Seasonal Dynamics of Water Quality and Pollution in Urban Streams of Tunduma, Tanzania

Seasonal Dynamics of Water Quality and Pollution in Urban Streams of Tunduma, Tanzania | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Seasonal Dynamics of Water Quality and Pollution in Urban Streams of Tunduma, Tanzania Matungwa William, Zacharia Katambara This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8491963/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Understanding seasonal water quality variation is essential for sustainable water resource management in rapidly urbanising towns such as Tunduma, Tanzania. This study investigated temporal dynamics in physicochemical and microbial quality across six surface water points (SWPs) through monthly sampling over one year. Parameters analysed included pH, electrical conductivity (EC), turbidity, total dissolved solids (TDS), nitrate, phosphate, biochemical oxygen demand (BOD), and microbial indicators (faecal and total coliforms). Descriptive statistics revealed distinct wet-season increases in turbidity (up to 25.24 NTU), total suspended solids (> 44.17 mg/L), nitrate (11.59–25.57 mg/L), phosphate (1.40–1.49 mg/L), BOD (13.96–24.53 mg/L), and microbial contamination (faecal coliforms 10.58 CFU/100 mL; total coliforms 26.00 CFU/100 mL). Shapiro–Wilk tests (W = 0.64–0.95) confirmed non-normality of most variables, reflecting event-driven pollution. Pearson correlation (r > 0.65) indicated strong associations between nutrients and microbial indicators, suggesting agricultural runoff, pit latrines, and greywater as common sources. Mann–Kendall trend tests identified increasing trends in EC, TDS, and nitrate, with declining turbidity and sulphate at some sites. Principal Component Analysis (PCA) extracted three components explaining 77.9% of total variance: Component 1 linked nitrate, phosphate, BOD, and coliforms to nutrient and organic pollution; Component 2 captured turbidity and suspended solids, indicating sediment inputs during rainfall; while Component 3 reflected site-specific variability. PCA biplots revealed clear seasonal clustering, with wet-season months (March–May, November) associated with elevated contaminant loads, and dry-season months near baseline conditions. Several parameters exceeded World Health Organization (WHO) drinking water guidelines, particularly during peak rainfall, posing risks to human health and aquatic ecosystems. These findings underscore the need for continuous monitoring, seasonally adaptive management, and pollution control strategies to safeguard surface water quality in Tunduma and similar rapidly growing urban environments. Water Quality Seasonal Variation Microbial Contamination PCA Trend Analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Freshwater streams are essential for ecosystem balance, providing critical services such as biodiversity support, clean water for domestic use, and irrigation for local (Oberdorff, 2022 ). In rapidly growing border towns like Tunduma, Tanzania, streams are increasingly polluted by agricultural runoff, unmanaged waste disposal, and seasonal flooding linked to informal development and poor sanitation infrastructure assessment in Dar es Salaam (Mbuligwe & Kaseva, 2005 ). These stressors contribute to significant temporal variability in physicochemical and microbial water quality parameters (Fang et al., 2018 ). Temporal variation, encompassing both diurnal and seasonal fluctuations, influences water quality through mechanisms such as surface runoff during rainy seasons, sediment resuspension, and enhanced microbial activity at elevated temperatures (Rochelle-Newall et al., 2016 ). Physicochemical parameters, such as pH, electrical conductivity (EC), turbidity, nitrate, and phosphate, often vary in response to changes in rainfall, land use, and urban encroachment. Simultaneously, microbial indicators like faecal coliform (FC) and total coliform (TC) tend to spike during wet periods due to runoff, leaky sanitation infrastructure, and livestock intrusion into streams (Onifade et al., 2025 ) Recent studies in East Africa and comparable tropical regions have emphasised the relationship between seasonal shifts and water quality deterioration. For example, in Cameroon, seasonal changes were linked to significant fluctuations in dissolved oxygen, heavy metals, and benthic microbial diversity, driven by rainfall and anthropogenic inputs (Ndourwe Far Bolivar et al., 2025). In Tanzania, streams near mining sites and expanding towns have shown increases in microbial load and nutrient concentrations, underscoring the urgency of localised, time-sensitive monitoring (Focus et al., 2025 ). To address the complexity of interrelated water quality variables, Principal Component Analysis (PCA) is widely applied as a multivariate technique. PCA is a powerful multivariate tool used to reduce data dimensionality and identify dominant patterns in complex environmental datasets, particularly in water quality studies (Rodionova et al., 2021 ). In Tanzania, PCA has proven effective in identifying pollution pathways in both surface and groundwater. For example, an analysis of shallow wells in Tunduma’s Half‑London Ward revealed surface runoff, pit latrines, and fertilizer use as dominant contamination sources(William & Katambara, 2025 ), while in coastal Dar es Salaam aquifers, it identified seasonal salinization and nitrate inputs. PCA applications in the Pangani and Zigi river basins have revealed that seasonal changes in land use and rainfall drive variations in nutrient load, ionic strength, and microbial contamination (Nyambukah & Mihale, 2022a ). In the Pangani basin, PCA identified geogenic drivers (carbonate weathering) and anthropogenic influences (agricultural runoff and domestic effluent), accounting for over 60% of the observed seasonal variance (Hellar-Kihampa et al., 2013 ). Meanwhile, studies in Uganda’s River Rwizi showed that the first two PCA factors explained 81.2% of dry-season variability and 69.2% of wet-season variability, with main contributors including turbidity, TSS, EC, and microbial loads tied to erosion and wastewater inputs (Oketola et al., 2013 ). Principal Component Analysis (PCA) has proven to be a valuable tool in deciphering complex water quality datasets by identifying key pollution indicators and isolating dominant contaminant sources. For instance, in Morocco’s Oum Er Rbia River, PCA effectively differentiated anthropogenic pollution inputs by highlighting nitrate, phosphate, and biochemical oxygen demand (BOD) as primary markers of industrial and domestic contamination (Barakat et al., 2016 ). Such multivariate techniques offer a nuanced understanding of seasonal water quality dynamics, enabling researchers to distinguish between natural geochemical backgrounds and human-induced pressures. Despite the ecological significance and socio-economic role of Tunduma as a cross-border hub between Tanzania and Zambia, the region remains critically underrepresented in empirical water quality studies. This research gap poses a challenge for evidence-based water resource governance and public health risk management. Therefore, the present study applies an integrated methodology combining long-term temporal sampling, PCA, and Mann-Kendall trend analysis to assess the spatial and seasonal variability of physicochemical and microbial water quality indicators. By uncovering pollution signatures and temporal co-variations in stream water, this study aims to inform the development of localized, data-driven strategies for sustainable water management in rapidly urbanizing towns like Tunduma. 2. Materials and Methods This study employed a comprehensive approach to assess the temporal dynamics, inter-parameter relationships, and pollution signatures of stream water quality. A combination of systematic field sampling, laboratory analysis, and multivariate statistical techniques was used to address the stated research objectives. 2.1 Description of the study area Figure 1 shows the study area within Tunduma Town, a rapidly growing border town in Momba District, Songwe Region, Tanzania. The study area covers approximately 141 square kilometres and had a population of over 219,000 as of 2022, according to the National Bureau of Statistics. It is situated at around 1,500 meters above sea level within the Lake Rukwa catchment and experiences a tropical sub-humid climate with distinct wet (November to April) and dry (May to October) seasons. The town receives an average annual rainfall of approximately 1,200 mm, and its hydrogeology is dominated by Precambrian basement rocks, resulting in shallow, unconfined aquifers with depths typically less than 15 meters. These aquifers are predominantly recharged by rainfall and surface runoff. Rapid urban expansion, poor sanitation infrastructure, and widespread reliance on unlined pit latrines have significantly increased the vulnerability of both surface and groundwater resources to contamination. Streams frequently receive direct discharge of household and greywater effluents, especially during the rainy season, elevating the risk of pollutant transport and hydraulic connectivity between surface streams and shallow wells. Such urban hydrological settings are prone to nutrient enrichment, organic load, and microbial contamination patterns consistently reported in similar urban centres across Sub-Saharan Africa (Pandey et al., 2014 ). 2.2 Study Design, Sample Collection and Analytical Procedures This study employed a longitudinal design to assess temporal and spatial variability in stream water quality across six monitoring sites (SWP 1, SWP 2, SWP 3, SWP 4, SWP 5 and SWP 6) in Tunduma, Tanzania. Sampling was conducted monthly from March 2024 to February 2025, yielding 72 samples representative of areas impacted by agricultural runoff, domestic wastewater, and unplanned urban development. Field procedures followed the Standard Methods for the Examination of Water and Wastewater (APHA, 2017). In-situ measurements of pH, temperature, dissolved oxygen (DO), turbidity, and electrical conductivity (EC) were obtained using calibrated multi-parameter probes. Sterile polyethylene bottles were used to collect 1 L samples for physicochemical analysis and 500 mL for microbial analysis. Samples were preserved at approximately 4°C in insulated coolers, transported to the laboratory within 6 hours, and processed within 24 hours to ensure integrity. Laboratory analysis included spectrophotometric determination of nutrients (nitrate, phosphate, sulphate, and ammonia), and BOD5 measurement using the Winkler method. Total dissolved solids (TDS) were determined gravimetrically. Microbial quality was assessed by membrane filtration and the Most Probable Number (MPN) method, using m-FC and m-Endo agar for faecal and total coliforms, incubated at 44.5°C and 37°C, respectively. These procedures ensured high-quality data for robust interpretation of temporal and spatial water quality trends. 2.3 Pearson Correlation Analysis To investigate potential linkages among water quality parameters, Pearson correlation analysis was employed. This method is appropriate for examining linear relationships between continuous, normally distributed variables. The correlation matrix enabled the identification of co-varying patterns, such as positive correlations between nutrient concentrations and microbial indicators, which may suggest shared pollution sources or standard seasonal drivers. Pearson’s approach was selected due to its broad applicability in hadrochemical datasets and its interpretability for identifying both direct and indirect pollution pathways (Kawo et al., 2018 ). 2.4 Temporal Trend Analysis To explore long-term trends in the dataset, the Mann-Kendall test was used as a non-parametric method suitable for detecting monotonic changes over time in environmental data. The seasonal Kendall variant was applied to account for repeated monthly measurements, enhancing the test’s ability to distinguish genuine trends from seasonal fluctuations (Hirsch et al., 1982 ). 2.5 Comparison with WHO Standards Each measured water quality parameter was evaluated in relation to the World Health Organization (WHO) drinking water guidelines to assess its suitability for human consumption. Parameters such as nitrate, phosphate, turbidity, BOD, and microbial indicators (faecal and total coliforms) were reviewed to identify exceedances of recommended thresholds. Instances where concentrations surpassed WHO limits were considered potential public health concerns, particularly during the wet season when contamination levels were elevated. This comparison provided a benchmark for determining water safety and highlighted areas requiring urgent intervention or improved management practices. 2.6 Principal Component Analysis (PCA) To identify the most influential variables and unravel potential pollution sources across space and time, Principal Component Analysis (PCA) was applied to the standardized dataset comprising physicochemical and microbial water quality parameters. Prior to analysis, all variables were normalized to zero mean and unit variance to mitigate scale-related distortions. The PCA was conducted using the correlation matrix, and components with eigenvalues greater than one (Kaiser criterion) were retained. Varimax orthogonal rotation was employed to maximize the interpretability of component loadings and to clarify the underlying structure of pollutant interactions. PCA biplots were constructed to visualize the clustering of water quality parameters and sampling months, enabling the identification of co-occurring pollutants and dominant pollution signatures. Additionally, site score plots were generated to trace the spatial and temporal variability of pollution profiles across monitoring locations. This analytical framework aligns with previous applications of PCA in environmental water quality studies. For example, (Bengraı̈ne & Marhaba, 2003 ) utilized PCA to differentiate between solute constituents, nutrient concentrations, and organic pollutants in river systems, effectively mapping spatiotemporal pollution trends in the Passaic River, New Jersey. These precedents underscore the utility of PCA in disentangling complex water quality datasets and supporting evidence-based water resource management. 2.7 Data Analysis Tools All statistical analyses were conducted using Jamovi (v2.6.44) and R (v4.5.1), employing key packages such as trend, FactoMineR, and ggplot2. Descriptive statistics summarized seasonal variations, while the Shapiro-Wilk test assessed normality. Pearson correlation identified inter-parameter relationships. Mann-Kendall tests detected temporal trends. Principal Component Analysis (PCA) with varimax rotation was applied to standardized data to identify pollution sources and seasonal patterns. These tools ensured analytical rigor and insight into water quality dynamics at the six surface water points in Tunduma. 3. Results and discussion 3.1 Descriptive statistics Showing the Seasonal Variations among Water Quality Parameters at Different Surface Water Points Table 1 a. Descriptive Statistics of Seasonal Variations in Physicochemical and Microbial Water Quality Parameters at SWP 1 pH EC Tem Col Tur TSS TDS Hd Alk Sal Cl − PO 3 − SO 4 2− NO 3 − BOD Ca 2+ Mg 2+ Fe 2+ FC TC Mean 6.69 326.10 22.99 0.70 7.90 0.08 130.55 175.01 165.43 0.33 43.27 1.40 4.66 3.77 3.39 20.71 29.02 1.44 3.33 7.67 Standard deviation 0.16 24.35 0.70 0.24 2.51 0.13 10.60 12.44 11.28 0.18 5.40 0.56 1.19 0.98 1.09 2.52 3.50 0.40 1.72 2.71 Minimum 6.40 274.55 21.40 0.34 2.88 0.01 110.20 145.80 140.00 0.19 36.22 0.19 2.70 1.80 1.02 15.60 20.66 1.05 1.00 5.00 Maximum 6.90 359.60 23.60 1.24 12.60 0.45 145.00 193.20 176.23 0.80 54.00 2.30 6.33 5.23 4.90 24.99 34.55 2.11 6.00 12.00 Skewness -0.15 -0.84 -1.26 0.84 -0.41 2.60 -0.32 -1.08 -1.20 2.03 0.88 -0.49 -0.63 -0.24 -0.89 -0.26 -0.88 0.86 -0.24 0.19 Kurtosis -0.71 0.48 0.93 1.32 1.02 7.30 -0.66 1.74 1.02 4.62 0.13 1.02 -0.77 0.14 0.55 0.54 2.48 -0.84 -1.19 -1.88 Shapiro-Wilk W 0.93 0.93 0.81 0.95 0.91 0.64 0.93 0.92 0.87 0.77 0.93 0.95 0.90 0.94 0.93 0.96 0.92 0.83 0.90 0.80 The descriptive statistics for water quality parameters at SWP 1 revealed distinct seasonal variations shaped by climatic conditions, land use, and anthropogenic pressures in Tunduma (Table 1 a). The mean pH (6.69) remained within the WHO acceptable range (6.5–8.5), with low skewness (-0.15) and near-normal distribution (Shapiro-Wilk W = 0.93), in line with findings from urban streams in northern Nigeria (Khurshid et al., 2025 ). Electrical conductivity (EC) averaged 326.10 µS/cm, indicating moderate ionic concentration. It showed left-skewness (-0.84) and normality (W = 0.93), which suggests relatively stable ion levels typical of non-industrial urban environments. This value is lower than those reported in Addis Ababa streams, indicating lower salinity stress in Tunduma (Rahman et al., 2014 ). Water temperature (mean = 22.99°C) displayed strong negative skew (-1.26) and poor normality (W = 0.81), a pattern attributed to cooler readings during wet months. Similar seasonal cooling trends, affecting microbial activity and water clarity, have been reported by (Coffey et al., 2020 ). The extremely high kurtosis observed in TSS (7.30) and low Shapiro–Wilk W (0.64) suggest rare but intense sedimentation events likely driven by surface runoff and erosion similar to patterns reported in Kampala’s peri‑urban streams, where soil erosion was identified as a primary contributor to episodic sediment influx(Ssewankambo et al., 2023 ). Nutrient parameters, including nitrate (3.77 mg/L) and phosphate (1.40 mg/L), exceeded background levels, reflecting non-point source pollution from fertilizers and domestic waste. Their skewness was moderate, and Shapiro-Wilk W values (0.94–0.95) indicate approximate normality. These trends are comparable to nutrient influxes reported in Morogoro, Tanzania (Focus et al., 2025 ). BOD (mean value of 3.39 mg/L) showed left-skewness (-0.89) and normal distribution (W = 0.93), suggesting moderate organic pollution, especially during early rainfall events. Elevated BOD during wet seasons has also been observed in Cameroonian urban streams(Ndourwe Far Bolivar et al., 2025). Microbial contamination was significant. Faecal coliform (mean = 3.33 MPN/100 mL) and total coliform (7.67 MPN/100 mL) values exceeded WHO thresholds in several instances. Their non-normal distributions (W = 0.90 and 0.80) reflect episodic pollution likely linked to poor sanitation and livestock intrusion. Similar microbial spikes in wet periods have been reported in urban rivers in Kenya (Kamal et al., 2025 ). In conclusion, the combined interpretation of skewness, kurtosis, and Shapiro-Wilk W values confirms that many parameters at SWP 1 were non-normally distributed and seasonally influenced. These findings support evidence from broader East African studies (Díaz et al., 2025 ; Sonar et al., 2024 ), reinforcing the importance of targeted, seasonal water quality monitoring in urban border towns like Tunduma. Table 1 b. Descriptive Statistics of Seasonal Variations in Physicochemical and Microbial Water Quality Parameters at SWP 2 pH EC Tem Col Tur TSS TDS Hd Alk Sal Cl − PO₄³⁻ SO 4 2− NO 3 − BOD Ca 2+ Mg 2+ Fe 2+ FC TC Mean 6.62 492.51 21.03 5.20 25.45 44.00 309.45 161.11 157.17 0.14 63.20 1.49 14.57 25.57 22.38 26.45 14.53 2.27 10.58 26.00 Standard deviation 0.20 20.47 1.23 2.15 3.36 13.67 22.50 5.45 10.79 0.09 7.86 0.55 1.68 7.76 4.27 3.77 3.16 0.65 3.15 2.83 Minimum 6.20 460.22 19.25 1.90 18.55 19.50 260.80 150.00 135.30 0.04 50.22 0.45 11.20 4.26 11.33 20.30 7.50 1.33 4.00 21.00 Maximum 6.80 520.00 23.50 8.90 30.30 59.40 340.00 170.00 174.00 0.30 79.10 2.74 16.55 34.90 26.00 34.57 18.64 3.30 14.00 29.00 Skewness -0.79 0.00 0.37 -0.01 -0.84 -0.93 -0.63 -0.55 -0.74 0.65 0.48 0.59 -0.78 -1.98 -2.05 0.42 -0.91 0.32 -1.07 -0.84 Kurtosis -0.24 -1.11 0.26 -0.58 0.55 -0.42 0.44 0.33 0.32 -0.53 0.33 2.19 -0.11 5.43 3.86 1.01 0.86 -0.97 0.25 -0.71 Shapiro-Wilk W 0.84 0.92 0.95 0.97 0.93 0.87 0.90 0.97 0.93 0.92 0.97 0.93 0.93 0.81 0.72 0.95 0.93 0.94 0.84 0.85 Table 1 b presents the descriptive statistics of physicochemical and microbial water quality parameters for Surface Water Point 2 (SWP 2), highlighting seasonal variability. Parameters analysed include central tendency (mean), dispersion (standard deviation, minimum, and maximum), and distribution characteristics (skewness, kurtosis, and Shapiro-Wilk W statistic). These indicators provide insight into the temporal trends and suitability of the water for domestic and environmental use. The pH values at SWP 2 ranged from 6.20 to 6.80, with a mean of 6.62, indicating mildly acidic conditions. The data were negatively skewed (− 0.79) and slightly platykurtic (kurtosis = − 0.24), suggesting more frequent values below the mean. The Shapiro-Wilk test result (W = 0.84) confirmed a deviation from normality. Similar seasonal patterns have been observed in southern Tanzanian surface waters (Shimba, 2017); (Nyambukah & Mihale, 2022b ). Electrical conductivity (EC) had a narrow range (460.22–520.00 µS/cm), with a mean of 492.51 µS/cm. It was symmetrically distributed (skew = 0.00) but platykurtic (kurtosis = − 1.11), and not normally distributed (W = 0.92), indicating consistent ionic concentration throughout seasons. Temperature (mean = 21.03°C) was slightly right-skewed (skew = 0.37), with W = 0.95, suggesting approximate normality. Physical water quality indicators, including colour, turbidity, TSS, and TDS, showed moderate to strong negative skewness (− 0.01 to − 0.93), indicating seasonal reductions during periods of dilution, especially in the wet season. These parameters had near-normal kurtosis values and varied Shapiro-Wilk W values (0.87–0.97), reflecting seasonal variability in sediment and suspended matter due to runoff (Nyambukah & Mihale, 2022b ). Nutrient-related parameters demonstrated significant departures from normal distribution. Phosphate (PO₄³⁻) (mean = 1.49 mg/L), nitrate (NO₃⁻) (mean = 25.57 mg/L), and sulphate (SO₄²⁻) (mean = 14.57 mg/L) exhibited high negative skewness (up to − 1.98 for NO₃⁻) and elevated kurtosis values (e.g., 5.43 for NO₃⁻). These distributions suggest the occurrence of extreme values due to sporadic runoff events or agricultural inputs, consistent with findings in Tanzanian catchments (Alex et al., 2021 ). The biological oxygen demand (BOD) was high (mean = 22.38 mg/L), exceeding WHO standards. It was strongly negatively skewed (− 2.05) and leptokurtic (kurtosis = 3.86), with a low Shapiro-Wilk value (W = 0.72), indicating pollution spikes, potentially from untreated wastewater or decaying organic matter during stagnant flow periods. These characteristics align with observations from urbanized and peri-urban streams in East (Alphayo & Sharma, 2018 ). Microbial indicators revealed persistent contamination, with faecal coliform (FC) and total coliform (TC) counts averaging 10.58 and 26.00 CFU/100 mL exceeding WHO surface water guidelines. Both microbial parameters exhibited negative skewness (FC = − 1.07; TC = − 0.84) and light tails (kurtosis ≈ − 0.7), failing normality (Shapiro–Wilk W = 0.84 and 0.85). This suggests episodic faecal pollution tied to sanitation infrastructure and seasonal runoff. Similar patterns were observed in peri-urban streams in Kampala, Uganda, where both faecal coliform and total coliform counts spiked during wet seasons due to pit-latrine leaching and stormwater runoff (Ronoh et al., 2020 ). The Shapiro-Wilk test revealed that most water quality variables significantly deviated from normality (W < 0.95). This justifies the application of non-parametric statistical methods-such as Kendall’s tau, Spearman correlation, or Wilcoxon signed-rank tests-for robust trend and relationship analysis (Nyambukah & Mihale, 2022a ). The pronounced skewness and kurtosis observed in critical pollutants such as BOD, NO₃⁻, and FC indicate episodic or event-driven pollution patterns, reinforcing the need for seasonally adaptive water management strategies in Tunduma and similar semi-urban settlements. Table 1 c. Descriptive Statistics of Seasonal Variations in Physicochemical and Microbial Water Quality Parameters at SWP 3 pH EC Tem Col Tur TSS TDS Hd Alk Sal Cl − PO₄³⁻ SO 4 2− NO 3 − BOD Ca 2+ Mg 2+ Fe2+ FC TC Mean 6.51 522.22 21.66 8.24 25.03 44.34 319.59 143.92 141.69 0.32 56.53 1.41 16.85 19.41 24.53 17.35 12.61 2.63 2.17 17.92 Standard deviation 0.21 16.42 1.11 1.70 2.88 13.43 19.41 7.81 15.22 0.20 6.06 0.50 1.91 6.53 4.19 3.95 2.70 0.79 1.75 2.81 Minimum 6.10 480.98 20.10 5.20 19.30 17.30 270.42 130.90 110.20 0.10 42.46 1.05 12.30 3.45 13.90 10.30 7.55 1.80 0.00 13.00 Maximum 6.80 535.00 23.60 10.30 28.00 60.50 340.90 160.50 167.55 0.85 65.54 2.80 19.00 29.33 27.99 23.52 16.20 4.22 5.00 21.00 Skewness -0.36 -1.84 0.09 -0.82 -1.05 -1.23 -1.55 0.69 -0.33 1.70 -1.04 2.30 -1.29 -1.11 -1.89 -0.40 -0.37 0.83 0.06 -0.78 Kurtosis 0.08 2.87 -0.83 -0.41 0.17 0.74 3.09 0.89 0.54 3.83 1.79 5.71 1.94 2.68 3.36 -0.56 -0.76 -0.51 -1.37 -0.81 Shapiro-Wilk W 0.93 0.72 0.95 0.88 0.88 0.85 0.87 0.94 0.97 0.85 0.93 0.70 0.89 0.91 0.76 0.94 0.94 0.86 0.91 0.87 Table 1 c summarizes the seasonal variation of physicochemical and microbial water quality parameters at SWP 3. Most parameters exhibited deviations from normal distribution, with notable skewness and kurtosis reflecting the influence of seasonal rainfall, anthropogenic activities, and catchment characteristics. Physicochemical parameters such as pH (mean = 6.51), temperature (mean = 21.66°C), EC (mean = 522 µS/cm), salinity, turbidity, colour, TDS, TSS, alkalinity, and hardness displayed moderate to strong skewness (− 1.84 to 1.70), elevated kurtosis (up to 5.71), and poor normality (Shapiro-Wilk W = 0.72–0.97). These patterns suggest sediment influx, ion accumulation, and sporadic pollution events, particularly during or after rains (Coffey et al., 2020 ). Nutrient parameters (phosphate and nitrate) displayed non-normal distributions with high skewness and kurtosis, indicative of episodic loading from agricultural runoff, pit latrines, or organic matter decay patterns consistent with seasonal nutrient surges observed in Tanzania’s Pangani River Basin (Selemani et al., 2017 ). Biological indicators-including biochemical oxygen demand (BOD; mean = 24.53 mg/L), faecal coliforms (FC), and total coliforms (TC) consistently exceeded recommended thresholds and displayed marked non-normal distributions (Shapiro–Wilk W = 0.76–0.91) with skewness and leptokurtosis, reflecting episodic faecal contamination driven by poor sanitation and seasonal runoff. These findings align with seasonal microbial surges documented in Tanzania’s Serengeti rivers, where coliform levels peaked following wet-weather events linked to wildlife and livestock faecal loading (Kanyerere et al., 2012 ). Table 1 d. Descriptive Statistics of Seasonal Variations in Physicochemical and Microbial Water Quality Parameters at SWP 4 pH EC Tem Col Tur TSS TDS Hd Alk Sal Cl − PO₄³⁻ SO 4 2− NO 3 − BOD Ca 2+ Mg 2+ Fe2+ FC TC Mean 6.66 409.30 22.01 2.95 16.68 22.04 168.06 161.30 0.23 53.24 1.44 9.62 14.67 12.88 23.58 21.77 1.85 6.96 16.83 220.00 Standard deviation 0.16 21.81 0.88 1.14 2.78 6.87 8.24 9.33 0.11 6.16 0.52 1.33 4.16 2.55 3.02 3.22 0.44 2.05 2.44 16.40 Minimum 6.40 367.38 20.88 1.23 10.71 9.77 152.90 143.25 0.12 43.96 0.69 7.20 3.83 6.17 19.15 14.08 1.27 3.50 13.50 185.50 Maximum 6.85 439.80 23.55 5.07 19.80 29.93 181.60 174.45 0.48 63.25 2.52 11.16 20.06 15.28 29.78 26.59 2.71 9.50 20.50 242.50 Skewness -0.24 -0.46 0.04 0.20 -1.10 -0.92 -0.39 -0.77 1.19 0.32 0.48 -0.82 -1.59 -2.12 0.27 -0.98 1.02 -0.34 0.09 -0.51 Kurtosis -1.24 -0.22 -0.88 -0.28 0.73 -0.42 -0.37 -0.25 0.76 -1.06 0.45 -0.71 3.95 4.22 0.41 2.28 0.38 -1.24 -1.53 0.11 Shapiro-Wilk W 0.93 0.95 0.92 0.98 0.89 0.87 0.96 0.91 0.87 0.95 0.95 0.88 0.86 0.70 0.94 0.92 0.87 0.92 0.90 0.90 Table 1 d presents seasonal statistics for physicochemical and microbial parameters at SWP 4. pH ranged from 6.40 to 6.85 (mean = 6.66) with near-normal distribution (W = 0.93), suggesting stable acid–base conditions (Masese et al., 2017). EC averaged 409.30 µS/cm (W = 0.95), indicating moderate ionic strength with seasonal dilution effects (Mwegoha et al., 2010). Temperature was stable (mean = 22.01°C, W = 0.92), consistent with tropical thermal patterns (Nyambukah & Mihale, 2022a ). Suspended solids, colour, turbidity (mean = 16.68 NTU), and TSS (22.04 mg/L) showed moderate variability, with turbidity skewed (− 1.10), indicating higher dry-season loads (Kimani-Murage & Ngindu, 2007 ). TDS averaged 168.06 mg/L with slight skew (− 0.39), reflecting consistent mineral presence. Alkalinity (161.30 mg/L CaCO₃) and hardness (53.24 mg/L) were stable; alkalinity’s positive skew (1.19) suggests episodic base cation influx. Nutrients varied seasonally: phosphate (1.44 mg/L) and nitrate (12.88 mg/L) were non-normally distributed, with skewed and leptokurtic patterns indicating irregular nutrient pulses from runoff and waste (Syamsir et al., 2019). Sulphate (9.62 mg/L) showed slight negative skew. BOD (mean = 23.58 mg/L) indicated moderate organic pollution, while calcium and magnesium levels were stable. Iron averaged 6.96 mg/L (W = 0.92), consistent with reductive processes in groundwater-fed streams (Nkotagu, 1996). Microbial indicators revealed contamination: FC (16.83 CFU/100 mL) and TC (220 CFU/100 mL) exceeded limits, with TC showing non-normal distribution highlighting sanitation deficiencies (Dzwairo et al., 2006 ). Non-normal distributions across many parameters (Shapiro–Wilk W < 0.95) reinforced the use of non-parametric analyses. Observed skewness and kurtosis patterns suggest episodic pollution linked to rainfall and human activities consistent with documented trends in East African catchments (Fang et al., 2018 ). Table 1 e. Descriptive Statistics of Seasonal Variations in Physicochemical and Microbial Water Quality Parameters at SWP 5 pH EC Tem Col Tur TSS TDS Hd Alk Sal Cl − PO₄³⁻ SO 4 2− NO 3 − BOD Ca 2+ Mg 2+ Fe2+ FC TC Mean 6.56 507.37 21.35 6.72 25.24 44.17 314.52 152.52 149.43 0.23 59.86 1.45 15.71 22.49 23.45 21.90 13.57 2.45 6.38 21.96 Standard deviation 0.15 17.47 1.11 1.82 3.05 13.42 19.80 4.76 11.31 0.13 6.81 0.43 1.75 6.99 4.15 3.66 2.82 0.59 2.18 2.75 Minimum 6.35 473.13 19.68 3.55 18.93 18.95 265.61 145.45 122.75 0.09 46.34 0.78 11.75 3.85 12.62 15.30 7.53 1.70 2.50 17.50 Maximum 6.80 527.50 23.15 9.55 28.65 59.95 339.75 161.90 161.28 0.51 72.32 2.22 17.50 32.11 26.82 29.05 17.42 3.76 9.00 25.00 Skewness 0.28 -0.82 0.01 -0.52 -1.11 -1.11 -1.36 0.32 -1.18 1.00 -0.12 0.65 -1.09 -1.71 -2.13 0.02 -0.76 1.21 -0.44 -0.92 Kurtosis -0.83 -0.09 -0.62 -0.36 0.49 0.17 2.58 -0.17 1.51 0.62 0.63 0.18 0.98 4.68 4.10 0.57 0.47 1.23 -1.07 -0.75 Shapiro-Wilk W 0.93 0.90 0.95 0.95 0.88 0.85 0.90 0.97 0.89 0.91 0.99 0.93 0.89 0.84 0.68 0.97 0.94 0.88 0.92 0.80 Table 1 e presents the seasonal statistics for physicochemical and microbial parameters at SWP 5. The pH ranged from 6.35–6.80 (mean = 6.56, W = 0.93), reflecting slightly acidic to neutral conditions typical of tropical streams. Temperature averaged 21.35°C (W = 0.95), indicating minimal seasonal thermal variation, consistent with trends observed in tropical headwaters (Coffey et al., 2020 ). Electrical conductivity (mean = 507.37 µS/cm, W = 0.90) exhibited moderate negative skew, suggesting ionic accumulation during dry seasons due to reduced dilution and mineral leaching (Mbaka et al., 2017 ). Turbidity (mean = 25.24 NTU) and total suspended solids (TSS; mean = 44.17 mg/L) exhibited strong negative skewness, signalling dry-season sediment surges driven by erosion and reduced flow similar trends have been reported in Tanzanian rivers affected by seasonal hydrology and land-use pressures (Nyagushuge et al., 2023). Total dissolved solids (TDS) averaged 314.52 mg/L, with strong left skew and elevated kurtosis, pointing to episodic dissolved load increases. Alkalinity (mean = 152.52 mg/L CaCO₃) and hardness (mean = 149.43 mg/L) indicated moderate mineral levels, while salinity showed right-skewed distribution, reflecting occasional saltwater influx or anthropogenic input. Phosphate concentrations averaged 1.45 mg/L and followed near-normal distribution, while nitrate (mean = 22.49 mg/L) exhibited high negative skew and leptokurtosis, consistent with sporadic nutrient loading from agricultural runoff and domestic waste. Biochemical oxygen demand (BOD; mean = 23.45 mg/L) exceeded WHO (2017) limits, showing strong negative skew and high kurtosis, indicating episodic organic pollution events likely from untreated wastewater and stagnant flow conditions(Kimani-Murage & Ngindu, 2007 ). Calcium and magnesium averaged 21.90 and 13.57 mg/L respectively, with variability due to geogenic factors. Iron (Fe²⁺) averaged 2.45 mg/L, with strong positive skew indicating occasional mobilisation under reducing conditions. Faecal coliforms (mean = 6.38 CFU/100 mL) and total coliforms (mean = 21.96 CFU/100 mL) showed non-normal distributions and moderate skewness, confirming widespread faecal contamination, a risk heightened during wet seasons due to runoff (Dzwairo et al., 2006 ). Overall, non-normality (W < 0.95) in several parameters, especially TSS, turbidity, nutrients, BOD, and microbial indicators, justified the application of non-parametric tests. These patterns of skewness and kurtosis confirm episodic, rainfall-linked contamination influenced by land use and poor sanitation, in agreement with other East African watershed studies (Kimani-Murage & Ngindu, 2007 ). Table 1 f. Descriptive Statistics of Seasonal Variations in Physicochemical and Microbial Water Quality Parameters at SWP 6 pH EC Tem Col Tur TSS TDS Hd Alk Sal Cl − PO₄³⁻ SO 4 2− NO 3 − BOD Ca 2+ Mg 2+ Fe2+ FC TC Mean 6.60 424.16 22.32 4.47 16.47 22.21 225.07 159.46 153.56 0.32 49.90 1.41 10.75 11.59 13.96 19.03 20.82 2.04 2.75 12.79 Standard deviation 0.13 19.07 0.83 0.92 2.60 6.75 13.58 8.68 11.75 0.19 5.30 0.43 1.40 3.61 2.59 3.06 2.87 0.58 1.73 2.32 Minimum 6.35 387.70 21.30 2.88 11.09 8.66 190.31 138.35 135.10 0.15 40.08 0.72 7.75 3.43 7.46 13.70 14.11 1.52 0.50 9.00 Maximum 6.75 447.30 23.55 5.72 20.30 30.48 242.25 173.50 171.38 0.82 58.16 2.30 12.66 17.28 16.45 24.25 25.38 3.17 5.50 16.50 Skewness -0.57 -1.16 -0.17 -0.60 -0.93 -1.21 -1.56 -1.13 -0.29 1.97 -0.29 0.70 -0.85 -0.76 -1.75 -0.26 -0.81 0.86 -0.08 -0.21 Kurtosis -0.40 0.65 -1.45 -0.56 0.67 0.72 3.42 2.76 -1.11 4.62 -0.33 0.62 0.39 1.42 3.05 -0.11 2.05 -0.71 -1.25 -0.89 Shapiro-Wilk W 0.93 0.85 0.87 0.92 0.92 0.85 0.88 0.91 0.93 0.80 0.97 0.95 0.93 0.95 0.81 0.96 0.92 0.84 0.92 0.95 The seasonal descriptive statistics for physicochemical and microbial water quality parameters at Surface Water Point 6 (SWP 6) are summarized in Table 1 f. The seasonal descriptive statistics at SWP 6 showed a mean pH of 6.60 (range 6.35–6.75), reflecting slightly acidic to near-neutral conditions typical of tropical freshwater systems. The pH distribution was slightly negatively skewed (–0.57) and near-normal (W = 0.93), indicating stable acid-base buffering. Temperature averaged 22.32°C with minor skewness and slight deviation from normality (W = 0.87), aligning with the region’s relatively stable seasonal thermal regime (Mason et al., 2012). Colour (mean 4.47 Pt-Co), turbidity (16.47 NTU), and total suspended solids (22.21 mg/L) all exhibited moderate negative skewness (− 0.60 to − 1.21) and moderate deviations from normality, reflecting episodic particulate increases during dry seasons likely caused by soil erosion and reduced flow (Moyo, 2013 ). Total dissolved solids (TDS) averaged 225.07 mg/L, with strong negative skewness (− 1.56), indicating intermittent elevated mineral loads. Alkalinity (159.46 mg/L CaCO₃) and hardness (153.56 mg/L CaCO₃) were stable with slight negative skewness and near-normal distributions, indicating buffering capacity maintained by carbonate species (Ganiyu et al., 2021 ; Nwanosike et al., 2014 ). Salinity showed strong positive skew (1.97), possibly from anthropogenic sources or local geology. Nutrient levels showed temporal fluctuations: phosphate averaged 1.41 mg/L with slight positive skewness, while nitrate (11.59 mg/L) had moderate negative skewness, reflecting nutrient inputs from agricultural runoff and sewage effluents (Hellar-Kihampa et al., 2013 ). Sulphate concentrations (mean = 10.75 mg/L) exhibited mild negative skewness, aligning with typical mineral weathering processes in tropical watersheds. The observed BOD levels (13.96 mg/L), negatively skewed and moderately peaked, point to intermittent organic loading likely driven by domestic wastewater discharges an observation consistent with findings from urban stream assessments in sub-Saharan Africa (Ssewankambo et al., 2023 ). Calcium and magnesium averaged 19.03 and 20.82 mg/L respectively, with minor skewness, reflecting lithogenic sources. Iron (Fe²⁺) concentrations (mean 2.04 mg/L) had positive skewness, suggesting occasional mobilization under anoxic conditions, a common feature in tropical groundwater-influenced systems. Microbial contamination was relatively low compared to other sites, with faecal coliform averaging 2.75 CFU/100 mL and total coliform 12.79 CFU/100 mL, both showing near-normal distributions. This suggests relatively better sanitation and/or dilution effects at SWP 6, although microbial presence still poses potential health risks consistent with studies that found contamination can persist even in improved systems (Amin et al., 2019a ). Many parameters deviated from normality (Shapiro–Wilk W < 0.95), highlighting the necessity for non-parametric analytical approaches. This suggests relatively better sanitation or dilution at SWP 6, although detectable microbial contamination continues to present health risks even in areas with improved infrastructure a phenomenon observed in many low-income settings (Bain et al., 2014 ). Many parameters deviated from normality (Shapiro–Wilk W < 0.95), making non-parametric methods necessary for accurate interpretation. Seasonal patterns of skewness and kurtosis at SWP 6 reveal episodic pollution tied to hydrological variability and human impacts, mirroring observations in tropical watersheds like Ghana’s Volta Basin (Lukhabi et al., 2023 ). These results underscore the imperative for continuous water quality monitoring and integrated catchment management to safeguard essential water resources. 4.2 Correlation Coefficient Indicating the Relationship of Water Quality Parameters at Different Surface Water Points The Pearson correlation matrix (Fig. 2 a) illustrates significant relationships among physicochemical and microbial water quality parameters across the surface water points. Electrical conductivity (EC) showed strong positive correlations with nitrate (r = 0.63), total dissolved solids (TDS, r = 0.53), calcium (Ca²⁺, r = 0.54), magnesium (Mg²⁺, r = 0.63), and faecal coliform (FC, r = 0.77), indicating that ionic strength is closely linked to nutrient and microbial contamination. This pattern aligns with observations from tropical and subtropical watersheds, where elevated EC frequently reflects nutrient-rich agricultural runoff and wastewater inputs (Lam et al., 2011 ). Phosphate (PO₄³⁻) correlated positively with chloride (Cl⁻, r = 0.77) and faecal coliform (r = 0.67), suggesting nutrient enrichment alongside faecal contamination, consistent with studies linking nutrient spikes to anthropogenic pollution (Syamsir et al., 2019). Biochemical oxygen demand (BOD) showed strong correlations with alkalinity (r = 0.68) and hardness (r = 0.66), indicating that organic pollution significantly influences water chemistry an effect also reported in tropical rivers where increasing BOD is closely aligned with elevated alkalinity and hardness values(Ogunribido, 2017 ). Temperature showed moderate correlations with microbial parameters such as total coliform (TC, r = 0.54), highlighting the role of seasonal temperature changes in microbial growth (Amin et al., 2019b ). Turbidity was positively correlated with total suspended solids (TSS, r = 0.61) and alkalinity (r = 0.83), indicating sediment inputs affect water quality, consistent with erosion impacts observed in tropical catchments (Moyo, 2013 ). Overall, Fig. 2 a confirms interconnected dynamics among water quality parameters influenced by both natural processes and human activities, emphasizing the need for integrated management strategies (Su et al., 2015 ). The Pearson correlation matrix at SWP 2 (Fig. 2 b) shows strong positive correlations between electrical conductivity (EC) and biochemical oxygen demand (BOD, r = 0.65), total coliform (TC, r = 0.67), and total dissolved solids (TDS, r = 0.58), indicating that ionic content increases alongside organic pollution and microbial contamination. These findings are consistent with previous research demonstrating that elevated EC often signals anthropogenic inputs such as wastewater and agricultural runoff in tropical catchments (Hellar-Kihampa et al., 2013 ). Notably, pH correlated positively with nitrate (NO₃⁻, r = 0.54) and negatively with turbidity (r = − 0.38) and iron (Fe²⁺, r = − 0.63), suggesting acid-base balance is influenced by nutrient levels and particulate matter. The negative correlation of Fe²⁺ with pH aligns with known iron solubility decreases at higher pH values. Turbidity correlated strongly with sulphate (SO₄²⁻, r = 0.81), indicating that suspended solids may transport sulphate-rich sediments, similar to observations in other African rivers affected by soil erosion (Moyo, 2013 ). Phosphate (PO₄³⁻) showed strong correlations with alkalinity (r = 0.81), calcium (Ca²⁺, r = 0.75), and chloride (Cl⁻, r = 0.74), reflecting nutrient interactions with mineral content in the water, as reported by (Ganiyu et al., 2021 ). Overall, microbial indicators such as faecal coliform (FC) showed moderate positive correlations with turbidity and chloride, reinforcing the influence of runoff and sanitation. These correlations underscore the complex interplay between physicochemical parameters and microbial contamination in surface waters impacted by seasonal and anthropogenic factors, echoing results from tropical watershed studies where land use and rainfall-driven dynamics strongly influenced contaminant relationships (Ahada & Suthar, 2018 ). The Pearson correlation matrix for SWP 3 (Fig. 2 c) highlights significant interrelationships among water quality parameters. Electrical conductivity (EC) exhibited strong positive correlations with biochemical oxygen demand (BOD, r = 0.90), total dissolved solids (TDS, r = 0.87), and total coliform (TC, r = 0.72), suggesting that increased ionic strength is closely linked to elevated levels of organic matter and microbial contamination. Similar trends have been reported in other tropical freshwater environments, where EC serves as an effective indicator of anthropogenic influence and wastewater intrusion(Shrestha & Kazama, 2007 ). Interestingly, temperature negatively correlated with EC (r = − 0.77) and BOD (r = − 0.76), suggesting seasonal temperature variation influences the degradation of organic matter and solute concentration, consistent with reports by (Song et al., 2025a ). Faecal coliform (FC) showed a strong positive correlation with pH (r = 0.62) and moderate correlation with magnesium (Mg²⁺, r = 0.63), highlighting the complex interaction between microbial presence and water chemistry as also observed by (Amin et al., 2019c ). Turbidity correlated highly with sulphate (SO₄²⁻, r = 0.86) and total suspended solids (TSS, r = 0.09), indicating particulate matter as a carrier of sulphate, which aligns with Moyo et al.’s (2019) findings on sediment-bound contaminants in tropical watersheds. Overall, these correlations highlight dynamic physicochemical-microbial interactions influenced by seasonal and anthropogenic factors. At SWP 4, analysis of the Pearson correlation matrix (Fig. 2 d) highlights important interactions reflecting both natural geochemical influences and anthropogenic pressures on water quality. Electrical conductivity (EC) shows strong positive relationships with faecal coliform (FC; r = 0.74), total coliform (TC; r = 0.58), and nitrate (NO₃⁻; r = 0.67), suggesting that increased ionic strength coincides with microbial contamination and nutrient inputs, likely from agricultural runoff and domestic waste (Chen et al., 2019a ; Pandey et al., 2014 ). This observation parallels findings in tropical catchments where EC is frequently a proxy for pollution load. Additionally, alkalinity correlates strongly with hardness (Hd; r = 0.59) and salinity (Sal; r = 0.87), indicating mineral weathering processes, such as carbonate dissolution, significantly influence water chemistry(Minh Nguyen, 2018 ). Negative or weak correlations between pH and coliform bacteria and turbidity imply dilution effects during high flow periods that reduce bacterial counts and slightly acidify the water via organic matter decomposition. Magnesium (Mg²⁺) exhibits a negative correlation with pH (r = -0.39), consistent with enhanced metal solubility under acidic conditions(Mahapatra et al., 2012 ). Moderate positive correlations of phosphate (PO₄³⁻) with hardness (r = 0.87) and salinity (r = 0.46) suggest nutrient enrichment from mineral or fertilizer sources (Ningrum, 2018 ). Collectively, these patterns reflect a complex interplay between natural and human-induced factors, mirroring other tropical surface water systems and emphasizing the need for integrated pollution control approaches (Minh Nguyen, 2018 ) The Pearson correlation analysis at SWP 5 (Fig. 2 e) reveals significant interrelationships among key water quality parameters, reflecting complex environmental and anthropogenic influences. Notably, EC exhibits strong positive correlations with pH (r = 0.78), BOD (r = 0.78), and TDS (r = 0.73), indicating that increased ionic concentrations coincide with higher organic pollution levels and dissolved solids. This pattern aligns with findings from studies in agricultural watersheds where fertilizer application and wastewater discharge contribute to elevated EC and nutrient loads (Oki & Akana, 2016 ). The inverse correlations between EC and temperature (r = -0.65) and turbidity (r = -0.41) suggest seasonal dilution effects during rainfall periods, consistent with observations in subtropical river systems where runoff modulates water chemistry (Pandey et al., 2014 ). Phosphate's strong positive correlations with hardness (r = 0.82) and chloride (r = 0.90) further emphasize mineral weathering and fertilizer runoff as major nutrient sources, echoing results from catchment studies affected by intensive agriculture. The moderate association of faecal coliform with EC (r = 0.36) and iron (r = 0.59) highlights the dual impact of microbial contamination and iron mobilization in surface water, which is supported by similar research in tropical urban watersheds(Manini et al., 2022 ). Collectively, these correlations underscore the influence of both natural geochemical processes and anthropogenic activities such as agriculture and sanitation on the water quality at SWP 5. These findings corroborate the broader understanding of water quality dynamics in tropical environments, where seasonal variations and land use patterns significantly affect physicochemical and microbial parameters (Van Horne et al., 2019 ). At SWP 6, the Pearson correlation matrix (Fig. 2 f) reveals key interactions between physicochemical and microbial parameters, indicating the combined influence of natural processes and anthropogenic activities on water quality. Electrical conductivity (EC) is strongly correlated with pH (r = 0.72), BOD (r = 0.75), total coliform (TC; r = 0.77), and faecal coliform (FC; r = 0.70), suggesting that higher ion concentrations coincide with increased organic pollution and microbial contamination. This pattern aligns with previous studies in tropical watersheds affected by urban runoff and sewage discharge (Quero et al., 2024 ). The inverse relationship between EC and temperature (r = -0.62) likely reflects dilution effects during wetter, cooler seasons, consistent with observations in monsoonal climates. Phosphate (PO₄³⁻) demonstrates strong positive correlations with salinity (r = 0.89) and chloride (r = 0.79), implicating agricultural runoff and mineral dissolution as primary nutrient sources, as documented in similar catchments with fertilizer inputs (Hellar-Kihampa et al., 2013 ). The associations of hardness, alkalinity, and sulphate with phosphate and chloride further underscore geogenic contributions to water chemistry. Moderate correlations between faecal coliform and nutrient parameters (ranging from 0.48 to 0.66) highlight the interplay between microbial pollution and nutrient enrichment, often driven by insufficient sanitation infrastructure (Kifanyi et al., 2024 ). Collectively, these findings suggest that water quality at SWP 6 is shaped by both natural geochemical factors and human activities such as agriculture and wastewater discharge, corroborating previous regional studies emphasizing seasonal variability and land use impacts on tropical surface waters(Ojok et al., 2017 ). 4.3 Mann-Kendall Trend Analysis of Seasonal Changes in Water Quality Parameters at Different Surface Water Points The Mann-Kendall trend analysis reveals mostly non-significant but directionally informative patterns in the water quality parameters as shown in Fig. 3 a. Electrical conductivity (EC) shows a moderate increasing trend (Tau = 0.38, p = 0.11), suggesting a gradual accumulation of dissolved ions, likely due to intensified agricultural runoff and urban wastewater inputs. This aligns with findings by (De Troyer et al., 2016 ), who observed rising EC trends linked to expanding urbanization in East African watersheds. Similarly, slight upward trends in total suspended solids (TSS; Tau = 0.29), total dissolved solids (TDS; Tau = 0.29), phosphate (PO₄³⁻; Tau = 0.29), nitrate (NO₃⁻; Tau = 0.22), magnesium (Mg; Tau = 0.23), and microbial indicators such as faecal coliform (FC; Tau = 0.20) and total coliform (TC; Tau = 0.24) are consistent with ongoing nutrient enrichment and microbial contamination observed in tropical catchments affected by agricultural intensification and poor sanitation (Kifanyi et al., 2024 ). Conversely, parameters such as pH (Tau = -0.21), hardness (Hd; Tau = -0.38), alkalinity (Alk; Tau = -0.31), sulphate (SO₄²⁻; Tau = -0.11), and biological oxygen demand (BOD; Tau = -0.14) tend to decrease over time. These declines may reflect seasonal dilution during wet periods or geochemical buffering through carbonate dissolution and biological processes reducing organic load, as similarly reported by (Minh Nguyen, 2018 ). The reduction in hardness and alkalinity particularly supports the influence of increased rainfall dilution on mineral content, consistent with patterns found by(Song et al., 2025b ) in tropical river systems. Although none of these trends reach statistical significance (p > 0.05), their directional tendencies correspond well with the dual impact of anthropogenic pressures and natural seasonal variability documented in other tropical environments. The observed increase in microbial contamination and nutrients aligns with studies linking poor sanitation and fertilizer runoff to water quality degradation (Amin et al., 2019d ). Meanwhile, geochemical parameters decreasing over time highlight the moderating role of natural processes in shaping water chemistry. Together, these results emphasize the complexity of managing tropical surface water systems, where both land use changes and climatic seasonality interact to influence water quality trends (Minh Nguyen, 2018 ). Regular monitoring and integrated watershed management are essential to mitigate pollutant inputs and preserve water resources. The Mann-Kendall trend analysis shown in Fig. 3 b reveals notable shifts in several water quality parameters over time. Turbidity (Tau = -0.62, p = 0.007) and sulphate (SO₄²⁻; Tau = -0.46, p = 0.046) show statistically significant decreasing trends, indicating improved water clarity and possible reductions in sulphate inputs or changes in geochemical conditions. This decline in turbidity aligns with findings by (Pandey et al., 2014 ), who reported decreasing turbidity trends following implementation of erosion control in tropical watersheds. Parameters such as electrical conductivity (EC; Tau = 0.34) and total dissolved solids (TDS; Tau = 0.33) display non-significant but increasing trends, suggesting a gradual accumulation of dissolved ions potentially linked to anthropogenic activities like fertilizer application and urban runoff, consistent with observations by (Hellar-Kihampa et al., 2013 ). Similarly, slight increases in chloride (Cl; Tau = 0.28), salinity (Sal; Tau = 0.25), and nutrients such as nitrate (NO₃⁻; Tau = 0.20) and phosphate (PO₄³⁻; Tau = 0.11) reflect ongoing nutrient loading pressures seen in agricultural catchment. Microbial indicators show mixed trends; faecal coliform (FC; Tau = -0.21) and colour (Col; Tau = -0.21) tend to decrease, while total coliform (TC; Tau = 0.03) shows a slight increase. These trends suggest some improvement in microbial contamination but persistent challenges, possibly due to intermittent pollution sources, a pattern noted by (Kifanyi et al., 2024 ) in regions with variable sanitation infrastructure. Other parameters such as pH (Tau = 0.22) show a mild increasing trend, consistent with natural buffering capacity in tropical waters, while temperature (Tem; Tau = -0.06) remains stable. The biological oxygen demand (BOD; Tau = 0.05) does not exhibit a significant trend, indicating relatively steady organic pollution levels. Overall, these results reflect a complex interplay between anthropogenic nutrient inputs and natural seasonal/geochemical processes affecting water quality, paralleling findings in similar tropical watersheds (Minh Nguyen, 2018 ). The significant decrease in turbidity and sulphate is encouraging but highlights the need for ongoing monitoring and targeted management to address persistent nutrient and microbial contamination. Figure 3 c presents the Mann-Kendall trend analysis results, revealing diverse temporal patterns in water quality parameters. Notably, turbidity (Tau = -0.615, p = 0.007) and sulphate (SO₄²⁻; Tau = -0.462, p = 0.046) exhibit statistically significant decreasing trends, likely influenced by sedimentation processes and either improved land use management or seasonal dilution effects. Comparable reductions in turbidity have been reported in watersheds experiencing reforestation, erosion control interventions, or changing hydrological regimes (Ochoa-Tocachi et al., 2016 ). Conversely, parameters such as electrical conductivity (EC; Tau = 0.344), total dissolved solids (TDS; Tau = 0.326), and chloride (Cl⁻; Tau = 0.277) show increasing trends, though not statistically significant. This pattern suggests gradual accumulation of ions due to anthropogenic inputs such as urban runoff or fertilizer leaching, consistent with findings by (Minh Nguyen, 2018 ). Similarly, pH and phosphate (PO₄³⁻) exhibit weak upward trends, aligning with observations in tropical basins where buffer capacity increases during dry periods due to overconcentration. Biological parameters such as faecal coliform (FC; Tau = -0.209) and total coliform (TC; Tau = 0.032) did not show significant trends but suggest variable microbial pollution likely driven by seasonal sanitation impacts and rainfall events. The increasing trends in calcium, magnesium, and BOD may reflect a rise in mineral content and organic load, resonating with findings from urbanizing catchments (Guan et al., 2018 )(Guan et al., 2018 ). Overall, the mixed trends in Fig. 3 c highlight both natural hydrological cycles and human-induced pressures affecting water quality, underscoring the importance of integrated watershed management strategies (Chen et al., 2019b ). Figure 3 d illustrates the Mann-Kendall trend analysis results, revealing subtle but noteworthy temporal changes in water quality parameters. Turbidity exhibited a strong decreasing trend (Tau = -0.443, p = 0.054), suggesting improved water clarity likely due to sedimentation or enhanced riparian zone management. This trend mirrors findings from riparian buffer restoration efforts in Southeast (Li et al., 2023 ). In contrast, Electrical Conductivity (EC; Tau = 0.313) and Total Dissolved Solids (TDS; Tau = 0.295) demonstrated increasing tendencies, reflecting gradual salinization and higher ionic concentrations often linked to agricultural runoff and land use changes, consistent with patterns reported by (Minh Nguyen, 2018 ). Hardness (Tau = -0.290) and alkalinity (Tau = -0.229) showed mild decreasing trends, potentially indicating seasonal flow variability and changing geochemical interactions, as discussed by (Van Binh et al., 2025 ). While other parameters, including pH, calcium (Ca²⁺), magnesium (Mg²⁺), chloride (Cl⁻), phosphate (PO₄³⁻), nitrate (NO₃⁻), BOD, faecal coliform (FC), and total coliform (TC), exhibited weak and statistically insignificant trends, their directions suggest a complex interplay of anthropogenic influences and natural processes. Overall, the results underscore both progress in sediment control and continuing challenges from diffuse pollution sources in tropical river systems undergoing urban and agricultural intensification. As illustrated in Fig. 3 e, the Mann–Kendall trend analysis reveals notable seasonal shifts in several water quality parameters, indicating both natural variability and anthropogenic pressures. Turbidity (Tau = − 0.543, p = 0.019) and sulphate (SO₄²⁻; Tau = − 0.443, p = 0.054) exhibited significant or near-significant decreasing trends, possibly reflecting improved sediment management or reduced runoff from surrounding catchments. In contrast, pH displayed a moderately increasing trend (Tau = 0.413, p = 0.082), which may be attributed to reduced acidifying pollutants or increased biological activity-a pattern comparable to water quality improvements (Pritchard et al., 2007 ). Other parameters such as EC (Tau = 0.219), TDS (Tau = 0.215), and salinity (Tau = 0.308) also showed upward trends, though not statistically significant, suggesting gradual ionic accumulation likely linked to agricultural runoff or evaporation, as previously noted by (El-fadl & Development, 2018 ). Moreover, nitrate (NO₃⁻) showed a positive trend (Tau = 0.295), consistent with increasing nutrient loading from fertilizers, a common issue in intensively cultivated regions such as those documented by (Hong et al., 2007 ). The lack of significant trends in microbial indicators like faecal and total coliforms suggests irregular contamination patterns, potentially influenced by seasonal sanitation dynamics or stormwater flushing. These results highlight that while sediment-related parameters show signs of improvement, nutrient and ionic buildup remains a concern at SWP 5. Figure 3 f presents the Mann-Kendall trend results for seasonal water quality at SWP 6, highlighting both improving and deteriorating conditions. A statistically significant decreasing trend in turbidity (Tau = − 0.543, p = 0.019) suggests improved clarity, likely due to sediment control efforts or reduced erosion during the wet season-an outcome that aligns with the findings of (Bakure et al., 2020 ) who observed similar improvements following riparian vegetation restoration in Ethiopian highlands. Parameters such as electrical conductivity (EC; Tau = 0.219), salinity (Tau = 0.308), and nitrate (NO₃⁻; Tau = 0.295) showed positive trends, although not statistically significant. These increases may reflect cumulative effects of agricultural runoff and evaporative concentration, consistent with studies in peri-urban watersheds by (Coffey et al., 2020 ). On the other hand, microbial indicators such as faecal coliform (FC; Tau = − 0.142) and total coliform (TC; Tau = − 0.083) demonstrated declining trends, which, although not statistically significant, may indicate seasonal dilution or enhanced sanitation practices. In summary, the trends at SWP 6 point toward marginal improvements in microbial and sediment-related parameters, while nutrient and salinity-related indicators suggest ongoing anthropogenic pressure in the area of study. 4.4 Biplot of Principal Component Analysis (PCA) Showing Seasonal Distribution of Water Quality Figure 4 a illustrates the Principal Component Analysis (PCA) biplot for Surface Water Point 1 (SWP 1), summarizing seasonal variations and associations among key water quality parameters. The first two dimensions explain a combined 77.90% of the total variance (Dim 1 = 49.51%; Dim 2 = 28.39%), indicating that most of the variation in the dataset can be interpreted within this two-dimensional space. Total Suspended Solids (TSS) loads heavily along Dim 2, isolated from other variables. This suggests episodic sediment influxes, possibly driven by heavy rainfall and erosion during wet months, a pattern frequently observed in tropical catchments (Yang et al., 2024 ). The clustering of nutrient and microbial parameters-phosphate (PO₄³⁻), nitrate (NO₃⁻), BOD, faecal coliforms (FC), and total coliforms (TC)-on the positive side of Dim 1 reflects common sources, such as domestic wastewater, fertilizer runoff, and open defecation during rainy seasons. These variables are notably linked with months like June and August, highlighting temporal loading effects due to intensified anthropogenic pressure and runoff. Meanwhile, months like May and October are positioned near the origin, reflecting lower values across most pollutants and suggesting background or baseflow water conditions. Their position indicates minimal anthropogenic or hydrological disturbance, consistent with seasonal dilution effects during drier periods. The directional strength of vectors such as PO₄³⁻ and FC emphasizes their significant contribution to the variance and potential as sentinel indicators of water pollution in urban streams. These findings closely align with regional studies across Africa and Asia where PCA has revealed pollution signatures linked to urban expansion and seasonal fluctuations (Sahoo et al., 2015 ). Figure 4 b shows the spatial-temporal relationships among water quality variables and sampling months at Surface Water Point 2 (SWP 2). The first two principal components explain 43.09% (Dim 1) and 23.53% (Dim 2) of the total variance, jointly accounting for approximately 66.62% of the dataset's variability. Notably, nutrient and microbial parameters-including phosphate (PO₄³⁻), nitrate (NO₃⁻), faecal coliform (FC), and total coliform (TC)-cluster along the positive side of Dim 1, especially in July, August, and September, indicating peak contamination during mid to late wet season. This aligns with seasonal runoff and sanitation-related influxes, supporting findings from Egypt and Uganda that show increased microbial and nutrient loads during rainy months due to agricultural leaching and inadequate waste infrastructure (El-Bady & Metwally, 2016 ; Kamal et al., 2025 ). Total Suspended Solids (TSS) are positioned distinctly on the far right of Dim 1, showing strong independence and heavy influence on the component, indicating sporadic sedimentation peaks, possibly linked to erosion episodes or flood pulse. In contrast, physicochemical indicators such as pH, EC, DO, and temperature cluster near the origin or in Africa the lower-left quadrant with sampling months like February, May, and October, showing less seasonal fluctuation and contributing less to overall variance. The placement of turbidity and BOD in proximity to microbial indicators reinforces the interdependence of organic load and microbial contamination, consistent with findings from tropical catchments where faecal pollution and BOD co-occur under runoff-driven dynamics (Fang et al., 2018 ). Furthermore, the scattering of salinity and sulphate in the upper-left quadrant, alongside variables such as magnesium and alkalinity, may point to geological or domestic wastewater signatures during early dry months. This spatial segregation supports earlier PCA interpretations in other East African watersheds that distinguish between baseflow-driven geogenic inputs and event-based pollutant spikes (De Troyer et al., 2016 ). Collectively, PCA at SWP 2 reveals distinct seasonal clustering of sampling months and parameters, demonstrating that nutrient and microbial loads are dominant during wet seasons, whereas ionic indicators prevail in drier conditions. This insight supports the strategic application of seasonal monitoring and pollutant source differentiation to guide local water resource management in Tunduma. Figure 4 c illustrates the Principal Component Analysis (PCA) biplot for SWP 3, capturing seasonal patterns and key variable interrelationships in shallow groundwater quality. Dim 1 and Dim 2 explain 43.08% and 24.08% of the total variance, respectively. The spatial orientation of variables reveals pollutant groupings and their seasonal loadings. Faecal coliform (FC) appears distinctly in the upper right quadrant along Dim 2, suggesting strong microbial contamination spikes during months like August. This separation implies FC is influenced by episodic sources such as pit latrine leakage or livestock activity near the wellhead, particularly during the dry to wet seasonal transition a finding consistent with (Kifanyi et al., 2024 ). Nitrate (NO₃⁻), salinity (Sal), and iron (Fe) also load positively on Dim 1 and to a lesser extent on Dim 2, clustering near July–September. Parameters such as phosphate (PO₄³⁻), calcium (Ca²⁺), magnesium (Mg²⁺), and biochemical oxygen demand (BOD) cluster centrally, overlapping with mid-year months (e.g., April to August). Their grouping suggests persistent but moderate inputs of nutrients and organic matter throughout seasons, influenced by both natural background levels and diffuse pollution sources (Hellar-Kihampa et al., 2013 ). In contrast, turbidity (Tur), sulphate (SO₄²⁻), and alkalinity cluster around January to February, suggesting peak rainfall dilution and sediment flushing. This pattern mirrors seasonal recharge effects described in tropical Africa, where early wet-season runoff increases alkalinity and sulphate while reducing particulate loads (Frank et al., 2021 ). Total suspended solids (TSS) appear isolated in the bottom-right quadrant largely uncorrelated with other variables and months indicative of episodic sediment inputs from surface runoff infiltration that do not co-vary with microbial or nutrient parameters. Similar patterns have been reported in groundwater settings, where TSS dynamics reflect distinct transport mechanisms compared to dissolved contaminants (Hughes, 2019 ). Collectively, the PCA biplot underscores the dual influence of seasonal hydrology and anthropogenic activities on shallow well water quality. FC, NO₃⁻, and salinity emerge as critical indicators of contamination risks during dry seasons or early rains, necessitating protective zoning around wells and improved sanitation infrastructure. This integrative PCA approach confirms the temporal-spatial pollutant dynamics emphasized in recent East African groundwater monitoring studies (Innocent et al., 2024 ). Figure 4 d displays a Principal Component Analysis (PCA) biplot for Shallow Well 5 (SW5), visualizing the spatio-temporal distribution of water quality parameters and their correlations during different months. The first two principal components (PC1 and PC2) explain 37.61% and 28.65% of the total variance, respectively, offering a consolidated view of the primary drivers of water quality variability at this site. Nutrient pollutants like nitrate (NO₃⁻) and phosphate (PO₄³⁻) are strongly projected on the upper right quadrant, indicating their dominance in PC2 and suggesting distinct seasonal pulses, likely from agricultural runoff or pit latrines. Total suspended solids (TSS) appear isolated in the far right of PC1, indicating it is a key independent contributor to variance, potentially linked to erosion or sediment-laden flows during wet seasons. Faecal coliform (FC) and total coliform (TC) cluster near phosphate and nitrate vectors, pointing to a potential shared source such as surface runoff contaminated by human or animal waste. These microbial and nutrient variables align with elevated values in warm months like November and March, indicating enhanced microbial growth and leaching during higher temperatures and stormwater influxes. Conversely, parameters like pH, TDS, DO, and temperature are centrally located near the origin, showing weak loadings and less contribution to the variability captured by the first two dimensions. These may remain relatively stable across seasons or be influenced more subtly. Seasonal grouping of months is evident, with dry months (e.g., February and May) clustering on the lower left, indicating more stable water quality, while transitional or wet months (e.g., March, November) scatter farther from the centre, highlighting episodic pollutant influx. These results align with earlier PCA-based studies showing that microbial and nutrient contaminants significantly influence groundwater quality under urbanization and seasonal fluxes (Barakat et al., 2016 ). Figure 4 e presents a PCA biplot for SWP5, summarizing spatio-temporal variation in water quality parameters across different months. The biplot captures 66.47% of the total variance, with Dimension 1 (37.61%) and Dimension 2 (28.86%) representing the most influential axes of variation. Key parameters such as turbidity (Tur), total suspended solids (TSS), phosphate (PO₄³⁻), nitrate (NO₃⁻), and salinity (Sal) are located in the upper right quadrant, indicating strong positive contributions to both dimensions. Their proximity suggests a common seasonal influence most likely from agricultural runoff and erosion during rainy months, consistent with findings by (Selemani et al., 2017 ), who demonstrated nutrient and TSS spikes following precipitation in the catchments. These patterns align with wet season months (March–May), located in the same quadrant. The microbial parameters faecal coliforms (FC) and total coliforms (TC) are grouped in the upper left quadrant, correlating with months like August and October, indicating sanitation-related contamination during transitional rainfall periods. This clustering is supported by (Amin et al., 2019e ) who emphasized microbial contamination risks from leaky sanitation infrastructure in Sub-Saharan towns. Conversely, February and January, located in the lower left quadrant, are associated with low loading of contaminants and correlate negatively with key pollutants. Interestingly, chloride (Cl), temperature (Temp), and electrical conductivity (EC) are centred and near the origin, suggesting moderate influence across seasons. These parameters, often geogenic or minimally impacted by short-term surface processes, are typical of baseflow contributions in urban groundwater(Athamena et al., 2022 ). Overall, PCA effectively reveals the influence of rainfall-driven events, land use, and sanitation infrastructure on the spatial-temporal distribution of pollutants. The distinct seasonal clustering confirms the need for adaptive monitoring frameworks that consider hydrological shifts and urban development pressures particularly in vulnerable towns like Tunduma where stream–groundwater interactions amplify contaminant mobilization. Figure 4 f presents a Principal Component Analysis (PCA) biplot illustrating the relationships among physicochemical and microbial water quality parameters from Shallow Well 5 (SW5) across different months. The first two principal components (Dim 1 and Dim 2) explain 37.61% and 21.74% of the variance, respectively. The microbial parameter faecal coliform (FC) is strongly associated with Dim 1 and lies far from the origin in the lower right quadrant, indicating high variability and distinct influence compared to other parameters. Its spatial separation suggests episodic contamination events, possibly due to sanitation infrastructure failures or livestock access, a pattern consistent with other urban settings where poorly managed waste and pit latrines influence groundwater (Graham & Polizzotto, 2013 ). Turbidity (Tur), total suspended solids (TSS), and nitrate (NO₃⁻) also show a strong positive loading on Dim 1, implying seasonal runoff influence or fertilizer leaching, particularly during the rainy months. This aligns with prior studies in Addis Ababa and Niamey, which found elevated nitrates and turbidity in shallow wells during high rainfall due to surface-groundwater connectivity and urban waste mismanagement (Kawo & Karuppannan, 2018 ). Conversely, January and February (dry months) cluster tightly near the origin, indicating relatively stable and lower levels of contaminants, possibly due to reduced surface infiltration and dilution effects. Nutrient parameters such as phosphate (PO₄³⁻) and sulphate (SO₄²⁻) show moderate loadings, suggesting intermittent point-source contributions from household detergents or greywater. Clustered physicochemical parameters near the centre like electrical conductivity (EC), pH, total hardness (Hd), and alkalinity (Alk) imply minimal seasonal fluctuation and consistent geogenic origins (e.g., mineral dissolution), as reported in urban aquifer studies (Alex et al., 2021 ). This PCA biplot underscores that SW5 is affected by a complex mix of anthropogenic and natural influences, with key contamination events occurring seasonally. The spatial alignment of critical parameters (FC, NO₃⁻, TSS) supports earlier findings that shallow wells in rapidly urbanizing areas are vulnerable to episodic contamination, particularly during wet seasons (“Water Quality in Selected Shallow Wells in Dar Es Salaam,” 2012). Conclusion This study provides a comprehensive assessment of temporal variations in physicochemical and microbial water quality in Tunduma’s stream systems, revealing clear seasonal patterns influenced by rainfall, land use, and inadequate sanitation infrastructure. Elevated levels of turbidity, nutrients, BOD, and microbial contamination during the wet season point to significant pollutant loading from urban runoff and domestic waste discharge. Descriptive statistics and Shapiro-Wilk tests confirmed non-normal, event-driven data distributions. Correlation analysis highlighted strong relationships between nutrient and microbial indicators, indicating shared contamination pathways. Trend analysis showed mixed changes across parameters, with increasing levels of EC, TDS, and nitrate suggesting long-term accumulation of pollutants. PCA and biplot visualizations effectively grouped parameters and seasons, illustrating pollution signatures linked to both organic and sediment-based sources. Several parameters exceeded WHO drinking water standards, particularly during peak rainfall months, posing potential health and environmental risks. These findings emphasize the need for seasonally adaptive water quality monitoring, improved sanitation infrastructure, and catchment-wide pollution control strategies. Ensuring safe water resources in urbanizing regions like Tunduma will require integrated efforts across municipal planning, community engagement, and environmental regulation. Declarations Conflict of interest: The authors declare no conflict of interest. Clinical trial number not applicable Ethics, Consent to Participate, and Consent to Publish declarations not applicable Funding: The study was financially supported by Mbeya University of Science and Technology as part of PhD. studies under grant number MUST-PF309. Author Contribution Zacharia Katambara contributed 50%Matungwa William contributed 50% Acknowledgement The authors extend their heartfelt gratitude to the Management of Mbeya University of Science and Technology, particularly Prof. Aloys Mvuma, Prof. Godliving Mtui and Prof. Vuai Said for their invaluable support and granting permission to pursue this research. We deeply appreciate their unwavering guidance and encouragement throughout the preparation of this research article. References Ahada, C. P. S., & Suthar, S. (2018). Groundwater nitrate contamination and associated human health risk assessment in southern districts of Punjab, India. Environmental Science and Pollution Research , 25 (25), 25336–25347. https://doi.org/10.1007/s11356-018-2581-2 Alex, R., Kitalika, A., Mogusu, E., & Njau, K. (2021). Sources of Nitrate in Ground Water Aquifers of the Semiarid Region of Tanzania. Geofluids , 2021 , 1–20. https://doi.org/10.1155/2021/6673013 Alphayo, S. M., & Sharma, M. P. (2018). Water Quality Assessment of Ruvu River in Tanzania Using NSFWQI. Journal of Scientific Research and Reports , 20 (3), 1–9. https://doi.org/10.9734/jsrr/2018/44324 Amin, R., Zaidi, M. B., Bashir, S., Khanani, R., Nawaz, R., Ali, S., & Khan, S. (2019a). Microbial contamination levels in the drinking water and associated health risks in Karachi, Pakistan. Journal of Water, Sanitation and Hygiene for Development , 9 (2), 319–328. https://doi.org/10.2166/washdev.2019.147 Amin, R., Zaidi, M. B., Bashir, S., Khanani, R., Nawaz, R., Ali, S., & Khan, S. (2019b). Microbial contamination levels in the drinking water and associated health risks in Karachi, Pakistan. Journal of Water, Sanitation and Hygiene for Development , 9 (2), 319–328. https://doi.org/10.2166/washdev.2019.147 Amin, R., Zaidi, M. B., Bashir, S., Khanani, R., Nawaz, R., Ali, S., & Khan, S. (2019c). Microbial contamination levels in the drinking water and associated health risks in Karachi, Pakistan. Journal of Water, Sanitation and Hygiene for Development , 9 (2), 319–328. https://doi.org/10.2166/washdev.2019.147 Amin, R., Zaidi, M. B., Bashir, S., Khanani, R., Nawaz, R., Ali, S., & Khan, S. (2019d). Microbial contamination levels in the drinking water and associated health risks in Karachi, Pakistan. Journal of Water, Sanitation and Hygiene for Development , 9 (2), 319–328. https://doi.org/10.2166/washdev.2019.147 Amin, R., Zaidi, M. B., Bashir, S., Khanani, R., Nawaz, R., Ali, S., & Khan, S. (2019e). Microbial contamination levels in the drinking water and associated health risks in Karachi, Pakistan. Journal of Water, Sanitation and Hygiene for Development , 9 (2), 319–328. https://doi.org/10.2166/washdev.2019.147 Athamena, A., Gaagai, A., Aouissi, H. A., Burlakovs, J., Bencedira, S., Zekker, I., & Krauklis, A. E. (2022). Chemometrics of the Environment: Hydrochemical Characterization of Groundwater in Lioua Plain (North Africa) Using Time Series and Multivariate Statistical Analysis. Sustainability , 15 (1), 20. https://doi.org/10.3390/su15010020 Bain, R., Cronk, R., Hossain, R., Bonjour, S., Onda, K., Wright, J., Yang, H., Slaymaker, T., Hunter, P., Prüss‐Ustün, A., & Bartram, J. (2014). Global assessment of exposure to faecal contamination through drinking water based on a systematic review. Tropical Medicine & International Health , 19 (8), 917–927. https://doi.org/10.1111/tmi.12334 Bakure, B. Z., Fikadu, S., & Malu, A. (2020). Analysis of physicochemical water quality parameters for streams under agricultural, urban and forest land-use types: in the case of gilgel Gibe catchment, Southwest Ethiopia. Applied Water Science , 10 (11), 234. https://doi.org/10.1007/s13201-020-01318-9 Barakat, A., El Baghdadi, M., Rais, J., Aghezzaf, B., & Slassi, M. (2016). Assessment of spatial and seasonal water quality variation of Oum Er Rbia River (Morocco) using multivariate statistical techniques. International Soil and Water Conservation Research , 4 (4), 284–292. https://doi.org/10.1016/j.iswcr.2016.11.002 Bengraı̈ne, K., & Marhaba, T. F. (2003). Using principal component analysis to monitor spatial and temporal changes in water quality. Journal of Hazardous Materials , 100 (1–3), 179–195. https://doi.org/10.1016/S0304-3894(03)00104-3 Chen, L., Han, L., Tan, J., Zhou, M., Sun, M., Zhang, Y., Chen, B., Wang, C., Liu, Z., & Fan, Y. (2019a). Water Environmental Capacity Calculated Based on Point and Non-Point Source Pollution Emission Intensity under Water Quality Assurance Rates in a Tidal River Network Area. International Journal of Environmental Research and Public Health , 16 (3), 428. https://doi.org/10.3390/ijerph16030428 Chen, L., Han, L., Tan, J., Zhou, M., Sun, M., Zhang, Y., Chen, B., Wang, C., Liu, Z., & Fan, Y. (2019b). Water Environmental Capacity Calculated Based on Point and Non-Point Source Pollution Emission Intensity under Water Quality Assurance Rates in a Tidal River Network Area. International Journal of Environmental Research and Public Health , 16 (3), 428. https://doi.org/10.3390/ijerph16030428 Coffey, R., Butcher, J., Benham, B., & Johnson, T. (2020). Modeling the Effects of Future Hydroclimatic Conditions on Microbial Water Quality and Management Practices in Two Agricultural Watersheds. Transactions of the ASABE , 63 (3), 753–770. https://doi.org/10.13031/trans.13630 De Troyer, N., Mereta, S., Goethals, P., & Boets, P. (2016). Water Quality Assessment of Streams and Wetlands in a Fast Growing East African City. Water , 8 (4), 123. https://doi.org/10.3390/w8040123 Díaz, J. A., Montecinos, M., Vega, A., Gironás, J., Molinos, M., & Pastén, P. (2025). Impacts of Droughts on Water Quality: Processes and Monitoring (pp. 89–124). https://doi.org/10.1007/978-3-031-85040-0_6 Dzwairo, B., Hoko, Z., Love, D., & Guzha, E. (2006). Assessment of the impacts of pit latrines on groundwater quality in rural areas: A case study from Marondera district, Zimbabwe. Physics and Chemistry of the Earth , 31 (15–16), 779–788. https://doi.org/10.1016/j.pce.2006.08.031 El-Bady, M. S. M., & Metwally, H. I. (2016). Evaluation of water quality of the surface water of the damietta nile branch, damietta governorate, Egypt. International Journal of ChemTech Research , 9 (5), 119–134. El-fadl, M. M. A., & Development, L. (2018). Monitoring of water-rock interaction and its impact on groundwater salinization at El Goura area , Northeast Sinai , Egypt . November . https://doi.org/10.13140/RG.2.2.22989.69605 Fang, H., Vergara, G. G. R., Goh, S. G., Ang, C. Y. L., Gu, X., & Gin, K. Y. (2018). Effect of Rainfall on the Microbial Water Quality of a Tropical Urban Catchment. Journal of Environmental Quality , 47 (5), 1242–1248. https://doi.org/10.2134/jeq2018.03.0099 Focus, E., Mhozya, M., & Jackson, M. (2025). Occupational Health Risk Assessment of Microbiological Air Quality in Buhemba Small Scale Gold Mines, Tanzania. Environment and Pollution , 14 (1), 17. https://doi.org/10.5539/ep.v14n1p17 Frank, S., Goeppert, N., & Goldscheider, N. (2021). Improved understanding of dynamic water and mass budgets of high‐alpine karst systems obtained from studying a well‐defined catchment area. Hydrological Processes , 35 (4). https://doi.org/10.1002/hyp.14033 Ganiyu, S. A., Mabunmi, A. A., Olurin, O. T., Adeyemi, A. A., Jegede, O. A., & Okeh, A. (2021). Assessment of microbial and heavy metal contamination in shallow hand-dug wells bordering Ona River, Southwest Nigeria. Environmental Monitoring and Assessment , 193 (3). https://doi.org/10.1007/s10661-021-08910-9 Graham, J. P., & Polizzotto, M. L. (2013). Pit latrines and their impacts on groundwater quality: A systematic review. Environmental Health Perspectives , 121 (5), 521–530. https://doi.org/10.1289/ehp.1206028 Guan, B., Guo, L., Gibson, M., & Li, Z. (2018). The derivation of water quality criteria for bisphenol A for the protection of marine species in China. Water Quality Research Journal , 53 (3), 156–165. https://doi.org/10.2166/wqrj.2018.035 Hellar-Kihampa, H., De Wael, K., Lugwisha, E., & Van Grieken, R. (2013). Water quality assessment in the Pangani River basin, Tanzania: natural and anthropogenic influences on the concentrations of nutrients and inorganic ions. International Journal of River Basin Management , 11 (1), 55–75. https://doi.org/10.1080/15715124.2012.759119 Hirsch, R. M., Slack, J. R., & Smith, R. A. (1982). Techniques of trend analysis for monthly water quality data. Water Resources Research , 18 (1), 107–121. https://doi.org/10.1029/WR018i001p00107 Hong, N., White, J. G., Weisz, R., Gumpertz, M. L., Duffera, M. G., & Cassel, D. K. (2007). Groundwater Nitrate Spatial and Temporal Patterns and Correlations. Vadose Zone Journal , 6 (1). Hughes, D. A. (2019). Facing a future water resources management crisis in sub-Saharan Africa. Journal of Hydrology: Regional Studies , 23 , 100600. https://doi.org/10.1016/j.ejrh.2019.100600 Innocent, O. N., James, K., Susan, M., & Salome, G. (2024). Groundwater pollution and climate change vulnerability in Kenya: A review. International Journal of Water Resources and Environmental Engineering , 16 (1), 17–31. https://doi.org/10.5897/IJWREE2024.1079 Kamal, M. A. M., Khalf, M. A., Mohamed, Z. A., Eljakee, J., Soliman, S. M., Alhotan, R. A., Hussein, E. O., Galik, B., Shukry, M., & Saleh, A. (2025). Seasonal Variations Alter Drinking Water Quality at Different Points in The water Distribution Systems of Cattle Farms in Egypt. Egyptian Journal of Veterinary Sciences , 56 (4), 757–768. https://doi.org/10.21608/ejvs.2024.276555.1908 Kanyerere, T., Levy, J., Xu, Y., & Saka, J. (2012). Assessment of microbial contamination of groundwater in upper Limphasa River catchment, located in a rural area of northern Malawi. Water SA , 38 (4), 581–596. https://doi.org/10.4314/wsa.v38i4.14 Kawo, N. S., & Karuppannan, S. (2018). Groundwater quality assessment using water quality index and GIS technique in Modjo River Basin, central Ethiopia. Journal of African Earth Sciences , 147 (June), 300–311. https://doi.org/10.1016/j.jafrearsci.2018.06.034 Kawo, N. S., Zhou, Y., Magalso, R., & Salvacion, L. (2018). Optimization of an artificial-recharge–pumping system for water supply in the Maghaway Valley, Cebu, Philippines. Hydrogeology Journal , 26 (3), 963–977. https://doi.org/10.1007/s10040-017-1693-y Khurshid, A., Korke, S., Kothari, Y., Alone, S., & Bais, K. (2025). Improving water quality parameter prediction with multi-level linear regression model and hybrid feature selection. International Journal of Electrical and Computer Engineering (IJECE) , 15 (2), 2381. https://doi.org/10.11591/ijece.v15i2.pp2381-2391 Kifanyi, G., Kasambala, H., Kavishe, R., & Moyo, M. (2024). Analysis of Spatial Bacteriological Quality Variation of Domestic Water Source Points in Mbarali District, Tanzania. Mbeya University of Science and Technology Journal of Research and Development , 5 (3), 873–885. https://doi.org/10.62277/mjrd2024v5i300557 Kimani-Murage, E. W., & Ngindu, A. M. (2007). Quality of water the slum dwellers use: The case of a Kenyan slum. Journal of Urban Health , 84 (6), 829–838. https://doi.org/10.1007/s11524-007-9199-x Lam, A., Karssenberg, D., M. Van Den Hurk, B. J. J., & Bierkens, M. F. P. (2011). Spatial and temporal connections in groundwater contribution to evaporation. Hydrology and Earth System Sciences . https://doi.org/10.5194/hess-15-2621-2011 Li, C., Hao, J., Zhang, G., Fang, H., Wang, Y., & Lu, H. (2023). Runoff variations affected by climate change and human activities in Yarlung Zangbo River, southeastern Tibetan Plateau. CATENA , 230 , 107184. https://doi.org/10.1016/j.catena.2023.107184 Lukhabi, D. K., Mensah, P. K., Asare, N. K., Pulumuka-Kamanga, T., & Ouma, K. O. (2023). Adapted Water Quality Indices: Limitations and Potential for Water Quality Monitoring in Africa. Water , 15 (9), 1736. https://doi.org/10.3390/w15091736 Mahapatra, S. S., Sahu, M., Patel, R. K., & Panda, B. N. (2012). Prediction of Water Quality Using Principal Component Analysis. Water Quality, Exposure and Health , 4 (2), 93–104. https://doi.org/10.1007/s12403-012-0068-9 Manini, E., Baldrighi, E., Ricci, F., Grilli, F., Giovannelli, D., Intoccia, M., Casabianca, S., Capellacci, S., Marinchel, N., Penna, P., Moro, F., Campanelli, A., Cordone, A., Correggia, M., Bastoni, D., Bolognini, L., Marini, M., & Penna, A. (2022). Assessment of Spatio-Temporal Variability of Faecal Pollution along Coastal Waters during and after Rainfall Events. Water , 14 (3), 502. https://doi.org/10.3390/w14030502 Mbaka, P. K., Mwangi, J. K., & Kiptum, C. K. (2017). Assessment of water quality in selected shallow wells of Keiyo Highlands, Kenya. African Journal of Science, Technology, Innovation and Development , 9 (3), 329–338. https://doi.org/10.1080/20421338.2017.1327476 Mbuligwe, S. E., & Kaseva, M. E. (2005). Pollution and Self-Cleansing of an Urban River in a Developing Country: A Case Study in Dar es Salaam, Tanzania. Environmental Management , 36 (2), 328–342. https://doi.org/10.1007/s00267-003-0068-4 Minh Nguyen, T. T. (2018). Integrated Evaluation Model for Surface Water Quality a Case Study in Ho Chi Minh City, Vietnam. Vietnam Journal of Science and Technology , 54 (4B), 232. https://doi.org/10.15625/2525-2518/54/4b/12046 Moyo, N. A. G. (2013). An analysis of the chemical and microbiological quality of ground water from boreholes and shallow wells in Zimbabwe. Physics and Chemistry of the Earth , 66 . https://doi.org/10.1016/j.pce.2013.06.003 Ndourwe Far Bolivar, Edioh Pem Frederic, Fosso Kengne Loraine Priscillia, Mogue Kamdem Gwladis, Betsi Noel Christiane Wifreid, Gwos Nhiomock Serge Roger, Foto Menbohan Samuel, & Zébazé Togouet Serge Hubert. (2025). Biodiversity of Benthic Macroinvertebrates and water quality as a tool to the ecological study of three forest streams in the littoral zone (Cameroon). World Journal of Advanced Research and Reviews , 25 (1), 1722–1734. https://doi.org/10.30574/wjarr.2025.25.1.0034 Ningrum, S. O. (2018). Analysis Quality of Water River and Quality of Well Water in The Surrounding of Rejo Agung Baru Sugar Factory Madiun. Jurnal Kesehatan Lingkungan , 10 (1), 1. https://doi.org/10.20473/jkl.v10i1.2018.1-12 Nwanosike, A. A., Ameh, M., & Adinoyi, O. Y. (2014). Bacteriological and Physico-chemical Analysis of Well Water from Villages in Edati , Niger State , North-central Nigeria . 10 (3), 10–16. Nyambukah, R., & Mihale, M. J. (2022a). Seasonal Variability of Water Quality in the Zigi River, Northern Tanzania. HURIA JOURNAL OF THE OPEN UNIVERSITY OF TANZANIA , 28 (1). https://doi.org/10.61538/huria.v28i1.974 Nyambukah, R., & Mihale, M. J. (2022b). Seasonal Variability of Water Quality in the Zigi River, Northern Tanzania. HURIA JOURNAL OF THE OPEN UNIVERSITY OF TANZANIA , 28 (1). https://doi.org/10.61538/huria.v28i1.974 Oberdorff, T. (2022). Time for decisive actions to protect freshwater ecosystems from global changes. Knowledge & Management of Aquatic Ecosystems , 423 , 19. https://doi.org/10.1051/kmae/2022017 Ochoa‐Tocachi, B. F., Buytaert, W., De Bièvre, B., Célleri, R., Crespo, P., Villacís, M., Llerena, C. A., Acosta, L., Villazón, M., Guallpa, M., Gil‐Ríos, J., Fuentes, P., Olaya, D., Viñas, P., Rojas, G., & Arias, S. (2016). Impacts of land use on the hydrological response of tropical Andean catchments. Hydrological Processes , 30 (22), 4074–4089. https://doi.org/10.1002/hyp.10980 Ogunribido, T. (2017). Bacteriological and hydrogeochemical investigation of surface water and groundwater in Ikare- Akoko, Nigeria. International Journal of Advanced Geosciences , 6 (1), 27. https://doi.org/10.14419/ijag.v6i1.8635 Ojok, W., Wasswa, J., & Ntambi, E. (2017). Assessment of Seasonal Variation in Water Quality in River Rwizi Using Multivariate Statistical Techniques, Mbarara Municipality, Uganda. Journal of Water Resource and Protection , 09 (01), 83–97. https://doi.org/10.4236/jwarp.2017.91007 Oketola, A. A., Adekolurejo, S. M., & Osibanjo, O. (2013). Water Quality Assessment of River Ogun Using Multivariate Statistical Techniques. Journal of Environmental Protection , 04 (05), 466–479. https://doi.org/10.4236/jep.2013.45055 Oki, A. O., & Akana, T. S. (2016). Quality Assessment of Groundwater in Yenagoa, Niger Delta, Nigeria. Geosciences , 6 (1), 1–12. https://doi.org/10.5923/j.geo.20160601.01 Onifade, O., Lawal, Z. K., Shamsuddin, N., Abas, P. E., Lai, D. T. C., & Gӧdeke, S. H. (2025). Impact of Seasonal Variation and Population Growth on Coliform Bacteria Concentrations in the Brunei River: A Temporal Analysis with Future Projection. Water , 17 (7), 1069. https://doi.org/10.3390/w17071069 Pandey, P. K., Kass, P. H., Soupir, M. L., Biswas, S., & Singh, V. P. (2014). Contamination of water resources by pathogenic bacteria. AMB Express , 4 (1), 1–16. https://doi.org/10.1186/s13568-014-0051-x Pritchard, M., Mkandawire, T., & O’Neill, J. G. (2007). Biological, chemical and physical drinking water quality from shallow wells in Malawi: Case study of Blantyre, Chiradzulu and Mulanje. Physics and Chemistry of the Earth , 32 (15–18). https://doi.org/10.1016/j.pce.2007.07.013 Quero, G. M., Guicciardi, S., Penna, P., Catenacci, G., Brandinelli, M., Bolognini, L., & Luna, G. M. (2024). Increasing trends in faecal pollution revealed over a decade in the central Adriatic Sea (Italy). Water Research , 262 , 122083. https://doi.org/10.1016/j.watres.2024.122083 Rahman, S. H., Fakhruddin, A., Uddin, M. J., Zaman, M. S., Talukder, A., Adyel, T. M., & Sarker, M. M. R. (2014). Water quality of shallow tube wells as affected by sanitary latrines and groundwater flow. Journal of Bangladesh Academy of Sciences , 37 (2). https://doi.org/10.3329/jbas.v37i2.17565 Rochelle-Newall, E. J., Ribolzi, O., Viguier, M., Thammahacksa, C., Silvera, N., Latsachack, K., Dinh, R. P., Naporn, P., Sy, H. T., Soulileuth, B., Hmaimum, N., Sisouvanh, P., Robain, H., Janeau, J.-L., Valentin, C., Boithias, L., & Pierret, A. (2016). Effect of land use and hydrological processes on Escherichia coli concentrations in streams of tropical, humid headwater catchments. Scientific Reports , 6 (1), 32974. https://doi.org/10.1038/srep32974 Rodionova, O., Kucheryavskiy, S., & Pomerantsev, A. (2021). Efficient tools for principal component analysis of complex data— a tutorial. Chemometrics and Intelligent Laboratory Systems , 213 , 104304. https://doi.org/10.1016/j.chemolab.2021.104304 Ronoh, P., Furlong, C., Kansiime, F., Mugambe, R., & Brdjanovic, D. (2020). Are There Seasonal Variations in Faecal Contamination of Exposure Pathways? An Assessment in a Low–Income Settlement in Uganda. International Journal of Environmental Research and Public Health , 17 (17), 6355. https://doi.org/10.3390/ijerph17176355 Sahoo, M. M., Patra, K. C., & Khatua, K. K. (2015). Inference of Water Quality Index Using ANFIA and PCA. Aquatic Procedia , 4 , 1099–1106. https://doi.org/10.1016/j.aqpro.2015.02.139 Selemani, J. R., Zhang, J., Muzuka, A. N. N., Njau, K. N., Zhang, G., Maggid, A., Mzuza, M. K., Jin, J., & Pradhan, S. (2017). Seasonal water chemistry variability in the Pangani River basin, Tanzania. Environmental Science and Pollution Research , 24 (33), 26092–26110. https://doi.org/10.1007/s11356-017-0221-x Shrestha, S., & Kazama, F. (2007). Assessment of surface water quality using multivariate statistical techniques: A case study of the Fuji river basin, Japan. Environmental Modelling & Software , 22 (4), 464–475. https://doi.org/10.1016/j.envsoft.2006.02.001 Sonar, C., Al Hammadi, A. M., & Padme, Y. L. (2024). Water Quality Assessment Using Principal Component Analysis (pp. 88–97). https://doi.org/10.1007/978-3-031-74701-4_7 Song, Z., Khaksari, M., Lilleskov, E. A., Kane, E. S., & Doskey, P. V. (2025a). Seasonal effects of plant functional groups on molecular biogeochemistry of dissolved organic matter in porewater of a poor fen. Frontiers in Environmental Science , 13 . https://doi.org/10.3389/fenvs.2025.1549320 Song, Z., Khaksari, M., Lilleskov, E. A., Kane, E. S., & Doskey, P. V. (2025b). Seasonal effects of plant functional groups on molecular biogeochemistry of dissolved organic matter in porewater of a poor fen. Frontiers in Environmental Science , 13 . https://doi.org/10.3389/fenvs.2025.1549320 Ssewankambo, G., Kabenge, I., Nakawuka, P., Wanyama, J., Zziwa, A., Bamutaze, Y., Gwapedza, D., Palmer, C. T., Tanner, J., Mantel, S., & Tessema, B. (2023). Assessing soil erosion risk in a peri-urban catchment of the Lake Victoria basin. Modeling Earth Systems and Environment , 9 (2), 1633–1649. https://doi.org/10.1007/s40808-022-01565-6 Su, X., Xu, W., Yang, F., & Zhu, P. (2015). Using new mass balance methods to estimate gross surface water and groundwater exchange with naturally occurring tracer 222Rn in data poor regions: A case study in northwest China. Hydrological Processes , 29 (6), 979–990. https://doi.org/10.1002/hyp.10208 Syamsir, Birawida, A. B., & Faisal, A. (2019). Development of Water Quality Index of Island Wells in Makassar City. Journal of Physics: Conference Series , 1155 (1). https://doi.org/10.1088/1742-6596/1155/1/012106 Van Binh, D., Nguyen, B. Q., Nguyen, T.-T.-H., Le, X.-H., Tuan, L. A., Le, M.-H., Kantoush, S. A., Nguyen, T. V., Dinh, V. N., Luan, N. T., Ahmed, M. F., & Sumi, T. (2025). Quantifying the Impacts of Climate Change and Human Interventions on Flow Alterations in a Tropical River. Water Resources Management , 39 (7), 3537–3552. https://doi.org/10.1007/s11269-025-04121-w Van Horne, Y. O., Parks, J., Tran, T., Abrell, L., Reynolds, K. A., & Beamer, P. I. (2019). Seasonal variation of water quality in unregulated domestic wells. International Journal of Environmental Research and Public Health , 16 (9). https://doi.org/10.3390/ijerph16091569 Water Quality in Selected Shallow Wells in Dar es Salaam. (2012). Huria - Journal of the Open University of Tanzania , 11 (1). William, M., & Katambara, Z. (2025). Assessment of Spatial Water Quality Variations in Shallow Wells Using Principal Component Analysis in Half London Ward, Tanzania. Journal of Water Resource and Protection , 17 (02), 108–143. https://doi.org/10.4236/jwarp.2025.172007 Yang, Y., Yu, Y., & Chen, X. (2024). Arsenic-methylating microbial community in sediment along the water flow is correlated with the distance to a low-temperature hot spring. Water Supply , 24 (3), 918–930. https://doi.org/10.2166/ws.2024.045 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 31 Jan, 2026 Editor assigned by journal 29 Jan, 2026 Submission checks completed at journal 29 Jan, 2026 First submitted to journal 31 Dec, 2025 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-8491963","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":583620440,"identity":"9774c5c2-d18b-4d3b-bcc0-6ddd689d9485","order_by":0,"name":"Matungwa William","email":"","orcid":"","institution":"Mbeya University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Matungwa","middleName":"","lastName":"William","suffix":""},{"id":583620448,"identity":"afbd8fab-7a36-4e84-80b1-784b0466ccab","order_by":1,"name":"Zacharia Katambara","email":"data:image/png;base64,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","orcid":"","institution":"Mbeya University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Zacharia","middleName":"","lastName":"Katambara","suffix":""}],"badges":[],"createdAt":"2025-12-31 19:38:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8491963/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8491963/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105035333,"identity":"bed269ba-fe22-4907-85bc-53dfc8b164ce","added_by":"auto","created_at":"2026-03-20 07:25:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":917986,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the study area indicating and sampling point layout of the streams in Tunduma watershed.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8491963/v1/895fba46bad8494c614a2436.png"},{"id":104984124,"identity":"9c32bb4a-a553-4687-a7cd-130d87c6c837","added_by":"auto","created_at":"2026-03-19 14:03:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":396781,"visible":true,"origin":"","legend":"\u003cp\u003e\u0026nbsp;See image above for figure legend.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8491963/v1/5ffca6b71b2e5224c973bcb6.png"},{"id":104984126,"identity":"c42f5814-64f6-4333-88e1-51e1bbb59f46","added_by":"auto","created_at":"2026-03-19 14:03:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":330915,"visible":true,"origin":"","legend":"\u003cp\u003e\u0026nbsp;See image above for figure legend.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8491963/v1/0b45769bdbb3aa91ad9c8a84.png"},{"id":105035481,"identity":"387bd093-8c95-40af-b67a-159838b33b4a","added_by":"auto","created_at":"2026-03-20 07:26:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":207356,"visible":true,"origin":"","legend":"\u003cp\u003e\u0026nbsp;See image above for figure legend.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8491963/v1/70381d51364b9d3dd8861568.png"},{"id":105036853,"identity":"3a32251a-9228-413b-be31-cda6f418e501","added_by":"auto","created_at":"2026-03-20 07:36:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3203108,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8491963/v1/ddfda4df-4e65-49b8-a611-e2fd17c56467.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Seasonal Dynamics of Water Quality and Pollution in Urban Streams of Tunduma, Tanzania","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eFreshwater streams are essential for ecosystem balance, providing critical services such as biodiversity support, clean water for domestic use, and irrigation for local (Oberdorff, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In rapidly growing border towns like Tunduma, Tanzania, streams are increasingly polluted by agricultural runoff, unmanaged waste disposal, and seasonal flooding linked to informal development and poor sanitation infrastructure assessment in Dar es Salaam (Mbuligwe \u0026amp; Kaseva, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). These stressors contribute to significant temporal variability in physicochemical and microbial water quality parameters (Fang et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTemporal variation, encompassing both diurnal and seasonal fluctuations, influences water quality through mechanisms such as surface runoff during rainy seasons, sediment resuspension, and enhanced microbial activity at elevated temperatures (Rochelle-Newall et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Physicochemical parameters, such as pH, electrical conductivity (EC), turbidity, nitrate, and phosphate, often vary in response to changes in rainfall, land use, and urban encroachment. Simultaneously, microbial indicators like faecal coliform (FC) and total coliform (TC) tend to spike during wet periods due to runoff, leaky sanitation infrastructure, and livestock intrusion into streams (Onifade et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eRecent studies in East Africa and comparable tropical regions have emphasised the relationship between seasonal shifts and water quality deterioration. For example, in Cameroon, seasonal changes were linked to significant fluctuations in dissolved oxygen, heavy metals, and benthic microbial diversity, driven by rainfall and anthropogenic inputs (Ndourwe Far Bolivar et al., 2025). In Tanzania, streams near mining sites and expanding towns have shown increases in microbial load and nutrient concentrations, underscoring the urgency of localised, time-sensitive monitoring (Focus et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo address the complexity of interrelated water quality variables, Principal Component Analysis (PCA) is widely applied as a multivariate technique. PCA is a powerful multivariate tool used to reduce data dimensionality and identify dominant patterns in complex environmental datasets, particularly in water quality studies (Rodionova et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In Tanzania, PCA has proven effective in identifying pollution pathways in both surface and groundwater. For example, an analysis of shallow wells in Tunduma\u0026rsquo;s Half‑London Ward revealed surface runoff, pit latrines, and fertilizer use as dominant contamination sources(William \u0026amp; Katambara, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), while in coastal Dar es Salaam aquifers, it identified seasonal salinization and nitrate inputs. PCA applications in the Pangani and Zigi river basins have revealed that seasonal changes in land use and rainfall drive variations in nutrient load, ionic strength, and microbial contamination (Nyambukah \u0026amp; Mihale, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e). In the Pangani basin, PCA identified geogenic drivers (carbonate weathering) and anthropogenic influences (agricultural runoff and domestic effluent), accounting for over 60% of the observed seasonal variance (Hellar-Kihampa et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Meanwhile, studies in Uganda\u0026rsquo;s River Rwizi showed that the first two PCA factors explained 81.2% of dry-season variability and 69.2% of wet-season variability, with main contributors including turbidity, TSS, EC, and microbial loads tied to erosion and wastewater inputs (Oketola et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrincipal Component Analysis (PCA) has proven to be a valuable tool in deciphering complex water quality datasets by identifying key pollution indicators and isolating dominant contaminant sources. For instance, in Morocco\u0026rsquo;s Oum Er Rbia River, PCA effectively differentiated anthropogenic pollution inputs by highlighting nitrate, phosphate, and biochemical oxygen demand (BOD) as primary markers of industrial and domestic contamination (Barakat et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Such multivariate techniques offer a nuanced understanding of seasonal water quality dynamics, enabling researchers to distinguish between natural geochemical backgrounds and human-induced pressures. Despite the ecological significance and socio-economic role of Tunduma as a cross-border hub between Tanzania and Zambia, the region remains critically underrepresented in empirical water quality studies. This research gap poses a challenge for evidence-based water resource governance and public health risk management. Therefore, the present study applies an integrated methodology combining long-term temporal sampling, PCA, and Mann-Kendall trend analysis to assess the spatial and seasonal variability of physicochemical and microbial water quality indicators. By uncovering pollution signatures and temporal co-variations in stream water, this study aims to inform the development of localized, data-driven strategies for sustainable water management in rapidly urbanizing towns like Tunduma.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003eThis study employed a comprehensive approach to assess the temporal dynamics, inter-parameter relationships, and pollution signatures of stream water quality. A combination of systematic field sampling, laboratory analysis, and multivariate statistical techniques was used to address the stated research objectives.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Description of the study area\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the study area within Tunduma Town, a rapidly growing border town in Momba District, Songwe Region, Tanzania. The study area covers approximately 141 square kilometres and had a population of over 219,000 as of 2022, according to the National Bureau of Statistics. It is situated at around 1,500 meters above sea level within the Lake Rukwa catchment and experiences a tropical sub-humid climate with distinct wet (November to April) and dry (May to October) seasons. The town receives an average annual rainfall of approximately 1,200 mm, and its hydrogeology is dominated by Precambrian basement rocks, resulting in shallow, unconfined aquifers with depths typically less than 15 meters. These aquifers are predominantly recharged by rainfall and surface runoff. Rapid urban expansion, poor sanitation infrastructure, and widespread reliance on unlined pit latrines have significantly increased the vulnerability of both surface and groundwater resources to contamination. Streams frequently receive direct discharge of household and greywater effluents, especially during the rainy season, elevating the risk of pollutant transport and hydraulic connectivity between surface streams and shallow wells. Such urban hydrological settings are prone to nutrient enrichment, organic load, and microbial contamination patterns consistently reported in similar urban centres across Sub-Saharan Africa (Pandey et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Study Design, Sample Collection and Analytical Procedures\u003c/h2\u003e \u003cp\u003eThis study employed a longitudinal design to assess temporal and spatial variability in stream water quality across six monitoring sites (SWP 1, SWP 2, SWP 3, SWP 4, SWP 5 and SWP 6) in Tunduma, Tanzania. Sampling was conducted monthly from March 2024 to February 2025, yielding 72 samples representative of areas impacted by agricultural runoff, domestic wastewater, and unplanned urban development. Field procedures followed the Standard Methods for the Examination of Water and Wastewater (APHA, 2017). In-situ measurements of pH, temperature, dissolved oxygen (DO), turbidity, and electrical conductivity (EC) were obtained using calibrated multi-parameter probes. Sterile polyethylene bottles were used to collect 1 L samples for physicochemical analysis and 500 mL for microbial analysis. Samples were preserved at approximately 4\u0026deg;C in insulated coolers, transported to the laboratory within 6 hours, and processed within 24 hours to ensure integrity.\u003c/p\u003e \u003cp\u003eLaboratory analysis included spectrophotometric determination of nutrients (nitrate, phosphate, sulphate, and ammonia), and BOD5 measurement using the Winkler method. Total dissolved solids (TDS) were determined gravimetrically. Microbial quality was assessed by membrane filtration and the Most Probable Number (MPN) method, using m-FC and m-Endo agar for faecal and total coliforms, incubated at 44.5\u0026deg;C and 37\u0026deg;C, respectively. These procedures ensured high-quality data for robust interpretation of temporal and spatial water quality trends.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Pearson Correlation Analysis\u003c/h2\u003e \u003cp\u003eTo investigate potential linkages among water quality parameters, Pearson correlation analysis was employed. This method is appropriate for examining linear relationships between continuous, normally distributed variables. The correlation matrix enabled the identification of co-varying patterns, such as positive correlations between nutrient concentrations and microbial indicators, which may suggest shared pollution sources or standard seasonal drivers. Pearson\u0026rsquo;s approach was selected due to its broad applicability in hadrochemical datasets and its interpretability for identifying both direct and indirect pollution pathways (Kawo et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.4 Temporal\u003c/b\u003e Trend \u003cb\u003eAnalysis\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eTo explore long-term trends in the dataset, the Mann-Kendall test was used as a non-parametric method suitable for detecting monotonic changes over time in environmental data. The seasonal Kendall variant was applied to account for repeated monthly measurements, enhancing the test\u0026rsquo;s ability to distinguish genuine trends from seasonal fluctuations (Hirsch et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1982\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Comparison with WHO Standards\u003c/h2\u003e \u003cp\u003eEach measured water quality parameter was evaluated in relation to the World Health Organization (WHO) drinking water guidelines to assess its suitability for human consumption. Parameters such as nitrate, phosphate, turbidity, BOD, and microbial indicators (faecal and total coliforms) were reviewed to identify exceedances of recommended thresholds. Instances where concentrations surpassed WHO limits were considered potential public health concerns, particularly during the wet season when contamination levels were elevated. This comparison provided a benchmark for determining water safety and highlighted areas requiring urgent intervention or improved management practices.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.6 Principal\u003c/b\u003e Component \u003cb\u003eAnalysis (PCA)\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eTo identify the most influential variables and unravel potential pollution sources across space and time, Principal Component Analysis (PCA) was applied to the standardized dataset comprising physicochemical and microbial water quality parameters. Prior to analysis, all variables were normalized to zero mean and unit variance to mitigate scale-related distortions. The PCA was conducted using the correlation matrix, and components with eigenvalues greater than one (Kaiser criterion) were retained. Varimax orthogonal rotation was employed to maximize the interpretability of component loadings and to clarify the underlying structure of pollutant interactions. PCA biplots were constructed to visualize the clustering of water quality parameters and sampling months, enabling the identification of co-occurring pollutants and dominant pollution signatures. Additionally, site score plots were generated to trace the spatial and temporal variability of pollution profiles across monitoring locations. This analytical framework aligns with previous applications of PCA in environmental water quality studies. For example, (Bengraı̈ne \u0026amp; Marhaba, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) utilized PCA to differentiate between solute constituents, nutrient concentrations, and organic pollutants in river systems, effectively mapping spatiotemporal pollution trends in the Passaic River, New Jersey. These precedents underscore the utility of PCA in disentangling complex water quality datasets and supporting evidence-based water resource management.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Data Analysis Tools\u003c/h2\u003e \u003cp\u003eAll statistical analyses were conducted using Jamovi (v2.6.44) and R (v4.5.1), employing key packages such as trend, FactoMineR, and ggplot2. Descriptive statistics summarized seasonal variations, while the Shapiro-Wilk test assessed normality. Pearson correlation identified inter-parameter relationships. Mann-Kendall tests detected temporal trends. Principal Component Analysis (PCA) with varimax rotation was applied to standardized data to identify pollution sources and seasonal patterns. These tools ensured analytical rigor and insight into water quality dynamics at the six surface water points in Tunduma.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Descriptive statistics Showing the Seasonal Variations among Water Quality Parameters at Different Surface Water Points\u003c/h2\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ea. Descriptive Statistics of Seasonal Variations in Physicochemical and Microbial Water Quality Parameters at SWP 1\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTem\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCol\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTur\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTSS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTDS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHd\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAlk\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCl\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBOD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCa\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMg\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFe\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTC\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\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e326.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e130.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e175.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e165.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStandard deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e274.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e110.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e145.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e140.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e359.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e145.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e193.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e176.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSkewness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKurtosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShapiro-Wilk W\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eThe descriptive statistics for water quality parameters at SWP 1 revealed distinct seasonal variations shaped by climatic conditions, land use, and anthropogenic pressures in Tunduma (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea). The mean pH (6.69) remained within the WHO acceptable range (6.5\u0026ndash;8.5), with low skewness (-0.15) and near-normal distribution (Shapiro-Wilk W\u0026thinsp;=\u0026thinsp;0.93), in line with findings from urban streams in northern Nigeria (Khurshid et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). Electrical conductivity (EC) averaged 326.10 \u0026micro;S/cm, indicating moderate ionic concentration. It showed left-skewness (-0.84) and normality (W\u0026thinsp;=\u0026thinsp;0.93), which suggests relatively stable ion levels typical of non-industrial urban environments. This value is lower than those reported in Addis Ababa streams, indicating lower salinity stress in Tunduma (Rahman et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). Water temperature (mean\u0026thinsp;=\u0026thinsp;22.99\u0026deg;C) displayed strong negative skew (-1.26) and poor normality (W\u0026thinsp;=\u0026thinsp;0.81), a pattern attributed to cooler readings during wet months. Similar seasonal cooling trends, affecting microbial activity and water clarity, have been reported by (Coffey et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). The extremely high kurtosis observed in TSS (7.30) and low Shapiro\u0026ndash;Wilk W (0.64) suggest rare but intense sedimentation events likely driven by surface runoff and erosion similar to patterns reported in Kampala\u0026rsquo;s peri‑urban streams, where soil erosion was identified as a primary contributor to episodic sediment influx(Ssewankambo et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Nutrient parameters, including nitrate (3.77 mg/L) and phosphate (1.40 mg/L), exceeded background levels, reflecting non-point source pollution from fertilizers and domestic waste. Their skewness was moderate, and Shapiro-Wilk W values (0.94\u0026ndash;0.95) indicate approximate normality. These trends are comparable to nutrient influxes reported in Morogoro, Tanzania (Focus et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). BOD (mean value of 3.39 mg/L) showed left-skewness (-0.89) and normal distribution (W\u0026thinsp;=\u0026thinsp;0.93), suggesting moderate organic pollution, especially during early rainfall events. Elevated BOD during wet seasons has also been observed in Cameroonian urban streams(Ndourwe Far Bolivar et al., 2025). Microbial contamination was significant. Faecal coliform (mean\u0026thinsp;=\u0026thinsp;3.33 MPN/100 mL) and total coliform (7.67 MPN/100 mL) values exceeded WHO thresholds in several instances. Their non-normal distributions (W\u0026thinsp;=\u0026thinsp;0.90 and 0.80) reflect episodic pollution likely linked to poor sanitation and livestock intrusion. Similar microbial spikes in wet periods have been reported in urban rivers in Kenya (Kamal et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). In conclusion, the combined interpretation of skewness, kurtosis, and Shapiro-Wilk W values confirms that many parameters at SWP 1 were non-normally distributed and seasonally influenced. These findings support evidence from broader East African studies (D\u0026iacute;az et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sonar et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), reinforcing the importance of targeted, seasonal water quality monitoring in urban border towns like Tunduma.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eb. Descriptive Statistics of Seasonal Variations in Physicochemical and Microbial Water Quality Parameters at SWP 2\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTem\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCol\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTur\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTSS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTDS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHd\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAlk\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCl\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePO₄\u0026sup3;⁻\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBOD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCa\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMg\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFe\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTC\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\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e492.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e309.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e161.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e157.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStandard deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e460.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e260.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e150.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e135.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e520.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e340.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e170.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e174.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSkewness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKurtosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShapiro-Wilk W\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eb presents the descriptive statistics of physicochemical and microbial water quality parameters for Surface Water Point 2 (SWP 2), highlighting seasonal variability. Parameters analysed include central tendency (mean), dispersion (standard deviation, minimum, and maximum), and distribution characteristics (skewness, kurtosis, and Shapiro-Wilk W statistic). These indicators provide insight into the temporal trends and suitability of the water for domestic and environmental use. The pH values at SWP 2 ranged from 6.20 to 6.80, with a mean of 6.62, indicating mildly acidic conditions. The data were negatively skewed (\u0026minus;\u0026thinsp;0.79) and slightly platykurtic (kurtosis\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.24), suggesting more frequent values below the mean. The Shapiro-Wilk test result (W\u0026thinsp;=\u0026thinsp;0.84) confirmed a deviation from normality. Similar seasonal patterns have been observed in southern Tanzanian surface waters (Shimba, 2017); (Nyambukah \u0026amp; Mihale, \u003cspan class=\"CitationRef\"\u003e2022b\u003c/span\u003e). Electrical conductivity (EC) had a narrow range (460.22\u0026ndash;520.00 \u0026micro;S/cm), with a mean of 492.51 \u0026micro;S/cm. It was symmetrically distributed (skew\u0026thinsp;=\u0026thinsp;0.00) but platykurtic (kurtosis\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1.11), and not normally distributed (W\u0026thinsp;=\u0026thinsp;0.92), indicating consistent ionic concentration throughout seasons. Temperature (mean\u0026thinsp;=\u0026thinsp;21.03\u0026deg;C) was slightly right-skewed (skew\u0026thinsp;=\u0026thinsp;0.37), with W\u0026thinsp;=\u0026thinsp;0.95, suggesting approximate normality. Physical water quality indicators, including colour, turbidity, TSS, and TDS, showed moderate to strong negative skewness (\u0026minus;\u0026thinsp;0.01 to \u0026minus;\u0026thinsp;0.93), indicating seasonal reductions during periods of dilution, especially in the wet season. These parameters had near-normal kurtosis values and varied Shapiro-Wilk W values (0.87\u0026ndash;0.97), reflecting seasonal variability in sediment and suspended matter due to runoff (Nyambukah \u0026amp; Mihale, \u003cspan class=\"CitationRef\"\u003e2022b\u003c/span\u003e). Nutrient-related parameters demonstrated significant departures from normal distribution. Phosphate (PO₄\u0026sup3;⁻) (mean\u0026thinsp;=\u0026thinsp;1.49 mg/L), nitrate (NO₃⁻) (mean\u0026thinsp;=\u0026thinsp;25.57 mg/L), and sulphate (SO₄\u0026sup2;⁻) (mean\u0026thinsp;=\u0026thinsp;14.57 mg/L) exhibited high negative skewness (up to \u0026minus;\u0026thinsp;1.98 for NO₃⁻) and elevated kurtosis values (e.g., 5.43 for NO₃⁻). These distributions suggest the occurrence of extreme values due to sporadic runoff events or agricultural inputs, consistent with findings in Tanzanian catchments (Alex et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). The biological oxygen demand (BOD) was high (mean\u0026thinsp;=\u0026thinsp;22.38 mg/L), exceeding WHO standards. It was strongly negatively skewed (\u0026minus;\u0026thinsp;2.05) and leptokurtic (kurtosis\u0026thinsp;=\u0026thinsp;3.86), with a low Shapiro-Wilk value (W\u0026thinsp;=\u0026thinsp;0.72), indicating pollution spikes, potentially from untreated wastewater or decaying organic matter during stagnant flow periods. These characteristics align with observations from urbanized and peri-urban streams in East (Alphayo \u0026amp; Sharma, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Microbial indicators revealed persistent contamination, with faecal coliform (FC) and total coliform (TC) counts averaging 10.58 and 26.00 CFU/100 mL exceeding WHO surface water guidelines. Both microbial parameters exhibited negative skewness (FC = \u0026minus;\u0026thinsp;1.07; TC = \u0026minus;\u0026thinsp;0.84) and light tails (kurtosis \u0026asymp; \u0026minus;\u0026thinsp;0.7), failing normality (Shapiro\u0026ndash;Wilk W\u0026thinsp;=\u0026thinsp;0.84 and 0.85). This suggests episodic faecal pollution tied to sanitation infrastructure and seasonal runoff. Similar patterns were observed in peri-urban streams in Kampala, Uganda, where both faecal coliform and total coliform counts spiked during wet seasons due to pit-latrine leaching and stormwater runoff (Ronoh et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). The Shapiro-Wilk test revealed that most water quality variables significantly deviated from normality (W\u0026thinsp;\u0026lt;\u0026thinsp;0.95). This justifies the application of non-parametric statistical methods-such as Kendall\u0026rsquo;s tau, Spearman correlation, or Wilcoxon signed-rank tests-for robust trend and relationship analysis (Nyambukah \u0026amp; Mihale, \u003cspan class=\"CitationRef\"\u003e2022a\u003c/span\u003e). The pronounced skewness and kurtosis observed in critical pollutants such as BOD, NO₃⁻, and FC indicate episodic or event-driven pollution patterns, reinforcing the need for seasonally adaptive water management strategies in Tunduma and similar semi-urban settlements.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ec. Descriptive Statistics of Seasonal Variations in Physicochemical and Microbial Water Quality Parameters at SWP 3\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTem\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCol\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTur\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTSS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTDS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHd\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAlk\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCl\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePO₄\u0026sup3;⁻\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBOD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCa\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMg\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFe2+\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTC\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\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e522.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e319.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e143.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e141.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStandard deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e480.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e270.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e130.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e110.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e535.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e340.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e160.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e167.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSkewness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKurtosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShapiro-Wilk W\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ec summarizes the seasonal variation of physicochemical and microbial water quality parameters at SWP 3. Most parameters exhibited deviations from normal distribution, with notable skewness and kurtosis reflecting the influence of seasonal rainfall, anthropogenic activities, and catchment characteristics. Physicochemical parameters such as pH (mean\u0026thinsp;=\u0026thinsp;6.51), temperature (mean\u0026thinsp;=\u0026thinsp;21.66\u0026deg;C), EC (mean\u0026thinsp;=\u0026thinsp;522 \u0026micro;S/cm), salinity, turbidity, colour, TDS, TSS, alkalinity, and hardness displayed moderate to strong skewness (\u0026minus;\u0026thinsp;1.84 to 1.70), elevated kurtosis (up to 5.71), and poor normality (Shapiro-Wilk W\u0026thinsp;=\u0026thinsp;0.72\u0026ndash;0.97). These patterns suggest sediment influx, ion accumulation, and sporadic pollution events, particularly during or after rains (Coffey et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Nutrient parameters (phosphate and nitrate) displayed non-normal distributions with high skewness and kurtosis, indicative of episodic loading from agricultural runoff, pit latrines, or organic matter decay patterns consistent with seasonal nutrient surges observed in Tanzania\u0026rsquo;s Pangani River Basin (Selemani et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eBiological indicators-including biochemical oxygen demand (BOD; mean\u0026thinsp;=\u0026thinsp;24.53 mg/L), faecal coliforms (FC), and total coliforms (TC) consistently exceeded recommended thresholds and displayed marked non-normal distributions (Shapiro\u0026ndash;Wilk W\u0026thinsp;=\u0026thinsp;0.76\u0026ndash;0.91) with skewness and leptokurtosis, reflecting episodic faecal contamination driven by poor sanitation and seasonal runoff. These findings align with seasonal microbial surges documented in Tanzania\u0026rsquo;s Serengeti rivers, where coliform levels peaked following wet-weather events linked to wildlife and livestock faecal loading (Kanyerere et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ed. Descriptive Statistics of Seasonal Variations in Physicochemical and Microbial Water Quality Parameters at SWP 4\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTem\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCol\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTur\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTSS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTDS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHd\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAlk\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCl\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePO₄\u0026sup3;⁻\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBOD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCa\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMg\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFe2+\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTC\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\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e409.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e168.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e161.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e220.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStandard deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e367.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e152.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e143.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e185.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e439.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e181.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e174.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e242.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSkewness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKurtosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShapiro-Wilk W\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ed presents seasonal statistics for physicochemical and microbial parameters at SWP 4. pH ranged from 6.40 to 6.85 (mean\u0026thinsp;=\u0026thinsp;6.66) with near-normal distribution (W\u0026thinsp;=\u0026thinsp;0.93), suggesting stable acid\u0026ndash;base conditions (Masese et al., 2017). EC averaged 409.30 \u0026micro;S/cm (W\u0026thinsp;=\u0026thinsp;0.95), indicating moderate ionic strength with seasonal dilution effects (Mwegoha et al., 2010). Temperature was stable (mean\u0026thinsp;=\u0026thinsp;22.01\u0026deg;C, W\u0026thinsp;=\u0026thinsp;0.92), consistent with tropical thermal patterns (Nyambukah \u0026amp; Mihale, \u003cspan class=\"CitationRef\"\u003e2022a\u003c/span\u003e). Suspended solids, colour, turbidity (mean\u0026thinsp;=\u0026thinsp;16.68 NTU), and TSS (22.04 mg/L) showed moderate variability, with turbidity skewed (\u0026minus;\u0026thinsp;1.10), indicating higher dry-season loads (Kimani-Murage \u0026amp; Ngindu, \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e). TDS averaged 168.06 mg/L with slight skew (\u0026minus;\u0026thinsp;0.39), reflecting consistent mineral presence. Alkalinity (161.30 mg/L CaCO₃) and hardness (53.24 mg/L) were stable; alkalinity\u0026rsquo;s positive skew (1.19) suggests episodic base cation influx. Nutrients varied seasonally: phosphate (1.44 mg/L) and nitrate (12.88 mg/L) were non-normally distributed, with skewed and leptokurtic patterns indicating irregular nutrient pulses from runoff and waste (Syamsir et al., 2019). Sulphate (9.62 mg/L) showed slight negative skew. BOD (mean\u0026thinsp;=\u0026thinsp;23.58 mg/L) indicated moderate organic pollution, while calcium and magnesium levels were stable. Iron averaged 6.96 mg/L (W\u0026thinsp;=\u0026thinsp;0.92), consistent with reductive processes in groundwater-fed streams (Nkotagu, 1996). Microbial indicators revealed contamination: FC (16.83 CFU/100 mL) and TC (220 CFU/100 mL) exceeded limits, with TC showing non-normal distribution highlighting sanitation deficiencies (Dzwairo et al., \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e). Non-normal distributions across many parameters (Shapiro\u0026ndash;Wilk W\u0026thinsp;\u0026lt;\u0026thinsp;0.95) reinforced the use of non-parametric analyses. Observed skewness and kurtosis patterns suggest episodic pollution linked to rainfall and human activities consistent with documented trends in East African catchments (Fang et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ee. Descriptive Statistics of Seasonal Variations in Physicochemical and Microbial Water Quality Parameters at SWP 5\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTem\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCol\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTur\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTSS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTDS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHd\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAlk\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCl\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePO₄\u0026sup3;⁻\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBOD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCa\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMg\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFe2+\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTC\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\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e507.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e314.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e152.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e149.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStandard deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e473.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e265.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e145.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e122.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e527.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e339.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e161.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e161.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSkewness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKurtosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShapiro-Wilk W\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ee presents the seasonal statistics for physicochemical and microbial parameters at SWP 5. The pH ranged from 6.35\u0026ndash;6.80 (mean\u0026thinsp;=\u0026thinsp;6.56, W\u0026thinsp;=\u0026thinsp;0.93), reflecting slightly acidic to neutral conditions typical of tropical streams. Temperature averaged 21.35\u0026deg;C (W\u0026thinsp;=\u0026thinsp;0.95), indicating minimal seasonal thermal variation, consistent with trends observed in tropical headwaters (Coffey et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Electrical conductivity (mean\u0026thinsp;=\u0026thinsp;507.37 \u0026micro;S/cm, W\u0026thinsp;=\u0026thinsp;0.90) exhibited moderate negative skew, suggesting ionic accumulation during dry seasons due to reduced dilution and mineral leaching (Mbaka et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). Turbidity (mean\u0026thinsp;=\u0026thinsp;25.24 NTU) and total suspended solids (TSS; mean\u0026thinsp;=\u0026thinsp;44.17 mg/L) exhibited strong negative skewness, signalling dry-season sediment surges driven by erosion and reduced flow similar trends have been reported in Tanzanian rivers affected by seasonal hydrology and land-use pressures (Nyagushuge et al., 2023). Total dissolved solids (TDS) averaged 314.52 mg/L, with strong left skew and elevated kurtosis, pointing to episodic dissolved load increases. Alkalinity (mean\u0026thinsp;=\u0026thinsp;152.52 mg/L CaCO₃) and hardness (mean\u0026thinsp;=\u0026thinsp;149.43 mg/L) indicated moderate mineral levels, while salinity showed right-skewed distribution, reflecting occasional saltwater influx or anthropogenic input. Phosphate concentrations averaged 1.45 mg/L and followed near-normal distribution, while nitrate (mean\u0026thinsp;=\u0026thinsp;22.49 mg/L) exhibited high negative skew and leptokurtosis, consistent with sporadic nutrient loading from agricultural runoff and domestic waste. Biochemical oxygen demand (BOD; mean\u0026thinsp;=\u0026thinsp;23.45 mg/L) exceeded WHO (2017) limits, showing strong negative skew and high kurtosis, indicating episodic organic pollution events likely from untreated wastewater and stagnant flow conditions(Kimani-Murage \u0026amp; Ngindu, \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e). Calcium and magnesium averaged 21.90 and 13.57 mg/L respectively, with variability due to geogenic factors. Iron (Fe\u0026sup2;⁺) averaged 2.45 mg/L, with strong positive skew indicating occasional mobilisation under reducing conditions. Faecal coliforms (mean\u0026thinsp;=\u0026thinsp;6.38 CFU/100 mL) and total coliforms (mean\u0026thinsp;=\u0026thinsp;21.96 CFU/100 mL) showed non-normal distributions and moderate skewness, confirming widespread faecal contamination, a risk heightened during wet seasons due to runoff (Dzwairo et al., \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e). Overall, non-normality (W\u0026thinsp;\u0026lt;\u0026thinsp;0.95) in several parameters, especially TSS, turbidity, nutrients, BOD, and microbial indicators, justified the application of non-parametric tests. These patterns of skewness and kurtosis confirm episodic, rainfall-linked contamination influenced by land use and poor sanitation, in agreement with other East African watershed studies (Kimani-Murage \u0026amp; Ngindu, \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ef. Descriptive Statistics of Seasonal Variations in Physicochemical and Microbial Water Quality Parameters at SWP 6\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTem\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCol\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTur\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTSS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTDS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHd\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAlk\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCl\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePO₄\u0026sup3;⁻\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBOD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCa\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMg\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFe2+\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTC\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\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e424.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e225.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e159.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e153.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStandard deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e387.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e190.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e138.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e135.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e447.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e242.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e173.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e171.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSkewness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKurtosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShapiro-Wilk W\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eThe seasonal descriptive statistics for physicochemical and microbial water quality parameters at Surface Water Point 6 (SWP 6) are summarized in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ef. The seasonal descriptive statistics at SWP 6 showed a mean pH of 6.60 (range 6.35\u0026ndash;6.75), reflecting slightly acidic to near-neutral conditions typical of tropical freshwater systems. The pH distribution was slightly negatively skewed (\u0026ndash;0.57) and near-normal (W\u0026thinsp;=\u0026thinsp;0.93), indicating stable acid-base buffering. Temperature averaged 22.32\u0026deg;C with minor skewness and slight deviation from normality (W\u0026thinsp;=\u0026thinsp;0.87), aligning with the region\u0026rsquo;s relatively stable seasonal thermal regime (Mason et al., 2012). Colour (mean 4.47 Pt-Co), turbidity (16.47 NTU), and total suspended solids (22.21 mg/L) all exhibited moderate negative skewness (\u0026minus;\u0026thinsp;0.60 to \u0026minus;\u0026thinsp;1.21) and moderate deviations from normality, reflecting episodic particulate increases during dry seasons likely caused by soil erosion and reduced flow (Moyo, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). Total dissolved solids (TDS) averaged 225.07 mg/L, with strong negative skewness (\u0026minus;\u0026thinsp;1.56), indicating intermittent elevated mineral loads. Alkalinity (159.46 mg/L CaCO₃) and hardness (153.56 mg/L CaCO₃) were stable with slight negative skewness and near-normal distributions, indicating buffering capacity maintained by carbonate species (Ganiyu et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Nwanosike et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). Salinity showed strong positive skew (1.97), possibly from anthropogenic sources or local geology. Nutrient levels showed temporal fluctuations: phosphate averaged 1.41 mg/L with slight positive skewness, while nitrate (11.59 mg/L) had moderate negative skewness, reflecting nutrient inputs from agricultural runoff and sewage effluents (Hellar-Kihampa et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). Sulphate concentrations (mean\u0026thinsp;=\u0026thinsp;10.75 mg/L) exhibited mild negative skewness, aligning with typical mineral weathering processes in tropical watersheds. The observed BOD levels (13.96 mg/L), negatively skewed and moderately peaked, point to intermittent organic loading likely driven by domestic wastewater discharges an observation consistent with findings from urban stream assessments in sub-Saharan Africa (Ssewankambo et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Calcium and magnesium averaged 19.03 and 20.82 mg/L respectively, with minor skewness, reflecting lithogenic sources. Iron (Fe\u0026sup2;⁺) concentrations (mean 2.04 mg/L) had positive skewness, suggesting occasional mobilization under anoxic conditions, a common feature in tropical groundwater-influenced systems. Microbial contamination was relatively low compared to other sites, with faecal coliform averaging 2.75 CFU/100 mL and total coliform 12.79 CFU/100 mL, both showing near-normal distributions. This suggests relatively better sanitation and/or dilution effects at SWP 6, although microbial presence still poses potential health risks consistent with studies that found contamination can persist even in improved systems (Amin et al., \u003cspan class=\"CitationRef\"\u003e2019a\u003c/span\u003e). Many parameters deviated from normality (Shapiro\u0026ndash;Wilk W\u0026thinsp;\u0026lt;\u0026thinsp;0.95), highlighting the necessity for non-parametric analytical approaches. This suggests relatively better sanitation or dilution at SWP 6, although detectable microbial contamination continues to present health risks even in areas with improved infrastructure a phenomenon observed in many low-income settings (Bain et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). Many parameters deviated from normality (Shapiro\u0026ndash;Wilk W\u0026thinsp;\u0026lt;\u0026thinsp;0.95), making non-parametric methods necessary for accurate interpretation. Seasonal patterns of skewness and kurtosis at SWP 6 reveal episodic pollution tied to hydrological variability and human impacts, mirroring observations in tropical watersheds like Ghana\u0026rsquo;s Volta Basin (Lukhabi et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). These results underscore the imperative for continuous water quality monitoring and integrated catchment management to safeguard essential water resources.\u003c/p\u003e\n \u003ch2\u003e4.2 Correlation Coefficient Indicating the Relationship of Water Quality Parameters at Different Surface Water Points\u003c/h2\u003e\n \u003cp\u003eThe Pearson correlation matrix (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea) illustrates significant relationships among physicochemical and microbial water quality parameters across the surface water points. Electrical conductivity (EC) showed strong positive correlations with nitrate (r\u0026thinsp;=\u0026thinsp;0.63), total dissolved solids (TDS, r\u0026thinsp;=\u0026thinsp;0.53), calcium (Ca\u0026sup2;⁺, r\u0026thinsp;=\u0026thinsp;0.54), magnesium (Mg\u0026sup2;⁺, r\u0026thinsp;=\u0026thinsp;0.63), and faecal coliform (FC, r\u0026thinsp;=\u0026thinsp;0.77), indicating that ionic strength is closely linked to nutrient and microbial contamination. This pattern aligns with observations from tropical and subtropical watersheds, where elevated EC frequently reflects nutrient-rich agricultural runoff and wastewater inputs (Lam et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e). Phosphate (PO₄\u0026sup3;⁻) correlated positively with chloride (Cl⁻, r\u0026thinsp;=\u0026thinsp;0.77) and faecal coliform (r\u0026thinsp;=\u0026thinsp;0.67), suggesting nutrient enrichment alongside faecal contamination, consistent with studies linking nutrient spikes to anthropogenic pollution (Syamsir et al., 2019). Biochemical oxygen demand (BOD) showed strong correlations with alkalinity (r\u0026thinsp;=\u0026thinsp;0.68) and hardness (r\u0026thinsp;=\u0026thinsp;0.66), indicating that organic pollution significantly influences water chemistry an effect also reported in tropical rivers where increasing BOD is closely aligned with elevated alkalinity and hardness values(Ogunribido, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). Temperature showed moderate correlations with microbial parameters such as total coliform (TC, r\u0026thinsp;=\u0026thinsp;0.54), highlighting the role of seasonal temperature changes in microbial growth (Amin et al., \u003cspan class=\"CitationRef\"\u003e2019b\u003c/span\u003e). Turbidity was positively correlated with total suspended solids (TSS, r\u0026thinsp;=\u0026thinsp;0.61) and alkalinity (r\u0026thinsp;=\u0026thinsp;0.83), indicating sediment inputs affect water quality, consistent with erosion impacts observed in tropical catchments (Moyo, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). Overall, Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea confirms interconnected dynamics among water quality parameters influenced by both natural processes and human activities, emphasizing the need for integrated management strategies (Su et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe Pearson correlation matrix at SWP 2 (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb) shows strong positive correlations between electrical conductivity (EC) and biochemical oxygen demand (BOD, r\u0026thinsp;=\u0026thinsp;0.65), total coliform (TC, r\u0026thinsp;=\u0026thinsp;0.67), and total dissolved solids (TDS, r\u0026thinsp;=\u0026thinsp;0.58), indicating that ionic content increases alongside organic pollution and microbial contamination. These findings are consistent with previous research demonstrating that elevated EC often signals anthropogenic inputs such as wastewater and agricultural runoff in tropical catchments (Hellar-Kihampa et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). Notably, pH correlated positively with nitrate (NO₃⁻, r\u0026thinsp;=\u0026thinsp;0.54) and negatively with turbidity (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.38) and iron (Fe\u0026sup2;⁺, r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.63), suggesting acid-base balance is influenced by nutrient levels and particulate matter. The negative correlation of Fe\u0026sup2;⁺ with pH aligns with known iron solubility decreases at higher pH values. Turbidity correlated strongly with sulphate (SO₄\u0026sup2;⁻, r\u0026thinsp;=\u0026thinsp;0.81), indicating that suspended solids may transport sulphate-rich sediments, similar to observations in other African rivers affected by soil erosion (Moyo, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). Phosphate (PO₄\u0026sup3;⁻) showed strong correlations with alkalinity (r\u0026thinsp;=\u0026thinsp;0.81), calcium (Ca\u0026sup2;⁺, r\u0026thinsp;=\u0026thinsp;0.75), and chloride (Cl⁻, r\u0026thinsp;=\u0026thinsp;0.74), reflecting nutrient interactions with mineral content in the water, as reported by (Ganiyu et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Overall, microbial indicators such as faecal coliform (FC) showed moderate positive correlations with turbidity and chloride, reinforcing the influence of runoff and sanitation. These correlations underscore the complex interplay between physicochemical parameters and microbial contamination in surface waters impacted by seasonal and anthropogenic factors, echoing results from tropical watershed studies where land use and rainfall-driven dynamics strongly influenced contaminant relationships (Ahada \u0026amp; Suthar, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe Pearson correlation matrix for SWP 3 (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ec) highlights significant interrelationships among water quality parameters. Electrical conductivity (EC) exhibited strong positive correlations with biochemical oxygen demand (BOD, r\u0026thinsp;=\u0026thinsp;0.90), total dissolved solids (TDS, r\u0026thinsp;=\u0026thinsp;0.87), and total coliform (TC, r\u0026thinsp;=\u0026thinsp;0.72), suggesting that increased ionic strength is closely linked to elevated levels of organic matter and microbial contamination. Similar trends have been reported in other tropical freshwater environments, where EC serves as an effective indicator of anthropogenic influence and wastewater intrusion(Shrestha \u0026amp; Kazama, \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e). Interestingly, temperature negatively correlated with EC (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.77) and BOD (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.76), suggesting seasonal temperature variation influences the degradation of organic matter and solute concentration, consistent with reports by (Song et al., \u003cspan class=\"CitationRef\"\u003e2025a\u003c/span\u003e). Faecal coliform (FC) showed a strong positive correlation with pH (r\u0026thinsp;=\u0026thinsp;0.62) and moderate correlation with magnesium (Mg\u0026sup2;⁺, r\u0026thinsp;=\u0026thinsp;0.63), highlighting the complex interaction between microbial presence and water chemistry as also observed by (Amin et al., \u003cspan class=\"CitationRef\"\u003e2019c\u003c/span\u003e). Turbidity correlated highly with sulphate (SO₄\u0026sup2;⁻, r\u0026thinsp;=\u0026thinsp;0.86) and total suspended solids (TSS, r\u0026thinsp;=\u0026thinsp;0.09), indicating particulate matter as a carrier of sulphate, which aligns with Moyo et al.\u0026rsquo;s (2019) findings on sediment-bound contaminants in tropical watersheds. Overall, these correlations highlight dynamic physicochemical-microbial interactions influenced by seasonal and anthropogenic factors.\u003c/p\u003e\n \u003cp\u003eAt SWP 4, analysis of the Pearson correlation matrix (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ed) highlights important interactions reflecting both natural geochemical influences and anthropogenic pressures on water quality. Electrical conductivity (EC) shows strong positive relationships with faecal coliform (FC; r\u0026thinsp;=\u0026thinsp;0.74), total coliform (TC; r\u0026thinsp;=\u0026thinsp;0.58), and nitrate (NO₃⁻; r\u0026thinsp;=\u0026thinsp;0.67), suggesting that increased ionic strength coincides with microbial contamination and nutrient inputs, likely from agricultural runoff and domestic waste (Chen et al., \u003cspan class=\"CitationRef\"\u003e2019a\u003c/span\u003e; Pandey et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). This observation parallels findings in tropical catchments where EC is frequently a proxy for pollution load. Additionally, alkalinity correlates strongly with hardness (Hd; r\u0026thinsp;=\u0026thinsp;0.59) and salinity (Sal; r\u0026thinsp;=\u0026thinsp;0.87), indicating mineral weathering processes, such as carbonate dissolution, significantly influence water chemistry(Minh Nguyen, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Negative or weak correlations between pH and coliform bacteria and turbidity imply dilution effects during high flow periods that reduce bacterial counts and slightly acidify the water via organic matter decomposition. Magnesium (Mg\u0026sup2;⁺) exhibits a negative correlation with pH (r = -0.39), consistent with enhanced metal solubility under acidic conditions(Mahapatra et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). Moderate positive correlations of phosphate (PO₄\u0026sup3;⁻) with hardness (r\u0026thinsp;=\u0026thinsp;0.87) and salinity (r\u0026thinsp;=\u0026thinsp;0.46) suggest nutrient enrichment from mineral or fertilizer sources (Ningrum, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Collectively, these patterns reflect a complex interplay between natural and human-induced factors, mirroring other tropical surface water systems and emphasizing the need for integrated pollution control approaches (Minh Nguyen, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e\n \u003cp\u003eThe Pearson correlation analysis at SWP 5 (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ee) reveals significant interrelationships among key water quality parameters, reflecting complex environmental and anthropogenic influences. Notably, EC exhibits strong positive correlations with pH (r\u0026thinsp;=\u0026thinsp;0.78), BOD (r\u0026thinsp;=\u0026thinsp;0.78), and TDS (r\u0026thinsp;=\u0026thinsp;0.73), indicating that increased ionic concentrations coincide with higher organic pollution levels and dissolved solids. This pattern aligns with findings from studies in agricultural watersheds where fertilizer application and wastewater discharge contribute to elevated EC and nutrient loads (Oki \u0026amp; Akana, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). The inverse correlations between EC and temperature (r = -0.65) and turbidity (r = -0.41) suggest seasonal dilution effects during rainfall periods, consistent with observations in subtropical river systems where runoff modulates water chemistry (Pandey et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). Phosphate\u0026apos;s strong positive correlations with hardness (r\u0026thinsp;=\u0026thinsp;0.82) and chloride (r\u0026thinsp;=\u0026thinsp;0.90) further emphasize mineral weathering and fertilizer runoff as major nutrient sources, echoing results from catchment studies affected by intensive agriculture. The moderate association of faecal coliform with EC (r\u0026thinsp;=\u0026thinsp;0.36) and iron (r\u0026thinsp;=\u0026thinsp;0.59) highlights the dual impact of microbial contamination and iron mobilization in surface water, which is supported by similar research in tropical urban watersheds(Manini et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Collectively, these correlations underscore the influence of both natural geochemical processes and anthropogenic activities such as agriculture and sanitation on the water quality at SWP 5. These findings corroborate the broader understanding of water quality dynamics in tropical environments, where seasonal variations and land use patterns significantly affect physicochemical and microbial parameters (Van Horne et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eAt SWP 6, the Pearson correlation matrix (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ef) reveals key interactions between physicochemical and microbial parameters, indicating the combined influence of natural processes and anthropogenic activities on water quality. Electrical conductivity (EC) is strongly correlated with pH (r\u0026thinsp;=\u0026thinsp;0.72), BOD (r\u0026thinsp;=\u0026thinsp;0.75), total coliform (TC; r\u0026thinsp;=\u0026thinsp;0.77), and faecal coliform (FC; r\u0026thinsp;=\u0026thinsp;0.70), suggesting that higher ion concentrations coincide with increased organic pollution and microbial contamination. This pattern aligns with previous studies in tropical watersheds affected by urban runoff and sewage discharge (Quero et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). The inverse relationship between EC and temperature (r = -0.62) likely reflects dilution effects during wetter, cooler seasons, consistent with observations in monsoonal climates. Phosphate (PO₄\u0026sup3;⁻) demonstrates strong positive correlations with salinity (r\u0026thinsp;=\u0026thinsp;0.89) and chloride (r\u0026thinsp;=\u0026thinsp;0.79), implicating agricultural runoff and mineral dissolution as primary nutrient sources, as documented in similar catchments with fertilizer inputs (Hellar-Kihampa et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). The associations of hardness, alkalinity, and sulphate with phosphate and chloride further underscore geogenic contributions to water chemistry. Moderate correlations between faecal coliform and nutrient parameters (ranging from 0.48 to 0.66) highlight the interplay between microbial pollution and nutrient enrichment, often driven by insufficient sanitation infrastructure (Kifanyi et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Collectively, these findings suggest that water quality at SWP 6 is shaped by both natural geochemical factors and human activities such as agriculture and wastewater discharge, corroborating previous regional studies emphasizing seasonal variability and land use impacts on tropical surface waters(Ojok et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e4.3 Mann-Kendall Trend Analysis of Seasonal Changes in Water Quality Parameters at Different Surface Water Points\u003c/h2\u003e\n \u003cp\u003eThe Mann-Kendall trend analysis reveals mostly non-significant but directionally informative patterns in the water quality parameters as shown in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea. Electrical conductivity (EC) shows a moderate increasing trend (Tau\u0026thinsp;=\u0026thinsp;0.38, p\u0026thinsp;=\u0026thinsp;0.11), suggesting a gradual accumulation of dissolved ions, likely due to intensified agricultural runoff and urban wastewater inputs. This aligns with findings by (De Troyer et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e), who observed rising EC trends linked to expanding urbanization in East African watersheds. Similarly, slight upward trends in total suspended solids (TSS; Tau\u0026thinsp;=\u0026thinsp;0.29), total dissolved solids (TDS; Tau\u0026thinsp;=\u0026thinsp;0.29), phosphate (PO₄\u0026sup3;⁻; Tau\u0026thinsp;=\u0026thinsp;0.29), nitrate (NO₃⁻; Tau\u0026thinsp;=\u0026thinsp;0.22), magnesium (Mg; Tau\u0026thinsp;=\u0026thinsp;0.23), and microbial indicators such as faecal coliform (FC; Tau\u0026thinsp;=\u0026thinsp;0.20) and total coliform (TC; Tau\u0026thinsp;=\u0026thinsp;0.24) are consistent with ongoing nutrient enrichment and microbial contamination observed in tropical catchments affected by agricultural intensification and poor sanitation (Kifanyi et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Conversely, parameters such as pH (Tau = -0.21), hardness (Hd; Tau = -0.38), alkalinity (Alk; Tau = -0.31), sulphate (SO₄\u0026sup2;⁻; Tau = -0.11), and biological oxygen demand (BOD; Tau = -0.14) tend to decrease over time. These declines may reflect seasonal dilution during wet periods or geochemical buffering through carbonate dissolution and biological processes reducing organic load, as similarly reported by (Minh Nguyen, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). The reduction in hardness and alkalinity particularly supports the influence of increased rainfall dilution on mineral content, consistent with patterns found by(Song et al., \u003cspan class=\"CitationRef\"\u003e2025b\u003c/span\u003e) in tropical river systems. Although none of these trends reach statistical significance (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), their directional tendencies correspond well with the dual impact of anthropogenic pressures and natural seasonal variability documented in other tropical environments. The observed increase in microbial contamination and nutrients aligns with studies linking poor sanitation and fertilizer runoff to water quality degradation (Amin et al., \u003cspan class=\"CitationRef\"\u003e2019d\u003c/span\u003e). Meanwhile, geochemical parameters decreasing over time highlight the moderating role of natural processes in shaping water chemistry. Together, these results emphasize the complexity of managing tropical surface water systems, where both land use changes and climatic seasonality interact to influence water quality trends (Minh Nguyen, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Regular monitoring and integrated watershed management are essential to mitigate pollutant inputs and preserve water resources.\u003c/p\u003e\n \u003cp\u003eThe Mann-Kendall trend analysis shown in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb reveals notable shifts in several water quality parameters over time. Turbidity (Tau = -0.62, p\u0026thinsp;=\u0026thinsp;0.007) and sulphate (SO₄\u0026sup2;⁻; Tau = -0.46, p\u0026thinsp;=\u0026thinsp;0.046) show statistically significant decreasing trends, indicating improved water clarity and possible reductions in sulphate inputs or changes in geochemical conditions. This decline in turbidity aligns with findings by (Pandey et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e), who reported decreasing turbidity trends following implementation of erosion control in tropical watersheds. Parameters such as electrical conductivity (EC; Tau\u0026thinsp;=\u0026thinsp;0.34) and total dissolved solids (TDS; Tau\u0026thinsp;=\u0026thinsp;0.33) display non-significant but increasing trends, suggesting a gradual accumulation of dissolved ions potentially linked to anthropogenic activities like fertilizer application and urban runoff, consistent with observations by (Hellar-Kihampa et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). Similarly, slight increases in chloride (Cl; Tau\u0026thinsp;=\u0026thinsp;0.28), salinity (Sal; Tau\u0026thinsp;=\u0026thinsp;0.25), and nutrients such as nitrate (NO₃⁻; Tau\u0026thinsp;=\u0026thinsp;0.20) and phosphate (PO₄\u0026sup3;⁻; Tau\u0026thinsp;=\u0026thinsp;0.11) reflect ongoing nutrient loading pressures seen in agricultural catchment. Microbial indicators show mixed trends; faecal coliform (FC; Tau = -0.21) and colour (Col; Tau = -0.21) tend to decrease, while total coliform (TC; Tau\u0026thinsp;=\u0026thinsp;0.03) shows a slight increase. These trends suggest some improvement in microbial contamination but persistent challenges, possibly due to intermittent pollution sources, a pattern noted by (Kifanyi et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) in regions with variable sanitation infrastructure. Other parameters such as pH (Tau\u0026thinsp;=\u0026thinsp;0.22) show a mild increasing trend, consistent with natural buffering capacity in tropical waters, while temperature (Tem; Tau = -0.06) remains stable. The biological oxygen demand (BOD; Tau\u0026thinsp;=\u0026thinsp;0.05) does not exhibit a significant trend, indicating relatively steady organic pollution levels. Overall, these results reflect a complex interplay between anthropogenic nutrient inputs and natural seasonal/geochemical processes affecting water quality, paralleling findings in similar tropical watersheds (Minh Nguyen, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). The significant decrease in turbidity and sulphate is encouraging but highlights the need for ongoing monitoring and targeted management to address persistent nutrient and microbial contamination.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ec presents the Mann-Kendall trend analysis results, revealing diverse temporal patterns in water quality parameters. Notably, turbidity (Tau = -0.615, p\u0026thinsp;=\u0026thinsp;0.007) and sulphate (SO₄\u0026sup2;⁻; Tau = -0.462, p\u0026thinsp;=\u0026thinsp;0.046) exhibit statistically significant decreasing trends, likely influenced by sedimentation processes and either improved land use management or seasonal dilution effects. Comparable reductions in turbidity have been reported in watersheds experiencing reforestation, erosion control interventions, or changing hydrological regimes (Ochoa-Tocachi et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). Conversely, parameters such as electrical conductivity (EC; Tau\u0026thinsp;=\u0026thinsp;0.344), total dissolved solids (TDS; Tau\u0026thinsp;=\u0026thinsp;0.326), and chloride (Cl⁻; Tau\u0026thinsp;=\u0026thinsp;0.277) show increasing trends, though not statistically significant. This pattern suggests gradual accumulation of ions due to anthropogenic inputs such as urban runoff or fertilizer leaching, consistent with findings by (Minh Nguyen, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Similarly, pH and phosphate (PO₄\u0026sup3;⁻) exhibit weak upward trends, aligning with observations in tropical basins where buffer capacity increases during dry periods due to overconcentration. Biological parameters such as faecal coliform (FC; Tau = -0.209) and total coliform (TC; Tau\u0026thinsp;=\u0026thinsp;0.032) did not show significant trends but suggest variable microbial pollution likely driven by seasonal sanitation impacts and rainfall events. The increasing trends in calcium, magnesium, and BOD may reflect a rise in mineral content and organic load, resonating with findings from urbanizing catchments (Guan et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e)(Guan et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Overall, the mixed trends in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ec highlight both natural hydrological cycles and human-induced pressures affecting water quality, underscoring the importance of integrated watershed management strategies (Chen et al., \u003cspan class=\"CitationRef\"\u003e2019b\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ed illustrates the Mann-Kendall trend analysis results, revealing subtle but noteworthy temporal changes in water quality parameters. Turbidity exhibited a strong decreasing trend (Tau = -0.443, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.054), suggesting improved water clarity likely due to sedimentation or enhanced riparian zone management. This trend mirrors findings from riparian buffer restoration efforts in Southeast (Li et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). In contrast, Electrical Conductivity (EC; Tau\u0026thinsp;=\u0026thinsp;0.313) and Total Dissolved Solids (TDS; Tau\u0026thinsp;=\u0026thinsp;0.295) demonstrated increasing tendencies, reflecting gradual salinization and higher ionic concentrations often linked to agricultural runoff and land use changes, consistent with patterns reported by (Minh Nguyen, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Hardness (Tau = -0.290) and alkalinity (Tau = -0.229) showed mild decreasing trends, potentially indicating seasonal flow variability and changing geochemical interactions, as discussed by (Van Binh et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). While other parameters, including pH, calcium (Ca\u0026sup2;⁺), magnesium (Mg\u0026sup2;⁺), chloride (Cl⁻), phosphate (PO₄\u0026sup3;⁻), nitrate (NO₃⁻), BOD, faecal coliform (FC), and total coliform (TC), exhibited weak and statistically insignificant trends, their directions suggest a complex interplay of anthropogenic influences and natural processes. Overall, the results underscore both progress in sediment control and continuing challenges from diffuse pollution sources in tropical river systems undergoing urban and agricultural intensification.\u003c/p\u003e\n \u003cp\u003eAs illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ee, the Mann\u0026ndash;Kendall trend analysis reveals notable seasonal shifts in several water quality parameters, indicating both natural variability and anthropogenic pressures. Turbidity (Tau = \u0026minus;\u0026thinsp;0.543, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019) and sulphate (SO₄\u0026sup2;⁻; Tau = \u0026minus;\u0026thinsp;0.443, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.054) exhibited significant or near-significant decreasing trends, possibly reflecting improved sediment management or reduced runoff from surrounding catchments. In contrast, pH displayed a moderately increasing trend (Tau\u0026thinsp;=\u0026thinsp;0.413, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.082), which may be attributed to reduced acidifying pollutants or increased biological activity-a pattern comparable to water quality improvements (Pritchard et al., \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e). Other parameters such as EC (Tau\u0026thinsp;=\u0026thinsp;0.219), TDS (Tau\u0026thinsp;=\u0026thinsp;0.215), and salinity (Tau\u0026thinsp;=\u0026thinsp;0.308) also showed upward trends, though not statistically significant, suggesting gradual ionic accumulation likely linked to agricultural runoff or evaporation, as previously noted by (El-fadl \u0026amp; Development, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Moreover, nitrate (NO₃⁻) showed a positive trend (Tau\u0026thinsp;=\u0026thinsp;0.295), consistent with increasing nutrient loading from fertilizers, a common issue in intensively cultivated regions such as those documented by (Hong et al., \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e). The lack of significant trends in microbial indicators like faecal and total coliforms suggests irregular contamination patterns, potentially influenced by seasonal sanitation dynamics or stormwater flushing. These results highlight that while sediment-related parameters show signs of improvement, nutrient and ionic buildup remains a concern at SWP 5.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ef presents the Mann-Kendall trend results for seasonal water quality at SWP 6, highlighting both improving and deteriorating conditions. A statistically significant decreasing trend in turbidity (Tau = \u0026minus;\u0026thinsp;0.543, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019) suggests improved clarity, likely due to sediment control efforts or reduced erosion during the wet season-an outcome that aligns with the findings of (Bakure et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) who observed similar improvements following riparian vegetation restoration in Ethiopian highlands. Parameters such as electrical conductivity (EC; Tau\u0026thinsp;=\u0026thinsp;0.219), salinity (Tau\u0026thinsp;=\u0026thinsp;0.308), and nitrate (NO₃⁻; Tau\u0026thinsp;=\u0026thinsp;0.295) showed positive trends, although not statistically significant. These increases may reflect cumulative effects of agricultural runoff and evaporative concentration, consistent with studies in peri-urban watersheds by (Coffey et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). On the other hand, microbial indicators such as faecal coliform (FC; Tau = \u0026minus;\u0026thinsp;0.142) and total coliform (TC; Tau = \u0026minus;\u0026thinsp;0.083) demonstrated declining trends, which, although not statistically significant, may indicate seasonal dilution or enhanced sanitation practices. In summary, the trends at SWP 6 point toward marginal improvements in microbial and sediment-related parameters, while nutrient and salinity-related indicators suggest ongoing anthropogenic pressure in the area of study.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e4.4 Biplot of Principal Component Analysis (PCA) Showing Seasonal Distribution of Water Quality\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea illustrates the Principal Component Analysis (PCA) biplot for Surface Water Point 1 (SWP 1), summarizing seasonal variations and associations among key water quality parameters. The first two dimensions explain a combined 77.90% of the total variance (Dim 1\u0026thinsp;=\u0026thinsp;49.51%; Dim 2\u0026thinsp;=\u0026thinsp;28.39%), indicating that most of the variation in the dataset can be interpreted within this two-dimensional space. Total Suspended Solids (TSS) loads heavily along Dim 2, isolated from other variables. This suggests episodic sediment influxes, possibly driven by heavy rainfall and erosion during wet months, a pattern frequently observed in tropical catchments (Yang et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). The clustering of nutrient and microbial parameters-phosphate (PO₄\u0026sup3;⁻), nitrate (NO₃⁻), BOD, faecal coliforms (FC), and total coliforms (TC)-on the positive side of Dim 1 reflects common sources, such as domestic wastewater, fertilizer runoff, and open defecation during rainy seasons. These variables are notably linked with months like June and August, highlighting temporal loading effects due to intensified anthropogenic pressure and runoff. Meanwhile, months like May and October are positioned near the origin, reflecting lower values across most pollutants and suggesting background or baseflow water conditions. Their position indicates minimal anthropogenic or hydrological disturbance, consistent with seasonal dilution effects during drier periods. The directional strength of vectors such as PO₄\u0026sup3;⁻ and FC emphasizes their significant contribution to the variance and potential as sentinel indicators of water pollution in urban streams. These findings closely align with regional studies across Africa and Asia where PCA has revealed pollution signatures linked to urban expansion and seasonal fluctuations (Sahoo et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb shows the spatial-temporal relationships among water quality variables and sampling months at Surface Water Point 2 (SWP 2). The first two principal components explain 43.09% (Dim 1) and 23.53% (Dim 2) of the total variance, jointly accounting for approximately 66.62% of the dataset\u0026apos;s variability. Notably, nutrient and microbial parameters-including phosphate (PO₄\u0026sup3;⁻), nitrate (NO₃⁻), faecal coliform (FC), and total coliform (TC)-cluster along the positive side of Dim 1, especially in July, August, and September, indicating peak contamination during mid to late wet season. This aligns with seasonal runoff and sanitation-related influxes, supporting findings from Egypt and Uganda that show increased microbial and nutrient loads during rainy months due to agricultural leaching and inadequate waste infrastructure (El-Bady \u0026amp; Metwally, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kamal et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). Total Suspended Solids (TSS) are positioned distinctly on the far right of Dim 1, showing strong independence and heavy influence on the component, indicating sporadic sedimentation peaks, possibly linked to erosion episodes or flood pulse. In contrast, physicochemical indicators such as pH, EC, DO, and temperature cluster near the origin or in Africa the lower-left quadrant with sampling months like February, May, and October, showing less seasonal fluctuation and contributing less to overall variance. The placement of turbidity and BOD in proximity to microbial indicators reinforces the interdependence of organic load and microbial contamination, consistent with findings from tropical catchments where faecal pollution and BOD co-occur under runoff-driven dynamics (Fang et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Furthermore, the scattering of salinity and sulphate in the upper-left quadrant, alongside variables such as magnesium and alkalinity, may point to geological or domestic wastewater signatures during early dry months. This spatial segregation supports earlier PCA interpretations in other East African watersheds that distinguish between baseflow-driven geogenic inputs and event-based pollutant spikes (De Troyer et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). Collectively, PCA at SWP 2 reveals distinct seasonal clustering of sampling months and parameters, demonstrating that nutrient and microbial loads are dominant during wet seasons, whereas ionic indicators prevail in drier conditions. This insight supports the strategic application of seasonal monitoring and pollutant source differentiation to guide local water resource management in Tunduma.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ec illustrates the Principal Component Analysis (PCA) biplot for SWP 3, capturing seasonal patterns and key variable interrelationships in shallow groundwater quality. Dim 1 and Dim 2 explain 43.08% and 24.08% of the total variance, respectively. The spatial orientation of variables reveals pollutant groupings and their seasonal loadings. Faecal coliform (FC) appears distinctly in the upper right quadrant along Dim 2, suggesting strong microbial contamination spikes during months like August. This separation implies FC is influenced by episodic sources such as pit latrine leakage or livestock activity near the wellhead, particularly during the dry to wet seasonal transition a finding consistent with (Kifanyi et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Nitrate (NO₃⁻), salinity (Sal), and iron (Fe) also load positively on Dim 1 and to a lesser extent on Dim 2, clustering near July\u0026ndash;September. Parameters such as phosphate (PO₄\u0026sup3;⁻), calcium (Ca\u0026sup2;⁺), magnesium (Mg\u0026sup2;⁺), and biochemical oxygen demand (BOD) cluster centrally, overlapping with mid-year months (e.g., April to August). Their grouping suggests persistent but moderate inputs of nutrients and organic matter throughout seasons, influenced by both natural background levels and diffuse pollution sources (Hellar-Kihampa et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). In contrast, turbidity (Tur), sulphate (SO₄\u0026sup2;⁻), and alkalinity cluster around January to February, suggesting peak rainfall dilution and sediment flushing. This pattern mirrors seasonal recharge effects described in tropical Africa, where early wet-season runoff increases alkalinity and sulphate while reducing particulate loads (Frank et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Total suspended solids (TSS) appear isolated in the bottom-right quadrant largely uncorrelated with other variables and months indicative of episodic sediment inputs from surface runoff infiltration that do not co-vary with microbial or nutrient parameters. Similar patterns have been reported in groundwater settings, where TSS dynamics reflect distinct transport mechanisms compared to dissolved contaminants (Hughes, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Collectively, the PCA biplot underscores the dual influence of seasonal hydrology and anthropogenic activities on shallow well water quality. FC, NO₃⁻, and salinity emerge as critical indicators of contamination risks during dry seasons or early rains, necessitating protective zoning around wells and improved sanitation infrastructure. This integrative PCA approach confirms the temporal-spatial pollutant dynamics emphasized in recent East African groundwater monitoring studies (Innocent et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ed displays a Principal Component Analysis (PCA) biplot for Shallow Well 5 (SW5), visualizing the spatio-temporal distribution of water quality parameters and their correlations during different months. The first two principal components (PC1 and PC2) explain 37.61% and 28.65% of the total variance, respectively, offering a consolidated view of the primary drivers of water quality variability at this site. Nutrient pollutants like nitrate (NO₃⁻) and phosphate (PO₄\u0026sup3;⁻) are strongly projected on the upper right quadrant, indicating their dominance in PC2 and suggesting distinct seasonal pulses, likely from agricultural runoff or pit latrines. Total suspended solids (TSS) appear isolated in the far right of PC1, indicating it is a key independent contributor to variance, potentially linked to erosion or sediment-laden flows during wet seasons. Faecal coliform (FC) and total coliform (TC) cluster near phosphate and nitrate vectors, pointing to a potential shared source such as surface runoff contaminated by human or animal waste. These microbial and nutrient variables align with elevated values in warm months like November and March, indicating enhanced microbial growth and leaching during higher temperatures and stormwater influxes. Conversely, parameters like pH, TDS, DO, and temperature are centrally located near the origin, showing weak loadings and less contribution to the variability captured by the first two dimensions. These may remain relatively stable across seasons or be influenced more subtly. Seasonal grouping of months is evident, with dry months (e.g., February and May) clustering on the lower left, indicating more stable water quality, while transitional or wet months (e.g., March, November) scatter farther from the centre, highlighting episodic pollutant influx. These results align with earlier PCA-based studies showing that microbial and nutrient contaminants significantly influence groundwater quality under urbanization and seasonal fluxes (Barakat et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ee presents a PCA biplot for SWP5, summarizing spatio-temporal variation in water quality parameters across different months. The biplot captures 66.47% of the total variance, with Dimension 1 (37.61%) and Dimension 2 (28.86%) representing the most influential axes of variation. Key parameters such as turbidity (Tur), total suspended solids (TSS), phosphate (PO₄\u0026sup3;⁻), nitrate (NO₃⁻), and salinity (Sal) are located in the upper right quadrant, indicating strong positive contributions to both dimensions. Their proximity suggests a common seasonal influence most likely from agricultural runoff and erosion during rainy months, consistent with findings by (Selemani et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e), who demonstrated nutrient and TSS spikes following precipitation in the catchments. These patterns align with wet season months (March\u0026ndash;May), located in the same quadrant. The microbial parameters faecal coliforms (FC) and total coliforms (TC) are grouped in the upper left quadrant, correlating with months like August and October, indicating sanitation-related contamination during transitional rainfall periods. This clustering is supported by (Amin et al., \u003cspan class=\"CitationRef\"\u003e2019e\u003c/span\u003e) who emphasized microbial contamination risks from leaky sanitation infrastructure in Sub-Saharan towns. Conversely, February and January, located in the lower left quadrant, are associated with low loading of contaminants and correlate negatively with key pollutants. Interestingly, chloride (Cl), temperature (Temp), and electrical conductivity (EC) are centred and near the origin, suggesting moderate influence across seasons. These parameters, often geogenic or minimally impacted by short-term surface processes, are typical of baseflow contributions in urban groundwater(Athamena et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Overall, PCA effectively reveals the influence of rainfall-driven events, land use, and sanitation infrastructure on the spatial-temporal distribution of pollutants. The distinct seasonal clustering confirms the need for adaptive monitoring frameworks that consider hydrological shifts and urban development pressures particularly in vulnerable towns like Tunduma where stream\u0026ndash;groundwater interactions amplify contaminant mobilization.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ef presents a Principal Component Analysis (PCA) biplot illustrating the relationships among physicochemical and microbial water quality parameters from Shallow Well 5 (SW5) across different months. The first two principal components (Dim 1 and Dim 2) explain 37.61% and 21.74% of the variance, respectively. The microbial parameter faecal coliform (FC) is strongly associated with Dim 1 and lies far from the origin in the lower right quadrant, indicating high variability and distinct influence compared to other parameters. Its spatial separation suggests episodic contamination events, possibly due to sanitation infrastructure failures or livestock access, a pattern consistent with other urban settings where poorly managed waste and pit latrines influence groundwater (Graham \u0026amp; Polizzotto, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). Turbidity (Tur), total suspended solids (TSS), and nitrate (NO₃⁻) also show a strong positive loading on Dim 1, implying seasonal runoff influence or fertilizer leaching, particularly during the rainy months. This aligns with prior studies in Addis Ababa and Niamey, which found elevated nitrates and turbidity in shallow wells during high rainfall due to surface-groundwater connectivity and urban waste mismanagement (Kawo \u0026amp; Karuppannan, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Conversely, January and February (dry months) cluster tightly near the origin, indicating relatively stable and lower levels of contaminants, possibly due to reduced surface infiltration and dilution effects. Nutrient parameters such as phosphate (PO₄\u0026sup3;⁻) and sulphate (SO₄\u0026sup2;⁻) show moderate loadings, suggesting intermittent point-source contributions from household detergents or greywater. Clustered physicochemical parameters near the centre like electrical conductivity (EC), pH, total hardness (Hd), and alkalinity (Alk) imply minimal seasonal fluctuation and consistent geogenic origins (e.g., mineral dissolution), as reported in urban aquifer studies (Alex et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). This PCA biplot underscores that SW5 is affected by a complex mix of anthropogenic and natural influences, with key contamination events occurring seasonally. The spatial alignment of critical parameters (FC, NO₃⁻, TSS) supports earlier findings that shallow wells in rapidly urbanizing areas are vulnerable to episodic contamination, particularly during wet seasons (\u0026ldquo;Water Quality in Selected Shallow Wells in Dar Es Salaam,\u0026rdquo; 2012).\u003c/p\u003e\n\u003c/div\u003e"},{"header":" Conclusion","content":" \u003cp\u003eThis study provides a comprehensive assessment of temporal variations in physicochemical and microbial water quality in Tunduma\u0026rsquo;s stream systems, revealing clear seasonal patterns influenced by rainfall, land use, and inadequate sanitation infrastructure. Elevated levels of turbidity, nutrients, BOD, and microbial contamination during the wet season point to significant pollutant loading from urban runoff and domestic waste discharge.\u003c/p\u003e \u003cp\u003eDescriptive statistics and Shapiro-Wilk tests confirmed non-normal, event-driven data distributions. Correlation analysis highlighted strong relationships between nutrient and microbial indicators, indicating shared contamination pathways. Trend analysis showed mixed changes across parameters, with increasing levels of EC, TDS, and nitrate suggesting long-term accumulation of pollutants.\u003c/p\u003e \u003cp\u003ePCA and biplot visualizations effectively grouped parameters and seasons, illustrating pollution signatures linked to both organic and sediment-based sources. Several parameters exceeded WHO drinking water standards, particularly during peak rainfall months, posing potential health and environmental risks.\u003c/p\u003e \u003cp\u003eThese findings emphasize the need for seasonally adaptive water quality monitoring, improved sanitation infrastructure, and catchment-wide pollution control strategies. Ensuring safe water resources in urbanizing regions like Tunduma will require integrated efforts across municipal planning, community engagement, and environmental regulation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflict of interest:\u003c/h2\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003ch2\u003eClinical trial number\u003c/h2\u003e\n\u003cp\u003enot applicable\u003c/p\u003e\n\u003ch2\u003eEthics, Consent to Participate, and Consent to Publish declarations\u003c/h2\u003e\n\u003cp\u003enot applicable\u003c/p\u003e\n\u003ch2\u003eFunding:\u003c/h2\u003e\n\u003cp\u003eThe study was financially supported by Mbeya University of Science and Technology as part of PhD. studies under grant number MUST-PF309.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eZacharia Katambara contributed 50%Matungwa William contributed 50%\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThe authors extend their heartfelt gratitude to the Management of Mbeya University of Science and Technology, particularly Prof. Aloys Mvuma, Prof. Godliving Mtui and Prof. Vuai Said for their invaluable support and granting permission to pursue this research. We deeply appreciate their unwavering guidance and encouragement throughout the preparation of this research article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAhada, C. P. S., \u0026amp; Suthar, S. (2018). Groundwater nitrate contamination and associated human health risk assessment in southern districts of Punjab, India. \u003cem\u003eEnvironmental Science and Pollution Research\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(25), 25336\u0026ndash;25347. https://doi.org/10.1007/s11356-018-2581-2\u003c/li\u003e\n \u003cli\u003eAlex, R., Kitalika, A., Mogusu, E., \u0026amp; Njau, K. (2021). Sources of Nitrate in Ground Water Aquifers of the Semiarid Region of Tanzania. \u003cem\u003eGeofluids\u003c/em\u003e, \u003cem\u003e2021\u003c/em\u003e, 1\u0026ndash;20. https://doi.org/10.1155/2021/6673013\u003c/li\u003e\n \u003cli\u003eAlphayo, S. M., \u0026amp; Sharma, M. P. (2018). Water Quality Assessment of Ruvu River in Tanzania Using NSFWQI. \u003cem\u003eJournal of Scientific Research and Reports\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(3), 1\u0026ndash;9. https://doi.org/10.9734/jsrr/2018/44324\u003c/li\u003e\n \u003cli\u003eAmin, R., Zaidi, M. B., Bashir, S., Khanani, R., Nawaz, R., Ali, S., \u0026amp; Khan, S. (2019a). Microbial contamination levels in the drinking water and associated health risks in Karachi, Pakistan. \u003cem\u003eJournal of Water, Sanitation and Hygiene for Development\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(2), 319\u0026ndash;328. https://doi.org/10.2166/washdev.2019.147\u003c/li\u003e\n \u003cli\u003eAmin, R., Zaidi, M. B., Bashir, S., Khanani, R., Nawaz, R., Ali, S., \u0026amp; Khan, S. (2019b). Microbial contamination levels in the drinking water and associated health risks in Karachi, Pakistan. \u003cem\u003eJournal of Water, Sanitation and Hygiene for Development\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(2), 319\u0026ndash;328. https://doi.org/10.2166/washdev.2019.147\u003c/li\u003e\n \u003cli\u003eAmin, R., Zaidi, M. B., Bashir, S., Khanani, R., Nawaz, R., Ali, S., \u0026amp; Khan, S. (2019c). Microbial contamination levels in the drinking water and associated health risks in Karachi, Pakistan. \u003cem\u003eJournal of Water, Sanitation and Hygiene for Development\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(2), 319\u0026ndash;328. https://doi.org/10.2166/washdev.2019.147\u003c/li\u003e\n \u003cli\u003eAmin, R., Zaidi, M. B., Bashir, S., Khanani, R., Nawaz, R., Ali, S., \u0026amp; Khan, S. (2019d). Microbial contamination levels in the drinking water and associated health risks in Karachi, Pakistan. \u003cem\u003eJournal of Water, Sanitation and Hygiene for Development\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(2), 319\u0026ndash;328. https://doi.org/10.2166/washdev.2019.147\u003c/li\u003e\n \u003cli\u003eAmin, R., Zaidi, M. B., Bashir, S., Khanani, R., Nawaz, R., Ali, S., \u0026amp; Khan, S. (2019e). Microbial contamination levels in the drinking water and associated health risks in Karachi, Pakistan. \u003cem\u003eJournal of Water, Sanitation and Hygiene for Development\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(2), 319\u0026ndash;328. https://doi.org/10.2166/washdev.2019.147\u003c/li\u003e\n \u003cli\u003eAthamena, A., Gaagai, A., Aouissi, H. A., Burlakovs, J., Bencedira, S., Zekker, I., \u0026amp; Krauklis, A. E. (2022). Chemometrics of the Environment: Hydrochemical Characterization of Groundwater in Lioua Plain (North Africa) Using Time Series and Multivariate Statistical Analysis. \u003cem\u003eSustainability\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(1), 20. https://doi.org/10.3390/su15010020\u003c/li\u003e\n \u003cli\u003eBain, R., Cronk, R., Hossain, R., Bonjour, S., Onda, K., Wright, J., Yang, H., Slaymaker, T., Hunter, P., Pr\u0026uuml;ss‐Ust\u0026uuml;n, A., \u0026amp; Bartram, J. (2014). Global assessment of exposure to faecal contamination through drinking water based on a systematic review. \u003cem\u003eTropical Medicine \u0026amp; International Health\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(8), 917\u0026ndash;927. https://doi.org/10.1111/tmi.12334\u003c/li\u003e\n \u003cli\u003eBakure, B. Z., Fikadu, S., \u0026amp; Malu, A. (2020). Analysis of physicochemical water quality parameters for streams under agricultural, urban and forest land-use types: in the case of gilgel Gibe catchment, Southwest Ethiopia. \u003cem\u003eApplied Water Science\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(11), 234. https://doi.org/10.1007/s13201-020-01318-9\u003c/li\u003e\n \u003cli\u003eBarakat, A., El Baghdadi, M., Rais, J., Aghezzaf, B., \u0026amp; Slassi, M. (2016). Assessment of spatial and seasonal water quality variation of Oum Er Rbia River (Morocco) using multivariate statistical techniques. \u003cem\u003eInternational Soil and Water Conservation Research\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(4), 284\u0026ndash;292. https://doi.org/10.1016/j.iswcr.2016.11.002\u003c/li\u003e\n \u003cli\u003eBengraı̈ne, K., \u0026amp; Marhaba, T. F. (2003). Using principal component analysis to monitor spatial and temporal changes in water quality. \u003cem\u003eJournal of Hazardous Materials\u003c/em\u003e, \u003cem\u003e100\u003c/em\u003e(1\u0026ndash;3), 179\u0026ndash;195. https://doi.org/10.1016/S0304-3894(03)00104-3\u003c/li\u003e\n \u003cli\u003eChen, L., Han, L., Tan, J., Zhou, M., Sun, M., Zhang, Y., Chen, B., Wang, C., Liu, Z., \u0026amp; Fan, Y. (2019a). Water Environmental Capacity Calculated Based on Point and Non-Point Source Pollution Emission Intensity under Water Quality Assurance Rates in a Tidal River Network Area. \u003cem\u003eInternational Journal of Environmental Research and Public Health\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(3), 428. https://doi.org/10.3390/ijerph16030428\u003c/li\u003e\n \u003cli\u003eChen, L., Han, L., Tan, J., Zhou, M., Sun, M., Zhang, Y., Chen, B., Wang, C., Liu, Z., \u0026amp; Fan, Y. (2019b). Water Environmental Capacity Calculated Based on Point and Non-Point Source Pollution Emission Intensity under Water Quality Assurance Rates in a Tidal River Network Area. \u003cem\u003eInternational Journal of Environmental Research and Public Health\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(3), 428. https://doi.org/10.3390/ijerph16030428\u003c/li\u003e\n \u003cli\u003eCoffey, R., Butcher, J., Benham, B., \u0026amp; Johnson, T. (2020). Modeling the Effects of Future Hydroclimatic Conditions on Microbial Water Quality and Management Practices in Two Agricultural Watersheds. \u003cem\u003eTransactions of the ASABE\u003c/em\u003e, \u003cem\u003e63\u003c/em\u003e(3), 753\u0026ndash;770. https://doi.org/10.13031/trans.13630\u003c/li\u003e\n \u003cli\u003eDe Troyer, N., Mereta, S., Goethals, P., \u0026amp; Boets, P. (2016). Water Quality Assessment of Streams and Wetlands in a Fast Growing East African City. \u003cem\u003eWater\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(4), 123. https://doi.org/10.3390/w8040123\u003c/li\u003e\n \u003cli\u003eD\u0026iacute;az, J. A., Montecinos, M., Vega, A., Giron\u0026aacute;s, J., Molinos, M., \u0026amp; Past\u0026eacute;n, P. (2025). \u003cem\u003eImpacts of Droughts on Water Quality: Processes and Monitoring\u003c/em\u003e (pp. 89\u0026ndash;124). https://doi.org/10.1007/978-3-031-85040-0_6\u003c/li\u003e\n \u003cli\u003eDzwairo, B., Hoko, Z., Love, D., \u0026amp; Guzha, E. (2006). Assessment of the impacts of pit latrines on groundwater quality in rural areas: A case study from Marondera district, Zimbabwe. \u003cem\u003ePhysics and Chemistry of the Earth\u003c/em\u003e, \u003cem\u003e31\u003c/em\u003e(15\u0026ndash;16), 779\u0026ndash;788. https://doi.org/10.1016/j.pce.2006.08.031\u003c/li\u003e\n \u003cli\u003eEl-Bady, M. S. M., \u0026amp; Metwally, H. I. (2016). Evaluation of water quality of the surface water of the damietta nile branch, damietta governorate, Egypt. \u003cem\u003eInternational Journal of ChemTech Research\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(5), 119\u0026ndash;134.\u003c/li\u003e\n \u003cli\u003eEl-fadl, M. M. A., \u0026amp; Development, L. (2018). \u003cem\u003eMonitoring of water-rock interaction and its impact on groundwater salinization at El Goura area , Northeast Sinai , Egypt\u003c/em\u003e. \u003cem\u003eNovember\u003c/em\u003e. https://doi.org/10.13140/RG.2.2.22989.69605\u003c/li\u003e\n \u003cli\u003eFang, H., Vergara, G. G. R., Goh, S. G., Ang, C. Y. L., Gu, X., \u0026amp; Gin, K. Y. (2018). Effect of Rainfall on the Microbial Water Quality of a Tropical Urban Catchment. \u003cem\u003eJournal of Environmental Quality\u003c/em\u003e, \u003cem\u003e47\u003c/em\u003e(5), 1242\u0026ndash;1248. https://doi.org/10.2134/jeq2018.03.0099\u003c/li\u003e\n \u003cli\u003eFocus, E., Mhozya, M., \u0026amp; Jackson, M. (2025). Occupational Health Risk Assessment of Microbiological Air Quality in Buhemba Small Scale Gold Mines, Tanzania. \u003cem\u003eEnvironment and Pollution\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(1), 17. https://doi.org/10.5539/ep.v14n1p17\u003c/li\u003e\n \u003cli\u003eFrank, S., Goeppert, N., \u0026amp; Goldscheider, N. (2021). Improved understanding of dynamic water and mass budgets of high‐alpine karst systems obtained from studying a well‐defined catchment area. \u003cem\u003eHydrological Processes\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e(4). https://doi.org/10.1002/hyp.14033\u003c/li\u003e\n \u003cli\u003eGaniyu, S. A., Mabunmi, A. A., Olurin, O. T., Adeyemi, A. A., Jegede, O. A., \u0026amp; Okeh, A. (2021). Assessment of microbial and heavy metal contamination in shallow hand-dug wells bordering Ona River, Southwest Nigeria. \u003cem\u003eEnvironmental Monitoring and Assessment\u003c/em\u003e, \u003cem\u003e193\u003c/em\u003e(3). https://doi.org/10.1007/s10661-021-08910-9\u003c/li\u003e\n \u003cli\u003eGraham, J. P., \u0026amp; Polizzotto, M. L. (2013). Pit latrines and their impacts on groundwater quality: A systematic review. \u003cem\u003eEnvironmental Health Perspectives\u003c/em\u003e, \u003cem\u003e121\u003c/em\u003e(5), 521\u0026ndash;530. https://doi.org/10.1289/ehp.1206028\u003c/li\u003e\n \u003cli\u003eGuan, B., Guo, L., Gibson, M., \u0026amp; Li, Z. (2018). The derivation of water quality criteria for bisphenol A for the protection of marine species in China. \u003cem\u003eWater Quality Research Journal\u003c/em\u003e, \u003cem\u003e53\u003c/em\u003e(3), 156\u0026ndash;165. https://doi.org/10.2166/wqrj.2018.035\u003c/li\u003e\n \u003cli\u003eHellar-Kihampa, H., De Wael, K., Lugwisha, E., \u0026amp; Van Grieken, R. (2013). Water quality assessment in the Pangani River basin, Tanzania: natural and anthropogenic influences on the concentrations of nutrients and inorganic ions. \u003cem\u003eInternational Journal of River Basin Management\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(1), 55\u0026ndash;75. https://doi.org/10.1080/15715124.2012.759119\u003c/li\u003e\n \u003cli\u003eHirsch, R. M., Slack, J. R., \u0026amp; Smith, R. A. (1982). Techniques of trend analysis for monthly water quality data. \u003cem\u003eWater Resources Research\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(1), 107\u0026ndash;121. https://doi.org/10.1029/WR018i001p00107\u003c/li\u003e\n \u003cli\u003eHong, N., White, J. G., Weisz, R., Gumpertz, M. L., Duffera, M. G., \u0026amp; Cassel, D. K. (2007). Groundwater Nitrate Spatial and Temporal Patterns and Correlations. \u003cem\u003eVadose Zone Journal\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(1).\u003c/li\u003e\n \u003cli\u003eHughes, D. A. (2019). Facing a future water resources management crisis in sub-Saharan Africa. \u003cem\u003eJournal of Hydrology: Regional Studies\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e, 100600. https://doi.org/10.1016/j.ejrh.2019.100600\u003c/li\u003e\n \u003cli\u003eInnocent, O. N., James, K., Susan, M., \u0026amp; Salome, G. (2024). Groundwater pollution and climate change vulnerability in Kenya: A review. \u003cem\u003eInternational Journal of Water Resources and Environmental Engineering\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(1), 17\u0026ndash;31. https://doi.org/10.5897/IJWREE2024.1079\u003c/li\u003e\n \u003cli\u003eKamal, M. A. M., Khalf, M. A., Mohamed, Z. A., Eljakee, J., Soliman, S. M., Alhotan, R. A., Hussein, E. O., Galik, B., Shukry, M., \u0026amp; Saleh, A. (2025). Seasonal Variations Alter Drinking Water Quality at Different Points in The water Distribution Systems of Cattle Farms in Egypt. \u003cem\u003eEgyptian Journal of Veterinary Sciences\u003c/em\u003e, \u003cem\u003e56\u003c/em\u003e(4), 757\u0026ndash;768. https://doi.org/10.21608/ejvs.2024.276555.1908\u003c/li\u003e\n \u003cli\u003eKanyerere, T., Levy, J., Xu, Y., \u0026amp; Saka, J. (2012). Assessment of microbial contamination of groundwater in upper Limphasa River catchment, located in a rural area of northern Malawi. \u003cem\u003eWater SA\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e(4), 581\u0026ndash;596. https://doi.org/10.4314/wsa.v38i4.14\u003c/li\u003e\n \u003cli\u003eKawo, N. S., \u0026amp; Karuppannan, S. (2018). Groundwater quality assessment using water quality index and GIS technique in Modjo River Basin, central Ethiopia. \u003cem\u003eJournal of African Earth Sciences\u003c/em\u003e, \u003cem\u003e147\u003c/em\u003e(June), 300\u0026ndash;311. https://doi.org/10.1016/j.jafrearsci.2018.06.034\u003c/li\u003e\n \u003cli\u003eKawo, N. S., Zhou, Y., Magalso, R., \u0026amp; Salvacion, L. (2018). Optimization of an artificial-recharge\u0026ndash;pumping system for water supply in the Maghaway Valley, Cebu, Philippines. \u003cem\u003eHydrogeology Journal\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(3), 963\u0026ndash;977. https://doi.org/10.1007/s10040-017-1693-y\u003c/li\u003e\n \u003cli\u003eKhurshid, A., Korke, S., Kothari, Y., Alone, S., \u0026amp; Bais, K. (2025). Improving water quality parameter prediction with multi-level linear regression model and hybrid feature selection. \u003cem\u003eInternational Journal of Electrical and Computer Engineering (IJECE)\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(2), 2381. https://doi.org/10.11591/ijece.v15i2.pp2381-2391\u003c/li\u003e\n \u003cli\u003eKifanyi, G., Kasambala, H., Kavishe, R., \u0026amp; Moyo, M. (2024). Analysis of Spatial Bacteriological Quality Variation of Domestic Water Source Points in Mbarali District, Tanzania. \u003cem\u003eMbeya University of Science and Technology Journal of Research and Development\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(3), 873\u0026ndash;885. https://doi.org/10.62277/mjrd2024v5i300557\u003c/li\u003e\n \u003cli\u003eKimani-Murage, E. W., \u0026amp; Ngindu, A. M. (2007). Quality of water the slum dwellers use: The case of a Kenyan slum. \u003cem\u003eJournal of Urban Health\u003c/em\u003e, \u003cem\u003e84\u003c/em\u003e(6), 829\u0026ndash;838. https://doi.org/10.1007/s11524-007-9199-x\u003c/li\u003e\n \u003cli\u003eLam, A., Karssenberg, D., M. Van Den Hurk, B. J. J., \u0026amp; Bierkens, M. F. P. (2011). Spatial and temporal connections in groundwater contribution to evaporation. \u003cem\u003eHydrology and Earth System Sciences\u003c/em\u003e. https://doi.org/10.5194/hess-15-2621-2011\u003c/li\u003e\n \u003cli\u003eLi, C., Hao, J., Zhang, G., Fang, H., Wang, Y., \u0026amp; Lu, H. (2023). Runoff variations affected by climate change and human activities in Yarlung Zangbo River, southeastern Tibetan Plateau. \u003cem\u003eCATENA\u003c/em\u003e, \u003cem\u003e230\u003c/em\u003e, 107184. https://doi.org/10.1016/j.catena.2023.107184\u003c/li\u003e\n \u003cli\u003eLukhabi, D. K., Mensah, P. K., Asare, N. K., Pulumuka-Kamanga, T., \u0026amp; Ouma, K. O. (2023). Adapted Water Quality Indices: Limitations and Potential for Water Quality Monitoring in Africa. \u003cem\u003eWater\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(9), 1736. https://doi.org/10.3390/w15091736\u003c/li\u003e\n \u003cli\u003eMahapatra, S. S., Sahu, M., Patel, R. K., \u0026amp; Panda, B. N. (2012). Prediction of Water Quality Using Principal Component Analysis. \u003cem\u003eWater Quality, Exposure and Health\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(2), 93\u0026ndash;104. https://doi.org/10.1007/s12403-012-0068-9\u003c/li\u003e\n \u003cli\u003eManini, E., Baldrighi, E., Ricci, F., Grilli, F., Giovannelli, D., Intoccia, M., Casabianca, S., Capellacci, S., Marinchel, N., Penna, P., Moro, F., Campanelli, A., Cordone, A., Correggia, M., Bastoni, D., Bolognini, L., Marini, M., \u0026amp; Penna, A. (2022). Assessment of Spatio-Temporal Variability of Faecal Pollution along Coastal Waters during and after Rainfall Events. \u003cem\u003eWater\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(3), 502. https://doi.org/10.3390/w14030502\u003c/li\u003e\n \u003cli\u003eMbaka, P. K., Mwangi, J. K., \u0026amp; Kiptum, C. K. (2017). Assessment of water quality in selected shallow wells of Keiyo Highlands, Kenya. \u003cem\u003eAfrican Journal of Science, Technology, Innovation and Development\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(3), 329\u0026ndash;338. https://doi.org/10.1080/20421338.2017.1327476\u003c/li\u003e\n \u003cli\u003eMbuligwe, S. E., \u0026amp; Kaseva, M. E. (2005). Pollution and Self-Cleansing of an Urban River in a Developing Country: A Case Study in Dar es Salaam, Tanzania. \u003cem\u003eEnvironmental Management\u003c/em\u003e, \u003cem\u003e36\u003c/em\u003e(2), 328\u0026ndash;342. https://doi.org/10.1007/s00267-003-0068-4\u003c/li\u003e\n \u003cli\u003eMinh Nguyen, T. T. (2018). Integrated Evaluation Model for Surface Water Quality a Case Study in Ho Chi Minh City, Vietnam. \u003cem\u003eVietnam Journal of Science and Technology\u003c/em\u003e, \u003cem\u003e54\u003c/em\u003e(4B), 232. https://doi.org/10.15625/2525-2518/54/4b/12046\u003c/li\u003e\n \u003cli\u003eMoyo, N. A. G. (2013). An analysis of the chemical and microbiological quality of ground water from boreholes and shallow wells in Zimbabwe. \u003cem\u003ePhysics and Chemistry of the Earth\u003c/em\u003e, \u003cem\u003e66\u003c/em\u003e. https://doi.org/10.1016/j.pce.2013.06.003\u003c/li\u003e\n \u003cli\u003eNdourwe Far Bolivar, Edioh Pem Frederic, Fosso Kengne Loraine Priscillia, Mogue Kamdem Gwladis, Betsi Noel Christiane Wifreid, Gwos Nhiomock Serge Roger, Foto Menbohan Samuel, \u0026amp; Z\u0026eacute;baz\u0026eacute; Togouet Serge Hubert. (2025). Biodiversity of Benthic Macroinvertebrates and water quality as a tool to the ecological study of three forest streams in the littoral zone (Cameroon). \u003cem\u003eWorld Journal of Advanced Research and Reviews\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(1), 1722\u0026ndash;1734. https://doi.org/10.30574/wjarr.2025.25.1.0034\u003c/li\u003e\n \u003cli\u003eNingrum, S. O. (2018). Analysis Quality of Water River and Quality of Well Water in The Surrounding of Rejo Agung Baru Sugar Factory Madiun. \u003cem\u003eJurnal Kesehatan Lingkungan\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(1), 1. https://doi.org/10.20473/jkl.v10i1.2018.1-12\u003c/li\u003e\n \u003cli\u003eNwanosike, A. A., Ameh, M., \u0026amp; Adinoyi, O. Y. (2014). \u003cem\u003eBacteriological and Physico-chemical Analysis of Well Water from Villages in Edati , Niger State , North-central Nigeria\u003c/em\u003e. \u003cem\u003e10\u003c/em\u003e(3), 10\u0026ndash;16.\u003c/li\u003e\n \u003cli\u003eNyambukah, R., \u0026amp; Mihale, M. J. (2022a). Seasonal Variability of Water Quality in the Zigi River, Northern Tanzania. \u003cem\u003eHURIA JOURNAL OF THE OPEN UNIVERSITY OF TANZANIA\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(1). https://doi.org/10.61538/huria.v28i1.974\u003c/li\u003e\n \u003cli\u003eNyambukah, R., \u0026amp; Mihale, M. J. (2022b). Seasonal Variability of Water Quality in the Zigi River, Northern Tanzania. \u003cem\u003eHURIA JOURNAL OF THE OPEN UNIVERSITY OF TANZANIA\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(1). https://doi.org/10.61538/huria.v28i1.974\u003c/li\u003e\n \u003cli\u003eOberdorff, T. (2022). Time for decisive actions to protect freshwater ecosystems from global changes. \u003cem\u003eKnowledge \u0026amp; Management of Aquatic Ecosystems\u003c/em\u003e, \u003cem\u003e423\u003c/em\u003e, 19. https://doi.org/10.1051/kmae/2022017\u003c/li\u003e\n \u003cli\u003eOchoa‐Tocachi, B. F., Buytaert, W., De Bi\u0026egrave;vre, B., C\u0026eacute;lleri, R., Crespo, P., Villac\u0026iacute;s, M., Llerena, C. A., Acosta, L., Villaz\u0026oacute;n, M., Guallpa, M., Gil‐R\u0026iacute;os, J., Fuentes, P., Olaya, D., Vi\u0026ntilde;as, P., Rojas, G., \u0026amp; Arias, S. (2016). Impacts of land use on the hydrological response of tropical Andean catchments. \u003cem\u003eHydrological Processes\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(22), 4074\u0026ndash;4089. https://doi.org/10.1002/hyp.10980\u003c/li\u003e\n \u003cli\u003eOgunribido, T. (2017). Bacteriological and hydrogeochemical investigation of surface water and groundwater in Ikare- Akoko, Nigeria. \u003cem\u003eInternational Journal of Advanced Geosciences\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(1), 27. https://doi.org/10.14419/ijag.v6i1.8635\u003c/li\u003e\n \u003cli\u003eOjok, W., Wasswa, J., \u0026amp; Ntambi, E. (2017). Assessment of Seasonal Variation in Water Quality in River Rwizi Using Multivariate Statistical Techniques, Mbarara Municipality, Uganda. \u003cem\u003eJournal of Water Resource and Protection\u003c/em\u003e, \u003cem\u003e09\u003c/em\u003e(01), 83\u0026ndash;97. https://doi.org/10.4236/jwarp.2017.91007\u003c/li\u003e\n \u003cli\u003eOketola, A. A., Adekolurejo, S. M., \u0026amp; Osibanjo, O. (2013). Water Quality Assessment of River Ogun Using Multivariate Statistical Techniques. \u003cem\u003eJournal of Environmental Protection\u003c/em\u003e, \u003cem\u003e04\u003c/em\u003e(05), 466\u0026ndash;479. https://doi.org/10.4236/jep.2013.45055\u003c/li\u003e\n \u003cli\u003eOki, A. O., \u0026amp; Akana, T. S. (2016). Quality Assessment of Groundwater in Yenagoa, Niger Delta, Nigeria. \u003cem\u003eGeosciences\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(1), 1\u0026ndash;12. https://doi.org/10.5923/j.geo.20160601.01\u003c/li\u003e\n \u003cli\u003eOnifade, O., Lawal, Z. K., Shamsuddin, N., Abas, P. E., Lai, D. T. C., \u0026amp; Gӧdeke, S. H. (2025). Impact of Seasonal Variation and Population Growth on Coliform Bacteria Concentrations in the Brunei River: A Temporal Analysis with Future Projection. \u003cem\u003eWater\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(7), 1069. https://doi.org/10.3390/w17071069\u003c/li\u003e\n \u003cli\u003ePandey, P. K., Kass, P. H., Soupir, M. L., Biswas, S., \u0026amp; Singh, V. P. (2014). Contamination of water resources by pathogenic bacteria. \u003cem\u003eAMB Express\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(1), 1\u0026ndash;16. https://doi.org/10.1186/s13568-014-0051-x\u003c/li\u003e\n \u003cli\u003ePritchard, M., Mkandawire, T., \u0026amp; O\u0026rsquo;Neill, J. G. (2007). Biological, chemical and physical drinking water quality from shallow wells in Malawi: Case study of Blantyre, Chiradzulu and Mulanje. \u003cem\u003ePhysics and Chemistry of the Earth\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(15\u0026ndash;18). https://doi.org/10.1016/j.pce.2007.07.013\u003c/li\u003e\n \u003cli\u003eQuero, G. M., Guicciardi, S., Penna, P., Catenacci, G., Brandinelli, M., Bolognini, L., \u0026amp; Luna, G. M. (2024). Increasing trends in faecal pollution revealed over a decade in the central Adriatic Sea (Italy). \u003cem\u003eWater Research\u003c/em\u003e, \u003cem\u003e262\u003c/em\u003e, 122083. https://doi.org/10.1016/j.watres.2024.122083\u003c/li\u003e\n \u003cli\u003eRahman, S. H., Fakhruddin, A., Uddin, M. J., Zaman, M. S., Talukder, A., Adyel, T. M., \u0026amp; Sarker, M. M. R. (2014). Water quality of shallow tube wells as affected by sanitary latrines and groundwater flow. \u003cem\u003eJournal of Bangladesh Academy of Sciences\u003c/em\u003e, \u003cem\u003e37\u003c/em\u003e(2). https://doi.org/10.3329/jbas.v37i2.17565\u003c/li\u003e\n \u003cli\u003eRochelle-Newall, E. J., Ribolzi, O., Viguier, M., Thammahacksa, C., Silvera, N., Latsachack, K., Dinh, R. P., Naporn, P., Sy, H. T., Soulileuth, B., Hmaimum, N., Sisouvanh, P., Robain, H., Janeau, J.-L., Valentin, C., Boithias, L., \u0026amp; Pierret, A. (2016). Effect of land use and hydrological processes on Escherichia coli concentrations in streams of tropical, humid headwater catchments. \u003cem\u003eScientific Reports\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(1), 32974. https://doi.org/10.1038/srep32974\u003c/li\u003e\n \u003cli\u003eRodionova, O., Kucheryavskiy, S., \u0026amp; Pomerantsev, A. (2021). Efficient tools for principal component analysis of complex data\u0026mdash; a tutorial. \u003cem\u003eChemometrics and Intelligent Laboratory Systems\u003c/em\u003e, \u003cem\u003e213\u003c/em\u003e, 104304. https://doi.org/10.1016/j.chemolab.2021.104304\u003c/li\u003e\n \u003cli\u003eRonoh, P., Furlong, C., Kansiime, F., Mugambe, R., \u0026amp; Brdjanovic, D. (2020). Are There Seasonal Variations in Faecal Contamination of Exposure Pathways? An Assessment in a Low\u0026ndash;Income Settlement in Uganda. \u003cem\u003eInternational Journal of Environmental Research and Public Health\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(17), 6355. https://doi.org/10.3390/ijerph17176355\u003c/li\u003e\n \u003cli\u003eSahoo, M. M., Patra, K. C., \u0026amp; Khatua, K. K. (2015). Inference of Water Quality Index Using ANFIA and PCA. \u003cem\u003eAquatic Procedia\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e, 1099\u0026ndash;1106. https://doi.org/10.1016/j.aqpro.2015.02.139\u003c/li\u003e\n \u003cli\u003eSelemani, J. R., Zhang, J., Muzuka, A. N. N., Njau, K. N., Zhang, G., Maggid, A., Mzuza, M. K., Jin, J., \u0026amp; Pradhan, S. (2017). Seasonal water chemistry variability in the Pangani River basin, Tanzania. \u003cem\u003eEnvironmental Science and Pollution Research\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(33), 26092\u0026ndash;26110. https://doi.org/10.1007/s11356-017-0221-x\u003c/li\u003e\n \u003cli\u003eShrestha, S., \u0026amp; Kazama, F. (2007). Assessment of surface water quality using multivariate statistical techniques: A case study of the Fuji river basin, Japan. \u003cem\u003eEnvironmental Modelling \u0026amp; Software\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(4), 464\u0026ndash;475. https://doi.org/10.1016/j.envsoft.2006.02.001\u003c/li\u003e\n \u003cli\u003eSonar, C., Al Hammadi, A. M., \u0026amp; Padme, Y. L. (2024). \u003cem\u003eWater Quality Assessment Using Principal Component Analysis\u003c/em\u003e (pp. 88\u0026ndash;97). https://doi.org/10.1007/978-3-031-74701-4_7\u003c/li\u003e\n \u003cli\u003eSong, Z., Khaksari, M., Lilleskov, E. A., Kane, E. S., \u0026amp; Doskey, P. V. (2025a). Seasonal effects of plant functional groups on molecular biogeochemistry of dissolved organic matter in porewater of a poor fen. \u003cem\u003eFrontiers in Environmental Science\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e. https://doi.org/10.3389/fenvs.2025.1549320\u003c/li\u003e\n \u003cli\u003eSong, Z., Khaksari, M., Lilleskov, E. A., Kane, E. S., \u0026amp; Doskey, P. V. (2025b). Seasonal effects of plant functional groups on molecular biogeochemistry of dissolved organic matter in porewater of a poor fen. \u003cem\u003eFrontiers in Environmental Science\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e. https://doi.org/10.3389/fenvs.2025.1549320\u003c/li\u003e\n \u003cli\u003eSsewankambo, G., Kabenge, I., Nakawuka, P., Wanyama, J., Zziwa, A., Bamutaze, Y., Gwapedza, D., Palmer, C. T., Tanner, J., Mantel, S., \u0026amp; Tessema, B. (2023). Assessing soil erosion risk in a peri-urban catchment of the Lake Victoria basin. \u003cem\u003eModeling Earth Systems and Environment\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(2), 1633\u0026ndash;1649. https://doi.org/10.1007/s40808-022-01565-6\u003c/li\u003e\n \u003cli\u003eSu, X., Xu, W., Yang, F., \u0026amp; Zhu, P. (2015). Using new mass balance methods to estimate gross surface water and groundwater exchange with naturally occurring tracer 222Rn in data poor regions: A case study in northwest China. \u003cem\u003eHydrological Processes\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(6), 979\u0026ndash;990. https://doi.org/10.1002/hyp.10208\u003c/li\u003e\n \u003cli\u003eSyamsir, Birawida, A. B., \u0026amp; Faisal, A. (2019). Development of Water Quality Index of Island Wells in Makassar City. \u003cem\u003eJournal of Physics: Conference Series\u003c/em\u003e, \u003cem\u003e1155\u003c/em\u003e(1). https://doi.org/10.1088/1742-6596/1155/1/012106\u003c/li\u003e\n \u003cli\u003eVan Binh, D., Nguyen, B. Q., Nguyen, T.-T.-H., Le, X.-H., Tuan, L. A., Le, M.-H., Kantoush, S. A., Nguyen, T. V., Dinh, V. N., Luan, N. T., Ahmed, M. F., \u0026amp; Sumi, T. (2025). Quantifying the Impacts of Climate Change and Human Interventions on Flow Alterations in a Tropical River. \u003cem\u003eWater Resources Management\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e(7), 3537\u0026ndash;3552. https://doi.org/10.1007/s11269-025-04121-w\u003c/li\u003e\n \u003cli\u003eVan Horne, Y. O., Parks, J., Tran, T., Abrell, L., Reynolds, K. A., \u0026amp; Beamer, P. I. (2019). Seasonal variation of water quality in unregulated domestic wells. \u003cem\u003eInternational Journal of Environmental Research and Public Health\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(9). https://doi.org/10.3390/ijerph16091569\u003c/li\u003e\n \u003cli\u003eWater Quality in Selected Shallow Wells in Dar es Salaam. (2012). \u003cem\u003eHuria - Journal of the Open University of Tanzania\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(1).\u003c/li\u003e\n \u003cli\u003eWilliam, M., \u0026amp; Katambara, Z. (2025). Assessment of Spatial Water Quality Variations in Shallow Wells Using Principal Component Analysis in Half London Ward, Tanzania. \u003cem\u003eJournal of Water Resource and Protection\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(02), 108\u0026ndash;143. https://doi.org/10.4236/jwarp.2025.172007\u003c/li\u003e\n \u003cli\u003eYang, Y., Yu, Y., \u0026amp; Chen, X. (2024). Arsenic-methylating microbial community in sediment along the water flow is correlated with the distance to a low-temperature hot spring. \u003cem\u003eWater Supply\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(3), 918\u0026ndash;930. https://doi.org/10.2166/ws.2024.045\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":"Water Quality, Seasonal Variation, Microbial Contamination, PCA, Trend Analysis","lastPublishedDoi":"10.21203/rs.3.rs-8491963/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8491963/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnderstanding seasonal water quality variation is essential for sustainable water resource management in rapidly urbanising towns such as Tunduma, Tanzania. This study investigated temporal dynamics in physicochemical and microbial quality across six surface water points (SWPs) through monthly sampling over one year. Parameters analysed included pH, electrical conductivity (EC), turbidity, total dissolved solids (TDS), nitrate, phosphate, biochemical oxygen demand (BOD), and microbial indicators (faecal and total coliforms). Descriptive statistics revealed distinct wet-season increases in turbidity (up to 25.24 NTU), total suspended solids (\u0026gt;\u0026thinsp;44.17 mg/L), nitrate (11.59\u0026ndash;25.57 mg/L), phosphate (1.40\u0026ndash;1.49 mg/L), BOD (13.96\u0026ndash;24.53 mg/L), and microbial contamination (faecal coliforms 10.58 CFU/100 mL; total coliforms 26.00 CFU/100 mL). Shapiro\u0026ndash;Wilk tests (W\u0026thinsp;=\u0026thinsp;0.64\u0026ndash;0.95) confirmed non-normality of most variables, reflecting event-driven pollution. Pearson correlation (r\u0026thinsp;\u0026gt;\u0026thinsp;0.65) indicated strong associations between nutrients and microbial indicators, suggesting agricultural runoff, pit latrines, and greywater as common sources. Mann\u0026ndash;Kendall trend tests identified increasing trends in EC, TDS, and nitrate, with declining turbidity and sulphate at some sites. Principal Component Analysis (PCA) extracted three components explaining 77.9% of total variance: Component 1 linked nitrate, phosphate, BOD, and coliforms to nutrient and organic pollution; Component 2 captured turbidity and suspended solids, indicating sediment inputs during rainfall; while Component 3 reflected site-specific variability. PCA biplots revealed clear seasonal clustering, with wet-season months (March\u0026ndash;May, November) associated with elevated contaminant loads, and dry-season months near baseline conditions. Several parameters exceeded World Health Organization (WHO) drinking water guidelines, particularly during peak rainfall, posing risks to human health and aquatic ecosystems. These findings underscore the need for continuous monitoring, seasonally adaptive management, and pollution control strategies to safeguard surface water quality in Tunduma and similar rapidly growing urban environments.\u003c/p\u003e","manuscriptTitle":"Seasonal Dynamics of Water Quality and Pollution in Urban Streams of Tunduma, Tanzania","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-19 14:03:19","doi":"10.21203/rs.3.rs-8491963/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-31T19:54:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-29T07:10:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-29T07:07:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Monitoring and Assessment","date":"2025-12-31T19:33:54+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":"1ff99122-4561-487e-813f-3865e3b12cdf","owner":[],"postedDate":"March 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T14:09:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-19 14:03:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8491963","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8491963","identity":"rs-8491963","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}