 331.50 Color NTU 29 <LQD 120.00 41.42 33.40 60.60 138.79 75 Chromium (cr) mg/L 45 <LQD 0.01 0.001 <LQD <LQD <LQD 0.05 Bod mg/L 37 <LQD 5.00 1.24 <LQD 2.50 7.50 5 Chemical oxygen demand (cod) mg/L 37 <LQD 77.50 11.89 9.00 12.60 23.15 Total hardness mg/L 41 <LQD 31.10 5.31 6.17 6.95 18.10 Iron (fe) mg/L 45 <LQD 12.00 2.22 1.56 2.58 4.80 Phosphate mg/L 37 <LQD 0.22 0.05 0.05 0.09 0.19 0.003 Magnesium (mg) mg/L 45 <LQD 1.08 0.41 0.45 0.53 0.83 Manganese (mn) mg/L 45 <LQD 0.37 0.01 <LQD 0.01 0.03 0.1 Nitrate mg/L 37 <LQD 10.12 0.43 <LQD 0.28 0.59 Nitrite mg/L 37 <LQD 0.09 0.00 <LQD <LQD <LQD Dissolved oxygen (do) mg/L 25 5 Ph 21 3.7 6.36 5.39 5.48 5.96 7.71 6–9 Potassium (k) mg/L 45 <LQD 5.81 1.61 1.40 2.16 4.12 Eh mV 21 <LQD 348.70 146.99 107.10 225.90 412.35 Sodium (na) mg/L 45 <LQD 18.40 4.22 4.05 5.40 10.21 Tds mg/L 25 <LQD 151.00 47.20 36.00 69.00 115.38 500 Sulfate mg/L 37 <LQD 23.99 3.92 1.14 4.63 12.72 250 Temperature °C 21 25.17 30.90 26.72 26.35 26.80 29.19 Turbidity NTU 25 4.325 102.00 21.59 12.80 26.25 34.71 100 Zinc (zn) mg/L 45 <LQD 0.62 0.03 0.004 0.02 0.03 0.18 Table 2 highlights the cumulative frequency of results for the 70%, 90%, and 95% percentiles during dry and rainy seasons, providing a NBL for surface and groundwater to be compared with regulatory resolutions and reference limits. Again, many of the analyzed elements lack established maximum permissible values for comparison. However, it is worth noting that parameters such as total dissolved solids (TDS), sulfate, chloride, and nitrate remained within acceptable limits, while anomalies were detected for pH, iron, and aluminum, with varying responses depending on the season. Regarding pH, 70% of the surface water data was below the regulatory limit during the dry season. For groundwater, although CONAMA Resolution No. 420 (Brasil, 2009) does not establish reference parameters, acidic values were observed across all percentiles in both dry and rainy seasons. Iron and aluminum concentrations exceeded reference limits for both surface and groundwater across all percentiles and seasons. Table 2 Cumulative frequency for surface and groundwater considering the 70%, 90%, and 95% percentiles, compared to reference limits. Parameter Unit Surface water Groundwater Potable water Dry season Rainy season (1) Dry season Rainy season (2) (3) (4) 70% 90% 95% 70% 90% 95% 70% 90% 95% 70% 90% 95% pH - 5.90 6.38 6.61 6.87 7.60 9.95 6–9 5.46 6.07 6.36 5.15 5.74 6.03 - 6.5–8.5 - Eh mV 193.00 246.00 272.00 184.00 248.00 278.00 - 239.00 345.00 396.00 239.00 345.00 396.00 - - - EC uS/cm 100.00 144.00 164.00 173.00 238.00 269.00 - 107.00 188.00 227.00 254.00 418.00 496.00 - - - TDS mg/L 96.73 131.66 148.42 46.57 65.78 75.00 500.00 128.02 175.54 197.96 144.36 223.41 261.33 - 1 000.00 500.00 Iron (Fe) mg/L 2.87 7.60 10.94 2.87 3.45 4.88 0.30 16.90 47.20 54.60 14.30 47.30 95.40 2.45 0.30 0.30 Aluminum (Al) mg/L 0.64 0.99 1.16 0.58 0.89 1.03 0.10 10.50 19.29 23.51 6.60 12.45 15.25 3.50 0.20 0.20 Calcium (Ca) mg/L 3.00 4.35 5.00 2.00 2.70 3.00 - 3.50 5.98 7.17 8.57 15.25 18.46 - - - Sulfate (SO₄²⁻) mg/L 7.43 13.95 16.22 8.17 13.02 14.77 250.00 59.48 109.64 133.70 44.85 85.26 104.65 - 250.00 250.00 Manganese (Mn) mg/L 0.045 0.071 0.084 0.016 0.02 0.022 - 0.04 0.071 0.084 0.078 0.14 0.16 0.4 0.1 0.1 Magnesium (Mg) mg/L 0.56 0.71 0.74 0.48 0.58 0.64 - 1.8 3.08 3.69 1.66 2.8 3.22 - - - Chloride (Cl⁻) mg/L 9.46 12.01 14.07 6.7 8.97 10.06 250 20.95 35.44 42.39 19.43 30.4 35.66 - 250 250 Potassium (K) mg/L 2.44 3.28 3.67 2.13 2.95 3.34 - 6.77 11.85 14.3 5.25 8.86 10.59 - - - Total hardness mgCaCO 3 21.81 28.63 31.9 12.08 16.12 18.05 - 20.36 31.42 36.73 27.87 44.89 53.05 - 450 - Nitrate (NO₃⁻) mg/L 3.39 5.79 6.94 0.32 0.37 0.4 10 2.23 3.64 4.32 2.48 4.1 4.88 10 10 10 COD mg/L 11 14.73 16.53 26 39.02 45.26 - 31.18 55.33 66.91 37.94 60.53 71.38 - - - BOD mg/L 5.11 7.98 9.36 3.44 5.15 5.97 5 8.1 14.93 18.21 10.22 17.42 20.87 - - - (1) CONAMA n°357 (Brasil, 2005); (2) CONAMA n°420 (Brasil, 2009); (3) GB − 5749–2006; (4) MS n°888 (Brasil, 2021). Well-developed latosoils were collected using soil augers and subsequently analyzed. These soils predominate in a region with flat topography presenting minimal variation. Like water data, soil analyses (Fig. 4 ) indicated anomalies in iron and aluminum concentrations, particularly at shallow depths (0.2 to 0.5 m). Compounds such as cadmium, sulfide, nitrate, and nitrite were almost always below the detection limit. However, localized anomalies of nitrate and surface sulfide, as well as sulfide anomalies at a depth of 30 cm, were detected. Surface waters: overall characterization and geogenic and anthropogenic influences The mechanisms governing the chemical composition of surface waters can be identified using the Gibbs Diagram (Gibbs, 1970 ) (Fig. 5 ). When comparing variations in the salinization mechanisms of surface waters between the dry and rainy seasons (Fig. 5 ), only slight differences are observed. However, in general, these mechanisms are dominated by precipitation and weathering. The influence of precipitation and water-rock interactions in the study area is evident in Fig. 6. As observed (Figs. 6a and 6b), elements such as iron and aluminum tend to exhibit higher concentrations following significant precipitation events. On the other hand, increased concentrations of compounds such as ammoniacal nitrogen (Fig. 6c), nitrate, and nitrite are associated with sampling conducted during periods of heavy rainfall. Ammoniacal nitrogen measurements (Fig. 6c) indicate that, in certain instances, this compound can reach elevated levels. However, these increases are not consistent over time and are linked to higher precipitation events in the study area. pH is another key factor in understanding the distribution of iron and nitrogen compounds in water. The pH–pE diagram allows for the assessment of multiple influences on these parameters, considering variations in pH and Eh. As seen in Fig. 7 a, most samples are located near the (NO₃⁻ / NO₂⁻ = 1) and NH₄⁺ line, where the stable nitrogen form is either nitrate (NO₃⁻) or ammoniacal nitrogen. This result is strongly correlated with the oxidizing environment in the natural section of the river, where, during periods with Eh values close to zero, ammoniacal nitrogen is the dominant form. Conversely, in periods with higher Eh values—and thus a more oxidizing environment—nitrate, and occasionally nitrite, become the stable nitrogen forms in the system. Iron (Fig. 7 b) is predominantly present as ferrous iron (Fe²⁺), originating from the dissolution of the lateritic crust of the Barreiras Group in a slightly acidic and oxidizing environment. DISCUSSION A quarterly analysis conducted over four years of surface and groundwater in the area surrounding the CTPR Sanitary Landfill enabled a geochemical investigation and characterization in the Igarapé Pau-Grande River sub-basin. These waters exhibit slightly acidic pH, particularly during the dry season (Table 2 ), mild temperatures, low EC, and Eh ranging from mildly to slightly oxidizing (Fig. 2 ). Regarding background values, iron and aluminum naturally occur at levels exceeding those deemed suitable for human consumption. This is evident both in the calculated background values for surface waters (Table 1 ), particularly following heavy precipitation events (Fig. 6), and in soil analysis (Fig. 4 ), as well as cumulative frequency analysis of surface and groundwater (Table 2 ). In this context, results indicate the chemical composition of surface waters in the study area is inherently linked to water-rock interactions (Fig. 5 ), though it is also highly influenced by seasonal variations. The similarity of these findings to studies conducted in areas affected by landfills in Brazil (Medeiros et al. 2008 , Pereira et al. 2013 ) reinforces the importance of continuous monitoring, as interactions between leachates and water bodies can increase environmental vulnerability. First, determining background values enabled the characterization of waters in this region and facilitated monitoring the impact of chemical elements present at concentrations much lower than potable water standards. In this regard, elements such as zinc, chloride, sulfate, turbidity, TDS, chromium, and barium have significantly lower upper limits than those established by regulations. Studies such as Aragão et al. ( 2020 ) highlight the importance of determining background levels of chloride, nitrate, sulfate, and phosphate, as areas exceeding these values may be subject to anthropogenic pollution from urban centers or agricultural activities—an issue not identified in the study area. Additionally, the determination of NBL (Table 2 ) allowed for the identification of seasonal influences—like the findings of Lucon et al. (2018) in the karstic watershed of the São Miguel River, MG—on the concentration of naturally elevated elements, regardless of established reference values. Notably, pH, iron, and aluminum exhibited high levels in both surface and groundwater, with variations across different seasons. In the Gibbs diagram (Fig. 5 ), no samples were plotted in the evaporation dominance zone. This can be attributed to the region’s high precipitation rates and well-developed drainage network, consistent with the findings of Yu et al. ( 2017 ) for the Yongding River in China. Conversely, water-rock interactions and precipitation constitute key factors in determining water chemistry. The TDS values exhibited the greatest variation when compared to the Na/(Na + Ca) and Cl/(Cl + HCO₃) ratios. This variation is also associated with the region’s high precipitation rates, which have a greater impact on TDS than on dissolved metals. Moreover, the evaluation of salinization mechanisms (Fig. 5 ) revealed a pronounced seasonal influence, similar to the studies of Gibbs ( 1970 ) on Amazon River tributaries and those of Gunes & Balci ( 2022 ) on water-rock interactions in highly variable climatic environments. During the dry season, the chemical composition of surface waters is predominantly governed by water-rock interactions, whereas in the rainy season, dilution from precipitation reduces dissolved salt concentrations, as also reported by Prasanna et al. ( 2010 ) and Lalaoui et al. ( 2020 ). It is noteworthy that pH, iron, and aluminum naturally exhibited high values in both surface and groundwater, fluctuating with the seasons and reflecting the ongoing interplay of regional hydrogeochemical and hydrodynamic processes (Odukoya et al. 2013 ). The Piper diagram (Fig. 3 ) also indicated a chemical evolution between seasons, where rainfall recharge facilitates water mass renewal and mixing, diluting ions accumulated during the dry season. In contrast, in the absence of significant rainfall, the waters exhibit higher mineralization as a result of intensified geochemical interactions and decreased recharge or fresh water input. These results underscore the importance of considering seasonality in hydrogeochemical monitoring campaigns, as processes such as dilution, mixing, and ion exchange vary significantly under different climatic conditions, influencing groundwater quality and typology. Integrated analyses of temperature, pH, Eh, and EC in surface and groundwater (Fig. 2 ) reveal significant seasonal variations that directly impact water quality and availability. During the dry season, evaporation intensifies solute concentrations, increasing EC and creating less oxygenated conditions, which can alter redox balance and enhance metal solubility, as demonstrated by Lalaoui et al. ( 2020 ) and Gunes & Balci ( 2022 ). In the rainy season, dilution from precipitation promotes greater homogeneity in parameters such as temperature and pH, creating more favorable conditions for ecological processes, as observed by Prasanna et al. ( 2010 ) and Odukoya et al. ( 2013 ). However, abrupt fluctuations in these parameters may indicate anthropogenic inputs or rapid changes in hydrological regimes, warranting close monitoring for potential contamination risks and biogeochemical imbalances (Setia et al. 2021 ). In groundwater, the relative stability of parameters reflects the protective role of geological formations, which act as natural barriers against external variations, as highlighted by Mongelli et al. ( 2013 ). However, localized peaks in EC, pH, or Eh variations may indicate specific pollution sources, emphasizing the need for continuous monitoring to detect and mitigate potential impacts on water quality (Lucon et al. 2018, Muller et al. 2006 ). Elements such as iron and aluminum exhibit higher concentrations following significant precipitation events (Figs. 6a and 6b). Several authors report elevated iron and aluminum levels in groundwater within the Barreiras Group (Picanço et al. 2002 , Matta 2002 , Cabral 2004 , Oliveira Filho et al. 2018, Filho 2018). According to these studies, the high iron and aluminum content in groundwater is associated with the geological composition of this aquifer and the enhanced dissolution and mobilization of iron in slightly acidic and oxidizing water. Consequently, ferrous iron (Fe²⁺) or bivalent iron originates from the dissolution of the lateritic crust of the Barreiras Group. Additionally, elevated concentrations of these elements in surface waters can be explained by groundwater contributions to effluent rivers and by leaching and subsequent dissolution of these elements by meteoric waters. The increase in their concentration in surface waters results from interactions between meteoric water and the Barreiras Group rocks within the unsaturated and saturated zones of the aquifer, followed by exudation as base flow. Therefore, this increase occurs after intense precipitation events. CONAMA Resolution No. 001 (Brasil 1986), in its Article 1, defines environmental impact as any alteration of the physical, chemical, and biological properties of the environment caused by any form of matter or energy resulting from human activities, either directly or indirectly. Accordingly, monitoring the environmental quality of water resources in areas influenced by the ASG aims to assess the impact of landfill disposal activities and other operational units on the environmental quality of regional surface waters. Similar findings were reported in studies by Bacellar & Catapreta ( 2010 ) on a landfill in the Belo Horizonte metropolitan area, Siqueira & Aprile ( 2013 ) in Belém, PA, and Alves & Bertolo ( 2012 ) on the geochemistry of landfill-impacted waters. Increased concentrations of compounds such as ammoniacal nitrogen (Fig. 6c), nitrate, and nitrite are associated with samples collected during heavy precipitation periods. Ammoniacal nitrogen levels (Fig. 6c) suggest that this compound occasionally reaches elevated concentrations, and its weak correlation with other water chemical parameters indicates that it is linked to isolated events unrelated to landfill operations. Siqueira et al. ( 2012 ) emphasize the influence of urban expansion along the Parauapebas River in areas near riparian reserves as a major factor affecting groundwater chemistry. Thus, water quality in each basin also reflects land use and occupation patterns, as observed by Muller et al. ( 2006 ) in a watershed in northern Scotland. This study identified certain elements and parameters as key indicators of the hydrological characteristics of the Igarapé Pau-Grande River sub-basin (Table 3 ), all of which exhibit anomalous concentrations with diverse origins and are influenced by seasonal variations. Table 3 Characteristic parameters and elements of the Igarapé Pau-Grande River sub-basin, their origins, and seasonal influences. Parameter / Element Concentration NBL90% (dry / rainy period) Origen Sazonality Fe 2+ High Surface water 7.60 / 3.45 mg/L Geogenic (Barreiras Group) ↑ dry periods Groundwater 47.2 / 47.3 mg/L Al 3+ High Surface water 0.99 / 0.89 mg/L Geogenic (Barreiras Group) ↑ dry periods Groundwater 19.29 / 12.45 mg/L NH₃ Occasionally high Anthropogenic ↑ rainy periods pH Acid Surface water 6.38 / 7.60 Geogenic (Barreiras Group) ↑ rainy periods ↓ dry periods Groundwater 6.07 / 5.74 . CONCLUSION This study demonstrates that the chemical composition of the area surrounding the Guamá Waste Treatment Landfill is inherently linked to the coalescence of two natural processes: the interaction between water and rock and geochemical processes associated with the region's high precipitation levels. These characteristics align with the low erosion and highly leaching environment of the Amazon, where the most soluble elements are rapidly removed. Statistical analysis and the subsequent definition of reference values indicate that chemical elements such as iron, aluminum, phosphate, and the color and BOD naturally occur at levels exceeding potable water standards, rendering the waters unsuitable for consumption. The high concentrations of iron and aluminum are associated with the interaction of rainwater with layers enriched in these elements within the Barreiras Group. Consequently, the presence of these elements in surface waters is linked to dissolution and leaching processes at the surface and subsurface, as well as the exudation of the aquifer through baseflow. Finally, the establishment of reference values and systematic monitoring in the area surrounding the CPTR Marituba Sanitary Landfill confirms that the landfill has been operating without altering the environmental quality classification of the region’s water resources. . Declarations Author Contribution Lucas Salles (Federal University of Bahia) – Conceptualization; Methodology; Investigation; Formal analysis; Writing – Original Draft; Project administration.Lucas Salles designed the research framework, conducted the hydrogeochemical interpretation, performed statistical and geochemical analyses, and led the manuscript writing process.Luiz Rogério Bastos Leal (Federal University of Bahia) – Supervision; Scientific review; Validation; Methodological support.Luiz Rogério Bastos Leal provided senior scientific supervision, contributed to the conceptual refinement of the hydrogeochemical interpretation, and validated methodological and analytical procedures.Giovana Rebelo Diório – Data curation; Visualization; Database management.Giovana Rebelo Diório organized and structured the analytical database, performed data treatment support, and prepared the figures and graphical representations presented in the manuscript.Paulo Galvão (Federal University of Minas Gerais) – Review & Editing; Critical revision; Final validation.Paulo Galvão conducted the final technical and scientific review of the manuscript, contributing to the refinement of the discussion, conceptual integration, and overall coherence of the final version.All authors have read and approved the final manuscript and agree with its submission. 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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-8979562","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":600504327,"identity":"77212f85-99e7-48ad-8ce2-83d3d933ed36","order_by":0,"name":"Lucas Salles","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYBACPigtA2QwPkgwsJED8Q48wKOFDUrzABnMBh8q0ozBWhKI1MImOePM4cQGEBevFonkhx9//LHjYWNvfyDN25aWPj/s8EOgLXZyug24tKQZA1Um87DxnDEw5m2zyd14O80AqCXZ2OwALi05DNKMDcw8IEYy0JbcjbMTQFoOJG7DrYX5548/9Txs8s8fHOZtO5xuODv9AyEtbBI8bIeBtjAYNgK9nyAvnUPAFp5nZta8bceBfskxZgAGsuEG6ZyCAwkGuP3Cz578+OaPP9Vy/OzHn/8ARqW8/Oz0zR8+VNjJ4dKCCQzAKg2IVQ4C8g2kqB4Fo2AUjIKRAADFYVqL83k5TgAAAABJRU5ErkJggg==","orcid":"","institution":"Universidade Federal da Bahia Universidade Federal da Bahia, Instituto de Geociências","correspondingAuthor":true,"prefix":"","firstName":"Lucas","middleName":"","lastName":"Salles","suffix":""},{"id":600504328,"identity":"3e5ac70e-5a42-402c-8b99-228108daf2bd","order_by":1,"name":"Luiz Rogério Bastos Leal","email":"","orcid":"","institution":"Universidade Federal da Bahia Universidade Federal da Bahia, Instituto de Geociências","correspondingAuthor":false,"prefix":"","firstName":"Luiz","middleName":"Rogério Bastos","lastName":"Leal","suffix":""},{"id":600504329,"identity":"61b2da4e-c7a4-4ca0-ad97-cba429585d03","order_by":2,"name":"Giovana Rebelo Diório Brazil","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Giovana","middleName":"Rebelo Diório","lastName":"Brazil","suffix":""},{"id":600504330,"identity":"b9d06f7b-354f-43df-82af-b55da940a8e7","order_by":3,"name":"Paulo Galvão","email":"","orcid":"","institution":"Universidade Federal de Minas Gerias Universidade Federal de Minas Gerais, CPMTC-IGC, Laboratório de Estudos Hidrogeológicos – LEHID","correspondingAuthor":false,"prefix":"","firstName":"Paulo","middleName":"","lastName":"Galvão","suffix":""}],"badges":[],"createdAt":"2026-02-26 15:38:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8979562/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8979562/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104402261,"identity":"fc168a5a-afd3-436c-a047-b9672c775189","added_by":"auto","created_at":"2026-03-11 12:14:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":849701,"visible":true,"origin":"","legend":"\u003cp\u003eContext of studied area. (a) Location of the Guamá River Basin in the state of Pará, northern Brazil, within the Legal Amazon. (b) Location of the CPTR Marituba sanitary landfill in the northern portion of the Guamá River Basin. (c) Local hydrography (SEIRH 2019) and geological map (CPRM 2008). (d) Groundwater and surface water monitoring points (dark and light blue circles, respectively).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8979562/v1/8543286ae9ef16010461c1f1.png"},{"id":104015957,"identity":"1199d48a-3cbe-4205-991c-c7b8d2dc0ef0","added_by":"auto","created_at":"2026-03-05 17:00:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":199413,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of physico-chemical parameter values for surface (27 data points) and groundwater (148 data points) during dry and rainy seasons: (a) temperature, (b) pH, (c) Eh, and (d) EC.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8979562/v1/311e6235a42314751f850097.png"},{"id":104402435,"identity":"88794c66-f5bf-4286-987f-9b604cd4ba16","added_by":"auto","created_at":"2026-03-11 12:15:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":256763,"visible":true,"origin":"","legend":"\u003cp\u003ePiper diagram distinguishing rainy season (blue) from dry season (red).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8979562/v1/07e02b1c2029d57b875609d8.png"},{"id":104015961,"identity":"20733742-9c60-4c75-ae40-5fdc6aeb1329","added_by":"auto","created_at":"2026-03-05 17:00:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":44381,"visible":true,"origin":"","legend":"\u003cp\u003eAverage results of soil chemical analysis by depth.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8979562/v1/ce463b8c902e8571e7e0ebfa.png"},{"id":104401931,"identity":"31ff6322-17f2-4bc5-accb-cb12dd00e0af","added_by":"auto","created_at":"2026-03-11 12:13:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":181332,"visible":true,"origin":"","legend":"\u003cp\u003eGibbs Diagram of surface water samples from the area surrounding the Guamá Waste Treatment Landfill. Red points represent samples collected during dry periods, while blue points correspond to samples collected during rainy ones.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8979562/v1/c72964a6f40f363ca78f5285.png"},{"id":104015963,"identity":"842f5999-9ab1-45d2-838b-b76562e9253c","added_by":"auto","created_at":"2026-03-05 17:00:22","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":222922,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal variation comparing the chemical analysis results of water samples (AS-01 in orange, AS-02 in gray, AS-03 in yellow, AS-04 in light blue, AS-05 in green, AS-06 in dark blue, and AS-07 in brown) as a function of precipitation for (a) iron, (b) aluminum, with reference values according to Ordinance No. 36, and (c) ammoniacal nitrogen.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8979562/v1/47c2df47864ac2dfebe07997.png"},{"id":104015962,"identity":"22be6805-4941-474b-b8c5-1e655ddfe204","added_by":"auto","created_at":"2026-03-05 17:00:22","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":112455,"visible":true,"origin":"","legend":"\u003cp\u003epH–pE diagram for surface waters highlighting (a) the distribution of nitrogen compounds and (b) the distribution of ferrous compounds. Modified from Appelo \u0026amp; Postma (2005).\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8979562/v1/74940ed52dbd634a9a28c414.png"},{"id":104408455,"identity":"966750a3-a973-44aa-b3ce-0a28d9390398","added_by":"auto","created_at":"2026-03-11 12:42:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2678120,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8979562/v1/7c1abce4-68d5-428c-adec-85e3bd7a06d0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eGeochemistry and Natural Background Values of Waters in the Area Surrounding the Guamá Waste Treatment Landfill, Marituba (Pa), Amazon Region\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eIn Brazil, 47% of municipalities rely exclusively on surface water for supply, 39% on groundwater, and 14% on both sources (ANA 2010), underscoring the importance of preserving this resource. Both surface and groundwater are subject to natural and anthropogenic processes that may alter their quality (Meyber \u0026amp; Helmer 1992, Bartram \u0026amp; Ballance \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1996\u003c/span\u003e), as they are vulnerable to anthropogenic contamination sources, such as agricultural activities, industrialization, and urbanization (Meyber \u0026amp; Helmer 1992, Bartram \u0026amp; Ballance \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1996\u003c/span\u003e, Huang et al. 2014, Alibuyog \u0026amp; Pastor \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). However, aquifers possess a certain degree of natural protection provided by the unsaturated zone, which influences the mobility of contaminants, or in cases of confined aquifers covered by impermeable layers (Aller et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1987\u003c/span\u003e, Foster \u0026amp; Hirata \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1988\u003c/span\u003e, Civita et al. 1994, Franc\u0026eacute;s et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). The hydrogeochemical characteristics of both water resources are determined by rock composition, soil type, climate, biological activity, land use, residence time, watershed drainage capacity, discharge of domestic and industrial effluents, and dam construction (Trofimov et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, Batista \u0026amp; Gastmans \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Dinka \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe combination of natural and anthropogenic processes tends to create distinct hydrochemical signatures in water bodies (Singh et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2006\u003c/span\u003e, Aghazadeh et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In many cases, both natural mineral dissolution and human-induced pollution coexist, resulting in complex geochemical patterns (Kamel et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Identifying these patterns is essential for effective water quality management and the development of environmental protection and remediation strategies (Mountassir \u0026amp; Bahir \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this context, water contamination represents one of the primary environmental impacts associated with the disposal of solid waste (Bacellar \u0026amp; Catapreta \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, Alves \u0026amp; Bertolo \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, Siqueira \u0026amp; Aprile \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Leachate, the liquid effluent generated by waste decomposition, is the principal pollutant affecting both surface and groundwater in the vicinity of landfills during operation, closure and post-closure (Bispo 2004, Gallas et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, Bittencourt \u0026amp; Rigoti 2011). This contaminant triggers various biogeochemical processes that vary according to leachate composition, environmental conditions, and the geological, geomorphological, and pedogenetic characteristics of a region (Christensen et al. 2001, Alves \u0026amp; Bertolo \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTherefore, in order to support sustainable planning and environmental protection in areas with high pollution potential, it is crucial to implement an efficient monitoring plan (Ladeira 2005, Mondelli et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), conduct geochemical and surface water studies (Jalali 2007, Boyacioglu \u0026amp; Boyacioglu \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), monitor water bodies over extended periods and across different seasons (e.g., Horbe \u0026amp; Santos \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, Trindade et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and establish reference values for surface water quality assessment.\u003c/p\u003e \u003cp\u003eDetermining local reference values (background values) is fundamental to understand a region's geochemical profile, enabling early detection and monitoring of potential geogenic or anthropogenic impacts associated with land use (BRIDGE 2006, 2009, ISPRA 2009). Additionally, these values allow for the identification of chemical elements that naturally occur above potable water standards, as well as the monitoring of elements that lack legally established maximum concentration limits.\u003c/p\u003e \u003cp\u003eIn Brazil, these reference values are defined by the National Environmental Council (CONAMA) through its resolutions (Brasil 2005, Brasil 2008, Brasil 2009, Brasil 2011). However, given that surface water composition is inherently influenced by the interplay of multiple processes, nationally or internationally established reference values (VROM 1994, USEPA 1996) may not accurately reflect the chemical characteristics of a specific locality.\u003c/p\u003e \u003cp\u003eFor instance, pH, iron, manganese, and sulfate levels exceeding legal standards have been observed in Crici\u0026uacute;ma (SC) (Sim\u0026atilde;o et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Arag\u0026atilde;o et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) showed that the establishment of Natural Background Levels (NBLs) is essential for distinguishing geogenic hydrochemical signatures from anthropogenic inputs in karst aquifers, demonstrating that parameters such as sulfate are predominantly controlled by geological sources (e.g., sulfide oxidation), while localized exceedances of nitrate and phosphate are associated with diffuse land-use pressures in areas of moderate to high intrinsic vulnerability. Thus, defining reference values in any project with potential environmental impacts on water resources is an essential strategy for protecting these resources - particularly in regions with limited geochemical studies, such as the Amazon region, in the northern region of Brazil.\u003c/p\u003e \u003cp\u003eThe study area is located in the Amazon region of northern Brazil, within the Igarap\u0026eacute; Pau-Grande sub-basin (a tributary of the Guam\u0026aacute; River) across the municipalities of Benevides, Marituba, and Ananindeua, where the CPTR Marituba sanitary landfill operates in a landscape marked by intense rainfall (\u0026asymp;\u0026thinsp;2,500\u0026ndash;2,700 mm/year) and year-round hydrological activity. This climatic setting, combined with the local geology dominated by the Barreiras Group (ferruginous sandstones and sandy\u0026ndash;clayey units) and Quaternary/post-Barreiras deposits, favors strong leaching, rapid mobilization of iron and aluminum, and naturally acidic waters\u0026mdash;processes that can produce concentrations exceeding drinking-water standards even in the absence of anthropogenic contamination. At the same time, landfills represent high pollution potential due to leachate generation, and the regional knowledge of hydrogeochemistry in smaller Amazonian tributaries remains limited, making it difficult to separate natural signals from possible impacts linked to waste disposal and surrounding land use. Establishing Natural Background Levels (NBL) in this context is therefore essential to (i) define a defensible baseline for surface and groundwater quality, (ii) distinguish geogenic enrichment and seasonal dilution effects from contamination fingerprints, and (iii) support monitoring design and public policy decisions in landfill-influenced environments, reducing uncertainty in compliance assessment and environmental management.\u003c/p\u003e \u003cp\u003eThis study investigates the geochemistry of surface and groundwater in the area surrounding the Sanitary Landfill of the Central Waste Processing and Treatment Facility (CTPR), specifically within the sub-basin of the Igarap\u0026eacute; Pau-Grande River, a tributary of the Guam\u0026aacute; River, which extends across the municipalities of Benevides, Marituba, and Ananindeua in the state of Par\u0026aacute;, Brazil (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). By analyzing the physicochemical properties of surface water, groundwater, and soil, as well as employing multiple hydrochemical tools and establishing Natural Background Levels (NBL) for surface and groundwater, this study seeks to elucidate the hydrogeochemical, physical, and anthropogenic processes influencing water composition. Such understanding is essential for informing public policy development in areas with landfill sites in northern Brazil. Notably, most studies on surface water geochemistry in the Amazon region focus on the Solim\u0026otilde;es, Amazon, and Negro Rivers, while knowledge regarding the chemical composition of smaller tributaries remains limited and fragmented.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eSITE DESCRIPTION\u003c/h3\u003e\n\u003cp\u003eThe study area is located in the Eastern Amazon region, in the State of Par\u0026aacute;, encompassing the surroundings of the CPTR Marituba sanitary landfill and situated within the Igarap\u0026eacute; Pau-Grande sub-basin, a tributary of the Guam\u0026aacute; River, with drainage distributed across the municipalities of Benevides, Marituba, and Ananindeua in the Metropolitan Region of Bel\u0026eacute;m. The area is characterized by low relief and high hydrological connectivity, marked by a network of small drainage channels (igarap\u0026eacute;s and streams) that respond rapidly to rainfall events and maintain direct interaction with the shallow aquifer. This hydrodynamic setting is particularly relevant for interpreting seasonal variations in parameters such as pH, Eh, and electrical conductivity. Geologically, the area is dominated by units of the Barreiras Group and Post-Barreiras/Quaternary deposits, composed of sandy to sandy-clayey materials that promote intense leaching under a high annual rainfall regime (\u0026asymp;\u0026thinsp;2,500\u0026ndash;2,700 mm/year). This combination of characteristics \u0026mdash; proximity to a high pollution-potential facility (landfill), presence of low-order watercourses, and strong influence of the hydrological cycle \u0026mdash; provides essential context for understanding the controls on local water chemistry.The spatial distribution of lithologies in the study area is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec.\u003c/p\u003e \u003cp\u003eGeologically, the Barreiras Group comprises a continental and marine sedimentary cover that extends along Brazil's coastal zone, from the state of Amap\u0026aacute; to Rio de Janeiro, characterized by its almost continuous occurrence and geomorphological regularity (Arai \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Lithologically, it consists of marls; micritic, biohermal, and dolomicritic limestones; as well as biocalcirudites and biocalcarenites from the Pirabas Formation (Menezes 2000). Overlying this unit is the Barreiras Formation, composed of ferruginous sandstones, fine to medium silty and clayey sands. Notably, the CPTR Marituba sanitary landfill was established in an area previously exploited for sand, gravel, and clay extraction, all belonging to this geological Group.\u003c/p\u003e \u003cp\u003ePost-Barreiras sediments consist of unconsolidated, yellowish sandy-clayey material that unconformably overlies the Barreiras Group. These deposits were formed by major fluctuations in sea level (transgressive and regressive episodes) associated with climate changes during the Quaternary (Martin et al. 1980; Martin et al. 1981; Esquivel 2006). Finally, modern sediments correspond to alluvial deposits along the Igarap\u0026eacute; Pau-Grande River channel, forming the alluvial plains.\u003c/p\u003e \u003cp\u003eThe region's climate is classified as Tropical, with annual precipitation ranging between 2,500 mm and 2,700 mm, distributed throughout the year without a defined dry season. The highest precipitation levels occur between January and May, with March being the wettest month, while November records the lowest rainfall. From January to September, water surplus conditions prevail, linked to increased precipitation, particularly in February and March. However, in October and November, water withdrawal from the environment leads to a hydrological deficit due to reduced rainfall levels. The mean temperature of the warmest month exceeds 26.7\u0026deg;C, whereas the coldest month typically records temperatures below 24.5\u0026deg;C (INMET 2020).\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003eA total of 27 hydrochemical monitoring campaigns were conducted in the study area between May 2018 and May 2021, covering both groundwater and surface water at the sampling points highlighted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed. Seven groundwater monitoring campaigns were carried out during the rainy season, with a total of 88 collected samples, while eight took place during the dry season, with a total of 90 samples. For surface water, six campaigns were conducted in the dry season (30 samples) and six in the rainy season (25 samples). In total, 233 samples were collected, with 120 obtained during the dry season and 113 during the rainy season.\u003c/p\u003e \u003cp\u003eSurface water sampling followed the guidelines established by ABNT NBR 9898, ensuring appropriate sampling and preservation methods for water bodies. For groundwater, a low-flow (minimal drawdown) groundwater sampling procedure was employed, as per ABNT NBR 15516, in shallow wells (up to 15 m) to minimize interference with sample quality. Soil samples were collected and prepared following ABNT NBR 6457, ensuring proper representativeness and storage conditions. All sampling activities were conducted under constant field parameter monitoring and by qualified professionals.\u003c/p\u003e \u003cp\u003eAs a result, a database was compiled with quarterly chemical analyses. Aliquots intended for analysis were filtered using a Millipore system with 0.45 \u0026micro;m filters, and the sealed containers were kept refrigerated before being sent to the laboratory. Chemical analyses were performed at the LP Anal\u0026iacute;tica Laboratory, following the methods established in Standard Methods for the Examination of Water and Wastewater (Rice et al. 2017). Cation (e.g., Ca\u0026sup2;⁺, Mg\u0026sup2;⁺, Na⁺, K⁺, Fe\u0026sup2;⁺) and anion (e.g., F⁻, Cl⁻, NO₃⁻, NO₂⁻, SO₄\u0026sup2;⁻) concentrations were determined using ion chromatography (IC), while alkalinity (HCO₃⁻ and CO₃\u0026sup2;⁻) was assessed through titration. To ensure analytical accuracy, blank and duplicate samples were randomly collected during sampling campaigns.\u003c/p\u003e \u003cp\u003eSubsequently, groundwater and surface water analysis results underwent basic statistical processing, along with equilibrium diagram construction using Microsoft Excel, to assess and interpret the physical, chemical, and anthropogenic processes influencing the study area.\u003c/p\u003e \u003cp\u003eTo determine local reference values for surface waters, a statistical treatment was applied, presenting the number of samples, minimum, maximum, mean, median, and upper limit (UL), based on descriptive statistical analysis using box plot diagrams. In this assessment, values exceeding the upper limit were considered anomalies, also referred to as outliers. The term \u003cem\u003eoutlier\u003c/em\u003e was initially described by Hawkins (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1980\u003c/span\u003e) as an observation that significantly deviates from others, raising suspicion that it was generated by a distinct mechanism, which in the context of water quality may indicate contamination.\u003c/p\u003e \u003cp\u003eThe selection of chemical parameters for reference value determination was based on systematic studies of leachate composition in Brazilian landfills conducted by Souto and Povinelli (2007). The NBL was also calculated for both surface and groundwater, considering cumulative frequency percentiles of 70%, 90%, and 95% for both dry and rainy seasons, following the method of Lucon et al. (2018) and Arag\u0026atilde;o et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA comprehensive analysis of physicochemical parameters, including temperature, pH, redox potential (Eh), and electrical conductivity (EC), was conducted to assess geochemical processes and the quality of surface and groundwater resources (Hem \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; APHA 2012). These parameters reflect both environmental conditions and potential anthropogenic interferences, encompassing seasonal variations (dry and rainy periods) and episodic contamination events (Stumm \u0026amp; Morgan \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Finally, maximum values were compared against drinking water standards established by CONAMA Resolution No. 357 (Brasil 2005), Ministry of Health Ordinance No. 36, CONAMA Resolution No. 420 (Brasil 2009), GB-5749\u0026ndash;2006, and Ministry of Health Ordinance No. 888 (Brasil 2021).\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eClassification and physico-chemical parameters of surface and groundwater\u003c/h2\u003e\n \u003cp\u003eOverall, results from monitoring campaigns conducted since 2018 indicate a slightly acidic pH, ranging from 4.58 to 7.46, while temperature remained at moderate levels, between 25.70 and 32.29\u0026deg;C. In terms of salinity, the rivers exhibited low electrical conductivity (EC) values, with a maximum of 274 \u0026micro;S/cm, characterizing a low-salinity environment. Eh varied between slightly oxidizing and mildly reducing conditions, indicating a naturally balanced system, albeit subject to both climatic and anthropogenic influences.\u003c/p\u003e\n \u003cp\u003eIntegrated analyses of temperature, pH, Eh, and EC in surface and groundwater (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) reveal significant seasonal variations that directly affect water quality and availability.\u003c/p\u003e\n \u003cp\u003eThe temperature distribution (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea) indicates that surface waters exhibit greater variability, especially during the dry season, whereas groundwater temperatures remain more stable. The concentration of temperature within the 26\u0026deg;C to 30\u0026deg;C range reflects local thermal equilibrium characteristics. Surface waters display a wider temperature range, with higher values during the dry season, likely due to direct exposure to solar heating. Groundwater, on the other hand, shows less variation, maintaining more stable temperatures due to the thermal insulation provided by the aquifer.\u003c/p\u003e\n \u003cp\u003eA comparison of pH distribution by water type and season (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb) reveals that groundwater exhibits less variation, tending to be more acidic. In contrast, surface waters show greater dispersion, with significantly higher pH values during the rainy season. Notably, surface water displays greater pH variability compared to groundwater, particularly in the rainy season, where more alkaline samples are observed. These differences may be attributed to the increased interaction of surface waters with external sources (such as organic matter, waste, and the alkalinity of rainwater), while groundwater is more influenced by geochemical processes and buffering mechanisms within the aquifer.\u003c/p\u003e\n \u003cp\u003eMost Eh values (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ec) are concentrated in lower ranges, around 0 to 500 mV, particularly in groundwater, indicating more reducing conditions. Surface water shows a wider range of Eh values, especially during the rainy season, suggesting the influence of oxidative processes due to increased circulation and mixing with the atmosphere. Surface waters generally exhibit higher and more dispersed Eh values, implying greater exposure to oxidation during the rainy period. In contrast, groundwater remains more stable, predominantly controlled by reducing conditions within the aquifer.\u003c/p\u003e\n \u003cp\u003eFinally, most EC values (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ed) fall within low ranges (0\u0026ndash;500 \u0026micro;S/cm), with a higher density in groundwater, suggesting minimal external influences compared to surface water. Surface water during the rainy season displays greater variability, indicating the influence of surface runoff and mixing. Groundwater exhibits lower EC variation, with outliers potentially linked to localized contamination sources or high mineralization. In contrast, surface waters show greater dispersion, particularly in the rainy season, reinforcing the contribution of external water inputs rich in dissolved salts or particles during this period.\u003c/p\u003e\n \u003cp\u003eAdditionally, Piper diagram analysis (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) reveals differences in the chemical composition of groundwater between dry and rainy seasons. During the rainy season (represented by blue points), samples tend to reflect greater ion dilution due to increased recharge from precipitation, generally exhibiting bicarbonate-dominated waters with lower salt concentrations. In contrast, during the dry season (red points), a higher concentration of dissolved compounds is observed, which may be associated with evapotranspiration processes, longer water residence time within the matrix, or more concentrated anthropogenic inputs.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eChemistry, background values, and reference limits\u003c/h3\u003e\n\u003cp\u003eThe calculated background values, along with the reference limits (upper limit) for surface waters within the area influenced by Guam\u0026aacute; Waste Treatment are presented in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Many chemical elements do not have maximum allowable values established by CONAMA Resolution No. 357 (Brasil, 2005), making direct comparison with drinking water standards unfeasible. However, certain chemical parameters, such as phosphate, biochemical oxygen demand (BOD), and color, naturally exceed the legally established values.\u003c/p\u003e\n\u003cp\u003eFurthermore, when considering Ordinance No. 36 of the Ministry of Health, it becomes evident that elements such as iron (upper limit\u0026thinsp;=\u0026thinsp;4.8 mg/L) and aluminum (upper limit\u0026thinsp;=\u0026thinsp;0.84 mg/L) naturally occur at levels exceeding those deemed suitable for human consumption, which are 0.3 mg/L and 0.1 mg/L, respectively.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults of statistical treatment and comparison with reference values for the analyzed data.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnit\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAverage\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e3\u0026deg;quartile\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUpper limit\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBrasil, 2005\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlkalinity due to bicarbonates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emgCaCO\u003csub\u003e3\u003c/sub\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAluminum (al)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBarium (ba)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCadmium (cd)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCalcium (ca)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal organic carbon (toc)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLead (pb)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChloride\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCopper (cu)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConductivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026micro;S/cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e331.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eColor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNTU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e138.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChromium (cr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBod\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChemical oxygen demand (cod)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal hardness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIron (fe)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhosphate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMagnesium (mg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eManganese (mn)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNitrate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNitrite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDissolved oxygen (do)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u0026ndash;9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePotassium (k)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e348.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e146.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e225.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e412.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSodium (na)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e151.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSulfate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTemperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTurbidity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNTU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZinc (zn)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;LQD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e highlights the cumulative frequency of results for the 70%, 90%, and 95% percentiles during dry and rainy seasons, providing a NBL for surface and groundwater to be compared with regulatory resolutions and reference limits. Again, many of the analyzed elements lack established maximum permissible values for comparison. However, it is worth noting that parameters such as total dissolved solids (TDS), sulfate, chloride, and nitrate remained within acceptable limits, while anomalies were detected for pH, iron, and aluminum, with varying responses depending on the season.\u003c/p\u003e\n\u003cp\u003eRegarding pH, 70% of the surface water data was below the regulatory limit during the dry season. For groundwater, although CONAMA Resolution No. 420 (Brasil, 2009) does not establish reference parameters, acidic values were observed across all percentiles in both dry and rainy seasons. Iron and aluminum concentrations exceeded reference limits for both surface and groundwater across all percentiles and seasons.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCumulative frequency for surface and groundwater considering the 70%, 90%, and 95% percentiles, compared to reference limits.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eUnit\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eSurface water\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eGroundwater\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePotable water\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eDry season\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eRainy season\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eDry season\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eRainy season\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u0026ndash;9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.5\u0026ndash;8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e193.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e246.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e272.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e184.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e248.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e278.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e239.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e345.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e396.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e239.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e345.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e396.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003euS/cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e144.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e164.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e173.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e238.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e269.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e188.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e227.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e254.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e418.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e496.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e131.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e148.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e500.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e128.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e175.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e197.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e144.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e223.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e261.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 000.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e500.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIron (Fe)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAluminum (Al)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCalcium (Ca)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSulfate (SO₄\u0026sup2;⁻)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e109.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e133.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eManganese (Mn)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMagnesium (Mg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChloride (Cl⁻)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePotassium (K)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal hardness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emgCaCO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNitrate (NO₃⁻)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBOD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e(1) \u003cem\u003eCONAMA n\u0026deg;357\u003c/em\u003e (Brasil, 2005); \u003cem\u003e(2) CONAMA n\u0026deg;420\u003c/em\u003e (Brasil, 2009); \u003cem\u003e(3) GB\u0026thinsp;\u0026minus;\u0026thinsp;5749\u0026ndash;2006; (4) MS n\u0026deg;888\u003c/em\u003e (Brasil, 2021).\u003c/p\u003e\n\u003cp\u003eWell-developed latosoils were collected using soil augers and subsequently analyzed. These soils predominate in a region with flat topography presenting minimal variation. Like water data, soil analyses (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) indicated anomalies in iron and aluminum concentrations, particularly at shallow depths (0.2 to 0.5 m). Compounds such as cadmium, sulfide, nitrate, and nitrite were almost always below the detection limit. However, localized anomalies of nitrate and surface sulfide, as well as sulfide anomalies at a depth of 30 cm, were detected.\u003c/p\u003e\n\u003ch3\u003eSurface waters: overall characterization and geogenic and anthropogenic influences\u003c/h3\u003e\n\u003cp\u003eThe mechanisms governing the chemical composition of surface waters can be identified using the Gibbs Diagram (Gibbs, \u003cspan class=\"CitationRef\"\u003e1970\u003c/span\u003e) (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). When comparing variations in the salinization mechanisms of surface waters between the dry and rainy seasons (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e), only slight differences are observed. However, in general, these mechanisms are dominated by precipitation and weathering.\u003c/p\u003e\n\u003cp\u003eThe influence of precipitation and water-rock interactions in the study area is evident in Fig.\u0026nbsp;6. As observed (Figs.\u0026nbsp;6a and 6b), elements such as iron and aluminum tend to exhibit higher concentrations following significant precipitation events. On the other hand, increased concentrations of compounds such as ammoniacal nitrogen (Fig.\u0026nbsp;6c), nitrate, and nitrite are associated with sampling conducted during periods of heavy rainfall. Ammoniacal nitrogen measurements (Fig.\u0026nbsp;6c) indicate that, in certain instances, this compound can reach elevated levels. However, these increases are not consistent over time and are linked to higher precipitation events in the study area.\u003c/p\u003e\n\n\u003cp\u003epH is another key factor in understanding the distribution of iron and nitrogen compounds in water. The pH\u0026ndash;pE diagram allows for the assessment of multiple influences on these parameters, considering variations in pH and Eh. As seen in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ea, most samples are located near the (NO₃⁻ / NO₂⁻ = 1) and NH₄⁺ line, where the stable nitrogen form is either nitrate (NO₃⁻) or ammoniacal nitrogen. This result is strongly correlated with the oxidizing environment in the natural section of the river, where, during periods with Eh values close to zero, ammoniacal nitrogen is the dominant form. Conversely, in periods with higher Eh values\u0026mdash;and thus a more oxidizing environment\u0026mdash;nitrate, and occasionally nitrite, become the stable nitrogen forms in the system. Iron (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eb) is predominantly present as ferrous iron (Fe\u0026sup2;⁺), originating from the dissolution of the lateritic crust of the Barreiras Group in a slightly acidic and oxidizing environment.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eA quarterly analysis conducted over four years of surface and groundwater in the area surrounding the CTPR Sanitary Landfill enabled a geochemical investigation and characterization in the Igarap\u0026eacute; Pau-Grande River sub-basin. These waters exhibit slightly acidic pH, particularly during the dry season (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), mild temperatures, low EC, and Eh ranging from mildly to slightly oxidizing (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegarding background values, iron and aluminum naturally occur at levels exceeding those deemed suitable for human consumption. This is evident both in the calculated background values for surface waters (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), particularly following heavy precipitation events (Fig.\u0026nbsp;6), and in soil analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), as well as cumulative frequency analysis of surface and groundwater (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In this context, results indicate the chemical composition of surface waters in the study area is inherently linked to water-rock interactions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), though it is also highly influenced by seasonal variations. The similarity of these findings to studies conducted in areas affected by landfills in Brazil (Medeiros et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Pereira et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) reinforces the importance of continuous monitoring, as interactions between leachates and water bodies can increase environmental vulnerability.\u003c/p\u003e \u003cp\u003eFirst, determining background values enabled the characterization of waters in this region and facilitated monitoring the impact of chemical elements present at concentrations much lower than potable water standards. In this regard, elements such as zinc, chloride, sulfate, turbidity, TDS, chromium, and barium have significantly lower upper limits than those established by regulations. Studies such as Arag\u0026atilde;o et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) highlight the importance of determining background levels of chloride, nitrate, sulfate, and phosphate, as areas exceeding these values may be subject to anthropogenic pollution from urban centers or agricultural activities\u0026mdash;an issue not identified in the study area.\u003c/p\u003e \u003cp\u003eAdditionally, the determination of NBL (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) allowed for the identification of seasonal influences\u0026mdash;like the findings of Lucon et al. (2018) in the karstic watershed of the S\u0026atilde;o Miguel River, MG\u0026mdash;on the concentration of naturally elevated elements, regardless of established reference values. Notably, pH, iron, and aluminum exhibited high levels in both surface and groundwater, with variations across different seasons.\u003c/p\u003e \u003cp\u003eIn the Gibbs diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), no samples were plotted in the evaporation dominance zone. This can be attributed to the region\u0026rsquo;s high precipitation rates and well-developed drainage network, consistent with the findings of Yu et al. (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) for the Yongding River in China. Conversely, water-rock interactions and precipitation constitute key factors in determining water chemistry. The TDS values exhibited the greatest variation when compared to the Na/(Na\u0026thinsp;+\u0026thinsp;Ca) and Cl/(Cl\u0026thinsp;+\u0026thinsp;HCO₃) ratios. This variation is also associated with the region\u0026rsquo;s high precipitation rates, which have a greater impact on TDS than on dissolved metals.\u003c/p\u003e \u003cp\u003eMoreover, the evaluation of salinization mechanisms (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) revealed a pronounced seasonal influence, similar to the studies of Gibbs (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1970\u003c/span\u003e) on Amazon River tributaries and those of Gunes \u0026amp; Balci (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) on water-rock interactions in highly variable climatic environments. During the dry season, the chemical composition of surface waters is predominantly governed by water-rock interactions, whereas in the rainy season, dilution from precipitation reduces dissolved salt concentrations, as also reported by Prasanna et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and Lalaoui et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). It is noteworthy that pH, iron, and aluminum naturally exhibited high values in both surface and groundwater, fluctuating with the seasons and reflecting the ongoing interplay of regional hydrogeochemical and hydrodynamic processes (Odukoya et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Piper diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) also indicated a chemical evolution between seasons, where rainfall recharge facilitates water mass renewal and mixing, diluting ions accumulated during the dry season. In contrast, in the absence of significant rainfall, the waters exhibit higher mineralization as a result of intensified geochemical interactions and decreased recharge or fresh water input. These results underscore the importance of considering seasonality in hydrogeochemical monitoring campaigns, as processes such as dilution, mixing, and ion exchange vary significantly under different climatic conditions, influencing groundwater quality and typology.\u003c/p\u003e \u003cp\u003eIntegrated analyses of temperature, pH, Eh, and EC in surface and groundwater (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) reveal significant seasonal variations that directly impact water quality and availability. During the dry season, evaporation intensifies solute concentrations, increasing EC and creating less oxygenated conditions, which can alter redox balance and enhance metal solubility, as demonstrated by Lalaoui et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Gunes \u0026amp; Balci (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In the rainy season, dilution from precipitation promotes greater homogeneity in parameters such as temperature and pH, creating more favorable conditions for ecological processes, as observed by Prasanna et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and Odukoya et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). However, abrupt fluctuations in these parameters may indicate anthropogenic inputs or rapid changes in hydrological regimes, warranting close monitoring for potential contamination risks and biogeochemical imbalances (Setia et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn groundwater, the relative stability of parameters reflects the protective role of geological formations, which act as natural barriers against external variations, as highlighted by Mongelli et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). However, localized peaks in EC, pH, or Eh variations may indicate specific pollution sources, emphasizing the need for continuous monitoring to detect and mitigate potential impacts on water quality (Lucon et al. 2018, Muller et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eElements such as iron and aluminum exhibit higher concentrations following significant precipitation events (Figs.\u0026nbsp;6a and 6b). Several authors report elevated iron and aluminum levels in groundwater within the Barreiras Group (Pican\u0026ccedil;o et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2002\u003c/span\u003e, Matta \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2002\u003c/span\u003e, Cabral \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2004\u003c/span\u003e, Oliveira Filho et al. 2018, Filho 2018). According to these studies, the high iron and aluminum content in groundwater is associated with the geological composition of this aquifer and the enhanced dissolution and mobilization of iron in slightly acidic and oxidizing water. Consequently, ferrous iron (Fe\u0026sup2;⁺) or bivalent iron originates from the dissolution of the lateritic crust of the Barreiras Group. Additionally, elevated concentrations of these elements in surface waters can be explained by groundwater contributions to effluent rivers and by leaching and subsequent dissolution of these elements by meteoric waters. The increase in their concentration in surface waters results from interactions between meteoric water and the Barreiras Group rocks within the unsaturated and saturated zones of the aquifer, followed by exudation as base flow. Therefore, this increase occurs after intense precipitation events.\u003c/p\u003e \u003cp\u003eCONAMA Resolution No. 001 (Brasil 1986), in its Article 1, defines environmental impact as any alteration of the physical, chemical, and biological properties of the environment caused by any form of matter or energy resulting from human activities, either directly or indirectly. Accordingly, monitoring the environmental quality of water resources in areas influenced by the ASG aims to assess the impact of landfill disposal activities and other operational units on the environmental quality of regional surface waters. Similar findings were reported in studies by Bacellar \u0026amp; Catapreta (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) on a landfill in the Belo Horizonte metropolitan area, Siqueira \u0026amp; Aprile (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) in Bel\u0026eacute;m, PA, and Alves \u0026amp; Bertolo (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) on the geochemistry of landfill-impacted waters.\u003c/p\u003e \u003cp\u003eIncreased concentrations of compounds such as ammoniacal nitrogen (Fig.\u0026nbsp;6c), nitrate, and nitrite are associated with samples collected during heavy precipitation periods. Ammoniacal nitrogen levels (Fig.\u0026nbsp;6c) suggest that this compound occasionally reaches elevated concentrations, and its weak correlation with other water chemical parameters indicates that it is linked to isolated events unrelated to landfill operations. Siqueira et al. (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) emphasize the influence of urban expansion along the Parauapebas River in areas near riparian reserves as a major factor affecting groundwater chemistry. Thus, water quality in each basin also reflects land use and occupation patterns, as observed by Muller et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) in a watershed in northern Scotland.\u003c/p\u003e \u003cp\u003eThis study identified certain elements and parameters as key indicators of the hydrological characteristics of the Igarap\u0026eacute; Pau-Grande River sub-basin (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), all of which exhibit anomalous concentrations with diverse origins and are influenced by seasonal variations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristic parameters and elements of the Igarap\u0026eacute; Pau-Grande River sub-basin, their origins, and seasonal influences.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter / Element\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConcentration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNBL90%\u003c/p\u003e \u003cp\u003e\u003cem\u003e(dry / rainy period)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrigen\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSazonality\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFe \u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSurface water\u003c/p\u003e \u003cp\u003e\u003cem\u003e7.60 / 3.45 mg/L\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGeogenic\u003c/p\u003e \u003cp\u003e(Barreiras Group)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e dry periods\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGroundwater\u003c/p\u003e \u003cp\u003e\u003cem\u003e47.2 / 47.3 mg/L\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAl \u003csup\u003e3+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSurface water\u003c/p\u003e \u003cp\u003e\u003cem\u003e0.99 / 0.89 mg/L\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGeogenic\u003c/p\u003e \u003cp\u003e(Barreiras Group)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e dry periods\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGroundwater\u003c/p\u003e \u003cp\u003e\u003cem\u003e19.29 / 12.45 mg/L\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNH₃\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eOccasionally high\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnthropogenic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e rainy periods\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAcid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSurface water\u003c/p\u003e \u003cp\u003e\u003cem\u003e6.38 / 7.60\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGeogenic\u003c/p\u003e \u003cp\u003e(Barreiras Group)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e rainy periods\u003c/p\u003e \u003cp\u003e\u003cb\u003e\u0026darr;\u003c/b\u003e dry periods\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGroundwater\u003c/p\u003e \u003cp\u003e\u003cem\u003e6.07 / 5.74\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study demonstrates that the chemical composition of the area surrounding the Guam\u0026aacute; Waste Treatment Landfill is inherently linked to the coalescence of two natural processes: the interaction between water and rock and geochemical processes associated with the region's high precipitation levels. These characteristics align with the low erosion and highly leaching environment of the Amazon, where the most soluble elements are rapidly removed.\u003c/p\u003e \u003cp\u003eStatistical analysis and the subsequent definition of reference values indicate that chemical elements such as iron, aluminum, phosphate, and the color and BOD naturally occur at levels exceeding potable water standards, rendering the waters unsuitable for consumption. The high concentrations of iron and aluminum are associated with the interaction of rainwater with layers enriched in these elements within the Barreiras Group. Consequently, the presence of these elements in surface waters is linked to dissolution and leaching processes at the surface and subsurface, as well as the exudation of the aquifer through baseflow.\u003c/p\u003e \u003cp\u003eFinally, the establishment of reference values and systematic monitoring in the area surrounding the CPTR Marituba Sanitary Landfill confirms that the landfill has been operating without altering the environmental quality classification of the region\u0026rsquo;s water resources.\u003c/p\u003e \u003cp\u003e.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLucas Salles (Federal University of Bahia) \u0026ndash; Conceptualization; Methodology; Investigation; Formal analysis; Writing \u0026ndash; Original Draft; Project administration.Lucas Salles designed the research framework, conducted the hydrogeochemical interpretation, performed statistical and geochemical analyses, and led the manuscript writing process.Luiz Rog\u0026eacute;rio Bastos Leal (Federal University of Bahia) \u0026ndash; Supervision; Scientific review; Validation; Methodological support.Luiz Rog\u0026eacute;rio Bastos Leal provided senior scientific supervision, contributed to the conceptual refinement of the hydrogeochemical interpretation, and validated methodological and analytical procedures.Giovana Rebelo Di\u0026oacute;rio \u0026ndash; Data curation; Visualization; Database management.Giovana Rebelo Di\u0026oacute;rio organized and structured the analytical database, performed data treatment support, and prepared the figures and graphical representations presented in the manuscript.Paulo Galv\u0026atilde;o (Federal University of Minas Gerais) \u0026ndash; Review \u0026amp; Editing; Critical revision; Final validation.Paulo Galv\u0026atilde;o conducted the final technical and scientific review of the manuscript, contributing to the refinement of the discussion, conceptual integration, and overall coherence of the final version.All authors have read and approved the final manuscript and agree with its submission.\u003c/p\u003e\u003ch2\u003eACKNOWLEDGMENTS\u003c/h2\u003e \u003cp\u003eThe authors thank Solv\u0026iacute; Essencis for supporting this research and the Graduate Program in Geology at the Federal University of Bahia.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAghazadeh N, Chitsazan M, Golestan Y (2016) Hydrochemistry and quality assessment of groundwater in the Ardabil area, Iran. 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Water 9(7):453. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/w9070453\u003c/span\u003e\u003cspan address=\"10.3390/w9070453\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-earth-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enge","sideBox":"Learn more about [Environmental Earth Sciences](https://www.springer.com/journal/12665)","snPcode":"12665","submissionUrl":"https://submission.nature.com/new-submission/12665/3","title":"Environmental Earth Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Groundwater, Surface water, Baseline, Brazilian Amazon","lastPublishedDoi":"10.21203/rs.3.rs-8979562/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8979562/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe definition of natural background values is essential to distinguish natural geochemical processes from potential anthropogenic influences, thereby supporting the assessment of surface and groundwater quality. This study evaluates the hydrogeochemistry of waters surrounding the Central Waste Processing and Treatment Facility (CPTR) landfill in Marituba, Par\u0026aacute;, Brazil, with the objective of establishing Natural Background Levels (NBL) and supporting the development of public policies in landfill-influenced areas. Sampling campaigns and statistical analyses of physicochemical parameters were conducted, including pH, temperature, electrical conductivity, redox potential, and inorganic and organic constituents. The results indicate slightly acidic pH (4.58\u0026ndash;7.46), moderate temperatures (25.7\u0026ndash;32.3\u0026deg;C), low electrical conductivity (\u0026le;\u0026thinsp;274 \u0026micro;S/cm), and mildly oxidizing conditions. Elevated concentrations of iron and aluminum are attributed to water\u0026ndash;rock interaction and intensified leaching driven by high Amazonian rainfall. Parameters such as phosphorus, color, and biochemical oxygen demand exceed drinking water standards, rendering the water unsuitable for consumption. Although localized variations in ammoniacal nitrogen were observed, systematic monitoring indicates that the CPTR landfill has not significantly altered regional water quality. These findings underscore the importance of continuous monitoring and the technical distinction between natural geochemical conditions and potential contamination sources in landfill-affected environments.\u003c/p\u003e","manuscriptTitle":"Geochemistry and Natural Background Values of Waters in the Area Surrounding the Guamá Waste Treatment Landfill, Marituba (Pa), Amazon Region","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-05 17:00:17","doi":"10.21203/rs.3.rs-8979562/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-24T01:03:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-23T14:21:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-13T16:30:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"51658958073383400180219559014013801910","date":"2026-03-13T08:50:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"116352570540967286378912162658764990325","date":"2026-03-13T03:32:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"36425063474225873423498517623983508060","date":"2026-03-03T02:13:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-02T08:36:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-02T00:42:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-28T07:34:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Earth Sciences","date":"2026-02-26T15:22:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-earth-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enge","sideBox":"Learn more about [Environmental Earth Sciences](https://www.springer.com/journal/12665)","snPcode":"12665","submissionUrl":"https://submission.nature.com/new-submission/12665/3","title":"Environmental Earth Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"44d845f2-13ab-45e1-9311-b524e7e80f46","owner":[],"postedDate":"March 5th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-03-24T01:08:46+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-05 17:00:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8979562","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8979562","identity":"rs-8979562","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}