Water Quality Assessment of the São Bartolomeu Stream (Viçosa, Brazil) Using the Water Quality Index (WQI) and Hierarchical Cluster Analysis (HCA): Seasonal Variation and Anthropogenic Influence

Water Quality Assessment of the São Bartolomeu Stream (Viçosa, Brazil) Using the Water Quality Index (WQI) and Hierarchical Cluster Analysis (HCA): Seasonal Variation and Anthropogenic Influence | 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 Systematic Review Water Quality Assessment of the São Bartolomeu Stream (Viçosa, Brazil) Using the Water Quality Index (WQI) and Hierarchical Cluster Analysis (HCA): Seasonal Variation and Anthropogenic Influence Letícia Said Marangon, Larissa Quartaroli This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7943771/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose This study assessed the physicochemical and microbiological quality of the São Bartolomeu Stream (Viçosa, Minas Gerais, Brazil) using the Water Quality Index (WQI) and Hierarchical Cluster Analysis (HCA). The aim was to identify spatial and seasonal water quality patterns associated with land use and anthropogenic pressure, including conditions at the drinking-water abstraction point. Methods Water samples were collected at four sites—P1 (rural upstream), C (drinking-water abstraction point), P2 (semi-urban), and P3 (urban downstream)—during the rainy and dry seasons of 2022. Nine parameters were analyzed according to APHA (2017) . The WQI was calculated following IGAM guidelines, and HCA (Ward’s linkage, Euclidean distance) was applied to detect spatial and temporal similarity patterns among sites. Results The WQI ranged from fair to poor, with the intake point (C) showing good water quality in both seasons, while urban sites (P2 and P3) exhibited degraded conditions. HCA revealed two main clusters, distinguishing rural–intake from urbanized sectors, reflecting hydrochemical gradients driven by anthropogenic inputs. Conclusions The combined use of WQI and multivariate analysis provided a robust diagnostic framework for surface water assessment. The drinking-water abstraction point maintained good quality but remains vulnerable to urban expansion, highlighting the need for continuous monitoring and integrated water management strategies. Water Quality Index Cluster analysis Drinking-water abstraction Multivariate analysis Anthropogenic impact Surface water Brazil Figures Figure 1 Figure 2 1 Introduction Surface water quality deterioration in developing countries often stems from rapid urbanization, inadequate sanitation infrastructure, and agricultural runoff (Silva et al., 2020). Small tropical watersheds are especially susceptible to contamination from untreated domestic sewage, industrial discharges, and diffuse sources, leading to nutrient enrichment, oxygen depletion, and microbial pollution (Santos et al., 2021; Souza et al., 2019). The Water Quality Index (WQI) condenses multiple physicochemical and microbiological indicators into a single numerical value, offering a practical tool for environmental monitoring and decision-making (Souza et al., 2019; Von Sperling, 2014). However, the WQI alone may not capture subtle spatial or temporal patterns; hence, multivariate statistical tools such as Hierarchical Cluster Analysis (HCA) arem recommended to identify hidden structures and hydrochemical similarities (Johnson & Wichern, 2019; Singh et al., 2018). In Brazil, national water quality standards are established by CONAMA Resolution 357/2005 (Brasil, 2005) and implemented through monitoring programs by IGAM and CETESB. Despite this, local-scale studies are essential to identify pollution sources and evaluate compliance with legislation. The São Bartolomeu Stream, located in Viçosa (MG), exemplifies a small watershed under mixed rural and urban pressures. This study integrates WQI and HCA to: (i) evaluate seasonal and spatial variation in water quality, (ii) identify anthropogenic impacts along the river continuum, and (iii) provide data to support sustainable water resource management. 2 Materials and Methods 2.1 Study Area The São Bartolomeu Stream lies within the Turvo Sujo River Basin, southeastern Brazil (20°44′–20°51′ S; 42°50′–42°55′ W). The region has a humid tropical climate with well-defined dry (May–September) and rainy (October–April) seasons. Four sampling sites were defined: P1: rural headwater with low anthropogenic disturbance; C: drinking-water abstraction point for municipal supply; P2: semi-urban zone receiving partial domestic wastewater; P3: urban downstream reach affected by sewage and surface runoff. This spatial distribution allows the assessment of hydrochemical transitions from preserved upstream areas to degraded urban sectors. 2.2 Sampling Sampling was conducted during the rainy (March–April) and dry (August–September) seasons of 2022. Samples were collected in sterilized polyethylene bottles, transported at 4°C, and analyzed within 24 h following APHA (2017) procedures. 2.3 Analytical Procedures Parameters measured included pH, temperature, turbidity, total solids, dissolved oxygen (DO), biochemical oxygen demand (BOD₅), total nitrogen (Ntotal), total phosphorus (Ptotal), and thermotolerant coliforms. Data were compared with the limits defined by CONAMA Resolution 357/2005 for Class 2 freshwater bodies (Brasil, 2005). 2.4 Water Quality Index (WQI) The WQI was computed as a weighted geometric mean of nine parameters following IGAM (2012) and CETESB (2021) criteria, Eq. 1: \(\:WQI=\prod\:_{i=1}^{n}{q}_{i}^{{w}_{i}}\) Equation 1 Where q i is the normalized sub-index (0–100) and w i is the corresponding weight (Σw i = 1). Weights: DO (0.17), thermotolerant coliforms (0.15), pH (0.12), BOD (0.10), Ntotal (0.10), Ptotal (0.10), temperature (0.10), turbidity (0.08), and total solids (0.08). WQI classification: Excellent (90–100), Good (70–90), Fair (50–70), Poor (25–50), Very Poor (0–25). 2.5 Statistical Analysis To identify similarity patterns, Hierarchical Cluster Analysis (HCA) was applied using Ward’s linkage and Euclidean distance after z-score normalization (Johnson & Wichern, 2019). Analyses were performed in OriginPro 2024 . Dendrograms were used to interpret spatial–seasonal relationships among sites and to classify clusters according to hydrochemical behavior. 3 Results and Discussion 3.1 Physicochemical and Microbiological Parameters The pH values ranged from 6.8 to 7.4, remaining within the acceptable range (6–9) established by CONAMA Resolution 357/2005 (Brasil, 2005). Water temperature varied from 18 to 27°C, reflecting seasonal climatic conditions typical of tropical watersheds. Total solids and turbidity were higher during the rainy season, evidencing the influence of surface runoff. The maximum turbidity (29 NTU) was recorded at P2 in March, likely due to soil erosion and sediment resuspension. BOD₅ values (1.4–8.7 mg·L⁻¹) and DO concentrations (3.1–7.9 mg·L⁻¹) indicated moderate organic pollution, with occasional oxygen depletion near urbanized sections (P2, P3). Elevated BOD₅ and low DO values suggest active microbial decomposition of organic matter (APHA, 2017). Ntotal and Ptotal concentrations were notably higher at P2 and P3—up to 13 mg·L⁻¹ and 2.6 mg·L⁻¹, respectively, exceeding the limits for Class 2 waters (2.18 mg·L⁻¹ N; 0.10 mg·L⁻¹ P) (Brasil, 2005). These nutrient surpluses are consistent with wastewater discharges and diffuse urban pollution. Thermotolerant coliforms reached levels of 10⁹ NMP·100 mL⁻¹ in urban zones, far above the maximum allowable value of 1000 NMP·100 mL⁻¹ for recreational or supply uses, confirming fecal contamination from sewage inputs. These results are consistent with findings by Silva et al. (2020) and Santos et al. (2021), who reported similar physicochemical profiles in urban basins subjected to unregulated effluent discharge. 3.2 Water Quality Index (WQI) The WQI results (Table 1 ) revealed spatial and seasonal variations consistent with land use patterns and pollution sources. Table 1 WQI values and classification for each sampling site Season P1 C P2 P3 Rainy 55 – Fair 79 – Good 48 – Poor 43 – Poor Dry 51 – Fair 88 – Good 40 – Poor 35 – Poor The drinking-water abstraction point (C) exhibited the best conditions in both seasons, suggesting effective catchment protection. P2 and P3, located in urbanized areas, consistently recorded WQI < 50, reflecting strong sewage influence. P1 maintained fair quality, indicating moderate agricultural runoff. The overall mean WQI was 55 ± 18, classifying the stream as fair (IGAM, 2012). Comparable patterns were observed in similar Brazilian basins (Costa et al., 2020; Luz et al., 2020; Santos et al., 2021), confirming that land-use intensity is a primary determinant of WQI variation. 3.3 Seasonal and Anthropogenic Influence During the rainy season, dilution slightly improved WQI at P1 and C, whereas stormwater runoff degraded P2 and P3 due to pollutant transport. In the dry season, low discharge concentrated nutrients and coliforms downstream, accentuating water quality deterioration. These results align with studies by Silva et al. (2020) and Souza et al. (2019), which reported that seasonal hydrology modulates pollutant loading and biogeochemical processes in tropical streams. Such dynamics highlight the need for continuous monitoring and integration of rainfall–runoff models in watershed management frameworks. 3.4 Cluster Analysis (HCA) The HCA (Fig. 1 ) identified two major clusters representing distinct hydrochemical regimes. Cluster 1 grouped sites 1, 5, 9, 7, 10, 14, and 2, characterized by high dissolved oxygen, low nutrient concentrations, and minimal microbial loads, suggesting preserved or less impacted conditions, grouped P1 and C. Cluster 2 comprised sites 3, 4, 8, 11, 12, 16, 13, and 15, marked by elevated nitrogen and phosphorus levels and higher coliform counts, indicating urban influence and wastewater intrusion, comprised P2 and P3. Subclusters exhibited temporal consistency, as samples from the same location tended to group together irrespective of sampling season, reflecting stable hydrochemical behavior. Minor seasonal shifts were attributed to dilution processes during the rainy period and concentration effects during droughts. The central observation point acted as a transitional node between rural and urban clusters, evidencing intermediate hydrochemical characteristics and potential susceptibility to anthropogenic pressure. These results are consistent with previous studies (Singh et al., 2018; Luz et al., 2020), confirming the robustness of hierarchical cluster analysis (HCA) in distinguishing pollution gradients and supporting targeted water quality management strategies. 4 Conclusions The São Bartolomeu Stream exhibited predominantly fair to poor water quality, particularly in the urbanized reaches (P2 and P3). The drinking-water abstraction point (C) maintained good quality, but its proximity to impacted zones necessitates permanent monitoring. HCA revealed clear spatial segregation between rural–intake and urban clusters, confirming progressive downstream degradation driven by anthropogenic pressure. The combined use of WQI and multivariate analysis provided an integrated approach to evaluate water quality and prioritize remediation actions. To ensure long-term sustainability, this study recommends expanding wastewater treatment coverage, controlling diffuse agricultural pollution, and implementing public environmental education in Viçosa and surrounding municipalities. Declarations Funding Not applicable. Author Contribution LM drafted the manuscript. LQ supervised the development of the study. All authors reviewed and approved the final version of the manuscript. Acknowledgments The authors would like to thank the supporting institutions and laboratories for providing technical and analytical assistance. Data Availability “The data that support the findings of this study are available from the corresponding author upon reasonable request.” References APHA (2017). Standard Methods for the Examination of Water and Wastewater (23rd ed.). American Public Health Association, Washington, D.C. Brasil (2005). CONAMA Resolution 357/2005 . Ministério do Meio Ambiente, Brasília. Brasil (2020). Sistema Nacional de Informações sobre Saneamento (SNIS) . Ministério do Desenvolvimento Regional, Brasília. Costa, L., et al. (2020). Assessment of surface water quality near landfill areas in Pará, Brazil. Environmental Monitoring and Assessment , 192(3), 1–15. IGAM (2012). Índice de Qualidade da Água – IQA: Manual Técnico . Instituto Mineiro de Gestão das Águas, Belo Horizonte. Luz, C. R., et al. (2020). Evaluation of water quality using WQI in Brazilian rivers under anthropogenic influence. Sustainability , 12(9), 3684. Santos, M., et al. (2021). Surface water quality assessment of Tangará da Serra, MT, Brazil. Water, Air, & Soil Pollution , 232(2), 55. Silva, R. A., et al. (2020). Monitoring of water contamination in tropical basins: a Brazilian perspective. Environmental Systems Research , 9(2), 1–12. Souza, F., et al. (2019). Application of water quality indices for environmental assessment in Brazilian rivers. Ecological Indicators , 104, 180–189. Von Sperling, M. (2014). Introduction to Water Quality and Treatment Systems . DESA/UFMG, Belo Horizonte. Johnson, R. A., & Wichern, D. W. (2019). Applied Multivariate Statistical Analysis (7th ed.). Pearson Education. Singh, K. P., et al. (2018). Multivariate techniques for water quality assessment: a case study in India. Environmental Monitoring and Assessment , 190(7), 396. CETESB (2021). Qualidade das Águas Interiores no Estado de São Paulo – Relatório 2021 . Companhia Ambiental do Estado de São Paulo, São Paulo. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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18:13:18","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":29821,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7943771/v1/e9ee9e2e725d2a2c809ed26d.html"},{"id":94583195,"identity":"8cf9dc51-6a94-41a4-a4d8-253802cef68e","added_by":"auto","created_at":"2025-10-28 18:13:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":592401,"visible":true,"origin":"","legend":"\u003cp\u003eCollection points along the watershed\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7943771/v1/65f011002a8e1a6104b8d04e.png"},{"id":94583423,"identity":"6c0b7f67-894e-4d62-b83c-9017a87279c5","added_by":"auto","created_at":"2025-10-28 18:14:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":18690,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 1. Dendrogram of sampling sites and campaigns for the São Bartolomeu Stream.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7943771/v1/73471b0758b6a1b77a59a5d7.png"},{"id":98425256,"identity":"0029e600-7800-4880-b615-b4e13c8a7beb","added_by":"auto","created_at":"2025-12-17 16:34:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1081329,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7943771/v1/90aec492-cc05-46bd-a818-db3d2b54faa0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Water Quality Assessment of the São Bartolomeu Stream (Viçosa, Brazil) Using the Water Quality Index (WQI) and Hierarchical Cluster Analysis (HCA): Seasonal Variation and Anthropogenic Influence","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eSurface water quality deterioration in developing countries often stems from rapid urbanization, inadequate sanitation infrastructure, and agricultural runoff (Silva et al., 2020). Small tropical watersheds are especially susceptible to contamination from untreated domestic sewage, industrial discharges, and diffuse sources, leading to nutrient enrichment, oxygen depletion, and microbial pollution (Santos et al., 2021; Souza et al., 2019).\u003c/p\u003e\u003cp\u003eThe Water Quality Index (WQI) condenses multiple physicochemical and microbiological indicators into a single numerical value, offering a practical tool for environmental monitoring and decision-making (Souza et al., 2019; Von Sperling, 2014). However, the WQI alone may not capture subtle spatial or temporal patterns; hence, multivariate statistical tools such as Hierarchical Cluster Analysis (HCA) arem recommended to identify hidden structures and hydrochemical similarities (Johnson \u0026amp; Wichern, 2019; Singh et al., 2018).\u003c/p\u003e\u003cp\u003eIn Brazil, national water quality standards are established by CONAMA Resolution 357/2005 (Brasil, 2005) and implemented through monitoring programs by IGAM and CETESB. Despite this, local-scale studies are essential to identify pollution sources and evaluate compliance with legislation. The S\u0026atilde;o Bartolomeu Stream, located in Vi\u0026ccedil;osa (MG), exemplifies a small watershed under mixed rural and urban pressures. This study integrates WQI and HCA to: (i) evaluate seasonal and spatial variation in water quality, (ii) identify anthropogenic impacts along the river continuum, and (iii) provide data to support sustainable water resource management.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Area\u003c/h2\u003e\u003cp\u003eThe S\u0026atilde;o Bartolomeu Stream lies within the Turvo Sujo River Basin, southeastern Brazil (20\u0026deg;44\u0026prime;\u0026ndash;20\u0026deg;51\u0026prime; S; 42\u0026deg;50\u0026prime;\u0026ndash;42\u0026deg;55\u0026prime; W). The region has a humid tropical climate with well-defined dry (May\u0026ndash;September) and rainy (October\u0026ndash;April) seasons. Four sampling sites were defined: P1: rural headwater with low anthropogenic disturbance; C: drinking-water abstraction point for municipal supply; P2: semi-urban zone receiving partial domestic wastewater; P3: urban downstream reach affected by sewage and surface runoff.\u003c/p\u003e\u003cp\u003eThis spatial distribution allows the assessment of hydrochemical transitions from preserved upstream areas to degraded urban sectors.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Sampling\u003c/h2\u003e\u003cp\u003eSampling was conducted during the rainy (March\u0026ndash;April) and dry (August\u0026ndash;September) seasons of 2022. Samples were collected in sterilized polyethylene bottles, transported at 4\u0026deg;C, and analyzed within 24 h following \u003cem\u003eAPHA (2017)\u003c/em\u003e procedures.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Analytical Procedures\u003c/h2\u003e\u003cp\u003eParameters measured included pH, temperature, turbidity, total solids, dissolved oxygen (DO), biochemical oxygen demand (BOD₅), total nitrogen (Ntotal), total phosphorus (Ptotal), and thermotolerant coliforms. Data were compared with the limits defined by CONAMA Resolution 357/2005 for Class 2 freshwater bodies (Brasil, 2005).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Water Quality Index (WQI)\u003c/h2\u003e\u003cp\u003eThe WQI was computed as a weighted geometric mean of nine parameters following IGAM (2012) and CETESB (2021) criteria, Eq.\u0026nbsp;1:\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"2\"\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\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:WQI=\\prod\\:_{i=1}^{n}{q}_{i}^{{w}_{i}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEquation 1\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\u003eWhere \u003cem\u003eq\u003csub\u003ei\u003c/sub\u003e\u003c/em\u003e is the normalized sub-index (0\u0026ndash;100) and \u003cem\u003ew\u003csub\u003ei\u003c/sub\u003e\u003c/em\u003e is the corresponding weight (Σw\u003csub\u003ei\u003c/sub\u003e = 1). Weights: DO (0.17), thermotolerant coliforms (0.15), pH (0.12), BOD (0.10), Ntotal (0.10), Ptotal (0.10), temperature (0.10), turbidity (0.08), and total solids (0.08).\u003c/p\u003e\u003cp\u003eWQI classification: Excellent (90\u0026ndash;100), Good (70\u0026ndash;90), Fair (50\u0026ndash;70), Poor (25\u0026ndash;50), Very Poor (0\u0026ndash;25).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e\u003cp\u003eTo identify similarity patterns, Hierarchical Cluster Analysis (HCA) was applied using Ward\u0026rsquo;s linkage and Euclidean distance after z-score normalization (Johnson \u0026amp; Wichern, 2019). Analyses were performed in \u003cem\u003eOriginPro 2024\u003c/em\u003e. Dendrograms were used to interpret spatial\u0026ndash;seasonal relationships among sites and to classify clusters according to hydrochemical behavior.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results and Discussion","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Physicochemical and Microbiological Parameters\u003c/h2\u003e\u003cp\u003eThe pH values ranged from 6.8 to 7.4, remaining within the acceptable range (6\u0026ndash;9) established by CONAMA Resolution 357/2005 (Brasil, 2005). Water temperature varied from 18 to 27\u0026deg;C, reflecting seasonal climatic conditions typical of tropical watersheds.\u003c/p\u003e\u003cp\u003eTotal solids and turbidity were higher during the rainy season, evidencing the influence of surface runoff. The maximum turbidity (29 NTU) was recorded at P2 in March, likely due to soil erosion and sediment resuspension.\u003c/p\u003e\u003cp\u003eBOD₅ values (1.4\u0026ndash;8.7 mg\u0026middot;L⁻\u0026sup1;) and DO concentrations (3.1\u0026ndash;7.9 mg\u0026middot;L⁻\u0026sup1;) indicated moderate organic pollution, with occasional oxygen depletion near urbanized sections (P2, P3). Elevated BOD₅ and low DO values suggest active microbial decomposition of organic matter (APHA, 2017).\u003c/p\u003e\u003cp\u003eNtotal and Ptotal concentrations were notably higher at P2 and P3\u0026mdash;up to 13 mg\u0026middot;L⁻\u0026sup1; and 2.6 mg\u0026middot;L⁻\u0026sup1;, respectively, exceeding the limits for Class 2 waters (2.18 mg\u0026middot;L⁻\u0026sup1; N; 0.10 mg\u0026middot;L⁻\u0026sup1; P) (Brasil, 2005). These nutrient surpluses are consistent with wastewater discharges and diffuse urban pollution.\u003c/p\u003e\u003cp\u003eThermotolerant coliforms reached levels of 10⁹ NMP\u0026middot;100 mL⁻\u0026sup1; in urban zones, far above the maximum allowable value of 1000 NMP\u0026middot;100 mL⁻\u0026sup1; for recreational or supply uses, confirming fecal contamination from sewage inputs.\u003c/p\u003e\u003cp\u003eThese results are consistent with findings by Silva et al. (2020) and Santos et al. (2021), who reported similar physicochemical profiles in urban basins subjected to unregulated effluent discharge.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Water Quality Index (WQI)\u003c/h2\u003e\u003cp\u003eThe WQI results (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) revealed spatial and seasonal variations consistent with land use patterns and pollution sources.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eWQI values and classification for each sampling site\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\u003eSeason\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRainy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55 \u0026ndash; Fair\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79 \u0026ndash; Good\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48 \u0026ndash; Poor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e43 \u0026ndash; Poor\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDry\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51 \u0026ndash; Fair\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e88 \u0026ndash; Good\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40 \u0026ndash; Poor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e35 \u0026ndash; Poor\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\u003eThe drinking-water abstraction point (C) exhibited the best conditions in both seasons, suggesting effective catchment protection. P2 and P3, located in urbanized areas, consistently recorded WQI\u0026thinsp;\u0026lt;\u0026thinsp;50, reflecting strong sewage influence. P1 maintained fair quality, indicating moderate agricultural runoff. The overall mean WQI was 55\u0026thinsp;\u0026plusmn;\u0026thinsp;18, classifying the stream as \u003cem\u003efair\u003c/em\u003e (IGAM, 2012).\u003c/p\u003e\u003cp\u003eComparable patterns were observed in similar Brazilian basins (Costa et al., 2020; Luz et al., 2020; Santos et al., 2021), confirming that land-use intensity is a primary determinant of WQI variation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Seasonal and Anthropogenic Influence\u003c/h2\u003e\u003cp\u003eDuring the rainy season, dilution slightly improved WQI at P1 and C, whereas stormwater runoff degraded P2 and P3 due to pollutant transport. In the dry season, low discharge concentrated nutrients and coliforms downstream, accentuating water quality deterioration.\u003c/p\u003e\u003cp\u003eThese results align with studies by Silva et al. (2020) and Souza et al. (2019), which reported that seasonal hydrology modulates pollutant loading and biogeochemical processes in tropical streams. Such dynamics highlight the need for continuous monitoring and integration of rainfall\u0026ndash;runoff models in watershed management frameworks.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Cluster Analysis (HCA)\u003c/h2\u003e\u003cp\u003eThe HCA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e) identified two major clusters representing distinct hydrochemical regimes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eCluster 1 grouped sites 1, 5, 9, 7, 10, 14, and 2, characterized by high dissolved oxygen, low nutrient concentrations, and minimal microbial loads, suggesting preserved or less impacted conditions, grouped P1 and C. Cluster 2 comprised sites 3, 4, 8, 11, 12, 16, 13, and 15, marked by elevated nitrogen and phosphorus levels and higher coliform counts, indicating urban influence and wastewater intrusion, comprised P2 and P3.\u003c/p\u003e\u003cp\u003eSubclusters exhibited temporal consistency, as samples from the same location tended to group together irrespective of sampling season, reflecting stable hydrochemical behavior. Minor seasonal shifts were attributed to dilution processes during the rainy period and concentration effects during droughts.\u003c/p\u003e\u003cp\u003eThe central observation point acted as a transitional node between rural and urban clusters, evidencing intermediate hydrochemical characteristics and potential susceptibility to anthropogenic pressure. These results are consistent with previous studies (Singh et al., 2018; Luz et al., 2020), confirming the robustness of hierarchical cluster analysis (HCA) in distinguishing pollution gradients and supporting targeted water quality management strategies.\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Conclusions","content":"\u003cp\u003eThe S\u0026atilde;o Bartolomeu Stream exhibited predominantly fair to poor water quality, particularly in the urbanized reaches (P2 and P3). The drinking-water abstraction point (C) maintained good quality, but its proximity to impacted zones necessitates permanent monitoring.\u003c/p\u003e\u003cp\u003eHCA revealed clear spatial segregation between rural\u0026ndash;intake and urban clusters, confirming progressive downstream degradation driven by anthropogenic pressure. The combined use of WQI and multivariate analysis provided an integrated approach to evaluate water quality and prioritize remediation actions.\u003c/p\u003e\u003cp\u003eTo ensure long-term sustainability, this study recommends expanding wastewater treatment coverage, controlling diffuse agricultural pollution, and implementing public environmental education in Vi\u0026ccedil;osa and surrounding municipalities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLM drafted the manuscript. LQ supervised the development of the study. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e\u003cp\u003eThe authors would like to thank the supporting institutions and laboratories for providing technical and analytical assistance.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003e\u0026ldquo;The data that support the findings of this study are available from the corresponding author upon reasonable request.\u0026rdquo;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAPHA (2017). \u003cem\u003eStandard Methods for the Examination of Water and Wastewater\u003c/em\u003e (23rd ed.). American Public Health Association, Washington, D.C.\u003c/li\u003e\n\u003cli\u003eBrasil (2005). \u003cem\u003eCONAMA Resolution 357/2005\u003c/em\u003e. Minist\u0026eacute;rio do Meio Ambiente, Bras\u0026iacute;lia.\u003c/li\u003e\n\u003cli\u003eBrasil (2020). \u003cem\u003eSistema Nacional de Informa\u0026ccedil;\u0026otilde;es sobre Saneamento (SNIS)\u003c/em\u003e. Minist\u0026eacute;rio do Desenvolvimento Regional, Bras\u0026iacute;lia.\u003c/li\u003e\n\u003cli\u003eCosta, L., et al. (2020). Assessment of surface water quality near landfill areas in Par\u0026aacute;, Brazil. \u003cem\u003eEnvironmental Monitoring and Assessment\u003c/em\u003e, 192(3), 1\u0026ndash;15.\u003c/li\u003e\n\u003cli\u003eIGAM (2012). \u003cem\u003e\u0026Iacute;ndice de Qualidade da \u0026Aacute;gua \u0026ndash; IQA: Manual T\u0026eacute;cnico\u003c/em\u003e. Instituto Mineiro de Gest\u0026atilde;o das \u0026Aacute;guas, Belo Horizonte.\u003c/li\u003e\n\u003cli\u003eLuz, C. R., et al. (2020). Evaluation of water quality using WQI in Brazilian rivers under anthropogenic influence. \u003cem\u003eSustainability\u003c/em\u003e, 12(9), 3684.\u003c/li\u003e\n\u003cli\u003eSantos, M., et al. (2021). Surface water quality assessment of Tangar\u0026aacute; da Serra, MT, Brazil. \u003cem\u003eWater, Air, \u0026amp; Soil Pollution\u003c/em\u003e, 232(2), 55.\u003c/li\u003e\n\u003cli\u003eSilva, R. A., et al. (2020). Monitoring of water contamination in tropical basins: a Brazilian perspective. \u003cem\u003eEnvironmental Systems Research\u003c/em\u003e, 9(2), 1\u0026ndash;12.\u003c/li\u003e\n\u003cli\u003eSouza, F., et al. (2019). Application of water quality indices for environmental assessment in Brazilian rivers. \u003cem\u003eEcological Indicators\u003c/em\u003e, 104, 180\u0026ndash;189.\u003c/li\u003e\n\u003cli\u003eVon Sperling, M. (2014). \u003cem\u003eIntroduction to Water Quality and Treatment Systems\u003c/em\u003e. DESA/UFMG, Belo Horizonte.\u003c/li\u003e\n\u003cli\u003eJohnson, R. A., \u0026amp; Wichern, D. W. (2019). \u003cem\u003eApplied Multivariate Statistical Analysis\u003c/em\u003e (7th ed.). Pearson Education.\u003c/li\u003e\n\u003cli\u003eSingh, K. P., et al. (2018). Multivariate techniques for water quality assessment: a case study in India. \u003cem\u003eEnvironmental Monitoring and Assessment\u003c/em\u003e, 190(7), 396.\u003c/li\u003e\n\u003cli\u003eCETESB (2021). \u003cem\u003eQualidade das \u0026Aacute;guas Interiores no Estado de S\u0026atilde;o Paulo \u0026ndash; Relat\u0026oacute;rio 2021\u003c/em\u003e. Companhia Ambiental do Estado de S\u0026atilde;o Paulo, S\u0026atilde;o Paulo.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Water Quality Index, Cluster analysis, Drinking-water abstraction, Multivariate analysis, Anthropogenic impact, Surface water, Brazil","lastPublishedDoi":"10.21203/rs.3.rs-7943771/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7943771/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003eThis study assessed the physicochemical and microbiological quality of the S\u0026atilde;o Bartolomeu Stream (Vi\u0026ccedil;osa, Minas Gerais, Brazil) using the Water Quality Index (WQI) and Hierarchical Cluster Analysis (HCA). The aim was to identify spatial and seasonal water quality patterns associated with land use and anthropogenic pressure, including conditions at the drinking-water abstraction point.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWater samples were collected at four sites\u0026mdash;P1 (rural upstream), C (drinking-water abstraction point), P2 (semi-urban), and P3 (urban downstream)\u0026mdash;during the rainy and dry seasons of 2022. Nine parameters were analyzed according to \u003cem\u003eAPHA (2017)\u003c/em\u003e. The WQI was calculated following IGAM guidelines, and HCA (Ward\u0026rsquo;s linkage, Euclidean distance) was applied to detect spatial and temporal similarity patterns among sites.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe WQI ranged from fair to poor, with the intake point (C) showing good water quality in both seasons, while urban sites (P2 and P3) exhibited degraded conditions. HCA revealed two main clusters, distinguishing rural\u0026ndash;intake from urbanized sectors, reflecting hydrochemical gradients driven by anthropogenic inputs.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThe combined use of WQI and multivariate analysis provided a robust diagnostic framework for surface water assessment. The drinking-water abstraction point maintained good quality but remains vulnerable to urban expansion, highlighting the need for continuous monitoring and integrated water management strategies.\u003c/p\u003e","manuscriptTitle":"Water Quality Assessment of the São Bartolomeu Stream (Viçosa, Brazil) Using the Water Quality Index (WQI) and Hierarchical Cluster Analysis (HCA): Seasonal Variation and Anthropogenic Influence","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-28 16:22:34","doi":"10.21203/rs.3.rs-7943771/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6eabc043-d57b-4178-ad94-de85150ddcc7","owner":[],"postedDate":"October 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-11T17:09:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-28 16:22:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7943771","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7943771","identity":"rs-7943771","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}