Impacts of urbanization on the quality of surface water in a watershed in the Brazilian Cerrado

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Water samples were collected at 11 points along the rivers, and eight physical-chemical parameters (electrical conductivity, pH, alkalinity, apparent and true color, turbidity, dissolved oxygen and biochemical oxygen demand) and three microbiological indicators (heterotrophic bacteria, total and thermotolerant coliforms) were analyzed. Spatio-temporal variation was assessed using the multivariate techniques of Principal Component Analysis/Factorial Analysis (PCA/FA) and Hierarchical Cluster Analysis (HCA). The results of the PCA/FA highlighted eight of the eleven parameters as the main ones responsible for the variations in water quality, with the greatest increase in these parameters being observed in the rainy season, especially among the points influenced by sewage discharges and by the influence of the urban area. The CA grouped the results from 11 points into three main groups: group 1 corresponded to points influenced by sewage discharges; group 2 grouped points with mainly urban influences; and group 3 grouped points in rural areas. These groupings showed the negative influence of urbanization and also statistically significant variations between the groups and periods. The most degraded conditions were in group 1, and the least degraded conditions in group 3. Assessment of the variations between the monitoring periods showed that rainfall had a significant impact on the increase or decrease in the parameters assessed, as a result of surface runoff linked to urbanization and increased river flow. Surface runoff urbanized rivers sanitary sewage multivariate statistics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Rivers play a fundamental role in both the maintenance of ecosystems and the socio-economic development of a region. However, in recent decades, these environments have faced challenges resulting from human activities, such as climate change, population growth and the expansion of agricultural and urban areas. (Best, 2019 ; Singh et al., 2020 ; Li et al., 2022 ; Wu et al., 2023 ; Çankaya et al., 2023 ). In urbanized rivers, water quality is impaired by the increase in impervious areas, which results in greater surface runoff, transporting diffuse pollutants such as organic matter, urban solid waste and untreated effluents (Gomes & Wai 2020 ). Furthermore, population growth unaccompanied by basic sanitation infrastructure generates point sources of pollution, with inadequate discharge of effluents at irregular disposal points (Santana Pereira et al., 2021 ; Ferreira et al., 2021 ). Furthermore, the diffuse activities of agricultural activities can introduce various polluting substances into rivers, causing significant changes in water quality (Cerqueira et al., 2019 ; Ren et al., 2023 ). These concentrations of pollutants, whether point source or diffuse, vary seasonally according to rainfall (Bastos et al., 2021 ). In the face of climate change and human activities, water quality in rivers varies spatially and temporally. Therefore, it is essential to monitor its quality with punctual and periodic samples that are representative, allowing an understanding of the spatio-temporal dynamics of variations in the physical-chemical and microbiological parameters of water quality (Varekar et al., 2015 ; Yang et al., 2021 ; Giri, 2021 ; Luz et al., 2022 ). Multivariate statistical techniques such as Principal Component Analysis/Factor Analysis (PCA/FA) and Hierarchical Cluster Analysis (HCA) have been widely used (Luo et al., 2017 ; Alves et al., 2018a ; Mohtar et al., 2019 ; Singh et al., 2020 ; Jo et al., 2022 ) and are efficient tools for understanding the dynamics of changes in river water quality over time. In this scenario, the western region of Bahia stands out, characterized by an extensive hydrographic network cut by the tributaries of the left bank of the São Francisco River, with vast areas of plateau relief. These characteristics have historically driven socio-economic growth in the region, mainly linked to agriculture, which has resulted in a notable population and urban increase (Moreira, 2013 ). Located in the western region of the state of Bahia, Brazil, the city of Barreiras is crossed by the Grande and Ondas rivers, which play a crucial role in the region. The rivers are essential for human supply and recreation, as well as being used by agriculture and industry. The municipality of Barreiras has the largest population and urban area in the entire region (IBGE, 2022). However, urban expansion near these rivers has brought with it significant environmental challenges, especially related to preserving water quality (Nascimento et al., 2020 ). This situation is exacerbated by the contamination of water resources through the runoff of pollutants from agricultural practices, as well as the inadequate disposal of solid waste and sanitary effluents, negatively impacting water quality (EMBASA, 2020 ). In view of the factors mentioned above, there is a clear need to carry out studies that assess water quality on a spatial-temporal basis in the municipality of Barreiras. This analysis is fundamental to understanding the variations in the water characteristics of the Grande and Ondas rivers. Therefore, the main objective of this study was to evaluate the spatial-temporal variations in the physical-chemical and microbiological parameters of water quality in the Grande and Ondas rivers, monitoring at points upstream, downstream and in the urban area of the city of Barreiras/BA, in order to identify the parameters that had the greatest impact between the periods and monitoring points. Material and methods Characterization of the study area From the dynamics of agricultural development and urban growth in the mesoregion of the far west of the state of Bahia, the municipalities of Barreiras and São Desidério stand out, which are the localities covered by the study area. The municipality of Barreiras has a territorial area of 8,051.274 km² and a population of 159,743 inhabitants in 2022, with an urbanized area of 31.72 km2 in 2019 (IBGE, 2023a). The municipal seat, the main hub of regional urban and economic development, is crossed by the Grande River, which is an important tributary of the São Francisco River, accounting for 16.6% of the latter's annual flow (INEMA, 2022 ). The Ondas River, which is the city's main water supply, meets the Grande River in the urban area. With regard to sanitary sewage, in 2021 the municipality had a collection rate of 68.8%, with 100% of the sanitary sewage collected being treated (SNIS, 2021 ). The municipality of São Desidério, which borders Barreiras to the north, has a territorial area of 15,156.712 km² and a population of 32,828 in 2022, with an urbanized area of 9.09 km2 (IBGE, 2022d ). The municipal seat lies within the perimeter of the São Desiderio River basin, which passes through the city's urban area and has its headwaters on the right bank of the Grande River. The municipality does not have sanitary sewage collection and treatment, and part of the sanitary sewage from the urban area is discharged into the São Desidério river, a tributary of the Grande River (SNIS, 2021 ). The Grande River rises in the Serra Geral de Goiás, and its watershed has a drainage area of 77,209 km², until it meets the São Francisco River in the municipality of Barra, state of Bahia. The left bank of the Grande River is fed by the Ondas River, which has a drainage area of approximately 5,465 km² (Fistarol et al., 2015 ). The Ondas River is the source of water for the urban supply of the municipality of Barreiras, as well as an important leisure area in the region. The land is heavily used for rainfed and irrigated agriculture and livestock farming (INEMA, 2022 ). The climate in the region is dry sub-humid with two well-defined seasons, dry and rainy. The rivers are located over the Urucuia aquifer, which feeds the perennial and voluminous flow of the rivers with its bottom discharge, with a constant flow even during the dry season. Sampling and monitoring To assess the surface water quality of the rivers, 11 monitoring points were selected (Fig. 1 ). Eight points were chosen along the Grande River, the first four coded as G1 to G4, located before the confluence with the Ondas River, while the subsequent four points, named G5 to G8, are located after this intersection. In addition, three points were selected on the Ondas River, identified as O1 to O3. On the Grande River, point G1 is located in the community of Morrão, in São Desidério, outside the influence of the urban area. Point G2 is located after the São Desidério river enters the urban area, while point G3 is located in the community of Barrocão de Baixo, an intermediate point before entering the urban area of Barreiras. Point G4, near the urban area of Barreiras and before the junction with the Ondas River. Continuing along the urban perimeter of the city of Barreiras, point G5 is in the Geraldo Rocha Exhibition Park and point G6 after a rainwater drainage channel. Point G7 is located just after the discharge of water from the city's sewage treatment plant. Point G8 is located after the urban perimeter of Barreiras. On the Ondas River, point O1 is located in the community of Val da Boa Esperança, 26.82 km upstream from the urban perimeter. Point O2 is located after several recreational areas along the river and before a soy-derived food industry. Point O3 is adjacent to the urban perimeter of Barreiras, just before the confluence with the Grande River (Fig. 1 ). The points were monitored bimonthly, with the dry period comprising the months of June, August and October 2022 (Table S1 , Figure S1 in the supplementary file). The rainy season comprised the months of December 2022, February and April 2023 (Table S2, figure S1 in the supplementary file). At each point, one liter of samples was collected for physicochemical analysis and stored in polyethylene bottles, and 250 mL of samples were collected in autoclaved glass vials for microbiological analysis. The sampling and preservation procedures followed the guidelines of the National Guide to Sample Collection and Preservation (CETESB; ANA, 2011 ). After each collection, the samples were sent in thermal containers to the Environmental Sanitation Laboratory of the Federal University of Western Bahia (UFOB), in the municipality of Barreiras/Brazil, for laboratory analysis. Physico-chemical and microbiological analysis The physicochemical parameters analyzed using APHA ( 2017 ) methods were: dissolved oxygen (4500-O G); pH (4500-H + B); electrical conductivity (2510 B); turbidity (2130 B); apparent and true color (2120 B); alkalinity (2320 B); and biochemical oxygen demand (5210 B). The microbiological analyses carried out were total coliforms, thermotolerant coliforms and heterotrophic bacteria. For coliform analysis, the Most Probable Number per 100 mL of sample (MPN/100 mL) technique was used, as established in technical standard L5.202 (CETESB, 2018 ) adapted from APHA ( 2017 ). The multiple dilution test adapted for three tubes with dilutions of 10 − 1 , 10 − 2 , 10 − 3 was used for the three stages of analysis. After obtaining the number of positive tubes for each confirmatory test, the MPN/mL of sample was calculated by applying the MPN table for three tubes (FDA, 2020 ). Heterotrophic bacteria were counted using the technique of counting the number of colony-forming units per mL of sample (CFU/mL) in accordance with technical standard L5.201 (CETESB, 2018 ), adapted from APHA ( 2017 ). Statistical analysis Multivariate principal component analysis and factor analysis (PCA/FA) were carried out to identify the main parameters influencing water quality and hierarchical cluster analysis (CA) to identify spatial patterns in water quality based on the points sampled. Initially, the suitability of the data for the application of multivariate methods was assessed using the Kaiser-Meyer-Olkin (KMO) test and Bartlett's test of sphericity, both at a 5% significance level (Jo & Kwon, 2023 ). Next, the eigenvalues of the correlation matrix were calculated and the principal components (PC) selected according to the criterion of eigenvalues > 1.0 and the criterion of cumulative percentage of total variance between 70 and 99% (Jo & Kwon, 2023 ; Guedes, et al., 2012 ). For the PCA, the factor loadings matrix was rotated using the varimax method with Kaiser normalization, a tool used to simplify the interpretation of the PC (Alves, et al., 2018b ) and, for terms of explanation, the criterion of strong factor loadings with results greater than 0.75 was followed (Liu et al., 2003 ; Rocha & Pereira, 2015 ; Jo & Kwon, 2023 ). From the correlation matrix of the PCA/AF, hierarchical cluster analysis (CA) was applied using the Euclidean distance and Ward's method, which minimizes the loss of information between groups. To group the sampling points by dissimilarity according to spatial and temporal variations (Luo, et al., 2017 ; Jo & Kwon, 2023 ). Shapiro Wilk's normality and Levene's homogeneity tests were applied to the groups formed in the CA. The parameters that met the assumptions of normality and homogeneity were submitted to analysis of variance (ANOVA) with 5% significance. Parameters that lacked normality and homogeneity were subjected to the Kruskal-Wallis non-parametric multiple comparison test, which assessed the variance between groups and periods at a 5% significance level (Cruz et al., 2019 ). Results and discussion Multivariate exploratory analysis of physico-chemical and microbiological water quality parameters The Kaiser-Meyer-Olkin test of data suitability was 0.606 and Bartlett's test of sphericity was p < 0.01, indicating that the data is suitable for the application of the PCA/AF. The results of the PCA/AF (Table 1 ) indicated that it was appropriate to consider four components (PC1, PC2, PC3 and PC4) to represent water quality and explain 77.80% of the total variance of the data, with strong loadings in 8 of the 11 parameters evaluated. Table 1 Matrix of factor loadings from PCA/FA Parameter PC1 PC2 PC3 PC4 DO 0.4865 0.7330 0.0348 -0.0345 pH 0.1102 -0.9024 -0.0082 0.1397 EC 0.3493 -0.1689 0.2571 0.8197* Alkalinity -0.1530 -0.0867 0.1113 0.9205* Turbidity 0.9063* -0.0713 0.2428 -0.0025 Apparent Color 0.9066* -0.0517 0.2451 0.0193 True Color 0.8116* 0.2539 0.0855 0.1056 BOD 5,20 -0.4224 0.4053 0.4847 -0.2783 Total Coliforms 0.2981 0.0466 0.8653* 0.0973 Thermotolerant coliforms 0.2737 -0.0226 0.8587* 0.1714 Heterotrophic bacteria 0.0337 0.0119 0.4413 0.3193 Eigenvalues 3.822805 2.103510 1.640594 0.991499 Variance (%) 34.75277 19.12282 14.91449 9.01363 Cumulative variance (%) 34.75280 53.87560 68.79010 77.80370 * High load factor The first PC1 component, which explains 34.75% of the variance in the data, indicates turbidity, apparent color and true color as strong load factors, results that are mainly associated with rainfall that facilitates increased river flow, surface runoff and transport of suspended particles to water bodies (Passos et al., 2021 ; Alves et al., 2018b ). This behavior can be seen in Fig. 2 , where the highest elevations were found in the rainy season (Fig. 2 c) between points G2 and G8 within the urban area, with the highest records at points G7 and G8 (Fig. 2 b). The second component (PC2), which accounts for 19.12% of the total variance of the data, explained pH with a strong load. The greatest increases in pH were observed in the dry season (Fig. 2 c) as a result of the urban area (Fig. 2 b), which emphasizes the influence of intense urban occupation on changes in this parameter (Freire et al., 2021 ). The third component (PC3), which explains 14.91% of the total variance of the data, shows total coliforms and thermotolerant coliforms with strong load factors, which indicates that high concentrations of coliforms were recorded during the study period, especially in the rainy season. These results may be associated with possible sanitary sewage discharges (Alves et al., 2018a ). These conditions can be observed at points G6, G7 and G8 in this study, which showed the greatest increases. On the other hand, Bastos et al. ( 2021 ) observed a significant correlation in the concentration of total and thermotolerant coliforms in the base flows of rivers, which result from the percolation of water in the soil and interaction with the soil matrix, influencing the quantity and quality of water supply sources and the base flow of rivers. The fourth component (PC4), which represents 9.01% of the variance of the data, explains the EC and alkalinity components with strong loads, with increases being observed in the rainy season, denoting the seasonal influence on the variation of this parameter, which in turn is intensified by surface runoff in urbanized areas, soil sealing (Luo et al., 2017 ), as well as improper discharges of sanitary sewage (Ferreira et al., 2021 ). These characteristics were observed between points G4, G6, G7 and G8, which showed the highest elevations during the rainy season. The CA dendrogram grouped the sampling points into three main groups (Fig. 3 ). Group 1 corresponds to the points that receive contributions from point pollutants (G6, G7 and G8). Group 2, on the other hand, contains the points that receive contributions mainly from the urban area, except for point G3, which in addition to the urban contribution, also receives input from a stream in a rural community with the possible discharge of untreated sanitary sewage used for animal feed. Group 3 corresponds mainly to the points located in less urbanized areas of the Ondas River (O1, O2 and O3) and the point upstream of the contributions from urban areas, located on the Grande River (G1). In the supplementary file, Table S5 shows the descriptive statistics for each group performed by CA. The analysis of variance indicated that there were statistically significant differences between the groups performed by the CA (F(22, 106) = 7.87, p = 0.01), with the greatest impacts in Group 1, resulting mainly from point source discharges of sanitary sewage, conditions that are widely discussed in the literature (Fonseca & Tibiriçá, 2019 ; Dantas et al., 2021 ; Souza et al., 2020 ; Yang et al., 2021 ). Group 2 shows the influence of the urban area on river water quality, with significant changes in the parameters assessed after entering the urbanized area (Ferreira et al., 2021 ; Gomes & Wai, 2020 ; Cruz et al., 2019 ). Group 3 presented data from less urbanized areas, which recorded the smallest variations in parameters, especially in the Ondas River. This finding may indicate that the Ondas River had less impact on water quality, considering that it is less urbanized than the Grande River. It is important to note that the Ondas River has undergone significant transformations in its native vegetation cover and has experienced exponential agricultural growth in its watershed over the last 36 years. This dynamic, even in the absence of intense urbanization, can have a strong impact on river conditions and water quality (Rego et al., 2023 ). Spatio-temporal variation of physico-chemical parameters The seasonal variability analyses indicated that there was a statistically significant increase in the rainy season when compared to the dry season (Table S3, S4 in the supplementary file) for the parameters EC (H (2) = 40, 36 p < 0.01), alkalinity (H (1) = 4.93 p = 0.02), apparent color (H (1) = 36.95 p < 0.01), true color (H (1) = 28.60, p < 0.01), turbidity (H(1) = 20.50, p < 0.01) and DO (F (2) = 12.58 p = 0.01). These increases in the rainy season can be attributed to concentrations of diffuse pollution by surface runoff during the rains, resulting in an increase in organic matter constituents, nutrients and dissolution of minerals, compounds and solutes by soil erosion, which in times of precipitation show great variability (Libânio 2010 ; Silva et al., 2018 ; Memet, 2019 ; Zhu et al., 2020 ; Boyd, 2020 ; Milz et al., 2022 ). In both the dry and rainy periods, the highest values were recorded between points G2 to G8 and O3, which were inserted after contributions associated with the urban perimeter (Fig. 4 ). In the rainy season, even with increased values, the DO concentrations showed a downward trend at the points on the urban perimeter (Fig. 4 a). Conditions that in urban centers are often associated with contributions of pollutants derived from the improper discharge of sanitary sewage and industrial effluents (Calazans et al., 2018 ; Santana Pereira et al., 2021 ; Yang et al. 2021 ). This situation was observed at points G6, associated with the contribution of a rainwater drainage channel, which even during the dry season has a constant flow of water, and at points G7 and G8, affected by the discharge of effluents from the sewage treatment plant in the city of Barreiras. The pH observed during the study period showed a significant decrease (F (4) = 3.83 p = 0.01) in the rainy season, a decrease that was observed at all the points analyzed (Fig. 4 e), with more acidic conditions in the areas with less urbanization (G1, O1 and O2). These results in the rainy season can be associated with surface runoff and erosion from the type of soil with acidic characteristics in the region, as well as the presence of CO2 in the rainwater (Freitas et al., 2014 ; Damasceno et al., 2015 ; Girardi et al., 2016 ; Medeiros et al., 2018 ). On the other hand, the points located in areas with greater urbanization tended to have less acidic values, conditions that are found in regions with greater urban occupation, influenced by the discharge of domestic sewage (Qiao et al., 2016 ). In the rainy season there was a decrease in BOD (Fig. 4 h), which can be attributed to the increase in flow, which contributes to the dilution capacity of the river (Monfared et al., 2017 ). This decrease did not show significant differences (H (1) = 1.87, p < 0.17), being within the maximum and minimum values (18.70 mgO 2 /L and 0.59 mgO 2 /L) recorded in the dry season. The results can be attributed to variations in flow and possible discharges of untreated effluents near the urban perimeter of the rivers (Trindade et al., 2016 ; Passos et al., 2021 ). Figure 4 h shows that after the contribution of the Ondas River, there is a dilution of organic matter (G5) with a reduction in BOD in both periods. This result contrasts with those recorded for EC (Fig. 4 a), where it is also possible to observe a decrease at point G5 for both periods. Spatio-temporal variation in microbiological parameters The seasonal evaluation of the parameter total coliforms (H (1) = 8.85 p = 0.01), thermotolerant coliforms (H (1) = 15.92 p = 0.01) and heterotrophic bacteria (H (1) = 22.09, p < 0.01) showed a statistically significant increase in the rainy season (Fig. 5 , table S3 and S4 in the supplementary file). These behaviors can be associated with the increase in river flow, the urbanization of the drainage area studied, the discharge of sewage from the treatment plant and the discharge of improper sanitary sewage (Silva et al., 2018 ; Souza et al., 2020 ; Assis et al., 2020 ; Goshu et al., 2021 ). These conditions were observed mainly between points G6 and G8 (Fig. 5 ). It is important to note that during the visits it was found that the area around point G6 is used as a leisure and fishing spot by the population of the surrounding neighborhoods. On the other hand, point G8 recorded the highest dispersion for both periods, as it is located after the urbanization area of the city of Barreiras. Heterotrophic bacteria are essential for maintaining aquatic ecosystems, but their excess concentration can indicate high nutrient levels in water bodies. Obtaining data on the abundance and functional activity of heterotrophic bacteria in aquatic ecosystems is important, even though they are widely studied in marine environments and lakes, they are little known in urbanized rivers (Manahan, 2013 ; Kopylov et al., 2015 ; Faure et al., 2015 ; Romanova et al., 2022 ). Conclusion It can be seen that the urban area of the municipality of Barreiras influences the reduction of water quality, especially in the Grande River, through specific contributions. The results of the ACP/AF indicated that turbidity, apparent and true color, DO, pH, total coliforms, thermotolerant coliforms, EC and alkalinity were the most relevant indicators of water quality in the Grande and Ondas rivers, due to spatio-temporal variations, indicating the greatest changes at the points in the urban area and showing the influence of rainfall on these changes. The CA analysis separated the sampling points into three groups based on their spatial characteristics. Group 1 corresponded to points influenced by point source discharges of untreated sanitary sewage. Group 2 with points with diffuse influences from the urban area. Group 3 with points with reduced urbanization. Analysis of the spatio-temporal variations showed that there were significant variations in the water quality parameters in the rainy season when compared to the dry season, results associated mainly with urban runoff. Declarations Funding The study was funded under the Brazil Scholarship Program - PAEC OEA-GCUB, call for Proposals OEA/GCUB 001/2020, supported by the Cooperation Agreement between the Organization of American States (OEA) and the Agreement between the Organization of American States (OEA) and the International Cooperation Group of Brazilian Universities (GCUB). Author Contribution TG: Sampling survey, laboratory analysis and original manuscript writing. GL, NG and SJ: Sampling survey, laboratory analysis.EJ and SS: laboratory analysis. DC: Prepared figure 1. CV and MC :Support for analysis methodologies. RB and MC:Supervision, revision and editing of the original manuscript. Data Availability The data will be made available according to requests. References Alves, D. D., Riegel, R. P., Quevedo, D. M., Osório, D. M., Costa, G. M., Nascimento, C. A., & Telöken, F. (2018b). 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Water quality assessment and pollution source apportionment using multivariate statistical and PMF receptor modeling techniques in a sub-watershed of the upper Yangtze River, Southwest China. Environmental Geochemistry and Health . doi:https://doi.org/10.1007/s10653-023-01477-z Rocha, C. H., & Pereira, A. M. (2015). Análise multivariada para seleção de parâmetros de monitoramento em manancial de Juiz de Fora, Minas Gerais. Ambiente & Água . doi:doi:10.4136/ambi-agua.1590 Romanova, N. D., Boltenkova, M. A., Polukhin, A. A., Bezzubova, E. M., & Shchuka, S. A. (2022). Heterotrophic Bacteria of the Ob River Estuary during Growing Season: Spatial and Temporal Variability. Oceanology (62). doi:https://doi.org/10.1134/S0001437022030109 Santana Pereira, M. C., Scarati Martins, J. R., Ferreira Nogueira, F., Bento Magalhães, A. A., Silva, P. d., & Fábio. (2021). Improvement of water quality in urban rivers: new paradigms to explore – Pinheiros river basin São Paulo, Brazil. 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Enviromental technology , págs. 4286-4295. doi:https://doi.org/10.1080/09593330.2020.1754922 Trindade, A. L., Almeida, K. C., Barbosa, P. E., & Oliveira, S. M. (2016). Temporal and spatial trends of surface water quality of Velhas River sub-basin, Minas Gerais state. Engenharia Sanitaria e Ambiental . doi:https://doi.org/10.1590/S1413-41522016131457 Varekar, V., Karmakar, S., Jha, R., & Ghosh, N. C. (2015). Design of sampling locations for river water quality monitoring considering seasonal variation of point and diffuse pollution loads. Environmental Monitoring and Assessment . doi:https://doi.org/10.1007/s10661-015-4583-6 Medeiros, W. M., Silva, C. E., & Lins, R. P. (2018). Avaliação sazonal e espacial da qualidade das águas superficiais da bacia hidrográfica do rio Longá, Piauí, Brasil. Ambiente & Água . doi:https://doi.org/10.4136/ambi-agua.2054 Wu, Q., Ke, L., Wang, J., Pavelsky, T. M., Allen, G. H., Sheng, Y., Song, C. (2023). Satellites reveal hotspots of global river extent change. Nature Communicationsn (14). doi:https://doi.org/10.1038/s41467-023-37061-3 Yang, S., Liang, M., Qin, Z., Qian, Y., Li, M., & Cao, Y. (2021). A novel assessment considering spatial and temporal variations of water quality to identify pollution sources in urban rivers. Scientific Reports: https://doi.org/10.1038/s41598-021-87671-4 Zhu, G., Xiong, N., Wang, X., Hursthouse, A. S., & Marr, A. (2020). Correlation Characteristics of Electrical Conductivity of Surface Waters with the Fluorescence Excitation-Emission Matrix Spectroscopy-Parallel Factor Components of Dissolved Organic Matter. Journal of Fluorescence . doi:https://doi.org/10.1007/s10895-020-02628-6 Additional Declarations No competing interests reported. Supplementary Files Supplementaryfile.docx Cite Share Download PDF Status: Published Journal Publication published 07 Oct, 2024 Read the published version in Environmental Monitoring and Assessment → Version 1 posted Editorial decision: Revision requested 30 Jul, 2024 Editor assigned by journal 26 Jul, 2024 Submission checks completed at journal 26 Jul, 2024 First submitted to journal 18 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4601767","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":333978947,"identity":"4d2943e9-7c69-4b3e-91cc-cb720081f48b","order_by":0,"name":"Terly Gabriela Quiñonez 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2","display":"","copyAsset":false,"role":"figure","size":117619,"visible":true,"origin":"","legend":"\u003cp\u003eBiplot graph for Principal Components 1 and 2 in the load factor plane\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4601767/v1/2b9d0dfe5ce48f23d29fb0d9.png"},{"id":63025693,"identity":"8f92d91f-fae3-424b-8325-2d98b752b3a2","added_by":"auto","created_at":"2024-08-22 08:24:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":285488,"visible":true,"origin":"","legend":"\u003cp\u003eDissimilarity of sampling points\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4601767/v1/221503d9ed7d57cf5a4352e4.png"},{"id":63025692,"identity":"e0bd7163-1d94-4711-a1ef-eb5f5795fa1c","added_by":"auto","created_at":"2024-08-22 08:24:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":448256,"visible":true,"origin":"","legend":"\u003cp\u003eSpatio-temporal variation in physico-chemical parameters\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4601767/v1/371f32bff905828db95b4ec1.png"},{"id":63025696,"identity":"db4ae9d0-61a9-4479-8f37-2d235ff0f71f","added_by":"auto","created_at":"2024-08-22 08:24:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":171760,"visible":true,"origin":"","legend":"\u003cp\u003eVariação Spatio-temporal microbiological parameters\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4601767/v1/ec043b998afe5ad44f3ce6c8.png"},{"id":66598085,"identity":"55bb8739-4e3e-499e-a6c2-771aa71c032c","added_by":"auto","created_at":"2024-10-14 16:12:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2021710,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4601767/v1/ede3b0ae-7ca1-419c-98d9-024beb7cab10.pdf"},{"id":63026440,"identity":"d7f490b3-7cb2-440e-a9a6-cf16abd326f1","added_by":"auto","created_at":"2024-08-22 08:32:05","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":368042,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-4601767/v1/f41e399ea8bdda578092d4fb.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impacts of urbanization on the quality of surface water in a watershed in the Brazilian Cerrado","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRivers play a fundamental role in both the maintenance of ecosystems and the socio-economic development of a region. However, in recent decades, these environments have faced challenges resulting from human activities, such as climate change, population growth and the expansion of agricultural and urban areas. (Best, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; \u0026Ccedil;ankaya et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn urbanized rivers, water quality is impaired by the increase in impervious areas, which results in greater surface runoff, transporting diffuse pollutants such as organic matter, urban solid waste and untreated effluents (Gomes \u0026amp; Wai \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Furthermore, population growth unaccompanied by basic sanitation infrastructure generates point sources of pollution, with inadequate discharge of effluents at irregular disposal points (Santana Pereira et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ferreira et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, the diffuse activities of agricultural activities can introduce various polluting substances into rivers, causing significant changes in water quality (Cerqueira et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ren et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese concentrations of pollutants, whether point source or diffuse, vary seasonally according to rainfall (Bastos et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the face of climate change and human activities, water quality in rivers varies spatially and temporally. Therefore, it is essential to monitor its quality with punctual and periodic samples that are representative, allowing an understanding of the spatio-temporal dynamics of variations in the physical-chemical and microbiological parameters of water quality (Varekar et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Giri, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Luz et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Multivariate statistical techniques such as Principal Component Analysis/Factor Analysis (PCA/FA) and Hierarchical Cluster Analysis (HCA) have been widely used (Luo et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Alves et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e; Mohtar et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Jo et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and are efficient tools for understanding the dynamics of changes in river water quality over time.\u003c/p\u003e \u003cp\u003eIn this scenario, the western region of Bahia stands out, characterized by an extensive hydrographic network cut by the tributaries of the left bank of the S\u0026atilde;o Francisco River, with vast areas of plateau relief. These characteristics have historically driven socio-economic growth in the region, mainly linked to agriculture, which has resulted in a notable population and urban increase (Moreira, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Located in the western region of the state of Bahia, Brazil, the city of Barreiras is crossed by the Grande and Ondas rivers, which play a crucial role in the region. The rivers are essential for human supply and recreation, as well as being used by agriculture and industry. The municipality of Barreiras has the largest population and urban area in the entire region (IBGE, 2022).\u003c/p\u003e \u003cp\u003eHowever, urban expansion near these rivers has brought with it significant environmental challenges, especially related to preserving water quality (Nascimento et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This situation is exacerbated by the contamination of water resources through the runoff of pollutants from agricultural practices, as well as the inadequate disposal of solid waste and sanitary effluents, negatively impacting water quality (EMBASA, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn view of the factors mentioned above, there is a clear need to carry out studies that assess water quality on a spatial-temporal basis in the municipality of Barreiras. This analysis is fundamental to understanding the variations in the water characteristics of the Grande and Ondas rivers. Therefore, the main objective of this study was to evaluate the spatial-temporal variations in the physical-chemical and microbiological parameters of water quality in the Grande and Ondas rivers, monitoring at points upstream, downstream and in the urban area of the city of Barreiras/BA, in order to identify the parameters that had the greatest impact between the periods and monitoring points.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003eCharacterization of the study area\u003c/p\u003e \u003cp\u003eFrom the dynamics of agricultural development and urban growth in the mesoregion of the far west of the state of Bahia, the municipalities of Barreiras and S\u0026atilde;o Desid\u0026eacute;rio stand out, which are the localities covered by the study area. The municipality of Barreiras has a territorial area of 8,051.274 km\u0026sup2; and a population of 159,743 inhabitants in 2022, with an urbanized area of 31.72 km2 in 2019 (IBGE, 2023a). The municipal seat, the main hub of regional urban and economic development, is crossed by the Grande River, which is an important tributary of the S\u0026atilde;o Francisco River, accounting for 16.6% of the latter's annual flow (INEMA, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The Ondas River, which is the city's main water supply, meets the Grande River in the urban area. With regard to sanitary sewage, in 2021 the municipality had a collection rate of 68.8%, with 100% of the sanitary sewage collected being treated (SNIS, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe municipality of S\u0026atilde;o Desid\u0026eacute;rio, which borders Barreiras to the north, has a territorial area of 15,156.712 km\u0026sup2; and a population of 32,828 in 2022, with an urbanized area of 9.09 km2 (IBGE, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022d\u003c/span\u003e). The municipal seat lies within the perimeter of the S\u0026atilde;o Desiderio River basin, which passes through the city's urban area and has its headwaters on the right bank of the Grande River. The municipality does not have sanitary sewage collection and treatment, and part of the sanitary sewage from the urban area is discharged into the S\u0026atilde;o Desid\u0026eacute;rio river, a tributary of the Grande River (SNIS, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Grande River rises in the Serra Geral de Goi\u0026aacute;s, and its watershed has a drainage area of 77,209 km\u0026sup2;, until it meets the S\u0026atilde;o Francisco River in the municipality of Barra, state of Bahia. The left bank of the Grande River is fed by the Ondas River, which has a drainage area of approximately 5,465 km\u0026sup2; (Fistarol et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The Ondas River is the source of water for the urban supply of the municipality of Barreiras, as well as an important leisure area in the region. The land is heavily used for rainfed and irrigated agriculture and livestock farming (INEMA, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The climate in the region is dry sub-humid with two well-defined seasons, dry and rainy. The rivers are located over the Urucuia aquifer, which feeds the perennial and voluminous flow of the rivers with its bottom discharge, with a constant flow even during the dry season.\u003c/p\u003e \u003cp\u003eSampling and monitoring\u003c/p\u003e \u003cp\u003eTo assess the surface water quality of the rivers, 11 monitoring points were selected (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Eight points were chosen along the Grande River, the first four coded as G1 to G4, located before the confluence with the Ondas River, while the subsequent four points, named G5 to G8, are located after this intersection. In addition, three points were selected on the Ondas River, identified as O1 to O3.\u003c/p\u003e \u003cp\u003eOn the Grande River, point G1 is located in the community of Morr\u0026atilde;o, in S\u0026atilde;o Desid\u0026eacute;rio, outside the influence of the urban area. Point G2 is located after the S\u0026atilde;o Desid\u0026eacute;rio river enters the urban area, while point G3 is located in the community of Barroc\u0026atilde;o de Baixo, an intermediate point before entering the urban area of Barreiras. Point G4, near the urban area of Barreiras and before the junction with the Ondas River. Continuing along the urban perimeter of the city of Barreiras, point G5 is in the Geraldo Rocha Exhibition Park and point G6 after a rainwater drainage channel. Point G7 is located just after the discharge of water from the city's sewage treatment plant. Point G8 is located after the urban perimeter of Barreiras.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOn the Ondas River, point O1 is located in the community of Val da Boa Esperan\u0026ccedil;a, 26.82 km upstream from the urban perimeter. Point O2 is located after several recreational areas along the river and before a soy-derived food industry. Point O3 is adjacent to the urban perimeter of Barreiras, just before the confluence with the Grande River (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe points were monitored bimonthly, with the dry period comprising the months of June, August and October 2022 (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e in the supplementary file). The rainy season comprised the months of December 2022, February and April 2023 (Table S2, figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e in the supplementary file). At each point, one liter of samples was collected for physicochemical analysis and stored in polyethylene bottles, and 250 mL of samples were collected in autoclaved glass vials for microbiological analysis. The sampling and preservation procedures followed the guidelines of the National Guide to Sample Collection and Preservation (CETESB; ANA, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). After each collection, the samples were sent in thermal containers to the Environmental Sanitation Laboratory of the Federal University of Western Bahia (UFOB), in the municipality of Barreiras/Brazil, for laboratory analysis.\u003c/p\u003e \u003cp\u003ePhysico-chemical and microbiological analysis\u003c/p\u003e \u003cp\u003eThe physicochemical parameters analyzed using APHA (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) methods were: dissolved oxygen (4500-O G); pH (4500-H\u003csup\u003e+\u003c/sup\u003e B); electrical conductivity (2510 B); turbidity (2130 B); apparent and true color (2120 B); alkalinity (2320 B); and biochemical oxygen demand (5210 B).\u003c/p\u003e \u003cp\u003eThe microbiological analyses carried out were total coliforms, thermotolerant coliforms and heterotrophic bacteria. For coliform analysis, the Most Probable Number per 100 mL of sample (MPN/100 mL) technique was used, as established in technical standard L5.202 (CETESB, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) adapted from APHA (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The multiple dilution test adapted for three tubes with dilutions of 10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e, 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e was used for the three stages of analysis. After obtaining the number of positive tubes for each confirmatory test, the MPN/mL of sample was calculated by applying the MPN table for three tubes (FDA, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Heterotrophic bacteria were counted using the technique of counting the number of colony-forming units per mL of sample (CFU/mL) in accordance with technical standard L5.201 (CETESB, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), adapted from APHA (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eMultivariate principal component analysis and factor analysis (PCA/FA) were carried out to identify the main parameters influencing water quality and hierarchical cluster analysis (CA) to identify spatial patterns in water quality based on the points sampled. Initially, the suitability of the data for the application of multivariate methods was assessed using the Kaiser-Meyer-Olkin (KMO) test and Bartlett's test of sphericity, both at a 5% significance level (Jo \u0026amp; Kwon, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Next, the eigenvalues of the correlation matrix were calculated and the principal components (PC) selected according to the criterion of eigenvalues\u0026thinsp;\u0026gt;\u0026thinsp;1.0 and the criterion of cumulative percentage of total variance between 70 and 99% (Jo \u0026amp; Kwon, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Guedes, et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor the PCA, the factor loadings matrix was rotated using the varimax method with Kaiser normalization, a tool used to simplify the interpretation of the PC (Alves, et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e) and, for terms of explanation, the criterion of strong factor loadings with results greater than 0.75 was followed (Liu et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Rocha \u0026amp; Pereira, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Jo \u0026amp; Kwon, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). From the correlation matrix of the PCA/AF, hierarchical cluster analysis (CA) was applied using the Euclidean distance and Ward's method, which minimizes the loss of information between groups. To group the sampling points by dissimilarity according to spatial and temporal variations (Luo, et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Jo \u0026amp; Kwon, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eShapiro Wilk's normality and Levene's homogeneity tests were applied to the groups formed in the CA. The parameters that met the assumptions of normality and homogeneity were submitted to analysis of variance (ANOVA) with 5% significance. Parameters that lacked normality and homogeneity were subjected to the Kruskal-Wallis non-parametric multiple comparison test, which assessed the variance between groups and periods at a 5% significance level (Cruz et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results and discussion","content":"\u003cp\u003eMultivariate exploratory analysis of physico-chemical and microbiological water quality parameters\u003c/p\u003e\n\u003cp\u003eThe Kaiser-Meyer-Olkin test of data suitability was 0.606 and Bartlett\u0026apos;s test of sphericity was p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, indicating that the data is suitable for the application of the PCA/AF. The results of the PCA/AF (Table \u003cspan\u003e1\u003c/span\u003e) indicated that it was appropriate to consider four components (PC1, PC2, PC3 and PC4) to represent water quality and explain 77.80% of the total variance of the data, with strong loadings in 8 of the 11 parameters evaluated.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eMatrix of factor loadings from PCA/FA\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePC1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePC2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePC3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePC4\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\u003eDO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.0345\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=\"char\"\u003e\n \u003cp\u003e0.1102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.9024\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.0082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1397\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=\"char\"\u003e\n \u003cp\u003e0.3493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.1689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.8197*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlkalinity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.1530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.0867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.9205*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTurbidity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.9063*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.0713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.0025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eApparent Color\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.9066*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.0517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0193\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrue Color\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.8116*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBOD\u003csub\u003e5,20\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.4224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.2783\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal Coliforms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.8653*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0973\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThermotolerant coliforms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.0226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.8587*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1714\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeterotrophic bacteria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3193\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEigenvalues\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.822805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.103510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.640594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.991499\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariance (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.75277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.12282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.91449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.01363\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCumulative variance (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.75280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53.87560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68.79010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77.80370\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e* High load factor\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe first PC1 component, which explains 34.75% of the variance in the data, indicates turbidity, apparent color and true color as strong load factors, results that are mainly associated with rainfall that facilitates increased river flow, surface runoff and transport of suspended particles to water bodies (Passos et al., \u003cspan\u003e2021\u003c/span\u003e; Alves et al., \u003cspan\u003e2018b\u003c/span\u003e). This behavior can be seen in Fig. \u003cspan\u003e2\u003c/span\u003e, where the highest elevations were found in the rainy season (Fig. \u003cspan\u003e2\u003c/span\u003ec) between points G2 and G8 within the urban area, with the highest records at points G7 and G8 (Fig. \u003cspan\u003e2\u003c/span\u003eb).\u003c/p\u003e\n\u003cp\u003eThe second component (PC2), which accounts for 19.12% of the total variance of the data, explained pH with a strong load. The greatest increases in pH were observed in the dry season (Fig. \u003cspan\u003e2\u003c/span\u003ec) as a result of the urban area (Fig. \u003cspan\u003e2\u003c/span\u003eb), which emphasizes the influence of intense urban occupation on changes in this parameter (Freire et al., \u003cspan\u003e2021\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe third component (PC3), which explains 14.91% of the total variance of the data, shows total coliforms and thermotolerant coliforms with strong load factors, which indicates that high concentrations of coliforms were recorded during the study period, especially in the rainy season. These results may be associated with possible sanitary sewage discharges (Alves et al., \u003cspan\u003e2018a\u003c/span\u003e). These conditions can be observed at points G6, G7 and G8 in this study, which showed the greatest increases. On the other hand, Bastos et al. (\u003cspan\u003e2021\u003c/span\u003e) observed a significant correlation in the concentration of total and thermotolerant coliforms in the base flows of rivers, which result from the percolation of water in the soil and interaction with the soil matrix, influencing the quantity and quality of water supply sources and the base flow of rivers.\u003c/p\u003e\n\u003cp\u003eThe fourth component (PC4), which represents 9.01% of the variance of the data, explains the EC and alkalinity components with strong loads, with increases being observed in the rainy season, denoting the seasonal influence on the variation of this parameter, which in turn is intensified by surface runoff in urbanized areas, soil sealing (Luo et al., \u003cspan\u003e2017\u003c/span\u003e), as well as improper discharges of sanitary sewage (Ferreira et al., \u003cspan\u003e2021\u003c/span\u003e). These characteristics were observed between points G4, G6, G7 and G8, which showed the highest elevations during the rainy season.\u003c/p\u003e\n\u003cp\u003eThe CA dendrogram grouped the sampling points into three main groups (Fig. \u003cspan\u003e3\u003c/span\u003e). Group 1 corresponds to the points that receive contributions from point pollutants (G6, G7 and G8). Group 2, on the other hand, contains the points that receive contributions mainly from the urban area, except for point G3, which in addition to the urban contribution, also receives input from a stream in a rural community with the possible discharge of untreated sanitary sewage used for animal feed. Group 3 corresponds mainly to the points located in less urbanized areas of the Ondas River (O1, O2 and O3) and the point upstream of the contributions from urban areas, located on the Grande River (G1).\u003c/p\u003e\n\u003cp\u003eIn the supplementary file, Table S5 shows the descriptive statistics for each group performed by CA. The analysis of variance indicated that there were statistically significant differences between the groups performed by the CA (F(22, 106)\u0026thinsp;=\u0026thinsp;7.87, p\u0026thinsp;=\u0026thinsp;0.01), with the greatest impacts in Group 1, resulting mainly from point source discharges of sanitary sewage, conditions that are widely discussed in the literature (Fonseca \u0026amp; Tibiri\u0026ccedil;\u0026aacute;, \u003cspan\u003e2019\u003c/span\u003e; Dantas et al., \u003cspan\u003e2021\u003c/span\u003e; Souza et al., \u003cspan\u003e2020\u003c/span\u003e; Yang et al., \u003cspan\u003e2021\u003c/span\u003e). Group 2 shows the influence of the urban area on river water quality, with significant changes in the parameters assessed after entering the urbanized area (Ferreira et al., \u003cspan\u003e2021\u003c/span\u003e; Gomes \u0026amp; Wai, \u003cspan\u003e2020\u003c/span\u003e; Cruz et al., \u003cspan\u003e2019\u003c/span\u003e). Group 3 presented data from less urbanized areas, which recorded the smallest variations in parameters, especially in the Ondas River. This finding may indicate that the Ondas River had less impact on water quality, considering that it is less urbanized than the Grande River. It is important to note that the Ondas River has undergone significant transformations in its native vegetation cover and has experienced exponential agricultural growth in its watershed over the last 36 years. This dynamic, even in the absence of intense urbanization, can have a strong impact on river conditions and water quality (Rego et al., \u003cspan\u003e2023\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eSpatio-temporal variation of physico-chemical parameters\u003c/p\u003e\n\u003cp\u003eThe seasonal variability analyses indicated that there was a statistically significant increase in the rainy season when compared to the dry season (Table S3, S4 in the supplementary file) for the parameters EC (H (2)\u0026thinsp;=\u0026thinsp;40, 36 p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), alkalinity (H (1)\u0026thinsp;=\u0026thinsp;4.93 p\u0026thinsp;=\u0026thinsp;0.02), apparent color (H (1)\u0026thinsp;=\u0026thinsp;36.95 p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), true color (H (1)\u0026thinsp;=\u0026thinsp;28.60, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), turbidity (H(1)\u0026thinsp;=\u0026thinsp;20.50, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and DO (F (2)\u0026thinsp;=\u0026thinsp;12.58 p\u0026thinsp;=\u0026thinsp;0.01). These increases in the rainy season can be attributed to concentrations of diffuse pollution by surface runoff during the rains, resulting in an increase in organic matter constituents, nutrients and dissolution of minerals, compounds and solutes by soil erosion, which in times of precipitation show great variability (Lib\u0026acirc;nio \u003cspan\u003e2010\u003c/span\u003e; Silva et al., \u003cspan\u003e2018\u003c/span\u003e; Memet, \u003cspan\u003e2019\u003c/span\u003e; Zhu et al., \u003cspan\u003e2020\u003c/span\u003e; Boyd, \u003cspan\u003e2020\u003c/span\u003e; Milz et al., \u003cspan\u003e2022\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIn both the dry and rainy periods, the highest values were recorded between points G2 to G8 and O3, which were inserted after contributions associated with the urban perimeter (Fig. \u003cspan\u003e4\u003c/span\u003e). In the rainy season, even with increased values, the DO concentrations showed a downward trend at the points on the urban perimeter (Fig. \u003cspan\u003e4\u003c/span\u003ea). Conditions that in urban centers are often associated with contributions of pollutants derived from the improper discharge of sanitary sewage and industrial effluents (Calazans et al., \u003cspan\u003e2018\u003c/span\u003e; Santana Pereira et al., \u003cspan\u003e2021\u003c/span\u003e; Yang et al. \u003cspan\u003e2021\u003c/span\u003e). This situation was observed at points G6, associated with the contribution of a rainwater drainage channel, which even during the dry season has a constant flow of water, and at points G7 and G8, affected by the discharge of effluents from the sewage treatment plant in the city of Barreiras.\u003c/p\u003e\n\u003cp\u003eThe pH observed during the study period showed a significant decrease (F (4)\u0026thinsp;=\u0026thinsp;3.83 p\u0026thinsp;=\u0026thinsp;0.01) in the rainy season, a decrease that was observed at all the points analyzed (Fig. \u003cspan\u003e4\u003c/span\u003ee), with more acidic conditions in the areas with less urbanization (G1, O1 and O2). These results in the rainy season can be associated with surface runoff and erosion from the type of soil with acidic characteristics in the region, as well as the presence of CO2 in the rainwater (Freitas et al., \u003cspan\u003e2014\u003c/span\u003e; Damasceno et al., \u003cspan\u003e2015\u003c/span\u003e; Girardi et al., \u003cspan\u003e2016\u003c/span\u003e; Medeiros et al., \u003cspan\u003e2018\u003c/span\u003e). On the other hand, the points located in areas with greater urbanization tended to have less acidic values, conditions that are found in regions with greater urban occupation, influenced by the discharge of domestic sewage (Qiao et al., \u003cspan\u003e2016\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIn the rainy season there was a decrease in BOD (Fig. \u003cspan\u003e4\u003c/span\u003eh), which can be attributed to the increase in flow, which contributes to the dilution capacity of the river (Monfared et al., \u003cspan\u003e2017\u003c/span\u003e). This decrease did not show significant differences (H (1)\u0026thinsp;=\u0026thinsp;1.87, p\u0026thinsp;\u0026lt;\u0026thinsp;0.17), being within the maximum and minimum values (18.70 mgO\u003csub\u003e2\u003c/sub\u003e /L and 0.59 mgO\u003csub\u003e2\u003c/sub\u003e /L) recorded in the dry season. The results can be attributed to variations in flow and possible discharges of untreated effluents near the urban perimeter of the rivers (Trindade et al., \u003cspan\u003e2016\u003c/span\u003e; Passos et al., \u003cspan\u003e2021\u003c/span\u003e). Figure \u003cspan\u003e4\u003c/span\u003eh shows that after the contribution of the Ondas River, there is a dilution of organic matter (G5) with a reduction in BOD in both periods. This result contrasts with those recorded for EC (Fig. \u003cspan\u003e4\u003c/span\u003ea), where it is also possible to observe a decrease at point G5 for both periods.\u003c/p\u003e\n\u003cp\u003eSpatio-temporal variation in microbiological parameters\u003c/p\u003e\n\u003cp\u003eThe seasonal evaluation of the parameter total coliforms (H (1)\u0026thinsp;=\u0026thinsp;8.85 p\u0026thinsp;=\u0026thinsp;0.01), thermotolerant coliforms (H (1)\u0026thinsp;=\u0026thinsp;15.92 p\u0026thinsp;=\u0026thinsp;0.01) and heterotrophic bacteria (H (1)\u0026thinsp;=\u0026thinsp;22.09, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) showed a statistically significant increase in the rainy season (Fig. \u003cspan\u003e5\u003c/span\u003e, table S3 and S4 in the supplementary file).\u003c/p\u003e\n\u003cp\u003eThese behaviors can be associated with the increase in river flow, the urbanization of the drainage area studied, the discharge of sewage from the treatment plant and the discharge of improper sanitary sewage (Silva et al., \u003cspan\u003e2018\u003c/span\u003e; Souza et al., \u003cspan\u003e2020\u003c/span\u003e; Assis et al., \u003cspan\u003e2020\u003c/span\u003e; Goshu et al., \u003cspan\u003e2021\u003c/span\u003e). These conditions were observed mainly between points G6 and G8 (Fig. \u003cspan\u003e5\u003c/span\u003e). It is important to note that during the visits it was found that the area around point G6 is used as a leisure and fishing spot by the population of the surrounding neighborhoods. On the other hand, point G8 recorded the highest dispersion for both periods, as it is located after the urbanization area of the city of Barreiras. Heterotrophic bacteria are essential for maintaining aquatic ecosystems, but their excess concentration can indicate high nutrient levels in water bodies. Obtaining data on the abundance and functional activity of heterotrophic bacteria in aquatic ecosystems is important, even though they are widely studied in marine environments and lakes, they are little known in urbanized rivers (Manahan, \u003cspan\u003e2013\u003c/span\u003e; Kopylov et al., \u003cspan\u003e2015\u003c/span\u003e; Faure et al., \u003cspan\u003e2015\u003c/span\u003e; Romanova et al., \u003cspan\u003e2022\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIt can be seen that the urban area of the municipality of Barreiras influences the reduction of water quality, especially in the Grande River, through specific contributions. The results of the ACP/AF indicated that turbidity, apparent and true color, DO, pH, total coliforms, thermotolerant coliforms, EC and alkalinity were the most relevant indicators of water quality in the Grande and Ondas rivers, due to spatio-temporal variations, indicating the greatest changes at the points in the urban area and showing the influence of rainfall on these changes.\u003c/p\u003e \u003cp\u003eThe CA analysis separated the sampling points into three groups based on their spatial characteristics. Group 1 corresponded to points influenced by point source discharges of untreated sanitary sewage. Group 2 with points with diffuse influences from the urban area. Group 3 with points with reduced urbanization. Analysis of the spatio-temporal variations showed that there were significant variations in the water quality parameters in the rainy season when compared to the dry season, results associated mainly with urban runoff.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was funded under the Brazil Scholarship Program - PAEC OEA-GCUB, call for Proposals OEA/GCUB 001/2020, supported by the Cooperation Agreement between the Organization of American States (OEA) and the Agreement between the Organization of American States (OEA) and the International Cooperation Group of Brazilian Universities (GCUB).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eTG: Sampling survey, laboratory analysis and original manuscript writing. GL, NG and SJ: Sampling survey, laboratory analysis.EJ and SS: laboratory analysis. DC: Prepared figure 1. CV and MC :Support for analysis methodologies. RB and MC:Supervision, revision and editing of the original manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data will be made available according to requests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlves, D. D., Riegel, R. P., Quevedo, D. M., Os\u0026oacute;rio, D. M., Costa, G. M., Nascimento, C. A., \u0026amp; Tel\u0026ouml;ken, F. (2018b). Seasonal assessment and apportionment of surface water pollution using multivariate statistical methods: Sinos River, southern Brazil. \u003cem\u003eEnvironmental Monitoring and Assessment\u003c/em\u003e. doi:https://doi.org/10.1007/s10661-018-6759-3\u003c/li\u003e\n\u003cli\u003eAlves, R. I., Machado, C. S., Beda, C. F., Fregonesi, B. M., Nadal, M., Sierra, J. Segura-Mu\u0026ntilde;oz, S. I. (2018a). 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Satellites reveal hotspots of global river extent change. \u003cem\u003eNature Communicationsn\u003c/em\u003e(14). doi:https://doi.org/10.1038/s41467-023-37061-3\u003c/li\u003e\n\u003cli\u003eYang, S., Liang, M., Qin, Z., Qian, Y., Li, M., \u0026amp; Cao, Y. (2021). \u003cem\u003eA novel assessment considering spatial and temporal variations of water quality to identify pollution sources in urban rivers.\u003c/em\u003e Scientific Reports: https://doi.org/10.1038/s41598-021-87671-4\u003c/li\u003e\n\u003cli\u003eZhu, G., Xiong, N., Wang, X., Hursthouse, A. S., \u0026amp; Marr, A. (2020). Correlation Characteristics of Electrical Conductivity of Surface Waters with the Fluorescence Excitation-Emission Matrix Spectroscopy-Parallel Factor Components of Dissolved Organic Matter. \u003cem\u003eJournal of Fluorescence\u003c/em\u003e. doi:https://doi.org/10.1007/s10895-020-02628-6\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Surface runoff, urbanized rivers, sanitary sewage, multivariate statistics","lastPublishedDoi":"10.21203/rs.3.rs-4601767/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4601767/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe aim of this study was to assess the spatio-temporal variation in water quality in the Grande River and the Ondas River, in the city of Barreiras, Bahia, Brazil. Water samples were collected at 11 points along the rivers, and eight physical-chemical parameters (electrical conductivity, pH, alkalinity, apparent and true color, turbidity, dissolved oxygen and biochemical oxygen demand) and three microbiological indicators (heterotrophic bacteria, total and thermotolerant coliforms) were analyzed. Spatio-temporal variation was assessed using the multivariate techniques of Principal Component Analysis/Factorial Analysis (PCA/FA) and Hierarchical Cluster Analysis (HCA). The results of the PCA/FA highlighted eight of the eleven parameters as the main ones responsible for the variations in water quality, with the greatest increase in these parameters being observed in the rainy season, especially among the points influenced by sewage discharges and by the influence of the urban area. The CA grouped the results from 11 points into three main groups: group 1 corresponded to points influenced by sewage discharges; group 2 grouped points with mainly urban influences; and group 3 grouped points in rural areas. These groupings showed the negative influence of urbanization and also statistically significant variations between the groups and periods. The most degraded conditions were in group 1, and the least degraded conditions in group 3. Assessment of the variations between the monitoring periods showed that rainfall had a significant impact on the increase or decrease in the parameters assessed, as a result of surface runoff linked to urbanization and increased river flow.\u003c/p\u003e","manuscriptTitle":"Impacts of urbanization on the quality of surface water in a watershed in the Brazilian Cerrado","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-22 08:24:00","doi":"10.21203/rs.3.rs-4601767/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-31T01:55:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-26T18:30:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-26T18:29:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Monitoring and Assessment","date":"2024-06-18T19:02:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"232dce97-9eb3-49a5-80d0-d5b85b8b8cf0","owner":[],"postedDate":"August 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-10-14T16:10:28+00:00","versionOfRecord":{"articleIdentity":"rs-4601767","link":"https://doi.org/10.1007/s10661-024-13198-6","journal":{"identity":"environmental-monitoring-and-assessment","isVorOnly":false,"title":"Environmental Monitoring and Assessment"},"publishedOn":"2024-10-07 15:57:50","publishedOnDateReadable":"October 7th, 2024"},"versionCreatedAt":"2024-08-22 08:24:00","video":"","vorDoi":"10.1007/s10661-024-13198-6","vorDoiUrl":"https://doi.org/10.1007/s10661-024-13198-6","workflowStages":[]},"version":"v1","identity":"rs-4601767","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4601767","identity":"rs-4601767","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}